\" + response+\"
\"\n\n\nadmin_site.register(Resource, ResourceAdmin)\n\nclass End_PointAdmin(OSMGeoAdmin):\n pass\n\nadmin_site.register(End_Point, End_PointAdmin)\n\nclass PublisherAdmin(OSMGeoAdmin):\n search_fields = ('name',)\n pass\nadmin_site.register(Publisher, PublisherAdmin)\n\nclass Community_InputAdmin(OSMGeoAdmin):\n list_display = [\"resource\",\"date\",\"name\", \"email\"]\n raw_id_fields = (\"resource\",)\nadmin_site.register(Community_Input, Community_InputAdmin)\n\nclass Georeference_RequestAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Georeference_Request, Georeference_RequestAdmin)\n\n\nclass OwnerAdmin(OSMGeoAdmin):\n # enable a full_name overwrite when available\n list_display=[\"name\",\"full_name\"]\n search_fields = (\"name\",\"full_name\")\n\nadmin_site.register(Owner, OwnerAdmin)\n\nclass TypeAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Type, TypeAdmin)\n\nclass Geometry_TypeAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Geometry_Type, Geometry_TypeAdmin)\n\nclass FormatAdmin(OSMGeoAdmin):\n pass\nadmin_site.register(Format, FormatAdmin)\n\n\nclass Category_KeywordsInline(admin.StackedInline):\n model = Category_Keywords.category.through\n extra = 0\n\nclass Category_KeywordsAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Category_Keywords, Category_KeywordsAdmin)\n\nclass CategoryAdmin(OSMGeoAdmin):\n inlines = [\n Category_KeywordsInline\n ]\nadmin_site.register(Category, CategoryAdmin)\n\n\nclass TagAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Tag, TagAdmin)\n\nclass PlaceAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Place, PlaceAdmin)\n\nclass Named_PlaceAdmin(OSMGeoAdmin):\n search_fields = ('name',)\nadmin_site.register(Named_Place, Named_PlaceAdmin)\n\nclass URL_TypeAdmin(OSMGeoAdmin):\n list_display = ('name', 'ref', 'service', '_class', '_method')\n\nadmin_site.register(URL_Type,URL_TypeAdmin)\n\nclass URLAdmin(OSMGeoAdmin):\n list_filter = (\"url_type\",)\n\nadmin_site.register(URL,URLAdmin)\n\n","repo_name":"GeospatialCentroid/geoportal-manager","sub_path":"resources/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":15060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"41738584902","text":"from copy import deepcopy\n\nimport math\nimport time\nimport heapq\n\n\n\n\ndef a_star(start, goal):\n if not is_solveable(start):\n print(\"Not solvable\")\n return\n visited = set()\n path = {tuple(flatten_matrix(start)): None}\n mhd = calculate_manhatan_distance(start, goal)\n heap = [(mhd,start)]\n heapq.heapify(heap)\n while heap:\n _, current = heapq.heappop(heap)\n if current == goal:\n break\n current_state_flatten = tuple(flatten_matrix(current))\n if current_state_flatten not in visited:\n visited.add(current_state_flatten)\n for entry in get_childs_of_state(current):\n [direction, child_node] = entry\n child_state_flatten = tuple(flatten_matrix(child_node))\n if child_state_flatten in visited:\n continue\n path[child_state_flatten] = (direction, current_state_flatten)\n mhd = calculate_manhatan_distance(child_node, goal)\n visited.add(child_state_flatten)\n heapq.heappush(heap, (mhd, child_node))\n if current != goal:\n print(\"Not solveable\")\n return \n result = []\n current = tuple(flatten_matrix(current))\n while path[current]:\n result.append(path[current])\n current = path[current][1]\n count = 0\n for (direction, state) in result[::-1]:\n print_state(create_matrix_from_list(state))\n print(direction)\n count += 1\n print_state(goal) \n print(count)\ndef print_state(matrix):\n for i in range(len(matrix)):\n for j in range(len(matrix[i])):\n print(matrix[i][j], end=' ')\n print()\n\ndef create_matrix_from_list(list):\n matrix = [[]]\n matrix_row_col = math.sqrt(len(list))\n current_row = 0\n for i,num in enumerate(list):\n if i != 0 and i % matrix_row_col == 0:\n current_row += 1\n matrix.append([])\n matrix[current_row].append(num)\n return matrix\n\ndef get_childs_of_state(state):\n x = -1\n y = -1\n for i in range(len(state)):\n for j in range(len(state[0])):\n if state[i][j] == 0:\n x = i \n y = j\n possible_moves = {(x + 1,y): 'U', (x - 1,y): 'D', (x,y - 1): 'R', (x, y + 1): 'L'}\n new_states = []\n for move, direction in possible_moves.items():\n if move[0] < 0 or len(state[0]) <= move[0]:\n continue\n elif move[1] < 0 or len(state) <= move[1]:\n continue\n temp = deepcopy(state)\n temp[move[0]][move[1]], temp[x][y] = temp[x][y], temp[move[0]][move[1]]\n new_states.append([direction,temp])\n return new_states\n\n\ndef get_positions_state(state):\n goal_positions = {}\n for i in range(len(state)):\n for j in range(len(state)):\n if state[i][j] == 0:\n continue\n goal_positions[state[i][j]] = [i,j]\n return goal_positions\ndef calculate_manhatan_distance(state, goal):\n dist = 0\n goal_positions = get_positions_state(goal)\n\n for i in range(len(state)):\n for j in range(len(state[0])):\n if state[i][j] == 0:\n continue\n dist += abs(goal_positions[state[i][j]][0] - i) + abs(goal_positions[state[i][j]][1] - j)\n return dist\n\ndef flatten_matrix(board):\n return [item for lst in board for item in lst]\n\ndef is_solveable(state):\n count_inversions = 0\n flat_list = [item for lst in state for item in lst]\n for i,item in enumerate(flat_list):\n if item == 0:\n continue\n inversions = [el for el in flat_list[i+1:] if item > el and el != 0]\n count_inversions += len(inversions)\n return count_inversions % 2 == 0\n\ndef get_goal_state(num_row_cols, blocks, empty_index):\n goal_state = []\n nums = list(range(1,blocks + 1))\n if empty_index == -1:\n nums.append(0)\n else:\n nums.insert(empty_index, 0)\n for i in range(0,len(nums),num_row_cols):\n goal_state.append(nums[i: i + num_row_cols]) \n return goal_state\n\ndef get_start_state(row_cols):\n start_state = []\n for _ in range(row_cols):\n row_input = input()\n row = [int(x) for x in row_input.split()]\n start_state.append(row)\n return start_state \ndef main():\n blocks = int(input(\"Enter number of non empty blocks: \"))\n empty_index = input(\"Enter the index of the empty block(by default is down right): \") or -1\n empty_index = int(empty_index)\n row_cols = int(math.sqrt(blocks) + 1)\n start_state = get_start_state(row_cols)\n start_time = time.time()\n goal_state = get_goal_state(row_cols, blocks, empty_index)\n a_star(start_state, goal_state)\n print(f\"Time elapsed since program start: {time.time() - start_time} seconds\")\nmain()\n\n\n","repo_name":"VladislavSpassov/ai_hw","sub_path":"hw_1/hw_1.py","file_name":"hw_1.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"35394957031","text":"import sys\nfrom base import make_app\n\n\nif __name__ == '__main__':\n\toptions = {}\n\tif len(sys.argv) > 1:\n\t\tif sys.argv[1] == 'upgradedb':\n\t\t\tfrom alembic.config import main\n\t\t\tmain('upgrade head'.split(' '), 'alembic')\n\t\n\t\t\texit(0)\n\t\tfor arg in sys.argv[1:]:\n\t\t\tk, v = arg.strip('--').split('=', 1)\n\t\t\toptions[k] = v\n\tapp = make_app(options)\n\tapp.run()\n","repo_name":"yyotsuba/session_book","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"3095917147","text":"# 37\r\nname = input(\"name:\")\r\nage = input(\"age:\")\r\naddress = input(\"address:\")\r\n\r\nprint(name)\r\nprint(age)\r\nprint(address)\r\n\r\n# 38\r\nx = float(input(\"x:\"))\r\ny = float(input(\"y:\"))\r\nprint((x + y) ** 2)\r\n\r\n# 39\r\namount = float(input(\"amount:\"))\r\ninterest = float(input(\"interest:\")) / 100\r\nyears = int(input(\"years:\"))\r\n\r\nsum = amount\r\nfor i in range(years):\r\n\tsum += interest * sum\r\n\r\nprint(round(sum, 2))\r\n\r\n# 40\r\nfrom math import sqrt\r\nx1 = float(input(\"x1:\"))\r\nx2 = float(input(\"x2:\"))\r\ny1 = float(input(\"y1:\"))\r\ny2 = float(input(\"y2:\"))\r\n\r\nx = x1 - y1\r\ny = x2 - y2\r\n\r\nprint(sqrt((x ** 2) + (y ** 2)))\r\n\r\n# 41\r\nfrom os import path\r\nprint(path.exists(input(\"file:\")))\r\n\r\n# 42\r\nfrom sys import maxsize\r\nprint(maxsize > 2 ** 32)\r\n\r\n# 43\r\nfrom os import name\r\nfrom platform import system, release\r\nprint(system(), name, release())\r\n\r\n# 44\r\nfrom sysconfig import get_paths\r\nprint(get_paths()[\"purelib\"])\r\n\r\n# 45\r\nfrom subprocess import call\r\ncall([\"ls\", \"-l\"])\r\n\r\n# 46\r\nprint(__file__)","repo_name":"RedGuy12/python","sub_path":"3/37-46.py","file_name":"37-46.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"14958532073","text":"\"\"\"\nA chef has collected data on the satisfaction level of his n dishes. Chef can cook any dish in 1 unit of time.\n\nLike-time coefficient of a dish is defined as the time taken to cook that dish including previous dishes multiplied\nby its satisfaction level i.e. time[i] * satisfaction[i].\n\nReturn the maximum sum of like-time coefficient that the chef can obtain after dishes preparation.\n\nDishes can be prepared in any order and the chef can discard some dishes to get this maximum value.\n\n\n\nExample 1:\n\nInput: satisfaction = [-1,-8,0,5,-9] Output: 14 Explanation: After Removing the second and last dish, the maximum\ntotal like-time coefficient will be equal to (-1*1 + 0*2 + 5*3 = 14). Each dish is prepared in one unit of time.\nExample 2:\n\nInput: satisfaction = [4,3,2]\nOutput: 20\nExplanation: Dishes can be prepared in any order, (2*1 + 3*2 + 4*3 = 20)\nExample 3:\n\nInput: satisfaction = [-1,-4,-5]\nOutput: 0\nExplanation: People do not like the dishes. No dish is prepared.\n\n\nConstraints:\n\nn == satisfaction.length\n1 <= n <= 500\n-1000 <= satisfaction[i] <= 1000\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def maxSatisfaction(self, satisfaction: List[int]) -> int:\n # Like-time coefficient of a dish is defined as the time taken to cook that dish including previous dishes\n # multiplied by its satisfaction level i.e. time[i] * satisfaction[i].\n\n max_satisfaction = sorted(satisfaction, reverse=True)\n\n # result -> sum of time to cook the positive reviewed dish [Note: +ve -> positive like time to cook same dish\n # considering previous time to cook the previous dish]\n # like time -> it is like time coefficient of the dish prepared by chef in a day[given satisfaction array]\n result, like_time = 0, 0\n\n # loop throughout dish time in list.\n for dish_time in max_satisfaction:\n\n # like time -> if positive, add time to like time coefficient to given dish time\n like_time += dish_time\n\n # like time -> if negative, then there is no need of consideration\n if like_time < 0:\n break\n\n # result -> add result to like time coefficient\n result += like_time\n\n return result\n\n\nsolution = Solution()\nassert 14 == solution.maxSatisfaction(satisfaction=[-1, -8, 0, 5, -9])\nassert 20 == solution.maxSatisfaction(satisfaction=[4, 3, 2])\nassert 0 == solution.maxSatisfaction(satisfaction=[-1, -4, -5])\n","repo_name":"ImSakunthala/leetcode","sub_path":"Advance_level/reducing_dishes.py","file_name":"reducing_dishes.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"70418661635","text":"\"\"\"\nCollection of all business logic the application must be able to\nperform.\n\"\"\"\nfrom contextlib import contextmanager\nfrom sqlalchemy.exc import IntegrityError\nfrom database import Session\nfrom models import User\n\n\n@contextmanager\ndef session_scope():\n \"\"\"\n Context for dealing with sessions. This allows the developer not to have to\n worry perse about closing and creating the session.\n \"\"\"\n session = Session()\n try:\n yield session\n session.commit()\n except:\n session.rollback()\n raise\n finally:\n session.close()\n\n\ndef name_registered(name):\n \"\"\"\n Confirm or deny the uniqueness of the given user in the database.\n \"\"\"\n with session_scope() as session:\n if session.query(User).filter(User.name == name).one_or_none():\n return True\n return False\n\n\ndef email_registered(email):\n \"\"\"\n Confirm or deny the uniqueness of the given email in the database.\n \"\"\"\n with session_scope() as session:\n if session.query(User).filter(User.email == email).one_or_none():\n return True\n return False\n\n\ndef register_user(name, email):\n \"\"\"\n Register a user in the database by a name and email.\n \"\"\"\n with session_scope() as session:\n new_user = User(name, email)\n session.add(new_user)\n try:\n session.commit()\n except IntegrityError:\n session.rollback()\n raise\n else:\n return new_user.id\n ","repo_name":"Drvanon/FlaskBoilerplate","sub_path":"controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"70576710275","text":"from typing import Dict\n\nimport os\nimport numpy as np\nimport torch\nfrom torch import nn\nimport urllib.request\nimport tarfile\n\nfrom ml_foundations.models.deep_models.layers import GraphAttentionLayer\n\n\nclass CoraDataset:\n \"\"\"\n content [\n ['31336' '0' '0' ... '0' '0' 'Neural_Networks'],\n ['1061127' '0' '0' ... '0' '0' 'Rule_Learning'],\n ...\n ]\n citations [\n [35 1033]\n [35 103482]\n ...\n ]\n \"\"\"\n\n labels: torch.Tensor\n classes: Dict[str, int]\n features: torch.Tensor\n adj_mat: torch.Tensor\n\n @staticmethod\n def _download():\n data_dir = \"./data/\"\n if not os.path.exists(data_dir):\n os.makedirs(data_dir)\n url = \"https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz\"\n filename = \"cora.tgz\"\n urllib.request.urlretrieve(url, os.path.join(data_dir, filename))\n with tarfile.open(os.path.join(data_dir, filename), \"r:gz\") as tar:\n tar.extractall(data_dir)\n\n def __init__(self, include_edges: bool = True):\n self.include_edges = include_edges\n self._download()\n content = np.genfromtxt(\"./data/cora/cora.content\", dtype=np.dtype(str))\n citations = np.genfromtxt(\"./data/cora/cora.cites\", dtype=np.int32)\n features = torch.Tensor(np.array(content[:, 1:-1], dtype=np.float32))\n self.features = features / features.sum(dim=1, keepdim=True)\n self.classes = {s: i for i, s in enumerate(set(content[:, -1]))}\n self.labels = torch.tensor(\n [self.classes[i] for i in content[:, -1]], dtype=torch.long\n )\n paper_ids = np.array(content[:, 0], dtype=np.int32)\n ids_to_idx = {id_: i for i, id_ in enumerate(paper_ids)}\n self.adj_mat = torch.eye(len(self.labels), dtype=torch.bool)\n\n if self.include_edges:\n for e in citations:\n e1, e2 = ids_to_idx[e[0]], ids_to_idx[e[1]]\n self.adj_mat[e1][e2] = True\n self.adj_mat[e2][e1] = True\n\n\nclass GAT(nn.Module):\n def __init__(\n self,\n in_features: int,\n n_hidden: int,\n n_classes: int,\n n_heads: int,\n dropout: float,\n ):\n super().__init__()\n self.layer1 = GraphAttentionLayer(\n in_features, n_hidden, n_heads, is_concat=True, dropout=dropout\n )\n self.activation = nn.ELU()\n self.output = GraphAttentionLayer(\n n_hidden, n_classes, 1, is_concat=True, dropout=dropout\n )\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, x, adj_mat):\n x = self.dropout(x)\n x = self.layer1(x, adj_mat)\n x = self.activation(x)\n x = self.dropout(x)\n return self.output(x, adj_mat)\n\n\ndef accuracy(output: torch.Tensor, labels: torch.Tensor):\n return output.argmax(dim=-1).eq(labels).sum().item() / len(labels)\n\n\ndef main():\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n dataset = CoraDataset(include_edges=True)\n in_features = dataset.features.shape[1]\n n_hidden = 64\n n_classes = len(dataset.classes)\n n_heads = 8\n dropout = 0.6\n model = GAT(in_features, n_hidden, n_classes, n_heads, dropout).to(device)\n epochs = 1000\n optimizer = torch.optim.Adam(model.parameters(), lr=5e-3)\n loss_func = nn.CrossEntropyLoss()\n\n features = dataset.features.to(device)\n labels = dataset.labels.to(device)\n edges_adj = dataset.adj_mat.to(device)\n edges_adj = edges_adj.unsqueeze(-1) # add a third dim for heads\n\n idx_rand = torch.randperm(len(labels))\n idx_train = idx_rand[:500]\n idx_valid = idx_rand[500:]\n\n for epoch in range(epochs):\n model.train()\n optimizer.zero_grad()\n output = model(features, edges_adj)\n loss = loss_func(output[idx_train], labels[idx_train])\n loss.backward()\n optimizer.step()\n train_accuracy = accuracy(output[idx_train], labels[idx_train])\n\n model.eval()\n with torch.no_grad():\n output = model(features, edges_adj)\n val_loss = loss_func(output[idx_valid], labels[idx_valid])\n val_accuracy = accuracy(output[idx_valid], labels[idx_valid])\n\n print(\n f\"epoch {epoch + 1} / {epochs}, train loss: {loss}, train accuracy: {train_accuracy}, valid loss: {val_loss}, valid accuracy: {val_accuracy}\"\n )\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"akashsonowal/ml-foundations","sub_path":"ml_foundations/pipelines/tabular/graph_learning/research_paper_classification.py","file_name":"research_paper_classification.py","file_ext":"py","file_size_in_byte":4491,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"61"}
+{"seq_id":"23562486371","text":"import sys\n\nname = \"A-small\"\npath = \"\"\n\npath2 = '/Users/daniel/Downloads/B-small-attempt0'\n\nsys.stdin = open(path2 +'.in')\nsys.stdout = open(path2 + \".out\", \"w\")\n\ntestCases = int(input())\n\n\ndef check_non_decreasing(num):\n num = str(num)\n prev_d = num[0]\n for d in range(1, len(num)):\n if int(num[d]) >= int(prev_d):\n prev_d = num[d]\n continue\n return False\n return True\n\n\nfor testCase in range(1, testCases + 1):\n str_input = str(input())\n highest = 1\n for i in range(int(str_input) + 1):\n if check_non_decreasing(i):\n highest = i\n\n print('Case #{}: {}'.format(testCase, highest))","repo_name":"dr-dos-ok/Code_Jam_Webscraper","sub_path":"solutions_python/Problem_200/4741.py","file_name":"4741.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"12633691501","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy as spy\nfrom scipy.stats import norm\nfrom scipy.optimize import linprog\nimport math\n\n##################################################################################\n\n### global variables\nn_sim = 30\netha = 1\nret_sec_bank = 0.18\nret_sec_bank_sigma = 0.03\nret_sec_shbank = 0.18\nrfree = 0.18\nrfree_min = 0.20\nrfree_max = 0.25\nrfree_vector = [f'{rfree}']\nintrinsic_value = 100\np_market = intrinsic_value + np.random.normal(0)\np_market_old = p_market\np_market_max = 150\np_market_min = 80\np_market_vector = [f'{p_market}']\nret_sec_bank_vector = [f'{ret_sec_bank}']\n\nevery = 0\nevery_thing_vector = [f'{every}']\n\nevery1 = 0\nevery1_thing_vector = [f'{every1}']\n\nevery2 = p_market\nevery2_thing_vector = [f'{every2}']\n\n\n########################################################################\n# defining the agents: banks, shadow banks\n\n\nclass Bank:\n def __init__(self, bank_cash, lend_to_banks, lend_to_loans, bank_sec, deposits, borrow_from_banks, equity,\n alpha_min, provision_per, phi, zeta, car, xs, xbl, xl, etha_max):\n self.bankrupt = False\n self.bank_cash = bank_cash\n self.lend_to_banks = lend_to_banks\n self.lend_to_loans = lend_to_loans\n self.bank_sec = bank_sec\n self.deposits = deposits\n self.borrow_from_banks = borrow_from_banks\n self.equity = equity\n\n self.alpha_min = alpha_min\n self.provision_per = provision_per\n self.zeta = zeta\n self.total_assets = bank_cash + lend_to_banks + lend_to_loans + bank_sec\n self.xs = xs\n self.xbl = xbl\n self.xl = xl\n self.car = car\n self.ret_on_sec = np.random.normal(ret_sec_bank, ret_sec_bank_sigma)\n self.stock = bank_sec / p_market\n self.security_sale = 0\n self.supply_of_stock_b = 0\n self.demand_of_stock_b = 0\n\n ##### income and expense of bank\n self.phi = phi\n self.sigma = phi * (self.deposits + self.equity)\n # self.profit = float(np.random.normal(self.net_income, self.sigma, 1))\n # self.pd = float(norm.ppf((-self.net_income - self.equity) / (self.sigma)))\n self.pd = np.random.beta(1, 20)\n self.etha_max = etha_max\n\n\nclass Shadow_Bank:\n def __init__(self, participation, shadow_bank_cash, security, s_alpha, s_provision):\n self.exit = False\n self.participation = participation\n self.shadow_bank_cash = shadow_bank_cash\n self.security = security\n self.s_alpha = s_alpha\n self.s_provision = s_provision\n self.int_value = np.random.normal(intrinsic_value)\n self.redemption = 0\n self.stock = security / p_market\n\n ##### income and expense of shadow bank\n\n\n###############################################################\n# introduction of Iranian Banks\n\nbank_melli = Bank(2.9, 22.9, 66.3, 33.5, 102.3, 20.2, 3.2, 0.1, 0.1, 0.1, 0.4, 0.07, 0.05, 0.05, 0.05, 1)\nbank_seppah = Bank(0.8, 6, 17.4, 8.8, 14.7, 6.3, 12.1, 0.1, 0.1, 0.1, 0.4, 0.07, 0.05, 0.05, 0.05, 1)\nbank_tosesaderat = Bank(8.3, 3.2, 22.8, 21.5, 5.7, 8.2, 42, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_maskan = Bank(3.4, 0, 54.2, 9.3, 6.9, 9.8, 50.3, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_sanatmadan = Bank(6.7, 0, 106, 18.3, 8.9, 42.7, 79.3, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_keshavarzi = Bank(25.3, 3.2, 88, 47.4, 33.6, 2.1, 128.2, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_tosetavon = Bank(1.3, 0.2, 16.9, 4.4, 5.6, 2.3, 14.8, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_post = Bank(0.6, 4.4, 11.1, 1.3, 13.3, 4, 0.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_eghtesadnovin = Bank(4, 6.2, 44, 10.9, 51.1, 7.3, 6.7, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_parsian = Bank(9.5, 27.6, 94.6, 26.4, 33.3, 11.3, 113.5, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_karafarin = Bank(4.8, 7.5, 52.8, 13, 61.3, 8.7, 8.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_saman = Bank(0.5, 14.1, 22.6, 18, 46.3, 4.9, 4, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_sina = Bank(2, 2.6, 16, 3.8, 21.6, 0.2, 2.6, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_khavarmiane = Bank(0.2, 5.2, 11.8, 4, 12.7, 5.3, 3.2, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_shahr = Bank(0.8, 9.9, 43.7, 21.4, 85.8, 5.1, -15.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_dey = Bank(1, 3.2, 8.2, 15.1, 27.8, 8.4, -8.7, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_saderat = Bank(16.3, 45.5, 204.3, 60.4, 231.3, 41.6, 53.6, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_tejarat = Bank(13.1, 46.4, 133.8, 51.6, 197.6, 22.3, 25, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_mellat = Bank(21.2, 50.8, 311.3, 68.4, 269.6, 127.1, 55, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_refah = Bank(2.4, 3.1, 19.1, 4.5, 25.9, 0.2, 3.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_ayandeh = Bank(1.5, 21.8, 97.6, 79.3, 186.1, 22.8, -8.7, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_gardeshgary = Bank(1, 0.2, 19.8, 29.7, 38.4, 8.4, 3.8, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_iranzamin = Bank(0.3, 4.1, 3.5, 29.2, 33.6, 6.1, -2.6, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_sarmaye = Bank(0.5, 2.7, 5.4, 5.4, 18.3, 21.7, -26.1, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 0)\nbank_pasargad = Bank(18.7, 21.6, 79.9, 45.8, 116.9, 19.3, 29.9, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\nbank_melal = Bank(0.2, 2.3, 10.8, 21.1, 15.2, 15, 4, 0.1, 0.1, 0.1, 0.1, 0.07, 0.05, 0.05, 0.05, 1)\n\n# introduction of Iranian Shadow Banks\n\nshadow1 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.01, 0.01)\nshadow2 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.3, 0.3)\nshadow3 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.91, 0.01)\nshadow4 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow5 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow6 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow7 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow8 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow9 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow10 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow11 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow12 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow13 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow14 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\nshadow15 = Shadow_Bank(np.random.normal(45), np.random.normal(3.5), np.random.normal(41.5), 0.1, 0.1)\n\n\n#####################################################\n\n#####################################################\n# 1-BL 2-S 3-BB 4-L 5-C\n# optimixation phase\n### objective function of banks\n\ndef optimize_bank(mmm):\n if mmm.bankrupt == False:\n c = np.array([-rfree, -mmm.ret_on_sec, ((rfree * mmm.borrow_from_banks) / (1 - mmm.zeta * mmm.pd)), 0, 0])\n A_ub = np.array([[(-1 + mmm.car * mmm.xbl), (-1 + mmm.car * mmm.xs), 1, (-1 + mmm.car * mmm.xl), -1],\n [-1, 0, (mmm.alpha_min + mmm.provision_per), 0, -1], [0, 0, 0, 0, -1], [0, 1, 0, 0, 0]])\n b_ub = np.array([-mmm.deposits, -(mmm.alpha_min + mmm.provision_per) * mmm.deposits,\n -(mmm.alpha_min + mmm.provision_per) * mmm.deposits, mmm.etha_max * mmm.total_assets])\n A_eq = np.array([[1, 1, -1, 1, 1], [0, 0, -1, 2, 0]])\n b_eq = np.array([mmm.deposits + mmm.equity, mmm.deposits + mmm.equity])\n x0_bounds = (0, None)\n x1_bounds = (0, None)\n x2_bounds = (0, None)\n x3_bounds = (0, None)\n x4_bounds = (0, None)\n bounds = [x0_bounds, x1_bounds, x2_bounds, x3_bounds, x4_bounds]\n result = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq, bounds=bounds)\n mmm.bank_cash = result.x[4]\n mmm.lend_to_banks = result.x[0]\n mmm.lend_to_loans = result.x[3]\n bank_sec_old = mmm.bank_sec\n mmm.bank_sec = result.x[1]\n mmm.borrow_from_banks = result.x[2]\n mmm.ret_on_sec = float(np.random.normal(ret_sec_bank, ret_sec_bank_sigma))\n mmm.net_income = (mmm.ret_on_sec * mmm.bank_sec) + (rfree * mmm.lend_to_banks) - (\n (rfree * mmm.borrow_from_banks) / (1 - mmm.zeta * mmm.pd))\n mmm.sigma = abs(mmm.phi * (mmm.deposits + mmm.equity))\n mmm.profit = float(np.random.normal(mmm.net_income, mmm.sigma, 1))\n\n mmm.sec_sale = bank_sec_old - mmm.bank_sec\n if mmm.sec_sale > 0:\n mmm.demand_of_stock_b = 0\n mmm.supply_of_stock_b = mmm.security_sale\n elif mmm.security_sale < 0:\n mmm.demand_of_stock_b = mmm.security_sale\n mmm.supply_of_stock_b = 0\n else:\n mmm.demand_of_stock_b = 0\n mmm.supply_of_stock_b = 0\n\n mmm = Bank(result.x[4], result.x[0], result.x[3], result.x[1], result.x[2], mmm.deposits, mmm.equity,\n mmm.alpha_min, mmm.provision_per, mmm.phi, mmm.zeta, mmm.car, mmm.xs, mmm.xbl, mmm.xl, mmm.etha_max)\n else:\n mmm = Bank(0, 0, 0, 0, 0, 0, 0, mmm.alpha_min, mmm.provision_per, mmm.phi, mmm.zeta, mmm.car, mmm.xs, mmm.xbl,\n mmm.xl, mmm.etha_max)\n\n ########################################################\n # optimixation phase\n ### objective function of shadow banks\n # 1- security 2-cash\n\n\ndef optimize_shadow_bank(nnn):\n c_s = np.array([np.random.normal(-ret_sec_shbank, 0.01), 0])\n A_ub_s = np.array([[0, -1]])\n b_ub_s = np.array([-(nnn.s_alpha + nnn.s_provision) * nnn.participation])\n A_eq_s = np.array([[1, 1]])\n b_eq_s = np.array([nnn.participation])\n x0_bounds_s = (0, None)\n x1_bounds_s = (0, None)\n\n bounds_s = [x0_bounds_s, x1_bounds_s]\n result_s = linprog(c_s, A_ub=A_ub_s, b_ub=b_ub_s, A_eq=A_eq_s, b_eq=b_eq_s, bounds=bounds_s)\n nnn.shadow_bank_cash = result_s.x[1]\n nnn.security_old = nnn.security\n nnn.security = result_s.x[0]\n nnn.shadow_bank_cash = result_s.x[1]\n\n nnn.security_sale = nnn.security_old - nnn.security\n if nnn.security_sale > 0:\n nnn.demand_of_stock = 0\n nnn.supply_of_stock = nnn.security_sale\n elif nnn.security_sale < 0:\n nnn.demand_of_stock = abs(nnn.security_sale)\n nnn.supply_of_stock = 0\n else:\n nnn.demand_of_stock = 0\n nnn.supply_of_stock = 0\n\n nnn = Shadow_Bank((result_s.x[0] + result_s.x[1]), result_s.x[0], result_s.x[1], nnn.s_alpha, nnn.s_provision)\n\n\n######################################################################################################\n\ndef redemption(www):\n p_change = 1 - (p_market / p_market_old)\n if p_change < 0:\n www.redemption = 0\n else:\n www.redemption = www.participation * ((math.exp(etha * p_change) - 1))\n\n if www.redemption < www.shadow_bank_cash:\n www.shadow_bank_cash = www.shadow_bank_cash - www.redemption\n www.participation = www.participation - www.redemption\n\n elif www.shadow_bank_cash < www.redemption < www.security + www.shadow_bank_cash:\n www.shadow_bank_cash = 0\n www.security = www.security + www.shadow_bank_cash - www.redemption\n www.participation = www.participation - www.redemption\n elif www.security + www.shadow_bank_cash < www.redemption:\n www.exit = True\n www.participation = 0\n www.security = 0\n www.shadow_bank_cash = 0\n\n\n###############################################################\n##### dynamics of model\n# the name of bank which is source of the shock\n\nshock_hit = bank_melli\n\nsig = 0.5\nshock = sig * (shock_hit.deposits + shock_hit.borrow_from_banks)\n\n\ndef dynamic_bank(www):\n if shock <= www.bank_cash:\n www.equity = www.equity - shock\n www.bank_cash = www.bank_cash - shock\n elif (www.bank_cash + www.lend_to_banks) >= shock >= www.bank_cash:\n landa = (sig * (www.deposits + www.borrow_from_banks)) - www.bank_cash\n www.equity = www.equity - www.bank_cash\n www.bank_cash = 0\n www.lend_to_banks = www.lend_to_banks - (shock - www.bank_cash)\n elif (www.bank_cash + www.lend_to_banks + www.bank_sec) >= shock >= (www.bank_cash + www.lend_to_banks):\n delta = www.bank_cash + www.lend_to_banks + www.bank_sec - shock\n www.bank_cash = 0\n www.equity = www.equity - www.bank_cash\n www.lend_to_banks = 0\n www.bank_sec = www.bank_sec - delta\n www.stock = www.bank_sec / p_market\n elif (www.bank_cash + www.lend_to_banks + www.bank_sec) <= shock:\n www.bank_cash = 0\n www.equity = 0\n www.lend_to_banks = 0\n www.bank_sec = 0\n www.borrow_from_banks = 0\n www.bankrupt = True\n\n\n###############################################################\n################ start the simulation\n################\ndynamic_bank(shock_hit)\nfor ttt in range(n_sim):\n\n # first banks\n optimize_bank(bank_melli)\n optimize_bank(bank_seppah)\n optimize_bank(bank_tosesaderat)\n optimize_bank(bank_maskan)\n optimize_bank(bank_sanatmadan)\n optimize_bank(bank_keshavarzi)\n optimize_bank(bank_tosetavon)\n optimize_bank(bank_post)\n optimize_bank(bank_eghtesadnovin)\n optimize_bank(bank_parsian)\n optimize_bank(bank_karafarin)\n optimize_bank(bank_saman)\n optimize_bank(bank_sina)\n optimize_bank(bank_khavarmiane)\n optimize_bank(bank_shahr)\n optimize_bank(bank_dey)\n optimize_bank(bank_saderat)\n optimize_bank(bank_tejarat)\n optimize_bank(bank_mellat)\n optimize_bank(bank_refah)\n optimize_bank(bank_ayandeh)\n optimize_bank(bank_gardeshgary)\n optimize_bank(bank_iranzamin)\n optimize_bank(bank_sarmaye)\n optimize_bank(bank_pasargad)\n optimize_bank(bank_melal)\n\n # second shadow banks determine the redemptions because it is almost a legal issue the they optimize which is a ecnomic behaviour\n\n redemption(shadow1)\n redemption(shadow2)\n redemption(shadow3)\n redemption(shadow4)\n redemption(shadow5)\n redemption(shadow6)\n redemption(shadow7)\n redemption(shadow8)\n redemption(shadow9)\n redemption(shadow10)\n redemption(shadow11)\n redemption(shadow12)\n redemption(shadow13)\n redemption(shadow14)\n redemption(shadow15)\n\n ## optimization of shadow banks\n\n optimize_shadow_bank(shadow1)\n optimize_shadow_bank(shadow2)\n optimize_shadow_bank(shadow3)\n optimize_shadow_bank(shadow4)\n optimize_shadow_bank(shadow5)\n optimize_shadow_bank(shadow6)\n optimize_shadow_bank(shadow7)\n optimize_shadow_bank(shadow8)\n optimize_shadow_bank(shadow9)\n optimize_shadow_bank(shadow10)\n optimize_shadow_bank(shadow11)\n optimize_shadow_bank(shadow12)\n optimize_shadow_bank(shadow13)\n optimize_shadow_bank(shadow14)\n optimize_shadow_bank(shadow15)\n\n demand_of_banks = bank_melli.borrow_from_banks + bank_seppah.borrow_from_banks + bank_tosesaderat.borrow_from_banks + bank_maskan.borrow_from_banks + bank_sanatmadan.borrow_from_banks + bank_keshavarzi.borrow_from_banks + bank_tosetavon.borrow_from_banks + bank_post.borrow_from_banks + bank_eghtesadnovin.borrow_from_banks + bank_parsian.borrow_from_banks + bank_karafarin.borrow_from_banks + bank_saman.borrow_from_banks + bank_saman.borrow_from_banks + bank_sina.borrow_from_banks + bank_khavarmiane.borrow_from_banks + bank_shahr.borrow_from_banks + bank_dey.borrow_from_banks + bank_saderat.borrow_from_banks + bank_tejarat.borrow_from_banks + bank_mellat.borrow_from_banks + bank_refah.borrow_from_banks + bank_ayandeh.borrow_from_banks + bank_gardeshgary.borrow_from_banks + bank_iranzamin.borrow_from_banks + bank_sarmaye.borrow_from_banks + bank_sarmaye.borrow_from_banks + bank_pasargad.borrow_from_banks + bank_melal.borrow_from_banks\n supply_of_banks = bank_melli.lend_to_banks + bank_seppah.lend_to_banks + bank_tosesaderat.lend_to_banks + bank_maskan.lend_to_banks + bank_sanatmadan.lend_to_banks + bank_keshavarzi.lend_to_banks + bank_tosetavon.lend_to_banks + bank_post.lend_to_banks + bank_eghtesadnovin.lend_to_banks + bank_parsian.lend_to_banks + bank_karafarin.lend_to_banks + bank_saman.lend_to_banks + bank_saman.lend_to_banks + bank_sina.lend_to_banks + bank_khavarmiane.lend_to_banks + bank_shahr.lend_to_banks + bank_dey.lend_to_banks + bank_saderat.lend_to_banks + bank_tejarat.lend_to_banks + bank_mellat.lend_to_banks + bank_refah.lend_to_banks + bank_ayandeh.lend_to_banks + bank_gardeshgary.lend_to_banks + bank_iranzamin.lend_to_banks + bank_sarmaye.lend_to_banks + bank_sarmaye.lend_to_banks + bank_pasargad.lend_to_banks + bank_melal.lend_to_banks\n diference = demand_of_banks - supply_of_banks\n\n if demand_of_banks > supply_of_banks:\n # rrr = (demand_of_banks/supply_of_banks) - 1\n # rfree = (1+rrr) * rfree\n rfree = (rfree + rfree_max) / 2\n elif demand_of_banks < supply_of_banks:\n # rrr = (supply_of_banks / demand_of_banks) - 1\n # rfree = (1 - rrr) * rfree\n rfree = (rfree + rfree_min) / 2\n else:\n rfree = rfree\n\n rfree_vector.append(rfree)\n\n stock_supply_of_banks = bank_melli.supply_of_stock_b + bank_seppah.supply_of_stock_b + bank_tosesaderat.supply_of_stock_b + bank_maskan.supply_of_stock_b + bank_sanatmadan.supply_of_stock_b + bank_keshavarzi.supply_of_stock_b + bank_tosetavon.supply_of_stock_b + bank_post.supply_of_stock_b + bank_eghtesadnovin.supply_of_stock_b + bank_parsian.supply_of_stock_b + bank_karafarin.supply_of_stock_b + bank_saman.supply_of_stock_b + bank_saman.supply_of_stock_b + bank_sina.supply_of_stock_b + bank_khavarmiane.supply_of_stock_b + bank_shahr.supply_of_stock_b + bank_dey.supply_of_stock_b + bank_saderat.supply_of_stock_b + bank_tejarat.supply_of_stock_b + bank_mellat.supply_of_stock_b + bank_refah.supply_of_stock_b + bank_ayandeh.supply_of_stock_b + bank_gardeshgary.supply_of_stock_b + bank_iranzamin.supply_of_stock_b + bank_sarmaye.supply_of_stock_b + bank_sarmaye.supply_of_stock_b + bank_pasargad.supply_of_stock_b + bank_melal.supply_of_stock_b\n stock_demand_of_banks = bank_melli.demand_of_stock_b + bank_seppah.demand_of_stock_b + bank_tosesaderat.demand_of_stock_b + bank_maskan.demand_of_stock_b + bank_sanatmadan.demand_of_stock_b + bank_keshavarzi.demand_of_stock_b + bank_tosetavon.demand_of_stock_b + bank_post.demand_of_stock_b + bank_eghtesadnovin.demand_of_stock_b + bank_parsian.demand_of_stock_b + bank_karafarin.demand_of_stock_b + bank_saman.demand_of_stock_b + bank_saman.demand_of_stock_b + bank_sina.demand_of_stock_b + bank_khavarmiane.demand_of_stock_b + bank_shahr.demand_of_stock_b + bank_dey.demand_of_stock_b + bank_saderat.demand_of_stock_b + bank_tejarat.demand_of_stock_b + bank_mellat.demand_of_stock_b + bank_refah.demand_of_stock_b + bank_ayandeh.demand_of_stock_b + bank_gardeshgary.demand_of_stock_b + bank_iranzamin.demand_of_stock_b + bank_sarmaye.demand_of_stock_b + bank_sarmaye.demand_of_stock_b + bank_pasargad.demand_of_stock_b + bank_melal.demand_of_stock_b\n stock_demand_of_shadow_banks = shadow1.demand_of_stock + shadow2.demand_of_stock + shadow3.demand_of_stock + shadow4.demand_of_stock + shadow5.demand_of_stock + shadow6.demand_of_stock + shadow7.demand_of_stock + shadow8.demand_of_stock + shadow9.demand_of_stock + shadow10.demand_of_stock + shadow11.demand_of_stock + shadow12.demand_of_stock + shadow13.demand_of_stock + shadow14.demand_of_stock + shadow15.demand_of_stock\n stock_supply_of_shadow_banks = shadow1.supply_of_stock + shadow2.supply_of_stock + shadow3.supply_of_stock + shadow4.supply_of_stock + shadow5.supply_of_stock + shadow6.supply_of_stock + shadow7.supply_of_stock + shadow8.supply_of_stock + shadow9.supply_of_stock + shadow10.supply_of_stock + shadow11.supply_of_stock + shadow12.supply_of_stock + shadow13.supply_of_stock + shadow14.supply_of_stock + shadow15.supply_of_stock\n total_stock_demand = stock_demand_of_banks + stock_demand_of_shadow_banks\n total_stock_supply = stock_supply_of_banks + stock_supply_of_shadow_banks\n\n p_market_old = p_market\n\n if total_stock_demand > total_stock_supply:\n p_market = (p_market + p_market_max) / 2\n elif demand_of_banks < supply_of_banks:\n p_market = (p_market + p_market_min) / 2\n else:\n p_market = p_market\n\n ret_on_sec = p_market / p_market_old - 1\n ret_sec_shbank = ret_on_sec\n p_market_vector.append(p_market)\n ret_sec_bank_vector.append(ret_on_sec)\n\n every = total_stock_demand\n every_thing_vector.append(every)\n\n every1 = total_stock_supply\n every1_thing_vector.append(every1)\n\n every2 = p_market\n every2_thing_vector.append(every2)\n\n# rfree_plot = []\n# for i in range(0, len(rfree_vector)):\n# rfree_plot.append([float(rfree_vector[i])])\n#\n# p_market_plot = []\n# for i in range(0, len(p_market_vector)):\n# p_market_plot.append([float(p_market_vector[i])])\n#\n# ret_on_sec_plot = []\n# for i in range(0, len(ret_sec_bank_vector)):\n# ret_on_sec_plot.append([float(ret_sec_bank_vector[i])])\n\nevery_thing_plot = []\nfor i in range(0, len(every_thing_vector)):\n every_thing_plot.append([float(every_thing_vector[i])])\n\nevery1_thing_plot = []\nfor i in range(0, len(every1_thing_vector)):\n every1_thing_plot.append([float(every1_thing_vector[i])])\n\nevery2_thing_plot = []\nfor i in range(0, len(every2_thing_vector)):\n every2_thing_plot.append([float(every2_thing_vector[i])])\n\n# print(ret_on_sec_plot)\n# print(p_market_plot)\n# print(rfree_plot)\n# print(every_thing_vector)\n\n\n# plt.plot(rfree_plot)\n# plt.show()\n# plt.plot(p_market_plot)\n# plt.show()\n# plt.plot(ret_on_sec_plot)\n# plt.show()\n\nplt.plot(every_thing_plot)\nplt.plot(every1_thing_plot)\nplt.plot(every2_thing_plot)\n\nplt.show()\n#\n# plt.plot(every2_thing_plot)\n# plt.show()\n\n\n# print(every_thing_plot)\n# print(every1_thing_plot)\n# print(every2_thing_plot)","repo_name":"vahidito/banking-system-model","sub_path":"banking system model.py","file_name":"banking system model.py","file_ext":"py","file_size_in_byte":22345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"40041528526","text":"\"\"\"train_config.py: Parse training arguments and create config dictionnary.\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport logging\nimport sys\nimport six\n\nfrom nmt_chainer.utilities import argument_parsing_tools\n\nlogging.basicConfig()\nlog = logging.getLogger(\"rnns:train_config\")\nlog.setLevel(logging.INFO)\n\n_CONFIG_SECTION_TO_DESCRIPTION = {\"model\": \"Model Description\",\n \"training\": \"Training Parameters\",\n \"training_management\": \"Training Management and Monitoring\"}\n\n\ndef define_parser(parser):\n parser.add_argument(\"data_prefix\", nargs=\"?\",\n action=argument_parsing_tools.ArgumentActionNotOverwriteWithNone,\n help=\"prefix of the training data created by make_data.py\")\n parser.add_argument(\"save_prefix\", nargs=\"?\",\n action=argument_parsing_tools.ArgumentActionNotOverwriteWithNone,\n help=\"prefix to be added to all files created during the training\")\n\n model_description_group = parser.add_argument_group(_CONFIG_SECTION_TO_DESCRIPTION[\"model\"])\n model_description_group.add_argument(\"--Ei\", type=int, default=640, help=\"Source words embedding size.\")\n model_description_group.add_argument(\"--Eo\", type=int, default=640, help=\"Target words embedding size.\")\n model_description_group.add_argument(\"--Hi\", type=int, default=1024, help=\"Source encoding layer size.\")\n model_description_group.add_argument(\"--Ho\", type=int, default=1024, help=\"Target hidden layer size.\")\n model_description_group.add_argument(\"--Ha\", type=int, default=1024, help=\"Attention Module Hidden layer size.\")\n model_description_group.add_argument(\"--Hl\", type=int, default=512, help=\"Maxout output size.\")\n model_description_group.add_argument(\"--encoder_cell_type\", default=\"lstm\", help=\"cell type of encoder. format: type,param1:val1,param2:val2,...\") # where type is in [%s]\"%(\" \".join(rnn_cells.cell_dict.keys())))\n model_description_group.add_argument(\"--decoder_cell_type\", default=\"lstm\", help=\"cell type of decoder. format same as for encoder\")\n model_description_group.add_argument(\"--lexical_probability_dictionary\", help=\"lexical translation probabilities in zipped JSON format. Used to implement https://arxiv.org/abs/1606.02006\")\n model_description_group.add_argument(\"--lexicon_prob_epsilon\", default=1e-3, type=float, help=\"epsilon value for combining the lexical probabilities\")\n model_description_group.add_argument(\"--use_deep_attn\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--use_accumulated_attn\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--init_orth\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--use_bn_length\", default=0, type=int)\n model_description_group.add_argument(\"--use_goto_attention\", default=False, action=\"store_true\")\n \n model_description_group.add_argument(\"--use_ff_model\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--ff_d_model\", type=int, default=512, help=\"FF model d_model\")\n model_description_group.add_argument(\"--ff_n_heads\", type=int, default=8, help=\"FF model number of attention heads\")\n model_description_group.add_argument(\"--ff_nb_layers_src\", type=int, default=6, help=\"FF model number of source layers\")\n model_description_group.add_argument(\"--ff_nb_layers_tgt\", type=int, default=6, help=\"FF model number of target layers\")\n model_description_group.add_argument(\"--ff_dropout\", type=float, help=\"FF model dropout\")\n model_description_group.add_argument(\"--ff_d_ff\", type=int, default=2048, help=\"FF model d_ff\")\n model_description_group.add_argument(\"--ff_use_exp_relu\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--ff_residual_mode\", default=\"normal\", choices=\"normal none after\".split())\n model_description_group.add_argument(\"--ff_no_normalize\", default=False, action=\"store_true\")\n model_description_group.add_argument(\"--use_own_layer_normalization\", default=False, action=\"store_true\")\n \n training_paramenters_group = parser.add_argument_group(_CONFIG_SECTION_TO_DESCRIPTION[\"training\"])\n training_paramenters_group.add_argument(\"--mb_size\", type=int, default=64, help=\"Minibatch size\")\n training_paramenters_group.add_argument(\"--nb_batch_to_sort\", type=int, default=20, help=\"Sort this many batches by size.\")\n training_paramenters_group.add_argument(\"--noise_on_prev_word\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--l2_gradient_clipping\", type=float, default=1, help=\"L2 gradient clipping. 0 for None\")\n training_paramenters_group.add_argument(\"--hard_gradient_clipping\", type=float, nargs=2, help=\"hard gradient clipping.\")\n training_paramenters_group.add_argument(\"--weight_decay\", type=float, help=\"Weight decay value. \")\n training_paramenters_group.add_argument(\"--optimizer\", choices=[\"sgd\", \"rmsprop\", \"rmspropgraves\",\n \"momentum\", \"nesterov\", \"adam\", \"scheduled_adam\", \"adagrad\", \"adadelta\"],\n default=\"adam\", help=\"Optimizer type.\")\n training_paramenters_group.add_argument(\"--learning_rate\", type=float, default=0.01, help=\"Learning Rate\")\n training_paramenters_group.add_argument(\"--momentum\", type=float, default=0.9, help=\"Momentum term\")\n training_paramenters_group.add_argument(\"--randomized_data\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--no_shuffle_of_training_data\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--use_reinf\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--use_previous_prediction\", default=0, type=float)\n training_paramenters_group.add_argument(\"--curiculum_training\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--reverse_src\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--reverse_tgt\", default=False, action=\"store_true\")\n \n training_paramenters_group.add_argument(\"--use_soft_prediction_feedback\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--use_gumbel_for_soft_predictions\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--temperature_for_soft_predictions\", type=float, default=1.0)\n\n\n training_paramenters_group.add_argument(\"--dynamic_batching\", default=False, action=\"store_true\")\n training_paramenters_group.add_argument(\"--dynamic_batching_max_elems\", type=int, default=10000)\n training_paramenters_group.add_argument(\"--dynamic_batching_nb_sent_to_sort\", type=int, default=5000)\n \n training_paramenters_group.add_argument(\"--load_initial_source_embeddings\")\n training_paramenters_group.add_argument(\"--load_initial_target_embeddings\")\n\n training_monitoring_group = parser.add_argument_group(_CONFIG_SECTION_TO_DESCRIPTION[\"training_management\"])\n training_monitoring_group.add_argument(\"--config\", help=\"load a training config file\")\n training_monitoring_group.add_argument(\"--data_prefix\", dest=\"data_prefix\",\n action=argument_parsing_tools.ArgumentActionNotOverwriteWithNone,\n help=\"same as positional argument --data_prefix\")\n training_monitoring_group.add_argument(\"--save_prefix\", dest=\"save_prefix\",\n action=argument_parsing_tools.ArgumentActionNotOverwriteWithNone,\n help=\"same as positional argument --save_prefix\")\n training_monitoring_group.add_argument(\"--gpu\", type=int, help=\"specify gpu number to use, if any\")\n training_monitoring_group.add_argument(\"--load_model\", help=\"load the parameters of a previously trained model\")\n training_monitoring_group.add_argument(\"--load_optimizer_state\", help=\"load previously saved optimizer states\")\n training_monitoring_group.add_argument(\"--load_trainer_snapshot\", help=\"load previously saved trainer states\")\n training_monitoring_group.add_argument(\"--use_memory_optimization\", default=False, action=\"store_true\",\n help=\"Experimental option that could strongly reduce memory used.\")\n training_monitoring_group.add_argument(\"--max_nb_iters\", type=int, default=None, help=\"maximum number of iterations\")\n training_monitoring_group.add_argument(\"--max_nb_epochs\", type=int, default=None, help=\"maximum number of epochs\")\n training_monitoring_group.add_argument(\"--max_src_tgt_length\", type=int, help=\"Limit length of training sentences\")\n training_monitoring_group.add_argument(\"--report_every\", type=int, default=200, help=\"report every x iterations\")\n training_monitoring_group.add_argument(\"--no_resume\", default=False, action=\"store_true\")\n training_monitoring_group.add_argument(\"--no_report_or_save\", default=False, action=\"store_true\")\n training_monitoring_group.add_argument(\"--sample_every\", default=200, type=int)\n training_monitoring_group.add_argument(\"--save_ckpt_every\", default=4000, type=int)\n training_monitoring_group.add_argument(\"--save_initial_model_to\", help=\"save the initial model parameters to given file in npz format\")\n training_monitoring_group.add_argument(\"--reshuffle_every_epoch\", default=False, action=\"store_true\", help=\"reshuffle training data at the end of each epoch\")\n training_monitoring_group.add_argument(\"--resume\", default=False, action=\"store_true\", help=\"resume training from checkpoint config\")\n training_monitoring_group.add_argument(\"--timer_hook\", default=False, action=\"store_true\", help=\"activate timer hook for profiling\")\n training_monitoring_group.add_argument(\"--force_overwrite\", default=False, action=\"store_true\", help=\"Do not ask before overwiting existing files\")\n training_monitoring_group.add_argument(\"--description\", help=\"Optional message to be stored in the configuration file\")\n\n training_monitoring_group.add_argument(\"--set_false_in_config\", nargs=\"*\", help=\"Forcing some options to be false\")\n \n training_monitoring_group.add_argument(\"--update_old_config_file_with_default_values\", \n default=False, action=\"store_true\", help=\"When using older config files\")\n\n training_monitoring_group.add_argument(\"--generate_computation_graph\", help=\"will generate computation graph of the first loss computed\")\n\n training_monitoring_group.add_argument(\"--disable_cudnn_softmax\", default=False, action=\"store_true\")\n training_monitoring_group.add_argument(\"--use_chainerx\", default=False, action=\"store_true\", help=\"use chainerx\")\n\nclass CommandLineValuesException(Exception):\n pass\n\n#\n# def load_training_config_file(filename):\n# file_content = json.load(open(filename))\n\n\ndef get_parse_option_orderer():\n description_to_config_section = dict((v, k) for (k, v) in six.iteritems(_CONFIG_SECTION_TO_DESCRIPTION))\n por = argument_parsing_tools.ParseOptionRecorder(group_title_to_section=description_to_config_section,\n ignore_positional_arguments=set([\"save_prefix\", \"data_prefix\"]))\n define_parser(por)\n return por\n\n\ndef convert_cell_string(config_training, no_error=False):\n import nmt_chainer.models.rnn_cells_config\n\n try:\n if \"encoder_cell_type\" in config_training[\"model\"] and config_training[\"model\"][\"encoder_cell_type\"] is not None:\n config_training[\"model\"][\"encoder_cell_type\"] = nmt_chainer.models.rnn_cells_config.create_cell_config_from_string(\n config_training[\"model\"][\"encoder_cell_type\"])\n\n if \"decoder_cell_type\" in config_training[\"model\"] and config_training[\"model\"][\"decoder_cell_type\"] is not None:\n config_training[\"model\"][\"decoder_cell_type\"] = nmt_chainer.models.rnn_cells_config.create_cell_config_from_string(\n config_training[\"model\"][\"decoder_cell_type\"])\n except BaseException:\n if not no_error:\n raise\n\n\ndef load_config_train(filename, readonly=True, no_error=False):\n config = argument_parsing_tools.OrderedNamespace.load_from(filename)\n if \"metadata\" not in config: # older config file\n parse_option_orderer = get_parse_option_orderer()\n config_training = parse_option_orderer.convert_args_to_ordered_dict(config[\"command_line\"], args_is_namespace=False)\n\n convert_cell_string(config_training, no_error=no_error)\n\n assert \"data\" not in config_training\n config_training[\"data\"] = argument_parsing_tools.OrderedNamespace()\n config_training[\"data\"][\"data_fn\"] = config[\"data\"]\n config_training[\"data\"][\"Vi\"] = config[\"Vi\"]\n config_training[\"data\"][\"Vo\"] = config[\"Vo\"]\n config_training[\"data\"][\"voc\"] = config[\"voc\"]\n\n assert \"metadata\" not in config_training\n config_training[\"metadata\"] = argument_parsing_tools.OrderedNamespace()\n config_training[\"metadata\"][\"config_version_num\"] = 0.9\n config_training[\"metadata\"][\"command_line\"] = None\n config_training[\"metadata\"][\"knmt_version\"] = None\n config = config_training\n elif config[\"metadata\"][\"config_version_num\"] != 1.0:\n raise ValueError(\"The config version of %s is not supported by this version of the program\" % filename)\n\n # Compatibility with intermediate verions of config file\n if \"data_prefix\" in config and \"data_prefix\" not in config[\"training_management\"]:\n config[\"training_management\"][\"data_prefix\"] = config[\"data_prefix\"]\n del config[\"data_prefix\"]\n\n if \"train_prefix\" in config and \"train_prefix\" not in config[\"training_management\"]:\n config[\"training_management\"][\"train_prefix\"] = config[\"train_prefix\"]\n del config[\"train_prefix\"]\n\n if readonly:\n config.set_readonly()\n return config\n\n\ndef find_which_command_line_arguments_were_given(argument_list):\n pwndan = argument_parsing_tools.ParserWithNoneDefaultAndNoGroup()\n define_parser(pwndan)\n args_given_set = pwndan.get_args_given(argument_list)\n return args_given_set\n\n\ndef make_config_from_args(args, readonly=True):\n config_base = None\n if args.config is not None:\n log.info(\"loading training config file %s\", args.config)\n config_base = load_config_train(args.config, readonly=False)\n\n if args.set_false_in_config is not None:\n for option_name in args.set_false_in_config:\n path_option = option_name.split(\".\")\n last_dict = config_base\n for level in six.moves.range(len(path_option) -1):\n last_dict = config_base[path_option[level]]\n last_dict[path_option[-1]] = False\n \n\n parse_option_orderer = get_parse_option_orderer()\n config_training = parse_option_orderer.convert_args_to_ordered_dict(args)\n\n convert_cell_string(config_training)\n\n if config_base is not None:\n args_given_set = find_which_command_line_arguments_were_given(\n args.__original_argument_list)\n for argname in set(args_given_set):\n if getattr(args, argname) is None:\n args_given_set.remove(argname)\n\n print(\"args_given_set\", args_given_set)\n config_base.update_recursive(config_training, valid_keys=args_given_set, add_absent_keys=args.update_old_config_file_with_default_values)\n config_training = config_base\n else:\n assert \"data\" not in config_training\n assert \"metadata\" not in config_training\n\n# config_data_fn = config_training[\"data_prefix\"] + \".data.config\"\n\n if config_training[\"training_management\"][\"data_prefix\"] is None or config_training[\"training_management\"][\"save_prefix\"] is None:\n raise CommandLineValuesException(\"save_prefix and data_prefix need to be set either on the command line or in a config file\")\n\n config_training.add_metadata_infos(version_num=1, overwrite=args.config is not None)\n\n if readonly:\n config_training.set_readonly()\n\n return config_training\n\n\n# def load_config_train(filename, readonly = True):\n# config_as_ordered_dict = json.load(open(filename), object_pairs_hook=OrderedDict)\n#\n# config = OrderedNamespace.load_from(filename)\n# if \"metadata\" not in config_as_ordered_dict: # older config file\n# parse_option_orderer = get_parse_option_orderer()\n# config_training = parse_option_orderer.convert_args_to_ordered_dict(config_as_ordered_dict[\"command_line\"], args_is_namespace = False)\n#\n# assert \"data\" not in config_training\n# config_training[\"data\"] = argument_parsing_tools.OrderedNamespace()\n# config_training[\"data\"][\"data_fn\"] = config_as_ordered_dict[\"data\"]\n# config_training[\"data\"][\"Vi\"] = config_as_ordered_dict[\"Vi\"]\n# config_training[\"data\"][\"Vo\"] = config_as_ordered_dict[\"Vo\"]\n# config_training[\"data\"][\"voc\"] = config_as_ordered_dict[\"voc\"]\n#\n# assert \"metadata\" not in config_training\n# config_training[\"metadata\"] = argument_parsing_tools.OrderedNamespace()\n# config_training[\"metadata\"][\"config_version_num\"] = 0.9\n# config_training[\"metadata\"][\"command_line\"] = None\n# config_training[\"metadata\"][\"knmt_version\"] = None\n# elif config_as_ordered_dict[\"metadata\"][\"config_version_num\"] == 1.0:\n# argument_parsing_tools.OrderedNamespace.convert_to_ordered_namespace(config_as_ordered_dict)\n# config_training = config_as_ordered_dict\n# else:\n# raise ValueError(\"The config version of %s is not supported by this version of the program\" % filename)\n#\n# if readonly:\n# config_training.set_readonly()\n# return config_training\n\ndef command_line(arguments=None):\n import argparse\n parser = argparse.ArgumentParser(description=\"Train a RNNSearch model\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n define_parser(parser)\n args = parser.parse_args(args=arguments)\n\n do_train(args)\n\n\ndef do_train(args):\n import nmt_chainer.training_module.train\n config = make_config_from_args(args, readonly=False)\n nmt_chainer.training_module.train.do_train(config)\n\n\nif __name__ == '__main__':\n command_line()\n","repo_name":"fabiencro/knmt","sub_path":"nmt_chainer/training_module/train_config.py","file_name":"train_config.py","file_ext":"py","file_size_in_byte":18496,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"61"}
+{"seq_id":"33421048483","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat May 28 09:39:17 2022\r\n\r\n@author: kundan\r\n\"\"\"\r\n\r\nclass node:\r\n def __init__(self,value):\r\n self.value=value\r\n self.next=None\r\n self.top=1\r\n \r\n \r\n \r\nclass stack:\r\n \r\n def __init__(self):\r\n self.head=None\r\n self.top = 1\r\n \r\n \r\n def push(self,value):\r\n new_node = node(value)\r\n new_node.next = self.head\r\n self.head=new_node\r\n self.top += 1\r\n return value\r\n \r\n \r\n \r\n def pop(self):\r\n if self.head is None:\r\n return False\r\n temp=self.head\r\n self.head=temp.next\r\n self.top -= 1\r\n return True\r\n \r\n \r\n \r\n def display(self):\r\n temp = self.head\r\n while temp is not None:\r\n print(temp.value)\r\n temp=temp.next\r\n \r\n print()\r\n \r\n \r\n \r\n\r\n \r\n \r\nobj = stack()\r\nobj.push(30)\r\nobj.push(50)\r\nobj.push(80)\r\nobj.push(60)\r\nobj.push(10)\r\nobj.push(20)\r\n\r\nobj.display()\r\n\r\nobj.pop()\r\nobj.display()\r\n\r\nobj.pop()\r\nobj.display()","repo_name":"kundankx6090/Data-structures-and-algorithms","sub_path":"stack.py","file_name":"stack.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"20020810790","text":"from django.contrib import admin\n\nfrom .models import Setting, ContactMessage, Events\n\n\nclass SettingAdmin(admin.ModelAdmin):\n list_display = ['title', 'update_at', 'status']\n list_filter = ['title']\n\nclass ContactMessageAdmin(admin.ModelAdmin):\n list_display = ['name', 'update_at', 'status']\n list_filter = ['status']\n readonly_fields = ('name', 'update_at', 'subject', 'your_email', 'your_message', 'ip')\n\nclass EventsAdmin(admin.ModelAdmin):\n list_display = ['title', 'update_at', 'status']\n list_filter = ['status']\n prepopulated_fields = {\"slug\": (\"title\",)}\n\n\nadmin.site.register(Setting, SettingAdmin)\nadmin.site.register(ContactMessage, ContactMessageAdmin)\nadmin.site.register(Events, EventsAdmin)\n\n# Register your models here.\n","repo_name":"Killman03/mylibsite","sub_path":"home/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"5134181261","text":"# SPDX-License-Identifier: MIT\n# © 2020-2022 ETH Zurich and other contributors, see AUTHORS.txt for details\n\nfrom gdl_apps.EmotionRecognition.utils.io import load_model\nfrom gdl.datasets.ImageTestDataset import TestData\nimport gdl\nimport numpy as np\nimport os\nimport torch\nfrom pathlib import Path\nimport matplotlib.pyplot as plt\nfrom torch.functional import F\nfrom gdl.datasets.AffectNetDataModule import AffectNetExpressions\nfrom gdl.utils.other import get_path_to_assets\nfrom tqdm import tqdm\n\ndef load_dir(lmspath, framepath, start, end):\n lmss = []\n imgs_paths = []\n for i in range(start, end):\n if os.path.isfile(os.path.join(lmspath, str(i) + '.lms')):\n lms = np.loadtxt(os.path.join(\n lmspath, str(i) + '.lms'), dtype=np.float32)\n lmss.append(lms)\n imgs_paths.append(os.path.join(framepath, str(i) + '.jpg'))\n lmss = np.stack(lmss)\n lmss = torch.as_tensor(lmss).cuda()\n return imgs_paths\n\nclass EMOCA_tracker:\n def __init__(self):\n \n model_name = 'ResNet50'\n path_to_models = get_path_to_assets() /\"EmotionRecognition\"\n\n path_to_models = path_to_models / \"image_based_networks\"\n\n self.model = load_model(Path(path_to_models) / model_name)\n print(self.model)\n self.model.cuda()\n self.model.eval()\n\n def __call__(self, images, tform=None):\n\n codedict = self.model(images)\n\n return codedict\n \n def save_images(self, batch, predictions, output_folder):\n # Save the images\n\n softmax = F.softmax(predictions[\"expr_classification\"])\n top_expr = torch.argmax(softmax, dim=1)\n for i in range(len(batch[\"image\"])):\n img = batch[\"image\"][i].cpu().detach().numpy()\n img = img.transpose(1, 2, 0)\n img = img * 255\n img = img.astype(np.uint8)\n\n plt.figure()\n # plot the image with matplotlib \n plt.imshow(img)\n # write valence and arousal to the image\n expr = AffectNetExpressions(int(top_expr[i].item()))\n text = \"Predicted emotion:\\n\"\n text += f'Arousal: {predictions[\"arousal\"][i].item():.2f} \\nValence: {predictions[\"valence\"][i].item():.2f}'\n text += f\"\\nExpression: {expr.name}, {softmax[i][expr.value].item()*100:.2f}%\"\n plt.title(text)\n out_fname = Path(output_folder) / f\"{batch['image_name'][i]}.png\"\n # save the image to the output folder\n \n # axis off \n plt.axis('off')\n plt.savefig(out_fname)\n plt.close()\n\n\ndef emotion_detection(dataset_base, emotion_dir):\n '''\n Face tracker using FLAME model.\n Used to have geometry prior for nerf sampling.\n '''\n\n id_dir = dataset_base\n debug_emotions = os.path.join(id_dir, 'debug', 'emotions_imgs')\n Path(debug_emotions).mkdir(parents=True, exist_ok=True)\n\n emoca_tracker = EMOCA_tracker()\n\n # Run deca on all frames\n testdata = TestData(os.path.join(id_dir, 'frames'), face_detector=\"fan\", max_detection=20)\n \n for i, data in enumerate(tqdm(testdata)):\n batch = testdata[i]\n batch[\"image\"] = batch[\"image\"].cuda()\n predictions = emoca_tracker(batch)\n npy_pred = {k: v.cpu().detach().numpy() for k,v in predictions.items()}\n np.save(os.path.join(emotion_dir, '%5d.npy' % i), npy_pred)\n\n emoca_tracker.save_images(batch, predictions, debug_emotions)\n\nif __name__ == '__main__':\n\n dataset_base = '/media/apennino/EmotionDetection/Test/Greta/'\n emotion_dir = '/media/apennino/EmotionDetection/Test/Greta/emotions/'\n emotion_detection(dataset_base, emotion_dir)\n","repo_name":"mediatechnologycenter/AvatarForge","sub_path":"motion-gan-pipeline/preprocessing/emoca_tracker.py","file_name":"emoca_tracker.py","file_ext":"py","file_size_in_byte":3711,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"61"}
+{"seq_id":"33322591725","text":"# coding=utf-8\n__author__ = 'Feely'\n\nimport time\nimport multiprocessing\nimport sys\n\nimport DrawNO\nimport conn\nimport GDSFC\n\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\nsys.excepthook = lambda *args: None\nSTDERR = sys.stderr\n\n\n#重庆时时彩\ndef ssc_drawnumber(ssc_type,db_ssc_type):\n returndate=''\n while True:\n #调用爬虫，获取开奖信息\n assert isinstance(ssc_type, str)\n draw_date,draw_code, draw_time_str= DrawNO.drawnumber(ssc_type)\n if draw_code == '0' or draw_date <= returndate:\n pass\n else:\n returndate=conn.kjdata(t2=draw_code,cid=db_ssc_type,t1=draw_date,t3=draw_time_str)\n time.sleep(180)\n # draw_time = datetime.strptime(draw_time_str, \"%Y-%m-%d %H:%M\")\n # ms.IsInfoExists(SPname='ibc.dbo.IsInfoExists',lottery_type=db_ssc_type,lottery_num=draw_date,kjCodes=draw_code,kjtime=draw_time,addtime=datetime.now())\n # time.sleep(1)\n # ms.SYSPaiJiang(SPname='ibc.dbo.SYSPaiJiang',kjExpect=draw_date,kjTime=draw_time_str,kjCode=draw_code,ltType=db_ssc_type)\n time.sleep(30)\n\ndef main():\n \"\"\"\n\n :rtype : Null\n \"\"\"\n #重庆时时彩\n ssc_type='cqssc'\n db_ssc_type='1'\n jobs=[]\n for i in range(2):\n p_cq=multiprocessing.Process(name='CQSSC',target=ssc_drawnumber,args=(ssc_type,db_ssc_type,))\n jobs.append(p_cq)\n p_cq.start()\n p_cq.join(timeout=10)\nif __name__ == \"__main__\":\n main()","repo_name":"FeelySong/SJLottery","sub_path":"kj/SSC/sjNumber.py","file_name":"sjNumber.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"61"}
+{"seq_id":"569337338","text":"import random\r\n\r\ndef main():\r\n num1 = random.randint(0,9)\r\n print(\"you have 3 guesses to find the number \")\r\n i = 3\r\n while i >0 :\r\n print(\"please guess a number\")\r\n guess = int(input())\r\n if(guess == num1):\r\n print(\"you have guessed correctly\")\r\n print(f\"your score is \",i)\r\n print(\"would you like to play again : y/n (y stands for yes , n stands for no)\")\r\n r=input()\r\n if (r=='y'):\r\n main()\r\n else:\r\n print(\"you have gussed incorrectly \")\r\n print(f\"you have\", i-1, \"chances\")\r\n i = i-1\r\n\r\n if(i==0):\r\n print(\"you have lost the game\")\r\n print(f\"the correct number is \", num1)\r\n print(\"would you like to play again : y/n (y stands for yes , n stands for no)\")\r\n s=input()\r\n if (s=='y'):\r\n main()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"praneeth-vemula/python-project","sub_path":"guessgame.py","file_name":"guessgame.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"34093717952","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"These functions calculate the similarity of two images of the same size.\"\"\"\n\n\nimport cv2\nfrom .utils import img_mat_rgb_2_gray\n\n\ndef cal_ccoeff_confidence(im_source, im_search):\n \"\"\"求取两张图片的可信度，使用TM_CCOEFF_NORMED方法.\"\"\"\n # 扩展置信度计算区域\n im_search = cv2.copyMakeBorder(im_search, 10,10,10,10,cv2.BORDER_REPLICATE)\n \n im_source, im_search = img_mat_rgb_2_gray(im_source), img_mat_rgb_2_gray(im_search)\n res = cv2.matchTemplate(im_source, im_search, cv2.TM_CCOEFF_NORMED)\n min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n\n return max_val\n\n\ndef cal_rgb_confidence(img_src_rgb, img_sch_rgb):\n \"\"\"同大小彩图计算相似度.\"\"\"\n # 扩展置信度计算区域\n img_sch_rgb = cv2.copyMakeBorder(img_sch_rgb, 10,10,10,10,cv2.BORDER_REPLICATE)\n # 转HSV强化颜色的影响\n img_src_rgb = cv2.cvtColor(img_src_rgb, cv2.COLOR_BGR2HSV)\n img_sch_rgb = cv2.cvtColor(img_sch_rgb, cv2.COLOR_BGR2HSV)\n src_bgr, sch_bgr = cv2.split(img_src_rgb), cv2.split(img_sch_rgb)\n\n # 计算BGR三通道的confidence，存入bgr_confidence:\n bgr_confidence = [0, 0, 0]\n # 加入取值范围干扰，防止算法过于放大微小差异\n src_bgr[0][0,0] = sch_bgr[0][0,0] = 0\n src_bgr[0][0,1] = sch_bgr[0][0,1] = 255\n for i in range(3):\n res_temp = cv2.matchTemplate(src_bgr[i], sch_bgr[i], cv2.TM_CCOEFF_NORMED)\n min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res_temp)\n bgr_confidence[i] = max_val\n\n return min(bgr_confidence)\n","repo_name":"manito-666/air_uitest","sub_path":"lib/python3.9/site-packages/airtest/aircv/cal_confidence.py","file_name":"cal_confidence.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"61"}
+{"seq_id":"41791803995","text":"import time\n\n\nclass TwoCaptchaSolver():\n\n TWOCAPTCHA_APIKEY = \"e1c237a87652d7d330c189f71c00ec0b\"\n\n INPUT_URL = 'http://2captcha.com/in.php'\n OUTPUT_URL = 'http://2captcha.com/res.php?key={api_key}&action=get&id={captcha_id}'\n DELAY = 5\n MAX_RETRIES = 30\n\n def __init__(self, scraper, captcha_url, disable_log=False):\n self.scraper = scraper\n self.captcha_url = captcha_url\n self.add_lh_log('solve captcha from {}'.format(captcha_url))\n\n def extract_first(self, xpath_expr, default_value=None):\n data = self.scraper.tree_html.xpath(xpath_expr)\n return data[0] if data else default_value\n\n def add_lh_log(self, value):\n if self.scraper.lh:\n self.scraper.lh.add_list_log('errors', value)\n\n def send_request(self, data):\n for _ in range(3):\n req = self.scraper._request(\n self.INPUT_URL,\n verb='post',\n data=data\n )\n if req.status_code == 200:\n status = req.text.strip().split('|')[0]\n if status == 'OK':\n captcha_server_id = req.text.strip().split('|')[-1]\n return captcha_server_id\n else:\n self.add_lh_log('Can\\'t upload captcha to server, status_code: {}'.format(req.status_code))\n print('Can\\'t upload captcha to server, status_code: {}'.format(req.status_code))\n\n def get_captcha_answer(self, captcha_server_id):\n for _ in range(self.MAX_RETRIES):\n req = self.scraper._request(\n self.OUTPUT_URL.format(\n api_key=self.TWOCAPTCHA_APIKEY,\n captcha_id=captcha_server_id\n )\n )\n if req.status_code == 200:\n status = req.text.strip().split('|')[0]\n if status == 'OK':\n answer = req.text.strip().split('|')[-1]\n self.add_lh_log('Solution found')\n print('Solution found')\n return answer\n elif status == 'ERROR_CAPTCHA_UNSOLVABLE':\n self.add_lh_log(status)\n print('Can\\'t solve captcha, server answer: {}'.format(status))\n else:\n self.add_lh_log(status)\n print('Captcha status: {}'.format(status))\n time.sleep(self.DELAY)\n\n def recaptchaV2(self):\n site_key = self.extract_first('//div[@data-sitekey]/@data-sitekey')\n data = {\n \"key\": self.TWOCAPTCHA_APIKEY,\n \"method\": \"userrecaptcha\",\n \"googlekey\": site_key,\n \"pageurl\": self.captcha_url\n }\n captcha_server_id = self.send_request(data)\n if captcha_server_id:\n answer = self.get_captcha_answer(captcha_server_id)\n return answer\n\n\n\n\n","repo_name":"aprosdev/ecom-predictor","sub_path":"special_crawler/two_captcha_solver.py","file_name":"two_captcha_solver.py","file_ext":"py","file_size_in_byte":2893,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"61"}
+{"seq_id":"24748458957","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 23 06:14:31 2022\n\n@author: docker\n\"\"\"\nimport copy\nimport random\nimport itertools\nimport collections\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom gldadec import utils\n\nclass SetData():\n def __init__(self,verbose=True):\n self.verbose = verbose\n self.raw_df = None\n self.marker_dic = None\n self.final_int = None\n self.input_mat = None\n \n def set_expression(self,df):\n \"\"\"\n Set gene expression data.\n It is better to keep as many genes as possible.\n ----------\n df : DataFrame\n Genes in rows and samples in columns.\n \"\"\"\n df.index = [t.upper() for t in df.index.tolist()] # re-index\n self.raw_df = df\n if self.verbose:\n a,b = self.raw_df.shape\n print(a,'genes')\n print(b,'samples')\n \n def set_marker(self,marker_dic:dict):\n \"\"\"\n Set marker list for each cell\n ----------\n marker_dic : dict\n \n \"\"\"\n # convert uppercase\n new_v = []\n new_k = []\n for i,k in enumerate(marker_dic):\n if len(marker_dic.get(k)) > 0:\n tmp_v = sorted([t.upper() for t in marker_dic.get(k)])\n new_v.append(tmp_v)\n new_k.append(k)\n else:\n pass\n marker_dic2 = dict(zip(new_k,new_v))\n self.marker_dic = marker_dic2\n if self.verbose:\n print(len(self.marker_dic),'cells')\n print(len(marker_dic)-len(self.marker_dic),'cells were removed (markers were not registered)')\n \n def marker_info_processing(self,do_plot=True):\n # reflect expression data\n marker_dic = self.marker_dic\n genes = self.raw_df.index.tolist()\n new_v = []\n new_k = []\n for i,k in enumerate(marker_dic):\n marker = marker_dic.get(k)\n tmp_common = sorted(list(set(marker) & set(genes)))\n if len(tmp_common) > 0:\n tmp_v = [t.upper() for t in tmp_common]\n new_v.append(tmp_v)\n new_k.append(k)\n else:\n pass\n marker_dic3 = dict(zip(new_k,new_v))\n self.marker_dic = marker_dic3\n marker_genes = set(list(itertools.chain.from_iterable(list(self.marker_dic.values()))))\n if self.verbose:\n print('--- reflect genes in expression ---')\n print(len(self.marker_dic),'cells')\n print(len(marker_dic)-len(self.marker_dic),'cells were removed (markers were not registered)')\n print(len(marker_genes),'genes were registered')\n \n # plot the original registered marker size\n if do_plot:\n y = [len(t) for t in self.marker_dic.values()]\n x = [i for i in range(len(y))]\n plt.bar(x,y)\n plt.xticks(x,self.marker_dic.keys(),rotation=75)\n plt.title('Original Marker Size')\n plt.show()\n \n # detect cell specific markers\n count_dic = dict(collections.Counter(list(itertools.chain.from_iterable(list(self.marker_dic.values())))))\n sort_count = sorted(count_dic.items(),key=lambda x : x[1])\n unique_marker = [] # no overlap\n for t in sort_count:\n if t[1] == 1:\n unique_marker.append(t[0])\n else:\n pass\n new_v = []\n new_k = []\n for i,k in enumerate(self.marker_dic):\n tmp_v = sorted(list(set(self.marker_dic.get(k)) & set(unique_marker)))\n if len(tmp_v) > 0:\n new_v.append(tmp_v)\n new_k.append(k)\n else:\n pass\n self.spe_marker_dic = dict(zip(new_k,new_v))\n spe_marker_genes = set(list(itertools.chain.from_iterable(list(self.spe_marker_dic.values()))))\n if self.verbose:\n print('--- extract cell specific marker ---')\n print(len(self.spe_marker_dic),'cells')\n print(set(self.marker_dic.keys())-set(self.spe_marker_dic.keys()),'cells were removed (no marker after removing overlap)')\n print(len(spe_marker_genes),'genes were registered')\n \n # plot the cell specific marker size\n if do_plot:\n y = [len(t) for t in self.spe_marker_dic.values()]\n x = [i for i in range(len(y))]\n plt.bar(x,y)\n plt.xticks(x,self.spe_marker_dic.keys(),rotation=75)\n plt.title('Specific Marker Size')\n plt.show()\n \n def set_random(self,random_sets:list):\n \"\"\"\n Random states list\n ----------\n random_sets : list\n e.g. [1448, 1632, 5913, 7927, 8614,...]\n \"\"\"\n self.random_sets = random_sets\n \n def expression_processing(self,random_genes=None,random_n=0,specific=True,random_s=None,prior_norm=True,norm_scale=1000):\n \"\"\"\n 1. Determine if the markers are cell specific.\n 2. Add non-marker gene at random.\n 3. Process expression data into a format for analysis\n ----------\n random_n : int\n DESCRIPTION. The default is 0.\n specific : bool\n DESCRIPTION. The default is True.\n \"\"\"\n if specific:\n if self.verbose:\n print('use specific markers')\n self.marker_final_dic = self.spe_marker_dic\n else:\n if self.verbose:\n print('use overlap markers')\n self.marker_final_dic = self.marker_dic\n \n genes = list(itertools.chain.from_iterable(list(self.marker_final_dic.values()))) # marker genes\n \n raw_df = copy.deepcopy(self.raw_df)\n if random_s is None:\n random_s = self.random_sets[0]\n random.seed(random_s)\n random_candidates = sorted(list(set(raw_df.index.tolist()) - set(genes))) # total genes - marker genes\n if random_genes is None:\n random_genes = random.sample(random_candidates,random_n) # choose genes from non-marker genes\n if self.verbose:\n print(len(random_genes),'genes were added at random')\n else:\n pass\n \n union = sorted(list(set(random_genes) | set(genes)))\n common = sorted(list(set(raw_df.index.tolist()) & set(union))) # fix the output gene order\n target_df = raw_df.loc[common]\n\n # prior information normalization\n if prior_norm:\n linear_norm = utils.freq_norm(target_df,self.marker_final_dic)\n linear_norm = linear_norm.loc[sorted(linear_norm.index.tolist())]\n final_df = linear_norm/norm_scale\n else:\n final_df = target_df/norm_scale\n self.final_int = final_df.astype(int) # convert int\n self.input_mat = np.array(self.final_int.T,dtype='int64')\n\n # seed-topic preparation\n gene_names = [t.upper() for t in self.final_int.index.tolist()]\n self.gene2id = dict((v, idx) for idx, v in enumerate(gene_names))\n self.random_genes = random_genes\n \n def expression_processing2(self,specific=True):\n \"\"\"\n 1. Determine if the markers are cell specific.\n 2. Add non-marker gene at random to each topic.\n 3. Process expression data into a format for analysis\n ----------\n specific : bool\n DESCRIPTION. The default is True.\n \"\"\"\n if specific:\n if self.verbose:\n print('use specific markers')\n self.marker_final_dic = self.spe_marker_dic\n else:\n if self.verbose:\n print('use overlap markers')\n self.marker_final_dic = self.marker_dic\n \n marker_final_dic = copy.deepcopy(self.marker_final_dic)\n genes = list(itertools.chain.from_iterable(list(marker_final_dic.values()))) # marker genes\n raw_df = copy.deepcopy(self.raw_df)\n\n random_list = []\n new_list = []\n for i,k in enumerate(marker_final_dic):\n m = marker_final_dic.get(k)\n random_candidates = sorted(list(set(raw_df.index.tolist()) - set(genes))) # total genes - marker genes\n random.seed(i)\n random_gene = random.sample(random_candidates,len(m))\n m.extend(random_gene)\n new_list.append(sorted(m))\n random_list.append(random_gene)\n genes.extend(random_gene)\n new_dic = dict(zip(list(marker_final_dic.keys()), new_list))\n # FIXME: overwrite\n self.marker_final_dic = new_dic\n \n common = list(itertools.chain.from_iterable(list(new_dic.values()))) # marker genes\n final_df = raw_df.loc[common]\n self.final_int = final_df.astype(int) # convert int\n self.input_mat = np.array(self.final_int.T,dtype='int64')\n\n # seed-topic preparation\n gene_names = [t.upper() for t in self.final_int.index.tolist()]\n self.gene2id = dict((v, idx) for idx, v in enumerate(gene_names))\n #self.random_genes = random_genes\n \n def seed_processing(self):\n \"\"\"\n Prepare seed information for use as a guide.\n \n input_mat : np.array\n samples are in rows and genes (markers) are in columns.\n array([[7, 4, 5, ..., 4, 9, 4],\n [7, 4, 5, ..., 5, 8, 4],\n [6, 4, 4, ..., 4, 9, 5],\n ...,\n [7, 4, 4, ..., 4, 8, 4],\n [7, 4, 5, ..., 4, 9, 4],\n [8, 4, 4, ..., 4, 9, 4]])\n seed_topics : dict\n seed_topics: dict\n e.g.{0: [4,3],\n 1: [4],\n 2: [1],\n 3: [1,3,5],\n 4: [1],\n 5: [7]}\n seed_k : list\n [1,3,5,7,9,11,13]\n marker_final_dic : dcit\n {'B cells memory': ['AIM2', 'CR2', 'JCHAIN'],\n 'B cells naive': ['BCL7A', 'CD24', 'FCER2', 'IL4R', 'PAX5', 'TCL1A'],\n 'Monocytes': ['ALOX5AP','C5AR1','CCR2','CD14','CD163','CD274',...]}\n\n \"\"\"\n if self.marker_final_dic is None:\n raise ValueError('!! Final Marker Candidates were not defined !! --> run expression_processing()')\n # seed_topic preparation\n genes = list(itertools.chain.from_iterable(list(self.marker_final_dic.values())))\n target = list(self.marker_final_dic.keys())\n seed_topic_list = [self.marker_final_dic.get(t) for t in target]\n seed_topics = {}\n finish_genes = []\n for t_id, st in enumerate(seed_topic_list):\n for gene in st:\n try:\n if gene in finish_genes:\n tmp = seed_topics[self.gene2id[gene]]\n seed_topics[self.gene2id[gene]] = tmp + [t_id]\n else:\n seed_topics[self.gene2id[gene]] = [t_id]\n finish_genes.append(gene)\n except:\n # not included in target expression table\n print(gene)\n pass\n \n # reliable gene\n genes = list(itertools.chain.from_iterable(list(self.marker_final_dic.values())))\n seed_k = []\n for g in genes:\n if self.gene2id.get(g) is None:\n #print(g)\n pass\n else:\n seed_k.append(self.gene2id.get(g))\n\n self.seed_topics = seed_topics\n seed_k = sorted(list(set(seed_k)))\n self.seed_k = seed_k\n \n if self.verbose:\n print(\"final genes:\",len(self.final_int))\n print('seed number:',len(self.seed_topics))\n print(\"seed_k:\",len(self.seed_k))\n\ndef main():\n raw_df = pd.read_csv('/mnt/AzumaDeconv/github/GLDADec/data/GSE65133/GSE65133_expression.csv',index_col=0)\n marker_dic = pd.read_pickle('/mnt/AzumaDeconv/github/GLDADec/data/domain_info/human_PBMC_CellMarker_8cell_raw_dic_v1.pkl')\n random_sets = pd.read_pickle('/mnt/AzumaDeconv/github/GLDADec/data/random_info/100_random_sets.pkl')\n\n SD = SetData()\n SD.set_expression(df=raw_df) \n SD.set_marker(marker_dic=marker_dic)\n SD.marker_info_processing(do_plot=True)\n SD.set_random(random_sets=random_sets)\n SD.expression_processing(random_n=0,specific=True)\n SD.seed_processing()\n \n # Collect data to be used in later analyses\n input_mat = SD.input_mat\n final_int = SD.final_int\n seed_topics = SD.seed_topics\n marker_final_dic = SD.marker_final_dic\n \n # save\n out_path = '/mnt/AzumaDeconv/github/GLDADec/Dev/test_data/'\n pd.to_pickle(final_int,out_path+'final_int.pkl')\n pd.to_pickle(seed_topics,out_path+'seed_topics.pkl')\n pd.to_pickle(marker_final_dic,out_path+'marker_final_dic.pkl')\n\nif __name__ == '__main__':\n main()","repo_name":"mizuno-group/GLDADec","sub_path":"run/dev1_set_data.py","file_name":"dev1_set_data.py","file_ext":"py","file_size_in_byte":12913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"72831479234","text":"\"\"\"\nFor description of the script, see the README.md\n\"\"\"\n\nimport numpy as np\nfrom printind.printind_function import printi\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\n\n\nclass ComputeForce(object):\n def __init__(self, verbose=0, rho=1000.0, added_AOA_camber_deg=0.0, radius_sphere=0.010):\n self.verbose = verbose\n self.rho = rho\n self.added_AOA_camber_deg = added_AOA_camber_deg\n self.radius_sphere = radius_sphere\n\n def set_vertical_velocity(self, vertical_velocity):\n self.vertical_velocity = vertical_velocity\n\n def set_angular_frequency(self, angular_frequency):\n self.angular_frequency = angular_frequency\n self.angular_rate = 2 * np.pi * self.angular_frequency\n\n def set_interpolator_Cl(self, interpolator_Cl):\n self.interpolator_Cl = interpolator_Cl\n\n def set_interpolator_Cd(self, interpolator_Cd):\n self.interpolator_Cd = interpolator_Cd\n\n def set_seed_profile(self, wing_instance):\n self.wing_instance = wing_instance\n\n def set_wing_pitch(self, wing_pitch):\n self.wing_pitch = wing_pitch\n\n def set_wing_chord(self, wing_chord):\n self.wing_chord = wing_chord\n\n def compute_force_all_elements(self):\n self.list_crrt_R = []\n self.list_projected_vertical_velocity = []\n self.list_AOA_deg = []\n self.list_total_velocity = []\n self.list_wind_angle_rad = []\n self.list_drag = []\n self.list_lift = []\n self.list_Cd = []\n self.list_Cl = []\n self.list_forward_force = []\n self.list_vertical_force = []\n self.list_forward_force_lift = []\n self.list_forward_force_drag = []\n self.list_vertical_force_lift = []\n self.list_vertical_force_drag = []\n self.list_base_coeff = []\n self.list_crrt_rotation_velocity = []\n self.list_forward_moments = []\n\n for (crrt_size, crrt_R, crrt_phi) in zip(self.wing_instance.list_size_element, self.wing_instance.list_R, self.wing_instance.list_phi):\n # horizontal velocity due to rotation of the seed\n crrt_rotation_velocity = self.angular_rate * crrt_R\n\n # angle of attack\n projected_vertical_velocity = self.vertical_velocity * np.cos(crrt_phi * np.pi / 180.0)\n wind_angle_rad = np.arctan2(projected_vertical_velocity, crrt_rotation_velocity)\n crrt_AOA_deg = wind_angle_rad * 180.0 / np.pi - self.wing_pitch\n\n # total velocity magnitude\n total_velocity = np.sqrt(crrt_rotation_velocity**2 + projected_vertical_velocity**2)\n\n # base coefficient for computation of lift and drag\n base_coeff = 0.5 * self.rho * (total_velocity**2) * self.wing_chord * crrt_size\n\n # compute lift and drag; careful about the orientation of crrt_AOA_deg! This is because of\n # direction of rotation vs. the sketches\n Cd = self.interpolator_Cd.return_interpolated(crrt_AOA_deg + self.added_AOA_camber_deg)\n Cl = self.interpolator_Cl.return_interpolated(crrt_AOA_deg + self.added_AOA_camber_deg)\n crrt_lift = base_coeff * Cl\n crrt_drag = base_coeff * Cd\n crrt_forward = np.sin(wind_angle_rad) * crrt_lift \\\n - np.cos(wind_angle_rad) * crrt_drag\n crrt_vertical = np.cos(wind_angle_rad) * crrt_lift \\\n + np.sin(wind_angle_rad) * crrt_drag\n\n crrt_forward_projected = crrt_forward\n crrt_vertical_projected = crrt_vertical * np.cos(crrt_phi * np.pi / 180.0)\n\n self.list_crrt_R.append(crrt_R)\n self.list_projected_vertical_velocity.append(projected_vertical_velocity)\n self.list_crrt_rotation_velocity.append(crrt_rotation_velocity)\n self.list_wind_angle_rad.append(wind_angle_rad)\n self.list_total_velocity.append(total_velocity)\n self.list_AOA_deg.append(crrt_AOA_deg)\n self.list_base_coeff.append(base_coeff)\n self.list_drag.append(crrt_drag)\n self.list_lift.append(crrt_lift)\n self.list_Cd.append(Cd)\n self.list_Cl.append(Cl)\n self.list_forward_force.append(crrt_forward_projected)\n self.list_vertical_force.append(crrt_vertical_projected)\n self.list_forward_moments.append(crrt_forward_projected * crrt_R)\n\n def compute_resultant_force(self):\n self.resultant_forward_moment = sum(self.list_forward_moments)\n self.resultant_vertical_force = sum(self.list_vertical_force)\n\n def display_reduced_information(self, title_base=None, gray_region=None):\n linewidth = 3.0\n color = (0.5, 0.5, 0.5, 0.5)\n \n \n \n # figure with profile and angle of attack ------------------------------\n fig, ax1 = plt.subplots()\n \n if gray_region is not None:\n rect = Rectangle((gray_region[0], -0.002), gray_region[1] - gray_region[0], 0.06, color=color)\n ax1.add_patch(rect)\n \n ax1.plot(self.wing_instance.list_R, self.wing_instance.list_h, 'b', linewidth=linewidth * 2.0, label=\"seed wing\", linestyle=\"-\")\n ax1.set_xlabel('R [m]')\n # Make the y-axis label, ticks and tick labels match the line color.\n ax1.set_ylabel('h [m]', color='b')\n ax1.tick_params('y', colors='b')\n plt.ylim([-0.002, 0.052])\n plt.xlim([-0.002, 0.052])\n\n ax2 = ax1.twinx()\n ax2.plot(self.wing_instance.list_R, self.list_AOA_deg, 'r', linewidth=linewidth, linestyle=\"--\", label=\"Angle of attack\")\n ax2.set_ylabel('local angle of attack [deg]', color='r')\n ax2.tick_params('y', colors='r')\n \n ax2.plot([], [], 'b', linewidth=linewidth, linestyle=\"-\", label=\"seed wing\")\n \n plt.legend(loc=\"lower right\")\n\n fig.tight_layout()\n \n plt.savefig(title_base + \"combined_fig_1.pdf\")\n # done -----------------------------------------------------------------\n \n \n \n # figure with vertical force and moment --------------------------------\n fig, ax1 = plt.subplots()\n \n if gray_region is not None:\n rect = Rectangle((gray_region[0], -0.5), gray_region[1] - gray_region[0], 1.0, color=color)\n ax1.add_patch(rect)\n \n ax1.plot(self.wing_instance.list_R, np.array(self.list_vertical_force) / np.array(self.wing_instance.list_size_element), 'b', linewidth=linewidth, linestyle=\"-\", label=\"vertical force\")\n ax1.plot([0.0, 0.049], [0.0, 0.0], 'k', linewidth=linewidth * 0.75)\n ax1.set_xlabel('R [m]')\n # Make the y-axis label, ticks and tick labels match the line color.\n ax1.set_ylabel('Vertical force distribution [N/m]', color='b')\n ax1.tick_params('y', colors='b')\n plt.ylim([-0.3, 0.3])\n plt.xlim([0.00, 0.049])\n\n ax2 = ax1.twinx()\n ax2.plot(self.wing_instance.list_R, np.array(self.list_forward_moments) / np.array(self.wing_instance.list_size_element), 'r', linewidth=linewidth, linestyle=\"--\", label=\"forward moment\")\n ax2.set_ylabel('Forward moment distribution [N.m / m]', color='r')\n ax2.tick_params('y', colors='r')\n plt.ylim([-0.0044, 0.0045])\n \n ax2.plot([], [], 'b', linewidth=linewidth, linestyle=\"-\", label=\"vertical force\")\n \n plt.legend(loc=\"lower left\")\n\n fig.tight_layout()\n \n plt.savefig(title_base + \"combined_fig_2.pdf\")\n # done -----------------------------------------------------------------\n \n \n \n fig = plt.figure()\n ax = fig.add_subplot(111)\n if gray_region is not None:\n rect = Rectangle((gray_region[0], -100), gray_region[1] - gray_region[0], 200, color=color)\n ax.add_patch(rect)\n plt.plot(self.wing_instance.list_R, np.array(self.list_vertical_force) / np.array(self.wing_instance.list_size_element), linewidth=linewidth)\n # plt.legend()\n plt.grid()\n plt.xlabel(\"R [m]\")\n plt.ylabel(\"Vertical force distribution [N/m]\")\n plt.ylim([-0.3, 0.4])\n plt.tight_layout()\n if title_base is not None:\n plt.savefig(title_base + \"_forceDistribution.pdf\")\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n if gray_region is not None:\n rect = Rectangle((gray_region[0], -100), gray_region[1] - gray_region[0], 200, color=color)\n ax.add_patch(rect)\n plt.plot(self.wing_instance.list_R, np.array(self.list_forward_force) / np.array(self.wing_instance.list_size_element), linewidth=linewidth)\n # plt.legend()\n plt.grid()\n plt.ylim([-0.2, 0.1])\n plt.xlabel(\"R [m]\")\n plt.ylabel(\"Forward force distribution [N/m]\")\n plt.tight_layout()\n if title_base is not None:\n plt.savefig(title_base + \"_forwardForceDistribution.pdf\")\n plt.show()\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n if gray_region is not None:\n rect = Rectangle((gray_region[0], -100), gray_region[1] - gray_region[0], 200, color=color)\n ax.add_patch(rect)\n plt.plot(self.wing_instance.list_R, np.array(self.list_forward_moments) / np.array(self.wing_instance.list_size_element), linewidth=linewidth)\n # plt.legend()\n plt.grid()\n plt.xlabel(\"R [m]\")\n plt.ylabel(\"Forward moment distribution [N.m / m]\")\n plt.ylim([-0.008, 0.004])\n plt.tight_layout()\n if title_base is not None:\n plt.savefig(title_base + \"_momentDistribution.pdf\")\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n if gray_region is not None:\n rect = Rectangle((gray_region[0], -100), gray_region[1] - gray_region[0], 200, color=color)\n ax.add_patch(rect)\n plt.plot(self.wing_instance.list_R, self.list_AOA_deg, linewidth=linewidth)\n # plt.legend()\n plt.grid()\n plt.xlabel(\"R [m]\")\n plt.ylabel(\"local angle of attack [deg]\")\n plt.ylim([-20, 50])\n plt.tight_layout()\n if title_base is not None:\n plt.savefig(title_base + \"_AOADistribution.pdf\")\n \n \n \n \n \n plt.show()\n\n def return_forward_moment(self):\n return(self.resultant_forward_moment)\n\n def return_vertical_force(self):\n return(self.resultant_vertical_force)\n\n def display_all_results(self):\n plt.figure()\n plt.plot(self.wing_instance.list_R, np.array(self.list_AOA) * 180.0 / np.pi, label=\"AOA\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, np.array(self.list_wind_angle) * 180.0 / np.pi, label=\"wind angle\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_total_velocity, label=\"total_velocity\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_crrt_rotation_velocity, label=\"crrt_rotation_velocity\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_Cl, label=\"Cl\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_Cd, label=\"Cd\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_base_coeff, label=\"base_coeff\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_lift, label=\"lift\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_drag, label=\"drag\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, np.array(self.list_lift) / np.array(self.list_drag), label=\"lift/drag\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, self.list_forward_force_lift, label=\"forward lift\")\n plt.plot(self.wing_instance.list_R, self.list_forward_force_drag, label=\"forward drag\")\n plt.plot(self.wing_instance.list_R, self.list_vertical_force_lift, label=\"vertical lift\")\n plt.plot(self.wing_instance.list_R, self.list_vertical_force_drag, label=\"vertical drag\")\n plt.legend()\n plt.plot()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, np.array(self.list_vertical_force) / np.array(self.wing_instance.list_size_element), label=\"Vertical force\")\n plt.legend()\n plt.show()\n\n plt.figure()\n plt.plot(self.wing_instance.list_R, np.array(self.list_forward_force) / np.array(self.wing_instance.list_size_element), label=\"Forward force\")\n plt.legend()\n plt.show()\n\n\n\"\"\"\nnote that in compute_force_on_seed, both the rotation and the vertical velocity are positive for 'normal seed'. I.e.\npositive vertical velocity means seed going down, positive frequency means seed rotating as in experiments.\n\"\"\"\n","repo_name":"jerabaul29/EffectFoldAngleAutorotatingSeeds","sub_path":"model/compute_force_on_seed.py","file_name":"compute_force_on_seed.py","file_ext":"py","file_size_in_byte":13161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"9307183701","text":"from typing import List, Optional, Tuple\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom pydantic import BaseModel, ValidationError\r\n\r\nfrom classification_model.config.core import config\r\n\r\n# from marshmallow import Schema, ValidationError, fields\r\n\r\n\r\ndef replace_interrogation_marks(*, input_data: pd.DataFrame) -> pd.DataFrame:\r\n \"\"\"replace interrogation marks by NaN values\"\"\"\r\n\r\n replaced_interrogation_data = input_data.copy().replace(\"?\", np.nan)\r\n\r\n return replaced_interrogation_data\r\n\r\n\r\ndef validate_inputs(*, input_data: pd.DataFrame) -> Tuple[pd.DataFrame, Optional[dict]]:\r\n \"\"\"Check model inputs for unprocessable values.\"\"\"\r\n\r\n # convert syntax error field names (beginning with numbers)\r\n input_data.rename(columns=config.model_config.variables_to_rename, inplace=True)\r\n\r\n # replace interrogation marks by NaN values\r\n validated_data = replace_interrogation_marks(input_data=input_data)\r\n\r\n errors = None\r\n\r\n # cast numerical variables as floats\r\n for var in config.model_config.numerical_vars:\r\n validated_data[var] = validated_data[var].astype(\"float\")\r\n\r\n try:\r\n # replace numpy nans so that pydantic can validate\r\n MultipleTitanicDataInputs(\r\n inputs=validated_data.replace({np.nan: None}).to_dict(orient=\"records\")\r\n )\r\n except ValidationError as error:\r\n errors = {e[\"loc\"][-1]: e[\"msg\"] for e in error.errors()}\r\n\r\n return validated_data, errors\r\n\r\n\r\nclass TitanicDataInputSchema(BaseModel):\r\n pclass: Optional[int]\r\n name: Optional[str]\r\n sex: Optional[str]\r\n age: Optional[float]\r\n sibsp: Optional[int]\r\n parch: Optional[int]\r\n ticket: Optional[str]\r\n fare: Optional[float]\r\n cabin: Optional[str]\r\n embarked: Optional[str]\r\n boat: Optional[str]\r\n body: Optional[float]\r\n home_dest: Optional[str]\r\n\r\n\r\nclass MultipleTitanicDataInputs(BaseModel):\r\n inputs: List[TitanicDataInputSchema]\r\n","repo_name":"vinaykumarkonda/deployment_and_testing_of_machine_learning_model","sub_path":"classification_model/processing/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"24404590871","text":"# Lane Testing\n\nfrom LaneFollower import LaneFollower\nfrom CarControl import CarControl\nfrom matplotlib import pyplot as plt\nimport argparse\nimport imutils\nimport numpy as np\nimport cv2\n\nleftColorMin = [90, 245, 180] # Yellow - Determined by plotting imgHSV and hovering over the colors\nleftColorMax = [100, 255, 210] # Yellow\nrightColorMin = [5, 15, 170] # White\nrightColorMax = [20, 40, 230] # White\n\nLF = LaneFollower()\n\nvs = cv2.VideoCapture(\"/dev/video2\", cv2.CAP_V4L) # ls -ltr /dev/video*\n# M = np.load(\"M.npy\")\n\nwhile True:\n # read the next frame from the file\n (grabbed, frame) = vs.read()\n \n # if the frame was not grabbed, then we have reached the end\n # of the stream\n if not grabbed:\n break\n\n\n LF.update_picture(frame)\n\n img = LF.convert_to_HSV(frame)\n leftMask = LF.filter_by_color(img, leftColorMin, leftColorMax)\n rightMask = LF.filter_by_color(img, rightColorMin, rightColorMax)\n\n leftCanny = LF.canny_img(leftMask)\n rightCanny = LF.canny_img(rightMask)\n\n leftCropped = LF.crop_image(leftCanny)\n rightCropped = LF.crop_image(rightCanny)\n\n leftLines = LF.hough_lines(leftCropped)\n rightLines = LF.hough_lines(rightCropped)\n\n\n try:\n left_line_x, left_line_y, right_line_x, right_line_y = LF.find_lines(leftLines, rightLines)\n\n leftFinal, left_points = LF.calculate_lines(rawImg, left_line_x, left_line_y, 1)\n rightFinal, right_points = LF.calculate_lines(leftFinal, right_line_x, right_line_y, 1)\n except:\n rightFinal = img\n\n image = rightFinal\n\n\n cv2.imshow(\"Camera Feed\", image)\n key = cv2.waitKey(1) & 0xFF\n\n if key == ord(\"q\"):\n break\n \nvs.release()\ncv2.destroyAllWindoes()\n\n\n\n","repo_name":"kalinnorman/SDCars","sub_path":"class_code/laneTesting.py","file_name":"laneTesting.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"36805208294","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom challenges.models import CommunityChallenge\n\ndef challenges(request):\n num_challenge_posts = CommunityChallenge.objects.all().count()\n posts = CommunityChallenge.objects.order_by('-published_date')\n\n context = {\n 'num_challenge_posts': num_challenge_posts,\n 'challenges': posts,\n }\n\n\n return render(request, 'challenges.html', context=context)","repo_name":"katiehrenchir/you-go-girl","sub_path":"challenges/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"35426911542","text":"#Base class\nclass Dog:\n intro = \"This is default intro for Dog\"\n def __init__(self, name, age):\n self.name = name\n self.age = age\n\n#Child class\nclass BullDog(Dog):\n \"This is bulldog\"\n\nprint(Dog.intro)\nprint(BullDog.intro)\n\ndog1 = Dog(\"James\", 10)\nprint(dog1.name)\nbulldog1 = BullDog(\"Andy\", 17)\nprint(bulldog1.name)\n","repo_name":"NehaDua-BITS/python-learning","sub_path":"oops/inheritance.py","file_name":"inheritance.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"12958861331","text":"#! python3\r\n# Reads text files, put them into lists and then input into an excel file\r\nimport openpyxl, os\r\ndef TextToExcel(folder):\r\n wb = openpyxl.Workbook()\r\n sheet = wb.active\r\n num_column = 0\r\n # Going through the file\r\n for foldername, subfolders, filenames in os.walk(folder):\r\n for fl_int in range(len(filenames)):\r\n filename = list(filenames)\r\n file_ = open(foldername + '\\\\' + filename[fl_int],'r')\r\n # Acquiring the text form .txt\r\n text_ = file_.readlines()\r\n text_ = text_[0].split(' ')\r\n for num_row in range(len(text_)):\r\n sheet.cell(row = num_row + 1, column = num_column + 1).value = text_[num_row]\r\n print(text_[num_row])\r\n num_column += 1\r\n wb.save('TextToExcel.xlsx')\r\n\r\nTextToExcel(r'C:\\Users\\Dr. Wan Asna\\Desktop\\Python Projects\\Automate the Boring Stuff\\Ch.13 - Working with Excel Spreadsheets\\num')","repo_name":"QaisZainon/Learning-Coding","sub_path":"Automate the Boring Stuff/Ch.13 - Working with Excel Spreadsheets/TextFilestoSpreadsheet.py","file_name":"TextFilestoSpreadsheet.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"36916714242","text":"#Wavelet Analysis and Transform minipackage\r\n\r\n#Written by Doug Keller\r\n\r\nimport numpy as np\r\n\r\ndef kw(x,a,b,n): #complex frequency function windowed by a super Gaussian function\r\n return (np.exp(((2*np.pi*(x-b)/a)+np.pi/2)*(-1)**.5)*(.01)**((2*(x-b)/a)**(2*n))).real #modified Morlet wavelet but with a super Gaussian (only taking the real part or the part that forms a sine wave in the real domain)\r\n\r\ndef dgw(x,a,b,n): #Super Gaussian function first derivative wavelet\r\n return ((x-b)/a)**(2*n-1)*np.exp(.5-.5*((x-b)/a)**(2*n))\r\n\r\ndef sgw(x,a,b,n,y_a): #Super Gaussian function wavelet\r\n return y_a ** ((x - b) *2/a) ** (2*n)\r\n \r\ndef kwwt(f,x,a,b,n): #wavelet transform with the sine/super Gaussian wavelet\r\n phi = np.zeros((x.size,b.size,a.size))\r\n for ai in range(0,a.size):\r\n for bi in range(0,b.size):\r\n for xi in range(0,x.size):\r\n phi[xi,bi,ai] = kw(x[xi],a[ai],b[bi],n)\r\n fphi = np.tensordot(f,phi,1)\r\n wt = np.transpose(np.zeros(fphi.shape))\r\n for ai in range(0,a.size):\r\n wt[ai,:] = fphi[:,ai]/a[ai]**.5\r\n return wt\r\n \r\n#def kwwt1(f,x,a,b,n): #wavelet transform with the sine/super Gaussian wavelet\r\n# wt = np.zeros((a.size,b.size))\r\n# for ai in range(0,a.size):\r\n# for bi in range(0,b.size):\r\n# for xi in range(0,x.size):\r\n# wt[ai,bi] += f[xi] * kw(x[xi],a[ai],b[bi],n) / a[ai]**.5\r\n# return wt\r\n\r\ndef sgwwt(f,x,a,b,n,y_a): #wavelet transform with the Gaussian function wavelet\r\n phi = np.zeros((x.size,b.size,a.size))\r\n for ai in range(0,a.size):\r\n for bi in range(0,b.size):\r\n for xi in range(0,x.size):\r\n phi[xi,bi,ai] = sgw(x[xi],a[ai],b[bi],n,y_a)\r\n fphi = np.tensordot(f,phi,1)\r\n wt = np.transpose(np.zeros(fphi.shape))\r\n for ai in range(0,a.size):\r\n wt[ai,:] = fphi[:,ai]/a[ai]**.5\r\n return wt\r\n\r\ndef clip_wavelet_kw(wt,a,b):\r\n for i in range(a.size):\r\n for j in range(b.size):\r\n if b[j] < b[0] + a[i]/2 or b[j] > b[-1] - a[i]/2:\r\n wt[i,j] = np.nan\r\n return wt","repo_name":"dkllrjr/PyKeller","sub_path":"build/lib/signal_processing/wavelets.py","file_name":"wavelets.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"61"}
+{"seq_id":"12816261862","text":"from django.conf.urls import url\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nfrom . import views\n\napp_name = \"website\"\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^login$', views.login_user, name='login'),\n url(r'^logout$', views.user_logout, name='logout'),\n url(r'^register$', views.register, name='register'),\n url(r'^addmember$', views.addmember, name='addmember'), \n url(r'^adddetainee$', views.add_detainee, name='detainee'),\n url(r'^addsession$', views.session, name='addsession'),\n url(r'^session/(?P