diff --git "a/4150.jsonl" "b/4150.jsonl" new file mode 100644--- /dev/null +++ "b/4150.jsonl" @@ -0,0 +1,704 @@ +{"seq_id":"31215565029","text":"# coding: utf-8\n\nfrom __future__ import unicode_literals\nfrom functools import partial\n\nfrom modules.models.airmaterial.in_bound import InBound\nfrom .base import AirmaterialViewBase\n\n\nclass _InBoundView(AirmaterialViewBase):\n # 入库列表视图应显示的内容\n column_list = [\n 'form_number', 'form_user_name', 'form_date',\n 'statusName', 'operation',\n ]\n # 对应内容的中文翻译\n column_labels = {\n 'form_number': '编号',\n 'form_user_name': '制表人',\n 'form_date': '日期',\n 'statusName': '状态',\n 'operation': '操作',\n }\n\n @staticmethod\n def operation_formatter(view, context, model, name):\n pass\n\n # 一些特殊的列，比如不存在的列（operation）需要自定义格式方式\n column_formatters = {\n 'operation': operation_formatter\n }\n\n\nInBoundView = partial(\n _InBoundView, InBound, name='入库'\n)\n","repo_name":"GSIL-Monitor/wxk","sub_path":"modules/views/airmaterial/in_bound.py","file_name":"in_bound.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"4556898278","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 16 15:37:27 2019\n\n@author: Eirik Nordgård\n\"\"\"\n\n\"\"\"\nUsing Neural Network to solve equation\nu_xx = u_t\n\"\"\"\nimport numpy as np\nfrom matplotlib import cm\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nfrom mpl_toolkits.mplot3d import axes3d\n\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\n# define initial condition\ndef initial(x):\n return tf.sin(np.pi*x)\n\ndef exact(x, t):\n return np.exp(-np.pi**2*t)*np.sin(np.pi*x)\n\ndef solve(init_func, T=0.08, Nx=100, Nt=10, L=1, learning_rate=1e-3, num_iter=1e3):\n tf.set_random_seed(4155)\n # resetting neural network\n tf.reset_default_graph()\n \n # Defining grid interval\n dx = L/(Nx - 1)\n dt = T/(Nt - 1)\n\n x = np.linspace(0, L, Nx)\n t = np.linspace(0, T, Nt)\n\n # Create mesh and convert to tensors\n X, T = np.meshgrid(x, t)\n\n x_ = (X.ravel()).reshape(-1, 1)\n t_ = (T.ravel()).reshape(-1, 1)\n\n x_tf = tf.convert_to_tensor(x_)\n t_tf = tf.convert_to_tensor(t_)\n\n points = tf.concat([x_tf, t_tf], 1)\n\n # setup of neural network\n\n num_hidden_neurons = [20,20]\n num_hidden_layers = np.size(num_hidden_neurons)\n\n with tf.variable_scope('nn', reuse=tf.AUTO_REUSE):\n # input layer\n previous_layer = points\n\n # hidden layers\n for l in range(num_hidden_layers):\n current_layer = tf.layers.dense(previous_layer,\n num_hidden_neurons[l],\n activation=tf.nn.sigmoid)\n previous_layer = current_layer\n\n # output layer\n nn_output = tf.layers.dense(previous_layer, 1)\n\n # set up cost function \n\n with tf.name_scope('cost'):\n # define trial funcition\n trial = (1-t_tf)*initial(x_tf) + x_tf*(1-x_tf)*t_tf*nn_output\n\n # calculate gradients\n trial_dt = tf.gradients(trial, t_tf)\n trial_d2x = tf.gradients(tf.gradients(trial, x_tf), x_tf)\n\n # calculate cost function\n err = tf.square(trial_dt[0] - trial_d2x[0])\n cost = tf.reduce_sum(err, name='cost')\n\n # define learning rate and minimization of cost function\n with tf.name_scope('train'):\n optimizer = tf.train.AdamOptimizer(learning_rate)\n training_op = optimizer.minimize(cost)\n\n # definie itialization of all nodes\n init = tf.global_variables_initializer()\n\n # storage value for the solution\n u_nn = None\n\n # solve pde\n with tf.Session() as session:\n # Initialize the computational graph\n init.run()\n #print('Initial cost: %g'%cost.eval())\n for i in range(int(num_iter)):\n session.run(training_op)\n #print('Final cost: %g'%cost.eval())\n u_nn = trial.eval()\n\n # reshape arrays\n U_nn = u_nn.reshape((Nt, Nx))\n return U_nn, x\n#%%\n\"\"\"\nsns.set()\nsns.set_style(\"whitegrid\")\nsns.set_palette(\"Set2\")\nplt.rc(\"text\", usetex=True)\nplt.rc(\"font\", family=\"serif\")\n\nfigdir = \"../figures/\"\n\nu, x = solve(initial, T=0.2, Nt=30)\n\nprint(\"==== For t = 0.2 ====\")\nprint(f\"MSE = {np.mean((u[-1, :]-exact(x, t[-1]))**2)}\")\nprint(\"==== For t = 0.02 ====\")\nprint(f\"MSE = {np.mean((u[2, :]-exact(x, t[2]))**2)}\")\n\nfig, ax = plt.subplots(1, 1)\n\nax.plot(x, u[-1, :], color=\"b\", ls=\"dashed\", label=\"Computed\")\nax.plot(x, exact(x, t[-1]), color=\"b\", ls=\"dotted\", lw=4, label=\"Exact\")\nax.plot(x, u[2, :], color=\"r\", ls=\"dashed\")\nax.plot(x, exact(x, t[2]), color=\"r\", ls=\"dotted\", lw=4)\n\nax.set_xlabel(\"x\", fontsize=20)\nax.set_ylabel(\"u(x, t)\", fontsize=20)\nfig.legend(ncol=2, frameon=False, loc=\"upper center\", fontsize=20)\n#plt.savefig(figdir + \"nn.png\")\nplt.show()\n\"\"\"\n#%%\n#plot solutions \n\nfigdir = \"../figures/\"\nu1, x1 = solve(initial, 0.01, Nt=10) #dx = 1/100\nu2, x2 = solve(initial, 0.3, Nt=10) #dx = 1/100\nu3, x3 = solve(initial, 0.01, Nt=10) #dx = 1/10\nu4, x4 = solve(initial, 0.3, Nt=10) #dx = 1/10\n\nfig, ax = plt.subplots(1, 1)\n\nax.plot(x1, u1[-1, :], color=\"b\", ls=\"dashed\", label=\"dx=0.01\")\nax.plot(x3, u3[-1, :], color=\"b\", ls=\":\", label=\"dx=0.1\")\nax.plot(x1, exact(x1, 0.01), color=\"b\", ls=\"dotted\", lw=4, label=\"Exact\")\nax.plot(x2, u2[-1, :], color=\"r\", linestyle=\"dashed\")\nax.plot(x4, u4[-1, :], color=\"r\", ls=\":\")\nax.plot(x2, exact(x2, 0.3), color=\"r\", linestyle=\"dotted\", lw=4)\n\nax.set_xlabel(\"x\", fontsize=20)\nax.set_ylabel(\"u(x, t)\", fontsize=20)\nfig.legend(ncol=3, loc=\"upper center\", frameon=False, fontsize=15)\n#plt.savefig(figdir + \"NN.png\")\nplt.show()\n\n#OBS!! This plot gives negative solution in some parts of the domain. STRANGE.\n#dont know what causes this.\n#%%\n# compute MSE of the error for the different cases:\nprint(\"\")\nprint(\"---------For t = 0.02:---------\")\nprint(f\"dx = 0.01 | MSE = {np.mean((u1[-1, :]-exact(x1,0.02))**2)}\")\nprint(\"---------For t = 0.2-----------\")\nprint(f\"dx = 0.01 | MSE = {np.mean((u2[-1, :]-exact(x2,0.2))**2)}\")\nprint(\"---------For t = 0.02:---------\")\nprint(f\"dx = 0.1 | MSE = {np.mean((u3[-1, :]-exact(x3,0.02))**2)}\")\nprint(\"---------For t = 0.2-----------\")\nprint(f\"dx = 0.1 | MSE = {np.mean((u4[-1, :]-exact(x4,0.2))**2)}\")\n\n#%%\n#plot 3D solutions \n\nNx = 100; Nt = 10\nx = np.linspace(0, 1, Nx) \nt = np.linspace(0,1,Nt)\n\nU_nn,x = solve(initial,T=0.1)\n\nfigdir = \"../figures/\"\n\n# Surface plot of the solutions\n\nX,T = np.meshgrid(t, x)\n\nfig = plt.figure(figsize=(10,10))\nax = fig.gca(projection='3d')\nax.set_title(\"Neural Network solution\",fontsize=35)\ns = ax.plot_surface(T,X,U_nn,linewidth=0,antialiased=False,cmap=cm.viridis)\nax.set_xlabel('Time $t$',fontsize=35)\nax.set_ylabel('Position $x$',fontsize=35);\n#fig.savefig(figdir + \"dnn.png\")\n\nfig = plt.figure(figsize=(10,10))\nax = fig.gca(projection='3d')\nax.set_title('Exact solution',fontsize=35)\ns = ax.plot_surface(T,X,U_e,linewidth=0,antialiased=False,cmap=cm.viridis)\nax.set_xlabel('Time $t$',fontsize=35)\nax.set_ylabel('Position $x$',fontsize=35);\n#fig.savefig(figdir + \"exact.png\")\n\nfig.show()\n","repo_name":"eirikngard/FYS-STK4155","sub_path":"Project_3/code/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":5902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"39767854946","text":"#중앙은 노란색, 테두리는 갈색, 격자수가 주어지고 카펫의 가로 세로 크기를 구해라\ndef solution(brown, yellow):\n n=2 #가로길이\n answer = []\n if yellow==1:\n answer=[3,3]\n else:\n while 1: #여기서 brown 갯수도 파악해줘야된다.\n if yellow%n==0:\n if (n+2)*2+int(yellow/n)*2 ==brown:#수식으로 해결.\n answer.append(n + 2)\n answer.append(int(yellow/n)+2)\n break\n\n n+=1#여기가 문제였다. 1개씩 추가 해줘도 전부다 확인이 가능!\n #not n+=2\n if answer[0] pd.Series:\n distance_keys = pd.MultiIndex.from_arrays(\n [left_indices, right_indices]\n )\n key_pairs = distance_keys\n return pd.Series(\n self.distance_normalizer.transform(\n self._get_raw_distance_values(key_pairs)\n ),\n index=key_pairs,\n dtype=np.float64,\n )\n\n def _get_raw_distance_values(self, key_pairs) -> np.ndarray:\n # if relation points to nonexistent ind\n # keyerror here\n side_values = [\n self[side].loc[key_pairs.get_level_values(side), :].values\n for side in range(2)\n ]\n\n return np.concatenate(\n [\n dist_fun(\n side_values[0][:, idx],\n side_values[1][:, idx],\n )[:, np.newaxis]\n for idx, dist_fun in enumerate(self.distance_metrics)\n ],\n axis=1,\n )\n\n\n@dataclass(repr=False)\nclass MotifPair(PairBase):\n\n entity_types_of_columns: Optional[list] = None\n\n def extend(\n self, relation_pair: \"RelationPair\", source_col=None, inverse=False\n ) -> \"MotifPair\":\n\n return relation_pair.megre(\n self,\n source_col if source_col is not None else self.n_cols - 1,\n inverse,\n )\n\n @classmethod\n def root_from_indices(\n cls,\n left_indices,\n right_indices,\n entity_type,\n ) -> \"MotifPair\":\n return cls(\n df1=pd.DataFrame({0: left_indices}),\n df2=pd.DataFrame({0: right_indices}),\n name=entity_type,\n entity_types_of_columns=[entity_type],\n )\n\n @property\n def leaf_entity_type(self):\n return self.entity_types_of_columns[-1]\n\n\n@dataclass(repr=False)\nclass RelationPair(PairBase):\n\n entity_types_of_columns: Optional[list] = None\n\n def __post_init__(self):\n if self.name is None:\n if self.df1.index.name is None:\n self.name = \"-\".join(map(str, self.df1.columns))\n else:\n self.name = self.df1.index.name\n if self.entity_types_of_columns is None:\n self.entity_types_of_columns = self.df1.columns.tolist()\n\n def megre(\n self,\n nh_pair: MotifPair,\n source_col: int,\n inverse=False,\n ) -> MotifPair:\n\n _cs = -1 if inverse else 1\n\n new_name = f\"{nh_pair.name}--{self.name}\"\n new_type = self.entity_types_of_columns[int(not inverse)]\n etypes = nh_pair.entity_types_of_columns + [new_type]\n\n neigh_dfs = [\n neighdf.merge(\n reldf.rename(\n columns=dict(\n zip(reldf.columns[::_cs], [source_col, nh_pair.n_cols])\n )\n ),\n how=\"inner\",\n )\n for reldf, neighdf in zip(self, nh_pair)\n ]\n\n return MotifPair(*neigh_dfs, new_name, etypes)\n\n\ndef _dosample(df, size):\n if isinstance(size, (list, np.ndarray, pd.Index)):\n return df.loc[size, :]\n return df if (size is None or size > df.shape[0]) else df.sample(size)\n\n\ndef _dodrop(df, todrop):\n if todrop is None:\n return df\n return df.drop(todrop)\n","repo_name":"endremborza/encoref","sub_path":"encoref/core/pair_classes.py","file_name":"pair_classes.py","file_ext":"py","file_size_in_byte":6176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"35035698486","text":"from PyQt5.QtWidgets import QDialog\nfrom PyQt5.QtWidgets import QVBoxLayout, QLabel, QSpinBox, QDialogButtonBox\n\nfrom .CompInputBoxes import CompInputBoxes\n\nclass PopUpMixtureUi (QDialog):\n \"\"\"Class handling a constrution of the pop-up dialog window\"\"\"\n def __init__(self):\n \"\"\"PopUpMixture constructor\"\"\"\n super().__init__()\n self.setWindowTitle('Adding a new mixture')\n\n # General layout for pop-up window\n self.generalComponentsLayout = QVBoxLayout()\n self.setLayout(self.generalComponentsLayout)\n\n # QLabel with basic info about QSpinBox\n self.componentsInfoLabel = QLabel('Number of components')\n # QSpinBox - number of components selection\n self.componentsNumberSelector = QSpinBox()\n # QDialogButtonBox - OK and Cancel buttons\n self.standardButtons = QDialogButtonBox()\n\n # text input area\n self.componentsInputBoxes = CompInputBoxes()\n\n # Building dialog buttons\n self.standardButtons.setStandardButtons(QDialogButtonBox.Cancel | QDialogButtonBox.Ok)\n\n # Attach info label & QSpinBox to the general layout\n self.generalComponentsLayout.addWidget(self.componentsInfoLabel)\n self.generalComponentsLayout.addWidget(self.componentsNumberSelector)\n # Attach inputBoxes to the general layout\n self.componentsInputBoxes.attachCompInputBoxes(self.generalComponentsLayout)\n # Attach 'Cancel' and 'Ok' buttons\n self.generalComponentsLayout.addWidget(self.standardButtons)\n\n # Customize QSpinBox\n self.componentsNumberSelector.setMaximum(10)\n self.componentsNumberSelector.setMinimum(2)\n\n # Setting basic dialog buttons functionality\n self.standardButtons.rejected.connect(self.reject)\n self.standardButtons.accepted.connect(self.accept)\n\n def getInputBoxes(self):\n \"\"\"Returns the current instance of CompInputBoxes\"\"\"\n return self.componentsInputBoxes\n\n def getSpinBox(self):\n \"\"\"Returns the current instance of QSpinBox\"\"\"\n return self.componentsNumberSelector\n\n def getStandardButtons(self):\n \"\"\"Returns standard QDialog buttons - 'Cancel, 'Ok' (QDialogButtonBox)\"\"\"\n return self.standardButtons\n\n \n","repo_name":"SzymonJakubiak/mixtures-calc","sub_path":"lib/PopUpMixtureUi.py","file_name":"PopUpMixtureUi.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"34154863281","text":"from setuptools import setup, find_packages\n\nwith open(\"requirements.txt\") as f:\n\tinstall_requires = f.read().strip().split(\"\\n\")\n\n# get version from __version__ variable in assignment_test/__init__.py\nfrom assignment_test import __version__ as version\n\nsetup(\n\tname=\"assignment_test\",\n\tversion=version,\n\tdescription=\"Customisations in Item Doctype\",\n\tauthor=\"Aashish Vashisht\",\n\tauthor_email=\"aashishvashisht6@gmail.com\",\n\tpackages=find_packages(),\n\tzip_safe=False,\n\tinclude_package_data=True,\n\tinstall_requires=install_requires\n)\n","repo_name":"aashishvashisht6/Assignment","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"2895051351","text":"\"\"\"Server for demo2 Game\"\"\"\n\nimport sys\nimport random\n\nimport engine.time as time\nfrom engine.log import log\nimport engine.server\nimport engine.geometry as geo\n\n\nclass Server(engine.server.Server):\n \"\"\"Extends engine.server.Server\"\"\"\n\n def __init__(self, args):\n \"\"\"Extends __init__()\n\n Adds Attributes: (incomplete list but these are the important ones)\n\n self['mode'] which contains one of three values:\n 1) waitingForPlayers: Game has not started.\n - Waiting for all players to Join AND\n - Waiting for all players to send \"ready\" msg.\n 2) gameOn: All players have joined and game.\n 3) gameOver: Game objective is complete.\n\n self['quitAfter'] which tells the server when to quit. Set\n when mode == gameOver so players will have time to see the\n game has been won before everything quits.\n \"\"\"\n\n super().__init__(args)\n \"\"\"Set up game data, default values, and set objects on maps\"\"\"\n\n # server will quit after this time.\n self['quitAfter'] = sys.float_info.max\n self['gameStartSec'] = 0\n self['mode'] = \"waitingForPlayers\"\n self['redPoints'] = 0\n self['bluePoints'] = 0\n\n self['GAMETIME'] = 60.0 * 10 # game leangth in seconds\n self['MAXHEALTH'] = 100.0\n self['HEALTHREGENSEC'] = 60.0 # seconds to regen full health from 0\n self['MAXENDUR'] = 3.0 # seconds\n self['ENDURREGENSEC'] = self['MAXENDUR'] * 5.0 # seconds to regen full run from 0\n self['RUNSPEED'] = 2.0 # multiplier of running vs. normal speed.\n\n self.setCollsion()\n self.randomizeWeapons()\n self.createDoors()\n self.createKeys()\n\n log(f\"Server __init__ complete. Server Attributes:{engine.log.dictToStr(self, 1)}\", \"VERBOSE\")\n\n def setCollsion(self):\n \"\"\"Change collision type of monsters and players to be circle.\n also, players only collide with outOfBounds layer while moving\"\"\"\n for mapName in self['maps']:\n map = self['maps'][mapName]\n for o in map['sprites']:\n if o['type'] == 'player':\n o['collisionType'] = 'circle'\n o['checkLocationOn'] = ['outOfBounds']\n elif o['type'] == 'monster':\n o['collisionType'] = 'circle'\n\n def randomizeWeapons(self):\n \"\"\"Move weapons from the hidden map to random weaponlocaion points. Then set them up as holdables\"\"\"\n # find all weapons\n weapons = []\n map = self['maps']['hidden']\n for holdable in map['reference']:\n if holdable['type'] == 'holdable' and holdable['prop-holdable-type'] == 'weapon':\n weapons.append(holdable)\n\n # remove weapons from current locations. (Don't really need to this since\n # the hidden map is used but doing it to be very clean)\n for weapon in weapons:\n map.removeObjectFromAllLayers(weapon)\n\n # find all weapon location and shuffle the list\n weaponLocations = []\n for mapName in self['maps']:\n map = self['maps'][mapName]\n for o in map['reference']:\n if o['name'] == 'weaponLocation':\n weaponLocations.append(o)\n random.shuffle(weaponLocations)\n\n # put weapons in new locations and set up as holdables.\n for weapon in weapons:\n location = weaponLocations.pop()\n map = self['maps'][location['mapName']]\n map.addObject(weapon)\n map.setObjectLocationByAnchor(weapon, location['anchorX'], location['anchorY'])\n map.addHoldableTrigger(weapon)\n\n def createDoors(self):\n \"\"\" Create doors. Doors are created from rects on the triggers layer of type == 'lockedDoor' \"\"\"\n\n lockNumbers = random.sample(range(10, 99), 20)\n\n doorTiles = []\n map = self['maps']['hidden']\n for doorTile in map['reference']:\n if doorTile['name'] == 'doorTile':\n doorTiles.append(doorTile)\n\n self['lockedDoors'] = []\n # find all doors\n for mapName in self['maps']:\n map = self['maps'][mapName]\n for doorTrigger in map['triggers']:\n if doorTrigger['type'] == 'lockedDoor':\n self['lockedDoors'].append(doorTrigger)\n doorTrigger['lockNumber'] = lockNumbers.pop()\n\n doorCopy = doorTrigger.copy()\n doorCopy['fillColor'] = \"#008800\"\n # Add to outOfBounds so it blocks players\n map.addObject(doorCopy, map['outOfBounds'])\n # Add to spries adn color it so players can see it\n map.addObject(doorCopy)\n\n # grow trigger so players can step on it even with doorCopy on outOfBounds layer.\n doorTrigger['x'] -= 10\n doorTrigger['y'] -= 10\n doorTrigger['width'] += 20\n doorTrigger['height'] += 20\n\n # add doorTile icon graphic\n doorTile = doorTiles[random.randrange(0, len(doorTiles))]\n door = doorTile.copy()\n map.addObject(door)\n # add 0.1 so tile render on top of door rect on sprite layer.\n map.setObjectLocationByAnchor(door, doorCopy['anchorX'] + 0.1, doorCopy['anchorY'] + 0.1)\n doorTrigger['doNotTrigger'] = [doorCopy, door]\n\n def createKeys(self):\n \"\"\"Create a key for each door and put them in keylocaions.\"\"\"\n\n # find of keyTiles\n keyTiles = []\n map = self['maps']['hidden']\n for keyTile in map['reference']:\n if keyTile['name'] == 'keyTile':\n keyTiles.append(keyTile)\n\n # find all key locations and shuffle the list\n keyLocations = []\n for mapName in self['maps']:\n map = self['maps'][mapName]\n for o in map['reference']:\n if o['name'] == 'keyLocation':\n keyLocations.append(o)\n random.shuffle(keyLocations)\n\n # create a key for each lockedDoor\n for door in self['lockedDoors']:\n keyLocation = keyLocations.pop()\n map = self['maps'][keyLocation['mapName']]\n keyTile = keyTiles[random.randrange(0, len(keyTiles))]\n key = keyTile.copy()\n key['lockNumber'] = door['lockNumber']\n key['name'] = f\"Key {door['lockNumber']}\"\n map.addObject(key)\n map.setObjectLocationByAnchor(key, keyLocation['anchorX'], keyLocation['anchorY'])\n map.addHoldableTrigger(key)\n\n ########################################################\n # Networking - GAME MESSAGES\n ########################################################\n\n def msgReadyRequest(self, ip, port, ipport, msg):\n \"\"\"msgReadyRequest()\n\n Record player is ready and call updateWaiting() which will start game if all players are ready.\n \"\"\"\n if self['mode'] != \"waitingForPlayers\":\n return\n\n if ipport in self['players']:\n self['players'][ipport]['ready'] = True\n self.updateWaiting()\n return {'type': 'readyReply'}\n\n def msgPlayerMove(self, ip, port, ipport, msg):\n \"\"\"Extends msgPlayerMove()\n\n ignore playerMove msgs until all players have joined game,\n self['mode'] == \"gameOn\".\n\n Once all players have joined game, if a player moves then\n remove their marqueeTest.\n \"\"\"\n if self['mode'] == \"waitingForPlayers\":\n return\n elif self['mode'] == 'gameOn' and ipport in self['players']:\n # clear start marqueeText if player has moved and game is ongoing.\n self.delPlayerMarqueeText(self['players'][ipport]['playerNumber'])\n\n return super().msgPlayerMove(ip, port, ipport, msg)\n\n def msgPlayerAction(self, ip, port, ipport, msg):\n \"\"\"Extends msgPlayerAction()\n\n ignore playerAction msgs until all players have joined game.\n \"\"\"\n if self['mode'] == \"waitingForPlayers\":\n return\n\n return super().msgPlayerAction(ip, port, ipport, msg)\n\n def msgRun(self, ip, port, ipport, msg):\n \"\"\"Increase players speed. Player must already be moving and have an endur value > 0\"\"\"\n\n if self['mode'] == \"waitingForPlayers\":\n return\n\n if ipport in self['players']:\n sprite = self['players'][ipport]['sprite']\n if 'move' in sprite and self['players'][ipport]['endur'] > 0:\n sprite['move']['s'] *= self['RUNSPEED']\n sprite['move']['run'] = True\n\n def msgFire(self, ip, port, ipport, msg):\n \"\"\"Fire players weapon, player must have a weapon and not have fired it for 1 second\"\"\"\n\n if self['mode'] == \"waitingForPlayers\":\n return\n\n if ipport in self['players']:\n player = self['players'][ipport]\n sprite = player['sprite']\n map = self['maps'][sprite['mapName']]\n\n # player must have weapon and can only fire once per second\n if 'weapon' not in sprite or player['lastFired'] > time.perf_counter() - 1:\n return\n\n # ensure player is not firing at themselves\n if geo.distance(sprite['anchorX'], sprite['anchorY'], msg['fireDestX'], msg['fireDestY']) < sprite['width']:\n return\n\n angle = geo.angle(sprite['anchorX'], sprite['anchorY'], msg['fireDestX'], msg['fireDestY'])\n\n if sprite['prop-team'] == 'blue':\n color = \"#4444ff\"\n else:\n color = \"#ff4444\"\n\n if sprite['weapon']['name'] == \"Bow\":\n map.createArrow(sprite['anchorX'], sprite['anchorY'], angle, sprite['width'], color)\n elif sprite['weapon']['name'].startswith(\"Throwing \"):\n map.createStars(sprite['anchorX'], sprite['anchorY'], angle, sprite['width'], color)\n elif sprite['weapon']['name'] == \"Magic Wand\":\n map.createRay(sprite['anchorX'], sprite['anchorY'], angle, sprite['width'], color)\n else:\n log(f\"Unrecognized weapon type: {sprite['weapon']['name']}\", ERROR)\n\n player['lastFired'] = time.perf_counter()\n\n ########################################################\n # Networking - STEP MESSAGES\n ########################################################\n\n def getStepMsg(self, player):\n \"\"\"Extends engine.getStepMsg()\n\n Adds game specific data based on game mode (self['mode']).\n \"\"\"\n\n msg = super().getStepMsg(player)\n\n # Add game specific data to step message\n if self['mode'] == \"gameOn\":\n timeRemaining = self['GAMETIME'] - (time.perf_counter() - self['gameStartSec'])\n elif self['mode'] == 'gameOver':\n timeRemaining = 0.0\n else:\n timeRemaining = self['GAMETIME']\n msg.update({\n 'health': player['health'],\n 'endur': player['endur'],\n 'redPoints': self['redPoints'],\n 'bluePoints': self['bluePoints'],\n 'timeRemaining': timeRemaining\n })\n\n if self['mode'] == \"waitingForPlayers\":\n if 'actionText' in msg:\n del msg['actionText']\n else:\n # add to or create action text\n addonActionText = \"\"\n if 'weapon' in player['sprite']:\n addonActionText = addonActionText + \" Fire (f)\"\n if player['endur'] > 0:\n addonActionText = addonActionText + \" Run (r)\"\n if addonActionText != \"\":\n if 'actionText' in msg:\n msg['actionText'] = msg['actionText'] + addonActionText\n else:\n msg['actionText'] = addonActionText.lstrip()\n\n return msg\n\n ########################################################\n # GAME LOGIC\n ########################################################\n\n def stepServer(self):\n \"\"\"OVERRIDE stepServer()\n\n Take the game one \"step\" forward in time. Unlike engine.server.Server.stepServer()\n this function steps every map on every step, not just maps with players. This allows\n monsters to chase players through map doors.\n \"\"\"\n self.stepServerStart()\n\n for mapName in self['maps']:\n self['maps'][mapName].stepMap()\n\n self.stepServerEnd()\n\n def updateWaiting(self):\n \"\"\"Detect if all players are ready and switch mode to gameOn. Update players as each player becomes ready.\"\"\"\n\n # count how many players are ready.\n totalPlayers = len(self['unassignedPlayerSprites']) + len(self['players'])\n ready = 0\n for ipport in self['players']:\n if self['players'][ipport]['ready'] == True:\n ready += 1\n\n # if all players are ready.\n if ready == totalPlayers:\n self['mode'] = \"gameOn\"\n self['gameStartSec'] = time.perf_counter()\n marqueeText = \"GAME ON!!!\\n\\nClick to move.\"\n log(\"GAME ON: All players have joined.\")\n elif len(self['unassignedPlayerSprites']) == 0:\n # all players have joined but not all ready.\n marqueeText = f\"Waiting for {totalPlayers-ready} players to be ready.\"\n else:\n # waiting for players to both join and be ready.\n marqueeText = f\"Waiting for {len(self['unassignedPlayerSprites'])} to players to join and {totalPlayers-ready} players to be ready.\"\n\n for playerNumber in self['playersByNum']:\n self.setPlayerMarqueeText(playerNumber, marqueeText)\n\n def stepServerStart(self):\n \"\"\"Extends stepServerStart()\"\"\"\n\n super().stepServerStart()\n\n # check stats of players\n for playerNumber in self['playersByNum']:\n player = self['playersByNum'][playerNumber]\n\n if player['health'] <= 0:\n # Player died. respawn the player\n self.respawnPlayer(playerNumber)\n elif player['health'] < self['MAXHEALTH']:\n # regenerate health\n player['health'] += self['MAXHEALTH'] / (self['HEALTHREGENSEC'] * self['fps'])\n player['changed'] = True\n if player['health'] > self['MAXHEALTH']:\n player['health'] = self['MAXHEALTH']\n\n if 'move' in player['sprite'] and 'run' in player['sprite']['move']:\n # if player is running then reduce endurance.\n player['endur'] -= 1.0 / self['fps']\n player['changed'] = True\n # if player ran endurance to 0 then stop running and set endurance to negative number.\n if player['endur'] <= 0:\n del player['sprite']['move']['run']\n player['sprite']['move']['s'] /= self['RUNSPEED']\n player['endur'] = -self['MAXENDUR']\n else:\n # regenerate endurance\n if player['endur'] < self['MAXENDUR']:\n player['endur'] += self['MAXENDUR'] / (self['ENDURREGENSEC'] * self['fps'])\n player['changed'] = True\n if player['endur'] > self['MAXENDUR']:\n player['endur'] = self['MAXENDUR']\n\n # check if the game has ended\n if self['mode'] == \"gameOn\":\n timeRemaining = self['GAMETIME'] - (time.perf_counter() - self['gameStartSec'])\n if timeRemaining < 0:\n self['mode'] = 'gameOver'\n self['quitAfter'] = time.perf_counter() + 30\n log(\"GAME OVER: Quiting in 30 seconds\")\n\n if self['redPoints'] > self['bluePoints']:\n winnerText = \"RED WINS!\"\n elif self['redPoints'] == self['bluePoints']:\n winnerText = \"IT'S A TIE!\"\n else:\n winnerText = \"BLUE WINS!\"\n for playerNumber in self['playersByNum']:\n self.setPlayerMarqueeText(playerNumber,\n f\"Game Over\\n\\nBlue={self['bluePoints']} Red={self['redPoints']}\\n\\n{winnerText}\")\n\n # check if it is time for server to quit\n if self['quitAfter'] < time.perf_counter():\n log(\"Sending quitting msg to all clients.\")\n for ipport in self['players']:\n self['socket'].sendMessage(\n msg={'type': 'quitting'},\n destinationIP=self['players'][ipport]['ip'],\n destinationPort=self['players'][ipport]['port']\n )\n engine.server.quit()\n\n ########################################################\n # PLAYER\n ########################################################\n\n def addPlayer(self, ip, port, ipport, msg):\n \"\"\"Extends addPlayer() and adds game specific data\"\"\"\n\n super().addPlayer(ip, port, ipport, msg)\n\n sprite = self['players'][ipport]['sprite']\n self['players'][ipport].update({\n 'ready': False,\n # Next 3 lines are to remember where player started\n 'startMapName': sprite['mapName'],\n 'startAnchorX': sprite['anchorX'],\n 'startAnchorY': sprite['anchorY'],\n 'lastFired': 0 # time (in sec) that player last fired weapon.\n })\n self.restoreStats(sprite['playerNumber'])\n self.updateWaiting()\n\n def respawnPlayer(self, playerNumber):\n \"\"\"Move player, and all held items, back to where they started and restore player health, endur, etc...\"\"\"\n\n player = self['playersByNum'][playerNumber]\n sprite = player['sprite']\n\n # give point to other team\n if sprite['prop-team'] == 'red':\n self['bluePoints'] += 1\n else:\n self['redPoints'] += 1\n\n # put held items back to where they were picked up.\n for item in ('weapon', 'key', 'idol'):\n if item in sprite:\n drop = sprite[item]\n destmap = self['maps'][drop['mapName']]\n destmap.dropHoldable(sprite, item)\n\n # put player back to where they started.\n destMap = self['maps'][player['startMapName']]\n if sprite['mapName'] != player['startMapName']:\n map = self['maps'][sprite['mapName']]\n map.setObjectMap(sprite, destMap)\n destMap.setObjectLocationByAnchor(sprite, player['startAnchorX'], player['startAnchorY'])\n destMap.delMoveLinear(sprite)\n\n self.restoreStats(playerNumber)\n destMap.setSpriteSpeechText(sprite, \"I died but I have been reborn!\", time.perf_counter() + 4)\n\n def restoreStats(self, playerNumber):\n \"\"\" Set player health and endurance to max\"\"\"\n player = self['playersByNum'][playerNumber]\n player['health'] = self['MAXHEALTH']\n player['endur'] = self['MAXENDUR']\n player['changed'] = True # player changed and step message must be sent.\n\n def resetPlayerChanged(self, player):\n \"\"\"Extends resetPlayerChanged()\"\"\"\n\n player['changed'] = False\n super().resetPlayerChanged(player)\n\n def getPlayerChanged(self, player):\n \"\"\"Extends getPlayerChanged()\n\n Detect if player changed based on player['changed']\n\n Also says player has change if player has not been send a step message\n in 1 second. This makes sure player can see time changing every second.\n \"\"\"\n\n if player['changed']:\n return True\n\n if player['lastStepMsgSent'] + 1 < time.perf_counter():\n # send a least one update per sec so player can see time increasing.\n return True\n\n return super().getPlayerChanged(player)\n","repo_name":"dbakewel/lan-caster","sub_path":"src/demo2/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":19958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"37199330148","text":"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.utils.data\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport torch.nn.parallel # for multi gpus\nimport numpy as np\n\n\nclass T_Net_kd(nn.Module):\n \"\"\"T-Net :\n\n predict an affine transformation matrix\n learning the transform(translation / rotation)\n of point cloud or feature.\n\n Attributes:\n k: is an integer, set 3 for input point cloud and\n 64 for feature after MLP.\n \"\"\"\n def __init__(self, k=3):\n super(T_Net_kd, self).__init__()\n self.conv1 = nn.Conv1d(k, 64, 1)\n self.conv2 = nn.Conv1d(64, 128, 1)\n self.conv3 = nn.Conv1d(128, 1024, 1)\n self.fc1 = nn.Linear(1024, 512)\n self.fc2 = nn.Linear(512, 256)\n self.fc3 = nn.Linear(256, k*k) # matrix [k, k]\n self.relu = nn.ReLU()\n\n self.bn1 = nn.BatchNorm1d(64)\n self.bn2 = nn.BatchNorm1d(128)\n self.bn3 = nn.BatchNorm1d(1024)\n self.bn4 = nn.BatchNorm1d(512)\n self.bn5 = nn.BatchNorm1d(256)\n\n self.k = k\n\n def forward(self, x):\n batch_size = x.size()[0]\n x = F.relu(self.bn1(self.conv1(x))) # [B, 64, N]\n x = F.relu(self.bn2(self.conv2(x))) # [B, 128, N]\n x = F.relu(self.bn3(self.conv3(x))) # [B, 1024, N]\n x = torch.max(x, 2, keepdim=True)[0] # MaxPooling [B, 1024, 1]\n x = x.view(-1, 1024)\n\n x = F.relu(self.bn4(self.fc1(x))) # [B, 512]\n x = F.relu(self.bn5(self.fc2(x))) # [B, 256]\n x = self.fc3(x) # [B, k*k]\n # print(x.size())\n\n # bias = 1 ->> identity\n iden = Variable(torch.from_numpy(np.eye(self.k).flatten().astype(np.float32))).\\\n view(1, self.k*self.k).repeat(batch_size, 1)\n if x.is_cuda:\n iden = iden.cuda()\n x += iden # Linear func: wx + bias\n x = x.view(-1, self.k, self.k)\n return x\n\n\nclass point_net_feature(nn.Module):\n \"\"\"Extract feature using MLP + MaxPooling\n Attributes:\n global_feature: return 1*1024 for classification or\n n*1024 for segmentation.\n feature_transform: do transform by T-net or not.\n \"\"\"\n def __init__(self, global_feature=True, feature_transform=False):\n super(point_net_feature, self).__init__()\n self.tn3d = T_Net_kd()\n self.conv1 = nn.Conv1d(3, 64, 1) # 1*1 convolution for lift dims\n self.conv2 = nn.Conv1d(64, 128, 1)\n self.conv3 = nn.Conv1d(128, 1024, 1)\n self.bn1 = nn.BatchNorm1d(64)\n self.bn2 = nn.BatchNorm1d(128)\n self.bn3 = nn.BatchNorm1d(1024)\n self.global_feature = global_feature\n self.feature_transform = feature_transform\n if self.feature_transform:\n self.tn_feature = T_Net_kd(k=64)\n\n def forward(self, x):\n num_points = x.size()[2] # x: tensor, shape [B, 3, N]\n trans_matrix = self.tn3d(x)\n # do transform for points:\n x = x.transpose(2, 1) # shape [B, N, 3]\n x = torch.bmm(x, trans_matrix)\n x = x.transpose(2, 1) # [B, 3, N]\n x = F.relu(self.bn1(self.conv1(x))) # [B, 64, N]\n\n if self.feature_transform:\n trans_matrix_feat = self.tn_feature(x)\n x = x.transpose(2, 1) # shape [B, N, 64]\n x = torch.bmm(x, trans_matrix_feat)\n x = x.transpose(2, 1) # [B, 64N]\n else:\n trans_matrix_feat = None\n\n points_feature = x # [B, 64, N]\n # print(\"points_feature:\", points_feature.shape)\n x = F.relu(self.bn2(self.conv2(x))) # [B, 128, N]\n x = self.bn3(self.conv3(x)) # [B, 1024, N]\n x = torch.max(x, 2, keepdim=True)[0] # MaxPooling,(values, indices)\n x = x.view(-1, 1024) # global feature [B*1, 1024]\n if self.global_feature:\n return x, trans_matrix, trans_matrix_feat\n else:\n # print(x.size())\n x = x.view(-1, 1024, 1).repeat(1, 1, num_points) # [B, 1024, N]\n # print(x.size())\n return torch.cat([x, points_feature], 1), trans_matrix, trans_matrix_feat\n\n\nclass point_net_cls(nn.Module):\n def __init__(self, k=2, feature_transform=False):\n super(point_net_cls, self).__init__()\n self.feature_transform = feature_transform\n self.feature = point_net_feature(global_feature=True, feature_transform=feature_transform)\n self.fc1 = nn.Linear(1024, 512)\n self.fc2 = nn.Linear(512, 256)\n self.fc3 = nn.Linear(256, k)\n self.dropout = nn.Dropout(p=0.3)\n self.bn1 = nn.BatchNorm1d(512)\n self.bn2 = nn.BatchNorm1d(256)\n self.relu = nn.ReLU()\n\n def forward(self, x):\n x, trans_matrix, trans_matrix_feat = self.feature(x)\n x = F.relu(self.bn1(self.fc1(x))) # [B, 512]\n x = F.relu(self.bn2(self.fc2(x))) # [B, 256]\n x = self.fc3(x) # [B, k=2]\n return F.log_softmax(x, dim=1), trans_matrix, trans_matrix_feat\n\n\nclass point_net_seg(nn.Module):\n def __init__(self, k=2, feature_transform=False):\n super(point_net_seg, self).__init__()\n self.k = k\n self.feature_transform = feature_transform\n self.feature = point_net_feature(global_feature=False, feature_transform=feature_transform)\n self.conv1 = nn.Conv1d(1088, 512, 1)\n self.conv2 = nn.Conv1d(512, 256, 1)\n self.conv3 = nn.Conv1d(256, 128, 1)\n self.conv4 = nn.Conv1d(128, self.k, 1)\n self.bn1 = nn.BatchNorm1d(512)\n self.bn2 = nn.BatchNorm1d(256)\n self.bn3 = nn.BatchNorm1d(128)\n\n def forward(self, x):\n batch_size = x.size()[0]\n num_points = x.size()[2]\n x, trans_matrix, trans_matrix_feat = self.feature(x)\n x = F.relu(self.bn1(self.conv1(x)))\n x = F.relu(self.bn2(self.conv2(x)))\n x = F.relu(self.bn3(self.conv3(x))) # [B, 128, N]\n x = self.conv4(x) # [B, k=2, N]\n x = x.transpose(2, 1).contiguous() # [B, N, 2]\n x = x.view(-1, self.k) # [B*N, 2]\n x = F.log_softmax(x, dim=-1)\n x = x.view(batch_size, num_points, self.k)\n return x, trans_matrix, trans_matrix_feat\n\ndef feature_transform_regularizer(trans):\n \"\"\"\n\n :param trans: [B, k, k]\n \"\"\"\n dims = trans.size()[1]\n batchsize = trans.size()[0]\n I = torch.eye(dims)[None, :, :]\n if trans.is_cuda:\n I = I.cuda()\n loss = torch.mean(torch.norm(torch.bmm(trans, trans.transpose(2, 1)) - I, dim=(1, 2)))\n return loss\n\n\n\nif __name__ == '__main__':\n sim_data = Variable(torch.rand(32, 3, 2500))\n trans = T_Net_kd()\n trans_matrix = trans(sim_data)\n print(\"T-Net:\", trans_matrix.size())\n\n point_feature = point_net_feature(global_feature=True)\n out, _, _ = point_feature(sim_data)\n print(\"global feature:\", out.size())\n\n point_feature = point_net_feature(global_feature=False)\n out, _, _ = point_feature(sim_data)\n print(\"point feature:\", out.size())\n\n cls = point_net_cls(k=5)\n out, _, _ = cls(sim_data)\n print(\"class:\", out.size())\n\n seg = point_net_seg(k=3)\n out, _, _ = seg(sim_data)\n print(\"seg:\", out.size())\n\n\n\n\n","repo_name":"adrien-Chen/pointnet_by_dw","sub_path":"pointnet/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"36575683234","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport sys\nimport argparse\nfrom collections import deque\nimport time\nimport os\nimport csv\nimport numpy as np\nimport pygame\nfrom pygame.locals import *\n\nimport subprocess\n# import pipes\n# import RPi.GPIO as GPIO\n\n\ndef main(args):\n\n delay_obj = Delays(args.stdev_thresh, args.sample_size)\n song_obj = Songs(args.bpm_file, args.pct_change_trigger)\n \n pygame.init()\n BLACK = (0,0,0)\n WIDTH = 100\n HEIGHT = 100\n windowSurface = pygame.display.set_mode((WIDTH, HEIGHT), 0, 32)\n windowSurface.fill(BLACK)\n \n while True:\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n if event.type == KEYDOWN:\n key = event.key\n if key == pygame.K_a:\n strike_event_handler(delay_obj, song_obj)\n elif key == pygame.K_q:\n song_obj.player.terminate()\n pygame.quit()\n sys.exit()\n\ndef strike_event_handler(delay_obj, song_obj):\n delay_obj.add_event(time.time())\n bpm = delay_obj.calc_bpm()\n print('Detected bpm: {0}'.format(bpm))\n if bpm:\n song_obj.select_song(bpm)\n \n\nclass Delays(object):\n def __init__(self, stdev_thresh, sample_size):\n self.stdev_thresh = stdev_thresh\n self.delays = deque(maxlen=sample_size)\n self.last_timestamp = 0\n self.bpm = None\n\n def add_event(self, event_time):\n # debounce\n if event_time - self.last_timestamp < 0.2:\n return\n\n self.delays.append(event_time - self.last_timestamp )\n self.last_timestamp = event_time\n \n #print('delays: {0}'.format(self.delays))\n #print('stdev: {0}'.format(np.std(self.delays)))\n\n def calc_bpm(self):\n if len(self.delays) == self.delays.maxlen and np.std(self.delays) < self.stdev_thresh:\n bps = len(self.delays) / sum(self.delays)\n self.bpm = bps * 60\n return self.bpm\n else:\n return None\n\nclass Songs(object):\n def __init__(self, bpm_filename, pct_change_trigger):\n self.music_dir = os.path.dirname(os.path.abspath(bpm_filename))\n # Load BPM file\n with open(bpm_filename, 'rb') as bpm_file:\n bpm_reader = csv.reader(bpm_file, delimiter='\\t')\n self.bpm_dict = {float(bpm): name for name, bpm in bpm_reader}\n self.bpm_array = np.array(self.bpm_dict.keys())\n self.pct_change_trigger = pct_change_trigger\n null = open('/dev/null', 'wb')\n self.player = subprocess.Popen(['mpg123', '--mono', '--quiet', '--remote', '--fifo', '/tmp/fifo'], stdin=null, stdout=null, stderr=null)\n\n self.now_playing = None\n self.now_playing_bpm = 0\n self.playing = False # Flag to store the play/pause state of the player because mpg123 uses a toggle mode\n\n #def _send_command(self, args):\n #print(' '.join(['echo',] + args))\n #self.command_pipe.append(' '.join(['echo',] + args), '--')\n #fifo_handle = self.command_pipe.open('/tmp/fifo', 'w')\n #fifo_handle.close()\n\n def _send_command(self, args):\n with open('/tmp/fifo', 'w') as fifo:\n fifo.write(' '.join(args)+'\\n')\n \n def select_song(self, bpm):\n pct_change = np.abs((self.now_playing_bpm - bpm) / bpm)\n print('Now playing: {0}, BPM: {1}, % Change: {2}'.format(self.now_playing_bpm, bpm, pct_change))\n if pct_change > self.pct_change_trigger:\n nearest_index = (np.abs(self.bpm_array-bpm)).argmin()\n song_file = self.bpm_dict[self.bpm_array[nearest_index]]\n song_bpm = self.bpm_array[nearest_index]\n if song_file != self.now_playing:\n print('Playing song: {0} with BPM {1}'.format(song_file, song_bpm))\n self.play_song(song_file, song_bpm)\n return self.now_playing\n\n def play_song(self, song_file, song_bpm):\n if self.playing: \n self._fade_out()\n self._send_command(['loadpaused', os.path.join(self.music_dir, song_file)])\n self.playing = False\n self._fade_in()\n self.now_playing = song_file\n self.now_playing_bpm = song_bpm\n\n def _true_play(self):\n # send toggle command only if already paused\n if self.playing == False:\n self._send_command(['pause'])\n self.playing = True\n\n def _true_pause(self):\n # send toggle command only if not playing\n if self.playing == True:\n self._send_command(['pause'])\n self.playing = False\n\n def _fade_in(self):\n self._send_command(['volume', '0'])\n self._true_play()\n for i in range(100):\n self._send_command(['volume', str(i+1)])\n time.sleep(1/100)\n\n def _fade_out(self):\n for i in range(100,0,-1):\n self._send_command(['volume', str(i-1)])\n time.sleep(1/100)\n self._send_command(['stop'])\n self.playing = False\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('bpm_file', type=str, help='TSV file with bpm values in form: filename \\t bpm')\n parser.add_argument('--sample_size', type=int, default=4, help='Number of inter-strike delay values to average for BPM calculation')\n parser.add_argument('--stdev_thresh', type=float, default=0.05, help='Max threshold of standard dev of delay counts to trigger a bpm calculation')\n parser.add_argument('--pct_change_trigger', type=float, default=0.10, help='The percent change in BPM to trigger a song change')\n return parser\n\nif __name__ == '__main__':\n args = parse_args().parse_args()\n main(args)\n","repo_name":"alexholman/musical_hammer","sub_path":"hammer_bpm_laptop.py","file_name":"hammer_bpm_laptop.py","file_ext":"py","file_size_in_byte":5821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"2283501263","text":"from XDSM import XDSM\n\nopt = 'Optimization'\ndat = 'DataInter'\n\nLarge=\"\\Large \"\n\ndef build_tubepod(xdsm): \n x.addComp('solver', 'MDA', Large + 'Solver')\n\n x.addComp('Tube', 'Function', Large + r'Tube')\n x.addComp('Pod', 'Function', Large + r'Pod')\n\n x.addDep('Tube', 'Pod', dat, \"$n, b, b_o$\")\n x.addDep('Pod', 'Tube', dat, \"$n, b, b_o$\")\n\nx = XDSM()\nbuild_tubepod(x)\nx.write('tube_and_pod',True)\n","repo_name":"goldanguyen/MagnePlane","sub_path":"paper/images/xdsm/tube_and_pod_xdsm.py","file_name":"tube_and_pod_xdsm.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"27"} +{"seq_id":"36043321298","text":"def update_server_config(file_path, key, value):\n # Read the existing content of the server configuration file\n with open(file_path, 'r') as file:\n lines = file.readlines()\n\n # Update the configuration value for the specified key\n with open(file_path, 'w') as file:\n for line in lines:\n # Check if the line starts with the specified key\n if key in line:\n # Update the line with the new value\n file.write(key + \"=\" + value + \"\\n\")\n else:\n # Keep the existing line as it is\n file.write(line)\n\n# Path to the server configuration file\nserver_config_file = 'server.conf'\n\n# Key and new value for updating the server configuration\nkey_to_update = 'MAX_CONNECTIONS'\nnew_value = '600' # New maximum connections allowed\n\n# Update the server configuration file\nupdate_server_config(server_config_file, key_to_update, new_value)\n","repo_name":"iam-veeramalla/python-for-devops","sub_path":"Day-12/update_server.py","file_name":"update_server.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","stars":852,"dataset":"github-code","pt":"27"} +{"seq_id":"35341562250","text":"'''\nfunctions for color correction\n'''\n\nimport os\nimport sys\nimport numpy as np\nimport atpy\nimport pyhdust.phc as phc\nimport pyhdust.beatlas as bat\nfrom glob import glob\nfrom scipy.interpolate import griddata\nfrom konoha.constants import G, pi, mH, Msun, Rsun, Lsun, pc\nfrom scipy import stats\n\n\ndef jy2cgs(flux, lbd, inverse=False):\n '''\n Converts from Jy units to erg/s/cm2/micron, and vice-versa\n\n [lbd] = micron\n\n Usage:\n flux_cgs = jy2cgs(flux, lbd, inverse=False)\n '''\n if not inverse:\n flux_new = 3e-9 * flux / lbd**2\n else:\n flux_new = lbd**2 * flux / 3e-9\n\n return flux_new\n\n\n\n\n\n\n# COLOR CORRECTION\ndef color_corr(lbd, flux, flag):\n '''\n Computes color correction factors iteractively\n\n [lbd]=micron\n [flux]=erg/s/cm2/micron\n flag='akari' OR 'iras' OR 'wise'\n\n if 'wise':\n K = {int R lbd dlbd}/{int R g(lbd) lbd dlbd}\n else:\n K = {int R (lbd/lbdi)^-1 dlbd}/{int R g(lbd) dlbd}\n\n where\n g(lbd)=exp[Pn(ln lbd_i,ln f_i)]\n g(lbd_i)=flux_i\n norma = int R dlambda\n\n Usage:\n flux_corr = color_corr(lbd, flux, flag):\n '''\n from .utils import integral, poly_interp\n\n # make sure input are numpy arrays\n lbd = np.array([lbd]).reshape((-1))\n flux = np.array([flux]).reshape((-1))\n\n # path to bandpasses transmission files\n dir0 = '/home/amanda/anaconda3/lib/python3.7/site-packages/konoha/defs/'\n\n # either select filter files, or apply default correction\n # for single fluxes\n if (flag.lower() == 'akari') or (flag.lower() == 'akari/irc'):\n if len(lbd) == 1:\n print('Just one data point: assuming slope=3')\n K = np.array([1.096, 0.961])\n lbdi = np.array([9., 18.])\n flux_corr = flux / K[np.where(np.abs(lbd - lbdi) \\\n == np.min(np.abs(lbd - lbdi)))]\n return flux_corr\n else:\n band = [dir0 + 'bandpasses/irc-s9w.dat', \\\n dir0 + 'bandpasses/irc-l18w.dat']\n elif flag.lower() == 'iras':\n if len(lbd) == 1:\n print('Just one data point: assuming slope=3')\n K = np.array([1.25, 1.23, 1.15, 1.04])\n lbdi = np.array([12., 25., 60., 100.])\n flux_corr = flux / K[np.where(np.abs(lbd - lbdi) \\\n == np.min(np.abs(lbd - lbdi)))]\n return flux_corr\n else:\n band = []\n lbd_arr = np.array(['12', '25', '60', '100'])\n delt = 9.\n for i in range(len(lbd_arr)):\n if (np.abs(lbd - np.float(lbd_arr[i])) < delt).any():\n band.append(dir0 + 'bandpasses/IRAS_IRAS.' \\\n + lbd_arr[i] + 'mu.dat')\n elif flag.lower() == 'wise':\n if len(lbd) == 1:\n print('Just one data point: assuming slope=3')\n K = np.array([0.9961, 0.9976, 0.9393, 0.9934])\n lbdi = np.array([3.3526, 4.6028, 11.5608, 22.0883])\n flux_corr = flux / K[np.where(np.abs(lbd - lbdi) \\\n == np.min(np.abs(lbd - lbdi)))]\n return flux_corr\n else:\n band = []\n lbdi = np.array([3.3526, 4.6028, 11.5608, 22.0883])\n delt = 2.\n for i in range(4):\n if (np.abs(lbd - lbdi[i]) < delt).any():\n band.append(dir0 + 'bandpasses/RSR-W' \\\n + '{:1d}'.format(i + 1) + '.txt')\n else:\n print('unknown input flag')\n flux_corr = np.zeros(len(flux))\n return flux_corr\n\n # iterative color-correction\n nband = len(band)\n K = np.ones(nband)\n K1 = np.zeros(nband)\n delt = 1e-5\n res = 1.\n while res > delt:\n for iband in range(nband):\n tab = np.loadtxt(band[iband])\n lbdi, R = tab[:, 0], tab[:, 1]\n logG = poly_interp(np.log(lbd), np.log((flux/K) \\\n / (flux[iband] / K[iband])), np.log(lbdi))\n G = np.exp(logG)\n if flag.lower() == 'wise':\n K1[iband] = integral(lbdi, R * G * lbdi) \\\n / integral(lbdi, R * lbdi)\n else:\n K1[iband] = integral(lbdi, R * G) \\\n / integral(lbdi, R * (lbd[iband] / lbdi))\n\n res = np.sum(np.abs(K - K1) / K1)\n K = K1\n\n flux_corr = flux / K\n\n return flux_corr\n\n\n\n\n\n# VOSA TO CATALOGUE VALUES\ndef vosa2catvalues(lbd, flux, flag):\n '''\n Converts fluxes back to the catalog values,\n i.e., at the formal nominal wavelengths\n\n currently available flags: 'akari', 'iras', 'wise'\n\n Usage:\n lbd_cat, flux_cat = vosa2catvalues(lbd_vosa, flux_vosa, flag)\n '''\n\n # make sure everybody is a numpy array\n lbd = np.array([lbd]).reshape((-1))\n flux = np.array([flux]).reshape((-1))\n\n # definitions\n lcat = np.zeros(len(lbd))\n fcat = np.zeros(len(flux))\n\n # wavelength tolerance range\n delt = 1. # wavelength tolerance range\n\n # conversion\n if (flag.lower() == 'akari') or (flag.lower() == 'akari/irc'):\n i09 = np.abs(lbd - 8.22) < delt\n i18 = np.abs(lbd - 17.61) < delt\n if i09.any():\n lcat[i09] = 9.\n fcat[i09] = flux[i09] * (lbd[i09] / 9.)**2\n if i18.any():\n lcat[i18] = 18.\n fcat[i18] = flux[i18] * (lbd[i18] / 18.)**2\n elif flag.lower() == 'akari/fis':\n i65 = np.abs(lbd - 62.95) < delt\n i90 = np.abs(lbd - 76.90) < delt\n i140 = np.abs(lbd - 140.86) < delt\n i160 = np.abs(lbd - 159.47) < delt\n if i65.any():\n lcat[i65] = 65.\n fcat[i65] = flux[i65] * (lbd[i65] / 65.)**2\n if i90.any():\n lcat[i90] = 90.\n fcat[i90] = flux[i90] * (lbd[i90] / 90.)**2\n if i140.any():\n lcat[i140] = 140.\n fcat[i140] = flux[i140] * (lbd[i140] / 140.)**2\n if i160.any():\n lcat[i160] = 160.\n fcat[i160] = flux[i160] * (lbd[i160] / 160.)**2\n elif flag.lower() == 'iras':\n i12 = np.abs(lbd - 10.15) < delt\n i25 = np.abs(lbd - 21.73) < delt\n i60 = np.abs(lbd - 51.99) < delt\n i100= np.abs(lbd - 95.30) < delt\n if i12.any():\n lcat[i12] = 12.\n fcat[i12] = flux[i12] * (lbd[i12] / 12.)**2\n if i25.any():\n lcat[i25] = 25.\n fcat[i25] = flux[i25] * (lbd[i25] / 25.)**2\n if i60.any():\n lcat[i60] = 60.\n fcat[i60] = flux[i60] * (lbd[i60] / 60.)**2\n if i100.any():\n lcat[i100] = 100.\n fcat[i100] = flux[i100] * (lbd[i100] / 100.)**2\n elif flag.lower() == 'wise':\n lcat = lbd\n fcat = flux\n else:\n print('unknown input flag')\n lcat, fcat = lbd, flux\n\n return lcat, fcat\n","repo_name":"inthetimevortex/konoha","sub_path":"sed_tools.py","file_name":"sed_tools.py","file_ext":"py","file_size_in_byte":6798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"72407319433","text":"#!/usr/bin/env python3.6\nfrom os import path\nfrom sys import exit\nfrom argparse import ArgumentParser\nfrom shemutils.logger import Logger\nfrom shemutils.database import *\nfrom database import t1\n\nlog = Logger(\"HashQuery\")\n\n\nclass HashQuery(object):\n def __init__(self, *args, **kwargs):\n self.args = args[0]\n\n if not self.args.file or not path.exists(self.args.file):\n log.critial(\"No database file to search for hashes or words.\")\n exit(1)\n\n self.database_file = Database(self.args.file)\n\n if self.args.word is not None:\n search = t1.search(\"PLAIN_TEXT\", self.args.word)\n elif self.args.hash is not None:\n search = t1.search(\"HASH\", self.args.hash)\n else:\n log.critical(\"Neither hash nor word arguments had values.\")\n exit(1)\n\n self.database_file.controller.execute(search)\n results = self.database_file.controller.get()\n for result in results:\n id, word, hash = result\n self._present_data((word, hash))\n\n\n @staticmethod\n def _present_data(data):\n print(\"Plain Text ....: %s\\nHash ..........: %s\\n\\n\" % (data[0], data[1]))\n return 0\n\n\ndef main():\n parser = ArgumentParser()\n parser.add_argument(\"-f\", \"--file\", help=\"Database file to read data.\", required=True)\n parser.add_argument(\"-w\", \"--word\", help=\"Word to search for hashes.\")\n parser.add_argument(\"-e\", \"--hash\", help=\"Hash to search for words.\")\n args = parser.parse_args()\n HashQuery(args)\n return 0\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"0x00-0x00/hashbank","sub_path":"hashquery.py","file_name":"hashquery.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"72772641352","text":"from pydub import AudioSegment, playback\r\nimport numpy as np\r\n\r\ndef change_pitch(sound, semitones):\r\n semitone_ratio = 2 ** (semitones / 12.0)\r\n \r\n # Pitch shift using interpolation\r\n samples = np.array(sound.get_array_of_samples())\r\n pitch_shifted_samples = np.interp(\r\n np.arange(0, len(samples), semitone_ratio),\r\n np.arange(0, len(samples)), samples).astype(np.int16)\r\n \r\n # Create a new sound with the same parameters\r\n new_sound = AudioSegment(\r\n pitch_shifted_samples.tobytes(),\r\n frame_rate=sound.frame_rate,\r\n sample_width=sound.sample_width,\r\n channels=sound.channels\r\n )\r\n \r\n return new_sound\r\n\r\ndef main():\r\n # Load the audio file\r\n audio_file = \"C:/Users/Gerric Villanueva/Documents/Voice_test/Gerric/Gerric_add.wav\"\r\n sound = AudioSegment.from_file(audio_file)\r\n\r\n # Parameters for pitch adjustment (adjust as needed)\r\n semitones_shift = -1 # Adjusted for making the pitch higher (like a kid's voice)\r\n\r\n # Apply pitch shift\r\n high_pitch_sound = change_pitch(sound, semitones_shift)\r\n \r\n # Play the modified sound with the same speed\r\n #playback.play(high_pitch_sound)\r\n\r\n # Export the modified sound to a new file\r\n output_file = \"C:/Users/Gerric Villanueva/Documents/Voice_test/Gerric/Add.wav\"\r\n high_pitch_sound.export(output_file, format=\"wav\")\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"GerricVill/CPE-027-Case-Study-Voice-Changer","sub_path":"Code.py","file_name":"Code.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"6889303126","text":"import time\nimport re\nimport subprocess\n\nfrom PIL import Image\nfrom io import BytesIO\nfrom utils.config import Config\nfrom utils.filer import touch_dir\n\n\nCONFIG = Config('adb').conf\ntouch_dir(CONFIG['tmp_dir'])\n\nclass ADB():\n def __init__(self):\n self.path = CONFIG['adb_path']\n\n def run(self, cmd):\n print('{} {}'.format(self.path, cmd))\n return subprocess.Popen('{} {}'.format(self.path, cmd), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n def screenshot(self):\n process = self.run('shell screencap -p')\n binary_screenshot = process.stdout.read()\n try:\n return Image.open(BytesIO(binary_screenshot))\n except OSError:\n pass\n try:\n return Image.open(BytesIO(binary_screenshot.replace(b'\\r\\n', b'\\n')))\n except OSError:\n pass\n try:\n return Image.open(BytesIO(binary_screenshot.replace(b'\\r\\r\\n', b'\\n')))\n except OSError:\n tmp_dir = CONFIG['tmp_dir'].rstrip('/').rstrip('\\\\')\n self.run('shell screencap -p /sdcard/screencap.png')\n self.run('pull /sdcard/screencap.png {}'.format(tmp_dir))\n return Image.open('{}/screencap.png'.format(tmp_dir))\n\n def get_devices(self):\n process = self.run('devices')\n result = process.stdout.read().decode('utf-8')\n devices = re.findall(r'\\r\\n(\\w+?)\\tdevice', result)\n unauthorized = re.findall(r'\\r\\n(\\w+?)\\tunauthorized', result)\n return devices, unauthorized\n\n def is_connected(self):\n devices, unauthorized = self.get_devices()\n if len(devices) == 1:\n return True\n elif len(devices) > 1:\n print('当前有多个设备连接，请断开不必要的设备连接')\n return False\n else:\n if len(unauthorized) > 0:\n print('请在手机上授权 USB 调试')\n return False\n else:\n print('请您确定已经开启开发者模式与 USB 调试')\n return False\n\n def click(self, point):\n self.run('shell input tap {} {}'.format(point[0], point[1]))\n\n def swipe(self, point_1, point_2, duration=''):\n self.run('shell input swipe {} {} {} {} {}'.format(point_1[0], point_1[1], point_2[0], point_2[1], duration))\n","repo_name":"vaopen/blogs","sub_path":"Codes/pynotex/drivers/adb.py","file_name":"adb.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"13938082282","text":"import torch\nimport torch.nn as nn\nimport numpy as np\nfrom tqdm import tqdm\n\nimport os\nimport csv\nimport re\nimport json\n\nimport utils\nimport opts\nfrom train import train_model, eval_model, eval_logits, eval_model_tta, eval_logits_tta\nfrom model import *\nfrom dataloader import TestDataset, my_transform, test_transform\nfrom sync_batchnorm import convert_model\n\ndef main(opt):\n if torch.cuda.is_available():\n device = torch.device('cuda')\n torch.cuda.set_device(opt.gpu_id)\n else:\n device = torch.device('cpu')\n\n if opt.cadene:\n model = cadene_model(opt.classes, model_name=opt.network)\n elif opt.network == 'resnet':\n model = resnet(opt.classes, opt.layers)\n elif opt.network == 'resnext':\n model = resnext(opt.classes, opt.layers)\n elif opt.network == 'resnext_wsl':\n # resnext_wsl must specify the opt.battleneck_width parameter\n opt.network = 'resnext_wsl_32x' + str(opt.battleneck_width) +'d'\n model = resnext_wsl(opt.classes, opt.battleneck_width)\n elif opt.network == 'resnext_swsl':\n model = resnext_swsl(opt.classes, opt.layers, opt.battleneck_width)\n elif opt.network == 'vgg':\n model = vgg_bn(opt.classes, opt.layers)\n elif opt.network == 'densenet':\n model = densenet(opt.classes, opt.layers)\n elif opt.network == 'inception_v3':\n model = inception_v3(opt.classes, opt.layers)\n elif opt.network == 'dpn':\n model = dpn(opt.classes, opt.layers)\n elif opt.network == 'effnet':\n model = effnet(opt.classes, opt.layers)\n elif opt.network == 'pnasnet_m':\n model = pnasnet_m(opt.classes, opt.layers, opt.pretrained)\n elif opt.network == 'senet_m':\n model = senet_m(opt.classes, opt.layers, opt.pretrained)\n\n\n # model = nn.DataParallel(model, device_ids=[0, 1, 2, 3])\n model = nn.DataParallel(model, device_ids=[0, 1, 2, 3, 4, 5, 6, 7])\n # model = nn.DataParallel(model, device_ids=[1, 2, 3, 4, 5, 6, 7, 0])\n # model = nn.DataParallel(model, device_ids=[4, 5, 6, 7])\n # model = convert_model(model)\n model = model.to(device)\n\n # for param in model.module.model.parameters():\n for param in model.parameters():\n param.requires_grad = False\n\n if opt.classes > 2:\n images, names = utils.read_test_data(os.path.join(opt.root_dir, opt.test_dir))\n else:\n images, names = utils.read_test_ice_snow_data(\n os.path.join(opt.root_dir, opt.test_dir),\n os.path.join(opt.results_ts, opt.res8))\n\n dict_= {}\n\n for crop_size in [opt.crop_size+256]:\n if opt.tta:\n transforms = test_transform(crop_size)\n else:\n transforms = my_transform(False, crop_size)\n\n dataset = TestDataset(images, names, transforms)\n loader = torch.utils.data.DataLoader(dataset,\n batch_size=opt.batch_size,\n shuffle=False, num_workers=4)\n state_dict = torch.load(\n opt.model_dir+'/'+opt.network+'-'+str(opt.layers)+'-'+str(crop_size)+'_model.ckpt')\n if opt.network == 'densenet':\n pattern = re.compile(\n r'^(.*denselayer\\d+\\.(?:norm|relu|conv))\\.((?:[12])\\.(?:weight|bias|running_mean|running_var))$')\n for key in list(state_dict.keys()):\n res = pattern.match(key)\n if res:\n new_key = res.group(1) + res.group(2)\n state_dict[new_key] = state_dict[key]\n del state_dict[key]\n model.load_state_dict(state_dict)\n if opt.vote:\n if opt.tta:\n im_names, labels = eval_model_tta(loader, model, device=device)\n else:\n im_names, labels = eval_model(loader, model, device=device)\n else:\n if opt.tta:\n im_names, labels = eval_logits_tta(loader, model, device=device)\n else:\n im_names, labels = eval_logits(loader, model, device)\n im_labels = []\n # print(im_names)\n for name, label in zip(im_names, labels):\n if name in dict_:\n dict_[name].append(label)\n else:\n dict_[name] = [label]\n\n\n header = ['filename', 'type']\n utils.mkdir(opt.results_dir)\n utils.mkdir(opt.results_ts)\n result = opt.network + '-' +str(opt.layers) + '-'+str(crop_size)+ '_result.csv'\n if opt.classes == 9:\n filename = os.path.join(opt.results_dir, result)\n else:\n result = str(opt.classes) + '-' + result\n filename = os.path.join(opt.results_ts, result)\n with open(filename, 'w', encoding='utf-8') as f:\n f_csv = csv.writer(f)\n f_csv.writerow(header)\n for key in dict_.keys():\n # val = np.max(np.sum(np.array(dict_[key]), axis=0))\n # if val > 0.5: continue\n # v = np.argmax(np.sum(np.array(dict_[key]), axis=0)) + 1\n v = list(np.sum(np.array(dict_[key]), axis=0))\n # f_csv.writerow([key, val])\n f_csv.writerow([key, v])\n\nopt = opts.parse_args()\nmain(opt)\n\n\n\n\n\n\n\n","repo_name":"Algolzw/WeatherClassification","sub_path":"evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":5145,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"27"} +{"seq_id":"23465360116","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.forms import UserCreationForm\t\n\n\ndef register(request):\n\tif request.method == 'POST':\n\t\tform = UserCreationForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect('login')\n\telse:\n\t\tform = UserCreationForm()\n\tcontext = dict(form=form)\n\treturn render(request, 'registration/register.html', context)","repo_name":"johnjullies/tindero","sub_path":"tindero/registration/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"27"} +{"seq_id":"36355888533","text":"from collections import deque\nimport logging\nimport bit_utils\nimport math\nimport random\nimport pygame\nimport config\n\npygame.init()\nbleep = pygame.mixer.Sound('bleep.wav')\n\n\nlogger = logging.getLogger(__name__)\nlogging.basicConfig(level=logging.DEBUG)\nfh = logging.FileHandler('last.log', mode='w')\nfh.setLevel(logging.INFO)\nlogger.addHandler(fh)\n\n\nclass UnknownOpcodeException(Exception):\n def __init__(self, opcode):\n super(UnknownOpcodeException, self).__init__(\n \"The opcode isn't valid {}\".format(hex(opcode)))\n logger.error(\"The opcode isn't valid {}\".format(hex(opcode)))\n\n\nclass CPU:\n def __init__(self, ram, display):\n self.registers = [0] * 16 # The CHIP8 has 16 registers\n self.ram = ram\n self.memory = ram.memory\n self.fontset = ram.fontset\n self.display = display\n self.offset = ram.offset\n self.I = 0 # Adress register\n self.pc = 0 # Currently executing address\n self.delay_timer = 0 # 8-bit timer register\n self.sound_timer = 0 # When this isn't zero there should be a beep\n # The chip8 supports 16 levels of nested subroutines\n self.stack = deque(maxlen=16)\n self.stack_pointer = -1 # Points to the top-level stack instruction\n self.opcode = 0\n\n # Opcode lookup table decyphered by looking at the first byte\n\n self.operation_lookup = {\n 0x0: self.zero_opcodes, # opcodes starting with zero\n 0x1: self.jmp_to_addr,\n 0x2: self.call_subroutine,\n 0x3: self.branch_if_equal_val,\n 0x4: self.branch_if_not_equal_val,\n 0x5: self.branch_if_equal_reg,\n 0x6: self.set_reg_to_val,\n 0x7: self.add_to_reg,\n 0x8: self.logical_operations,\n 0xD: self.draw_pixel_to_display,\n 0xA: self.set_I_to_address,\n 0xF: self.other_operations,\n 0xC: self.generate_random_number,\n 0xE: self.input_handler,\n 0x9: self.skip_if_regs_not_equal,\n 0xB: self.jmp_to_val_plus_v0\n }\n\n # basically opcodes starting with 8 the last byte is the operation\n self.logical_operation_lookup = {\n 0x0: self.set_reg_to_reg,\n 0x1: self.bitwise_or,\n 0x2: self.bitwise_and,\n 0x3: self.bitwise_xor,\n 0x4: self.add_reg_to_reg,\n 0x5: self.sub_reg_from_reg,\n 0x6: self.right_shift if not config.shift_quirk else self.right_shift_quirk,\n 0x7: self.subn_reg_from_reg,\n 0xE: self.left_shift if not config.shift_quirk else self.left_shift_quirk\n }\n\n self.other_operation_lookup = {\n 0x33: self.bin_coded_dec,\n 0x65: self.load_mem_to_registers if not config.load_quirk else self.load_mem_to_registers_quirk,\n 0x29: self.load_sprite_from_memory,\n 0x15: self.set_delay_timer_to_reg,\n 0x07: self.set_reg_to_delay_timer,\n 0x18: self.set_sound_timer_to_reg,\n 0x55: self.load_registers_in_memory if not config.load_quirk else self.load_registers_in_memory_quirk,\n 0x1E: self.add_reg_to_I,\n 0x0A: self.wait_for_keypress\n }\n\n def load_fontset(self):\n for i, (_, _) in enumerate(zip(self.memory, self.fontset)):\n self.memory[i] = self.fontset[i]\n\n def load_rom(self, filename):\n cart = open(filename, 'rb')\n rom = cart.read()\n i = 0\n while i < len(rom):\n self.memory[i + self.offset] = rom[i]\n i += 1\n cart.close()\n\n def dump_memory(self, path):\n \"\"\"\n Dumps the cpu memory into a file\n \"\"\"\n with open(path, 'wb') as file:\n for byte in self.memory:\n file.write(bytes([byte]))\n logger.info(\"Dumped memory into {}\".format(path))\n\n def fetch_opcode(self):\n self.opcode = self.memory[self.pc] << 8 | self.memory[self.pc + 1]\n\n def decode_opcode(self):\n # This makes sure I get only the first byte\n operation = (self.opcode & 0xF000) >> 12\n try:\n self.operation_lookup[operation]()\n except KeyError:\n raise UnknownOpcodeException(self.opcode)\n\n def update_timers(self):\n if (self.delay_timer > 0):\n self.delay_timer -= 1\n if (self.sound_timer > 0):\n self.sound_timer -= 1\n bleep.play()\n\n def run_cycle(self):\n self.fetch_opcode()\n self.decode_opcode()\n self.pc += 2\n\n def initalize_cpu(self, filename):\n self.load_fontset()\n self.pc = self.offset\n self.load_rom(filename)\n\n def reset_cpu(self, rom):\n self.registers = [0] * 16\n self.memory = self.ram.memory\n self.I = 0\n self.pc = 200\n self.delay_timer = 0\n self.sound_timer = 0\n self.stack = deque(maxlen=16)\n self.stack_pointer = -1\n self.display.clear_display()\n self.load_rom(rom)\n\n def return_middle_registers(self, opcode):\n \"\"\"\n Return a tuple consisting of the registers in between an opcode\n\n >>> return_middle_registers(8231)\n (2, 3)\n \"\"\"\n registers = (opcode & 0x0FF0) >> 4\n register_x = (registers & 0xF0) >> 4\n register_y = registers & 0x0F\n return (register_x, register_y)\n\n # Here are the operations:\n\n def return_registers(self):\n return self.registers\n\n def zero_opcodes(self):\n \"\"\" Opeartions that start with 0 are either\n 0nnn - Jump to machine routine at nnn (ignored)\n 00E0 - Clear the display\n 00EE - Return from a subroutine.\n \"\"\"\n if self.opcode == 0x00E0:\n self.display.clear_display()\n logger.info(\"Cleared display\")\n elif self.opcode == 0x00EE:\n logger.info(\"Returned from subroutine at {}\".format(hex(self.pc)))\n self.pc = self.stack[self.stack_pointer]\n self.stack.pop()\n self.stack_pointer -= 1\n logger.info(\"to address at {}\".format(hex(self.pc)))\n\n def input_handler(self):\n if self.opcode & 0xFF == 0xA1:\n self.skip_if_key_not_pressed()\n elif self.opcode & 0xFF == 0x9E:\n self.skip_if_key_pressed()\n\n def logical_operations(self):\n operation = self.opcode & 0xF\n try:\n self.logical_operation_lookup[operation]()\n except KeyError:\n raise UnknownOpcodeException(self.opcode)\n\n def other_operations(self):\n operation = self.opcode & 0xFF\n try:\n self.other_operation_lookup[operation]()\n except KeyError:\n raise UnknownOpcodeException(self.opcode)\n\n def jmp_to_addr(self):\n \"\"\"\n 1nnn - Jump to location nnn.\n The interpreter sets the program counter to nnn.\n \"\"\"\n self.pc = self.opcode & 0x0FFF\n logger.info(\"Jumped to address at {}\".format(hex(self.pc)))\n # PC gets incremented after every instruction this counteracts that\n self.pc -= 2\n\n def call_subroutine(self):\n \"\"\"0x2nnn:\n The interpreter increments the stack pointer, then puts the current\n PC on the top of the stack. The PC is then set to nnn.\n \"\"\"\n # Might be an issue didn't test it yet.\n self.stack_pointer += 1\n self.stack.append(self.pc)\n self.pc = (self.opcode & 0x0FFF) - 2\n logger.info(\"Called subroutine at {}\".format(hex(self.pc)))\n\n def branch_if_equal_val(self):\n \"\"\"\n 3xkk - Skip next instruction if Vx = kk.\n The interpreter compares register Vx to kk, and if they are equal,\n increments the program counter by 2.\n \"\"\"\n register = (self.opcode & 0x0F00) >> 8\n value = self.opcode & 0xFF\n if self.registers[register] == value:\n self.pc += 2\n logger.info(\"Skipped {} because V{} and {} are equal\".format(\n hex(self.pc - 2),\n register,\n value))\n\n def branch_if_not_equal_val(self):\n \"\"\"\n 4xkk - Skip next instruction if Vx != kk.\n The interpreter compares register Vx to kk, and if they are not\n equal, increments the program counter by 2.\n \"\"\"\n register = (self.opcode & 0x0F00) >> 8\n value = self.opcode & 0xFF\n if self.registers[register] != value:\n self.pc += 2\n logger.info(\n \"Didn't skip {} because V{} and {} are not equal\".format(\n hex(self.pc - 2),\n register,\n value))\n\n def branch_if_equal_reg(self):\n \"\"\"\n 5xy0 - Skip next instruction if Vx = Vy.\n The interpreter compares register Vx to register Vy, and if they\n are equal, increments the program counter by 2.\n \"\"\"\n registers = self.return_middle_registers(self.opcode)\n\n if self.registers[registers[0]] == self.registers[registers[1]]:\n self.pc += 2\n logger.info(\n \"Skipped {} because register V{} and V{} are equal to {}\".format(\n hex(self.pc - 2),\n registers[0],\n registers[1],\n self.registers[registers[0]]))\n\n def set_reg_to_val(self):\n \"\"\"\n 6xkk - Set Vx = kk.\n The interpreter puts the value kk into register Vx.\n \"\"\"\n register = (self.opcode & 0x0F00) >> 8\n value = self.opcode & 0x00FF\n self.registers[register] = value\n logger.info(\"Set register V{} to {}\".format(register, value))\n\n def add_to_reg(self):\n \"\"\"\n 7xkk - Set Vx = Vx + kk.\n Adds the value kk to the value of register Vx, then stores the\n result in Vx.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n value = self.opcode & 0xFF\n sum = self.registers[register] + value\n if sum > 0xFF:\n sum = bit_utils.wrap_around(sum, 0xFF + 1)\n self.registers[register] = sum\n logger.info(\"Added {} to register V{}\".format(value, register))\n\n def set_reg_to_reg(self):\n \"\"\"\n 8xy0 - Set Vx = Vy.\n Stores the value of register Vy in register Vx.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n self.registers[register[0]] = self.registers[register[1]]\n logger.info(\"Set register V{} to V{}\".format(register[0], register[1]))\n\n def bitwise_or(self):\n \"\"\"\n 8xy1 - Set Vx = Vx OR Vy.\n Performs a bitwise OR on the values of Vx and Vy, then stores the\n result in Vx. A bitwise OR compares the corrseponding bits from two\n values, and if either bit is 1, then the same bit in the result is\n also 1. Otherwise, it is 0.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n self.registers[register[0]] = (\n self.registers[register[0]] | self.registers[register[1]])\n logger.info(\"Bitwise OR on V{} and V{} for {}\".format(\n register[0],\n register[1],\n self.registers[register[0]]))\n\n def bitwise_and(self):\n \"\"\"\n 8xy2 - Set Vx = Vx AND Vy.\n Performs a bitwise AND on the values of Vx and Vy, then stores the\n result in Vx. A bitwise AND compares the corrseponding bits from\n two values, and if both bits are 1, then the same bit in the result\n is also 1. Otherwise, it is 0.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n self.registers[register[0]] = (\n self.registers[register[0]] & self.registers[register[1]])\n logger.info(\"Bitwise AND on V{} and V{} for {}\".format(\n register[0],\n register[1],\n self.registers[register[0]]))\n\n def bitwise_xor(self):\n \"\"\"\n 8xy3 - Set Vx = Vx XOR Vy.\n Performs a bitwise exclusive OR on the values of Vx and Vy, then\n stores the result in Vx. An exclusive OR compares the corrseponding\n bits from two values, and if the bits are not both the same, then\n the corresponding bit in the result is set to 1. Otherwise, it is 0\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n self.registers[register[0]] = (\n self.registers[register[0]] ^ self.registers[register[1]])\n logger.info(\"Bitwise XOR on V{} and V{} for {}\".format(\n register[0],\n register[1],\n self.registers[register[0]]))\n\n def add_reg_to_reg(self):\n \"\"\"\n 8xy4 - Set Vx = Vx + Vy, set VF = carry.\n The values of Vx and Vy are added together. If the result is\n greater than 8 bits (i.e., > 255,) VF is set to 1, otherwise 0.\n Only the lowest 8 bits of the result are kept, and stored in Vx.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n sum = self.registers[register[0]] + self.registers[register[1]]\n if sum > 0xFF:\n self.registers[0xF] = 1\n self.registers[register[0]] = sum & 0xFF\n else:\n self.registers[0xF] = 0\n self.registers[register[0]] = sum\n logger.info(\"Added V{} to V{} and got {}\".format(\n register[0],\n register[1],\n self.registers[register[0]]))\n\n def sub_reg_from_reg(self):\n \"\"\"\n 8xy5 - Set Vx = Vx - Vy, set VF = NOT borrow.\n If Vx > Vy, then VF is set to 1, otherwise 0. Then Vy is subtracted\n from Vx, and the results stored in Vx.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n if self.registers[register[0]] > self.registers[register[1]]:\n self.registers[0xF] = 1\n self.registers[register[0]] -= self.registers[register[1]]\n else:\n self.registers[0xF] = 0\n self.registers[register[0]] = (\n 256\n + self.registers[register[0]]\n - self.registers[register[1]])\n # the 256 is there to simulate a wrap around of an unsigned integer\n logger.info(\"Subtracted V{} from V{} and got {}\".format(\n register[1],\n register[0],\n self.registers[register[0]]))\n\n def draw_pixel_to_display(self):\n \"\"\"\n Dxyn - Display n-byte sprite starting at memory location I at\n (Vx, Vy), set VF = collision.\n\n The interpreter reads n bytes from memory, starting at the address\n stored in I. These bytes are then displayed as sprites on screen at\n coordinates (Vx, Vy). Sprites are XORed onto the existing screen.\n If this causes any pixels to be erased, VF is set to 1, otherwise\n it is set to 0. If the sprite is positioned so part of it is\n outside the coordinates of the display, it wraps around to the\n opposite side of the screen.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n x = self.registers[register[0]]\n y = self.registers[register[1]]\n height = self.opcode & 0xF\n\n self.registers[0xF] = 0\n\n x = bit_utils.wrap_around(x, self.display.width)\n y = bit_utils.wrap_around(y, self.display.height)\n\n for yline in range(0, height):\n pixels = self.memory[self.I + yline]\n y1 = bit_utils.wrap_around(y + yline, self.display.height)\n for xline in range(0, 8):\n x1 = bit_utils.wrap_around(x + xline, self.display.width)\n if pixels & (0x80 >> xline) != 0:\n if self.display.set_pixel(x1, y1):\n self.registers[0xF] = 1\n\n self.display.draw_flag = True\n logger.info(\"Drawing sprite from {} to {} at {}, {}\".format(\n hex(self.I),\n hex(self.I + height),\n x, y))\n\n def set_I_to_address(self):\n \"\"\"\n Annn - Set I = nnn.\n The value of register I is set to nnn.\n \"\"\"\n self.I = self.opcode & 0xFFF\n logger.info(\"Set I to {}\".format(hex(self.I)))\n\n def right_shift(self):\n \"\"\"\n 8x06 - Set Vx = Vx SHR 1.\n If the least-significant bit of Vx is 1, then VF is set to 1,\n otherwise 0. Then Vx is divided by 2.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n bits = self.registers[register]\n \"\"\"if bits & 0b1 == 1:\n self.registers[0xF] = 1\n else:\n self.registers[0xF] = 0\n \"\"\"\n self.registers[0xF] = bits & 0b1\n self.registers[register] = self.registers[register] >> 1\n logger.info(\"Shifted register V{} 1 bit to the right got {}\".format(\n register,\n hex(self.registers[register])))\n\n def right_shift_quirk(self):\n \"\"\"\n 8xy6 - Set Vx = Vy SHR 1.\n If the least-significant bit of Vy is 1, then VF is set to 1,\n otherwise 0. Then Vy is divided by 2.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n bits = self.registers[register[1]]\n self.registers[0xF] = bits & 0b1\n self.registers[register[0]] = self.registers[register[1]] >> 1\n logger.info(\"Shifted register V{} to the right into V{}({})\".format(\n register[1],\n register[0],\n hex(self.registers[register[0]])))\n\n def subn_reg_from_reg(self):\n \"\"\"\n 8xy7 - Set Vx = Vy - Vx, set VF = NOT borrow.\n If Vy > Vx, then VF is set to 1, otherwise 0. Then Vx is subtracted\n from Vy, and the results stored in Vx.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n if self.registers[register[1]] > self.registers[register[0]]:\n self.registers[0xF] = 1\n self.registers[register[0]] = (\n self.registers[register[1]]\n - self.registers[register[0]])\n else:\n self.registers[0xF] = 0\n self.registers[register[0]] = (\n 256\n + self.registers[register[1]]\n - self.registers[register[0]])\n\n logger.info(\"Subtracted V{} from V{} and got {}\".format(\n register[1],\n register[0],\n self.registers[register[0]]))\n\n def left_shift(self):\n \"\"\"\n 8x0E - Set Vx = Vx SHL 1.\n If the most-significant bit of Vx is 1, then VF is set to 1,\n otherwise to 0. Then Vx is multiplied by 2.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n bits = self.registers[register]\n \"\"\"if bits & 0b1 == 1:\n self.registers[0xF] = 1\n else:\n self.registers[0xF] = 0\n \"\"\"\n self.registers[0xF] = bits & 0x80\n self.registers[register] = self.registers[register] << 1\n logger.info(\"Shifted register V{} 1 bit to the left got {}\".format(\n register,\n hex(self.registers[register])))\n\n def left_shift_quirk(self):\n \"\"\"\n 8xyE - Set Vx = Vy SHL 1.\n If the most-significant bit of Vy is 1, then VF is set to 1,\n otherwise 0. Then Vy is multiplied by 2.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n bits = self.registers[register[1]]\n self.registers[0xF] = bits & 0x80\n self.registers[register[0]] = self.registers[register[1]] << 1\n logger.info(\"Shifted register V{} to the left into V{}({})\".format(\n register[1],\n register[0],\n hex(self.registers[register[0]])))\n\n def bin_coded_dec(self):\n \"\"\"\n Fx33 - Store BCD representation of Vx in memory locations I, I+1,\n and I+2.\n The interpreter takes the decimal value of Vx, and places the\n hundreds digit in memory at location in I, the tens digit at\n location I+1, and the ones digit at location I+2.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n value = self.registers[register]\n self.memory[self.I] = int(math.floor(value / 100))\n self.memory[self.I + 1] = int(math.floor(value % 100 / 10))\n self.memory[self.I + 2] = value % 10\n logger.info(\"Stored BCD of V{}({}) starting at {}\".format(\n register,\n self.registers[register],\n hex(self.I)))\n\n def load_mem_to_registers(self):\n \"\"\"\n Fx65 - Read registers V0 through Vx from memory starting at location I.\n The interpreter reads values from memory starting at location I into\n registers V0 through Vx.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n for x in range(register+1):\n self.registers[x] = self.memory[self.I + x]\n logger.info(\n \"Loaded memory from {} to {} in registers till V{}\".format(\n hex(self.I),\n hex((self.I + register)),\n register))\n\n def load_mem_to_registers_quirk(self):\n \"\"\"\n Fx65 - Read registers V0 through Vx from memory starting at location I.\n The interpreter reads values from memory starting at location I into\n registers V0 through Vx.\n I is set to I + X + 1 after operation\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n for x in range(register+1):\n self.registers[x] = self.memory[self.I + x]\n self.I += register + 1\n logger.info(\n \"Loaded memory from {} to {} in registers till V{}\".format(\n hex(self.I),\n hex((self.I + register)),\n register))\n\n def load_sprite_from_memory(self):\n \"\"\"\n Fx29 - Set I = location of sprite for digit Vx.\n The value of I is set to the location for the hexadecimal sprite\n corresponding to the value of Vx. See section 2.4, Display, for\n more information on the Chip-8 hexadecimal font.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n self.I = self.registers[register] * 5\n\n logger.info(\"Loaded sprite at memory location {}\".format(hex(self.I)))\n\n def set_delay_timer_to_reg(self):\n \"\"\"\n Fx15 - Set delay timer = Vx.\n DT is set equal to the value of Vx.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n self.delay_timer = self.registers[register]\n\n logger.info(\"Set delay timer to register V{} = {}\".format(\n register,\n self.registers[register]))\n\n def set_reg_to_delay_timer(self):\n \"\"\"\n Fx07 - Set Vx = delay timer value.\n The value of DT is placed into Vx.\n \"\"\"\n register = (self.opcode & 0xFFF) >> 8\n self.registers[register] = self.delay_timer\n logger.info(\"Set register V{} to delay timer {}\".format(\n register,\n self.registers[register]))\n\n def generate_random_number(self):\n \"\"\"\n Cxkk - Set Vx = random byte AND kk.\n The interpreter generates a random number from 0 to 255, which is\n then ANDed with the value kk. The results are stored in Vx.\n \"\"\"\n value = random.randint(0, 0xFF)\n register = (self.opcode & 0xF00) >> 8\n to_and = self.opcode & 0xFF\n self.registers[register] = value & to_and\n logger.info(\"Set V{} to random number {}\".format(\n register,\n self.registers[register]))\n\n def skip_if_key_not_pressed(self):\n \"\"\"\n ExA1 - Skip next instruction if key with the value of Vx is not\n pressed.\n Checks the keyboard, and if the key corresponding to the value of\n Vx is currently in the up position, PC is increased by 2.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n key = self.registers[register]\n keys = pygame.key.get_pressed()\n if not keys[ord(config.keys[key])]:\n self.pc += 2\n logger.info(\"Skipped {} because {} wasn't pressed\".format(\n hex(self.memory[self.pc - 2]),\n key))\n\n def set_sound_timer_to_reg(self):\n \"\"\"\n Fx18 - Set sound timer = Vx.\n ST is set equal to the value of Vx.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n self.sound_timer = self.registers[register]\n logger.info(\"Set sound timer to V{} = {}\".format(\n register,\n self.registers[register]))\n\n def load_registers_in_memory(self):\n \"\"\"\n Fx55 - Store registers V0 through Vx in memory starting at\n location I.\n The interpreter copies the values of registers V0 through Vx into\n memory, starting at the address in I.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n for x in range(register+1):\n self.memory[self.I + x] = self.registers[x]\n logger.info(\"Loaded registers from V0 to V{} into {}\".format(\n register,\n hex(self.I)))\n\n def load_registers_in_memory_quirk(self):\n \"\"\"\n Fx55 - Store registers V0 through Vx in memory starting at\n location I.\n The interpreter copies the values of registers V0 through Vx into\n memory, starting at the address in I.\n I is set to I + X + 1 after operation\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n for x in range(register+1):\n self.memory[self.I + x] = self.registers[x]\n self.I += register + 1\n logger.info(\"Loaded registers from V0 to V{} into {}\".format(\n register,\n hex(self.I)))\n\n def add_reg_to_I(self):\n \"\"\"\n Fx1E - Set I = I + Vx.\n The values of I and Vx are added, and the results are stored in I.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n value = bit_utils.wrap_around(\n self.registers[register] + self.I,\n 0xffff + 1)\n self.I = value\n logger.info(\"Added V{}({}) to I\".format(\n register,\n self.registers[register]))\n\n def skip_if_key_pressed(self):\n \"\"\"\n Ex9E - Skip next instruction if key with the value of Vx is\n pressed.\n Checks the keyboard, and if the key corresponding to the value of\n Vx is currently in the down position, PC is increased by 2.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n key = self.registers[register]\n keys = pygame.key.get_pressed()\n if keys[ord(config.keys[key])]:\n self.pc += 2\n logger.info(\"Skipped {} because {} was pressed\".format(\n self.memory[self.pc + 2],\n key))\n\n def skip_if_regs_not_equal(self):\n \"\"\"\n 9xy0 - Skip next instruction if Vx != Vy.\n The values of Vx and Vy are compared, and if they are not equal,\n the program counter is increased by 2.\n \"\"\"\n register = self.return_middle_registers(self.opcode)\n value_x = self.registers[register[0]]\n value_y = self.registers[register[1]]\n if value_x != value_y:\n self.pc += 2\n logger.info(\"Skipped {} because V{} = V{}\".format(\n hex(self.pc - 2),\n register[0],\n register[1]))\n\n def jmp_to_val_plus_v0(self):\n \"\"\"\n Bnnn - Jump to location nnn + V0.\n The program counter is set to nnn plus the value of V0.\n \"\"\"\n addr = self.opcode & 0xFFF\n self.pc = addr + self.registers[0]\n logger.info(\"Jumped to {} + V0 = {}\".format(\n hex(addr),\n hex(self.pc)))\n\n def wait_for_keypress(self):\n \"\"\"\n Fx0A - Wait for a key press, store the value of the key in Vx.\n All execution stops until a key is pressed, then the value of that\n key is stored in Vx.\n \"\"\"\n register = (self.opcode & 0xF00) >> 8\n key_pressed = False\n while not key_pressed:\n event = pygame.event.wait()\n if event.type == pygame.QUIT:\n pygame.quit()\n if event.type == pygame.KEYDOWN:\n inv_keys = {v: k for k, v in config.keys.items()}\n try:\n self.registers[register] = inv_keys[chr(event.key)]\n key_pressed = True\n except KeyError:\n pass\n\n logger.info(\"Stored key {} into V{}\".format(\n self.registers[register],\n register))\n","repo_name":"MeArio/Chipy38","sub_path":"cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":28749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"23971733865","text":"from psycopg2.extras import execute_values\n\nfrom dagster import (solid, String, Output, OutputDefinition)\nfrom dagster_pandas import DataFrame\n\nfrom db_toolkit.postgres import count_sql\n##########################################################\n# Craete the required apache tables in postgres\n# #########################################################\n\n@solid(required_resource_keys={'postgres_warehouse'})\ndef create_postgres_tables(context):\n client = context.resources.postgres_warehouse.get_connection(context)\n\n if client is not None:\n\n cursor = client.cursor()\n\n try:\n\n # Create the booking_step configuration table, containing a name, value pair:\n # Step number and name for each step in the booking flow\n create_apache_tracking_table_SQL = \"\"\"CREATE TABLE IF NOT EXISTS apache_tracking\n (\n loaded_file text PRIMARY KEY,\n loaded_date timestamp\n ) \"\"\"\n cursor.execute(create_apache_tracking_table_SQL)\n client.commit()\n\n create_bs_table_query = \"\"\"CREATE TABLE IF NOT EXISTS booking_step (\n step_number integer NOT NULL,\n step_name text NOT NULL,\n CONSTRAINT \"PK_booking_step\" PRIMARY KEY (step_number)\n )\n \"\"\"\n cursor.execute(create_bs_table_query)\n client.commit()\n\n booking_step_dic = {'step_number': [1, 2, 3, 4, 5, 6],\n 'step_name': ['Search', 'Selection', 'Summary', 'Traveler Details', 'Payment',\n 'Confirmation']\n }\n\n booking_step_df = DataFrame(booking_step_dic, columns=['step_number', 'step_name'])\n\n # Insert the configuration data\n insert_bs_query = \"\"\"INSERT INTO booking_step(\n step_number, \n step_name)\n VALUES %s \n ON CONFLICT DO NOTHING \"\"\"\n tuples = [tuple(x) for x in booking_step_df.values]\n\n cursor.execute(count_sql('booking_step'))\n result = cursor.fetchone()\n pre_len = result[0]\n\n execute_values(cursor, insert_bs_query, tuples)\n client.commit()\n\n cursor.execute(count_sql('booking_step'))\n result = cursor.fetchone()\n post_len = result[0]\n\n context.log.info(f'Inserted {post_len - pre_len} records in the booking_step table')\n\n # create table\n\n create_apache_session_table_SQL = '''CREATE TABLE IF NOT EXISTS apache_session (\n id SERIAL PRIMARY KEY,\n ip_address TEXT NOT NULL,\n session_id TEXT NOT NULL,\n channel TEXT NOT NULL,\n session_start_time TIMESTAMP NOT NULL,\n session_end_time TIMESTAMP NOT NULL,\n session_duration INT, \n first_step SMALLINT,\n last_step SMALLINT,\n num_pages_accessed SMALLINT,\n continent_code TEXT,\n country_code TEXT,\n country_name TEXT, \n city_name TEXT,\n latitude TEXT,\n longitude TEXT,\n timezone TEXT\n ) '''\n ############################################################################################################\n # Removed CONSTRAINT UK_apache_session UNIQUE(ip_address,session_id) due to UK violation\n # when a session is across 2 days\n # , CONSTRAINT constraint_name UNIQUE (ip_address, session_id)\n # Also\n ############################################################################################################\n\n context.log.info(f'{create_apache_session_table_SQL}')\n cursor.execute(create_apache_session_table_SQL)\n client.commit()\n\n create_apache_index1_SQL = ''' CREATE INDEX IF NOT EXISTS idx_apache_session_start\n ON apache_session(session_start_time)\n '''\n context.log.info(f'{create_apache_index1_SQL}')\n cursor.execute(create_apache_index1_SQL)\n client.commit()\n\n create_apache_index2_SQL = ''' CREATE INDEX IF NOT EXISTS idx_apache_session_step\n ON apache_session(last_step)\n '''\n context.log.info(f'{create_apache_index2_SQL}')\n cursor.execute(create_apache_index2_SQL)\n client.commit()\n\n finally:\n # tidy up\n cursor.close()\n client.close_connection()\n","repo_name":"philtap/Apache_logs","sub_path":"Apache_logs/solids/create_apache_tables_nodes.py","file_name":"create_apache_tables_nodes.py","file_ext":"py","file_size_in_byte":4786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22848788089","text":"import pandas as pd\r\nimport openpyxl as pyxl\r\nimport numpy as np\r\nimport glob\r\nimport datetime\r\n\r\n#2G first\r\nkpi_dict = { '2g_kpis': {\r\n 'thrput': 'EGPRS_LLC_THROUGHPUT_IR_Kbps_2G'\r\n ,'payload' : 'PAYLOAD_LLC_TOTAL_KBYTE_IR_KB_2G'\r\n ,'avail': 'TCH_AVAILABILITY_IR_2G'\r\n },\r\n '3g_kpis': {\r\n 'thrput': 'Throughput_HS_DC_NodeB_kbps_IR_3G'\r\n ,'payload': 'PAYLOAD_TOTAL_3G_KBYTE_IR_KB_3G'\r\n ,'avail': 'Cell_Avail_Sys_IR_3G'\r\n },\r\n '4g_kpis': {\r\n 'thrput': 'Throughput_UE_DL_kbps_IR_Kbps_4G'\r\n ,'payload': 'PAYLOAD_TOTAL_KBYTE_IR_KB_4G'\r\n ,'avail': 'CELL_AVAIL_SYS_IR_4G'\r\n }\r\n }\r\n\r\ndef readFiles():\r\n path = r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122'\r\n print('Reading input files in the path ' + path + ' ...')\r\n all_files = glob.glob(path + \"/*.csv\")\r\n\r\n print('Found these files in the path ', all_files)\r\n\r\n dfListRawFiles = []\r\n\r\n for filename in all_files:\r\n print('Reading ', filename , ' ...')\r\n df = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',',index_col=None, header=0)\r\n dfListRawFiles.append(df)\r\n\r\n print('All Read, now Concatenating them...')\r\n df_big = pd.concat(dfListRawFiles, axis=0, ignore_index=True)\r\n print('Concatenated successfully...')\r\n\r\n df_big.rename(columns={\"2G_EGPRS_LLC_THROUGHPUT_IR(Kbps)\": \"EGPRS_LLC_THROUGHPUT_IR_Kbps_2G\",\r\n \"2G_PAYLOAD_LLC_TOTAL_KBYTE_IR(KB)\": \"PAYLOAD_LLC_TOTAL_KBYTE_IR_KB_2G\",\r\n \"2G_TCH_AVAILABILITY_IR(%)\": \"TCH_AVAILABILITY_IR_2G\",\r\n \"3G_Throughput_HS_DC_NodeB_kbps_IR(%)\": \"Throughput_HS_DC_NodeB_kbps_IR_3G\",\r\n \"3G_PAYLOAD_TOTAL_3G_KBYTE_IR(KB)\": \"PAYLOAD_TOTAL_3G_KBYTE_IR_KB_3G\",\r\n \"3G Cell_Avail_Sys_IR(%)\": \"Cell_Avail_Sys_IR_3G\",\r\n \"4G_Throughput_UE_DL_kbps_IR(Kbps)\": \"Throughput_UE_DL_kbps_IR_Kbps_4G\",\r\n \"4G_PAYLOAD_TOTAL_KBYTE_IR(KB)\": \"PAYLOAD_TOTAL_KBYTE_IR_KB_4G\",\r\n \"4G_CELL_AVAIL_SYS_IR\":\"CELL_AVAIL_SYS_IR_4G\"}, inplace=True)\r\n\r\n #filtering out invalid values which are a few but they cause type errors in KPI examination code.\r\n # for i in range(2, 5):\r\n # print('Cleaning up ' + str(i) + 'G Data...')\r\n # tech_label = str(i) + 'g_kpis'\r\n # tech_kpi_dict = kpi_dict[tech_label]\r\n # throughput_kpi = tech_kpi_dict['thrput']\r\n df_big.query('~Throughput_UE_DL_kbps_IR_Kbps_4G.str.contains(\"-,\", na=False)', engine='python', inplace=True)\r\n #df_big.to_csv(r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122\\cleaned.csv', index=False, header=True)\r\n\r\n return df_big\r\n\r\n# verification functions ###############################################################\r\ndef check_count(df:pd.DataFrame):\r\n df_group_by = df.pivot_table(values='SITE', index='Time', aggfunc=pd.Series.nunique)\r\n df_count_check = df_group_by[df_group_by['SITE'] <= 10900]\r\n\r\n return df_count_check\r\n\r\ndef check_thrput(df:pd.DataFrame, kpi_dict):\r\n throughput_kpi = kpi_dict['thrput']\r\n payload_kpi = kpi_dict['payload']\r\n avail_kpi = kpi_dict['avail']\r\n\r\n df_check_thrput = df[((df[throughput_kpi].isna())\r\n | (pd.to_numeric(df[throughput_kpi], errors='coerce') <= 0) )\r\n &\r\n ((df[payload_kpi] > 0)\r\n #commented as avail doesn't correlate directly to payload or throughput\r\n #| (pd.to_numeric(df[avail_kpi], errors='coerce') > 0)\r\n )\r\n ]\r\n df_check_thrput = df_check_thrput.reset_index(drop=True)\r\n\r\n return df_check_thrput\r\n\r\ndef check_payload(df:pd.DataFrame, kpi_dict):\r\n throughput_kpi = kpi_dict['thrput']\r\n payload_kpi = kpi_dict['payload']\r\n avail_kpi = kpi_dict['avail']\r\n\r\n #throughputMBps = round(df[throughput_kpi]/8/1024,2)\r\n df_check_payload = df[((df[payload_kpi].isna())\r\n | (df[payload_kpi] <= 0))\r\n &\r\n (round(pd.to_numeric(df[throughput_kpi], errors='coerce')/8/1024,2) > 0)\r\n ]\r\n df_check_payload = df_check_payload.reset_index(drop=True)\r\n return df_check_payload\r\n\r\ndef check_avail(df:pd.DataFrame, kpi_dict):\r\n throughput_kpi = kpi_dict['thrput']\r\n payload_kpi = kpi_dict['payload']\r\n avail_kpi = kpi_dict['avail']\r\n\r\n df_check_avail = df[((df[avail_kpi].isna())\r\n | (pd.to_numeric(df[avail_kpi], errors='coerce') <= 0))\r\n &\r\n ((round(pd.to_numeric(df[throughput_kpi], errors='coerce')/8/1024,2) > 0)\r\n | (df[payload_kpi] > 0))\r\n ]\r\n\r\n df_check_avail = df_check_avail.reset_index(drop=True)\r\n return df_check_avail\r\n\r\ndef check_all_KPIs(df:pd.DataFrame, kpi_dict):\r\n throughput_kpi = kpi_dict['thrput']\r\n payload_kpi = kpi_dict['payload']\r\n avail_kpi = kpi_dict['avail']\r\n\r\n df_check_all_KPIs = df[((df[avail_kpi].isna())\r\n | (pd.to_numeric(df[avail_kpi], errors='coerce') <= 0))\r\n &\r\n ((df[throughput_kpi].isna())\r\n | (round(pd.to_numeric(df[throughput_kpi], errors='coerce')/8/1024,2) <= 0))\r\n &\r\n ((df[payload_kpi].isna())\r\n | (df[payload_kpi] <= 0))\r\n ]\r\n\r\n df_check_all_KPIs = df_check_all_KPIs.reset_index(drop=True)\r\n return df_check_all_KPIs\r\n\r\n\r\ndef summarize_all_KPIs_count(df:pd.DataFrame, kpi_dict):\r\n df_summarize_all_KPIs_count = df_check_all_KPIs.pivot_table(values='Time', index='SITE', aggfunc=pd.Series.count, )\r\n # df_summarize_all_KPIs_count.sort_values(by='Time', ascending=False)\r\n df_summarize_all_KPIs_count = df_summarize_all_KPIs_count.reset_index()\r\n\r\n\r\n return df_summarize_all_KPIs_count\r\n\r\ndef excludeNotSOACsites( df_raw_input: pd.DataFrame\r\n ,df_morning: pd.DataFrame\r\n ,i):\r\n\r\n if len(df_raw_input.index) > 0:\r\n # print(\"Adding SiteID column with this format TXXXX ...\")\r\n df_raw_input['SiteID'] = df_raw_input['SITE'].str.extract(r'([A-Z]\\d{4})') #to unify the site id format to join\r\n\r\n #print(\"Merging data frames for SOAC check of \", i , \"G ...\")\r\n df_merged = pd.merge(df_raw_input, df_morning, on='SiteID', how='inner')\r\n\r\n if i == 2: #2G\r\n df_merged = df_merged[df_merged['2G SOAC Date'].notnull()]\r\n elif i == 3: #3G\r\n df_merged = df_merged[(df_merged['U2100 SOAC Date'].notnull()) | (df_merged['U900 SOAC Date'].notnull())]\r\n elif i == 4: #4G\r\n df_merged = df_merged[(df_merged['L1800 SOAC Date'].notnull()) | (df_merged['L2600 SOAC Date'].notnull())\r\n | (df_merged['L900 SOAC Date'].notnull())]\r\n return df_merged\r\n else:\r\n return df_raw_input #just return it without any modification\r\n\r\ndef excludeDeactivatedSites(df_raw_input: pd.DataFrame\r\n ,df_deactivateList: pd.DataFrame\r\n ,i):\r\n\r\n if len(df_raw_input.index) > 0:\r\n # print(\"Adding SiteID column with this format TXXXX ...\")\r\n df_raw_input['SiteID'] = df_raw_input['SITE'].str.extract(r'([A-Z]\\d{4})') #to unify the site id format to join\r\n df_deactivateList['SiteID'] = df_deactivateList['MOENTITYNAME'].str.extract(r'([A-Z]\\d{4})') #to unify the site id format to join\r\n\r\n df_merged = pd.merge(df_raw_input, df_deactivateList, on='SiteID', how='left')\r\n\r\n df_deactived_sites = df_merged[df_merged['MOENTITYNAME'].notnull()]\r\n if len(df_deactived_sites.index) > 0:\r\n print('Excluding ', len(df_deactived_sites.index), ' number of deactivated sites.')\r\n print('samples:')\r\n print(df_deactived_sites)\r\n print('before deactivate: ', len(df_merged.index))\r\n\r\n df_merged = df_merged[df_merged['MOENTITYNAME'].isnull()] # where site is not in deactivated list\r\n\r\n df_merged.reset_index()\r\n\r\n if len(df_deactived_sites.index) > 0:\r\n print('After deactivate: ', len(df_merged.index))\r\n\r\n return df_merged\r\n else:\r\n return df_raw_input #just return it without any modification\r\n\r\ndef excludeUnsyncSites( df_raw_input: pd.DataFrame\r\n ,df_unsync: pd.DataFrame\r\n ,i):\r\n\r\n if len(df_raw_input.index) > 0:\r\n # print(\"Adding SiteID column with this format TXXXX ...\")\r\n regexPattern = r'([A-Z]\\d{4})'\r\n df_raw_input['SiteID'] = df_raw_input['SITE'].str.extract(regexPattern) #to unify the site id format to join\r\n df_unsync = df_unsync.melt(id_vars=['site_id'], var_name='DateHour', value_name='unsync_flag')\r\n df_unsync['SiteID'] = df_unsync['site_id'].str.extract(regexPattern) #to unify the site id format to join\r\n\r\n #reformatting time column to match with raw data.\r\n df_unsync['Time'] = df_unsync['DateHour'].str[:4] + '-' + df_unsync['DateHour'].str[4:6] + '-' + df_unsync['DateHour'].str[6:8] \\\r\n + ' ' + df_unsync['DateHour'].str[8:10] + ':00:00'\r\n\r\n #print('Total number of raw data rows:', len(df_raw_input.index))\r\n #print(\"Merging data frames for SOAC check of \", i , \"G ...\")\r\n\r\n print(df_raw_input.columns)\r\n print(df_unsync.columns)\r\n df_merged = pd.merge(df_raw_input, df_unsync, on=['SiteID','Time'], how='left')\r\n\r\n #simply remove the ones with unsync flag = 1\r\n df_merged = df_merged[df_merged['unsync_flag'] != 1]\r\n df_merged.reset_index()\r\n\r\n return df_merged\r\n else:\r\n return df_raw_input #just return it without any modification\r\n\r\n\r\ndef readMorningReport():\r\n print(\"Reading morning report...\")\r\n df_morning = pd.read_excel(r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122\\RAN Morning Report 2021-01-23.xlsx'\r\n , sheet_name='On-Air Sites') #, skiprows=1\r\n\r\n df_morning = df_morning[['Site ID', '2G SOAC Date', 'U2100 SOAC Date', 'U900 SOAC Date', 'L1800 SOAC Date'\r\n ,'L2600 SOAC Date', 'L900 SOAC Date']]\r\n\r\n df_morning.rename(columns={'Site ID': 'SiteID'}, inplace=True)\r\n\r\n return df_morning\r\n\r\ndef readDeactivatedSitesReport():\r\n print(\"Reading Deactivated list report...\")\r\n df_deactivatedSites = pd.read_excel(r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122\\MAPS_active_deactive_lilst.xls'\r\n , sheet_name='title_1')\r\n\r\n return df_deactivatedSites\r\n\r\ndef readUnsyncList():\r\n print(\"Reading Unsync list ...\")\r\n df_UnsyncList = pd.read_excel(r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122\\unsync_comp_20210122.xlsx'\r\n , sheet_name='unsync_list')\r\n\r\n df_UnsyncList = df_UnsyncList.drop(['DAILY_SUMMATION'],axis=1)\r\n\r\n return df_UnsyncList\r\n\r\ndef read4GThrputCounters():\r\n print(\"Reading 4G Throughput files ...\")\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\ericsson_4g_thrput_counters_2019122911_eae6362b63244251bbc6d161ada2e2f0.csv'\r\n df_4gthrput_ericsson = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_4gthrput_ericsson.rename(columns={\"Ericsson_ENodeB\": \"ENODEB\"}, inplace=True)\r\n print(df_4gthrput_ericsson.columns)\r\n\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\huawei_4g_thrput_counters_2019122911_ff9bb89a509044d0997ddee5aced7b80.csv'\r\n df_4gthrput_huawei = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_4gthrput_huawei.rename(columns={\"Huawei_LTE_eNodeB\": \"ENODEB\"}, inplace=True)\r\n print(df_4gthrput_huawei.columns)\r\n\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\nokia_4g_thrput_counters_2019122911_af2d9f49651d4e54bc6d79075f00a4d6.csv'\r\n df_4gthrput_nokia = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_4gthrput_nokia.rename(columns={\"NSN_FDD_LNBTS\": \"ENODEB\"}, inplace=True)\r\n print(df_4gthrput_nokia.columns)\r\n\r\n df_merged_1 = pd.merge(df_4gthrput_ericsson, df_4gthrput_huawei, on=['ENODEB','Time'], how='outer')\r\n\r\n df_merged_2 = pd.merge(df_merged_1, df_4gthrput_nokia, on=['ENODEB','Time'], how='outer')\r\n\r\n print(df_merged_2.columns)\r\n\r\n regexPattern = r'([A-Z]{1}\\d{4}[A-Z])'\r\n df_merged_2['SiteID'] = df_merged_2['ENODEB'].str.extract(regexPattern) # to unify the site format to join\r\n\r\n return df_merged_2\r\n\r\n\r\ndef read2GThrputCounters():\r\n print(\"Reading 2G Throughput files ...\")\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\ericsson_2g_thrput_counters_2019122911_c8e1db436bfa4c059bb15c4da2aeeee3.csv'\r\n df_2gthrput_ericsson = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_2gthrput_ericsson.rename(columns={\"Ericsson_BTS\": \"BTS\"}, inplace=True)\r\n print(df_2gthrput_ericsson.columns)\r\n\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\huawei_2g_thrput_counters_2019122911_a3aa33a1688a4253ba714be36d194784.csv'\r\n df_2gthrput_huawei = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_2gthrput_huawei.rename(columns={\"Huawei_BTS\": \"BTS\"}, inplace=True)\r\n print(df_2gthrput_huawei.columns)\r\n\r\n filename = r'E:\\data\\data_verification\\20191227\\thrput counters\\nokia_2g_thrput_counters_2019122911_720b161101734ef5af8929f10ced1952.csv'\r\n df_2gthrput_nokia = pd.read_csv(filename, low_memory=False, skiprows=1, thousands=r',', index_col=None, header=0)\r\n df_2gthrput_nokia.rename(columns={\"NSN_BTS\": \"BTS\"}, inplace=True)\r\n print(df_2gthrput_nokia.columns)\r\n\r\n df_merged_1 = pd.merge(df_2gthrput_ericsson, df_2gthrput_huawei, on=['BTS','Time'], how='outer')\r\n\r\n df_merged_2 = pd.merge(df_merged_1, df_2gthrput_nokia, on=['BTS','Time'], how='outer')\r\n\r\n print(df_merged_2.columns)\r\n\r\n regexPattern = r'([A-Z]{1}\\d{4})'\r\n df_merged_2['SiteID'] = df_merged_2['BTS'].str.extract(regexPattern) # to unify the site format to join\r\n\r\n return df_merged_2\r\n\r\n\r\ndef include4GThroughputCountres(df_check_thrput, df_4g_thrput):\r\n print(\"Including 4g throughput counters...\")\r\n df_merged = pd.merge(df_check_thrput, df_4g_thrput, on=['SiteID', 'Time'], how='left')\r\n\r\n return df_merged\r\n\r\ndef include2GThroughputCountres(df_check_thrput, df_2g_thrput):\r\n print(\"Including 2g throughput counters...\")\r\n df_merged = pd.merge(df_check_thrput, df_2g_thrput, on=['SiteID', 'Time'], how='left')\r\n\r\n return df_merged\r\n\r\n\r\n#start here\r\n\r\nprint('Started at: ', datetime.datetime.now())\r\n\r\nwriter = pd.ExcelWriter(r'D:\\workspace_python\\main_kpi_verifier\\data\\20210122\\verification_report_20210122.xlsx')\r\n\r\n\r\n# df_4g_thrput = read4GThrputCounters()\r\n# df_2g_thrput = read2GThrputCounters()\r\ndf = readFiles()\r\ndf_morning = readMorningReport()\r\ndf_deactivatedList = readDeactivatedSitesReport()\r\ndf_unsyncList = readUnsyncList()\r\n\r\nprint('Now verifying data...')\r\n\r\ntotal_rows = len(df.index)\r\n\r\narr_result_summary_data = []\r\n\r\nprint('Total rows: ', total_rows)\r\narr_result_summary_data.append(['Total Rows',total_rows])\r\n\r\ndf_result_summary = pd.DataFrame()\r\ndf_result_summary.to_excel(writer, sheet_name='Test Result Summary')\r\n\r\n# 1) site count issues\r\ndf_count_check = check_count(df)\r\nrowsWithCountIssue = len(df_count_check.index)\r\nprint('Rows with count issue: ', rowsWithCountIssue)\r\ndf_count_check.to_excel(writer, sheet_name='Count check - less than 10900')\r\narr_result_summary_data.append(['Rows with count issue',rowsWithCountIssue])\r\n\r\n# 2) per KPI and per technology\r\nfor i in range(2,5):\r\n print('Verifying ' + str(i) + 'G KPIs...' )\r\n tech_label = str(i) + 'g_kpis'\r\n tech_kpi_dict = kpi_dict[tech_label]\r\n\r\n df_check_thrput = check_thrput(df, tech_kpi_dict)\r\n df_check_payload = check_payload(df, tech_kpi_dict)\r\n df_check_avail = check_avail(df, tech_kpi_dict)\r\n df_check_all_KPIs = check_all_KPIs(df, tech_kpi_dict)\r\n df_summarize_all_KPIs_count = summarize_all_KPIs_count(df_check_all_KPIs, tech_kpi_dict)\r\n\r\n print(\"Excluding Not SOAC sites for \" + str(i) + \"G ...\")\r\n df_check_thrput = excludeNotSOACsites(df_check_thrput, df_morning,i)\r\n df_check_payload = excludeNotSOACsites(df_check_payload, df_morning,i)\r\n df_check_avail = excludeNotSOACsites(df_check_avail, df_morning,i)\r\n df_check_all_KPIs = excludeNotSOACsites(df_check_all_KPIs, df_morning,i)\r\n df_summarize_all_KPIs_count = excludeNotSOACsites(df_summarize_all_KPIs_count, df_morning,i)\r\n\r\n if i == 2:\r\n # deactived sites are only available for 2G at the moment\r\n print(\"Excluding deactivated sites...\")\r\n df_check_thrput = excludeDeactivatedSites(df_check_thrput, df_deactivatedList, i)\r\n df_check_payload = excludeDeactivatedSites(df_check_payload, df_deactivatedList, i)\r\n df_check_avail = excludeDeactivatedSites(df_check_avail, df_deactivatedList, i)\r\n df_check_all_KPIs = excludeDeactivatedSites(df_check_all_KPIs, df_deactivatedList, i)\r\n df_summarize_all_KPIs_count = excludeDeactivatedSites(df_summarize_all_KPIs_count, df_deactivatedList, i)\r\n\r\n # adding 2g throughput counters for ease the investigation of the issues\r\n # df_check_thrput = include2GThroughputCountres(df_check_thrput, df_2g_thrput)\r\n\r\n print(\"Excluding Unsync sites for \" + str(i) + \"G ...\")\r\n df_check_thrput = excludeUnsyncSites(df_check_thrput, df_unsyncList,i)\r\n df_check_payload = excludeUnsyncSites(df_check_payload, df_unsyncList,i)\r\n df_check_avail = excludeUnsyncSites(df_check_avail, df_unsyncList,i)\r\n df_check_all_KPIs = excludeUnsyncSites(df_check_all_KPIs, df_unsyncList,i)\r\n\r\n # if i == 4: # adding 4g throughput counters for ease the investigation of the issues\r\n # df_check_thrput = include4GThroughputCountres(df_check_thrput, df_4g_thrput)\r\n\r\n ##Because summarized sheet doesn't have a datetime column, it cannot be sent to unsync exclusion method\r\n ##df_summarize_all_KPIs_count = excludeUnsyncSites(df_summarize_all_KPIs_count, df_unsyncList,i)\r\n\r\n df_check_thrput.reset_index(inplace=True, drop=True)\r\n df_check_payload.reset_index(inplace=True, drop=True)\r\n df_check_avail.reset_index(inplace=True, drop=True)\r\n df_check_all_KPIs.reset_index(inplace=True, drop=True)\r\n df_summarize_all_KPIs_count.reset_index(inplace=True, drop=True)\r\n\r\n rowsWithThroughputIssue = len(df_check_thrput.index)\r\n rowsWithPayloadIssue = len(df_check_payload.index)\r\n rowsWithAvailIssue = len(df_check_avail.index)\r\n rowsWithAllKPIsIssue = len(df_check_all_KPIs.index)\r\n rowsWithSummarizedAllKPIsIssue = len(df_summarize_all_KPIs_count.index)\r\n\r\n testThrput = 'Rows with ' + str(i) + 'g throughput issue: '\r\n testPayload = 'Rows with ' + str(i) + 'g payload issue: '\r\n testAvail = 'Rows with ' + str(i) + 'g availability issue: '\r\n testAllKpis = 'Rows with all ' + str(i) + 'g KPIs issue: '\r\n testSummarizedAllKpis = 'Sites with all ' + str(i) + 'g KPIs issue: '\r\n\r\n print(testThrput , rowsWithThroughputIssue)\r\n print(testPayload, rowsWithPayloadIssue)\r\n print(testAvail, rowsWithAvailIssue)\r\n print(testAllKpis, rowsWithAllKPIsIssue)\r\n print(testSummarizedAllKpis, rowsWithSummarizedAllKPIsIssue)\r\n\r\n arr_result_summary_data.append([testThrput, rowsWithThroughputIssue])\r\n arr_result_summary_data.append([testPayload, rowsWithPayloadIssue])\r\n arr_result_summary_data.append([testAvail, rowsWithAvailIssue])\r\n arr_result_summary_data.append([testAllKpis, rowsWithAllKPIsIssue])\r\n arr_result_summary_data.append([testSummarizedAllKpis, rowsWithSummarizedAllKPIsIssue])\r\n\r\n print('Exporting into excel report for ' + tech_label + '...')\r\n df_check_thrput.to_excel(writer, sheet_name='Throughput ' + str(i) + 'g issues')\r\n df_check_payload.to_excel(writer, sheet_name='Payload ' + str(i) + 'g issues')\r\n df_check_avail.to_excel(writer, sheet_name='Availability ' + str(i) + 'g issues')\r\n\r\n# if i != 4: #because for 4G, we have so many sites which don't have support for 4G at all. so no need to report them as issue. morever, they put heavy burden on the program and the output file.\r\n df_check_all_KPIs.to_excel(writer, sheet_name='All ' + str(i) + 'g KPIs issues')\r\n df_summarize_all_KPIs_count.to_excel(writer, sheet_name='Summarized ' + str(i) + 'g Sites with issues')\r\n\r\ndf_result_summary = pd.DataFrame(data=arr_result_summary_data, columns=['Test', 'Result'])\r\ndf_result_summary.to_excel(writer, sheet_name='Test Result Summary')\r\n\r\nwriter.save()\r\n\r\nprint('All Completed.')\r\nprint('Completed at: ', datetime.datetime.now())\r\n","repo_name":"network-performance-repo/Main-KPIs-verifier","sub_path":"Verifier.py","file_name":"Verifier.py","file_ext":"py","file_size_in_byte":21259,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"} +{"seq_id":"33464327014","text":"import logging\nimport os\n\nfrom sqlalchemy.engine import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nlogger = logging.getLogger(__name__)\n\nengine = None\nSesaion = None\n\n_cockroach_engine = None\n_cockroach_session = None\n\n\ndef get_cockroach_engine():\n global _cockroach_engine\n if _cockroach_engine is None:\n connection_string = os.environ.get(\"COCKROACH_CONNECTION_STRING\")\n if not connection_string:\n logger.error(\"No connection string available; please set COCKROACH_CONNECTION_STRING environment variable.\")\n exit()\n _cockroach_engine = create_engine(connection_string)\n return _cockroach_engine\n\n\ndef get_cockroach_sessionmaker():\n global _cockroach_session\n if _cockroach_session is None:\n engine = get_cockroach_engine()\n _cockroach_session = sessionmaker(engine)\n return _cockroach_session\n","repo_name":"f3peakcity/f3_bot","sub_path":"sheets_task/src/sheets_task/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"37262176432","text":"# import gi\n# gi.require_version('Gtk', '3.0')\n# from gi.repository import Gtk\n#\n# win = Gtk.Window()\n# win.connect(\"delete-event\", Gtk.main_quit)\n# win.show_all()\n# Gtk.main()\n\n\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk,Gdk\n\nclass LineTypeSelector(Gtk.ListBoxRow):\n def __init__(self,id,cbs):\n # self.cbs = {\n # 'onRemove':\n # 'onTypeChoose':\n # }\n self.id = id\n self.cbs = cbs\n\n super(Gtk.ListBoxRow, self).__init__()\n\n hbox = Gtk.Box(spacing=6)\n self.hasLightBox = False\n close = Gtk.Button.new_with_label(\"x\")\n close.connect(\"clicked\", self.on_close_click)\n hbox.pack_start(close, False, False, 0)\n\n button1 = Gtk.RadioButton.new_with_label_from_widget(None, \"Dividing\")\n button1.connect(\"toggled\", self.on_button_toggled, \"P\" )\n hbox.pack_start(button1, False, False, 0)\n\n button2 = Gtk.RadioButton.new_from_widget(button1)\n button2.set_label(\"Front\")\n button2.connect(\"toggled\", self.on_button_toggled, \"R\" )\n hbox.pack_start(button2, False, False, 0)\n self.add(hbox)\n self.hbox = hbox\n # hbox.pack_start(LineColorSelector(), False, False, 0)\n def on_button_toggled(self, button, name):\n if button.get_active():\n state = \"on\"\n if name == \"P\":\n if(self.hasLightBox):\n print(\"destroy\")\n self.lightBox.destroy()\n else:\n print(\"front\")\n self.hasLightBox = True\n self.lightBox = LineColorSelector(lambda name: self.cbs['onTypeChoose'](self.id,name))\n self.hbox.pack_start(self.lightBox, False, False, 0)\n self.hbox.show_all()\n self.cbs['onTypeChoose'](self.id,name)\n else:\n state = \"off\"\n print(\"Button\", name, \"was turned\", state)\n def toggleGreyscale(self, *args):\n global greyscale\n greyscale = ~ greyscale\n\n def on_close_click(self,button):\n if 'onRemove' in self.cbs:\n self.cbs['onRemove'](self.id)\n True\n def rm(self):\n self.destroy()\nclass LineColorSelector(Gtk.Box):\n def get_color(self,rgb):\n colorh=rgb\n color=Gdk.RGBA()\n color.parse(colorh)\n color.to_string()\n return color\n def __init__(self,cb):\n super(Gtk.Box, self).__init__(spacing=6)\n self.cb = cb\n hbox = self\n button1 = Gtk.RadioButton.new_with_label_from_widget(None, \"R\")\n # button1.modify_bg(Gtk.StateType.PRELIGHT, Gdk.color_parse('#234fdb'))\n\n button1.override_background_color(Gtk.StateFlags.NORMAL, self.get_color(\"#FF0000\"))\n button1.connect(\"toggled\", self.on_button_toggled, \"R\")\n hbox.pack_start(button1, False, False, 0)\n\n button2 = Gtk.RadioButton.new_with_mnemonic_from_widget(button1,\"Y\")\n button2.override_background_color(Gtk.StateFlags.NORMAL, self.get_color(\"#FFFF00\"))\n button2.connect(\"toggled\", self.on_button_toggled, \"Y\")\n hbox.pack_start(button2, False, False, 0)\n\n button3 = Gtk.RadioButton.new_with_mnemonic_from_widget(button1,\"G\")\n button3.override_background_color(Gtk.StateFlags.NORMAL, self.get_color(\"#00FF00\"))\n button3.connect(\"toggled\", self.on_button_toggled, \"G\")\n hbox.pack_start(button3, False, False, 0)\n\n def on_button_toggled(self, button, name):\n if button.get_active():\n state = \"on\"\n else:\n state = \"off\"\n self.cb(name)\n print(\"Button\", name, \"was turned\", state)\n","repo_name":"partus/traffic-violation-detection-demo","sub_path":"ui2.py","file_name":"ui2.py","file_ext":"py","file_size_in_byte":3644,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"27"} +{"seq_id":"70670105991","text":"import numpy as np #Importamos matrices\r\nimport random #Importamos números aleatorios\r\n\r\nclass Grafo(): #Creamos la clase de los vectores\r\n def __init__(self,nodos): #Fijamos parametros principales\r\n\r\n self.nodos = nodos #Volvemos el número de nodos una variable global en la clase\r\n self.nodos_debug = 5\r\n self.vector = np.zeros((nodos,nodos)) #Creamos una matriz cuadrada con todos los valores en 0 coincidiendo el número de nodos y el tamaño de las filas y columnas. Esta sera una matriz de adyascencias\r\n\r\n self.vector_debug = np.array([[0,10,-1,30,100],\r\n [10,0,50,-1,-1],\r\n [-1,50,0,20,10],\r\n [30,-1,20,0,60],\r\n [100,-1,10,60,0]]) #Creamos una matriz para debug\r\n\r\n self.dist_min=0\r\n self.camino_min=[]\r\n\r\n def imprimir(self): #Creamos una función que imprima la matriz\r\n\r\n print(self.vector) #Imprimimos la matriz\r\n \r\n def imprimir_debug(self): #Creamos una función que imprima la matriz debug\r\n\r\n print(self.vector_debug) #Imprimimos la matriz debug\r\n\r\n def fijar_valores(self): #Creamos la clase para fijar valores\r\n for i in range(self.nodos): #Recorremos la matriz\r\n for j in range(self.nodos):\r\n if j != i and self.vector[i,j] == 0: #Comprobamos si es una de la diagonal o ya se ha rellenado\r\n cero = True \r\n while cero: #Hacemos un bucle\r\n dist = int(input('Distancia entre el nodo '+str(i+1)+' y el '+str(j+1)+' (si no estan conectados escribe -1) ')) #Le preguntamos la distancia entre 2 vectores\r\n if dist != 0: #Si la respuesta no es 0\r\n cero = False #Paramos el bucle\r\n self.vector[i,j] = dist #Fijamos la distancia entre estos 2 a la matriz\r\n self.vector[j,i] = dist #Como es un esquema sin direcciones es una matriz simetrica, por lo que la fijamos a la de la posición opuesta\r\n else: #Si la respuesta es 0\r\n print()\r\n print('La distancia entre 2 nodos no puede ser 0') # Decimos que es imposible y como no hemos quitado el bucle volveremos a preguntar\r\n print()\r\n \r\n def texto(self): #Función que pasa de txt a matriz\r\n\r\n f = open ('matriz.txt','r') #Abrimos el archivo de texto\r\n mensaje = f.read() #Coje lo que contiene el archivo\r\n f.close() #Cerramos el archivo\r\n activa='' #Variable en la que iremos almacenando cada división poco a poco\r\n filas=[] #Lista en la que se almacena las divisiones\r\n for i in mensaje: #Por cada letra del mensaje\r\n if i != '\\n': #Si no hay un enter\r\n activa = activa+i #Archivamos letras\r\n if i == ']': #Las divisiones estan hechas con ] por lo que si estamos en ese caracter\r\n filas.append(activa) #Metemos la división en la lista\r\n activa='' #Reseteamos la variable de almacenamiento\r\n\r\n x=0 #Fijamos las variables que diran la posición de la matriz en la que estamos\r\n y=0\r\n for j in filas: #Recorremos la lista\r\n for i in j: #Por cada letra de la división\r\n if i == ',' or i == ']': #Miramos si hay alguna división\r\n self.vector[x,y]=int(activa) #Meteriamos la división en la matriz\r\n y+=1 #Pasariamos a la siguiente casilla\r\n if y>self.nodos-1:\r\n y=0\r\n x+=1\r\n activa='' #Reseteamos la variable de almacenamiento\r\n elif i != '[': #Saltamos el caracte [\r\n activa = activa+i\r\n\r\n def generar_vector(self):\r\n\r\n file = open(\"matriz.txt\", \"w\") #Abrimos o creamos el archivo matriz.txt\r\n matrizG = np.zeros((self.nodos,self.nodos)) #Creamos una matriz\r\n for i in range(self.nodos): #Durante todos los nodos\r\n for j in range(self.nodos): #En fila y columnas\r\n if matrizG[i,j] == 0 and j!= i: #Si no se ha puesto ningún número\r\n matrizG[i,j] = str(random.randint(20,200)) #Sera un número aleatorio entre 20 y 200\r\n matrizG[j,i] = matrizG[i,j] #En el opuesto tambien ya que es simétrica\r\n\r\n #La escribimos\r\n\r\n for i in matrizG: \r\n file.write('[')\r\n for x,j in enumerate(i):\r\n if x != 0:\r\n file.write(',')\r\n file.write(str(int(j)))\r\n\r\n file.write(']\\n') #Esto pone un ] y un enter\r\n file.close()\r\n \r\n def TSP(self,inicio):\r\n\r\n self.camino_min=[]\r\n self.dist_min=-1\r\n camino = []\r\n for i in range(self.nodos-1):\r\n camino.append(0)\r\n while camino[0] != self.nodos:\r\n poder = True\r\n for x,i in enumerate(camino):\r\n for y,j in enumerate(camino):\r\n if i==j and x != y or j == inicio:\r\n poder = False\r\n if poder:\r\n self.comprobamos(inicio,camino)\r\n camino[-1]+=1\r\n for i in range(len(camino)):\r\n if camino[len(camino)-i-1]==self.nodos:\r\n if len(camino)-i-1 != 0:\r\n camino[len(camino)-i-1]=0\r\n camino[len(camino)-i-2]+=1\r\n print(self.camino_min)\r\n print(self.dist_min)\r\n\r\n def comprobamos(self,inicio,camino):\r\n\r\n camino_completo=camino.copy()\r\n camino_completo.append(inicio)\r\n camino_completo.insert(0,inicio)\r\n dist=0\r\n for i in range(len(camino_completo)-1):\r\n dist+= self.vector[camino_completo[i],camino_completo[i+1]]\r\n if dist 5:\n msg = \"Error: Too many problems.\"\n return msg\n else:\n for problemValues in inputValues:\n counterFor = 1\n tempValue = \"\"\n primaryValue = True\n for problemValue in problemValues:\n if (problemValue.isnumeric() == True and primaryValue == True): \n tempValue += problemValue\n counterFor += 1\n elif problemValue.isnumeric() == False:\n if problemValue == \" \":\n if tempValue != \"\":\n if len(tempValue) <= 4:\n primaryValues.append(tempValue)\n primaryValue = False\n counterFor += 1\n tempValue = \"\"\n else:\n return \"Error: Numbers cannot be more than four digits.\"\n elif problemValue != \" \":\n if problemValue == \"+\" or problemValue == \"-\":\n operatorValue.append(problemValue)\n elif problemValue.isalpha() == True:\n return \"Error: Numbers must only contain digits.\"\n else: \n return \"Error: Operator must be '+' or '-'.\"\n if (problemValue.isnumeric() == True and primaryValue == False): \n tempValue += problemValue\n counterFor += 1\n if counterFor == (len(problemValues)-1):\n if len(tempValue) <= 4:\n secondaryValues.append(tempValue)\n else:\n return \"Error: Numbers cannot be more than four digits.\"\n return primaryValues + operatorValue + secondaryValues\n \n#Checked\n#actual = arithmetic_arranger([\"3 / 855\", \"3801 - 2\", \"45 + 43\", \"123 + 49\"])\n#actual = arithmetic_arranger([\"44 + 815\", \"909 - 2\", \"45 + 43\", \"123 + 49\", \"888 + 40\", \"653 + 87\"])\n#actual = arithmetic_arranger([\"3 / 855\", \"3801 - 2\", \"45 + 43\", \"123 + 49\"])\n#actual = arithmetic_arranger([\"98 + 3g5\", \"3801 - 2\", \"45 + 43\", \"123 + 49\"])\n#actual = arithmetic_arranger([\"24 + 8521\", \"3801 - 2\", \"45 + 43\", \"123 + 40009\"])\nactual = arithmetic_arranger([\"3 + 855\", \"3801 - 2\", \"45 + 43\", \"123 + 49\"])\nprint(actual)\n","repo_name":"nano0018/freecodecamp-projects","sub_path":"Python Data Science/arithmetic_arranger.py","file_name":"arithmetic_arranger.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"23108773798","text":"#!/usr/local/bin/python\n#-*- coding: UTF-8 -*-\n\nfrom ui import *\nimport time\nfrom PyQt4 import QtCore, QtGui ,QtNetwork\nfrom PyQt4.QtCore import *\nfrom ctypes import *\nfrom PyQt4.QtGui import *\n#import QtNetwork\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\nimport url_cms_QTX #CMS指纹识别\n\n#import win32ui\n#user32 = windll.LoadLibrary('user32.dll') # 加载动态链接库\ntry:\n _fromUtf8 = QtCore.QString.fromUtf8\nexcept AttributeError:\n _fromUtf8 = lambda s: s\n\nclass Start(QtGui.QMainWindow):\n def __init__(self, parent=None):\n QtGui.QWidget.__init__(self, parent)\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n flags = 0 #设置禁止最大化\n flags|= Qt.WindowMinimizeButtonHint #设置禁止最大化\n self.setWindowFlags(flags) #设置禁止最大化\n self.ini() #初始化\n\n self.threads0 = url_cms_QTX.url_cms_QTX(self.ui,self.model) #CMS指纹识别\n self.threads0.start()\n\n #初始化\n def ini(self): #初始化\n self.setWindowTitle(u'CMS(KEY关键字--文件MD5)指纹识别 本程序采用单线程识别 V:1.0 BY:神龙 农民工写代码 http://www.hacked90.com/') #设置标题\n self.model = QStandardItemModel()\n self.model.setColumnCount(4) #列\n self.model.setRowCount(0) #行 len(node)\n self.model.setHorizontalHeaderLabels([u'URL地址',u'CMS名称',u'识别方式',u'URL链接文件(地址)',u'关键字KEY or 文件MD5'])\n self.ui.tableView_1.setModel(self.model)\n self.ui.tableView_1.setColumnWidth(0,120) #设置表格的各列的宽度值\n self.ui.tableView_1.setColumnWidth(1,80) #设置表格的各列的宽度值\n self.ui.tableView_1.setColumnWidth(2,60) #设置表格的各列的宽度值\n self.ui.tableView_1.setColumnWidth(3,150) #设置表格的各列的宽度值\n self.ui.tableView_1.setColumnWidth(4,200) #设置表格的各列的宽度值\n for i in range(0): #调整行高度 len(node)\n self.ui.tableView_1.setRowHeight(i, 20)\n self.ui.tableView_1.setEditTriggers(QTableWidget.NoEditTriggers) #设置表格的单元为只读属性，即不能编辑\n self.ui.tableView_1.setSelectionBehavior(QTableWidget.SelectRows) #点击选择是选择行//设置选中时为整行选中\n self.ui.tableView_1.setSelectionMode(QTableWidget.SingleSelection) #禁止多行选择\n self.ui.tableView_1.setAlternatingRowColors(True) #还是只可以选择单行（单列）\n #self.ui.tableView_1.verticalHeader().hide() #隐藏行头\n #self.ui.messagebox_textEdit.setEnabled(0) #给改成禁用\n self.ui.messagebox_textEdit.append(u\"CMS(KEY关键字--文件MD5)\\n指纹识别 V:1.0 \\nTIME:%s\"%(time.strftime('%Y.%m.%d-%H.%M.%S')))\n self.ui.webView.setUrl(QtCore.QUrl(\"http://hi.baidu.com/alalmn\"))\n self.ui.progressBar.setValue(0) #设置进度\n\nif __name__ == \"__main__\":\n app = QtGui.QApplication(sys.argv)\n #lang = QtCore.QTranslator()\n #lang.load(\"qt_zh_CN.qm\")\n #app.installTranslator(lang)#载入中文字体需要从qt安装目录里复制PyQt4\\translations\\qt_zh_CN.qm\n myapp = Start()\n myapp.show()\n sys.exit(app.exec_())\n\n","repo_name":"webxscan/Webcrawle","sub_path":"WEB指纹扫描FILE_MD5/界面版本CMS识别/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"zh","doc_type":"code","stars":20,"dataset":"github-code","pt":"27"} +{"seq_id":"39886527068","text":"import os.path\nimport datetime\nimport time\nfilename = \"test.txt\"\nhere = os.path.dirname(os.path.realpath(__file__))\nsubdir = \"data\"\ncompleteName = os.path.join(here,subdir,filename)\nfile = open(completeName, 'a')\n\nwhile True:\n now = datetime.datetime.now()\n dt_string = now.strftime(\"%d-%m-%Y %H:%M:%S\")\n print(now.strftime(\"%d-%m-%Y %H:%M:%S\"))\n file.write(str(dt_string)+ '\\n')\n file.close()\n time.sleep(86400) # every 24 hours the scripts run\n \n","repo_name":"robtec/k8s-python-exec-cli","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22156433011","text":"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport os\n#1print(os.getcwd())\n\n#\"Class\" attribute: 1 means approved, 0 means not approved\ndataset = pd.read_csv('Credit_Card_Applications.csv')\n\nX = dataset.iloc[:, :-1].values\n#print(type(X))\ny = dataset.iloc[:, -1].values\n\n#feature scaling\nfrom sklearn.preprocessing import MinMaxScaler\nsc = MinMaxScaler(feature_range=(0,1))\n#normalized X\nX = sc.fit_transform(X)\n\n#train the SOM with a third party library (https://pypi.org/project/MiniSom/#history), which is minisom.py\nfrom minisom import MiniSom\nsom = MiniSom(x = 10, y = 10, input_len= 15, sigma = 1.0, learning_rate=0.5)\n\n#initialize the weights\nsom.random_weights_init(X)\n\n#train the som\nsom.train_random(data = X, num_iteration=100)\n\n#visualize the result\nfrom pylab import bone, pcolor, colorbar, plot, show\nbone()\n#som.distance_map returns all MID (mean into winning node distances) for all nodes. The higher the more likely to be fraud (or outlier).\npcolor(som.distance_map().T)\ncolorbar()\n#add markers\nmarkers = ['o', 's']\ncolors = ['r', 'g']\nfor i, x in enumerate (X):\n w = som.winner(x)\n# if the customer gets approval, y[i] = 1 and markers[y[i]] = 's', if the customer did not get approval, y[i] = 0 and markers[y[i]] = 'o'\n plot(w[0] + 0.5, w[1] + 0.5, \n markers[y[i]], \n markeredgecolor = colors[y[i]],\n markerfacecolor = 'None',\n markersize = 10,\n markeredgewidth = 2)\nshow()\n\n#find the frauds\nmappings = som.win_map(X)\n#You need to manually determine the outlier winning node coordinate from the visualized figure\n#potentialFrauds = np.concatenate((mappings[7,4]), axis = 0)\npotentialFrauds = mappings[7,4]\n#unscale the elements of all customers\nunscaledPotentialFrauds = sc.inverse_transform(potentialFrauds)\n\n\n\n\n\n\n\n\n\n","repo_name":"kensaku-okada/DeepLearningA-Z","sub_path":"deepNNMain/Volume 2 - Unsupervised Deep Learning/Part 4 - Self Organizing Maps (SOM)/Section 16 - Building a SOM/Self_Organizing_Maps/mySom.py","file_name":"mySom.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"13768953836","text":"class Solution:\n def wordBreak(self, s, wordDict):\n \"\"\"\n :type s: str\n :type wordDict: List[str]\n :rtype: List[str]\n \"\"\"\n\n reserve = dict()\n\n def dfs(s, wordDict, start):\n\n if start in reserve.keys():\n return reserve[start]\n\n final = []\n\n alternative = []\n\n for word in wordDict:\n if len(word) <= len(s) and s[:len(word)] == word:\n alternative.append(word)\n\n if not len(alternative):\n return None\n\n for alt in alternative:\n if len(alt) == len(s):\n final.append([alt])\n continue\n\n next = dfs(s[len(alt):], wordDict, start + len(alt))\n if next:\n for t in next:\n final.append([alt] + t)\n reserve[start] = final\n return final\n\n re = dfs(s, wordDict, 0)\n if not re:\n return []\n\n return [' '.join(s) for s in re]\n\n\nif __name__ == \"__main__\":\n print(Solution().wordBreak(\"aaa\", [\"a\", \"aa\"]))\n","repo_name":"Apeoud/LeetCode","sub_path":"140_wordseg.py","file_name":"140_wordseg.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"35301177191","text":"#!/usr/bin/env python3.5\nimport pygame as pg\nimport sys\nimport pygame.locals as pg_local\nimport random\nimport time\nimport pygame.gfxdraw\nrandom.seed(int((time.time())%1000))\nclass Board:\n\n\tdef __init__(self, disp_surf = None, height = 480, width = 640, step = 20,\n\t\t \t\t\tback_color = pg.Color(143, 252, 131), \n\t \t\t\t\tline_color = pg.Color(255, 255, 255), \n\t \t\t\t\tline_thickness = 2, disp_board_grid = True):\n\t\tself._height = height\n\t\tself._width = width\n\t\tself._step = step\n\t\tself._back_color = back_color\n\t\tself._line_color = line_color\n\t\tself._disp_surf = disp_surf\n\t\tself._line_thickness = line_thickness\n\t\tself.disp_board_grid = disp_board_grid\n\n\tdef draw(self):\n\t\tself._disp_surf.fill(self._back_color)\n\t\tif self.disp_board_grid == True:\n\t\t\tfor i in range(0, self._width, self._step):\n\t\t\t\tpg.draw.line(self._disp_surf, self._line_color, (i, 0), (i, self._height), self._line_thickness)\n\t\t\tfor i in range(0, self._height, self._step):\n\t\t\t\tpg.draw.line(self._disp_surf, self._line_color, (0, i), (self._width, i), self._line_thickness)\n\n\tdef get_box_form(self):\n\t\treturn (int(self._width / self._step), int(self._height / self._step))\n\n\tdef conv_left_top(self, boxx, boxy):\n\t\treturn (boxx * self._step, boxy * self._step)\n\n\tdef conv_box(self, left, top):\n\t\treturn (int(left / self._step), int(top, self._step))\n\n\tdef box_dim(self):\n\t\treturn (self._step, self._step)\n\n\tdef get_dim(self):\n\t\treturn (self._width, self._height)\n\n\nclass Apple:\n\tdef __init__(self, disp_surf, board, snake):\n\t\tself._board = board\n\t\tself.snake = snake\n\t\tself._disp_surf = disp_surf\n\t\tself._horizontal_boxes, self._vertical_boxes = self._board.get_box_form()\n\t\tself._coords_of_apple = self.__generate_random()\n\t\tself._color_inner = pg.Color(252, 92, 68)\n\t\tself._length,self._width = self._board.box_dim()\n\t\tself._color_outer = pg.Color(178, 68, 51)\n\t\tself.coords = self._coords_of_apple\n\t\tself._radius = 8\n\n\tdef __generate_random(self):\n\t\tbody = self.snake.get_body()\n\t\tbody_x = [x[0] for x in body]\n\t\tbody_y = [x[1] for x in body]\n\t\tseq = [(i, j) for i in range(5, self._horizontal_boxes-5) for j in range(5, self._vertical_boxes-5) if not (i in body_x and j in body_y)]\n\t\t#print(\"Sequence\" + str(seq))\n\t\tch = random.choice(seq)\n\t\treturn (ch[0], ch[1])\n\n\tdef draw(self, generate_new = False):\n\t\tif generate_new == True:\n\t\t\tself._coords_of_apple = self.coords = self.__generate_random()\n\t\tcoords = self._board.conv_left_top(self._coords_of_apple[0], self._coords_of_apple[1])\n\t\tpg.draw.rect(self._disp_surf, self._color_outer, (coords[0], coords[1], self._length, self._width))\n\t\tpg.draw.rect(self._disp_surf, self._color_inner, (coords[0]+3, coords[1]+3, self._length-6, self._width-6))\n\t\t\"\"\"pg.gfxdraw.filled_circle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius, self._color_inner)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius, self._color_outer)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius-1, self._color_outer)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius-2, self._color_outer)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius, self._color_outer)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius-1, self._color_outer)\n\t\tpg.gfxdraw.aacircle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius-2, self._color_outer)\"\"\"\n\n\tdef get_coords(self):\n\t\treturn self._coords_of_apple\n\n\nclass Snake:\n\tdef __init__(self, disp_surf, board):\n\t\tself._disp_surf = disp_surf\n\t\tself._board = board\n\t\tself._horizontal_boxes, self._vertical_boxes = self._board.get_box_form()\n\t\tself._length,self._width = self._board.box_dim()\n\t\tself.head = self.__generate_random()\n\t\tself.body = [{'x': self.head[0], 'y':self.head[1]},\n\t\t\t\t\t{'x': self.head[0]-1, 'y':self.head[1]},\n\t\t\t\t\t{'x': self.head[0]-2, 'y':self.head[1]}]\n\t\tself._color_inner = pg.Color(38, 168, 15)\n\t\tself._color_outer = pg.Color(36, 104, 17)\n\t\tself.cur_dir = 'right'\n\t\tself._radius = 9\n\n\tdef specify_head(self, coords):\n\t\tself.head = coords\n\t\tself.body = [{'x': self.head[0], 'y':self.head[1]},\n\t\t\t\t\t{'x': self.head[0]-1, 'y':self.head[1]},\n\t\t\t\t\t{'x': self.head[0]-2, 'y':self.head[1]}]\n\tdef draw(self):\n\t\tfor i in range(len(self.body)):\n\t\t\tcoords = self._board.conv_left_top(self.body[i]['x'], self.body[i]['y'])\n\t\t\tpg.draw.rect(self._disp_surf, self._color_outer, (coords[0], coords[1], self._length, self._width))\n\t\t\tpg.draw.rect(self._disp_surf, self._color_inner, (coords[0]+3, coords[1]+3, self._length-6, self._width-6))\n\t\t\t#pg.gfxdraw.filled_circle(self._disp_surf, coords[0] + int(self._width/2), coords[1] + int(self._length/2), self._radius, self._color_inner)\n\n\tdef __generate_random(self):\n\t\treturn (random.randint(10, self._horizontal_boxes-10), \n\t\t\t\trandom.randint(10, self._vertical_boxes-10))\n\n\tdef cross_over(self):\n\t\tif self.head[0] < 0 or self.head[0] >= self._horizontal_boxes or \\\n\t\t self.head[1] < 0 or self.head[1] >= self._vertical_boxes:\n\t\t \treturn True\n\t\tfor i in range(1, len(self.body)):\n\t\t\tif self.body[i]['x'] == self.head[0] and self.body[i]['y'] == self.head[1]:\n\t\t\t\treturn True\n\t\treturn False\n\n\tdef remove_tail(self):\n\t\tif len(self.body) != 0:\n\t\t\t\tself.body.pop()\n\n\tdef push_coords(self, coords, on_tail = False):\n\t\tif on_tail == True:\n\t\t\tself.body.append({'x': coords[0], 'y':coords[1]})\n\t\t\treturn\n\t\tself.body.insert(0, {'x': coords[0], 'y': coords[1]})\n\t\tself.head = coords\n\n\tdef _get_possible(self, val):\n\t\tif val in ('up', 'down'):\n\t\t\treturn ['left', 'right']\n\t\telif val in ('left', 'right'):\n\t\t\treturn ['up', 'down']\n\n\tdef change_dir(self, new_dir):\n\t\tpossibles = self._get_possible(self.cur_dir)\n\t\tif not new_dir in possibles:\n\t\t\treturn\n\t\telse:\n\t\t\tself.cur_dir = new_dir\n\t\tself.make_changes()\n\n\tdef make_changes(self, remove_tail_bool = True):\n\t\tif remove_tail_bool == True:\n\t\t\tself.remove_tail()\n\t\tto_add = None\n\t\tif self.cur_dir == 'up':\n\t\t\tto_add = (self.head[0], self.head[1] - 1)\n\t\telif self.cur_dir == 'down': \n\t\t\tto_add = (self.head[0], self.head[1] + 1)\n\t\telif self.cur_dir == 'left':\n\t\t\tto_add = (self.head[0] - 1, self.head[1])\n\t\telif self.cur_dir == 'right':\n\t\t\tto_add = (self.head[0] + 1, self.head[1])\n\t\tself.push_coords(to_add)\n\n\tdef move(self, generate_tail = False):\n\t\tif generate_tail == False:\n\t\t\tself.make_changes()\n\t\telse:\n\t\t\tself.make_changes(remove_tail_bool = False)\n\n\tdef get_body(self):\n\t\tto_ret = []\n\t\tfor i in self.body:\n\t\t\tto_ret.append((i['x'], i['y']))\n\t\treturn to_ret\n\nclass ComicWrites:\n\tdef __init__(self, disp_surf, font_spec = \"./fonts/font_to_use.ttf\", font_size = 32):\n\t\tself.font_spec = font_spec\n\t\tself.font_size = font_size\n\t\tself.disp_surf = disp_surf\n\t\tself.__font_obj = pg.font.Font(self.font_spec, self.font_size)\n\n\tdef get_write(self, to_write, coords, anti_alias = True, text_color = pg.Color(46, 173, 53)):\n\t\ttext_surf_obj = self.__font_obj.render(to_write, anti_alias, text_color)\n\t\ttext_rect_obj = text_surf_obj.get_rect()\n\t\ttext_rect_obj.center = coords\n\t\tself.disp_surf.blit(text_surf_obj, text_rect_obj)\n\t\treturn self.disp_surf\n\nclass SnakeGame:\n\tdef __init__(self, frame_count = 30, save_moves = True, play_ai = False, disp_board_grid = True):\n\t\tpg.init()\n\t\tself.disp_surf = pg.display.set_mode((640, 480))\n\t\tpg.display.set_caption((\"Twister\"))\n\t\tself.board = Board(self.disp_surf, disp_board_grid = disp_board_grid)\n\t\tself.fps_clock = pg.time.Clock()\n\t\tself.FPS = frame_count\n\t\tself.apple = None\n\t\tself.snake = None\n\t\tself.score = 0\n\t\tself.state_capture = Capture(\"SnakeEvents.txt\")\n\t\tself.score_board = ComicWrites(self.disp_surf, font_size = 16)\n\t\tself.save_moves = save_moves\n\t\tself.play_ai = play_ai\n\t\tif self.play_ai == True:\n\t\t\tself.random_move_gen = MoveGenerate()\n\n\tdef new_pieces(self):\n\t\tsnake = Snake(self.disp_surf, self.board)\n\t\tapple = Apple(self.disp_surf, self.board, snake)\n\t\tscore = 0\n\t\treturn (apple, snake, 0)\n\n\tdef main_loop(self):\n\t\tself.start_game()\n\t\twhile True:\n\t\t\tself.run()\n\t\t\tself.state_capture.submit_event(request_save = True)\n\t\t\tself.game_over()\n\n\tdef run(self):\n\t\tself.apple, self.snake, self.score = self.new_pieces()\n\t\tdim = self.board.get_dim()\n\t\tcount = -1\n\t\tevent_detected = False\n\t\tkey = None\n\t\tchange_dir = False\n\t\twhile True:\t\n\t\t\tevent_detected = False\n\t\t\tcount+=1\n\t\t\tchange_dir = False\n\t\t\tkey = None\n\t\t\tfor event in pg.event.get():\n\t\t\t\tevent_detected = True\n\t\t\t\tif event.type == pg_local.QUIT:\n\t\t\t\t\tpg.quit()\n\t\t\t\t\tsys.exit()\n\t\t\t\tif event.type == pg_local.KEYUP and self.play_ai == False:\n\t\t\t\t\tchange_dir = True\n\t\t\t\t\tif event.key in (pg_local.K_LEFT, pg_local.K_a):\n\t\t\t\t\t\t#self.snake.change_dir(key)\t\n\t\t\t\t\t\tkey = 'left'\n\t\t\t\t\telif event.key in (pg_local.K_RIGHT, pg_local.K_d):\n\t\t\t\t\t\t#self.snake.change_dir(\"right\")\n\t\t\t\t\t\tkey = 'right'\n\t\t\t\t\telif event.key in (pg_local.K_UP, pg_local.K_w):\n\t\t\t\t\t\t#self.snake.change_dir('up')\n\t\t\t\t\t\tkey = 'up'\n\t\t\t\t\telif event.key in (pg_local.K_DOWN, pg_local.K_s):\n\t\t\t\t\t\t#self.snake.change_dir('down')\n\t\t\t\t\t\tkey = 'down'\n\t\t\t\tif self.save_moves\t == True:\n\t\t\t\t\tself.state_capture.submit_event(self.get_status(key))\n\t\t\t\tself.snake.change_dir(key)\n\t\t\tif self.play_ai == True:\n\t\t\t\tkey = self.translate_number_to_event(self.random_move_gen.gen_move())\n\t\t\t\t#print('gen >> {!s}'.format(key))\n\t\t\t\tself.snake.change_dir(key)\n\t\t\tself.board.draw()\n\t\t\tself.snake.draw()\n\t\t\tif self.snake.cross_over():\n\t\t\t\tbreak\n\t\t\tif self.snake.head == self.apple.coords:\n\t\t\t\tself.score+=1;\n\t\t\t\tself.apple.draw(generate_new = True)\n\t\t\t\tself.snake.move(generate_tail = True)\n\t\t\telse:\n\t\t\t\tself.apple.draw()\n\t\t\t\tself.snake.move()\n\t\t\tself.score_board.get_write(\"Score: {!s}\".format(self.score),(dim[0]-50, 10), text_color = pg.Color(0, 41, 39))\n\t\t\tpg.display.update()\n\t\t\tself.fps_clock.tick(self.FPS)\n\n\tdef game_over(self):\n\t\tdim = self.board.get_dim()\n\t\tgame_over_surf = pg.Surface(dim)\n\t\tgame_over_surf = game_over_surf.convert_alpha()\n\t\tback_color = pg.Color(0, 0, 0, 200)\n\t\tgame_over_surf.fill(back_color)\n\t\tgame_over_font = ComicWrites(game_over_surf, font_size = 60)\n\t\tgame_over_surf = game_over_font.get_write(\"Game over\", (dim[0]/2, dim[1]/2))\n\t\tself.disp_surf.blit(game_over_surf, (0, 0))\n\t\twhile True:\n\t\t\tfor event in pg.event.get():\n\t\t\t\tif event.type == pg_local.QUIT:\n\t\t\t\t\tpg.quit()\n\t\t\t\t\tsys.exit()\n\t\t\t\tif event.type == pg_local.KEYUP:\n\t\t\t\t\treturn\n\t\t\tpg.display.update()\n\n\tdef start_game(self):\n\t\tdim = self.board.get_dim()\n\t\tgame_start_surf = pg.Surface(dim).convert_alpha()\n\t\tback_color = pg.Color(35, 147, 46, 200)\n\t\tgame_start_surf.fill(back_color)\n\t\tgame_start_font = ComicWrites(game_start_surf, font_size = 50)\n\t\tgame_start_press_any_font = ComicWrites(game_start_surf, font_size = 20)\n\t\tgame_start_surf = game_start_font.get_write(\"Snakky!\", (dim[0]/2, dim[1]/2), text_color = pg.Color(125, 237, 127))\n\t\tgame_start_surf = game_start_press_any_font.get_write(\"Press any key to continue...\", (dim[0]/2, dim[1]-20))\n\t\tself.disp_surf.blit(game_start_surf, (0, 0))\n\t\tsnake = Snake(game_start_surf, self.board)\n\t\twhile True:\n\t\t\tfor event in pg.event.get():\n\t\t\t\tif event.type == pg_local.QUIT:\n\t\t\t\t\tpg.quit()\n\t\t\t\t\tsys.exit()\n\t\t\t\tif event.type == pg_local.KEYUP:\n\t\t\t\t\treturn\n\t\t\tpg.display.update()\n\n\tdef get_status(self, event):\n\t\t#print(\"inside get_status \\<-->/\")\n\t\tsnake_body_coords = self.snake.get_body()\n\t\tapple_coords = self.apple.get_coords()\n\t\tto_append = []\n\t\tto_append.append(self.translate_event_to_number(event))\n\t\tto_append.append(apple_coords[0])\n\t\tto_append.append(apple_coords[1])\n\t\tfor i in snake_body_coords:\n\t\t\tto_append.append(i[0])\n\t\t\tto_append.append(i[1])\n\t\t#print(to_append)\n\t\treturn to_append\n\n\tdef translate_event_to_number(self, event):\n\t\tif event == None:\n\t\t\treturn 0\n\t\telif event == 'left':\n\t\t\treturn 1\n\t\telif event == 'right':\n\t\t\treturn 2\n\t\telif event == 'up':\n\t\t\treturn 3\n\t\telif event == 'down':\n\t\t\treturn 4\n\n\tdef translate_number_to_event(self, number):\n\t\tif number == 0:\n\t\t\treturn None\n\t\telif number == 1:\n\t\t\treturn 'left'\n\t\telif number == 2:\n\t\t\treturn 'right'\n\t\telif number == 3:\n\t\t\treturn 'up'\n\t\telif number == 4:\n\t\t\treturn 'down'\n\nclass Capture:\n\tdef __init__(self, filename=\"cap_events.csv\"):\n\t\tself.filename = filename\n\t\tself.events = []\n\t\tself.cur_size = 0\n\t\tself.max_size = 60\n\n\tdef submit_event(self, status = None, request_save = False):\n\t\tif not status == None:\n\t\t\tself.events.append(status)\n\t\t\tself.cur_size+=1\n\t\tif self.cur_size >= self.max_size or request_save == True:\n\t\t\ttry:\n\t\t\t\twith open(self.filename, 'a') as fout:\n\t\t\t\t\tto_write = ''\n\t\t\t\t\tfor i in self.events:\n\t\t\t\t\t\tto_write = ', '.join([str(v) for v in i]) + '\\n'\n\t\t\t\t\t\tfout.write(to_write)\n\t\t\t\tself.events = []\n\t\t\t\tself.cur_size = 0\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"[Error] >> {!s}\".format(e))\n\nclass MoveGenerate:\n\tdef __init__(self, method = 'random'):\n\t\tself.method = method\n\t\tself.valid = [1, 2, 3, 4]\n\n\tdef gen_move(self):\n\t\tif self.method == 'random':\n\t\t\treturn self.__random_move()\n\t\telse:\n\t\t\treturn self.__ai_move()\n\n\tdef __random_move(self):\n\t\treturn random.choice(self.valid)\n\n\tdef __ai_move(self):\n\t\tpass","repo_name":"vermakartik/Snake-AI","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":13134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"30551672925","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom .models import BlogModel, CommentModel, AddBlogModel\nfrom django.views.generic import CreateView, UpdateView, ListView, DeleteView\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom .forms import AddBlogContentForm\nfrom django.contrib.auth.models import User\n\n\nclass HomePage(ListView):\n model = BlogModel\n template_name = 'blog/home.html'\n ordering = [\n '-date_posted_on'\n ]\n context_object_name = 'blogs'\n\n\n@login_required()\ndef blogDetail(request, slug):\n user = User.objects.get(id=request.user.id)\n\n post_all = BlogModel.objects.all()[:5]\n post = BlogModel.objects.filter(slug=slug).first()\n if not post:\n return render(request, 'blog/pageNotFound.html', context=None)\n additional_content = AddBlogModel.objects.filter(blog=post)\n if request.method == 'POST':\n comment_object = CommentModel(blog=post, comment_author=request.user, comment=request.POST['commentText'])\n comment_object.save()\n return redirect(f'/blog/{slug}/')\n\n if post in user.post_likes.all():\n has_liked = True\n else:\n has_liked = False\n\n context = {\n 'blog': post,\n 'username': request.user,\n 'all_post': post_all,\n 'additional_content': additional_content,\n 'has_liked': has_liked,\n }\n return render(request, 'blog/blogDetail.html', context=context)\n\n\nclass CreateBlogView(CreateView):\n model = BlogModel\n fields = ['blog_title', 'blog_short_desc', 'blog_image', 'blog_complete_desc', 'blog_language',]\n template_name = 'blog/createBlog.html'\n\n def form_valid(self, form):\n form.instance.blog_author = self.request.user\n return super().form_valid(form)\n\n\nclass UpdateBlogView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n model = BlogModel\n fields = ['blog_title', 'blog_short_desc','blog_image','blog_complete_desc','blog_language']\n template_name = 'blog/updateBlog.html'\n\n def form_valid(self, form):\n form.instance.author = self.request.user\n return super().form_valid(form)\n\n def test_func(self):\n post = self.get_object()\n if post.blog_author == self.request.user:\n return True\n return False\n\n\nclass DeleteBlogView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = BlogModel\n template_name = 'blog/postDelete.html'\n success_url = '/blog'\n\n def test_func(self):\n post = self.get_object()\n if post.blog_author == self.request.user:\n return True\n return False\n\n\n# class AddContentView(CreateView):\n# template_name = 'blog/addcontent.html'\n# model = AddBlogModel\n# fields = ['title', 'image', 'body']\n#\n# def form_valid(self, form):\n# form.instance.author = self.request.user\n# form.instance.blog = self.kwargs.get('id')\n# return super().form_valid(form)\n\n\ndef addContent(request, slug):\n post = BlogModel.objects.filter(slug=slug)\n\n if request.method == 'POST':\n content = AddBlogContentForm(request.POST, request.FILES)\n if content.is_valid():\n content.instance.author = request.user\n content.instance.blog = post.first()\n content.save()\n return redirect(f'/blog/{slug}')\n else:\n content = AddBlogContentForm()\n return render(request, 'blog/addcontent.html', context={'form': content})\n\n\n@login_required()\ndef like(request, slug):\n blog = BlogModel.objects.filter(slug=slug)\n if not blog:\n return redirect(request, 'blog/pageNotFound.html', context=None)\n else:\n blog = blog.first()\n if request.method == 'POST':\n blog.likes.add(request.user)\n return redirect(blog.get_absolute_url())\n\n\n@login_required()\ndef dislike(request, slug):\n blog = BlogModel.objects.filter(slug=slug)\n if not blog:\n return redirect(request, 'blog/pageNotFound.html', context=None)\n else:\n blog = blog.first()\n if request.method == 'POST':\n blog.likes.remove(request.user)\n return redirect(blog.get_absolute_url())\n","repo_name":"Godwin-Metri/GabrielPost","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"75001988550","text":"import cv2\nimport numpy as np\nfrom skimage import measure, morphology\nfrom skimage.measure import regionprops\nfrom crop_image import ImageCropper\n\ndef make_mask(image):\n \"\"\"\n create a mask that the bright parts are marked as 255, the rest as 0.\n\n params\n ------\n image: numpy array\n\n return\n ------\n frame_threshold: numpy array\n \"\"\"\n low_threshold=(0, 0, 250)\n high_threshold=(255, 255, 255)\n frame_HSV = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n frame_threshold = cv2.inRange(\n frame_HSV, low_threshold, high_threshold\n )\n return frame_threshold\n\n\ndef extract(mask):\n \"\"\"\n params\n ------\n mask: numpy array\n\n return\n ------\n labeled_image: numpy array\n The labeled image.\n \"\"\"\n condition = mask > mask.mean()\n labels = measure.label(condition, background=1)\n\n total_pixels = 0\n nb_region = 0\n average = 0.0\n for region in regionprops(labels):\n if region.area > 10:\n total_pixels += region.area\n nb_region += 1\n \n if nb_region > 1:\n average = total_pixels / nb_region\n # small_size_outlier is used as a threshold value to remove pixels\n # are smaller than small_size_outlier\n small_size_outlier = average * 3 + 100\n\n # big_size_outlier is used as a threshold value to remove pixels\n # are bigger than big_size_outlier\n big_size_outlier = small_size_outlier * 15\n\n # remove small pixels\n labeled_image = morphology.remove_small_objects(labels, small_size_outlier)\n # remove the big pixels\n component_sizes = np.bincount(labeled_image.ravel())\n too_small = component_sizes > (big_size_outlier)\n too_small_mask = too_small[labeled_image]\n labeled_image[too_small_mask] = 0\n\n labeled_mask = np.full(labeled_image.shape, 255, dtype=\"uint8\")\n labeled_mask = labeled_mask * (labeled_image == 0)\n else:\n labeled_mask = mask\n\n return labeled_mask\n\n\ndef detect_signature(image):\n # Create a binary mask of the input image using adaptive thresholding.\n mask = make_mask(image)\n #Extract connected components from a binary mask and label them.\n labeled_mask = extract(mask)\n #Initialize the ImageCropper with the minimum region size and border ratio.\n cropper = ImageCropper()\n \n results = cropper.run(labeled_mask)\n return results","repo_name":"Bagzhan/detect_signature","sub_path":"detect.py","file_name":"detect.py","file_ext":"py","file_size_in_byte":2406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"29176440021","text":"#!/usr/bin/env python\n\n\nimport argparse\nimport shutil\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\nimport urllib.parse\nimport m3u8\nfrom pathlib import Path\nimport re\n\n\ndef download(video_url):\n\tvideo_player_url_prefix = 'https://twitter.com/i/videos/tweet/'\n\tvideo_host = ''\n\toutput_dir = './output'\n\n\t# Parse the tweet ID\n\ttweet_user = video_url.split('/')[3]\n\ttweet_id = video_url.split('/')[5]\n\ttweet_dir = Path(output_dir + '/' + tweet_user + '/' + tweet_id)\n\tPath.mkdir(tweet_dir, parents = True, exist_ok = True)\n\n\t# Grab the video client HTML\n\tvideo_player_url = video_player_url_prefix + tweet_id\n\tvideo_player_response = requests.get(video_player_url)\n\n\t# Get the JS file with the Bearer token to talk to the API.\n\t# Twitter really changed things up.\n\tjs_file_soup = BeautifulSoup(video_player_response.text, 'html.parser')\n\tjs_file_url = js_file_soup.find('script')['src']\n\tjs_file_response = requests.get(js_file_url)\n\n\t# Pull the bearer token out\n\tbearer_token_pattern = re.compile('Bearer ([a-zA-Z0-9%-])+')\n\tbearer_token = bearer_token_pattern.search(js_file_response.text)\n\tbearer_token = bearer_token.group(0)\n\n\t# Talk to the API to get the m3u8 URL\n\tplayer_config = requests.get('https://api.twitter.com/1.1/videos/tweet/config/' + tweet_id + '.json', headers={'Authorization': bearer_token})\n\tm3u8_url_get = json.loads(player_config.text)\n\tm3u8_url_get = m3u8_url_get['track']['playbackUrl']\n\n\t# Get m3u8\n\tm3u8_response = requests.get(m3u8_url_get, headers = {'Authorization': bearer_token})\n\n\tm3u8_url_parse = urllib.parse.urlparse(m3u8_url_get)\n\tvideo_host = m3u8_url_parse.scheme + '://' + m3u8_url_parse.hostname\n\n\tm3u8_parse = m3u8.loads(m3u8_response.text)\n\n\tif m3u8_parse.is_variant:\n\t\tprint('Multiple resolutions found. Slurping all resolutions.')\n\n\t\tfor playlist in m3u8_parse.playlists:\n\t\t\tresolution = str(playlist.stream_info.resolution[0]) + 'x' + str(playlist.stream_info.resolution[1])\n\t\t\tresolution_dir = Path(tweet_dir) / Path(resolution)\n\t\t\tPath.mkdir(resolution_dir, parents = True, exist_ok = True)\n\n\t\t\tplaylist_url = video_host + playlist.uri\n\n\t\t\tts_m3u8_response = requests.get(playlist_url)\n\t\t\tts_m3u8_parse = m3u8.loads(ts_m3u8_response.text)\n\n\t\t\tts_list = []\n\n\t\t\tfor ts_uri in ts_m3u8_parse.segments.uri:\n\t\t\t\tprint('[+] Downloading ' + resolution)\n\n\t\t\t\tts_file = requests.get(video_host + ts_uri)\n\t\t\t\tfname = ts_uri.split('/')[-1]\n\t\t\t\tts_path = resolution_dir / Path(fname)\n\t\t\t\tts_list.append(ts_path)\n\n\t\t\t\tts_path.write_bytes(ts_file.content)\n\n\n\t\t\tts_full_file = Path(resolution_dir) / Path(resolution + '.ts')\n\n\t\t\t# Shamelessly taken from https://stackoverflow.com/questions/13613336/python-concatenate-text-files/27077437#27077437\n\t\t\twith open(str(ts_full_file), 'wb') as wfd:\n\t\t\t\tfor f in ts_list:\n\t\t\t\t\twith open(f, 'rb') as fd:\n\t\t\t\t\t\tshutil.copyfileobj(fd, wfd, 1024 * 1024 * 10)\n\n\nif __name__ == '__main__':\n\timport sys\n\n\tif sys.version_info[0] == 2:\n\t\tprint('Python3 is required.')\n\t\tsys.exit(1)\n\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-v', '--video', dest='video_url', help='The video URL on Twitter (https://twitter.com//status/).', required=True)\n\n\targs = parser.parse_args()\n\n\tdownload(args.video_url)\n","repo_name":"FLYSTEPHEN/scribe-twitter","sub_path":"others/twitter-video-downloader.py","file_name":"twitter-video-downloader.py","file_ext":"py","file_size_in_byte":3199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37053889813","text":"import torch\nimport numpy as np\n\nfrom model.DCRNN.model import DCRNNModel\nfrom lib.utils import masked_MAE, masked_RMSE, masked_MAPE\nimport pytorch_lightning as pl\n\n\nclass Supervisor(pl.LightningModule):\n def __init__(self, adj_mx, scaler, hparams):\n super().__init__()\n self._model_kwargs = hparams.get('MODEL')\n self._tf_kwargs = hparams.get('TEACHER_FORCING')\n self._optim_kwargs = hparams.get('OPTIMIZER')\n self._metric_kwargs = hparams.get('METRIC')\n # data set\n self.standard_scaler = scaler\n self.input_dim = int(\n self._model_kwargs.get(\n 'input_dim',\n 1)) # for the encoder\n self.output_dim = int(self._model_kwargs.get('output_dim', 1))\n self.use_curriculum_learning = bool(\n self._tf_kwargs.get('use_curriculum_learning', False))\n self.horizon = int(self._model_kwargs.get(\n 'horizon', 1)) # for the decoder\n\n # setup model\n self.model = DCRNNModel(\n adj_mx,\n **self._model_kwargs)\n self.monitor_metric_name = self._metric_kwargs['monitor_metric_name']\n self.training_metric_name = self._metric_kwargs['training_metric_name']\n\n # optimizer setting\n self.lr = self._optim_kwargs['base_lr']\n self.example_input_array = torch.rand(\n 64, self.input_dim, adj_mx.size(0), 12)\n self.save_hyperparameters('hparams')\n\n def on_train_start(self):\n self.logger.log_hyperparams(\n self.hparams, {\n self.monitor_metric_name: 0})\n\n def forward(self, x):\n x = self.model(x)\n return x\n\n def validation_step(self, batch, idx):\n x, y = batch\n pred = self.forward(x)\n return {'true': y, 'pred': pred}\n\n def validation_epoch_end(self, outputs):\n true = torch.cat([output['true'] for output in outputs], dim=0)\n pred = torch.cat([output['pred'] for output in outputs], dim=0)\n loss = self._compute_loss(true, pred)\n self.log_dict({self.monitor_metric_name: loss,\n 'step': float(self.current_epoch)}, prog_bar=True)\n\n def training_step(self, batch, idx):\n batches_seen = self.current_epoch * self.trainer.num_training_batches + idx\n sampling_p = self._compute_sampling_threshold(\n self._tf_kwargs['cl_decay_steps'], batches_seen)\n self.log('training/teacher_forcing_probability',\n float(sampling_p), prog_bar=False)\n x, y = batch\n output = self.model(\n x, y, lambda: self.teacher_forcing(\n sampling_p, self.use_curriculum_learning))\n loss = self._compute_loss(y, output)\n self.log(self.training_metric_name, loss, prog_bar=False)\n return loss\n\n def test_step(self, batch, idx):\n x, y = batch\n pred = self.forward(x)\n return {'true': y, 'pred': pred}\n\n def test_epoch_end(self, outputs):\n true = torch.cat([output['true'] for output in outputs], dim=0)\n pred = torch.cat([output['pred'] for output in outputs], dim=0)\n losses = self._compute_all_loss(true, pred)\n loss = {'mae': losses[0],\n 'rmse': losses[1],\n 'mape': losses[2]}\n\n # error for each horizon\n for h in range(len(loss[\"mae\"])):\n print(f\"Horizon {h+1} ({5*(h+1)} min) - \", end=\"\")\n print(f\"MAE: {loss['mae'][h]:.2f}\", end=\", \")\n print(f\"RMSE: {loss['rmse'][h]:.2f}\", end=\", \")\n print(f\"MAPE: {loss['mape'][h]:.2f}\")\n if self.logger:\n for m in loss:\n self.logger.experiment.add_scalar(\n f\"Test/{m}\", loss[m][h], h)\n\n # aggregated error\n print(\"Aggregation - \", end=\"\")\n print(f\"MAE: {loss['mae'].mean():.2f}\", end=\", \")\n print(f\"RMSE: {loss['rmse'].mean():.2f}\", end=\", \")\n print(f\"MAPE: {loss['mape'].mean():.2f}\")\n\n self.test_results = pred.cpu()\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(\n self.model.parameters(), lr=self.lr, eps=self._optim_kwargs['eps'])\n lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=self._optim_kwargs['milestones'],\n gamma=self._optim_kwargs['gamma'])\n\n return [optimizer], [lr_scheduler]\n\n @ staticmethod\n def _compute_sampling_threshold(cl_decay_steps, batches_seen):\n return cl_decay_steps / (\n cl_decay_steps + np.exp(batches_seen / cl_decay_steps))\n\n @ staticmethod\n def teacher_forcing(sampling_p, curricular_learning_flag):\n go_flag = (torch.rand(1).item() < sampling_p) & (\n curricular_learning_flag)\n return go_flag\n\n def _prepare_data(self, x, y):\n x, y = self._get_x_y(x, y)\n x, y = self._get_x_y_in_correct_dims(x, y)\n return x, y\n\n def _compute_loss(self, y_true, y_predicted, dim=(0, 1, 2, 3)):\n y_predicted = self.standard_scaler.inverse_transform(y_predicted)\n y_true = self.standard_scaler.inverse_transform(y_true)\n return masked_MAE(y_predicted, y_true, dim=dim)\n\n def _compute_all_loss(self, y_true, y_predicted, dim=(0, 1, 2)):\n y_predicted = self.standard_scaler.inverse_transform(y_predicted)\n y_true = self.standard_scaler.inverse_transform(y_true)\n return (masked_MAE(y_predicted, y_true, dim=dim),\n masked_RMSE(y_predicted, y_true, dim=dim),\n masked_MAPE(y_predicted, y_true, dim=dim))\n","repo_name":"semink/DCRNN_PyTorchLightning","sub_path":"model/DCRNN/supervisor.py","file_name":"supervisor.py","file_ext":"py","file_size_in_byte":5616,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"27"} +{"seq_id":"7809041124","text":"import argparse\r\n\r\nfrom dbexplorer.extracting.postgres_like import PostgresLikeDbExtractor\r\nfrom dbexplorer.extracting.mysql import MysqlDbExtractor\r\nfrom dbexplorer.extracting.teradata import TeradataDbExtractor\r\n\r\nfrom dbexplorer.visualizing import DbVisualizer\r\n\r\nparser = argparse.ArgumentParser(description='Database explorer')\r\nparser.add_argument('-e', '--extended', action='store_true', help='Generate extended report')\r\nparser.add_argument('-s', '--server', help='Server\\'s address', type=str, required=True)\r\nparser.add_argument('-p', '--port', help='Server\\'s port', type=int)\r\nparser.add_argument('-n', '--database_name', help='Database name', type=str, required=True)\r\nparser.add_argument('-u', '--user', help='Username', type=str, required=True)\r\nparser.add_argument('-pass', '--password', help='Password', type=str, required=True)\r\nparser.add_argument('-t', '--database_type', help='Database type (Postgres, MySQL, Redshift, Teradata', type=str,\r\n required=True)\r\nparser.add_argument('-o', '--output', help='Output HTML path', type=str, required=True)\r\nparser.add_argument('-top', '--top_number', help='Number of desired most frequent values', type=int, default=5)\r\nparser.add_argument('-m', '--max_text_length',\r\n help='Max length of text in given column that will be taken into account', type=int, default=100)\r\nparser.add_argument('-sc', '--schema', help='Schema for postgres', type=str, default='public')\r\nparser.add_argument('-d', '--odbc_driver', help='ODBC driver name for teradata', type=str)\r\n\r\nargs = parser.parse_args()\r\n\r\n\r\ndef main():\r\n db_type = args.database_type.lower()\r\n if db_type == 'postgres':\r\n extractor = PostgresLikeDbExtractor\r\n elif db_type == 'mysql':\r\n extractor = MysqlDbExtractor\r\n elif db_type == 'redshift':\r\n extractor = PostgresLikeDbExtractor\r\n elif db_type == 'teradata':\r\n extractor = TeradataDbExtractor\r\n else:\r\n raise ValueError\r\n if db_type == 'teradata' and args.odbc_driver is None:\r\n raise Exception(\"Please provide odbc driver for teradata\")\r\n elif db_type != 'teradata' and args.port is None:\r\n raise Exception(\"Please provide port for connection\")\r\n\r\n extractor = extractor(server_address=args.server,\r\n port=args.port,\r\n db_name=args.database_name,\r\n user=args.user,\r\n password=args.password,\r\n extended=args.extended,\r\n top_number=args.top_number,\r\n schema=args.schema,\r\n odbc_driver=args.odbc_driver,\r\n max_text_len=args.max_text_length,\r\n )\r\n visualizer = DbVisualizer(extractor.extract_to_dict(), args.output)\r\n visualizer.generate_report()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"mi2-warsaw/dbexplorer","sub_path":"dbexplorer/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"9793117781","text":"import torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.utils.data as Data\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nimport scipy.io as io\nimport psutil\nfrom sklearn.cluster import KMeans\n\n\ndef dataProcess(path_x, path_y):\n \"\"\"\n 数据处理，包括数据下载、输出矩阵的输出提取以及格式的转换\n :param path_x: 网络输入的路径\n :param path_y: 网络输出的路径\n :return: 可作为网络训练、测试的输入输出数据\n \"\"\"\n X_dic = io.loadmat(path_x)\n Y_dic = io.loadmat(path_y)\n X_all = X_dic['in_mat']\n Y_all = Y_dic['out_mat']\n # 选取预测的值（0-3为4个奇异值，4为噪声，5-8为除了噪声之后的奇异值）\n Y = Y_all[:, :4]\n\n\n k=3\n kmeans = KMeans(n_clusters=k)\n kmeans.fit(X_all)\n # 获取每个数据点所属聚类簇的标签\n labels = kmeans.labels_\n\n # 假设我们要取第1个聚类簇的所有数据\n cluster_index = 0\n cluster_mask = (labels == cluster_index) # 获取指定聚类簇的掩码\n cluster_data = X_all[cluster_mask] # 取出所有符合条件的数据\n \n X_train, X_test, Y_train, Y_test = train_test_split(cluster_data, Y[cluster_mask], test_size=0.2, random_state=21)\n # X_train, X_test, Y_train, Y_test = train_test_split(X_all, Y, test_size=0.2, random_state=21)\n # 通过去除均值和缩放方差，使得特征数据可以更好地适应模型的训练\n scale = StandardScaler()\n X_train_s = scale.fit_transform(X_train)\n X_test_s = scale.transform(X_test)\n\n # 转换为32位浮点数的张量\n X_train = torch.from_numpy(X_train_s.astype(np.float32))\n Y_train = torch.from_numpy(Y_train.astype(np.float32))\n X_test = torch.from_numpy(X_test_s.astype(np.float32))\n Y_test = torch.from_numpy(Y_test.astype(np.float32))\n\n return X_train, X_test, Y_train, Y_test\n\n\ndef LossMSERevise(label, output):\n \"\"\"\n 该损失函数为修改版MSE,给四个奇异值的平方差加上权重\n :param label: 真实值\n :param output: 预测值\n :return: 损失函数值\n \"\"\"\n Var = torch.square(label-output) * torch.tensor([1, 0, 0, 0]) # 4个奇异值的分配权重【5 2 2 1】\n lossValue = torch.mean(Var)\n return lossValue\n\n\ndef MAESingularValue(label, output):\n \"\"\"\n 计算四个奇异值的绝对误差\n :return: 4个误差的值\n \"\"\"\n Var = torch.mean(torch.abs(output - label), dim=0)\n return Var\n\n\ndef trainFunc(model, X_train, Y_train, num_Epoch, batch):\n \"\"\"\n DNN模型训练\n :param model: 模型\n :param X_train: 训练输入数据\n :param Y_train: 训练输出数据\n :param num_Epoch: 总训练轮数\n :param batch: 每次抽取的批量大小\n :return: model训练好的模型\n \"\"\"\n count = 0 # 用于判断是否输出优化参数\n train_data = Data.TensorDataset(X_train, Y_train)\n train_loader = Data.DataLoader(\n dataset=train_data, # 使用的数据集\n batch_size=batch, # 批处理的样本大小\n shuffle=True, # 每次迭代前打乱数据\n num_workers=0, # 使用一个线程\n drop_last=True\n )\n model.train()\n for batch_idx, (data_input, data_out) in enumerate(train_loader): # batch_idx 表征了取batch的次���\n\n optimizer.zero_grad()\n predicValue = model(data_input)\n # train_loss = LossMSERevise(data_out, predicValue) # 损失函数的计算\n loss_fn = nn.L1Loss() # 定义损失函数\n train_loss = loss_fn(predicValue, data_out) # 计算损失\n train_loss.backward() # 损失的后向传播，计算梯度\n optimizer.step() # 使用梯度进行优化\n # 每一个batch输出一次\n # print('Train Epoch:{} [{}/{}]\\tLoss:{:.6f}'.format\n # (num_Epoch, (batch_idx + 1) * len(data_input), len(train_loader.dataset), train_loss.item()))\n\n MAEVar = MAESingularValue(data_out, predicValue)\n # print('MAE value:', MAEVar[0].item(), MAEVar[1].item(), MAEVar[2].item(), MAEVar[3].item())\n count += 1\n\n # 每5次输出一下模型参数\n # if count % 5 == 0:\n # print('Parameter value :', model.hidden1.weight[0, :5])\n # # 获取系统内存信息\n # mem = psutil.virtual_memory()\n # print(f\"Total: {mem.total}, Available: {mem.available}, Used: {mem.used}\")\n #\n # # 获取当前进程内存占用情况\n # process = psutil.Process()\n # mem_info = process.memory_info()\n # print(f\"Resident Set Size: {mem_info.rss}, Virtual Memory Size: {mem_info.vms}\")\n return model\n\n\ndef testFunc(model, X_test, Y_test):\n \"\"\"\n 模型测试\n \"\"\"\n model.eval()\n batch = 1\n test_loss = 0\n MAE_test = torch.zeros((4, ))\n labValue = torch.zeros((1, 4))\n testdata = Data.TensorDataset(X_test, Y_test)\n loader = Data.DataLoader(\n dataset=testdata,\n batch_size=batch,\n drop_last=True\n )\n with torch.no_grad():\n for data_input, data_out in loader:\n predicValue = model.forward(data_input)\n loss = LossMSERevise(data_out, predicValue)\n test_loss += loss\n\n MAEper = MAESingularValue(data_out, predicValue)\n MAE_test += MAEper\n labValue += data_out\n test_loss /= len(loader.dataset)\n MAE_test /= len(loader.dataset)\n labValue /= len(loader.dataset)\n RelativeMAE = MAE_test / labValue\n print('The average loss of the data for test is ', test_loss.item())\n print('MAE value :', MAE_test[0].item(), MAE_test[1].item(), MAE_test[2].item(), MAE_test[3].item())\n print('Relative MAE value :', RelativeMAE[0, 0].item(), RelativeMAE[0, 1].item(),\n RelativeMAE[0, 2].item(), RelativeMAE[0, 3].item())\n return test_loss, MAE_test\n\nclass ResidualBlock(nn.Module):\n def __init__(self, input_dim, hidden1_dim, hidden2_dim):\n super(ResidualBlock, self).__init__()\n self.fc1 = nn.Linear(input_dim, hidden1_dim)\n self.bn1 = nn.BatchNorm1d(hidden1_dim)\n self.relu = nn.ReLU()\n self.fc2 = nn.Linear(hidden1_dim, hidden2_dim)\n self.bn2 = nn.BatchNorm1d(hidden2_dim)\n self.relu2 = nn.ReLU()\n self.fc3 = nn.Linear(hidden2_dim, input_dim)\n\n def forward(self, x):\n residual = x\n out = self.fc1(x)\n out = self.bn1(out)\n out = self.relu(out)\n out = self.fc2(out)\n out = self.bn2(out)\n out = self.relu2(out)\n out = self.fc3(out)\n out += residual\n return out\n \nclass MyNet(nn.Module):\n \"\"\"\n 网络的定义\n \"\"\"\n def __init__(self):\n super(MyNet, self).__init__()\n \n # 定义第一个隐藏层\n self.hidden1 = nn.Linear(21, 200)\n self.active1 = nn.ELU()\n self.bn0 = nn.BatchNorm1d(200)\n \n self.resblock1= ResidualBlock(200, 100, 200)\n self.active_res1 = nn.ELU()\n self.bn1 = nn.BatchNorm1d(200)\n \n self.resblock2= ResidualBlock(200, 100, 200)\n self.active_res2 = nn.ELU()\n self.bn2 = nn.BatchNorm1d(200)\n \n self.resblock3= ResidualBlock(200, 100, 200)\n self.active_res3 = nn.ELU()\n self.bn3 = nn.BatchNorm1d(200)\n # 定义预测回归层\n self.regression = nn.Linear(200, 4)\n self.dp = nn.Dropout1d(0.3)\n\n # 定义网络的前向传播\n def forward(self, x):\n x = self.hidden1(x)\n x = self.bn0(x)\n x = self.active1(x)\n \n x = self.resblock1(x)\n x = self.bn1(x)\n x = self.active_res1(x)\n \n x = self.resblock2(x)\n x = self.bn2(x)\n x = self.active_res2(x)\n \n x = self.resblock3(x)\n x = self.bn3(x)\n x = self.active_res3(x)\n # x = self.dp(x)\n outValue = self.regression(x)\n return outValue\n\n\nif __name__ == '__main__':\n\n pathX = 'Data_input_6w.mat'\n pathY = 'Data_output_6w.mat'\n [XTrain, XTest, YTrain, YTest] = dataProcess(pathX, pathY)\n seed = 42\n torch.manual_seed(seed) # 设定随机数种子\n model = MyNet()\n\n # 定义好网络之后，可以对已经准备好的数据集进行训练\n # 对回归模型mynet进行训练并输出损失函数的变化情况，定义优化器和损失函数\n optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)\n total_epoch = 40\n batchSize = 512\n # # 用于记录每个epoch中训练与测试loss的值\n # ListLossTrain = []\n # ListLossTest = []\n\n for epoch in range(total_epoch):\n # 进行网络模型的训练`\n print('Net Training . . . ')\n\n model = trainFunc(model, XTrain, YTrain, epoch+1, batchSize)\n # 测试模型性能\n print('Net Testing . . . ')\n [Loss2, MAE] = testFunc(model, XTest, YTest)\n # ListLossTest.append(Loss2)\n\n # # 用于模型的保存\n # torch.save(model.state_dict(), './model_xxx.pt')\n","repo_name":"Xyy-tj/CZX_Net","sub_path":"DNNpredict.py","file_name":"DNNpredict.py","file_ext":"py","file_size_in_byte":9016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22189372066","text":"import sqlite3\nimport numpy as np\n\nclass Database:\n # Burada kullanıcı görselini (x, y, z) boyutundan (m, n) boyutuna çeviriyoruz.\n # Bu işlemi (R, G, B) kodlarını tek bir değer olarak alıp string veri tipine çevirerek yapıyoruz.\n # Böylece kullanıcı göresli veri tabanına aktarmaya uygun hala getiriyoruz.\n def get3Dto2D(self, img): \n \n Data = list()\n \n for i in range(len(img)):\n \n templist = list()\n for j in range(len(img[i])):\n temp = str(img[i][j][0]) + \"-\" + str(img[i][j][1]) + \"-\" + str(img[i][j][2])\n templist.append(temp)\n \n Data.append(templist)\n \n return Data\n\n # Kullanıcı göreslini uygun hale getirdikten sonra, tablo ismi ve görsel data veri tabanına aktarılıyor.\n # Burada tablo ismi aynı zamanda username olmaktadır. \n def createTable(self, tableName, veri): \n try:\n columnsSize = len(veri)\n columnNames = \"\"\n for i in range(columnsSize): # Veri tabını tablo sütun isimlerinin oluşturulması : (0, 1, 2, 3, ...) --> (_0, _1, _2, ...)\n columnNames += \"{} STRING, \".format(\"_\" + str(i))\n \n \n connect = sqlite3.connect(\"DatabaseLoginVideo.db\")\n cur = connect.cursor()\n \n cur.execute(\"\"\"CREATE TABLE {} ({})\"\"\".format(tableName, columnNames[:-2]))\n \n\n for i in range(len(veri[0])): # Tabloda ki her satır tek tek tabloya eklemek için düzenleniyor.\n row = \"\"\n for j in range(len(veri)):\n row += \"'{}',\".format(veri[j][i])\n \n cur.execute(\"INSERT INTO {} VALUES ({})\".format(tableName, row[:-1])) # Düzenlenen satırlar tabloya ekleniyor.\n \n connect.commit()\n connect.close()\n\n return True # Eğer ekleme başarılı ise True değeri döndürüyor.\n except:\n return False # Eğer ekleme başarısız ise False değeri döndürüyor.\n\n # username (tablo ismi) 'i parametre alarak veri tabanında o tablo mevcut ise tabloyu çeker ve tablo yu döndürür.\n # Eğer mevcut değilse False değerini döndürür.\n def databaseExtraction(self, tableName): \n try:\n con = sqlite3.connect(\"DatabaseLoginVideo.db\")\n cur = con.cursor()\n \n columnNames = list(map(lambda x: x[0], cur.execute(\"SELECT * FROM {}\".format(tableName)).description)) # Tabloda ki sürun isimlerini çeker\n \n valList = list()\n for i, j in enumerate(columnNames): # Sırayla sütunda ki verileri çeker bir listeye atar.\n data = cur.execute(\"SELECT {} FROM {}\".format(j, tableName))\n \n array = list()\n for i in data:\n array.append(i[0])\n \n valList.append(array) # Litenin son hali\n \n con.commit()\n con.close()\n\n return valList\n except:\n return False\n\n \n # Veri tabanından çekilen veriler tekrardan yüz karşılaştırma işlemlerine uygun formata çevirmek için. (np.array, unit8)\n def get2Dto3D(self, Data): \n \n lastList = list()\n for i in Data:\n tempList = list()\n \n for ind in range(len(i)):\n row = i[ind].split(\"-\")\n \n for _ in range(len(row)):\n row[_] = int(row[_])\n \n tempList.append(row)\n \n lastList.append(tempList)\n \n array = np.array(lastList, dtype = \"uint8\")\n\n return array","repo_name":"rrumark/access-control-with-face-recognition","sub_path":"database_.py","file_name":"database_.py","file_ext":"py","file_size_in_byte":3835,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70597526793","text":"import os, sys\nimport time\nfrom random import randint\nfrom src.mobs import Mobs\nfrom src.colision import Colision\n\nclass mobSlime(Mobs):\n def __init__(self, settings, player, background, spawnPosX):\n super().__init__(settings, player, background, spawnPosX, 0)\n\n self.__newInit()\n\n def __newInit(self):\n self.__newLoadVariables()\n self.__newLoadImages()\n\n def __newLoadVariables(self):\n self.haveAttack = True\n self.qntImageAttack = 19\n self.velocityMobAttack = 0.1 #miliseg\n self.inAttack = False\n self.posPlayer = self.player.getPlayerPosX()\n\n #dano do ataque\n self.slimeAttack0Damage = self.mobDamage*3\n\n #Delay Attack\n self.delayAttack = randint(1,10)\n self.inDelayAttack = False\n\n self.startChangeImageAttackDelay = time.time()\n self.endChangeImageAttackDelay = time.time()\n\n def __newLoadImages(self):\n self.__imageMobAttack0 = [] #Slime\n for i in range(self.qntImageAttack):\n tempImage = self.settings.load_Images(\"Slime\"+str(i)+\".png\", \"Monstros/ID\"+str(self.mobID)+\"/Attack\", -1)\n self.__imageMobAttack0.append(tempImage)\n\n self.__imageMobAttack1 = [] #Tentacles\n for i in range(self.qntImageAttack):\n tempImage = self.settings.load_Images(\"Tentacle\"+str(i)+\".png\", \"Monstros/ID\"+str(self.mobID)+\"/Attack\", -1)\n self.__imageMobAttack1.append(tempImage)\n\n self.__currentImageMobAttack0 = self.__imageMobAttack0[0]\n self.__currentImageMobAttack1 = self.__imageMobAttack1[0]\n self.__rectMobAttack0 = self.__currentImageMobAttack0.get_rect()\n self.__rectMobAttack1 = self.__currentImageMobAttack0.get_rect()\n\n def __setImageMobAttack(self, numImg):\n self.__currentImageMobAttack0 = self.__imageMobAttack0[numImg]\n self.__currentImageMobAttack1 = self.__imageMobAttack1[numImg]\n self.numCurrentImageMob = numImg\n self.__rectMobAttack0 = self.__currentImageMobAttack0.get_rect()\n self.__rectMobAttack1 = self.__currentImageMobAttack0.get_rect()\n\n def getRectMobAttack1(self):\n tempRect = self.__rectMobAttack1.copy()\n tempRect.x = self.posPlayer\n return tempRect\n\n def __setProxImageMobAttack(self):\n if self.numCurrentImageMob == self.qntImageAttack-1:\n self.numCurrentImageMob = 0\n self.inAttack = False\n self.inDelayAttack = True\n self.delayAttack = randint(1,10)\n else:\n self.__setImageMobAttack(self.numCurrentImageMob+1)\n\n def __updateImageMobAttack(self):\n self.endChangeImage = time.time()\n if self.endChangeImage - self.startChangeImage >= self.velocityMobAttack:\n self.startChangeImage = time.time()\n self.__setProxImageMobAttack()\n\n def update(self):\n self.__updateDelayAttack()\n self.__checkAttack()\n self.__checkDamagePlayer()\n if not self.inAttack:\n super().update()\n else:\n self.__updateImageMobAttack()\n\n def __checkDamagePlayer(self):\n #Numeros baseados no numero da imagem que o tentaculo sai do chão\n if self.inAttack and self.numCurrentImageMob >= 8 and self.numCurrentImageMob <= 14:\n Colision.colisionSlimeAttackPlayer(self.player, self)\n\n def __updateDelayAttack(self):\n if self.inAttack:\n self.startChangeImageAttackDelay = time.time()\n self.endChangeImageAttackDelay = time.time()\n if self.endChangeImageAttackDelay - self.startChangeImageAttackDelay >= self.delayAttack:\n self.startChangeImageAttackDelay = time.time()\n self.inDelayAttack = False\n\n def __checkAttack(self):\n if self.inAttack or self.inDelayAttack:\n return\n disRan = randint(0,100)\n if Colision.colisionSlimePlayer(self.player, self, disRan):\n if not self.inAttack:\n self.posPlayer = self.player.getPlayerPosX()\n #print (\"Mob attack distancia = %d\" % (disRan))\n self.inAttack = True\n self.numCurrentImageMob = 0\n return\n\n def setDamage(self):\n self.endImunityTime = time.time()\n if self.endImunityTime - self.startImunityTime >= 2:\n self.startImunityTime = time.time()\n if self.mobLife - self.player.playerDamage <= 0:\n self.mobLife = 0\n else:\n self.mobLife -= self.player.playerDamage\n if self.settings.generalInfo:\n print (\"Mob Damage %d | Life %d\" % (self.player.playerDamage, self.mobLife))\n if not self.inAttack:\n self.setKnockback()\n\n def draw(self, camera):\n if not self.inAttack:\n super().draw(camera)\n else:\n camera.draw(self.__currentImageMobAttack0, (self.currentMobPosX, self.settings.valuePosY-self.__rectMobAttack0.h))\n if self.mobVelocity > 0:\n camera.draw(self.__currentImageMobAttack1, (self.posPlayer, self.settings.valuePosY-self.__rectMobAttack1.h-38))\n else:\n camera.draw(self.__currentImageMobAttack1, (self.posPlayer, self.settings.valuePosY-self.__rectMobAttack1.h-38))","repo_name":"JoseLooLo/Defeat-the-Night","sub_path":"src/Mobs/mobSlime.py","file_name":"mobSlime.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"30698366695","text":"from es_search import es_search\nfrom send_message import telegram\nimport subprocess\n\nif es_search():\n ip_dict = es_search()\n for ip, quantity in ip_dict.items():\n if quantity >= 4:\n message = 'From IP {} server was attacked {} times. Banish him!'.format(ip, quantity)\n cmd = 'iptables -A INPUT -s {} -p tcp --dport 22 -j DROP'.format(ip)\n telegram(message)\n print(message + '\\n' + cmd)\n subprocess.call(cmd, shell=True)\n\n\n","repo_name":"PavelvKalinin/elasticpy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"33427132282","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport time\n\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n\n# plot package\nfrom sklearn import tree\nimport graphviz\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom dtreeviz.trees import dtreeviz # remember to load the package\nfrom tqdm import tqdm\nfrom matplotlib.colors import ListedColormap\n\nimport plotly.express as px\nimport plotly.graph_objs as go\nimport plotly.figure_factory as ff\nfrom plotly.subplots import make_subplots\n\n# model packages\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.dummy import DummyClassifier, DummyRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.neural_network import MLPClassifier, MLPRegressor\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC, SVR\nfrom sklearn.gaussian_process import GaussianProcessClassifier, GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF\nfrom sklearn.gaussian_process.kernels import DotProduct, WhiteKernel\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, RandomForestRegressor, AdaBoostRegressor \nfrom sklearn.naive_bayes import GaussianNB\n\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\ndef compute_fig_from_df(model_type, result, metrics):\n if model_type == 'C':\n return compute_fig_from_classification_df(result, metrics)\n elif model_type == 'R':\n return compute_fig_from_regression_df(result, metrics)\n else:\n logger.warn(f'Unrecognized model_type {model_type}, use regression instead')\n return compute_fig_from_regression_df(result, metrics)\n\ndef compute_fig_from_classification_df(result, metrics):\n result = result.loc[:, result.columns != 'value']\n mean_result = result.groupby('model').mean().sort_values('test_roc_auc')\n std_result = result.groupby('model').std().loc[mean_result.index]\n\n # plot\n traces = []\n\n for i in metrics:\n traces.append(go.Bar(\n x = mean_result.index,#['model'],\n y = mean_result['test_'+i],\n error_y= dict(\n type= 'data',\n array= std_result['test_'+i],\n visible= True\n ),\n name = i,\n ))\n \n fig = go.Figure(traces)\n return fig \n\ndef compute_fig_from_regression_df(result, metrics):\n result = result.loc[:, result.columns != 'value']\n mean_result = result.groupby('model').mean().sort_values('test_neg_mean_absolute_error')\n std_result = result.groupby('model').std().sort_values('test_neg_mean_absolute_error').loc[mean_result.index]\n\n # plot\n traces = []\n\n for i in metrics:\n traces.append(go.Bar(\n x = mean_result.index,#['model'],\n y = mean_result['test_'+i],\n error_y= dict(\n type= 'data',\n array= std_result['test_'+i],\n visible= True\n ),\n name = i,\n ))\n \n fig = go.Figure(traces)\n \n return fig\n\ndef plot_decision_tree(dt, dat, dat_y):\n # DOT data\n dot_data = tree.export_graphviz(dt, out_file=None, \n feature_names=dat.columns, \n class_names='target',\n filled=True)\n # Draw graph\n graph = graphviz.Source(dot_data, format=\"svg\").source\n\n # viz plot\n viz = dtreeviz(dt, dat, dat_y,\n target_name=\"target\",\n feature_names=dat.columns,\n class_names=list('target'))\n\n return graph, viz \n\ndef plot_model_comparison(self, x_results: list , y_results: list, ptype: str='plot', title: str='', save_path: str=None, id: int=None):\n \"\"\"Creates a plot of the given columns in the dataframe. Saves the plot at a given path.\n \n Keyword arguments:\n x_results -- x coordinates\n y_results -- y coordinates\n ptype -- type of plot, should be in ['plot', 'bar']\n title -- title of the plot\n save_path -- path where to save the plot\n id -- ID of the plot\n \"\"\"\n n_figures = len(y_results)\n fig, axs = plt.subplots(n_figures, constrained_layout=True, figsize=(10, 10))\n fig.suptitle(title)\n fig = plt.figure()\n\n if ptype == 'plot':\n for i in range(len(y_results)):\n if len(x_results) == 1:\n axs[i].plot(x_results[0], y_results[i])\n else:\n axs[i].plot(x_results[i], y_results[i])\n elif ptype == 'bar':\n for i in range(len(x_results)):\n if len(x_results) == 1:\n axs[i].bar(x_results[0], y_results[i])\n axs[i].tick_params(axis='x', labelrotation=45)\n axs[i].set_ylim([0, 1.1])\n axs[i].set_yticks(np.arange(0, 1.5, 0.25))\n else:\n axs[i].bar(x_results[i], y_results[i])\n axs[i].tick_params(axis='x', labelrotation=45)\n axs[i].set_ylim([0, 1.1])\n axs[i].set_yticks(np.arange(0, 1.5, 0.25))\n plt.show()\n if save_path:\n if not os.path.exists(save_path):\n os.mkdir(save_path)\n plt.savefig(save_path + '/' + '_'.join(cols) + '_' + str(id), transparent=True)\n\ndef plot_feature_importance_of_random_forest(forest_importances):\n\n\n # plot\n trace1 = go.Bar(\n x = forest_importances.index,\n y = forest_importances.values,\n )\n\n data = [trace1]\n fig = go.Figure(data=data)\n return fig\n","repo_name":"sdsc-bw/DataFactory","sub_path":"datafactory/db/plotting/model_plotting.py","file_name":"model_plotting.py","file_ext":"py","file_size_in_byte":5682,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"37146137904","text":"from pyspark.sql import SparkSession\nfrom pyspark.sql.functions import col\nfrom pyspark.ml.feature import StringIndexer, IndexToString, VectorAssembler, VectorIndexer\nfrom pyspark.ml.regression import GBTRegressor, RandomForestRegressor, DecisionTreeRegressor, GeneralizedLinearRegression\nfrom pyspark.ml.evaluation import RegressionEvaluator\nfrom pyspark.ml.tuning import CrossValidator, ParamGridBuilder\nfrom pyspark.ml import Pipeline, Model\nfrom timeit import default_timer as timer\n\nspark = SparkSession.builder.getOrCreate()\n\nflights = spark.read\\\n .format('org.apache.spark.sql.execution.datasources.csv.CSVFileFormat')\\\n .option('header', 'true')\\\n .option('inferSchema', 'true')\\\n .load('s3a://eduardomtz/flights/input_data/flights/flights.csv')\n\ndf_flights = flights.filter(\"CANCELLED = 0\") \\\n .select(\"DEPARTURE_DELAY\",\n \"MONTH\", \n \"DAY\",\n \"DAY_OF_WEEK\", \n \"AIRLINE\",\n \"ORIGIN_AIRPORT\", \n \"DESTINATION_AIRPORT\", \n \"TAXI_OUT\", \n \"SCHEDULED_TIME\",\n \"ELAPSED_TIME\", \n \"AIR_TIME\", \n \"DISTANCE\", \n \"TAXI_IN\", \n \"ARRIVAL_DELAY\",\n \"DIVERTED\")\n \ndf_flights = df_flights.na.drop(subset=[\"DEPARTURE_DELAY\",\n \"MONTH\",\n \"DAY\",\n \"DAY_OF_WEEK\",\n \"AIRLINE\",\n \"ORIGIN_AIRPORT\", \n \"DESTINATION_AIRPORT\", \n \"TAXI_OUT\", \n \"SCHEDULED_TIME\",\n \"ELAPSED_TIME\", \n \"AIR_TIME\", \n \"DISTANCE\", \n \"TAXI_IN\", \n \"ARRIVAL_DELAY\",\n \"DIVERTED\"])\n \ndf_flights = df_flights.select(col(\"DEPARTURE_DELAY\").cast(\"integer\").alias(\"label\"),\n col(\"MONTH\").cast(\"integer\"), \n col(\"DAY\").cast(\"integer\"),\n col(\"DAY_OF_WEEK\").cast(\"integer\"), \n \"AIRLINE\",\n \"ORIGIN_AIRPORT\", \n \"DESTINATION_AIRPORT\", \n col(\"TAXI_OUT\").cast(\"integer\"), \n col(\"SCHEDULED_TIME\").cast(\"integer\"), \n col(\"ELAPSED_TIME\").cast(\"integer\"), \n col(\"AIR_TIME\").cast(\"integer\"), \n col(\"DISTANCE\").cast(\"integer\"), \n col(\"TAXI_IN\").cast(\"integer\"), \n col(\"ARRIVAL_DELAY\").cast(\"integer\"),\n col(\"DIVERTED\").cast(\"integer\"))\n\ndf_flights.show()\n\ntrain_flights, test_flights = df_flights.randomSplit([0.7, 0.3])\n\nprint(\"Número de registros de entrenamiento: \" + str(train_flights.count()))\nprint(\"Número de registros de prueba: \" + str(test_flights.count()))\n\naerolin_indexada = StringIndexer(inputCol=\"AIRLINE\", outputCol=\"AIRLINE_NUM\").setHandleInvalid(\"skip\")\norigen_indexada = StringIndexer(inputCol=\"ORIGIN_AIRPORT\", outputCol=\"ORIGIN_AIRPORT_NUM\").setHandleInvalid(\"skip\")\ndestino_indexada = StringIndexer(inputCol=\"DESTINATION_AIRPORT\", outputCol=\"DESTINATION_AIRPORT_NUM\").setHandleInvalid(\"skip\")\nvectorAssembler_features = VectorAssembler(inputCols=[\"MONTH\",\n \"DAY\",\n \"DAY_OF_WEEK\",\n \"AIRLINE_NUM\",\n \"ORIGIN_AIRPORT_NUM\",\n \"DESTINATION_AIRPORT_NUM\",\n \"TAXI_OUT\",\n \"SCHEDULED_TIME\",\n \"ELAPSED_TIME\",\n \"AIR_TIME\",\n \"DISTANCE\",\n \"TAXI_IN\", \n \"ARRIVAL_DELAY\",\n \"DIVERTED\"],\n outputCol=\"features\")\n\n\nmetod = {\n 'gbt': GBTRegressor(labelCol=\"label\", featuresCol=\"features\", maxBins = 640),\n 'mlg': GeneralizedLinearRegression(labelCol=\"label\", featuresCol=\"features\", family=\"gaussian\", link=\"identity\")\n}\n\ngrid = {\n 'gbt': ParamGridBuilder() \\\n .addGrid(metod['gbt'].maxDepth, [2, 5, 7])\\\n .addGrid(metod['gbt'].maxIter, [3, 5, 9])\\\n .build(),\n \n 'mlg': ParamGridBuilder() \\\n .addGrid(metod['mlg'].regParam, [0.1, 0.3, 0.5])\\\n .addGrid(metod['mlg'].maxIter, [3, 5, 9])\\\n .build()\n}\n\n\ndef magic_loop(metod_ejec, metod, grid, train):\n ## Parametros:\n ## metod_ejec: Lista de modelos a ejecutar\n ## metod: Diccionario de modelos que se consideraran\n ## grid: ParamGrid con los parametros de los modelos a considerar\n ## train: datos de entrenamiento\n \n ## Se inicializan variables\n mejor_score = 0\n mejor_modelo = ''\n \n print(\"Modelo\\tMetrica_desemp\")\n \n for index, mdl in enumerate([metod[x] for x in metod_ejec]):\n\n pipeline_gral = Pipeline(stages=[aerolin_indexada, \n origen_indexada,\n destino_indexada,\n vectorAssembler_features,\n mdl])\n\n crossval = CrossValidator(estimator=pipeline_gral,\n estimatorParamMaps=grid[metod_ejec[index]],\n evaluator=RegressionEvaluator(),\n numFolds=10) \n\n cvModel = crossval.fit(train)\n\n print(\"{}\\t{}\".format(metod_ejec[index], cvModel.avgMetrics[0]))\n \n ## Se condiciona a seleccionar entre los modelos el mejor entre los modelos probados:\n if (mejor_score == 0) or (cvModel.avgMetrics[0] < mejor_score):\n mejor_score = cvModel.avgMetrics[0]\n mejor_modelo = cvModel\n \n return mejor_modelo\n\n## Modelos que se ejecutaran\nmetod_ejec=['mlg','gbt']\n\n## Ejecucion de la funcion funcion Magic Loop\nstart = timer()\nejec_magicloop = magic_loop(metod_ejec, metod, grid, train_flights)\nend = timer()\n\nprint(\"Tiempo de ejecucion en minutos:\", (end - start)/60)\n\nejec_magicloop.avgMetrics[0]\n\npredict = ejec_magicloop.transform(test_flights)\npredict.select('features', 'prediction').show()\n\nevaluator = RegressionEvaluator(labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n\nrmse = evaluator.evaluate(predict)\nprint(\"Error cuadrático medio sobre los datos de prueba = %g\" % rmse)","repo_name":"abraham314/MGE18","sub_path":"alumnos/eduardo_martinez/tarea_7/tarea7.py","file_name":"tarea7.py","file_ext":"py","file_size_in_byte":7151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"7528950155","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"honey\", views.honey, name=\"honey\"),\n path(\"home\", views.home, name=\"home\"),\n path(\"home/\", views.greet, name=\"greet\"),\n]\n\n","repo_name":"vishwajeethaldar/python","sub_path":"Lecture_3_DJango/lec3/hellow/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"15220034067","text":"#categories of functions\r\n#based on arguments\r\n\r\n\r\n#1:Positional arguments\r\ndef function1(num1,num2,num3,num4):\r\n print(\"num1\",num1,\"num2\",num2,\"num3\",num3,\"num4\",num4)\r\nfunction1(10,20,30,40)\r\nfunction1(100,200,300,400)\r\n\r\n\r\n#2:Keyword arguments\r\ndef function2(num1,num2,num3,num4):\r\n print(\"num1\",num1,\"num2\",num2,\"num3\",num3,\"num4\",num4)\r\nfunction2(num4=10,num1=20,num2=30,num3=40)\r\nfunction2(num4=10,num1=10,num2=30,num3=40)\r\n\r\n\r\n#3:Default arguments\r\ndef function3(name,rollno,branch,collegename):\r\n print(name,rollno,branch,collegename)\r\nfunction3(\"Sapneswar\",270,\"CSE\",\"GIET\")\r\nfunction3(\"Atul\",37,\"ECE\",\"GIET\")\r\n\r\n\r\ndef function3(name,rollno,branch,collegename=\"GIET\"):\r\n print(name,rollno,branch,collegename)\r\nfunction3(\"Sapneswar\",270,\"CSE\")\r\nfunction3(\"Atul\",37,\"ECE\")\r\n\r\n\r\n#4:Variable no. of arguments\r\ndef function4(*var):#tuple=\r\n for i in var:\r\n print(i,end=' ')\r\nfunction4(10,20)\r\nprint()\r\nfunction4(10,20,30,40)\r\nprint()\r\nfunction4(10,20,30,40,50,60)\r\nprint()\r\n\r\n\r\ndef add(*var):#tuple=\r\n sum1=0\r\n for i in var:\r\n sum1=sum1+i\r\n return(sum1)\r\nprint(add(10,20))\r\nprint(add(10,20,30,40))\r\nprint(add(10,20,30,40,50,60))\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"SAPNESWAR/PYTHON-OOPS","sub_path":"Day 1/Function_categories.py","file_name":"Function_categories.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"20673253274","text":"import pandas as pd\nimport numpy as np\n\ndef forecast():\n \n #Get training dataframe and the list of distinct number of days before departure\n training_file_name = 'airline_booking_trainingData.csv' #training_file_name = input('Enter file name:')\n df_train, distinct_diff_days = get_diff_between_bookingDate_and_departureDate(training_file_name)\n \n #Get the mean of the total number of sold tickets on each day before departure\n cum_bookings_hist_mean = booking_mean(df_train, distinct_diff_days)\n \n #Get the validation dataframe\n valid_file_name = 'airline_booking_validationData.csv' #valid_file_name = input(ter file name:')\n df_valid, diff_days_valid = get_diff_between_bookingDate_and_departureDate(valid_file_name)\n \n #Get the days before departure of each case in validation data\n validation_days_before_departure = df_valid.difference\n \n #Get the mean of the total number of sold tickets on the departure day\n final_ticket_index = distinct_diff_days.index(0)\n final_sold_ticket = cum_bookings_hist_mean[final_ticket_index]\n \n #Predict the result using pure additive method and pure multiplicative respectively\n result_additive = additive_model(distinct_diff_days, cum_bookings_hist_mean, validation_days_before_departure, final_sold_ticket)\n result_multiplicative = multiplicative_model(distinct_diff_days, cum_bookings_hist_mean, validation_days_before_departure, final_sold_ticket)\n \n #Use the results of both additive model and multiplicative model to predict the forecast\n forecast_result = combined_model(validation_days_before_departure, result_additive, result_multiplicative, df_valid)\n df_valid['my_forecast'] = forecast_result\n \n #Calculate MASE\n diff_between_true_naive_forecast = np.absolute(df_valid[df_valid.difference != 0]['naive_forecast'] - df_valid[df_valid.difference != 0]['final_demand'])\n diff_between_true_final_forecast = np.absolute(df_valid[df_valid.difference != 0]['my_forecast'] - df_valid[df_valid.difference != 0]['final_demand'])\n MASE = np.sum(diff_between_true_final_forecast) / np.sum(diff_between_true_naive_forecast)\n \n return MASE\n\n \n \n \ndef get_diff_between_bookingDate_and_departureDate(f):\n df = pd.read_csv(f)\n \n #Get the number of difference between departure date and the current date in each case\n difference = (pd.to_datetime(df.departure_date) - pd.to_datetime(df.booking_date)).dt.days\n df['difference'] = difference\n \n #Get the distinct number of difference between departure date and the current date\n distinct_diff_days = list(set(difference))\n \n return df, distinct_diff_days\n\n\ndef booking_mean(df, distinct_diff_days):\n cum_bookings_mean = []\n \n for days_before_departure in distinct_diff_days:\n diff_df = df[df['difference'] == days_before_departure]\n ticket_accum = diff_df['cum_bookings']\n cum_bookings_mean.append(np.mean(ticket_accum))\n \n return cum_bookings_mean \n \n\ndef additive_model(X_train, y_train, target_date, final_ticket):\n result = []\n dic= {}\n \n for i in range(len(X_train)):\n dic[X_train[i]] = final_ticket - y_train[i]\n \n for td in target_date:\n result.append(dic[td])\n \n return result\n \n\ndef multiplicative_model(X_train, y_train, target_date, final_ticket):\n result = []\n dic = {}\n \n for i in range(len(X_train)):\n dic[X_train[i]] = y_train[i] / final_ticket\n \n for td in target_date:\n result.append(dic[td])\n \n return result\n\n\ndef combined_model(lyst, additive, multiplicative, df):\n final_result = []\n for i in range(len(lyst)):\n if lyst[i] == 0:\n final_result.append(np.nan)\n elif lyst[i] < 8: #Use additive model result when the difference between current date and departure date is less than 8 days\n final_result.append((additive[i] + df.cum_bookings[i])*0.8 + df.cum_bookings[i] / multiplicative[i] * 0.2)\n else: #Use multiplicative model result when the difference between current date and departure date is at least 8 days\n final_result.append((additive[i] + df.cum_bookings[i])*0.2 + df.cum_bookings[i] / multiplicative[i] * 0.8)\n return final_result\n\nprint(forecast())","repo_name":"lihalyf/Homework","sub_path":"Group_Project_Han.py","file_name":"Group_Project_Han.py","file_ext":"py","file_size_in_byte":4289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"10379518944","text":"def binConversion(s):\n\t# n = 0\n\t# for i in range(len(s)):\n\t# \tif s[i] == '1':\n\t# \t\tn += 2**(len(s)-i-1)\n\tn = int(s, 2)\n\t\n\treturn (oct(n)[2:], str(n), hex(n)[2:])\n\nif __name__==\"__main__\":\n\ts = input()\n\tprint(binConversion(s))\n","repo_name":"tgds03/helloGithub","sub_path":"py_lab2/my_pkg/binConversion.py","file_name":"binConversion.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"19375777402","text":"import os\r\nimport locale\r\nimport glob\r\nimport re\r\n\r\n# where is coca?\r\ncoca_path = \"D:\\corpora\\COCA\"\r\ncoca_fiction_path = os.path.join(coca_path, \"text_fiction_awq\")\r\ncoca_source_index = os.path.join(coca_path, \"sources/coca-sources.txt\")\r\njuvenile_fiction_subgenre_code = 117\r\n\r\noutput_file_juvenile = os.path.join(coca_path, \"thomas/juvenile_fiction.txt\")\r\n\r\n\r\n# find all fiction text ids\r\njuvenile_fiction_ids = list()\r\nother_fiction_ids = list()\r\n\r\nencoding = locale.getpreferredencoding()\r\n\r\n\r\ndef get_id_lists():\r\n index = open(coca_source_index, encoding=encoding, errors='ignore')\r\n for line in index:\r\n trimmed_line = line.strip()\r\n if trimmed_line != \"\":\r\n data = trimmed_line.split()\r\n genre = data[2]\r\n if genre == \"FIC\":\r\n id = data[0]\r\n subgenre = data[3]\r\n if subgenre == \"117\":\r\n juvenile_fiction_ids.append(id)\r\n else:\r\n other_fiction_ids.append(id)\r\n\r\n\r\ndef print_id_lists():\r\n print(\"\\nJUVENILE FICTION IDS\\n\")\r\n for id in juvenile_fiction_ids:\r\n print(id)\r\n\r\n print(\"\\nNON-JUVENILE FICTION IDS\\n\")\r\n for id in other_fiction_ids:\r\n print(id)\r\n\r\n\r\ndef clean_line(line):\r\n cleaned = line.strip().lower()\r\n cleaned = re.sub('(<.>|@ @ @ @ @ @ @ @ @ @)', '', cleaned)\r\n return cleaned\r\n\r\n\r\ndef scan_file_for_fiction(file_name):\r\n input_file_name = os.path.join(coca_fiction_path, file_name)\r\n juvenile_output_file_name = os.path.join(coca_fiction_path, \"juvenile\", file_name)\r\n other_output_file_name = os.path.join(coca_fiction_path, \"other\", file_name)\r\n input_file = open(input_file_name, encoding=encoding, errors='ignore')\r\n juvenile_output_file = open(juvenile_output_file_name, 'w')\r\n other_output_file = open(other_output_file_name, 'w')\r\n for line in input_file:\r\n m = re.match(r'^##([0-9]+) ', line)\r\n if m is not None and m.groups() is not None and m.groups()[0] is not None:\r\n id = m.groups()[0]\r\n if id in other_fiction_ids:\r\n other_output_file.write(clean_line(line[m.end():]) + \"\\n\")\r\n elif id in juvenile_fiction_ids:\r\n juvenile_output_file.write(clean_line(line[m.end():]) + \"\\n\")\r\n input_file.close()\r\n juvenile_output_file.close()\r\n other_output_file.close()\r\n\r\n\r\n# start here\r\nget_id_lists()\r\n#print_id_lists()\r\nfile_list = glob.glob(os.path.join(coca_fiction_path, \"*.txt\"))\r\nnum_files = len(file_list)\r\ni = 0\r\n\r\nprint(\"\\nStarting with {} juvenile fiction sources and {} non-juvenile fiction sources\".format(len(juvenile_fiction_ids), len(other_fiction_ids)))\r\n\r\nfor file_name in file_list:\r\n i += 1\r\n print(\"scanning: \" + file_name + \", \" + str(i) + \" of \" + str(num_files))\r\n scan_file_for_fiction(os.path.basename(file_name))\r\n\r\n\r\n","repo_name":"unicornmafia/MLPRegisterAnalysisFinal","sub_path":"makeReferenceCorporaFromCOCA.py","file_name":"makeReferenceCorporaFromCOCA.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"11507000268","text":"import speech_recognition as sr\nimport pyttsx3\nimport datetime\nimport pywhatkit\nimport wikipedia\n\n\nlistener = sr.Recognizer()\nalexa = pyttsx3.init()\n\nvoices = alexa.getProperty('voices')\nalexa.setProperty('voice', 'english')\n\ndef talk(text):\n alexa.say(text)\n alexa.runAndWait()\n\ndef take_command():\n for cmd in ['a.wav', 'c.wav', 'd.wav']:\n with sr.AudioFile(cmd) as source:\n print('Device is listening, please speak...')\n voice = listener.listen(source)\n command = listener.recognize_google(voice)\n command = command.lower()\n if 'alexa' in command:\n command = command.replace('alexa', '')\n yield command\n\ndef run_alexa():\n\n for command in take_command():\n talk(command)\n\n if 'time' in command:\n time = datetime.datetime.now().strftime('%I:%M %p')\n print('Current time is ' + time)\n talk('Current time is ' + time)\n elif 'how are you' in command:\n info = \"I am Fine.\"\n print(info)\n talk(info)\n elif 'play' in command:\n song = command.replace('play', '')\n talk('playing ' + song)\n pywhatkit.playonyt(song)\n elif 'tell me about' in command:\n wiki = command.replace('tell me about', '')\n info = wikipedia.summary(wiki, 2)\n print(info)\n talk(info)\n else:\n talk('Sorry I didnot get your question, I can search it from google')\n pywhatkit.search(command)\n\nrun_alexa()\n","repo_name":"saadmk11/PySpeechAssistant","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"39767741966","text":"\n#graph를 y축 기준으로 정렬, 그러면 y값에 따른 x값이 들어감, 이걸 기준으로 heapq에 값을 넣어준다?\nimport heapq, sys\ninput=sys.stdin.readline\n\nnode, final_y=map(int,input().split()) #징검다리 수, 결승선 y좌표\ngraph=[[] for _ in range(final_y+1)]\nINF=float(1e9)\ndist = [[] for _ in range(final_y+1)]\ndef find_way(graph):\n heap=[]\n heapq.heappush(heap,[float(0),0,0]) # cost, x좌표, y좌표 값을 넣는다 초기엔 다 0임\n while heap:\n # print(heap)\n distance, x_point, y_point=heapq.heappop(heap)\n for i in range(-2,3): #-2~2까지의 범위의 y값을 검색\n next_y=y_point+i\n if next_y <0 or next_y>final_y: #y 좌표가 -거나 결승점을 넘어가면 안됨\n continue\n for j in range(len(graph[next_y])):#y 좌표와 연결된 x 좌표를 확인한다.\n next_x=graph[next_y][j]\n # print(next_x, next_y)\n if abs(next_x-x_point)<=2: # 비교 지점이 아니고 x값의 차이가 2이하면\n next_distacne = ((x_point-next_x)**(2) + (y_point-next_y)**(2))**(0.5)\n # print(next_x, next_y,x_point,y_point)\n # print(next_distacne)\n if dist[next_y][j]> next_distacne+distance and next_distacne>0:\n dist[next_y][j]=next_distacne+distance\n heapq.heappush(heap,[next_distacne+distance,next_x,next_y])\n\nstart_point=[0,0]\ngraph[0].append(0)\ndist[0].append(0)\nfor _ in range(node):#노드 갯수만큼 좌표가 주어짐\n x,y=map(int,input().split())#징검다리들의 좌표\n graph[y].append(x) #y좌표와 연결된 x 좌표\n dist[y].append(INF)\n\n# print(dist)\nfind_way(graph)\n# print(dist)\nif round(min(dist[-1]))==INF or round(min(dist[-1]))==0:\n print(-1)\nelse:\n print(round(min(dist[-1])))\n #x,y좌표가 2이하로 차이나는 좌표로만 이동가능하다!\n\n#graph를 y축 기준으로 정렬, 그러면 y값에 따른 x값이 들어감, 이걸 기준으로 heapq에 값을 넣어준다?\n#","repo_name":"Gyeo1/CodeTest_Practice","sub_path":"백준/최단거리/징검다리 건너기2.py","file_name":"징검다리 건너기2.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"3840570024","text":"def p_np_solver():\n # today we will solve the p vs np problem through brute force\n p = 0\n n = 0\n np = n * p\n while (p != np):\n p += 1\n np = n * p\n while (p != np):\n n += 1\n np = n * p\n # the loop will end if we have found the solution to p vs np\n return (n, p)\n\n# if you run this function, it will terminate. Therefore, p = np. QED.\n","repo_name":"Piratach/p_np_solver","sub_path":"p_np_solver.py","file_name":"p_np_solver.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"27"} +{"seq_id":"16878365269","text":"import requests\nfrom typing import List\nfrom cloud_src import file_manipulation\nfrom cloud_src.reddit_post import RedditPost\n\n\ndef get_url(subreddit):\n return \"https://www.reddit.com/r/\" + subreddit + \"/new.json\"\n\n\nSUBREDDIT = \"buildapcsales\"\nURL = get_url(SUBREDDIT)\n\n\ndef get_json_data(url):\n json_data = requests.get(url, headers={\"User-agent\": \"build_a_pc_scraper.1\"}).json()\n return json_data\n\n\ndef parse_posts_from_json(json_data):\n posts = []\n post_dicts = json_data[\"data\"][\"children\"]\n for post_dict in post_dicts:\n posts.append(RedditPost.__from_web__(post_dict[\"data\"]))\n return posts\n\n\ndef get_new_posts(posts: List[RedditPost], last_known_post: RedditPost):\n try:\n new_posts = posts[: posts.index(last_known_post)]\n except ValueError:\n raise ValueError(\"Couldn't find last know post in passed posts list\")\n return new_posts\n\n\ndef get_last_known_post(file_name: str, new_last_post: RedditPost):\n last_post = file_manipulation.get_last_known_post(file_name)\n file_manipulation.update_last_known_post(file_name, new_last_post)\n return last_post\n\n\ndef get_fresh_posts(last_know_post_file_name: str):\n json_data = get_json_data(URL)\n all_posts = parse_posts_from_json(json_data)\n last_known_post = get_last_known_post(last_know_post_file_name, all_posts[0])\n return get_new_posts(all_posts, last_known_post)\n","repo_name":"theheuman/reddit-alerts","sub_path":"cloud_src/reddit_api_handler.py","file_name":"reddit_api_handler.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"36469925785","text":"class Solution:\r\n def twoSum(self, nums, target):\r\n\r\n # There is exactly one solution\r\n # If the value in the array is above the target value we can skip over it\r\n\r\n # Use tuples to save the current index value in the list\r\n indexed_nums = [(value, index) for index, value in enumerate(nums)]\r\n # 'enumerate' returns an iterable that yields pairs of the form (index, value)\r\n # The 'for' iterates over the pairs of index, value\r\n # '(value, index)' is a tuple being constructed for each iteration of the loop\r\n\r\n\r\n # Sort values of the list\r\n sorted_indexed_nums = sorted(indexed_nums, key=lambda x: x[0])\r\n # The 'sorted()' function returns an array in ascending order\r\n # The 'key=' parameter specifies a custom function to be used to define how the elements should be compared to determine thier sort order\r\n # The 'lambda x: x[0]' is an anonymous (or lambda) function that takes a single argument x and returns it's first element\r\n\r\n # Create cursors \r\n left = 0\r\n right = len(sorted_indexed_nums) - 1\r\n\r\n # Bring the right cursor left if it is greater than or equal to the target value\r\n if target >= 0:\r\n while (sorted_indexed_nums[right][0] > target and right > left):\r\n right -= 1\r\n else:\r\n while (sorted_indexed_nums[left][0] < target and left < right):\r\n left += 1\r\n\r\n # Begin searching for combination that matches target value\r\n currentSum = sorted_indexed_nums[left][0] + sorted_indexed_nums[right][0]\r\n while (currentSum != target):\r\n\r\n # If the current sum is less than the target value, bring the left cursor to the right\r\n if currentSum < target:\r\n left += 1\r\n \r\n # Otherwise, bring the right cursor to the left\r\n else:\r\n right -= 1\r\n\r\n # Find the new current sum of the values at the left and right cursors\r\n currentSum = sorted_indexed_nums[left][0] + sorted_indexed_nums[right][0]\r\n\r\n # Find the original indexes from the indexes for the sorted_indexed_nums list\r\n left = sorted_indexed_nums[left][1]\r\n right = sorted_indexed_nums[right][1]\r\n\r\n # return an array with the left and right cursors which point to values that sum to the target value\r\n return [left, right]\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Feddockh/Leetcode_Practice","sub_path":"Two_Sum.py","file_name":"Two_Sum.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"2199586638","text":"import random\nfrom time import sleep\n\nfrom django.core.cache import caches\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views.decorators.cache import cache_page\n\nfrom app.models import LoadPic\n\n\ndef upload(request):\n if request.method == \"POST\":\n useranem = request.POST.get(\"username\")\n imgfile = request.FILES.get(\"img\")\n load = LoadPic()\n load.load_name = useranem\n load.load_img = imgfile\n load.save()\n print(load.load_img.url)\n return redirect(r\"../../static/media_root/\"+load.load_img.url)\n else:\n return render(request,\"upload.html\")\n\n@cache_page(30,cache=\"redis_backend\")\ndef showCache(request):\n sleep(5)\n response = HttpResponse(\"haloman\")\n return response\n\n@cache_page(30,cache=\"redis_backend\")\ndef getMi10(request):\n num = random.randrange(100)\n sleep(5)\n if num >= 98:\n response = HttpResponse(\"恭喜哦！您抢到了一部小米手机！\")\n else:\n response = HttpResponse(\"不好意思！您没有抢到！\")\n return response","repo_name":"happyQiao-PS/Django-","sub_path":"Django基础入门/缓存和中间件/untitled1/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"42230563390","text":"#\t\t\t\t\t\t\t\t\t\tFLOWER PREDICTION BASED ON IRIS FLOWER DATASET\n#\t\t\t\t\t\t\t\t\t\t (Importing from CSV file)\n# Link to Reference used-->https://machinelearningmastery.com/machine-learning-in-python-step-by-step/\n#This program is used to learn about Ml, python libraries and their functioning etc...\n#To understand, I am doing documentation about new things i will encounter.\n#I am also going to add some more modifications in this program\n#Potential modifications:- \n# 1) Using timenow and timeit it calculate the execution time\n# 2) Plotting more types of graphs, 3d graphs if possible \n# 3) Applying dataset to more algorithms\n\n#Importing the modules\n\n#Creating an alias\nimport pandas as pd\nimport sklearn as sk\nimport matplotlib.pyplot as plt\n\nfrom pd.tools.plotting import scatter_matrix\nfrom sk import model_selection\nfrom sk.metrics import classification_report, confusion_matrix, accuracy_score\nfrom sk.linear_model import LogisticRegression\nfrom sk.tree import DecisionTreeClassifier\nfrom sk.neighbors import KNeighborsClassifier\nfrom sk.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sk.naive_bayes import GaussianNB\nfrom sk.svm import SVC\n\n# Loading the dataset\n#Provide a url from which to take the CSV file\nurl = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n\n#Store the name of the attributes in form of list\nnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n\n#Using the read_csv function from pandas to read the CSV file \ndataset = pd.read_csv(url, names=names)\n\n# shape, we will see (150,5), 150 is instances and 5 is attributes\nprint(dataset.shape)\n\n# head-20 is 20 rows of data\nprint(dataset.head(20))\n\n# descriptions-This includes the count, mean, the min and max values as well as some percentiles.\nprint(dataset.describe())\n\n# class distribution-number of instances (rows) that belong to each class\nprint(dataset.groupby('class').size())\n\n\n#DATA Visualization\n\n#univariate plots-plots of each individual variable.\n# box and whisker plots\ndataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\nplt.show()#-->gives us a much clearer idea of the distribution of the input attributes\n#histograms\ndataset.hist()\nplt.show()#-->It looks like perhaps two of the input variables have a Gaussian distribution. This is useful to note as we can use algorithms that can exploit this assumption.\n\n#Multivariable plots--interaction between the variables\n# scatter plot matrix-helpful to spot structured relationships between input variables.\nscatter_matrix(dataset)\nplt.show()#-->Note the diagonal grouping of some pairs of attributes. This suggests a high correlation and a predictable relationship.\n\n#NOTED-->to see the type of relations from looking at the graphs\n\n#EVALUATING THE ALGORITHMS\n\n# Split-out validation dataset\narray = dataset.values\nX = array[:,0:4]\nY = array[:,4]\nvalidation_size = 0.20\nseed = 7\nX_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)\n\n# Test options and evaluation metric\n#We will use 10-fold cross validation to estimate accuracy.\n#This will split our dataset into 10 parts, train on 9 and test on 1 and repeat for all combinations of train-test splits.\nseed = 7\nscoring = 'accuracy'\n#Accuracy is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage \n\n# Spot Check Algorithms-as we don't know which algorithm will be better\nmodels = []\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('LDA', LinearDiscriminantAnalysis()))\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('CART', DecisionTreeClassifier()))\nmodels.append(('NB', GaussianNB()))\nmodels.append(('SVM', SVC()))\n# evaluate each model in turn\nresults = []\nnames = []\nfor name, model in models:\n\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n\tcv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n\tresults.append(cv_results)\n\tnames.append(name)\n\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n\tprint(msg)\n#Select the best model!--Here it is KNN\n\n# Compare Algorithms\nfig = plt.figure()\nfig.suptitle('Algorithm Comparison')\nax = fig.add_subplot(111)\nplt.boxplot(results)\nax.set_xticklabels(names)\nplt.show()\n\n# Make predictions on validation dataset\nknn = KNeighborsClassifier()\nknn.fit(X_train, Y_train)\npredictions = knn.predict(X_validation)\nprint(accuracy_score(Y_validation, predictions))\nprint(confusion_matrix(Y_validation, predictions))\nprint(classification_report(Y_validation, predictions))\n","repo_name":"qifanyyy/JupyterNotebook","sub_path":"new_algs/Graph+algorithms/Nearest+neighbour+algorithm/Load_from_CSV.py","file_name":"Load_from_CSV.py","file_ext":"py","file_size_in_byte":4690,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"27"} +{"seq_id":"26020096155","text":"num_of_cars = int(input())\ncars = {}\nfor _ in range(num_of_cars):\n car, mileage, fuel = input().split('|')\n if car not in cars:\n cars[car] = {'mileage': 0,\n 'fuel': 0}\n cars[car]['mileage'] += int(mileage)\n cars[car]['fuel'] += int(fuel)\n\ncommand = input()\nwhile command != 'Stop':\n command_args = command.split(' : ')\n if command_args[0] == 'Drive':\n car = command_args[1]\n distance = int(command_args[2])\n fuel = int(command_args[3])\n\n if fuel > cars[car]['fuel']:\n print('Not enough fuel to make that ride')\n else:\n cars[car]['mileage'] += distance\n cars[car]['fuel'] -= fuel\n print(f'{car} driven for {distance} kilometers. {fuel} liters of fuel consumed.')\n\n if cars[car]['mileage'] >= 100000:\n print(f'Time to sell the {car}!')\n cars.pop(car)\n\n elif command_args[0] == 'Refuel':\n car = command_args[1]\n fuel = int(command_args[2])\n current_fuel = cars[car]['fuel']\n cars[car]['fuel'] += fuel\n if cars[car]['fuel'] > 75:\n cars[car]['fuel'] = 75\n fuel_refilled = 75 - current_fuel\n print(f'{car} refueled with {fuel_refilled} liters')\n else:\n print(f'{car} refueled with {fuel} liters')\n\n elif command_args[0] == 'Revert':\n car = command_args[1]\n kilometers = int(command_args[2])\n cars[car]['mileage'] -= kilometers\n if cars[car]['mileage'] < 10000:\n cars[car]['mileage'] = 10000\n else:\n print(f'{car} mileage decreased by {kilometers} kilometers')\n command = input()\n\nfor car in cars.keys():\n mileage = cars[car]['mileage']\n fuel = cars[car]['fuel']\n print(f'{car} -> Mileage: {mileage} kms, Fuel in the tank: {fuel} lt.')\n","repo_name":"georgi-eh/python-fundamentals-softuni","sub_path":"exam_preparation/need_for_speed_3.py","file_name":"need_for_speed_3.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"5851170360","text":"import logging\nimport os\nfrom subprocess import CalledProcessError\n\nfrom qemu.qmp import QEMUMonitorProtocol\nfrom utils.machine import Machine, localRoot\nfrom utils.shell_utils import run_command_output, run_command_check, run_command_remote, run_command_async, run_command\nfrom time import sleep\nfrom tempfile import NamedTemporaryFile\nimport signal\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\nclass VM(Machine):\n BOOTUP_WAIT = 50 # 15\n POWEROFF_WAIT = 3\n USER = \"user\"\n\n def __init__(self, path, guest_ip, host_ip):\n super(VM, self).__init__(guest_ip, self.USER)\n self.path = path\n self.ip_guest = guest_ip\n self.ip_host = host_ip\n self.bootwait = self.BOOTUP_WAIT\n self.netperf_test_params = \"\"\n self.guest_configure_commands = list()\n\n def get_info(self, old_info=None):\n DISK_PATH = \"disk_path\"\n info = super().get_info(old_info)\n\n if not self.enabled:\n info.update({k: old_info[k] for k in (DISK_PATH,)})\n else:\n info[DISK_PATH] = self.path\n return info\n\n def _run(self):\n raise NotImplementedError()\n\n def configure_guest(self):\n for cmd in self.guest_configure_commands:\n self.remote_command(cmd)\n\n def shutdown(self):\n self.remote_root_command(\"poweroff\")\n sleep(self.POWEROFF_WAIT)\n\n def run(self, configure_guest=True):\n logger.info(\"Running VM: %s\", self)\n self._run()\n sleep(self.bootwait)\n if configure_guest:\n self.configure_guest()\n\n\nclass Qemu(VM):\n QEMU_EXE = \"\"\n\n QEMU_E1000_DEBUG_PARAMETERS_FILE = \"/tmp/e1000_debug_parameters\"\n\n QEMU_E1000 = \"e1000\"\n QEMU_VIRTIO = \"virtio-net-pci\"\n\n BOOTUP_WAIT = 30\n\n def __init__(self, disk_path, guest_ip, host_ip, cpu_to_pin=\"2\"):\n super(Qemu, self).__init__(disk_path, guest_ip, host_ip)\n self._pid = None\n\n self.cpu_to_pin = cpu_to_pin\n # self.cpu_num = cpu_num\n self.mac_address = \"52:54:00:a0:e5:1c\"\n self.vnc_number = \"10\"\n\n self.ethernet_dev = self.QEMU_E1000 # can be \"virtio-net-pci\" or \"e1000\"\n self.vhost = False\n self.sidecore = False\n self.mem = \"8192\"\n\n self.io_thread_cpu = \"\"\n\n # auto config\n self.tap_device = ''\n self.pidfile = None\n\n self.qemu_config = dict()\n self.bridge = None\n self.exe = self.QEMU_EXE\n\n self.is_io_thread_nice = False\n self.io_nice = 1 # nice value to set\n\n self.root = Machine(self._remote_ip, \"root\")\n\n # self.kernel = r\"/home/bdaviv/repos/e1000-improv/linux-3.13.0/arch/x86/boot/bzImage\"\n # self.kernel = r\"/homes/bdaviv/repos/msc-ng/linux-4.13.9/arch/x86/boot/bzImage\"\n self.kernel = r\"../vms/vmlinuz\" #r\"../linux/arch/x86/boot/bzImage\"\n # self.initrd = r\"../vms/initrd.img\"\n # self.initrd = r\"/homes/bdaviv/repos/msc-ng/vm-files/kernels/initrd.img-4.13.9-ng+\"\n self.initrd = r\"../vms/initrd.img\" # r\"../vms/initrd.img\"\n self.kernel_cmdline = r\"BOOT_IMAGE=/vmlinuz-5.4.0-73-generic root=/dev/mapper/ubuntu--vg-ubuntu--lv ro maybe-ubiquity\"\n self.kernel_cmdline_additional = \"\"\n\n self.nic_additionals = \"\"\n self.qemu_additionals = \"\"\n\n self.disable_kvm_poll = False\n\n self.guest_e1000_ng_flag = 0\n\n self.qmp = None\n\n def get_info(self, old_info=None):\n KERNEL_FILE = \"kernel_file\"\n KERNEL_GIT = \"kernel_git\"\n KERNEL_CMD = \"kernel_cmd\"\n\n QEMU_FILE = \"qemu_file\"\n QEMU_GIT = \"qemu_git\"\n\n NIC_TYPE = \"nic_type\"\n CPU_PIN = \"cpu_pin\"\n MEM = \"mem\"\n NICE = \"nice\"\n\n keys = (KERNEL_FILE, KERNEL_GIT, KERNEL_CMD, QEMU_FILE, QEMU_GIT, NIC_TYPE, CPU_PIN, MEM, NICE)\n\n info = super().get_info(old_info)\n\n if not self.enabled:\n info.update({k: old_info[k] for k in keys})\n else:\n #info[KERNEL_FILE] = self.kernel\n #info[KERNEL_GIT] = run_command_output(\"git -C {directory} rev-parse --short HEAD\".format(\n # directory=os.path.dirname(self.kernel)\n #)).strip()\n info[KERNEL_CMD] = self.kernel_cmdline\n\n info[QEMU_FILE] = self.exe\n #info[QEMU_GIT] = run_command_output(\"git -C {directory} rev-parse --short HEAD\".format(\n # directory=os.path.dirname(self.exe)\n #)).strip()\n\n info[NIC_TYPE] = self.ethernet_dev\n info[CPU_PIN] = self.cpu_to_pin\n info[MEM] = self.mem\n info[NICE] = (self.is_io_thread_nice, self.io_nice)\n return info\n\n def run(self, configure_guest=True):\n super().run(configure_guest)\n\n def create_tun(self):\n \"\"\"\n create tun device and assign it an IP\n \"\"\"\n current_user = os.environ[\"USER\"]\n\n output = run_command_output(\"sudo tunctl -u {user}\".format(user=current_user))\n self.tap_device = output.split(\"'\")[1]\n assert self.tap_device == 'tap0'\n\n run_command_check(\"sudo ip link set {tap} up\".format(tap=self.tap_device))\n if self.ip_host and not self.bridge:\n run_command_check(\"sudo ip a a {host_ip}/24 dev {tap}\".format(host_ip=self.ip_host,\n tap=self.tap_device))\n if self.bridge:\n run_command_check(\"sudo brctl addif {br} {iff}\".format(br=self.bridge, iff=self.tap_device))\n\n def delete_tun(self):\n if self.bridge:\n run_command(\"sudo brctl delif {br} {iff}\".format(br=self.bridge, iff=self.tap_device))\n while True:\n try:\n run_command_check(\"sudo tunctl -d {tap}\".format(tap=self.tap_device))\n break\n except:\n sleep(1)\n\n def load_kvm(self):\n run_command_check(\"sudo modprobe kvm-intel\")\n if self.disable_kvm_poll:\n run_command_check(\"echo 0 | sudo tee /sys/module/kvm/parameters/halt_poll_ns\")\n\n def unload_kvm(self):\n sleep(1)\n run_command(\"sudo modprobe -r kvm-intel\")\n\n def _clean_cpu(self):\n return\n run_command(\"echo 0 |sudo tee /sys/devices/system/cpu/cpu{cpu}/online\".format(\n cpu=self.cpu_to_pin\n ), shell=True)\n run_command(\"echo 1 |sudo tee /sys/devices/system/cpu/cpu{cpu}/online\".format(\n cpu=self.cpu_to_pin\n ), shell=True)\n\n def setup(self):\n super().setup()\n self.load_kvm()\n self.create_tun()\n self._configure_host()\n self._clean_cpu()\n\n def teardown(self):\n try:\n self.shutdown()\n self.qmp.close()\n except:\n pass\n self.qmp = QEMUMonitorProtocol(('127.0.0.1', 1235))\n self._pid = None\n self._reset_host_configuration()\n sleep(20)\n self.delete_tun()\n sleep(2)\n self.unload_kvm()\n super().teardown()\n\n def _get_temp_nic_additional(self):\n return \"\"\n\n def _run(self):\n assert self.exe\n\n self.qmp = QEMUMonitorProtocol(('127.0.0.1', 1235))\n self.pidfile = NamedTemporaryFile()\n\n if self.vhost:\n vhost_param = \",vhost=on\"\n # HACK HACK HACK\n localRoot.remote_command(\"chown :kvm /dev/vhost-net\")\n localRoot.remote_command(\"chmod 660 /dev/vhost-net\")\n else:\n vhost_param = \"\"\n\n if self.sidecore:\n sidecore_param = \"-enable-e1000-sidecore\"\n else:\n sidecore_param = \"\"\n\n self.pidfile.close()\n self.pidfile = NamedTemporaryFile()\n\n kernel_spicific_boot = \"\"\n kernel_command_line = self.kernel_cmdline_additional\n if \"e1000.NG_flags\" not in self.kernel_cmdline_additional and self.guest_e1000_ng_flag != 0:\n kernel_command_line += \" e1000.NG_flags={}\".format(self.guest_e1000_ng_flag)\n if self.kernel:\n kernel_spicific_boot = \"-kernel {kernel} -initrd {initrd} -append '{cmdline} {cmdline_more}'\".format(\n kernel=self.kernel,\n initrd=self.initrd,\n cmdline=self.kernel_cmdline,\n cmdline_more=kernel_command_line\n )\n\n qemu_command = \"numactl -C {cpu} -m 0 {qemu_exe} -enable-kvm {sidecore} -k en-us -m {mem} \" \\\n \"{kernel_additions} \" \\\n \"{qemu_additionals} \" \\\n \"-drive file='{disk}',if=none,id=drive-virtio-disk0,format=qcow2 \" \\\n \"-device virtio-blk-pci,scsi=off,bus=pci.0,addr=0x5,drive=drive-virtio-disk0,id=virtio-disk0,bootindex=1 \" \\\n \"-netdev tap,ifname={tap},id=net0,script=no{vhost} \" \\\n \"-object iothread,id=iothread0 \" \\\n \"-device {dev_type},netdev=net0,mac={mac}{nic_additionals} \" \\\n \"-vnc :{vnc} \" \\\n \"-pidfile {pidfile} \" \\\n \"-monitor tcp:127.0.0.1:1234,server,nowait,nodelay \" \\\n \"-qmp tcp:127.0.0.1:1235,server,nowait,nodelay \" \\\n \"\".format( # -monitor tcp:1234,server,nowait,nodelay\n cpu=self.cpu_to_pin,\n qemu_exe=self.exe,\n sidecore=sidecore_param,\n kernel_additions=kernel_spicific_boot,\n qemu_additionals=self.qemu_additionals,\n disk=self.path,\n tap=self.tap_device,\n vhost=vhost_param,\n dev_type=self.ethernet_dev,\n mac=self.mac_address,\n nic_additionals=self.nic_additionals + self._get_temp_nic_additional(),\n pidfile=self.pidfile.name,\n vnc=self.vnc_number,\n mem=self.mem,\n# \"-pidfile {pidfile} \" \\\n\n \n )\n run_command_async(qemu_command)\n sleep(1)\n if self.qemu_config:\n self.change_qemu_parameters()\n sleep(1)\n if self.io_thread_cpu:\n command = \"sudo taskset -p -c {} {}\".format(self.io_thread_cpu, self.get_pid())\n run_command_check(command)\n if self.is_io_thread_nice:\n self.set_iothread_nice()\n sleep(1)\n self.qmp.connect()\n\n def set_iothread_nice(self, nice=None):\n if nice is None:\n nice = self.io_nice\n run_command_remote(\"127.0.0.1\", \"root\", \"renice -n {} -p {}\".format(nice, self.get_pid()))\n\n def change_qemu_parameters(self, config=None):\n if config:\n self.qemu_config.update(config)\n\n with open(self.QEMU_E1000_DEBUG_PARAMETERS_FILE, \"w\") as f:\n for name, value in self.qemu_config.items():\n f.write(\"{} {}\\n\".format(name, value))\n logger.debug(\"set qemu option: %s=%s\", name, value)\n self._signal_qemu()\n\n def get_pid(self):\n if self._pid:\n return self._pid\n with open(self.pidfile.name, \"r\") as f:\n pid = int(f.read().strip())\n self._pid = pid\n return pid\n\n def _signal_qemu(self):\n pid = self.get_pid()\n os.kill(pid, signal.SIGUSR1)\n\n def configure_guest(self):\n super().configure_guest()\n\n def _configure_host(self):\n pass\n\n def _reset_host_configuration(self):\n pass\n\n\nclass QemuE1000Max(Qemu):\n def __init__(self, *args, **kargs):\n super(QemuE1000Max, self).__init__(*args, **kargs)\n self.qemu_config = {\n \"no_tso_loop_on\": 1,\n \"no_tcp_csum_on\": 1,\n \"zero_copy_on\": 1,\n \"tdt_handle_on_iothread\": 1,\n \"interrupt_mitigation_multiplier\": 10,\n\n \"smart_interrupt_mitigation\": 0,\n \"smart_interrupt_mitigarion\": 0,\n \"latency_itr\": 0,\n\n \"tx_packets_per_batch\": 0,\n\n \"drop_packet_every\": 8000,\n }\n self.ethernet_dev = self.QEMU_E1000\n self.addiotional_guest_command = None\n self.nic_additionals = ',pcix=true'\n\n def _configure_host(self):\n try:\n run_command_check(\"echo 1 | sudo tee /proc/sys/debug/tun/no_tcp_checksum_on\", shell=True)\n except CalledProcessError:\n pass\n\n def _reset_host_configuration(self):\n try:\n run_command_check(\"echo 0 | sudo tee /proc/sys/debug/tun/no_tcp_checksum_on\", shell=True)\n except CalledProcessError:\n pass\n\n def configure_guest(self):\n super().configure_guest()\n commands = (\n \"echo 1 | sudo tee /proc/sys/debug/kernel/srtt_patch_on\",\n \"echo 1 | sudo tee /proc/sys/net/ipv4/tcp_srtt_patch\",\n )\n for cmd in commands:\n try:\n self.remote_command(cmd)\n except CalledProcessError:\n pass\n\n if self.addiotional_guest_command:\n self.remote_command(self.addiotional_guest_command)\n\n\nclass QemuLargeRing(QemuE1000Max):\n def configure_guest(self):\n super(QemuLargeRing, self).configure_guest()\n self.remote_command(\"sudo ethtool -G eth0 rx 4096\")\n self.remote_command(\"sudo ethtool -G eth0 tx 4096\")\n\n\nclass QemuNG(Qemu):\n QEMU_E1000_BETTER = 'e1000-82545em'\n # BOOTUP_WAIT = 50\n\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n self.e1000_options = dict()\n self.large_queue = False\n self.static_itr = False\n self.queue_size = 0\n\n def _get_temp_nic_additional(self):\n return \",\" + \",\".join((\"%s=%s\" % (k, v) for k, v in self.e1000_options.items()))\n\n def configure_guest(self):\n logger.info(\"**********: %s\", self.guest_configure_commands)\n super().configure_guest()\n if self.large_queue:\n self.remote_command(\"sudo ethtool -G eth0 rx {}\".format(self.queue_size))\n self.remote_command(\"sudo ethtool -G eth0 tx {}\".format(self.queue_size))\n if self.static_itr:\n self.remote_command(\"sudo ethtool -C eth0 rx-usecs 4000\")\n\n def get_info(self, old_info=None):\n E1000_OPTIONS = \"e1000_options\"\n LARGE_QUEUE = \"large_queue\"\n STATIC_ITR = \"static_itr\"\n QUEUE_SIZE = \"queue_size\"\n keys = (E1000_OPTIONS, LARGE_QUEUE, STATIC_ITR, QUEUE_SIZE)\n\n info = super().get_info(old_info)\n\n if not self.enabled:\n info.update({k: old_info[k] for k in keys})\n else:\n info.update({k: getattr(self, k) for k in keys})\n\n return info\n\n\nclass QemuE1000NG(QemuNG):\n def __init__(self, *args, **kargs):\n super(QemuE1000NG, self).__init__(*args, **kargs)\n self.e1000_options = {\n \"NG_no_checksum\": \"on\",\n \"NG_no_tcp_seg\": \"on\",\n \"NG_pcix\": \"on\",\n \"NG_tx_iothread\": \"on\",\n \"NG_vsend\": \"on\",\n \"NG_tso_offloading\": \"on\",\n \"NG_drop_packet\": \"off\",\n \"NG_interrupt_mul\": 10, #1, # 10\n \"NG_interrupt_mode\": 0, # 0 - normal,\n # 1 - batch,\n # 2 - smart timer reference to end of recv,\n # 3 - smart timer reference to last interrupt (as normal)\n \"NG_parabatch\": \"off\",\n \"NG_vcpu_send_latency\": \"on\", # skip context switch to iothread if low latency mode detected\n # \"NG_interuupt_momentum\": 1,\n # \"NG_interuupt_momentum_max\": 20,\n \"NG_disable_iothread_lock\": \"off\", # disable taking iothread lock in e1000 mmio\n\n \"NG_disable_TXDW\": \"off\" # disable TXDW interrupt on TX finish\n }\n\n self.ethernet_dev = self.QEMU_E1000\n # self.ethernet_dev = 'e1000-82545em'\n\n self.addiotional_guest_command = None\n self.is_io_thread_nice = False\n self.io_nice = 5 # nice value to set\n # self.kernel = r\"/homes/bdaviv/repos/msc-ng/linux-4.13.9/arch/x86/boot/bzImage\"\n # self.initrd = r\"/homes/bdaviv/repos/msc-ng/vm-files/kernels/initrd.img-4.13.9-ng+\"\n\n def configure_guest(self):\n super().configure_guest()\n commands = (\n \"echo 1 | sudo tee /proc/sys/debug/kernel/srtt_patch_on\", # old location\n # \"echo 1 | sudo tee /proc/sys/net/ipv4/tcp_srtt_patch\", # new location\n # \"echo 1 | sudo tee /proc/sys/net/ipv4/tcp_srtt_patch\", # new location\n )\n for cmd in commands:\n try:\n self.remote_command(cmd)\n except CalledProcessError:\n pass\n\n if self.addiotional_guest_command:\n self.remote_command(self.addiotional_guest_command)\n\n\nclass QemuE1000NGBest(QemuE1000NG):\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n self.e1000_options.update({\n \"NG_no_checksum\": \"on\",\n \"NG_no_tcp_seg\": \"on\",\n \"NG_tx_iothread\": \"on\",\n \"NG_parahalt\": \"on\",\n \"NG_interrupt_mode\": 0,\n \"NG_interrupt_mul\": 0,\n \"mitigation\": \"off\",\n \"NG_disable_rdt_jump\": \"on\",\n \"NG_vsend\": \"on\",\n \"NG_pcix\": \"on\",\n })\n self.guest_e1000_ng_flag = 1\n\n\nclass QemuE1000NGAdaptive(QemuE1000NG):\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n self.e1000_options.update({\n \"NG_no_checksum\": \"on\",\n \"NG_no_tcp_seg\": \"on\",\n \"NG_tx_iothread\": \"on\",\n # \"NG_parahalt\": \"on\",\n \"NG_interrupt_mode\": 3,\n \"NG_interrupt_mul\": 1,\n # \"mitigation\": \"off\",\n \"NG_disable_rdt_jump\": \"on\",\n \"NG_vsend\": \"on\",\n \"NG_pcix\": \"on\",\n })\n self.guest_e1000_ng_flag = 1\n\n\nclass QemuLargeRingNG(QemuE1000NG):\n def __init__(self, *args, **kargs):\n super().__init__(*args, **kargs)\n self.ethernet_dev = 'e1000-82545em'\n self.large_queue = True\n self.static_itr = True\n\n\nclass QemuVirtioNG(QemuNG):\n def __init__(self):\n super().__init__()\n self.e1000_options = {\n \"NG_drop_packet\": \"on\"\n }\n self.ethernet_dev = self.QEMU_VIRTIO\n\n\nclass VMware(VM):\n BOOTUP_WAIT = 15\n\n def setup(self):\n super().setup()\n run_command_check(\"sudo service vmware start\")\n sleep(1)\n\n def _run(self):\n command = \"vmrun -T ws start {} nogui\".format(self.path)\n run_command_check(command)\n\n def teardown(self):\n self.shutdown()\n run_command_check(\"sudo service vmware stop\")\n sleep(1)\n super().teardown()\n\n\nvirtualBox_count = 0\nclass VirtualBox(VM):\n BOOTUP_WAIT = 30\n POWEROFF_WAIT = 10\n\n def setup(self):\n global virtualBox_count\n super().setup()\n if virtualBox_count == 0:\n run_command_check(\"sudo rcvboxdrv start\")\n sleep(1)\n run_command_check(\"VBoxManage hostonlyif create\")\n run_command_check(\"VBoxManage hostonlyif ipconfig vboxnet0 --ip 192.168.56.1\")\n run_command_check(\n \"VBoxManage dhcpserver modify --ifname vboxnet0 --ip 192.168.56.100 --netmask 255.255.255.0 --lowerip 192.168.56.101 --upperip 192.168.56.150 --enable\")\n virtualBox_count += 1\n\n def teardown(self):\n try:\n self.shutdown()\n except:\n pass\n run_command(\"VBoxManage controlvm {} acpipowerbutton\".format(self.path))\n global virtualBox_count\n virtualBox_count -= 1\n if virtualBox_count == 0:\n run_command(\"sudo rcvboxdrv stop\")\n sleep(1)\n super().teardown()\n\n def _run(self):\n command = \"VBoxManage startvm {} --type headless\".format(self.path)\n run_command_check(command)\n","repo_name":"baraveh/E1000_Virtio_test_env","sub_path":"utils/vms.py","file_name":"vms.py","file_ext":"py","file_size_in_byte":19791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"71378718152","text":"import pandas as pd\nimport numpy as np\n\nimport Feature_Engineering.Binning as Binning\n\n\"\"\"\nTo achieve more reliable results, the following methods will be applied in this project:\n - Missing numeric values will be replaced with mean value in the column\n - Missing strings will be replaces with most frequencist value in the column\n - If the missing values is in the target column, then the row will be dropped\n\"\"\"\n\ndef clean_missing(df, target = None):\n # Get headers\n headers = df.columns.values\n\n # Identify missing values -assuming missing values represented '?' symbol in dataset-\n df.replace(\"?\", np.nan, inplace = True)\n\n # Count missing values per column\n missing_data = df.isnull()\n missing_data_counts = []\n\n for column in missing_data.columns.values.tolist():\n missing_data_counts.append(missing_data[column].value_counts())\n\n avg = (df.isnull().sum()).sum()/(len(df.axes[0])*len(df.axes[1])*100)\n\n # print(\"The missing value percentage: %{:.3f}\".format(avg))\n\n # Missing data in target column\n if target != None:\n df.dropna(subset=[target], axis=0, inplace=True)\n\n for header in headers:\n if Binning.is_numeric(df[header]) :\n avg = df[header].astype('float').mean(axis=0) \n\n avg = int(avg) # year, age, model cannot be float\n df[header].replace(np.nan, avg, inplace= True)\n else:\n most_common = df[header].value_counts().idxmax()\n df[header].replace(np.nan, most_common, inplace=True)\n\n return df\n","repo_name":"busetosuner/IBM-Project","sub_path":"Data_Cleaning/Handle_Missing_Values.py","file_name":"Handle_Missing_Values.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"10703483632","text":"from typing import List\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\nfrom processing.text_to_data import BagOfWords\n\n\nclass EmbeddingModel:\n\n def __init__(self, vocab_size: int = 10_000, tokenizer=None):\n self.bow = BagOfWords(vocab_size=vocab_size, tokenizer=tokenizer)\n self._vocab_size = vocab_size\n self.model = None\n\n def load_newline_txt(self, label: str, filepath: str, encoding: str = 'utf-8'):\n self.bow.load_newline_txt(label, filepath, encoding=encoding)\n\n def load_model(self, model_path: str, vocab_path: str, labels: List[str]):\n self.model = self._create_model()\n self.model.load_weights(model_path)\n self.bow.load_vocab_labels(vocab_path, labels)\n\n def save_model(self, model_name: str):\n if self.model:\n self.bow.save_vocab('{}.pkl'.format(model_name))\n self.model.save('{}.h5'.format(model_name))\n\n def _create_model(self):\n model = keras.Sequential()\n model.add(keras.layers.Embedding(self._vocab_size, 16))\n model.add(keras.layers.GlobalAveragePooling1D())\n model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n return model\n\n def _unpack_data(self, test_split: float):\n train, test = self.bow.test_train_split(test_split=test_split)\n return train[0], train[1], test[0], test[1]\n\n @staticmethod\n def _validation_split(train_data, train_labels, validation_split: int):\n x_val, partial_x_train = train_data[:validation_split], train_data[validation_split:]\n y_val, partial_y_train = train_labels[:validation_split], train_labels[validation_split:]\n return x_val, partial_x_train, y_val, partial_y_train\n\n def train_model(self, validation_split: int, padding: int = 256, test_split: float = 0.1):\n self.model = self._create_model()\n self.bow.prepare_data()\n train_data, train_labels, test_data, test_labels = self._unpack_data(test_split)\n train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=padding)\n test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=padding)\n x_val, partial_x_train, y_val, partial_y_train = self._validation_split(train_data, test_labels,\n validation_split)\n self.model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val),\n verbose=1)\n results = self.model.evaluate(test_data, test_labels)\n print('Loss: {}'.format(results[0]))\n print('Accuracy: {}'.format(results[1]))\n\n def predict(self, sentence: str):\n if not self.model:\n raise AttributeError('No model trained or loaded')\n sent = self.bow.sentence_to_data(sentence)\n result = self.model.predict(sent)\n prob, label_int = result[0][0], result[1][0]\n label = sorted(self.bow._labels)[int(label_int)]\n return prob, label\n\n\nif __name__ == '__main__':\n model = EmbeddingModel()\n #model.load_newline_txt('russian', 'russian.txt')\n #model.load_newline_txt('bulgarian', 'bulgarian.txt')\n #model.train_model(5000)\n #model.save_model('my_model')\n model.load_model('my_model.h5', 'my_model.pkl', ['russian', 'bulgarian'])\n result = model.predict('Привет меня зовут')\n print(result)\n result = model.predict('Автобус се заби на спирка в Пловдив, шофьорът издъхна')\n print(result)\n","repo_name":"EdmundMartin/keras_text_classification","sub_path":"embedding_model.py","file_name":"embedding_model.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"2826647338","text":"import urllib.request\r\nfrom bs4 import BeautifulSoup\r\n\r\nweb = urllib.request.urlopen('http://www.swu.ac.kr/www/swuniversity.html')\r\nsoup = BeautifulSoup(web, 'html.parser')\r\ntmp=soup.select('li')\r\nprint(\"*** 서울여자대학교 학과 및 홈페이지 ***\")\r\nprint(\"학과 홈페이지\")\r\nfor i in tmp[:-3]:\r\n if i.text==\"공동기기실\" or i.text==\"컴퓨터학과(*)\" or i.text==\"콘텐츠디자인학과(*)\":\r\n continue\r\n print(i.text,end=\" \")\r\n web2=urllib.request.urlopen('http://www.swu.ac.kr'+i.select_one(\"a\")[\"href\"])\r\n soup2=BeautifulSoup(web2,'html.parser')\r\n tmp2=soup2.find(\"a\", {'class':\"btn btn_xl btn_blue_gray\"})\r\n if tmp2.text==\"홈페이지바로가기\" or tmp2.text==\"홈페이지 바로가기\":\r\n print(tmp2['href'])\r\n else:\r\n print(\"홈페이지가 존재하지 않음\")\r\n\r\n\r\n\r\n","repo_name":"Hosim33/Python","sub_path":"major_parser.py","file_name":"major_parser.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"20870820146","text":"# Modify the previous program so that it can process any number of values. The input\n# terminates when the user just pressed \"Enter\" at the prompt rather than entering a\n# value.\n\na=[]\nb=0\nwhile True:\n d=input(\"Enter a number: \")\n if d!=\"\":\n p=int(d)\n # print(type(p))\n a.append(p)\n else:\n break\nprint(max(a),min(a),sum(a)//len(a))","repo_name":"shredpap/FOCP","sub_path":"week4/programs04/Q8.py","file_name":"Q8.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"73861577991","text":"import random \r\n\r\n#main variables and lists\r\nst = \"stein\"\r\nsc = \"schere\"\r\npa = \"papier\"\r\nec = \"echse\"\r\nsp = \"spock\"\r\nrandomlist = ['schere', 'stein', 'papier', 'echse', 'spock']\r\n\r\nerrormessage = \"Entschuldigung aber dieses Handzeichen kenne ich nicht :/\"\r\nwelcomemessage = \"\"\"Hallo und Herzlich willkommen zu 'Schere, Stein, Papier, Echse, Spock' aus der Serie 'The Big Bang Theory'.\r\nIch hoffe dir gefällt es! \r\n\r\nHier vorab noch mal die Regeln:\r\n\r\nAlso ... \r\n Schere schneidet Papier \r\n Papier bedeckt Stein \r\n Stein zerquetscht Echse\r\n Echse vergiftet Spock\r\n Spock zertrümmert Schere\r\n Schere köpft Echse\r\n Echse frisst Papier\r\n Papier widerlegt Spock\r\n Spock verdampft Stein\r\n Stein schleift Schere\r\n\r\n Falls du noch mehr hilfe brauchst schreib einfach \"help\".\r\n Für den Fall, dass du keine Lust mehr hast schreib einfach \"exit\".\r\n\r\n\r\nDa wir dies nun hoffentlich erledigt haben, fangen wir am besten direkt mit dem Spiel an.\r\n\r\nAlso, ... \"\"\"\r\nwinsmessage = \"Du hast scheinbar gewonnen ... Naja egal, herzlichen Glückwunsch und noch viel spaß =)\"\r\nlosemessage = \"Oh wie's aussieht hast du scheinbar verloren gegen mich verloren >=D ... naja probiers gerne nochmal =)\"\r\ndrawmessage = \"Unentschieden ! Probiers doch gerne nochmal =)\"\r\ncommandlist = [\"help: shows all comands\",\r\n\"exit: leaves rounds\",\r\n\"rules: opens game rules\",\r\n]\r\n\r\n\r\n\r\n#welcome message\r\nprint(welcomemessage)\r\n\r\n\r\n#main loop\r\nwhile (True):\r\n \r\n#input command and random output for opponent\r\n inpu = input(\"Schere, Stein, Papier, Echse oder Spock ? \")\r\n randomoutput = random.choice(randomlist)\r\n\r\n#string-method .lower for input\r\n inpu = inpu.lower()\r\n\r\n wins = 0\r\n loses = 0\r\n draws = 0\r\n\r\n# input error definition\r\n input_error = True\r\n\r\n#scissors query\r\n if inpu == sc:\r\n \r\n #wins\r\n if randomoutput == pa:\r\n wins += 1 \r\n elif randomoutput == ec:\r\n wins += 1\r\n \r\n #loses\r\n elif randomoutput == st:\r\n loses += 1\r\n elif randomoutput == sp:\r\n loses += 1\r\n \r\n #draws\r\n elif randomoutput == sc:\r\n draws +=1\r\n \r\n #correctivity check\r\n input_error = False\r\n\r\n#stone query\r\n elif inpu == st:\r\n \r\n #wins\r\n if randomoutput == sc:\r\n wins += 1 \r\n elif randomoutput == ec:\r\n wins += 1\r\n \r\n #loses\r\n elif randomoutput == sp:\r\n loses += 1\r\n elif randomoutput == pa:\r\n loses += 1\r\n \r\n #draws\r\n elif randomoutput == st:\r\n draws +=1\r\n \r\n #correctivity check\r\n input_error = False\r\n\r\n#paper query\r\n elif inpu == pa:\r\n \r\n #wins\r\n if randomoutput == st:\r\n wins += 1 \r\n elif randomoutput == sp:\r\n wins += 1\r\n \r\n #loses\r\n elif randomoutput == sc:\r\n loses += 1\r\n elif randomoutput == ec:\r\n loses += 1\r\n \r\n #draws\r\n elif randomoutput == pa:\r\n draws +=1\r\n\r\n #correctivity check\r\n input_error = False\r\n\r\n#lizard query\r\n elif inpu == ec:\r\n \r\n #wins\r\n if randomoutput == pa:\r\n wins += 1 \r\n elif randomoutput == sp:\r\n wins += 1\r\n \r\n #loses\r\n elif randomoutput == sc:\r\n loses += 1\r\n elif randomoutput == st:\r\n loses += 1\r\n \r\n #draws\r\n elif randomoutput == ec:\r\n draws +=1\r\n \r\n #correctivity check\r\n input_error = False\r\n\r\n#spock query\r\n elif inpu == sp:\r\n \r\n #wins\r\n if randomoutput == sc:\r\n wins += 1 \r\n elif randomoutput == st:\r\n wins += 1\r\n \r\n #loses\r\n elif randomoutput == pa:\r\n loses += 1\r\n elif randomoutput == ec:\r\n loses += 1\r\n \r\n #draws\r\n elif randomoutput == sp:\r\n draws +=1\r\n \r\n #correctivity check\r\n input_error = False\r\n \r\n\r\n#commands\r\n elif inpu == (\"exit\"):\r\n break\r\n\r\n elif inpu == (\"help\"):\r\n print(\"\")\r\n print(commandlist)\r\n\r\n inpu = inpu.capitalize()\r\n randomoutput = randomoutput.capitalize()\r\n\r\n print(\"\")\r\n#result output\r\n \r\n #User input and bt output\r\n if input_error == False:\r\n print(\"Dein Zug: \" + inpu)\r\n print(\"Der Zug des Computers: \" + randomoutput)\r\n if input_error == True:\r\n print(errormessage)\r\n \r\n #win\r\n if wins > 0:\r\n print(winsmessage) \r\n \r\n #lose\r\n elif loses > 0:\r\n print(losemessage)\r\n \r\n #draw\r\n elif draws > 0:\r\n print(drawmessage)\r\n\r\n print(\"\")\r\n\r\n\r\n#play again \r\n '''print(\"\")\r\n playagain = input(\"Willst du nochmal spielen ? \")\r\n playagain = playagain.lower()\r\n\r\n if playagain == (\"ja\"):\r\n print()\r\n elif playagain == (\"nein\"):\r\n break\r\n elif playagain == (\"exit\"):\r\n break\r\n else:\r\n print(\"\")\r\n print(\"Probiere es nochmal\")\r\n print(\"\") '''\r\n \r\n\r\n#loop ending\r\n \r\n\r\n'''print(\"\") \r\nprint(\"Wins\", wins)\r\nprint(\"\")\r\nprint(\"Loses\", loses)\r\nprint(\"\")\r\nprint(\"Draws\", draws)\r\nprint(\"\")'''\r\n","repo_name":"Hoetti/rock-paper-scissors-lizard-spock","sub_path":"schere stein papier echse spock.py","file_name":"schere stein papier echse spock.py","file_ext":"py","file_size_in_byte":5264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"37208519186","text":"from typing import List\r\n\r\n# Aclaración: Debido a la versión de Python del CMS, para el tipo Lista, la sintaxis de la definición de tipos que deben usar es la siguiente:\r\n# l: List[int] <--Este es un ejemplo para una lista de enteros.\r\n# Respetar esta sintaxis, ya que el CMS dirá que no pasó ningún test si usan otra notación.\r\ndef mesetaMasLarga(l: List[int]) -> int :\r\n # Implementar esta funcion\r\n maximaMeseta = 0\r\n\r\n for x in range(0, len(l), 1):\r\n meseta = detectarMeseta(x, l)\r\n if meseta > maximaMeseta:\r\n maximaMeseta = meseta\r\n\r\n return maximaMeseta\r\n\r\ndef detectarMeseta(i: int, l: List[int]) -> int:\r\n meseta = 1\r\n\r\n if (i == len(l) - 1): return 1\r\n if (i == len(l) - 2):\r\n if (l[i] == l[i + 1]):\r\n return 2\r\n else:\r\n return 1\r\n\r\n for x in range(i, (len(l) - 1), 1):\r\n if (l[x] == l[x + 1]):\r\n meseta = meseta + 1\r\n else:\r\n break\r\n\r\n return meseta\r\n\r\n\r\nif __name__ == '__main__':\r\n x = input()\r\n print(mesetaMasLarga([int(j) for j in x.split()]))","repo_name":"santiago-paz/haskell","sub_path":"CMS-2/mesetaMasLarga.py","file_name":"mesetaMasLarga.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"} +{"seq_id":"6351779355","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[30]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom astropy.modeling import models, fitting\nfrom astropy.io import fits\n\n\n# In[31]:\n\n\ndat=fits.open(\"C:/Users/ANANDHU/Downloads/modtest_image.fits\")[0].data\n\n\n# In[32]:\n\n\nplt.figure()\nplt.imshow(dat)\nplt.colorbar()\nplt.plot(dat)\nplt.show()\n\n\n# In[33]:\n\n\nplt.plot(dat)\nplt.show()\n\n\n# In[34]:\n\n\nx = np.arange(-1, 1, 2/dat.shape[1])\ny = np.arange(-1, 1, 2/dat.shape[0])\nxx, yy = np.meshgrid(x, y)\n\n\n# In[35]:\n\n\nfit = fitting.LevMarLSQFitter()\n\n\n# In[36]:\n\n\nmymod = models.Sersic2D()\n\n\n# In[37]:\n\n\np = fit(mymod,xx,yy,dat)\np\n\n\n# In[38]:\n\n\nplt.imshow(p(xx,yy))\n\n\n# In[39]:\n\n\nplt.plot(p(xx,yy))\nplt.show()\n\n\n# In[40]:\n\n\nprint(p)\n\n\n# In[41]:\n\n\nmymod = models.Gaussian2D()\n\n\n# In[42]:\n\n\nq = fit(mymod,xx,yy,dat)\nq\n\n\n# In[43]:\n\n\nplt.imshow(q(xx,yy))\n\n\n# In[44]:\n\n\nplt.plot(q(xx,yy))\nplt.show()\n\n\n# In[45]:\n\n\nprint(q)\n\n\n# In[46]:\n\n\nprint(p)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"abanend/abanend","sub_path":"Untitled21.py","file_name":"Untitled21.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"46280285844","text":"from random_runner import RandomRunner\nfrom task_generator import TaskGenerator\n\n\ndef test_only_one_task_in_parallel():\n task_generator = TaskGenerator(\n min_task_duration_minutes=2, max_task_duration_minutes=3\n )\n\n runner = RandomRunner(\n task_generator,\n max_task_in_parallel=1,\n max_rand_sleep_period_sec=100,\n num_task_to_execute=3,\n )\n\n runner.start()\n runner.join()\n\n\ndef test_two_task_in_parallel():\n task_generator = TaskGenerator(\n min_task_duration_minutes=1, max_task_duration_minutes=3\n )\n\n runner = RandomRunner(\n task_generator,\n max_task_in_parallel=2,\n max_rand_sleep_period_sec=10,\n num_task_to_execute=6,\n )\n\n runner.start()\n runner.join()\n\n\nif __name__ == \"__main__\":\n test_only_one_task_in_parallel()\n test_two_task_in_parallel()\n","repo_name":"lambdazy/lzy","sub_path":"pylzy/tests/stress/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"27"} +{"seq_id":"74974406792","text":"# numbers in a file, find which add two 2020 and then multiply them to find answer\n\ninpFile = open('numbers.txt', 'r')\nlines = inpFile.readlines()\nlines = sorted(lines)\nfound = False\nfor i in lines:\n\ti = int(i)\n\tfor j in lines:\n\t\tj = int(j)\n\t\tfor aids in lines:\n\t\t\taids = int(aids)\n\t\t\tif i + j + aids == 2020:\n\t\t\t\tprint(i * j * aids)\n\t\t\t\tfound = True\n\t\t\t\tbreak\n\t\tif found:\n\t\t\tbreak\n\tif found:\n\t\tbreak\n","repo_name":"potatokuka/advent_2020","sub_path":"day01/day01.py","file_name":"day01.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"75280577350","text":"import re\nfrom pprint import pprint\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef parse_inputfile(inputfile):\n # position=< 9, 1> velocity=< 0, 2>\n x_pos = []\n y_pos = []\n vx = []\n vy = []\n for line in open(inputfile):\n m = re.search(r\"position=<(.*\\d+),(.*\\d+)> velocity=<(.*\\d+),(.*\\d+)>\", line)\n x_pos.append(int(m.group(1)))\n y_pos.append(int(m.group(2)))\n vx.append(int(m.group(3)))\n vy.append(int(m.group(4)))\n\n return x_pos, y_pos, vx, vy\n\nif __name__ == \"__main__\":\n\n input_file = \"input.txt\"\n x_pos, y_pos, vx, vy = parse_inputfile(input_file)\n\n boxsizes = []\n for i in range(30000):\n # x_pos, y_pos = updateposition(x_pos, y_pos, vx, vy)\n for j in range(len(x_pos)):\n x_pos[j] += vx[j]\n y_pos[j] += vy[j]\n boxsizes.append(\n max(x_pos) - min(x_pos) + max(y_pos) - min(y_pos)\n )\n\n imin = np.argmin(boxsizes)\n\n print(\"Have to wait %s seconds\" % (imin + 1))\n x_pos, y_pos, vx, vy = parse_inputfile(input_file)\n\n for i in range(len(x_pos)):\n x_pos[i] = x_pos[i] + ((imin + 1) * vx[i])\n y_pos[i] = (\n y_pos[i] + ((imin + 1) * vy[i])\n ) * -1 # Flip the image\n\n plt.plot(x_pos, y_pos, \"bo\")\n plt.show()\n\n\n# RPNNXFZR\n# 10946\n","repo_name":"bvanhoewijk/adventofcode","sub_path":"day10/day10.py","file_name":"day10.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"1938788527","text":"#!/usr/bin/env python\nimport rospy\nimport ros_numpy\nimport numpy\nimport numpy as np\n\n\nfrom sensor_msgs.msg import PointCloud2\nimport std_msgs.msg\nimport sensor_msgs.point_cloud2 as pcl2\nfrom sensor_msgs import point_cloud2\nfrom sensor_msgs.msg import PointField\nfrom std_msgs.msg import Header\n\n\n\nminRange = pow(10,10)\npcl = PointCloud2()\npclHeader = None\npclFields = None \n\noriginal_pcl = PointCloud2()\n\nshortestPcl = 0\n#need to have installed ros_numpy \ndef shortestPoint(array, origin):\n shortestDist = pow(10,10)\n closestPoint = array[0]\n for x in array: \n #print(x)\n dist = numpy.sqrt(numpy.sum((origin-x)**2))\n if (dist < shortestDist):\n shortestDist = dist\n closestPoint = x\n return closestPoint\n\ndef callback(msg):\n global test\n global shortestPcl\t\n global pclFields\n global pclHeader\n global pcl\n arr = ros_numpy.point_cloud2.pointcloud2_to_xyz_array(msg)\n print(arr)\n origin = numpy.array((0 ,0, 0))\n \n #find shortest dist in each array, and print it per callback \n dist = numpy.sqrt(numpy.sum((origin-arr[0])**2))\n #print(dist)\n\n pcl = msg\n shortestPcl = shortestPoint(arr, origin)\n pclFields = msg.fields \n pclHeader = msg.header\n\n #print(shortestPoint(arr, origin)) \n\n print('\\n')\n\n\n\n \n\ndef loop():\n rospy.init_node('ranges')\n pub = rospy.Publisher('/PointCloudTest', PointCloud2, queue_size=1)\n pub_two = rospy.Publisher('/CarlaPointCloud', PointCloud2, queue_size=1)\n\n #minRange = pow(10, 10)\n maxRange = 0\n #sub = rospy.Subscriber('/carla/hero/lidar/front/point_cloud', PointCloud2, callback, (maxRange))\n sub = rospy.Subscriber('/carla/hero/lidar/front/point_cloud', PointCloud2, callback)\n #rospy.spin()\n #rate = rospy.Rate(pow(10,10)) #original\n rate = rospy.Rate(10)\n \n header = std_msgs.msg.Header()\n header.stamp = rospy.Time.now()\n header.frame_id = 'map'\n scaled_polygon_pcl = None\n new_pcl = None \n \n while not rospy.is_shutdown():\n # cloud_points = [[shortestPcl]]\n # cloud_points = [[1.0, 1.0, 0.0]]\n if (isinstance(shortestPcl, numpy.ndarray)):\n cloud_points = [ [shortestPcl[0], shortestPcl[1], shortestPcl[2]] ]\n #scaled_polygon_pcl = pcl2.create_cloud_xyz32(header, cloud_points)\n new_pcl = point_cloud2.create_cloud(pclHeader,pclFields, cloud_points)\n print(new_pcl)\n pub.publish(new_pcl) \n\n\n #pub_two.publish(pcl)\n #print(cloud_points)\n #pub.publish(scaled_polygon_pcl)\n #print(scaled_polygon_pcl)\n \n \n rate.sleep()\n\n\n\nif __name__ == '__main__':\n loop()\n\n\n\n\n# def shortestPoint(array, origin):\n# shortestDist = pow(10,10)\n# for x in np.nditer(array):\n# # dist = numpy.sqrt(numpy.sum((origin-x)**2))\n# # if (dist < shortestDist):\n# # shortestDist = dist\n# print(x)\n# print('\\n')\n# return shortestDist","repo_name":"Janmart10/hello-world","sub_path":"carla-ros-bridge/catkin_ws/src/carla_packages/src/scripts/carla_pcl.py","file_name":"carla_pcl.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"43972230975","text":"import pandas as pd\nimport numpy as np\n\n# for stats\nimport scipy.stats as stats\nfrom sklearn import preprocessing\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom sklearn.model_selection import train_test_split\nimport statsmodels.api as sm\nfrom sklearn import linear_model\nfrom sklearn import metrics\n\n# for plotting and tables\nfrom tabulate import tabulate\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport seaborn as sns\nimport matplotlib as mpl\nmpl.rcParams.update({\n 'font.size' : 18.0,\n 'axes.titlesize' : 'medium',\n 'axes.labelsize' : 'medium',\n 'xtick.labelsize' : 'small',\n 'ytick.labelsize' : 'small',\n 'legend.fontsize' : 'medium',\n})\n\n\ndef calculate_vif(X, thresh=10.0):\n '''\n Values with VIF > thresh are too collinear and will be removed from dataframe\n Standard thresh is 10\n INPUT:\n X = DataFrame\n thresh = float\n OUTPUT:\n dataframe\n '''\n dropped=True\n while dropped:\n col = X.columns\n dropped = False\n vif = [variance_inflation_factor(X[col].values, col.get_loc(var)) for var in col]\n\n max_vif = max(vif)\n if max_vif > thresh:\n value = vif.index(max_vif)\n print(f'Dropping {X.columns[value]} with vif={max_vif}')\n X = X.drop([X.columns.tolist()[value]], axis=1)\n dropped=True\n return X\n\ndef scale_data(X):\n '''\n Standardize and scale data\n INPUT:\n X = array\n OUTPUT:\n array\n '''\n x = X.values\n snd_scaler = preprocessing.StandardScaler()\n mm_scaler = preprocessing.MinMaxScaler()\n x_scaled = mm_scaler.fit_transform(x)\n X_scaled = pd.DataFrame(data=x_scaled,columns=X.columns, index=X.index)\n\n return X_scaled\n\ndef make_model(X_train, y_train):\n '''\n Makes a logistic regression model and returns probabilities, predictions, coefficients,\n and the classification and confusion matrices\n INPUT:\n X_train = dataframe\n y_train = dataframe\n OUTPUT:\n array, array, array, array, array, array\n '''\n model = linear_model.LogisticRegression()\n model.fit(X_train, y_train)\n probabilities = model.predict_proba(X_test)[:,1]\n y_pred = model.predict(X_test)\n\n # find coeffiecents, classification matrix, and confusion matrix\n coef = model.coef_\n precision = metrics.precision_score(y_test, y_pred)\n recall = metrics.recall_score(y_test, y_pred)\n cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n\n return probabilities, y_pred, coef, precision, recall, cnf_matrix\n\ndef roc(probabilities, labels):\n '''\n Take a numpy array of the predicted probabilities and a numpy array of the\n true labels.\n Return the True Positive Rates, False Positive Rates and Thresholds for the\n ROC curve.\n INPUT:\n probabilities = numpy array\n labels = numpy array\n OUTPUT:\n list, list, list\n '''\n\n thresholds = np.sort(probabilities)\n tprs = []\n fprs = []\n\n num_positive_cases = sum(labels)\n num_negative_cases = len(labels) - num_positive_cases\n\n for threshold in thresholds:\n predicted_positive = probabilities >= threshold\n true_positives = np.sum(predicted_positive * labels)\n false_positives = np.sum(predicted_positive) - true_positives\n\n tpr = true_positives / float(num_positive_cases)\n fpr = false_positives / float(num_negative_cases)\n\n fprs.append(fpr)\n tprs.append(tpr)\n\n return tprs, fprs, thresholds.tolist()\n\ndef plot_roc(fpr,tpr,title):\n '''\n Plot ROC curve\n INPUT:\n fpr, tpr = array\n title = string\n OUTPUT:\n plt.plot\n '''\n plt.plot(fpr, tpr,label='auc = {0:.2f}'.format(metrics.auc(fpr,tpr)))\n plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n label='Luck')\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title(title)\n plt.legend()\n plt.tight_layout()\n plt.savefig('plotting_roc')\n plt.show()\n\n# for plotting and tables:\ndef heatplot(X):\n '''\n Plot correlation heatmap\n INPUT:\n matrix = DataFrame\n OUTPUT:\n sns.heatmap\n '''\n f, ax = plt.subplots(figsize=(11, 9))\n\n cmap = sns.diverging_palette(220, 10, as_cmap=True)\n mask = np.zeros_like(X, dtype=np.bool)\n mask[np.triu_indices_from(mask)] = True\n\n ax = sns.heatmap(X, mask=mask, cmap=cmap, vmax=1.0, center=0,\n square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n ax.set_title('Pearson Correlation Coeficient Matrix')\n plt.tight_layout()\n plt.savefig('corr_plot')\n plt.tight_layout()\n plt.show()\n\ndef to_dataframe(lst,col = None):\n '''\n convert list to dataframe\n INPUT:\n lst = list\n col = string\n OUTPUT:\n dataframe\n '''\n arr = np.array([lst])\n return pd.DataFrame(arr, columns=col)\n\ndef to_markdown(df, round_places=3):\n '''\n Returns a markdown, rounded representation of a dataframe\n INPUT:\n df = DataFrame\n OUTPUT:\n markdown table\n '''\n print(tabulate(df.round(round_places), headers='keys', tablefmt='pipe'))\n\n\nif __name__ == \"__main__\":\n df = pd.read_csv('data/cleaned.csv')\n X = df.iloc[0:1000].drop('Unnamed: 0', axis=1)\n y = X.pop('koi_pdisposition')\n\n # splitting data\n X_scaled = scale_data(X)\n X= calculate_vif(X_scaled,thresh=10)\n X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=130)\n model_table = to_markdown(X_train.iloc[0:10])\n\n # Compute pairwise correlation of remaining columns\n correlated = X_train.corr(method='pearson', min_periods=1)\n heatplot(correlated)\n\n # Make predictive model and plot roc curve\n probabilities, y_pred, coef, precision, recall, cnf_matrix = make_model(X_train, y_train)\n fpr, tpr, thresholds = metrics.roc_curve(y_test, probabilities)\n plot_roc(fpr,tpr,title='ROC plot of Model')\n\n # Put info into tables\n df_prec_recall = to_dataframe([precision, recall],col = ['precision', 'recall'])\n prec_table = to_markdown(df_prec_recall)\n","repo_name":"emle2899/Capstone1-Exoplanets","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":6036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37693184453","text":"#!/bin/python3\n# -*- coding: utf-8 -*-\n\n####\n# Program: hex2bin\n# Author: Orange233\n# Date: 2023 Jul 14th\n####\n\n# !WIP: 可以增加追加写入结果。\n# !WIP: 增加选项，设置程序仅识别大写字母，并启用非安全检查模式来加快解码速度。\n\nimport os\nimport sys\n\ndef __proc_direct(origin: str) -> tuple[str, str]:\n\treturn origin, None\n\nhex_proc_functions = {\n\t'direct': __proc_direct\n}\n\ndef __drop_warn(warn: str) -> None:\n\tpass\n\ndef __hex_to_halfbyte(hex: str) -> int:\n\thex = ord(hex)\n\tif ord('0') <= hex and hex <= ord('9'):\n\t\treturn hex - ord('0')\n\telif ord('A') <= hex and hex <= ord('F'):\n\t\treturn hex - ord('A') + 10\n\telif ord('a') <= hex and hex <= ord('f'):\n\t\treturn hex - ord('a') + 10\n\ndef __hex_to_byte(high: str, low: str) -> int:\n\tr = (__hex_to_halfbyte(high) << 4) | __hex_to_halfbyte(low)\n\treturn r\n\ndef __decode(hex_str: str, warn_func) -> tuple[bytes, str]:\n\tif len(hex_str) % 2 == 1:\n\t\twarn_func('Numbers are not paired. Automatically supplded a 0 as low bit.')\n\t\thex_str += '0'\n\n\tr = bytearray()\n\ti = 0\n\twhile i < len(hex_str):\n\t\thigh = hex_str[i]\n\t\tlow = hex_str[i + 1]\n\n\t\tbyte = __hex_to_byte(high, low)\n\t\tr.append(byte)\n\n\t\ti = i + 2\n\treturn bytes(r)\n\ndef decode_main(hex_str: str, mode: str, warn_func) -> tuple[bytes, str]:\n\tif warn_func is None:\n\t\twarn_func = __drop_warn\n\n\t# 先重新组合字符串\n\tmode = mode.lower()\n\tif mode not in hex_proc_functions:\n\t\treturn None, 'Mode \"' + mode + '\" not found.'\n\thex_str, err = hex_proc_functions[mode](hex_str)\n\tif err is not None:\n\t\treturn None, err\n\n\t#解码\n\tr = __decode(hex_str, warn_func)\n\treturn r, None\n\ndef __print_warn(warn: str) -> None:\n\tprint(warn)\n\n\ndef __save(result: bytes, output_path: str) -> None:\n\t# [head:1]\n\t# -> is_exists:\n\t# *y -> ask_overwrite:\n\t# *y -> goto save.\n\t# *N -> [ask:2]\n\t# -> ask_continue_save:\n\t# *Y -> ask_new_path:\n\t# -> goto head.\n\t# *n -> done.\n\t# *n -> [save:3]\n\t# -> try_open_file:\n\t# *err -> goto ask.\n\t# *ok -> save:\n\t# -> done.\n\t# [done:0]\n\n\tflag = 1\n\twhile flag > 0:\n\t\tif flag == 1: #[head]\n\t\t\tif os.path.exists(output_path): #is_exists\n\t\t\t\t# *y:\n\t\t\t\t# ask_overwrite:\n\t\t\t\tinput_str = input('Output file has been exists. Do you want to overwrite it? [yes/No] ')\n\t\t\t\tinput_str = input_str.lower()\n\n\t\t\t\tif input_str not in ['yes', 'y']:\n\t\t\t\t\t# *N:\n\t\t\t\t\tflag = 2 #goto ask.\n\t\t\t\telse:\n\t\t\t\t\t# *y:\n\t\t\t\t\tflag = 3 #goto save.\n\t\t\telse:\n\t\t\t\t# *n:\n\t\t\t\tflag = 3 #goto save.\n\t\t\n\t\telif flag == 2: #[ask]\n\t\t\t# ask_continue_save:\n\t\t\tinput_str = input('Do you want to continue saving (use other path to save the result)? [Yes/no] ')\n\t\t\tinput_str = input_str.lower()\n\n\t\t\tif input_str not in ['no', 'n']:\n\t\t\t\t# *Y:\n\t\t\t\t# ask_new_path:\n\t\t\t\toutput_path = input('Path to save: ')\n\t\t\t\tflag = 1 #goto head.\n\t\t\telse: \n\t\t\t\t# *n:\n\t\t\t\tflag = 0 #done.\n\n\t\telif flag == 3: #[save]\n\t\t\t# try_open_file:\n\t\t\tfile = None\n\t\t\ttry:\n\t\t\t\tfile = open(output_path, 'wb')\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n\t\t\t\n\t\t\tif file is None:\n\t\t\t\t# *err:\n\t\t\t\tflag = 2 #goto ask.\n\t\t\telse:\n\t\t\t\t# *ok:\n\t\t\t\t# save:\n\n\t\t\t\tfile.write(result)\n\t\t\t\tfile.close()\n\n\t\t\t\tprint('Saved to \"' + output_path + '\".')\n\n\t\t\t\tflag = 0 #done.\n\t\telse:\n\t\t\t# unknown:\n\t\t\ttry:\n\t\t\t\traise Exception('Unknown error.')\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)\n\t\t\tflag = 2 #goto ask.\n\nif __name__ == '__main__':\n\t# `python hex2bin.py out.bin direct \"a1\" `\n\n\t__hex_str = sys.argv[3]\n\t__mode = sys.argv[2]\n\t__output_path = sys.argv[1]\n\n\t__result, __err = decode_main(__hex_str, __mode, __print_warn)\n\n\tif __err is not None:\n\t\tprint('Error: ' + __err)\n\t\tos._exit(-1)\n\t\n\t__save(__result, __output_path)\n\n\tos._exit(0)","repo_name":"Orange23333/hex2bin","sub_path":"hex2bin.py","file_name":"hex2bin.py","file_ext":"py","file_size_in_byte":3720,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"8628390731","text":"from django.core.urlresolvers import reverse\nfrom core.models import Recipe\nfrom .test_utils import TestDataProvider, RecipeTestCase\n\n\nclass RecipeDeleteViewTest(RecipeTestCase):\n\n def setUp(self):\n TestDataProvider.create_channels()\n TestDataProvider.create_recipe()\n self.user = TestDataProvider.create_user()\n self.client.force_login(self.user)\n\n def test_get(self):\n\n testRecipe = Recipe.objects.first()\n res = self.client.get(reverse(\"recipes:delete\", args=(testRecipe.id,)))\n self.assertTemplate(\"recipes/recipe_delete.html\", res)\n self.assertEqual(testRecipe, res.context['recipe'])\n\n def test_post__keep(self):\n\n recipeCountBefore = Recipe.objects.all().count()\n testRecipe = Recipe.objects.first()\n\n postData = {\"action\": \"keep\"}\n res = self.client.post(reverse(\"recipes:delete\",\n args=(testRecipe.id,)),\n data=postData,\n follow=True)\n\n recipeCountAfter = Recipe.objects.all().count()\n self.assertEqual(recipeCountBefore, recipeCountAfter)\n\n allRecipes = Recipe.objects.all()\n self.assertIn(testRecipe, allRecipes)\n\n def test_post__delete(self):\n\n recipeCountBefore = Recipe.objects.all().count()\n testRecipe = Recipe.objects.first()\n\n postData = {\"action\": \"delete\"}\n res = self.client.post(reverse(\"recipes:delete\",\n args=(testRecipe.id,)),\n data=postData,\n follow=True)\n\n expectedCount = recipeCountBefore - 1\n recipeCountAfter = Recipe.objects.all().count()\n self.assertEqual(expectedCount, recipeCountAfter)\n\n allRecipes = Recipe.objects.all()\n self.assertNotIn(testRecipe, allRecipes)\n","repo_name":"daisychainme/daisychain","sub_path":"daisychain/recipes/tests/test_RecipeDeleteView.py","file_name":"test_RecipeDeleteView.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"} +{"seq_id":"10796084275","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/11/21 10:26\n# @Author : liuyun\n# @File : process_bar2.py\nimport threading\nimport time\n\nimport requests\nimport os\nfrom datetime import datetime\n\ndownload_list = list()\n\n\nclass DownloadBar():\n\n def __init__(self, url, filename):\n\n self.url = url\n self.filename = filename\n\n def down(self):\n self.resp = requests.get(self.url, stream=True)\n self.total_size = int(self.resp.headers[\"content-Length\"])\n down_t = threading.Thread(target=self.download_file, args=[])\n\n down_t.start()\n self.download_progress()\n\n def download_file(self):\n # url = \"https://qd.myapp.com/myapp/qqteam/pcqq/QQ9.0.7_1.exe\"\n self.start = datetime.now()\n with open(self.filename, 'wb') as f:\n for chunk in self.resp.iter_content(512):\n if chunk:\n f.write(chunk)\n\n def download_progress(self):\n # 已下载/花费总时间\n self.file_size = 0\n print(\"开始下载:总大小:%s Mb\" % (self.total_size / 1024 / 1024))\n while self.file_size < self.total_size:\n self.file_size = os.path.getsize(self.filename)\n now = datetime.now()\n cost_time = (now - self.start).seconds\n if cost_time != 0:\n self.rate = self.file_size / 1024 / 1024 / cost_time\n else:\n self.rate = 0\n if self.rate != 0:\n self.remain_time = int((self.total_size - self.file_size) / (self.rate * 1024 * 1024))\n else:\n self.remain_time = \"..\"\n self.view_bar()\n time.sleep(1)\n\n def view_bar(self):\n num = int(50 * (self.file_size / float(self.total_size)))\n blank = 50 - num\n self.rate = \"%.2f\" % float(self.rate)\n\n bar = \" [%s%s]%.2f%% 速度:%s Mb/s 剩余: %ss \" % (\n \"#\" * num, \" \" * blank, num * 100 / 50, self.rate, self.remain_time)\n return bar\n\n\ndef print_bar():\n while download_list:\n bar_str = ''\n for download in download_list:\n bar_str += download.view_bar()\n # bar_str += '\\n'\n print ('\\r %s' % bar_str),\n time.sleep(1)\n\n\ndownbar = DownloadBar(\"https://qd.myapp.com/myapp/qqteam/pcqq/QQ9.0.7_1.exe\", \"QQ9.0.7_1.exe\")\nthreading.Thread(target=downbar.down).start()\ndownload_list.append(downbar)\ndownbar2 = DownloadBar(\"https://qd.myapp.com/myapp/qqteam/pcqq/QQ9.0.7_1.exe\", \"QQ9.0.7_2.exe\")\ndownload_list.append(downbar2)\nthreading.Thread(target=downbar2.down).start()\ndownbar3 = DownloadBar(\"https://qd.myapp.com/myapp/qqteam/pcqq/QQ9.0.7_1.exe\", \"QQ9.0.7_3.exe\")\ndownload_list.append(downbar3)\nthreading.Thread(target=downbar3.down).start()\n\nthreading.Timer(10, print_bar).start()\n","repo_name":"helloworld28/demos","sub_path":"pythondemo/module1/process_bar2.py","file_name":"process_bar2.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"39627509654","text":"# -*- coding: utf8 -*-\n\nimport shlex\nimport subprocess\n\n\n# command line test\ndef test_cmdline():\n \"\"\"Get usage and help functions\"\"\"\n result = subprocess.Popen(shlex.split('./dyndnsupdate.py --help'), stdout=subprocess.PIPE)\n stdout, stderr = result.communicate()\n assert 'usage:' in stdout.decode()\n\n result = subprocess.Popen(shlex.split('./dyndnsupdate.py --version'), stdout=subprocess.PIPE)\n stdout, stderr = result.communicate()\n assert 'DynDNS client version' in stdout.decode()\n\ndef test_cmdline_exit_with_2():\n \"\"\"Feed a bad url must produce a 2 return code\"\"\"\n result = subprocess.Popen(shlex.split('./dyndnsupdate.py --dyn-address 1.1.1.1 --dyn-server ftp://www.ovh.com --dyn-hostname d'), stdout=subprocess.PIPE)\n stdout, stderr = result.communicate()\n assert result.returncode == 2\n","repo_name":"Turgon37/DynDNSUpdate","sub_path":"tests/test_cmdline.py","file_name":"test_cmdline.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"9945374464","text":"flagMap = {}\n\ndef initDB(fn):\n try:\n log = open(fn, \"r\")\n for line in log:\n idd, _, frm, to, _, key, _, pwd = line.split(\" \")\n pwd = pwd.strip()\n flagMap[to] = (key, pwd)\n \n log.close()\n except Exception as e:\n print(e)\n \ninitDB(\"passwords-b-dump.txt\")\n\n# frm, key pairs - continue from\ndef findEntries():\n l = []\n for key, value in flagMap.items():\n l.append((key, value[1]))\n return l\n\n# used to reduce redundancy during the search\ndef already(n):\n return n in flagMap\n \ndef getID(name):\n import pwn\n pwn.context.log_level = 'warn'\n p = pwn.process('./output/'+name)\n key, pwd = flagMap[name]\n \n # send decrypt password\n if key != \"None\":\n p.send(bytes.fromhex(key))\n p.recvline()\n \n # send password\n p.send(bytes.fromhex(pwd))\n \n v = 0\n try:\n v = int(p.recvline().strip().split(b\"ID: \")[1])\n except Exception as e:\n print(\"[!] \"+name+\" could not be parsed correctly\") \n v = 0\n p.close()\n return v\n \ndef makeImage():\n imageMap = {}\n \n # part (b) binaries\n i = 0\n for key, value in flagMap.items():\n if i % 1000 == 0:\n print(str(i)+\" binaries processed\")\n idd = getID(key)\n imageMap[idd] = bytes.fromhex(value[1])\n i = i + 1\n \n # backup (b) binary mapping\n log = open(\"passwords-b.txt\", \"w\")\n for key, value in imageMap.items(): \n log.write(str(key)+\" \"+value.hex()+\"\\n\")\n log.close()\n \n # solution from (a)\n log = open(\"passwords-a.txt\", \"r\")\n for line in log:\n idd, pwd = line.split(\" \")\n pwd = bytes.fromhex(pwd.strip())\n idd = int(idd)\n imageMap[idd] = pwd\n log.close()\n \n # check for missing index stuff\n for i in range(len(imageMap)):\n if not (i in imageMap):\n print(\"[!] \"+str(i)+\" is missing in the map\")\n\n \n # write all bytes in order\n img = open(\"output2.bmp\", \"wb\")\n for i in range(len(imageMap)):\n if not (i in imageMap): \n img.write(b\"\\x00\"*8)\n else:\n img.write(imageMap[i]) \n img.close()","repo_name":"Pusty/writeups","sub_path":"DEFCONCTFQuals2023/flagcombiner.py","file_name":"flagcombiner.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"27"} +{"seq_id":"20974917950","text":"import os\nimport logging\nimport util\nfrom dotenv import load_dotenv\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\ndef main():\n load_dotenv()\n SECRET_KEY_SJ = os.getenv('SECRET_KEY_SJ')\n\n programming_langs = ['C#',\n 'Go',\n 'C++',\n 'PHP',\n 'Ruby',\n 'Java',\n 'Scala',\n 'Swift',\n 'Python',\n 'TypeScript',\n 'JavaScript'] \n\n stat_hh = util.get_statistics_salarys_hh(programming_langs)\n stat_sj = util.get_statistics_salarys_sj(programming_langs, SECRET_KEY_SJ)\n\n util.rendering_statiс_data(stat_hh, 'HeadHunter')\n util.rendering_statiс_data(stat_sj, 'SuperJob')\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"rosoporto/job-rating","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22104252473","text":"import json\nimport re\n\n\ndef split_camel_case(str):\n return ' '.join(re.findall(r'[A-Z](?:[a-z]+|[A-Z]*(?=[A-Z]|$))', str))\n\n\ndef prepare_candidates(l):\n return list(set(filter(lambda x: x is not None, l)))\n\n\ndef prepare_string(str: str):\n if str:\n return str.lower().strip()\n return None\n\n\ndef describe_column(col, prev_col_path, prefixes, type):\n if \"Localization\" in col.get('tableName', '') or \"ChangeTracking\" in col.get('tableName', '') or col['name'] == \"IsDeleted\":\n return None\n candidates = prepare_candidates([prepare_string(col['caption']), prepare_string(split_camel_case(col['name']))])\n if prefixes:\n candidates = [f\"{c} of {p}\" for c, p in zip(candidates, prefixes)]\n return {\n \"name\": f\"{prev_col_path}{col['name']}\",\n \"refs\": candidates,\n \"type\": type\n }\n\n\ndef process_columns(columns, prev_col, prefixes, type):\n return filter(lambda x: x is not None, (map(lambda x: describe_column(x, prev_col, prefixes, type), columns)))\n\n\ndef process_all_columns(model, structure, depth = 0, prev_col = \"\", prefixes = []):\n if depth > 1:\n return []\n columns_ref = [\n *process_columns(model.get('booleanFieldsStructure', []), prev_col, prefixes, 'bool'),\n *process_columns(model.get('textFieldsStructure', []), prev_col, prefixes, 'text'),\n *process_columns(model.get('numberFieldsStructure', []), prev_col, prefixes, 'number'),\n *process_columns(model.get('dateTimeFieldsStructure', []), prev_col, prefixes, 'date'),\n *process_columns(model.get('entityCollectionsStructure', []), prev_col, prefixes, 'collections')\n ]\n\n for col in model.get('lookupFieldsStructure', []):\n next_model = list(filter(lambda x: x['tableName'] == col['tableName'], structure))\n if next_model:\n prefs = prepare_candidates([prepare_string(next_model[0]['caption']), prepare_string(split_camel_case(next_model[0]['tableName']))])\n columns_ref.extend(process_all_columns(next_model[0], structure, depth=depth + 1, prev_col=f\"{col['name']}.\", prefixes=prefs))\n \n return columns_ref\n\n\ndef describe_model(model, structure):\n if \"Localization\" in model['tableName'] or \"ChangeTracking\" in model['tableName']:\n return None\n\n description = {}\n\n model_names_candidates = prepare_candidates([prepare_string(model['caption']), prepare_string(split_camel_case(model['tableName']))])\n\n description[\"name\"] = model['tableName']\n description[\"refs\"] = model_names_candidates\n description[\"cols\"] = process_all_columns(model, structure)\n\n return description\n\n\ndef generate():\n with open('src/structure/structure.json', 'r') as structure_file:\n structure = json.load(structure_file)\n\n descriptions = list(filter(lambda x: x is not None, map(lambda m: describe_model(m, structure), structure)))\n\n with open('src/structure/structure-description.json', 'w+') as f:\n json.dump(descriptions, f)\n\n\nif __name__ == \"__main__\":\n generate()","repo_name":"Dymasik/ai-code-generation","sub_path":"src/structure-description-generator.py","file_name":"structure-description-generator.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"11901227837","text":"#!/usr/bin/python\n\nimport sys\nimport re\nfrom xml.etree import ElementTree as ET\n\n\"\"\" Merges xml files so that values from patchFile are put in file \"\"\"\ndef patch(filename, patchFilename):\n baseTree = ET.parse(filename)\n patchTree = ET.parse(patchFilename)\n patchLeaves(baseTree, patchTree.getroot(), \"\")\n baseTree.write(sys.stdout)\n\ndef patchLeaves(baseTree, node, path):\n if (len(list(node)) == 0):\n matchingNodes = baseTree.findall(\".\" + path)\n if (len(matchingNodes) > 1):\n raise RuntimeError(\"Multiple matches\")\n else:\n target = baseTree.find(\".\" + path)\n if (target is None):\n raise RuntimeError(\"Base tree node not found: \" + path)\n else:\n target.text = node.text\n else:\n for child in node:\n patchLeaves(baseTree, child, path + \"/\" + child.tag)\n\npatch(sys.argv[1], sys.argv[2])\n\n","repo_name":"nebulostore/nebulostore","sub_path":"resources/conf/merge_xml.py","file_name":"merge_xml.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"42914681503","text":"from array import array\r\nfrom random import *\r\n\r\nimport sys\r\nimport time\r\n\r\nclass image:\r\n\tdef __init__(self, width, height):\r\n\t\tself.width = width\r\n\t\tself.height = height\r\n\r\n\t\t## TGA header\r\n\t\tself.hdr = array('B')\r\n\t\tself.hdr.fromstring(\"\\x00\\x00\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\")\r\n\t\tself.hdr.append((self.width >> 0) & 0xff)\r\n\t\tself.hdr.append((self.width >> 8) & 0xff)\r\n\t\tself.hdr.append((self.height >> 0) & 0xff)\r\n\t\tself.hdr.append((self.height >> 8) & 0xff)\r\n\t\tself.hdr.append(24)\r\n\t\tself.hdr.append(0x30)\r\n\t\tself.rgb = array('B')\r\n\r\n\t\t## start with all channels fully white\r\n\t\tfor i in xrange(width * height * 3):\r\n\t\t\tself.rgb.append(255)\r\n\r\n\tdef write_tga(self, f):\r\n\t\tself.hdr.tofile(f)\r\n\t\tself.rgb.tofile(f)\r\n\t\tf.close()\r\n\r\n\tdef generate_random_noise(self):\r\n\t\tfor y in xrange(self.height):\r\n\t\t\tfor x in xrange(self.width):\r\n\t\t\t\tpxl_idx = (y * self.width + x) * 3\r\n\r\n\t\t\t\tself.rgb[pxl_idx + 0] = int(random() * 255)\r\n\t\t\t\tself.rgb[pxl_idx + 1] = int(random() * 255)\r\n\t\t\t\tself.rgb[pxl_idx + 2] = int(random() * 255)\r\n\r\n\tdef generate_stepped_gradient(self):\r\n\t\tfor y in xrange(self.height):\r\n\t\t\tfor x in xrange(self.width):\r\n\t\t\t\tpxl_col = int(round((x / (self.width / 10)) * (255.0 / (10 - 1))))\r\n\t\t\t\tpxl_idx = (y * self.width + x) * 3\r\n\r\n\t\t\t\tself.rgb[pxl_idx + 0] = pxl_col\r\n\t\t\t\tself.rgb[pxl_idx + 1] = pxl_col\r\n\t\t\t\tself.rgb[pxl_idx + 2] = pxl_col\r\n\r\n\tdef voronoi_crystallize_image(self, cell_size):\r\n\t\trows = self.height / cell_size\r\n\t\tcols = self.width / cell_size\r\n\r\n\t\tif ((self.height % cell_size) != 0):\r\n\t\t\trows += 1\r\n\t\tif ((self.width % cell_size) != 0):\r\n\t\t\tcols += 1\r\n\r\n\t\tpoints = [0, 0] * (rows * cols)\r\n\t\tpindex = 0\r\n\r\n\t\t## pick random points as the cell centers\r\n\t\tfor row in xrange(rows):\r\n\t\t\tfor col in xrange(cols):\r\n\t\t\t\tpoints[pindex ] = randint(col * cell_size, (col + 1) * cell_size - 1)\r\n\t\t\t\tpoints[pindex + 1] = randint(row * cell_size, (row + 1) * cell_size - 1)\r\n\t\t\t\tpindex += 2\r\n\r\n\t\tdef sq_distance(x0, y0, x1, y1): return ((x1 - x0) ** 2.0 + (y1 - y0) ** 2.0)\r\n\t\tdef distance(x0, y0, x1, y1): return (sq_distance(x0, y0, x1, y1) ** 0.5)\r\n\r\n\t\tdef get_ngb_cell_indices(x, y, r = 5):\r\n\t\t\tcell_x = x / cell_size\r\n\t\t\tcell_y = y / cell_size\r\n\r\n\t\t\tcells = [-1] * (r * r)\r\n\t\t\tindex = 0\r\n\r\n\t\t\t## grab all pixels in the vicinity of (x, y)\r\n\t\t\tfor j in xrange(r):\r\n\t\t\t\tfor i in xrange(r):\r\n\t\t\t\t\tpxl_x = cell_x - 2 + i\r\n\t\t\t\t\tpxl_y = cell_y - 2 + j\r\n\r\n\t\t\t\t\tif (pxl_x < 0 or pxl_x >= cols): continue\r\n\t\t\t\t\tif (pxl_y < 0 or pxl_y >= rows): continue\r\n\r\n\t\t\t\t\tcells[index] = (pxl_y * cols) + pxl_x\r\n\t\t\t\t\tindex += 1\r\n\r\n\t\t\treturn cells\r\n\r\n\t\tdef cell_color(x, y):\r\n\t\t\tngbs = get_ngb_cell_indices(x, y)\r\n\t\t\tindx = ngbs[0] * 2\r\n\r\n\t\t\tmin_indx = 0\r\n\t\t\tmin_dist = sq_distance(x, y, points[indx], points[indx + 1])\r\n\r\n\t\t\t## figure out which cell this pixel is closest to\r\n\t\t\tfor cell_idx in ngbs:\r\n\t\t\t\tif (cell_idx == -1):\r\n\t\t\t\t\tbreak\r\n\r\n\t\t\t\tindx = cell_idx * 2\r\n\t\t\t\tdist = sq_distance(x, y, points[indx], points[indx + 1])\r\n\r\n\t\t\t\tif (dist < min_dist):\r\n\t\t\t\t\tmin_dist = dist\r\n\t\t\t\t\tmin_indx = indx\r\n\r\n\t\t\txj = points[min_indx ]\r\n\t\t\tyj = points[min_indx + 1]\r\n\r\n\t\t\t## return the color of this cell\r\n\t\t\tk = (yj * self.width + xj) * 3\r\n\t\t\tr = self.rgb[k + 0]\r\n\t\t\tg = self.rgb[k + 1]\r\n\t\t\tb = self.rgb[k + 2]\r\n\t\t\treturn r, g, b\r\n\r\n\t\tfor y in xrange(self.height):\r\n\t\t\tfor x in xrange(self.width):\r\n\t\t\t\tr, g, b = cell_color(x, y)\r\n\t\t\t\ti = (y * self.width + x) * 3\r\n\r\n\t\t\t\tself.rgb[i + 0] = r\r\n\t\t\t\tself.rgb[i + 1] = g\r\n\t\t\t\tself.rgb[i + 2] = b\r\n\r\n\r\ndef gen_random_noise_image(xsize, ysize, csize, name):\r\n\timg = image(xsize, ysize)\r\n\r\n\timg.generate_random_noise()\r\n\timg.write_tga(open(name + (\"_original_%dx%d.tga\" % (xsize, ysize)), \"wb\"))\r\n\r\n\timg.voronoi_crystallize_image(csize)\r\n\timg.write_tga(open(name + (\"_crystals_%dx%d.tga\" % (xsize, ysize)), \"wb\"))\r\n\r\ndef gen_stepped_gradient_image(xsize, ysize, csize, name):\r\n\timg = image(xsize, ysize)\r\n\r\n\timg.generate_stepped_gradient()\r\n\timg.write_tga(open(name + (\"_original_%dx%d.tga\" % (xsize, ysize)), \"wb\"))\r\n\r\n\timg.voronoi_crystallize_image(csize)\r\n\timg.write_tga(open(name + (\"_crystals_%dx%d.tga\" % (xsize, ysize)), \"wb\"))\r\n\r\n\r\ndef main(argc, argv):\r\n\txsize = ((argc >= 2) and int(argv[1])) or 320\r\n\tysize = ((argc >= 3) and int(argv[2])) or 200\r\n\tcsize = ((argc >= 4) and int(argv[3])) or 20\r\n\r\n\tt0 = time.time()\r\n\tgen_random_noise_image(xsize, ysize, csize, \"random_noise\")\r\n\tgen_stepped_gradient_image(xsize, ysize, csize, \"stepped_gradient\")\r\n\tt1 = time.time()\r\n\r\n\tprint(\"[main] xs=%d ys=%d cs=%d dt=%f\" % (xsize, ysize, csize, t1 - t0))\r\n\treturn 0\r\n\r\nsys.exit(main(len(sys.argv), sys.argv))\r\n\r\n","repo_name":"rrti/misc","sub_path":"python/voronoi_image_crystallizer.py","file_name":"voronoi_image_crystallizer.py","file_ext":"py","file_size_in_byte":4566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"37618025778","text":"import os\nfrom flask import Flask, request\nfrom webhookhandler import WebhookHandler\n\napp = Flask(__name__)\nhandler = WebhookHandler()\n\n@app.route('/webhook', methods=['POST'])\ndef handle_webhook():\n data = request.get_json()\n handler.handle_webhook(data)\n return '', 200\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))\n","repo_name":"spinjob/gpt-integration-eng","sub_path":"projects/0757ea4c-5bb8-4630-82c5-5d15f380c447/archive/20230816_170109/workspace/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"24586374755","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom random import randint\r\n\r\npd.options.display.max_columns = 12\r\n\r\ndef header(msg) :\r\n print('-' * 50)\r\n print(msg)\r\n \r\n\r\ntrain_df = pd.read_csv(\"train.csv\")\r\ntest_df = pd.read_csv(\"test.csv\")\r\ncombine = [train_df, test_df]\r\n\r\nheader(\"Feature Names:\")\r\nprint(combine[0].columns.tolist())\r\n\r\nheader(\"Categorical Features:\")\r\nprint(combine[0].select_dtypes(include=['object']).columns.tolist())\r\n\r\nheader(\"Numerical Features:\")\r\nprint(combine[0].select_dtypes(include=[np.number]).columns.tolist())\r\n\r\nheader(\"Features NaN Values:\")\r\nprint(combine[0].columns[combine[0].isnull().any()].tolist())\r\n\r\nheader(\"Data Types:\")\r\nprint(combine[0].dtypes)\r\n\r\nheader(\"Properties of Numerical Features:\")\r\nprint(combine[0][combine[0].columns[3:]].select_dtypes(include=[np.number]).describe())\r\n\r\nheader(\"Properties of Categorical Features:\")\r\ncombine[0]['PassengerId'] = combine[0].PassengerId.astype('object')\r\ncombine[0]['Survived'] = combine[0].Survived.astype('object')\r\ncombine[0]['Pclass'] = combine[0].Pclass.astype('object')\r\nprint(combine[0].select_dtypes(include=['object']).describe())\r\n\r\nheader(\"Correlation of Pclass = 1 and Survived\")\r\ncombine[0]['Survived'] = combine[0].Survived.astype(int)\r\ncombine[0]['Pclass'] = combine[0].Pclass.astype(int)\r\n\r\nprint(train_df[\"Survived\"][train_df[\"Pclass\"] == 1].value_counts(normalize = True))\r\n\r\nheader(\"Gender Survival Rates:\")\r\nheader(\"Female\")\r\nprint(train_df[\"Survived\"][train_df[\"Sex\"] == \"female\"].value_counts(normalize = True))\r\nheader(\"Male\")\r\nprint(train_df[\"Survived\"][train_df[\"Sex\"] == \"male\"].value_counts(normalize = True))\r\n#print(train_df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False))\r\n\r\nage_hist = train_df.loc[:, ['Survived', 'Age']]\r\nage_hist.hist(column = 'Age',by='Survived', bins = 20)\r\nplt.savefig('image.png')\r\n\r\nthree_hist = train_df.loc[:, ['Survived', 'Age', 'Pclass']]\r\nthree_hist.hist(column = 'Age', by=['Survived','Pclass'],sharex = True, bins = 15)\r\nplt.savefig('image2.png')\r\n\r\n\r\nsurvived_df = train_df.loc[train_df['Survived'] == 1]\r\nnot_survived_df = train_df.loc[train_df['Survived'] == 0]\r\n\r\nsns.set(font_scale=1)\r\ng = sns.factorplot(x=\"Sex\", y=\"Fare\", col='Embarked',\r\n data=survived_df, saturation=.5,\r\n kind=\"bar\", ci=None, aspect=.6)\r\n(g.set_axis_labels(\"\", \"Fare\")\r\n .set_xticklabels([\"Men\", \"Women\"])\r\n .set_titles(\"{col_name} {col_var}\")\r\n .despine(left=True)) \r\nplt.subplots_adjust(top=0.8)\r\ng.fig.suptitle('Survived');\r\nplt.savefig('image3.png')\r\n\r\nsns.set(font_scale=1)\r\ng = sns.factorplot(x=\"Sex\", y=\"Fare\", col='Embarked',\r\n data=not_survived_df, saturation=.5,\r\n kind=\"bar\", ci=None, aspect=.6)\r\n(g.set_axis_labels(\"\", \"Fare\")\r\n .set_xticklabels([\"Men\", \"Women\"])\r\n .set_titles(\"{col_name} {col_var}\")\r\n .despine(left=True)) \r\nplt.subplots_adjust(top=0.8)\r\ng.fig.suptitle('Did Not Survive');\r\nplt.savefig('image4.png')\r\n\r\nheader(\"Null valuse in Cabin Feature:\")\r\nprint(train_df['Cabin'].isnull().sum() + test_df['Cabin'].isnull().sum())\r\n\r\nheader(\"Replacing sex values with 0's or 1's:\")\r\ntrain_df['Sex'].replace(['female','male'],[1,0], inplace=True)\r\ntrain_df.rename(columns={'Sex' : 'Gender'}, inplace=True)\r\nprint(train_df['Gender'].head())\r\n\r\nheader(\"Filling Age Values\")\r\ndef fillNaN_with_unifrand(df):\r\n a = df.values\r\n m = np.isnan(a) # mask of NaNs\r\n mu, sigma = df.mean(), df.std()\r\n a[m] = np.random.normal(mu, sigma, size=m.sum())\r\n return df\r\n\r\nfillNaN_with_unifrand(train_df['Age'])\r\nprint(train_df['Age'])\r\n\r\nheader(\"Filling Embarked Values\")\r\ntrain_df['Embarked'].fillna(value='S' ,inplace=True)\r\n\r\nheader(\"Getting Fare's Mode\")\r\nprint(train_df['Fare'].mode())\r\n\r\nheader(\"Converting Fare to Ordinal\")\r\ntrain_df.loc[train_df['Fare'].between(-0.001, 7.91), 'Fare'] = 0\r\ntrain_df.loc[train_df['Fare'].between(7.910000000001,14.454), 'Fare'] = 1\r\ntrain_df.loc[train_df['Fare'].between(14.45400000001,31.0), 'Fare'] = 2\r\ntrain_df.loc[train_df['Fare'].between(31.00000000001,512.329), 'Fare'] = 3\r\nprint(train_df.head())\r\n","repo_name":"jschultz1995/Datamining-HW1","sub_path":"datamining hw1.py","file_name":"datamining hw1.py","file_ext":"py","file_size_in_byte":4293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"35795946200","text":"#Imports\nimport streamlit as st\nfrom dataclasses import dataclass\nfrom typing import Any, List\nfrom web3 import Web3\nw3 = Web3(Web3.HTTPProvider('HTTP://127.0.0.1:7545'))\n\n################################################################################\n# Streamlit application headings\nst.markdown(\"# Health Records\")\nst.markdown(\"## Select a Patient\")\nst.text(\" \\n\")\n\n# Patients' Personal Information\n\n# Database of multiple patients' personal information\npatients_database = {\n \"George\": [\"George\", \"16\", \"5'11\", \"80\", \"Images/George.jpg\"],\n \"Jane\": [\"Jane\", \"21\", \"5'4\", \"50\", \"Images/Jane.jpg\"],\n \"Karen\": [\"Karen\", \"55\", \"5'7\", \"30\", \"Images/Karen.jpg\"],\n \"Luke\": [\"Luke\", \"1\", \"2\", \"100\", \"Images/Luke.jpg\"],\n \"Tom\": [\"Tom\", \"65\", \"6'2\", \"50\", \"Images/Tom.jpg\"]\n}\n\n# A list of the patients' first names\npeople = [\"George\", \"Jane\", \"Karen\", \"Luke\", \"Tom\"]\n\n\ndef get_people(w3):\n \"\"\"Display the database of the patients information.\"\"\"\n db_list = list(patients_database.values())\n\n for number in range(len(people)):\n\n st.image(db_list[number][4], width=200)\n st.write(\"Name: \", db_list[number][0])\n st.write(\"Age: \", db_list[number][1])\n st.write(\"Height: \", db_list[number][2])\n st.write(\"Health Score: \", db_list[number][3])\n st.text(\" \\n\")\n\n################################################################################\n# Streamlit Code\nget_people(w3)\n\n\n################################################################################\n# Streamlit Sidebar Code - Start\n\nst.sidebar.markdown(\"## Patients' Details\")\n\n\n# Create a select box to select a patient\nperson = st.sidebar.selectbox('Select a Person', people)\n\n# Create a slider to measure each patient's health score in a range of 0 through 100.\nhealth_score = st.sidebar.slider(\"Range of Health Score:\", value=(0, 100))\n\n# Create a textbox for doctors to leave additional information for each patient. \ntext = st.sidebar.text_area(\"Any additional information\")\n\nif st.sidebar.button('Save'):\n st.sidebar.write('Saved')\nelse:\n st.sidebar.write('Edit')\n\n# Create a button for parties to upload any additional information. \nst.sidebar.markdown(\"## Upload Additional Information!\")\n\ndata = uploaded_files = st.sidebar.file_uploader(\"Choose a CSV file\", accept_multiple_files=True)\nfor uploaded_file in uploaded_files:\n bytes_data = uploaded_file.read()\n st.write(\"filename:\", uploaded_file.name)\n st.write(bytes_data)\n\n# Create a download text file.\ntext_contents = '''This is text file'''\nst.sidebar.download_button('Download text file', text_contents)\n\n\n# Sending Information to Blockchain\nif st.sidebar.button(\"Send Information\"):\n transaction_hash = send_transaction(\"name\", \"age\", \"health_score\")\n\n st.sidebar.markdown(\"#### Validated Transaction Hash\")\n st.sidebar.write(transaction_hash)\n \n st.balloons()","repo_name":"baigal17/Project-3","sub_path":"Project.py","file_name":"Project.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"13854283320","text":"\"\"\"\r\nBehavioral Finance\r\n\r\nConsider a Barberis, Huang and Santos (2001) economy with the following parameter choices \r\nfor the investor's utility function:\r\ndelta = 0.99, gamma = 1, lambda = 2\r\n\r\nConsumption growth has a lognormal distribution:\r\nln(g) = 0.02 +0.02*epsilon\r\nwhere epsilon is a standard normal random variable. \r\n\r\nWith these parameter choices, the risk-free rate is constant at 1.0303 per year. \r\nSimulate the distribution for consumption growth with at least 10,000 random draws for epsilon. \r\n\r\nDefine x as one plus the dividend-price ratio for the market portfolio:\r\nx = (1+P/D)*(D/P) = 1+D/P\r\n\r\nand define the error term:\r\ne(x) = 0.99 * b0 * E[nvhat(x*g)] + 0.99*x - 1\r\nwhere utility from financial gain or loss is given by:\r\nnvhat(R) = R - 1.0303 for R>=1.0303\r\nnvhat(R) = 2 * (R - 1.0303) for R <1.0303\r\n\r\nCalculate the equilibrium values of x for b0 in the range [0, 10], using an iterative procedure known as bisection search:\r\nStep1:\r\n Set x– = 1 and x+ = 1.1. Use the simulated distribution of consumption growth to confirm that e(x–) < 0 and e(x+) > 0. \r\n Hence solution for x must lie between x– and x+.\r\nStep2: \r\n Set x = 0.5*(x– + x+), and use the simulated distribution of consumption growth to calculate e(x).\r\nStep3: \r\n If |e(x)| < 10–4, then x is (close enough to) the solution.\r\nStep4:\r\n Otherwise, if e(x) < 0, then the solution lies between x and x+, so repeat the procedure from step 2 with x– = x.\r\nStep5:\r\n Otherwise, if e(x) > 0, then the solution lies between x– and x, so repeat the procedure from step 2 with x+ = x.\r\n\r\nUse x to calculate the price-dividend ratio for the market portfolio:\r\nP/D = 1/(x-1)\r\nPlot the price-dividend ratio (on the vertical axis) vs b0 (on the horizontal axis). \r\n\r\nAlso, calculate the expected market return:\r\nE[R(m)] = E[x*g]\r\nPlot the equity premium (on the vertical axis) vs b0 (on the horizontal axis). \r\n\r\nBriefly explain the economic significance of the investor's utility function for financial gain or loss [i.e., nuhat(R)], \r\nas well as the economic significance of the parameters b0 and lambda.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nDelta, Gamma, Lambda = 0.99, 1, 2\r\nrf = 1.0303\r\nnum_1 = 10000\r\nepsilon = np.random.standard_normal(num_1)\r\nconsumption_growth = np.exp(0.02+0.02*epsilon)\r\nnum_2 = 201\r\nb0 = np.linspace(0,10,num_2)\r\nnvhat = np.zeros(num_1)\r\nx_solution = np.zeros(num_2)\r\n\r\nfor n in range(num_2):\r\n x_bi = {\"x-\":1, \"x+\":1.1}\r\n ex = {\"x-\":np.nan,\"x+\":np.nan,\"x\":1}\r\n while abs(ex[\"x\"]) >= 10**(-4):\r\n x = (x_bi[\"x-\"]+x_bi[\"x+\"])/2\r\n x_bi[\"x\"] = x\r\n for i,j in x_bi.items():\r\n for m in range(num_1):\r\n if j*consumption_growth[m]>=rf:\r\n nvhat[m] = j*consumption_growth[m]-rf\r\n else:\r\n nvhat[m] = 2*(j*consumption_growth[m]-rf)\r\n ex[i] = 0.99*b0[n]*np.mean(nvhat)+0.99*j-1\r\n if ex[\"x\"] < 0:\r\n x_bi[\"x-\"] = x_bi[\"x\"]\r\n elif ex[\"x\"] > 0:\r\n x_bi[\"x+\"] = x_bi[\"x\"]\r\n else:\r\n xa = x_bi[\"x\"]\r\n x_solution[n] = xa\r\n\r\nPD_ratio = 1/(x_solution-1)\r\n\r\nfig,ax = plt.subplots(figsize=(10,8))\r\nax.plot(b0,PD_ratio)\r\nplt.xlabel('scale factor b0')\r\nplt.ylabel('Price-dividend Ratio')\r\nplt.title('Relation between Price-dividend Ratio and scale factor b0')\r\n\r\nMkt_rt = x_solution*np.mean(consumption_growth)\r\nEquity_premium = Mkt_rt - 1.0303\r\nfig,ax = plt.subplots(figsize=(10,8))\r\nax.plot(b0,Equity_premium)\r\nplt.xlabel('scale factor b0')\r\nplt.ylabel('Equity Premium')\r\nplt.title('Relation between Equity Premium and scale factor b0')\r\n\r\n","repo_name":"Robert-Ruilin/Asset-Pricing-Projects","sub_path":"Behavioral Finance.py","file_name":"Behavioral Finance.py","file_ext":"py","file_size_in_byte":3642,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"} +{"seq_id":"26556100254","text":"from telegram import Update, Bot\r\nfrom telegram.ext import Updater, CommandHandler\r\n\r\nfrom credits import bot_token\r\n\r\nbot = Bot(token=bot_token)\r\nupdater = Updater(token=bot_token, use_context=True)\r\ndispatcher = updater.dispatcher\r\n\r\n\r\ndef start(update, context):\r\n context.bot.send_message(update.effective_chat.id, \"Приветствую)\")\r\n\r\ndef info(update, context):\r\n context.bot.send_message(update.effective_chat.id, \"Автор бота Юнусова Ульяна\")\r\n\r\n\r\n\r\nstart_handler = CommandHandler('start', start)\r\ninfo_handler = CommandHandler('info', info)\r\n\r\ndispatcher.add_handler(start_handler)\r\ndispatcher.add_handler(info_handler)\r\n\r\nupdater.start_polling()\r\nupdater.idle()","repo_name":"Ulyanaaa/Telegram_bots","sub_path":"Telegram (хэндлеры2).py","file_name":"Telegram (хэндлеры2).py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"15785345636","text":"from setuptools import find_packages, setup\n\nversion = '4.0.3'\n\nsetup(\n name='alerta-timeout',\n version=version,\n description='Alerta plugin to permit a global custom timeout to be supplied via alerta config',\n url='https://github.com/alerta/alerta-contrib',\n license='MIT',\n author='Thomas Collins',\n author_email='thomaswcollins@gmail.com',\n packages=find_packages(),\n py_modules=['alerta_timeout'],\n install_requires=[],\n include_package_data=True,\n zip_safe=True,\n entry_points={\n 'alerta.plugins': [\n 'timeout = alerta_timeout:Timeout'\n ]\n }\n)\n","repo_name":"alerta/alerta-contrib","sub_path":"plugins/timeout/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"27"} +{"seq_id":"71275259913","text":"import albumentations as A\r\nimport cv2\r\nfrom matplotlib import pyplot as plt\r\nimport os\r\nimport numpy as np\r\nimport codecs\r\nimport json\r\nfrom glob import glob\r\nimport shutil\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nKEYPOINT_COLOR = (0, 255, 0) # Green\r\n\r\ndef vis_keypoints(image, keypoints, color=KEYPOINT_COLOR, diameter=15):\r\n image = image.copy()\r\n\r\n for (x, y) in keypoints:\r\n cv2.circle(image, (int(x), int(y)), diameter, (0, 255, 0), -1)\r\n\r\n plt.figure(figsize=(8, 8))\r\n plt.axis('off')\r\n plt.imshow(image)\r\n\r\n\r\ndef data_aug():\r\n # Declare an augmentation pipeline\r\n transform = A.Compose([\r\n# A.RandomCrop(width=450, height=450),\r\n A.HorizontalFlip(p=0.5),\r\n# A.VerticalFlip(p=0.5),\r\n# A.RandomBrightnessContrast(p=0.2),\r\n ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']), keypoint_params=A.KeypointParams(format='xy'))\r\n\r\n # 3-\r\n #image = cv2.imread(\"images/labelme/1639726903.jpg\")\r\n image = cv2.imread(\"images/labelme/1639726322.jpg\")\r\n #cv2.imwrite(\"./sss1.jpg\", image)\r\n #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n\r\n # bboxes = [\r\n # [0, 117, 638, 247],\r\n # ]\r\n # keypoints = [\r\n # (638,119),\r\n # (0, 253),\r\n # (1, 157),\r\n # (637, 124),\r\n # (636, 238),\r\n # (2, 250),\r\n # ]\r\n\r\n bboxes = [\r\n [0, 208, 639, 314],\r\n ]\r\n keypoints = [\r\n (0, 208),\r\n (639, 314),\r\n (0, 214),\r\n (637, 207),\r\n (639, 307),\r\n (0, 314),\r\n ]\r\n image1 = image.copy()\r\n cv2.rectangle(image, (0, 208),(639, 314), (0, 255, 0))\r\n #cv2.circle(image, (0, 208), 5, (0, 255, 0), -1)\r\n #cv2.circle(image, (639, 314), 5, (0, 255, 0), -1)\r\n cv2.circle(image, (0, 214), 5, (0, 255, 255), -1)\r\n cv2.circle(image, (637, 207), 5, (0, 255, 255), -1)\r\n cv2.circle(image, (639, 307), 5, (0, 255, 255), -1)\r\n cv2.circle(image, (0, 314), 5, (0, 255, 255), -1)\r\n cv2.imwrite(\"./sss1.jpg\", image)\r\n\r\n class_labels = ['false']\r\n #class_categories = ['animal', 'animal', 'item']\r\n # 4-\r\n transformed = transform(image=image1, bboxes=bboxes, class_labels=class_labels, keypoints=keypoints)\r\n transformed_image = transformed['image']\r\n transformed_bboxes = transformed['bboxes']\r\n transformed_keypoints = transformed['keypoints']\r\n transformed_class_labels = transformed['class_labels']\r\n diameter = 3\r\n\r\n cv2.rectangle(transformed_image, (int(transformed_keypoints[0][0]), int(transformed_keypoints[0][1])), (int(transformed_keypoints[1][0]), int(transformed_keypoints[1][1])), (0, 255, 0))\r\n for (x, y) in transformed_keypoints:\r\n print(int(x))\r\n print(int(y))\r\n cv2.circle(transformed_image, (int(x), int(y)), diameter, (0, 255, 0), -1)\r\n #vis_keypoints(transformed_image, transformed_keypoints)\r\n cv2.imwrite(\"./sss.jpg\", transformed_image)\r\n\r\n\r\ndef data_aug_data(p_0, p_1, pic, rename):\r\n # Declare an augmentation pipeline\r\n\r\n if rename == '_horizon':\r\n transform = A.Compose([\r\n # A.RandomCrop(width=450, height=450),\r\n A.HorizontalFlip(p=1.0),\r\n # A.VerticalFlip(p=0.5),\r\n #A.RandomBrightnessContrast(p=0.2),\r\n ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']),\r\n keypoint_params=A.KeypointParams(format='xy'))\r\n\r\n if rename == '_Blur':\r\n transform = A.Compose([\r\n # A.RandomCrop(width=450, height=450),\r\n # A.HorizontalFlip(p=0.5),\r\n # A.VerticalFlip(p=0.5),\r\n # A.RandomBrightnessContrast(p=0.2),\r\n A.OneOf([\r\n A.MotionBlur(p=0.5), # 使用随机大小的内核将运动模糊应用于输入图像。\r\n A.MedianBlur(blur_limit=3, p=0.5), # 中值滤波\r\n A.Blur(blur_limit=3, p=0.5), # 使用随机大小的内核模糊输入图像。\r\n ], p=1.0),\r\n ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']), keypoint_params=A.KeypointParams(format='xy'))\r\n\r\n\r\n # 3-\r\n image = cv2.imread(pic)\r\n #cv2.imwrite(\"./sss1.jpg\", image)\r\n #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n\r\n bboxes = [\r\n [min(p_0[:, 0]), min(p_0[:, 1]), max(p_0[:, 0]), max(p_0[:, 1])],\r\n ]\r\n\r\n keypoints = [\r\n (min(p_0[:, 0]), min(p_0[:, 1])),\r\n (max(p_0[:, 0]), max(p_0[:, 1])),\r\n (p_1[0, 0],p_1[0, 1]),\r\n (p_1[1, 0], p_1[1, 1]),\r\n (p_1[2, 0], p_1[2, 1]),\r\n (p_1[3, 0], p_1[3, 1]),\r\n ]\r\n\r\n class_labels = ['false']\r\n #class_categories = ['animal', 'animal', 'item']\r\n # 4-\r\n transformed = transform(image=image, bboxes=bboxes, class_labels=class_labels, keypoints=keypoints)\r\n transformed_image = transformed['image']\r\n transformed_bboxes = transformed['bboxes']\r\n transformed_keypoints = transformed['keypoints']\r\n transformed_class_labels = transformed['class_labels']\r\n return transformed_keypoints, transformed_image\r\n\r\ndef data_aug_pic( pic, rename):\r\n # Declare an augmentation pipeline\r\n\r\n if rename == '_horizon':\r\n transform = A.Compose([\r\n # A.RandomCrop(width=450, height=450),\r\n A.HorizontalFlip(p=1.0),\r\n # A.VerticalFlip(p=0.5),\r\n #A.RandomBrightnessContrast(p=0.2),\r\n ])\r\n\r\n if rename == '_Blur':\r\n transform = A.Compose([\r\n # A.RandomCrop(width=450, height=450),\r\n # A.HorizontalFlip(p=0.5),\r\n # A.VerticalFlip(p=0.5),\r\n # A.RandomBrightnessContrast(p=0.2),\r\n A.OneOf([\r\n A.MotionBlur(p=0.5), # 使用随机大小的内核将运动模糊应用于输入图像。\r\n A.MedianBlur(blur_limit=3, p=0.5), # 中值滤波\r\n A.Blur(blur_limit=3, p=0.5), # 使用随机大小的内核模糊输入图像。\r\n ], p=1.0),\r\n ])\r\n\r\n\r\n # 3-\r\n image = cv2.imread(pic)\r\n #cv2.imwrite(\"./sss1.jpg\", image)\r\n #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n\r\n\r\n #class_categories = ['animal', 'animal', 'item']\r\n # 4-\r\n transformed = transform(image=image)\r\n transformed_image = transformed['image']\r\n\r\n return transformed_image\r\n\r\n\r\ndef labelme_json_voc_widerface(rename):\r\n # 1.标签路径\r\n labelme_path = \".\\\\labelme_to_voc_widerface\\\\labelme\\\\\" # 原始labelme标注数据路径\r\n saved_path = \".\\\\labelme_to_voc_widerface\\\\VOC2007\\\\\" # 保存路径\r\n #labelme_path = \".\\\\images\\\\labelme\\\\\" # 原始labelme标注数据路径\r\n #saved_path = \".\\\\images\\\\VOC2007\\\\\" # 保存路径\r\n\r\n # 2.创建要求文件夹\r\n if not os.path.exists(saved_path + \"Annotations\"):\r\n os.makedirs(saved_path + \"Annotations\")\r\n if not os.path.exists(saved_path + \"JPEGImages/\"):\r\n os.makedirs(saved_path + \"JPEGImages/\")\r\n if not os.path.exists(saved_path + \"ImageSets/Main/\"):\r\n os.makedirs(saved_path + \"ImageSets/Main/\")\r\n\r\n # 3.获取待处理文件\r\n files = glob(labelme_path + \"*.json\")\r\n files = [i.split(\"\\\\\")[-1].split(\".json\")[0] for i in files]\r\n\r\n #file_handle = open(\".\\\\images\\\\save_widerface_result_horizon1.txt\", mode='w')\r\n if rename == '_horizon':\r\n file_handle = open(\"labelme_to_voc_widerface/save_widerface_result_horizon.txt\", mode='w')\r\n if rename == '_Blur':\r\n file_handle = open(\"labelme_to_voc_widerface/save_widerface_result_blur.txt\", mode='w')\r\n\r\n # 4.读取标注信息并写入 xml\r\n for json_file_ in files:\r\n json_filename = labelme_path + json_file_ + \".json\"\r\n json_file = json.load(open(json_filename, \"r\", encoding=\"utf-8\"))\r\n height, width, channels = cv2.imread(labelme_path + json_file_ + \".jpg\").shape\r\n with codecs.open(saved_path + \"Annotations/\" + json_file_ + rename+\".xml\", \"w\", \"utf-8\") as xml:\r\n xml.write('\\n')\r\n xml.write('\\t' + 'STOP_LINE' + '\\n')\r\n xml.write('\\t' + json_file_ +rename+ \".jpg\" + '\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\tThe STOP LINE Database\\n')\r\n xml.write('\\t\\tPASCAL VOC\\n')\r\n xml.write('\\t\\tflickr\\n')\r\n xml.write('\\t\\tNULL\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\tNULL\\n')\r\n xml.write('\\t\\tLine\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\t' + str(width) + '\\n')\r\n xml.write('\\t\\t' + str(height) + '\\n')\r\n xml.write('\\t\\t' + str(channels) + '\\n')\r\n xml.write('\\t\\n')\r\n xml.write('\\t0\\n')\r\n # print(json_file[\"shapes\"][0][\"points\"])\r\n\r\n points_0 = np.array(json_file[\"shapes\"][0][\"points\"])\r\n points_1 = np.array(json_file[\"shapes\"][1][\"points\"])\r\n transform_data, new_img = data_aug_data(points_0, points_1, labelme_path + json_file_ + \".jpg\",rename)\r\n\r\n cv2.imwrite(saved_path + \"JPEGImages\\\\\"+json_file_+ rename+\".jpg\", new_img)\r\n\r\n xmin = min(transform_data[0][0],transform_data[1][0])\r\n xmax = max(transform_data[0][0],transform_data[1][0])\r\n ymin = min(transform_data[0][1],transform_data[1][1])\r\n ymax = max(transform_data[0][1],transform_data[1][1])\r\n label = json_file[\"shapes\"][0][\"label\"]\r\n if xmax <= xmin:\r\n pass\r\n elif ymax <= ymin:\r\n pass\r\n else:\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\t' + str(label) + '\\n')\r\n xml.write('\\t\\tUnspecified\\n')\r\n xml.write('\\t\\t1\\n')\r\n xml.write('\\t\\t0\\n')\r\n xml.write('\\t\\t\\n')\r\n xml.write('\\t\\t\\t' + str(int(xmin)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(ymin)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(xmax)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(ymax)) + '\\n')\r\n xml.write('\\t\\t\\n')\r\n xml.write('\\t\\n')\r\n\r\n\r\n label_1 = json_file[\"shapes\"][1][\"label\"]\r\n x1 = transform_data[2][0]\r\n y1 = transform_data[2][1]\r\n x2 = transform_data[3][0]\r\n y2 = transform_data[3][1]\r\n x3 = transform_data[4][0]\r\n y3 = transform_data[4][1]\r\n x4 = transform_data[5][0]\r\n y4 = transform_data[5][1]\r\n\r\n\r\n\r\n\r\n '''\r\n xml.write('\\t\\n')\r\n xml.write('\\t\\t'+str(label_1)+'\\n')\r\n xml.write('\\t\\tUnspecified\\n')\r\n xml.write('\\t\\t1\\n')\r\n xml.write('\\t\\t0\\n')\r\n xml.write('\\t\\t\\n')\r\n xml.write('\\t\\t\\t' + str(int(x1)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(y1)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(x2)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(y2)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(x3)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(y3)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(x4)) + '\\n')\r\n xml.write('\\t\\t\\t' + str(int(y4)) + '\\n')\r\n xml.write('\\t\\t\\n')\r\n xml.write('\\t\\n')\r\n '''\r\n # print(json_file_ + \".jpg\",xmin,ymin,xmax,ymax,label,x1,y1,x2,y2,x3,y3,x4,y4)\r\n if rename == '_Blur':\r\n txt_file = \"# \" + json_file_ + rename+\".jpg\" + '\\n' + str(int(xmin)) + \" \" + str(int(ymin)) + \" \" + str(\r\n int(xmax-xmin)) + \" \" + str(int(ymax-ymin)) + \" \" + str(int(label)) + \" \" + str(int(x1)) + \" \" + str(\r\n int(y1)) + \" \" + str(int(x2)) + \" \" + str(int(y2)) + \" \" + str(int(x3)) + \" \" + str(\r\n int(y3)) + \" \" + str(int(x4)) + \" \" + str(int(y4)) + \" \" + str(int(label_1))\r\n if rename == '_horizon':\r\n txt_file = \"# \" + json_file_ + rename + \".jpg\" + '\\n' + str(int(xmin)) + \" \" + str(\r\n int(ymin)) + \" \" + str(int(xmax - xmin)) + \" \" + str(int(ymax - ymin)) + \" \" + str(int(label)) + \" \" + str(\r\n int(x2)) + \" \" + str(int(y2)) + \" \" + str(int(x1)) + \" \" + str(int(y1)) + \" \" + str(int(x4)) + \" \" + str(\r\n int(y4)) + \" \" + str(int(x3)) + \" \" + str(int(y3)) + \" \" + str(int(label_1))\r\n file_handle.write(txt_file + '\\n')\r\n xml.write('')\r\n\r\n # 5.复制图片到 VOC2007/JPEGImages/下\r\n #image_files = glob(labelme_path + \"*.jpg\")\r\n #print(\"copy image files to VOC007/JPEGImages/\")\r\n # for image in image_files:\r\n # new_name = os.path.basename(image).split(\".\")[0]+\"_abl.jpg\"\r\n # shutil.copy(image, saved_path + \"JPEGImages\\\\\"+new_name)\r\n\r\n # 6.split files for txt\r\n txtsavepath = saved_path + \"ImageSets\\\\Main\\\\\"\r\n ftrainval = open(txtsavepath + '\\\\trainval.txt', 'w')\r\n ftest = open(txtsavepath + '\\\\test.txt', 'w')\r\n ftrain = open(txtsavepath + '\\\\train.txt', 'w')\r\n fval = open(txtsavepath + '\\\\val.txt', 'w')\r\n #total_files = glob(\".\\\\images\\\\VOC2007\\\\Annotations\\\\*.xml\")\r\n total_files = glob(\".\\\\labelme_to_voc_widerface\\\\VOC2007\\\\Annotations\\\\*.xml\")\r\n total_files = [i.split(\"\\\\\")[-1].split(\".xml\")[0] for i in total_files]\r\n # test_filepath = \"\"\r\n for file in total_files:\r\n #os.path.basename(file).split(\".\")[0] + \"_abl.jpg\"\r\n ftrainval.write(file + \"\\n\")\r\n # test\r\n # for file in os.listdir(test_filepath):\r\n # ftest.write(file.split(\".jpg\")[0] + \"\\n\")\r\n # split\r\n train_files, val_files = train_test_split(total_files, test_size=0.15, random_state=42)\r\n # train\r\n for file in train_files:\r\n ftrain.write(file +\"\\n\")\r\n # val\r\n for file in val_files:\r\n fval.write(file + \"\\n\")\r\n\r\n ftrainval.close()\r\n ftrain.close()\r\n fval.close()\r\n\r\ndef labelme_json_voc_widerface_copy():\r\n # 1.标签路径\r\n labelme_path = \"F:\\\\stop_line_data\\\\train\\\\\" # 原始labelme标注数据路径\r\n saved_path = \"F:\\\\stop_line_data\\\\label_1\\\\\" # 保存路径\r\n\r\n\r\n\r\n # 3.获取待处理文件\r\n files = glob(labelme_path + \"*.json\")\r\n files = [i.split(\"\\\\\")[-1].split(\".json\")[0] for i in files]\r\n\r\n file_handle = open(\"labelme_to_voc_widerface/save_widerface_result_horizon1.txt\", mode='w')\r\n # file_handle = open(\"labelme_to_voc_widerface/save_widerface_result_blur1.txt\", mode='w')\r\n\r\n # 4.读取标注信息并写入 xml\r\n for json_file_ in files:\r\n json_filename = labelme_path + json_file_ + \".json\"\r\n json_file = json.load(open(json_filename, \"r\", encoding=\"utf-8\"))\r\n height, width, channels = cv2.imread(labelme_path + json_file_ + \".jpg\").shape\r\n label = json_file[\"shapes\"][0][\"label\"]\r\n if int(label) > 0:\r\n shutil.copy(labelme_path + json_file_ + \".jpg\", saved_path)\r\n shutil.copy(json_filename, saved_path)\r\n\r\n\r\ndef val_horizon_data():\r\n #txt_path = \".\\\\labelme_to_voc_widerface\\\\save_widerface_result_horizon.txt\"\r\n txt_path = \".\\\\labelme_to_voc_widerface\\\\save_widerface_result_blur.txt\"\r\n f = open(txt_path, 'r')\r\n lines = f.readlines()\r\n isFirst = True\r\n imgs_path = []\r\n words = []\r\n labels = []\r\n for line in lines:\r\n line = line.rstrip()\r\n if line.startswith('#'):\r\n if isFirst is True:\r\n isFirst = False\r\n else:\r\n labels_copy = labels.copy()\r\n words.append(labels_copy)\r\n #labels.clear()\r\n path = line[2:]\r\n #path = txt_path.replace('save_widerface_result_horizon.txt', 'VOC2007\\\\JPEGImages\\\\') + path\r\n path = txt_path.replace('save_widerface_result_blur.txt', 'VOC2007\\\\JPEGImages\\\\') + path\r\n imgs_path.append(path)\r\n else:\r\n line = line.split(' ')\r\n label = [float(x) for x in line]\r\n labels.append(label)\r\n\r\n for index in range(0, len(imgs_path)):\r\n img_raw = cv2.imread(imgs_path[index])\r\n #print(img_raw.shape)\r\n #print(int(labels[index][0]))\r\n\r\n cv2.rectangle(img_raw, (int(labels[index][0]), int(labels[index][1])), (int(labels[index][0])+int(labels[index][2]), int(labels[index][1])+int(labels[index][3])), (0, 0, 255), 2)\r\n\r\n cv2.circle(img_raw, (int(labels[index][5]), int(labels[index][6])), 2, (255, 255, 0), 8)\r\n cv2.circle(img_raw, (int(labels[index][7]), int(labels[index][8])), 2, (0, 255, 255), 8)\r\n cv2.circle(img_raw, (int(labels[index][9]), int(labels[index][10])), 2, (255, 0, 255), 8)\r\n cv2.circle(img_raw, (int(labels[index][11]), int(labels[index][12])), 2, (0, 255, 0), 8)\r\n #saved_path = os.path.join(os.path.dirname(imgs_path[index]), os.path.basename(imgs_path[index]).split(\".\")[0]+\"_1.jpg\")\r\n saved_path = os.path.join(\".\\\\labelme_to_voc_widerface\\\\save_result\", os.path.basename(imgs_path[index]).split(\".\")[0]+\"_1.jpg\")\r\n cv2.imwrite(saved_path, img_raw)\r\n #shutil.copy(imgs_path[index], saved_path)\r\n\r\n\r\n\r\ndef data_album(dataDir, rename):\r\n list_dirs = os.walk(dataDir)\r\n for root, dirs, files in list_dirs:\r\n # for d in dirs:\r\n # print(\"@@@@@@@@@@@@@@@@@@@@\")\r\n # print(os.path.join(root,d))\r\n for f in files:\r\n #print(\"********************\")\r\n #./images\\VOC2007\\ImageSets\\Main\r\n #print(root)\r\n path_name = root\r\n #./images\\VOC2007\\ImageSets\\Main\\val.txt\r\n #print(os.path.join(root,f))\r\n pic_path = os.path.join(root,f)\r\n\r\n new_img = data_aug_pic(pic_path,rename)\r\n cv2.imwrite(os.path.join(\".\\\\data_album\", os.path.basename(pic_path).split(\".\")[0]+rename+\".jpg\"), new_img)\r\n\r\n\r\n#data_aug()\r\n#将label为1的 jpg和json复制在新的文件夹\r\n#labelme_json_voc_widerface_copy()\r\n\r\n#验证水平翻转和模糊后关键点和框的正确性，画出来\r\n#val_horizon_data()\r\n\r\n#单纯的对图片做模糊和水平翻转，没有坐标值和数据\r\n# data_album(\".\\\\data_album\",\"_horizon\")\r\ndata_album(\".\\\\data_album\",\"_Blur\")\r\n\r\n#对labelme标注数据做增强，并将增强的数据转换成voc和widerface格式\r\n#labelme_json_voc_widerface(\"_horizon\")\r\n# labelme_json_voc_widerface(\"_Blur\")\r\n\r\n\r\n\r\n\r\n","repo_name":"Aruen24/Pytorch_Retinaface_Stop_Line","sub_path":"Albumentations_data.py","file_name":"Albumentations_data.py","file_ext":"py","file_size_in_byte":18895,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"11622056842","text":"import itertools\nimport math\n\n\"\"\"Maximize It!\n\nExercise from https://www.hackerrank.com/challenges/maximize-it/problem\n\nYou are given a function . You are also given lists. The list consists of elements.\n\nYou have to pick one element from each list so that the value from the equation below is maximized: \n\nS = (f(X1) + f(X2) + ... + f(Xk)) % M\n\nXi denotes the element picked from the list . Find the maximized value obtained Smax.\n\n% denotes the modulo operator.\n\nNote that you need to take exactly one element from each list, not necessarily the largest element. \nYou add the squares of the chosen elements and perform the modulo operation. \nThe maximum value that you can obtain, will be the answer to the problem.\n\nInput Format\n\nThe first line contains 2 space separated integers K and M. \nThe next K lines each contains an integer Ni, denoting the number of elements in the ith list,\nfollowed by Ni space separated integers denoting the elements in the list.\n\nConstraints\n\n1≤K≤7\n1≤M≤1000\n1≤Ni≤7\n1≤Magnitude of elements in list≤9\n\nOutput Format\n\nOutput a single integer denoting the value .\n\nSample Input\n\n3 1000\n2 5 4\n3 7 8 9\n5 5 7 8 9 10\n\nSample Output\n206\n\n\"\"\"\n\nsample_input = \"\"\"3 1000\n2 5 4\n3 7 8 9\n5 5 7 8 9 10\"\"\"\n\n# Get input from sample_input\n# map applies a function to all the items in a list\nK, M = map(int, sample_input.split(\"\\n\")[0].split(\" \"))\nmy_input = []\nfor line in sample_input.split(\"\\n\")[1:]:\n numbers = list(map(int, line.split(\" \")))\n my_input.append(numbers)\n\nprint(f\"K = {K}\")\nprint(f\"M = {M}\")\nprint(f\"Lists = {my_input}\")\n\n\ndef f(x):\n return x * x\n\n\ndef s(args):\n to_sum = [f(x) for x in args]\n return sum(to_sum) % M\n\n\ndef magnitude(x):\n return int(math.log10(x))\n\n\n# Validate the input checking the constraints\n# First constraint 1≤K≤7\nif K not in range(1, 8):\n print(\"\\n\\nThe value of K is not in the range 1-7\")\n exit()\n\n# Second constraint 1≤M≤1000\nif M not in range(1, 1001):\n print(\"\\n\\nThe value of M is not in the range 1-1000\")\n exit()\n\n# Third constraint 1≤Ni≤7\nfor index, _list in enumerate(my_input):\n if len(_list) not in range(1, 8):\n print(f\"\\n\\nThe list {index + 1} is not in range 1-7\")\n exit()\n\n # Fourth constraint 1≤Magnitude of elements in list≤9\n for e in _list:\n if magnitude(e) not in range(0, 10):\n print(f\"\\n\\nElement {e} not in range 1-10^9\")\n exit()\n\nif __name__ == \"__main__\":\n results = []\n for element in itertools.product(*my_input):\n elements_as_list = list(element)\n results.append(s(elements_as_list))\n maximum = max(results)\n print(f\"\\n\\nThe result is: {maximum}\\n\\n\")\n","repo_name":"fnkap/shiny-happiness","sub_path":"MaximizeIt/Maximize It.py","file_name":"Maximize It.py","file_ext":"py","file_size_in_byte":2682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"29781961702","text":"import numpy as np\nimport pyglet\n\n# pyglet.options['debug_gl'] = False # Disable error checking for increased performance\n\nfrom pyglet.gl import *\nfrom pyglet.window import key\n\nfrom isosurface import make_cube, tetra_triangles, surface_from_cube\n\nwindow = pyglet.window.Window(resizable=True)\n\n\n@window.event\ndef on_resize(width, height):\n\n rfix = 2 # mac OSX retina fix. Should be addressed by pyglet v1.4+\n glViewport(0, 0, width * rfix, height * rfix)\n window.aspect = width / float(height)\n return pyglet.event.EVENT_HANDLED\n\n\ndef enter_draw_2D():\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n glOrtho(0, window.width * 2, 0, window.height * 2, -1, 1)\n\n glMatrixMode(GL_MODELVIEW)\n glLoadIdentity()\n\n\ndef enter_draw_3D():\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(50., window.aspect, .1, 1000.)\n\n glMatrixMode(GL_MODELVIEW)\n glLoadIdentity()\n glTranslatef(0, 0, -3)\n glRotatef(window.rz, 0, 0, 1)\n glRotatef(window.ry, 0, 1, 0)\n glRotatef(window.rx, 1, 0, 0)\n\n\ndef draw_tetras(origin, side):\n\n verts = make_cube(origin, side)\n flat_verts = [v for xyz in verts for v in xyz]\n inflated_tetras = [[tetra[i] for triangle in template for i in triangle] for tetra in tetras]\n for index, should_draw in enumerate(window.flags):\n if should_draw:\n pyglet.graphics.draw_indexed(8, pyglet.gl.GL_TRIANGLES, inflated_tetras[index], ('v3f', flat_verts),\n ('c3f', [x for io in window.inout for x in ((1, 0, 0) if io else (0, 0, 0))]))\n\n\ndef draw_wireframe_cube(origin, side):\n\n verts = make_cube(origin, side)\n faces3 = [[0, 1, 2, 3], [0, 1, 6, 7], [0, 3, 4, 7]]\n indexes = [(i,j) for face3 in faces3 for face in [face3, list(set(range(8)) - set(face3))] for i, j in zip(face, face[1:] + [face[0]])]\n flattened_coords = [xyz for i, j in indexes for vert in [verts[i], verts[j]] for xyz in vert]\n pyglet.graphics.draw(len(flattened_coords) // 3, pyglet.gl.GL_LINES, ('v3f', flattened_coords), ('c3f', np.zeros_like(flattened_coords)))\n\n\ndef test_triangle():\n\n pyglet.gl.glColor4f(1,1,1,1)\n coords = [0.8,0.7,0.7,0.7,0.8,0.7,0.7,0.7,0.8]\n pyglet.graphics.draw(3, pyglet.gl.GL_TRIANGLES, ('v3f', coords), ('c3f', [1,0,0,0,1,0,0,0,1]))\n pyglet.graphics.draw(3, pyglet.gl.GL_TRIANGLES, ('v3f', coords[::-1]), ('c3f', [1,0,0,0,1,0,0,0,1]))\n\n\n@window.event\ndef on_draw():\n\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n enter_draw_3D()\n\n oos3 = 1 / np.sqrt(3)\n draw_tetras((-oos3,) * 3, 2 * oos3)\n draw_wireframe_cube((-oos3,) * 3, 2 * oos3)\n tris = surface_from_cube((-oos3,) * 3, 2 * oos3, [float(io) for io in window.inout], 0.5)\n pyglet.graphics.draw(len(tris), pyglet.gl.GL_TRIANGLES, ('v3f', [v for xyz in tris for v in xyz]), ('c3f', [0, 1, 0] * len(tris)))\n pyglet.graphics.draw(len(tris), pyglet.gl.GL_TRIANGLES, ('v3f', [v for xyz in tris[::-1] for v in xyz]), ('c3f', [0, 0, 0] * len(tris)))\n\n\n@window.event\ndef on_key_press(symbol, modifiers):\n\n numbers = (key._1, key._2, key._3, key._4, key._5, key._6)\n if symbol in numbers:\n index = numbers.index(symbol)\n window.flags[index] ^= True\n\n qwerasdf = (key.Q, key.W, key.E, key.R, key.A, key.S, key.D, key.F)\n if symbol in qwerasdf:\n index = qwerasdf.index(symbol)\n window.inout[index] ^= True\n\n\ndef update(dt):\n\n if window.rotate:\n window.rx, window.ry, window.rz = ((angle + dt * speed) % 360\n for angle, speed in zip((window.rx, window.ry, window.rz), (1, 80, 30)))\n\n\ndef init():\n\n glClearColor(1, 1, 1, 1)\n glEnable(GL_DEPTH_TEST)\n glEnable(GL_CULL_FACE)\n\n window.rotate = True\n window.rx = window.ry = window.rz = 0\n window.flags = [True] * 6\n window.inout = [True] * 8\n window.wireframe = False\n\n pyglet.clock.schedule(update)\n\n\ninit()\npyglet.app.run()\n","repo_name":"r1cc4rdo/python_projects","sub_path":"marching_tetrahedra/v0/marching_tetrahedra_test_pyglet.py","file_name":"marching_tetrahedra_test_pyglet.py","file_ext":"py","file_size_in_byte":3928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"23488491509","text":"r\"\"\"Combine multiple training corpus into a single training corpus.\n\nCurrently only support the case that multiple corpus share the same\nconfigurables except the \"modules\" field.\n\nUsage: we'd like to combine training corpus corpus1 and corpus2 into\ncombinedcorpus; we first structure the files as follows:\n\ncombinedcorpus\ncombinedcorpus/corpus1\ncombinedcorpus/corpus2\n\nRunning this script with\n\npython3 \\\ncompiler_opt/tools/combine_training_corpus.py \\\n --root_dir=$PATH_TO_combinedcorpus\n\ngenerates combinedcorpus/corpus_description.json file. In this way corpus1\nand corpus2 are combined into combinedcorpus.\n\"\"\"\n\nfrom absl import app\nfrom absl import flags\n\nfrom compiler_opt.tools import combine_training_corpus_lib\n\nflags.DEFINE_string('root_dir', '', 'root dir of module paths to combine.')\n\nFLAGS = flags.FLAGS\n\n\ndef main(argv):\n if len(argv) > 1:\n raise app.UsageError('Too many command-line arguments.')\n\n combine_training_corpus_lib.combine_corpus(FLAGS.root_dir)\n\n\nif __name__ == '__main__':\n app.run(main)\n","repo_name":"google/ml-compiler-opt","sub_path":"compiler_opt/tools/combine_training_corpus.py","file_name":"combine_training_corpus.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":537,"dataset":"github-code","pt":"27"} +{"seq_id":"13011484891","text":"import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\n\n# Current solution requires volume to be a global variable, this keeps us from making volume an array of arrays\n# which was initially hampering me from proper plotting... Having a very hard time generating a sphere, looks much more\n# like a diamond... not exactly sure how I can improve without using more sophisticated methods\nvolume = []\n\n\ndef main():\n\n\tradius = 14\n\tstep = 1\n\thorizontal_step = 0\n\thorizontal_max = False\n\n\tfor i in range(0, radius + 1, step):\n\t\tif not horizontal_max:\n\t\t\thorizontal_step += 1\n\t\telse:\n\t\t\thorizontal_step -= 1\n\t\tif horizontal_step >= radius // 2:\n\t\t\thorizontal_max = True\n\t\tbottom_sphere_section_generator(step, radius, i, horizontal_step)\n\t\ttop_sphere_section_generator(step, radius, i, horizontal_step)\n\n\tplotting_volume = np.array(volume)\n\n\tfig = plt.figure()\n\tax = fig.add_subplot(111, projection='3d')\n\n\tax.scatter(plotting_volume[:, 0], plotting_volume[:, 1], plotting_volume[:, 2])\n\n\tax.set_xbound(-(radius-6), radius-6)\n\n\tplt.show()\n\n\n# Similar code as 3D volume generator prototype 3 except this time im looking to generate sections of a sphere instead\n# of a cube\ndef bottom_sphere_section_generator(step, radius, height, horizontal_step):\n\tshrinking_circle = radius\n\tfor i in range(1, horizontal_step + 1, step):\n\t\tshrinking_circle -= 2\n\t\tfor j in range(-shrinking_circle, shrinking_circle, step):\n\t\t\tvolume.append([i, j, height, 0])\n\n\ndef top_sphere_section_generator(step, radius, height, horizontal_step):\n\tshrinking_circle = radius\n\tfor i in range(0, -horizontal_step, -step):\n\t\tshrinking_circle -= 2\n\t\tfor j in range(-shrinking_circle, shrinking_circle, step):\n\t\t\tvolume.append([i, j, height, 0])\n\n\nmain()\n","repo_name":"StephenPacker/Random-Slice","sub_path":"Random Slice Prototype/Sphere Generator.py","file_name":"Sphere Generator.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"74772909190","text":"from datetime import datetime\nfrom web3 import Web3\n\n\n# user exception classes for later use\n#\nclass ProviderError(Exception):\n def __init__(self, hook):\n self.blockchain = hook\n\n\nclass BlockError(Exception):\n def __init__(self, blockchain, block):\n self.blockchain = blockchain\n self.block = block\n\n\nclass ReceiptError(Exception):\n def __init__(self, blockchain, transaction):\n self.blockchain = blockchain\n self.transaction = transaction\n\n\n# connect to existing chain\n#\ndef connect_chain(http_hook=None, ipc_hook=None, ws_hook=None):\n if http_hook:\n method = 'HTTP'\n provider = Web3.HTTPProvider\n hook = http_hook\n elif ipc_hook:\n method = 'IPC'\n provider = Web3.IPCProvider\n hook = ipc_hook\n elif ws_hook:\n method = 'Websocket'\n provider = Web3.WebsocketProvider\n hook = ws_hook\n else:\n method = 'IPC'\n provider = Web3.IPCProvider\n hook = \"\\\\\\\\.\\\\pipe\\\\geth.ipc\"\n\n try:\n w3 = Web3(provider(hook))\n if w3.isConnected():\n print(\"Connected to %s: %s with latest block %d.\" % (method, hook, w3.eth.blockNumber))\n print()\n return w3\n else:\n print('%s connection to %s failed.' % (method, hook))\n return None\n\n except ProviderError(hook):\n pass\n\n\n# read block from the blockchain\n#\ndef read_block(block_number, blockchain):\n\n try:\n raw_block = blockchain.getBlock(block_number, full_transactions=True)\n\n except BlockError(blockchain, block_number):\n print('Reading block %d failed.' % block_number)\n return None\n\n block = dict()\n block['number'] = raw_block.number\n if raw_block.timestamp != 0:\n block['timestamp'] = datetime.fromtimestamp(raw_block.timestamp)\n else:\n block['timestamp'] = raw_block.timestamp\n block['hash'] = raw_block.hash.hex()\n block['parentHash'] = raw_block.parentHash.hex()\n block['gasUsed'] = raw_block.gasUsed\n block['miner'] = raw_block.miner\n block['transactions'] = raw_block.transactions\n\n return block\n\n\n# create transaction and read its receipt from the blockchain\n#\ndef read_transaction(tx, timestamp, blockchain):\n\n transaction = dict()\n transaction['hash'] = tx.hash.hex()\n transaction['blockNumber'] = tx.blockNumber\n transaction['nonce'] = tx.nonce\n transaction['timestamp'] = timestamp\n transaction['from'] = tx['from']\n transaction['to'] = tx.to\n transaction['value'] = str(tx.value)\n transaction['input'] = tx.input\n\n try:\n raw_receipt = blockchain.getTransactionReceipt(tx.hash)\n transaction['gas_used'] = raw_receipt.gasUsed\n transaction['cost'] = str(raw_receipt.gasUsed * tx.gasPrice)\n transaction['contractAddress'] = raw_receipt.contractAddress\n logs = []\n for raw_log in raw_receipt.logs:\n if not raw_log.removed:\n log = dict()\n log['address'] = raw_log.address\n log['topics'] = raw_log.topics\n log['data'] = raw_log.data\n logs.append(log)\n transaction['logs'] = logs\n\n except ReceiptError(blockchain, tx.hash):\n print(\"Error reading receipt for tx: %s\" % hash)\n transaction['gas_used'] = 0\n transaction['cost'] = 0\n transaction['contractAddress'] = None\n transaction['logs'] = None\n\n return transaction\n","repo_name":"tmierzwa/EtherDB","sub_path":"chain.py","file_name":"chain.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"10037345571","text":"# ukol-03\n# V tomto úkolu využiješ znalosti z kapitoly Slovníky a cykly\n\n# Zadání\n# Soubor body.json je JSON, který obsahuje informace o získaných bodech z písemky.\n\n# Soubor si ulož a načti do slovníku.\n\n# Z písemky nebude známka, ale jen Pass/Fail hodnocení neboli prospěl(a)/neprospěl(a). Vytvoř nový slovník. Jeho klíče budou opět jména žáků, a hodnotou bude řetězec \"Pass\", pokud má jednotlivec alespoň než 60 bodů. Pokud má méně než 60, hodnota bude \"Fail\".\n\n# Výsledný slovník ulož jako JSON do souboru prospech.json.\n\n# Nepovinný bonus\n# Krom souboru s body si ulož a načti do druhého slovníku ještě soubor bonusy.json. Obsahuje bonusové body získané během semestru. Pozor, bonusové body získali jen někteří žáci.\n\n# Tvým úkolem je žákům přiřadit známky na základě součtu bodů z písemky a bonusových bodů. Bodová rozhraní (vztahují se na součet) najdeš zde:\n\n# 1: 90 a více\n# 2: 70-89\n# 3: 50-69\n# 4: 30-49\n# 5: 29 a méně\n# Výsledný slovník ulož jako JSON do souboru znamky.json.\n\nimport json\n\nwith open('ukoly/body.json', encoding='utf-8') as Inputdata:\n body = json.load(Inputdata)\n# print(body)\nprospech = {}\nfor zak, vysledek in body.items():\n # print(f\"{zak}, {vysledek}\")\n if vysledek >= 60:\n prospech[zak] = \"pass\"\n else:\n prospech[zak] = \"fail\"\n# print(prospech)\n\nwith open('ukoly/prospech.json', \"w\", encoding='utf-8') as OutputData: #formatovani vystupu, co člověk to řádek a s diakritikou tak jako jsou vstupní data, nefunguje. Jak to vyřešit?\n json.dump(prospech, OutputData)\n\n# BONUS\n\nwith open('ukoly/bonusy.json', encoding='utf-8') as Inputdata:\n bonusy = json.load(Inputdata)\n# print(bonusy)\nznamky = {}\nfor zak, vysledek in body.items():\n znamky[zak] = vysledek\n for zak2, bonus in bonusy.items():\n if zak == zak2:\n znamky[zak] = vysledek + bonus\n\nwith open('ukoly/body_celkem.json', \"w\", encoding='utf-8') as OutputData: #formatovani vystupu, co člověk to řádek a s diakritikou tak jako jsou vstupní data, nefunguje. Jak to vyřešit?\n json.dump(znamky, OutputData)\n\nfor zak, vysledek in znamky.items():\n if vysledek >= 90:\n znamky[zak] = 1\n elif vysledek >= 70:\n znamky[zak] = 2\n elif vysledek >= 50:\n znamky[zak] = 3\n elif vysledek >= 30:\n znamky[zak] = 4\n else:\n znamky[zak] = 5\n \nwith open('ukoly/znamky.json', \"w\", encoding='utf-8') as OutputData: #formatovani vystupu, co člověk to řádek a s diakritikou tak jako jsou vstupní data, nefunguje. Jak to vyřešit?\n json.dump(znamky, OutputData)\n","repo_name":"MisaLiska/Python1_Misa_2023","sub_path":"ukoly/ukol_03.py","file_name":"ukol_03.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"70703261511","text":"#!/bin/python\nimport time\nfrom django.db import connection\nfrom .. import config, logger\nfrom ..common.util import run, parse_package\n\ndef repo_remove(db, pkgname):\n output = run(['repo-remove', db, pkgname], capture_output=True, check=False)\n stderr = output.stderr.decode('utf-8')\n if output.returncode != 0 and not f\"Package matching '{pkgname}' not found.\" in stderr:\n raise Exception(f'Failed to remove {pkgname} from {db}.')\n time.sleep(1)\n\ndef remove_package(package, repository=None):\n pkgname, _, _, _, arch, _ = parse_package(package)\n oldfiles = []\n oldfiles.append(f'{config[\"publisher\"][\"path\"]}/{arch}/{package}')\n oldfiles.append(oldfiles[-1] + '.sig')\n\n if repository:\n db = repository / arch / f\"{config['pacman']['repository']}.db.tar.gz\"\n repo_remove(db, pkgname)\n\n if arch == 'any':\n for arch in config['pacman']['archs'].split(' '):\n oldfiles.append(f'{config[\"publisher\"][\"path\"]}/{arch}/{package}')\n oldfiles.append(oldfiles[-1] + '.sig')\n if repository:\n db = repository / arch / f\"{config['pacman']['repository']}.db.tar.gz\"\n repo_remove(db, pkgname)\n\n run(['ssh', 'repository', 'rm', '-f'] + oldfiles)\n logger.debug('Deleted %s.', oldfiles)\n\nif __name__ == '__main__':\n from pathlib import Path\n from ..models import Status, Package\n\n repository = Path('repository')\n\n logger.info('Removing old packages ...')\n for package in Package.objects.filter(age__gt=config['publisher']['max-age']):\n remove_package(package.package)\n package.delete()\n\n if not repository.exists():\n run(['rsync', '-avP', '--exclude', '*.pkg*', f'repository:{config[\"publisher\"][\"path\"]}/*', repository])\n\n with open(repository / 'lastupdate', 'w') as f:\n f.write(str(int(time.time())))\n\n run(['sh', '-c', f'rsync -avP repository/* repository:{config[\"publisher\"][\"path\"]}'])\n\n logger.info('Removing dropped packages ...')\n status = Status.objects.all()\n for package in Package.objects.exclude(key__in=status):\n if not repository.exists():\n run(['rsync', '-avP', '--exclude', '*.pkg*', f'repository:{config[\"publisher\"][\"path\"]}/*', repository])\n\n remove_package(package.package, repository)\n\n with open(repository / 'lastupdate', 'w') as f:\n f.write(str(int(time.time())))\n\n run(['sh', '-c', f'rsync -avP repository/* repository:{config[\"publisher\"][\"path\"]}'])\n package.delete()\n logger.info('Removed %s', package.key)\n","repo_name":"arch4edu/cactus","sub_path":"publisher/clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":2591,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"27"} +{"seq_id":"71546883913","text":"import os\nimport platform\nimport sys\nimport time\nimport pytest\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\nclass TestRunnerHandler(FileSystemEventHandler):\n def on_any_event(self, event):\n if event.is_directory:\n return None\n elif event.event_type == 'modified' and event.src_path.endswith('.py') and '__pycache__' not in event.src_path:\n self.run_tests()\n\n def run_tests(self):\n # Clear the screen\n system_platform = platform.system()\n if system_platform == \"Windows\":\n os.system('cls')\n else:\n os.system('clear')\n\n print(\"\\n\" + \"=\"*40 + \"\\nRunning tests...\\n\" + \"=\"*40 + \"\\n\")\n sys.stdout.flush() # Ensure print statements come out in the right order\n exit_code = pytest.main(['-q']) # '-q' for quiet mode\n if exit_code == 0:\n print(\"\\nTests Passed!\\n\")\n else:\n print(\"\\nTests Failed!\\n\")\n\nif __name__ == \"__main__\":\n path = '.' # Monitor current directory\n event_handler = TestRunnerHandler()\n observer = Observer()\n observer.schedule(event_handler, path, recursive=True)\n observer.start()\n\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n\n observer.join()\n","repo_name":"jasoncalalang/python-tdd","sub_path":"watch_tests.py","file_name":"watch_tests.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"41090393526","text":"import functools\nfrom tkinter import * \nfrom tkinter import ttk\nfrom gmail import grab_emails\nfrom decimal import Decimal, getcontext\nimport webbrowser\nfrom datetime import datetime, timedelta\n\ndef ematcher(emailmatches, transaction, paypal_txns, amazon_txns):\n t = Toplevel()\n t.title(\"Select Matching Email\")\n\n #getcontext().prec=2\n search_str = Decimal.copy_abs(transaction[\"amount\"])\n\n\n if not emailmatches:\n emailmatches = grab_emails(str(search_str))\n for emailmatch in emailmatches:\n emailmatch[\"link\"] = \"No Info\"\n \n content = ttk.Frame(t, width=1200, height=100, border=2, relief=RIDGE, style=\"BW.TFrame\")\n frame = ttk.Frame(t, width=1200, height=300, border=2, relief=RIDGE, style=\"BW.TFrame\")\n buttonframe = ttk.Frame(t, style=\"GREY.TFrame\")\n content.grid(column=0, row=0)\n frame.grid(column=0, row=1)\n buttonframe.grid(column=0, row=2, sticky=\"e w\")\n value = None \n\n def do_ok(event=None):\n t.destroy()\n\n def do_cancel():\n t.destroy()\n \n ok = ttk.Button(buttonframe, text=\"ok\", command=do_ok)\n cancel = ttk.Button(buttonframe, text=\"cancel\", command=do_cancel)\n accountlbl = ttk.Label(content, text=\"Account\", background=\"white\", foreground=\"grey50\")\n datelbl = ttk.Label(content, text=\"Date\", background=\"white\", foreground=\"grey50\")\n amountlbl = ttk.Label(content, text=\"Amount\", background=\"white\", foreground=\"grey50\")\n desclbl = ttk.Label(content, text=\"Description\", background=\"white\", foreground=\"grey50\")\n memolbl = ttk.Label(content, text=\"Memo\", background=\"white\", foreground=\"grey50\")\n balancedlbl = ttk.Label(content, text=\"Balanced\", background=\"white\", foreground=\"grey50\")\n s = ttk.Separator(content, orient=HORIZONTAL)\n ttk.Label(content, text=transaction[\"account\"], style=\"BLUE.TLabel\").grid(column=0, row=2, sticky='w e')\n ttk.Label(content, text=transaction[\"date\"], style=\"BLUE.TLabel\").grid(column=1, row=2, sticky='w e')\n ttk.Label(content, text=transaction[\"amount\"], style=\"BLUE.TLabel\").grid(column=2, row=2, sticky='w e')\n ttk.Label(content, text=transaction[\"desc\"], style=\"BLUE.TLabel\").grid(column=3, row=2, sticky='w e')\n ttk.Label(content, text=transaction[\"memo\"], style=\"BLUE.TLabel\").grid(column=4, row=2, sticky='w e')\n ttk.Label(content, text=transaction[\"amount\"], style=\"BLUE.TLabel\").grid(column=5, row=2, sticky='w e')\n\n confidencelbl = ttk.Label(frame, text=\"Confidence\", background=\"white\", foreground=\"grey50\")\n date2lbl = ttk.Label(frame, text=\"Date\", background=\"white\", foreground=\"grey50\")\n subjectlbl = ttk.Label(frame, text=\"Subject\", background=\"white\", foreground=\"grey50\")\n linklbl = ttk.Label(frame, text=\"Link to Invoice\", background=\"white\", foreground=\"grey50\")\n ss = ttk.Separator(frame, orient=HORIZONTAL)\n \n i = 0\n subjectlbls = []\n linklbls = []\n for emailmatch in emailmatches:\n if i == 0:\n mystyle = \"BLUE.TLabel\"\n else:\n mystyle = \"BW.TLabel\"\n date = \"\"\n subject = \"\"\n for item in emailmatch[\"payload\"][\"headers\"]:\n if item[\"name\"] == \"Subject\":\n subject = item[\"value\"]\n if item[\"name\"] == \"Date\":\n date = item[\"value\"]\n link = invoice_matcher(transaction ,emailmatch, paypal_txns, amazon_txns)\n print(link)\n ttk.Label(frame, text=emailmatch[\"confidence\"], style=mystyle).grid(column=0, row=2 + i, sticky=\"w e\")\n ttk.Label(frame, text=date.split(\" \")[:3], style=mystyle).grid(column=1, row=2 + i, sticky=\"w e\")\n subjectlbls.append(ttk.Label(frame, text=subject, style=mystyle))\n subjectlbls[i].grid(column=2, row=2 + i, sticky=\"w e\")\n subjectlbls[i].bind(\"