', methods=['PUT', 'PATCH', 'POST'])\ndef update_ship(id):\n try:\n get_access_token(request)\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 403\n \n try:\n id = int(id)\n if id not in db_load_or_install.ships:\n raise Exception()\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 404\n\n try:\n ships = request.json\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 400\n\n db_load_or_install.ships.update(db_load_or_install.ships[id], name=ships['name'],\n type=ships['type'], country=ships['country'])\n db_load_or_install.ships.commit()\n\n return '', 200\n\n\n@app.route('/ships/', methods=['POST'])\ndef insert_ship():\n try:\n get_access_token(request)\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 403\n \n try:\n ships = request.json\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 400\n\n id = db_load_or_install.ships.insert(name=ships['name'], type=ships['type'],\n country=ships['country'])\n db_load_or_install.ships.commit()\n\n return '', 201, {\n 'Location': '/ships/{}'.format(id)\n }\n\n\n@app.route('/sailors/', methods=['POST'])\ndef insert_sailor():\n try:\n get_access_token(request)\n except:\n return '', 403\n\n \n\n try:\n sailor = request.json\n\n if int(sailor['ship_empl']) not in db_load_or_install.sailors:\n raise Exception()\n\n hiredate = string_to_datetime(sailor['hiredate'])\n except:\n traceback.print_exc(file=sys.stdout)\n return '', 400\n\n id = db_load_or_install.sailors.insert(firstname=sailor['firstname'],\n lastname=sailor['lastname'], speciality=sailor['speciality'],\n hiredate=hiredate, ship_empl=int(sailor['ship_empl']))\n db_load_or_install.sailors.commit()\n\n return '', 201, {\n 'Location': '/sailors/{}'.format(id)\n }\n\n\n@app.route('/me', methods=['GET'])\ndef get_me():\n try:\n access_token = get_access_token(request)\n except:\n return '', 403\n\n user_id = db_load_or_install.token(access=access_token)[0]['user_id']\n\n return json.dumps({\n 'login': db_load_or_install.user[user_id]['login'],\n 'name': db_load_or_install.user[user_id]['name'],\n 'email': db_load_or_install.user[user_id]['email'],\n 'phone': db_load_or_install.user[user_id]['phone'],\n }, indent=4), 200, {\n 'Content-Type': 'application/json;charset=UTF-8'\n }\n\n\ndef string_to_datetime(string_date):\n return datetime.strptime(string_date, \"%Y-%m-%d %H:%M:%S.%f\")\n\n\ndef datetime_to_string(date_time):\n return date_time.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\n\n\ndef get_access_token(request):\n access_token = request.headers.get('Authorization', '')[len('Bearer '):]\n if not db_load_or_install.token(access=access_token) or db_load_or_install.token(access=access_token)[0]['expire_time'] < datetime.now():\n raise Exception()\n return access_token\n\nif __name__ == '__main__':\n app.run(port=5050, debug=True)\n\n\n\n\n","repo_name":"kirillovsky/rsoi_lab2","sub_path":"service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":15964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38436889687","text":"from evennia import logger\n\n\nclass QuestHandler(object):\n \"\"\"Controls all quests for one player.\"\"\"\n\n def __init__(self, player):\n self.player = player\n\n def send(self, event):\n \"\"\"Dispatches an event to each open quest.\n\n Arguments:\n event - an arbitrary entity communicating information to\n the quests\"\"\"\n\n for quest in self.player.db.quests:\n try:\n quest.send(event)\n except Exception:\n logger.log_trace()\n\n def add(self, quest):\n \"\"\"Registers a quest with the handler if it is unregistered.\"\"\"\n player = self.player.db\n\n if callable(quest):\n quest = quest(self.player)\n\n if quest not in player.closed_quests:\n player.open_quests.add(quest)\n","repo_name":"AgoraNomic/Nomeria","sub_path":"nomeria/world/quests/questhandler.py","file_name":"questhandler.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9063698236","text":"import cv2\nimport numpy as np\nimport torch.nn as nn\n\n\ndef sigmoid(x):\n return 1 / (1 + np.exp(-x))\n\n\ndef simple_dice(img1: np.array, img2: np.array) -> float:\n img1 = np.asarray(img1).astype(np.bool)\n img2 = np.asarray(img2).astype(np.bool)\n intersection = np.logical_and(img1, img2)\n return 2.0 * intersection.sum() / (img1.sum() + img2.sum())\n\n\ndef single_dice_coef(y_pred_bin, y_true):\n if not isinstance(y_pred_bin, np.ndarray):\n y_pred_bin = y_pred_bin.cpu().detach().numpy()\n y_pred_bin = sigmoid(y_pred_bin)\n y_pred_bin = y_pred_bin > 0.5\n if not isinstance(y_true, np.ndarray):\n y_true = y_true.cpu().detach().numpy()\n intersection = np.sum(y_true * y_pred_bin)\n if (np.sum(y_true) == 0) and (np.sum(y_pred_bin) == 0):\n return 1\n return (2 * intersection) / (np.sum(y_true) + np.sum(y_pred_bin))\n\n\ndef mean_dice_coef(y_pred_bin, y_true, **kwargs):\n # shape of y_true and y_pred_bin: (n_samples, height, width, n_channels)\n # actual shape (Batch, channels, height, width)\n batch_size = y_true.shape[0]\n channel_num = y_true.shape[1]\n mean_dice_channel = 0.0\n for i in range(batch_size):\n for j in range(channel_num):\n channel_dice = single_dice_coef(y_pred_bin[i, j, :, :], y_true[i, j, :, :])\n mean_dice_channel += channel_dice\n mean_dice_channel /= channel_num * batch_size\n return mean_dice_channel\n\n\nclass DiceLoss(nn.Module):\n __name__ = \"dice_loss\"\n\n def __init__(self, eps=1e-7, activation=\"sigmoid\"):\n super().__init__()\n self.activation = activation\n self.eps = eps\n\n def forward(self, y_pr, y_gt):\n return 1 - mean_dice_coef(y_pred_bin=y_pr, y_true=y_gt)\n\n\nclass BCEDiceLossCustom(DiceLoss):\n __name__ = \"bce_dice_loss\"\n\n def __init__(self, eps=1e-7, activation=\"sigmoid\"):\n super().__init__(eps, activation)\n self.bce = nn.BCEWithLogitsLoss(reduction=\"mean\")\n\n def forward(self, y_pr, y_gt):\n dice = super().forward(y_pr, y_gt)\n bce = self.bce(y_pr, y_gt)\n return dice + bce\n\n\ndef post_process(probability, threshold, min_size):\n \"\"\"\n Post processing of each predicted mask, components with lesser number of pixels\n than `min_size` are ignored\n \"\"\"\n # don't remember where I saw it\n mask = cv2.threshold(probability, threshold, 1, cv2.THRESH_BINARY)[1]\n num_component, component = cv2.connectedComponents(mask.astype(np.uint8))\n predictions = np.zeros((350, 525), np.float32)\n num = 0\n for c in range(1, num_component):\n p = component == c\n if p.sum() > min_size:\n predictions[p] = 1\n num += 1\n return predictions, num\n\n\ndef mask2rle(img):\n \"\"\"\n Convert mask to rle.\n img: numpy array, 1 - mask, 0 - background\n Returns run length as string formated\n \"\"\"\n pixels = img.T.flatten()\n pixels = np.concatenate([[0], pixels, [0]])\n runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n runs[1::2] -= runs[::2]\n return \" \".join(str(x) for x in runs)\n","repo_name":"munachisonwadike/tuunv2","sub_path":"pipelines/cloud-segmentation-pipeline/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3049,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"21814212230","text":"# shared.py contains data that should be shared among modules and helpful functions\n\n# dictionary for levels, contains the text and expected value for goal variable\n# the second half of the levels are quite hard to solve in pyblocks, but possible\nLEVEL_DATA = {\n 1: (\"Set 'goal' to the result of 5 + 5 * 5\", 30),\n 2: (\"Set 'goal' to be the smallest prime factor of 12345678\", 2),\n 3: (\"Set 'goal' to be the sum of all odd numbers under 100\", 2500),\n 4: (\"Find the sum of all the multiples of 3 or 5 below 1000.\", 233168),\n 5: (\"Each new term in the Fibonacci sequence is generated by adding the previous two terms. Start with 1, 2, 3, 5 and so on. Find the sum of the even fibonacci numbers less than 4,000,000.\", 4613732),\n 6: (\"The first prime numbers are 2, 3, 5, _. What is the 10 001st prime number?\", 104743),\n 7: (\"The Collatz sequence is created with the following rules: n/2 if n is even, otherwise 3*n+1. For example, starting with 13: 13 -> 40 -> 20 -> 10. Find the starting number with the longest sequence under 1,000,000.\", 837799),\n 8: (\"Start on the top left of a 20x20 grid lattice. If you can only move down or to the right, how many paths to the bottom left are there?\", 137846528820),\n 9: (\"Two numbers are amicable if the sum of the PROPER divisors of number A is equal to number B, and vice versa. A cannot equal B. Find the sum of all amicable numbers under 1000.\", 31626),\n 10: (\"Congratulations! You've completed all of the levels.\", None),\n}\n\n# blocks that will appear on the insert menu\nINSERT_OPTIONS = [\n \"StartBlock\",\n \"NumBlock\", \"TextBlock\", \"TrueBlock\", \"FalseBlock\",\n \"PrintBlock\", \"RetBlock\",\n \"AddBlock\", \"SubBlock\", \"MulBlock\", \"DivBlock\", \"ModBlock\",\n \"EqBlock\", \"NEqBlock\", \"GrBlock\", \"LsBlock\", \"AndBlock\", \"OrBlock\",\n \"NotBlock\", \"RndBlock\", \"FlrBlock\", \"CelBlock\",\n \"IncBlock\", \"DecBlock\",\n \"VarBlock\", \"SetBlock\",\n \"FuncBlock\", \"CallBlock\",\n \"IfBlock\", \"WhileBlock\", \"ForBlock\",\n]\n\n# checks collision between rectangle and point\ndef check_collision(pos, size, point):\n rx, ry = pos\n px, py = point\n w, h = size\n return (px > rx and px < rx + w) and (py > ry and py < ry + h)\n\n# returns a list of strings that are all less than 70 characters (which is about 600 pixels in the font)\ndef wrap_text(text):\n words = text.split()\n ret = []\n\n curr = \"\"\n for word in words:\n if len(curr) + len(word) >= 70 or word == \"[BREAK]\":\n ret.append(curr)\n curr = \"\" if word == \"[BREAK]\" else word\n else:\n curr += f\" {word}\"\n ret.append(curr)\n return ret\n\n# clamps num into min/max\ndef clamp(n, mi, ma):\n return min(ma, max(mi, n))\n\n","repo_name":"05st/pyblocks","sub_path":"shared.py","file_name":"shared.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32816871744","text":"from django.forms.fields import EmailField, CharField\nfrom django.shortcuts import render\nfrom django.contrib.auth.forms import BaseUserCreationForm, UsernameField\nfrom django.contrib.auth.admin import User\nfrom django.urls import reverse_lazy\nfrom django.views import generic\n\n# Create your views here.\nclass UserSignUpForm(BaseUserCreationForm):\n class Meta:\n model = User\n fields = [\n \"username\",\n \"first_name\",\n \"last_name\",\n \"email\"\n ]\n\n field_classes = {\n \"username\": UsernameField,\n \"first_name\": CharField,\n \"last_name\": CharField,\n \"email\": EmailField\n }\n\n\nclass SignUpView(generic.CreateView):\n form_class = UserSignUpForm\n success_url = reverse_lazy(\"login\")\n template_name = \"registration/signup.html\"\n\n\n# class UserView(generic.DetailView):\n# model = User \n# template_name = \"accounts/profile.html\"\n#\n# def get_queryset(self):\n# return User.objects.filter(username__startswith=self.slug)\n \n","repo_name":"ece-mohammad/django-polls-plus","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7126629396","text":"def calculate(homework, assessment, final):\n homework = (homework / 25) * 100\n assessment = (assessment / 50) * 100\n total = (homework + assessment + final) / 3\n return total\n\n\ndef numberToLetter(grade):\n if grade >= 90:\n return \"A\"\n elif grade >= 80:\n return \"B\"\n elif grade >= 70:\n return \"C\"\n elif grade >= 60:\n return \"D\"\n else:\n return \"F\"\n\n\nhomework = float(input(\"Please enter your homework grade out of 25: \"))\nassessment = float(input(\"Please enter your assessment grade out of 50: \"))\nfinal = float(input(\"Please enter your final exam grade out of 100: \"))\n\ngrade = calculate(homework, assessment, final)\nprint(\"Your final grade is: \", grade)\nprint(\"Your final letter grade is: \", numberToLetter(grade))\n","repo_name":"jamesbryer/ams_repo","sub_path":"python/functions/grade_calculator.py","file_name":"grade_calculator.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11379567820","text":"import argparse\nimport gc\n\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nimport pytorch_lightning as pl\n\n\nNUM_FEATURES_DEFAULT = 20\n\n\ndef feature_cols(n_features=NUM_FEATURES_DEFAULT):\n col_subset = [f\"f_{i}\" for i in range(0, n_features)]\n return col_subset\n\n\ndef idx_cols():\n col_subset = [\"time_id\", \"investment_id\"]\n return col_subset\n\n\ndef load_df(\n idx_cols, feature_cols, target=\"target\", fn=\"train_low_mem.parquet\", small=False\n):\n train = pd.read_parquet(fn, columns=idx_cols + feature_cols + [\"target\"])\n if small:\n train = train.loc[train.investment_id <= 100]\n train.reset_index(inplace=True, drop=True)\n return train\n\n\ndef window_dict(df, win_len=4):\n groups = df.groupby(\"investment_id\").groups\n d = {}\n for group in tqdm(groups.values()):\n for i, idx in enumerate(group):\n d[idx] = np.array(group[max(0, i - win_len + 1) : i + 1])\n return d\n\n\ndef prepend_slice(ids, df, win_len):\n df_ids = df.loc[df.investment_id.isin(ids)]\n groups = df_ids.groupby(\"investment_id\").groups\n idxs = np.concatenate([i.values[(-1) * (win_len - 1) :] for i in groups.values()])\n df_slice = df_ids.loc[idxs]\n return df_slice\n\n\nclass WinDataset(Dataset):\n def __init__(self, df, win_len=4, prepend_df=None):\n\n # groups = df.groupby('investment_id').groups\n if prepend_df is not None:\n df_slice = prepend_slice(df.investment_id.unique(), prepend_df, win_len)\n df = pd.concat([df_slice, df])\n df.reset_index(inplace=True, drop=True)\n n_prepend_rows = df_slice.shape[0]\n\n self.n_prepend_rows = 0 if prepend_df is None else n_prepend_rows\n self.win_len = win_len\n self.win_dict = window_dict(df, win_len=self.win_len)\n\n # self.df_index = df.index\n self.features = df[feature_cols()].values\n if \"target\" in list(df.columns):\n self.targets = df[\"target\"]\n self.with_targets = True\n else:\n self.with_targets = False\n\n def __getitem__(self, i):\n if self.n_prepend_rows > 0:\n i = i + self.n_prepend_rows\n features = self.features[self.win_dict[i]]\n n_rows = features.shape[0]\n if n_rows < self.win_len:\n # features = np.pad(features, ((self.win_len-n_rows,0), (0, 0)), mode='constant',\n # constant_values = (-1, -1))\n features = np.pad(\n features, ((self.win_len - n_rows, 0), (0, 0)), mode=\"mean\"\n )\n if self.with_targets:\n target = self.targets[i]\n return torch.tensor(features, dtype=torch.float32), torch.tensor(\n target, dtype=torch.float32\n )\n else:\n return torch.tensor(features, dtype=torch.float32)\n\n def __len__(self):\n return self.features.shape[0] - self.n_prepend_rows\n\n\nclass TimeDataset(Dataset):\n def __init__(self, df, win_len=2, prepend_df=None):\n\n # groups = df.groupby('investment_id').groups\n if prepend_df is not None:\n df_slice = prepend_slice(df.investment_id.unique(), prepend_df, win_len)\n df = pd.concat([df_slice, df])\n df.reset_index(inplace=True, drop=True)\n n_prepend_rows = df_slice.shape[0]\n\n self.n_prepend_rows = 0 if prepend_df is None else n_prepend_rows\n time_df = df.groupby(\"time_id\")[feature_cols()].mean()\n\n self.win_len = win_len\n self.win_dict = window_dict(df, win_len=self.win_len)\n\n self.df_index = df.index\n self.time_ids = df.time_id.values\n\n self.time_map = {k: v for v, k in enumerate(df.time_id.unique())}\n self.time_features = torch.tensor(\n time_df.astype(\"float32\").values, dtype=torch.float32\n )\n self.features = torch.tensor(df[feature_cols()].values, dtype=torch.float32)\n if \"target\" in list(df.columns):\n self.targets = torch.tensor(df[\"target\"], dtype=torch.float32)\n self.with_targets = True\n else:\n self.with_targets = False\n\n def __getitem__(self, i):\n if self.n_prepend_rows > 0:\n i = i + self.n_prepend_rows\n features = self.features[self.win_dict[i]]\n # print(features)\n n_rows = features.shape[0]\n if n_rows < self.win_len:\n # features = np.pad(features, ((self.win_len-n_rows,0), (0, 0)), mode='constant',\n # constant_values = (-1, -1))\n # features = np.pad(\n # features, ((self.win_len - n_rows, 0), (0, 0)), mode=\"mean\"\n features = F.pad(\n features, (0, 0, self.win_len - n_rows, 0), mode=\"constant\"\n ) # )\n time_features = self.time_features[[self.time_map[self.time_ids[i]]]]\n # print(time_features)\n # features = np.concatenate([features, time_features])\n features = torch.cat([features, time_features])\n if self.with_targets:\n target = self.targets[i]\n\n # return torch.tensor(features, dtype=torch.float32), torch.tensor(\n # target, dtype=torch.float32\n # )\n return features, target\n else:\n # return torch.tensor(features, dtype=torch.float32)\n return features\n\n def __len__(self):\n return self.features.shape[0] - self.n_prepend_rows\n\n\nclass LinBnDrop(nn.Sequential):\n \"Module grouping `BatchNorm1d`, `Dropout` and `Linear` layers\"\n\n def __init__(self, n_in, n_out, bn=True, p=0.0, act=None, lin_first=False):\n layers = [nn.BatchNorm1d(n_out if lin_first else n_in)] if bn else []\n if p != 0:\n layers.append(nn.Dropout(p))\n lin = [nn.Linear(n_in, n_out, bias=not bn)]\n if act is not None:\n lin.append(act)\n layers = lin + layers if lin_first else layers + lin\n super().__init__(*layers)\n\n\ndef predict(model, test_dl, device):\n model.to(device)\n model.eval()\n val_preds, ys = [], []\n for xb, yb in tqdm(test_dl):\n xb, yb = xb.to(device), yb.to(device)\n logits = model(xb)\n val_preds.append(logits.squeeze().reshape(-1).detach().numpy())\n ys.append(yb.squeeze().reshape(-1).detach().numpy())\n return np.concatenate(val_preds), np.concatenate(ys)\n\n\ndef do_predict(model, df, df_test, win_len=4):\n test_ds = WinDataset(df_test, prepend_df=df, win_len=win_len)\n test_dl = DataLoader(test_ds, batch_size=128, shuffle=False)\n preds, ys = predict(model, test_dl, torch.device(\"cpu\"))\n torch.cuda.empty_cache()\n return preds\n\n\ndef do_iterative_prediction(model, df, df_test, win_len=4):\n time_id_groups = df_test.groupby(\"time_id\").groups\n all_preds = []\n for group in time_id_groups.values():\n df_t = df_test.loc[group]\n # df_t['time_id'] = df_t['row_id'].transform(lambda x: int(x.split('_'[0])))\n preds = do_predict(model, df, df_t, win_len)\n all_preds.append(preds)\n if df is not None:\n df = pd.concat([df, df_t])\n df.reset_index(inplace=True, drop=True)\n\n return np.concatenate(all_preds)\n\n\nclass WinDS(Dataset):\n def __init__(self, features, idcs, win_dict, targets=None, win_len=1, **kwargs):\n self.features, self.idcs, self.win_dict = features, idcs, win_dict\n self.targets, self.win_len = targets, win_len\n\n def __getitem__(self, i):\n features = self.features[self.win_dict[self.idcs[i]]]\n if features.shape[0] < self.win_len:\n features = np.pad(\n features, ((self.win_len - features.shape[0], 0), (0, 0)), mode=\"mean\"\n )\n if self.targets is None:\n return features\n else:\n return features, self.targets[self.idcs[i]]\n\n def __len__(self):\n return len(self.idcs)\n\n\ndef get_win_features(features, win_dict, i, win_len=4):\n win_features = features[win_dict[i]]\n if win_features.shape[0] < win_len:\n win_features = np.pad(\n win_features, ((win_len - win_features.shape[0], 0), (0, 0)), mode=\"mean\"\n )\n if win_len == 1:\n win_features = np.expand_dims(win_features, axis=0)\n return win_features\n\n\ndef get_time_features(time_features, time_ids, time_map, i, time_win_len=1):\n if time_win_len == 0:\n return np.array([], dtype=\"float32\")\n time_idx = time_map[time_ids[i]]\n time_win_features = time_features[\n max(0, time_idx - time_win_len + 1) : time_idx + 1\n ]\n if time_win_features.shape[0] < time_win_len:\n time_win_features = np.pad(\n time_win_features,\n ((time_win_len - time_win_features.shape[0], 0), (0, 0)),\n mode=\"mean\",\n )\n return time_win_features\n\n\nclass TimeDS(Dataset):\n def __init__(\n self,\n features,\n time_features,\n idcs,\n win_dict,\n time_ids,\n time_map,\n targets=None,\n win_len=1,\n time_win_len=1,\n **kwargs,\n ):\n self.features, self.time_features, self.targets = (\n features,\n time_features,\n targets,\n )\n self.idcs = idcs\n self.win_dict, self.win_len = win_dict, win_len\n self.time_ids, self.time_map, self.time_win_len = (\n time_ids,\n time_map,\n time_win_len,\n )\n\n def __getitem__(self, i):\n df_idx = self.idcs[i]\n # print(df_idx)\n features = get_win_features(self.features, self.win_dict, df_idx, self.win_len)\n # time_step aggregated features\n\n time_features = get_time_features(\n self.time_features, self.time_ids, self.time_map, df_idx, self.time_win_len\n )\n # print(features)\n # print(time_features)\n # features = np.concatenate([features, time_features])\n # features_full = np.concatenate([features, time_features])\n if self.targets is not None:\n target = self.targets[df_idx]\n return features, time_features, target\n else:\n return features, time_features\n\n def __len__(self):\n return len(self.idcs)\n\n\ndef default_args():\n args = argparse.Namespace(\n df_path=\"train_low_mem.parquet\",\n win_len=1,\n time_win_len=0,\n dset_type=\"time_ds\",\n num_features=20,\n split_time_id=1000,\n batch_size=256,\n num_workers=0,\n small_df=True,\n pin_memory=False,\n )\n return args\n\n\nclass WinDM(pl.LightningDataModule):\n def __init__(self, args: argparse.Namespace):\n super().__init__()\n args = vars(args) if args is not None else None\n\n self.num_features = args.get(\"num_features\")\n self.df_path = args.get(\"df_path\")\n self.win_len = args.get(\"win_len\")\n self.time_win_len = args.get(\"time_win_len\")\n self.dset_type = args.get(\"dset_type\")\n self.split_time_id = args.get(\"split_time_id\")\n self.batch_size = args.get(\"batch_size\")\n self.num_workers = args.get(\"num_workers\")\n self.small_df = args.get(\"small_df\")\n self.pin_memory = args.get('pin_memory', False)\n\n def setup(self):\n self.feature_cols = feature_cols(self.num_features)\n df = load_df(\n idx_cols(), self.feature_cols, fn=self.df_path, small=self.small_df\n )\n # self.df = df\n self.features = df[self.feature_cols].values.astype(\"float32\")\n if \"target\" in df.columns:\n self.targets = df[\"target\"].values\n else:\n self.targets = None\n if self.win_len > 1:\n self.win_dict = window_dict(df, self.win_len)\n else:\n self.win_dict = {k: k for k in df.index}\n if self.split_time_id == -1:\n self.train_idcs = df.index\n self.val_idcs = df.loc[df.time_id == df.time_id.max()].index\n else:\n self.train_idcs = df.loc[df.time_id <= self.split_time_id].index\n self.val_idcs = df.loc[df.time_id > self.split_time_id].index\n self.test_idcs = self.val_idcs\n\n # time aggregate mapping\n print(self.dset_type)\n if self.dset_type == \"time_ds\":\n print(\"time ds\")\n time_df = df.groupby(\"time_id\")[self.feature_cols].mean()\n self.time_ids = df.time_id.values\n self.time_map = {k: v for v, k in enumerate(df.time_id.unique())}\n self.time_features = time_df.astype(\"float32\").values\n else:\n self.time_ids, self.time_map, self.time_features = None, None, None\n\n dset_cls = TimeDS if self.dset_type == \"time_ds\" else WinDS\n self.train_dset = dset_cls(\n self.features,\n self.time_features,\n self.train_idcs,\n self.win_dict,\n time_ids=self.time_ids,\n time_map=self.time_map,\n targets=self.targets,\n win_len=self.win_len,\n time_win_len=self.time_win_len,\n )\n self.valid_dset = dset_cls(\n self.features,\n self.time_features,\n self.val_idcs,\n self.win_dict,\n time_ids=self.time_ids,\n time_map=self.time_map,\n targets=self.targets,\n win_len=self.win_len,\n time_win_len=self.time_win_len,\n )\n self.test_dset = dset_cls(\n self.features,\n self.time_features,\n self.val_idcs,\n self.win_dict,\n time_ids=self.time_ids,\n time_map=self.time_map,\n targets=None,\n win_len=self.win_len,\n time_win_len=self.time_win_len,\n )\n\n gc.collect()\n\n def train_dataloader(self):\n return DataLoader(\n self.train_dset,\n batch_size=self.batch_size,\n shuffle=True,\n pin_memory=self.pin_memory,\n num_workers=self.num_workers,\n )\n\n def val_dataloader(self):\n return DataLoader(\n self.valid_dset,\n batch_size=self.batch_size,\n shuffle=False,\n pin_memory=self.pin_memory,\n num_workers=self.num_workers,\n )\n\n def test_dataloader(self):\n return DataLoader(\n self.test_dset,\n batch_size=self.batch_size,\n shuffle=False,\n pin_memory=self.pin_memory,\n num_workers=self.num_workers,\n )\n\n @staticmethod\n def add_to_argparse(parser):\n parser.add_argument(\n \"--batch_size\",\n type=int,\n default=256,\n help=\"Number of examples to operate on per forward step.\",\n )\n parser.add_argument(\n \"--num_workers\",\n type=int,\n default=0,\n help=\"Number of additional processes to load data.\",\n )\n parser.add_argument(\n \"--df_path\",\n type=str,\n default='train_low_mem.parquet',\n help=\"training dataframe\",\n )\n parser.add_argument(\n \"--num_features\", type=int, default=20, help=\"number of features\"\n )\n parser.add_argument(\n \"--win_len\", type=int, default=1, help=\"length of considered investment_id window\"\n )\n parser.add_argument(\n \"--time_win_len\", type=int, default=0, help=\"length of considered time_id window\"\n )\n parser.add_argument(\n \"--split_time_id\", type=int, default=1100, help=\"time_id split point for validation, -1 for full data\")\n parser.add_argument(\n \"--small_df\", dest='small_df', default=False, action=\"store_true\"\n )\n parser.add_argument(\n \"--n_folds\", type=int, default=1, help=\"end index of test set\"\n )\n parser.add_argument(\n \"--fold\", type=int, default=1, help=\"end index of test set\"\n )\n parser.add_argument(\n \"--dset_type\", type=str, default='time_ds', help=\"dataset type\"\n )\n parser.add_argument(\n \"--pin_memory\", dest='pin_memory', default=False, action=\"store_true\"\n )\n # parser.add_argument(\n # \"--augments\", type=str, nargs='+', action='append',\n # default=AUGMENTS, help='tfms for the groups list of lists, use like --augments noise scale --augments all --augments integer_noise --augments all'\n # )\n parser.add_argument(\n \"--data_dir\",\n type=str,\n default='./',\n help=\"directory of the input dataframe\",\n )\n parser.add_argument(\n \"--log_dir\", type=str, default='./lightning_logs', help=\"directory of the log files\"\n )\n return parser\n","repo_name":"Takezo87/ubm","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":16824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30570678706","text":"# -* encode UTF-8\r\nimport numpy as np\r\nfrom numpy import dtype\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nimport seaborn as sns\r\nimport codecs\r\nimport csv\r\nfrom collections import defaultdict\r\nimport json\r\n\r\n# 自定义numpy数据类型\r\nmovie = dtype([('movieid', int), ('title', str, 70)])\r\nrate = dtype([('movieid', int), ('rating', float)])\r\nfilm_rating = dtype([('title', str, 70), ('rating', float)])\r\nfilm_genres = dtype([('title', str, 70), ('genres', str, 70)])\r\nfilm_genres_rating = dtype([('title', str, 70), ('genres', str, 70), ('rating', float)])\r\nuser_rating = dtype([('userid', int), ('rating', float)])\r\n\r\n\r\ndef countGenres(genres_nd):\r\n '''\r\n :param genres_nd:\r\n :return:\r\n 统计得到字典： {genres:出现次数}\r\n 以及列表：包含出现的所有标签\r\n '''\r\n dictionary = defaultdict(int)\r\n lst = []\r\n for _ in genres_nd:\r\n for __ in _['genres'].split(\"|\"):\r\n dictionary[__] += 1\r\n lst.append(__)\r\n\r\n # print(dictionary)\r\n return dictionary, lst\r\n\r\n\r\ndef merge(nd1: 'ndarray include id and title', nd2: 'ndarray include id and rating') -> 'ndarray with id and average rating':\r\n '''\r\n :param nd1: id and title\r\n :param nd2: id and rating\r\n :return: ndarray id and rating\r\n '''\r\n res = np.array([], dtype=rate)\r\n for _ in nd1:\r\n # 筛选条件：movieid相等\r\n condition = nd2['movieid'] == _[\"movieid\"]\r\n if np.all(condition == False) == True:\r\n continue\r\n # 筛选出id相等的movie的所有评分\r\n total_rating = np.compress(condition, nd2)[\"rating\"]\r\n # 计算均值\r\n mean_rating = np.mean(total_rating)\r\n # 生成 rate对象\r\n temp = np.array([(_['movieid'], mean_rating)], dtype=rate)\r\n # 组合\r\n res = np.concatenate((res, temp), 0)\r\n return res\r\n\r\n\r\ndef extract(nd1: 'ndarray with id and rating', nd2: 'ndarray with id and title')-> 'ndarray with title and rating':\r\n '''\r\n :param nd1: id and rating\r\n :param nd2: id and title\r\n :return: ndarray with title and rating\r\n '''\r\n result = np.array([], dtype=film_rating)\r\n # print(nd2)\r\n\r\n for _ in nd1:\r\n condition = nd2['movieid'] == _['movieid']\r\n # print(condition)\r\n if np.all(condition == False) == True:\r\n continue\r\n # 通过id筛选得到title，这里肯定唯一\r\n tt = np.compress(condition, nd2)['title'][0]\r\n # 生成film对象\r\n temp = np.array([(tt, _['rating'])], dtype=film_rating)\r\n # 组合\r\n result = np.concatenate((result, temp), 0)\r\n return result\r\n\r\n\r\ndef union(nd1: 'ndarray with title and rating', nd2: 'ndarray with title and genres')-> 'ndarray with title and genres':\r\n '''\r\n :param nd1: title and rating\r\n :param nd2: title and genres\r\n :return: ndarray title and genres\r\n '''\r\n result = np.array([], dtype=film_genres)\r\n\r\n for _ in nd1:\r\n condition = nd2['title'] == _['title']\r\n # print(condition)\r\n if np.all(condition == False) == True:\r\n continue\r\n # 通过title筛选得到genres，这里肯定唯一\r\n gen = np.compress(condition, nd2)['genres'][0]\r\n # 生成film对象\r\n temp = np.array([(_['title'], gen)], dtype=film_genres)\r\n # 组合\r\n result = np.concatenate((result, temp), 0)\r\n return result\r\n\r\n\r\ndef union2(nd1: 'ndarray with title and rating', nd2: 'ndarray with title and genres')-> 'ndarray with title and genres and rating':\r\n '''\r\n :param nd1: title and rating(average)\r\n :param nd2: title and genres\r\n :return: ndarray title and genres and rating\r\n '''\r\n result = np.array([], dtype=film_genres_rating)\r\n\r\n for _ in nd1:\r\n condition = nd2['title'] == _['title']\r\n # print(condition)\r\n if np.all(condition == False) == True:\r\n continue\r\n # 通过title筛选得到genres，这里肯定唯一\r\n gen = np.compress(condition, nd2)['genres'][0]\r\n # 生成film对象\r\n temp = np.array([(_['title'], gen, _['rating'])], dtype=film_genres_rating)\r\n # 组合\r\n result = np.concatenate((result, temp), 0)\r\n return result\r\n\r\n\r\n# 读取电影id和名字，使用numpy.loadtxt有些行会读错！不知为何\r\n# movieid, title = np.loadtxt('movies.csv', dtype=str, delimiter=',', skiprows=1, usecols=(0, 1),\r\n# unpack=True, encoding='cp936')\r\n# 不知为何读不出来\r\n# movieid, title, genres = np.genfromtxt(file, dtype=str, delimiter=',', skip_header=1, usecols=(0, 1, 3),\r\n# unpack=True)\r\n# movieid2, rating = np.loadtxt('ratings.csv', dtype=float, delimiter=',', skiprows=1, usecols=(1, 2), unpack=True,\r\n# converters={1: lambda x: int(x), 2: lambda x: float(x)}, encoding='cp936')\r\n\r\n# 使用csv读取正确无误\r\n# 读取电影id和title以及genres\r\nwith codecs.open('movies.csv', 'r', encoding='cp936') as f:\r\n reader = csv.reader(f)\r\n movies = np.array(list(reader))\r\n movieid = movies[1:, 0]\r\n title = movies[1:, 1]\r\n genres = movies[1:, 2]\r\n\r\n# 读取电影id和rating\r\nwith codecs.open('ratings.csv', 'r', encoding='cp936') as f:\r\n reader = csv.reader(f)\r\n movies = np.array(list(reader))\r\n userid = movies[1:, 0]\r\n movieid2 = movies[1:, 1]\r\n rating = movies[1:, 2]\r\n\r\n\r\n# i: id-title\r\n# ii: id-rating\r\n# iii: title-genres\r\n# iiii: userid-rating\r\ni = np.array(list(zip(map(int, movieid), title)), dtype=movie)\r\nii = np.array(list(zip(map(int, movieid2), rating)), dtype=rate)\r\niii = np.array(list(zip(title, genres)), dtype=film_genres)\r\niiii = np.array(list(zip(map(int, userid), rating)), dtype=user_rating)\r\n# print(i)\r\n# print(ii)\r\n# print(iii)\r\n\r\n\r\n# 获取所有电影id以及其平均评分的数组\r\nres = merge(i, ii)\r\n\r\n# 计算所有电影的平均分\r\nmean_rating = np.mean(res['rating'])\r\n\r\n\r\ndef figure1():\r\n # 图1\r\n fig = plt.figure(1)\r\n # 绘制散点图，显示离散数据点\r\n plt.subplot(121)\r\n plt.scatter(res['movieid'][:], res['rating'][:], c=res['rating'][:]*100, s=res['rating'][:]*0.1)\r\n\r\n # 百分位数\r\n p25 = np.percentile(res['rating'], 25)\r\n p50 = np.percentile(res['rating'], 50)\r\n p75 = np.percentile(res['rating'], 75)\r\n\r\n plt.axhline(p25, label='25% line', c='r')\r\n plt.axhline(p50, label='50% line', c='g')\r\n plt.axhline(p75, label='75% line', c='b')\r\n\r\n plt.xlabel('movie id')\r\n plt.ylabel('rating')\r\n\r\n plt.title('movie rating')\r\n\r\n # 箱线图\r\n plt.subplot(122)\r\n plt.boxplot(res['rating'], 0, 'gx', vert=False)\r\n\r\n plt.legend(['Q5', 'Q4', '', 'Q2',\r\n 'Q1', 'median value', 'x stand for outlier'], loc='best')\r\n plt.title('show outlier')\r\n\r\n\r\ndef figure2():\r\n # 直方图，显示rating分布\r\n fig = plt.figure(2)\r\n plt.autoscale()\r\n\r\n # 调整字体\r\n matplotlib.rcParams.update({'font.size': 7})\r\n\r\n plt.subplot(211)\r\n # 柱数目，0.1分一个桶，从0-5分共有51个桶\r\n buckets = [x/10+0.05 for x in range(0, 51)];buckets[0] -= 0.05;buckets[-1] -= 0.05\r\n # buckets = [x/10 for x in range(0, 51)]\r\n # print(buckets)\r\n plt.hist(res['rating'], buckets, facecolor='orange', edgecolor='black')\r\n\r\n # 高于平均分的和低于平均分的以不同颜色区分，有错误\r\n x = np.array([x/10 for x in range(0, 51)])\r\n # plt.fill_betweenx(x, x > mean_rating, res['rating'], facecolor=\"green\", alpha=0.4)\r\n # plt.fill_betweenx(x, x <= mean_rating, res['rating'], facecolor=\"red\", alpha=0.4)\r\n\r\n # 平均评分线\r\n plt.axvline(mean_rating, label='mean rating', c='r')\r\n\r\n plt.xlabel('rating')\r\n plt.ylabel('amount')\r\n # x轴\r\n plt.xticks(x)\r\n plt.legend(loc='best')\r\n plt.title('rating amount')\r\n\r\n plt.subplot(212)\r\n sns.set_style('darkgrid')\r\n sns.distplot(res['rating'], kde=True, hist_kws=None, kde_kws=None)\r\n # fit参数可以指定概率分布\r\n # sns.distplot(d, fit=stats.laplace, kde=False)\r\n\r\n plt.xlabel('rating')\r\n plt.ylabel('amount')\r\n # x轴\r\n plt.xticks(x)\r\n\r\n\r\ndef figure3_4_5():\r\n # 计算各个分段得的数组\r\n res09 = np.compress(res['rating'] <= 0.9, res)\r\n res19 = np.compress(1.0 <= res['rating'], res)\r\n res19 = np.compress(1.9 >= res19['rating'], res19)\r\n res29 = np.compress(2.0 <= res['rating'], res)\r\n res29 = np.compress(2.9 >= res29['rating'], res29)\r\n res39 = np.compress(3.0 <= res['rating'], res)\r\n res39 = np.compress(3.9 >= res39['rating'], res39)\r\n res50 = np.compress(4.0 <= res['rating'], res)\r\n res50 = np.compress(5.0 >= res50['rating'], res50)\r\n\r\n resgm = np.compress(res['rating'] >= mean_rating, res)\r\n reslm = np.compress(res['rating'] < mean_rating, res)\r\n\r\n # 图3，各个分段的饼图\r\n fig = plt.figure(3)\r\n\r\n plt.subplot(121)\r\n # 饼图标签\r\n labels = '0-0.9', '1.0-1.9', '2.0-2.9', '3.0-3.9', '4.0-5.0'\r\n\r\n # 各部分占比\r\n size1 = res09.size\r\n size2 = res19.size\r\n size3 = res29.size\r\n size4 = res39.size\r\n size5 = res50.size\r\n\r\n sizes = [size1, size2, size3, size4, size5]\r\n\r\n # 0.1表示将Hogs那一块凸显出来\r\n explode = (0.1, 0.1, 0.1, 0, 0.1)\r\n\r\n # startangle表示饼图的起始角度\r\n plt.pie(sizes, explode=explode, labels=labels, autopct='%2.2f%%', labeldistance=0.8, shadow=True, startangle=90)\r\n # 饼图等宽\r\n plt.axis('equal')\r\n plt.title('Proportion of each part: 0-0.9, 1.0-1.9, 2.0-2.9, 3.0-3.9, 4.0-5.0')\r\n\r\n plt.subplot(122)\r\n labels = '>= mean rating', '< mean rating'\r\n size1 = resgm.size\r\n size2 = reslm.size\r\n sizes = [size1, size2]\r\n\r\n plt.pie(sizes, labels=labels, autopct='%2.2f%%', labeldistance=0.8, shadow=True, startangle=80)\r\n # 饼图等宽\r\n plt.axis('equal')\r\n plt.title('Proportion of two part: >= mean rating, < mean rating')\r\n\r\n # 图4，展示各个评分段的散点图\r\n fig = plt.figure(4)\r\n\r\n plt.subplot(231)\r\n plt.scatter(res09[\"movieid\"], res09[\"rating\"], c=res09[\"rating\"] * 100, s=res09['rating'][:] * 0.3)\r\n plt.title('0-0.9')\r\n\r\n plt.subplot(232)\r\n plt.scatter(res19[\"movieid\"], res19[\"rating\"], c=res19[\"rating\"] * 100, s=res19['rating'][:] * 0.1)\r\n plt.title('1.0-1.9')\r\n\r\n plt.subplot(233)\r\n plt.scatter(res29[\"movieid\"], res29[\"rating\"], c=res29[\"rating\"] * 100, s=res29['rating'][:] * 0.1)\r\n plt.title('2.0-2.9')\r\n\r\n plt.subplot(234)\r\n plt.scatter(res39[\"movieid\"], res39[\"rating\"], c=res39[\"rating\"] * 100, s=res39['rating'][:] * 0.1)\r\n plt.title('3.0-3.9')\r\n\r\n plt.subplot(235)\r\n plt.scatter(res50[\"movieid\"], res50[\"rating\"], c=res50[\"rating\"] * 100, s=res50['rating'][:] * 0.1)\r\n plt.title('4.0-5.0')\r\n\r\n # 图五，低于平均和高于平均的散点图\r\n fig = plt.figure(5)\r\n\r\n plt.subplot(121)\r\n plt.scatter(resgm[\"movieid\"], resgm[\"rating\"], c=resgm[\"rating\"] * 100, s=res['rating'][:] * 0.1)\r\n plt.title('greater than mean rating')\r\n\r\n plt.subplot(122)\r\n plt.scatter(reslm[\"movieid\"], reslm[\"rating\"], c=reslm[\"rating\"] * 100, s=res['rating'][:] * 0.1)\r\n plt.title('less than mean rating')\r\n\r\n\r\ndef figure6():\r\n # 图6，五个分段的直方图\r\n fig = plt.figure(6)\r\n\r\n # sns.distplot(res['rating'], bins=5, kde=True)\r\n plt.hist(res['rating'], bins=[0, 1, 2, 3, 4, 5], edgecolor='white')\r\n\r\n\r\ndef figure7():\r\n # 分析一下5分的电影的分类\r\n # 5.0分电影，id-rating\r\n rating5_0 = np.compress(res['rating'] == 5.0, res)\r\n\r\n # 5.0分电影，title-rating\r\n film5_0 = extract(rating5_0, i)\r\n\r\n # 5.0分电影，title-genres\r\n genres5_0 = union(film5_0, iii)\r\n # 存文件\r\n # with open('genres5_0.csv', 'w', encoding='cp936', newline='') as f:\r\n # writer = csv.writer(f)\r\n # for _ in genres5_0:\r\n # # print(list(_))\r\n # writer.writerow(list(_))\r\n\r\n # 统计5.0分电影的标签以及出现次数，保存到json文件\r\n # genres5_0count 字典, genres5_0_total列表\r\n genres5_0count, genres5_0total = countGenres(genres5_0)\r\n # 保存到文件，已存，将注释\r\n # with open('genres5_0count.json', 'w', encoding='utf-8') as f:\r\n # print(json.dump(genres5_0count, f))\r\n\r\n # 图7，显示5.0电影标签的直方图\r\n fig = plt.figure(7)\r\n\r\n # 直方图，显示各个标签出现的次数\r\n plt.subplot(211)\r\n\r\n plt.hist(genres5_0total, bins=len(genres5_0count.keys()), edgecolor='black', facecolor='pink')\r\n\r\n plt.xlabel(\"movie genres\")\r\n plt.ylabel(\"count\")\r\n\r\n # 饼图，同上显示比例\r\n plt.subplot(212)\r\n\r\n plt.pie(genres5_0count.values(), explode=None, labels=None, autopct='%2.2f%%', startangle=90,\r\n labeldistance=1.1)\r\n\r\n plt.legend(genres5_0count.keys(), loc='best')\r\n\r\n\r\ndef figure8():\r\n # <=1.2超级烂片\r\n res1_2 = np.compress(res['rating'] <= 1.2, res)\r\n # 生成词云，依据tag\r\n # 。。。暂不会\r\n\r\n # <=1.2分电影, title and rating\r\n film1_2 = extract(res1_2, i)\r\n\r\n # <=1.2分电影，title-genres-rating\r\n genres1_2 = union2(film1_2, iii)\r\n\r\n # 存文件，使用numpy.savetxt会串行！不知为何\r\n # with open('genres1_2.csv', 'w', encoding='cp936', newline='') as f:\r\n # writer = csv.writer(f)\r\n # for _ in genres1_2:\r\n # writer.writerow(list(_))\r\n\r\n\r\ndef figure9_10_11():\r\n '''\r\n 分析，随机抽12个用户，给出这些用户的所有评分\r\n '''\r\n # 样本用户\r\n # userid_sample = np.random.randint(low=int(np.min(userid)), high=int(np.max(userid)), size=4)\r\n userid_sample = np.random.randint(low=1, high=671, size=12)\r\n users = []\r\n\r\n for _ in userid_sample:\r\n condition = iiii['userid'] == _\r\n # 筛选不到元素\r\n if np.all(condition == False) == True:\r\n continue\r\n users.append(np.compress(condition, iiii))\r\n\r\n # 图9\r\n plt.figure(9)\r\n plt.title('user sample')\r\n\r\n plt.subplot(221)\r\n plt.scatter(np.arange(len(users[0])), users[0]['rating'], s=users[0]['rating']*1)\r\n plt.title('user ' + str(users[0]['userid'][0]))\r\n\r\n plt.subplot(222)\r\n plt.scatter(np.arange(len(users[1])), users[1]['rating'], s=users[1]['rating']*1)\r\n plt.title('user ' + str(users[1]['userid'][1]))\r\n\r\n plt.subplot(223)\r\n plt.scatter(np.arange(len(users[2])), users[2]['rating'], s=users[2]['rating']*1)\r\n plt.title('user ' + str(users[2]['userid'][2]))\r\n\r\n plt.subplot(224)\r\n plt.scatter(np.arange(len(users[3])), users[3]['rating'], s=users[3]['rating']*1)\r\n plt.title('user ' + str(users[3]['userid'][3]))\r\n\r\n # 图10\r\n plt.figure(10)\r\n plt.title('user sample')\r\n\r\n plt.subplot(221)\r\n plt.scatter(np.arange(len(users[4])), users[4]['rating'], s=users[4]['rating'] * 1)\r\n plt.title('user ' + str(users[4]['userid'][4]))\r\n\r\n plt.subplot(222)\r\n plt.scatter(np.arange(len(users[5])), users[5]['rating'], s=users[5]['rating'] * 1)\r\n plt.title('user ' + str(users[5]['userid'][5]))\r\n\r\n plt.subplot(223)\r\n plt.scatter(np.arange(len(users[6])), users[6]['rating'], s=users[6]['rating'] * 1)\r\n plt.title('user ' + str(users[6]['userid'][6]))\r\n\r\n plt.subplot(224)\r\n plt.scatter(np.arange(len(users[7])), users[7]['rating'], s=users[7]['rating'] * 1)\r\n plt.title('user ' + str(users[7]['userid'][7]))\r\n\r\n # 图11\r\n plt.figure(11)\r\n plt.title('user sample')\r\n\r\n plt.subplot(221)\r\n plt.scatter(np.arange(len(users[8])), users[8]['rating'], s=users[8]['rating'] * 1)\r\n plt.title('user ' + str(users[8]['userid'][8]))\r\n\r\n plt.subplot(222)\r\n plt.scatter(np.arange(len(users[9])), users[9]['rating'], s=users[9]['rating'] * 1)\r\n plt.title('user ' + str(users[9]['userid'][9]))\r\n\r\n plt.subplot(223)\r\n plt.scatter(np.arange(len(users[10])), users[10]['rating'], s=users[10]['rating'] * 1)\r\n plt.title('user ' + str(users[10]['userid'][10]))\r\n\r\n plt.subplot(224)\r\n plt.scatter(np.arange(len(users[11])), users[11]['rating'], s=users[11]['rating'] * 1)\r\n plt.title('user ' + str(users[11]['userid'][11]))\r\n\r\n\r\n# 总体概况\r\nfigure1()\r\nfigure2()\r\n\r\n# 各个分段详情\r\nfigure3_4_5()\r\nfigure6()\r\n\r\n# 5.0分电影\r\nfigure7()\r\n\r\n# <=1.2 超级烂片，注释掉，暂无图像\r\n# figure8()\r\n\r\n# 分析用户及其评分\r\nfigure9_10_11()\r\n\r\n\r\n# 自动调整坐标轴大小\r\nplt.autoscale()\r\n\r\nplt.show()","repo_name":"RichardcLee/self-made-tools","sub_path":"ml-latest-small/learn.py","file_name":"learn.py","file_ext":"py","file_size_in_byte":16441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20137782794","text":"import torchaudio\nimport torch as tr\nimport torch.nn as nn\nfrom torch import Tensor\nimport torch.nn.functional as F\nimport numpy as np\nimport math\n\n\nclass MultiChannelClipperModel(nn.Module):\n def __init__(self, min_val: float, max_val: float, gain_val: float) -> None:\n super().__init__()\n self.min_val = min_val\n self.max_val = max_val\n self.gain_val = gain_val\n\n def forward(self, x: Tensor) -> Tensor:\n for i in range(x.shape[-1]):\n for channel in range(x.shape[1]):\n for c in range(x.shape[0]):\n x[c][channel][i] = tr.min(\n tr.max(\n x[c][channel][i],\n tr.tensor(self.gain_val) * tr.tensor(-self.min_val),\n ),\n tr.tensor(self.gain_val) * tr.tensor(self.max_val),\n )\n # print(x[c][channel][i])\n return x\n\n\nclass ChannelKillerOriginal(nn.Module):\n def forward(self, x: Tensor) -> Tensor:\n for i in range(x.shape[-1]):\n print(f\"i:{i}\")\n for channel in range(x.shape[1]):\n for c in range(x.shape[0]):\n if channel == 0:\n x[c][channel][i] = x[c][channel][i] * 1\n else:\n x[c][channel][i] = x[c][channel][i] * 0.5\n return x\n\n\nclass ChannelKiller(nn.Module):\n def forward(self, x: Tensor) -> Tensor:\n for c in range(x.shape[0]):\n # print(f\"c: {c}\")\n for channel in range(x.shape[1]):\n # print(f\"i:{channel}\")\n for sample in range(x.shape[-1]):\n if channel == 0:\n x[c][channel][sample] = x[c][channel][sample] * 1\n else:\n x[c][channel][sample] = x[c][channel][sample] * 0\n return x\n\n\nclass ClipperModel(nn.Module):\n def __init__(self, min_val: float, max_val: float, gain_val: float) -> None:\n super().__init__()\n self.min_val = min_val\n self.max_val = max_val\n self.gain_val = gain_val\n\n def forward(self, x: Tensor) -> Tensor:\n for c in range(x.shape[0]):\n # print(f\"c: {c}\")\n for channel in range(x.shape[1]):\n # print(f\"i:{channel}\")\n for sample in range(x.shape[-1]):\n x[c][channel][sample] = tr.min(\n tr.max(\n x[c][channel][sample],\n tr.tensor(self.gain_val) * tr.tensor(-self.min_val),\n ),\n tr.tensor(self.gain_val) * tr.tensor(self.max_val),\n )\n # print(x[c][channel][i])\n return x\n\n\nclass Net(nn.Module):\n def __init__(self, num_channels):\n super(Net, self).__init__()\n\n self.num_channels = num_channels\n\n self.conv1 = nn.Conv2d(3, self.num_channels, 3, stride=1, padding=1)\n self.conv2 = nn.Conv2d(\n self.num_channels, self.num_channels * 2, 3, stride=1, padding=1\n )\n self.conv3 = nn.Conv2d(\n self.num_channels * 2, self.num_channels * 4, 3, stride=1, padding=1\n )\n\n self.fc1 = nn.Linear(self.num_channels * 4 * 8 * 8, self.num_channels * 4)\n self.fc2 = nn.Linear(self.num_channels * 4, 6)\n\n def forward(self, x):\n # Empieza 3x64x64\n x = self.conv1(x) # num_channels x 64 x 64\n x = F.relu(F.max_pool2d(x, 2)) # num_channels x 32 x 32\n x = self.conv2(x) # num_channels*2 x 32 x32\n x = F.relu(F.max_pool2d(x, 2)) # num_channels*2 x 16 x 16\n x = self.conv3(x) # num_channels*4 x16x16\n x = F.relu(F.max_pool2d(x, 2)) # num_channels*4 x 8 x 8\n\n # flatten\n x = x.view(-1, self.num_channels * 4 * 8 * 8)\n\n # fc\n x = self.fc1(x)\n x = F.relu(x)\n x = self.fc2(x)\n\n # log_softmax\n\n x = F.log_softmax(x, dim=1)\n\n return x\n\n\nclass Effects(nn.Module):\n @staticmethod\n def _apply_effects(x: Tensor, fs: int, block_size: int, n_channels: int):\n effects = [\n [\"lowpass\", \"-1\", \"300\"],\n [\"rate\", f\"{fs}\"],\n [\"reverb\", \"-w\"],\n ]\n\n x, _ = torchaudio.sox_effects.apply_effects_tensor(\n x.reshape(n_channels, block_size).float(), fs, effects\n )\n\n return x\n\n def forward(self, x: Tensor, fs: int, block_size: int, n_channels: int) -> Tensor:\n x = self._apply_effects(x, fs, block_size, n_channels)\n return x\n\n\nclass ArcTanDistortion(nn.Module):\n def forward(self, x: Tensor) -> Tensor:\n gain = 67\n for c in range(x.shape[0]):\n # print(f\"c: {c}\")\n for channel in range(x.shape[1]):\n # print(f\"i:{channel}\")\n for sample in range(x.shape[-1]):\n\n out = (2.0 / math.pi) * math.atan(gain * x[c][channel][sample])\n out = out / math.log(gain)\n # print(x[c][channel][sample], \"--->\", out)\n x[c][channel][sample] = tr.tensor(out)\n\n return x\n\n\nclass ClipperModelFixed(nn.Module):\n def __init__(\n self, min_val: float = 0.5, max_val: float = 1.0, gain_val: float = 1.0\n ) -> None:\n super().__init__()\n self.min_val = min_val\n self.max_val = max_val\n self.gain_val = gain_val\n\n def forward(self, x: Tensor) -> Tensor:\n for c in range(x.shape[0]):\n # print(f\"c: {c}\")\n for channel in range(x.shape[1]):\n # print(f\"i:{channel}\")\n for sample in range(x.shape[-1]):\n x[c][channel][sample] = tr.min(\n tr.max(\n x[c][channel][sample],\n tr.tensor(self.gain_val) * tr.tensor(-self.min_val),\n ),\n tr.tensor(self.gain_val) * tr.tensor(self.max_val),\n )\n # print(x[c][channel][i])\n return x\n","repo_name":"rodoortiz/Modelizer","sub_path":"audio-models/models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6126,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"44402330616","text":"# Assignment 1-Part 1\r\n# Linked Lists\r\n\r\n# Single Node Class\r\nfrom cgi import test\r\n\r\n\r\nclass Node:\r\n def __init__(self, data):\r\n self.data = data # Assign data to node\r\n self.next = None # Node initially does not point to any other node\r\n \r\n# Linked List class\r\nclass LinkedList:\r\n # Initializing the linked list object. Head points to None.\r\n def __init__(self):\r\n self.head = None\r\n\r\n # Already provided\r\n def print_list(self):\r\n traverse = self.head\r\n LinkedList = []\r\n while(traverse):\r\n LinkedList.append(traverse.data)\r\n traverse = traverse.next\r\n return LinkedList\r\n\r\n #helper-function-1 : returns the tail node\r\n def get_tail_node(self):\r\n if not self.head:\r\n return None\r\n else:\r\n temp = self.head\r\n while temp.next:\r\n temp = temp.next\r\n \r\n return temp\r\n\r\n #helper-function-2: returns any Node at position n if it exists, None otherwise\r\n def get_node(self, pos):\r\n \r\n length = self.get_length()\r\n\r\n if (pos >= length) or (pos < 0):\r\n return None\r\n else:\r\n temp_node = self.head\r\n for i in range(pos):\r\n temp_node = temp_node.next\r\n \r\n return temp_node\r\n\r\n \r\n \r\n \r\n # To-do Function 1\r\n #time-complexity = O(1)\r\n def get_head(self):\r\n if not(self.head):\r\n return None\r\n else:\r\n return (self.head).data\r\n \r\n # To-do Function 2\r\n #time-complexity = O(n)\r\n def get_tail(self):\r\n tail_node = self.get_tail_node()\r\n\r\n if tail_node:\r\n return tail_node.data\r\n else:\r\n return None\r\n \r\n # To-do Function 3\r\n #time-complexity = O(1)\r\n def is_empty(self):\r\n if self.head:\r\n\r\n #list is not empty\r\n return False\r\n\r\n else: \r\n return True\r\n \r\n # To-do Function 4\r\n #time-complexity = O(1)\r\n def insert_at_head(self, data):\r\n\r\n #creates a new node\r\n new_node = Node(data)\r\n\r\n #new node now points to where head was pointing\r\n (new_node).next = self.head\r\n\r\n #now head point to new node\r\n self.head = new_node\r\n\r\n # To-do Function 5\r\n #time-complexity = O(n) \r\n def insert_at_tail(self, data):\r\n\r\n #creates a new node\r\n new_node = Node(data)\r\n\r\n tail_node = self.get_tail_node()\r\n\r\n if tail_node:\r\n tail_node.next = new_node\r\n else: \r\n self.head = new_node\r\n\r\n\r\n # To-do Function 6\r\n # Assume both the node to be inserted after and before exist in the right order in the test set\r\n #time-complexity = O(n)\r\n def insert_in_between(self, data, after, before):\r\n \r\n new_node = Node(data)\r\n\r\n temp_node = self.head\r\n\r\n while temp_node.data != after:\r\n temp_node = temp_node.next\r\n \r\n new_node.next = temp_node.next\r\n temp_node.next = new_node\r\n\r\n pass\r\n \r\n # To-do Function 7 \r\n # time-complexity = O(1) \r\n def delete_head(self):\r\n if self.head:\r\n\r\n #making sure the second node exists\r\n if (self.head).next:\r\n\r\n #make a new node, have it point to the second node (the node that head was pointing to)\r\n new_node = (self.head).next\r\n\r\n #head points to the new node\r\n self.head = new_node\r\n\r\n #lucky me, python takes care of garbage collection\r\n else:\r\n self.head = None\r\n else:\r\n return None\r\n\r\n \r\n # To-do Function 8\r\n # time-complexity = O(n)\r\n def delete_tail(self):\r\n if self.is_empty():\r\n return None\r\n else:\r\n before_tail = self.head\r\n\r\n #if the list has one element, delete the only node\r\n if not before_tail.next:\r\n self.head = None\r\n\r\n else:\r\n while (before_tail.next).next:\r\n before_tail = before_tail.next\r\n\r\n #after this loop the var before_tail stores the second last node\r\n before_tail.next = None\r\n\r\n # To-do Function 9\r\n #time-complexity = O(n)\r\n def delete_any(self, data):\r\n \r\n if self.get_head() == data:\r\n self.delete_head()\r\n\r\n elif self.get_tail() == data:\r\n self.delete_tail()\r\n\r\n else:\r\n node_to_be_deleted = self.head.next\r\n node_before = self.head\r\n\r\n while node_to_be_deleted.data != data and node_to_be_deleted:\r\n node_to_be_deleted = node_to_be_deleted.next\r\n node_before = node_before.next\r\n\r\n # *poof*\r\n node_before.next = node_to_be_deleted.next\r\n\r\n # To-do Function 10\r\n #time-complexity = O(n)\r\n def get_length(self):\r\n\r\n #for ease\r\n head = self.head\r\n length = 1\r\n\r\n if not head:\r\n return 0\r\n elif not head.next:\r\n return 1\r\n else:\r\n while (head.next):\r\n length += 1\r\n head = head.next\r\n\r\n return length\r\n \r\n # To-do Function 11\r\n #time-complexity = O(n)\r\n def get_element(self, data):\r\n if not self.head:\r\n return False\r\n else: \r\n temp_node = self.head\r\n temp_data = temp_node.data\r\n\r\n while temp_node:\r\n if temp_data == data:\r\n return data\r\n else:\r\n temp_node = temp_node.next\r\n \r\n if not temp_node:\r\n return False\r\n\r\n temp_data = temp_node.data\r\n\r\n \r\n # To-do Function 12\r\n #time-complexity = O(n)\r\n def reverse_list(self):\r\n if self.is_empty():\r\n return None\r\n elif self.get_length() == 1:\r\n pass\r\n elif self.get_length() == 2:\r\n\r\n head = self.head\r\n next_node = head.next\r\n \r\n next_node.next = head\r\n head.next = None\r\n \r\n self.head = next_node\r\n \r\n else:\r\n head = self.head\r\n\r\n #storing the first three nodes in temp vars\r\n temp_var_1 = head\r\n temp_var_2 = temp_var_1.next\r\n temp_var_3 = temp_var_2.next\r\n\r\n head.next = None\r\n\r\n \r\n while temp_var_3:\r\n temp_var_2.next = temp_var_1\r\n\r\n temp_var_1 = temp_var_2\r\n temp_var_2 = temp_var_3\r\n temp_var_3 = temp_var_3.next\r\n\r\n temp_var_2.next = temp_var_1\r\n self.head = temp_var_2\r\n\r\n\r\n\r\n\r\n\r\n# Testing Area ---\r\n\r\n# You can create a class Linked List object below and check the implementation of your functions.\r\n# Make sure to comment out the code before running the tests else it might mix up the results.\r\n# def main():\r\n# # Create an object here to test the functions.\r\n# test_list = LinkedList()\r\n# # Write code here\r\n# test_list.insert_at_head('a')\r\n# test_list.insert_at_head('b')\r\n# test_list.insert_at_head('c')\r\n# test_list.insert_at_tail('d')\r\n \r\n# print(test_list.get_head(), '\\n')\r\n# #print(test_list.head, '\\n')\r\n# print(test_list.get_tail(), '\\n')\r\n# print(test_list.is_empty(), '\\n')\r\n \r\n\r\n# print(test_list.print_list(), test_list.get_length(), '\\n')\r\n# print(test_list.get_node(0).data, '\\n')\r\n# print(test_list.get_node(1).data, '\\n')\r\n# print(test_list.get_node(2).data, '\\n')\r\n# print(test_list.get_node(3).data, '\\n')\r\n\r\n# test_list.insert_in_between('r', 'a', 'd')\r\n# print(test_list.print_list(), test_list.get_length(), '\\n')\r\n\r\n# test_list.delete_any('c')\r\n# print(test_list.print_list(), test_list.get_length(), '\\n')\r\n# test_list.reverse_list()\r\n# print(test_list.print_list(), test_list.get_length(), '\\n')\r\n \r\n \r\n \r\n \r\n# # Comment out the line below before running your test files.\r\n# main()","repo_name":"soomro-abd/Data-Structures","sub_path":"Assignment 1/Part1.py","file_name":"Part1.py","file_ext":"py","file_size_in_byte":8203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22461818891","text":"import numpy as np\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom mmcv.runner import get_dist_info\n\nfrom easycv.framework.errors import KeyError, ValueError\nfrom easycv.utils.checkpoint import load_checkpoint\nfrom easycv.utils.logger import get_root_logger\nfrom easycv.utils.preprocess_function import gaussianBlur, randomGrayScale\nfrom .. import builder\nfrom ..base import BaseModel\nfrom ..registry import MODELS\n\n\n@MODELS.register_module\nclass SWAV(BaseModel):\n\n def __init__(self,\n backbone,\n train_preprocess=[],\n neck=None,\n config=None,\n pretrained=None):\n super(SWAV, self).__init__()\n self.pretrained = pretrained\n self.backbone = builder.build_backbone(backbone)\n\n self.preprocess_key_map = {\n 'randomGrayScale': randomGrayScale,\n 'gaussianBlur': gaussianBlur\n }\n self.train_preprocess = [\n self.preprocess_key_map[i] for i in train_preprocess\n ]\n self.neck = builder.build_neck(neck)\n self.config = config\n\n self.prototypes = None\n nmb_prototypes = self.config['nmb_prototypes']\n if isinstance(nmb_prototypes, list):\n self.prototypes = MultiPrototypes(neck['out_channels'],\n nmb_prototypes)\n elif nmb_prototypes > 0:\n self.prototypes = nn.Linear(\n neck['out_channels'], nmb_prototypes, bias=False)\n\n self.feat_dim = neck['out_channels']\n\n self.l2norm = True\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n self.softmax = nn.Softmax(dim=1).cuda()\n self.use_the_queue = False\n self.init_weights()\n self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))\n\n def init_weights(self):\n if isinstance(self.pretrained, str):\n logger = get_root_logger()\n load_checkpoint(\n self.backbone, self.pretrained, strict=False, logger=logger)\n else:\n self.backbone.init_weights()\n self.neck.init_weights(init_linear='kaiming')\n\n # if torch.load(pretrained).get(\"prototypes.weight\", None) is not None:\n # self.prototypes.weight.data = torch.load(pretrained)['state_dict'].get(\"prototypes.weight\")\n\n def forward_backbone(self, img):\n feature_list = self.backbone(img)\n return feature_list\n\n def forward_train_model(self, inputs):\n if not isinstance(inputs, list):\n inputs = [inputs]\n idx_crops = torch.cumsum(\n torch.unique_consecutive(\n torch.tensor([inp.shape[-1] for inp in inputs]),\n return_counts=True,\n )[1], 0\n ) # this is a split operation, get the different shape input index\n\n start_idx = 0\n for end_idx in idx_crops:\n img = torch.cat(inputs[start_idx:end_idx]).cuda(non_blocking=True)\n for preprocess in self.train_preprocess:\n img = preprocess(img)\n\n _out = self.forward_backbone(img)[\n 0] # resnet return [[n,c,h,w],[],]\n _out = self.avgpool(_out)\n _out = torch.flatten(_out, 1)\n\n if start_idx == 0:\n output = _out\n else:\n output = torch.cat((output, _out))\n start_idx = end_idx\n\n output = self.neck([output])[0]\n\n if self.l2norm:\n output = nn.functional.normalize(output, dim=1, p=2)\n\n if self.prototypes is not None:\n return output, self.prototypes(output)\n return output\n\n def forward_train(self, inputs):\n self.backbone.train()\n self.neck.train()\n self.prototypes.train()\n # normalize the prototypes\n with torch.no_grad():\n w = self.prototypes.weight.data.clone()\n w = nn.functional.normalize(w, dim=1, p=2)\n self.prototypes.weight.copy_(w)\n embedding, output = self.forward_train_model(inputs)\n embedding = embedding.detach()\n bs = inputs[0].size(0)\n # swav loss\n loss = 0\n for i, crop_id in enumerate(self.config['crops_for_assign']):\n with torch.no_grad():\n out = output[bs * crop_id:bs * (crop_id + 1)]\n\n # time to use the queue\n if getattr(self, 'queue', None) is not None:\n if self.use_the_queue or not torch.all(\n self.queue[i, -1, :] == 0):\n self.use_the_queue = True\n out = torch.cat(\n (torch.mm(self.queue[i],\n self.prototypes.weight.t()), out))\n # fill the queue\n self.queue[i, bs:] = self.queue[i, :-bs].clone()\n self.queue[i, :bs] = embedding[crop_id * bs:(crop_id + 1) *\n bs]\n # get assignments\n q = torch.exp(out / self.config['epsilon']).t()\n q = distributed_sinkhorn(\n q, self.config['sinkhorn_iterations'])[-bs:]\n\n # cluster assignment prediction\n subloss = 0\n for v in np.delete(\n np.arange(np.sum(self.config['num_crops'])), crop_id):\n p = self.softmax(output[bs * v:bs * (v + 1)] /\n self.config['temperature'])\n subloss -= torch.mean(torch.sum(q * torch.log(p), dim=1))\n loss += subloss / (np.sum(self.config['num_crops']) - 1)\n loss /= len(self.config['crops_for_assign'])\n losses = dict()\n losses['loss'] = loss\n return losses\n\n def forward_test(self, img, **kwargs):\n pass\n\n def forward_feature(self, img, **kwargs):\n \"\"\"Forward backbone\n\n Returns:\n x (torch.Tensor): feature tensor\n \"\"\"\n return_dict = {}\n x = self.backbone(img)\n return_dict['backbone'] = x\n\n if hasattr(self, 'neck') and self.neck is not None:\n feature = self.neck([self.avg_pool(i) for i in x])[0]\n else:\n feature = self.avg_pool(x[-1])\n return_dict['neck'] = feature\n\n return return_dict\n\n def forward(self,\n img,\n gt_label=None,\n mode='train',\n extract_list=['neck'],\n **kwargs):\n if mode == 'train':\n return self.forward_train(img, **kwargs)\n elif mode == 'test':\n return self.forward_test(img, **kwargs)\n\n elif mode == 'extract':\n rd = self.forward_feature(img)\n rv = {}\n for name in extract_list:\n if name in rd.keys():\n rv[name] = rd[name]\n else:\n raise ValueError(\n 'Extract %s is not support in classification models' %\n name)\n if gt_label is not None:\n rv['gt_labels'] = gt_label.cpu()\n return rv\n else:\n raise KeyError('No such mode: {}'.format(mode))\n\n\nclass MultiPrototypes(nn.Module):\n\n def __init__(self, output_dim, nmb_prototypes):\n super(MultiPrototypes, self).__init__()\n self.nmb_heads = len(nmb_prototypes)\n for i, k in enumerate(nmb_prototypes):\n self.add_module('prototypes' + str(i),\n nn.Linear(output_dim, k, bias=False))\n\n def forward(self, x):\n out = []\n for i in range(self.nmb_heads):\n out.append(getattr(self, 'prototypes' + str(i))(x))\n return out\n\n\ndef distributed_sinkhorn(Q, nmb_iters):\n rank, world_size = get_dist_info()\n with torch.no_grad():\n sum_Q = torch.sum(Q)\n dist.all_reduce(sum_Q)\n Q /= sum_Q\n\n # u = torch.zeros(Q.shape[0]).cuda(non_blocking=True)\n r = torch.ones(Q.shape[0]).cuda(non_blocking=True) / Q.shape[0]\n c = torch.ones(Q.shape[1]).cuda(non_blocking=True) / (\n world_size * Q.shape[1])\n\n curr_sum = torch.sum(Q, dim=1)\n dist.all_reduce(curr_sum)\n\n for it in range(nmb_iters):\n u = curr_sum\n Q *= (r / u).unsqueeze(1)\n Q *= (c / torch.sum(Q, dim=0)).unsqueeze(0)\n curr_sum = torch.sum(Q, dim=1)\n dist.all_reduce(curr_sum)\n return (Q / torch.sum(Q, dim=0, keepdim=True)).t().float()\n","repo_name":"alibaba/EasyCV","sub_path":"easycv/models/selfsup/swav.py","file_name":"swav.py","file_ext":"py","file_size_in_byte":8521,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"27266777663","text":"import cv2\r\nimport numpy as np\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image, ImageOps\r\ninput_file=sys.argv[1]\r\nimage=cv2.imread(input_file)\r\nimage_open=image.copy()\r\npart1 =image_open.copy()[0:200,0:190]\r\npart2 =image_open.copy()[200:410,0:190]\r\npart3 =image_open.copy()[150:330,515:700]\r\npart4 =image_open.copy()[370:421,370:798]\r\ng1=part1.copy()[:,:,1]\r\nb1=part1.copy()[:,:,0]\r\npart1[:,:,0]=g1\r\npart1[:,:,1]=b1\r\nimage_open[370:421,370:798]=np.flip(part4,0)\r\n#image_open[190:390,0:190]=part1\r\nimage_open[150:330,515:700]=np.flip(part3,1)\r\npart2=np.flip(part2,0)\r\n\r\npart1_padd=cv2.copyMakeBorder(part1,0,8,0,0,cv2.BORDER_REPLICATE)\r\nimage_open[200:408,0:190,:]=part1_padd\r\npart5=cv2.copyMakeBorder(image_open[410:412,0:190,:],2,0,0,0,cv2.BORDER_REFLECT)\r\nimage_open[408:412,0:190,:]=part5\r\nimage_open[0:200,0:190,:]=part2[0:200,0:190,:]\r\n#directory=r'C:\\Users\\Teja\\Downloads\\EE604-Assign1-pictures'\r\n#print('before saving image')\r\n#print(os.listdir(directory))\r\n#os.chdir(directory)\r\ncwd = os.getcwd()\r\ncv2.imwrite('saved image.jpg',image_open)\r\ncv2.imshow('image',image_open)\r\nprint('after saving image')\r\n#print(os.listdir(directory))\r\nos.chdir(directory)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n\r\n","repo_name":"NaveentejaB/Image-Processing-by-python","sub_path":"jigsolver.py","file_name":"jigsolver.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8066676636","text":"from dataclasses import dataclass, field\nfrom typing import Optional\n\nfrom hostingde.model import Model\n\n\n@dataclass\nclass ExchangeRatio(Model):\n base_currency: str\n currency: str\n exchange_date: str\n exchange_ratio: int\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n@dataclass\nclass DomainPrice(Model):\n create: int\n create_duration: int\n currency: str\n domain_suffix: str\n owner_change: Optional[int]\n period_of_notice: int\n renew: int\n renew_duration: int\n restore: Optional[int]\n transfer: Optional[int]\n transfer_duration: Optional[int]\n update: int\n vat_rate: int\n exchange_ratio: Optional[ExchangeRatio]\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n","repo_name":"cancom/python-hostingde","sub_path":"hostingde/model/billing.py","file_name":"billing.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"29348208295","text":"with open('cryptography_caesar_input.txt', 'r') as file:\n encrypted = file.read().replace('\\n', '').replace(' ', '')\n\nalphabet = '+-*0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n\nfor char in range(len(alphabet)):\n \n decrypted = ''\n\n for eChar in encrypted:\n num = alphabet.index(eChar)\n num = num - char\n if num < 0:\n num += len(alphabet)\n decrypted += alphabet[num]\n print(f'key #{char}: {decrypted}')\n","repo_name":"kstonekuan/fbsghack2020","sub_path":"cipher/caesar.py","file_name":"caesar.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41658982379","text":"server = ['wms', 'tms', 'kml']\n\nwms = dict(\n image_formats = ['image/png', 'image/jpeg', 'image/gif', 'image/GeoTIFF', 'image/tiff'],\n srs = set(['EPSG:4326', 'EPSG:4258', 'CRS:84', 'EPSG:900913', 'EPSG:3857']),\n strict = False,\n request_parser = 'default',\n client_request = 'default',\n concurrent_layer_renderer = 1,\n max_output_pixels = 4000*4000,\n)\ndebug_mode = False\n\nsrs = dict(\n # user sets\n axis_order_ne = set(),\n axis_order_en = set(),\n # default sets, both will be combined in config:load_base_config\n axis_order_ne_ = set(['EPSG:4326', 'EPSG:4258', 'EPSG:31466', 'EPSG:31467', 'EPSG:31468']),\n axis_order_en_ = set(['CRS:84', 'EPSG:900913', 'EPSG:25831', 'EPSG:25832', 'EPSG:25833']),\n)\n\nimage = dict(\n # nearest, bilinear, bicubic\n resampling_method = 'bicubic',\n jpeg_quality = 90,\n stretch_factor = 1.15,\n max_shrink_factor = 4.0,\n paletted = True,\n transparent_color_tolerance = 5,\n font_dir = None,\n)\n# number of concurrent requests to a tile source\n\n\nservices_conf = 'services.yaml'\nlog_conf = 'log.ini'\n\n# directory with mapproxy/service/templates/* files\ntemplate_dir = None\n\ncache = dict(\n base_dir = './cache_data',\n lock_dir = './cache_data/tile_locks',\n max_tile_limit = 500,\n concurrent_tile_creators = 2,\n meta_size = (4, 4),\n meta_buffer = 80,\n minimize_meta_requests = False,\n link_single_color_images = False,\n sqlite_timeout = 30,\n)\n\ngrid = dict(\n tile_size = (256, 256),\n)\n\ngrids = dict(\n GLOBAL_GEODETIC=dict(\n srs='EPSG:4326', origin='sw', name='GLOBAL_GEODETIC'\n ),\n GLOBAL_MERCATOR=dict(\n srs='EPSG:900913', origin='sw', name='GLOBAL_MERCATOR'\n ),\n GLOBAL_WEBMERCATOR=dict(\n srs='EPSG:3857', origin='nw', name='GLOBAL_WEBMERCATOR'\n )\n)\n\ntiles = dict(\n expires_hours = 72,\n)\n\nhttp = dict(\n ssl_ca_certs = None,\n ssl_no_cert_checks = False,\n client_timeout = 60,\n concurrent_requests = 0,\n method = 'AUTO',\n access_control_allow_origin = '*',\n hide_error_details = True,\n manage_cookies = False,\n)\n","repo_name":"mapproxy/mapproxy","sub_path":"mapproxy/config/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":489,"dataset":"github-code","pt":"60"}
+{"seq_id":"73807215231","text":"from typing import Dict\n\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\n\nfrom apps.cinema.constants import CINEPLANET\nfrom apps.cinema.utils import movie_exists\n\n\ndef scrapp_schedules(\n cinema_link: str = CINEPLANET, movies: Dict[str, str] = {}\n) -> Dict[str, str]:\n driver = webdriver.Chrome(ChromeDriverManager().install())\n driver.get(f\"{cinema_link}/la-medium\")\n page = driver.page_source\n raw_movie_list = page.split(\"movie-schedule\")\n for raw_movie in raw_movie_list[1:]:\n movie = (\n raw_movie.split(' ')[0]\n .split('\">')[-1]\n .upper()\n )\n showings = raw_movie.count('
')\n does_exists, movie_key = movie_exists(movie, movies)\n if does_exists:\n movies[movie_key] += showings\n else:\n movies[movie_key] = showings\n print(movies)\n driver.close()\n return movies\n","repo_name":"elcobalto/cinema-showings","sub_path":"apps/cinema/services/cineplanet/scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3157066248","text":"import cv2\r\nimport numpy as np\r\nvid=cv2.VideoCapture(0)\r\nimg1=cv2.imread('resource/bag.jpg')\r\nimg2=cv2.imread('resource/bag2.jpg')\r\nimg3=cv2.imread('resource/bag3.jpg')\r\nimg4=cv2.imread('resource/bag4.jpg')\r\nimg5=cv2.imread('resource/bag5.jpg')\r\nimg6=cv2.imread('resource/bag6.jpg')\r\ni=int(input())\r\nwhile True:\r\n flag,frame=vid.read()\r\n if not flag:\r\n print('cant access')\r\n break\r\n if i==1:\r\n img1 = cv2.resize(img1, (frame.shape[1], frame.shape[0]))\r\n blended_img1 = cv2.addWeighted(frame, 0.8, img1, 0.3, gamma=0.1)\r\n cv2.imshow('blended1', blended_img1)\r\n cv2.waitKey(10)\r\n elif i==2:\r\n img2 = cv2.resize(img2, (frame.shape[1], frame.shape[0]))\r\n blended_img2 = cv2.addWeighted(frame, 0.8, img2, 0.3, gamma=0.1)\r\n cv2.imshow('blended2', blended_img2)\r\n cv2.waitKey(10)\r\n elif i==3:\r\n img3 = cv2.resize(img3, (frame.shape[1], frame.shape[0]))\r\n blended_img3 = cv2.addWeighted(frame, 0.8, img3, 0.3, gamma=0.1)\r\n cv2.imshow('blended3', blended_img3)\r\n cv2.waitKey(10)\r\n elif i==4:\r\n img4 = cv2.resize(img4, (frame.shape[1], frame.shape[0]))\r\n blended_img4 = cv2.addWeighted(frame, 0.8, img4, 0.3, gamma=0.1)\r\n cv2.imshow('blended4', blended_img4)\r\n cv2.waitKey(10)\r\n elif i==5:\r\n img5 = cv2.resize(img5, (frame.shape[1], frame.shape[0]))\r\n blended_img5 = cv2.addWeighted(frame, 0.8, img5, 0.3, gamma=0.1)\r\n cv2.imshow('blended5', blended_img5)\r\n cv2.waitKey(10)\r\n elif i==6:\r\n img6 = cv2.resize(img6, (frame.shape[1], frame.shape[0]))\r\n blended_img6 = cv2.addWeighted(frame, 0.8, img6, 0.3, gamma=0.1)\r\n cv2.imshow('blended6', blended_img6)\r\n cv2.waitKey(10)\r\n k=cv2.waitKey(10)\r\n if k & 0xFF==ord('q'):\r\n break","repo_name":"18121A0472/kalyan","sub_path":"video adding.py","file_name":"video adding.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39607201179","text":"import sys\nsys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\nimport argparse\nfrom PIL import Image\nimport signal\nfrom matplotlib import pyplot as plt\nimport time\nimport numpy as np\nimport cv2\nimport threading\nfrom ctypes import cdll\nimport open3d as o3d\nimport ctypes\nfrom numpy.ctypeslib import ndpointer\nlib = cdll.LoadLibrary('./viewer_opengl.so')\nst = lib.Foo_start\nt0 = threading.Thread(target=st)\nt0.start()\nend = lib.Foo_end\ndataread =lib.Foo_dataread\ndataread_color =lib.Foo_dataread_color\ndataread_depth =lib.Foo_dataread_depth\ndataread_get_pointcloud_xyz = lib.Foo_get_pointcloud_xyz\ndataread_get_pointcloud_rgb = lib.Foo_get_pointcloud_rgb\n\ndataread_color_to_depth =lib.Foo_dataread_color_to_depth\ndataread.restype = ndpointer(dtype=ctypes.c_uint16, shape=(720,1280))\ndataread_color.restype = ndpointer(dtype=ctypes.c_uint8, shape=(720,1280,4))\ndataread_depth.restype = ndpointer(dtype=ctypes.c_uint16, shape=(512,512))#ctypes.POINTE\ndataread_color_to_depth.restype = ndpointer(dtype=ctypes.c_uint8, shape=(512,512,4))\ndataread_get_pointcloud_xyz.restype = ndpointer(dtype=ctypes.c_int16, shape=(720*1280,3))\ndataread_get_pointcloud_rgb.restype = ndpointer(dtype=ctypes.c_uint8, shape=(720*1280,3))\n\n#convert_2d_3d = lib.Foo_convert_2d_3d\n#convert_2d_3d.restype = ndpointer(dtype=ctypes.c_float, shape=(3))#ctypes.POINTE\ndef signal_handler(signal, frame):\n # Press Ctrl + \\\n end()\n sys.exit(0)\n\nsignal.signal(signal.SIGINT, signal_handler)\ndef detect_img():\n\n n = 0\n while True:\n color_data =np.array(dataread_color(),dtype=np.uint8)\n depth_data =np.array(dataread_depth(),dtype=np.uint16)\n color_image_to_depth_camera_data =np.array(dataread_color_to_depth(),dtype=np.uint16)\n depth_image_to_color_camera_data =np.array(dataread(),dtype=np.uint8)\n pointCloud_xyz = np.array(dataread_get_pointcloud_xyz(),dtype=np.int16)\n pointCloud_rgb = np.array(np.array(dataread_get_pointcloud_rgb(),dtype=np.uint8),np.float)/255.0\n #print(np.max(pointCloud_rgb))\n xyz = np.reshape(pointCloud_xyz,(720*1280,3))\n rgb = np.reshape(pointCloud_rgb,(720*1280,3))\n #print(np.mean(rgb))\n #pcd = o3d.geometry.PointCloud()\n # pcd.points = o3d.utility.Vector3dVector(xyz)\n #pcd.colors = o3d.utility.Vector3dVector(rgb)\n\n #o3d.io.write_point_cloud(\"sync.ply\", pcd)\n color_img = color_data.copy()\n depth_img = cv2.convertScaleAbs(depth_data)\n color_image_to_depth_camera_img = cv2.convertScaleAbs(color_image_to_depth_camera_data)\n depth_image_to_color_camera_img = cv2.convertScaleAbs(depth_image_to_color_camera_data)\n\n cv2.imshow(\"Color\", color_data)\n cv2.imshow(\"Depth\", depth_img)\n\n cv2.imshow(\"Color_to_Depth\", color_image_to_depth_camera_img)\n cv2.imshow(\"Depth_to_Color\", depth_image_to_color_camera_img)\n cv2.waitKey(1)\n\nFLAGS = None\n\nif __name__ == '__main__':\n t1 = threading.Thread(target=detect_img)\n t1.start()\n\n","repo_name":"MinchangSung0223/Azure_Kinect_python_Ubuntu16.04","sub_path":"python_example/capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72929782911","text":"# SETUP\n\nfrom flask import Flask, request, redirect, render_template, session, flash\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://WhatsInMyFridge:food@localhost:8889/WhatsInMyFridge'\napp.config['SQLALCHEMY_ECHO'] = True\napp.secret_key = 'y337kGcys&zP3B'\n\ndb = SQLAlchemy(app)\n\nclass Grocery(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(50))\n quantity = db.Column(db.Integer)\n date = db.Column(db.String(50))\n category = db.Column(db.String(50))\n\n def __init__(self, name, quantity, date, category):\n self.name = name\n self.quantity = quantity\n self.date = date\n self.category = category\n\n# CONTROLLERS\n\n@app.route(\"/\")\ndef index():\n groceries = Grocery.query.all()\n\n return render_template(\"index.html\", groceries=groceries)\n\n@app.route(\"/add\", methods=[\"POST\", \"GET\"])\ndef add():\n\n if request.method == \"GET\":\n return render_template(\"add.html\")\n\n if request.method == \"POST\":\n\n # get values out of requst object\n\n name = request.form['name']\n quantity = request.form['quantity']\n date = request.form['date']\n category = request.form['category']\n\n # create new object with those values\n\n grocery = Grocery(name, quantity, date, category)\n\n # add it to the database\n\n db.session.add(grocery)\n db.session.commit()\n\n return redirect(\"/\")\n\n# RUN\n\nif __name__ == '__main__':\n app.run()","repo_name":"danduval/whatsInMyFridge","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6990875764","text":"\"\"\"\nWolt Summer 2023 Engineering Internships\nPreliminary Assignment for Engineering Positions\nBackend\n\nCreator: Jukka Pelli, jukka.pelli@tuni.fi\n\nThis module contains the tests for the API\n\"\"\"\nfrom fastapi.testclient import TestClient\nfrom api.api import app\n\ntest_client = TestClient(app)\n\ndef test_api_methods():\n \"\"\"Function tests the allowed methods\"\"\"\n\n # Testing payload\n test_payload = {\"cart_value\":790, \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}\n\n # Store test values to a list and iterate through it\n # first element is the method, second the method to test, third the expected response and fourth the expected response\n test_list = [\n (\"GET\", test_client.get(\"/\"), 405, {\"detail\": \"Method Not Allowed\"}),\n (\"POST\", test_client.post(\"/\", json=test_payload), 200, {\"delivery_fee\": 710}),\n (\"PUT\", test_client.put(\"/\", json=test_payload), 405, {\"detail\": \"Method Not Allowed\"}),\n (\"DELETE\", test_client.delete(\"/\"), 405, {\"detail\": \"Method Not Allowed\"})\n ]\n\n for test in test_list:\n print(\"Testing\", test[0], \"method\")\n response = test[1]\n assert response.status_code == test[2]\n assert response.json() == test[3]\n\n\ndef test_request_payload_validation():\n \"\"\"Function tests the basic responses of the REST API with different request payloads\"\"\"\n # Store test values to a list of tuples and iterate through it\n # first element of each tuple is the api call and the second is the basic response expected\n test_list = [\n ({\"cart_value\":790, \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 200),\n ({\"cart_value\":\"test\", \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"cart_value\":1500, \"delivery_distance\":\"test\", \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"cart_value\":1000, \"delivery_distance\":2235, \"number_of_items\":\"k\", \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"delivery_distance\":5000, \"number_of_items\":20, \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"cart_value\":500, \"number_of_items\":20, \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"cart_value\":500, \"delivery_distance\":5000, \"time\":\"2021-10-12T13:00:00Z\"}, 422),\n ({\"cart_value\":500, \"delivery_distance\":5000, \"number_of_items\":20}, 422)\n ]\n\n for test in test_list:\n response = test_client.post(\"/\", json=test[0])\n assert response.status_code == test[1]\n\n\ndef test_response_is_json():\n \"\"\"Function tests that the return payload is json\"\"\"\n response = test_client.post(\"/\", json={\"cart_value\":1000, \"delivery_distance\":2235, \"number_of_items\":6, \"time\":\"2021-10-12T13:00:00Z\"})\n assert response.json()\n assert response.headers.get(\"content-type\") == \"application/json\"\n\n\ndef test_response_value_is_correct():\n # Store test values to a list of tuples and iterate through it\n # first element of each tuple is the api call and the second is the expected delivery fee value\n test_list = [\n ({\"cart_value\":790, \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 710),\n ({\"cart_value\":790, \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2023-01-13T15:05:00Z\"}, 852),\n ({\"cart_value\":1000, \"delivery_distance\":1000, \"number_of_items\":4, \"time\":\"2023-01-13T15:05:00Z\"}, 240),\n ({\"cart_value\":15000, \"delivery_distance\":2235, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 0),\n ({\"cart_value\":1000, \"delivery_distance\":1000, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 200),\n ({\"cart_value\":1000, \"delivery_distance\":1000, \"number_of_items\":5, \"time\":\"2021-10-12T13:00:00Z\"}, 250),\n ({\"cart_value\":1000, \"delivery_distance\":1000, \"number_of_items\":10, \"time\":\"2021-10-12T13:00:00Z\"}, 500),\n ({\"cart_value\":1000, \"delivery_distance\":1000, \"number_of_items\":13, \"time\":\"2021-10-12T13:00:00Z\"}, 770),\n ({\"cart_value\":1000, \"delivery_distance\":1499, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 300),\n ({\"cart_value\":1000, \"delivery_distance\":1500, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 300),\n ({\"cart_value\":1000, \"delivery_distance\":1501, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 400),\n ({\"cart_value\":1000, \"delivery_distance\":5000, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 1000),\n ({\"cart_value\":1000, \"delivery_distance\":10000, \"number_of_items\":4, \"time\":\"2021-10-12T13:00:00Z\"}, 1500),\n ({\"cart_value\":50, \"delivery_distance\":10000, \"number_of_items\":50, \"time\":\"2021-10-12T13:00:00Z\"}, 1500)\n ]\n\n for test in test_list:\n response = test_client.post(\"/\", json=test[0])\n assert response.json() == {\"delivery_fee\": test[1]}\n","repo_name":"jugipe/defapi","sub_path":"src/api/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":4817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17811113029","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@author:lpf\n@file: ShanghaiTechDensityMap.py\n@time: 2020/03/08\n\"\"\"\nimport h5py\nimport scipy.io as io\nimport PIL.Image as Image\nimport numpy as np\nimport os\nimport glob\nfrom matplotlib import pyplot as plt\nfrom scipy.ndimage.filters import gaussian_filter\nimport scipy\nimport json\nfrom matplotlib import cm as CM\nfrom image import *\nfrom model import CSRNet\nimport torch\nfrom tqdm import tqdm\n#https://github.com/davideverona/deep-crowd-counting_crowdnet\ndef gaussian_filter_density(gt):\n print(gt.shape)\n density = np.zeros(gt.shape, dtype=np.float32)\n gt_count = np.count_nonzero(gt)\n print(gt_count)\n if gt_count == 0:\n return density\n\n pts = np.array(list(zip(np.nonzero(gt)[1], np.nonzero(gt)[0])))\n leafsize = 2048\n\n # build kdtree 寻找最临近点\n tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)\n # query kdtree\n distances, locations = tree.query(pts, k=4)\n\n print('generate density...')\n for i, pt in enumerate(pts):\n pt2d = np.zeros(gt.shape, dtype=np.float32)\n pt2d[pt[1],pt[0]] = 1.\n if gt_count > 1:\n sigma = (distances[i][1]+distances[i][2]+distances[i][3])*0.1\n else:\n sigma = np.average(np.array(gt.shape))/2./2. #case: 1 point\n density += scipy.ndimage.filters.gaussian_filter(pt2d, sigma, mode='constant')\n print('done.')\n return density\n\nroot = '/home/lpf/PycharmProjects/MakeDensityMap/'\npart_A_train = os.path.join(root,'ShanghaiTech_Crowd_Counting_Dataset/part_A_final/train_data','images')\npart_A_test = os.path.join(root,'ShanghaiTech_Crowd_Counting_Dataset/part_A_final/test_data','images')\npart_B_train = os.path.join(root,'ShanghaiTech_Crowd_Counting_Dataset/part_B_final/train_data','images')\npart_B_test = os.path.join(root,'ShanghaiTech_Crowd_Counting_Dataset/part_B_final/test_data','images')\npath_sets = [part_A_train,part_A_test]\n\n#获取路径下所有图片的路径\nimg_paths = []\nfor path in path_sets:\n for img_path in glob.glob(os.path.join(path, '*.jpg')):\n img_paths.append(img_path)\n# 一,ShanghaiTech_DataSet\n# 1,partA部分\nfor img_path in img_paths:\n #产生图像对应mat路径\n mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground_truth').replace('IMG_','GT_IMG_'))\n print(mat,type(mat)) #dict类型\n #读取程numpy数据\n img= plt.imread(img_path)\n #构建一个和img相同维度的numpy\n k = np.zeros((img.shape[0],img.shape[1]))\n #读取mat文件内容\n gt = mat[\"image_info\"][0,0][0,0][0]\n print('gt',gt) #坐标系内容\n for i in range(0,len(gt)):\n if int(gt[i][1]) \")\nimg1 = Image.open('photos/' + img1_name)\nimg1= img1.resize((1000, 1000), PIL.Image.ANTIALIAS)\nnew_name1 = img1_name[:-4] + 'R' + '.png'\nimg1.save(new_name1)\n\nimg2_name = input(\"What is the second photo?> \")\nimg2 = Image.open('photos/' + img2_name)\nimg2= img2.resize((1000, 1000), PIL.Image.ANTIALIAS)\nnew_name2 = img2_name[:-4] + 'R' + '.png'\nimg2.save(new_name2)\n\nimg1 = rgb2gray(mpimg.imread(new_name1))\n\nimg2 = rgb2gray(mpimg.imread(new_name2))\n\nrows, cols = img1.shape\n\ndef mse(x, y):\n return np.linalg.norm(x - y)\n\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4),\n sharex=True, sharey=True)\nax = axes.ravel()\n\nmse_img1 = mse(img1, img1)\nssim_img1 = ssim(img1, img1, data_range=img1.max() - img1.min())\n\nmse_img2 = mse(img1, img2)\nssim_img2 = ssim(img1, img2,\n data_range=img2.max() - img2.min())\n\nlabel = 'MSE: {:.2f}, SSIM: {:.2f}'\n\nax[0].imshow(img1, cmap=plt.cm.gray, vmin=0, vmax=1)\nax[0].set_xlabel(label.format(mse_img1, ssim_img1))\nax[0].set_title(new_name1)\n\nax[1].imshow(img2, cmap=plt.cm.gray, vmin=0, vmax=1)\nax[1].set_xlabel(label.format(mse_img2, ssim_img2))\nax[1].set_title(new_name2)\n\nplt.tight_layout()\nplt.show()\n","repo_name":"patrickhuang112/HandwritingClassifier","sub_path":"Excess python files/comparer.py","file_name":"comparer.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"21094922453","text":"from base_app.choices import CurrencyChoices\nfrom base_app.validators import validate_date_on_future\nfrom django.core.validators import MinValueValidator\nfrom django.db import models\n\n\nclass CreditType(models.Model):\n '''Credit type model.\n Detailed information about credit types.\n '''\n\n name = models.CharField('Name', max_length=128)\n percent = models.FloatField('Percent')\n credit_term = models.IntegerField('Credit term', validators=[MinValueValidator(0)])\n currency = models.CharField('Currency', choices=CurrencyChoices.choices, max_length=5)\n min_downpayment = models.DecimalField(\n 'Minimal downpayment',\n max_digits=21,\n decimal_places=2,\n validators=[MinValueValidator(0)]\n )\n max_downpayment = models.DecimalField(\n 'Maximum downpayment',\n max_digits=21,\n decimal_places=2,\n null=True,\n validators=[MinValueValidator(0)]\n )\n is_annuity_payment = models.BooleanField('Is annuity payment')\n\n class Meta:\n ordering = ['percent']\n verbose_name = 'Credit type'\n verbose_name_plural = 'Credit types'\n\n def __str__(self):\n return f'{self.name} | {self.percent} | {self.credit_term} | {self.currency}'\n\n\nclass CreditContract(models.Model):\n '''Credit contract model.\n Detailed information about credit contracts.\n '''\n\n credit_type = models.ForeignKey(\n CreditType,\n verbose_name='Credit type',\n on_delete=models.RESTRICT,\n related_name='contracts'\n )\n starts_at = models.DateField('Start date', validators=[validate_date_on_future])\n ends_at = models.DateField('End date', validators=[validate_date_on_future])\n is_ended = models.BooleanField('Is credit ended', default=False)\n credit_amount = models.DecimalField(\n 'Credit amount',\n max_digits=21,\n decimal_places=2,\n validators=[MinValueValidator(0)]\n )\n client = models.ForeignKey(\n 'client_app.Client',\n verbose_name='Client',\n on_delete=models.RESTRICT,\n related_name='credit_contracts'\n )\n main_bank_account = models.ForeignKey(\n 'bank_account_app.BankAccount',\n verbose_name='Main bank account',\n on_delete=models.RESTRICT,\n related_name='credit_contracts_main'\n )\n credit_bank_account = models.ForeignKey(\n 'bank_account_app.BankAccount',\n verbose_name='Credit bank account',\n on_delete=models.RESTRICT,\n related_name='credit_contracts_credit'\n )\n special_bank_account = models.ForeignKey(\n 'bank_account_app.BankAccount',\n verbose_name='Special bank account',\n on_delete=models.RESTRICT,\n related_name='credit_contracts_special'\n )\n\n class Meta:\n ordering = ['starts_at']\n verbose_name = 'Credit contract'\n verbose_name_plural = 'Credit contract'\n\n def __str__(self):\n return f'{self.starts_at} - {self.ends_at} | ({self.client}) | {self.credit_amount}'\n","repo_name":"yurymarozau/bank_system","sub_path":"bank/src/credit_app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1865035125","text":"from pascal_valid_functions import *\nimport xml.etree.ElementTree as ET\nimport os\n\n\ndef pascal_validator_one_file(path: str):\n filname = os.path.basename(path)\n if syntax_validator(path, filname) is not False:\n tree = ET.parse(path)\n root = tree.getroot()\n if check_if_empty_value(tree, filname) is False:\n if correct_tag(tree, filname) is not False:\n size = root.find(\"size\")\n width = float(size.find(\"width\").text)\n height = float(size.find(\"height\").text)\n str_jpg = root.find('filename').text\n check_extension(str_jpg, root, filname)\n correct_filename(root, filname)\n root_tag(root, filname)\n great_bnd_validator(root, width, height, filname)\n values_validator(root, 'depth', {'0', '3'}, filname)\n values_validator(root, 'truncated', {'0', '1'}, filname)\n values_validator(root, 'difficult', {'0', '1'}, filname)\n values_validator(root, 'pose', {'Unspecified', 'Rear', 'Frontal', 'Left', 'Right'}, filname)\n\ndef pascal_validator_one_file_syntax(path: str):\n filname = os.path.basename(path)\n syntax_validator(path, filname)","repo_name":"projektmmad/walidacjaMMAD","sub_path":"scripts/pascal_validator_one_file.py","file_name":"pascal_validator_one_file.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40021794650","text":"try: from cmu_cs3_graphics import *\nexcept: from cmu_graphics import *\n\nfrom runAppWithScreens import *\nfrom State import *\nimport math\nfrom readingInputs import *\nfrom Buttons import *\nfrom boardSolver import boardSolverMain\n##################################\n# boardScreen\n##################################\n\n#Todo\n# inputing numbers function\n\ndef boardScreen_onScreenStart(app):\n app.boardLeft = app.width*0.1\n app.boardTop = app.height*0.15\n boardSideLen = min(app.width*0.8,app.height*0.8)\n app.boardWidth = boardSideLen\n app.boardHeight = boardSideLen\n app.cellBorderWidth = 2\n app.lineColor = 'gray'\n app.boarderColor ='black'\n app.sectionBoxesColor = 'black'\n app.competitionMode = False\n restartBoardScreen(app)\n\ndef restartBoardScreen(app):\n app.boardContent =loadRandomBoard(app.currMode)\n app.currInputMode = 'normal' #other option include mouse, key\n app.boardScreenButtons = []\n #load board\n newBoard(app)\n app.selectedCell = (0,0)\n app.inputingLegals =False\n \n\ndef newBoard(app):\n \n app.state = State(app.boardContent)\n #move to mode\n if app.currMode == 'easy':\n app.usingAutoLegals = False\n else: \n app.usingAutoLegals =True\n app.solvedBoard = boardSolverMain(app.state) #will not modify\n setAllButtons(app)\n app.highlightCells = []\n app.state.gameStarted = True\n app.prevStepLegals = None\n app.state.errorList = []\n\n#optional if switch board\ndef loadNewBoard(app, boardContent):\n app.boardContent = boardContent\n newBoard(app)\n\ndef boardScreen_onKeyPress(app, key):\n boardScreenKeyPress(app, key)\n\ndef boardScreenKeyPress(app, key):\n if key == 'j':\n boardPath = loadBoardPaths(['test.txt'])\n loadNewBoard(app,getBoardIn2dList(boardPath[0]))\n if key == 'c':\n app.competitionMode = not app.competitionMode\n if not app.competitionMode:\n if key == 'o':\n highlightHint(app)\n\n elif key == 'p':\n doHint(app)\n app.highlightCells = []\n if key =='s' and app.currMode != 'easy':\n #play singleton\n app.state.playHint1()\n if key =='u':\n app.state.undo()\n if key == 'r':\n app.state.redo()\n if key == 'h':\n print('help')\n setActiveScreen('helpScreen')\n\n if key =='m':\n app.currInputMode = 'mouse'\n elif key =='n':\n app.currInputMode = 'normal'\n elif key == 'k':\n app.currInputMode = 'key'\n if app.currInputMode != 'mouse':\n if key == 'space': setActiveScreen('mainScreen')\n if key == 'backspace' or key == '0':\n app.state.undoSet(*app.selectedCell, app.prevStepLegals)\n if app.selectedCell in app.state.errorList:\n app.state.errorList.remove(app.selectedCell)\n elif key.isdigit(): #not including 0\n num =int(key)\n doInputNum(app, num)\n\n if key =='l':\n app.inputingLegals =True\n if key =='a': \n app.usingAutoLegals =not app.usingAutoLegals\n \n #up down left right\n \n if key == 'left': moveSelection(app, 0, -1)\n elif key == 'right': moveSelection(app, 0, +1)\n elif key == 'up': moveSelection(app ,-1, 0)\n elif key == 'down': moveSelection(app, +1, 0) \n #modified, from https://cs3-112-f22.academy.cs.cmu.edu/notes/4189\n\ndef doInputNum(app, num):\n row,col = app.selectedCell\n app.prevStepLegals = app.state.getLegals(row, col)\n if not app.state.cellInOriginalBoard(row,col):\n app.highlightCells = []\n \n if app.inputingLegals: \n if not app.usingAutoLegals:\n app.state.inputLegals(row, col, num)\n else:\n app.state.set(row, col,num )\n findErrors(app, row, col)\n \n \n\ndef moveSelection(app, drow, dcol):\n if app.selectedCell != None:\n selectedRow, selectedCol = app.selectedCell\n newSelectedRow = (selectedRow + drow) % app.state.rows\n newSelectedCol = (selectedCol + dcol) % app.state.cols\n app.selectedCell = (newSelectedRow, newSelectedCol)\n#modified, from https://cs3-112-f22.academy.cs.cmu.edu/notes/4189\n\n\ndef boardScreen_onKeyRelease(app, key):\n boardScreenKeyRelease(app,key)\n\ndef boardScreenKeyRelease(app,key):\n if key =='l':\n app.inputingLegals =False\n\ndef boardScreen_onMousePress(app,mouseX, mouseY):\n \n buttonClickedIndex = getButtonClicked(app.boardScreenButtons, mouseX, mouseY)\n if buttonClickedIndex ==0:\n setActiveScreen('mainScreen')\n elif buttonClickedIndex ==1 and app.currMode != 'easy' and not app.competitionMode:\n app.state.playHint1()\n elif buttonClickedIndex ==2:\n restartBoardScreen(app)\n elif buttonClickedIndex ==3:\n app.usingAutoLegals = not app.usingAutoLegals \n boardScreenDoMousePress(app,mouseX, mouseY)\n \ndef boardScreenDoMousePress(app,mouseX, mouseY):\n #used by both board screen and 2 player mode\n selectedCell = getCell(app, mouseX, mouseY)\n if selectedCell != None:\n if selectedCell != app.selectedCell:\n app.selectedCell = selectedCell\n if app.currInputMode == 'mouse':\n #check for numPad\n numPadCell = getNumPadCell(app, mouseX, mouseY)\n if numPadCell!=None:\n if numPadCell == 0:\n print('toggle setting legals') #add setting candidate toggle\n app.inputingLegals = not app.inputingLegals\n else:\n doInputNum(app, numPadCell)\n\n\ndef boardScreen_onMouseMove(app, mouseX, mouseY):\n boardScreenMouseMove(app,mouseX,mouseY)\n\ndef boardScreenMouseMove(app,mouseX,mouseY):\n buttonClickedIndex = getButtonClicked(app.boardScreenButtons, mouseX, mouseY)\n if buttonClickedIndex != None:\n app.boardScreenButtons[buttonClickedIndex]['hover'] =True\n else:\n setAllButtonHoverFalse(app.boardScreenButtons)\n\ndef boardScreen_redrawAll(app):\n drawBackground(app)\n drawAllButtons(app.boardScreenButtons)\n redrawBoardScreen(app)\n\ndef redrawBoardScreen(app):\n boardScreen_drawBoard(app)\n boardScreen_drawBoardBorder(app)\n boardScreen_DrawSectionBoxes(app)\n drawSudokuNumbers(app, app.state.userBoard)\n drawAllLegals(app)\n drawMsg(app)\n drawAllRedDot(app)\n #mouse only\n if app.currInputMode == 'mouse':\n drawNumPad(app)\n if app.competitionMode:\n drawLabel('Competition Mode On', 650, 150)\n ########################################################\n # Buttons #\n ########################################################\n\ndef setAllButtons(app):\n y = 40\n setButton(app.boardScreenButtons, 'Back',50 , y, length =60, height =40)\n setButton(app.boardScreenButtons, 'Singleton',125 , y,length =100, height =40)\n setButton(app.boardScreenButtons, 'New Game',250 , y,length =100, height =40)\n setButton(app.boardScreenButtons, 'Auto/Manual Legals',375 , y,length =150, height =40) #make togging button\n \n ########################################################\n # Hints #\n ########################################################\ndef highlightHint(app):\n hint1Res = app.state.getHint1()\n if hint1Res != None:\n app.highlightCells = [hint1Res]\n else:\n hint2Res = app.state.getHint2()\n if hint2Res != None:\n app.highlightCells = hint2Res\n # row, col = hint2Res\n else:\n print('found NO hint')\n return\n #sets app.selection to this cell\n # print('found hint')\n\n # app.highlightCells = row, col\n\ndef doHint(app):\n if app.state.playHint1() ==None:\n app.state.playHint2()\n\n ########################################################\n # HELPERS #\n ########################################################\ndef drawAllRedDot(app):\n #competitionMode\n if app.competitionMode and app.state.errorList !=[]:\n print('Crashing')\n assert(False) #crash if red\n for row, col in app.state.errorList:\n drawRedDot(app, row, col)\n\ndef findErrors(app, row, col):\n if (row, col) in app.state.errorList:\n if app.state.userBoard[row][col] == 0:\n app.state.errorList.remove((row,col))\n else:\n userVal = app.state.userBoard[row][col]\n correctVal = app.state.solvedBoard[row][col]\n if userVal != 0 and userVal != correctVal:\n app.state.errorList.append((row,col))\n\n\n\ndef drawRedDot(app,row, col):\n cellLeft, cellTop = getCellLeftTop(app, row, col)\n cellWidth, cellHeight = getCellSize(app)\n drawCircle(cellLeft+0.8*cellWidth, cellTop +0.8*cellHeight, 0.1*cellWidth, fill = 'red')\n\ndef drawMsg(app):\n if app.state.gameOver:\n drawRect(app.boardLeft+app.boardWidth/2, app.boardTop + app.boardHeight/2, 500, 50, align = 'center', fill = app.settingDict['Game Over Color']) #gameOverColor\n drawLabel('Congrats, you finished the game', app.boardLeft+app.boardWidth/2, app.boardTop + app.boardHeight/2, size = 20, bold = True, fill = 'white') \n writeFile(f'finished.txt', getStandardFormat(app.state.userBoard))\n\ndef getStandardFormat(board):\n res = ''\n for rowList in board:\n for colCell in rowList:\n res+=f'{colCell} '\n res = res[:-1]\n res+='\\n'\n return res\n\ndef drawNumPad(app):\n startTop, w, h = getNumPadInfo(app)\n #draw the legal button\n \n for num in range(0,10): #numbers 1 to 9\n drawNumPadCell(app,num, startTop, w, h)\n drawNumPadNumbers(app,num, startTop, w,h )\n\ndef getNumPadInfo(app):\n #startTop, w, h\n return (app.boardTop, 50,50)\n\ndef drawNumPadCell(app,num, startTop, w,h ):\n rectX = app.width -w\n rectY = startTop +(num-1)*h\n color =None\n if num ==0 and app.inputingLegals:\n color = rgb(183, 202, 241)\n drawRect(rectX, rectY, w, h, fill =color, border ='black')\n\ndef drawNumPadNumbers(app,num, startTop, w,h ):\n numX = app.width -w/2\n numY = startTop +(num-1)*h +h/2\n msg = str(num)\n if num ==0:\n msg = 'Legal'\n drawLabel(msg, numX, numY, size = app.height//40, bold = True)\n\ndef getNumPadCell(app, mouseX, mouseY):\n startTop, w, h = getNumPadInfo(app)\n for num in range(0,10):\n rectX = app.width - w\n rectY = startTop + (num-1)*h\n if rectX <= mouseX <= rectX+w and rectY <= mouseY <= rectY+h:\n return num\n return None\n\ndef drawSudokuNumbers(app, boardToDraw):\n cellWidth, cellHeight = getCellSize(app)\n for row in range(app.state.rows):\n for col in range(app.state.cols):\n color = rgb(175, 125, 119) if not app.state.cellInOriginalBoard(row,col) else app.settingDict['Inital Values Color']\n cellLeft, cellTop = getCellLeftTop(app, row, col)\n cellX = cellLeft+cellWidth/2\n cellY = cellTop +cellHeight/2\n num =boardToDraw[row][col]\n if num!=0:\n drawLabel(str(num),cellX, cellY, size = app.height//25, bold = True, fill =color)\n \ndef drawAllLegals(app):\n if app.usingAutoLegals:\n legals = app.state.legals\n else:\n legals = app.state.userLegals\n for row in range(app.state.rows):\n for col in range(app.state.cols):\n if app.state.userBoard[row][col]==0:\n legalsSet = legals[row][col]\n drawLegalsInCell(app, row, col, legalsSet)\n\ndef drawLegalsInCell(app, row, col, legalsSet):\n cellLeft, cellTop =getCellLeftTop(app, row, col)\n cellWidth, cellHeight =getCellSize(app)\n for num in range(1,10): #from 1 to 9\n if num in legalsSet:\n legalRow = (num-1)//3\n legalCol = (num-1)%3\n legalHeight =cellHeight/3\n legalWidth = cellWidth/3\n legalLeft = cellLeft + legalCol * legalHeight\n legalTop = cellTop + legalRow * legalWidth\n legalX = legalLeft + legalHeight/2\n legalY = legalTop + legalWidth/2 #from logic of drawSudokuNumbers and getCellleftTop\n \n drawLabel(str(num), legalX,legalY)\n'''\ndef getCellLeftTop(app, row, col):\n cellWidth, cellHeight = getCellSize(app)\n cellLeft = app.boardLeft + col * cellWidth\n cellTop = app.boardTop + row * cellHeight\n return (cellLeft, cellTop)\n\n cellLeft, cellTop = getCellLeftTop(app, row, col)\n cellX = cellLeft+cellWidth/2\n cellY = cellTop +cellHeight/2\n'''\ndef drawBackground(app):\n drawRect(0,0, app.width, app.height, fill = app.settingDict['Background Color'])\n\ndef boardScreen_drawBoard(app):\n for row in range(app.state.rows):\n for col in range(app.state.cols):\n boardScreen_drawCell(app, row, col)\n#modified, originally from https://cs3-112-f22.academy.cs.cmu.edu/notes/4187\n\ndef boardScreen_drawBoardBorder(app):\n # draw the board outline (with double-thickness):\n drawRect(app.boardLeft, app.boardTop, app.boardWidth, app.boardHeight,\n fill=None, border=app.boarderColor,\n borderWidth=2*app.cellBorderWidth)\n#modified, originally from https://cs3-112-f22.academy.cs.cmu.edu/notes/4187\n\ndef boardScreen_DrawSectionBoxes(app):\n for sectionRow in range(0,app.state.rows,3):\n for sectionCol in range(0,app.state.cols,3):\n cellLeft, cellTop = getCellLeftTop(app, sectionRow, sectionCol)\n cellWidth, cellHeight = getCellSize(app)\n cellWidth*=3\n cellHeight*=3\n drawRect(cellLeft, cellTop, cellWidth, cellHeight,\n fill=None, border= app.sectionBoxesColor,\n borderWidth=app.cellBorderWidth)\n#modified, originally from https://cs3-112-f22.academy.cs.cmu.edu/notes/4187\n\ndef boardScreen_drawCell(app, row, col):\n cellLeft, cellTop = getCellLeftTop(app, row, col)\n \n cellWidth, cellHeight = getCellSize(app)\n color = getCellColor(app, row, col)\n drawRect(cellLeft, cellTop, cellWidth, cellHeight,\n fill=color, border= app.lineColor,\n borderWidth=app.cellBorderWidth)\n#modified, originally from https://cs3-112-f22.academy.cs.cmu.edu/notes/4187\n\ndef getCellColor(app, row, col):\n selectedRow, selectedCol = app.selectedCell\n color = app.settingDict['Empty Cell Color']\n \n if (row, col) == app.selectedCell:\n color = app.settingDict['Selected Cell Color'] #selectedCellC\n elif isInBox(app, row,col) or row == selectedRow or col == selectedCol:\n color = app.settingDict['Selected Region Color'] \n if (row,col) in app.highlightCells:\n color = 'cyan'\n return color\n\ndef getSelectBoxRegion(app, row,col):\n #find startRow and startCol\n boxSize =3\n startRow = row//boxSize *3\n startCol = col//boxSize *3\n return startRow, startCol\n \n\n\ndef isInBox(app, row,col):\n #given arb row and col, determine if should be highlighted\n selectedRow, selectedCol = app.selectedCell\n startRow, startCol = getSelectBoxRegion(app, selectedRow, selectedCol)\n #if inside the box of this\n return startRow<=row', views.ClassifyRecordView.as_view(), name='process_record'),\r\n path('records/', views.RecordsView.as_view(), name='records'),\r\n path('records/delete/', views.RecordsDeleteView.as_view(), name='records_delete'),\r\n path('admin/', views.AdminView.as_view(), name='admin'),\r\n path('admin/delete/', views.AdminDeleteView.as_view(), name='admin_delete'),\r\n path('django-admin/', admin.site.urls),\r\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\r\n","repo_name":"LuoBingjun/Pic-demo","sub_path":"demo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16159571295","text":"from flask import Blueprint, request, jsonify\nfrom ..models import db, User, Review\nfrom app.models import db, Favorite\n\nfavorite_routes = Blueprint('favorites', __name__)\n\n# ***************************************************************\n# Endpoint to Create or Delete a Favorite\n# ***************************************************************\n@favorite_routes.route('/', methods=['POST'])\ndef create_or_delete_favorite():\n data = request.json\n if not data.get('user_id') or not data.get('restaurant_id'):\n return {\"error\": \"Missing required fields\"}, 400\n\n user_id = data['user_id']\n restaurant_id = data['restaurant_id']\n\n existing_favorite = Favorite.query.filter_by(user_id=user_id, restaurant_id=restaurant_id).first()\n if existing_favorite:\n try:\n db.session.delete(existing_favorite)\n db.session.commit()\n return {\"action\": \"removed\", \"favorite\": existing_favorite.to_dict()}, 200\n except Exception as e:\n db.session.rollback()\n return {\"error\": f\"Favorite deletion failed: {str(e)}\"}, 500\n\n new_favorite = Favorite(\n user_id=user_id,\n restaurant_id=restaurant_id\n )\n\n try:\n db.session.add(new_favorite)\n db.session.commit()\n return {\"action\": \"added\", \"favorite\": new_favorite.to_dict()}, 201\n except Exception as e:\n db.session.rollback()\n return {\"error\": f\"Favorite creation failed: {str(e)}\"}, 500\n\n\n# ***************************************************************\n# Endpoint to Retrieve All Favorites\n# ***************************************************************\n@favorite_routes.route('/')\ndef get_favorites():\n \"\"\"\n Get all favorites for a specific user.\n\n Returns:\n list: A list of all favorite objects in the database for a specific user.\n \"\"\"\n user_id = request.args.get('user_id')\n\n if not user_id:\n return jsonify(error=\"User ID is required\"), 400\n\n favorites = Favorite.query.filter_by(user_id=user_id).all()\n return jsonify([favorite.to_dict() for favorite in favorites])\n\n# ***************************************************************\n# Endpoint to Check if a Favorite Exists\n# ***************************************************************\n@favorite_routes.route('/check', methods=['GET'])\ndef check_favorite():\n \"\"\"\n Check if a favorite exists for a specific user and restaurant.\n\n Requires:\n user_id (int): The ID of the user.\n restaurant_id (int): The ID of the restaurant.\n\n Returns:\n dict: A response indicating whether the favorite exists.\n \"\"\"\n user_id = request.args.get('user_id')\n restaurant_id = request.args.get('restaurant_id')\n\n # Check if a favorite exists with the given user_id and restaurant_id\n favorite = Favorite.query.filter_by(user_id=user_id, restaurant_id=restaurant_id).first()\n\n if favorite:\n return {\"exists\": True}\n else:\n return {\"exists\": False}\n","repo_name":"amalakkad93/GothamEat","sub_path":"app/api/favorite_routes.py","file_name":"favorite_routes.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20195959267","text":"import numpy as np\nimport numpy\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pandas import read_csv\nimport math\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\n# import tensorflow as tf\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n\ndef create_dataset(dataset, look_back=24):#從第24HR開始 試試24 48 72HR前\n\tdataX, dataY = [], []\n\tfor i in range(len(dataset)-look_back-1):\n\t\ta = dataset[i:(i+look_back), 0]\n\t\tdataX.append(a)\n\t\tdataY.append(dataset[i + look_back, 0])\n\treturn numpy.array(dataX), numpy.array(dataY)\n\n#讀資料近來分階\ndata = pd.read_csv('CSIE-6F-0823-0912.csv',parse_dates =[\"Time\"], index_col =\"Time\")\ndata = data.resample(\"1H\").sum()\n# print(data.iloc[0])\ndata_array = np.array(data)\n# print(data_array)\nsmallest = 1\nbiggest = 0\nfor i in range(len(data)):\n\tif data_array[i] > biggest:\n\t\tbiggest = data_array[i]\n\tif data_array[i] < smallest:\n\t\tsmallest = data_array[i]\nlevel = biggest - smallest\nlevel /= 15\nfor i in range(len(data)):\n\tdata_array[i] //= level\n\n\nscaler = MinMaxScaler(feature_range=(0, 1))\ndataset = scaler.fit_transform(data_array)\n# print(dataset)\n# 2/3 資料為訓練資料， 1/3 資料為測試資料\nlook_back = 168\ntrain_size = 336\ntest_size = len(dataset) - train_size\ntrain, test = dataset[0:train_size,:], dataset[train_size-look_back:len(dataset),:]\n\n# 產生 (X, Y) 資料集, Y 是下一期的乘客數(reshape into X=t and Y=t+1)\ntrainX, trainY = create_dataset(train, look_back)\ntestX, testY = create_dataset(test, look_back)\n# print(trainX)\n# reshape input to be [samples, time steps, features]\ntrainX = numpy.reshape(trainX, (trainX.shape[0], 1,trainX.shape[1]))\n# trainX = trainX.astype('float64')\n# print(\"======================\",trainX)\ntestX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n\n# 建立及訓練 LSTM 模型\nmodel = Sequential()\nmodel.add(LSTM(4, input_shape=(1, look_back)))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(trainX, trainY, epochs=50, batch_size=16, verbose=2)\n\n# 預測\ntrainPredict = model.predict(trainX)\ntestPredict = model.predict(testX)\n\n# 回復預測資料值為原始數據的規模\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n\n# print(testX)\n# print(testY)\n# calculate 均方根誤差(root mean squared error)\nty = 0\npy = 0\nfor i in range(len(trainY[0])):\n\tty += trainY[0][i]\n\tpy += trainPredict[i]\ntempy = (ty-py)\nprint('Train Total MAE: %.2f' %abs(tempy))\ntrainScore = (mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Average MAE: %.2f ' % (trainScore))\ntx = 0\npx = 0\nfor i in range(len(trainX[0])):\n\ttx += testY[0][i]\n\tpx += testPredict[i]\ntempx = (tx-px)\nprint('Test Total MAE: %.2f' %abs(tempx))\ntestScore = (mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Average MAE: %.2f ' % (testScore))\n# print(\"MAE :\",mean_squared_error(predict_MAE,answer_MAE, squared=False ))\n# 畫訓練資料趨勢圖\n# shift train predictions for plotting\ntrainPredictPlot = numpy.empty_like(dataset)\ntrainPredictPlot[:, :] = numpy.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n\n# 畫測試資料趨勢圖\n# shift test predictions for plotting\ntestPredictPlot = numpy.empty_like(dataset)\ntestPredictPlot[:, :] = numpy.nan\ntestPredictPlot[len(trainPredict)+(look_back)+1:len(dataset)-1, :] = testPredict\n\n# 畫原始資料趨勢圖\n# plot baseline and predictions\nplt.plot(scaler.inverse_transform(dataset))\nplt.plot(trainPredictPlot)\nplt.plot(testPredictPlot)\nplt.show()","repo_name":"hank95179/water-diapenser-scheduling","sub_path":"1211/CSIE6F_LSTM.py","file_name":"CSIE6F_LSTM.py","file_ext":"py","file_size_in_byte":3726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5082546656","text":"# 배열을 시계방향으로 90도 회전 하는 함수\ndef turn(x, rList): # 배열의 길이 x, 기존의 배열 rList\n new_arr = [[0] * N for a in range(N)]\n for i in range(N):\n for j in range(N):\n new_arr[i][j] = rList[N - j - 1][i]\n return new_arr\n\n\n# up을 기준으로 하는 코드이므로, 밀고 싶은 방향을 up으로 맞춰주기\n# up 0번, left 1번, down 2번, right 3번 회전시켜주면 된다.\n# 회전시켜주는 함수\ndef switch(directions):\n change = {\n 'up': 0,\n 'left': 1,\n 'down': 2,\n 'right': 3,\n }\n directionNum = change.get(directions)\n return directionNum\n\n\n# T = 1\nT = int(input())\nfor t in range(1, T + 1):\n N, direction = input().split() # N 배열크기, direction 방향\n N = int(N)\n arr = [list(map(int, input().split())) for i in range(N)] # N X N 배열만들기\n\n # # 확인-----\n # print('입력값 확인')\n # print(N, direction)\n # for i in arr:\n # print(i)\n # print()\n # # -----확인\n swdirec = switch(direction)\n for k in range(swdirec):\n arr = turn(N, arr)\n\n # # 확인 -----\n # print('회전확인')\n # for i in arr:\n # print(i)\n # print()\n # # -----확인\n\n # up 기준\n # 0을 제외한 나머지 같은 숫자들 합치기\n for i in range(N):\n for j in range(N - 1):\n chk = 1\n while arr[j][i] != 0 and (j + chk) < N: # 첫번째 위치가 0이면 생략 j+chk 가 N보다 크거나 같을경우 out of range 오류 이므로\n if arr[j][i] == arr[j + chk][i]: # 첫번째와 두번째가 같으면\n arr[j][i] *= 2 # 첫번째를 두배\n arr[j + chk][i] = 0 # 두번째를 0\n break # 확인 종료\n elif arr[j + chk][i] != 0: # 두번째가 0이 아니면\n break # 확인 종료\n chk += 1 # 두번째가 0이면 첫번째와 세번째를 비교하기 위해 chk 를 1 더해준다\n\n # # 확인-----\n # print('숫자합치기 확인')\n # for i in arr:\n # print(i)\n # print()\n # # -----확인\n\n # 0을 제외한 나머지 숫자들을 up방향으로 밀기\n for i in range(N):\n for j in range(N - 1):\n if arr[j][i] == 0: # 선택한 점이 0일때\n chk = 1\n while j + chk < N:\n if arr[j + chk][i] != 0: # 그 다음점이 0이 아니면\n arr[j][i] = arr[j + chk][i] # 다음점을 선택한 점에 덮어쓰기\n arr[j + chk][i] = 0 # 그리고 다음점은 0\n break # 체크 종료\n chk += 1 # 그 다음점도 0이라면 그 다다음점을 확인하기 위해 chk +1\n\n # # 확인-----\n # print('숫자밀기 확인')\n # for i in arr:\n # print(i)\n # print()\n # # -----확인\n\n # 기존의 배열을 회전했으므로 다시 원래대로 돌리기 위해 4-swich(dirction) 번 회전시켜줌\n if swdirec != 0: # up일경우 할 필요가 없다.\n for k in range(4 - swdirec):\n arr = turn(N, arr)\n\n # # 확인-----\n # print('최종 확인')\n # for i in arr:\n # print(i)\n # print()\n # # -----확인\n\n # 답 출력하기\n print('#{}'.format(t))\n for i in range(N):\n result = \"\"\n for j in range(N):\n result += str(arr[i][j]) + ' '\n print(result)","repo_name":"deok2kim/algorithm","sub_path":"SWEA/D4/6109. 추억의 2048게임.py","file_name":"6109. 추억의 2048게임.py","file_ext":"py","file_size_in_byte":3501,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35492162758","text":"# Challenge #30\n# Calculate the number of digits in the total number of IPv6 addresses. \n# Each IPv6 uses 128 bits and the total number of IPv6 is 2 raised to the power of 128.\n# Save the number of digits in the total number of IPv6 in a variable called no_of_digits.\n\nno_of_digits = len(str(2**128))\nprint(no_of_digits)\n\n# Challenge #31\n# Consider the following dictionary: phone = {'Brand':'Samsung', 'Price':650.2, 'Seller': 'Nile'}\n# Using a single method add the following key:value pairs to the dictionary: 'OS':'Android' and 'Color': 'Black'\n\nphone = {'Brand':'Samsung', 'Price':650.2, 'Seller': 'Nile'}\nphone.update({'OS':'Android', 'Color': 'Black'})\nprint(phone)\n\n# Challenge #32\n# Consider the following dictionary: phone = {'Brand':'Samsung', 'Price':650.2, 'Seller': 'Nile'}\n# Your task is to extract the price in a variable called price and calculate the VAT knowing that \n# the VAT percentage is 19%. Use only 2 decimal points for the VAT value.\n# The VAT value will be stored in a variable called vat and will be a float.\n\nphone = {'Brand':'Samsung', 'Price':650.2, 'Seller': 'Nile'}\nprice = phone['Price']\nprice_vat = round(price * .19, 2)\nprint(f'price: {price}, vat: {price_vat}')\n\n# Challenge #33\n# Consider the following list: my_list = [1, 2.3, 'abc', (5, 6, 'x', 'y')]\n# Create a variable called my_var that contains the second element of the list as a string ('2.3') \n# concatenated to the first letter of the third element of the list which is 'a' and the last element \n# of the tuple which is the last element of the list.\n# my_var should store the string: 2.3ay\n\nmy_list = [1, 2.3, 'abc', (5, 6, 'x', 'y')]\nmy_var = str(my_list[1]) + my_list[2][0] + my_list[3][3]\nprint(my_var)\n\n# Challenge #34\n# Consider the following list: languages = ['English', 'Python', 'Java', 'Golang', 'German']\n# Using list slicing, eliminate the first and the last element from the list called language.\n\nlanguages = ['English', 'Python', 'Java', 'Golang', 'German']\nlanguages = languages[1:len(languages)-1]\nprint(languages)\n\n# Challenge #35\n# Using the range() built-in function create a list called my_list that stores \n# the following numbers: -20, -17, -14, -11, -8, -5, -2, 1, 4, 7\n\nmy_list = list(range(-20, 10, 3))\nprint(my_list)\n\n# Challenge #36\n# Consider the following list: days = [10, 11, 13, 14, 15]\n# Using a list method insert the value 12 between 11 and 13.\n\ndays = [10, 11, 13, 14, 15]\ndays.insert(2, 12)\nprint(days)\n\n# Challenge #37\n# Consider the following string: message = 'Wow! Python is great'\n# Using list comprehension create a list called no_vowels that contains as elements \n# the characters from the string above that are not vowels or whitespace. We consider vowels: 'aeio'\n\nmessage = 'Wow! Python is great'\nno_vowels = [char for char in message if char not in 'aeiouAEIOU ']\nprint(no_vowels)\n\n# Challenge #38\n# Consider the following sets: \n# names1 = {'John', 'Marry', 'Lena', 'Pollux' and \n# names2 = {'Dan', 'Arthur', 'Marry', 'Lena', 'Castor'}\n# Using set methods and operations create a list called names that contains the \n# elements that belong to both sets. names will be ['Lena', 'Marry']\n\nnames1 = {'John', 'Marry', 'Lena', 'Pollux'} \nnames2 = {'Dan', 'Arthur', 'Marry', 'Lena', 'Castor'}\nnames = names1.intersection(names2)\nprint(names)\n\n# Challenge #39\n# Consider the following lists: \n# phone1 = ['1111', '2222', '2222', '1111'] and \n# phone2 = ['1111', '3333', '3333', '1111']\n# Using set methods create a new set called phone_numbers that contains all unique elements present in both lists.\n\nphone1 = ['1111', '2222', '2222', '1111']\nphone2 = ['1111', '3333', '3333', '1111']\nphone_numbers = set(phone1).union(set(phone2))\nprint(phone_numbers)\n\n# Challenge #40\n# Consider the following string variable: my_str = 'wlo1 Link encap:Ethernet HWaddr b4:6d:83:77:85:f3'\n# Using string methods and list operations create a variable of type string called interface_mac that stores \n# the name of the interface which is wlo1, an exclamation mark, and the MAC address which is b4:6d:83:77:85:f3\n\nmy_str = 'wlo1 Link encap:Ethernet HWaddr b4:6d:83:77:85:f3'\ntmp = my_str.split(' ')\ninterface_mac = tmp[0] + '!' + tmp[-1]\nprint(interface_mac)\n\n# Challenge #41\n# Write a function called my_function() that takes exactly one argument which is an integer written between single \n# or double quotes (this is in fact a string). If the argument is integer 'n', the function should return the result of n + nn + nnn\n# Example: my_function('5') returns 5 + 55 + 555 which is 615 and my_function('9') returns 9 + 99 + 999 which is 1107\n\ndef my_function(x):\n res = int(x) + int(x*2) + int(x*3)\n return res\n\nprint(my_function('5'))\nprint(my_function('9'))\n\n# Challenge #42\n# Consider the following list with duplicates what stores MAC addresses: \n# mac = ['b4:6d:83:77:85:f3', 'b4:6d:83:77:85:f3', 'a4:6d:83:77:85:f4', 'c4:6d:83:77:85:f3', 'b4:6d:83:77:85:f3']\n# Write a Python script that uses a for loop, iterates over the list and creates a new list called mac_unique that \n# stores only unique MAC addresses (unique elements).\n\nmac = ['b4:6d:83:77:85:f3', 'b4:6d:83:77:85:f3', 'a4:6d:83:77:85:f4', 'c4:6d:83:77:85:f3', 'b4:6d:83:77:85:f3']\nmac_unique = []\nfor adr in mac:\n if adr not in mac_unique :\n mac_unique .append(adr)\nprint(mac_unique )\n\n# Challenge #43\n# Consider the following list with duplicate elements: years = [2010, 2010, 2011, 2011, 2012, 2012, 2012]\n# Write a Python script that uses list comprehension and appends only unique elements from the years list to a new list called years_unique.\n\nyears = [2010, 2010, 2011, 2011, 2012, 2012, 2012]\nyears_unique = []\n[years_unique.append(item) for item in years if item not in years_unique]\nprint(years_unique)\n\n# Challenge #44\n# Consider the following list of words: words = ['Anna', 'Car', 'Civic', 'Screen', 'Level', 'Cat', 'Mom']\n# Some words are palindromes and some are not. Using list comprehension, create a list called palindromes \n# that contains only words from the list above that are palindromes. Ignore the letter case when checking for palindromes.\n\nwords = ['Anna', 'Car', 'Civic', 'Screen', 'Level', 'Cat', 'Mom']\npalindromes = [word for word in words if word.lower() == word[::-1].lower()]\nprint(palindromes)\n\n# Challenge #45\n# Create a function called reverse() that accepts a string as an argument and returns a new string with all characters reversed.\n\ndef reverse(s):\n return s[::-1]\n\nprint(reverse('Python'))\n\n# Challenge #46\n# Write a function called remove_from_list() that takes 2 arguments: a list and the value that should be removed from the list. \n# After calling the function, all occurrences of the second argument should have been removed from the list.\nlist1 = [1, 2, 1, 1, 3]\ndef remove_from_list(l, v):\n while v in l:\n l.remove(v)\n\nremove_from_list(list1, 1)\nprint(list1)\n\n# Challenge #47\n# Write a function called vowel_count() that takes a string as an argument and returns a dictionary with the keys as the vowels in the \n# string and the values as the count of times a vowel appears in the string. The function ignores the case and considers only lower-case letters.\n\ndef vowel_count(s):\n my_dict = {}\n for vowel in 'aeiou':\n if vowel in s.lower():\n my_dict[vowel] = s.lower().count(vowel)\n return my_dict\n\nprint(vowel_count('Python JAVA Go'))\n\n# Challenge #48\n# Write a function called counter() that takes a string as an argument and returns the number of vowels and consonants in the string as a tuple.\n# Letter case doesn't matter (both 'a' and 'A' will be counted as a vowel).\n\ndef counter(s):\n vowels = 'aeiou'\n vowel_count = 0\n for vowel in vowels:\n if vowel in s:\n vowel_count += s.count(vowel)\n return (len(s)-vowel_count, vowel_count)\n\nprint(counter('Python'))\nprint(counter('Beautiful'))\n\n# Challenge #49\n# Consider the following list of countries with duplicates: \n# countries = ['USA', 'UK', 'France', 'Romania', 'France', 'Germany', 'USA', 'Canada', 'India', 'UK']\n# Your task is to eliminate duplicates and sort the list in alphabetic order.\n# P.S. Solve the exercise by writing only two lines of code.\n\ncountries = ['USA', 'UK', 'France', 'Romania', 'France', 'Germany', 'USA', 'Canada', 'India', 'UK']\ncountries = list(set(countries))\ncountries.sort()\nprint(countries)\n\n# Challenge #50\n# Having the show_arp.txt file below, create a Python script that reads the file and extracts IP and MAC addresses \n# in a list called ip_mac. Each element of the list should be a tuple with 2 items: (ip, mac)\n\nwith open('python/challenges/files/show_arp.txt') as f:\n content = f.read().splitlines()\n result = []\n head = [element for element in content[0].split(' ') if element != '']\n print(head)\n for line in content[1:]:\n tmp = line.split(' ')\n tmp2 = [element for element in tmp if element != '']\n ip = tmp2[head.index('Address')]\n mac = tmp2[head.index('Hardware')-1]\n result.append((ip, mac))\n\nprint(result)\n\n# Challenge #51\n# Create a lambda expression that calculates the area of a square. Lambda has one argument, the length of one side.\n# Assign the lambda expression to a variable called area.\n\narea = lambda x: x*x\nprint(area(2))\n\n# Challenge #52\n# Consider the value of constant PI with many decimal points: PI = 3.141592653589793\n# Using the format() built-in function, convert PI to a value formatted with 5 decimals. PI will be 3.14159 and remains a float.\n\nPI = 3.141592653589793\nPI = float(format(PI, '.5f'))\nprint(PI)\n\n# Challenge #53\n# Consider the following dictionary: \n# countries = {'us': 'United States of America', 'br': 'Brazil', 'de': 'Germany', 'at': 'Austria'}\n# Write a Python script that prints out the values of the dictionary sorted by the keys in alphabetical order. Each value should be on its own line.\n\ncountries = {'us': 'United States of America', 'br': 'Brazil', 'de': 'Germany', 'at': 'Austria'}\ncountries_sorted = sorted(countries, key=lambda x:countries[x])\nfor country in countries_sorted:\n print(countries[country])\n\n# Challenge #54\n# Consider the following dictionary. The keys are the names of the employees and the values are their salaries before taxes.\n# salaries = {'John': 50000, 'Anne': 66000, 'Antonio': 48000}\n# Using dictionary comprehension create a new dictionary called taxes that stores the names of the employees as keys and the tax for each employee as value. We consider the tax as being 10% of the salary.\n\nsalaries = {'John': 50000, 'Anne': 66000, 'Antonio': 48000}\ntaxes = {key:salary*.10 for key, salary in salaries.items()}\nprint(taxes)\n\n# Challenge #55\n# Consider a text file called a.txt in the current working directory.\n# Create a Python script that opens the file in read-only mode, and reads only the word \"first\" (the 2nd word) from the file in a variable called word.\n\nwith open('python/challenges/files/a.txt') as file:\n word = file.read().splitlines()[0].split(' ')[1]\n\nprint(word)\n\n# Challenge #56\n# Consider a text file called workout.txt in the current working directory. \n# This is used by a fitness application written in Python that writes to the file the date and how many steps per day a person takes.\n# Take care not to overwrite the file!\n\nwith open('python/challenges/files/workout.txt', 'a') as file:\n file.write('May 25:8800\\n')\n","repo_name":"leoopd/learning-python","sub_path":"challenges/challenges_23.py","file_name":"challenges_23.py","file_ext":"py","file_size_in_byte":11327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43970034073","text":"\nclass RepeatedDigitCalculator:\n def __init__(self):\n print(\"This python program works out the range of numbers that have repeated digits\")\n print(\"In between starting_number and final_number (they are not included)\")\n\n self.run()\n\n def get_input(self):\n \n self.starting_number = int(input(\"Enter in the starting number: \"))\n self.final_number = int(input(\"Enter in the final number: \"))\n\n def make_list_n_z(self):\n range_list = []\n for value in range(self.starting_number + 1,self.final_number):\n range_list.append(value)\n\n return range_list\n \n def repeated_digit_finder(self,range_list):\n repeated_digit_count = 0\n for item in range_list:\n char_dict = {}\n for char in str(item):\n if char not in char_dict:\n char_dict[char] = 1 \n elif char in char_dict:\n char_dict[char] += 1\n\n for key in char_dict.keys():\n if char_dict[key] > 1:\n repeated_digit_count += 1\n break\n\n return repeated_digit_count\n \n\n def run(self):\n while True:\n self.get_input()\n range_list = self.make_list_n_z()\n print(range_list)\n repeated_digit_count = self.repeated_digit_finder(range_list)\n print(repeated_digit_count)\n\n \nRepeatedDigitCalculator()\n","repo_name":"Reis200/Repeated-Digit-Calculator","sub_path":"RepeatedDigitFinder.py","file_name":"RepeatedDigitFinder.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24417389784","text":"import os\nimport re\nimport logging\nfrom flask import Flask\nfrom slack import WebClient\nfrom slackeventsapi import SlackEventAdapter\nfrom randombot import RandomBot\n\n# Initialize a Flask app to host the events adapter\napp = Flask(__name__)\n# Create an events adapter and register it to an endpoint in the slack app for event injestion.\nslack_events_adapter = SlackEventAdapter(os.environ.get(\"SLACK_EVENTS_TOKEN\"), \"/slack/events\", app)\n\n# Initialize a Web API client\nslack_web_client = WebClient(token=os.environ.get(\"SLACKBOT_TOKEN\"))\n\ndef random_action(channel, action=None, **kwargs):\n \"\"\"Determine which action to perform based on parameter. For roll die if \n a kwarg of sides is passed in and it's a valid integer roll a dSIDES die\n \"\"\"\n # Create a new CoinBot\n random_bot = RandomBot(channel)\n\n if action == \"coin\":\n message = random_bot.flip_coin()\n elif action == \"die\":\n sides = kwargs.get(\"sides\", None)\n if sides is None or isinstance(sides, int) is False:\n message = random_bot.roll_die()\n else:\n print(f\"We got here. Sides: {sides}\")\n message = random_bot.roll_die(sides)\n elif action == \"card\":\n message = random_bot.random_card()\n\n # Post the onboarding message in Slack\n slack_web_client.chat_postMessage(**message)\n\n\n# When a 'message' event is detected by the events adapter, forward that payload\n# to this function.\n@slack_events_adapter.on(\"message\")\ndef message(payload):\n \"\"\"Parse the message event, and if the activation string is in the text,\n simulate a coin flip and send the result.\n \"\"\"\n\n # Get the event data from the payload\n event = payload.get(\"event\", {})\n\n # Get the text from the event that came through\n text = event.get(\"text\")\n\n # Check and see if the activation phrase was in the text of the message.\n # If so, execute the code to flip a coin.\n if \"flip a coin\" in text.lower():\n # Since the activation phrase was met, get the channel ID that the event\n # was executed on\n channel_id = event.get(\"channel\")\n # Execute the random action as a coin flip\n return random_action(channel_id, action=\"coin\")\n elif \"roll a die\" in text.lower() or \"roll a dice\" in text.lower():\n # Since the activation phrase was met, get the channel ID that the event\n # was executed on\n channel_id = event.get(\"channel\")\n # Execute the random action as a coin flip\n return random_action(channel_id, action=\"die\")\n elif \"pick a card\" in text.lower() or \"choose a card\" in text.lower():\n # Since the activation phrase was met, get the channel ID that the event\n # was executed on\n channel_id = event.get(\"channel\")\n # Execute the random action as a coin flip\n return random_action(channel_id, action=\"card\")\n elif \"roll a d\" in text.lower():\n # Since the activation phrase was met, get the channel ID that the event\n # was executed on\n channel_id = event.get(\"channel\")\n\n # Strip out the number from the command\n droll = text.split(\"roll a\")[1].strip().split()[0]\n try:\n int(droll[1:])\n except ValueError:\n pass\n else:\n return random_action(channel_id, action=\"die\", sides=int(droll[1:]))\n\nif __name__ == \"__main__\":\n # Create the logging object\n logger = logging.getLogger()\n\n # Set the log level to DEBUG. This will increase verbosity of logging messages\n logger.setLevel(logging.DEBUG)\n\n # Add the StreamHandler as a logging handler\n logger.addHandler(logging.StreamHandler())\n\n # Run our app on our externally facing IP address on port 3000 instead of\n # running it on localhost, which is traditional for development.\n app.run(host='0.0.0.0', port=8080)","repo_name":"MasonEgger/python-slackbot-sampleapp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"31042622991","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\n\r\ndef gethtml(url):\r\n r = requests.get(url)\r\n r.raise_for_status()\r\n r.encoding = r.apparent_encoding\r\n return r.text\r\n\r\ndef maketxt(txt):\r\n soup = BeautifulSoup(txt, \"html.parser\")\r\n\r\n str = ''\r\n p = soup.find_all(\"p\")\r\n for k,i in enumerate(p):\r\n try:\r\n if(i[\"class\"] == [\"p1\",]):#通过debug，发现在存储的时候是以列表形式存储的，所以如果判断其是否为字符串p1会出现错误\r\n continue\r\n except:\r\n pass\r\n str += i.get_text()\r\n return str\r\n\r\ndef savetxt(str, i):\r\n with open(r\"E:\\python\\scrapy\\rmrb\\rmrb{}.txt\".format(i), \"w\", encoding = \"utf-8\") as f:\r\n f.write(str)\r\n\r\n\r\nif __name__ == '__main__':\r\n url = \"http://paper.people.com.cn/rmrb/html/{0:4}-{1:02}/{2:02}/nw.D110000renmrb_{0:4}{1:02}{2:02}_{3}-{4:02}.htm\"#(2018)(12)(01-31)(2018)(12)(05) (1,2,3) (01-24)\r\n \"http://paper.people.com.cn/rmrb/html/2017-01/01/nw.D110000renmrb_20170101_1-01.htm\"\r\n strs = ''\r\n count = 1\r\n for year in (2017, 2018):\r\n for month in range(1, 13):\r\n for day in range (1,32):\r\n i = 0\r\n j = 0\r\n while(True):\r\n try:\r\n j += 1\r\n while(True):\r\n i += 1\r\n try:\r\n txt = gethtml(url.format(year, month, day, i, j))\r\n str = maketxt(txt)\r\n savetxt(str, count)\r\n count += 1\r\n strs += str\r\n print(count) #测试\r\n except:\r\n raise Exception\r\n break\r\n except:\r\n break\r\n\r\n savetxt(strs, 0)","repo_name":"zhoubay/-","sub_path":"rmrb.py","file_name":"rmrb.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9936788920","text":"from csv_cti.blueprints.web_api import web_api\nfrom flask import request,current_app\nfrom csv_cti.blueprints.op.md5_token import encrypt_md5\nfrom csv_cti.blueprints.op.queues import Queues_op\nfrom csv_cti.blueprints.op.csv_esl import Send_commands\n\n\ndef send_command(cmd_type,host,port,passwd,crm_uuid=None,extensin_number=None,customer_number=None,product_code=None):\n new_send_commands=Send_commands(cmd_type,host,port,passwd,crm_uuid,extensin_number,customer_number,product_code)\n new_send_commands.send_call()\n # return_data['data']=new_send_commands.job_status\n#queue\n@web_api.route('/queue-out-call/',methods=['POST'])\ndef queue_out_call():\n '''\n 队列名称\n 客户列表\n 并发数量\n 接听策略\n '''\n pass\n\n@web_api.route('/queues-add/',methods=['POST'])\ndef queues_add():\n return_data={}\n r_token=request.json.get('token')\n if r_token in encrypt_md5(current_app.config['MD5_KEY']):\n r_data=request.json.get('data')\n '''\n {\n\t\t\t\"token\":\"aecsv@88tech.net\",\n\t\t\t\"data\":\n [{\n \"name\":\"test\",//必选\n \"group\":\"C68\"//必选\n \n }]\n\t\t}\n '''\n \n try:\n Queues_op.add(r_data)\n except Exception as e:\n current_app.logger.debug(\"/queues-add/ 数据库操作失败：%s\",e)\n return_data['msg']='Voice abnormal, Please contact the Voice engineer'\n return return_data,500\n else:\n current_app.logger.info(\"/queues-add/ 添加成功\")\n return_data['msg']='Add OK'\n return return_data,200\n else:\n return_data['msg']='Auth Fail'\n return return_data,401\n\n@web_api.route('/queues-rm/',methods=['POST'])\ndef queues_rm():\n return_data={}\n r_token=request.json.get('token')\n if r_token in encrypt_md5(current_app.config['MD5_KEY']):\n r_data=request.json.get('data')\n '''\n {\n\t\t\t\"token\":\"aecsv@88tech.net\",\n\t\t\t\"data\"://留空查所有\n [{\n \"name\":\"test\",//必选\n \"group\":\"C68\"//必选\n }]\n\t\t}\n '''\n try:\n Queues_op.remove(r_data)\n except Exception as e:\n current_app.logger.debug(\"/queues-rm/ 数据库操作失败：%s\",e)\n return_data['msg']='Voice abnormal, Please contact the Voice engineer'\n return return_data,500\n else:\n current_app.logger.info(\"/queues-rm/ 删除成功\")\n return_data['msg']='Remove OK'\n return return_data,200\n else:\n return_data['msg']='Auth Fail'\n return return_data,401\n\n@web_api.route('/queues-list/',methods=['POST'])\ndef queues_list():\n return_data={}\n r_token=request.json.get('token')\n if r_token in encrypt_md5(current_app.config['MD5_KEY']):\n r_data=request.json.get('data')\n '''\n {\n\t\t\t\"token\":\"aecsv@88tech.net\",\n\t\t\t\"data\"://留空字符串查所有\n {\n \"name\":\"x\",//可选\n \"group\":\"C68\"//可选\n }\n\t\t}\n '''\n try:\n list=Queues_op.query(r_data)\n except Exception as e:\n current_app.logger.debug(\"/queues-list/ 数据库操作失败：%s\",e)\n return_data['msg']='Voice abnormal, Please contact the Voice engineer'\n return return_data,500\n else:\n current_app.logger.info(\"/queues-list/ 查询成功\")\n return_data['msg']='Query OK'\n return_data['data']=list[0:-1]\n return_data['total']=list[-1]\n return_data['page_size']=r_data['page_size']\n return_data['page_index']=r_data['page_index']\n return return_data,200\n else:\n return_data['msg']='Auth Fail'\n return return_data,401\n\n@web_api.route('/queues-reload/',methods=['POST'])\ndef queues_reload():\n '''\n {\n\t\t\t\"token\":\"36ad10c7b8ded102658aeb4b241f48cc\",\n\t\t\t\"data\":{}留空\n\t\t}\n '''\n return_data={}\n r_token=request.json.get('token')\n if r_token in encrypt_md5(current_app.config['MD5_KEY']):\n try:\n send_command('reload_mod_callcenter',current_app.config['ESL_DOMAIN'],current_app.config['ESL_PORT'],current_app.config['ESL_PASSWD'])\n except Exception as e:\n current_app.logger.debug(\"/queues-reload/ 连接ESL失败：%s\",e)\n return_data['msg']='Voice abnormal, Please contact the Voice engineer'\n return return_data,500\n else:\n current_app.logger.info(\"/queues-reload/ 接口调用成功:reload_mod_callcenter\")\n return_data['msg']='Call OK'\n return return_data,200\n else:\n return_data['msg']='Auth Fail'\n return return_data,401","repo_name":"Osmond1689/csv-cti","sub_path":"csv_cti/blueprints/web_api/views/queues.py","file_name":"queues.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40012403860","text":"import xml.dom.minidom as minidom\n\ndef parsestores(xml):\n dom = minidom.parseString(xml) \n storearr = []\n \n stores = getstores(dom)\n for store in stores:\n storearr.append(parsestore(store))\n \n return storearr\n\ndef parsestore(store):\n storeobj = {}\n for node in store.childNodes:\n if node.nodeType == node.ELEMENT_NODE:\n storeobj[node.nodeName] = getText(node.firstChild)\n\n return storeobj\n\ndef parseproducts(xml):\n dom = minidom.parseString(xml)\n productsarr = []\n \n products = getproducts(dom)\n for product in products:\n productsarr.append(parseproduct(product))\n \n return productsarr\n\ndef parseproduct(product):\n productobj = {}\n for node in product.childNodes:\n if node.nodeType == node.ELEMENT_NODE:\n productobj[node.nodeName] = getText(node.firstChild)\n \n return productobj\n\ndef getstores(storesdom):\n return storesdom.getElementsByTagName('Store')\n\ndef getproducts(productsdom):\n return productsdom.getElementsByTagName('Product')\n\ndef getText(el):\n if el == None:\n return \"\"\n return el.data\n ","repo_name":"jarobb3/aircarta","sub_path":"aparser.py","file_name":"aparser.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32988227362","text":"from django.shortcuts import redirect, render\nfrom .models import player\nfrom .substring import Substr\n#IPL players\n\nfrom Backend.Runs import runs_info\n\ndef home(request):\n if request.method == 'POST':\n name=request.POST['name']\n team=request.POST['team']\n no=request.POST['no']\n image=request.FILES['image']\n\n obj = player()\n obj.Name = name\n obj.Team = team\n obj.No = no\n obj.Image = image\n obj.save()\n data=player.objects.all()\n return render(request,'home.html',locals())\n data = player.objects.all()\n return render(request,'home.html',locals())\n\n\ndef update(request,id):\n data=player.objects.get(id=id)\n if request.method == \"POST\":\n name=request.POST['name']\n team=request.POST['team']\n no=request.POST['no']\n try:\n image=request.FILES['image']\n except:\n image = data.Image\n\n data.Name = name\n data.Team = team\n data.No = no\n data.Image = image\n data.save()\n return redirect('home')\n\n return render(request,'update.html',locals())\n\n\ndef delete(request,id):\n data = player.objects.get(id=id)\n data.delete()\n return redirect('home')\n\ndef table(request):\n if request.method == \"POST\":\n name = request.POST['search']\n Db = player.objects.all()\n\n player_list = []\n for n in Db:\n player_list.append(n.Name)\n\n sorted_list = []\n for crew in player_list:\n str_obj = Substr()\n if str_obj.Search(crew.lower(),name.lower()) != \"\":\n sorted_list.append(crew)\n\n data=[]\n for i in sorted_list:\n details = player.objects.get(Name = i)\n data.append(details)\n return render(request,'table.html',locals())\n\n data = player.objects.all()\n return render (request,'table.html',locals())\n\n\ndef generate_player_info(request,id):\n data = player.objects.get(id=id)\n name = data.Name\n obj = runs_info(name)\n print(obj.total_runs())\n return render(request,'players.html',locals())","repo_name":"Senthilvadivel-20/Deme_DB","sub_path":"player/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4827136701","text":"set1 = {} #empty dictionary\nset2 = set() #empty set\n# print(type(set2))\n\nset3 = {1,2,3,4,5} #creating a set\nprint(set3)\n\nfor ele in set3: #traversing a set\n print(ele)\n\nset3.add(6) #add an element to set\nl1 = [8,9]\nset3.update(l1) #add multiple elements to the set\nprint(set3)\n\nset3.discard(8) #remove element from set\nresult = set3.pop() #pop deletes from the first element\n\nprint(set3, \" \", result)\n\nset4 = {2,3,5,10}\n\nprint(set3.symmetric_difference(set4)) #operations on set\n\n\n\n","repo_name":"preetu391/master-in-python-with-dsa","sub_path":"set.py","file_name":"set.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36261736785","text":"\"\"\"change source_checksum and periodic_task schema\n\nRevision ID: a37b35b25ceb\nRevises: 4d6b97fac8ed\nCreate Date: 2022-06-06 12:27:17.675738\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.orm import Session\nfrom app.models import Task, SourceChecksum\n\n\n# revision identifiers, used by Alembic.\nrevision = \"a37b35b25ceb\"\ndown_revision = \"4d6b97fac8ed\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n bind = op.get_bind()\n session = sa.orm.Session(bind=bind)\n\n op.add_column(\n \"periodic_task\",\n sa.Column(\"id\", sa.Integer(), autoincrement=True, nullable=True),\n )\n op.drop_constraint(\"periodic_task_pkey\", \"periodic_task\", \"primary\")\n\n tasks = session.execute(f\"SELECT * FROM periodic_task;\")\n for index, row in enumerate(tasks):\n session.execute(\n f\"UPDATE periodic_task SET id={index+1} WHERE name='{row.name}';\"\n )\n\n op.alter_column(\"periodic_task\", \"id\", nullable=False)\n\n op.create_primary_key(\"periodic_task_pkey\", \"periodic_task\", [\"id\"])\n op.alter_column(\"periodic_task\", \"name\", existing_type=sa.VARCHAR(), nullable=True)\n\n op.add_column(\n \"source_checksum\",\n sa.Column(\"id\", sa.Integer(), autoincrement=True, nullable=True),\n )\n op.drop_constraint(\"source_checksum_pkey\", \"source_checksum\", \"primary\")\n\n checksums = session.execute(f\"SELECT * FROM source_checksum;\")\n for index, row in enumerate(checksums):\n session.execute(\n f\"UPDATE source_checksum SET id={index+1} WHERE name='{row.source}';\"\n )\n\n op.alter_column(\"source_checksum\", \"id\", nullable=False)\n op.create_primary_key(\"source_checksum_pkey\", \"source_checksum\", [\"id\"])\n op.alter_column(\n \"source_checksum\", \"source\", existing_type=sa.VARCHAR(), nullable=True\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.alter_column(\n \"source_checksum\", \"source\", existing_type=sa.VARCHAR(), nullable=False\n )\n op.drop_column(\"source_checksum\", \"id\")\n op.create_primary_key(\"source_checksum_pkey\", \"source_checksum\", [\"source\"])\n\n op.alter_column(\"periodic_task\", \"name\", existing_type=sa.VARCHAR(), nullable=False)\n op.drop_column(\"periodic_task\", \"id\")\n op.create_primary_key(\"periodic_task_pkey\", \"periodic_task\", [\"name\"])\n # ### end Alembic commands ###\n","repo_name":"FigureHook/hook_api","sub_path":"alembic/versions/a37b35b25ceb_change_source_checksum_and_periodic_task_schema.py","file_name":"a37b35b25ceb_change_source_checksum_and_periodic_task_schema.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18461108490","text":"#I wrote this code in aug2020, I git-push this in june 2022\nfrom ptprotoplus import adc\nfrom time import sleep\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nfrom pprint import pprint\n\n\n##settings:##\nlight_sensor = adc.ADCProbe()\nscope = [\"https://spreadsheets.google.com/feeds\",'https://www.googleapis.com/auth/spreadsheets',\"https://www.googleapis.com/auth/drive.file\",\"https://www.googleapis.com/auth/drive\"]\ncreds = ServiceAccountCredentials.from_json_keyfile_name(\"creds.json\", scope)\nclient = gspread.authorize(creds)\n\n##code:##\nsheet = client.open(\"SMARTLIGHT_BM\").sheet1\nsheet_report = client.open(\"SMARTLIGHT_BM\").get_worksheet(1)\n\nwhile True:\n\tif light_sensor.read_value(0) < 100:\n\t\tsheet.update_cell(4,4, \"cheak report\")\n\t\t#report_log\n\t\tBAD_LIGHT = [\"over the sensor\", \"LED\", \"9W\", \"NOT OK\", \"replace!\" ]\n\t\tsheet_report.insert_row(BAD_LIGHT,2)\n\t\tsleep(3)\n\t\tbreak\n\telse:\n\t\tsheet.update_cell(4,4, \"ok\")\n\t\tsleep(3)\n","repo_name":"ChipLuxury-EWA/smart_light","sub_path":"light_sensor_pi_top.py","file_name":"light_sensor_pi_top.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40087363396","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.backends.backend_pdf import PdfPages\r\n\r\nxposition1 = np.arange(3)\r\nxposition2 = np.arange(4,7)\r\nxposition3 = np.arange(8,11)\r\nbar_width=0.25\r\n#y_axis_range = np.arange(0.0,0.8,0.2)\r\n\r\nfname = '1.txt'\r\n\r\nfile=open(fname, 'r')\r\nlines=list(file.readlines())\r\ntemp_file=open('temp_file.txt','w')\r\nfor line in lines:\r\n new_line=line[1:len(line)-2]\r\n print(new_line,file=temp_file)\r\ntemp_file.close()\r\ndata=np.loadtxt('temp_file.txt',delimiter=',')\r\n\r\nprint(fname)\r\n\r\nplt.bar(x=xposition2, height=data[2][3:6], label='SLSQP', color='b',align='center',width=bar_width)\r\nplt.bar(x=xposition2+bar_width, height=data[1][3:6], label='GDS', color='g',align='center',width=bar_width)\r\nplt.bar(x=xposition2+2*bar_width, height=data[0][3:6], label='ranRFL', color='r',align='center',width=bar_width)\r\n\r\nplt.bar(x=xposition3, height=data[2][6:9], color='b',align='center',width=bar_width)\r\nplt.bar(x=xposition3+bar_width, height=data[1][6:9],color='g',align='center',width=bar_width)\r\nplt.bar(x=xposition3+2*bar_width, height=data[0][6:9],color='r',align='center',width=bar_width)\r\n\r\nplt.bar(x=xposition1, height=data[2][0:3], color='b',align='center',width=bar_width)\r\nplt.bar(x=xposition1+bar_width, height=data[1][0:3],color='g',align='center',width=bar_width)\r\nplt.bar(x=xposition1+2*bar_width, height=data[0][0:3],color='r',align='center',width=bar_width)\r\n\r\nfontx={'size':22, 'weight':'normal'}\r\nfonty={'size':23, 'weight':'normal'}\r\n# plt.xlabel(\"Different F Distribution\",fontx)\r\nplt.ylabel('Average Error Rate',fonty)\r\nplt.xticks(list(xposition1+bar_width)+list(xposition2+bar_width)+list(xposition3+bar_width),labels=('$S_1$','$M_1$','$L_1$','$S_2$','$M_2$','$L_2$','$S_3$','$M_3$','$L_3$'),fontsize=21)\r\nplt.yticks(fontsize=22)\r\nplt.legend(fontsize = 16)\r\nplt.tight_layout()\r\n#plt.gca().axes.get_yaxis().set_visible(False) #隐藏y轴\r\n\r\npdf=PdfPages(fname+'.pdf')\r\npdf.savefig()\r\npdf.close()\r\n\r\nplt.show()","repo_name":"Huage001/Improving-Reliability-for-Federated-Learning-in-Mobile-Edge-Networks","sub_path":"柱状图画图.py","file_name":"柱状图画图.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"38595545889","text":"class GameStats:\n #Track game stats for Alien Invasion\n def __init__(self, ai_game):\n #initialize game stats\n self.high_score = 0\n self.settings = ai_game.settings\n self.reset_stats()\n\n\n #start Alien Invasion in an active state\n self.game_active = False\n\n def reset_stats(self):\n #initialize stats that can change during gameplay\n self.ships_left = self.settings.ship_limit\n self.score = 0\n self.level = 1","repo_name":"BBode11/CIT228","sub_path":"Lesson6/AlienInvasion/game_stats.py","file_name":"game_stats.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"866827151","text":"a = True\nb = False\nx = ( 'bear', 'bunny', 'tree', 'sky', 'rain' )\ny = 'bear'\n\nif a and b:\n print('expression is true')\nelse:\n print('expression is false')\n\n# boolean operators\n# and: both values are true\n# or: one of the values is true\n# not: the value is not true\n# in: check if is in a collection (arrays)\n# is: check if are the same\n","repo_name":"AngelBFdev/Python-course-linkedin","sub_path":"Chap05/boolean.py","file_name":"boolean.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71037799551","text":"# for module compiling\nfrom building import *\nimport os\n\ncwd = GetCurrentDir()\n\nsrc = Split('''\nDAP/DAP.c\nDAP/SW_DP.c\nSWD_host/SWD_host.c\n''')\n\npath = []\npath += [cwd + '/DAP']\npath += [cwd + '/SWD_host']\n\n\ngroup = DefineGroup('DAPLink', src, depend = [''], CPPPATH = path)\n\nReturn('group')\n","repo_name":"initdc/hmi-board-daplink","sub_path":"ports/DAPLink/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74062421951","text":"# -*- coding: utf-8 -*-\nimport time\nfrom datetime import datetime, timedelta\nfrom dateutil import relativedelta\nimport babel\nfrom odoo import api, fields, models, tools, _\nfrom odoo.exceptions import UserError, ValidationError\nfrom odoo.tools.safe_eval import safe_eval\nfrom reportlab.lib.pagesizes import landscape, letter, A2, A4\nfrom reportlab.platypus import SimpleDocTemplate\nfrom reportlab.lib.styles import ParagraphStyle\nfrom reportlab.lib.enums import TA_JUSTIFY, TA_CENTER, TA_RIGHT, TA_LEFT\nfrom decimal import *\nfrom math import modf\n\n\nclass HrPayslipExt(models.Model):\n _inherit = 'hr.payslip'\n\n @api.multi\n def imprimir_boleta(self):\n self.ensure_one()\n dias_no_laborados,dias_laborados,first,second,dias_faltas = 0,0,0,0,0\n payslips = self.env['hr.payslip'].search([('payslip_run_id','=',self.payslip_run_id.id),('employee_id','=',self.employee_id.id)])\n planilla_ajustes = self.env['planilla.ajustes'].search([], limit=1)\n try:\n ruta = self.env['main.parameter.hr'].search([])[0].dir_create_file\n except:\n raise UserError('Falta configurar un directorio de descargas en el menu Configuracion/Parametros/Directorio de Descarga')\n\n archivo_pdf = SimpleDocTemplate(\n ruta+\"planilla_tmp.pdf\", pagesize=A4, rightMargin=10, leftMargin=10, topMargin=10, bottomMargin=5)\n\n elements = []\n company = self.env['res.company'].search([], limit=1)\n categories = self.env['hr.salary.rule.category'].search(\n [('aparece_en_nomina', '=', True)], order=\"secuencia\")\n\n for payslip in payslips:\n dias_no_laborados += int(payslip.worked_days_line_ids.search([('code', '=', planilla_ajustes.cod_dias_no_laborados.codigo if planilla_ajustes else ''),\n ('payslip_id', '=', payslip.id)], limit=1).number_of_days)\n for payslip in payslips:\n if not payslip.contract_id.hourly_worker:\n dias_laborados += int(payslip.worked_days_line_ids.search([('code', '=', planilla_ajustes.cod_dias_laborados.codigo if len(planilla_ajustes) > 0 else ''),\n ('payslip_id', '=', payslip.id)], limit=1).number_of_days)\n dias_laborados=dias_laborados-self.feriados if dias_laborados > 0 else 0\n if not planilla_ajustes.cod_dias_subsidiados:\n raise UserError('Falta configurar codigos de dias subsidiados en Parametros de Boleta.')\n wd_codes = planilla_ajustes.cod_dias_subsidiados.mapped('codigo')\n dias_subsidiados = 0\n for payslip in payslips:\n wds = filter(lambda l:l.code in wd_codes and l.payslip_id == payslip,payslip.worked_days_line_ids)\n dias_subsidiados += sum([int(i.number_of_days) for i in wds])\n\n query_horas_sobretiempo = '''\n select sum(number_of_days) as dias ,sum(number_of_hours) as horas ,sum(minutos) as minutos from hr_payslip_worked_days\n where (code = 'HE25' OR code = 'HE35' or code = 'HE100')\n and payslip_id in (%s)\n ''' % (','.join(str(i) for i in payslips.mapped('id')))\n\n self.env.cr.execute(query_horas_sobretiempo)\n total_sobretiempo = self.env.cr.dictfetchone()\n for payslip in payslips:\n dias_faltas += self.env['hr.payslip.worked_days'].search([('code', '=', planilla_ajustes.cod_dias_no_laborados.codigo if planilla_ajustes else ''),\n ('payslip_id', '=', payslip.id)], limit=1).number_of_days\n if self.employee_id.calendar_id:\n total = self.employee_id.calendar_id.average_hours if self.employee_id.calendar_id.average_hours > 0 else 8\n else:\n total = 8\n\n total_horas_jornada_ordinaria = 0\n for payslip in payslips:\n if payslip.contract_id.hourly_worker:\n total_horas_jornada_ordinaria += sum(payslip.worked_days_line_ids.filtered(lambda l:l.code == planilla_ajustes.cod_dias_laborados.codigo).mapped('number_of_hours'))\n\n if self.employee_id.calendar_id:\n total = self.employee_id.calendar_id.average_hours if self.employee_id.calendar_id.average_hours > 0 else 8\n else:\n total = 8\n\n total_horas_minutos = modf(int(dias_laborados-dias_faltas)*total) if total_horas_jornada_ordinaria == 0 else total_horas_jornada_ordinaria\n total_horas_jornada_ordinaria = total_horas_minutos[1]\n total_minutos_jornada_ordinaria = Decimal(str(total_horas_minutos[0] * 60)).quantize(Decimal('1.'), rounding=ROUND_HALF_UP)\n\n payslip_run = self.env['hr.payslip.run']\n\n payslip_run.genera_boleta_empleado(self.date_from, self.date_to, payslips, str(dias_no_laborados), str(int(dias_laborados - dias_faltas)), str(total_horas_jornada_ordinaria), str(total_minutos_jornada_ordinaria), (total_sobretiempo), str(dias_subsidiados), elements,\n company, categories, planilla_ajustes)\n\n elements = elements*2\n archivo_pdf.build(elements)\n\n import sys\n reload(sys)\n sys.setdefaultencoding('iso-8859-1')\n import os\n vals = {\n 'output_name': 'Boleta-%s.pdf' % (payslip[0].employee_id.name+'-'+payslip[0].date_from+'-'+payslip[0].date_to),\n 'output_file': open(ruta+\"planilla_tmp.pdf\", \"rb\").read().encode(\"base64\"),\n }\n sfs_id = self.env['planilla.export.file'].create(vals)\n return {\n \"type\": \"ir.actions.act_window\",\n \"res_model\": \"planilla.export.file\",\n \"views\": [[False, \"form\"]],\n \"res_id\": sfs_id.id,\n \"target\": \"new\",\n }","repo_name":"dhp-denero/DTDATA_A","sub_path":"bloomcker_addons/project_bloomcker_ATF_req/models/hr_payslip_ext.py","file_name":"hr_payslip_ext.py","file_ext":"py","file_size_in_byte":5759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37355735646","text":"from django.urls import path\nfrom shortener import views\n\napp_name = \"shortener\"\n\nurlpatterns = [\n path(\"\", views.URLShortenerListView.as_view(), name=\"list\"),\n path(\"create/\", views.URLShortenerCreateView.as_view(), name=\"create\"),\n path(\"/\", views.RedirectURLView.as_view(), name=\"url\"),\n]\n","repo_name":"EhsanSkh/Django-URL-Shortening","sub_path":"src/shortener/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21471161835","text":"# -*- Python -*-\n#\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n# Jiao Lin\n# California Institute of Technology\n# (C) 2006-2011 All Rights Reserved\n#\n# {LicenseText}\n#\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n\nimport luban\n\nfrom ....DemoPanelActor import Actor as base\nclass Actor(base):\n\n title='replaceContent'\n description = [\n 'action to replace the interior of a UI element a new element'\n ]\n rank = 10004\n \n def createDemoPanel(self, **kwds):\n container = luban.e.document()\n \n doc = container.document(\n title = 'the document for which the interior with be replaced', \n id=luban.uuid())\n doc.paragraph(text='interior')\n \n newdoc = luban.e.document(title = 'new interior')\n \n button = container.button(label = 'click me')\n button.onclick = luban.a.select(element = doc).replaceContent(newcontent = newdoc)\n return container\n \n\n\n# End of file \n","repo_name":"yxqd/luban","sub_path":"timber/aokuang.timber/aokuang.timber/actors/elementbase/replaceContent.py","file_name":"replaceContent.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13816029773","text":"import boto3\r\nimport logging\r\n\r\ndef lambda_handler(event, context):\r\n s3 = boto3.client('s3')\r\n bucket='9514-loaaccelerator-prod'\r\n response = s3.list_objects(\r\n Bucket=bucket,\r\n Prefix='PTOAccrued_to_Process'\r\n )\r\n try:\r\n subfolder_filename=(response['Contents'][1]['Key'])\r\n split_ff=subfolder_filename.split(\"/\")\r\n filename=split_ff[1]\r\n print(filename)\r\n #logging.info(filename)\r\n destination = \"PTOAccrued/\"+filename\r\n if filename is not None or (filename == 0):\r\n #subprocess.run('aws s3 mv s3://$filename s3://9514-loaaccelerator-prod/PTOAccrued/')\r\n copy_S3 = s3.copy_object(\r\n Bucket=bucket,\r\n CopySource={'Bucket': bucket, 'Key': subfolder_filename},\r\n Key=destination\r\n )\r\n print(copy_S3)\r\n #logging.info(copy_S3)\r\n delete_S3 = s3.delete_object(\r\n Bucket=bucket,\r\n Key=subfolder_filename\r\n )\r\n print(delete_S3)\r\n #logging.info(delete_S3)\r\n except IndexError:\r\n print(\"no objects found\")\r\n #logging.info(\"no objects found\")","repo_name":"lekha73/Python_Automation_NPM_Lambda","sub_path":"S3_File_Move_Scheduler.py","file_name":"S3_File_Move_Scheduler.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41885900089","text":"from NN_models import my_models\nfrom print_stat import print_stat\nimport numpy as np\nimport numpy.matlib as npm\nimport matplotlib.pyplot as plt\nfrom utility_func import sum_power\nfrom plotters import plot_scatter_colored \nfrom plotters import plot_cmp\nfrom sklearn import preprocessing\n\ndropout_pr = 0.5\nNN_scaling = False\nfine_tuning = False\n\ntry:\n load_data_flag \nexcept :\n load_data_flag = True \n\nif load_data_flag:\n from load_data import load\n train_x_s, train_y_s, val_x_s, val_y_s, train_x_t, train_y_t, \\\n val_x_t, val_y_t, test_x_t, test_y_t = load()\n\n# This way we preserve the statistics of the signal\n\nx_s = np.concatenate((train_x_s, val_x_s), axis = 0)\nx_t = np.concatenate((train_x_s, val_x_t), axis = 0)\n\n\nprint('Kernel mean matching')\n# =============================================================================\nkmm_kernel = 'lin'\nB = 1\n# =============================================================================\n\n# from kernel_mean_matching import eprimical_kmm as ekmm\n# coef_s, coef_t = ekmm(x_t, x_s, kern = kmm_kernel, B = B)\n\nfrom kernel_mean_matching import kernel_mean_matching as kmm\ncoef_s = kmm(x_t, x_s, kern = kmm_kernel, B = B)\n\n# from kernel_mean_matching import eprimical_kmm_emb as ekmm_emb\n# coef_s, coef_t = ekmm_emb(x_t, x_s, kern = kmm_kernel, B = B,\n# embedder_type = 'autoencoder', n_components = 10)\n\n#coef_s, coef_t = ekmmd(x_t, x_s, kern = kmm_kernel, B = B)\n\nprint('Done')\n\n# =============================================================================\ncoef_val_s = coef_s[train_y_s.shape[0]:]\ncoef_s = coef_s[:train_y_s.shape[0]]\n\n# =============================================================================\ntraining_weights = npm.repmat(coef_s, 1 , train_y_s.shape[1])\ntraining_val_weights = npm.repmat(coef_val_s, 1 , val_y_s.shape[1])\nnum_weights = training_weights.shape[1]\n\nnum_inputs = train_x_s.shape[1]# input layer size\n# =============================================================================\nif 'model' in locals():\n del model\nif 'w_model_obj' in locals():\n del w_model_obj\n \nw_model_obj = my_models(num_inputs, num_weights = num_weights,\n model_type='weighted', dropout = dropout_pr)\nmodel = w_model_obj.build_model()\nmodel = w_model_obj.fit(train_x_s, train_y_s, val_x_s, val_y_s, \n val_w = training_val_weights ,\n scale = NN_scaling, training_w = training_weights)\n\nerror_sample_bias, ssbc_train_loss, ssbc_val_loss, ssbc_test_loss =\\\n w_model_obj.evaluate(train_x_t, train_y_t, val_x_t, val_y_t,\n test_x_t, test_y_t, scale = NN_scaling)\n \nif fine_tuning:\n training_weights = np.ones(train_y_t.shape)\n training_val_weights = np.ones(val_y_t.shape)\n model = w_model_obj.fit(train_x_t, train_y_t, val_x_t, val_y_t, \n val_w = training_val_weights ,\n scale = NN_scaling, training_w = training_weights)\n\n error_sample_bias_f, ssbc_train_loss_f, ssbc_val_loss_f, ssbc_test_loss_f =\\\n w_model_obj.evaluate(train_x_t, train_y_t, val_x_t, val_y_t,\n test_x_t, test_y_t, scale = NN_scaling)\ntitle = 'sample selection bias'\nprint_stat(title, error_sample_bias, ssbc_train_loss, ssbc_val_loss, ssbc_test_loss)\n\nif fine_tuning:\n title = 'sample selection bias with fine_tuning'\n print_stat(title, error_sample_bias_f, ssbc_train_loss_f, ssbc_val_loss_f, ssbc_test_loss_f)\n# =============================================================================\nfrom plotters import error_dist , plot_cdf, plot_embeding\nerror_dist(train_x_s, train_y_s, train_x_t, train_y_t, error_sample_bias,\n test_y_t, weights=coef_s, title = title)\nplt.show()\n\nplot_embeding(train_x_s, train_x_t, coef_s, train_y_s, train_y_t)\n\n\nplot_cdf(error_sample_bias, 100) \nplt.show()\n\nprint_stat(title, error_sample_bias, ssbc_train_loss, ssbc_val_loss, ssbc_test_loss)\n\n\ndel model\ndel w_model_obj","repo_name":"saeedshojaee/transfer_learning","sub_path":"sample_selection_bias_by_unlabeled_tranining.py","file_name":"sample_selection_bias_by_unlabeled_tranining.py","file_ext":"py","file_size_in_byte":3957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16283209001","text":"from collections import OrderedDict\n\nfrom fairseq import utils\nfrom fairseq.models import FairseqMultiModel, register_model, register_model_architecture, BaseFairseqModel\n\nfrom fairseq.models.transformer import (\n base_architecture,\n Embedding,\n TransformerEncoder,\n TransformerDecoder,\n TransformerModel,\n)\n\nfrom .masked_attention_decoder_layer import MaskedAttentionDecoderLayer\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass XTransformerEncoder(TransformerEncoder):\n\n def __init__(self, args, dictionary, embed_tokens):\n super().__init__(args, dictionary, embed_tokens)\n self.mask_idx = dictionary.mask_index\n\n def forward(self, src_tokens, src_lengths,\n source_sent_ids=None, target_sent_ids=None):\n\n x = self.embed_scale * self.embed_tokens(src_tokens)\n if self.embed_positions is not None:\n x += self.embed_positions(src_tokens)\n\n x = self.dropout_module(x)\n\n # B x T x C -> T x B x C\n x = x.transpose(0, 1)\n\n # compute padding mask\n encoder_padding_mask = src_tokens.eq(self.padding_idx) | src_tokens.eq(self.mask_idx)\n if not encoder_padding_mask.any():\n encoder_padding_mask = None\n\n # encoder layers\n for layer in self.layers:\n x = layer(x, encoder_padding_mask)\n\n if self.layer_norm:\n x = self.layer_norm(x)\n \n return {\n 'encoder_out': x, # T x B x C\n 'encoder_padding_mask': encoder_padding_mask, # B x T\n 'source_sent_ids': source_sent_ids # B x S\n }\n \n def reorder_encoder_out(self, encoder_out, new_order):\n \"\"\"\n Reorder encoder output according to *new_order*.\n Args:\n encoder_out: output from the ``forward()`` method\n new_order (LongTensor): desired order\n Returns:\n *encoder_out* rearranged according to *new_order*\n \"\"\"\n if encoder_out['encoder_out'] is not None:\n encoder_out['encoder_out'] = \\\n encoder_out['encoder_out'].index_select(1, new_order)\n if encoder_out['encoder_padding_mask'] is not None:\n encoder_out['encoder_padding_mask'] = \\\n encoder_out['encoder_padding_mask'].index_select(0, new_order)\n if encoder_out['source_sent_ids'] is not None:\n encoder_out['source_sent_ids'] = \\\n encoder_out['source_sent_ids'].index_select(0, new_order) \n \n return encoder_out\n\n\nclass XTransformerDecoder(TransformerDecoder):\n\n def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):\n super().__init__(args, dictionary, embed_tokens, no_encoder_attn)\n \n self.layers = nn.ModuleList([])\n self.layers.extend([\n MaskedAttentionDecoderLayer(args, no_encoder_attn)\n for _ in range(args.decoder_layers)\n ])\n self.cnt = 0\n \n def forward(self, prev_output_tokens, encoder_out=None,\n incremental_state=None, positions=None):\n if encoder_out is not None and type(encoder_out) == type(dict()) \\\n and 'source_sent_ids' in encoder_out.keys() and encoder_out['source_sent_ids'] is not None:\n\n src_len = encoder_out['source_sent_ids'].size()[-1]\n tgt_len = prev_output_tokens.size()[1]\n beam_batch_size = prev_output_tokens.size()[0]\n\n source_sent_ids = encoder_out['source_sent_ids']\n is_sep = prev_output_tokens.eq(5).int()\n target_sent_ids = is_sep.cumsum(dim=1)\n \n # T is current time step\n s = source_sent_ids.unsqueeze(1).repeat(1, tgt_len, 1)\n t = target_sent_ids.unsqueeze(2).repeat(1, 1, src_len)\n sent_mask = torch.ne(s, t) \n sent_mask = sent_mask[:, -1, :]\n sent_mask = sent_mask.unsqueeze(1)\n encoder_out['encoder_padding_mask'] = sent_mask\n\n # embed positions\n positions = self.embed_positions(\n prev_output_tokens,\n incremental_state=incremental_state,\n positions=positions,\n ) if self.embed_positions is not None else None\n\n if incremental_state is not None:\n prev_output_tokens = prev_output_tokens[:, -1:]\n if positions is not None:\n positions = positions[:, -1:]\n\n # embed tokens and positions\n x = self.embed_scale * self.embed_tokens(prev_output_tokens)\n\n if self.project_in_dim is not None:\n x = self.project_in_dim(x)\n\n if positions is not None:\n x += positions\n x = self.dropout_module(x)\n\n # B x T x C -> T x B x C\n x = x.transpose(0, 1)\n attn, attns = None, []\n\n inner_states = [x]\n\n # decoder layers\n for layer in self.layers:\n x, attn, _ = layer(\n x,\n encoder_out['encoder_out'] if encoder_out is not None else None,\n encoder_out['encoder_padding_mask'] if encoder_out is not None else None,\n incremental_state,\n self_attn_mask=self.buffered_future_mask(x) if incremental_state is None else None,\n need_attn=True,\n )\n inner_states.append(x)\n attns.append(attn)\n\n if self.layer_norm:\n x = self.layer_norm(x)\n\n # T x B x C -> B x T x C\n x = x.transpose(0, 1)\n\n if self.project_out_dim is not None:\n x = self.project_out_dim(x)\n\n if self.adaptive_softmax is None:\n # project back to size of vocabulary\n if self.share_input_output_embed:\n x = F.linear(x, self.embed_tokens.weight)\n else:\n x = F.linear(x, self.embed_out)\n\n return x, {'attn': attn, 'inner_states': inner_states, 'attns': attns}\n\n\n@register_model('xtransformer')\nclass XTransformerModel(BaseFairseqModel):\n def __init__(self, encoders, decoders, eval_lang_pair=None):\n super().__init__()\n self.encoders = nn.ModuleDict(encoders)\n self.decoders = nn.ModuleDict(decoders)\n self.tgt_key = None\n if eval_lang_pair is not None:\n self.source_lang = eval_lang_pair.split('-')[0]\n self.target_lang = eval_lang_pair.split('-')[1]\n\n def get_normalized_probs(self, net_output, log_probs, sample=None):\n \"\"\"Get normalized probabilities (or log probs) from a net's output.\"\"\"\n if hasattr(self, 'decoder'):\n return self.decoder.get_normalized_probs(net_output, log_probs, sample)\n elif hasattr(self, 'decoders'):\n return self.decoders[self.tgt_key].get_normalized_probs(net_output, log_probs, sample)\n elif torch.is_tensor(net_output):\n logits = net_output.float()\n if log_probs:\n return F.log_softmax(logits, dim=-1)\n else:\n return F.softmax(logits, dim=-1)\n raise NotImplementedError\n\n def max_positions(self):\n return None\n\n def max_decoder_positions(self):\n return min(decoder.max_positions() for decoder in self.decoders.values())\n\n def forward(self, src_tokens, src_lengths, prev_output_tokens,\n source_sent_ids, target_sent_ids, src_key, tgt_key, positions=None):\n\n encoder_out = self.encoders[src_key](src_tokens, src_lengths)\n\n input_encoder_out = encoder_out['encoder_out']\n input_encoder_padding_mask = encoder_out['encoder_padding_mask']\n\n src_len = src_tokens.size()[1]\n tgt_len = prev_output_tokens.size()[1]\n # (B, S) -> (B,1,S) -> (B,T,S)\n s = source_sent_ids.unsqueeze(1).repeat(1, tgt_len, 1)\n # (B, T) -> (B,T,1) -> (B,T,S)\n t = target_sent_ids.unsqueeze(2).repeat(1, 1, src_len)\n\n sent_mask = torch.ne(s, t)\n encoder_out['encoder_padding_mask'] = sent_mask\n\n decoder_out = self.decoders[tgt_key](\n prev_output_tokens,\n encoder_out=encoder_out,\n positions=positions\n )\n self.tgt_key = tgt_key\n return decoder_out\n\n def add_args(parser):\n TransformerModel.add_args(parser)\n parser.add_argument('--share-encoders', action='store_true',\n help='share encoders across languages')\n parser.add_argument('--share-decoders', action='store_true',\n help='share decoders across languages')\n\n @classmethod\n def build_model(cls, args, task):\n langs = [lang for lang in args.langs]\n\n embed_tokens = {}\n for lang in langs:\n if len(embed_tokens) == 0 or args.share_all_embeddings is False:\n embed_token = build_embedding(\n task.dicts[lang], args.encoder_embed_dim, args.encoder_embed_path,\n )\n embed_tokens[lang] = embed_token\n else:\n embed_tokens[lang] = embed_tokens[langs[0]]\n\n args.share_decoder_input_output_embed = True\n encoders, decoders = {}, {}\n\n for lang in langs:\n encoder_embed_tokens = embed_tokens[lang]\n decoder_embed_tokens = encoder_embed_tokens\n if lang in args.source_langs:\n encoder = XTransformerEncoder(args, task.dicts[lang], encoder_embed_tokens)\n encoders[lang] = encoder\n if lang in args.target_langs:\n decoder = XTransformerDecoder(args, task.dicts[lang], decoder_embed_tokens)\n decoders[lang] = decoder\n return XTransformerModel(encoders, decoders, args.eval_lang_pair)\n\n @property\n def decoder(self):\n return self.decoders[self.target_lang]\n\n @property\n def encoder(self):\n return self.encoders[self.source_lang]\n\n\n@register_model_architecture('xtransformer', 'xtransformer')\ndef base_x_transformer(args):\n base_architecture(args)\n\n\ndef build_embedding(dictionary, embed_dim, path=None):\n num_embeddings = len(dictionary)\n padding_idx = dictionary.pad()\n emb = Embedding(num_embeddings, embed_dim, padding_idx)\n # if provided, load from preloaded dictionaries\n if path:\n embed_dict = utils.parse_embedding(path)\n utils.load_embedding(embed_dict, dictionary, emb)\n return emb\n","repo_name":"microsoft/muzic","sub_path":"songmass/mass/xtransformer.py","file_name":"xtransformer.py","file_ext":"py","file_size_in_byte":10335,"program_lang":"python","lang":"en","doc_type":"code","stars":3825,"dataset":"github-code","pt":"60"}
+{"seq_id":"2215125020","text":"import pyotherside\n\nimport sys\nimport logging\n\nfrom client import MatrixClient\nfrom api import MatrixRequestError\nfrom requests.exceptions import MissingSchema\n\n\n\nclass PyClient:\n client = MatrixClient(host)\n\n try:\n client.login_with_password(username, password)\n except MatrixRequestError as e:\n print(e)\n if e.code == 403:\n print(\"Bad username or password.\")\n sys.exit(4)\n else:\n print(\"Check your sever details are correct.\")\n sys.exit(2)\n except MissingSchema as e:\n print(\"Bad URL format.\")\n print(e)\n sys.exit(3)\n\n #return \"success\"\n print(\"success\")\n try:\n room = client.join_room(room_id_alias)\n except MatrixRequestError as e:\n print(e)\n if e.code == 400:\n print(\"Room ID/Alias in the wrong format\")\n sys.exit(11)\n else:\n print(\"Couldn't find room.\")\n sys.exit(12)\n # Called when a message is recieved.\n\n def on_message(room, event):\n if event['type'] == \"m.room.member\":\n if event['membership'] == \"join\":\n pyotherside.send(\"{0} joined\".format(event['content']['displayname']))\n elif event['type'] == \"m.room.message\":\n if event['content']['msgtype'] == \"m.text\":\n pyotherside.send(\"{0}: {1}\".format(event['sender'], event['content']['body']))\n else:\n pyotherside.send(event['type'])\n\n\n room.add_listener(on_message)\n client.start_listener_thread()\n\n\n\n\n","repo_name":"DylanVanAssche/harbour-matriksi","sub_path":"qml/matriksi.py","file_name":"matriksi.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"14040276313","text":"import sys\nfrom collections import namedtuple as basenamedtuple\nfrom typing import Any, Set\n\nfrom django.db import transaction, connections\nfrom django.db.models import QuerySet\n\n\ndef namedtuple(*args, **kwargs):\n \"\"\"\n Changes namedtuple to support defaults parameter as python 3.7 does\n https://docs.python.org/3.7/library/collections.html#collections.namedtuple\n See https://stackoverflow.com/questions/11351032/namedtuple-and-default-values-for-optional-keyword-arguments\n :return: namedtuple class\n \"\"\"\n if sys.version_info < (3, 7):\n defaults = kwargs.pop('defaults', ())\n TupleClass = basenamedtuple(*args, **kwargs)\n TupleClass.__new__.__defaults__ = (None,) * (len(TupleClass._fields) - len(defaults)) + tuple(defaults)\n return TupleClass\n else:\n return basenamedtuple(*args, **kwargs)\n\n\ndef django_pg_returning_available(using: str) -> bool:\n \"\"\"\n Checks if django-pg-returning library is installed and can be used with given databse\n :return: Boolean\n \"\"\"\n try:\n import django_pg_returning # noqa: F401\n return connections[using].vendor == 'postgresql'\n except ImportError:\n return False\n\n\ndef update_returning_pk(qs: QuerySet, updates: dict) -> Set[Any]:\n \"\"\"\n Updates QuerySet items returning primary key values.\n This method should not depend on database engine, though can have optimization performances for some engines.\n :param qs: QuerySet to update\n :param updates: Update items as passed to QuerySet.update(**updates) method\n :return: A set of primary keys\n \"\"\"\n qs._for_write = True\n if django_pg_returning_available(qs.db) and hasattr(qs, 'update_returning'):\n pk_name = qs.model._meta.pk.name\n qs = qs.only(pk_name).update_returning(**updates)\n pks = set(qs.values_list(pk_name, flat=True))\n else:\n with transaction.atomic(using=qs.db):\n pks = set(qs.select_for_update().values_list('pk', flat=True))\n QuerySet.update(qs, **updates)\n\n return pks\n","repo_name":"carrotquest/django-clickhouse","sub_path":"src/django_clickhouse/compatibility.py","file_name":"compatibility.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"60"}
+{"seq_id":"9216815729","text":"import sys\nimport os\n\nsys.path.insert(\n 0, os.path.abspath(os.path.join(os.path.dirname(__file__), \"../src\"))\n)\n\nimport glob\nfrom pathlib import Path\nfrom multiprocessing import Event\n\nfrom analysis.audio import AudioStream\nfrom analysis.analysis import AnalysisCoordinator\nfrom analysis.acoustic_indices import get_supported_indices\n\nimport logging\n\nlogging.basicConfig(\n format=\"[%(asctime)s] [%(levelname)s] %(name)s: %(message)s\",\n handlers=[\n logging.FileHandler(\"demo_info.log\"),\n logging.NullHandler(),\n ],\n level=logging.INFO,\n)\nlogger = logging.getLogger(__name__)\n\nif __name__ == \"__main__\":\n csv_folder = \"D:/Part II Study/temp\"\n file_folder = \"D:/Part II Study/temp\"\n indices = get_supported_indices()\n\n event = Event()\n coordinator = AnalysisCoordinator(callback_event=event)\n\n try:\n for file in glob.iglob(file_folder + \"/*.wav\", recursive=True):\n file_name = Path(file).stem\n if (\n not os.path.exists(csv_folder + \"/\" + file_name + \".csv\")\n and (os.path.getsize(file) >> 20) > 100\n ):\n event.clear()\n print(file_name)\n stream = AudioStream(file=file)\n coordinator.load_stream(\n stream,\n *indices,\n save_file=csv_folder + \"/\" + file_name + \".csv\"\n )\n event.wait()\n finally:\n coordinator.shutdown()\n","repo_name":"VivianDLi/birdsong-visualizer","sub_path":"demos/create_csvs_demo.py","file_name":"create_csvs_demo.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22499457536","text":"# Installed packages\nfrom fastapi import FastAPI\n\n# Internal packages\nfrom main import get_movie, tranform_movie\n\napp = FastAPI()\n\n\n@app.post(\"/create_movie\")\ndef read_root():\n movie = get_movie('The Matrix')\n\n print(tranform_movie(movie))\n return {\"Hello\": \"World\"}\n","repo_name":"AN-Xiang-yu/mlp-1","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6507810877","text":"import sys\nsys.path.append('..')\n\nfrom cannon.tools.git_controller import GitController\n\n\nif __name__ == '__main__':\n git = GitController('./test_repo', './experiment_repo')\n h = git.commit_experiment()\n print(\"commit hash: {}\".format(h))","repo_name":"AntonioCarta/cannon","sub_path":"tests_long/test_git.py","file_name":"test_git.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"41129482934","text":"import krpc\nimport time\nimport math\nfrom kRPC_Automation.log_setup import logger\nimport operator\n\nexpression_shorthand = {\n \"<\" : operator.lt,\n \"<=\": operator.le,\n \">\" : operator.gt,\n \">=\": operator.ge,\n \"==\": operator.eq,\n \"!=\": operator.ne,\n \"+\" : operator.add,\n \"-\" : operator.sub,\n \"*\" : operator.mul,\n \"/\" : operator.truediv,\n \"%\" : operator.mod,\n \"^\" : operator.pow,\n}\n\ndef log_and_print(message):\n logger.info(message)\n print(message)\n\ndef is_part_engine(part):\n for m in part.modules:\n if m.name == \"ModuleEngines\":\n return True\n return False\n\nclass Stage:\n def __init__(self, decouple_stage_number, conn, vessel):\n self.decouple_stage = decouple_stage_number\n self.parts = vessel.parts.in_decouple_stage(self.decouple_stage)\n self.engines = []\n for p in self.parts:\n if is_part_engine(p):\n self.engines.append(p.engine)\n\n def has_fuel(self):\n for eng in self.engines:\n if eng.has_fuel:\n return True\n\n return False\n\n def wait_for_no_fuel(self):\n while self.has_fuel():\n time.sleep(.25)\n return True\n\n\n\nclass MissionData:\n def __init__(self, conn, vessel):\n self.conn = conn\n self.vessel = vessel\n self.stages = []\n self.stage_has_fuel = []\n # Universal Time\n self.ut = conn.add_stream(getattr, conn.space_center, 'ut')\n\n # Flight Data\n self.surface_altitude = conn.add_stream(getattr, vessel.flight(), 'surface_altitude')\n self.mean_altitude = conn.add_stream(getattr, vessel.flight(), 'mean_altitude')\n self.latitude = conn.add_stream(getattr, vessel.flight(), 'latitude')\n self.longitude = conn.add_stream(getattr, vessel.flight(), 'longitude')\n self.velocity = conn.add_stream(getattr, vessel.flight(), 'velocity')\n self.direction = conn.add_stream(getattr, vessel.flight(), 'direction')\n self.prograde = conn.add_stream(getattr, vessel.flight(), 'prograde')\n self.retrograde = conn.add_stream(getattr, vessel.flight(), 'retrograde')\n\n # Orbit Data\n self.apoapsis = conn.add_stream(getattr, vessel.orbit, 'apoapsis_altitude')\n self.periapsis = conn.add_stream(getattr, vessel.orbit, 'periapsis_altitude')\n self.semi_major_axis = conn.add_stream(getattr, vessel.orbit, 'semi_major_axis')\n self.orbital_speed = conn.add_stream(getattr, vessel.orbit, 'orbital_speed')\n self.orbital_period = conn.add_stream(getattr, vessel.orbit, 'period')\n self.time_to_apoapsis = conn.add_stream(getattr, vessel.orbit, 'time_to_apoapsis')\n self.time_to_periapsis = conn.add_stream(getattr, vessel.orbit, 'time_to_periapsis')\n\n self.n_stages = self.vessel.control.current_stage\n\n for s in range(self.n_stages):\n stage = Stage(s, self.conn, self.vessel)\n self.stages.append(stage)\n\n self.current_stage_number = self.vessel.control.current_stage - 1\n self.current_stage = self.stages[self.current_stage_number]\n\n # Resource Data\n self.fuel_amount = {}\n self.fuel_amount['LiquidFuel'] = conn.add_stream(vessel.resources.amount, 'LiquidFuel')\n self.fuel_amount['SolidFuel'] = conn.add_stream(vessel.resources.amount, 'SolidFuel')\n\n\n def wait_for(self, stream_name, expr_symbol, value_waiting_for, changing_value=False, factor=1):\n try:\n stream = getattr(self, stream_name)\n except:\n log_and_print(f\"{stream_name} data stream not available\")\n return False\n eval_func = expression_shorthand[expr_symbol]\n count = 0\n if changing_value:\n target_value = factor * value_waiting_for()\n else:\n target_value = factor * value_waiting_for\n\n prev_value = target_value\n time_to_sleep = 1\n log_and_print(f\"Waiting for {stream_name} {expr_symbol} {target_value} (changing: {changing_value}) (currently: {stream()})\")\n while eval_func(stream(), target_value) is not True:\n if changing_value:\n log_and_print(f\"Waiting for {stream_name} {expr_symbol} {target_value} (changing: {changing_value}) (currently: {stream()})\")\n count += 1\n log_and_print(f\"sleep for {time_to_sleep} second(s) and see change\")\n time.sleep(time_to_sleep)\n target_value = factor * value_waiting_for()\n if count == 1:\n time_to_sleep = .1 * math.fabs((target_value-stream()) / (target_value - prev_value))\n log_and_print(f\"set sleep interval to {time_to_sleep} seconds\")\n\n return True\n\n def activate_stage(self):\n self.vessel.control.activate_next_stage()\n self.current_stage_number -= 1\n self.current_stage = self.stages[self.current_stage_number]\n\n\n\nif __name__ == \"__main__\":\n pass\n","repo_name":"SamuelStuver/KSP_Automation","sub_path":"data_collection/data_streams.py","file_name":"data_streams.py","file_ext":"py","file_size_in_byte":4968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41381447080","text":"import struct\nimport itertools\nimport unittest\n\nclass Offsets(object):\n '''\n classdocs\n '''\n\n\n def __init__(self, actions, actors, scenes, reperts, sounds, unknown):\n '''\n Constructor\n '''\n self.actions = actions\n self.actors = actors\n self.scenes = scenes\n self.reperts = reperts\n self.sounds = sounds\n self.unknown = unknown\n \n \n def __str__(self):\n outstr = \"Offsets collection:\"\n outstr += str(len(self.actions)) + \" Actions, \"\n outstr += str(len(self.actors)) + \" Actors, \"\n outstr += str(len(self.scenes)) + \" Scenes, \"\n outstr += str(len(self.reperts)) + \" Scenes, \"\n outstr += str(len(self.sounds)) + \" Sounds, \"\n outstr += str(len(self.unknown)) + \" Unknowns\"\n return outstr\n \n \ndef loadOffsets(filename):\n fileobj = open(filename, \"rb\")\n \n scenes = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 1000)), fileobj.read(4*1000))\n actors = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 500)), fileobj.read(4*500))\n actions = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 1000)), fileobj.read(4*1000))\n reperts = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 150)), fileobj.read(4*150))\n sounds = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 500)), fileobj.read(4*500))\n unknown = struct.unpack(\">\"+\"\".join(itertools.repeat(\"I\", 375)), fileobj.read(4*375)) \n \n fileobj.close()\n \n return Offsets(actions, actors, scenes, reperts, sounds, unknown)\n\nclass TestLoadXML(unittest.TestCase):\n def test_load_offsets(self):\n offsets = loadOffsets(\"test/offsets.\")\n print(offsets)\n print(offsets.actors)\n off2 = loadOffsets(\"test/off2.\")\n print(off2)\n print(off2.actors)\n \n","repo_name":"fHachenberg/pyecstaticalib","sub_path":"Ecstatica/Offsets.py","file_name":"Offsets.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"29783611602","text":"import os\nimport re\n\nimport pandas as pd\nimport tabula\nimport requests\nimport boto3\n\nclass DataExtractor:\n ''' \n DataExtractor - a class for extracting data from multiple data sources.\n =======================================================================\n\n DataExtractor, contains methods for retrieving and extracting data from AWS RDS \n and S3 buckets, .pdf, .csv files, and web APIs. \n Extracted data returned as pandas data frames. \n '''\n def read_rds_table(self, table, engine):\n '''\n This function is used to read and download from AWS RDS. This function\n is linked to the list_db_tables( ) function from DatabaseConnector Class.\n Reads the sql table and returns pandas dataframe object.\n \n Args:\n table (str): the name of table to retrieve data from.\n engine (str): name of the engine created to connect to the AWS RDS.\n\n Returns:\n df (pd.DataFrame): returns sql table as a pandas dataframe object.\n '''\n user_df = pd.read_sql_table(f\"{table}\", engine)\n return user_df\n \n def retrieve_pdf_data(self):\n '''\n This function is used to read and extract data from .pdf files. \n\n Returns:\n df (pd.DataFrame): returns concatenated list of df from pdf as a pandas dataframe object.\n '''\n pdf_path = \"https://data-handling-public.s3.eu-west-1.amazonaws.com/card_details.pdf\"\n card_details_df = tabula.read_pdf(pdf_path, pages= 'all')\n card_details_df = pd.concat(card_details_df) #join list of df into a pandas dataframe\n return card_details_df\n \n def list_number_of_stores(self, endpoint, header):\n '''\n This function is used to find the number of stores listed on the web API. \n\n Args:\n endpoint (str): the url of the web API endpoint to retrieve number of stores.\n header (dict): a dictionary of the API access key and value.\n\n Returns:\n Number of stores (int): returns the response of the web api request as a integer. \n '''\n #endpoint = 'https://aqj7u5id95.execute-api.eu-west-1.amazonaws.com/prod/number_stores'\n #header = {'x-api-key':'yFBQbwXe9J3sd6zWVAMrK6lcxxr0q1lr2PT6DDMX'}\n response = requests.get(f\"{endpoint}\", headers=header)\n Num_of_stores = response.json()\n return Num_of_stores\n\n def retrieve_stores_data(self, Num_of_stores, endpoint):\n '''\n This function is used to retrieve store data from web API, \n by iterating through all stores by number of stores. \n\n Args:\n Num_of_stores (dict): Dictionary of response list_number_of_stores(), with value of number of stores data to download from web API.\n endpoint (str): the url of the web API endpoint to retrieve stores data.\n \n Returns:\n df (pd.DataFrame): returns the response of the web API requests as a pandas data frame.\n '''\n header = {'x-api-key':'yFBQbwXe9J3sd6zWVAMrK6lcxxr0q1lr2PT6DDMX'}\n number_of_stores = Num_of_stores\n store_number = number_of_stores['number_stores']\n store_data = []\n #Iterating through store numbers to download each store data\n for i in range(0, store_number):\n response = requests.get(f\"{endpoint}{i}\", headers= header)\n data = response.json()\n store_data.append(data)\n \n store_data_df = pd.DataFrame(store_data)\n return store_data_df\n \n def extract_from_s3(self, s3address, file_download_path = \"\"):\n '''\n Thus function is used for downloading data from AWS s3 bucket. \n Using boto3 to connect to AWS s3 client, and downloading data from s3 address.\n Add path of location to download data (default = current working directory). \n\n Args:\n s3address (str): Url path string representation, s3 bucket and target file for downloading.\n file_download_path (str): String representation of local path to download target file to.\n\n Returns:\n df (pd.Dataframe): returns the read target file as a pandas data frame. \n '''\n s3 = boto3.client('s3')\n #s3address = 'https://data-handling-public.s3.eu-west-1.amazonaws.com/date_details.json'\n #s3address = 's3://data-handling-public/products.csv'\n s3_params = re.split('[//.]', s3address)\n #bucket name from url\n bucket = s3_params[2] \n #file name by accessing last two elements of split string list\n delimiter = '.'\n target_file = delimiter.join(s3_params[-2:])\n file_download_path = os.getcwd() + '\\Data'\n s3.download_file(f'{bucket}', f'{target_file}', f'{file_download_path}\\{target_file}')\n \n def read_in_file(target_file):\n '''\n This function determines the file method for reading in target file. Either .csv or .json file.\n '''\n if '.csv' in target_file:\n s3_df = pd.read_csv(f'{target_file}')\n if '.json' in target_file:\n s3_df = pd.read_json(f'{target_file}')\n return s3_df\n \n res_df = read_in_file(f'{file_download_path}\\{target_file}')\n return res_df","repo_name":"amysw13/multinational-retail-data-centralisation","sub_path":"Classes/data_extraction.py","file_name":"data_extraction.py","file_ext":"py","file_size_in_byte":5283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2209874296","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.list_courses, name='list-courses'),\n path('create/', views.create_course, name='create-course'),\n path('update//', views.update_course, name='update-course'),\n path('delete//', views.delete_course, name='delete-course'),\n # Add more URL patterns as needed\n]\n","repo_name":"Prashantrathour/progressio.com","sub_path":"progressio_backend/progressio_main/courses/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23217015040","text":"# nome, titulo, data_nasc, mae, pai\nimport os\n\ndef solicitar_dados():\n nome = input('Nome: ')\n titulo = input('Titulo de eleitor: ')\n nascimento = input('Data de nascimento: ')\n mae = input('Nome da mãe: ')\n pai = input('Nome do pai: ')\n votou = input('Votou na ultima eleição? (S/N)')\n dados = (nome, mae, pai, nascimento, titulo, votou)\n return dados\n\ndef criar_base_dados(caminho):\n\n if not os.path.exists(caminho):\n\n colunas = ['nome', 'mae', 'pai', 'data_nasc', 'titulo', 'votou']\n\n arquivo = open(caminho, 'w')\n # JEITO PUNK\n # linha = ''\n # for coluna in colunas:\n # if \n # linha = linha + f'{coluna}, '\n # print(linha)\n\n linha = ','.join(colunas)\n arquivo.write(linha + '\\n')\n arquivo.close()\n\ndef cadastrar_eleitor(dados, caminho = 'base_eleitores.csv'):\n \n if os.path.exists(caminho):\n \n arquivo = open(caminho, 'a')\n arquivo.write(','.join(dados) + '\\n')\n arquivo.close()\n \n else:\n\n criar_base_dados(caminho)\n\n\ndef validar_dados_eleitor(eleitor, base = 'base_eleitores.csv'):\n\n # arquivo = open(base, 'r')\n # linha = arquivo.readlines()\n pass\n\n\n\n\n\nif __name__ == \"__main__\":\n \n criar_base_dados('base_eleitores.csv')\n\n dados_eleitor = solicitar_dados()\n\n cadastrar_eleitor(dados_eleitor)\n","repo_name":"pedrodamas0803/py_jeito_certo","sub_path":"projeto/projeto.py","file_name":"projeto.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}