diff --git "a/3465.jsonl" "b/3465.jsonl" new file mode 100644--- /dev/null +++ "b/3465.jsonl" @@ -0,0 +1,995 @@ +{"seq_id":"36170038088","text":"import math\nimport os\nimport os.path as osp\nimport time\nfrom collections import deque\n\nimport joblib\nimport numpy as np\n# noinspection PyPackageRequirements\nimport tensorflow as tf\n\nfrom agents.common import get_throttle\nfrom sim.action import Action\nfrom vendor.openai.baselines import logger\n\nfrom vendor.openai.baselines.common.math_util import explained_variance\n\nimport config as c\n\nTF_VAR_SCOPE = 'ppo2model'\n\n\nclass Model(object):\n def __init__(self, *, policy, ob_space, ac_space, nbatch_act, nbatch_train,\n nsteps, ent_coef, vf_coef, max_grad_norm, **kwargs):\n sess = tf.get_default_session()\n\n act_model = policy(sess, ob_space, ac_space, nbatch_act, 1, reuse=False, **kwargs)\n train_model = policy(sess, ob_space, ac_space, nbatch_train, nsteps, reuse=True, **kwargs)\n\n A = train_model.pdtype.sample_placeholder([None])\n ADV = tf.placeholder(tf.float32, [None])\n R = tf.placeholder(tf.float32, [None])\n OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])\n OLDVPRED = tf.placeholder(tf.float32, [None])\n LR = tf.placeholder(tf.float32, [])\n CLIPRANGE = tf.placeholder(tf.float32, [])\n\n neglogpac = train_model.pd.neglogp(A)\n entropy = tf.reduce_mean(train_model.pd.entropy())\n\n vpred = train_model.vf\n vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED, - CLIPRANGE, CLIPRANGE)\n vf_losses1 = tf.square(vpred - R)\n vf_losses2 = tf.square(vpredclipped - R)\n vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))\n ratio = tf.exp(OLDNEGLOGPAC - neglogpac)\n pg_losses = -ADV * ratio\n pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE, 1.0 + CLIPRANGE)\n pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))\n approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))\n clipfrac = tf.reduce_mean(tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))\n loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef\n with tf.variable_scope(TF_VAR_SCOPE):\n params = tf.trainable_variables()\n grads = tf.gradients(loss, params)\n if max_grad_norm is not None:\n grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)\n grads = list(zip(grads, params))\n trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)\n _train = trainer.apply_gradients(grads)\n\n def train(lr, cliprange, obs, returns, masks, actions, values, neglogpacs, states=None):\n advs = returns - values\n if len(advs) > 1:\n advs = (advs - advs.mean()) / (advs.std() + 1e-8)\n for _adv in advs:\n if math.isnan(_adv):\n print('huh oh nan time')\n td_map = {train_model.X:obs, A:actions, ADV:advs, R:returns, LR:lr,\n CLIPRANGE:cliprange, OLDNEGLOGPAC:neglogpacs, OLDVPRED:values}\n if states is not None:\n td_map[train_model.S] = states\n td_map[train_model.M] = masks\n # print('running backprop')\n ret = sess.run(\n [pg_loss, vf_loss, entropy, approxkl, clipfrac, _train],\n td_map\n )[:-1]\n return ret\n self.loss_names = ['policy_loss', 'value_loss', 'policy_entropy', 'approxkl', 'clipfrac']\n\n def save(save_path):\n print('saving model to %s' % save_path)\n ps = sess.run(params)\n joblib.dump(ps, save_path)\n\n def load(load_path):\n print('loading weights from %s' % load_path)\n loaded_params = joblib.load(load_path)\n restores = []\n for p, loaded_p in zip(params, loaded_params):\n restores.append(p.assign(loaded_p))\n sess.run(restores)\n # If you want to load weights, also save/load observation scaling inside VecNormalize\n\n self.train = train\n self.train_model = train_model\n self.act_model = act_model\n self.step = act_model.step\n self.value = act_model.value\n self.initial_state = act_model.initial_state\n self.save = save\n self.load = load\n tf.global_variables_initializer().run(session=sess) #pylint: disable=E1101\n\n\ndef mis(action_probs, rewards):\n \"\"\" Mistake importance scaling\n It seems that taking the log probability in Policy Gradient reverses the amount of learning you would want for\n negative rewards. i.e. We learn much more from unlikely bad actions, than we do likely ones. Whereas this is what\n we want for positive rewards - to learn more from unlikely good actions, we would want the opposite for negative\n rewards - learn more from likely bad actions because our goal is for bad actions and states to be unlikely.\n I've tested these ideas a bit in baselines and the results seem to be good.\n Although I'm sort of duct-taping on the idea by scaling negative rewards inversely to their odds to reverse\n the effect of taking the log. I also notice that DQN, which does not scale the gradient by log likelihood,\n does better than PG methods on Atari games with mostly negative rewards, i.e. DoubleDunk, ice hockey, and surround,\n with skiing being an exception to this rule - but the score for skiing is weird.\"\"\"\n mis_rewards = []\n for i, reward in enumerate(rewards):\n if 'SCALE_ALL_REWARDS' in os.environ:\n mis_rewards.append(reward * 1.8) # Works (in pong), but not as well as scaling by odds\n else:\n if reward < 0:\n scale = 1 + action_probs[i] / (1 - action_probs[i])\n scale = min(scale, 3)\n mis_rewards.append(reward * scale)\n else:\n mis_rewards.append(reward)\n return mis_rewards\n\n\nclass Runner(object):\n\n def __init__(self, *, env, model, nsteps, gamma, lam):\n self.env = env\n self.model = model\n nenv = env.num_envs\n self.obs = np.zeros((nenv,) + env.observation_space.shape, dtype=model.train_model.X.dtype.name)\n self.obs[:] = env.reset()\n self.gamma = gamma\n self.lam = lam\n self.nsteps = nsteps\n self.states = model.initial_state\n self.dones = [False for _ in range(nenv)]\n\n def run(self):\n mb_obs, mb_rewards, mb_actions, mb_values, mb_dones, mb_neglogpacs = [],[],[],[],[],[]\n mb_states = self.states\n epinfos = []\n for _ in range(self.nsteps):\n actions, values, self.states, neglogpacs, action_probs = self.model.step(self.obs, self.states, self.dones)\n\n mb_obs.append(self.obs.copy())\n mb_actions.append(actions)\n mb_values.append(values)\n mb_neglogpacs.append(neglogpacs)\n mb_dones.append(self.dones)\n\n self.obs[:], rewards, self.dones, infos = self.env.step(actions)\n\n rewards = mis(action_probs, rewards)\n\n for info in infos:\n maybe_episode_info = info.get('episode') if info else None\n if maybe_episode_info: epinfos.append(maybe_episode_info)\n\n mb_rewards.append(rewards)\n #batch of steps to batch of rollouts\n mb_obs = np.asarray(mb_obs, dtype=self.obs.dtype)\n mb_rewards = np.asarray(mb_rewards, dtype=np.float32)\n mb_actions = np.asarray(mb_actions)\n mb_values = np.asarray(mb_values, dtype=np.float32)\n mb_neglogpacs = np.asarray(mb_neglogpacs, dtype=np.float32)\n mb_dones = np.asarray(mb_dones, dtype=np.bool)\n last_values = self.model.value(self.obs, self.states, self.dones)\n #discount/bootstrap off value fn\n mb_returns = np.zeros_like(mb_rewards)\n mb_advs = np.zeros_like(mb_rewards)\n lastgaelam = 0\n for t in reversed(range(self.nsteps)):\n if t == self.nsteps - 1:\n nextnonterminal = 1.0 - self.dones\n nextvalues = last_values\n else:\n nextnonterminal = 1.0 - mb_dones[t+1]\n nextvalues = mb_values[t+1]\n delta = mb_rewards[t] + self.gamma * nextvalues * nextnonterminal - mb_values[t]\n mb_advs[t] = lastgaelam = delta + self.gamma * self.lam * nextnonterminal * lastgaelam\n mb_returns = mb_advs + mb_values\n\n # TODO(py27): Python versions < 3.5 do not support starred expressions in tuples, lists, and sets\n return (*map(sf01, (mb_obs, mb_returns, mb_dones, mb_actions, mb_values, mb_neglogpacs)),\n mb_states, epinfos)\n\n def process_actions(self, actions):\n action = Action.from_gym(actions)\n action.throttle = get_throttle(actual_speed=self.obs['speed'], target_speed=(8 * 100))\n actions = action.as_gym()\n return actions\n\n\n# obs, returns, masks, actions, values, neglogpacs, states = runner.run()\n\n\ndef sf01(arr):\n \"\"\"\n swap and then flatten axes 0 and 1\n \"\"\"\n s = arr.shape\n return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:])\n\n\ndef constfn(val):\n def f(_):\n return val\n return f\n\n\ndef learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,\n vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,\n log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,\n save_interval=0, eval_only=False, **kwargs):\n\n if isinstance(lr, float): lr = constfn(lr)\n else: assert callable(lr)\n if isinstance(cliprange, float): cliprange = constfn(cliprange)\n else: assert callable(cliprange)\n total_timesteps = int(total_timesteps)\n\n nenvs = env.num_envs\n ob_space = env.observation_space\n\n ac_space = env.action_space\n nbatch = nenvs * nsteps\n\n if nenvs < nminibatches and 'lstm' in policy.__name__.lower():\n # We aren't running enough environments to split our observations across\n nbatch_train = nbatch\n else:\n nbatch_train = nbatch // nminibatches\n\n make_model = lambda : Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,\n nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,\n max_grad_norm=max_grad_norm, **kwargs)\n if save_interval and logger.get_dir():\n import cloudpickle\n with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:\n fh.write(cloudpickle.dumps(make_model))\n model = make_model()\n if c.PPO_RESUME_PATH is not None:\n model.load(c.PPO_RESUME_PATH)\n\n runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)\n\n epinfobuf = deque(maxlen=100)\n tfirststart = time.time()\n\n nupdates = total_timesteps//nbatch\n for update in range(1, nupdates + 1):\n assert nbatch % nminibatches == 0\n nbatch_train = nbatch // nminibatches\n tstart = time.time()\n frac = 1.0 - (update - 1.0) / nupdates\n lrnow = lr(frac)\n cliprangenow = cliprange(frac)\n\n obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632\n\n if eval_only:\n continue\n\n epinfobuf.extend(epinfos)\n mblossvals = []\n if states is None: # nonrecurrent version\n inds = np.arange(nbatch)\n for _ in range(noptepochs):\n np.random.shuffle(inds)\n for start in range(0, nbatch, nbatch_train):\n end = start + nbatch_train\n minibatch_indxs = inds[start:end]\n slices = (arr[minibatch_indxs] for arr in (obs, returns, masks, actions, values, neglogpacs))\n mblossvals.append(model.train(lrnow, cliprangenow, *slices))\n else: # recurrent version\n # assert nenvs % nminibatches == 0\n # envsperbatch = nenvs // nminibatches\n envinds = np.arange(nenvs)\n flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)\n envsperbatch = nbatch_train // nsteps # ((nevns * nsteps) // nminibatches) // nsteps\n envsperbatch = max(envsperbatch, 1)\n for _ in range(noptepochs):\n np.random.shuffle(envinds)\n for start in range(0, nenvs, envsperbatch):\n end = start + envsperbatch\n mbenvinds = envinds[start:end]\n mbflatinds = flatinds[mbenvinds].ravel()\n slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))\n mbstates = states[mbenvinds]\n\n # TODO(py27): Python versions < 3.5 do not allow positional arguments after *expression\n mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))\n\n lossvals = np.mean(mblossvals, axis=0)\n tnow = time.time()\n fps = int(nbatch / (tnow - tstart))\n if update % log_interval == 0 or update == 1:\n ev = explained_variance(values, returns)\n logger.logkv(\"serial_timesteps\", update * nsteps)\n logger.logkv(\"nupdates\", update)\n logger.logkv(\"total_timesteps\", update * nbatch)\n logger.logkv(\"fps\", fps)\n logger.logkv(\"explained_variance\", float(ev))\n logger.logkv('eprewmean', safemean([epinfo['reward'] for epinfo in epinfobuf]))\n logger.logkv('eplenmean', safemean([epinfo['length'] for epinfo in epinfobuf]))\n logger.logkv('time_elapsed', tnow - tfirststart)\n for (lossval, lossname) in zip(lossvals, model.loss_names):\n logger.logkv(lossname, lossval)\n logger.dumpkvs()\n # input('continue?')\n if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():\n checkdir = osp.join(logger.get_dir(), 'checkpoints')\n os.makedirs(checkdir, exist_ok=True)\n savepath = osp.join(checkdir, '%.5i'%update)\n print('Saving to', savepath)\n model.save(savepath)\n env.close()\n\n\ndef safemean(xs):\n return np.nan if len(xs) == 0 else np.mean(xs)\n","repo_name":"deepdrive/deepdrive","sub_path":"vendor/openai/baselines/ppo2/ppo2.py","file_name":"ppo2.py","file_ext":"py","file_size_in_byte":14112,"program_lang":"python","lang":"en","doc_type":"code","stars":862,"dataset":"github-code","pt":"82"} +{"seq_id":"38186946374","text":"import numpy as np\n\n\nclass Tuple:\n id = 0\n\n def __init__(self, pid=0, offset=0, qid_atts=None):\n self.pid = Tuple.id\n Tuple.id += 1\n if qid_atts is None:\n qid_atts = {}\n self.qidAtts = qid_atts\n self.pid = pid\n self.offset = offset\n\n def __str__(self):\n return str(self.pid)\n\n\nclass Cluster:\n\n \"\"\"\n tuples: list\n list of tuples in the cluster: [t1, t2, ...]\n genAtts: dict\n dictionary with minimum and maximum of each pid in Cluster that will be used in calculation of Tau.\n The key is the attribute's name, and the value is a a tuple (min, max) containing the minimum and maximum values\n in the cluster -> {att_name: (min, max)}.\n \"\"\"\n\n id = 0\n\n def __init__(self, tuple_):\n self.id = Cluster.id\n Cluster.id += 1\n self.tuples = [tuple_]\n self.genAtts = {}\n # Transforming {key, value} in {key, (value, value)}\n for key, value in tuple_.qidAtts.items():\n self.genAtts[key] = (value, value)\n\n # add tuple to [tuples] and update min max in each attribute in genAtts\n def add_tuple(self, tuple_):\n \"\"\"\n Adds a new tuple in the cluster and then calls put_values(tuple_.qidAtts).\n\n :param tuple_: new tuple\n \"\"\"\n self.tuples.append(tuple_)\n self.put_values(tuple_.qidAtts)\n\n def put_values(self, qids):\n \"\"\"\n Updates the genAtts list, i.e. the (min, max) of each attribute.\n :param qids: Attributes of a new tuple used to update genAtts.\n \"\"\"\n for key in qids.keys():\n # if key in qidAtts, check if value < minimum or value > maximum.\n if key in self.genAtts:\n minimum, maximum = self.genAtts[key]\n\n if qids[key] < minimum:\n minimum = qids[key]\n elif qids[key] > maximum:\n maximum = qids[key]\n self.genAtts[key] = (minimum, maximum)\n else:\n self.genAtts[key] = (qids[key], qids[key])\n\n def centroid(self):\n \"\"\"\n Calculates the cluster's centroid as the average of each attribute.\n\n :return: average of attributes from tuples in cluster.\n \"\"\"\n sum_att = np.zeros(len(self.genAtts))\n for tuple_ in self.tuples:\n\n for i, att in enumerate(tuple_.qidAtts.values()):\n sum_att[i] += att\n\n mean_atts = sum_att/len(self.tuples)\n return mean_atts\n\n def __len__(self):\n \"\"\"\n :return: number of tuples in the cluster.\n \"\"\"\n return len(self.tuples)\n\n\n# Setting min and max from all pids in all stream\nclass QidAttsDomain:\n \"\"\"\n qidAtts: dict\n dictionary with minimum and maximum of each element in the stream so far that will be used in calculation of Tau\n The key is the attribute's name, and the value is a tuple (min, max) containing the minimum and maximum values\n -> {att_name: (min, max)}.\n \"\"\"\n def __init__(self, qid_atts={}):\n self.qidAtts = {}\n # Transforming {key, value} in {key, (value, value)}\n for key, value in qid_atts.items():\n self.qidAtts[key] = (value, value)\n\n # consider qid = {qid,value}\n def put_values(self, qids):\n \"\"\"\n Updates the genAtts list, i.e. the (min, max) of each attribute.\n :param qids: Attributes of a new tuple used to update genAtts.\n \"\"\"\n for key in qids.keys():\n # if key in qidAtts, check if value < minimum or value > maximum.\n\n if key in self.qidAtts:\n minimum, maximum = self.qidAtts[key]\n if qids[key] < minimum:\n minimum = qids[key]\n elif qids[key] > maximum:\n maximum = qids[key]\n\n self.qidAtts[key] = (minimum, maximum)\n else:\n self.qidAtts[key] = (qids[key], qids[key])\n\n\nclass Tau:\n \"\"\"\n Keeps track of the last mi cluster published, and calculates the average of their info_loss.\n \"\"\"\n def __init__(self, mi=0, value=0):\n \"\"\"\n :param mi: number of published clusters to be used to calculate Tau\n :param value: the info_loss average of the last mi published clusters\n \"\"\"\n self.value = value\n self.last_clusters_info_loss = []\n self.mi = mi\n\n def update(self, cluster_info_loss):\n \"\"\"\n Updates tau value.\n\n :param cluster_info_loss: info loss from last published cluster\n \"\"\"\n\n if len(self.last_clusters_info_loss) < self.mi:\n self.last_clusters_info_loss.append(cluster_info_loss)\n # if anonymizedClusters size is >= mi, should pop the oldest one before adding\n else:\n self.last_clusters_info_loss.pop(0)\n self.last_clusters_info_loss.append(cluster_info_loss)\n\n self.value = sum(self.last_clusters_info_loss) / len(self.last_clusters_info_loss)\n\n\n\n","repo_name":"israelcvidal/doca","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18799979014","text":"import asyncio\nimport logging\n\nimport websockets\n\n\nasync def server_main(websocket, path):\n logging.info(f'server_main, path:{path}')\n while True:\n rx_msg = await websocket.recv()\n logging.info(f'< {rx_msg}')\n\n if rx_msg.startswith('start_bm'):\n tokens = rx_msg.split()\n assert len(tokens) == 3\n\n size = int(tokens[1])\n cnt = int(tokens[2])\n logging.info(f'size:{size}, cnt:{cnt}')\n tx_msg = 'a' * size\n for i in range(cnt):\n await websocket.send(tx_msg)\n await websocket.send(f'end_bm')\n else:\n tx_msg = f'echo {rx_msg!r}'\n await websocket.send(tx_msg)\n logging.info(f'> {tx_msg}')\n\nif __name__ == '__main__':\n # debug\n LOG_FORMAT = '%(pathname)s:%(lineno)03d | %(asctime)s | %(levelname)s | %(message)s'\n # LOG_LEVEL = logging.DEBUG # DEBUG(10), INFO(20), (0~50)\n LOG_LEVEL = logging.INFO # DEBUG(10), INFO(20), (0~50)\n\n logging.basicConfig(format=LOG_FORMAT, level=LOG_LEVEL)\n\n start_server = websockets.serve(server_main, \"localhost\", 8080)\n asyncio.get_event_loop().run_until_complete(start_server)\n asyncio.get_event_loop().run_forever()\n\n","repo_name":"nhlsm/websocket_benchmark","sub_path":"w41_1_python_websocket_server.py","file_name":"w41_1_python_websocket_server.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24664343104","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 22 18:13:24 2017\n\n@author: andraa\n\"\"\"\nimport datetime\nimport pandas as pd\nfrom os.path import isfile, join\nfrom os import listdir\n \n\nfolder = '/media/andraa/10160545101605452/kaggle/WSDM-kaggle/data/intermediate_data/user_log_merge'\n\nmerge_file = '/media/andraa/10160545101605452/kaggle/WSDM-kaggle/data/intermediate_data/user_log_members.csv'\n\n\nfiles = [f for f in listdir(folder) if isfile(join(folder, f))]\nf_out = open(merge_file, 'w')\nf_init = open(join(folder, files[0]))\n\nprint(files[0])\nfor l in f_init.readlines():\n f_out.write(l)\n\nfor f_in in files[1:]:\n print(f_in)\n for l in open(join(folder, f_in)).readlines()[1:]:\n f_out.write(l)\n\n\nf_out.close()","repo_name":"AndraAnoaica/kaggle_music","sub_path":"merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39538017537","text":"from base64 import encode\nimport socket\nimport threading\nimport tkinter\nimport tkinter.scrolledtext\nfrom tkinter import simpledialog\n\n# It needs to match the server port\nHOST = '127.0.0.1' \nPORT = 9090\n\n''' You can also use your public IP address to host on the web instead locally\n The user will have to specify the public IP address in order to connect\n Need to open ports on the server side as well\n'''\n\n# Creating a client that has a socket that connects with host and port\nclass Client:\n\n def __init__(self, host, port):\n\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.connect((host, port))\n\n msg = tkinter.TK()\n msg.withdraw()\n\n # Getting nickname from the user\n self.nickname = simpledialog.askstring(\"Nickname\", \"Please choose a nickname\", parent=msg)\n\n self.gui_done = False\n self.running = True\n\n # Threads to build the GUI and maintains the GUI and connects to the server\n gui_thread = threading.Thread(target=self.gui_loop)\n receive_thread = threading.Thread(target=self.receive)\n\n gui_thread.start()\n receive_thread.start()\n\n # Build the GUI\n def gui_loop(self):\n self.win = tkinter.Tk()\n self.win.configure(bg=\"lightgray\")\n\n self.chat_label = tkinter.Label(self.win, text=\"Chat:\", bg=\"lightgray\")\n self.chat_label.config(font=(\"Arial\", 12))\n self.chat_label.pack(padx=20, pady=5)\n\n self.text_area = tkinter.scrolledtext.ScrolledText(self.win)\n self.text_area.pack(padx=20, pady=5)\n\n # Disabled means the content cannot be changed. To change it, revert it to enabled make \n # the changes and revert back to disabled.\n self.text_area.config(state='disabled') \n\n self.msg_label = tkinter.Label(self.win, text=\"Message:\", bg=\"lightgray\")\n self.msg_label.config(font=(\"Arial\", 12))\n self.msg_label.pack(padx=20, pady=5)\n\n self.input_area = tkinter.Text(self.win, height=3)\n self.input_area.pack(padx=20, pady=5)\n\n self.send_button = tkinter.Button(self.win, text=\"Send, command=self.write\")\n self.send_button.config(font=(\"Arial\", 12))\n self.send_button.pack(padx=20, pady=5)\n\n self.gui_done = True\n\n self.win.protocol(\"WM_DELETE_WINDOW\", self.stop)\n\n self.win.mainloop()\n\n\n def write(self):\n message = f\"{self.nickname}: {self.input_area.get('1.0', 'end')}\"\n self.sock.send(message.encode('utf-8'))\n self.input_area.delete('1.0', 'end')\n\n\n def stop(self):\n self.running = False\n self.win.destroy()\n self.sock.close()\n exit(0)\n\n\n def receive(self):\n while self.running:\n try:\n message = self.sock.recv(1024).decode('utf-8')\n if message == 'NICK':\n self.sock.send(self.nickname.encode('utf-8'))\n\n else:\n if self.gui_done:\n self.text_area.config(state='normal')\n self.text_area.config('end', message)\n self.text_area.yview('end')\n self.text_area.config(state='disabled')\n except ConnectionAbortedError:\n break\n except:\n print('Error')\n self.sock.close()\n break\n\nclient = Client(HOST, PORT)\n","repo_name":"nicolasnkGH/Simple-GUI-Chat","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34939346944","text":"# -*- coding: utf-8 -*-\n\"\"\"\ncollection_list_requestor.py\n\nA time queue action to periodically request a list of collections\n\"\"\"\nimport logging\nimport os\nimport time\n\n_collection_polling_interval = float(os.environ.get(\n \"NIMBUSIO_ANTI_ENTROPY_COLLECTION_POLLING_INTERVAL\", \"86400.0\")\n)\n\nclass CollectionListRequestor(object):\n \"\"\"\n A time queue action to periodically request a list of collections\n \"\"\"\n def __init__(self, state):\n self._log = logging.getLogger(\"CollectionListRequestor\")\n self._state = state\n\n @classmethod\n def next_run(cls):\n return time.time() + _collection_polling_interval\n\n def run(self, halt_event):\n \"\"\"\n request a list of collection ids from the local database\n \"\"\"\n if halt_event.is_set():\n self._log.info(\"halt-event is set, exiting\")\n return\n\n collection_id_generator = \\\n self._state[\"central-database-connection\"].generate_all_rows(\n \"\"\"\n select id \n from nimbusio_central.collection\n where deletion_time is null\n \"\"\"\n )\n for (collection_id, ) in collection_id_generator:\n self._state[\"collection-ids\"].add(collection_id)\n\n self._log.info(\"%s known collection ids\" % (\n len(self._state[\"collection-ids\"]), \n ))\n \n return [(self.run, self.next_run(), )]\n\n","repo_name":"jocelyn-monitor/nimbus.io","sub_path":"anti_entropy_server/collection_list_requestor.py","file_name":"collection_list_requestor.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35268882624","text":"from flask import Flask, request, jsonify, render_template\nimport os\nfrom flask_cors import CORS, cross_origin\nfrom datetime import datetime\n\nfrom services.card_detector.application.ai.inference.prediction import CardsDetector\nfrom services.card_detector.application.ai.utils.utils import decodeImage\n\nos.putenv('LANG', 'en_US.UTF-8')\nos.putenv('LC_ALL', 'en_US.UTF-8')\n\napp = Flask(__name__)\nCORS(app)\n\n\n@app.route(\"/\")\ndef home():\n return render_template(\"index.html\")\n\n\n@app.route(\"/predict\", methods=['POST'])\n@cross_origin()\ndef predictRoute():\n try:\n image = request.json['image']\n image_name = \"input_image_\" + str(datetime.now()).split(':')[-1] + \".jpg\"\n cards_detector.settings.logger.info(\"Received Post Request for inference--!!\")\n decodeImage(image, image_name, cards_detector.settings.INPUT_IMAGE_PATH)\n cards_detector.settings.logger.info(\n \"Image stored in directory -- \" + cards_detector.settings.INPUT_IMAGE_PATH + \"--with image name--\" + str(\n image_name))\n result = cards_detector.predict(cards_detector.settings.INPUT_IMAGE_PATH + image_name)\n return jsonify(result)\n except BaseException as ex:\n cards_detector.settings.logger.error(\"Following Error occurred while inference---!!\", str(ex))\n return jsonify(str(ex))\n\n\nif __name__ == \"__main__\":\n cards_detector = CardsDetector()\n port = 9000\n app.run(host='127.0.0.1', port=port)\n","repo_name":"R-aryan/Cards_Detection_Using_FASTER-RCNN","sub_path":"services/card_detector/api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38690213007","text":"import importlib\nimport time\nfrom datetime import datetime\nimport asyncio\n#from asyncio import get_event_loop_policy\nfrom pyrogram import idle\nfrom uvloop import install\nfrom Ubotlibs import *\nfrom Ubot import BOTLOG_CHATID, aiosession, bots, app, ids, LOOP\nfrom platform import python_version as py\nfrom Ubot.logging import LOGGER\nfrom pyrogram import __version__ as pyro\nfrom Ubot.modules import ALL_MODULES\nfrom Ubotlibs import *\nfrom Ubot.core.db.activedb import *\nfrom Ubot.core.db.usersdb import *\nfrom config import SUPPORT, CHANNEL, CMD_HNDLR, ADMIN1_ID\nimport os\nfrom dotenv import load_dotenv\n\n\nMSG_BOT = \"\"\"\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n• **Alive\n• **Phython**: `{}`\n• **Pyrogram**: `{}`\n• **Users**: `{}`\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n\"\"\"\n\nMSG_ON = \"\"\"\n**pyRainger Actived ✅**\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n• **Versi** : `{}`\n• **Phython** : `{}`\n• **Pyrogram** : `{}`\n• **Masa Aktif** : `{}`\n• **Akan Berakhir**: `{}`\n**Ketik** `{}alive` **untuk Mengecheck Bot**\n╼┅━━━━━━━━━━╍━━━━━━━━━━┅╾\n\"\"\"\n\nMSG = \"\"\"\n**Users**: `{}`\n**ID**: `{}`\n\"\"\"\n\n\nasync def main():\n await app.start()\n LOGGER(\"Ubot\").info(\"Memulai Ubot Pyro..\")\n for all_module in ALL_MODULES:\n importlib.import_module(\"Ubot.modules\" + all_module)\n for bot in bots:\n try:\n await bot.start()\n ex = await bot.get_me()\n user_id = ex.id\n await buat_log(bot)\n botlog_chat_id = await get_botlog(user_id)\n LOGGER(\"Info\").info(\"Startup Completed\")\n LOGGER(\"√\").info(f\"Started as {ex.first_name} | {ex.id} \")\n await join(bot)\n await bot.send_message(botlog_chat_id, MSG_ON.format(BOT_VER, py(), pyro))\n ids.append(ex.id)\n except Exception as e:\n LOGGER(\"X\").info(f\"{e}\")\n await idle()\n await aiosession.close()\n await app.stop()\n \n\nif __name__ == \"__main__\":\n LOGGER(\"Ubot\").info(\"Starting Ubot\")\n install()\n LOOP.run_until_complete(main())\n","repo_name":"RaingerXD/pyRaingerV1_heroku","sub_path":"Ubot/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73832773708","text":"def _eval(newSentences, label_map, args, eval_dataset):\n tag2Idx = {v: k for k, v in label_map.items()}\n trueEntityID, predEntityID = entityIDGeneration(newSentences)\n\n f1_record = []\n if args.determine_entity:\n labels = []\n preds = []\n for sent in newSentences:\n for token_info in sent:\n labels.append(token_info[1])\n preds.append(token_info[2])\n assert len(labels) == len(preds)\n p, r, f1 = compute_token_f1(labels, preds)\n f1_record.append(f1)\n print(\"Entity: Precision: {}, Recall: {}, F1: {}\".format(p, r, f1))\n else:\n if args.flag == 'ALL' or args.inference:\n flags = [f for f in tag2Idx.keys()][1:-2]\n for flag in flags:\n precision, recall, f1 = compute_precision_recall_f1(trueEntityID, predEntityID, flag, tag2Idx[flag])\n print(flag + \": Precision: {}, Recall: {}, F1: {}\".format(precision, recall, f1))\n overall_precision, overall_recall, overall_f1 = compute_overall_precision_recall_f1(trueEntityID, predEntityID, tag2Idx)\n f1_record.append(overall_f1)\n print(\"OVERALL: Precision: {}, Recall: {}, F1: {}\".format(overall_precision, overall_recall, overall_f1))\n else:\n p, r, f1 = compute_precision_recall_f1(trueEntityID, predEntityID, args.flag, 1)\n f1_record.append(f1)\n print(args.flag + \": Precision: {}, Recall: {}, F1: {} on {}\".format(p, r, f1, eval_dataset))\n\n return sum(f1_record)\n\n\ndef entityIDGeneration(sentences):\n sent_id = 0\n type_ = \"#\"\n flag = -1\n\n label_start_id = 0\n pred_start_id = 0\n\n true_entities = []\n pred_entities = []\n for sentence in sentences:\n # print(\"sentence\")\n # print(sentence)\n pre_label = \"O\"\n sent_true_entities = []\n sent_pred_entities = []\n for i, (word, label, pred) in enumerate(sentence):\n if label == \"O\":\n if not pre_label == \"O\":\n label_end_id = i - 1\n # print(\"entity label: \", sent_id, label_start_id, label_end_id, type)\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n else:\n if \"B-\" in label:\n label = label.split(\"-\")[-1]\n if not pre_label == \"O\":\n label_end_id = i - 1\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n label_start_id = i\n type_ = label\n else:\n continue\n pre_label = label\n if not pre_label == \"O\":\n label_end_id = len(sentence) - 1\n # print(\"entity label: \", sent_id, label_start_id, label_end_id, type)\n sent_true_entities.append(\"_\".join([str(i) for i in [sent_id, label_start_id, label_end_id]] + [type_]))\n\n pre_pred = 1\n for i, (word, label, pred) in enumerate(sentence):\n if pred == 1:\n if not pre_pred == 1:\n pred_end_id = i - 1\n # print(\"entity pred: \", sent_id, pred_start_id, pred_end_id, flag)\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n else:\n if not pre_pred == pred:\n if not pre_pred == 1:\n pred_end_id = i - 1\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n pred_start_id = i\n flag = pred\n else:\n continue\n pre_pred = pred\n\n if not pre_pred == 1:\n pred_end_id = len(sentence) - 1\n # print(\"entity pred: \", sent_id, pred_start_id, pred_end_id, flag)\n sent_pred_entities.append(\"_\".join([str(i) for i in [sent_id, pred_start_id, pred_end_id, flag]]))\n\n sent_id += 1\n true_entities.append(sent_true_entities)\n pred_entities.append(sent_pred_entities)\n return true_entities, pred_entities\n\n\ndef compute_token_f1(labels, preds):\n # recall = tp/(tp + fn)\n # precision = tp/(tp + fp)\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n\n assert len(labels) == len(preds)\n for i in range(len(labels)):\n if (labels[i].startswith(\"B\") or labels[i].startswith(\"I\")) and preds[i] == 1:\n tp += 1\n elif (labels[i].startswith(\"B\") or labels[i].startswith(\"I\")) and preds[i] == 0:\n fn += 1\n elif labels[i].startswith(\"O\") and preds[i] == 0:\n tn += 1\n elif labels[i].startswith(\"O\") and preds[i] == 1:\n fp += 1\n if tp == 0:\n recall = 0\n precision = 0\n else:\n recall = float(tp) / (float(tp) + float(fn))\n precision = float(tp) / (float(tp) + float(fp))\n if recall == 0 or precision == 0:\n f1 = 0\n else:\n f1 = (2 * precision * recall) / (precision + recall)\n return precision, recall, f1\n\n\ndef compute_precision_recall_f1(true_entities, pred_entities, flag, pflag):\n tp = 0\n np_ = 0\n pp = 0\n for i in range(len(true_entities)):\n sent_true = true_entities[i]\n sent_pred = pred_entities[i]\n for e in sent_true:\n if flag in e:\n np_ += 1\n temp = e.replace(flag, str(pflag))\n if temp in sent_pred:\n tp += 1\n for e in sent_pred:\n if int(e.split(\"_\")[-1]) == pflag:\n pp += 1\n if pp == 0:\n p = 0\n else:\n p = float(tp) / float(pp)\n if np_ == 0:\n r = 0\n else:\n r = float(tp) / float(np_)\n if p == 0 or r == 0:\n f1 = 0\n else:\n f1 = float(2 * p * r) / float((p + r))\n return p, r, f1\n\n\ndef compute_overall_precision_recall_f1(true_entities, pred_entities, tag2Idx):\n tp = 0\n np_ = len(sum(true_entities, []))\n pp = len(sum(pred_entities, []))\n temp = ' '\n\n assert len(true_entities) == len(pred_entities)\n for i in range(len(true_entities)):\n sent_true = true_entities[i]\n sent_pred = pred_entities[i]\n for e in sent_true:\n for flag in tag2Idx:\n if flag in e:\n temp = e.replace(flag, str(tag2Idx[flag]))\n if temp in sent_pred:\n tp += 1\n if pp == 0:\n p = 0\n else:\n p = float(tp) / float(pp)\n if np_ == 0:\n r = 0\n else:\n r = float(tp) / float(np_)\n if p == 0 or r == 0:\n f1 = 0\n else:\n f1 = float(2 * p * r) / float((p + r))\n return p, r, f1\n","repo_name":"kangISU/Conf-MPU-BERT-DS-NER","sub_path":"metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":6799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30418344733","text":"from enum import Enum\nimport time\nimport weakref\n\nfrom .backend.QtCore import pyqtSignal, QLineF, QRect, Qt\nfrom .backend.QtWidgets import QGraphicsScene, QGraphicsSceneMouseEvent\n\n\nclass MouseEventState(Enum):\n OFF = 0\n ON = 1\n ENTER = 2\n EXIT = 3\n\n\nclass MouseDragEvent:\n \"\"\"Mouse event delivered by :class:`GraphicsScene` when a item is dragged.\n \"\"\"\n def __init__(self, move_ev, press_ev, last_ev,\n state: MouseEventState = MouseEventState.ON):\n self._state = state\n self.accepted = False\n self.current_item = None\n self._button_down_scene_pos = {}\n self._button_down_screen_pos = {}\n for btn in [Qt.MouseButton.LeftButton,\n Qt.MouseButton.MiddleButton,\n Qt.MouseButton.RightButton]:\n self._button_down_scene_pos[btn] = move_ev.buttonDownScenePos(btn)\n self._button_down_screen_pos[btn] = move_ev.buttonDownScreenPos(btn)\n self._scene_pos = move_ev.scenePos()\n self._screen_pos = move_ev.screenPos()\n if last_ev is None:\n self._last_scene_pos = press_ev.scenePos()\n self._last_screen_pos = press_ev.screenPos()\n else:\n self._last_scene_pos = last_ev.scenePos()\n self._last_screen_pos = last_ev.screenPos()\n self._buttons = move_ev.buttons()\n self._button = press_ev.button()\n self._modifiers = move_ev.modifiers()\n self.accepted_item = None\n\n def accept(self):\n \"\"\"An item should call this method if it can handle the event.\n\n This will prevent the event being delivered to any other items.\"\"\"\n self.accepted = True\n self.accepted_item = self.current_item\n\n def ignore(self):\n \"\"\"An item should call this method if it cannot handle the event.\n\n This will allow the event to be delivered to other items.\"\"\"\n self.accepted = False\n\n def isAccepted(self):\n return self.accepted\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position (pixels relative to widget) of the mouse.\"\"\"\n return self._screen_pos\n\n def buttonDownScenePos(self, btn=None):\n \"\"\"\n Return the scene position of the mouse at the time *btn* was pressed.\n If *btn* is omitted, then the button that initiated the drag is assumed.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self._button_down_scene_pos[btn]\n\n def buttonDownScreenPos(self, btn=None):\n \"\"\"\n Return the screen position (pixels relative to widget) of the mouse at the time *btn* was pressed.\n If *btn* is omitted, then the button that initiated the drag is assumed.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self._button_down_screen_pos[btn]\n\n def lastScenePos(self):\n \"\"\"\n Return the scene position of the mouse immediately prior to this event.\n \"\"\"\n return self._last_scene_pos\n\n def lastScreenPos(self):\n \"\"\"\n Return the screen position of the mouse immediately prior to this event.\n \"\"\"\n return self._last_screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def button(self):\n \"\"\"Return the button that initiated the drag (may be different from the buttons currently pressed)\n (see QGraphicsSceneMouseEvent::button in the Qt documentation)\n\n \"\"\"\n return self._button\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def buttonDownPos(self, btn=None):\n \"\"\"\n Return the position of the mouse at the time the drag was initiated\n in the coordinate system of the item that the event was delivered to.\n \"\"\"\n if btn is None:\n btn = self.button()\n return self.current_item.mapFromScene(self._button_down_scene_pos[btn])\n\n def entering(self):\n \"\"\"Whether this event is the first one since a drag was initiated.\"\"\"\n return self._state == MouseEventState.ENTER\n\n def exiting(self):\n \"\"\"Whether this event is the last one since a drag was initiated.\"\"\"\n return self._state == MouseEventState.EXIT\n\n def __repr__(self):\n if self.current_item is None:\n lp = self._last_scene_pos\n p = self._scene_pos\n else:\n lp = self.lastPos()\n p = self.pos()\n return \"(%g,%g) buttons=%d entering=%s existing=%s>\" % (\n lp.x(), lp.y(), p.x(), p.y(), int(self.buttons()), str(self.entering()), str(self.exiting()))\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n\n \"\"\"\n return self._modifiers\n\n\nclass MouseClickEvent:\n \"\"\"\n Instances of this class are delivered to items in a :class:`GraphicsScene `\n via their mouseClickEvent() method when the item is clicked.\n \"\"\"\n\n def __init__(self, pressEvent, double=False):\n self.accepted = False\n self.current_item = None\n self._double = double\n self._scene_pos = pressEvent.scenePos()\n self._screen_pos = pressEvent.screenPos()\n self._button = pressEvent.button()\n self._buttons = pressEvent.buttons()\n self._modifiers = pressEvent.modifiers()\n self._time = time.time()\n self.accepted_item = None\n\n def accept(self):\n \"\"\"An item should call this method if it can handle the event.\n\n This will prevent the event being delivered to any other items.\"\"\"\n self.accepted = True\n self.accepted_item = self.current_item\n\n def ignore(self):\n \"\"\"An item should call this method if it cannot handle the event.\n\n This will allow the event to be delivered to other items.\"\"\"\n self.accepted = False\n\n def isAccepted(self):\n return self.accepted\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position (pixels relative to widget) of the mouse.\"\"\"\n return self._screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def button(self):\n \"\"\"Return the mouse button that generated the click event.\n (see QGraphicsSceneMouseEvent::button in the Qt documentation)\n \"\"\"\n return self._button\n\n def double(self):\n \"\"\"Return True if this is a double-click.\"\"\"\n return self._double\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n \"\"\"\n return self._modifiers\n\n def __repr__(self):\n try:\n if self.current_item is None:\n p = self._scene_pos\n else:\n p = self.pos()\n return \"\" % (p.x(), p.y(), int(self.button()))\n except:\n return \"\" % (int(self.button()))\n\n def time(self):\n return self._time\n\n\nclass HoverEvent:\n \"\"\"\n Instances of this class are delivered to items in a :class:`GraphicsScene ` via their hoverEvent() method when the mouse is hovering over the item.\n This event class both informs items that the mouse cursor is nearby and allows items to\n communicate with one another about whether each item will accept *potential* mouse events.\n\n It is common for multiple overlapping items to receive hover events and respond by changing\n their appearance. This can be misleading to the user since, in general, only one item will\n respond to mouse events. To avoid this, items make calls to event.acceptClicks(button)\n and/or acceptDrags(button).\n\n Each item may make multiple calls to acceptClicks/Drags, each time for a different button.\n If the method returns True, then the item is guaranteed to be\n the recipient of the claimed event IF the user presses the specified mouse button before\n moving. If claimEvent returns False, then this item is guaranteed NOT to get the specified\n event (because another has already claimed it) and the item should change its appearance\n accordingly.\n\n event.isEnter() returns True if the mouse has just entered the item's shape;\n event.isExit() returns True if the mouse has just left.\n \"\"\"\n\n def __init__(self, ev: QGraphicsSceneMouseEvent, state: MouseEventState):\n self._state = state\n self.enter = False\n self.exit = False\n self.__click_items = weakref.WeakValueDictionary()\n self.__drag_items = weakref.WeakValueDictionary()\n self.current_item = None\n if ev is not None:\n self._scene_pos = ev.scenePos()\n self._screen_pos = ev.screenPos()\n self._last_scene_pos = ev.lastScenePos()\n self._last_screen_pos = ev.lastScreenPos()\n self._buttons = ev.buttons()\n self._modifiers = ev.modifiers()\n else:\n self.exit = True\n\n def isEnter(self):\n \"\"\"Returns True if the mouse has just entered the item's shape\"\"\"\n return self.enter\n\n def isExit(self):\n \"\"\"Returns True if the mouse has just exited the item's shape\"\"\"\n return self.exit\n\n def acceptClicks(self, button: Qt.MouseButton):\n \"\"\"Inform the scene that the item (that the event was delivered to)\n would accept a mouse click event if the user were to click before\n moving the mouse again.\n\n Returns True if the request is successful, otherwise returns False (indicating\n that some other item would receive an incoming click).\n \"\"\"\n if self._state == MouseEventState.EXIT:\n return False\n\n if button not in self.__click_items:\n self.__click_items[button] = self.current_item\n return True\n return False\n\n def acceptDrags(self, button: Qt.MouseButton):\n \"\"\"Inform the scene that the item (that the event was delivered to)\n would accept a mouse drag event if the user were to drag before\n the next hover event.\n\n Returns True if the request is successful, otherwise returns False (indicating\n that some other item would receive an incoming drag event).\n \"\"\"\n if self._state == MouseEventState.EXIT:\n return False\n\n if button not in self.__drag_items:\n self.__drag_items[button] = self.current_item\n return True\n return False\n\n def scenePos(self):\n \"\"\"Return the current scene position of the mouse.\"\"\"\n return self._scene_pos\n\n def screenPos(self):\n \"\"\"Return the current screen position of the mouse.\"\"\"\n return self._screen_pos\n\n def lastScenePos(self):\n \"\"\"Return the previous scene position of the mouse.\"\"\"\n return self._last_scene_pos\n\n def lastScreenPos(self):\n \"\"\"Return the previous screen position of the mouse.\"\"\"\n return self._last_screen_pos\n\n def buttons(self):\n \"\"\"\n Return the buttons currently pressed on the mouse.\n (see QGraphicsSceneMouseEvent::buttons in the Qt documentation)\n \"\"\"\n return self._buttons\n\n def pos(self):\n \"\"\"\n Return the current position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._scene_pos)\n\n def lastPos(self):\n \"\"\"\n Return the previous position of the mouse in the coordinate system of the item\n that the event was delivered to.\n \"\"\"\n return self.current_item.mapFromScene(self._last_scene_pos)\n\n def __repr__(self):\n if self.exit:\n return \"\"\n\n if self.current_item is None:\n lp = self._last_scene_pos\n p = self._scene_pos\n else:\n lp = self.lastPos()\n p = self.pos()\n return \"(%g,%g) buttons=%d enter=%s exit=%s>\" % (\n lp.x(), lp.y(), p.x(), p.y(), int(self.buttons()), str(self.isEnter()), str(self.isExit()))\n\n def modifiers(self):\n \"\"\"Return any keyboard modifiers currently pressed.\n (see QGraphicsSceneMouseEvent::modifiers in the Qt documentation)\n \"\"\"\n return self._modifiers\n\n def clickItems(self):\n return self.__click_items\n\n def dragItems(self):\n return self.__drag_items\n\n\nclass GraphicsScene(QGraphicsScene):\n \"\"\"Extension of QGraphicsScene that implements a complete, parallel mouse event system.\n\n (It would have been preferred to just alter the way QGraphicsScene creates and delivers \n events, but this turned out to be impossible because the constructor for QGraphicsMouseEvent\n is private)\n \n * Generates MouseClicked events in addition to the usual press/move/release events. \n (This works around a problem where it is impossible to have one item respond to a \n drag if another is watching for a click.)\n * Adjustable radius around click that will catch objects so you don't have to click *exactly* over small/thin objects\n * Global context menu--if an item implements a context menu, then its parent(s) may also add items to the menu.\n * Allows items to decide _before_ a mouse click which item will be the recipient of mouse events.\n This lets us indicate unambiguously to the user which item they are about to click/drag on\n * Eats mouseMove events that occur too soon after a mouse press.\n * Reimplements items() and itemAt() to circumvent PyQt bug\n \n Mouse interaction is as follows:\n \n 1) Every time the mouse moves, the scene delivers both the standard hoverEnter/Move/LeaveEvents \n as well as custom HoverEvents. \n 2) Items are sent HoverEvents in Z-order and each item may optionally call event.acceptClicks(button), \n acceptDrags(button) or both. If this method call returns True, this informs the item that _if_ \n the user clicks/drags the specified mouse button, the item is guaranteed to be the \n recipient of click/drag events (the item may wish to change its appearance to indicate this).\n If the call to acceptClicks/Drags returns False, then the item is guaranteed to *not* receive\n the requested event (because another item has already accepted it). \n 3) If the mouse is clicked, a mousePressEvent is generated as usual. If any items accept this press event, then\n No click/drag events will be generated and mouse interaction proceeds as defined by Qt. This allows\n items to function properly if they are expecting the usual press/move/release sequence of events.\n (It is recommended that items do NOT accept press events, and instead use click/drag events)\n Note: The default implementation of QGraphicsItem.mousePressEvent will *accept* the event if the \n item is has its Selectable or Movable flags enabled. You may need to override this behavior.\n 4) If no item accepts the mousePressEvent, then the scene will begin delivering mouseDrag and/or mouseClick events.\n If the mouse is moved a sufficient distance (or moved slowly enough) before the button is released, \n then a mouseDragEvent is generated.\n If no drag events are generated before the button is released, then a mouseClickEvent is generated. \n 5) Click/drag events are delivered to the item that called acceptClicks/acceptDrags on the HoverEvent\n in step 1. If no such items exist, then the scene attempts to deliver the events to items near the event. \n ClickEvents may be delivered in this way even if no\n item originally claimed it could accept the click. DragEvents may only be delivered this way if it is the initial\n move in a drag.\n \"\"\"\n # Emitted a list of objects under the cursor when the mouse is\n # moved over the scene.\n mouse_hover_sgn = pyqtSignal(object)\n # Emitted when the mouse cursor moves over the scene. The position\n # is given in the scene coordinate system.\n mouse_moved_sgn = pyqtSignal(object)\n # Emitted when the mouse is clicked over the scene. Use ev.pos() to\n # get the click position relative to the item that was clicked on,\n # or ev.scenePos() to get the click position in scene coordinates.\n mouse_clicked_sgn = pyqtSignal(object)\n\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n self._click_radius = 2\n self._move_distance = 5\n\n self.click_events = []\n self.drag_buttons = []\n self.drag_item = None\n self.last_drag = None\n self.hover_items = weakref.WeakKeyDictionary()\n self.last_hover_event = None\n self.min_drag_time = 0.5 # drags shorter than 0.5 sec are interpreted as clicks\n\n def mousePressEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n super().mousePressEvent(ev)\n\n if self.mouseGrabberItem() is None: # nobody claimed press; we are free to generate drag/click events\n if self.last_hover_event is not None:\n # If the mouse has moved since the last hover event, send a new one.\n # This can happen if a context menu is open while the mouse is moving.\n if ev.scenePos() != self.last_hover_event.scenePos():\n self.sendHoverEvents(ev)\n \n self.click_events.append(MouseClickEvent(ev))\n \n # set focus on the topmost focusable item under this click\n items = self.items(ev.scenePos())\n for i in items:\n if i.isEnabled() and i.isVisible() and (i.flags() & i.GraphicsItemFlag.ItemIsFocusable):\n i.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n \n def mouseMoveEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n self.mouse_moved_sgn.emit(ev.scenePos())\n\n # First allow QGraphicsScene to deliver hoverEnter/Move/ExitEvents\n super().mouseMoveEvent(ev)\n \n # Next deliver our own HoverEvents\n self.sendHoverEvents(ev)\n \n if ev.buttons(): # button is pressed; send mouseMoveEvents and mouseDragEvents\n # FIXME: duplicated?\n super().mouseMoveEvent(ev)\n if self.mouseGrabberItem() is None:\n now = time.time()\n init = False\n # keep track of which buttons are involved in dragging\n for btn in [Qt.MouseButton.LeftButton,\n Qt.MouseButton.MiddleButton,\n Qt.MouseButton.RightButton]:\n if not (ev.buttons() & btn):\n continue\n if btn not in self.drag_buttons: # see if we've dragged far enough yet\n cev = [e for e in self.click_events if e.button() == btn]\n if cev:\n cev = cev[0]\n dist = QLineF(ev.scenePos(), cev.scenePos()).length()\n if dist == 0 or (dist < self._move_distance and now - cev.time() < self.min_drag_time):\n continue\n # If this is the first button to be dragged, then init=True\n init = init or (len(self.drag_buttons) == 0)\n self.drag_buttons.append(btn)\n\n # If we have dragged buttons, deliver a drag event\n if len(self.drag_buttons) > 0:\n if self.sendDragEvent(\n ev, MouseEventState.ENTER if init\n else MouseEventState.ON):\n ev.accept()\n\n def mouseReleaseEvent(self, ev: QGraphicsSceneMouseEvent) -> None:\n \"\"\"Override.\"\"\"\n if self.mouseGrabberItem() is None:\n if ev.button() in self.drag_buttons:\n if self.sendDragEvent(ev, MouseEventState.EXIT):\n ev.accept()\n self.drag_buttons.remove(ev.button())\n else:\n cev = [e for e in self.click_events if e.button() == ev.button()]\n if cev:\n if self.sendClickEvent(cev[0]):\n ev.accept()\n self.click_events.remove(cev[0])\n\n if not ev.buttons():\n self.drag_item = None\n self.drag_buttons = []\n self.click_events = []\n self.last_drag = None\n\n super().mouseReleaseEvent(ev)\n \n self.sendHoverEvents(ev) # let items prepare for next click/drag\n\n def mouseDoubleClickEvent(self, ev: QGraphicsSceneMouseEvent):\n \"\"\"Override.\"\"\"\n super().mouseDoubleClickEvent(ev)\n\n if self.mouseGrabberItem() is None: # nobody claimed press; we are free to generate drag/click events\n self.click_events.append(MouseClickEvent(ev, double=True))\n \n def sendHoverEvents(self, ev: QGraphicsSceneMouseEvent,\n state: MouseEventState = MouseEventState.ON):\n \"\"\"Send out HoverEvent.\n\n :param ev:\n :param state:\n \"\"\"\n if state == MouseEventState.EXIT:\n items = []\n event = HoverEvent(ev, False)\n else:\n # if we are in mid-drag, do not allow items to accept the hover event.\n event = HoverEvent(ev, not ev.buttons())\n items = self.itemsNearEvent(event, hoverable=True)\n self.mouse_hover_sgn.emit(items)\n \n prev_items = list(self.hover_items.keys())\n \n for item in items:\n if hasattr(item, 'hoverEvent'):\n event.current_item = item\n if item not in self.hover_items:\n self.hover_items[item] = None\n event.enter = True\n else:\n prev_items.remove(item)\n event.enter = False\n \n item.hoverEvent(event)\n \n event.enter = False\n event.exit = True\n for item in prev_items:\n event.current_item = item\n\n if item.scene() is self:\n item.hoverEvent(event)\n del self.hover_items[item]\n \n # Update last hover event unless:\n # - mouse is dragging (move+buttons); in this case we want the dragged\n # item to continue receiving events until the drag is over\n # - event is not a mouse event (QEvent.Leave sometimes appears here)\n if (ev.type() == ev.Type.GraphicsSceneMousePress or\n (ev.type() == ev.Type.GraphicsSceneMouseMove and not ev.buttons())):\n self.last_hover_event = event # save this so we can ask about accepted events later.\n\n def sendDragEvent(self,\n ev: QGraphicsSceneMouseEvent,\n state: MouseEventState):\n \"\"\"Send out a MouseDragEvent.\n\n to the current drag_item or to items near the beginning of the drag.\n\n :param ev:\n :param state:\n \"\"\"\n event = MouseDragEvent(ev, self.click_events[0], self.last_drag, state=state)\n if state == MouseEventState.ENTER and self.drag_item is None:\n if self.last_hover_event is not None:\n accepted_item = self.last_hover_event.dragItems().get(event.button(), None)\n else:\n accepted_item = None\n \n if accepted_item is not None and accepted_item.scene() is self:\n self.drag_item = accepted_item\n event.current_item = self.drag_item\n self.drag_item.mouseDragEvent(event)\n \n else:\n for item in self.itemsNearEvent(event):\n if not item.isVisible() or not item.isEnabled():\n continue\n if hasattr(item, 'mouseDragEvent'):\n event.current_item = item\n item.mouseDragEvent(event)\n if event.isAccepted():\n self.drag_item = item\n if item.flags() & item.GraphicsItemFlag.ItemIsFocusable:\n item.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n elif self.drag_item is not None:\n event.current_item = self.drag_item\n self.drag_item.mouseDragEvent(event)\n\n self.last_drag = event\n \n return event.isAccepted()\n\n def sendClickEvent(self, ev: QGraphicsSceneMouseEvent):\n # if we are in mid-drag, click events may only go to the dragged item.\n if self.drag_item is not None and hasattr(self.drag_item, 'MouseDragEvent'):\n ev.current_item = self.drag_item\n self.drag_item.mouseClickEvent(ev)\n \n # otherwise, search near the cursor\n else:\n if self.last_hover_event is not None:\n accepted_item = self.last_hover_event.clickItems().get(ev.button(), None)\n else:\n accepted_item = None\n if accepted_item is not None:\n ev.current_item = accepted_item\n accepted_item.mouseClickEvent(ev)\n else:\n for item in self.itemsNearEvent(ev):\n if not item.isVisible() or not item.isEnabled():\n continue\n if hasattr(item, 'mouseClickEvent'):\n ev.current_item = item\n item.mouseClickEvent(ev)\n\n if ev.isAccepted():\n if item.flags() & item.GraphicsItemFlag.ItemIsFocusable:\n item.setFocus(Qt.FocusReason.MouseFocusReason)\n break\n self.mouse_clicked_sgn.emit(ev)\n return ev.isAccepted()\n \n def items(self, *args):\n return QGraphicsScene.items(self, *args)\n\n def selectedItems(self, *args):\n return QGraphicsScene.selectedItems(self, *args)\n\n def itemAt(self, *args):\n return super().itemAt(*args)\n\n def itemsNearEvent(self,\n event,\n selMode=Qt.ItemSelectionMode.IntersectsItemShape,\n sortOrder=Qt.SortOrder.DescendingOrder,\n hoverable=False):\n \"\"\"\n Return an iterator that iterates first through the items that directly intersect point (in Z order)\n followed by any other items that are within the scene's click radius.\n \"\"\"\n view = self.views()[0]\n tr = view.viewportTransform()\n r = self._click_radius\n rect = view.mapToScene(QRect(0, 0, 2*r, 2*r)).boundingRect()\n \n if hasattr(event, 'buttonDownScenePos'):\n point = event.buttonDownScenePos()\n else:\n point = event.scenePos()\n\n items = self.items(point, selMode, sortOrder, tr)\n \n # remove items whose shape does not contain point (scene.items() apparently sucks at this)\n items2 = []\n for item in items:\n if hoverable and not hasattr(item, 'hoverEvent'):\n continue\n if item.scene() is not self:\n continue\n shape = item.shape() # Note: default shape() returns boundingRect()\n if shape is None:\n continue\n if shape.contains(item.mapFromScene(point)):\n items2.append(item)\n \n # Sort by descending Z-order (don't trust scene.itms() to do this either)\n # use 'absolute' z value, which is the sum of all item/parent ZValues\n def absZValue(item):\n if item is None:\n return 0\n return item.zValue() + absZValue(item.parentItem())\n \n items2.sort(key=absZValue, reverse=True)\n \n return items2\n","repo_name":"zhujun98/foamgraph","sub_path":"foamgraph/graphics_scene.py","file_name":"graphics_scene.py","file_ext":"py","file_size_in_byte":29443,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"6888734420","text":"from setuptools import setup, find_packages\nimport glob\nimport ntpath\n\ndef get_module_name(module_path):\n \"\"\"\n Return the module name of the module path\n \"\"\"\n return ntpath.split(module_path)[1].split(\".\")[0]\n\ndef snake_to_camel(word):\n \"\"\"\n Convert a word from snake_case to CamelCase\n \"\"\"\n return ''.join(x.capitalize() or '_' for x in word.split('_'))\n\nsetup(\n name='fn_secureworks_ctp',\n version='1.0.0',\n license='MIT',\n author_email='',\n url='https://ibm.com/mysupport',\n description=\"Resilient Circuits Components for 'fn_secureworks_ctp'\",\n long_description=\"Resilient Circuits Components for 'fn_secureworks_ctp'\",\n install_requires=[\n 'resilient_circuits>=30.0.0',\n 'resilient-lib>=35.0.0'\n ],\n packages=find_packages(),\n include_package_data=True,\n platforms='any',\n classifiers=[\n 'Programming Language :: Python',\n ],\n entry_points={\n \"resilient.circuits.components\": [\n \"fn_secureworks_ctpFunctionComponent = fn_secureworks_ctp.components.scwx_ctp_poll:SecureworksCTPPollComponent\",\n \"funct_secureworks_ctp_close_ticketFunctionComponent = fn_secureworks_ctp.components.funct_secureworks_ctp_close_ticket:FunctionComponent\"\n ],\n \"resilient.circuits.configsection\": [\"gen_config = fn_secureworks_ctp.util.config:config_section_data\"],\n \"resilient.circuits.customize\": [\"customize = fn_secureworks_ctp.util.customize:customization_data\"],\n \"resilient.circuits.selftest\": [\"selftest = fn_secureworks_ctp.util.selftest:selftest_function\"]\n }\n)\n","repo_name":"ibmresilient/resilient-community-apps","sub_path":"fn_secureworks_ctp/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"82"} +{"seq_id":"16633474707","text":"# Authentication\nimport time\nimport os\nimport random\n\nimport connexion\nimport six\nimport toolz as T\nfrom werkzeug.exceptions import (BadRequest,\n InternalServerError)\n\nfrom google.cloud import storage\n\nfrom flask import request\n\nfrom salsa.db import db\nfrom salsa.permission import get_user, get_user_id_from_user\n\nNUM_FILES_ERROR_MSG = 'Upload up to 5 images in a request'\nBUCKET_CONNECTION_ERROR_MSG = 'Error in connecting to bucket'\nBUCKET_UPLOAD_ERROR_MSG = 'Error in uploading image to bucket'\nIMAGE_TOO_LARGE_ERROR_MSG = 'Images can have a maximum size of 5 MB'\nFILE_SAVE_ERROR_MSG = 'Error in saving the file'\nFILE_SIZE_CHECK_ERROR_MSG = 'Error in checking size of the file'\nFILE_REMOVAL_ERROR_MSG = 'Error in removing file from temp location'\n\nLOCAL_IMAGE_STORE_PATH = 'salsa/image_store/'\nBUCKET_URL_PREFIX = 'https://storage.googleapis.com/chinese_goods/image_store'\n#5MB\nMAX_FILE_SIZE = 5000000\n\n\ndef _current_timestamp() -> int:\n return int(time.time())\n\ndef upload(**kwargs):\n \"\"\"\n Upload images to google bucket, and return the urls\n\n file_name = user_name + timestamp + file_index\n \"\"\"\n user_id = get_user_id_from_user(get_user(kwargs))\n\n # Validate number of files in the request\n uploaded_files = request.files.getlist(\"file\")\n\n if len(uploaded_files) == 0 or len(uploaded_files) > 5:\n raise BadRequest(description=NUM_FILES_ERROR_MSG)\n\n # Try to connect to the bucket\n try:\n storage_client = storage.Client.from_service_account_json(\n os.environ.get('STORAGE_BUCKET_CREDENTIAL_PATH'))\n\n bucket = storage_client.get_bucket('chinese_goods')\n except Exception as error:\n raise InternalServerError(\n description=f'{BUCKET_CONNECTION_ERROR_MSG}: {error}')\n\n # Try to upload the images to the bucket\n file_urls = []\n for idx, file in enumerate(uploaded_files):\n # Save file to disk\n try:\n file_path = f'{user_id}_{_current_timestamp()}_{idx}.png'\n local_file_path = LOCAL_IMAGE_STORE_PATH + file_path\n file.save(local_file_path, buffer_size=65536)\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_SAVE_ERROR_MSG}: {error}')\n\n # Check the size of the files\n try:\n file_length = os.stat(local_file_path).st_size\n if file_length > MAX_FILE_SIZE:\n raise BadRequest(description=IMAGE_TOO_LARGE_ERROR_MSG)\n file.close()\n except BadRequest as error:\n raise error\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_SIZE_CHECK_ERROR_MSG}: {error}')\n\n # Upload the file to bucket and get URL\n try:\n image_loc = bucket.blob(f'image_store/{file_path}')\n image_loc.upload_from_filename(filename=local_file_path)\n file_urls.append(f'{BUCKET_URL_PREFIX}/{file_path}')\n except Exception as error:\n raise InternalServerError(\n description=f'{BUCKET_UPLOAD_ERROR_MSG}: {error}')\n\n # Remove file from disk\n try:\n os.remove(local_file_path)\n except Exception as error:\n raise InternalServerError(\n description=f'{FILE_REMOVAL_ERROR_MSG}: {error}')\n\n return {'status_code': 201,\n 'message': 'Image upload success!',\n 'file_urls': file_urls}\n\n","repo_name":"charlieouyang/salsa","sub_path":"salsa/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":3470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40869064674","text":"from designer import *\nimport random\nimport time\nimport pygame\nfrom cisc108 import assert_equal\n\nSNAKE_SPEED_HORIZONTAL = 11.2\nSNAKE_SPEED_VERTICAL = 11.24\ninputs_list = []\ntimed = []\ncounter = []\nslowmotion_duration = []\nregenerating_slowmotion = []\n\nWorld = {'snake': [DesignerObject],\n 'snake_speed_horizontal': int,\n 'snake_speed_vertical': int,\n 'food': DesignerObject,\n 'border': DesignerObject,\n 'score': int,\n 'counter': DesignerObject,\n 'shield': DesignerObject,\n 'shielded': bool,\n 'shieldicon': DesignerObject,\n 'slowmotion': bool,\n 'slowmotion_icon': DesignerObject,\n 'slowmotion_time_left': int,\n 'slowmotion_timer': DesignerObject,\n 'instructions': [DesignerObject],\n 'obstacles': [DesignerObject],\n 'invisible_box': DesignerObject\n}\n\ndef create_world() -> World:\n '''\n creates a world with a snake segment as the head, snake vertical and horizontal speeds, a food for the snake,\n the border fo the game, score, and a counter. \n \n Args:\n None\n \n Returns:\n World: A World with a snake, snake speeds, food, border, score and a counter\n '''\n return {'snake': [create_snake()],\n 'snake_speed_horizontal': SNAKE_SPEED_HORIZONTAL,\n 'snake_speed_vertical': SNAKE_SPEED_VERTICAL,\n 'food': create_food(),\n 'border': rectangle('black', 784, 562, get_width()/2, get_height()/2, border = 10),\n 'score': 0,\n 'counter': text('black', '', 20, get_width()/2, 10),\n 'shield': None,\n 'shielded': False,\n 'shieldicon': None,\n 'slowmotion': False,\n 'slowmotion_icon': create_slowmotion_icon(),\n 'slowmotion_time_left': 4,\n 'slowmotion_timer': text('black', '4', 20, 625, 10),\n 'instructions': [text('black', 'Press WASD or Arrow Keys to move.',20, get_width()/2, 150),\n text('black', 'Press Space for Slowmotion.', 20, get_width()/2, 175)],\n 'obstacles': [],\n 'invisible_box': create_invisible_box()\n }\n\ndef create_snake() -> DesignerObject:\n '''\n creates a snake segment\n \n Args:\n None\n \n Returns:\n DesignerObject: An image of a snake segment\n '''\n snake = image('red square.jpeg')\n snake['scale_x'] = .02\n snake['scale_y'] = .02\n snake['anchor'] = 'center'\n return snake\n\ndef create_invisible_box() -> DesignerObject:\n '''\n creates a box that is not visible\n \n Args:\n None\n \n Returns:\n DesignerObject: A not visile box\n '''\n box = rectangle('green', 125, 125)\n box['visible'] = False\n return box\n \ndef moving_snake(world: World):\n '''\n causes the snake to move constantly, controls the invisible box to move constantly,\n will enable slowmotion if world['slowmotion'] == True\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n if not world['slowmotion']:\n if world['snake_speed_vertical'] == 0:\n world['snake'][0]['x'] += world['snake_speed_horizontal']\n world['invisible_box']['x'] += world['snake_speed_horizontal']\n if world['snake_speed_horizontal'] == 0:\n world['snake'][0]['y'] += world['snake_speed_vertical']\n world['invisible_box']['y'] += world['snake_speed_vertical']\n elif world['slowmotion'] and len(timed)%2 == 0:\n if world['snake_speed_vertical'] == 0:\n world['snake'][0]['x'] += world['snake_speed_horizontal']\n world['invisible_box']['x'] += world['snake_speed_horizontal']\n if world['snake_speed_horizontal'] == 0:\n world['snake'][0]['y'] += world['snake_speed_vertical']\n world['invisible_box']['y'] += world['snake_speed_vertical']\n\ndef head_up(world: World):\n '''\n causes the snake to move upwards at the speed -snake_speed_vertical\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = -SNAKE_SPEED_VERTICAL\n world['snake_speed_horizontal'] = 0\n\ndef head_down(world: World):\n '''\n causes the snake to move downwards at the speed snake_speed_vertical\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = SNAKE_SPEED_VERTICAL\n world['snake_speed_horizontal'] = 0\n \ndef head_left(world: World):\n '''\n causes the snake to move left at the speed -snake_speed_horizontal\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_horizontal'] = -SNAKE_SPEED_HORIZONTAL\n world['snake_speed_vertical'] = 0\n\ndef head_right(world: World):\n '''\n causes the snake to move right at the speed snake_speed_horizontal\n \n Args:\n world(World): A World\n \n Returns:\n None\n '''\n world['snake_speed_vertical'] = 0\n world['snake_speed_horizontal'] = SNAKE_SPEED_HORIZONTAL\n\ndef control_snake(world: World, key: str):\n '''\n controls the snake to move in the direction inputted on the keyboard using the arrow keys\n or WASD, also adds to a list to determine what inputs were made, and removes the instructions\n after an input.\n \n Args:\n world(World): A World\n \n key(str): A string key inputted when typing\n \n Returns:\n None\n '''\n if key == \"W\" or key == \"up\":\n if not inputs_list:\n head_up(world)\n inputs_list.append(1)\n world['instructions'].clear()\n elif world['snake_speed_vertical'] == 0:\n head_up(world)\n inputs_list.append(1)\n elif key == \"A\" or key == \"left\":\n if not inputs_list:\n head_left(world)\n inputs_list.append(2)\n world['instructions'].clear()\n elif world['snake_speed_horizontal'] == 0:\n head_left(world)\n inputs_list.append(2)\n elif key == \"S\" or key == \"down\":\n if not inputs_list:\n head_down(world)\n inputs_list.append(3)\n world['instructions'].clear()\n elif world['snake_speed_vertical'] == 0:\n head_down(world)\n inputs_list.append(3)\n elif key == \"D\" or key == \"right\":\n if not inputs_list:\n head_right(world)\n inputs_list.append(3)\n world['instructions'].clear()\n elif world['snake_speed_horizontal'] == 0:\n head_right(world)\n inputs_list.append(4)\n\ndef create_food() -> DesignerObject:\n '''\n creates a food DesignerObject\n \n Args:\n None\n \n Returns:\n DesignerObject: the image of a food\n '''\n food = image('red square.jpeg')\n food['scale_x'] = .025\n food['scale_y'] = .025\n food['anchor'] = 'topleft'\n food['x'] = random.randint(get_width()-784, 772)\n food['y'] = random.randint(get_height()-562, 560)\n return food\n\ndef teleport_food_new_segments_new_obstacles(world: World):\n '''\n teleports food after a food is eaten, adds 4 new segments behind the snake head and\n creates an obstacle 50% of the time after a food is eaten\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['food']['anchor'] = 'topleft'\n if colliding(world['snake'][0], world['food']):\n new_segment = create_snake()\n move_behind(new_segment, world['snake'][-1])\n new_segment2 = create_snake()\n move_behind(new_segment2, new_segment)\n new_segment3 = create_snake()\n move_behind(new_segment3, new_segment2)\n new_segment2\n new_segment4 = create_snake()\n move_behind(new_segment4, new_segment3)\n world['snake'].append(new_segment)\n world['snake'].append(new_segment2)\n world['snake'].append(new_segment3)\n world['snake'].append(new_segment4)\n obstacle_creator = random.randint(0,1)\n if obstacle_creator == 0 and random.randint(0,1) == 1:\n world['obstacles'].append(create_obstacle1())\n while colliding_with_snake(world, world['obstacles'][-1]):\n world['obstacles'][-1]['x'] = random.randint(get_width()-780, 765)\n world['obstacles'][-1]['y'] = random.randint(get_height()-555, 545)\n if obstacle_creator == 1 and random.randint(0,1) == 1:\n world['obstacles'].append(create_obstacle2())\n while colliding_with_snake(world, world['obstacles'][-1]):\n world['obstacles'][-1]['x'] = random.randint(get_width()-780, 765)\n world['obstacles'][-1]['y'] = random.randint(get_height()-555, 545) \n while colliding_with_snake(world, world['food']):\n world['food']['x'] = random.randint(get_width()-784, 772)\n world['food']['y'] = random.randint(get_height()-562, 550)\n\n\ndef move_behind(snake_tail: DesignerObject, snake_head: DesignerObject):\n '''\n moves the snake tail (a new segment) behind the segment before it the snake_head\n \n Args:\n snake_tail(DesignerObject): the segment you want to move behind the previous one\n \n snake_head(DesignerObject): the segment in front of the segment you want to be placed behind\n \n Returns:\n None\n '''\n if inputs_list:\n if inputs_list[-1] == 1:\n snake_tail['y'] = snake_head['y'] + snake_head['height']\n snake_tail['x'] = snake_head['x']\n elif inputs_list[-1] == 2:\n snake_tail['y'] = snake_head['y']\n snake_tail['x'] = snake_head['x'] + snake_head['width']\n elif inputs_list[-1] == 3:\n snake_tail['y'] = snake_head['y'] - snake_head['height']\n snake_tail['x'] = snake_head['x']\n elif inputs_list[-1] == 4:\n snake_tail['y'] = snake_head['y']\n snake_tail['x'] = snake_head['x'] - snake_head['width']\n\ndef move_snake_segments(world: World):\n '''\n Takes the snake segment's location and moves the snake segment behind it into its own position\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n snake_segments = world['snake']\n index = range(len(snake_segments))\n listx = []\n listy = []\n for segment in snake_segments:\n listx.append(segment['x'])\n listy.append(segment['y'])\n if not world['slowmotion']:\n for i in index:\n if i > 0:\n snake_segments[i]['x'] = listx[i-1]\n snake_segments[i]['y'] = listy[i-1]\n elif world['slowmotion'] and len(timed)%2 == 0:\n for i in index:\n if i > 0:\n snake_segments[i]['x'] = listx[i-1]\n snake_segments[i]['y'] = listy[i-1]\n\ndef timer():\n '''\n appends to a list for every update (30 in a second) and will clear out the the timer and inputs_list lists\n every 1/3 second (after 10 updates) leaving the inputs_list only with the last directional input\n \n Args:\n None\n \n Returns:\n None\n '''\n if inputs_list:\n timed.append(1)\n last_input = inputs_list[-1]\n if len(timed)%10 == 0 and inputs_list:\n inputs_list.clear()\n inputs_list.append(last_input)\n timed.clear()\n\ndef snake_hits_border(world: World) -> bool:\n '''\n checks if the snake head has collided with the border of the world\n \n Args:\n world(World): A world\n \n Returns:\n bool: whether the snake collided with the border or not\n '''\n collided = False\n snake_head = world['snake'][0]\n if snake_head['x'] > 772 or snake_head['x'] < get_width()-772 or snake_head['y'] > 562 or snake_head['y'] < get_height()-562:\n collided = True\n return collided\n\ndef snake_hits_self(world: World) -> bool:\n '''\n checks if the snake has collided with any of its segments, if the snake is shielded the snake will\n pass through a single segment and lose the shield\n \n Args:\n world(World): A world\n \n Returns:\n bool: whether the snake collided or not with itself\n '''\n collided = False\n snake_segments = world['snake']\n snake_head = snake_segments[0]\n for i in range(len(snake_segments)):\n if i > 0 and i > 1:\n if colliding(snake_head, snake_segments[i]):\n if not world['shielded']:\n collided = True\n elif world['shielded']:\n world['shieldicon']['scale'] = 0\n world['shielded'] = False\n return collided\n\ndef snake_hits_obstacle(world: World) -> bool:\n '''\n checks to see if the snake head has collided with an obstacle, if the snake is shielded\n the obstacle is removed and the snake loses its shield\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether the snake collided or not with an obstacle\n '''\n collided = False\n obstacle_list = world['obstacles']\n for i in range(len(obstacle_list)):\n hit_obstacle = obstacle_list[i]\n if colliding(world['snake'][0], hit_obstacle):\n if not world['shielded']:\n collided = True\n elif world['shielded']:\n world['shieldicon']['scale'] = 0\n world['shielded'] = False\n obstacle_list[i] = None\n return collided\n\ndef score_counter(world: World):\n '''\n counts the score for every second that passes\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n counter.append(1)\n if len(counter)%30 == 0 and inputs_list:\n world['score'] = world['score'] + 1\n counter.clear()\n\ndef update_score(world: World):\n '''\n updates the score coaunter in world['counter'] with the score for the time and\n total length of the snake\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['counter']['text'] = 'Time: ' + str(world['score']) + ' Length: ' + str(len(world['snake']))\n \ndef create_shield_powerup() -> DesignerObject:\n '''\n creates a shield powerup\n \n Args:\n None\n \n Returns\n DesignerObject: image of a shield\n '''\n shield = image('shield.png')\n shield['scale_x'] = .02\n shield['scale_y'] = .02\n return shield\n\ndef generate_shield(world: World):\n '''\n creates a shield in a random location with a 1/1500 chance that is rolled 30 times a second as long\n as the snake is not shielded and a shield doesn't currently exist\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n if random.randint(0,1500) == 0 and not world['shielded'] and not world['shield'] and inputs_list:\n shield = create_shield_powerup()\n world['shield'] = shield\n world['shield']['x'] = random.randint(get_width()-784, 772)\n world['shield']['y'] = random.randint(get_height()-562, 560)\n while colliding_with_snake(world, world['shield']):\n world['shield']['x'] = random.randint(get_width()-784, 772)\n world['shield']['y'] = random.randint(get_height()-562, 560)\n\ndef create_shieldicon() -> DesignerObject:\n '''\n creates a shield icon at the top right ish area of the screen\n \n Args:\n None\n \n Returns:\n DesignerObject: A shield image at the top right ish area of the screen\n '''\n shieldicon = image('shield.png')\n shieldicon['scale_x'] = .03\n shieldicon['scale_y'] = .03\n shieldicon['x'] = 600\n shieldicon['y'] = 10\n return shieldicon\n\ndef shielded_snake(world: World):\n '''\n If the snake is shielded the shield icon will appear at the top right ish area of the screen\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n if colliding(world['snake'][0], world['shield']):\n world['shielded'] = True\n world['shield'] = None\n world['shieldicon'] = create_shieldicon()\n \ndef create_slowmotion_icon() -> DesignerObject:\n '''\n creates an icon for the slowmotion timer\n \n Args:\n None\n \n Returns:\n DesignerObject: Image of the slowmotion clock icon\n '''\n slowmotion_icon = image('slow.png')\n slowmotion_icon['scale'] = .05\n slowmotion_icon['x'] = 625\n slowmotion_icon['y'] = 10\n return slowmotion_icon\n\ndef space_is_held(world: World) -> bool:\n '''\n determines if space is being held down\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether space is being held or not\n '''\n keys = pygame.key.get_pressed()\n return keys[pygame.K_SPACE]\n\ndef space_is_released(world: World) -> bool:\n '''\n determines if space is being released\n \n Args:\n world(World): A world\n \n Returns:\n bool: Whether space is being released or not\n '''\n keys = pygame.key.get_pressed()\n return not keys[pygame.K_SPACE]\n\ndef run_when_space_held(world: World):\n '''\n when space is being held down the list of the cooldown for the slowmtion will be cleared, a number will be appended\n to the slowmotion duration list, if the list length is between 0-30 the duration is 4 seconds , 30-60, 3 seconds, 60-90,\n 2 seconds, 90-119, 1 second, and 120, 0.\n If the list reaches 120 slowmtion ends.\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n regenerating_slowmotion.clear()\n if world['slowmotion_time_left'] > 0 and len(slowmotion_duration) < 120:\n world['slowmotion'] = True\n slowmotion_duration.append(1)\n if len(slowmotion_duration) > 0 and len(slowmotion_duration) < 30:\n world['slowmotion_time_left'] = 4\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 30 and len(slowmotion_duration) < 60:\n world['slowmotion_time_left'] = 3\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 60 and len(slowmotion_duration) < 90:\n world['slowmotion_time_left'] = 2\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 90 and len(slowmotion_duration) < 120:\n world['slowmotion_time_left'] = 1\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n elif len(slowmotion_duration) >= 120:\n world['slowmotion'] = False\n world['slowmotion_time_left'] = 0\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n\ndef run_when_space_released(world: World):\n '''\n when space is released slowmotion is not active and if slowmotion is used the regenerating list will have a number added to it.\n If the regenerating slowmotion list reaches a length of 300 (takes 5 seconds) the slowmotion duration list will start to decrease.\n If slowmotion duration was 120 slowmotion will become useable again and eventually reach \"4\" which is full duration. If slowmotion\n is used after regenerating starts 5 seconds must be waited for slowmotion to begin regenerating again.\n \n Args:\n world(World): A world\n \n Returns:\n None\n '''\n world['slowmotion'] = False\n if slowmotion_duration:\n if len(regenerating_slowmotion) < 150:\n regenerating_slowmotion.append(1)\n if len(regenerating_slowmotion) == 150:\n del slowmotion_duration[-1]\n if len(slowmotion_duration) > 0 and len(slowmotion_duration) < 15:\n world['slowmotion_time_left'] = 4\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 15 and len(slowmotion_duration) < 30:\n world['slowmotion_time_left'] = 3\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 30 and len(slowmotion_duration) < 60:\n world['slowmotion_time_left'] = 2\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n if len(slowmotion_duration) >= 60 and len(slowmotion_duration) <= 90:\n world['slowmotion_time_left'] = 1\n world['slowmotion_timer']['text'] = world['slowmotion_time_left']\n\ndef colliding_with_snake(world: World, designerobject: DesignerObject) -> bool:\n '''\n tests if a designerobject is colliding with an object in the world, including a snake segment\n an obstacle and the invisible box.\n \n Args:\n world(World): A world\n \n designerobject: A DesignerObject\n \n Returns:\n bool: if the DesignerObject was colliding with a snake segment, an obstacle, or the invisible box\n '''\n collided = False\n for segment in world['snake']:\n if colliding(designerobject, segment):\n collided = True\n if not designerobject in world['obstacles']:\n for obstacle in world['obstacles']:\n if colliding(designerobject, obstacle):\n collided = True\n if colliding(designerobject, world['invisible_box']):\n collided = True\n return collided\n \ndef create_obstacle1() -> DesignerObject:\n '''\n creates an obstacle that is wide\n \n Args:\n None\n \n Returns:\n DesignerObject: A wide obstacle\n '''\n obstacle = rectangle('black', 10, 30)\n obstacle['x'] = random.randint(get_width()-784, 772)\n obstacle['y'] = random.randint(get_height()-562, 550)\n return obstacle\n\ndef create_obstacle2() -> DesignerObject:\n '''\n creates a tall obstacle\n \n Args:\n None\n \n Returns:\n DesignerObject: A tall obstacle\n '''\n obstacle = rectangle('black', 30, 10)\n obstacle['x'] = random.randint(get_width()-784, 772)\n obstacle['y'] = random.randint(get_height()-562, 550)\n return obstacle\n\n \n \nwhen('starting', create_world)\nwhen('updating', moving_snake)\nwhen('typing', control_snake)\nwhen('updating', teleport_food_new_segments_new_obstacles)\nwhen('updating', move_snake_segments)\nwhen('updating', timer)\nwhen('updating', score_counter)\nwhen('updating', update_score)\nwhen('updating', generate_shield)\nwhen('updating', shielded_snake)\nwhen(space_is_held, run_when_space_held)\nwhen(space_is_released, run_when_space_released)\nwhen(snake_hits_border, pause)\nwhen(snake_hits_self, pause)\nwhen(snake_hits_obstacle, pause)\nstart()","repo_name":"Igneyy/Snake-Game","sub_path":"SnakeGame.py","file_name":"SnakeGame.py","file_ext":"py","file_size_in_byte":22472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28250793675","text":"#!/usr/bin/python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n#default parameters\nnpoints = 100 #numbers of shells\nlength = 15 #length maximum of pair distance \n\n#read coordination from POSCAR\nf = open(\"POSCAR\", \"r+\")\ntmp = f.readlines()\natom_num = int(tmp[6])\naxis = []\nfor i in [2,3,4]:\n axis.append(list(map(float, tmp[i].split())))\naxis = np.array(axis)\nscale_factor = float(tmp[1])\n\natoms = []\nfor i in range(8,8+atom_num):\n atoms.append(list(map(float, tmp[i].split())))\natoms = np.array(atoms)\nf.close()\n\n#get area of cell\ntmp = np.array([axis[0,1]*axis[1,2]-axis[0,2]*axis[1,1],axis[0,2]*axis[1,0]-\\\n axis[0,0]*axis[1,2],axis[0,0]*axis[1,1]-axis[0,1]*axis[1,0]])\narea = np.linalg.norm(tmp)\narea *= scale_factor**2\n\n#calculating rdf\ng = np.zeros(npoints)\ndelta = length/npoints #distance between adjent shells\n\nfor i in range(atom_num):\n for j in range(i+1,atom_num):\n a = atoms[i].tolist()\n b = atoms[j].tolist()\n for k in range(3):\n if a[k] - b[k]> 0.5:\n a[k] -= 1\n if a[k] - b[k]<-0.5:\n b[k] -= 1\n a = np.dot(a,axis)*scale_factor\n b = np.dot(b,axis)*scale_factor\n tmp = a-b\n tmp = np.linalg.norm(tmp[:2])\n num = int(tmp/delta)\n if num < npoints:\n g[num] += 2\n\n#averaging\nrho = atom_num/area\nfor i in range(npoints):\n g[i] /= 2*np.pi*(i+1)*delta*delta*rho\n\n#plot\nx = [delta*(i+1) for i in range(npoints)]\nplt.plot(x,g)\nplt.show()\n","repo_name":"ponychen123/MD","sub_path":"2drdf.py","file_name":"2drdf.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32964585669","text":"import requests, json\n\nurl = ('http://newsapi.org/v2/top-headlines?'\n 'country=us&'\n 'apiKey=ff0d334a96854958932475eb3d5e381a')\n\nresponse = json.loads(requests.get(url).text)\nprint(response['totalResults'])\n\nfor author in response['articles']:\n print(author['author'])","repo_name":"ZZmarkus/DataSience","sub_path":"hw-ch05/getting data.py","file_name":"getting data.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1893665262","text":"#!/usr/bin/env python\n'''\nfile: mtsp_json\nauthor: adh\ncreated_at: 6/14/21 12:03 PM\n'''\nimport pandas as pd\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef json_to_df(path):\n logger.debug(f\"Reading json data from {path}\")\n df = pd.read_json(path,orient=\"index\")\n return df\n\n\ndef clean_df(df):\n logger.debug(\"Cleaning data\")\n cols = ['path', 'name', 'disclosure_date', 'type', 'description', 'platform', 'arch', 'mod_time']\n\n df = df.reset_index().rename(columns={'index': 'filepath'})\n df['mod_time'] = pd.to_datetime(df['mod_time'])\n\n # references is a column of lists\n # need to break it into one row per item in each list\n df2 = (pd.melt(df.references.apply(pd.Series).reset_index(),\n id_vars=['index'],\n value_name='references')\n .set_index(['index'])\n .drop('variable', axis=1)\n .dropna()\n .sort_index()\n )\n # merge the broken out rows back into the original data\n df3 = df[cols].join(df2).dropna()\n df3 = df3.rename(columns={'references': 'reference', })\n df3['reference'] = df3['reference'].str.strip()\n df3 = df3.set_index('reference')\n df3 = df3.sort_values(by=\"mod_time\",ascending=True)\n\n return df3\ndef only_cves(df):\n return filter_by_vulid(df, vulid_pfx='CVE')\n\ndef filter_by_vulid(df,vulid_pfx=\"CVE\"):\n logger.debug(f\"filtering for {vulid_pfx} records\")\n df2 = pd.DataFrame(df.loc[df.index.str.startswith(vulid_pfx)])\n return df2\n\n\ndef main():\n pass\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"CERTCC/metasploit_json_parser","sub_path":"mtsp_parser/mtsp_json.py","file_name":"mtsp_json.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"21355215562","text":"#Calcular el número de divisores de un número, mayor o igual que la unidad.\n\nnumero = int(input('Introduzca un numero igual o mayor que 1: \\r\\n'))\n\nif numero >= 1:\n \n divisores = 0\n print (f'Los divisores de {numero}:')\n for divisor in range(1, numero + 1):\n if (numero % divisor) == 0:\n print(f'{divisor} es divisor')\n divisores += 1\n \nelse:\n print(f'ERROR. {numero} no es mayor o igual que 1')","repo_name":"smr1-Jaime/CLASE","sub_path":"python/batería ejercicios ester/Ej25.py","file_name":"Ej25.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31841021092","text":"arr = [1,4,3,2]\n\n# Slicing\nprint(arr[::-1])\n\n# Using reversed()\nprint([i for i in reversed(arr)])\n\n# Brute Force\nindex = len(arr)\nnewList = [0]* index\nfor i in arr:\n index = index - 1\n newList[index] = i\nprint(newList)\n","repo_name":"SMony-L/HackerRank-Solution","sub_path":"HackerRank Solution/Data Structures/Arrays/Arrays - DS.py","file_name":"Arrays - DS.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8226234782","text":"import asyncio\nimport json\nimport os\nimport datetime as dt\nimport data_hub\nimport saver\n\nclass Main:\n def __init__(self, config_file, data_hub, saver):\n if not os.path.exists(\"data\"):\n os.makedirs(\"data\")\n\n configs = get_configs_from(config_file)\n self.data_hub = data_hub\n self.saver = saver\n self.time_frame_size = configs[\"time_frame_size\"]\n self.time_from = date_from(configs[\"time_from\"])\n self.time_to = date_from(configs[\"time_to\"])\n self.data_type = configs[\"data_type\"]\n self.category = configs[\"category\"]\n\n async def execute(self):\n stations = await self.data_hub.stations_for(self.category)\n time_frames = time_frames_from(self.time_from, self.time_to, self.time_frame_size)\n\n for station in stations:\n for frame_index in range(len(time_frames) - 1):\n start_time = time_frames[frame_index]\n end_time = time_frames[frame_index + 1]\n records = await self.data_hub.records_for(self.category, station, self.data_type, start_time, end_time)\n self.saver.add(records)\n print_record(self.category, station, self.data_type, records, start_time, end_time)\n\n self.saver.save()\n\ndef get_configs_from(configs_file):\n with open(configs_file) as configfile:\n return json.loads(\"\".join(configfile.readlines()))\n\ndef time_frames_from(start, end, time_frame_size):\n actual = end\n while True:\n if actual <= start:\n start = actual\n break\n actual -= dt.timedelta(days=1)\n\n date_range = range(0, (end - start).days + 1, time_frame_size)\n return reverse([end - dt.timedelta(days=x) for x in date_range])\n\ndef reverse(elements):\n return elements[::-1]\n\ndef date_from(date_as_string):\n return dt.datetime.strptime(date_as_string, '%Y-%m-%d')\n\ndef print_record(category, station, data_type, records, start_time, end_time):\n print(\"obtained %i records for %s %s %s (%s -> %s)\" % (\n len(records),\n category,\n station[\"id\"],\n data_type,\n start_time.strftime('%Y-%m-%d'),\n end_time.strftime('%Y-%m-%d')))\n\nif __name__ == \"__main__\":\n data_hub = data_hub.DataHub()\n saver = saver.Saver('data/dataset.csv')\n\n app = Main(\"config.json\", data_hub, saver)\n asyncio.run(app.execute())\n","repo_name":"giacomo-montibeller/ml-workflow-data-layer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38465926102","text":"from typing import List\n\n\ndef merge(left: List[int], right: List[int]) -> List[int]:\n result = []\n left_idx, right_idx = 0, 0\n\n while left_idx < len(left) and right_idx < len(right):\n if left[left_idx] < right[right_idx]:\n result.append(left[left_idx])\n left_idx += 1\n else:\n result.append(right[right_idx])\n right_idx += 1\n\n result.extend(left[left_idx:])\n result.extend(right[right_idx:])\n return result\n\n\ndef merge_sort(arr: List[int]) -> List[int]:\n if len(arr) < 2:\n return arr\n\n mid = len(arr) // 2\n left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])\n return merge(left, right)\n\n\ndef not_in_place_quick_sort(arr):\n if len(arr) < 2:\n return arr\n\n pivot = arr.pop()\n lower = [x for x in arr if x < pivot]\n greater = [x for x in arr if x > pivot]\n return not_in_place_quick_sort(lower) + [pivot] + not_in_place_quick_sort(greater)\n","repo_name":"alefeans/algs4","sub_path":"algs4/part1/week3/sorting.py","file_name":"sorting.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10253585569","text":"import RPi.GPIO as GPIO\nfrom time import sleep\nimport adafruit_mcp3xxx.mcp3008 as MCP\nfrom adafruit_mcp3xxx.analog_in import AnalogIn\nimport busio\nimport digitalio\nimport board\nfrom leds import PwmPin\n\nr = 14 #GPIO14 r\nb = 15 #GPIO15 b\ng = 18 #GPIO18 g\nclk = 25 #GPIO25 clk\ndout = 8 #GPIO8 dout\ndin = 7#GPIO7 din\ncs_pin = 1 #GPIO1 cs\n\n# create the spi bus\nspi = busio.SPI(clock=clk, MISO=dout, MOSI=din)\n\n# create the cs (chip select)\ncs = digitalio.DigitalInOut(cs_pin)\n\n# create the mcp object\nmcp = MCP.MCP3008(spi, cs)\n\ndef remap_range(value, left_min, left_max, right_min, right_max):\n # this remaps a value from original (left) range to new (right) range\n # Figure out how 'wide' each range is\n left_span = left_max - left_min\n right_span = right_max - right_min\n\n # Convert the left range into a 0-1 range (int)\n valuescaled = int(value - left_min) / int(left_span)\n\n # Convert the 0-1 range into a value in the right range.\n return int(right_min + (valuescaled * right_span))\n\n\nclass Channel:\n _mcp_object = mcp\n channel_map = {\n \"0\": MCP.P0,\n \"1\": MCP.P1,\n \"2\": MCP.P2,\n \"3\": MCP.P3,\n \"4\": MCP.P4,\n \"5\": MCP.P5,\n \"6\": MCP.P6,\n \"7\": MCP.P7\n }\n def __init__(self, channel):\n self.channel = AnalogIn(self._mcp_object, self.channel_map[channel])\n\nclass AnalogPin:\n _last_value = 0\n _tolerance = 250\n def __init__(self, channel, pin):\n self.channel = channel\n self.pin = pin\n\n def value_change_check(self, current_value):\n return abs(current_value - self._last_value) > self._tolerance\n\n def check_value(self):\n current_value = self.channel.value\n if self.value_change_check(current_value):\n return remap_range(current_value, 0, 65535, 0, 100)\n return self._last_value\n\n def change_led(self):\n self.pin.pwm_cdc()\n\n\nif __name__ == \"__main__\":\n GPIO.setmode(GPIO.BCM)\n PinRed = PwmPin(r, \"out\")\n PinGreen = PwmPin(g, \"out\")\n PinBlue = PwmPin(b, \"out\")\n pins = {PinRed, PinGreen, PinBlue}","repo_name":"jonguz6/leds","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18068183427","text":"import view\n\nfrom PIL import Image\nfrom pathlib import Path\n\n\nclass Color:\n def __init__(self, red, green, blue, alpha):\n self.red = red\n self.green = green\n self.blue = blue\n self.alpha = alpha\n\n\ndef GetFileName(path):\n return Path(path).stem\n\n\ndef GetFolderOfFile(path):\n return str(Path(path).parent) + \"/\"\n\n\ndef GetAbsoFolderOfFile(path):\n return str(Path(path).resolve())\n\n\ndef FileIsTxt(path):\n file = Path(path)\n if file.suffix == '.txt':\n view.CheckingType(file)\n return True\n else:\n view.Error(2)\n view.CheckingType(file)\n return False\n\n\ndef FileIsPng(path):\n file = Path(path)\n if file.suffix == '.png':\n view.CheckingType(file.suffix)\n return True\n else:\n view.Error(2)\n view.CheckingType(file.suffix)\n return False\n\n\ndef FindFile(path):\n view.CheckingFile(path)\n\n file = Path(path)\n if file.is_file():\n abso_path = str(file.resolve())\n view.FileFound(abso_path)\n return True\n else:\n view.Error(1)\n return False\n\n\ndef ImageOpened(path):\n return Image.open(path)\n","repo_name":"GregoryHue/EncodeTextImage","sub_path":"app/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41277387760","text":"\"\"\"Operator to flag anomalies in a metric batch.\"\"\"\n\nfrom typing import Sequence, Any\n\nfrom airflow.models.baseoperator import BaseOperator\nfrom airflow.providers.google.cloud.hooks.bigquery import BigQueryHook\n\n\nclass BigQueryMetricBatchAlertOperator(BaseOperator):\n \"\"\"\n Runs some sql to flag anomalies.\n\n :param alert_status_sql: sql to be executed when flagging anomalies\n :type alert_status_sql: str\n \"\"\"\n\n template_fields: Sequence[str] = [\"alert_status_sql\"]\n template_fields_renderers = {\"alert_status_sql\": \"sql\"}\n\n def __init__(self, alert_status_sql: str, **kwargs) -> None:\n super().__init__(**kwargs)\n self.alert_status_sql = alert_status_sql\n \n def execute(self, context: Any):\n\n metric_batch_name = context['params']['metric_batch_name']\n\n bigquery_hook = BigQueryHook(context['params']['gcp_connection_id'])\n\n df_alert = bigquery_hook.get_pandas_df(\n sql=self.alert_status_sql,\n dialect='standard'\n )\n df_alert = df_alert.dropna()\n df_alert['metric_timestamp'] = df_alert['metric_timestamp'].astype(str)\n\n self.log.info(f'len(df_alert)={len(df_alert)}')\n\n # push df_alert to xcom to by picked up by downstream notify task\n context['ti'].xcom_push(key=f'df_alert_{metric_batch_name}', value=df_alert.to_dict('records'))\n","repo_name":"andrewm4894/airflow-provider-anomaly-detection","sub_path":"airflow_anomaly_detection/operators/bigquery/metric_batch_alert_operator.py","file_name":"metric_batch_alert_operator.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"4257922492","text":"# Databricks notebook source\n# MAGIC %md\n# MAGIC # Import dependencies\n\n# COMMAND ----------\n\nimport pandas as pd\nimport numpy as np\nimport mlflow\nimport tensorflow\nfrom tensorflow import keras\nimport mlflow.keras\nfrom sklearn.metrics import f1_score,confusion_matrix\nfrom sklearn.model_selection import train_test_split\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Retrieve Data\n\n# COMMAND ----------\n\n\npandas_df = pd.read_csv('/dbfs/FileStore/shared_uploads/blasa.matthew@yahoo.com/training_data.csv')\nX=pandas_df.iloc[:,:-1]\nY=pandas_df.iloc[:,-1]\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=4284, stratify=Y)\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Set Experiment\n\n# COMMAND ----------\n\n\nexperiment_name = \"/Experiments/ml_flow_run_xgboost\"\nmlflow.set_experiment(experiment_name)\nmlflow.tensorflow.autolog()\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Create Model\n\n# COMMAND ----------\n\n\nmodel = keras.Sequential([\n keras.layers.Dense(\n units=36,\n activation='relu',\n input_shape=(X_train.shape[-1],)\n ),\n keras.layers.BatchNormalization(),\n keras.layers.Dense(units=1, activation='sigmoid'),\n])\n\nmodel.compile(\n optimizer=keras.optimizers.Adam(lr=0.001),\n loss=\"binary_crossentropy\",\n metrics=\"Accuracy\"\n)\n\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC # Run the Model\n\n# COMMAND ----------\n\nwith mlflow.start_run(run_name='keras_model_baseline') as run:\n model.fit(\n X_train,\n y_train,\n epochs=20,\n validation_split=0.05,\n shuffle=True,\n verbose=0\n )\n preds = model.predict(X_test)\n y_pred = np.where(preds>0.5,1,0)\n f1 = f1_score(y_test, y_pred)\n mlflow.log_metric(key=\"f1_experiment_score\", value=f1)\n\n\n# COMMAND ----------\n\n\n","repo_name":"mattblasa/mlegineering_mlflow","sub_path":"src/第4章/mlflow_run_keras.py","file_name":"mlflow_run_keras.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33265437874","text":"# Snowpark\nimport snowflake.connector\nimport streamlit as st\nimport pandas as pd\nimport plotly.express as px\nimport re\nimport string\nfrom model import GeneralModel\n\n\ncovid_dict = {\n \"test positive, but don't have COVID\": 'test positive',\n \"test negative, but do have COVID\": 'test negative'\n}\n\nbank_dict = {\n \"bank places hold on credit card, but no fraud occurred\": 'credit hold',\n \"bank doesn't place a hold, but there was fraud!\": 'no-hold, but fraud!'\n}\n\nschool_dict = {\n \"Rejection letter, but it's a mistake and you were actually admitted!\": 'false rejection',\n \"Acceptance letter, but you were actually mean to be rejected!\": 'false acceptance'\n}\n\n\ndef insert_row_into_snowflake(vote_choice, table_name):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"insert into {table_name} values ('{vote_choice}')\")\n my_cnx.close()\n return\n\n\ndef grab_data_from_snowflake(table_name):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"select * from {table_name}\")\n output = pd.DataFrame(my_cur.fetchall())\n my_cnx.close()\n return output\n\n\ndef grab_and_plot_data(table_name, values):\n votes = grab_data_from_snowflake(table_name)\n if len(votes) >= 2:\n # transform votes\n counts = votes.value_counts()\n data_dict = {'choice': values, 'count': [counts[values[0]], counts[values[1]]]}\n final_df = pd.DataFrame(data_dict)\n # plot\n fig = px.pie(final_df, values='count', names='choice', title='Voting Results')\n st.plotly_chart(fig, use_container_width=True)\n else:\n st.write('waiting for votes')\n return\n\n\ndef generate_question_column(table_name, data_dict, question, num):\n col1, col2 = st.columns(2)\n with st.container():\n with col1:\n st.subheader(question)\n output = st.radio(\"Which is less desirable?\",\n tuple(data_dict.keys()))\n if not st.button('Vote', key=num):\n st.write('please vote')\n else:\n st.write(f'thanks for voting!')\n insert_row_into_snowflake(data_dict[output], table_name)\n\n with col2:\n grab_and_plot_data(table_name, values=list(data_dict.values()))\n return\n\n\ndef insert_new_words(words_list):\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n for word in words_list:\n my_cur.execute(f\"insert into gpt_words values ('{word}')\")\n my_cnx.close()\n return\n\n\ndef word_counter(new_words_list):\n insert_new_words(new_words_list)\n # grab all words and plot frequency\n my_cnx = snowflake.connector.connect(**st.secrets['snowflake'])\n with my_cnx.cursor() as my_cur:\n my_cur.execute(f\"select * from gpt_words\")\n word_df = pd.DataFrame(my_cur.fetchall())\n my_cnx.close()\n fig = px.histogram(word_df)\n st.plotly_chart(fig) #, use_container_width=True)\n return\n \n\ndef app():\n\n # Creating an object of prediction service\n pred = GeneralModel()\n\n api_key = st.sidebar.text_input(\"APIkey\", type=\"password\")\n \n # Add header and a subheader\n st.title('Streamlit Voting Demo')\n st.subheader(\n \"Powered by Snowpark for Python and GPT-3 | Made with Streamlit\")\n st.header(\"Vote for the situations you think are less desirable!\")\n\n tab1, tab2, tab3, tab4 = st.tabs(['COVID', 'BANK', 'SCHOOL', \"'Roll the dice!\"])\n # COVID section\n with tab1:\n question = 'Bob thinks he may have contracted COVID-19, and goes to get tested.'\n generate_question_column(\"COVID_VOTES\", covid_dict, question, 1)\n\n # Bank section\n with tab2:\n question = 'ABC Bank monitors credit card usage to detect any fraudulent activity.'\n generate_question_column(\"BANK_VOTES\", bank_dict, question, 2)\n\n # SCHOOL section\n with tab3:\n question = \"It's your senior year of highschool and you recieve an admissions letter from your dream school.\"\n generate_question_column(\"SCHOOL_VOTES\", school_dict, question, 3)\n \n # GPT-3 Section\n with tab4:\n # Using the streamlit cache\n @st.cache\n def process_prompt(input):\n\n return pred.model_prediction(input=input.strip() , api_key=api_key)\n\n if api_key:\n\n # Setting up the Title\n st.title(\"Write a poem based on these words\")\n\n # st.write(\"---\")\n\n s_example = \"Birds, flowers, love, sun\"\n input = st.text_area(\n \"Use the example below or input your own text in English\",\n value=s_example,\n max_chars=150,\n height=100,\n )\n\n if st.button(\"Submit\"):\n with st.spinner(text=\"In progress\"):\n report_text = process_prompt(input)\n st.markdown(report_text)\n word_list = re.sub('['+string.punctuation+']', '', report_text.lower()).split()\n # remove specified words\n spec_words = re.sub('['+string.punctuation+']', '', input.lower()).split()\n for word in spec_words:\n word_list.remove(word)\n word_counter(word_list)\n \n \n else:\n st.error(\"🔑 Please enter API Key\")\n","repo_name":"rmorton8/streamlit-gpt-voting","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":5497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72110921549","text":"from __future__ import annotations\n\nimport logging\nimport re\nimport sys\nimport typing as t\nfrom datetime import datetime\nfrom datetime import timezone\n\nif t.TYPE_CHECKING:\n from _typeshed.wsgi import WSGIEnvironment\n from .wrappers.request import Request\n\n_logger: logging.Logger | None = None\n\n\nclass _Missing:\n def __repr__(self) -> str:\n return \"no value\"\n\n def __reduce__(self) -> str:\n return \"_missing\"\n\n\n_missing = _Missing()\n\n\ndef _wsgi_decoding_dance(s: str) -> str:\n return s.encode(\"latin1\").decode(errors=\"replace\")\n\n\ndef _wsgi_encoding_dance(s: str) -> str:\n return s.encode().decode(\"latin1\")\n\n\ndef _get_environ(obj: WSGIEnvironment | Request) -> WSGIEnvironment:\n env = getattr(obj, \"environ\", obj)\n assert isinstance(\n env, dict\n ), f\"{type(obj).__name__!r} is not a WSGI environment (has to be a dict)\"\n return env\n\n\ndef _has_level_handler(logger: logging.Logger) -> bool:\n \"\"\"Check if there is a handler in the logging chain that will handle\n the given logger's effective level.\n \"\"\"\n level = logger.getEffectiveLevel()\n current = logger\n\n while current:\n if any(handler.level <= level for handler in current.handlers):\n return True\n\n if not current.propagate:\n break\n\n current = current.parent # type: ignore\n\n return False\n\n\nclass _ColorStreamHandler(logging.StreamHandler):\n \"\"\"On Windows, wrap stream with Colorama for ANSI style support.\"\"\"\n\n def __init__(self) -> None:\n try:\n import colorama\n except ImportError:\n stream = None\n else:\n stream = colorama.AnsiToWin32(sys.stderr)\n\n super().__init__(stream)\n\n\ndef _log(type: str, message: str, *args: t.Any, **kwargs: t.Any) -> None:\n \"\"\"Log a message to the 'werkzeug' logger.\n\n The logger is created the first time it is needed. If there is no\n level set, it is set to :data:`logging.INFO`. If there is no handler\n for the logger's effective level, a :class:`logging.StreamHandler`\n is added.\n \"\"\"\n global _logger\n\n if _logger is None:\n _logger = logging.getLogger(\"werkzeug\")\n\n if _logger.level == logging.NOTSET:\n _logger.setLevel(logging.INFO)\n\n if not _has_level_handler(_logger):\n _logger.addHandler(_ColorStreamHandler())\n\n getattr(_logger, type)(message.rstrip(), *args, **kwargs)\n\n\n@t.overload\ndef _dt_as_utc(dt: None) -> None:\n ...\n\n\n@t.overload\ndef _dt_as_utc(dt: datetime) -> datetime:\n ...\n\n\ndef _dt_as_utc(dt: datetime | None) -> datetime | None:\n if dt is None:\n return dt\n\n if dt.tzinfo is None:\n return dt.replace(tzinfo=timezone.utc)\n elif dt.tzinfo != timezone.utc:\n return dt.astimezone(timezone.utc)\n\n return dt\n\n\n_TAccessorValue = t.TypeVar(\"_TAccessorValue\")\n\n\nclass _DictAccessorProperty(t.Generic[_TAccessorValue]):\n \"\"\"Baseclass for `environ_property` and `header_property`.\"\"\"\n\n read_only = False\n\n def __init__(\n self,\n name: str,\n default: _TAccessorValue | None = None,\n load_func: t.Callable[[str], _TAccessorValue] | None = None,\n dump_func: t.Callable[[_TAccessorValue], str] | None = None,\n read_only: bool | None = None,\n doc: str | None = None,\n ) -> None:\n self.name = name\n self.default = default\n self.load_func = load_func\n self.dump_func = dump_func\n if read_only is not None:\n self.read_only = read_only\n self.__doc__ = doc\n\n def lookup(self, instance: t.Any) -> t.MutableMapping[str, t.Any]:\n raise NotImplementedError\n\n @t.overload\n def __get__(\n self, instance: None, owner: type\n ) -> _DictAccessorProperty[_TAccessorValue]:\n ...\n\n @t.overload\n def __get__(self, instance: t.Any, owner: type) -> _TAccessorValue:\n ...\n\n def __get__(\n self, instance: t.Any | None, owner: type\n ) -> _TAccessorValue | _DictAccessorProperty[_TAccessorValue]:\n if instance is None:\n return self\n\n storage = self.lookup(instance)\n\n if self.name not in storage:\n return self.default # type: ignore\n\n value = storage[self.name]\n\n if self.load_func is not None:\n try:\n return self.load_func(value)\n except (ValueError, TypeError):\n return self.default # type: ignore\n\n return value # type: ignore\n\n def __set__(self, instance: t.Any, value: _TAccessorValue) -> None:\n if self.read_only:\n raise AttributeError(\"read only property\")\n\n if self.dump_func is not None:\n self.lookup(instance)[self.name] = self.dump_func(value)\n else:\n self.lookup(instance)[self.name] = value\n\n def __delete__(self, instance: t.Any) -> None:\n if self.read_only:\n raise AttributeError(\"read only property\")\n\n self.lookup(instance).pop(self.name, None)\n\n def __repr__(self) -> str:\n return f\"<{type(self).__name__} {self.name}>\"\n\n\n_plain_int_re = re.compile(r\"-?\\d+\", re.ASCII)\n\n\ndef _plain_int(value: str) -> int:\n \"\"\"Parse an int only if it is only ASCII digits and ``-``.\n\n This disallows ``+``, ``_``, and non-ASCII digits, which are accepted by ``int`` but\n are not allowed in HTTP header values.\n\n Any leading or trailing whitespace is stripped\n \"\"\"\n value = value.strip()\n if _plain_int_re.fullmatch(value) is None:\n raise ValueError\n\n return int(value)\n","repo_name":"pallets/werkzeug","sub_path":"src/werkzeug/_internal.py","file_name":"_internal.py","file_ext":"py","file_size_in_byte":5542,"program_lang":"python","lang":"en","doc_type":"code","stars":6451,"dataset":"github-code","pt":"82"} +{"seq_id":"41674716812","text":"import requests\r\nimport os\r\nimport easyquotation\r\n# os.environ['NO_PROXY'] = 'hq.sinajs.cn'\r\n\r\nclass Price_Grabber(object):\r\n def __init__(self):\r\n self.interface_name = 'tencent'\r\n self.quotation = easyquotation.use(self.interface_name) # 新浪 ['sina'] 腾讯 ['tencent', 'qq']\r\n # self.interface_url = 'http://hq.sinajs.cn/list='\r\n\r\n def grab(self, stocks_code):\r\n stocks_dict = self.quotation.real(stocks_code)\r\n # url = self.interface_url + stock_code\r\n # r = requests.get(url)\r\n return self.parse_dict(stocks_dict)\r\n\r\n def parse_dict(self, stocks_dict):\r\n # print(stocks_dict)\r\n res_dicts = []\r\n for code in stocks_dict:\r\n single_stock_dict = stocks_dict[code]\r\n stock_name = single_stock_dict['name']\r\n if code[0] in ['5', '1']:\r\n price_s = '%.3f' % single_stock_dict['now']\r\n else:\r\n price_s = '%.2f' % single_stock_dict['now']\r\n if self.interface_name == 'tencent':\r\n ratio_s = '%.2f%%' % single_stock_dict['涨跌(%)']\r\n else:\r\n ratio_f = (single_stock_dict['now'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n ratio_s = '%.2f%%' % ratio_f\r\n high_ratio = (single_stock_dict['high'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n high_ratio_s = '%.2f%%' % high_ratio\r\n low_ratio = (single_stock_dict['low'] - single_stock_dict['close']) / single_stock_dict['close'] * 100.0\r\n low_ratio_s = '%.2f%%' % low_ratio\r\n if self.interface_name == 'tencent':\r\n current_date = str(single_stock_dict['datetime'].date())\r\n current_time = str(single_stock_dict['datetime'].time())\r\n else:\r\n current_date = single_stock_dict['date']\r\n current_time = single_stock_dict['time']\r\n res_dict = dict(stock_name=stock_name, ratio=ratio_s, current_price=price_s,\r\n today_high=high_ratio_s, today_low=low_ratio_s,\r\n current_date=current_date, current_time=current_time)\r\n res_dicts.append(res_dict)\r\n return res_dicts\r\n\r\n def parse_text(self, text: str):\r\n try:\r\n left_start_idx = text.index('=\"') + 2\r\n ts_code_idx = left_start_idx - 8\r\n ts_code = text[ts_code_idx:ts_code_idx+6]\r\n info_text = text[left_start_idx:]\r\n s_texts = info_text.split(',')\r\n last_day_price_f = float(s_texts[2])\r\n current_price_f = float(s_texts[3])\r\n if ts_code[0] in ['5', '1']:\r\n price_s = '%.3f' % current_price_f\r\n else:\r\n price_s = '%.2f' % current_price_f\r\n ratio = (current_price_f - last_day_price_f) / last_day_price_f * 100\r\n ratio_s = '%.2f%%' % ratio\r\n today_high_ratio = (float(s_texts[4]) - last_day_price_f) / last_day_price_f * 100\r\n high_ratio_s = '%.2f%%' % today_high_ratio\r\n today_low_ratio = (float(s_texts[5]) - last_day_price_f) / last_day_price_f * 100\r\n low_ratio_s = '%.2f%%' % today_low_ratio\r\n res_dict = dict(stock_name=s_texts[0], ratio=ratio_s, current_price=price_s,\r\n today_high=high_ratio_s, today_low=low_ratio_s,\r\n current_date=s_texts[30], current_time=s_texts[31])\r\n except:\r\n print(text)\r\n res_dict = dict(stock_name='Error', ratio=\"No\", current_price='data',\r\n today_high='returned', today_low='Check',\r\n current_date='request', current_time='text')\r\n return res_dict\r\n\r\n\r\nif __name__ == '__main__':\r\n pg = Price_Grabber()\r\n dict = pg.grab(['sz000001', 'sh600000'])\r\n print(dict)\r\n","repo_name":"inSight-mk1/ssimple_stock_viewer","sub_path":"price_grabber.py","file_name":"price_grabber.py","file_ext":"py","file_size_in_byte":3914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"72182677068","text":"class SimpleGraph:\n def __init__(self):\n self.edges = {}\n \n def neighbors(self, id):\n return self.edges[id]\n\n\nimport collections\n\nclass Queue:\n def __init__(self):\n self.elements = collections.deque()\n \n def empty(self):\n return len(self.elements) == 0\n \n def put(self, x):\n self.elements.append(x)\n \n def get(self):\n return self.elements.popleft()\n\n\ndef breadth_first_search(graph, start):\n # print out what we find\n open_list = Queue()\n open_list.put(start)\n visited = {}\n visited[start] = True\n \n while not open_list.empty():\n current = open_list.get()\n print(\"Visiting %r\" % current)\n for next in graph.neighbors(current):\n if next not in visited:\n open_list.put(next)\n visited[next] = True\n\n\n\nif __name__ == '__main__':\n\texample_graph = SimpleGraph()\n\texample_graph.edges = {\n \t'A': ['B'],\n \t'B': ['A', 'C', 'D'],\n \t'C': ['A'],\n \t'D': ['E', 'A'],\n \t'E': ['B']\n\t}\n\tbreadth_first_search(example_graph, 'A')","repo_name":"starkblaze01/Algorithms-Cheatsheet-Resources","sub_path":"Python/bfs.py","file_name":"bfs.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":334,"dataset":"github-code","pt":"82"} +{"seq_id":"7798352452","text":"from collections import OrderedDict\r\n\r\ndef get_input():\r\n n = int(input())\r\n\r\n psw_text_pair = OrderedDict()\r\n\r\n for i in range(n):\r\n m = int(input())\r\n psw = tuple(input().split()[0:m])\r\n text = input()\r\n psw_text_pair[psw] = text\r\n\r\n if i != n-1:\r\n input()\r\n\r\n return psw_text_pair\r\n\r\ndef calc_psw(keys):\r\n positions = []\r\n for k in keys:\r\n a, b = 0, 0\r\n for i, char in enumerate(k):\r\n a += ord(char) & 2**i\r\n tmpb = ord(char) >> ((i+3)%6)\r\n tmpb = tmpb & 1\r\n b += tmpb * (2**i)\r\n\r\n positions.append(a)\r\n positions.append(b)\r\n\r\n return positions\r\n\r\ndef find_code(psw_text_pair):\r\n for k, v in psw_text_pair.items():\r\n positions = calc_psw(k)\r\n for pos in positions:\r\n print(v[pos], end='')\r\n print()\r\n\r\ndef main():\r\n find_code(get_input())\r\n\r\nif __name__ == '__main__':\r\n # http://www.spoj.com/problems/HS12HDPW\r\n main()\r\n","repo_name":"chao98/Python","sub_path":"SPOJ/SPOJ12206.py","file_name":"SPOJ12206.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"17429488578","text":"import sqlite3\nimport csv\nimport sys\n\nimport unicodecsv as u_csv\n\n\ndef import_and_export():\n resultname = filename.replace('.csv', '') + '_translation.csv'\n with open(resultname, 'wb') as nf:\n w = u_csv.writer(nf, encoding='GBK')\n with open(filename, 'r', encoding='UTF-8') as of:\n reader = csv.reader(of)\n first = 1\n for row in reader:\n if first:\n w.writerow(row)\n first = 0\n else:\n try:\n if translate(row[0]) is not None:\n name, description, solution = translate(row[0])\n w.writerow([row[0], row[1], row[2], row[3], row[4], row[5], row[6], name, row[8],\n description, solution, row[11], row[12]])\n else:\n w.writerow(row)\n except:\n print(\"报错ID：\"+row[0])\n \n\ndef translate(plugin_id):\n conn = sqlite3.connect(\"vulLib.db\")\n conn.text_factory = lambda x: str(x, 'gbk', 'ignore')\n cursor = conn.cursor()\n for row in cursor.execute(\"select * from VULNDB where Plugin_ID=?\", (plugin_id,)):\n if row is not None:\n return row[1], row[3], row[4]\n else:\n return None\n\n\nif __name__ == '__main__':\n filename = sys.argv[1]\n print('请耐心等待！')\n import_and_export()\n print('翻译结束！')\n","repo_name":"Largemage/nessus-report-translater","sub_path":"translater.py","file_name":"translater.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9141700963","text":"\n\nimport os, sys\nimport numpy as np\nfrom IPython import embed\nfrom collections import defaultdict\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nfilenames = [ 'idtmp_regrasp_010_500.log', 'idtmp_regrasp_005_500.log']\n# filenames = [ 'idtmp_regrasp_010_fc_500.log', 'idtmp_regrasp_010_500.log', 'idtmp_regrasp_005_500.log', 'idtmp_regrasp_005_fc_500.log']\ndata_pd = defaultdict(list)\n# data_pd_010 = defaultdict(list)\n\nfor filename in filenames:\n if os.path.exists('tmp.txt'):\n os.remove('tmp.txt')\n os.system(f'grep -i \"motion refine failed\\|task and motion plan found\\|current task plan is infeasible\\|current task plan is feasible\" {filename} >> tmp.txt')\n\n with open('tmp.txt', 'r') as f:\n mp_times = []\n fc_times = []\n\n mp_num = 0\n fc_num = 0\n pointer = 0\n for line in f:\n # print(line)\n if 'current task plan is infeasible' in line:\n fc_num += 1\n if 'fc' in filename and 'current task plan is feasible' in line:\n mp_num += 1\n if 'fc' not in filename and 'motion refine failed' in line:\n mp_num += 1\n if 'task and motion plan found' in line:\n if mp_num==0:\n continue\n if '005' in filename:\n data_pd['mp_times'].append(mp_num)\n data_pd['resolution'].append('idtmp_005')\n\n if 'fc' in filename:\n data_pd['feasible_check'].append('yes')\n else:\n data_pd['feasible_check'].append('no')\n\n if '010' in filename:\n data_pd['mp_times'].append(mp_num)\n data_pd['resolution'].append('idtmp_010')\n\n mp_times.append(mp_num)\n fc_times.append(fc_num+mp_num)\n mp_num = 0\n fc_num = 0\n\ndata_pd = pd.DataFrame(data_pd)\n\nmedianprops = dict(markerfacecolor='r', color='r')\nmax_y = max(data_pd['mp_times']) * 1.1\n\n# Initialize the figure with a logarithmic x axis\nf, ax = plt.subplots(figsize=(5, 6))\n# ax.set_title(\"total planning time\", fontdict=dict(fontsize=20))\n# Plot the orbital period with horizontal boxes\n# hue='feasible_check',\nsns.boxplot(x=\"resolution\", y='mp_times', data=data_pd, orient=\"v\", \n linewidth=1, medianprops=medianprops, whis=5, width=0.5,\n fliersize=0, color=[1,1,1])\n\nax.set_ylabel(\"motion planner calling\", fontdict=dict(fontsize=14))\nax.set_xlabel(\"\", fontdict=dict(fontsize=14))\n\nax.set_ylim([0,max_y])\nxlabels = ['idtmp_010','idtmp_005']\nax.set_xticklabels(xlabels, fontdict=dict(fontsize=14))\nax.tick_params(labelrotation=30)\nplt.tight_layout()\n# sns.despine(trim=True, left=True)\n# plt.savefig(\"total_planning_time_idtmp_fc.pdf\")\nplt.show()\n\n","repo_name":"Drrreistein/idtmp-py","sub_path":"examples/Darias/TASK_regrasp/log2/plot_mp_time.py","file_name":"plot_mp_time.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"42456409465","text":"from vizualization import Visualizer\nfrom datasets import get_datamodule\nfrom configs import get_config\n\nif __name__ == \"__main__\":\n\n\n DATASET = \"waymo\"\n ITER_OVER = \"test\"\n\n cfg = get_config(\"../configs/models/slim.yaml\", dataset=DATASET)\n data_module = get_datamodule(name=DATASET, data_path=None, cfg=cfg)\n data_module.setup()\n\n if ITER_OVER == \"train\":\n dl = data_module.train_dataloader()\n elif ITER_OVER == \"test\":\n dl = data_module.test_dataloader()\n else:\n raise ValueError()\n\n dl.num_workers = 0\n\n # Model\n # Loading config\n #cfg = get_config(\"../configs/slim.yaml\", dataset=\"waymo\")\n #model = SLIM(config=cfg, dataset=\"waymo\")\n #model = model.load_from_checkpoint(\"../models/waymo12k.ckpt\")\n # Wrap the dataloader into visualizer\n dl = Visualizer(dl, visualize=\"seg3d\", model=None)\n\n for idx, (x, flow, T_gt) in enumerate(dl):\n continue\n\n","repo_name":"simonpokorny/MotionFeatureLearning","sub_path":"scripts/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40588919835","text":"# -*- coding: utf-8 -*-\n# by xieshichang\n# modified from packages.statistical.twogroup_CI\n# and packages.statistical.mul_posthoc\nimport math\nimport scipy\nimport itertools\nimport pandas as pd\nfrom scipy.stats.distributions import t\nfrom numpy import mean\nfrom collections import Counter\nfrom mbio.packages.statistical.QTable import QTable\n\n\ndef group_detail(groupfile, get_member=False, mul=False):\n group = pd.read_csv(groupfile, sep='\\t', header=None, comment='#')\n\n group_num = Counter(group[1])\n \n if mul:\n N = len(group[1]) # 样本的个数\n g_num = len(group_num) # 分组的个数\n dfN = g_num - 1\n dfD = N - g_num\n return N, dfN, dfD, group_num\n\n if not get_member:\n return group_num\n\n group_member = {}\n for gname in group_num:\n group_member[gname] = list(group[group[1] == gname][0])\n return group_num, group_member\n\n\ndef stat_info(statfile, gnames):\n stat = pd.read_csv(statfile, sep='\\t', index_col=0)\n mean_dict = {}\n sd_dict = {}\n taxon_list = stat.index\n\n for gname in gnames:\n gmean = gname + '-mean'\n gsd = gname + '-sd'\n mean_dict[gname] = stat[gmean]\n sd_dict[gname] = stat[gsd]\n\n return mean_dict, sd_dict, taxon_list\n\n\ndef student(statfile, groupfile, coverage):\n group_num_dict = group_detail(groupfile)\n gnames = sorted(group_num_dict.keys())\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n\n with open('student_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for tx in taxon_list:\n meanG1 = mean_dict[gnames[0]][tx]\n meanG2 = mean_dict[gnames[1]][tx]\n dp = meanG1 - meanG2\n varG1 = (sd_dict[gnames[0]][tx]**2)\n varG2 = (sd_dict[gnames[1]][tx]**2)\n n1 = group_num_dict[gnames[0]]\n n2 = group_num_dict[gnames[1]]\n\n dof = n1 + n2 - 2\n pooledVar = ((n1 - 1)*varG1 + (n2 - 1)*varG2) / dof\n sqrtPooledVar = math.sqrt(pooledVar)\n denom = sqrtPooledVar * math.sqrt(1.0/n1 + 1.0/n2)\n tCritical = t.isf(0.5 * (1.0-coverage), dof)\n lowerCI = dp - tCritical*denom\n upperCI = dp + tCritical*denom\n\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(tx, dp, lowerCI, upperCI))\n\n\ndef welch(statfile, groupfile, coverage):\n group_num_dict = group_detail(groupfile)\n gnames = sorted(group_num_dict.keys())\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n\n with open('welch_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for tx in taxon_list:\n meanG1 = mean_dict[gnames[0]][tx]\n meanG2 = mean_dict[gnames[1]][tx]\n dp = meanG1 - meanG2\n varG1 = (sd_dict[gnames[0]][tx]**2)\n varG2 = (sd_dict[gnames[1]][tx]**2)\n n1 = group_num_dict[gnames[0]]\n n2 = group_num_dict[gnames[1]]\n\n normVarG1 = varG1 / n1\n normVarG2 = varG2 / n2\n unpooledVar = normVarG1 + normVarG2\n sqrtUnpooledVar = math.sqrt(unpooledVar)\n dof = (unpooledVar**2) / ((normVarG1**2) /\n (n1-1) + (normVarG2**2)/(n2-1))\n tCritical = t.isf(0.5 * (1.0 - coverage), dof)\n lowerCI = dp - tCritical*sqrtUnpooledVar\n upperCI = dp + tCritical*sqrtUnpooledVar\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(tx, dp, lowerCI, upperCI))\n\n\ndef bootstrap(intable, groupfile, coverage):\n group_num_dict, group_member_dict = group_detail(\n groupfile, get_member=True)\n gnames = sorted(group_num_dict.keys())\n intable = pd.read_csv(intable, sep='\\t', index_col=0)\n\n profile = (intable + 0.0) / intable.sum()\n\n scipy.random.seed(1234)\n with open('mann_CI.xls', 'w') as w:\n w.write('\\teffectsize\\tlowerCI\\tupperCI\\n')\n for index in profile.index:\n distribution = []\n for _ in xrange(0, 999):\n samplesGroup = {}\n for gname in gnames:\n sampleSize = group_num_dict[gname]\n samples = group_member_dict[gname]\n choices = scipy.random.randint(0, sampleSize, sampleSize)\n samplesGroup[gname] = profile.loc[index, samples][choices]\n diffOfMeanProp = samplesGroup[gnames[0]].mean() -\\\n samplesGroup[gnames[1]].mean()\n distribution.append(diffOfMeanProp*100)\n dp = profile.loc[index, group_member_dict[gnames[0]]].mean() -\\\n profile.loc[index, group_member_dict[gnames[1]]].mean()\n distribution.sort()\n dp *= 100\n lowerCI = distribution[max(\n 0, int(math.floor(0.5*(1.0-coverage)*len(distribution))))]\n upperCI = distribution[min(\n len(distribution) - 1,\n int(math.ceil((coverage+0.5*(1.0-coverage))*len(distribution)))\n )]\n w.write('{}\\t{}\\t{}\\t{}\\n'.format(index, dp, lowerCI, upperCI))\n\n\n# 多组posthoc test计算CI\ndef scheffe(statfile, groupfile, coverage, outfile):\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_scheffe_%s.xls' % groups, 'w') as w:\n cv = dfN*distributions.f.ppf(coverage, dfN, dfD)\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n # calculate within group variance\n withinGroupVar = 0\n for name in group_num_dict.keys():\n withinGroupVar += (group_num_dict[name] -\n 1)*(sd_dict[name][tx]**2)\n withinGroupVar /= dfD\n withinGroupStdDev = math.sqrt(withinGroupVar)\n if withinGroupVar == 0:\n # degenerate case: within group variance is zero; set to 1e-6.\n withinGroupVar = 1e-6\n es = mean_dict[g[0]][i] - mean_dict[g[1]][tx]\n invSampleSize = 1.0 / \\\n (group_num_dict[g[0]]) + 1.0/(group_num_dict[g[1]])\n Fs = (es * es) / (withinGroupVar*invSampleSize)\n pValue = 1.0 - distributions.f.cdf(Fs / dfN, dfN, dfD)\n # confidence interval\n confInter = math.sqrt(cv*invSampleSize)*withinGroupStdDev\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n\n\ndef welchuncorrected(statfile, groupfile, coverage, outfile):\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n # the numbers of post-hoc test\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_welchuncorrected_%s.xls' % groups, 'w') as w:\n cv = dfN*distributions.f.ppf(coverage, dfN, dfD)\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n dp = meanG1 - meanG2\n varG1 = sd_dict[g[0]][tx]**2\n varG2 = sd_dict[g[1]][tx]**2\n n1 = group_num_dict[g[0]]\n n2 = group_num_dict[g[1]]\n normVarG1 = varG1 / n1\n normVarG2 = varG2 / n2\n unpooledVar = normVarG1 + normVarG2\n sqrtUnpooledVar = math.sqrt(unpooledVar)\n if unpooledVar != 0:\n # p-value\n T_statistic = -1 * abs(meanG1 - meanG2) / sqrtUnpooledVar\n dof = unpooledVar**2 / \\\n ((normVarG1**2)/(n1-1) + (normVarG2**2)/(n2-1))\n pValue = t.cdf(T_statistic, dof) * 2\n # CI\n tCritical = t.isf(0.5 * (1.0-coverage), dof)\n # 0.5 factor accounts from symmetric nature of distribution\n lowerCI = dp - tCritical*sqrtUnpooledVar\n upperCI = dp + tCritical*sqrtUnpooledVar\n else:\n if meanG1 != meanG2:\n pValue = 0.0\n # the difference (at least according to these samples) must be true as there is no variance\n else:\n pValue = 0.5\n lowerCI = dp\n upperCI = dp\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, dp, lowerCI, upperCI, pValue))\n\n\ndef tukeykramer(statfile, groupfile, coverage, outfile, preferences=None):\n qtable = QTable(preferences)\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n k = len(group_num_dict)\n q_cv = qtable.cv(1.0-coverage, k, dfD)\n cv001 = qtable.cv(0.001, k, dfD)\n cv01 = qtable.cv(0.01, k, dfD)\n cv02 = qtable.cv(0.02, k, dfD)\n cv05 = qtable.cv(0.05, k, dfD)\n cv1 = qtable.cv(0.1, k, dfD)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_tukeykramer_%s.xls' % groups, 'w') as w:\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n # calculate within group variance\n withinGroupVar = 0\n for name in group_num_dict.keys():\n withinGroupVar += (group_num_dict[name] -\n 1)*(sd_dict[name][tx]**2)\n withinGroupVar /= dfD\n withinGroupStdDev = math.sqrt(withinGroupVar)\n if withinGroupStdDev == 0:\n # degenerate case: within group variance is zero; set to 1e-6.\n withinGroupStdDev = 1e-6\n sqrtInvSampleSize = math.sqrt(\n (1.0/group_num_dict[g[0]] + 1.0/group_num_dict[g[1]]) / 2.0\n )\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n es = meanG1 - meanG2\n qs = abs(es) / (withinGroupStdDev*sqrtInvSampleSize)\n if qs > cv001:\n pValue = '< 0.001'\n elif qs > cv01:\n pValue = '< 0.01'\n elif qs > cv02:\n pValue = '< 0.05' # < 0.02\n elif qs > cv05:\n pValue = '< 0.05'\n elif qs > cv1:\n pValue = '< 0.1'\n else:\n pValue = '>= 0.1'\n confInter = q_cv * withinGroupStdDev * sqrtInvSampleSize\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n\n\ndef gameshowell(statfile, groupfile, coverage, outfile, preferences=None):\n qtable = QTable(preferences)\n (N, dfN, dfD, group_num_dict) = group_detail(groupfile, mul=True)\n gnames = group_num_dict.keys()\n (mean_dict, sd_dict, taxon_list) = stat_info(statfile, gnames)\n k = len(group_num_dict)\n two_hoc = list(itertools.combinations(gnames, 2))\n for one in two_hoc:\n g = list(one)\n g.sort()\n groups = '-'.join(g)\n with open(outfile + '_gameshowell_%s.xls' % groups, 'w') as w:\n w.write('\\t%s_effectsize\\t%s_lowerCI\\t%s_upperCI\\t%s_pvalue\\n' %\n (groups, groups, groups, groups))\n for tx in taxon_list:\n meanG1 = mean_dict[g[0]][tx]\n meanG2 = mean_dict[g[1]][tx]\n # effect size\n es = meanG1 - meanG2\n varG1 = sd_dict[g[0]][tx]**2\n varG2 = sd_dict[g[1]][tx]**2\n n1 = group_num_dict[g[0]]\n n2 = group_num_dict[g[1]]\n vn1 = varG1 / n1\n vn2 = varG2 / n2\n if vn1 == 0:\n vn1 = 1e-6\n if vn2 == 0:\n vn2 = 1e-6\n df = (vn1 + vn2) * (vn1 + vn2)\n df /= (vn1*vn1)/(n1-1) + (vn2*vn2)/(n2-1)\n q_cv = qtable.cvInterpolate(1.0-coverage, k, df)\n cv001 = qtable.cvInterpolate(0.001, k, df)\n cv01 = qtable.cvInterpolate(0.01, k, df)\n cv02 = qtable.cvInterpolate(0.02, k, df)\n cv05 = qtable.cvInterpolate(0.05, k, df)\n cv1 = qtable.cvInterpolate(0.1, k, df)\n # calculate Games-Howell unequal variance adjustment\n varAdj = math.sqrt((vn1 + vn2) / 2.0)\n # p-value\n qs = abs(es) / varAdj\n if qs > cv001:\n pValue = '< 0.001'\n elif qs > cv01:\n pValue = '< 0.01'\n elif qs > cv02:\n pValue = '< 0.05' # < 0.02\n elif qs > cv05:\n pValue = '< 0.05'\n elif qs > cv1:\n pValue = '< 0.1'\n else:\n pValue = '>= 0.1'\n # confidence interval\n confInter = q_cv * varAdj\n lowerCI = es - confInter\n upperCI = es + confInter\n w.write('%s\\t%s\\t%s\\t%s\\t%s\\n' %\n (tx, es, lowerCI, upperCI, pValue))\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/packages/bac_comp_genome/groups_CI.py","file_name":"groups_CI.py","file_ext":"py","file_size_in_byte":14189,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"9216630745","text":"import argparse\nimport os\nfrom os.path import join\nimport sys\n\nimport joblib\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nmatplotlib.use('TkAgg')\n\nsys.path.append('.')\n\nfrom project.models.common import get_errors, get_model_details_for_algorithm, get_color, init_scale_from_train_set\nfrom project.models.details import get_model_filepath, ModelDetails\nfrom project.models.scale import transform_x, inverse_transform_y\nfrom project.utils.app_ids import app_name_to_id\nfrom project.utils.logger import logger\nfrom project.definitions import ROOT_DIR\nfrom project.models.data import (\n get_data_frame,\n DataFrameColumns,\n)\n\nparser = argparse.ArgumentParser(description='Model training and validation.')\nparser.add_argument('--app_name', required=True, type=str, help='app name')\nparser.add_argument('--alg', required=True, type=str, help='algorithm')\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n logger.info(args)\n app_id = app_name_to_id.get(args.app_name, None)\n\n if app_id is None:\n raise ValueError(f'missing app \"{args.app_name}\" from app map={str(app_name_to_id)}')\n\n results_filepath = join(ROOT_DIR, '..', 'execution_results/results.csv')\n results_test_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_test.csv')\n results_train_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_train.csv')\n df, df_err = get_data_frame(results_filepath, app_id)\n df_test, df_test_err = get_data_frame(results_test_filepath, app_id)\n df_train, df_train_err = get_data_frame(results_train_filepath, app_id)\n\n if df_err is not None or df_test_err is not None or df_train_err is not None:\n raise ValueError(f'data frame load err')\n\n x_origin = df.loc[:, df.columns != DataFrameColumns.EXECUTION_TIME]\n x_test = df_test.loc[:, df_test.columns != DataFrameColumns.EXECUTION_TIME]\n x_train = df_train.loc[:, df_train.columns != DataFrameColumns.EXECUTION_TIME]\n y = df.loc[:, df.columns == DataFrameColumns.EXECUTION_TIME]\n y_test = df_test.loc[:, df_test.columns == DataFrameColumns.EXECUTION_TIME]\n y_train = df_train.loc[:, df_train.columns == DataFrameColumns.EXECUTION_TIME]\n x_plot_train = x_train[DataFrameColumns.OVERALL_SIZE]\n y_plot_train = x_train[DataFrameColumns.CPUS]\n z_plot_train = y_train[DataFrameColumns.EXECUTION_TIME]\n x_plot_test = x_test[DataFrameColumns.OVERALL_SIZE]\n y_plot_test = x_test[DataFrameColumns.CPUS]\n z_plot_test = y_test[DataFrameColumns.EXECUTION_TIME]\n # plot data points\n ax = plt.axes(projection='3d')\n ax.set_xlabel('over', linespacing=0.1, labelpad=-12)\n ax.set_ylabel('cpus', linespacing=0.1, labelpad=-12)\n ax.set_zlabel('t', linespacing=0.1, labelpad=-15)\n ax.tick_params(\n axis='both', # changes apply to the x-axis\n which='both', # both major and minor ticks are affected\n bottom=False, # ticks along the bottom edge are off\n top=False,\n left=False, # ticks along the bottom edge are off\n right=False,\n labelbottom=False,\n labeltop=False,\n labelright=False,\n labelleft=False\n )\n ax.dist = 8\n ax.scatter(x_plot_train, y_plot_train, z_plot_train, c='#2ca02c', alpha=1, label='training points')\n ax.scatter(x_plot_test, y_plot_test, z_plot_test, label='test points', c='#cc0000', alpha=1)\n # Load model details\n model_details = get_model_details_for_algorithm(args.app_name, args.alg)\n\n if model_details.scale:\n init_scale_from_train_set(model_details, app_id)\n\n x_test = pd.DataFrame(transform_x(x_test), columns=x_test.columns)\n x = pd.DataFrame(transform_x(x_origin), columns=x_origin.columns)\n # Load model\n model_filepath, err = get_model_filepath(args.alg, model_details)\n\n if err is not None:\n raise ValueError(err)\n\n model = joblib.load(model_filepath)\n z_all = model.predict(x)\n # Efficiency\n z_test = model.predict(x_test)\n z_test_inverse = inverse_transform_y(z_test)\n y_test_list = list(y_test[DataFrameColumns.EXECUTION_TIME])\n y_train_list = list(y_train[DataFrameColumns.EXECUTION_TIME])\n errors, errors_rel = get_errors(y_test_list, z_test_inverse)\n logger.info('############### SUMMARY ##################')\n logger.info('avg time [s] = %s' % str(sum(y_test_list) / len(y_test_list)))\n logger.info('avg error [s] = %s' % str(sum(errors) / len(errors)))\n logger.info('avg error relative [percentage] = %s' % str(sum(errors_rel) / len(errors_rel)))\n logger.info(f'best params: {str(model.get_params())}')\n # Plot prediction surface\n z_inverse = inverse_transform_y(z_all)\n x_plot = x_origin[DataFrameColumns.OVERALL_SIZE].to_numpy()\n y_plot = x_origin[DataFrameColumns.CPUS].to_numpy()\n ax.plot_trisurf(x_plot, y_plot, z_inverse, alpha=0.5, color=get_color(args.alg))\n fake_legend_point = matplotlib.lines.Line2D([0], [0], linestyle=\"solid\", c=get_color(args.alg))\n\n plt.margins()\n plt.gcf().autofmt_xdate()\n handles, labels = ax.get_legend_handles_labels()\n handles.append(fake_legend_point)\n labels.append(args.alg)\n ax.legend(handles, labels, loc='upper left')\n ax.view_init(elev=20., azim=140)\n model_scheme = ModelDetails(args.app_name, 1.0, True, False)\n fig_path = os.path.join(ROOT_DIR, 'models', 'figures', '_'.join([args.alg, args.app_name, 'surf.png']))\n plt.savefig(fig_path, bbox_inches='tight', pad_inches=0)\n","repo_name":"K4liber/execution_time_estimation","sub_path":"project/models/plot_surface_multi.py","file_name":"plot_surface_multi.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36953452166","text":"import pandas as pd\n\ndef main() :\n csv = \"cleaning_copy.csv\"\n\n file = pd.read_csv(csv, index_col=0)\n df = pd.DataFrame(file)\n\n df[\"hour\"] = \"00:00\"\n #print(df.iloc[0])\n\n print(df.iloc[4])\n del df.iloc[4]\n\n\n add_to_csv(df,csv)\n\ndef add_to_csv(df,csv) :\n\n print(\"add to csv ? y/n\")\n addcsv = input()\n if addcsv == \"y\" :\n df.to_csv(csv, index=False)\n\nmain()\n","repo_name":"MugicaLaurendi/Simplon","sub_path":"Pandas/exo01/ovni.py","file_name":"ovni.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22235106407","text":"# best average worst\r\n# O{n^2) O(n^2) O(n^2)\r\ndef exe_selection_sort(arr):\r\n for i in range( len(arr), 0, -1):\r\n max_pos = 0\r\n for j in range (0, i):\r\n if arr[j] > arr[max_pos]:\r\n max_pos = j\r\n tmp = arr[j]\r\n arr[j] = arr[max_pos]\r\n arr[max_pos]= tmp\r\n\r\ndef selection_sort(arr):\r\n\r\n # For every slot in array\r\n for fillslot in range(len(arr)-1,0,-1):\r\n positionOfMax=0\r\n\r\n # For every set of 0 to fillslot+1\r\n # find the largest element and swap it with the fillslot\r\n for location in range(1,fillslot+1):\r\n # Set maximum's location\r\n if arr[location]>arr[positionOfMax]:\r\n positionOfMax = location\r\n\r\n temp = arr[fillslot]\r\n arr[fillslot] = arr[positionOfMax]\r\n arr[positionOfMax] = temp\r\n\r\n\r\n\r\narr = [3,5,2,7,6,8,12,40,21]\r\n#selection_sort(arr)\r\nexe_selection_sort(arr)\r\nprint (arr)","repo_name":"shaokangtan/python_sandbox","sub_path":"sort and search/selection_sort.py","file_name":"selection_sort.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40363543646","text":"import math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n\r\ndef timeConversion(s):\r\n h=int(s[0:2])\r\n \r\n if s[-2]=='P' or s[-2]=='p':\r\n if h!=12:\r\n h=12+h\r\n else:\r\n h=h \r\n return(str(h)+s[2:8])\r\n else:\r\n if h==12:\r\n return(\"00\"+s[2:8])\r\n else:\r\n return(s[:8])\r\n\r\n \r\n # Write your code here\r\n\r\nif __name__ == '__main__':\r\n \r\n s = input()\r\n\r\n result = timeConversion(s)\r\n\r\n print(result + '\\n')\r\n\r\n\r\n","repo_name":"Rutuja-Deshmukh-2091999/MyWorkplace","sub_path":"HackerRank/TIme-format.py","file_name":"TIme-format.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25537848057","text":"import random\nimport time\nfrom multiprocessing import Pool\ndef sum_random_numbers(n):\n total_sum = 0\n for i in range(n):\n total_sum += random.randint(1, 100)\n return total_sum\ndef sequential_execution():\n start_time = time.time()\n result = sum_random_numbers(10000000)# Генерация случайные чисела\n end_time = time.time()\n print(f\"Результаты последовательного: {result}\")\n print(f\"Результаты последовательного time: {end_time - start_time}s\")\ndef parallel_execution():\n start_time = time.time()\n with Pool(processes=4) as pool:\n result = pool.map(sum_random_numbers, [2500000] * 4)# Генерация случайные чисела\n end_time = time.time()\n print(f\"Результаты параллельного: {sum(result)}\")\n print(f\"Результаты параллельного time: {end_time - start_time}s\")\nif __name__ == '__main__':\n sequential_execution()\n parallel_execution()\n","repo_name":"sem7655/seti","sub_path":"2cod.py","file_name":"2cod.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31281928800","text":"import torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\nimport numpy as np\nimport platform\n\n\ndef auto_select_device():\n if platform.system() == \"Darwin\" and platform.processor() == \"arm64\":\n # M-series Macs\n return torch.device(\"mps\")\n elif torch.cuda.is_available():\n return torch.device(\"cuda\")\n else:\n return torch.device(\"cpu\")\n\n\ndevice = auto_select_device()\n\n\nclass GaussianNLLLoss(nn.Module):\n def __init__(self):\n super(GaussianNLLLoss, self).__init__()\n\n def forward(self, mu, sigma, target):\n neg_log_likelihood = 0.5 * (\n torch.log(sigma**2) + ((target - mu) ** 2) / (sigma**2)\n )\n return neg_log_likelihood.mean()\n\n\nclass DataWrapper(Dataset):\n \"\"\"\n Used for wrapping raw NumPy data. Training and testing sets should be wrapped separately.\n \"\"\"\n\n def __init__(self, data, n_species):\n self.data = data\n self.n_species = n_species\n\n def __getitem__(self, index):\n \"\"\"\n Inputs contain all information: time, species concentrations, reaction rates.\n Targets only contain species concentrations.\n \"\"\"\n # species_concentration_indices = [0, 1, 2]\n species_concentration_indices = list(range(1, self.n_species + 1))\n\n inputs = self.data[index, :-1, :].astype(np.float32)\n targets = self.data[index, 1:, species_concentration_indices].astype(np.float32)\n targets = np.transpose(targets, (1, 0))\n\n return (torch.from_numpy(inputs), torch.from_numpy(targets))\n\n def __len__(self):\n return len(self.data)\n\n\nclass MDN(nn.Module):\n def __init__(\n self, input_size, hidden_size, num_layers, output_size, dropout_rate=0.0\n ):\n super(MDN, self).__init__()\n\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n\n self.lstm = nn.LSTM(\n input_size, hidden_size, num_layers, batch_first=True, dropout=dropout_rate\n )\n\n self.fc1 = nn.Linear(hidden_size, hidden_size)\n self.fc2 = nn.Linear(hidden_size, hidden_size)\n\n self.fc_out = nn.Linear(hidden_size, 2 * output_size) # mu and sigma\n\n self.relu = nn.ReLU()\n\n def forward(self, x):\n h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)\n\n out, _ = self.lstm(x, (h0, c0))\n\n out = self.fc1(out)\n out = self.relu(out)\n\n out = self.fc2(out)\n out = self.relu(out)\n\n out = self.fc_out(out)\n\n mu, sigma = torch.chunk(out, 2, dim=-1)\n sigma = torch.exp(sigma)\n\n return mu, sigma\n\n\nclass MdnManager:\n def __init__(self, n_species):\n self.n_species = n_species\n # self.n_parameters = n_parameters\n\n self.model = MDN(\n # input_size=1 + self.n_species + self.n_parameters,\n input_size=1 + self.n_species,\n hidden_size=50,\n num_layers=2,\n output_size=n_species,\n ).to(device)\n\n def load_data(self, data):\n self.simulation_data = data\n\n def prepare_data_loaders(self, batch_size=64, split=0.8):\n split_index = int(len(self.simulation_data) * split)\n train_data = self.simulation_data[:split_index]\n test_data = self.simulation_data[split_index:]\n\n train_dataset = DataWrapper(train_data, self.n_species)\n test_dataset = DataWrapper(test_data, self.n_species)\n\n self.train_loader = DataLoader(\n train_dataset, batch_size=batch_size, shuffle=True\n )\n self.test_loader = DataLoader(\n test_dataset, batch_size=batch_size, shuffle=False\n )\n\n def save_model(self, filepath):\n torch.save(self.model.state_dict(), filepath)\n print(f\"Model saved to {filepath}\")\n\n def load_model(self, filepath):\n self.model.load_state_dict(torch.load(filepath, map_location=device))\n self.model.to(device) # Move the model to the device\n self.model.eval()\n print(f\"Model loaded from {filepath} and moved to {device}\")\n\n def get_model_weights(self):\n return self.model.state_dict()\n\n def save_model_to_onnx(self, destination):\n dummy_input = torch.randn(1, 1, 1 + self.n_species).to(device)\n torch.onnx.export(self.model, dummy_input, destination, verbose=True)\n print(\"Model exported to model.onnx\")\n\n def load_onnx_model(self, filepath):\n self.model = torch.jit.load(filepath, map_location=device)\n self.model.to(device)\n\n def set_model_weights(self, weights):\n self.model.load_state_dict(weights)\n\n def train(\n self,\n exec_context,\n n_epochs=20,\n # loss_criterion=nn.MSELoss(),\n loss_criterion=GaussianNLLLoss(),\n patience=5,\n ):\n optimizer = torch.optim.Adam(self.model.parameters())\n\n # train model\n best_loss = float(\"inf\")\n epochs_no_improve = 0\n\n # progress visualisation\n progress = 0\n progress_step = 100 / n_epochs / 100\n\n for epoch in range(n_epochs):\n if exec_context.is_canceled():\n print(\"Execution cancelled.\")\n break\n\n exec_context.set_progress(progress)\n for i, (inputs, targets) in enumerate(self.train_loader):\n inputs = inputs.to(device)\n targets = targets.to(device)\n\n mu, sigma = self.model(inputs) # Get mu and sigma\n loss = loss_criterion(mu, sigma, targets) # Compute Gaussian NLL\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n progress += progress_step\n\n print(f\"Epoch [{epoch+1}/{n_epochs}], Loss: {loss.item():.4f}\")\n\n if loss.item() < best_loss:\n best_loss = loss.item()\n epochs_no_improve = 0\n torch.save(self.model.state_dict(), \"model_best_state_dict.pth\")\n else:\n epochs_no_improve += 1\n\n if epochs_no_improve == patience:\n print(\"Early stopping due to no improvement in loss.\")\n break\n\n def validate(self):\n self.model.eval()\n running_loss = 0.0\n criterion = GaussianNLLLoss()\n with torch.no_grad():\n for i, (inputs, targets) in enumerate(self.test_loader):\n inputs = inputs.float().to(device)\n targets = targets.float().to(device)\n\n mu, sigma = self.model(inputs) # Get mu and sigma\n\n loss = criterion(mu, sigma, targets)\n running_loss += loss.item()\n\n average_loss = running_loss / len(self.test_loader)\n print(f\"Validation Loss of the model on test data : {average_loss}\")\n\n def simulate(\n self,\n init_conditions,\n exec_context,\n time_step,\n n_steps=10,\n n_sims_per_condition=1,\n ):\n self.model.eval()\n all_trajectories = []\n\n progress = 0\n progress_step = 100 / len(init_conditions) / n_sims_per_condition / 100\n\n for i, init_condition in enumerate(init_conditions):\n print(\n f\"Generating trajectories for init_condition {i+1} / {len(init_conditions)}\"\n )\n\n for sim in range(n_sims_per_condition):\n if exec_context.is_canceled():\n print(\"Execution cancelled.\")\n break\n\n print(f\" Simulating trajectory {sim+1} / {n_sims_per_condition}\")\n exec_context.set_progress(progress)\n\n trajectory = [init_condition[: self.n_species + 1]]\n current_state = self.convert_numpy_to_torch(init_condition)\n timestamp = 0.0\n\n for j in range(n_steps):\n mu, sigma = self.model(current_state) # Get mu and sigma\n next_state_array = (\n mu.squeeze().detach().cpu().numpy()\n ) # Use mu as the next state\n\n timestamp += time_step\n next_state_array = np.concatenate(([timestamp], next_state_array))\n trajectory.append(np.round(next_state_array))\n\n current_state = self.convert_numpy_to_torch(next_state_array)\n\n trajectory = np.array(trajectory)\n all_trajectories.append(trajectory)\n\n progress += progress_step\n\n return np.array(all_trajectories)\n\n def convert_numpy_to_torch(self, state):\n i = torch.from_numpy(state).float().to(device)\n i = torch.unsqueeze(i, 0)\n i = torch.unsqueeze(i, 0) # emulate a batch of size 1\n return i\n","repo_name":"iusethemouse/deep-abstractions","sub_path":"extension/src/utils/mdn_manager.py","file_name":"mdn_manager.py","file_ext":"py","file_size_in_byte":8842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20068887064","text":"from spack import *\nimport os\n\nclass Paraver(Package):\n \"\"\"\"A very powerful performance visualization and analysis tool\n based on traces that can be used to analyse any information that\n is expressed on its input trace format. Traces for parallel MPI,\n OpenMP and other programs can be genereated with Extrae.\"\"\"\n homepage = \"http://www.bsc.es/computer-sciences/performance-tools/paraver\"\n url = \"http://www.bsc.es/ssl/apps/performanceTools/files/paraver-sources-4.5.2.tar.gz\"\n\n version('4.5.2', 'ea463dd494519395c99ebae294edee17')\n\n depends_on(\"boost\")\n #depends_on(\"extrae\")\n depends_on(\"wx\")\n depends_on(\"wxpropgrid\")\n\n def install(self, spec, prefix):\n os.chdir(\"ptools_common_files\")\n configure(\"--prefix=%s\" % prefix)\n make()\n make(\"install\")\n\n os.chdir(\"../paraver-kernel\")\n\t\t#\"--with-extrae=%s\" % spec['extrae'].prefix,\n configure(\"--prefix=%s\" % prefix, \"--with-ptools-common-files=%s\" % prefix, \"--with-boost=%s\" % spec['boost'].prefix, \"--with-boost-serialization=boost_serialization\")\n make()\n make(\"install\")\n\n os.chdir(\"../paraver-toolset\")\n configure(\"--prefix=%s\" % prefix)\n make()\n make(\"install\")\n\n os.chdir(\"../wxparaver\")\n\t\t#\"--with-extrae=%s\" % spec['extrae'].prefix,\n configure(\"--prefix=%s\" % prefix, \"--with-paraver=%s\" % prefix, \"--with-boost=%s\" % spec['boost'].prefix, \"--with-boost-serialization=boost_serialization\", \"--with-wxdir=%s\" % spec['wx'].prefix.bin)\n make()\n make(\"install\")\n\n","repo_name":"utkarshayachit/spack","sub_path":"var/spack/packages/paraver/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"73600619467","text":"# imports\nimport time\nimport numpy as np\nfrom tensorflow import keras\nimport tensorflow as tf\nimport math\nimport sys\nassert sys.version_info >= (3, 5)\n\n\n# TensorFlow ≥2.0 is required\nassert tf.__version__ >= \"2.0\"\n\n################################ Load data and initial conditions #######################\n# Load data and set initial condition\nnx, ny = 300, 300\nT = np.zeros([nx, ny], dtype=np.float64)\ngamma = 40\n# initialise t:\nx0 = 0\ny0 = -50\nx = np.zeros([1, nx], dtype=np.float64)\ny = np.zeros([1, ny], dtype=np.float64)\n\nfor ii in range(nx):\n x[0][ii] = -150 + 300/nx*ii\n y[0][ii] = -150 + 300/nx*ii\n\n# boundary excluded: range 1-299 x 1-299, I suppose we are using Dirichlet boundary condition\nfor i in range(1, 299):\n for j in range(1, 299):\n temp1 = -((x[0][i] - x0)**2 + (y[0][j] - y0)**2)\n temp2 = 2*gamma**2\n T[i][j] = math.exp(temp1/temp2)\n\ninput_shape = (1, nx, ny, 1) # (1,300,300,1) as original problem size\n\n# default data type of np.zeros is np.float64\nmesh = np.zeros(input_shape, dtype=np.float64)\n\n# generate Gaussian with a blob\nfor i in range(nx):\n for j in range(ny):\n mesh[0][i][j][0] = T[i][j] # + Z1[i][j] + Z2[i][j] + Z3[i][j]*0.5\n\n# generate Gaussian with a blob\nfor i in range(50):\n for j in range(50):\n mesh[0][i+225][j+125][0] = mesh[0][i+225][j+125][0] + 1\n\n# values = tf.convert_to_tensor(mesh,dtype=np.float64)\nvalues = mesh\n\n################################ Initializations ####################################\nstart_time = time.perf_counter()\n\n# weight matrices\nw1 = ([[[[0.0], # upwind\n [0.2],\n [0.0]],\n\n [[0.3],\n [-1.0],\n [0.2]],\n\n [[0.0],\n [0.3],\n [0.0]]]])\n\nw2 = ([[[[0.0], # central\n [0.15],\n [0.0]],\n\n [[0.25],\n [-0.8],\n [0.15]],\n\n [[0.0],\n [0.25],\n [0.0]]]])\n\n# print(np.array(w1).shape) # shape (1,3,3,1)\ninit_kernel_1 = w1\ninit_kernel_2 = w2\n\ninit_bias = np.zeros((1,)) # filters - need change to exact value for bias\n\nkernel_initializer_1 = tf.keras.initializers.constant(\n init_kernel_1) # initializer which initialize constant tensor\nkernel_initializer_2 = tf.keras.initializers.constant(init_kernel_2)\n\nbias_initializer = tf.keras.initializers.constant(init_bias)\n\n# CNN 2D layers: now I generate CNN filters for each subdomains\n# filter 1\nCNN2D_1 = keras.models.Sequential([\n keras.layers.InputLayer(input_shape=(nx, ny, 1)),\n tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n kernel_initializer=kernel_initializer_1,\n bias_initializer=bias_initializer),\n # tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n # kernel_initializer=kernel_initializer_2,\n # bias_initializer=bias_initializer),\n])\n\n# filter 2\nCNN2D_2 = keras.models.Sequential([\n keras.layers.InputLayer(input_shape=(nx, ny, 1)),\n tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n kernel_initializer=kernel_initializer_2,\n bias_initializer=bias_initializer),\n # tf.keras.layers.Conv2D(1, kernel_size=3, strides=1, padding='SAME',\n # activation='relu',\n # kernel_initializer=kernel_initializer_2,\n # bias_initializer=bias_initializer),\n])\n\n# here set up the hyperparameters to tune in the later training process\nCNN2D_1.compile(loss=\"mse\",\n optimizer=keras.optimizers.Nadam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999))\nCNN2D_2.compile(loss=\"mse\",\n optimizer=keras.optimizers.Nadam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999))\n\nl1_norms = np.array([])\nl2_norms = np.array([])\nlinf_norms = np.array([])\n\nfor t in range(1000):\n # one-step scheme with central scheme\n # a = CNN2D_2.predict(values)\n # values += a\n\n # two-step scheme with central scheme\n a = CNN2D_2.predict(values)\n b = (a + values)\n c = (b + values)*0.5\n d = CNN2D_2.predict(c)\n values += d\n\n # if t %10 == 0: # save the l1 norm and l2 norm of result per 10 timesteps\n l1_norms = np.append(l1_norms, np.linalg.norm(\n values.reshape(300, 300), ord=1)/90000)\n l2_norms = np.append(l2_norms, np.linalg.norm(\n values.reshape(300, 300), ord=2)/90000)\n linf_norms = np.append(linf_norms, np.linalg.norm(\n values.reshape(300, 300), ord=np.inf)/90000)\n # np.save(\"/content/serial_steps/AD_2D_serial_step_{}\".format(t),values.reshape(nx, ny)) # save the resultant mesh of one time step\n\n# Visualization omitted\n# if t == 0:\n# plt.imshow(values[0,:,:,0], vmin=0, vmax=1.0)\n# plt.axis('off')\n# fig1_name = \"paper_figure/figure_1/up_2nd_\"+str(t)+\".jpg\"\n# plt.savefig(fig1_name, dpi=200, bbox_inches='tight')\n# plt.close()\n# elif t ==250 or t == 500 or t == 1000:\n# plt.imshow(values[0,:,:,0], vmin=0, vmax=1.0)\n# plt.axis('off')\n# fig1_name = \"paper_figure/figure_1/up_2nd_\"+str(t)+\".jpg\"\n# plt.savefig(fig1_name, dpi=200, bbox_inches='tight')\n# plt.close()\n\n\nend_time = time.perf_counter()\nprint(\n f\"[INFO] Problem solved in {end_time - start_time:0.4f} seconds using serial solution.\")\n\n# save the final result to text file\nnp.save(\"/content/output/AD_2D_serial\", values.reshape(nx, ny))\nnp.save(\"/content/norms/AD_2D_serial_l1_norms\", l1_norms)\nnp.save(\"/content/norms/AD_2D_serial_l2_norms\", l2_norms)\nnp.save(\"/content/norms/AD_2D_serial_linf_norms\", linf_norms)\n","repo_name":"bc1chen/AI-HFM-Solver","sub_path":"Anisotropic Resistivity Tomography/scripts/advection_diffusion_2D.py","file_name":"advection_diffusion_2D.py","file_ext":"py","file_size_in_byte":5876,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70022595150","text":"import cgi\nimport datetime\nfrom collections import Counter\n\nfrom pyexpat.errors import messages\n\nfrom django.db.models import Q\nfrom django.shortcuts import render, redirect, get_object_or_404\n\n# Create your views here.\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.generic import FormView\n\nfrom dreams.forms import DreamForm\nfrom dreams.models import DreamModel\nfrom account.models import CustomUser\n@csrf_exempt\ndef createDream(request):\n if request.method == 'POST':\n form = DreamForm(request.POST)\n if form.is_valid():\n dream = form.save(commit=False)\n dream.created = datetime.datetime.now()\n dream.read_cnt = 0\n dream.author = request.user\n dream.save()\n return redirect('dream:detail', id=dream.id)\n else:\n form = DreamForm()\n return render(request,'dreams/new.html',{'form':form})\n\ndef viewDream(request, id):\n # dream 게시물 가져오기\n dream = get_object_or_404(DreamModel,id=id)\n # 제목 키워드 분석\n\n from konlpy.tag import Okt\n okt = Okt()\n keywords = okt.nouns(dream.title)\n # 해당 해몽들 보여주기\n import urllib.request\n from bs4 import BeautifulSoup\n\n results =[]\n # 꿈 단어 제외하고 검색결과 가져오기\n for key in keywords[:-1]:\n word = urllib.parse.quote_plus(key+'꿈해몽')\n\n url = f'https://search.naver.com/search.naver?date_from=&date_option=0&date_to=&dup_remove=1&nso=&post_blogurl=&post_blogurl_without=&query={word}&sm=tab_pge&srchby=all&st=sim&where=post&start=5'\n html = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(html, 'html.parser')\n\n titles = soup.find_all(class_='api_txt_lines total_tit')\n\n results = []\n for index, title in enumerate(titles):\n if index == 6: break\n results.append((''.join((title.find_all(text=True))),title.attrs['href']))\n # print(''.join((title.find_all(text=True))))\n # print(title.attrs['href'])\n\n if len(results)==0:\n results.append(('제목에 해당하는 해몽을 찾지 못했습니다.',''))\n\n isUser = request.user == dream.author\n return render(request,'dreams/view.html',{'dream':dream, 'isUser':isUser, 'results':results})\n\ndef mainPage(request):\n # dream 전체 게시물 가져오기\n dream = DreamModel.objects.all()\n return render(request, 'dreams/main.html',{'dream_list':dream})\n\n\ndef search(request):\n datas = DreamModel.objects.values_list('color', flat=True)\n counter = dict(Counter(datas))\n sorted_color = sorted(counter, key=lambda x: x[1], reverse=True)[:5]\n context = {}\n context['colors'] = sorted_color\n return render(request, 'dreams/search.html', context)\n\ndef search_color(request, color_id):\n dream_list = DreamModel.objects.filter(color__contains=color_id)\n context = {'dream_list':dream_list}\n return render(request, 'dreams/main.html',context)\n\n\ndef search_title(request):\n context = {}\n # 검색\n search_word = request.GET.get('search_word','')\n\n dream_list = DreamModel.objects.filter(title__contains=search_word)\n context['dream_list'] = dream_list\n\n return render(request, 'dreams/main.html', {'dream_list':dream_list})\n\n\n# 수정하기\ndef modify(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n if request.user != dream.author:\n messages.error(request, '수정권한이 없습니다.')\n return redirect(request,'dream:')\n\n if request.method == \"POST\":\n form = DreamForm(request.POST, instance=dream)\n if form.is_valid():\n movie = form.save(commit=False)\n movie.save()\n return redirect('dream:detail', id=dream.id)\n else:\n form = DreamForm(instance=dream)\n context = {'form': form,'dream':dream}\n return render(request, 'dreams/modify.html', context)\n\n# 카운트 업데이트\ndef read_count(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n dream.read_cnt = dream.read_cnt+1\n dream.save()\n return redirect('dream:detail',id=dream.id)\n\n# 삭제하기\ndef delete(request,id):\n dream = get_object_or_404(DreamModel,pk=id)\n dream.delete()\n return redirect('dream:main')","repo_name":"iruyj/DreamColor","sub_path":"dreams/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42375612627","text":"import cv2\nimport numpy as np\n\n\n\"\"\"def regroup(scale, imgarray):\n rows = len(imgarray)\n cols = len(imgarray[0])\n rowsavailable = len(imgarray[0])\n width = imgarray[0][0].shape[1]\n height = imgarray[0][0].shape[0]\n\n widthscale = width*scale\n heightscale = height*scale\n\n \n if rowsavailable:\n for x in range (0, rows):\n for y in range (0, cols):\n if imgarray[x][y].shape[:2] == imgarray[0][0].shape[:2]:\n imgarray[x][y] = cv2.resize(imgarray[x][y], (widthscale, heightscale), None)\n else:\n imgarray[x][y] = cv2.resize(imgarray[x][y], (imgarray[0][0].shape[1], imgarray[0][0].shape[0]), None, scale, scale)\n if len(imgarray[x][y].shape) == 2:\n imgarray[x][y] = cv2.cvtColor( imgarray[x][y], cv2.COLOR_GRAY2BGR)\n\n imageblank = np.zeros((height, width, 3), np.uint8)\n hor = [imageblank]*rows\n horcon = [imageblank]*rows\n for x in range (0, rows):\n hor[x] = np.hstack(imgarray[x])\n ver = np.vstack(hor)\n else:\n for x in range (0, rows):\n if imgarray[x].shape[:2] == imgarray[0].shape[:2]:\n imgarray[x] = cv2.resize(imgarray[x], (widthscale, heightscale), None)\n else:\n imgarray[x] = cv2.resize(imgarray[x], (imgarray[0].shape[1], imgarray[0].shape[0]), None, scale, scale)\n if len(imgarray[x].shape) == 2:\n imgarray[x] = cv2.cvtColor( imgarray[x], cv2.COLOR_GRAY2BGR)\n hor = np.hstack(imgarray)\n ver = hor\n return ver\"\"\"\n\ncap = cv2.VideoCapture(0)\n\n\"\"\"while True:\n _, frame =cap.read()\n\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\n lower_blue = np.array([38, 86, 0])\n upper_blue = np.array([121, 255, 255])\n mask = cv2.inRange(hsv, lower_blue, upper_blue)\n\n a , contour = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n cv2.drawContours(frame, a, -1, (0, 0, 255), 3)\n \n cv2.imshow(\"frame\", frame)\n cv2.imshow(\"mask\", mask)\n\n key = cv2.waitKey(1)\n if key == ord(\"q\"):\n break\"\"\"\n\nwhile True:\n def empty(o):\n pass\n\n\n cv2.namedWindow(\"truc\")\n cv2.resizeWindow(\"truc\", 640,240)\n\n cv2.createTrackbar(\"gauss 1\", \"truc\", 5, 10, empty)\n cv2.createTrackbar(\"gauss 2\", \"truc\", 5, 10, empty)\n cv2.createTrackbar(\"canny 1\", \"truc\", 50, 100, empty)\n cv2.createTrackbar(\"canny 2\", \"truc\", 210, 400, empty)\n\n\n gauss1 = cv2.getTrackbarPos(\"gauss 1\", \"truc\")\n gauss2 = cv2.getTrackbarPos(\"gauss 2\", \"truc\")\n canny1 = cv2.getTrackbarPos(\"canny 1\", \"truc\")\n canny2 = cv2.getTrackbarPos(\"canny 2\", \"truc\")\n\n \n _,frame = cap.read()\n\n gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n\n gauss = cv2.GaussianBlur(gray, (5, 5), 0)\n\n canny = cv2.Canny(gauss, 60, 210)\n\n cont = canny.copy()\n\n contr, hier = cv2.findContours(cont, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n for cnt in contr:\n area = cv2.contourArea(cnt)\n if area>400:\n cv2.drawContours(frame, cnt, -1, (0,0, 255), 2)\n\n cv2.imshow(\"contours\", frame)\n\n key = cv2.waitKey(1)\n if key == ord(\"q\"):\n break \n\n\n\ncap.release()\n\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n \n \n \n \n","repo_name":"fablab3lapins/tirelire_ai","sub_path":"aide debug/contour.py","file_name":"contour.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38700044957","text":"import pyowm\r\n\r\nprint(\"Введите \\\"stop\\\" для выхода!\")\r\nwhile True:\r\n try:\r\n city = input(\"Какой город вас интересует?: \")\r\n if city == 'stop':\r\n break\r\n else:\r\n owm = pyowm.OWM('a99967bc9ee70d5b4bd387902982f400', language=\"RU\")\r\n observation = owm.weather_at_place(city)\r\n w = observation.get_weather()\r\n\r\n temperature = w.get_temperature('celsius')['temp']\r\n\r\n print(\"В городе \" + city + \" сейчас температура: \" + str(temperature) + \" по Цельсию.\")\r\n print('Погода в указаном городе: ' + w.get_detailed_status())\r\n except:\r\n print(\"Что-то пошло не так\")\r\n","repo_name":"ra-110110/mini","sub_path":"weather/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33869235354","text":"import sys\nfrom math import acos, asin, cos, pi, sin, sqrt\n\nimport inkex\n\nX, Y = range(2)\n\ndef draw_SVG_tri(point1, point2, point3, offset, width, name, parent):\n style = {'stroke': '#000000', 'stroke-width': str(width), 'fill': 'none'}\n elem = parent.add(inkex.PathElement())\n elem.update(**{\n 'style': style,\n 'inkscape:label': name,\n 'd': 'M ' + str(point1[X] + offset[X]) + ',' + str(point1[Y] + offset[Y]) +\n ' L ' + str(point2[X] + offset[X]) + ',' + str(point2[Y] + offset[Y]) +\n ' L ' + str(point3[X] + offset[X]) + ',' + str(point3[Y] + offset[Y]) +\n ' L ' + str(point1[X] + offset[X]) + ',' + str(point1[Y] + offset[Y]) + ' z'})\n return elem\n\n\ndef angle_from_3_sides(a, b, c): # return the angle opposite side c\n cosx = (a * a + b * b - c * c) / (2 * a * b) # use the cosine rule\n return acos(cosx)\n\n\ndef third_side_from_enclosed_angle(s_a, s_b, a_c): # return the side opposite a_c\n c_squared = s_a * s_a + s_b * s_b - 2 * s_a * s_b * cos(a_c)\n if c_squared > 0:\n return sqrt(c_squared)\n else:\n return 0 # means we have an invalid or degenerate triangle (zero is caught at the drawing stage)\n\n\ndef pt_on_circ(radius, angle): # return the x,y coordinate of the polar coordinate\n x = radius * cos(angle)\n y = radius * sin(angle)\n return [x, y]\n\n\ndef v_add(point1, point2): # add an offset to coordinates\n return [point1[X] + point2[X], point1[Y] + point2[Y]]\n\n\ndef is_valid_tri_from_sides(a, b, c): # check whether triangle with sides a,b,c is valid\n return (a + b) > c and (a + c) > b and (b + c) > a and a > 0 and b > 0 and c > 0 # two sides must always be greater than the third\n # no zero-length sides, no degenerate case\n\n\ndef draw_tri_from_3_sides(s_a, s_b, s_c, offset, width, parent): # draw a triangle from three sides (with a given offset\n if is_valid_tri_from_sides(s_a, s_b, s_c):\n a_b = angle_from_3_sides(s_a, s_c, s_b)\n\n a = (0, 0) # a is the origin\n b = v_add(a, (s_c, 0)) # point B is horizontal from the origin\n c = v_add(b, pt_on_circ(s_a, pi - a_b)) # get point c\n c[1] = -c[1]\n\n offx = max(b[0], c[0]) / 2 # b or c could be the furthest right\n offy = c[1] / 2 # c is the highest point\n offset = (offset[0] - offx, offset[1] - offy) # add the centre of the triangle to the offset\n\n draw_SVG_tri(a, b, c, offset, width, 'Triangle', parent)\n else:\n inkex.errormsg('Invalid Triangle Specifications.')\n\n\nclass Triangle(inkex.EffectExtension):\n def add_arguments(self, pars):\n pars.add_argument(\"--unit\", default=\"mm\", help=\"Units\")\n pars.add_argument(\"--s_a\", type=float, default=100.0, help=\"Side Length a\")\n pars.add_argument(\"--s_b\", type=float, default=100.0, help=\"Side Length b\")\n pars.add_argument(\"--s_c\", type=float, default=100.0, help=\"Side Length c\")\n pars.add_argument(\"--a_a\", type=float, default=60.0, help=\"Angle a\")\n pars.add_argument(\"--a_b\", type=float, default=30.0, help=\"Angle b\")\n pars.add_argument(\"--a_c\", type=float, default=90.0, help=\"Angle c\")\n pars.add_argument(\"--mode\", default='3_sides', help=\"Side Length c\")\n\n def effect(self):\n tri = self.svg.get_current_layer()\n offset = self.svg.namedview.center\n self.options.s_a = self.svg.unittouu(str(self.options.s_a) + self.options.unit)\n self.options.s_b = self.svg.unittouu(str(self.options.s_b) + self.options.unit)\n self.options.s_c = self.svg.unittouu(str(self.options.s_c) + self.options.unit)\n stroke_width = self.svg.unittouu('1px')\n\n if self.options.mode == '3_sides':\n s_a = self.options.s_a\n s_b = self.options.s_b\n s_c = self.options.s_c\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_ab_a_c':\n s_a = self.options.s_a\n s_b = self.options.s_b\n a_c = self.options.a_c * pi / 180 # in rad\n\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_ab_a_a':\n s_a = self.options.s_a\n s_b = self.options.s_b\n a_a = self.options.a_a * pi / 180 # in rad\n\n if (a_a < pi / 2.0) and (s_a < s_b) and (s_a > s_b * sin(a_a)): # this is an ambiguous case\n ambiguous = True # we will give both answers\n else:\n ambiguous = False\n\n sin_a_b = s_b * sin(a_a) / s_a\n\n if (sin_a_b <= 1) and (sin_a_b >= -1): # check the solution is possible\n a_b = asin(sin_a_b) # acute solution\n a_c = pi - a_a - a_b\n error = False\n else:\n sys.stderr.write('Error:Invalid Triangle Specifications.\\n') # signal an error\n error = True\n\n if not error and (a_b < pi) and (a_c < pi): # check that the solution is valid, if so draw acute solution\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n if not error and ((a_b > pi) or (a_c > pi) or ambiguous): # we want the obtuse solution\n a_b = pi - a_b\n a_c = pi - a_a - a_b\n s_c = third_side_from_enclosed_angle(s_a, s_b, a_c)\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_a_a_ab':\n s_a = self.options.s_a\n a_a = self.options.a_a * pi / 180 # in rad\n a_b = self.options.a_b * pi / 180 # in rad\n\n a_c = pi - a_a - a_b\n s_b = s_a * sin(a_b) / sin(a_a)\n s_c = s_a * sin(a_c) / sin(a_a)\n\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n elif self.options.mode == 's_c_a_ab':\n s_c = self.options.s_c\n a_a = self.options.a_a * pi / 180 # in rad\n a_b = self.options.a_b * pi / 180 # in rad\n\n a_c = pi - a_a - a_b\n s_a = s_c * sin(a_a) / sin(a_c)\n s_b = s_c * sin(a_b) / sin(a_c)\n\n draw_tri_from_3_sides(s_a, s_b, s_c, offset, stroke_width, tri)\n\n\nif __name__ == '__main__':\n Triangle().run()\n","repo_name":"eridur-de/mightyscape-1.1-deprecated","sub_path":"extensions/fablabchemnitz/triangle/triangle.py","file_name":"triangle.py","file_ext":"py","file_size_in_byte":6407,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"82"} +{"seq_id":"11345461777","text":"from qgis.PyQt.QtCore import QUrl\nfrom qgis.PyQt.QtNetwork import QNetworkRequest\n\nfrom qgis.gui import QgisInterface\nfrom qgis.core import (\n QgsApplication,\n QgsBlockingNetworkRequest,\n QgsFetchedContent,\n QgsLocatorResult,\n QgsFeedback,\n)\nfrom swiss_locator.core.filters.swiss_locator_filter import (\n SwissLocatorFilter,\n)\nfrom swiss_locator.core.filters.filter_type import FilterType\nfrom swiss_locator.core.results import WMSLayerResult\n\nimport xml.etree.ElementTree as ET\nimport urllib.parse\n\n\nclass SwissLocatorFilterWMTS(SwissLocatorFilter):\n def __init__(self, iface: QgisInterface = None, crs: str = None, capabilities=None):\n super().__init__(FilterType.WMTS, iface, crs)\n\n self.capabilities = capabilities\n self.capabilities_url = f\"https://wmts.geo.admin.ch/EPSG/{self.crs}/1.0.0/WMTSCapabilities.xml?lang={self.lang}\"\n\n # do this on main thread only?\n if self.capabilities is None and iface is not None:\n\n self.content = QgsApplication.networkContentFetcherRegistry().fetch(\n self.capabilities_url\n )\n self.content.fetched.connect(self.handle_capabilities_response)\n\n self.info(self.content.status())\n\n if self.content.status() == QgsFetchedContent.ContentStatus.Finished:\n file_path = self.content.filePath()\n self.info(\n f\"Swisstopo capabilities already downloaded. Reading from {file_path}\"\n )\n self.capabilities = ET.parse(file_path).getroot()\n else:\n self.content.download()\n\n def clone(self):\n if self.capabilities is None:\n self.content.cancel()\n nam = QgsBlockingNetworkRequest()\n request = QNetworkRequest(QUrl(self.capabilities_url))\n nam.get(request, forceRefresh=True)\n reply = nam.reply()\n if (\n reply.attribute(QNetworkRequest.HttpStatusCodeAttribute) == 200\n ): # other codes are handled by NetworkAccessManager\n self.capabilities = ET.fromstring(reply.content().data().decode(\"utf8\"))\n else:\n self.info(\n self.tr(\n \"The Swiss Locator filter for WMTS layers could not fetch capabilities.\"\n )\n )\n\n return SwissLocatorFilterWMTS(crs=self.crs, capabilities=self.capabilities)\n\n def displayName(self):\n return self.tr(\"Swiss Geoportal WMTS Layers\")\n\n def prefix(self):\n return \"chw\"\n\n def handle_capabilities_response(self):\n if self.content.status() == QgsFetchedContent.ContentStatus.Finished:\n self.info(\n f\"Swisstopo capabilities has been downloaded. Reading from {self.content.filePath()}\"\n )\n self.capabilities = ET.parse(self.content.filePath()).getroot()\n\n def perform_fetch_results(self, search: str, feedback: QgsFeedback):\n namespaces = {\n \"wmts\": \"http://www.opengis.net/wmts/1.0\",\n \"ows\": \"http://www.opengis.net/ows/1.1\",\n }\n\n if len(search) < 2:\n return\n\n if self.capabilities is None:\n self.info(\n self.tr(\n \"The Swiss Locator filter for WMTS layers could not fetch capabilities.\"\n )\n )\n return\n\n # Search for layers containing the search term in the name or title\n for layer in self.capabilities.findall(\".//wmts:Layer\", namespaces):\n layer_title = layer.find(\".//ows:Title\", namespaces).text\n layer_abstract = layer.find(\".//ows:Abstract\", namespaces).text\n layer_identifier = layer.find(\".//ows:Identifier\", namespaces).text\n dimensions = dict()\n for dim in layer.findall(\".//wmts:Dimension\", namespaces):\n identifier = dim.find(\"./ows:Identifier\", namespaces).text\n default = dim.find(\"./wmts:Default\", namespaces).text\n dimensions[identifier] = default\n dimensions = \"&\".join([f\"{k}={v}\" for (k, v) in dimensions.items()])\n dimensions = urllib.parse.quote(dimensions)\n\n results = {}\n\n if layer_identifier:\n if search in layer_identifier.lower():\n score = 1\n elif search in layer_title.lower():\n score = 2\n elif search in layer_abstract.lower():\n score = 3\n else:\n continue\n\n tile_matrix_set = layer.find(\".//wmts:TileMatrixSet\", namespaces).text\n _format = layer.find(\".//wmts:Format\", namespaces).text\n style = layer.find(\".//wmts:Style/ows:Identifier\", namespaces).text\n\n result = QgsLocatorResult()\n result.filter = self\n result.icon = QgsApplication.getThemeIcon(\"/mActionAddWmsLayer.svg\")\n\n result.displayString = layer_title\n result.description = layer_abstract\n result.userData = WMSLayerResult(\n layer=layer_identifier,\n title=layer_title,\n url=self.capabilities_url,\n tile_matrix_set=tile_matrix_set,\n _format=_format,\n style=style,\n tile_dimensions=dimensions,\n ).as_definition()\n\n results[result] = score\n\n # sort the results with score\n results = sorted([result for (result, score) in results.items()])\n\n for result in results[0 : self.settings.value(\"wmts_limit\")]:\n self.resultFetched.emit(result)\n self.result_found = True\n","repo_name":"opengisch/qgis-swiss-locator","sub_path":"swiss_locator/core/filters/swiss_locator_filter_wmts.py","file_name":"swiss_locator_filter_wmts.py","file_ext":"py","file_size_in_byte":5822,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"82"} +{"seq_id":"30711014045","text":"# -*- coding:utf-8 -*-\n# __author__ = 'gupan'\nfrom Atm.common.tools import *\nfrom Atm.core.CONSTANT import *\n\nimport json\npath_account = Root_path().get_root_path() + \"\\\\db\\\\accounts\"\npath_name_dir = Root_path().get_root_path() + \"\\\\db\\\\accounts_records\\\\\"\n#print(path_account)\n\ndef test_login(func):\n def decorator(*args, **kwargs):\n R_Flag = False\n name = kwargs[\"name\"]\n pwd = kwargs[\"pwd\"]\n\n with open(path_account, \"r\") as f_account:\n data = f_account.read()\n if not data:\n print(\"系统尚无用户注册\")\n return False\n accounts = json.loads(data)\n if accounts.get(name) and accounts[name][PWD] == pwd:\n R_Flag = True\n if not R_Flag:\n print(\"登陆失败\")\n return R_Flag\n\n if not accounts[name][STATUS]:\n print(\"\\033[31;1m账户被冻结，请联系管理员解除冻结状态\\033[0m\")\n return False\n\n R_Flag = func(*args, **kwargs)\n if not R_Flag:\n print(\"{name}失败\".format(name = func.__name__))\n return R_Flag\n return decorator\n\n # if Flag:\n # self.balance = int(self.accounts[name][BALANCE])\n # self.name = name","repo_name":"gupan2018/pythonProjects","sub_path":"Atm/core/decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74750812109","text":"import random\n\n\nclass countries:\n string = str()\n country = ''\n list1 = list()\n\n @staticmethod\n def random_country():\n Asia = [\n \"Afghanistan\", \"Armenia\", \"Azerbaijan\", \"Bahrain\", \"Bangladesh\", \"Bhutan\", \"Brunei\", \"Cambodia\", \"China\",\n \"Cyprus\", \"Georgia\", \"India\", \"Indonesia\", \"Iran\", \"Iraq\", \"Israel\", \"Japan\", \"Jordan\", \"Kazakhstan\",\n \"Kuwait\", \"Kyrgyzstan\", \"Laos\", \"Lebanon\", \"Malaysia\", \"Maldives\", \"Mongolia\", \"Myanmar\", \"Nepal\",\n \"North Korea\", \"Oman\", \"Pakistan\", \"Palestine\", \"Philippines\", \"Qatar\", \"Saudi Arabia\", \"Singapore\",\n \"South Korea\", \"Sri Lanka\", \"Syria\", \"Taiwan\", \"Tajikistan\", \"Thailand\", \"Timor Leste\", \"Turkey\",\n \"Turkmenistan\", \"United Arab Emirates\", \"Uzbekistan\", \"Vietnam\", \"Yemen\"\n ]\n\n copy = random.choice(Asia)\n a = copy.lower()\n return a\n\n @staticmethod\n def print_space(country):\n new_list = list()\n for i in country:\n if i != ' ':\n new_list.append(\"_\")\n else:\n new_list.append(\" \")\n new_str = \"\".join(new_list)\n return new_str\n\n @staticmethod\n def input_method(country, space):\n con_list = list(country)\n spa_list = list(space)\n con_str = \"\".join(con_list)\n\n try:\n while True:\n a = str(input(\"Guess word:-\"))\n\n if len(a) > 1:\n print(\"Please enter character or only one character!\\n\")\n continue\n elif a in con_str:\n occurrences = [i for i, letter in enumerate(con_list) if letter == a]\n\n for ek in occurrences:\n spa_list[ek] = a\n\n new = \" \".join(spa_list).upper()\n print(new, \"\\n\") # Move this line outside the loop\n\n if spa_list == con_list:\n print(\"Congratulations!\\nYou guessed the correct Country\")\n break\n\n else:\n print(\"Incorrect guess! Try again\\n\")\n except Exception as ex:\n print(type(ex))\n print(ex)\n\n\n# import pycountry\n#\n# all_countries = list(pycountry.countries)\n# list1 = list()\n#\n# for country in all_countries:\n# list1.append(country.name)\n#\n# print(list1)\n\n\n\"\"\"\n 1. Make a method to choose a country\n 2. Create a method to print len(country) space like ______\n 3. After that create a method to take input and exception handling\n\"\"\"\n\nc = countries()\n\nb = c.random_country()\nd = c.print_space(b)\ne = \" \".join(d)\nprint(e)\nc.input_method(b, d)\n","repo_name":"Ranjit2002/pythonProgram","sub_path":"Exercises/Word_Guessing_game.py","file_name":"Word_Guessing_game.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70603925389","text":"#!/usr/bin/env python3\r\n\r\n\"\"\"Search locations in text form and convert them to geographic coordinates\r\n\"\"\"\r\nfrom numpy.core.numeric import NaN\r\nimport spacy\r\nimport geocoder\r\nimport re\r\nimport pandas as pd\r\nfrom collections import Counter\r\n\r\n# Choose language\r\nnlp = spacy.load('en_core_web_sm')\r\n\r\n# Read data\r\ndf = pd.read_csv('quantitiesdone.csv')\r\nlocationlist = pd.read_csv('locationlist.csv')\r\n\r\n# Add new location columns\r\ndf['location_spacy'] = [[]] * df.shape[0] # empty list​\r\ndf['location_osm'] = [[]] * df.shape[0]\r\ndf['coordinates'] = [[]] * df.shape[0]\r\ndf['lat'] = [[]] * df.shape[0]\r\ndf['lon'] = [[]] * df.shape[0]\r\ndf['location_found_from'] = [[]] * df.shape[0]\r\ndf['location_match'] = [[]] * df.shape[0]\r\n\r\n# Remove NaN values\r\ndf = df.fillna('')\r\ndf = df.replace(r'\\s',' ', regex=True) \r\n\r\n# Column names from which location is searched\r\nlocation_columns = ['location', 'description', 'reptile']\r\n\r\n# Iterating trough dataframe rows\r\nfor index, row in df.iterrows():\r\n location_found = False\r\n location = str(df.iloc[index]['location'])\r\n # Uses Natural language processing to find location entities from the location column\r\n doc = nlp(location)\r\n for ent in doc.ents:\r\n if not location_found:\r\n # If an entity is geographic find it's coordinates from osm with geocoder and add them to data\r\n if ent.label_ == 'GPE':\r\n df.at[index, 'location_spacy'] = doc\r\n osm = geocoder.osm(str(ent))\r\n df.at[index, 'location_osm'] = osm\r\n if osm.country is not None:\r\n country_osm = geocoder.osm(osm.country)\r\n if country_osm.latlng is not None:\r\n df.at[index, 'coordinates'] = country_osm.latlng\r\n df.at[index, 'Country'] = country_osm.country\r\n df.at[index, 'lat'] = float(country_osm.latlng[0])\r\n df.at[index, 'lon'] = float(country_osm.latlng[1])\r\n df.loc[index, 'location_found_from'] = 'location'\r\n location_found = True\r\n # if ent.label_ == 'GPE':\r\n # df.at[index, 'location_spacy'] = doc\r\n # osm = geocoder.osm(str(ent))\r\n # df.at[index, 'location_osm'] = osm\r\n # if osm.latlng is not None:\r\n # df.at[index, 'coordinates'] = osm.latlng\r\n # df.at[index, 'country'] = osm.country\r\n # df.at[index, 'lat'] = float(osm.latlng[0])\r\n # df.at[index, 'lon'] = float(osm.latlng[1])\r\n # df.loc[index, 'location_found_from'] = 'location'\r\n # df.loc[index, 'location_match'] = str(row['location'])\r\n # \r\n \r\n # If location is not found search keywords from locationlist matching text in df\r\n if not location_found:\r\n for search_column in location_columns:\r\n if not location_found:\r\n for col in locationlist.columns:\r\n if not location_found:\r\n for i, r in locationlist.iterrows():\r\n if not location_found:\r\n location = re.findall(str(r[col]), str(row[search_column]), re.IGNORECASE)\r\n if location != '[]' and str(r[col]) != 'nan' and location:\r\n df.loc[index, 'location_spacy'] = col\r\n osm = geocoder.osm(str(ent))\r\n df.at[index, 'location_osm'] = osm\r\n if osm.country is not None:\r\n country_osm = geocoder.osm(osm.country)\r\n if country_osm.latlng is not None:\r\n df.at[index, 'coordinates'] = country_osm.latlng\r\n df.at[index, 'Country'] = country_osm.country\r\n df.at[index, 'lat'] = float(country_osm.latlng[0])\r\n df.at[index, 'lon'] = float(country_osm.latlng[1]) \r\n\r\n df.loc[index, 'location_found_from'] = search_column\r\n df.loc[index, 'location_match'] = str(r[col])\r\n location_found = True\r\n \r\n\r\n# Drop any data entries that dont have species information (These should not exist anymore at this point)\r\ndf['Species'].replace('', float('NaN'), inplace=True)\r\ndf.dropna(subset = ['Species'], inplace=True)\r\n\r\n\r\n#Remove duplicates, where Seller_id, location, quantity, price, currency, intent and species are indentical\r\ndf['Seller_id'] = df['Seller_id'].astype(str)\r\ndf['location_spacy'] = df['location_spacy'].astype(str)\r\n\r\ndf_noseller_id = df[df['Seller_id']=='[]']\r\ndf_withseller_id = df[df['Seller_id']!='[]']\r\n\r\n\r\nsubset_columns = ['Species', 'Quantity', 'Price', 'Currency', 'Intent', 'Seller_id', 'location_spacy']\r\n\r\nnon_empty_columns = df_withseller_id[subset_columns].apply(lambda x: sum(item != '[]' for item in x), axis=1)\r\n\r\n# Determine how many of the columns has to have data so the deduplication is taken into consideration\r\n# How many empty columns is allowed? Enter here:\r\nempty_columns = 0\r\n\r\n# Create a mask to identify rows that match the condition of empty columns allowed\r\ncondition = non_empty_columns >= len(subset_columns) - empty_columns\r\n\r\n# Perform deduplication only on rows that satisfy the condition\r\ndeduplicated_df = df_withseller_id[condition].drop_duplicates(subset=subset_columns)\r\n\r\n# Combine the deduplicated rows with the rows that don't meet the condition\r\ndf_withseller_id = pd.concat([deduplicated_df, df_withseller_id[~condition]])\r\n\r\n\r\n# Combine rows with seller id and rest of the roews\r\ndf = pd.concat([df_withseller_id, df_noseller_id])\r\n\r\n# Drop columns that might have personal information\r\ncolumns = df[['original_datarow', 'Species', 'Quantity', 'Price', 'Currency', 'Intent', 'Seller_id', 'Country', 'lat', 'lon']]\r\n\r\nnew_df = columns.copy()\r\n# Saves to file\r\nnew_df.to_csv(\"results.csv\")\r\n","repo_name":"JooelRinne/wildlifetrade","sub_path":"Data_processing/locations.py","file_name":"locations.py","file_ext":"py","file_size_in_byte":6191,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"35735003220","text":"import esprima\nfrom esprima.nodes import (\n Identifier,\n Literal,\n ComputedMemberExpression,\n ExpressionStatement,\n)\nimport escodegen\nfrom typing import Any, Callable, Dict, List, NewType, Tuple\n\nTransformation = Callable[[Dict[str, Any]], Any]\n\n\nclass VariableRenamer(esprima.NodeVisitor):\n \"\"\"\n Renames variables in the expression to the ones in the variable_mapping.\n\n Example:\n variable_mapping = {c: 'knactor.io/v1/checkouts'}\n expr = 'c.cost'\n output = scope['knactor.io/v1/checkouts']['cost']\n \"\"\"\n\n def __init__(self, alias: Dict[str, str] = {}) -> None:\n self.alias = alias\n self.variables = []\n\n def visit_StaticMemberExpression(self, node):\n if node.object.name in self.alias:\n new_object = ComputedMemberExpression(\n object=Identifier(name=\"scope\"),\n property=Literal(\n value=self.alias[node.object.name],\n raw=f\"'{self.alias[node.object.name]}'\",\n ),\n )\n dict_node = ComputedMemberExpression(\n object=new_object,\n property=Literal(\n value=node.property.name, raw=f\"'{node.property.name}'\"\n ),\n )\n self.variables.append(\n f\"{self.alias[node.object.name]}.{node.property.name}\"\n )\n\n for attr in vars(node):\n setattr(node, attr, getattr(dict_node, attr))\n\n\ndef parse_expr(expr: str, alias: Dict) -> Tuple[Tuple[str, ...], ExpressionStatement]:\n \"\"\"\n Parses the given expression string and returns a tuple containing the variables used in the expression and a transformation function.\n\n Args:\n expr (str): The expression string to parse.\n alias (Dict): A dictionary containing variable name mappings.\n\n Returns:\n Tuple[Tuple[str, ...], Transformation]: A tuple containing the variables used in the expression and a transformation function.\n \"\"\"\n if not expr:\n return (), ExpressionStatement(expression=Literal(value=None, raw=\"null\"))\n\n renamer = VariableRenamer(alias)\n parsed_expression = esprima.parseScript(expr).body[0]\n transformed_expression = renamer.visit(parsed_expression)\n variables = renamer.variables\n return tuple(variables), transformed_expression\n","repo_name":"knactor/cast","sub_path":"driver/runtime/redis/parsing.py","file_name":"parsing.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38069500891","text":"\n'''\nID: smaylni1\nLANG: PYTHON3\nTASK: dualpal\n'''\n\ndef divine(number, base):\n global num\n if (number // base == 0):\n num = str(number % base)\n return number % base\n else:\n divine(number // base, base)\n num += str(number % base)\n return number % base\n\ndef ispalindrome(num):\n if len(num) <= 1:\n return True\n else:\n return num[0] == num[-1] and ispalindrome(num[1:-1])\n\nf = open('dualpal.in', 'r')\ninp = list(map(int, f.read().split(' ')))\nf.close()\n\nf = open('dualpal.out', 'a')\nr = 0\n\nwhile (r < inp[0]):\n k = 0\n inp[1] += 1\n for i in range(2, 11):\n divine(inp[1], i)\n if ispalindrome(num):\n k += 1\n else:\n continue\n if (k >= 2):\n r += 1\n f.write(str(inp[1]) + '\\n')\n\nf.close()","repo_name":"smaylninja/usaco_dualpal","sub_path":"dualpal.py","file_name":"dualpal.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19385882616","text":"#!/usr/bin/python3\n\nimport requests, time\nfrom pwn import *\n\nurl = \"http://localhost:1234/index.php\"\n\np1 = log.progress(\"SQLi blind\")\np2 = log.progress(\"Database name\")\n\nsession = requests.Session()\n\ndic_letters = \"abcdefghijklmnopqrstuvwxyz0123456789.+!$#-_<>~}:\\\"\\'{*][%,&/\\)(=ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\nresult = \"\"\n\n# Recorremos como si la palabra encontrada tuviera 15 caracteres\nfor position in range(1, 16):\n # Probamos con cada letra de nuestro diccionario\n for letter in dic_letters:\n # Obtenemos el tiempo antes de la peticion\n time_now = time.time()\n \n # Validamos X letra en N posicion\n payload = \"?method=select&\"\n payload += \"username=administrator' and if(substr(database(),%d,1)='%s',sleep(3),1) and '1'='1&\" % (position, letter)\n payload += \"table=passwords\"\n\n p1.status(payload)\n r = session.get(url + payload)\n\n # Obtenemos el tiempo despues de la peticion\n time_after = time.time()\n\n # Si la diferencia de tiempos en mayor a 3, sabemos que la letra que probo esta en la base de datos, asi que la guardamos\n if time_after - time_now > 2:\n result += letter\n p2.status(result)\n break\n\np1.success(\"Done\")\np2.success(result)\n","repo_name":"lanzt/lanzt.github.io","sub_path":"assets/scripts/HTB/breadcrumbs/process_SQLi/extract_db_name.py","file_name":"extract_db_name.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"es","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"23780718456","text":"def tic_tac_toe(field):\n WINS = ((0, 1, 2), (3, 4, 5), (6, 7, 8), (0, 3, 6),\n (1, 4, 7), (2, 5, 8), (0, 4, 8), (2, 4, 6))\n\n sfield = \"\"\n for i in range(3):\n sfield += \"\".join(field[i])\n win_exist = False\n for win in WINS:\n if sfield[win[0]] == sfield[win[1]] == sfield[win[2]] != '.':\n print('{0} win'.format(sfield[win[0]]))\n win_exist = True\n break\n if not win_exist:\n print('draw')\n\n\ndata = \"\"\"0 - 0\nx x x\n0 0 -\"\"\"\n\nfield = [line.split() for line in data.split('\\n')]\n\ntic_tac_toe(field)\n","repo_name":"AndLvG/Python","sub_path":"Lyceum/2019 2 полугодие/p5.py","file_name":"p5.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70414821710","text":"class NameChunks:\n def __init__(self, root):\n self.root = root\n\n def __enter__(self):\n return self\n\n def __exit__(self, type, value, traceback):\n pass\n\n def apply(self):\n counts = {}\n for chunk in self.root.chunks:\n if chunk.type in counts:\n counts[chunk.type] += 1\n else:\n counts[chunk.type] = 1\n if \"name\" not in chunk.options:\n chunk.options[\"name\"] = (\n self.root.options[\"name\"]\n + \"-\"\n + chunk.type\n + str(counts[chunk.type])\n )\n if chunk.type == \"group\":\n with NameChunks(chunk) as p:\n p.apply()\n","repo_name":"yitzchak/metys","sub_path":"metys/Processors/NameChunks.py","file_name":"NameChunks.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29512123918","text":"from exobuilder.contracts.futureschain import FuturesChain\nfrom exobuilder.contracts.futurecontract import FutureContract\nfrom exobuilder.tests.assetindexdict import AssetIndexDicts\nfrom datetime import datetime, date, timedelta, time as dttime\nfrom exobuilder.contracts.instrument import Instrument\nfrom exobuilder.data.datasource_mongo import DataSourceMongo\nfrom exobuilder.data.datasource_sql import DataSourceSQL\nfrom exobuilder.data.assetindex_mongo import AssetIndexMongo\nfrom exobuilder.data.exostorage import EXOStorage\nfrom exobuilder.exo.exoenginebase import ExoEngineBase\nfrom exobuilder.exo.transaction import Transaction\nimport time\nfrom exobuilder.algorithms.rollover_helper import RolloverHelper\n\nclass EXOBrokenwingCollar(ExoEngineBase):\n def __init__(self, symbol, direction, date, datasource, log_file_path=''):\n self._direction = direction\n self._symbol = symbol\n\n if self._direction != 1 and self._direction != -1:\n raise ValueError('self._direction != 1 and self._direction != -1')\n\n super().__init__(symbol, direction, date, datasource, log_file_path=log_file_path)\n\n @staticmethod\n def direction_type():\n return 0\n\n @staticmethod\n def names_list(symbol):\n return [symbol + '_BullishCollarBW', symbol + '_BearishCollarBW']\n\n @property\n def exo_name(self):\n if self._direction == 1:\n return self._symbol + '_BullishCollarBW'\n elif self._direction == -1:\n return self._symbol + '_BearishCollarBW'\n\n def is_rollover(self):\n if len(self.position) != 0:\n for p in self.position.legs.values():\n rh = RolloverHelper(p.instrument)\n if rh.is_rollover(p):\n return True\n return False\n\n def process_rollover(self):\n trans_list = self.position.close_all_translist()\n return trans_list\n\n\n def process_day(self):\n \"\"\"\n Main EXO's position management method\n :return: list of Transactions to process\n \"\"\"\n\n\n if len(self.position) == 0:\n instr = self.datasource.get(self._symbol, self.date)\n rh = RolloverHelper(instr)\n fut, opt_chain = rh.get_active_chains()\n if fut is None or opt_chain is None:\n if self.debug_mode:\n self.logger.write(\n 'Futures contract or option chain not found.\\n\\tFuture: {0}\\tOption chain: {1}\\n'.format(\n fut,\n opt_chain\n ))\n return []\n\n if self._direction == 1:\n # the bullish broken wings are long the -5 put , long the future, short the + 5 call and long the +9 call\n put_dn5 = opt_chain[-5].P\n call_up5 = opt_chain[5].C\n call_up9 = opt_chain[9].C\n\n\n return [\n Transaction(put_dn5, self.date, 1.0, put_dn5.price, leg_name='opt_otm_leg'),\n Transaction(fut, self.date, 1.0, fut.price, leg_name='fut_leg'),\n Transaction(call_up5, self.date, -1.0, call_up5.price, leg_name='call_up5_short_leg'),\n Transaction(call_up9, self.date, 1.0, call_up9.price, leg_name='call_up9_long_leg'),\n ]\n if self._direction == -1:\n # the bearish BW long the -9 put, short the -5 put , short the future, long the + 5 call\n call_up5 = opt_chain[5].C\n put_dn9 = opt_chain[-9].P\n put_dn5 = opt_chain[-5].P\n\n return [\n Transaction(call_up5, self.date, 1.0, call_up5.price, leg_name='opt_otm_leg'),\n Transaction(fut, self.date, -1.0, fut.price, leg_name='fut_leg'),\n Transaction(put_dn9, self.date, 1.0, put_dn9.price, leg_name='put_dn9_long_leg'),\n Transaction(put_dn5, self.date, -1.0, put_dn5.price, leg_name='put_dn5_short_leg'),\n ]\n","repo_name":"trendmanagement/tmqrexo_alexveden","sub_path":"exobuilder/algorithms/exo_brokenwing.py","file_name":"exo_brokenwing.py","file_ext":"py","file_size_in_byte":4034,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"41750808924","text":"# Method-3: Two Pointer variables\n\ndef twoNumberSum(array, target):\n array.sort()\n left = 0\n right = len(array) - 1\n while(left < right):\n currentSum = array[left] + array[right]\n if(currentSum == target):\n return [array[left], array[right]]\n elif(currentSum < target):\n left += 1\n elif(currentSum > target):\n right -= 1\n\n return []\n\na = [1,2,3,4,5]\ntarget = 5\n\nresult = twoNumberSum(a,target)\n\nif(result):\n print(result)\nelse:\n print(\"There are no elements whose sum is {}\".format(target))\n\n\n# Time Complexity: O(nlogn)\n# Space Complexity: O(1)","repo_name":"OrionJoshi/Competitive_Programming","sub_path":"1.Two Number Of Sum/Method-3.py","file_name":"Method-3.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"82"} +{"seq_id":"4636494176","text":"use_relative_paths = True\n\ndeps = {\n\n \"build\": \"https://chromium.googlesource.com/chromium/src/build.git@acf607f7d345915ea2ecca208bc516677d298463\",\n\n \"buildtools\": \"https://chromium.googlesource.com/chromium/buildtools.git@5fd66957f08bb752dca714a591c84587c9d70762\",\n\n \"tools/gyp\": \"https://chromium.googlesource.com/external/gyp.git@c61b0b35c8396bfd59efc6cfc11401d912b0f510\",\n\n}\n\nhooks = [{\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=win32',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/win/gn.exe.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_win'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=darwin',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/mac/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_mac'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=linux*',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/linux32/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_linux32'\n}, {\n 'action': [\n 'download_from_google_storage',\n '--no_resume',\n '--platform=linux*',\n '--no_auth',\n '--bucket',\n 'chromium-gn',\n '-s',\n 'minimal-gn-project/buildtools/linux64/gn.sha1'\n ],\n 'pattern': '.',\n 'name': 'gn_linux64'\n}]\n","repo_name":"skopf/minimal-gn-project","sub_path":"DEPS","file_name":"DEPS","file_ext":"","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"35393935513","text":"\"\"\"\nvirtstrapcore.main\n==================\n\nThe main controller for virtstrap after the \nbootstrapping process has completed. \n\nVirtStrap Core ensures that the core sections\nof virtstrap are handled before any extension sections. \nFor now this seems to be the best way to handle it\n\"\"\"\nimport logging\nvs_logger = logging.getLogger(\"virtstrap\")\n\nclass CoreUninitialized(Exception):\n pass\n\nclass VirtStrapCore(object):\n def __init__(self, options=None, args=None, settings=None):\n self._settings = settings\n self._options = options\n self._args = args\n self._initialized = False\n self._core_sections = []\n\n def initialize(self, options, args, settings):\n vs_logger.debug(\"Initializing Core\")\n # Parse the settings\n self._initialized = True\n self._settings = settings\n self._options = options\n self._args = args\n core_sections = self._core_sections\n for section in core_sections:\n section_settings = section.settings\n if not section_settings:\n continue\n settings.parse_section(section_settings)\n\n def execute_command(self):\n \"\"\"Executes command from command line\"\"\"\n if not self._initialized:\n raise CoreUninitialized()\n args = self._args\n arg_length = len(args)\n command = \"default\"\n if arg_length > 0:\n command = args[0]\n core_sections = self._core_sections\n settings = self._settings\n for section in core_sections:\n section_command = getattr(section, command, None)\n if section_command:\n vs_logger.debug(\"Section {0} running command {1}\".format(\n section.name, command))\n section_command(settings)\n \n def register_core_sections(self, *core_sections):\n self._core_sections.extend(core_sections)\n\n\n","repo_name":"ravenac95/virtstrap-resources","sub_path":"packages/virtstrapcore/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"943511705","text":"import time\nfrom turtle import Screen\nfrom snake import Snake\nfrom food import Food\nfrom scoreboard import Scoreboard\n\n# Screen setup\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.bgcolor(\"black\")\nscreen.title(\"Snake Game\")\nscreen.tracer(0)\n\n# create game objects\nsnake = Snake()\nfood = Food()\nscoreboard = Scoreboard()\n\n# listen for inputs\nscreen.listen()\nscreen.onkey(fun=snake.up, key=\"Up\")\nscreen.onkey(fun=snake.down, key=\"Down\")\nscreen.onkey(fun=snake.left, key=\"Left\")\nscreen.onkey(fun=snake.right, key=\"Right\")\n\n# Game Loop\ngame_is_on = True\nwhile game_is_on:\n # draw screen\n screen.update()\n time.sleep(0.2)\n\n snake.move()\n\n # Detect collision with food\n if snake.head.distance(food) < 15:\n food.refresh()\n snake.extend()\n scoreboard.increase_score()\n scoreboard.update_display()\n\n # Detect collision with wall\n if snake.head.xcor() > 280 or snake.head.xcor() < -280 or snake.head.ycor() > 280 \\\n or snake.head.ycor() < -280:\n scoreboard.reset_scoreboard()\n snake.reset_snake()\n\n # Detect collision with tail\n for segment in snake.segments[1:]:\n if snake.head.distance(segment) < 10:\n scoreboard.reset_scoreboard()\n snake.reset_snake()\n\nscreen.exitonclick()\n","repo_name":"EderLukas/python_portfolio","sub_path":"snake/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39280008878","text":"#!/usr/bin/env python3\nimport sys\nimport argparse\nimport doctest\nimport slyther.types\nimport slyther.parser\nimport slyther.interpreter\nimport slyther.builtins\nimport slyther.evaluator\n\n\ndef module_globals(obj):\n if isinstance(obj, type(sys)):\n pass\n elif hasattr(obj, '__module__'):\n obj = sys.modules[obj.__module__]\n else:\n raise TypeError('Input is not a module or an element with __module__')\n return {\n k: getattr(obj, k)\n for k in dir(obj)\n }\n\n\nd1 = [\n slyther.types.ConsCell,\n slyther.types.ConsCell.__eq__,\n slyther.types.ConsCell.__repr__,\n slyther.types.ConsList,\n slyther.types.ConsList.__init__,\n slyther.types.ConsList.from_iterable,\n slyther.types.ConsList.__getitem__,\n slyther.types.ConsList.cells,\n slyther.types.ConsList.__len__,\n slyther.types.ConsList.__contains__,\n slyther.types.ConsList.__reversed__,\n slyther.types.ConsList.__eq__,\n slyther.types.ConsList.__repr__,\n slyther.types.SExpression,\n slyther.types.cons,\n slyther.types.LexicalVarStorage,\n slyther.types.LexicalVarStorage.fork,\n slyther.types.LexicalVarStorage.put,\n slyther.types.LexicalVarStorage.__getitem__,\n]\n\nd2 = [\n slyther.parser.tokenize,\n slyther.parser.parse,\n slyther.parser.parse_strlit,\n slyther.parser,\n]\n\nd3 = [\n slyther.evaluator,\n slyther.evaluator.lisp_eval,\n slyther.types.UserFunction,\n slyther.types.UserFunction.__init__,\n slyther.types.UserFunction.__call__,\n slyther.types.UserFunction.__repr__,\n slyther.interpreter,\n slyther.interpreter.Interpreter,\n slyther.builtins,\n slyther.builtins.add,\n slyther.builtins.sub,\n slyther.builtins.mul,\n slyther.builtins.div,\n slyther.builtins.floordiv,\n slyther.builtins._list,\n slyther.builtins.car,\n slyther.builtins.cdr,\n slyther.builtins.define,\n slyther.builtins.lambda_func,\n slyther.builtins.let,\n slyther.builtins.if_expr,\n slyther.builtins.cond,\n slyther.builtins._and,\n slyther.builtins._or,\n slyther.builtins._set,\n slyther.builtins._eval,\n slyther.builtins._parse,\n]\n\n\ndef tco_test(code):\n \"\"\"\n Test that tail call optimization works.\n\n >>> f = open(\"examples/carmichael.scm\")\n >>> tco_test(f.read()) # doctest: +ELLIPSIS\n 561\n 1105\n 1729\n 2465\n ...\n >>> f.close()\n \"\"\"\n import signal\n\n def handler(signum, frame):\n raise TimeoutError\n\n try:\n signal.signal(signal.SIGALRM, handler)\n signal.alarm(240)\n interp = slyther.interpreter.Interpreter()\n interp.exec(code)\n except TimeoutError:\n return\n signal.alarm(0)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--d1',\n action='store_true',\n help='Run tests for D1'\n )\n parser.add_argument(\n '--d2',\n action='store_true',\n help='Run tests for D2'\n )\n parser.add_argument(\n '--d3',\n action='store_true',\n help='Run tests for D3'\n )\n parser.add_argument(\n '--tco',\n action='store_true',\n help='Run tail-call optimization tests (takes 4 minutes!)'\n )\n parser.add_argument(\n '-v', '--verbose',\n action='store_true',\n help='Print all test results, not just failures'\n )\n args = parser.parse_args()\n\n tests = []\n if args.d1:\n tests += d1\n if args.d2:\n tests += d2\n if args.d3:\n tests += d3\n if args.tco:\n tests += [tco_test]\n\n if not tests:\n parser.error('You must specify at least one of --d{1,2,3} or --tco.')\n\n for t in tests:\n if t is tco_test:\n print(\"Running tail-call optimization test... \"\n \"(takes 4 minutes if it works!)\")\n print(\"Essentially, if after 4-minutes of preforming all sorts\\n\"\n \"of tail calls, your program does not break, call it good.\")\n doctest.run_docstring_examples(\n t,\n globs=module_globals(t),\n verbose=args.verbose,\n name=getattr(t, '__name__', None) or str(t))\n","repo_name":"geraldung/SlytherLisp","sub_path":"run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27973309416","text":"import json\nimport collections\nfrom collections import OrderedDict\n\nclass Law(object):\n pid = \"\"\n city = \"\"\n state = \"\"\n implementingSectorName = \"\"\n date = \"\"\n typeName = \"\"\n policyName = \"\"\n url = \"\"\n category = \"\"\n\n # The class \"constructor\" - It's actually an initializer \n def __init__(self, pid, city, state, implementingSectorName, date, typeName, policyName, url, category):\n self.pid = pid\n self.city = city\n self.state = state\n self.implementingSectorName = implementingSectorName\n self.date = date\n self.typeName = typeName\n self.policyName = policyName\n self.url = url\n self.category = category\n\ndef make_law(pid, city, state, implementingSectorName, date, typeName, policyName, url, category):\n law = Law(pid, city, state, implementingSectorName, date, typeName, policyName, url, category)\n return law\n\ncities_list = []\ncode_list = []\nid_list = []\nlaw_list = []\ntype_list = []\nprogramId2 = [\"programId\", \"city\", \"state\", \"implementingSectorName\", \"tempCity\"] \n\ndef ogDumpclean(obj):\n if type(obj) == dict:\n for k, v in obj.items():\n if hasattr(v, '__iter__'):\n print (k.encode(\"utf-8\"))\n ogDumpclean(v)\n else:\n \tprint (('%s : %s' % (k, v)).encode(\"utf-8\"))\n elif type(obj) == list:\n for v in obj:\n if hasattr(v, '__iter__'):\n ogDumpclean(v)\n else:\n print (v.encode(\"utf-8\"))\n else:\n print (obj.encode(\"utf-8\"))\n\n\ndef dumpcleaner(obj):\n\tif type(obj) == dict:\n\t\tmainObj = obj.get(\"data\")\n\t\tfor objs in mainObj:\n\t\t\tprogramId = str(objs.get(\"ProgramId\"))\n\t\t\tcode_list.append(programId)\n\t\t\tstate = str(objs.get(\"State\"))\n\t\t\timplementingSectorName = str(objs.get(\"ImplementingSectorName\"))\n\t\t\tcityName = \"\"\n\t\t\tcities = objs.get(\"Cities\")\n\t\t\tcontacts = objs.get(\"Contacts\")\n\t\t\tif cities:\n\t\t\t\tcityName = str(cities[0].get(\"name\"))\n\t\t\telif contacts:\n\t\t\t\tcounter = 0\n\t\t\t\twhile ((len(contacts) > counter) & ((cityName.strip() == \"\") | (cityName == \"None Specified\"))):\n\t\t\t\t\tcontact = contacts[counter].get(\"contact\")\n\t\t\t\t\tcity = contact.get(\"city\")\n\t\t\t\t\tstateObject = contact.get(\"stateObject\")\n\t\t\t\t\tstateName = stateObject.get(\"name\")\n\t\t\t\t\tif ((state == str(stateName)) & ((cityName.strip() == \"\") | (cityName == \"None Specified\"))):\n\t\t\t\t\t\tif str(city) == \"None\":\n\t\t\t\t\t\t\tcityName = \"None Specified\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tcityName = str(city)\n\t\t\t\t\telse:\n\t\t\t\t\t\tcityName = \"None Specified\"\n\t\t\t\t\tcounter += 1\n\t\t\telse:\n\t\t\t\tcityName = \"None Specified\"\n\t\t\tif cityName.strip() == \"\":\n\t\t\t\tcityName = \"None Specified\"\n\t\t\tcategory = objs.get(\"CategoryName\")\n\t\t\tdate = objs.get(\"StartDate\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = objs.get(\"enactedDate\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = objs.get(\"enactedDateDisplay\")\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = str(objs.get(\"enactedText\"))\n\t\t\tif ((date == \"\") | (str(date) == \"None\")):\n\t\t\t\tdate = str(objs.get(\"LastUpdate\"))\n\t\t\tif str(date) == \"None\":\n\t\t\t\tdate = \"None given\"\n\t\t\ttypeName = str(objs.get(\"TypeName\")).encode(\"utf-8\")\n\t\t\tpolicyName = str(objs.get(\"Name\")).encode(\"utf-8\")\n\t\t\tif not typeName in type_list:\n\t\t\t\tif implementingSectorName == \"Utility\":\n\t\t\t\t\ttype_list.append(typeName)\n\t\t\turl = str(objs.get(\"WebsiteUrl\"))\n\t\t\tif url.strip() == \"\":\n\t\t\t\turl = \"None\"\n\t\t\tif cityName != \"None Specified\":\n\t\t\t\tcities_list.append(cityName)\n\t\t\tlaw_list.append(make_law(programId, cityName, state, implementingSectorName, \n\t\t\t\tdate, typeName, policyName, url, category))\n\nwith open('data_DSIRE.json') as f:\n data = json.load(f)\n\ndumpcleaner(data)\n#law_list.append(make_law(code_list[-1], programId[1], programId[2]))\n#print(\"number of cities\" + str(len(cities_list)))\nprint(\"number of codes\" + str(len(code_list)))\nprint(\"programCode = \" + code_list[-1])\nf = open(\"parsed_DSIRE_data2.txt\", \"w+\")\nfor x in law_list:\n\tf.write(x.category + \"\\n\")\n#\tf.write(str(x.policyName)[2:-1] + \"\\n\")\n#\tprint(x.pid + \" \" + x.city + \" \" + x.state + \" \" + x.implementingSectorName+ \n#\t\t\" \" + x.date + \" \" + str(x.typeName)[2:-1] + \" \" + str(x.policyName)[2:-1]) \nprint(len(type_list))\nprint(len(law_list))\n\nf.close()","repo_name":"pmohamma/Summer-2018-Climate-Policy-Research","sub_path":"src/climate_policy/DSIRE_dataset_parsing.py","file_name":"DSIRE_dataset_parsing.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7182530927","text":"import os\nimport csv\nimport numpy as np\n\nfolder = \"memoryPerformance/\"\nciphers = [\"SABER=1\", \"KYBER=1\", \"NTRU=1\", \"NTRUP=1\", \"FRODO=1\"]\nmassiffile = [\"saber/saber\", \"kyber/kyber\", \"ntru/ntru\", \"ntrup/ntrup\", \"frodo/frodo\"]\noperation = [\"TOTAL=1\", \"KEYGEN=1\", \"ENC=1\", \"DEC=1\"]\next = \".out\"\nrm = \"rm test\"\nmake = \"make test \"\nvalgrind = \"valgrind --tool=massif --stacks=yes --massif-out-file=\"\nN = 1\n\ndef measureMemory():\n # For each cipher, measure its memory consumption\n for i in range(5):\n for j in range(N):\n # make with the memory option enabled, and desired operation\n for k in range(1):\n # Remove test binary\n os.system(rm)\n cmd = make + \"test \" + ciphers[i] + \" MEMORY=1 DEBUG=1 RPI=1\" + operation[k]\n os.system(cmd)\n\n # Profile memory with valgrind\n cmd = valgrind + folder + massiffile[i] + \"_\" + operation[k].split(\"=\")[0] + \"_\" + str(j) + ext + \" ./test\"\n print(cmd)\n os.system(cmd)\n\ndef readThreeLines(file):\n file.readline()\n file.readline()\n file.readline()\n\ndef getTotalMemory(file):\n heap = int(file.readline().split(\"=\")[1])\n heapExtra = int(file.readline().split(\"=\")[1])\n stack = int(file.readline().split(\"=\")[1])\n return heap + heapExtra + stack\n\ndef getMemoryUsageAll():\n # Read the generated output file, and get the maximum amount of memory used per KEM\n for i in range(4):\n # Get the array of values per operation\n memUsageAll = []\n for k in range(4):\n # Get the maximum value for each iteration\n memUsage = [] # Stores the max values for all files\n for j in range(N):\n fileN = folder + massiffile[i] + \"_\" + operation[k].split(\"=\")[0] + \"1_\" + str(j) + ext\n print(fileN)\n memUsageOp = []\n with open(fileN, \"r\") as file:\n # Read the first three lines:\n readThreeLines(file)\n # Find the max value\n for line in file:\n # Search the following '#' character\n if line[0] == \"#\":\n # Read the following three lines\n readThreeLines(file)\n # Get the total amount of memory\n total = getTotalMemory(file)\n memUsageOp.append(total)\n memUsage.append(memUsageOp.copy())\n print(memUsage)\n memUsageAll.append(memUsage.copy())\n print(memUsageAll)\n return memUsageAll\n\ndef getMemoryUsageMax():\n \"\"\"\n Get the maximum memory used for all files.\n \"\"\"\n # Read the generated output file, and get the maximum amount of memory used\n memUsageAll = []\n for i in range(4):\n memUsage = [] # Stores the max values for each file\n for j in range(N):\n fileN = memory + massiffile[i] + \"_\" + str(j) + ext\n maxM = 0\n with open(fileN, \"r\") as file:\n # Read the first three lines:\n readThreeLines(file)\n # Find the max value\n for line in file:\n # Search the following '#' character\n if line[0] == \"#\":\n # Read the following three lines\n readThreeLines(file)\n # Get the total amount of memory\n total = getTotalMemory(file)\n if (total > maxM):\n maxM = total\n memUsage.append(maxM)\n memUsageAll.append(memUsage.copy())\n return memUsageAll\n\ndef getMemoryUsageKEM():\n \"\"\"\n Get the data of memory usage per KEM, all the operations\n \"\"\"\n # For each kem\n totalValues = []\n for i in range(5):\n fileN = folder + massiffile[i] + \"_TOTAL_0\" + ext\n values = []\n print(fileN)\n with open(fileN, \"r\") as file:\n readThreeLines(file)\n for line in file:\n if line[0] == \"#\":\n readThreeLines(file)\n total = getTotalMemory(file)\n values.append(total)\n totalValues.append(values.copy())\n return totalValues\n\ndef saveData(data, file, delimiter, op=True):\n m = np.array(data, dtype=object)\n mT = m.transpose()\n with open(file, \"w\") as csvfile:\n writer = csv.writer(csvfile, delimiter=delimiter)\n cipherS = [\"LightSaber\", \"Kyber512\", \"NTRUhps2048509\", \"NTRULPr653\", \"Frodo640\"]\n writer.writerow(cipherS)\n if op:\n operationS = [c.split(\"=\")[0] for c in operation]\n writer.writerow(operationS)\n writer.writerows(mT)\n\nif __name__ == '__main__':\n #measureMemory()\n m = getMemoryUsageKEM()\n saveData(m, \"memoryPerformance/memoryPerformance.csv\", \",\", False)\n","repo_name":"Septien/PQSPerformance","sub_path":"measureMemory.py","file_name":"measureMemory.py","file_ext":"py","file_size_in_byte":4966,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"26770925232","text":"from django.db import models\n\n\nclass Company(models.Model):\n CONTINENTS = (('AF', 'Africa'),\n ('NA', 'North America'),\n ('OC', 'Oceania'),\n ('AN', 'Antarctica'),\n ('AS', 'Asia'),\n ('EU', 'Europe'),\n ('SA', 'South America'))\n\n name = models.CharField(max_length=100)\n location = models.CharField(max_length=2, choices=CONTINENTS, default='NA')\n\n def __str__(self):\n return str(self.name) + \", \" + str(self.location)\n\n\nclass Part(models.Model):\n company = models.ForeignKey(Company, on_delete=models.CASCADE, default=0)\n name = models.CharField(max_length=50)\n on_hand = models.IntegerField()\n price = models.FloatField()\n min = models.IntegerField()\n max = models.IntegerField()\n\n\nclass Product(models.Model):\n company = models.ForeignKey(Company, on_delete=models.CASCADE, default=0)\n name = models.CharField(max_length=50)\n parts = models.ManyToManyField(Part)\n on_hand = models.IntegerField()\n price = models.FloatField()\n min = models.IntegerField()\n max = models.IntegerField()\n","repo_name":"zpillman/django-inventory-project","sub_path":"inventory/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16125909275","text":"'''\nchat room 客户端\n'''\n\nfrom socket import *\nimport os,sys\n\n#服务端地址,客户端ｙｉｉｄｎｇ会��收服务端地址\nADDR = ('127.0.0.1',8888)\n\n#发消息的函数\ndef send_msg(s,name):\n while True:\n #捕获掉用户如果按ctrl+c的异常\n try:\n text = input('>>')\n except (KeyboardInterrupt,SyntaxError):\n text = 'quit'\n #退出的情况\n if text.strip() == 'quit':\n msg = 'Q' + name\n s.sendto(msg.encode(),ADDR)\n sys.exit('退出聊天室') #进程退出，打印出:退出聊天室\n\n msg = 'C %s %s'%(name,text)\n s.sendto(msg.encode(),ADDR)\n\n#接收消息\ndef recv_msg(s):\n while True:\n data,addr = s.recvfrom(4096)\n #发送消息的进程退出(表明用户要退出)，接收消息的进程也得跟着退出\n if data.decode() == 'EXIT':\n sys.exit()\n print(data.decode()+'\\n>>',end='') #\\n>>表示换行打印出光标\n\n\n#搭建网络\ndef main():\n s = socket(AF_INET,SOCK_DGRAM)\n\n #进入聊天室\n while True:\n name = input('请输入昵称：')\n msg = 'L ' + name #通信协议与服务端确定好\n s.sendto(msg.encode(),ADDR)\n #接收反馈\n data,addr = s.recvfrom(128)\n # 登录成功，服务端会返回OK\n if data == b'OK':\n print('您已进入聊天室')\n break\n #登录失败，重新输入用户名\n else:\n print(data.decode())\n\n #已经进入聊天室,创建一个进程收消息，一个进程发消息，避免消息堵塞\n pid = os.fork()\n if pid < 0:\n sys.exit('Error!')\n elif pid == 0:\n send_msg(s,name)\n else:\n recv_msg(s)\n\n\n\n\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n\n\n\n\n","repo_name":"codefish-yu/socket-chatroom","sub_path":"chat_client.py","file_name":"chat_client.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2386072542","text":"\"\"\"\nCreated on Sun Dec 28 20:16:29 2014\n\n@author: aashishsatya\n\nDescription: Script that handles primitive procedures\n\n\"\"\"\n\n# Classifier.py finds the type of Scheme expression \n# that was given as input\nfrom Classifier import *\n# selectors for the given Scheme expression\nfrom Selectors import *\n\nprimitive_operators = ['+', '-', '*', '/', '=', '<', '>', '<=', '>=',\n 'and', 'or', 'not', 'eq?', 'equal?']\n \nprimitive_list_operators = ['cons', 'car', 'cdr', 'null?', 'list?', 'list',\n 'append']\n\ndef make_list(arguments):\n return arguments\n \ndef convert_to_scheme_expression(val):\n \n \"\"\"\n Prints Python expression val as an expression in Scheme\n \"\"\"\n \n # some code taken from Peter Norvig's implementation of the same\n # thanks Mr. Norvig\n # list_repn stands for list representation\n if type(val) == list:\n list_repn = '(' + ' '.join(map(convert_to_scheme_expression, val)) + ')'\n return list_repn\n\n elif str(val) == 'True':\n return '#t'\n elif str(val) == 'False':\n return '#f'\n else:\n return str(val)\n \ndef is_shortened_list_operation(operation_name):\n \n \"\"\"\n Checks if an operation is a shortened list operation such as caar, cadddr\n etc.\n Input: A string\n Output: True or False depending on whether the operation is a shortened\n list operation\n \"\"\"\n \n if operation_name[0] != 'c' or operation_name[-1] != 'r':\n return False\n \n for character in operation_name[1:-1]:\n if character != 'a' and character != 'd':\n return False\n \n return True\n \ndef expand_list_operation(list_op, args):\n \n \"\"\"\n Converts a shortened list operation to its expanded form.\n This is done because the program can only work on expanded forms of\n list operations.\n Input: A list operation in its short form and the arguments\n Output: Expanded form of the list operation along with the arguments.\n \"\"\"\n \n # args[0] because internal representation will be of the form\n # [[, , ...]] <= notice the double parens\n args = args[0]\n for index in range(len(list_op) - 2, 0, -1):\n if list_op[index] == 'a':\n args = ['car', args]\n elif list_op[index] == 'd':\n args = ['cdr', args]\n \n return args \n\ndef raise_argument_error(function_name, error_type, error_arg):\n \n \"\"\"\n Raises an error of type error_type with error_arg as the object\n being printed as responsible for the error.\n \"\"\"\n \n raise error_type('The object ' + convert_to_scheme_expression(error_arg) + ', passed as an argument to ' + function_name + ', is not the correct type.')\n \ndef raise_argument_count_error(correct_number, error_number, procedure_name):\n \n \"\"\"\n Raises an error of type TypeError with a message containing the \n input number and the required number of arguments.\n \"\"\"\n \n if type(procedure_name) == str:\n raise TypeError('The procedure ' + procedure_name + ' has been called with ' + str(error_number) + ' argument(s); it requires exactly ' + str(correct_number) + ' argument(s).')\n # in case lambdas are being directly used (then they don't have a name)\n raise TypeError('The procedure has been called with ' + str(error_number) + ' argument(s); it requires exactly ' + str(correct_number) + ' argument(s).') \n\ndef apply_list_procedure(list_operation, args):\n \n \"\"\"\n Applies list operations to given arguments\n \"\"\"\n \n if list_operation == 'cons':\n if len(args) != 2:\n raise_argument_count_error(2, len(args), 'cons')\n if not type(args[1]) == list:\n raise_argument_error(list_operation, TypeError, convert_to_scheme_expression(args[1]))\n return make_list([args[0]] + args[1])\n \n elif list_operation == 'append':\n if not type(args[0]) == list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n if not type(args[1]) == list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n appended_lists = []\n for arg in args:\n appended_lists += arg\n return appended_lists\n \n elif list_operation == 'list':\n return args\n \n if len(args) != 1:\n raise_argument_count_error(1, len(args), list_operation)\n \n if list_operation == 'list?':\n return type(args[0]) == list\n \n if type(args[0]) != list:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n \n if list_operation == 'car':\n # []\n # extra parens because of the [1:] from get_arguments() earlier in PyScheme.py\n # same goes for cdr and null? as well\n if args[0] == []:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n return args[0][0]\n \n elif list_operation == 'cdr':\n if args[0] == []:\n raise_argument_error(list_operation, ValueError, convert_to_scheme_expression(args[0]))\n return args[0][1:]\n \n elif list_operation == 'null?':\n return args[0] == [] \n \n\ndef apply_arithmetic_operator(op, arguments):\n \n \"\"\"\n Applies an arithmetic operator (+, -, /, *) to the arguments.\n Input: An operator type and arguments\n Output: Value after applying operator op to its arguments.\n \"\"\"\n \n running_value = op(arguments[0], arguments[1])\n \n if len(arguments) == 2:\n return running_value\n \n # has more than two arguments, so process them\n remaining_arguments = arguments[2:]\n for argument in remaining_arguments:\n running_value = op(running_value, argument) \n \n return running_value\n \ndef apply_logic_operator(op, arguments):\n \n \"\"\"\n Applies a logic operator (>, <, == etc.) to the arguments\n Input: An operator and arguments\n Output: A True or False boolean value after applying op to its arguments\n \"\"\"\n \n running_value = op(arguments[0], arguments[1])\n \n if len(arguments) == 2:\n return running_value\n \n index = 2\n while running_value and index < len(arguments):\n running_value = running_value and op(arguments[index - 1], arguments[index])\n index += 1\n \n return running_value\n \ndef apply_operators(op, arguments):\n \n # op stands for the operator given as string\n \n \"\"\"\n Applies operator to given arguments (may be more than two; see below)\n \"\"\"\n \n import operator\n \n # checking error in arguments\n for arg in arguments:\n if arg not in (True, False) and op in ('and', 'or', 'not'):\n raise_argument_error(op, TypeError, arg)\n if type(arg) != int and op == 'modulo':\n raise_argument_error(op, TypeError, arg)\n if op not in ('eq?', 'and', 'or', 'not', 'modulo', 'equal?') and type(arg) not in (int, float):\n raise_argument_error(op, TypeError, arg)\n \n if op in ('modulo', 'eq?', 'equal?') and len(arguments) != 2:\n raise_argument_count_error(2, len(arguments), op)\n \n \n if op == 'and':\n current_op = operator.and_\n elif op == 'or':\n current_op = operator.or_\n elif op == 'not':\n if len(arguments) != 1:\n raise_argument_count_error(1, len(arguments), 'not')\n return operator.not_(arguments[0])\n elif op == 'modulo': \n return operator.mod(arguments[0], arguments[1])\n elif op == 'eq?':\n if type(arguments[0]) == str and type(arguments[1]) == str:\n return arguments[0] == arguments[1]\n else:\n return id(arguments[0]) == id(arguments[1])\n elif op == 'equal?':\n if type(arguments[0]) in (int, float) and type(arguments[1]) in (int, float): \n if type(arguments[1]) != type(arguments[0]):\n return False\n else:\n return arguments[0] == arguments[1]\n return str(arguments[0]) == str(arguments[1])\n \n # find the type of the operator\n if op == '+':\n current_op = operator.add\n elif op == '-':\n current_op = operator.sub\n elif op == '*':\n current_op = operator.mul\n elif op == '/':\n current_op = operator.div\n elif op == '=':\n current_op = operator.eq\n elif op == '<':\n current_op = operator.lt\n elif op == '>':\n current_op = operator.gt\n elif op == '<=':\n current_op = operator.le\n elif op == '>=':\n current_op = operator.ge\n \n if op in ['+', '-', '*', '/']:\n return apply_arithmetic_operator(current_op, arguments)\n \n return apply_logic_operator(current_op, arguments)\n ","repo_name":"aashishsatya/PyScheme","sub_path":"PrimitiveProcedures.py","file_name":"PrimitiveProcedures.py","file_ext":"py","file_size_in_byte":8892,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"74098004108","text":"#coding:utf8\nimport time\nimport pandas as pd\nimport lightgbm as lgb\nfrom sklearn.metrics import log_loss\nimport matplotlib.pyplot as plt\nimport warnings\n\n\n#将unix时间戳value改为指定的format格式\ndef timestamp_datetime(value):\n format = '%Y-%m-%d %H:%M:%S'\n value = time.localtime(value)\n dt = time.strftime(format, value)\n return dt\n\n#数据预处理\ndef convert_data(data):\n #将data里面的'context_timestamp'列属性换算成2018-09-09 13:59:59格式\n data['time'] = data.context_timestamp.apply(timestamp_datetime)\n #截取2018-09-09 13:59:59格式对应位置的数值\n data['day'] = data.time.apply(lambda x: int(x[8:10]))\n data['hour'] = data.time.apply(lambda x: int(x[11:13]))\n data['minute'] = data.time.apply(lambda x: int(x[14:16]))\n data['second'] = data.time.apply(lambda x: int(x[17:]))\n #不同user_id有197693条\n #groupby()方法能总结出不重复的按指定columns分组的记录，此处意为某用户在某一天的数据,共229465条\n #.size()方法能总结出groupby之后，数据出现的次数，也就是某用户在某一天浏览（或购买）过的商品次数\n #.reset_index()方法能给groupby.size之后的df重新设置索引，从零开始\n #.rename方法将size（）方法生成的列标签\"0\"改为user_query_day\n user_query_day = data.groupby(['user_id', 'day']).size(\n ).reset_index().rename(columns={0: 'user_query_day'})\n\n #merge介绍url\n #https://blog.csdn.net/weixin_37226516/article/details/64137043\n #‘left’只的是以data里面的columns为基准对齐\n data = pd.merge(data, user_query_day, 'left', on=['user_id', 'day'])\n user_query_day_hour = data.groupby(['user_id', 'day', 'hour']).size().reset_index().rename(\n columns={0: 'user_query_day_hour'})\n data = pd.merge(data, user_query_day_hour, 'left',\n on=['user_id', 'day', 'hour'])\n\n return data\n\n\nif __name__ == \"__main__\":\n #忽略警告\n warnings.filterwarnings(\"ignore\")\n online = False# 这里用来标记是 线下验证 还是 在线提交\n\n data = pd.read_csv('round1_ijcai_18_train_20180301.txt', sep=' ')\n data.drop_duplicates(inplace=True)\n data['item_category_list'] = data['item_category_list'].map(lambda x: int(str(x).split(';')[1]))\n #data = convert_data(data)\n item_category_list_index_time=[]\n\n #sort已弃用，要用sort_index或者sort_values\n #data = data[['user_id','item_category_list','time','is_trade']]\n data.sort_values(by=['user_id','item_category_list','context_timestamp'], ascending=[0, 1,2], inplace=True)\n #data=data.reset_index()\n #print(data.reset_index())\n data = data.reset_index(drop=True)\n\n # print(data.user_id[0])\n index_user=1\n index_item=1\n new_ss=[]\n\n for row in data.iterrows():\n if (row[1]['user_id']==index_user) and (row[1]['item_category_list']==index_item):\n continue\n else:\n index_user=row[1]['user_id']\n index_item=row[1]['item_category_list']\n s= data[(data.user_id==row[1]['user_id']) &(data.item_category_list==row[1]['item_category_list'])]\n if len(s)==1:\n new_ss.append(1)\n else:\n for new_s in range((len(s)-1)):\n new_ss.append(2)\n new_ss.append(3)\n # print(new_ss)\n\n data=pd.concat([data,pd.DataFrame({'item_sees':new_ss})],axis=1)\n data.to_csv('2new_data.csv',index=False,sep=' ')\n print(data)\n\n\n\n","repo_name":"zhmi1204/alimama","sub_path":"submited_plan/user_sorted_by_frequency_of_item_id_1_2_3.py","file_name":"user_sorted_by_frequency_of_item_id_1_2_3.py","file_ext":"py","file_size_in_byte":3503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31626695333","text":"import numpy as np\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.utils.validation import check_X_y, check_array, check_is_fitted\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import f1_score\n\n\nclass RandomForestPredictor(RandomForestClassifier, TransformerMixin):\n \"\"\"\n Classification with randomized decision trees\n \"\"\"\n def __init__(self, n_estimators=20, n_jobs=4,\n class_weight='balanced_subsample', debug=False):\n self.debug = debug\n super(RandomForestPredictor, self).__init__(\n n_estimators=n_estimators, n_jobs=n_jobs, class_weight=class_weight)\n\n def fit(self, X, y):\n X, y = check_X_y(X, y, multi_output=True)\n super(RandomForestPredictor, self).fit(X, y)\n if self.debug:\n print(\"Fitting {} samples:\".format(X.shape[0]))\n print(\"\\t- feature importance: {}\".format(self.feature_importances_))\n return self\n\n def predict(self, X):\n check_is_fitted(self, [\"estimators_\"])\n X = check_array(X)\n prediction = super(RandomForestPredictor, self).predict(X)\n if self.debug:\n print(\"Predicting {} samples\".format(X.shape[0]))\n return prediction.astype(int)\n\n def score(self, X, y):\n prediction = self.predict(X)\n score = f1_score(prediction, y, average='micro')\n if self.debug:\n print(\"Score for {} samples: {}\".format(X.shape[0], score))\n return score\n","repo_name":"puttak/ETH-ml-project","sub_path":"ml_project/models/classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40582364305","text":"# -*- coding: utf-8 -*-\n# __author__ = 'moli.zhou'\nimport datetime\nimport random\nfrom biocluster.api.database.base import Base, report_check\nimport re\nfrom biocluster.config import Config\nfrom pymongo import MongoClient\nimport gridfs\nfrom mainapp.libs.param_pack import param_pack\nfrom bson import ObjectId\n\n\nclass SgPaternityTest(Base):\n '''\n 将亲子鉴定的结果内容存入数据库中\n '''\n def __init__(self, bind_object):\n super(SgPaternityTest, self).__init__(bind_object)\n # self.mongo_client = Config().mongo_client\n # self.database = self.mongo_client[Config().MONGODB+'_paternity_test']\n # self.mongo_client_ref = Config().biodb_mongo_client\n # self.database_ref = self.mongo_client_ref['sanger_paternity_test_ref'] # 正式机的参考库\n self._project_type = \"pt\"\n\n @report_check\n def add_sg_father(self,dad,mom,preg,batch_id,member_id,type=None):\n '''\n 添加father主表，一个批次有多少个样本就有多少个主表\n :param dad:父本id\n :param mom:母本id\n :param preg:胎儿id\n :param batch_id:批次表的_id\n :param member_id:前端传入的用户id\n :return:返回值为主表的_id\n '''\n temp_d = re.search(\"WQ([0-9]*)-F.*\", dad)\n temp_m = re.search(\".*-(M.*)\", mom)\n temp_s = re.search(\".*-(S.*)\", preg)\n if type == 'free': # 自由交互的主表需要完整记录样本名\n name = 'check_' + dad + '_' + mom + '_' + preg\n else:\n name = dad + \"-\" + temp_m.group(1) + \"-\" + temp_s.group(1)\n # 信息增加modify by zhouxuan 20170705\n\n pt_collection = self.db[\"sg_pt_customer\"]\n # -T 表示重上机信息不变 # modify by zhouxuan 20170728\n # 根据现在的样本名绑定家系信息中的name方便查找家系信息\n if temp_d and temp_m and temp_s:\n if re.match('(.*-T)([0-9])', dad):\n dad_ = ('-').join(dad.split('-')[:-1])\n else:\n dad_ = dad\n if re.match('(.*-T)([0-9])', mom):\n mom_ = ('-').join(mom.split('-')[:-1])\n temp_m_ = re.search(\".*-(M.*)\", mom_)\n else:\n temp_m_ = temp_m\n message_id = dad_ + \"-\" + temp_m_.group(1) # 只有父本和母本的名字\n else:\n message_id = 'free_WQ'\n # 对于自由组合而言，只有正确的家系生成的message_id才会有用\n\n result = pt_collection.find_one({\"name\": message_id}) # 自由组合可能没有这个\n if result:\n report_status = result['report_status']\n accept = result['accept_time']\n if report_status == '是':\n report_status = '1'\n else:\n report_status = '0'\n time = accept.split('-')\n accept_time = datetime.datetime(int(time[0]), int(time[1]), int(time[2]), 0, 0)\n report_time = accept_time + datetime.timedelta(days=5)\n else: # 当自由组合找不到的时候，\n report_status = '0'\n report_time = datetime.datetime.now()\n self.bind_object.logger.info('该家系信息不全，请查看：{}'.format(message_id))\n # 这边不需要raise 可以忍受有错误，一方面前面已经判断过一次了，所以不用再次进行判断，另一方面自由组合不需要一定符合家系信息\n if len(str(report_time.month)) == 1:\n ti = str(report_time.year) + '0' + str(report_time.month)\n else:\n ti = str(report_time.year) + str(report_time.month)\n if len(str(report_time.day)) == 1:\n ti = ti + '0' + str(report_time.day)\n else:\n ti += str(report_time.day)\n insert_data = {\n \"dad_id\": dad,\n \"mom_id\": mom,\n \"preg_id\": preg,\n \"family_id\": temp_d.group(1),\n \"name\": name,\n \"created_ts\": datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n \"batch_id\": ObjectId(batch_id),\n \"member_id\": member_id,\n \"message_id\": message_id,\n \"report_time\": ti,\n \"report_status\": report_status,\n }\n try:\n collection = self.db['sg_father']\n father_id = collection.insert_one(insert_data).inserted_id\n except Exception as e:\n self.bind_object.logger.error('导入家系主表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入家系主表成功\")\n return father_id\n\n def add_father_result(self,father_id,pt_father_id, dad_id):\n '''\n 将最终的分析匹配结果和交互表id添加到主表当中去，方便网页展示时取数据以及筛选数据\n :param father_id:father主表的_id\n :param pt_father_id:pt_father交互表的_id\n :param dad_id:父本id\n '''\n self.bind_object.logger.info(\"father主表更新1\")\n collection_result = self.db['sg_pt_father_analysis']\n collection = self.db['sg_father']\n # case = collection.find_one({\"_id\":father_id})\n # dad_id = case['dad_id']\n if collection_result.find_one({'dad_id':dad_id}):\n self.bind_object.logger.info(\"dad_id存在\")\n result_case = collection_result.find_one({'pt_father_id':pt_father_id, \"dad_id\":dad_id})\n else:\n result_case = collection_result.find_one({'pt_father_id': pt_father_id, \"dad_id\": 'NA'})\n self.bind_object.logger.info(\"dad_id为NA\")\n result = result_case['result']\n\n try:\n collection.update({\"_id\": father_id}, {'$set': {\"pt_father_id\": pt_father_id,'result':result}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新father主表结果出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新father主表结果成功\")\n\n def update_infoshow(self, pt_father_id,mom,preg):\n '''\n 如果分析结果有问题，如样本深度不够等等。即在结果表中标记qc字段为不合格\n '''\n collection_result = self.db['sg_pt_father_result_info']\n insert={\n \"pt_father_id\":pt_father_id,\n \"qc\":\"unqualified\",\n \"mom_id\":mom,\n \"preg_id\":preg\n }\n\n try:\n collection_result.insert_one(insert)\n except Exception as e:\n self.bind_object.logger.error('更新有问题的母子信息表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新有问题的母子信息表成功\")\n\n def add_father_qc(self, father_id, pt_father_id):\n '''\n 将分析出的样本qc值加入到主表中，方便页面展示\n '''\n collection_result = self.db['sg_pt_father_result_info']\n collection = self.db['sg_father']\n\n result_case = collection_result.find_one({'pt_father_id': pt_father_id})\n qc = result_case['qc']\n\n try:\n collection.update({\"_id\": father_id}, {'$set': {'qc': qc}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新father主表家系质控出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新father主表家系质控成功\")\n\n def update_sg_pt_father(self, pt_father_id):\n '''\n 流程结束时更新交互主表的状态\n\n '''\n try:\n collection = self.db['sg_pt_father']\n # collection.update({\"_id\": pt_father_id}, {'$set': {\"status\": \"end\"}})\n collection.update({\"_id\": pt_father_id}, {'$set': {\"status\": \"end\"}}, multi=True)\n except Exception as e:\n self.bind_object.logger.error('更新pt_father主表状态出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"更新pt_father主表状态成功\")\n\n\n\n # \"status\": \"end\",\n # @report_check\n def add_pt_father(self, father_id, err_min, dedup):\n '''\n 增加交互主表。第一次运行时自动添加一个（即主表生成时，交互主表也生成）。后在交互页面投递任务时，\n 每一个任务对应一个交互主表。每一个主表可能对应不同的交互主表（视交互次数而定，但至少对应一个）\n '''\n params = dict()\n params['err_min'] = err_min\n params['dedup'] = dedup\n name = 'err-' + str(err_min) + '_dedup-'+ str(dedup)\n insert_data = {\n \"father_id\": father_id,\n \"name\": name,\n \"status\": \"start\"\n }\n\n collection = self.db['sg_pt_father']\n new_params = param_pack(params)\n insert_data[\"params\"] = new_params\n # collection.insert_data[\"params\"] = params\n try:\n pt_father_id = collection.insert_one(insert_data).inserted_id\n # collection.insert_one(insert_data)\n except Exception as e:\n self.bind_object.logger.error('导入交互主表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入交互主表成功\")\n return pt_father_id\n\n @report_check\n def add_sg_ref_file(self,father_id, ref_fasta,targets_bedfile,ref_point,fastq_path):\n '''\n 参考文件的记录\n\n '''\n insert_data={\n \"father_id\": father_id,\n \"ref_fasta\": ref_fasta,\n \"targets_bedfile\": targets_bedfile,\n \"ref_point\": ref_point,\n \"fastq_path\":fastq_path,\n }\n try:\n collection = self.db['sg_pt_ref_file']\n collection.insert_one(insert_data)\n # collection.insert_one(insert_data)\n except Exception as e:\n self.bind_object.logger.error('导入参考文件表出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入参考文件表成功\")\n\n @report_check\n def add_sg_pt_father_detail(self,file_path,pt_father_id):\n '''\n 调试表的导入\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"chrom\":\n continue\n # if line[44] == 'Mis':\n # Mis = '错配'\n # else:\n # Mis = '-'\n if line[8] == 'NA':\n dad_rf = 'NA'\n else:\n dad_rf = round(float(line[8]),8)\n if line[17] == 'NA':\n preg_rf = 'NA'\n else:\n preg_rf = round(float(line[17]),8)\n if line[26] == 'NA':\n mom_rf = 'NA'\n else:\n mom_rf = round(float(line[26]),8)\n\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"chrom\": line[0],\n \"pos\":line[1],\n \"dad_id\": line[2],\n \"dad_ref\": line[3],\n \"dad_alt\": line[4],\n \"dad_dp\": line[5],\n \"dad_ref_dp\": line[6],\n \"dad_alt_dp\": line[7],\n \"dad_rf\": dad_rf,\n \"dad_geno\": line[9],\n \"dad_geno_bases\": line[10],\n \"preg_id\": line[11],\n \"preg_ref\": line[12],\n \"preg_alt\": line[13],\n \"preg_dp\": line[14],\n \"preg_ref_dp\": line[15],\n \"preg_alt_dp\": line[16],\n \"preg_rf\": preg_rf,\n \"preg_geno\": line[18],\n \"preg_geno_bases\": line[19],\n \"mom_id\": line[20],\n \"mom_ref\": line[21],\n \"mom_alt\": line[22],\n \"mom_dp\": line[23],\n \"mom_ref_dp\": line[24],\n \"mom_alt_dp\": line[25],\n \"mom_rf\": mom_rf,\n \"mom_geno\": line[27],\n \"mom_geno_bases\": line[28],\n \"reg\": line[29],\n \"from\": line[30],\n \"to\": line[31],\n \"rs\": line[32],\n \"hapmap_rf\": line[33],\n \"hapmap_geno\": line[34],\n \"n\": line[35],\n \"mj_ref\": line[36],\n \"pA\": line[37],\n \"pG\": line[38],\n \"pC\": line[39],\n \"pT\": line[40],\n \"mj_dp\": line[41],\n \"mj_gene\": line[42],\n \"is_test\": line[43],\n \"is_mis\": line[44],\n \"mustbe\": line[45],\n \"mustnotbe\": line[46],\n \"good\": line[47],\n \"pi\": line[48]\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_detail']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入调试页面表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入调试页面表格成功\")\n\n @report_check\n def add_pt_father_figure(self, file_dir,pt_father_id):\n '''\n 导入结果图片\n '''\n fs = gridfs.GridFS(self.db)\n family_fig = fs.put(open(file_dir + '_family.png', 'r'))\n figure1 = fs.put(open(file_dir + '_fig1.png', 'r'))\n figure2 = fs.put(open(file_dir + '_fig2.png', 'r'))\n preg_percent = fs.put(open(file_dir + '_preg_percent.png', 'r'))\n update_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n 'family_fig': family_fig,\n 'figure1': figure1,\n 'figure2': figure2,\n 'preg_percent': preg_percent\n }\n try:\n collection = self.db['sg_pt_father_figure']\n figure_id = collection.insert_one(update_data).inserted_id\n except Exception as e:\n self.bind_object.logger.error('导入图片表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入图片表格成功\")\n return figure_id\n\n @report_check\n def add_analysis_tab(self, file_path,pt_father_id):\n '''\n 结果信息存入表格，包括测试位点数，有效率无效率等等\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"dad.id\":\n continue\n temp_fp = eval(line[4])\n RCP = temp_fp / (temp_fp + 1)\n if RCP > 0.5:\n rcp_result = \"> 99.99%\"\n else:\n rcp_result = \"< 0.01%\"\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"dad_id\": line[0],\n \"test_pos_n\": line[1],\n \"err_pos_n\": line[2],\n \"err_rate\": line[3],\n \"fq\": line[4],\n \"dp\": line[5],\n \"eff_rate\": line[6],\n \"ineff_rate\": line[7],\n \"result\": line[8],\n \"rcp\": rcp_result,\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_analysis']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入是否匹配表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入是否匹配表格成功\")\n\n @report_check\n def add_info_detail(self, file_path,pt_father_id):\n '''\n 基本信息存入数据库，包括母本胎儿是否匹配，胎儿信号比例等等\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"bed.preg.id\":\n continue\n if line[1] >= 30 and line[0] >= 4 and line[7] >= 95:\n qc = 'qualified'\n else:\n qc = 'unqualified'\n if str(line[7]) == 'NA':\n mom_preg = line[7]\n else:\n if eval(line[7]) >= 95:\n mom_preg = '{} Yes'.format(line[7])\n else:\n mom_preg = '{} No'.format(line[7])\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"preg_id\": line[0],\n \"dp_preg\": line[1],\n \"percent\": line[2],\n \"error\": line[3],\n \"s_signal\": line[4],\n \"mom_id\": line[5],\n \"dp_mom\": line[6],\n \"mom_preg\": mom_preg,\n \"qc\": qc\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_result_info']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入基本信息表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入基本信息表格成功\")\n\n # @report_check\n def add_test_pos(self, file_path, pt_father_id):\n '''\n 测试位点信息导入数据库\n '''\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n for line in f:\n line = line.strip()\n line = line.split('\\t')\n if line[0] == \"检测位点编号\":\n continue\n if line[5] == 'Mis':\n Mis = '错配'\n else:\n Mis = '-'\n insert_data = {\n # \"task_id\": self.bind_object.id,\n \"pt_father_id\": pt_father_id,\n \"test_no\": line[0],\n \"chrom\": line[1],\n \"dad_geno\": line[2],\n \"mom_geno\": line[3],\n \"preg_geno\": line[4],\n \"is_mis\": Mis\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_test_pos']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入位点信息表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入位点信息表格成功\")\n\n def has_problem(self,pt_father_id,dad_id):\n '''\n 如果在分析家系时，有样本质检不过关，此时不绘制结果图，匹配结果字段做异常标记\n '''\n collection = self.db['sg_pt_father_analysis']\n if collection.find_one({'dad_id':dad_id}):\n collection.update({\"pt_father_id\":pt_father_id,'dad_id':dad_id},{\"$set\":{\"result\":'MARK'}}, multi=True)\n else:\n collection.update({\"pt_father_id\": pt_father_id, 'dad_id': 'NA'}, {\"$set\": {\"result\": 'MARK'}}, multi=True)\n\n def check_pt_message(self, family_id_, member_id_, type):\n collection = self.db[\"sg_pt_customer\"]\n if type == 'mom':\n m = collection.find_one({\"pt_serial_number\": family_id_, 'mom_id_': member_id_})\n else:\n m = collection.find_one({\"pt_serial_number\": family_id_, 'dad_id_': member_id_})\n if m:\n return 'True'\n else:\n return 'False'\n\n def import_dedup_data(self, file_path, pt_father_id):\n sg_pt_family_detail = list()\n with open(file_path, 'r') as f:\n data = f.readlines()[1:]\n for line in data:\n line = line.strip().split('\\t')\n temp_fp = eval(line[4])\n RCP = float(temp_fp) / (float(temp_fp) + 1)\n if RCP > 0.5:\n rcp_result = \">99.99%\"\n else:\n rcp_result = \"<0.01%\"\n result = self.ref_db['sg_pt_ref_main'].find_one({\"sample_id\": line[0],\n \"storage_time\": {\"$exists\": True}}) # 正式机\n # result = self.database['sg_pt_ref_main'].find_one({\"sample_id\": line[0],\n # \"storage_time\": {\"$exists\": True}}) # 测试机\n if result:\n dad_time = result['storage_time'] # 改样本的入库时间，之前的都没有(在sanger上跑过的才会有)\n else:\n dad_time = ''\n insert_data = {\n \"pt_father_id\": pt_father_id,\n \"dad_id\": line[0],\n \"test_pos_n\": line[1],\n \"err_pos_n\": line[2],\n \"err_rate\": line[3],\n \"fq\": format(eval(line[4]), '.2e'), # 科学计数法保留两位\n \"dp\": line[5],\n \"eff_rate\": line[6],\n \"ineff_rate\": line[7],\n \"result\": line[8],\n \"rcp\": rcp_result,\n \"dad_time\": dad_time\n }\n sg_pt_family_detail.append(insert_data)\n try:\n collection = self.db['sg_pt_father_analysis']\n collection.insert_many(sg_pt_family_detail)\n except Exception as e:\n self.bind_object.logger.error('导入查重表格出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入查重表格成功\")\n\n def sample_size(self, sample_id, batch_id, tab_none=None):\n collection = self.db['sg_pt_problem_sample']\n # self.mongo_client_ref = Config().biodb_mongo_client # 线上\n # self.database_ref = self.mongo_client_ref['sanger_paternity_test_ref']\n ref_data = self.ref_db['sg_pt_ref_main'].find_one({\"sample_id\": sample_id}) # 线上\n # ref_data = self.database['sg_pt_ref_main'].find_one({\"sample_id\": sample_id}) # 线下\n split_data_name = ref_data[\"split_data_name\"]\n try:\n collection.insert_one({'sample_id': sample_id,\n 'split_data_name': split_data_name,\n 'batch_id': batch_id,\n 'time': datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"),\n 'tab_none': tab_none})\n except Exception as e:\n self.bind_object.logger.error('导入问题样本出错：{}'.format(e))\n raise Exception('导入问题样本出错：{}'.format(e))\n else:\n self.bind_object.logger.info(\"导入问题样本成功\")\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/api/database/sg_paternity_test.py","file_name":"sg_paternity_test.py","file_ext":"py","file_size_in_byte":23551,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"39132338638","text":"#!/usr/bin/python3\n# -*-coding:utf-8-*-\n\n__author__ = \"Bannings\"\n\nimport os, logging, re\n\nif __name__ == '__main__':\n root_directory = os.path.dirname(os.path.abspath(__file__))\n\n # 找出所有的 markdown 文章\n post_directory = os.path.join(root_directory, '_posts')\n posts = [os.path.join(post_directory, file) for file in os.listdir(post_directory) if file[-2:]==\"md\"]\n\n if os.path.exists(\"output.log\"):\n os.remove(\"output.log\")\n logging.basicConfig(filename=\"output.log\", level=logging.DEBUG, format='%(message)s')\n\n # 添加别名\n for post in posts:\n with open(post, \"r+\", encoding=\"utf-8\") as file:\n logging.debug(post)\n lines = file.readlines()\n\n alias = os.path.splitext(post)[0]\n alias = os.path.basename(alias).replace(\"-\", \"/\")\n alias = alias.replace(\" \", \"-\")\n alias = alias.replace(\"#\", \"\")\n lines.insert(2, f\"redirect_from: /{alias}/\\n\")\n \n file.seek(0)\n file.truncate()\n file.writelines(lines)\n file.flush()\n # # 移除多余的文本\n # regex = re.compile(\"本周选择的算法题是：\\[.*?\\]\\(.*?\\)(（.*?）)\")\n # for post in posts:\n # with open(post, \"r+\", encoding=\"utf-8\") as file:\n # logging.debug(post)\n # lines = file.readlines()\n # changed = False\n # for i, line in enumerate(lines):\n # result = regex.match(line)\n # if result:\n # changed = True\n # start, end = result.span(1)\n # lines[i] = line[:start]+line[end:]\n # logging.debug(f\" {i} {lines[i]}\")\n # break\n \n # if changed:\n # file.seek(0)\n # file.truncate()\n # file.writelines(lines)\n # file.flush()\n ","repo_name":"zhangao0086/zhangao0086.github.io","sub_path":"update_front_matter.py","file_name":"update_front_matter.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19401363655","text":"from torch import nn\n\nfrom modules.encoderlayer import EncoderLayer\n\n\nclass Encoder(nn.Module):\n def __init__(self, model_dim, filter_dim, layer_num):\n super(Encoder, self).__init__()\n self.layers = nn.ModuleList([EncoderLayer(model_dim, filter_dim) for i in range(layer_num)])\n\n def forward(self, inputs, attn_mask):\n for layer in self.layers:\n inputs = layer(inputs, attn_mask)\n return inputs","repo_name":"royyoung388/srl","sub_path":"src/modules/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14345481666","text":"# Write a recursive function to check if a string is a palindrome.\n\nword = input(\"Please enter a word yo check if it's a palindrome: \")\n\ndef is_palindrome(w):\n if len(w) <= 1:\n return True\n else:\n if w[0].lower() == w[-1].lower():\n return is_palindrome(w[1:-1])\n else:\n return False\n \nresult = is_palindrome(word)\nprint(result)\n","repo_name":"Mati-Bouchet/entry_level_exercises","sub_path":"Functions/exercise32.py","file_name":"exercise32.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42507809985","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as pl\nfrom sklearn import datasets\n\nclass ANN:\n def __init__(self, inputsize, hiddensize, outputsize):\n \"\"\" setup the network \"\"\"\n # add an additional node to the input and hidden layers as bias nodes\n inputsize += 1\n hiddensize += 1\n\n # thetas hold the weights for connections between nodes \n # theta1 matrix holds weights for connections between input and hidden nodes\n # theta2 matrix for hidden to output nodes\n # initially set the weights randomly\n np.random.seed(123)\n self.theta1 = np.random.normal(0,0.5,[inputsize,hiddensize])\n self.theta2 = np.random.normal(0,0.5,[hiddensize,outputsize])\n\n # change matricies to hold onto the last change in weight for the thetas\n # this is used for incorporating momentum into the weight updates in backprop\n self.theta1change = np.zeros([inputsize,hiddensize])\n self.theta2change = np.zeros([hiddensize,outputsize])\n\n def feedforward(self, x):\n \"\"\" push feature vector x through the network, return each layers output \"\"\"\n # set inputlayer output to x plus 1.0 bias node\n inputlayer = np.append(1.0,np.tanh(x))\n\n # calculate hidden layer vector output, set bias node to 1.0\n z2 = np.dot(inputlayer,self.theta1)\n hiddenlayer = np.tanh(z2)\n hiddenlayer[0] = 1.0\n\n # calculate output layer vector\n z3 = np.dot(hiddenlayer,self.theta2)\n outputlayer = np.tanh(z3)\n\n return inputlayer,hiddenlayer,outputlayer\n\n def backpropagation(self, targets, inputlayer, hiddenlayer, outputlayer, alpha, momentum):\n \"\"\" utilize backprop to update theta1 and theta2 weights \"\"\"\n # alpha is the learning rate, or how much to update theta per training\n # momentum is the what we add to the theta change to prevent getting stuck in a local minimum\n\n # dtanh returns the derivative of the tanh function\n dtanh = lambda y: 1.0 - y ** 2\n\n # calculate the errors between the expected result and the result of the output and hidden layers\n # delta matricies determine how much and in what direction to \"correct\" weights \n outputerrors = targets - outputlayer\n outputdeltas = dtanh(outputlayer) * outputerrors\n\n hiddenerrors = np.dot(outputdeltas,self.theta2.T)\n hiddendeltas = dtanh(hiddenlayer) * hiddenerrors\n\n # for each theta:\n # use the deltas to calculate the change gradient\n # update the weights for thetas to correct the errors\n change = np.array(np.matrix(hiddenlayer).T * np.matrix(outputdeltas))\n self.theta2 = self.theta2 + (alpha * change) + (momentum * self.theta2change)\n self.theta2change = change\n\n change = np.array(np.matrix(inputlayer).T * np.matrix(hiddendeltas))\n self.theta1 = self.theta1 + (alpha * change) + (momentum * self.theta1change)\n self.theta1change = change\n\n def predict(self, x):\n \"\"\" given feature vector x return the learned outputlayer \"\"\"\n inputlayer,hiddenlayer,outputlayer = self.feedforward(x)\n return outputlayer\n \n def train(self, x, y, alpha=0.5, momentum=0.3):\n \"\"\" train a single example (x) with expected output (y) \"\"\"\n # the learning rate (alpha) and the momentum values may need to be adjusted so they are not too high/low\n\n # first get the outputs of all the layers from pushing x through the network\n inputlayer,hiddenlayer,outputlayer = self.feedforward(x)\n # then use backprop to adjust the weights so the network's output is closer to y\n self.backpropagation(y,inputlayer,hiddenlayer,outputlayer,alpha,momentum)\n\n\ndef digits():\n \"\"\" teach the neural network what digits look like \"\"\"\n # use the digits dataset from the sklearn library\n # the images are 8x8 bitmaps of handwritten digits {0,9}\n # when 'unrolled' each image becomes a 1x64 matrix\n digits = datasets.load_digits()\n X = digits.data\n Y = digits.target\n\n classes = list(set(Y))\n\n # use each pixel as an input node\n # using 12 hidden nodes/neurons\n # output layer contains 10 nodes, one for each digit\n inputsize = X.shape[1]\n hiddensize = 12\n outputsize = len(classes)\n ann = ANN(inputsize,hiddensize,outputsize)\n\n # for this example, im only training with the first 12 examples\n for n in xrange(400):\n for i in xrange(12):\n x,y = X[i],Y[i]\n target = np.zeros(len(classes))\n target[classes.index(y)] = 1\n ann.train(x,target,alpha=0.1,momentum=0.2)\n\n # see how well the trained examples were learned\n for i in xrange(12):\n x,y = X[i],Y[i]\n results = ann.predict(x)\n prediction = results.argmax()\n pl.subplot(3,4,i+1)\n color = pl.cm.gray if prediction == y else pl.cm.Reds_r\n pl.imshow(digits.images[i], cmap=color)\n pl.title('Predicted=%i vs. Actual=%i' % (prediction,y))\n\n pl.show()\n\nif __name__ == '__main__':\n digits()\n","repo_name":"liamgriffiths/learning","sub_path":"ann.py","file_name":"ann.py","file_ext":"py","file_size_in_byte":5095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25435724815","text":"import os\nimport sys\nimport time\nimport json\nimport logging\nfrom functools import partial\n\nfrom feed import bitstamp\nfrom datasink import Datasink, stdout_logger\n\n\nCONFIG_FILE = 'tick.conf'\n\n\ndef write_tick_to_sink(record, sink):\n rec = json.loads(record)\n fields = ['id', 'price', 'amount', 'timestamp']\n msg = ','.join([str(rec[field]) for field in fields])\n sink.write(msg)\n\n\ndef main(\n *,\n root='cryptle-exchange/bitstamp-tick',\n pairs=('btcusd', 'bchusd', 'ethusd', 'xrpusd'),\n resolution=Datasink.MINUTE,\n backend='os'\n ):\n header = ['id', 'price', 'amount', 'time']\n header = ','.join(header)\n ext = 'csv'\n\n # Prepare sinks\n sinks = {}\n for pair in pairs:\n sinks[pair] = Datasink(\n root='-'.join([root, pair]),\n ext=ext,\n header=header,\n namemode=2,\n resolution=resolution,\n backend=backend,\n )\n\n conn = bitstamp.BitstampFeed()\n conn.connect()\n\n for pair in pairs:\n conn.onTrade(pair, partial(write_tick_to_sink, sink=sinks[pair]))\n\n while True:\n try:\n while conn.is_connected():\n time.sleep(0.2)\n except ConnectionError:\n # reconnect\n conn.connect()\n except KeyboardInterrupt:\n print('\\rTerminating...')\n conn.close()\n return 0\n except Exception:\n logging.error('Uncaught exception %s', e)\n return 1\n\n\nif __name__ == '__main__':\n config = {}\n if os.path.isfile(CONFIG_FILE):\n with open(CONFIG_FILE) as f:\n for line in f:\n name, var = line.partition('=')[::2]\n config[name.strip()] = var.strip()\n logging.basicConfig(level=logging.INFO)\n stdout_logger()\n sys.exit(main(**config))\n","repo_name":"pinealan/crypto-data","sub_path":"scripts/tick.py","file_name":"tick.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"69810621709","text":"from cgitb import text\nfrom sqlite3 import Cursor\nfrom tkinter import*\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom PIL import Image,ImageTk\nfrom tkinter import messagebox\nimport mysql.connector\nimport cv2\nimport os\nimport numpy as np\n# from main import Face_Recognition_System\n\n\nclass TrainImages:\n def __init__(self,root):\n self.root=root\n self.root.geometry(\"1530x790+0+0\")\n self.root.title(\"train Images\")\n\n \n butn_frame=Frame(self.root,bd=2,pady=3,relief=RIDGE,bg=\"white\")\n butn_frame.place(x=0,y=200,width=660,height=100)\n\n back_button=Button(self.root,text=\"Back\",command=self.Back_to_main,cursor=\"hand2\")\n back_button.place(x=1265,y=100,width=90,height=30)\n\n train_Button=Button(butn_frame,command=self.trainClassifier,text=\"Train Data\",font=(\"times new roman\",13,\"bold\"),padx=10,width=14,bg=\"blue\",fg=\"white\")\n train_Button.grid(row = 0,column = 0)\n\n \n\n\n def trainClassifier(self):\n data_dir=(\"Student faces\")\n path=[os.path.join(data_dir,file) for file in os.listdir(data_dir)]\n\n faces=[]\n ids=[]\n for image in path:\n img=Image.open(image).convert('L') # gray scale image\n imageNp=np.array(img,'uint8')\n id=int(os.path.split(image)[1].split('.')[1])\n\n\n faces.append(imageNp)\n ids.append(id)\n cv2.imshow(\"Training\",imageNp)\n cv2.waitKey(1)==13\n ids=np.array(ids)\n\n\n\n # ************************* train classifier save****************\n clf=cv2.face.LBPHFaceRecognizer_create()\n clf.train(faces,ids)\n clf.write(\"classifier.xml\")\n cv2.destroyAllWindows()\n messagebox.showinfo(\"Result\",\"Training datasets completed!!\")\n\n def Back_to_main(self):\n # self.old_window=Toplevel(self.root)\n # cv2.destroyAllWindows()\n self.root.destroy()\n \n\n\n\n\n\n\nif __name__==\"__main__\":\n root=Tk()\n obj=TrainImages(root)\n root.mainloop()\n","repo_name":"jeevandaka/Face_recognition","sub_path":"trainImages.py","file_name":"trainImages.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24454942747","text":"import cv2\nimport matplotlib.pyplot as plt\n\nimg = cv2.imread('./images/street.jpeg', cv2.IMREAD_GRAYSCALE)\nret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n# kernel\nkernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))\n# closing\nclosed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n# plotting\nfig, axs = plt.subplots(1, 2, figsize=(10, 5))\naxs[0].imshow(thresh, cmap='gray')\naxs[0].set_title('Binary Image')\naxs[1].imshow(closed, cmap='gray')\naxs[1].set_title('Closed Image')\nplt.show()\n","repo_name":"Raaulsthub/ImgProcessingStudies","sub_path":"codes/morph/closing.py","file_name":"closing.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"37517192695","text":"# -*- coding: utf-8 -*-\r\n# when : 2021.09.09\r\n# who : [sori-machi]\r\n# what : \r\n# *日射量(地上/衛星)、出力、アメダス積雪深の関係性\r\n# *冬季期間における、富山・福井の日射量影響の把握/冬季期間においては、積雪により、日射量が過小評価になりがちなことを、plotlyで確認。\r\n#---------------------------------------------------------------------------\r\n# basic-module\r\nimport matplotlib.pyplot as plt\r\nimport sys,os,re,glob\r\nfrom matplotlib import rcParams\r\nrcParams['font.family'] = 'sans-serif'\r\nrcParams['font.size'] = 18\r\nrcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic', 'IPAPGothic', 'VL PGothic', 'Noto Sans CJK JP']\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datetime import datetime, timedelta\r\nimport warnings\r\nwarnings.simplefilter('ignore')\r\n\r\n# \r\nimport matplotlib.colors as mcolors\r\n_color = [ mcolors.rgb2hex(plt.cm.get_cmap('tab10')(i)) for i in range(10)]\r\n\r\nfrom tqdm import tqdm\r\nimport seaborn as sns\r\nimport math\r\n# https://www.python.ambitious-engineer.com/archives/1140\r\n#---------------------------------------------------\r\n# sori -module\r\nsys.path.append('/home/ysorimachi/tool')\r\nfrom getErrorValues import me,rmse,mae,r2 #(x,y)\r\n# from convSokuhouData import conv_sfc #(df, ave=minutes,hour)\r\nfrom convAmedasData import conv_amd\r\n#amedas relate 2020.02,04 making...\r\nfrom tool_AMeDaS import code2name, name2code\r\nfrom tool_110570 import get_110570,open_110570\r\nfrom tool_100571 import get_100571,open_100571\r\n#(code,ini_j,out_d)/(code,path,csv_path)\r\n#---------------------------------------------------\r\nimport subprocess\r\nfrom utils_plotly import plotly_2axis#(df,col1,col2,html_path, title=\"sampe\"):\r\n# outd ='/home/griduser/work/sori-py2/deep/out/0924_01'\r\n# os.makedirs(outd, exist_ok=True)\r\n# subprocess.run('rm -f *.png', cwd=outd, shell=True)\r\n#------------------------\r\n# 2021.09.09 \r\nsys.path.append(\"..\")\r\nfrom tmp.amedas import get_List\r\nfrom teleme.reg_PU_001 import load_teleme\r\nfrom smame.reg_PU_001 import load_smame,get_a_b_c_d #(month,cate) #(\"surplus\",\"month\")\r\nfrom teleme.utils_teleme import teleme_max#(code=None,cate =\"max\")\r\nfrom utils import load_rad#(month=\"201904\",cate=\"obs\", lag=30)\r\n\r\n#------------------------\r\n# from mapbox import map_lonlat_multi#(_df,html_path,size=4,zoom=4)\r\nfrom mapbox import map_lonlat2,map_lonlat# (_df,text=\"name\",html_path,size=4,zoom=4)\r\n\r\nDHOME=\"/work/ysorimachi/hokuriku/snow_hosei/rad210524\" #8now0/sfc\r\nDAT_1MIN=\"/home/ysorimachi/work/hokuriku/dat/rad/111600_2sites\"\r\nTMP=\"/home/ysorimachi/work/hokuriku/tmp/tmp210524\"\r\npoint_hash = {\"cpnt15\": \"47607\",\"cpnt18\": \"47616\"}\r\nname_hash = {\"cpnt15\": \"TOYAMA\",\"cpnt18\": \"FUKUI\"}\r\n\r\n#settig loop\r\ndef load_month(isWinter=False):\r\n if isWinter:\r\n return list([202012,202101,202102,202103])\r\n else:\r\n return list([202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103])\r\n \r\n\r\n# set\r\n\r\ndef check_rad(code,month):\r\n \"\"\"\r\n 2021.05.24 \r\n 個別地点、個別の月において、日射量と積雪について確認する\r\n \"\"\"\r\n #local function ---------------\r\n def clensing(df):\r\n for c in [\"rad\",\"H0\",\"8now0\"]:\r\n df[c] = df[c].apply(lambda x: np.nan if x< 0 or x>1400 else x)\r\n df = df.dropna(subset=[\"rad\",\"H0\",\"8now0\"])\r\n return df\r\n \r\n def load_sfc(scode,month):\r\n path = f\"{DHOME}/sfc/sfc_10minh_{month}_{scode}.csv\"\r\n df = pd.read_csv(path)\r\n df = conv_sfc(df, ave=False)\r\n df[\"time\"] = df[\"time\"].apply(lambda x:x.strftime(\"%Y%m%d%H%M\"))\r\n df = df[[\"time\",\"snowDepth\"]]\r\n df = df.replace(9999,np.nan)\r\n return df\r\n \r\n df = pd.read_csv(f\"{DHOME}/dataset/{code}_{month}.csv\")\r\n df[\"time\"] = df[\"time\"].astype(str)\r\n # df = clensing(df)\r\n scode = point_hash[code]\r\n ame = load_sfc(scode,month)\r\n \r\n # print(df)\r\n df = df.merge(ame, on=\"time\",how=\"inner\")\r\n df[\"time\"] = pd.to_datetime(df[\"time\"])\r\n html_path = f\"{TMP}/{code}_{month}_rad_SNOW.html\"\r\n plotly_2axis(df,[\"rad\",\"8now0\"],[\"snowDepth\"],html_path, title=f\"{code}_{month}_rad_SNOW\")\r\n print(df.head())\r\n sys.exit()\r\n\r\n\r\ndef plot_map():\r\n \"\"\"\r\n 2021.09.09 : 日射量/アメダス観測点を表示する\r\n \"\"\"\r\n df = get_List()\r\n df[\"flg\"] = df[\"name\"].isnull()\r\n df.loc[df[\"flg\"]==False, \"cate\"] = \"日\"\r\n print(df[\"cate\"].unique())\r\n _df,_text = [],[]\r\n for i,c in enumerate(df[\"cate\"].unique()):\r\n tmp = df[df[\"cate\"]==c]\r\n tmp[\"color\"] = i\r\n _df.append(tmp)\r\n _text.append(c)\r\n \r\n df = pd.concat(_df,axis=0)\r\n # df[\"text\"] = df[\"code\"].astype(str) + \"-\" + df[\"cate\"] + \"-\" + df[\"name\"]\r\n df[\"text\"] = df[\"code\"].astype(str) +\"-\" + df[\"cate\"]\r\n # print(df.head(50))\r\n # sys.exit()\r\n\r\n out_d= \"/home/ysorimachi/work/hokuriku/out/snow/html\"\r\n html_path = f\"{out_d}/map_amedas.html\"\r\n # map_lonlat_multi(_df,_text,html_path)\r\n map_lonlat(df,html_path =html_path, text=\"text\",size=4,size_col=\"color\",zoom=4)\r\n\r\n\r\n\r\n\r\n#---------------------------------\r\n#-----------------\r\ndef get_snw(month,code):\r\n local = \"/home/ysorimachi/work/hokuriku/dat/snow/amedas\"\r\n # path = f\"{local}/amd_10minh_{month}_{code}.csv\"\r\n # if not os.path.exists(path):\r\n # subprocess.run(\"sh amd_get.sh {} {} {}\".format(month,code,local), shell=True)\r\n # else:\r\n # print(f\"already get ..{month} {code}\")\r\n if 202004 <= int(month) <= 202006:\r\n path = f\"{local}/snow_2004.csv\"\r\n elif 202007 <= int(month) <= 202009:\r\n path = f\"{local}/snow_2007.csv\"\r\n elif 202010 <= int(month) <= 202012:\r\n path = f\"{local}/snow_2010.csv\"\r\n elif 202101 <= int(month) <= 202103:\r\n path = f\"{local}/snow_2101.csv\"\r\n else:\r\n path = \"not found\"\r\n df = pd.read_csv(path)\r\n \r\n for c in df.columns[1:]:\r\n df[c] = df[c].apply(lambda x: x if x>0 else 0)\r\n \r\n df[\"time\"] = pd.to_datetime(df[\"time\"])\r\n df = df.set_index(\"time\")\r\n if code == \"55056\":\r\n return df[\"魚津\"]\r\n if code == \"55151\":\r\n return df[\"富山\"]\r\n if code == \"56286\":\r\n return df[\"白山河内\"]\r\n\r\ndef loop_month(st = \"201904\", ed=\"202104\"):\r\n _t = pd.date_range(start = f\"{st}300000\",end = f\"{ed}300000\", freq=\"M\")\r\n _t = [ t.strftime(\"%Y%m\") for t in _t]\r\n _t = _t[:-1]\r\n return _t\r\n\r\ndef get_pv(cate,month,pv_name):\r\n #--------------\r\n if cate == \"teleme\":\r\n df = load_teleme(month)\r\n max_val = teleme_max(pv_name)\r\n \r\n df[\"max\"] = max_val\r\n df = df[[pv_name,\"max\"]]\r\n df.columns = [\"PV\",\"max\"]\r\n return df\r\n\r\n if cate == \"surplus\":\r\n # 事前に、/home/ysorimachi/work/hokuriku/py/smame のdetails_smame2.pyを実行して、対象地点のみの合算ファイルを作成しておくことが必要\r\n DHOME_TMP=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/tmp_smame_month\"\r\n path = f\"{DHOME_TMP}/{cate}_{month}.csv\"\r\n df = pd.read_csv(path)\r\n df[\"time\"]= df[\"time\"].astype(str)\r\n df[\"time\"] = df[\"time\"].apply(lambda x: x[0:8] + \"0000\" if x[8:10] == \"24\" else x)\r\n df[\"time\"] = pd.to_datetime(df[\"time\"].astype(str))\r\n df = df.set_index(\"time\")\r\n df[\"sum\"] *=2 #30分間隔なので\r\n # df = df.rename(columns={\"sum\":\"PV\"})\r\n return df[[\"sum\",\"max\"]]\r\n\r\n\r\ndef details_effect(NAME):\r\n if NAME==\"AREA001\":\r\n rad_name,pv_name,snow_code,cate = \"unyo001\",\"telm007\",\"55056\",\"teleme\"\r\n if NAME==\"AREA002\":\r\n rad_name,pv_name,snow_code,cate = \"unyo001\",\"telm007\",\"55056\",\"teleme\"\r\n if NAME==\"AREA003\":\r\n rad_name,pv_name,snow_code,cate = \"unyo012\",\"telm007\",\"56286\",\"surplus\"\r\n \r\n return rad_name,pv_name,snow_code,cate\r\n\r\ndef snow_effect(month=\"202103\"):\r\n \"\"\"\r\n 2021 .09.09\r\n 積雪深が個別のPV出力に影響を与えていたのかを調査\r\n \"\"\"\r\n HTML_HOME=\"/home/ysorimachi/work/hokuriku/out/snow/html\"\r\n # area setting ---------\r\n # NAME=\"AREA001\"\r\n NAME=\"AREA003\"\r\n radname = \"obs\"\r\n \r\n rad_name,pv_name,snow_code,cate = details_effect(NAME)\r\n png_title = f\"{NAME}({rad_name}/{pv_name}/{snow_code})\"\r\n # print(png_title)\r\n # sys.exit()\r\n \r\n # mk dataset ---------\r\n sn_df= get_snw(month,snow_code)# dataget\r\n pv_df = get_pv(cate,month,pv_name) #teleme&smame\r\n\r\n rad_df = load_rad(month=month,cate=radname, lag=30) #rad_\r\n df = pd.concat([rad_df[rad_name],pv_df,sn_df],axis=1)\r\n \r\n df.columns = [\"rad\",\"PV\",\"max\",\"snow\"]\r\n df[\"snow\"] = df[\"snow\"].fillna(method = \"pad\")\r\n df = df.dropna()\r\n \r\n # calc -----\r\n df[\"p.u\"] = df[\"PV\"]/df[\"max\"]\r\n df[\"rad\"] /=1000\r\n \r\n DOUT=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/point\"\r\n df.to_csv(f\"{DOUT}/{NAME}_{month}.csv\")\r\n \r\n if 0: #plotly \r\n df = df.reset_index()\r\n html_path = f\"{HTML_HOME}/ts_{NAME}.html\"\r\n plotly_2axis(df,[\"rad\",\"p.u\"],[\"snow\"],html_path, title=png_title, vmax=1)\r\n \r\n if 0: #png\r\n df = df.reset_index()\r\n png_d =\"/home/ysorimachi/work/hokuriku/out/snow/png\"\r\n from plot1m import plot1m_2axis#(df,_col,_sub_col=False,month=False,_ylim=[0,1000,0,100],title=False,step=6)\r\n f = plot1m_2axis(df,_col=[\"rad\",\"p.u\"],_sub_col=[\"snow\"],month=month,_ylim=[0,1.1,0,120],title=False,step=6)\r\n f.savefig(f\"{png_d}/ts_{month}_{NAME}.png\",bbox_inches=\"tight\")\r\n print(png_d, month)\r\n return\r\n\r\ndef plot_pu(NAME):\r\n \"\"\"\r\n 2021.09.12\r\n 事前にデータセットを作成して置く必要がある\r\n \"\"\"\r\n DHOME=\"/home/ysorimachi/work/hokuriku/dat/snow/csv/point\"\r\n png_d =\"/home/ysorimachi/work/hokuriku/out/snow/png\"\r\n \r\n _path = sorted(glob.glob(f\"{DHOME}/{NAME}*.csv\"))\r\n _df = [pd.read_csv(path) for path in _path]\r\n df = pd.concat(_df,axis=0)\r\n N=df.shape[0]\r\n \r\n # f,ax = plt.subplots(1,3,figsize=(18,5))\r\n f,ax = plt.subplots(figsize=(9,9))\r\n \r\n _h = [0,5,20]\r\n \r\n ax.scatter(df[\"rad\"],df[\"p.u\"],color=\"gray\", s=1, alpha=0.3,label=\"全データ\")\r\n for i,h in enumerate(_h):\r\n tmp = df[df[\"snow\"]>h]\r\n title = f\"積雪別日射量とp.uの関係\"\r\n percent = np.round(tmp.shape[0]*100/N,1)\r\n color = _color[i]\r\n if i==2:\r\n size = 50\r\n marker=\"o\"\r\n color=\"r\"\r\n else:\r\n size = 12\r\n marker=\"o\"\r\n \r\n ax.scatter(tmp[\"rad\"],tmp[\"p.u\"],color=color, s=size,marker=marker, alpha=1, label=f\"SNOW({h}cm超-{percent}[%])\")\r\n \r\n \r\n if 1:\r\n a,b,c,d = get_a_b_c_d(\"202101\",\"8now0\")\r\n _x = np.linspace(0,1,1000)\r\n _y = a*_x**3 +b*_x**2 + c*_x + d\r\n ax.plot(_x, _y, color=\"g\", lw=2, label=\"p.u回帰曲線(1月)\")\r\n # print(a,b,c,d)\r\n # sys.exit()\r\n \r\n ax.plot(_x, _x, color=\"k\", lw=1)\r\n ax.set_xlabel(\"日射量[kW/m2]\") \r\n ax.set_ylabel(\"p.u[-]\") \r\n ax.set_xlim(0,1) \r\n ax.set_ylim(0,1)\r\n ax.set_title(title)\r\n plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0, fontsize=18)\r\n \r\n f.savefig(f\"{png_d}/scatter_{NAME}.png\", bbox_inches=\"tight\") \r\n \r\n print(\"DIRECTR\",png_d)\r\n sys.exit()\r\n \r\n\r\n\r\nif __name__ == \"__main__\":\r\n #---------------------------------------\r\n # #all months -------------\r\n if 0:\r\n plot_map()\r\n \r\n if 0: #\"make dataset \r\n for month in loop_month()[12:]:\r\n # month=\"202010\"\r\n snow_effect(month=month)\r\n print(datetime.now(), \"[END]\", month)\r\n # sys.exit()\r\n if 1:\r\n NAME=\"AREA001\" #teleme\r\n NAME=\"AREA003\" #smame\r\n plot_pu(NAME)\r\n ","repo_name":"soriiieee/analysis_stock","sub_path":"py_hokuriku/py/snow/tmp_210909.py","file_name":"tmp_210909.py","file_ext":"py","file_size_in_byte":11210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"4738531426","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom collections import OrderedDict\n\n\nclass _DenseLayer(nn.Sequential):\n def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):\n super(_DenseLayer, self).__init__()\n self.add_module('norm1', nn.BatchNorm3d(num_input_features)),\n self.add_module('relu1', nn.ReLU(inplace=True)),\n self.add_module('conv1', nn.Conv3d(num_input_features, bn_size *\n growth_rate, kernel_size=1, stride=1, bias=False)),\n self.add_module('norm2', nn.BatchNorm3d(bn_size * growth_rate)),\n self.add_module('relu2', nn.ReLU(inplace=True)),\n self.add_module('conv2', nn.Conv3d(bn_size * growth_rate, growth_rate,\n kernel_size=3, stride=1, padding = 1, bias=False)),\n self.drop_rate = drop_rate\n\n def forward(self, x):\n new_features = super(_DenseLayer, self).forward(x)\n if self.drop_rate > 0:\n new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)\n return torch.cat([x, new_features], 1)\n\n\nclass _DenseBlock(nn.Sequential):\n def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):\n super(_DenseBlock, self).__init__()\n for i in range(num_layers):\n layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)\n self.add_module('denselayer%d' % (i + 1), layer)\n\n\nclass _Strider(nn.Sequential):\n def __init__(self, num_input_features, num_output_features):\n super(_Strider, self).__init__()\n self.add_module('norm', nn.BatchNorm3d(num_input_features))\n self.add_module('relu', nn.ReLU(inplace=True))\n self.add_module('conv', nn.Conv3d(num_input_features, num_output_features,\n kernel_size=3, stride=1, bias=False)) ## reduce the size of the feature map\n self.add_module('pool', nn.Conv3d(num_output_features, num_output_features,\n kernel_size=2, stride=2)) ## removed the average pooling layer.\n\n\n# class OutputTransition(nn.Module): ##### original vnet implementation\n# def __init__(self, inChans, nll=True):\n# super(OutputTransition, self).__init__()\n# self.conv1 = nn.Conv3d(inChans, 2, kernel_size=5, padding=2)\n# self.bn1 = nn.BatchNorm3d(2)\n# self.conv2 = nn.Conv3d(2, 2, kernel_size=1)\n# self.relu1 = nn.ReLU(inplace=True)\n# if nll:\n# self.softmax = F.log_softmax\n# else:\n# self.softmax = F.softmax\n\n# def forward(self, x):\n# # convolve 32 down to 2 channels\n# out = self.relu1(self.bn1(self.conv1(x)))\n# out = self.conv2(out)\n\n# # make channels the last axis\n# out = out.permute(0, 2, 3, 4, 1).contiguous()\n# # flatten\n# out = out.view(out.numel() // 2, 2)\n# out = self.softmax(out)\n# # treat channel 0 as the predicted output\n# return out\n\nclass OutputTransition(nn.Module):\n def __init__(self, inChans, out_number):\n super(OutputTransition, self).__init__()\n self.bn1 = nn.BatchNorm3d(inChans)\n self.relu1 = nn.ReLU(inplace=True)\n self.conv1 = nn.Conv3d(inChans, out_number, kernel_size=5)\n self.conv3 = nn.Conv3d(4, out_number, kernel_size=2)\n\n\n def forward(self, x):\n out = self.conv1(self.relu1(self.bn1(x)))\n # out = self.conv3(self.relu1(out))\n # print (out)\n return out\n\nclass PhenotypeLayer(nn.Module):\n \"\"\"docstring for PhenotypeLayer\"\"\"\n def __init__(self):\n super(PhenotypeLayer, self).__init__()\n self.layer1_c = nn.Linear(80, 32)\n self.layer1_a = nn.Linear(1, 32)\n self.layer1_t = nn.Linear(1, 32)\n self.layer2 = nn.Linear(32, 2)\n\n def forward(self, _class, _age, _tiv):\n out_c = self.layer1_c(_class)\n out_a = self.layer1_a(_age)\n out_t = self.layer1_t(_tiv)\n out = out_c + out_t + out_a\n out = self.layer2(out)\n return out\n\nclass DenseNet3D(nn.Module):\n \"\"\"Densenet-BC model class, based on\n `\"Densely Connected Convolutional Networks\" `_\n\n Args:\n growth_rate (int) - how many filters to add each layer (`k` in paper)\n block_config (list of 4 ints) - how many layers in each pooling block\n num_init_features (int) - the number of filters to learn in the first convolution layer\n bn_size (int) - multiplicative factor for number of bottle neck layers\n (i.e. bn_size * k features in the bottleneck layer)\n drop_rate (float) - dropout rate after each dense layer\n out_number (int) - number of classification classe\n \"\"\"\n def __init__(self, growth_rate=4, block_config=(1, 2, 3),\n num_init_features=8, bn_size=4, drop_rate=0.2, out_number=10):\n\n super(DenseNet3D, self).__init__()\n\n # First convolution\n self.features = nn.Sequential(OrderedDict([\n ('conv0', nn.Conv3d(1, num_init_features, kernel_size=3, stride=1, padding=2, bias=False)),\n ('norm0', nn.BatchNorm3d(num_init_features)),\n ('relu0', nn.ReLU(inplace=True)),\n ('pool0', nn.AvgPool3d(kernel_size=3, stride=2, padding=1)),# Average Pooling layer\n ]))\n\n # Each denseblock\n num_features = num_init_features\n for i, num_layers in enumerate(block_config):\n block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,\n bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)\n self.features.add_module('db%d' % (i + 1), block)\n num_features = num_features + num_layers * growth_rate\n if i != len(block_config) - 1:\n trans = _Strider(num_input_features=num_features, num_output_features=num_features // 2)\n self.features.add_module('strider%d' % (i + 1), trans)\n num_features = num_features // 2\n\n # Final batch norm\n # self.features.add_module('norm5', nn.BatchNorm3d(num_features)) ### added OutputTransition\n\n # Linear layer\n # self.Linear_classifier = nn.Linear(8*8*8, num_classes)\n self.classifier = OutputTransition(num_features, out_number)\n\n # Official init from torch repo.\n for m in self.modules():\n if isinstance(m, nn.Conv3d):\n nn.init.kaiming_normal(m.weight.data)\n elif isinstance(m, nn.BatchNorm3d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n elif isinstance(m, nn.Linear):\n m.bias.data.zero_()\n\n def forward(self, x, age, tiv):\n # print (\"DATA: \", x.size())\n features = self.features(x)\n # print (\"features: \", features.size())\n out = F.relu(features, inplace=True)\n out = self.classifier(out)\n # print (\"classifier: \", out.size())\n out = out.view(out.size(0), -1)\n # print (\"linear: \", out.size())\n out = PhenotypeLayer().cuda()(out, age, tiv)\n # print (\"phType: \", out.size())\n out = F.softmax(out)\n return out\n","repo_name":"koriavinash1/PAC18","sub_path":"src/DensenetModels.py","file_name":"DensenetModels.py","file_ext":"py","file_size_in_byte":7300,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"72993383627","text":"import tkinter as tk\nfrom tkinter.filedialog import askopenfilename\nfrom design import *\nfrom crypt import encrypt_message, decrypt_message\nimport datetime\n\n\nroot = tk.Tk()\nroot.title('Final Project')\nroot.geometry('1080x720')\nroot.resizable(False,False)\nroot.configure(bg=WINDOW_BACKGROUND)\n\n#Frame Creations:\nleft_frame = tk.Frame(root, bg=WINDOW_BACKGROUND)\nleft_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=tk.YES)\n\nright_frame = tk.Frame(root, bg=WINDOW_BACKGROUND)\nright_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=tk.YES)\n\n#Title Creations:\ntitle_encryption = tk.Label(left_frame, text='Encryption', font=TITLE_DESIGN)\ntitle_encryption.pack(side=tk.TOP)\n\ntitle_entry_encrypt = tk.Label(left_frame, text = 'Text to Encrypt: ',font=SECONDARY_TITLE_DESIGN)\ntitle_entry_encrypt.pack(side=tk.TOP)\n\ntitle_decryption = tk.Label(right_frame, text='Decryption', font=TITLE_DESIGN)\ntitle_decryption.pack(side=tk.TOP)\n\ntitle_entry_decrypt = tk.Label(right_frame, text = 'Text to Decrypt: ',font=SECONDARY_TITLE_DESIGN)\ntitle_entry_decrypt.pack(side=tk.TOP)\n\n#Entry Creations\nvariable_encrypt = tk.StringVar(left_frame)\nentry_encrypt = tk.Entry(left_frame, width=72,\n textvariable=variable_encrypt)\nentry_encrypt.pack(side=tk.TOP)\n\nvariable_decrypt = tk.StringVar(right_frame)\nentry_decrypt = tk.Entry(right_frame, width=72,\n textvariable=variable_decrypt)\nentry_decrypt.pack(side=tk.TOP)\n\n#Results:\nresult_text_encrypt = tk.Label(left_frame, text=\"\", font=TEXT_DESIGN, bg=WINDOW_BACKGROUND)\nresult_text_encrypt.pack(side=tk.TOP, pady=100)\n\nresult_text_decrypt = tk.Label(right_frame, text=\"\", font=TEXT_DESIGN, bg=WINDOW_BACKGROUND)\nresult_text_decrypt.pack(side=tk.TOP, pady=100)\n\n#Functions of Buttons/File Saves:\ndef save_file(folder_name, text_to_write):\n file_name = str(datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n file_to_write = open(f'{folder_name}\\\\{file_name}.txt', 'w')\n file_to_write.write(f'{text_to_write}')\n file_to_write.close()\n if 'encrypt' in folder_name.lower():\n result_text_encrypt.configure(text=f\"Encrypt Text Saved!\")\n elif 'decrypt' in folder_name.lower():\n result_text_decrypt.configure(text=f\"Decrypt Text Saved!\")\n\ndef action_encrypt_text(input_type, output_type, text_to_encrypt=None):\n if input_type == 'text':\n encrypted_text = encrypt_message(text_to_encrypt)\n if output_type == \"text\":\n result_text_encrypt.configure(text=encrypted_text)\n elif output_type == \"file\":\n save_file('EncryptedMessages', encrypted_text)\n elif input_type =='file':\n file_to_open = askopenfilename()\n file_to_read = open(file_to_open, 'r')\n file_content = file_to_read.read()\n file_to_read.close()\n if output_type == \"text\":\n encrypted_text = encrypt_message(file_content)\n result_text_encrypt.configure(text=encrypted_text)\n elif output_type == \"file\":\n encrypted_text = encrypt_message(file_content)\n save_file('EncryptedMessages', encrypted_text)\n\n\ndef action_decrypt_text(input_type, output_type, text_to_decrypt=None):\n if input_type == 'text':\n decrypted_text = decrypt_message(text_to_decrypt)\n if output_type == \"text\":\n result_text_decrypt.configure(text=decrypted_text)\n elif output_type == \"file\":\n save_file('DecryptedMessages', decrypted_text)\n elif input_type =='file':\n file_to_open = askopenfilename()\n file_to_read = open(file_to_open, 'r')\n file_content = file_to_read.read()\n file_to_read.close()\n if output_type == \"text\":\n decrypted_text = decrypt_message(file_content)\n result_text_decrypt.configure(text=decrypted_text)\n elif output_type == \"file\":\n decrypted_text = decrypt_message(file_content)\n save_file('DecryptedMessages', decrypted_text)\n\n\n\n#Button Creations:\n#Encrypt Buttons:\nencrypt_text_to_text = tk.Button(left_frame, text='Encrypt Text!',\n font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n text_to_encrypt=variable_encrypt.get(),\n input_type='text',\n output_type='text'\n ))\nencrypt_text_to_text.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_to_file = tk.Button(left_frame, text='Encrypt Text to File!',\n bg=ENCRYPT_BUTTON_BACKGROUND,\n font=BUTTON_DESIGN,\n command=lambda: action_encrypt_text(\n text_to_encrypt=variable_encrypt.get(),\n input_type='text',\n output_type='file'\n ))\nencrypt_to_file.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_from_file = tk.Button(left_frame, text='Encrypt from File!', font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n input_type='file',\n output_type='text'\n ))\nencrypt_from_file.pack(side=tk.TOP, padx=10, pady = 20)\n\nencrypt_from_file_to_file = tk.Button(left_frame, text='Encrypt from File to Another File',\n font=BUTTON_DESIGN,\n bg=ENCRYPT_BUTTON_BACKGROUND,\n command=lambda: action_encrypt_text(\n input_type='file',\n output_type='file'\n ))\nencrypt_from_file_to_file.pack(side=tk.TOP, padx=10, pady = 20)\n\n#Decrypt Buttons:\ndecrypt_text = tk.Button(right_frame, text='Decrypt Text!', font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='text',\n output_type='text',\n text_to_decrypt=variable_decrypt.get()\n ))\ndecrypt_text.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_text_to_file = tk.Button(right_frame, text='Decrypt Text to File!', font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='text',\n output_type='file',\n text_to_decrypt=variable_decrypt.get()\n ))\ndecrypt_text_to_file.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_from_file = tk.Button(right_frame,text=\"Decrypt from File!\",font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='file',\n output_type='text',\n ))\ndecrypt_from_file.pack(side=tk.TOP, padx=10, pady=20)\n\ndecrypt_from_file_to_file = tk.Button(right_frame,text=\"Decrypt from File to Another File!\",font=BUTTON_DESIGN,\n bg=DECRYPT_BUTTON_BACKGROUND,\n command=lambda: action_decrypt_text(\n input_type='file',\n output_type='file'\n ))\ndecrypt_from_file_to_file.pack(side=tk.TOP, padx=10, pady=20)\n\nroot.mainloop()","repo_name":"JLessons/Transposition","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12714830653","text":"orders = int(input())\ntotal_order = 0\n\nfor order in range(1, orders + 1):\n price_per_capsule = float(input())\n days = int(input())\n capsules_per_day = int(input())\n if price_per_capsule < 0.01 or price_per_capsule > 100:\n continue\n elif days < 1 or days > 31:\n continue\n elif capsules_per_day < 1 or capsules_per_day > 2000:\n continue\n else:\n price_order = price_per_capsule * days * capsules_per_day\n total_order += price_order\n print(f\"The price for the coffee is: ${price_order:.2f}\")\nprint(f\"Total: ${total_order:.2f}\")\n","repo_name":"BorislavRaynov/SoftUniModules","sub_path":"02_fundamentals_python/05_basic_syntax_conditional_statements_and_loops/05_exercises/05_orders.py","file_name":"05_orders.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27911673445","text":"\"\"\"\nPlotting functions.\n\"\"\"\nimport typing as tp\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\nimport numpy as np\nfrom . import errors as err\n\n\ndef plot_multiple(data_x, *args, **kwargs):\n \"\"\"\n Plot multiple data curves against a same x-axis on mulitple subplots.\n\n Arguments:\n datax (darray): the data point on the x-axis.\n *args: each entry of args is a list containing multiple sets of data\n and parameters that will be plotted in the same subplot.\n An entry should follow the format `(data_1, param_1, ...)`,\n where each of the `data_i` is a numpy array, and each of the\n `param_i` is a `dict` of the parameters for ploting `data_i` against\n `data_x`. Alternatively, an entry can simply be an numpy array. In\n this case, only one curve will be plotted in the corresponding\n subplot.\n\n Keyword Arguments:\n figw (float): the figure width.\n figh (float): the figure height.\n xlabel (str): the label of the x-axis.\n\n The additional keyword arguments will propagate into the private\n plotting method `_plot`, and eventually into the `pyplot.plot` method.\n \"\"\"\n\n def _plot(axe, data_x, data_y, **kwargs):\n \"\"\"\n Arguments:\n axe (matplotlib.Axes.axe): the axe of the subplot.\n data_x (darray): the data point along the x-axis.\n data_y (darray): the data point along the y-axis.\n\n Keyword Arguments:\n xlim (tuple): a tuple-like with two entries of limits of the x-axis.\n ylim (tuple): a tuple-like with two entries of limits of the y-axis.\n spike (bool): specify if `data_y` is a spike sequence.\n ylabel (str): the label of the y-axis.\n ds_rate (int): the downsample rate of the data.\n\n The additional keyword arguments will propagate into the\n `pyplot.plot` method. For example, one could use `label` to add a\n legend to a curve.\n \"\"\"\n xlim = kwargs.pop(\"xlim\", None)\n ylim = kwargs.pop(\"ylim\", None)\n spike = kwargs.pop(\"spike\", False)\n ylabel = kwargs.pop(\"ylabel\", None)\n ds_rate = kwargs.pop(\"ds_rate\", None)\n\n if spike:\n ylim = [0, 1.2]\n ylabel = ylabel or \"Spike Train\"\n axe.yaxis.set_ticklabels([\" \"])\n\n if ds_rate is not None:\n data_x = data_x[::ds_rate]\n data_y = data_y[::ds_rate]\n\n axe.plot(data_x, data_y, **kwargs)\n\n if xlim:\n axe.set_xlim(xlim)\n if ylim:\n axe.set_ylim(ylim)\n if ylabel:\n axe.set_ylabel(ylabel)\n\n figw = kwargs.pop(\"figw\", 5)\n figh = kwargs.pop(\"figh\", 2)\n xlabel = kwargs.pop(\"xlabel\", \"Time, [s]\")\n\n num = len(args)\n\n fig, axes = plt.subplots(num, 1, figsize=(figw, num * figh))\n\n if not hasattr(axes, \"__len__\"):\n axes = [axes]\n\n for i, (dataset, axe) in enumerate(zip(args, axes)):\n axe.grid()\n if i < num - 1:\n axe.xaxis.set_ticklabels([])\n\n if isinstance(dataset, np.ndarray):\n param_list = [{}]\n data_list = [dataset]\n else:\n param_list = dataset[1::2]\n data_list = dataset[0::2]\n\n has_legend = False\n for data_y, subkwargs in zip(data_list, param_list):\n for key, val in kwargs.items():\n if not key in subkwargs:\n subkwargs[key] = val\n has_legend = has_legend or (\"label\" in subkwargs)\n _plot(axe, data_x, data_y, **subkwargs)\n if has_legend:\n axe.legend()\n\n axes[-1].set_xlabel(xlabel)\n plt.tight_layout()\n\n return fig, axes\n\n\ndef plot_spikes(\n spikes: np.ndarray,\n dt: float = None,\n t: np.ndarray = None,\n ax: plt.Axes = None,\n markersize: int = None,\n color: tp.Union[str, tp.Any] = \"k\",\n) -> plt.Axes:\n \"\"\"\n Plot Spikes in raster format\n Arguments:\n spikes: the spike states in binary format, where 1 stands for a spike.\n The shape of the spikes should either be (N_times, ) or (N_trials, N_times)\n dt: time resolution of the time axis.\n t: time axes for the spikes, use arange if not provided\n\n .. note::\n\n If `t` is specified, it is assumed to have the same\n length as `mat.shape[1]`, which is used to find the x coordinate of\n the spiking values of the data. If `t` is\n not specified, the time-axis is formated by resolution `dt`.\n `dt` is assumed to be 1 if not specified.\n\n ax: which axis to plot into, create one if not provided\n markersize: size of raster\n color: color for the raster. Any acceptable type of `matplotlib.pyplot.plot`'s\n color argument is accepted.\n Returns:\n ax: the axis that the raster is plotted into\n \"\"\"\n spikes = np.atleast_2d(spikes)\n if spikes.ndim != 2:\n raise err.NeuralPlotError(\n f\"matrix need to be of ndim 2, (channels x time), got ndim={spikes.ndim}\"\n )\n\n if t is not None:\n if len(t) != spikes.shape[1]:\n raise err.NeuralPlotError(\n \"Time vector 't' does not have the same shape as the matrix.\"\n f\" Expected length {spikes.shape[1]} but got {len(t)}\"\n )\n else:\n if dt is None:\n dt = 1.0\n else:\n if not np.isscalar(dt):\n raise err.NeuralPlotError(\"dt must be a scalar value.\")\n t = np.arange(spikes.shape[1]) * dt\n\n if ax is None:\n fig = plt.gcf()\n ax = fig.add_subplot()\n\n neu_idx, t_idx = np.nonzero(spikes)\n\n try:\n ax.plot(t[t_idx], neu_idx, \"|\", c=color, markersize=markersize)\n except ValueError as e:\n raise err.NeuralPlotError(\n \"Raster plot failed, likely an issue with color or markersize setting\"\n ) from e\n except IndexError as e:\n raise err.NeuralPlotError(\n \"Raster plot failed, likely an issue with spikes and time vector mismatch\"\n ) from e\n except Exception as e:\n raise err.NeuralPlotError(\"Raster plot failed due to unknown error\") from e\n ax.set_xlim([t.min(), t.max()])\n return ax\n\n\ndef plot_mat(\n mat: np.ndarray,\n dt: float = None,\n t: np.ndarray = None,\n ax: plt.Axes = None,\n cax=None,\n vmin: float = None,\n vmax: float = None,\n cbar_kw: dict = None,\n cmap: tp.Any = None,\n) -> tp.Union[tp.Tuple[plt.Axes, tp.Any], plt.Axes]:\n \"\"\"\n Plot Matrix with formatted time axes\n\n Arguments:\n mat: the matrix to be plotted, it should of shape (N, Time)\n dt: time resolution of the time axis.\n t: time axes for the spikes, use arange if not provided.\n\n .. note::\n\n If `t` is specified, it is assumed to have the same\n length as `mat.shape[1]`. Consequently, the x-axis will be formatted\n to take the corresponding values from `t` based on index. If `t` is\n not specified, the time-axis is formated by resolution `dt`.\n If neither are specified, `dt` is assumed to be 1.\n\n ax: which axis to plot into, create one if not provided\n cax: which axis to plot colorbar into\n - if instance of axis, plot into that axis\n - if is True, steal axis from `ax`\n vmin: minimum value for the imshow\n vmax: maximum value for the imshow\n cbar_kw: keyword arguments to be passed into the colorbar creation\n cmap: colormap to use\n\n Returns:\n ax: the axis that the raster is plotted into\n cbar: colorbar object\n - only returned if cax is `True` or a `plt.Axes` instance\n\n Example:\n >>> dt, dur, start, stop = 1e-4, 2, 0.5, 1.0\n >>> t = np.arange(0, dur, dt)\n >>> amps = np.arange(0, 100, 10)\n >>> wav = utils.generate_stimulus('step', dt, dur, (start, stop), amps)\n >>> ax,cbar = plot_mat(wav, t=t, cax=True, vmin=10, vmax=100, cbar_kw={'label':'test'}, cmap=plt.cm.gnuplot)\n >>> ax, = plot_mat(wav, t=t, cax=False, vmin=10, vmax=100, cbar_kw={'label':'test'}, cmap=plt.cm.gnuplot)\n \"\"\"\n mat = np.atleast_2d(mat)\n if mat.ndim != 2:\n raise err.NeuralPlotError(\n \"matrix need to be of ndim 1 (N_time),or ndim 2 (N_trials x N_times),\"\n f\" got ndim={mat.ndim}\"\n )\n if t is not None:\n if len(t) != mat.shape[1]:\n raise err.NeuralPlotError(\n \"Time vector 't' does not have the same shape as the matrix.\"\n f\" Expected length {mat.shape[1]} but got {len(t)}\"\n )\n\n @ticker.FuncFormatter\n def major_formatter(x, pos):\n return \"{:.1f}\".format(np.interp(x, np.arange(len(t)), t))\n\n else:\n if dt is None:\n dt = 1\n\n @ticker.FuncFormatter\n def major_formatter(x, pos):\n return \"{:.1f}\".format(dt * x)\n\n if ax is None:\n fig = plt.gcf()\n ax = fig.add_subplot()\n\n cim = ax.imshow(\n mat,\n aspect=\"auto\",\n interpolation=\"none\",\n origin=\"lower\",\n vmin=vmin,\n vmax=vmax,\n cmap=cmap,\n )\n ax.xaxis.set_major_formatter(major_formatter)\n\n if cax:\n if cbar_kw is None:\n cbar_kw = {}\n if not isinstance(cax, plt.Axes):\n cbar = plt.colorbar(cim, ax=ax, **cbar_kw)\n else:\n cbar = plt.colorbar(cim, cax, **cbar_kw)\n return ax, cbar\n else:\n return (ax,)\n\n\ndef yyaxis(ax: plt.Axes, c: \"color\" = \"red\") -> plt.Axes:\n \"\"\"Create A second axis with colored spine/ticks/label\n\n Note:\n This method will only make the twinx look like the color in\n MATLAB's :code:`yyaxis` function. However, unlike in MATLAB,\n it will not set the linestyle and linecolor of the lines that\n are plotted after twinx creation.\n\n Arguments:\n ax: the main axis to generate a twinx from\n c: color of the twinx, see https://matplotlib.org/stable/gallery/color/color_demo.html\n for color specifications accepted by matplotlib.\n \"\"\"\n ax2 = ax.twinx()\n ax2.spines[\"right\"].set_color(c)\n ax2.tick_params(axis=\"y\", colors=c)\n ax2.yaxis.label.set_color(c)\n return ax2\n","repo_name":"chungheng/neural","sub_path":"neural/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":10345,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"27424444671","text":"\r\n# Write a program that accepts multiple number of sentences as input and prints the lines after making all characters in the sentence capitalized.\r\n\r\ndef MutipleInput():\r\n p,q =input(\"Enter your First String: \\n\"),input(\"Enter the Second String:\\n\").capitalize()\r\n words = p.upper()\r\n wordCount = q.upper()\r\n print(wordCount,words)\r\nMutipleInput()\r\n\r\n#2) Write a program that accepts a sequence of whitespace separated words as input and prints the words after removing all duplicate words and sorting them alphanumerically.\r\n\r\nfrom collections import OrderedDict\r\n\r\ndef Duplicat(string):\r\n word= (' '.join(OrderedDict((w,w) for w in string.split()).keys()))\r\n told=word.split()\r\n told.sort()\r\n return \"\".join(told)\r\nstring=\"hello world and practice makes perfect and hello world again\"\r\nprint(Duplicat(string))\r\n\r\n\r\n#3) We have a count 35 heads and 94 legs among the chickens and rabbits in a farm. How many rabbits and how many chickens do we have? Write a program to get the answer,\r\n\r\ndef RabbitChicken(sum,leg):\r\n for rabbit in range(sum+1): #According to the first equation:- x+y=34\r\n chicken=sum-rabbit #According to the second equation:- 4x +2y=94\r\n if 2*chicken+4*rabbit==leg: #Multiply the first equation by 2\r\n return chicken,rabbit\r\n return None,None\r\n\r\nif __name__ == '__main__':\r\n try:\r\n heads=int(input(\"Enter the number of head:\\n\"))\r\n legs=int(input(\"Enter the number of leg:\\n\"))\r\n res=RabbitChicken(heads,legs)\r\n print(\"number of rabbit %d and number of chicken%d\"%res)\r\n except TypeError:\r\n print(\"invalid\")\r\n\r\n\r\n#4) Create a function that accepts single list containing letters or may be words. Total number of elements in a list may vary. In turn, it counts the number of occurrences in a list for each element and returns user a dictionary with total number of counts for each element. Please remember to include case-sensitive match i.e. 'user1' is not equal to 'User1' word.\r\n\r\ndef show(mylist):\r\n dict1 = {} # empty dictionary\r\n for item in mylist:\r\n if (item in dict1):\r\n dict1[item] += 1\r\n else:\r\n dict1[item] = 1\r\n for key, value in dict1.items():\r\n print (\"% s : % s\"%(key, value))\r\n\r\nif __name__ == \"__main__\":\r\n mylist =['python', 'pyhton3', 'user1', 'assignment', 'user', 'user1', 'python', 'User1']\r\n show(mylist)\r\n\r\n#5) Create a function that accepts a list containing integers. Total number of elements in list may vary. Your method should return back the list removing duplicates from list. So lets say if user passes a following list to your function as input:\r\n\r\n\r\ndef hack(mylsit):\r\n res=[]\r\n for i in mylsit:\r\n if i not in res:\r\n res.append(i)\r\n return res\r\nmylist= [1,2,55,1,3,2,34,55]\r\nprint(hack(mylist))","repo_name":"saveplanet18/-Ashesh-Sutha","sub_path":"Ashes Suthar.py","file_name":"Ashes Suthar.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"16710995034","text":"from django.shortcuts import render\nfrom .models import Cities\nfrom .serializers import CitiesSerializer, CitiesGeoJSONSerializer\nfrom rest_framework import viewsets\nfrom rest_framework_gis.filters import InBBoxFilter,TMSTileFilter,DistanceToPointFilter\n\nclass CitiesViewSet(viewsets.ModelViewSet):\n queryset = Cities.objects.all()\n serializer_class = CitiesSerializer\n\n\ndef home(request):\n allcities = Cities.objects.all()\n contenxt = {\n 'allcities':allcities\n }\n return render(request, 'home.html',contenxt)\n\n\nclass CitiesGeoJSONViewSet(viewsets.ModelViewSet):\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer \n\n\nclass CitiesInBBOX(viewsets.ModelViewSet):\n\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer\n bbox_filter_field = 'geometry'\n filter_backends = (InBBoxFilter,)\n bbox_filter_include_overlapping = True # Optional\n\nclass CitiesInTMS(viewsets.ModelViewSet):\n\n queryset = Cities.objects.all()\n serializer_class = CitiesGeoJSONSerializer\n bbox_filter_field = 'geometry'\n filter_backends = (TMSTileFilter,)\n bbox_filter_include_overlapping = True # Optional\n","repo_name":"krishnaglodha/spatial-apis-25-min","sub_path":"pokestar/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22342504654","text":"import pm4py\n\n\ndef execute_script():\n # example where the log skeleton is manullay built, and not automatically discovered from the log.\n\n log = pm4py.read_xes(\"../tests/input_data/running-example.xes\")\n\n log_skeleton = {\"always_after\": set(), \"always_before\": set(), \"equivalence\": set(), \"never_together\": set(),\n \"directly_follows\": set(), \"activ_freq\": dict()}\n\n for act in pm4py.get_event_attribute_values(log, \"concept:name\"):\n # initially sets that every activity of the log can occur from 0 to 10 times\n # (without this constraints, conformance checking will signal deviations for every event)\n log_skeleton[\"activ_freq\"][act] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n\n # sets that the 'reinitiate request' activity should not occur (so it occurs 0 times)\n log_skeleton[\"activ_freq\"][\"reinitiate request\"] = {0}\n\n # sets that the 'pay compensation' activity should occur somewhen after the 'decide' activity.\n log_skeleton[\"always_after\"].add(('decide', 'pay compensation'))\n\n # gets the conformance checking results. The first describes for each case of the log the exact deviations\n detailed_conf_results = pm4py.conformance_log_skeleton(log, log_skeleton)\n print(detailed_conf_results)\n\n # the second provides a summary (as a dataframe) of the fitness per case\n summary_df = pm4py.conformance_log_skeleton(log, log_skeleton, return_diagnostics_dataframe=True)\n print(summary_df)\n\n\nif __name__ == \"__main__\":\n execute_script()\n","repo_name":"pm4py/pm4py-core","sub_path":"examples/log_skeleton_manual_constraints.py","file_name":"log_skeleton_manual_constraints.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":604,"dataset":"github-code","pt":"82"} +{"seq_id":"6347014062","text":"# --------------------\r\n# (S)GD on Stiefel manifold in `A feasible method for optimization with orthogonality constraints'\r\n# (https://link.springer.com/article/10.1007/s10107-012-0584-1)\r\n# This algorithm is termed as `Momentumless Stiefel SGD' in our paper\r\n# --------------------\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch.optim import Optimizer\r\nimport torch.nn as nn\r\nimport numpy as np\r\nimport math\r\nfrom torch import Tensor\r\nfrom typing import List, Optional\r\n# torch.set_default_tensor_type(torch.DoubleTensor)\r\n\r\nclass _RequiredParameter(object):\r\n \"\"\"Singleton class representing a required parameter for an Optimizer.\"\"\"\r\n def __repr__(self):\r\n return \"\"\r\n\r\nrequired = _RequiredParameter()\r\n\r\nclass MomentumlessStiefelSGD(Optimizer):\r\n def __init__(self, params, lr=required, method='NAG-SC', other_params=None, if_cayley=True):\r\n r'''\r\n Arguments:\r\n net: must be a plain fully connected nn. Recommand generated with class OrthogonalNN\r\n gamma: gamma in the AISTAT paper (momentum)\r\n\r\n '''\r\n if lr is not required and lr < 0.0:\r\n raise ValueError(\"Invalid learning rate: {}\".format(lr))\r\n\r\n defaults = dict(lr=lr, method=method, other_params=other_params, if_cayley=if_cayley)\r\n super(MomentumlessStiefelSGD, self).__init__(params, defaults)\r\n def __setstate__(self, state):\r\n super(MomentumlessStiefelSGD, self).__setstate__(state)\r\n\r\n @torch.no_grad()\r\n \r\n def step(self):\r\n \"\"\"Performs a single optimization step.\r\n\r\n buf: xi in algorithm 2\r\n p: R in algotithm 2\r\n\r\n Arguments:\r\n closure (callable, optional): A closure that reevaluates the model\r\n and returns the loss.\r\n \"\"\"\r\n loss = None\r\n\r\n for group in self.param_groups:\r\n lr = group['lr']\r\n \r\n for p_raw in group['params']:\r\n if p_raw.grad is None:\r\n continue\r\n p=p_raw.view(p_raw.size()[0],-1)\r\n p_grad=p_raw.grad.view(p_raw.size()[0],-1)\r\n if p.shape[0] 16:\n print(\"Grade 16+\")\nelse:\n print(\"Grade: \", index)","repo_name":"Minta-Ra/CS50x_2021","sub_path":"Pset_6/Readability/readability.py","file_name":"readability.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41465313936","text":"import torch\nimport torch.nn as nn\nfrom torchsummary import summary\n\n\nimport torch.nn as nn\n\n\nclass ResnetBlock(nn.Module):\n expansion = 1\n\n def __init__(self, in_channels, out_channels, stride=1, padding='same'):\n super(ResnetBlock, self).__init__()\n self.conv1 = nn.Conv2d(\n in_channels,\n out_channels,\n kernel_size=3,\n stride=stride,\n padding=padding,\n bias=False,\n )\n self.bn1 = nn.BatchNorm2d(out_channels)\n self.conv2 = nn.Conv2d(\n out_channels,\n out_channels,\n kernel_size=3,\n stride=stride,\n padding=padding,\n )\n self.bn2 = nn.BatchNorm2d(out_channels)\n\n self.shortcut = nn.Sequential()\n\n if stride != 1 or in_channels != self.expansion * out_channels:\n self.shortcut = nn.Sequential(\n nn.Conv2d(\n in_channels,\n self.expansion * out_channels,\n kernel_size=1,\n stride=stride,\n bias=False,\n ),\n nn.BatchNorm2d(self.expansion * out_channels),\n )\n\n def forward(self, x):\n out = nn.ReLU()(self.bn1(self.conv1(x)))\n out = self.bn2(self.conv2(out))\n out += self.shortcut(x)\n out = nn.ReLU()(out)\n return out\n\n\nclass ResnetBackBone(nn.Module):\n def __init__(self):\n\n \n super(ResnetBackBone, self).__init__()\n \n # Conv1_x\n self.conv1_1 = nn.Conv2d(\n 1, 32, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n self.conv1_2 = nn.Conv2d(\n 32, 64, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n\n # Conv2_x\n self.conv2_pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)\n self.conv2_resnet1 = ResnetBlock(in_channels=64, out_channels=128)\n # self.conv2_resnet2 = ResnetBlock(in_channels=128, out_channels=128)\n self.conv2_1 = nn.Conv2d(\n 128, 128, stride=(1, 1), padding=(1, 1), kernel_size=(3, 3)\n )\n\n # Conv3_x\n self.conv3_pool = nn.MaxPool2d(2, stride = 2, padding = 0)\n self.conv3_1 = ResnetBlock(in_channels=128, out_channels=256)\n self.conv3_2 = ResnetBlock(in_channels=256, out_channels=256)\n\n # Conv4_x\n self.conv4_pool = nn.MaxPool2d(2, stride=(2,1), padding=(0,1))\n self.conv4_1 = ResnetBlock(in_channels=256, out_channels=512)\n self.conv4_2 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_3 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_4 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_5 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv4_11 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)\n\n # Conv5_x\n self.conv5_1 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_2 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_3 = ResnetBlock(in_channels=512, out_channels=512)\n self.conv5_7 = nn.Conv2d(512, 512, kernel_size=2, stride=(2,1), padding=(0,1))\n self.conv5_8 = nn.Conv2d(512, 512, kernel_size=2, stride=(1,1), padding = 0)\n\n\n\n\n def forward(self, x):\n out = self.conv1_1(x)\n out = self.conv1_2(out)\n\n out = self.conv2_pool(out)\n out = self.conv2_resnet1(out)\n out = self.conv2_1(out)\n\n out = self.conv3_pool(out)\n out = self.conv3_1(out)\n out = self.conv3_2(out)\n\n out = self.conv4_pool(out)\n out = self.conv4_1(out)\n out = self.conv4_2(out)\n out = self.conv4_3(out)\n out = self.conv4_4(out)\n out = self.conv4_5(out)\n out = self.conv4_11(out)\n\n out = self.conv5_1(out)\n out = self.conv5_2(out)\n out = self.conv5_3(out)\n out = self.conv5_7(out)\n out = self.conv5_8(out)\n return out\n\ndef main():\n summary(ResnetBackBone().to('cpu'), (1, 64, 256), batch_size=1)\n\nif __name__ == '__main__':\n main()","repo_name":"rogAKAnn/image-2-latex","sub_path":"image2latex/resnet32.py","file_name":"resnet32.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"7717114536","text":"dp = {0:0, 1:1, 2:1}\n\ndef fib (n, k):\n\tif n in dp:\n\t\treturn dp[n]\n\telse:\n\t\tdp[n] = fib(n-1,k) + k*fib(n-2,k)\n\t\treturn dp[n]\n\nn, k = map(int,input().split(\" \"))\nfib(n,k)\nprint(dp[n])","repo_name":"nadide/Bioinformatics_Lab","sub_path":"rosalind/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9877386379","text":"import datetime\n\nfrom aka.utils import datefromstring, datetostring\nfrom aka.utils import format_filesize\nfrom django.test import SimpleTestCase\n\n\nclass BasicTestCase(SimpleTestCase):\n def setUp(self):\n pass\n\n # Test module utils.\n # -------------------\n def test_utils_1(self):\n datestring = '2018-05-13'\n dd = datefromstring(datestring)\n self.assertTrue(type(dd) is datetime.datetime)\n self.assertEqual(dd.year, 2018)\n self.assertEqual(dd.month, 5)\n self.assertEqual(dd.day, 13)\n\n def test_utils_2(self):\n try:\n datefromstring('2018-20-20')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_3(self):\n try:\n datefromstring('2018-02-50')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_4(self):\n try:\n datefromstring('2018-02')\n self.fail('Failed to catch ValueError.')\n except ValueError:\n self.assertTrue(True)\n\n def test_utils_5(self):\n datestring1 = '2018-02-01'\n date = datefromstring(datestring1)\n datestring2 = datetostring(date)\n self.assertEqual(datestring1, datestring2)\n\n def test_format_filesize(self):\n self.assertEqual(\"100 B\", format_filesize(100))\n self.assertEqual(\"1.0 kB\", format_filesize(1000))\n self.assertEqual(\"1.5 kB\", format_filesize(1500))\n self.assertEqual(\"12.3 MB\", format_filesize(12345678))\n self.assertEqual(\"12.35 MB\", format_filesize(12345678, 2))\n self.assertEqual(\"1.0 MiB\", format_filesize(1024**2, 1, False))\n self.assertEqual(\"1.5 MiB\", format_filesize(1.5*1024**2, 1, False))\n self.assertEqual(\"1.0 GiB\", format_filesize(1024**3, SI=False))\n self.assertEqual(\"1.0 GB\", format_filesize(1000**3, SI=True))\n","repo_name":"magenta-aps/aka-selvbetjening","sub_path":"backend/aka/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9684273882","text":"def sort012(array, size):\n '''\n https://www.geeksforgeeks.org/sort-an-array-of-0s-1s-and-2s/\n '''\n low = 0\n mid = 0\n high = size - 1\n while mid <= high:\n if array[mid] == 0:\n array[low], array[mid] = array[mid], array[low]\n low = low + 1\n mid = mid + 1\n elif array[mid] == 1:\n mid = mid + 1\n else:\n array[mid], array[high] = array[high], array[mid]\n high = high - 1\n return array\n\n\narr = [0, 1, 1, 0, 1, 2, 1, 2, 0, 0, 0, 1]\narr = sort012(arr, len(arr))\n\nprint(arr)\n","repo_name":"jsjain/DSA_GeeksforGeeks_Random-utility-python-codes","sub_path":"geeksforgeeks/sort 0s, 1s and 2s.py","file_name":"sort 0s, 1s and 2s.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"24093546797","text":"from bs4 import BeautifulSoup\n\ndef parse_xml(file_name,table_name):\n with open(file_name, \"r\") as markup:\n soup = BeautifulSoup(markup, \"xml\")\n table = soup.find_all('table', {'name': table_name})\n \n ret = []\n\n with open(file_name, \"r\") as markup:\n soup = BeautifulSoup(markup, \"xml\")\n table = soup.find_all('table', {'name': table_name})\n for row in table:\n column = row.find_all('column')\n ret_dict = {}\n for value in column:\n key = value['name']\n ret_dict.setdefault(key,[]).append(value.text)\n ret.append(ret_dict)\n return ret\n","repo_name":"JohnlNguyen/xml-parser","sub_path":"parse/parse_xml.py","file_name":"parse_xml.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5552479522","text":"# A basic, and pretty fast (for python) way of generating all kmers of length x (only argument)\n\"\"\"\nSome speed benchmarks:\n$ for i in {1..10..2} ; do\n echo \"k =\" $i\n time python mer-permutations.py $i > /dev/null\n done\n\nk = 1\nreal 0m0.022s\nuser 0m0.016s\nsys 0m0.004s\n\nk = 3\nreal 0m0.022s\nuser 0m0.004s\nsys 0m0.016s\n\nk = 5\nreal 0m0.024s\nuser 0m0.016s\nsys 0m0.004s\n\nk = 7\nreal 0m0.054s\nuser 0m0.048s\nsys 0m0.004s\n\nk = 9\nreal 0m0.519s\nuser 0m0.504s\nsys 0m0.016s\n\"\"\"\n\nimport sys\nimport itertools\n\ncombinations = itertools.product(\n *itertools.repeat([\"A\", \"T\", \"C\", \"G\"], int(sys.argv[1]))\n)\nfor i, k in enumerate(combinations):\n # print('>Kmer_' + str(i) + '\\n' + ''.join(k) )\n print(\"\".join(k))\n","repo_name":"jrjhealey/bioinfo-tools","sub_path":"kmer-permutations.py","file_name":"kmer-permutations.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"82"} +{"seq_id":"25741916837","text":"\"\"\"`argparse` to create a cli\"\"\"\nimport argparse\nimport os\nimport sqlite3\n\nfrom parse_quran_db import q_trans_main, q_ar_trans, \\\n translations_data, chapters_data, multi_lang_chapters, lang_data\nfrom parse_hadith_db import hadiths\n\nparser = argparse.ArgumentParser(description=\"Formats and saves quran and hadith data\")\n\nparser.add_argument(\"--q_trans_main\", action=\"store_true\")\nparser.add_argument(\"--q_ar_trans\", action=\"store_true\")\nparser.add_argument(\"--translations\", action=\"store_true\")\nparser.add_argument(\"--chapters\", action=\"store_true\")\nparser.add_argument(\"--multi_lang_chapters\", action=\"store_true\")\nparser.add_argument(\"--lang\", action=\"store_true\")\n\nparser.add_argument(\"--hadiths\", action=\"store_true\")\n\nargs = parser.parse_args()\n\ntry:\n os.mkdir('../quran')\n os.mkdir('../hadith')\nexcept:\n pass\n\nif args.q_trans_main or args.q_ar_trans or args.translations or args.chapters or args.multi_lang_chapters or args.lang:\n conn = sqlite3.connect(\"../quran/quran.db\");\n conn_c = conn.cursor()\n\n\nif args.q_trans_main:\n q_trans_main()\n\nif args.q_ar_trans:\n q_ar_trans()\n\nif args.translations:\n translations_data()\n\nif args.chapters:\n chapters_data()\n\nif args.multi_lang_chapters:\n multi_lang_chapters()\n\nif args.lang:\n lang_data()\n\nif args.hadiths:\n hadiths()\n\nif args.q_trans_main or args.q_ar_trans or args.translations or args.chapters or args.multi_lang_chapters or args.lang:\n conn_c.close()\n conn.close()\n","repo_name":"Islamic-OS/qitab_data_repo","sub_path":"parser/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32369998823","text":"from ah.website.db_con import DbCon as db_con\nimport pandas as pd\n\noa_con = db_con.con_oa\ndb_gp = db_con.con_gp\n\n\n# 获取源GP 所有表信息\ndef gp_tables():\n gp_table_query = f\"\"\"\n SELECT \n table_name,\n table_catalog \n FROM information_schema.tables\n \"\"\"\n gp_table_detail = pd.read_sql(gp_table_query, db_gp)\n return gp_table_detail\n\n\n# 获取源OA 所有表信息\ndef oa_tables():\n oa_table_query = f\"\"\"\n SELECT \n table_name,\n table_catalog \n FROM information_schema.tables\n \"\"\"\n oa_table_detail = pd.read_sql(oa_table_query, oa_con)\n return oa_table_detail\n\n\n# 获取所有任务信息\ndef current_system_tasks():\n current_system_query = f'''\n select \n job_name,\n job_db,\n job_sql,\n job_status,\n job_owner,\n job_desc,\n job_frequency,\n job_time,\n job_type,\n job_level,\n level_sort,\n job_sql \n from ods_task_job_schedule_pool\n '''\n current_system_tasks_detail = pd.read_sql(current_system_query, db_gp)\n return current_system_tasks_detail\n\n\n# 获取所有任务对应log信息\ndef current_system_logs():\n current_system_query = f'''\n SELECT \n job_name,\n job_result,\n job_db,\n job_level,\n job_owner\n FROM ods_task_job_execute_log\n where date(end_time)=date(current_date)\n '''\n current_system_logs_detail = pd.read_sql(current_system_query, db_gp)\n return current_system_logs_detail\n","repo_name":"Aapche5200/dataflow","sub_path":"website/templates/data_work/getalltable_info.py","file_name":"getalltable_info.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35822931247","text":"# word = input('Input a word:')\n# word_list = list(word)\n# print(word_list)\n# for i in range(len(word_list)):\n# print(word_list.pop(), end='') #end는 줄바꿈 없이. 전체주석 ctrl + /\n#\n\n# Decimal to\nTwo_list = list()\nDeci = 1\ncnt = 0\nwhile cnt != 10:\n Deci = int(input(\"Input a decimal:\"))\n cnt += 1\n if (Deci > 1):\n while Deci != 0:\n Deci_re = Deci % 2\n Deci = Deci // 2\n Two_list.append(str(Deci_re))\n for i in range(len(Two_list)):\n print(Two_list.pop(),end='')\n print('\\n')\n else : break\n\n# Two = 1\n# cnt = 0\n# while cnt != 10:\n# Two = int(input(\"Input a Two:\"))\n# Two_list = list(str(Two))\n# cnt += 1\n# deci = 0\n# if (Two != 0):\n# for i in range(len(Two_list)):\n# deci = deci + (Two_list(i))*2**i\n# print(deci)\n# else : break\n","repo_name":"HyunchanMOON/Python","sub_path":"stack_example.py","file_name":"stack_example.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25982556255","text":"from auth import AuthError\nfrom flask import Flask, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate, migrate\nfrom flask_cors import CORS\nfrom actors_blueprint import actors_blueprint\nfrom castings_blueprint import castings_blueprint\nfrom genders_blueprint import genders_blueprint\nfrom movies_blueprint import movies_blueprint\nfrom models import get_migrate, setup_db, get_db\nimport os\n\ndb = SQLAlchemy()\nmigrate = Migrate()\ntest_mode = os.getenv('TEST_MODE')\n\n\ndef create_app(test_config=None):\n # create and configure the app\n app = Flask(__name__)\n app.register_blueprint(actors_blueprint)\n app.register_blueprint(movies_blueprint)\n app.register_blueprint(genders_blueprint)\n app.register_blueprint(castings_blueprint)\n CORS(app)\n if test_mode == 1:\n setup_db(app, test_mode=True)\n else:\n setup_db(app)\n db = get_db()\n migrate = get_migrate()\n\n return app\n\n\nAPP = create_app(test_config=test_mode)\n\n\n@APP.after_request\ndef after_request(response):\n response.headers.add('Access-Control-Allow-Headers',\n 'Content-Type, Authorization, true')\n response.headers.add(\n 'Access-Control-Allow-Methods', 'GET, OPTIONS, PATCH, DELETE, POST')\n return response\n\n\n@APP.errorhandler(404)\ndef error_404(error):\n message = 'not found'\n return jsonify({\n 'success': False,\n 'error': 404,\n 'message': message.lower()\n }), 404\n\n\n@APP.errorhandler(401)\ndef error_401(error):\n message = 'unauthorized'\n return jsonify({\n 'success': False,\n 'error': 401,\n 'message': message.lower()\n }), 401\n\n\n@APP.errorhandler(403)\ndef error_403(error):\n message = 'forbidden'\n return jsonify({\n 'success': False,\n 'error': 403,\n 'message': message.lower()\n }), 401\n\n\n@APP.errorhandler(405)\ndef error_405(error):\n message = 'not allowed'\n return jsonify({\n 'success': False,\n 'error': 405,\n 'message': message.lower()\n }), 405\n\n\n@APP.errorhandler(422)\ndef error_422(error):\n message = 'unprocessable'\n return jsonify({\n 'success': False,\n 'error': 422,\n 'message': message.lower()\n }), 422\n\n\n@APP.errorhandler(400)\ndef error_400(error):\n message = 'bad request'\n return jsonify({\n 'success': False,\n 'error': 400,\n 'message': message.lower()\n }), 400\n\n\n@APP.errorhandler(500)\ndef error_500(error):\n message = 'server error'\n return jsonify({\n 'success': False,\n 'error': 500,\n 'message': message.lower()\n }), 500\n\n\n@APP.errorhandler(AuthError)\ndef auth_error(error):\n error_data = error.format()\n return jsonify({\n 'success': False,\n 'error': error_data['code'],\n 'message': error_data['message']\n }), error_data['code']\n\n\nif __name__ == '__main__':\n APP.run(host='0.0.0.0', port=8080, debug=True)\n","repo_name":"GiftXXVI/FSND_Capstone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25372690041","text":"\nimport unittest\n\nfrom ..pintaformas.inicio.operaciones_eventos.tipos import AtributoPygameEvent\nfrom ..pintaformas.dependencias import nombres_pygame, crear_evento_pygame\nfrom ..pintaformas.inicio.operaciones_eventos.posproceso_eventos import PostprocesadorEventos\n\nDictPygameEvent = dict[str, AtributoPygameEvent]\n\nDICC_MOVIMIENTO_RATON: DictPygameEvent = dict(\n pos=(44, 486), rel=(44, -61), buttons=(0, 0, 0)\n)\n\n\nclass TestPrepararEventosParaGuardado(unittest.TestCase):\n\n def test_convertir_formato_eventos(self) -> None:\n\n lista_1 = [\n crear_evento_pygame(nombres_pygame.MOUSEMOTION, DICC_MOVIMIENTO_RATON),\n crear_evento_pygame(nombres_pygame.ACTIVEEVENT, dict(gain=1, state=1)),\n\n ]\n lista_2 = [\n crear_evento_pygame(nombres_pygame.KEYDOWN, dict(unicode='t', key=116, mod=0, scancode=20)),\n\n ]\n eventos_totales = [\n lista_1,\n lista_2,\n ]\n salida_esperada = [\n [\n {\n 'tipo': nombres_pygame.MOUSEMOTION,\n 'dicc': DICC_MOVIMIENTO_RATON\n },\n {\n 'tipo': nombres_pygame.ACTIVEEVENT,\n 'dicc': dict(gain=1, state=1)\n },\n ],\n [\n {\n 'tipo': nombres_pygame.KEYDOWN,\n 'dicc': dict(unicode='t', key=116, mod=0, scancode=20)\n },\n ],\n ]\n posprocesador = PostprocesadorEventos(eventos_totales)\n self.assertEqual(\n posprocesador.convertir_formato_eventos(), salida_esperada\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/tests/test_inicio_preparar_eventos.py","file_name":"test_inicio_preparar_eventos.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"25372552851","text":"from typing import Union, TypedDict\n\nfrom ...dependencias import PygameEvent\nfrom ...core.tipos import Tuple2Int, Tuple3Int\n\nValorBasico = Union[str, int]\nValorOrigenJSON = Union[ValorBasico, list[int], None]\nAtributoPygameEvent = Union[ValorBasico, Tuple2Int, Tuple3Int, None]\n\nDiccEvento = dict[str, AtributoPygameEvent]\n\nDiccionarioStrObject = dict[str, object]\n\n\nclass EventoJson(TypedDict):\n '''Contiene listas'''\n tipo: int\n dicc: dict[str, ValorOrigenJSON]\n\n\nclass EventoParaJson(TypedDict):\n '''Contiene tuplas'''\n tipo: int\n dicc: dict[str, AtributoPygameEvent]\n\n\n# Los eventos JSON contienen listas en vez de tuplas\nEventosJSONDeUnCiclo = list[EventoJson]\nEventosJSONTotales = list[EventosJSONDeUnCiclo]\n\n# Los eventos para JSON contienen tuplas\nListaTotalEventosParaJSON = list[list[EventoParaJson]]\n\nEventosDeUnCiclo = list[PygameEvent]\nListaTotalDeEventos = list[EventosDeUnCiclo]\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/pintaformas/inicio/operaciones_eventos/tipos.py","file_name":"tipos.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"74187984267","text":"import random\nimport string\n\nimport itertools\nfrom dataclasses import dataclass\nfrom typing import List, Dict\n\nfrom rlbot.utils.structures.game_data_struct import GameTickPacket, PlayerInfo\nfrom rlbot_action_client.models import BotAction\nfrom twitchbroker.action_and_server_id import AvailableActionsAndServerId, ActionAndServerId\n\n\nclass NumberedAction:\n def __init__(self, number: int, action: BotAction):\n self.number = number\n self.action = action\n\n\n@dataclass\nclass CommandAcknowledgement:\n username: str\n description: str\n status: str\n id: str\n voters: List[str]\n\n\n\n@dataclass\nclass VoteTracker:\n votes_needed: int\n original_menu_id: str\n voters: List[str]\n start_time: float\n deadline: float # The game seconds (instant in time) at which this vote tracker should expire\n entity_name: str # This is used to retrieve config. Useful in situations where we're replacing the vote tracker with a new one.\n five_second_warning: bool # The UI can use this to start flashing when we're close to the deadline.\n\n def register_vote(self, username):\n if username not in self.voters:\n self.voters.append(username)\n\n def has_needed_votes(self):\n return len(self.voters) >= self.votes_needed\n\n\ndef create_section(act_and_server: AvailableActionsAndServerId, counter: itertools.count):\n return CommandSection(header=act_and_server.available_actions.entity_name,\n entity_name=act_and_server.available_actions.entity_name,\n action_server_id=act_and_server.action_server_id,\n actions=[NumberedAction(next(counter), a) for a in\n act_and_server.available_actions.available_actions])\n\n\ndef generate_menu_id():\n return ''.join(random.choice(string.ascii_uppercase) for _ in range(2))\n\n\ndef generate_menu(list: List[AvailableActionsAndServerId], menu_id: str,\n recent_commands: List[CommandAcknowledgement], packet: GameTickPacket,\n vote_trackers: Dict[str, VoteTracker]) -> 'OverlayData':\n\n raw_players = [packet.game_cars[i] for i in range(packet.num_cars)]\n players = [PlayerData(p.name, p.team) for p in raw_players if p.name]\n counter = itertools.count(1)\n return OverlayData(menu_id=menu_id, sections=[create_section(s, counter) for s in list],\n recent_commands=recent_commands, players=players, vote_trackers=vote_trackers,\n is_menu_active=packet.game_info.is_round_active, chat_users_involved=[],\n creation_time=packet.game_info.seconds_elapsed)\n\n\n@dataclass\nclass CommandSection:\n header: str\n entity_name: str # Probably the same as the header for now.\n action_server_id: str\n actions: List[NumberedAction]\n\n\n@dataclass\nclass PlayerData:\n name: str\n team: int\n\n\n@dataclass\nclass OverlayData:\n menu_id: str\n sections: List[CommandSection]\n recent_commands: List[CommandAcknowledgement]\n players: List[PlayerData]\n vote_trackers: Dict[str, VoteTracker]\n is_menu_active: bool\n chat_users_involved: List[str]\n creation_time: float\n\n def retrieve_choice(self, choice_num: int) -> ActionAndServerId:\n for section in self.sections:\n for action in section.actions:\n if action.number == choice_num:\n return ActionAndServerId(action.action, section.entity_name, section.action_server_id)\n return None\n\n def num_actions(self) -> int:\n count = 0\n for section in self.sections:\n count += len(section.actions)\n return count\n\n\ndef serialize_for_overlay(o):\n if hasattr(o, 'to_dict'):\n return o.to_dict()\n return o.__dict__\n","repo_name":"RLBot/RLBotPack","sub_path":"RLBotPack/TwitchInteraction/TwitchBroker/twitchbroker/overlay_data.py","file_name":"overlay_data.py","file_ext":"py","file_size_in_byte":3768,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"82"} +{"seq_id":"2716146213","text":"# import networkx as nx\nimport GraphCreator\nimport matplotlib.pyplot as plt\nimport pylab\n\"\"\"\nScript to calculate the average salary of every actor with age ranges of 10 years starting from 0\nReturns a scatter plot of the values to spot any trends\n\"\"\"\n\n\ng = GraphCreator.json_to_graph(\"data.json\")\n\nsums = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\ncounts = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n\nfor i in g.nodes(data=True):\n offset = -1\n if i[1]['json_class'] == 'Actor':\n if i[1]['age'] < 0:\n continue\n elif i[1]['age'] < 10:\n offset = 0\n elif i[1]['age'] < 20:\n offset = 1\n elif i[1]['age'] < 30:\n offset = 2\n elif i[1]['age'] < 40:\n offset = 3\n elif i[1]['age'] < 50:\n offset = 4\n elif i[1]['age'] < 60:\n offset = 5\n elif i[1]['age'] < 70:\n offset = 6\n elif i[1]['age'] < 80:\n offset = 7\n elif i[1]['age'] < 90:\n offset = 8\n elif i[1]['age'] < 100:\n offset = 9\n\n if offset > 0:\n sums[offset] += i[1]['total_gross']\n counts[offset] += 1\n\naverages = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\nfor i in range(0, 9):\n if counts[i] is not 0:\n averages[i] = (sums[i]/counts[i])\n\nages = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\nplt.figure(figsize=(17, 6))\nplt.scatter(ages, averages)\npylab.xlabel('Ages')\npylab.ylabel('Average Gross')\n\nlabels = ['0-10', '10-20', '20-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100', ]\nplt.ylim(-100, 60534868)\nplt.xticks(ages, labels)\nplt.show()\n","repo_name":"shrujancheruku/Programming-Studio","sub_path":"Assignment2.1/AgeSalary.py","file_name":"AgeSalary.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40599790292","text":"import random\ndef sanitize_phone_number(phone):\n new_phone = (\n phone.strip()\n .removeprefix(\"+\")\n .replace(\"(\", \"\")\n .replace(\")\", \"\")\n .replace(\"-\", \"\")\n .replace(\" \", \"\")\n )\n print (new_phone)\n\n list_phones = []\n for i in range(10):\n list_phones.append(new_phone)\n \n print (list_phones)\n\nsanitize_phone_number(\"+45645 64-64\")\n\n\n\n#def get_phone_numbers_for_countries(list_phones):\n\n\n#get_phone_numbers_for_countries():","repo_name":"evgeniytr1509/Dell","sub_path":"phone.py","file_name":"phone.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33242500324","text":"from KongMing.Archiver.SingleNNArchiver import SingleNNArchiver\nfrom KongMing.ModelFactory.SingleNNModelFactory import SingleNNModelFactory\nfrom KongMing.Trainer.VGGTrainer import VGGTrainer\n\nfrom KongMing.Models.BaseNNModel import BaseNNModel\n\nfrom KongMing.Utils.CaseInsensitiveContainer import CaseInsensitiveList, CaseInsensitiveDict\n\nimport torch\nimport torchvision\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models.vgg import VGG16_Weights\n\nclass VGG16(BaseNNModel):\n def __init__(self, inNumClasses=10):\n super().__init__()\n self.features = nn.Sequential(\n # Block 1\n nn.Conv2d(3, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 2\n nn.Conv2d(64, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(128, 128, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 3\n nn.Conv2d(128, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 4\n nn.Conv2d(256, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n\n # Block 5\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2),\n )\n\n self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\n\n self.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, inNumClasses),\n )\n\n def forward(self, inX):\n inX = self.features(inX)\n inX = self.avgpool(inX)\n inX = torch.flatten(inX, 1)\n inX = self.classifier(inX)\n return inX\n\nclass VGGModelFactory(SingleNNModelFactory) :\n def __init__(\n self,\n inNumClasses,\n inLearningRate,\n inModelRootFolderPath\n ) :\n self.VGG = VGG16(inNumClasses)\n\n Trainer = VGGTrainer(inLearningRate)\n\n super().__init__(self.VGG, Trainer, inModelRootFolderPath)\n\n print(\"Sum of Params:{:,} \".format(self._SumParameters(self.VGG)))\n\n def NewTrain(self, inDataLoader, inEpochIterCount : int, inArgs : CaseInsensitiveList = None, inKVArgs : CaseInsensitiveDict = None) -> None:\n if \"LoadPretrained\" in inArgs:\n print(\"Load Pretranined Begin........\")\n PreTrainedModel = torchvision.models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1)\n print(\"\\t Load Features\")\n self.VGG.features.load_state_dict(PreTrainedModel.features.state_dict())\n print(\"\\t Load Avgpool\")\n self.VGG.avgpool.load_state_dict(PreTrainedModel.avgpool.state_dict())\n for i in range(6):\n print(\"\\t Load classifier[{}]\".format(i))\n self.VGG.classifier[i].load_state_dict(PreTrainedModel.classifier[i].state_dict())\n print(\"Load Pretranined Finished........\")\n\n super().NewTrain(inDataLoader=inDataLoader, inEpochIterCount=inEpochIterCount, inArgs=inArgs, inKVArgs=inKVArgs)\n\n def Eval(self, inEpoch, inArgs : CaseInsensitiveList = None, inKVArgs : CaseInsensitiveDict = None) :\n if (super().Eval(inEpoch, inArgs, inKVArgs) == False) :\n return False\n\n TestDataLoader = inKVArgs.get(\"inDataLoader\")\n if (TestDataLoader is None) :\n return False\n\n self.VGG.eval()\n\n correct = 0\n total = 0\n with torch.no_grad():\n for data in TestDataLoader:\n images, labels = data[0].to(self.Device), data[1].to(self.Device)\n outputs = self.VGG(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\n print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))\n\n return True\n","repo_name":"shanhaobo/StudyAI","sub_path":"KongMing/ModelFactory/Classifier/VGGModelFactory.py","file_name":"VGGModelFactory.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26234767793","text":"\"\"\"\ndeepdataspace.server.settings\n\nThe django settings.\n\"\"\"\n\nimport os.path\nfrom pathlib import Path\n\nfrom corsheaders.defaults import default_headers\nfrom corsheaders.defaults import default_methods\n\nfrom deepdataspace import constants\nfrom deepdataspace import environs\n\nBASE_DIR = os.path.abspath(Path(__file__).resolve().parent)\n\nDJANGO_DIR = environs.DJANGO_DIR\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = environs.DJANGO_SECRET\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = environs.DEBUG\n\nis_local = environs.ENV == constants.RunningEnv.Local\n\nALLOWED_HOSTS = [\"*\"]\n\n# Application definition\nINSTALLED_APPS = [\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"whitenoise.runserver_nostatic\",\n \"django.contrib.staticfiles\",\n \"rest_framework\",\n \"corsheaders\",\n \"deepdataspace.server\",\n]\n\nMIDDLEWARE = [\n \"corsheaders.middleware.CorsMiddleware\",\n \"whitenoise.middleware.WhiteNoiseMiddleware\",\n \"django.middleware.http.ConditionalGetMiddleware\",\n \"deepdataspace.server.middlewares.RequestPerfMiddleware\",\n]\n\nROOT_URLCONF = \"deepdataspace.server.urls\"\n\nTEMPLATES = [\n {\n \"BACKEND\" : \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\" : [],\n \"APP_DIRS\": True,\n \"OPTIONS\" : {\n \"context_processors\": [\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.contrib.messages.context_processors.messages\",\n ],\n },\n },\n]\n\n# Static files\nSTATIC_ROOT = f\"{BASE_DIR}/static\"\nSTATIC_URL = \"/static/\"\n\n# Database\nif environs.DB_ENGIN == \"sqlite3\":\n if is_local:\n default_db = {\n \"NAME\": os.path.join(DJANGO_DIR, f\"{environs.DB_NAME}.sqlite3\"),\n }\n else:\n default_db = {\n \"NAME\": os.path.join(BASE_DIR, f\"{environs.DB_NAME}.sqlite3\"),\n }\nelse:\n default_db = {\n \"NAME\" : environs.DB_NAME,\n \"USER\" : environs.DB_USER,\n \"PASSWORD\": environs.DB_PASS,\n \"HOST\" : environs.DB_HOST,\n \"PORT\" : environs.DB_PORT,\n }\ndefault_db[\"ENGINE\"] = f\"django.db.backends.{environs.DB_ENGIN}\"\nDATABASES = {\"default\": default_db}\n\n# Internationalization\nLANGUAGE_CODE = \"en-us\"\nTIME_ZONE = \"UTC\"\nUSE_I18N = True\nUSE_TZ = True\n\n# Default primary key field type\nDEFAULT_AUTO_FIELD = \"django.db.models.BigAutoField\"\n\n# For Logging\nLOGGING = {\n \"version\" : 1,\n \"disable_existing_loggers\": False,\n \"formatters\" : {\n \"simple\" : {\n \"format\": \"%(asctime)s %(levelname)s [%(name)s] %(message)s\"\n },\n \"verbose\": {\n \"format\": \"%(asctime)s %(levelname)s [%(filename)s:%(funcName)s:%(lineno)s] %(process)d %(thread)d %(message)s\"\n },\n },\n \"handlers\" : {\n \"console\": {\n \"class\" : \"logging.StreamHandler\",\n \"formatter\": \"simple\",\n }\n },\n \"root\" : {\n \"level\" : \"INFO\",\n \"handlers\" : [\"console\"] if environs.VERBOSE else [],\n \"propagate\": True,\n },\n \"loggers\" : {\n \"django\": {\n \"level\" : \"INFO\",\n \"handlers\" : [\"console\"],\n \"propagate\": True,\n },\n }\n}\n\nif is_local:\n LOGGING[\"handlers\"][\"django\"] = {\n \"level\" : \"INFO\",\n \"class\" : \"logging.handlers.RotatingFileHandler\",\n \"filename\" : environs.DJANGO_LOG_PATH,\n \"maxBytes\" : 1024 * 1024 * 100, # 100 mb\n \"formatter\": \"verbose\",\n }\n LOGGING[\"loggers\"][\"django\"][\"handlers\"].append(\"django\")\n\n# For DRF\nREST_FRAMEWORK = {\n \"DEFAULT_AUTHENTICATION_CLASSES\": [],\n \"DEFAULT_PERMISSION_CLASSES\" : [],\n \"EXCEPTION_HANDLER\" : \"deepdataspace.utils.http.handle_api_exception\",\n \"DEFAULT_RENDERER_CLASSES\" : [\"rest_framework.renderers.JSONRenderer\", ],\n \"DEFAULT_PARSER_CLASSES\" : [\"rest_framework.parsers.JSONParser\", ]\n}\n\n# For CORS\nCORS_ORIGIN_ALLOW_ALL = True\nCORS_ALLOW_CREDENTIALS = True\nCORS_ALLOW_METHODS = list(default_methods)\nCORS_ALLOW_HEADERS = list(default_headers) + [\n \"Token\",\n]\n\n# For django running behind a proxy\nSECURE_PROXY_SSL_HEADER = (\"HTTP_X_FORWARDED_PROTO\", \"https\")\n\n# For Login and Token\nTOKEN_AGE = 3600 * 24\n","repo_name":"IDEA-Research/deepdataspace","sub_path":"deepdataspace/server/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4442,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"82"} +{"seq_id":"5920501197","text":"\"\"\"add guild event column\n\nRevision ID: a4aab942bc52\nRevises: 71237a836be1\nCreate Date: 2022-09-25 21:12:07.350204\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a4aab942bc52'\ndown_revision = '71237a836be1'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('attendance', sa.Column('is_guild_event', sa.Boolean(), nullable=True))\n op.execute(\"UPDATE attendance SET is_guild_event = false\")\n op.alter_column('attendance', 'is_guild_event', nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('attendance', 'is_guild_event')\n # ### end Alembic commands ###\n","repo_name":"waliens/bloude-clockin","sub_path":"src/alembic/versions/a4aab942bc52_add_guild_event_column.py","file_name":"a4aab942bc52_add_guild_event_column.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20242392069","text":"import os\nimport sys\nimport json\n\nbasename = os.path.basename(sys.argv[1]).split('.')[0]\n\nwith open(sys.argv[1], 'rb') as f:\n o_data = f.read().split('\\n')[:-1]\n\n# dict_data = {}\nlist_data = []\nfor row in o_data:\n row = row.split(' :: ')\n found = row[2] == 'True'\n # if not found:\n # continue\n fixations = int(row[1])\n setsize = int(row[0].split('_')[0][7:])\n trialnum = int(row[0].split('_')[1][:-4])\n tup = (setsize, trialnum, fixations)\n list_data.append(tup)\n\nwith open('tuples/{}.json'.format(basename), 'wb') as f:\n json.dump(list_data, f, indent=4)","repo_name":"matthewr6/visual-search-model","sub_path":"gdrivesets/old/fixation_tuples.py","file_name":"fixation_tuples.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"46682523202","text":"import numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\n\n\"\"\"\n@X: input data\n@k: number of clusters\n\"\"\"\ndef kmeans_wrapper(X, k, image_as_input = False):\n if not image_as_input:\n X = np.float32(X)\n else:\n orig_shape = X.shape\n # flatten the image into a vector of BGR entries\n # so an n x 3 -> this is why -1 as first argument\n X = X.reshape((-1, 3))\n # data must be float32\n X = np.float32(X)\n type_ = cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER\n max_iter = 10\n epsilon = 1.0\n criteria = (type_, max_iter, epsilon)\n # labels returns the index of the cluster they belong in\n compactness, labels, centers = cv2.kmeans(data = X,\n K = k,\n bestLabels = None,\n criteria = criteria,\n attempts = 10,\n flags = cv2.KMEANS_RANDOM_CENTERS)\n if not image_as_input:\n # the final clusters - Kmeans output\n S = []\n for l in labels:\n S.append(X[labels.ravel() == l])\n return S, centers\n else:\n # convert data back to image\n centers = np.uint8(centers)\n # same as for l in flat labels: res.append(center[l])\n res = centers[labels.flatten()]\n res2 = res.reshape((orig_shape))\n return res2, centers\n\n\ndef main():\n x1 = np.random.randint(25,54,(25,4))\n x2 = np.random.randint(45,75,(25,4))\n # concatenate them in one (50,4) array\n X = np.vstack((x1, x2))\n S, centers = kmeans_wrapper(X, 2)\n # with an image\n im = cv2.imread('../kmeans/santorini.jpg') \n cv2.imshow('input', im)\n cv2.waitKey()\n cv2.destroyAllWindows()\n assert im is not None, \"Invalid input image\"\n quant, centers = kmeans_wrapper(im, 8, True)\n cv2.imshow('output', quant)\n cv2.waitKey()\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main()\n","repo_name":"leonmavr/journal","sub_path":"computer-vision/segmentation/src/src/kmeans/k_means_cv.py","file_name":"k_means_cv.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"35702099226","text":"import numpy as np\nfrom scipy.linalg import inv, det\n\n\ndef glr(x, y, theta=1.82):\n xm = multivariate_normal.logpdf(\n x, np.mean(x, axis=0), np.cov(x, rowvar=False))\n ym = multivariate_normal.logpdf(\n y, np.mean(y, axis=0), np.cov(y, rowvar=False))\n z = np.vstack((x, y))\n zm = multivariate_normal.logpdf(\n z, np.mean(z, axis=0), np.cov(z, rowvar=False))\n return (np.sum(zm) - np.sum(np.hstack((xm, ym)))) / len(z)**theta\n\n\ndef glr2(x, y, theta=1.0):\n cx = np.cov(x, rowvar=0)\n cy = np.cov(y, rowvar=0)\n nx = x.shape[0]\n ny = y.shape[0]\n n = nx + ny\n d = -0.5 * (nx * np.log(det(cx)) + ny * np.log(det(cy)) -\n n * np.log(det((nx / n) * cx + (ny / n) * cy)))\n return d\n\n\ndef bic(x, y, theta=1.0, params={}):\n px = np.log(det(np.cov(x, rowvar=0)))\n py = np.log(det(np.cov(x, rowvar=0)))\n z = np.vstack((x, y))\n pz = np.log(det(np.cov(z, rowvar=0)))\n d = 0.5 * (z.shape[0] * pz - x.shape[0] * px - y.shape[0] * py)\n p = z.shape[1]\n corr = theta * 0.25 * p * (p + 3) * np.log(z.shape[0])\n return d - corr\n\n\ndef kl2(x, y):\n cx = np.cov(x, rowvar=0)\n cy = np.cov(y, rowvar=0)\n cix = inv(cx)\n ciy = inv(cy)\n dxy = np.mean(x, axis=0) - np.mean(y, axis=0)\n d = 0.5 * (np.trace((cx - cy) * (ciy - cix)) +\n np.trace((ciy + cix) * np.outer(dxy, dxy)))\n return d\n","repo_name":"cilsat/scribe","sub_path":"segment/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24616202950","text":"import pyxel\r\nimport time\r\nimport random\r\n\r\n# Define the Chessboard Status\r\nclass ChessboardStatus:\r\n EMPTY = 0 # The Chess Cell not Taken\r\n PLAYER1 = 1 # Taken by Player 1 (The Human)\r\n PLAYER2 = 2 # Taken by Player 2 (The Bot)\r\n\r\n# The Bot Player Functioningg Part\r\nclass DBot:\r\n # Initiate the Chessboard Status for the Bot\r\n def __init__(self, board_size):# Initiate the Chessboard Status for the Bot\r\n self.BOARD_SIZE = board_size\r\n\r\n # Check If There is a Winner\r\n def if_winner(self, board):\r\n # Horizontally Check if 5 Chess Pieces in a Row\r\n for row in board:\r\n for i in range(self.BOARD_SIZE - 4):\r\n if row[i] == row[i + 1] == row[i + 2] == row[i + 3] == row[i + 4] != 0:\r\n return row[i]\r\n\r\n # Vertically Check if 5 Chess Pieces in a Row\r\n for col in range(self.BOARD_SIZE):\r\n for i in range(self.BOARD_SIZE - 4):\r\n if board[i][col] == board[i + 1][col] == board[i + 2][col] == board[i + 3][col] == board[i + 4][col] != 0:\r\n return board[i][col]\r\n\r\n # Top-Left and Bottom-Right if 5 Chess Pieces in a Row\r\n for i in range(self.BOARD_SIZE - 4):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if board[i][j] == board[i + 1][j + 1] == board[i + 2][j + 2] == board[i + 3][j + 3] == board[i + 4][j + 4] != 0:\r\n return board[i][j]\r\n\r\n # Top-Right and Bottom-Left if 5 Chess Pieces in a Row\r\n for i in range(self.BOARD_SIZE - 1, 3, -1):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if board[i][j] == board[i - 1][j + 1] == board[i - 2][j + 2] == board[i - 3][j + 3] == board[i - 4][j + 4] != 0:\r\n return board[i][j]\r\n\r\n # No Winner, Place 1 Chess Piece on Chessboard\r\n return 0\r\n\r\n # Check Where to Put the Chess\r\n def near_complete(self, board, x, y):\r\n # Check a Cell and Surrounding 8 Cells for an Empty One\r\n for dx in [-1, 0, 1]:\r\n for dy in [-1, 0, 1]:\r\n if dx == 0 and dy == 0:\r\n continue\r\n # Pick up a Cell to Place the Chess Piece\r\n new_x = x + dx\r\n new_y = y + dy\r\n\r\n # Make Sure to Place Chess inside the Chessboard \r\n if (\r\n new_x >= 0\r\n and new_x < self.BOARD_SIZE\r\n and new_y >= 0\r\n and new_y < self.BOARD_SIZE\r\n and board[new_y][new_x] != 0\r\n ):\r\n return True\r\n return False\r\n\r\n # Check if a Move Would Win the Round\r\n def win_move(self, board, x, y, player):\r\n if x <= self.BOARD_SIZE - 5 and all(board[y][i] == player for i in range(x, x + 5)):\r\n return True\r\n\r\n if y <= self.BOARD_SIZE - 5 and all(board[i][x] == player for i in range(y, y + 5)):\r\n return True\r\n\r\n if x <= self.BOARD_SIZE - 5 and y <= self.BOARD_SIZE - 5 and all(\r\n board[y + i][x + i] == player for i in range(5)\r\n ):\r\n return True\r\n\r\n if x <= self.BOARD_SIZE - 5 and y >= 4 and all(\r\n board[y - i][x + i] == player for i in range(5)\r\n ):\r\n return True\r\n\r\n return False\r\n\r\n # Defines How Bot Makes a Move\r\n def bot_turn(self, board, current_player):\r\n # Empty Cell\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == ChessboardStatus.EMPTY and self.win_move(board, x, y, current_player):\r\n return x, y\r\n\r\n # Cell Taken by Player 1's Chess Pieces\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == 0 and self.win_move(board, x, y, ChessboardStatus.PLAYER1):\r\n return x, y\r\n\r\n # Intercept Player 1's Chess Pieces or Placing Its Own\r\n # Detect Horizontal Lines of 3 Chess Pieces Placed by Human Player\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y][x + 1] == ChessboardStatus.PLAYER1\r\n and board[y][x + 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y][x + 1] == ChessboardStatus.PLAYER2\r\n and board[y][x + 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if x > 0 and board[y][x - 1] == 0:\r\n return x - 1, y\r\n if x + 3 < self.BOARD_SIZE and board[y][x + 3] == 0:\r\n return x + 3, y\r\n\r\n # Detect Vertical Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if y > 0 and board[y - 1][x] == 0:\r\n return x, y - 1\r\n if y + 3 < self.BOARD_SIZE and board[y + 3][x] == 0:\r\n return x, y + 3\r\n\r\n # Detect Top-Left to Botton-Right Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE - 2):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x + 1] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x + 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x + 1] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x + 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if (y > 0 and x > 0) and board[y - 1][x - 1] == 0:\r\n return x - 1, y -1\r\n if (y + 3 < self.BOARD_SIZE and x + 3 < self.BOARD_SIZE) and board[y + 3][x + 3] == 0:\r\n return x + 3, y + 3\r\n\r\n # Detect Top-Right to Botton-Left Lines of 3 Chess Pieces Placed by Human Player\r\n for x in range(self.BOARD_SIZE - 2):\r\n for y in range(self.BOARD_SIZE - 2):\r\n if (\r\n board[y][x] == ChessboardStatus.PLAYER1\r\n and board[y + 1][x - 1] == ChessboardStatus.PLAYER1\r\n and board[y + 2][x - 2] == ChessboardStatus.PLAYER1\r\n ) or (\r\n board[y][x] == ChessboardStatus.PLAYER2\r\n and board[y + 1][x - 1] == ChessboardStatus.PLAYER2\r\n and board[y + 2][x - 2] == ChessboardStatus.PLAYER2\r\n ):\r\n # Add Considered Places to Intercept or Add Own\r\n if (y > 0 and x < self.BOARD_SIZE) and board[y - 1][x + 1] == 0:\r\n return x + 1, y - 1\r\n if (y + 3 < self.BOARD_SIZE and x - 3 > 0) and board[y + 3][x - 3] == 0:\r\n return x - 3, y + 3\r\n\r\n # Valid Move to Place Bot's Chess Pieces in a Cell Next to Player 1's Chess Pieces or Connect Own Lines\r\n valid_moves = []\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n # Add a Choice to Consider\r\n if board[y][x] == 0 and self.near_complete(board, x, y):\r\n valid_moves.append((x, y))\r\n if valid_moves:\r\n return random.choice(valid_moves)\r\n\r\n # Valid Move to Place Bot's Chess Pieces in a Cell w/ No Player 1's Chess Pieces Around\r\n valid_moves = []\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if board[y][x] == 0:\r\n valid_moves.append((x, y))\r\n if valid_moves:\r\n return random.choice(valid_moves)\r\n\r\n return None\r\n\r\n# The Execution of the Chess Game\r\nclass App:\r\n # Initialize the Chessboard Status\r\n def __init__(self):\r\n # Key Details of the Solid Figures\r\n self.BOARD_SIZE = 15\r\n self.CELL_SIZE = 32\r\n self.SCREEN_WIDTH = self.BOARD_SIZE * self.CELL_SIZE\r\n self.SCREEN_HEIGHT = self.BOARD_SIZE * self.CELL_SIZE + 40\r\n\r\n self.board = [[ChessboardStatus.EMPTY] * self.BOARD_SIZE for _ in range(self.BOARD_SIZE)]\r\n self.current_player = ChessboardStatus.PLAYER1\r\n self.player1_score = 0\r\n self.player2_score = 0\r\n self.game_over = False\r\n\r\n self.countdown_time = 0\r\n self.countdown_duration = 5\r\n self.turn_time = 20\r\n self.turn_start_time = 0\r\n\r\n self.bot = DBot(self.BOARD_SIZE)\r\n\r\n # Show Instructions for Playing as Human for 5 Seconds\r\n self.show_instructions = True\r\n self.instructions_time = time.time()\r\n\r\n # Initlize the Entire Program\r\n pyxel.init(self.SCREEN_WIDTH, self.SCREEN_HEIGHT)\r\n pyxel.run(self.update, self.draw)\r\n\r\n # Check if there is a Winner Between Player 1 and 2 by Seeking 5 Chess Pieces Form a Horizontal, Vertical or Diagonal Line\r\n def if_winner(self):\r\n for row in self.board:\r\n for i in range(self.BOARD_SIZE - 4):\r\n if row[i] == row[i + 1] == row[i + 2] == row[i + 3] == row[i + 4] != 0:\r\n return row[i]\r\n\r\n for col in range(self.BOARD_SIZE):\r\n for i in range(self.BOARD_SIZE - 4):\r\n if self.board[i][col] == self.board[i + 1][col] == self.board[i + 2][col] == self.board[i + 3][col] == self.board[i + 4][col] != 0:\r\n return self.board[i][col]\r\n\r\n for i in range(self.BOARD_SIZE - 4):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if self.board[i][j] == self.board[i + 1][j + 1] == self.board[i + 2][j + 2] == self.board[i + 3][j + 3] == self.board[i + 4][j + 4] != 0:\r\n return self.board[i][j]\r\n\r\n for i in range(self.BOARD_SIZE - 1, 3, -1):\r\n for j in range(self.BOARD_SIZE - 4):\r\n if self.board[i][j] == self.board[i - 1][j + 1] == self.board[i - 2][j + 2] == self.board[i - 3][j + 3] == self.board[i - 4][j + 4] != 0:\r\n return self.board[i][j]\r\n\r\n return 0\r\n\r\n # After a Winner is Confirmed, Reset the Chessboard Status\r\n def reset_game(self):\r\n self.board = [[0] * self.BOARD_SIZE for _ in range(self.BOARD_SIZE)]\r\n self.current_player = ChessboardStatus.PLAYER1\r\n self.game_over = False\r\n self.countdown_time = 0\r\n\r\n # A 5-Second Countdown before Next Round Begins\r\n def start_countdown(self):\r\n self.countdown_time = time.time()\r\n\r\n # The Start of a New Round and Reset\r\n def new_round(self):\r\n self.reset_game()\r\n self.game_over = False\r\n\r\n # Display of Game's Instructions\r\n def instructions(self):\r\n pyxel.cls(7)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 60, self.SCREEN_HEIGHT // 2 - 20, \"Keyboard Instructions:\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2, \"Space and Mouse Cursor: Place your chess\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2 + 10, \"Q: Quit the game\", 0)\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 80, self.SCREEN_HEIGHT // 2 + 20, \"R: Skip the waiting time between rounds\", 0)\r\n\r\n # What to do After Each Prerequisite Met\r\n def update(self):\r\n # Un-Display the Instruction and Show the Chessboard\r\n if self.show_instructions:\r\n if time.time() - self.instructions_time > self.countdown_duration:\r\n self.show_instructions = False\r\n self.start_countdown()\r\n return\r\n\r\n # Press \"Q\" to Quit the Program\r\n if pyxel.btnp(pyxel.KEY_Q):\r\n pyxel.quit()\r\n\r\n # Press \"R\" to Skip the 5-Second Countdown and to the Next Round\r\n if self.game_over:\r\n if pyxel.btnp(pyxel.KEY_R):\r\n self.new_round()\r\n return\r\n\r\n # Bot Player Acts\r\n if self.current_player == ChessboardStatus.PLAYER2:\r\n if not self.game_over:\r\n # If Chess Piece not Placed by Bot Before Pesudo Countdown Ends, It Is Player 1's Turn\r\n if time.time() - self.turn_start_time > self.turn_time:\r\n self.current_player = ChessboardStatus.PLAYER1\r\n return\r\n\r\n # Time Needed for Bot to Place Chess, in This Case, It Is 1 Second. Also Can be Replaced by random.randint(1, 20), But Countdown Does Not Move\r\n time.sleep(1)\r\n\r\n # Bot Make the Move\r\n bot_move = self.bot.bot_turn(self.board, self.current_player)\r\n x, y = bot_move\r\n\r\n # Add 1 to the Score Count of Player Who Wins the Current Round\r\n self.board[y][x] = self.current_player\r\n winner = self.bot.if_winner(self.board)\r\n if winner != ChessboardStatus.EMPTY:\r\n self.game_over = True\r\n if winner == ChessboardStatus.PLAYER1:\r\n self.player1_score += 1\r\n else:\r\n self.player2_score += 1\r\n self.start_countdown()\r\n else:\r\n self.current_player = ChessboardStatus.PLAYER1\r\n\r\n # Human Player Acts\r\n if pyxel.btnp(pyxel.KEY_SPACE) and self.current_player == ChessboardStatus.PLAYER1:\r\n if self.board[pyxel.mouse_y // self.CELL_SIZE][pyxel.mouse_x // self.CELL_SIZE] == ChessboardStatus.EMPTY:\r\n x = pyxel.mouse_x // self.CELL_SIZE\r\n y = pyxel.mouse_y // self.CELL_SIZE\r\n\r\n # Player Which Placed Recent Chess Piece and Got 5-In-a-Row Is the Winner\r\n self.board[y][x] = self.current_player\r\n winner = self.bot.if_winner(self.board)\r\n # After Human Player Won the Round, Add 1 to Player's Score Count and Start the 5-Second Countdown to Next Round\r\n if winner != 0:\r\n self.game_over = True\r\n self.player1_score += 1\r\n self.start_countdown()\r\n # It Is Bot's Turn Now and Start the Pseudo Countdown \r\n else:\r\n self.current_player = ChessboardStatus.PLAYER2\r\n self.turn_start_time = time.time()\r\n\r\n # What to Show\r\n def draw(self):\r\n if self.show_instructions:\r\n self.instructions()\r\n return\r\n\r\n # A top Rectangle for Displaying the Messages\r\n pyxel.cls(9)\r\n pyxel.rect(0, 0, self.SCREEN_WIDTH, 40, 0)\r\n\r\n # Display the Current Turn is for Who's Move\r\n player_turn_text = f\"Player {self.current_player}'s turn\"\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 32, 10, player_turn_text, 7)\r\n\r\n # Add a Pseudo-Thinking Time of 20 Seconds for the Bot and Display it\r\n if self.current_player == ChessboardStatus.PLAYER2 and not self.game_over:\r\n remaining_time = self.turn_time - (time.time() - self.turn_start_time)\r\n countdown_text = f\"Time left: {int(remaining_time)} seconds\"\r\n pyxel.text(self.SCREEN_WIDTH - 284, 20, countdown_text, 7)\r\n\r\n # Draw Lines to Boarder the Cells on Chessboard\r\n for i in range(self.BOARD_SIZE):\r\n pyxel.line(0, i * self.CELL_SIZE + 40, self.SCREEN_WIDTH, i * self.CELL_SIZE + 40, 0)\r\n pyxel.line(i * self.CELL_SIZE, 40, i * self.CELL_SIZE, self.SCREEN_HEIGHT, 0)\r\n\r\n # Define the Color of Chess Pieces Placed by Each Player\r\n for y in range(self.BOARD_SIZE):\r\n for x in range(self.BOARD_SIZE):\r\n if self.board[y][x] == 1:\r\n pyxel.circ(x * self.CELL_SIZE + self.CELL_SIZE // 2, y * self.CELL_SIZE + self.CELL_SIZE // 2 + 40, 10, 1)\r\n elif self.board[y][x] == 2:\r\n pyxel.circ(x * self.CELL_SIZE + self.CELL_SIZE // 2, y * self.CELL_SIZE + self.CELL_SIZE // 2 + 40, 10, 7)\r\n\r\n # Display How Many Rounds by Player 1 and 2 in Top-Left and Top-Right Corner\r\n pyxel.text(10, 10, f\"Player 1: {self.player1_score}\", 7)\r\n pyxel.text(self.SCREEN_WIDTH - 90, 10, f\"Player 2: {self.player2_score}\", 7)\r\n\r\n # Make Sure the Displayed Cursor Exactly Fit inside the Cell and Draw the Cursor\r\n cursor_x = pyxel.mouse_x // self.CELL_SIZE\r\n cursor_y = pyxel.mouse_y // self.CELL_SIZE\r\n pyxel.line(cursor_x * self.CELL_SIZE, cursor_y * self.CELL_SIZE + 40, (cursor_x + 1) * self.CELL_SIZE, (cursor_y + 1) * self.CELL_SIZE + 40, 2)\r\n pyxel.line((cursor_x + 1) * self.CELL_SIZE, cursor_y * self.CELL_SIZE + 40, cursor_x * self.CELL_SIZE, (cursor_y + 1) * self.CELL_SIZE + 40, 2)\r\n\r\n # What to do after Someone Won the Round\r\n if self.game_over:\r\n winner = self.if_winner()\r\n # Display who is the Winner\r\n if winner != 0:\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 25, 30, f\"Player {winner} wins!\", 3)\r\n pyxel.play(0, 0)\r\n # This Part is Basically Useless Since there is not Going to be a Draw in Gomoku\r\n else:\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 20, self.SCREEN_HEIGHT // 7 - 4, \"It's a draw!\", 3)\r\n\r\n # The Execution of 5-Second Countdown\r\n if self.countdown_time > 0:\r\n # How Much Time Left\r\n remaining_time = self.countdown_duration - (time.time() - self.countdown_time)\r\n # Display How Much Time Left\r\n if remaining_time > 0:\r\n countdown_text = f\"{int(remaining_time)} seconds to next round\"\r\n pyxel.text(self.SCREEN_WIDTH // 2 - 45, 20, countdown_text, 7)\r\n # A New Round if the Countdown is Over\r\n else:\r\n self.new_round()\r\n\r\n# Sound for Mentioning if Someone Wins \r\ndef esound():\r\n pyxel.sound(0).set(\"c3e3g3c4c4\", \"s\", \"7\", (\"n\" * 4), 7)\r\n\r\napp = App()\r\n","repo_name":"RenElsa/FIT_2_Project_S23","sub_path":"Gomoku_Show.py","file_name":"Gomoku_Show.py","file_ext":"py","file_size_in_byte":18844,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"29759644347","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n\r\n\r\n'''\r\n 使用逻辑回归算法，进行二分类\r\n'''\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score\r\nbreask_cancer = pd.read_csv(r'D:\\自己用\\项目\\R语言\\课程\\实验三\\breast_cancer.csv',header =None)\r\n#print(breask_cancer.head())\r\n\r\n#进行数据处理，删除’？’所在的行\r\nprint(breask_cancer.shape)\r\ndata = breask_cancer.replace(to_replace='?',value=np.nan)\r\n# data = breask_cancer.replace(to_replace='',value=np.nan)\r\ndata = data.dropna(how='any')\r\nprint(data.shape)\r\n\r\nx = data.iloc[:,1:10]\r\ntarget =data.loc[:,10]\r\n#将数据集拆分成训练集和测试集\r\nx_train,x_test,y_train,y_test = train_test_split(x,target,test_size=0.25,random_state=33)\r\n\r\n#创建线性回归模型\r\nfrom sklearn.linear_model import LogisticRegression\r\n#solver,选择更新参数使用何种方式，sag随机梯度下降，lbfas拟牛顿算法，newton-cg牛顿法\r\n#max_iter 更新多少次参数\r\n#tol误差小于tol时停止更新参数，默认值1e-4\r\nlr = LogisticRegression(solver='sag',max_iter=3000)\r\nlr.fit(x_train,y_train)\r\nlrpredict = lr.predict(x_test)\r\n# print(lrpredict)\r\n# print(y_test)\r\nprint(accuracy_score(y_test,lrpredict))\r\nfrom sklearn import metrics\r\nprint(metrics.confusion_matrix(y_test,lrpredict))\r\nprint(y_test.shape)\r\nprint((70+99)/(70+1+99+1))\r\n\r\nres = lr.predict([[2\t,1,\t1,\t1,\t2,\t1\t,2,\t1,\t1]])\r\nprint(res)\r\nres = lr.predict([[8\t,7,\t5\t,10\t,7,\t9\t,5\t,5\t,4]])\r\nprint(res)\r\n\r\n\r\n","repo_name":"fulequn/PythonLearning","sub_path":"机器学习/20200411-01.py","file_name":"20200411-01.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27782892463","text":"#!/usr/bin/env python\r\n# Author: Tyler Sanderson \r\n#\r\n# This file is part of PyBST.\r\n#\r\n# PyBST is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n#\r\n# PyBST is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with PyBST. If not, see .\r\n\r\nimport collections\r\nimport bstree\r\n\r\nNode = bstree.Node\r\nBSTree = bstree.BSTree\r\n\r\nclass AVLNode(Node):\r\n \"\"\"Represents a node of a balanced AVL Tree\"\"\"\r\n def __init__(self,key,value):\r\n \"\"\"Initializes a BST node, then add height and balance attributes\"\"\"\r\n Node.__init__(self,key,value)\r\n self.height = 0\r\n self.balance = 0\r\n\r\nclass AVLTree(BSTree):\r\n \"\"\"\r\n AVLTree implements a self-balancing AVL Tree.\r\n\r\n An AVL Tree is an ordered node based tree key structure\r\n in which each node has at most two children, and the heights\r\n of each children of a node differ by at most one.\r\n\r\n For more information regarding AVL Trees, see:\r\n http://en.wikipedia.org/wiki/Avl_tree\r\n\r\n Constructors:\r\n\r\n AVLTree() -> Creates a new empty AVL Tree\r\n AVLTree(seq) -> Creates a new AVL Tree from the elements in sequence [(k1,v1),(k2,v2),...,(kn,vn)]\r\n\r\n For further explanation of some functions or their source code, see bstree.py.\r\n \"\"\"\r\n def __init__(self,*args):\r\n \"\"\"Initializes tree the same as a BST\"\"\"\r\n BSTree.__init__(self,*args)\r\n\r\n def is_valid(self, *args):\r\n \"\"\"\r\n T.is_valid(...) -> Boolean. Produces True if and only if\r\n T is a valid AVL Tree. Raises an exception otherwise.\r\n \"\"\"\r\n if len(args) == 0:\r\n node = self.Root\r\n else:\r\n node = args[0]\r\n\r\n if not node:\r\n return True\r\n\r\n expected_height = self.get_height(node)\r\n expected_balance = self.get_balance(node)\r\n\r\n if not (node.height == expected_height):\r\n raise Exception(\"Height of node \" + str(node.key) + \" is \" + str(node.height) + \" and should be \" + str(expected_height))\r\n\r\n if not (node.balance == expected_balance):\r\n raise Exception(\"Balance of node \" + str(node.key) + \" is \" + str(node.balance) + \" and should be \" + str(expected_balance))\r\n\r\n if abs(expected_balance) > 1:\r\n raise Exception(\"Tree is unbalanced at node \" + str(node.key))\r\n\r\n if node.left:\r\n if not node.left.parent == node:\r\n raise Exception(\"Left child of node \" + str(node.key) + \" is adopted by another node!\")\r\n\r\n if node.right:\r\n if not node.right.parent == node:\r\n raise Exception(\"Right child of node \" + str(node.key) + \" is adopted by another node!\")\r\n\r\n if node.parent and node.parent.left == node:\r\n if node.key > node.parent.key:\r\n raise Exception(\"Node \" + str(node.key) + \" is to the left of \" + str(node.parent.key) + \" but is larger\")\r\n\r\n if node.parent and node.parent.right == node:\r\n if node.key < node.parent.key:\r\n raise Exception(\"Node \" + str(node.key) + \" is to the right of \" + str(node.parent.key) + \" but is smaller\")\r\n\r\n return (self.is_valid(node.left) and self.is_valid(node.right))\r\n\r\n def preorder(self,*args):\r\n \"\"\"\r\n T.preorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in preorder.\r\n \"\"\"\r\n return BSTree.preorder(self,*args)\r\n\r\n def inorder(self,*args):\r\n \"\"\"\r\n T.inorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in inorder.\r\n \"\"\"\r\n return BSTree.inorder(self,*args)\r\n\r\n def postorder(self,*args):\r\n \"\"\"\r\n T.postorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in postorder.\r\n \"\"\"\r\n return BSTree.postorder(self,*args)\r\n\r\n def levelorder(self):\r\n \"\"\"\r\n T.levelorder(...) -> Sequence. Produces a sequence of the Nodes\r\n in T, obtained in levelorder.\r\n \"\"\"\r\n return BSTree.levelorder(self,*args)\r\n\r\n def get_node(self,key,*args):\r\n \"\"\"\r\n T.get_node(key,...) -> Node. Produces the Node in T with key\r\n attribute key. If there is no such Node, produces None.\r\n \"\"\"\r\n return BSTree.get_node(self,key,*args)\r\n\r\n def insert(self,key,value,*args):\r\n \"\"\"\r\n T.insert(key,value...) <==> T[key] = value. Inserts\r\n a new Node with key attribute key and value attribute\r\n value into T. Balances if necessary.\r\n \"\"\"\r\n if not isinstance(key,(int,long,float)):\r\n raise TypeError(str(key) + \" is not a number\")\r\n else:\r\n if not self.Root:\r\n self.Root = AVLNode(key,value)\r\n elif len(args) == 0:\r\n if not self.get_node(key,self.Root):\r\n self.insert(key,value,self.Root)\r\n else:\r\n child = AVLNode(key,value)\r\n parent = args[0]\r\n if child.key > parent.key:\r\n if not parent.right:\r\n parent.right = child\r\n child.parent = parent\r\n self._update_height(parent)\r\n self._update_balance(parent)\r\n node = child\r\n while node and abs(node.balance) <=1:\r\n node = node.parent\r\n if node:\r\n self._balance(node)\r\n else:\r\n self.insert(key,value,parent.right)\r\n else:\r\n if not parent.left:\r\n parent.left = child\r\n child.parent = parent\r\n self._update_height(parent)\r\n self._update_balance(parent)\r\n node = child\r\n while abs(node.balance) <=1:\r\n node = node.parent\r\n if not node:\r\n break\r\n if node:\r\n self._balance(node)\r\n else:\r\n self.insert(key,value,parent.left)\r\n\r\n def insert_from(self,seq):\r\n \"\"\"\r\n T.insert_from(seq). For every key, value pair in seq,\r\n inserts a new Node into T with key and value attributes\r\n as given.\r\n \"\"\"\r\n BSTree.insert_from(self,seq)\r\n\r\n def get_max(self,*args):\r\n \"\"\"\r\n T.get_max(...) -> Node. Produces the Node that has the maximum\r\n key attribute in T.\r\n \"\"\"\r\n return BSTree.get_max(self,*args)\r\n\r\n def get_min(self,*args):\r\n \"\"\"\r\n T.get_min(...) -> Node. Produces the Node that has the minimum\r\n key attribute in T.\r\n \"\"\"\r\n return BSTree.get_min(self,*args)\r\n\r\n def get_element_count(self,*args):\r\n \"\"\"\r\n T.get_element_count(...) -> Nat. Produces the number of elements\r\n in T.\r\n \"\"\"\r\n return BSTree.get_element_count(self,*args)\r\n\r\n def get_height(self,*args):\r\n \"\"\"\r\n T.get_height(...) -> Nat. Produces the height of T, defined\r\n as one added to the height of the tallest subtree.\r\n \"\"\"\r\n return BSTree.get_height(self,*args)\r\n\r\n def get_balance(self,*args):\r\n \"\"\"\r\n T.get_balance(...) -> Nat. Produces the balance of T, defined\r\n as the height of the right subtree taken away from the height\r\n of the left subtree.\r\n \"\"\"\r\n if len(args) == 0:\r\n node = self.Root\r\n else:\r\n node = args[0]\r\n\r\n return ((node.left.height if node.left else -1) -\r\n (node.right.height if node.right else -1))\r\n\r\n def _update_height(self,node):\r\n \"\"\"\r\n T._update_height(node). Updates the height attribute\r\n of Nodes in T starting from node backtracking up to the root.\r\n \"\"\"\r\n if not node:\r\n pass\r\n else:\r\n new_height = self.get_height(node)\r\n if node.height == new_height:\r\n pass\r\n else:\r\n node.height = new_height\r\n self._update_height(node.parent)\r\n\r\n def _update_balance(self,node):\r\n \"\"\"\r\n T._update_balance(node). Updates the balance attribute\r\n of Nodes in T starting from node backtracking up to the root.\r\n \"\"\"\r\n if not node:\r\n pass\r\n else:\r\n new_balance = self.get_balance(node)\r\n if node.balance == new_balance:\r\n pass\r\n else:\r\n node.balance = new_balance\r\n self._update_balance(node.parent)\r\n\r\n def _rotate_left(self,pivot):\r\n \"\"\"\r\n T._rotate_left(pivot). Performs a left tree rotation in T\r\n around the Node pivot.\r\n \"\"\"\r\n old_root = pivot\r\n par_node = old_root.parent\r\n\r\n new_root = old_root.right\r\n temp = new_root.right\r\n old_root.right = new_root.left\r\n\r\n if (old_root.right):\r\n old_root.right.parent = old_root\r\n new_root.left = old_root\r\n old_root.parent = new_root\r\n\r\n if par_node is None:\r\n self.Root = new_root\r\n self.Root.parent = None\r\n else:\r\n if par_node.right and par_node.right.key == old_root.key:\r\n par_node.right = new_root\r\n new_root.parent = par_node\r\n elif par_node.left and par_node.left.key == old_root.key:\r\n par_node.left = new_root\r\n new_root.parent = par_node\r\n\r\n self._update_height(new_root.left)\r\n self._update_height(par_node)\r\n self._update_balance(new_root.left)\r\n self._update_balance(par_node)\r\n\r\n def _rotate_right(self,pivot):\r\n \"\"\"\r\n T._rotate_right(pivot). Performs a right tree rotation in T\r\n around the Node pivot.\r\n \"\"\"\r\n old_root = pivot\r\n par_node = old_root.parent\r\n\r\n new_root = old_root.left\r\n temp = new_root.left\r\n old_root.left = new_root.right\r\n\r\n if (old_root.left):\r\n old_root.left.parent = old_root\r\n\r\n new_root.right = old_root\r\n old_root.parent = new_root\r\n\r\n if par_node is None:\r\n self.Root = new_root\r\n self.Root.parent = None\r\n else:\r\n if par_node.right and par_node.right.key == old_root.key:\r\n par_node.right = new_root\r\n new_root.parent = par_node\r\n elif par_node.left and par_node.left.key == old_root.key:\r\n par_node.left = new_root\r\n new_root.parent = par_node\r\n\r\n self._update_height(new_root.right)\r\n self._update_height(par_node)\r\n self._update_balance(new_root.right)\r\n self._update_balance(par_node)\r\n\r\n def _balance(self,pivot):\r\n \"\"\"\r\n T._balance(pivot). Balances T at Node pivot, performing\r\n appropriate tree rotations to ensure T remains a valid AVL Tree.\r\n \"\"\"\r\n weight = self.get_balance(pivot)\r\n\r\n if weight == -2:\r\n if self.get_balance(pivot.right) == -1 or self.get_balance(pivot.right) == 0:\r\n self._rotate_left(pivot)\r\n\r\n elif self.get_balance(pivot.right) == 1:\r\n self._rotate_right(pivot.right)\r\n self._rotate_left(pivot)\r\n\r\n elif weight == 2:\r\n if self.get_balance(pivot.left) == 1 or self.get_balance(pivot.left) == 0:\r\n self._rotate_right(pivot)\r\n\r\n elif self.get_balance(pivot.left) == -1:\r\n self._rotate_left(pivot.left)\r\n self._rotate_right(pivot)\r\n\r\n def _delete_leaf(self,node):\r\n \"\"\"\r\n T._delete_leaf_parent(node). Deletes node from T, treating it\r\n as a Node with only one child.\r\n \"\"\"\r\n par_node = node.parent\r\n\r\n if par_node:\r\n if par_node.left == node:\r\n par_node.left = None\r\n else:\r\n par_node.right = None\r\n\r\n del node\r\n\r\n self._update_height(par_node)\r\n self._update_balance(par_node)\r\n to_balance = par_node\r\n\r\n while to_balance and abs(to_balance.balance) <=1:\r\n to_balance = to_balance.parent\r\n if to_balance:\r\n self._balance(to_balance)\r\n\r\n else:\r\n self.Root = None\r\n\r\n def _delete_leaf_parent(self,node):\r\n \"\"\"\r\n T._delete_leaf_parent(node). Deletes node from T, treating it\r\n as a Node with only one child.\r\n \"\"\"\r\n par_node = node.parent\r\n\r\n if node.key == self.Root.key:\r\n if node.right:\r\n self.Root = node.right\r\n node.right = None\r\n else:\r\n self.Root = node.left\r\n node.left = None\r\n\r\n else:\r\n if par_node.right == node:\r\n if node.right:\r\n par_node.right = node.right\r\n par_node.right.parent = par_node\r\n node.right = None\r\n else:\r\n par_node.right = node.left\r\n par_node.right.parent = par_node\r\n node.left = None\r\n else:\r\n\r\n if node.right:\r\n par_node.left = node.right\r\n par_node.left.parent = par_node\r\n node.right = None\r\n else:\r\n par_node.left = node.left\r\n par_node.left.parent = par_node\r\n node.left = None\r\n\r\n del node\r\n\r\n self._update_height(par_node)\r\n self._update_balance(par_node)\r\n to_balance = par_node\r\n\r\n while to_balance and abs(to_balance.balance) <=1:\r\n to_balance = to_balance.parent\r\n if to_balance:\r\n self._balance(to_balance)\r\n\r\n def _switch_nodes(self,node1,node2):\r\n \"\"\"\r\n T._switch_nodes(node1,node2). Switches positions\r\n of node1 and node2 in T.\r\n \"\"\"\r\n BSTree._switch_nodes(self,node1,node2)\r\n\r\n def _delete_node(self,node):\r\n \"\"\"\r\n T._delete_node(node). Deletes node from T, treating it as\r\n a Node with two children.\r\n \"\"\"\r\n if self.get_height(node.left) > self.get_height(node.right):\r\n to_switch = self.get_max(node.left)\r\n self._switch_nodes(node,to_switch)\r\n\r\n if not (to_switch.right or to_switch.left):\r\n to_delete = self.get_max(node.left)\r\n self._delete_leaf(to_delete)\r\n else:\r\n to_delete = self.get_max(node.left)\r\n self._delete_leaf_parent(to_delete)\r\n else:\r\n to_switch = self.get_min(node.right)\r\n self._switch_nodes(node,to_switch)\r\n\r\n if not (to_switch.right or to_switch.left):\r\n to_delete = self.get_min(node.right)\r\n self._delete_leaf(to_delete)\r\n else:\r\n to_delete = self.get_min(node.right)\r\n self._delete_leaf_parent(to_delete)\r\n\r\n def delete(self,key):\r\n \"\"\"T.delete(key) <==> del T[key]. Deletes the Node\r\n with key attribute key from T.\r\n \"\"\"\r\n node = self.get_node(key,self.Root)\r\n\r\n if node:\r\n if not (node.left or node.right):\r\n self._delete_leaf(node)\r\n\r\n elif not (node.left and node.right):\r\n self._delete_leaf_parent(node)\r\n\r\n else:\r\n self._delete_node(node)\r\n\r\n def delete_from(self,seq):\r\n \"\"\"\r\n T.delete_from(seq). For every keyin seq, deletes\r\n the Node with that key attribute from T.\r\n \"\"\"\r\n if isinstance(seq,collections.Iterable):\r\n for x in seq:\r\n self.delete(x)\r\n else:\r\n raise TypeError(str(iter) + \" is not iterable\")","repo_name":"TylerSandman/py-bst","sub_path":"pybst/avltree.py","file_name":"avltree.py","file_ext":"py","file_size_in_byte":16515,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"82"} +{"seq_id":"846835799","text":"# Importer les bibliothèques nécessaires\nprint('Importation des librairies...\\n')\nimport pandas as pd\nimport numpy as np\nimport re\nimport json\nimport os\nimport librosa\nimport random\n\n############################################################################################################################\ndef Sup_columns(Dataframe):\n column_to_dtop = ['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accents', 'variant', 'locale', 'segment']\n Dataframe.drop(column_to_dtop, inplace=True, axis = 1)\n print(len(column_to_dtop),'colonnes supprimées.\\n')\n return Dataframe\n\ndef add_fullaudio_path (Dataframe,clips_path):\n Dataframe['path'] = clips_path + '//' + Dataframe['path'] # Créer une nouvelle colonne 'path' en concaténant path_audio avec le contenu de 'path'\n new_df = Dataframe[['path', 'sentence']] # Créer un nouveau DataFrame avec les colonnes 'path' et 'sentence' uniquement\n # Afficher le premier chemin audio et la première phrase\n #print(new_df['path'][0])\n #print(new_df['sentence'][0])\n return new_df\n\n\"\"\"\ndef treat_sentence(Dataframe):\n Dataframe[\"sentence\"] = Dataframe[\"sentence\"].apply(clean_sentence)\n # Afficher le premier chemin audio et la première phrase\n #print(Dataframe['path'][0])\n #print(Dataframe['sentence'][0])\n return Dataframe\n\ndef clean_sentence(text):\n text = re.sub(r\"[:,-?!;.@#+*$£%<>_°)(&=\\[\\]\\^\\\"]\", \"\", text) # Supprimer les caractères spéciaux\n text = text.lower() # Normaliser en lettres minuscules\n return text\n\ndef create_vocab(Dataframe): # UNIQUEMENT SUR LE TRAIN DATASET\n \n print(\"Extraction du vocabulaire..\")\n sentence_concatenated = \"\".join(Dataframe[\"sentence\"])\n letters = sorted(list(set(sentence_concatenated))) # Lettres distinctes\n letters.append(\"|\") # Ajouter le caractère spécial pour l'espace\n letters.append(\"\") # Ajouter le token \"inconnu\"\n letters.append(\"\") # Ajouter le token de remplissage\n\n vocab = {char: idx for idx, char in enumerate(letters)}\n print('\\nVocabulaire extrait :',vocab)\n\n print(\"\\nTokenization..\")\n Dataframe[\"tokens\"] = Dataframe[\"sentence\"].apply(tokenize_text, args=(vocab,))\n \n\n with open(f\"{new_dataset_path}vocabulaire.json\", \"w\") as f:\n json.dump(vocab, f)\n \n return Dataframe\n\ndef tokenize_text(text, vocab):\n tokens = []\n for char in text:\n if char in vocab:\n tokens.append(vocab[char])\n else:\n tokens.append(vocab[\"\"]) # Caractère inconnu\n return tokens\n\"\"\"\n\ndef View_samplingRate(Dataframe):\n print(\"\\nRandom sampling rate :\")\n for i in range(0,4):\n r = random.randint(0,10000)\n signal, sr = librosa.load(Dataframe['path'][r], sr=None) # Charger l'audio avec le sampling rate d'origine\n print(f\"audio_file n°{r} : {sr}\")\n\n\"\"\" \ndef process_audio(file_path, target_sr):\n signal, sr = librosa.load(file_path, sr=None) # Charger l'audio avec le sampling rate d'origine\n if sr != target_sr:\n signal = librosa.resample(signal, sr, target_sr) # Resampler l'audio au sampling rate cible\n\n return np.asarray(signal)\n\ndef resample_audio(Dataframe):\n target_sr = 16000 # Sampling rate cible\n\n # Parcourir tous les fichiers audio et les traiter\n for index, row in Dataframe.iterrows():\n audio_file = row[\"path\"]\n processed_audio = process_audio(audio_file, target_sr)\n\"\"\" \n\n############################################################################################################################\n\n# Chemin vers les fichiers contenant les datas\ndataset_path = '//Users//charles-albert//Desktop//Projet Ingénieur//datas//fr//'\nclips_path = '//Users//charles-albert//Desktop//Projet Ingénieur//datas//fr//clips'\nnew_dataset_path = '//Users//charles-albert//Desktop/Projet Ingénieur//treat_datas//'\n\ntrain_path = f'{dataset_path}train.tsv'\ntest_path = f'{dataset_path}test.tsv'\n\n# Charger les fichiers TSV dans un DataFrame pandas\nprint('Creation des dataframes...\\n')\ntrain_df = pd.read_csv(train_path, delimiter='\\t')\ntest_df = pd.read_csv(test_path, delimiter='\\t')\n\nprint('\\n',len(train_df),'Train samples trouvés\\n')\ntrain_df.info()\n\nprint('\\n',len(test_df),'Test samples trouvés\\n')\ntest_df.info()\n\n# Supression des colonnes inutiles - Modification colonne 1 (path) - Modification colonne 2 (sentence)\nprint('\\nTraitement des Datas...')\nprint('\\nSupression des colonnes inutiles...')\ntrain_df = Sup_columns(train_df)\ntest_df = Sup_columns(test_df)\n\nprint('\\nTraitement de la colonne \\'path\\'...')\ntrain_df = add_fullaudio_path(train_df,clips_path)\ntest_df = add_fullaudio_path(test_df,clips_path)\n\n\"\"\"\nprint('\\nTraitement de la colonne \\'sentence\\'...')\ntrain_df = treat_sentence(train_df)\ntest_df = treat_sentence(test_df)\n\nprint('\\nCreation d\\'un vocabulaire et d\\'un Tokenizer...')\ntrain_df = create_vocab(train_df)\n\"\"\"\n\nprint('\\nVisualisation des Sampling Rate...')\nView_samplingRate(train_df)\nView_samplingRate(test_df)\n\nprint('\\nVisualisation des nouveaux dataframes...')\nprint(train_df.head(5))\nprint(test_df.head(5))\n\nprint('\\nSauvegarde...')\ntrain_df.to_csv(f\"{new_dataset_path}train.tsv\", sep=\"\\t\", index=False)\ntest_df.to_csv(f\"{new_dataset_path}test.tsv\", sep=\"\\t\", index=False)\nprint('\\nSauvegarde Terminée.')","repo_name":"CAprogs/French-Speech-Recognition","sub_path":"01_pretraitement_datas.py","file_name":"01_pretraitement_datas.py","file_ext":"py","file_size_in_byte":5295,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10879863204","text":"from typing import Any\n\nclass Node:\n \"\"\"\n models a node in a doubly linked list\n \"\"\"\n\n def __init__(self, data: Any) -> None:\n self.data = data\n self.next_node = None\n self.previous_node = None\n\n\nclass DoublyLinkedList:\n \"\"\"\n models a doubly linked list data structure\n \"\"\"\n\n def __init__(self) -> None:\n self.head = None\n self.tail = None\n self.number_of_nodes = 0\n\n #0(1) operation\n def insert_end(self, data: Any) -> None:\n new_node = Node(data)\n self.number_of_nodes += 1\n\n # if linked list is empty\n if not self.head:\n self.head = new_node\n self.tail = new_node\n # there is at least one item\n else:\n self.tail.next_node = new_node\n new_node.previous_node = self.tail\n\n self.tail = new_node\n\n\n # 0(n) operation. Remember, doubly linked lists could be traversed\n # in both directions.\n def traverse_forward(self) -> None:\n place_holder_node = self.head\n\n while place_holder_node is not None:\n print(place_holder_node.data)\n place_holder_node = place_holder_node.next_node\n\n # O(n) operation\n def traverse_backward(self) -> None:\n place_holder_node = self.tail\n\n while place_holder_node is not None:\n print(place_holder_node.data)\n place_holder_node = place_holder_node.previous_node\n\n\nif __name__ == \"__main__\":\n doubly_list = DoublyLinkedList()\n\n doubly_list.insert_end(1)\n doubly_list.insert_end(2)\n doubly_list.insert_end(3)\n\n doubly_list.traverse_forward()\n print(\"______________________________\")\n doubly_list.traverse_backward()\n","repo_name":"nyior/algorithms-and-datastructures-python","sub_path":"data_structures/linked_lists/doubly_linked_list/implementation.py","file_name":"implementation.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70054911630","text":"import os\nimport sys\nfrom sentence_transformers import SentenceTransformer, util\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\n# find the most similar posts to a given question in a given tag\n# return as user prompt\ndef findSimilar(course, tag, question):\n print(\"Encoding similar archived posts...\")\n\n # get all posts from same tag as question\n # with student's question as the first entry\n posts = []\n posts.append(question)\n\n # go thru files in tag folder to collect posts\n folder = './data/' + course + '/' + tag + '/'\n for filename in os.listdir(folder):\n filepath = folder + filename\n file = open(filepath, 'r')\n po = file.read().split('###question ')\n po = po[1:] # get rid of empty first entry\n po = [p.strip() for p in po] # get rid of trailing newlines\n posts.extend(po)\n\n # get only questions\n questions = []\n questions.append(question)\n questions.extend([p.split(\"#\")[0] for p in posts[1:]])\n\n # encode all questions\n embeddings = model.encode(questions)\n\n # compute cosine similarity for all \n cos_sim = util.cos_sim(embeddings[0], embeddings)\n\n # add all pairs to a list with their cosine similarity score\n most_similar_posts = []\n for i in range(len(cos_sim[0])):\n most_similar_posts.append([cos_sim[0][i], i])\n\n # sort list by the highest cosine similarity score\n most_similar_posts = sorted(most_similar_posts, key=lambda x: x[0], reverse=True)\n\n # delete question from most similar posts bc it's the same post\n most_similar_posts = most_similar_posts[1:]\n\n ### DEPRECATED CODE START: this used to just print out something to copypaste\n ### keeping for testing purposes\n # # print question\n # print(\"QUESTION:\\n\" + question + \"\\n\")\n\n # # print top 5 most relevant posts\n # print(\"INFORMATION:\")\n # for score, i in most_similar_posts[0:5]:\n # #print(\"{} \\t {:.4f}\".format(posts[i], score))\n # print(posts[i])\n # print(\"confidence score: {:.3f}\\n\".format(score))\n ### DEPRECATED CODE END\n\n # create user prompt\n # question\n user_prompt = \"QUESTION:\\n\" + question + \"\\n\\n\"\n # top 5 most relevant posts\n user_prompt += \"INFORMATION:\\n\"\n for score, i in most_similar_posts[0:5]:\n user_prompt += posts[i] + \"\\n\"\n user_prompt += \"similarity score: {:.3f}\\n\\n\".format(score)\n\n # also get confidence score based on similarity of most relevant post\n confidence_score = \"{:.3f}\\n\\n\".format(most_similar_posts[0][0])\n\n print(\"Encoded!\")\n\n return user_prompt, confidence_score\n\n\n# prompt input\n# question = input(\"PASTE QUESTION HERE: \")\n# tag = input(\"ENTER CATEGORY: \")\n# print()\n\n# findSimilar(question, tag)","repo_name":"falseaxiom/cgbot","sub_path":"code/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6518789481","text":"from typing import Optional, List\n\nfrom sqlalchemy import select, delete\n\nfrom database import run_query, run_commit, add_item\nfrom database.tables.dt_blacklist import BlacklistType, DTBlacklistItem\n\nasync def get_blacklist_item(bl_type: BlacklistType, identifier: int) -> Optional[DTBlacklistItem]:\n statement = select(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier)\n result = await run_query(statement)\n return result.scalar_one_or_none()\n\nasync def is_on_blacklist(bl_type: BlacklistType, identifier: int) -> bool:\n result = await run_query(select(DTBlacklistItem.identifier).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier))\n return result.scalar_one_or_none() is not None\n\nasync def get_blacklist_items(bl_type: Optional[BlacklistType]=None) -> List[DTBlacklistItem]:\n if bl_type is not None:\n result = await run_query(select(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type)))\n return result.scalars().all()\n\n result = await run_query(select(DTBlacklistItem))\n return result.scalars().all()\n\nasync def create_blacklist_item(bl_type: BlacklistType, identifier: int, additional_data: Optional[str]=None) -> Optional[DTBlacklistItem]:\n item = await get_blacklist_item(bl_type, identifier)\n if item is not None: return None\n\n item = DTBlacklistItem(bl_type=BlacklistType(bl_type), identifier=identifier, additional_data=additional_data)\n await add_item(item)\n\n return item\n\nasync def remove_blacklist_item(bl_type: BlacklistType, identifier: int) -> bool:\n result = await run_query(delete(DTBlacklistItem).filter(DTBlacklistItem.bl_type == BlacklistType(bl_type), DTBlacklistItem.identifier == identifier))\n await run_commit()\n return result.rowcount > 0\n","repo_name":"Matesxs/DeepTownEventBot","sub_path":"database/dt_blacklist_repo.py","file_name":"dt_blacklist_repo.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39746818197","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\ndirect PAS\nPython Application Services\n----------------------------------------------------------------------------\n(C) direct Netware Group - All rights reserved\nhttps://www.direct-netware.de/redirect?pas;contentor\n\nThe following license agreement remains valid unless any additions or\nchanges are being made by direct Netware Group in a written form.\n\nThis program is free software; you can redistribute it and/or modify it\nunder the terms of the GNU General Public License as published by the\nFree Software Foundation; either version 2 of the License, or (at your\noption) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT\nANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\nFITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for\nmore details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program; if not, write to the Free Software Foundation, Inc.,\n51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n----------------------------------------------------------------------------\nhttps://www.direct-netware.de/redirect?licenses;gpl\n----------------------------------------------------------------------------\n#echo(pasContentorVersion)#\n#echo(__FILEPATH__)#\n\"\"\"\n\nfrom time import time\n\nfrom dNG.data.binary import Binary\nfrom dNG.data.data_linker import DataLinker\nfrom dNG.data.ownable_mixin import OwnableMixin as OwnableInstance\nfrom dNG.data.ownable_lockable_read_mixin import OwnableLockableReadMixin\nfrom dNG.database.instances.contentor_document import ContentorDocument as _DbContentorDocument\nfrom dNG.database.instances.text_entry import TextEntry as _DbTextEntry\nfrom dNG.database.lockable_mixin import LockableMixin\nfrom dNG.database.sort_definition import SortDefinition\n\nfrom .category import Category\n\nclass Document(DataLinker, LockableMixin, OwnableLockableReadMixin):\n \"\"\"\n\"Document\" represents a contentor entry.\n\n:author: direct Netware Group et al.\n:copyright: direct Netware Group - All rights reserved\n:package: pas\n:subpackage: contentor\n:since: v0.2.00\n:license: https://www.direct-netware.de/redirect?licenses;gpl\n GNU General Public License 2\n \"\"\"\n\n _DB_INSTANCE_CLASS = _DbContentorDocument\n \"\"\"\nSQLAlchemy database instance class to initialize for new instances.\n \"\"\"\n\n def __init__(self, db_instance = None):\n \"\"\"\nConstructor __init__(Document)\n\n:param db_instance: Encapsulated SQLAlchemy database instance\n\n:since: v0.2.00\n \"\"\"\n\n DataLinker.__init__(self, db_instance)\n LockableMixin.__init__(self)\n OwnableLockableReadMixin.__init__(self)\n\n self.set_max_inherited_permissions(OwnableLockableReadMixin.READABLE,\n OwnableLockableReadMixin.READABLE\n )\n #\n\n def delete(self):\n \"\"\"\nDeletes this entry from the database.\n\n:since: v0.2.00\n \"\"\"\n\n if (self.log_handler is not None): self.log_handler.debug(\"#echo(__FILEPATH__)# -{0!r}.delete()- (#echo(__LINE__)#)\", self, context = \"pas_datalinker\")\n\n with self:\n db_text_entry_instance = self.local.db_instance.rel_text_entry\n\n DataLinker.delete(self)\n if (db_text_entry_instance is not None): self.local.connection.delete(db_text_entry_instance)\n #\n #\n\n def _get_default_sort_definition(self, context = None):\n \"\"\"\nReturns the default sort definition list.\n\n:param context: Sort definition context\n\n:return: (object) Sort definition\n:since: v0.2.00\n \"\"\"\n\n if (self.log_handler is not None): self.log_handler.debug(\"#echo(__FILEPATH__)# -{0!r}._get_default_sort_definition({1})- (#echo(__LINE__)#)\", self, context, context = \"pas_datalinker\")\n\n return (DataLinker._get_default_sort_definition(self, context)\n if (context == \"DataLinker\") else\n SortDefinition([ ( \"position\", SortDefinition.ASCENDING ),\n ( \"title\", SortDefinition.ASCENDING )\n ])\n )\n #\n\n def _get_unknown_data_attribute(self, attribute):\n \"\"\"\nReturns the data for the requested attribute not defined for this instance.\n\n:param attribute: Requested attribute\n\n:return: (dict) Value for the requested attribute\n:since: v0.2.00\n \"\"\"\n\n if (attribute == \"content\" and self.local.db_instance.rel_text_entry is not None): _return = self.local.db_instance.rel_text_entry.content\n else: _return = DataLinker._get_unknown_data_attribute(self, attribute)\n\n return _return\n #\n\n def _insert(self):\n \"\"\"\nInsert the instance into the database.\n\n:since: v0.2.00\n \"\"\"\n\n with self.local.connection.no_autoflush:\n DataLinker._insert(self)\n\n if (self.local.db_instance.time_published is None): self.local.db_instance.time_published = int(time())\n\n is_acl_missing = (len(self.local.db_instance.rel_acl) == 0)\n is_data_missing = self.is_data_attribute_none(\"owner_type\", \"entry_type\")\n is_permission_missing = self.is_data_attribute_none(\"guest_permission\", \"user_permission\")\n\n parent_object = (self.load_parent() if (is_acl_missing or is_data_missing or is_permission_missing) else None)\n\n if (is_data_missing and (isinstance(parent_object, Category) or isinstance(parent_object, Document))):\n parent_data = parent_object.get_data_attributes(\"id_site\", \"entry_type\")\n\n if (self.local.db_instance.id_site is None and parent_data['id_site'] is not None): self.local.db_instance.id_site = parent_data['id_site']\n if (self.local.db_instance.entry_type is None): self.local.db_instance.entry_type = parent_data['entry_type']\n #\n\n if (isinstance(parent_object, OwnableInstance)):\n if (is_acl_missing): self._copy_acl_entries_from_instance(parent_object)\n if (is_permission_missing): self._copy_default_permission_settings_from_instance(parent_object)\n #\n #\n #\n\n def set_data_attributes(self, **kwargs):\n \"\"\"\nSets values given as keyword arguments to this method.\n\n:since: v0.2.00\n \"\"\"\n\n with self, self.local.connection.no_autoflush:\n DataLinker.set_data_attributes(self, **kwargs)\n\n if (\"entry_type\" in kwargs): self.local.db_instance.entry_type = kwargs['entry_type']\n if (\"owner_type\" in kwargs): self.local.db_instance.owner_type = kwargs['owner_type']\n if (\"author_id\" in kwargs): self.local.db_instance.author_id = kwargs['author_id']\n if (\"author_ip\" in kwargs): self.local.db_instance.author_ip = kwargs['author_ip']\n if (\"time_published\" in kwargs): self.local.db_instance.time_published = int(kwargs['time_published'])\n if (\"description\" in kwargs): self.local.db_instance.description = Binary.utf8(kwargs['description'])\n if (\"locked\" in kwargs): self.local.db_instance.locked = kwargs['locked']\n if (\"guest_permission\" in kwargs): self.local.db_instance.guest_permission = kwargs['guest_permission']\n if (\"user_permission\" in kwargs): self.local.db_instance.user_permission = kwargs['user_permission']\n\n if (\"content\" in kwargs):\n if (self.local.db_instance.rel_text_entry is None):\n self.local.db_instance.rel_text_entry = _DbTextEntry()\n self.local.db_instance.rel_text_entry.id = self.local.db_instance.id\n db_text_entry = self.local.db_instance.rel_text_entry\n else: db_text_entry = self.local.db_instance.rel_text_entry\n\n db_text_entry.content = Binary.utf8(kwargs['content'])\n #\n #\n #\n#\n","repo_name":"dNG-git/pas_contentor","sub_path":"src/dNG/data/contentor/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":7898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42585098895","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_lyric_data():\n URL = 'http://www.songlyrics.com/top-songs-lyrics.html'\n page = requests.get(URL)\n\n soup = BeautifulSoup(page.content, 'html.parser')\n results = soup.find(id='wrapper')\n links = str(results.find_all('td', class_='td-item td-last')).split(',')\n to_remove = []\n temp = []\n final = ''\n not_english = ['http://www.songlyrics.com/loonie/tao-lang-lyrics/',\n 'http://www.songlyrics.com/prince-royce/darte-un-beso-lyrics/',\n 'http://www.songlyrics.com/banda-el-recodo-de-cruz-lizarraga/vas-a-llorar-por-m-lyrics/',\n 'http://www.songlyrics.com/banda-los-recoditos/mi-ultimo-deseo-lyrics/',\n 'http://www.songlyrics.com/aventura/el-malo-lyrics/',\n 'http://www.songlyrics.com/slank/ku-tak-bisa-lyrics/',\n 'http://www.songlyrics.com/ron-henley/hagdan-lyrics/',\n 'http://www.songlyrics.com/stromae/tous-les-mmes-lyrics/',\n 'http://www.songlyrics.com/prince-royce/el-amor-que-perdimos-lyrics/',\n 'http://www.songlyrics.com/yeng-constantino/alaala-lyrics/',\n 'http://www.songlyrics.com/yeng-constantino/chinito-lyrics/',\n 'http://www.songlyrics.com/anitta/zen-lyrics/',\n 'http://www.songlyrics.com/sarah-geronimo/tayo-lyrics/',\n 'http://www.songlyrics.com/rio-febrian/jenuh-lyrics/']\n\n for i in range(len(links)):\n http = 0\n\n for _ in range(len(links[i]) - 3):\n end_http = links[i][_] + links[i][_ + 1] + links[i][_ + 2]\n if end_http == '/\" ':\n links[i] = links[i][42:_ + 1]\n http = 1\n break\n\n if http == 0:\n to_remove.append(i)\n\n for count, remove in enumerate(to_remove):\n links.remove(links[remove - count])\n\n for count, link in enumerate(links):\n songs = count\n print('Collecting lyrics from: ', link)\n page = requests.get(link)\n soup = BeautifulSoup(page.content, 'html.parser')\n song_lyrics = str(soup.find(id='songLyricsDiv')).split('
\\r\\n')\n\n for i in range(len(song_lyrics)):\n for _ in song_lyrics[i].split():\n if link not in not_english:\n temp.append(_)\n print(songs - 15, 'songs with lyrics found')\n\n for word in range(len(temp)):\n for z in range(temp[word].count('<')):\n to_remove.clear()\n tag = 0\n for _ in range(len(temp[word])):\n if tag == 1:\n if temp[word][_] == '>':\n to_remove.append(_)\n tag = 0\n break\n\n if temp[word][_] == '<':\n to_remove.append(_)\n tag = 1\n\n if len(to_remove) == 1:\n temp[word] = temp[word][to_remove[0] + 1:]\n\n if len(to_remove) == 2:\n temp[word] = temp[word][:to_remove[0]] + temp[word][to_remove[1] + 1:]\n\n to_remove.clear()\n\n for not_a_word in range(len(temp)):\n if temp[not_a_word] == 'p':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word] == 'span':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:4] == 'id=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:8] == 'iComment':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:10] == 'data-chunk':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:6] == 'href=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word][:7] == 'class=\"':\n to_remove.append(not_a_word)\n\n elif temp[not_a_word] == '':\n to_remove.append(not_a_word)\n\n for count, remove in enumerate(to_remove):\n temp.remove(temp[remove - count])\n\n print('Number of data points (in words): ', len(temp))\n for i in temp:\n final += i\n final += ' '\n\n return final\n","repo_name":"Tennis-Ball/AI-Singer","sub_path":"lyrics_modules/get_lyric_data.py","file_name":"get_lyric_data.py","file_ext":"py","file_size_in_byte":4103,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"34901953347","text":"# -*- coding: utf-8 -*-\nfrom os.path import join,isfile,isdir,dirname,basename\nfrom shutil import copyfile\nimport getopt,os,sys,re\nimport io,json\nimport global_m as gb\nfrom datetime import date\n\n\n'''\nOrganization: MDIBL\nAuthor: Lucie N. Hutchins\nContact: lucie.hutchins@mdibl.org\nDate: June 2019\n\n'''\ndef get_header():\n header='''\n****************** json_generator ********************************************************\n\nThe tool generates sample-specific json files for a given experiment\n\n***************************************************************************************\n '''\n return header\n\ndef prog_usage():\n usage=get_header()\n usage+='''\n\n Usage: PROG [-h] -c path2project_runID_main_config/cfgs/pipeline.cfg [-j path2project_runID_json_template/cfgs/template.json] [-s fastq]\n Where:\n -h To show the usage\n -c path2runID/cfgs/pipeline.cfg or --cfg=path2runID/cfgs/pipeline.cfg ... required, \n -j path2runID/cfgs/template.json or --jtemp=path2runID/cfgs/template.json ... optional\n (default - gets template path from pipeline.cfg), \n -s fatsq.gz or --suffix=fastq.gz ... optional(default fastq), reads files suffix \n \n What It Does: Uses the json template to generate sample-specific json files under \n the location specified in the pipeline.cfg for json files. \n\n Example: \n python PROG -c path2results/teamName/projectName/runID/cfgs/pipeline.cfg -s fastq\n OR \n python PROG -c path2results/teamName/projectName/runID/cfgs/pipeline.cfg \n -j path2results/teamName/projectName/runID/cfgs/template.json\n OR\n python PROG --cfg=path2results/teamName/projectName/runID/cfgs/pipeline.cfg \n \n ASSUMPTIONS: \n 1) User has full permission to create sample-specific json files\n 2) The json template has been generated in the same directory as the pipeline.cfg file\n '''\n print(\"%s\"%(usage))\n##\n# A data model to store sample info\n#\nclass SampleDOM:\n def __init__(self,sample_id,reads_list,reads_suffix):\n self.id=sample_id\n self.reads=[]\n self.set_sample(reads_list,reads_suffix)\n\n def set_sample(self,reads_list,reads_suffix):\n if reads_list:\n for read_file in reads_list:\n read_file=read_file.strip()\n if read_file.startswith(self.id) and read_file.endswith(reads_suffix):\n self.reads.append(read_file)\n \n def get_read_file(self,sampleID,read_number):\n # Logic:\n # if the len of sample_reads array is one, return the first element\n # else:\n # use the map-reduced algorithm to get the right file name\n #\n if len(self.reads)<=0: return None\n elif len(self.reads)<2: \n try: \n return self.reads[0].replace(\".gz\",\"\")\n except:pass\n else:\n # Map step\n # Create a list of string tokens using one string(read_file)\n ## we want our regular expression to capture both \"_\" and non-alphanumeric characters\n try:\n token_file=self.reads[0].replace(sampleID,\"sample\")\n tokens=re.split(r'[\\W+|_]',token_file)\n ##Based on our standars readID is field#2 in the name\n read_id=tokens[1]\n if read_id.startswith(\"R\"):read_number=\"R\"+read_number\n # Create a dictionary with read_file:read_file.tokens key:value pair\n reads={}\n for read_file in self.reads:\n token_file=read_file.replace(sampleID,\"sample\")\n reads[read_file]=re.split(r'[\\W+|_]',token_file)\n # Reduction step - reduce each dict>value using string tokens\n for token in tokens:\n if token in read_number: continue\n for read_file in reads:\n if token in reads[read_file]:reads[read_file].remove(token)\n # Assembly and quantification step\n except:pass\n read_file=None\n for read in reads:\n if read_number in reads[read]:read_file=read\n return read_file.replace(\".gz\",\"\")\n\nif __name__== \"__main__\":\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"hc:j:s:\", \n [\"help\", \"cfg=\",\"jtemp=\",\"suffix\"])\n except getopt.GetoptError as err:\n # print help information and exit:\n print(\"ERROR:%s\" % (str(err) )) # will print something like \"option -a not recognized\"\n prog_usage()\n sys.exit(1)\n #set program arguments\n json_template=None\n pipeline_config=None\n log_file=None\n json_base_dir=None\n design_file=None\n reads_suffix=\"fastq\"\n for o, a in opts:\n if o in (\"-c\", \"--cfg\"):pipeline_config = a\n elif o in (\"-j\",\"--jtemp\"):json_template = a\n elif o in (\"-s\",\"--suffix\"):reads_suffix = a\n elif o in (\"-h\", \"--help\"):\n prog_usage()\n sys.exit()\n else:\n assert False, \"unhandled option\"\n if pipeline_config is None or not isfile(pipeline_config):\n msg=\"ERROR: pipeline.cfg missing\"\n print(\"%s - Check %s\"%(msg,pipeline_config))\n prog_usage()\n sys.exit()\n #get project global environment variables \n # variables of interest for this step:\n # 1)LOG_BASE\n # 2)JSON_TEMPLATE\n # 3)PATH2_JSON_FILES\n # 4)DESIGN_FILE \n # 5)READS_BASE\n # 6)RUN_ID\n \n project_env=gb.loadEnv(pipeline_config) \n if not project_env[\"LOG_BASE\"]:\n print(\"ERROR: Log directory missing - see:%s\"%(project_env[\"LOG_BASE\"]))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not project_env[\"PATH2_JSON_FILES\"]:\n print(\"ERROR: Json files base directory missing - see:%s\"%(project_env[\"PATH2_JSON_FILES\"]))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not project_env[\"ORIGINAL_READS_BASE\"]:\n print(\"ERROR: Path to Reads files is incorrect - see:%s\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n sys.exit()\n\n if not isdir(project_env[\"LOG_BASE\"]):\n gb.mkdir_p(project_env[\"LOG_BASE\"])\n log_file=join(project_env[\"LOG_BASE\"],basename(__file__)+\".log\")\n if not isdir(project_env[\"PATH2_JSON_FILES\"]):\n gb.mkdir_p(project_env[\"PATH2_JSON_FILES\"])\n json_base_dir=project_env[\"PATH2_JSON_FILES\"]\n if json_template is None: \n json_template=project_env[\"JSON_TEMPLATE\"]\n design_file=project_env[\"DESIGN_FILE\"]\n project_run_id=\"\"\n if \"RUN_ID\" in project_env:\n project_run_id=project_env[\"RUN_ID\"]\n\n if not isdir(json_base_dir):\n print(\"ERROR: Json files base directory does not exist - see:%s\"%(json_base_dir))\n print(\"create the above directory and try again.\")\n sys.exit()\n if not isfile(design_file): \n print(\"ERROR: The design file is missing - see:%s\"%(design_file))\n sys.exit()\n if not isfile(json_template):\n print(\"ERROR: Json template file is missing - see:%s\"%(json_template))\n sys.exit()\n ## Load json template into an object\n json_obj=None\n with open(json_template) as f:\n json_obj=json.load(f)\n if json_obj is None:\n print(\"ERROR: Failed to open Json template - see:%s\"%(json_template))\n sys.exit()\n ##Enforce our standards by making a copy of json template under this runID cfgs directory if needed\n cfgs_base=dirname(pipeline_config).strip()\n json_template_base=dirname(json_template).strip()\n json_template_file=basename(json_template).strip()\n if cfgs_base not in json_template_base: \n copyfile(json_template,join(cfgs_base,json_template_file))\n \n log=open(log_file,'w') \n log.write(\"**********************************\\n\")\n log.write(\"**********************************\\n\")\n log.write(\"Date:%s\\n\"%( date.today()))\n log.write(\"\\n\")\n log.write(\"Log file:%s\\n\"%(log_file))\n log.write(\"Json template:%s\\n\"%(json_template)) \n log.write(\"Json files base directory:%s\\n\"%(json_base_dir)) \n log.write(\"Experiment Design File:%s\\n\"%(design_file))\n log.write(\"Experiment Reads base:%s\\n\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n log.write(\"Experiment Run config File:%s\\n\"%(pipeline_config))\n bad_format=False\n json_obj[\"project_run_id\"]=project_run_id\n ## get list of reads file names\n reads_base=project_env[\"ORIGINAL_READS_BASE\"]\n reads=[f for f in os.listdir(reads_base) if isfile(join(reads_base,f))]\n with open(design_file,'r') as f:\n try:\n for line in f.readlines():\n if \"Sample\" in line:continue\n if \"sample_id\" in line:continue\n #Remove leading and trailing whitespace from line\n line=line.strip()\n fields=line.split('\\t')\n sample=SampleDOM(fields[0].strip(),reads,reads_suffix)\n read_file_format=sample.id+\"[delimiter]readID[delimiter][...]\"+reads_suffix\n log.write(\"----------------------------\\n\")\n log.write(\"SampleID:%s\\n\"%(sample.id))\n log.write(\"Read files suffix:%s\\n\"%(reads_suffix))\n log.write(\"Number of Reads:%d\\n\"%(len(sample.reads)))\n \n if len(sample.reads)<=0:\n try:\n log.write(\"ERROR: Bad read files name - expected format - %s\\n\"%(read_file_format))\n log.write(\"Original reads files are expected under - %s\\n\"%(project_env[\"ORIGINAL_READS_BASE\"]))\n except:pass\n bad_format=True\n continue\n read1=join(project_env[\"READS_BASE\"],sample.get_read_file(sample.id,\"1\"))\n read2=None\n sample_json_obj=json_obj\n sample_json_file=join(json_base_dir,sample.id+\".\"+project_env[\"ORGANISM\"]+\".json\")\n sample_json_obj[\"input_fastq_read1_files\"][0][\"path\"]=read1\n if len(sample.reads)>1:read2=join(project_env[\"READS_BASE\"],sample.get_read_file(sample.id,\"2\"))\n log.write(\" READ1:%s\\n\"%(read1))\n if read2 is not None:\n log.write(\" READ2:%s\\n\"%(read2))\n sample_json_obj[\"input_fastq_read2_files\"][0][\"path\"]=read2\n log.write(\"Json file:%s\\n\"%(sample_json_file))\n try:\n to_unicode = unicode\n except NameError:\n to_unicode = str\n with io.open(sample_json_file, 'w', encoding='utf8') as outfile:\n str_ = json.dumps(sample_json_obj,indent=2, sort_keys=True,separators=(',', ': '), ensure_ascii=False)\n outfile.write(to_unicode(str_))\n print(\"Sample:%s\\nJson file:%s\\n\"%(sample.id,sample_json_file))\n except:pass\n if bad_format:\n log.write(\"Failed\\n\")\n print(\"Program failed - check the log file:%s\\n\"%(log_file))\n sys.exit(1)\n log.write(\"Program complete\\n\")\n print(\"Program complete\\n\")\n sys.exit()\n","repo_name":"mdibl/biocore_utils","sub_path":"src/python/json_generator.py","file_name":"json_generator.py","file_ext":"py","file_size_in_byte":11157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3630512358","text":"import turtle as t\nfrom turtle import Screen\n\ntim = t.Turtle()\nscreen = Screen()\n\n########### Challenge 2 - Draw a Dashed Line ########\nwin_width = screen.window_width()\ntim.shape(\"turtle\")\ntim.pencolor('red')\ntim.penup()\ntim.setx(-1 * (win_width / 2))\ntim.width(3)\ntim.pendown()\n\nfor _ in range(int(win_width / 20)):\n if abs(tim.xcor()) > win_width / 2:\n tim.right(90)\n\n tim.forward(10)\n tim.penup()\n tim.forward(10)\n tim.pendown()\n\n\nscreen.exitonclick()\n","repo_name":"cuauhtlahuac/100DaysOfPythonCode","sub_path":"day18/draw_dashed_line.py","file_name":"draw_dashed_line.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"27046373153","text":"import random\nimport sys\nn = 100\nif len(sys.argv) == 3:\n\tif int(sys.argv[2]) < 1:\n\t\tprint(\"TOO LOW OF VALUE FOR N\")\n\telse:\n\t\tn = int(sys.argv[2])\n\t\n\nf = open(sys.argv[1], encoding='utf-8')\nlyrics = f.readlines()\n\nstrings = \" \".join(lyrics)\nstrings = strings.lower()\nstrLyrics = \" \".join(lyrics)\nstrLyrics = strLyrics.replace(\"?\",\" \")\nstrLyrics = strLyrics.replace(\"\\n\",\" \")\nstrLyrics = strLyrics.replace('\"',\" \")\nstrLyrics = strLyrics.replace(\",\",\" \")\nstrLyrics = strLyrics.lower()\nstrLyrics = strLyrics.replace(\"-\",\" \")\nstrLyrics = strLyrics.replace(\".\",\" \")\nstrLyrics = strLyrics.replace(\":\",\" \")\nstrLyrics = strLyrics.replace(\";\",\" \")\nstrLyrics = strLyrics.replace(\"(\",\" \")\nstrLyrics = strLyrics.replace(\")\",\" \")\nstrLyrics = strLyrics.replace(\"!\",\" \")\nblack_sabbath = strLyrics.split(' ') \n\n\n#START OF UNIGRAMS\nunigrams = {} #empty Dictionary\n#This for loop creates a unigram dictionary and counts each word.\nfor ozzy_token in black_sabbath:\n\tif ozzy_token != \"\":\n\n\t\tif unigrams.get(ozzy_token) == None:\n\t\t\tunigrams[ozzy_token] = 1\n\n\t\telse:\n\t\t\tnum = unigrams[ozzy_token]\n\t\t\tunigrams[ozzy_token] = num + 1\n\n#Testing the Unigram Model\nkey = list(unigrams.keys())\nval = list(unigrams.values())\nsentence = random.choices(key, weights = val, k = n)\nstr2 = \" \".join(sentence)\n\n\n#UNCOMMENT\nprint(\"UNIGRAMS MODEL\")\nprint(str2)\n#UNCOMMENT\nprint(\" \")\n\n#UNIGRAMS END\n######################################################\n#BIGRAMS START\nprint(\"BIGRAMS MODEL\")\nbigrams = {}\nfor i in range(len(black_sabbath)):\n\tif black_sabbath[i] != \"\" and black_sabbath[i+1] != \"\":\n\t\t#if this bigram doesn't exist\n\n\t\tif bigrams.get(black_sabbath[i]) == None:\n\t\t\tsecond = {black_sabbath[i+1] :1}\n\t\t\tbigrams[black_sabbath[i]] = second\n\t\n\t\t#If this bigram exists with this word\n\t\telif bigrams.get(black_sabbath[i]) != None:\n\t\t\texisting = bigrams[black_sabbath[i]]\n\t\t\t#if existing first word doesn't have this second word\n\t\t\tif(existing.get(black_sabbath[i+1]) == None ):\n\t\t\t\texisting[black_sabbath[i + 1]] = 1\n\t\t\t\tbigrams[black_sabbath[i]].update(existing)\n\t\t\telse:\n\t\t\t#if this bigram occured already add one to its count\n\t\t\t\tnum = existing[black_sabbath[i+1]]\n\t\t\t\texisting[black_sabbath[i+1]] = num + 1\n\t\t\t\tbigrams[black_sabbath[i]] = existing\n\n\ni = 0\nuni = random.choices(key,weights = val, k = 1)\nnextWord = \" \".join(uni)\nsong = []\nwhile i < n:\n\tif bigrams.get(nextWord,None) == None:\n\t\tuni = random.choices(key,weights = val, k = 1)\n\t\tnextWord = \" \".join(uni)\n\telse:\n\t\ttemp = bigrams.get(nextWord) \t\n\t\tkey2 = (list(temp.keys())) \n\t\tvals2 = (list(temp.values()))\n\t\tphrase = random.choices(key2, weights = vals2, k = 1)\n\t\tsong.append(\" \" + \" \".join(phrase))\n\t\tnextWord = \" \".join(phrase)\n\t\ti+= 1\n\nprint(\"\".join(song).strip())\n\n\n\n#END OF BIGRAMS\n######################################################\n#START OF TRIGRAMS\ntrigrams = {} \nfor i in range(len(black_sabbath)):\n\tif black_sabbath[i] != \"\" and black_sabbath[i+1] != \"\" and black_sabbath[i+2] != \"\": \n\t\t#Case 1: First word in trigram doesn't exist\n\t\tif trigrams.get(black_sabbath[i]) == None:\n\t\t\tnext_two = {black_sabbath[i+1] :{black_sabbath[i+2]:1}} \n\t\t\ttrigrams[black_sabbath[i]] = next_two\t\n\n\t\t#Case 2: First word exists, second word doesn't exist.\n\t\telif trigrams.get(black_sabbath[i]) != None:\n\t\t\tfirst_word = trigrams[black_sabbath[i]]\n\t\t\t\n\t\n\t\t\t#if second word doesn't exist add it. \n\t\t\tif first_word.get(black_sabbath[i+1]) == None:\n\t\t\t\tnext_two = {black_sabbath[i+1] :{black_sabbath[i+2]:1}}\n\t\t\t\ttrigrams[black_sabbath[i]].update(next_two)\n\t\t\t#second Word is there third isn't\n\t\t\telif first_word.get(black_sabbath[i+1]) != None: \n\t\t\t\tsecond_word = trigrams[black_sabbath[i]][black_sabbath[i+1]]\t\t\t\t\n\t\t\t\t#HAS THIRD WORD\n\t\t\t\tif second_word.get(black_sabbath[i+2]) != None:\n\t\t\t\t\tnum = second_word[black_sabbath[i+2]]\n\t\t\t\t\ttrigrams[black_sabbath[i]][black_sabbath[i+1]][black_sabbath[i+2]] = num + 1\n\t\t\t\t#NO THIRD WORD\t \n\t\t\t\telif second_word.get(black_sabbath[i+2]) == None:\n\t\t\t\t\tlast = {black_sabbath[i+2]: 1}\n\t\t\t\t\ttrigrams[black_sabbath[i]][black_sabbath[i+1]].update(last)\n\n\n\n\t\nprint(\" \")\nprint(\"TRIGRAMS\")\ni = 0\nuni = random.choices(key,weights = val, k = 1)\nnextWord = \" \".join(uni)\nsong = []\nwhile i < n:\n\t##If no trigram starts with this.\n\tif trigrams.get(nextWord) == None:\t\t\n\t\tuni = random.choices(key,weights = val, k = 1)\n\t\tnextWord = \" \".join(uni)\t\n\telse: #If there is a trigram\n\t\tfirst = trigrams.get(nextWord) #FIRST WORD\n\t\ttemp = list(first.keys())\t\n\t\tsecond = random.choices(temp) #SECOND WORD\t\n\t\tthird = first.get(second[0]) #RN ITS A DICT\n\t\tthird_keys = list(third.keys())\n\t\tthird_vals = list(third.values())\n\t\tthird = random.choices(third_keys,weights=third_vals, k = 1)\n\t\ttri_list = [nextWord,second[0]]\n\t\tphrase = \" \".join(tri_list)\t\n\t\tsong.append(phrase)\n\t\tnextWord = \"\".join(third[0])\n\t\ti = i+1\n\n\n\t\n\n\t\n\n\nprint(\" \".join(song).strip())\n#END OF TRIGRAMS\n\n\n\n\n\n\n\n\n","repo_name":"MicahHarlan/BlackSabbathSongGenerator","sub_path":"mharlan_ngram.py","file_name":"mharlan_ngram.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9120140441","text":"# import threading\nimport concurrent.futures\nimport time\n\nstart = time.perf_counter()\n\n\ndef do_smth(sec):\n print(f\"Sleep {sec} second(s)!\")\n time.sleep(sec)\n return f\"Done sleeping {sec} second(s)\"\n\nwith concurrent.futures.ThreadPoolExecutor() as executor:\n secs = [5, 4, 3, 2, 1]\n results = executor.map(do_smth, secs)\n for result in results:\n print(result)\n\n\n\n # results = [executor.submit(do_smth, sec) for sec in secs]\n # for f in concurrent.futures.as_completed(results):\n # print(f.result())\n\n\n# threads = []\n# for _ in range(10):\n# t = threading.Thread(target=do_smth, args = [1.5])\n# t.start()\n# threads.append(t)\n\n# for thread in threads:\n# thread.join()\n\nprint(f\"Finished in {time.perf_counter() - start} seconds!\")","repo_name":"dkleptsov/small_projects","sub_path":"multithreading/multithread.py","file_name":"multithread.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16650273581","text":"\"\"\"Set of numbers displayed after selecting blue or purple after task.\"\"\"\n\nimport ipywidgets as widgets\nfrom ipywidgets import VBox, HBox\nfrom IPython.display import display\nimport time\n\n# Set of buttons shown after player selects blue or purple\nthree_button = widgets.Button(description='3')\nfour_button = widgets.Button(description='4')\nseven_button = widgets.Button(description='7')\neight_button = widgets.Button(description='8')\nout = widgets.Output()\n\n# Arranges the buttons into a 2x2 table\nleft_box = VBox([three_button, seven_button])\nright_box = VBox([four_button, eight_button])\nout_greater = HBox([left_box, right_box])\n \ndisplay(out_greater, out)\n\ndef three_chosen(clicked):\n \"\"\"After the three button is chosen, a statement corresponding \n to the three button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out: \n print('\\nHere is your task:\\n' \n '\\nEat an apple (or any fruit of your choice)\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the three button is clicked on \nthree_button.on_click(three_chosen)\n\ndef four_chosen(clicked):\n \"\"\"After the four button is chosen, a statement corresponding \n to the four button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nTreat yourself with your favorite dessert!\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n\n return print('Thanks for playing!')\n\n# Allows this function to occur only when the four button is clicked on \nfour_button.on_click(four_chosen)\n\ndef seven_chosen(clicked):\n \"\"\"After the seven button is chosen, a statement corresponding \n to the seven button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nTake a 5 minute break\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the seven button is clicked on\nseven_button.on_click(seven_chosen) \n\ndef eight_chosen(clicked):\n \"\"\"After the eight button is chosen, a statement corresponding \n to the eight button will be shown along with a thank you statement. \n \n Parameters\n ----------\n clicked : Button (ipywidgets.widgets.widget_button.Button)\n Allows function to run if button is clicked.\n \n Returns\n -------\n print('Thanks for playing!') : string\n A statement thanking the player.\n \"\"\" \n with out:\n print('\\nHere is your task:\\n' \n '\\nClean your desk\\n')\n # Delays return statement to give player time to read task\n time.sleep(1.5)\n \n return print('Thanks for playing!')\n\n# Allows this function to occur only when the eight button is clicked on\neight_button.on_click(eight_chosen) ","repo_name":"ckwon822/COGS18Final","sub_path":"task_greater_numbers.py","file_name":"task_greater_numbers.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10649739004","text":"#\n#\n\nimport isce\nimport isceobj\nimport stdproc\nfrom isceobj.Util.Poly2D import Poly2D\nimport logging\nfrom isceobj.Util.decorators import use_api\n\nimport os\nimport numpy as np\nimport shelve\n\nlogger = logging.getLogger('isce.insar.runResampleSubbandSlc')\n\n# Modified by V. Brancato 10.14.2019 added \"self\" as input parameter of resampleSLC\ndef resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, radarWavelength, coregDir,\n azoffname, rgoffname, azpoly = None, rgpoly = None, misreg=False):\n logger.info(\"Resampling secondary SLC\")\n\n imageSlc1 = referenceFrame.getImage().filename\n\n inimg = isceobj.createSlcImage()\n inimg.load(imageSlc2 + '.xml')\n inimg.setAccessMode('READ')\n\n prf = secondaryFrame.PRF\n\n doppler = secondaryFrame._dopplerVsPixel\n factor = 1.0 # this should be zero for zero Doppler SLC.\n coeffs = [factor * 2*np.pi*val/prf/prf for val in doppler]\n\n dpoly = Poly2D()\n dpoly.initPoly(rangeOrder=len(coeffs)-1, azimuthOrder=0, coeffs=[coeffs])\n\n rObj = stdproc.createResamp_slc()\n rObj.slantRangePixelSpacing = secondaryFrame.getInstrument().getRangePixelSize()\n #rObj.radarWavelength = secondaryFrame.getInstrument().getRadarWavelength()\n rObj.radarWavelength = radarWavelength\n rObj.dopplerPoly = dpoly \n\n # for now let's start with None polynomial. Later this should change to\n # the misregistration polynomial\n rObj.azimuthOffsetsPoly = azpoly\n rObj.rangeOffsetsPoly = rgpoly\n rObj.imageIn = inimg\n\n rngImg = isceobj.createImage()\n rngImg.load(rgoffname + '.xml')\n rngImg.setAccessMode('READ')\n\n aziImg = isceobj.createImage()\n aziImg.load(azoffname + '.xml')\n aziImg.setAccessMode('READ')\n\n width = rngImg.getWidth()\n length = rngImg.getLength()\n\n# Modified by V. Brancato on 10.14.2019 (if Rubbersheeting in range is turned on, flatten the interferogram during cross-correlation)\n if not self.doRubbersheetingRange:\n print('Rubber sheeting in range is turned off, flattening the interferogram during resampling')\n flatten = True\n print(flatten)\n else:\n print('Rubber sheeting in range is turned on, flattening the interferogram during interferogram formation')\n flatten=False\n print(flatten)\n# end of Modification\n \n rObj.flatten = flatten\n rObj.outputWidth = width\n rObj.outputLines = length\n rObj.residualRangeImage = rngImg\n rObj.residualAzimuthImage = aziImg\n\n if referenceFrame is not None:\n rObj.startingRange = secondaryFrame.startingRange\n rObj.referenceStartingRange = referenceFrame.startingRange\n rObj.referenceSlantRangePixelSpacing = referenceFrame.getInstrument().getRangePixelSize()\n rObj.referenceWavelength = radarWavelength\n \n # preparing the output directory for coregistered secondary slc\n #coregDir = self.insar.coregDirname\n\n os.makedirs(coregDir, exist_ok=True)\n\n # output file name of the coregistered secondary slc\n img = secondaryFrame.getImage() \n coregFilename = os.path.join(coregDir , os.path.basename(img.filename))\n\n imgOut = isceobj.createSlcImage()\n imgOut.setWidth(width)\n imgOut.filename = coregFilename\n imgOut.setAccessMode('write')\n\n rObj.resamp_slc(imageOut=imgOut)\n\n imgOut.renderHdr()\n\n return coregFilename\n\n\ndef runResampleSubbandSlc(self, misreg=False):\n '''Run method for split spectrum.\n '''\n\n if not self.doSplitSpectrum:\n print('Split spectrum not requested. Skipping...')\n return\n \n referenceFrame = self._insar.loadProduct( self._insar.referenceSlcCropProduct)\n secondaryFrame = self._insar.loadProduct( self._insar.secondarySlcCropProduct)\n\n# Modified by V. Brancato 10.14.2019\n\n if self.doRubbersheetingAzimuth:\n print('Using rubber in azimuth sheeted offsets for resampling sub-bands')\n azoffname = os.path.join( self.insar.offsetsDirname, self.insar.azimuthRubbersheetFilename)\n\n else:\n print('Using refined offsets for resampling sub-bands')\n azoffname = os.path.join( self.insar.offsetsDirname, self.insar.azimuthOffsetFilename)\n \n if self.doRubbersheetingRange:\n print('Using rubber in range sheeted offsets for resampling sub-bands')\n rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeRubbersheetFilename)\n else:\n print('Using refined offsets for resampling sub-bands')\n rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeOffsetFilename)\n# ****************** End of Modification\n \n # rgoffname = os.path.join( self.insar.offsetsDirname, self.insar.rangeOffsetFilename)\n azpoly = self.insar.loadProduct( os.path.join(self.insar.misregDirname, self.insar.misregFilename) + '_az.xml')\n rgpoly = self.insar.loadProduct( os.path.join(self.insar.misregDirname, self.insar.misregFilename) + '_rg.xml')\n\n\n imageSlc2 = os.path.join(self.insar.splitSpectrumDirname, self.insar.lowBandSlcDirname, \n os.path.basename(secondaryFrame.getImage().filename))\n\n wvlL = self.insar.lowBandRadarWavelength\n coregDir = os.path.join(self.insar.coregDirname, self.insar.lowBandSlcDirname)\n \n lowbandCoregFilename = resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, wvlL, coregDir,\n azoffname, rgoffname, azpoly=azpoly, rgpoly=rgpoly,misreg=False)\n\n imageSlc2 = os.path.join(self.insar.splitSpectrumDirname, self.insar.highBandSlcDirname,\n os.path.basename(secondaryFrame.getImage().filename))\n wvlH = self.insar.highBandRadarWavelength\n coregDir = os.path.join(self.insar.coregDirname, self.insar.highBandSlcDirname)\n\n highbandCoregFilename = resampleSlc(self,referenceFrame, secondaryFrame, imageSlc2, wvlH, coregDir, \n azoffname, rgoffname, azpoly=azpoly, rgpoly=rgpoly, misreg=False)\n\n self.insar.lowBandSlc2 = lowbandCoregFilename\n self.insar.highBandSlc2 = highbandCoregFilename\n \n","repo_name":"isce-framework/isce2","sub_path":"components/isceobj/StripmapProc/runResampleSubbandSlc.py","file_name":"runResampleSubbandSlc.py","file_ext":"py","file_size_in_byte":5961,"program_lang":"python","lang":"en","doc_type":"code","stars":431,"dataset":"github-code","pt":"82"} +{"seq_id":"1950601808","text":"# coding: utf-8\n\nfrom News.database import Monitor\nfrom News.database import session\n\n\ndef _record_error_log(item, error):\n m = Monitor(\n crawl_url=item[\"crawl_url\"],\n original_url=item[\"original_url\"],\n crawl_source=item[\"crawl_source\"],\n original_source=item[\"original_source\"],\n channel=item[\"channel\"],\n error=error,\n )\n try:\n session.add(m)\n session.commit()\n except Exception as e:\n session.rollback()\n","repo_name":"xiaol/NewsCrawlerPG","sub_path":"News/test/monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"40138059690","text":"'''Cree una función que retorne el número de palabras\npresentes en un String que le llega cómo parámetro.\n\n(obs: considere que toda palabra válida está separada\npor un espacio de la anterior)'''\n\ndef NumeroDePalabras():\n Oracion=input('Digite una oracion: ')\n\n if Oracion.isdigit():\n while True:\n print('No se admiten numeros...')\n Oracion=input('Digite una oracion: ')\n if not Oracion.isdigit():\n break\n\n espacios=Oracion.split(' ')\n print(len(espacios))\n\n\nNumeroDePalabras()","repo_name":"JPerez1005/Python","sub_path":"C/C1/Practicas Homework/5Funciones/NumeroDePalabras.py","file_name":"NumeroDePalabras.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34223387221","text":"FONT = (\"--X--XXX-XXX-X-X-XXX--XX-XXX-XXX--XX-XX--\"\n \"-XX----X---X-X-X-X---X-----X-X-X-X-X-X-X-\"\n \"--X---XX--X--XXX-XX--XXX--X--XXX-XXX-X-X-\"\n \"--X--X-----X---X---X-X-X-X---X-X---X-X-X-\"\n \"--X--XXX-XXX---X-XX---XX-X---XXX-XX---XX-\")\n\nfrom pprint import pprint \n\ndef divide_font(FONT):\n numbers = [FONT[num:num+40] for num in range(0,len(FONT), 41)]\n #pprint (numbers)\n num_arr = []\n for i in range(0,len(numbers[0]),4):\n a = [j[i:i+4] for j in numbers]\n a = [j for i in a for j in i]\n a = map(lambda x : 0 if x == '-' else 1, a)\n num_arr.append(a)\n #pprint(a)\n #print '\\n'\n #pprint(num_arr)\n #print len(num_arr)\n last = num_arr.pop()\n num_arr.insert(0, last)\n #num_arr[-1] + num_arr[:-1] \n pprint (num_arr)\n return num_arr\n\ndef checkio(image):\n num_img = ''\n numbers = divide_font(FONT)\n for i in range(0,len(image[0])-1,4):\n num = [j[i:i+4] for j in image]\n num = [j for i in num for j in i]\n #print num \n #print '\\n'\n for j,i in enumerate(numbers):\n #sum(k[0]!=k[1] for k in zip(num,i))\n if sum(k[0]!=k[1] for k in zip(num,i)) < 2 :\n #print sum(k[0]!=k[1] for k in zip(num,i))\n #print zip(num,i) \n #print 'bingo'\n #print j+1\n num_img += str(j)\n #print num_img\n return int(num_img)\n\nif __name__ == '__main__':\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert checkio([[0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0]]) == 394, \"394 clear\"\n assert checkio([[0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0],\n [0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],\n [0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0],\n [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0],\n [0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0]]) == 394, \"again 394 but with noise\"\n","repo_name":"a1ip/checkio-17","sub_path":"mono-captcha.py","file_name":"mono-captcha.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41519780965","text":"#!/usr/bin/python\nprint('Content-type: text/html\\n')\n\n'''\n\nSynergetic Lite\n\nremovePeriodReservation.py\n\nAllows the user to \"un-reserve\" a period for all students and teachers in a class.\n\nBy Nick Patrikeos on 22DEC17\n\n'''\n\nimport cgi\nimport cgitb; cgitb.enable()\nimport sqlite3\nfrom dbFunctions import *\nimport random\n\nform = cgi.FieldStorage()\nperiodNum = form.getvalue('periodNum')\nclassID = form.getvalue('classID')\nvalues = {'classID':classID, 'periodNum':periodNum}\n\ndb = sqlite3.connect('synergetic.db')\ncursor = db.cursor()\ncursor.execute('PRAGMA foreign_keys = ON')\n\ncursor.execute('SELECT Teacher FROM Classes WHERE Class_ID = :classID', values)\nteacherID = cursor.fetchall()[0][0]\n\nprint('')\n\ncursor.execute('DELETE FROM TeacherPeriods WHERE Class = :classID AND Period_Num = :periodNum', values)\n\ncursor.execute('SELECT Student FROM Enrolments WHERE Class = :classID', values)\nstudents = [i[0] for i in cursor.fetchall()]\n\nfor student in students:\n values['studentID'] = student\n cursor.execute('DELETE FROM StudentPeriods WHERE Class = :classID AND Student = :studentID AND Period_Num = :periodNum', values)\n\n\nprint('')\ndb.commit()\ndb.close()\n","repo_name":"NicktheGreek1985/PythonCGIProjects","sub_path":"Synergetic Lite/removePeriodReservation.py","file_name":"removePeriodReservation.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30672563012","text":"\r\nimport random\r\nimport pygame\r\nimport math\r\nfrom pygame import mixer\r\n\r\npygame.init()\r\nmixer.init()\r\n\r\n# create the screen\r\nscreen=pygame.display.set_mode((800,600))\r\n#icon and title\r\npygame.display.set_caption(\"pokecapture\")\r\nicon=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pokeball.png\")\r\npygame.display.set_icon(icon)\r\n\r\n#background\r\nbg=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bg1.png\")\r\n\r\n# player\r\npokeball=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pokeball.png\")\r\nplayerImg=pokeball\r\nplayerX=374\r\nplayerY=536\r\nplayerX_change=0\r\ndef player(x,y):\r\n screen.blit(playerImg,(x,y))\r\n#sound\r\nmixer.music.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bgsound.wav\")\r\nmixer.music.play(-1)\r\n #score\r\nscore=0\r\nfont=pygame.font.SysFont('inkfree.ttf',40)\r\ntextX=10\r\ntextY=10\r\n\r\ndef show_score():\r\n score1=font.render('SCORE:'+str(score),True, (0,0,0))\r\n screen.blit(score1,(30,30))\r\n#pokemons\r\nbullbasaur=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\bullbasaur.png\")\r\ncharmander=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\charmander.png\")\r\ndratini=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\dratini.png\")\r\neevee=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\eevee.png\")\r\njigglypuff=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\jigglypuff.png\")\r\nmeowth=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\meowth (2).png\")\r\npikachu=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\pikachu.png\")\r\npsyduck=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\psyduck.png\")\r\nsnorlax=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\snorlax.png\")\r\nsquirtle=pygame.image.load(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\squirtle.png\")\r\npoke=[bullbasaur,charmander,dratini,eevee,jigglypuff,meowth,pikachu,psyduck,snorlax,squirtle]\r\n\r\npokeImg=[meowth,pikachu]\r\npokeX=[]\r\npokeY=[]\r\npokeY_change=[1,1]\r\nfor i in range(8):\r\n n=random.randint(0,9)\r\n poke1=poke[n]\r\n pokeImg.append(poke1)\r\n pokeX.append(random.randint(0,768))\r\n pokeY.append(random.randint(-80,400))\r\n \r\n pokeY_change.append(1)\r\nfor i in range (2):\r\n pokeX.append(random.randint(0,768))\r\n pokeY.append(random.randint(-80,400))\r\ndef pokemon(x,y,i,l):\r\n screen.blit(l[i],(x,y))\r\n#collision\r\ndef collision(x,y,playerX,playerY):\r\n dist=math.sqrt((math.pow(x-playerX,2))+(math.pow(y-playerY,2)))\r\n if dist<=27:\r\n return True\r\n\r\n#game over\r\nover_font=pygame.font.SysFont('inkfree.ttf',60)\r\ndef gameover():\r\n overtext=over_font.render(\"GAME OVER\",True,(0,0,0))\r\n screen.blit(overtext,(190,300))\r\n screen.blit(pokeball,(130,400))\r\n#game loop\r\n\r\nrunning=True\r\nwhile running:\r\n \r\n screen.fill((0,0,0))\r\n screen.blit(bg,(0,0))\r\n for event in pygame.event.get(): \r\n if event.type==pygame.QUIT:\r\n running=False\r\n if event.type==pygame.KEYDOWN:\r\n if event.key==pygame.K_LEFT:\r\n playerX_change=-3\r\n if event.key==pygame.K_RIGHT:\r\n playerX_change=3\r\n if event.type==pygame.KEYUP:\r\n playerX_change=0\r\n \r\n playerX+=playerX_change\r\n if playerX<=0:\r\n playerX=0\r\n elif playerX>=736:\r\n playerX=736\r\n player(playerX,playerY)\r\n#show score\r\n \r\n for i in range(10):\r\n pokeY[i]+=pokeY_change[i]\r\n \r\n pokemon(pokeX[i],pokeY[i],i,pokeImg)\r\n col=collision(pokeX[i],pokeY[i],playerX,playerY)\r\n\r\n if pokeY[i]>=600:\r\n pokeY[i]=random.randint(-20,40)\r\n pokeX[i]=random.randint(0,768)\r\n if col:\r\n char=pokeImg[i]\r\n if char==pikachu:\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n score+=5\r\n elif char==meowth:\r\n for i in range(10):\r\n screen.fill((34,34,34))\r\n gameover()\r\n \r\n running=False\r\n else:\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n \r\n score+=5\r\n np=random.randint(0,9)\r\n pokeX[i]=random.randint(0,736)\r\n pokeY[i]=random.randint(0,40)\r\n pokemon(pokeX[i],pokeY[i],np,poke)\r\n cap=mixer.Sound(\"C:\\\\users\\\\khuhan rawat\\\\Desktop\\\\pokemon\\\\capture.wav\")\r\n cap.play()\r\n \r\n show_score()\r\n\r\n\r\n#Run=True\r\n\r\n \r\n\r\n pygame.display.update()\r\n","repo_name":"Mystic-miracle/Pokecapture","sub_path":"G!.py","file_name":"G!.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2999263502","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n\nimport re\nimport urllib\nimport json\nimport base64\nimport random\nimport datetime\nimport tornado\nfrom db import mongo\nfrom db import live_mongo\nfrom db import theme_mongo\nfrom pymongo import DESCENDING, ASCENDING\nfrom bson import objectid\nfrom libs import BaseHandler\nfrom conf import config\n\nnewlist = ['4e4d610cdf714d2966000002','4fb479f75ba1c65561000027','4e4d610cdf714d2966000003','4fb47a305ba1c60ca5000223','4ef0a35c0569795756000000','4e4d610cdf714d2966000001'] #风景、视觉、动漫、城市、情感、动物\nhotlist = ['4e4d610cdf714d2966000000','4e4d610cdf714d2966000002','4fb479f75ba1c65561000027','4fb47a465ba1c65561000028','4e4d610cdf714d2966000006','4e58c2570569791a19000000'] #美女、风景、视觉、物语、男人、影视\n\n\nclass CommendHandler(BaseHandler):\n def get(self):\n weekpaper = None #recommend paper at week\n weeklive = None # recommend live at week\n adimage = None\n\n #newest paper\n nlist = []\n for i in newlist:\n cid = objectid.ObjectId(i)\n img = mongo.image.find({'cid':cid},limit=1).sort('atime', DESCENDING)\n if img.count()>0:\n nlist.append(img[0])\n\n #hot paper\n try:\n _imglist, length = self.hot_image_cache.find_list(config.Cache.hot_image_cache, 0, 5)\n if _imglist:\n imglist = [json.loads(i) for i in _imglist]\n else:\n imglist = mongo.image.find(limit=6,skip=0).sort('rank',DESCENDING)\n except:\n imglist = mongo.image.find(limit=6,skip=0).sort('rank',DESCENDING)\n\n self.render(\"commend.html\",\n context=self.context,\n news=nlist,\n hots=imglist,\n )\n\n\nclass NewCommendHandler(BaseHandler):\n def get(self):\n self.render(\"newcommend.html\",\n context=self.context,\n )\n\nclass MoreNewPaperHandler(BaseHandler):\n def get(self):\n limit = 18\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n imglist = mongo.image.find(limit=limit,skip=skip).sort('atime',DESCENDING)\n images = []\n index = skip\n for i in imglist:\n if not self.session.hd:\n netimg = str(i['thumb_fobj'])\n elif self.session.net=='pc':\n netimg = str(i['fobjs']['640x480'])\n else: # self.session.net=='wifi':\n netimg = str(i['fobjs']['160x120'])\n\n images.append({\n 'id':str(i['_id']),\n 'image':netimg,\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':images})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass MoreNewLiveHandler(BaseHandler):\n def get(self):\n limit = 9 \n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n apklist = live_mongo.apk.find(skip=skip,limit=limit).sort('atime',DESCENDING)\n lives = []\n index = skip\n for i in apklist:\n lives.append({\n 'id':str(i['_id']),\n 'thumbid':str(i['thumbid'][0]),\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':lives})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass NewPaperDetailHandler(BaseHandler):\n def get(self):\n imgid = self.get_argument(\"imgid\",default=None)\n _skip = self.get_argument(\"skip\",default=0)\n ctype = self.get_argument(\"type\",default=\"date\")\n showmsg = self.session.show_msg\n self.session.show_msg = None\n\n try:\n skip=int(_skip)\n if skip < 0:\n skip = 0\n _skip = None\n except:\n skip=0\n\n img=None\n read_from_cache = False\n\n try:\n if _skip:\n if ctype=='date':\n img = mongo.image.find(skip=skip,limit=1).sort('atime',DESCENDING)[0]\n else:\n try:\n img = self.hot_image_cache.find_one(config.Cache.hot_image_cache, skip)\n if img:\n read_from_cache = True\n img = json.loads(img)\n else:\n img = mongo.image.find(skip=skip,limit=1).sort('rank',DESCENDING)[0]\n except:\n img = mongo.image.find(skip=skip,limit=1).sort('rank',DESCENDING)[0]\n\n else:\n iid = objectid.ObjectId(imgid)\n img = mongo.image.find_one({'_id':iid})\n if not img:\n raise\n except:\n raise\n return self.notfound()\n\n front = skip - 1\n end = skip + 1\n\n if not read_from_cache:\n if end>=mongo.image.count():\n end = -1\n else:\n if not self.hot_image_cache.find_one(config.Cache.hot_image_cache, end):\n end = -1\n\n if _skip == None:\n front = -1\n end = -1\n\n referer = urllib.quote(self.request.uri)\n isfav=-1\n if self.session.uid:\n pri=mongo.private.find_one({'uid':self.session.uid,'imgid': img['_id']})\n if pri:\n isfav=1\n else:\n isfav=0\n\n tags = mongo.img2tag.find({'imgid': objectid.ObjectId(img['_id'])}).sort('num', DESCENDING)\n tags = [i for i in tags]\n self.render(\"compaper_detail.html\",\n context=self.context,\n image=img,\n front=front,\n end=end,\n isfav=isfav,\n referer=referer,\n tags=tags,\n message=showmsg,\n type=ctype,\n )\n\nclass NewLiveDetailHandler(BaseHandler):\n def get(self):\n apkid = self.get_argument(\"apkid\",default=None)\n skip = self.get_argument(\"skip\",default=0)\n ctype = self.get_argument(\"type\", default=\"date\")\n\n try:\n skip=int(skip)\n except:\n skip=0\n\n apk=None\n try:\n if apkid==None:\n if ctype=='date':\n apks=live_mongo.apk.find(skip=skip,limit=1).sort('atime',DESCENDING)\n else:\n apks=live_mongo.apk.find(skip=skip,limit=1).sort('rank',DESCENDING)\n\n try:\n apk=apks[0]\n pid=apk['_id']\n except:\n raise\n else:\n pid = objectid.ObjectId(apkid)\n apk = live_mongo.apk.find_one({'_id':pid})\n if not apk:\n raise\n except:\n return self.notfound()\n\n #cal mark\n marks = live_mongo.mark2apk.find({'apkid':apk['_id']})\n msum = 0.0\n mcount = 0\n for m in marks:\n msum += m['mark']\n mcount += 1\n score = 0\n if mcount>0:\n score = round(msum/mcount)\n score = int(score)\n\n\n front = skip-1\n end = skip+1\n if end>=live_mongo.apk.count():\n end = -1\n\n referer = urllib.quote(self.request.uri)\n isfav=-1\n if self.session.uid:\n pri=live_mongo.private.find_one({'uid':self.session.uid,'apkid':pid})\n if pri:\n isfav=1\n else:\n isfav=0\n\n self.render(\"comlive_detail.html\",\n context=self.context,\n apk=apk,\n front=front,\n end=end,\n favstate=isfav,\n referer=referer,\n score=score,\n amount=mcount,\n type=ctype,\n )\n\nclass HotCommendHandler(BaseHandler):\n def get(self):\n self.render(\"hotcommend.html\",\n context=self.context,\n )\n\nclass MoreHotPaperHandler(BaseHandler):\n def get(self):\n limit = 18\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n try:\n _imglist, length = self.hot_image_cache.find_list(config.Cache.hot_image_cache, skip, limit-1)\n if _imglist:\n imglist = [json.loads(i) for i in _imglist]\n else:\n imglist = mongo.image.find(limit=limit,skip=skip).sort('rank',DESCENDING)\n except:\n imglist = mongo.image.find(limit=limit,skip=skip).sort('rank',DESCENDING)\n\n\n images = []\n index = skip\n for i in imglist:\n if not self.session.hd:\n netimg = str(i['thumb_fobj'])\n elif self.session.net=='pc':\n netimg = str(i['fobjs']['640x480'])\n else: # self.session.net=='wifi':\n netimg = str(i['fobjs']['160x120'])\n\n images.append({\n 'id':str(i['_id']),\n 'image':netimg,\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':images})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\nclass MoreHotLiveHandler(BaseHandler):\n def get(self):\n limit = 9\n skip = self.get_argument(\"skip\",default=0)\n try:\n skip = int(skip)\n except:\n skip = 0\n\n apklist = live_mongo.apk.find(skip=skip,limit=limit).sort('rank',DESCENDING)\n lives = []\n index = skip\n for i in apklist:\n lives.append({\n 'id':str(i['_id']),\n 'thumbid':str(i['thumbid'][0]),\n 'skip':index\n })\n index += 1\n\n self._buffer = json.dumps({'code':0,'resp':lives})\n callback = self.get_argument('jsoncallback', default=None)\n if callback:\n self._buffer = \"%s(%s)\" % (callback,self._buffer)\n self.write(self._buffer)\n\n","repo_name":"zytjm/tornado-mongo-based-webserver","sub_path":"mobile_server/app/commend.py","file_name":"commend.py","file_ext":"py","file_size_in_byte":10616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28785151500","text":"import copy\nfrom typing import Optional, Union\nimport unittest\nfrom google.protobuf import descriptor\nfrom google.protobuf import message\nfrom google.protobuf import text_format\n\n\ndef _clear_field(proto: message.Message, field_path: str) -> None:\n \"\"\"Clears field_path in proto.\n\n field_path contains field names separated by '.' into the proto, e.g.,\n my_sub_message.my_repeated_field.my_field.\n A field is removed by calling ClearField.\n\n Args:\n proto: A proto message to be modified.\n field_path: The path to the field to be cleared.\n \"\"\"\n\n next_field_name, _, path_suffix = field_path.partition(\".\")\n if next_field_name not in proto.DESCRIPTOR.fields_by_name:\n raise ValueError(\n f\"Field {next_field_name} in field path {field_path} does not refer to\"\n f\" a known field for message {proto.DESCRIPTOR.full_name}.\"\n )\n\n # root case, field_path was just a field\n if not path_suffix:\n proto.ClearField(next_field_name)\n return\n\n # next_field can refer to:\n # - a submessage (or oneof of submessages)\n # - a repeated field of messages\n next_field: descriptor.FieldDescriptor = proto.DESCRIPTOR.fields_by_name[\n next_field_name\n ]\n if next_field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n raise ValueError(\n f\"Field {next_field_name} in field path {field_path} does not refer to\"\n f\" a message field for message {proto.DESCRIPTOR.full_name}.\"\n )\n\n if next_field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n sub_field_list = getattr(proto, next_field_name)\n for sub_message in sub_field_list:\n _clear_field(sub_message, path_suffix)\n return\n\n if not proto.HasField(next_field_name):\n return\n sub_message = getattr(proto, next_field_name)\n _clear_field(sub_message, path_suffix)\n\n\ndef _sort_repeated_fields(proto: message.Message, deduplicate: bool) -> None:\n \"\"\"Sorts all repeated fields including in submessages.\n\n This is typically called to have a canonical order of repeated fields in the\n message for comparison. Thus no particular order is guaranteed, but only that\n the order is deterministic for multiple calls on equal messages.\n\n Args:\n proto: A proto message to be modified.\n deduplicate: Determines if duplicate elements in repeated fields should be\n removed.\n \"\"\"\n\n # recurse first, then sort\n field: descriptor.FieldDescriptor\n for field in proto.DESCRIPTOR.fields:\n if field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n continue\n # At this point field can be\n # - just a single message\n # - a repeated field (list) of messages\n # - a map to a scalar value\n # - a map to message values\n if field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n sub_field_list = getattr(proto, field.name)\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n and field.message_type.fields_by_name[\"value\"].type\n != descriptor.FieldDescriptor.TYPE_MESSAGE\n ):\n # this is a map to build in types (not to message) - nothing to recurse\n continue\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n # this is a map to messages\n for _, sub_message in sub_field_list.items():\n _sort_repeated_fields(sub_message, deduplicate)\n else:\n # this is just a repeated field of messages\n for sub_message in sub_field_list:\n _sort_repeated_fields(sub_message, deduplicate)\n elif proto.HasField(field.name):\n # a single message field\n sub_message = getattr(proto, field.name)\n _sort_repeated_fields(sub_message, deduplicate)\n\n # now, sort each field, where sub-fields are already sorted (and thus\n # canonical)\n for field in proto.DESCRIPTOR.fields:\n if field.label != descriptor.FieldDescriptor.LABEL_REPEATED:\n continue\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_MESSAGE\n and field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n continue # do not sort maps\n\n sub_field_list = getattr(proto, field.name)\n if not sub_field_list:\n continue\n\n if field.type == descriptor.FieldDescriptor.TYPE_MESSAGE:\n key_fn = text_format.MessageToString\n else:\n key_fn = lambda x: x\n sub_field_list.sort(key=key_fn)\n sub_field_list_no_duplicates = []\n prev = None\n for sub_msg in sub_field_list:\n if not deduplicate or (prev is None or key_fn(prev) != key_fn(sub_msg)):\n sub_field_list_no_duplicates.append(sub_msg)\n prev = sub_msg\n del sub_field_list[:]\n sub_field_list.extend(sub_field_list_no_duplicates)\n\n\ndef _floats_in_tolerance(value_a: float, value_b: float, rtol: float) -> bool:\n return abs(value_a - value_b) <= rtol * max(abs(value_a), abs(value_b))\n\n\ndef _equalize_floats_in_tolerance(\n proto_a: message.Message, proto_b: message.Message, rtol: float\n) -> None:\n \"\"\"Replaces all floats in proto_a with floats from proto_b, if both are in rtol.\n\n All equivalent floating point values (floats and doubles) in proto_a will be\n replaced by the exact values from proto_b, such that there will be no more\n difference between these two messages regarding floats within rtol. This is\n typically called to facilitate a readable diff including non-float fields.\n\n Args:\n proto_a: A proto message to be modified.\n proto_b: A given proto message.\n rtol: A relative tolerance defining if the floats are considered equivalent.\n rtol is considered as a proportion of the float with the larger magnitude.\n \"\"\"\n if proto_a.DESCRIPTOR != proto_b.DESCRIPTOR:\n return\n\n # Relevant fields to be handled by this function.\n # Directly:\n # - floats (float and double)\n # - repeated floats\n # - map to float\n # By recursion:\n # - message fields\n # - repeated messages\n # - map to messages\n proto_a_field_names = set(fd.name for fd, _ in proto_a.ListFields())\n proto_b_field_names = set(fd.name for fd, _ in proto_b.ListFields())\n for field_name in proto_a_field_names.intersection(proto_b_field_names):\n field: descriptor.FieldDescriptor = proto_a.DESCRIPTOR.fields_by_name[\n field_name\n ]\n\n value_a = getattr(proto_a, field.name)\n value_b = getattr(proto_b, field.name)\n\n if (\n field.type == descriptor.FieldDescriptor.TYPE_FLOAT\n or field.type == descriptor.FieldDescriptor.TYPE_DOUBLE\n ):\n if field.label != descriptor.FieldDescriptor.LABEL_REPEATED:\n # field is just a float\n if _floats_in_tolerance(value_a, value_b, rtol):\n setattr(proto_a, field.name, value_b)\n else:\n # field is a list of floats\n for index in range(min(len(value_a), len(value_b))):\n if _floats_in_tolerance(value_a[index], value_b[index], rtol):\n value_a[index] = value_b[index]\n\n if field.type != descriptor.FieldDescriptor.TYPE_MESSAGE:\n continue\n\n if field.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n if (\n field.message_type.has_options\n and field.message_type.GetOptions().map_entry\n ):\n value_type = field.message_type.fields_by_name[\"value\"]\n # field is a map\n for key, mapped_value_a in value_a.items():\n mapped_value_b = value_b.get(key)\n if mapped_value_b is None:\n continue\n if (\n value_type.type == descriptor.FieldDescriptor.TYPE_FLOAT\n or value_type.type == descriptor.FieldDescriptor.TYPE_DOUBLE\n ):\n # field is a map to floats\n if _floats_in_tolerance(mapped_value_a, mapped_value_b, rtol):\n value_a[key] = mapped_value_b\n elif value_type.type == descriptor.FieldDescriptor.TYPE_MESSAGE:\n # field is a map to messages - recurse\n _equalize_floats_in_tolerance(\n mapped_value_a, mapped_value_b, rtol=rtol\n )\n else:\n # field is a list of messages - recuse\n for sub_message_a, sub_message_b in zip(value_a, value_b):\n _equalize_floats_in_tolerance(sub_message_a, sub_message_b, rtol=rtol)\n else:\n # field is just a single message - recurse\n _equalize_floats_in_tolerance(value_a, value_b, rtol=rtol)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2Equal(\n testobj: unittest.case.TestCase,\n proto_a: Union[message.Message, str, bytes],\n proto_b: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n rtol: Optional[float] = None,\n) -> None:\n \"\"\"Asserts that two protos are equal.\n\n Args:\n testobj: The test case that called this comparison.\n proto_a: A proto to compare.\n proto_b: A proto to compare to.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n rtol: Relative tolerance to compare floating point values. If not set,\n floats are compared using string comparison.\n \"\"\"\n\n if isinstance(proto_a, str | bytes):\n proto_a = text_format.Parse(proto_a, proto_b.__class__())\n\n copied = False\n if ignored_fields is not None:\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n copied = True\n for field_path in ignored_fields:\n _clear_field(proto_a, field_path)\n _clear_field(proto_b, field_path)\n\n if rtol is not None:\n if not copied:\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n _equalize_floats_in_tolerance(proto_a, proto_b, rtol)\n\n txt_a = text_format.MessageToString(proto_a)\n txt_b = text_format.MessageToString(proto_b)\n testobj.assertMultiLineEqual(txt_a, txt_b)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2Contains(\n testobj: unittest.case.TestCase,\n proto_needle: Union[message.Message, str, bytes],\n proto_haystack: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n) -> None:\n \"\"\"Asserts that fields from proto_needle are set the same in proto_haystack.\n\n Args:\n testobj: The test case that called this comparison.\n proto_needle: A proto to compare with proto_haystack.\n proto_haystack: A proto that contains all fields in proto_needle and others.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n \"\"\"\n if isinstance(proto_needle, str | bytes):\n proto_needle = text_format.Parse(proto_needle, proto_haystack.__class__())\n else:\n proto_needle = copy.deepcopy(proto_needle)\n proto_haystack = copy.deepcopy(proto_haystack)\n if ignored_fields is not None:\n for field_path in ignored_fields:\n _clear_field(proto_needle, field_path)\n _clear_field(proto_haystack, field_path)\n\n proto_needle_full = copy.deepcopy(proto_haystack)\n proto_needle_full.MergeFrom(proto_needle)\n\n _sort_repeated_fields(proto_needle_full, deduplicate=True)\n _sort_repeated_fields(proto_haystack, deduplicate=True)\n\n txt_needle = text_format.MessageToString(proto_needle_full)\n txt_haystack = text_format.MessageToString(proto_haystack)\n testobj.assertMultiLineEqual(txt_needle, txt_haystack)\n\n\n# pylint:disable-next=invalid-name\ndef assertProto2SameElements(\n testobj: unittest.case.TestCase,\n proto_a: Union[message.Message, str, bytes],\n proto_b: message.Message,\n *,\n ignored_fields: Optional[list[str]] = None,\n keep_duplicate_values: Optional[bool] = None,\n) -> None:\n \"\"\"Asserts that fields from proto_a and proto_b are the same.\n\n For repeated fields, both messages must have the same items, but count or\n order does not matter.\n The semantics are similar to, e.g., absltest.assertSameElements.\n This method does not care about any duplicates unless keep_duplicate_values\n is set to true.\n\n Args:\n testobj: The test case that called this comparison.\n proto_a: A proto to compare with proto_b.\n proto_b: The proto to compare to.\n ignored_fields: List of field paths into the proto to be ignored during\n comparison.\n keep_duplicate_values: Keep duplicate values before comparing. If not set or\n set to false, duplicate values will be considered one value. This makes it\n possible to compare similar to set semantics.\n \"\"\"\n if isinstance(proto_a, str | bytes):\n proto_a = text_format.Parse(proto_a, proto_b.__class__())\n\n proto_a = copy.deepcopy(proto_a)\n proto_b = copy.deepcopy(proto_b)\n if ignored_fields is not None:\n for field_path in ignored_fields:\n _clear_field(proto_a, field_path)\n _clear_field(proto_b, field_path)\n\n deduplicate = True\n if keep_duplicate_values is not None and keep_duplicate_values:\n deduplicate = False\n\n _sort_repeated_fields(proto_a, deduplicate)\n _sort_repeated_fields(proto_b, deduplicate)\n\n txt_a = text_format.MessageToString(proto_a)\n txt_b = text_format.MessageToString(proto_b)\n testobj.assertMultiLineEqual(txt_a, txt_b)\n","repo_name":"intrinsic-dev/intrinsic_sdks","sub_path":"intrinsic/solutions/testing/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":12983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3655117611","text":"import os\nimport glob\nimport subprocess\n\n\npath = os.path.join(os.getcwd(),'sudokus')\n# subprocess.Popen('\"C:\\\\Program Files (x86)\\\\CodeBlocks\\\\MinGW\\\\bin\"\\\\gcc.exe sudoku.c -o sudoku.exe',shell=True)\n\nfile_number = 1\nfor filename in glob.glob(os.path.join(path, '*.dat')):\n\tf = open(filename, 'r')\n\ti = 0\n\tsudoku_str = \"{\"\n\tsudoku_str2 =\"\"\n\tfor line in f:\n\t\tif (i>1):\n\t\t\tsudoku_str+= \"{\"\n\t\t\tline = line.split()\t\t\t\t\t\t\n\t\t\t# print(line)\n\t\t\taux = 0\n\t\t\tfor number in line:\n\t\t\t\tsudoku_str += str(number)\n\t\t\t\tsudoku_str2 += str(number)\n\t\t\t\tif(aux < 8):\n\t\t\t\t\tsudoku_str+=\",\"\n\t\t\t\taux = aux + 1\n\t\t\tsudoku_str += \"}\"\n\t\t\tif(i<10):\n\t\t\t\tsudoku_str+=\",\"\n\t\ti+=1\n\tsudoku_str += \"}\"\n\tprint(sudoku_str2)\n\tsubprocess.Popen('sudoku.exe '+sudoku_str2+'>> output\\\\output_'+str(file_number)+'.txt',shell=True)\n\t\n\tprint('\\n')\n\tf.close()\n\tfile_number += 1\n","repo_name":"GuilhermeBorges/Sudoku","sub_path":"executa.py","file_name":"executa.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9878330565","text":"exp = []\nx = str(input('Digite uma expressão: ')).strip()\nfor c in x:\n if c == '(':\n exp.append('(')\n elif c == ')':\n if len(exp) > 0:\n exp.pop()\n else:\n exp.append(')')\n print(len(exp))\n break\nif len(exp) == 0:\n print('Expressão válida!')\nelse:\n print('Expressão invalida!')","repo_name":"Raphael-Azevedo/Exercicios_Python","sub_path":"Exercicios em Python/ex083.py","file_name":"ex083.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74764755149","text":"import sqlite3\nfrom googletrans import Translator\n\n\ndef get_top_results(update, context):\n conn = sqlite3.connect(\"data.db\")\n cursor = conn.cursor()\n\n cursor.execute(\"SELECT username, full_name, results FROM user_info ORDER BY results DESC LIMIT 5\")\n\n results = cursor.fetchall()\n\n conn.close()\n\n if not results:\n update.message.reply_text(text=\"Unfortunately, no one has run the test yet and has not shown any results😔Maybe you will be the first, click /quiz and test yourself🥹\", parse_mode=\"html\")\n else:\n message = \"\\n\".join([\n f\"{i + 1} {user[1]}'s result is {user[2]}\"\n for i, user in enumerate(results)\n ])\n update.message.reply_text(message, parse_mode=\"html\")\n\n\ndef get_my_level(update,context):\n conn=sqlite3.connect(\"data.db\")\n c=conn.cursor()\n c.execute(f\"WITH SortedUsers AS (SELECT username, full_name, user_id, results,ROW_NUMBER() OVER (ORDER BY results DESC) AS position FROM user_info) SELECT position, username, full_name, results FROM SortedUsers WHERE user_id = {update.message.from_user.id}\")\n\n results=c.fetchone()\n conn.close()\n if results is None:\n update.message.reply_text(\"But you haven't done the quiz yet. That's why you don't have any points. Please click the /quiz command first and collect points by starting the quiz😉\",parse_mode=\"html\")\n else:\n l=[i for i in results]\n update.message.reply_text(\n f\"Your level are {l[0]}🏆Dear {l[2]} your total score {l[3]} .Never stop🚫\",parse_mode=\"html\"\n )\ndef detect_language(text):\n translator = Translator()\n detected = translator.detect(text)\n return detected.lang\n\ndef lang_trans(update,context):\n\n if detect_language(update.message.text)=='en':\n translator = Translator()\n text=update.message.text\n # Translate text from one language to another\n result = translator.translate(f\"{text}\", src=\"en\", dest=\"uz\")\n\n # Access the translated text\n translated_text = result.text\n update.message.reply_text(text=translated_text)\n if detect_language(update.message.text)=='uz':\n translator = Translator()\n text = update.message.text\n # Translate text from one language to another\n result = translator.translate(f\"{text}\", src=\"uz\", dest=\"en\")\n\n # Access the translated text\n translated_text = result.text\n update.message.reply_text(text=translated_text)\n\n\n","repo_name":"umidyor/Quiz_bot_eng","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73760584909","text":"import speedtest\nfrom threading import Thread\nimport time\nfrom .db.speed_table import SpeedTable\nfrom datetime import datetime\n\nclass GetSpeedData(Thread):\n def __init__(self, log = False):\n self.log = log\n\n def run(self):\n while True:\n self.getData()\n time.sleep(60)\n\n def convertToMb(self, speed):\n return \"{:.2f}\".format(speed/1048576) # Bytes to MBytes\n\n def getData(self):\n st = speedtest.Speedtest()\n downloadSpeed = float(st.download())\n uploadSpeed = float(st.upload())\n timestamp = int(time.time())\n\n downloadSpeed = self.convertToMb(downloadSpeed)\n uploadSpeed = self.convertToMb(uploadSpeed)\n SpeedTable.insert(downloadSpeed, uploadSpeed, timestamp)\n\n if(self.log):\n dt_object = datetime.fromtimestamp(timestamp)\n print('{} --- Download speed: {} Mb/s --- Upload speed: {} Mb/s'.format(dt_object, downloadSpeed, uploadSpeed))","repo_name":"Joselsneto/Internet-Speed-Tracker","sub_path":"src/get_speed_data.py","file_name":"get_speed_data.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8625183216","text":"import cv2\nimport os\n\n\nclass Video:\n def __init__(self, path, reheight, rewidht):\n self.pathVideo = path\n self.capture = cv2.VideoCapture(path)\n self.resultsPath = \"results\"\n if not os.path.exists(self.resultsPath):\n os.makedirs(self.resultsPath)\n self.num_frames = int(self.capture.get(cv2.CAP_PROP_FRAME_COUNT))\n self.height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.fps = int(self.capture.get(cv2.CAP_PROP_FPS))\n self.rewidth = rewidht\n self.reheight = reheight\n\n\nif __name__ == '__main__':\n video = Video(\"videos/video_test.mp4\")\n","repo_name":"mcv-m6-video/mcv-m6-2021-team6","sub_path":"W4/Video.py","file_name":"Video.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"11006096923","text":"from django.shortcuts import render\n\n# Create your views here.\n\nimport highlighter.backend as backend\nfrom .models import SummaryEntry, LabelType\nfrom .form import SummaryForm\n\n\ndef highlighter_view(r, *args, **kwargs):\n\t\"\"\"\n\tmain discharge summary labeller view\n\t\"\"\"\n\n\t#vars: cleaned_data, labels\n\tprocessed_text = \"(Enter summary to see labels.)\"\n\tdefinition_html = \"(Enter summary to see definitions.)\"\n\tform = SummaryForm()\n\tif r.method == \"POST\":\n\t\tform = SummaryForm(r.POST)\n\t\tif form.is_valid():\n\t\t\tcleaned_data = form.cleaned_data\n\t\t\tlabels = cleaned_data.pop('labels')\n\t\t\ts = SummaryEntry.objects.create(**cleaned_data)\n\t\t\ts.labels.set(labels)\n\n\t\t\t# if using ML model, use backend.get_summary() function instead.\n\t\t\tprocessed_text, definition_html = backend.get_summary_scispacy(cleaned_data, labels)\n\t\t\ts.processed = processed_text\n\t\t\ts.save() #this step is key!!! :) saves it!\n\n\t\telse:\n\t\t\tprint (\"Post:\", r.POST, form.is_valid())\n\t\t\tform = SummaryForm()\n\t\t\tprint(\"Errors in form:\", form.errors)\n\telse:\n\t\tprint(\"Not a POST method.\")\n\n\tcontext={\n\t\t'form':form,\n\t\t'processed_text': processed_text,\n\t\t'definitions': definition_html,\n\t}\n\treturn render(r, 'highlighter_temp.html', context)\n\t# this is still relative to templates directory!!\n","repo_name":"gloriafang123/mitmlhc2020-public-discharge-labeller","sub_path":"mysite/highlighter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32605575490","text":"\"\"\"SpaceTravels URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('registration/', include('tourists.urls')), # Showing Django where he should searching for urlpatterns \n path('', include('SpaceTravels.views')), # Pointing into urlpatterns in views.py file in main folder\n path('api-tourists/', include('tourists.api.urls')), # Pointing on our api urls in tourists app\n path('api-flights/', include('flights.api.urls')), # Pointing on our api urls in flights app\n]\n","repo_name":"lukaszkania/SpaceTravelsBackEnd","sub_path":"SpaceTravels/SpaceTravels/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74656155468","text":"from numba import cuda\n\n\n@cuda.jit\ndef mutate(population, random_values, size_individual, mutation_rate):\n index = cuda.grid(1)\n if index < population.shape[0]:\n individual_mutate(population[index], random_values[index], size_individual, mutation_rate)\n\n\n@cuda.jit(device=True)\ndef individual_mutate(individual, random_values, size_individual, mutation_rate):\n for position_1 in range(size_individual):\n if random_values[0] < mutation_rate:\n position_2 = round(random_values[1] * (size_individual - 1))\n if not position_1 == position_2:\n swap_value = individual[position_1]\n individual[position_1] = individual[position_2]\n individual[position_2] = swap_value\n","repo_name":"TimLC/Genetic_Algorithm_GPU-CPU","sub_path":"optimized_genetic_algorithm/genetic/mutation.py","file_name":"mutation.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"807951755","text":"import pd\n\n\ndef py2pd(value):\n \"\"\"Convert a Python data type to a PureData type\"\"\"\n return value\n\ndef pd2py(value):\n \"\"\"Convert a PureData data type to a Python type\"\"\"\n return value\n\n\ndef pdlist2pylist(value):\n \"\"\"Convert a PureData list to a Python list\"\"\"\n # value is one list, make it a string\n try:\n s = ''\n for i in range(len(value)):\n s = s + str(value[i]) + \" \" \n s = s.replace(\" \", \",\")\n s = \"[\" + s + \"]\"\n lst = eval(s)\n return lst[0]\n except:\n pd.error(\"There is syntax error in the list\")\n return None\n\n\n\n\n\n\n\n\n\n\n","repo_name":"charlesneimog/py4pd","sub_path":"resources/scripts/src/convertion.py","file_name":"convertion.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"70063454348","text":"import numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport collections\nimport multiprocessing\nfrom pathos.multiprocessing import ProcessingPool as Pool\n\n\ndef dist(x,y):\n return np.sum((x-y)**2)\n\ndef ChooseInitialMeans(data,k):\n means = []\n for _ in range(k):\n random_centroid = []\n for i in range(data.shape[1]):\n a = min(data[:, i])\n b = max(data[:, i])\n random_centroid.append( np.random.uniform(a,b) )\n means.append(random_centroid)\n #means, clusters = mykmns.kmeans_main(X, k)\n return means\n\n\ndef kmeansOnceWeights(data,weights,k,n,n_per_cluster):\n means = ChooseInitialMeans(data, k)\n\n for iter in range(50):\n #print(iter)\n\n if iter>0:\n means = []\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n if len(indices) > 0:\n cut = np.take(data, indices, axis=0)\n means.append(np.apply_along_axis(np.mean, axis=0, arr=cut))\n\n clusters = dict(enumerate(means))\n ids = list(clusters.keys())\n diffs = []\n for id in ids:\n diffs.append(np.apply_along_axis(lambda x: dist(x, clusters[id]), axis=1, arr=data))\n\n diffs = np.asarray(diffs)\n\n clust_sizes = dict(zip(ids, np.zeros(len(ids))))\n closest_cluster = []\n for i in range(n):\n row = diffs[:, i]\n w0 = weights[i]\n inds_sorted = np.argsort(row)\n for id_opt in inds_sorted:\n if clust_sizes[id_opt] < n_per_cluster:\n closest_cluster.append(id_opt)\n clust_sizes[id_opt] += w0\n break\n\n inner_diffs = []\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n if len(indices) > 0:\n cut = np.take(diffs, indices, axis=1)\n inner_diffs.append(np.apply_along_axis(np.mean, axis=1, arr=cut)[k0])\n\n return ids, closest_cluster, sum(inner_diffs)\n\n\n\ndef kmeans(data,weights,k,n=None,n_per_cluster=None,B=10):\n\n if n is None:\n n = data.shape[0]\n\n if n_per_cluster is None:\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n results = []\n for b in range(B):\n print(b)\n results.append(kmeansOnceWeights(data,weights, k, n, n_per_cluster))\n\n inner_diffs = [r[2] for r in results]\n opt = np.argmin(inner_diffs)\n\n counter = collections.Counter(results[opt][1])\n print(counter)\n\n return results[opt][0], results[opt][1]\n\n\ndef kmeans_parallel(data,weights,k,n=None,n_per_cluster=None,B=10):\n\n if n is None:\n n = data.shape[0]\n\n if n_per_cluster is None:\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n def processInput(b):\n print(b)\n return kmeansOnceWeights(data, weights, k, n, n_per_cluster)\n\n # inputs = [(b, data, weights, k, n, n_per_cluster) for b in range(B)]\n inputs = range(B)\n num_cores = multiprocessing.cpu_count()\n\n with Pool(num_cores-1) as p:\n results = p.map(processInput, inputs)\n\n inner_diffs = [r[2] for r in results]\n opt = np.argmin(inner_diffs)\n\n counter = collections.Counter(results[opt][1])\n print(counter)\n\n return results[opt][0], results[opt][1]\n\n\nif __name__==\"__main__\":\n home_dir = \"/media/bruno/data/chatbot_project/sent2sent\"\n\n k = 2\n data = pickle.load(open(home_dir + \"/data.pickle\", \"rb\"))\n weights = pickle.load(open(home_dir + \"/weights.pickle\", \"rb\"))\n n = data.shape[0]\n\n n_per_cluster = int(np.ceil(sum(weights) / k))\n\n B = 10\n print(data.shape)\n ids, closest_cluster = kmeans_parallel(data, weights, k)\n\n for k0 in ids:\n indices = [i for i, cl in enumerate(closest_cluster) if cl == k0]\n cut = np.take(data, indices, axis=0)\n x, y = cut[:, 0], cut[:, 1]\n v = np.random.rand(3, 1)\n plt.scatter(x, y, c=tuple(v[:, 0]))\n # print(\"cluster \" + str(cl) + \" size = \" + str(len(clusters[cl])))\n\n plt.show()","repo_name":"BOpermanis/chatbot_project","sub_path":"sent2sent/kmeans3_weighted.py","file_name":"kmeans3_weighted.py","file_ext":"py","file_size_in_byte":4049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71720743948","text":"import cv2 \nimport numpy as np\nfrom random import randint\n\n#################################### PLACEHOLDER FUNCTIONS FOR LATER ########################################\ndef recieveMsg():\n \n x = randint(3,14)*100\n y = randint(3,9)*100\n \n return x,y,1\n\ndef getLocatorPhoto():\n \n photo = cv2.imread(\"slika0.jpg\")\n \n return photo\n\ndef extractMap(photo):\n \n MAP = cv2.imread(\"mapa.png\")\n \n return MAP\n\n\n\n#################################### REAL FUNCTIONS IN USE CURRENTLY ########################################\n\n# to know which jetcar is being traced, each ID is connected to its roof-marker color\ndef getColor(ID):\n \n if ID == 1:\n return [0,255,255]\n \n \n\n# coordinates recieved are extracted from a wide-angle lens camera. They need to be adjusted accordingly (un-fisheyed) \ndef undistortCoords(x,y):\n\n # makes an image with one white px, and un-distorts it\n img = np.zeros((2464,3264,3))\n img[y-1:y+1,x-1:x+1,:] = [255,255,255]\n img = undistort(img)\n \n #finds the position of the white px\n img = img[:,:,0]\n horizontal = img.sum(axis=0)\n vertical = img.sum(axis=1)\n \n x = np.argmax(horizontal)\n y = np.argmax(vertical)\n \n return x,y\n \n \n \ndef visualizeMarker(x,y,ID):\n \n x,y = undistortCoords(x,y)\n \n color = getColor(ID)\n marker = np.zeros_like(MAP)\n marker[y-5:y+5,x-5:x+5,:] = color\n \n return marker, x,y\n\n\n\ndef undistort(img, balance=1, dim2=(816,616), dim3=(1632,1332)):\n \n K=np.array([[403.5072678987361, 0.0, 390.5537285576421], [0.0, 403.056903943273, 303.0726428457018], [0.0, 0.0, 1.0]])\n D=np.array([[-0.02877771348636789], [-0.012216466999853827], [0.020949602322686396], [-0.015176688869367766]])\n \n\n dim1 = img.shape[:2][::-1] #dim1 is the dimension of input image to un-distort\n assert dim1[0]/dim1[1] == dim2[0]/dim2[1], \"Image to undistort needs to have same aspect ratio as the ones used in calibration\"\n if not dim2:\n dim2 = dim1\n if not dim3:\n dim3 = dim1\n scaled_K = K * dim1[0] / dim2[0] # The values of K is to scale with image dimension.\n scaled_K[2][2] = 1.0 # Except that K[2][2] is always 1.0\n \n # This is how scaled_K, dim2 and balance are used to determine the final K used to un-distort image. OpenCV document failed to make this clear!\n new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D, dim2, np.eye(3), balance=balance)\n map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)\n return cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)\n\n\n\n\n\n\n\n\nphoto = getLocatorPhoto() #FTTP\nphoto = undistort(photo)\nMAP = extractMap(photo)\n\nwhile True:\n \n x,y,ID = recieveMsg()\n marker,x,y = visualizeMarker(x,y,ID)\n \n cv2.imshow(\"Map with marker(s):\",marker+MAP)\n cv2.waitKey(1)\n print(x,y,end='\\r')\ncv2.destroyAllWindows()\n ","repo_name":"duspic/SmartCity_Model","sub_path":"L2S_communication/locator2server_server.py","file_name":"locator2server_server.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42272491474","text":"import pygame as pg\nfrom parameters import screen, W, H\nimport player\nimport globals\n\npg.init()\n\nclock = pg.time.Clock()\n\nplayer = player.Player(50, 50, 0.8)\n\ncolours = {'bg': \"#F2EDD7\", 'ground': \"#755139\"}\n\nrun = True\n\nwhile run:\n\n clock.tick(60)\n\n screen.fill(colours['bg'])\n\n pg.draw.rect(screen, colours['ground'], pg.Rect(0, globals.GROUND_LEVEL, W, H - globals.GROUND_LEVEL))\n\n player.update()\n player.draw()\n\n for event in pg.event.get():\n if event.type == pg.QUIT:\n run = False\n if event.type == pg.KEYDOWN:\n if event.key == pg.K_RIGHT:\n globals.moving_right = True\n stay = False\n if event.key == pg.K_LEFT:\n globals.moving_left = True\n stay = False\n if event.key == pg.K_UP:\n globals.jumping = True\n if event.type == pg.KEYUP:\n if event.key == pg.K_RIGHT:\n globals.moving_right = False\n stay = True\n if event.key == pg.K_LEFT:\n globals.moving_left = False\n stay = True\n\n pg.display.flip()\n","repo_name":"Oksana515/Platformer_walking_n_jumping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1363686842","text":"from outrankingDigraphs import *\nt = PerformanceTableau('zeitRanking2005')\n\ninput('Performance tableau')\nt.showHTMLPerformanceHeatmap(colorLevels=5,\\\n rankingRule=None,\\\n pageTitle='Performance Tableau \\'Zeit Ranking 2005\\'')\n\nfrom sortingDigraphs import *\nqs = QuantilesSortingDigraph(t,limitingQuantiles=7,LowerClosed=False)\ninput('7-tiles sorting')\nqs.showSorting()\ninput('7-tiles qunatile ordering')\nqs.showQuantileOrdering(strategy='average')\n\ninput('Ranking with heatmap')\nt.showHTMLPerformanceHeatmap(colorLevels=5,rankingRule='NetFlows',\n Correlations=True,pageTitle='Performance Tableau \\'Zeit Ranking 2006\\'')\n\n# absolute quantiles rating\nfrom performanceQuantiles import *\npq = PerformanceQuantiles(t,numberOfBins=9,LowerClosed=False)\nnqs = NormedQuantilesRatingDigraph(pq,t)\ninput('9-tiled rating heatmap')\nnqs.showHTMLRatingHeatmap(ndigits=0,colorLevels=5,Correlations=True,pageTitle='3-tiled rating of the universities')\n\n# best choice from preranked digraph\nfrom sparseOutrankingDigraphs import *\nprg = PreRankedOutrankingDigraph(t,5)\ninput('5-tiles preranked relation map')\nprg.showHTMLRelationMap()\ninput('Preranked Best choice recommendation')\nprg.showBestChoiceRecommendation()\n","repo_name":"rbisdorff/Digraph3","sub_path":"examples/zeit2005Demo.py","file_name":"zeit2005Demo.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"82"} +{"seq_id":"74814861067","text":"import requests\nimport json\nfrom hackernews.celery import app\nfrom pprint import pprint\nimport os\nfrom news.models import Item, Author\nfrom django.db.models import Max\nfrom datetime import datetime\nfrom pytz import timezone\nfrom django.db import IntegrityError\n\n\nutc = timezone(\"UTC\")\n\n\ndef get_max_item():\n resp = requests.get(\"https://hacker-news.firebaseio.com/v0/maxitem.json\")\n return resp.json()\n\n\n@app.task\ndef get_history():\n max_item_id = get_max_item()\n max_item_no_db = Item.objects.aggregate(max_item_id=Max(\"item_id\"))[\"max_item_id\"]\n print(f\"Max Item ID from API = {max_item_id}\")\n print(f\"Current Max Item ID from DB = {max_item_no_db}\")\n print(f\"Catching up with {max_item_id - max_item_no_db}\")\n stories_left = 100\n while max_item_no_db < max_item_id and stories_left > 0:\n max_item_no_db += 1\n get_item.delay(max_item_no_db)\n\n\n@app.task\ndef get_latest():\n resp = requests.get(\"https://hacker-news.firebaseio.com/v0/jobstories.json\")\n ids = resp.json()\n for id in ids:\n get_item.delay(id)\n\n\n@app.task\ndef get_item(id):\n resp = requests.get(f'https://hacker-news.firebaseio.com/v0/item/{id}.json')\n item = resp.json()\n\n parent = None\n if \"parent\" in item:\n try:\n parent = Item.objects.get(item_id=item[\"parent\"])\n except Item.DoesNotExist:\n get_item(item[\"parent\"])\n\n try:\n item_db = Item.objects.get(item_id=item[\"id\"])\n if item_db.category == \"story\" and item[\"type\"] != \"story\":\n item_db.category = item[\"type\"]\n \n except Item.DoesNotExist:\n item_db = Item(\n item_id = item[\"id\"],\n category = item[\"type\"],\n created_date = utc.localize(datetime.utcfromtimestamp(item[\"time\"])) if item.get(\"time\") else None, \n )\n \n item_db.parent = parent\n item_db.text = item.get(\"text\", \"\")\n item_db.url = item.get(\"url\")\n item_db.title = item.get(\"title\", \"\")\n item_db.score = item.get(\"score\")\n \n if \"by\" in item:\n item_db.author = get_user(item[\"by\"])\n \n try:\n item_db.save()\n except IntegrityError:\n pass\n\n # kids = []\n for kid_id in item.get(\"kids\", []):\n subitem = get_item(kid_id)\n # kids.append(subitem)\n # item[\"kids\"] = kids\n return item\n\n\ndef get_user(user_id):\n resp = requests.get(f'https://hacker-news.firebaseio.com/v0/user/{user_id}.json')\n data = resp.json()\n username = data[\"id\"]\n try:\n author = Author.objects.get(username=username)\n except Author.DoesNotExist:\n author = Author(\n username = username,\n created = utc.localize(datetime.utcfromtimestamp(data[\"created\"])),\n karma = data[\"karma\"],\n no_submitted = len(data.get(\"submitted\", []))\n )\n try:\n author.save()\n except IntegrityError:\n pass\n return author\n","repo_name":"adebisit/hacker-news-app","sub_path":"news/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20829653956","text":"class Solution:\n def brute_force(self, heights):\n \"\"\"\n TC:O(n^2) TLE\n SC:O(1)\n \"\"\"\n n=len(heights)\n max_area=0\n \n for i in range(n):\n min_height=float('inf')\n for j in range(i,n):\n min_height=min(min_height, heights[j])\n max_area=max(max_area, min_height * (j-i+1))\n return max_area\n def divide_and_conquer_helper(self,heights, start, end):\n \"\"\"\n TC:O(nlogn) TLE\n SC:O(n)\n \"\"\"\n if start>end:\n return 0\n \n min_index=start\n for i in range(start, end+1):\n if heights[min_index]>heights[i]:\n min_index=i\n \n res1=heights[min_index]*(end-start+1)\n res2=self.divide_and_conquer_helper(heights,start,min_index-1)\n res3=self.divide_and_conquer_helper(heights,min_index+1,end)\n \n max_area=max(res1, max(res2, res3))\n \n return max_area\n \n def divide_and_conquer(self, heights):\n return self.divide_and_conquer_helper(heights, start=0, end=len(heights)-1)\n def stack_helper(self, heights):\n \"\"\"\n TC: O(N)\n SC: O(N)\n \"\"\"\n n=len(heights)\n stack=list()\n max_area=0\n stack.append(-1)\n \n for i in range(n):\n while (stack[-1]!=-1 and heights[i]<=heights[stack[-1]]):\n temp_area=heights[stack.pop()] * (i-stack[-1]-1)\n max_area=max(max_area,temp_area)\n stack.append(i)\n \n while stack[-1]!=-1:\n max_area=max(max_area, heights[stack.pop()] * (n-stack[-1]-1))\n \n return max_area\n def largestRectangleArea(self, heights: List[int]) -> int:\n if not heights or len(heights)==0:\n return 0\n #return self.brute_force(heights)\n #return self.divide_and_conquer(heights)\n return self.stack_helper(heights)","repo_name":"akshatakulkarni98/ProblemSolving","sub_path":"DataStructures/stacks/histogram_heights.py","file_name":"histogram_heights.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24221493863","text":"\n__DEBUGING__ = False\n\nif not __DEBUGING__: \n from smbus2 import SMBus\n bus = SMBus(1)\n\nimport time\nimport threading\nimport random\n\n# Open i2c bus 1 and read one byte from address 80, offset 0\n\ntime.sleep(2)\n\n\nlocal_callback = None\n\nkeys = [ '1', '2', '3', 'A', '4', '5', '6', 'B', '7', '8', '9', 'C', '*', '0', '#', 'D' ]\nstates = [False] * len(keys)\n\ndef set_callback(callback):\n global local_callback\n\n # print(\"setting callback\")\n local_callback = callback\n\ndef reset_keys():\n global states\n\n states = [False] * len(keys)\n\n\ndef get_keys():\n global states\n\n # print(\"wheres the keys\")\n return states\n\n\ndef async_key_check():\n global states\n\n while True:\n if __DEBUGING__:\n theres_a_change = check_simulation_keys()\n else:\n theres_a_change = check_keys()\n if theres_a_change:\n local_callback(states)\n \n if __DEBUGING__:\n time.sleep(1)\n else:\n time.sleep(0.1)\n\n\ndef check_keys():\n global states\n\n try:\n b = bus.read_byte_data(0x2a, 0)\n if b != 0:\n char = chr(b)\n index = keys.index(char)\n states[index] = not states[index]\n # print(char, states[index])\n return True\n else:\n return False\n except:\n sad = \"No mames Hugo\"\n return False\n \n\ndef reset_key(index):\n global states\n\n states[index] = False\n\n\ndef check_simulation_keys():\n global states\n # print(\"checking keys\")\n\n theres_a_change = random.randint(0, 1)\n\n if theres_a_change:\n index = random.randint(0, len(states) - 1)\n char = keys[index]\n states[index] = not states[index]\n # print(char, states[index])\n return True\n else:\n return False\n","repo_name":"elastra21/ffa-controller","sub_path":"keyboard.py","file_name":"keyboard.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20829680856","text":"# https://leetcode.com/problems/palindrome-permutation/\n# TC:O(N)\n# SC:O(N)\n\nclass Solution:\n def canPermutePalindrome(self, s: str) -> bool:\n if not s:\n return False\n \n n=len(s)\n hash_map=dict()\n count=0\n \n for ch in s:\n hash_map[ch]=hash_map.get(ch,0)+1\n \n for k,v in hash_map.items():\n count = count + (v%2)\n \n return count<=1\n \n \n \n","repo_name":"akshatakulkarni98/ProblemSolving","sub_path":"DataStructures/strings/can_permute_palindrome.py","file_name":"can_permute_palindrome.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41263746856","text":"import argparse\nimport logging\nimport requests\nimport sys\n\nfrom crlite_query import CRLiteDB, CRLiteQuery, IntermediatesDB, parse_hosts_file\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom urllib.parse import urlparse\n\nlog = logging.getLogger(\"query_cli\")\n\n\ncrlite_collection_prod = (\n \"https://firefox.settings.services.mozilla.com/v1/buckets/security-state\"\n + \"/collections/cert-revocations/records\"\n)\ncrlite_collection_stage = (\n \"https://settings.stage.mozaws.net/v1/buckets/security-state\"\n + \"/collections/cert-revocations/records\"\n)\nintermediates_collection_prod = (\n \"https://firefox.settings.services.mozilla.com/v1/buckets/security-state\"\n + \"/collections/intermediates/records\"\n)\n\n\ndef find_attachments_base_url(urlstring):\n url = urlparse(urlstring)\n base_rsp = requests.get(f\"{url.scheme}://{url.netloc}/v1/\")\n return base_rsp.json()[\"capabilities\"][\"attachments\"][\"base_url\"]\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description=\"Query CRLite data\",\n epilog=\"\"\"\n The --db option should point to a folder containing a single filter file of\n the form \"YYYYMMDDnn.filter\" along with a collection of files of the form\n \"YYYYMMDDnn.stash\" which contain updates from that original filter. By\n default, if this tool believes it is out-of-date based on the local\n database, it will attempt to update itself before performing its checks.\n To avoid that behavior, pass --no-update on the command line.\n \"\"\",\n )\n parser.add_argument(\n \"--hosts\",\n help=\"Hosts to check, in the form host[:port] where \"\n + \"port is assumed 443 if not provided. Can be specified multiple times.\",\n action=\"append\",\n nargs=\"+\",\n default=[],\n metavar=\"host[:port]\",\n )\n parser.add_argument(\n \"--hosts-file\",\n help=\"File of hosts to check, in the form of 'host[:port]' each line, \"\n + \"where port is assumed 443 if not provided. Can be specified multiple \"\n + \" times.\",\n action=\"append\",\n default=[],\n type=Path,\n )\n parser.add_argument(\n \"files\", help=\"PEM files to load\", type=argparse.FileType(\"r\"), nargs=\"*\"\n )\n parser.add_argument(\n \"--db\",\n type=Path,\n default=Path(\"~/.crlite_db\"),\n help=\"Path to CRLite database folder\",\n )\n parser.add_argument(\n \"--no-update\", help=\"Do not attempt to update the database\", action=\"store_true\"\n )\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"--force-update\", help=\"Force an update to the database\", action=\"store_true\"\n )\n group.add_argument(\n \"--use-filter\",\n help=\"Use this specific filter file, ignoring the database\",\n type=Path,\n )\n parser.add_argument(\n \"--check-freshness\",\n help=\"Set exit code 0 if the database is more than this many hours old\",\n type=int,\n )\n parser.add_argument(\n \"--check-not-revoked\",\n help=\"Set exit code 0 if none of the supplied certificates are revoked\",\n action=\"store_true\",\n )\n parser.add_argument(\n \"--no-delete\",\n help=\"Do not attempt to delete old database files\",\n action=\"store_true\",\n )\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"--crlite-url\",\n default=crlite_collection_prod,\n help=\"URL to the CRLite records at Remote Settings.\",\n )\n group.add_argument(\n \"--crlite-staging\",\n action=\"store_true\",\n help=\"Use the staging URL for CRLite\",\n )\n parser.add_argument(\n \"--intermediates-url\",\n default=intermediates_collection_prod,\n help=\"URL to the CRLite records at Remote Settings.\",\n )\n parser.add_argument(\n \"--download-intermediates\",\n action=\"store_true\",\n help=\"Download all intermediate PEM files to the database\",\n )\n parser.add_argument(\n \"--verbose\", \"-v\", help=\"Be more verbose\", action=\"count\", default=0\n )\n parser.add_argument(\n \"--structured\",\n help=\"Emit log entries intended for structured loggers\",\n action=\"store_true\",\n )\n\n args = parser.parse_args()\n\n if args.crlite_staging:\n args.crlite_url = crlite_collection_stage\n\n if args.verbose > 1:\n logging.basicConfig(level=logging.DEBUG)\n if args.verbose > 2:\n from pyasn1 import debug\n\n debug.setLogger(debug.Debug(\"all\"))\n else:\n logging.basicConfig(level=logging.INFO)\n\n db_dir = args.db.expanduser()\n\n if not db_dir.is_dir():\n db_dir.expanduser().mkdir()\n\n last_updated_file = (db_dir / \".last_updated\").expanduser()\n if last_updated_file.exists() and not args.force_update:\n updated_file_timestamp = datetime.fromtimestamp(\n last_updated_file.stat().st_mtime\n )\n grace_time = datetime.now() - timedelta(hours=6)\n if last_updated_file.is_file() and updated_file_timestamp > grace_time:\n log.info(f\"Database was updated at {updated_file_timestamp}, skipping.\")\n log.debug(\n f\"Database was last updated {datetime.now() - updated_file_timestamp} ago.\"\n )\n args.no_update = True\n\n attachments_base_url = find_attachments_base_url(args.crlite_url)\n\n intermediates_db = IntermediatesDB(\n db_path=db_dir, download_pems=args.download_intermediates\n )\n crlite_db = CRLiteDB(db_path=args.db)\n\n try:\n if args.force_update or not args.no_update:\n if args.download_intermediates:\n log.info(\n \"Downloading all intermediate certificates. Look in \"\n + f\"{intermediates_db.intermediates_path}\"\n )\n\n intermediates_db.update(\n collection_url=args.intermediates_url,\n attachments_base_url=attachments_base_url,\n )\n crlite_db.update(\n collection_url=args.crlite_url,\n attachments_base_url=attachments_base_url,\n )\n last_updated_file.touch()\n except KeyboardInterrupt:\n log.warning(\"Interrupted.\")\n sys.exit(1)\n\n if args.use_filter:\n crlite_db.load_filter(path=args.use_filter)\n\n if not args.no_delete:\n crlite_db.cleanup()\n\n log.info(f\"Status: {intermediates_db}, {crlite_db}\")\n\n if args.check_freshness:\n freshness_limit = timedelta(hours=args.check_freshness)\n if crlite_db.age() > freshness_limit:\n log.error(\n f\"Database age is {crlite_db.age()}, which is larger than {freshness_limit}, \"\n + \"aborting!\"\n )\n sys.exit(1)\n\n query = CRLiteQuery(intermediates_db=intermediates_db, crlite_db=crlite_db)\n\n if not args.files and not args.hosts and not args.hosts_file:\n log.info(\"No PEM files or hosts specified to load. Run with --help for usage.\")\n\n to_test = list()\n\n for file in args.files:\n to_test.append((file.name, query.gen_from_pem(file)))\n\n host_strings = []\n for host_list in args.hosts:\n host_strings.extend(host_list)\n\n for path in args.hosts_file:\n with path.open(\"r\") as fd:\n host_strings.extend(parse_hosts_file(fd))\n\n for host_str in host_strings:\n parts = host_str.split(\":\")\n hostname = parts[0]\n port = 443\n if len(parts) > 1:\n port = int(parts[1])\n to_test.append((f\"{hostname}:{port}\", query.gen_from_host(hostname, port)))\n\n failures = list()\n\n for (name, generator) in to_test:\n for result in query.query(name=name, generator=generator):\n if args.structured:\n result.log_query_result()\n else:\n result.print_query_result(verbose=args.verbose)\n\n if args.check_not_revoked and result.is_revoked():\n failures.append(result)\n\n if failures:\n log.error(f\"{len(failures)} failures logged:\")\n for result in failures:\n log.error(result)\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"leplatrem/moz_crlite_query","sub_path":"crlite_query/query_cli.py","file_name":"query_cli.py","file_ext":"py","file_size_in_byte":8185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"21480201032","text":"import sys\nfrom collections import deque\ninput=sys.stdin.readline\n# 1시간 이상\n# bfs(x좌표,y좌표,지나온 흔적(str))\nh,w=map(int,input().split())\ngrid=[list(map(str,input().rstrip())) for _ in range(h)]\ndisc=[[0]*w for _ in range(h)]\ns_x,s_y,l_x,l_y=0,0,0,0\ntrans={}\ntrans['W']=[-1,0];trans['S']=[1,0];trans['A']=[0,-1];trans['D']=[0,1]\ndirection={}\ndirection[(-1,0)]='W';direction[(1,0)]='S';direction[(0,-1)]='A';direction[(0,1)]='D'\nfor i in range(h):\n for j in range(w):\n if grid[i][j]=='D':\n s_x,s_y=i,j\n if grid[i][j]=='Z':\n l_x,l_y=i,j\n\norder={}\nn=int(input())\nfor i in range(n):\n l=input().split()\n order[i+1]=[]\n for c in l:\n order[i+1].append(trans[c])\n\nans=[]\nq=deque()\nq.append([s_x,s_y,\"\"])\ntime=0\nwhile q:\n time+=1\n for _ in range(len(q)):\n a,b,ans=q.popleft()\n if a==l_x and b==l_y:\n print('YES')\n print(ans)\n sys.exit()\n if time>n:continue\n for x,y in order[time]:\n sam=ans[:]\n aa,bb=a+x,b+y\n if 0<=aa32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy\n Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.\n \"\"\"\n # fpath = os.path.join(fpath, str(ID) + '\\\\sve_' + str(ID) + '_')\n fpath = os.path.join(fpath, str(ID))\n bname = 'sve_' + str(ID) + '_'\n print(fpath) \n # specific leaf area (m2/kg) for converting leaf mass to leaf area \n # SLA = {'pine': 5.54, 'spruce': 5.65, 'decid': 18.46} # m2/kg, Kellomäki et al. 2001 Atm. Env.\n SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195\n \n # values to be set for 'open peatlands' and 'not forest land'\n nofor = {'vol': 0.1, 'ba': 0.01, 'height': 0.1, 'cf': 0.01, 'age': 0.0, \n 'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.01, 'bmroot': 0.01}\n opeatl = {'vol': 0.01, 'ba': 0.01, 'height': 0.1, 'cf': 0.1, 'age': 0.0,\n 'LAIpine': 0.01, 'LAIspruce': 0.01, 'LAIdecid': 0.1, 'bmroot': 0.01}\n\n # dem, set values outside boundaries to NaN\n dem, info, pos, cellsize, nodata = read_AsciiGrid(os.path.join(fpath, bname + 'dem_16m_aggr.asc'))\n # latitude, longitude arrays \n nrows, ncols = np.shape(dem)\n lon0 = np.arange(pos[0], pos[0] + cellsize*ncols, cellsize)\n lat0 = np.arange(pos[1], pos[1] + cellsize*nrows, cellsize)\n lat0 = np.flipud(lat0) # why this is needed to get coordinates correct when plotting?\n\n # catchment mask cmask ==1, np.NaN outside\n cmask = dem.copy()\n cmask[np.isfinite(cmask)] = 1.0\n\n # flowacc, D-infinity, nr of draining cells\n flowacc, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'Flow_accum_D-Inf_grids.asc'))\n flowacc = flowacc*cellsize**2 # in m2\n # slope, degrees\n slope, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'slope_16m.asc'))\n # twi\n twi, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'TWI_16m.asc'))\n \n \"\"\"\n Create soiltype grid and masks for waterbodies, streams, peatlands and rocks\n \"\"\"\n # Maastotietokanta water bodies: 1=waterbody\n stream, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'vesielementit_mtk.asc'))\n stream[np.isfinite(stream)] = 1.0\n # maastotietokanta peatlandmask\n peatm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'suo_mtk.asc'))\n peatm[np.isfinite(peatm)] = 1.0\n # maastotietokanta kalliomaski\n rockm, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kallioalue_mtk.asc'))\n rockm[np.isfinite(rockm)] = 1.0\n \n \"\"\"\n gtk soilmap: read and re-classify into 4 texture classes\n #GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)\n #Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000\n \"\"\"\n CoarseTextured = [195213, 195314, 19531421, 195313, 195310]\n MediumTextured = [195315, 19531521, 195215, 195214, 195601, 195411, 195112,\n 195311, 195113, 195111, 195210, 195110, 195312]\n FineTextured = [19531521, 195412, 19541221, 195511, 195413, 195410,\n 19541321, 195618]\n Peats = [195512, 195513, 195514, 19551822, 19551891, 19551892]\n Water = [195603]\n\n gtk_s, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'soil.asc')) \n \n r, c = np.shape(gtk_s)\n soil = np.ravel(gtk_s)\n #del gtk_s\n\n soil[np.in1d(soil, CoarseTextured)] = 1.0 # ; soil[f]=1; del f\n soil[np.in1d(soil, MediumTextured)] = 2.0\n soil[np.in1d(soil, FineTextured)] = 3.0\n soil[np.in1d(soil, Peats)] = 4.0\n soil[np.in1d(soil, Water)] = -1.0\n\n # reshape back to original grid\n soil = soil.reshape(r, c)\n del r, c\n soil[np.isfinite(peatm)] = 4.0\n # update waterbody mask\n ix = np.where(soil == -1.0)\n stream[ix] = 1.0\n \n # update catchment mask so that water bodies are left out (SL 20.2.18)\n #cmask[soil == -1.0] = np.NaN\n cmask[soil <= 0] = np.NaN\n soil = soil * cmask\n \n \"\"\" stand data (MNFI)\"\"\"\n # stand volume [m3ha-1]\n vol, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'tilavuus.asc'), setnans=False)\n vol = vol*cmask\n # indexes for cells not recognized in mNFI\n ix_n = np.where((vol >= 32727) | (vol == -9999) ) # no satellite cover or not forest land: assign arbitrary values \n ix_p = np.where((vol >= 32727) & (peatm == 1)) # open peatlands: assign arbitrary values\n ix_w = np.where((vol >= 32727) & (stream == 1)) # waterbodies: leave out\n cmask[ix_w] = np.NaN # NOTE: leaves waterbodies out of catchment mask\n vol[ix_n] = nofor['vol']\n vol[ix_p] = opeatl['vol']\n vol[ix_w] = np.NaN\n\n # basal area [m2 ha-1]\n ba, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'ppa.asc') )\n ba[ix_n] = nofor['ba']\n ba[ix_p] = opeatl['ba']\n ba[ix_w] = np.NaN\n\n # tree height [m]\n height, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'keskipituus.asc'))\n height = 0.1*height # m\n height[ix_n] = nofor['height']\n height[ix_p] = opeatl['height']\n height[ix_w] = np.NaN\n\n # canopy closure [-] \n cf, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'latvuspeitto.asc'))\n cf = 1e-2*cf\n cf[ix_n] = nofor['cf']\n cf[ix_p] = opeatl['cf']\n cf[ix_w] = np.NaN\n # cfd, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'lehtip_latvuspeitto.asc'))\n # cfd = 1e-2*cfd # percent to fraction\n\n # stand age [yrs]\n age, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname+'ika.asc'))\n age[ix_n] = nofor['age']\n age[ix_p] = opeatl['age']\n age[ix_w] = np.NaN\n\n # leaf biomasses and one-sided LAI\n bmleaf_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_neulaset.asc'))\n bmleaf_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_neulaset.asc'))\n bmleaf_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_neulaset.asc'))\n # bmleaf_pine[ix_n]=np.NaN; bmleaf_spruce[ix_n]=np.NaN; bmleaf_decid[ix_n]=np.NaN;\n\n LAI_pine = 1e-3*bmleaf_pine*SLA['pine'] # 1e-3 converts 10kg/ha to kg/m2\n LAI_pine[ix_n] = nofor['LAIpine']\n LAI_pine[ix_p] = opeatl['LAIpine']\n LAI_pine[ix_w] = np.NaN\n\n LAI_spruce = 1e-3*bmleaf_spruce*SLA['spruce']\n LAI_spruce[ix_n] = nofor['LAIspruce']\n LAI_spruce[ix_p] = opeatl['LAIspruce']\n LAI_spruce[ix_w] = np.NaN\n\n LAI_decid = 1e-3*bmleaf_decid*SLA['decid']\n LAI_decid[ix_n] = nofor['LAIdecid']\n LAI_decid[ix_p] = opeatl['LAIdecid']\n LAI_decid[ix_w] = np.NaN\n\n bmroot_pine, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_manty_juuret.asc'))\n bmroot_spruce, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_kuusi_juuret.asc'))\n bmroot_decid, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'bm_lehtip_juuret.asc')) \n bmroot = 1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) # 1000 kg/ha\n bmroot[ix_n] = nofor['bmroot']\n bmroot[ix_p] = opeatl['bmroot']\n bmroot[ix_w] = np.NaN\n\n # site types\n maintype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'paatyyppi.asc'))\n maintype = maintype*cmask\n sitetype, _, _, _, _ = read_AsciiGrid(os.path.join(fpath, bname + 'kasvupaikka.asc'))\n sitetype = sitetype*cmask\n \n # catchment outlet location and catchment mean elevation\n (iy, ix) = np.where(flowacc == np.nanmax(flowacc))\n loc = {'lat': lat0[iy], 'lon': lon0[ix], 'elev': np.nanmean(dem)}\n\n # dict of all rasters\n GisData = {'cmask': cmask, 'dem': dem, 'flowacc': flowacc, 'slope': slope,\n 'twi': twi, 'gtk_soilcode': gtk_s, 'soilclass': soil, 'peatm': peatm, 'stream': stream,\n 'rockm': rockm, 'LAI_pine': LAI_pine, 'LAI_spruce': LAI_spruce,\n 'LAI_conif': LAI_pine + LAI_spruce,\n 'LAI_decid': LAI_decid, 'bmroot': bmroot, 'ba': ba, 'hc': height,\n 'vol': vol, 'cf': cf, 'age': age, 'maintype': maintype, 'sitetype': sitetype,\n 'cellsize': cellsize, 'info': info, 'lat0': lat0, 'lon0': lon0, 'loc': loc} \n\n if plotgrids is True:\n # %matplotlib qt\n # xx, yy = np.meshgrid(lon0, lat0)\n plt.close('all')\n\n plt.figure()\n\n plt.subplot(221)\n plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)')\n plt.plot(ix, iy,'rs')\n plt.subplot(222)\n plt.imshow(twi); plt.colorbar(); plt.title('TWI')\n plt.subplot(223)\n plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')\n plt.subplot(224)\n plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')\n\n plt.figure(figsize=(6, 14))\n\n plt.subplot(221)\n plt.imshow(soil); plt.colorbar(); plt.title('soiltype')\n mask = cmask.copy()*0.0\n mask[np.isfinite(peatm)] = 1\n mask[np.isfinite(rockm)] = 2\n mask[np.isfinite(stream)] = 3\n\n plt.subplot(222)\n plt.imshow(mask); plt.colorbar(); plt.title('masks')\n plt.subplot(223)\n plt.imshow(LAI_pine+LAI_spruce + LAI_decid); plt.colorbar(); plt.title('LAI (m2/m2)')\n plt.subplot(224)\n plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')\n\n \n plt.figure(figsize=(6,11))\n plt.subplot(321)\n plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')\n plt.subplot(323)\n plt.imshow(height); plt.colorbar(); plt.title('hc (m)')\n #plt.subplot(223)\n #plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')\n plt.subplot(325)\n plt.imshow(age); plt.colorbar(); plt.title('age (yr)')\n plt.subplot(322)\n plt.imshow(1e-3*bmleaf_pine); plt.colorbar(); plt.title('pine needles (kg/m2)')\n plt.subplot(324)\n plt.imshow(1e-3*bmleaf_spruce); plt.colorbar(); plt.title('spruce needles (kg/m2)')\n plt.subplot(326)\n plt.imshow(1e-3*bmleaf_decid); plt.colorbar(); plt.title('decid. leaves (kg/m2)')\n\n if plotdistr is True:\n twi0 = twi[np.isfinite(twi)]\n vol = vol[np.isfinite(vol)]\n lai = LAI_pine + LAI_spruce + LAI_decid\n lai = lai[np.isfinite(lai)]\n soil0 = soil[np.isfinite(soil)]\n \n plt.figure(100)\n plt.subplot(221)\n plt.hist(twi0, bins=100, color='b', alpha=0.5, normed=True)\n plt.ylabel('f');plt.ylabel('twi')\n\n s = np.unique(soil0)\n colcode = 'rgcym'\n for k in range(0,len(s)):\n print(k)\n a = twi[np.where(soil==s[k])]\n a = a[np.isfinite(a)]\n plt.hist(a, bins=50, alpha=0.5, color=colcode[k], normed=True, label='soil ' +str(s[k]))\n plt.legend()\n plt.show()\n\n plt.subplot(222)\n plt.hist(vol, bins=100, color='k', normed=True); plt.ylabel('f'); plt.ylabel('vol')\n plt.subplot(223)\n plt.hist(lai, bins=100, color='g', normed=True); plt.ylabel('f'); plt.ylabel('lai')\n plt.subplot(224)\n plt.hist(soil0, bins=5, color='r', normed=True); plt.ylabel('f');plt.ylabel('soiltype')\n\n return GisData\n\ndef preprocess_soildata(pbu, psoil, soiltype, cmask, spatial=True):\n \"\"\"\n creates input dictionary for initializing BucketGrid\n Args:\n bbu - bucket parameters dict\n psoil - soiltype dict\n soiltype - soiltype code classified into 5 groups\n cmask - catchment mask\n \"\"\"\n # create dict for initializing soil bucket.\n # copy pbu into sdata and make each value np.array(np.shape(cmask))\n data = pbu.copy()\n data.update((x, y*cmask) for x, y in data.items())\n\n if spatial:\n for key in psoil.keys():\n c = psoil[key]['soil_id']\n ix = np.where(soiltype == c)\n data['poros'][ix] = psoil[key]['poros']\n data['fc'][ix] = psoil[key]['fc']\n data['wp'][ix] = psoil[key]['wp']\n data['ksat'][ix] = psoil[key]['ksat']\n data['beta'][ix] = psoil[key]['beta']\n del ix\n\n data['soilcode'] = soiltype\n return data\n \n\n\"\"\" ************ Reading and writing Ascii -grids ********* \"\"\" \n \ndef read_AsciiGrid(fname, setnans=True):\n \n \"\"\" reads AsciiGrid format in fixed format as below:\n \n ncols 750\n nrows 375\n xllcorner 350000\n yllcorner 6696000\n cellsize 16\n NODATA_value -9999\n -9999 -9999 -9999 -9999 -9999\n -9999 4.694741 5.537514 4.551162\n -9999 4.759177 5.588773 4.767114\n IN:\n fname - filename (incl. path)\n OUT:\n data - 2D numpy array\n info - 6 first lines as list of strings\n (xloc,yloc) - lower left corner coordinates (tuple)\n cellsize - cellsize (in meters?)\n nodata - value of nodata in 'data'\n Samuli Launiainen Luke 7.9.2016\n \"\"\"\n import numpy as np\n print(fname)\n fid = open(fname, 'r')\n info = fid.readlines()[0:6]\n fid.close()\n\n # print info\n # conversion to float is needed for non-integers read from file...\n xloc = float(info[2].split(' ')[-1])\n yloc = float(info[3].split(' ')[-1])\n cellsize = float(info[4].split(' ')[-1])\n nodata = float(info[5].split(' ')[-1])\n\n # read rest to 2D numpy array\n data = np.loadtxt(fname, skiprows=6)\n\n if setnans is True:\n data[data == nodata] = np.NaN\n nodata = np.NaN\n return data, info, (xloc, yloc), cellsize, nodata\n\n\ndef write_AsciiGrid(fname, data, info, fmt='%.18e'):\n \"\"\" writes AsciiGrid format txt file\n IN:\n fname - filename\n data - data (numpy array)\n info - info-rows (list, 6rows)\n fmt - output formulation coding\n \n Samuli Launiainen Luke 7.9.2016\n \"\"\"\n import numpy as np\n\n # replace nans with nodatavalue according to info\n nodata = int(info[-1].split(' ')[-1])\n data[np.isnan(data)] = nodata\n # write info\n fid = open(fname, 'w')\n fid.writelines(info)\n fid.close()\n\n # write data\n fid = open(fname, 'a')\n np.savetxt(fid, data, fmt=fmt, delimiter=' ')\n fid.close()\n\n\"\"\" ********* Flatten 2d array with nans to dense 1d array ********** \"\"\"\n\n\ndef matrix_to_array(x, nodata=None):\n \"\"\" returns 1d array and their indices in original 2d array\"\"\"\n\n s = np.shape(x)\n if nodata is None: # Nan\n ix = np.where(np.isfinite(x))\n else:\n ix = np.where(x != nodata)\n y = x[ix].copy()\n return y, ix, s\n\n\ndef array_to_matrix(y, ix, s, nodata=None):\n \"\"\"returns 1d array reshaped into 2d array x of shape s\"\"\"\n if nodata is None:\n x = np.ones(s)*np.NaN\n else:\n x = np.ones(s)*nodata\n x[ix] = y\n\n return x\n\n\ndef inputs_netCDF(ID, fname, data):\n \"\"\"\n Store gridded data required by SpaFHy into netCDF \n IN:\n ID -catchment id as str\n fname - filename\n data - dict with keys:\n cmask - catchment mask; integers within np.Nan outside\n LAI_conif [m2m-2]\n LAI_decid [m2m-2]\n hc, canopy closure [m]\n fc, canopy closure fraction [-]\n soil, soil type integer code 1-5\n flowacc - flow accumulation [units]\n slope - local surface slope [units]\n \n cellsize - gridcell size\n lon0 - x-grid\n lat0 - y-grid\n OUT:\n ncf - netCDF file handle. Initializes data\n ff - netCDF filename incl. path\n LAST EDIT 05.10.2018 / Samuli\n \"\"\"\n\n from netCDF4 import Dataset #, date2num, num2date\n from datetime import datetime\n\n print('**** creating SpaFHy input netCDF4 file: ' + fname + ' ****')\n \n # create dataset & dimensions\n ncf = Dataset(fname, 'w')\n ncf.description = 'SpatialData from : ' + str(ID)\n ncf.history = 'created ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n ncf.source = 'SpaFHy v.1.0 inputs'\n \n dlat, dlon = np.shape(data['cmask'])\n\n ncf.createDimension('dlon', int(dlon))\n ncf.createDimension('dlat', int(dlat))\n ncf.createDimension('scalar', 1)\n\n # create variables \n # call as createVariable(varname,type,(dimensions))\n cellsize = ncf.createVariable('cellsize', 'f4', ('scalar',))\n cellsize.units = 'm'\n lat = ncf.createVariable('lat', 'f4', ('dlat',))\n lat.units = 'ETRS-TM35FIN'\n lon = ncf.createVariable('lon', 'f4', ('dlon',))\n lon.units = 'ETRS-TM35FIN'\n\n cellsize[0] = data['cellsize']\n lon[:] = data['lon0']\n lat[:] = data['lat0']\n \n # required inputs\n cmask = ncf.createVariable('cmask', 'i4', ('dlat','dlon',))\n cmask.units = 'integer inside catchment, Nan outside'\n LAI_conif = ncf.createVariable('LAI_conif', 'f4', ('dlat','dlon',))\n LAI_conif.units = 'conifer LAI (m2m-2)'\n LAI_decid = ncf.createVariable('LAI_decid', 'f4', ('dlat','dlon',))\n LAI_decid.units = 'deciduous annual max LAI (m2m-2)' \n hc = ncf.createVariable('hc', 'f4', ('dlat','dlon',))\n hc.units = 'canopy height m' \n cf = ncf.createVariable('cf', 'f4', ('dlat','dlon',))\n cf.units = 'canopy closure (-)' \n \n soilclass = ncf.createVariable('soilclass', 'i4', ('dlat','dlon',))\n soilclass.units = 'soil class (1 - 5)'\n \n flowacc = ncf.createVariable('flowacc', 'f4', ('dlat','dlon',))\n flowacc.units = 'flow accumualtion area m2'\n slope = ncf.createVariable('slope', 'f4', ('dlat','dlon',))\n slope.units = 'local slope (deg)' \n \n for k in ['LAI_conif', 'LAI_decid', 'hc', 'cf', 'soilclass', 'flowacc', 'slope']:\n ncf[k][:,:] = data[k]\n \n print('**** done ****')\n\n\n# specific for MEOLO-sites\n\"\"\" ****************** creates gisdata dictionary from Vihti-koealue ************************ \"\"\"\n\ndef create_vihti_catchment(ID='Vihti', fpath='c:\\\\projects\\\\fotetraf\\\\spathy\\\\data', plotgrids=False, plotdistr=False):\n \"\"\" \n reads gis-data grids from selected catchments and returns numpy 2d-arrays\n IN: \n ID - SVE catchment ID (int or str)\n fpath - folder (str)\n plotgrids - True plots\n OUT:\n GisData - dictionary with 2d numpy arrays and some vectors/scalars.\n\n keys [units]:'dem'[m],'slope'[deg],'soil'[coding 1-4], 'cf'[-],'flowacc'[m2], 'twi'[log m??],\n 'vol'[m3/ha],'ba'[m2/ha], 'age'[yrs], 'hc'[m], 'bmroot'[1000kg/ha],'LAI_pine'[m2/m2 one-sided],'LAI_spruce','LAI_decid',\n 'info','lat0'[latitude, euref_fin],'lon0'[longitude, euref_fin],loc[outlet coords,euref_fin],'cellsize'[cellwidth,m],\n 'peatm','stream','cmask','rockm'[masks, 1=True] \n \n TODO (6.2.2017 Samuli): \n mVMI-datan koodit >32766 ovat vesialueita ja ei-metsäalueita (tiet, sähkölinjat, puuttomat suot) käytä muita maskeja (maastotietokanta, kysy\n Auralta tie + sähkölinjamaskit) ja IMPOSE LAI ja muut muuttujat ko. alueille. Nyt menevät no-data -luokkaan eikä oteta mukaan laskentaan.\n \"\"\"\n #from iotools import read_AsciiGrid\n\n fpath=os.path.join(fpath,str(ID)+'_')\n \n #specific leaf area (m2/kg) for converting leaf mass to leaf area \n # SLA={'pine':5.54, 'spruce': 5.65, 'decid': 18.46} #m2/kg, Kellomäki et al. 2001 Atm. Env.\n SLA = {'pine': 6.8, 'spruce': 4.7, 'decid': 14.0} # Härkönen et al. 2015 BER 20, 181-195\n\n #values to be set for 'open peatlands' and 'not forest land'\n nofor={'vol':0.1, 'ba':0.01, 'height':0.1, 'cf': 0.01, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}\n opeatl={'vol':0.01, 'ba':0.01, 'height':0.1, 'cf': 0.1, 'age': 0.0, 'LAIpine': 0.01, 'LAIspruce':0.01, 'LAIdecid': 0.01, 'bmroot':0.01}\n \n #dem, set values outside boundaries to NaN \n dem, info, pos, cellsize, nodata = read_AsciiGrid(fpath+'dem_16m.asc')\n #latitude, longitude arrays \n nrows, ncols=np.shape(dem) \n lon0=np.arange(pos[0], pos[0]+cellsize*ncols,cellsize)\n lat0=np.arange(pos[1], pos[1]+cellsize*nrows,cellsize)\n lat0=np.flipud(lat0) #why this is needed to get coordinates correct when plotting?\n\n #catchment mask cmask ==1, np.NaN outside\n cmask=dem.copy(); cmask[np.isfinite(cmask)]=1.0\n \n #flowacc, D-infinity, nr of draining cells\n flowacc, _, _, _, _ = read_AsciiGrid(fpath +'flowaccum_16m.asc')\n conv = np.nanmin(flowacc) # to correct units in file\n flowacc = flowacc / conv *cellsize**2 #in m2\n #slope, degrees\n slope, _, _, _, _ = read_AsciiGrid(fpath + 'slope_16m.asc')\n #twi\n twi, _, _, _, _ = read_AsciiGrid(fpath + 'twi_16m.asc')\n \n #Maastotietokanta water bodies: 1=waterbody\n stream, _, _, _, _ = read_AsciiGrid(fpath +'vesielementit_1_0.asc')\n stream[stream == 0.0] = np.NaN\n stream[np.isfinite(stream)]=1.0 \n #maastotietokanta peatlandmask\n #peatm, _, _, _, _ = read_AsciiGrid(fpath + 'suo_mtk.asc')\n peatm = np.ones([nrows, ncols])*np.NaN\n #peatm[np.isfinite(peatm)]=1.0 \n #maastotietokanta kalliomaski\n #rockm, _, _, _, _ = read_AsciiGrid(fpath +'kallioalue_mtk.asc')\n #rockm[np.isfinite(rockm)]=1.0 \n rockm = peatm.copy()\n \n \"\"\" stand data (MNFI)\"\"\"\n\n #stand volume [m3ha-1]\n vol, _, _, _, _ = read_AsciiGrid(fpath +'tilavuus.asc', setnans=False)\n vol=vol*cmask\n #indexes for cells not recognized in mNFI\n ix_n=np.where((vol>=32727) | (vol==-9999) ) #no satellite cover or not forest land: assign arbitrary values \n ix_p=np.where((vol>=32727) & (peatm==1))#open peatlands: assign arbitrary values\n ix_w=np.where((vol>=32727) & (stream==1)) #waterbodies: leave out\n cmask[ix_w]=np.NaN #*********** NOTE: leave waterbodies out of catchment mask !!!!!!!!!!!!!!!!!!!!!!\n vol[ix_n]=nofor['vol']; vol[ix_p]=opeatl['vol']; vol[ix_w]=np.NaN\n #basal area [m2 ha-1]\n ba, _, _, _, _ = read_AsciiGrid(fpath +'ppa.asc') \n ba[ix_n]=nofor['ba']; ba[ix_p]=opeatl['ba']; ba[ix_w]=np.NaN\n \n #tree height [m]\n height, _, _, _, _ = read_AsciiGrid(fpath +'keskipituus.asc')\n height=0.1*height #m \n height[ix_n]=nofor['height']; height[ix_p]=opeatl['height']; height[ix_w]=np.NaN\n \n #canopy closure [-] \n cf, _, _, _, _ = read_AsciiGrid(fpath +'latvuspeitto.asc') \n cfd, _, _, _, _ = read_AsciiGrid(fpath +'lehtip_latvuspeitto.asc')\n cf=1e-2*cf; cfd=1e-2*cfd; #in fraction\n cf[ix_n]=nofor['cf']; cf[ix_p]=opeatl['cf']; cf[ix_w]=np.NaN\n \n #stand age [yrs]\n age, _, _, _, _ = read_AsciiGrid(fpath +'ika.asc')\n age[ix_n]=nofor['age']; age[ix_p]=opeatl['age']; age[ix_w]=np.NaN\n \n #leaf biomasses and one-sided LAI\n bmleaf_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_neulaset.asc')\n bmleaf_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_neulaset.asc')\n bmleaf_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_neulaset.asc')\n # bmleaf_pine[ix_n]=np.NaN; bmleaf_spruce[ix_n]=np.NaN; bmleaf_decid[ix_n]=np.NaN;\n \n LAI_pine=1e-3*bmleaf_pine*SLA['pine'] #1e-3 converts 10kg/ha to kg/m2\n LAI_pine[ix_n]=nofor['LAIpine']; LAI_pine[ix_p]=opeatl['LAIpine']; age[ix_w]=np.NaN\n \n LAI_spruce=1e-3*bmleaf_spruce*SLA['spruce'] #1e-3 converts 10kg/ha to kg/m2\n LAI_spruce[ix_n]=nofor['LAIspruce']; LAI_spruce[ix_p]=opeatl['LAIspruce']; age[ix_w]=np.NaN\n \n LAI_conif = LAI_spruce + LAI_pine\n \n LAI_decid=1e-3*bmleaf_decid*SLA['decid'] #1e-3 converts 10kg/ha to kg/m2\n LAI_decid[ix_n]=nofor['LAIdecid']; LAI_decid[ix_p]=opeatl['LAIdecid']; age[ix_w]=np.NaN \n \n bmroot_pine, _, _, _, _ = read_AsciiGrid(fpath +'bm_manty_juuret.asc')\n bmroot_spruce, _, _, _, _ = read_AsciiGrid(fpath +'bm_kuusi_juuret.asc')\n bmroot_decid, _, _, _, _ = read_AsciiGrid(fpath +'bm_lehtip_juuret.asc') \n bmroot=1e-2*(bmroot_pine + bmroot_spruce + bmroot_decid) #1000 kg/ha \n bmroot[ix_n]=nofor['bmroot']; bmroot[ix_p]=opeatl['bmroot']; age[ix_w]=np.NaN \n \n \"\"\"\n gtk soilmap: read and re-classify into 4 texture classes\n #GTK-pintamaalaji grouped to 4 classes (Samuli Launiainen, Jan 7, 2017)\n #Codes based on maalaji 1:20 000 AND ADD HERE ALSO 1:200 000\n \"\"\"\n CoarseTextured = [195213,195314,19531421,195313,195310]\n MediumTextured = [195315,19531521,195215,195214,195601,195411,195112,195311,195113,195111,195210,195110,195312]\n FineTextured = [19531521, 195412,19541221,195511,195413,195410,19541321,195618]\n Peats = [195512,195513,195514,19551822,19551891,19551892]\n Water =[195603]\n\n gtk_s, _, _, _, _ = read_AsciiGrid(fpath +'soil.asc') \n\n r,c=np.shape(gtk_s);\n soil=np.ravel(gtk_s); del gtk_s\n soil[np.in1d(soil, CoarseTextured)]=1.0 #; soil[f]=1; del f\n soil[np.in1d(soil, MediumTextured)]=2.0\n soil[np.in1d(soil, FineTextured)]=3.0\n soil[np.in1d(soil, Peats)]=4.0\n soil[np.in1d(soil, Water)]=-1.0\n \n #soil[soil>4.0]=-1.0;\n #reshape back to original grid\n soil=soil.reshape(r,c)*cmask; del r,c\n soil[np.isfinite(peatm)]=4.0\n #update waterbody mask \n ix=np.where(soil==-1.0)\n stream[ix]=1.0 \n\n # update catchment mask so that water bodies are left out (SL 20.2.18)\n #cmask[soil == -1.0] = np.NaN\n cmask[soil <= 0] = np.NaN\n soil = soil * cmask\n \n #catchment outlet location\n (iy,ix)=np.where(flowacc==np.nanmax(flowacc));\n loc={'lat':lat0[iy],'lon':lon0[ix],'elev': np.nanmean(dem)}\n \n # harvester driving route and location of test sites\n\n route, _, _, _, _ = read_AsciiGrid(fpath +'route.asc')\n test_sites, _, _, _, _ = read_AsciiGrid(fpath +'test_sites.asc')\n \n GisData={'cmask':cmask, 'dem':dem, 'flowacc': flowacc, 'slope': slope, 'twi': twi, 'soilclass':soil,\n 'peatm':peatm, 'stream': stream, 'rockm': rockm,'LAI_pine': LAI_pine,\n 'LAI_spruce': LAI_spruce, 'LAI_conif': LAI_conif, 'LAI_decid': LAI_decid,\n 'bmroot': bmroot, 'ba': ba, 'hc': height, 'vol':vol,'cf':cf, 'cfd': cfd,\n 'age': age, 'route': route, 'test_sites': test_sites, \n 'cellsize': cellsize, 'info': info, 'lat0':lat0, 'lon0':lon0,'loc':loc} \n\n if plotgrids is True:\n #%matplotlib qt\n #xx,yy=np.meshgrid(lon0, lat0)\n plt.close('all')\n \n plt.figure() \n plt.subplot(221);plt.imshow(dem); plt.colorbar(); plt.title('DEM (m)');plt.plot(ix,iy,'rs')\n plt.subplot(222);plt.imshow(twi); plt.colorbar(); plt.title('TWI')\n plt.subplot(223);plt.imshow(slope); plt.colorbar(); plt.title('slope(deg)')\n plt.subplot(224);plt.imshow(flowacc); plt.colorbar(); plt.title('flowacc (m2)')\n #\n plt.figure()\n plt.subplot(221); plt.imshow(soil); plt.colorbar(); plt.title('soiltype')\n mask=cmask.copy()*0.0\n mask[np.isfinite(peatm)]=1; mask[np.isfinite(rockm)]=2; mask[np.isfinite(stream)]=3; \n plt.subplot(222); plt.imshow(mask); plt.colorbar(); plt.title('masks')\n plt.subplot(223); plt.imshow(LAI_pine+LAI_spruce + LAI_decid); plt.colorbar(); plt.title('LAI (m2/m2)')\n plt.subplot(224); plt.imshow(cf); plt.colorbar(); plt.title('cf (-)')\n \n plt.figure()\n plt.subplot(221);plt.imshow(vol); plt.colorbar(); plt.title('vol (m3/ha)')\n plt.subplot(222);plt.imshow(height); plt.colorbar(); plt.title('hc (m)')\n plt.subplot(223);plt.imshow(ba); plt.colorbar(); plt.title('ba (m2/ha)')\n plt.subplot(224);plt.imshow(age); plt.colorbar(); plt.title('age (yr)')\n \n if plotdistr is True:\n plt.figure() \n #twi\n twi0=twi[np.isfinite(twi)]; vol=vol[np.isfinite(vol)]; lai=LAI_pine + LAI_spruce + LAI_decid\n lai=lai[np.isfinite(lai)];soil0=soil[np.isfinite(soil)]\n \n plt.subplot(221); plt.hist(twi0,bins=100,color='b',alpha=0.5,normed=True); plt.ylabel('f');plt.ylabel('twi')\n \n s=np.unique(soil0); print(s)\n colcode='rgcym'\n for k in range(0,len(s)):\n print(k)\n a=twi[np.where(soil==s[k])]; a=a[np.isfinite(a)]\n plt.hist(a,bins=50,alpha=0.5,color=colcode[k], normed=True, label='soil ' +str(s[k]))\n plt.legend(); plt.show()\n \n plt.subplot(222); plt.hist(vol,bins=100,color='k',normed=True); plt.ylabel('f');plt.ylabel('vol')\n plt.subplot(223); plt.hist(lai,bins=100,color='g',normed=True); plt.ylabel('f');plt.ylabel('lai')\n plt.subplot(224); plt.hist(soil0, bins=5,color='r',normed=True); plt.ylabel('f');plt.ylabel('soiltype')\n\n \n return GisData\n \n\n\n\"\"\" ************************ Forcing data, sitefile ************************** \"\"\"\ndef read_FMI_weatherdata(forcfile, fyear,lyear, asdict=False):\n \"\"\" \n reads FMI interpolated daily weather data from file containing single point\n IN: \n forcfile- filename \n fyear & lyear - first and last years \n asdict=True if dict output, else pd.dataframe\n OUT: F -pd.DataFrame with columns (or dict with fields):\n time, doy, Ta, Tmin, Tmax (degC), Prec (mm/d), Rg (Wm-2), VPD (kPa), RH (%), esa (kPa), h2o (kPa), dds (degC, degree-day sum)\n \n \"\"\"\n \n #OmaTunniste;OmaItä;OmaPohjoinen;Kunta;siteid;vuosi;kk;paiva;longitude;latitude;t_mean;t_max;t_min;\n #rainfall;radiation;hpa;lamposumma_v;rainfall_v;lamposumma;lamposumma_cum\n #-site number\n #-date (yyyy mm dd)\n #-latitude (in KKJ coordinates, metres)\n #-longitude (in KKJ coordinates, metres)\n #-T_mean (degrees celcius)\n #-T_max (degrees celcius)\n #-T_min (degrees celcius)\n #-rainfall (mm)\n #-global radiation (per day in kJ/m2)\n #-H2O partial pressure (hPa)\n\n from datetime import datetime\n #forcfile='c:\\\\pyspace\\\\DATAT\\\\Topmodel_calibr\\\\FMI_saa_Porkkavaara.csv'\n\n #import forcing data\n dat=np.genfromtxt(forcfile,dtype=float,delimiter=';', usecols=(5,6,7,10,11,12,13,14,15,16))\n\n fi=np.where(dat[:,0]>=fyear); li=np.where(dat[:,0]<=lyear)\n ix=np.intersect1d(fi,li); #del fi, li\n #print min(ix), max(ix), np.shape(ix)\n tvec=dat[ix,0:3] #YYYY MM DD\n\n dat=dat[ix, 3:] \n\n time=[]; doy=[]\n for k in range(0,len(tvec)):\n time.append(datetime( int(tvec[k,0]), int(tvec[k,1]), int(tvec[k,2]), 0, 0) )\n doy.append(time[k].timetuple().tm_yday)\n \n time=np.array(time)\n doy=np.array(doy)\n \n Ta=dat[:,0];Tmax=dat[:,1]; Tmin=dat[:,2]; Prec=dat[:,3]; Rg=1e3*dat[:,4]/86400.0; Par=Rg*0.5 #from kJ/m2/d-1 to Wm-2 \n e=1e-1*dat[:,5]; #hPa-->kPa\n dds=dat[:,6] #temperature sum\n\n #saturated vapor pressure \n esa=0.6112*np.exp((17.67*Ta)/ (Ta +273.16 -29.66)) #kPa\n vpd=esa - e; #kPa \n vpd[vpd<0]=0.0\n rh=100.0*e/esa;\n rh[rh<0]=0.0; rh[rh>100]=100.0\n \n F={'Ta':Ta, 'Tmin':Tmin, 'Tmax':Tmax, 'Prec':Prec, 'Rg':Rg, 'Par': Par, 'VPD':vpd, 'RH':rh, 'esa':esa, 'h2o':e, 'dds':dds}\n\n F['time']=time\n F['doy']=doy\n \n ix=np.where(np.isnan(F['Prec'])); \n F['Prec'][ix]=0.0\n #del dat, fields, n, k, time\n \n if asdict is not True:\n #return pandas dataframe\n F=pd.DataFrame(F)\n cols=['time', 'doy', 'Ta', 'Tmin','Tmax', 'Prec', 'Rg', 'Par', 'VPD', 'RH', 'esa', 'h2o', 'dds']\n F=F[cols]\n return F\n \n# \"\"\" ******* functions to read Hyde data for CanopyGrid calibration ******** \"\"\"\n\n\n# def read_HydeDaily(filename):\n\n# cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','PrecSmear','Prec','U','Pamb',\n# 'SWE0','SWCh','SWCa','SWCb','SWCc', 'Tsh','Tsa','Tsb','Tsc','RnetFlag','Trfall','Snowdepth','Snowdepthstd','SWE','SWEstd','Roff1','Roff2'] \n \n# dat=pd.read_csv(filename,sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat.columns=cols\n# dat.index=dat['time']; dat=dat.drop(['time','SWE0'],axis=1)\n \n# forc=dat[['doy','Ta','VPD','Prec','Par','U']]; forc['Par']= 1/4.6*forc['Par']; forc['Rg']=2.0*forc['Par']\n# forc['VPD'][forc['VPD']<=0]=eps\n \n# #relatively extractable water, Hyde A-horizon\n# #poros = 0.45 \n# fc = 0.30\n# wp = 0.10\n# Wliq = dat['SWCa']\n# Rew = np.maximum( 0.0, np.minimum( (Wliq-wp)/(fc - wp + eps), 1.0) )\n# forc['Rew'] = Rew\n# forc['CO2'] = 380.0\n# # beta, soil evaporation parameter \n# #forc['beta'] = Wliq / fc\n# return dat, forc\n \n \n# def read_CageDaily(filepath):\n \n# cols=['time','doy','NEE','GPP','TER','ET','H','NEEflag','ETflag','Hflag','Par','Rnet','Ta','VPD','CO2','SWCa','PrecSmear','Prec','U','Pamb'] \n \n# dat1=pd.read_csv(filepath + 'HydeCage4yr-2000.txt',sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat1.columns=cols\n# dat1.index=dat1['time']; dat1=dat1.drop('time',axis=1)\n# forc1=dat1[['doy','Ta','VPD','Prec','Par','U']]; forc1['Par']= 1/4.6*forc1['Par']; forc1['Rg']=2.0*forc1['Par']\n \n# dat2=pd.read_csv(filepath + 'HydeCage12yr-2002.txt',sep='\\s+',header=None, names=None, parse_dates=[[0,1,2]], keep_date_col=False)\n# dat2.columns=cols\n# dat2.index=dat2['time']; dat2=dat2.drop('time',axis=1)\n# forc2=dat2[['doy','Ta','VPD','Prec','Par','U']]; forc2['Par']= 1/4.6*forc2['Par']; forc2['Rg']=2.0*forc2['Par']\n# return dat1, dat2,forc1,forc2\n","repo_name":"LukeEcomod/VMI_KAS","sub_path":"spafhy/spafhy_preprocessing.py","file_name":"spafhy_preprocessing.py","file_ext":"py","file_size_in_byte":34677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"70647421389","text":"import time\nimport copy\nimport warnings\n\nimport numpy as np\n\nfrom scipy.stats import multivariate_normal as mvn\n\nfrom helpers.cov_matrix import correct_hessian\nfrom helpers.distributions import product_multivariate_gaussian as pmvn\nfrom parameter.mcmc.mh_quasi_newton import QuasiNewtonMetropolisHastings\n\n\nclass QuasiNewtonMetropolisHastingsBenchmark(QuasiNewtonMetropolisHastings):\n \"\"\"Helper for checking the accuracy of the quasi-Newton estimate of the\n Hessian when mMALA is running the main chain.\"\"\"\n current_iter = 0\n start_time = 0\n time_offset = 0\n run_time = 0\n time_per_iter = 0\n no_hessians_corrected = 0\n iter_hessians_corrected = []\n\n def __init__(self, model, settings=None):\n \"\"\" Constructor. See the constructor for parameter.mcmc.mh_quasi_newton\n for all the settings. \"\"\"\n super().__init__(model, settings)\n self.type = 'qmh_benchmark'\n self.alg_type = 'qmh_benchmark'\n\n def _estimate_state(self, estimator, proposed_state, state_history):\n # Get adapted step sizes (if there are any) otherwise use fixed\n if 'adapted_step_size' in proposed_state:\n step_size_gradient = 0.5 * proposed_state['adapted_step_size']**2\n step_size_hessian = proposed_state['adapted_step_size']**2\n else:\n step_size_gradient = 0.5 * self.settings['step_size_gradient']**2\n step_size_hessian = self.settings['step_size_hessian']**2\n\n # Check if there is an empirical estimate of the Hessian to use\n # as the fallback\n if type(self.emp_hessian) is np.ndarray:\n alt_hess = self.emp_hessian\n else:\n alt_hess = self.settings['hess_corr_fallback']\n\n hess_corr = self.settings['hess_corr_method']\n\n # Run the smoother to get likelihood and state estimate\n warnings.filterwarnings(\"error\")\n try:\n self.model.store_free_params(proposed_state['params_free'])\n log_jacobian = self.model.log_jacobian()\n _, log_prior = self.model.log_prior()\n except:\n print(\"MH-QN-benchmark: Storing parameters failed...\")\n return False\n\n if self.settings['correlated_rvs'] and estimator.alg_type is not 'kalman':\n rvs = {'rvs': proposed_state['rvs']}\n smoother_completed = estimator.smoother(\n self.model, compute_hessian=True, rvs=rvs)\n else:\n smoother_completed = estimator.smoother(\n self.model, compute_hessian=True)\n\n if not smoother_completed:\n print(\"MH-QN-benchmark: Smoother failed...\")\n return False\n\n log_like = estimator.results['log_like']\n state_trajectory = estimator.results['state_trajectory']\n grad = estimator.results['gradient_internal']\n hess = np.linalg.inv(estimator.results['hessian_internal'])\n grad_copy = np.array(grad, copy=True)\n\n # Run benchmark with different Quasi-Newton proposals\n memory_length_vector = (5, 10, 15, 20, 25, 30, 35, 40, 45, 50)\n error_bfgs_fro = []\n error_ls_fro = []\n error_sr1_fro = []\n\n if self.current_iter > self.settings['memory_length']:\n for i, memory_length in enumerate(memory_length_vector):\n params_diffs, grads_diffs = self._qn_compute_diffs(\n state_history, memory_length=memory_length)\n\n init_hessian = self._qn_init_hessian(grad)\n init_hessian_ls = state_history\n\n hess_bfgs, _ = self._qn_bfgs(\n params_diffs, grads_diffs, init_hessian)\n hess_bfgs, _ = correct_hessian(\n hess_bfgs, alt_hess, hess_corr, verbose=False)\n\n hess_ls, _ = self._qn_ls(\n params_diffs, grads_diffs, init_hessian_ls)\n hess_ls, _ = correct_hessian(\n hess_ls, alt_hess, hess_corr, verbose=False)\n\n hess_sr1, _ = self._qn_sr1(\n params_diffs, grads_diffs, init_hessian)\n hess_sr1, _ = correct_hessian(\n hess_sr1, alt_hess, hess_corr, verbose=False)\n\n hess_direct = np.linalg.inv(\n estimator.results['hessian_internal_noprior'])\n\n error_bfgs_fro.append(np.linalg.norm(\n hess_direct - hess_bfgs, 'fro'))\n error_ls_fro.append(np.linalg.norm(\n hess_direct - hess_ls, 'fro'))\n error_sr1_fro.append(np.linalg.norm(\n hess_direct - hess_sr1, 'fro'))\n\n hess, fixed_hess = correct_hessian(\n hess, alt_hess, hess_corr, verbose=False)\n\n grad = estimator.results['gradient_internal']\n nat_grad = hess @ grad\n if np.isfinite(step_size_hessian) and np.isfinite(step_size_gradient):\n output_hess = np.array(hess, copy=True) * step_size_hessian\n output_nat_grad = np.array(\n nat_grad, copy=True) * step_size_gradient\n else:\n print(\"MH-QN-benchmark: Gradient or Hessian not finite.\")\n return False\n\n proposed_state.update({'params': self.model.get_params()})\n proposed_state.update({'state_trajectory': state_trajectory})\n proposed_state.update({'log_like': log_like})\n proposed_state.update({'log_jacobian': log_jacobian})\n proposed_state.update({'log_prior': log_prior})\n proposed_state.update({'log_target': log_prior + log_like})\n proposed_state.update({'gradient': grad_copy})\n proposed_state.update({'nat_gradient': output_nat_grad})\n proposed_state.update({'hessian': output_hess})\n proposed_state.update({'hessian_corrected': fixed_hess})\n proposed_state.update({'error_bfgs_fro': np.array(error_bfgs_fro)})\n proposed_state.update({'error_ls_fro': np.array(error_ls_fro)})\n proposed_state.update({'error_sr1_fro': np.array(error_sr1_fro)})\n return True\n\n def _qn_compute_diffs(self, state_history, memory_length):\n no_params = self.no_params_to_estimate\n\n # Extract parameters, gradients and log-target for the current length\n # of memory\n params = np.zeros((memory_length - 1, no_params))\n grads = np.zeros((memory_length - 1, no_params))\n losses = np.zeros((memory_length - 1, 1))\n j = 0\n for i in range(self.current_iter - memory_length + 1, self.current_iter):\n params[j, :] = state_history[i]['params_free'].flatten()\n grads[j, :] = state_history[i]['gradient'].flatten()\n losses[j, :] = float(state_history[i]['log_target'])\n losses[j, :] += float(state_history[i]['log_prior'])\n j += 1\n\n # Sort and compute differences\n idx = np.argsort(losses.flatten())\n params = params[idx, :]\n grads = grads[idx, :]\n\n params_diffs = np.zeros((memory_length - 2, no_params))\n grads_diffs = np.zeros((memory_length - 2, no_params))\n for i in range(len(idx) - 1):\n params_diffs[i, :] = params[i + 1, :] - params[i, :]\n grads_diffs[i, :] = grads[i + 1, :] - grads[i, :]\n\n return params_diffs, grads_diffs\n","repo_name":"compops/pmmh-qn","sub_path":"python/parameter/mcmc/mh_quasi_newton_benchmark.py","file_name":"mh_quasi_newton_benchmark.py","file_ext":"py","file_size_in_byte":7260,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"12770201613","text":"import base64\r\n\r\n\r\nimport PIL\r\n\r\n\r\n\r\n\r\ndef writeImageToDisk(base64Img,name):\r\n basemod = base64Img.replace(\"b'\",\"'\")\r\n img = bytes(basemod , encoding=\"UTF-8\")\r\n filename = name+\".jpg\"\r\n print(img)\r\n with open(filename, \"wb\") as fh:\r\n fh.write(base64.decodebytes(img))\r\n","repo_name":"othierie/SGF-Viya-Streaming-Integration","sub_path":"kafka-python-framework/src/dataUtils/ReadImage.py","file_name":"ReadImage.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34307325981","text":"# need to have this in the compiled gentle folder\nimport logging\nimport multiprocessing\nimport os\nimport sys\nimport gentle\n\ndef align_process(path_to_audio, lyrics_file):\n disfluencies = set(['uh', 'um'])\n \n def on_progress(p):\n for k,v in p.items():\n logging.debug(\"%s: %s\" % (k, v))\n\n with open(lyrics_file, encoding=\"utf-8\") as fh:\n transcript = fh.read()\n\n resources = gentle.Resources()\n \n with gentle.resampled(path_to_audio) as wavfile:\n aligner = gentle.ForcedAligner(resources, transcript, nthreads=multiprocessing.cpu_count(), disfluency=False, conservative=False, disfluencies=disfluencies)\n result = aligner.transcribe(wavfile, progress_cb=on_progress, logging=logging)\n\n return result.to_json(indent=2)\n\n#Testing...\nif __name__ == \"__main__\":\n # sample test\n result = align_process(\"audio.mp3\", \"words.txt\")\n print(result)","repo_name":"ST2-EV/lyrixy","sub_path":"force_align.py","file_name":"force_align.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"11909318058","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.models as models\n\n\nclass GroupNorm32(torch.nn.GroupNorm):\n def __init__(self, num_channels, num_groups=32, **kargs):\n super().__init__(num_groups, num_channels, **kargs)\n\n\nclass ResNet(nn.Module):\n def __init__(self, pretrained=False, num_classes=10, small_kernel=True, backbone='resnet18', args=None):\n super(ResNet, self).__init__()\n\n # Load the pretrained ResNet model\n if args.norm_type == 'bn':\n resnet_model = models.__dict__[backbone](pretrained=pretrained)\n else:\n resnet_model = models.__dict__[backbone](pretrained=pretrained, norm_layer=GroupNorm32)\n\n if small_kernel:\n conv1_out_ch = resnet_model.conv1.out_channels\n if args.dset in ['fmnist']:\n resnet_model.conv1 = nn.Conv2d(1, conv1_out_ch, kernel_size=3, stride=1, padding=1, bias=False) # Small dataset filter size used by He et al. (2015)\n else:\n resnet_model.conv1 = nn.Conv2d(3, conv1_out_ch, kernel_size=3, stride=1, padding=1, bias=False) # Small dataset filter size used by He et al. (2015)\n resnet_model.maxpool = nn.MaxPool2d(kernel_size=1, stride=1, padding=0)\n\n # Isolate the feature extraction layers\n self.features = nn.Sequential(*list(resnet_model.children())[:-1])\n\n # Isolate the classifier layer\n self.classifier = nn.Linear(resnet_model.fc.in_features, num_classes)\n\n if args.ETF_fc:\n weight = torch.sqrt(torch.tensor(num_classes / (num_classes - 1))) * (\n torch.eye(num_classes) - (1 / num_classes) * torch.ones((num_classes, num_classes)))\n weight /= torch.sqrt((1 / num_classes * torch.norm(weight, 'fro') ** 2))\n\n self.classifier.weight = nn.Parameter(torch.mm(weight, torch.eye(num_classes, resnet_model.fc.in_features)))\n self.classifier.weight.requires_grad_(False)\n\n if args.ckpt not in ['', 'null', 'none']:\n pretrain_wt = torch.load(args.ckpt)\n if args.load_fc: # load both feature extractor and fc\n pass\n else: # not load fc\n pretrain_wt = {k: v for k, v in pretrain_wt.items() if 'classifier' not in k}\n self.load_state_dict(pretrain_wt, strict=False)\n\n def forward(self, x, ret_feat=False):\n x = self.features(x)\n x = x.view(x.size(0), -1)\n out = self.classifier(x)\n\n if ret_feat:\n return out, x\n else:\n return out\n\n\nclass MLP(nn.Module):\n def __init__(self, hidden, depth=6, fc_bias=True, num_classes=10):\n # Depth means how many layers before final linear layer\n\n super(MLP, self).__init__()\n layers = [nn.Linear(3072, hidden), nn.BatchNorm1d(num_features=hidden), nn.ReLU()]\n for i in range(depth - 1):\n layers += [nn.Linear(hidden, hidden), nn.BatchNorm1d(num_features=hidden), nn.ReLU()]\n\n self.layers = nn.Sequential(*layers)\n self.fc = nn.Linear(hidden, num_classes, bias=fc_bias)\n print(fc_bias)\n\n def forward(self, x, ret_feat=False):\n x = x.view(x.shape[0], -1)\n x = self.layers(x)\n features = F.normalize(x)\n x = self.fc(x)\n if ret_feat:\n return x, features\n else:\n return x\n","repo_name":"glbreeze/neural_collapse","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"31737488365","text":"import numpy as np\nfrom Bio import SeqIO\nfrom Bio.Align import substitution_matrices\n\nH = np.int16(-11) # gap opening penalty\nG = np.int16(-1) # gap extension penalty\n\nS = np.array(substitution_matrices.load(\"BLOSUM62\"), dtype=np.int8) # Scoring matrix\n\nCONV_TABLE = { # Table for converting letter to index in BLOSUM62\n 'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4, 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9, 'L': 10, 'K': 11, 'M': 12, 'F': 13,\n 'P': 14, 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, 'B': 20, 'Z': 21, 'X': 22, '*': 23,\n}\n\n\ndef parse_fasta_file(fasta_file_path: str) -> tuple[np.ndarray, np.ndarray]:\n records = list(SeqIO.parse(fasta_file_path, 'fasta'))\n assert len(records) == 2, \"wrong number of records in the provided fasta file\"\n return np.array(records[0].seq), np.array(records[1].seq)\n\n\ndef sequence_to_indices(seq: np.ndarray):\n index_sequence = np.zeros(len(seq), dtype=np.int8)\n for i, letter in enumerate(seq):\n index_sequence[i] = CONV_TABLE[letter]\n return index_sequence\n\n\ndef construct_matrix(index_seq_a: np.ndarray, index_seq_b: np.ndarray) -> np.array:\n # Initialise matrix\n m = np.zeros((index_seq_b.size + 1, index_seq_a.size + 1, 3), dtype=np.int16)\n m[0][0] = [0, 0, 0]\n for j in range(1, index_seq_a.size + 1):\n m[0][j] = [-32768 / 2, (j - 1) * G + H, (j - 1) * G + H]\n for i in range(1, index_seq_b.size + 1):\n m[i][0] = [(i - 1) * G + H, (i - 1) * G + H, -32768 / 2]\n\n # Construct matrix\n for i in range(1, index_seq_b.size + 1):\n for j in range(1, index_seq_a.size + 1):\n m[i][j][0] = max(m[i - 1][j][0] + G, m[i - 1][j][1] + H)\n m[i][j][2] = max(m[i][j - 1][2] + G, m[i][j - 1][1] + H)\n m[i][j][1] = max(m[i - 1][j - 1][1] + S[index_seq_a[j - 1]][index_seq_b[i - 1]], m[i][j][0], m[i][j][2])\n\n # Return matrix\n return m\n\n\ndef global_alignment_score(fasta_file_path: str) -> int:\n seq_a, seq_b = parse_fasta_file(fasta_file_path)\n index_seq_a = sequence_to_indices(seq_a)\n index_seq_b = sequence_to_indices(seq_b)\n m = construct_matrix(index_seq_a, index_seq_b)\n return np.amax(m[-1][-1])\n\n\nDIRS = [(-1, 0), (-1, -1), (0, -1)]\n\n\ndef global_alignment(fasta_file_path: str) -> tuple[str, str]:\n # Parse sequences from file and construct matrix\n seq_a, seq_b = parse_fasta_file(fasta_file_path)\n index_seq_a = sequence_to_indices(seq_a)\n index_seq_b = sequence_to_indices(seq_b)\n m = construct_matrix(index_seq_a, index_seq_b)\n\n # Initialise traceback\n seq_a_aligned = []\n seq_b_aligned = []\n\n i = m.shape[0] - 1\n j = m.shape[1] - 1\n platform = 1\n\n # Traceback\n while i > 0 or j > 0:\n # Decide direction and platform\n i_dirs = platform\n if platform == 1:\n i_dirs = np.argmax([m[i][j][0], m[i - 1][j - 1][1] + S[index_seq_a[j - 1]][index_seq_b[i - 1]], m[i][j][2]])\n\n if i_dirs == 0:\n platform = 0 if m[i - 1][j][0] + G > m[i - 1][j][1] + H else 1\n elif i_dirs == 2:\n platform = 2 if m[i][j - 1][2] + G > m[i][j - 1][1] + H else 1\n\n seq_a_aligned.append('-' if i_dirs == 0 else seq_a[j - 1])\n seq_b_aligned.append('-' if i_dirs == 2 else seq_b[i - 1])\n\n i += DIRS[i_dirs][0]\n j += DIRS[i_dirs][1]\n\n seq_a_aligned = ''.join(np.flip(seq_a_aligned))\n seq_b_aligned = ''.join(np.flip(seq_b_aligned))\n\n return seq_a_aligned, seq_b_aligned\n","repo_name":"eliasnijs/global-alignment","sub_path":"global_alignment.py","file_name":"global_alignment.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73763127309","text":"import socket, time, pickle\n\n# Função que imprime a lista formatada\ndef imprime(l):\n print(f\"\"\"\n pid: {l['pid']}\n ip: {l['ip']}\n mem_total: {l['memoria_total']}\n mem_usado: {l['memoria_usada']}\n cpu: {l['cpu']}\n disco_total: {l['disco_total']}\n disco_usado: {l['disco_usado']}\n \n \"\"\")\n\n# Cria o socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ntry:\n # Tenta se conectar ao servidor\n s.connect((socket.gethostname(), 9999))\n msg = ' '\n for i in range(10):\n # Envia mensagem vazia apenas para indicar a requisição\n s.send(msg.encode('ascii'))\n bytes = s.recv(1024)\n # Converte os bytes para lista\n dicionario = pickle.loads(bytes)\n imprime(dicionario)\n time.sleep(2)\n msg = 'fim'\n s.send(msg.encode('ascii'))\nexcept Exception as erro:\n print(str(erro))\n\n# Fecha o socket\ns.close()\n\ninput(\"Pressione qualquer tecla para sair...\")","repo_name":"joselsantospqt/Python","sub_path":"Projeto_de_Bloco_Python/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2584013589","text":"# -*- coding: utf-8 -*-\n\"\"\"# Visualization\n\nThis module contains functions that produce plots and visualizations\nneeded for logging, data exploration and the final dashboards.\n\"\"\"\n\nimport itertools\nfrom typing import List, Optional, Tuple, Union\n\nimport einops\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom loguru import logger as log\nfrom matplotlib.colors import Colormap\nfrom torch.utils.data import Dataset\nfrom tqdm import tqdm\n\n\ndef plot_confusion_matrix(\n cm: Union[np.array, torch.tensor],\n classes: Optional[List[str]] = None,\n normalize: bool = False,\n title: str = \"Confusion Matrix\",\n cmap: Union[str, Colormap] = plt.cm.Blues,\n) -> plt.Figure:\n \"\"\"Create a matplotlib confusion matrix plot from a np.array or torch.tensor.\n\n Args:\n cm (np.array | torch.tensor): Raw Confusion Matrix as np.array or torch.tensor.\n classes (Optional[List[str]], optional): If defined replace class indices on axes with class labels. Defaults to None.\n normalize (bool, optional): If True, Normalize the count of each class to 1 to see percentages instead of absolute counts. Defaults to False.\n title (str, optional): Figure Title. Defaults to \"Confusion Matrix\".\n cmap ([str | plt.Colormap, optional): Matplotlib colormap. Defaults to plt.cm.Blues.\n\n Returns:\n plt.Figure: [description]\n \"\"\"\n if isinstance(cm, torch.Tensor):\n cm = cm.cpu().numpy()\n if normalize:\n cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n\n fig = plt.figure()\n plt.imshow(cm, interpolation=\"nearest\", cmap=cmap)\n plt.title(title)\n plt.colorbar()\n\n if classes:\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = \".2f\" if normalize else \".0f\" # \"d\" for integers\n thresh = cm.max() / 2.0\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(\n j,\n i,\n format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\",\n )\n\n plt.tight_layout()\n plt.ylabel(\"True label\")\n plt.xlabel(\"Predicted label\")\n\n return fig\n\n\ndef visualize_samples_from_dataset(\n dataset: Dataset,\n rows: int = 5,\n undo_normalization: Optional[Tuple[List[float], List[float]]] = (\n [0.3211, 0.2243, 0.1602],\n [0.2617, 0.1825, 0.1308],\n ),\n) -> plt.Figure:\n \"\"\"Visualize a grid of samples without titles/labels in a single plot.\n\n Args:\n dataset (torch.utils.data.Dataset): Dataset to visualize samples from.\n rows (int, optional): How many samples will be in one row. Total number of samples will be rows^2. Defaults to 5.\n\n Returns:\n plt.Figure: matplotlib figure\n \"\"\"\n log.info(\"logging samples from dataset\")\n\n fig = plt.figure(figsize=(rows * 2, rows * 2))\n\n for idx in tqdm(range(rows * rows)):\n plt.subplot(rows, rows, idx + 1)\n img = dataset[np.random.randint(0, len(dataset))][0]\n\n if undo_normalization:\n means, stds = undo_normalization\n means = torch.tensor(means).reshape(3, 1, 1)\n stds = torch.tensor(stds).reshape(3, 1, 1)\n img = torch.clamp(img * stds + means, 0.0, 1.0)\n\n plt.imshow(einops.rearrange(img.squeeze().numpy(), \"c w h -> w h c\"))\n plt.axis(\"off\")\n plt.tight_layout(pad=0.0)\n\n return fig\n\n\ndef visualize_signal_propagation(\n name_values: list, title: str, *args, **kwargs\n) -> plt.Figure:\n \"\"\"Visualize Signal Propagation Plot using matplolib and\n the utilities from the timm package.\n See: https://github.com/mehdidc/signal_propagation_plot/blob/main/signal_propagation_plot/pytorch.py\n\n Args:\n name_values (torch.nn.Module): pytorch model\n input_shape (List[int], optional): Input Size of the model.\n\n Returns:\n plt.Figure: matplotlib figure\n \"\"\"\n labels = [\".\".join(name.split(\".\")[-3:]) for name, _ in name_values]\n values = [value for _, value in name_values]\n depth = np.arange(len(labels))\n\n fig, ax = plt.subplots(figsize=(12, 6))\n\n plt.plot(depth, values, *args, **kwargs)\n plt.xticks(depth, labels, rotation_mode=\"anchor\")\n plt.grid()\n plt.title(title)\n plt.setp(\n ax.get_xticklabels(),\n rotation=45,\n horizontalalignment=\"right\",\n fontsize=6,\n )\n\n return fig\n","repo_name":"LaurenzBeck/ophthalmology","sub_path":"ophthalmology/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"82"} +{"seq_id":"32914914904","text":"import numpy as np\nimport pandas as pd\nfrom astropy.table import Table\nimport matplotlib.pyplot as plt\nimport logging\n\n# logging.basicConfig(filename='logs.log',\n# encoding='utf-8',\n# format='%(levelname)s (%(asctime)s): %(message)s (Line: %(lineno)d [%(filename)s])',\n# datefmt='%d/%m/%Y %I:%M:%S %p',\n# level=logging.INFO)\n#\n# df = pd.read_csv(\"clusterMembers/M79_memberStars_6.dat\", delimiter=\"\\t\", skiprows=2)\n#\n# df1 = pd.read_csv(\"nonMembers/M79_nonMembers.dat\", delimiter=\"\\t\", skiprows=2)\n#\n# starNums= df.iloc[:,0]\n#\n# starNums1 = df1.iloc[:,0]\n#\n# #print(starNums[0])\n#\n# lst = np.array([3524,3473,3491,3487,3353,2600,2363,1995, 816])\n#\n# # print(lst)\n#\n# starNums = np.array([starNums])\n#\n# starNums1 = np.array([starNums1])\n#\n# # print(starNums)\n#\n# a = np.intersect1d(starNums, lst)\n#\n# c = np.setdiff1d(lst, starNums)\n#\n# # print(a)\n# # print(c)\n#\n# b = np.intersect1d(starNums1, lst)\n#\n# # print(b)\n#\n# clusterName = \"M14\"\n# clusterNameFile = (\"{}.phot\".format(clusterName))\n#\n# dat = Table.read(clusterNameFile, format=\"ascii\")\n#\n# u = dat['col10']\n# b = dat['col4']\n# v = dat['col8']\n# i = dat['col6']\n# chi = dat['col12']\n# sharp = dat['col13']\n#\n# ind = np.where(dat['col1'] == 320)[0]\n# cond = np.logical_or.reduce((b>60,v>60, chi>3, abs(sharp)>0.5))\n# #cond = np.logical_and.reduce((b<60,v<60))\n# ind = np.where(cond)[0]\n#\n# # print(dat[ind])\n#\n# dat1 = dat[ind]\n#\n# #[5212 6215 6395 6458 6908]\n#\n# moo = np.where(dat1['col1'] == 6908)[0]\n# print(moo)\n\n# df2 = Table.read(\"nonMembers/M14_nonMembers_testing123.dat\", format=\"ascii\", delimiter=\"\\s\")\ndf3 = Table.read(\"clusterMembers/M9_memberStars_5Sigma.dat\", format=\"ascii\", delimiter=\"\\s\")\n\n\n\nvRaw = df3['col14']\nB = df3['col13']\nbvRaw = B-vRaw\nuRaw = df3['col12']\nv = df3['col11']\nb = df3['col10']\nu = df3['col9']\n# i = df2['col11']\nbv = b-v\n# vi = v-i\n\narr = [1226, 1459]\nprint(df3[arr])\n\n\n# print(df2)\n\n# if (vi[1543] > 0.331+1.444*bv[1543]):\n# print(0)\n# logging.error('Run unsuccessful')\n# else:\n# print(1)\n# logging.info('Run successful')\n\nlst1 = [10001,9243,8956,8812,8119,7645,7386,7075,6897,6908,6682,6458,6395,6215,5262,5212,5096,4987,4562,3006,1320]\n\n# print(df2['col1'])\n\n# for j in range(len(lst1)):\n# print(lst1[j])\n# print(np.where(df2['col1'] == lst1[j])[0])\n# print(np.where(df3['col1'] == lst1[j])[0])\n# print(\"================================\")\n\n# print(np.intersect1d(lst1, df2['col1']))\n\n#\n# vRaw1 = df3['col14']\n# B1 = df3['col13']\n# bvRaw1 = B1-vRaw1\n#\n# v1 = df3['col11']\n# b1 = df3['col10']\n# bv1 = b1-v1\n\ndef model_f(x,a,b,c,d,e,f,g,k):\n x=x+k\n return a*x**6+b*x**5+c*x**4+d*x**3+e*x**2+f*x+g\n\n\n# arr = [301, 323, 363]\narr = [1226, 1459]\n\nfig, ax = plt.subplots()\nax.scatter(bvRaw, vRaw, c='k', s=0.1)\n# ax.scatter(bvhb,vhb,c='b',s=2)\nax.scatter(bvRaw[arr], vRaw[arr], c='orangered', s=5, marker=\"o\")\n# xplot = np.linspace(bv1.min(), bv1.max(), len(bv1))\n# -3.74, 7.03, 6.83, -19.86, 8.98, -1.51, 0.35, -0.15\n# y = model_f(xplot, -3.74, 7.03, 6.83, -19.86, 8.98, -1.51, 0.35,-0.15)\n# ax.plot(bv1, y, color=\"red\", linestyle=\"--\")\n# ax.set_title(\"{} $E(B-V)$={:.2f} $m-M$={:.2f}\".format(clusterName, ebv, distModulus))\nax.set_xlim(-0.75, 1.6)\nax.set_ylim(22,12)\nax.set_xlabel('$B-V$')\nax.set_ylabel('$V$')\nplt.show()\n\n","repo_name":"akshat-chaturvedi/clusterFunc","sub_path":"commonChecker.py","file_name":"commonChecker.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16147875907","text":"#\n# @lc app=leetcode id=355 lang=python3\n#\n# [355] Design Twitter\n#\nimport collections\nimport heapq\n# @lc code=start\nclass Twitter:\n\n def __init__(self):\n \"\"\"\n Initialize your data structure here.\n \"\"\"\n self.user_and_posts = collections.defaultdict(list)\n self.user_and_followers = collections.defaultdict(set)\n self.no = 0\n self.recent = 10\n\n def postTweet(self, userId, tweetId):\n \"\"\"\n Compose a new tweet.\n \"\"\"\n self.user_and_posts[userId].append([-self.no, tweetId])\n self.no += 1\n \n def getNewsFeed(self, userId: int):\n \"\"\"\n Retrieve the 10 most recent tweet ids in the user's news feed. Each item in the news feed must be posted by users who the user followed or by the user herself. Tweets must be ordered from most recent to least recent.\n \"\"\"\n heap = []\n for following in self.user_and_followers[userId]:\n for i in range(len(self.user_and_posts[following]) - 1, max(-1, len(self.user_and_posts[following]) - self.recent - 1), -1):\n heapq.heappush(heap, self.user_and_posts[following][i])\n for i in range(len(self.user_and_posts[userId]) - 1, max(-1, len(self.user_and_posts[userId]) - self.recent - 1), -1):\n heapq.heappush(heap, self.user_and_posts[userId][i])\n ans = []\n c = 0\n while heap and c < self.recent:\n ans.append(heapq.heappop(heap)[1])\n c += 1\n return ans\n\n def follow(self, followerId: int, followeeId: int):\n \"\"\"\n Follower follows a followee. If the operation is invalid, it should be a no-op.\n \"\"\"\n if followerId != followeeId:\n self.user_and_followers[followerId].add(followeeId)\n \n def unfollow(self, followerId: int, followeeId: int):\n \"\"\"\n Follower unfollows a followee. If the operation is invalid, it should be a no-op.\n \"\"\"\n if self.user_and_followers[followerId] and followeeId in self.user_and_followers[followerId]:\n self.user_and_followers[followerId].remove(followeeId)\n\n\n# Your Twitter object will be instantiated and called as such:\n# obj = Twitter()\n# obj.postTweet(1,5)\n# param_2 = obj.getNewsFeed(1)\n# obj.follow(1,2)\n# obj.postTweet(2,6)\n# param_2 = obj.getNewsFeed(1)\n# print(param_2)\n# obj.unfollow(followerId,followeeId)\n# @lc code=end\n\n","repo_name":"610yilingliu/leetcode","sub_path":"Python3/355.design-twitter.py","file_name":"355.design-twitter.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"4824931448","text":"import json\nimport re\nfrom typing import NoReturn\n\nfrom bs4 import BeautifulSoup\n\nfrom back.services.core.parsers.response_handler import send_request\nfrom back.services.core.parsers.database_handler import TestPlacePusher\nfrom back.services.core.settings import DB_HOST, DB_LOGIN, DB_DATABASE, DB_PASSWORD\n\nPRICE_PCR = '1980'\nANTIBODIES_TEST_PRICE = '850'\nTIME_TILL_RES_DAYS = '3 дня'\n\ndef get_cites() -> dict:\n response = send_request(url=f'https://citilab.ru/local/components/reaspekt/reaspekt.geoip/'\n f'templates/my01/ajax_popup_city.php', payload={}, return_json=False)\n city_names = re.findall(r'(?<=title=\\\").*?(?=\\\")', response.text)\n city_codes = re.findall(r'(?<=data-code=\").*?(?=\\\")', response.text)\n cites = dict(zip(city_codes, city_names))\n return cites\n\n\ndef parse_citilab() -> NoReturn:\n db_pusher = TestPlacePusher(DB_HOST, DB_LOGIN, DB_PASSWORD, DB_DATABASE)\n db_pusher.get_or_add_med_org('Citilab')\n cites = get_cites()\n for code, city in cites.items():\n db_pusher.get_or_add_city(city)\n\n response = send_request(url=f'https://citilab.ru/{code}/medcentres/', payload={}, return_json=False)\n soup = BeautifulSoup(response.text, 'html.parser')\n\n try:\n data = soup.find_all('script')[42].string\n except IndexError:\n data = soup.find_all('script')[40].string\n\n json_data_raw = re.findall('(?<=var jsonData = ).*$', data)[0][:-1].replace('\\'', '\"')\n json_data = json.loads(json_data_raw)\n\n for place in json_data.get('mark'):\n if place['covid'] != '1':\n continue\n address = place['adr']\n coord = {'lat': place['lat'], 'lon': place['lng']}\n url = f'https://citilab.ru{place[\"url\"]}'\n db_pusher.add_test_place(city=city, med_org='Citilab', address=address, position=coord, url=url,\n pcr_test_price=PRICE_PCR,\n antibodies_test_price=ANTIBODIES_TEST_PRICE,\n time_of_completion=TIME_TILL_RES_DAYS)\n print(f\"Город : {city}\\n\"\n f\"Корона : {place['covid']}\\n\"\n f\"Адрес: {place['adr']}\\n\"\n f\"Координаты: {place['lat']} : {place['lng']}\\n\"\n f\"Цена: {PRICE_PCR}\\n\"\n f\"Срок готовности результатов: {TIME_TILL_RES_DAYS}\")\n print('--------')\n\n\nif __name__ == '__main__':\n parse_citilab()\n","repo_name":"techglove/sberhack","sub_path":"back/services/core/parsers/citilab.py","file_name":"citilab.py","file_ext":"py","file_size_in_byte":2572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"5296135971","text":"\"\"\"Streamlit App\n\nThis script allows the user to make predictions on sales prices for cars on CarsAndBids.\n\n\"\"\"\nfrom datetime import date\nimport pickle\nimport requests as rq\nimport plotly.express as px\nimport streamlit as st\nimport pandas as pd\nimport yfinance as yf\nfrom sqlalchemy import create_engine, text\nimport os\nimport boto3\nfrom streamlit_option_menu import option_menu\nimport altair as alt\nimport time\n\nsession = boto3.Session(\n aws_access_key_id = os.environ[\"ACCESS_KEY\"],\n aws_secret_access_key=os.environ[\"ACCESS_SECRET\"],\n region_name = os.environ[\"REGION\"])\n\n\n\n@st.cache_data(ttl=259200, max_entries=None)\ndef get_vin_info(vin, api_key = 'VA_DEMO_KEY', num_days = 90, mileage = 'average'):\n \"\"\"pulls data from vinaudit api \"\"\"\n vinaudit_url = f'https://marketvalue.vinaudit.com/getmarketvalue.php?key={api_key}&vin={vin}&format=json&period={num_days}&mileage={mileage}'\n req = rq.get(url = vinaudit_url)\n data = req.json()\n return data\n\n\n@st.cache_data(ttl=259200, max_entries=None)\ndef fetch_market_data():\n sp500 = yf.download(\"^GSPC\", start= '2023-2-1', end=str(date.today())) \n sp500 = pd.DataFrame(sp500)\n sp500 = sp500[\"Adj Close\"].iloc[0]\n return sp500\n\n# name = 'thismod'\ns3 = session.resource('s3')\nmodel_list = []\nfor i in s3.Bucket('carsalesmodel').objects.all():\n model_list.append(i.key)\n \nDATA_URI = os.environ[\"DATA_URI\"]\nengine = create_engine(DATA_URI)\n\nSERVER_URI = os.environ[\"SERVER_URI\"]\n\nMODEL_SQL_QUERY = 'SELECT DISTINCT \"model\" FROM \"cars_bids_listings\";'\nMAKE_SQL_QUERY = 'SELECT DISTINCT \"make\" FROM \"cars_bids_listings\";'\nENGINE_SQL_QUERY = 'SELECT DISTINCT \"engine\" FROM \"cars_bids_listings\";'\nTITLE_STATUS_SQL_QUERY = 'SELECT DISTINCT \"status\" FROM \"cars_bids_listings\";'\nDRIVE_TRAIN_SQL_QUERY = 'SELECT DISTINCT \"drivetrain\" FROM \"cars_bids_listings\";'\nTRANSMISSION_SQL_QUERY = 'SELECT DISTINCT \"transmission\" FROM \"cars_bids_listings\";'\nBODYSTYLE_SQL_QUERY = 'SELECT DISTINCT \"bodystyle\" FROM \"cars_bids_listings\";'\nSOLDTYPE_SQL_QUERY = 'SELECT DISTINCT \"soldtype\" FROM \"cars_bids_listings\";'\nYNRESERVE_SQL_QUERY = 'SELECT DISTINCT \"y_n_reserve\" FROM \"cars_bids_listings\";'\nVIN_SQL_QUERY = 'SELECT \"vin\" FROM \"cars_bids_listings\" LIMIT 1;'\n\nwith engine.connect() as connection:\n make_df = pd.read_sql_query(text(MAKE_SQL_QUERY), con = connection)\n model_df = pd.read_sql_query(text(MODEL_SQL_QUERY), con = connection)\n engine_df = pd.read_sql_query(text(ENGINE_SQL_QUERY), con = connection)\n title_status_df = pd.read_sql_query(TITLE_STATUS_SQL_QUERY, con = connection)\n drive_train_df = pd.read_sql_query(DRIVE_TRAIN_SQL_QUERY, con = connection)\n transmission_df = pd.read_sql_query(TRANSMISSION_SQL_QUERY, con = connection)\n bodyStyle_df = pd.read_sql_query(BODYSTYLE_SQL_QUERY, con=connection)\n soldType_df = pd.read_sql_query(SOLDTYPE_SQL_QUERY, con = connection)\n reserve_df = pd.read_sql_query(YNRESERVE_SQL_QUERY, con = connection)\n\nmake_df.sort_values(by='make', inplace=True)\nmodel_df.sort_values(by='model', inplace=True)\nengine_df.sort_values(by='engine', inplace=True)\ntitle_status_df.sort_values(by='status', inplace=True)\ndrive_train_df.sort_values(by='drivetrain', inplace=True)\ntransmission_df.sort_values(by='transmission', inplace=True)\nbodyStyle_df.sort_values(by='bodystyle', inplace=True)\nsoldType_df.sort_values(by='soldtype', inplace=True)\nreserve_df.sort_values(by='y_n_reserve', inplace=True)\n\nst.set_page_config(layout=\"wide\", page_title=\"Car Sale Value\")\nheadercol1, headercol2 = st.columns(2)\nwith st.container():\n with headercol1:\n st.markdown(\"

How Much is Your Collector Car Worth?

\", unsafe_allow_html=True)\n st.subheader('Use our API and predict a sale price for your vehicle!')\n with headercol2:\n st.image('https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTe1SBdlXWtJ96-zcUnN05YMaumzpJ-q2ei-A&usqp=CAU', width=500)\nselected_navbar = option_menu(None, [\"Predict\", \"FAQ\", \"API\"], orientation=\"horizontal\")\n\n \ndataset = st.container()\nmodel = st.container()\nyears = range(1980, 2023)\nchart = st.container()\n\n \nif selected_navbar == \"FAQ\":\n with st.container():\n with st.expander(\"What is Cars and Bids?\"):\n st.write('Cars and Bids is an online enthusiast car sales platform created by the automotive Youtuber Doug DeMuro. Most listings on the platform are sold in auction format.')\n with st.expander(\"What model is being used to predict sale price?\"):\n st.write('We are using a Gradient Boosted Regression Tree to predict sale price')\n with st.expander(\"How was the data collected?\"):\n st.write('All of the past listings from CarsAndBids.com were collected using webscraping via selenium. We collected estimated market price for each vehicle from VinAudit.com as well as overall market conditions at the time of sale via Yahoo Finance')\n with st.expander(\"How accurate are the predictions?\"):\n st.write('On our best model, we obtain an accuracy of about 75% (MSE .75)')\n with st.expander(\"Can I use this site commercially?\"):\n st.write('This site is not intended to be used commercially and should not be used commercially')\n with st.expander(\"Is the car price prediction sound financial advice?\"):\n st.write('No. This is a purely academic exercise; use the model output at your own discretion')\n\nif selected_navbar == \"Predict\":\n with st.container():\n st.text('CarsAndBids.com is a new auction website for collector cars from the 80s until now. With a rich history of auctions, we wanted to learn if we could predict\\nwhich cars would be good deals on the site by using features of the vehicle like Make, Model, Year, Engine (etc.) as well as car market data on the vehicle\\nand general market data, we fit a gradient boosted decision tree to predict the selling price of the car. To determine whether its a good deal, we compare the\\npredicted sale price against the market average for similar vehicles. Car market data comes from the VinAudit API\\n')\n form = st.form(key='uinput')\n with form:\n form_columns = st.columns(4)\n text_arr = [['Make', 'Model', 'Year'], ['Engine', 'Title', 'Drive'], ['Body Style', 'Reserve', 'Transmission'], ['Vin', 'Mileage']]\n options_arr = [[make_df, model_df, years], [engine_df, title_status_df, drive_train_df], [bodyStyle_df, reserve_df, transmission_df]]\n first_make = make_df.iloc[0][\"make\"]\n columns = []\n for i, col in enumerate(form_columns):\n if i < 3:\n for j in range(len(text_arr[i])):\n newcol = col.selectbox(text_arr[i][j], options_arr[i][j], key=(i*3)+j, index=0)\n columns.append(newcol)\n else:\n for j in range(len(text_arr[i])):\n newcol = col.text_input(text_arr[i][j], key=(i*3)+j)\n columns.append(newcol)\n newcol = col.selectbox('ML Model', model_list, key=(i*3)+j+1, index=0)\n columns.append(newcol)\n\n \n sp500 = fetch_market_data()\n \n m = st.markdown(\"\"\"\n \"\"\", unsafe_allow_html=True)\n \n \n \n button = st.form_submit_button(label=\"Submit\", use_container_width=True)\n \n if button:\n try:\n req = get_vin_info(columns[9])\n with st.spinner('Running Prediction...'):\n time.sleep(5)\n newres= rq.post(SERVER_URI, json={\"rows\": [{ \"make\": columns[0],\n \"model\": columns[1],\n \"mileage\": columns[10],\n \"status\": columns[4], \n \"engine\":columns[3],\n \"drivetrain\": columns[5],\n \"transmission\" :columns[8],\n \"bodystyle\": columns[6],\n \"y_n_reserve\":columns[7],\n \"year\":columns[2],\n 'market_value_mean': req[\"mean\"], \n 'market_value_std':req['stdev'], \n 'count_over_days':str(float(req['count']) / 90), \n 'Adj Close':sp500,\n 'tree_model': columns[11]}]}) \n response = newres.json()\n newres = response[0][0]\n shaps = pd.DataFrame(pd.Series(response[1]))\n shaps = pd.melt(shaps.reset_index(), id_vars=[\"index\"])\n st.subheader('Dollar Contribution of Each Feature to the Predicted Sale Price')\n chart = (\n alt.Chart(shaps)\n .mark_bar()\n .encode(\n x=alt.X(\"value\", type=\"quantitative\", title=\"Dollars\"),\n y=alt.Y(\"index\", type=\"nominal\", title=\"Features\"),\n color=alt.Color(\"variable\", type=\"nominal\", title=\"\", legend=None),\n order=alt.Order(\"variable\", sort=\"descending\")))\n st.altair_chart(chart, use_container_width=True)\n st.markdown(f\"# Predicted Price on CarsAndBids.com: **${round(newres)}**\")\n except:\n st.write('Unable to gather information from VIN. Please try a different vehicle')\n\napi_column1, api_column2, api_column3 = st.columns(3) \nif selected_navbar == \"API\":\n st.subheader(\"Our API is free to use and available via a POST request to http://collectorcarpricing.com:8080/predict\")\n st.write('The post request must include the following parameters:')\n api_data = { \"Name\": ['make', 'model', 'mileage', 'status', 'engine', 'bodystyle', 'y_n_reserve','year', 'drivetrain', 'transmission', 'vin'],\n \"Required\": ['yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes','yes', 'yes', 'yes', 'yes'],\n \"Data Type\": ['string', 'string', 'float', 'string', 'string', 'string', 'string','int', 'string', 'string', 'string'],\n \"Accepted Values\": [\"Any brand of auto manufacturer. If the brand doesnt exist in the training data make will not contribute to the prediction\",\n \"Any model from an auto manufacturer. If the model doesnt exist in the training data it will use the average price for the chosen make\",\n \"Any positive number (without commas)\",\n \"Clean, Salvage, Other\",\n \"One of the following: (P9, P8, V1, I6, Electric, I2, H6, I3, I5, Flat-2, I4, Flat-4, R6, H4, V6, W8, V2, Flat-6, V8). If not in this list the model will use the average price for the chosen make\",\n \"One of the following: (SUV/Crossover, Hatchback, Convertible, Van/Minivan, Sedan, Wagon, Truck, Coupe)\",\n \"One of the following: (Reserve, No Reserve)\",\n \"Any year from 1980 - present\",\n \"One of the following: (Rear-wheel drive, 4WD/AWD, Front-wheel drive)\",\n \"One of the following: (Manual, Automatic)\",\n \"Any valid VIN number\"]}\n st.table(pd.DataFrame(api_data))\n st.subheader('Ex:')\n st.text('''curl -d '{\"rows\": [{\"make\": \"Porsche\",\"model\": \"Cayenne\",\"mileage\": \"167500.0\",\"status\": \"Clean\" , \"engine\":\"3.6L V6\",\"drivetrain\": \"4WD/AWD\",\"transmission\" :\"Manual (6-Speed)\",\"bodystyle\":\" SUV/Crossover\", \"y_n_reserve\":\" No Reserve\",\"year\":\"2012.0\", \"vin\": \"5YJSA1DP4CFF00027\"}]}' -X POST http://collectorcarpricing.com:8080/predict''')\n \n\nst.write(\"Developed by Adam Lang and David Kim [Github Repo]('https://github.com/CodeSmithDSMLProjects/CarSalesModel')\")\nst.write(\"Contact us at adamglang96@gmail.com and koyykdy@gmail.com\")\n","repo_name":"CodeSmithDSMLProjects/CarSalesModel","sub_path":"public/stream_lit.py","file_name":"stream_lit.py","file_ext":"py","file_size_in_byte":12710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3251391737","text":"import telegram\n\nfrom dbdriver import DBDriver\n\ndatabase = DBDriver()\ndatabase.setup()\n\nclass ToDoBot:\n\n class UserData:\n def __init__(self, text, chat_id, items):\n self.text = text\n self.chat_id = chat_id\n self.items = items\n\n def __init__(self, todo_queue):\n \"\"\" The data in queue contains tuples with the text of the message received from\n the user and user's chat id in (text, chat_id) format \"\"\"\n self.queue = todo_queue\n self.calls = {\n '/list': self.call_list,\n '/done': self.call_delete_keyboard,\n '/start': self.call_start,\n '/clear': self.call_clear\n }\n\n def run(self):\n while not self.queue.empty():\n text, chat_id = self.queue.get()\n items = database.get_items(chat_id)\n userdata = self.UserData(text, chat_id, items)\n\n if userdata.text in self.calls:\n self.calls[userdata.text](userdata)\n\n elif userdata.text.startswith('/'):\n continue\n\n elif userdata.text in userdata.items:\n self.delete_item(userdata)\n\n else:\n database.add_item(userdata)\n\n\n def call_start(self, userdata):\n telegram.send_message(\"Welcome to your personal To Do list. Send any text to me and I'll store it as an\"\n \" item. Send /done to remove items\", userdata.chat_id)\n\n def call_list(self, userdata):\n if userdata.items:\n text_of_items = '\\n'.join(userdata.items)\n telegram.send_message(text_of_items, userdata.chat_id)\n else:\n telegram.send_message('The list is empty, type anything you want to add', userdata.chat_id)\n\n def call_clear(self, userdata):\n if userdata.items:\n database.clear_items(userdata)\n telegram.send_message('The list has been cleared', userdata.chat_id)\n else:\n telegram.send_message('The list is empty', userdata.chat_id)\n\n def call_delete_keyboard(self, userdata):\n if userdata.items:\n keyboard = telegram.build_keyboard(userdata.items)\n telegram.send_message('Select an item to delete', userdata.chat_id, keyboard)\n else:\n telegram.send_message('The list is empty', userdata.chat_id)\n\n def delete_item(self, userdata):\n database.delete_item(userdata)\n userdata.items.remove(userdata.text)\n self.call_delete_keyboard(userdata)\n\n","repo_name":"tonybruhh/ToDoBot","sub_path":"todobot.py","file_name":"todobot.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6183160869","text":"import cv2\r\nimport glob\r\nimport numpy as np\r\nimport pickle\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Activation, Dense, Conv2D , Flatten, MaxPool2D, BatchNormalization, Dropout, GlobalMaxPool2D\r\nfrom tensorflow.keras.optimizers import Adam, RMSprop\r\nfrom sklearn.metrics import precision_recall_fscore_support, accuracy_score\r\nfrom sklearn.utils import shuffle\r\n\r\n\r\n#Data Augmentation\r\nfromPath = '../test/'\r\naugPath = '../aug_test/'\r\nclassName = {0:'Aavad',1:'Chikoo',2:'Jamun',3:'Raat_Rani',4:'Umbaro'}\r\n\r\n\r\ndatagen = ImageDataGenerator(\r\n rotation_range=40,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True,\r\n fill_mode='nearest'\r\n)\r\n\r\nfor key,values in className.items() :\r\n for image in glob.glob(fromPath + values + '/*.jpg'):\r\n img = cv2.imread(image)\r\n # cv2.imshow('image',g)\r\n da_img = img.reshape(1,img.shape[0], img.shape[1], 3)\r\n print('new img shape: ',da_img.shape)\r\n i=0\r\n for batch in datagen.flow(da_img,save_to_dir=augPath + values,save_format='jpg'):\r\n i += 1\r\n if i>20:\r\n break\r\n\r\n#Saving Image Matrix into pickles\r\nli = []\r\nlabels =[]\r\nfor key,values in className.items() :\r\n for img in glob.glob(augPath + values + '/*.jpg'):\r\n g = cv2.imread(img)\r\n print(g.shape)\r\n # cv2.imshow('image',g)\r\n g = cv2.resize(g,(224,224))\r\n g = g.reshape(g.shape[0], g.shape[1], 3)\r\n print('g: ',g.shape)\r\n li.append(g)\r\n labels.append(key)\r\n\r\n\r\nfeatures = 'test'+\".pkl\"\r\nclass_labels = 'TestClassLabels'+\".pkl\"\r\n\r\nli = np.array(li)\r\nlabels = np.array(labels)\r\n\r\nfo = open(features, \"wb\")\r\npickle.dump(li, fo)\r\nfo.close()\r\n\r\nfo = open(class_labels, \"wb\")\r\npickle.dump(labels, fo)\r\nfo.close()\r\n\r\nprint(labels)\r\nprint(li.shape)\r\n'''\r\n\r\n\r\n\r\ngpus = tf.config.list_physical_devices('GPU')\r\nif gpus:\r\n # Restrict TensorFlow to only allocate 1GB of memory on the first GPU\r\n try:\r\n tf.config.set_logical_device_configuration(gpus[0],[tf.config.LogicalDeviceConfiguration(memory_limit=4500)])\r\n logical_gpus = tf.config.list_logical_devices('GPU')\r\n print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\r\n except RuntimeError as e:\r\n # Virtual devices must be set before GPUs have been initialized\r\n print(e)\r\n\r\n#Loading pickle files\r\nfp = open('train.pkl', \"rb\")\r\ntrain_features = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('TrainClassLabels.pkl', \"rb\")\r\ntrain_cls_labels = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('test.pkl', \"rb\")\r\ntest_features = pickle.load(fp)\r\nfp.close()\r\n\r\nfp = open('TestClassLabels.pkl', \"rb\")\r\ntest_cls_labels = pickle.load(fp)\r\nfp.close()\r\n\r\n#Normalizng data\r\nX_train = train_features/255\r\nX_test = test_features/255\r\nY_train = train_cls_labels\r\nY_test = test_cls_labels\r\nX_train, Y_train = shuffle(X_train, Y_train)\r\nX_test, Y_test = shuffle(X_test, Y_test)\r\nprint(X_train.shape, X_test.shape)\r\nprint(Y_train.shape, Y_test.shape)\r\n\r\n#Training of CNN model\r\nmodel = Sequential()\r\nmodel.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(224, 224, 3)))\r\nmodel.add(MaxPool2D(pool_size=(2, 2)))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(100, activation='relu'))\r\nmodel.add(Dense(5, activation='softmax'))\r\n\r\nmodel.compile(loss='sparse_categorical_crossentropy',optimizer=RMSprop(learning_rate=0.01),metrics=['accuracy'])\r\nmodel.fit(X_train,Y_train,epochs=10,validation_data=(X_test,Y_test))\r\nmodel.save('model.h5')\r\n\r\n#Checking accuracy\r\nY_pred = model.predict(X_test)\r\nY_pred = np.argmax(Y_pred,axis=1)\r\nacc = accuracy_score(Y_test, Y_pred)\r\nprint('testing accuracy: ',acc)\r\n'''","repo_name":"RajPanjwani-2001/Plant-Classification","sub_path":"Codes/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":3848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42074343385","text":"#Crie um programa que tenha uma tupla totalmente preenchida com uma contagem por extenso, de zero até vinte. Seu programa deverá ler um número pelo teclado (entre 0 e 20) e mostrá-lo por extenso\n\nextenso = ('Zero', 'Um', 'Dois', 'Tres', 'Quatro', 'Cinco', 'Seis', 'Sete', 'Oito', 'Nove', 'Dez', 'Onze', 'Doze', 'Treze', 'Quatorze', 'Quinze', 'Dezesseis', 'Dezessete','Dezoito', 'Dezenove', 'Vinte')\nwhile True:\n num = int(-1)\n while num not in range(0,len(extenso)):\n num = int(input('Digite um número de 0 a 20 para saber seus nome por extenso:\\n>>> '))\n \n print(f'O número {num} por extenso é: {extenso[num]}')\n \n answer = str(input('Deseja saber outro número? [S/N]\\n>>> '))[0]\n if answer in 'nN':\n break","repo_name":"LeonardoSextare/Curso-Python","sub_path":"Curso em Video - Guanabara/Mundo 3/!Exercicios/ex072 - Numero por Extenso.py","file_name":"ex072 - Numero por Extenso.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"25402384958","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nExtract certificate stored in the APK as PEM\n\"\"\"\n\n\nimport sys\nimport argparse\nfrom apk_parse.apk import APK\n\n\ndef main():\n # Parse command line arguments\n parser = argparse.ArgumentParser(description='Extracts PEM certificates from APK files')\n parser.add_argument('files', nargs=argparse.ZERO_OR_MORE, default=[], help='APK files')\n parser.add_argument('-t', dest='text', default=False, action='store_const', const=True,\n help='show also text representation')\n args = parser.parse_args()\n\n for file_name in args.files:\n apkf = APK(file_name)\n if args.text:\n print(apkf.cert_text)\n\n pem = apkf.cert_pem\n print(pem)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","repo_name":"ph4r05/codesign-analysis","sub_path":"codesign/android/apk2cert.py","file_name":"apk2cert.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"40310655475","text":"import uuid\nimport os\nimport speech_recognition as sr\nfrom pydub import AudioSegment\nimport tempfile\n\n\ndef decorator_remove_file(func):\n def wrapper(*args, **kwargs):\n rez = func(*args, **kwargs)\n try:\n os.remove('voice_message.ogg')\n os.remove('voice_message.wav')\n except:\n pass\n return rez\n return wrapper\n#\ndef convert_ogg_wav(file):\n wfn = file.replace('.ogg', '.wav')\n x = AudioSegment.from_file(file)\n x.export(wfn, format='wav')\n\n\nlanguage='ru_RU'\n\n\n@decorator_remove_file\ndef audio_to_text(file):\n r = sr.Recognizer()\n with sr.AudioFile(file) as source:\n audio = r.record(source)\n text = r.recognize_google(audio_data=audio, language=language)\n return text\n\n\n\ndef convert_and_recognize(file_path):\n # Создаем временный файл, который будет автоматически удаляться после закрытия\n with tempfile.NamedTemporaryFile(delete=True) as temp_wav:\n audio = AudioSegment.from_ogg(file_path)\n audio.export(temp_wav.name, format=\"wav\") # Экспортируем аудио в wav-формате во временный файл\n\n recognizer = sr.Recognizer()\n with sr.AudioFile(temp_wav.name) as source:\n # Записываем аудио из файла\n audio_file = recognizer.record(source)\n # Применяем распознавание речи с помощью Google Speech Recognition\n try:\n result = recognizer.recognize_google(audio_file, language='ru-RU')\n print('Распознан текст:', result)\n return result\n except sr.UnknownValueError:\n print(\"Google Speech Recognition не смог понять аудио\")\n except sr.RequestError:\n print(\"Could not request results from Google Speech Recognition service\")\n\n\nasync def dewnload_and_converted_audio_text(event):\n if event.message.voice:\n # Получаем голосовое сообщение\n voice_message = await event.message.download_media()\n\n # Создаем временный файл, который будет автоматически удаляться после закрытия\n with tempfile.NamedTemporaryFile(delete=True) as temp_ogg:\n # Копируем голосовое сообщение во временный файл\n with open(voice_message, 'rb') as file:\n temp_ogg.write(file.read())\n\n # Преобразуем и распознаем речь\n text = convert_and_recognize(temp_ogg.name)\n return f'👺 Voice\\n{text}'\n\nasync def esli_voice_to_text_ili_text_text(event):\n return f'💥🔊💭 {await dewnload_and_converted_audio_text(event)}\\n{event.message.message}' if event.message.voice else event.message.message\n # if event.message.voice:#если сообщение голосовое\n # text =f'💥🔊💭 {await dewnload_and_converted_audio_text(event)}'\n # else:\n # text = event.message.message # достаем только текст сообщени","repo_name":"nasket-it/sanchos","sub_path":"audio_text.py","file_name":"audio_text.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38685876642","text":"# -*- coding: utf-8 -*-\n\n\"\"\"the zimbra module provides an interface to interact with zimbra\n\"\"\"\n\nimport re\nimport json\n\nfrom bs4 import BeautifulSoup\n\nfrom dhbw.util import ImporterSession, reqget, reqpost, url_get_fqdn\nfrom dhbw.util import ServiceUnavailableException, LoginRequiredException\n\n#------------------------------------------------------------------------------#\n# H E L P E R - F U N C T I O N S\n#------------------------------------------------------------------------------#\n\ndef _entity_list(in_list, out_list, in_type):\n \"\"\"Adds entities to a list while converting an entity string to a dict.\n\n Parameters\n ----------\n in_list : List[str]\n Description\n out_list : List[Dict[str, str]]\n Description\n in_type : str\n Description\n Returns\n -------\n List[Dict[str, str]]\n\n \"\"\"\n\n if in_type == \"recipient\":\n temp = \"t\"\n elif in_type == \"cc\":\n temp = \"c\"\n else:\n temp = \"b\"\n\n for account in in_list:\n temp_dict = {}\n temp_dict[\"t\"] = temp\n temp_dict[\"a\"] = account\n out_list.insert(0, temp_dict)\n\n return out_list\n\n\ndef _fill_contacts_dict_elem(contact):\n \"\"\"Checks for existing keys inside the response contact dict and creates contact dict.\n\n Parameters\n ----------\n contact : Dict[str, str]\n\n Returns\n -------\n Dict\n\n \"\"\"\n temp = {}\n if \"email\" in contact.keys():\n temp[\"email\"] = contact[\"email\"]\n temp[\"id\"] = contact[\"id\"]\n temp[\"firstName\"] = None\n temp[\"lastName\"] = None\n temp[\"jobTitle\"] = None\n if \"firstName\" in contact.keys():\n temp[\"firstName\"] = contact[\"firstName\"]\n if \"lastName\" in contact.keys():\n temp[\"lastName\"] = contact[\"lastName\"]\n if \"jobTitle\" in contact.keys():\n temp[\"jobTitle\"] = contact[\"jobTitle\"]\n\n return temp\n\n#------------------------------------------------------------------------------#\n# Z I M B R A - H A N D L E R\n#------------------------------------------------------------------------------#\n\nclass ZimbraHandler(ImporterSession):\n \"\"\"Handler for interacting with zimbra.\n\n Attributes\n ----------\n url : str\n the given url for zimbra\n accountname : str\n the dhbw mail account\n contacts : List[Dict[str, str]]\n a list representing all contacts from zimbra\n realname : str\n the real name of the logged in user\n signatures : List[str]\n a list of all available signatures to the user\n\n Methods\n -------\n login(self): None\n creates a session for the user\n logout(self): None\n sends a logout request\n scrape(self): None\n scrape the wanted data from the website\n get_contacts(self): None\n import contacts from the default \"contact\" book\n new_contact(self, contact_dict): None\n create a new contact inside the default contact book\n remove_contact(self, contact_id): None\n remove an existing contact from the default contact book\n _create_entities_list(self, recipients, rec_cc, rec_bcc): List[Dict[str, str]]\n create a list with dictionary elements\n _generate_mail(self, mail_dict): Dict[str, Any]\n build the mail in the needed format for zimbra\n send_mail(self, mail_dict): None\n sends a mail to the soap backend of zimbra\n \"\"\"\n\n url = \"https://studgate.dhbw-mannheim.de/zimbra/\"\n\n __slots__ = (\"accountname\", \"contacts\", \"realname\", \"signatures\",)\n\n def __init__(self):\n super().__init__()\n self.accountname = \"\"\n self.contacts = []\n self.headers[\"Host\"] = url_get_fqdn(ZimbraHandler.url)\n self.realname = \"\"\n self.signatures = []\n\n async def login(self, username, password):\n \"\"\"Authenticate the user against zimbra.\n\n Parameters\n ----------\n username: str\n the username for the authentication process\n password: str\n the password for the authentication process\n\n Returns\n -------\n ZimbraHandler\n \"\"\"\n url = ZimbraHandler.url\n\n # add accountname\n self.accountname = username\n\n # set headers for post request\n self.headers[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n self.headers[\"Cookie\"] = \"ZM_TEST=true\"\n\n # form data\n payload = {\n \"client\": \"preferred\",\n \"loginOp\": \"login\",\n \"username\": username,\n \"password\": password\n }\n\n # LOGIN - POST REQUEST\n try:\n r_login = reqpost(\n url=url,\n headers=self.headers,\n payload=payload,\n allow_redirects=False,\n return_code=302\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n # drop content-type header\n self.drop_header(\"Content-Type\")\n\n # add authentication cookie to the headers\n self.auth_token = r_login.headers[\"Set-Cookie\"].split(\";\")[0]\n self.headers[\"Cookie\"] = self.headers[\"Cookie\"] + \"; \" + self.auth_token\n\n return self\n\n async def scrape(self):\n # TODO documentation?\n \"\"\"Scrape the selected data from zimbra.\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n\n try:\n r_home = reqget(\n url=url,\n headers=self.headers,\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n\n content_home = BeautifulSoup(r_home.text, \"lxml\")\n\n # improvement idea -> let it loop reversed, since needed content\n # is inside the last / one of the last script tag(s)\n try:\n tag_script_all = content_home.find_all(\"script\")\n except AttributeError as attr_err:\n raise LoginRequiredException() from attr_err\n\n for tag_script in tag_script_all:\n if \"var batchInfoResponse\" in str(tag_script.string):\n temp = re.search(\n r\"var\\ batchInfoResponse\\ =\\ \\{\\\"Header\\\":.*\\\"_jsns\\\":\\\"urn:zimbraSoap\\\"\\};\",\n str(tag_script.string)\n )\n break\n temp_json = json.loads(\n re.sub(r\"(var\\ batchInfoResponse\\ =\\ )|(;$)\", \"\", temp.group(0))\n )\n\n self.realname = temp_json[\"Body\"][\"BatchResponse\"][\"GetInfoResponse\"][0][\"attrs\"][\"_attrs\"][\"cn\"]\n\n self.scraped_data = temp_json\n\n def get_contacts(self):\n \"\"\"Import contacts from the default contact book.\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n # TODO query is limited to 100 contact entities --> query all contact entities\n\n query = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n }\n }\n },\n \"Body\": {\n \"SearchRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"sortBy\": \"nameAsc\",\n \"offset\": 0,\n \"limit\": 100,\n \"query\": \"in:contacts\",\n \"types\": \"contact\"\n }\n }\n }\n\n try:\n r_contacts = reqpost(\n url=origin + \"/service/soap/SearchRequest\",\n headers=self.headers,\n payload=json.dumps(query)\n ).json()\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n try:\n contacts = r_contacts[\"Body\"][\"SearchResponse\"][\"cn\"]\n except KeyError:\n contacts = []\n\n for contact in contacts:\n cnt = contact[\"_attrs\"]\n cnt[\"id\"] = contact[\"id\"]\n temp = _fill_contacts_dict_elem(cnt)\n if temp:\n self.contacts.append(temp)\n\n def new_contact(self, contact_dict):\n \"\"\"Create a new contact inside the default contact book.\n\n Parameters\n ----------\n contact_dict : Dict\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n contact_details = []\n for key, value in contact_dict.items():\n if value:\n contact_details.append(\n {\n \"n\": key,\n \"_content\": value\n }\n )\n\n contact = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n },\n \"Body\": {\n \"CreateContactRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"cn\": {\n \"l\": \"7\",\n \"a\": contact_details\n }\n }\n }\n }\n\n try:\n r_contact = reqpost(\n url=origin + \"/service/soap/CreateContactRequest\",\n headers=self.headers,\n payload=json.dumps(contact),\n ).json()\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n try:\n contact_dict[\"id\"] = r_contact[\"Body\"][\"CreateContactResponse\"][\"cn\"][0][\"id\"]\n except AttributeError as attr_err:\n raise LoginRequiredException() from attr_err\n\n self.contacts.append(contact_dict)\n\n def remove_contact(self, contact_id):\n \"\"\"remove an existing contact from the default contact book\n\n Parameters\n ----------\n contact_id : str\n\n \"\"\"\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n del_contact = {\n \"Header\": {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n },\n \"Body\": {\n \"ContactActionRequest\": {\n \"_jsns\": \"urn:zimbraMail\",\n \"action\": {\n \"id\": contact_id,\n \"l\": \"3\",\n \"op\": \"move\"\n }\n }\n }\n }\n\n try:\n reqpost(\n url=origin + \"/service/soap/ContactActionRequest\",\n headers=self.headers,\n payload=json.dumps(del_contact)\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n i = 0\n while i < len(self.contacts):\n if self.contacts[i][\"id\"] == contact_id:\n break\n i += 1\n\n del self.contacts[i]\n\n def _create_entities_list(self, recipients, rec_cc, rec_bcc):\n \"\"\"Create a list with dictionary elements.\n\n Parameters\n ----------\n recipients : List[str]\n\n rec_cc : List[str]\n\n rec_bcc : List[str]\n\n\n Returns\n -------\n List[Dict[str, str]]\n \"\"\"\n entities_list = [\n {\n \"t\": \"f\",\n \"a\": self.accountname,\n \"p\": self.realname\n }\n ]\n\n entities_list = _entity_list(rec_bcc, entities_list, \"bcc\")\n entities_list = _entity_list(rec_cc, entities_list, \"cc\")\n entities_list = _entity_list(recipients, entities_list, \"recipient\")\n\n return entities_list\n\n def _generate_mail(self, mail_dict):\n \"\"\"build the mail in the needed format for zimbra\n\n Parameters\n ----------\n mail_dict : Dict\n\n Returns\n -------\n Dict[str, Any]\n \"\"\"\n header_dict = {\n \"context\": {\n \"_jsns\": \"urn:zimbra\",\n \"account\": {\n \"_content\": self.accountname,\n \"by\": \"name\"\n },\n \"auth_token\": self.auth_token\n }\n }\n\n entities = self._create_entities_list(\n mail_dict[\"recipients\"],\n mail_dict[\"rec_cc\"],\n mail_dict[\"rec_bcc\"]\n )\n\n message_dict = {\n \"_jsns\": \"urn:zimbraMail\",\n \"m\": {\n \"e\": entities,\n \"su\": {\n \"_content\": mail_dict[\"subject\"]\n },\n \"mp\": {\n \"ct\": mail_dict[\"cttype\"],\n \"content\": {\n \"_content\": mail_dict[\"content\"]\n }\n }\n }\n }\n\n # join the dicts to create the whole mail\n mail = {\n \"Header\": header_dict,\n \"Body\": {\n \"SendMsgRequest\": message_dict\n }\n }\n\n return mail\n\n def send_mail(self, mail_dict):\n \"\"\"Sends a mail to the soap backend of zimbra.\n\n Parameters\n ----------\n mail_dict: SendMailDict\n a dictionary containing recipients, subject, content-type and the actual content\n\n Returns\n -------\n None\n \"\"\"\n # create mail\n mail = self._generate_mail(mail_dict)\n\n # IMPROVEMENT IDEA:\n # store mail_dict somewhere, in case that the service is unavailable\n\n url = ZimbraHandler.url\n origin = \"https://\" + url_get_fqdn(url)\n\n self.headers[\"Content-Type\"] = \"application/soap+xml; charset=utf-8\"\n self.headers[\"Referer\"] = url\n self.headers[\"Origin\"] = origin\n\n try:\n reqpost(\n url=origin + \"/service/soap/SendMsgRequest\",\n headers=self.headers,\n payload=json.dumps(mail),\n return_code=200\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n finally:\n self.drop_header(\"Content-Type\")\n\n def logout(self):\n \"\"\"sends a logout request\n\n Returns\n -------\n None\n \"\"\"\n url = ZimbraHandler.url\n\n try:\n reqget(\n url=url,\n headers=self.headers,\n params={\"loginOp\": \"logout\"},\n return_code=200\n )\n except ServiceUnavailableException as service_err:\n raise service_err\n\n self.auth_token = \"\"\n","repo_name":"Software-Engineering-DHBW/BonoboBoard","sub_path":"bonobo-board/modules/dhbw/zimbra.py","file_name":"zimbra.py","file_ext":"py","file_size_in_byte":15644,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"82"} +{"seq_id":"34336940367","text":"from pdfminer3.pdfpage import PDFPage\nfrom pdfminer3.pdfinterp import PDFResourceManager\nfrom pdfminer3.pdfinterp import PDFPageInterpreter\nfrom pdfminer3.converter import TextConverter\nimport io\nimport os\nimport shutil\nfrom PyPDF2 import PdfFileMerger\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\n\n\ndef scan_folder(parent, keyword):\n lista = []\n # iterate over all the files in directory 'parent'\n for file_name in os.listdir(parent):\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n if file_name.endswith(\".pdf\"):\n # if it's a txt file, print its name (or do whatever you want)\n arquivo = open(parent + \"/\" + file_name, 'rb')\n with arquivo as fh:\n\n for page in PDFPage.get_pages(fh,\n caching=True,\n check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n if (text.find(keyword) != -1):\n # print(file_name + \" TEEM\")\n lista.append(parent + \"/\" + file_name)\n # else:\n # print(file_name + \" NAOOOO\")\n converter.close()\n handle.close()\n else:\n current_path = \"\".join((parent, \"/\", file_name))\n if os.path.isdir(current_path):\n # if we're checking a sub-directory, recall this method\n scan_folder(current_path)\n return lista\n\n\ndef merger(output_path, input_paths):\n pdf_merger = PdfFileMerger()\n file_handles = []\n\n for path in input_paths:\n pdf_merger.append(path)\n\n with open(output_path, 'wb') as fileobj:\n pdf_merger.write(fileobj)\n\n\ndef searchPDF(parent, keyword):\n lista = []\n # iterate over all the files in directory 'parent'\n for file_name in parent:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n if (text.find(keyword) != -1):\n # print(file_name + \" TEEM\")\n lista.append(file_name)\n # else:\n # print(\"NAO\")\n converter.close()\n handle.close()\n return lista\n\n\ndef splitter(path, output_folder):\n for x in path:\n fname = os.path.splitext(os.path.basename(x))[0]\n pdf = PdfFileReader(x)\n for page in range(pdf.getNumPages()):\n pdf_writer = PdfFileWriter()\n pdf_writer.addPage(pdf.getPage(page))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(\n fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n\n\ndef splitterCustom(path, output_folder, doublepageslist,originalfile):\n for x in path:\n fname = os.path.splitext(os.path.basename(x))[0]\n pdf = PdfFileReader(x)\n print(x)\n filenumber = find_between(x, \"_file_\", \".pdf\")\n if filenumber not in originalfile:\n doublepageslist2 = []\n else:\n doublepageslist2 = doublepageslist\n b = True\n for page in range(pdf.getNumPages()):\n pagenamemerged = str(page + 1) + \";\" + filenumber\n print(pagenamemerged)\n pdf_writer = PdfFileWriter()\n if b:\n if pagenamemerged not in doublepageslist2:\n pdf_writer.addPage(pdf.getPage(page))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n b = True\n else:\n pdf_writer.addPage(pdf.getPage(page))\n pdf_writer.addPage(pdf.getPage(page + 1))\n pages = page + 1\n if page >= 99:\n pagename = str(pages)\n elif page >= 9:\n pagename = \"0\" + str(pages)\n else:\n pagename = \"00\" + str(pages)\n output_filename = output_folder + '/{}_page_{}.pdf'.format(\n fname, pagename)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n b = False\n else:\n b = True\n\n\ndef splitterNew(path, output_folder):\n for x in path:\n name = os.path.splitext(os.path.basename(x))[0]\n print(*name)\n pdf = PdfFileReader(x)\n for page in range(pdf.getNumPages()):\n pdf_writer = PdfFileWriter()\n pdf_writer.addPage(pdf.getPage(page))\n output_filename = output_folder + '/{}_{}.pdf'.format(\n page + 1, name)\n with open(output_filename, 'wb') as out:\n pdf_writer.write(out)\n # print('Created: {}'.format(output_filename))\n\n\ndef list_files_mac(dir):\n names = []\n for root, dirs, files in os.walk(dir):\n for file in files:\n if file.endswith('.pdf'):\n names.append(dir + \"/\" + file)\n return names\n\n\ndef list_files_win(dir):\n names = []\n for root, dirs, files in os.walk(dir):\n for file in files:\n if file.endswith('.pdf'):\n names.append(dir + \"/\" + file)\n return names\n\n\ndef list_files_walk(directory):\n fu = [os.path.join(dp, f) for dp, dn, filenames in os.walk(directory) for f in filenames if\n os.path.splitext(f)[1].lower() == '.pdf']\n return (fu)\n\n\ndef newScan(parent):\n lista = []\n f = open(\"designs.txt\", \"w+\")\n g = open(\"paths.txt\", \"w+\")\n # iterate over all the files in directory 'parent'\n for file_name in parent:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n page_interpreter.process_page(page)\n text = handle.getvalue()\n word = find_between(text, \"SKUPrice1\", \"$\")\n print(word)\n # number = has_sequence(word)\n # stringnumber = ''.join(map(str, number))\n # artwork = find_between(word,\"1\",stringnumber)\n\n f.write(word + \"\\n\")\n g.write(file_name + \"\\n\")\n converter.close()\n handle.close()\n f.close()\n g.close()\n return lista\n\n\ndef scanDoublePages(parent, galleryprices, dailyprices):\n daily = open(\"daily.txt\", \"w+\")\n gal = open(\"gallery.txt\", \"w+\")\n sweet = open(\"sweet.txt\", \"w+\")\n duplicatestest = open(\"duplicatestest.txt\", \"w+\")\n number = 0\n numberlist = []\n originalfile = []\n folder = \"temp\"\n cleanFolder(folder)\n # listing the files inside the folder\n parentnew = list_files_walk(parent)\n # creating a temporary folder\n os.mkdir(folder)\n # splitting the temporary files\n splitter(parentnew, folder)\n # getting the temporary files\n parentnew2 = list_files_walk(folder)\n # sorting the files by name\n parentnew2.sort()\n # iterate over all the files in directory 'parent'\n for file_name in parentnew2:\n resource_manager = PDFResourceManager()\n handle = io.StringIO()\n converter = TextConverter(resource_manager, handle)\n page_interpreter = PDFPageInterpreter(resource_manager, converter)\n arquivo = open(file_name, 'rb')\n if \"page_001.pdf\" in file_name:\n number = 0\n with arquivo as fh:\n for page in PDFPage.get_pages(fh, caching=True, check_extractable=True):\n booleangal = True\n booleanSweet = True\n page_interpreter.process_page(page)\n text = handle.getvalue()\n text = text[:-1]\n text = text + \"¬¬¬\"\n #print(text)\n # searching the reference number\n search = find_between(text, \"#\", \"Order\")\n # searching the order number\n search2 = find_between(text, \"# \", \"Order Date\")\n # Searching the design name\n name = find_between(text, \"SKUPrice1\", \"$\")\n # Prices\n price = find_between(text, name, \",\")\n # Products\n products = find_between(text, \"SKUPrice1\", \"¬¬¬\")\n #print(products)\n originalfilenumber = find_between(file_name, \"_file_\", \"_page\")\n print(originalfilenumber)\n if search == \"\":\n numberlist.append(str(number) + \";\" + originalfilenumber)\n originalfile.append(originalfilenumber)\n # print(result[number-1])\n # f.write(result[number - 1] + \"\\n\")\n else:\n duplicatestest.write(search2 + \"\\n\")\n for daprices in dailyprices:\n if products.find(daprices) != -1:\n print(search2 + \" Daily Shirt\")\n booleangal = False\n daily.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n break\n if booleangal:\n for gaprices in galleryprices:\n if products.find(gaprices) != -1:\n print(search2 + \" Gallery Shirt\")\n gal.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n booleanSweet = False\n break\n if booleanSweet:\n sweet.write(name + \"^\" + file_name + \"^\" + search2 + \"\\n\")\n print(search2 + \" Sweet Deal\")\n number = number + 1\n converter.close()\n handle.close()\n daily.close()\n gal.close()\n duplicatestest.close()\n sweet.close()\n cleanFolder(folder)\n print(originalfile)\n print(\"Files with double pages: \")\n print(numberlist)\n os.mkdir(folder)\n splitterCustom(parentnew, folder, numberlist,originalfile)\n\n\ndef find_between(s, first, last):\n try:\n start = s.index(first) + len(first)\n end = s.index(last, start)\n return s[start:end]\n except ValueError:\n return \"\"\n\n\ndef has_sequence(s):\n val = []\n number = []\n length = len(s)\n for x in range(length):\n try:\n prov = int(s[x])\n val.append(prov)\n\n except ValueError:\n val.append(\"%\")\n\n for x in range(length):\n if val[x] == \"%\":\n 1\n else:\n if val[x + 1] == \"%\":\n 1\n else:\n number.append(val[x])\n return number\n\n\ndef sortFiles(file_name):\n f = open(file_name + \".txt\", \"r\")\n contents = f.readlines()\n contents.sort()\n with open(file_name + \"_sorted.txt\", \"w+\") as g:\n for item in contents:\n g.write(item)\n f.close()\n g.close()\n\n\ndef cleanFolder(path):\n if os.path.exists(path):\n shutil.rmtree(path)\n\ndef checkIfDuplicates(listOfElems):\n for elem in listOfElems:\n if listOfElems.count(elem) > 1:\n return True\n return False","repo_name":"williamzu/PDF_Miner","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":12733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1999871848","text":"# -*- coding: utf-8 -*-\nfrom .models import Item\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom django.core import serializers\nimport json\n\ndef create_item(name, checklist):\n\tnewItem = Item(name=name, checklist_id=checklist)\n\tnewItem.save()\n\tmapped_item = item_mapper(newItem)\n\treturn json.dumps(mapped_item)\n\ndef get_all_items():\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\tall_items_orderd = all_items.order_by('createdtime')\n\tall_items_serialized = serializers.serialize('json', all_items_orderd)\n\treturn all_items_serialized\n\ndef get_all_items_by_checklist_id(checklist_id):\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(\n\t\t\tQ(checklist_id=float(checklist_id))\n\t\t\t& Q(\n\t\t\t\tQ(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\t\t\t)\n\t)\n\tall_items_orderd = all_items.order_by('createdtime')\n\tall_items_orderd = all_items_orderd.order_by('done')\n\tmappedItemList = []\n\tfor item in all_items_orderd:\n\t\tmappedItem = item_mapper(item)\n\t\tmappedItemList.append(mappedItem)\n\tall_items_json = json.dumps(mappedItemList)\n\t#all_items_serialized = serializers.serialize('json', all_items_orderd)\n\treturn all_items_json\n\ndef update_item(id, value):\n\tif value == \"1\":\n\t\tendt = timezone.now()\n\telse:\n\t\tendt = None\n\titem = Item.objects.filter(id=id)\n\titem.update(done = value)\n\titem.update(endtime = endt)\n\ndef remove_item(id):\n\titem = Item.objects.filter(id=id)\n\titem.delete()\n\ndef get_highest_soring_order():\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tall_items = Item.objects.filter(Q(endtime__range=[startdate, enddate]) | Q(endtime=None))\n\tindex = all_items.order_by(\"-ordernumber\")[0]\n\treturn index.ordernumber\n\n\ndef update_item_order(newvalue, id):\n\treorder_items(newvalue)\n\titem = Item.objects.filter(id=id)\n\titem.update(ordernumber=newvalue)\n\ndef reorder_items(i):\n\tstartdate = timezone.now() - timezone.timedelta(hours=1)\n\tenddate = timezone.now()\n\tfor item in items:\n\t\titem.update(ordernumber = F('ordernumber') + 1)\n\ndef item_mapper(rawItem):\n\titem = {}\n\titem['id'] = rawItem.id\n\titem['name'] = rawItem.name\n\titem['done'] = rawItem.done\n\treturn item","repo_name":"andreasastrom/mysocialclub","sub_path":"hello/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27589884887","text":"# -*- coding: utf-8 -*-\n\nimport asyncio\nimport traceback\nfrom datetime import datetime\nfrom typing import List\n\nfrom fastapi import APIRouter, Request, WebSocket\nfrom starlette.responses import HTMLResponse\n\nfrom current_log.RedisClient import RedisClient\n\nlog_router = APIRouter()\n\nhtml = \"\"\"\n\n\n \n \n 实时日志\n \n \n

实时日志

\n
\n \n \n\"\"\"\n\nredis_client = RedisClient()\n\n\nclass ConnectionManager:\n def __init__(self):\n self.active_connections: List[WebSocket] = []\n\n async def broadcast(self, system_name):\n while True:\n message = redis_client.lpop(system_name)\n if message:\n # await asyncio.gather(\n # *[ws.send_text(message) for ws in self.active_connections],\n # return_exceptions=False,\n # )\n for ws in self.active_connections:\n try:\n await ws.send_text(message)\n except:\n pass\n await asyncio.sleep(0.2)\n\n def start_broadcast(self, system_name):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n asyncio.get_event_loop().run_until_complete(manager.broadcast(system_name))\n # asyncio.get_event_loop().run_forever()\n\n\nmanager = ConnectionManager()\n\n\n@log_router.get(path='/logs')\ndef get_log(request: Request):\n try:\n run_host = str(request.url.netloc)\n user = datetime.now().strftime('%f')\n user_html = html\n user_html = user_html.format(host=run_host, user=user)\n return HTMLResponse(user_html)\n except:\n return {\"error\": traceback.format_exc()}\n\n\n@log_router.websocket(path=\"/log_connect/{user}\")\nasync def broadcast_log_redis(ws: WebSocket, user: str):\n await ws.accept()\n manager.active_connections.append(ws)\n try:\n while True:\n await ws.receive_text()\n await ws.send_text(\"pong\")\n except:\n pass\n finally:\n manager.active_connections.remove(ws)\n\n\n@log_router.get(path=\"/start_generate_log/{system_name}\")\ndef start_generate_log(system_name: str):\n import threading\n threading.Thread(target=manager.start_broadcast, args=(system_name,)).start()\n return {\"message\": \"success\"}\n\n\n@log_router.get(path='/')\ndef test():\n print(\"测试\")\n return {\"message\": \"aaaa\"}\n","repo_name":"cooldowntime/curentlog","sub_path":"current_log/current_log_router.py","file_name":"current_log_router.py","file_ext":"py","file_size_in_byte":3758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"13883538647","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nimport datetime\n\n\n# Create your views here.\n@api_view()\ndef get_detail(request):\n\n slack_name = request.GET.get('slack_name', None)\n\n track = request.GET.get('track', None)\n\n current_date = datetime.date.today()\n\n\n \n # Get the current UTC time\n current_utc_time = datetime.datetime.utcnow()\n\n # Define a time range of +/- 2 hours\n time_range = datetime.timedelta(hours=2)\n\n # Calculate the minimum and maximum allowed times\n min_time = current_utc_time - time_range\n max_time = current_utc_time + time_range\n\n # Get the current UTC time as a string\n min_time_str = min_time.strftime('%Y-%m-%d %H:%M:%S')\n max_time_str = max_time.strftime('%Y-%m-%d %H:%M:%S')\n\n# Get the name of the day of the week\n day_of_week = current_date.strftime('%A')\n\n detail = { \n \"slack_name\": slack_name,\n \"current_days\": day_of_week,\n \"utc_time\": \"Min. =>\"+ min_time_str + \" - Man =>\" + max_time_str,\n \"track\": track,\n \"github_file_url\": \"https://github.com/hussain4me/zuri-first-assignment/blob/main/api/views.py\",\n \"github_repo_url\": \"https://github.com/hussain4me/zuri-first-assignment\",\n \"status_code\": 200\n }\n\n return Response(detail)\n","repo_name":"hussain4me/zuri-first-assignment","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14461442187","text":"import tensorflow as tf\nimport helper\n \nmodel = tf.keras.models.load_model(\"pets\")\n\nwhile True:\n url = input(\"Please enter an image url:\")\n try:\n image = tf.keras.utils.get_file(origin=url)\n image = tf.keras.utils.load_img(image)\n break\n except:\n print(\"That is not a valid link\")\n\nhelper.show_predictions(url, model)","repo_name":"Powerlax/ImageSegmentation","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31564645195","text":"import sys\nfrom turtle import back\nfrom load import *\n\n# this function creates a 2D matrix with pre-determined scores for certain rows\ndef scoreMatrix(x, y, score):\n matrix = [[0] * (len(y)+1) for i in range(len(x)+1)]\n for i in range(len(x)+1):\n matrix[i][0] = i * score\n for j in range(len(y)+1):\n matrix[0][j] = j * score\n return matrix\n\n# this function creates a 2D matrix to backtrack previous locations\ndef backMatrix(x, y):\n matrix = [[0] * (len(y)+1) for i in range(len(x)+1)]\n for i in range(len(x)+1):\n matrix[i][0] = \"up\"\n for j in range(len(y)+1):\n matrix[0][j] = \"left\"\n matrix[0][0] = 0\n return matrix\n\n# this function decides which score to use for DNA scoring\ndef DNAscore(match, mismatch, seq1, seq2):\n if seq1 == seq2:\n return match\n else: \n return mismatch\n\n# this function calculates identities between 2 sequences\ndef identities(seq1, seq2):\n total = len(seq1)\n count = 0\n for i in range(len(seq1)):\n if seq1[i] == seq2[i]:\n count += 1\n \n num = str(count) + \"/\" + str(total)\n percent = \"(\" + str(int(count/total * 100)) + \"%)\"\n return num + \" \" + percent\n\n# this function takes 2 DNA sequences and use global alignment to decide the optimal score\ndef DNAglobal(scores, seq1, seq2):\n\n # get scores from the dnaMatrix file\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n # create a 2D matrix using list of list and fill in the basic gap penalties \n matrix = scoreMatrix(seq1, seq2, gapPenalty)\n\n # create another 2D matrix to save the information for backtracking\n backtrack = backMatrix(seq1, seq2)\n\n # fill in all the scores and backtracking info\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and use semi-global alignment to decide the optimal score\ndef DNAsemi_global(scores, seq1, seq2):\n\n # get scores from the dnaMatrix file\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n\n if i == len(seq1):\n left = matrix[i][j-1]\n else:\n left = matrix[i][j-1] + gapPenalty\n\n if j == len(seq2):\n up = matrix[i-1][j]\n else:\n up = matrix[i-1][j] + gapPenalty\n \n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n \n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and uses local alignment to decide the optimal score\ndef DNAlocal(scores, seq1, seq2):\n matchScore = scores.get(\"match score\")\n mismatchScore = scores.get(\"mismatch score\")\n gapPenalty = scores.get(\"gap penalty\")\n\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n max_score = 0\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + DNAscore(matchScore, mismatchScore, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n if matrix[i][j] < 0:\n matrix[i][j] = 0\n\n if matrix[i][j] > max_score:\n max_score = matrix[i][j]\n max_index = [i,j]\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score, max_score, max_index, matrix\n\n# this function uses backtracking info to create new aligned sequences\ndef aligned(seq1, seq2, src, type):\n l1 = len(seq1)\n l2 = len(seq2)\n back_matrix = src[0]\n s1 = \"\"\n s2 = \"\"\n \n if type == \"G\" or type == \"S\":\n score = src[1]\n while (l1 > 0 or l2 > 0):\n if back_matrix[l1][l2] == \"diagonal\":\n s1 = seq1[l1-1] + s1\n s2 = seq2[l2-1] + s2\n l1 -= 1\n l2 -= 1\n elif back_matrix[l1][l2] == \"left\":\n s1 = \"-\" + s1\n s2 = seq2[l2-1] + s2\n l2 -= 1\n else:\n s1 = seq1[l1-1] + s1\n s2 = \"-\" + s2\n l1 -= 1\n else:\n score = src[2]\n max_start = src[3]\n l1 = max_start[0]\n l2 = max_start[1]\n score_matrix = src[4]\n while (score_matrix[l1][l2] != 0):\n if back_matrix[l1][l2] == \"diagonal\":\n s1 = seq1[l1-1] + s1\n s2 = seq2[l2-1] + s2\n l1 -= 1\n l2 -= 1\n elif back_matrix[l1][l2] == \"left\":\n s1 = \"-\" + s1\n s2 = seq2[l2-1] + s2\n l2 -= 1\n else:\n s1 = seq1[l1-1] + s1\n s2 = \"-\" + s2\n l1 -= 1\n \n return s1, s2, score, l1, l2\n\n# this function determines which score to use from the BLOSUM file\ndef proteinScore(blosum, seq1, seq2):\n score = blosum.get(seq1).get(seq2)\n return score\n\n# this function creates a scoring matrix using global alignment for 2 protein sequences\ndef proteinGlobal(scores, seq1, seq2):\n gapPenalty = scores.get(\"gap penalty\")\n matrix = scoreMatrix(seq1, seq2, gapPenalty)\n backtrack = backMatrix(seq1, seq2)\n \n # fill in all the scores and backtracking info\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function creates a scoring matrix using semi-global alignment for 2 protein sequences\ndef proteinSemi_global(scores, seq1, seq2):\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n gapPenalty = scores.get(\"gap penalty\")\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n\n if i == len(seq1):\n left = matrix[i][j-1]\n else:\n left = matrix[i][j-1] + gapPenalty\n\n if j == len(seq2):\n up = matrix[i-1][j]\n else:\n up = matrix[i-1][j] + gapPenalty\n \n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n \n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score\n\n# this function takes 2 DNA sequences and uses local alignment to decide the optimal score\ndef proteinLocal(scores, seq1, seq2):\n matrix = scoreMatrix(seq1, seq2, 0)\n backtrack = backMatrix(seq1, seq2)\n gapPenalty = scores.get(\"gap penalty\")\n max_score = 0\n\n for i in range(1, len(seq1)+1):\n for j in range(1, len(seq2)+1):\n diagonal = matrix[i-1][j-1] + proteinScore(scores, seq1[i-1], seq2[j-1])\n left = matrix[i][j-1] + gapPenalty\n up = matrix[i-1][j] + gapPenalty\n matrix[i][j] = max(diagonal, left, up)\n\n if matrix[i][j] == diagonal:\n backtrack[i][j] = \"diagonal\"\n elif matrix[i][j] == left:\n backtrack[i][j] = \"left\"\n else:\n backtrack[i][j] = \"up\"\n\n if matrix[i][j] < 0:\n matrix[i][j] = 0\n\n if matrix[i][j] > max_score:\n max_score = matrix[i][j]\n max_index = [i,j]\n\n final_score = matrix[len(seq1)][len(seq2)]\n return backtrack, final_score, max_score, max_index, matrix\n\n# this function deals with all command-line arguments and put them in an ordered list\ndef param(seq1, seq2, output, proseq, atype):\n argv = sys.argv[1:]\n pars = [\"-i\", \"-j\", \"-o\", \"-p\", \"-atype\"]\n alist = [seq1, seq2, output, proseq, atype]\n\n for i in range(len(argv)):\n for j in range(len(pars)):\n if argv[i] == pars[j]:\n try:\n argv[i+1]\n except IndexError:\n print(\"Missing argument after the last index \" + pars[j] + \". Can't run the program!\")\n else:\n if argv[i+1] not in pars:\n alist[j] = argv[i+1]\n\n for i in range(len(alist)):\n if alist[i] == \"\":\n print(\"Missing \" + pars[i] + \" or missing argument after \" + pars[i] + \". Can't run the program!\")\n return None\n if alist[3] != 'T' and alist[3] != 'F':\n print(\"Wrong argument, can't run the program! It should be either T or F after '-p'.\")\n return None\n break\n if alist[4] != 'G' and alist[4] != 'S' and alist[4] != 'L':\n print(\"Wrong argument, can't run the program! It should be either G or S after '-atype'.\")\n return None\n break\n\n return alist\n\ndef main():\n alist = param(None, None, None, None, None)\n # print(alist)\n i = loadSeq(alist[0])\n j = loadSeq(alist[1])\n output = alist[2]\n proseq = alist[3]\n atype = alist[4]\n\n if alist != None:\n fout = open(output, \"w\")\n fout.write(\"\\n \\n\")\n if proseq == \"F\":\n scoring = loadMatrix(\"dnaMatrix.txt\")\n if atype == \"G\":\n source = DNAglobal(scoring, i, j)\n elif atype == \"S\":\n source = DNAsemi_global(scoring, i, j)\n else:\n source = DNAlocal(scoring, i, j)\n else:\n scoring = loadBLOSUM(\"BLOSUM45.txt\")\n if atype == \"G\":\n source = proteinGlobal(scoring, i, j)\n elif atype == \"S\":\n source = proteinSemi_global(scoring, i, j)\n else: \n source = proteinLocal(scoring, i, j)\n\n result = aligned(i, j, source, atype)\n align1 = result[0]\n align2 = result[1]\n score = str(result[2])\n if atype == \"G\" or atype == \"S\":\n fout.write(\"seq1: \" + str(1) + \" \" + align1 + \" \" + str(len(i)) + \"\\n\")\n fout.write(\"\\n\")\n fout.write(\"seq2: \" + str(1) + \" \" + align2 + \" \" + str(len(j)) + \"\\n\") \n else:\n last_idx = source[3]\n fout.write(\"seq1: \" + str(result[3]+1) + \" \" + align1 + \" \" + str(last_idx[0]+1) + \"\\n\")\n fout.write(\"\\n\")\n fout.write(\"seq2: \" + str(result[4]+1) + \" \" + align2 + \" \" + str(last_idx[1]+1) + \"\\n\") \n fout.write(\"\\n\")\n fout.write(\"Score: \" + score + \"\\n\")\n fout.write(\"Identities: \" + identities(align1, align2) + \"\\n\")\n fout.close()\nmain()","repo_name":"ninavu/pairwise_alignment","sub_path":"align.py","file_name":"align.py","file_ext":"py","file_size_in_byte":12452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71211003149","text":"# cook your dish here\ntry:\n import math as mt\n def great(a,b):\n if(a>=b):\n return a\n else:\n return b\n t=int(input())\n while(t):\n a,b=map(int,input().split())\n g=great(a,b)\n power=int((mt.log(g)/mt.log(2)))+1\n mx=a^b\n \n rotation=0\n mx_rotation=0\n for i in range(1,power):\n end=b&1\n b=b>>1\n b=b|int((end*(2**(power-1))))\n rotation+=1\n if((a^b)>mx):\n mx=a^b\n mx_rotation=rotation\n # print(power)\n # print(mx)\n print(mx_rotation,mx)\n t-=1\nexcept:\n pass\n","repo_name":"iamvedant/Campus-Chapters-1.0","sub_path":"Another Game Of Numbers (GAMENUM).py","file_name":"Another Game Of Numbers (GAMENUM).py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32624230813","text":"import turtle\nfrom turtle import Turtle, Screen\n\n\nclass LeftPaddle(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setpos(-400, 0)\n\t\tself.setheading(90)\n\t\tself.speed(0)\n\t\tself.color(61, 84, 103)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.8, 4)\n\t\tself.score = 0\n\n\tdef up(self):\n\t\tif self.ycor() < 290:\n\t\t\tself.setheading(90)\n\t\t\tself.forward(20)\n\n\tdef down(self):\n\t\tif self.ycor() > -290:\n\t\t\tself.setheading(270)\n\t\t\tself.forward(20)\n\n\nclass RightPaddle(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setpos(400, 0)\n\t\tself.setheading(90)\n\t\tself.speed(0)\n\t\tself.color(61, 84, 103)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.8, 4)\n\t\tself.score=0\n\n\tdef up(self):\n\t\tif self.ycor() < 290:\n\t\t\tself.setheading(90)\n\t\t\tself.forward(20)\n\n\tdef down(self):\n\t\tif self.ycor() > -290:\n\t\t\tself.setheading(270)\n\t\t\tself.forward(20)\n\n\nclass Ball(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.shape(\"circle\")\n\t\tself.speed(8)\n\t\tself.dx = 10\n\t\tself.dy = 10\n\n\tdef move(self):\n\t\tself.goto((self.xcor() + self.dx), (self.ycor() + self.dy))\n\n\tdef reset(self):\n\t\tself.goto(0, 0)\n\n\nclass ScoreBoard(Turtle):\n\tdef __init__(self, player, score, cord):\n\t\tsuper().__init__()\n\t\tself.player = player\n\t\tself.cord = cord\n\t\tself.score = score\n\t\tself.penup()\n\t\tself.hideturtle()\n\t\tself.goto(cord)\n\t\tself.color(219, 84, 97)\n\t\tself.write(f\"{self.player}: {self.score}\", True, align=\"center\", font=(\"Arial\", 30, \"normal\"))\n\n\nclass HalfCourt(Turtle):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.penup()\n\t\tself.setheading(90)\n\t\tself.color(138, 162, 158)\n\t\tself.shape(\"square\")\n\t\tself.shapesize(0.5, 1)\n","repo_name":"xalxnder/pong","sub_path":"pong_classes.py","file_name":"pong_classes.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14372327952","text":"import csv\nimport operator\nfrom random import choice\n\nfrom .classes import Node, Link, Network, Agent\n\n\ndef read_nodes(input_dir, node_list, internal_node_seq_no_dict,\n external_node_id_dict, zone_to_nodes_dict):\n \"\"\" step 1: read input_node \"\"\"\n with open(input_dir+'/node.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n node_seq_no = 0\n for line in reader:\n node = Node(node_seq_no, line['node_id'], line['zone_id'])\n node_list.append(node)\n internal_node_seq_no_dict[node.external_node_id] = node_seq_no\n external_node_id_dict[node_seq_no] = node.external_node_id\n if node.zone_id not in zone_to_nodes_dict.keys():\n zone_to_nodes_dict[int(node.zone_id)] = list()\n zone_to_nodes_dict[int(node.zone_id)].append(\n node.external_node_id\n )\n else:\n zone_to_nodes_dict[int(node.zone_id)].append(\n node.external_node_id\n )\n node_seq_no += 1\n print('the number of nodes is', node_seq_no)\n fp.close()\n\n\ndef read_links(input_dir, link_list, node_list, internal_node_seq_no_dict):\n \"\"\" step 2: read input_link \"\"\"\n with open(input_dir+'/link.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n link_seq_no = 0\n for line in reader:\n from_node_no = internal_node_seq_no_dict[int(line['from_node_id'])]\n to_node_no = internal_node_seq_no_dict[int(line['to_node_id'])]\n link = Link(link_seq_no, \n from_node_no, \n to_node_no,\n int(line['from_node_id']),\n int(line['to_node_id']),\n line['length'],\n line['lanes'],\n line['free_speed'],\n line['capacity'],\n line['link_type'],\n line['VDF_alpha1'],\n line['VDF_beta1'])\n node_list[link.from_node_seq_no].outgoing_link_list.append(link)\n node_list[link.to_node_seq_no].incoming_link_list.append(link)\n link_list.append(link)\n link_seq_no += 1\n print('the number of links is', link_seq_no)\n fp.close()\n \n\ndef read_agents(input_dir,\n agent_list,\n agent_td_list_dict,\n zone_to_nodes_dict):\n \"\"\" step 3:read input_agent \"\"\"\n with open(input_dir+'/demand.csv', 'r', encoding='utf-8') as fp:\n reader = csv.DictReader(fp)\n agent_id = 1\n agent_type = 'v'\n agent_seq_no = 0\n for line in reader:\n volume = line['volume']\n volume_agent_size = int(float(volume) + 1)\n \n # only test up to 10k\n if agent_id >= 10000 :\n break \n \n for i in range(volume_agent_size):\n agent = Agent(agent_id,\n agent_seq_no,\n agent_type,\n line['o_zone_id'], \n line['d_zone_id'])\n\n # step 3.1 generate o_node_id and d_node_id randomly according \n # to o_zone_id and d_zone_id \n if zone_to_nodes_dict.get(agent.o_zone_id, -1) == -1 : \n continue\n if zone_to_nodes_dict.get(agent.d_zone_id, -1) == -1 : \n continue \n \n agent.o_node_id = choice(zone_to_nodes_dict[agent.o_zone_id])\n agent.d_node_id = choice(zone_to_nodes_dict[agent.d_zone_id])\n \n # step 3.2 update agent_id and agent_seq_no\n agent_id += 1\n agent_seq_no += 1 \n\n # step 3.3: update the g_simulation_start_time_in_min and \n # g_simulation_end_time_in_min \n if agent.departure_time_in_min < g_simulation_start_time_in_min:\n g_simulation_start_time_in_min = agent.departure_time_in_min\n if agent.departure_time_in_min > g_simulation_end_time_in_min:\n g_simulation_end_time_in_min = agent.departure_time_in_min\n\n #step 3.4: add the agent to the time dependent agent list \n if agent.departure_time_in_simu_interval not in agent_td_list_dict.keys():\n agent_td_list_dict[agent.departure_time_in_simu_interval] = list()\n agent_td_list_dict[agent.departure_time_in_simu_interval].append(agent.agent_seq_no)\n else:\n agent_td_list_dict[agent.departure_time_in_simu_interval].append(agent.agent_seq_no)\n agent_list.append(agent)\n\n print('the number of agents is', len(agent_list))\n\n #step 3.6:sort agents by the departure time\n sort_fun = operator.attrgetter(\"departure_time_in_min\")\n agent_list.sort(key=sort_fun)\n for i, agent in enumerate(agent_list):\n agent.agent_seq_no = i\n\n\ndef read_network(input_dir='./'):\n network = Network()\n\n read_nodes(input_dir,\n network.node_list,\n network.internal_node_seq_no_dict,\n network.external_node_id_dict,\n network.zone_to_nodes_dict)\n\n read_links(input_dir, \n network.link_list,\n network.node_list,\n network.internal_node_seq_no_dict)\n\n read_agents(input_dir,\n network.agent_list,\n network.agent_td_list_dict,\n network.zone_to_nodes_dict)\n\n network.update()\n\n return network","repo_name":"asu-trans-ai-lab/Path4GMNS","sub_path":"path4gmns/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"24251040917","text":"\"\"\" Introduction to the Python Protocol \"\"\"\n\n# Suppose you have a function that calculates the total value of a product list, \n# where each product has the name, quantity, and price attributes:\nfrom typing import List\nclass Product:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\ndef calculate_total(items: List[Product]) -> int:\n return sum([prod.quantity * prod.price for prod in items])\n\nitems = [\n Product('Mouse', 2, 250),\n Product('Keyboard', 3, 550)\n]\n\nprint(calculate_total(items))\n\n# In this example, the calculate_total() function accepts a list of Product objects and \n# returns the total value.\n\n# When writing this function, you may want to calculate the total of a product list. But you \n# likely want to use it for other lists such as inventory lists in the future.\n\n# If you look closely at the calculate_total() function, it only uses the quantity and price \n# attributes.\n\n# To make the calculate_total() more dynamic while leveraging type hints, you can use the \n# Protocol from the typing module. The Protocol class has been available since Python 3.8, \n# described in PEP 544.\n\nfrom pprint import pprint\nfrom typing import Protocol\n# First, define an Item class that inherits from the Protocol with two \n# attributes: quantity and price:\nclass Item(Protocol):\n quantity: int\n price: float\n\nclass Product:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\nclass Inventory:\n def __init__(self, name, quantity, price):\n self.name = name\n self.quantity = quantity\n self.price = price\n\n# Second, change the calculate_total() function that accepts a list of Item objects \n# instead of a list of Product objects:\ndef calculate_total(items: List[Item]):\n return sum([item.quantity * item.price for item in items])\n\n# By doing this, you can pass any list of Item objects to the calculate_total() function \n# with the condition that each item has two attributes quantity and price.\ntotal = calculate_total([\n Product('Keyboard', 2, 800),\n Product('Mouse', 3, 250)\n])\nprint(total)\n\ntotal = calculate_total([\n Inventory('Food', 25, 150),\n Inventory('Stones', 150, 250)\n])\nprint(total)\n\n# In this example, the Product and Inventory class don’t need to subclass the Item \n# class but still can be used in the calculate_total() function.\n\n# This is called duck typing in Python. In duck typing, the behaviors and properties of an \n# object determine the object type, not the explicit type of the object.\n\n# For example, an object with the quantity and price will follow the Item protocol, \n# regardless of its explicit type.\n\n\"\"\" Summary \"\"\"\n# Use Python Protocol to define implicit interfaces.\n\n","repo_name":"Engr-Asad-Hussain/oop","sub_path":"single_inheritance/protocol.py","file_name":"protocol.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40581670455","text":"import os\nimport random\n\n\ndef search_file(directory, file, list_p):\n\n for i_elem in os.listdir(directory):\n path = os.path.join(directory, i_elem)\n if file == i_elem:\n list_p.append(path)\n elif os.path.isdir(path):\n search_file(path, file, list_p)\n\n return list_p\n\n\nlist_paths = list()\nmy_dir = 'Skillbox'\ndir_path = os.path.abspath(os.path.join('..', '..', '..', my_dir))\n\nprint('Ищем в: ', dir_path)\nfile_name = input('Имя файла: ')\n\n\nresult = search_file(dir_path, file_name, list_paths)\n\nif not result:\n print('Указанный файл в системе не найден.')\nelse:\n print('Найдены следующие пути:')\n for i_path in result:\n print(i_path)\n\nrandom_file = random.choice(result)\n\nfile = open(random_file, 'r', encoding='utf-8')\n\nprint('Вывод случайного файла из найденных, его путь', random_file)\nfor i_line in file:\n print(i_line, end='')\n\n","repo_name":"surma623/Module-Skillbox","sub_path":"22.3.2.py","file_name":"22.3.2.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12559472397","text":"import sys\r\nimport heapq\r\n\r\ninput = sys.stdin.readline\r\nn, k = map(int, input().split())\r\njewel_data = [tuple(map(int, input().split())) for _ in range(n)]\r\nbag_data = [int(input()) for _ in range(k)]\r\n\r\njewel_data.sort(reverse=True)\r\nbag_data.sort()\r\n\r\nh = []\r\n\r\nresult = 0\r\nfor c in bag_data:\r\n while jewel_data and jewel_data[-1][0] <= c:\r\n jewel = jewel_data.pop()\r\n heapq.heappush(h, -jewel[1])\r\n if h:\r\n result += -heapq.heappop(h)\r\n\r\nprint(result)","repo_name":"Charmull/Algorithm_Python","sub_path":"백준/Gold/1202. 보석 도둑/보석 도둑.py","file_name":"보석 도둑.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25864527041","text":"import pymongo\nfrom pymongo import MongoClient\nimport requests\nfrom bs4 import BeautifulSoup as soup\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup as BS\nimport re\n\nc = MongoClient()\ndb=c[\"mydatabase\"]\narticle = db.articles\n\ndef insertIntoDB(date,title, content, source, url):\n post_Data ={'date': date, 'title':title,'content':content,'source':source,'url':url,'score':'NA'}\n \"\"\"if(article.find({'title':title,'date':date,'source':source}).count()>0):\n print(\"already present\")\n else:\"\"\"\n result = article.insert_one(post_Data)\n\ndef updateScore():\n article.find({'score':'NA'})\n\t\ndef getGoodNetworkNews():\n url = 'https://www.goodnewsnetwork.org/'\n html = requests.get(url)\n soup = BS(html.text)\n table = soup.find_all('h3',{'class':\"entry-title td-module-title\"})\n for i in table:\n k=i.find('a')['href']\n #print(\"url\",k)\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n browser.get(k)\n html = browser.page_source\n soup = BS(html, 'html.parser')\n time = soup.find('time')\n #print(time.string)\n try:\n title_article=soup.find('h1',{'class':\"entry-title\"})\n #print(\"TITLE\",title_article.string)\n para = soup.find('div',{'class':'td-post-content'}).find_all('p')\n content=''\n for i in para:\n #if not(i.string is None):\n content=content+i.string\n insertIntoDB(time.string,title_article.string, content, \"Good\", content)\n except:\n print(\"removing video articles\") #article\n\t\t\t\ndef newsFromGuardian():\n main_url = \"https://newsapi.org/v2/everything?sources=the-guardian-uk&apiKey=fa6d77b861bc48c2a4bfd93ef6ceaeba\"\n open_bbc_page = requests.get(main_url).json()\n article = open_bbc_page[\"articles\"]\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n try:\n for ar in article:\n print(\"TITLE:\",ar[\"title\"])\n browser.get(ar[\"url\"])\n ans=''\n html = browser.page_source\n soup = BS(html, 'html.parser')\n table = soup.find('div',{'class':re.compile('content__article-body')}).find_all('p')\n for k in table:\n if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"the-guardian-uk\", ar[\"url\"])\n except:\n print(\"removing video articles\") #article\n \n \ndef newsFromBBC():\n main_url = \" https://newsapi.org/v2/everything?sources=bbc-news&apiKey=95465951cbf447369c10a005ded49a0b\"\n open_bbc_page = requests.get(main_url).json()\n article = open_bbc_page[\"articles\"]\n results = []\n links = []\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n for ar in article:\n print(\"TITLE:\",ar[\"title\"])\n print(\"DATE:\",ar['publishedAt'])\n try:\n browser.get(ar[\"url\"])\n ans=''\n html = browser.page_source\n soup = BS(html, 'html.parser')\n table = soup.find_all('div',{'class':\"story-body__inner\"})[0].find_all('p',{'class':\"aria-hidden\"})\n for div in table:\n div.decompose()\n table=soup.find_all('div',{'class':\"story-body__inner\"})[0].find_all('p')\n for k in table:\n #if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"BBC\", ar[\"url\"])\n except:\n print(\"removing video articles\")\n\t\t\n\t\t\t\ndef newsFromCNBC():\n main_url = \"https://newsapi.org/v2/everything?sources=cnbc&apiKey=cb28b795dd1e469ebbc02ea19535898a\"\n open_cnbc_page = requests.get(main_url).json()\n article = open_cnbc_page[\"articles\"]\n browser = webdriver.PhantomJS(executable_path=\"D:/sw/phantomjs-2.1.1-windows/bin/phantomjs\")\n for ar in article:\n browser.get(ar[\"url\"])\n ans=''\t\t\n html = browser.page_source\n soup = BS(html, 'html.parser')\n try:\n table = soup.find_all('div',{'class':'group-container'})[1].find_all('p')\n for k in table:\n if k.string is not None:\n ans=ans+k.string\n insertIntoDB(ar['publishedAt'],ar[\"title\"], ans, \"CNBC\", ar[\"url\"])\n except:\n print(\"removing video articles\")\n\n\t\t\t\nnewsFromBBC()\t\n#newsFromGuardian()\ngetGoodNetworkNews()\n#newsFromCNBC()\n","repo_name":"nivedita104/PosNews","sub_path":"mongo.py","file_name":"mongo.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36108116979","text":"import numpy as np\n\n\nclass HillClimbing:\n \"\"\"\n Maximization Problem.\n \"\"\"\n\n def __init__(self, method='vanilla', noise_scale=0.1, n_candidates=1, up_rate=2, down_rate=0.5, max_noise=2,\n min_noise=0.001):\n\n assert method in ['vanilla', 'steepest_ascent', 'simulated_annealing', 'adaptive_noise_scaling']\n\n self.x_best = None\n self.f_best = -np.inf\n self.noise_scale = noise_scale\n self.n_candidates = n_candidates\n self.down_rate = down_rate\n self.up_rate = up_rate\n self.max_noise = max_noise\n self.min_noise = min_noise\n self.method = method\n\n def step(self, xs, fs):\n\n xs_new = None\n\n if self.method == 'vanilla':\n if fs[0] > self.f_best:\n self.x_best = xs[0]\n self.f_best = fs[0]\n xs_new = [self.x_best + np.random.normal(loc=0, scale=self.noise_scale, size=xs[0].shape)]\n\n if self.method == 'steepest_ascent':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n if self.method == 'simulated_annealing':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n self.noise_scale /= self.down_rate\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n if self.method == 'adaptive_noise_scaling':\n best_indx = np.argmax(fs)\n\n if fs[best_indx] > self.f_best:\n self.x_best = xs[best_indx]\n self.f_best = fs[best_indx]\n self.noise_scale = max(self.noise_scale * self.down_rate, self.min_noise)\n else:\n self.noise_scale = min(self.noise_scale * self.up_rate, self.max_noise)\n\n xs_new = [self.x_best + np.random.normal(0, self.noise_scale, size=xs[0].shape) for _ in\n range(self.n_candidates)]\n\n return xs_new\n","repo_name":"m-fili/CartPole_HillClimbing","sub_path":"gradient_free/hill_climbing.py","file_name":"hill_climbing.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29466654927","text":"import argparse\nimport logging\nimport re\nimport os\nimport shutil\nfrom logging import getLogger\n\nimport MeCab\nimport wikipedia\nfrom Levenshtein import distance as D\nfrom pykakasi import kakasi\n\n\nlogging.basicConfig(level=logging.INFO)\nlog = getLogger(__name__)\n\n\nclass Gorgeous:\n \"\"\"\n 君のハートに、レボ☆リューション\n\n gorgeous = Gorgeous()\n gorgeous.revolution(\"まだ助かる\")\n\n >>> マダガスカル\n \"\"\"\n def __init__(self, **kwargs) -> None:\n k = kakasi()\n k.setMode('K', 'a')\n self.conv = k.getConverter()\n self.tagger = MeCab.Tagger()\n self.nations = self.read_nations(**kwargs)\n self.nations_roman = [\n self.romanize(nation) for nation in self.nations]\n self.nations_roman_vowel = [self.extract_vowel(\n self.romanize(nation)) for nation in self.nations]\n self.recent_answer = \"\"\n return\n\n def read_nations(self, fname=\"data/nations.csv\", **kwargs) -> list:\n \"\"\"\n Read csv file \n published on 『国コード一覧CSV ISO 3166-1』\n https://qiita.com/tao_s/items/32b90a2751bfbdd585ea\n \"\"\"\n assert os.path.exists(fname), f\"{fname} is not found\"\n with open(fname, \"r\") as f:\n nations = f.read().split(\"\\n\")\n\n nations = [re.split(\"[,|]\", nation)[0].replace(\"\\\"\", \"\") for nation in nations]\n nations.pop(0)\n return nations\n\n def read_csv_data(self, filepath, **kwargs) -> list:\n with open(filepath, \"r\") as f:\n data = f.read().split(\"\\n\")\n data = [re.split(\"[,|]\", area)[0].replace(\"\\\"\", \"\") for area in data]\n data.pop(0)\n return data\n\n def clean_str(self, s: str) -> str:\n return re.sub(r'[*\\s\\t\\n.,]', \"\", s)\n\n def katakanize(self, s: str, morph=False, **kwargs) -> str:\n \"\"\"\n convert \"kanji\" to \"katakana\"\n \"\"\"\n morphed = [re.split(r\"[,\\t\\s\\n]\", w) for w in self.tagger.parse(s).split(\"\\n\")]\n morphed.remove([\"\"])\n morphed.remove([\"EOS\"])\n \n k = [morph[-1] if morph[-1] != \"*\" else morph[0] for morph in morphed]\n\n if morph: # morphlogical analysed output\n return k\n\n return \"\".join(k)\n\n def romanize(self, s, **kwargs) -> list:\n \"\"\"\n convert \"katakana\" to \"romaji\" via kakasi\n (kanji - kana simple inverter)\n \"\"\"\n s = self.katakanize(s, **kwargs)\n if type(s) == str:\n s = [s]\n return [self.conv.do(w) for w in s]\n\n def extract_vowel(self, word: str, **kwargs) -> str:\n \"\"\"\n extract vowels from romanized words\n \"\"\"\n if type(word) == list:\n return [self.extract_vowel(w) for w in word]\n\n return \"\".join([l for l in word if l in [\"a\", \"i\", \"u\", \"e\", \"o\", \"n\"]])\n\n def revolution(self, sentence: str, app_use=False ,**kwargs):\n \"\"\"\n Revolution: Get Similar Nation Name from Word\n\n gorgeous.revolution(\"まだ助かる\")\n >>> マダガスカル\n\n args\n ----\n n_result : default=5 : lines of result print\n vowel : default=False : if true, word-distance will be calculated based on vowels\n app_use : default=False, if true, returns value of dict with some info\n \"\"\"\n\n # default kargs\n n_result = kwargs.get('n_result', 3)\n vowel = kwargs.get('vowel', False)\n\n answer = dict()\n\n log.info(f\"INPUT: {sentence}\")\n answer[\"input\"] = sentence\n # sentence -> [words] -> [katakana] -> [roman]\n word_roman = self.romanize(sentence, **kwargs)\n log.info(f\"ROMAN: {word_roman}\")\n answer[\"roman\"] = word_roman\n\n if vowel:\n word_vowel = self.extract_vowel(word_roman)\n log.info(f\"VOWEL: {word_vowel}\")\n answer[\"vowel\"] = word_vowel\n dists = [D(word_vowel[-1], nation[0]) for nation in self.nations_roman_vowel]\n else:\n dists = [D(word_roman[-1], nation[0]) for nation in self.nations_roman]\n answer[\"vowel\"] = \"\"\n idx = sorted(range(len(dists)), key=lambda k: dists[k])\n\n # logging\n log.info(\"RESULT:\")\n answer[\"results\"] = []\n for i in range(n_result):\n rank = idx[i]\n nation = self.nations[rank]\n dist = dists[rank]\n roman = self.nations_roman_vowel[rank] if vowel else self.nations_roman[rank]\n # calc score\n roman = roman[0] if type(roman) == list else roman # list -> str\n word_roman = word_roman[0] if type(word_roman) == list else word_roman # list -> str\n score = (len(word_roman) - int(dist)) / len(roman) if len(roman) != 0 else 0\n score = round(100 * score, 2)\n # build message for log and line bot\n msg = f\"No.{i+1} : {nation} ({roman}) : ({dist} : {score}%)\"\n log.info(\"\\t\" + msg)\n answer[\"results\"].append([nation, roman, dist, score])\n\n self.recent_answer = self.nations[idx[0]]\n answer[\"result\"] = self.nations[idx[0]]\n\n # Get meta info\n map_url = self.googlemap()\n log.info(f\"ここ！({map_url})\")\n answer[\"map\"] = map_url\n print(\"-\" * shutil.get_terminal_size()[0]) # draw line\n \n wiki = self.wikipedia()\n log.info(f\"{wiki[1]}！！\\n\")\n _, answer[\"wiki_summary\"], answer[\"wiki_url\"] = wiki\n print(u\"☆\" * shutil.get_terminal_size()[0]) # draw line\n\n # Answer\n if app_use: # returns dict value\n return answer\n return self.recent_answer\n\n def googlemap(self, place=None) -> str:\n \"\"\"generate Google Map Link\"\"\"\n if place is None:\n place = self.recent_answer\n return f\"https://www.google.com/maps/search/{place}/\"\n\n def wikipedia(self, place=None) -> tuple:\n \"\"\"Generate Wikipedia Link\"\"\"\n if place is None:\n place = self.recent_answer\n wikipedia.set_lang(\"ja\")\n p = wikipedia.page(wikipedia.search(place)[0])\n return (p.title, p.summary, p.url)\n\n def showtime(self, **kwargs) -> None:\n print(\"【ゴー☆ジャスのショータイム！】\")\n print(f\"\\n- 【お題】を入力してくれよな！\\n- ランキングを{kwargs.get('n_result', 3)}件表示するぞ！\\n- 地球義ではなく、GoogleMapとWikipediaの情報を出力するぞ！\")\n print(u\"☆\" * shutil.get_terminal_size()[0]) # draw line\n while True:\n place = input(\"\\n【お題】を入力: \")\n if place in [\"終了\", \"end\", \"終わり\"]:\n break\n self.revolution(place, **kwargs)\n print(\"また遊んでくれよな！\")\n return\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(\n description='キミも、ゴー☆ジャスになろう！')\n \n parser.add_argument('-N', '--n_line', help=\"結果表示数\", default=3, type=int)\n parser.add_argument('-F', '--file', help=\"nations.csv ファイルパス\",\n default='nations.csv')\n parser.add_argument('-V', '--vowel', help=\"母音モード\", action='store_true')\n\n args = parser.parse_args()\n gorgeous = Gorgeous(fname=args.file)\n gorgeous.showtime(vowel=args.vowel, n_result=args.n_line)\n","repo_name":"atsukoba/GorgeousApp","sub_path":"app/gorgeous.py","file_name":"gorgeous.py","file_ext":"py","file_size_in_byte":7351,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"40065351764","text":"import math\nl = input()\nwhile l:\n try:\n r,x,y = map(float, l.split())\n area = math.pi*r**2\n d = math.sqrt(x**2 + y**2)\n if d > r:\n print(\"miss\")\n else:\n #are of sector - area of triangle\n #area of circle - area of segment\n tmp = r-d\n o = 2*math.acos(d/r)\n seg = 0.5*(o - math.sin(o))*(r**2)\n print(area - seg,seg)\n\n l = input() \n\n except EOFError:\n break\n","repo_name":"nigelandrewquinn/Kattis","sub_path":"halfacookie.py","file_name":"halfacookie.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24916151396","text":"#Demonstrates a simple GUI\n\nfrom tkinter import *\n\n#Base window\nroot = Tk()\n\n#Editing the window\nroot.title(\"Простейший GUI\")\nroot.geometry(\"200x100\")\n\n#Frame\napp = Frame(root)\napp.grid()\n\n#A Label inside the Frame\nlbl = Label(app, text=\"Это я!\")\nlbl.grid()\n\n#Button1 inside the Frame\nbttn1 = Button(app, text=\"Я ничего не делаю!\")\nbttn1.grid()\n\n#Button2 inside the Frame\nbttn2 = Button(app)\nbttn2.grid()\nbttn2.configure(text=\"Я тоже!\")\n\n#Button3 inside the Frame\nbttn3 = Button(app)\nbttn3.grid()\nbttn3[\"text\"] = \"И я тоже!\"\n\n\nroot.mainloop()","repo_name":"Xomer89/LearningPy","sub_path":"untitled/GUI/simpleGUI.py","file_name":"simpleGUI.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"37443747793","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom app.libs.utils import safe_int\nfrom app.exception.http_error import MultiValidationException\n\nINPUT = {\n \"X\": [10, 20, 30, 40, 50, 60, 70],\n \"Y\": [12, 21, 46, 65, 90, 111, 148]\n}\n\nclass PolinomialNewton(object):\n xinput: int\n\n def __init__(self, data: dict) -> object:\n self.xinput = safe_int(data.get(\"xinput\", 0))\n \n def prevalidate(self) -> MultiValidationException:\n error = MultiValidationException()\n\n if self.xinput == 0 or \\\n (self.xinput < 10 or self.xinput > 70):\n error.push_error(\"input\", \"Invalid input. Range input: 10-70\")\n\n return error\n\n def to_dict(self) -> dict:\n return self.__polinom_newton()\n\n def __polinom_newton(self) -> dict:\n x = INPUT[\"X\"]\n y = INPUT[\"Y\"]\n n = len(x)-1\n ST = np.zeros((n+1, n+1))\n ST[:, 0] = y\n\n for k in range(1, n+1):\n for i in range(0, n-k+1):\n ST[i, k] = round((ST[i+1, k-1] - ST[i, k-1])/(x[i+k]-x[i]), 5)\n\n p = ST[0,0]\n for i in range(1, n+1):\n a = ST[0, i]\n\n for k in range(0, i):\n a = a * (self.xinput-x[k])\n\n p = p + a\n \n return {\n \"calculate\": ST.tolist(),\n \"result\": p,\n }\n","repo_name":"agprsty-utdi/metode-numerik-app","sub_path":"domain/polinomial_newton.py","file_name":"polinomial_newton.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16813782546","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function, absolute_import, division\n\nimport os\nimport sys\nimport time\nfrom pprint import pprint\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim\nimport torch.backends.cudnn as cudnn\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\n\nfrom opt import Options\n\nfrom model import LinearModel, weight_init\nfrom train import DatasetTrain, DatasetTest\nimport util\nimport log\n\n\n\ndef main(opt):\n start_epoch = 0\n err_best = 1000\n glob_step = 0\n lr_now = opt.lr\n\n # save options\n\n # create model\n print(\">>> creating model\")\n model = LinearModel()\n model = model.cuda()\n model.apply(weight_init)\n print(\">>> total params: {:.2f}M\".format(sum(p.numel() for p in model.parameters()) / 1000000.0))\n criterion = nn.MSELoss(size_average=True).cuda()\n optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)\n\n # load ckpt\n if opt.load:\n print(\">>> loading ckpt from '{}'\".format(opt.load))\n # import pdb; pdb.set_trace()\n ckpt = torch.load(opt.load)\n start_epoch = ckpt['epoch']\n err_best = ckpt['err']\n glob_step = ckpt['step']\n lr_now = ckpt['lr']\n model.load_state_dict(ckpt['state_dict'])\n optimizer.load_state_dict(ckpt['optimizer'])\n print(\">>> ckpt loaded (epoch: {} | err: {})\".format(start_epoch, err_best))\n \n\n # list of action(s)\n \n\n # data loading\n # test\n if opt.test:\n \n test_loader = DataLoader(DatasetTest('test.npy','label.npy'), batch_size =128,drop_last=False)\n \n hh=test(test_loader, model, criterion)\n \n print (\">>>>>> TEST results:\")\n \n sys.exit()\n\n # load dadasets for training\n test_loader = DataLoader(DatasetTest('train.npy','label.npy'), batch_size=128,drop_last=False)\n train_loader = DataLoader(DatasetTrain('train.npy','label.npy'), batch_size=128,drop_last=False, shuffle=True)\n print(\">>> data loaded !\")\n\n cudnn.benchmark = True\n for epoch in range(start_epoch, opt.epochs):\n print('==========================')\n print('>>> epoch: {} | lr: {:.5f}'.format(epoch + 1, lr_now))\n\n # per epoch\n glob_step, lr_now, loss_train = train(\n train_loader, model, criterion, optimizer,\n lr_init=opt.lr, lr_now=lr_now, glob_step=glob_step, lr_decay=opt.lr_decay, gamma=opt.lr_gamma,\n max_norm=opt.max_norm)\n loss_test = test(test_loader, model, criterion)\n\n # update log file\n \n\n # save ckpt\n is_best = loss_test < err_best\n err_best = min(loss_test, err_best)\n if is_best:\n \tlog.save_ckpt({'epoch': epoch + 1,\n 'lr': lr_now,\n 'step': glob_step,\n 'err': err_best,\n 'state_dict': model.state_dict(),\n 'optimizer': optimizer.state_dict()},\n ckpt_path=opt.ckpt,\n is_best=True)\n \n\n \n\n\ndef train(train_loader, model, criterion, optimizer,\n lr_init=None, lr_now=None, glob_step=None, lr_decay=None, gamma=None,\n max_norm=True):\n losses = util.AverageMeter()\n\n model.train()\n\n start = time.time()\n batch_time = 0\n \n for i, (inps, tars) in enumerate(train_loader):\n glob_step += 1\n if glob_step % lr_decay == 0 or glob_step == 1:\n lr_now = util.lr_decay(optimizer, glob_step, lr_init, lr_decay, gamma)\n inputs = Variable(inps.cuda())\n targets = Variable(tars.cuda())\n\n outputs = model(inputs)\n\n # calculate loss\n optimizer.zero_grad()\n loss = criterion(outputs, targets)\n losses.update(loss.item(), inputs.size(0))\n loss.backward()\n if max_norm:\n nn.utils.clip_grad_norm(model.parameters(), max_norm=1)\n optimizer.step()\n print (\">>> train error: {} <<<\".format(losses.avg))\n\n # update summary\n\n \n return glob_step, lr_now, losses.avg\n\n\ndef test(test_loader, model, criterion):\n losses = util.AverageMeter()\n\n model.eval()\n\n all_dist = []\n start = time.time()\n batch_time = 0\n results = list()\n for i, (inps, tars) in enumerate(test_loader):\n inputs = Variable(inps.cuda())\n targets = Variable(tars.cuda())\n\n \n outputs = model(inputs)\n results.append(outputs)\n\n # calculate loss\n outputs_coord = outputs\n loss = criterion(outputs_coord, targets)\n\n losses.update(loss.item(), inputs.size(0))\n\n final=torch.cat(results,dim=0)\n final=1000000*final.detach().cpu().numpy()[:,0]\n results_txt = open(\"results.txt\", \"w+\")\n for i in range(final.shape[0]):\n \tresults_txt.write(str(final[i]) + '\\n')\n # update summary\n \n print (\">>> error: {} <<<\".format(losses.avg))\n return losses.avg\n\n\nif __name__ == \"__main__\":\n option = Options().parse()\n main(option)\n\n","repo_name":"WendyBaiYunwei/CS5228-project","sub_path":"task1/nn_approach/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2278701279","text":"import matplotlib\nmatplotlib.use('agg')\nimport numpy as np\nimport pickle\nimport numpy\nfrom astropy.io import fits\nfrom galpy.util import bovy_conversion, bovy_coords, save_pickles, bovy_plot\nfrom galpy.potential import MWPotential2014, turn_physical_off, vcirc\nimport astropy.units as u\nfrom galpy.orbit import Orbit\nimport random\nimport pal5_util_MWfit\nimport MWPotential2014Likelihood\nimport os, os.path\nimport re\nimport glob\nimport pickle\nimport csv\nfrom optparse import OptionParser\n_REFR0, _REFV0= MWPotential2014Likelihood._REFR0, MWPotential2014Likelihood._REFV0\nro, vo= _REFR0, _REFV0\n\n\ndef get_options():\n usage = \"usage: %prog [options]\"\n parser = OptionParser(usage=usage)\n \n parser.add_option(\"--ind\",dest='ind',default=None,\n type='int',\n help=\"index of potential\")\n return parser\n\n\ndef determine_nburn(filename='../pal5_mcmc/mwpot14-fitsigma-0.dat',\n threshold=0.1,skip=50,\n return_nsamples=False):\n \"\"\"Function to detemrine an appropriate nburn for a given chain\"\"\"\n # Load the data\n data= numpy.loadtxt(filename,comments='#',delimiter=',')\n lndata= numpy.reshape(data[:,-1],(len(data[:,5])//nwalkers,nwalkers))\n # Perform a running diff wrt skip less\n diff= (lndata-numpy.roll(lndata,skip,axis=0))\n diff[:skip]= -100. # Make sure it's not within the first hundred\n maxln= numpy.nanmax(lndata)\n try:\n indx= (numpy.fabs(numpy.median(diff,axis=1)) < threshold)\\\n *((maxln-numpy.nanmax(lndata,axis=1)) < 1.25)\n if maxln > -22.5:\n indx*= numpy.std(lndata,axis=1) < 3.\n if return_nsamples:\n return len(data)-numpy.arange(len(lndata))[indx][0]*nwalkers\n else:\n return numpy.arange(len(lndata))[indx][0]*nwalkers\n except IndexError:\n if return_nsamples: return 100.\n else: return numpy.prod(lndata.shape)-100\n\n\nnwalkers= 12\n\n#from each MCMC chain file, pick nsamples\nnsamples= 2000\n\npot_ind=np.arange(0,32,1)\npot_ind=np.delete(pot_ind,14)\n\nt_age= np.linspace(0.,5.,1001)/bovy_conversion.time_in_Gyr(vo,ro)\n\nperi_all=[]\n\nparser= get_options()\noptions,args= parser.parse_args()\n\npindx=pot_ind[options.ind]\n\ncsvfo= open('pal5_mcmc_selected_chains_pot{}.dat'.format(pindx),'w')\nfowriter= csv.writer(csvfo,delimiter=',')\n\n# Load this potential\nfn= 'mwpot14-fitsigma-%i.dat' % pindx\nwith open(fn,'rb') as savefile:\n line1= savefile.readline()\npotparams= [float(s) for s in (line1.split(':'.encode())[1].split(','.encode()))]\n\ntnburn= determine_nburn(fn)\ntdata= numpy.loadtxt(fn,comments='#',delimiter=',')\ntdata= tdata[tnburn::]\n\nrand_indx=random.sample(range(len(tdata)),nsamples)\n\nperi=[]\n\nfor jj in rand_indx:\n \n tvo= tdata[jj][1]*_REFV0\n pot= MWPotential2014Likelihood.setup_potential(potparams,tdata[jj][0],False,False,\n pal5_util_MWfit._REFR0,tvo)\n\n # Now compute the stream model for this setup\n dist= tdata[jj][2]*22.\n pmra= -2.296+tdata[jj][3]+tdata[jj][4]\n pmdecpar= 2.257/2.296\n pmdecperp= -2.296/2.257\n pmdec= -2.257+tdata[jj][3]*pmdecpar+tdata[jj][4]*pmdecperp\n vlos= -58.7\n sigv= 0.4*numpy.exp(tdata[jj][5])\n \n \n prog= Orbit([229.018,-0.124,dist,pmra,pmdec,vlos],\n radec=True,ro=ro,vo=tvo,\n solarmotion=[-11.1,24.,7.25]).flip()\n \n prog.integrate(t_age,pot)\n peri=prog.rperi()\n \n out=[peri,tdata[jj][3],tdata[jj][0],tdata[jj][1],sigv]\n out.extend([229.018,-0.124,dist,pmra,pmdec,vlos])\n \n fowriter.writerow(out)\n \ncsvfo.flush()\ncsvfo.close()\n","repo_name":"nbanik/Baryonic-effects-on-Pal5","sub_path":"select_mcmc_chains.py","file_name":"select_mcmc_chains.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"2763084199","text":"from fastapi import FastAPI as FA\nfrom fastapi.logger import logger\nfrom aioredis import create_redis_pool, Redis\n\nfrom .auth.routes import auth_router, user_router\nfrom .budget.routes import budget_router, transactions_router\n\n\nclass FastAPI(FA):\n def __init__(self) -> None:\n super().__init__()\n self.redis: Redis\n\n\napp = FastAPI()\n\n\n@app.on_event('startup')\nasync def on_start():\n logger.info('App init')\n logger.info('Connecting to redis database ... ')\n redis = await create_redis_pool('redis://redis', db=3)\n app.redis = redis\n\n\n@app.on_event('shutdown')\nasync def on_shutdown():\n app.redis.close()\n await app.redis.wait_closed()\n\n\napp.include_router(auth_router)\napp.include_router(user_router)\napp.include_router(budget_router)\napp.include_router(transactions_router)\n","repo_name":"bensondomingo/budget-backend","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36396016914","text":"import os\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'CHANGES.rst')).read()\n\nrequires = [\n 'pyramid>=1.4',\n 'PyBrowserID',\n 'requests>=1.0',\n 'MarkupSafe',\n ]\n\nsetup(name='pyramid_persona',\n version='1.6.1',\n description='pyramid_persona',\n long_description=README + '\\n\\n' + CHANGES,\n classifiers=[\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 2\",\n \"Programming Language :: Python :: 3\",\n \"Framework :: Pyramid\",\n \"Topic :: Internet :: WWW/HTTP\",\n ],\n author='Georges Dubus',\n author_email='georges.dubus@gmail.com',\n url='https://github.com/madjar/pyramid_persona',\n keywords='web pyramid pylons authentication persona',\n packages=find_packages(),\n include_package_data=True,\n zip_safe=False,\n install_requires=requires,\n tests_require=requires,\n test_suite=\"pyramid_persona\",\n )\n","repo_name":"madjar/pyramid_persona","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"82"} +{"seq_id":"38799631444","text":"from django.contrib import admin\nfrom django.urls import path,include\nfrom blog import views\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('register/',views.register,name = 'register'),\n path('login/', views.login, name='login'),\n path('blog/',include('blog.urls')),\n path('home/', views.home_unlog,name=\"home_unlog\"),\n path('home/unlog',views.log_out,name=\"log_out\"),\n path('summernote/', include('django_summernote.urls')),\n path('jet/', include('jet.urls', 'jet')),\n path('jet/dashboard/', include('jet.dashboard.urls', 'jet-dashboard')),\n]","repo_name":"githubfqy/EasyDown","sub_path":"EasyDown/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29673402947","text":"import time\nimport sys\n\ndef timecount(fu):\n def counttime():\n fu()\n t=time.process_time()\n print(t)\n return counttime\n\n@timecount\n\ndef test():\n print('hello')\n time.sleep(1)\n \ntest()\n\ndef fibnq(n):\n a,b,count=0,1,0\n while count \"\n__source = \"https://github.com/pgdr/benchmcmc\"\n__webpage = __source\n__description = \"Use MCMC to do benchmark analysis\"\n\n\ndef _src(x):\n root = os.path.dirname(__file__)\n return os.path.abspath(os.path.join(root, x))\n\n\ndef _read_file(fname, op):\n with open(_src(fname), \"r\") as fin:\n return op(fin.readlines())\n\n\ndef readme():\n try:\n return _read_file(\"README.md\", lambda lines: \"\".join(lines))\n except Exception:\n return __description\n\n\nsetuptools.setup(\n name=\"benchmcmc\",\n version=\"0.0.7\",\n packages=[\"benchmcmc\"],\n description=__description,\n long_description=readme(),\n long_description_content_type=\"text/markdown\",\n author=\"PG Drange\",\n author_email=\"pgdr@equinor.com\",\n maintainer=__pgdr,\n url=__webpage,\n project_urls={\n \"Bug Tracker\": \"{}/issues\".format(__source),\n \"Documentation\": \"{}/blob/master/README.md\".format(__source),\n \"Source Code\": __source,\n },\n license=\"MIT\",\n keywords=\"mcmc, bayesian methods, statistics, benchmark analysis, disaster modeling, unix, command line tool\",\n install_requires=[\"matplotlib\", \"pymc3\"],\n entry_points={\"console_scripts\": [\"benchmcmc=benchmcmc:main\"]},\n)\n","repo_name":"pgdr/benchmcmc","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"13784482084","text":"from htd_utilities import *\nfrom htd_collaterals import *\nfrom hpl_clocks import *\nfrom htd_hpl_itpp_interface import *\nfrom htd_hpl_signal_manager import *\nfrom htd_hpl_sbftload_manager import *\nfrom hpl_tap_spf_api import *\nfrom hpl_tap_stpl_api import *\nfrom hpl_tap_dfx_api import *\nfrom htd_hpl_interactive_socket_interface import *\nfrom htd_hpl_spf_interface import *\nfrom htd_hpl_xdp_interface import *\nfrom htd_history_manager import *\n# -------------------------------------------------------------------------------------------------------\n# This class is used as a data container for passing arguments from TE Tap action toward HPL Tap manager\n# -------------------------------------------------------------------------------------------------------\n\n\nclass htd_tap_params(object):\n def __init__(self):\n self.ircode = -1\n self.irname = \"\"\n self.agent = \"\"\n self.read_modify_write = 0\n self.bfm_mode = \"normal\"\n self.check = 1\n self.refclock = \"\"\n self.read_type = 0\n self.waitcycles = -1\n self.eptarget = \"\"\n self.dr = htd_argument_containter() # data container storing fields and dri/dro assignment\n # arguments.get_argument(\"VIK\"): -> [ \"VIK[0:2]=2\",\"VIK[5:7]=7\"..... ]\n\n\n# --------------------------------------------------------------------\n# This class is used as an isolation interface between HPL and TE\n# --------------------------------------------------------------------\nclass htd_player_ui(object):\n\n def __init__(self):\n htdte_logger.callBack_for_extensions = self.add_comment\n self.current_action = None\n self.silent_mode = False\n self.interactive_mode = False\n self.labels_history_table_name = \"HPL_Labels\"\n self.current_ratio = 1\n self.default_ratio = 1\n self.ratio_clk = \"\"\n self.scan_memory_in_progress = 0\n # --------Clock managment\n # Take it from TE_cfg.....self.hpl_to_dut_interface\n clock_class_name = (\"hpl_%s_clocks\") % (\"interactive\" if (self.interactive_mode) else \"non_interactive\")\n # --------------------------------\n if(os.environ.get('HTD_TE_HELP_MODE') == \"1\"):\n self.hplClockMgr = eval(clock_class_name)(CFG, self)\n return # No low level object initilization in HELP arguments mode\n # -----------------------------\n if(\"HPL\" not in list(CFG.keys())):\n htdte_logger.error(\"Missing HPL configuration category in global CFG structure . \")\n if(\"execution_mode\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error(\"Missing \\\"execution_mode\\\" configuration entry in CFG[HPL] structure . \")\n # -------Interfaces--------\n self.hpl_to_dut_interface = None\n if(CFG[\"HPL\"][\"execution_mode\"] == \"itpp\"):\n self.hpl_to_dut_interface = hpl_itpp_interface(self.get_interface_file_name(), self)\n self.interactive_mode = False\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"interactive_socket\"):\n self.hpl_to_dut_interface = hpl_interactive_socket_interface(self)\n self.interactive_mode = True\n htdte_logger.setErrHndlrInterface(self.hpl_to_dut_interface)\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"spf\"):\n self.hpl_to_dut_interface = hpl_spf_interface(self.get_interface_file_name(), self)\n elif(CFG[\"HPL\"][\"execution_mode\"] == \"xdp\"):\n self.hpl_to_dut_interface = hpl_xdp_interface(self.get_interface_file_name(), self)\n else:\n htdte_logger.error((\"Not supported execution mode value -\\\"%s\\\" found in CFG[HPL][execution_mode]. Expected modes are: itpp\") % (CFG[\"HPL\"][\"execution_mode\"]))\n # ---------Plsyer BFM manager----\n self.hplSignalMgr = eval((\"hpl_SignalManager_%s\") % ((\"interactive\" if (self.interactive_mode) else \"non_interactive\")))(self.hpl_to_dut_interface, self)\n # ----------------------------------------\n self.hplSbftLoadMgr = eval(\"hpl_SbftLoadManager\")(self.hpl_to_dut_interface, self)\n # ----------------------------------------\n if(\"tap_api_selector\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error(\"Missing TAP API selector (expected in CFG[HPL][tap_api_selector]. \")\n self.hpl_tap_api = eval(cfg_HPL(\"tap_api_selector\"))()\n # -----------------------\n clock_class_name = (\"hpl_%s_clocks\") % (\"interactive\" if (self.interactive_mode) else \"non_interactive\")\n self.hplClockMgr = eval(clock_class_name)(CFG, self)\n # --Simoptimization\n if(\"signal_wait_mode\" not in list(CFG[\"HPL\"].keys())):\n htdte_logger.error((\" Missing obligatory CFG[\\\"HPL\\\"][\\\"signal_wait_mode\\\"] definition in TE_cfg.xml (Valid values are:sim_time or silicon).... \"))\n if(CFG[\"HPL\"][\"signal_wait_mode\"] not in [\"sim_time\", \"silicon\"]):\n htdte_logger.error((\" Invalid CFG[\\\"HPL\\\"][\\\"signal_wait_mode\\\"]=\\\"%s\\\" definition in TE_cfg.xml (Valid values are:sim_time or silicon).... \") % (CFG[\"HPL\"][\"signal_wait_mode\"]))\n self.signal_wait_mode = CFG[\"HPL\"][\"signal_wait_mode\"]\n # --------------------\n # Sync Enable\n if \"sync_enabled\" in CFG[\"HPL\"]:\n self.sync_enabled = CFG[\"HPL\"][\"sync_enabled\"]\n htdte_logger.inform(\"HPL Sync: %d\" % (self.sync_enabled))\n else:\n self.sync_enabled = 1\n htdte_logger.inform(\"HPL Sync Enabled by default\")\n htdte_logger.set_message_signal = self.set_message_signal\n # --------------------------------------------------\n\n def get_interface_file_name(self):\n mode = CFG[\"HPL\"][\"execution_mode\"]\n if(mode == \"itpp\"):\n return \"htd_test_stimulus.itpp\" if(\"ItppOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"ItppOutputFileName\")\n elif(mode == \"spf\"):\n return \"htd_test_stimulus.spf\" if(\"SpfOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"SpfOutputFileName\")\n elif(mode == \"xdp\"):\n return \"htd_test_stimulus.py\" if(\"XdpOutputFileName\" not in list(CFG[\"HPL\"].keys())) else cfg_HPL(\"XdpOutputFileName\")\n else:\n htdte_logger.error((\"Not supported execution mode value -\\\"%s\\\" found in CFG[HPL][execution_mode]. Expected modes are: itpp\") % (CFG[\"HPL\"][\"execution_mode\"]))\n\n def get_indexed_label(self, label, agent_filter=\"\"):\n if(not htd_history_mgr.parametric_has(self.labels_history_table_name, [label + agent_filter])):\n htd_history_mgr.parametric_capture(self.labels_history_table_name, [label + agent_filter], 0, \"HPL_ui\")\n return label\n else:\n indx = htd_history_mgr.parametric_get(self.labels_history_table_name, [label + agent_filter]) + 1\n htd_history_mgr.parametric_capture(self.labels_history_table_name, [label + agent_filter], indx, \"HPL_ui\")\n #htdte_logger.inform(\"current %s index is %d filter %s\" %(label, indx, agent_filter))\n return (\"%s_%d\") % (label, indx)\n\n def set_current_action(self, actionObj):\n self.current_action = actionObj\n\n def get_current_action(self): return self.current_action\n\n def close(self):\n self.hpl_to_dut_interface.close()\n\n def set_silent_mode(self):\n self.hpl_to_dut_interface.set_silent_mode()\n self.silent_mode = True\n\n def unset_silent_mode(self):\n self.hpl_to_dut_interface.unset_silent_mode()\n self.silent_mode = False\n # -------------------------------\n # Create HTML formatted help file\n # -------------------------------\n\n def create_hpl_help(self, file_name):\n html_file = open(file_name, 'w')\n # -----The short help is printed to screen , detailed help in html------------\n # --Create a bookmarks links for html\n html_file.write(\"\\n\\n\")\n html_file.write('\\n')\n html_file.write('

HTD Player (Output Interface) Help:

\\n')\n # --------------\n util_get_methods_prototypes_of_class(self.__class__.__name__).print_html(html_file, HelpListStreamEnum_all)\n html_file.close()\n # -------------------------------------------------------------------------------------------------------------------------\n # --To be used for comments printout to transactor : ITPP file or SIM/EMU or pattern comment to make a flow clarification\n # -------------------------------------------------------------------------------------------------------------------------\n\n def add_comment(self, line):\n self.hpl_to_dut_interface.add_comment(line)\n # -------------------------------------------------------------\n # Passing pattern information through Simulation,EMU or DP\n # -------------------------------------------------------------\n\n def set_pattern_info(self, message):\n self.hpl_to_dut_interface.set_pattern_info(message)\n # ---------------------------------------------------------\n # ---TAP CallBacks\n # --------------------------------------------------------\n # def tap_send_cmd(self,tap_obj): # todo vik close interface with (this is the entry point fomr htd_te end)\n # return self.hpl_tap.send_cmd(tap_obj)\n\n def get_ir_opcode_int(self, cmd, agent):\n return HTD_INFO.tap_info.get_ir_opcode_int(cmd, agent)\n\n def get_ir_name(self, ircode, agent):\n # return self.hpl_tap.api.get_ir_name(ircode,agent)\n return HTD_INFO.tap_info.get_ir_name(ircode, agent)\n\n def get_tapreg_fields(self, cmd, agent, eptarget):\n # return self.hpl_tap.api.get_ir_fields(cmd,agent)\n return HTD_INFO.tap_info.get_ir_fields(cmd, agent, eptarget)\n\n def tap_send_cmd(self, tap_params): # /nfs/iil/proj/mpgbd/vbhutani/CNL/HTD_TE/repo_latest/tools//htd_hpl/bin/htd_player_ui.pytodo vik close interface with (this is the entry point fomr htd_te end)\n return self.hpl_tap.send_cmd(tap_params)\n\n def verify_tap_eptarget(self, agent, eptarget):\n return self.hpl_tap.verify_tap_eptarget(agent, eptarget)\n\n def rtl_node_exists(self, cmd, agent, field):\n # return self.hpl_tap.api.rtl_node_exists(cmd,agent,field)\n return HTD_INFO.tap_info.rtl_node_exists(cmd, agent, field)\n # ---------------------------------------------------------\n # ---Signal CallBacks\n # ---------------------------------------------------------\n\n def is_intractive_simulation(self):\n return self.hplSignalMgr.is_interactive_mode()\n\n def get_full_signal_path(self, signal, lsb=-1, msb=-1, selector=\"\"):\n return HTD_INFO.signal_info.extract_full_signal_path(signal, lsb, msb, selector)\n\n def signal_module_exists(self, search_signal_or_module):\n return HTD_INFO.signal_info.signal_module_exists(search_signal_or_module)\n\n # ------------------------------------------------------------------------------------------------------------------------------------\n def wait_clock_num(self, cycles, clock=\"none\"):\n if (cycles == 0):\n return\n self.hpl_to_dut_interface.wait_clock_num(cycles, clock)\n # ---SYNC API\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def wait_clock_edge(self, clock, edge):\n supported_edges = [\"ar\", \"br\", \"af\", \"bf\"]\n if(edge not in supported_edges):\n htdte_logger.error((\"Not supported edge value received: \\\"%s\\\" - supported:% \") % (edge, supported_edges))\n delay = self.hplClockMgr.get_clock_edge_delay(clock_name, edge) # return a delay for a requested edge\n if(delay):\n self.hpl_to_dut_interface.wait_clock_num(delay, clock)\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def sync_to_clock_modulo(self, clock, modulo):\n # TODO Vik to fix in HPL_Clock colck Modulo API.\n # Make a delay until the target clock modulo constrain\n # moduloPatVecClock=self.hplClockMgr.clock_transpose(clock,modulo,CFG[\"HPL\"][\"PatVecClock\"])\n # self.hplClockMgr.wait_clock_modulo(clock,modulo) #Get the number of Pattern vector clock (\"bclks\") until the target clock will be modulo , Example core clock 1:22, requirement modulo 8 -> 3 bclk\n self.hpl_to_dut_interface.wait_clock_modulo(clock, modulo)\n\n #-------------- ratio commands----------------#\n def set_ratio(self, ratio, clock):\n if (self.ratio_clk != \"\" and self.ratio_clk != clock):\n htdte_logger.inform(\"ratio was set on clock %s, can't modify it to other clock %s\" % (self.ratio_clk, clock))\n if (ratio != self.current_ratio):\n self.tap_expandata(clock, ratio)\n self.current_ratio = ratio\n self.ratio_clk = clock\n\n def restore_ratio(self):\n if (self.ratio_clk == \"\"):\n htdte_logger.error(\"ratio clock was not set, can't restore properly\")\n\n if (self.current_ratio != self.default_ratio):\n self.tap_expandata(self.ratio_clk, self.default_ratio)\n self.current_ratio = self.default_ratio\n\n # ------------------------------------------------------------------------------------------------------------------------------------\n def write_itpp_cmd(self, cmd):\n self.hpl_to_dut_interface.write_itpp_cmd(cmd)\n\n def start_scan_memory(self):\n if(\"scan_group\" not in list(CFG[\"HPL\"].keys()) or CFG[\"HPL\"][\"scan_group\"] != \"\"):\n htdte_logger.inform(\"Trying to use start_scan command while the scan group is not defined in CFG[\\\"HPL\\\"][\\\"scan_group\\\"]\")\n if(self.scan_memory_in_progress):\n htdte_logger.inform(\"Trying to use start_scan command during active scan mode - (already has been called without stop_scan)\")\n self.write_itpp_cmd((\"start_scan: %s;\\n\") % (CFG[\"HPL\"][\"scan_group\"]))\n self.scan_memory_in_progress = 1\n\n def stop_scan_memory(self):\n if(\"scan_group\" not in list(CFG[\"HPL\"].keys()) or CFG[\"HPL\"][\"scan_group\"] != \"\"):\n htdte_logger.inform(\"Trying to use stop_scan command while the scan group is not defined in CFG[\\\"HPL\\\"][\\\"scan_group\\\"]\")\n if(self.scan_memory_in_progress == 0):\n htdte_logger.inform(\"Trying to call stop_scan cmd , while not started previously.\")\n self.write_itpp_cmd((\"stop_scan: %s;\\n\") % (CFG[\"HPL\"][\"scan_group\"]))\n self.scan_memory_in_progress = 0\n # ------------------------------------------------------------------------------------------------------------------------------------\n\n def set_message_signal(self, message_val):\n if(\"hvm_flow_tracking_signal\" in list(CFG[\"HPL\"].keys()) and CFG[\"HPL\"][\"hvm_flow_tracking_signal\"] != \"\"):\n for i in range(0, 16):\n val = util_get_int_sub_range(i * 32, (i + 1) * 32 - 1, message_val)\n self.hplSignalMgr.signal_set(CFG[\"HPL\"][\"hvm_flow_tracking_signal\"], i * 32, (i + 1) * 32 - 1, val)\n # --------------------\n\n def tap_compression_on(self): self.hpl_to_dut_interface.tap_compression_on()\n\n def tap_compression_off(self): self.hpl_to_dut_interface.tap_compression_off()\n\n def tap_expandata(self, clock, value):\n self.write_itpp_cmd(\"expandata: %s,%d;\" % (clock, value))\n self.write_itpp_cmd(\"delay: %s(%d);\" % (self.hplClockMgr.get_clock_rtl_path(CFG[\"HPL\"][\"PatVecClock\"]), 10))\n","repo_name":"mattpacey/pacman_core","sub_path":"tools/htd_hpl/bin/htd_player_ui.py","file_name":"htd_player_ui.py","file_ext":"py","file_size_in_byte":15425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3913325421","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport rospy\n\nfrom sensor_msgs.msg import JointState\n\nfrom catchrobo_manager.servo import Servo, Laser\n\n\n\nclass GripperID:\n NEAR = 0\n FAR = 1\n\nclass GripWay:\n LONG_GRIP = 0\n SMALL_GRIP = 1\n\n \n MAX = 2\n\nclass GripperManager():\n def __init__(self, color):\n self._grippers = [Servo(\"gripper1\"), Servo(\"gripper2\")]\n self._lasers = [Laser(\"laser1\"), Laser(\"laser2\")]\n self.RELEASE_WAIT_S = 0.5\n self.GRIP_WAIT_S = 0.5\n\n id_map = [0,1]\n if color == \"red\":\n id_map[GripperID.NEAR] = 0\n id_map[GripperID.FAR] = 1\n if color == \"blue\":\n id_map[GripperID.NEAR] = 1\n id_map[GripperID.FAR] = 0\n self._id_map = id_map\n\n self._grip_dist = rospy.get_param(\"grip_dist\")\n\n\n self._grip_status = [GripWay.MAX, GripWay.MAX]\n self.releaseBisco(0)\n self.releaseBisco(1)\n\n def getGripDist(self, target_gripper, grip_way):\n if grip_way == GripWay.LONG_GRIP:\n ret = self._grip_dist[\"long\"][target_gripper]\n else:\n ret = self._grip_dist[\"small\"][target_gripper]\n return ret\n\n def laser(self, laser_on):\n self._lasers[0].output(laser_on)\n self._lasers[1].output(laser_on)\n\n def graspBisco(self, target_gripper, grip_way,wait):\n target_gripper_id = self._id_map[target_gripper]\n dist = self.getGripDist(target_gripper, grip_way)\n\n self._grip_status[target_gripper] = grip_way\n\n if wait is True:\n wait_s = self.GRIP_WAIT_S\n else:\n wait_s = 0\n self._grippers[target_gripper_id].move(dist,wait_s)\n temp = \"gripper {}: {} cm\".format(target_gripper_id, dist)\n rospy.loginfo(temp)\n\n\n def releaseBisco(self, target_gripper):\n target_gripper_id = self._id_map[target_gripper]\n dist = self._grip_dist[\"max\"]\n self._grippers[target_gripper_id].move(dist,self.RELEASE_WAIT_S)\n \n rospy.loginfo(\"release\")\n\n","repo_name":"catchrobo2021/catchrobo_robot","sub_path":"catchrobo_manager/src/catchrobo_manager/gripper_manager.py","file_name":"gripper_manager.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"21494721484","text":"\nimport os\nimport sys\nimport time\nimport zlib\nimport logging\nlogger = logging.getLogger('data_gen')\nlogger.setLevel(logging.INFO)\n\nch = logging.StreamHandler()\nch.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nch.setFormatter(formatter)\nlogger.addHandler(ch)\n\nimport numpy as np\nimport tensorflow as tf\nimport random\nimport pandas as pd\n\nfrom tensorflow.keras.utils import Sequence\n\nimport SimpleITK as sitk\nfrom skimage.transform import resize \nfrom scipy import ndimage as ndi\n\nimport albumentations as A\n\ndef seed_everything(seed=4269):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n os.environ['TF_CUDNN_DETERMINISTIC'] = '1' # new flag present in tf 2.0+\n np.random.seed(seed)\n tf.random.set_seed(seed)\nseed_everything()\n\n \n# https://gist.github.com/mrajchl/ccbd5ed12eb68e0c1afc5da116af614a\ndef resample_img(itk_image, out_spacing=[2.0, 2.0, 2.0], is_label=False):\n \n # Resample images to 2mm spacing with SimpleITK\n original_spacing = itk_image.GetSpacing()\n original_size = itk_image.GetSize()\n\n out_size = [\n int(np.round(original_size[0] * (original_spacing[0] / out_spacing[0]))),\n int(np.round(original_size[1] * (original_spacing[1] / out_spacing[1]))),\n int(np.round(original_size[2] * (original_spacing[2] / out_spacing[2])))]\n\n resample = sitk.ResampleImageFilter()\n resample.SetOutputSpacing(out_spacing)\n resample.SetSize(out_size)\n resample.SetOutputDirection(itk_image.GetDirection())\n resample.SetOutputOrigin(itk_image.GetOrigin())\n resample.SetTransform(sitk.Transform())\n resample.SetDefaultPixelValue(itk_image.GetPixelIDValue())\n\n if is_label:\n resample.SetInterpolator(sitk.sitkNearestNeighbor)\n else:\n resample.SetInterpolator(sitk.sitkBSpline)\n\n return resample.Execute(itk_image)\n\ndef read_image(row): # responsible for reading, resampling, scaling intensity to (-1,1)\n \n file_path = row.file_path\n if row.dataset == 'ped-ct-seg':\n \n spacing=(2.0,2.0,2.0)\n\n reader= sitk.ImageFileReader()\n reader.SetFileName(file_path)\n img_obj = reader.Execute() \n img_obj = resample_img(img_obj, out_spacing=spacing, is_label=False)\n\n spacing = img_obj.GetSpacing()\n origin = img_obj.GetOrigin()\n size = img_obj.GetSize()\n direction = img_obj.GetDirection()\n\n img = sitk.GetArrayFromImage(img_obj)\n\n logger.debug(f'{origin},{direction}')\n logger.debug(f'{spacing},{size}')\n logger.debug(f'img.shape {img.shape}')\n\n MIN_VAL,MAX_VAL = -1000,1000\n img = img.astype(np.float16)\n img = ((img-MIN_VAL)/(MAX_VAL-MIN_VAL))\n img = (img-0.5)*2\n img = img.clip(-1,1)\n\n img = np.expand_dims(img,axis=-1)\n\n elif row.dataset == 'brats19':\n\n subject_id = os.path.basename(row.file_path)\n flair_path = os.path.join(row.file_path,f'{subject_id}_flair.nii.gz')\n t1_path = os.path.join(row.file_path,f'{subject_id}_t1.nii.gz')\n t1ce_path = os.path.join(row.file_path,f'{subject_id}_t1ce.nii.gz')\n t2_path = os.path.join(row.file_path,f'{subject_id}_t2.nii.gz')\n\n x_list = []\n spacing=(1,1,1)\n for file_path in [flair_path,t1_path,t2_path]:\n\n reader= sitk.ImageFileReader()\n reader.SetFileName(file_path)\n img_obj = reader.Execute() \n img_obj = resample_img(img_obj, out_spacing=spacing, is_label=False)\n\n spacing = img_obj.GetSpacing()\n origin = img_obj.GetOrigin()\n size = img_obj.GetSize()\n direction = img_obj.GetDirection()\n\n x = sitk.GetArrayFromImage(img_obj)\n\n logger.debug(f'{origin},{direction}')\n logger.debug(f'{spacing},{size}')\n logger.debug(f'x.shape {x.shape}')\n\n mu = np.mean(x[x>0])\n sd = np.std(x[x>0])\n x = (x-mu)/(3*sd)\n x = x.clip(-1,1)\n x_list.append(x)\n\n img = np.array(x_list)\n img = np.moveaxis(img, 0, -1)\n\n else:\n raise NotImplementedError()\n\n return img\n\nMIN_VAL = -1\naug_pipeline = A.Compose([\n A.ShiftScaleRotate(value=MIN_VAL,border_mode=0),\n])\n\nFILL_VAL = 0\ncutout_aug_pipeline = A.Compose([\n A.Cutout(p=0.5, num_holes=1,\n max_h_size=120, max_w_size=120, fill_value=FILL_VAL),\n])\n\ndef augment_2d(img,min_val):\n \n img = img.squeeze()\n\n assert(min_val==MIN_VAL)\n\n augmented = aug_pipeline(\n image=img,\n )\n img = augmented['image']\n\n cut_augmented = cutout_aug_pipeline(\n image=img,\n )\n aug_img = cut_augmented['image']\n\n img = np.expand_dims(img,axis=0)\n aug_img = np.expand_dims(aug_img,axis=0)\n\n return img,aug_img\n\ndef augment_3d(img,min_val):\n \n mydim = [6,8,8] # random rectangle cutouts\n np.random.shuffle(mydim)\n\n tmp = np.expand_dims(np.random.rand(*mydim),axis=-1)\n cutout = (tmp>0.9).astype(np.float) # cut out 10% of spaces.\n cutout = resize(cutout>0,img.shape,order=0,mode='edge',cval=min_val)\n\n aug_img = img.copy() # copy!!!\n aug_img[cutout==1] = min_val\n\n return img,aug_img\n\ndef augment(img,min_val):\n\n if img.shape[0]>1: # leverage albumentation if 1st dim == 1\n return augment_3d(img,min_val)\n else:\n return augment_2d(img,min_val)\n\nTHIS_DIR = os.path.dirname(os.path.abspath(__file__))\n\nclass DataGenerator(Sequence):\n def __init__(self,df,batch_size=8,shuffle=False,augment=False,output_shape=(32,128,128,1)):\n \n self.df = df.copy().reset_index() \n self.indices = np.arange(len(self.df))\n\n self.min_val = -1\n self.batch_size = batch_size\n self.shuffle = shuffle\n self.augment = augment\n self.output_shape = output_shape\n \n\n def on_epoch_end(self):\n if self.shuffle:\n np.random.shuffle(self.indices)\n\n def dataread(self, row):\n\n img = read_image(row)\n if self.output_shape: \n # orignal image shape\n i0,i1,i2,_ = img.shape\n\n # target image shape\n o0,o1,o2,_ = self.output_shape\n\n # we pad some values \n diff = np.array([i0-o0,i1-o1,i2-o2,0])\n if any(diff<0):\n padding = [(0,0) if x>=0 else (np.abs(x),np.abs(x)) for x in diff]\n img = np.pad(img,padding,'constant',constant_values=(self.min_val,self.min_val))\n \n i0,i1,i2,_ = img.shape\n\n # starting coordinate\n if i0-o0 == 0:\n s0 = 0\n else:\n s0 = random.choice(list(range(i0-o0))) \n if i1-o1 == 0:\n s1 = 0\n else:\n s1 = random.choice(list(range(i1-o1)))\n if i2-o2 == 0:\n s2 = 0\n else:\n s2 = random.choice(list(range(i2-o2)))\n\n img = img[s0:s0+o0,s1:s1+o1,s2:s2+o2,:]\n\n if self.augment:\n img, aug_img = augment(img,self.min_val)\n else:\n aug_img = img.copy()\n \n logger.debug(f'{img.shape},{aug_img.shape}')\n\n return img,aug_img\n\n def __len__(self):\n return int(np.floor(len(self.indices) / float(self.batch_size)))\n\n def __getitem__(self, idx):\n inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]\n batch_rows = self.df.iloc[inds,:]\n\n x_arr = []\n cutout_x_arr = []\n for n,row in batch_rows.iterrows():\n img, cutout_img = self.dataread(row)\n x_arr.append(img)\n cutout_x_arr.append(cutout_img)\n \n return np.array(cutout_x_arr), np.array(x_arr)\n\nif __name__ == \"__main__\":\n logging.basicConfig( level=\"DEBUG\" )\n \n df = pd.read_csv(sys.argv[1])\n mygen = DataGenerator(\n df,\n batch_size=8,output_shape=(1,240,240,4),\n shuffle=True,augment=True,\n )\n mygen.on_epoch_end()\n print(len(mygen))\n for n,(x,y) in zip(range(2),mygen):\n print(n,x.shape,y.shape)\n\n'''\npython data_gen.py ped-ct-seg.csv\n'''\n","repo_name":"pangyuteng/fc-vae-gan","sub_path":"data_gen.py","file_name":"data_gen.py","file_ext":"py","file_size_in_byte":8148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17089564038","text":"import numpy as np\n\nfrom vectorbt import _typing as tp\nfrom vectorbt.utils.docs import to_doc\n\n__all__ = [\n 'RangeStatus',\n 'DrawdownStatus',\n 'drawdown_dt',\n 'range_dt'\n]\n\n__pdoc__ = {}\n\n\n# ############# Enums ############# #\n\nclass RangeStatusT(tp.NamedTuple):\n Open: int\n Closed: int\n\n\nRangeStatus = RangeStatusT(*range(2))\n\"\"\"_\"\"\"\n\n__pdoc__['RangeStatus'] = f\"\"\"Range status.\n\n```json\n{to_doc(RangeStatus)}\n```\n\"\"\"\n\n\nclass DrawdownStatusT(tp.NamedTuple):\n Active: int\n Recovered: int\n\n\nDrawdownStatus = DrawdownStatusT(*range(2))\n\"\"\"_\"\"\"\n\n__pdoc__['DrawdownStatus'] = f\"\"\"Drawdown status.\n\n```json\n{to_doc(DrawdownStatus)}\n```\n\"\"\"\n\n# ############# Records ############# #\n\nrange_dt = np.dtype([\n ('id', np.int_),\n ('col', np.int_),\n ('start_idx', np.int_),\n ('end_idx', np.int_),\n ('status', np.int_)\n], align=True)\n\"\"\"_\"\"\"\n\n__pdoc__['range_dt'] = f\"\"\"`np.dtype` of range records.\n\n```json\n{to_doc(range_dt)}\n```\n\"\"\"\n\ndrawdown_dt = np.dtype([\n ('id', np.int_),\n ('col', np.int_),\n ('peak_idx', np.int_),\n ('start_idx', np.int_),\n ('valley_idx', np.int_),\n ('end_idx', np.int_),\n ('peak_val', np.float_),\n ('valley_val', np.float_),\n ('end_val', np.float_),\n ('status', np.int_),\n], align=True)\n\"\"\"_\"\"\"\n\n__pdoc__['drawdown_dt'] = f\"\"\"`np.dtype` of drawdown records.\n\n```json\n{to_doc(drawdown_dt)}\n```\n\"\"\"\n","repo_name":"polakowo/vectorbt","sub_path":"vectorbt/generic/enums.py","file_name":"enums.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":3319,"dataset":"github-code","pt":"82"} +{"seq_id":"29162052696","text":"\"\"\"\n문제 번호 : 2941\n단계 : 문자열\n제목 : 크로아티아 알파벳\n알고리즘 : 구현 / 문자열\n\"\"\"\n\n# 문자를 split, slicing\n\n# 문자 입력\nN = input()\n\n# 변경된 문자들\nletters = ['c=','c-','dz=','d-','lj','nj','s=','z=']\n\nfor i in letters:\n N = N.replace(i,'*')\nprint(len(N))","repo_name":"Gilbert9172/Gil_code","sub_path":"백준문제풀이/4주차/210829-09.py","file_name":"210829-09.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72142242827","text":"import sqlite3\nimport os, re, sys, json\nfrom collections import OrderedDict\n\nDATABASE_FILE = 'scorelib.dat'\n\ndef parsePeople(data):\n people = []\n for person in data:\n author = OrderedDict()\n author['name'] = person[2]\n author['born'] = person[0]\n author['died'] = person[1]\n people.append(author)\n return people\n\ndef parseVoices(voice_data):\n voices = OrderedDict()\n for voice in voice_data:\n v = OrderedDict()\n v['name'] = voice[2]\n v['range'] = voice[1]\n voices.update({str(voice[0]): v})\n\n return voices\n\ndef main():\n\n #author = '%' + 'Carl Maria' + '%'\n author = '%' + sys.argv[1] + '%'\n conn = sqlite3.connect(DATABASE_FILE)\n conn.text_factory = str\n cur = conn.cursor()\n RESULT_FOR_PRINT = []\n\n cur.execute('SELECT * FROM person WHERE person.name LIKE ?',(author,))\n authors = cur.fetchall()\n\n for author in authors:\n composer_id = author[0]\n composer_name = author[3]\n print_instance = OrderedDict()\n PRINTS = []\n RESULT = OrderedDict({composer_name: PRINTS})\n\n cur.execute('SELECT * FROM score JOIN (SELECT * FROM score_author JOIN person ON score_author.composer = person.id WHERE person.id = ?) ON score.id = score', (composer_id,))\n scores_data = cur.fetchall()\n\n for score in scores_data:\n print_instance = OrderedDict()\n score_id = score[0]\n title = score[1]\n genre = score[2]\n key = score[3]\n incipit = score[4]\n composition_year = score[5]\n\n cur.execute('SELECT born,died,name FROM score_author JOIN person ON score_author.composer = person.id WHERE score = ?', (score_id,))\n composers_arr = parsePeople(cur.fetchall())\n cur.execute('SELECT * FROM edition WHERE score = ?', (score_id,))\n editions_data = cur.fetchall()\n\n for edition in editions_data:\n\n edition_id = edition[0]\n edition_name = edition[2]\n cur.execute('SELECT born,died,name FROM edition_author JOIN person ON edition_author.editor = person.id WHERE edition = ?', (edition_id,))\n editors_array = parsePeople(cur.fetchall())\n cur.execute('SELECT * FROM print WHERE edition = ?', (edition_id,))\n print_data = cur.fetchone()\n cur.execute('SELECT number, range, name FROM voice WHERE score = ?', (score_id,))\n voice_data = cur.fetchall()\n\n\n print_instance['Print Number'] = print_data[0]\n print_instance['Composer'] = composers_arr\n print_instance['Title'] = title\n print_instance['Genre'] = genre\n print_instance['Key'] = key\n print_instance['Composition Year'] = composition_year\n print_instance['Edition'] = edition_name\n print_instance['Editor'] = editors_array\n print_instance['Voices'] = parseVoices(voice_data)\n print_instance['Partiture'] = True if print_data[1] == 'Y' else False\n print_instance['Incipit'] = incipit\n PRINTS.append(print_instance)\n\n if len(print_instance) != 0:\n RESULT_FOR_PRINT.append(RESULT)\n\n if len(RESULT_FOR_PRINT) != 0:\n hotovo = {}\n for i in RESULT_FOR_PRINT:\n for k,v in i.items():\n hotovo.update({k:v})\n print(json.dumps(hotovo, indent=2, ensure_ascii=False))\n else:\n print(json.dumps({}, indent=2, ensure_ascii=False))\n\n\n\n conn.close()\n\nmain()\n","repo_name":"anticol/python_uni_project","sub_path":"04-exercise/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20860538014","text":"import random\nimport requests\nG_url = 'https://www.google.com/'\nF_url = 'https://www.facebook.com/'\nT_url = 'https://twitter.com/'\nA_url = 'https://www.amazon.com/'\nAp_url = 'https://www.apple.com/'\nurls = [G_url, F_url, T_url, A_url, Ap_url]\na = random.randint(0,4)\nres = requests.get(urls[a])\nprint(res.status_code)\nprint(res.url)\n#print(res.text)\nprint(len(res.text))\n\n#temperature in your city\ncity = input('enter your city ')\nprint(city)\ngetc = requests.get(f'https://geocoding-api.open-meteo.com/v1/search?name={city}')\ncjson = getc.json()\n#print(cjson)\nclatitude = cjson['results'][0]['latitude']\n#print(clatitude)\nclongitude = cjson['results'][0]['longitude']\n#print(clongitude)\nforecast = requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={clatitude}&longitude={clongitude}¤t_weather=true')\n#print(forecast.json())\nprint(f'Weather today in {city}')\nprint(forecast.json()['current_weather']['temperature'])\n\n","repo_name":"LilianaLukash/PythonStudy","sub_path":"requests/req.py","file_name":"req.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"35294391896","text":"import pymysql\n\n# Se abre la conexión con el servidor de BD\ndb = pymysql.connect(\"localhost\", \"root\", \"\", \"msc2019\")\n\n# Creamos un objeto tipo cursor\ncursor = db.cursor()\n\nname = \"Isis Siomara\"\nsalary = 293765.28\n\n# Definir cadena SQL\nsql = \"INSERT INTO empelado(nombre, sueldo) VALUES ('{0}', {1})\".format(name, salary)\n\nprint(sql)\n\ntry:\n cursor.execute(sql)\n db.commit()\nexcept:\n db.rollback()\n\ndb.close()","repo_name":"JuandeDiosBarajasCorona/MSC2019-PythonHackathon","sub_path":"database/Insertar.py","file_name":"Insertar.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40305980333","text":"import numpy as np\n\n\ndef linear_phase(dt, T=1):\n phase = np.linspace(0, T, T / dt)[:, np.newaxis]\n \n return phase\n\n\ndef normalized_gaussian_basis(N, Z, dt):\n mu = np.linspace(0, 1, N)[:, np.newaxis]\n sigma = np.ones((N, 1)) / N\n \n basis = np.sqrt(2 * np.pi) * (1 / sigma.T) * np.exp(-0.5 * ((Z - mu.T) / sigma.T) ** 2)\n basis = basis / np.sum(basis, 1)[:, np.newaxis]\n\n basis = basis.T\n \n return basis\n","repo_name":"PedroIldefonso/baxter_project","sub_path":"ProMPs/build/lib.linux-x86_64-2.7/promp/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2536712457","text":"from datetime import datetime, timedelta\n\n\nDAG_ID = \"batched_update\"\nSTART_DATE = datetime(2023, 5, 1)\nSLACK_USERNAME = \"Upstream Batched Update\"\nSLACK_ICON = \":database:\"\n\nDEFAULT_BATCH_SIZE = 10_000\nDAGRUN_TIMEOUT = timedelta(days=31 * 3)\nSELECT_TIMEOUT = timedelta(hours=24)\nUPDATE_TIMEOUT = timedelta(days=30 * 3) # 3 months\n\n# Task IDs used for branching operator\nGET_EXPECTED_COUNT_TASK_ID = \"get_expected_update_count\"\nCREATE_TEMP_TABLE_TASK_ID = \"select_rows_to_update\"\n\n# Timeout for an individual batch, given in seconds\nDEFAULT_UPDATE_BATCH_TIMEOUT = 60 * 60 # 1 hour\n\nTEMP_TABLE_NAME = \"{query_id}_rows_to_update\"\nCREATE_TEMP_TABLE_QUERY = \"\"\"\n CREATE TABLE {temp_table_name} AS\n SELECT ROW_NUMBER() OVER() row_id, identifier\n FROM {table_name}\n {select_query};\n \"\"\"\nCREATE_TEMP_TABLE_INDEX_QUERY = \"CREATE INDEX ON {temp_table_name}(row_id)\"\nSELECT_TEMP_TABLE_COUNT_QUERY = \"\"\"\n SELECT COUNT(*)\n FROM {temp_table_name};\n \"\"\"\nUPDATE_BATCH_QUERY = \"\"\"\n UPDATE {table_name}\n {update_query}\n WHERE identifier in (\n SELECT identifier FROM {temp_table_name}\n WHERE row_id > {batch_start} AND row_id <= {batch_end}\n FOR UPDATE SKIP LOCKED\n );\n \"\"\"\nDROP_TABLE_QUERY = \"DROP TABLE IF EXISTS {temp_table_name} CASCADE;\"\nRETURN_ROW_COUNT = lambda c: c.rowcount # noqa: E731\n","repo_name":"WordPress/openverse","sub_path":"catalog/dags/database/batched_update/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"82"} +{"seq_id":"33287692328","text":"import functools\nimport random\nimport sys\nimport time\nimport unittest\n\nimport xtimeout\n\ntry:\n import _thread\n thread_enabled = True\n del _thread\nexcept ImportError:\n thread_enabled = False\n\nif thread_enabled:\n import threading\n\ndef busy(seconds):\n if seconds == -1:\n while 1:\n pass\n end = time.time() + seconds\n while time.time() < end:\n for i in range(random.randint(1000, 3000)):\n pass\n\n\nclass TimeoutError(Exception):\n pass\n\n\nclass TestMonitor(unittest.TestCase):\n def test_loop(self):\n def on_timeout(start_time):\n raise TimeoutError\n start = time.time()\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(200, on_timeout):\n busy(-1)\n elapsed = time.time() - start\n self.assertAlmostEqual(elapsed, 0.2, delta=0.05)\n\n def test_with(self):\n def on_timout(start_time):\n nonlocal called\n called = True\n\n called = False\n with xtimeout.check_context(10, on_timout):\n busy(0.1)\n self.assertTrue(called)\n\n def test_with_nest(self):\n def on_timeout_1(start_time):\n nonlocal called1\n self.assertGreaterEqual(time.time() - start_time, 0.05)\n called1 = True\n\n def on_timeout_2(start_time):\n nonlocal called2\n nonlocal count\n count += 1\n called2 = True\n\n called1 = False\n called2 = False\n count = 0\n\n start = time.time()\n with xtimeout.check_context(50, on_timeout_1):\n start1 = time.clock()\n while time.clock() - start1 < 0.1:\n for i in range(2):\n start2 = time.clock()\n with xtimeout.check_context(10, on_timeout_2):\n busy(0.1)\n\n self.assertEqual(count, 2)\n self.assertTrue(called1)\n self.assertTrue(called2)\n\n def test_break(self):\n def on_timeout(start_time):\n raise TimeoutError\n\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(0.1)\n\n def test_decorator(self):\n def on_timeout(start_time):\n raise Exception(\"Timeout\")\n\n @xtimeout.check_time(10, on_timeout)\n def func():\n busy(1)\n\n with self.assertRaises(Exception) as context:\n func()\n self.assertEqual(context.exception.args[0], \"Timeout\")\n\n def test_trace_recover(self):\n def dummy_trace(*args):\n pass\n\n def on_timeout(start_time):\n self.assertEqual(sys.gettrace(), dummy_trace)\n raise TimeoutError\n\n old_trace = sys.gettrace()\n sys.settrace(dummy_trace)\n try:\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(-1)\n finally:\n sys.settrace(old_trace)\n\n def test_reset(self):\n def ont_time(start_time):\n raise TimeoutError\n\n count = 0\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(250, ont_time) as context:\n for i in range(1, 5):\n count = i\n # timeout on 300ms\n busy(0.1 * i)\n context.reset()\n self.assertEqual(count, 3)\n\n\n@unittest.skipIf(not thread_enabled, \"no threading\")\nclass TestMultiThread(unittest.TestCase):\n def test_child_thread(self):\n def on_timeout(start_time):\n raise TimeoutError\n\n def thfunc():\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(0.1)\n\n th = threading.Thread(target=thfunc)\n th.start()\n th.join()\n\n def test_multi_threads(self):\n def on_timeout(start_time):\n nonlocal count\n count += 1\n raise TimeoutError\n count = 0\n def thfunc():\n with self.assertRaises(TimeoutError):\n with xtimeout.check_context(50, on_timeout):\n busy(-1)\n \n ths = []\n for i in range(4):\n th = threading.Thread(target=thfunc)\n th.start()\n ths.append(th)\n for th in ths:\n th.join()\n self.assertEqual(count, 4)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"amos402/py-xtimeout","sub_path":"xtimeout/tests/test_monitor.py","file_name":"test_monitor.py","file_ext":"py","file_size_in_byte":4513,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"13131500809","text":"from django.test import TestCase, Client\nfrom http import HTTPStatus\nfrom django.urls import reverse\n\n\nfrom posts.models import Group, Post, User\n\n\nclass PostURLTests(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.user = User.objects.create_user(username='test_user')\n cls.group = Group.objects.create(\n title='Тестовая группа',\n slug='test_slug',\n description='Тестовое описание',\n )\n cls.post = Post.objects.create(\n author=cls.user,\n text='Тестовый текст',\n )\n\n def setUp(self):\n self.guest_client = Client()\n self.authorized_client = Client()\n self.authorized_client.force_login(self.user)\n\n def test_url_exists_at_desired_location(self):\n \"\"\"Страницы доступны любому пользователю.\"\"\"\n url_names = {\n reverse('posts:index'): HTTPStatus.OK,\n reverse('posts:group_list', kwargs={\n 'slug': self.group.slug\n }): HTTPStatus.OK,\n reverse('posts:profile', kwargs={\n 'username': self.user\n }): HTTPStatus.OK,\n reverse('posts:post_detail', kwargs={\n 'post_id': self.post.id\n }): HTTPStatus.OK,\n }\n for url, status in url_names.items():\n with self.subTest(url=url):\n response = self.guest_client.get(url)\n self.assertEqual(response.status_code, status)\n","repo_name":"Saggitel/hw04_tests","sub_path":"yatube/posts/tests/test_urls.py","file_name":"test_urls.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"86644734630","text":"import datetime\nimport math\n\nimport numpy as np\nimport pandas as pd\n\nclass Strategy():\n def __init__(self, sotck_data):\n self.stock_data = sotck_data\n self.strategy_list = []\n self.constraint = []\n self.call_dict = {}\n self.set_func_dict()\n self.load_strategy_data()\n \n def set_func_dict(self):\n self.call_dict = {\n 'dropdown': self.dropdown,\n 'profit': self.profit,\n 'std': self.std,\n 'ma20': self.ma20,\n 'pe': self.pe,\n 'pb': self.pb,\n 'dividend': self.dividend,\n 'than60ma': self.than60ma,\n 'than120ma': self.than120ma,\n 'than_month': self.than_month,\n 'than_volume': self.than_volume\n }\n\n def load_strategy_data(self):\n # json file loading\n # create test data\n self.strategy_list = []\n a = {\n 'name': 'dropdown',\n 'period': 365 * 3,\n 'threshold': 50,\n 'operation': -1\n }\n b = {\n 'name': 'profit',\n 'period': 365 * 3,\n 'threshold': 10,\n 'operation': 1\n }\n c = {\n 'name': 'std',\n 'period': 365 * 3,\n 'threshold': 0.02,\n 'operation': -1\n }\n d = {\n 'name': 'ma20',\n 'period': 15,\n 'threshold': 100,\n 'operation': -1\n }\n e = {\n 'name': 'ma20',\n 'period': 15,\n 'threshold': 10,\n 'operation': 1\n }\n f = {\n 'name': 'pb',\n 'period': 15,\n 'threshold': 2,\n 'operation': -1\n }\n g = {\n 'name': 'pe',\n 'period': 15,\n 'threshold': 15,\n 'operation': -1\n }\n h = {\n 'name': 'pe',\n 'period': 15,\n 'threshold': 10,\n 'operation': 1\n }\n i = {\n 'name': 'than60ma',\n 'period': 15,\n 'threshold': 0,\n 'operation': 1\n }\n j = {\n 'name': 'than120ma',\n 'period': 15,\n 'threshold': 0,\n 'operation': 1\n }\n k = {\n 'name': 'dividend',\n 'period': 15,\n 'threshold': 4,\n 'operation': 1\n }\n l = {\n 'name': 'than_month',\n 'period': 15,\n 'threshold': 12,\n 'operation': 1\n }\n m = {\n 'name': 'than_volume',\n 'period': 15,\n 'threshold': 50,\n 'operation': 1\n }\n \n #self.strategy_list.append(a)\n #self.strategy_list.append(b)\n #self.strategy_list.append(c)\n #self.strategy_list.append(d)\n #self.strategy_list.append(e)\n self.strategy_list.append(f)\n self.strategy_list.append(g)\n #self.strategy_list.append(h)\n self.strategy_list.append(i)\n self.strategy_list.append(j)\n self.strategy_list.append(k)\n self.strategy_list.append(l)\n self.strategy_list.append(m)\n\n def combine_constraint(self, start_date):\n constraint_list = []\n for strategy in self.strategy_list:\n res = self.call_dict[strategy['name']](start_date, strategy['period'], strategy['threshold'], strategy['operation'])\n constraint_list.append(res)\n self.constraint = constraint_list[0]\n for i in range(1, len(constraint_list)):\n self.constraint = self.constraint & constraint_list[i]\n\n def get_constraint(self, start_date):\n self.combine_constraint(start_date)\n return self.constraint\n\n def dropdown(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data.cummax() - data).max()/data.max() * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def profit(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data.iloc[-1] / data.iloc[0] - 1) * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def std(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = (data / data.shift()).std()\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def ma20(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['20ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n def pb(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['PB'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n\n def pe(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['PE'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n\n def dividend(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n data = self.stock_data['dividend'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n result = data.iloc[-1]\n if operation < 0:\n return result[result < threshold].index.astype(str)\n elif operation == 0:\n return result[result == threshold].index.astype(str)\n else:\n return result[result > threshold].index.astype(str)\n \n def than60ma(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n close = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n ma60 = self.stock_data['60ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n close = close.iloc[-1]\n ma60 = ma60.iloc[-1]\n if operation < 0:\n res = np.where(close < ma60, True, False)\n else:\n res = np.where(close > ma60, True, False)\n result = pd.Series(res, index=close.index)\n return result[result == True].index.astype(str)\n\n def than120ma(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n close = self.stock_data['close'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n ma120 = self.stock_data['120ma'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n close = close.iloc[-1]\n ma120 = ma120.iloc[-1]\n if operation < 0:\n res = np.where(close < ma120, True, False)\n else:\n res = np.where(close > ma120, True, False)\n result = pd.Series(res, index=close.index)\n return result[result == True].index.astype(str)\n\n def than_month(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(600)\n month = self.stock_data['month'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n if len(month.index) <= 2 or len(month.index) <= threshold + 1:\n cur_month = month.iloc[-1]\n prev_month = month.iloc[-1]\n else:\n cur_month = month.iloc[-2]\n prev_month = month.iloc[-(threshold+1)]\n if operation < 0:\n res = np.where(cur_month < prev_month, True, False)\n else:\n res = np.where(cur_month > prev_month, True, False)\n result = pd.Series(res, index=cur_month.index)\n return result[result == True].index.astype(str)\n\n def than_volume(self, start_date, period, threshold, operation):\n prev_date = start_date - datetime.timedelta(period)\n volume = self.stock_data['volume'].truncate(prev_date.strftime('%Y-%m-%d'), start_date.strftime('%Y-%m-%d'))\n\n result = ((volume.iloc[-1] - volume.iloc[-2]) / volume.iloc[-2]) * 100\n if operation < 0:\n return result[result < threshold].index\n elif operation == 0:\n return result[result == threshold].index\n else:\n return result[result > threshold].index\n\n","repo_name":"mistysya/stock_backtest","sub_path":"strategy.py","file_name":"strategy.py","file_ext":"py","file_size_in_byte":10069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7797930293","text":"#!/usr/bin/python\n\nfrom utils.util import *\n\ndef deploy_tez_internal(default_conf, custom_conf, master, beaver_env):\n tez_vesrion = beaver_env.get(\"TEZ_VERSION\")\n download_tez(master, tez_vesrion)\n copy_tez_lib_to_hive(master)\n copy_tez_conf_to_hadoop(default_conf, custom_conf, [master], beaver_env)\n\ndef download_tez(node, version):\n print (colors.LIGHT_BLUE + \"Distribute \" + \"tar.gz file\" + \" for apache-tez-\" + version + \"-bin\" + colors.ENDC)\n download_url = \"http://\" + download_server + \"/software\"\n package = \"apache-tez-\" + version + \"-bin\" + \".tar.gz\"\n if not os.path.isfile(os.path.join(package_path, package)):\n print (colors.LIGHT_BLUE + \"\\tDownloading \" + package + \" from our repo...\" + colors.ENDC)\n os.system(\"wget --no-proxy -P \" + package_path + \" \" + download_url + \"/\" + package)\n else:\n print (colors.LIGHT_GREEN + \"\\t\" + package + \" has already exists in Beaver package\" + colors.ENDC)\n print (colors.LIGHT_BLUE + \"\\tCopy \" + package + \" to \" + node.hostname + \"...\" + colors.ENDC)\n ssh_execute(node, \"mkdir -p /opt/Beaver/\")\n ssh_copy(node, os.path.join(package_path, package), \"/opt/Beaver/\" + package)\n print (colors.LIGHT_BLUE + \"\\tUnzip \" + package + \" on \" + node.hostname + \"...\" + colors.ENDC)\n softlink = \"/opt/Beaver/tez\"\n cmd = \"rm -rf \" + softlink + \";\"\n cmd += \"rm -rf /opt/Beaver/tez-*;\"\n cmd += \"mkdir /opt/Beaver/tez-\" + version + \";\"\n cmd += \"tar zxf /opt/Beaver/\" + package + \" -C /opt/Beaver/tez-\" + version + \" --strip-components=1 > /dev/null\"\n ssh_execute(node, cmd)\n cmd = \"ln -s /opt/Beaver/tez-\" + version + \" \" + softlink + \";\"\\\n + \"rm -rf /opt/Beaver/\" + package\n ssh_execute(node, cmd)\n\ndef copy_tez_lib_to_hive(node):\n print (colors.LIGHT_BLUE + \"Copy Tez lib to Hive\" + colors.ENDC)\n cmd = \"yes|cp /opt/Beaver/tez/*.jar /opt/Beaver/hive/lib;\"\n cmd += \"yes|cp /opt/Beaver/tez/lib/*.jar /opt/Beaver/hive/lib\"\n ssh_execute(node, cmd)\n\ndef copy_tez_package_to_hadoop(node):\n print (colors.LIGHT_BLUE + \"Copy Tez package to Hadoop\" + colors.ENDC)\n cmd = \"hadoop dfsadmin -safemode wait;\"\n cmd += \"$HADOOP_HOME/bin/hadoop fs -mkdir /apps;\"\n cmd += \"$HADOOP_HOME/bin/hadoop fs -copyFromLocal /opt/Beaver/tez/share/tez.tar.gz /apps/\"\n ssh_execute(node, cmd)\n\ndef undeploy_tez(master):\n ssh_execute(master, \"rm -rf /opt/Beaver/tez*\")\n\ndef copy_tez_conf_to_hadoop(default_conf, custom_conf, master, beaver_env):\n output_tez_conf = update_tez_conf(default_conf, custom_conf, master)\n copy_configurations(master, output_tez_conf, \"hadoop\", beaver_env.get(\"HADOOP_HOME\"))\n\ndef update_tez_conf(default_conf, custom_conf, master):\n output_tez_conf = update_conf(\"tez\", default_conf, custom_conf)\n # for all conf files, replace the related value, eg, replace master_hostname with real hostname\n for conf_file in [file for file in os.listdir(output_tez_conf) if fnmatch.fnmatch(file, '*.xml')]:\n output_conf_file = os.path.join(output_tez_conf, conf_file)\n for node in master:\n dict = {'master_hostname': node.hostname}\n replace_conf_value(output_conf_file, dict)\n format_xml_file(output_conf_file)\n return output_tez_conf","repo_name":"Liu765940375/autohadoop","sub_path":"infra/tez.py","file_name":"tez.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32234734957","text":"# python3\r\n\r\nfrom collections import namedtuple\r\nfrom os.path import exists\r\n\r\n\r\nBracket = namedtuple(\"Bracket\", [\"char\", \"position\"])\r\n\r\n\r\nRESPONSE_TYPE_SUCCESS = \"Success\"\r\n\r\n\r\nOPENING_BRACKETS = \"([{\"\r\nCLOSING_BRACKETS = \")]}\"\r\n\r\n\r\ndef are_matching(left, right):\r\n return (left + right) in [\"()\", \"[]\", \"{}\"]\r\n\r\n\r\ndef find_mismatch(text):\r\n opening_brackets_stack = []\r\n\r\n for i, char in enumerate(text):\r\n if char in OPENING_BRACKETS:\r\n opening_brackets_stack.append(Bracket(char, i))\r\n\r\n if char in CLOSING_BRACKETS and (not opening_brackets_stack or not are_matching(opening_brackets_stack.pop().char, char)):\r\n return i + 1\r\n\r\n if opening_brackets_stack:\r\n return opening_brackets_stack[0].position + 1\r\n\r\n return False\r\n\r\n\r\ndef get_text():\r\n while True:\r\n user_choice = input(\"F for file OR I for input OR Q to quit: \").strip().lower()\r\n if user_choice == \"f\":\r\n file_path = input(\"Input file path: \")\r\n if not exists(file_path):\r\n print(\"Incorrect file path\")\r\n\r\n with open(file_path, \"r\", encoding=\"UTF-8\") as file:\r\n return file.read()\r\n elif user_choice == 'i':\r\n return input(\"Input text: \")\r\n elif user_choice == 'q':\r\n print(\"Exiting\")\r\n\r\n break\r\n else:\r\n print(\"No such option exists\")\r\n\r\n\r\ndef handle_mismatch(text):\r\n mismatch_found = find_mismatch(text)\r\n if mismatch_found:\r\n print(mismatch_found)\r\n else:\r\n print(RESPONSE_TYPE_SUCCESS)\r\n\r\n\r\ndef main():\r\n text = get_text()\r\n if not text:\r\n return\r\n\r\n handle_mismatch(text)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"DA-testa/steks-un-iekavas-jbolozdina-rtu","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"5765156478","text":"import requests\nimport json\nfrom config import API_KEY, currencies\n\n\nclass APIException(Exception):\n pass\n\n\nclass Converter:\n @staticmethod\n def get_convert(curr_from, curr_to, amount):\n try:\n curr_from_key = currencies[curr_from]\n except KeyError:\n raise APIException(f'Валюта {curr_from} не найдена!\\nСписок доступных валют см. /values')\n try:\n curr_to_key = currencies[curr_to]\n except KeyError:\n raise APIException(f'Валюта {curr_to} не найдена!\\nСписок доступных валют см. /values')\n if curr_from_key == curr_to_key:\n raise APIException(f'Невозможно перевести одинаковые валюты {curr_from}')\n try:\n amount = float(amount.replace(',', '.'))\n except ValueError:\n raise APIException(f'Неудалось обработать количество: {amount}')\n\n url = f\"https://api.apilayer.com/exchangerates_data/convert?to={curr_to_key}&from={curr_from_key}&amount={amount}\"\n payload = {}\n headers = {\"apikey\": API_KEY}\n r = requests.request(\"GET\", url, headers=headers, data=payload)\n resp = json.loads(r.content)\n result = resp['result']\n return round(result, 3)\n\n\n","repo_name":"ZhArtem/SF-TelegramBot","sub_path":"extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"ru","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"70119083148","text":"from django.http import JsonResponse, HttpResponse, FileResponse\nfrom watcher.models import *\nimport requests\nimport json\nfrom watcher.tools import *\nimport os\nfrom django.utils import timezone\nfrom django.shortcuts import render\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .serializers import *\nfrom rest_framework.response import Response\nfrom django.core.files.storage import default_storage #파일 저장 경로\nfrom google.cloud import vision\nfrom django.conf import settings\nimport traceback\nfrom django.db.models import Q\n\ndef send_seat_data(request):\n\t\"\"\"\n\tTodo : get camera id, store id\n\t\"\"\"\n\tbefore_pk_list = json.loads(request.POST['before_pk_list'])\n\n\tstring_data = request.POST['seat_data']\n\tcamera_pk = int(request.POST['camera_pk'])\n\tcamera = Camera.objects.get(pk=camera_pk)\n\tstore_pk = int(request.POST['store_pk'])\n\tstore = Store.objects.get(pk=store_pk)\n\tpicture_name = request.POST['picture_name']\n\treal_data = json.loads(string_data)\n\t\"\"\"\n\tcamera data set\n\t\"\"\"\n\tCamera.objects.filter(pk=camera_pk).update(cur_pic=picture_name)\n\t\"\"\"\n\treal data format\n\t{\n\t\t'is_elec': False, \n\t\t'capacity': '1', \n\t\t'position': {'f_x': 0.19416666666666665, 'f_y': 0.35, 's_x': 0.2808333333333333, 's_y': 0.46555555555555556}\n\t}\n\t\"\"\"\n\n\t\"\"\"\n\tTodo : cam addr setting : connection test\n\t\"\"\"\n\tcam_host = camera.cur_host\n\tcam_addr_test = '/'.join([cam_host, 'test'])\n\tif cam_host != 'test':\n\t\ttry:\n\t\t\tresponse = requests.get(cam_addr_test, timeout=5)\n\t\texcept Exception:\n\t\t\treturn HttpResponse('connection error')\n\t\tif response.text != 'ok':\n\t\t\treturn HttpResponse('connection error2')\n\t\n\tcur_pk_list = []\n\n\tto_cam_data_list = []\n\n\tfor elem in real_data:\n\t\tto_cam_data = {}\n\n\t\ttarget_data = {\n\t\t\t'pic_f_x' : round(elem['position']['f_x'],2),\n\t\t\t'pic_f_y' : round(elem['position']['f_y'],2),\n\t\t\t'pic_s_x' : round(elem['position']['s_x'],2),\n\t\t\t'pic_s_y' : round(elem['position']['s_y'],2),\n\t\t\t'is_elec' : elem['is_elec'],\n\t\t\t'capacity' : elem['capacity'],\n\t\t\t'camera' : camera,\n\t\t\t'store' : store,\n\t\t\t'pic_name': picture_name,\n\t\t}\n\t\tif bool(elem['pk']):\n\t\t\tcur_pk_list.append(elem['pk'])\n\t\t\tTable.objects.filter(pk=elem['pk']).update(**target_data)\n\t\t\tto_cam_data['pk'] = elem['pk']\n\t\t\tto_cam_data['position'] = elem['position']\n\t\t\tto_cam_data_list.append(to_cam_data)\n\t\telse:\n\t\t\ttable_tmp = Table.objects.create(**target_data)\n\t\t\tto_cam_data['pk'] = table_tmp.pk\n\t\t\tto_cam_data['position'] = elem['position']\n\t\t\tto_cam_data_list.append(to_cam_data)\n\n\t\"\"\"\n\tTodo : table delete\n\t\"\"\"\n\tfor before_pk in before_pk_list:\n\t\tif before_pk not in cur_pk_list:\n\t\t\tTable.objects.filter(pk=before_pk).delete()\n\t\n\t\"\"\"\n\tsend coord to cam\n\t\"\"\"\n\t\n\tcam_get_seat_info = '/'.join([camera.cur_host, 'get_seat_info'])\n\tif cam_host != 'test':\n\t\ttry:\n\t\t\toutput_data = {\n\t\t\t\t'is_updated' : True,\n\t\t\t\t'data' : to_cam_data_list\n\t\t\t}\n\t\t\tresponse = requests.post(cam_get_seat_info, data={'seat_data':json.dumps(output_data, indent=4)}, timeout=5)\n\t\texcept Exception:\n\t\t\treturn HttpResponse('connection error')\n\t\tif response.text != 'good':\n\t\t\treturn HttpResponse('connection error2')\n\n\treturn HttpResponse('good')\n\n\n#가게 정보 관련 \ndef get_store_info(request) :\n\tpk = int(request.GET['pk'])\n\tstore_info = Store.objects.get(pk=pk)\n\n\tdata = {\n\t\t'pk' : store_info.pk,\n\t\t'store_name' : store_info.store_name,\n\t\t'store_location' : store_info.store_location,\n\t}\n\n\treturn JsonResponse(json.dumps(store_info))\ndef delete_store_info(request) :\n\tpk = int(request.GET['pk'])\n\tstore = Store.objects.get(pk=pk)\n\tstore.delete()\n\treturn HttpResponse(\"delete success\")\n\ndef edit_store_info(request) :\n\tpk = int(request.POST.get('pk'))\n\tstore_name = request.POST['store_name']\n\tstore_location = request.POST.get('store_location')\n\tpicture_name = request.POST.get('picture_name')\n\tpic = request.FILES.get('img')\n\n\tstore = Store.objects.get(pk=pk)\n\t\n\tif pic :\n\t\tdefault_storage.delete('watcher/static/img/store/'+str(pk)+'/'+store.picture_name)\n\t\tdefault_storage.save('watcher/static/img/store/'+str(pk)+'/'+pic.name, pic)\n\n\tstore.store_name = store_name\n\tstore.store_location = store_location\n\tstore.picture_name = picture_name\n\tstore.save()\n\n\tstores = Store.objects.all()\n\tserialized_stores = StoreSerializer(stores,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_stores.data))\n\ndef add_store_list(request) :\n\tstore_name = request.POST.get('store_name')\n\tstore_location = request.POST.get('store_location')\n\tpicture_name = request.POST.get('picture_name',\"modal_cafe_img.jpg\")\n\tpic = request.FILES.get('img')\n\t\t\n\tstore = Store(store_name=store_name, store_location=store_location, picture_name=picture_name)\n\tstore.save()\n\n\tif pic :\n\t\tdefault_storage.save('watcher/media/img/store/'+str(store.pk)+'/'+pic.name, pic)\n\t\tstore.picture_name=pic.name\n\t\tstore.save()\n\t\n\tdata = {\n\t\t'pk' : store.pk,\n\t\t'store_name' : store.store_name,\n\t\t'store_location' : store.store_location,\n\t\t'picture_name' : store.picture_name,\n\t}\n\n\treturn JsonResponse(data, safe=False)\n\ndef upload_store_img(requset) :\n\tpicture_name = requset.GET.get('picture_name')\n\tprint(\"img_start\")\n\treturn HttpResponse('img good')\n\n\n\n#카메라 정보 관련\n\ndef get_camera_info(request):\n\tpk = int(request.POST.get('pk'))\n\tcamera = Camera.objects.get(pk=pk)\n\n\tdata = {\n\t\t'pk' : camera.pk,\n\t\t'store_id' : camera.store_id,\n\t\t'cur_pic' : camera.cur_pic,\n\t\t'mac_addr' : camera.mac_addr,\n\t\t'cur_host' : camera.cur_host,\n\t\t'description' : camera.description,\n\t} \n\treturn JsonResponse(data, safe=False)\n\ndef edit_camera_info(request) :\n\tstore_id = int(request.GET['store_id'])\n\tpk = int(request.GET['pk'])\n\tmac_addr = request.GET.get('mac_addr')\n\tdescription = request.GET['description']\n\tcur_host = request.GET.get('cur_host')\n\n\tcamera = Camera.objects.filter(pk=pk)\n\tcamera.update(mac_addr=mac_addr,description=description,cur_host=cur_host)\n\tcamera = Camera.objects.get(pk=pk)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\t\ndef add_camera_info(request) :\n\t#cur_pic = request.GET['cur_pic']\n\tdescription = request.GET['description']\n\tstore_id= int(request.GET['store_id'])\n\tcur_host = request.GET.get('cur_host')\n\tmac_addr = request.GET.get('mac_addr')\n\n\n\tcamera=Camera(description = description, store_id = store_id, cur_host= cur_host, mac_addr=mac_addr)\n\tcamera.save()\n\n\tdata = {\n\t\t'pk' : camera.pk,\n\t\t'store_id' : camera.store_id,\n\t\t'cur_pic' : camera.cur_pic,\n\t\t'mac_addr' : camera.mac_addr,\n\t\t'cur_host' : camera.cur_host,\n\t\t'description' : camera.description,\n\t\t'floor_id' : camera.floor_id,\n\t}\n\treturn JsonResponse(data, safe=False) \n\ndef get_camera_info_without_floor(request) :\n\tstore_id = int(request.GET['store_id'])\n\tcamera_floor_list = Camera.objects.filter(store_id= store_id, floor_id__isnull= True)\n\tcamera = camera_floor_list.first()\n\n\tdata = serializers.serialize(\"json\",camera_floor_list,fields=('id','cur_pic','description','mac_addr','cur_host','store_id','floor_id'))\n\t\n\treturn HttpResponse(data)\n\ndef delete_camera_list(request) :\n\n\tstore_id = int(request.GET['store_id'])\n\tpk = int(request.GET['pk'])\n\n\tcamera = Camera.objects.filter(pk=pk, store_id= store_id)\n\tcamera.delete()\n\n\t\n\treturn HttpResponse('delete success') #수정 필요 -> 2020-08-25 수정완료\n\ndef check_camera_connection(request) :\n\tcur_host = request.POST.get('cur_host')\n\n\ttry :\n\t\trq = requests.get(cur_host+'/test',timeout=5)\n\t\tif rq.text != 'ok' :\n\t\t\treturn HttpResponse('bad')\n\texcept Exception as e :\n\t\treturn HttpResponse('bad')\n\n\treturn HttpResponse ('good')\n\n\n\n\ndef check_camera_connection_row(request) :\n\tcur_host = request.POST.get('cur_host')\n\tpk = int(request.POST['pk'])\n\n\ttry :\n\t\trq = requests.get(cur_host+'/test',timeout=5)\n\t\tif rq.text != 'ok' :\n\t\t\tdata = {\n\t\t\t\t'pk' : pk,\n\t\t\t\t'con' : \"bad\",\n\t\t\t}\n\t\telse:\n\t\t\tdata = {\n\t\t\t\t'pk' : pk,\n\t\t\t\t'con' : 'good'\n\t\t\t}\n\t\treturn JsonResponse(data)\n\texcept Exception as e :\n\t\tdata = {\n\t\t\t'pk' :pk,\n\t\t\t'con' : \"bad\",\n\t\t}\n\t\treturn JsonResponse(data)\n\n\n#층 정보 관련\ndef add_floor_info(request) :\n\n\tstore_id = int(request.GET['store_id'])\n\tfloor_num = int(request.GET['floor_num'])\n\tfloor_name = request.GET['floor_name']\n\tdescription = request.GET['description']\n\tcamera_list = request.GET.getlist('camera_list[]')\n\n\n\tfloor=Floor(store_id=store_id, floor_num=floor_num, name=floor_name,description=description)\n\tfloor.save()\n\n\tfor c_list in camera_list :\n\t\tcamera = Camera.objects.filter(pk=int(c_list))\n\t\tcamera.update(floor_id=floor.pk)\n\n\tdata = {\n\t\t'pk' : floor.pk,\n\t\t'floor_num' : floor_num,\n\t\t'name' : floor.name,\n\t\t'description' : floor.description,\n\t}\n\treturn JsonResponse(data)\n\n\ndef edit_floor_id(request) :\n\n\tcamera_list = request.POST.getlist('camera_list[]')\n\tstore_id = int(request.POST.get('store_id'))\n\tfloor_id = int(request.POST.get('floor_id'))\n\n\tfor c_list in camera_list :\n\t\tcamera = Camera.objects.filter(pk=int(c_list))\n\t\tcamera.update(floor_id=floor_id)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\ndef delete_floor_info(request) :\n\n\tfloor_id = int(request.GET['floor_id'])\n\tstore_id = int(request.GET['store_id'])\n\n\tfloor = Floor.objects.filter(pk=floor_id)\n\tfloor.delete()\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\n\treturn HttpResponse(json.dumps(serialized_cameras.data))\n\ndef edit_floor_camera_list(request) :\n\n\tcamera_used_list = request.GET.getlist('camera_used[]')\n\tcamera_unused_list = request.GET.getlist('camera_unused[]')\n\tstore_id = int(request.GET['store_id'])\n\tfloor_id = int(request.GET['floor_id'])\n\tname = request.GET['floor_name']\n\tfloor_num = int(request.GET['floor_num'])\n\tdescription = request.GET['floor_description']\n\n\tfloor = Floor.objects.filter(pk=floor_id)\n\tfloor.update(name=name,floor_num=floor_num,description=description)\n\n\tfor lists in camera_used_list :\n\t\tcamera = Camera.objects.filter(pk=int(lists))\n\t\tcamera.update(floor_id=floor_id)\n\n\tfor lists in camera_unused_list :\n\t\tcamera = Camera.objects.filter(pk=int(lists))\n\t\tcamera.update(floor_id=None)\n\n\tcameras = Camera.objects.filter(store_id=store_id)\n\n\tserialized_cameras = CameraSerializer(cameras,many=True)\n\treturn HttpResponse(json.dumps(serialized_cameras.data)) \n\n\n\ndef get_file_from_cam(request):\n\t\"\"\"\n\tTodo : 카메라 pk, 가게 pk -> 카메라에 cur_pic 이름 저장\n\t\"\"\"\n\tcur_time = timezone.now().strftime(\"%Y%m%d%H%M%S\")\n\tcamera_pk = request.POST['camera_pk']\n\tcur_host = request.POST['host_addr']\n\ttarget_addr = '/'.join([cur_host, 'send_image'])\n\tcur_pic_name = cur_time+str(camera_pk)\n\tresponse = requests.get(target_addr, stream=True)\n\tif response.status_code == 200:\n\t\twith open('watcher/static/img/'+cur_pic_name+'.jpg', 'wb') as f:\n\t\t\tfor chunk in response:\n\t\t\t\tf.write(chunk)\n\t\n\treturn JsonResponse({\n\t\t'path' : '/static/img/'+ cur_pic_name + '.jpg',\n\t\t'pic_name' : cur_pic_name + '.jpg'\n\t})\n\n\ndef save_layout(request):\n\tlayout_pos_data = json.loads(request.POST['layout_pos_data'])\n\tbefore_pk_list = json.loads(request.POST['before_pk_list'])\n\tfloor_pk = int(request.POST['floor_pk'])\n\tfloor = Floor.objects.get(pk=floor_pk)\n\tcur_pk_list = []\n\tfor datum in layout_pos_data:\n\t\tsave_datum = {\n\t\t\t'floor':floor,\n\t\t\t'layout_f_x':datum['f_x'],\n\t\t\t'layout_f_y':datum['f_y'],\n\t\t\t'layout_s_x':datum['s_x'],\n\t\t\t'layout_s_y':datum['s_y']\n\t\t}\n\t\tTable.objects.filter(pk=int(datum['pk'])).update(**save_datum)\n\t\tcur_pk_list.append(datum['pk'])\n\n\t\n\tfor before_pk in before_pk_list:\n\n\t\tif before_pk not in cur_pk_list:\n\t\t\tsave_datum = {\n\t\t\t\t'floor':None,\n\t\t\t\t'layout_f_x':None,\n\t\t\t\t'layout_f_y':None,\n\t\t\t\t'layout_s_x':None,\n\t\t\t\t'layout_s_y':None\n\t\t\t}\n\t\t\tTable.objects.filter(pk=before_pk).update(**save_datum)\n\n\treturn HttpResponse('good')\n\n@csrf_exempt\ndef get_seat_inspection_result(request):\n\tinspection_result = json.loads(request.POST['input'])\n\tfor e in inspection_result:\n\t\tis_occupied = None\n\t\tif e['res'] == 'T':\n\t\t\tis_occupied = True\n\t\telse:\n\t\t\tis_occupied = False\n\t\tTable.objects.filter(pk=e['pk']).update(is_occupied=is_occupied)\n\n\treturn HttpResponse('good')\n\ndef localize_objects(request):\n\tpic_name = request.POST['pic_name']\n\tclient = vision.ImageAnnotatorClient()\n\tpath = 'watcher/static/img/'+pic_name\n\n\twith open(path, 'rb') as image_file:\n\t\tcontent = image_file.read()\n\timage = vision.types.Image(content=content)\n \n\tobjects = client.object_localization(image=image).localized_object_annotations\n\n\toutput_data = []\n\n\ttarget_data = ['Table', 'Tableware']\n\n\tfor object_ in objects:\n\t\tif object_.name in target_data:\n\t\t\tvertex_list = object_.bounding_poly.normalized_vertices\n\t\t\tdata = {\n\t\t\t\t'x' : vertex_list[0].x,\n\t\t\t\t'y' : vertex_list[0].y,\n\t\t\t\t'width' : abs(vertex_list[0].x - vertex_list[1].x),\n\t\t\t\t'height' : abs(vertex_list[0].y - vertex_list[3].y)\n\t\t\t}\n\t\t\toutput_data.append(data)\n\t\n\treturn HttpResponse(json.dumps(output_data))\n\ndef update_cam_addr(request):\n\tcamera_pk = int(request.POST['camera_pk'])\n\tcamera = Camera.objects.get(pk=camera_pk)\n\tcamera_mac_addr = camera.mac_addr\n\n\tif bool(camera_mac_addr) == False:\n\t\treturn HttpResponse('camera_mac_addr_failure')\n\n\tdeveloperkey = settings.REMOTE_IT_DEVELOPER_KEY\n\n\theaders = {\n\t\t'developerkey' : developerkey\n\t}\n\n\tbody = {\n\t\t'password' : settings.REMOTE_IT_PASSWORD,\n\t\t'username' : settings.REMOTE_IT_USERNAME\n\t}\n\n\turl = 'https://api.remot3.it/apv/v27/user/login'\n\n\tresponse = requests.post(url, data=json.dumps(body), headers=headers)\n\tresponse_body = response.json()\n\n\tif response_body['status'] == 'false':\n\t\treturn HttpResponse('connection_failure')\n\n\ttoken = response_body['token']\n\n\theaders = {\n \t\"developerkey\":developerkey,\n\t \"token\":token\n\t}\n\n\tbody = {\n \t\"deviceaddress\": camera_mac_addr,\n \t\"wait\":\"true\",\n\t}\n\n\turl = \"https://api.remot3.it/apv/v27/device/connect\"\n\n\tresponse = requests.post(url, data=json.dumps(body), headers=headers)\n\tresponse_body = response.json()\n\n\tif response_body['status'] == 'false':\n\t\treturn HttpResponse('addr_update_failure')\n\t\n\tcamera.cur_host = response_body['connection']['proxy']\n\tcamera.save()\n\n\treturn HttpResponse('update_success')\n\ndef add_category_info(request) :\n\tstore_id=request.GET.get('store_id')\n\tname=request.GET.get('category_name')\n\n\tcategory=Category(store_id=store_id,name=name)\n\tcategory.save();\n\n\treturn HttpResponse(\"good\")\n\ndef add_store_menu_info(request) :\n\tprice=request.GET.get('price')\n\tname=request.GET.get('name')\n\tcategory_id=request.GET.get('category_id')\n\tcategory_name=request.GET.get('category_name')\n\tstore_id=request.GET.get('store_id')\n\n\tmenu=Menu(name=name,store_id=store_id,price=price,category_id=category_id,category_name=category_name)\n\tmenu.save()\n\n\treturn HttpResponse(\"good\")\n\ndef edit_store_menu_info(request) :\n\tpk=request.GET.get('pk')\n\tprice=request.GET.get('price')\n\tname=request.GET.get('name')\n\tcategory_id=request.GET.get('category_id')\n\tcategory=Category.objects.get(pk=category_id)\n\n\tprint(name)\n\tprint(category_id)\n\tmenu=Menu.objects.get(pk=pk)\n\tmenu.price=price\n\tmenu.name=name\n\tmenu.category_id=category_id\n\tmenu.category_name=category.name\n\tmenu.save()\n\treturn HttpResponse(\"good\")\n\ndef delete_store_menu_info(request) :\n\tpk=int(request.GET.get('pk'))\n\tmenu=Menu.objects.get(pk=pk)\n\tmenu.delete()\n\n\treturn HttpResponse(\"good\")\n\n","repo_name":"YoonRyeol/SEAT_WATCHER","sub_path":"watcher/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":15244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32052148426","text":"import Atrium_class as AC\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport scipy.stats as sp\nresults1 = pickle.load(open(\"risk_curve_0.02-0.1.p\",\"rb\"))\nresults2 = pickle.load(open(\"risk_curve_0.11-0.15.p\",\"rb\"))\nresults3 = pickle.load(open(\"risk_curve_0.16-0.20.p\",\"rb\"))\nresults4 = pickle.load(open(\"risk_curve_0.21-0.25.p\",\"rb\"))\nresults5 = pickle.load(open(\"risk_curve_0.26-0.30.p\",\"rb\"))\nresults6 = pickle.load(open(\"risk_curve_0.16.p\",\"rb\"))\nresults7 = pickle.load(open(\"risk_curve_0.17.p\",\"rb\"))\nresults8 = pickle.load(open(\"risk_curve_0.18.p\",\"rb\"))\nresults9 = pickle.load(open( \"risk_curve_0.16-0.19.p\", \"rb\" ) )\nK_results = pickle.load(open(\"Kishan_data_risk_curve.p\",\"rb\"))\nL = 200\ndelta = 0.05\ntau = 50\nnus = np.array([0.02, 0.04, 0.06, 0.08, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3,0.5,1])\nT_risk = (1 - ((1-(1-nus)**tau)**(delta*L*L)))\nresults_nu = np.concatenate((results1[0],results2[0],results9[0],results3[0][-1:],results4[0],results5[0]))\nresults_risk = np.concatenate((results1[1],results2[1],results9[1],results3[1][-1:],results4[1],results5[1]))\nresults_repeats = np.concatenate((results1[2],results2[2],results9[2],results3[2][-1:],results4[2],results5[2]))\nplt.scatter(results_nu,results_risk,c='r',marker = 'x', label = \"My Data\")\nplt.scatter(K_results[0],K_results[1],c='b',marker = 'x', label = \"Kishan's Data\" )\nplt.plot(nus,T_risk, 'g', label = 'Theoretical Data')\nplt.show()\nprint(results_repeats)\ndata = [results_nu,results_risk,results_repeats]\npickle.dump(data,open( \"Risk_Curve_keep.p\", \"wb\" ) )\n","repo_name":"GwynethMatthews/AF-Work","sub_path":"OldCode/Code2/Trial1.py","file_name":"Trial1.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"27883850946","text":"def mas_s(pal):\n\tif pal[len(pal) - 1] in ['a','e','o']:\n\t\treturn True\n\treturn False\n\ndef chxx(pal):\n\t\n\tsplited = pal.split(\"ch\")\n\t\n\taux = []\n\taux2 = [splited[0]]\n\t\n\tfor frag in splited[1:]:\n\t\t\n\t\taux = []\n\t\tfor s in aux2:\n\t\t\taux.append(s + 'x' + frag)\n\t\t\taux.append(s + 'ch' + frag)\n\t\t\n\t\taux2 = []\n\t\tfor s in aux:\n\t\t\taux2.append(s)\n\t\n\treturn aux\n\ndef reducir_silabas(pal):\n\tsplited = pal.split(\"a\")\n\tnew_pal = splited[0]\n\tfor frag in splited[1:]:\n\t\tif frag == \"\":\n\t\t\tnew_pal += 'a' + frag\n\t\n\tfor silaba in ['e','i','o','u']:\n\t\tsplited = new_pal.split(silaba)\n\t\tnew_pal = splited[0]\n\t\tfor frag in splited[1:]:\n\t\t\tif frag != \"\":\n\t\t\t\tnew_pal += silaba + frag\n\t\n\treturn new_pal","repo_name":"sapphi20/Analizador-de-garabatos-Twitter","sub_path":"funciones_auxiliares/generadores_de_variaciones.py","file_name":"generadores_de_variaciones.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29369061732","text":"import os\nfrom turtle import pendown\nimport cv2\nimport tkinter as tk\nfrom tkinter import messagebox\nfrom tkinter.filedialog import askopenfilename\n\nfrom importlib.resources import path\nimport numpy as np\nfrom glob import glob\nimport shutil\n\nfrom PIL import ImageTk, Image\nfrom PIL import ImageGrab\nfrom PIL import Image\n\nimport time\nfrom time import gmtime, strftime\nfrom datetime import datetime\nimport tensorflow as tf\n\n# comment out below line to enable tensorflow outputs\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\nphysical_devices = tf.config.experimental.list_physical_devices('GPU')\n\nif len(physical_devices) > 0:\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n\nfrom absl import app, flags, logging\nfrom absl.flags import FLAGS\nimport core.utils as utils\nfrom core.yolov4 import filter_boxes\nfrom core.functions import *\nfrom tensorflow.python.saved_model import tag_constants\nfrom tensorflow.compat.v1 import ConfigProto\nfrom tensorflow.compat.v1 import InteractiveSession\n\n\nimport face_recognition\nimport csv\n\n\nflags.DEFINE_string('framework', 'tf', '(tf, tflite, trt')\nflags.DEFINE_string('weights', './checkpoints/yolov4-416',\n 'path to weights file')\nflags.DEFINE_integer('size', 416, 'resize images to')\nflags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')\nflags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')\nflags.DEFINE_string('video', './data/video/video.mp4', 'path to input video or set to 0 for webcam')\nflags.DEFINE_string('output', None, 'path to output video')\nflags.DEFINE_string('output_format', 'XVID', 'codec used in VideoWriter when saving video to file')\nflags.DEFINE_float('iou', 0.70, 'iou threshold')\nflags.DEFINE_float('score', 0.70, 'score threshold')\nflags.DEFINE_boolean('count', False, 'count objects within video')\nflags.DEFINE_boolean('dont_show', False, 'dont show video output')\nflags.DEFINE_boolean('info', False, 'print info on detections')\nflags.DEFINE_boolean('crop', False, 'crop detections from images')\nflags.DEFINE_boolean('plate', False, 'perform license plate recognition')\nflags.DEFINE_boolean('person', False, 'perform person detection')\nflags.DEFINE_boolean('frames', False, 'get the frames with persons')\nflags.DEFINE_boolean('identify', False, 'Identify the target person')\n\n\npath = os.getcwd()\nd = 'People'\nfiles = os.path.join(path, d)\nisdir = os.path.isdir(files)\nif not isdir:\n os.mkdir(files)\n\n\n\nclass Application(tk.Frame):\n\n def __init__(self, master=None):\n super().__init__(master)\n\n self.pack()\n self.create_front()\n\n\n def create_front(self):\n\n def gui_destroy():\n root.destroy()\n\n self.uname_label = tk.Label(root, text=\"In-Video Person Detection System\", font=('calibre',14,'bold'), bg='medium aquamarine')\n self.uname_label.place(x=110, y=40)\n\n self.go_upload = tk.Button(\n root, text=\"Upload Image of Person\", font=('calibre',12,'bold'), bg='skyblue2', command=self.create_upload)\n self.go_upload.place(x=160, y=100)\n\n\n self.go_verify = tk.Button(\n root, text=\"Detect Person in Video\", font=('calibre',12,'bold'), bg='gold2', command=self.create_verify)\n self.go_verify.place(x=170, y=160)\n\n\n quit = tk.Button(root, text=\"Exit\", font=('calibre',12,'bold'), bg = \"tomato\", width=5, command=root.destroy)\n quit.place(x=220, y=220)\n\n\n def create_upload(self):\n\n def image_save(window, path0, path1):\n xx = self.name_entry.get()\n print(\"\\n User name is : \" + xx + \"\\n\")\n\n sign_list = [path1]\n\n if path0=='':\n messagebox.showerror(\"Warning!\", \"Username can not be empty!\")\n\n else:\n ff = 0\n for ee in sign_list:\n file_exists = os.path.exists(ee)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\",\n \"You must upload all 5 signatures!\")\n break\n else:\n ff += 1\n\n if ff==1:\n cc = 0\n for i in sign_list:\n file = i\n cc += 1\n s_file = 'People/' + xx + '.jpg'\n print(\"\\n\", file, \"\\n\")\n image = cv2.imread(file)\n\n cv2.imwrite(s_file, image)\n\n # cv2.imshow(str(cc), image)\n # cv2.waitKey(0)\n\n # cv2.destroyAllWindows()\n\n dataset = tk.Label(root_u, text=\"Display Uploaded Image\", font=('calibre',12,'bold'))\n dataset.place(x=130, y=460)\n \n s_file1 = './People/' + path0 + '.jpg'\n img1 = Image.open(s_file1)\n img1 = img1.resize((80, 80), Image.ANTIALIAS)\n img1 = ImageTk.PhotoImage(img1)\n panel1 = tk.Label(root_u, image=img1)\n panel1.place(x=20, y=500)\n\n messagebox.showinfo(\"Success!\",\n \"Image Uploaded!!\")\n \n dataset.destroy()\n\n def check_data():\n xx = self.name_entry.get()\n print(\"\\n User name is : \" + xx + \"\\n\")\n\n file = 'People/' + xx + '.jpg'\n # print(\"\\n\", file, \"\\n\")\n file_exists = os.path.exists(file)\n\n if not file_exists:\n messagebox.showwarning(\"Checked!\",\n \"User doesn't exist! Please Continue Uploading for Registration!\")\n else:\n messagebox.showinfo(\"Checked!\",\n \"User exists! You can exit to Verify or Continue Upload and Update!\")\n\n def browsefunc(ent):\n filename = askopenfilename(filetypes=([\n (\"image\", \".jpeg\"),\n (\"image\", \".png\"),\n (\"image\", \".jpg\"),\n ]))\n ent.delete(0, tk.END)\n ent.insert(tk.END, filename) # add this\n\n def gui_destroy():\n root_u.destroy()\n\n\n root_u = tk.Toplevel(self)\n root_u.title('Image Upload')\n root_u.geometry('500x350+650+50')\n\n self.uname_label = tk.Label(root_u, text=\"Upload Person Image\", font=('calibre',14,'bold'), bg='skyblue2')\n self.uname_label.place(x=135, y=25)\n\n # creating a label for name using widget Label\n self.name_label = tk.Label(root_u, text = 'Person Name:', font=('calibre',10,'bold'))\n self.name_label.place(x=60, y=90)\n\n # creating a entry for input name using widget Entry\n self.name_entry = tk.Entry(root_u, bd=3, font=('calibre',10,'normal'))\n self.name_entry.place(x=170, y=90)\n\n # creating a button using the widget button that will call the submit function\n sub_btn = tk.Button(root_u, text = 'Check Data', font=('calibre',10,'normal'), command = check_data)\n sub_btn.place(x=350, y=88)\n\n\n # Image 1\n self.img_message = tk.Label(root_u, text=\"Image:\", font=('calibre',10,'bold'))\n self.img_message.place(x=60, y=140)\n # Image Submit\n self.image_path_entry1 = tk.Entry(root_u, bd=3, font=('calibre',10,'normal'))\n self.image_path_entry1.place(x=170, y=140)\n # Browse Button\n self.img_browse_button = tk.Button(\n root_u, text=\"Browse\", font=('calibre',10,'normal'), command=lambda: browsefunc(ent=self.image_path_entry1))\n self.img_browse_button.place(x=350, y=138)\n\n\n # registered Button\n self.register_button = tk.Button(\n root_u, text=\"Register\", font=('calibre',12,'bold'), bg='gold2', command=lambda: image_save(window=root_u,\n path0=self.name_entry.get(),\n path1=self.image_path_entry1.get(),), width=8)\n self.register_button.place(x=198, y=200)\n\n\n # Exit Button\n go_exit = tk.Button(\n root_u, text=\"Exit\", font=('calibre',12,'bold'), bg='tomato', command=lambda: gui_destroy(), width=5)\n go_exit.place(x=214, y=250)\n\n root_u.mainloop()\n\n\n def create_verify(self):\n # Mach Threshold\n THRESHOLD = 50\n\n root_v=tk.Toplevel(self)\n root_v.title(\"Person Detection\")\n\n # setting the windows size\n root_v.geometry(\"500x500+650+50\")\n\n\n # defining a function that will get the name and password and print them on the screen\n def view_data():\n name = self.name_entry.get()\n \n print(\"\\n The name is : \" + name + \"\\n\")\n\n for i in range(1):\n file = 'People/' + name + '.jpg'\n print(\"\\n\", file, \"\\n\")\n image = cv2.imread(file)\n\n image = cv2.resize(image, (300, 300))\n\n cv2.imshow(str(i+1), image)\n cv2.waitKey(0)\n\n cv2.destroyAllWindows()\n\n\n def check_data():\n name = self.name_entry.get()\n print(\"\\n Person name is : \" + name + \"\\n\")\n\n file = 'People/' + name + '.jpg'\n # print(\"\\n\", file, \"\\n\")\n file_exists = os.path.exists(file)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\",\n \"User doesn't exist! Please Enter Correct Username!\")\n else:\n messagebox.showinfo(\"Checked!\",\n \"User exists! Please Continue Upload to Verify!\")\n\n\n def browsefunc(ent):\n filename = askopenfilename(filetypes=([\n (\"video\", \".mp4\"),\n (\"video\", \".avi\"),\n (\"video\", \".mkv\"),\n ]))\n ent.delete(0, tk.END)\n ent.insert(tk.END, filename) # add this\n\n\n def checkSimilarity(window, path0, path1):\n\n pending = tk.Label(root_v, text=\"Video Processing ...\", font=('calibre',12,'bold'))\n pending.place(x=140, y=300)\n\n if path0=='' or path1=='':\n messagebox.showerror(\"Warning!\", \"Username or Uploaded Image can not be empty while varifying!\")\n\n else:\n ch_file = './People/' + path0 + '.jpg'\n file_exists = os.path.exists(ch_file)\n\n if not file_exists:\n messagebox.showerror(\"Warning!\", \"User does not exist in Database! Please enter Username correctly for verifying! Or, Exit and Go to User Registration\")\n\n else:\n\n def main(_argv):\n\n yy = strftime(\"%d-%b-%Y_%H-%M\", gmtime())\n # print(yy)\n\n # Source Images\n images = []\n classNames = []\n\n\n path = 'People'\n name = path0\n cl = name + \".jpg\"\n curImg = cv2.imread(f'{path}/{cl}')\n images.append(curImg)\n classNames.append(os.path.splitext(cl)[0])\n\n # Face Encodings\n def findEncodings(images):\n encodeList = []\n for img in images:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n encode = face_recognition.face_encodings(img)[0]\n encodeList.append(encode)\n return encodeList\n\n encodeListKnown = findEncodings(images)\n # print('Encoding Complete')\n # print(\"Number of Records: \",len(encodeListKnown))\n\n\n ###############################################################\n\n FLAGS.weights = './checkpoints/yolov4-416'\n FLAGS.model = 'yolov4'\n FLAGS.size = 416\n\n\n # For only video\n FLAGS.video = path1\n FLAGS.output = \"./detections/video_output.mp4\"\n\n # FLAGS.person = True\n FLAGS.crop = True\n FLAGS.frames = True\n FLAGS.identify = True\n\n person_count = 0\n\n config = ConfigProto()\n config.gpu_options.allow_growth = True\n session = InteractiveSession(config=config)\n STRIDES, ANCHORS, NUM_CLASS, XYSCALE = utils.load_config(FLAGS)\n input_size = FLAGS.size\n video_path = FLAGS.video\n # get video name by using split method\n video_name = video_path.split('/')[-1]\n video_name = video_name.split('.')[0]\n\n\n if FLAGS.framework == 'tflite':\n interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)\n interpreter.allocate_tensors()\n input_details = interpreter.get_input_details()\n output_details = interpreter.get_output_details()\n print(input_details)\n print(output_details)\n else:\n saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])\n infer = saved_model_loaded.signatures['serving_default']\n\n # begin video capture\n try:\n vid = cv2.VideoCapture(int(video_path))\n except:\n vid = cv2.VideoCapture(video_path)\n\n out = None\n\n\n if FLAGS.output:\n # by default VideoCapture returns float instead of int\n width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = int(vid.get(cv2.CAP_PROP_FPS))\n vid_fps = fps\n # print(vid_fps)\n codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)\n out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))\n\n ##############################################################################\n # Main Loop Starts\n frame_num = -1\n\n while True:\n return_value, frame = vid.read()\n if return_value:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n frame_num += 1\n image = Image.fromarray(frame)\n\n else:\n print('\\n--- Video has ended or failed. Check the output or try a different video format! ---')\n break\n \n frame_size = frame.shape[:2]\n image_data = cv2.resize(frame, (input_size, input_size))\n image_data = image_data / 255.\n image_data = image_data[np.newaxis, ...].astype(np.float32)\n start_time = time.time()\n\n if FLAGS.framework == 'tflite':\n interpreter.set_tensor(input_details[0]['index'], image_data)\n interpreter.invoke()\n pred = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]\n if FLAGS.model == 'yolov3' and FLAGS.tiny == True:\n boxes, pred_conf = filter_boxes(pred[1], pred[0], score_threshold=0.25,\n input_shape=tf.constant([input_size, input_size]))\n else:\n boxes, pred_conf = filter_boxes(pred[0], pred[1], score_threshold=0.25,\n input_shape=tf.constant([input_size, input_size]))\n else:\n batch_data = tf.constant(image_data)\n pred_bbox = infer(batch_data)\n for key, value in pred_bbox.items():\n boxes = value[:, :, 0:4]\n pred_conf = value[:, :, 4:]\n\n boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(\n boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),\n scores=tf.reshape(\n pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),\n max_output_size_per_class=50,\n max_total_size=50,\n iou_threshold=FLAGS.iou,\n score_threshold=FLAGS.score\n )\n \n # format bounding boxes from normalized ymin, xmin, ymax, xmax ---> xmin, ymin, xmax, ymax\n original_h, original_w, _ = frame.shape\n bboxes = utils.format_boxes(boxes.numpy()[0], original_h, original_w)\n\n pred_bbox = [bboxes, scores.numpy()[0], classes.numpy()[0], valid_detections.numpy()[0]]\n\n # read in all class names from config\n class_names = utils.read_class_names(cfg.YOLO.CLASSES)\n\n # by default allow all classes in .names file\n allowed_classes = list(class_names.values())\n \n # custom allowed classes (uncomment line below to allow detections for only people)\n allowed_classes = ['person']\n\n\n # if crop flag is enabled, crop each detection and save it as new image\n if FLAGS.crop:\n crop_rate = int(vid_fps/2) # capture images every so many frames (ex. crop photos every 150 frames)\n crop_path = os.path.join(os.getcwd(), 'detections', 'crop_' + yy)\n try:\n os.mkdir(crop_path)\n except FileExistsError:\n pass\n if frame_num % crop_rate == 0:\n final_path = os.path.join(crop_path, 'frame_' + str(frame_num))\n try:\n os.mkdir(final_path)\n except FileExistsError:\n pass \n crop_objects(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), pred_bbox, final_path, allowed_classes)\n else:\n pass\n\n\n if FLAGS.frames:\n for file in glob(\"./detections/crop_\" + yy + \"/*/\", recursive = True):\n # Get the file names\n ff = os.path.normpath(file)\n xx = os.path.basename(ff)\n\n # Get all the images in the folders\n for i in os.listdir(file):\n # print(i)\n ii = './detections/crop_' + yy + '/' + xx + '/' + i\n image_i = cv2.imread(ii)\n cv2.imwrite('./detections/crop_' + yy + '/' + xx + '_' + i, image_i)\n shutil.rmtree(file)\n\n\n if FLAGS.identify:\n file = \"./detections/crop_\" + yy + \"/\"\n\n for i in os.listdir(file):\n img_name = i.split('.')[0]\n # print(img_name)\n try:\n frame_i = cv2.imread(\"./detections/crop_\" + yy + \"/\" + i)\n frame_i = cv2.cvtColor(frame_i, cv2.COLOR_BGR2RGB)\n facesCurFrame = face_recognition.face_locations(frame_i)\n encodesCurFrame = face_recognition.face_encodings(frame_i, facesCurFrame)\n\n person_path = os.path.join(os.getcwd(), 'detections', 'person_' + yy)\n isdir = os.path.isdir(person_path)\n if not isdir:\n os.mkdir(person_path)\n\n for encodeFace,faceLoc in zip(encodesCurFrame, facesCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown, encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown, encodeFace)\n # print(faceDis)\n matchIndex = np.argmin(faceDis)\n\n if matches[matchIndex]:\n name = classNames[matchIndex].upper()\n # print(name, 'is present in the video.\\n')\n\n y1,x2,y2,x1 = faceLoc\n # y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4\n cv2.rectangle(frame_i,(x1,y1),(x2,y2),(0,255,0),1)\n cv2.rectangle(frame_i,(x1-30,y2+20),(x2+30,y2),(0,255,0),cv2.FILLED)\n cv2.putText(frame_i, name, (x1-28,y2+18), cv2.FONT_HERSHEY_COMPLEX,0.7,(255,255,255),2)\n \n frame_i = cv2.cvtColor(frame_i, cv2.COLOR_BGR2RGB)\n cv2.imwrite(person_path + '/' + img_name + '_' + name + '.jpg', frame_i)\n\n person_count += 1\n\n except:\n pass\n\n if FLAGS.count:\n # count objects found\n counted_classes = count_objects(pred_bbox, by_class = True, allowed_classes=allowed_classes)\n # loop through dict and print\n for key, value in counted_classes.items():\n pass\n # print(\"Number of {}s: {}\".format(key, value))\n image = utils.draw_bbox(frame, pred_bbox, FLAGS.info, counted_classes, allowed_classes=allowed_classes, read_plate=FLAGS.plate)\n else:\n image = utils.draw_bbox(frame, pred_bbox, FLAGS.info, allowed_classes=allowed_classes, read_plate=FLAGS.plate)\n\n\n if FLAGS.person:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n facesCurFrame = face_recognition.face_locations(frame)\n encodesCurFrame = face_recognition.face_encodings(frame,facesCurFrame)\n\n person_path = os.path.join(os.getcwd(), 'detections', 'person_' + yy)\n isdir = os.path.isdir(person_path)\n if not isdir:\n os.mkdir(person_path)\n\n for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):\n matches = face_recognition.compare_faces(encodeListKnown,encodeFace)\n faceDis = face_recognition.face_distance(encodeListKnown,encodeFace)\n # print(faceDis)\n matchIndex = np.argmin(faceDis)\n\n if matches[matchIndex]:\n name = classNames[matchIndex].upper()\n print(name,'is present in the video.\\n')\n\n final_path = os.path.join(person_path, 'frame_' + str(frame_num) + '_' + name)\n\n y1,x2,y2,x1 = faceLoc\n # y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4\n cv2.rectangle(frame,(x1,y1),(x2,y2),(0,255,0),1)\n cv2.rectangle(frame,(x1-30,y2+20),(x2+30,y2),(0,255,0),cv2.FILLED)\n cv2.putText(frame, name, (x1-28,y2+18), cv2.FONT_HERSHEY_COMPLEX,0.7,(255,255,255),2)\n\n cv2.imwrite(final_path + '.jpg', frame)\n\n\n fps = 1.0 / (time.time() - start_time)\n # print(\"FPS: %.2f\" % fps)\n result = np.asarray(image)\n cv2.namedWindow(\"result\", cv2.WINDOW_AUTOSIZE)\n result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n \n if not FLAGS.dont_show:\n cv2.imshow(\"result\", result)\n \n if FLAGS.output:\n out.write(result)\n\n if cv2.waitKey(1) & 0xFF == ord('q'): break\n\n\n # dataset = tk.Label(root_v, text=\"Display Images in Database\", font=('calibre',12,'bold'))\n # dataset.place(x=140, y=300)\n \n # s_file1 = './People/' + path0 + '.jpg'\n # img1 = Image.open(s_file1)\n # img1 = img1.resize((80, 80), Image.ANTIALIAS)\n # img1 = ImageTk.PhotoImage(img1)\n # panel1 = tk.Label(root_v, image=img1)\n # panel1.place(x=20, y=340)\n\n # d_result = tk.Label(root_v, text=\"Detection Result\", font=('calibre',13,'bold'))\n # d_result.place(x=20, y=450)\n\n\n d_result = tk.Label(root_v, text=\"Detection Result\", font=('calibre',13,'bold'))\n d_result.place(x=20, y=370)\n\n if person_count != 0:\n print('\\n\\n--- Result: Positive! ', name, 'is present in the video. ---\\n')\n\n got_frames = \"./detections/person_\" + yy + \"/\"\n\n print('The captured frames are:')\n for f in os.listdir(got_frames):\n fr_name = f.split('.')[0]\n print(fr_name)\n print()\n\n valid_result = tk.Label(root_v, text=\"Result: Positive! \" + name + \" is present in the video!!\", font=('calibre',11,'bold'), bg='green')\n valid_result.place(x=20, y=420)\n messagebox.showinfo(\"Success: Person Detected!\",\n \"Target person is present in the video!!\")\n valid_result.destroy()\n\n else:\n print('\\n\\n--- Result: Negative! ', name, 'is not present in the video. ---\\n\\n')\n\n fail_result = tk.Label(root_v, text=\"Result: Negative! \" + name + \" is not present in the video!!\", font=('calibre',11,'bold'), bg='red')\n fail_result.place(x=20, y=420)\n messagebox.showerror(\"Failure: Person Not Detected.\",\n \"Target person is not present in the video!!\")\n fail_result.destroy()\n\n cv2.destroyAllWindows()\n\n pending.destroy()\n d_result.destroy()\n\n\n if __name__ == '__main__':\n try:\n app.run(main)\n except SystemExit:\n pass\n\n\n return True\n\n\n def gui_destroy():\n root_v.destroy()\n\n\n\n self.uname_label = tk.Label(root_v, text=\"In-video Person Detection\", font=('calibre',14,'bold'), bg='medium aquamarine')\n self.uname_label.place(x=140, y=25)\n\n # creating a label for name using widget Label\n self.name_label = tk.Label(root_v, text = 'Username:', font=('calibre',10,'bold'))\n self.name_label.place(x=60, y=90)\n\n # creating an entry for input name using widget Entry\n self.name_entry = tk.Entry(root_v, bd=3, font=('calibre',10,'normal'))\n self.name_entry.place(x=170, y=90)\n\n # creating a button using the widget button that will check the available data\n self.sub_btn = tk.Button(root_v, text = 'Check Data', font=('calibre',10,'normal'), command = check_data)\n self.sub_btn.place(x=340, y=85)\n\n\n # Upload\n self.img_message = tk.Label(root_v, text=\"Input Video:\", font=('calibre',10,'bold'))\n self.img_message.place(x=60, y=140)\n # Image Submit\n self.image_path_entry1 = tk.Entry(root_v, bd=3, font=('calibre',10,'normal'))\n self.image_path_entry1.place(x=170, y=140)\n # Browse Button\n self.img_browse_button = tk.Button(\n root_v, text=\"Browse\", font=('calibre',10,'normal'), command=lambda: browsefunc(ent=self.image_path_entry1))\n self.img_browse_button.place(x=340, y=135)\n\n\n\n # Verify Button\n self.verify_button = tk.Button(\n root_v, text=\"Verify\", font=('calibre',12,'bold'), bg='gold2', command=lambda: checkSimilarity(window=root_v, path0=self.name_entry.get(), path1=self.image_path_entry1.get(),), width=8)\n self.verify_button.place(x=198, y=190)\n\n\n # Exit Button\n self.go_exit = tk.Button(\n root_v, text=\"Exit\", bg='tomato', font=('calibre',12,'bold'), command=lambda: gui_destroy(), width=5)\n self.go_exit.place(x=215, y=240)\n\n\n # performing an infinite loop for the window to display\n root_v.mainloop()\n\n\n\nroot = tk.Tk()\nroot.configure(bg='wheat1')\nroot.geometry(\"500x300+50+50\")\n\napp_g = Application(master=root)\napp_g.master.title(\"Person Detection & Identification System\")\napp_g.mainloop()\n","repo_name":"MZayed47/Specific-Person-Detector","sub_path":"detector_gui.py","file_name":"detector_gui.py","file_ext":"py","file_size_in_byte":31571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40161538009","text":"#Rock, Paper, Scissors- Compare User Choice and Computer Choice with one another and show user results\n#When I put it in the base component the 'xxx' end game would not work\n\nrps_list = [\"rock\", \"paper\", \"scissors\"]\ncomputer_index = 0\nfor item in rps_list:\n user_index = 0\n for item in rps_list:\n user_choice = rps_list[user_index]\n computer_choice = rps_list[computer_index]\n user_index += 1\n\n #Compare options\n\n if user_choice == \"rock\":\n\n if computer_choice == \"rock\":\n result = \"It is a draw\"\n\n elif computer_choice == \"paper\":\n result = \"You lost\"\n\n else:\n result = \"You Won\"\n\n elif user_choice == \"paper\":\n\n if computer_choice == \"rock\":\n result = \"You Won\"\n\n elif computer_choice == \"paper\":\n result = \"It is a draw\"\n\n else:\n result = \"You lost (Better luck next time)\"\n\n else:\n\n if computer_choice == \"rock\":\n result = \"You lost (Better luck next time)\"\n\n elif computer_choice == \"paper\":\n result = \"You Won\"\n\n else:\n result = \"It is a draw\"\n\n\n print(\"You chose {} the computer chose {}. \\nResult: {}. \".format(user_choice, computer_choice, result))\n\n\n computer_index += 1\n print()\n\n","repo_name":"KarlYapBuller/02-Rock-Paper-Scissors-game-Ncea-Level-1-Programming","sub_path":"06_RPS_User_Computer_Choice_Compare_v1.py","file_name":"06_RPS_User_Computer_Choice_Compare_v1.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74077860427","text":"from requests import Response\n\n\nclass LexofficeException(Exception):\n msg: str\n\n def __init__(self, response: Response, message: str):\n super().__init__(message)\n status_code = response.status_code\n body = response.json()\n self.msg = f\"status={status_code}, msg={body['message']}\"\n print(self.msg)\n\n def msg(self):\n return self.msg","repo_name":"maikerlab/lexoffice_api","sub_path":"src/lexoffice/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36566985248","text":"\"\"\"Contains the tools needed to get weights for the model\nusing the L-BFGS optimization algorithm.\"\"\"\nimport numpy as np\ntry:\n import cupy as cp\nexcept:\n pass\n\nclass sgdModelFit:\n \"\"\"This class contains all the tools needed to fit a model\n whose hyperparameters have already been tuned using implicit\n SGD. This will be slower than preconditioned CG if the\n preconditioner is good (low ratio), but can outperform\n preconditioned CG with a high-ratio preconditioner.\n\n Attributes:\n lambda_ (float): The noise hyperparameter shared across all kernels.\n verbose (bool): If True, print regular updates.\n device_ (str): One of 'cpu', 'gpu'. Indicates where calculations\n will be performed.\n n_epoch (int): The number of epochs.\n n_iter (int): The number of datapoints traversed in current epoch.\n \"\"\"\n\n def __init__(self, lambda_, device, verbose):\n \"\"\"Class constructor.\n\n Args:\n lambda_ (float): The noise hyperparameter shared across all kernels.\n device (str): One of 'cpu', 'gpu'. Indicates where calculations\n will be performed.\n verbose (bool): If True, print regular updates.\n \"\"\"\n self.lambda_ = lambda_\n self.verbose = verbose\n self.device = device\n self.n_iter = 0\n self.n_epoch = 0\n self.n_iter = 0\n self.mbatch_size = 250\n if self.device == \"cpu\":\n self.empty = np.empty\n self.zeros = np.zeros\n else:\n self.empty = cp.empty\n self.zeros = cp.zeros\n\n\n def fit_model(self, dataset, kernel, tol = 1e-6, max_epochs = 40,\n preconditioner = None, manual_lr = None):\n \"\"\"Finds an optimal set of weights using the information already\n provided to the class constructor.\n\n Args:\n dataset: An OnlineDataset or OfflineDatset containing all the\n training data.\n kernel: A kernel object that can generate random features for\n the Dataset.\n tol (float): The threshold for convergence.\n max_epochs (int): The number of epochs. Used to set the learning\n rate schedule.\n random_state (int): A seed for the random number generator.\n manual_lr (float): Either None or a float. If not None, this\n is a user-specified initial learning rate. If None, find\n a good initial learning rate using autotuning.\n mbatch_lr_check (int): The number of minibatches after which\n to check that the loss is not diverging (and reset the\n learning rate if it is).\n\n Returns:\n wvec: A cupy or numpy array depending on device that contains the\n best set of weights found. A 1d array of length self.kernel.get_num_rffs().\n \"\"\"\n losses = []\n\n #Key sgd hyperparameters.\n likely_lr = [3**i * 1e-9 for i in range(25)]\n if self.device == \"cpu\":\n likely_lr = np.asarray(likely_lr)\n else:\n likely_lr = cp.asarray(likely_lr)\n\n\n full_grad, wvec, z_trans_y, zty_norm = self.initialize(dataset, kernel)\n\n current_grad, last_wvec = full_grad.copy(), wvec.copy()\n sg_last_grad, sg_current_grad = self.zeros((kernel.get_num_rffs())), \\\n self.zeros((kernel.get_num_rffs()))\n if manual_lr is not None:\n step_size = manual_lr\n else:\n step_size = self.autotune(dataset, kernel, full_grad, wvec,\n last_wvec, preconditioner, z_trans_y, likely_lr)\n\n\n for self.n_epoch in range(max_epochs):\n end_epoch = False\n while not end_epoch:\n xbatch, _, end_epoch = dataset.get_next_minibatch(self.mbatch_size)\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n sg_current_grad[:] = xbatch.T @ (xbatch @ wvec) + self.lambda_**2 * wvec\n sg_last_grad[:] = xbatch.T @ (xbatch @ last_wvec) + self.lambda_**2 * last_wvec\n\n current_grad[:] = full_grad + (sg_current_grad - sg_last_grad)\n if preconditioner is not None:\n wvec -= step_size * preconditioner.batch_matvec(current_grad[:,None])[:,0]\n else:\n wvec -= step_size * current_grad / dataset.get_ndatapoints()\n\n\n dataset.reset_index()\n self.update_full_gradient(dataset, kernel, full_grad,\n wvec, z_trans_y)\n last_wvec[:] = wvec\n current_grad[:] = full_grad\n loss = full_grad / zty_norm\n loss = np.sqrt(float( loss.T @ loss ) )\n losses.append(loss)\n if len(losses) > 2:\n if losses[-1] - losses[-2] > 0:\n print(\"Reducing learning rate by 50%\")\n step_size *= 0.5\n\n if losses[-1] < tol:\n break\n\n if self.verbose and self.n_epoch % 1 == 0:\n print(f\"Epoch {self.n_epoch} complete; loss {losses[-1]}\")\n #The number of epochs is 2 x self.n_epoch because we perform\n #a gradient \"snapshot\" for each actual epoch.\n return wvec.copy(), 2 * self.n_epoch + 1, losses\n\n\n\n def initialize(self, dataset, kernel):\n full_grad = self.zeros((kernel.get_num_rffs()))\n wvec = self.zeros((kernel.get_num_rffs()))\n z_trans_y = self.zeros((kernel.get_num_rffs()))\n zty_norm = 0.0\n\n for xdata, ydata in dataset.get_chunked_data():\n if not dataset.pretransformed:\n xdata = kernel.transform_x(xdata)\n z_trans_y += xdata.T @ ydata\n\n zty_norm = np.sqrt(float(z_trans_y.T @ z_trans_y))\n full_grad[:] = -z_trans_y\n return full_grad, wvec, z_trans_y, zty_norm\n\n\n def update_full_gradient(self, dataset, kernel, full_grad, wvec,\n z_trans_y):\n full_grad[:] = -z_trans_y + self.lambda_**2 * wvec\n for xdata in dataset.get_chunked_x_data():\n if not dataset.pretransformed:\n xdata = kernel.transform_x(xdata)\n full_grad += xdata.T @ (xdata @ wvec)\n\n\n def autotune(self, dataset, kernel, full_grad, wvec,\n last_wvec, precond, z_trans_y, likely_lr):\n \"\"\"Uses a simple heuristic to tune the learning rate over the first 10\n minibatches in an arbitrarily designated epoch. Maybe not the best\n possible way to do this, but seems to work...\"\"\"\n wvec_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n last_wvec_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n gradient_batch = self.empty((kernel.get_num_rffs(), likely_lr.shape[0]))\n losses = self.zeros((kernel.get_num_rffs(), likely_lr.shape[0]))\n wvec_batch[:] = wvec[:,None]\n last_wvec_batch[:] = last_wvec[:,None]\n\n #Recall that we check under \"fit\" that the dataset has at least\n #10 minibatches...Using more might lead to more accurate tuning\n #but would make tuning more expensive...it's a tradeoff.\n end_epoch = False\n while not end_epoch:\n gradient_batch[:] = full_grad[:,None]\n xbatch, _, end_epoch = dataset.get_next_minibatch(self.mbatch_size)\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n gradient_batch += xbatch.T @ (xbatch @ wvec_batch)\n gradient_batch -= xbatch.T @ (xbatch @ last_wvec_batch)\n gradient_batch += kernel.get_lambda()**2 * (wvec_batch - last_wvec_batch)\n\n if precond is not None:\n wvec_batch -= likely_lr * precond.batch_matvec(gradient_batch)\n else:\n wvec_batch -= likely_lr[None,:] * gradient_batch / dataset.get_ndatapoints()\n\n dataset.reset_index()\n\n losses[:] = -z_trans_y[:,None] + self.lambda_**2 * wvec_batch\n for xbatch in dataset.get_chunked_x_data():\n if not dataset.pretransformed:\n xbatch = kernel.transform_x(xbatch)\n losses += xbatch.T @ (xbatch @ wvec_batch)\n\n\n losses[np.isnan(losses)] = np.inf\n losses = (losses**2).sum(axis=0)\n best_idx = int(losses.argmin())\n return likely_lr[best_idx]\n","repo_name":"jlparkI/xGPR","sub_path":"xGPR/fitting_toolkit/sgd_fitting_toolkit.py","file_name":"sgd_fitting_toolkit.py","file_ext":"py","file_size_in_byte":8382,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"4814410858","text":"import datetime\nimport json\nimport time\n\nfrom django.http import HttpResponseNotFound\nfrom django.shortcuts import render\n\nfrom establishment.funnel.base_views import JSONErrorResponse, JSONResponse, global_renderer, single_page_app\nfrom establishment.funnel.utils import GlobalObjectCache\n\n\ndef render_ui_widget(request, widget_class, state=None, page_title=None, widget_require=None, widget_options={}):\n context = {}\n if state:\n context[\"state\"] = state.dumps()\n else:\n context[\"state\"] = \"{}\"\n\n if widget_class == \"MessagesPanel\":\n widget_class = \"DocFlowApp\"\n widget_require = \"Bundle\"\n\n # TODO: DEFAULT_PAGE_TITLE should be an option in settings\n context[\"page_title\"] = page_title or \"CS Academy\"\n context[\"widget_class\"] = widget_class\n context[\"widget_require\"] = widget_require or widget_class\n context[\"widget_options\"] = json.dumps(widget_options)\n\n return render(request, \"docmanager/base.html\", context)\n\n\ndef render_csa_app(request):\n return render_ui_widget(request, \"CSAApp\", state={}, widget_require=\"Bundle\")\n\n\n@single_page_app\ndef generic_error_response(request, title, message):\n return JSONResponse({\"title\": title, \"message\": message})\n\n\nglobal_renderer.render_ui_widget = render_ui_widget\nglobal_renderer.render_single_page_app = render_csa_app\nglobal_renderer.render_error_message = generic_error_response\n\n\n@single_page_app\ndef index(request):\n return render(request, \"docsmanager/base.html\", {})\n\n if not request.is_ajax():\n return render_ui_widget(request, \"CSAApp\", state={}, widget_require=\"Bundle\")\n\n # This should be shared with the blog\n state = GlobalObjectCache(user=request.user)\n\n blog_posts = BlogEntry.objects.filter(visible=True).prefetch_related(\"article\")\n blog_posts = blog_posts.order_by(\"-id\")[:5]\n\n for blog_post in blog_posts:\n state.add(blog_post)\n article = blog_post.article\n state.add(article)\n\n top_users = CSAUser.objects.all().order_by(\"global_rating_rank\")[:10]\n\n for user in top_users:\n state.add(PublicUserSummary(user))\n\n upcoming_contests = Contest.objects.filter(end_date__gt=datetime.datetime.now(), is_visible=True)\n state.add_all(upcoming_contests)\n contest_users = ContestUser.objects.filter(contest__in=upcoming_contests.filter(system_generated=True))\n state.add_all(contest_users)\n\n return JSONResponse({\"state\": state})\n\n\ndef about(request):\n return render_csa_app(request)\n\n\ndef policy(request):\n return render(request, \"docmanader/policy.html\", {})\n\n\ndef maintenance_mode(request):\n if request.is_ajax():\n return JSONErrorResponse(\"Website in maintenance mode!\")\n return render(request, \"docmanader/maintenance.html\", {})\n\n\ndef admin_chat(request):\n if not request.user.is_superuser:\n return HttpResponseNotFound()\n\n widget_options = {\n \"chatId\": 1,\n \"style\": {\n \"padding-left\": \"12%\",\n \"padding-right\": \"12%\",\n }\n }\n\n return render_ui_widget(request, \"DelayedChat\", widget_require=\"IndexAuthenticated\", widget_options=widget_options)\n\n\ndef server_time(request):\n return JSONResponse({\"time\": time.time()})\n\n","repo_name":"TechGovRo/DocumentFlow","sub_path":"docmanager/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42487710217","text":"import sys\n\n\ndef add_sys(arg_list):\n integer_list = [int(x) for x in arg_list]\n return sum(integer_list)\n\n\nif __name__ == \"__main__\":\n args = sys.argv\n result = add_sys(args[1:])\n print(\"addition is\", result)\n","repo_name":"devika-aigalikar/first-git-project","sub_path":"accept_cmd.py","file_name":"accept_cmd.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1046917825","text":"from plagiarism import plagiarism_check_minhash_permutations, plagiarism_check_minhash\nfrom shingling import shingle_files\nimport argparse\nimport time\n\ndef main():\n\n parser = argparse.ArgumentParser(description='Running MinHash for plagiarism detection')\n parser.add_argument('path_files', metavar='f', type=str,\n help='path to files to check plagiarism')\n parser.add_argument('k_shingle', metavar='k', type=int, help='number of k for shringling')\n parser.add_argument('num_permutations', metavar='n_perm', type=int, help='number of permutations for minhash')\n\n args = parser.parse_args()\n files_path = args.path_files\n k_arg = args.k_shingle\n num_permutations = args.num_permutations\n\n st = time.time()\n shingle_files(files_path, './shingles.pkl', k_arg)\n # plagiarism_check_minhash_permutations('./shingles.pkl', num_permutations)\n plagiarism_check_minhash('./shingles.pkl', num_hash=num_permutations)\n print(\"Done in \",time.time() - st, \" seconds\")\n\nif __name__ == '__main__':\n main()","repo_name":"marichka-dobko/Plagiarism_detection_texts","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20227341430","text":"'''\nserver.py\n\nSimple Flask server to allow connections with the MongoDB Atlas database.\n\nNOTE: In order for this server to work:\n - The database cluster must have a database named 'counters'\n - The 'counters' databas must have 2 documents:\n One with name: red\n One with name: blue\n Both of them need a count: field with an integer type\n - This file must be run from the same folder as a file 'config.ini' which needs:\n [Database]\n DB_URI = \n\nUSAGE: \n - Run this server somewhere\n - Connect to the server over http\n - Use one of the following requests to interact with the database:\n - /get-red, /get-blue (GET)\n Returns the MongoDB document for the red or blue counter\n - /set-red, /set-blue (PUT)\n Body: { redCount/blueCount: }\n Sets the 'count' field of the red or blue counter document in MongoDB\n - / (GET)\n Returns 'Hello World!' html to verify the Flask server is working\n\nIf you are having trouble connecting to this server when it is hosted on AWS, try seeing if you are\nconnecting to this server with http or https. You may have to connect only with http.\n\n@author Alex Wills\n@author PyMongo tutorial: https://www.mongodb.com/compatibility/setting-up-flask-with-mongodb\n@date April 4, 2023\n'''\nimport configparser # Reading the config file\nimport os\nfrom flask import Flask, current_app, g, request # Creating a flask server\nfrom flask_pymongo import PyMongo # Connecting to MongoDB\nfrom flask_cors import CORS, cross_origin # CORS connections\nfrom werkzeug.local import LocalProxy # Something from MongoDB tutorial\nimport certifi # Creating a network certificate\n\n\n# ------------ Setting up the Flask app ------------ #\n\n# Create a global flask app\napp = Flask(__name__)\nCORS(app)\n\n# Read config.ini\nconfig = configparser.ConfigParser()\nconfig.read(os.path.abspath(os.path.join(\"config.ini\")))\n\n# Configure flask app\napp.config[\"MONGO_URI\"] = config['Database']['DB_URI']\napp.config[\"DEBUG\"] = False\n\n# Create network certificate\ncertificate = certifi.where()\n\n\n# Configure the database (from MongoDB Tutorial: https://www.mongodb.com/compatibility/setting-up-flask-with-mongodb)\ndef get_db():\n \"\"\"\n Configuration method to return db instance\n \"\"\"\n db = getattr(g, \"_database\", None)\n\n if db is None:\n\n db = g._database = PyMongo(current_app, tlsCAFile=certificate).db\n \n return db\n\n# Use LocalProxy to read the global db instance with just `db`\ndb = LocalProxy(get_db)\n\n\n# ------------ Routing requests to the Flask app ------------ #\n\n@app.route(\"/\")\ndef home_page():\n '''\n Returns html for the home page of the server.\n '''\n return \"

Hello World!

\"\n\n\n@app.route(\"/get-red\")\n@cross_origin()\ndef get_red_counter():\n '''\n Accesses the red counter from the database.\n @return - the red counter document\n '''\n query = {'name': 'red'} # Query for PyMongo\n counter = db.counters.find_one(query, {\"_id\": False}) # Search with the query, removing the _id field\n return counter # Return the database entry\n\n@app.route(\"/get-blue\")\n@cross_origin()\ndef get_blue_counter():\n '''\n Accesses the blue counter from the database.\n @return - the blue counter document\n '''\n query = {'name': 'blue'} # Query for PyMongo\n counter = db.counters.find_one(query, {\"_id\": False}) # Search with the query, removing the _id field\n return counter # Return the database entry\n\n@app.put(\"/set-red\")\n@cross_origin()\ndef set_red_counter():\n '''\n Updates the red counter value in the database.\n Request should contain a body with a 'redCount' field that has\n the value to set the red counter to.\n '''\n\n # Get the PUT request body and access the 'redCount' field\n body = request.json\n redCount = body['redCount']\n\n # Update 1 document matching the query by setting the value of count\n query = {'name': 'red'}\n db.counters.update_one(query, {'$set': {'count': redCount}})\n\n # Return nothing\n return \"\"\n\n@app.put(\"/set-blue\")\n@cross_origin()\ndef set_blue_counter():\n '''\n Updates the blue counter value in the database.\n Request should contain a body with a 'blueCount' field that has\n the value to set the blue counter to.\n '''\n\n # Get the PUT request body and access the 'blueCount' field\n body = request.json\n blueCount = body['blueCount']\n\n # Update 1 document matching the query by setting the value of count\n query = {'name': 'blue'}\n db.counters.update_one(query, {'$set': {'count': blueCount}})\n\n # Return nothing\n return \"\"\n\n\nif __name__ == \"__main__\":\n app.run()","repo_name":"AlexWills37/Fullstack-Tutorial-Code","sub_path":"backend-flask/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74252363469","text":"\"\"\"148. Sort List\n\nSort a linked list in O(n log n) time using constant space complexity.\n\nExample 1:\n\nInput: 4->2->1->3\nOutput: 1->2->3->4\n\nExample 2:\n\nInput: -1->5->3->4->0\nOutput: -1->0->3->4->5\n\"\"\"\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def sortList(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: ListNode\n \"\"\"\n # merge-sort\n # See LC 21 merge two lists\n \n # Nothing needs to be done for linked list with <= 1 nodes\n if not head or not head.next:\n return head\n \n # Use turtle-rabbit two pointers to find the mid-point\n turtle = head\n rabbit = head.next\n \n while rabbit.next:\n rabbit = rabbit.next\n turtle = turtle.next\n if rabbit.next:\n rabbit = rabbit.next\n \n # []------> ... ------> []--------->[]----------> ... -------->[]\n # head turtle turtle.next \n # <=========lower========> <===========upper===========> \n # ^\n # mid-point\n \n # step 1:\n # recurisvely sort `upper`:\n #\n # upper: []----------> ... -------->[]---->None\n \n # step 2:\n # disconnect `lower` from `upper`\n #\n # []------> ... ------> []--->None []----------> ... -------->[]\n # head turtle turtle.next \n # <=========lower========> <===========upper===========> \n \n # step 3:\n # recursively sort `lower`:\n #\n # lower: []------> ... ------> []--->None \n \n # step 4:\n # merged the two sorted list `lower` and `upper`\n \n upper = self.sortList(turtle.next) \n turtle.next = None\n lower = self.sortList(head)\n \n merged = self.mergeTwoLists(lower, upper)\n return merged\n \n def mergeTwoLists(self, l1, l2):\n if not l1:\n return l2\n elif not l2:\n return l1\n \n # `dummy.next` points to the start of a merged list (initially empty)\n #\n # `node` points the end of merged list (initially empty)\n \n \n # [] =======> [] =====> ... ====> [] ======> None\n # dummy node\n \n # [] ===> .... ===> [] ===> ... ===>[] ===> None\n # l1\n #\n # [] ===> .... ===> [] ===> ... ===>[] ===> None\n # l2 \n node = dummy = ListNode(0)\n \n while l1 and l2:\n # `l1` and `l2` points to the head of the two linked lists\n # We pick whichever is smaller, and step the corresponding pointer, `l1` or `l2`\n \n if l1.val < l2.val:\n node.next = l1\n l1 = l1.next\n else:\n node.next = l2\n l2 = l2.next\n \n # We always step `node` \n node = node.next\n \n if l1:\n node.next = l1\n if l2:\n node.next = l2\n \n return dummy.next \n","repo_name":"chao-ji/LeetCode","sub_path":"python/linked_list/lc148.py","file_name":"lc148.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"42470768684","text":"from statsmodels.tsa.seasonal import seasonal_decompose\n\n\ndef seasonal_decomp(df,column,model=\"additive\"):\n sea_decomp_df = df.copy(deep=True)\n sea_decomp_df.index=sea_decomp_df.index.to_timestamp()\n\n s_dec_add = seasonal_decompose(sea_decomp_df[f'{column}'],model=model, period=1).plot()\n s_dec_add.set_size_inches(20, 8)\n return s_dec_add","repo_name":"obaidagh/crypto-analysis-forcasting","sub_path":"analysis/seasonal_decomp.py","file_name":"seasonal_decomp.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17153360160","text":"import traceback\nfrom typing import Any, NamedTuple, Optional\n\nimport graphviz\nimport torch\nimport torch.fx\n\n\nclass TensorMetadata(NamedTuple):\n # TensorMetadata is a structure containing pertinent information\n # about a tensor within a PyTorch program.\n\n # General Tensor metadata\n shape: torch.Size\n dtype: torch.dtype\n requires_grad: bool\n memory_format: Optional[torch.memory_format]\n\n def __repr__(self):\n return \"×\".join(map(str, self.shape))\n\n\ndef _extract_tensor_metadata(result: torch.Tensor) -> TensorMetadata:\n \"\"\"\n Extract a TensorMetadata NamedTuple describing `result`.\n \"\"\"\n shape = result.shape\n dtype = result.dtype\n requires_grad = result.requires_grad\n\n memory_formats = {\n torch.contiguous_format,\n torch.channels_last,\n torch.channels_last_3d,\n }\n\n memory_format = None\n\n for query_format in memory_formats:\n if result.is_contiguous(memory_format=query_format):\n memory_format = query_format\n break\n\n return TensorMetadata(shape, dtype, requires_grad, memory_format)\n\n\nclass ResultProbe(torch.fx.Interpreter):\n def run_node(self, n: torch.fx.Node) -> Any:\n try:\n result = super().run_node(n)\n except Exception:\n traceback.print_exc()\n raise RuntimeError(\n f\"ShapeProp error for: \"\n f\"node={n.format_node()} with \"\n f\"meta={n.meta}\"\n )\n find_tensor_in_result = False\n\n def extract_tensor_meta(obj):\n if isinstance(obj, torch.Tensor):\n nonlocal find_tensor_in_result\n find_tensor_in_result = True\n return _extract_tensor_metadata(obj)\n else:\n return obj\n\n n.meta[\"result\"] = torch.fx.node.map_aggregate(result, extract_tensor_meta)\n n.meta[\"find_tensor_in_result\"] = find_tensor_in_result\n return result\n\n\ndef html_table(*content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + \"\\n\".join(content) + \"
\"\n\n\ndef html_tr(*content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + \"\\n\".join(content) + \"\"\n\n\ndef html_td(content, **kwargs):\n kwargs_pairs = [f'{k}=\"{v}\"' for k, v in kwargs.items()]\n return f'' + str(content) + \"\"\n\n\ndef node_label_html(model, node):\n name = node._pretty_print_target(node.target)\n result = node.meta[\"result\"]\n\n cols = [[html_td(result)]]\n\n if node.op == \"call_module\":\n module = model.get_submodule(node.target)\n head = str(module)\n cols[0] = [html_td(name, rowspan=len(cols)), *cols[0]]\n elif node.op == \"call_method\":\n head = f\".{name}()\"\n elif node.op == \"get_attr\":\n head = f\".{name}\"\n elif node.op == \"call_function\":\n head = f\"{name}()\"\n else:\n head = name\n\n head_kwargs = dict(colspan=len(cols[0]))\n if not node.meta[\"find_tensor_in_result\"]:\n head_kwargs[\"bgcolor\"] = \"lightgray\"\n\n html = html_table(\n html_tr(html_td(head, **head_kwargs)),\n *[html_tr(*c) for c in cols],\n border=0,\n cellborder=1,\n cellspacing=0,\n )\n return f\"<{html}>\"\n\n\ndef single_node(model: torch.nn.Module, graph: graphviz.Digraph, node: torch.fx.Node):\n node_label = node_label_html(model, node)\n node_kwargs = dict(shape=\"plaintext\")\n graph.node(node.name, node_label, **node_kwargs)\n for in_node in node.all_input_nodes:\n edge_kwargs = dict()\n if (\n not node.meta[\"find_tensor_in_result\"]\n or not in_node.meta[\"find_tensor_in_result\"]\n ):\n edge_kwargs.update(dict(style=\"dashed\", color=\"lightgrey\"))\n graph.edge(in_node.name, node.name, **edge_kwargs)\n\n\ndef model_graph(model: torch.nn.Module, *args, **kwargs) -> graphviz.Digraph:\n symbolic_traced: torch.fx.GraphModule = torch.fx.symbolic_trace(model)\n ResultProbe(symbolic_traced).run(*args, **kwargs)\n symbolic_traced.graph.print_tabular()\n graph = graphviz.Digraph(\"model\", format=\"svg\", node_attr={\"shape\": \"plaintext\"})\n for node in symbolic_traced.graph.nodes:\n single_node(model, graph, node)\n return graph\n\n\ndef _test():\n torch.set_grad_enabled(False)\n import networks\n\n model = networks.DISCNet(cond_in_channels=3)\n graph = model_graph(model, torch.randn(1, 3, 512, 512), torch.randn(1, 3, 512, 512))\n graph.render(directory=\"test\", view=True)\n\n\nif __name__ == \"__main__\":\n _test()\n","repo_name":"budui/flare_removal_pytorch","sub_path":"tools/module_graph.py","file_name":"module_graph.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"40885707613","text":"# coding:utf8\r\n\r\n\"\"\"\r\nDescription:LSA/LSI 潜在语义分析/索引\r\nAuthor：伏草惟存\r\nPrompt: code in Python3 env\r\n\"\"\"\r\n\r\nfrom mydict import *\r\nfrom gensim import corpora, models\r\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\r\nimport pickle as pkl\r\n# python的pickle模块实现了基本的数据序列和反序列化。\r\n# 通过pickle模块的序列化操作我们能够将程序中运行的对象信息保存到文件中去，永久存储。\r\n# 通过pickle模块的反序列化操作，我们能够从文件中创建上一次程序保存的对象。\r\n\r\n'''\r\n作者：黄小猿\r\n主题模型（LDA）(一)--通俗理解与简单应用\r\nhttps://blog.csdn.net/qq_39422642/article/details/78730662\r\n\r\n什么是LDA？\r\n它是一种无监督的贝叶斯模型。\r\n是一种主题模型，它可以将文档集中的每篇文档按照概率分布的形式给出。\r\n是一种无监督学习，在训练时不需要手工标注的训练集，需要的是文档集和指定主题的个数。\r\n是一种典型的词袋模型，它认为一篇文档是由一组词组成的集合，词与词之间没有顺序和先后关系。\r\n'''\r\n\r\n# LSA 潜在语义分析\r\ndef gensim_Corpus(corpus=None):\r\n dictionary = corpora.Dictionary(corpus)\r\n # 1 doc_bow转化成tfidf向量\r\n doc_bow_corpus = [dictionary.doc2bow(doc_cut) for doc_cut in corpus]\r\n tfidf_model = models.TfidfModel(dictionary=dictionary) # 生成tfidf模型\r\n tfidf_corpus = [tfidf_model[doc_bow] for doc_bow in doc_bow_corpus] # 将每doc_bow转换成对应的tfidf_doc向量\r\n print('doc_bow转换成对应的tfidf_doc向量:\\n',tfidf_corpus)\r\n\r\n # 2 生成lsi model\r\n lsi_model = models.LsiModel(corpus=tfidf_corpus, id2word=dictionary, num_topics=10)\r\n # 转换成lsi向量\r\n lsi_corpus = [lsi_model[tfidf_doc] for tfidf_doc in tfidf_corpus]\r\n print('LSA生成主题:\\n',lsi_corpus)\r\n\r\n # 3 将lsi模型存储到磁盘上\r\n savepath =r'../dataSet/files/lsi_model.pkl'\r\n lsi_file = open(savepath, 'wb')\r\n pkl.dump(lsi_model, lsi_file)\r\n lsi_file.close()\r\n print('--- lsi模型已经生成 ---')\r\n\r\n\r\nif __name__=='__main__':\r\n # corpus参数样例数据如下：\r\n corpus,classVec = loadDataSet()\r\n gensim_Corpus(corpus)\r\n","repo_name":"bainingchao/DataProcess","sub_path":"GensimVec/LSA.py","file_name":"LSA.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"zh","doc_type":"code","stars":64,"dataset":"github-code","pt":"82"} +{"seq_id":"8820253946","text":"#STEP 14\r\n# https://github.com/puolival/multipy is used\r\n#from statsmodels.stats.tests.test_multi import fdrcorrection\r\nfrom multipy.fdr import lsu\r\nfrom multipy.data import neuhaus\r\nimport numpy as np\r\nimport os\r\ndef step14():\r\n\tprint ('step 14 start')\r\n\ttry:\r\n\t\tff = \"data/SNA_driver_gene_list_FDR5.tsv\"\r\n\t\tif os.path.exists(ff) and os.path.getsize(ff) > 1024:\r\n\t\t\tpass\r\n\t\telse:\r\n\t\t\tp = []\r\n\t\t\tgenes = []\r\n\t\t\twith open(\"data/SNA_classification_genes_NSEI_HISR_Pvalues.tsv\",'r') as f:\r\n\t\t\t\tf.readline()\r\n\t\t\t\tfor line in f:\r\n\t\t\t\t\tgenes.append(line.split(\"\\t\")[0])\r\n\t\t\t\t\tp.append(float(line.split(\"\\t\")[-1]))\r\n\t\t\tp = np.array(p)\r\n\t\t\t#p_fdr = fdrcorrection(p, alpha=0.05)\r\n\r\n\t\t\tp1 = lsu(p, q=0.05)\r\n\t\t\ttrue = []\r\n\t\t\tfor i in range (len(p)):\r\n\t\t\t\tif p1[i] == True:\r\n\t\t\t\t\ttrue.append(genes[i])\r\n\r\n\t\t\twith open(\"data/SNA_classification_genes_NSEI_HISR_Pvalues.tsv\",'r') as f:\r\n\t\t\t\twith open(\"data/SNA_driver_gene_list_FDR5.tsv\",'w') as out:\r\n\t\t\t\t\tout.write(f.readline())\r\n\t\t\t\t\tfor line in f:\r\n\t\t\t\t\t\tif line.split(\"\\t\")[0] in true:\r\n\t\t\t\t\t\t\tout.write(line)\r\n\texcept:\r\n\t\tprint ('check your file in data/ to execute script')","repo_name":"belikov-av/SNADRIF","sub_path":"step_14.py","file_name":"step_14.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"9904746190","text":"# https://leetcode.com/problems/valid-palindrome/\n# https://leetcode.com/problems/valid-palindrome/submissions/1097932406/\nclass Solution:\n chars = ()\n def isPalindrome(self, s: str) -> bool:\n def ignore_char(char:str) -> bool:\n return char.isalnum() == False or char == ' '\n\n start = 0\n end = len(s) - 1\n\n if end <= 0:\n return True # empty string\n\n\n\n while start <= end:\n beginning_char = s[start].lower()\n end_char = s[end].lower()\n\n if ignore_char(beginning_char):\n start = start + 1\n continue\n if ignore_char(end_char):\n end = end - 1\n continue\n\n if beginning_char != end_char:\n return False\n\n else:\n start += 1\n end -= 1\n\n return True\n\n # cheese way lol\n # return input == input[:-1]\n\n\nprint(Solution().isPalindrome(\"race a car\"))\n","repo_name":"Rash20000/leetcode","sub_path":"Two Pointers/valid-palindrome.py","file_name":"valid-palindrome.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19443381281","text":"import segyio\nimport numpy as np\nimport sys\n\n\ndef create_varsize(path, ilines_number, xlines_number, samples_number):\n \"\"\" File with provided dimensions filled wih some data\"\"\"\n spec = segyio.spec()\n\n spec.sorting = 2\n spec.format = 1\n spec.ilines = range(int(ilines_number))\n spec.xlines = range(int(xlines_number))\n spec.samples = range(int(samples_number))\n\n print(\"Creating file with dimensions {}:{}:{}\".format(\n ilines_number, xlines_number, samples_number))\n\n # We use scaling constant of -10, meaning that values will be divided by 10\n # note that lines are not perpendicular\n il_step_x = int(1.1 * 10)\n il_step_y = int(0 * 10)\n xl_step_x = int(0 * 10)\n xl_step_y = int(3.3 * 10)\n ori_x = int(1 * 10)\n ori_y = int(3 * 10)\n\n with segyio.create(path, spec) as f:\n data = -5\n tr = 0\n for il in spec.ilines:\n for xl in spec.xlines:\n f.header[tr] = {\n segyio.su.iline: il,\n segyio.su.xline: xl,\n segyio.su.cdpx:\n (il - spec.ilines[0]) * il_step_x +\n (xl - spec.xlines[0]) * xl_step_x +\n ori_x,\n segyio.su.cdpy:\n (il - spec.ilines[0]) * il_step_y +\n (xl - spec.xlines[0]) * xl_step_y +\n ori_y,\n segyio.su.scalco: -10,\n }\n data = data + 0.00001\n f.trace[tr] = np.linspace(\n start=data, stop=data+2, num=len(spec.samples), dtype=np.single)\n tr += 1\n\n f.bin.update(tsort=segyio.TraceSortingFormat.INLINE_SORTING)\n\n\nif __name__ == \"__main__\":\n path = sys.argv[1]\n ilines = sys.argv[2]\n xlines = sys.argv[3]\n samples = sys.argv[4]\n create_varsize(path, ilines, xlines, samples)\n","repo_name":"equinor/vds-slice","sub_path":"testdata/varsize/make_varsize.py","file_name":"make_varsize.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"42643246017","text":"from django.urls import path\nfrom questions import views\n\napp_name = 'questions'\nurlpatterns = [\n path('', views.QuestionDetailAPI.as_view(), name='question_detail'),\n # path('student_exams', views.StudentExamPrivateListAPI.as_view(), name='student_exam_detail'),\n # path('submit', views.SubmitExamAPI.as_view()),\n path('create', views.CreateQuestionAPI.as_view()),\n path('edit', views.EditQuestionAPI.as_view()),\n path('questions-by-teacher', views.GetQuestionsByTeacher.as_view()),\n path('questions-teacher-remain', views.GetQuestionsByTeacherRemain.as_view()),\n path('add-to-exam', views.AddToExam.as_view())\n\n]","repo_name":"Natsu1270/Ucourse","sub_path":"UCourse/questions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"13376102511","text":"#!/usr/bin/env python\n\nimport argparse\nfrom timedomain.filters import *\nfrom timedomain.iterators import *\nfrom timedomain.sp_utils import *\nimport sys\n\n\n__version__=0.1\n\ndef main(args):\n \"\"\" Main entry point of the app \"\"\"\n print(\"Start \", args)\n logic = getattr(sys.modules[__name__], args.logic)\n iterator = getattr(sys.modules[__name__], args.iterator)\n\n # make this none for results to appear in the notebook\n spdf = [\"diff\",logic.__name__,args.subdir,args.trunk,args.date]\n for pspectra0,pspectra1 in iterator(args.date,subdir=args.subdir,trunk=args.trunk, verbose=True):\n # which of these are real targets\n triggered, diff = logic.filter(pspectra0,pspectra1, norm=True,ston_cut=5)\n\n # plot triggered objects\n if triggered.sum() > 0:\n\n wheretriggered = np.where(triggered)[0]\n\n for sig in wheretriggered.flat:\n SkyPortal.postCandidate(sig, diff.fibermap)\n targetid = diff.fibermap['TARGETID'].data[sig].astype('str')\n SkyPortal.postSpectra(targetid, diff)\n SkyPortal.postSpectra(targetid, pspectra0)\n SkyPortal.postSpectra(targetid, pspectra1)\n logic.plotter(sig,pspectra0, pspectra1, diff, savepdf=spdf)\n print(\"End\")\n \nif __name__ == \"__main__\":\n \n # ./diff.py 20201223 CVLogic Date_SpectraPairs_Iterator daily coadd\n # ./diff.py 20201223 CVLogic Date_TargetPairs_Iterator daily spectra\n \n# date = \"20201223\"\n# subdir = 'daily'\n# trunk='coadd'\n \"\"\" This is executed when run from the command line \"\"\"\n parser = argparse.ArgumentParser()\n\n # Required positional argument\n parser.add_argument(\"date\", help=\"Required positional argument\")\n parser.add_argument(\"logic\", help=\"Required positional argument\") \n parser.add_argument(\"iterator\", help=\"Required positional argument\")\n parser.add_argument(\"subdir\", help=\"Required positional argument\")\n parser.add_argument(\"trunk\", help=\"Required positional argument\")\n \n \n #If there are more than 1 obsdate, provide a 2D array\n parser.add_argument('-o', '--obsdates_tilenumbers', nargs='+', type=str,default=None,\n help='str array with columns obsdate, tilenumber, separated by |')\n \n # Optional argument flag which defaults to False\n# parser.add_argument('-f', '--flag', action=\"store_true\", default=False)\n\n # Optional argument which requires a parameter (eg. -d test)\n# parser.add_argument(\"-n\", \"--name\", action=\"store\", dest=\"name\")\n# parser.add_argument('-i','--iargs', nargs='+', action=\"store\", dest=\"iargs\")\n\n\n # Optional verbosity counter (eg. -v, -vv, -vvv, etc.)\n# parser.add_argument(\n# '-v',\n# '--verbose',\n# action='count',\n# default=0,\n# help=\"Verbosity (-v, -vv, etc)\")\n\n# # Specify output of '--version'\n# parser.add_argument(\n# '--version',\n# action='version',\n# version='%(prog)s (version {version})'.format(version=__version__))\n\n args = parser.parse_args()\n main(args)","repo_name":"desihub/timedomain","sub_path":"timedomain/bin/diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"24008418879","text":"from flask import Flask, render_template, make_response, request, url_for, redirect\nimport random\nimport sys\nimport os\nimport glob\n\napp = Flask(__name__)\n\n@app.route('/')\ndef temp_page():\n response = make_response(render_template('main.html',\n camera = url_for('camera'), led = url_for('led'), water = url_for('water')))\n return response\n \n@app.route('/camera')\ndef camera():\n response = make_response(render_template('camera.html',\n camera_1 = url_for('camera_1'), camera_2 = url_for('camera_2'),\n camera_3 = url_for('camera_3'), camera_4 = url_for('camera_4'),\n camera_5 = url_for('camera_5'), back = url_for('temp_page')))\n return response\n\n@app.route('/led')\ndef led():\n response = make_response(render_template('led.html',\n led_1 = url_for('led_1'), led_2 = url_for('led_2'),\n led_3 = url_for('led_3'), back = url_for('temp_page')))\n return response\n\n@app.route('/water')\ndef water():\n response = make_response(render_template('water.html',\n water_1 = url_for('water_1'), water_2 = url_for('water_2'),\n water_3 = url_for('water_3'), back = url_for('temp_page')))\n return response\n\n@app.route('/camera_1')\ndef camera_1():\n\n pictures = glob.glob(os.path.join(os.getcwd(), \"static/img/*.jpg\"))\n i=0\n for picture in pictures:\n pictures[i] = os.path.basename(pictures[i])\n i+=1\n pictures.sort()\n\n response = make_response(render_template('picture_list.html', back = url_for('camera'),\n image = url_for('image'), pictures = pictures))\n return response\n\n@app.route('/camera_2')\ndef camera_2():\n os.system(os.path.join(os.getcwd(), \"static/img/camera\"))\n\n pictures = glob.glob(os.path.join(os.getcwd(), \"static/img/*.jpg\"))\n i=0\n for picture in pictures:\n pictures[i] = os.path.basename(pictures[i])\n i+=1\n pictures.sort()\n\n response = make_response(render_template('catch.html', picture = url_for('temp_page')+\"static/img/\"+pictures[len(pictures)-1],\n back = url_for('camera')))\n return response\n\n@app.route('/camera_3')\ndef camera_3():\n return redirect(url_for('minute'))\n\n@app.route('/camera_4')\ndef camera_4():\n os.system(\"crontab -r\")\n f = open(\"camera.txt\", 'w')\n f.close()\n what = 'c'\n response = make_response(render_template('cancle.html', back = url_for('camera'), what = 'C'))\n return response\n\n@app.route('/camera_5')\ndef camera_5():\n f = open(\"camera.txt\", 'r')\n minute = \"\"\n hour = \"\"\n week = \"\"\n check = 0\n asc = f.read()\n if len(asc) == 0:\n response = make_response(render_template('time_of_camera_fail.html'), back=url_for('camera'))\n return response\n while asc[check] != ' ':\n check = check + 1\n minute = asc[0:check]\n asc = asc[check+1:]\n check = 0\n while asc[check] != ' ':\n check = check + 1\n hour = asc[0:check]\n asc = asc[check+5:]\n check = 0\n while asc[check] != ' ':\n check = check + 1\n week = asc[0:check]\n\n response = make_response(render_template('time_of_camera.html'), week=week, hour=hour, minute=minute,\n back = url_for('camera'))\n return response\n\n@app.route('/image')\n@app.route('/image/')\ndef image(name):\n response = make_response(render_template('image.html', name=name, delete=url_for('delete_page'),\n list=url_for('camera_1'), back=url_for('temp_page')))\n return response\n\n@app.route('/delete_page')\n@app.route('/delete_page/')\ndef delete_page(name):\n response = make_response(render_template('delete_page.html', delete=url_for('delete'),\n name=name, list=url_for('camera_1')))\n return response\n\n@app.route('/delete')\n@app.route('/delete/')\ndef delete(name):\n os.system(\"rm -r ./static/img/\"+name)\n return redirect(url_for('camera_1'))\n\n@app.route('/minute')\ndef minute():\n response = make_response(render_template('minute.html', cal = url_for('minute_cal')))\n return response\n\n@app.route('/minute_cal', methods=['POST'])\ndef minute_cal():\n f = open(\"camera.txt\", 'w')\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str)\n\n f.close()\n return redirect(url_for('hour'))\n\n@app.route('/hour')\ndef hour():\n response = make_response(render_template('hour.html', cal = url_for('hour_cal')))\n return response\n\n@app.route('/hour_cal', methods=['POST'])\ndef hour_cal():\n f = open(\"camera.txt\", 'a')\n f.write(\" \")\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str)\n\n f.close()\n return redirect(url_for('week'))\n\n@app.route('/week')\ndef week():\n response = make_response(render_template('week.html', cal = url_for('week_cal')))\n return response\n\n@app.route('/week_cal', methods=['POST'])\ndef week_cal():\n f = open(\"camera.txt\", 'a')\n f.write(\" * * \")\n\n list = request.form.getlist('o[]')\n str = \"\"\n\n if list:\n start = 1\n str = str + list[0]\n while start < len(list):\n str = str + \",\" + list[start]\n start = start + 1\n else:\n str = \"*\"\n\n f.write(str+ \" \" +os.path.join(os.getcwd(), \"static/img/camera\\n\"))\n\n f.close()\n os.system(\"crontab -r\")\n os.system(\"crontab camera.txt\")\n return redirect(url_for('camera'))\n\n@app.route('/led_1')\ndef led_1():\n response = make_response(render_template('led_1.html', cal = url_for('led_1_cal')))\n return response\n\n@app.route('/led_2')\ndef led_2():\n os.system(\"/home/pi/embeded/dht -200 100\")\n response = make_response(render_template('cancle.html', back = url_for('led'), what = 'L'))\n return response\n\n@app.route('/led_3')\ndef led_3():\n return redirect(url_for('led'))\n\n@app.route('/led_1_cal', methods=['POST'])\ndef led_2_cal():\n os.system(\"/home/pi/embeded/dht \"+request.form['degree']+\" 100\")\n return redirect('led')\n\n\n@app.route('/water_1')\ndef water_1():\n return redirect(url_for('water'))\n\n@app.route('/water_2')\ndef water_2():\n return redirect(url_for('water'))\n\n@app.route('/water_3')\ndef water_3():\n response = make_response(render_template('cancle.html', back = url_for('water'), what = 'W'))\n return response\n\n@app.route('/water_4')\ndef water_4():\n return redirect(url_for('water'))\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=5000)\n","repo_name":"saemm/embedded-caffeinaddicted","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":6873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8819441566","text":"# -*- coding: utf-8 -*-\nimport os\nimport argparse\nfrom collections import Counter\nimport shutil\nimport gzip\n\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom utils.utils import check_filesize, check_hash, download_ftp\n\n\ndef to_patient(barcode):\n return '-'.join(barcode.split('-')[:3])\n\n'''\ndef filter_val(cna_val, other_val):\n if cna_val * other_val > 0:\n if np.abs(cna_val) == 1 or np.abs(other_val) == 1:\n result = 1 * np.sign(cna_val)\n else:\n result = 2 * np.sign(cna_val)\n else:\n result = 0\n return np.int16(result)\n'''\n\ndef filter_val(cna_val, other_val):\n if cna_val * other_val > 0:\n if np.abs(other_val) == 1:\n result = 1 * np.sign(cna_val)\n else:\n result = 2 * np.sign(cna_val)\n else:\n result = 0\n return np.int16(result)\n\n\ndef decide_by_rows(row1, row2):\n row1 = np.array(row1)\n row2 = np.array(row2)\n return np.max([row1, row2], axis=0)\n\n\ndef find_dtypes(df_path):\n with open(df_path, 'r') as df_file:\n first_line = df_file.readline().replace('\\n', '')\n column_names = first_line.split('\\t')\n dtype_dict = {}\n for column in column_names:\n if 'TCGA' in column:\n dtype_dict[column] = np.int16\n else:\n dtype_dict[column] = str\n return dtype_dict\n\n\ndef step12(input_dir: str = 'data', output_folder_path: str = 'data'):\n\n print('Step 12')\n\n cna_path = os.path.join(input_dir, 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted.tsv')\n rna_path = os.path.join(input_dir, 'EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2-v2.geneExp_primary_whitelisted_median.tsv')\n mi_rna_path = os.path.join(input_dir, 'pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16_primary_whitelisted_median.tsv')\n gene_annot_path = os.path.join(input_dir, 'Homo_sapiens.gene_info')\n\n if not os.path.isfile(gene_annot_path):\n download_link = 'ftp://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz'\n download_ftp(download_link, gene_annot_path+'.gz')\n\n # unzip file\n with gzip.open(gene_annot_path+'.gz', 'rb') as f_in:\n with open(gene_annot_path, 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n\n # remove archive\n os.remove(gene_annot_path+'.gz')\n\n # Check size\n size_pass = check_filesize(gene_annot_path)\n if not size_pass:\n raise Exception(f'file: {gene_annot_path} has wrong size, please check input file and do this step again')\n\n output_file_path = os.path.join(output_folder_path, 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted_RNAfiltered.tsv')\n\n if not os.path.isdir(output_folder_path):\n os.makedirs(output_folder_path)\n\n if not os.path.isfile(output_file_path):\n\n vect_filter_val = np.vectorize(filter_val)\n\n print('Reading gene annotation table')\n required_columns = ['GeneID', 'Symbol', 'Synonyms']\n gene_corresp_df = pd.read_csv(gene_annot_path, sep='\\t', usecols=required_columns)\n\n print('CNA file reading and preprocessing')\n cna_dtypes = find_dtypes(cna_path)\n cna_df = pd.read_csv(cna_path, sep='\\t', header=0, dtype=cna_dtypes)\n cna_df.index = np.array(cna_df['Locus ID'], dtype=int)\n cna_df.drop(columns=['Locus ID'], inplace=True)\n cna_df.rename(columns={'Gene Symbol': 'gene_name'})\n num_columns_cna = len(cna_df.columns)\n cna_df.columns = list(map(to_patient, list(cna_df.columns)))\n cna_df.index.name = 'gene_id'\n\n # not unique genes deduplication\n cna_df = cna_df.loc[cna_df.index.drop_duplicates(keep=False)]\n\n print('RNA file reading and preprocessing')\n rna_dtypes = find_dtypes(rna_path)\n rna_df = pd.read_csv(rna_path, sep='\\t', header=0, dtype=rna_dtypes)\n rna_df.columns = list(map(to_patient, list(rna_df.columns)))\n rna_df.insert(loc=0, column='gene_name', value=[s.split('|')[0].upper() for s in rna_df['gene_id']])\n rna_df['gene_id'] = [int(s.split('|')[1].upper()) for s in rna_df['gene_id']]\n rna_df.index = rna_df['gene_id']\n rna_df.index.name = 'gene_id'\n rna_df.drop(columns=['gene_id'], inplace=True)\n\n # not unique patients investigation\n rna_counter = Counter(list(rna_df.columns))\n not_unique_patients = [patient for patient, count in rna_counter.items() if count > 1]\n # remove those patients\n rna_df.drop(columns=not_unique_patients, inplace=True)\n\n # not unique genes deduplication\n rna_df = rna_df.loc[rna_df.index.drop_duplicates(keep=False)]\n\n # we choose common patients and genes, and apply rule to them\n print('CNA - RNA filtering')\n CNA_RNA_patients = sorted(list(set(rna_df.columns).intersection(set(cna_df.columns))))\n CNA_RNA_genes = sorted(list(set(rna_df.index).intersection(set(cna_df.index))))\n\n #cna_df.loc[CNA_RNA_genes, CNA_RNA_patients] = vect_filter_val(cna_df.loc[CNA_RNA_genes, CNA_RNA_patients],\n # rna_df.loc[CNA_RNA_genes, CNA_RNA_patients])\n cna_df.loc[CNA_RNA_genes, CNA_RNA_patients] = cna_df.loc[CNA_RNA_genes, CNA_RNA_patients]\\\n .combine(rna_df.loc[CNA_RNA_genes, CNA_RNA_patients], vect_filter_val)\n\n print('miRNA file reading and preprocessing')\n mi_rna_dtypes = find_dtypes(mi_rna_path)\n mi_rna_df = pd.read_csv(mi_rna_path, sep='\\t', header=0, dtype=mi_rna_dtypes)\n mi_rna_df.columns = list(map(to_patient, list(mi_rna_df.columns)))\n mi_rna_df['gene_id'] = [x.replace('hsa-', '') for x in mi_rna_df['gene_id']]\n mi_rna_df = mi_rna_df.rename(columns={'gene_id': 'gene_name'})\n mi_rna_df = mi_rna_df.sort_values(by='gene_name')\n mi_rna_df.index = mi_rna_df['gene_name']\n mi_rna_df.drop(columns=['gene_name'], inplace=True)\n\n # find 3-prime, 5-prime pairs\n sorted_gene_names = list(mi_rna_df.index)\n corresp_3_to_5 = dict()\n for row_ix in range(len(sorted_gene_names) - 1):\n if sorted_gene_names[row_ix].replace('3p', '5p') == sorted_gene_names[row_ix + 1]:\n corresp_3_to_5[sorted_gene_names[row_ix]] = sorted_gene_names[row_ix + 1]\n # choose max among them\n for gene_name_3p in corresp_3_to_5:\n gene_name_5p = corresp_3_to_5[gene_name_3p]\n mi_rna_df.loc[gene_name_3p] = decide_by_rows(mi_rna_df.loc[gene_name_3p].values,\n mi_rna_df.loc[gene_name_5p].values)\n # delete unnecessary 5-prime\n indexes_to_delete = list(corresp_3_to_5.values())\n mi_rna_df.drop(index=indexes_to_delete, inplace=True)\n mi_rna_df.index = [x.replace('-3p', '').replace('-5p', '') for x in mi_rna_df.index]\n\n #finally match genes\n corresp_ids = []\n for query_gene in mi_rna_df.index:\n found = False\n for ref_ix, ref_gene_name in enumerate(gene_corresp_df['Symbol']):\n ref_gene_name = ref_gene_name.lower()\n query_gene = query_gene.replace('-', '').lower()\n if 'mir' + query_gene == ref_gene_name or query_gene == ref_gene_name:\n corresp_ids.append(gene_corresp_df['GeneID'][ref_ix])\n found = True\n if not found:\n corresp_ids.append(None)\n\n mi_rna_df = mi_rna_df.reset_index().rename(columns={'index': 'gene_name'})\n mi_rna_df.insert(loc=0, column='gene_id', value=corresp_ids)\n mi_rna_df = mi_rna_df[mi_rna_df['gene_id'].notna()]\n mi_rna_df.index = mi_rna_df['gene_id'].astype(int)\n mi_rna_df = mi_rna_df.drop(columns=['gene_id'])\n\n # not unique patients investigation\n mi_rna_counter = Counter(list(mi_rna_df.columns))\n not_unique_patients = [patient for patient, count in mi_rna_counter.items() if count > 1]\n # remove them\n mi_rna_df.drop(columns=not_unique_patients, inplace=True)\n\n # not unique genes deduplication\n mi_rna_df = mi_rna_df.loc[mi_rna_df.index.drop_duplicates(keep=False)]\n\n\n print('CNA - miRNA filtering')\n CNA_miRNA_patients = sorted(list(set(mi_rna_df.columns).intersection(set(cna_df.columns))))\n CNA_miRNA_genes = sorted(list(set(mi_rna_df.index).intersection(set(cna_df.index))))\n\n #cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients] = vect_filter_val(cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients],\n # mi_rna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients])\n cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients] = cna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients]\\\n .combine(mi_rna_df.loc[CNA_miRNA_genes, CNA_miRNA_patients], vect_filter_val)\n\n print('saving the results')\n cna_df.to_csv(output_file_path, sep = '\\t', header=True, index=True)\n\n # Check for filesize\n size_pass = check_filesize(output_file_path)\n if not size_pass:\n raise Exception(f'file: {output_file_path} has wrong size, please check input file and do this step again')\n\n print('OK')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='This script takes ' +\n 'ISAR_GISTIC.all_thresholded.by_genes_primary_whitelisted.tsv file (CNA),' + '\\n' + \\\n 'EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2-v2.geneExp_primary_whitelisted_quartiles.tsv file (RNA),' + '\\n' + \\\n 'pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16_primary_whitelisted_quartiles.tsv file (miRNA),' \\\n + '\\n' + 'and basically filters CNA file where possible.' + '\\n' + \\\n 'If output folder does not exist, script will create it.')\n parser.add_argument('-i', '--input_dir', type=str, help='full path to input folder', default='data')\n parser.add_argument('-o', '--output_folder', type=str, help='full path to output folder', default='data')\n\n args = parser.parse_args()\n\n step12(args.input_dir , args.output_folder)\n","repo_name":"belikov-av/GECNAV","sub_path":"step_12.py","file_name":"step_12.py","file_ext":"py","file_size_in_byte":10329,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70483094988","text":"# discounting.py: Discounted return functions\n#\n# (C) 2020, Daniel Mouritzen\n\nfrom typing import Optional, Tuple, Union\n\nimport tensorflow as tf\n\nfrom .general import move_dim, scan\n\n\ndef discounted_return(rewards: tf.Tensor,\n discount: Union[tf.Tensor, float],\n final_value: Optional[tf.Tensor] = None,\n axis: int = 1,\n stop_gradient: bool = True,\n ) -> tf.Tensor:\n \"\"\"\n Calculate discounted return as per this formula:\n V[t] = sum(discount**n * rewards[t+n] for n in range(len(rewards)-t)) + discount**(len(rewards)-t) * final_value\n\n For numerical stability, it is implemented recursively:\n V[last+1] = final_value\n V[t] = rewards[t] + discount * V[t + 1]\n \"\"\"\n if isinstance(discount, (float, int)) or discount.shape.num_elements() == 1:\n if discount == 1:\n return_ = tf.reduce_sum(rewards, axis)\n if final_value is not None:\n return_ += final_value\n return return_\n discount = discount * tf.ones_like(rewards)\n else:\n assert rewards.shape == discount.shape, (rewards.shape, discount.shape)\n if final_value is None:\n final_value = tf.zeros_like(rewards[-1])\n return_ = scan(fn=lambda accumulated, current: current[0] + current[1] * accumulated,\n elems=(rewards, discount),\n initializer=final_value,\n back_prop=not stop_gradient,\n axis=axis,\n reverse=True)\n if stop_gradient:\n return_ = tf.stop_gradient(return_)\n return return_\n\n\ndef lambda_return(rewards: tf.Tensor,\n values: tf.Tensor,\n discount: Union[tf.Tensor, float],\n lambda_: float,\n final_value: Optional[tf.Tensor] = None,\n axis: int = 1,\n stop_gradient: bool = True,\n ) -> tf.Tensor:\n \"\"\"\n Calculate lambda return as per this formula:\n dr(t, n) = discounted_return(reward[t:t + n], discount[t:t + n], values[t + n])[0]\n V[t] = ((1 - lambda_) * sum(lambda_**(n - 1) * dr(t, n) for n in range(1, T - t))\n + lambda_**(T - t - 1) * dr(t, T - t))\n\n For numerical stability, it is implemented recursively:\n V[last+1] = final_value\n V[t] = rewards[t] + discount * ((1 - lambda_) * values[t + 1] + lambda * V[t + 1])\n\n Setting lambda=1 gives a discounted Monte Carlo return.\n Setting lambda=0 gives a fixed 1-step return.\n \"\"\"\n if isinstance(discount, (int, float)) or discount.shape.num_elements() == 1:\n discount = discount * tf.ones_like(rewards)\n assert rewards.shape == values.shape == discount.shape, 'Incompatible shapes!'\n rewards, values, discount = move_dim((rewards, values, discount), axis, 0)\n if final_value is None:\n final_value = tf.zeros_like(values[-1])\n next_values = tf.concat([values[1:], final_value[tf.newaxis]], 0)\n\n def fn(accumulated: tf.Tensor, current: Tuple[tf.Tensor, tf.Tensor, tf.Tensor]) -> tf.Tensor:\n reward, next_value, d = current\n return reward + d * ((1 - lambda_) * next_value + lambda_ * accumulated)\n\n return_ = scan(fn=fn,\n elems=(rewards, next_values, discount),\n initializer=final_value,\n back_prop=not stop_gradient,\n axis=0,\n reverse=True)\n if stop_gradient:\n return_ = tf.stop_gradient(return_)\n return move_dim(return_, 0, axis)\n","repo_name":"danmou/MerCur-Re","sub_path":"project/util/tf/discounting.py","file_name":"discounting.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"40689092916","text":"\"\"\"Global fixture functions.\"\"\"\n\n# pylint: disable = redefined-outer-name\n\nfrom collections.abc import AsyncGenerator, Callable, Generator\n\nimport aiohttp\nimport pytest\nfrom aioresponses import aioresponses\n\nfrom pyalarmdotcomajax import AlarmController\nfrom pyalarmdotcomajax import const as c\nfrom pyalarmdotcomajax.devices.registry import AttributeRegistry, DeviceType\nfrom pyalarmdotcomajax.extensions import CameraSkybellControllerExtension\n\nfrom .responses import get_http_body_html, get_http_body_json\n\n\n@pytest.fixture\ndef response_mocker() -> Generator:\n \"\"\"Yield aioresponses.\"\"\"\n with aioresponses() as mocker:\n yield mocker\n\n\n@pytest.fixture\n@pytest.mark.asyncio\nasync def adc_client() -> AsyncGenerator:\n \"\"\"Build and return dummy controller for testing without Alarm.com API.\"\"\"\n\n async with aiohttp.ClientSession() as websession:\n yield AlarmController(\n username=\"test-username\",\n password=\"hunter2\", # noqa: S106\n websession=websession,\n twofactorcookie=\"test-cookie\",\n )\n\n\n@pytest.fixture\ndef all_base_ok_responses(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"Shortcut for including all mocked success responses immediately.\"\"\"\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef all_base_ok_responses_callable(response_mocker: aioresponses) -> Callable:\n \"\"\"Shortcut for including all mocked success responses on demand.\"\"\"\n\n def _load_mocks(repeat: bool = True) -> None:\n ############\n ### META ###\n ############\n\n response_mocker.get(\n url=c.TROUBLECONDITIONS_URL_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"trouble_conditions_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_SYSTEMS_URL_TEMPLATE.format(c.URL_BASE),\n status=200,\n body=get_http_body_json(\"available_systems_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=c.IDENTITIES_URL_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"identities_ok\"),\n repeat=repeat,\n )\n\n ###############\n ### DEVICES ###\n ###############\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.SYSTEM)[\"primary\"].format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"system_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.IMAGE_SENSOR)[\"primary\"].format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"image_sensors_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AttributeRegistry.get_endpoints(DeviceType.SCENE)[\"primary\"].format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"scenes_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_DEVICES_URL_TEMPLATE.format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"device_catalog_ok\"),\n repeat=repeat,\n )\n\n response_mocker.get(\n url=AlarmController.ALL_RECENT_IMAGES_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"recent_images_ok\"),\n repeat=repeat,\n )\n\n ##################\n ### EXTENSIONS ###\n ##################\n\n response_mocker.get(\n url=CameraSkybellControllerExtension.ENDPOINT.format(c.URL_BASE),\n status=200,\n body=get_http_body_html(\"camera_settings_skybell\"),\n repeat=True,\n )\n\n return _load_mocks\n\n\n@pytest.fixture\ndef image_sensors_no_permission(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"No permission to view devices.\"\"\"\n\n response_mocker.get(\n url=AlarmController.ALL_RECENT_IMAGES_TEMPLATE.format(c.URL_BASE, \"\"),\n status=200,\n body=get_http_body_json(\"processing_error\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef skybell_missing_video_quality_field(\n response_mocker: aioresponses, all_base_ok_responses_callable: Callable\n) -> None:\n \"\"\"Shortcut for including all mocked success responses.\"\"\"\n\n ##################\n ### EXTENSIONS ###\n ##################\n\n response_mocker.get(\n url=CameraSkybellControllerExtension.ENDPOINT.format(c.URL_BASE),\n status=200,\n body=get_http_body_html(\"camera_settings_skybell_missing_video_quality_field\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n\n\n@pytest.fixture\ndef device_catalog_no_permissions(response_mocker: aioresponses, all_base_ok_responses_callable: Callable) -> None:\n \"\"\"Shortcut for including all mocked success responses.\"\"\"\n\n response_mocker.get(\n url=AlarmController.ALL_DEVICES_URL_TEMPLATE.format(c.URL_BASE, \"id-system\"),\n status=200,\n body=get_http_body_json(\"no_permissions_or_invalid_antiforgery\"),\n repeat=True,\n )\n\n all_base_ok_responses_callable()\n","repo_name":"pyalarmdotcom/pyalarmdotcomajax","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"82"} +{"seq_id":"43666182996","text":"from typing import List\nimport nltk\nimport numpy as np\nfrom gensim.models.doc2vec import TaggedDocument, Doc2Vec\nfrom numpy.linalg import norm\n\ntry:\n nltk.data.find('tokenizers/punkt')\nexcept LookupError:\n nltk.download('punkt')\n\nDEFAULT_PARAMS = dict(vector_size=99, window=5, min_count=1, workers=4)\n\n\nclass Doc2VecModel:\n \"\"\"\n Doc2Vec maps documents, or a collection of words, to vectors in R^d space. The inverse distance between two vectors\n in this high-dimensional space gives us a concept of similarity between documents.\n\n Reference: https://radimrehurek.com/gensim/models/doc2vec.html\n \"\"\"\n\n def __init__(self, params=DEFAULT_PARAMS):\n self.model = Doc2Vec(**params)\n\n def train_model(self, documents: List[str]):\n print(\"Training doc2vec model...\", end=\" \")\n word_tokens = [nltk.tokenize.word_tokenize(r) for r in documents]\n documents = [TaggedDocument(r, [i]) for i, r in enumerate(word_tokens)]\n self.model.build_vocab(documents)\n self.model.train(documents, total_examples=len(documents), epochs=10)\n print(\"Done!\\n\")\n\n def to_vector(self, doc: str) -> np.ndarray:\n tokens = nltk.tokenize.word_tokenize(doc)\n return self.model.infer_vector(tokens)\n\n def pairwise_similarity(self, doc: str, other_doc: str) -> float:\n distance = self.to_vector(doc) - self.to_vector(other_doc)\n return 1 / norm(distance)\n","repo_name":"TheShiya/yelp-review-summarizer","sub_path":"summarize/doc2vec.py","file_name":"doc2vec.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"25945832289","text":"import logging\nimport vtk, qt\n\nimport slicer\nfrom slicer.ScriptedLoadableModule import *\nfrom slicer.util import VTKObservationMixin\n\nimport numpy as np\n\n'''=================================================================================================================='''\n'''=================================================================================================================='''\n'''------------------------- STRING Macro of sl__US_SeqViewer ------------------------------------------------------'''\n'''------------------------------------------------------------------------------------------------------------------'''\nINT_SliderFrameIndex_Min = 1 # StartingValue of slider_FrameIndex, increase from 1\nINT_FRAME_INDEX_SLIDER_DEFAULT = 50 # Default slider_FrameIndex value\nINT_FRAME_INDEX_SLIDER_DEFAULT_MAX = 99 # Default slider_FrameIndex maximum\n\n# ReferenceRole\nSTR_SeqBrowserNode_RefRole_Selected = 'SeqBrowser_Ref_CurSelected'\n\n\n\n'''=================================================================================================================='''\n#\n# sl__US_SeqViewer\n#\nclass sl__US_SeqViewer(ScriptedLoadableModule):\n\n def __init__(self, parent):\n ScriptedLoadableModule.__init__(self, parent)\n self.parent.title = \"sl__US_SeqViewer\"\n self.parent.categories = [\"SL_Tutorials\"] # Set categories (the module shows up in the module selector)\n self.parent.dependencies = [\"Markups\"] # Add here list of module names that this module requires\n self.parent.contributors = [\"Sen Li (École de Technologie Supérieure)\"]\n # TODO: 10. update with a link to online module Tutorial\n self.parent.helpText = \"\"\"This is sl__US_SeqViewer ! \"\"\"\n self.parent.helpText += self.getDefaultModuleDocumentationLink()\n self.parent.acknowledgementText = 'Step-by-step tutorial on 3D Slicer extension development. ' \\\n '\\nThis file was originally developed by Sen Li, LATIS, École de techonologie supérieure. ' \\\n '\\nSen.Li.1@ens.etsmtl.ca'\n\n print(\"sl__US_SeqViewer(ScriptedLoadableModule): __init__(self, parent)\")\n\n'''=================================================================================================================='''\nclass sl__US_SeqViewerWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):\n\n def __init__(self, parent=None):\n \"\"\" Called when the user opens the module the first time and the widget is initialized. \"\"\"\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None\n self._parameterNode = None # Singleton initialized through self.setParameterNode(self.logic.getParameterNode())\n self._updatingGUIFromParameterNode = False\n\n print(\"**Widget.__init__(self, parent)\")\n\n def setup(self):\n print(\"**Widget.setup(self), \\tSL_Developer\")\n \"\"\" 00. Called when the user opens the module the first time and the widget is initialized. \"\"\"\n ScriptedLoadableModuleWidget.setup(self)\n\n # 01. Load widget from .ui file (created by Qt Designer).\n # Additional widgets can be instantiated manually and added to self.layout.\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/sl__US_SeqViewer.ui'))\n self.layout.addWidget(uiWidget)\n self.ui = slicer.util.childWidgetVariables(uiWidget)\n\n # 02. Set scene in MRML widgets. Make sure that in Qt designer the\n # top-level qMRMLWidget's \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to\n # each MRML widget's \"setMRMLScene(vtkMRMLScene*)\" slot.\n uiWidget.setMRMLScene(slicer.mrmlScene)\n\n # 03. Create logic class. Logic implements all computations that should be possible to run\n # in batch mode, without a graphical user interface.\n self.logic = sl__US_SeqViewerLogic()\n\n # 04. Connections, ensure that we update parameter node when scene is closed\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)\n self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)\n\n # 05. SL_Developer. Connect Signal-Slot, ensure that whenever user changes some settings on the GUI,\n # that is saved in the MRML scene (in the selected parameter node).\n self.ui.sequenceSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelectedNodeChanged)\n slicer.modules.sequences.toolBar().activeBrowserNodeChanged.connect(self.onSelectedNodeChanged)\n\n self.ui.slider_SeqFrame.connect(\"valueChanged(int)\", self.onSliderFrameIndex_ValueChanged)\n\n\n # 06. Needed for programmer-friendly Module-Reload where the Module had already been enter(self)-ed;\n # Otherwise, will initial through function enter(self)\n if self.parent.isEntered:\n self.initializeParameterNode() # Every-Module own a Singleton ParameterNode track by **Logic.moduleName!\n\n # ------------------------------------------------------------------------------------------------------------------\n def cleanup(self):\n \"\"\" Called when the application closes and the module widget is destroyed. \"\"\"\n print(\"**Widget.cleanup(self)\")\n self.removeObservers()\n\n # ------------------------------------------------------------------------------------------------------------------\n def enter(self):\n \"\"\" Called each time the user opens this module. \"\"\"\n print(\"**Widget.enter(self)\")\n\n # 01. Slicer. SL__Note: Every-Module own a Singleton ParameterNode that can be identified by\n # self._parameterNode.GetAttribute('ModuleName')! Need to initial every Entry!\n self.initializeParameterNode()\n\n # ------------------------------------------------------------------------------------------------------------------\n def exit(self):\n \"\"\" Called each time the user opens a different module. \"\"\"\n print(\"**Widget.exit(self)\")\n # Slicer. Do not react to parameter node changes (GUI will be updated when the user enters into the module)\n self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSceneStartClose(self, caller, event):\n \"\"\" Called just before the scene is closed. \"\"\"\n print(\"**Widget.onSceneStartClose(self, caller, event)\")\n\n # Slicer. Parameter node will be reset, do not use it anymore\n self.setParameterNode(None)\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSceneEndClose(self, caller, event):\n \"\"\" Called just after the scene is closed. \"\"\"\n print(\"**Widget.onSceneEndClose(self, caller, event)\")\n # If this module is shown while the scene is closed then recreate a new parameter node immediately\n if self.parent.isEntered:\n self.initializeParameterNode()\n\n # ------------------------------------------------------------------------------------------------------------------\n def initializeParameterNode(self):\n \"\"\" Ensure parameter node exists and observed. \"\"\"\n # 01. Slicer-Initial: the Singleton ParameterNode stores all user choices in param-values, node selections...\n # so that when the scene is saved and reloaded, these settings are restored.\n self.setParameterNode(self.logic.getParameterNode())\n\n # 02. SL_Developer. To update ParameterNode and attach observers\n pass\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def setParameterNode(self, inputParameterNode):\n \"\"\" SL_Notes: Set and observe the Singleton ParameterNode.\n Observation is needed because when ParameterNode is changed then the GUI must be updated immediately.\n \"\"\"\n print(\"**Widget.setParameterNode(self, inputParameterNode)\")\n if inputParameterNode:\n if not inputParameterNode.IsSingleton():\n raise ValueError(f'SL__Allert! \\tinputParameterNode = \\n{inputParameterNode.__str__()}')\n self.logic.setDefaultParameters(inputParameterNode)\n\n # 01. Unobserve previously selected Singleton ParameterNode;\n if self._parameterNode is not None:\n self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n # 02. Set new Singleton ParameterNode and Add an observer to the newly selected\n self._parameterNode = inputParameterNode\n if self._parameterNode is not None:\n self.addObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n # 03. Initial GUI update; need to do this GUI update whenever there is a change from the Singleton ParameterNode\n self.updateGUIFromParameterNode()\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section I: get, set, obtain ================================\n # ------------------------------------------------------------------------------------------------------------------\n def getSelectedItemNumber_FromGUI_Slider(self):\n # Slider FrameIndex starts from 1, but idx_SelectedItemNumber starts 0.\n idx_CurSeqBrowser_SelectedItemNumber = self.ui.slider_SeqFrame.value - INT_SliderFrameIndex_Min\n return idx_CurSeqBrowser_SelectedItemNumber\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section II-A: updateGUIFromParameterNode__ & Slots that call uiUpdate =\n # ------------------------------------------------------------------------------------------------------------------\n def updateGUIFromParameterNode(self, caller=None, event=None):\n \"\"\" This method is called whenever parameter node is changed.\n The module GUI is updated to show the current state of the parameter node. \"\"\"\n # 00. Check self._updatingGUIFromParameterNode to prevent from GUI changes\n # (it could cause infinite loop: GUI change -> UpdateParamNode -> Update GUI -> UpdateParamNode)\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # I. Open-Brace: Make sure GUI changes do not call updateParameterNodeFromGUI__ (it could cause infinite loop)\n self._updatingGUIFromParameterNode = True\n # --------------------------------------------------------------------------------------------------------------\n # II. SL_Developer, C: In-Brace, Update UI widgets ()\n print(\"**Widget.updateGUIFromParameterNode(self, caller=None, event=None), \\tSL_Developer\")\n # II-01. Update Values of Node-Selectors (qMRMLNodeComboBox)\n nodeSeqBrowser_Selected = self._parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n self.ui.sequenceSelector.setCurrentNode(nodeSeqBrowser_Selected)\n # II-02. Update Status of slider_SeqFrame, and label_FrameIndex: QLabel, Sliders (ctkSliderWidget)\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(nodeSeqBrowser_Selected)\n\n # --------------------------------------------------------------------------------------------------------------\n # III. Close-Brace: All the GUI updates are done;\n self._updatingGUIFromParameterNode = False\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def onSliderFrameIndex_ValueChanged(self, caller=None, event=None):\n ''' SL_Notes: Not UserOnly function, can be called when a target_ControlPoint is selected! ''' ''''''\n # 00. Check Singleton ParameterNode: in case of enter() or onSceneStartClose()\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # 01. LogicUpdate: nodeSeqBrowser's Current-SelectedItemNumber\n idx_CurFrame = self.getSelectedItemNumber_FromGUI_Slider()\n self.logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(idx_CurFrame)\n print(f'\\t**Widget.onSliderFrameIndex_ValueChanged,\\tidx_CurFrame = {idx_CurFrame}')\n\n # 02. uiUpdate: LandmarkPositionLabels\n self._updatingGUIFromParameterNode = True # I. Open-Brace: Avoid updateParameterNodeFromGUI__ (infinite loop)\n self.uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex() # II. In-Brace: uiUpdate\n self._updatingGUIFromParameterNode = False # III. Close-Brace: All the GUI updates are done;\n\n # ------------------------------------------------------------------------------------------------------------------\n def onSelectedNodeChanged(self, node_NewActiveBrowser=None, event=None):\n ''' SL_Notes: Not UserOnly function, can be called when a target_ControlPoint is selected! ''' ''''''\n print(f\"\\nBeginning of **Widget.onSelectedNodeChanged(): \\tnode_NewActiveBrowser =\"\n f\" {node_NewActiveBrowser.GetID() if node_NewActiveBrowser else type(node_NewActiveBrowser)}\")\n # 00-A. Check Singleton ParameterNode: important test for every NodeChange Slot, in case of onSceneStartClose()\n # Check _updatingGUIFromParameterNode: avoid bugs introduced by Slicer (PointAdded, PointPositionDefined)\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n # 00-B. Check the validity of node_NewActiveBrowser\n if not node_NewActiveBrowser:\n return\n\n # 01. LogicUpdate\n self.updateParameterNodeFromGUI__Set_RefRoleNodeID(STR_SeqBrowserNode_RefRole_Selected, node_NewActiveBrowser.GetID())\n\n # 02. uiUpdate: update slider_SeqFrame\n if self.parent.isEntered:\n # I. Open-Brace: Make sure GUI changes do not call updateParameterNodeFromGUI__ (it could cause infinite loop)\n self._updatingGUIFromParameterNode = True\n # --------------------------------------------------------------------------------------------------------------\n # II-02-A. Re-Set sequenceSelector, just in case the Signal sender is Sequences.toolBar()\n self.ui.sequenceSelector.setCurrentNode(node_NewActiveBrowser)\n # II-02-B. Re-Set modules.sequences active SeqBrowser, just in case the Signal sender is Laminae-Labeling\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(node_NewActiveBrowser)\n # II-02-C. Push Slicer Screen refresh before uiUpdate\n self.uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser(node_NewActiveBrowser)\n # II-02-D. Start uiUpdate\n self.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(node_NewActiveBrowser)\n # --------------------------------------------------------------------------------------------------------------\n # III. Close-Brace: All the GUI updates are done;\n self._updatingGUIFromParameterNode = False\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ----------- Section II-B: Sub-Functions called by updateGUIFromParameterNode__ or Slot functions ---\n # ----- 1. All sub-functions starts with uiUpdate ----------------------------------------------------\n # ----- 2. All uiUpdate functions canNOT set self._updatingGUIFromParameterNode ----\n # ----- 3. The superior function who call uiUpdate function MUST set self._updatingGUIFromParameterNode ----\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(self, nodeSeqBrowser_Selected):\n ''' **Widget.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(self, nodeSeqBrowser_Selected) ''' ''''''\n if nodeSeqBrowser_Selected:\n str_CurSeqBrowser_ID = 'nodeSeqBrowser_Selected.GetID() = ' + nodeSeqBrowser_Selected.GetID()\n str_NumberOfItems = '.GetNumberOfItems() = ' + str(nodeSeqBrowser_Selected.GetNumberOfItems())\n str_idxFrame = f', \\tidxFrame = {self.logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(nodeSeqBrowser_Selected)}'\n else:\n str_CurSeqBrowser_ID = 'CurSeqBrowser.GetID() = ' + str(type(nodeSeqBrowser_Selected))\n str_NumberOfItems = ''\n str_idxFrame = ''\n print(f\"\\t**Widget.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(), {str_CurSeqBrowser_ID}, {str_NumberOfItems}{str_idxFrame}\")\n\n if nodeSeqBrowser_Selected and nodeSeqBrowser_Selected.GetNumberOfItems() > 0:\n self.ui.slider_SeqFrame.enabled = True\n self.ui.slider_SeqFrame.minimum = INT_SliderFrameIndex_Min\n self.ui.slider_SeqFrame.maximum = nodeSeqBrowser_Selected.GetNumberOfItems()\n self.ui.slider_SeqFrame.value = self.logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(nodeSeqBrowser_Selected)\n self.ui.label_FrameIndex.setText(str(self.ui.slider_SeqFrame.value))\n else:\n # No SequenceBrowser_Node available, so we disable the slider_SeqFrame, and set label_FrameIndex 'N/A'\n self.ui.slider_SeqFrame.enabled = False\n self.ui.slider_SeqFrame.minimum = INT_SliderFrameIndex_Min\n self.ui.slider_SeqFrame.maximum = INT_FRAME_INDEX_SLIDER_DEFAULT_MAX\n self.ui.slider_SeqFrame.value = INT_FRAME_INDEX_SLIDER_DEFAULT\n self.ui.label_FrameIndex.setText('N/A')\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(self, node_NewActiveBrowser):\n ''' **Widget.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(self, nodeTarget_SeqBrowser) ''' ''''''\n # 00-A. Check if the module isEntered\n if not self.parent.isEntered: return\n # 00-B. Check the validity of nodeTarget_SeqBrowser\n if not node_NewActiveBrowser: return\n\n # 01. Update slider_SeqFrame\n if node_NewActiveBrowser:\n str_CurSeqBrowser_ID = 'node_NewActiveBrowser.GetID() = ' + node_NewActiveBrowser.GetID()\n str_NumberOfItems = 'idx_SeqBrowserSelectedItem = ' + str(node_NewActiveBrowser.GetNumberOfItems())\n else:\n str_CurSeqBrowser_ID = 'node_NewActiveBrowser.GetID() = ' + str(type(node_NewActiveBrowser))\n str_NumberOfItems = ''\n print(f\"\\t**Widget.uiUpdate_SwitchSelection_ChangeSeqBrowser_RemainFrameIndex(), {str_CurSeqBrowser_ID}, {str_NumberOfItems}\")\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(node_NewActiveBrowser)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self):\n ''' **Widget.uiUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self) \n There are two modes to trigger this uiUpdate: UI modified / Non-UI (node) modified.\n To guarantee the Non-UI mode, we will update all UI widgets (including the possible TriggerMan UI widget).\n All uiUpdate can be done by logicUpdated nodeSeqBrowser_Selected, thus argument idx_TargetFrame NotRequired.\n ''' ''''''\n # 00-A. Check if the module isEntered\n if not self.parent.isEntered: return\n # 00-B. Check the validity of nodeSeqBrowser_Selected\n nodeSeqBrowser_Selected = self._parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n if not nodeSeqBrowser_Selected: return\n\n # 01. Update the uiSlider\n self.uiUpdate_Slider_SeqFrame__by__nodeSeqBrowser_Selected(nodeSeqBrowser_Selected)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n def uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser(self, nodeTarget_SeqBrowser):\n print(f' **Widget.uiUpdate_PushSlicerScreenUpdate_by_ShakeTargetSeqBrowser()')\n if nodeTarget_SeqBrowser:\n # Let's push Slicer to update by Setting current selected frame back and forth\n idx_curFrame = nodeTarget_SeqBrowser.GetSelectedItemNumber()\n\n nodeTarget_SeqBrowser.SetSelectedItemNumber(max(idx_curFrame - 1, 0))\n nodeTarget_SeqBrowser.SetSelectedItemNumber(min(idx_curFrame + 1, nodeTarget_SeqBrowser.GetNumberOfItems() - 1))\n nodeTarget_SeqBrowser.SetSelectedItemNumber(idx_curFrame)\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section IV: updateParameterNodeFromGUI__ ==============================\n # ------------------------------------------------------------------------------------------------------------------\n def updateParameterNodeFromGUI__Set_RefRoleNodeID(self, STR_RefRole, str_NodeID):\n \"\"\" Read GUI Method: Method updateParameterNodeFromGUI__ is called when users makes any change in the GUI.\n Changes are saved into the parameter node (so that they are restored when the scene is saved and loaded).\n **Widget.updateParameterNodeFromGUI__Set_RefRoleNodeID(self, STR_RefRole, str_NodeID） \"\"\"\n if self._parameterNode is None or self._updatingGUIFromParameterNode:\n return\n\n # I. Before updating the Singleton ParameterNode; Disable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n wasModified = self._parameterNode.StartModify() # Modify all properties in a single batch\n\n # II. Update the Singleton ParameterNode; No updateGUIFromParameterNode triggered in this step\n node_BeforeChange = self._parameterNode.GetNodeReference(STR_RefRole)\n if node_BeforeChange: str_NodeBeforeChange = self._parameterNode.GetNodeReference(STR_RefRole).GetID()\n else: str_NodeBeforeChange = \"\"\n print(f'\\tBefore Update: {str_NodeBeforeChange}')\n self._parameterNode.SetNodeReferenceID(STR_RefRole, str_NodeID)\n print(f'\\tAfter Update: {self._parameterNode.GetNodeReference(STR_RefRole).GetID()}')\n\n # III. After updating the Singleton ParameterNode; Enable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n self._parameterNode.EndModify(wasModified)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n''' ================================================================================================================='''\n#\n# sl__US_SeqViewerLogic\n#\nclass sl__US_SeqViewerLogic(ScriptedLoadableModuleLogic):\n \"\"\" The Logic class is : to facilitate dynamic reloading of the module without restarting the application.\n This class should implement all the actual computation done by your module. \n The interface should be such that other python code can import this class \n and make use of the functionality without requiring an instance of the Widget.\n Uses ScriptedLoadableModuleLogic base class, available at:\n https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py\n \"\"\"\n\n def __init__(self):\n \"\"\" Called when the logic class is instantiated. Can be used for initializing member variables. \"\"\"\n ScriptedLoadableModuleLogic.__init__(self)\n\n self._isSwitchingSeqBrowser = False\n\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VI: get, set, obtain, & createNewNode =====================\n # ------------------------------------------------------------------------------------------------------------------\n def obtain_idxSliderCurFrame_from_TargetSeqBrowser(self, nodeTarget_SeqBrowser):\n ''' **Logic.obtain_idxSliderCurFrame_from_TargetSeqBrowser(self, nodeTarget_SeqBrowser) ''' ''''''\n idx_SliderCurFrame = nodeTarget_SeqBrowser.GetSelectedItemNumber() + INT_SliderFrameIndex_Min\n return idx_SliderCurFrame\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VII-A: logicUpdate & Functions that call paramNodeUpdate ====\n # ------------------------------------------------------------------------------------------------------------------\n def setDefaultParameters(self, parameterNode):\n \"\"\" SL_Developer, B: Initialize parameter node, Re-Enter, Re-Load. \"\"\"\n print(\"**Logic.setDefaultParameters(self, parameterNode), \\tSL_Developer, B\");\n # I. Before updating the Singleton ParameterNode; Disable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n wasModified = parameterNode.StartModify() # Modify all properties in a single batch\n # --------------------------------------------------------------------------------------------------------------\n # II. Update the Singleton ParameterNode; No updateGUIFromParameterNode triggered in this step\n # II-01. Set NodeRef for curSelected SeqBrowser, select the first if not selected\n if not parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected):\n node_SeqBrowser_First = slicer.mrmlScene.GetFirstNodeByClass(\"vtkMRMLSequenceBrowserNode\")\n if node_SeqBrowser_First:\n # II-01-A. Set NodeRefID for paramNode\n parameterNode.SetNodeReferenceID(STR_SeqBrowserNode_RefRole_Selected, node_SeqBrowser_First.GetID())\n # II-01-B. Synchronize with modules.sequences's SequenceBrowser active node\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(node_SeqBrowser_First)\n else:\n # II-01-C. Already got NodeRefID for paramNode, we only need to Synchronize with modules.sequences\n nodeSeqBrowser_Selected = parameterNode.GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n slicer.modules.sequences.widgetRepresentation().setActiveBrowserNode(nodeSeqBrowser_Selected)\n\n # --------------------------------------------------------------------------------------------------------------\n # III. After updating the Singleton ParameterNode; Enable Modify events, e.g., vtk.vtkCommand.ModifiedEvent\n parameterNode.EndModify(wasModified)\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ----------- Section VII-B: Sub-Functions with prefix/surfix paramNodeUpdate ----------------------\n # ----- 1. All sub-functions prefix/surfix with paramNodeUpdate; --------------------------------------\n # ------2. All paramNodeUpdate functions canNOT self.getParameterNode().StartModify() ---\n # ------3. The superior function who call paramNodeUpdate function MUST self.getParameterNode().StartModify() ---\n # ------------------------------------------------------------------------------------------------------------------\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # ==================================================================================================================\n # ==================================================================================================================\n # =========== SL_Developer, Section VIII: Other Logic Functions ===============================\n # ------------------------------------------------------------------------------------------------------------------\n # --------------- Section VIII-01: Boolean (is_) Functions ---------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def isValid_idxTargetFrame(self, nodeSeqBrowser, idx_TargetFrame):\n ''' **Logic.isValid_idxTargetFrame(self, nodeSeqBrowser, idx_TargetFrame) ''' ''''''\n if nodeSeqBrowser and idx_TargetFrame >=0 and idx_TargetFrame < nodeSeqBrowser.GetNumberOfItems():\n return True;\n else:\n return False\n\n\n # ------------------------------------------------------------------------------------------------------------------\n # --------------- Section VIII-02: Set / Update Functions ---------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n # ------------------------------------------------------------------------------------------------------------------\n def logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self, idx_TargetFrame):\n ''' **Logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex(self, idx_TargetFrame) ''' ''''''\n # 00-A. Check the validity of nodeSeqBrowser_Selected and idx_TargetFrame\n nodeSeqBrowser_Selected = self.getParameterNode().GetNodeReference(STR_SeqBrowserNode_RefRole_Selected)\n if not nodeSeqBrowser_Selected: return\n # 00-B. Check the validity of idx_TargetFrame\n if not self.isValid_idxTargetFrame(nodeSeqBrowser_Selected, idx_TargetFrame):\n raise ValueError(f'SL_Alert! Invalid idx_TargetFrame = {idx_TargetFrame}'); return\n\n # 01. Update nodeSeqBrowser along with its the Current-SelectedItemNumber\n nodeSeqBrowser_Selected.SetSelectedItemNumber(idx_TargetFrame)\n print(f\"\\t\\t**Logic.logicUpdate_SwitchSelection_SelectedSeqBrowser_ChangeFrameIndex()\\t\"\n f\"nodeSeqBrowser_Selected.GetID() = {nodeSeqBrowser_Selected.GetID()}, idx_TargetFrame = {idx_TargetFrame}\")\n\n\n\n\n''' ================================================================================================================='''\n''' ================================================================================================================='''\n''' ================================================================================================================='''\n#\n# sl__US_SeqViewerTest\n#\nclass sl__US_SeqViewerTest(ScriptedLoadableModuleTest):\n \"\"\" This is the test case for your scripted module.\n Uses ScriptedLoadableModuleTest base class, available at:\n https://github.com/Slicer/Slicer/blob/master/Base/Python/slicer/ScriptedLoadableModule.py \"\"\"\n def setUp(self):\n \"\"\" Do whatever is needed to reset the state - typically a scene clear will be enough. \"\"\"\n slicer.mrmlScene.Clear()\n\n def runTest(self):\n \"\"\"Run as few or as many tests as needed here. \"\"\"\n self.setUp()\n self.test_sl__US_SeqViewer1()\n\n def test_sl__US_SeqViewer1(self):\n \"\"\" Ideally you should have several levels of tests. At the lowest level\n tests should exercise the functionality of the logic with different inputs\n (both valid and invalid). At higher levels your tests should emulate the\n way the user would interact with your code and confirm that it still works\n the way you intended.\n One of the most important features of the tests is that it should alert other\n developers when their changes will have an impact on the behavior of your\n module. For example, if a developer removes a feature that you depend on,\n your test should break so they know that the feature is needed.\n \"\"\"\n\n self.delayDisplay(\"Starting the test\")\n\n pass\n\n self.delayDisplay('Test passed')\n\n\n","repo_name":"SenonETS/3DSlicerTutorial_ExtensionModuleDevelopment","sub_path":"04__CodeStyle_MethodGroups_&_US_SeqViewer/sl__US_SeqViewer/sl__US_SeqViewer.py","file_name":"sl__US_SeqViewer.py","file_ext":"py","file_size_in_byte":34834,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"5836473752","text":"import sys\nimport argparse\nfrom Bio import SeqIO\nfrom Bio import Seq\nfrom Bio.Seq import MutableSeq\n\ndescription = \"\"\"\nModify a reference genome to methylate positions according to the input recognition set\n\"\"\"\n\nparser = argparse.ArgumentParser(description=description, epilog='')\nparser.add_argument('input', action='store', help='the input reference file')\nparser.add_argument('--recognition', action='store', help='the recognition set')\nargs = parser.parse_args()\n\nrecognition_sites = list()\nrecognition_sites_methylated = list()\n\nif args.recognition == \"cpg\":\n recognition_sites = [\"CG\"]\n recognition_sites_methylated = [\"MG\"]\nelif args.recognition == \"dam\":\n recognition_sites = [\"GATC\"]\n recognition_sites_methylated = [\"GMTC\"]\nelif args.recognition == \"dcm\":\n recognition_sites = [\"CCAGG\", \"CCTGG\"]\n recognition_sites_methylated = [\"CMAGG\", \"CMTGG\"]\nelse:\n sys.stderr.write(\"unknown recognition: \" + args.recognition)\n sys.exit(1)\n\nrecognition_length = len(recognition_sites[0])\n\n# \nfor rec in SeqIO.parse(args.input, \"fasta\"):\n outseq = rec.seq.tomutable()\n for bi in xrange(0, len(rec) - 1):\n\n for s,m in zip(recognition_sites, recognition_sites_methylated):\n if str(rec.seq[bi:bi + recognition_length]) == s:\n outseq[bi:bi + recognition_length] = m\n rec.seq = outseq\n SeqIO.write(rec, sys.stdout, \"fasta\")\n","repo_name":"xchromosome219/methylation-analysis","sub_path":"methylate_reference.py","file_name":"methylate_reference.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"28426961855","text":"from janome.tokenizer import Tokenizer as janome_tokenizer\nimport jieba\nimport MeCab\nfrom tqdm import tqdm\nfrom random import sample\ntry:\n from resource.langconv import Converter\n from resource.load_zh_jp_transfer import load_transfer\n\n\n zh2jp, jp2zh = load_transfer()\n\n def convert_ja2zh(line):\n opt_line = []\n for ch in line:\n if ch in jp2zh:\n opt_line.append(jp2zh[ch])\n else:\n opt_line.append(ch)\n return \"\".join(opt_line)\nexcept BaseException:\n pass\n\n\nj_t = janome_tokenizer()\ndef segment_janome(line):\n seg = [token for token in j_t.tokenize(line, wakati=True)]\n return [word for word in seg if word != \"\"]\n\nm_t = MeCab.Tagger()\ndef segment_mecab(line):\n m = m_t.parseToNode(line)\n output_list = []\n while m:\n if not m.surface == \"\":\n output_list.append(m.surface)\n m = m.next\n return [word for word in output_list if word != \"\"]\n\ndef segment_jieba(line):\n try:\n line = Converter('zh-hans').convert(line).encode('utf-8')\n except BaseException:\n pass\n seg = list(jieba.cut(line))\n return [word for word in seg if word != \"\"]\n\ndef main(zh_method=\"jieba\", ja_method=\"janome\"):\n with open(\"./text/zh.txt\", \"r\", encoding=\"utf-8\") as f:\n zh_lines = f.readlines()\n seg_zh_lines = [\" \".join(segment_jieba(line)) for line in tqdm(zh_lines)]\n\n with open(\"./text/ja.txt\", \"r\", encoding=\"utf-8\") as f:\n ja_lines = f.readlines()\n \"\"\"\n Mecab is at least 30x quicker than janome.\n \"\"\"\n if ja_method == \"janome\":\n seg_ja_lines = [\" \".join(segment_janome(line)) for line in tqdm(ja_lines)]\n if ja_method == \"mecab\":\n seg_ja_lines = [\" \".join(segment_mecab(line)) for line in tqdm(ja_lines)]\n\n ja_lines_new = [convert_ja2zh(line) for line in seg_ja_lines]\n\n total_count = len(seg_zh_lines)\n test_index = sample(range(total_count), 1000)\n\n with open(\"./text/zh_segment.txt\", \"w\", encoding=\"utf-8\") as f:\n with open(\"./text/zh_segment_test.txt\", \"w\", encoding=\"utf-8\") as f_test:\n for index, line in enumerate(seg_zh_lines):\n if not index in test_index:\n f.write(line.strip() + \"\\n\")\n else:\n f_test.write(line.strip() + \"\\n\")\n\n with open(\"./text/ja_segment.txt\", \"w\", encoding=\"utf-8\") as f:\n with open(\"./text/ja_segment_test.txt\", \"w\", encoding=\"utf-8\") as f_test:\n for index, line in enumerate(ja_lines_new):\n if not index in test_index:\n f.write(line.strip() + \"\\n\")\n else:\n f_test.write(line.strip() + \"\\n\")\n\nif __name__ == \"__main__\":\n main(zh_method=\"jieba\", ja_method=\"mecab\")\n\n","repo_name":"GanjinZero/ACG_translator","sub_path":"tokenize_util.py","file_name":"tokenize_util.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"70766422667","text":"#!/usr/bin/env python3\n\n\"\"\"\n.. codeauthor:: Tsuyoshi Hombashi \n\"\"\"\n\nimport os\nimport sys\nfrom textwrap import dedent, indent\n\nfrom subprocrunner import CalledProcessError, SubprocessRunner\n\n\ndef main() -> int:\n env = dict(os.environ, LC_ALL=\"C.UTF-8\")\n\n proc = SubprocessRunner(\"sqlitebiter -h\")\n try:\n proc.run(env=env, check=True)\n except CalledProcessError as e:\n print(f\"[ERROR] {e}\\n{e.stderr}\", file=sys.stderr)\n sys.exit(1)\n\n assert proc.stdout\n help_file_path = \"pages/usage/help.txt\"\n print(help_file_path)\n\n with open(help_file_path, \"w\") as f:\n f.write(\n dedent(\n \"\"\"\\\n ::\n\n \"\"\"\n )\n )\n\n f.write(indent(proc.stdout, \" \"))\n\n for subcommand in [\"file\", \"gs\", \"url\", \"stdin\"]:\n proc = SubprocessRunner(f\"sqlitebiter {subcommand:s} -h\")\n proc.run(env=env, check=True)\n assert proc.stdout\n help_file_path = f\"pages/usage/{subcommand:s}/help.txt\"\n\n print(help_file_path)\n\n with open(help_file_path, \"w\") as f:\n f.write(\n dedent(\n \"\"\"\\\n ``sqlitebiter {:s}`` subcommand help\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n ::\n\n \"\"\".format(\n subcommand\n )\n )\n )\n\n f.write(indent(proc.stdout, \" \"))\n\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"thombashi/sqlitebiter","sub_path":"docs/update_command_help.py","file_name":"update_command_help.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":794,"dataset":"github-code","pt":"82"} +{"seq_id":"22015683193","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nUW, CSEP 573, Win19\n\"\"\"\n\nfrom pomdp import POMDP\nfrom onlineSolver import OnlineSolver\nfrom typing import List, Optional, Set\nimport numpy as np\n\n\nclass Node:\n def __init__(self):\n self.U: float = float('inf')\n self.L: float = float('-inf')\n \nclass ActionNode(Node):\n def __init__(self, ai: int, parent: Node, reward: float):\n super(Node, self).__init__()\n if not isinstance(parent, BeliefNode):\n raise Exception(\"Needs to be a belief node!\")\n \n self.parent: BeliefNode = parent # Belief node that points to this action node\n self.ai = ai # Action index\n self.reward: float = reward # Arc from belief node = R(b, a)\n self.children: List[BeliefNode] = [] # Children belief nodes\n \n \nclass BeliefNode(Node):\n def __init__(\n self,\n belief,\n parent: Optional[Node],\n depth: int,\n gamma_d: float,\n oi: int,\n obs_prob: float,\n obs_prob_cumulative: float,\n ):\n super(Node, self).__init__()\n if parent and not isinstance(parent, ActionNode):\n raise Exception(\"Needs to be an action node!\")\n \n self.parent: ActionNode = parent # action node pointing to this belief node\n self.belief = belief # numpy array\n self.children: List[ActionNode] = [] # action nodes that this points to\n self.depth = depth # depth\n self.gamma_d = gamma_d # discount^depth\n self.oi = oi # observation index\n self.obs_prob = obs_prob # probability of seeing the observation P(z | b, a) (arc from AND node)\n self.obs_prob_cumulative = obs_prob_cumulative # Pi[P(z|b,a) * P(a|b)] from i=0 to i=depth\n self.chosen_action_index = None\n \n \nclass AEMS2(OnlineSolver):\n def __init__(self, pomdp, lb_solver, ub_solver, precision = .001, action_selection_time = .1):\n super(AEMS2, self).__init__(pomdp, precision, action_selection_time)\n self.lb_solver = lb_solver\n self.ub_solver = ub_solver\n \"\"\"\n *****Your code\n You can add any attribute you want\n \"\"\"\n \n # Collections of all belief and action nodes\n self.belief_nodes: Set[BeliefNode] = set()\n self.action_nodes: Set[ActionNode] = set()\n \n # Initial belief comes from the prior\n initial_belief = np.copy(self.pomdp.prior)\n initial_L = self.lb_solver.getValue(initial_belief)\n initial_U = self.ub_solver.getValue(initial_belief)\n \n # After updating root, we increment this!\n self.depthOffset = 0\n self.gammaDivisor = 1.0\n self.rewardConst = self.getRewardConst()\n \n self.root: BeliefNode = BeliefNode(\n belief=initial_belief,\n parent=None,\n depth=0,\n gamma_d=1,\n oi=0,\n obs_prob=1.0,\n obs_prob_cumulative=1.0\n )\n self.root.L = initial_L\n self.root.U = initial_U\n self.belief_nodes.add(self.root)\n \n # Non-changing part of reward\n def getRewardConst(self):\n RT = np.multiply(self.pomdp.R[:,:,:,0], self.pomdp.T)\n sum = np.sum(RT, 2)\n return np.swapaxes(sum, 0, 1)\n #\n # Choose\n #\n def is_fringe_node(self, belief_node: BeliefNode) -> bool:\n if not belief_node.children:\n return True\n return False\n \n def get_all_fringe_nodes(self) -> List[BeliefNode]:\n fringe_nodes: List[BeliefNode] = []\n for bn in self.belief_nodes:\n fringe_nodes.append(bn)\n return fringe_nodes\n \n def select_best_fringe_node(self) -> BeliefNode:\n # If root is a fringe node, then return it\n if self.is_fringe_node(self.root):\n return self.root\n \n # Otherwise, select the next actions\n fringe_nodes = self.get_all_fringe_nodes()\n max = float('-inf')\n max_i = -1\n \n # b* ← arg maxb∈FRINGE(G) E(b)\n for i, bn in enumerate(fringe_nodes):\n e = self.E(bn)\n if e > max:\n max = e\n max_i = i\n return fringe_nodes[max_i]\n \n # E(b) = gamma^d * P(b) * e_hat(b)\n def E(self, bn: BeliefNode) -> float:\n return bn.gamma_d * self.e_hat(bn) * self.P(bn)\n \n def e_hat(self, bn: BeliefNode) -> float:\n return bn.U - bn.L\n \n def P(self, bn: BeliefNode):\n return bn.obs_prob_cumulative\n\n #\n # Expand\n #\n def expand(self, bn: BeliefNode):\n L_a_max = float('-inf')\n U_a_max = float('-inf')\n \n for ai, action in enumerate(self.pomdp.actions):\n L_a = U_a = reward = self.R_b_a(bn, ai)\n \n # Create new action node\n new_an = ActionNode(ai=ai, parent=bn, reward=reward)\n \n for oi, obs in enumerate(self.pomdp.observations):\n prob_arc_val = self.P_o_b_a(bn, ai, oi)\n \n # Calculate new belief\n # TODO!! Should oi be from the current observation or from the past (bn.oi)?\n new_belief = self.NewBelief(bn=bn, ai=ai, oi=oi)\n \n # Use heuristics to get U and L\n L = self.lb_solver.getValue(new_belief)\n U = self.ub_solver.getValue(new_belief)\n \n # TODO: Is this correct?\n # Equation 2 - set the action node L and U\n L_a += self.pomdp.discount * prob_arc_val * L\n U_a += self.pomdp.discount * prob_arc_val * U\n \n # TODO: Is this correct?\n # P(b^d) = Pi[P(o|b,a)*P(a|b)\n obs_prob_cumulative = bn.obs_prob_cumulative * bn.obs_prob\n \n # TODO: Create a new belief node and append to action node\n new_bn = BeliefNode(\n belief=new_belief,\n parent=new_an,\n depth=bn.depth+1,\n gamma_d=bn.gamma_d * self.pomdp.discount / self.gammaDivisor, # divisor for updateRoot\n oi=oi,\n obs_prob=prob_arc_val,\n obs_prob_cumulative=obs_prob_cumulative,\n )\n new_bn.L = L\n new_bn.U = U\n \n # Add the new BN to the new AN and to the belief node set (for searching)\n new_an.children.append(new_bn)\n self.belief_nodes.add(new_bn)\n \n # Get the max vals to update the current bn\n if L_a > L_a_max:\n L_a_max = L_a\n if U_a > U_a_max:\n U_a_max = U_a\n \n # Configure the action node and append to chosen bn (created by eq 2)\n new_an.L = L_a\n new_an.U = U_a\n bn.children.append(new_an)\n \n # TODO: Does this actually need to happen? Where is this in the paper?\n bn.U = U_a_max\n bn.L = L_a_max\n \n def R_b_a(self, bn: BeliefNode, ai: int) -> float:\n return float(np.dot(bn.belief, self.rewardConst[:, ai]))\n # ASS = np.einsum('ijkl->ijk', self.pomdp.R)\n # AS = np.einsum('ijk->ij', ASS)\n # S = AS[ai]\n # res = np.dot(S, bn.belief)\n # return float(res)\n \n # total = 0.0\n # for si in range(len(self.pomdp.states)):\n # # What should these values be???\n # total += self.pomdp.R[ai, si, 0, 0]\n # return total\n \n # Calculates EQ 3 in the paper\n def P_o_b_a(self, bn: BeliefNode, ai: int, oi: int) -> float:\n # TODO: This is probably wrong!\n # total = 0.0\n # for s_prime in range(len(self.pomdp.states)):\n # O = self.pomdp.O[ai, s_prime, oi]\n # s_sum = sum(self.pomdp.T[ai, si, s_prime]*bn.belief[si] for si in range(len(self.pomdp.states)))\n # total += O * s_sum\n # return total\n current_belief = np.matmul(bn.belief, self.pomdp.T[ai, :, :])\n current_belief = np.dot(current_belief, self.pomdp.O[ai, :, oi])\n return float(current_belief)\n \n # This calculates b'(s') using EQ 1 from the paper\n def NewBelief(self, bn: BeliefNode, ai: int, oi: int):\n b_prime = np.zeros(len(self.pomdp.states))\n \n for s_prime in range(len(self.pomdp.states)):\n O = self.pomdp.O[ai, s_prime, oi]\n s_sum = sum((self.pomdp.T[ai, si, s_prime]*bn.belief[si]) for si in range(len(self.pomdp.states)))\n b_prime[s_prime] = O * s_sum\n \n # Apply normalization\n nf = np.sum(b_prime)\n if nf:\n b_prime = np.divide(b_prime, nf)\n return b_prime\n \n #\n # Backtrack\n #\n def backtrack(self, bn: BeliefNode, L_old: float, U_old: float):\n while bn != self.root:\n an = bn.parent\n an.L += self.pomdp.discount * bn.obs_prob * (bn.L - L_old)\n an.U += self.pomdp.discount * bn.obs_prob * (bn.U - U_old)\n \n # TODO: is there really a typing error here?\n bn = an.parent\n L_old, U_old = self.update_belief_node(bn)\n \n def update_belief_node(self, bn: BeliefNode):\n L_old, U_old = bn.L, bn.U\n max_ai = -1\n U_max = L_max = float('-inf')\n \n for ai, an in enumerate(bn.children):\n if an.U > U_max:\n U_max = an.U\n max_ai = ai\n if an.L > L_max:\n L_max = an.L\n \n bn.L = L_max\n bn.U = U_max\n bn.chosen_action_index = max_ai\n return L_old, U_old\n \n def expandOneNode(self):\n \"\"\"\n *****Your code\n \"\"\"\n # Choose\n best_fringe_node = self.select_best_fringe_node()\n L_old, U_old = best_fringe_node.L, best_fringe_node.U\n \n # Expand\n self.expand(best_fringe_node)\n \n # Backtrack\n self.backtrack(bn=best_fringe_node, L_old=L_old, U_old=U_old)\n \n # Consider using termination condition\n return True\n\n def chooseAction(self) -> int:\n \"\"\"\n *****Your code\n \"\"\"\n if self.root.chosen_action_index is not None:\n return self.root.chosen_action_index\n \n max_U = float('-inf')\n max_ai = -1\n for ai, an in enumerate(self.root.children):\n if an.U > max_U:\n max_ai = ai\n max_U = an.U\n \n return max_ai\n \n \n def updateRoot(self, action, observation):\n \"\"\"\n ***Your code \n \"\"\"\n # TODO: May want to throw a bunch of stuff away (plus their children) instead of keeping it in memory\n chosen_an = self.root.children[action]\n throwaway_action_nodes = [an for an in self.root.children if an != chosen_an]\n chosen_bn = chosen_an.children[observation]\n throwaway_belief_nodes = [bn for bn in chosen_an.children if bn != chosen_bn]\n \n # Update root\n self.belief_nodes.remove(self.root)\n self.root = chosen_bn\n self.root.parent = None\n self.depthOffset += 1\n self.gammaDivisor *= self.pomdp.discount\n","repo_name":"kail/csep573","sub_path":"pomdp/aems.py","file_name":"aems.py","file_ext":"py","file_size_in_byte":11533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"28181948674","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n__author__ = 'MFC'\n__time__ = '18/4/22 23:45'\n\n\"\"\"\n11-1 python 中的 GIL\nGIL = global interpreter lock (in cpython)\npython中一个线程对应于c语言中的一个线程\ngil使得同一个时刻只有一个线程在一个cpu上执行字节码，无法将多个线程映射到多个cpu上执行\npypy是去gil化的 \n\"\"\"\n\nimport dis\n\n\ndef add(a):\n a = a + 1\n return a\n\n\nprint(dis.dis(add))\n","repo_name":"yudn/Python3Scripts","sub_path":"imooc/AdvancePythonIO/chapter11/python_gil.py","file_name":"python_gil.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"zh","doc_type":"code","dataset":"github-code","pt":"82"} +{"seq_id":"20645251938","text":"import mindspore\nimport mindspore as ms\nimport mindspore.ops as ops\nimport numpy as np\nimport pytest\nfrom mindspore import nn\nfrom mindspore.common.initializer import initializer\n\nfrom mindediting import is_ascend\nfrom mindediting.models.common.tunable_conv import TunableConv2d, TunableParameter\n\nms.set_seed(1)\nnp.random.seed(1)\n\n\n@pytest.mark.parametrize(\"num_params\", [2, 3])\n@pytest.mark.parametrize(\"default_input\", [initializer(init=\"uniform\", shape=(1, 1, 1), dtype=ms.float32)])\ndef test_tunable_parameter(default_input, num_params):\n assert num_params > 1\n batch_size = num_params\n gamma = TunableParameter(\n default_input=default_input, name=\"gamma\", requires_grad=True, num_params=num_params, mode=\"linear\"\n )\n px = ops.eye(num_params, num_params, ms.float32)\n g = gamma(px)\n print(gamma)\n assert g.shape == (batch_size, *default_input.shape)\n\n\n@pytest.mark.parametrize(\"num_params\", [3])\n@pytest.mark.parametrize(\"kernel_size\", [3])\n@pytest.mark.parametrize(\"stride\", [1])\n@pytest.mark.parametrize(\"group\", [1])\ndef test_tunable_conv2d(num_params, kernel_size, stride, group):\n\n assert num_params > 1\n mse = nn.MSE()\n batch_size = num_params\n b, c, h, w, d = batch_size, 16, 24, 24, 32\n x = ops.normal(shape=(b, c, h, w), mean=ms.Tensor(0.0), stddev=ms.Tensor(1.0))\n px = ops.eye(num_params, num_params, ms.float32)\n\n tunable_conv = TunableConv2d(\n c, d, kernel_size, stride=stride, group=group, has_bias=True, num_params=num_params, mode=\"linear\"\n )\n conv = nn.Conv2d(c, d, kernel_size, stride=stride, group=group, has_bias=True)\n print(tunable_conv)\n y = tunable_conv(x, px)\n for p in range(num_params):\n conv.weight = tunable_conv.weight[0, p, ...]\n conv.bias = tunable_conv.bias[0, p, ...]\n y_p = conv(x[p : p + 1, ...])\n if is_ascend():\n assert mse(y[p : p + 1, ...], y_p) < 1e-4\n else:\n assert mse(y[p : p + 1, ...], y_p) < 1e-6\n","repo_name":"mindspore-lab/mindediting","sub_path":"tests/st/test_tunable_cell.py","file_name":"test_tunable_cell.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"82"} +{"seq_id":"1540497852","text":"from Small_UnetGated import UNetLWGated,ConvHis\nimport torch\nimport tensorrt as trt\nimport common\nimport numpy as np\nimport torch.nn as nn\nfrom config import mdevice\nTRT_LOGGER = trt.Logger(trt.Logger.WARNING)\nmodel_his = ConvHis()\nmodel_his.load_state_dict(torch.load(\"totalModel.140.pth.tar\")[\"state_dict\"],strict=False)\nmodel_his=model_his.to(mdevice).eval()\nfor key in model_his.state_dict():\n print(key)\nprint(model_his.state_dict()[\"convHis3.7.running_mean\"])\ndef build_engine():\n with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network:\n builder.max_workspace_size = common.GiB(1)\n input_tensor = network.add_input(name=\"test\",dtype=trt.float32,shape=(1,1,2,2))\n upsample1=network.add_resize(input=input_tensor)\n upsample1.resize_mode = trt.ResizeMode.NEAREST\n upsample1.shape=(1,1,4,4)\n upsample1.scales=[1,1,2,2]\n network.mark_output(tensor=upsample1.get_output(0))\n return builder.build_cuda_engine(network)\nwith build_engine() as engine:\n inputs, outputs, bindings, stream = common.allocate_buffers(engine)\n inputs[0].host=np.arange(1,5).reshape((1,1,2,2)).astype(np.float32)\n m_upsample=nn.Upsample(scale_factor=2, mode='nearest')\n print(inputs[0].host)\n print(m_upsample(torch.from_numpy(inputs[0].host)))\n with engine.create_execution_context() as context:\n [output] = common.do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)\n print(output.reshape(1,1,4,4))\n","repo_name":"fuxihao66/ExtraNetTRTInference","sub_path":"deploy_trt_manual.py","file_name":"deploy_trt_manual.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"18348088775","text":"import re\n\nfrom triple_quote_clean import TripleQuoteCleaner\n\nfrom ai_template_style_transfer import transfer\n\ntqc = TripleQuoteCleaner()\n\ndescription = (\n tqc\n << \"\"\"\n *Background*\n\n Solution files uploaded into AWS will be loaded into Databricks tables. We\n will assume that the ground truth for the data loaded into Databricks will\n match the output from a set of local extraction samples. (i.e. the\n extraction end to end on a local device. Note I am assuming that the local\n extraction has already been sufficiently tested). For each loaded table, for\n each row, metadata columns will be added showing the file source and\n ingestion time.\n\n *Method*\n\n using the added meta-data columns,\n\n - we will confirm that all source files are ingested into Databricks\n - ensure that when compared to a local extraction.\n - row counts match.\n - all data matches.\n - no duplicates are found.\n\n *Narrative*\n\n As a Validator, I want to ensure the data is accurate and consistent with\n the ground truth from local extraction samples.\n\n *Acceptance Criteria*\n\n Given solution files are uploaded into AWS and loaded into Databricks\n tables, When comparing the data in Databricks with local extraction samples,\n Then the following conditions should be\n\n met:\n\n - All source files are ingested into Databricks using the added metadata\n columns\n - Row counts match between Databricks tables and local extraction samples *\n All data matches between Databricks tables and local extraction samples\n - No duplicate records are found in Databricks tables*\n \"\"\"\n)\n\nassertion_failed_message = \"template conditions not met\"\n\n\ndef test_transfer__jira_issue():\n style_transferred = transfer.jira_issue(description).lower()\n\n print(style_transferred)\n\n assert (\n len(re.findall(\"as a\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(r\"\\\\*narrative\\\\*\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"i want\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(r\"\\\\*acceptance criteria\\\\*\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"given that\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"when the\", style_transferred)) > 0\n ), assertion_failed_message\n\n assert (\n len(re.findall(\"then the\", style_transferred)) > 0\n ), assertion_failed_message\n\n\nif __name__ == \"__main__\":\n test_transfer__jira_issue()\n","repo_name":"Chr1sC0de/template-style-transfer","sub_path":"tests/test__transfer_style.py","file_name":"test__transfer_style.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"47674546782","text":"from unittest import TestSuite, makeSuite\nfrom Products.CMFCore.utils import getToolByName\n\nfrom pmr2.app.workspace.tests.storage import DummyStorage\n\nfrom pmr2.app.exposure.content import ExposureContainer, Exposure\nfrom pmr2.app.exposure.adapter import *\n\nfrom pmr2.app.exposure.tests.base import ExposureUnitTestCase\n\n\nclass TestAdapters(ExposureUnitTestCase):\n\n def afterSetUp(self):\n self.portal['exposure'] = ExposureContainer('exposure')\n tester = Exposure('tester')\n self.portal.exposure['tester'] = tester\n\n def test_000_original_adapter(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'cake'\n workspace = ExposureToWorkspaceAdapter(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_001_fullpath_adapter(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'/plone/workspace/cake'\n workspace = ExposureToWorkspaceAdapter(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_010_original_traverse(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'cake'\n workspace = ExposureToWorkspaceTraverse(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n def test_011_fullpath_traverse(self):\n tester = self.portal.exposure.tester\n self.assertEqual(tester.workspace, None)\n tester.workspace = u'/plone/workspace/cake'\n workspace = ExposureToWorkspaceTraverse(tester)\n self.assertEqual(workspace.absolute_url_path(), \n '/plone/workspace/cake')\n\n\nclass TestExposureStorageAdapter(ExposureUnitTestCase):\n \"\"\"\\\n This tests the dummy framework and implementation, along with the\n adapter with manual registration.\n \"\"\"\n\n def setUp(self):\n ExposureUnitTestCase.setUp(self)\n self.portal['exposure'] = ExposureContainer('exposure')\n self.workspace = self.portal.workspace.blank\n tester = Exposure('tester')\n tester.commit_id = u'0'\n tester.workspace = u'/plone/workspace/blank'\n self.portal.exposure['tester'] = tester\n self.exposure = self.portal.exposure['tester']\n\n def test_010_storage_adapter_failure(self):\n # but workspace has storage unspecified\n self.assertRaises(ValueError, ExposureStorageAdapter, self.exposure)\n\n def test_020_storage_adapter_success(self):\n self.workspace.storage = 'dummy_storage'\n # storage adapter should now return.\n result = ExposureStorageAdapter(self.exposure)\n self.assert_(isinstance(result, DummyStorage))\n self.assertEqual(result.rev, '0')\n\n\ndef test_suite():\n suite = TestSuite()\n suite.addTest(makeSuite(TestAdapters))\n suite.addTest(makeSuite(TestExposureStorageAdapter))\n return suite\n","repo_name":"PMR2/pmr2.app","sub_path":"pmr2/app/exposure/tests/test_adapters.py","file_name":"test_adapters.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"38096320183","text":"# -*- coding: utf-8 -*-\n# @Time : 2018/10/31 15:22\n# @Author : Alex\n# @Site : \n# @File : runtest.py\n# @Software: PyCharm\n\nimport unittest\n\n# 利用TestLoader类中提供的discover（）方法\n# 定义测试用例的目录为当前目录\ntest_dir = './test_case'\ndiscover = unittest.defaultTestLoader.discover(test_dir, pattern='test*.py')\n\nif __name__ == '__main__':\n runner = unittest.TextTestRunner()\n runner.run(discover)\n","repo_name":"Crazy-bear/mytest","sub_path":"test_web/runtest.py","file_name":"runtest.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"13232357069","text":"#-----------------------------------------------------------------------\n# Author: Kyle Thomas\n# My python program for calculating taxes and room charges for a hotel\n# This program requires the Zelle Graphics Library which can be found at\n# http://mcsp.wartburg.edu/zelle/python/\n#-----------------------------------------------------------------------\n\n\nfrom graphics import Entry, Image, GraphWin, Point, Rectangle, Text\n\n\n# Tax Rates:\nTAX_RATE = 1.103 # 1 + 8.3% sales + 2% occupancy tax\nFLAT_TAX = 2 # $2 per night city/county tax\n\n\n# Purpose: To calculate the total charges after tax\n# Input: The room rate (float)\n# Output: The total after tax (float)\ndef calc_tax(rate):\n return rate * TAX_RATE + FLAT_TAX\n\n\n# Purpose: To calculate just the room charges without tax\n# Input: The number of nights (int) and the total charges (float)\n# Output: The total room charges (minus tax) (float)\ndef calc_rate(nights, total):\n return ((total / nights) - FLAT_TAX) / TAX_RATE\n\n\n# Purpose: To calculate the total tax paid\n# Input: The rate (float), the total and the number of nights (int)\n# Output: The tax paid (float)\ndef calc_diff(rate, total, nights):\n return total - (rate * nights)\n\n\n# Purpose: To determine whether a point is in a rectangle or not\n# Input: A rectangle and a point\n# Output: A True or False whether the point is in the rectangle or not\ndef is_pt_in_rect(rectangle, point):\n point1 = rectangle.getP1()\n point1X = point1.getX()\n point1Y = point1.getY()\n point2 = rectangle.getP2()\n point2X = point2.getX()\n point2Y = point2.getY()\n sideOneLength = abs(point1X - point2X)\n sideTwoLength = abs(point1Y - point2Y)\n pointXvalue = point.getX()\n pointYvalue = point.getY()\n\n if ((abs(point1X - pointXvalue) <= sideOneLength\n and abs(point2X - pointXvalue) <= sideOneLength)\n and (abs(point1Y - pointYvalue) <= sideTwoLength\n and abs(point2Y - pointYvalue) <= sideTwoLength)):\n\n inFlag = True\n\n else:\n inFlag = False\n\n return inFlag\n\n\ndef main():\n window = GraphWin(\"Tax Calculator\", 300, 350)\n window.setBackground(\"White\")\n\n banner = Text(Point(150, 20), \"Tax Calculator\")\n banner.setStyle(\"bold\")\n banner.setFace(\"courier\")\n banner.setSize(18)\n banner.draw(window)\n\n rateText = Text(Point(60,80), \"Rate:\")\n rateText.setFace(\"courier\")\n rateText.draw(window)\n\n rateBox = Entry(Point(200, 80), 7)\n rateBox.setFill(\"White\")\n rateBox.setText(\"0\")\n rateBox.draw(window)\n\n nightText = Text(Point(50, 140), \"Nights:\")\n nightText.setFace(\"courier\")\n nightText.draw(window)\n\n nightBox = Entry(Point(200, 140), 7)\n nightBox.setFill(\"White\")\n nightBox.setText(\"1\")\n nightBox.draw(window)\n\n totalText = Text(Point(56, 200), \"Total:\")\n totalText.setFace(\"courier\")\n totalText.draw(window)\n\n totalBox = Entry(Point(200, 200), 7)\n totalBox.setFill(\"White\")\n totalBox.setText(\"0\")\n totalBox.draw(window)\n\n calc = Image(Point(150, 310), \"button.png\")\n calc.draw(window)\n\n calcButton = Rectangle(Point(68,288), Point(232, 332))\n\n calcFlag = False # Flag of whether or not a calculation has been performed\n\n while True:\n errorFlag = False\n try:\n mouseClick = window.getMouse()\n\n except:\n window.close()\n break\n\n if (is_pt_in_rect(calcButton, mouseClick)):\n try:\n rate = float(rateBox.getText())\n nights = int(nightBox.getText())\n total = float(totalBox.getText())\n\n except: # Reset boxes and clear totals\n totalBox.setText(\"0\")\n nightBox.setText(\"1\")\n rateBox.setText(\"0\")\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n errorFlag = True\n\n # Make sure values are \"sane\"\n if ((not errorFlag) and (rate < 0 or nights < 1 or total < 0)):\n totalBox.setText(\"0\")\n nightBox.setText(\"1\")\n rateBox.setText(\"0\")\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n errorFlag = True\n\n if (not errorFlag):\n if (rate > 0):\n total = round(calc_tax(rate) * nights, 2)\n totalBox.setText(str(total))\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n nightlyTax = Text(Point(150, 245),\n \"Nightly Tax: \"\n + str(round(calc_tax(rate) - rate, 2)))\n\n nightlyTax.setFill(\"red\")\n nightlyTax.setFace(\"courier\")\n nightlyTax.draw(window)\n\n totalTax = Text(Point(150, 270),\n \"Total Tax: \"\n + str(round(\n calc_diff(rate, total, nights), 2)))\n\n totalTax.setFill(\"red\")\n totalTax.setFace(\"courier\")\n totalTax.draw(window)\n\n calcFlag = True\n\n elif (total > 0):\n rate = round(calc_rate(nights, total), 2)\n rateBox.setText(str(rate))\n if calcFlag:\n totalTax.undraw()\n nightlyTax.undraw()\n\n nightlyTax = Text(Point(150, 245),\n \"Nightly Tax: \"\n + str(round(calc_tax(rate) - rate, 2)))\n\n nightlyTax.setFill(\"red\")\n nightlyTax.setFace(\"courier\")\n nightlyTax.draw(window)\n\n totalTax = Text(Point(150, 270),\n \"Total Tax: \"\n + str(round(\n calc_diff(rate, total, nights), 2)))\n\n totalTax.setFill(\"red\")\n totalTax.setFace(\"courier\")\n totalTax.draw(window)\n\n calcFlag = True\n return\nmain()\n","repo_name":"kthomas422/Tax-Calculator","sub_path":"tax_calc.pyw","file_name":"tax_calc.pyw","file_ext":"pyw","file_size_in_byte":6261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32302019756","text":"from mysql.connector import Error\nimport pymysql\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nfrom datetime import datetime, date, timedelta\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.common.exceptions import SessionNotCreatedException\nimport os\nimport calendar\nimport time\nimport sys\nfrom sys import platform\n\n\ndef get_all_tickers():\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT ticker FROM all_securities\"\"\"\n\n cursor.execute(sql_insert_query)\n tickers = cursor.fetchall()\n all_tickers = []\n for i in tickers:\n all_tickers.append(i[0])\n cursor.close()\n connection_hosted.close()\n return all_tickers\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n\n\ndef get_all_s_and_p():\n url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n page = urlopen(url)\n soup = BeautifulSoup(page, 'html.parser')\n table_body = soup.find('table', {'id': 'constituents'}).find('tbody')\n rows = table_body.find_all('tr')\n tickers = []\n for row in rows:\n cols = row.find_all('td')\n if len(cols) > 0:\n tickers.append(cols[0].text.replace('\\n', ''))\n return tickers\n\n\ndef manage_intraday_updates():\n all_s_p = get_all_s_and_p()\n for i in get_all_tickers():\n in_sp = i in all_s_p\n record_dt = datetime.today()\n change_dollar, change_percent, market_cap, current_price = get_intraday_data(i)\n replace_data(get_security_id(i), change_dollar, change_percent, market_cap, current_price, record_dt, in_sp)\n\n\ndef get_intraday_data(ticker):\n print(ticker)\n url = 'https://finance.yahoo.com/quote/' + ticker + '?p=' + ticker + '&.tsrc=fin-srch'\n page = urlopen(url)\n soup = BeautifulSoup(page, 'html.parser')\n changes = soup.find('span', {'data-reactid': '51'}).text.split(\" \")\n change_dollar = float(changes[0].replace(\"+\", \"\"))\n change_percent = float(changes[1].replace(\"(\", \"\").replace(\"%\", \"\").replace(\")\", \"\").replace(\"+\", \"\"))\n market_cap = calc_market_cap(soup.find('span', {'data-reactid': '139'}).text)\n current_price = float(soup.find('span', {'data-reactid': '50'}).text)\n return change_dollar, change_percent, market_cap, current_price\n\n\ndef calc_market_cap(mk_str):\n last_char = mk_str[-1]\n if last_char == 'T':\n val = float(mk_str[:-1]) * 1000000000000\n elif last_char == 'B':\n val = float(mk_str[:-1]) * 1000000000\n else:\n val = float(mk_str[:-1]) * 1000000\n return val\n\n\ndef replace_data(sec_id, change_dollar, change_percent, market_cap, current_price, record_dt, in_sp):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_delete_query = \"\"\"DELETE FROM today_data WHERE sec_id = %s\"\"\"\n sql_insert_query = \"\"\"INSERT INTO today_data\n (sec_id, change_dollar, change_percent, market_cap, current_price, record_dt, in_s_p)\n VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\n cursor.execute(sql_delete_query, sec_id)\n cursor.executemany(sql_insert_query, [(sec_id, change_dollar, change_percent, market_cap, current_price,\n record_dt, in_sp)])\n connection_hosted.commit()\n print(\"Data inserted successfully table\")\n cursor.close()\n connection_hosted.close()\n print(\"MySQL connection is closed\")\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n\n\ndef manage_updates():\n for i in get_all_tickers():\n print(i)\n dates, prices = get_historic_data(i, datetime.today().timestamp(),\n get_most_recent_dt(get_security_id(i)))\n add_historic_price(get_security_id(i), prices, dates)\n\n\ndef get_most_recent_dt(sec_id):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT DISTINCT record_dt FROM historic_data\n WHERE sec_id = %s\"\"\"\n\n cursor.executemany(sql_insert_query, (sec_id,))\n try:\n data = cursor.fetchall()\n most_recent = (max(data) + timedelta(days=1)).timestamp()\n except TypeError:\n most_recent = datetime(2000, 1, 1, 0, 0).timestamp()\n cursor.close()\n connection_hosted.close()\n return most_recent\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n print(\"MySQL connection is closed\")\n\n\ndef get_historic_data(ticker, end_dt, begin_dt=datetime(1980, 1, 1, 0, 0).timestamp()):\n dates = []\n close_prices = []\n try:\n if platform == \"linux\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver 2 linux')\n elif platform == \"darwin\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver 3')\n else:\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver.exe')\n os.environ[\"webdriver.chrome.driver\"] = chromedriver\n options = Options()\n options.headless = True\n driver = webdriver.Chrome(executable_path=chromedriver, options=options)\n url = 'https://finance.yahoo.com/quote/' + ticker + '/history?period1=' + str(int(begin_dt)) + '&period2=' + \\\n str(int(end_dt)) + '&interval=1d&filter=history&frequency=1d'\n num_scrolls = int((end_dt-begin_dt)/(86400*50))\n driver.get(url)\n except SessionNotCreatedException:\n if platform == \"linux\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver linux 85')\n elif platform == \"darwin\":\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver mac 85')\n else:\n chromedriver = os.path.join(sys.path[0], 'chromedriver/chromedriver win 85.exe')\n os.environ[\"webdriver.chrome.driver\"] = chromedriver\n options = Options()\n options.headless = True\n driver = webdriver.Chrome(executable_path=chromedriver, options=options)\n url = 'https://finance.yahoo.com/quote/' + ticker + '/history?period1=' + str(int(begin_dt)) + '&period2=' + \\\n str(int(end_dt)) + '&interval=1d&filter=history&frequency=1d'\n num_scrolls = int((end_dt-begin_dt)/(86400*50))\n driver.get(url)\n time.sleep(1)\n for i in range(0, num_scrolls):\n driver.execute_script(\"window.scrollTo(1,1000000)\")\n time.sleep(.05)\n soup = BeautifulSoup(driver.page_source, 'html.parser')\n table_body = soup.find('table', {'class': 'W(100%) M(0)'}).find('tbody')\n rows = table_body.find_all('tr')\n for row in rows:\n cols = row.find_all('td')\n if len(cols) > 2 and cols[4].text != '-':\n cur_date = cols[0].text.replace(',', '').split(' ')\n dates.append(date(int(cur_date[2]), list(calendar.month_abbr).index(cur_date[0]), int(cur_date[1])))\n close_prices.append(cols[4].text.replace(',', ''))\n driver.close()\n return dates, close_prices\n\n\ndef add_historic_price(sec_id, prices, dates):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n records_to_insert = []\n for i in range(0, len(prices)):\n records_to_insert.append((sec_id, dates[i], prices[i]))\n sql_insert_query = \"\"\"INSERT INTO historic_data\n (sec_id, record_dt, close_price) VALUES (%s,%s,%s)\"\"\"\n\n cursor.executemany(sql_insert_query, records_to_insert)\n connection_hosted.commit()\n print(\"Data inserted successfully table\")\n\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n cursor.close()\n connection_hosted.close()\n print(\"MySQL connection is closed\")\n\n\ndef get_security_id(ticker):\n try:\n connection_hosted = pymysql.connect(host='investmentport.c1xr79lgjc2q.us-east-1.rds.amazonaws.com',\n db='investment_portfolio',\n user='investPort',\n passwd='InvestPortPass')\n\n cursor = connection_hosted.cursor()\n sql_insert_query = \"\"\"SELECT sec_id FROM all_securities\n WHERE ticker = %s\"\"\"\n\n cursor.execute(sql_insert_query, (ticker,))\n try:\n sec_id = cursor.fetchone()[0]\n except TypeError:\n sec_id = None\n except Error as error:\n print(\"parameterized query failed {}\".format(error))\n finally:\n cursor.close()\n connection_hosted.close()\n return sec_id\n","repo_name":"mactaggart-t/InvestmentPortfolio","sub_path":"flask-backend/sql/update_data.py","file_name":"update_data.py","file_ext":"py","file_size_in_byte":9945,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"70781756747","text":"import numpy as np\r\nimport h5py\r\nimport os\r\n\r\ndef setupTime(tStart,tEnd,dt):\r\n\t\r\n\tN = int((tEnd-tStart)/dt+1)\r\n\ttime,dt = np.linspace(tStart,tEnd,N,retstep=True)\r\n\t\r\n\treturn time,dt,N\r\n\r\ndef limit(var,varLimit):\r\n\t# apply varLimit to each index of var \r\n\t# where the magnitude of var exceeds the limit\r\n\t\r\n\tkLimit = abs(var) > varLimit\r\n\tvar[kLimit] = np.sign(var[kLimit]) * varLimit\r\n\treturn var\r\n\r\n# 4th Order Runge Kutta Calculation\r\ndef RK4(f,x,u,dt):\r\n # Inputs: x[k], u[k], dt (time step, seconds)\r\n # Returns: x[k+1]\r\n \r\n # Calculate slope estimates\r\n K1 = f(x, u)\r\n K2 = f(x + K1 * dt / 2, u)\r\n K3 = f(x + K2 * dt / 2, u)\r\n K4 = f(x + K3 * dt, u)\r\n \r\n # Calculate x[k+1] estimate using combination of slope estimates\r\n x_next = x + 1/6 * (K1 + 2*K2 + 2*K3 + K4) * dt\r\n \r\n return x_next,K1\r\n\t\r\ndef saveData(filename,myData):\r\n\t# get full path for filename\r\n\tsavePath = os.path.dirname(os.path.realpath(__file__))\r\n\tfilepath = f'{savePath}\\{filename}'\r\n\t\r\n\tprint(\"Saving to...\")\r\n\tprint(f\"\\t{filepath}\")\r\n\twith h5py.File(filepath,'w') as myFile:\r\n\t\tdList = []\r\n\t\tfor myKey in myData.keys():\r\n\t\t\tdList.append(myFile.create_dataset(myKey,data=myData[myKey]))\r\n\tprint('Done')\r\n\t\t\r\n\t\r\n\r\n","repo_name":"astroHaoPeng/rotational-dynamics","sub_path":"python/simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71228550987","text":"\"\"\"\nHolds a Tsuro referee capable of running a single game of Tsuro.\n\"\"\"\nfrom copy import deepcopy\nfrom typing import Dict, Iterator, List, Optional, Set\n\nfrom Common.board import Board\nfrom Common.color import AllColors, ColorString\nfrom Common.moves import InitialMove, IntermediateMove\nfrom Common.player_interface import PlayerInterface\nfrom Common.result import Result, error, ok\nfrom Common.rules import RuleChecker\nfrom Common.tiles import Tile, load_tiles_from_json\nfrom Common.tsuro_types import GameResult\nfrom Common.util import silenced_object, timeout\nfrom Common.validation import validate_types\nfrom Admin.game_observer import RefereeObserver\n\nTIMEOUT = 3\n\ndef deterministic_tile_iterator() -> Iterator[Tile]:\n \"\"\"\n A simple deterministic infinite tile iterator that yields tiles in order by their\n TileIndex.\n\n :return: An iterator of tiles\n \"\"\"\n while True:\n for _, tile in load_tiles_from_json():\n yield tile\n\n\nclass Referee:\n \"\"\"\n Represents the referee for a game of Tsuro. Runs a full game with the specified players. A instance of the\n Referee class can only be used for a single game and cannot be reused.\n\n In the event of abnormal conditions, the referee will handle them in the following ways:\n - Player cheats -> Player is eliminated from the game\n - Player raises exception -> Player is eliminated from the game\n - Player returns an error -> Player is eliminated from the game\n - Player takes too long -> Not currently handled\n \"\"\"\n\n _players: Dict[ColorString, PlayerInterface]\n _rule_checker: Optional[RuleChecker]\n _tile_iterator: Optional[Iterator[Tile]]\n _cheaters: Set[ColorString]\n _leaderboard: List[Set[ColorString]]\n _observers: List[RefereeObserver]\n\n def __init__(self) -> None:\n \"\"\"\n Create a new Referee.\n \"\"\"\n self._players = {}\n self._rule_checker = None\n self._tile_iterator = None\n self._leaderboard = []\n self._cheaters: Set[ColorString] = set()\n self._observers = []\n\n @validate_types\n def add_observer(self, observer: RefereeObserver):\n self._observers.append(observer)\n\n @validate_types\n def set_players(self, players: List[PlayerInterface]) -> Result[List[ColorString]]:\n \"\"\"\n Add the given players (ordered by age, decreasing) to the game. Ties for age can be represented in either order.\n Returns an error if `len(players)` is greater than 5 or less than 3, or if the method has already been called.\n\n :param players: Players for this referee to use in a game. Allows only 3-5 players.\n :return: The list of colors that will be assigned to those players.\n \"\"\"\n if self._players:\n return error(\"players have already been set for this game.\")\n if len(players) < 3 or len(players) > 5:\n return error(f\"there must be between 3 and 5 players, not {len(players)}.\")\n if len(set(players)) != len(players):\n return error(\n f\"the given set of players contains duplicates (or players that do not \"\n f\"implement __hash__, __eq__)\"\n )\n\n assigned_colors = AllColors[: len(players)]\n \n self._players = {\n color: silenced_object(player)\n for color, player in zip(assigned_colors, players)\n }\n \n for observer in self._observers:\n observer.players_added(assigned_colors)\n return ok(assigned_colors)\n\n @validate_types\n def set_rule_checker(self, rule_checker: RuleChecker) -> None:\n \"\"\"\n Set the rule checker to be used by this referee. Must be called prior to calling run_game().\n\n :param rule_checker: The rule checker that the referee should use.\n \"\"\"\n self._rule_checker = rule_checker\n\n @validate_types\n def set_tile_iterator(self, tile_iterator: Iterator[Tile]) -> None:\n \"\"\"\n Set the iterator of tiles to be used by this referee. Must be infinite and only be used by this referee.\n\n :param tile_iterator: The infinite tile iterator to be used by this referee.\n \"\"\"\n self._tile_iterator = tile_iterator\n\n def run_game(self) -> Result[GameResult]:\n \"\"\"\n Run an entire game of Tsuro with the players that have been added to this referee. Returns the result\n of the game.\n\n A list of players, a rule checker, and a tile iterator must have already been set on this referee\n prior to calling run_game.\n\n :return: The GameResult at the end of the game, or an error if something goes wrong.\n \"\"\"\n if not self._players:\n return error(\"must add players to this referee\")\n if not self._rule_checker:\n return error(\"must add a rule checker to this referee\")\n if not self._tile_iterator:\n return error(\"must add a tile iterator to this referee\")\n\n self._initialize_players()\n board = Board()\n\n # Run the initial turns\n r = self._run_game_initial_turns(board)\n if r.is_error():\n return error(r.error())\n\n # Run the intermediate turns\n while True:\n if len(board.live_players) <= 1:\n break\n\n r = self._run_game_single_round(board)\n if r.is_error():\n return error(r.error())\n\n return self._generate_game_result(board)\n\n def _initialize_players(self) -> None:\n \"\"\"\n Initialize the players contained within this referee according to the player interface\n \"\"\"\n assert self._rule_checker\n for color, player in self._players.items():\n self._handle_player_timeout(color, lambda: player.set_color(color))\n self._handle_player_timeout(color, lambda: player.set_players(list(set(self._players.keys()) - {color})))\n\n def _generate_game_result(self, board: Board) -> Result[GameResult]:\n \"\"\"\n Generate a GameResult once a game of Tsuro is complete based off of the data in self.cheaters,\n self.players_eliminated_in_round, and board. Must only be called once the game is over and 0 or 1\n players remain on the board.\n\n :param board: The board at the end of the game\n :return: The game result which contains a leaderboard and a list of cheaters\n \"\"\"\n # Add the last man standing to the list of eliminated players\n self._leaderboard.append(set(board.live_players.keys()))\n leaderboard = [x for x in reversed(self._leaderboard) if x]\n\n # Return the leaderboard and the cheaters and notify observers\n results = deepcopy((leaderboard, self._cheaters))\n for observer in self._observers:\n observer.game_result(results)\n return ok(results)\n\n def _remove_cheaters(self, board: Board) -> None:\n \"\"\"\n Remove anyone in self.cheaters from the list of players and from the list of currently live players\n\n :param board: The board to remove players from\n \"\"\"\n for player in self._cheaters:\n if player in self._players:\n del self._players[player]\n if player in board.live_players:\n board.remove_player(player)\n for observer in self._observers:\n observer.cheater_removed(player, board.get_board_state())\n\n def _get_tiles(self, num_tiles: int) -> List[Tile]:\n \"\"\"\n Get N tiles from the internal tile iterator\n :param num_tiles: The number of tiles\n :return: A list of retrieved tiles\n \"\"\"\n if self._tile_iterator is None:\n raise ValueError(\n \"Cannot call _get_tiles(n) prior to setting the tile iterator!\"\n )\n\n return [next(self._tile_iterator) for _ in range(num_tiles)]\n \n def _confirm_all_components(self) -> Result[None]:\n \"\"\"\n Checks that all components required for the referee to run (players, rules, tile iterator) exist\n \"\"\"\n if not self._players:\n return error(\"must add players to this referee\")\n if not self._rule_checker:\n return error(\"must add a rule checker to this referee\")\n if not self._tile_iterator:\n return error(\"must add a tile iterator to this referee\")\n return ok(None)\n\n def _run_game_initial_turns(self, board: Board) -> Result[None]:\n \"\"\"\n Run the first step of a Tsuro game: prompting every player for their initial move. Apply the\n changes to this board to the fields contained within this referee.\n\n :param board: The board to run the game on\n :return: A result containing either None or an error\n \"\"\"\n r_components = self._confirm_all_components()\n if r_components.is_error(): return r_components\n\n for color, player in self._players.items():\n tiles = self._get_tiles(3)\n for observer in self._observers:\n observer.initial_move_offered(color, tiles, board.get_board_state())\n\n r_initial_move = self._get_check_initial_move(board, color, player, tiles)\n if r_initial_move.is_error(): continue\n \n r = board.initial_move(r_initial_move.value())\n if r.is_error():\n return error(r.error())\n\n self._remove_cheaters(board)\n return ok(None)\n\n def _get_check_initial_move(\n self, board: Board, color: ColorString, player: PlayerInterface, tiles: List[Tile]\n ) -> Result[InitialMove]:\n \"\"\"\n Gets the initial move from the player and checks whether it is valid based on the rulechecker. If any errors, the player is added as a cheater.\n Returns an error if cheating, or the chosen move if it is valid\n \"\"\"\n r_initial_move = self._handle_player_timeout(color, lambda: player.generate_first_move(deepcopy(tiles), board.get_board_state()))\n if r_initial_move.is_error():\n self._cheaters.add(color)\n return error(r_initial_move.error())\n\n pos, tile, port = r_initial_move.value()\n initial_move = InitialMove(pos, tile, port, color)\n\n for observer in self._observers:\n observer.initial_move_played(color, tiles, board.get_board_state(), initial_move)\n\n r_rule = self._rule_checker.validate_initial_move(board.get_board_state(), tiles, initial_move)\n if r_rule.is_error():\n self._cheaters.add(color)\n return error(r_initial_move.error())\n\n return ok(initial_move)\n\n def _run_game_single_round(self, board: Board) -> Result[None]:\n \"\"\"\n Run the second step of a Tsuro game: prompting every player for an intermediate move. Apply the\n changes to this board to the fields contained within this referee.\n\n :param board: The board to run the game on\n :return: A result containing either None or an error\n \"\"\"\n r_components = self._confirm_all_components()\n if r_components.is_error(): return r_components\n\n alive_at_start_of_round = set(board.live_players.keys())\n for color, player in list(self._players.items()):\n if color not in board.live_players.keys():\n # They were killed by someone else so continue\n continue\n\n tiles = self._get_tiles(2)\n for observer in self._observers:\n observer.intermediate_move_offered(color, tiles, board.get_board_state())\n\n r_intermediate_move = self._get_check_intermediate_move(color, board, tiles, player)\n if r_intermediate_move.is_error(): continue\n\n r = board.intermediate_move(r_intermediate_move.value())\n if r.is_error():\n return error(r.error())\n\n alive_at_end_of_round = set(board.live_players.keys())\n self._leaderboard.append(set())\n self._handle_players_lost_in_round(board, alive_at_start_of_round, alive_at_end_of_round)\n \n return ok(None)\n\n def _handle_players_lost_in_round(\n self, board: Board, alive_at_start_of_round: Set, alive_at_end_of_round: Set\n ):\n \"\"\"\n Removes all players who died during a round from the game and adds them to the leaderboard.\n \"\"\"\n for killed_player in (alive_at_start_of_round - alive_at_end_of_round - self._cheaters):\n self._leaderboard[-1].add(killed_player)\n del self._players[killed_player]\n \n for observer in self._observers:\n observer.player_eliminated(killed_player, board.get_board_state())\n\n self._remove_cheaters(board)\n\n def _get_check_intermediate_move(\n self, color:ColorString, board: Board, tiles: List[Tile], player: PlayerInterface\n ) -> Result[IntermediateMove]:\n \"\"\"\n Gets the intermediate move from the player and checks whether it is valid based on the rulechecker. If any errors, the player is added as a cheater.\n Returns an error if cheating, or the chosen move if it is valid\n \"\"\"\n r_move = self._handle_player_timeout(color, lambda: player.generate_move(deepcopy(tiles), board.get_board_state()))\n if r_move.is_error():\n self._cheaters.add(color)\n return error(r_move.error())\n\n intermediate_move = IntermediateMove(r_move.value(), color)\n r_rule = self._rule_checker.validate_move(board.get_board_state(), tiles, intermediate_move)\n\n for observer in self._observers:\n observer.intermediate_move_played(color, tiles, board.get_board_state(), intermediate_move, r_rule.is_ok())\n\n if r_rule.is_error():\n self._cheaters.add(color)\n return error(r_rule.error())\n\n return ok(intermediate_move)\n\n def _handle_player_timeout(self, color, func):\n \"\"\"\n Calls the method on a player with a timeout. Returns the same value\n as the given function if the function takes less than three seconds\n to run. Otherwise, the player took too long to play, and is added\n to the list of cheaters.\n \"\"\"\n try:\n with timeout(TIMEOUT):\n return func()\n except:\n self._cheaters.add(color)\n","repo_name":"feliciazhang/tsuro","sub_path":"Admin/referee.py","file_name":"referee.py","file_ext":"py","file_size_in_byte":14373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2155636465","text":"from flask import Flask, request\nfrom flask.templating import render_template\nfrom .models.processing import process_image, process_video\n\nimport shutil\nimport warnings\nwarnings.filterwarnings(action='ignore')\n\napp = Flask(__name__)\napp.debug = True\n\n### Main page ###\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n### Face detection ###\n@app.route('/face_detect_get')\ndef face_detect_get():\n return render_template('face_detect_get.html')\n\n@app.route('/face_detect_post', methods=['GET', 'POST'])\ndef face_detect_post():\n if request.method == 'POST':\n face_image = request.files['face_img']\n face_image.save('./front/static/input/'+ str(face_image.filename))\n face_image_path = './front/static/input/' + str(face_image.filename)\n\n known_face_encoding = process_image(face_image_path)\n\n video_file = request.files['object_file']\n video_file.save('./front/static/input/' + str(video_file.filename))\n video_file_path = '/front/static/input/' + str(video_file.filename)\n\n fin_video = process_video(video_path=video_file_path, known_face=known_face_encoding)\n shutil.copy(fin_video, './front/static/output.mp4')\n\n return render_template('face_detect_post.html' , detected=fin_video)\n","repo_name":"jaehwan-AI/video_editer","sub_path":"front/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31463694707","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom diffusion_base import Diffusion\nfrom utils import ConditionalEmbedding\nimport numpy as np\n\n\nclass GaussianDiffusionTrainer(Diffusion):\n def __init__(self, model, config):\n super().__init__(config)\n\n self.model = model\n self.config = config\n # reggister buffer\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'sqrt_alphas_bar', self.sqrt_alphas_bar_)\n self.register_buffer(\n 'sqrt_one_minus_alphas_bar', self.sqrt_one_minus_alphas_bar_)\n\n\n def forward(self, x_0, cemb):\n \"\"\"\n Algorithm 1. with label embedding\n \"\"\"\n \n\n # x_0.shape = [batch_size, channels, height, width]\n # select a batch of timesteps\n t = torch.randint(self.T, size=(x_0.shape[0], ), device=x_0.device)\n noise = torch.randn_like(x_0)\n x_t = (\n self.extract(self.sqrt_alphas_bar, t, x_0.shape) * x_0 +\n self.extract(self.sqrt_one_minus_alphas_bar, t, x_0.shape) * noise) \n loss = F.mse_loss(self.model(x_t, t, cemb), noise, reduction='none')\n return loss\n\n\nclass DDPM_Sampler(Diffusion):\n def __init__(self, model, config):\n \n super().__init__(config)\n\n self.model = model\n self.w = config.evaluate.w\n\n self.register_buffer('betas', self.betas_)\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'sqrt_recip_alphas_bar', self.sqrt_recip_alphas_bar_)\n self.register_buffer(\n 'sqrt_recipm1_alphas_bar', self.sqrt_recipm1_alphas_bar_)\n \n # calculations for posterior q(x_{t-1} | x_t, x_0)\n self.register_buffer(\n 'posterior_var',\n self.betas_ * (1. - self.alphas_bar_prev_) / (1. - self.alphas_bar_))\n \n # below: log calculation clipped because the posterior variance is 0 at\n # the beginning of the diffusion chain\n self.register_buffer(\n 'posterior_log_var_clipped',\n torch.log(\n torch.cat([self.posterior_var[1:2], self.posterior_var[1:]]))) # replace the first element with the second element\n \n self.register_buffer(\n 'posterior_mean_coef1',\n torch.sqrt(self.alphas_bar_prev_) * self.betas_ / (1. - self.alphas_bar_))\n self.register_buffer(\n 'posterior_mean_coef2',\n torch.sqrt(self.alphas_) * (1. - self.alphas_bar_prev_) / (1. - self.alphas_bar_))\n \n def predict_x0_from_eps(self, x_t, t, eps):\n assert x_t.shape == eps.shape\n return (\n self.extract(self.sqrt_recip_alphas_bar, t, x_t.shape) * x_t -\n self.extract(self.sqrt_recipm1_alphas_bar, t, x_t.shape) * eps\n )\n\n def q_mean_variance(self, x_0, x_t, t):\n \"\"\"\n Compute the mean and variance of the diffusion posterior\n q(x_{t-1} | x_t, x_0)\n \"\"\"\n assert x_0.shape == x_t.shape\n posterior_mean = (\n self.extract(self.posterior_mean_coef1, t, x_t.shape) * x_0 +\n self.extract(self.posterior_mean_coef2, t, x_t.shape) * x_t\n )\n posterior_log_var_clipped = self.extract(self.posterior_log_var_clipped, t, x_t.shape)\n return posterior_mean, posterior_log_var_clipped\n\n \n def p_sample(self, x_t, t, cemb): # p_theta(x_{t-1} | x_t)\n # below: only log_variance is used in the KL computations\n model_log_var = {\n # for fixedlarge, we set the initial (log-)variance like so to\n # get a better decoder log likelihood\n 'fixedlarge': torch.log(torch.cat([self.posterior_var[1:2],\n self.betas[1:]])),\n 'fixedsmall': self.posterior_log_var_clipped,\n }[self.var_type]\n\n model_log_var = self.extract(model_log_var, t, x_t.shape)\n\n # Mean parameterization\n eps_cond = self.model(x_t, t, cemb)\n nu_emb = torch.zeros(cemb.shape, device = eps_cond.device)\n eps_uncond = self.model(x_t, t, nu_emb)\n eps = (1+self.w)*eps_cond - self.w*eps_uncond\n\n x_0 = self.predict_x0_from_eps(x_t, t, eps=eps)\n mean, log_var = self.q_mean_variance(x_0, x_t, t)\n \n # x_0 = torch.clip(x_0, -1., 1.)\n # no noise when t == 0\n time_step = t[0]\n if time_step > 0:\n noise = torch.randn_like(x_t) # for a batch of images\n else:\n noise = 0\n\n x_prev = mean + torch.exp(0.5 * log_var) * noise\n return x_prev\n \n def forward(self, x_T, cemb):\n \"\"\"\n Algorithm 2.\n \"\"\"\n x_t = x_T\n for time_step in reversed(range(self.T)):\n t = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * time_step \n x_t = self.p_sample(x_t, t, cemb)\n x_0 = x_t\n return torch.clip(x_0, -1, 1)\n \nclass DDIM_Sampler(Diffusion):\n def __init__(self, model, config):\n \n super().__init__(config)\n\n self.model = model\n self.w = config.evaluate.w\n\n self.register_buffer('ddim_sigma', self.ddim_sigma_)\n\n self.register_buffer('ddim_steps', self.ddim_steps_)\n\n # calculations for diffusion q(x_t | x_{t-1}) and others\n self.register_buffer(\n 'ddim_sqrt_recip_alphas_bar', self.ddim_sqrt_recip_alphas_bar_)\n self.register_buffer(\n 'ddim_sqrt_recipm1_alphas_bar', self.ddim_sqrt_recipm1_alphas_bar_)\n \n self.register_buffer(\n 'posterior_mean_coef1',\n torch.sqrt(self.ddim_alpha_prev_))\n self.register_buffer(\n 'posterior_mean_coef2',\n torch.sqrt(1-self.ddim_alpha_prev_-self.ddim_sigma_**2))\n\n def predict_x0_from_eps(self, x_t, idx, eps):\n assert x_t.shape == eps.shape\n return (\n self.extract(self.ddim_sqrt_recip_alphas_bar, idx, x_t.shape) * x_t -\n self.extract(self.ddim_sqrt_recipm1_alphas_bar, idx, x_t.shape) * eps\n )\n\n def q_mean_variance(self, x_0, x_t, idx, eps):\n \"\"\"\n Compute the mean and variance of the diffusion posterior\n q(x_{t-1} | x_t, x_0)\n \"\"\"\n assert x_0.shape == x_t.shape\n posterior_mean = (\n self.extract(self.posterior_mean_coef1, idx, x_t.shape) * x_0 +\n self.extract(self.posterior_mean_coef2, idx, x_t.shape) * eps\n )\n # return sigma here not variance\n posterior_sigma = self.extract(self.ddim_sigma, idx, x_t.shape)\n return posterior_mean, posterior_sigma\n \n def p_sample(self, x_t, t, idx, cemb):\n\n eps_cond = self.model(x_t, t, cemb)\n nu_emb = torch.zeros(cemb.shape, device = eps_cond.device)\n eps_uncond = self.model(x_t, t, nu_emb)\n eps = (1+self.w)*eps_cond - self.w*eps_uncond\n\n x_0 = self.predict_x0_from_eps(x_t, idx, eps)\n\n mean, sigma = self.q_mean_variance(x_0, x_t, idx, eps)\n \n x_prev = mean + sigma * eps\n \n return x_prev\n\n def forward(self, x_T, cemb):\n \n x_t = x_T\n for idx, time_step in enumerate(reversed(self.ddim_steps)):\n t = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * time_step \n idx = x_t.new_ones([x_T.shape[0], ], dtype=torch.long) * (len(self.ddim_steps) - idx - 1)\n x_t = self.p_sample(x_t, t, idx, cemb)\n x_0 = x_t\n return torch.clip(x_0, -1, 1)\n\n \n\n\n \n\n ","repo_name":"SoloChe/cls-free-diff","sub_path":"diffusion.py","file_name":"diffusion.py","file_ext":"py","file_size_in_byte":7571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19478884077","text":"# D. Заботливая мама\n# ID успешной посылки 65663041\n\nclass Node: \n def __init__(self, value, next_item=None):\n self.value = value\n self.next_item = next_item\n\n\ndef solution(node, elem):\n count = 0\n while node.value != elem:\n if node.value != elem and node.next_item is None:\n count = -1\n break\n else:\n count = count + 1\n node = node.next_item\n return count\n\n\ndef test():\n node3 = Node(\"node3\", None)\n node2 = Node(\"node2\", node3)\n node1 = Node(\"node1\", node2)\n node0 = Node(\"node0\", node1)\n solution(node0, \"node4\")\n # result is idx == 2\n\n\nif __name__ == '__main__':\n test()\n","repo_name":"master-cim/algorithm","sub_path":"tasks_sprints_12/d_caring_mother.py","file_name":"d_caring_mother.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"31141042741","text":"import tkinter as tk\nimport configparser\nimport os\nfrom pynput.keyboard import Key, Listener\nimport pyautogui\nimport time\nimport pytesseract\nfrom PIL import Image,ImageOps,ImageGrab\nimport cv2\nimport numpy as np\n\n\n\n################################################################\n# #\n# Script #\n# #\n################################################################\n\ncounter = 1\n\ndef create_ini_file():\n config_name = config_name_entry.get()\n if config_name:\n config = configparser.ConfigParser()\n with open('./configurations/deplacement/'+config_name + '.ini', 'w') as configfile:\n config.write(configfile)\n log_status(f\"Le fichier {config_name}.ini a été créé.\")\n update_file_list()\n else:\n log_status(\"Veuillez entrer un nom de configuration valide.\")\n\ndef update_file_list():\n # Clear the listbox\n file_listbox.delete(0, tk.END)\n # Get a list of all .ini files in the current directory\n ini_files = [f for f in os.listdir('./configurations/deplacement') if f.endswith('.ini')]\n # Add each file to the listbox\n for f in ini_files:\n file_listbox.insert(tk.END, f)\n\ndef select_ini_file(event):\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Set the config name entry to the selected file's name\n config_name_entry.delete(0, tk.END)\n # Display a message indicating which configuration is being used\n log_status(f\"Vous utilisez la configuration : {selection}\")\n\ndef delete_ini_file():\n selection = file_listbox.get(file_listbox.curselection())\n os.remove('./configurations/deplacement/'+selection)\n update_file_list()\n log_status(f\"Le fichier {selection} a été supprimé.\")\n \ndef update_selected_file():\n global counter\n # Check if an item in the listbox is selected\n if file_listbox.curselection():\n # Get the current position of the mouse\n x, y = pyautogui.position()\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Update the selected file with the mouse position\n config = configparser.ConfigParser()\n config.read('./configurations/deplacement/'+selection)\n config[f\"{counter}\"] = {'x': f\"{x}\", 'y': f\"{y}\"}\n with open('./configurations/deplacement/'+selection, 'w') as configfile:\n config.write(configfile)\n # Display a message indicating that the file has been updated\n log_status(f\"Le fichier {selection} a été mis à jour avec la position du curseur.\")\n # Increment the counter\n \n counter += 1\n else:\n # Display a message indicating that no file has been selected\n log_status(f\"Veuillez sélectionner un fichier dans la liste.\")\n \ndef process_selected_file():\n if file_listbox.curselection():\n # Get the name of the selected file\n selection = file_listbox.get(file_listbox.curselection())\n # Read the selected file\n config = configparser.ConfigParser()\n config.read('././configurations/deplacement/'+selection)\n # Get the initial coordinates\n current_coordinate = extract_coordinates()\n # Move the mouse to each position specified in the file\n for section in config.sections():\n x = int(config[section]['x'])\n y = int(config[section]['y'])\n # Move the mouse to the position\n pyautogui.moveTo(x, y)\n # Perform a left click\n pyautogui.click(button='left')\n # Wait for a short time to allow the click to complete\n time.sleep(0.1)\n log_status(f\"Moved mouse to ({x}, {y})\")\n # Continuously check the coordinates until they change\n while current_coordinate == extract_coordinates():\n time.sleep(0.001)\n # Update the current coordinates\n current_coordinate = extract_coordinates()\n # Display a message indicating that the file has been processed\n log_status(f\"Le fichier {selection} a été traité.\")\n else:\n # Display a message indicating that no file has been selected\n log_status(\"Veuillez sélectionner un fichier dans la liste.\")\n \n\n\ndef extract_coordinates():\n \n\n # Take a screenshot of the top left of the screen\n start_time_extract_coordinates = time.time()\n screenshot = ImageGrab.grab(bbox=(0, 0, 500, 300))\n rgb_image = screenshot.convert('RGB')\n rgb_image.save('my_image.jpg', format='JPEG')\n\n # Find white pixels and create a mask\n white_tolerance = 50 # adjust this as needed\n mask = Image.new('1', screenshot.size, 0)\n for x in range(screenshot.width):\n for y in range(screenshot.height):\n r, g, b = rgb_image.getpixel((x, y))\n if abs(r - 255) <= white_tolerance and abs(g - 255) <= white_tolerance and abs(b - 255) <= white_tolerance:\n mask.putpixel((x, y), 1)\n # convert the mask image to mode \"L\"\n mask = mask.convert(\"L\")\n # Apply the mask to the original image to keep only white pixels\n result = ImageOps.colorize(mask, (0, 0, 0), (255, 255, 255))\n\n # Save the result as a JPEG file\n result.save('my_image2.jpg', 'JPEG')\n\n # Use pytesseract to read text from the image\n string = pytesseract.image_to_string(result)\n \n # Find the start index of the coordinates string\n start_index = string.find(\"Coordonnées :\") + len(\"Coordonnées : \")\n\n # Find the end index of the coordinates string\n end_index = string.find(\"\\n\", start_index)\n\n # Extract the coordinates string\n coordinates_string = string[start_index:end_index]\n\n # Split the coordinates string into a list of two strings\n coordinates_list = coordinates_string.split(\", \")\n\n # Convert the coordinate strings to integers\n x_coord = int(coordinates_list[0])\n y_coord = int(coordinates_list[1])\n\n # print the time taken to execute the script\n end_time_extract_coordinates = time.time()\n log_status(f\"Execution time (extract_coordinates): {end_time_extract_coordinates - start_time_extract_coordinates:.2f} seconds\")\n\n\n # Delete the temporary files\n os.remove('my_image.jpg')\n os.remove('my_image2.jpg')\n print(x_coord, y_coord)\n # Return the coordinates as a tuple\n return [x_coord, y_coord]\n\n\ndef on_press(key):\n if key == Key.shift:\n update_selected_file()\n elif key == Key.ctrl_l:\n process_selected_file()\n \n \n \n\n################################################################\n# #\n# Interface #\n# #\n################################################################\n\n\n# Create the GUI\nroot = tk.Tk()\nroot.title(\"Création de fichier INI\")\nroot.geometry(\"600x500\")\n\n# Configuration name label and entry\nconfig_name_label = tk.Label(root, text=\"Nom de la configuration:\")\nconfig_name_label.pack()\nconfig_name_entry = tk.Entry(root)\nconfig_name_entry.pack()\n\n# Create file button\ncreate_button = tk.Button(root, text=\"Créer fichier\", command=create_ini_file)\ncreate_button.pack()\n\n# File listbox and delete button\nfile_frame = tk.Frame(root)\nfile_frame.pack(fill=tk.BOTH, expand=True)\n\nfile_listbox = tk.Listbox(file_frame)\nfile_listbox.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)\nupdate_file_list()\nfile_listbox.bind(\"<>\", select_ini_file)\n\ndelete_button = tk.Button(file_frame, text=\"Supprimer fichier\", command=delete_ini_file)\ndelete_button.pack(side=tk.BOTTOM)\n\n# Status text box\nstatus_text = tk.Text(root, height=5)\nstatus_text.pack(fill=tk.BOTH, expand=True)\n\ndef log_status(message):\n # Insert the message at the end of the text box\n status_text.insert(tk.END, message + '\\n')\n # Scroll the text box to show the latest message\n status_text.see(tk.END)\n \n# Mouse listener\nlistener = Listener(on_press=on_press)\nlistener.start()\n\n\n# Run the GUI\nroot.mainloop()\n","repo_name":"Misa-10/Prytaek","sub_path":"modules/deplacement.py","file_name":"deplacement.py","file_ext":"py","file_size_in_byte":8258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"71096521547","text":"\n\ndef recurse(n, s):\n \"\"\"\n Funções recursiva exemplo.\n :param n:tamanho da recursividade.\n :param s:mostra o tamanho da recursividade ao final\n :return:valores pro recursividade,\n \"\"\"\n if n == 0:\n print(n, s)\n else:\n print(n, s)\n recurse(n-1, n+s)\n\n\nrecurse(n=3, s=0)\n\n\"\"\"1. O que aconteceria se você chamasse esta função desta forma: recurse(-1, 0)?\"\"\"\n# seria uma recursividade infinita.\n\n\"\"\"2. Escreva uma docstring que explique tudo o que alguém precisaria saber para usar esta\nfunção (e mais nada).\"\"\"\n","repo_name":"FelipeDreissig/PenseEmPy","sub_path":"Cap 5/Ex5.4.py","file_name":"Ex5.4.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33715816678","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis module contains functions for thresholding matrices\nand outputting links/networks.\n\"\"\"\nimport numpy as np\nimport igraph\nimport dataio\n\n\ndef get_graph_from_bare_data(corr_mat_fname, blacklist_fname, density,\n include_mst=False, weighted=False):\n \"\"\"\n Extracts a graph from raw data.\n\n Parameters\n ----------\n corr_mat_fname : str\n path to the file containing the correlation matrix.\n blacklist_fname : str\n path to the bool blacklist\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n weighted : bool\n whether to consider the network as weighted\n\n Returns\n -------\n net : igraph.Graph\n the network\n \"\"\"\n corr_mat = dataio.load_adj_matrix_from_mat(corr_mat_fname)\n ok_nodes = dataio.get_ok_nodes(blacklist_fname)\n net = make_net_from_unfiltered_data(\n corr_mat,\n ok_nodes,\n density,\n include_mst=include_mst,\n weighted=weighted)\n return net\n\n\ndef _get_filtered_triu_adj_mat_copy(matrix, ok_nodes):\n \"\"\"\n Takes only the nodes listed in ok_nodes into account.\n\n Parameters\n ----------\n matrix : np.array\n 2D matrix with bad nodes\n ok_nodes : numpy bool array\n\n Returns\n -------\n m : np.array\n a copy of the matrix where the bad nodes have been removed\n \"\"\"\n m = matrix.copy()\n m = m[ok_nodes, :]\n m = m[:, ok_nodes]\n return np.triu(m, 1)\n\n\ndef make_net_from_unfiltered_data(corr_mat, ok_nodes, density, include_mst=False,\n weighted=False):\n \"\"\"\n Constructs a net from unfiltered data.\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n weighted : bool\n whether to consider the network as weighted\n\n Returns\n -------\n net : igraph.Graph\n \"\"\"\n assert 0 <= density <= 1\n edgelist = sort_links_by_weight(corr_mat, ok_nodes, include_mst)\n\n nNodes = sum(ok_nodes)\n nLinksMax = (nNodes * (nNodes - 1)) / 2\n nLinks = int(nLinksMax * density)\n edgelist = edgelist[:nLinks]\n\n return make_net(edgelist, nNodes, weighted)\n\n\ndef get_treshold_value(corr_mat, ok_nodes, density, include_mst=False):\n \"\"\"\n Constructs a net from unfiltered data.\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n density : float\n the network density to use\n include_mst : bool\n whether to include the maximum spanning tree\n\n Returns\n -------\n threshold: float\n the weight corresponding to the last considered link\n (i.e. no threshold)\n \"\"\"\n assert 0 <= density <= 1\n edgelist = sort_links_by_weight(corr_mat, ok_nodes, include_mst)\n n_nodes = sum(ok_nodes)\n n_links_max = (n_nodes * (n_nodes - 1)) / 2\n n_links = int(n_links_max * density)\n return edgelist[n_links]['weight']\n\n\ndef make_net(edgelist, nNodes, weighted):\n '''\n Create the network given the edgelist and number of nodes\n\n Parameters\n ----------\n weighted : (boolean)\n Whether weights are to be considered or not\n '''\n graph = igraph.Graph(nNodes)\n\n graph.add_edges(zip(edgelist['node1'], edgelist['node2']))\n if weighted is True:\n # graph.es['weight'] = 1\n graph.es['weight'] = edgelist['weight']\n\n # for n1, n2, w in edgelist:\n # graph[n1, n2] = w\n return graph\n\n\ndef make_full_weighted_net_from_weight_mat(matrix, ok_nodes, return_weights=False):\n \"\"\"\n Takes in an adjacency/correlation matrix, and constructs an undirected\n weighted network\n \"\"\"\n nNodes = np.sum(ok_nodes)\n graph = igraph.Graph(nNodes)\n\n triu_indices = np.triu_indices_from(matrix, 1)\n edgelist = np.array(triu_indices).T\n graph.add_edges(edgelist)\n weights = matrix[triu_indices]\n graph.es[\"weight\"] = weights\n if return_weights:\n return graph, weights\n return graph\n\n\ndef sort_links_by_weight(corr_mat, ok_nodes, include_mst):\n \"\"\"\n Sort the links by their link-weight\n\n Parameters\n ----------\n corr_mat : np.array\n 2D numpy array with bad nodes.\n ok_nodes : np.array\n the bool blacklist (whitelist)\n include_mst : Bool\n If true add the maximum spanning tree to the begining of sorted list\n\n Returns\n -------\n edgelist : numpy structrued array (node1, node2, weight)\n array([(0, 1, 1.0), (0, 3, 0.5), (2, 3, 0.5), (0, 4, 0.7), (1, 4, 0.4)],\n dtype=[('node1', '1:\n return 0,\n return sum(ind),\n\ntoolbox = base.Toolbox()\ntoolbox.register(\"particle\", generate, size=10, pmin=-1, pmax=1, smin=-1, smax=1)\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.particle)\ntoolbox.register(\"update\", updateParticle, phi1=1.0, phi2=1.0)\ntoolbox.register(\"evaluate\", evalOneMax)\n\nfits= []\ndef main():\n pop = toolbox.population(n=20)\n GEN = 100\n best = None\n\n for g in range(GEN):\n for part in pop:\n part.fitness.values = toolbox.evaluate(part)\n if not part.best or part.best.fitness < part.fitness:\n part.best = creator.Particle(part)\n part.best.fitness.values = part.fitness.values\n if not best or best.fitness < part.fitness:\n best = creator.Particle(part)\n best.fitness.values = part.fitness.values\n for part in pop:\n toolbox.update(part, best)\n\n print(best,best.fitness.values) \n fits.append(best.fitness.values)\n return pop, best\n\nif __name__ == \"__main__\":\n main()\n plt.plot(fits)\n plt.show()\n","repo_name":"shohei/emt-3108","sub_path":"particle_swarm_optimization/pso_deap.py","file_name":"pso_deap.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18518005232","text":"import datetime\nfrom matplotlib import pyplot as plt\nimport tensorflow as tf\nfrom tensorflow import keras\nimport tensorflow_addons as tfa\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nimport pandas as pd\nimport numpy as np\nfrom tensorflow import keras\nfrom keras.preprocessing.text import Tokenizer\n\nclass BiLSTMCRF(tf.keras.Model):\n def __init__(self, vocab_size, num_tags, embedding_dim, lstm_units):\n super(BiLSTMCRF, self).__init__()\n self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n self.bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm_units, return_sequences=True))\n self.dense = tf.keras.layers.Dense(num_tags)\n # self.crf = tfa.layers.CRF(num_tags)\n \n def call(self, inputs, training=None, mask=None):\n embeddings = self.embedding(inputs)\n lstm_outputs = self.bilstm(embeddings)\n outputs = self.dense(lstm_outputs)\n # outputs = self.crf(outputs)\n return outputs\n\ndf = pd.read_csv('./datasets/cleaned2.csv')\ntext_list = df['simple_sentence'].tolist()\ntokenizer = tf.keras.preprocessing.text.Tokenizer()\ntokenizer.fit_on_texts(text_list)\nvocab_size = len(tokenizer.word_index) + 1\nmax_length = max([len(s.split()) for s in text_list])\nword_index = tokenizer.word_index\n\nx_train, x_val, y_train, y_val = train_test_split(df['simple_sentence'], df['truth_value'], test_size=0.33, random_state=42)\n\n\ntrain_sequences = tokenizer.texts_to_sequences(x_train)\nval_sequences = tokenizer.texts_to_sequences(x_val)\ntrain_padded = tf.keras.preprocessing.sequence.pad_sequences(train_sequences, maxlen=max_length, padding='post', truncating='post')\nvalidation_padded = tf.keras.preprocessing.sequence.pad_sequences(val_sequences, maxlen=max_length, padding='post', truncating='post')\nlabel_tokenizer = tf.keras.preprocessing.text.Tokenizer()\nlabel_tokenizer.fit_on_texts(df['truth_value'].tolist())\n\ntraining_label_seq = np.array(label_tokenizer.texts_to_sequences(y_train))\nvalidation_label_seq = np.array(label_tokenizer.texts_to_sequences(y_val))\nembedding_dim = 100\nnum_tags = 2 + 1\nlstm_units = 4\nnum_epochs = 1\n\ndataset = tf.data.experimental.make_csv_dataset('./datasets/cleaned2.csv', batch_size=32, num_epochs=1, label_name='truth_value', ignore_errors=True)\n\n\n# log_dir = \"./logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\nwith tf.device(\"/cpu:0\"):\n model = BiLSTMCRF(vocab_size, num_tags, embedding_dim, lstm_units)\n model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics= ['accuracy', 'loss'])\n history = model.fit(dataset, epochs=num_epochs, verbose=2)\n\ndef plot_graphs(history, string):\n plt.plot(history.history[string])\n plt.plot(history.history['val_'+string])\n plt.xlabel(\"Epochs\")\n plt.ylabel(string)\n plt.legend([string, 'val_'+string])\n plt.show()\n \nplot_graphs(history, \"accuracy\")\nplot_graphs(history, \"loss\")","repo_name":"Karun842002/fyp-data-scraper","sub_path":"tfmodel.py","file_name":"tfmodel.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"29171580760","text":"import os\nmenu2=True\npt=12000\npp=14000\npa=17000\nmenu1=True\ncont1=0\ncont2=0\ncont3=0\ndcto=0\ncance=0\nwhile menu1:\n print(\"****bienvenido a Pizzeria Douc****\")\n print(\"Elija una de las opciones disponibles: \")\n print(\"1-Pizza Tradicional\")\n print(\"2-Pizza Peperoni\")\n print(\"3-Pizza All Carnes\")\n print(\"4-Salir\")\n try:\n opcion=int(input(\"Que deseas pedir : \"))\n if(opcion >0 and opcion <5) :\n if opcion ==1:\n print(\"Usted ingreso opcion 1\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pt*pedido}\\n\")\n print(\"Quieres ordenar algo mas? \")\n cont1=(pt*pedido)\n\n mp=int(input(\"1-Si 2-No :\"))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==2:\n print(\"Usted ingreso opcion 2\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pp*pedido}\") \n print(\"Quieres ordenar algo mas? \")\n cont2=(pp*pedido)\n mp=int(input(\"1-Si 2-No :\"))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==3:\n print(\"Usted ingreso opcion 3\")\n pedido=int(input(\"¿cuantas pizzas quieres?\"))\n print(f\"total a pagar:{pa*pedido}\")\n print(\"Quieres ordenar algo mas? \")\n cont3=(pa*pedido)\n mp=int(input(\"1-Si 2-No :\" ))\n if mp ==1:\n menu1=True\n if mp==2:\n menu1=False\n os.system(\"cls\")\n if opcion ==4:\n print(\"hasta luego\")\n menu1=False\n for acumulador in range (pedido):\n acumulador=cont1+cont2+cont3\n print(\"Deseas seguir con la compra ? \")\n cancel1=int(input(\"1-Si 2-No : \"))\n if cancel1==2:\n menu1=True\n os.system(\"cls\")\n continue\n if cancel1==1:\n os.system(\"cls\")\n except:\n print(\"Ocurrio un error\")\nprint(f\"total a cancelar : {acumulador} \")\nwhile menu2:\n print(\"Selecciona tu jornada : \")\n print(\"1-Diurno\")\n print(\"2-Vespertino\")\n print(\"3-Administrativo\")\n try:\n dcto=int(input(\"tu jornada es : \"))\n if(opcion >0 and opcion <4) :\n if dcto ==1:\n print(\"Descuento para jornada diurna 12%\")\n menu2=False\n if dcto ==2:\n print(\"Descuento para jornada vespertina 10%\")\n menu2=False\n if dcto ==3:\n print(\"Administrativo no corresponde descuento\")\n menu2=False\n except:\n print(\"algo salio mal , vuelve a intentarlo\")\n\nos.system(\"cls\")\n\nprint(\"*************Pizzeria Duoc**************\")\nif cont1 > 0:\n print(f\"Pizza Tradicional: $ {cont1}\")\nif cont2 > 0:\n print(f\"Pizza Peperoni: $ {cont2}\")\nif cont3 > 0:\n print(f\"Pizza All Carnes: $ {cont3}\")\nprint(\"*****************************************\")\nprint(f\"Subtotal: $ {acumulador} \")\nif dcto==1:\n des=12\n print(\"Descuento: 12%\")\n d=acumulador*des//100\nif dcto==2:\n des=10\n print(\"Descuento: 10%\")\n d=acumulador*des//100\nif dcto==3:\n des=0\n print(\"Descuento: 0%\")\n d=acumulador*des//100\nprint(\"******************************************\")\nprint(f\"Total a pagar: $ {acumulador-d} \")\n\nprint(\"**********Gracias por su compra***********\")\n","repo_name":"ConstanzapGaete/PYTHON","sub_path":"pruebatipoB.py","file_name":"pruebatipoB.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6624076079","text":"import os\nimport sys\nimport re\nfrom datetime import datetime,timedelta\nimport numpy as np\nfrom proc_satellite_class import Satellite_Process\n\nclass Interp(Satellite_Process):\n\n def __init__(self):\n super().__init__()\n self._freeze()\n\n def run(self):\n # Start process\n super().run()\n\n # Check files/folders\n start_dtim = datetime.strptime(self.start_date,self.date_fmt)\n end_dtim = datetime.strptime(self.end_date,self.date_fmt)\n first_dtim = datetime.strptime(self.first_date,self.date_fmt)\n last_dtim = datetime.strptime(self.last_date,self.date_fmt)\n trg_bnam = '{:%Y%m%d}_{:%Y%m%d}'.format(first_dtim,last_dtim)\n if not os.path.exists(self.s2_data):\n os.makedirs(self.s2_data)\n if not os.path.isdir(self.s2_data):\n raise ValueError('{}: error, no such folder >>> {}'.format(self.proc_name,self.s2_data))\n\n # Interpolate data\n ystr = '{:%Y}'.format(first_dtim)\n dnam = os.path.join(self.s2_data,'interp',ystr)\n if not os.path.exists(dnam):\n os.makedirs(dnam)\n if not os.path.isdir(dnam):\n raise IOError('Error, no such folder >>> {}'.format(dnam))\n command = self.python_path\n if self.values['atcor_flag']:\n command += ' \"{}\"'.format(os.path.join(self.scr_dir,'sentinel2_interp_atcor.py'))\n command += ' --inpdir \"{}\"'.format(os.path.join(self.s2_data,'atcor'))\n command += ' --nmax {}'.format(self.values['nmax'])\n command += ' --rthr {}'.format(self.values['rthr'])\n else:\n command += ' \"{}\"'.format(os.path.join(self.scr_dir,'sentinel2_interp.py'))\n command += ' --inpdir \"{}\"'.format(os.path.join(self.s2_data,'parcel'))\n command += ' --dstdir \"{}\"'.format(os.path.join(self.s2_data,'interp'))\n command += ' --tendir \"{}\"'.format(os.path.join(self.s2_data,'tentative_interp'))\n command += ' --data_tmin {:%Y%m%d}'.format(first_dtim)\n command += ' --data_tmax {:%Y%m%d}'.format(last_dtim)\n command += ' --tmgn {}'.format(self.values['tmgn'])\n command += ' --tstp 1'\n command += ' --smooth=\"{}\"'.format(self.values['p_smooth'])\n command += ' --ethr {}'.format(self.values['cflag_thr'])\n if self.values['csv_flag']:\n command += ' --out_csv'\n iflag = self.list_labels['oflag'].index('interp')\n if self.values['oflag'][iflag]:\n command += ' --overwrite'\n iflag = self.list_labels['oflag'].index('tentative interp')\n if self.values['oflag'][iflag]:\n command += ' --tentative_overwrite'\n if self.values['eflag']:\n command += ' --extrapolate'\n command += ' --fignam \"{}\"'.format(os.path.join(dnam,'{}_interp.pdf'.format(trg_bnam)))\n command += ' --nfig 10'\n command += ' --debug'\n command += ' --batch'\n self.run_command(command,message='<<< Interpolate data between {:%Y-%m-%d} - {:%Y-%m-%d} >>>'.format(first_dtim,last_dtim))\n\n # Finish process\n super().finish()\n return\n","repo_name":"nahiro/satellite_analysis","sub_path":"run_satellite_interp.py","file_name":"run_satellite_interp.py","file_ext":"py","file_size_in_byte":3125,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"42907646202","text":"\"\"\"\nThis module is used for loading and preparing intents data\n\"\"\"\nimport json\n\nimport nltk\n\n\ndef load_intents(path: str):\n \"\"\"\n Loads a specified intents JSON file\n\n :param path: Path to the intents file\n :return: An Intents object\n \"\"\"\n try:\n with open(path) as file:\n intents = json.loads(file.read())\n\n words = []\n classes = []\n documents = []\n\n for intent in intents['intents']:\n for pattern in intent['patterns']:\n word_list = nltk.word_tokenize(pattern)\n words.extend(word_list)\n documents.append((word_list, intent['tag']))\n if intent['tag'] not in classes:\n classes.append(intent['tag'])\n\n return Intents(words, classes, documents)\n\n except FileNotFoundError:\n print(\"Intents file not found.\")\n return Intents([], [], [])\n \n \ndef load_entities(path):\n \"\"\"\n Load the entities and return an dict carrying all relevant info\n \"\"\"\n try:\n with open(path) as file:\n entities = json.loads(file.read())\n\n ents = {}\n\n for e in entities['entities']:\n ents[e['entity']] = {'opening hours':e['opening hours'],\n 'location':e['location'],\n 'contact':e['contact'],\n 'link':e['link']\n }\n return ents \n\n except FileNotFoundError:\n print(\"Entities file not found.\")\n return {}\n \n\n\nclass Intents:\n \"\"\"\n A class containing the words, classes and documents information\n loaded from an Intents JSON file.\n \"\"\"\n \n def __init__(self, words, classes, documents):\n self.words = words\n self.classes = classes\n self.documents = documents\n","repo_name":"team28COSC310/chat-bot-cosc-310","sub_path":"src/data_importer.py","file_name":"data_importer.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"2717749628","text":"#!/usr/bin/python3\n\"\"\"Script using REST API for an employee ID\"\"\"\n\nimport requests\nimport sys\n\n\nif __name__ == '__main__':\n if len(sys.argv) != 2:\n print(\"Usage: {} \".format(sys.argv[0]))\n sys.exit(1)\n\n employee_id = sys.argv[1]\n base_url = \"https://jsonplaceholder.typicode.com/users\"\n url = base_url + \"/\" + employee_id\n\n response = requests.get(url)\n if response.status_code != 200:\n print(\"Employee not found.\")\n sys.exit(1)\n\n username = response.json().get('username')\n\n todo_url = url + \"/todos\"\n response = requests.get(todo_url)\n tasks = response.json()\n\n with open('{}.csv'.format(employee_id), 'w') as file:\n for task in tasks:\n file.write('\"{}\",\"{}\",\"{}\",\"{}\"\\n'\n .format(employee_id, username, task.get('completed'),\n task.get('title')))\n","repo_name":"sylvieshimwauwase/alx-system_engineering-devops","sub_path":"0x15-api/1-export_to_CSV.py","file_name":"1-export_to_CSV.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26695637427","text":"# build_update.py\n# copy the contents from patch to AOSP build directory \nimport sys\nsys.path.append('./python_scripts')\nfrom replace import *\nfrom distutils.dir_util import copy_tree\nfrom read_configuration import *\ncwd = os.getcwd()\n\n# reading the configuration variables \n\ndata = read_configuration ()\n\n\nprint(\"build_update.py executing from : \"+ cwd +'/nxp')\nprint(\"\\n\")\nsource = data['src_dir_build']+'/'+'make'+'/core'+'/soong_config.mk'\ntarget = data['dst_dir_build']+'/'+'make'+'/'+'/core'+'/soong_config.mk'\n\n\nprint (\"source path:=\"+str(source))\nprint (\"target path:=\"+str(target)+\"\\n\")\ncopyfile(source,target)\nprint (\"file copied successfuly....\\n\")\n\n\nsource = data['src_dir_build']+'/'+'soong'+'/android'+'/variable.go'\ntarget = data['dst_dir_build']+'/'+'soong'+'/android'+'/variable.go'\n\n\nprint (\"source path:=\"+str(source))\nprint (\"target path:=\"+str(target)+\"\\n\")\ncopyfile(source,target)\nprint (\"file copied successfuly....\")\n\n","repo_name":"mohankadali/personal_files","sub_path":"android_poting_scripts/scripts_android_upgrade/script_backup/nxp/build_update.py","file_name":"build_update.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16732975030","text":"#!/usr/bin/env python\n\n# server program for client sending requests to execute tasks\n\n# run this program and then client (rps_monitor_client.py) either on same node or different node\n# on local network. Server and client can also be run on two different networks but client must\n# call 'scheduler.peer' method appropriately.\n\nimport sys\nimport pycos\n# import netpycos to use distributed version of Pycos\nimport pycos.netpycos\n\n# PyPI / pip packaging adjusts assertion below for Python 3.7+\nif sys.version_info.major == 3:\n assert sys.version_info.minor < 7, \\\n ('\"%s\" is not suitable for Python version %s.%s; use file installed by pip instead' %\n (__file__, sys.version_info.major, sys.version_info.minor))\n\n\n# when client invokes RPS in this program, function below is used to start a task\ndef rps_server(a, b=1, task=None):\n pycos.logger.debug('running %s with %s, %s', task, a, b)\n # receive message from client\n msg = yield task.receive(timeout=2)\n # to illustrate how client's monitor can receive exit values or exceptions, exception is\n # raised if given b is not a positve number, otherwise task sleeps for b seconds and exits\n # with msg\n if isinstance(b, (int, float)) and b > 0 and isinstance(msg, str):\n yield task.sleep(b)\n # (remote) monitor (if any) gets back msg (to be interpreted as normal termination)\n raise StopIteration(msg)\n else:\n # (remote) monitor (if any) gets this exception\n raise Exception('invalid invocation: %s' % b)\n\n\nif __name__ == '__main__':\n pycos.logger.setLevel(pycos.Logger.DEBUG)\n # 'secret' is set so only peers that use same secret can communicate\n scheduler = pycos.Pycos(name='server', secret='test')\n # register rps_server so remote clients can request execution\n rps = pycos.RPS(rps_server)\n rps.register()\n\n if sys.version_info.major > 2:\n read_input = input\n else:\n read_input = raw_input\n while True:\n try:\n line = read_input().strip().lower()\n if line in ('quit', 'exit'):\n break\n except Exception:\n break\n","repo_name":"pgiri/pycos","sub_path":"examples/rps_monitor_server.py","file_name":"rps_monitor_server.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"82"} +{"seq_id":"15261975429","text":"# from flask import Flask, render_template, current_app\nfrom flask import Flask, render_template\nfrom flask_wtf.csrf import CSRFProtect\nfrom flask_bootstrap import Bootstrap\nfrom flask_assets import Environment\nfrom .assets import create_assets\nfrom .routes import auth_bp, api_bp, main_bp, auth_pages, movies_bp\nfrom flask_login import LoginManager\nfrom .login_manager import manage_login\n\n\n# auth_blueprint = Blueprint('auth', __name__, url_prefix='/auth')\nfrom .routes.admin_panel import admin_panel_pb\nfrom .routes.user_profile import user_profile_pb\n\n\ndef create_app(config_name):\n app = Flask(__name__)\n Bootstrap(app)\n app.secret_key = b'\\xdf\\xc0\\xe8\\xb0\\x14\\xb2\\xad\\x9f\\x1c\\xc19\\x87/4\\x19v\\x11\\xa8%I\\xad=\\x8f\\x86'\n # for testing\n # app.config['WTF_CSRF_ENABLED'] = False\n\n csrf = CSRFProtect()\n csrf.init_app(app)\n\n login_mng = LoginManager()\n login_mng.init_app(app)\n\n manage_login(login_mng)\n\n assets = Environment(app)\n create_assets(assets)\n\n app.register_blueprint(auth_bp)\n app.register_blueprint(api_bp)\n app.register_blueprint(main_bp)\n app.register_blueprint(auth_pages)\n app.register_blueprint(movies_bp)\n app.register_blueprint(admin_panel_pb)\n app.register_blueprint(user_profile_pb)\n\n register_error_pages(app)\n\n return app\n\n\ndef register_error_pages(app):\n\n @app.errorhandler(403)\n def page_not_found(e):\n return render_template('403.html'), 403\n","repo_name":"P4yBill/MovieFlix","sub_path":"flask-app/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7493868835","text":"import math\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef poisson_pmf(x, lambd):\n return (math.exp(-lambd) * (lambd**x)) / math.factorial(x)\n\n# Parameters\ncalls_per_hour_5 = 5 # Average rate of 5 calls per hour\ncalls_per_hour_10 = 10 # Average rate of 10 calls per hour\ncalls_per_hour_15 = 15 # Average rate of 15 calls per hour\n\nmax_calls = 10 # Maximum number of calls\n\n# Calculate probabilities\nx = np.arange(max_calls + 1)\npmf_5 = [poisson_pmf(i, calls_per_hour_5) for i in x]\npmf_10 = [poisson_pmf(i, calls_per_hour_10) for i in x]\npmf_15 = [poisson_pmf(i, calls_per_hour_15) for i in x]\n\n\n# Calculate and print probabilities\nprint(\"Number of Calls (x) | Probability (P(x; λ=5)) | Probability (P(x; λ=10)) | Probability (P(x; λ=15))\")\nprint(\"---------------------------------------------------------------------------------------------------------------\")\nfor i in range(len(x)):\n print(f\"{x[i]:<21} | {pmf_5[i]:<30.4e}| {pmf_10[i]:<30.4e}| {pmf_15[i]:<.4e}\")\nprint(\"---------------------------------------------------------------------------------------------------------------\")\n\n# Plotting the PMF graph\nplt.bar(x, pmf_5, label='λ = 5')\nplt.bar(x, pmf_10, label='λ = 10', alpha=0.5)\nplt.bar(x, pmf_15, label='λ = 15', alpha=0.2)\nplt.xlabel('Number of Calls')\nplt.ylabel('Probability')\nplt.title('Probability Mass Function (PMF)')\nplt.xticks(x)\nplt.legend()\nplt.show()\n","repo_name":"Rakibul73/Simulation_Modeling_Code","sub_path":"masud_sir_part/same_python_file/poisson_distribution.py","file_name":"poisson_distribution.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"82"} +{"seq_id":"27270167628","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def sortList(self, head: ListNode) -> List:\n if head is None:\n return head\n t = []\n b = head\n while b:\n t.append(b.val)\n b = b.next\n \n t.sort()\n tail = head\n for i in t:\n tail.next = ListNode(i)\n tail = tail.next\n return head.next \n","repo_name":"javokhirbek1999/leetcode","sub_path":"Python/Data Structures/Linked List/Sort-List.py","file_name":"Sort-List.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"17388608730","text":"import sys\nfrom unittest.util import _MAX_LENGTH\n\n\ndef parkereso(fiuk, lanyok):\n if(len(fiuk) != len(lanyok)):\n return False\n result = {}\n for i in fiuk:\n for j in lanyok:\n if i[0] == j[0]:\n result[i] = j\n fiuk.remove(i)\n lanyok.remove(j)\n break\n for i in fiuk:\n for j in lanyok:\n if i[len(i)-1] == j[len(j)-1] == 'i':\n result[i] = j\n fiuk.remove(i)\n lanyok.remove(j)\n break\n minimumdiff = 100000\n for i in fiuk:\n lany = \"\"\n minimumdiff = 100000\n for j in lanyok:\n diff = abs(len(i) - len(j))\n if diff < minimumdiff:\n minimumdiff = diff\n lany = j\n result[i] = lany\n lanyok.remove(lany)\n return result","repo_name":"berypurda/ScriptDict","sub_path":"src/feladat.py","file_name":"feladat.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8862874310","text":"from argparse import ArgumentParser, RawDescriptionHelpFormatter\nfrom sys import argv\nfrom pathlib import Path\nfrom typing import List\n\nfrom camel_tools.ner import NERecognizer\nfrom p_tqdm import p_uimap\n\nfrom eis1600.helper.repo import TRAINING_DATA_REPO\nfrom eis1600.processing.preprocessing import get_yml_and_miu_df\nfrom eis1600.processing.postprocessing import reconstruct_miu_text_with_tags\n\n\ndef ner_to_md(toponym_labels: List[str]) -> List[List[str]]:\n md_tags = []\n prev = None\n for label in toponym_labels:\n if label == 'B-TOPD' or prev == 'O' and label == 'I-TOPD':\n if prev == 'B-TOPD':\n md_tags.append(['ETOPD BTOPD'])\n else:\n md_tags.append(['BTOPD'])\n elif (prev == 'I-TOPD' or prev == 'B-TOPD') and label == 'O':\n md_tags.append(['ETOPD'])\n else:\n md_tags.append(None)\n \n prev = label\n \n return md_tags \n\n\ndef annotate_miu(file: str) -> str:\n outpath = file.replace('gold_standard', 'topo_descriptions')\n \n with open(file, 'r', encoding='utf-8') as miu_file_object:\n yml_handler, df = get_yml_and_miu_df(miu_file_object)\n\n toponym_labels = NERecognizer('EIS1600_Pretrained_Models/camelbert-ca-toponyms-description/').predict_sentence(df['TOKENS'].fillna('-').to_list())\n if 'B-TOPD' in toponym_labels:\n df['TAGS_LISTS'] = ner_to_md(toponym_labels)\n print(list(zip(toponym_labels, df['TAGS_LISTS'])))\n \n yml_handler.unset_reviewed()\n updated_text = reconstruct_miu_text_with_tags(df[['SECTIONS', 'TOKENS', 'TAGS_LISTS']]) \n \n with open(outpath, 'w', encoding='utf-8') as ofh:\n ofh.write(str(yml_handler) + updated_text)\n\n return outpath\n\n\ndef main():\n arg_parser = ArgumentParser(\n prog=argv[0], formatter_class=RawDescriptionHelpFormatter,\n description='''Script to annotate onomastic information in gold-standard MIUs.'''\n )\n arg_parser.add_argument('-D', '--debug', action='store_true')\n\n args = arg_parser.parse_args()\n debug = args.debug\n\n with open(TRAINING_DATA_REPO + 'gold_standard.txt', 'r', encoding='utf-8') as fh:\n files_txt = fh.read().splitlines()\n\n infiles = [TRAINING_DATA_REPO + 'gold_standard/' + file for file in files_txt if Path(\n TRAINING_DATA_REPO + 'gold_standard/' + file\n ).exists()]\n\n res = []\n if debug:\n for i, file in enumerate(infiles):\n print(i, file)\n res.append(annotate_miu(file))\n else:\n res += p_uimap(annotate_miu, infiles)\n\n print('Done')\n","repo_name":"EIS1600/eis1600-pkg","sub_path":"eis1600/helper/annotate_topd.py","file_name":"annotate_topd.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10868696933","text":"import func\nimport requests\nimport os\nfrom datetime import datetime, timedelta\nfrom model import *\n\n\ndef get_employee_activity(start_date: str, end_date: str, id_employee: str) -> list[EmployeeActivity]:\n employee_activities = []\n\n for date in func.get_range_date(start_date, end_date):\n request = requests.get(F\"{os.environ.get('API')}/activity\", params={\n 'apikey': os.environ.get(\"APIKEY\"),\n 'begin': date.strftime('%Y%m%d'),\n 'end': func.get_next_day(date).strftime('%Y%m%d'),\n 'employees': id_employee\n })\n\n json = request.json()\n\n if 'status' in json:\n employee_activities.append(EmployeeActivity(\n day_week=func.get_name_week_day(date),\n date=date.strftime('%d.%m.%Y'),\n total_time=func.convert_from_sec(\n json['items'][0]['totalTime']),\n active_time=func.convert_from_sec(\n json['items'][0]['activeTime']),\n procent=F\"{func.get_percent(json['items'][0]['activeTime'], json['items'][0]['totalTime'])} %\"\n ))\n\n return employee_activities\n\n\ndef get_employees() -> list[Employee] | None:\n request = requests.get(F\"{os.environ.get('API')}/employees\", params={\n 'apikey': os.environ.get(\"APIKEY\"),\n 'active': 'true'\n })\n\n json = request.json()\n if 'status' in json:\n return [Employee(id=item['id'], fio=F\"{item['lastName']} {item['firstName']}\") for item in json['items']]\n\n\ndef get_employee_activity_to_xlsx(date_start: str, date_end: str, ids_employee: str) -> list[EmployeeActivityXLSX]:\n employees = get_employees()\n employeeActivityXLSX = []\n \n for id in ids_employee.split(','):\n employeeActivityXLSX.append(EmployeeActivityXLSX(\n date_start=date_start,\n date_end=date_end,\n fio=func.filter_employee_by_id(id, employees),\n activity=get_employee_activity(date_start, date_end, id)\n ))\n \n return employeeActivityXLSX\n\n\ndef get_productivity_smartboard(date: str, smartboards: list[Employee]) -> list:\n smardboard_ids = ','.join([str(smartboard.id)\n for smartboard in smartboards])\n\n request = requests.get(F\"{os.environ.get('API')}productivity\", params={\n 'apikey': os.environ.get('APIKEY'),\n 'begin': date,\n 'end': func.get_next_day(datetime.strptime(date, '%Y%m%d')).strftime('%Y%m%d'),\n 'employees': smardboard_ids\n })\n\n json = request.json()\n\n if json['status'] == 'success':\n data = []\n for item in json['items']:\n smartboard_active = get_employee_activity(date, date, item['id'])[0]\n active_time = func.get_seconds_from_str_time(\n smartboard_active.active_time)\n total_time = func.get_seconds_from_str_time(\n smartboard_active.total_time)\n\n data.append({\n 'corpus': func.parse_name_smartboard(item['name'], 1),\n 'cabinet': func.parse_name_smartboard(item['name'], 2),\n 'name': 'Smart Board',\n 'date': datetime.strptime(date, '%Y%m%d').strftime('%d.%m.%Y'),\n 'totalTime': func.parse_time_smartboard(total_time),\n 'productiveTime': func.parse_time_smartboard(item['productiveTime']),\n 'unproductiveTime': func.parse_time_smartboard(item['unproductiveTime']),\n 'neutralTime': func.parse_time_smartboard(item['totalTime'] - item['productiveTime']),\n 'percent_productive': F\"{func.get_percent(item['productiveTime'], active_time)} %\",\n 'time_workday': F\"{func.get_percent(total_time, timedelta(minutes=480).total_seconds())} %\"\n })\n\n return data\n else:\n return json\n","repo_name":"alexnsidorov/bitcop_custom_report","sub_path":"getter_data.py","file_name":"getter_data.py","file_ext":"py","file_size_in_byte":3840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"21047293782","text":"# This is a Python Program to read a number n and print an identity matrix of the desired size.\n\nn=int(input('Enter a number: '))\nfor i in range(0,n):\n for j in range(0,n):\n if(i==j):\n print('1', sep=' ', end=' ')\n else:\n print('0', sep=' ', end=' ')\n print()","repo_name":"Maaitrayo/Python-Programming-Basics","sub_path":"program18.py","file_name":"program18.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"34540653974","text":"import torch\nimport nibabel as nib\nimport torch.nn.functional as F\n\nfrom src.runner.predictors import BasePredictor\n\n\nclass VipcupSegPredictor(BasePredictor):\n \"\"\"The VIPCUP predictor for the segmentation task.\n Args:\n saved_pred (bool): Whether to save the prediction (default: False).\n \"\"\"\n\n def __init__(self, saved_pred=False, **kwargs):\n super().__init__(**kwargs)\n if self.test_dataloader.batch_size != 1:\n raise ValueError(f'The testing batch size should be 1. Got {self.test_dataloader.batch_size}.')\n\n self.saved_pred = saved_pred\n self.output_dir = self.saved_dir / 'prediction'\n if not self.output_dir.is_dir():\n self.output_dir.mkdir(parents=True)\n\n def _test_step(self, batch):\n if self.test_dataloader.dataset.csv_name == 'testing.csv':\n input, target = batch['input'].to(self.device), batch['target']\n output = F.interpolate(self.net(input),\n size=target.size()[2:],\n mode='trilinear',\n align_corners=False)\n cross_entropy_loss = torch.tensor(float('nan'))\n dice_loss = torch.tensor(float('nan'))\n loss = torch.tensor(float('nan'))\n dice = torch.tensor(tuple(float('nan') for _ in range(3)))\n else:\n input, target = batch['input'].to(self.device), batch['target'].to(self.device)\n output = F.interpolate(self.net(input),\n size=target.size()[2:],\n mode='trilinear',\n align_corners=False)\n cross_entropy_loss = self.loss_fns.cross_entropy_loss(output, target.squeeze(dim=1))\n dice_loss = self.loss_fns.dice_loss(output, target)\n loss = (self.loss_weights.cross_entropy_loss * cross_entropy_loss\n + self.loss_weights.dice_loss * dice_loss)\n dice = self.metric_fns.dice(F.softmax(output, dim=1), target)\n\n if self.saved_pred:\n (affine,), (header,), (name,) = batch['affine'], batch['header'], batch['name']\n _, pred = F.softmax(output, dim=1).max(dim=1)\n pred = pred.squeeze(dim=0).permute(1, 2, 0).contiguous()\n nib.save(\n nib.Nifti1Image(\n pred.cpu().numpy(),\n affine.numpy(),\n header\n ),\n (self.output_dir / name).as_posix()\n )\n return {\n 'loss': loss,\n 'losses': {\n 'CrossEntropyLoss': cross_entropy_loss,\n 'DiceLoss': dice_loss\n },\n 'metrics': {\n 'Dice': dice[1:].mean(),\n }\n }\n","repo_name":"cmlab-mira/MedicalPro","sub_path":"src/runner/predictors/vipcup_seg_predictor.py","file_name":"vipcup_seg_predictor.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"82"} +{"seq_id":"3503934315","text":"#################################################\r\n# hw12.py\r\n#\r\n# Your name:\r\n# Your andrew id:\r\n#################################################\r\n\r\nfrom pyexpat.errors import XML_ERROR_RECURSIVE_ENTITY_REF\r\nimport cs112_n22_hw12_linter\r\nimport math, copy\r\n\r\n#################################################\r\n# Helper functions\r\n#################################################\r\n\r\ndef almostEqual(d1, d2, epsilon=10**-7):\r\n # note: use math.isclose() outside 15-112 with Python version 3.5 or later\r\n return (abs(d2 - d1) < epsilon)\r\n\r\nimport decimal\r\ndef roundHalfUp(d):\r\n # Round to nearest with ties going away from zero.\r\n rounding = decimal.ROUND_HALF_UP\r\n # See other rounding options here:\r\n # https://docs.python.org/3/library/decimal.html#rounding-modes\r\n return int(decimal.Decimal(d).to_integral_value(rounding=rounding))\r\n\r\n#################################################\r\n# Functions for you to write\r\n#################################################\r\n\r\ndef evalPrefixNotation(L):\r\n if len(L) == 1:\r\n return L[0]\r\n for element in L:\r\n if not isinstance(element, int):\r\n if element not in [\"+\", \"-\", \"*\"]:\r\n raise Exception('Unknown operator: ' + operator)\r\n L1 = []\r\n operator = L.pop(0)\r\n intCount = 0\r\n opCount = 1\r\n while intCount != opCount:\r\n value = L.pop(0)\r\n if isinstance(value, int):\r\n intCount += 1\r\n else:\r\n opCount += 1\r\n L1.append(value)\r\n if operator == \"+\":\r\n return evalPrefixNotation(L1) + evalPrefixNotation(L)\r\n elif operator == \"-\":\r\n return evalPrefixNotation(L1) - evalPrefixNotation(L)\r\n elif operator == \"*\":\r\n return evalPrefixNotation(L1) * evalPrefixNotation(L)\r\n\r\n\r\n'''def possibleMoves(rows, cols, crow, ccol):\r\n xmove = (2, -2, 1, -1)\r\n ymove = ((1, -1), (1, -1), (2, -2), (2, -2))\r\n possibleCoords = []\r\n for x in range(len(xmove)):\r\n for y in range(len(ymove[x])):\r\n if ((crow + xmove[x] < rows) and (ccol + ymove[x][y] < cols)):\r\n possibleCoords.append((xmove[x], ymove[y]))\r\n return possibleCoords'''\r\n\r\ndef printBoard(board):\r\n for row in board:\r\n print(row)\r\n\r\ndef knightsTourHelper(rows, cols, crow, ccol, visited, count):\r\n possMoves = [(2, 1), (2, -1), (-2, 1), (-2, -1), \r\n (1, 2), (1, -2), (-1, 2), (-1, -2)]\r\n for row in visited:\r\n for element in row:\r\n if element == (rows*cols):\r\n return visited\r\n for move in possMoves:\r\n drow = move[0]\r\n dcol = move[1];\r\n tempRow = crow + drow\r\n tempCol = ccol + dcol\r\n if ((0 <= tempRow < rows) and (0 <= tempCol < cols)):\r\n if (visited[tempRow][tempCol] == 0):\r\n #print(\"hi\")\r\n count += 1\r\n visited[tempRow][tempCol] = count\r\n if knightsTourHelper(rows, cols, tempRow, tempCol, \r\n visited, count):\r\n #print(\"1\")\r\n return visited\r\n count -= 1 \r\n visited[tempRow][tempCol] = 0\r\n return None\r\n\r\ndef knightsTour(rows, cols):\r\n board = [[0] * cols for _ in range(rows)]\r\n board[0][0] = 1\r\n result = knightsTourHelper(rows, cols, 0, 0, board, 1)\r\n return result\r\n\r\n#################################################\r\n# Test Functions\r\n#################################################\r\n\r\ndef testEvalPrefixNotation():\r\n print('Testing evalPrefixNotation()...', end='')\r\n assert(evalPrefixNotation([42]) == 42) # (42)\r\n assert(evalPrefixNotation(['+', 3, 4]) == 7) # (3 + 4)\r\n assert(evalPrefixNotation(['-', 3, 4]) == -1) # (3 - 4)\r\n assert(evalPrefixNotation(['-', 4, 3]) == 1) # (4 - 3)\r\n assert(evalPrefixNotation(['+', 3, '*', 4, 5]) == 23) # (3 + (4 * 5))\r\n\r\n # ((2 * 3) + (4 * 5))\r\n assert(evalPrefixNotation(['+', '*', 2, 3, '*', 4, 5]) == 26)\r\n # ((2 + 3) * (4 + 5))\r\n assert(evalPrefixNotation(['*', '+', 2, 3, '+', 4, 5]) == 45)\r\n # ((2 + (3 * (8 - 7))) * ((2 * 2) + 5))\r\n assert(evalPrefixNotation(['*', '+', 2, '*', 3, '-', 8, 7,\r\n '+', '*', 2, 2, 5]) == 45)\r\n \r\n #Make sure to raise an error for operators that are not +, -, or *\r\n raisedAnError = False\r\n try:\r\n evalPrefixNotation(['^', 2, 3])\r\n except:\r\n raisedAnError = True\r\n assert(raisedAnError == True)\r\n print('Passed.')\r\n\r\n\r\ndef testKnightsTour():\r\n print('Testing knightsTour()....', end='')\r\n def checkDims(rows, cols, ok=True):\r\n T = knightsTour(rows, cols)\r\n s = f'knightsTour({rows},{cols})'\r\n if (not ok):\r\n if (T is not None):\r\n raise Exception(f'{s} should return None')\r\n return True\r\n if (T is None):\r\n raise Exception(f'{s} must return a {rows}x{cols}' +\r\n ' 2d list (not None)')\r\n if ((rows != len(T)) or (cols != (len(T[0])))):\r\n raise Exception(f'{s} must return a {rows}x{cols} 2d list')\r\n d = dict()\r\n for r in range(rows):\r\n for c in range(cols):\r\n d[ T[r][c] ] = (r,c)\r\n if (sorted(d.keys()) != list(range(1, rows*cols+1))):\r\n raise Exception(f'{s} should contain numbers' +\r\n ' from 1 to {rows*cols}')\r\n prevRow, prevCol = d[1]\r\n for step in range(2, rows*cols+1):\r\n row,col = d[step]\r\n distance = abs(prevRow - row) + abs(prevCol - col)\r\n if (distance != 3):\r\n raise Exception(f'{s}: from {step-1} to {step}' +\r\n ' is not a legal move')\r\n prevRow, prevCol = row,col\r\n return True\r\n assert(checkDims(4, 3))\r\n assert(checkDims(4, 4, ok=False))\r\n assert(checkDims(4, 5))\r\n assert(checkDims(3, 4))\r\n assert(checkDims(3, 6, ok=False))\r\n assert(checkDims(3, 7))\r\n assert(checkDims(5, 5))\r\n print('Passed!')\r\n\r\n#################################################\r\n# testAll and main\r\n#################################################\r\n\r\ndef testAll():\r\n testEvalPrefixNotation()\r\n testKnightsTour()\r\ndef main():\r\n cs112_n22_hw12_linter.lint()\r\n testAll()\r\n\r\nif (__name__ == '__main__'):\r\n main()\r\n","repo_name":"davidchung29/CMU-Summer-2022","sub_path":"cis 15-112/hw/hw12/hw12.py","file_name":"hw12.py","file_ext":"py","file_size_in_byte":6376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7030053481","text":"import sys\nimport time\n\n\nresults = {}\ndef collatz(n):\n n_start = n\n if results.get(n_start, 0) > 0:\n return results[n_start]\n \n answer = 1\n if n == 1:\n answer = 1\n elif n % 2 == 1:\n answer = collatz(3*n + 1) + 1\n else:\n answer = collatz(n/2) + 1\n results[n_start] = answer\n return answer\n\nstart = time.time()\nfor line in sys.stdin:\n pair = [int(x) for x in line.split()]\n left = min(pair[0], pair[1])\n right = max(pair[0], pair[1])\n max_cycle = 1\n for n in range(left, right+1):\n max_cycle = max(max_cycle, collatz(n))\n print(pair[0], pair[1], max_cycle)\nend = time.time()\nelapsed_seconds = float(\"%.2f\" % (end - start))\nprint('elapsed=', elapsed_seconds)\n","repo_name":"qswitcher/algorithms_design_manual","sub_path":"3np1.py","file_name":"3np1.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25534431754","text":"from rock_paper_scissors import play1, play2\n\ndef printMenu():\n print('Welcome to Rock Paper Scissors Game:')\n print('Game one: You vs Computer: you pick one of three possibilities and check your luck vs computer')\n print('Game two: You set how many games will be played in Computer vs Computer game and then you watch.')\n \n \ndef getChoice():\n while True:\n print ('\\nInput 1: You vs Computer')\n print ('Input 2: Computer vs Computer')\n choice = input(\"\\nPlease make a choice (1/2): \")\n \n if choice in ('1', '2'):\n return choice\n else:\n print('\\nInvalid value: please enter 1 or 2')\n \ndef main():\n printMenu()\n \n while True:\n choice = getChoice()\n \n if choice == '1':\n result = play1()\n print('\\n' + result)\n elif choice == '2':\n result = play2()\n \n \n escape = input('\\nContinue? Press Y to continue or N to exit: ').lower()\n while escape not in ('y','n'):\n print (\"Please enter correct value: \")\n escape = input('Continue? Press Y to continue or N to exit: ').lower()\n if escape != 'y':\n print(\"\\nThank you for playing!\")\n break\n \nif __name__ == \"__main__\":\n main()","repo_name":"DarekW90/Python_traning_programs","sub_path":"1_Easy_projects/2_Rock_Paper_Scissors/Updated_Version/main_rock_paper_scissors.py","file_name":"main_rock_paper_scissors.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40605963675","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@time : 2018/10/25 15:33\n@file : diff_gene_anno.py\n@author : zhipeng.zhao\n@contact: 757049042@qq.com\n\"\"\"\nimport glob\nimport os\nimport json\nimport time\nimport unittest\n\nimport pandas as pd\n\nfrom biocluster.workflow import Workflow\nfrom biocluster.file import getsize, exists\nfrom biocluster.file import download\n\n# from src.biocluster.workflow import Workflow\nfrom bson import ObjectId\n\n\nclass DiffGeneAnnoWorkflow(Workflow):\n\n def __init__(self, wsheet_object):\n # 初始化网端参数\n self._sheet = wsheet_object\n super(DiffGeneAnnoWorkflow, self).__init__(wsheet_object)\n\n options = [\n # 基因列表文件和注释文件\n dict(name=\"gene_list\", type=\"infile\", format=\"prok_rna.diff_gene_list\"),\n dict(name=\"anno_matrix\", type=\"infile\", format=\"prok_rna.diff_anno_matrix\"),\n dict(name='task_id', type='string', default='tsg_32038'),\n dict(name='diff_main_id', type='string'),\n dict(name='update_info', type='string'),\n ]\n # 获取参数\n self.add_option(options)\n self.set_options(self._sheet.options())\n\n # 输出设置\n self.filepath = os.path.join(self.output_dir, 'diff_gene_annotation.xls')\n # self.option('outdir', outdir)\n # self.option('outfile_name', os.path.basename(self.filepath))\n\n self.module = self.add_module(\"prok_rna.diff_gene_anno\")\n self.db_tool = self.api.api(\"prok_rna.all_exp\")\n\n def run(self):\n self.module.on(\"end\", self.set_db)\n self.run_module()\n super(DiffGeneAnnoWorkflow, self).run()\n\n def set_db(self):\n \"\"\"\n 保存结果表到mongo数据库中\n def diff_gene_anno_to_db(\n self, outpath, task_id=None, main_id=None, query_dict: dict = None,\n project_sn='prok_rna', main_table_name='diff_geen_anno'):\n \"\"\"\n # add result info\n self.db_tool.diff_gene_anno_to_db(\n self.filepath, task_id=self.option('task_id'), main_id=self.option('diff_main_id'),\n main_table_name='diff_gene_anno_extr'\n )\n self.end()\n\n def end(self):\n # result_dir = self.add_upload_dir(self.tool.output_dir)\n # result_dir.add_relpath_rules([\n # [\".\", \"\", \"差异分析结果目录\"],\n # ])\n super(DiffGeneAnnoWorkflow, self).end()\n\n def run_module(self):\n options = {\n 'gene_list': self.option('gene_list'),\n 'anno_matrix': self.option('anno_matrix'),\n 'outdir': self.output_dir,\n 'outfile_name': os.path.basename(self.filepath),\n 'pool': 1\n }\n self.module.set_options(options)\n self.module.run()\n\n\nif __name__ == '__main__':\n from biocluster.wsheet import Sheet\n import random\n\n main_id = str(ObjectId(\"5bd907a8a4e1af0a8255a566\"))\n\n data = {\n \"id\": \"diff_gene_extract_\" + str(random.randint(1, 10000)),\n \"type\": \"workflow\",\n \"name\": \"prok_rna.diff_gene_anno\",\n \"instant\": False,\n \"options\": {\n 'gene_list': r'/mnt/ilustre/users/sanger-dev/sg-users/zhaozhipeng/gene.list',\n 'anno_matrix': r'/mnt/ilustre/users/sanger-dev/sg-users/zhaozhipeng/all_anno_detail.xls',\n 'task_id': 'tsg_32038',\n 'diff_main_id': str(main_id),\n 'update_info': json.dumps({'main_id': str(main_id)})\n }\n }\n\n wsheet = Sheet(data=data)\n wf = DiffGeneAnnoWorkflow(wsheet)\n wf.run()\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/workflows/prok_rna/report/diff_gene_anno.py","file_name":"diff_gene_anno.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"38394963903","text":"import functools\nimport json\nimport logging\nimport os\nfrom urllib.parse import quote\n\nimport requests\nfrom flask import Flask, request\nfrom flask import send_from_directory, render_template\nfrom twilio.rest import Client\nfrom twilio.twiml.messaging_response import MessagingResponse\nfrom twilio.twiml.voice_response import Gather, VoiceResponse\n\nTWILIO_ACCOUNT = os.getenv('TWILIO_ACCOUNT')\nTWILIO_AUTH = os.getenv('TWILIO_AUTH')\n\n\n@functools.lru_cache(maxsize=1)\ndef get_twilio_client():\n return Client(TWILIO_ACCOUNT, TWILIO_AUTH)\n\n\nTWILIO_FROM_PHONE = os.getenv('TWILIO_FROM_PHONE', '+441803500679')\n\nSMS_HISTORY = {}\nMSG_STORE = {}\n\nLOGREADER_ADDRESS = os.environ.get(\"LOG_PARSER_ADDRESS\", \"localhost\")\nLOGREADER_PORT = os.environ.get(\"LOG_PARSER_PORT\", 8888)\n\napp = Flask(__name__)\n\n\ndef style_from_state(state):\n if state == \"to_manually_dispatch\":\n return \"danger\"\n elif state == \"to_acknowledge\":\n return \"warning\"\n elif state == \"in_progress\":\n return \"info\"\n else:\n return \"success\"\n\n\ndef style_to_text(state):\n if state == \"to_manually_dispatch\":\n return \"to be dispatched\"\n elif state == \"to_acknowledge\":\n return \"to be acknowledged\"\n elif state == \"in_progress\":\n return \"in progress\"\n else:\n return state.replace(\"_\", \" \")\n\n\n@app.route('/static/')\ndef serve_static(path):\n return send_from_directory('static', path)\n\n\n@app.route(\"/set_manual/\", methods=['POST'])\ndef manually_checkbock_toggled(enabled):\n url = \"http://{}:{}/manual_mode\".format(LOGREADER_ADDRESS, LOGREADER_PORT)\n print(\"Sending request to\", url, \" with manual_mode:\", enabled)\n response = requests.post(url, {\"manual_mode\": enabled})\n return \"Success\"\n\n\n@app.route(\"/\")\n@app.route('/alerts/')\ndef alerts():\n url = \"http://{}:{}/get_all\".format(LOGREADER_ADDRESS, LOGREADER_PORT)\n try:\n response = requests.get(url)\n except requests.exceptions.ConnectionError as e:\n return render_template('alerts.html', error=True, message=\"An error occourred\")\n else:\n alerts = list(response.json().values())\n for alert in alerts:\n if \"INTRUDER\" in alert[\"label\"]:\n alert[\"isIntruder\"] = True\n if \"ARMED\" in alert[\"label\"]:\n alert[\"isArmed\"] = True\n if \"SENSOR\" in alert[\"name\"]:\n alert[\"isSensor\"] = True\n\n alert[\"style\"] = style_from_state(alert[\"state\"])\n alert[\"state_text\"] = style_to_text(alert[\"state\"])\n return render_template('alerts.html', error=False, alerts=alerts, teams=[1,2,3,4])\n\n\n@app.route('/alert/')\ndef alert(id):\n url = \"http://{}:{}/get_single?uuid={}\".format(LOGREADER_ADDRESS, LOGREADER_PORT, id)\n try:\n response = requests.get(url)\n except requests.exceptions.ConnectionError as e:\n return render_template('alerts.html', error=True, message=e.response.text)\n else:\n return render_template('alert.html', error=False, alert=response.json(), id=id)\n\n\n@app.route('/teams_or_rangers/')\ndef teams_or_rangers():\n return render_template('teams_or_rangers.html')\n\n\n@app.route('/teams/')\ndef teams():\n return render_template('teams.html')\n\n\n@app.route('/team/')\ndef team(name):\n return render_template('team.html', name=name)\n\n\n@app.route('/rangers/')\ndef rangers():\n lone = {'name': 'Lone'}\n texas = {'name': 'Texas'}\n power = {'name': 'Power'}\n lone1 = {'name': 'John'}\n texas1 = {'name': 'Jack'}\n power1 = {'name': 'Sophie'}\n lone2 = {'name': 'Lone'}\n texas2 = {'name': 'Texas'}\n power2 = {'name': 'Power'}\n return render_template('rangers.html', rangers=[lone, texas, power, lone1, texas1, power1, lone2, texas2, power2])\n\n\n@app.route('/ranger/')\ndef ranger(name):\n return render_template('ranger.html', name=name)\n\n\n@app.route('/')\ndef hello():\n return 'SmartAlert'\n\n\n# TWILIO SMS SERVICE\n\n@app.route('/sms', methods=['POST'])\ndef sms():\n msg, to, uuid = get_contact_user()\n SMS_HISTORY[to] = uuid\n body = '''{}\nTEXT 1 to ACCEPT!'''.format(msg)\n message = get_twilio_client().messages.create(to=to, from_=TWILIO_FROM_PHONE, body=body)\n call_id = voice_call()\n return json.dumps({'message': message.sid, 'call': call_id})\n\n\ndef get_contact_user():\n uuid, to, msg = request.form['uuid'], request.form['to'], request.form['msg']\n if to.startswith('00'):\n to = '+{}'.format(to[2:])\n return msg, to, uuid\n\n\n@app.route(\"/sms_respond\", methods=['POST'])\ndef sms_reply():\n body = request.form['Body']\n from_ = request.form['From']\n uuid = SMS_HISTORY.get(from_)\n app.logger.info('got response {} {} {}'.format(uuid, from_, body))\n\n if uuid is not None and body:\n requests.post('http://localhost:8888', data={'uuid': uuid,\n \"old_state\": 'to_acknowledge',\n \"new_state\": 'in_progress'})\n\n resp = MessagingResponse()\n resp.message(accept_alert(uuid))\n return str(resp)\n\n return None\n\n\n@app.route(\"/get_single\", methods=['GET'])\ndef get_status():\n uuid = request.args.get('uuid')\n response = requests.get(\"http://{}:{}/get_single\".format(LOGREADER_ADDRESS, LOGREADER_PORT), data={'uuid': uuid})\n return json.dumps(response.json())\n\ndef accept_alert(uuid):\n return '{} ACCEPTED'.format(uuid)\n\n\n@app.route(\"/voice_respond\", methods=['POST'])\ndef voice_call():\n msg, to, uuid = get_contact_user()\n MSG_STORE[uuid, to] = msg\n url = \"{}/voice_handle?uuid={}&to={}\".format('http://precocial-tang-6014.dataplicity.io',\n quote(uuid),\n quote(to))\n app.logger.warn('{} - {} - {} at {}'.format(uuid, to, msg, url))\n call = get_twilio_client().calls.create(\n to=to,\n from_=TWILIO_FROM_PHONE,\n url=url\n )\n return call.sid\n\n\n@app.route(\"/voice_handle\", methods=['GET', 'POST'])\ndef voice_handle():\n uuid, to = request.args.get('uuid'), request.args.get('to')\n resp = VoiceResponse()\n if 'Digits' in request.values:\n choice = request.values['Digits']\n if choice == '1':\n resp.say('Alert accepted!')\n get_twilio_client().messages.create(to=to, from_=TWILIO_FROM_PHONE, body=accept_alert(uuid))\n return str(resp)\n elif choice == '2':\n resp.say('Try again!')\n return str(resp)\n else:\n # If the caller didn't choose 1 or 2, apologize and ask them again\n resp.say(\"Sorry, I don't understand that choice.\")\n gather = Gather(num_digits=1)\n body = '''{}, press 1 to accept!'''.format(MSG_STORE[uuid, to])\n gather.say(body)\n resp.append(gather)\n resp.redirect('/voice_respond', method='POST')\n return str(resp)\n\n\n@app.errorhandler(500)\ndef server_error(e):\n # Log the error and stacktrace.\n logging.exception('An error occurred during a request. {}'.format(e))\n return 'An internal error occurred.', 500\n\n\napp.logger.addHandler(logging.StreamHandler())\napp.logger.setLevel(logging.INFO)\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n","repo_name":"vittorioromeo/zoohackathon2017","sub_path":"ui-flask/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28937238720","text":"import base64\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport pandas as pd\nfrom dash import dash_table, Dash\n\nfrom .. import WebvizPluginABC, EncodedFile\nfrom ..webviz_store import webvizstore\nfrom ..common_cache import CACHE\n\n\nclass DataTable(WebvizPluginABC):\n \"\"\"Adds a table to the webviz instance, using tabular data from a provided csv file.\nIf feature is requested, the data could also come from a database.\n\n---\n\n* **`csv_file`:** Path to the csv file containing the tabular data. Either absolute \\\n path or relative to the configuration file.\n* **`sorting`:** If `True`, the table can be sorted interactively based \\\n on data in the individual columns.\n* **`filtering`:** If `True`, the table can be filtered based on values in the \\\n individual columns.\n* **`pagination`:** If `True`, only a subset of the table is displayed at once. \\\n Different subsets can be viewed from 'previous/next' buttons\n\"\"\"\n\n def __init__(\n self,\n app: Dash,\n csv_file: Path,\n sorting: bool = True,\n filtering: bool = True,\n pagination: bool = True,\n ):\n\n super().__init__()\n\n self.csv_file = csv_file\n self.df = get_data(self.csv_file)\n self.sorting = sorting\n self.filtering = filtering\n self.pagination = pagination\n\n self.set_callbacks(app)\n\n def add_webvizstore(self) -> List[tuple]:\n return [(get_data, [{\"csv_file\": self.csv_file}])]\n\n @property\n def layout(self) -> dash_table.DataTable:\n return dash_table.DataTable(\n columns=[{\"name\": i, \"id\": i} for i in self.df.columns],\n data=self.df.to_dict(\"records\"),\n sort_action=\"native\" if self.sorting else \"none\",\n filter_action=\"native\" if self.filtering else \"none\",\n page_action=\"native\" if self.pagination else \"none\",\n )\n\n def set_callbacks(self, app: Dash) -> None:\n @app.callback(self.plugin_data_output, self.plugin_data_requested)\n def _user_download_data(data_requested: Optional[int]) -> Optional[EncodedFile]:\n return (\n {\n \"filename\": \"data-table.csv\",\n \"content\": base64.b64encode(\n get_data(self.csv_file).to_csv(index=False).encode()\n ).decode(\"ascii\"),\n \"mime_type\": \"text/csv\",\n }\n if data_requested\n else None\n )\n\n\n@CACHE.memoize()\n@webvizstore\ndef get_data(csv_file: Path) -> pd.DataFrame:\n return pd.read_csv(csv_file)\n","repo_name":"equinor/webviz-config","sub_path":"webviz_config/generic_plugins/_data_table.py","file_name":"_data_table.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"82"} +{"seq_id":"14509749209","text":"import threading\nimport time\n\n\nclass RepeatedTimer:\n def __init__(self, interval, function, *args, **kwargs):\n self._timer = None\n self.interval = interval\n self.function = function\n self.args = args\n self.kwargs = kwargs\n self.is_running = False\n self.start()\n\n def start(self):\n if self.is_running:\n return\n self.is_running = True\n threading.Thread(target=self._run).start()\n\n def stop(self):\n self.is_running = False\n\n def _run(self):\n old_time = time.perf_counter_ns()\n while True:\n new_time = time.perf_counter_ns()\n if not self.is_running:\n break\n\n if new_time - old_time > self.interval*1000000000:\n self.function(*self.args, **self.kwargs)\n old_time = new_time\n\n time.sleep(0.001)\n\n\n","repo_name":"Tobias-Glauser/PO-ELECTRO","sub_path":"timerV2.py","file_name":"timerV2.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"4936289379","text":"# ======================================================================\n# https://rafatieppo.github.io/\n# 13-06-2020\n# file to manage transactions\n# ======================================================================\n\nclass managtransac:\n def __init__(self, connection):\n self.connection = connection\n\n def find_byid(self, id_trans):\n connection = self.connection\n self.id_trans = id_trans\n with connection:\n cursor = connection.cursor()\n query = \"SELECT * FROM transacao WHERE transacao_id=?\"\n result = cursor.execute(query, (id_trans,))\n row = result.fetchone()\n if row is not None:\n return {'transacao': {'id': row[0], 'tipo': row[1], 'conta':row[3]}}\n else:\n return {'transacao': {'id': [-99], 'tipo': [-99], 'conta': [-99]}}\n print('Transacao ' + str(id_trans) + ' nao existe')\n\n def insert(self, tipo_id, data, conta_id, categoria_id,\n subcategoria_id, valor, obs):\n connection = self.connection\n with connection:\n cursor = connection.cursor()\n if tipo_id == 1:\n valor = valor * -1\n query = \"INSERT INTO transacao VALUES (NULL, ?,?,?,?,?,?,?);\"\n cursor.execute(query, (tipo_id, data, conta_id,\n categoria_id, subcategoria_id, valor, obs,))\n connection.commit()\n print('Transacao' + 'registrada com sucesso')\n\n def delete(self, id_trans, conta):\n contafound = self.find_byid(id_trans)\n print(contafound['transacao']['conta'])\n print('conta digitada é ', str(conta) + 'e conta encontrada é ' + str(contafound['transacao']['conta']))\n if str(contafound['transacao']['conta']) == str(conta):\n connection = self.connection\n with connection:\n cursor = connection.cursor()\n query = \"DELETE FROM transacao WHERE transacao_id=?;\"\n cursor.execute(query, (id_trans,))\n connection.commit()\n print('Transacao ' + str(id_trans) + ' excluida com sucesso')\n else:\n print('Transacao ' + str(id_trans) + ' nao existe ou não corresponde com a conta')\n","repo_name":"rafatieppo/lucycashflow","sub_path":"models/managtransac.py","file_name":"managtransac.py","file_ext":"py","file_size_in_byte":2354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"42074574725","text":"# Declarar uma lista (array)\n\narray = [] # Vazio\n\narray = [1, 2, 3, 4, 5] # Com Valores\n\nprint(array)\nprint(array[3])\n\narray.append(10) # Adiciona o elemento na ultima posição\nprint(array)\n\narray.insert(0, 'Cu') # Adiciona no indice 0 o parametro dps da virgula\nprint(array)\n\ndel array[4] # Deleta o indice 4\narray.pop(4) # Deleta o indice 4\n\nif 'Cu' in array: # Verifica se o valor passado está na lista\n array.remove('Cu') # Remove o valor da lista\n\narray.pop() # Elimina o ultmo elemento da lista\n\nvalores = list(range(4, 11)) # list() cria uma lista\n\nvalores.sort() # Ordena os valores\n\nvalores.sort(reverse=True) # Ordena os valores na ordem reversa\n\nprint(len(valores)) #Retorna quantos elementos estão na lista\n\nfor i,v in enumerate(valores):\n print(i,v)\n \n\na = [1,2,3,4]\nb = a # vincula o B ao A, se mudar o B o A muda e vice-versa = [:] não vincula, somente passa os valores\nprint(a)\nprint(b)\nb[2] = 6\nprint(a)\nprint(b)\n","repo_name":"LeonardoSextare/Curso-Python","sub_path":"Curso em Video - Guanabara/Mundo 3/Aula 17 - Listas.py","file_name":"Aula 17 - Listas.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"20662532499","text":"from typing import List\n\nfrom spil.conf import sip, ors\nfrom spil.sid.core import query_helper\n\n\ndef execute(sids: List[str]) -> List[str]:\n \"\"\"\n Runs or_op (the \"or operator\") on a list of Sids.\n\n Args:\n sids: a list of Sid strings to edit\n\n Returns: the list of Sid strings, edited\n \"\"\"\n result = []\n for sid in sids:\n result.extend(or_op(sid))\n\n return result\n\n\ndef or_op(sid: str) -> List[str]:\n \"\"\"\n or_op (the \"or operator\") transforms a string containing the \"or\" sign\n into a list of strings, each one individually representing the options without the or.\n\n Note: the ors sign can be configured, it is called \"ors\" in the config.\n Typically it is a comma \",\".\n\n Example:\n\n >>> or_op('bla/s/bla/A,B/**/one,two?test=X,Y,Z')\n ['bla/s/bla/A/**/one?test=X', 'bla/s/bla/A/**/one?test=Y', 'bla/s/bla/A/**/one?test=Z', 'bla/s/bla/B/**/one?test=X', 'bla/s/bla/B/**/one?test=Y', 'bla/s/bla/B/**/one?test=Z', 'bla/s/bla/A/**/two?test=X', 'bla/s/bla/A/**/two?test=Y', 'bla/s/bla/A/**/two?test=Z', 'bla/s/bla/B/**/two?test=X', 'bla/s/bla/B/**/two?test=Y', 'bla/s/bla/B/**/two?test=Z']\n\n Args:\n sid: a sid string\n\n Returns: a list of Sid strings\n \"\"\"\n sid = str(sid)\n if not sid.count(ors): # no \"or\" operators in sid.\n return [sid]\n\n if sid.count(\"?\"): # sid contains Query ending. We put it aside, and later append it back\n sid, query = sid.split(\"?\", 1)\n else:\n query = \"\"\n\n sids = or_on_path(sid)\n\n result = []\n if query:\n uris = or_on_query(query)\n for s in sids:\n for u in uris:\n result.append(\"{}?{}\".format(s, u))\n else:\n result = sids\n\n return result\n\n\ndef or_on_path(sid):\n \"\"\"\n Applies the or_op on the path part of the Sid.\n\n Example:\n\n >>> or_on_path('bla/s/bla/A,B,C/**/one,two,three')\n ['bla/s/bla/A/**/one', 'bla/s/bla/B/**/one', 'bla/s/bla/C/**/one', 'bla/s/bla/A/**/two', 'bla/s/bla/B/**/two', 'bla/s/bla/C/**/two', 'bla/s/bla/A/**/three', 'bla/s/bla/B/**/three', 'bla/s/bla/C/**/three']\n\n Args:\n sid: sid string\n\n Returns: list of sid string\n \"\"\"\n\n _start = \"--start--\"\n\n parts = sid.split(sip)\n\n found = [_start]\n for part in parts:\n current = found.copy()\n if ors in part:\n for alt in part.split(ors):\n alt = alt.strip()\n for sid in current.copy():\n new = sid + sip + alt\n # print 'replace', sid, ' --> ', new, ' -- ', sid in found, '?'\n #\n if sid in found:\n found[found.index(sid)] = new # replace (of the first element)\n else:\n found.append(new) # replace (of the first element)\n\n # found.remove()\n else:\n for sid in found.copy():\n new = sid + sip + part\n # print new\n found[found.index(sid)] = new # replace (of the first element)\n\n result = []\n for sid in found:\n if not sid in result:\n result.append(sid.replace(_start + sip, \"\"))\n\n # no type check needed\n return result\n\n\ndef or_on_query(query):\n \"\"\"\n Applies the or operator to values of the query, creating unique uris without the operator.\n\n Example:\n\n >>> or_on_query('titi=tata,blip&roger=vadim,bom,tom, tata')\n ['titi=tata&roger=vadim', 'titi=blip&roger=vadim', 'titi=tata&roger=bom', 'titi=blip&roger=bom', 'titi=tata&roger=tom', 'titi=blip&roger=tom', 'titi=tata&roger=tata', 'titi=blip&roger=tata']\n\n Args:\n query:\n\n Returns:\n\n \"\"\"\n query_dict = query_helper.to_dict(query)\n result = [query_dict.copy()]\n for key, value in query_dict.items():\n if value.count(ors):\n new_result = []\n for i in value.split(ors):\n for d in result.copy():\n new_dict = d.copy()\n new_dict[key] = i\n new_result.append(new_dict)\n result = new_result\n # print(result)\n\n return [query_helper.to_string(d) for d in result]\n\n\nif __name__ == \"__main__\":\n\n import doctest\n\n doctest.testmod()\n","repo_name":"MichaelHaussmann/spil","sub_path":"spil/sid/read/unfolders/or_op.py","file_name":"or_op.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"82"} +{"seq_id":"23846279305","text":"import os\nfrom datetime import datetime\nfrom sklearn.model_selection import StratifiedKFold\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom ray import tune\nfrom collections import Counter\n# from imblearn.over_sampling import RandomOverSampler\nfrom Antispoofing.AntispoofHelpers.antispoof_model_helper import create_vit_b32\nfrom Antispoofing.AntispoofHelpers.dataset_helper import get_random_selection_on_aug_category, get_antispoof_frame, \\\n get_train_validation_generator, get_test_generator, Y_COL, Z_COL, X_COL, test_split\nfrom Antispoofing.AntispoofHelpers.spoof_metric import determine_spoof_metrics, PROTOCOL_COL\nimport os\nos.environ['TUNE_RESULT_DELIM'] = '/'\nAUG_PERCENTAGES = [0.05,0.1,0.2, 0.30]\n\ndef initialise_tf():\n import tensorflow as tf\n try:\n # fix memory issues\n gpus = tf.config.experimental.list_physical_devices('GPU')\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n except:\n pass\n\ndef combine_with_augmentation(train_frame, aug_frame, aug_root, categories, aug_percentage, stratified_name_list_func=None, use_last_only=False, must_remove_normal=True, must_use_normal_only=False): # determine how many frames are in the dataset\n total_frames = train_frame.shape[0]\n # (Itot / aug %) / (1 - aug %)\n temp = []\n for cat in categories:\n if \"-\" in cat:\n temp2 = cat.split('-')\n for t in temp2:\n temp.append(t)\n else:\n temp.append(cat)\n categories = temp\n\n\n tempcategories = [] #['ASUS', 'IP7P', 'IPP2017', 'SGS8']\n for cat in categories:\n if cat == \"R\":\n tempcategories.append('ASUS')\n tempcategories.append('IP7P')\n tempcategories.append('IPP2017')\n tempcategories.append('SGS8')\n elif \"N\" in cat:\n if not must_remove_normal:\n tempcategories.append(cat)\n else:\n tempcategories.append(cat)\n\n categories = tempcategories\n\n if must_use_normal_only:\n tempcategories = []\n for cat in categories:\n if \"N\" in cat:\n tempcategories.append(cat)\n categories = tempcategories\n\n if use_last_only:\n num_categories = 1\n else:\n num_categories = len(categories)\n num_augmentation_files = round(((total_frames * aug_percentage)/ (1 - aug_percentage))/num_categories)\n # file_path, ground_truth df\n # to augment with only N\n random_aug_frame = get_random_selection_on_aug_category(aug_frame, categories, aug_root, num_augmentation_files, seed=None, stratified_name_list_func=stratified_name_list_func, use_last_only=use_last_only)\n return random_aug_frame\n\ndef video_based_results(single_frame, protocol_name, fold_index,protocol_number, fold_save_metrics_root, save_metric_name):\n temp_single = single_frame.copy()\n def categorise_video(row):\n return os.path.basename(os.path.dirname(row['file_paths']))\n temp_single['video_name'] = temp_single.apply(lambda row: categorise_video(row), axis=1)\n video_names = temp_single['video_name'].unique()\n video_list = []\n for name in video_names:\n temp_df = temp_single.query(f\"video_name == '{name}'\")\n real = 0\n spoof = 1\n spoof_pred_count = temp_df[(temp_df.predicted == 1)].count()[\"predicted\"]\n real_pred_count = temp_df[(temp_df.predicted == 0)].count()[\"predicted\"]\n if spoof_pred_count > real_pred_count:\n predicted = 1\n else:\n predicted = 0\n ground_truth = temp_df['ground_truth'].tolist()[0]\n video_list.append({\"video_name\": name, f'spoof({spoof})_pred_count': spoof_pred_count, f'real({real}_pred_count': real_pred_count, \"predicted\": predicted, \"ground_truth\": ground_truth})\n multi_frame = pd.DataFrame.from_dict(video_list)\n multi_frame.to_csv(os.path.join(fold_save_metrics_root, f\"test_{protocol_name}_multi_frame_results.csv\"), index=False)\n predicted = multi_frame['predicted'].tolist()\n ground_truth = multi_frame['ground_truth'].tolist()\n metric_dic = determine_spoof_metrics(ground_truth, predicted, protocol_name, fold_index,protocol_number, save_dir=os.path.join(fold_save_metrics_root, f\"{save_metric_name}_{protocol_name}_Multi_Metrics\"), must_show=False)\n metric_dic = dict((\"{}_{}\".format(\"Multi\",k),v) for k,v in metric_dic.items())\n return metric_dic\n\n\ndef antispoof(config):\n use_hsv = config['use_hsv']\n must_remove_normal = config['must_remove_normal']\n aug_after_split = config['aug_after_split']\n must_use_normal_only = config['must_use_normal_only']\n initialise_tf()\n import tensorflow as tf\n tf.keras.backend.set_image_data_format('channels_last')\n include_traditional_aug = config['include_traditional_aug']\n stratified_name_list_func=config['stratified_name_list_func']\n n_folds = config['n_folds']\n current_fold = config['current_fold']\n dataset_root = config['dataset_root']\n original_dataset_root = config['original_dataset_root']\n train_subject_number = config[\"train_subject_number\"]\n test_subject_number = config[\"test_subject_number\"]\n get_train_frame_func = config[\"get_train_frame_func\"]\n get_stratified_name_col_func = config[\"get_stratified_name_col_func\"]\n get_protocol_frame_dic_func = config[\"get_protocol_frame_dic_func\"]\n process_dataset_metrics_func = config[\"process_dataset_metrics_func\"]\n repeat_number = config[\"HP_REPEAT\"]\n # get the config variables\n attack_type_combination = config['HP_COMB']\n aug_percentage = config['HP_AUG_PER']\n use_last_only = config['use_last_only']\n\n run_folder = f\"{attack_type_combination}_aug_{aug_percentage}_run_{repeat_number}\"\n epochs = config['epochs']\n save_metrics_root = os.path.join(config['save_metrics_root'], run_folder)\n save_checkpoints_root = os.path.join(config['save_checkpoints_root'], run_folder)\n save_tb_root = os.path.join(config['save_tb_root'], run_folder)\n\n experiment_dirs = [save_metrics_root, save_checkpoints_root, save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n\n dataset_name = config['dataset_name']\n dataset_csv_name = config['dataset_csv_name']\n aug_root = config['aug_root']\n aug_csv = config['aug_csv']\n\n combinations = []\n # split the attack type combination\n if \",\" in attack_type_combination:\n attack_type_combination = attack_type_combination.split(\",\")\n for comb in attack_type_combination:\n combinations.append(comb.split(\"@\")[1])\n elif \"-\" in attack_type_combination:\n combinations.append(attack_type_combination.split(\"@\")[1])\n\n else:\n combinations.append(attack_type_combination.split(\"@\")[1])\n # get the train dataset frame\n train_frame = get_train_frame_func(dataset_root, dataset_csv_name, combinations, train_subject_number)\n\n stratified_name = get_stratified_name_col_func(combinations)\n train_frame = get_antispoof_frame(train_frame, dataset_root, stratified_name=stratified_name)\n\n\n\n sss = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=0)\n\n\n if aug_after_split:\n if Z_COL in train_frame.columns:\n splits = sss.split(train_frame[X_COL], train_frame[Z_COL])\n else:\n splits = sss.split(train_frame[X_COL], train_frame[Y_COL])\n\n for i in range(n_folds):\n train_index, val_index = next(splits)\n if i == current_fold:\n break\n\n\n\n fold_index = str(current_fold)\n fold_save_metrics_root = os.path.join(save_metrics_root, fold_index)\n fold_save_checkpoints_root = os.path.join(save_checkpoints_root, fold_index)\n fold_save_tb_root = os.path.join(save_tb_root, fold_index)\n\n experiment_dirs = [fold_save_metrics_root, fold_save_checkpoints_root, fold_save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n fold_train_frame = train_frame.iloc[train_index]\n fold_val_frame = train_frame.iloc[val_index]\n\n # add the augmentation files to the train frame\n if aug_percentage > 0:\n aug_frame = pd.read_csv(os.path.join(aug_root, aug_csv))\n aug_frame = combine_with_augmentation(fold_train_frame, aug_frame, aug_root, combinations, aug_percentage,\n stratified_name_list_func, use_last_only, must_remove_normal, must_use_normal_only)\n fold_train_frame = pd.concat([fold_train_frame, aug_frame])\n else:\n # add the augmentation files to the train frame\n if aug_percentage > 0:\n aug_frame = pd.read_csv(os.path.join(aug_root, aug_csv))\n aug_frame = combine_with_augmentation(train_frame, aug_frame, aug_root, combinations, aug_percentage,\n stratified_name_list_func, use_last_only, must_remove_normal,must_use_normal_only)\n train_frame = pd.concat([train_frame, aug_frame])\n\n if Z_COL in train_frame.columns:\n splits = sss.split(train_frame[X_COL], train_frame[Z_COL])\n else:\n splits = sss.split(train_frame[X_COL], train_frame[Y_COL])\n\n for i in range(n_folds):\n train_index, val_index = next(splits)\n if i == current_fold:\n break\n\n fold_index = str(current_fold)\n fold_save_metrics_root = os.path.join(save_metrics_root, fold_index)\n fold_save_checkpoints_root = os.path.join(save_checkpoints_root, fold_index)\n fold_save_tb_root = os.path.join(save_tb_root, fold_index)\n\n experiment_dirs = [fold_save_metrics_root, fold_save_checkpoints_root, fold_save_tb_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n fold_train_frame = train_frame.iloc[train_index]\n fold_val_frame = train_frame.iloc[val_index]\n\n\n\n if save_metrics_root is not None:\n fold_train_frame.to_csv(f\"{save_metrics_root}/fold_{fold_index}_train_frame.csv\", index=False)\n fold_val_frame.to_csv(f\"{save_metrics_root}/fold_{fold_index}_val_frame.csv\", index=False)\n test_split(fold_train_frame, fold_val_frame, X_COL)\n train_generator, valid_generator = get_train_validation_generator(fold_train_frame, fold_val_frame, use_hsv=use_hsv)\n\n # create the model\n model = create_vit_b32(include_traditional=include_traditional_aug)\n learning_rate = 1e-4\n weight_decay = 1e-5\n\n optimiser = tf.keras.optimizers.Adam(learning_rate=learning_rate)#, beta_2=weight_decay)\n\n model.compile(optimizer=optimiser, loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2),\n metrics=['accuracy'])\n # For future work\n # reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = 'val_accuracy',\n # factor = 0.2,\n # patience = 2,\n # verbose = 1,\n # min_delta = 1e-4,\n # min_lr = 1e-6,\n # mode = 'max')\n earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n min_delta=1e-4,\n patience=15,\n mode='min',\n restore_best_weights=True,\n verbose=1)\n checkpoint_path = os.path.join(fold_save_checkpoints_root, \"best.ckpt\")\n checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n monitor='val_loss',\n verbose=1,\n save_best_only=True,\n save_weights_only=True,\n mode='min')\n tensorboard_callback = tf.keras.callbacks.TensorBoard(\n log_dir=fold_save_tb_root, histogram_freq=1, update_freq='epoch')\n callbacks = [earlystopping, checkpointer, tensorboard_callback] # ,reduce_lr ]\n\n train_step_size = train_generator.n // train_generator.batch_size\n validation_step_size = valid_generator.n // valid_generator.batch_size\n history = model.fit(x=train_generator,\n steps_per_epoch=train_step_size,\n validation_data=valid_generator,\n validation_steps=validation_step_size,\n epochs=epochs,\n callbacks=callbacks)\n\n loss = history.history['loss']\n val_loss = history.history['val_loss']\n\n epochs = range(len(loss))\n with plt.ioff():\n plt.figure()\n\n plt.plot(epochs, loss, 'b', label='Training Loss')\n plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n plt.title('Training and validation loss')\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.legend()\n plt.savefig(os.path.join(fold_save_metrics_root, f\"fold_{fold_index}_train_history.png\"))\n\n print(\"restoring top model\")\n # load best model\n latest = tf.train.latest_checkpoint(fold_save_checkpoints_root)\n print(latest)\n model = create_vit_b32()\n model.load_weights(latest)\n\n save_metric_name = \"\"\n lookup_dataset_root =\"\"\n if original_dataset_root is None:\n protocol_frame_dic = get_protocol_frame_dic_func(dataset_root, dataset_csv_name, combinations, test_subject_number)\n lookup_dataset_root = dataset_root\n else:\n protocol_frame_dic = get_protocol_frame_dic_func(original_dataset_root, dataset_csv_name, combinations, test_subject_number)\n lookup_dataset_root = original_dataset_root\n if test_subject_number is not None:\n save_metric_name = f\"S{test_subject_number}\"\n for protocol_name, protocol_number_frame in protocol_frame_dic.items():\n protocol_number = protocol_number_frame[\"protocol_number\"]\n protocol_frame = protocol_number_frame[\"frame\"]\n test_frame = get_antispoof_frame(protocol_frame, lookup_dataset_root)\n # test if there is bias\n test_split(fold_train_frame, test_frame, X_COL)\n\n test_generator = get_test_generator(test_frame, use_hsv=use_hsv)\n test_step_size = test_generator.n // test_generator.batch_size\n predicted = np.argmax(model.predict(test_generator, test_step_size, verbose=1), axis=1)\n ground_truth = test_generator.classes\n temp_dic = {\"file_paths\": test_generator.filepaths, \"ground_truth\": ground_truth, \"predicted\": list(predicted)}\n df = pd.DataFrame.from_dict(temp_dic, orient='index').transpose()\n df.to_csv(os.path.join(fold_save_metrics_root, f\"test_{protocol_name}_results.csv\"), index=False)\n metric_dic = determine_spoof_metrics(ground_truth, predicted, protocol_name, fold_index,protocol_number, save_dir=os.path.join(fold_save_metrics_root, f\"{save_metric_name}_{protocol_name}_Metrics\"), must_show=False)\n multi_metric_dic = video_based_results(df, protocol_name, fold_index,protocol_number, fold_save_metrics_root, save_metric_name)\n metric_dic.update(multi_metric_dic)\n if config['is_ray']:\n tune.report(**metric_dic)\n\n\ndef start_antispoofing(dataset_root, dataset_csv_name, aug_root, aug_csv, save_metrics_root, save_checkpoints_root,\n save_tb_root, save_tune_root, aug_folder_combinations, tune_gpu, tune_cpu, epochs,\n get_train_frame_func, get_protocol_frame_dic_func, get_stratified_name_col_func, process_dataset_metrics_func,tune_experiment_name=None,\n aug_percentages=None, repeat_run_list=None, train_subject_number=None,\n test_subject_number=None, must_resume_from_last_experiment=True, is_ray=True, n_k_folds=3,\n original_dataset_root=None, stratified_name_list_func=None, is_traditional=False,\n use_last_only=False,is_single_folder=True, include_traditional_aug=False, aug_after_split=False\n , must_remove_normal=False, mode_info=\"\", must_use_normal_only=False, error_only=False, use_hsv=False):\n if aug_percentages is None and repeat_run_list is None:\n raise TypeError(\"Please specify either the aug_percentages or repeat_run_list\")\n return\n\n # get the dataset name\n if \".csv\" not in dataset_csv_name:\n dataset_csv_name += \".csv\"\n\n dataset_name = os.path.basename(dataset_root)\n\n\n # test if the dataset creator csv file is present in the dataset root\n dataset_csv_location = os.path.join(dataset_root, dataset_csv_name)\n aug_csv_location = os.path.join(aug_root, aug_csv)\n if not os.path.exists(dataset_csv_location):\n raise TypeError(f\"Could not find the dataset csv file: {dataset_csv_location}\")\n\n if not os.path.exists(aug_csv_location) and aug_percentages is not None:\n raise TypeError(f\"Could not find the aug csv file: {aug_csv_location}\")\n\n tune_antispoof_csv = f\"{dataset_name}_antispoof_tune.csv\"\n\n training_type = \"\"\n if is_single_folder:\n training_type += \"Single\"\n else:\n training_type += \"Multi\"\n\n\n save_tune_root = os.path.join(save_tune_root, training_type)\n if tune_experiment_name is None:\n if must_resume_from_last_experiment:\n existing_dirs = []\n if os.path.exists(save_tune_root):\n existing_dirs = os.listdir(save_tune_root)\n\n if len(existing_dirs) > 0:\n # run from the last directory\n existing_dirs.sort(reverse=True)\n tune_experiment_name = os.path.basename(existing_dirs[0])\n\n if tune_experiment_name is None:\n tune_experiment_name = f\"{dataset_name}_antispoof_\" + datetime.now().strftime(\"%m_%d_%Y_%H_%M_%S\")\n\n save_metrics_root = os.path.join(save_metrics_root,training_type, tune_experiment_name)\n save_checkpoints_root = os.path.join(save_checkpoints_root,training_type, tune_experiment_name)\n save_tb_root = os.path.join(save_tb_root, training_type, tune_experiment_name)\n\n\n\n experiment_dirs = [save_metrics_root, save_checkpoints_root, save_tb_root, save_tune_root]\n # create the directories\n for _dir in experiment_dirs:\n if not os.path.exists(_dir):\n os.makedirs(_dir)\n # return\n k_fold_list = [i for i in range(n_k_folds)]\n tune_config = {\n \"HP_COMB\": tune.grid_search(aug_folder_combinations),\n # \"HP_COMB\": aug_folder_combinations[0],\n 'must_remove_normal' : must_remove_normal,\n \"epochs\": epochs,\n \"save_metrics_root\": save_metrics_root,\n \"save_checkpoints_root\": save_checkpoints_root,\n \"save_tb_root\": save_tb_root,\n \"dataset_root\": dataset_root,\n \"dataset_name\": dataset_name,\n \"dataset_csv_name\": dataset_csv_name,\n \"aug_root\": aug_root,\n \"aug_csv\": aug_csv,\n \"train_subject_number\" : train_subject_number,\n \"test_subject_number\" : test_subject_number,\n \"get_train_frame_func\": get_train_frame_func,\n \"get_protocol_frame_dic_func\": get_protocol_frame_dic_func,\n 'is_ray': is_ray,\n 'get_stratified_name_col_func': get_stratified_name_col_func,\n 'process_dataset_metrics_func': process_dataset_metrics_func,\n 'stratified_name_list_func': stratified_name_list_func,\n 'n_folds' : n_k_folds,\n 'current_fold': tune.grid_search(k_fold_list),\n 'HP_REPEAT': tune.grid_search(repeat_run_list),\n 'original_dataset_root': original_dataset_root,\n 'use_last_only': use_last_only,\n 'include_traditional_aug':include_traditional_aug,\n 'aug_after_split':aug_after_split,\n 'mode_info': mode_info,\n 'must_use_normal_only': must_use_normal_only,\n 'use_hsv': use_hsv,\n }\n\n if aug_percentages is None:\n tune_config[\"HP_AUG_PER\"] = tune.grid_search([0])\n else:\n tune_config[\"HP_AUG_PER\"] = tune.grid_search(aug_percentages)\n\n if is_ray:\n if error_only:\n analysis = tune.run(antispoof, config=tune_config, local_dir=save_tune_root, name=tune_experiment_name,\n resources_per_trial={\"cpu\": tune_cpu, \"gpu\": tune_gpu}, resume=\"ERRORED_ONLY\")\n else:\n analysis = tune.run(antispoof, config=tune_config, local_dir=save_tune_root, name=tune_experiment_name,\n resources_per_trial={\"cpu\": tune_cpu, \"gpu\": tune_gpu}, resume=\"AUTO\")\n df = analysis.results_df\n df.to_csv(os.path.join(save_tune_root,tune_experiment_name, tune_antispoof_csv))\n process_dataset_metrics_func(df, os.path.join(save_tune_root,tune_experiment_name))\n else:\n if aug_percentages is None:\n aug_per = 0\n else:\n aug_per = aug_percentages[0]\n for combination in aug_folder_combinations:\n antispoof({\n \"HP_COMB\": combination,\n 'must_remove_normal':must_remove_normal,\n \"epochs\": epochs,\n \"save_metrics_root\": save_metrics_root,\n \"save_checkpoints_root\": save_checkpoints_root,\n \"save_tb_root\": save_tb_root,\n \"dataset_root\": dataset_root,\n \"dataset_name\": dataset_name,\n \"dataset_csv_name\": dataset_csv_name,\n \"aug_root\": aug_root,\n \"aug_csv\": aug_csv,\n \"train_subject_number\" : train_subject_number,\n \"test_subject_number\" : test_subject_number,\n \"get_train_frame_func\": get_train_frame_func,\n \"get_protocol_frame_dic_func\": get_protocol_frame_dic_func,\n \"stratified_name_list_func\": stratified_name_list_func,\n \"HP_AUG_PER\": aug_per,\n \"HP_REPEAT\": 1,\n 'is_ray' : is_ray,\n 'get_stratified_name_col_func': get_stratified_name_col_func,\n 'process_dataset_metrics_func': process_dataset_metrics_func,\n 'n_folds' : n_k_folds,\n 'current_fold' : k_fold_list[0],\n 'original_dataset_root': original_dataset_root,\n 'use_last_only': use_last_only,\n 'include_traditional_aug': include_traditional_aug,\n 'aug_after_split': aug_after_split,\n 'mode_info':mode_info,\n 'must_use_normal_only': must_use_normal_only,\n 'use_hsv': use_hsv,\n\n })\n\n\n\n\n\n","repo_name":"Jayz-o/OrfaoDissertation","sub_path":"Antispoofing/AntispoofHelpers/hyper_perameter_helper.py","file_name":"hyper_perameter_helper.py","file_ext":"py","file_size_in_byte":23004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"11192075287","text":"#!/usr/bin/python3\n\n# lftp impacts@ghrc.nsstc.nasa.gov\n# cd goes16-2023/Mesoscale-1\n# lcd /home/disk/bob/impacts/raw/goes16/Mesoscale-1\n# mirror -R\n\nimport os\n\ninDirBase = '/home/disk/bob/impacts/daac/WRF'\n\nfor init in os.listdir(inDirBase):\n if (init.startswith('GFS') or init.startswith('NAM') ) and os.path.isdir(inDirBase+'/'+init):\n initDir = inDirBase+'/'+init\n for date in os.listdir(initDir):\n if date.startswith('202') and os.path.isdir(initDir+'/'+date):\n dateDir = initDir+'/'+date\n os.chdir(dateDir)\n command = 'lftp -c \"open ftp://impacts:snowfallATLANTIC2020\\!@ghrc.nsstc.nasa.gov; cd wrf-2023; cd '+init+'; cd '+date+'; mirror -R\"'\n os.system(command)\n","repo_name":"srbrodzik/impacts-scripts","sub_path":"lftp_to_daac_wrf.py","file_name":"lftp_to_daac_wrf.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31340210251","text":"#!/usr/bin/env python3\n\n#********* data analysis functions *********#\n# transform data to a smooth curve\n# 1. fitting: use least-square curve fitting\n# 2. smooth_SG: smooth curve by locally fitting\n# 3. smooth_AA: sum[i-hw:i+hw] or running avg\n\ndef func_fitted(x, a, b, c): # fitting curves\n return a + b * x**c\ndef fitting(x, y):\n import scipy.optimize as opt\n return opt.curve_fit(func_fitted, x, y, guess0)[0]\n\ndef smooth_SG(x, pwin=41, pord=2): # smooth data by Savitzky-Golay\n from scipy.signal import savgol_filter\n return savgol_filter(x, pwin, pord)\n\ndef smooth_AA(x, pwin=41): # smooth data by adjacent averaging\n import numpy\n hw= int(pwin/2.0) # half window\n smoothed= []\n for i in range(len(x)):\n smoothed.extend(numpy.mean(x[max(0, i-hw):min(len(x), i+hw+1)]))\n return smoothed\n#####***** data analysis functions *****#####\n\nfrom sys import argv\nfrom os.path import isfile\n\nif len(argv) != 2 and len(argv) != 3:\n print(\"\\nSmooth curve by Savitzky-Golay or adjacent averaging\")\n exit(\"Usage: %s name(optional)\\n\" % argv[0])\n\n# input parameters\nN_col= input(\"Insert number of columns\\n\")\ndata= []\nfor i in range(int(N_col)): data.append([])\n\nin_isc= input(\"Do you need chop vacuum data (rm zeros)? (yes/no)\\n\")\nif in_isc==\"yes\": is_chop= True\nelse: is_chop= False\n\nin_pwin= input(\"Insert number of points of window (dafault:41)\\n\")\nif in_pwin.isdigit(): N_pwin= int(in_pwin)\nelse: N_pwin= 41\n\n# open output file\nname_out= \"out\"\nif len(argv)==3: name_out= name_out + argv[2]\nOFILE= open(name_out, \"w\")\n\n# read\nif not isfile(argv[1]): exit(\"%s doesn\\'t exist!\" % argv[1])\nwith open(argv[1], \"r\") as IFILE: # Reading his file\n for line in IFILE:\n indata = line.strip().split()\n for i in range(len(data)):\n data[i].append(float(indata[i]))\n\n#chop zero\nTOL_BOUND= 0.05 # smaller than this considerd vacuum\nleft= -1; right= len(data[-1]) \nfor i in range(len(data[-1])):\n if data[-1][i]>TOL_BOUND: left = i; break\nfor i in range(len(data[-1])-1, -1, -1):\n if data[-1][i]>TOL_BOUND: right= i; break\n\n# calculate\nif is_chop:\n dataSM= [0]*left\n dataSM.extend(smooth_SG(data[-1][left:right+1], N_pwin))\n dataSM.extend([0]*(len(data[-1])-right-1))\nelse:\n dataSM= smooth_SG(data[-1], N_pwin)\n\nif len(dataSM) != len(data[0]): print(\"wrong dataSM: \", len(dataSM), len(data[0]))\n# output\nfor i in range(len(data[0])):\n for j in range(len(data)-1):\n print(data[j][i], end=\" \", file= OFILE)\n print(dataSM[i], file= OFILE)\n\n","repo_name":"skyhuang1208/kmctools_surface","sub_path":"data_smooth.py","file_name":"data_smooth.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2581388143","text":"from bs4 import BeautifulSoup\nimport requests\nfrom tqdm import tqdm\nimport csv\n\n\nURL = \"https://ignition.devpost.com/project-gallery\"\nN_PAGES = 21\nOUTER_CLASS = \"large-4 small-12 columns gallery-item\"\nINNER_CLASS = \"software-entry-name entry-body\"\nLINK_CLASS = \"block-wrapper-link fade link-to-software\"\nLIKE_CLASS = \"count like-count\"\nOUTFILE_HEADERS= ['name', 'url', 'likes', 'description']\n\npage_urls = []\nfor n in range(1,N_PAGES+1):\n page_urls.append(URL + f'?page={n}')\n\nnames = []\nurls = []\nlikes = []\ndescriptions = []\n\nprint('scraping project data...')\nfor _url in tqdm(page_urls):\n page = requests.get(_url).content\n soup = BeautifulSoup(page, 'html.parser')\n entries = soup.find_all(class_=OUTER_CLASS)\n\n for entry in entries:\n data = entry.find(class_=LINK_CLASS)\n\n urls.append(data.get('href'))\n likes.append(int(data.footer.find(class_=LIKE_CLASS).get_text().strip(' \\n')))\n names.append(data.find(class_=INNER_CLASS).h5.get_text().strip(' \\n'))\n descriptions.append(data.find(class_=INNER_CLASS).p.get_text().strip(' \\n'))\n\n# -- debugging --\n# print(names)\n# print(urls)\n# print(likes)\n# print(descriptions)\n\nproject_data = set(zip(names, urls, likes, descriptions))\n# print(project_data)\n\nprint('saving data to csv...')\nwith open('projects.csv','w') as out:\n csv_out = csv.writer(out)\n csv_out.writerow(OUTFILE_HEADERS)\n for row in tqdm(list(project_data)):\n csv_out.writerow(row)\n\nprint('done!')\n\n\n\n\n","repo_name":"jeremyongws/ignition-scrape","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"20978384141","text":"import json\nimport jsonschema\nfrom jsonschema import validate\n\n\nclass JsonProcessing:\n \"\"\"\n A class to represent a data file JSON.\n \"\"\"\n def __init__(self, json_content):\n self.json_content = json_content\n\n def validate(self):\n \"\"\"\n Checks if the json file matches the pattern in the \"schema.json\" file.\n :param: JSON file\n :return: (bool) True or False\n \"\"\"\n json_data = self.json_content\n\n with open('schema.json', 'r') as scheme:\n googleapis_schema = json.load(scheme)\n try:\n validate(instance=json_data, schema=googleapis_schema)\n except jsonschema.exceptions.ValidationError:\n return False\n return True\n\n def parse_and_extract(self):\n \"\"\"\n Parse JSON content from www.googleapis.com/books/v1/volumes and extracts relevant data.\n :param: JSON content\n :return parsed_data: a list of volumes\n \"\"\"\n list_of_volume_info = []\n for item in self.json_content['items']:\n auxiliary_list = []\n for info in item:\n if info == 'volumeInfo' or info == 'id':\n auxiliary_list.append(item[info])\n\n auxiliary_list[1]['bookid'] = auxiliary_list[0]\n list_of_volume_info.append(auxiliary_list[1])\n\n searched_information = [\"title\", \"authors\", \"published_date\", \"categories\",\n \"average_rating\", \"ratings_count\", \"thumbnail\", \"bookid\"]\n\n extracted_data = []\n for raw_information in list_of_volume_info:\n needed_info = {}\n for info in raw_information:\n\n if info in searched_information:\n needed_info[info] = raw_information[info]\n elif info == \"imageLinks\":\n needed_info[\"thumbnail\"] = raw_information[info][\"thumbnail\"]\n elif info in [\"publishedDate\", \"averageRating\", \"ratingsCount\"]:\n for char in info:\n if char != char.lower():\n needed_info[info.replace(char, \"_\"+char.lower())] = raw_information[info]\n\n extracted_data.append(needed_info)\n\n parsed_data = []\n for book in extracted_data:\n parsed_data.append(self.add_missing_info(book, searched_information))\n return parsed_data\n\n @staticmethod\n def add_missing_info(book_information, searched_information):\n \"\"\"\n parse_and_extract() helper.\n \"\"\"\n for info in searched_information:\n if info not in book_information:\n if info in ['average_rating', 'ratings_count']:\n book_information[info] = None\n elif info in ['authors', 'categories']:\n book_information[info] = []\n else:\n book_information[info] = \"\"\n\n return book_information\n","repo_name":"czesky90/books-rest-api","sub_path":"app/json_processing.py","file_name":"json_processing.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12858715895","text":"#!/usr/bin/env python3\r\n\r\n#splitting the numbers at a space; specifically said to separate with a space for user clarity.\r\nnum1, num2 = input(\"Please enter two numbers and separate them with a space. \").split()\r\n\r\n#converting to integers\r\nnum1 = (int(num1))\r\nnum2 = (int(num2))\r\n\r\n#printing out numbers to make sure user knows what they entered.\r\nprint(\"First number: \" + str(num1))\r\nprint(\"Second number: \" + str(num2))\r\n#function to do calculations so it can be called\r\ndef numbercheck():\r\n a = num1 % 3\r\n if a == 0:\r\n print(\"The first number is divisible by three \" + str((num1/3)) + \" times.\")\r\n else:\r\n print(\"The first number is NOT divisible by three.\")\r\n\r\n b = num2 % 3\r\n if a == 0:\r\n print(\"The second number is divisible by three \" + str(round((num2/3), 1)) + \" times.\")\r\n else:\r\n print(\"The second number is NOT divisible by three.\")\r\n\r\nnumbercheck()","repo_name":"sophtank/binf-2111","sub_path":"lab10/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74188693387","text":"#!/usr/bin/env python\n\n# distance between two atoms\n# input the two coordinates[x,y,z]\ndef distance(coord1, coord2):\n dist = 0.0\n for i in range(3):\n dist += (coord1[i] - coord2[i])**2.0\n dist = dist**0.5\n return dist\n\n# geometric center of a molecule\n# input the coordinates [[x1,y1,z1], [x2,y2,z2], ...]\ndef geom_center(coord):\n geocent = [0.0, 0.0, 0.0]\n leng = len(coord)\n for i in range(leng):\n for j in range(3):\n geocent[j] += coord[i][j]/leng\n return geocent\n","repo_name":"yang2076/AMOEBA_Seminario","sub_path":"src/geomFunction.py","file_name":"geomFunction.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38118200643","text":"# coding=UTF-8\nimport torch as t\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data as dataloader \nimport torch.optim as optim\nimport pickle\nimport random\nimport numpy as np\nimport time\nimport dgl\nfrom dgl import DGLGraph\nimport scipy.sparse as sp\nfrom scipy.sparse import csr_matrix\nimport argparse\nimport os\n\nfrom ToolScripts.TimeLogger import log\nfrom ToolScripts.tools import sparse_mx_to_torch_sparse_tensor\nfrom Interface.BPRData import BPRData \nimport Interface.evaluate as evaluate\nfrom model import MODEL\nfrom MV_MIL.informax import Informax\n\nmodelUTCStr = str(int(time.time()))\ndevice_gpu = t.device(\"cuda\")\n\n\nisLoadModel = False\nLOAD_MODEL_PATH = r\"SR-HAN_Yelp_1599990303_hide_dim_8_layer_dim_[8,8,8]_lr_0.05_reg_0.02_topK_10_lambda1_0_lambda2_0\"\n\n\nclass Hope():\n def __init__(self,args,data,metaPath,subGraph):\n self.args = args \n self.metaPath = metaPath\n\n #train data and test data\n trainMat, testData, _, _, _ = data\n self.userNum, self.itemNum = trainMat.shape\n train_coo = trainMat.tocoo()\n train_u, train_v, train_r = train_coo.row, train_coo.col, train_coo.data\n assert np.sum(train_r == 0) == 0\n train_data = np.hstack((train_u.reshape(-1,1),train_v.reshape(-1,1))).tolist()#//(u,v)list\n test_data = testData\n \n train_dataset = BPRData(train_data, self.itemNum, trainMat, 1, True) #num_negtive samples\n test_dataset = BPRData(test_data, self.itemNum, trainMat, 0, False)\n self.train_loader = dataloader.DataLoader(train_dataset, batch_size=self.args.batch, shuffle=True, num_workers=0) \n self.test_loader = dataloader.DataLoader(test_dataset, batch_size=1024*1000, shuffle=False,num_workers=0) #test batch=1024\n\n #user metaPath: UU UIU UITIU ITI IUI\n self.uu_graph = dgl.graph(self.metaPath['UU'], ntype='user', etype='social')\n self.uiu_graph = dgl.graph(self.metaPath['UIU'], ntype='user', etype='rating')\n self.uitiu_graph = dgl.graph(self.metaPath['UITIU'], ntype='user', etype='rating') \n # self.user_graph =[self.uu_graph, self.uiu_graph, self.uitiu_graph] #7 cases\n\n #item metapath IUI ITI\n self.iti_graph = dgl.graph(self.metaPath['ITI'], ntype='item', etype='category')\n self.iui_graph = dgl.graph(self.metaPath['IUI'], ntype='item', etype='raitng')\n # self.item_graph =[self.iui_graph, self.iti_graph] #3 cases\n \n #according args to append metapath graph to user graph or item graph\n self.graph_dict={}\n self.graph_dict['uu']=self.uu_graph\n self.graph_dict['uiu']=self.uiu_graph\n self.graph_dict['uitiu']=self.uitiu_graph\n self.graph_dict['iui']=self.iui_graph\n self.graph_dict['iti']=self.iti_graph\n\n print(\"user metaPath: \"+self.args.user_graph_indx)\n user_graph_list = self.args.user_graph_indx.split('_')\n self.user_graph = []\n for i in range(len(user_graph_list)):\n self.user_graph.append(self.graph_dict[user_graph_list[i]])\n\n print(\"item metaPath: \"+self.args.item_graph_indx)\n item_graph_list = self.args.item_graph_indx.split('_')\n self.item_graph = []\n for i in range(len(item_graph_list)):\n self.item_graph.append(self.graph_dict[item_graph_list[i]])\n del self.graph_dict, self.uu_graph, self.uiu_graph, self.uitiu_graph, self.iui_graph, self.iti_graph\n \n #informax\n if self.args.informax == 1:\n (self.ui_graphAdj,self.ui_subGraphAdj) = subGraph\n self.ui_subGraphAdj_Tensor = sparse_mx_to_torch_sparse_tensor(self.ui_subGraphAdj).cuda()\n self.ui_subGraphAdj_Norm =t.from_numpy(np.sum(self.ui_subGraphAdj,axis=1)).float().cuda()\n self.ui_graph = DGLGraph(self.ui_graphAdj)\n\n #data for plot \n self.train_losses = []\n self.test_hr = []\n self.test_ndcg = []\n \n def prepareModel(self):\n np.random.seed(args.seed)\n t.manual_seed(args.seed)\n t.cuda.manual_seed(args.seed)\n random.seed(args.seed)\n self.out_dim = self.args.hide_dim + sum(eval(self.args.layer_dim))\n #metapath encoder model\n self.model = MODEL(len(self.user_graph),\n len(self.item_graph),\n self.userNum,\n self.itemNum,\n self.args.hide_dim,\n eval(self.args.layer_dim)).cuda()\n #informax\n if self.args.informax == 1:\n if self.args.informax_graph_act == 'sigmoid':\n informaxGraphAct = nn.Sigmoid()\n elif self.args.informax_graph_act == 'tanh':\n informaxGraphAct = nn.Tanh()\n print('informax graph-level Act funciton: '+self.args.informax_graph_act )\n self.ui_informax = Informax(self.ui_graph,self.out_dim, self.out_dim, nn.PReLU(), informaxGraphAct,self.ui_graphAdj).cuda()\n self.opt = optim.Adam([\n {'params':self.model.parameters(),'weight_decay':0},\n {'params':self.ui_informax.parameters(),'weight_decay':0},\n ],lr=self.args.lr)\n else:\n self.opt = optim.Adam(self.model.parameters(),lr=self.args.lr)\n\n def predictModel(self,user, pos_i, neg_j, isTest=False):\n if isTest:\n pred_pos = t.sum(user * pos_i, dim=1)\n return pred_pos\n else:\n pred_pos = t.sum(user * pos_i, dim=1)\n pred_neg = t.sum(user * neg_j, dim=1)\n return pred_pos, pred_neg\n\n def adjust_learning_rate(self):\n # lr = self.lr * (self.args.decay**epoch)\n if self.opt != None:\n for param_group in self.opt.param_groups:\n param_group['lr'] = max(param_group['lr'] * self.args.decay, self.args.minlr)\n # print(param_group['lr'])\n\n def getModelName(self):\n title = \"SR-HAN\" + \"_\"\n ModelName = title + self.args.dataset + \"_\" + modelUTCStr +\\\n \"_hide_dim_\" + str(self.args.hide_dim) +\\\n \"_layer_dim_\" + str(self.args.layer_dim) +\\\n \"_lr_\" + str(self.args.lr) +\\\n \"_reg_\" + str(self.args.reg) +\\\n \"_topK_\" + str(self.args.topk) +\\\n \"_graph_\" + str(self.args.user_graph_indx) +\"_\"+ str(self.args.item_graph_indx) +\\\n \"_useInformax_\" + str(self.args.informax) +\\\n \"_\"+str(self.args.k_hop_num) + \"hopSubGraph\"+\\\n \"_lambda1_\" + str(self.args.lambda1) +\\\n \"_lambda2_\" + str(self.args.lambda2)\n return ModelName\n\n def saveHistory(self): \n history = dict()\n history['loss'] = self.train_losses\n history['hr'] = self.test_hr\n history['ndcg'] = self.test_ndcg\n ModelName = self.getModelName()\n\n with open(r'./History/' + dataset + r'/' + ModelName + '.his', 'wb') as fs:\n pickle.dump(history, fs)\n\n def saveModel(self): \n ModelName = self.getModelName()\n history = dict()\n history['loss'] = self.train_losses\n history['hr'] = self.test_hr\n history['ndcg'] = self.test_ndcg\n savePath = r'./Model/' + dataset + r'/' + ModelName + r'.pth'\n params = {\n 'model': self.model,\n 'epoch': self.curEpoch,\n 'args': self.args,\n 'opt': self.opt,\n 'history':history\n }\n t.save(params, savePath)\n log(\"save model : \" + ModelName)\n\n def loadModel(self, modelPath):\n checkpoint = t.load(r'./Model/' + dataset + r'/' + modelPath + r'.pth')\n self.curEpoch = checkpoint['epoch'] + 1\n self.model = checkpoint['model']\n self.args = checkpoint['args']\n self.opt = checkpoint['opt']\n\n history = checkpoint['history']\n self.train_losses = history['loss']\n self.test_hr = history['hr']\n self.test_ndcg = history['ndcg']\n log(\"load model %s in epoch %d\"%(modelPath, checkpoint['epoch']))\n\n def trainModel(self):\n epoch_loss = 0\n epoch_informax_loss=0\n self.train_loader.dataset.ng_sample() \n for user, item_i, item_j in self.train_loader: \n ##a batch\n bpr_loss = 0\n\n user = user.long().cuda() \n item_i =item_i.long().cuda()\n item_j = item_j.long().cuda()\n self.userEmbed,self.itemEmbed = self.model(self.user_graph, self.item_graph)\n\n #predict\n pred_pos, pred_neg = self.predictModel(self.userEmbed[user], self.itemEmbed[item_i], self.itemEmbed[item_j])\n bprloss = -(pred_pos.view(-1) - pred_neg.view(-1)).sigmoid().log().sum()\n bpr_loss += bprloss\n \n epoch_loss += bpr_loss.item()\n regLoss=(t.norm(self.userEmbed[user])**2+t.norm(self.itemEmbed[item_i])**2+t.norm(self.itemEmbed[item_j])**2) \n loss = 0.5*(bpr_loss + regLoss*self.args.reg)/self.args.batch\n\n #DGIloss \n if self.args.informax == 1:\n ui_informax_loss = 0\n self.allEmbed = t.cat([self.userEmbed,self.itemEmbed],dim=0) \n if self.args.lambda1 != 0 or self.args.lambda2 != 0:\n res = self.ui_informax(self.allEmbed, self.ui_subGraphAdj, self.ui_subGraphAdj_Tensor,self.ui_subGraphAdj_Norm)\n Mask = t.zeros((self.userNum+self.itemNum)).cuda()\n Mask[user]=1\n Mask[self.userNum+item_i] = 1\n Mask[self.userNum+item_j] = 1\n informax_loss = self.args.lambda1*(((Mask*res[0]).sum()+(Mask*res[1]).sum())/t.sum(Mask))\\\n +self.args.lambda2*(((Mask*res[2]).sum()+(Mask*res[3]).sum())/t.sum(Mask)+res[4])\n epoch_informax_loss += informax_loss.item()\n loss = loss + informax_loss \n self.opt.zero_grad()\n loss.backward()\n self.opt.step()\n return epoch_loss \n\n def testModel(self):\n HR=[]\n NDCG=[]\n with t.no_grad():\n self.userEmbed,self.itemEmbed = self.model(self.user_graph, self.item_graph)\n\n for test_u, test_i in self.test_loader:\n test_u = test_u.long().cuda()\n test_i = test_i.long().cuda()\n pred = self.predictModel(self.userEmbed[test_u], self.itemEmbed[test_i], None, isTest=True)\n batch = int(test_u.cpu().numpy().size/100)\n for i in range(batch):\n batch_socres=pred[i*100:(i+1)*100].view(-1)\n _,indices=t.topk(batch_socres,self.args.topk) \n tmp_item_i=test_i[i*100:(i+1)*100]\n recommends=t.take(tmp_item_i,indices).cpu().numpy().tolist()\n gt_item=tmp_item_i[0].item()\n HR.append(evaluate.hit(gt_item,recommends))\n NDCG.append(evaluate.ndcg(gt_item,recommends))\n return np.mean(HR),np.mean(NDCG)\n\n def run(self):\n self.prepareModel()\n if isLoadModel:\n self.loadModel(LOAD_MODEL_PATH)\n HR,NDCG = self.testModel()\n log(\"HR@10=%.4f, NDCG@10=%.4f\"%(HR, NDCG))\n return \n \n loss = 0\n self.curEpoch = 0\n best_hr=-1\n best_ndcg=-1\n best_epoch=-1\n\n wait=0\n\n for e in range(args.epochs+1):\n self.curEpoch = e\n #train\n log(\"**************************************************************\")\n epoch_loss = self.trainModel()\n self.train_losses.append(epoch_loss)\n log(\"epoch %d/%d, epoch_loss=%.2f\"%(e, args.epochs, epoch_loss))\n\n #test\n HR, NDCG = self.testModel()\n self.test_hr.append(HR)\n self.test_ndcg.append(NDCG)\n log(\"epoch %d/%d, HR@10=%.4f, NDCG@10=%.4f\"%(e, args.epochs, HR, NDCG))\n\n self.adjust_learning_rate() \n if HR>best_hr:\n best_hr,best_ndcg,best_epoch=HR,NDCG,e\n wait=0\n self.saveModel()\n else:\n wait+=1\n print('wait=%d'%(wait))\n \n self.saveHistory()\n if wait==self.args.patience:\n log('Early stop! best epoch = %d'%(best_epoch))\n self.loadModel(self.getModelName())\n break\n\n print(\"*****************************\")\n log(\"best epoch = %d, HR= %.4f, NDCG=%.4f\"% (best_epoch,best_hr,best_ndcg)) \n print(\"*****************************\") \n print(self.args)\n log(\"model name : %s\"%(self.getModelName()))\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='SR-HAN main.py')\n parser.add_argument('--dataset', type=str, default='CiaoDVD')\n parser.add_argument('--batch', type=int, default=8192, metavar='N', help='input batch size for training')\n parser.add_argument('--seed', type=int, default=29, metavar='int', help='random seed')\n parser.add_argument('--decay', type=float, default=0.97, metavar='LR_decay', help='decay')\n parser.add_argument('--lr', type=float, default=0.05, metavar='LR', help='learning rate')\n parser.add_argument('--minlr', type=float,default=0.0001)\n parser.add_argument('--reg', type=float, default=0.05) \n parser.add_argument('--epochs', type=int, default=400, metavar='N', help='number of epochs to train')\n parser.add_argument('--patience', type=int, default=5, metavar='int', help='early stop patience')\n parser.add_argument('--topk', type=int, default=10)\n parser.add_argument('--hide_dim', type=int, default=16, metavar='N', help='embedding size')\n parser.add_argument('--layer_dim',nargs='?', default='[16]', help='Output size of every layer') \n parser.add_argument('--user_graph_indx', nargs=r\"?\", default=\"uu_uiu_uitiu\", help='user graph')\n parser.add_argument('--item_graph_indx', nargs=r\"?\", default=\"iui_iti\", help='item graph')\n parser.add_argument('--gcn_act', default='prelu',help='metaPath gcn activation function')\n #informax\n parser.add_argument('--informax', type=int, default=1, help=\"whether use informax model block\")\n parser.add_argument('--informax_graph_act',default='sigmoid',help='informax graph activation function')\n parser.add_argument('--lambda1', type=float, default=0.06, help='weight of loss with informax')\n parser.add_argument('--lambda2', type=float, default=0.002, help='weight of loss with informax')\n parser.add_argument('--k_hop_num',type=int,default=2,help='k-hop of subgraph')\n\n args = parser.parse_args()\n print(args)\n dataset = args.dataset\n\n with open(r'dataset/'+args.dataset+'/metaPath.pkl', 'rb') as fs:\n metaPath = pickle.load(fs)\n with open(r'dataset/'+args.dataset+'/data.pkl', 'rb') as fs:\n data = pickle.load(fs)\n\n subGraphPath=r'dataset/'+args.dataset+'/'+str(args.k_hop_num)+'hop_ui_subGraph.pkl'\n if not os.path.exists(subGraphPath):\n print('please run '+'dataset/'+args.dataset+'/GenerateSubGraph.py first!')\n exit()\n else: \n with open(subGraphPath,'rb') as fs:\n subGraph = pickle.load(fs)\n hope = Hope(args,data,metaPath,subGraph)\n\n modelName = hope.getModelName()\n print('ModelName = ' + modelName) \n hope.run()\n \n\n \n\n \n\n","repo_name":"SocialRecsys/SMIN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15328,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"82"} +{"seq_id":"71528954187","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport pickle\nfrom tqdm import tqdm\nfrom boneik import kinematics, solvers, utils, draw, criteria, io\nfrom boneik import bvh\n\n\ndef create_human_body() -> kinematics.Body:\n b = kinematics.BodyBuilder()\n b.add_bone(\n \"torso\",\n \"chest\",\n tip_to_base=utils.make_tip_to_base(1.17965, \"-x,z,y\"),\n dofs={\"rx\": np.deg2rad([-10.0, 90.0])},\n ).add_bone(\n \"chest\",\n \"neck\",\n tip_to_base=utils.make_tip_to_base(2.0279, \"x,y,z\"),\n dofs={\"ry\": np.deg2rad([-90.0, 90.0])},\n ).add_bone(\n \"neck\",\n \"head\",\n tip_to_base=utils.make_tip_to_base(0.73577, \"-x,y,-z\"),\n ).add_bone(\n \"neck\",\n \"shoulder.L\",\n tip_to_base=utils.make_tip_to_base(0.71612, \"-z,-x,y\"),\n ).add_bone(\n \"shoulder.L\",\n \"elbow.L\",\n tip_to_base=utils.make_tip_to_base(1.8189, \"x,y,z\"),\n dofs={\n \"rx\": np.deg2rad([-90.0, 30.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-90.0, 90.0]),\n },\n ).add_bone(\n \"elbow.L\",\n \"hand.L\",\n tip_to_base=utils.make_tip_to_base(1.1908, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([-135.0, 0.0])},\n ).add_bone(\n \"neck\",\n \"shoulder.R\",\n tip_to_base=utils.make_tip_to_base(0.71612, \"z,x,y\"),\n ).add_bone(\n \"shoulder.R\",\n \"elbow.R\",\n tip_to_base=utils.make_tip_to_base(1.8189, \"x,y,z\"),\n dofs={\n \"rx\": np.deg2rad([-90.0, 30.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-90.0, 90.0]),\n },\n ).add_bone(\n \"elbow.R\",\n \"hand.R\",\n tip_to_base=utils.make_tip_to_base(1.1908, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([0.0, 135.0])},\n ).add_bone(\n \"torso\",\n \"hip.L\",\n tip_to_base=utils.make_tip_to_base(1.1542, \"-y,x,z\"),\n ).add_bone(\n \"hip.L\",\n \"knee.L\",\n tip_to_base=utils.make_tip_to_base(2.2245, \"x,-z,y\"),\n dofs={\n \"rx\": np.deg2rad([-20.0, 20.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-20.0, 20.0]),\n },\n ).add_bone(\n \"knee.L\",\n \"foot.L\",\n tip_to_base=utils.make_tip_to_base(1.7149, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([0.0, 90.0])},\n ).add_bone(\n \"torso\",\n \"hip.R\",\n tip_to_base=utils.make_tip_to_base(1.1542, \"y,-x,z\"),\n ).add_bone(\n \"hip.R\",\n \"knee.R\",\n tip_to_base=utils.make_tip_to_base(2.2245, \"x,-z,y\"),\n dofs={\n \"rx\": np.deg2rad([-20.0, 20.0]),\n \"ry\": np.deg2rad([-90.0, 90.0]),\n \"rz\": np.deg2rad([-20.0, 20.0]),\n },\n ).add_bone(\n \"knee.R\",\n \"foot.R\",\n tip_to_base=utils.make_tip_to_base(1.7149, \"x,y,z\"),\n dofs={\"rz\": np.deg2rad([-90.0, 0.0])},\n ).add_bone(\n \"root\",\n \"torso\",\n tip_to_base=torch.eye(4),\n # dofs={\"rx\", \"ry\", \"rz\", \"tx\", \"ty\", \"tz\"},\n dofs={\"rx\", \"ry\", \"rz\"},\n )\n\n body = b.finalize(\n [\n \"head\",\n \"neck\",\n \"shoulder.R\",\n \"elbow.R\",\n \"hand.R\",\n \"shoulder.L\",\n \"elbow.L\",\n \"hand.L\",\n \"hip.R\",\n \"knee.R\",\n \"foot.R\",\n \"hip.L\",\n \"knee.L\",\n \"foot.L\",\n \"torso\",\n \"chest\",\n \"root\",\n ]\n )\n\n return body\n\n\ndef main():\n import argparse\n from pathlib import Path\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"input\", type=Path, help=\"Pickled 3D joint predictions (NxMx3)\")\n parser.add_argument(\"-body\", type=Path, help=\"Kinematic description file\")\n parser.add_argument(\"-input-fps\", type=int, default=30, help=\"Input FPS\")\n parser.add_argument(\"-input-step\", type=int, default=1, help=\"Fit every nth frame\")\n parser.add_argument(\n \"-scale\", type=float, help=\"Scale anchors of first frame to this\"\n )\n parser.add_argument(\n \"-max-loss\", type=float, default=0.3, help=\"max loss to accept in fitting\"\n )\n parser.add_argument(\n \"-crit\",\n type=str,\n choices=[\"euclidean\", \"parallel\"],\n default=\"parallel\",\n help=\"Loss criterium to apply\",\n )\n parser.add_argument(\"-output\", type=Path, default=Path(\"./tmp/human.bvh\"))\n parser.add_argument(\"-show\", type=int, default=1, help=\"visualize every nth frame\")\n\n args = parser.parse_args()\n assert args.input.is_file()\n\n if args.body is not None:\n assert args.body.is_file()\n body = io.load_json(args.body)\n else:\n body = create_human_body()\n N = body.graph.number_of_nodes()\n frame_data = pickle.load(open(r\"C:\\dev\\bone-solve-ik\\etc\\frames_raw.pkl\", \"rb\"))\n if args.scale is not None:\n scale_factor = utils.find_scale_factor(frame_data[0]) * args.scale\n else:\n scale_factor = 1.0\n\n poses = [body.fk()] # important to start from rest-pose for bvh export.\n\n solver = solvers.IKSolver(body)\n if args.crit == \"parallel\":\n crit = criteria.ParallelSegmentCriterium(torch.zeros((N, 3)), torch.ones(N))\n else:\n crit = criteria.EuclideanDistanceCriterium(torch.zeros((N, 3)), torch.ones(N))\n crit.weights[-1] = 0 # root joint never has a corresponding anchor.\n\n axes_ranges = [[-20, 20], [-20, 20], [-2, 5]]\n fig, ax = draw.create_figure3d(axes_ranges=axes_ranges)\n prev_pose = body.fk()\n for i in tqdm(range(0, len(frame_data), args.input_step)):\n crit.anchors[: N - 1] = torch.from_numpy(frame_data[i]).float() * scale_factor\n torso = crit.anchors[-3].clone()\n crit.anchors[: N - 1] -= torso # torso at 0/0/0\n loss = solver.solve(crit, history_size=10, max_iter=10)\n if loss > args.max_loss:\n # retry from rest-pose\n body.reset_()\n loss = solver.solve(crit)\n if loss < args.max_loss:\n delta = body[\"root\", \"torso\"].get_delta()\n body[\"root\", \"torso\"].set_delta(\n [\n delta[0],\n delta[1],\n delta[2],\n torso[0],\n torso[1],\n torso[2],\n ]\n )\n new_pose = body.fk()\n poses.append(new_pose)\n prev_pose = new_pose\n else:\n body.reset_()\n poses.append(prev_pose) # Do not skip any frames, unhandled by BVH\n crit.anchors[: N - 1] += torso\n if (i // args.input_step) % args.show == 0:\n ax.cla()\n ax.set_xlim(*axes_ranges[0])\n ax.set_ylim(*axes_ranges[1])\n ax.set_zlim(*axes_ranges[2])\n draw.draw_kinematics(\n ax,\n body=body,\n fk=body.fk(),\n anchors=crit.anchors,\n draw_vertex_labels=False,\n draw_local_frames=False,\n draw_root=False,\n )\n # fig.savefig(f\"tmp/{i:05d}.png\", bbox_inches=\"tight\")\n plt.show(block=False)\n plt.pause(0.01)\n\n bvh.export_bvh(\n path=args.output, body=body, poses=poses, fps=(args.input_fps / args.input_step)\n )\n\n\nif __name__ == \"__main__\":\n main()\n # makefile()\n","repo_name":"cheind/bone-solve-ik","sub_path":"boneik/examples/fit.py","file_name":"fit.py","file_ext":"py","file_size_in_byte":7382,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"74227311949","text":"from flask import Flask, request, render_template\n\nimport random\nimport string\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n return render_template('index.html')\n\n\n@app.route('/', methods=['POST'])\ndef index_post():\n target = request.form['target']\n population = len(target)\n return find_fittest(target, population)\n\n\ndef get_random():\n return random.choice(string.ascii_uppercase + ' ')\n\n\n# 1. Start with a random string of characters the same length as the target\ndef get_individuals(random_characters, population):\n return ''.join(random_characters for _ in range(population))\n\n\n# 2. Make 100 copies of the string (reproduce)\ndef reproduce(individuals):\n return [individuals] * 100\n\n\n# 3. For each character in each of the 100 copies, with a probability of 5%, replace (mutate) the character with a new random character.\ndef mutate(individuals):\n new_individual = ''\n for c in individuals:\n if random.random() < 0.05:\n new_individual += ''.join(get_random())\n else:\n new_individual += c\n return new_individual\n\n\n# 4. Compare each new string with the target string, and give each a score (the number of letters in the string that are correct and in the correct position).\ndef fitness_score(individuals, target, population):\n fitness_score = 0\n for i in range(population):\n if individuals[i] == target[i]:\n fitness_score += 1\n return fitness_score\n\n\n# 5. If any of the new strings has a perfect score (target length), halt. Otherwise, take the highest scoring string, and go to step 2.\ndef find_fittest(target, population):\n \"\"\"\n While the score of new random characters doesn't match the target character length:\n\n 1. Create 100 empty strings and None scores\n 2. For each sequence in 100, assign empty copies at that sequence to mutated individuals and empty scores to the score of mutated individuals\n 3. Check the highest score (target char length) and append the individuals at this best score's index to the empty array\n 4. Increment loop index to see how many iterations it took to match the target\n \"\"\"\n fittest = []\n generation = 0\n new_individuals = get_individuals(get_random(), population)\n while fitness_score(new_individuals, target, population) != population:\n individual_list = reproduce('')\n fitness_score_list = reproduce(None)\n for i in range(100):\n individual_list[i] = mutate(new_individuals)\n fitness_score_list[i] = fitness_score(individual_list[i], target, population)\n high_score = max(fitness_score_list)\n new_individuals = individual_list[fitness_score_list.index(high_score)]\n fittest.append(new_individuals)\n generation += 1\n return render_template('index.html', output='
'.join(fittest), index=generation)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","repo_name":"erinmyoung/hackery","sub_path":"programming-problems/dawkins-weasel/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31212376160","text":"# https://github.com/zhangqianhui/Conditional-GAN\nimport tensorflow\nfrom tensorflow.contrib.layers.python.layers import variance_scaling_initializer, batch_norm\nfrom tensorflow.contrib.layers.python.layers import xavier_initializer\nimport numpy\nimport keras\nimport cv2\nimport skimage\nimport skimage.io\nimport matplotlib.pyplot as plt\n\n\n\ndef lrelu(x, alpha=2e-1):\n return tensorflow.maximum(x, alpha * x)\n\ndef conv2d(input_, output_dim, k_h=3, k_w=3, d_h=2, d_w=2, name='conv2d'):\n with tensorflow.variable_scope(name):\n w = tensorflow.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim], initializer=variance_scaling_initializer())\n conv = tensorflow.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')\n b = tensorflow.get_variable('b', [output_dim], initializer=tensorflow.constant_initializer(0.0))\n conv = tensorflow.reshape(tensorflow.nn.bias_add(conv, b), conv.get_shape())\n return conv, w\n\n\ndef de_conv2d(input_, output_shape, k_h=3, k_w=3, d_h=2, d_w=2, stddev=2e-2, name='deconv2d', with_w=False, initializer=variance_scaling_initializer()):\n with tensorflow.variable_scope(name):\n w = tensorflow.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]], initializer=initializer)\n deconv = tensorflow.nn.conv2d_transpose(input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1])\n b = tensorflow.get_variable('b', [output_shape[-1]], initializer=tensorflow.constant_initializer(0.0))\n deconv = tensorflow.reshape(tensorflow.nn.bias_add(deconv, b), deconv.get_shape())\n if with_w:\n return deconv, w, b\n else:\n return deconv\n\n\ndef fully_connected(input_, output_size, scope=None, with_w=False, initializer=variance_scaling_initializer()):\n shape = input_.get_shape().as_list()\n with tensorflow.variable_scope(scope or 'Linear'):\n matrix = tensorflow.get_variable('Matrix', [shape[1], output_size], tensorflow.float32, initializer=initializer)\n b = tensorflow.get_variable('b', [output_size], initializer=tensorflow.constant_initializer(0.0))\n if with_w:\n return tensorflow.matmul(input_, matrix) + b, matrix, b\n else:\n return tensorflow.matmul(input_, matrix) + b\n\n\ndef conv_cond_concat(x, y):\n x_shapes = x.get_shape()\n y_shapes = y.get_shape()\n return tensorflow.concat([x, y * tensorflow.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3)\n\n\ndef batch_normal(input_, scope='scope', reuse=False):\n return batch_norm(input_, epsilon=1e-5, decay=9e-1, scale=True, scope=scope, reuse=reuse, updates_collections=None)\n\n\ndef sample_label():\n num = 64\n label_vector = numpy.zeros((num, 10), dtype=numpy.float32)\n for i in range(0, num):\n label_vector[i, int(i/8)] = 1.0\n return label_vector\n\n\ndef merge(images, size):\n h, w = images.shape[1], images.shape[2]\n img = numpy.zeros(h * size[0], w * size[1], 3)\n for idx, image in enumerate(images):\n i = idx % size[1]\n j = idx // size[1]\n img[j*h: j*h + h, i*w: i*w + w, :] = image\n return img\n\n\ndef save_image(images, size, path):\n skimage.io.imsave(path, merge(images, size))\n\n\ndef inverse_transform(image):\n return (image + 1.0) / 2.0\n\n\ndef save_images(images, size, image_path):\n return save_image(inverse_transform(images), size, image_path)\n\n\ndef vis_square(vis_path, data, type):\n data = (data - data.min()) / (data.max() - data.min())\n n = int(numpy.ceil(numpy.sqrt(data.shape[0])))\n padding = (((0, n ** 2 - data.shape[0]),\n (0, 1), (0, 1)) + ((0, 0),) * (data.ndim - 3))\n data = numpy.pad(data, padding, mode='constant', constant_values=1)\n data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n plt.imshow(data[:, :, 0])\n plt.axis('off')\n if type:\n plt.savefig('./{}/weights.png'.format(vis_path), format='png')\n else:\n plt.savefie('./{}/activation.png'.format(vis_path), format='png')\n\n\nclass CGAN(object):\n def __init__(self, data_ob, sample_dir, output_size, learn_rate, batch_size, z_dim, y_dim, log_dir, model_path, visua_path):\n self.data_ob = data_ob\n self.sample_dir = sample_dir\n self.output_size = output_size\n self.learn_rate = learn_rate\n self.batch_size = batch_size\n self.z_dim = z_dim\n self.y_dim = y_dim\n self.log_dir = log_dir\n self.model_path = model_path\n self.visua_path = visua_path\n self.channels = self.data_ob.shape[2]\n self.images = tensorflow.placeholder(tensorflow.float32, [batch_size, self.output_size, self.output_size, self.channels])\n self.z = tensorflow.placeholder(tensorflow.float32, [self.batch_size, self.z_dim])\n self.y = tensorflow.placeholder(tensorflow.float32, [self.batch_size, self.y_dim])\n\n def gern_net(self, z, y):\n with tensorflow.variable_scope('generator') as scope:\n yb = tensorflow.reshape(y, shape=[self.batch_size, 1, 1, self.y_dim])\n z = tensorflow.concat([z, y], 1)\n c1, c2 = int(self.output_size / 4), int(self.output_size / 2)\n d1 = tensorflow.nn.relu(batch_normal(fully_connected(z, output_size=1024, scope='gen_fully1'), scope='gen_bn1'))\n d1 = tensorflow.concat([d1, y], 1)\n d2 = tensorflow.nn.relu(batch_normal(fully_connected(d1, output_size=7*7*2*64, scope='gen_fully2'), scope='gen_bn2'))\n d2 = tensorflow.reshape(d2, [self.batch_size, c1, c1, 64 * 2])\n d2 = conv_cond_concat(d2, yb)\n d3 = tensorflow.nn.relu(batch_normal(de_conv2d(d2, output_shape=[self.batch_size, c2, c2, 128], name='gen_deconv1'), scope='gen_bn3'))\n d3 = conv_cond_concat(d3, yb)\n d4 = de_conv2d(d3, output_shape=[self.batch_size, self.output_size, self.output_size, self.channels], name='gen_deconv2', initializer=xavier_initializer())\n return tensorflow.nn.sigmoid(d4)\n\n def dis_net(self, images, y, reuse=False):\n with tensorflow.variable_scope('discriminator') as scope:\n if reuse == True:\n scope.reuse_variables()\n\n yb = tensorflow.reshape(y, shape=[self.batch_size, 1, 1, self.y_dim])\n concat_data = conv_cond_concat(images, yb)\n conv1, w1 = conv2d(concat_data, output_dim=10, name='dis_conv1')\n tensorflow.add_to_collection('weight_1', w1)\n conv1 = lrelu(conv1)\n conv1 = conv_cond_concat(conv1, yb)\n tensorflow.add_to_collection('ac_1', conv1)\n conv2, w2 = conv2d(conv1, output_dim=64, name='dis_conv2')\n tensorflow.add_to_collection('weight_2', w2)\n conv2 = lrelu(batch_normal(conv2, scope='dis_bn1'))\n tensorflow.add_to_collection('ac_2', conv2)\n f1 = lrelu(batch_normal(fully_connected(conv2, output_size=1024, scope='dis_fully1'), scope='dis_bn2', reuse=reuse))\n f1 = tensorflow.concat([f1, y], 1)\n out = fully_connected(f1, output_size=1, scope='dis_fully2', initializer=xavier_initializer())\n return tensorflow.nn.sigmoid(out), out\n\n def test(self):\n init = tensorflow.initialize_all_variables()\n with tensorflow.Session() as sess:\n sess.run(init)\n self.saver.restore(sess, self.model_path)\n sample_z = numpy.random.uniform(1, -1, size=[self.batch_size, self.z_dim])\n output = sess.run(self.fake_images, feed_dict={self.z: sample_z, self.y: sample_label()})\n save_images(output, [8, 8], './{}/test{:02d}_{:04d}.png'.format(self.sample_dir, 0, 0))\n image = cv2.imread('./{}/test{:02d}_{:04d}.png'.format(self.sample_dir, 0, 0), 0)\n cv2.imshow('test', image)\n cv2.waitKey(-1)\n print('Test Finish!')\n\n def visual(self):\n init = tensorflow.initialize_all_variables()\n with tensorflow.Session() as sess:\n sess.run(init)\n self.saver.restore(sess, self.model_path)\n real_batch_array, real_labels = self.data_ob.getNext_batch(0)\n batch_z = numpy.random.uniform(-1, 1, size=[self.batch_size, self.z_dim])\n conv_weights = sess.run([tensorflow.get_collection('weight_2')])\n vis_square(self.visua_path, conv_weights[0][0].transpose(3, 0, 1, 2), type=1)\n ac = sess.run([tensorflow.get_collection('ac_2')],\n feed_dict={self.images: real_batch_array[:64], self.z: batch_z, self.y: sample_label()})\n vis_square(self.visua_path, ac[0][0].transpose(3, 1, 2, 0), type=0)\n print('The visualization finish!')\n\n def build_model(self):\n self.fake_images = self.gern_net(self.z, self.y)\n G_image = tensorflow.summary.image('G_out', self.fake_images)\n D_pro, D_logits = self.dis_net(self.images, self.y, False)\n D_pro_sum = tensorflow.summary.histogram('D_pro', D_pro)\n G_pro, G_logits = self.dis_net(self.fake_images, self.y, True)\n G_pro_sum = tensorflow.summary.histogram('G_pro', G_pro)\n\n D_fake_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.zeros_like(G_pro), logits=G_logits))\n D_real_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.ones_like(D_pro), logits=D_logits))\n G_fake_loss = tensorflow.reduce_mean(tensorflow.nn.sigmoid_cross_entropy_with_logits(labels=tensorflow.ones_like(G_pro), logits=G_logits))\n self.D_loss = D_real_loss + D_fake_loss\n self.G_loss = G_fake_loss\n loss_sum = tensorflow.summary.scalar('D_loss', self.D_loss)\n G_loss_sum = tensorflow.summary.scalar('G_loss', self.G_loss)\n self.merged_summary_op_d = tensorflow.summary.merge([loss_sum, D_pro_sum])\n self.merged_summary_op_g = tensorflow.summary.merge([G_loss_sum, G_pro_sum, G_image])\n t_vars = tensorflow.trainable_variables()\n self.d_var = [var for var in t_vars if 'dis' in var.name]\n self.g_var = [var for var in t_vars if 'gen' in var.name]\n self.saver = tensorflow.train.Saver()\n\n def train(self, epochs=20):\n opti_D = tensorflow.train.AdamOptimizer(learning_rate=self.learn_rate, beta1=5e-1).minimize(self.D_loss, var_list=self.d_var)\n opti_G = tensorflow.train.AdamOptimizer(learning_rate=self.learn_rate, beta1=5e-1).minimize(self.G_loss, var_list=self.g_var)\n init = tensorflow.global_variables_initializer()\n config = tensorflow.ConfigProto()\n config.gpu_options.allow_growth = True\n with tensorflow.Session(config=config) as sess:\n sess.run(init)\n summary_writer = tensorflow.summary.FileWriter(self.log_dir, graph=sess.graph)\n step = 0\n while step < epochs:\n real_batch_array, real_labels = self.data_ob.getNext_batch(step)\n batch_z = numpy.random.uniform(-1, 1, size=[self.batch_size, self.z_dim])\n _, summary_str = sess.run([opti_D, self.merged_summary_op_d],\n feed_dict={self.images: real_batch_array, self.z: batch_z, self.y: real_labels})\n summary_writer.add_summary(summary_str, step)\n _, summary_str = sess.run([opti_G, self.merged_summary_op_g],\n feed_dict={self.z: batch_z, self.y: real_labels})\n if step % 2 == 0:\n D_loss = sess.run(self.D_loss, feed_dict={self.images: real_batch_array, self.z: batch_z, self.y: real_labels})\n fake_loss = sess.run(self.G_loss, feed_dict={self.z: batch_z, self.y: real_labels})\n print(\"Step %d: D: loss = %.7f G: loss=%.7f\" % (step, D_loss, fake_loss))\n\n if numpy.mod(step, 50) == 1 and step != 0:\n sample_images = sess.run(self.fake_images, feed_dict={self.z: batch_z, self.y: sample_label()})\n save_images(sample_images, [8, 8], './{}/train_{:04d}.png'.format(self.sample_dir, step))\n self.saver.save(sess, self.model_path)\n step = step + 1\n save_path = self.saver.save(sess, self.model_path)\n print('Model saved in file: %s' % save_path)\n\n\n","repo_name":"MustafaHalimehHH/DeepLearning","sub_path":"Examples/example_23.py","file_name":"example_23.py","file_ext":"py","file_size_in_byte":12407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26289202720","text":"from scrapy.crawler import CrawlerProcess\nfrom scrapy.utils.project import get_project_settings\n\nprocess = CrawlerProcess(get_project_settings())\n\nfor spider_name in process.spiders.list():\n print (\"Running spider %s\" % (spider_name))\n process.crawl(spider_name)\n\nprocess.start()","repo_name":"coldperformer/web-scrapers","sub_path":"worldometers/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27992263154","text":"import json\nimport tempfile\nimport heapq\n\nfrom django.db import transaction\nfrom django.http import JsonResponse, HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, render\nfrom django.urls import reverse\n\nfrom rdkit import Chem\nfrom rdkit.Chem.rdmolfiles import SDMolSupplier\nfrom rdkit.Chem.Draw import rdMolDraw2D\nfrom rdkit.Chem.rdmolfiles import MolToMolBlock, MolFromSmiles\n\nfrom cspace.forms import UploadSDFForm, CreateChemicalSetForm, \\\n CreateFacetJobForm\nfrom cspace.utils import MethodSplitView, load_mol, get_distance_func, \\\n big_qs_iterator, DuckChem\nfrom cspace.models import *\n\ndef tag_index(request):\n tags = ChemicalTag.objects.all()\n\n return render(request, 'cspace/tag-index.html', {\n 'tags': tags\n })\n\ndef chemical_set_index(request):\n sets = ChemicalSet.objects.all()\n\n return render(request, 'cspace/chemical-set-index.html', {\n 'sets': sets\n })\n\ndef facet_index(request):\n facets = ChemicalSetFacet.objects.all()\n\n return render(request, 'cspace/facet-index.html', {\n 'facets': facets\n })\n\nclass ChemicalSetDetail(MethodSplitView):\n def GET(self, request, sid):\n chem_set = get_object_or_404(ChemicalSet, pk=sid)\n create_facet_form = CreateFacetJobForm(\n initial={'chemical_set': chem_set}\n )\n\n return render(request, 'cspace/chemical-set-details.html', {\n 'chem_set': chem_set,\n 'create_facet_form': create_facet_form,\n 'jobs': chem_set.computefacetjob_set.filter(status__lt=2)\n })\n\n def POST(self, request, sid):\n chem_set = get_object_or_404(ChemicalSet, pk=sid)\n create_facet_form = CreateFacetJobForm(request.POST)\n\n if create_facet_form.is_valid():\n job = ComputeFacetJob.objects.create(\n chemical_set=create_facet_form.cleaned_data['chemical_set'],\n sim_measure=create_facet_form.cleaned_data['sim_measure'],\n embedding=create_facet_form.cleaned_data['embedding'],\n )\n\n return HttpResponseRedirect(reverse('chemical-set', args=(sid,)))\n else:\n return render(request, 'cspace/chemical-set-details.html', {\n 'chem_set': chem_set,\n 'create_facet_form': create_facet_form,\n 'jobs': chem_set.computefacetjob_set.filter(status__lt=2)\n })\n\ndef get_facet_data(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, id=fid)\n\n points = []\n all_tags = set(facet.chemical_set.tags.all())\n max_dist_from_origin = 0\n\n echems = (EmbeddedChemical.objects\n .filter(facet=facet)\n .select_related('chemical'))\n\n for echem in echems:\n tags = all_tags & set(echem.chemical.tags.all())\n chem = echem.chemical\n position = json.loads(echem.position)\n\n dist_from_origin = sum([c*c for c in position])\n max_dist_from_origin = max(dist_from_origin, max_dist_from_origin)\n\n points.append({\n 'name': chem.chem_name,\n 'chem_id': chem.pk,\n 'mol_weight': chem.mol_weight,\n 'tpsa': chem.tpsa,\n 'smiles': chem.smiles,\n 'pos': position,\n 'tags': [tag.name for tag in tags],\n 'pubchem_cid': chem.props.get('PUBCHEM_COMPOUND_CID', None),\n 'formula': chem.props.get('PUBCHEM_MOLECULAR_FORMULA', None),\n 'svg_url': reverse('draw-chem', args=(chem.pk,))\n })\n\n return JsonResponse({\n 'facet': {\n 'id': facet.pk,\n 'name': facet.name,\n 'simMeasure': facet.sim_measure,\n 'embedding': facet.embedding,\n 'maxDistFromOrigin': max_dist_from_origin ** 0.5,\n 'tags': sorted([tag.name for tag in all_tags])\n },\n 'points': points,\n })\n\ndef draw_chemical(request, chem_id):\n chem = get_object_or_404(Chemical, id=chem_id)\n mol = chem.get_mol()\n mc = Chem.Mol(mol.ToBinary())\n\n try:\n Chem.Kekulize(mc)\n except:\n mc = Chem.Mol(mol.ToBinary())\n\n if not mc.GetNumConformers():\n Chem.rdDepictor.Compute2DCoords(mc)\n\n drawer = rdMolDraw2D.MolDraw2DSVG(300,200)\n drawer.DrawMolecule(mc)\n drawer.FinishDrawing()\n svg = drawer.GetDrawingText().replace('svg:','')\n\n return JsonResponse({'data' : svg})\n\ndef facet_page(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, id=fid)\n\n return render(request, 'cspace/space-viewer.html', {\n 'facet_id': facet.pk\n })\n\nclass CreateChemicalSet(MethodSplitView):\n def GET(self, request):\n return render(request, 'cspace/create-chemical-set.html', {\n 'form': CreateChemicalSetForm()\n })\n\n @transaction.atomic\n def POST(self, request):\n form = CreateChemicalSetForm(request.POST)\n\n if form.is_valid():\n tags = form.cleaned_data['tags']\n\n chem_set = ChemicalSet.objects.create(\n name=form.cleaned_data['name'],\n description=form.cleaned_data['description']\n )\n chem_set.save()\n\n chems = Chemical.objects.filter(tags__in=tags)\n chem_set.chemical_set.set(chems)\n chem_set.tags.set(tags)\n\n return HttpResponseRedirect(reverse('chemical-set-index'))\n\n\nclass UploadSDF(MethodSplitView):\n def GET(self, request):\n return render(request, 'cspace/upload-sdf.html', {\n 'form': UploadSDFForm()\n })\n\n @transaction.atomic\n def POST(self, request):\n form = UploadSDFForm(request.POST, request.FILES)\n\n if form.is_valid():\n upload = request.FILES['sdf_file']\n tf = tempfile.NamedTemporaryFile()\n for chunk in upload.chunks():\n tf.write(chunk)\n\n tf.flush()\n tf.seek(0)\n molecules = SDMolSupplier(tf.name)\n\n tag, created = ChemicalTag.objects.get_or_create(\n name=form.cleaned_data['tag'])\n\n loaded = 0\n skipped = 0\n for mol in molecules:\n result = load_mol(mol, tag)\n\n if result == -1:\n skipped += 1\n else:\n loaded += 1\n\n return HttpResponseRedirect(reverse('tag-index'))\n\n else:\n return render(request, 'cspace/upload-sdf.html', {\n 'form': form\n })\n\ndef edit_smiles(request):\n smiles = request.GET.get('SMILES', '')\n mol = MolFromSmiles(smiles)\n\n return render(request, 'cspace/chemical-editor.html', {\n 'molblock': MolToMolBlock(mol)\n })\n\ndef sim_search(request, fid):\n facet = get_object_or_404(ChemicalSetFacet, pk=fid)\n chem_set = facet.chemical_set\n\n smiles = request.GET.get('SMILES', None)\n if not smiles:\n return JsonResponse({\n 'status': 'FAILED'\n }, status=400)\n\n make_representation, distf = get_distance_func(facet.sim_measure)\n qrep = make_representation(DuckChem(smiles))\n\n sim_heap = []\n\n i = 0\n for chem in big_qs_iterator(chem_set.chemical_set.all(), batch_size=200):\n rep = make_representation(chem)\n sim = 1.0 - distf(qrep, rep)\n if i < 10:\n i += 1\n heapq.heappush(sim_heap, (sim, chem.pk, chem))\n else:\n heapq.heappushpop(sim_heap, (sim, chem.pk, chem))\n\n sim_heap.sort(reverse=True)\n\n return JsonResponse({\n 'qrep': qrep.ToBase64(),\n 'query': smiles,\n 'status': 'OK',\n 'results': dict(((pk, sim) for sim, pk, _ in sim_heap)),\n })\n\n\n","repo_name":"Lanny/cspace","sub_path":"cspace/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"12042575271","text":"from torchtext.legacy.data import Iterator, BucketIterator\r\nfrom torchtext.legacy import data\r\nimport torch\r\n\r\ndef data_loader(batch_size=32, device=\"cuda\", data_path='data', vectors=None):\r\n TEXT = data.Field(batch_first=True, include_lengths=True, lower=True)\r\n LABEL = data.LabelField(batch_first=True)\r\n TREE = None\r\n\r\n fields = {'sentence1': ('premise', TEXT),\r\n 'sentence2': ('hypothesis', TEXT),\r\n 'gold_label': ('label', LABEL)}\r\n\r\n train_data, dev_data, test_data = data.TabularDataset.splits(\r\n path = data_path,\r\n train='snli_1.0_train.jsonl',\r\n validation='snli_1.0_dev.jsonl',\r\n test='snli_1.0_test.jsonl',\r\n format ='json',\r\n fields = fields,\r\n filter_pred=lambda ex: ex.label != '-'\r\n )\r\n TEXT.build_vocab(train_data, vectors=vectors, unk_init=torch.Tensor.normal_)\r\n LABEL.build_vocab(dev_data)\r\n train_iter, dev_iter = BucketIterator.splits(\r\n (train_data, dev_data),\r\n batch_sizes=(batch_size, batch_size),\r\n device=device,\r\n sort_key=lambda x: len(x.premise) + len(x.hypothesis),\r\n sort_within_batch=True,\r\n repeat=False,\r\n shuffle=True\r\n )\r\n\r\n test_iter = Iterator(\r\n test_data,\r\n batch_size=batch_size,\r\n device=device,\r\n sort=False,\r\n sort_within_batch=False,\r\n repeat=False,\r\n shuffle=False\r\n )\r\n\r\n return train_iter, dev_iter, test_iter, TEXT, LABEL","repo_name":"Raki-j/nlp-beginner-Raki","sub_path":"task3 基于注意力机制的文本匹配/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"70483084748","text":"# encoder.py: Encoder network\n#\n# (C) 2019, Daniel Mouritzen\n\nimport functools\nfrom typing import Iterable, Mapping, Optional, Type, Union, cast\n\nimport gin\nimport tensorflow as tf\n\nfrom project.util.tf import auto_shape\n\n\n@gin.configurable(whitelist=['activation', 'batch_norm'])\nclass Encoder(auto_shape.Layer):\n \"\"\"Encoder with architecture from World Models (D. Ha and J. Schmidhuber)\"\"\"\n def __init__(self,\n image_input: str = 'image',\n vector_inputs: Optional[Iterable[str]] = None,\n activation: Union[None, str, Type[tf.keras.layers.Layer]] = auto_shape.ReLU,\n batch_norm: bool = False,\n name: str = 'image_encoder') -> None:\n super().__init__(name=name)\n self._image_input = image_input\n self._vector_inputs = [] if vector_inputs is None else list(vector_inputs)\n kwargs = dict(kernel_size=4, strides=2)\n filter_counts = [32, 64, 128, 256]\n layers = []\n for i, filters in enumerate(filter_counts):\n layers.append(auto_shape.Conv2D(filters=filters, **kwargs, name=f'{name}_conv_{i}'))\n if activation is not None:\n if isinstance(activation, str):\n activation = cast(Type[tf.keras.layers.Layer], functools.partial(auto_shape.Activation, activation))\n layers.append(activation(name=f'{name}_activation_{i}'))\n if batch_norm:\n layers.append(auto_shape.BatchNormalization())\n layers.append(auto_shape.Flatten(name=f'{name}_flatten'))\n self._image_enc = auto_shape.Sequential(layers, name=f'{name}_sequential')\n self._concat = auto_shape.Concatenate(axis=-1)\n\n def call(self, inputs: Mapping[str, tf.Tensor]) -> tf.Tensor:\n vectors = [inputs[key] for key in self._vector_inputs]\n return self._concat([self._image_enc(inputs[self._image_input])] + vectors)\n","repo_name":"danmou/MerCur-Re","sub_path":"project/networks/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"37771395084","text":"\nclass Solution:\n def isAnagram(self, s: str, t: str) -> bool:\n # if len(s) != len(t):\n # return False\n # hashmap = dict()\n # for i in s:\n # if i not in hashmap.keys():\n # hashmap[i] = 1\n # else:\n # hashmap[i] = hashmap[i] + 1\n # for j in t:\n # if j not in hashmap.keys():\n # return False\n # if hashmap[j] == 0:\n # return False\n # hashmap[j] -= 1\n # return True\n # if tuple(sorted(s)) == tuple(sorted(t)):\n # return True\n # return False\n if len(s) != len(t):\n return False\n ss= [0]*26\n tt= [0]*26\n for i in range(len(s)):\n ss[ord(s[i])-ord('a')] += 1\n tt[ord(t[i])-ord('a')] += 1\n if tuple(ss) == tuple(tt):\n return True\n return False\n\n\n\n#%%\na = \" adsd\"\n# for i in range(len(a)):\nfor i in a:\n print(i)\n# %%\na = dict()\na['1'] = [2]\na['2'] = [4,5]\n# a.get('1',0)\nlist(a.values())\na['1'].append(3)\na['1']\n# %%\na=[1,2,3]\ni = 1\n# a.index(2,0,1)\n# a.index(2,2,3)\na.append(4)\na\n# %%\na = set()\na.add(1)\na.add(2)\nlist(a)\n# %%\nimport collections\ncollections.defaultdict(list)\n# %%\ntuple([0] * 26)\n# %%\nord(\"a\")\n# %%\na='abbcca'\nsorted(a)\n# %%\nimport collections\na=collections.defaultdict(int)\na[0] += 1\na[0]\n# %%\n\nsorted([1,5,3])\n# %%\n","repo_name":"AbigailCY/neetcode150","sub_path":"242.py","file_name":"242.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14615557756","text":"#9-1\nclass Restaurant: \n \"\"\"Create Class for a Mexican restaurant called Wapos.\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initialize name and type attributes\"\"\"\n self.restaurant_name = restaurant_name\n self.cuisine_type = cuisine_type\n def describe_restaurant(self):\n \"\"\"Print the two attributes\"\"\"\n print(f\"{self.restaurant_name.title()} serves {self.cuisine_type.title()} food\")\n def open_restaurant(self):\n \"\"\"Declare that the restaurant is open\"\"\"\n print(f\"{self.restaurant_name.title()} is now open\")\nrestaurant = Restaurant('wapos', 'mexican')\nrestaurant.describe_restaurant()\nrestaurant.open_restaurant()\n\n#9-2\n#Create three separate instances and call describe restaurant for each \nbirdhouse = Restaurant('birdhouse', 'ramen')\nlucilles = Restaurant('lucilles', 'cajun')\necho = Restaurant('echo', 'italian')\n\nbirdhouse.describe_restaurant()\nlucilles.describe_restaurant()\necho.describe_restaurant()\n\n#9-3\nclass User:\n \"\"\"Create user class with several user categories\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type,):\n \"\"\"Initialize user attributes\"\"\"\n self.first_name = first_name\n self.last_name = last_name\n self.user_name = user_name\n self.user_type = user_type\n\n def describe_user(self):\n \"\"\"User summary response to command\"\"\"\n print(f\"{self.first_name.title()} {self.last_name.title()} is a {self.user_type.title()} level user that will appear in the system as {self.user_name}.\")\n def greet_user(self):\n \"\"\"Greeting to user\"\"\"\n print(f\"Hello, {self.first_name.title()} {self.last_name.title()}! Welcome to the system. Your username is {self.user_name} and your access level is {self.user_type.title()}.\")\n#Create several instances for different users and print both messages\nallen = User('allen', 'murner', 'amurner', 'entry')\nmiles = User('miles', 'miglia', 'mmiglia', 'intermediate')\nlex = User('lex', 'tucky', 'ltucky', 'master')\n\nallen.describe_user()\nallen.greet_user()\nmiles.describe_user()\nmiles.greet_user()\nlex.describe_user()\nlex.greet_user()\n\n#9-4\nclass Restaurant: \n \"\"\"Create Class for a Mexican restaurant called Wapos.\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initialize name and type attributes\"\"\"\n self.restaurant_name = restaurant_name\n self.cuisine_type = cuisine_type\n self.number_served = 0\n def describe_restaurant(self):\n \"\"\"Print the two attributes\"\"\"\n print(f\"{self.restaurant_name.title()} serves {self.cuisine_type.title()} food\")\n def open_restaurant(self):\n \"\"\"Declare that the restaurant is open\"\"\"\n print(f\"{self.restaurant_name.title()} is now open\")\n def set_number_served(self):\n \"\"\"\"attribute counting the number of served patrons\"\"\"\n print(f\"There has been {self.number_served} customers served.\")\n def update_number_served(self, customers):\n \"\"\"set the number of customers served to a given value\"\"\"\n self.number_served = customers\n def increment_number_served(self, customer):\n \"\"\"Add the given amount to the number of customers served\"\"\"\n self.number_served += customer\n\nrestaurant = Restaurant('wapos', 'mexican')\nrestaurant.describe_restaurant()\nrestaurant.open_restaurant()\nrestaurant.update_number_served(16)\nrestaurant.set_number_served()\nrestaurant.increment_number_served(62)\nrestaurant.set_number_served() \n\n#9-5 Adding attibute login_attempts, method called increment_login_attempts, method reset_login_attempts, printing login attempts\nclass User:\n \"\"\"Create user class with several user categories\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type, login_attempts,):\n \"\"\"Initialize user attributes\"\"\"\n self.first_name = first_name\n self.last_name = last_name\n self.user_name = user_name\n self.user_type = user_type\n self.login_attempts = 0\n\n def describe_user(self):\n \"\"\"User summary response to command\"\"\"\n print(f\"{self.first_name.title()} {self.last_name.title()} is a {self.user_type.title()} level user that will appear in the system as {self.user_name}.\")\n def greet_user(self):\n \"\"\"Greeting to user\"\"\"\n print(f\"Hello, {self.first_name.title()} {self.last_name.title()}! Welcome to the system. Your username is {self.user_name} and your access level is {self.user_type.title()}.\")\n def increment_login_attempts(self, logins):\n \"\"\"increment value of login_attempts by 1\"\"\"\n self.login_attempts += logins\n def reset_login_attempts(self):\n \"\"\"resets login_attempts to 0\"\"\"\n self.login_attempts = 0\n#Create several instances for different users and print both messages\nallen = User('allen', 'murner', 'amurner', 'entry', '0')\nmiles = User('miles', 'miglia', 'mmiglia', 'intermediate', '0')\nlex = User('lex', 'tucky', 'ltucky', 'master', '0')\n\nallen.describe_user()\nallen.greet_user()\nmiles.describe_user()\nmiles.greet_user()\nlex.describe_user()\nlex.greet_user()\n\n#make instance of user class and call increment_login_attempts(). Show that it is incrementing correctly, then call reset to 0, show that is has reset. \nallen.increment_login_attempts(1)\nallen.login_attempts\nallen.increment_login_attempts(1)\nallen.login_attempts\nallen.increment_login_attempts(1)\nallen.login_attempts\nprint(f\"Allen has attempted to login {allen.login_attempts} times.\")\nallen.reset_login_attempts()\nallen.login_attempts\nprint(f\"Allen has now attempted to login {allen.login_attempts} times.\")\n\n#9-6 Inherit class from Restaurant above. \nclass IceCreamStand(Restaurant):\n \"\"\"New class, adding flavors as an attribute\"\"\"\n def __init__(self, restaurant_name, cuisine_type):\n \"\"\"initial parent class attributes\"\"\"\n super().__init__(restaurant_name, cuisine_type)\n self.flavors = ['vanilla', 'butterscotch', 'strawberry', 'chocolate']\n def list_flavors(self):\n \"\"\"Print a statement stating the available ice cream flavors\"\"\"\n print(f\"We have the following ice cream flavors: {self.flavors}.\")\n#create instance and call method to list ice cream flavors\nshow_flavors = IceCreamStand('wapos', 'mexican')\nshow_flavors.list_flavors()\n\n#9-7\nclass Admin(User):\n \"\"\"Admin account intaking User class settings, with additional changes unique to Admin users\"\"\"\n def __init__(self, first_name, last_name, user_name, user_type, login_attempts):\n \"\"\"Initialize User attributes and adding Admin attributes\"\"\"\n super().__init__(first_name, last_name, user_name, user_type, login_attempts)\n#add below instance for 9-8\n self.privileges = Privileges()\n#(originally in 9-7, changed for 9-8) self.privileges = ['can add post', 'can delete post', 'can ban user']\n#(originally in 9-7, changed for 9-8) def show_privileges(self):\n#(originally in 9-7, changed for 9-8) \"\"\"Print out list of privileges for Admin users\"\"\"\n#(originally in 9-7, changed for 9-8) print(f\"As an admin user, you are able to do the following actions: {self.privileges}.\")\n#(originally in 9-7, changed for 9-8)admin_privileges = Admin('cody', 'baggins', 'cbaggins', 'admin', '1')\n#(originally in 9-7, changed for 9-8)admin_privileges.show_privileges()\n\n#9-8\nclass Privileges:\n \"\"\"Privileges class with only one attribute\"\"\"\n def __init__(self, privileges = ['can add post', 'can delete post', 'can ban user']):\n \"\"\"list privileges\"\"\"\n self.privileges = privileges\n def show_privileges(self):\n \"\"\"Print out list of privileges for Admin users\"\"\"\n print(f\"As an admin user, you are able to do the following actions: {self.privileges}.\")\nadmin_privileges = Admin('cody', 'baggins', 'cbaggins','admin', '1')\nadmin_privileges.privileges.show_privileges()\n\n#9-9\nclass Car():\n \"\"\"A simple attempt to represent a car.\"\"\"\n\n def __init__(self, manufacturer, model, year):\n \"\"\"Initialize attributes to describe a car.\"\"\"\n self.manufacturer = manufacturer\n self.model = model\n self.year = year\n self.odometer_reading = 0\n \n def get_descriptive_name(self):\n \"\"\"Return a neatly formatted descriptive name.\"\"\"\n long_name = str(self.year) + ' ' + self.manufacturer + ' ' + self.model\n return long_name.title()\n \n def read_odometer(self):\n \"\"\"Print a statement showing the car's mileage.\"\"\"\n print(\"This car has \" + str(self.odometer_reading) + \" miles on it.\")\n \n def update_odometer(self, mileage):\n \"\"\"\n Set the odometer reading to the given value.\n Reject the change if it attempts to roll the odometer back.\n \"\"\"\n if mileage >= self.odometer_reading:\n self.odometer_reading = mileage\n else:\n print(\"You can't roll back an odometer!\")\n \n def increment_odometer(self, miles):\n \"\"\"Add the given amount to the odometer reading.\"\"\"\n self.odometer_reading += miles\n\nclass Battery():\n \"\"\"A simple attempt to model a battery for an electric car.\"\"\"\n\n def __init__(self, battery_size=60):\n \"\"\"Initialize the batteery's attributes.\"\"\"\n self.battery_size = battery_size\n\n def describe_battery(self):\n \"\"\"Print a statement describing the battery size.\"\"\"\n print(\"This car has a \" + str(self.battery_size) + \"-kWh battery.\")\n\n \n def get_range(self):\n \"\"\"Print a statement about the range this battery provides.\"\"\"\n if self.battery_size == 60:\n range = 140\n elif self.battery_size == 85:\n range = 185\n \n message = \"This car can go approximately \" + str(range)\n message += \" miles on a full charge.\"\n print(message)\n\n#9-10 Importing the Restaurant class into that program file. Saved as restaurant.py\n\n\n def upgrade_battery(self):\n \"\"\"Upgrade the battery if possible.\"\"\"\n if self.battery_size == 60:\n self.battery_size = 85\n print(\"Upgraded the battery to 85 kWh.\")\n else:\n print(\"The battery is already upgraded.\")\n \n \nclass ElectricCar(Car):\n \"\"\"Models aspects of a car, specific to electric vehicles.\"\"\"\n\n def __init__(self, manufacturer, model, year):\n \"\"\"\n Initialize attributes of the parent class.\n Then initialize attributes specific to an electric car.\n \"\"\"\n super().__init__(manufacturer, model, year)\n self.battery = Battery()\n\n\nprint(\"Make an electric car, and check the battery:\")\nmy_tesla = ElectricCar('tesla', 'model s', 2016)\nmy_tesla.battery.describe_battery()\n\nprint(\"\\nUpgrade the battery, and check it again:\")\nmy_tesla.battery.upgrade_battery()\nmy_tesla.battery.describe_battery()\n\nprint(\"\\nTry upgrading the battery a second time.\")\nmy_tesla.battery.upgrade_battery()\nmy_tesla.battery.describe_battery()\n\n#9-10 work is done in Restaurant.py\n\n#9-11 work is done in 9_11_tiy.py\n\n#9-12 work is done in multiple 9_12 files\n\n#9-13\nfrom random import randint\nclass Die:\n \"\"\"Features related to a standard 6 sided die\"\"\"\n def __init__(self, sides = 6):\n \"\"\"initialize die attributes\"\"\"\n self.sides = sides\n def roll_die(self):\n \"\"\"method to roll a random die side number\"\"\"\n return randint (1,self.sides)\nside6 = Die() \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 6-sided die:\")\nprint(results)\n\n#Make the die 10 sided, roll 10 times\nside6 = Die(sides=10) \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 10-sided die:\")\nprint(results)\n\n#make the die 20 sided, roll 10 times\nside6 = Die(sides = 20) \nresults = []\nfor roll_num in range(10):\n result = side6.roll_die()\n results.append(result)\nprint(\"10 rolls of a 20-sided die:\")\nprint(results)\n\n#9-14\nfrom random import choice\nlotto_list = ['3', '16', '20', '23', '29', '37', '42', '48', '55', '67', 'A', 'G', 'N', 'Q', 'Y']\ndigit_one = choice(lotto_list)\ndigit_two = choice(lotto_list)\ndigit_three = choice(lotto_list)\ndigit_four = choice(lotto_list)\nprint(f\"The winning lottery ticket has the following figures: {digit_one}, {digit_two}, {digit_three}, and {digit_four}.\")\n\n\n# 9-15\nfrom random import choice\n\ndef get_winning_ticket(possibilities):\n \"\"\"Return a winning ticket from a set of possibilities.\"\"\"\n winning_ticket = []\n\n # We don't want to repeat winning numbers or letters, so we'll use a\n # while loop.\n while len(winning_ticket) < 4:\n pulled_item = choice(possibilities)\n\n # Only add the pulled item to the winning ticket if it hasn't\n # already been pulled.\n if pulled_item not in winning_ticket:\n winning_ticket.append(pulled_item)\n\n return winning_ticket\n\ndef check_ticket(played_ticket, winning_ticket):\n # Check all elements in the played ticket. If any are not in the \n # winning ticket, return False.\n for element in played_ticket:\n if element not in winning_ticket:\n return False\n\n # We must have a winning ticket!\n return True\n\ndef make_random_ticket(possibilities):\n \"\"\"Return a random ticket from a set of possibilities.\"\"\"\n ticket = []\n # We don't want to repeat numbers or letters, so we'll use a while loop.\n while len(ticket) < 4:\n pulled_item = choice(possibilities)\n\n # Only add the pulled item to the ticket if it hasn't already\n # been pulled.\n if pulled_item not in ticket:\n ticket.append(pulled_item)\n\n return ticket\n\n\npossibilities = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'a', 'b', 'c', 'd', 'e']\nwinning_ticket = get_winning_ticket(possibilities)\n\nplays = 0\nwon = False\n\n# Let's set a max number of tries, in case this takes forever!\nmax_tries = 1_000_000\n\nwhile not won:\n new_ticket = make_random_ticket(possibilities)\n won = check_ticket(new_ticket, winning_ticket)\n plays += 1\n if plays >= max_tries:\n break\n\nif won:\n print(\"We have a winning ticket!\")\n print(f\"Your ticket: {new_ticket}\")\n print(f\"Winning ticket: {winning_ticket}\")\n print(f\"It only took {plays} tries to win!\")\nelse:\n print(f\"Tried {plays} times, without pulling a winner. :(\")\n print(f\"Your ticket: {new_ticket}\")\n print(f\"Winning ticket: {winning_ticket}\")","repo_name":"bwengerDU/python_crash_course","sub_path":"chapter_exercises/Chapter9/Ch9_TIY.py","file_name":"Ch9_TIY.py","file_ext":"py","file_size_in_byte":14368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40606714185","text":"# -*- coding: utf-8 -*-\n# __author__ = 'zhaobinbin'\n# modified 20200416\n\nimport os\nimport re\nimport math\nimport time\nfrom biocluster.workflow import Workflow\nfrom biocluster.core.exceptions import OptionError\n\n\nclass BarBreakWorkflow(Workflow):\n \"\"\"\n 相关性小工具工作流\n \"\"\"\n def __init__(self, wsheet_object):\n self._sheet = wsheet_object\n super(BarBreakWorkflow, self).__init__(wsheet_object)\n options = [\n {\"name\": \"bar_table\", \"type\": \"infile\", \"format\": \"tool_lab.simple\"},\n {\"name\": \"set_group\", \"type\": \"string\", \"default\": True},\n {\"name\": \"ishape\", \"type\": \"string\", \"default\": \"sd\"},\n {\"name\": \"group_table\", \"type\": \"infile\", \"format\": \"tool_lab.simple\"},\n {\"name\": \"low_point\", \"type\": \"float\"}, # 下断点值\n {\"name\": \"high_point\", \"type\": \"float\"}, # 上断点值\n {\"name\": \"main_id\", \"type\": \"string\"},\n {'name': \"update_info\", 'type': 'string'},\n ]\n self.add_option(options)\n self.revise_infiles()\n self.set_options(self._sheet.options())\n self.bar_break = self.add_tool(\"tool_lab.bar_break\")\n\n def check_options(self):\n if not self.option(\"bar_table\"):\n raise OptionError(\"必须输入snp_table文件\")\n if not self.option(\"low_point\"):\n raise OptionError(\"必须输入下断点值\")\n if not self.option(\"high_point\"):\n raise OptionError(\"必须输入上断点值\")\n if not self.option(\"ishape\"):\n raise OptionError(\"必须输入ishape取值\")\n if not self.option(\"set_group\"):\n raise OptionError(\"必须输入set_group取值\")\n\n def run_bar_break(self):\n options = {\n \"bar_table\": self.option('bar_table'),\n \"group_table\": self.option('group_table'),\n \"low_point\": self.option('low_point'),\n 'high_point': self.option('high_point'),\n \"main_id\": self.option('main_id'),\n \"ishape\":self.option('ishape'),\n }\n self.bar_break.set_options(options)\n self.bar_break.on(\"end\", self.set_output, \"bar_break\")\n self.bar_break.run()\n\n def set_output(self, event):\n obj = event['bind_object']\n if event['data'] == 'bar_break':\n self.linkdir(obj.output_dir, 'bar_break')\n\n def linkdir(self, dirpath, dirname):\n allfiles = os.listdir(dirpath)\n newdir = os.path.join(self.output_dir, dirname)\n if not os.path.exists(newdir):\n os.mkdir(newdir)\n oldfiles = [os.path.join(dirpath, i) for i in allfiles]\n newfiles = [os.path.join(newdir, i) for i in allfiles]\n for newfile in newfiles:\n if os.path.exists(newfile):\n if os.path.isfile(newfile):\n os.remove(newfile)\n else:\n os.system('rm -r %s' % newfile)\n # self.logger.info('rm -r %s' % newfile)\n for i in range(len(allfiles)):\n if os.path.isfile(oldfiles[i]):\n os.link(oldfiles[i], newfiles[i])\n elif os.path.isdir(oldfiles[i]):\n # self.logger.info('cp -r %s %s' % (oldfiles[i], newdir))\n os.system('cp -r %s %s' % (oldfiles[i], newdir))\n time.sleep(1)\n self.end()\n\n def run(self):\n self.run_bar_break()\n super(BarBreakWorkflow, self).run()\n\n def end(self):\n result_dir = self.add_upload_dir(self.output_dir)\n result_dir.add_relpath_rules([\n [\".\", \"\", \"结果输出目录\"],\n ])\n result_dir.add_regexp_rules([\n [\"\", \"\", \"\"]\n ])\n super(BarBreakWorkflow, self).end()\n","repo_name":"bensonlew/rnawl","sub_path":"src/mbio/workflows/tool_lab/bar_break.py","file_name":"bar_break.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"82"} +{"seq_id":"17586799772","text":"from aiogram import types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.types import ReplyKeyboardMarkup, KeyboardButton\nfrom states import MusicState\nfrom loader import dp\nfrom utils.misc.throttling import rate_limit\n\n\n@dp.message_handler(text=\"Выход\", state=MusicState.get_back_menu)\nasync def stop_cast_playlist(message: types.Message, state: FSMContext):\n await state.finish()\n await message.answer(text=f\"Ты вышел \\nДля просмотра доступных команд используй /help \\nИли \\\"/\\\" в чат\")\n\n\n@dp.message_handler(commands=['music'], state=\"*\")\n@rate_limit(limit=5, key='music')\nasync def music(message: types.Message):\n spotify = KeyboardButton(\"Spotify\")\n vk = KeyboardButton(\"VK\")\n playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spotify).add(vk)\n await message.answer('На какой платформе будем слушать?',\n reply_markup=playlists_markup, )\n await MusicState.choose_music_platform.set() #\n\n\n@dp.message_handler(state=MusicState.get_back_menu)\nasync def music(message: types.Message):\n spotify = KeyboardButton(\"Spotify\")\n vk = KeyboardButton(\"VK\")\n playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spotify).add(vk)\n await message.answer('На какой платформе будем слушать?',\n reply_markup=playlists_markup, )\n await MusicState.choose_music_platform.set() # задано состояние выбора платформы(состояние 1)\n\n\n@dp.message_handler(state=MusicState.choose_music_platform)\n# вместо текста или команды фильтром выступает параметр state,\n# определяющий в каком состоянии находится пользователь\nasync def bot_message(message: types.Message):\n spoti_plotniy_playlist = KeyboardButton('ПЛОТНЫЙ РЭП')\n spoti_witch_house = KeyboardButton('ViVoDDn3#2')\n vk_morgen_album = KeyboardButton('MORGENSHTERN - MILLION DOLLAR: HAPPINESS')\n spoti_playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n spoti_plotniy_playlist).add(spoti_witch_house)\n vk_playlists_markup = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n vk_morgen_album)\n if message.text == 'Spotify':\n await message.answer(\"Вот все доступные плейлисты в спотифай\",\n reply_markup=spoti_playlists_markup)\n if message.text == 'VK':\n await message.answer(\"Вот все доступные плейлисты VK\",\n reply_markup=vk_playlists_markup)\n await MusicState.choose_music_album.set() # установка состояния выхода в меню выбора(состояние 2)\n\n\n@dp.message_handler(state=MusicState.choose_music_album)\nasync def choose_music(message: types.Message):\n get_back_button = KeyboardButton('Вернуться к выбору плейлиста')\n exit_button = KeyboardButton('Выход')\n exit_menu = ReplyKeyboardMarkup(resize_keyboard=True, one_time_keyboard=True).add(\n get_back_button).add(exit_button)\n if message.text == 'ПЛОТНЫЙ РЭП':\n await message.answer(\"https://open.spotify.com/playlist/5BQemH4tSKWnOeUjOGGCJW\",\n reply_markup=exit_menu)\n if message.text == 'VivoDDn3#2':\n await message.answer(\"https://open.spotify.com/playlist/2U9iYP0tAtDM8j5Zm3Eiv0?si=301482f1b4d54e85\",\n reply_markup=exit_menu)\n if message.text == 'MORGENSHTERN - MILLION DOLLAR: HAPPINESS':\n await message.answer(\"https://vk.com/music/album/-2000517727_11517727_acba018a8ba0af12f6\",\n reply_markup=exit_menu)\n await MusicState.get_back_menu.set()","repo_name":"formorter/tBot","sub_path":"handlers/users/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"23165867840","text":"import numpy as np\nimport scipy\nimport matplotlib.pyplot as plt\n\n# 读取数据\n# data = np.hstack((t,xs,r_observe,xs_ckf1,xs_ckf3))\ndata=np.loadtxt('.\\data\\calculte_result_2.txt')\na_test=1e-2\n\nt = data[:,0]\nxs = data[:,1:7]\nr_observe = data[:,7:10]\nxs_ckf = data[:,10:22]\nxs_rts = data[:,22:34]\n\nx1 = xs_rts[1000:-1,6]\nn = 3 # 滤波器阶数\ncut_off = 2*1e-3 # 截止频率\nb, a = scipy.signal.butter(n, cut_off, 'low')\nx1_filtered = scipy.signal.filtfilt(b, a, x1)\n\nx2 = xs_rts[:,6]\nn = 3 # 滤波器阶数\ncut_off = 2*1e-3 # 截止频率\nb, a = scipy.signal.butter(n, cut_off, 'low')\nx2_filtered = scipy.signal.filtfilt(b, a, x2)\n\na_x=np.zeros(len(t))\nfor i in range(len(t)):\n v_norm=np.linalg.norm(xs[i,3:6])\n a_x[i]=a_test*xs[i,3]/v_norm\n\n# 绘制结果\nplt.figure()\nplt.plot(t, x2, label='Before')\nplt.plot(t, x2_filtered, label='After2')\nplt.plot(t, a_x, label='Track')\nplt.legend()\nplt.show()\n\n# 绘制结果\nplt.figure()\nplt.plot(t[1000:-1], x1, label='Before')\nplt.plot(t[1000:-1], x1_filtered, label='After')\n# plt.plot(t[1000:-1], x2_filtered[1000:-1], label='After2')\nplt.plot(t[1000:-1], a_x[1000:-1], label='Track')\nplt.xlabel('t(s)')\nplt.ylabel('ax(m/s^2)')\nplt.legend()\nplt.show()\n\n# # 绘制结果\n# plt.figure()\n# plt.plot(t[1000:-1], x[1000:-1], label='Before')\n# plt.plot(t[1000:-1], x_filtered[1000:-1], label='After')\n# plt.plot(t[1000:-1], a_x[1000:-1], label='Track')\n# plt.legend()\n# plt.show()\n\ndebug = 1","repo_name":"LeanWu/myKF","sub_path":"result_fft.py","file_name":"result_fft.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33683816262","text":"import hashlib\nimport ssl\nimport io\nimport os\nimport distutils.util\nimport urllib3.request\nfrom typing import List\nfrom PIL import Image\n\nfrom flask import Flask, make_response, abort, redirect, request\nfrom flask_ldapconn import LDAPConn\nfrom flask_apscheduler import APScheduler\n\n\nclass Config:\n LDAP_SERVER = os.environ.get('LDAP_SERVER')\n LDAP_PORT = int(os.environ.get('LDAP_PORT', default=389))\n LDAP_BINDDN = os.environ.get('LDAP_BINDDN', default=None)\n LDAP_SECRET = os.environ.get('LDAP_BINDPW', default=None)\n LDAP_USE_SSL = bool(distutils.util.strtobool(os.environ.get('LDAP_SSL', default='False')))\n LDAP_USE_TLS = bool(distutils.util.strtobool(os.environ.get('LDAP_TLS', default='True')))\n LDAP_SEARCH_BASE = os.environ.get('LDAP_SEARCH_BASE')\n LDAP_CONNECT_TIMEOUT = 10 # Honored when the TCP connection is being established\n LDAP_READ_ONLY = True\n LDAP_TLS_VERSION = ssl.PROTOCOL_TLSv1_2\n FORCE_ATTRIBUTE_VALUE_AS_LIST = True\n CALCULATE_HASHES_DELAY = int(os.environ.get('CALCULATE_HASHES_DELAY', default=600))\n SCHEDULER_API_ENABLED = True\n MAX_SIZE = 2048\n\n\nldap = LDAPConn()\n\n\nclass User(ldap.Entry):\n base_dn = Config.LDAP_SEARCH_BASE\n object_classes = ['inetOrgPerson']\n\n emails: List[str] = ldap.Attribute('mail')\n photo: List[bytes] = ldap.Attribute('jpegPhoto')\n\n\ndef create_app():\n app = Flask(__name__)\n app.config.from_object(Config)\n\n # TODO: use sqlite/redis/memcached\n mail_hashes_md5 = dict()\n mail_hashes_sha256 = dict()\n\n scheduler = APScheduler()\n\n ldap.init_app(app)\n scheduler.init_app(app)\n\n @app.get(\"/avatar/\")\n def get_photo(mail_hash):\n mail = None\n\n if mail_hash in mail_hashes_md5:\n mail = mail_hashes_md5.get(mail_hash)\n elif mail_hash in mail_hashes_sha256:\n mail = mail_hashes_sha256.get(mail_hash)\n\n s = None\n if 's' in request.args:\n s = request.args.get('s', type=int, default=80)\n if 'size' in request.args:\n s = request.args.get('size', type=int, default=80)\n\n if not s:\n size = 80\n elif s < 1:\n size = 80\n elif s > Config.MAX_SIZE:\n size = Config.MAX_SIZE\n else:\n size = s\n\n # default action\n d = None # default\n if 'd' in request.args:\n d = request.args.get('d', type=str)\n if 'default' in request.args:\n d = request.args.get('default', type=str)\n\n # force\n f = None # default\n if 'f' in request.args:\n f = request.args.get('f', type=str)\n\n if mail and not f:\n user: User = User.query.filter(\"mail:{}\".format(mail)).first()\n if user and len(user.photo) > 0:\n photo = user.photo[0]\n if photo and len(photo) > 0:\n pil_photo: Image.Image = Image.open(io.BytesIO(photo))\n pil_photo = make_square(pil_photo)\n pil_photo = pil_photo.resize((size, size), Image.ANTIALIAS)\n photo_new = io.BytesIO()\n pil_photo.save(photo_new, format='PNG')\n # TODO: store photo in cache\n response = make_response(photo_new.getvalue())\n response.headers.set('Content-Type', 'image/png')\n return response\n\n # if no image was found, but a default action is given\n if d == '404':\n return abort(404)\n\n param_dict = dict()\n if d:\n param_dict['d'] = d\n if f:\n param_dict['f'] = f\n if s:\n param_dict['s'] = s\n\n url = \"https://cdn.libravatar.org/avatar/{}\".format(mail_hash)\n\n params = urllib3.request.urlencode(param_dict)\n if params:\n url += \"?{}\".format(params)\n\n return redirect(url, 302)\n\n @scheduler.task('interval', seconds=Config.CALCULATE_HASHES_DELAY)\n def calculate_hashes():\n print(\"calculating hashes of known mail addresses\")\n\n with app.app_context():\n\n users = User.query.all()\n for index, user in enumerate(users):\n email_addresses = []\n\n for email in user.emails:\n email.strip().lower()\n if email not in email_addresses:\n email_addresses.append(email)\n\n for email in email_addresses:\n email_md5 = hashlib.md5(email.encode('ascii')).hexdigest()\n email_sha256 = hashlib.sha256(email.encode('ascii')).hexdigest()\n\n mail_hashes_md5[email_md5] = email\n mail_hashes_md5[email_sha256] = email\n\n calculate_hashes()\n scheduler.start()\n\n return app\n\n\n# copied from https://stackoverflow.com/a/44231784 and adapted\ndef make_square(im: Image.Image, fill_color=(0, 0, 0, 0)) -> Image.Image:\n x, y = im.size\n size = min(x, y)\n new_im = Image.new('RGBA', (size, size), fill_color)\n new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\n return new_im\n","repo_name":"jasperroloff/libravatar-ldap-jpegphoto","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72697349069","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# \"Open\n\n# In[ ]:\n\n\nimport csv\nimport random\nfrom collections import defaultdict, OrderedDict\nfrom operator import add\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom tensorflow import keras\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nimport sklearn\nimport sklearn.metrics as sm\nfrom sklearn import svm, tree\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import BernoulliNB, GaussianNB, MultinomialNB\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport pandas as pd\n\n\n# In[ ]:\n\n\ndef shuffle_data(X, y):\n combined = list(zip(X, y))\n random.shuffle(combined)\n X[:], y[:] = zip(*combined)\n return X, y\n\n\n# # Get Glove Data\n\n# In[ ]:\n\n\ndef get_glove_data():\n comments = []\n y = []\n dataset_filename='Final Dataset/cleaned_tweets_16K.csv'\n \n with open(dataset_filename,newline='',encoding=\"utf8\") as csvfile:\n csv_reader = csv.reader(csvfile, delimiter=',')\n line_count = 0\n \n for row in csv_reader:\n if line_count == 0:\n print(','.join(row))\n else:\n comments.append(row[1])\n y.append(int(row[0]))\n line_count += 1\n \n num_comments = len(comments)\n print(\"splitting data........\")\n word_arrays = []\n for s in comments:\n word_arrays.append(s.split(' '))\n \n print(\"Getting GLOVE embeddings size 50..\")\n file = open('glove.6B/glove.6B.50d.txt',errors='ignore').readlines()\n gloveDict = {}\n for line in file:\n info = line.split(' ')\n key = info[0]\n vec = []\n for elem in info[1:]:\n vec.append(elem.rstrip())\n gloveDict[key] = vec\n print(len(gloveDict),\"words in the GLOVE dictionary\\n\")\n \n #VECTORISE WORDS\n print(\"converting comments to lists of vectors........\")\n word_vectors = []\n for sentence in word_arrays:\n temp = []\n for word in sentence:\n if word in gloveDict:\n temp.append(gloveDict[word])\n word_vectors.append(temp)\n \n MAX_LEN = 32\n \n print(\"padding vectors to maxlen = \",MAX_LEN,\".....\")\n padded_word_vecs = np.array(pad_sequences(word_vectors, padding='pre', maxlen=MAX_LEN, dtype='float32'))\n padded_word_vecs = padded_word_vecs.reshape((num_comments, -1))\n \n print(\"DONE PRE-PROCESSING\\n\")\n \n #CLASSIFYING\n print(\"splitting\")\n X_train,X_test,y_train,y_test = train_test_split(padded_word_vecs,y,test_size=0.20)\n \n return X_train, X_test, y_train, y_test\n\n\n# # Logistic Regression\n\n# In[ ]:\n\n\n#Logistic Regression\nX_train_logistic, X_test_logistic, y_train_logistic, y_test_logistic = get_glove_data()\n\ngrid_searching = False\nclf = sklearn.linear_model.LogisticRegression(penalty=\"l2\", max_iter=100, solver=\"liblinear\")\nclf = clf.fit(X_train_logistic, y_train_logistic)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_logistic = clf.predict(X_test_logistic)\nprint(y_pred_logistic)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_logistic,y_pred_logistic))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_logistic,y_pred_logistic), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_random, y_pred_random), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_random, y_pred_random), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_random, y_pred_random), 4))\n\n\n# # Random Forest\n\n# In[ ]:\n\n\n#Logistic Regression\nX_train_random, X_test_random, y_train_random, y_test_random = get_glove_data()\ngrid_searching = False\nclf = RandomForestClassifier(n_estimators=100, max_depth=4)\nclf = clf.fit(X_train_random, y_train_random)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_random = clf.predict(X_test_random)\nprint(y_pred_random)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_random,y_pred_random))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_random,y_pred_random), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_random, y_pred_random), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_random, y_pred_random), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_random, y_pred_random), 4))\n\n\n# # Bernoulli Naive Bayes\n\n# In[ ]:\n\n\nX_train_bayes, X_test_bayes, y_train_bayes, y_test_bayes = get_glove_data()\ngrid_searching = False\nclf = BernoulliNB()\nclf = clf.fit(X_train_bayes, y_train_bayes)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_bayes = clf.predict(X_test_bayes)\nprint(y_pred_bayes)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_bayes,y_pred_bayes))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_bayes,y_pred_bayes), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_bayes, y_pred_bayes), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_bayes, y_pred_bayes), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_bayes, y_pred_bayes), 4))\n\n\n# # KNN\n\n# In[ ]:\n\n\nX_train_knn, X_test_knn, y_train_knn, y_test_knn = get_glove_data()\ngrid_searching = False\nclf = KNeighborsClassifier(n_neighbors=3)\nclf = clf.fit(X_train_knn, y_train_knn)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_knn = clf.predict(X_test_knn)\nprint(y_pred_knn)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_knn,y_pred_knn))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_knn,y_pred_knn), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_knn, y_pred_knn), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_knn, y_pred_knn), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_knn, y_pred_knn), 4))\n\n\n# # Adaboost Classifier\n\n# In[ ]:\n\n\nX_train_adaboost, X_test_adaboost, y_train_adaboost, y_test_adaboost = get_glove_data()\ngrid_searching = False\nclf = AdaBoostClassifier()\nclf = clf.fit(X_train_adaboost, y_train_adaboost)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_adaboost = clf.predict(X_test_adaboost)\nprint(y_pred_adaboost)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_adaboost,y_pred_adaboost))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_adaboost,y_pred_adaboost), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_adaboost, y_pred_adaboost), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_adaboost, y_pred_adaboost), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_adaboost, y_pred_adaboost), 4))\n\n\n# # SVM\n\n# In[ ]:\n\n\nX_train_svm, X_test_svm, y_train_svm, y_test_svm = get_glove_data()\ngrid_searching = False\nclf = svm.SVC(C=10, kernel=\"rbf\", gamma=0.001)\nclf = clf.fit(X_train_svm, y_train_svm)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_svm = clf.predict(X_test_svm)\nprint(y_pred_svm)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_svm,y_pred_svm))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_svm,y_pred_svm), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_svm, y_pred_svm), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_svm, y_pred_svm), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_svm, y_pred_svm), 4))\n\n\n# # Decision Tree\n\n# In[ ]:\n\n\nX_train_tree, X_test_tree, y_train_tree, y_test_tree = get_glove_data()\ngrid_searching = False\nclf = clf = tree.DecisionTreeClassifier()\nclf = clf.fit(X_train_tree, y_train_tree)\n\n#PREDICT\nprint(\"\\nevaluating\")\ny_pred_tree = clf.predict(X_test_tree)\nprint(y_pred_tree)\n\n\n# In[ ]:\n\n\n# EVALUATE\nprint(\"confusion matrix:\\n\", sm.confusion_matrix(y_test_tree,y_pred_tree))\nprint(\"accuracy:\", round(sm.accuracy_score(y_test_tree,y_pred_tree), 4))\n\n\n# In[ ]:\n\n\nprint(\"recall:\", round(sm.recall_score(y_test_tree, y_pred_tree), 4))\nprint(\"precision:\", round(sm.precision_score(y_test_tree, y_pred_tree), 4))\nprint(\"f1 score:\", round(sm.f1_score(y_test_tree, y_pred_tree), 4))\n\n","repo_name":"tarekhemdan/Cyberbullying","sub_path":"Twitter_16k_Glove.py","file_name":"Twitter_16k_Glove.py","file_ext":"py","file_size_in_byte":8289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27880176236","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout, Activation\nfrom keras.layers.convolutional import Convolution2D\n\n#Load Training Data\nX_Train = np.load('X_Train_Raw.npy')\ny_Train = np.load('y_Train_Raw.npy')\nprint(X_Train.shape)\nprint(y_Train.shape)\n\n# Modified PilotNet\nkeep_prob = 0.5 #Dropout keep probability\nbatch_size = 64 #Batch size\nmodel = Sequential()\n# Normalization\nmodel.add(Lambda(lambda x: x / 255.0 - 0.5,\n\tinput_shape=(160,320,3)))\n# Crop\nmodel.add(Cropping2D(cropping=((50,25),(0,0))))\n# Start conv layers\nmodel.add(Convolution2D(24,5,5, subsample=(2,2), init='normal'))\nmodel.add(Convolution2D(36,5,5, subsample=(2,2), init='normal'))\nmodel.add(Convolution2D(48,5,5, subsample=(2,2), \n\tinit='normal', activation='elu'))\nmodel.add(Dropout(keep_prob)) # Dropout #1\nmodel.add(Convolution2D(64,3,3, subsample=(1,1), init='normal'))\nmodel.add(Convolution2D(64,3,3, subsample=(1,1), \n\tinit='normal', activation='elu'))\nmodel.add(Dropout(keep_prob)) # Dropout #2\n# Start of fully connected layers\nmodel.add(Flatten())\nmodel.add(Dense(100, init='normal', activation='tanh'))\nmodel.add(Dropout(keep_prob)) # Dropout #3\nmodel.add(Dense(50, init='normal'))\nmodel.add(Dense(10, init='normal'))\nmodel.add(Dense(1, init='normal'))\n\n# Train Model (MSE + Adam)\nmodel.compile(loss='mse', optimizer='adam')\nmodel.fit(X_Train, y_Train, validation_split=0.2, \nshuffle=True, nb_epoch=1, batch_size=batch_size)\n\nmodel.save('model.h5')\nexit()","repo_name":"t-mccawley/Udacity-SelfDrivingCar-T1P3","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14415160738","text":"import json\nimport boto3\nimport datetime\nfrom botocore.exceptions import ClientError\nfrom boto3.dynamodb.conditions import Key\n\n\ndef lambda_handler(event, context):\n try:\n dynamodb = boto3.resource('dynamodb')\n patientId = event['pathParameters']['id']\n patient_table = dynamodb.Table('patients')\n\n patient_response = patient_table.query(\n KeyConditionExpression=Key('patient_id').eq(patientId)\n )\n\n patient = patient_response['Items'][0]\n return {\n 'statusCode': 200,\n 'headers': {\n 'Content-Type': 'application/json',\n 'Access-Control-Allow-Headers': '*',\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': '*',\n },\n 'body': json.dumps({'Patient': patient})\n }\n except Exception as e:\n return {\n 'statusCode': 400,\n 'headers': {\n 'Content-Type': 'application/json',\n 'Access-Control-Allow-Headers': '*',\n 'Access-Control-Allow-Origin': '*',\n 'Access-Control-Allow-Methods': '*',\n },\n 'body': json.dumps({'Patient': f\"Error: {e}\"})\n }\n","repo_name":"Weiyao-Li/CureSphere","sub_path":"Lambda/getPatient.py","file_name":"getPatient.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"14141004005","text":"class Solution(object):\n def moveZeroes(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: None Do not return anything, modify nums in-place instead.\n \"\"\"\n front = 0 \n back = len(nums)-1\n counter = 0 \n \n # sorts the array and puts number in place \n for number in nums:\n if number != 0:\n nums[front] = number \n front += 1\n else:\n counter += 1\n \n # add the zeros to the end of the array\n for i in range(counter):\n nums[back] = 0\n back -= 1\n\n return nums","repo_name":"AndrewIO47/leetcode","sub_path":"leetcode-comeback/array/move-zeroes.py","file_name":"move-zeroes.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34422067937","text":"#! /usr/bin/python 3\n# -*- coding:UTF8 -*-\nfrom time import sleep\n\nfrom practice.web.base import Base\n\n\nclass TestJs(Base):\n def test_js(self):\n self.driver.get(\"http://www.baidu.com\")\n self.driver.find_element_by_id(\"kw\").send_keys(\"selenium测试\")\n # execute_script执行js，return 返回js的返回结果\n ele = self.driver.execute_script(\"return document.getElementById('su')\")\n ele.click()\n # 获取当前页面的滚动条纵坐标位置\n # 滚动到底部，点击下一页\n self.driver.execute_script(\"document.documentElement.scrollTop=10000\")\n self.driver.find_element_by_xpath(\"//*[@id='page']/div/a[10]\").click()\n sleep(3)\n for code in[\n 'return document.title','return JSON.stringify(performance.timing)'\n ]:\n print(self.driver.execute_script(code))\n\n\n # 修改时间控件：时间控件一般都是readonly\n # 取消日期的readonly属性\n # 给value赋值\n def test_js_time(self):\n self.driver.get(\"https://www.12306.cn/index/\")\n self.driver.execute_script(\"document.getElementById('train_date')\")\n print(self.driver.execute_script(\n \"return a = document.getElementById('train_date') ,document.getElementById('train_date').removeAttribute('readonly'),a.value = '2020-12-03'\"))\n","repo_name":"chensaijun/HogwartsSDET15-homework","sub_path":"test/web/test_json.py","file_name":"test_json.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40251943614","text":"from django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.paginator import Paginator\nfrom django.db.models import Count, Q\nfrom django.http import Http404, HttpRequest, HttpResponse\nfrom django.shortcuts import redirect, render\nfrom django.views.decorators.http import require_GET, require_POST\n\nfrom tag.models import Tag\nfrom utils.pagination import pagination\n\nfrom .models import Author, Church, Engraving, Painting\n\n\n@require_GET\ndef home(request: HttpRequest) -> HttpResponse:\n current_page = int(request.GET.get('page', 1))\n paintings = Painting.objects.filter(is_published=True).order_by('-id')\n paintings = paintings.select_related('church', 'post_author') \\\n .defer('description', 'is_published')\n paintings = paintings.prefetch_related('engraving', 'author') \\\n .defer('engraving__book', 'engraving__cover')\n\n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/home.html', {\n 'page':page,\n 'obras':'-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise as obras pelo nome ou pelo resumo',\n })\n\n@require_GET\ndef detail_painting(request: HttpRequest, painting_id: int) -> HttpResponse:\n try:\n painting = Painting.objects.select_related('church', 'post_author').prefetch_related('engraving__author', 'author').get(pk=painting_id, is_published=True)\n engravings = painting.engraving.all()\n except ObjectDoesNotExist:\n raise Http404('Objects not found in database')\n\n return render(request, 'museum/pages/detail_painting.html', {\n 'painting': painting,\n 'engravings':engravings,\n 'range': [i+1 for i in range(engravings.count())],\n 'isDetailPage': True,\n 'searchbar': False,\n \n })\n\n@require_GET\ndef tags_paintings(request, slug):\n current_page = int(request.GET.get('page', 1)) \n paintings = Painting.objects.filter(tag__slug=slug)\n tag_name = Tag.objects.get(slug=slug).name\n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/tag_paintings.html', {\n 'page':page,\n 'tag_name': tag_name,\n })\n\n@require_GET\ndef churches(request:HttpRequest) -> HttpResponse:\n churches_paintings = []\n churches = Church.objects.filter(painting__is_published = True).distinct().order_by('-id')\n churches = churches.annotate(\n num_paintings=Count('painting')\n )\n for church in churches:\n paintings_number = church.num_paintings\n if paintings_number > 0:\n churches_paintings.append((church, paintings_number))\n \n return render(request, 'museum/pages/search_church.html',{\n 'churches': churches_paintings,\n 'filterChurch': 'selected',\n 'igrejas': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise as igrejas pelo nome, cidade ou estado',\n })\n\n\n@require_GET\ndef detail_church(request: HttpRequest, id_church: int) -> HttpResponse:\n filter = request.GET.get('filter', '')\n current_page = int(request.GET.get('page', 1))\n paintings = Painting.objects.filter(church__id=id_church, is_published=True).order_by('-id')\n paintings = paintings.select_related('church', 'post_author')\n paintings = paintings.prefetch_related('engraving', 'author')\n\n \n if not paintings:\n raise Http404(\"there are no paintings related to this church id\")\n church = paintings.first().church\n \n if filter == 'churches':\n search = request.GET.get('q', '')\n paintings = paintings.filter(Q(\n Q(name__icontains=search) | Q(summary__icontains=search)\n ))\n \n page = pagination(paintings, current_page)\n return render(request, 'museum/pages/church.html', {\n 'page':page,\n 'church': church,\n 'filterChurch': 'selected',\n 'placeholder': 'Pesquise pelas obras dessa igreja.',\n 'limparPesquisa': True if filter == 'churches' else False,\n 'search_result': search if filter == 'churches' else False,\n \n })\n\n@require_GET\ndef painters(request: HttpRequest) -> HttpResponse:\n painter_paintings = []\n painters = Author.objects.filter(painting__is_published = True).distinct().order_by('-id')\n painters = painters.annotate(\n num_paintings = Count('painting')\n )\n for painter in painters:\n paintings_number = painter.num_paintings\n painter_paintings.append((painter, paintings_number))\n \n return render(request, 'museum/pages/search_painter.html',{\n 'painters': painter_paintings,\n 'filterPainter': 'selected',\n 'pintores': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise os pintores pelo nome',\n })\n\n@require_GET\ndef detail_painter(request: HttpRequest, id_painter: int)-> HttpResponse:\n filter = request.GET.get('filter', '')\n try:\n painter = Author.objects.get(pk=id_painter)\n paintings_this_painter = painter.painting_set.filter(is_published=True).order_by('-id')\n paintings_this_painter = paintings_this_painter.select_related('church', 'post_author').defer('church__city','church__state', 'description')\n paintings_this_painter = paintings_this_painter.prefetch_related('engraving', 'author').defer('engraving__book', 'author__biography')\n \n except ObjectDoesNotExist:\n raise Http404(\"Painter doesn't found in this database!\")\n \n current_page = int(request.GET.get('page', 1))\n \n if filter == 'painters':\n search = request.GET.get('q', '')\n paintings_this_painter = paintings_this_painter.filter(Q(\n Q(name__icontains=search) | Q(summary__icontains=search)\n ))\n\n page = pagination(paintings_this_painter, current_page)\n return render(request, 'museum/pages/painter.html', {\n 'painter': painter,\n 'page': page,\n 'filterPainter': 'selected',\n 'placeholder': 'Pesquise pelas obras desse pintor.',\n 'limparPesquisa': True if filter == 'painters' else False,\n 'search_result': search if filter == 'painters' else False,\n\n })\n\n@require_GET\ndef engravings(request: HttpRequest) -> HttpResponse:\n engraving_paintings = []\n engravings = Engraving.objects.filter(painting__is_published = True).distinct().order_by('-id')\n for engraving in engravings:\n paintings_number = engraving.painting_set.filter(is_published=True).count()\n engraving_paintings.append((engraving, paintings_number))\n \n return render(request, 'museum/pages/search_engraving.html',{\n 'engravings': engraving_paintings,\n 'filterEngraving': 'selected',\n 'gravuras': '-selected',\n 'search_action': 'painting:search',\n 'placeholder': 'Pesquise a gravura pelo nome ou livro',\n })\n\n@require_GET\ndef detail_engraving(request: HttpRequest, id_engraving:int) -> HttpResponse:\n try:\n engraving = Engraving.objects.get(pk=id_engraving)\n if not engraving.painting_set.filter(is_published=True).exists():\n raise Http404(\"Gravura não encontrada\")\n except ObjectDoesNotExist:\n raise Http404(\"Gravura não encontrada\")\n \n return render(request, 'museum/pages/detail_engraving.html', {\n 'engraving': engraving,\n 'search': False,\n })\n\n@require_GET\ndef search(request: HttpRequest)-> HttpResponse:\n filter = request.GET.get(\"filter\", \"paintings\")\n search = request.GET.get(\"q\", \"\")\n current_page = int(request.GET.get('page', 1))\n \n if filter == 'paintings':\n template = 'museum/pages/search_painting.html'\n paintings = Painting.objects.filter(\n Q(\n Q(name__icontains=search) | Q(summary__icontains=search) \n ) & Q(is_published=True)\n ).order_by('-id')\n paintings = paintings.select_related('church', 'post_author').prefetch_related('engraving', 'author')\n page = pagination(paintings, current_page)\n return render(request, template, {\n 'page': page,\n 'search_result': search,\n 'is_search': True,\n 'filter': filter,\n 'obras': '-selected',\n 'placeholder': 'Pesquise as obras pelo nome ou pelo resumo',\n })\n\n if filter == 'churches':\n template = 'museum/pages/search_church.html'\n churches_with_paintings_published = []\n churches = Church.objects.filter(\n Q(\n Q(name__icontains=search) | Q(city__icontains=search) | Q(state__icontains=search)\n ) \n ).order_by('-id')\n \n\n for church in churches:\n painting_this_church = church.painting_set.filter(is_published=True).count()\n if painting_this_church > 0:\n churches_with_paintings_published.append((church, painting_this_church))\n\n return render(request, template,{\n 'churches': churches_with_paintings_published,\n 'search_result': search,\n 'filterChurch': 'selected',\n 'igrejas': '-selected',\n 'placeholder': 'Pesquise as igrejas pelo nome, cidade ou estado',\n })\n \n if filter == \"painters\":\n template = 'museum/pages/search_painter.html'\n painters_with_paintings_published = []\n authors = Author.objects.filter(name__icontains=search).order_by('-id')\n\n for painter in authors:\n paintings_this_painter = painter.painting_set.filter(is_published=True).count()\n if paintings_this_painter > 0:\n painters_with_paintings_published.append((painter, paintings_this_painter))\n \n\n return render(request, template,{\n 'painters': painters_with_paintings_published,\n 'search_result': search,\n 'filterPainter': 'selected',\n 'pintores': '-selected',\n 'placeholder': 'Pesquise os pintores pelo nome',\n })\n\n@require_GET\ndef detail_painting_not_published(request: HttpRequest, painting_id: int) -> HttpResponse:\n try:\n painting = Painting.objects.select_related('church', 'post_author').prefetch_related('author', 'engraving__author').get(pk=painting_id, is_published=False)\n engravings = painting.engraving.all()\n except ObjectDoesNotExist:\n raise Http404('Objects not found in database')\n \n return render(request, 'museum/pages/detail_painting_edit.html', {\n 'painting': painting,\n 'engravings': engravings,\n 'range': [i+1 for i in range(engravings.count())],\n 'isDetailPage': True,\n 'search':False,\n 'edit': True,\n \n })\n\n@require_GET\ndef info(request: HttpRequest) -> HttpResponse:\n return render(request, 'museum/pages/info.html', {\n 'searchbar': False,\n 'sobre': '-selected',\n })\n","repo_name":"GuilhermeGonSoares/Art-Museum","sub_path":"museum/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29612539004","text":"class Settings:\r\n \"\"\"Settings of the game\"\"\"\r\n\r\n def __init__(self):\r\n # Screen settings\r\n self.screen_width = 1200\r\n self.screen_height = 700\r\n self.background_color = (255, 255, 255)\r\n # Spaceship settings\r\n self.spaceship_speed = 0.7\r\n self.spaceship_lives = 3\r\n # Bullet settings\r\n self.bullet_speed = 1\r\n self.bullet_width = 4\r\n self.bullet_height = 10\r\n self.bullet_color = (60, 60, 60)\r\n self.bullets_allowed_scr = 3\r\n # Alien settings\r\n self.alien_speed = 1\r\n self.fleet_speed_drop = 10\r\n self.fleet_direction = 1\r\n","repo_name":"Portos97/Alien_shooter","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"45191633569","text":"import math\n\nlength, k = list(map(int, input().split()))\narr = list(map(lambda x: int(x) % k, input().split()))\n\nsetLength = 0\n\nfor i in range(1, int(math.ceil(k / 2))):\n count = arr.count(i)\n complementaryCount = arr.count(k - i)\n setLength += max(count, complementaryCount)\n\nif k / 2 in arr:\n setLength += 1\nif 0 in arr:\n setLength += 1\n\nprint(setLength)\n\n# correct\n","repo_name":"hasnain-cyber/competitive-programming","sub_path":"hackerrank/non-divisible-subset.py","file_name":"non-divisible-subset.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29247931115","text":"#Name:Pravalika Rao Chitneni\n#CompletionDate:18/11/2023 10.40AM\n\nimport random\n\n#is_prime(parameter)\n#Accepts usrInput\n#Returns boolean true/false\n#This method checks passed parameter is primeNumber or Not\ndef is_prime(generatedNumber):\n if generatedNumber <= 1:\n return False\n for i in range(2, int(generatedNumber**0.5) + 1):\n if generatedNumber % i == 0:\n return False\n return True\n\n#generate_random_number(parameter1,parameter2)\n#Accepts two userInputs\n#Returns primeNumber which is generated randomly\ndef generate_random_number(usrInput_2, usrInput_3):\n while True:\n generated_RandomNumber = random.randint(usrInput_2, usrInput_3)\n if is_prime(generated_RandomNumber):\n return generated_RandomNumber\n\n#countCheck(parameter_1,parameter_2)\n#it will checks length of the primesnumbers count and checks count\n\ndef countCheck(usrInput_2, usrInput_3):\n count = 0\n for num in range(usrInput_2, usrInput_3 + 1):\n if is_prime(num):\n count += 1\n return count\n\n#number_of_primes_in_the_range(userInput1, userInput2, userInput3)\n#In this method we need to collect the primeNumbers which are generated randomly\n#Here we used set to collect the unique numbers\n#And the set was type casted to list\n#List was manipulated\n#printed list and reversed list and find the max and min of the list\ndef number_of_primes_in_the_range(userInput1, userInput2, userInput3):\n primes = set()\n while len(primes) < userInput1:\n random_prime = generate_random_number(userInput2, userInput3)\n primes.add(random_prime)\n resultList= list(primes)\n print(\"The generated random number list:\")\n print(resultList)\n print(resultList[::-1])\n print(\"The minimum and maximum prime numbers are :\",max(resultList),\"and\",min(resultList))\n\n\n#main()\n#Is's more like a wrapper method\n#because we have called all the methods\n#and achieved the output\ndef main(input_values):\n\n while True:\n try:\n if len(input_values) != 3:\n raise ValueError()\n\n usrInput_1,usrInput_2, usrInput_3=input_values\n if usrInput_1 <= 0:\n raise ValueError()\n\n if usrInput_2 >= usrInput_3:\n raise ValueError()\n\n if countCheck(usrInput_2,usrInput_3) < usrInput_1:\n raise ValueError()\n break\n except ValueError as e:\n print()\n\n number_of_primes_in_the_range(usrInput_1,usrInput_2,usrInput_3)\n\nif __name__ == \"__main__\":\n print(\"Provide inputs of 3 integers from the keyboard\")\n print(\"The 1st is the number of unique prime numbers to be created;\")\n print(\"The 2nd and 3rd define the range of those prime numbers.\")\n input_values = list(map(int, input(\"Enter those 3 integers seperated by space :\").split()))\n main(input_values)\n\n\n\n\n","repo_name":"thappeta/PyhtonAssignment","sub_path":"Lab5BCoding/Lab5B02.py","file_name":"Lab5B02.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72578027148","text":"\"\"\"\n\"\"\"\n\nfrom server.utils import *\nfrom server.models.product import Product\nfrom server.data.product_data import ProductData\n\n\nclass ProductController(object):\n _productData = ProductData()\n\n def __init__(self):\n pass\n\n def get_list(self, q=None, offset=0, fetch=20):\n product_list = None\n try:\n product_list = self._productData.get_list(q, offset, fetch)\n except:\n raise\n\n return product_list\n\n\n def get(self, product_id):\n product_dict = None\n try:\n product_dict = self._productData.get(product_id)\n except:\n raise\n\n return product_dict\n\n\n def create(self, product):\n product_dict = None\n try:\n product_dict = self._productData.create(product)\n except:\n raise\n\n return product_dict\n\n\n def update(self, product):\n product_dict = None\n try:\n return self._productData.update(product)\n except:\n raise\n \n return product_dict\n\n \n def delete(self, product_id):\n return self._productData.delete(product_id)\n","repo_name":"hyoungsookim/banzee","sub_path":"server/controller/product_controller.py","file_name":"product_controller.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"37748043114","text":"from accommodations.models import *\nfrom accommodations.serializers import *\nfrom accounts.models import *\nfrom accounts.serializers import *\n\n\ndef accommodation_list(accommodations):\n context = []\n for accommodation in accommodations:\n rooms = Room.objects.filter(building=accommodation, is_verified=True).order_by('rent')\n perks = Perk.objects.filter(building=accommodation)\n if rooms.exists():\n serialized_accommodation = BuildingSerializer(accommodation).data\n serialized_accommodation['starting_rent'] = rooms[0].rent\n serialized_accommodation['perks'] = PerkSerializer(perks, many=True).data\n context.append(serialized_accommodation)\n return context\n\n\ndef accommodation_detail(accommodation, owner=False):\n context = BuildingSerializer(accommodation).data\n if owner:\n rooms = Room.objects.filter(building=accommodation)\n else:\n rooms = Room.objects.filter(building=accommodation, is_verified=True)\n perks = Perk.objects.filter(building=accommodation)\n photos = BuildingPhoto.objects.filter(building=accommodation)\n context['rooms'] = RoomSerializer(rooms, many=True).data\n context['photos'] = BuildingPhotoSerializer(photos, many=True).data\n context['perks'] = PerkSerializer(perks, many=True).data\n return context\n\n\ndef room_detail(room):\n context = RoomSerializer(room).data\n bookings = Booking.objects.filter(room=room)\n context['bookings'] = []\n for booking in bookings:\n seeker = booking.user\n context['bookings'].append(\n {\n 'booking_no': booking.booking_no,\n 'user': seeker.user.username,\n 'first_name': seeker.user.first_name,\n 'last_name': seeker.user.last_name,\n 'phone_number': seeker.user.phone_number,\n 'booking_date': booking.booking_date\n }\n )\n return context\n\n\ndef bookmark_list(bookmarks):\n context = BookmarkSerializer(bookmarks, many=True).data\n for bookmark in context:\n building_id = bookmark['building']\n building = Building.objects.filter(id=building_id)\n bookmark['building'] = accommodation_list(building)[0]\n return context\n\n\ndef booking_details(booking):\n context = {}\n room = booking.room\n building = room.building\n owner = building.owner.user\n context['booking'] = BookingSerializer(booking).data\n context['room'] = RoomSerializer(room).data\n context['building'] = BuildingSerializer(building).data\n context['owner'] = {\n 'email': owner.username,\n 'first_name': owner.first_name,\n 'last_name': owner.last_name,\n 'phone_number': owner.phone_number\n }\n return context\n","repo_name":"Accomple/APIs","sub_path":"custom/responses.py","file_name":"responses.py","file_ext":"py","file_size_in_byte":2752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7474293536","text":"import tensorflow as tf\nimport numpy as np\nimport data.dataset as data\n\nfrom lib.autoencoder import AutoEncoder\nfrom lib.colors import colors\n\nfrom functools import partial\n\n__dim__ = 100\n\nknown_labels = [\n 'K-Pop', \n 'Drum and Bass', \n 'Symphonic Metal', \n 'Trance',\n 'Progressive Rock'\n ]\n\ndef transform_labels (labels):\n global known_labels\n\n return [1 if x in labels else 0 for x in known_labels]\n\ndef decode_labels (labels, threshold=0.5):\n global known_labels\n\n return [known_labels[i] for i in range(len(labels)) if labels[i] > threshold]\n\ndef transform_input_one_hot (input, depth):\n \"\"\"\n Transforms a vector of features into a one-hot vector.\n\n Ex:\n\n input = [3, 4, 1, 1]\n depth = 5\n\n # [0, 1, 0, 1, 1]\n \"\"\"\n\n result = tf.zeros(depth)\n\n for scalar in input:\n result += tf.one_hot(scalar, depth)\n\n return result\n\n\"\"\" Main stub \"\"\"\n\nprint(colors.BOLD, 'Loading dataset...', colors.ENDC)\n\ndata_set, labels = data.get_data()\n\nprint(colors.BOLD, 'Transforming data...', colors.ENDC, end='')\n\ndata_set = list(map(partial(transform_input_one_hot, depth=__dim__), data_set))\ndata_set = tf.stack(data_set)\ndata_set = tf.map_fn(lambda t: t / tf.reduce_max(t), data_set)\n\ntrain = data_set[:800]\nvalidation = data_set[800:]\n\ntf.InteractiveSession()\n\ntrain_np = train.eval()\nvalidation_np = validation.eval()\n\nprint(colors.OKGREEN, 'OK', colors.ENDC)\n\nae = AutoEncoder(data_dimension=__dim__, encoding_dimension=65, verbose=True)\n\nae.initialize_model()\n\nprint(colors.BOLD, 'Fitting autoencoder model...', colors.ENDC)\nae.fit(train_np, validation_np, epochs=300)\nprint(colors.OKGREEN, 'Autoencoder fitting done.', colors.ENDC)\n\nae.extend_model(loss_function='binary_crossentropy')\n\nlabels = np.array(list(map(transform_labels, labels)))\n\ntrain_labels = labels[:800]\ntest_labels = labels[800:]\n\nprint(colors.BOLD, 'Fitting full model...', colors.ENDC)\nae.fit_full_model(train_np, train_labels, \n test_data=validation_np,\n test_labels=test_labels,\n epochs=300)\nprint(colors.OKGREEN, 'Training complete.', colors.ENDC)\n\nlabels = ae.full_model.predict(validation_np[-17:])\ndemo_labels = test_labels[-17:]\n\nprint()\nprint(colors.OKBLUE, '=======', colors.ENDC)\nprint(colors.HEADER, 'Training results:', colors.ENDC)\nprint()\n\nfor idx in range(len(demo_labels)):\n print(colors.BOLD, 'Expected:', colors.ENDC, end='')\n print(', '.join(decode_labels(demo_labels[idx])))\n\n print(colors.BOLD, 'Got:', colors.ENDC, end='')\n print(', '.join(decode_labels(labels[idx])))\n print()","repo_name":"brotheroftux/genres-classifier","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36380697453","text":"from aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup\r\nfrom keyboards.kb_fabric import statistic_callback, chooce_type_callback\r\n\r\nkb_choice = InlineKeyboardMarkup(row_width=2, inline_keyboard=[\r\n [\r\n InlineKeyboardButton(text=\"Послематчевое табло\", callback_data=chooce_type_callback.new(type='summary')),\r\n InlineKeyboardButton(text=\"Командная аналитика\", callback_data=chooce_type_callback.new(type=\"team\")),\r\n InlineKeyboardButton(text=\"Персональная статистика\", callback_data=chooce_type_callback.new(type=\"personal\"))\r\n ]\r\n])","repo_name":"Stkos95/Statbot","sub_path":"keyboards/inline.py","file_name":"inline.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34218701248","text":"#!/usr/bin/env python\n\nimport rospy\nimport numpy as np\nimport cv2\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom simple_webcam.srv import Image_Proc,Image_ProcResponse\nimport actionlib\nfrom simple_webcam.msg import image_procAction,image_procGoal,image_procFeedback,image_procResult\n\nbridge = CvBridge()\n\nimg_rec_flag = False\n\nclass my_img:\n\tdef __init__(self):\n\t\tself.img = bridge.cv2_to_imgmsg(np.zeros((500,500,3), np.uint8), \"bgr8\")\n\t\tself.img_rec_flag = False\n\tdef get_img(self):\n\t\treturn(self.img)\n\tdef set_img(self,img):\n\t\tself.img = img\n\tdef set_flag(self):\n\t\tself.img_rec_flag = True\n\tdef get_flag(self):\n\t\treturn(self.img_rec_flag)\n\nI = my_img()\n\ndef video_callback(Image_msg):\n\tI.set_img(Image_msg)\n\tI.set_flag()\n\ndef callback_feedback(feedback):\n\trospy.loginfo(\"Feedback recieved\")\t\n\tcv_feedback = bridge.imgmsg_to_cv2(feedback.image_inter, \"bgr8\")\n\tcv2.namedWindow(\"Camera 1 Blur feedback\")\n\tcv2.imshow('Camera 1 Blur feedback',cv_feedback)\n\tcv2.waitKey(1)\n\ndef proc_act_client():\n\tclient = actionlib.SimpleActionClient('image_proc', image_procAction)\n\n\t# Waits until the action server has started up and started\n\t# listening for goals.\n\tclient.wait_for_server()\n\n\t# Creates a goal to send to the action server.\n\tgoal = image_procGoal(I.get_img())\n\n\t# Sends the goal to the action server.\n\tclient.send_goal(goal,feedback_cb=callback_feedback)\n\n\t# Waits for the server to finish performing the action.\n\tclient.wait_for_result()\n\t\n\trospy.loginfo(\"Result recieved\")\n\tcv2.destroyAllWindows()\n\treturn client.get_result()\n\nif __name__ == '__main__':\n\trospy.init_node('proc_act_client', anonymous=True, disable_signals = True)\n\trospy.loginfo(\"Starting Image Processing Action Client\")\n\trospy.Subscriber(\"video_stream\", Image, video_callback)\n\twhile not I.get_flag():\n\t\tpass\n\t# try:\n\tcv_result = bridge.imgmsg_to_cv2(proc_act_client().image_out, \"bgr8\")\n\t# cv2.namedWindow(\"Camera 1 Blur result\")\n\t# cv2.imshow('Camera 1 Blur result',cv_result)\n\t# cv2.waitKey(0)\n\t# # except rospy.ROSInterruptException:\n\t# \trospy.loginfo(\"program interrupted before completion\")\n\tcv2.destroyAllWindows()\n\trospy.signal_shutdown(\"Recieved responce. Shutting down client\")\n","repo_name":"sohambhave/simple_webcam","sub_path":"scripts/proc_act_client.py","file_name":"proc_act_client.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3589764051","text":"#!/usr/bin/python3\n\nimport os,sys,requests,threading\nfrom sys import platform as _platform\n\nprint(\"PID: \",os.getpid())\n\n\nif _platform == \"linux\":\n\tprint(os.getloadavg())\n\nload1, load5, load15 = os.getloadavg()\n\nprint(\"Load average over the last 5 minute:\", load5)\n\nnproc = os.cpu_count() \n \nprint(\"Number of CPUs in the system:\", nproc) \n\n\nif (nproc - load5 < 1):\n\tsys.exit()\n\nurls=['https://api.github.com​', 'http://bilgisayar.mu.edu.tr/​', 'https://www.python.org/​ ', 'http://akrepnalan.com/ceng2034​', 'https://github.com/caesarsalad/wow​']\n\ndef check_url(url):\n\tx=requests.get(url)\n\tprint(x.status_code)\n\tif(x.status_code>=200 and x.status_code <=300):\n\t\tprint(\"The url:\",url,\"is valid.\")\n\telif(x.status_code>=404):\n\t\tprint(\"The url:\",url,\" is not valid.\")\n\n\nthreads = [threading.Thread(target=check_url, args=(url,)) for url in urls]\n\nfor thread in threads:\n thread.start()\n\n","repo_name":"cilememre24/ceng_2034_2020_midterm","sub_path":"ceng_2034_answer.py","file_name":"ceng_2034_answer.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"11974725480","text":"\"\"\"\nСделал более универсально, чем описано в задаче (без захардкоженных списков)\n\"\"\"\n\nimport csv\nimport os\nimport re\n\nimport chardet\n\nSOURCE_DIR = 'source_files'\nSEARCH_STRINGS = [\n 'Изготовитель системы',\n 'Название ОС',\n 'Код продукта',\n 'Тип системы'\n]\n\n\nclass NoDataInFile(Exception):\n def __init__(self, search_string, file_name):\n self.search_string = search_string\n self.file_name = file_name\n\n\ndef get_data(source_dir: str) -> list:\n \"\"\"\n Функция парсинга файлов в заданной директории\n :param source_dir: название директории\n :return: список словарей спаршенных данных\n \"\"\"\n\n extention = 'txt'\n\n files = list()\n for file in os.listdir(source_dir):\n if file.endswith(extention):\n files.append(file)\n\n result_list = list()\n\n for file in files:\n full_path = os.path.join(source_dir, file)\n\n # кодировка файла\n with open(full_path, 'rb') as fb:\n encoding = chardet.detect(fb.read())['encoding']\n\n # парсинг файла в словарь\n file_dict = dict()\n with open(full_path, encoding=encoding) as f:\n for line in f.readlines():\n line = line.strip() # исключение переноса строки\n for search_string in SEARCH_STRINGS:\n if re.search(search_string, line):\n # если в строке нашлась подстрока поиска, заполняем словарь\n file_dict[search_string] = re.sub(search_string + ':' + ' +', '', line)\n\n # Проверка, что полученные данные полны\n for search_string in SEARCH_STRINGS:\n if search_string not in file_dict.keys():\n raise NoDataInFile(search_string, file)\n\n result_list.append(file_dict)\n\n return result_list\n\n\ndef write_to_csv(target_file):\n \"\"\"\n Функция записи данных в csv-файл\n :param target_file: имя файла для записи\n :return: None\n \"\"\"\n data = get_data(source_dir=SOURCE_DIR)\n with open(target_file, 'w', encoding='utf-8') as f:\n writer = csv.DictWriter(f, fieldnames=SEARCH_STRINGS)\n writer.writeheader()\n for row in data:\n writer.writerow(row)\n\n\nif __name__ == '__main__':\n csv_file = 'result_01.csv'\n try:\n write_to_csv(os.path.join(SOURCE_DIR, csv_file))\n except NoDataInFile as e:\n print(f'Ошибка: нет строки во входном файле: {e}')\n","repo_name":"tkvitko/study_python_async","sub_path":"homework_02/task_01.py","file_name":"task_01.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28160627726","text":"input = [input() for i in range(2)]\nN = input[0]\nCards = list(map(int, input[1].split()))\n\nAlice = 0\nBob = 0\n\nif len(Cards) % 2 == 1:\n idx = Cards.index(min(Cards))\n lastNum = Cards.pop(idx)\n Alice += lastNum\n\nturns = len(Cards) / 2\nfor i in range(int(turns)):\n first = Cards.index(max(Cards))\n largerNum = Cards.pop(first)\n Alice += largerNum\n\n second = Cards.index(max(Cards))\n smallerNum = Cards.pop(second)\n Bob += smallerNum\n\nprint(Alice - Bob)\n ","repo_name":"KokiIto-45/atcoder_python","sub_path":"contest/abc088/b/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"14023071707","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render, redirect\nfrom user.forms.profile_form import ProfileForm\nfrom user.models import Profile, UserHistory\nfrom django.contrib.auth import (authenticate, login)\n\n\ndef register(request):\n if request.user.is_authenticated:\n return redirect('/')\n next = request.GET.get('next')\n form = UserCreationForm(request.POST or None)\n if form.is_valid():\n user = form.save()\n username = form.cleaned_data.get('username')\n password = form.cleaned_data.get('password1')\n new_user = authenticate(username=username, password=password)\n login(request, user)\n if next:\n return redirect(next)\n return redirect('/')\n\n context = {\n 'form': form\n }\n\n return render(request, 'user/register.html', context)\n\n\ndef edit_profile(request):\n profile = Profile.objects.filter(user=request.user).first()\n if request.method == 'POST':\n form = ProfileForm(instance=profile, data=request.POST)\n if form.is_valid():\n profile = form.save(commit=False)\n profile.user = request.user\n profile.save()\n return redirect('profile')\n return render(request, 'user/edit_profile.html', {\n 'form': ProfileForm(instance=profile)\n })\n\n\ndef profile(request):\n if not request.user.is_authenticated:\n return redirect('/')\n\n user_history = UserHistory.objects.filter(user_id=request.user.id).order_by('-date')\n print(user_history[:10])\n return render(request, '../templates/user/profile.html', {\n 'user_history': user_history[:4]\n })\n\n\ndef history(request):\n if not request.user.is_authenticated:\n return redirect('/')\n\n user_history = UserHistory.objects.filter(user_id=request.user.id).order_by('-date')\n return render(request, '../templates/user/history.html', {\n 'history': user_history\n })\n","repo_name":"runarlevi/VN2_hopur54","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"39823507228","text":"#\n#\n# |∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕—\\|∕\n# ⓖ REDUCE CONDITIONAL PROBABILITY DISTRIBUTION. Start with a 'quiescent' conditional-probability distribution. Marginalize out \n# a specified conditioning variable with a specified distribution. Return the reduced probability distribution.\n#\n# Our inputs are:\n# ⧐ digraph, A Networkx digraph object containing the vertex and its predecessors;\n# ⧐ var_states, the variable state;\n# ⧐ vertex, The target vertex for which the reduced conditional probability is desired; and \n# ⧐ cond_dist, A dictionary object containing baseline conditional-probability distributions for the target\n# vertex and its conditioning variables.\n#\n# Our logic performs the following steps.\n# Ⓐ Create a list of predecessors to vertex.\n# Ⓑ Build the conditional-probability table for vertex and for its predecessors for which probability distributions\n# are specified.\n# Ⓒ Merge the CPTs to perform factor multiplication.\n# Ⓓ Perform the factor multiplication. \n# Ⓔ Marginalize out the specified vertices. Use groupby-sum.\n# Ⓕ Return the ID-root-reduced probability-distribution as a list.\n#\n# Ⓐ Create a list of predecessors to vertex.\n#\ndef reduce_cpd(digraph, var_states, vertex, cond_dist):\n\tparent_verts = digraph.predecessors(vertex)\n#\n# Ⓑ Build the conditional-probability table for vertex. Use internally-defined function state_df\n# to build the variable states for each variable. We then use a constant-unit-valued join key\n# to make our merge perform like a cartesian product. We drop unneded attributes from the colum.\n\tbase_cpt = fct.reduce(lambda x, y: pd.merge(left = x, right = y),\n\t\t\t\t\t\t\t\t\t[state_df(state_var = vert,\n\t\t\t\t\t\t\t\t\t\t\tvar_states = var_states.drop('UNMEASURED',axis = 0)['CAT_LEVEL_IDX'])\n\t\t\t\t\t\t\t\t\tfor vert in digraph.predecessors(vertex) + [vertex]])\\\n\t\t\t\t\t.assign(MEAS = cond_dist.get(vertex))\\\n\t\t\t\t\t.drop(labels = 'join_key',\n\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t.rename(columns = {'MEAS' : 'P_' + vertex})\n#\n# Ⓒ Merge the CPTs to perform factor multiplication. We simultaneously build the CPTs for the ID-root vertices.\n\tfactor_prod = fct.reduce(lambda x, y: pd.merge(left = x, right = y),\n\t\t\t\t\t\t\t[base_cpt] +\\\n\t\t\t\t\t\t\t[state_df(state_var = vert,\n\t\t\t\t\t\t\t\t\t\tvar_states = var_states.drop('UNMEASURED',axis = 0)['CAT_LEVEL_IDX'])\\\n\t\t\t\t\t\t\t\t\t.assign(MEAS = cond_dist.get(vert))\\\n\t\t\t\t\t\t\t\t\t.drop(labels = 'join_key',\n\t\t\t\t\t\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t\t\t\t\t.rename(columns = {'MEAS' : 'P_' + vert})\n\t\t\t\t\t\t\tfor vert in list(cond_dist.keys())\n\t\t\t\t\t\t\tif vert != vertex] )\n#\n# Ⓓ Perform the factor multiplication. \n\tfactor_prod = factor_prod.assign(factor_prod = factor_prod[[fact_col for fact_col in factor_prod.columns.tolist()\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif 'P_' in fact_col]].product(axis = 1).tolist())\\\n\t\t\t\t\t\t\t.drop(labels = [fact_col for fact_col in factor_prod.columns.tolist()\n\t\t\t\t\t\t\t\t\t\t\tif 'P_' in fact_col],\n\t\t\t\t\t\t\t\taxis = 1)\\\n\t\t\t\t\t\t\t.rename(columns = {'factor_prod' : 'P_' + vertex})\n#\n# Ⓔ Marginalize out the ID-root vertices.\n\tred_cpt = factor_prod.groupby(by = list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex],\n\t\t\t\t\t\t\t\taxis = 0,\n\t\t\t\t\t\t\t\tas_index = False)['P_' + vertex].sum()\\\n\t\t\t\t\t\t\t[list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex] + ['P_'+ vertex]]\\\n\t\t\t\t\t\t\t.sort_values(by = list(set(parent_verts) - set(list(cond_dist.keys()))) + [vertex],\n\t\t\t\t\t\t\t\t\t\taxis = 0)\n#\n# Ⓕ Return the ID-root-reduced probability-distribution as a list.\n\treturn red_cpt['P_' + vertex].tolist()\n#","repo_name":"hamlett-neil-ur/diagnostic_cognitive_model","sub_path":"PrototypeSubroutinesInR/REDUCE_CPD.py","file_name":"REDUCE_CPD.py","file_ext":"py","file_size_in_byte":3765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"69888996427","text":"# Avazu CTR prediction\n# SGD Logistic regression + hashing trick.\n\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime, date, time\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.feature_extraction import FeatureHasher\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import log_loss\nimport scipy as sp\n\ncols = ['tweet']\n\n# add two columns for hour and weekday\ndef dayhour(timestr):\n d = datetime.strptime(str(x), \"%y%m%d%H\")\n return [float(d.weekday()), float(d.hour)]\n\ndef textClean(s):\n remove = ['\\\\t','\\\\n',' ']\n # s = s.replace(i, Noneunct)\n\n for i in remove:\n s = re.sub(i,'',s)\n s = s.lower()\n s = s.split()\n return s\n\ndef textCleaner(value):\n for i in parenthesis:\n value = value.replace(i, '')\n # print value\n for word in value.split(' '):\n if '#' in word:\n if word[0] == '#':\n value = re.sub(word,\"\",value)\n if '@' in word:\n value = re.sub(word,\"\",value)\n # print word\n if 'http://' in word or 'http' in word or '.com' in word:\n value = re.sub(word,\"\",value)\n # print word\n for i in string.punctuation:\n value = value.replace(i, '')\n return value\n\nfh = FeatureHasher(n_features = 2**20, input_type=\"string\", non_negative=True)\n\n# Train classifier\nclf = MultinomialNB()\ntrain = pd.read_csv(\"newtrain.csv\", chunksize = 50000, iterator = True)\nall_classes = np.array([0, 1])\nfor chunk in train:\n y_train = chunk[\"polarity\"]\n chunk = chunk[cols]\n chunk['tweet'] = textCleaner(chunk['tweet'])\n chunk['tweet'] = textClean(chunk['tweet'])\n Xcat = fh.transform(np.asarray(chunk.astype(str)))\n clf.partial_fit(Xcat, y_train, classes=all_classes)\n \n# Create a submission file\nusecols = cols + [\"id\"]\nX_test = pd.read_csv(\"newtest.csv\", usecols=usecols)\n\nX_enc_test = fh.transform(np.asarray(X_test.astype(str)))\n\ny_act = pd.read_csv(\"newtest.csv\", usecols=['click'])\ny_pred = clf.predict_proba(X_enc_test)[:, 1]\n\nwith open('logloss.txt','a') as f:\n f.write('\\n'+str(log_loss(y_act, y_pred))+'\\tMultinomialNB')\n\nwith open(\"sentiment.csv\", \"w\") as f:\n f.write(\"id,tweet,sentiment\\n\")\n for idx, xid in enumerate(X_test.id):\n f.write(str(xid) + \",\" + str(idx) + ',' + \"{0:.10f}\".format(y_pred[idx]) + \"\\n\")\nf.close()","repo_name":"antiDigest/UserClassify","sub_path":"MultinomialNB.py","file_name":"MultinomialNB.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"8858820470","text":"import unittest\n\nfrom qemu_tcp_wrapper import qemu_oneshot_test\n\nexpected_wrapper_errors_output = [\n 'Truncated RegisterAs: -2',\n 'Truncated WhoIs: -2',\n 'Too short RegisterAs: -3',\n 'Too short WhoIs: -3',\n 'Not found WhoIs: -4',\n]\n\nexpected_too_many = [\n 'RegisterAs before too many: 0',\n 'RegisterAs one too many: -4'\n]\n\nexpected_happypath = [\n 'Basic RegisterAs/WhoIs: Me: 2, Task in question: 2',\n 'Other task registers: Them: 4, Task registered: 4',\n 'Overwriting task: Them: 68, Task registered: 68',\n 'I\\'m 2 and registered Task1 and Task3. Nameserver says Task1 and Task3 are 2 and 2'\n]\n\n\nclass TestNameserver(unittest.TestCase):\n def test_wrapper_errors(self):\n terminal_output = qemu_oneshot_test('test_nameserver_wrapper_errors', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_wrapper_errors_output))\n for i, exp in enumerate(expected_wrapper_errors_output):\n self.assertEqual(lines[i], exp)\n\n def test_too_many(self):\n terminal_output = qemu_oneshot_test('test_nameserver_too_many', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_too_many))\n for i, exp in enumerate(expected_too_many):\n self.assertEqual(lines[i], exp)\n\n def test_happypath(self):\n terminal_output = qemu_oneshot_test('test_nameserver_happypath', '', 10)\n lines = list(filter(lambda x: x != '', terminal_output.split('\\n\\r')))\n self.assertEqual(len(lines), len(expected_happypath))\n for i, exp in enumerate(expected_happypath):\n self.assertEqual(lines[i], exp)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"thechrisu/trains","sub_path":"test/e2e/test_nameserver.py","file_name":"test_nameserver.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"9672394744","text":"'''\n SEIR model plotting\n\n 2020-03-28\n'''\nimport numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pylab as plt\nfrom matplotlib.pylab import rcParams\nplt.style.use('seaborn-colorblind')\n\nN = 60480000 # community size\nt_max = 300 \ntspan = np.linspace(0.0, t_max, t_max + 1)\n\n# parameters to fit\nr0 = 2.67 #Reproduction number\nbeta = 0.205 # infection force\nI0 = 7123 # Init Infected patients\ngamma = 0.154 # average rate or death (Hubei)\nsigma = 1/7 # incubation average (7.0 days)\n\ndef seir(v,t):\n global r0, beta, sigma, gamma\n # v = [S, E, I, R]\n x = beta*v[0]*v[2]/N # infected rate of the day\n dS = -x # Susceptible\n dE = x - sigma * v[1] #Exposed\n dI = sigma * v[1] - gamma * v[2] #Infected\n dR = gamma * v[1] # Removed\n dN = dI +dR\n return np.array([dS, dE, dI, dR, dN])\n\nini_state = [N-I0,I0,0, 0,0]\n\n#rcParams['figure.figsize'] = 12,7\node_int = odeint(seir, ini_state, tspan)\n\nfor n in range(len(ode_int)):\n ode = ode_int[n]\n S = int(ode[0])\n E = int(ode[1])\n I = int(ode[2])\n R = int(ode[3])\n N = int(ode[4])\n print(n,N)\n\n","repo_name":"IchiroYoshida/python_public","sub_path":"covid/calc/italy/seir_out.py","file_name":"seir_out.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"5211538150","text":"from tensorflow.python.keras.utils.data_utils import Sequence\nimport numpy as np\nfrom tqdm import tqdm\nfrom random import shuffle \nimport cv2\nimport os\n\n\n\ndef process_data(data_dir, dog_image_list, cat_image_lst, IMG_SIZE):\n ## Helper for manual_pre_process\n data_df = []\n labels = []\n cat_count, dog_count = 0, 0\n DATA_FOLDER = data_dir\n for img in tqdm(dog_image_list):\n path = os.path.join(DATA_FOLDER, img)\n label = 1\n img = cv2.imread(path, cv2.IMREAD_COLOR)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n data_df.append([np.array(img), np.array(label), path])\n for img in tqdm(cat_image_lst):\n path = os.path.join(DATA_FOLDER, img)\n label = 0\n img = cv2.imread(path, cv2.IMREAD_COLOR)\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n data_df.append([np.array(img), np.array(label), path])\n # DATA_FOLDER = aug_dir\n # for img in tqdm(dog_aug_lst):\n # path = os.path.join(DATA_FOLDER, img)\n # label = 1\n # img = cv2.imread(path, cv2.IMREAD_COLOR)\n # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n # data_df.append([np.array(img), np.array(label), path])\n # for img in tqdm(cat_aug_lst):\n # path = os.path.join(DATA_FOLDER, img)\n # label = 0\n # img = cv2.imread(path, cv2.IMREAD_COLOR)\n # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))\n # data_df.append([np.array(img), np.array(label), path])\n shuffle(data_df)\n return data_df\n\n\ndef manual_pre_process(data_dir, IMG_SIZE, DATA_SAMPLE_SIZE, isTrain=True):\n dog_image_lst = [file for file in os.listdir(data_dir) if 'dog' in file][:int(DATA_SAMPLE_SIZE/2)]\n cat_image_lst = [file for file in os.listdir(data_dir) if 'cat' in file][:int(DATA_SAMPLE_SIZE/2)]\n # dog_aug_lst = [file for file in os.listdir(aug_dir) if 'dog' in file][:int(AUG_SAMPLE_SIZE/2)]\n # cat_aug_lst = [file for file in os.listdir(aug_dir) if 'cat' in file][:int(AUG_SAMPLE_SIZE/2)]\n # dog_image_lst = [file for file in os.listdir(dir) if 'dog' in file]\n # cat_image_lst = [file for file in os.listdir(dir) if 'cat' in file]\n data_df = process_data(data_dir, dog_image_lst, cat_image_lst, IMG_SIZE)\n X = np.array([i[0] for i in data_df]).reshape(-1, IMG_SIZE, IMG_SIZE, 3)\n y = np.array([i[1] for i in data_df])\n files = np.array([i[2] for i in data_df])\n return X, y, files\n\n\n\nclass DatasetSequence(Sequence):\n ## Take the processed data and make it easiy digestible for model training\n\n def __init__(self, x_set, y_set, batch_size, augmentations=None):\n self.x, self.y = x_set, y_set\n self.batch_size = batch_size\n self.augment = augmentations\n\n def __len__(self):\n return int(np.ceil(len(self.x) / float(self.batch_size)))\n\n def __getitem__(self, idx):\n batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n \n if self.augment == None:\n return batch_x, batch_y\n else:\n return np.stack([\n self.augment(image=x)[\"image\"] for x in batch_x\n ], axis=0), np.array(batch_y)","repo_name":"reiffd7/gradcam_cats-dogs","sub_path":"pre_processing.py","file_name":"pre_processing.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72403250505","text":"import re\n\n\ndef main():\n html = input(\"HTML: \")\n print(parse(html))\n\n\ndef parse(s):\n if re.search(r\"<\\/iframe>\", s):\n url_pattern = re.search(\n r\"https?:\\/\\/(www\\.)?youtube\\.com\\/embed\\/(\\w+)\", s)\n if url_pattern:\n split_url = url_pattern.group(2)\n return \"https://youtu.be/\" + split_url\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Damz1/CS50-Python","sub_path":"pset7/watch/watch.py","file_name":"watch.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73605880265","text":"from datetime import datetime\nfrom unittest.mock import MagicMock, patch\n\nfrom airflow import DAG\nfrom airflow.models import TaskInstance\n\n# from airflow_salesforce_plugin.operators import SalesforceAttachmentToS3Operator, SalesforceToFileOperator, SalesforceToS3Operator\n\n\ndef test_salesforce_to_file_operator(\n soql_params, csv_dir, sql_file, salesforce_to_file_operator\n):\n csv_filename = f\"{sql_file.name}.csv\"\n operator = salesforce_to_file_operator.operator\n target = salesforce_to_file_operator.target\n operator.hook = MagicMock()\n # with patch(\"airflow_salesforce_plugin.hooks.salesforce_hook.SalesforceHook\") as mock_hook:\n soql_params = \",\".join(soql_params)\n operator.execute(context={})\n # operator.hook.assert_called_once_with(conn_id=operator.conn_id)\n operator.hook.export.assert_called_once_with(\n sql_file.sql_text, target, soql_params.split(\",\"), True\n )\n","repo_name":"techalchemy/airflow-salesforce-plugin","sub_path":"tests/test_operators.py","file_name":"test_operators.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"17255187495","text":"from manim import *\r\nfrom BinomHelpers import *\r\nimport random\r\n\r\nimport scipy.stats \r\nimport itertools as it\r\n\r\n\r\ndef get_row_of_boxes(height = 0.5, n = 4, **kwargs):\r\n boxes = VGroup()\r\n for _ in range(n):\r\n box = Square(color = LIGHT_GREY, stroke_width = 2, **kwargs)\r\n box.set(height = height)\r\n boxes.add(box)\r\n boxes.arrange(RIGHT, buff = 0.25)\r\n return boxes\r\n\r\ndef get_general_bin_formula():\r\n formula = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula.remove(formula.get_part_by_tex(\"\\\\over\"))\r\n formula.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n return formula\r\n\r\n\r\nclass MultipleChoice(Scene):\r\n def construct(self):\r\n\r\n\r\n self.medicine_question()\r\n self.pass_this_test()\r\n\r\n\r\n def medicine_question(self):\r\n task1 = Tex(\"Frage 1:\", font_size = 60)\\\r\n .to_corner(UL)\\\r\n .set_color_by_gradient(TEAL, TEAL_A, TEAL)\\\r\n .save_state()\r\n uline = Underline(task1, color = DARK_GREY)\r\n\r\n task1.scale(1.6).center()\r\n\r\n body = SVGMobject(SVG_DIR + \"human_body_back\")\r\n body.set(height = 6)\r\n body.to_edge(RIGHT)\r\n gluteus = body[-4:-2]\r\n for muscle in body:\r\n muscle.save_state()\r\n body.center()\r\n\r\n colors = [BLUE, YELLOW, PINK, TEAL]\r\n self.play(DrawBorderThenFill(body, rate_func = rush_into), run_time = 5)\r\n self.play(\r\n AnimationGroup(\r\n *[FadeToColor(body[index], color = random.choice(colors), rate_func = there_and_back) for index in range(1, len(body))], \r\n lag_ratio = 0.02\r\n ), run_time = 4\r\n )\r\n\r\n\r\n # body to side, show number of questions\r\n questions = VGroup(*[Tex(\"Frage \", str(x), \":\", ) for x in range(1, 11)])\r\n questions.arrange(DOWN, aligned_edge = LEFT)\r\n questions.to_corner(UL)\r\n\r\n self.play(\r\n AnimationGroup(\r\n *[Restore(muscle, path_arc = np.pi/3) for muscle in body], lag_ratio = 0.05\r\n ),\r\n LaggedStartMap(FadeIn, questions, shift = 0.25*RIGHT, lag_ratio = 0.05, rate_func = there_and_back), \r\n run_time = 4\r\n )\r\n\r\n\r\n # show question number 1\r\n question = Tex(\"Welche Antwort ist falsch?\\\\\\\\ Der \", \"Musculus gluteus maximus\", \"...\", tex_environment=\"flushleft\")\r\n question.to_corner(UL).shift(2.5*RIGHT)\r\n\r\n self.play(\r\n AnimationGroup(Restore(task1, path_arc = -np.pi/3), Create(uline), lag_ratio = 0.2),\r\n Write(question),\r\n run_time = 2\r\n )\r\n self.wait(0.5)\r\n self.play(\r\n AnimationGroup(*[FadeToColor(mob, PINK) for mob in [question[1], gluteus]], lag_ratio = 0.25),\r\n run_time = 2.5\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n answers_list = [\r\n \"wird vom Nervus gluteus inferior aus \\\\\\\\dem Plexus sacralis innerviert\", \r\n \"hat seinen Ursprung u.a. an der Spina \\\\\\\\iliaca posterior superior\", \r\n \"fällt bei Schädigungen durch das \\\\\\\\Trendelenburg-Zeichen auf\", \r\n \"ist ein wichtiger Extensor im Hüftgelenk \\\\\\\\und ermöglicht das Treppensteigen\"\r\n ]\r\n answers = VGroup(*[Tex(answer, tex_environment=\"flushleft\") for answer in answers_list])\r\n answers.arrange(DOWN, buff = 0.45, aligned_edge = LEFT)\r\n answers.to_edge(LEFT, buff = 1.5)\r\n answers.shift(0.75*DOWN)\r\n\r\n boxes = VGroup(*[Square() for x in range(4)])\r\n for box, answer in zip(boxes, answers):\r\n box.set(height = 0.5)\r\n box.set_color(TEAL_A)\r\n box.next_to(answer, LEFT, buff = 0.75, aligned_edge=UL)\r\n\r\n self.play(FadeIn(boxes, shift = RIGHT, lag_ratio = 0.1), run_time = 2)\r\n self.play(LaggedStartMap(FadeIn, answers, lag_ratio = 0.1), run_time = 2)\r\n self.wait()\r\n\r\n # randomize answer & highlight options\r\n cross = Cross(boxes[0], stroke_color = YELLOW_D, stroke_width = 3)\r\n def randomize_cross(mob):\r\n choices = range(4)\r\n num = random.choice(choices)\r\n mob.move_to(boxes[num])\r\n\r\n for x in range(len(answers)):\r\n highlight = answers[x]\r\n make_dark = [answers[i] for i in range(4) if i != x]\r\n self.play(\r\n FadeToColor(highlight, WHITE),\r\n *[FadeToColor(mob, DARK_GREY) for mob in make_dark], \r\n UpdateFromFunc(cross, randomize_cross),\r\n run_time = 3\r\n )\r\n self.wait()\r\n self.play(FadeToColor(answers, WHITE))\r\n self.wait(3)\r\n\r\n\r\n self.fade_out_group = VGroup(task1, uline, question, answers, cross)\r\n self.first_boxes = boxes\r\n self.body = body\r\n\r\n def pass_this_test(self):\r\n boxes = VGroup(*[self.get_row() for x in range(10)])\r\n boxes.arrange(DOWN, buff = 0.25)\r\n boxes.set_color(TEAL_A)\r\n\r\n for box, index in zip(self.first_boxes, range(4)):\r\n box.generate_target()\r\n box.target.become(boxes[0][index])\r\n\r\n numbers = VGroup(*[Tex(str(num)) for num in range(1, 11)])\r\n for num, row in zip(numbers, boxes):\r\n num.set(height = row.height - 0.25)\r\n num.next_to(row, LEFT, buff = 0.5)\r\n\r\n crosses = VGroup()\r\n for row in boxes:\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n choices = range(4)\r\n value = random.choice(choices)\r\n\r\n cross.move_to(row[value])\r\n crosses.add(cross)\r\n\r\n bools = 3*[False] + 2*[True] + 3*[False] + [True] + [False]\r\n cac = get_checks_and_crosses(bools)\r\n for row, mark in zip(boxes, cac):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.5)\r\n\r\n\r\n self.play(\r\n FadeOut(self.fade_out_group, rate_func = squish_rate_func(smooth, 0, 0.4)),\r\n AnimationGroup(*[MoveToTarget(box) for box in self.first_boxes], lag_ratio = 0.05),\r\n LaggedStartMap(FadeIn, boxes[1:], lag_ratio = 0.1),\r\n run_time = 3\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(\r\n LaggedStartMap(GrowFromCenter, crosses, lag_ratio = 0.1),\r\n FadeIn(numbers, shift = 0.5*RIGHT, lag_ratio = 0.1),\r\n run_time = 2\r\n )\r\n\r\n self.play(FadeIn(cac, shift = 0.5*LEFT, lag_ratio = 0.1), run_time = 1.5)\r\n self.wait()\r\n\r\n\r\n prop = ValueTracker(0)\r\n prop_dec = Integer(edge_to_fix = RIGHT, unit = \"\\\\%\")\r\n prop_dec.set_color(RED)\r\n prop_dec.set(height = 1)\r\n prop_dec.move_to(4*LEFT + 2.5*UP)\r\n prop_dec.add_updater(lambda dec: dec.set_value(prop.get_value()))\r\n prop_dec.add_updater(lambda dec: dec.set_color(self.parameter_to_color(prop.get_value(), 0, 80, [RED, GREEN])))\r\n\r\n mt = Tex(\"mehr als\")\r\n mt.set(width = prop_dec.width)\r\n mt.next_to(prop_dec, UP, buff = 0.1)\r\n\r\n self.add(prop_dec)\r\n self.play(\r\n prop.animate.set_value(80),\r\n Write(mt, rate_func = squish_rate_func(smooth, 0.75, 1)),\r\n run_time = 4\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n certi = self.get_certificate()\r\n certi.next_to(prop_dec, DOWN, buff = 2.5)\r\n\r\n arrow = Arrow(prop_dec.get_bottom(), certi.get_top(), color = YELLOW_D)\r\n self.play(GrowArrow(arrow))\r\n\r\n\r\n self.play(LaggedStartMap(Create, certi, lag_ratio = 0.1), run_time = 2)\r\n self.play(DrawBorderThenFill(self.body.copy(), rate_func = rush_into), run_time = 5)\r\n self.wait(3)\r\n\r\n # functions \r\n def get_row(self, height = 0.5, n = 4):\r\n boxes = VGroup()\r\n for _ in range(n):\r\n box = Square(color = GREY_B, stroke_width = 2)\r\n box.set(height = height)\r\n boxes.add(box)\r\n boxes.arrange(RIGHT, buff = 0.25)\r\n \r\n return boxes\r\n\r\n def get_certificate(self):\r\n rect = Rectangle(width = 2, height = 2*1.414, stroke_width = 1.5)\r\n text = Tex(\"Zertifikat\")\r\n text.set(width = rect.width - 0.2)\r\n text.next_to(rect.get_top(), DOWN, buff = 0.2)\r\n\r\n lines = VGroup(*[Line() for x in range(10)])\r\n for line in lines:\r\n line.set(width = rect.width - 0.3)\r\n line.set_stroke(width = 1)\r\n lines.arrange(DOWN, buff = 0.1)\r\n\r\n passed = Tex(\"Bestanden\")\r\n passed.set_color(C_COLOR)\r\n passed.set(width = rect.width - 0.5)\r\n passed.next_to(rect.get_corner(DR), UL, aligned_edge=RIGHT)\r\n\r\n result = VGroup(rect, text, lines, passed)\r\n return result\r\n\r\n def parameter_to_color(self, value, min, max, colors):\r\n alpha = inverse_interpolate(min, max, value)\r\n index, sub_alpha = integer_interpolate(0, len(colors) - 1, alpha)\r\n\r\n return interpolate_color(colors[index], colors[index + 1], sub_alpha)\r\n\r\n\r\nclass IntroduceFormula(Scene):\r\n def construct(self):\r\n\r\n\r\n self.get_to_three_questions()\r\n self.explain_xek()\r\n self.bernoulli_trail()\r\n\r\n\r\n def get_to_three_questions(self):\r\n formula = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula.scale(1.5)\r\n formula.move_to(0.75*UP)\r\n formula.remove(formula.get_part_by_tex(\"\\\\over\"))\r\n formula.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n name = Tex(\"Bernoulli\", \"$-$\", \"Formel\")\\\r\n .set_color_by_gradient(GREEN, YELLOW_D, RED)\\\r\n .set_fill(GREY, 0.3)\\\r\n .set_stroke(width = 1.5)\\\r\n .set(width = formula.width)\\\r\n .to_edge(DOWN)\r\n\r\n self.play(Write(formula), run_time = 1.5)\r\n self.wait()\r\n sur_rects = VGroup(*[\r\n SurroundingRectangle(formula, buff = buff, stroke_width = 2).set_color([GREEN, YELLOW_D, RED])\r\n for buff in reversed(np.linspace(0.1, 0.55, 11))\r\n ])\r\n self.play(FadeIn(sur_rects, scale = 2, lag_ratio = 0.05), rate_func = lambda t: smooth(1-t), run_time = 2)\r\n self.play(DrawBorderThenFill(name, rate_func = rush_into), run_time = 2)\r\n self.wait()\r\n\r\n\r\n meaning_list = [\r\n \"Anzahl der\\\\\\\\ Erfolge\", \r\n \"Anzahl, die zu\\\\\\\\ $k$ Erfolgen führen\", \r\n \"Wkt. für \\\\\\\\ Erfolge\", \r\n \"Wkt. für \\\\\\\\ Misserfolge\"\r\n ]\r\n targets = [formula[2:5], formula[7:11], formula[12:14], formula[15:]]\r\n meanings = VGroup(*[Tex(mean) for mean in meaning_list])\r\n for tex, target, dir in zip(meanings, targets, [UP, DOWN, UP, DOWN]):\r\n tex.next_to(target, dir, buff = 0.75)\r\n\r\n target_rects = VGroup(*[\r\n DashedVMobject(SurroundingRectangle(obj, color = BLUE_E), num_dashes=50)\r\n for obj in targets\r\n ])\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, meanings, lag_ratio = 0.25, rate_func = there_and_back_with_pause),\r\n LaggedStartMap(Create, target_rects, lag_ratio = 0.25),\r\n run_time = 6\r\n )\r\n self.wait()\r\n\r\n\r\n www = VGroup(*[Tex(word, font_size = 72) for word in [\"Wann?\", \"Wie?\", \"Warum?\"]])\r\n www.arrange(RIGHT, buff = 1)\r\n www.to_edge(UP, buff = 0.75)\r\n self.play(\r\n AnimationGroup(\r\n *[ReplacementTransform(rect, word) for rect, word in zip(target_rects[1:], www)], \r\n lag_ratio = 0.2\r\n ),\r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n\r\n www.generate_target()\r\n www.target.arrange(RIGHT, buff = 2).scale(0.6).to_edge(UP, buff = 0.25)\r\n \r\n formula.save_state()\r\n formula.generate_target()\r\n formula.target.scale(0.5).to_edge(DOWN, buff = 0.25)\r\n \r\n xek = formula[2:5].copy()\r\n self.add(xek)\r\n self.play(\r\n MoveToTarget(www),\r\n MoveToTarget(formula), \r\n FadeOut(name, shift = 3*DOWN), \r\n run_time = 2\r\n )\r\n\r\n self.formula, self.xek, self.dash_rect = formula, xek, target_rects[0]\r\n\r\n def explain_xek(self):\r\n xeks = VGroup(*[MathTex(\"X\", \"=\", str(k)) for k in [0,1,2,3,4,5]])\r\n for tex in xeks:\r\n tex.scale(1.5)\r\n tex[-1].set_color(GREEN_A)\r\n xeks.arrange(DOWN)\r\n xeks[:2].next_to(self.xek, UP, buff = 0.35, aligned_edge=LEFT)\r\n xeks[2:].next_to(self.xek, DOWN, buff = 0.35, aligned_edge=LEFT)\r\n\r\n boxes = VGroup(*[get_row_of_boxes(height = 0.3, n = 4) for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.shift(3.5*RIGHT)\r\n\r\n correct = [1, 2, 2, 0, 3, 3, 1, 2, 0, 0]\r\n cross_places = [0, 2, 3, 1, 2, 3, 1, 0, 2, 3]\r\n bools = [False] + [True] + 3*[False] + 2*[True] + 3*[False]\r\n\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n crosses = VGroup(*[cross.copy() for x in range(10)])\r\n cac = get_checks_and_crosses(bools)\r\n\r\n for mark, row in zip(cac, boxes):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.35)\r\n\r\n for row, i_correct, cross, i_place in zip(boxes, correct, crosses, cross_places):\r\n row[i_correct].generate_target()\r\n row[i_correct].target.set_fill(C_COLOR, 0.4)\r\n cross.move_to(row[i_place])\r\n\r\n self.play(LaggedStartMap(FadeIn, xeks, shift = RIGHT, lag_ratio = 0.1), run_time = 2.5)\r\n self.wait()\r\n self.play(FadeIn(boxes, lag_ratio = 0.1), run_time = 2)\r\n self.play(FadeIn(crosses, lag_ratio = 0.1), run_time = 2)\r\n self.play(\r\n AnimationGroup(\r\n *[MoveToTarget(row[i_correct]) for row, i_correct in zip(boxes, correct)], \r\n lag_ratio = 0.1\r\n ), \r\n run_time = 2\r\n )\r\n self.bring_to_front(crosses)\r\n self.wait()\r\n\r\n self.play(FadeIn(cac, shift = LEFT, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n num_of_succ = Tex(\"\\\\#\", CMARK_TEX, \" = \", \"3\", tex_template = myTemplate)\r\n num_of_succ.scale(1.5)\r\n num_of_succ.next_to(xeks[3], RIGHT, buff = 1)\r\n num_of_succ[:2].set_color(C_COLOR)\r\n self.play(self.dash_rect.animate.move_to(xeks[3]), run_time = 2)\r\n self.play(Write(num_of_succ))\r\n self.wait(2)\r\n\r\n\r\n self.boxes, self.crosses = boxes, crosses\r\n self.test_group = VGroup(boxes, crosses, cac)\r\n\r\n self.play(\r\n FadeOut(self.dash_rect), \r\n FadeOut(num_of_succ),\r\n FadeOut(xeks),\r\n FadeOut(self.xek), \r\n run_time = 3\r\n )\r\n\r\n def bernoulli_trail(self):\r\n first_row = get_row_of_boxes(height = 0.75)\r\n first_row[1].set_fill(C_COLOR, 0.4)\r\n first_row.move_to(2.25*LEFT + UP)\r\n cross = Cross(first_row[1], stroke_color = YELLOW_D, stroke_width = 3)\r\n\r\n bern_trail = Tex(\"Bernoulli\", \"$-$\", \"Kette\", \"?\", font_size = 60)\r\n bern_trail.next_to(first_row, UP)\r\n\r\n self.play(Write(bern_trail))\r\n self.wait()\r\n\r\n\r\n self.play(FadeIn(first_row, shift = 0.25 * RIGHT, lag_ratio = 0.1), run_time = 1.5)\r\n self.play(GrowFromCenter(cross), run_time = 1.5)\r\n self.wait()\r\n\r\n self.play(cross.animate.move_to(first_row[0]), run_time = 1.5)\r\n cmark = Tex(CMARK_TEX, color = C_COLOR, tex_template = myTemplate)\r\n xmark = Tex(XMARK_TEX, color = X_COLOR, tex_template = myTemplate)\r\n for mark, box in zip([xmark, cmark], first_row[:2]):\r\n mark.match_width(first_row[0])\r\n mark.next_to(box, DOWN)\r\n\r\n self.play(FadeIn(VGroup(cmark, xmark), shift = 0.5*UP, lag_ratio = 0.2), run_time = 2)\r\n self.wait(2)\r\n\r\n\r\n self.play(\r\n AnimationGroup(\r\n TransformFromCopy(first_row, self.boxes[0]), \r\n TransformFromCopy(cross, self.crosses[0]),\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n arrow_kwargs = {\"color\": PINK, \"stroke_width\": 2.5, \"tip_length\": 0.2}\r\n arrows = VGroup(*[CurvedArrow(self.boxes[k].get_left(), self.boxes[k+1].get_left(), **arrow_kwargs) for k in range(9)])\r\n arrows.shift(0.15*LEFT)\r\n\r\n braces = VGroup(*[Brace(self.boxes[:k], LEFT, buff = 0.75) for k in range(10)])\r\n braces_nums = VGroup(*[MathTex(str(k)).next_to(brace, LEFT) for k, brace in zip(range(10), braces)])\r\n\r\n for k in range(len(self.boxes) - 1):\r\n if k < 9:\r\n added_anims = [Create(arrows[k])]\r\n else:\r\n added_anims = []\r\n self.play(\r\n *added_anims,\r\n GrowFromCenter(self.crosses[k]), \r\n ReplacementTransform(braces[k], braces[k + 1]),\r\n ReplacementTransform(braces_nums[k], braces_nums[k + 1]),\r\n run_time = 0.5\r\n )\r\n self.wait(0.1)\r\n final_brace = Brace(self.boxes, LEFT, buff = 0.75)\r\n final_number = MathTex(\"10\").next_to(final_brace, LEFT)\r\n self.play(\r\n ReplacementTransform(braces[-1], final_brace), \r\n ReplacementTransform(braces_nums[-1], final_number), \r\n run_time = 0.5\r\n )\r\n self.wait(2)\r\n\r\n\r\n length = MathTex(\"n\", \"=\", \"10\")\r\n prob = MathTex(\"p\", \"=\", \"0{,}25\")\r\n prob[0].set_color(YELLOW_D)\r\n\r\n for mob in length, prob:\r\n mob.next_to(cmark, DOWN, buff = 0.75)\r\n mob.align_to(bern_trail, LEFT)\r\n prob.shift(0.75*DOWN)\r\n\r\n self.play(FadeIn(length, shift = LEFT), run_time = 3)\r\n self.wait()\r\n self.play(FadeIn(prob, shift = LEFT), run_time = 3)\r\n self.wait()\r\n\r\n sur_rects = VGroup(*[SurroundingRectangle(mob, color = BLUE_E) for mob in [VGroup(bern_trail, prob), self.formula]])\r\n self.play(Create(sur_rects[0]), run_time = 5)\r\n self.wait(0.5)\r\n self.play(ReplacementTransform(sur_rects[0], sur_rects[1]), run_time = 3)\r\n self.wait(3)\r\n\r\n\r\nclass ConnectToBinomTree(MovingCameraScene):\r\n def construct(self):\r\n\r\n self.setup_old_scene()\r\n self.binom_tree()\r\n self.pfad_probability(pfad_nums = [1, 4, 11, 24])\r\n self.calc_probability1()\r\n self.pfad_probability(pfad_nums = [0, 3, 8, 18])\r\n self.calc_probability2()\r\n self.bring_back_formula()\r\n\r\n def setup_old_scene(self):\r\n formula = get_general_bin_formula()\r\n formula.scale(0.75)\r\n formula.to_corner(DL)\r\n formula.save_state()\r\n formula.center().to_edge(DOWN, buff = 0.25)\r\n rect = SurroundingRectangle(formula, color = BLUE_E)\r\n\r\n www = VGroup(*[Tex(word, font_size = 72) for word in [\"Wann?\", \"Wie?\", \"Warum?\"]])\r\n www.arrange(RIGHT, buff = 2)\r\n www.scale(0.6).to_edge(UP, buff = 0.25)\r\n\r\n\r\n self.add(formula, rect, www)\r\n self.wait(2)\r\n\r\n\r\n formula.generate_target()\r\n formula.target.scale(4/3 * 1.5).center()\r\n rect.generate_target()\r\n rect.target.set(width = formula.target.width + 3).set(height = formula.target.height + 0.2).center()\r\n\r\n self.play(\r\n MoveToTarget(formula),\r\n MoveToTarget(rect, rate_func = rush_into),\r\n run_time = 3\r\n )\r\n self.play(\r\n FadeOut(rect, scale = 4, run_time = 1), \r\n FadeOut(www[0], shift = UP, run_time = 1.5),\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(formula[12:].animate.shift(UP), run_time = 1.5)\r\n self.play(FadeOut(www[1], shift = UP))\r\n self.wait()\r\n\r\n self.play(\r\n formula[7:11].animate(rate_func = there_and_back_with_pause).shift(DOWN),\r\n FadeOut(www[2], shift = UP, rate_func = squish_rate_func(smooth, 0.5, 1)),\r\n formula[12:].animate(rate_func = squish_rate_func(smooth, 0.5, 1)).shift(DOWN),\r\n run_time = 4\r\n )\r\n self.wait()\r\n self.play(Restore(formula), run_time = 1.5)\r\n\r\n self.formula = formula\r\n\r\n def binom_tree(self):\r\n boxes = VGroup(*[get_row_of_boxes(height = 0.5, n = 4) for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.to_corner(UR)\r\n boxes.shift(0.75*LEFT)\r\n\r\n correct = [1, 2, 2, 0, 3, 3, 1, 2, 0, 0]\r\n cross_places = [0, 2, 3, 1, 2, 3, 1, 0, 2, 3]\r\n\r\n cross = Cross(boxes[0][0], stroke_color = YELLOW_D, stroke_width = 3)\r\n crosses = VGroup(*[cross.copy() for x in range(10)])\r\n\r\n bools = [False] + [True] + 3*[False] + 2*[True] + 3*[False]\r\n cac = get_checks_and_crosses(bools)\r\n for mark, row in zip(cac, boxes):\r\n mark.match_height(row)\r\n mark.next_to(row, RIGHT, buff = 0.35)\r\n\r\n for row, true_place, cross, cross_place in zip(boxes, correct, crosses, cross_places):\r\n row[true_place].generate_target()\r\n row[true_place].target.set_fill(C_COLOR, 0.4)\r\n cross.move_to(row[cross_place])\r\n\r\n self.play(LaggedStartMap(FadeIn, boxes, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n self.play(LaggedStartMap(GrowFromCenter, crosses, lag_ratio = 0.05), run_time = 3)\r\n self.wait()\r\n\r\n\r\n # build the tree\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n #tree_nums = tree.get_pfad_nums()\r\n #self.add(tree_nums)\r\n\r\n path_indis = [0, 3, 8, 18]\r\n added_anims = [FocusOn(tree.lines[0], rate_func = squish_rate_func(smooth, 0.5, 1), run_time = 3)]\r\n for k in range(4):\r\n\r\n self.play(\r\n MoveToTarget(boxes[k][correct[k]]), \r\n *added_anims\r\n )\r\n self.wait()\r\n\r\n added_anims = [] # make sure not to Focus On line every time\r\n\r\n path_indi = path_indis[k]\r\n line = tree.lines[path_indi]\r\n self.play(Create(line), run_time = 1.5)\r\n\r\n cx_mark = tree.cx_marks[path_indi]\r\n self.play(GrowFromCenter(cx_mark), run_time = 1.5)\r\n self.wait()\r\n self.wait(2)\r\n\r\n\r\n # swap decisions\r\n new_cross_places = [1, 0, 2, 3, 1, 0, 2, 1, 3, 1]\r\n for cross, row, c_place in zip(crosses, boxes, new_cross_places):\r\n cross.generate_target()\r\n cross.target.move_to(row[c_place])\r\n\r\n self.play(LaggedStartMap(MoveToTarget, crosses, path_arc = np.pi/3, lag_ratio = 0.1), run_time = 5)\r\n self.wait()\r\n\r\n path_indis = [1,4,11,24]\r\n for k in range(4):\r\n self.play(Circumscribe(boxes[k][correct[k]], time_width = 0.75, fade_out = True, color = PINK))\r\n\r\n path_indi = path_indis[k]\r\n line = tree.lines[path_indi]\r\n self.play(Create(line), run_time = 1.5)\r\n\r\n cx_mark = tree.cx_marks[path_indi]\r\n self.play(GrowFromCenter(cx_mark), run_time = 1.5)\r\n self.wait()\r\n self.wait(2)\r\n\r\n tree_copy = tree.copy()\r\n self.play(FadeIn(tree_copy, lag_ratio = 0.1), run_time = 3)\r\n self.add(tree)\r\n self.remove(tree_copy)\r\n self.wait(3)\r\n\r\n # show big tree just to remove it afterwards\r\n big_tree = BinomTree(width = tree.width * 2.57, height = tree.height * 1.02, num_events = 10)\r\n big_tree.next_to(tree.get_left(), RIGHT, buff = 0)\r\n\r\n frame = self.camera.frame\r\n frame.save_state()\r\n\r\n self.play(\r\n FadeIn(big_tree.lines[30:], lag_ratio = 0.01), \r\n frame.animate.set(width = big_tree.width + 2).move_to(big_tree.get_center()),\r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n self.play(\r\n Restore(frame), \r\n FadeOut(big_tree.lines[30:], lag_ratio = 0.01), \r\n run_time = 4\r\n )\r\n self.wait()\r\n\r\n\r\n # get rid of boxes[4:]\r\n top_boxes = VGroup(boxes[:4], crosses[:4]).copy()\r\n top_boxes.generate_target()\r\n top_boxes.target.scale(0.6).to_corner(UL, buff = 0.2)\r\n self.remove(*boxes[:4], *crosses[:4])\r\n self.add(top_boxes)\r\n self.play(\r\n LaggedStartMap(FadeOut, boxes[4:], shift = 3*RIGHT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeOut, crosses[4:], shift = 3*RIGHT, lag_ratio = 0.1),\r\n MoveToTarget(top_boxes, path_arc = np.pi),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n self.boxes, self.tree = top_boxes, tree\r\n\r\n def pfad_probability(self, pfad_nums):\r\n tree, boxes = self.tree, self.boxes\r\n\r\n # pfad_nums = [1,4,11,24]\r\n moving_dot = Dot(point = tree.lines[0].get_start()).set_fill(opacity = 0)\r\n pfad = tree.get_pfad(pfad_nums)\r\n trace = TracedPath(moving_dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n\r\n\r\n self.add(trace)\r\n self.play(MoveAlongPath(moving_dot, pfad, run_time = 3))\r\n self.remove(trace)\r\n self.wait()\r\n\r\n tree_probs = tree.get_pfad_prob(texp = \"0.25\", texq = \"0.75\", use_prob_values=True)\r\n pfad_probs = [tree_probs[k] for k in pfad_nums]\r\n\r\n for k, num in enumerate(pfad_nums):\r\n self.play(\r\n FadeToColor(tree.lines[num], color = YELLOW_D),\r\n FadeIn(pfad_probs[k], scale = 0.5),\r\n )\r\n self.wait(0.5)\r\n self.wait()\r\n\r\n def calc_probability1(self):\r\n tree = self.tree\r\n\r\n calc = MathTex(\"0{,}25\", \"\\\\cdot\", \"0{,}75\", \"\\\\cdot\", \"0{,}25\", \"\\\\cdot\", \"0{,}75\")\\\r\n .next_to(tree.lines[24], RIGHT, buff = 0.75)\r\n\r\n self.play(ShowIncreasingSubsets(calc), run_time = 3)\r\n self.wait()\r\n\r\n calc2 = MathTex(\"0{,}25^\", \"2\", \"\\\\cdot\", \"0{,}75^\", \"2\")\\\r\n .move_to(calc, aligned_edge=DOWN)\r\n\r\n self.play(TransformMatchingTex(calc, calc2))\r\n self.wait(0.5)\r\n self.play(Circumscribe(calc2, color = YELLOW_D, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait()\r\n\r\n self.result1 = calc2\r\n\r\n def calc_probability2(self):\r\n tree = self.tree\r\n calc = MathTex(\"0{,}75\", \"\\\\cdot\", \"0{,}25\", \"\\\\cdot\", \"0{,}75\", \"\\\\cdot\", \"0{,}75\")\\\r\n .next_to(tree.lines[18], RIGHT, buff = 0.75)\r\n\r\n self.play(ShowIncreasingSubsets(calc), run_time = 3)\r\n self.wait()\r\n\r\n calc2 = MathTex(\"0{,}25^\", \"1\", \"\\\\cdot\", \"0{,}75^\", \"3\")\\\r\n .move_to(calc, aligned_edge=DOWN)\r\n\r\n self.play(TransformMatchingTex(calc, calc2))\r\n self.wait(0.5)\r\n self.play(Circumscribe(calc2, color = YELLOW_D, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait(2)\r\n\r\n self.result2 = calc2\r\n\r\n def bring_back_formula(self):\r\n formula, result1, result2 = self.formula, self.result1, self.result2\r\n\r\n formula.add_background_rectangle(buff = 0.25, opacity = 0.85)\r\n self.bring_to_front(formula)\r\n\r\n dest = result2.get_part_by_tex(\"\\\\cdot\").get_center() + 2*DOWN\r\n self.play(\r\n formula.animate.scale(4/3 * 1.25, about_point = formula[15].get_center()).shift(dest - formula[15].get_center()), \r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n cac1 = get_checks_and_crosses([True, False, True, False], width = result1.width)\r\n cac1.next_to(result1, UP)\r\n\r\n cac2 = get_checks_and_crosses([False, True, False, False], width = result2.width)\r\n cac2.next_to(result2, UP)\r\n\r\n\r\n\r\n\r\n # transform c_marks, x_marks into exponents first example\r\n self.play(FadeIn(cac1, shift = DOWN, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n self.play(\r\n FadeToColor(result1[0], YELLOW_D),\r\n FadeToColor(result1[1], C_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result1[1], path_arc = np.pi/3) for check in cac1 if check.positive],\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 3\r\n )\r\n self.wait(0.5)\r\n\r\n\r\n self.play(\r\n FadeToColor(result1[4], X_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result1[4], path_arc = -np.pi/3) for check in cac1 if not check.positive],\r\n lag_ratio = 0.2\r\n ),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n # transform c_marks, x_marks into exponents first example\r\n self.play(FadeIn(cac2, shift = DOWN, lag_ratio = 0.1), run_time = 3)\r\n self.wait()\r\n\r\n\r\n self.play(\r\n FadeToColor(result2[0], YELLOW_D),\r\n FadeToColor(result2[1], C_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result2[1], path_arc = np.pi/3) for check in cac2 if check.positive],\r\n lag_ratio = 0.2\r\n ), \r\n run_time = 1.5\r\n )\r\n self.wait(0.5)\r\n\r\n self.play(\r\n FadeToColor(result2[4], X_COLOR), \r\n AnimationGroup(\r\n *[FadeOut(check, target_position = result2[4], path_arc = -np.pi/3) for check in cac2 if not check.positive],\r\n lag_ratio = 0.2\r\n ),\r\n run_time = 3\r\n )\r\n self.wait()\r\n\r\n\r\n\r\n # # highlight Exponent for success and failure\r\n self.play(Circumscribe(formula[14], color = PINK, shape = Circle, time_width = 0.75, run_time = 3))\r\n self.wait()\r\n\r\n self.play(\r\n *[\r\n Circumscribe(expo, color = PINK, shape = Circle, fade_in = True ,time_width = 0.75, run_time = 3) \r\n for expo in [result1[1], result2[1]]\r\n ],\r\n )\r\n self.wait()\r\n\r\n\r\n self.play(Circumscribe(formula[-1], color = PINK, shape = Circle, time_width = 0.75, fade_out = True, run_time = 3))\r\n self.wait()\r\n self.play(\r\n *[\r\n Circumscribe(expo, color = PINK, shape = Circle, time_width = 0.75, run_time = 3) \r\n for expo in [result1[4], result2[4], formula[-1]]\r\n ],\r\n )\r\n self.wait()\r\n\r\n\r\n # Fadeout result2 + this is not P(X=2)\r\n self.play(FadeOut(result2, shift = 2*RIGHT), run_time = 2)\r\n self.wait()\r\n\r\n not_equal = MathTex(\"\\\\neq\", color = RED)\\\r\n .scale(1.25)\\\r\n .next_to(result1, DOWN)\r\n\r\n text = MathTex(\"P\", \"(\", \"X\", \"=\", \"2\", \")\")\\\r\n .scale(1.25)\\\r\n .next_to(not_equal, DOWN)\r\n\r\n self.play(Write(not_equal))\r\n self.play(Write(text))\r\n self.wait(2)\r\n\r\n\r\n self.play(Circumscribe(formula[8:12], color = PINK, fade_out = True, run_time = 3))\r\n self.wait(3)\r\n\r\n\r\nclass NChooseK(MovingCameraScene):\r\n def construct(self):\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = self.tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n\r\n self.choose_2_out_of_4()\r\n self.choose_1_out_of_4()\r\n self.how_many_are_there()\r\n\r\n\r\n def choose_2_out_of_4(self):\r\n tree = self.tree\r\n\r\n pfad_lists = [[1, 5, 12, 26], [1, 4, 11, 24], [1, 4, 10, 23], [0, 3, 9, 20], [0, 3, 8, 19], [0, 2, 7, 17]]\r\n two_pfads = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n self.add(two_pfads)\r\n self.add(tree.circles, tree.cx_marks)\r\n\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.wait(2)\r\n\r\n\r\n # Moving Camera to tree \r\n self.camera.frame.save_state()\r\n self.play(\r\n self.camera.frame.animate.set(height = tree.height).move_to(tree).shift(1.75*RIGHT), \r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n\r\n # Add checks and crosses according to tree\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists, two_pfads)\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[0], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[0], add_anim)\r\n self.wait()\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[1], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[1], add_anim)\r\n self.wait()\r\n\r\n\r\n for cac in cac_group[2:]:\r\n self.play(ShowIncreasingSubsets(cac))\r\n self.wait()\r\n\r\n c_list1 = [0,0,0,1,1,2] # this is super hacky\r\n c_list2 = [1,2,3,2,3,3] # ....\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list1)], \r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n brace_tex = brace.get_tex(str(choose(4,2)))\r\n\r\n self.play(Create(brace))\r\n self.play(Write(brace_tex))\r\n self.wait(2)\r\n\r\n self.clear()\r\n\r\n def choose_1_out_of_4(self):\r\n tree = self.tree\r\n\r\n pfad_lists = [[1, 4, 10, 22], [0, 3, 8, 18], [0, 2, 7, 16], [0, 2, 6, 15]]\r\n one_pfads = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n\r\n self.add(one_pfads)\r\n self.add(tree.circles, tree.cx_marks)\r\n\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.wait(2)\r\n\r\n # Add checks and crosses according to tree\r\n bool_lists = self.get_bool_lists(4, 1)\r\n cac_group = self.get_orientated_cac_group(bool_lists, one_pfads)\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[0], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[0], add_anim)\r\n self.wait()\r\n\r\n add_anim = [ShowIncreasingSubsets(cac_group[1], run_time = 3)]\r\n self.animate_trace_along_path(pfad_lists[1], add_anim)\r\n self.wait()\r\n\r\n for cac in cac_group[2:]:\r\n self.play(ShowIncreasingSubsets(cac))\r\n self.wait()\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n brace_tex = brace.get_tex(str(choose(4,1)))\r\n\r\n self.play(Create(brace))\r\n self.play(Write(brace_tex))\r\n self.wait(2)\r\n\r\n\r\n self.num_of_combs = brace_tex\r\n\r\n def how_many_are_there(self):\r\n\r\n hm = Tex(\"Wie viele Möglichkeiten?\")\\\r\n .align_to(self.num_of_combs, RIGHT)\\\r\n .shift(2.5*UP)\r\n\r\n hm2 = Tex(\"1 Erfolg auf 4 mögliche Plätze zu verteilen\")\\\r\n .set(width = hm.width - 0.75)\\\r\n .next_to(hm, DOWN, buff = 0.1)\\\r\n .set_color(GREY_B)\r\n\r\n arrow = CurvedArrow(self.num_of_combs.get_corner(UR), hm.get_corner(DR), color = YELLOW_D, stroke_width = 3, tip_length = 0.25)\r\n self.play(Create(arrow), run_time = 1.5)\r\n self.play(Write(hm))\r\n self.play(FadeIn(hm2))\r\n self.wait(3)\r\n\r\n\r\n # functions\r\n\r\n def get_remove_pfad_nums_list(self, pfad_lists):\r\n # Create one list containing all number --> this include duplicates\r\n combine_pfad_lists = [x for lists in pfad_lists for x in lists]\r\n\r\n # Create target list --> add only those, you are not already in that list\r\n remove_duplicates = []\r\n [remove_duplicates.append(x) for x in combine_pfad_lists if x not in remove_duplicates]\r\n remove_duplicates.sort()\r\n\r\n final_numbers = []\r\n [final_numbers.append(x) for x in list(range(30)) if x not in remove_duplicates]\r\n\r\n return final_numbers\r\n\r\n def get_bool_lists(self, n, k):\r\n combs = list(it.combinations(range(n), k))\r\n bool_lists = [\r\n [i in comb for i in range(n)]\r\n for comb in combs\r\n ]\r\n return bool_lists\r\n\r\n def get_orientated_cac_group(self, bool_lists, pfads):\r\n cac_group = VGroup(*[\r\n get_checks_and_crosses(bool_list, width = 1.5) \r\n for bool_list in bool_lists\r\n ])\r\n\r\n for cac, pfad in zip(cac_group, pfads):\r\n cac.set(height = self.tree.cx_marks[0].height) # compare height with first cx_mark\r\n cac.next_to(pfad.get_end(), RIGHT, buff = 1) # place cac next to end of pfad line\r\n\r\n return cac_group\r\n\r\n def animate_trace_along_path(self, pfad_list, added_anims = None):\r\n moving_dot = Dot(point = self.tree.lines[0].get_start()).set_fill(opacity = 0)\r\n pfad = self.tree.get_pfad(pfad_list)\r\n trace = TracedPath(moving_dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n\r\n if added_anims is None:\r\n added_anims = []\r\n\r\n self.add(trace)\r\n self.play(\r\n MoveAlongPath(moving_dot, pfad, run_time = 3), \r\n *added_anims\r\n )\r\n self.remove(trace)\r\n\r\n\r\nclass BinomialCoefficient(Scene):\r\n def construct(self):\r\n\r\n slots = VGroup(*[Line() for x in range(4)])\r\n for slot in slots:\r\n slot.set(width = 1)\r\n slot.set_stroke(width = 3)\r\n slot.set_color(GREY_B)\r\n slots.arrange(RIGHT, buff = 1)\r\n slots.shift(0.5*UP)\r\n\r\n\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists)\r\n\r\n for cac in cac_group:\r\n for cx, slot in zip(cac, slots):\r\n cx.match_width(slot)\r\n cx.next_to(slot, UP)\r\n\r\n first_row = cac_group[0]\r\n\r\n comb_counter = 1\r\n comb_dec = Integer(comb_counter)\\\r\n .scale(2.5)\\\r\n .next_to(slots, DOWN, buff = 1.5)\\\r\n .add_updater(lambda dec: dec.set_value(comb_counter))\r\n\r\n self.play(\r\n Create(slots, lag_ratio = 0.2, run_time = 2), \r\n LaggedStartMap(DrawBorderThenFill, first_row, lag_ratio = 0.2, run_time = 3), \r\n Write(comb_dec, run_time = 1)\r\n )\r\n self.wait()\r\n\r\n for k in range(1, len(cac_group)):\r\n comb_counter +=1\r\n\r\n new_cac = cac_group[k]\r\n first_row.become(new_cac)\r\n self.wait(0.75)\r\n self.wait()\r\n\r\n equals = MathTex(\"=\", font_size = 96)\r\n equals.next_to(comb_dec, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", \"2\", \"\\\\right)\", font_size = 96)\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n bin_tex = Tex(\"Bi\", \"nomialkoeffizient\", font_size = 72)\r\n bin_tex[0].set_color(YELLOW_D)\r\n bin_tex[1].set_color(BLUE_B)\r\n bin_tex.next_to(bin_coeff, DOWN)\r\n\r\n self.play(FadeIn(equals, shift=LEFT))\r\n self.play(\r\n Write(bin_coeff), \r\n Write(bin_tex)\r\n )\r\n self.wait()\r\n\r\n bin_speech = Tex(\"Vier\\\\\\\\\", \"über\\\\\\\\\", \"Zwei\", font_size = 96)\r\n bin_speech[1].scale(0.5)\r\n bin_speech.match_height(bin_coeff)\r\n bin_speech.next_to(bin_coeff, RIGHT)\r\n\r\n self.play(LaggedStartMap(FadeIn, bin_speech, rate_func = there_and_back_with_pause), run_time = 3)\r\n self.wait()\r\n\r\n self.play(\r\n *[mob.animate.shift(5*LEFT) for mob in [comb_dec, equals, bin_coeff]], \r\n run_time = 1.5\r\n )\r\n self.wait()\r\n\r\n bin_def = MathTex(\"{\" + \"n\", \"!\", \"\\\\over\", \"k\", \"!\", \"\\\\cdot\", \"(\", \"n\", \"-\", \"k\", \")\", \"!\" + \"}\", font_size = 96)\r\n bin_def.next_to(bin_coeff, RIGHT, buff = 1)\r\n bin_def.align_to(bin_tex, ORIGIN)\r\n\r\n bin_def2 = MathTex(\"{\" + \"4\", \"!\", \"\\\\over\", \"2\", \"!\", \"\\\\cdot\", \"(\", \"4\", \"-\", \"2\", \")\", \"!\" + \"}\", font_size = 96)\r\n bin_def2.next_to(bin_coeff, RIGHT, buff = 1)\r\n bin_def2.align_to(bin_tex, ORIGIN)\r\n\r\n self.play(Write(bin_def), run_time = 2)\r\n self.wait()\r\n\r\n self.play(Transform(bin_def, bin_def2, lag_ratio = 0.2), run_time = 2)\r\n self.wait(2)\r\n\r\n rect = SurroundingRectangle(comb_dec).set_color([YELLOW_D, BLUE])\r\n self.play(Create(rect), run_time = 3)\r\n self.wait(0.5)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n\r\n # functions \r\n def get_bool_lists(self, n, k):\r\n combs = list(it.combinations(range(n), k))\r\n bool_lists = [\r\n [i in comb for i in range(n)]\r\n for comb in combs\r\n ]\r\n return bool_lists\r\n\r\n def get_orientated_cac_group(self, bool_lists):\r\n cac_group = VGroup(*[\r\n get_checks_and_crosses(bool_list, width = 1.5) \r\n for bool_list in bool_lists\r\n ])\r\n return cac_group\r\n\r\n\r\nclass ShowAllBinCoeffInBinomTree(NChooseK):\r\n def construct(self):\r\n\r\n self.show_coeffs()\r\n self.bring_back_2()\r\n self.add_them_all_up()\r\n\r\n\r\n def show_coeffs(self):\r\n tree_config = {\"width\": 6, \"height\": 6, \"num_events\": 4}\r\n tree = self.tree = BinomTree(**tree_config)\r\n tree.shift(2*LEFT + 0.5*UP)\r\n\r\n # tree_nums = tree.get_pfad_nums()\r\n # self.add(tree, tree_nums)\r\n\r\n\r\n self.pfad_lists_0 = [[0, 2, 6, 14]]\r\n self.pfad_lists_1 = [[1, 4, 10, 22], [0, 3, 8, 18], [0, 2, 7, 16], [0, 2, 6, 15]]\r\n self.pfad_lists_2 = [[1, 5, 12, 26], [1, 4, 11, 24], [1, 4, 10, 23], [0, 3, 9, 20], [0, 3, 8, 19], [0, 2, 7, 17]]\r\n self.pfad_lists_3 = [[0, 3, 9, 21], [1, 4, 11, 25], [1, 5, 12, 27], [1, 5, 13, 28]]\r\n self.pfad_lists_4 = [[1, 5, 13, 29]]\r\n\r\n all_pfad_lists = [lists for lists in [self.pfad_lists_0, self.pfad_lists_1, self.pfad_lists_2, self.pfad_lists_3, self.pfad_lists_4]]\r\n\r\n for x, pfad_lists in enumerate(all_pfad_lists):\r\n pfade = VGroup(*[tree.get_pfad(numbers) for numbers in pfad_lists])\r\n cx_remove = self.get_remove_pfad_nums_list(pfad_lists)\r\n\r\n\r\n bool_lists = self.get_bool_lists(4, x)\r\n cac_group = self.get_orientated_cac_group(bool_lists, pfade)\r\n if x == 4:\r\n cac_group.scale_to_fit_width(width = 1, about_point = cac_group.get_left())\r\n\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n num_of_pfads = brace.get_tex(str(choose(4, x)))\r\n\r\n equals = MathTex(\"=\").next_to(num_of_pfads, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", str(x), \"\\\\right)\")\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n if x == 0:\r\n self.play(Create(pfade), run_time = 1.5)\r\n self.add(tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.play(\r\n AnimationGroup(\r\n *[ShowIncreasingSubsets(cac) for cac in cac_group], \r\n lag_ratio = 0.2\r\n ), \r\n run_time = 2\r\n )\r\n self.remove(*[cac for cac in cac_group])\r\n self.add(cac_group)\r\n self.play(\r\n Create(brace), \r\n Write(num_of_pfads), \r\n FadeIn(equals, shift = UP),\r\n FadeIn(bin_coeff, shift = LEFT), \r\n run_time = 2\r\n )\r\n else:\r\n self.add(pfade, tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.add(cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n self.wait(2)\r\n\r\n\r\n self.remove(pfade, tree.circles, tree.cx_marks, cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n self.clear()\r\n\r\n\r\n\r\n # # Moving Camera to tree \r\n # self.camera.frame.save_state()\r\n # self.play(\r\n # self.camera.frame.animate.set(height = tree.height).move_to(tree).shift(1.75*RIGHT), \r\n # run_time = 2\r\n # )\r\n # self.wait()\r\n\r\n\r\n # # Add checks and crosses according to tree\r\n # \r\n\r\n def bring_back_2(self):\r\n tree = self.tree\r\n\r\n pfade = VGroup(*[tree.get_pfad(numbers) for numbers in self.pfad_lists_2])\r\n cx_remove = self.get_remove_pfad_nums_list(self.pfad_lists_2)\r\n\r\n bool_lists = self.get_bool_lists(4, 2)\r\n cac_group = self.get_orientated_cac_group(bool_lists, pfade)\r\n\r\n brace = Brace(cac_group, RIGHT, color = GREY)\r\n num_of_pfads = brace.get_tex(str(choose(4, 2)))\r\n\r\n equals = MathTex(\"=\").next_to(num_of_pfads, RIGHT)\r\n\r\n bin_coeff = MathTex(\"\\\\left(\", \"4\", \"\\\\over\", \"2\", \"\\\\right)\")\r\n bin_coeff.remove(bin_coeff.get_part_by_tex(\"\\\\over\"))\r\n bin_coeff.next_to(equals, RIGHT)\r\n\r\n\r\n self.add(pfade, tree.circles, tree.cx_marks)\r\n self.remove(*[tree.cx_marks[k] for k in cx_remove])\r\n self.add(cac_group, brace, num_of_pfads, equals, bin_coeff)\r\n\r\n bin_tex1 = Tex(\"Binomialkoeffizient\")\\\r\n .set(width = bin_coeff.width * 3.5)\\\r\n .next_to(bin_coeff, UP, buff = 0.5)\\\r\n .set_color(BLUE_D)\r\n bin_tex2 = Tex(\"Anzahl der Pfade, die\\\\\\\\\", \"zu $k$ Erfolgen führen\")\\\r\n .set(width = bin_tex1.width)\\\r\n .next_to(bin_tex1, UP)\\\r\n .set_color(GREY)\r\n\r\n self.add(bin_tex1, bin_tex2)\r\n\r\n self.wait(2)\r\n\r\n\r\n\r\n moving_dots = VGroup(*[Dot(point = self.tree.lines[0].get_start()).set_fill(opacity = 0) for x in range(choose(4, 2))])\r\n traces = VGroup(*[\r\n TracedPath(dot.get_center, dissipating_time=0.75, stroke_opacity=[0, 1, 0], stroke_color = YELLOW_D, stroke_width = 8)\r\n for dot in moving_dots\r\n ])\r\n\r\n self.add(traces)\r\n self.play(\r\n AnimationGroup(\r\n *[MoveAlongPath(dot, pfad, run_time = 5) for dot, pfad in zip(moving_dots, pfade)], \r\n lag_ratio = 0.1\r\n ), \r\n run_time = 5\r\n )\r\n self.remove(traces)\r\n self.wait(2)\r\n\r\n\r\n self.cac_group, self.coeff = cac_group, num_of_pfads\r\n\r\n def add_them_all_up(self):\r\n bg = Rectangle(width = self.tree.width + 0.5, height = self.tree.height + 0.5)\r\n bg.set_stroke(width = 0)\r\n bg.set_fill(BLACK, 0.8)\r\n bg.move_to(self.tree)\r\n\r\n cac_group = self.cac_group.copy()\r\n cac_group.generate_target()\r\n cac_group.target.scale(1.5).arrange(DOWN, buff = 0.5).move_to(bg.get_center())\r\n\r\n prob = MathTex(\"P\", \"(\", \"X\", \"=\", \"2\", \")\")\r\n prob.set_color_by_tex_to_color_map({\"2\": C_COLOR})\r\n prob.next_to(cac_group.target, LEFT, buff = 1.5, aligned_edge = UP)\r\n prob.shift(0.05*UP)\r\n\r\n self.play(\r\n FadeIn(bg),\r\n Create(prob, rate_func = squish_rate_func(smooth, 0.5, 1)),\r\n )\r\n self.wait(0.25)\r\n\r\n self.play(MoveToTarget(cac_group, lag_ratio = 0.2), run_time = 3)\r\n\r\n\r\n ps = VGroup()\r\n left_braces = VGroup()\r\n right_braces = VGroup()\r\n pluses = VGroup()\r\n\r\n for cac in cac_group:\r\n left_brace = MathTex(\"(\")\\\r\n .set(height = cac.height + 0.1)\\\r\n .next_to(cac, LEFT, buff = 0.1)\r\n right_brace = MathTex(\")\")\\\r\n .set(height = cac.height + 0.1)\\\r\n .next_to(cac, RIGHT, buff = 0.1)\r\n p = MathTex(\"P\")\\\r\n .match_height(cac)\\\r\n .next_to(left_brace, LEFT, buff = 0.1)\r\n plus = MathTex(\"+\")\\\r\n .next_to(p, LEFT)\\\r\n .set_color(BLUE_D)\r\n\r\n ps.add(p)\r\n left_braces.add(left_brace)\r\n right_braces.add(right_brace)\r\n pluses.add(plus)\r\n\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, right_braces, shift = LEFT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeIn, left_braces, shift = RIGHT, lag_ratio = 0.1),\r\n LaggedStartMap(FadeIn, ps, shift = RIGHT, lag_ratio = 0.1),\r\n run_time = 1.5\r\n )\r\n\r\n equals = MathTex(\"=\").move_to(pluses[0])\r\n pluses[0] = equals\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, pluses, scale = 0.2, lag_ratio = 0.1),\r\n run_time = 1.5\r\n )\r\n self.wait(2)\r\n\r\n\r\n line = Line(stroke_width = 3, color = GREY)\r\n line.set(width = VGroup(pluses, right_braces).height + 0.5)\r\n line.next_to(VGroup(pluses, right_braces), DOWN, buff = 0.2)\r\n self.play(Create(line))\r\n\r\n\r\n coeff = self.coeff.copy()\r\n coeff.generate_target()\r\n coeff.target.scale(1.25).next_to(pluses, DOWN, buff = 0.7).set_color(BLUE_D)\r\n\r\n\r\n rest = MathTex(\"\\\\cdot \\\\\", \"0.25^\", \"2\", \"\\\\cdot\", \"0.75^\", \"2\")\r\n rest.scale(1.25)\r\n rest[-1].set_color(X_COLOR)\r\n rest[2].set_color(C_COLOR)\r\n rest[1].set_color(YELLOW_D)\r\n rest.next_to(coeff.target, RIGHT, aligned_edge = DOWN)\r\n\r\n self.play(Write(rest[:3]))\r\n c_list1 = [0,0,0,1,1,2] # this is super hacky\r\n c_list2 = [1,2,3,2,3,3] # ....\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list1)], \r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, c_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait()\r\n\r\n self.play(Write(rest[3:]))\r\n x_list1 = [2,1,1,0,0,0]\r\n x_list2 = [3,3,2,3,2,1]\r\n self.play(\r\n AnimationGroup(\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, x_list1)],\r\n *[row[k].animate(rate_func = there_and_back).shift(0.2*UP) for row, k in zip(cac_group, x_list2)],\r\n lag_ratio = 0.05\r\n ),\r\n run_time = 2\r\n )\r\n self.wait(2)\r\n\r\n\r\n self.play(MoveToTarget(coeff, path_arc = -np.pi/4), run_time = 3)\r\n rect = SurroundingRectangle(VGroup(coeff, rest)).set_color([BLUE_D, RED, GREEN, YELLOW_D])\r\n self.play(Create(rect), run_time = 3)\r\n self.wait(0.25)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\nclass CalculateProbability(Scene):\r\n def construct(self):\r\n\r\n self.setup_scene()\r\n self.prob_2_out_of_4()\r\n self.prob_1_out_of_4()\r\n self.prob_8_out_of_10()\r\n\r\n\r\n def setup_scene(self):\r\n # title = Tex(\"Wahrscheinlichkeit für \", \"2\", \" Erfolge bei 4 Wiederholungen\")\r\n # title.set(width = config[\"frame_width\"] - 3)\r\n # title.set_color_by_gradient(YELLOW_D, BLUE_B, LIGHT_GREY, BLUE_B, YELLOW_D)\r\n # title.to_edge(UP)\r\n\r\n # uline = Line(color = DARK_GREY, stroke_width = 2)\r\n # uline.set_length(config[\"frame_width\"])\r\n # uline.next_to(title, DOWN, buff = 0.1)\r\n\r\n # self.add(title, uline)\r\n\r\n formula_gen = MathTex(\r\n \"P\", \"(\", \"X\", \"=\", \"k\", \")\", \"=\",\r\n \"\\\\left(\", \"{\" + \"n\", \"\\\\over\", \"k\" + \"}\", \"\\\\right)\", \"\\\\cdot\", \r\n \"p^\", \"k\", \"\\\\cdot\", \r\n \"(\", \"1\", \"-\", \"p\", \")^\", \"{\" + \"n-k\" + \"}\"\r\n )\r\n formula_gen.scale(1.5)\r\n formula_gen.move_to(1.25*UP + 0.5*LEFT)\r\n formula_gen.remove(formula_gen.get_part_by_tex(\"\\\\over\"))\r\n formula_gen.set_color_by_tex_to_color_map({\"p\": YELLOW_D, \"k\": C_COLOR, \"n-k\": X_COLOR})\r\n\r\n\r\n meaning_list = [\r\n \"Anzahl der\\\\\\\\ Erfolge\", \r\n \"Anzahl der Pfade, die\\\\\\\\ zu $k$ Erfolgen führen\", \r\n \"Wkt. für \\\\\\\\ Erfolge\", \r\n \"Wkt. für \\\\\\\\ Misserfolge\"\r\n ]\r\n targets = [formula_gen[2:5], formula_gen[7:11], formula_gen[12:14], formula_gen[15:]]\r\n meanings = VGroup(*[Tex(mean) for mean in meaning_list])\r\n for tex, target, dir in zip(meanings, targets, [UP, DOWN, UP, DOWN]):\r\n tex.next_to(target, dir, buff = 0.75)\r\n\r\n target_rects = VGroup(*[\r\n DashedVMobject(SurroundingRectangle(obj, color = BLUE_E), num_dashes=50)\r\n for obj in targets\r\n ])\r\n\r\n self.play(\r\n LaggedStartMap(FadeIn, meanings, lag_ratio = 0.25),\r\n LaggedStartMap(Create, target_rects, lag_ratio = 0.25),\r\n run_time = 4\r\n )\r\n self.wait()\r\n self.play(\r\n FadeIn(formula_gen, lag_ratio = 0.1), run_time = 3 \r\n )\r\n self.wait()\r\n\r\n target_positions = [\r\n formula_gen[2:5].get_center() + 1.5*UP, \r\n formula_gen[7:11].get_center() + 1.5*UP + 0.5*LEFT, \r\n formula_gen[12:14].get_center() + 1.5*UP + 0.25*DOWN, \r\n formula_gen[15:].get_center() + 1.5*UP + 0.25*DOWN\r\n ]\r\n\r\n for meaning, pos in zip(meanings, target_positions):\r\n meaning.generate_target()\r\n meaning.target.scale(0.5).move_to(pos)\r\n \r\n self.play(LaggedStartMap(MoveToTarget, meanings, lag_ratio = 0.2), run_time = 1.5)\r\n self.wait()\r\n\r\n\r\n self.formula_gen = formula_gen\r\n\r\n def prob_2_out_of_4(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 4, 0.25, 2\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.play(Write(formula[:6]))\r\n self.wait(0.5)\r\n self.play(FadeIn(formula[6:11]))\r\n self.wait()\r\n\r\n self.play(Write(formula[11:14]))\r\n self.wait()\r\n self.play(Write(formula[14:]))\r\n self.wait(2)\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(FadeIn(approx, shift = LEFT))\r\n self.play(Write(result))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n self.formula, self.result = formula, result\r\n\r\n def prob_1_out_of_4(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 4, 0.25, 1\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.play(FadeTransform(self.formula, formula, lag_ratio = 0.2), run_time = 2)\r\n self.wait()\r\n\r\n self.play(Flash(self.formula[-2], color = RED, flash_radius = 0.2))\r\n self.wait()\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(Transform(self.result, result, lag_ratio = 0.1))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\n self.old_formula = formula\r\n\r\n def prob_8_out_of_10(self):\r\n formula_gen = self.formula_gen\r\n\r\n n, p, k = 10, 0.25, 8\r\n\r\n formula = get_binom_formula(n, p, k)\r\n formula.scale(1.5)\r\n formula.next_to(formula_gen, DOWN, buff = 1)\r\n\r\n formula[:6].align_to(formula_gen[:6], RIGHT)\r\n formula[6:].align_to(formula_gen[6:], LEFT)\r\n\r\n self.remove(self.old_formula)\r\n self.wait()\r\n\r\n\r\n self.play(Write(formula[:6]))\r\n self.wait(0.5)\r\n self.play(FadeIn(formula[6:11]))\r\n self.wait()\r\n\r\n self.play(Write(formula[11:14]))\r\n self.wait()\r\n self.play(Write(formula[14:]))\r\n self.wait(2)\r\n\r\n self.play(Flash(formula[-1], color = RED, flash_radius = 0.2))\r\n self.wait()\r\n\r\n approx = MathTex(\"\\\\approx\")\\\r\n .scale(1.5)\\\r\n .to_edge(DOWN, buff = 1)\\\r\n .shift(1.5*RIGHT)\r\n\r\n result_num = get_binom_result(n, p, k)\r\n result = MathTex(str(result_num)).scale(1.5).next_to(approx, RIGHT)\r\n\r\n self.play(Transform(self.result, result))\r\n rect = SurroundingRectangle(VGroup(approx, result)).set_color([YELLOW_D, GREEN, RED])\r\n self.play(Create(rect), run_time = 3)\r\n self.play(FadeOut(rect, scale = 4))\r\n self.wait(3)\r\n\r\n\r\nclass DealingWithMoreThen8(Scene):\r\n def construct(self):\r\n title1 = Tex(\"Einzelwahrscheinlichkeit\", font_size = 72, color = GREY_B)\r\n title1.move_to(2*LEFT + 3*UP)\r\n\r\n eq1 = MathTex(\"P\", \"(\", \"X\", \"=\", \"8\", \")\", \"=\", \"\\\\ldots\", font_size = 72)\r\n eq1.next_to(title1, DOWN, buff = 0.5, aligned_edge=LEFT)\r\n eq1.shift(0.5*RIGHT)\r\n\r\n title2 = Tex(\"??? Wahrscheinlichkeit\", font_size = 72, color = GREY_B)\r\n title2.move_to(2*LEFT + 0.5*DOWN)\r\n title2.align_to(title1, LEFT)\r\n\r\n eq2 = MathTex(\"P\", \"(\", \"X\", \">\", \"8\", \")\", \"=\", \"\\\\ldots\", font_size = 72)\r\n eq2.next_to(title2, DOWN, buff = 0.5, aligned_edge=LEFT)\r\n eq2.shift(0.5*RIGHT)\r\n\r\n exp1 = Tex(\"genau \", \"8\", font_size = 60)\\\r\n .next_to(eq1, DOWN, buff = 0.5, aligned_edge=LEFT)\\\r\n .shift(3*RIGHT)\r\n\r\n exp2 = Tex(\"mehr als \", \"8\", font_size = 60)\\\r\n .next_to(eq2, DOWN, buff = 0.5, aligned_edge=LEFT)\\\r\n .shift(3*RIGHT)\r\n\r\n for eq in eq1, eq2:\r\n eq[3].set_color(MAROON)\r\n eq[4].set_color(C_COLOR)\r\n\r\n for exp in exp1, exp2:\r\n exp[0].set_color(MAROON)\r\n exp[1].set_color(C_COLOR)\r\n\r\n arrow1 = CurvedArrow(exp1.get_left() + 0.15*LEFT, eq1[3].get_bottom() + 0.15*DOWN, angle=-TAU / 4, tip_length = 0.25)\r\n arrow2 = CurvedArrow(exp2.get_left() + 0.15*LEFT, eq2[3].get_bottom() + 0.15*DOWN, angle=-TAU / 4, tip_length = 0.25)\r\n\r\n\r\n self.add(title1, eq1, eq2)\r\n self.wait()\r\n\r\n self.play(Create(arrow1))\r\n self.play(Write(exp1))\r\n self.wait()\r\n\r\n\r\n self.play(Create(arrow2))\r\n self.play(Write(exp2))\r\n self.wait()\r\n\r\n # self.play(Write(title2))\r\n # self.wait()\r\n\r\n tbc = Tex(\"...to be continued...\", color = GREY)\\\r\n .next_to(VGroup(eq2, exp2), RIGHT, buff = 1)\r\n for x in range(3):\r\n new_tbc = tbc.copy()\r\n self.play(FadeIn(new_tbc, rate_func = there_and_back), run_time = 2)\r\n self.wait(3)\r\n\r\n\r\nclass NextVideo(Scene):\r\n def construct(self):\r\n fsr = ScreenRectangle(height = 5, stroke_width = 3, stroke_color = DARK_GREY)\r\n fsr.to_edge(DOWN, buff = 1)\r\n\r\n title = Tex(\"Nächstes Video\", font_size = 72, color = GREY_A)\r\n title.to_edge(UP)\r\n\r\n uline = Line(color = GREY, stroke_width = 3)\r\n uline.set(width = config[\"frame_width\"] - 5)\r\n uline.next_to(title, DOWN, buff = 0.1)\r\n\r\n self.play(\r\n Create(uline, run_time = 3),\r\n Write(title, run_time = 1),\r\n Create(fsr, run_time = 3),\r\n )\r\n self.wait(5)\r\n\r\n\r\nclass Thumbnail(Scene):\r\n def construct(self):\r\n formula = get_general_bin_formula()\r\n formula.set(width = config[\"frame_width\"] - 1)\r\n formula.add_background_rectangle(buff = 0.2, opacity = 0.85, stroke_opacity = 1, stroke_width = 4, stroke_color = PINK)\r\n formula.shift(1.5*DOWN)\r\n\r\n body = SVGMobject(SVG_DIR + \"human_body_back\")\r\n body.set(height = 7)\r\n body.to_corner(UL)\r\n\r\n colors = [BLUE, YELLOW, PINK, TEAL]\r\n for n in range(1, len(body)):\r\n color = random.choice(colors)\r\n body[n].set_fill(color, 0.3)\r\n body[0].set_fill(opacity = 0).set_stroke(width = 1)\r\n body[-4:-2].set_fill(PINK, 0.75)\r\n\r\n\r\n tree = BinomTree(width = 0.4*config[\"frame_width\"], height = body.height / 2.25, num_events=3)\r\n tree.next_to(formula, UP)\r\n\r\n boxes = VGroup(*[get_row_of_boxes() for x in range(10)])\r\n boxes.arrange(DOWN)\r\n boxes.set(height = body.height)\r\n boxes.to_corner(UR)\r\n\r\n correct = [1, 2, 2, 0, 3, 1, 1, 2, 0, 2]\r\n for row, index in zip(boxes, correct):\r\n row[:].set_fill(X_COLOR, 0.15)\r\n row[index].set_fill(C_COLOR, 0.3)\r\n\r\n title = Tex(\"Die Bernoulli\", \"$-$\",\"Formel\")\r\n title.to_edge(UP)\r\n title.scale(1.5)\r\n title.set_fill(WHITE, opacity = 0.2)\r\n title.set_stroke(width = 1.5)\r\n\r\n\r\n self.add(body, tree, boxes, title, formula)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"visual-x/manim-projects","sub_path":"2022/Binomial Distribution/Binom-03-BFormula.py","file_name":"Binom-03-BFormula.py","file_ext":"py","file_size_in_byte":61613,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"81"} +{"seq_id":"70252839304","text":"from nthp_api.nthp_build import schema\n\n\nclass TestPersonGraduated:\n def test_from_year_1999_estimated(self):\n assert schema.PersonGraduated.from_year(\n 1999, estimated=True\n ) == schema.PersonGraduated(\n year_title=\"1999\", year_decade=199, year_id=\"98_99\", estimated=True\n )\n\n def test_from_year_2000_actual(self):\n assert schema.PersonGraduated.from_year(\n 2000, estimated=False\n ) == schema.PersonGraduated(\n year_title=\"2000\", year_decade=199, year_id=\"99_00\", estimated=False\n )\n\n def test_from_year_2001_estimated(self):\n assert schema.PersonGraduated.from_year(\n 2001, estimated=True\n ) == schema.PersonGraduated(\n year_title=\"2001\", year_decade=200, year_id=\"00_01\", estimated=True\n )\n","repo_name":"newtheatre/nthp-api","sub_path":"tests/test_nthp_build/test_schema.py","file_name":"test_schema.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"32853480586","text":"#El usuario ingresa la fecha en un formato específico.\n\nfecha= input(\"Ingrese la fecha actual en el siguiente formato : \")\nweek= [\"lunes\", \"martes\", \"miercoles\", \"jueves\", \"viernes\", \"sabado\", \"domingo\"]\ntotal= 0\namount= \"\"\ntariff= \"\"\nday= \"\"\nnum_day= \"\"\nnum_month= \"\"\naux= \"\"\n\n\n#Se compruba que el usuiario haya brindado los datos solicitados y el formato correcto de ésto.\n\nhelper= fecha.split(\", \")\nif (len(helper) != 2):\n exit(\"Por favor, ingrese la fecha en el formato indicado\")\nelse:\n day= helper[0].lower()\n aux= helper[1]\n if (aux[1].isdigit() != True):\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n else:\n num_day= aux[0] + aux[1]\n if (len(aux) != 4):\n if (aux[4].isdigit() != True):\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n else:\n num_month= aux[3] + aux[4]\n else:\n exit(\"Por favor, ingrese el número del día usando dos numeros. (Ejemplo: 06/09)\")\n\nfor i in week:\n if (day == i):\n aux= True\n break\n else:\n aux= False\n\nif (aux == True):\n print(\"Nombre del día: Aceptado\")\nelif (aux == False):\n exit(\"Día inexistente, por favor, ingrese un día válido\")\naux= \"\"\n\nif (num_day.isdigit()):\n num_day= int(num_day)\n if (num_day <= 31 and num_day > 0):\n print(\"Número del día: Aceptado\")\n else:\n exit(\"Número del día inválido\")\nelse:\n exit(\"Por favor, asegurece de que lo que ingresó sea un número\")\n\nif (num_month.isdigit()):\n num_month= int(num_month)\n if (num_month <= 12 and num_month > 0):\n print(\"Número del mes: Aceptado\")\n else:\n exit(\"Número del mes inválido\")\nelse:\n exit(\"Por favor, asegurece de que lo que ingresó sea un número\")\n\n#El usuario ingresa los datos relacionados a la actividad del día.\n\nprint(\"¿El día de hoy a qué actividad corresponde? (A= Exámenes de Nivel Inicial, Intermedio, Avanzado; B= Práctica Hablada; C= Inglés para Pasajeros)\")\nchoose= input().lower()\n\nif (choose == \"a\"):\n choose= input(\"Ingrese la cantidad de alumnos que aprobaron el exámen: \")\n if (choose.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose_2= input(\"Ingrese la cantidad de alumnos que desaprobaron el exámen: \")\n if (choose_2.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose= int(choose)\n choose_2= int(choose_2)\n average= (choose + choose_2) / 2\n print(f\"El porcentaje de alumnos aprobados es de {average}%\")\nelif (choose == \"b\"):\n choose= input(\"Ingrese el porcentaje de asistencia de la clase (sin el símbolo <%>): \")\n if (choose.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n choose= int(choose)\n if (choose > 50):\n print(\"Asistío la mayoría\")\n else:\n print(\"No asistió la mayoría\")\nelif (choose == \"c\"):\n aux_d= int(helper[1][1])\n aux_m= int(helper[1][4])\n if ((aux_d == 1) and ((aux_m == 1) or (aux_m == 7))):\n print(\"Comienzo de nuevo ciclo\")\n amount= input(\"Ingrese la cantidad de alumnos del nuevo ciclo: \")\n if (amount.isdigit() != True):\n exit(\"Por favor, ingrese un número entero\")\n else:\n amount= int(amount)\n tariff= input(\"Ingrese el arancel por acada alumno: \")\n if (tariff.isdigit() != True):\n exit(\"Por favor, ingrese un número real\")\n else:\n tariff= int(tariff)\n total= tariff * amount\n print(f\"El ingreso total es de {total}$\")\n else:\n print(\"error\")\nelse:\n exit(\"Por favor, ingrese una de las opciones propuestas, es decir, , ó \")","repo_name":"Joako64110/TrabajosS1-ProgramacionI-Comision4","sub_path":"Ejercicio en clase - Condicionales (R).py","file_name":"Ejercicio en clase - Condicionales (R).py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"40186232286","text":"import sys, time\nimport multiprocessing\nDELAY = 0.1\nDISPLAY = [ '|', '/', '-', '\\\\' ]\ndef spinner_func(before='', after=''):\n write, flush = sys.stdout.write, sys.stdout.flush\n pos = -1\n while True:\n pos = (pos + 1) % len(DISPLAY)\n msg = before + DISPLAY[pos] + after\n write(msg); flush()\n write('\\x08' * len(msg))\n time.sleep(DELAY)\ndef long_computation():\n # emulate a long computation\n time.sleep(2)\n","repo_name":"ottacom/infinitylxd","sub_path":"client-commands/spinner.py","file_name":"spinner.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73673404746","text":"import cv2\nimport numpy as np\nimport os\nimport torch.utils.data as dutils\nfrom torchvision.transforms import Normalize\nimport torch\nfrom loguru import logger\n\nfrom human_models.smplLayer import SMPL\nfrom commons.keypoints import dset_to_body_model, get_part_idxs\nfrom commons.keyps_utils import mapping_keypoints\nfrom commons.im_utils import rgb_preprocessing\nfrom commons.bbox_utils import keyps_to_bbox, bbox_to_center_scale\nfrom commons.keyps_utils import j2d_processing\nfrom commons.human_models_utils import pose_processing, convert_aa_to_rot_mat_SMPL, aa_SMPLX\n\nclass ThreedpwEval(dutils.Dataset):\n def __init__(self, cfg, phase='test'):\n super(ThreedpwEval, self).__init__()\n dset_conf = cfg['3dpw-eval']\n if phase=='test':\n data_path = os.path.join(dset_conf['npz_dir'], '3dpw_test.npz')\n elif phase=='validation':\n data_path = os.path.join(dset_conf['npz_dir'], '3dpw_valid.npz')\n data = np.load(data_path, allow_pickle=True)\n data = {key: data[key] for key in data.keys()} \n self.root_dir = dset_conf['imgs_dir']\n self.imgs_name = np.asarray(data['imgname'], dtype=np.string_)\n # self.smplx = self.data['smplx']\n self.poses = data['pose']\n self.shapes = data['shape']\n self.scales = data['scale']\n self.centers = data['center']\n self.genders = data['gender']\n \n self.len_data = len(self.imgs_name)\n mean = [0.485, 0.456, 0.406]\n std =[0.229, 0.224, 0.225]\n \n self.norm = Normalize(mean=mean, std=std)\n self.smpl_neutral = SMPL('data/human_models/smpl/', gender='neutral', create_transl=False)\n self.smpl_male = SMPL('data/human_models/smpl/', gender='male', create_transl=False)\n self.smpl_female = SMPL('data/human_models/smpl/', gender='female', create_transl=False)\n self.SMPL_J_regressor = torch.from_numpy(np.load('data/J_regressor_h36m.npy')).float()\n if phase=='test':\n logger.debug('Load 3DPW test dataset ')\n elif phase=='validation':\n logger.debug('Load 3DPW validation dataset ')\n def __len__(self):\n return self.len_data\n def get_data(self, img_fn, center, scale):\n flip = 0 \n pn = np.ones(3) \n rot = 0\n sc = 1 \n try:\n img = cv2.imread(img_fn)[:,:,::-1].copy()\n except TypeError:\n print(img_fn)\n orig_shape = np.array(img.shape)[:2]\n\n img = rgb_preprocessing(img, center, scale * sc, rot, flip, pn)\n return img\n def __getitem__(self, index):\n item = {}\n\n '''------------Load from list-----------------'''\n img_fn = self.imgs_name[index].decode('utf-8')\n pose = self.poses[index]\n shape = self.shapes[index]\n center = self.centers[index]\n scale = self.scales[index]\n\n '''--------------Process data------------------'''\n img_fn = os.path.join(self.root_dir, img_fn)\n shape = np.array(shape).astype(np.float32)\n pose = np.array(pose).astype(np.float32)\n raw_pose = pose\n pose = pose.reshape(-1, 3)\n gp = pose[0]\n \n img = self.get_data(img_fn, center, scale)\n\n '''------------Make it tensor--------------'''\n img = torch.from_numpy(img).float()\n img = self.norm(img)\n pose_param = convert_aa_to_rot_mat_SMPL(gp, pose)\n shape = torch.from_numpy(shape).float()\n pose = torch.from_numpy(pose).float()\n raw_pose = torch.from_numpy(raw_pose).float()\n shape = shape.unsqueeze(dim=0)\n body_pose = pose_param['body_pose'].unsqueeze(dim=0)\n global_orient = pose_param['global_pose'].unsqueeze(dim=0)\n #--Get SMPL from GT\n if self.genders[index][0] =='m':\n human_mesh = self.smpl_male(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n elif self.genders[index][0] =='f':\n human_mesh = self.smpl_female(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n else:\n human_mesh = self.smpl_neutral(betas=shape, body_pose=body_pose, \n global_orient=global_orient, pose2rot=False)\n vertices = human_mesh.vertices\n reg_smpl = self.SMPL_J_regressor[None, :].expand(vertices.shape[0], -1, -1)\n lsp_14_joints = torch.matmul(reg_smpl, vertices)\n item['img'] = img \n item['j3d'] = lsp_14_joints[0]\n # item['shape'] = shape\n # item['global_orient'] = pose_param['global_pose']\n # item['body_pose'] = pose_param['body_pose']\n # item['raw_pose'] = raw_pose\n item['gender'] = self.genders[index][0]\n return item ","repo_name":"joshuajano/SSHHMA","sub_path":"datasets/threedpw_eval.py","file_name":"threedpw_eval.py","file_ext":"py","file_size_in_byte":4831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"42901615216","text":"#!/usr/bin/env python\n\"\"\"\nbuilds the .json source files for all base16 themes\n\n$0 clone: clones them into the current directory, before building\n$0 : only builds\n\"\"\"\n\n\nimport requests\nimport yaml, os, sys, json\nimport threading\n\nall = 'https://raw.githubusercontent.com/chriskempson/base16-schemes-source/main/list.yaml'\n\n\ndef get_yaml(url):\n s = requests.get(url).text\n return yaml.unsafe_load(s)\n\n\ndef clone(r):\n os.system(f'git clone \"{r}\"')\n\n\ndef collect(d):\n for fn in os.listdir(d):\n if fn.rsplit('.', 1)[-1] in {'yaml', 'yml'}:\n sr = open(f'{d}/{fn}').read()\n s = yaml.unsafe_load(sr)\n fnr = fn.rsplit('.', 1)[0] + '.json'\n if 'base00' in s and 'base0F' in s:\n with open(fnr, 'w') as fd:\n fd.write(json.dumps(s, indent=2, sort_keys=True))\n\n\ndef main(clone=True):\n if clone:\n clone_all()\n\n for d in os.listdir('.'):\n if os.path.exists(f'./{d}/.git'):\n collect(d)\n\n\ndef clone_all():\n y = get_yaml(all)\n t = [threading.Thread(target=clone, args=(r,)) for r in y.values()]\n [i.start() for i in t]\n [i.join() for i in t]\n\n\nif __name__ == '__main__':\n main(clone='clone' in sys.argv)\n","repo_name":"axiros/terminal_markdown_viewer","sub_path":"mdv/b16/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":1737,"dataset":"github-code","pt":"81"} +{"seq_id":"26297334495","text":"import base64\nimport os\nimport random\n\nfrom flask import request, g, has_request_context\n\nfrom .delayed_deferred import defer\nimport event_bus\n\n\nEVENT_BUS_PARTITIONS = ''\nAPI_SERVER = ''\n\n\ndef queue_deferred(*args, **kwargs):\n if has_request_context():\n queue_deferred_after_request(*args, **kwargs)\n else:\n queue_deferred_eventbus(*args, **kwargs)\n\n\ndef queue_deferred_after_request(*args, **kwargs):\n if os.getenv('ENVIRONMENT', 'development') == 'test':\n return None\n\n task = _build_event_bus_task(*args, **kwargs)\n delayed_tasks = getattr(g, 'delayed_tasks', [])\n delayed_tasks.append(task)\n g.delayed_tasks = delayed_tasks\n\n\ndef queue_deferred_eventbus(*args, **kwargs):\n if os.getenv('ENVIRONMENT', 'development') == 'test':\n return None\n\n task = _build_event_bus_task(*args, **kwargs)\n event_bus.send_task(task)\n\n\ndef _build_event_bus_task(*args, **kwargs):\n reserved_args = ['countdown', 'eta', 'name', 'target', 'queue', 'retry_options', 'key', 'max_retry', 'timeout']\n taskargs = dict((x, kwargs.pop(('_%s' % x), None)) for x in reserved_args)\n queue = taskargs.get('queue', None)\n func = args[0]\n url = '/_ah/eb_queue/deferred_flask'\n if hasattr(func, '__name__'):\n url = url + '/{}'.format(func.__name__)\n taskargs['_url'] = url\n payload = defer(*args, **kwargs)\n\n key = taskargs.get('key', None)\n if key is None:\n key = random.randint(0, EVENT_BUS_PARTITIONS)\n\n task = {\n 'payload': base64.encodestring(payload).decode(),\n 'topic': queue,\n 'key': str(key),\n 'url': API_SERVER + url,\n }\n max_retry = taskargs.get('max_retry', None)\n if max_retry is not None:\n task['max_retry'] = max_retry\n timeout = taskargs.get('timeout', None)\n if timeout:\n task['timeout'] = timeout\n\n countdown = taskargs.get('countdown', None)\n if countdown:\n task['delay'] = str(countdown * 1000)\n return task","repo_name":"gotitinc/code-samples","sub_path":"misc/eventbus/queue_deferred.py","file_name":"queue_deferred.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"69821725706","text":"from pathlib import Path\nfrom pytest import fixture, raises\n\nfrom fizzbuzz import fizzbuzz\n\ndivisible_by_3 = set(range(0, 100, 3))\ndivisible_by_5 = set(range(0, 100, 5))\n\n\ndef test_divisible_by_3_only():\n for number in divisible_by_3 - divisible_by_5:\n suffix = fizzbuzz.classify(number)\n assert suffix == \"fizz\"\n\n\ndef test_divisible_by_5_only():\n for number in divisible_by_5 - divisible_by_3:\n suffix = fizzbuzz.classify(number)\n assert suffix == \"buzz\"\n\n\ndef test_divisible_by_3_and_5():\n for number in divisible_by_3.intersection(divisible_by_5):\n suffix = fizzbuzz.classify(number)\n assert suffix == \"fizzbuzz\"\n\n\ndef test_divisible_by_neither_3_nor_5():\n divisible_by_neither = set(range(0, 100)) - divisible_by_3 - divisible_by_5\n for number in divisible_by_neither:\n suffix = fizzbuzz.classify(number)\n assert not suffix\n\n\ndef test_append():\n assert fizzbuzz.append(\"0\") == \"0 fizzbuzz\"\n assert fizzbuzz.append(\"1\") == \"1\"\n assert fizzbuzz.append(\"2\") == \"2\"\n assert fizzbuzz.append(\"3\") == \"3 fizz\"\n assert fizzbuzz.append(\"4\") == \"4\"\n assert fizzbuzz.append(\"5\") == \"5 buzz\"\n assert fizzbuzz.append(\"15\") == \"15 fizzbuzz\"\n\n\n@fixture\ndef fixture_files(request):\n test_dir = Path(request.module.__file__).parent\n files_dir = Path(test_dir, 'fixture_files')\n text_files = files_dir.glob(\"*.txt\")\n return {text_file.name: text_file for text_file in text_files}\n\n\ndef assert_files_equal(a_file, b_file):\n with open(a_file) as a:\n a_text = a.read()\n\n with open(b_file) as b:\n b_text = b.read()\n\n assert a_text == b_text\n\n\ndef test_classify_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'classify_out.txt')\n fizzbuzz.classify_lines(fixture_files['classify_in.txt'], out_file)\n assert_files_equal(out_file, fixture_files['classify_expected.txt'])\n\n\ndef test_filter_fizz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_fizz_out.txt')\n fizzbuzz.filter_lines(fixture_files['classify_expected.txt'], out_file, 'fizz')\n assert_files_equal(out_file, fixture_files['filter_fizz_expected.txt'])\n\n\ndef test_filter_buzz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_buzz_out.txt')\n fizzbuzz.filter_lines(fixture_files['filter_fizz_expected.txt'], out_file, 'buzz')\n assert_files_equal(out_file, fixture_files['filter_buzz_expected.txt'])\n\n\ndef test_main_help():\n with raises(SystemExit) as exception_info:\n fizzbuzz.main([\"--help\"])\n assert 0 in exception_info.value.args\n\n\ndef test_main_invalid_input():\n with raises(SystemExit) as exception_info:\n fizzbuzz.main([\"invalid\"])\n assert 2 in exception_info.value.args\n\n\ndef test_main_classify_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'classify_out.txt')\n exit_code = fizzbuzz.main(\n [fixture_files['classify_in.txt'].as_posix(), out_file.as_posix(), \"classify\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['classify_expected.txt'])\n\n\ndef test_main_filter_fizz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_fizz_out.txt')\n exit_code = fizzbuzz.main([fixture_files['classify_expected.txt'].as_posix(\n ), out_file.as_posix(), \"filter\", \"--substring\", \"fizz\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['filter_fizz_expected.txt'])\n\n\ndef test_main_filter_buzz_lines(fixture_files, tmp_path):\n out_file = Path(tmp_path, 'filter_buzz_out.txt')\n exit_code = fizzbuzz.main([fixture_files['filter_fizz_expected.txt'].as_posix(\n ), out_file.as_posix(), \"filter\", \"--substring\", \"buzz\"])\n assert not exit_code\n assert_files_equal(out_file, fixture_files['filter_buzz_expected.txt'])\n","repo_name":"benjamin-heasly/proceed","sub_path":"tests/fizzbuzz/test_fizzbuzz.py","file_name":"test_fizzbuzz.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"72929515786","text":"from sys import stdin\nfrom re import sub\n\n\ndef main():\n def input():\n return stdin.readline().rstrip()\n\n t = int(input())\n for _ in range(t):\n n = int(input())\n tels = [sub('[^0-9]', '', input()) for _ in range(n)]\n tels.sort()\n res = True\n for i in range(1, n):\n if tels[i - 1] == tels[i][:len(tels[i - 1])]:\n res = False\n break\n print(['NO', 'YES'][res])\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"boorooksus/Algorithm-Study","sub_path":"백준/CH22-trie/G4-5052-TEL_List2.py","file_name":"G4-5052-TEL_List2.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"40174285918","text":"from collections import Counter\n\nEMPTY_GRID = [[0 for _ in range(9)] for _ in range(9)]\n\n\nclass Grid:\n \"\"\"Grid to hold values of a sudoku puzzle.\"\"\"\n\n def __init__(self, values: list[list[int]] = EMPTY_GRID):\n self._values = values\n\n def __getitem__(self, key: tuple[int, int]) -> int:\n return self._values[key[1]][key[0]]\n\n def __setitem__(self, key: tuple[int, int], value: int) -> None:\n self._values[key[1]][key[0]] = value\n\n def __str__(self) -> str:\n grid_string = \"\\n\"\n for j in range(9):\n for i in range(9):\n grid_string += str(self[i, j]) + \" \"\n if i == 2 or i == 5:\n grid_string += \"| \"\n elif i == 8:\n grid_string += \"\\n\"\n if j == 2 or j == 5:\n grid_string += \"-\" * 6 + \"+\" + \"-\" * 7 + \"+\" + \"-\" * 6 + \"\\n\"\n return grid_string\n\n\nclass Sudoku:\n \"\"\"Sudoku solver class.\"\"\"\n\n def __init__(self, grid: Grid = Grid()):\n self.grid = grid\n self.solve_attempted = False\n self.solve_successful = False\n\n def solve(self) -> None:\n self.solve_attempted = True\n if not self.is_valid_grid():\n self.solve_successful = False\n return\n self._solve()\n self.solve_successful = all(self.grid[i, j] for i in range(9) for j in range(9))\n\n def _solve(self) -> bool:\n for row, column in (\n (i, j) for i in range(9) for j in range(9) if self.grid[(i, j)] == 0\n ):\n for value in range(1, 10):\n if self._is_valid_input((row, column), value):\n self.grid[row, column] = value\n if self._solve():\n return True\n self.grid[row, column] = 0\n return False # No values work\n return True # Solution found\n\n def _is_valid_input(self, position: tuple[int, int], value: int) -> bool:\n row, column = position\n if value in (self.grid[row, j] for j in range(9)):\n return False\n if value in (self.grid[i, column] for i in range(9)):\n return False\n box_row = (row // 3) * 3\n box_column = (column // 3) * 3\n if value in (\n self.grid[i, j]\n for i in range(box_row, box_row + 3)\n for j in range(box_column, box_column + 3)\n ):\n return False\n return True\n\n def is_valid_grid(self) -> bool:\n for row in range(9):\n counter = Counter(self.grid[row, j] for j in range(9) if self.grid[row, j])\n if counter:\n if max(counter.values()) > 1:\n return False\n for col in range(9):\n counter = Counter(self.grid[i, col] for i in range(9) if self.grid[i, col])\n if counter:\n if max(counter.values()) > 1:\n return False\n for row, col in ((3 * i, 3 * j) for i in range(3) for j in range(3)):\n counter = Counter(\n self.grid[row + i, col + j]\n for i in range(3)\n for j in range(3)\n if self.grid[row + i, col + j]\n )\n if counter:\n if max(counter.values()) > 1:\n return False\n return True\n\n def __str__(self) -> str:\n return str(self.grid)\n\n\nif __name__ == \"__main__\":\n test_grid = Grid(\n [\n [0, 0, 3, 0, 2, 0, 6, 0, 0],\n [9, 0, 0, 3, 0, 5, 0, 0, 1],\n [0, 0, 1, 8, 0, 6, 4, 0, 0],\n [0, 0, 8, 1, 0, 2, 9, 0, 0],\n [7, 0, 0, 0, 0, 0, 0, 0, 8],\n [0, 0, 6, 7, 0, 8, 2, 0, 0],\n [0, 0, 2, 6, 0, 9, 5, 0, 0],\n [8, 0, 0, 2, 0, 3, 0, 0, 9],\n [0, 0, 5, 0, 1, 0, 3, 0, 0],\n ]\n )\n sudoku = Sudoku(test_grid)\n print(sudoku)\n sudoku.solve()\n print(sudoku)\n print(sudoku.solve_attempted)\n print(sudoku.solve_successful)\n","repo_name":"Luke943/SudokuSolver","sub_path":"src/sudoku_solver.py","file_name":"sudoku_solver.py","file_ext":"py","file_size_in_byte":3997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"823602165","text":"# import torch, torchvision\n# from torchvision import datasets, transforms\nfrom torch import nn#, optim\n\n\n# Net class with a 2D CNN \nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n\n self.conv_layers = nn.Sequential(\n nn.Conv2d(1, 10, kernel_size=5),\n nn.MaxPool2d(2),\n nn.ReLU(),\n nn.Conv2d(10, 20, kernel_size=5),\n nn.Dropout(),\n nn.MaxPool2d(2),\n nn.ReLU(),\n )\n self.fc_layers = nn.Sequential(\n nn.Linear(320, 50),\n nn.ReLU(),\n nn.Dropout(),\n nn.Linear(50, 10),\n nn.Softmax(dim=1)\n )\n\n def forward(self, x):\n x = self.conv_layers(x)\n x = x.view(-1, 320)\n x = self.fc_layers(x)\n return x\n","repo_name":"HelmholtzAI-Consultants-Munich/XAI-Tutorials","sub_path":"data_and_models/model_net.py","file_name":"model_net.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"81"} +{"seq_id":"8594361191","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 6 15:27:04 2016\n\n@author: alex\n\"\"\"\n\nfrom AlexRobotics.planning import RandomTree as RPRT\nfrom AlexRobotics.dynamic import Manipulator as M\nfrom AlexRobotics.control import ComputedTorque as CTC\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\"\"\" Define system \"\"\"\n\nR = M.TwoLinkManipulator()\n\nx_start = np.array([3,0,0,0])\nx_goal = np.array([0,0,0,0])\n\nRRT = RPRT.RRT( R , x_start )\n\nT = 12 # torque\n\nRRT.U = np.array([[T,0],[0,0],[-T,0],[0,T],[0,-T],[T,T],[-T,-T],[-T,T],[T,-T]])\n\nRRT.dt = 0.1\nRRT.goal_radius = 0.8\nRRT.max_nodes = 12000\nRRT.max_solution_time = 8\n\n#RRT.compute_steps(1000,True)\nRRT.find_path_to_goal( x_goal )\n\n# Assign controller\nCTC_controller = CTC.ComputedTorqueController( R )\nCTC_controller.load_trajectory( RRT.solution )\nR.ctl = CTC_controller.ctl\n\nCTC_controller.w0 = 1.0\nCTC_controller.zeta = 0.7\nCTC_controller.traj_ref_pts = 'closest'\n#CTC_controller.traj_ref_pts = 'interpol'\n\n#R.ctl = RRT.trajectory_controller\n#RRT.traj_ctl_kp = 25\n#RRT.traj_ctl_kd = 10\n\n\"\"\" Simulation and plotting \"\"\"\n\n# Plot\ntf = RRT.time_to_goal + 5\nn = int( np.round( tf / 0.05 ) ) + 1\nR.plotAnimation( x_start , tf , n , solver = 'euler' )\nR.Sim.plot_CL('x') \nR.Sim.plot_CL('u')\n#R.phase_plane_trajectory([0,0],x_start,tf,True,True,True,True)\nRRT.plot_2D_Tree()\n\n# Hold figures alive\n#plt.show()","repo_name":"ali493/pyro","sub_path":"old/examples/twolinkmanipulator_with_RRT_and_CTC.py","file_name":"twolinkmanipulator_with_RRT_and_CTC.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15817032709","text":"from flask import Flask, request\nfrom flask import jsonify\nfrom flask.logging import default_handler\n\nimport logging\nimport os\n\n\ndef setup_logging(flask_app):\n \"\"\"Perform the setup of logging for this application.\"\"\"\n if not flask_app.debug:\n handler = logging.StreamHandler()\n handler.setFormatter(logging.Formatter(\n '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'))\n log_level = os.environ.get('FLASK_LOGGING_LEVEL', logging.getLevelName(logging.INFO))\n handler.setLevel(log_level)\n\n flask_app.logger.removeHandler(default_handler)\n flask_app.logger.addHandler(handler)\n flask_app.logger.setLevel(logging.DEBUG)\n\n\ndef create_app(test_config=None):\n app = Flask(__name__)\n\n setup_logging(app)\n\n @app.route(\"/submit\", methods=['POST'])\n def submit():\n body = request.get_json()\n app.logger.info(str(body))\n return jsonify({'result': 'success'})\n\n return app\n","repo_name":"msehnout/http-collector","sub_path":"http_collector/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"2216798897","text":"import smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.image import MIMEImage\nfrom email.mime.multipart import MIMEMultipart\nimport config\n\n# 发送email\ndef send_mail():\n receive = config.receive\n image = config.project_dir + 'luckball.png'\n \n body = \"\"\"\n

抱抱龙和笑笑龙给您送幸运号码啦！

\n \n \"\"\"\n msg = MIMEMultipart() \n msg['Subject'] = 'LuckBall'\n msg.attach(MIMEText(body, 'html', 'utf-8'))\n # 二进制模式读取图片\n with open(image, 'rb') as f:\n msgImage = MIMEImage(f.read())\n # 定义图片ID\n msgImage.add_header('Content-ID', '')\n msg.attach(msgImage)\n\n # 连接到SMTP服务器\n smtpObj = smtplib.SMTP(config.smtp,25)\n smtpObj.ehlo()\n smtpObj.starttls()\n\n # 登录发送邮箱\n smtpObj.login(config.sendusername, config.sendpassword)\n\n # 发送\n smtpObj.sendmail(config.sendusername, receive, msg.as_string())\n\n # 从SMTP服务器断开\n smtpObj.quit()","repo_name":"asillyrabbit/LuckBall","sub_path":"send_mail.py","file_name":"send_mail.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18563156865","text":"from time import time\nimport datetime\nimport pandas as pd\nfrom influxdb import DataFrameClient\nimport math\nimport json\n\nrecords = [\n {\n 'tag_name': 'VIB_CRBTS.WF.X_TDW',\n 'timestamp': [(1619101366968 + i) * 1000000 for i in range(1)],\n 'values': [float(i) for i in range(1)]\n },\n {\n 'tag_name': 'VIB_CRBTS.WF.X_TDW',\n 'timestamp': [(1619101366968 + i) * 1000000 for i in range(1)],\n 'values': [float(i) for i in range(1)]\n }\n\n]\n\n\n\n\ndef retry(max_attempts: int, interval_delay_ms: int = 1000,\n raise_in_limit: bool = False, verbose: bool = False):\n \"\"\"Retry a function call if it throw an error.\n\n If you decorate a function with @retry(n), every time you invoke it, if it\n raise an error in some part of its body, the decorator will try execute it\n again, and so on until the max_attempts was reached.\n\n After error the main thread will sleep for interval_delay_ms (ms) before\n the function was called again.\n\n If all attempts result in errors and raise_in_limit is True, then the last\n caught error will be threw as well. If raise_in_limit is False, the thread\n will continue to the next line after the function call.\n\n On every handled error, if verbose is True, e message log will be printed\n for the standard output.\n\n Despite all these parameters are set when you define the function, at\n runtime, when the function is invoked, all these parameters can be overrode\n using the next keyword arguments:\n - override_max_attempts\n - override_delay_ms\n - override_raise\n - override_verbose\n \"\"\"\n\n def decorator(function: callable):\n @functools.wraps(function)\n def wrapper(*args, **kwargs):\n iterations = max_attempts\n delay = interval_delay_ms\n raise_ = raise_in_limit\n verbose_ = verbose\n if \"override_max_attempts\" in kwargs:\n iterations = kwargs[\"override_max_attempts\"]\n del kwargs[\"override_max_attempts\"]\n if \"override_delay_ms\" in kwargs:\n delay = kwargs[\"override_delay_ms\"]\n del kwargs[\"override_delay_ms\"]\n if \"override_raise\" in kwargs:\n raise_ = kwargs[\"override_raise\"]\n del kwargs[\"override_raise\"]\n if \"override_verbose\" in kwargs:\n verbose_ = kwargs[\"override_verbose\"]\n del kwargs[\"override_verbose\"]\n for i in range(iterations):\n try:\n return function(*args, **kwargs)\n except Exception as err:\n if verbose_ is True:\n log(\n title=\"{}\".format(function.__name__),\n type_=LogType.ERROR,\n message=\"Attempt: {}\\n{}\".format(i + 1, err)\n )\n if raise_ is True and i + 1 == iterations:\n raise\n time.sleep(delay / 1000)\n\n return wrapper\n\n return decorator\n\n\ndef get_df_client(**kwargs):\n client = DataFrameClient(\n host=kwargs[\"host\"] if \"host\" in kwargs else \"localhost\",\n port=kwargs[\"port\"] if \"port\" in kwargs else 8086,\n username=kwargs[\"username\"] if \"username\" in kwargs else \"root\",\n password=kwargs[\"password\"] if \"password\" in kwargs else \"root\",\n database=kwargs[\"database\"] if \"database\" in kwargs else None,\n ssl=kwargs[\"ssl\"] if \"ssl\" in kwargs else False,\n verify_ssl=kwargs[\"verify_ssl\"] if \"verify_ssl\" in kwargs else False,\n timeout=kwargs[\"timeout\"] if \"timeout\" in kwargs else None,\n retries=1,\n use_udp=kwargs[\"use_udp\"] if \"use_udp\" in kwargs else False,\n udp_port=kwargs[\"udp_port\"] if \"udp_port\" in kwargs else 4444,\n proxies=kwargs[\"proxies\"] if \"proxies\" in kwargs else None,\n pool_size=kwargs[\"pool_size\"] if \"pool_size\" in kwargs else 10,\n path=kwargs[\"path\"] if \"path\" in kwargs else \"\",\n cert=kwargs[\"cert\"] if \"cert\" in kwargs else None,\n gzip=kwargs[\"gzip\"] if \"gzip\" in kwargs else False,\n session=kwargs[\"session\"] if \"session\" in kwargs else None,\n headers=kwargs[\"headers\"] if \"headers\" in kwargs else None\n )\n\n return client\n\n\n# @decorators.retry(3, 100, False)\ndef write_df_points(**kwargs):\n \"\"\"Write to multiple time series names.\"\"\"\n client = get_df_client(**kwargs)\n f_args = client.write_points.__code__.co_varnames\n wp_args = {key: val for key, val in kwargs.items() if key in f_args}\n\n return client.write_points(**wp_args)\n\n\ndef saving_influxdb(_records: list, batch_limit: int = 1000):\n try:\n records = _records\n if len(records) > 0:\n for obj in records:\n if type(obj['timestamp']) is list and type(obj['values']) is list:\n if len(obj['timestamp']) == len(obj['values']):\n df = pd.DataFrame(obj)\n elif len(obj['timestamp']) > len(obj['values']):\n obj['timestamp'] = obj['timestamp'][:len(\n obj['values'])]\n df = pd.DataFrame(obj)\n else:\n obj['values'] = obj['values'][:len(obj['timestamp'])]\n df = pd.DataFrame(obj)\n\n df['asset'] = df['tag_name'].map(lambda x: x.split('.')[0])\n df['tag_name'] = df['tag_name'].map(\n lambda x: '___'.join(x.split('.')[1:])\n )\n asset = df['asset'][0]\n df = df.set_index('asset').loc[df['asset'][0]]\n df['timestamp'] = pd.to_datetime(\n df['timestamp'], unit='ns'\n )\n df.set_index('timestamp', inplace=True)\n df = df.pivot_table(columns='tag_name',\n values='values',\n index='timestamp'\n )\n write_df_points(host='192.168.43.147',\n port=8086,\n username='sdc',\n password='sbrQp10',\n database='sorba_sde',\n dataframe=df,\n measurement=asset,\n time_precision='ms',\n batch_size=batch_limit)\n else:\n raise TypeError(\n 'Data format invalid, expected timestamp and values as list')\n return True\n except Exception as msg:\n raise Exception(\n '[ERROR] Runtime error writing in influxdb {}'.format(msg)\n )\n\n\nif __name__ == '__main__':\n ts = datetime.datetime.now().timestamp()\n with open(\"/home/eduardo/Descargas/Testing_FFT/records.json\") as data:\n _records = json.loads(data.readlines()[0])\n\n \n temp = _records.copy()\n for obj in temp:\n obj[\"timestamp\"] = []\n for pos in range(len(obj[\"values\"])):\n obj[\"timestamp\"].append(((ts * 1000) + pos) * 1000)\n \n \n x = saving_influxdb(temp)\n if x:\n print('Hola')\n","repo_name":"ciromolina86/python-testing","sub_path":"saving_influxdb.py","file_name":"saving_influxdb.py","file_ext":"py","file_size_in_byte":7430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"74358384906","text":"class ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\nclass Solution:\n def oddEvenList(self, head: ListNode) -> ListNode:\n\n if head is None:\n return None\n\n index = 0\n\n odd_head = None\n previous_odd = None\n odd_tail = None\n\n even_head = None\n previous_even = None\n even_tail = None\n\n while head:\n if index % 2 == 0:\n if odd_head is None:\n odd_head = head\n previous_odd = odd_head\n else:\n previous_odd.next = head\n previous_odd = head\n odd_tail = head\n else:\n if even_head is None:\n even_head = head\n previous_even = even_head\n else:\n previous_even.next = head\n previous_even = head\n even_tail = head\n\n if head.next:\n head = head.next\n else:\n head.next = None\n break\n\n index += 1\n\n if even_tail:\n even_tail.next = None\n\n odd_tail.next = even_head\n\n return odd_head\n\n\none = ListNode(1)\ntwo = ListNode(2)\nthree = ListNode(3)\nfour = ListNode(4)\nfive = ListNode(5)\nsix = ListNode(6)\nseven = ListNode(7)\neight = ListNode(8)\n\n\ns = Solution()\ns.oddEvenList(one)\n\nprint()\n","repo_name":"SergeySatunin/leetcode","sub_path":"linked_list/odd_even_linked_list.py","file_name":"odd_even_linked_list.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15045314660","text":"sayilar = [1,3,5,7,9,12,19,21]\r\nsonuc = []\r\n\r\n'''\r\nfor x in sayilar: # 3 ebölünebilen sayıları yazdırır.\r\n if x%3==0:\r\n sonuc.append(x)\r\n else:\r\n print(\"None\")\r\nprint(sonuc)\r\n'''\r\n\r\n'''\r\na=0\r\nfor x in sayilar: # sayıların toplamını yazdırır.\r\n a += x \r\nprint(a)\r\n'''\r\n\r\n'''\r\nfor x in sayilar: # tek sayıların karesini yazdırır.\r\n if x%2==1:\r\n sonuc.append(x**2)\r\n else:\r\n print(\"None\")\r\nprint(sonuc)\r\n'''\r\n\r\n'''\r\nsehirler = ['kocaeli','istanbul','ankara','izmir','rize'] # verilen şehirlerden en fazla 5 karakterli olanları ekrara yazdırır.\r\n\r\nfor x in sehirler:\r\n if (len(x)<=5):\r\n print(x)\r\n'''\r\n\r\nurunler = [\r\n {'name':'samsung S6', 'price': '3000' },\r\n {'name':'samsung S7', 'price': '4000' },\r\n {'name':'samsung S8', 'price': '5000' },\r\n {'name':'samsung S9', 'price': '6000' },\r\n {'name':'samsung S10', 'price': '7000' }\r\n]\r\n\r\n'''\r\na =0 # verilen telefonların fiyatlarının toplamını verir.\r\nfor x in urunler:\r\n b = int(x['price'])\r\n a += b\r\nprint(a)\r\n'''\r\n\r\n''' \r\nfor x in urunler: # verilen telefonların fiyatlarının en fazla 5000 olan ürünleri gösterir.\r\n b = int(x['price'])\r\n if b<=5000:\r\n sonuc.append(x)\r\n print(x)\r\n'''\r\n\r\n","repo_name":"ilkerozmen/python-works","sub_path":"for-demo.py","file_name":"for-demo.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"tr","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"27633002881","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\nfrom indicoio import text_tags\nfrom .indico_text_base import TextTest\n\nclass TextTagsTest(TextTest):\n\n def test_batch_texttags(self):\n test_data = [\"On Monday, president Barack Obama will be...\"]\n response = text_tags(test_data)\n self.assertTrue(isinstance(response, list))\n\n def test_text_tags(self):\n text = \"On Monday, president Barack Obama will be...\"\n results = text_tags(text)\n max_keys = sorted(results.keys(), key=lambda x:results.get(x), reverse=True)\n assert 'political_discussion' in max_keys[:5]\n results = text_tags(text, top_n=5)\n assert len(results) is 5\n results = text_tags(text, threshold=0.1)\n for v in results.values():\n assert v >= 0.1\n","repo_name":"EliabWoldeyes/Qhacks2017","sub_path":"IndicoIo-Python/tests/text/test_texttags.py","file_name":"test_texttags.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"456274598","text":"\"\"\"\nAuthorisation module for motley_cue API.\n\"\"\"\nfrom typing import Optional\nfrom enum import Enum\nimport logging\n\nfrom fastapi import Request\nfrom flaat import AuthWorkflow\nfrom flaat.config import AccessLevel\nfrom flaat.fastapi import Flaat\nfrom flaat.requirements import CheckResult, Requirement\nfrom flaat.user_infos import UserInfos\nfrom flaat.exceptions import FlaatException\n\nfrom .config import Config, ConfigAuthorisation, canonical_url\nfrom .exceptions import Unauthorised\n\n\nlogger = logging.getLogger(__name__)\n\n\n# We dynamically load the requirement in is_satisfied_by\nclass AuthRequirement(Requirement):\n \"\"\"Base class for authorisation requirements corresponding to an OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def __init__(self, authorisation: ConfigAuthorisation):\n self.authorisation = authorisation\n\n\nclass AuthenticatedUserRequirement(AuthRequirement):\n \"\"\"Requirement for a user to be able to login at the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return CheckResult(True, \"OP is configured\")\n\n\nclass AuthorisedUserRequirement(AuthRequirement):\n \"\"\"Requirement for a user to meet the configured authorisation for the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return op_authz.get_user_requirement().is_satisfied_by(user_infos)\n\n\nclass AuthorisedAdminRequirement(AuthRequirement):\n \"\"\"Requirement for an admin to meet the configured authorisation for the OP.\"\"\"\n\n # pylint: disable=too-few-public-methods\n def is_satisfied_by(self, user_infos: UserInfos) -> CheckResult:\n \"\"\"override method to use configured authorisation\"\"\"\n op_authz = self.authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"OP is not configured\")\n\n return op_authz.get_admin_requirement().is_satisfied_by(user_infos)\n\n\nclass AuthorisationType(Enum):\n \"\"\"Class to describe authorisation for an OP.\"\"\"\n\n NOT_SUPPORTED = (\"not supported\", \"OP is not supported.\")\n NOT_CONFIGURED = (\n \"not configured\",\n \"OP is supported but no authorisation is configured.\",\n )\n ALL_USERS = (\"all users\", \"All users from this OP are authorised.\")\n INDIVIDUAL_USERS = (\n \"individual users\",\n \"Users are authorised on an individual basis. \"\n \"Please contact a service administrator to request access.\",\n )\n VO_BASED = (\"VO-based\", \"Users who are in {} of the supported VOs are authorised\")\n\n def __init__(self, mode, info):\n \"\"\"Create authorisation type\"\"\"\n self.__mode = mode\n self.__info = info\n\n def description(self, vo_match=\"one\", audience=\"\"):\n \"\"\"Return a description of the authorisation as a dict\"\"\"\n desc_dict = {\n \"authorisation_type\": self.__mode,\n \"authorisation_info\": self.__info.format(vo_match),\n }\n if audience is not None and audience != \"\" and audience != []:\n desc_dict[\"audience\"] = audience\n return desc_dict\n\n\nclass Authorisation(Flaat):\n \"\"\"Extension for Flaat:\n\n - configures Flaat parameters in given config file\n - more flexible authorisation:\n - per OP configuration\n - individual user authorisation\n - stringify authorisation info\n \"\"\"\n\n def __init__(self, config: Config):\n \"\"\"Initialise Authorisation from given Config object\"\"\"\n super().__init__()\n self.set_trusted_OP_list(config.trusted_ops)\n self.set_verbosity(config.verbosity)\n self.__authorisation = config.authorisation\n self.access_levels = [\n AccessLevel(\n \"authenticated_user\", AuthenticatedUserRequirement(self.__authorisation)\n ),\n AccessLevel(\n \"authorised_user\", AuthorisedUserRequirement(self.__authorisation)\n ),\n AccessLevel(\n \"authorised_admin\", AuthorisedAdminRequirement(self.__authorisation)\n ),\n ]\n\n def info(self, request: Request) -> dict:\n \"\"\"Return authorisation information for issuer of token.\n OIDC Access Token should be found in request headers.\n \"\"\"\n # get OP from request\n try:\n user_infos = self.get_user_infos_from_request(request)\n except FlaatException as ex:\n logger.info(\"Error while trying to get user infos from request: %s\", ex)\n user_infos = None\n if user_infos is None:\n raise Unauthorised(\"Could not get user infos from request.\")\n op_authz = self.__authorisation.get_op_authz(user_infos)\n # if OP not supported\n if op_authz is None:\n return {\n \"OP\": user_infos.issuer,\n **AuthorisationType.NOT_SUPPORTED.description(),\n }\n # if all users from this OP are authorised\n if op_authz.authorise_all:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.ALL_USERS.description(audience=op_authz.audience),\n }\n # if authorised VOs are specified\n if len(op_authz.authorised_vos) > 0:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.VO_BASED.description(\n vo_match=op_authz.vo_match, audience=op_authz.audience\n ),\n \"supported_VOs\": op_authz.authorised_vos,\n }\n # if individual users are specified\n if len(op_authz.authorised_users) > 0:\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.INDIVIDUAL_USERS.description(\n audience=op_authz.audience\n ),\n }\n\n # OP is supported but no authorisation is configured\n return {\n \"OP\": op_authz.op_url,\n **AuthorisationType.NOT_CONFIGURED.description(audience=op_authz.audience),\n }\n\n def authenticated_user_required(self, func):\n \"\"\"Decorator that only allows users from supported OPs.\n OIDC Access Token should be found in request headers.\n \"\"\"\n return self.access_level(\"authenticated_user\")(func)\n\n def authorised_user_required(self, func):\n \"\"\"Decorator that only allows users from supported OPs that meet the\n configured authorisation requirements.\n OIDC Access Token should be found in request headers.\n \"\"\"\n return self.access_level(\"authorised_user\")(func)\n\n def authorised_admin_required(self, func):\n \"\"\"Decorator that only allows admins from supported OPs that meet the\n configured authorisation requirements.\n OIDC Access Token should be found in request headers.\n \"\"\"\n\n def _check_request(user_infos: UserInfos, *_, **kwargs) -> CheckResult:\n user_iss = kwargs.get(\"iss\", \"\")\n if user_iss != \"\":\n op_authz = self.__authorisation.get_op_authz(user_infos)\n if op_authz is None:\n return CheckResult(False, \"No OP config\")\n\n if not op_authz.authorise_admins_for_all_ops and canonical_url(\n op_authz.op_url\n ) != canonical_url(user_iss):\n return CheckResult(\n False,\n f\"Admin from issuer {op_authz.op_url} is not authorised to manage \"\n f\"users of issuer '{user_iss}'\",\n )\n\n return CheckResult(True, \"Request is authorised\")\n\n auth_flow = AuthWorkflow(\n self,\n user_requirements=self._get_access_level_requirement(\"authorised_admin\"),\n request_requirements=_check_request,\n )\n return auth_flow.decorate_view_func(func)\n\n def get_user_infos_from_access_token(\n self, access_token, issuer_hint=\"\"\n ) -> Optional[UserInfos]:\n \"\"\"Get a (flaat) UserInfos object from given OIDC Access Token.\"\"\"\n user_infos = super().get_user_infos_from_access_token(access_token, issuer_hint)\n if (\n user_infos is not None\n and user_infos.user_info is not None\n and user_infos.access_token_info is not None\n ):\n # HACK for wlcg OP: copy groups from AT body in 'wlcg.groups' claim\n # to 'groups' claim in userinfo; also needed by feudalAdapter\n wlcg_groups = user_infos.access_token_info.body.get(\"wlcg.groups\", None)\n if wlcg_groups is not None:\n if \"groups\" in user_infos.user_info:\n user_infos.user_info[\"groups\"] += [\n g\n for g in wlcg_groups\n if g not in user_infos.user_info[\"groups\"]\n ]\n else:\n user_infos.user_info[\"groups\"] = wlcg_groups\n return user_infos\n\n def get_uid_from_request(self, request: Request):\n \"\"\"Get a (flaat) UserInfos object from given request.\n OIDC Access Token should be found in request headers.\n \"\"\"\n try:\n user_infos = self.get_user_infos_from_request(request)\n except Exception: # pylint: disable=broad-except\n return None\n if user_infos is None:\n return None\n return {\"sub\": user_infos.subject, \"iss\": user_infos.issuer}\n","repo_name":"dianagudu/motley_cue","sub_path":"motley_cue/mapper/authorisation.py","file_name":"authorisation.py","file_ext":"py","file_size_in_byte":9914,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"} +{"seq_id":"5922860236","text":"import mainey\n#подключаем готовую схему чисел через созданный файл\n\nem_num = int ( input ( 'Число сотруднников: ' ) )\nsumm = 0\ndistance_list = []\nprice_list = []\n\nfor i in range( em_num ):\n distance = int( input( 'Расстояние до дома {0}-го сотрудника: '.format( i+1 ) ) )\n distance_list.append( distance )\n\nfor i in range( em_num ):\n price = int( input( 'Тариф {0}-го такси: '.format( i+1 ) ) )\n price_list.append( price )\n\n#Создаём копии списков\ndis_list = distance_list[:] \nprice_list = price_list[:]\n\n#Так же создаем списки для хранения индексов наших значений\nind_list_a = []\nind_list_b = []\n\n#В порядке возрастания и убывания значений переменных заполним списки индексами\nmax_dist = 0\nfor i in range( em_num ): \n index = dis_list.index( max( dis_list ) )\n ind_list_a.append( index )\n dis_list[ index ] = 0\n\nfor i in range( em_num ):\n index = price_list.index( min ( price_list ) )\n ind_list_b.append( index )\n price_list[ index ] = 10**10\n \n#Поиск для каждого i-го в паре списков с индексами нужный индекс такси\nprint('Такси для клиента 1, 2, 3, ...:')\n\nfor i in range( em_num ):\n taxi_num = ind_list_b[ ind_list_a.index ( i ) ]\n print( taxi_num+1 )\nfor i in range( em_num ):\n summ += distance_list[ ind_list_a[ i ] ]*price_list[ ind_list_b[ i ] ]\n\n#вывод итоговой суммы\nprint(summ)\nprint('Итоговая сумма:')\nmainey.out(summ)","repo_name":"ste1wallF/labz8","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18134612885","text":"def up_to_vowel(s):\n '''(str) -> str\n return a substring of s from index 0 up to but not including the first vowel in s.\n '''\n i=0\n before_vowel =''\n while i < len(s) and not(s[i] in 'aeiouAEIOU'):\n before_vowel= before_vowel + s[i]\n i = i + 1\n\n return before_vowel\n \ndef remove_vowel(s):\n i=0\n before=''\n while i joint angles\n :param reset_args: dicts containing a randomized parameter set for altering the mujoco model params\n if None, a new set of model params is sampled\n :return: initial observation\n \"\"\"\n assert reset_args is None or type(reset_args) == dict, \"reset_args must be a dict containing mujoco model params\"\n\n self.time_step = 1\n # reset number of steps taken\n self.n_steps = 0\n\n # The first time reset is called -> sample and fix the mujoco parameters\n if self.fix_params and not self.parameters_already_fixed:\n self.sample_and_fix_parameters()\n\n elif not self.parameters_already_fixed:\n self.save_parameters()\n\n if self.fix_params and reset_args is not None:\n warnings.warn(\"Environment parameters are fixed - reset_ars does not have any effect\", UserWarning)\n\n # set mujoco model parameters\n elif (not self.fix_params) and (reset_args is not None):\n self.reset_mujoco_parameters(reset_args)\n elif not self.fix_params:\n # sample parameter set\n reset_args = self.sample_env_params(1)[0]\n self.reset_mujoco_parameters(reset_args)\n\n self.reset_mujoco(init_state)\n self.model.forward()\n self.current_com = self.model.data.com_subtree[0]\n self.dcom = np.zeros_like(self.current_com)\n obs = self.get_current_obs()\n return obs\n\n def reset_mujoco_parameters(self, param_dict):\n for param, param_val in param_dict.items():\n param_variable = getattr(self.model, param)\n assert param_variable.shape == param_val.shape, 'shapes of new parameter value and old one must match'\n setattr(self.model, param, param_val)\n\n def fix_parameters(self, param_dict):\n assert self.fix_params, \"requires sample_and_fix_parameters to be True\"\n self.parameters_already_fixed = True\n self.reset_mujoco_parameters(param_dict)\n\n def sample_and_fix_parameters(self):\n assert hasattr(self, 'sample_env_params'), \"class must implement the sample_env_params method\"\n assert self.fix_params, \"requires sample_and_fix_parameters to be True\"\n param_dict = self.sample_env_params(1)[0]\n self.fix_parameters(param_dict)\n return self\n\n def save_parameters(self):\n assert not self.fix_params\n self.init_params = {}\n if 'body_mass' in self.rand_params:\n self.init_params['body_mass'] = self.model.body_mass\n\n # body_inertia\n if 'body_inertia' in self.rand_params:\n self.init_params['body_inertia'] = self.model.body_inertia\n\n # damping -> different multiplier for different dofs/joints\n if 'dof_damping' in self.rand_params:\n self.init_params['dof_damping'] = self.model.dof_damping\n\n # friction at the body components\n if 'geom_friction' in self.rand_params:\n self.init_params['geom_friction'] = self.model.geom_friction\n\n self.parameters_already_fixed = True\n\n def sample_env_params(self, num_param_sets, log_scale_limit=None):\n \"\"\"\n generates randomized parameter sets for the mujoco env\n :param num_param_sets: number of parameter sets to obtain\n :param log_scale_limit: lower / upper limit for uniform sampling in logspace of base 2\n :return: array of length num_param_sets with dicts containing a randomized parameter set\n \"\"\"\n assert hasattr(self, 'random_state'), \"random_state must be set in the constructor\"\n\n if log_scale_limit is None:\n log_scale_limit = self.log_scale_limit\n\n param_sets = []\n\n for _ in range(num_param_sets):\n # body mass -> one multiplier for all body parts\n\n new_params = {}\n\n if 'body_mass' in self.rand_params:\n body_mass_multiplyers = np.array(1.5)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.body_mass.shape)\n new_params['body_mass'] = self.init_params['body_mass'] * body_mass_multiplyers\n\n # body_inertia\n if 'body_inertia' in self.rand_params:\n body_inertia_multiplyers = np.array(1.5)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.body_inertia.shape)\n new_params['body_inertia'] = body_inertia_multiplyers * self.init_params['body_inertia']\n\n # damping -> different multiplier for different dofs/joints\n if 'dof_damping' in self.rand_params:\n dof_damping_multipliers = np.array(1.3)**self.random_state.uniform(-log_scale_limit, log_scale_limit, size=self.model.dof_damping.shape)\n new_params['dof_damping'] = np.multiply(self.init_params['dof_damping'], dof_damping_multipliers)\n\n # friction at the body components\n if 'geom_friction' in self.rand_params:\n dof_damping_multipliers = np.array(1.5) ** self.random_state.uniform(-log_scale_limit, log_scale_limit,\n size=self.model.geom_friction.shape)\n new_params['geom_friction'] = np.multiply(self.init_params['geom_friction'], dof_damping_multipliers)\n\n param_sets.append(new_params)\n\n return param_sets\n\n def seed(self, random_seed):\n self.random_seed = random_seed\n self.random_state = np.random.RandomState(random_seed)","repo_name":"jonasrothfuss/model_ensemble_meta_learning","sub_path":"sandbox/ours/envs/mujoco/base_env_rand_param.py","file_name":"base_env_rand_param.py","file_ext":"py","file_size_in_byte":7952,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"81"} +{"seq_id":"22516590687","text":"# Definition for a binary tree node.\nclass TreeNode(object):\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n\nclass Solution(object):\n def tree2str(self, root):\n if not root:\n return \"\"\n\n res = \"\"\n res += str(root.val)\n\n leftStr = self.tree2str(root.left)\n rightStr = self.tree2str(root.right)\n\n if leftStr == \"\" and rightStr == \"\":\n return res\n elif leftStr == \"\":\n return res + \"()\" + \"(\" + rightStr + \")\"\n elif rightStr == \"\":\n return res + \"(\" + leftStr + \")\"\n else:\n return res + \"(\" + leftStr + \")\" + \"(\" + rightStr + \")\"\n","repo_name":"abedmohammed/leetcode","sub_path":"606ConstructStringFromBinaryTree.py","file_name":"606ConstructStringFromBinaryTree.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"15443063528","text":"# -*- coding: utf-8 -*-\nfrom typing import List\n\"\"\"\nCreated on Thu Sep 17 14:05:24 2020\n\n@author: c0096\n\n Display Table of Food Orders in a Restaurant\n Given the array orders, which represents the orders that customers have done in a restaurant. More specifically orders[i]=[customerNamei,tableNumberi,foodItemi] where customerNamei is the name of the customer, tableNumberi is the table customer sit at, and foodItemi is the item customer orders.\n\nReturn the restaurant's “display table”. The “display table” is a table whose row entries denote how many of each food item each table ordered. The first column is the table number and the remaining columns correspond to each food item in alphabetical order. The first row should be a header whose first column is “Table”, followed by the names of the food items. Note that the customer names are not part of the table. Additionally, the rows should be sorted in numerically increasing order.\n\n \n\nExample 1:\n\nInput: orders = [[\"David\",\"3\",\"Ceviche\"],[\"Corina\",\"10\",\"Beef Burrito\"],[\"David\",\"3\",\"Fried Chicken\"],[\"Carla\",\"5\",\"Water\"],[\"Carla\",\"5\",\"Ceviche\"],[\"Rous\",\"3\",\"Ceviche\"]]\nOutput: [[\"Table\",\"Beef Burrito\",\"Ceviche\",\"Fried Chicken\",\"Water\"],[\"3\",\"0\",\"2\",\"1\",\"0\"],[\"5\",\"0\",\"1\",\"0\",\"1\"],[\"10\",\"1\",\"0\",\"0\",\"0\"]] \nExplanation:\nThe displaying table looks like:\nTable,Beef Burrito,Ceviche,Fried Chicken,Water\n3 ,0 ,2 ,1 ,0\n5 ,0 ,1 ,0 ,1\n10 ,1 ,0 ,0 ,0\nFor the table 3: David orders \"Ceviche\" and \"Fried Chicken\", and Rous orders \"Ceviche\".\nFor the table 5: Carla orders \"Water\" and \"Ceviche\".\nFor the table 10: Corina orders \"Beef Burrito\". \n\nExample 2:\n\nInput: orders = [[\"James\",\"12\",\"Fried Chicken\"],[\"Ratesh\",\"12\",\"Fried Chicken\"],[\"Amadeus\",\"12\",\"Fried Chicken\"],[\"Adam\",\"1\",\"Canadian Waffles\"],[\"Brianna\",\"1\",\"Canadian Waffles\"]]\nOutput: [[\"Table\",\"Canadian Waffles\",\"Fried Chicken\"],[\"1\",\"2\",\"0\"],[\"12\",\"0\",\"3\"]] \nExplanation: \nFor the table 1: Adam and Brianna order \"Canadian Waffles\".\nFor the table 12: James, Ratesh and Amadeus order \"Fried Chicken\".\n\nExample 3:\n\nInput: orders = [[\"Laura\",\"2\",\"Bean Burrito\"],[\"Jhon\",\"2\",\"Beef Burrito\"],[\"Melissa\",\"2\",\"Soda\"]]\nOutput: [[\"Table\",\"Bean Burrito\",\"Beef Burrito\",\"Soda\"],[\"2\",\"1\",\"1\",\"1\"]]\n\n \n\nConstraints:\n\n 1 <= orders.length <= 5 * 10^4\n orders[i].length == 3\n 1 <= customerNamei.length, foodItemi.length <= 20\n customerNamei and foodItemi consist of lowercase and uppercase English letters and the space character.\n tableNumberi is a valid integer between 1 and 500.\n\n\n\"\"\"\n\nclass Solution:\n def displayTable(self, orders:List[List[str]]) -> List[List[str]]:\n tables = dict()\n foods = list()\n display = list()\n \n for order in orders:\n table = order[1]\n food = order[2]\n \n if food not in foods:\n foods.append(food)\n \n if table not in tables: \n tables[table] = dict()\n tables[table][food] = 1\n elif food not in tables[table]:\n tables[table][food] = 1\n else:\n tables[table][food] += 1\n \n foods.sort()\n head = ['Table']\n head.extend(foods)\n display.append(head)\n table_index = sorted(map(lambda x:int(x), tables.keys()))\n table_index = map(lambda x:str(x),table_index)\n \n for ta in table_index:\n result = [ta]\n for Fd in foods:\n if tables[ta].get(Fd) == None:\n result.append('0')\n else:\n result.append(str(tables[ta].get(Fd)))\n \n display.append(result)\n \n return display\n \n\n","repo_name":"sqluo2972/Algorithm","sub_path":"others/Display Table of Food Orders in a Restaurant..py","file_name":"Display Table of Food Orders in a Restaurant..py","file_ext":"py","file_size_in_byte":3822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"17450844556","text":"import sqlite3\r\nimport sys\r\nimport six\r\nimport base64\r\nimport uuid\r\nimport pyodbc\r\n\r\ndef encryption(key, string):\r\n encoded_chars = []\r\n for i in range(len(string)):\r\n key_c = key[i % len(key)]\r\n encoded_c = chr(ord(string[i]) + ord(key_c) % 256)\r\n encoded_chars.append(encoded_c)\r\n encoded_string = ''.join(encoded_chars)\r\n encoded_string = encoded_string.encode('latin') if six.PY3 else encoded_string\r\n return base64.urlsafe_b64encode(encoded_string).rstrip(b'=')\r\n\r\n\r\ndef decryption(key, string):\r\n string = base64.urlsafe_b64decode(string + '===')\r\n string = string.decode('latin') if six.PY3 else string\r\n encoded_chars = []\r\n for i in range(len(string)):\r\n key_c = key[i % len(key)]\r\n encoded_c = chr((ord(string[i]) - ord(key_c) + 256) % 256)\r\n encoded_chars.append(encoded_c)\r\n encoded_string = ''.join(encoded_chars)\r\n return encoded_string\r\n\r\n\r\n######\r\nhardkey = \"dashboard\"\r\n######\r\n\r\n## check if the user ACTUALLY wants to do this\r\n\r\ncheck = input(\"### This will overwrite the current connection settings. Do you want to change them now? (Y/N): \")\r\n\r\nif check not in (\"Y\", \"y\"):\r\n input(\"### Please run this exe file again if settings need to change\")\r\n sys.exit()\r\n\r\n## let's go and check the database and/or create it if required\r\n\r\ndb = sqlite3.connect(\"PiConnections.db\")\r\ncursor = db.cursor()\r\ncursor.execute(\"DROP TABLE IF EXISTS tblConnStr\")\r\ncursor.execute(\"CREATE TABLE IF NOT EXISTS tblConnStr(strSQLServer TEXT, strSage TEXT, strSoftkey TEXT )\")\r\ndb.commit()\r\n\r\n## go and get a unique key\r\n\r\nsoftkey = str(uuid.uuid4())\r\n\r\n## stick it in the db\r\n\r\ndata = (\"INSERT INTO tblConnStr(strSoftkey) VALUES('%s')\" % (softkey))\r\ncursor.execute(data)\r\ndb.commit()\r\n\r\n## just make sure we're using the softkey that is stored to encrypt\r\n\r\ncursor.execute(\"SELECT strSoftkey FROM tblConnStr\")\r\nsoftkey = cursor.fetchone()[0]\r\n\r\nkey = hardkey + softkey\r\n\r\n## Now go and get the SQL SERVER details from the user and enter in our db\r\n\r\nwhile True:\r\n print(\"\\n### Please enter the SQL Server details ###\")\r\n strServer = input(\"Enter SQL Server Host Name (e.g. localhost\\SQLEXPRESS): \")\r\n strDBName = input(\"Enter SQL Database Name (e.g. PI_Sage50): \")\r\n strUser = input(\"Enter SQL UserName: \")\r\n strPass = input(\"Enter SQL Password: \")\r\n\r\n SQLstring = (\"Driver={ODBC Driver 17 for SQL Server};Server=%s;Database=%s;UID=%s;PWD=%s;\" % (strServer,strDBName,strUser,strPass))\r\n\r\n\r\n ## encrypt the string and store in the db\r\n\r\n en = encryption(key, SQLstring)\r\n en = en.decode('ASCII')\r\n\r\n data = (\"UPDATE tblConnStr SET strSQLServer = '%s' WHERE strSoftkey = '%s'\" % (en, softkey))\r\n\r\n cursor.execute(data)\r\n db.commit()\r\n\r\n ## Now go and get the Sage 50 details from the user and enter in our db\r\n\r\n print(\"\\n### Please enter the Sage 50 details ###\")\r\n strUser = input(\"Enter Sage 50 UserName: \")\r\n strPass = input(\"Enter Sage 50 Password: \")\r\n\r\n filename = \"PIDataSources.txt\"\r\n S50String = (\"UID=%s;PWD=%s; \" % (strUser, strPass))\r\n en = encryption(key, S50String)\r\n en = en.decode('ASCII')\r\n data = (\"UPDATE tblConnStr SET strSage = '%s' WHERE strSoftkey = '%s' \" % (en, softkey))\r\n cursor.execute(data)\r\n db.commit()\r\n\r\n # Test the connection details by inserting audit table\r\n try:\r\n\r\n with open(filename) as f:\r\n source = f.readlines()\r\n source = [x.strip() for x in source]\r\n\r\n comcount = 0\r\n for strDSN in source:\r\n print(strDSN)\r\n DSNcheck = strDSN\r\n strDSN = \"DSN=\" + strDSN +\";\"\r\n db = sqlite3.connect(\"PiConnections.db\")\r\n cursor = db.cursor()\r\n cursor.execute(\"SELECT strSoftkey FROM tblConnStr\")\r\n connsoftkey = str(cursor.fetchone()[0])\r\n key = hardkey + connsoftkey\r\n\r\n cursor.execute(\"SELECT strSQLServer FROM tblConnStr\")\r\n sqlstring = str(cursor.fetchone()[0])\r\n\r\n cursor.execute(\"SELECT strSage FROM tblConnStr\")\r\n s50string = str(cursor.fetchone()[0])\r\n\r\n sqlconn = decryption(key, sqlstring)\r\n s50conn = strDSN + decryption(key, s50string)\r\n Sage50_conn = pyodbc.connect(s50conn)\r\n SQL_conn = pyodbc.connect(sqlconn)\r\n\r\n cursor1 = Sage50_conn.cursor()\r\n SQLData = SQL_conn.cursor()\r\n if comcount == 0:\r\n auditcheck = (\"IF OBJECT_ID('dbo.SAGE50_ETL_AUDIT', 'U') IS NOT NULL DROP TABLE dbo.SAGE50_ETL_AUDIT\")\r\n\r\n SQLData.execute(auditcheck)\r\n SQLData.commit()\r\n\r\n audittable = (\"CREATE TABLE [dbo].[SAGE50_ETL_AUDIT]([ID] [int] IDENTITY(1,1) NOT NULL,\"\r\n \"[Table_Name] [varchar](200) NULL,\"\r\n \"[Started_Update] [datetime] NULL,\"\r\n \"[Completed_Update] [datetime] NULL,\"\r\n \"[PI_ID] INT NULL,\"\r\n \")\")\r\n\r\n SQLData.execute(audittable)\r\n SQLData.commit()\r\n\r\n for table in cursor1.tables():\r\n tb = table.table_name\r\n auditsetup = (\"INSERT INTO [dbo].[SAGE50_ETL_AUDIT] (Table_Name,PI_ID) VALUES (?,?)\")\r\n SQLData.execute(auditsetup, tb, comcount)\r\n SQLData.commit()\r\n comcount = comcount + 1\r\n SQLData.close()\r\n Sage50_conn.close()\r\n input(\"\\n### Credentials Updated Successfully ### \\n Press any key to continue\")\r\n db.close()\r\n break\r\n\r\n except Exception as e:\r\n check = \"driver\"\r\n if check in str(e):\r\n print(\"\\n### ERROR:\\n\" + str(e))\r\n print(DSNcheck + \": Is this the correct Data Source Name?\")\r\n print(\"\\nPlease check that the Microsoft ODBC Driver 17 for SQL Server \"\r\n \"and Sage 50 ODBC drivers are both installed and configured correctly.\")\r\n print(\"\\n### Please check and re-enter details below ###\")\r\n else:\r\n print(\"\\n### ERROR:\\n\" + str(e))\r\n print(\"\\n### DETAILS INCORRECT ### \\n### Please check and re-enter details below ###\")\r\n\r\n\r\n","repo_name":"Panintelligence/sage-50-etl","sub_path":"Sage50_Connections.py","file_name":"Sage50_Connections.py","file_ext":"py","file_size_in_byte":6243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28993925169","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # **Práctica 4: Métricas de distancia (datos estandarizados)**\n# \n# Nombre:\n# \n# Número de cuenta:\n# \n# Email:\n\n# **Objetivo.** Obtener las matrices de distancia (Euclidiana, Chebyshev, Manhattan, Minkowski) a partir de una matriz de datos.\n# \n# \n# **Fuente de datos:**\n# \n# * ingresos: son ingresos mensuales de 1 o 2 personas, si están casados.\n# * gastos_comunes: son gastos mensuales de 1 o 2 personas, si están casados. \n# * pago_coche\n# * gastos_otros\n# * ahorros\n# * vivienda: valor de la vivienda.\n# * estado_civil: 0-soltero, 1-casado, 2-divorciado\n# * hijos: cantidad de hijos menores (no trabajan).\n# * trabajo: 0-sin trabajo, 1-autonomo, 2-asalariado, 3-empresario, 4-autonomos, 5-asalariados, 6-autonomo y asalariado, 7-empresario y autonomo, 8-empresarios o empresario y autónomo \n# * comprar: 0-alquilar, 1-comprar casa a través de crédito hipotecario con tasa fija a 30 años.\n# \n\n# #### **1) Importar las bibliotecas necesarias**\n# \n\n# In[1]:\n\n\nimport pandas as pd \n# Para la manipulación y análisis de datos\nimport numpy as np \n# Para crear vectores y matrices n dimensionales\nimport matplotlib.pyplot as plt \n# Para generar gráficas a partir de los datos\nfrom scipy.spatial.distance import cdist \n# Para el cálculo de distancias\nfrom scipy.spatial import distance\n\n\n# #### **2) Importar los datos**\n\n# In[2]:\n\n\nHipoteca = pd.read_csv(\"Hipoteca.csv\")\nHipoteca\n\n\n# In[3]:\n\n\nHipoteca.info()\n\n\n# **Estandarización de datos**\n# \n# En los algoritmos basados en distancias es fundamental escalar o normalizar los datos para que cada una de las variables contribuyan por igual en el análisis.\n\n# In[42]:\n\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler \nestandarizar = StandardScaler() # Se instancia el objeto StandardScaler o MinMaxScaler\n# Con MinMaxScaler tenemos valores entre 0 y 1\nMEstandarizada = estandarizar.fit_transform(Hipoteca) # Se calculan la media y desviación y se escalan los datos\n\n\n# In[43]:\n\n\npd.DataFrame(MEstandarizada) \n\n\n# #### **3) Matrices de distancia**\n\n# **a) Matriz de distancias: Euclidiana**\n\n# In[44]:\n\n\nDstEuclidiana = cdist(MEstandarizada, MEstandarizada, metric='euclidean')\nMEuclidiana = pd.DataFrame(DstEuclidiana)\n\n\n# In[45]:\n\n\nprint(MEuclidiana)\n#MEuclidiana \n\n\n# In[46]:\n\n\nprint(MEuclidiana.round(3))\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[47]:\n\n\nDstEuclidiana = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='euclidean')\nMEuclidiana = pd.DataFrame(DstEuclidiana)\nprint(MEuclidiana) \n\n\n# Distancia entre dos objetos\n\n# In[48]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstEuclidiana = distance.euclidean(Objeto1,Objeto2)\ndstEuclidiana \n\n\n# **b) Matriz de distancias: Chebyshev**\n\n# In[49]:\n\n\nDstChebyshev = cdist(MEstandarizada, MEstandarizada, metric='chebyshev')\nMChebyshev = pd.DataFrame(DstChebyshev)\n\n\n# In[50]:\n\n\nprint(MChebyshev)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[51]:\n\n\nDstChebyshev = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='chebyshev')\nMChebyshev = pd.DataFrame(DstChebyshev)\nprint(MChebyshev)\n\n\n# Distancia entre dos objetos\n\n# In[52]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstChebyshev = distance.chebyshev(Objeto1,Objeto2)\ndstChebyshev\n\n\n# **c) Matriz de distancias: Manhattan**\n\n# In[53]:\n\n\nDstManhattan = cdist(MEstandarizada, MEstandarizada, metric='cityblock')\nMManhattan = pd.DataFrame(DstManhattan)\n\n\n# In[54]:\n\n\nprint(MManhattan)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[55]:\n\n\nDstManhattan = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='cityblock')\nMManhattan = pd.DataFrame(DstManhattan)\nprint(MManhattan)\n\n\n# Distancia entre dos objetos\n\n# In[56]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstManhattan = distance.cityblock(Objeto1,Objeto2)\ndstManhattan\n\n\n# **d) Matriz de distancias: Minkowski**\n\n# In[57]:\n\n\nDstMinkowski = cdist(MEstandarizada, MEstandarizada, metric='minkowski', p=1.5)\nMMinkowski = pd.DataFrame(DstMinkowski)\n\n\n# In[58]:\n\n\nprint(MMinkowski)\n\n\n# Matriz de distancias de una parte del total de objetos\n\n# In[59]:\n\n\nDstMinkowski = cdist(MEstandarizada[0:10], MEstandarizada[0:10], metric='minkowski', p=1.5)\nMMinkowski = pd.DataFrame(DstMinkowski)\nprint(MMinkowski)\n\n\n# Distancia entre dos objetos\n\n# In[60]:\n\n\nObjeto1 = MEstandarizada[0]\nObjeto2 = MEstandarizada[1]\ndstMinkowski = distance.minkowski(Objeto1,Objeto2, p=1.5)\ndstMinkowski\n\n","repo_name":"Palatio93/palatia","sub_path":"IA-Práctica4-MétricasDistancia(DatosEstandarizados).py","file_name":"IA-Práctica4-MétricasDistancia(DatosEstandarizados).py","file_ext":"py","file_size_in_byte":4583,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"16345015234","text":"# 문제\n# 주민등록번호 뒷 자리 7자리 중 첫째 자리는 성별을 나타내는데, 1, 3은 남자 2, 4는 여자를 의미한다. 사용자로부터 13자리의 주민등록번호를 입력 받은 후 성별 (남자, 여자)를 출력하는 프로그램을 작성하라.\n\n# >> 주민등록번호: 821010-1635210\n# 남자\n\ni = input(\"주민등록번호를 입력하세요:\")\n\nif \"1\" ==i[7] or \"3\" ==i[7]:\n print(\"남자\")\nelse:\n \"2\" ==i[7] or \"4\" == i[7]\n print(\"여자\")\n\n# 결과값\n# 주민등록번호를 입력하세요 : 898989-3030215\n# 여자","repo_name":"SEONGJAE-YOO/python-project.","sub_path":"pythonbasic/if02.py","file_name":"if02.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"72769723784","text":"#! ~/anaconda3/bin/python\n\nimport sys,argparse\nfrom src.main import main\nfrom src.model.models import MODELS, \\\n showModels, \\\n Clustering_Exception\nfrom src.model.kmer import Kmer_Exception\nfrom src.utils.extract import Extract_Exception\n\n\nDEFAULTMODEL = 'dbscan'\nDEFAULTKMER = 11\nDEFAULTNORM = 'l2'\nDEFAULTMINREADS = 5\nDEFAULTCOMP = 2\nDEFAULTEPS = 0.0\nDEFAULTTRIM = 0.1\nDEFMINLEN = 50\nDEFMAXLEN = 25000\nDEFAULTPREFIX = './clustered'\n\nparser = argparse.ArgumentParser(prog='ClusterAmplicons.py', description='Clustering by kmer counts')\nsubparsers = parser.add_subparsers(title='subcommands')\nparser_main = subparsers.add_parser('cluster', help='cluster reads')\nparser_main.set_defaults(func=main)\nparser_main.set_defaults(prog=f'{sys.argv[0]} cluster')\nparser_desc = subparsers.add_parser('describe', help='describe models')\nparser_desc.set_defaults(func=showModels)\nparser_desc.set_defaults(prog=f'{sys.argv[0]} describe')\n#describe\nparser_desc.add_argument('-M','--model', dest='model', choices=MODELS.keys(), type=str, default=None,\n help='Show argmap and defaults for specfic model. Default None (show all)')\n\n#cluster\nparser_main.add_argument('-b','--inBAM', dest='inBAM', type=str, default=None,\n help='input BAM of CCS alignments')\nparser_main.add_argument('-Q','--inFastq', dest='inFastq', type=str, default=None,\n help='input BAM of CCS alignments')\nparser_main.add_argument('-j,--njobs', dest='njobs', type=int, default=None,\n help='j parallel jobs (only for some models). Default 1')\nkmer = parser_main.add_argument_group('kmers')\nkmer.add_argument('-k','--kmer', dest='kmer', type=int, default=DEFAULTKMER,\n help=f'kmer size for clustering. Default {DEFAULTKMER}')\nkmer.add_argument('-z','--minimizer', dest='minimizer', type=int, default=0,\n help='group kmers by minimizer of length z. Default 0 (no minimizer)')\nkmer.add_argument('-H','--noHPcollapse', dest='hpCollapse', nargs='?', type=int, default=1, const=0,\n help='Collapse all HP to max H length. Default 1 (collapse all HP to length 1)')\nkmer.add_argument('-T','--trim', dest='trim', type=float, default=DEFAULTTRIM,\n help=f'Trim kmers with freq < trim or freq > (1-trim). Default {DEFAULTTRIM:.2f}')\nkmer.add_argument('--trimLow', dest='trimLow', type=float, default=None,\n help=f'Trim kmers with frequency < trim. Over-rides -T. Default None')\nkmer.add_argument('--trimHigh', dest='trimHigh', type=float, default=None,\n help=f'Trim kmers with frequency > trimHigh. Over-rides -T. Default None')\nclust = parser_main.add_argument_group('cluster')\nclust.add_argument('-M','--model', dest='model', type=str, choices=MODELS.keys(), default=DEFAULTMODEL,\n help=f'clustering model. See https://scikit-learn.org/stable/modules/clustering.html. Default {DEFAULTMODEL}')\nclust.add_argument('-a','--agg', dest='agg', type=str, choices=['pca','featagg'],default='pca',\n help='Feature reduction method. Default pca')\nclust.add_argument('-c','--components', dest='components', type=int, default=DEFAULTCOMP,\n help=f'Use first c components of PCA/FeatAgg for clustering. Set to 0 for no reduction. Default {DEFAULTCOMP}')\nclust.add_argument('-e','--eps', dest='eps', type=float, default=None,\n help='eps cluster tolerance. Default None')\nclust.add_argument('-m','--minReads', dest='minReads', type=int, default=DEFAULTMINREADS,\n help=f'Minimum reads to be a cluster. Default {DEFAULTMINREADS}')\nclust.add_argument('-n','--normalize', dest='normalize', type=str, choices=['l1','l2','none'], default=DEFAULTNORM,\n help=f'normalization of kmer counts. Default {DEFAULTNORM}')\nclust.add_argument('-i','--ignoreEnds', dest='ignoreEnds', type=int, default=0,\n help='ignore i bases at ends of amplicons for clustering. Default 0')\nclust.add_argument('-P','--params', dest='params', type=str, default=None,\n help='json file of parameters for specific model. Order of precedence: json > CL-opts > defaults. Default None')\nfilt = parser_main.add_argument_group('filter')\nfilt.add_argument('-r','--region', dest='region', type=str, default=None,\n help='Target region for selection of reads, format \\'[chr]:[start]-[stop]\\'. Example \\'4:3076604-3076660\\'. \\nDefault all reads (no region)')\nfilt.add_argument('--extractReference', dest='reference', type=str, default=None,\n help='Extract subsequence at region coordinates for clustering using fasta reference (must have .fai). Maps 100nt on either side of region to each read and extracts sequence inbetween for kmer counting. \\nDefault None (use full read)')\nfilt.add_argument('-q','--minQV', dest='minQV', type=float, default=0.99,\n help='Minimum quality [0-1] to use for clustering. Default 0.99')\nfilt.add_argument('-l','--minLength', dest='minLength', type=int, default=DEFMINLEN,\n help=f'Minimum length read to use for clustering. Default {DEFMINLEN}')\nfilt.add_argument('-L','--maxLength', dest='maxLength', type=int, default=DEFMAXLEN,\n help=f'Maximum length read to use for clustering. Default {DEFMAXLEN}')\nfilt.add_argument('-w','--whitelist', dest='whitelist', type=str, default=None,\n help='whitelist of read names to cluster. Default None')\nfilt.add_argument('-N','--nReads', dest='nReads', type=int, default=0,\n help='Randomly downsample to nReads after filtering. Default 0 (all avail reads)')\nfilt.add_argument('-f','--flanks', dest='flanks', type=str, default=None,\n help='fasta of flanking/primer sequence. Reads not mapping to both will be filtered. Default None')\nfilt.add_argument('-A','--noArtifactFilter', dest='palfilter', action='store_false', default=True,\n help='Turn off palindromic-artifact filtering. Default use artifact filter')\nfilt.add_argument('-s','--seed', dest='seed',type=int, default=17,\n help='Random seed for downsampling. Default 17')\nout = parser_main.add_argument_group('output')\nout.add_argument('-p','--prefix', dest='prefix', type=str, default=DEFAULTPREFIX,\n help=f'Output prefix. Default {DEFAULTPREFIX}')\nout.add_argument('-S','--splitBam', dest='splitBam', action='store_true',\n help='split clusters into separate bams (noise and no-cluster dropped). Default one bam')\nout.add_argument('-x','--noBam', dest='noBam', action='store_true',\n help='Do not export HP-tagged bam of clustered reads')\nout.add_argument('-F','--fastq', dest='fastq', action='store_true',\n help='Export one fastq per cluster')\nout.add_argument('-d','--drop', dest='drop', action='store_true',\n help='Drop reads with no cluster in output bam. Default keep all reads.')\nout.add_argument('-t','--testPlot', dest='testPlot', action='store_true',\n help='Plot reads vs dist to nearest m-neighbors without clustering')\nout.add_argument('-g','--plotReads', dest='plotReads', type=int, default=None,\n help='Write pairplot of first g reduced axes for each read. Default None (no plot)')\nout.add_argument('-X','--exportKmerTable', dest='exportKmerTable', action='store_true',default=False,\n help='Export kmer count table after trimming. Default False')\n\ntry:\n args = parser.parse_args()\n if hasattr(args,'inBAM'):\n if args.inBAM=='-' and not args.noBam:\n raise Clustering_Exception('Retagging streamed bam is not supported. Please use -x option')\n if args.inBAM and args.inFastq:\n raise Clustering_Exception('Please use either BAM or Fastq, not both')\n if not args.inFastq is None:\n if not args.noBam:\n print('Fastq Input. Turning off bam output (-x)')\n args.noBam = True\n if args.palfilter:\n print('Fastq Input. Turning off artifact filter (-A)')\n args.palfilter = False\n if args.region:\n print('Fastq Input. Ignoring region')\n if hasattr(args,'plotReads'):\n if args.plotReads == 1:\n raise Clustering_Exception('PlotReads argument cannot be 1. Must be 0 (no plot) or >=2')\n if hasattr(args,'reference'):\n if args.reference and args.flanks:\n raise Clustering_Exception('Extracting subsequence and requiring explicit flanks is redundant. One or the other!')\n args.func(args)\nexcept (Clustering_Exception,Kmer_Exception,Extract_Exception) as e:\n print(f'ERROR: {e}')\n sys.exit(1)\n","repo_name":"PacificBiosciences/pbampliconclustering","sub_path":"ClusterAmplicons.py","file_name":"ClusterAmplicons.py","file_ext":"py","file_size_in_byte":8747,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"81"} +{"seq_id":"40428300007","text":"import re\nfrom itertools import chain\nfrom dulwich import objects\nfrom subprocess import Popen, PIPE\n\nfrom vcs.conf import settings\nfrom vcs.backends.base import BaseChangeset, EmptyChangeset\nfrom vcs.exceptions import (\n RepositoryError, ChangesetError, NodeDoesNotExistError, VCSError,\n ChangesetDoesNotExistError, ImproperArchiveTypeError\n)\nfrom vcs.nodes import (\n FileNode, DirNode, NodeKind, RootNode, SubModuleNode,\n ChangedFileNodesGenerator, AddedFileNodesGenerator, RemovedFileNodesGenerator\n)\nfrom vcs.utils import (\n safe_unicode, safe_str, safe_int, date_fromtimestamp\n)\nfrom vcs.utils.lazy import LazyProperty\n\n\nclass GitChangeset(BaseChangeset):\n \"\"\"\n Represents state of the repository at single revision.\n \"\"\"\n\n def __init__(self, repository, revision):\n self._stat_modes = {}\n self.repository = repository\n\n try:\n commit = self.repository._repo[revision]\n if isinstance(commit, objects.Tag):\n revision = commit.object[1]\n commit = self.repository._repo.get_object(commit.object[1])\n except KeyError:\n raise RepositoryError(\"Cannot get object with id %s\" % revision)\n self.raw_id = revision\n self.id = self.raw_id\n self.short_id = self.raw_id[:12]\n self._commit = commit\n self._tree_id = commit.tree\n self._committer_property = 'committer'\n self._author_property = 'author'\n self._date_property = 'commit_time'\n self._date_tz_property = 'commit_timezone'\n self.revision = repository.revisions.index(revision)\n\n self.nodes = {}\n self._paths = {}\n\n @LazyProperty\n def message(self):\n return safe_unicode(self._commit.message)\n\n @LazyProperty\n def committer(self):\n return safe_unicode(getattr(self._commit, self._committer_property))\n\n @LazyProperty\n def author(self):\n return safe_unicode(getattr(self._commit, self._author_property))\n\n @LazyProperty\n def date(self):\n return date_fromtimestamp(getattr(self._commit, self._date_property),\n getattr(self._commit, self._date_tz_property))\n\n @LazyProperty\n def _timestamp(self):\n return getattr(self._commit, self._date_property)\n\n @LazyProperty\n def status(self):\n \"\"\"\n Returns modified, added, removed, deleted files for current changeset\n \"\"\"\n return self.changed, self.added, self.removed\n\n @LazyProperty\n def tags(self):\n _tags = []\n for tname, tsha in self.repository.tags.iteritems():\n if tsha == self.raw_id:\n _tags.append(tname)\n return _tags\n\n @LazyProperty\n def branch(self):\n\n heads = self.repository._heads(reverse=False)\n\n ref = heads.get(self.raw_id)\n if ref:\n return safe_unicode(ref)\n\n def _fix_path(self, path):\n \"\"\"\n Paths are stored without trailing slash so we need to get rid off it if\n needed.\n \"\"\"\n if path.endswith('/'):\n path = path.rstrip('/')\n return path\n\n def _get_id_for_path(self, path):\n\n # FIXME: Please, spare a couple of minutes and make those codes cleaner;\n if not path in self._paths:\n path = path.strip('/')\n # set root tree\n tree = self.repository._repo[self._tree_id]\n if path == '':\n self._paths[''] = tree.id\n return tree.id\n splitted = path.split('/')\n dirs, name = splitted[:-1], splitted[-1]\n curdir = ''\n\n # initially extract things from root dir\n for item, stat, id in tree.iteritems():\n if curdir:\n name = '/'.join((curdir, item))\n else:\n name = item\n self._paths[name] = id\n self._stat_modes[name] = stat\n\n for dir in dirs:\n if curdir:\n curdir = '/'.join((curdir, dir))\n else:\n curdir = dir\n dir_id = None\n for item, stat, id in tree.iteritems():\n if dir == item:\n dir_id = id\n if dir_id:\n # Update tree\n tree = self.repository._repo[dir_id]\n if not isinstance(tree, objects.Tree):\n raise ChangesetError('%s is not a directory' % curdir)\n else:\n raise ChangesetError('%s have not been found' % curdir)\n\n # cache all items from the given traversed tree\n for item, stat, id in tree.iteritems():\n if curdir:\n name = '/'.join((curdir, item))\n else:\n name = item\n self._paths[name] = id\n self._stat_modes[name] = stat\n if not path in self._paths:\n raise NodeDoesNotExistError(\"There is no file nor directory \"\n \"at the given path '%s' at revision %s\"\n % (path, self.short_id))\n return self._paths[path]\n\n def _get_kind(self, path):\n obj = self.repository._repo[self._get_id_for_path(path)]\n if isinstance(obj, objects.Blob):\n return NodeKind.FILE\n elif isinstance(obj, objects.Tree):\n return NodeKind.DIR\n\n def _get_filectx(self, path):\n path = self._fix_path(path)\n if self._get_kind(path) != NodeKind.FILE:\n raise ChangesetError(\"File does not exist for revision %s at \"\n \" '%s'\" % (self.raw_id, path))\n return path\n\n def _get_file_nodes(self):\n return chain(*(t[2] for t in self.walk()))\n\n @LazyProperty\n def parents(self):\n \"\"\"\n Returns list of parents changesets.\n \"\"\"\n return [self.repository.get_changeset(parent)\n for parent in self._commit.parents]\n\n @LazyProperty\n def children(self):\n \"\"\"\n Returns list of children changesets.\n \"\"\"\n rev_filter = settings.GIT_REV_FILTER\n cmd = \"rev-list %s --children | grep '^%s'\" % (rev_filter, self.raw_id)\n so, se = self.repository.run_git_command(cmd)\n\n children = []\n for l in so.splitlines():\n childs = l.split(' ')[1:]\n children.extend(childs)\n return [self.repository.get_changeset(cs) for cs in children]\n\n def next(self, branch=None):\n\n if branch and self.branch != branch:\n raise VCSError('Branch option used on changeset not belonging '\n 'to that branch')\n\n def _next(changeset, branch):\n try:\n next_ = changeset.revision + 1\n next_rev = changeset.repository.revisions[next_]\n except IndexError:\n raise ChangesetDoesNotExistError\n cs = changeset.repository.get_changeset(next_rev)\n\n if branch and branch != cs.branch:\n return _next(cs, branch)\n\n return cs\n\n return _next(self, branch)\n\n def prev(self, branch=None):\n if branch and self.branch != branch:\n raise VCSError('Branch option used on changeset not belonging '\n 'to that branch')\n\n def _prev(changeset, branch):\n try:\n prev_ = changeset.revision - 1\n if prev_ < 0:\n raise IndexError\n prev_rev = changeset.repository.revisions[prev_]\n except IndexError:\n raise ChangesetDoesNotExistError\n\n cs = changeset.repository.get_changeset(prev_rev)\n\n if branch and branch != cs.branch:\n return _prev(cs, branch)\n\n return cs\n\n return _prev(self, branch)\n\n def diff(self, ignore_whitespace=True, context=3):\n rev1 = self.parents[0] if self.parents else self.repository.EMPTY_CHANGESET\n rev2 = self\n return ''.join(self.repository.get_diff(rev1, rev2,\n ignore_whitespace=ignore_whitespace,\n context=context))\n\n def get_file_mode(self, path):\n \"\"\"\n Returns stat mode of the file at the given ``path``.\n \"\"\"\n # ensure path is traversed\n self._get_id_for_path(path)\n return self._stat_modes[path]\n\n def get_file_content(self, path):\n \"\"\"\n Returns content of the file at given ``path``.\n \"\"\"\n id = self._get_id_for_path(path)\n blob = self.repository._repo[id]\n return blob.as_pretty_string()\n\n def get_file_size(self, path):\n \"\"\"\n Returns size of the file at given ``path``.\n \"\"\"\n id = self._get_id_for_path(path)\n blob = self.repository._repo[id]\n return blob.raw_length()\n\n def get_file_changeset(self, path):\n \"\"\"\n Returns last commit of the file at the given ``path``.\n \"\"\"\n return self.get_file_history(path, limit=1)[0]\n\n def get_file_history(self, path, limit=None):\n \"\"\"\n Returns history of file as reversed list of ``Changeset`` objects for\n which file at given ``path`` has been modified.\n\n TODO: This function now uses os underlying 'git' and 'grep' commands\n which is generally not good. Should be replaced with algorithm\n iterating commits.\n \"\"\"\n self._get_filectx(path)\n cs_id = safe_str(self.id)\n f_path = safe_str(path)\n\n if limit:\n cmd = 'log -n %s --pretty=\"format: %%H\" -s -p %s -- \"%s\"' % (\n safe_int(limit, 0), cs_id, f_path\n )\n\n else:\n cmd = 'log --pretty=\"format: %%H\" -s -p %s -- \"%s\"' % (\n cs_id, f_path\n )\n so, se = self.repository.run_git_command(cmd)\n ids = re.findall(r'[0-9a-fA-F]{40}', so)\n return [self.repository.get_changeset(id) for id in ids]\n\n def get_file_history_2(self, path):\n \"\"\"\n Returns history of file as reversed list of ``Changeset`` objects for\n which file at given ``path`` has been modified.\n\n \"\"\"\n self._get_filectx(path)\n from dulwich.walk import Walker\n include = [self.id]\n walker = Walker(self.repository._repo.object_store, include,\n paths=[path], max_entries=1)\n return [self.repository.get_changeset(sha)\n for sha in (x.commit.id for x in walker)]\n\n def get_file_annotate(self, path):\n \"\"\"\n Returns a generator of four element tuples with\n lineno, sha, changeset lazy loader and line\n\n TODO: This function now uses os underlying 'git' command which is\n generally not good. Should be replaced with algorithm iterating\n commits.\n \"\"\"\n cmd = 'blame -l --root -r %s -- \"%s\"' % (self.id, path)\n # -l ==> outputs long shas (and we need all 40 characters)\n # --root ==> doesn't put '^' character for bounderies\n # -r sha ==> blames for the given revision\n so, se = self.repository.run_git_command(cmd)\n\n for i, blame_line in enumerate(so.split('\\n')[:-1]):\n ln_no = i + 1\n sha, line = re.split(r' ', blame_line, 1)\n yield (ln_no, sha, lambda: self.repository.get_changeset(sha), line)\n\n def fill_archive(self, stream=None, kind='tgz', prefix=None,\n subrepos=False):\n \"\"\"\n Fills up given stream.\n\n :param stream: file like object.\n :param kind: one of following: ``zip``, ``tgz`` or ``tbz2``.\n Default: ``tgz``.\n :param prefix: name of root directory in archive.\n Default is repository name and changeset's raw_id joined with dash\n (``repo-tip.``).\n :param subrepos: include subrepos in this archive.\n\n :raise ImproperArchiveTypeError: If given kind is wrong.\n :raise VcsError: If given stream is None\n\n \"\"\"\n allowed_kinds = settings.ARCHIVE_SPECS.keys()\n if kind not in allowed_kinds:\n raise ImproperArchiveTypeError('Archive kind not supported use one'\n 'of %s', allowed_kinds)\n\n if prefix is None:\n prefix = '%s-%s' % (self.repository.name, self.short_id)\n elif prefix.startswith('/'):\n raise VCSError(\"Prefix cannot start with leading slash\")\n elif prefix.strip() == '':\n raise VCSError(\"Prefix cannot be empty\")\n\n if kind == 'zip':\n frmt = 'zip'\n else:\n frmt = 'tar'\n _git_path = settings.GIT_EXECUTABLE_PATH\n cmd = '%s archive --format=%s --prefix=%s/ %s' % (_git_path,\n frmt, prefix, self.raw_id)\n if kind == 'tgz':\n cmd += ' | gzip -9'\n elif kind == 'tbz2':\n cmd += ' | bzip2 -9'\n\n if stream is None:\n raise VCSError('You need to pass in a valid stream for filling'\n ' with archival data')\n popen = Popen(cmd, stdout=PIPE, stderr=PIPE, shell=True,\n cwd=self.repository.path)\n\n buffer_size = 1024 * 8\n chunk = popen.stdout.read(buffer_size)\n while chunk:\n stream.write(chunk)\n chunk = popen.stdout.read(buffer_size)\n # Make sure all descriptors would be read\n popen.communicate()\n\n def get_nodes(self, path):\n if self._get_kind(path) != NodeKind.DIR:\n raise ChangesetError(\"Directory does not exist for revision %s at \"\n \" '%s'\" % (self.revision, path))\n path = self._fix_path(path)\n id = self._get_id_for_path(path)\n tree = self.repository._repo[id]\n dirnodes = []\n filenodes = []\n als = self.repository.alias\n for name, stat, id in tree.iteritems():\n if objects.S_ISGITLINK(stat):\n dirnodes.append(SubModuleNode(name, url=None, changeset=id,\n alias=als))\n continue\n\n obj = self.repository._repo.get_object(id)\n if path != '':\n obj_path = '/'.join((path, name))\n else:\n obj_path = name\n if obj_path not in self._stat_modes:\n self._stat_modes[obj_path] = stat\n if isinstance(obj, objects.Tree):\n dirnodes.append(DirNode(obj_path, changeset=self))\n elif isinstance(obj, objects.Blob):\n filenodes.append(FileNode(obj_path, changeset=self, mode=stat))\n else:\n raise ChangesetError(\"Requested object should be Tree \"\n \"or Blob, is %r\" % type(obj))\n nodes = dirnodes + filenodes\n for node in nodes:\n if not node.path in self.nodes:\n self.nodes[node.path] = node\n nodes.sort()\n return nodes\n\n def get_node(self, path):\n if isinstance(path, unicode):\n path = path.encode('utf-8')\n path = self._fix_path(path)\n if not path in self.nodes:\n try:\n id_ = self._get_id_for_path(path)\n except ChangesetError:\n raise NodeDoesNotExistError(\"Cannot find one of parents' \"\n \"directories for a given path: %s\" % path)\n\n _GL = lambda m: m and objects.S_ISGITLINK(m)\n if _GL(self._stat_modes.get(path)):\n node = SubModuleNode(path, url=None, changeset=id_,\n alias=self.repository.alias)\n else:\n obj = self.repository._repo.get_object(id_)\n\n if isinstance(obj, objects.Tree):\n if path == '':\n node = RootNode(changeset=self)\n else:\n node = DirNode(path, changeset=self)\n node._tree = obj\n elif isinstance(obj, objects.Blob):\n node = FileNode(path, changeset=self)\n node._blob = obj\n else:\n raise NodeDoesNotExistError(\"There is no file nor directory \"\n \"at the given path '%s' at revision %s\"\n % (path, self.short_id))\n # cache node\n self.nodes[path] = node\n return self.nodes[path]\n\n @LazyProperty\n def affected_files(self):\n \"\"\"\n Get's a fast accessible file changes for given changeset\n \"\"\"\n added, modified, deleted = self._changes_cache\n return list(added.union(modified).union(deleted))\n\n @LazyProperty\n def _diff_name_status(self):\n output = []\n for parent in self.parents:\n cmd = 'diff --name-status %s %s --encoding=utf8' % (parent.raw_id,\n self.raw_id)\n so, se = self.repository.run_git_command(cmd)\n output.append(so.strip())\n return '\\n'.join(output)\n\n @LazyProperty\n def _changes_cache(self):\n added = set()\n modified = set()\n deleted = set()\n _r = self.repository._repo\n\n parents = self.parents\n if not self.parents:\n parents = [EmptyChangeset()]\n for parent in parents:\n if isinstance(parent, EmptyChangeset):\n oid = None\n else:\n oid = _r[parent.raw_id].tree\n changes = _r.object_store.tree_changes(oid, _r[self.raw_id].tree)\n for (oldpath, newpath), (_, _), (_, _) in changes:\n if newpath and oldpath:\n modified.add(newpath)\n elif newpath and not oldpath:\n added.add(newpath)\n elif not newpath and oldpath:\n deleted.add(oldpath)\n return added, modified, deleted\n\n def _get_paths_for_status(self, status):\n \"\"\"\n Returns sorted list of paths for given ``status``.\n\n :param status: one of: *added*, *modified* or *deleted*\n \"\"\"\n added, modified, deleted = self._changes_cache\n return sorted({\n 'added': list(added),\n 'modified': list(modified),\n 'deleted': list(deleted)}[status]\n )\n\n @LazyProperty\n def added(self):\n \"\"\"\n Returns list of added ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return list(self._get_file_nodes())\n return AddedFileNodesGenerator([n for n in\n self._get_paths_for_status('added')], self)\n\n @LazyProperty\n def changed(self):\n \"\"\"\n Returns list of modified ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return []\n return ChangedFileNodesGenerator([n for n in\n self._get_paths_for_status('modified')], self)\n\n @LazyProperty\n def removed(self):\n \"\"\"\n Returns list of removed ``FileNode`` objects.\n \"\"\"\n if not self.parents:\n return []\n return RemovedFileNodesGenerator([n for n in\n self._get_paths_for_status('deleted')], self)\n","repo_name":"codeinn/vcs","sub_path":"vcs/backends/git/changeset.py","file_name":"changeset.py","file_ext":"py","file_size_in_byte":19413,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"81"} +{"seq_id":"16545385097","text":"from firebase import Firebase\nimport geocoder\n\nfrom datetime import datetime\n\nnow = datetime.now()\n\nconfig = {\n \"apiKey\": \"AIzaSyD4EwJFr8MTh0lIQmlMMVn2H365WOV08es\",\n \"authDomain\": \"demo1-e68cf.firebaseapp.com\",\n \"databaseURL\": \"https://demo1-e68cf.firebaseio.com\",\n \"storageBucket\": \"demo1-e68cf.appspot.com\"\n}\n\ndef write_to_firebase(driver_name):\n location = geocoder.ip('me').city\n firebase = Firebase(config)\n db = firebase.database()\n current_time = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n data = {\"location\": location}\n res = db.child(\"sleep_list\").child(driver_name).child(current_time).set(data)\n print(res)\n","repo_name":"vignesh5698/accident-avoider","sub_path":"write_to_firebase.py","file_name":"write_to_firebase.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"24021616095","text":"from typing import List, Any, Tuple, Optional, Callable\n\n\nimport glob\nimport os.path as osp\n\nimport torch\nimport torchvision\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import transforms\nfrom torch.utils.data import Dataset, DataLoader, ConcatDataset\nfrom torch.utils.data import DataLoader, random_split\nfrom torchvision.transforms.transforms import CenterCrop, Normalize, \\\n RandomErasing, RandomHorizontalFlip\nfrom torchvision.datasets import DatasetFolder\nimport os.path\n\nimport lightning as pl\nfrom utils import setup_logger\n\nlogger = setup_logger(__name__)\n\nIMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp',\n '.pgm', '.tif', '.tiff', '.webp')\n\nTRAIN_VAL_SPLIT = 0.0 # proportion of training set to use for train, the remainder for validation\n\ndef pil_loader(path: str) -> Image.Image:\n # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n with open(path, 'rb') as f:\n img = Image.open(f)\n return img.convert('RGB')\n\nclass SingleHeadDataset(DatasetFolder):\n def __init__(\n self,\n root: str,\n transform: Optional[Callable] = None,\n target_transform: Optional[Callable] = None,\n loader: Callable[[str], Any] = pil_loader,\n is_valid_file: Optional[Callable[[str], bool]] = None,\n ):\n super(SingleHeadDataset, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,\n transform=transform,\n target_transform=target_transform,\n is_valid_file=is_valid_file)\n self.imgs = self.samples\n\n def __getitem__(self, index):\n path, target = self.samples[index]\n sample = self.loader(path)\n if self.transform is not None:\n sample_1 = self.transform(sample)\n\n return sample_1, target\n\n # def __len__(self):\n # return 100\n\n\nclass DualHeadsDataset(Dataset):\n def __init__(\n self,\n root: str,\n mode: str,\n raw_data_path: str,\n transform: Optional[Callable] = None,\n loader: Callable[[str], Any] = pil_loader,\n is_valid_file: Optional[Callable[[str], bool]] = None,\n ):\n super(DualHeadsDataset, self).__init__()\n self.mode = mode\n self.transform = transform\n self.loader = loader\n valid_ext = IMG_EXTENSIONS if is_valid_file is None else None\n \n self.samples = []\n for ext in valid_ext:\n self.samples.extend(glob.glob(osp.join(root, '*', f'*{ext}')))\n \n self._load_coarse_labels(raw_data_path)\n\n def _load_coarse_labels(self, raw_data_path):\n def unpickle(file):\n import pickle\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n return dict\n data_path = raw_data_path + self.mode\n data_dict = unpickle(data_path)\n del data_dict[b'data']\n coarse_labels = np.array(data_dict[b'coarse_labels'])\n filenames = data_dict[b'filenames']\n filenames = [x.decode(\"utf-8\")for x in filenames]\n self.coarse_label_dict = dict(zip(filenames, coarse_labels))\n\n def __getitem__(self, index):\n path = self.samples[index]\n sample = self.loader(path)\n if self.transform is not None:\n sample = self.transform(sample)\n target = int(path.split('/')[-2])\n coarselabel = self.coarse_label_dict[path.split('/')[-1]]\n # logging.debug(\"labels --------- \" + str(target) + ', ' + str(coarselabel))\n data = {\"image\": sample, \"fine\": target, \"coarse\": coarselabel}\n return data\n \n def __len__(self):\n return len(self.samples)\n\nclass DataModule(pl.LightningDataModule):\n def __init__(self, \n mode_heads: str = 'both',\n train_dir: str = \"path/to/dir\", \n test_dir: str=\"path/to/dir\",\n raw_data_dir: str=\"path/to/dir\",\n batch_size: int = 32,\n num_workers:int = 4):\n super().__init__()\n self.train_dir = self.get_data_dir(mode_heads, train_dir)\n self.test_dir = self.get_data_dir(mode_heads, test_dir)\n self.raw_data_dir = raw_data_dir\n self.batch_size = batch_size\n self.num_workers = num_workers\n self.mode_heads = mode_heads\n\n def get_data_dir(self, mode_heads, base_folder):\n \"\"\"\n Returns the path to the data directory\n\n If there are two heads (fine and coarse), then get the 'fine' labels from the image file\n and get the coarse labelsl from the raw data file\n\n If there is only one head, then get the labels from the image file\n \"\"\"\n if mode_heads == 'fine' or mode_heads == 'both':\n suffix = 'fine'\n elif mode_heads == 'coarse':\n suffix = 'coarse'\n else:\n raise ValueError(f\"mode_out {mode_heads} not recognized\")\n\n folder_name = os.path.join(base_folder, suffix)\n\n return folder_name\n\n def setup_data_dual_heads(self, train_transforms, base_transforms):\n logger.debug(\"DataModule - setup double heads\")\n\n train_set = DualHeadsDataset(\n mode='train',\n root=self.train_dir,\n raw_data_path=self.raw_data_dir,\n transform=train_transforms)\n \n # special case of no split, then use test set for validation\n if TRAIN_VAL_SPLIT == 0.0 or TRAIN_VAL_SPLIT == None:\n logger.debug(\"Using test set for validation\")\n\n val_set = DualHeadsDataset(\n mode='test',\n root=self.test_dir,\n raw_data_path=self.raw_data_dir,\n transform=base_transforms)\n \n self.val_set = val_set\n self.train_set = train_set\n else:\n logger.debug(f\"Using {TRAIN_VAL_SPLIT} proportion of train set for train, remainder for validation\")\n self.train_set, self.val_set = self.get_train_val_splits(train_set) \n\n self.test_set = DualHeadsDataset(\n mode='test',\n root=self.test_dir,\n raw_data_path=self.raw_data_dir,\n transform=base_transforms)\n \n def setup_data_single_head(self, train_transforms, base_transforms):\n logger.debug(\"DataModule - setup single head\")\n\n train_set = SingleHeadDataset(\n root=self.train_dir,\n transform=train_transforms)\n\n # special case of no split, then use test set for validation\n if TRAIN_VAL_SPLIT == 0.0 or TRAIN_VAL_SPLIT == None:\n logger.debug(\"Using test set for validation\")\n\n val_set = SingleHeadDataset(\n root=self.test_dir,\n transform=base_transforms)\n \n self.val_set = val_set\n self.train_set = train_set\n else:\n logger.debug(f\"Using {TRAIN_VAL_SPLIT} proportion of train set for train, remainder for validation\")\n self.train_set, self.val_set = self.get_train_val_splits(train_set) \n\n self.test_set = SingleHeadDataset(\n root=self.test_dir,\n transform=base_transforms)\n \n def get_train_val_splits(self, train_set):\n train_set_size = int(len(train_set) * TRAIN_VAL_SPLIT)\n valid_set_size = len(train_set) - train_set_size\n train_set, val_set = random_split(train_set, [train_set_size, valid_set_size])\n return train_set, val_set\n\n def setup(self, stage: Optional[str] = None):\n logger.debug(\"*********** DataModule - setup ***********\")\n train_transform = transforms.Compose([\n transforms.RandomCrop(32, padding=4, padding_mode='reflect'), \n transforms.RandomHorizontalFlip(),\n transforms.Resize(32),\n transforms.ToTensor(),\n transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n ])\n base_transforms = transforms.Compose([\n transforms.Resize(32),\n transforms.ToTensor(),\n transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2675, 0.2565, 0.2761))\n ])\n \n if self.mode_heads == 'both':\n self.setup_data_dual_heads(train_transform, base_transforms)\n else:\n self.setup_data_single_head(train_transform, base_transforms)\n\n logger.debug(f\"Train set size: {len(self.train_set)}\")\n # logger.debug(f\"Train set transformation: {self.train_set.transform}\")\n\n logger.debug(f\"Validation set size: {len(self.val_set)}\")\n # logger.debug(f\"Validation set transformation: {self.val_set.transform}\")\n\n logger.debug(f\"Test set size: {len(self.test_set)}\")\n # logger.debug(f\"Test set transformation: {self.test_set.transform}\")\n\n def train_dataloader(self):\n return DataLoader(self.train_set,\n shuffle=True,\n batch_size=self.batch_size, \n num_workers=self.num_workers)\n\n def val_dataloader(self):\n return DataLoader(self.val_set,\n shuffle=False, \n batch_size=self.batch_size,\n num_workers=self.num_workers)\n\n def test_dataloader(self):\n return DataLoader(self.test_set,\n shuffle=False,\n batch_size=self.batch_size,\n num_workers=self.num_workers)\n ","repo_name":"Cerenaut/bilateral-brain","sub_path":"datamodule.py","file_name":"datamodule.py","file_ext":"py","file_size_in_byte":9671,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"40308944627","text":"\nfrom __future__ import absolute_import, print_function\nfrom .common import nil, PY2, breakpoint, throw, Exc, error\n\nif PY2:\n from funcsigs import signature\nelse:\n from inspect import signature\nfrom .casting import cast, is_cls\n\nimport time\n\n\ndef flatten_dotted_arg_from_dict(d, pth=None):\n while isinstance(d, dict):\n if not len(d) == 1:\n return\n k = list(d.keys())[0]\n pth = (k,) if not pth else pth + (k,)\n d = d.get(k)\n if d == 'is_set':\n return '.'.join(pth)\n\n\ndef fix_dotted_cli_vals(cli):\n \"\"\"arguments can be given positionally, i.e. without keyname\n e.g. just appname foo.bar.baz. The CLI pre_parser puts this into\n {foo:{bar:{baz: is_set}}}\n We recreate into foo.bar.baz=is_set like other position vals here:\n \"\"\"\n r = ()\n for K, d in cli:\n kv = flatten_dotted_arg_from_dict(d)\n if kv:\n K, d = '%s.%s' % (K, kv), 'is_set'\n r += ((K, d),)\n return r\n\n\ndef map_args_to_func_sig(\n f, cli, ctx, map_from=-1, prefer_positional=True, deep=True, cast=cast\n):\n \"\"\"\n inspect.signature based version.\n\n Py2: This WOULD work using the signature simulation for py2 (funcsigs)\n from https://funcsigs.readthedocs.io/en/0.4/\n but perf is lousy. See below, for 2 we do it in getargspec\n\n Maybe it can be done more effective, not sure yet:\n\n # update: We use funcsigs now for py2... maybe later add the argspec\n # version back again.\n\n \"\"\"\n cli = fix_dotted_cli_vals(cli)\n if map_from == -1:\n # signature does not deliver cls or self, so no effort here:\n map_from = 0\n sig_dict = signature(f).parameters\n va_pos, have_va, pos_params = 0, False, []\n i = 0\n for n, p in sig_dict.items():\n i += 1\n if i <= map_from:\n continue\n if p.kind == p.VAR_POSITIONAL:\n have_va = True\n break\n va_pos += 1\n pos_params.append(n)\n\n args = [nil for i in pos_params]\n kw = {}\n\n def default(n):\n d = sig_dict.get(n)\n if not d:\n return nil\n return d.default if d.default != d.empty else nil\n\n idx, leng = -1, len(cli) - 1\n while idx < leng:\n idx += 1\n n, v = cli[idx]\n if v != 'is_set':\n d = default(n)\n if d != nil:\n v = cast(v, d, {'for_param': n})\n if have_va and n in pos_params:\n args[pos_params.index(n)] = v\n pos_params.remove(n)\n else:\n kw[n] = v\n pos_params.remove(n) if n in pos_params else None\n else:\n # v = is_set when no key is given, just value:\n if pos_params:\n vv = n # the value\n n = pos_params.pop(0) # the key\n d = default(n)\n v = cast(vv, d, {'for_param': n}) if d != nil else vv\n if have_va:\n app = True\n for i in range(0, len(args)):\n if args[i] == nil:\n args[i] = v\n app = False\n break\n if app:\n args.append(n)\n else:\n kw[n] = v\n offset = args.index(nil) if nil in args else 0\n for n in list(pos_params):\n p = sig_dict[n]\n if p.default != p.empty and not is_cls(p):\n if have_va:\n args[pos_params.index(n) + offset] = p.default\n pos_params.remove(n)\n else:\n kw[n] = p.default\n pos_params.remove(n)\n\n argt = ()\n for a in args:\n if a != nil:\n argt += (a,)\n\n allow_types = ctx.get('allow_type_args', False)\n if ctx.get('req_args_complete'):\n\n ps, err = [], Exc.require_value\n for p in pos_params:\n d = default(p)\n if default(p) == nil:\n ps.append({'param': p})\n if not allow_types and type(d) == type:\n ps.append({'param': p, 'type': d.__name__})\n if not allow_types:\n for k, v in kw.items():\n if type(v) == type:\n ps.append({'param': k, 'type': v.__name__})\n if ps:\n [error(err, **p) for p in ps[:-1]]\n throw(err, **ps[-1])\n\n if prefer_positional:\n for p in list(sig_dict.keys())[map_from:]:\n v = sig_dict[p]\n if v.kind != v.POSITIONAL_OR_KEYWORD:\n break\n vm = kw.pop(p, nil)\n if vm != nil:\n argt += (vm,)\n\n return argt, kw\n\n\ndef pretty_type(sigstr):\n s = str(sigstr)\n for f, t in (('=0:\n #get the position of the middle item in the list (should be 3) as floor division gives the lowest whole number\n middle = first + ((last - first)//2)\n #check if this is the value\n if listIn[middle] == key:\n return middle\n #if the key is less than middle value set the last value to 1 position below middle\n elif key < listIn[middle]:\n last = middle - 1\n #if the key is more than middle value set the last value to 1 position more than middle\n else:\n first = middle +1\n return -1\n\nprint(binarySearch(myList,31))\n\n#Binary Search 2\ndef binary_search(v, L):\n#set the lowest index\n low = 0\n#set the highest index -1\n high = len(L)-1\n\n while (low <= high):\n#floor divide to get a int\n mid = (low+high)//2\n#check if the value (14) at position mid (7) is equal to v\n if L[mid] == v:\n return mid\n elif L[mid] < v:\n low = mid + 1\n else:\n high = mid - 1\n\n return len(L)\n# Driver code ...\nkeys = [2, 4, 5, 7, 8, 9, 12, 14, 17, 19, 22, 25, 27, 28, 33, 37]\nargument = int(input(\"Enter a target value: \"))\n\nresult = binary_search(argument, keys)\n#check if result is not equal to 15 then the key has been found\nif (result != len(keys)):\n print(\"%d found at position %d\" %(argument, result))\nelse:\n print(\"%d not found. Return value is %d\" %(argument, result))\n","repo_name":"mrroberts-mslt/Computer-Science-for-Leaving-Certificate-Solutions","sub_path":"Chapter 9/pg211_Search.py","file_name":"pg211_Search.py","file_ext":"py","file_size_in_byte":2867,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"} +{"seq_id":"20731923728","text":"import requests, wget, os, time\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\n\ntimestmp = str(datetime.now().replace(microsecond=0))\n\nheaders = {\n 'Connection': 'keep-alive',\n 'Upgrade-Insecure-Requests': '1',\n 'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n 'Referer': 'http://urctfd.domain/admin/config',\n 'Accept-Language': 'en-US,en;q=0.9,id;q=0.8',\n}\n\ns = requests.Session()\nurl = \"http://urctfd.domain/login\"\n\nr = s.get(url)\nsoup = BeautifulSoup(r.content, \"html.parser\")\n\nnonce = soup.find(\"input\",{\"name\":\"nonce\"})['value']\n\ndata = {\n 'name': 'yourCTFdAdmin',\n 'password': '*****',\n '_submit': 'Submit',\n 'nonce': nonce\n}\n\nprint('Logging in to '+url+' . .')\n\ns.post(url, data=data, allow_redirects=True)\ncookies = s.cookies.get_dict()\n\nprint('Downloading the file . .')\nbackup = requests.get('http://urctfd.domain/admin/export', headers=headers, cookies=cookies, allow_redirects=True, stream=True)\npath = 'backup_dir/'\n\nfile_name = os.path.join(path, 'ctfd_backup'+timestmp+'.zip')\n\nif not os.path.exists(path):\n\tos.makedirs(path)\n\nopen(file_name, 'wb').write(backup.content) \n\nprint('Successfully Exported ['+file_name+']')\n","repo_name":"rexyfahrezi/ctfd-auto-backup","sub_path":"ctfd-auto-backup.py","file_name":"ctfd-auto-backup.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"30892225797","text":"import json\nimport pickle\n\nimport pandas as pd\nimport numpy as np\n\nimport datetime\nclass User:\n def __init__(self, path: str):\n with open(path, 'r') as fp:\n self._user_log = json.load(fp)\n \n self._session_id = self._user_log['session']['session_id']\n self._learner_id = float(self._user_log['session']['learner_id'].replace('NaN', ''))\n self._event_df = pd.DataFrame()\n \n def createEventDataFrame(self):\n event_df = []\n for event in self._user_log['events']:\n timestamp = event['timestamp']\n timestamp = datetime.datetime.fromtimestamp(timestamp / 1e3)\n year = timestamp.year\n month = timestamp.month\n day = timestamp.day\n hour = timestamp.hour\n minute = timestamp.minute\n second = timestamp.second\n \n event_name = event['event'].replace('capacitorLabBasics.', '')\n event_name = event_name.replace('capacitanceScreen.', '')\n \n event_type = event['data']['eventType']\n if 'parameters' in event['data']:\n if 'method' in event['data']['parameters']:\n method_name = event['data']['parameters']['method']\n else:\n method_name = 'null'\n # parameters_event = event['data']['eventType']\n if 'phetioID' in event['data']:\n phetio_id = event['data']['phetioID']\n else:\n phetio_id = 'null'\n \n\n data = event['data']\n \n event_df.append([event_name, event_type, method_name, phetio_id, timestamp, year, month, day, hour, minute, second, data])\n \n event_df = pd.DataFrame(event_df)\n event_df.columns = ['event_name', 'event_type', 'method', 'phetio_id', 'timestamp', 'year', 'month', 'day', 'hour', 'minute', 'second', 'data']\n\n event_df = event_df.sort_values(['year', 'month', 'day', 'hour', 'minute', 'second'])\n \n self._event_df = event_df\n\n def save(self, version=''):\n name = str(self._session_id) + '_' + str(self._learner_id) + version + '_UserObject.pkl'\n name = '../Objects/users/' + name\n with open(name, 'wb') as fp:\n pickle.dump(self, fp)\n \n \n ","repo_name":"epfl-ml4ed/beerslaw-lab","sub_path":"src/extractors/parser/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"35283500160","text":"from caster_dashboard_2.version import get_current_version\r\nfrom dashboard.models.models import LeagueGroup\r\n\r\n\r\ndef version_context(request):\r\n return {\r\n \"version\": get_current_version(),\r\n \"theme\": \"dark\",\r\n }\r\n\r\n\r\ndef profile_context(request):\r\n is_league_admin = False\r\n user = request.user\r\n league_groups = LeagueGroup.objects.all()\r\n for lg in league_groups:\r\n if lg.user == user and lg.rank == 'admin':\r\n is_league_admin = True\r\n\r\n return {\r\n \"is_league_admin\": is_league_admin\r\n }\r\n","repo_name":"sthorsten/CasterDashboard2","sub_path":"backend/src/caster_dashboard_2/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"81"} +{"seq_id":"4435833791","text":"n = int(input())\r\n\r\nx = list(map(int, input().split()))\r\n\r\nnum = [x[0]]\r\n\r\nfor i in range(1, n):\r\n num.append(num[i-1] + x[i])\r\n \r\nanswer = 0 \r\n\r\nfor i in range(n):\r\n answer += x[i] * (num[n - 1] - num[i])\r\n \r\nprint(answer)","repo_name":"MarkSon-42/BackjoonHub","sub_path":"백준/Silver/14929. 귀찮아 （SIB）/귀찮아 （SIB）.py","file_name":"귀찮아 （SIB）.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"10549660232","text":"from bs4 import BeautifulSoup\nimport requests\n\ndef subcategoryCheki():\n site = 'https://www.cheki.com.ng'\n subUrl = []\n\n page_response = requests.get(site, headers={'User-Agent': 'Mozilla/5.0'})\n page_content = BeautifulSoup(page_response.content, \"html.parser\")\n\n subcategory = page_content.find('ul',{\"class\":\"vehicleIcons\"}).findAll('li')\n\n for item in subcategory:\n subCategoryUrl = item.find('a').get('href')\n\n subUrl.append(\n subCategoryUrl\n )\n\n return subUrl\n\n#print(subcategoryCheki())\n\n\ndef getAllPage():\n subUrl = subcategoryCheki()\n page = []\n maxPage = 16\n id = list(range(maxPage))\n del id[0]\n for url in subUrl:\n for item in id:\n link = url + \"?page=\" + str(item)\n page.append({\n 'url': link\n })\n return page\n\n#print(getAllPage())\n\ndef scrapCheki(origin):\n\n site = 'https://www.cheki.com.ng'\n page = getAllPage()\n produits = []\n\n for link in page:\n page_response = requests.get(link[\"url\"], headers={'User-Agent': 'Mozilla/5.0'})\n page_content = BeautifulSoup(page_response.content, \"html.parser\")\n\n logo = 'http://137.74.199.121/img/logo/ng/cheki.jpg'\n logoS='http://137.74.199.121/img/logo/ng/logoS/cheki.jpg'\n\n annonce = page_content.find_all(\"li\", {\"class\": \"listing-unit\"})\n\n for item in annonce:\n try:\n url = item.get(\"data-url\").replace('\\n','').replace(' ','')\n lib = item.find('div', {\"class\": \"listing-unit__title\"}).find(\"a\").text.replace('\\n','')\n img = item.find('div', {\"class\": \"listing-unit__image-container\"}).findAll(\"img\")[0].get(\"data-lazy\")\n desc = item.find('div', {\"class\": \"listing-unit__detail-container\"}).text.replace('\\n','')\n try:\n prix = int(item.find(\"div\", {\"class\": \"listing-unit__price\"}).text.replace(u',', '').replace(u'₦', ''))\n except:\n prix=0\n\n produits.append(\n {\n 'libProduct': lib,\n 'slug': '',\n 'descProduct': desc,\n 'priceProduct': prix,\n 'imgProduct': img,\n 'numSeller': '',\n 'src': site,\n 'urlProduct': site + url,\n 'logo': logo,\n 'logoS':logoS,\n 'origin': origin,\n 'country':'ng'\n }\n )\n\n except:\n continue\n\n return produits\n\nproduits = scrapCheki(origin=1)\nurl = 'http://api.comparez.co/ads/insert-product/'\nfor item in produits:\n response = requests.post(url, data=item)\n # api response\n print(response.json())\n","repo_name":"sysall/WebScrapping","sub_path":"Sites/Nigeria/Cheki.py","file_name":"Cheki.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"41401450468","text":"class HeapSort:\n \"\"\"\n HeapSort\n Responsible for heapsort sorting\n \"\"\"\n\n def max_heapify(self, array, heap_size, position):\n left_element_position = 2 * position + 1\n right_element_position = 2 * position + 2\n\n if (left_element_position <= heap_size - 1 and array[left_element_position] > array[position]):\n largest = left_element_position\n else:\n largest = position\n\n if (right_element_position <= heap_size - 1 and array[right_element_position] > array[largest]):\n largest = right_element_position\n\n if (largest != position):\n array[position], array[largest] = array[largest], array[position]\n self.max_heapify(array, heap_size, largest)\n\n def heap_sort(self, array):\n heap_size = len(array)\n for i in range((heap_size - 1) // 2, -1, -1):\n self.max_heapify(array, heap_size, i)\n\n for i in range(heap_size - 1, 0, -1):\n array[i], array[0] = array[0], array[i]\n self.max_heapify(array, i, 0)\n","repo_name":"s22446/ASD-project1","sub_path":"heap_sort/heap_sort.py","file_name":"heap_sort.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28425393585","text":"class Solution:\n def minimumPossibleSum(self, n: int, target: int) -> int:\n seen=set()\n num=1\n while len(seen) List[str]:\n return [ship.kind for ship in self.board.ships if not is_ship_destroyed(ship)]\n\n\n@dataclass\nclass Game:\n player_1: Player\n player_2: Player\n\n @property\n def has_ended(self) -> bool:\n return any(has_all_ships_destroyed(player.board) for player in self.players)\n\n @property\n def players(self) -> List[Player]:\n return [self.player_1, self.player_2]\n\n\nDEFAULT_GAME_OPTION: GameOption = {\n \"AIR\": AvailableShip(kind=\"aircraft-carrier\", length=5, quantity=1),\n \"BTL\": AvailableShip(kind=\"battleship\", length=4, quantity=1),\n \"SUB\": AvailableShip(kind=\"submarine\", length=3, quantity=1),\n \"DES\": AvailableShip(kind=\"destroyer\", length=3, quantity=1),\n \"PTL\": AvailableShip(kind=\"patrol-ship\", length=2, quantity=1),\n}\n\n\ndef retrieve_available_ships(game_option: GameOption) -> List[str]:\n return [ship_slug for ship_slug, ship_option in game_option.items() if ship_option[\"quantity\"] > 0]\n\n\ndef parse_line_input(line: str, game_option: GameOption) -> Tuple[AvailableShip, Position, ShipDirection]:\n try:\n ship_slug, x, y, direction_slug = line.split(maxsplit=3)\n return (\n game_option[ship_slug],\n Position(int(x), int(y)),\n ShipDirection[direction_slug],\n )\n except (ValueError, KeyError):\n print(\n \"\"\"\n Couldn't understand the given input, please input in the following format:\n - if horizontally: SLG X Y H\n - if vertically: SLG X Y V \n \n For example, if you have a destroyer (DES) and you want to put at position (0, 0) horizontally, write:\n DES 0 0 H \n \"\"\"\n )\n raise InputWithError\n\n\ndef place_ship(player: Player, chosen_ship: AvailableShip, position: Position, direction: ShipDirection):\n if chosen_ship[\"quantity\"] < 1:\n print(\"Ship is unavailable!\")\n raise UnavailableShip\n\n try:\n place_ship_on_board(Ship(chosen_ship[\"kind\"], chosen_ship[\"length\"]), player.board, position, direction)\n chosen_ship[\"quantity\"] -= 1\n except CannotOccupyPositions:\n print(\"Couldn't place ship on given position!\")\n raise\n\n\ndef prepare_player_game(player: Player):\n print(\n \"\"\"\n To place a ship, input in the following format:\n - if horizontally: SLG X Y H\n - if vertically: SLG X Y V\n\n For example, if you have a destroyer (DES) and you want to put at position (0, 0) horizontally, write:\n DES 0 0 H\n\n Input them until there's no available ships!\n \"\"\"\n )\n available_ships = retrieve_available_ships(player.game_option)\n\n while available_ships:\n print(f\"Available ships: {available_ships}\")\n\n with suppress(InputWithError, CannotOccupyPositions, UnavailableShip):\n line = input()\n chosen_ship, position, direction = parse_line_input(line, player.game_option)\n place_ship(player, chosen_ship, position, direction)\n logger.debug(\n \"\"\"Updated %s board:\n %s\n \"\"\",\n player.name,\n player.board,\n )\n available_ships = retrieve_available_ships(player.game_option)\n\n\ndef prepare_game(game_option: Optional[GameOption] = None) -> Game:\n player_1, player_2 = Player(\"Player 1\", game_option=game_option or DEFAULT_GAME_OPTION), Player(\n \"Player 2\", game_option=game_option or DEFAULT_GAME_OPTION\n )\n\n print(\"Player 1, please place your Ships on the board!\")\n prepare_player_game(player_1)\n\n print(\"Player 2, please place your Ships on the board!\")\n prepare_player_game(player_2)\n\n return Game(player_1, player_2)\n\n\ndef get_random_position(length: int, width: int) -> Position:\n return Position(randint(0, length), randint(0, width))\n\n\ndef print_outcome(player: Player, outcome: BombOutcome, position: Position):\n hit_something, destroyed_ship = (\n convert_boolean_to_yes_no(outcome.has_hit_something),\n convert_boolean_to_yes_no(outcome.has_destroyed_a_ship),\n )\n print(\n f\"\"\"\n {player.name} attacked position ({position.x}, {position.y})...\n Outcome:\n - hit something: {hit_something}\n - destroyed ship: {destroyed_ship}\n \"\"\"\n )\n\n\ndef start(game: Game):\n print(\"Time to battle!\")\n attacking_player, attacked_player = game.player_1, game.player_2\n\n while not game.has_ended:\n with suppress(CannotBombPosition):\n position = get_random_position(attacked_player.board.length, attacked_player.board.width)\n bomb_outcome = bomb_position(attacked_player.board, position)\n print_outcome(attacking_player, bomb_outcome, position)\n attacking_player, attacked_player = attacked_player, attacking_player\n\n print(f\"Battle result: {attacked_player.name} won!\")\n print(f\"Remaining ships: {attacked_player.remaining_ships}\", end=\"\\n\\n\")\n\n\ndef show_final_boards(game: Game):\n for player in game.players:\n print(f\"Final board from {player.name}\")\n print(str(player.board), end=\"\\n\\n\")\n","repo_name":"fabiohk/naval-warfare","sub_path":"naval_warfare/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":6597,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"82"} +{"seq_id":"20691509178","text":"import socket\nfrom parser.constants import RecordTypes\nfrom parser.parsers import parse_answers\nfrom parser.common_parsers import str_to_hex, domain_to_bytes_str\nimport argparse\n\n\ndef insert_info_into_request(domain, q_type):\n return \"4a 4a 01 00 00 01 00 00 00 00 00 00 {} 00 {} 00 01\".format(domain_to_bytes_str(domain),\n RecordTypes.get_hex_str_form_str(q_type))\n\n\nclass Client:\n def __init__(self, q_name, q_type):\n if q_type not in RecordTypes.ValidRequestType:\n raise ValueError(\"Invalid type value\")\n self.data = str_to_hex(insert_info_into_request(q_name, q_type))\n self.address = \"127.0.0.1\"\n self.port = 53\n with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n s.sendto(self.data, (self.address, self.port))\n data, sender = s.recvfrom(256)\n answer = parse_answers(data)\n print(answer)\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Client for cash dns')\n parser.add_argument(\"domain\", help=\"domain that you search\")\n parser.add_argument(\"type\", default=\"A\", help=\"Type of record that you search (A, AAAA, NS, TXT, MX)\")\n args = parser.parse_args()\n Client(args.domain, args.type)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","repo_name":"YosaRem/CacheDNS","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"10361669679","text":"city = input()\ntype_pack = input()\nvip_discount = input()\nnumber_days = int(input())\ntotal_sum = 0\n\nif city == 'Bansko' or city == 'Borovets':\n if type_pack == 'withEquipment':\n total_sum = 100\n if vip_discount == 'yes':\n total_sum *= 0.8\n elif type_pack == 'noEquipment':\n total_sum = 80\n if vip_discount == 'yes':\n total_sum *= 0.95\nelif city == 'Varna' or city == 'Burgas':\n if type_pack == 'withBreakfast':\n total_sum = 130\n if vip_discount == 'yes':\n total_sum *= 0.88\n elif type_pack == 'noBreakfast':\n total_sum = 100\n if vip_discount == 'yes':\n total_sum *= 0.93\n\nif number_days < 1:\n print(\"Days must be positive number!\")\nelse:\n if total_sum == 0:\n print(\"Invalid input!\")\n else:\n if number_days > 7:\n number_days -= 1\n print(f\"The price is {number_days * total_sum:.2f}lv! Have a nice time!\")\n","repo_name":"vasilevamanoela/Python_Basics","sub_path":"PB_exam_preparation/03. Travel Agency.py","file_name":"03. Travel Agency.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29999624626","text":"import socket\r\nfrom IPy import IP\r\nimport os\r\n\r\n\r\ntry:\r\n\tos.system(\"clear\")\r\nexcept:\r\n\tos.system(\"cls\")\r\n\r\nprint('''\\033[0;37m\r\n╭━━━╮╱╱╱╱╭╮╱╭━━━╮\r\n┃╭━╮┃╱╱╱╭╯╰╮┃╭━╮┃\\033[0;31mv2.9\r\n┃╰━╯┣━━┳┻╮╭╯┃╰━━┳━━┳━━┳━╮╭━╮╭━━┳━╮\r\n┃╭━━┫╭╮┃╭┫┃╱╰━━╮┃╭━┫╭╮┃╭╮┫╭╮┫┃━┫╭╯\r\n\\033[0;37m┃┃╱╱┃╰╯┃┃┃╰╮┃╰━╯┃╰━┫╭╮┃┃┃┃┃┃┃┃━┫┃\r\n╰╯╱╱╰━━┻╯╰━╯╰━━━┻━━┻╯╰┻╯╰┻╯╰┻━━┻╯\r\n\r\n\\033[1;36m =============================================\\033[1;m\r\n\\033[0;33m|\\033[0;32m# Code By Pinindu Tharushan \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m Contact On Whatsapp +94702801713 \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By Cyber Master \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By xOR \\033[0;33m|\r\n\\033[0;33m|\\033[0;32m# Tutorial By Black hat \\033[0;33m|\r\n\\033[1;36m =============================================\\033[1;m\r\n\\033[1;33m| BEST WEB PORT SCANNER |\r\n\\033[1;36m =============================================\\033[00m''')\r\n\r\ntry:\r\n print(\"[1] Scan With Web\")\r\n print(\"[2] Scan With IP\")\r\n print(\"[3] Exit Tool\")\r\n print()\r\n print(\"\\033[0;32m[~] Enter Your choose \")\r\n menu = input(\"\\033[0;36m[~]=======>> \\033[0;37m\")\r\n\r\n def main(ipaddress):\r\n def scan_port(ipaddress,port):\r\n try:\r\n sock = socket.socket()\r\n sock.settimeout(0.5)\r\n sock.connect((ipaddress, port))\r\n print(\"\\033[0;32m[+] Port \" + str(port) + \" is Open\")\r\n except:\r\n print(\"\\033[0;31m[-] Port \" + str(port) + \" is Closed\")\r\n\r\n print()\r\n count = int(input(\"Enter How Many Ports Do You Want Scan: \"))\r\n for port in range(1, int(count)+1):\r\n scan_port(ipaddress,port)\r\n\r\n def web():\r\n print()\r\n web = input(\"Enter Web: \")\r\n ipaddress = socket.gethostbyname(web)\r\n print(\"This Web IP is \" + ipaddress)\r\n main(ipaddress)\r\n\r\n def ip():\r\n print()\r\n ipaddress = input(\"Enter Web: \")\r\n print(\"Your IP Is \" + ipaddress)\r\n main(ipaddress)\r\n\r\n try:\r\n if menu == 1:\r\n web()\r\n elif menu == 2:\r\n ip()\r\n elif menu == 3:\r\n print()\r\n print(\"Thank You For Use This Tool..!\")\r\n exit()\r\n else:\r\n web()\r\n except:\r\n print()\r\n print(\"Typing Error\")\r\n\r\nexcept InterruptedError:\r\n print()\r\n print(\"Stoped By User\")\r\n print(\"Thank You For Use this tool...\")\r\n\r\nexcept:\r\n print()\r\n print(\"Script Error...!\")\r\n print(\"contact us.\")","repo_name":"Pinindu-Tharushan/Port-Scanner-Socket","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3195942769","text":"#!/usr/bin/env python3\n\n#main module to communicate with ROS\nimport rospy\n\n#from standard messages, \"Float32\" msg type is imported\nfrom std_msgs.msg import Float32\n\n# Setting the initial speed val to 0 (global variable)\nSPEED = 0\n\n\n#callback function for the subscriber of '/change_speed' topic\ndef update_speed(val):\n\t#accessing the global variable\n\tglobal SPEED\n\t#updating the global variable value; round funtion is to round the float number\n\tSPEED = round(val.data, 1)\n\n\n#publisher function\ndef pub_speed_val():\n\t#accessing the global variable\n\tglobal SPEED\n\n\t#creating a publisher class object to publish speed values\n\t#'speed' -> topic name; Float32 -> msg type; 'queue_size' -> outgoing message queue used for asynchronous publishing\n\tspeed_pub = rospy.Publisher('speed', Float32, queue_size=10)\n\n\t#creating a Rate class object, at 10Hz, 10 times per second\n\trate = rospy.Rate(10)\n\n\t#checking the ros master is alive\n\twhile not rospy.is_shutdown():\n\t\t#publishing the global variable; msg type -> Float32\n\t\tspeed_pub.publish(SPEED)\n\t\t#sleeping 0.1 seconds since Rate is 5 Hz\n\t\trate.sleep()\n\n\nif __name__ == \"__main__\":\n\ttry:\n\t\t#initializing a new node\n\t\trospy.init_node('speed_pub_node')\n\n\t\t#creating a subscriber for the topic \"change_speed\"\n\t\t#received data type -> Float32; callback func -> update_speed\n\t\trospy.Subscriber(\"change_speed\", Float32, update_speed)\n\n\t\t#main function for publisher\n\t\tpub_speed_val()\n\t\t\n\t\t#to keep alive the node, continuous spinning\n\t\trospy.spin()\n\n\t#only ROS related exceptions will be captured\n\texcept rospy.ROSInterruptException as e:\n\t\tprint(e)","repo_name":"Otabek8866/robot-x","sub_path":"scripts/speed_pub_node.py","file_name":"speed_pub_node.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"24040984028","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.spatial import distance\r\nfrom scipy.signal import argrelextrema\r\nimport matplotlib.patches as patches\r\n\r\n\r\nclass Nodo():\r\n\t'''Esta clase genera nodos aleatorios y en base a estos encuentra la mejor ruta \r\n\ten base al algortimo Bee swarm'''\r\n\r\n\t'''Para su constructor se necesita conocer la meta y la posicion en la que se \r\n\tpartira, así mismo se debe seleccionar un numero de nodos, por default\r\n\tel programa tendra 25 nodos.'''\r\n\tdef __init__(self, meta, pos_inicial, mpx1, mpx2, mpy1, mpy2, num_nodos=25):\r\n\r\n\t\tself.num_nodos = num_nodos\r\n\t\tself.meta = meta\r\n\t\tself.pos_inicial = pos_inicial\r\n\t\tself.pos = np.array([pos_inicial])\r\n\r\n\t\tself.sizex = abs(mpx2-1)\r\n\t\tself.sizey = abs(mpy2-1)\r\n\r\n\t\t\r\n\t\t'''Se definen las variables que se utilizaran globalmente, las cuales\r\n\t\tson el arreglo ruta, en donde se guardaran los nodos seleccionados.\r\n\t\tMientras que la matriz nodo es la que contendra los nodos generados\r\n\t\taleatoriamente, por ultimo la matriz muertos obtiene los nodos\r\n\t\tdesechados. '''\r\n\t\tself.muertos = np.zeros([1,2])\r\n\t\tself.ruta = np.array([[self.pos[0][0],self.pos[0][1]]])\r\n\t\tself.nodo = np.zeros([self.num_nodos,2])\r\n\r\n\tdef Restricciones(self,valorx,valory,iter):\r\n\t\tcont = 0\r\n\t\t# La primera restriccion es que la posicion y el nodo no \r\n\t\t# deben compartir el mismo espacio.\r\n\r\n\t\tif ((self.pos_inicial[0]==valorx)&(self.pos_inicial[1]==valory)):\r\n\t\t\tcont += 1\r\n\r\n\t\t# La segunda restriccion es que la meta y el nodo no \r\n\t\t# deben compartir el mismo espacio.\r\n\t\tif ((self.meta[0]==valorx)&(self.meta[1]==valory)):\r\n\t\t\tcont += 1\r\n\r\n\t\t# La tercera restriccion es que los nodos no deben \r\n\t\t# repetirse.\r\n\t\tMaux = np.copy(self.nodo[:iter])\r\n\t\tfor i in range(iter):\r\n\t\t\tif ((Maux[i][0]==valorx)&(Maux[i][1]==valory)):\r\n\t\t\t\tcont += 1\r\n\r\n\t\t# La cuarta restriccion es que los nodos no deben existir\r\n\t\t# en los obstaculos\r\n\r\n\t\t''' Se necesita contar con conocimiento del mapa para este caso \r\n\t\tse usaran los obstaculos puntuale del archivo obstacles.npy'''\r\n\r\n\t\tobs = np.load('obstacles.npy')\r\n\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif ((obs[i][0]==valorx)&(obs[i][1]==valory)):\r\n\t\t\t\tcont += 1\r\n\r\n\t\tif cont>0:\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\treturn True\r\n\r\n\r\n\tdef Puntos(self):\r\n\t\t\r\n\t\t# Se generan puntos en todo el mapa con la funcion choice se obtienen\r\n\t\t# aleatoriamente y de forma discreta.\r\n\t\tfor i in range(self.num_nodos):\r\n\t\t\tres = False\r\n\t\t\twhile (res==False):\r\n\t\t\t\tx = np.random.choice(self.sizex)\r\n\t\t\t\ty = np.random.choice(self.sizey)\r\n\t\t\t\tres = self.Restricciones(x,y,i)\r\n\t\t\tself.nodo[i][0] = x\r\n\t\t\tself.nodo[i][1] = y\r\n\t\t\t\r\n\r\n\r\n\tdef Grafica_NPM0(self):\r\n\r\n\t\t# Este grafico es unicamente para ver la posicion de los nodos\r\n\t\t# Figura\r\n\t\tfig, ax = plt.subplots()\r\n\t\r\n\t\t# Meta y posicion, estos valores son fijos\r\n\t\tplt.plot(self.meta[0],self.meta[1], marker=\"o\", color=\"b\",label = \"Meta\")\r\n\t\tplt.plot(self.pos_inicial[0],self.pos_inicial[1], marker=\"o\", color=\"g\",label = \"Posición\")\r\n\t\t# Grafica nodos\r\n\t\tfor i in range(int(np.size(self.nodo)/2)):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(self.nodo[i][0],self.nodo[i][1], marker=\"o\", color=\"red\",label = \"Nodos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(self.nodo[i][0],self.nodo[i][1], marker=\"o\", color=\"red\")\r\n\t\t# Grafica de obstaculos\r\n\t\tobs = np.load('obstacles.npy')\r\n\t\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\",label = \"Obstaculos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\")\r\n\t\t# Parametros de la grafica\r\n\t\tplt.title(\"Grafico de NODOS\")\r\n\t\tplt.xlabel(\"Eje X\") # Inserta el título del eje X\r\n\t\tplt.ylabel(\"Eje Y\") # Inserta el título del eje Y\r\n\t\tplt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\r\n\t\tplt.tight_layout()\r\n\r\n\r\n\r\n\r\n\tdef Grafica_NPM2(self,num_r = 1):\r\n\t\t# Esta funcion retorna de manera grafica la ruta obtenida\r\n\t\t# Figura\r\n\t\tfig, ax = plt.subplots()\r\n\t\t\r\n\t\t# Grafica ruta\r\n\t\tfor i in range(self.ruta.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot([self.ruta[i][0],self.ruta[i+1][0]],[self.ruta[i][1],self.ruta[i+1][1]], marker=\"o\", color=\"y\",label= \"Ruta\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot([self.ruta[i][0],self.ruta[i+1][0]],[self.ruta[i][1],self.ruta[i+1][1]], marker=\"o\", color=\"y\")\r\n\t\tplt.plot(self.meta[0],self.meta[1], marker=\"o\", color=\"b\",label = \"Meta\")\r\n\t\tplt.plot(self.pos_inicial[0],self.pos_inicial[1], marker=\"o\", color=\"g\",label = \"Posición\")\r\n\r\n\t\t# Grafica obstaculos\r\n\t\tobs = np.load('obstacles.npy')\r\n\t\tfor i in range(obs.shape[0]-1):\r\n\t\t\tif i==0:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\",label = \"Obstaculos\")\r\n\t\t\telse:\r\n\t\t\t\tplt.plot(obs[i][0],obs[i][1], marker=\"o\", color=\"k\")\r\n\t\t\t\r\n\t\t# Parametros de la grafica\r\n\t\tplt.title(\"Grafico de la Ruta\"+str(num_r))\r\n\t\tplt.xlabel(\"Eje X\") # Inserta el título del eje X\r\n\t\tplt.ylabel(\"Eje Y\") # Inserta el título del eje Y\r\n\t\tplt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\r\n\t\tplt.tight_layout()\r\n\r\n\tdef Funcion_Distancia(self):\r\n\t\tcontw = 0\r\n\t\tcont = 0\r\n\t\t# Esta funcion encuentra la mejor ruta analizando cada nodo\r\n\t\twhile (1):\r\n\t\t\tcont+=1\r\n\t\t\tPopCost = np.zeros(self.nodo.shape[0]+1,dtype=np.float64)\r\n\t\t\tPopCostM = np.zeros(3)\r\n\t\t\tfor i in range(self.nodo.shape[0]):\r\n\t\t\t\tPopCost[i] = distance.euclidean(self.pos, self.nodo[i])\r\n\t\t\tPopCost[self.nodo.shape[0]] = distance.euclidean(self.pos, self.meta)\r\n\t\t\t\r\n\t\t\tordenados = np.array(sorted(PopCost),dtype=np.float64)\r\n\t\t\targ_orden = np.zeros(3,dtype=np.uint32)\r\n\t\t\tnodo_m=np.zeros([3,2])\r\n\t\t\ti=0\r\n\t\t\tfor i in range(3):\r\n\t\t\t\taux_arg = np.where(PopCost==ordenados[i])[0]\r\n\t\t\t\tif aux_arg.shape[0]>1:\r\n\t\t\t\t\taux_arg = aux_arg[0]\r\n\t\t\t\targ_orden[i] = int(aux_arg)\r\n\t\t\t\tif arg_orden[i] == PopCost.shape[0]-1:\r\n\t\t\t\t\taux = np.array([[self.meta[0],self.meta[1]]])\r\n\t\t\t\t\tself.ruta = np.append(self.ruta,aux,axis=0)\r\n\t\t\t\t\tcontw+=1\r\n\t\t\t\t\tbreak\r\n\t\t\t\taux = np.array([[self.nodo[int(arg_orden[i])][0],self.nodo[int(arg_orden[i])][1]]])\r\n\t\t\t\tnodo_m[i]=self.nodo[int(arg_orden[i])]\r\n\t\t\t\tself.muertos = np.append(self.muertos,aux,axis=0)\r\n\t\t\tif contw>0:\r\n\t\t\t\tbreak\r\n\t\t\tfor i in range(3):\r\n\t\t\t\tPopCostM[i] = distance.euclidean(self.meta, nodo_m[i])\r\n\r\n\t\t\tordenados = np.array(sorted(PopCostM))\r\n\r\n\t\t\targ = np.where(PopCostM==ordenados[0])[0]\r\n\t\t\tif arg.shape[0]>1:\r\n\t\t\t\t\targ = arg[0]\r\n\t\t\taux = np.array([[nodo_m[int(arg)][0],nodo_m[int(arg)][1]]])\r\n\r\n\t\t\t\r\n\t\t\tself.ruta = np.append(self.ruta,aux,axis=0)\r\n\t\t\tself.pos = aux\r\n\t\t\tself.nodo = np.delete(self.nodo, [int(arg_orden[0]),int(arg_orden[1]),int(arg_orden[2])],axis=0)\r\n\r\n\t\treturn self.ruta\r\n\t\t\r\n\tdef distancia_min(self):\r\n\t\tdis = 0\r\n\t\tfor i in range(self.ruta.shape[0]-1):\r\n\t\t\tdis += distance.euclidean(self.ruta[i], self.ruta[i+1])\r\n\t\treturn dis\r\n\r\n\tdef Rango(self,num,min,max):\r\n\t\tif ((num>=min)&(num<=max)):\r\n\t\t\treturn False\r\n\t\telse:\r\n\t\t\treturn True\r\n\r\n\r\n\r\n''' Para ejecutar el codigo defina el numero de nodos, rutas, meta y posicion'''\r\nn_nodos = 25\r\nnum_rutas = 100\r\nmeta = np.array([17,18])\r\nposicion = np.array([1,2])\r\n''' Para obtener el mejor resultado se deben plantear diversas rutas, para eso \r\ncree una lista de objetos'''\r\nnodo = []\r\n\r\n'''Para saber cual es la mejor ruta, primero asigne un valor muy grande a una variable\r\nse recomienda colocar infinito'''\r\nbestruta = np.inf\r\naux_indice = 0\r\n\r\n'''Cree un for con el numero de rutas deseado en este defina los objetos y sus metodos'''\r\n\r\nfor i in range(num_rutas):\r\n\tnodo += [i + 1] # incrementa el tamaño de la lista\r\n\tnodo[i]=Nodo(meta,posicion,0,20,0,20,num_nodos=n_nodos)\r\n\tnodo[i].Puntos() # Obtenemos los nodos \r\n\t# nodo[i].Grafica_NPM0() # Se grafican los nodos\r\n\tauxr = nodo[i].Funcion_Distancia() # Se calcula la ruta\r\n\t# nodo[i].Grafica_NPM2(i+1) # Se grafica la ruta\r\n\tif bestruta > nodo[i].distancia_min(): # Se guarda la ruta mas baja \r\n\t\tbestruta = nodo[i].distancia_min()\r\n\t\taux_indice = i\r\n\t\truta = auxr\r\n\r\n''' Ahora solo imprima los valores encontrados '''\r\n\r\nprint(\"La mejor ruta es la:\",aux_indice+1,\"Con una distancia de:\",bestruta,\"metros\")\r\nnodo[aux_indice].Grafica_NPM2(aux_indice+1)\r\nprint(ruta)\r\nplt.show()\r\n","repo_name":"ftrujillo36/VeranoUG2021","sub_path":"Codigo/Abejas/ABC.py","file_name":"ABC.py","file_ext":"py","file_size_in_byte":7997,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14642398487","text":"def collatz(number):\n\tglobal a\n\tif number % 2 == 1:\n\t\ta = 3 * number + 1\n\t\tprint(a)\n\telif number % 2 == 0:\n\t\ta = number // 2\n\t\tprint(a)\n\na = 0\nprint('Enter number: ')\nwhile True:\n\tif a == 1:\n\t\tbreak\n\telse:\n\t\tnumber = int(input())\t\t\t\n\t\tcollatz(number)\n\n","repo_name":"zzz6/-Automate-the-Boring-Stuff-with-Python","sub_path":"Part 1/Chapter 3/Collatz sequence.py","file_name":"Collatz sequence.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31282934947","text":"import functools\nimport torch\nfrom torch import nn\n\n\nclass VaeBasicModel(torch.nn.Module):\n \"\"\"\n This is the basic VAE model class, called by all other VAE son classes.\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n norm_type,\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n ):\n \"\"\"\n Initialize the VAE basic class.\n \"\"\"\n # input tensor\n super().__init__()\n\n # define the network\n self.netEmbed = define_VAE(\n omics_dims,\n norm_type,\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n )\n\n def forward(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var = self.netEmbed.forward(data_e, data_m, data_c)\n latent = mean.detach()\n return z, recon_omics, mean, log_var, latent\n\n\nclass FCBlock(nn.Module):\n \"\"\"\n Linear => Norm1D => LeakyReLU\n \"\"\"\n\n def __init__(\n self,\n input_dim,\n output_dim,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n activation=True,\n normalization=True,\n activation_name=\"LeakyReLU\",\n ):\n \"\"\"\n Construct a fully-connected block\n Parameters:\n input_dim (int) -- the dimension of the input tensor\n output_dim (int) -- the dimension of the output tensor\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n activation (bool) -- need activation or not\n normalization (bool) -- need normalization or not\n activation_name (str) -- name of the activation function used in the FC block\n \"\"\"\n super(FCBlock, self).__init__()\n # Linear\n self.fc_block = [nn.Linear(input_dim, output_dim)]\n # Norm\n if normalization:\n # FC block doesn't support InstanceNorm1d\n if (\n isinstance(norm_layer, functools.partial)\n and norm_layer.func == nn.InstanceNorm1d\n ):\n norm_layer = nn.BatchNorm1d\n self.fc_block.append(norm_layer(output_dim))\n # Dropout\n if 0 < dropout_p <= 1:\n self.fc_block.append(nn.Dropout(p=dropout_p))\n # LeakyReLU\n if activation:\n if activation_name.lower() == \"leakyrelu\":\n self.fc_block.append(\n nn.LeakyReLU(negative_slope=leaky_slope, inplace=True)\n )\n elif activation_name.lower() == \"tanh\":\n self.fc_block.append(nn.Tanh())\n else:\n raise NotImplementedError(\n \"Activation function [%s] is not implemented\" % activation_name\n )\n\n self.fc_block = nn.Sequential(*self.fc_block)\n\n def forward(self, x):\n output = self.fc_block(x)\n return output\n\n\n# FcVae\nclass FcVaeABC(nn.Module):\n \"\"\"\n Defines a fully-connected variational autoencoder for multi-omics dataset\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n dim_1B=384,\n dim_1A=384,\n dim_1C=384,\n latent_dim=256,\n ):\n \"\"\"\n Construct a fully-connected variational autoencoder\n Parameters:\n omics_dims (list) -- the list of input omics dimensions\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space\n \"\"\"\n\n super(FcVaeABC, self).__init__()\n self.A_dim = omics_dims[0]\n self.B_dim = omics_dims[1]\n self.C_dim = omics_dims[2]\n self.dim_1B = dim_1B\n self.dim_1A = dim_1A\n self.dim_1C = dim_1C\n\n # ENCODER\n # Layer 1\n self.encode_fc_1B = FCBlock(\n self.B_dim,\n dim_1B,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n self.encode_fc_1A = FCBlock(\n self.A_dim,\n dim_1A,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n self.encode_fc_1C = FCBlock(\n self.C_dim,\n dim_1C,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n # Layer 4\n self.encode_fc_mean = FCBlock(\n dim_1C + dim_1B + dim_1A,\n latent_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.encode_fc_log_var = FCBlock(\n dim_1C + dim_1B + dim_1A,\n latent_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n # DECODER\n # Layer 1\n self.decode_fc_z = FCBlock(\n latent_dim,\n dim_1C + dim_1B + dim_1A,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n\n # Layer 4\n self.decode_fc_4B = FCBlock(\n dim_1B,\n self.B_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.decode_fc_4A = FCBlock(\n dim_1A,\n self.A_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n self.decode_fc_4C = FCBlock(\n dim_1C,\n self.C_dim,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n def encode(self, data_e, data_m, data_c):\n level_2_A = self.encode_fc_1A(data_e)\n level_2_B = self.encode_fc_1B(data_m)\n level_2_C = self.encode_fc_1C(data_c)\n\n level_3 = torch.cat((level_2_B, level_2_A, level_2_C), 1)\n latent_mean = self.encode_fc_mean(level_3)\n latent_log_var = self.encode_fc_log_var(level_3)\n\n return latent_mean, latent_log_var\n\n def reparameterize(self, mean, log_var):\n std = torch.exp(0.5 * log_var)\n eps = torch.randn_like(std)\n return eps.mul(std).add_(mean)\n\n def decode(self, z):\n level_1 = self.decode_fc_z(z)\n\n level_2_B = level_1.narrow(1, 0, self.dim_1B)\n level_2_A = level_1.narrow(1, self.dim_1B, self.dim_1A)\n level_2_C = level_1.narrow(1, self.dim_1B + self.dim_1A, self.dim_1C)\n\n recon_B = self.decode_fc_4B(level_2_B)\n recon_A = self.decode_fc_4A(level_2_A)\n recon_C = self.decode_fc_4C(level_2_C)\n\n return [recon_A, recon_B, recon_C]\n\n def get_last_encode_layer(self):\n return self.encode_fc_mean\n\n def forward(self, data_e, data_m, data_c):\n mean, log_var = self.encode(data_e, data_m, data_c)\n z = self.reparameterize(mean, log_var)\n recon_x = self.decode(z)\n return z, recon_x, mean, log_var\n\n\nclass VaeClassifierModel(VaeBasicModel):\n \"\"\"\n This class implements the VAE classifier model, using the VAE framework with the classification downstream task.\n \"\"\"\n\n def __init__(\n self,\n omics_dims,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n class_dim_1,\n leaky_slope,\n ):\n \"\"\"\n Initialize the VAE_classifier class.\n \"\"\"\n VaeBasicModel.__init__(\n self,\n omics_dims,\n \"batch\",\n leaky_slope,\n dropout_p,\n latent_space_dim,\n dim_1B,\n dim_1A,\n dim_1C,\n )\n # specify the training losses you want to print out.\n\n # define the network\n self.netDown = define_down(\n \"batch\", leaky_slope, dropout_p, latent_space_dim, 1, class_dim_1\n )\n\n def classify(self, data_e, data_m, data_c):\n _, _, _, _, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n # Get the output tensor\n y_out = self.netDown(latent)\n return y_out\n\n def encode(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var, _ = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n return z, recon_omics, mean, log_var\n\n def encode_and_classify(self, data_e, data_m, data_c):\n # Get the output tensor\n z, recon_omics, mean, log_var, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n y_out = self.netDown(latent)\n return z, recon_omics, mean, log_var, y_out\n\n def forward(self, data_e, data_m , data_c):\n _, _, _, _, latent = VaeBasicModel.forward(\n self, data_e, data_m, data_c\n )\n # Get the output tensor\n return self.netDown(latent)\n\n\ndef define_down(\n norm_type=\"batch\",\n leaky_slope=0.2,\n dropout_p=0,\n latent_dim=256,\n class_num=2,\n class_dim_1=128,\n):\n \"\"\"\n Create the downstream task network\n Parameters:\n norm_type (str) -- the name of normalization layers used in the network, default: batch\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space and the input layer of the classifier\n class_num (int) -- the number of class\n Returns a downstream task network\n The default downstream task network is a multi-layer fully-connected classifier.\n The generator has been initialized by .\n :param class_dim_2:\n :param class_dim_1:\n \"\"\"\n\n net = None\n\n # get the normalization layer\n norm_layer = get_norm_layer(norm_type=norm_type)\n\n net = MultiFcClassifier(\n class_num, latent_dim, norm_layer, leaky_slope, dropout_p, class_dim_1\n )\n\n return net\n\n\ndef get_norm_layer(norm_type=\"batch\"):\n \"\"\"\n Return a normalization layer\n Parameters:\n norm_type (str) -- the type of normalization applied to the model, default to use batch normalization, options: [batch | instance | none ]\n \"\"\"\n if norm_type == \"batch\":\n norm_layer = functools.partial(\n nn.BatchNorm1d, affine=True, track_running_stats=True\n )\n elif norm_type == \"instance\":\n norm_layer = functools.partial(\n nn.InstanceNorm1d, affine=False, track_running_stats=False\n )\n else:\n raise NotImplementedError(\"normalization method [%s] is not found\" % norm_type)\n return norm_layer\n\n\n# Class for downstream task\nclass MultiFcClassifier(nn.Module):\n \"\"\"\n Defines a multi-layer fully-connected classifier\n \"\"\"\n\n def __init__(\n self,\n class_num=2,\n latent_dim=256,\n norm_layer=nn.BatchNorm1d,\n leaky_slope=0.2,\n dropout_p=0,\n class_dim_1=128,\n ):\n \"\"\"\n Construct a multi-layer fully-connected classifier\n Parameters:\n class_num (int) -- the number of class\n latent_dim (int) -- the dimensionality of the latent space and the input layer of the classifier\n norm_layer -- normalization layer\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n layer_num (int) -- the layer number of the classifier, >=3\n \"\"\"\n super(MultiFcClassifier, self).__init__()\n\n self.input_fc = FCBlock(\n latent_dim,\n class_dim_1,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=dropout_p,\n activation=True,\n )\n\n # create a list to store fc blocks\n mul_fc_block = []\n self.mul_fc = nn.Sequential(*mul_fc_block)\n\n # the output fully-connected layer of the classifier\n self.output_fc = FCBlock(\n class_dim_1,\n class_num,\n norm_layer=norm_layer,\n leaky_slope=leaky_slope,\n dropout_p=0,\n activation=False,\n normalization=False,\n )\n\n def forward(self, x):\n x1 = self.input_fc(x)\n x2 = self.mul_fc(x1)\n y = self.output_fc(x2)\n return y\n\n\ndef define_VAE(\n omics_dims,\n norm_type=\"batch\",\n leaky_slope=0.2,\n dropout_p=0,\n latent_dim=256,\n dim_1B=384,\n dim_1A=384,\n dim_1C=384,\n):\n \"\"\"\n Create the VAE network\n Parameters:\n omics_dims (list) -- the list of input omics dimensions\n norm_type (str) -- the name of normalization layers used in the network, default: batch\n leaky_slope (float) -- the negative slope of the Leaky ReLU activation function\n dropout_p (float) -- probability of an element to be zeroed in a dropout layer\n latent_dim (int) -- the dimensionality of the latent space\n Returns a VAE\n The default backbone of the VAE is one dimensional convolutional layer.\n The generator has been initialized by .\n \"\"\"\n\n net = None\n # get the normalization layer\n norm_layer = get_norm_layer(norm_type=norm_type)\n net = FcVaeABC(\n omics_dims,\n norm_layer,\n leaky_slope,\n dropout_p,\n dim_1B=dim_1B,\n dim_1A=dim_1A,\n dim_1C=dim_1C,\n latent_dim=latent_dim,\n )\n return net\n","repo_name":"thauptmann/Multi-Omics-Analysis","sub_path":"src/models/omiEmbed_model.py","file_name":"omiEmbed_model.py","file_ext":"py","file_size_in_byte":14582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18042206011","text":"#课本p268 例12.8.3\r\nimport math\r\ndef f1(n):\r\n return (n*n-n-1)/(2*n-1)\r\ndef f2(n):\r\n if abs(n*n-n-1)<1e-8:\r\n return 1\r\n else:\r\n return 0\r\nx = 2\r\ncount = 1\r\nn = 100\r\nx1 = x - f1(x)\r\nwhile(abs(x-x1)>1e-8 and n!=0):\r\n x = x1\r\n x1 = x - f1(x)\r\n print('迭代第' + str(count) + '次,','x=',str(x1))\r\n count+=1\r\n n-=1\r\nif n==0:\r\n print('迭代失败！')\r\n","repo_name":"JYSNL/Grade2-numerical-calculation","sub_path":"数值计算实验2-牛顿迭代法求方程的根.py","file_name":"数值计算实验2-牛顿迭代法求方程的根.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41823570649","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport base64\nimport datetime\nimport hashlib\nimport hmac\nimport json\nimport time\nimport urllib\nimport urllib.parse\nimport urllib.request\nimport requests\nimport coincurve\nfrom urllib import parse\nfrom binascii import hexlify, unhexlify\n\n# 此处填写APIKEY\n\nACCESS_KEY = \"R8gHSqzBiSX\"\nSECRET_KEY = \"dad44786f7adb2132cfedb76f3491b52421a0695d9d2387258c3c963c1334c67\"\n\n\n\n# API 请求地址\nMARKET_URL = \"http://47.96.116.164:6602\"\nTRADE_URL = \"http://47.96.116.164:6602\"\n\n# 首次运行可通过get_accounts()获取acct_id,然后直接赋值,减少重复获取。\nACCOUNT_ID = None\n\n#'Timestamp': '2017-06-02T06:13:49'\n\ndef http_request(url, params,method, add_to_headers=None):\n headers = {\n \"Content-type\": \"application/json\",\n }\n if add_to_headers:\n headers.update(add_to_headers)\n # postdata = urllib.parse.urlencode(params)\n postdata = json.dumps(params)\n # print(postdata)\n # print(url)\n if method == 'POST':\n response = requests.post(url,data = postdata,headers= headers )\n else:\n response = requests.get(url) \n try:\n \n if response.status_code == 200:\n print(response.json()) \n else:\n print(response.text) \n except BaseException as e:\n print(\"httpGet failed, detail is:%s,%s\" %(response.text,e))\n return\n\ndef ecsign(rawhash, key):\n pk = coincurve.PrivateKey(key)\n signature = pk.sign_recoverable(rawhash, hasher=None)\n signature = base64.b64encode(signature)\n return signature\n\ndef hmacsha256(message):\n bmsg = str.encode(message)\n return hmac.new(key=b'', msg=bmsg, digestmod=hashlib.sha256).digest();\n\n\ndef api_key_req(method,params, request_path):\n timestamp = str(int(time.time()))\n \n params_to_sign = {\n 'SignatureMethod': 'HmacSHA256',\n 'SignatureVersion': '1',\n 'apiKey': ACCESS_KEY,\n 'Timestamp': timestamp}\n if method == 'GET':\n params_to_sign.update(params) \n host_url = TRADE_URL\n host_name = urllib.parse.urlparse(host_url).hostname\n params_sort = sorted(params_to_sign.items(), key=lambda d: d[0], reverse=False)\n # print(params_sort)\n # host_name = host_name.lower()\n signature = createSign(params_sort, method, host_name, request_path, SECRET_KEY)\n # print(params_to_sign['Signature'])\n # print(params_to_sign)\n url = host_url + request_path + '?' + urllib.parse.urlencode(params_sort) + '&'+'Signature='+parse.quote(signature)\n\n return http_request(url, params,method)\n\n\ndef createSign(pParams, method, host_url, request_path, secret_key): \n encode_params = urllib.parse.urlencode(pParams)\n payload = [method, host_url, request_path, encode_params]\n payload = '\\n'.join(payload)\n hashed = hmacsha256(payload)\n # print(hashed)\n\n signature = ecsign(hashed,unhexlify(secret_key))\n return signature\n\ndef order_req():\n method = 'POST'\n url = \"/test1/R8gHSqzBiSX/orders/batch/create\"\n\n params = {\"ords\":[\n {\"side\":\"S\",\"mkt\":\"ETC_ETH\",\"price\":\"1\",\"qty\":\"2\"}]}\n\n api_key_req(method,params, url)\n\ndef query_trd():\n method = 'GET'\n url = \"/test1/R8gHSqzBiSX/records/ordnum\"\n params = {'ordNum':'2018111921899018240'}\n api_key_req(method,params, url)\n\ndef main():\n begin_time = time.time();\n count =0\n# while(time.time() - begin_time < 1):\n for i in range(12):\n query_trd()\n count +=1\n print(count)\n\nif __name__ == '__main__':\n main()","repo_name":"gitkai2333/hello-word","sub_path":"AccessFunc.py","file_name":"AccessFunc.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"26859017268","text":"import json\n\nimport pkg_resources\nfrom loguru import logger\n\n\ndef get_resource(fname: str = \"\", package: str = \"resources\"):\n fullname = pkg_resources.resource_filename(\"ds4biz_textractor.\" + package,\n fname) # f\"resources/{fname}\") # @Undefined\n return fullname\n\n\ndef get_secret(secret_name: str):\n try:\n with open('/run/secrets/{0}'.format(secret_name), 'r') as secret_file:\n value = secret_file.read()\n value = json.loads(value)\n except Exception as inst:\n logger.warn(f\"can't find secrets with name {secret_name}\")\n value = {}\n finally:\n return value\n","repo_name":"loko-ai/loko-textractor","sub_path":"utils/resources_utils.py","file_name":"resources_utils.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22342857044","text":"'''\n This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).\n\n PM4Py is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n PM4Py is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with PM4Py. If not, see .\n'''\nimport copy\n\nfrom pm4py.objects.petri_net.utils import petri_utils as pn_utils\nfrom pm4py.objects.petri_net.obj import PetriNet\nfrom typing import Optional, Dict, Any\n\n\ndef _short_circuit_petri_net(net):\n \"\"\"\n Creates a short circuited Petri net,\n whether an unique source place and sink place are there,\n by connecting the sink with the source\n\n Parameters\n ---------------\n net\n Petri net\n\n Returns\n ---------------\n boolean\n Boolean value\n \"\"\"\n s_c_net = copy.deepcopy(net)\n no_source_places = 0\n no_sink_places = 0\n sink = None\n source = None\n for place in s_c_net.places:\n if len(place.in_arcs) == 0:\n source = place\n no_source_places += 1\n if len(place.out_arcs) == 0:\n sink = place\n no_sink_places += 1\n if (sink is not None) and (source is not None) and no_source_places == 1 and no_sink_places == 1:\n # If there is one unique source and sink place, short circuit Petri Net is constructed\n t_1 = PetriNet.Transition(\"short_circuited_transition\", \"short_circuited_transition\")\n s_c_net.transitions.add(t_1)\n # add arcs in short-circuited net\n pn_utils.add_arc_from_to(sink, t_1, s_c_net)\n pn_utils.add_arc_from_to(t_1, source, s_c_net)\n return s_c_net\n else:\n return None\n\n\ndef apply(net: PetriNet, parameters: Optional[Dict[Any, Any]] = None) -> bool:\n \"\"\"\n Checks if a Petri net is a workflow net\n\n Parameters\n ---------------\n net\n Petri net\n parameters\n Parameters of the algorithm\n\n Returns\n ---------------\n boolean\n Boolean value\n \"\"\"\n if parameters is None:\n parameters = {}\n\n import networkx as nx\n\n scnet = _short_circuit_petri_net(net)\n if scnet is None:\n return False\n nodes = scnet.transitions | scnet.places\n graph = nx.DiGraph()\n while len(nodes) > 0:\n element = nodes.pop()\n graph.add_node(element.name)\n for in_arc in element.in_arcs:\n graph.add_node(in_arc.source.name)\n graph.add_edge(in_arc.source.name, element.name)\n for out_arc in element.out_arcs:\n graph.add_node(out_arc.target.name)\n graph.add_edge(element.name, out_arc.target.name)\n if nx.algorithms.components.is_strongly_connected(graph):\n return True\n else:\n return False\n","repo_name":"pm4py/pm4py-core","sub_path":"pm4py/algo/analysis/workflow_net/variants/petri_net.py","file_name":"petri_net.py","file_ext":"py","file_size_in_byte":3141,"program_lang":"python","lang":"en","doc_type":"code","stars":604,"dataset":"github-code","pt":"82"} +{"seq_id":"14070090314","text":"class Solution:\n def arrayRankTransform(self, arr: [int]) -> [int]:\n arr2=[]\n res=[]\n for ch in arr:\n arr2.append(ch)\n arr2=list(set(arr2))\n arr2.sort()\n for ch in arr:\n res.append(arr2.index(ch)+1)\n return res\nif __name__ == '__main__':\n s = Solution()\n print(s.arrayRankTransform([8,3,1,4]))\n","repo_name":"QingTiao/leetcode","sub_path":"1331_arrayRankTransform.py","file_name":"1331_arrayRankTransform.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73195163789","text":"#!/usr/bin/python3\n\n# Python program to find current weather details \n# of an location using openweathermap api\n\n\nimport os\nimport shutil\n#import time\nfrom datetime import datetime\nfrom sqlalchemy import create_engine, text\nimport requests, json\nimport pandas as pd\nimport geopandas as gpd\n \n# curl query:\n# curl \"https://api.openweathermap.org/data/2.5/onecall?lat=51.509865&lon=-0.118092&exclude=minutely,hourly,alerts&appid=65d4508050d5008b768b660a688651ad\" | python -mjson.tool\n# Turkey Fields: 40.137442, 28.383499\n \n# Enter your API key here\napi_key = \"65d4508050d5008b768b660a688651ad\"\n \n# base_url variable to store url\nbase_url = \"https://api.openweathermap.org/data/2.5/onecall?\"\n\n# Variables\nlocation = [\"Kassow\",\"Karacabey\",\"Les Moeres\"]\nuni = [\"ru\",\"bursa\",\"ugent\"]\npostgreSQLTable = [\"ru_weather\",\"bursa_weather\",\"ugent_weather\"]\n\nlon = [\"12.079214\",\"28.383499\",\"2.55874\"]\nlat = [\"53.869024\",\"40.137442\",\"51.02979\"]\n\ndf_out = None\n\nfor i in range(0, 3):\n print(postgreSQLTable[i])\n\n # complete_url variable to store complete url address\n complete_url = base_url + \"appid=\" + api_key + \"&lat=\" + lat[i] + \"&lon=\" + lon[i] + \"&exclude=minutely,hourly,alerts\"\n\n # get method of requests module return response object\n response = requests.get(complete_url)\n\n # convert json format data into python format data:\n x = response.json()\n\n #print(type(x))\n #print(json.dumps(x, sort_keys=True, indent=4))\n\n # read the response (x) into a dataframe:\n df = pd.DataFrame(x['daily'])\n\n # normalize nested 'temp' data:\n df_temp = pd.json_normalize(df['temp'])\n\n # add rain column, if not exists\n if 'rain' not in df.columns:\n df[\"rain\"] = 0\n\n # subset of the dataframe:\n df = df[[\"dt\",\"rain\",\"humidity\",\"dew_point\",\"wind_speed\",\"clouds\",\"uvi\"]]\n df_temp = df_temp['day']\n \n # concat the two dataframes horizontally:\n df = pd.concat([df, df_temp], axis=1)\n\n # add lat & lon & location\n df['lon'] = lon[i]\n df['lat'] = lat[i]\n df['location'] = location[i]\n\n # rename time column:\n df = df.rename(columns={\"dt\":\"date\",\"day\":\"temp\"})\n \n # kelvin to celsius\n df['temp'] = df['temp'] - 273.15\n df['dew_point'] = df['dew_point'] - 273.15\n\n # fill NaN with Null\n df = df.fillna(0)\n\n # convert from unix time to python datetime:\n df['date'] = pd.to_datetime(df['date'],unit='s')\n df['date'] = df['date'].dt.date\n\n #print(df.dtypes)\n #print(df)\n\n ## Upload to local database\n alchemyEngine = create_engine('postgresql+psycopg2://postgres:postgres@127.0.0.1:5432/postgres');\n postgreSQLConnection = alchemyEngine.connect();\n try:\n frame = df.to_sql(postgreSQLTable[i], alchemyEngine, index=False, if_exists='append')\n print(\"append sucessfull\") \n # Delete duplicates: (ctid > t.ctid -> delete original row ; ctid < t.ctid -> delete new row)\n SQL = (\"DELETE FROM {} t WHERE EXISTS (SELECT FROM {} WHERE date = t.date AND ctid > t.ctid);\"\n .format(postgreSQLTable[i],postgreSQLTable[i]))\n #print(SQL)\n with alchemyEngine.connect() as con:\n con.execute(text(SQL))\n con.commit()\n except TypeError:\n print(\"trying to create table\", postgreSQLTable[i])\n frame = df.to_sql(postgreSQLTable[i], alchemyEngine, index=False, if_exists='fail');\n finally:\n postgreSQLConnection.close();\n \n ## Save as a shapefile\n folder = 'outputFiles/current_' + uni[i] + '_weather_forecast'\n file = 'current_' + uni[i] + '_weather_forecast'\n \n # Transform python datetime object to an string (shapefile can only read str and numbers)\n df['date'] = df['date'].astype(str)\n\n # Transform DataFrame into a GeoDataFrame\n gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat))\n\n # Add projection\n gdf.crs = 'epsg:4326'\n\n # Create a new directory if it does not exist\n isExist = os.path.exists(folder)\n if not isExist:\n os.makedirs(folder)\n\n # Export data as shapefile\n print('exporting: current_weather_forecast.shp')\n gdf.to_file(folder , driver='ESRI Shapefile')\n\n ## Upload the data\n # create and open (temporary) zip file)\n shutil.make_archive(folder, 'zip', folder)\n\n # Upload to geonode\n try:\n with open(folder +'.zip', 'rb') as f:\n data = f.read()\n url = 'https://geoportal.addferti.eu/geoserver/rest/workspaces/' \n response = requests.put(\n url + 'geonode/datastores/' + file + '/file.shp',\n headers={'Content-type': 'application/zip'},\n data=data,\n verify=False,\n auth=('admin', 'addferti')\n )\n print(folder +'.zip uploaded' )\n except FileNotFoundError:\n print(folder + \" file not found\")\n\n","repo_name":"AlexSteiger/fertigationMap","sub_path":"rainForecastAdapter/openWeatherMapAPI.py","file_name":"openWeatherMapAPI.py","file_ext":"py","file_size_in_byte":4551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16318233606","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\nimport torch\nimport pandas as pd\nimport numpy as np\nimport random\nimport itertools\nimport math\nimport os\n\nimport matplotlib\n#matplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.nn.init as init\n\nfrom torch.autograd import Variable\nfrom torch.utils.data.dataset import Dataset\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\n\nfrom neural_network import Attention_Net\nfrom neural_network import Linear_Net\nfrom datasets import GlobalModelDataset\n\nimport sklearn\nfrom sklearn.decomposition import PCA\n\n\n########### FUNCTIONS\n\ndef l1_penalty(var):\n return torch.abs(var).sum()\n\ndef l2_penalty(var):\n return torch.sqrt(torch.pow(var,2).sum())\n\ndef get_inner_class_distance(df, sample_list, order = 2):\n distance = 0\n list_num = len(sample_list)\n combines = itertools.combinations(sample_list,2)\n com_number = 0\n for combine in combines:\n #print(\"haha\")\n d = df.loc[combine[0],combine[1]]\n com_number += 1\n #print(combine)\n distance += pow(float(d), order)\n #print(com_number)\n distance = distance/float(com_number)\n return distance\n\ndef get_inter_class_distance(df, class_1_list, class_2_list, order = 2):\n distance = 0\n class_1_num = len(class_1_list)\n class_2_num = len(class_2_list)\n for name_i in class_1_list:\n inter_d = 0\n for name_j in class_2_list:\n d = df.loc[str(name_i),str(name_j)]\n inter_d += pow(float(d), order)\n inter_d = inter_d/class_2_num\n distance += inter_d\n distance = distance/class_1_num\n return distance\n\n\ndef get_entropy(dist_list):\n data_number = len(dist_list)\n data_dimension = len(dist_list[0])\n prob_list = []\n entropy = 0\n for i in range(data_dimension):\n prob_list.append(float(0))\n for dist in dist_list:\n prob_list[dist.index(max(dist))] += 1\n for i in range(data_dimension):\n prob_list[i] = float(prob_list[i])/data_number\n for prob in prob_list:\n if prob != float(0):\n entropy += - prob * math.log(prob)\n return entropy\n\n\ndef evaluate_model_inner_inter_distance(model, sample_number = 10, combines = (4,4), order = 2):\n class_1_inner_distance_list = []\n class_2_inner_distance_list = []\n class_1_star_inner_distance_list = []\n class_2_star_inner_distance_list = []\n inter_distance_list = []\n class_1_num = 8\n class_2_num = 8\n class_1_star_num = combines[0]\n class_2_star_num = combines[1]\n name_list = model.item_list\n \n for i in range(sample_number):\n class_1_sample = random.sample(range(32),class_1_num)\n class_2_sample = random.sample(range(32,64),class_2_num)\n class_1_star_sample = random.sample(class_1_sample, class_1_star_num)\n class_2_star_sample = random.sample(class_2_sample, class_2_star_num)\n star_sample = class_1_star_sample + class_2_star_sample\n\n cross_sample_name_list = []\n class_1_sample_name_list = []\n class_2_sample_name_list = []\n class_1_star_sample_name_list = []\n class_2_star_sample_name_list = []\n\n for i in class_1_star_sample:\n class_1_star_sample_name_list.append(name_list[i])\n for i in class_2_star_sample:\n class_2_star_sample_name_list.append(name_list[i])\n for i in star_sample:\n cross_sample_name_list.append(name_list[i])\n for i in class_1_sample:\n class_1_sample_name_list.append(name_list[i])\n for i in class_2_sample:\n class_2_sample_name_list.append(name_list[i])\n\n\n ## Test for debug\n #print(class_1_star_sample_name_list)\n #print(class_2_star_sample_name_list)\n #print(cross_sample_name_list)\n #print(class_1_sample_name_list)\n #print(class_2_sample_name_list)\n\n\n \n cross_test_input = []\n for name in name_list:\n if name in cross_sample_name_list:\n cross_test_input.append(1)\n else:\n cross_test_input.append(0)\n\n\n class_1_test_input = []\n for name in name_list:\n if name in class_1_sample_name_list:\n class_1_test_input.append(1)\n else:\n class_1_test_input.append(0)\n\n class_2_test_input = []\n for name in name_list:\n if name in class_2_sample_name_list:\n class_2_test_input.append(1)\n else:\n class_2_test_input.append(0)\n\n ### Debug\n #print(len(cross_test_input))\n #print(sum(cross_test_input))\n #print(len(class_1_test_input))\n #print(sum(class_1_test_input))\n #print(len(class_2_test_input))\n #print(sum(class_2_test_input))\n\n ##### Create Input\n cross_test_input = torch.from_numpy(np.array(cross_test_input)).unsqueeze(0).float()\n class_1_test_input = torch.from_numpy(np.array(class_1_test_input)).unsqueeze(0).float()\n class_2_test_input = torch.from_numpy(np.array(class_2_test_input)).unsqueeze(0).float()\n\n #### Get Output\n cross_test_output = model.forward(cross_test_input)\n class_1_test_output = model.forward(class_1_test_input)\n class_2_test_output = model.forward(class_2_test_input)\n\n #### Get Output Matrix\n cross_matrix = model.get_output_matrix(cross_test_input, cross_test_output, pandas = True)\n class_1_matrix = model.get_output_matrix(class_1_test_input, class_1_test_output, pandas = True)\n class_2_matrix = model.get_output_matrix(class_2_test_input, class_2_test_output, pandas = True)\n\n\n #### Get D11, D22, D11*, D22*, D12*\n class_1_inner_distance = get_inner_class_distance(class_1_matrix, class_1_sample_name_list, order = order)\n class_2_inner_distance = get_inner_class_distance(class_2_matrix, class_2_sample_name_list, order = order)\n cross_class_1_inner_distance = get_inner_class_distance(cross_matrix, class_1_star_sample_name_list, order = order)\n cross_class_2_inner_distance = get_inner_class_distance(cross_matrix, class_2_star_sample_name_list, order = order)\n cross_inter_distance = get_inter_class_distance(cross_matrix, class_1_star_sample_name_list, class_2_star_sample_name_list, order = order)\n\n class_1_inner_distance_list.append(class_1_inner_distance)\n class_2_inner_distance_list.append(class_2_inner_distance)\n class_1_star_inner_distance_list.append(cross_class_1_inner_distance)\n class_2_star_inner_distance_list.append(cross_class_2_inner_distance)\n inter_distance_list.append(cross_inter_distance)\n\n #### Get Average Distance\n class_1_inner_distance = np.mean(class_1_inner_distance_list)\n class_2_inner_distance = np.mean(class_2_inner_distance_list)\n class_1_star_inner_distance = np.mean(class_1_star_inner_distance_list)\n class_2_star_inner_distance = np.mean(class_2_star_inner_distance_list)\n inter_distance = np.mean(inter_distance_list)\n return class_1_inner_distance, class_2_inner_distance, class_1_star_inner_distance, class_2_star_inner_distance, inter_distance\n \n\n##########################################################################\nlifelog_itemlist = \"/home/li/datasets/lifelog/itemlist.csv\"\nlifelog_data = pd.read_csv(lifelog_itemlist)\ngroup_path = \"/home/li/datasets/lifelog/Group1_64.txt\"\ngroup_list = []\ngroup_item_name_list = []\n\n\nwith open(group_path,\"r\") as g_f:\n for line in g_f.readlines():\n group_list.append(int(line.strip()))\n group_item_name_list.append(lifelog_data.loc[int(line.strip()) - 1,\"Name\"])\n\n################## PARAMS\n## Constant\nADAM = \"Adam\"\nSGD = \"SGD\"\nL0 = \"L0\"\nL1 = \"L1\"\nL2 = \"L2\"\nMSE = \"MSE\"\nWD = \"000001\"\nATTENTION = \"attention_net\"\nLINEAR = \"linear_net\"\nRELU = \"relu\"\nSIGMOID = \"sigmoid\"\n\n\n## Train Params\nNET = ATTENTION\nBATCH_SIZE = 10\nLEARNING_RATE = 0.05\nWEIGHT_DECAY = torch.tensor(0.000001).float()\nQUERY_DIM = 9\nKEY_DIM = 6\nFEATURE_DIM = 5\nEPOCH = 10000\nMOMENTUM = 0.9\nREG = L0\nACT = SIGMOID\nOPTIMIZER = SGD\n\n## Evaluation Params\nEVA_SAMPLE_NUMBER = 30\nBETAS = (0.9,0.999)\nLOSS = MSE\nCV_NUM = 2\n\nTEST_NUMBER = 100\n\n\nif __name__ == '__main__':\n ############## Data Preparation ###################\n username = \"artificial\"\n\n #extra = \"LI_Mofei_Data_200_R_1_NL_00_LogF_True_epoch_\" + str(EPOCH)\n extra = \"Artificial_Data_LogF_True_epoch_\" + str(EPOCH)\n model_path = \"/home/li/torch/model/\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \"_CV.model\" \n train_log_path = \"/home/li/torch/model/train_log/\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".txt\" \n\n #input_csv = \"/home/li/torch/data/Data_Input_200_LI_Mofei_20190518.csv\"\n #output_csv = \"/home/li/torch/data/Data_Output_200_LI_Mofei_20190518.csv\"\n\n input_csv = \"/home/li/torch/artificial_data/artificial_data_200_20190911_input.csv\"\n output_csv = \"/home/li/torch/artificial_data/artificial_data_200_20190911_output.csv\"\n dataset = GlobalModelDataset(input_csv, output_csv, log_function = True)\n\n print(dataset.data_num)\n plot_path = \"/home/li/torch/plot/20190911/\"\n\n #eva_extra = \"LogF_True_NL_00_li_mofei\"\n eva_extra = \"LogF_True_Artificial_epoch_\" + str(EPOCH)\n evaluation_path = \"/home/li/torch/evaluation/datanumber_600_K_\" + str(KEY_DIM) + \"_\" + str(REG) + str(eva_extra) + \"/\"\n #coeff_path = \"/home/li/torch/artificial_data/coefficient_logF_True_LI_Mofei_200_NL_00_test_\" + str(NET) + \"_epoch_\" + str(EPOCH) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_WD_\" + str(WD) + \".txt\"\n coeff_path = \"/home/li/torch/artificial_data/coefficient_logF_True_\" + str(NET) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_WD_\" + str(WD) + \"_EPOCH_\" + str(EPOCH) + \".txt\"\n\n if not os.path.exists(evaluation_path):\n os.mkdir(evaluation_path)\n\n if not os.path.exists(plot_path):\n os.mkdir(plot_path)\n\n data_num = dataset.data_num\n sample_data_num = int(data_num/CV_NUM)\n\n if CV_NUM == 1:\n train_data_num = sample_data_num\n test_data_num = 0\n else:\n train_data_num = data_num - sample_data_num\n test_data_num = sample_data_num\n\n splits_list = []\n for i in range(CV_NUM):\n splits_list.append(sample_data_num)\n splits_list = tuple(splits_list)\n\n datasets = torch.utils.data.random_split(dataset, splits_list)\n\n dataloader_list = []\n for ds in datasets:\n dataloader = DataLoader(dataset = ds,\n batch_size = BATCH_SIZE,\n shuffle = True,\n num_workers = 0)\n dataloader_list.append(dataloader)\n\n data_num = dataset.data_num\n\n sample_data_num = int(data_num/CV_NUM)\n\n params = (QUERY_DIM,KEY_DIM,FEATURE_DIM)\n\n ## Attention Net\n if NET == ATTENTION:\n net = Attention_Net(dataset, params, activation = ACT)\n ## Linear Net\n elif NET == LINEAR:\n net = Linear_Net(dataset, FEATURE_DIM)\n\n\n\n ## Optimizer\n if OPTIMIZER == SGD:\n optimizer = torch.optim.SGD(net.parameters(), lr = LEARNING_RATE, momentum = MOMENTUM)\n elif OPTIMIZER == ADAM:\n optimizer = torch.optim.Adam(net.parameters(), lr = LEARNING_RATE, betas = BETAS)\n\n ## Loss\n loss_function = torch.nn.MSELoss()\n\n #### Print Parameters\n #for name,param in net.named_parameters():\n # if param.requires_grad:\n # print(name)\n #print(param)\n ###################### Training ############### Cross Validation\n #attention_net.train()\n #print(dataloader)\n train_loss_list = []\n test_loss_list = []\n test_loss_log_list = []\n\n entropy_list = []\n \n for epoch in range(EPOCH):\n train_loss_each_epoch_list = []\n test_loss_each_epoch_list = []\n\n dist_list = []\n\n for i in range(CV_NUM):\n test_dataloader = dataloader_list[i]\n train_dataloader_list = dataloader_list[:i] + dataloader_list[i+1:] \n\n net.train()\n #print(len(train_dataloader_list))\n ###### Train\n train_loss_each_sample_list = []\n for dataloader in train_dataloader_list:\n \n train_loss_each = 0\n for im,label in dataloader:\n l0_regularization = torch.tensor(0).float()\n l1_regularization = torch.tensor(0).float()\n l2_regularization = torch.tensor(0).float()\n\n if NET == ATTENTION:\n out,dist = net.forward(im)\n elif NET == LINEAR:\n out = net.forward(im)\n mse_loss = loss_function(out,label)\n\n ## Regularization\n for param in net.parameters():\n l1_regularization += WEIGHT_DECAY * torch.norm(param,1)\n l2_regularization += WEIGHT_DECAY * torch.norm(param,2)\n\n if REG == L0:\n loss = mse_loss + l0_regularization\n elif REG == L1:\n loss = mse_loss + l1_regularization\n elif REG == L2:\n loss = mse_loss + l2_regularization\n \n train_loss_each += mse_loss.item()/sample_data_num\n \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n train_loss_each_sample_list.append(train_loss_each)\n #print(len(train_loss_each_sample_list))\n #print(len(train_loss_each_sample_list))\n train_loss_each_epoch_list.append(np.mean(train_loss_each_sample_list))\n\n\n ############ Test\n test_loss_each = 0\n net.eval()\n for im,label in test_dataloader:\n if NET == ATTENTION:\n out,dis = net.forward(im)\n dist = list(dis[0].detach().numpy())\n dist_list.append(dist)\n elif NET == LINEAR:\n out = net.forward(im)\n #out = linear_net.forward(im)\n mse_loss = loss_function(out,label)\n test_loss_each += mse_loss.item()/sample_data_num\n\n test_loss_each_epoch_list.append(test_loss_each)\n\n entropy = get_entropy(dist_list)\n entropy_list.append(entropy)\n\n train_loss = np.mean(train_loss_each_epoch_list)\n test_loss = np.mean(test_loss_each_epoch_list)\n train_loss_list.append(train_loss)\n test_loss_list.append(test_loss)\n test_loss_log_list.append(math.log(test_loss))\n\n info1 = \"Epoch: \" + str(epoch) + \" , Train Loss: \" + str(train_loss)\n info2 = \"Epoch: \" + str(epoch) + \" , Test Loss: \" + str(test_loss)\n print(info1)\n print(info2)\n if NET == ATTENTION:\n info3 = \"Epoch: \" + str(epoch) + \" , Distribution: \" + str(dis)\n print(info3)\n \n \n\n print(model_path)\n torch.save(net.state_dict(), model_path)\n\n model = net\n\n #### PLOT\n figure = \"Learning_Curve\" \n plt_file = plot_path + str(figure) + \"_\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".png\"\n #plt.plot(range(len(train_loss_list)), train_loss_list, label = \"train loss\")\n plt.plot(range(len(test_loss_log_list)), test_loss_log_list, label = \"log train loss\")\n plt.legend(loc = \"upper right\")\n plt.savefig(plt_file)\n plt.close('all')\n\n figure = \"Entropy_Curve\"\n plt_file = plot_path + str(figure) + \"_\" + str(extra) + \"_\" + str(NET) + \"_u_\" + str(username) + \"_Q_\" + str(QUERY_DIM) + \"_K_\" + str(KEY_DIM) + \"_F_\" + str(FEATURE_DIM) + \"_REG_\" + str(REG) + \"_ACT_\" + str(ACT) + \"_WD_\" + str(WD) + \".png\"\n plt.scatter(test_loss_log_list, entropy_list, label = \"Entropy\")\n #plt.xlim((0,0.005))\n plt.ylim((0,1))\n plt.legend(loc = \"upper right\")\n plt.savefig(plt_file)\n plt.close('all')\n\n\n ##### Test\n item_list = model.item_list\n\n class_1_list = item_list[:32]\n class_2_list = item_list[32:]\n\n print(item_list)\n \n #embedding = MDS(n_components = 2, dissimilarity = \"precomputed\")\n\n d11_list = []\n d22_list = []\n d11_star_list = []\n d22_star_list = []\n d12_star_list = []\n\n dist_list = []\n \n\n for i in range(TEST_NUMBER):\n ## Group 1\n\n group1_list = random.sample(class_1_list, 8)\n\n input_test = []\n for item in item_list:\n if item in group1_list:\n input_test.append(1)\n else:\n input_test.append(0)\n \n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n d11 = get_inner_class_distance(output_large_matrix, group1_list, order = 1)\n d11_list.append(d11)\n #pos = embedding.fit_transform(output_matrix)\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group1_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group1_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n ## Group 2\n group2_list = random.sample(class_2_list, 8)\n\n input_test = []\n for item in item_list:\n if item in group2_list:\n input_test.append(1)\n else:\n input_test.append(0)\n\n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n d22 = get_inner_class_distance(output_large_matrix, group2_list, order = 1)\n d22_list.append(d22)\n #pos = embedding.fit_transform(output_matrix)\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group2_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group2_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n\n ## Group 3\n\n group31_list = random.sample(group1_list, 4)\n group32_list = random.sample(group2_list, 4)\n group3_list = group31_list + group32_list\n\n input_test = []\n for item in item_list:\n if item in group3_list:\n input_test.append(1)\n else:\n input_test.append(0)\n\n input_torch = torch.from_numpy(np.array(input_test)).unsqueeze(0).float()\n output,dist = model.forward(input_torch)\n output_large_matrix = model.get_output_matrix(input_torch, output, pandas = True)\n output_matrix = model.get_output_small_matrix(input_torch, output, pandas = False)\n output_df = model.get_output_small_matrix(input_torch, output, pandas = True)\n\n \n d11_star = get_inner_class_distance(output_large_matrix, group31_list, order = 1)\n d11_star_list.append(d11_star)\n d22_star = get_inner_class_distance(output_large_matrix, group32_list, order = 1)\n d22_star_list.append(d22_star)\n d12_star = get_inter_class_distance(output_large_matrix, group31_list, group32_list, order = 1)\n d12_star_list.append(d12_star)\n\n dist = list(dist[0].detach().numpy())\n dist_list.append(dist)\n\n csv_path = evaluation_path + \"group3_test\" + str(i) + \".csv\"\n if i % 10 == 0:\n output_df.to_csv(csv_path)\n\n bar_path = evaluation_path + \"group3_test_bar\" + str(i) + \".png\"\n plt.bar(range(len(dist)), dist, color = 'b')\n if i % 10 == 0:\n plt.savefig(bar_path)\n plt.close('all')\n\n d11_mean = np.mean(d11_list)\n d22_mean = np.mean(d22_list)\n d11_star_mean = np.mean(d11_star_list)\n d22_star_mean = np.mean(d22_star_list)\n d12_star_mean = np.mean(d12_star_list)\n\n c1 = d11_star_mean / d11_mean\n c2 = d22_star_mean / d22_mean\n c3 = (d11_star_mean + d22_star_mean)/ (2 * d12_star_mean)\n\n info0 = \"Model: \" + str(model_path)\n info01 = \"d11 : \" + str(d11_mean) + \" , d22 : \" + str(d22_mean)\n info02 = \"d11* : \" + str(d11_star_mean) + \" , d22* : \" + str(d22_star_mean)\n info03 = \"d12* : \" + str(d12_star_mean)\n info1 = \"c1: \" + str(c1)\n info2 = \"c2: \" + str(c2)\n info3 = \"c3: \" + str(c3)\n info4 = \"Entropy: \" + str(entropy)\n\n ############## PCA\n pca = PCA(n_components = \"mle\")\n pca.fit(dist_list)\n feature = pca.transform(dist_list)\n print(pca.explained_variance_ratio_)\n \n figure = \"PCA_Test\"\n plt_file = plot_path + str(extra) + \"_\" + str(figure) + \".png\"\n plt.scatter(feature[:,0], feature[:,1])\n plt.grid()\n #plt.xlim(-1,1)\n #plt.ylim(-1,1)\n plt.savefig(plt_file)\n plt.close('all')\n\n \n with open(coeff_path, \"w\") as log_f:\n log_f.write(info0 + \"\\r\\n\")\n log_f.write(info01 + \"\\r\\n\")\n log_f.write(info02 + \"\\r\\n\")\n log_f.write(info03 + \"\\r\\n\")\n log_f.write(info1 + \"\\r\\n\")\n log_f.write(info2 + \"\\r\\n\")\n log_f.write(info3 + \"\\r\\n\")\n log_f.write(info4 + \"\\r\\n\")\n log_f.write(\"Variance Ratio:\" + str(pca.explained_variance_ratio_) + \"\\r\\n\")\n\n\n \n","repo_name":"nixidekaoya/global_model","sub_path":"nn_attention_global_cv.py","file_name":"nn_attention_global_cv.py","file_ext":"py","file_size_in_byte":22585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"15230093548","text":"# Author: Nicolas Agudelo\n# Instructions for this project can be found at: https://www.codecademy.com/paths/computer-science/tracks/cspath-cs-101/modules/cspath-boredless-tourist/projects/the-boredless-tourist\n\n# Global Variables\n\nfrom webbrowser import get\n\n\ndestinations = ['Paris, France', 'Shanghai, China','Los Angeles, USA', 'São Paulo, Brazil', 'Cairo, Egypt']\n\ntest_traveler = ['Erin Wilkes', 'Shanghai, China', ['historical site', 'art']]\n\nattractions = [[] for destination in destinations]\nattractions_with_interest = []\n\n# Functions\ndef get_destination_index(destination):\n destination_index = destinations.index(destination)\n return destination_index\n\ndef get_traveler_location(traveler):\n traveler_destination = test_traveler[1]\n traveler_destination_index = get_destination_index(traveler_destination)\n return traveler_destination_index\n\ndef add_attraction(destination, attraction):\n destination_index = get_destination_index(destination)\n attractions_for_destination = attractions[destination_index]\n attractions_for_destination.append(attraction)\n\ndef find_attractions(destination, interests):\n destination_index = get_destination_index(destination)\n attractions_in_city = attractions[destination_index]\n for attraction in attractions_in_city:\n possible_attraction = attraction\n attraction_tags = attraction[1]\n for interest in interests:\n if interest in attraction_tags: attractions_with_interest.append(possible_attraction[0])\n \n return attractions_with_interest\n\ndef get_attractions_for_traveler(traveler):\n traveler_name = traveler[0]\n traveler_destination = traveler[1]\n traveler_interests = traveler[2]\n interests_string = ''\n traveler_attractions = find_attractions(traveler_destination, traveler_interests)\n interests_string = \"Hi \" + traveler_name + \", we think you'll like these places around \" + traveler_destination +\":\"\n for attraction in traveler_attractions: interests_string += '\\n- ' + attraction\n attractions_with_interest.clear()\n return interests_string\n\n# print(get_destination_index('Los Angeles, USA'))\n# print(get_destination_index('Paris, France'))\n# print(get_destination_index('“Hyderabad, India”'))\n\n# test_destination_index = get_traveler_location(test_traveler)\n\n# print(test_destination_index)\n\nadd_attraction('Los Angeles, USA', ['Venice Beach', ['beach']])\nadd_attraction(\"Paris, France\", [\"the Louvre\", [\"art\", \"museum\"]])\nadd_attraction(\"Paris, France\", [\"Arc de Triomphe\", [\"historical site\", \"monument\"]])\nadd_attraction(\"Shanghai, China\", [\"Yu Garden\", [\"garden\", \"historical site\"]])\nadd_attraction(\"Shanghai, China\", [\"Yuz Museum\", [\"art\", \"museum\"]])\nadd_attraction(\"Shanghai, China\", [\"Oriental Pearl Tower\", [\"skyscraper\", \"viewing deck\"]])\nadd_attraction(\"Los Angeles, USA\", [\"LACMA\", [\"art\", \"museum\"]])\nadd_attraction(\"São Paulo, Brazil\", [\"São Paulo Zoo\", [\"zoo\"]])\nadd_attraction(\"São Paulo, Brazil\", [\"Pátio do Colégio\", [\"historical site\"]])\nadd_attraction(\"Cairo, Egypt\", [\"Pyramids of Giza\", [\"monument\", \"historical site\"]])\nadd_attraction(\"Cairo, Egypt\", [\"Egyptian Museum\", [\"museum\"]])\n# print(attractions)\n\n# la_arts = find_attractions(\"Los Angeles, USA\", ['art'])\n# print(la_arts)\n\nsmills_france = get_attractions_for_traveler(['Dereck Smill', 'Paris, France', ['monument']])\n\nprint (smills_france)\nprint (get_attractions_for_traveler(test_traveler))","repo_name":"nicolasagudelo/the_boredless_tourist","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27018862526","text":"import uvicorn\nimport odrpc\nimport base64\nimport logging\nimport asyncio\nfrom fastapi import status, FastAPI, WebSocket, WebSocketDisconnect\nfrom fastapi.responses import Response\nfrom fastapi.staticfiles import StaticFiles\nfrom concurrent.futures import ThreadPoolExecutor\n\n\nclass API():\n def __init__(self, config, doods):\n self.config = config\n self.doods = doods\n self.api = FastAPI()\n # Borrow the uvicorn logger because it's pretty.\n self.logger = logging.getLogger(\"doods.api\")\n\n @self.api.get(\"/detectors\", response_model=odrpc.DetectorsResponse, response_model_exclude_none=True)\n async def detectors():\n return odrpc.DetectorsResponse(detectors=self.doods.detectors())\n\n @self.api.post(\"/detect\", response_model=odrpc.DetectResponse, response_model_exclude_none=True)\n async def detect(detect_request: odrpc.DetectRequest, response: Response):\n # logger.info('detect request: %s', detect_request)\n detect_response = self.doods.detect(detect_request)\n if detect_response.error:\n response.status_code = status.HTTP_400_BAD_REQUEST\n # If we requested an image, base64 encode it back to the user\n if detect_request.image:\n detect_response.image = base64.b64encode(detect_response.image)\n return detect_response\n \n @self.api.websocket(\"/detect\")\n async def detect_stream(websocket: WebSocket):\n await websocket.accept()\n detect_responses = asyncio.Queue()\n executor = ThreadPoolExecutor()\n async def detect_handle(detect_request: odrpc.DetectRequest):\n try:\n detect_response = self.doods.detect(detect_request)\n if detect_request.image:\n detect_response.image = base64.b64encode(detect_response.image)\n await detect_responses.put(detect_response)\n except asyncio.TimeoutError:\n self.logger.error(\"Detector timeout error\")\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n\n def detect_thread(detect_request: odrpc.DetectRequest):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n try:\n loop.run_until_complete(detect_handle(detect_request))\n loop.close()\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n loop.close()\n\n async def send_detect_responses():\n try:\n while True:\n detect_response = await detect_responses.get()\n await websocket.send_json(detect_response.asdict(include_none=False))\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n\n send_detect_responses_task = asyncio.create_task(send_detect_responses())\n \n while True:\n try:\n detect_config = await websocket.receive_json()\n detect_request = odrpc.DetectRequest(**detect_config)\n executor.submit(detect_thread, detect_request)\n except TypeError:\n await detect_responses.put(odrpc.DetectResponse(error='could not parse request body'))\n except WebSocketDisconnect:\n send_detect_responses_task.cancel()\n executor.shutdown()\n break\n except Exception as e:\n self.logger.error(\"Exception({0}):{1!r}\".format(type(e).__name__, e.args))\n send_detect_responses_task.cancel()\n executor.shutdown()\n break\n\n @self.api.post(\"/image\")\n async def image(detect_request: odrpc.DetectRequest, response: Response):\n # logger.info('image request: %s', detect_request)\n if not detect_request.image:\n detect_request.image = \".jpg\"\n detect_response = self.doods.detect(detect_request)\n if detect_response.error:\n return Response(status_code=status.HTTP_400_BAD_REQUEST, content=detect_response.error)\n return Response(content=detect_response.image, media_type=\"image/jpeg\")\n\n # Mount the UI directory - must be last\n self.api.mount(\"/\", StaticFiles(directory=\"html\", html=True), name=\"static\")\n \n def run(self):\n log_config = uvicorn.config.LOGGING_CONFIG\n log_config[\"formatters\"][\"access\"][\"fmt\"] = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n log_config[\"formatters\"][\"default\"][\"fmt\"] = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n log_config[\"loggers\"][\"uvicorn\"][\"propagate\"] = False # Fix a bug in logging\n uvicorn.run(self.api, host=self.config.host, port=self.config.port, log_config=log_config) \n\n","repo_name":"danielkaiser/MiniDoods","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"74187708107","text":"from routines import *\r\nfrom cutil.ctools import *\r\nfrom cutil.cutils import *\r\n\r\nclass reposition:\r\n def __init__(self, desired_distance = 3000, ball_going_into_our_net = False, offense_defense = 0):\r\n self.goto = goto(Vector3(0, 0, 0))\r\n self.target = None\r\n self.intercept_location = None\r\n self.intercept_time = None\r\n self.direction_vector = None\r\n self.desired_distance = desired_distance\r\n self.unmodded_reposition_target = None\r\n self.returning_to_goal = False\r\n self.ball_going_into_our_net = ball_going_into_our_net\r\n self.offense_defense = offense_defense\r\n\r\n def run(self, agent):\r\n if self.target is None:\r\n self.find_target(agent)\r\n if self.target.flat_dist(agent.me.location) < 100:\r\n self.target = self.intercept_location\r\n self.goto.arrival_time = self.intercept_time\r\n if self.returning_to_goal and self.offense_defense < 0:\r\n self.goto.urgent = True\r\n self.goto.slow_down = True\r\n if agent.time > self.intercept_time:\r\n agent.pop()\r\n\r\n car_to_final_target = (self.target - agent.me.location).flatten()\r\n distance_remaining = car_to_final_target.magnitude()\r\n\r\n self.goto.target = self.target\r\n self.goto.vector = self.direction_vector\r\n #print(self.goto.slow)\r\n agent.line(self.intercept_location - Vector3(0,0,500),self.intercept_location + Vector3(0,0,500),[255,0,0])\r\n agent.line(self.unmodded_reposition_target - Vector3(0, 0, 500), self.unmodded_reposition_target + Vector3(0,0,500), [0, 255, 0])\r\n\r\n self.goto.run(agent)\r\n\r\n def find_target(self, agent):\r\n slices = get_slices(agent, 6)\r\n\r\n earliest_intercept, intercept_vector_location = find_intercept_time_with_detour(agent.me, agent, return_intercept_location_too=True, ball_prediction_slices=slices, time_to_subtract=0.5)\r\n if earliest_intercept is None:\r\n intercept_location = slices[-1].physics.location\r\n earliest_intercept = slices[-1].game_seconds\r\n intercept_vector_location = Vector3(intercept_location.x, intercept_location.y, intercept_location.z)\r\n\r\n\r\n my_goal_to_ball = (intercept_vector_location - agent.friend_goal.location).flatten().normalize()\r\n ball_to_their_goal = (agent.foe_goal.location - intercept_vector_location).flatten().normalize()\r\n car_to_ball, car_to_ball_distance = (intercept_vector_location - agent.me.location).flatten().normalize(True)\r\n ball_to_goal_magnitude = (intercept_vector_location - agent.friend_goal.location).flatten().magnitude()\r\n\r\n if agent.closest_onside_to_ball and not agent.pull_back and len(agent.friends) >= 1 or (len(agent.friends) == 0 and self.offense_defense == 1):\r\n direction_vector = lerp(-ball_to_their_goal, -my_goal_to_ball, 0.75)\r\n else:\r\n direction_vector = -my_goal_to_ball\r\n\r\n reposition_target = intercept_vector_location.flatten() + direction_vector * min(self.desired_distance, ball_to_goal_magnitude - 150)\r\n\r\n # print(\"Intercept location: \" + str(intercept_vector_location))\r\n # print(\"Reposition target: \" + str(reposition_target))\r\n reposition_target.x = cap(reposition_target.x, -3796, 3796)\r\n reposition_target.y = cap(reposition_target.y, -5120, 5120)\r\n final_target = reposition_target\r\n self.unmodded_reposition_target = reposition_target\r\n\r\n # near_goal = abs(agent.me.location[1] - agent.friend_goal.location[1]) < 3000\r\n # side_shift = 400 if near_goal else 1800\r\n # points = [reposition_target + Vector3(side_shift, 0, 0), reposition_target - Vector3(side_shift, 0, 0)]\r\n # #print(\"Points: \" + str(points))\r\n # final_target = closest_point(reposition_target, points) if near_goal else furthest_point(reposition_target, points)\r\n # if abs(intercept_vector_location[0]) < 1000 or car_to_ball_distance < 1000:\r\n # final_target = closest_point(agent.me.location, points)\r\n # #print(\"Final Target: \" + str(final_target))\r\n #\r\n if (final_target.y * side(agent.team)) > 4000 or self.ball_going_into_our_net:\r\n final_target = agent.friend_goal.location\r\n #final_target.y = 4400 * utils.side(agent.team)\r\n self.returning_to_goal = True\r\n else:\r\n final_target.x = cap(final_target.x, -3400, 3400)\r\n final_target.y = cap(final_target.y, -4800, 4800)\r\n\r\n car_to_final_target = (final_target - agent.me.location).flatten()\r\n final_target_to_intercept_direction = -(final_target - intercept_vector_location).flatten().normalize()\r\n\r\n\r\n\r\n\r\n self.target = final_target\r\n if self.offense_defense > -1:\r\n self.direction_vector = final_target_to_intercept_direction\r\n self.intercept_location = intercept_vector_location.flatten()\r\n self.intercept_time = earliest_intercept","repo_name":"RLBot/RLBotPack","sub_path":"RLBotPack/CheeseBot Family/CheeseBot/cutil/croutines.py","file_name":"croutines.py","file_ext":"py","file_size_in_byte":5003,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"82"} +{"seq_id":"31698101383","text":"from numpy.core.fromnumeric import resize, shape\nimport plotly.express as px\nimport pandas as pd\nimport numpy as np\nimport plotly.graph_objects as go\nimport matplotlib.pyplot as plt\n\n\ndef load_csv(file_path):\n return pd.read_csv(file_path, sep=',', header=0)\n\n\nclass Figures():\n def __init__(self, data) -> None:\n self.data = data\n self.cmin = -0.1\n self.cmax = 2.5\n\n self.adjust_layout()\n self.rootCause_to_symptom()\n\n def adjust_layout(self):\n df = [self.data['Root cause High level'], self.data['Symptom Vis']]\n\n result = pd.concat(df, axis=1)\n\n categorical_dimensions = ['Root cause High level', 'Symptom Vis']\n dimensions = [dict(values=result[label], label=label)\n for label in categorical_dimensions]\n\n dimensions[0]['label'] = 'Root Causes'\n dimensions[1]['label'] = 'Symptoms'\n\n result[\"Symptom Vis\"] = result[\"Symptom Vis\"].map({'Segmentation Fault': 1, 'Crash': 2, 'Unexpected Behavior': 3, 'Resource Consumption': 4,\n 'Others': 5})\n\n color = result['Symptom Vis'].values\n\n colorscale = [[0, 'blue'], [0.33, 'red'], [0.33, 'aliceblue'], [\n 0.66, 'green'], [0.66, 'black'], [1.0, 'darkcyan']]\n layout = go.Layout(\n autosize=False,\n width=1400,\n height=600,\n\n xaxis=go.layout.XAxis(linecolor='red',\n linewidth=10,\n mirror=True),\n\n yaxis=go.layout.YAxis(linecolor='black',\n linewidth=10,\n mirror=True),\n\n margin=go.layout.Margin(\n l=300,\n r=500,\n b=100,\n t=100,\n pad=8\n )\n )\n\n trace1 = go.Parcats(dimensions=dimensions,\n line={'colorscale': colorscale, 'cmin': self.cmin, 'cmax': self.cmax, 'color': color, 'shape': 'hspline'})\n data = [trace1]\n\n fig = go.Figure(data=data, layout=layout)\n return fig, result, color, colorscale\n\n def rootCause_to_symptom(self):\n fig, result, color, colorscale = self.adjust_layout()\n colors = {\n 'background': 'white',\n 'text': 'black'\n }\n fig.update_layout(\n plot_bgcolor=colors['background'],\n paper_bgcolor=colors['background'],\n font_color=colors['text'],\n font_size=20\n )\n\n fig.show()\n fig.write_html(\"./rootcauseSymptom.html\")\n\n\ndef main():\n file_path = './benchmark.csv'\n data = load_csv(file_path)\n\n data = data[data['Root cause High level'] != 'Others']\n\n data = data[data['Root cause High level'] != 'others']\n\n data = data[data['Symptom Vis'] != 'Others']\n\n data = data[data['Symptom Vis'] != 'others']\n Figures(data)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"cse19922021/Deep-Learning-Security-Vulnerabilities","sub_path":"ppScript.py","file_name":"ppScript.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"21835759770","text":"import sys\nfrom os import path as op\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import Qt\n\napp = QApplication([])\n\napp.setStyle('Fusion')\n\nwindow = QWidget()\nwindow.setWindowTitle('Hider')\nwindow.setFixedWidth(600)\nwindow.setFixedHeight(400)\n\nlayout = QHBoxLayout()\nleft_layout = QVBoxLayout()\nform_layout = QFormLayout()\n\nimgPath = QLineEdit()\nimgPath.setFixedHeight(380)\nimgPath.setAlignment(Qt.AlignTop)\nimgPath.setPlaceholderText('Img\\'s Path:')\n\nlayout.addWidget(imgPath)\n\nmsg_box = QLineEdit()\nmsg_box.setFixedWidth(130)\nmsg_box.setPlaceholderText('Message')\n\ndef write_msg():\n alert_box = QMessageBox()\n\n if(op.isfile(imgPath.text()) and op.splitext(imgPath.text())[1] == '.jpeg'):\n with open(imgPath.text(), 'ab') as fp:\n fp.write(b'%a' % msg_box.text())\n alert_box.setWindowTitle('Success!')\n alert_box.setText('\\'{}\\' was written with success!'.format(msg_box.text()))\n elif(not op.isfile(imgPath.text())):\n alert_box.setWindowTitle('Error')\n alert_box.setText('{} is not a file'.format(imgPath.text()))\n elif(not op.splitext(imgPath.text())[1] == '.jpeg'):\n alert_box.setWindowTitle('Error')\n alert_box.setText('{} files are not supported'.format(op.splitext(imgPath.text())[1]))\n alert_box.exec()\n\nwrite_btn = QPushButton('Write')\nwrite_btn.clicked.connect(write_msg)\n\nimg_info = QLineEdit()\nimg_info.setReadOnly(True)\nimg_info.setPlaceholderText('Output')\n\ndef read_info():\n alert_box = QMessageBox()\n alert_box.setWindowTitle('Error')\n\n if(op.isfile(imgPath.text()) and op.splitext(imgPath.text())[1] == '.jpeg'):\n with open(imgPath.text(), 'rb') as fp:\n content = fp.read()\n offset = content.index(bytes.fromhex('FFD9'))\n\n fp.seek(offset + 2)\n img_info.setText(fp.read().decode('utf-8'))\n elif(not op.isfile(imgPath.text())):\n alert_box.setText('{} does not exist'.format(imgPath.text()))\n alert_box.exec()\n elif(not op.splitext(imgPath.text())[1] == '.jpeg'):\n alert_box.setText('{} files are not supported'.format(op.splitext(imgPath.text())[1]))\n alert_box.exec()\n\nread_btn = QPushButton('Read')\nread_btn.setFixedWidth(130)\nread_btn.clicked.connect(read_info)\n\nform_layout.addRow(msg_box, write_btn)\nform_layout.addRow(read_btn, img_info)\n\ndef clear_text():\n imgPath.clear()\n msg_box.clear()\n img_info.clear()\n\nclear_btn = QPushButton('Clear')\nclear_btn.clicked.connect(clear_text)\n\ndef exit_app():\n sys.exit()\n\nexit_btn = QPushButton('Exit')\nexit_btn.clicked.connect(exit_app)\n\nleft_layout.addLayout(form_layout)\nleft_layout.addWidget(clear_btn)\nleft_layout.addWidget(exit_btn)\n\nlayout.addLayout(left_layout)\n\nwindow.setLayout(layout)\n\nwindow.show()\napp.exec_()\n","repo_name":"MeEu1/img_writer","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72874767627","text":"from prjstore.db.schemas import product as schemas\nfrom prjstore.db.api.components.base import APIBase\n\n\nclass API_Product(APIBase[schemas.CreateProduct, schemas.Product, schemas.Product]):\n prefix = 'prod'\n schema = schemas.Product\n list_schema = schemas.ListProducts\n\n def __init__(self, headers):\n super().__init__(headers)\n","repo_name":"igol84/myproject","sub_path":"prjstore/db/api/components/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"2506455508","text":"import logging\nfrom collections import OrderedDict\nfrom math import floor\nfrom GEMEditor.analysis.statistics.ui import Ui_StatisticsDialog\nfrom PyQt5 import QtCore\nfrom PyQt5.QtWidgets import QDialog, QGridLayout, QGroupBox, QLabel, QFileDialog, QDialogButtonBox, \\\n QPushButton\n\nlogger = logging.getLogger(__name__)\n\n\nclass DisplayStatisticsDialog(QDialog, Ui_StatisticsDialog):\n \"\"\" Display model statistics\n\n Show model statistics one groupboxes per item type\n containing all computed numbers. The user has the\n choice to save the displayed statistics to a file.\n\n Parameters\n ----------\n statistics: OrderedDict,\n Dictionary containing the statistics grouped in categories\n\n \"\"\"\n\n def __init__(self, statistics):\n super(DisplayStatisticsDialog, self).__init__()\n self.setupUi(self)\n self.statistics = statistics\n self.setWindowTitle(\"Statistics\")\n self.save_button = QPushButton(\"Save\")\n self.buttonBox.addButton(self.save_button, QDialogButtonBox.ActionRole)\n self.save_button.clicked.connect(self.save_statistics)\n self.update_statistics()\n\n def update_statistics(self):\n \"\"\" Populate the dialogs with numbers from\n the statistics dictionary. \"\"\"\n\n # Delete existing child widgets\n for i in reversed(range(self.mainLayout.count())):\n current_widget = self.mainLayout.itemAt(i).widget()\n self.mainLayout.removeWidget(current_widget)\n current_widget.setParent(None)\n\n # Populate layout with new widgets\n for i, item in enumerate(self.statistics.items()):\n key, value = item\n # Generate group box per item\n group_widget = QGroupBox()\n group_widget.setTitle(key)\n\n # Set group layout\n group_layout = QGridLayout()\n group_widget.setLayout(group_layout)\n\n # Add groupbox to main layout (3 columns)\n self.mainLayout.addWidget(group_widget, floor(i/3), i % 3)\n\n # Add items to groupbox\n for n, item in enumerate(value.items()):\n # Add description\n group_layout.addWidget(QLabel(item[0]), n, 0, QtCore.Qt.AlignTop)\n # Add count\n group_layout.addWidget(QLabel(str(item[1])), n, 1, QtCore.Qt.AlignTop | QtCore.Qt.AlignRight)\n\n # Stretch last row to make rows align at top\n group_layout.setRowStretch(n, 1)\n\n @QtCore.pyqtSlot()\n def save_statistics(self):\n \"\"\" Write stats to file \"\"\"\n filename, filter = QFileDialog.getSaveFileName(self, self.tr(\"Save statistics\"), None,\n self.tr(\"Text file (*.txt)\"))\n if filename:\n write_stats_to_file(filename, self.statistics)\n\n\ndef write_stats_to_file(path, model_stats):\n \"\"\" Write the statistics to file\n\n Parameters\n ----------\n path: str\n File path to save statistics to\n model_stats: OrderedDict\n Dictionary containing the statistics grouped in categories\n\n \"\"\"\n with open(path, \"w\") as open_file:\n for category, statistics in model_stats.items():\n for description, count in statistics.items():\n open_file.write(\"\\t\".join((category, description, str(count)))+\"\\n\")\n","repo_name":"JuBra/GEMEditor","sub_path":"GEMEditor/analysis/statistics/dialog.py","file_name":"dialog.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"72993389067","text":"import random\n\n\n'''points_for = 126\npoints_against = 40\n\nexponent = 2.37\nseason_length = 16'''\n\n\ndef pythagorean_expecation(points_for=1,points_against=1, exponent=2.37, season_length=16):\n numer = points_for ** exponent\n denom = points_for ** exponent + points_against ** exponent\n\n return int(season_length * (numer / denom) + .5)\n\n\ndef judgement(e_wins):\n if e_wins >= 14:\n verdict = [\"legendary status!\",\n \"we got some dogs!\",\n \"i don't want to get ahead of myself, but this team beats the Pats!\",\n \"destined for greatness!\",\n \"this team will go down in the history books!\",\n \"championship aspirations!\",\n \"someone's getting a raise!\",\n \"this is a model organization with a high caliber track record!\",\n \"lesser men wouldn't be this good!\"\n ]\n elif e_wins >= 8:\n verdict = ['we got a solid team!',\n 'proud of the effort!',\n 'when we play well, not many teams can hang with us!',\n 'if we just play our game, we have a chance to win the ship!',\n 'awesome!'\n ]\n elif e_wins >= 4:\n verdict = [\"we'll get this on thing track!\",\n \"we're not too far out of the playoff raise!\",\n \"just trying to squeak by over here\",\n \"some positive momentum is needed!\",\n \"we have good players, just gotta get it together!\",\n \"not exactly what we expected, but keep grinding!\"]\n else:\n verdict = ['yikes!',\n 'do you know benching the qb is an option?',\n 'your GM isn\\'t gonna be thrilled!',\n 'wtf is Sachi Brown coaching this team!',\n 'you\\'re closer to the \\'19 dolphins than the \\'72 team',\n 'uhhh, trust the process I guess!',\n 'Tank for Tua!',\n \"don't bother going to another game!\",\n \"i'm gonna be real with you chief, this team is trash!\"\n ]\n\n return random.choice(verdict)\n\n\ndef football_expectation(points_for=1,points_against=1, exponent=2.37, season_length=16):\n e_wins = pythagorean_expecation(points_for,points_against, exponent, season_length)\n\n result = 'you\\'re performing at a {}-{} level, {}'.format(e_wins, season_length-e_wins, judgement(e_wins))\n return {\"points_for\": points_for,\n \"points_against\": points_against,\n \"verdict\": result\n }\n\n\nif __name__ == '__main__':\n football_expectation(points_for, points_against)\n","repo_name":"jletienne/jletienne.com","sub_path":"cool_projects/football_expectations.py","file_name":"football_expectations.py","file_ext":"py","file_size_in_byte":2717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"38222187976","text":"# Exercise 1\n# Instructions\n# Write a script that inserts an item at a defined index in a list.\nimport math\n\nfrom functools import reduce\n\n\nmy_list = [4, 6, 45, 6]\n\n\ndef insert_item(list_items, value, position):\n new_list = []\n if position <= len(list_items):\n new_list = list_items[0:position] + [value] + my_list[position: len(list_items)]\n else:\n new_list = my_list\n print(\"Index out of range\")\n\n return new_list\n\n\nresult = insert_item(my_list, 123, 3)\n\nprint(result)\n\n\n# Exercise 2\n# Instructions\n# Write a script that counts the number of spaces in a string.\n\n\ndef count_spaces(string):\n count = 0\n for char in string:\n if ord(char) == 32:\n count += 1\n if count == 0:\n count = 0\n return count\n\n\nspaces = count_spaces(\"Write a script that counts the number of spaces in a string\")\nprint(f\"There are {spaces} spaces in the string.\")\n\n\n# Exercise 3\n# Instructions\n# Write a script that calculates the number of upper case\n# letters and lower case letters in a string.\n\n\ndef is_upper(char):\n if char == char.upper() and ('a' <= char <= 'z' or 'A' <= char <= 'Z'):\n return True\n else:\n return False\n\n\ndef calc_upper_lower(sentence):\n lowers = 0\n uppers = 0\n for letter in sentence:\n if is_upper(letter):\n uppers += 1\n else:\n lowers += 1\n return uppers, lowers\n\n\nupper, lower = calc_upper_lower(\"Write A scRipt TO be in tHe lOOp\")\nprint(f\"Number of uppers is {upper} and number of lowers is {lower}\")\n\n\n# Exercise 4\n# Instructions\n# Write a function to find the sum of an array without using\n# the built in function:\n\n\ndef my_sum(list_param):\n sum_list = 0\n for i in range(len(list_param)):\n sum_list += list_param[i]\n return sum_list\n\n\nprint(my_sum([1, 5, 4, 2]))\n\n\n# Exercise 5\n# Instructions\n# Write a function to find the max number in a list\n\n\ndef find_max(list_param):\n max_num = list_param[0]\n for i in range(1, len(list_param)):\n if list_param[i] > max_num:\n max_num = list_param[i]\n return max_num\n\n\nprint(find_max([0, 1, 3, 50]))\n\n\n# Exercise 6\n# Instructions\n# Write a function that returns factorial of a number\n\n\ndef factorial(n):\n if n <= 1:\n return 1\n else:\n return n * factorial(n - 1)\n\n\nprint(factorial(12))\n\n\n# Exercise 7\n# Instructions\n# Write a function that counts an element\n# in a list (without using the count method):\n# list_count(['a','a','t','o'],'a')\n\ndef list_count(list_param, key):\n count = 0\n for item in list_param:\n if item == key:\n count += 1\n return count\n\n\nprint(list_count(['a', 'a', 't', 'o'], 'a'))\n\n# Exercise 8\n# Instructions\n# Write a function that returns the L2-norm (square root of the sum of squares)\n# of the sum of a list:\n# >>>norm([1,2,2])\n# >>>3\n\n\ndef norm(list_param):\n squared_norm = 0\n for num in list_param:\n squared_norm += num**2\n return math.sqrt(squared_norm)\n\n\nprint(norm([1, 2, 2]))\n\n\n# Exercise 9\n# Instructions\n# Write a function to find if an array is monotonic\n# (sorted either ascending of descending)\n# >>>is_mono([7,6,5,5,2,0])\n# >>>True\n# >>>is_mono([2,3,3,3])\n# >>>True\n# >>>is_mono([1,2,0,4])\n# >>>False\n\ndef is_mono(list_param):\n if list_param == sorted(list_param) or list_param == sorted(list_param, reverse=True):\n return True\n else:\n return False\n\nprint(is_mono([7,6,5,5,2,0]))\nprint(is_mono([2,3,3,3]))\nprint(is_mono([1,2,0,4]))\n\n# Exercise 10 Write a function that prints the longest word in a list.\n\n\ndef longuest_word(list_param):\n maximum = list_param[0]\n for i in range(len(list_param)-1):\n if len(list_param[i+1]) > len(maximum):\n maximum = list_param[i+1]\n return maximum\n\nsentence = \"Write a function that prints the longest word in a list\".split(' ')\nprint(sentence)\nprint(longuest_word(sentence))\n\n# Exercise 11\n# Instructions\n# Given a list of integers and strings, put all the integers in one list,\n# and all the strings in another one.\n\n# Exercise 12 Palidrome\n\n\ndef is_palindrome(word):\n if word == word[::-1]:\n return True\n else:\n return False\n\nprint(is_palindrome('radar'))\nprint(is_palindrome('John'))\n\n# Exercise 13 Write a function that returns the amount of words\n# in a sentence with length > k:\n# >>>sentence = 'Do or do not there is no try'\n# >>>k=2\n# >>>sum_over_k(sentence,k)\n# >>>3\n\ndef sum_over_k(sentence, k):\n count = 0\n sentence_to_list = sentence.split(\" \")\n for word in sentence_to_list:\n if len(word) > k:\n count += 1\n return count\n\nsentence = 'Do or do not there is no try'\nk = 2\nprint(sum_over_k(sentence, k))\n\n\n# Exercise 14 Write a function that returns the average value\n# in a dictionary (assume the values are numeric):\n# >>>dict_avg({'a': 1,'b':2,'c':8,'d': 1})\n# >>>3\n\ndef dict_avg(dict_param):\n sum_values = 0\n items = 0\n for key, value in dict_param.items():\n sum_values += value\n items += 1\n return sum_values / items\n\n\nprint(dict_avg({'a': 1,'b':2,'c':8,'d': 1}))\n\n# Exercise 15 Write a function that returns common divisors of 2 numbers:\n# >>>common_div(10,20)\n# >>>[2,5,10]\n\n\ndef common_div(num1, num2):\n divisors = []\n lower_num = min(num1, num2)\n higher_num = max(num1, num2)\n for i in range(2, lower_num+1):\n if lower_num % i == 0 and higher_num % i == 0:\n divisors.append(i)\n return divisors\n\nprint(common_div(30, 60))\n\n\n# Exercise 16\n# Instructions\n# Write a function that test if a number is prime.\n# >>>is_prime(11)\n# >>>True\ndef is_prime(num):\n divisors = []\n for i in range(2, num):\n if num % i == 0:\n divisors.append(num)\n if len(divisors) == 0:\n return True\n else:\n return False\n\nprint(is_prime(23))\n\n# Exercise 17\n# Instructions\n# Write a function that prints elements of a list\n# if the index and the value are even:\n# >>>weird_print([1,2,2,3,4,5])\n# >>>[2,4]\n\n\ndef weird_print(list_param):\n weird_list = []\n for i in range(len(list_param)):\n if i % 2 == 0 and list_param[i] % 2 == 0:\n weird_list.append(list_param[i])\n return weird_list\n\n\nprint(weird_print([1,2,2,3,4,5]))\n\n\n# Exercise 18\n# Instructions\n# Write a function that accepts an undefined number of keyworded\n# arguments and return the count of different types:\n# >>>type_count(a=1,b='string',c=1.0,d=True,e=False)\n# >>>int: 1, str:1 , float:1, bool:2\n\ndef type_count(**kwargs):\n type_values = {}\n values_list = []\n freq = {}\n for key, value in kwargs.items():\n type_values[key] = type(value)\n for value in type_values.values():\n values_list.append(value)\n for item in values_list:\n if item in freq:\n freq[item] += 1\n else:\n freq[item] = 1\n return freq\n\n\nprint(type_count(a=1,b='string',c=1.0,d=True,e=False))\n\n\n# Exercise 19 mimics the split function\n\nprint(\"word/show\".split(\"/\"))\n\n\ndef split_mimic(string_param, separator=' '):\n list_param = []\n for i in range(len(string_param)):\n if string_param[i] == separator:\n list_param.append(string_param[:i])\n i += 1\n return list_param\nprint(split_mimic(\"word show again\", \" \"))\n\n\n\n# Exercise 20 Convert a string into password format\n\ndef convert_to_password(word):\n print('*'*len(word))\n\nconvert_to_password(word=\"mypassword\")\n\n\n\n","repo_name":"gbedad/diexs","sub_path":"week_7/pythonProject/day_5/challenges1.py","file_name":"challenges1.py","file_ext":"py","file_size_in_byte":7449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"32462230943","text":"from tkinter import *\nfrom tkinter import filedialog\nimport pygame\nimport time\nfrom mutagen.mp3 import MP3\nimport tkinter.ttk as ttk\n\nroot =Tk()\nroot.title(\"Rspotify\")\nroot.geometry(\"500x400\")\n\n#initialize pygame\npygame.mixer.init()\n\n#craete a function to deal with time\ndef play_time():\n #check to see if song is stopped\n if stopped:\n return \n\n #grab curr song time\n current_time = pygame.mixer.music.get_pos() / 1000\n #convert song time to time format\n converted__current_time=time.strftime('%M:%S', time.gmtime(current_time))\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #find curr song length\n song_mut = MP3(song)\n global song_length\n song_length = song_mut.info.length\n #convert to time format\n converted_song_length = time.strftime('%M:%S', time.gmtime(song_length))\n \n #check to see if song is over\n if int(song_slider.get()) == int(song_length):\n stop()\n #check to see if paused, if so pass \n elif paused:\n pass\n \n else:\n #Move slider along one sec at a time\n next_time = int(song_slider.get()) + 1\n #o/p new time value to slider and to length of a song\n song_slider.config(to=song_length,value=next_time)\n\n #convert slider pos to time format\n converted__current_time=time.strftime('%M:%S', time.gmtime(int(song_slider.get())))\n\n #o/p slider\n status_bar.config(text=f'Time Elapsed: {converted__current_time} of {converted_song_length} ')\n\n\n #add curr time to status bar\n if(current_time>=1):\n status_bar.config(text=f'Time Elapsed: {converted__current_time} of {converted_song_length} ')\n #create loop to check the time every second\n status_bar.after(1000,play_time)\n\n#add 1 song to playlist\ndef add_song():\n song = filedialog.askopenfilename(initialdir='audio/', title=\"Choose A Song\", filetype=((\"mp3 Files\", \"*.mp3\"), ) )\n #my_label.config(text=song)\n #strip out dir structure and .mp3 from song\n song=song.replace(\"D:/mp3/audio/\", \"\")\n song=song.replace(\".mp3\", \"\")\n #add to end of playlist\n playlist_box.insert(END, song)\n\n\n#add many songs to playlist\ndef add_many_song():\n songs = filedialog.askopenfilenames(initialdir='audio/', title=\"Choose A Song\", filetype=((\"mp3 Files\", \"*.mp3\"), ) )\n #my_label.config(text=song)\n\n #loop through song list and replace directory structure mp2 from song name\n for song in songs:\n #strip out dir structure and .mp3 from song\n song=song.replace(\"D:/mp3/audio/\", \"\")\n song=song.replace(\".mp3\", \"\")\n #add to end of playlist\n playlist_box.insert(END, song)\n\n#create a func to del a song from playlist\ndef delete_song():\n #delete highlighted song from playlist\n playlist_box.delete(ANCHOR)\n\n#create a func to del all song from playlist\ndef delete_all_song():\n #del all songs\n playlist_box.delete(0, END)\n\ndef play():\n #set stopped to False since a song is now playing \n global stopped \n stopped = False\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #my_label.config(text=song)\n\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n #get song time\n play_time()\n\n#create stopped var\nglobal stopped\nstopped = False\n#create stop function\ndef stop():\n #stop a song\n pygame.mixer.music.stop()\n #clear playlist bar\n playlist_box.selection_clear(ACTIVE)\n status_bar.config(text='')\n #set slider value to 0\n song_slider.config(value=0)\n #set stop var to true\n global stopped\n stopped = True\n\n#create fun to play the next song\ndef forward():\n #reset slider pos and status bar\n status_bar.config(text='')\n song_slider.config(value=0)\n #get current song number\n next_one=playlist_box.curselection()\n #my_label.config(text=next_one)\n #add one to the current song number\n next_one=next_one[0] + 1 \n #grab the song title from playlist \n song = playlist_box.get(next_one)\n #add directory structure stuff to the song \n song =f'D:/mp3/audio/{song}.mp3'\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n #clear acrive bar in playlist\n playlist_box.selection_clear(0,END)\n #move active bar to next song\n playlist_box.activate(next_one)\n #set active bar to next song \n playlist_box.selection_set(next_one,last=None)\n\ndef previous():\n #reset slider pos and status bar\n status_bar.config(text='')\n song_slider.config(value=0)\n #get current song number\n next_one=playlist_box.curselection()\n #my_label.config(text=next_one)\n #add one to the current song number\n next_one=next_one[0] - 1 \n #grab the song title from playlist \n song = playlist_box.get(next_one)\n #add directory structure stuff to the song \n song =f'D:/mp3/audio/{song}.mp3'\n #play song woth pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0)\n\n #clear acrive bar in playlist\n playlist_box.selection_clear(0,END)\n #move active bar to next song\n playlist_box.activate(next_one)\n #set active bar to next song \n playlist_box.selection_set(next_one,last=None)\n\n#create paused var\nglobal paused \npaused = False\n\n#create pause function\ndef pause(is_paused):\n global paused\n paused = is_paused\n\n if paused:\n #unpause\n pygame.mixer.music.unpause()\n paused=False\n else:\n #pause\n pygame.mixer.music.pause()\n paused=True\n\n#create vol func\ndef volume(x):\n pygame.mixer.music.set_volume(volume_slider.get())\n\n#create slide func for song pos\ndef song_slide(x):\n #reconstruct song with dir structure stuff\n song = playlist_box.get(ACTIVE)\n song =f'D:/mp3/audio/{song}.mp3'\n #my_label.config(text=song)\n\n #play song with pygame mixer\n pygame.mixer.music.load(song)\n #play a sing with pygame mixer\n pygame.mixer.music.play(loops=0, start=song_slider.get())\n\n#Create main frame\nmain_frame=Frame(root)\nmain_frame.pack(pady=20)\n\n#create playlist box\nplaylist_box=Listbox(main_frame, bg=\"yellow\", fg=\"green\", width=60, selectbackground=\"green\", selectforeground=\"yellow\")\nplaylist_box.grid(row=0,column=0)\n\n#Create volume slider frame\nvolume_frame = LabelFrame(main_frame, text=\"Volume\")\nvolume_frame.grid(row=0,column=1,padx=20)\n\n#create volume slider \nvolume_slider=ttk.Scale(volume_frame,from_=1, to=0,orient=VERTICAL,value=1,length=125,command=volume)\nvolume_slider.pack(pady=10)\n\n#create song slider\nsong_slider = ttk.Scale(main_frame,from_=0, to=100,orient=HORIZONTAL,value=0,length=360,command=song_slide)\nsong_slider.grid(row=2,column=0,pady=20)\n\n#define button images for controls\nback_btn_img=PhotoImage(file='images/back.png ')\nforward_btn_img=PhotoImage(file='images/forward.png')\nplay_btn_img=PhotoImage(file='images/play.png')\npause_btn_img=PhotoImage(file='images/pause.png')\nstop_btn_img=PhotoImage(file='images/stop.png')\n\n#create button frame\ncontrol_frame=Frame(main_frame)\ncontrol_frame.grid(row=1,column=0,pady=20)\n\n#create play/pause etc button \nback_button=Button(control_frame, image=back_btn_img,borderwidth=0,command=previous)\nplay_button=Button(control_frame, image=play_btn_img,borderwidth=0, command=play)\npause_button=Button(control_frame, image=pause_btn_img,borderwidth=0, command=lambda: pause(paused))\nstop_button=Button(control_frame, image=stop_btn_img,borderwidth=0 , command=stop)\nforward_button=Button(control_frame, image=forward_btn_img,borderwidth=0, command=forward)\n\nback_button.grid(row=0,column=0,padx=10)\nforward_button.grid(row=0,column=4,padx=10)\nplay_button.grid(row=0,column=1,padx=10)\npause_button.grid(row=0,column=2,padx=10)\nstop_button.grid(row=0,column=3,padx=10)\n\n#create menu\nmy_menu = Menu(root)\nroot.config(menu=my_menu)\n\n#create add song menu dropdown\nadd_song_menu=Menu(my_menu, tearoff=0)\nmy_menu.add_cascade(label=\"Add Songs\",menu=add_song_menu)\n#add one song to playlist\nadd_song_menu.add_command(label=\"Add one song to playlist\",command=add_song)\n#add many song to playlist\nadd_song_menu.add_command(label=\"Add multiple songs to playlist\",command=add_many_song)\n\n#Create delete song menu dropdowns\nremove_song_menu = Menu(my_menu, tearoff=0)\nmy_menu.add_cascade(label=\"Remove Songs\", menu = remove_song_menu)\nremove_song_menu.add_command(label=\"Delete A Song From Playlist\", command=delete_song)\nremove_song_menu.add_command(label=\"Delete All Song From Playlist\", command=delete_all_song)\n\n#create status bar\nstatus_bar = Label(root, text='', bd=1, relief=GROOVE, anchor=E)\nstatus_bar.pack(fill=X, side=BOTTOM, ipady=2)\n\n\n\n#Temporary label\nmy_label = Label(root, text=\"\")\nmy_label.pack(pady=20)\n\nroot.mainloop()","repo_name":"rahulravisankar1108/MP3-player","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":8915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"40250951093","text":"from collections import defaultdict, Counter\n\nwith open(\"input\", \"r\") as f:\n#with open(\"t\", \"r\") as f:\n lines = [line for line in f.read().split(\"\\n\\n\")]\n\npolymer = lines[0]\n\nreactions = defaultdict(lambda: \"\")\n\nfor reaction in [chem.split(\" -> \") for chem in lines[1].strip().split(\"\\n\")]:\n reactions[reaction[0]] = reaction[1]\n\ncount = Counter()\n\ndef split(chain):\n parts = []\n for i in range(2, len(chain) + 1):\n parts.append(chain[i - 2: i])\n return (parts)\n\n# Populate the dictionary\nfor snip in split(polymer):\n count[snip] += 1\n\nfor i in range(40):\n counter_aux = Counter()\n for pair in count:\n counter_aux[pair[0] + reactions[pair]] += count[pair]\n counter_aux[reactions[pair] + pair[1]] += count[pair]\n count = counter_aux\n\nchar_freq = Counter()\nfor pair in count:\n char_freq[pair[0]] += count[pair]\n\n# Must count the final character\nchar_freq[polymer[-1]] += 1\n\nprint(max(char_freq.values()) - min(char_freq.values()))\n","repo_name":"EnriqueSLeeK/Advent-of-Code","sub_path":"2021/14 day/phase_2/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29508406338","text":"'''\nFutures data updates script, all futures contracts\n'''\n#import os, sys, inspect\n\n# realpath() will make your script run, even if you symlink it :)\n# cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))\n# if cmd_folder not in sys.path:\n# sys.path.insert(0, cmd_folder)\n#\n# # Use this if you want to include modules from a subfolder\n# cmd_subfolder = os.path.realpath(\n# os.path.abspath(os.path.join(os.path.split(inspect.getfile(inspect.currentframe()))[0], \"subfolder\")))\n# if cmd_subfolder not in sys.path:\n# sys.path.insert(0, cmd_subfolder)\n\nfrom tmqrscripts.historical_options.import_options_data import *\n\n#run_all_options()\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--instrument\", help=\"an instrument you want to backfill\", type=str)\nargs = parser.parse_args()\n\nif args.instrument is None:\n print('run all')\n run_full_options()\nelse:\n print('run ',args.instrument)\n run_full_options_selected_instrument(args.instrument)\n","repo_name":"trendmanagement/Tmqr-framework-2","sub_path":"tmqrscripts/historical_options/run_all_history_options_update.py","file_name":"run_all_history_options_update.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"10862477653","text":"'''\nWrite a function that will check list of the options, that should be marked/checked in the dropdown list and mark them.\nAdd an additional verification if user wants to mark All options\n'''\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n\ndef select_all_values(options_list, value):\n if not value[0].lower() == 'all':\n for ele in drop_list:\n for k in range(len(value)):\n if ele.text == value[k]:\n ele.click()\n break\n else:\n try:\n for ele in options_list:\n ele.click()\n except Exception as e:\n print(e)\n\n\ndriver = webdriver.Chrome(ChromeDriverManager().install())\ndriver.get('https://www.jqueryscript.net/demo/Drop-Down-Combo-Tree/')\n\ndriver.find_element(By.ID, 'justAnInputBox').click()\n\ndrop_list = driver.find_element(By.CSS_SELECTOR, 'span.comboTreeItemTitle')\nvalues_list = ['all']\n# values_list = ['choice 2', 'choice 3', 'choice 6 2 1']\n\nselect_all_values(drop_list, values_list)\n","repo_name":"AlexNovsky/Interview_tasks","sub_path":"SelectAllValuesInDropDownHandling.py","file_name":"SelectAllValuesInDropDownHandling.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1703450070","text":"from doctest import REPORTING_FLAGS\nfrom ntpath import realpath\nimport yaml\nimport os\n#from config.config import ROOT_DIR\n\n#print(__file__) #文件 当前路径\n# curpath = os.path.realpath(__file__)\n# # #print(curpath)\n# root_dir = os.path.dirname(os.path.dirname(curpath))#获取根文件路径\n# # print(root_dir)\n# yaml_path = os.path.join(root_dir,\"data\",\"test_data_login.yml\") #获取yaml文件路径\n# print(yaml_path)\n\ndef readyml(data_name):\n '''读取yaml文件内容\n 参数path: 相对路径，起始路径：项目的根目录\n realPath: 文件的真实路径,绝对路径地址 '''\n\n curpath = os.path.realpath(__file__)\n root_dir = os.path.dirname(os.path.dirname(curpath))#获取根文件路径\n #print(\"root_dir = %s\" %root_dir)\n yamlPath = os.path.join(root_dir,\"data\",data_name)\n #print(\"yaml_path = %s\" %yamlPath)\n\n if not os.path.isfile(yamlPath):\n raise FileNotFoundError(\"文件路径不存在，请检查路径是否正确：%s\" % yamlPath)\n # open方法打开直接读出来\n \n f = open(yamlPath, 'r', encoding='utf-8')\n cfg = f.read()\n d = yaml.safe_load(cfg)\n f.close()\n\n # 用load方法转字典\n #print(\"读取的测试文件数据：%s\"%d)\n return d\n\nif __name__ == '__main__':\n #yaml_path = os.path.join(root_dir,\"data\",\"sigin.yml\")\n data_name = \"check_data.yml\"\n d = readyml(data_name)\n print(d)\n\n\n\n","repo_name":"copyg/ezcloudgit","sub_path":"ezmanager/common/read_yaml.py","file_name":"read_yaml.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28426453445","text":"\nfrom bisect import insort, bisect_right\nfrom typing import List\nclass Solution:\n def findLeastGreater(self, n : int, arr : List[int]) -> List[int]:\n ans=[-1]*n\n temp=[]\n for i in range(n-1,-1,-1):\n insort(temp,arr[i])\n index=bisect_right(temp,arr[i])\n if index None:\n pass\n def Input(self,n):\n arr=[int(i) for i in input().strip().split()]#array input\n return arr\n def Print(self,arr):\n for i in arr:\n print(i,end=\" \")\n print()\n\n\nif __name__==\"__main__\":\n t = int(input())\n for _ in range(t):\n \n n = int(input())\n \n \n arr=IntArray().Input(n)\n \n obj = Solution()\n res = obj.findLeastGreater(n, arr)\n \n IntArray().Print(res)\n \n\n# } Driver Code Ends","repo_name":"GANJINAVEEN14161416/Leetcode_DSA","sub_path":"Replace every element with the least greater element on its right - GFG/replace-every-element-with-the-least-greater-element-on-its-right.py","file_name":"replace-every-element-with-the-least-greater-element-on-its-right.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36089434925","text":"import pygame\nfrom point import Point\nfrom mesh import ClothMesh\n\n# define constants\nWINDOW_TITLE = \"My Simulation\"\nWINDOW_SIZE = (800, 800)\nFPS = 60\n\n# initialize pygame\npygame.init()\n\n# create window\nwindow = pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\npygame.display.set_caption(WINDOW_TITLE)\n\nclock = pygame.time.Clock()\n\n# Game objects\nclothMesh = ClothMesh(topLeft=(200, 10), size=(22, 22))\n\nt = pygame.time.get_ticks()\nfont = pygame.font.SysFont(\"arial\", 20)\n# main loop\nrunning = True\nwhile running:\n # set frames per second\n clock.tick(FPS)\n dt = (pygame.time.get_ticks() - t) / 1000.0\n t = pygame.time.get_ticks()\n\n # handle inputs\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n # toggle fullscreen\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_f or event.key == pygame.K_RETURN:\n if window.get_flags() & pygame.FULLSCREEN:\n pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\n pygame.display.set_mode(WINDOW_SIZE, pygame.RESIZABLE)\n else:\n resolutions = pygame.display.list_modes()\n pygame.display.set_mode(resolutions[1], pygame.FULLSCREEN)\n elif event.key == pygame.K_r:\n clothMesh.reset()\n if pygame.mouse.get_pressed()[0]:\n clothMesh.isMouseDown = True\n else:\n clothMesh.isMouseDown = False\n\n clothMesh.update()\n\n # update the window\n pygame.display.flip()\n window.fill((0, 0 ,0))\n\n text = font.render(f\"{round(1/dt)} fps\", True, (255, 255, 255))\n window.blit(text, (0, 0))\n text = font.render(f\"press R to reset\", True, (255, 255, 255))\n window.blit(text, (0, 20))\n \n\npygame.quit()","repo_name":"starcreep48/cloth-simulation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"7137252758","text":"'''\nhedgeit.strategy.factory\n\nContains:\n Class StrategyFactory\n'''\n\nfrom trends import *\nfrom countertrends import *\n\nclass StrategyFactory(object):\n '''\n classdocs\n '''\n\n def __init__(self):\n pass\n\n factoryMethods = {}\n\n @classmethod\n def register(cls, name, method):\n StrategyFactory.factoryMethods[name] = method\n \n @classmethod\n def create(cls, name, barFeed, symbols = None, broker = None, cash = 1000000,\n compounding = True, parms = None):\n if not StrategyFactory.factoryMethods.has_key(name):\n raise Exception('No strategy %s registered' % name)\n return StrategyFactory.factoryMethods[name](barFeed, \n symbols = symbols,\n broker = broker,\n cash = cash,\n compounding = compounding,\n parms = parms)\n \nStrategyFactory.register('breakout', BreakoutStrategy)\nStrategyFactory.register('macross', MACrossStrategy)\n \nStrategyFactory.register('rsireversal', RSIReversalStrategy)\nStrategyFactory.register('connorsrsi', ConnorsRSIStrategy)\nStrategyFactory.register('split7s', Split7sStrategy)\nStrategyFactory.register('rsireversal2', RSIReversal2Strategy)\nStrategyFactory.register('cumrsi', CumRSIStrategy)\n ","repo_name":"wilki2021/hedgeit","sub_path":"hedgeit/strategy/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"82"} +{"seq_id":"24718955569","text":"import math\n\ndef check_prime(n):\n root = math.floor(math.sqrt(n))\n for i in range(2,root + 1):\n if (n % i == 0):\n return False\n\n return True\n\nn = int(input())\n\nif(check_prime(n)):\n print(n,\"IS PRIME\")\nelse:\n print(n,\"IS NOT PRIME\")","repo_name":"BlackEagle12/CompititiveCoding","sub_path":"Begineer problem solving on python/Ans-4.py","file_name":"Ans-4.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3881457078","text":"\"\"\"\n Configuration file management\n If you want to create a new configuration create a class with type decorators, and register it\n\n @Configuration.register('foo')\n class Foo(Settings):\n class Bar(Settings):\n name: str\n id: int = 0\n\n name: str = Required\n number: int\n bars: list[Bar]\n\n This will load the following yaml file\n ---\n foo:\n name: \"hi\"\n bars:\n - name: \"mo\"\n id: 1\n - name: \"mi\"\n id: 2\n\n You can register settings as repeated and provide multiple documents, or even include other documents.\n\n @Configuration.register('bar', repeated=True)\n class Bar(Settings):\n name: str = Required\n\n ---\n bar:\n name: \"bar1\"\n ---\n bar:\n name: \"bar2\"\n #include \"some_other.yaml\"\n\"\"\"\nfrom typing import get_type_hints, get_origin, get_args\nfrom carla_utils.util import Printable\n\n__all__ = [\"Configuration\", \"Required\", \"Settings\"]\n\n\ndef load_file_with_include(fn):\n import re\n with open(fn, 'r') as f:\n return re.sub('\\#include +[\"<](.*)[\">]', lambda m: load_file_with_include(fn.parent / m.group(1)), f.read())\n\n\nclass ForAll:\n def __init__(self, objects):\n self._objects = objects\n\n def __getitem__(self, item):\n return ForAll([o[item] for o in self._objects])\n\n def __setitem__(self, key, value):\n for o in self._objects:\n o[key] = value\n\n def __getattr__(self, item):\n if item[0] != '_':\n return ForAll([getattr(o, item) for o in self._objects])\n\n def __setattr__(self, name, value):\n if name[0] == '_':\n return super().__setattr__(name, value)\n else:\n for o in self._objects:\n setattr(o, name, value)\n\n\nclass EllipsisList(list):\n def __getitem__(self, item):\n if item is ...:\n return ForAll(list(self))\n else:\n return super().__getitem__(item)\n\n\nclass Configuration:\n _all = {}\n\n @staticmethod\n def register(name, repeated=False):\n def _wrapper(cls):\n Configuration._all[name] = (cls, repeated)\n cls.__str__ = Printable.__str__\n return cls\n return _wrapper\n\n @staticmethod\n def _find(name):\n return Configuration._all[name][0] if name in Configuration._all else None\n\n @staticmethod\n def _is_repeated(name):\n return Configuration._all[name][1] if name in Configuration._all else None\n\n @staticmethod\n def _has(name):\n return name in Configuration._all\n\n @staticmethod\n def from_file(*fns):\n from pathlib import Path\n settings = []\n for fn in fns:\n fn = Path(fn)\n if fn.name.endswith('.json'):\n import json\n with open(fn, 'r') as f:\n settings.append(json.load(f))\n elif fn.name.endswith('.yaml'):\n import yaml\n settings.extend(list(yaml.load_all(load_file_with_include(fn), Loader=yaml.FullLoader)))\n else:\n raise ValueError('Unknown file extension for configuration \"%s\"' % fn.name)\n return Configuration.from_dict(*settings)\n\n @staticmethod\n def from_dict(*settings):\n r = Configuration()\n for o in settings:\n for k, v in o.items():\n tpe = Configuration._find(k)\n assert tpe is not None, 'No configuration found for {!r}'.format(k)\n if Configuration._is_repeated(k):\n if hasattr(r, k):\n getattr(r, k).append(tpe(**v))\n else:\n setattr(r, k, EllipsisList([tpe(**v)]))\n else:\n assert not hasattr(r, k), 'Multiple configurations for {!r} found'.format(k)\n setattr(r, k, tpe(**v))\n return r\n\n def validate(self):\n # Add default configs for the ones missing\n for k, (cls, repeated) in Configuration._all.items():\n if not hasattr(self, k):\n setattr(self, k, [] if repeated else cls())\n\n # Make sure all configs are complete\n for v in vars(self):\n if hasattr(v, 'validate'):\n v.validate()\n elif isinstance(v, list):\n for vv in v:\n if hasattr(vv, 'validate'):\n vv.validate()\n\n @staticmethod\n def add_argument(parser):\n parser.add_argument('--config', nargs='+', default=[])\n parser.add_argument('--config_override', nargs='+', default=[])\n\n @staticmethod\n def from_args(args):\n cfg = Configuration.from_file(*args.config)\n # TODO: override in a less hacky way\n for o in args.config_override:\n exec(o, {k: getattr(cfg, k) for k in vars(cfg)})\n cfg.validate()\n return cfg\n\n\nclass Required:\n pass\n\n\nclass Settings(Printable):\n def __init__(self, **kwargs):\n th = get_type_hints(self.__class__)\n for k, v in kwargs.items():\n if k in th:\n setattr(self, k, convert_to(v, th[k], type(self).__name__+'.'+k))\n assert hasattr(self, k), 'Unknown settings value {!s}.{!s}'.format(type(self).__name__, k)\n\n def validate(self):\n for k in vars(self.__class__):\n assert getattr(self, k) != Required, \"Required configuration value {!r} not specified\".format(k)\n\n\ndef convert_to(o, tpe, dname=''):\n # Make sure v is of decorated type tpe\n if tpe is None:\n return o\n if get_origin(tpe) == list:\n assert isinstance(o, list), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o)\n l_tpe, *_ = *get_args(tpe), None\n return [convert_to(i, l_tpe, dname) for i in o]\n if get_origin(tpe) == dict:\n assert isinstance(o, dict), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o)\n k_tpe, v_tpe, *_ = *get_args(tpe), None, None\n return {convert_to(k, k_tpe, dname + '.' + k): convert_to(v, v_tpe, dname + '.' + k) for k, v in o.items()}\n if isinstance(tpe, type) and issubclass(tpe, Settings):\n assert isinstance(o, dict), 'Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, dict, type(o), o)\n return tpe(**o)\n try:\n return tpe(o)\n except (ValueError, TypeError) as e:\n raise ValueError('Setting {!r} expected type {!r} got {!r} ({!r})'.format(dname, tpe, type(o), o))\n raise ValueError('Setting {!r} decorator not a type {!r}'.format(dname, tpe))\n","repo_name":"philkr/carla_utils","sub_path":"carla_utils/recording/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":6598,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"21864798314","text":"\"\"\"\nGiven an integer number n, return the difference between the product of its digits and the sum of its digits.\n\nExample 1:\n Input: n = 234\n Output: 15\nExplanation:\n Product of digits = 2 * 3 * 4 = 24\n Sum of digits = 2 + 3 + 4 = 9\n Result = 24 - 9 = 15\n\nExample 2:\n Input: n = 4421\n Output: 21\nExplanation:\n Product of digits = 4 * 4 * 2 * 1 = 32\n Sum of digits = 4 + 4 + 2 + 1 = 11\n Result = 32 - 11 = 21\n\nConstraints:\n 1 <= n <= 10^5\n\"\"\"\n\nfrom functools import reduce\n\n\nclass Solution:\n \"\"\"\n Runtime: 52 ms, faster than 5.52% of Python3\n Memory Usage: 14.4 MB, less than 9.83% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n digits = [int(digit) for digit in str(n)]\n product = reduce(lambda a, b: a * b, digits)\n return product - sum(digits)\n\n\nclass Solution2:\n \"\"\"\n Runtime: 48 ms, faster than 7.21% of Python3\n Memory Usage: 14.3 MB, less than 9.83% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n sum_ = 0\n product = 1\n for digit in str(n):\n sum_ += int(digit)\n product *= int(digit)\n return product - sum_\n\n\nclass Solution3:\n \"\"\"\n Using modulo to operate with least significant digit of a number at each iteration, then reducing problem on it.\n\n Runtime: 36 ms, faster than 14.42% of Python3\n Memory Usage: 14.1 MB, less than 69.64% of Python3\n \"\"\"\n\n def subtractProductAndSum(self, n: int) -> int:\n sum_ = 0\n product = 1\n while n:\n # e.g., for 234 last_digit will == 4 and n for next iteration will become 23\n last_digit = n % 10\n sum_ += last_digit\n product *= last_digit\n n //= 10\n return product - sum_\n\n\nif __name__ == '__main__':\n solutions = [Solution(), Solution2(), Solution3()]\n tc = (\n (234, 15),\n (4421, 21),\n (1, 0),\n (100000, -1),\n )\n for sol in solutions:\n for inp_n, exp_result in tc:\n assert sol.subtractProductAndSum(inp_n) == exp_result\n","repo_name":"niki4/leetcode_py3","sub_path":"easy/1281_subtract_the_product_and_sum_of_digits_of_an_integer.py","file_name":"1281_subtract_the_product_and_sum_of_digits_of_an_integer.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"15727044232","text":"# -*- coding: utf-8 -*-\n\"\"\"\npytorch_custom_datasets.ipynb\n\n## PyTorch Custom Datasets\n\n## 0. Import PyTorch & Setting Up Device-Agnostic Code\n\"\"\"\n\nimport os\nimport time\nimport torch\nimport random\nimport requests\nimport zipfile\nimport pathlib\nimport torchinfo\nimport torchvision\nimport numpy as np\nimport pandas as pd\n\nfrom torch import nn\nfrom PIL import Image\nfrom pathlib import Path\nfrom tqdm.auto import tqdm\nfrom torchinfo import summary\nfrom matplotlib import pyplot as plt\nfrom typing import Tuple, Dict, List\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\n# Download helper functions\nif Path(\"helper_functions.py\").is_file():\n print(\"helper_functions.py already exists, skipping download\")\nelse:\n print(\"Downloading helper_functions.py\")\n request = requests.get(\"https://raw.githubusercontent.com/UygarKAYA/DeepLearning/main/utils/helper_functions.py\")\n with open(\"helper_functions.py\", \"wb\") as f:\n f.write(request.content)\n \nfrom helper_functions import accuracy_func\nfrom helper_functions import execution_time\n\n# Setup Device Agnostic Code\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\ndevice\n\n\"\"\"\n## 1. Get Data from GitHub\n\n * Our Dataset is a Subset of the Food101 Dataset.\n * Food101 Starts 101 Different Classes of Food and 1000 Images Per Class (750 Training, 250 Testing).\n * Our Dataset Starts with 3 Classes of Food and Only 10% of the Images (~75 Training, 25 Testing).\n\"\"\"\n\ndata_path = Path(\"data/food101_dataset\")\nzip_data_path = 'data/food101_dataset.zip'\n\nif data_path.is_dir():\n print(f\"{data_path} directory already exist... skipping download\")\nelse:\n print(f\"{data_path} does not exist, creating one...\")\n data_path.mkdir(parents=True, exist_ok=True)\n\n# Download Food101 Dataset from GitHub Repository\nwith open(zip_data_path, 'wb') as f:\n request = requests.get('https://github.com/UygarKAYA/DeepLearning/raw/main/data/food101_dataset.zip')\n f.write(request.content)\n\n# Unzip Food101 Dataset\nwith zipfile.ZipFile(zip_data_path, 'r') as zip:\n zip.extractall(data_path)\n\n\"\"\"## 2. Data Preparation\"\"\"\n\n# Setup Train and Testing Paths\ntrain_path = data_path/'train'\ntest_path = data_path/'test'\n\n# Write a Transform for Image\ndata_transform = transforms.Compose([\n # Resize Our Images to 64x64\n transforms.Resize(size=(64, 64)),\n\n # Flip the Images Randomly on the Horizontal\n transforms.RandomHorizontalFlip(p=0.5),\n\n # Turn the Image into torch.Tensor\n transforms.ToTensor()\n])\n\ntrain_data = datasets.ImageFolder(root=train_path,\n transform=data_transform,\n target_transform=None)\n\ntest_data = datasets.ImageFolder(root=test_path,\n transform=data_transform)\n\ntrain_data, test_data\n\nclass_names = train_data.classes\nclass_dict = train_data.class_to_idx\nclass_names, class_dict\n\n\"\"\"## 2.1 Prepare DataLoader\"\"\"\n\n# Setup the Batch Size Hyperparameter\nBATCH_SIZE=32\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ntrain_dataloader = DataLoader(dataset=train_data,\n batch_size=BATCH_SIZE, \n shuffle=SHUFFLE_TRAIN_DATASET)\n\ntest_dataloader = DataLoader(dataset=test_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\n\n# Let's check out what what we've created\nprint(f\"DataLoaders: {train_dataloader, test_dataloader}\")\nprint(f\"Length of train_dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}...\")\nprint(f\"Length of test_dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}...\")\n\n\"\"\"\n## 2.2 Option 1: Loading Image Data with a Custom Dataset\n\n1. Want to be able to load images from file\n2. Want to be able to get class names from the Dataset\n3. Want to be able to get classes as dictionary from the Dataset\n\nPros:\n * Can create a Dataset out of almost anything\n * Not limited to PyTorch pre-built Dataset functions\n\nCons:\n* Even though you could create Dataset out of almost anything, it doesn't mean it will work...\n* Using a custom Dataset often results in us writing more code, which could be prone to errors or performance issues\n\n## 2.2.1 Creating a Helper Function to Get Class Names\n\"\"\"\n\ndef find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:\n \"\"\"Finds the Class Folder Names in a Target Directory.\"\"\"\n # 1. Get the class names by scanning the target directory\n class_names = sorted(class_.name for class_ in os.scandir(directory) if class_.is_dir())\n\n # 2. Raise an error if class names could not be found\n if not class_names:\n raise FileNotFoundError(f\"Couldn't find any classes in {directory}... please check file structure.\")\n \n # 3. Create a dictionary of index labels\n class_to_idx = {class_name: idx for idx, class_name in enumerate(class_names)}\n\n return class_names, class_to_idx\n\n\"\"\"\n# 2.2.2 Create a Custom Dataset to Replicate ImageFolder\n\nTo create our own custom dataset, we want to:\n\n1. Subclass `torch.utils.data.Dataset`\n2. Init our subclass with a target directory (the directory we'd like to get data from) as well as a transform if we'd like to transform our data.\n3. Create several attributes:\n* paths - paths of our images\n* transform - the transform we'd like to use\n* classes - a list of the target classes\n* class_to_idx - a dict of the target classes mapped to integer labels\n\n4. Create a function to `load_images()`, this function will open an image\n5. Overwrite the `__len()__` method to return the length of our dataset\n6. Overwrite the `__getitem()__` method to return a given sample when passed an index\n\"\"\"\n\n# 1. Subclass torch.utils.data.Dataset\nclass CustomImageFolder(Dataset):\n # 2. Initialize our custom dataset\n def __init__(self, \n targ_dir: str, \n transform=None):\n # 3. Create class attributes\n # Get all of the image paths\n self.paths = list(pathlib.Path(targ_dir).glob(\"*/*.jpg\"))\n # Setup transform\n self.transform = transform\n # Create classes and class_to_idx attributes\n self.classes, self.class_to_idx = find_classes(targ_dir)\n\n # 4. Create a function to load images\n def load_image(self, index: int) -> Image.Image:\n \"Opens an image via a path and returns it.\"\n image_path = self.paths[index]\n return Image.open(image_path)\n\n # 5. Overwrite __len__()\n def __len__(self) -> int:\n \"Returns the total number of samples.\"\n return len(self.paths)\n \n # 6. Overwrite __getitem__() method to return a particular sample\n def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n \"Returns one sample of data, data and label (X, y).\"\n img = self.load_image(index)\n class_name = self.paths[index].parent.name # expects path in format: data_folder/class_name/image.jpg\n class_idx = self.class_to_idx[class_name]\n\n # Transform if necessary\n if self.transform:\n return self.transform(img), class_idx # return data, label (X, y)\n else:\n return img, class_idx # return untransformed image and label\n\ntrain_data_transform = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.RandomHorizontalFlip(p=0.5),\n transforms.ToTensor()\n])\n\ntest_data_transform = transforms.Compose([\n transforms.Resize(size=(64,64)),\n transforms.ToTensor()\n])\n\n# Test out CustomImageFolder\n\ncustom_train_data = CustomImageFolder(targ_dir=train_path,\n transform=train_data_transform)\n\ncustom_test_data = CustomImageFolder(targ_dir=test_path,\n transform=test_data_transform)\n\ncustom_train_data, custom_test_data\n\n\"\"\"\n## 2.3 Create a Helper Function to Display Random Images\n\n1. Take in a Dataset and a number of other parameters such as class names and how many images to visualize.\n2. To prevent the display getting out of hand, let's cap the number of images to see at 10.\n3. Set the random seed for reproducibility\n4. Get a list of random sample indexes from the target dataset.\n5. Setup a matplotlib plot.\n6. Loop through the random sample indexes and plot them with matploltib.\n7. Make sure the dimensions of our images line up with matplotlib (HWC)\n\"\"\"\n\n# 1. Create a function to take in a dataset\ndef display_random_images(dataset: torch.utils.data.Dataset,\n classes: List[str]=None,\n n: int=10,\n display_shape: bool=False,\n seed: int=None):\n # 2. Adjust display if n is too high\n if n > 10:\n n = 10\n display_shape = False\n print(f\"For display, purposes, n shouldn't be larger than 10, setting to 10 and removing shape display.\")\n\n # 3. Set the seed\n if seed:\n random.seed(seed)\n\n # 4. Get random sample indexes\n random_samples_idx = random.sample(range(len(dataset)), k=n)\n\n # 5. Setup plot\n plt.figure(figsize=(16, 8))\n\n # 6. Loop through random indexes and plot them with matplotlib\n for idx, targ_sample in enumerate(random_samples_idx):\n targ_image, targ_label = dataset[targ_sample][0], dataset[targ_sample][1]\n\n # 7. Adjust tensor dimensions for plotting\n targ_image_adjust = targ_image.permute(1, 2, 0) # [color_channels, height, width] -> [height, width, color_channels]\n\n # Plot adjusted samples\n plt.subplot(1, n, idx+1)\n plt.imshow(targ_image_adjust)\n if classes:\n title = f\"{classes[targ_label]}\"\n if display_shape:\n title = title + f\"\\n{targ_image_adjust.shape}\"\n plt.title(title)\n\n# Display random images from the ImageFolder created Dataset\ndisplay_random_images(train_data,\n n=5, \n classes=class_names,\n seed=42)\n\n# Display random images from the ImageFolderCustom Dataset\ndisplay_random_images(custom_train_data,\n n=5,\n classes=class_names,\n seed=42)\n\n\"\"\"## 2.4 Turn Custom Loaded Images Into DataLoader's\"\"\"\n\n# Setup the Batch Size Hyperparameter\nBATCH_SIZE=1\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ncustom_train_dataloader = DataLoader(dataset=custom_train_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TRAIN_DATASET)\n\ncustom_test_dataloader = DataLoader(dataset=custom_test_data,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\ncustom_train_dataloader, custom_test_dataloader\n\n\"\"\"\n## 3. Other Forms of Transforms (Data Augmentation)\n\n1. Data augmentation is the process of artificially adding diversity to your training data.\n\n2. In the case of image data, this may mean applying various image transformations to the training images.\n\n3. This practice hopefully results in a model that's more generalizable to unseen data.\n\n4. Let's take a look at one particular type of data augmentation used to train PyTorch vision models to state of the art levels...\n\n## 3.1 Create Transform and Train & Test Dataset's with Data Augmentation\n\"\"\"\n\ntrain_transform_data_aug = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.TrivialAugmentWide(num_magnitude_bins=31),\n transforms.ToTensor()\n])\n\ntest_transform_data_aug = transforms.Compose([\n transforms.Resize(size=(64, 64)),\n transforms.ToTensor()\n])\n\ntrain_data_augmented = datasets.ImageFolder(root=train_path,\n transform=train_transform_data_aug)\n\ntest_data_augmented = datasets.ImageFolder(root=test_path,\n transform=test_transform_data_aug)\n\n\n# Setup the Batch Size Hyperparameter for DataLoader\nBATCH_SIZE=1\nSHUFFLE_TRAIN_DATASET=True\nSHUFFLE_TEST_DATASET=False\n\ntrain_dataloader_augmented = DataLoader(dataset=train_data_augmented,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TRAIN_DATASET)\n\ntest_dataloader_augmented = DataLoader(dataset=test_data_augmented,\n batch_size=BATCH_SIZE,\n shuffle=SHUFFLE_TEST_DATASET)\n\ntrain_dataloader_augmented, test_dataloader_augmented\n\n# Display random images from the ImageFolder created Dataset\ndisplay_random_images(train_data,\n n=5, \n classes=class_names,\n seed=42)\n\n# Display random images from the Dataset with Data Augmentation\ndisplay_random_images(train_data_augmented,\n n=5,\n classes=class_names,\n seed=42)\n\n\"\"\"## 4. ModelV0: TinyVGG without Data Augmentation\"\"\"\n\nfrom torch.nn.modules.pooling import MaxPool2d\n# Create a Convolutional Neural Network - ConvNets\n# Setup Hyperparameters\nKERNEL_SIZE=3\nSTRIDE=1\nPADDING=0\n\nclass TinyVGG(nn.Module):\n \"\"\"\n Model Architecture that Replicates the TinyVGG Model from images/ConvNets.png\n \"\"\"\n def __init__(self, \n input_channels: int,\n input_shape: int, \n hidden_neurons: int,\n output_shape: int):\n super().__init__()\n self.first_conv_block = nn.Sequential(\n nn.Conv2d(in_channels=input_channels,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=(KERNEL_SIZE-1))\n )\n self.second_conv_block = nn.Sequential(\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.Conv2d(in_channels=hidden_neurons,\n out_channels=hidden_neurons,\n kernel_size=KERNEL_SIZE,\n stride=STRIDE,\n padding=PADDING),\n nn.ReLU(),\n nn.MaxPool2d(kernel_size=(KERNEL_SIZE-1))\n )\n self.fully_connected_layer = nn.Sequential(\n nn.Flatten(),\n nn.Linear(in_features=hidden_neurons*input_shape,\n out_features=output_shape)\n )\n\n def forward(self, x:torch.Tensor) -> torch.Tensor:\n return self.fully_connected_layer(self.second_conv_block(self.first_conv_block(x)))\n\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Create an Instance of TinyVGG\nconvNetsModelV0 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\n\"\"\"## 4.1 Use `torchinfo` to Get an Idea of the Shapes Going Through Our Model\"\"\"\n\nsummary(convNetsModelV0, input_size=[32, 3, 64, 64])\n\n\"\"\"## 4.2 Functionizing Training & Testing/Inference Loops\"\"\"\n\n# Create Training Step\ndef train_model(model: torch.nn.Module,\n train_data_loader: torch.utils.data.DataLoader, \n loss_func: torch.nn.Module,\n optimizer: torch.optim.Optimizer,\n accuracy_func,\n device: torch.device=device):\n \"\"\"\n Performs a Training with Model Trying to Learn on DataLoader\n \"\"\"\n \n train_loss, train_accuracy = 0, 0\n\n # Put Model into Training Phase\n model.train()\n\n # Add a Loop to Loop Through the Training Batches\n for batch, (train_image, train_label) in enumerate(train_data_loader):\n \n # Put Data to Target Device\n train_image, train_label = train_image.to(device), train_label.to(device)\n \n # 1. Forward Pass\n train_logits = model.forward(train_image)\n train_preds = torch.argmax(torch.softmax(train_logits, dim=1), dim=1)\n\n # 2. Calculate Loss & Accuracy Per Batch\n train_loss_ = loss_func(train_logits, train_label)\n train_loss += train_loss_.item() # Accumulate Train Loss\n train_accuracy += accuracy_func(train_preds, train_label)\n\n # 3. Optimizer Zero Grad\n optimizer.zero_grad()\n\n # 4. Loss Backward\n train_loss_.backward()\n\n # 5. Optimizer Step\n optimizer.step()\n\n # Divide Total Train Loss & Accuracy by Length of Train DataLoader\n train_loss /= len(train_data_loader)\n train_accuracy /= len(train_data_loader)\n\n # print(f\"Train Accuracy: {train_accuracy:.2f} | Train Loss: {train_loss:.2f}\")\n return train_loss, train_accuracy\n\n# Create Testing Step\ndef test_model(model: torch.nn.Module,\n test_data_loader: torch.utils.data.DataLoader,\n loss_func: torch.nn.Module, \n accuracy_func,\n device: torch.device=device):\n \"\"\"\n Performs a Testing Loop Step on Model Going Over DataLoader\n \"\"\"\n\n test_loss, test_accuracy = 0, 0\n\n # Put the Model in Eval Mode\n model.eval()\n\n # Turn on Inference Mode Context Manager\n with torch.inference_mode():\n for batch, (test_image, test_label) in enumerate(test_data_loader):\n\n # Send the Data Target Device\n test_image, test_label = test_image.to(device), test_label.to(device)\n\n # 1. Forward Pass\n test_logits = model.forward(test_image)\n test_preds = torch.argmax(torch.softmax(test_logits, dim=1), dim=1)\n\n # 2. Calculate Loss & Accuracy Per Batch\n test_loss += loss_func(test_logits, test_label).item()\n test_accuracy += accuracy_func(test_preds, test_label)\n\n # Divide Total Test Loss & Accuracy by Length of Test DataLoader\n test_loss /= len(test_data_loader)\n test_accuracy /= len(test_data_loader)\n \n # print(f\"Test Accuracy: {test_accuracy:.2f} | Test Loss: {test_loss:.2f}\")\n return test_loss, test_accuracy\n\ndef train_step(model: torch.nn.Module,\n epochs: int, \n train_data: torch.utils.data.DataLoader,\n test_data: torch.utils.data.DataLoader,\n loss_func: torch.nn.Module,\n optimizer: torch.optim,\n accuracy_func,\n device: torch.device=device):\n\n # Create empty results dictionary for plotting the model result\n model_results_dict = {\"train_loss\": [], \"train_accuracy\": [], \"test_loss\": [], \"test_accuracy\": []}\n\n for epoch in tqdm(range(EPOCHS)):\n train_loss, train_accuracy = train_model(model=model,\n train_data_loader=train_data,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n \n test_loss, test_accuracy = test_model(model=model,\n test_data_loader=test_data,\n loss_func=loss_func,\n accuracy_func=accuracy_func,\n device=device)\n \n print(f\"Epoch: {epoch} | Train Accuracy: {train_accuracy:.2f} | Train Loss: {train_loss:.2f} | Test Accuracy: {test_accuracy:.2f} | Test Loss: {test_loss:.2f}\")\n\n # 5. Update results dictionary\n model_results_dict[\"train_loss\"].append(train_loss)\n model_results_dict[\"train_accuracy\"].append(train_accuracy)\n model_results_dict[\"test_loss\"].append(test_loss)\n model_results_dict[\"test_accuracy\"].append(test_accuracy)\n \n return model_results_dict\n\n\"\"\"## 4.4 Create Loss Function, Optimizer & Evaluation Function\"\"\"\n\n# Setup Random Seeds\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Recreate an Instance of TinyVGG\nconvNetsModelV0 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\n# Setup Hyperparameter\nEPOCHS=15\nLEARNING_RATE=0.001\nMODEL_PARAMETERS=convNetsModelV0.parameters()\n\n# Setup Loss Function & Optimizer\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(params=MODEL_PARAMETERS,\n lr=LEARNING_RATE)\n\n# Start the Timer\nexecution_start_time = time.time()\n\n# Train First TinyVGG Model\nconvNetsModelV0Result = train_step(model=convNetsModelV0,\n epochs=EPOCHS,\n train_data=train_dataloader,\n test_data=test_dataloader,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n\n# End the Timer & Print Out How Long It Took\nexecution_end_time = time.time()\nmodel_execution_time = execution_time(\n start_time=execution_start_time,\n end_time=execution_end_time,\n device=device\n)\n\n\"\"\"## 5. Plot the Loss & Accuracy Curves of Model\"\"\"\n\ndef plot_curves(results: Dict[str, List[float]], epochs: int):\n \"\"\"Plots Training & Testing Curves of a Results Dictionary\"\"\"\n \n # Generate a List of Epoch Values based on the Number of Epochs\n epochs = range(1, epochs + 1)\n\n # Setup a Plot\n plt.figure(figsize=(15, 7))\n\n # Plot the Train & Test Accuracy\n plt.subplot(1, 2, 1)\n plt.plot(epochs, results['train_accuracy'], label='Train Accuracy')\n plt.plot(epochs, results['test_accuracy'], label='Test Accuracy')\n plt.title('Train & Test Accuracy')\n plt.xlabel('Epochs')\n plt.ylabel('Accuracy')\n plt.legend()\n\n # Plot the Train & Test Loss\n plt.subplot(1, 2, 2)\n plt.plot(epochs, results['train_loss'], label='Train Loss')\n plt.plot(epochs, results['test_loss'], label='Test Loss')\n plt.title('Train & Test Loss')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.legend()\n\nplot_curves(results=convNetsModelV0Result, epochs=EPOCHS)\n\n\"\"\"\n## 5.1 What Should an Ideal Loss Curve Look Like?\n* Loss Curve is one of the most helpful ways to troubleshoot a model\n* https://developers.google.com/machine-learning/testing-debugging/metrics/interpretic\n\n\n\n## 5.1.1 How to Deal with Overfitting\n* Since the main problem with [overfitting](https://developers.google.com/machine-learning/glossary#overfitting) is that you're model is fitting the training data too well, you'll want to use techniques to \"reign it in\"\n* A common technique of preventing overfitting is known as [regularization](https://ml-cheatsheet.readthedocs.io/en/latest/regularization.html)\n\n\n\n## 5.1.2 How to Deal with Underfitting\n* When a model is [underfitting](https://developers.google.com/machine-learning/glossary#underfitting) it is considered to have poor predictive power on the training and test sets\n* In essence, an underfitting model will fail to reduce the loss values to a desired level\n\n\n\n## 6. Model 1: TinyVGG with Data Augmentation\n* Let's Try Another Modelling Experiment This Time Using the Same Model as Before with Some Data Augmentation\n\"\"\"\n\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Create an Instance of TinyVGG\nconvNetsModelV1 = TinyVGG(\n input_channels=3, # number of color channels in our image data\n input_shape=13*13,\n hidden_neurons=10,\n output_shape=len(class_names)\n).to(device)\n\nconvNetsModelV1\n\n# Setup Random Seeds\ntorch.manual_seed(42)\ntorch.cuda.manual_seed(42)\n\n# Setup Hyperparameter\nEPOCHS=15\nLEARNING_RATE=0.001\nMODEL_PARAMETERS=convNetsModelV1.parameters()\n\n# Setup Loss Function & Optimizer\nloss_func = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(params=MODEL_PARAMETERS,\n lr=LEARNING_RATE)\n\n# Start the Timer\nexecution_start_time = time.time()\n\n# Train First TinyVGG Model\nconvNetsModelV1Result = train_step(model=convNetsModelV1,\n epochs=EPOCHS,\n train_data=train_dataloader_augmented,\n test_data=test_dataloader_augmented,\n loss_func=loss_func,\n optimizer=optimizer,\n accuracy_func=accuracy_func,\n device=device)\n\n# End the Timer & Print Out How Long It Took\nexecution_end_time = time.time()\nmodel_execution_time = execution_time(\n start_time=execution_start_time,\n end_time=execution_end_time,\n device=device\n)\n\nplot_curves(results=convNetsModelV1Result, epochs=EPOCHS)\n\n\"\"\"\n## 7. Compare Model Results\n\nThere's a few different ways to do this:\n\n1. Hard coding (what we're doing)\n2. PyTorch + Tensorboard - https://pytorch.org/docs/stable/tensorboard.html\n3. Weights & Biases - https://wandb.ai/site/experiment-tracking\n4. MLFlow - https://mlflow.org/\n\"\"\"\n\nmodel0_dataFrame = pd.DataFrame(convNetsModelV0Result)\nmodel1_dataFrame = pd.DataFrame(convNetsModelV1Result)\n\n# Generate a List of Epoch Values based on the Number of Epochs\nepochs = range(1, EPOCHS + 1)\n\n# Setup a Plot\nplt.figure(figsize=(15, 10))\n\n# Plot the Train & Test Accuracy\nplt.subplot(2, 2, 1)\nplt.plot(epochs, model0_dataFrame['train_accuracy'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['train_accuracy'], label='convNetsModelV1')\nplt.title('Train Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Train Accuracy')\nplt.legend()\n\nplt.subplot(2, 2, 2)\nplt.plot(epochs, model0_dataFrame['test_accuracy'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['test_accuracy'], label='convNetsModelV1')\nplt.title('Test Accuracy')\nplt.xlabel('Epochs')\nplt.ylabel('Test Accuracy')\nplt.legend()\n\n# Plot the Train & Test Loss\nplt.subplot(2, 2, 3)\nplt.plot(epochs, model0_dataFrame['train_loss'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['train_loss'], label='convNetsModelV1')\nplt.title('Train Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Train Loss')\nplt.legend()\n\nplt.subplot(2, 2, 4)\nplt.plot(epochs, model0_dataFrame['test_loss'], label='convNetsModelV0')\nplt.plot(epochs, model1_dataFrame['test_loss'], label='convNetsModelV1')\nplt.title('Test Loss')\nplt.xlabel('Epochs')\nplt.ylabel('Test Loss')\nplt.legend()\n\n\"\"\"\n## 8. Making Prediction on Custom Image\n## 8.1 Loading in Custom Image with PyTorch\n\"\"\"\n\ncustom_image_path = train_path/\"pizza/5764.jpg\"\ncustom_image_transforms = transforms.Compose([\n transforms.Resize(size=(64, 64))\n])\n\ncustom_image = torchvision.io.read_image(str(custom_image_path)).type(torch.float32)\ncustom_image = custom_image / 255\ncustom_image_resize = custom_image_transforms(custom_image) # this will error for eval mode: no batch size\n\nplt.figure(figsize=(10, 5))\nplt.subplot(1, 2, 1)\nplt.title('Original Image')\nplt.imshow(custom_image.permute(1, 2, 0))\n\nplt.subplot(1, 2, 2)\nplt.title('Resized Image')\nplt.imshow(custom_image_resize.permute(1, 2, 0))\n\n# To Avoid the Error, Add Batch Size\ncustom_image_resize = custom_image_resize.unsqueeze(0)\n\n\"\"\"\n## To Make Prediction on Custom Image We Had To:\n\n* Load the image and turn it into a tensor\n* Make sure the image was the same datatype as the model (torch.float32)\n* Make sure the image was the same shape as the data the model was trained on (3, 64, 64) with a batch size... (1, 3, 64, 64)\n* Make sure the image was on the same device as our model\n\n## 8.2 Putting Custom Image Prediction Together: Building a Function\n\n* Function Where We Pass an Image Path to and Have Our Model Predict on That Image and Plot the Image + Prediction\n\"\"\"\n\ndef make_prediction(model: torch.nn.Module,\n image_path: str,\n class_names: List[str] = None,\n transform=None,\n device=device):\n \"\"\"Makes a Prediction on a Target Image with a Trained Model & Plots the Image and Prediction\"\"\"\n\n # Load in the image\n target_image = torchvision.io.read_image(str(image_path)).type(torch.float32)\n\n # Divide the image pixel values by 255 to get them between [0, 1]\n target_image = target_image / 255.\n\n # Transform if necessary\n if transform:\n target_image = transform(target_image)\n\n # Make sure the model is on the target device\n model.to(device)\n\n # Turn on eval/inference mode and make a prediction\n model.eval()\n with torch.inference_mode():\n # Add an extra dimension to the image (this is the batch dimension, e.g. our model will predict on batches of 1x image)\n target_image = target_image.unsqueeze(0)\n\n # Make a prediction on the image with an extra dimension\n target_image_pred = model(target_image.to(device)) # make sure the target image is on the right device\n\n # Convert logits -> prediction probabilities\n target_image_pred_probs = torch.softmax(target_image_pred, dim=1)\n\n # Convert predction probabilities -> prediction labels\n target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)\n\n # Plot the image alongside the prediction and prediction probability\n plt.imshow(target_image.squeeze().permute(1, 2, 0)) # remove batch dimension and rearrange shape to be HWC\n if class_names:\n title = f\"Prediction: {class_names[target_image_pred_label.cpu()].capitalize()} | Probability: {target_image_pred_probs.max().cpu():.3f}\"\n else:\n title = f\"Prediction: {target_image_pred_label} | Probability: {target_image_pred_probs.max().cpu():.3f}\"\n plt.title(title)\n\n# Pred on Our Custom Image\nmake_prediction(model=convNetsModelV1,\n image_path=custom_image_path,\n class_names=class_names,\n transform=custom_image_transforms,\n device=device)","repo_name":"uygarkaya/DeepLearning","sub_path":"python/pytorch_custom_datasets.py","file_name":"pytorch_custom_datasets.py","file_ext":"py","file_size_in_byte":29758,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"15951599885","text":"from django.urls import path, include\n\nfrom rest_framework import routers\n\n\nfrom . import views\n\n\nrouter = routers.DefaultRouter()\nrouter.register(r'members',views.MemberViewSet)\nrouter.register(r'committeeroles',views.CommitteeRoleViewSet)\n\n\nurlpatterns = [\n path('member/id/', views.member, name=\"member_profile\"),\n path('members', views.member_list),\n path('member/update/id/', views.member_update, name=\"member_update\"),\n path('member/id//ajax_last_name', views.ajax_last_name_update),\n path('restapi/',include(router.urls))\n]\n","repo_name":"jamescruickshank/CS424-2018-19","sub_path":"clubsite/clubmanager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"21577517163","text":"# 난수\n# random 모듈\n\nimport random\n\n# def main():\n# for i in range(5):\n# print(\"randint = \", random.randint(1, 10)) # 1부터 10사이 정수 (10 포함)\n# for i in range(5):\n# print(\"randrange = \", random.randrange(1, 10)) # 1부터 9사이 정수 (10 미포함)\n# for i in range(5):\n# print(\"uniform = \", random.uniform(1, 10)) # 1부터 9사이 실수 (10 미포함)\n#\n# main()\n\n# def main():\n#\n# print(random.choice(food)) # 시퀀스에서 랜덤하게 요소 선택하여 리턴\n#\n# i = random.randrange(len(food))\n# print(food[i])\n#\n# main()\n\n# def main():\n# food = [\"짜장면\", \"짬뽕\", \"탕수육\", \"군만두\"]\n# print(food)\n# random.shuffle(food) # 시퀀스의 내용을 랜덤하게 섞음, 셔플의 리턴값은 없음. 즉 food 리스트가 shuffle로 인해 값이 바뀜\n# print(food)\n#\n# main()\n\ndef main():\n nums = random.sample(range(1, 46), 6)\n nums.sort()\n print(nums)\n\nmain()","repo_name":"hyunsooDii/TIL_Source","sub_path":"python/chapter12/ex12_02.py","file_name":"ex12_02.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"28507183122","text":"\"\"\"\nPersonal implementation of Playing Atari with Deep Reinforcement Learning, by Mnih et al. (2013)\nhttps://www.cs.toronto.edu/~vmnih/docs/dqn.pdf\n\nAnson Ho, 2021\n\"\"\"\n\nimport gym\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport random\nimport wandb\nfrom assets.memorybuffer import MemoryBuffer\n\nclass ffDQN(nn.Module):\n \"\"\"\n Creates a simple DQN with fully connected layers.\n This only works on simple environments, so that there\n is no need to learn from pixels\n\n Args\n - num_inputs depends on the environment \n - num_outputs is typically the size of the action space\n \"\"\"\n def __init__(self, num_inputs, num_outputs):\n super(ffDQN, self).__init__()\n self.fc1 = nn.Linear(num_inputs, config[\"l1_neurons\"])\n self.fc2 = nn.Linear(config[\"l1_neurons\"], config[\"l2_neurons\"])\n self.fc3 = nn.Linear(config[\"l2_neurons\"], config[\"l3_neurons\"])\n self.fc4 = nn.Linear(config[\"l3_neurons\"], num_outputs)\n self.loss_function = nn.SmoothL1Loss()\n\n torch.nn.init.normal_(self.fc1.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc2.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc3.weight, mean=0.0, std=0.1)\n torch.nn.init.normal_(self.fc4.weight, mean=0.0, std=0.1)\n \n def forward(self, state):\n x = F.relu(self.fc1(state))\n x = F.relu(self.fc2(x))\n x = F.relu(self.fc3(x))\n x = self.fc4(x)\n return x\n\ndef epsilon_greedy(epsilon, state):\n \"\"\"\n Select actions based on an \n epsilon-greedy policy\n \"\"\"\n\n q_values = net(state)\n max_q_value = torch.max(q_values).detach().numpy()\n\n # explore\n if random.uniform(0,1) < epsilon:\n action = env.action_space.sample()\n # exploit\n else: \n action = torch.argmax(q_values).item()\n\n return action, max_q_value\n\ndef optimise():\n \"\"\"\n Performs a single optimisation step,\n given a minibatch of transitions\n \"\"\"\n\n if len(memory) < config[\"minibatch_size\"]:\n return\n\n minibatch = memory.random_sample(config[\"minibatch_size\"])\n state_minibatch, action_minibatch, reward_minibatch, next_state_minibatch, done_minibatch = tuple(zip(*minibatch))\n state_minibatch, action_minibatch, reward_minibatch, next_state_minibatch, done_minibatch = torch.stack(state_minibatch), torch.tensor(action_minibatch), torch.tensor(reward_minibatch), torch.stack(next_state_minibatch), torch.tensor(done_minibatch)\n\n # create mask for non-terminal states\n mask = ~done_minibatch\n non_terminal_next_states = next_state_minibatch[mask]\n\n # predictions and targets\n next_q_values = torch.zeros((config[\"minibatch_size\"], config[\"action_space_size\"]))\n next_q_values[mask] = net(non_terminal_next_states)\n targets = torch.add(reward_minibatch, config[\"discount\"] * torch.max(next_q_values, dim=1)[0])\n pred_q_vals = net(state_minibatch)\n predictions = pred_q_vals.gather(1, action_minibatch.unsqueeze(1)).squeeze(1)\n \n # optimise\n loss = net.loss_function(predictions, targets)\n optimiser.zero_grad()\n loss.backward()\n for param in net.parameters():\n param.grad.data.clamp_(-1, 1)\n optimiser.step()\n\n return loss\n\ndef train(render_screen=True):\n\n epsilon = config[\"initial_epsilon\"]\n loss, avg_q_value, avg_reward, episode_time = 0, 0, 0, 0\n\n for episode in range(config[\"num_episodes\"]):\n observation = env.reset()\n episode_q_values = []\n episode_reward = 0\n\n for t in range(config[\"max_episode_time\"]):\n\n if render_screen:\n env.render()\n\n state = torch.tensor(observation)\n action, q_value = epsilon_greedy(epsilon, state)\n observation, reward, done, _ = env.step(action)\n next_state = torch.tensor(observation)\n\n memory.store_transition(state, action, reward, next_state, done)\n episode_q_values.append(q_value)\n episode_reward += reward\n loss = optimise()\n \n if epsilon >= config[\"final_epsilon\"]:\n epsilon -= (config[\"initial_epsilon\"] - config[\"final_epsilon\"]) / config[\"epsilon_anneal_frames\"]\n\n if WANDB:\n wandb.log({\n \"loss\": loss,\n \"epsilon\": epsilon,\n \"avg_q_value\": avg_q_value,\n \"avg_reward\": avg_reward,\n \"episode_time\": episode_time\n })\n\n if done:\n print(\"Episode {} finished after {} timesteps\".format(episode+1, t+1))\n episode_time = t + 1\n break\n\n avg_q_value = np.mean(episode_q_values)\n avg_reward = episode_reward\n\n env.close()\n\nif __name__ == \"__main__\":\n\n # classic_control_environments = [\"MountainCar-v0\", \"CartPole-v1\", \"Acrobot-v1\"]\n\n game = \"Acrobot-v1\"\n\n # make environment\n env = gym.make(game)\n\n config = {\n \"learning_rate\": 3e-4,\n \"minibatch_size\": 64,\n \"memory_capacity\": 5_000, \n \"num_episodes\": 10_000,\n \"max_episode_time\": 10_000,\n \"discount\": 0.99,\n \"initial_epsilon\": 1,\n \"final_epsilon\": 0.1,\n \"epsilon_anneal_frames\": 100_000,\n \"l1_neurons\": 32, \n \"l2_neurons\": 64,\n \"l3_neurons\": 64,\n \"observation_space_size\": env.observation_space.shape[0],\n \"action_space_size\": env.action_space.n\n }\n\n # logging results\n WANDB = 1\n if WANDB:\n wandb.init(project=game)\n wandb.config = config\n\n memory = MemoryBuffer(config[\"memory_capacity\"])\n net = ffDQN(config[\"observation_space_size\"], config[\"action_space_size\"])\n optimiser = torch.optim.RAdam(net.parameters(), lr=config[\"learning_rate\"])\n \n\n train()","repo_name":"ansonwhho/DQN","sub_path":"algorithms/ffDQN.py","file_name":"ffDQN.py","file_ext":"py","file_size_in_byte":5827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31449050347","text":"from flask import Flask, render_template, request, redirect, url_for, flash\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_login import UserMixin, LoginManager, login_user, login_required, logout_user, current_user\nimport sqlite3\n\napp = Flask(__name__)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\n\n# ---------------------------------LOGIN SETUP------------------------------------------\n\ndb = SQLAlchemy(app)\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n# set the type and location of the DB\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///robocupjr.db'\n# make sure this key stays secret\napp.config['SECRET_KEY'] = 'key'\n\n\n# class name has to match the table name\n# class variables must match the column names of the table\n# one column must be called 'id'\nclass judges(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String)\n password = db.Column(db.String)\n\n\n# should be set to refer to the class name as above\n@login_manager.user_loader\ndef load_user(id):\n return judges.query.get(id)\n\n\n@app.route('/')\n@app.route('/home')\ndef home_page():\n return render_template('home.html')\n\n\n@app.route('/criteria')\ndef criteria_page():\n return render_template('criteria.html')\n\n\n@app.route('/dancing')\ndef dancing_page():\n dance_results = dancing_table()\n print(dance_results)\n return render_template('dancing.html', product=dance_results)\n\n\ndef dancing_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM dancingscores\")\n return (c.fetchall())\n\n\n# --------TESTING INDIV DETAILS--------------------------------------------\n@app.route('/search//')\ndef search_id(id):\n name_first, name_last, team, category, year = details(id)\n return render_template('search.html', name_first=name_first, name_last=name_last,\n team=team, category=category, year=year)\n\n\n# ------ Placehodler for future searching function\n@app.route('/search', methods=['GET', 'POST'])\ndef search():\n if request.method == 'POST':\n id = request.form['ID']\n return redirect(url_for('search_id', id=id))\n else:\n return render_template('id_search.html')\n\n\ndef details(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT name_first, name_last, team, category, year FROM teams123 WHERE ID = ?\", (id,))\n name_first, name_last, team, category, year = c.fetchone()\n return (name_first, name_last, team, category, year)\n\n\n# --------------- End Ranking Page---------------\n@app.route('/teams')\ndef teams_page():\n teams_results = teams_page_table()\n print(teams_results)\n return render_template('teams.html', output=teams_results)\n\n\ndef teams_page_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123\")\n return (c.fetchall())\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login_page():\n if request.method == 'GET':\n return render_template('Login.html')\n else:\n try:\n my_judge = judges.query.filter_by(username=request.form['username']).first()\n except:\n return redirect(url_for('login_page'))\n\n if my_judge is not None:\n if my_judge.password == request.form['password']:\n login_user(my_judge)\n return render_template('admin.html')\n else:\n flash('An error occured. Please check Username and Password ')\n return redirect(url_for('login_page'))\n else:\n flash('An error occured. Please check Username and Password ')\n return redirect(url_for('login_page'))\n\n\n@app.route('/logout')\n@login_required\ndef log_me_out():\n logout_user()\n return render_template('logout.html')\n\n\n@app.route('/register')\ndef register_page():\n return render_template('register.html')\n\n\n@app.route('/admin')\ndef admin_page():\n if current_user.is_authenticated:\n return render_template('admin.html')\n else:\n return render_template('login.html')\n\n\n#\n# @app.route('/admin/scoring/')\n# def scoring_page(scrid):\n# scr_list = scr_lister()\n# return render_template('score.html')\n@app.route('/admin/delete_success', methods=['GET', 'POST'])\ndef character_delete_success():\n if request.method == 'POST':\n id = request.form['id']\n team_delete(id)\n return render_template('character_delete_success.html')\n else:\n return render_template('admin.html')\n\n\n@app.route('/admin/delete/')\ndef admin_delete(id):\n char_list = char_lister()\n id, team, score = query_profile_full(id)\n return render_template('character_delete.html',\n id=id,\n team=team,\n score=score,\n char_list=char_list\n )\n# a functyion that lists all the id and team names currently existing in the dancingscores table\ndef char_lister():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT id, team FROM dancingscores ORDER BY id\")\n char_list = c.fetchall()\n return char_list\n\n\ndef query_profile_full(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT id, team, score FROM dancingscores WHERE ID = ?\", (id,))\n id, team, score = c.fetchone()\n return (id, team, score)\n\n\ndef team_delete(id):\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"DELETE FROM dancingscores WHERE id =?\", (id,))\n\n\n@app.route('/register/team_add', methods=['GET', 'POST'])\ndef team_add():\n if request.method == 'POST':\n first_name = request.form['first_name']\n surname = request.form['surname']\n team_name = request.form['team_name']\n category = request.form['category']\n new_team_details = (first_name, surname, team_name, category)\n update_team_add(new_team_details)\n return redirect(url_for('register_page'))\n else:\n return render_template('register.html')\n\n\ndef update_team_add(new_team_details):\n sql_add_chr = \"\"\"INSERT INTO teams123 (name_first, name_last, team, category) \n VALUES (?,?,?,?)\"\"\"\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(sql_add_chr, new_team_details)\n connie.commit()\n\n\n# ---------------Ranking Page---------------\n@app.route('/rankings')\ndef rankings_page():\n rankings_results = rankings_table()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_table():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123\")\n return (c.fetchall())\n\n\n# -----------------------\n# -------2020 Ranking----------------------------\n@app.route('/rankings/2020')\ndef rankings2020():\n rankings_results = rankings_2020()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2020():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2020\")\n twenty_data = c.fetchall()\n return (twenty_data)\n\n\n# -----------------------------------------------\n# -------2019 Ranking----------------------------\n@app.route('/rankings/2019')\ndef rankings2019():\n rankings_results = rankings_2019()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2019():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2019\")\n nineteen_data = c.fetchall()\n return (nineteen_data)\n\n\n# -----------------------------------------------\n@app.route('/rankings/2018')\ndef rankings2018():\n rankings_results = rankings_2018()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2018():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2018\")\n eighteen_data = c.fetchall()\n return (eighteen_data)\n\n\n# -----------------------------------------------\n# -------2017 Ranking----------------------------\n@app.route('/rankings/2017')\ndef rankings2017():\n rankings_results = rankings_2017()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2017():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2017\")\n seventeen_data = c.fetchall()\n return (seventeen_data)\n\n\n# -----------------------------------------------\n# -------2016 Ranking----------------------------\n@app.route('/rankings/2016')\ndef rankings2016():\n rankings_results = rankings_2016()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2016():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2016\")\n sixteen_data = c.fetchall()\n return (sixteen_data)\n\n\n# -----------------------------------------------\n# -------2015 Ranking----------------------------\n@app.route('/rankings/2015')\ndef rankings2015():\n rankings_results = rankings_2015()\n return render_template('rankings.html', rank=rankings_results)\n\n\ndef rankings_2015():\n connie = sqlite3.connect('robocupjr.db')\n c = connie.cursor()\n c.execute(\"SELECT * FROM teams123 WHERE year = 2015\")\n fifteen_data = c.fetchall()\n return (fifteen_data)\n\n\nif __name__ == '__main__':\n app.run()\n","repo_name":"JerrySeinfeld02/carousel-01","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"39826216144","text":"\nclass Solution:\n def kthSmallestElement(self, matrix, k):\n row_l = len(matrix)-1\n col_l = len(matrix[0])-1\n min_v = matrix[0][0]\n max_v = matrix[row_l][col_l]\n\n while min_v < max_v:\n mid = min_v + int((max_v-min_v)/2)\n if self.possible(matrix, k, mid) >= k:\n max_v = mid \n else:\n min_v = mid + 1\n\n return min_v \n\n def possible(self, matrix, k, mid):\n i = len(matrix)-1\n j = 0\n count = 0\n while i >= 0 and j < len(matrix[0]):\n if matrix[i][j] <= mid:\n count += i+1\n j += 1\n else:\n i -= 1\n\n return count ","repo_name":"KJSui/leetcode-2020","sub_path":"kthsmallestelementinsortedarray.py","file_name":"kthsmallestelementinsortedarray.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"29975274747","text":"import os\nimport shutil\n\nfrom aidapy.hist import total_systematic_histogram\nfrom aidapy.hist import hist2array\n#from .style_mpl import atlas_mpl_style\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.ticker import AutoMinorLocator, MultipleLocator\nimport matplotlib as mpl\nimport matplotlib.gridspec as gsc\n#plt.style.use('classic')\nfrom pylab import setp\n#sty = atlas_mpl_style()\n#for key, val in sty.items():\n# mpl.rcParams[key] = val\nfrom matplotlib.font_manager import FontProperties\nfontBase = FontProperties()\nfontATLAS = fontBase.copy()\nfontATLAS.set_size(16)\nfontATLAS.set_style('italic')\nfontATLAS.set_weight('bold')\n\ndef canvas_with_ratio(figsize=(8,7),height_ratios=[3.65,1],\n xtitle='x title',ytitle='ytitle',ratio_title='Ratio'):\n fig = plt.figure(figsize=figsize)\n gs = gsc.GridSpec(2,1,height_ratios=height_ratios)\n gs.update(hspace=0.075)\n ax0 = fig.add_subplot(gs[0])\n ax1 = fig.add_subplot(gs[1],sharex=ax0)\n ax0.xaxis.set_minor_locator(AutoMinorLocator())\n ax0.yaxis.set_minor_locator(AutoMinorLocator())\n setp(ax0.get_xticklabels(),visible=False)\n ax0.set_ylabel(ytitle)\n ax1.set_ylabel(ratio_title)\n ax1.set_xlabel(xtitle)\n return fig, ax0, ax1\n\ndef hplot_mpl(root_file, hist_name='met_1pj', outdir='outs', xtitle='', ytitle='',logy=False,\n proc_names=['Wt','ttbar','Fakes','WW','Diboson','Ztautau','RareSM']):\n if os.path.exists(outdir):\n pass\n else:\n os.makedirs(outdir)\n nominals = { pname : root_file.Get(pname+'_FULL_main_nominal_'+hist_name) for pname in proc_names }\n nominals = { pname : hist2array(h,return_edges=True) for pname, h in nominals.items() }\n data = root_file.Get('Data_'+hist_name)\n data = hist2array(data)\n nom_h, total_band, edges, staterr = total_systematic_histogram(root_file,hist_name,proc_names,\n return_stat_error=True)\n centers = np.delete(edges,[0])-(np.ediff1d(edges)/2.0)\n\n to_stack = [nominals[name][0] for name in ['RareSM','Diboson','Fakes','WW','Wt','Ztautau','ttbar']]\n cols = ['darkred','black','gray','green','blue','orange','white']\n labels = [r'Rare SM',r'Diboson',r'Fake/NP (MC)',r'WW',r'Wt',r'$Z\\rightarrow\\tau\\tau$',r'$t\\bar{t}$']\n #to_stack = [nominals[name][0] for name in ['RareSM','Diboson','Fakes','WW','Ztautau','ttbar','Wt']]\n #cols = ['darkred','black','gray','green','orange','white','blue']\n #labels = [r'Rare SM',r'Diboson',r'Fake/NP (MC)',r'WW',r'$Z\\rightarrow\\tau\\tau$',r'$t\\bar{t}$',r'Wt']\n\n fig,ax,axerr = canvas_with_ratio()\n ax.errorbar(centers,data,yerr=np.sqrt(data),fmt='ko',label=r'Data')\n ax.hist([centers for _ in to_stack],weights=to_stack,bins=edges,stacked=True,\n color=cols,histtype='stepfilled',label=labels, ls='solid', lw=1, edgecolor='black')\n syspatches = []\n syspatches = [patches.Rectangle((c-w/2,v-err),w,err*2,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none')\n for c, v, err, w in zip(centers,nom_h,total_band,np.ediff1d(edges))]\n for p in syspatches: ax.add_patch(p)\n trashpatch = patches.Rectangle((0,0),0,0,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none',\n label=r'Systematics')\n ax.add_patch(trashpatch)\n ax.errorbar(centers,data,yerr=np.sqrt(data),fmt='ko')\n ax.legend(loc='upper right')\n l_handles, l_labels = ax.get_legend_handles_labels()\n l_handles = [l_handles[-1]] + l_handles[:-1]\n l_labels = [l_labels[-1]] + l_labels[:-1]\n ax.legend(l_handles,l_labels,loc='upper right',fontsize=12)\n ax.set_ylim([0,np.max(data)*1.3])\n ax.text(.05,.92,'ATLAS',transform=ax.transAxes,style='oblique',size=14,fontproperties=fontATLAS)\n ax.text(.185,.92,r'Internal, AIDA OS $e\\mu$, pre-fit',transform=ax.transAxes,size=14)\n ax.text(.05,.845,r'$\\sqrt{s}$ = 13 TeV, $\\int \\mathcal{L}$dt = 36.1 fb$^{-1}$',\n transform=ax.transAxes,size=14)\n ax.text(.05,.75,'',transform=ax.transAxes,size=14)\n domcErr = np.sqrt(1.0/(nom_h*nom_h)*data + data*data*staterr*staterr/(nom_h*nom_h*nom_h*nom_h))\n axerr.errorbar(centers,data/nom_h,yerr=domcErr,fmt='ko')#data/(nom_h*nom_h)*total_band\n errpatches = []\n errpatches = [patches.Rectangle((c-w/2,1-err),w,err*2,hatch='\\\\\\\\\\\\\\\\',fill=False,edgecolor='none')\n for c, v, err, w in zip(centers,data/nom_h,data/(nom_h*nom_h)*total_band,np.ediff1d(edges))]\n for p in errpatches: axerr.add_patch(p)\n axerr.set_ylim([0.5,1.5])\n axerr.set_xlim([edges[0],edges[-1]])\n axerr.plot(edges,np.array([1 for _ in edges]),'k-')\n log_axes = ['pT','_2bins','_3bins']\n if any(term in hist_name for term in log_axes):\n logy = True\n axerr.set_xlabel(xtitle,fontsize=14)\n if 'njets' in hist_name:\n axerr.xaxis.set_ticks(np.array([i for i in centers]))\n newxticklabels = [str(int(i)) for i in centers]\n newxticklabels[-1] = r'$\\geq '+str(int(centers[-1]))+'$'\n axerr.set_xticklabels(newxticklabels)\n ax.set_ylabel(ytitle,fontsize=14)\n if logy: ax.set_yscale('log'), ax.set_ylim([np.min(data)*.01,np.max(data)*500])\n fig.savefig(outdir+'/'+hist_name+'.pdf')\n fig.savefig(outdir+'/'+hist_name+'.png')\n #plt.show()\n","repo_name":"douglasdavis/aidapy","sub_path":"aidapy/plot/mpl.py","file_name":"mpl.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72591284748","text":"from . import utils\n\nimport functools\n\nimport torch\n\ndef make_cast_wrapper(orig_fn, cast_fn, handle,\n try_caching=False):\n @functools.wraps(orig_fn)\n def wrapper(*args, **kwargs):\n if not handle.is_active():\n return orig_fn(*args, **kwargs)\n \n input_types = [\n v.data.type() for v in list(args) + list(kwargs.values())\n if utils.is_fp_tensor(v)\n ]\n #print('wrapper: orig_fn:{}, input_types:{}'.format(orig_fn, input_types))\n input_type = input_types[0]\n\n if try_caching and handle.has_cache:\n args = list(args)\n for i in range(len(args)):\n if utils.should_cache(args[i]):\n args[i] = utils.cached_cast(cast_fn, args[i], handle.cache)\n for k in kwargs:\n if utils.should_cache(kwargs[k]):\n kwargs[k] = utils.cached_cast(cast_fn, kwargs[k], handle.cache)\n new_args = utils.casted_args(cast_fn,\n args,\n kwargs)\n output = orig_fn(*new_args, **kwargs)\n \n #if output.type() != input_type:\n # print('ori output type: {}, input type: {}'.format(output.type(), input_type))\n # return output.type(input_type) \n #return output\n return cast_output(output, input_type, verbose=False)\n\n return wrapper\n\ndef cast_output(output, input_type, verbose=False):\n if isinstance(output, dict):\n keys = output.keys()\n for k in keys:\n output[k] = cast_output(output[k], input_type)\n return output\n \n if utils.is_fp_tensor(output) and output.type() != input_type:\n if verbose:\n print('ori output type: {}, input type: {}'.format(output.type(), input_type))\n return output.type(input_type)\n return output\n\ndef cached_cast(mod, fn, cast_fn, handle,\n try_caching=False, verbose=False):\n if not utils.has_func(mod, fn):\n return\n\n orig_fn = utils.get_func(mod, fn)\n cast_fn = utils.verbosify(cast_fn, fn, verbose)\n wrapper = make_cast_wrapper(orig_fn, cast_fn, handle, try_caching)\n utils.set_func_save(handle, mod, fn, wrapper)\n\n","repo_name":"OpenGVLab/HumanBench","sub_path":"PATH/core/fp16/wrap.py","file_name":"wrap.py","file_ext":"py","file_size_in_byte":2257,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"82"} +{"seq_id":"39924492521","text":"# coding: utf-8\nfrom database import Store\nimport logging\nimport os\nimport glob\ntry:\n import cPickle as pickle\nexcept ImportError:\n import pickle\n\n\nclass Stock(Store):\n\n def __init__(self, mode, ind, dirs=None):\n super(Stock, self).__init__(mode, ind, dirs)\n self.logger = logging.getLogger('STOCK')\n self.logger.info('Init Stock')\n\n def trans(self, strs):\n if strs is None:\n return None\n\n arr = strs.split(',')\n\n op = float(arr[0])\n hi = float(arr[1])\n lw = float(arr[2])\n cl = float(arr[3])\n\n return (op, hi, lw, cl)\n\n # transfer inst\n def inst2inst(self, inst):\n inst = str(inst)\n if len(inst) == 7:\n if inst[1] == '6':\n ninst = 'SH' + inst[1:]\n if inst[1] == '3' or inst[1] == '0':\n ninst = 'SZ' + inst[1:]\n self.logger.info('Inst:{} Ninst:{}'.format(inst, ninst))\n return ninst\n\n # 计算给定标的在两个day之间的收益\n # day1: select day --> buyday open\n # day2: close\n def calRet2Days(self, inst, day1, day2):\n ret = None\n\n inst = self.inst2inst(inst)\n\n AA = self.getIndTdays(inst)\n if AA is None:\n return ret\n\n # select day to buy day\n day1 = self.getDay(inst, day1, AA, 1)\n if day1 is None:\n return None\n\n bar1 = self.trans(self.get(inst + '|' + day1))\n bar2 = self.trans(self.get(inst + '|' + day2))\n\n if bar1 is None or bar2 is None:\n return None\n\n op = bar1[0]\n cl = bar2[3]\n\n ret = 100 * (cl - op) / op\n self.logger.info('Inst:{} OP:{} CL:{} D1:{} D2:{}'.format(\n inst, op, cl, day1, day2))\n\n ret = \"{:.4f}\".format(ret)\n return ret\n\n def getIndTdays(self, inst):\n ret = None\n val = self.get(inst + '|indtdays', direct = 1)\n if val is None:\n return ret\n ind_tdays = pickle.loads(val)\n\n val = self.get(inst + '|tdaysind', direct = 1)\n if val is None:\n return ret\n tdays_ind = pickle.loads(val)\n return (ind_tdays, tdays_ind)\n\n # Get day base on Step\n def getDay(self, inst, baseday, AA, step=1):\n ret = None\n ind_tdays = AA[0]\n tdays_ind = AA[1]\n\n if baseday not in tdays_ind:\n return ret\n baseind = tdays_ind[baseday]\n\n buyday = baseind + step\n if buyday not in ind_tdays:\n return ret\n buyday = ind_tdays[buyday]\n return buyday\n\n # 计算给定标的未来一段时间的收益\n def calRet(self, inst, baseday, ndays):\n ret = None\n\n AA = self.getIndTdays(inst)\n if AA is None:\n return ret\n\n selday = baseday\n\n # get buy Day\n buyday = self.getDay(inst, baseday, AA, 1)\n if buyday is None:\n return ret\n\n # get sell day\n sellday = self.getDay(inst, baseday, AA, ndays)\n if sellday is None:\n return ret\n\n nbar = self.trans(self.get(inst + '|' + buyday))\n xbar = self.trans(self.get(inst + '|' + sellday))\n\n nopen = nbar[0]\n xclose = xbar[3]\n ret = (xclose - nopen) / nopen\n self.logger.info('Inst:{} Sday:{} Op:{} Cl:{} Bday:{} Sday:{}'.format(\n inst, selday, nopen, xclose, buyday, sellday))\n return ret\n\n def load(self, path):\n files = glob.glob(path + \"/*.csv\")\n for f in files:\n arr = f.split('.')\n if len(arr) > 2:\n continue\n fname = arr[0][-8:]\n self.logger.info('load stock {}'.format(fname))\n ind_tdays = {}\n tdays_ind = {}\n cnt = -1\n for line in open(path + '/' + fname + '.csv'):\n arr = line.strip().split(',')\n\n # raw data\n key = fname + '|' + arr[0]\n val = ','.join(arr[1:])\n self.add(key, val)\n\n # keep index of trade days\n if cnt == -1:\n cnt = cnt + 1\n continue\n ind_tdays[cnt] = arr[0]\n tdays_ind[arr[0]] = cnt\n cnt = cnt + 1\n\n self.add(fname + '|indtdays', pickle.dumps(ind_tdays))\n self.add(fname + '|tdaysind', pickle.dumps(tdays_ind))\n#\n","repo_name":"runrunliuliu/stockview","sub_path":"webserver/database/stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9040212473","text":"import dash\nfrom dash import dcc, html\n\ndash.register_page(__name__)\n\n\nclass UploadView:\n def __init__(self):\n self.layout = html.Div(\n\n # style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center'},\n style={'display': 'flex', 'flex-direction': 'column', 'justify-content': 'center', 'align-items': 'center'},\n\n children=[\n\n html.Div(\n [\n # html.Button(\n # \"text\",\n # id='generate-picture'\n # ),\n dcc.Input(id='manager_prompt', type='text', placeholder='Enter text here ...'),\n\n html.Button(\"Create Image\", id=\"create\"),\n\n\n ],\n style={'display': 'flex', 'gap': '5px'}\n ),\n\n html.Br(),\n\n dcc.Upload(\n id='upload-image',\n children=html.Button('Upload Image'),\n # style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center'},\n ),\n\n dcc.Loading(\n html.Div(\n id='output-image',\n style={'display': 'flex', 'align-items': 'center', 'justify-content': 'center',\n 'height': '65vh',\n 'max-height': '65vh'}\n ),\n ),\n\n dcc.Link(html.Button(\"Start Game\", id=\"start-game\", disabled=True), href=\"/final\", )\n ]\n )\n\n\nlayout = UploadView().layout\n","repo_name":"wzeyal/BrokenPhone","sub_path":"pages/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"9022106168","text":"import distances\nimport extractor\nimport schema\n\ntotalDistance = [0, 0, 0]\ntruckIdx = [0, 0, 0, 0, 0, 0]\n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.getTruckList()):\n extractor.getTruckList()[index][9] = ['8:00:00'][0]\n schema.kellie.append(extractor.getTruckList()[index])\n\n# Loops through twice at O(n^2) for set truck delivery\nfor index, outer in enumerate(schema.kellie):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.kellieDist.append(outer[0])\n schema.kellie[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for first truck O(n^2)\ndistances.routeLocater(schema.kellie, 1, 0)\n\ntruckIdx[0] = distances.getFirstIndexList(1)\ntruckIdx[1] = distances.getFirstIndexList(0)\n# Big O(n) for loop through truck load [array]\n\nfor index in range(len(truckIdx[0])):\n try:\n totalDistance[0] = distances.getDistance(int(truckIdx[0][index]), int(truckIdx[0][index + 1]), totalDistance[0])\n\n packsOut = distances.getTime(distances.getCurrent(int(truckIdx[0][index]), int(truckIdx[0][index + 1])), ['8:00:00'])\n truckIdx[1][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[1][index][0]), schema.kellie)\n except IndexError:\n pass\n\n# Second truck \n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.assignPacks()):\n extractor.assignPacks()[index][9] = ['9:10:00'][0]\n schema.wilson.append(extractor.assignPacks()[index])\n\n# Loops through twice at O(n^2) for set truck delivery\nfor index, outer in enumerate(schema.wilson):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.wilsonDist.append(outer[0])\n schema.wilson[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for Second truck\ndistances.routeLocater(schema.wilson, 2, 0)\n\n# Big O(n) for loop through truck load [array]\ntruckIdx[2] = distances.getSecIndexList(1)\ntruckIdx[3] = distances.getSecIndexList(0)\n\nfor index in range(len(truckIdx[2])):\n try:\n totalDistance[1] = distances.getDistance( int(truckIdx[2][index]), int(truckIdx[2][index + 1]), totalDistance[1])\n\n packsOut = distances.getTime(\n distances.getCurrent(int(truckIdx[2][index]), int(truckIdx[2][index + 1])), ['9:10:00'])\n truckIdx[3][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[3][index][0]), schema.wilson)\n except IndexError:\n pass\n\n\n# Loops through an enumerated trucklist O(n) \nfor index, value in enumerate(extractor.getPacks()):\n extractor.getPacks()[index][9] = ['11:00:00'][0]\n schema.subyam.append(extractor.getPacks()[index])\n\n# Will run through enumerated list @ O(n^2) to compare delivery address to address list\nfor index, outer in enumerate(schema.subyam):\n for inner in distances.getAddress():\n if (outer[2] == inner[2]):\n schema.subyamDist.append(outer[0])\n schema.subyam[index][1] = inner[0]\n\n# Call greedy algorithm to sort packages for Third truck\ndistances.routeLocater(schema.subyam, 3, 0)\ntotal_distance_tr3 = 0\n\n# Big O(n) for loop through truck load [array]\ntruckIdx[4] = distances.getThirdIndexList(1)\ntruckIdx[5] = distances.getThirdIndexList(0)\nfor index in range(len(truckIdx[4])):\n try:\n totalDistance[2] = distances.getDistance(int(truckIdx[4][index]), int(truckIdx[4][index + 1]), totalDistance[2])\n\n packsOut = distances.getTime(distances.getCurrent(int(truckIdx[4][index]),int(truckIdx[4][index + 1])), ['11:00:00'])\n truckIdx[5][index][10] = (str(packsOut))\n extractor.getHashTable().adjuster(int(truckIdx[5][index][0]), schema.subyam)\n except IndexError:\n pass\n\n## ----------------------------------Displays Package Data------------------------------------ ##\n\ndef total_distance_all_tr():\n return totalDistance[0] + totalDistance[1] + totalDistance[2]\n\ndef total_recall():\n print(\"Truck 1: Total distance: %s\" %(round(totalDistance[0], 1)))\n print(\"Truck 2: Total distance: %s\" %(round(totalDistance[1], 1)))\n print(\"Truck 3: Total distance: %s\" %(round(totalDistance[2], 1)))\n print(\"Total distance by all trucks: %s\" %(round(total_distance_all_tr(), 1)))\n\n## ------------------------------------------------------------------------------------------- ##","repo_name":"gccornejo441/school_assignment_packages","sub_path":"packages.py","file_name":"packages.py","file_ext":"py","file_size_in_byte":4383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"6707283157","text":"import csv\nimport copy\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nimport matplotlib.pyplot as plt\nimport math\nfrom sklearn import preprocessing as prep\nimport sys\nimport numpy as np;\nfrom matplotlib import cm\nfrom numpy import linalg as la\nfrom scipy import optimize as opt\nsys.path.append('/home/sanjay/home_work/study/ml/lib')\nimport mlutils as mu\n\n#main body\n\ndata = mu.readCSVFile('ex2data2.txt');\ndim = data.shape;\nprint(dim[0]);print(dim[1]);\n# put the data in input and output arrays\n# X keeps input, y is knonw output\nXOrg = data[:,0:2]\ny = data[:,2]\ny = y.reshape(dim[0],1)\nX = np.ones((dim[0],1))\nX = np.append(X,XOrg, axis=1)\n#print(X)\n# Data plotting\n\nXaxis = data[:,0:1]\nYaxis = data[:,1:2]\n#posInd = np.where(y==1)\n#negInd = np.where(y==0)\n\n#mu.printf(\"Xpos =%d, ypos=%d, xneg = %d, y =%d\\n\",len(Xaxis_pos),len(Yaxis_pos),len(Xaxis_neg),len(Yaxis_neg))\nfig = plt.figure(0);\nmu.plotData(X[:,1:3],y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\n#plt.scatter(Xaxis[posInd],Yaxis[posInd],marker='^')\n#plt.scatter(Xaxis[negInd],Yaxis[negInd],marker='o',c='r')\n#fig = plt.figure(3);\n#xr = np.arange(-10,10,0.1)\n#plt.plot(xr,sigmoid(xr))\n# =========== Part 1: Regularized Logistic Regression ============\n# In this part, you are given a dataset with data points that are not\n# Slinearly separable. However, you would still like to use logistic\n# regression to classify the data points.\n#\n# To do so, you introduce more features to use -- in particular, you add\n# polynomial features to our data matrix (similar to polynomial\n# regression).\n#\nX = mu.mapFeature(Xaxis, Yaxis);\ndim = np.shape(X)\nmu.printf(\"X dimensions after mapping = [%d,%d]\\n\",dim[0],dim[1]);\n# Initialize fitting parameters\ninitial_theta = np.zeros((1,dim[1]))\ntdim = np.shape(initial_theta);\nprint(tdim);\nmu.printf(\"inital theta dim = [%d ]\\n\", tdim[0]);\n# Set regularization parameter l to 1\nl = 1;\n\n# Compute and display initial cost and gradient for regularized logistic\n# regression\n#[cost, grad] = costFunctionReg(initial_theta, X, y, l);\ncost = mu.computeCostLogisticRegressionLR(initial_theta, X, y, l);\ngrad = mu.GradientDescentLogisticRegressionLR(initial_theta, X, y, l);\n\nprint(cost);\nmu.printf(\"Cost at initial theta (zeros): %.3f\\n\", cost);\nmu.printf(\"Expected cost (approx): 0.693\\n\");\nmu.printf(\"Gradient at initial theta (zeros) - first five values only:\\n\");\nprint(grad.shape)\nmu.printf(\" [%.4f %.4f %.4f %.4f %.4f] \\n\", grad[0],grad[1],grad[2],grad[3],grad[4]);\nmu.printf(\"Expected gradients (approx) - first five values only:\\n\");\nmu.printf(\" [0.0085 0.0188 0.0001 0.0503 0.0115]\\n\");\n\nmu.printf(\"\\nProgram paused. Press enter to continue.\\n\");\n\n# Compute and display cost and gradient\n# with all-ones theta and l = 10\nl=10;\ntest_theta = np.ones((1,dim[1]),dtype=np.float_);\n#[cost, grad] = costFunctionReg(test_theta, X, y, 10);\n\ncost = mu.computeCostLogisticRegressionLR(test_theta, X, y, l);\ngrad = mu.GradientDescentLogisticRegressionLR(test_theta, X, y, l);\n\nmu.printf('\\nCost at test theta (with l = 10): %.3f\\n', cost);\nmu.printf('Expected cost (approx): 3.16\\n');\nmu.printf('Gradient at test theta - first five values only:\\n');\nmu.printf(' [%.4f %.4f %.4f %.4f %.4f \\n', grad[0],grad[1],grad[2],grad[3],grad[4]);\nmu.printf('Expected gradients (approx) - first five values only:\\n');\nmu.printf(' [ 0.3460 0.1614 0.1948 0.2269 0.0922\\n');\n\nmu.printf('\\nProgram paused. Press enter to continue.\\n');\n\n# Set regularization parameter lambda to 1 (you should vary this)\nl = 1;\n# initial_theta = np.zeros((dim[1],1));\n\n#% Optimize\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC',jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x;\n\nplt.figure(1);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 1')\n\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"l=%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\n\n## ============= Part 3: Regularization and Accuracies Optional =============\n# Optional Exercise:\n# In this part, you will get to try different values of lambda and\n# see how regularization affects the decision coundart\n#\n#% Try the following values of lambda (0, 1, 10, 100).\n#%\n#% How does the decision boundary change when you vary lambda? How does\n#% the training set accuracy vary?\n#%\n\n#% Initialize fitting parameters\n#initial_theta = np.zeros((dim[0],dim[1]);\n\n#% Set regularization parameter lambda to 1 (you should vary this)\nl = 0;\n\n#% Set Options\n#options = optimset('GradObj', 'on', 'MaxIter', 400);\n\n#% Optimize\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC', jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x\n#[theta, J, exit_flag] = ...\n#\tfminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);\nprint(theta.shape)\nprint(X.shape)\nprint(y.shape)\n#% Plot Boundary\nplt.figure(2);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 0')\n\n#hold on;\n#title(smu.printf('lambda = %g', l))\n\n# Labels and Legend\n#xlabel('Microchip Test 1')\n#ylabel('Microchip Test 2')\n\n#legend('y = 1', 'y = 0', 'Decision boundary')\n#hold off;\n\n#% Compute accuracy on our training set\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"l=%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\n\nl = 100;\nres = opt.minimize(fun=mu.computeCostLogisticRegressionLR, x0=initial_theta, args = (X, y,l), method='TNC',jac=mu.GradientDescentLogisticRegressionLR)\ntheta= res.x\n#[theta, J, exit_flag] = ...\n#\tfminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);\n#\t% Plot Boundary\nplt.figure(3);\nmu.plotDecisionBoundary(theta, X, y);\nplt.xlabel('Microchip Test 1');\nplt.ylabel('Microchip Test 2')\nplt.title('lambda = 100')\n#\thold on;\n#\ttitle(smu.printf('lambda = %g', lambda))\n\n#\t% Labels and Legend\n#\txlabel('Microchip Test 1')\n#\tylabel('Microchip Test 2')\n\n#\tlegend('y = 1', 'y = 0', 'Decision boundary')\n#\thold off;\n\n#\t% Compute accuracy on our training set\np = mu.predict(theta, X);\ntmp = np.double(p == y)\nmu.printf(\"lambda =%d Train Accuracy: %.3f\\n\",l, tmp.mean() * 100);\nmu.printf(\"\\n\");\nplt.show()\n","repo_name":"skd73/study","sub_path":"ml/wk2/asspart2.py","file_name":"asspart2.py","file_ext":"py","file_size_in_byte":6298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"36078949221","text":"\"\"\"\nGiven a binary tree, you need to compute the length of the diameter \nof the tree. The diameter of a binary tree is the length of the \nlongest path between any two nodes in a tree. This path may or \nmay not pass through the root.\n\"\"\"\n\n\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def diameterOfBinaryTree(self, root: TreeNode):\n self.diameter = 0\n self.length(root)\n return self.diameter\n\n def length(self, root):\n if root:\n left = self.length(root.left)\n right = self.length(root.right)\n path = left + right\n if path > self.diameter:\n self.diameter = path\n return max(left, right) + 1\n return 0\n\n\nprint(diameterOfBinaryTree([1, 2, 3, 4, 5]))\nprint(diameterOfBinaryTree([1, 2, 3, 4, 5, 6, 7]))\nprint(diameterOfBinaryTree([1]))\nprint(\"The values above should be 3, 4, and 0.\")\n","repo_name":"alvinwang922/Data-Structures-and-Algorithms","sub_path":"Trees/Binary-Tree-Diameter.py","file_name":"Binary-Tree-Diameter.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"82"} +{"seq_id":"17962708616","text":"from __future__ import print_function\r\nimport numpy as np\r\nimport random\r\nimport pandas as pd\r\nimport sklearn\r\nimport sklearn.decomposition\r\nimport sklearn.ensemble\r\nimport sklearn.preprocessing\r\nimport sklearn.cluster\r\nfrom sklearn import *\r\nimport math\r\nimport gc\r\nimport os\r\nimport sys\r\nimport itertools\r\nimport threading\r\nfrom matplotlib import pyplot as plt\r\nimport matplotlib.colors\r\nimport tensorflow as tf\r\nimport re\r\nimport time\r\nimport pickle\r\nimport Constants\r\nimport gym\r\nimport copy\r\nimport gc\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn\r\nfrom sklearn.cluster import KMeans\r\nimport numpy as np\r\nfrom scipy.spatial.distance import cdist, pdist\r\n\r\n# Utility Function to return True / False regex matching\r\ndef pattern_match(patt, string):\r\n return re.findall(patt, string) != []\r\n# Utility Function to save objects in memory to a file\r\ndef save_memory(obj, path):\r\n return pickle.dump(obj, open(path, \"wb\"))\r\n# Utility Function to load objects from the harddisk\r\ndef load_memory(path):\r\n return pickle.load(open(path, \"rb\"))\r\n\r\ngc.collect()\r\n\r\ntry:\r\n os.mkdir(Constants.SAVE_PATH)\r\nexcept FileExistsError as e1:\r\n pass\r\nexcept OSError as e2:\r\n print('Failed to create directory {} - Incorrect syntax?'.format(Constants.SAVE_PATH))\r\nexcept:\r\n print('Error occurred - {}.'.format(sys.exc_info()[0]))\r\n\r\n############################ START BLACKJACK CLASS ############################\r\nclass Market(gym.Env):\r\n \r\n \"\"\"Trading Market environment\"\"\"\r\n \r\n def randomIndex(self):\r\n return random.randint(0, len(self.TRAIN)-self.DISCOUNT_STEPS - 10)\r\n \r\n def __init__(self, dataFile = None, COINS_IN = [], COINS_OUT = [], short = False):\r\n \r\n gc.collect()\r\n \r\n self.data = dataFile\r\n self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n cut_off_date = int(time.mktime(time.strptime('01/04/2018', \"%d/%m/%Y\"))) * 1000\r\n self.data = self.data[self.data.date > cut_off_date].reset_index(drop=True)\r\n\r\n self.data['reward_USD'] = 0\r\n if COINS_OUT == []:\r\n COINS_OUT = ['USD'] + [x.replace('close_','') for x in self.data.columns if \"close_\" in x]\r\n self.COINS = COINS_OUT\r\n print(\"{} rows & {} columns\".format(len(self.data), len(self.data.columns)))\r\n #--------------------------------------------------------------------------------------\r\n # Manual Options\r\n #--------------------------------------------------------------------------------------\r\n self.COMMISSION = 1e-10 # Commision % as a decimal to use in loss function\r\n self.NORMALIZE = True # Normalize Data\r\n self.ALLOW_SHORTS = True # Allow Shorts or not\r\n self.GAMMA = 0.5 # The discount factor\r\n self.DISCOUNT_STEPS = 5 # Number of periods to look ahead for discounting\r\n self.TRAIN_PERCENT = 0.75 # Percentage of data to use as training\r\n self.MULTS = 1 # How many future rewards to include in output\r\n #--------------------------------------------------------------------------------------\r\n # List of coins data to use as input variables. Set to [] to use all coins\r\n #--------------------------------------------------------------------------------------\r\n self.N_COINS = len(self.COINS)#( len(self.COINS) * 2 - 1 ) if self.ALLOW_SHORTS else len(self.COINS)\r\n #--------------------------------------------------------------------------------------\r\n # Create a list of X column names to use for modelling\r\n #--------------------------------------------------------------------------------------\r\n the_coins = []\r\n if COINS_IN == []:\r\n for c in self.data.columns:\r\n if \"reward_\" in c and c != \"reward_USD\" and not c.endswith(\"_S\") and c.replace(\"reward_\",\"\") not in the_coins:\r\n the_coins.append(c.replace(\"reward_\",\"\"))\r\n else:\r\n for c in self.data.columns:\r\n if \"reward_\" in c and c != \"reward_USD\" and not c.endswith(\"_S\") and c.replace(\"reward_\",\"\") not in the_coins:\r\n the_coin = c.replace(\"reward_\",\"\")\r\n if the_coin in COINS_IN:\r\n the_coins.append(the_coin)\r\n \r\n self.COINS_IN = the_coins\r\n \r\n in_cols = []\r\n for c in self.data.columns:\r\n if \"DAY_OF_WEEK\" in c or \"HOUR_OF_DAY\" in c:\r\n in_cols.append(c)\r\n continue\r\n for a in sorted(set(the_coins)):\r\n if \"_\"+a in c:\r\n in_cols.append(c)\r\n \r\n COLS_X = []\r\n for x in in_cols:\r\n if \"reward_\" in x or \"train_\" in x:\r\n continue\r\n COLS_X.append(x)\r\n \r\n #for c in self.data.columns:\r\n # if \"limit\" in c:\r\n # self.data[c.replace(\"limit\",\"reward\")] = self.data[c]\r\n #--------------------------------------------------------------------------------------\r\n # Create a list of Y column names to use for modelling\r\n #--------------------------------------------------------------------------------------\r\n COLS_Y = [] if \"USD\" not in self.COINS and \"USDT\" not in self.COINS else [\"reward_USD\"]\r\n\r\n for c in self.data.columns:\r\n added = False\r\n if 'reward' in c and (c != 'reward_USD' and c not in COLS_Y):\r\n\r\n if COINS_OUT == []:\r\n COLS_Y += [c]\r\n added = True\r\n\r\n else:\r\n for a in sorted(set(self.COINS)):\r\n if c == \"reward_\" + a and c not in COLS_Y:\r\n COLS_Y += [c]\r\n print(\"added reward:\", c)\r\n added = True\r\n if added:\r\n #self.data[c+\"_S\"] = self.data[c].apply(lambda x : math.log10(2-10**x))\r\n self.data[c+\"_S\"] = self.data[c].apply(lambda x : -x)\r\n \r\n if self.ALLOW_SHORTS:\r\n COLS_Y += [\"{}_S\".format(y) for y in COLS_Y if y != \"reward_USD\"]\r\n \r\n current_ys = copy.deepcopy(COLS_Y)\r\n for ahead in range(1, self.MULTS):\r\n for y in current_ys:\r\n c = y + \"_\" + str(ahead + 1)\r\n self.data[c] = self.data[y].shift(-ahead)\r\n COLS_Y.append(c)\r\n \r\n self.N_CRYPTO = len([1 for y in COLS_Y if y != \"reward_USD\" and not y.endswith(\"_S\")])\r\n \r\n PORT_W = [w.replace(\"reward_\", \"MARGIN_\") for w in COLS_Y]\r\n for p in PORT_W:\r\n self.data[p] = 0\r\n\r\n self.data[\"MARGIN_USD\"] = 1\r\n if self.COMMISSION != 0:\r\n COLS_X += PORT_W\r\n \r\n # Hard-code in spread\r\n for x in COLS_Y:\r\n if x in (\"train_USD\", \"reward_USD\"):\r\n continue\r\n self.data[x] = self.data[x].apply(lambda x : x + math.log10(1-0.0/4000))\r\n \r\n COLS_Y_TRAIN = [x.replace(\"reward_\",\"train_\") for x in COLS_Y]\r\n print(COLS_Y)\r\n print(COLS_Y_TRAIN)\r\n \r\n for y_pos in range(len(COLS_Y_TRAIN)):\r\n \r\n train_col = COLS_Y_TRAIN[y_pos]\r\n orig_col = COLS_Y[y_pos]\r\n stmt = \"self.data['{}'] = self.data['{}']\".format(train_col, orig_col)\r\n for ahead in range(1,self.DISCOUNT_STEPS+1):\r\n stmt += \"+(self.GAMMA**{}) * self.data['{}'].shift({})\".format(ahead, orig_col, -ahead)\r\n #for ahead in range(1,self.DISCOUNT_STEPS+1):\r\n # stmt += \"+((0.25*self.GAMMA)**{}) * self.data['{}'].shift({})\".format(ahead, orig_col, ahead)\r\n #stmt += \"+ math.log10(1 - 0.0001)\"\r\n print(\"Calculating Discount Rewards...\", end=\"\")\r\n exec(stmt)\r\n \r\n self.COLS_Y_TRAIN = COLS_Y_TRAIN\r\n self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n \r\n #for c in COLS_Y:\r\n # if \"USD\" in c:\r\n # continue\r\n # self.data[c] = self.data[c] + math.log10(1 - self.COMMISSION)\r\n\r\n #self.data = self.data.dropna(axis=0, how='any').reset_index(drop=True)\r\n #--------------------------------------------------------------------------------------\r\n # Split Train/Test\r\n #--------------------------------------------------------------------------------------\r\n train_idx = int( self.TRAIN_PERCENT * len(self.data) )\r\n #--------------------------------------------------------------------------------------\r\n # Normalizing the X columns. Scale using training data only\r\n #--------------------------------------------------------------------------------------\r\n self.SCALE_DICT = {}\r\n if self.NORMALIZE:\r\n '''print(\"Normalizing Data...\", end=\"\")\r\n scaler = sklearn.preprocessing.StandardScaler()\r\n print(\"Fitting Scaler: {}\".format(len(COLS_X)))\r\n scaler.fit( self.data[:train_idx][COLS_X] )\r\n print(\"Using Scaler: {}\".format(len(COLS_X)))\r\n self.data[COLS_X] = scaler.transform(self.data[COLS_X])\r\n self.SAVE_SCALER = scaler\r\n self.SAVE_SCALER_COLS = COLS_X'''\r\n \r\n #def scale_col(dat, x, mu, sd):\r\n # dat[x] = dat[x].apply(lambda x : (x-mu)/sd)\r\n # print(\"Scaled {}\".format(x))\r\n \r\n #norm_threads = []\r\n \r\n descriptions = self.data[:train_idx].describe()\r\n \r\n for i, x in enumerate(COLS_X):\r\n if \"MARGIN\" in x or 'date' in x or \"close_\" in x or \"open_\" in x or \"low_\" in x or \"high_\" in x:\r\n continue\r\n \r\n mu, sd = descriptions[x]['mean'], descriptions[x]['std']\r\n \r\n print(\"Normalizing {} - {} / {} {:.5f}, {:.5f}\".format(x, (i+1), len(COLS_X), mu, sd))\r\n self.SCALE_DICT[x] = (mu, sd)\r\n self.data[x] = self.data[x].apply(lambda x : (x-mu)/sd)\r\n #thr = threading.Thread(target=scale_col, args=(self.data, x, mu, sd))\r\n #norm_threads.append(thr)\r\n #norm_threads[-1].start()\r\n \r\n #for thr in norm_threads:\r\n # thr.join()\r\n \r\n print(\"Done\")\r\n \r\n self.TRAIN = self.data[:train_idx].reset_index(drop=True)\r\n #self.TEST = self.TRAIN\r\n self.TEST = self.data[train_idx:].reset_index(drop=True)\r\n \r\n \r\n fee_rate = 0.002/100\r\n self.TRAIN_HOLD = copy.deepcopy(self.TRAIN)\r\n self.TRAIN_HOLD[PORT_W] = 0 # Set all holdings to 0\r\n self.TRAIN_HOLD[PORT_W[0]] = 1 # Set first holding to 1\r\n self.TRAIN_HOLD[COLS_Y_TRAIN] += 2 * math.log10(1 - fee_rate) # Add transaction cost to all rewards\r\n self.TRAIN_HOLD[COLS_Y_TRAIN[0]] -= 2 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n for i, y in enumerate(COLS_Y):\r\n if i == 0:\r\n continue\r\n new = copy.deepcopy(self.TRAIN)\r\n new[PORT_W] = 0 # Set all holdings to 0\r\n new[PORT_W[i]] = 1 # Set first holding to 1\r\n new[COLS_Y_TRAIN] += 2 * math.log10(1 - fee_rate) # Add transaction cost to all rewards\r\n new[COLS_Y_TRAIN[i]] -= 2 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n if COLS_Y[0] == \"reward_USD\":\r\n new[COLS_Y_TRAIN[0]] -= 1 * math.log10(1 - fee_rate) # Remove it from the one we're holding\r\n self.TRAIN_HOLD = self.TRAIN_HOLD.append(new)\r\n \r\n self.TRAIN_HOLD.reset_index(inplace=True)\r\n \r\n self.COLS_X = COLS_X\r\n self.COLS_Y = COLS_Y\r\n self.N_IN = len(COLS_X)\r\n self.N_OUT = len(COLS_Y)\r\n \r\n self.holdings = {}\r\n for i, c in enumerate(sorted(self.COLS_Y)):\r\n self.holdings[c.replace(\"reward_\",\"\")] = 0\r\n self.holdings['USD'] = 1\r\n \r\n self.position = self.randomIndex()\r\n self.ACTIONS = [x.replace(\"reward_\",\"\") for x in self.COLS_Y]\r\n self.PORT_W = PORT_W\r\n \r\n self.N_CRYPTO_IN = len(self.COINS_IN)\r\n \r\n print(\"CRYPTO_IN:\" + str(self.COINS_IN))\r\n \r\n self.PREV_W_COLS = PORT_W\r\n \r\n gc.collect()\r\n \r\n print(\"Market Data Loaded\")\r\n \r\n def save(self):\r\n items = [ (self.SCALE_DICT, \"{}\\\\SCALE_DICT.save\".format(Constants.SAVE_PATH)),\r\n (self.PRICE_TENSOR_COLS, \"{}\\\\PRICE_TENSOR_COLS.save\".format(Constants.SAVE_PATH)),\r\n (self.PRICE_LAGS, \"{}\\\\PRICE_LAGS.save\".format(Constants.SAVE_PATH))]\r\n\r\n for i in items:\r\n try:\r\n save_memory(i[0], i[1])\r\n except:\r\n pass\r\n \r\n def step(self, action):\r\n \r\n rw = 0\r\n \r\n self.COMM_REWARD = math.log10(1 - self.COMMISSION)\r\n \r\n act_loc = M.ACTIONS.index(action)\r\n if self.TRAIN.at[self.position, self.PORT_W[act_loc]] == 1:\r\n rw = 0\r\n elif action in (\"USD\", \"USDT\") or self.TRAIN.at[self.position, \"MARGIN_USD\"] == 1:#\\\r\n #(self.TRAIN.at[self.position, \"MARGIN_USD\"] == 1 and action not in (\"USD\", \"USDT\")):\r\n rw = 1 * self.COMM_REWARD\r\n else:\r\n rw = 2 * self.COMM_REWARD\r\n \r\n rw += self.TRAIN.at[self.position, \"reward_{}\".format(action)]\r\n self.position += 1\r\n \r\n for w in self.PORT_W:\r\n self.TRAIN.set_value(self.position, w, 0)\r\n self.TRAIN.set_value(self.position, self.PORT_W[act_loc], 1)\r\n \r\n if np.isnan(rw):\r\n print(self.position, action, self.holdings)\r\n \r\n return rw\r\n \r\n############################ END MARKET CLASS ############################\r\n\r\nraw_data = pd.read_csv(\"Data/Crypto/5m/ALL_MOD.csv\")\r\n#raw_data = pd.read_csv(\"Data/Forex/15m/ALL_MOD.csv\")\r\n\r\n#M = Market(raw_data,\r\n# COINS_IN = ['BTC', 'EOS', 'ETC', 'ETH', 'IOTA', 'LTC', 'XRP'],\r\n# COINS_OUT = ['BTC', 'EOS', 'ETC', 'ETH', 'IOTA', 'LTC', 'XRP'])\r\n\r\n\r\nfx_pairs_in = ['AUDCAD', 'AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'EURAUD', 'EURCAD', 'EURGBP', \r\n 'EURJPY', 'EURNZD', 'EURUSD', 'GBPAUD', 'GBPCAD', 'GBPJPY', 'GBPNZD', 'GBPUSD', \r\n 'NZDCAD', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDJPY', 'USDOLLAR']\r\n\r\nfx_pairs_out = ['AUDCAD', 'AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'EURAUD', 'EURCAD', 'EURGBP', \r\n 'EURJPY', 'EURNZD', 'EURUSD', 'GBPAUD', 'GBPCAD', 'GBPJPY', 'GBPNZD', 'GBPUSD', \r\n 'NZDCAD', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDJPY']\r\n\r\nfx_pairs = ['USD', 'AUDUSD', 'EURUSD', 'GBPJPY', 'AUDJPY', 'GBPUSD', 'USDJPY', 'EURAUD', 'EURJPY']\r\n\r\nM = Market(raw_data,\r\n COINS_IN = [\"BMXBTCUSD\", \"BFXBTCUSDT\", \"BINBTCUSDT\", \"GDXBTCUSD\", \"BFXXRPUSDT\", \"BINETHUSDT\"],\r\n COINS_OUT = [\"BMXBTCUSD\"])\r\n\r\n\r\n#M = Market(raw_data,\r\n# COINS_IN = [\"AUDJPY\", \"AUDUSD\", \"GBPJPY\", \"GBPUSD\", \"EURUSD\", \"NZDUSD\", \"EURCAD\", \"USDJPY\"],\r\n# COINS_OUT = ['USDJPY'])\r\n\r\nX2 = []\r\nfor x in M.data.columns:\r\n \r\n channel_rank = 1000\r\n lag_rank = 1000\r\n \r\n channel_rank = 0 if \"L_CLOSE\" in x else channel_rank\r\n channel_rank = 1 if \"L_LOW\" in x else channel_rank\r\n channel_rank = 2 if \"L_HIGH\" in x else channel_rank\r\n channel_rank = 3 if \"L_VOLUME\" in x else channel_rank\r\n channel_rank = 4 if \"L_VOLPRICE\" in x else channel_rank\r\n \r\n channel_rank = 5 if \"L2_CLOSE\" in x else channel_rank\r\n channel_rank = 6 if \"L2_LOW\" in x else channel_rank\r\n channel_rank = 7 if \"L2_HIGH\" in x else channel_rank\r\n \r\n channel_rank = 8 if \"L3_CLOSE\" in x else channel_rank\r\n channel_rank = 9 if \"L3_LOW\" in x else channel_rank\r\n channel_rank = 10 if \"L3_HIGH\" in x else channel_rank\r\n \r\n channel_rank = 11 if \"SMACLOSE1\" in x else channel_rank\r\n channel_rank = 12 if \"SMACLOSE2\" in x else channel_rank\r\n channel_rank = 13 if \"SMACLOSE3\" in x else channel_rank\r\n channel_rank = 14 if \"SMACLOSE4\" in x else channel_rank\r\n channel_rank = 15 if \"SMACLOSE5\" in x else channel_rank\r\n \r\n channel_rank = 16 if \"SMALOW1\" in x else channel_rank\r\n channel_rank = 17 if \"SMALOW2\" in x else channel_rank\r\n channel_rank = 18 if \"SMALOW3\" in x else channel_rank\r\n channel_rank = 19 if \"SMALOW4\" in x else channel_rank\r\n channel_rank = 20 if \"SMALOW5\" in x else channel_rank\r\n \r\n channel_rank = 21 if \"SMAHIGH1\" in x else channel_rank\r\n channel_rank = 22 if \"SMAHIGH2\" in x else channel_rank\r\n channel_rank = 23 if \"SMAHIGH3\" in x else channel_rank\r\n channel_rank = 24 if \"SMAHIGH4\" in x else channel_rank\r\n channel_rank = 25 if \"SMAHIGH5\" in x else channel_rank\r\n \r\n channel_rank = 26 if \"RSI1\" in x else channel_rank\r\n channel_rank = 27 if \"RSI2\" in x else channel_rank\r\n channel_rank = 28 if \"RSI3\" in x else channel_rank\r\n channel_rank = 29 if \"RSI4\" in x else channel_rank\r\n channel_rank = 30 if \"RSI5\" in x else channel_rank\r\n \r\n #channel_rank = 29 if \"SUPPORT1\" in x else channel_rank\r\n\r\n #channel_rank = 30 if \"RESIST1\" in x else channel_rank\r\n \r\n channel_rank = 31 if \"LINEAR1\" in x else channel_rank\r\n channel_rank = 32 if \"LINEAR2\" in x else channel_rank\r\n channel_rank = 33 if \"LINEAR3\" in x else channel_rank\r\n\r\n \r\n S_COINS = sorted(M.COINS_IN)\r\n coin_rank = -1\r\n for i, c in enumerate(S_COINS):\r\n if x.endswith(\"_\"+c):\r\n coin_rank = i\r\n break\r\n \r\n try:\r\n lag_rank = int(\"\".join([ch for ch in x[x.index(\"_\"):] if ch in '0123456789']))\r\n lag_rank *= -1\r\n except:\r\n pass\r\n \r\n if coin_rank < 0:\r\n continue\r\n \r\n X2.append( (coin_rank, lag_rank, channel_rank, x) )\r\n \r\nX2.sort(key = lambda x : (x[0], x[1], x[2]))\r\n\r\nPRICE_TENSOR = [(x[-1], x[-2], x[-3]) for x in X2 if 0 <= x[2] < 1000]\r\n\r\ncols = list(M.data.columns)\r\nPRICE_LAGS = len(set([x[2] for x in PRICE_TENSOR]))\r\nPRICE_CHANNELS = len(set([x[1] for x in PRICE_TENSOR]))\r\nPRICE_TENSOR_COLS = [x[0] for x in PRICE_TENSOR]\r\nPRICE_TENSOR_IDX = [cols.index(x) for x in PRICE_TENSOR_COLS]\r\nM.PRICE_LAGS = PRICE_LAGS\r\nM.PRICE_TENSOR_COLS = PRICE_TENSOR_COLS\r\n\r\nMU_SD_TABLE = M.TRAIN[PRICE_TENSOR_COLS].describe()\r\n\r\nUSE_SIGMOID = True\r\nN_COINS = M.N_COINS\r\nN_CRYPTO = M.N_CRYPTO_IN\r\nN_IN = M.N_IN\r\nN_OUT = M.N_OUT\r\nTIMESTEP_DAYS = 86400000 / (M.data.date - M.data.date.shift(1)).describe()['50%']\r\n\r\nwith tf.device(\"/GPU:0\"):\r\n \r\n # PrevW\r\n HOLD_W = tf.placeholder(tf.float32, [None, N_OUT])\r\n HOLD_W = tf.reshape(HOLD_W, [-1, N_OUT])\r\n # Actual Rewards\r\n Y_ = tf.placeholder(tf.float32, [None, N_OUT])\r\n \r\n Q_TARGET = tf.placeholder(tf.float32, [None, N_OUT])\r\n Q_TARGET = tf.reshape(Q_TARGET, [-1, N_OUT])\r\n \r\n keep_p1 = tf.placeholder(tf.float32, name = 'keep1')\r\n keep_p2 = tf.placeholder(tf.float32, name = 'keep2')\r\n keep_p3 = tf.placeholder(tf.float32, name = 'keep3')\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Define Neural Network layers\r\n #--------------------------------------------------------------------------------------\r\n\r\n h_1 = 1\r\n w_1 = 1\r\n CH_OUT_1 = 20\r\n FILTER1 = [h_1, w_1, PRICE_CHANNELS, CH_OUT_1] # Filter 1 x 3 x 3, Input has 4 channels\r\n \r\n h_2 = 1\r\n w_2 = PRICE_LAGS - w_1 + 1\r\n CH_OUT_2 = 50\r\n FILTER2 = [h_2, w_2, CH_OUT_1, CH_OUT_2]\r\n \r\n # Final\r\n h_f = N_CRYPTO\r\n w_f = 1\r\n CH_OUT_f = 100\r\n FILTERf = [h_f, w_f, CH_OUT_2, CH_OUT_f]\r\n \r\n SDEV = 1\r\n BIAS_MULT = 0\r\n \r\n initializer = tf.contrib.layers.xavier_initializer()\r\n initializer_cnn = tf.contrib.layers.xavier_initializer_conv2d()\r\n \r\n X_PRICE_TENSOR = tf.placeholder(tf.float32, [None, len(PRICE_TENSOR_COLS)])\r\n X_PRICE_TENSOR_NN = tf.reshape(X_PRICE_TENSOR, [-1, N_CRYPTO, PRICE_LAGS, PRICE_CHANNELS])\r\n \r\n #X_PRICE_TENSOR_NN_AVG = tf.nn.avg_pool(X_PRICE_TENSOR_NN, [1,1,3,1], [1,1,3,1], 'VALID')\r\n \r\n X_SCALER = tf.Variable(tf.ones([N_CRYPTO, 1, PRICE_CHANNELS]))\r\n X_SCALER2 = tf.Variable(tf.ones([1, PRICE_LAGS, PRICE_CHANNELS]))\r\n \r\n X_PRICE_TENSOR_NN_AVG = X_PRICE_TENSOR_NN\r\n #X_PRICE_TENSOR_NN_AVG = tf.round(4 * X_PRICE_TENSOR_NN) / 4\r\n \r\n X_PRICE_TENSOR_NN_AVG = tf.multiply(X_PRICE_TENSOR_NN_AVG, X_SCALER)\r\n X_PRICE_TENSOR_NN_AVG = tf.multiply(X_PRICE_TENSOR_NN_AVG, X_SCALER2)\r\n \r\n LEAKY_ALPHA = 0.05\r\n \r\n # LAYER 1\r\n CW1 = tf.Variable(tf.random_normal(FILTER1, stddev = SDEV * (1/(h_1*w_1*PRICE_CHANNELS))**0.5 ))\r\n CB1 = tf.Variable(tf.zeros([CH_OUT_1]))\r\n CL1 = tf.nn.leaky_relu(tf.nn.conv2d(X_PRICE_TENSOR_NN_AVG, CW1, [1,1,1,1], padding=\"VALID\") + CB1 * BIAS_MULT, LEAKY_ALPHA)\r\n CL1 = tf.nn.dropout(CL1, keep_p1)\r\n \r\n # LAYER 2\r\n CW2 = tf.Variable(tf.random_normal(FILTER2, stddev = SDEV * (1/(h_2*w_2*CH_OUT_1))**0.5))\r\n CB2 = tf.Variable(tf.zeros([CH_OUT_2]))\r\n CL2 = tf.nn.leaky_relu(tf.nn.conv2d(CL1, CW2, [1,1,1,1], padding=\"VALID\") + CB2 * BIAS_MULT, LEAKY_ALPHA)\r\n \r\n CL2 = tf.nn.dropout(CL2, keep_p2)\r\n \r\n CW4 = tf.Variable(tf.random_normal(FILTERf, stddev = SDEV * (1/(h_f*w_f*CH_OUT_f))**0.5))\r\n CB4 = tf.Variable(tf.zeros([CH_OUT_f]))\r\n CL4 = tf.nn.relu(tf.nn.conv2d(CL2, CW4, [1,1,1,1], padding=\"VALID\") + CB4 * BIAS_MULT)\r\n \r\n CL4 = tf.nn.dropout(CL4, keep_p3)\r\n \r\n CL_flat = tf.reshape(CL4, (-1, CH_OUT_f * N_CRYPTO//h_f))\r\n CL_flat = tf.concat( [CL_flat, HOLD_W], -1)\r\n \r\n fc_w = tf.Variable( initializer([int(CL_flat.shape[-1]), 100]) )\r\n fc_b = tf.Variable( initializer([100]) )\r\n \r\n fc_w2 = tf.Variable( initializer([100, N_OUT]) )\r\n fc_b2 = tf.Variable( initializer([N_OUT]) )\r\n \r\n LOSS_L2 = tf.nn.l2_loss(fc_w)\r\n \r\n Q_UNSCALED1 = tf.nn.relu(tf.matmul(CL_flat, fc_w) + fc_b * BIAS_MULT)\r\n Q_UNSCALED1 = tf.nn.dropout(Q_UNSCALED1, keep_p3)\r\n \r\n Q_UNSCALED = tf.matmul(Q_UNSCALED1, fc_w2) + fc_b2 * BIAS_MULT\r\n Q_UNSCALED = tf.nn.dropout(Q_UNSCALED, keep_p3)\r\n \r\n if USE_SIGMOID:\r\n Q_PREDICT = tf.nn.sigmoid(Q_UNSCALED)\r\n else:\r\n #Q_PREDICT = Q_UNSCALED\r\n Q_PREDICT = tf.nn.softmax(Q_UNSCALED, 1)\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Define Loss Functions\r\n #--------------------------------------------------------------------------------------\r\n \r\n q_predict_mean, q_predict_var = tf.nn.moments(Q_PREDICT, axes=[1])\r\n all_returns = tf.reduce_sum(Q_PREDICT * Q_TARGET, 1)\r\n all_returns2 = tf.nn.relu( tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) ** 0.8 - \\\r\n tf.nn.relu( -tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) ** 0.8\r\n \r\n loss_func = -tf.reduce_mean(all_returns)\r\n r_mean, r_var = tf.nn.moments(all_returns, axes=[0])\r\n sharpe_loss = -r_mean / (r_var**0.5)\r\n \r\n winning_trades = tf.nn.relu(all_returns)\r\n winning_trades_mean = tf.reduce_mean(winning_trades)\r\n losing_trades = tf.nn.relu(-all_returns)\r\n losing_trades_mean = tf.reduce_mean(losing_trades)\r\n \r\n winning_trades2 = tf.nn.relu(all_returns2)\r\n winning_trades_mean2 = tf.reduce_mean(winning_trades2)\r\n losing_trades2 = tf.nn.relu(-all_returns2)\r\n losing_trades_mean2 = tf.reduce_mean(losing_trades2)\r\n \r\n #min_func = -tf.reduce_mean(tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) * math.e**-r_stdev\r\n opt_func = (winning_trades_mean2/losing_trades_mean2) * (-tf.reduce_mean(all_returns2) + 0.1 * tf.reduce_mean(losing_trades2))# - \\\r\n #opt_func = -tf.reduce_mean(winning_trades) / tf.reduce_mean(losing_trades)# - \\\r\n #1e-7 * tf.reduce_min(all_returns)\r\n \r\n #opt_func = -tf.reduce_mean(tf.reduce_sum(Q_PREDICT * Q_TARGET, 1) ) * math.e**-r_var\r\n\r\n #opt_func = -tf.reduce_sum(all_returns)# + 0.5*tf.reduce_sum(losing_trades)\r\n #opt_func = tf.reduce_sum(tf.square(Q_PREDICT - Q_TARGET), 0)\r\n opt_func = -tf.reduce_mean(all_returns2)\r\n\r\n \r\n #loss_func = -tf.reduce_sum(Q_PREDICT * Q_TARGET) \\\r\n # - math.log10(1-M.COMMISSION)*tf.reduce_sum( tf.abs(tf.reduce_sum(Q_PREDICT[1:,:] - Q_PREDICT[:-1,:], 1) ) )\r\n\r\n LR_START = 0.0005\r\n \r\n # Optimizer\r\n LEARNING_RATE = tf.Variable(LR_START, trainable=False)\r\n optimizer = tf.train.AdamOptimizer(LEARNING_RATE)#(LEARNING_RATE)\r\n train_step = optimizer.minimize(1e2 * opt_func)\r\n \r\n #--------------------------------------------------------------------------------------\r\n # Begin Tensorflow Session\r\n #--------------------------------------------------------------------------------------\r\n \r\n init = tf.global_variables_initializer()\r\n \r\n config = tf.ConfigProto()\r\n config.intra_op_parallelism_threads = 32\r\n config.log_device_placement = True\r\n \r\n sess = tf.Session(config=config)\r\n sess.run(init)\r\n\r\n# probability of picking a random action. This decays over time\r\nepsilon = 0.1\r\n\r\nall_rewards = [] # Holds all observed rewards. The rolling mean of rewards should improve as the network learns\r\nall_Qs = [] # Holds all predicted Q values. Useful as a sanity check once the network is trained\r\nall_losses = [] # Holds all the (Q_TARGET - Q_PREDICTED) values. The rolling mean of this should decrease\r\nQ_TARGETS = []\r\nQ_PREDS = []\r\nPRICE_STATES = []\r\nH_WEIGHTS = []\r\nQ_CONVERGE = {} # Not used yet\r\nprojections = []\r\nwatch = Constants.Stopwatch()\r\n\r\ntrain_losses, test_losses, transf_losses, opt_losses = [], [], [], []\r\ngc.collect()\r\n\r\nepisode = 0\r\nsmallest_loss = 1e6\r\nwhile episode < 1000000:\r\n \r\n init_pos = episode % (len(M.TRAIN)-50)#\r\n #init_pos = M.randomIndex()\r\n M.position = init_pos\r\n \r\n USD_STATE = None\r\n USD_PRICE_STATE = None\r\n Q_USD = 0\r\n W_USD = 0 \r\n \r\n '''if episode == 100:\r\n update_LR = tf.assign(LEARNING_RATE, 0.001)\r\n sess.run(update_LR)'''\r\n \r\n for w_index, starting_w in enumerate(M.PORT_W):\r\n \r\n watch.start('update_W')\r\n M.position = init_pos\r\n for w in M.PORT_W:\r\n M.TRAIN.set_value(M.position, w, 0)\r\n M.TRAIN.set_value(M.position, starting_w, 1)\r\n watch.end('update_W')\r\n \r\n watch.start('set_state')\r\n init_price_state = np.array(M.TRAIN.iloc[M.position, PRICE_TENSOR_IDX])\r\n watch.end('set_state')\r\n \r\n watch.start('Q_PREDICT')\r\n Q1 = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : np.reshape(init_price_state,(-1, len(PRICE_TENSOR_COLS)) ),\r\n HOLD_W : np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n watch.end('Q_PREDICT')\r\n if w_index == 0:\r\n USD_PRICE_STATE = init_price_state\r\n Q_USD = Q1\r\n W_USD = np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) )\r\n \r\n targetQ = list(Q1[0])\r\n \r\n for act_num, begin_act in enumerate(M.ACTIONS):\r\n \r\n M.position = init_pos\r\n for w in M.PORT_W:\r\n M.TRAIN.set_value(M.position, w, 0)\r\n M.TRAIN.set_value(M.position, starting_w, 1)\r\n #print(M.TRAIN.loc[M.position, M.PORT_W])\r\n \r\n watch.start(\"market_step\")\r\n #G = M.step(begin_act)\r\n #Gpercent = 100*(10**G-1)\r\n #G = math.log10(1+int(Gpercent*8)/800)\r\n profit = M.TRAIN.at[M.position, M.COLS_Y_TRAIN[act_num]]\r\n G = profit\r\n M.position += 1\r\n \r\n watch.end(\"market_step\")\r\n \r\n for t in range(0):#M.DISCOUNT_STEPS):\r\n \r\n state = np.array(M.TRAIN.loc[M.position, M.COLS_X])\r\n price_state = np.array(M.TRAIN.loc[M.position, PRICE_TENSOR_COLS])\r\n \r\n if random.random() < epsilon:\r\n act = random.choice(M.ACTIONS)\r\n else:\r\n Q = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : price_state.reshape(-1, len(PRICE_TENSOR_COLS)),\r\n HOLD_W : np.array(M.TRAIN.ix[M.position, M.PORT_W]).reshape( (-1, N_OUT) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n act = M.ACTIONS[np.argmax(Q)]\r\n \r\n if t == M.DISCOUNT_STEPS-1 and episode > 1000:\r\n G += M.GAMMA ** (t+1) * max(Q[0])\r\n else:\r\n G += M.GAMMA ** (t+1) * M.step(act)\r\n \r\n #for w in M.PORT_W:\r\n # M.TRAIN.set_value(M.position, w, 0)\r\n #M.TRAIN.set_value(M.position, M.PORT_W[M.ACTIONS.index(act)], 1)\r\n \r\n targetQ[act_num] = G\r\n \r\n PRICE_STATES.append(init_price_state)\r\n Q_PREDS.append(Q1)\r\n Q_TARGETS.append(targetQ)\r\n H_WEIGHTS.append(M.TRAIN.ix[init_pos, M.PORT_W])\r\n \r\n if w_index == 0:\r\n usd_target = copy.deepcopy(targetQ)\r\n break\r\n \r\n num_depth = 1+max(0, math.log(episode+1)-2)+len(M.TRAIN)#*0.15\r\n num_depth = len(M.TRAIN)\r\n #num_depth = 1024\r\n if len(Q_TARGETS) >= num_depth or True:\r\n \r\n COL_W = '\\033[0m' # white (normal)\r\n COL_R = '\\033[41m' # red\r\n COL_G = '\\033[42m' # green\r\n COL_O = '\\033[33m' # orange\r\n COL_B = '\\033[34m' # blue\r\n COL_P = '\\033[35m' # purple\r\n \r\n #update_drop_rt = tf.assign(tf_keep_prob, 0.7)\r\n #sess.run(update_drop_rt)\r\n \r\n #the_x = np.reshape( np.array(X_STATES), (-1, N_IN) )\r\n the_p = np.reshape( np.array(PRICE_STATES), (-1, len(PRICE_TENSOR_COLS)))\r\n the_q = np.reshape( np.array(Q_TARGETS), (-1, N_OUT))\r\n the_w = np.reshape( np.array(H_WEIGHTS), (-1, N_OUT))\r\n \r\n the_p = np.reshape(np.array(M.TRAIN_HOLD[PRICE_TENSOR_COLS]), (-1, len(PRICE_TENSOR_COLS)) )\r\n the_q = np.reshape(np.array(M.TRAIN_HOLD[M.COLS_Y_TRAIN]), (-1, len(M.COLS_Y_TRAIN)) )\r\n the_w = np.reshape(np.array(M.TRAIN_HOLD[M.PORT_W]), (-1, len(M.PORT_W)) )\r\n \r\n #for i in range(int(num_depth+0.5)):\r\n i = 0\r\n PR_KEEP_1, PR_KEEP_2, PR_KEEP_3 = 0.70, 0.70, 0.70\r\n use_sample = True\r\n while i < 2000000000:\r\n \r\n rates = {0 : 0.0005, \r\n 1e4 : 0.0001, \r\n 3e4 : 0.00003, \r\n 1e6 : 0.00001}\r\n \r\n if i in rates:\r\n update_LR = tf.assign(LEARNING_RATE, rates[i])\r\n sess.run(update_LR)\r\n\r\n opt = train_step\r\n\r\n #opt = train_step_start if i < 200 or random.random() < 0.02 else train_step\r\n #l_func = loss_func_start if i < 200 else loss_func\r\n #opt = train_step\r\n watch.start(\"Gradient_Update\")\r\n if use_sample:\r\n \r\n n_samples = min(i//100+500, round(0.2 * len(the_p)) )\r\n #n_samples = 50\r\n #samples = [int(random.random()**0.5 * len(the_p)) for _ in range(n_samples)]\r\n samples = random.sample(range(len(the_p)), n_samples)\r\n x_noise = np.random.normal(0, 0.15, the_p[samples,:].shape)\r\n #y_noise = np.random.normal(-1e-9, 1e-9, the_q[samples,:].shape)\r\n #samples = random.sample(range(len(the_p)), round(0.3*len(the_p)))\r\n sess.run(opt, \r\n feed_dict = {X_PRICE_TENSOR : the_p[samples,:] + x_noise,\r\n Q_TARGET : the_q[samples,:],\r\n HOLD_W : the_w[samples,:],\r\n keep_p1 : PR_KEEP_1, keep_p2 : PR_KEEP_2, keep_p3 : PR_KEEP_3})\r\n \r\n else:\r\n sess.run(opt, \r\n feed_dict = {X_PRICE_TENSOR : the_p,\r\n Q_TARGET : the_q,\r\n HOLD_W : the_w,\r\n keep_p1 : PR_KEEP_1, keep_p2 : PR_KEEP_2, keep_p3 : PR_KEEP_3} )\r\n \r\n watch.end(\"Gradient_Update\")\r\n if i % 100 == 0:\r\n \r\n train_loss = sess.run(loss_func, \r\n feed_dict = {X_PRICE_TENSOR : the_p,\r\n Q_TARGET : the_q,\r\n HOLD_W : the_w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n price_state = np.reshape(M.TEST[PRICE_TENSOR_COLS], (-1, len(PRICE_TENSOR_COLS)) )\r\n truth = np.reshape(M.TEST[M.COLS_Y], (-1, len(M.COLS_Y)) )\r\n w = np.reshape(M.TEST[M.PORT_W], (-1, len(M.PORT_W)) )\r\n \r\n test_loss, losing_mean, opt_loss = sess.run([loss_func, losing_trades_mean, opt_func], \r\n feed_dict = {X_PRICE_TENSOR : price_state,\r\n Q_TARGET : truth,\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )\r\n \r\n if test_loss < smallest_loss and i > 1000:\r\n # Add ops to save and restore all the variables.\r\n saver = tf.train.Saver()\r\n # Save the variables to disk.\r\n saver.save(sess, \"{}\\\\model.ckpt\".format(Constants.SAVE_PATH))\r\n print(\"Model saved in path: {}\".format(Constants.SAVE_PATH))\r\n M.save()\r\n smallest_loss = test_loss\r\n \r\n '''test_loss_trans = sess.run(l_func, \r\n feed_dict = {X_PRICE_TENSOR : price_state,\r\n Q_TARGET : np.reshape(M.TEST[M.COLS_Y_TRAIN], (-1, len(M.COLS_Y_TRAIN)) ),\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1} )'''\r\n \r\n train_losses.append(train_loss)\r\n test_losses.append(test_loss)\r\n transf_losses.append(losing_mean)\r\n opt_losses.append(opt_loss)\r\n \r\n fig, ax1 = plt.subplots()\r\n \r\n plot_window = 1000\r\n \r\n train_plot_data = pd.Series(train_losses[-plot_window:]).rolling(5).mean()\r\n test_plot_data = pd.Series(test_losses[-plot_window:]).rolling(5).mean()\r\n transf_plot_data = pd.Series(transf_losses[-plot_window:]).rolling(5).mean()\r\n opt_plot_data = pd.Series(opt_losses[-plot_window:]).rolling(5).mean()\r\n \r\n color = 'tab:red'\r\n ax1.set_xlabel('iteration')\r\n ax1.set_ylabel('train loss', color=color)\r\n \r\n ax1.plot(range(1, len(train_plot_data)+1), train_plot_data, color=color)\r\n ax1.tick_params(axis='y', labelcolor=color)\r\n \r\n ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:blue'\r\n ax2.set_ylabel('test loss', color=color) # we already handled the x-label with ax1\r\n ax2.plot(range(1, len(test_plot_data)+1), test_plot_data, color=color)\r\n ax2.tick_params(axis='y', labelcolor=color)\r\n \r\n ax3 = ax2.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:green'\r\n ax3.set_ylabel('L2 Loss', color=color) # we already handled the x-label with ax1\r\n ax3.plot(range(1, len(transf_plot_data)+1), transf_plot_data, color=color)\r\n ax3.tick_params(axis='y', labelcolor=color)\r\n \r\n ax4 = ax3.twinx() # instantiate a second axes that shares the same x-axis\r\n \r\n color = 'tab:orange'\r\n ax4.set_ylabel('Loss Value', color=color) # we already handled the x-label with ax1\r\n ax4.plot(range(1, len(opt_plot_data)+1), opt_plot_data, color=color)\r\n ax4.tick_params(axis='y', labelcolor=color)\r\n \r\n fig.tight_layout() # otherwise the right y-label is slightly clipped\r\n plt.show()\r\n \r\n DailyReturnTrain = 100 * (10**(-train_losses[-1] * TIMESTEP_DAYS) - 1)\r\n DailyReturnTest = 100 * (10**(-test_losses[-1] * TIMESTEP_DAYS) - 1) \r\n \r\n #DailyReturnTrain = -train_losses[-1] * TIMESTEP_DAYS\r\n #DailyReturnTest = -test_losses[-1] * TIMESTEP_DAYS\r\n \r\n print(\"Iteration: {:<10}, Train Loss: {:<.8f}, Test Loss: {:<.8f}, \"\r\n \"Test Daily Return: {}{:<.2f}%{}\".\r\n format(i,train_loss, test_loss, (COL_G if DailyReturnTest > 0 else COL_R), DailyReturnTest, COL_W))\r\n\r\n if i % 1000 == 0:\r\n gc.collect()\r\n watch.display()\r\n if i % 100000 == 0 and i > 0:\r\n \r\n '''M.TEST = D\r\n M.TEST['MARGIN_USD'] = 0\r\n M.TEST['MARGIN_BMXBTCUSD'] = 0\r\n M.TEST['MARGIN_BMXBTCUSD_S'] = 0\r\n \r\n M.TEST['reward_USD'] = 0\r\n M.TEST['reward_BMXBTCUSD'] = M.TEST['close_BMXBTCUSD'].shift(-1) / M.TEST['close_BMXBTCUSD']\r\n M.TEST['reward_BMXBTCUSD'] = M.TEST['reward_BMXBTCUSD'].apply(lambda x : math.log10(x))\r\n M.TEST['reward_BMXBTCUSD_S'] = M.TEST['reward_BMXBTCUSD'].apply(lambda x : -x)\r\n '''\r\n \r\n gc.collect()\r\n dat = M.TEST\r\n '''dat = M.data\r\n state = np.array(dat[M.COLS_X])\r\n price_state = np.array(dat[PRICE_TENSOR_COLS])\r\n w = np.array(dat[M.PORT_W])\r\n nn_outs, Q_pred = sess.run([CL_flat, Q_PREDICT], feed_dict = {\r\n X_PRICE_TENSOR : price_state.reshape(-1, len(PRICE_TENSOR_COLS) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n lst = []\r\n out_data = dat.copy()\r\n out_cols = []\r\n act_cols = []\r\n \r\n for idx in range(len(nn_outs[0])):\r\n lst = [x[idx] for x in nn_outs]\r\n c = \"NN_OUT_{}\".format(idx+1)\r\n out_data[c] = lst\r\n out_cols.append(c)\r\n \r\n for idx, action in enumerate(M.ACTIONS):\r\n print(idx, action)\r\n lst = [x[idx] for x in Q_pred]\r\n c = \"ACT_{}\".format(action)\r\n out_data[c] = lst\r\n out_cols.append(c)\r\n act_cols.append(c)\r\n if idx >= len(M.ACTIONS) / M.MULTS - 1:\r\n break\r\n \r\n out_cols += M.COLS_Y[ : len(M.COLS_Y) // M.MULTS ]\r\n out_data[out_cols].to_csv(\"Crypto Q Data.csv\",index=False)\r\n \r\n \r\n C = sklearn.cluster.KMeans(10)\r\n C.fit(out_data[:len(M.TRAIN)][act_cols])\r\n plt.plot(C.cluster_centers_, 'o')\r\n out_data['state'] = C.predict(out_data[act_cols])\r\n out_cols.append('state')\r\n out_data[out_cols].to_csv(\"Crypto Q Data.csv\",index=False)\r\n #(C.cluster_centers_ - out_data[act_cols])**2\r\n \r\n tr = out_data[:len(M.TRAIN)][act_cols]\r\n kMeansVar = [KMeans(n_clusters=k).fit(tr) for k in range(1, 20)]\r\n centroids = [X.cluster_centers_ for X in kMeansVar]\r\n k_euclid = [cdist(tr, cent) for cent in centroids]\r\n dist = [np.min(ke, axis=1) for ke in k_euclid]\r\n wcss = [sum(d**2) for d in dist]\r\n tss = sum(pdist(tr)**2)/tr.shape[0]\r\n bss = tss - wcss\r\n plt.plot(bss)\r\n plt.show()\r\n \r\n tr = out_data[:len(M.TRAIN)]\r\n \r\n Q = {}\r\n for st in set(out_data.state):\r\n for a in act_cols:\r\n Q[(st,a)] = {}\r\n for a2 in act_cols:\r\n Q[(st,a)][a2] = 0\r\n \r\n \r\n def getAction(state, epsilon=0.05, bestAct=False):\r\n if random.random() < epsilon:\r\n return random.choice((act_cols))\r\n elif bestAct == False:\r\n return np.random.choice(list(Q[state].keys()), p=softmax(list(Q[state].values())))\r\n else:\r\n best, best_v = None, 0\r\n for k,v in Q[state].items():\r\n if best is None:\r\n best = k\r\n best_v = v\r\n continue\r\n if v > best_v:\r\n best = k\r\n best_v = v\r\n return best\r\n \r\n num_iter = 0\r\n \r\n loop_forever = True\r\n while loop_forever:\r\n \r\n try:\r\n H = random.choice(act_cols)\r\n pos = random.randint(0, len(M.TRAIN)-2)\r\n current_state = tr.at[pos, \"state\"], H\r\n current_action = getAction(current_state, 0.1, False)\r\n \r\n reward = tr.ix[pos, current_action.replace(\"ACT\",\"reward\")]\r\n if H != current_action:\r\n reward += math.log10( 1 - 0.000 )\r\n \r\n new_state = tr.at[pos+1, \"state\"], current_action\r\n next_best_rw = max(Q[new_state].values())\r\n \r\n td_target = reward + 0.99 * next_best_rw\r\n td_error = td_target - Q[current_state][current_action]\r\n Q[current_state][current_action] += 0.1 * td_error\r\n \r\n num_iter += 1\r\n if num_iter % 20000 == 0:\r\n print(num_iter)\r\n #for k, v in Q[(3,\"ACT_IOTA\")].items():\r\n # print(k, v)\r\n \r\n if num_iter % 100000 == 0:\r\n \r\n H = \"ACT_USD\"\r\n tst = out_data[len(M.TRAIN):].reset_index(drop=True)\r\n raws, tcs, rewards = [], [], []\r\n \r\n for pos in range(0, len(tst)-1):\r\n \r\n current_state = tst.at[pos, \"state\"], H\r\n current_action = getAction(current_state, 0, True)\r\n \r\n reward = tr.ix[pos, current_action.replace(\"ACT\",\"reward\")]\r\n \r\n if H != current_action:\r\n tc = math.log10( 1 - 0.002 )\r\n else:\r\n tc = 0\r\n \r\n raws.append(reward)\r\n tcs.append(tc)\r\n rewards.append(reward+tc)\r\n \r\n H = current_action\r\n \r\n plt.plot(pd.Series(raws).cumsum())\r\n print(list(pd.Series(raws).cumsum())[-1])\r\n gc.collect()\r\n #plt.plot(pd.Series(rewards).cumsum())\r\n plt.show()\r\n \r\n except KeyboardInterrupt:\r\n loop_forever = False\r\n break'''\r\n \r\n print( len(dat) )\r\n M.position = 0\r\n dat[M.PORT_W] = 0\r\n dat[\"MARGIN_USD\"] = 1\r\n prevHoldings = None\r\n all_qs_out = []\r\n \r\n G = []\r\n profits, scaled_profits = [], []\r\n costs, n_switch = [], []\r\n Vs = []\r\n \r\n price_states = np.array(dat[PRICE_TENSOR_COLS])\r\n \r\n for test_pos in range(0, len(dat)-1):\r\n \r\n w = np.array(dat.loc[M.position, M.PORT_W]).reshape(-1, len(M.PORT_W))\r\n \r\n Q, V = sess.run([Q_PREDICT, Q_UNSCALED], feed_dict = {\r\n X_PRICE_TENSOR : price_states[test_pos].reshape(-1, len(PRICE_TENSOR_COLS) ),\r\n HOLD_W : w,\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n all_qs_out.append(np.round(Q[0], 3))\r\n act = M.ACTIONS[np.argmax(Q)]\r\n \r\n if USE_SIGMOID:\r\n binaries = np.apply_along_axis(lambda x : 1 if x > 0.5 else 0, 0, Q)\r\n else:\r\n binaries = [0] * len(M.ACTIONS)\r\n binaries[np.argmax(Q)] = 1\r\n binaries = np.array(binaries)\r\n \r\n profit = sum(binaries * dat.ix[M.position, M.COLS_Y])\r\n \r\n #if profits:\r\n #profit *= ( 10 ** pd.Series(profits).cumsum()[len(profits)-1] )\r\n \r\n tc = 0\r\n if prevHoldings is None:\r\n prevHoldings = binaries\r\n n_switch.append(0)\r\n else:\r\n chng = np.abs(binaries - prevHoldings)\r\n n_switch.append(chng.sum() > 0)\r\n chng = chng * math.log10(1-0.075/100)\r\n #chng = chng * -1\r\n tc = sum(chng)\r\n prevHoldings = binaries\r\n \r\n costs.append(tc)\r\n profits.append(profit)\r\n G.append(profit+tc)\r\n M.position += 1\r\n \r\n Vs.append( max(0, max(V[0]) ) )\r\n \r\n scaled_profits.append(profit * Vs[-1]**0.5 )\r\n \r\n #act = M.ACTIONS[np.random.choice(range(len(M.ACTIONS)), \r\n # p = softmax(Q[0]))]\r\n #G.append( M.stepTest(act) )\r\n \r\n \r\n \r\n for w in M.PORT_W:\r\n dat.set_value(M.position, w, 0)\r\n dat.set_value(M.position, \r\n M.PORT_W[M.ACTIONS.index(act)], \r\n 1)\r\n if test_pos % 1000 == 0 and test_pos > 0:\r\n print(\"Switch Rate: {:.2f}%\".format( 100.0 * sum(n_switch) / len(n_switch) ))\r\n plt.plot(pd.Series(profits).cumsum())\r\n #plt.plot(pd.Series(G).cumsum())\r\n plt.show()\r\n \r\n plt.plot(pd.Series(profits).cumsum())\r\n print(\"Switch Rate: {:.2f}%\".format( 100.0 * sum(n_switch) / len(n_switch) ))\r\n projections.append(pd.Series(G).cumsum())\r\n \r\n for num_p, p in enumerate(projections[::-1]):\r\n plt.plot(p)\r\n print(p[len(p)-1])\r\n if num_p >= 10:\r\n break\r\n plt.show()\r\n \r\n for idx in range(len(all_qs_out[0])):\r\n hold_data = [x[idx] for x in all_qs_out]\r\n plt.plot(pd.Series(hold_data).rolling(200).mean())\r\n #for c in M.PORT_W:\r\n # plt.plot(pd.rolling_mean(dat[c], 10))\r\n plt.legend(M.PORT_W)\r\n plt.show()\r\n i += 1\r\n watch.end(\"Gradient_Update\")\r\n all_losses.append(train_loss)\r\n rolling_window = 2000\r\n watch.start(\"rolling_loss\")\r\n rolling_loss = np.mean( all_losses[-rolling_window:] )\r\n watch.end(\"rolling_loss\")\r\n #update_drop_rt = tf.assign(tf_keep_prob, 1)\r\n #sess.run(update_drop_rt)\r\n \r\n Q_NEW = sess.run(Q_PREDICT, feed_dict = {\r\n X_PRICE_TENSOR : np.reshape(USD_PRICE_STATE,(-1, len(PRICE_TENSOR_COLS)) ),\r\n keep_p1 : 1, keep_p2 : 1, keep_p3 : 1\r\n } )\r\n \r\n print(\"Episode: {:<12}, Rolling Loss: {:.6f}, Position: {}\".format(\r\n episode, rolling_loss*10**5, init_pos))\r\n print(\"Target: {:<24}, Pred: {:<24}, Upd: {:<24}, Epsilon: {:.2f}%\".format(\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in usd_target])+\"]\",\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in Q_USD[0]])+\"]\",\r\n \"[\"+\"\".join([\"{}{:<6.3f}%\\033[0m \".format(COL_R if x < 0 else G, 100*(10**x-1)) \r\n for x in (Q_NEW-Q_USD)[0]])+\"]\",\r\n 100*epsilon))\r\n #print(episode, targetQ[0], Q1[0], (Q_NEW-Q1)[0], loss, \"{:.6f}\".format(epsilon))\r\n \r\n X_STATES, PRICE_STATES, Q_PREDS, Q_TARGETS = [], [], [], []\r\n \r\n epsilon = 10/((episode/500) + 10)\r\n epsilon = max(0.001, epsilon)\r\n epsilon = 0\r\n \r\n if episode % 500 == 0:\r\n watch.display()\r\n \r\n episode += 1\r\n","repo_name":"rendorHaevyn/Project_WinLife","sub_path":"Final Code/3. Train Network.py","file_name":"3. Train Network.py","file_ext":"py","file_size_in_byte":51940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"43504594773","text":"\"\"\" Immutable settings for <%= projectFullName %> project.\n\nCore settings configuration holds base, project independent Django settings.\nIf you need specific config, such as database of cache, use project settings.\n\n\"\"\"\nimport logging\nfrom os import path as op\n\nBASE_DIR = op.abspath(op.dirname(op.dirname(__file__)))\nPROJECT_NAME = \"<%= projectName %>\"\nENVIRONMENT_NAME = \"core\"\n\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '<%= secretKey %>'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\nTEMPLATE_DEBUG = True\nALLOWED_HOSTS = []\n\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': op.join(BASE_DIR, 'db.sqlite3'),\n 'USER': '',\n 'PASSWORD': '',\n 'TEST_CHARSET': 'utf8',\n }\n}\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n 'KEY_PREFIX': '_'.join((PROJECT_NAME, ENVIRONMENT_NAME))\n }\n}\n\nROOT_URLCONF = '<%= projectName %>.urls'\nWSGI_APPLICATION = '<%= projectName %>.wsgi.application'\nMESSAGE_STORAGE = 'django.contrib.messages.storage.cookie.CookieStorage'\n\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'UTC'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\nMEDIA_ROOT = op.join(BASE_DIR, 'media')\nSTATIC_ROOT = op.join(BASE_DIR, 'static')\nMEDIA_URL = '/media/'\nSTATIC_URL = '/static/'\n\nlogging.basicConfig(\n level=logging.DEBUG,\n format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',\n datefmt='%d.%m %H:%M:%S',\n)\n\nlogging.info(\"Core settings loaded.\")\n","repo_name":"pavlov99/generator-djangoproject","sub_path":"app/templates/project/settings/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"5051176584","text":"\"\"\"\nVariable to test the class\n\"\"\"\n\n# from sklearn.datasets import load_boston\n# X, y = load_boston(return_X_y=True)\n\n\n# # create string feature\n# feature = X[:,8].astype(str)\n\n\n\"\"\"\nThe goal of this class is to transform any categorical vector into either one-hot-encoding matrix either in a continuous vector.\n\nOne-Hot-Encoding:\n if c represents the number of different classes,\n The one-hot-encoding is build c-1 columns for these classes.\n The first class is ignored.\n \nTarget Encoding:\n The idea of target encoding has been used by CATBoost a famous gradient boosting algorithm specialised for categorical variables.\n It is proposed as one-hot-encoding my consum too much memory when the number of category is large.\n Additionnally, one-hot-encoding does not encode any specific information regarding the category unlike embeddings\n (Word2Vec for the most famous in NLP).\n \n The goal of target encoding is to use the target information to encode the categories. The value of the category will then\n be equal to the mean of response for this category. We have added a prior to the mean in order to account for low frequency\n of certain category. The prior account for 30 elements but can be changed by the user. The prior will consider the overall\n mean of the response.\n \nFrequency Encoding:\n The idea is similar to that of the target encoding. The frequency of the category is used instead of the response mean.\n \nInput:\n - any vector: it is assumed to be a categorical variable\n - the format needs to be numpy array\n \nOutputs:\n - one-hot-encoding: a numpy array containing c-1 columns where c represents the number of categories\n - target encoding or frequency encoding:\n - a continuous vector with the new categories embedding\n - a reference matrix to mappe the categories later (e.g., test data set)\n\"\"\"\n\n\n\n\"\"\"\nThe Class itself\n\"\"\"\nimport numpy as np\n\nclass CatTo:\n \n def OneHotEncoding(self, feature):\n cat = np.unique(feature) # list of the unique feature\n \n n = len(feature) # length of the vector\n one_hot = np.array([feature])\n\n for c in cat[1:]: # for all categories minus the first one!!\n \n one_hot_v = np.array([],dtype=int) # temporary array\n \n for i in range(0,n): # visit element of the array \n if feature[i] == c: # one hot creation\n one_hot_v = np.append(one_hot_v,1)\n else : one_hot_v = np.append(one_hot_v,0)\n \n one_hot = np.append(one_hot, [one_hot_v], axis = 0) # create one hot vector\n \n one_hot = np.transpose(one_hot)\n one_hot = one_hot[:,1:].astype(int)\n \n return one_hot\n \n \n \n def TargetEncoding(self, feature, y, prior = 30): # https://maxhalford.github.io/blog/target-encoding/\n cat = np.unique(feature)\n y_mean = y.mean()\n \n trg_enc = np.array(feature)\n \n cat_rec = np.array([[0,0]])\n \n for c in cat: \n sum_y = y[feature==c].sum() # class stat\n count_y = len(y[feature==c]) # class stat\n \n cat_val = (sum_y+prior*y_mean)/(count_y+prior) # encoding\n \n trg_enc[feature==c] = cat_val\n \n trg_enc = trg_enc.astype(float)\n \n cat_rec = np.concatenate((cat_rec, [[c,cat_val]]),0)\n \n cat_rec = cat_rec[1:,:]\n \n return trg_enc, cat_rec\n \n \n \n def FrequencyEncoding(self, feature, prior = 30): \n cat = np.unique(feature)\n \n n = len(feature)\n \n frq_enc = np.array(feature)\n \n cat_rec = np.array([[0,0]])\n \n for c in cat: \n\n count = len(feature[feature==c]) # class stat\n \n cat_val = (count + prior)/(n + prior) # encoding\n \n frq_enc[feature==c] = cat_val\n \n frq_enc = frq_enc.astype(float)\n \n cat_rec = np.concatenate((cat_rec, [[c,cat_val]]),0)\n \n cat_rec = cat_rec[1:,:]\n \n return frq_enc, cat_rec \n \n def Encode_by_mapping(self, feature, cat_mapping):\n enc = np.array(feature)\n \n for i in range(len(cat_mapping)):\n enc[feature==cat_mapping[i,0]] = cat_mapping[i,1]\n enc = enc.astype(float)\n \n return enc\n \n\n \n\"\"\"\nTest the class output\n\"\"\"\n \n# CatTo = CatTo()\n\n# test_OH = CatTo.OneHotEncoding(feature)\n# test_Trg, test2_Trg = CatTo.TargetEncoding(feature,y)\n# test_Frq, test2_Frq = CatTo.FrequencyEncoding(feature)\n\n# test = CatTo.Encode_by_mapping(feature, test2_Trg)\n\n#######\n# k-fold for tree and leave?\n# combine feature?\n","repo_name":"Maxime-Jo/RF-Python","sub_path":"CART_Tree/Transform_Categorical.py","file_name":"Transform_Categorical.py","file_ext":"py","file_size_in_byte":5177,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"15348666143","text":"\"\"\" File contains all of the inputs and validations for the\navailability and contact forms \"\"\"\nimport pytz\nfrom dateutil import parser\nfrom django import forms\nfrom django.forms import ModelForm\nfrom django.utils import timezone\nfrom django.core.exceptions import ValidationError\nfrom django.utils.translation import gettext_lazy as _\nfrom .models import Contact, Booking\n\n\nclass AvailabilityForm(ModelForm):\n \"\"\" Sets the inputs for the Availability form \"\"\"\n TABLE_LOCATION = (\n ('IN', 'INSIDE SEATING'),\n ('OUT', 'OUTSIDE SEATING')\n )\n\n table_location = forms.ChoiceField(\n choices=TABLE_LOCATION,\n required=True\n )\n\n first_name = forms.CharField(\n label='First Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'First Name'}),\n )\n\n def clean_first_name(self):\n \"\"\" Contains validation requirements for the first name input \"\"\"\n data = self.cleaned_data['first_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n last_name = forms.CharField(\n label='Last Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Last Name'}),\n )\n\n def clean_last_name(self):\n \"\"\" Contains validation requirements for last name input \"\"\"\n data = self.cleaned_data['last_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n people = forms.IntegerField(\n label='Number of people',\n required=True,\n widget=forms.NumberInput({'placeholder': 'Number of people'})\n )\n\n def clean_people(self):\n \"\"\" Contains validation for the people input \"\"\"\n data = self.cleaned_data['people']\n\n # Check only one number input\n if data > 8:\n raise ValidationError(_('Invalid number of people /n'\n '- please enter a number between 1 and 8'))\n\n # Check user has not entered 0\n if data <= 0:\n raise ValidationError(_('Invalid number of people /n'\n '- please enter a number between 1 and 8'))\n\n # Return the cleaned data\n return data\n\n booking_date_time_start = forms.DateTimeField(\n required=True,\n input_formats=['%d/%m/%YT%H:%M', ],\n widget=forms.DateTimeInput(attrs={'type': 'datetime-local'})\n )\n\n def clean_booking_date_time_start(self):\n \"\"\" Contains the validation requirements for the booking start time \"\"\"\n data = self.cleaned_data['booking_date_time_start']\n now = timezone.now()\n\n # Check if a date is not in the past.\n if data < now:\n raise ValidationError(_('Invalid date/time - please \\n'\n 'select a date and time in the future.'))\n\n # check that end time is within opening times\n # get time from datetime obj\n time = data.time()\n # get hour from time\n start_hour = time.hour\n\n # raise validation error if hour is bigger than or equal to closing\n if start_hour < 10:\n raise ValidationError(_('Invalid start time- restaurant \\n'\n 'opens at 10:00.'))\n\n # Return the cleaned data.\n return data\n\n booking_date_time_end = forms.DateTimeField(\n required=True,\n input_formats=['%d/%m/%YT%H:%M', ],\n widget=forms.DateTimeInput(attrs={'type': 'datetime-local'})\n )\n\n def clean_booking_date_time_end(self):\n \"\"\" Contains the validation requirements for the booking end time \"\"\"\n data = self.cleaned_data['booking_date_time_end']\n now = timezone.now()\n start_time_input = self.data['booking_date_time_start']\n # convert to datetime obj\n start_time_ntz = parser.parse(start_time_input)\n # add timezone\n t_z = pytz.timezone(\"UTC\")\n start_time = t_z.localize(start_time_ntz)\n\n # Check if a date is not in the past.\n if data < now:\n raise ValidationError(_('Invalid date/time - please select a \\n'\n 'date and time in the future.'))\n\n # check that date time end is after date time start\n if data < start_time:\n raise ValidationError(_('Invalid date/time - please make sure \\n'\n 'that booking end time is after \\n'\n 'booking start time.'))\n\n # check that slot is not longer than 2 hours\n # find differentce between start time and end time\n time_diff = data - start_time\n # find time in seconds\n tsecs = time_diff.total_seconds()\n # multiply to convert time to hours\n thrs = tsecs/(60*60)\n\n # raise validation error is difference is greater than 2 hours\n if thrs > 2:\n raise ValidationError(_('Invalid end time- maximum slot \\n'\n 'time is 2 hours.'))\n\n if thrs < 1:\n raise ValidationError(_('Invalid end time- minimum slot \\n'\n 'time is 1 hour.'))\n\n # check that end time is within opening times\n # get time from datetime obj\n time = data.time()\n # get hour from time\n end_hour = time.hour\n\n # raise validation error if hour is bigger than or equal to closing\n if end_hour >= 23:\n raise ValidationError(_('Invalid end time- restaurant closes \\n'\n 'at 23:00.'))\n\n # Return the cleaned data.\n return data\n\n additional_info = forms.CharField(\n label='Additional Info',\n required=False,\n widget=forms.Textarea(attrs={'placeholder': 'Please enter \\n'\n 'any additional information (max \\n'\n '400 characters)', 'rows': 2})\n )\n\n class Meta:\n \"\"\" Specifies to use the booking model \"\"\"\n model = Booking\n fields = ('first_name', 'last_name', 'table_location',\n 'people', 'booking_date_time_start',\n 'booking_date_time_end', 'additional_info')\n\n\nclass ContactForm(ModelForm):\n \"\"\" Contains for the information for the contact form \"\"\"\n first_name = forms.CharField(\n label='First Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'First Name'}),\n )\n\n def clean_first_name(self):\n \"\"\" Contains validation requirements for the first name input \"\"\"\n data = self.cleaned_data['first_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n last_name = forms.CharField(\n label='Last Name',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Last Name'}),\n )\n\n def clean_last_name(self):\n \"\"\" Contains validation requirements for last name input \"\"\"\n data = self.cleaned_data['last_name']\n\n if not data.isalpha():\n raise ValidationError(_('Please only enter letters'))\n\n return data\n\n contact_number = forms.IntegerField(\n label='Contact Number',\n required=True,\n widget=forms.NumberInput(attrs={'placeholder': 'Contact Number'}),\n )\n\n def clean_contact_number(self):\n \"\"\" Contains validation requirements for the contact number input \"\"\"\n data = self.cleaned_data['contact_number']\n\n # convert data to string and check length is phone number\n if not 11 >= len(str(data)) >= 12:\n raise ValidationError(_('Please enter a valid phone number'))\n\n return data\n\n email_address = forms.EmailField(\n label='Email Address',\n required=True,\n widget=forms.TextInput(attrs={'placeholder': 'Email Address'}),\n )\n\n message = forms.CharField(\n label='Message',\n required=True,\n widget=forms.Textarea(attrs={'placeholder': 'Please enter your \\n'\n 'message (max 400 characters)',\n 'rows': 3})\n )\n\n def clean_message(self):\n \"\"\" Contains validation information for the message input \"\"\"\n data = self.cleaned_data['message']\n\n # check to see that message isn't just spaces\n if data.isspace():\n raise ValidationError(_('Please enter a message'))\n\n if len(data) < 10:\n raise ValidationError(_('Minimum value 10 characters'))\n\n return data\n\n class Meta:\n \"\"\" Specifies to use contact model as a base \"\"\"\n model = Contact\n fields = ('first_name', 'last_name', 'email_address',\n 'contact_number', 'message')\n","repo_name":"ClDaly2904/restaurant-booking-system","sub_path":"restaurantbookings/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":8853,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"11565033023","text":"\"\"\"\nThird step of SIFT. Assigning orientation to keypoints.\n\"\"\"\n\nimport numpy as np\nfrom numpy import linalg as LA\n\nfrom SIFT.DoG_pyramid import gaussian_filter\n\n\ndef get_gradient(L, x, y):\n dy = L[min(L.shape[0] - 1, y + 1), x] - L[max(0, y - 1), x]\n dx = L[y, min(L.shape[1] - 1, x + 1)] - L[y, max(0, x - 1)]\n\n r = np.sqrt(dx ** 2 + dy ** 2)\n theta = (np.arctan2(dy, dx) + np.pi) * 180 / np.pi\n return r, theta\n\n\ndef fit_parabola(hist, bin_number, bin_width):\n centerval = bin_number * bin_width + bin_width / 2.\n\n if bin_number == len(hist) - 1:\n rightval = 360 + bin_width / 2.\n else:\n rightval = (bin_number + 1) * bin_width + bin_width / 2.\n\n if bin_number == 0:\n leftval = -bin_width / 2.\n else:\n leftval = (bin_number - 1) * bin_width + bin_width / 2.\n\n A = np.array([\n [centerval ** 2, centerval, 1],\n [rightval ** 2, rightval, 1],\n [leftval ** 2, leftval, 1]])\n b = np.array([\n hist[bin_number],\n hist[(bin_number + 1) % len(hist)],\n hist[(bin_number - 1) % len(hist)]])\n\n x = LA.lstsq(A, b, rcond=None)[0]\n if x[0] == 0: x[0] = 1e-6\n return -x[1] / (2 * x[0])\n\n\ndef assign_orientation(keypoints, octave, num_bins=36):\n new_keypoints = []\n bin_width = 360 // num_bins\n\n for keypoint in keypoints:\n cx, cy, s = int(keypoint[0]), int(keypoint[1]), int(keypoint[2])\n s = np.clip(s, 0, octave.shape[2] - 1)\n\n sigma = keypoint[2] * 1.5\n w = int(2 * np.ceil(sigma) + 1)\n kernel = gaussian_filter(sigma)\n\n L = octave[..., s]\n hist = np.zeros(num_bins, dtype=np.float32)\n\n for oy in range(-w, w + 1):\n for ox in range(-w, w + 1):\n x, y = cx + ox, cy + oy\n\n if x < 0 or x > octave.shape[1] - 1:\n continue\n elif y < 0 or y > octave.shape[0] - 1:\n continue\n\n m, theta = get_gradient(L, x, y)\n weight = kernel[oy + w, ox + w] * m\n\n bin = int(np.floor(theta) // (360 // num_bins))\n hist[bin] += weight\n\n max_bin = np.argmax(hist)\n new_keypoints.append([keypoint[0], keypoint[1], keypoint[2], fit_parabola(hist, max_bin, bin_width)])\n\n max_val = np.max(hist)\n for binno, val in enumerate(hist):\n if binno == max_bin: continue\n\n if .8 * max_val <= val:\n new_keypoints.append([keypoint[0], keypoint[1], keypoint[2], fit_parabola(hist, binno, bin_width)])\n\n return np.array(new_keypoints)\n","repo_name":"oxxford/feature_descriptors","sub_path":"SIFT/orientation.py","file_name":"orientation.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"73454972749","text":"import pygame\n\nfirst_run = True\nSCREEN_CLR = (75, 75, 75)\nBLACK = (0, 0, 0)\nBLUE = (0, 0, 255)\nRED = (255, 0, 0)\nWHITE = (255, 255, 255)\n\n\ndef draw_main_menu(screen):\n screen.fill(WHITE)\n pygame.font.init()\n title_font = pygame.font.SysFont(\"Comic Sans MS\", 30)\n textsurface = title_font.render(\"Tic-Tac-Toe\", False, (0, 0, 0))\n screen.blit(textsurface, (70, 100))\n button_font = pygame.font.SysFont(\"Comic Sans MS\", 15)\n buttonsurface = button_font.render(\"Start\", False, (0, 0, 0))\n pygame.draw.rect(screen, SCREEN_CLR, (30, 250, 80, 40), 0)\n screen.blit(buttonsurface, (50, 260))\n pygame.draw.rect(screen, SCREEN_CLR, (150, 250, 80, 40), 0)\n buttonsurface2 = button_font.render(\"Optionen\", False, (0, 0, 0))\n screen.blit(buttonsurface2, (160, 260))\n\n\ndef draw_options(screen):\n screen.fill(WHITE)\n pygame.draw.rect(screen, SCREEN_CLR, (250, 250, 40, 40))\n button_font = pygame.font.SysFont(\"Comic Sans MS\", 30)\n button_surface = button_font.render(\"<-\", False, (0, 0, 0))\n screen.blit(button_surface, (260, 247))\n pygame.draw.rect(screen, SCREEN_CLR, (20, 30, 80, 40), 0)\n button_surface2 = button_font.render(\"KI\", False, (0, 0, 0))\n screen.blit(button_surface2, (39, 28))\n options = True\n return options\n\n\ndef draw_ki(screen, ki_choice):\n if ki_choice is 0:\n pygame.draw.circle(screen, BLUE, (50, 50), 45, 3)\n if ki_choice is 1:\n pygame.draw.circle(screen, BLUE, (150, 50), 50, 5)\n if ki_choice is 2:\n pygame.draw.circle(screen, BLUE, (250, 50), 50, 5)\n if ki_choice is 3:\n pygame.draw.circle(screen, BLUE, (50, 150), 50, 5)\n if ki_choice is 4:\n pygame.draw.circle(screen, BLUE, (150, 150), 50, 5)\n if ki_choice is 5:\n pygame.draw.circle(screen, BLUE, (250, 150), 50, 5)\n if ki_choice is 6:\n pygame.draw.circle(screen, BLUE, (50, 250), 50, 5)\n if ki_choice is 7:\n pygame.draw.circle(screen, BLUE, (150, 250), 50, 5)\n if ki_choice is 8:\n pygame.draw.circle(screen, BLUE, (250, 250), 50, 5)\n\n\ndef draw_game_field(screen):\n screen.fill(SCREEN_CLR)\n pygame.draw.line(screen, BLACK, (0, 100), (300, 100), 5)\n pygame.draw.line(screen, BLACK, (0, 200), (300, 200), 5)\n pygame.draw.line(screen, BLACK, (100, 0), (100, 300), 5)\n pygame.draw.line(screen, BLACK, (200, 0), (200, 300), 5)\n","repo_name":"ndamb/tictactoe","sub_path":"drawing_stuff.py","file_name":"drawing_stuff.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"88519418","text":"import math\nimport random\nimport time\n\n\ndef get_function(x):\n return math.sin(math.pow(x, 2)/2)/math.log((x+4), 2)\n\n\ndef step_random_solution(x):\n step_sizes = [0.1, 0.01, -0.1, -0.01]\n size = random.sample(step_sizes, 1)[0]\n x_new = x + size\n return x_new, get_function(x_new)\n\n\ndef valid(a):\n return 0 <= a <= 10\n\n\ndef evaluate_p(x_new, x, temp):\n prob = math.pow(math.e, -(get_function(x) - get_function(x_new))/temp)\n #print('probability of accepting ', get_function(x_new), 'over current y which is ', get_function(x), ' is ', prob)\n pseudo_boltzmann = [True]*(int(prob*10000)) + [False]*(int((1-prob)*10000))\n accept_value = random.choice(pseudo_boltzmann)\n return accept_value\n\n\ndef cool(temperature, alpha):\n return alpha*temperature\n\n\ndef run_simulated_annealing(x_start, t_start, alpha):\n t = t_start\n x_current = x_start\n x_max = x_current\n y_current = get_function(x_current)\n y_max = get_function(x_current)\n #print('x-start', x_start, 'y-start : ', y_max)\n steps = 0\n while t > 0.00001:\n #print('T = ', t)\n rand_x, rand_y = step_random_solution(x_current)\n if valid(rand_x):\n #print('x: ', rand_x, 'y: ', rand_y)\n if rand_y > y_max:\n x_current = rand_x\n y_current = rand_y\n y_max = rand_y\n x_max = x_current\n #print('new max')\n if rand_y > y_current:\n x_current = rand_x\n y_current = rand_y\n #print('greater')\n else:\n if evaluate_p(rand_x, x_current, t):\n x_current = rand_x\n y_current = rand_y\n steps = steps + 1\n #print('less but accepted')\n #else:\n #print('less, not accepted')\n #else:\n #print('invalid x: ', rand_x, 'out of range')\n t = cool(t, alpha)\n\n return x_max, y_max, steps\n\n\ndef main():\n starting_temps = [100000]\n cooling_factors = [0.999]\n starting_points = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n for point in starting_points:\n for temp in starting_temps:\n for factor in cooling_factors:\n t0 = time.time()\n x, y, steps = run_simulated_annealing(point, temp, factor)\n te = time.time()\n diff = te - t0\n print(temp, ',', factor, ',', y, ',', diff, ',', steps, ',')\n\nmain()","repo_name":"metchel/COMP424","sub_path":"Assignment1/Q3b-simulated_annealing.py","file_name":"Q3b-simulated_annealing.py","file_ext":"py","file_size_in_byte":2485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27622918168","text":"from typing import List\nimport bisect\nfrom collections import Counter\n\n\nclass Solution:\n def numSmallerByFrequency(self, queries: List[str], words: List[str]) -> List[int]:\n\n wf = []\n for w in words:\n cnt = Counter(w)\n wf.append(cnt[min(w)])\n wf.sort()\n\n qf = []\n for q in queries:\n cnt = Counter(q)\n qf.append(cnt[min(q)])\n\n tot = 0\n res = []\n for v in qf:\n idx = bisect.bisect_right(wf, v)\n res.append(len(wf) - idx)\n return res\n\n\ns = Solution()\nprint(s.numSmallerByFrequency([\"bbb\", \"cc\"], [\"a\", \"aa\", \"aaa\", \"aaaa\"]))\n","repo_name":"srinathalla/python","sub_path":"algo/arrays/leet/medium/numSmallerByFreq.py","file_name":"numSmallerByFreq.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34234709437","text":"from ops import launch\n\n\ndef dist(planes, a, b):\n return abs(planes[a] - planes[b]) * 20\n\n\ndef find_letter(N, planes):\n if N <= 2: raise Exception()\n letter = None\n letter_save = None\n for i in range(1, N - 1):\n distance_saved = (dist(planes, i - 1, i) + dist(planes, i, i + 1)) - dist(planes, i - 1, i + 1)\n if letter is None or distance_saved > letter_save:\n letter = i\n letter_save = distance_saved\n return letter\n\n\ndef task(input, output):\n N = int(input.readline())\n planes = [int(x) for x in input.readline().split(' ')]\n assert N == len(planes)\n output.write(str(find_letter(N, planes)) + '\\n')\n\n\nlaunch(task, 'C-lehky.txt')\n# launch(task, None)\n","repo_name":"Kripner/Kasiopea","sub_path":"C/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"22046053297","text":"PROJECT_ID = \"aaet-geoscience-dev\"\r\n# The tmp folder is for lasio I/O purposes\r\nDATA_PATH = \"/home/airflow/gcs/data/tmp\"\r\n\r\n# Credential JSON key for accessing other projects\r\n# CREDENTIALS_JSON = \"gs://aaet_zexuan/flow/keys/composer_las_merge.json\"\r\nCREDENTIALS_JSON = \"keys/composer_las_merge.json\"\r\n\r\n# Bucket name for merged las files and spliced las files\r\nBUCKET_LAS_MERGE = \"las_merged\"\r\nBUCKET_LAS_SPLICE = \"us-central1-lithos-dev-94beb3d4-bucket\"\r\n\r\n# las_splice.py output to the composer data folder, as input of logqc\r\nCOMPOSER_FOLDER = \"data/logqc_landing\"\r\nTMP_FOLDER = \"data/tmp\"\r\n# for GCP web UI and Big Query Job Status Report\r\nBUCKET_JOB = \"log_splice_tool_jobs\"\r\nBIGQUERY_DATASET_ID = \"urc_jobs\"\r\nBIGQUERY_TABLE_ID = \"jobs\"\r\n\r\n# Workflow type\r\ntpt_workflow_type = \"tpt\"\r\nlogsplice_workflow_type = \"logsplice\"\r\nlogqc_workflow_type = \"logqc\"\r\ngeomech_workflow_type = \"geomech\"\r\n\r\n# Number of processors for las_merge_MP (multiprocessing).\r\nN_PROCESSORS = 16\r\n\r\n# The window size for moving average, e.g. 11 means the window covers a\r\n# point and 5 adjacent points on both sides\r\nMOVING_AVG_WINDOW_SIZE = 11\r\n\r\n# Default value for missing data, usually it is either -999.25 or -999.0\r\nMISSING = -999.0\r\n\r\n# COL_DICT: a dictionary of aliased curve names for log splicing. keys correspond to measurements\r\n# (e.g., 'density', 'gamma', 'resistivity', etc.),\r\n# and each value is a list of aliased column names that could potentially correspond\r\n# to those measurements. Each key is the aliased curve name before splicing,\r\n# each key's value is the standard curve name after splicing.\r\nCOL_DICT = {\r\n # Caliper\r\n \"cal\": [\"CAL\", \"CALI\", \"CALX\", \"HCAL\", \"TGS_CALX\", \"RAW_CALX\"],\r\n # Compressional Sonic Slowness\r\n \"dtc\": [\"DT\", \"DT24\", \"DTC\", 'TGS_DT', \"TGS_DTC\", \"RAW_DT\", \"RAW_DTC\"],\r\n # Deep Resistivity\r\n # 'rdeep' includes 'rdeep_ltrl' (laterolog), 'rdeep_indct' (induction), 'rdeep_unknown'.\r\n # A final 'rdeep' will be generated\r\n # with an additional 'rdeep_type' curve to denote the log type.\r\n \"rdeep\": ['ILT90', 'LLD', 'RDEEP', 'RES', 'RES_DEEP', 'AHT90', 'AT90', 'ILD', 'ILT90', 'LLD', 'ILO90', 'ILF90', 'LLMD'],\r\n # Density (Bulk)\r\n \"rhob\": [\"DEN\", \"RHOB\", \"RHOZ\", \"ZDEN\", \"ZDNC\", \"TGS_RHOB\", 'RAW_RHOB'],\r\n # Density (Correction)\r\n \"drho\": [\"DRHO\", \"HDRA\", \"ZCOR\"],\r\n # Gamma Ray\r\n \"gr\": [\"APC_GR_NRM\", \"GAMM\", \"GR\", \"GR_R\", \"GRR\", 'SGR', 'SGRR', 'CGR'],\r\n # Neutron Porosity\r\n \"nphil\": [\"CNCF\", \"NEU\", \"NPOR\", \"NPHI\", \"NPHIL\", \"TNPH\", 'TGS_NPHI', 'NPHI_LS', 'TNPH_LS', 'RAW_NPHI'],\r\n # Photoelectric effect\r\n \"pe\": [\"PE\", \"PEF\", \"PEFZ\", 'TGS_PE', 'RAW_PE'],\r\n}\r\n\r\n# LDD is laterolog\r\n# The rest are inductions\r\n# RDEEP, RES, RES_DEEP are of unknown origin\r\n# __log_type_rdeep = [log_type_enum.induction, #AHT90\r\n# log_type_enum.induction, #AT90\r\n# log_type_enum.induction, #ILD\r\n# log_type_enum.induction, #ILT90\r\n# log_type_enum.laterolog, #LLD\r\n# log_type_enum.induction, #M2R9\r\n# log_type_enum.unknown, #RDEEP\r\n# log_type_enum.unknown, #RES\r\n# log_type_enum.unknown] #RES_DEEP\r\n\r\nRDEEP_TYPE_LIST = [\"rdeep_ltrl\", \"rdeep_indct\", \"rdeep_unknown\"]\r\nRDEEP_TYPE_DICT = {\"rdeep_ltrl\": 1, \"rdeep_indct\": 2, \"rdeep_unknown\": 3}\r\n\r\n# curve description dictionary\r\nCURVE_DESC = {\r\n \"DEPT\": \"Depth\",\r\n \"CAL\": \"Caliper\",\r\n \"DRHO\": \"Density Correction\",\r\n \"DTC\": \"Compressional Wave Slowness\",\r\n \"DTS\": \"Shear Wave Slowness\",\r\n \"GR\": \"Gamma Ray\",\r\n \"NPHI\": \"Neutron Porosity\",\r\n \"NPHIL\": \"Neutron Porosity\",\r\n \"PE\": \"Photoelectric Effect\",\r\n \"RDEEP\": \"Deep Resistivity\",\r\n \"RDEEP_LTRL\": \"Laterolog Resistivity\",\r\n \"RDEEP_INDCT\": \"Induction Resistivity\",\r\n \"RDEEP_UNKNOWN\": \"Unknown Resistivity (Laterolog or Induction)\",\r\n \"RDEEP_TYPE\": \"RDEEP Type 1:Laterolog 2:Induction 3:Unknown\",\r\n \"RHOB\": \"Bulk Density\",\r\n \"RUGOSITY\": \"Borehole Rugosity\",\r\n \"RUGOSITY_BHF\": \"Rugosity Bad Hole Flag\",\r\n \"DRHO_BHF\": \"Density Correction Bad Hole Flag\",\r\n \"DTC_BHF\": \"Sonic Bad Hole Flag\",\r\n \"GR_BHF\": \"Gamma Ray Bad Hole Flag\",\r\n \"NPHIL_BHF\": \"Neutron Bad Hole Flag\",\r\n \"RHOB_BHF\": \"Density Bad Hole Flag\",\r\n \"LOG_RDEEP_BHF\": \"Resistivity Bad Hole Flag\",\r\n \"PE_BHF\": \"PE Bad Hole Flag\",\r\n \"RHOB_MCF\": \"Density Corrected from Multiwell Flag\",\r\n \"RHOB_SYN\": \"Density Estimation from Ensemble of Learners\",\r\n \"NPHI_MCF\": \"Neutron Corrected from Multiwell Flag\",\r\n \"NPHI_SYN\": \"Neutron Estimation from Ensemble of Learners\",\r\n \"DTC_MCF\": \"Sonic Corrected from Multiwell Flag\",\r\n \"DTC_SYN\": \"Sonic Estimation from Ensemble of Learners\",\r\n \"PE_MCF\": \"PE Corrected from Multiwell Flag\",\r\n \"PE_SYN\": \"PE Estimation from Ensemble of Learners\",\r\n \"RHOB_NCF\": \"Density No Correction Flag\",\r\n \"RHOB_CORR\": \"Density Corrected\",\r\n \"NPHI_NCF\": \"Neutron No Correction Flag\",\r\n \"NPHI_CORR\": \"Neutron Corrected\",\r\n \"DTC_NCF\": \"Sonic No Correction Flag\",\r\n \"DTC_CORR\": \"Sonic Corrected\",\r\n \"PE_NCF\": \"PE No Correction Flag\",\r\n \"PE_CORR\": \"PE Corrected\"\r\n}\r\n","repo_name":"dongzexuan/spark_demo","sub_path":"config/log_splice_config.py","file_name":"log_splice_config.py","file_ext":"py","file_size_in_byte":5193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19419814273","text":"#!/usr/bin/env python\n# -*- coding: utf-H -*-\n# @Time : 2020/1/12 下午11:10\n# @Author : alex\n# @File : BubbleSortCode.py\n# @Project : Algorithm\n# @Software : PyCharm\n\n# 在一个数组中，每一个数左边比当前数小的数累加起来，叫做这个数组的小和。\n\n\nimport copy\nimport random\n\n\ndef small_sum1(arr: list):\n return mergeSort(arr, 0, len(arr) - 1)\n\n\ndef mergeSort(arr: list, left: int, right: int):\n if left == right:\n return 0\n mid = left + ((right - left) >> 1)\n return mergeSort(arr, left, mid) + mergeSort(arr, mid + 1, right) + merge(arr, left, mid, right)\n\n\ndef merge(arr: list, left: int, mid: int, right: int):\n help_list = []\n p1, p2, res = left, mid + 1, 0\n\n while p1 <= mid and p2 <= right:\n if arr[p1] < arr[p2]:\n res += arr[p1] * (right - p2 + 1)\n help_list.append(arr[p1])\n p1 += 1\n else:\n help_list.append(arr[p2])\n p2 += 1\n\n while p1 <= mid:\n help_list.append(arr[p1])\n p1 += 1\n\n while p2 <= right:\n help_list.append(arr[p2])\n p2 += 1\n\n for j in range(len(help_list)):\n arr[left + j] = help_list[j]\n\n return res\n\n\ndef small_sum2(arr: list):\n res = 0\n for temp in range(1, len(arr)):\n for j in range(0, temp):\n res += arr[j] if arr[j] < arr[temp] else 0\n return res\n\n\ndef generate_random_test(max_size: int, max_value: int):\n result = []\n size = random.randint(0, max_size + 1)\n for i in range(size):\n result.append((int((max_value + 1) * random.random())) - int((max_value * random.random())))\n return result\n\n\nif __name__ == '__main__':\n test_num = 10\n max_size = 10\n max_value = 100\n flag = True\n\n for i in range(test_num):\n list1 = generate_random_test(max_size, max_value)\n list2 = copy.deepcopy(list1)\n list3 = copy.deepcopy(list1)\n res1 = small_sum1(list2)\n res2 = small_sum2(list3)\n if res1 != res2:\n flag = False\n print(list1)\n print(list2)\n print(list3)\n break\n\n print(\"Nice\" if flag else \"Fuck\")\n","repo_name":"koking0/Algorithm","sub_path":"算法与数据结构之美/Algorithm/Sort/MergeSort/小和问题/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"82"} +{"seq_id":"18742208574","text":"import json\nimport os\n\nimport common\nfrom tools.consolidated_reports import query_reports as query_reports\nfrom io import open\n\nDRC_BUCKET_PATH = query_reports.get_drc_bucket_path()\nDATASOURCES_PATH = 'curation_report/data/datasources.json'\n\n\ndef get_hpo_id(p):\n rel_path = p[len(DRC_BUCKET_PATH):]\n return rel_path[:rel_path.index('/')]\n\n\ndef get_report_path(p, hpo_id):\n return p.replace('datasources.json', hpo_id)\n\n\ndef get_submission_name(p):\n parts = p.split('/')\n for i in range(0, len(parts)):\n part = parts[i]\n if part == 'curation_report':\n return parts[i - 1]\n raise RuntimeError('Invalid submission path: %s' % p)\n\n\ndef transform_bq_list(uploads):\n \"\"\"\n Get paths to all most recent report files\n :param uploads: object representing loaded json data\n :return: a list of dictionaries which contains parsed data\n \"\"\"\n results = []\n for upload in uploads:\n dte, p = upload['upload_timestamp'], upload['file_path']\n hpo_id = get_hpo_id(p)\n report_path = p.replace('datasources.json', hpo_id)\n name = get_submission_name(p)\n result = {\n 'hpo_id': hpo_id,\n 'updated': dte,\n 'report_path': report_path,\n 'name': name\n }\n results.append(result)\n return results\n\n\ndef read_text(p):\n try:\n with open(p, 'r') as fp:\n return fp.read()\n except IOError as err:\n print(err)\n\n\ndef write_text(p, t):\n try:\n with open(p, 'w') as fp:\n fp.write(t)\n except IOError as err:\n print(err)\n\n\ndef write_json(pth, obj):\n try:\n with open(pth, 'w') as fp:\n json.dump(obj, fp, indent=4)\n except Exception as err:\n print(err)\n\n\ndef update_source_name(rpt):\n pth = 'curation_report/data/%s/person.json' % rpt['hpo_id']\n try:\n txt = read_text(pth).replace('my_source', rpt['hpo_id'])\n except Exception as err:\n txt = err\n\n print('Updating source name in %s...' % pth)\n write_text(pth, txt)\n\n\ndef datasource_for(rpt):\n return {'folder': rpt['hpo_id'], 'cdmVersion': 5, 'name': rpt['hpo_id']}\n\n\ndef update_datasources(rpts):\n datasources = []\n for rpt in rpts:\n datasource = datasource_for(rpt)\n datasources.append(datasource)\n obj = {'datasources': datasources}\n print('Saving datasources to %s...' % DATASOURCES_PATH)\n write_json(DATASOURCES_PATH, obj)\n\n\ndef download_report(path_dict):\n \"\"\"\n Download most recent report files\n :param path_dict: A Dictionary Which containing details of bucket parsed from the path.\n :return: None\n \"\"\"\n # Save it to curation_report/data/\n cdir = os.getcwd()\n try:\n os.mkdir('%s/curation_report/data' % cdir)\n\n except OSError:\n # log the exception but keep moving because it doesn't hurt your code.\n print(\"The file %s/result_data/%s already exists\", cdir,\n path_dict['hpo_id'])\n cmd = 'gsutil -m cp -r %s ./curation_report/data/' % (\n path_dict['report_path'])\n print('Downloading %s rpt with cmd: `%s`...' % (path_dict['hpo_id'], cmd))\n os.system(cmd)\n\n\ndef main():\n bq_list = query_reports.get_most_recent(\n report_for=common.REPORT_FOR_ACHILLES)\n reports = transform_bq_list(bq_list)\n for report in reports:\n print('processing report: \\n %s\\n...' % json.dumps(report, indent=4))\n download_report(report)\n update_source_name(report)\n update_datasources(reports)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"all-of-us/curation","sub_path":"data_steward/tools/consolidated_reports/get_all_achilles_reports.py","file_name":"get_all_achilles_reports.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"82"} +{"seq_id":"20525237411","text":"class FigureSetting:\n default = {\n \"dpi\": 300,\n \"bbox_inches\": \"tight\",\n \"pad_inches\": 0,\n }\n png = {\n **default,\n \"format\": \"png\",\n }\n tiff = {\n **default,\n \"format\": \"tiff\",\n }\n gif = {\n **default,\n \"format\": \"gif\",\n \"save_all\": True,\n }\n monochrome = {\n \"bbox_inches\": \"tight\",\n \"pad_inches\": 0,\n }\n monochrome_png = {\n **monochrome,\n \"format\": \"png\",\n }\n monochrome_tiff = {\n **monochrome,\n \"format\": \"tiff\",\n }\n\n\nclass PltPlotParameter:\n # area\n default = {\n \"figsize\": (8, 8),\n \"edgecolor\": \"black\",\n \"linewidth\": 0.7,\n }\n whole_area = {\n **default,\n \"facecolor\": \"white\",\n }\n scope_area = {\n **default,\n \"linewidth\": 0.3,\n \"facecolor\": \"HoneyDew\",\n }\n watershed = {\n **scope_area,\n \"edgecolor\": \"blue\",\n }\n catchment = {\n **watershed,\n \"facecolor\": \"blue\",\n }\n\n\nclass PltConfig:\n rc_config = {\n \"font.family\": \"Times New Roman\",\n \"font.size\": 16,\n \"figure.autolayout\": True,\n \"xtick.major.size\": 3,\n \"ytick.major.size\": 3,\n \"xtick.major.width\": 1,\n \"ytick.major.width\": 1,\n \"axes.linewidth\": 1,\n }\n","repo_name":"harukimine/research-target-area","sub_path":"src/common/figure_setting.py","file_name":"figure_setting.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"34433024392","text":"from flask import render_template, request, flash, redirect, url_for\n\nfrom ..app import app, login, db\nfrom ..modeles.donnees import Obelisque, Personne, Erige, Localisation, Authorship\nfrom ..modeles.utilisateurs import User\nfrom ..constantes import RESULTATS_PAR_PAGE\nfrom flask_login import login_user, current_user, logout_user, login_required\n# On importe or_ pour pouvoir filtrer des résultats sur de multiples éléments\nfrom sqlalchemy import or_\n\n\n# Page d'accueil\n@app.route(\"/\")\ndef accueil():\n \"\"\" Route vers la page d'accueil de l'application.\n :returns: template accueil.html \"\"\"\n\n erige = Erige.query.all()\n return render_template(\"pages/accueil.html\", erige=erige)\n\n\n# Page redirigeant vers les ajouts de pages\n@app.route(\"/add\")\ndef add():\n \"\"\" Route vers la page Contribuer, permettant un accès rapide de l'intégralité des formulaires d'ajouts.\n :returns: template add.html \"\"\"\n\n return render_template(\"pages/add.html\")\n\n\n# Routes vers les trois éléments principaux de la base\n\n# Les obélisques\n@app.route(\"/obelisque/\")\ndef obelisque(obelisque_id):\n \"\"\" Route vers une page obélisque.\n :param obelisque_id: identifiant de l'obélisque à afficher\n :type obelisque_id: integer\n :returns: template obelisque.html \"\"\"\n\n obelisque = Obelisque.query.filter(Obelisque.obelisque_id == obelisque_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_obelisque == obelisque_id)\n return render_template(\"pages/obelisque.html\", obelisque=obelisque, erige=erige)\n\n\n# Les personnes (commanditaires)\n@app.route(\"/personne/\")\ndef personne(personne_id):\n \"\"\" Route vers une page commanditaire.\n :param personne_id: identifiant du commanditaire à afficher\n :type personne_id: integer\n :returns: template personne.html \"\"\"\n\n personne = Personne.query.filter(Personne.personne_id == personne_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_personne == personne_id).order_by(Erige.erige_date)\n return render_template(\"pages/personne.html\", personne=personne, erige=erige)\n\n\n# Les localisations\n@app.route(\"/lieu/\")\ndef localisation(localisation_id):\n \"\"\" Route vers une page lieu.\n :param localisation_id: identifiant du lieu à afficher\n :type localisation_id: integer\n :returns: template lieu.html \"\"\"\n\n localisation = Localisation.query.filter(Localisation.localisation_id == localisation_id).first_or_404()\n erige = Erige.query.filter(Erige.erige_id_localisation == localisation_id).order_by(Erige.erige_date)\n return render_template(\"pages/lieu.html\", localisation=localisation, erige=erige)\n\n\n# Les pages d'index\n\n# L'index recensant l'intégralité des obélisques\n@app.route(\"/index_obelisques\")\ndef index_obelisques():\n \"\"\" Route vers l'index général des obélisques.\n :returns: template index_obelisques.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.order_by(Obelisque.obelisque_nom).paginate(page=page, per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques.html\", resultats=resultats)\n\n\n# L'index des obélisques égyptiens\n@app.route(\"/index_obelisques_egyptiens\")\ndef index_obelisques_egyptiens():\n \"\"\" Route vers l'index des obélisques égyptiens.\n :returns: template index_obelisques_egyptiens.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.filter(Obelisque.obelisque_type_commande == \"Égyptienne\").paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques_egyptiens.html\", resultats=resultats)\n\n\n# L'index des obélisques romains\n@app.route(\"/index_obelisques_romains\")\ndef index_obelisques_romains():\n \"\"\" Route vers l'index des obélisques romains.\n :returns: template index_obelisques_romains.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Obelisque.query.filter(Obelisque.obelisque_type_commande == \"Romaine\").paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_obelisques_romains.html\", resultats=resultats)\n\n\n# L'index des personnes (commanditaires)\n@app.route(\"/index_personnes\")\ndef index_personnes():\n \"\"\" Route vers l'index des commanditaires.\n :returns: template index_personnes.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Personne.query.order_by(Personne.personne_nom).paginate(page=page, per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_personnes.html\", resultats=resultats)\n\n\n# L'index des lieux (localisations)\n@app.route(\"/index_lieux\")\ndef index_lieux():\n \"\"\" Route vers l'index des lieux.\n :returns: template index_lieux.html \"\"\"\n\n page = request.args.get(\"page\", 1)\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n resultats = Localisation.query.order_by(Localisation.localisation_lieu).paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n return render_template(\"pages/index_lieux.html\", resultats=resultats)\n\n\n# Faire une recherche plein texte sur les pages obelisque.html\n@app.route(\"/recherche\")\ndef recherche():\n \"\"\" Route pour la recherche plein texte sur les pages obelisque.html.\n :returns: template recherche.html \"\"\"\n\n motclef = request.args.get(\"keyword\", None)\n page = request.args.get(\"page\", 1)\n\n if isinstance(page, str) and page.isdigit():\n page = int(page)\n else:\n page = 1\n\n # On crée une liste vide de résultat (qui restera vide par défaut\n # si on n'a pas de mot clé)\n resultats = []\n\n # On fait de même pour le titre de la page\n titre = \"Recherche\"\n if motclef:\n resultats = Obelisque.query.filter(or_(\n Obelisque.obelisque_nom.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_materiau.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_type_commande.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_notice.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_inscription_latine.like(\"%{}%\".format(motclef)),\n Obelisque.obelisque_inscription_latine_traduite.like(\"%{}%\".format(motclef)))).paginate(page=page,\n per_page=RESULTATS_PAR_PAGE)\n\n return render_template(\n \"pages/recherche.html\",\n resultats=resultats,\n titre=titre,\n keyword=motclef\n )\n\n\n# Gestion des comptes utilisateurs\n\n# Création d'un compte : l'inscription\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef inscription():\n \"\"\" Route vers le formulaire d'inscription.\n :returns: template inscription.html \"\"\"\n\n # Si on est en POST, cela veut dire que le formulaire a été envoyé\n if request.method == \"POST\":\n statut, donnees = User.creer(\n login=request.form.get(\"login\", None),\n email=request.form.get(\"email\", None),\n nom=request.form.get(\"nom\", None),\n motdepasse=request.form.get(\"motdepasse\", None)\n )\n if statut is True:\n flash(\"Enregistrement effectué. Identifiez-vous maintenant\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Les erreurs suivantes ont été rencontrées : \" + \",\".join(donnees), \"error\")\n return render_template(\"pages/inscription.html\")\n else:\n return render_template(\"pages/inscription.html\")\n\n\n# Connexion à un compte existant\n@app.route(\"/connexion\", methods=[\"POST\", \"GET\"])\ndef connexion():\n \"\"\" Route vers la page de connexion.\n :returns: template connexion.html \"\"\"\n\n if current_user.is_authenticated is True:\n flash(\"Vous êtes déjà connecté-e\", \"info\")\n return redirect(\"/\")\n # Si on est en POST, cela veut dire que le formulaire a été envoyé\n if request.method == \"POST\":\n utilisateur = User.identification(\n login=request.form.get(\"login\", None),\n motdepasse=request.form.get(\"motdepasse\", None)\n )\n if utilisateur:\n flash(\"Connexion effectuée\", \"success\")\n login_user(utilisateur)\n return redirect(\"/\")\n else:\n flash(\"Les identifiants n'ont pas été reconnus\", \"error\")\n\n return render_template(\"pages/connexion.html\")\n\n\nlogin.login_view = 'connexion'\n\n\n# Déconnexion\n@app.route(\"/deconnexion\", methods=[\"POST\", \"GET\"])\ndef deconnexion():\n \"\"\" Route de redirection après la déconnexion.\n :returns: redirection vers l'accueil \"\"\"\n\n if current_user.is_authenticated is True:\n logout_user()\n flash(\"Vous êtes déconnecté-e\", \"info\")\n return redirect(\"/\")\n\n\n# Gérer les pages d'erreurs\n\n# Erreur 404\n@app.errorhandler(404)\ndef not_found_error(error):\n \"\"\" Route en cas d'erreur 404.\n :returns: template erreur_404.html \"\"\"\n\n return render_template('erreurs/erreur_404.html'), 404\n\n\n# Erreur 500\n@app.errorhandler(500)\ndef internal_error(error):\n \"\"\" Route en cas d'erreur 500.\n :returns: template erreur_500.html \"\"\"\n\n return render_template('error/erreur_500.html'), 500\n\n\n# Ajouter, modifier ou supprimer une page\n\n# Ajouter une page\n\n# Ajouter une page obélisque\n\n@app.route(\"/obelisque/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef obelisque_add():\n \"\"\" Route pour le formulaire d'ajout d'un obélisque.\n :returns: template obelisque_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Obelisque.obelisque_add(\n obelisque_add_nom=request.form.get(\"obelisque_add_nom\", None),\n obelisque_add_hauteur=request.form.get(\"obelisque_add_hauteur\", None),\n obelisque_add_hauteur_avec_base=request.form.get(\"obelisque_add_hauteur_avec_base\", None),\n obelisque_add_materiau=request.form.get(\"obelisque_add_materiau\", None),\n obelisque_add_type_commande=request.form.get(\"obelisque_add_type_commande\", None),\n obelisque_add_notice=request.form.get(\"obelisque_add_notice\", None),\n obelisque_add_inscription_latine=request.form.get(\"obelisque_add_inscription_latine\", None),\n obelisque_add_inscription_latine_traduite=request.form.get(\"obelisque_add_inscription_latine_traduite\",\n None),\n obelisque_add_bibliographie=request.form.get(\"obelisque_add_bibliographie\", None),\n obelisque_add_image_url=request.form.get(\"obelisque_add_image_url\", None),\n obelisque_add_image_auteur=request.form.get(\"obelisque_add_image_auteur\", None),\n obelisque_add_image_licence=request.form.get(\"obelisque_add_image_licence\", None),\n obelisque_add_image_licence_url=request.form.get(\"obelisque_add_image_licence_url\", None)\n )\n\n if statut is True:\n flash(\"Nouvel obélisque ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/obelisque_form_add.html\")\n else:\n return render_template(\"pages/obelisque_form_add.html\")\n\n\n# Ajouter une page personne\n\n@app.route(\"/personne/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef personne_add():\n \"\"\" Route pour le formulaire d'ajout de commanditaire.\n :returns: template personne_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Personne.personne_add(\n personne_add_nom=request.form.get(\"personne_add_nom\", None),\n personne_add_fonction=request.form.get(\"personne_add_fonction\", None),\n personne_add_nationalite=request.form.get(\"personne_add_nationalite\", None)\n )\n\n if statut is True:\n flash(\"Nouveau commanditaire ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/personne_form_add.html\")\n else:\n return render_template(\"pages/personne_form_add.html\")\n\n\n# Ajouter une page lieu\n\n@app.route(\"/lieu/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef localisation_add():\n \"\"\" Route pour le formulaire d'ajout de lieu.\n :returns: template lieu_form_add.html \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Localisation.localisation_add(\n localisation_add_lieu=request.form.get(\"localisation_add_lieu\", None),\n localisation_add_ville=request.form.get(\"localisation_add_ville\", None),\n localisation_add_pays=request.form.get(\"localisation_add_pays\", None),\n localisation_add_latitude=request.form.get(\"localisation_add_latitude\", None),\n localisation_add_longitude=request.form.get(\"localisation_add_longitude\", None)\n )\n\n if statut is True:\n flash(\"Nouveau lieu ajouté à la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return render_template(\"pages/lieu_form_add.html\")\n else:\n return render_template(\"pages/lieu_form_add.html\")\n\n\n# Modifier une page\n\n# Modifier une page obélisque\n\n@app.route(\"/obelisque//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef obelisque_update(obelisque_id):\n \"\"\" Route pour le formulaire de mise à jour d'un obélisque.\n :param obelisque_id: identifiant de l'obélisque à modifier\n :type obelisque_id: integer\n :returns: template obelisque_form_update.html \"\"\"\n\n editable = Obelisque.query.get_or_404(obelisque_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"obelisque_nom\", \"\").strip():\n erreurs.append(\"Insérez un nom d'obélisque\")\n if not request.form.get(\"obelisque_hauteur\", \"\").strip():\n erreurs.append(\"Insérez une hauteur d'obélisque\")\n if not request.form.get(\"obelisque_hauteur_avec_base\", \"\").strip():\n erreurs.append(\"Insérez une hauteur avec base d'obélisque\")\n if not request.form.get(\"obelisque_materiau\", \"\").strip():\n erreurs.append(\"Insérez le matériau de l'obélisque\")\n if not request.form.get(\"obelisque_type_commande\", \"\").strip():\n erreurs.append(\"Insérez le type de commande de l'obélisque\")\n if not request.form.get(\"obelisque_notice\", \"\").strip():\n erreurs.append(\"Insérez une notice d'obélisque\")\n if not request.form.get(\"obelisque_bibliographie\", \"\").strip():\n erreurs.append(\"Insérez une bibliographie\")\n if not request.form.get(\"obelisque_image_url\", \"\").strip():\n erreurs.append(\"Insérez l'URL de l'image'\")\n if not request.form.get(\"obelisque_image_auteur\", \"\").strip():\n erreurs.append(\"Insérez le nom de l'auteur de l'image\")\n if not request.form.get(\"obelisque_image_licence\", \"\").strip():\n erreurs.append(\"Insérez les droits de réutilisation de l'image\")\n if not request.form.get(\"obelisque_image_licence_url\", \"\").strip():\n erreurs.append(\"Insérez l'URL de la licence de l'image\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.obelisque_nom = request.form[\"obelisque_nom\"]\n editable.obelisque_hauteur = request.form[\"obelisque_hauteur\"]\n editable.obelisque_hauteur_avec_base = request.form[\"obelisque_hauteur_avec_base\"]\n editable.obelisque_materiau = request.form[\"obelisque_materiau\"]\n editable.obelisque_type_commande = request.form[\"obelisque_type_commande\"]\n editable.obelisque_notice = request.form[\"obelisque_notice\"]\n editable.obelisque_bibliographie = request.form[\"obelisque_bibliographie\"]\n editable.obelisque_inscription_latine = request.form[\"obelisque_inscription_latine\"]\n editable.obelisque_inscription_latine_traduite = request.form[\"obelisque_inscription_latine_traduite\"]\n editable.obelisque_image_url = request.form[\"obelisque_image_url\"]\n editable.obelisque_image_auteur = request.form[\"obelisque_image_auteur\"]\n editable.obelisque_image_licence = request.form[\"obelisque_image_licence\"]\n editable.obelisque_image_licence_url = request.form[\"obelisque_image_licence_url\"]\n\n db.session.add(editable)\n db.session.add(Authorship(obelisque=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/obelisque_form_update.html\",\n obelisque=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Modifier une page personne\n\n@app.route(\"/personne//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef personne_update(personne_id):\n \"\"\" Route pour le formulaire de mise à jour d'un commanditaire.\n :param personne_id: identifiant du commanditaire à modifier\n :type personne_id: integer\n :returns: template personne_form_update.html \"\"\"\n\n editable = Personne.query.get_or_404(personne_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"personne_nom\", \"\").strip():\n erreurs.append(\"Insérez un nom\")\n if not request.form.get(\"personne_nationalite\", \"\").strip():\n erreurs.append(\"Insérez la nationalité de la personne\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.personne_nom = request.form[\"personne_nom\"]\n editable.personne_nationalite = request.form[\"personne_nationalite\"]\n editable.personne_fonction = request.form[\"personne_fonction\"]\n\n db.session.add(editable)\n db.session.add(Authorship(personne=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/personne_form_update.html\",\n personne=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Modifier une page lieu\n\n@app.route(\"/lieu//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef localisation_update(localisation_id):\n \"\"\" Route pour le formulaire de mise à jour d'un lieu.\n :param localisation_id: identifiant du lieu à modifier\n :type localisation_id: integer\n :returns: template lieu_form_update.html \"\"\"\n\n editable = Localisation.query.get_or_404(localisation_id)\n\n erreurs = []\n updated = False\n\n if request.method == \"POST\":\n if not request.form.get(\"localisation_lieu\", \"\").strip():\n erreurs.append(\"Insérez un nom de lieu\")\n if not request.form.get(\"localisation_ville\", \"\").strip():\n erreurs.append(\"Insérez la ville du lieu\")\n if not request.form.get(\"localisation_pays\", \"\").strip():\n erreurs.append(\"Insérez le pays du lieu\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.localisation_lieu = request.form[\"localisation_lieu\"]\n editable.localisation_ville = request.form[\"localisation_ville\"]\n editable.localisation_pays = request.form[\"localisation_pays\"]\n editable.localisation_latitude = request.form[\"localisation_latitude\"]\n editable.localisation_longitude = request.form[\"localisation_longitude\"]\n\n db.session.add(editable)\n db.session.add(Authorship(localisation=editable, user=current_user))\n db.session.commit()\n updated = True\n\n return render_template(\n \"pages/lieu_form_update.html\",\n localisation=editable,\n erreurs=erreurs,\n updated=updated\n )\n\n\n# Supprimer une page\n\n# Supprimer une page obélisque\n\n@app.route(\"/obelisque//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef obelisque_delete(obelisque_id):\n \"\"\" Route pour le formulaire de suppression d'un obélisque.\n :param obelisque_id: identifiant de l'obélisque à supprimer\n :type obelisque_id: integer\n :returns: template obelisque_form_delete.html en cas d'échec, retour à l'accueil en cas de réussite \"\"\"\n\n supprimable = Obelisque.query.get(obelisque_id)\n\n if request.method == \"POST\":\n statut = Obelisque.obelisque_delete(\n obelisque_id=obelisque_id\n )\n\n if statut is True:\n flash(\"L'obélisque a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/obelisque_form_delete.html\", supprimable=supprimable)\n\n\n# Supprimer une page personne\n\n@app.route(\"/personne//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef personne_delete(personne_id):\n \"\"\" Route pour le formulaire de suppression d'un commanditaire.\n :param personne_id: identifiant du commanditaire à supprimer\n :type personne_id: integer\n :returns: template personne_form_delete.html en cas d'échec, retour vers l'accueil en cas de réussite \"\"\"\n\n supprimable = Personne.query.get(personne_id)\n\n if request.method == \"POST\":\n statut = Personne.personne_delete(\n personne_id=personne_id\n )\n\n if statut is True:\n flash(\"Le commanditaire a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/personne_form_delete.html\", supprimable=supprimable)\n\n\n# Supprimer une page lieu\n\n@app.route(\"/lieu//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef localisation_delete(localisation_id):\n \"\"\" Route pour le formulaire de suppression d'un lieu.\n :param localisation_id: identifiant du lieu à supprimer\n :type localisation_id: integer\n :returns: template localisation_form_delete.html en cas d'échec, retour vers l'accueil en cas de réussite \"\"\"\n\n supprimable = Localisation.query.get(localisation_id)\n\n if request.method == \"POST\":\n statut = Localisation.localisation_delete(\n localisation_id=localisation_id\n )\n\n if statut is True:\n flash(\"Le lieu a été supprimé de la base\", \"success\")\n return redirect(\"/\")\n else:\n flash(\"Echec\", \"error\")\n return redirect(\"/\")\n else:\n return render_template(\"pages/lieu_form_delete.html\", supprimable=supprimable)\n\n\n# La page pour la gestion des élévations\n# Source : https://www.youtube.com/watch?v=XTpLbBJTOM4\n@app.route('/elevations')\n@login_required\ndef elevations():\n \"\"\" Route pour le tableau de gestion des élévations.\n :returns: template elevations.html \"\"\"\n\n erige = Erige.query.all()\n\n return render_template(\"pages/elevations.html\", erige=erige)\n\n\n# Ajouter une élévation\n\n@app.route(\"/erige/add\", methods=[\"GET\", \"POST\"])\n@login_required\ndef erige_add():\n \"\"\" Route pour le formulaire d'ajout d'une élévation.\n :returns: template elevations.html\n \"\"\"\n\n if request.method == \"POST\":\n statut, informations = Erige.erige_add(\n erige_add_id_obelisque=request.form.get(\"erige_add_id_obelisque\", None),\n erige_add_id_personne=request.form.get(\"erige_add_id_personne\", None),\n erige_add_id_localisation=request.form.get(\"erige_add_id_localisation\", None),\n erige_add_date=request.form.get(\"erige_add_date\", None),\n erige_add_actuel=request.form.get(\"erige_add_actuel\", None)\n )\n\n if statut is True:\n flash(\"Nouvelle élévation ajoutée à la base\", \"success\")\n return redirect(url_for('elevations'))\n else:\n flash(\"Echec : \" + \", \".join(informations), \"danger\")\n return redirect(url_for('elevations'))\n else:\n return redirect(url_for('elevations'))\n\n\n# Modifier une élévation\n\n@app.route(\"/erige//update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef erige_update(erige_id):\n \"\"\" Route pour le formulaire de modification d'une élévation.\n :param erige_id: identifiant de l'élévation à modifier\n :type erige_id: integer\n :returns: template elevations.html\n \"\"\"\n\n editable = Erige.query.get_or_404(erige_id)\n\n erreurs = []\n\n if request.method == \"POST\":\n if not request.form.get(\"erige_id_obelisque\", \"\").strip():\n erreurs.append(\"Insérez un id d'obélisque\")\n if not request.form.get(\"erige_id_personne\", \"\").strip():\n erreurs.append(\"Insérez un id de personne\")\n if not request.form.get(\"erige_id_localisation\", \"\").strip():\n erreurs.append(\"Insérez un id de lieu\")\n if not request.form.get(\"erige_date\", \"\").strip():\n erreurs.append(\"Insérez une date d'élévation\")\n\n if not erreurs:\n print(\"Faire ma modification\")\n editable.erige_id_obelisque = request.form[\"erige_id_obelisque\"]\n editable.erige_id_personne = request.form[\"erige_id_personne\"]\n editable.erige_id_localisation = request.form[\"erige_id_localisation\"]\n editable.erige_date = request.form[\"erige_date\"]\n editable.erige_actuel = request.form[\"erige_actuel\"]\n\n db.session.add(editable)\n db.session.add(Authorship(erige=editable, user=current_user))\n db.session.commit()\n flash(\"Elévation mise à jour avec succès\", \"success\")\n else:\n flash(\"Echec\", \"danger\")\n\n return redirect(url_for('elevations'))\n\n\n# Supprimer une élévation\n\n@app.route(\"/erige//delete\", methods=[\"POST\", \"GET\"])\n@login_required\ndef erige_delete(erige_id):\n \"\"\" Route pour le formulaire de suppression d'une élévation.\n :param erige_id: identifiant de l'élévation à supprimer\n :type erige_id: integer\n :returns: template elevations.html\n \"\"\"\n\n supprimable = Erige.query.get(erige_id)\n db.session.delete(supprimable)\n db.session.commit()\n flash(\"Elévation supprimée avec succès\", \"success\")\n\n return redirect(url_for('elevations'))\n","repo_name":"A-Menu/Obeliste","sub_path":"app/routes/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":27113,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"82"} +{"seq_id":"11716660427","text":"import pandas\nimport statistics\n\"\"\"\n reads the data from the file and puts it in a dictionary with the necessary features\n\"\"\"\ndef load_data(path,features):\n df = pandas.read_csv(path)\n data = df.to_dict(orient=\"list\")\n data = {x:data[x] for x in features}\n return data\n\"\"\"\n seperates the dictionary into two dictionaries by the value of each feature\n\"\"\"\n\n\ndef filter_by_feature(data, feature, values):\n data1 = deep_copy_data(data)\n data2 = deep_copy_data(data)\n for k in data:\n data1[k] = []\n data2[k] = []\n for i in range(len(data[feature])):\n if data[feature][i] in values:\n for k in data:\n data1[k].append(data[k][i])\n else:\n for k in data:\n data2[k].append(data[k][i])\n return data1, data2\n\n\n\n\n # data1 = deep_copy_data(data)\n #data2 = deep_copy_data(data)\n # if data[feature][i] in values:\n # for k in [\"cnt\",\"hum\",\"t1\",\"is_holiday\",\"season\"]:\n # data2[k].remove(data[k][i])\n # else:\n # for k in [\"cnt\",\"hum\", \"t1\", \"is_holiday\", \"season\"]:\n # data1[k].remove(data[k][i])\n\n\"\"\"\n prints the value of the statistic methods for each feature\n\"\"\"\ndef print_details(data, features, statistic_functions):\n temp = []\n for feature in features:\n for y in statistic_functions:\n temp.append(y(data[feature]))\n for i in range(len(temp)):\n temp[i] = \"%.2f\" % temp[i]\n print(\"{}: {}\".format(feature,','.join(temp)))\n temp.clear()\n return()\n\n\"\"\"\n prints the value of the statistic methods for for two features\n\"\"\"\n\n\ndef print_joint_details(data, features, statistic_functions, statistic_functions_names):\n values = [data[feature] for feature in features]\n for function, function_name in zip(statistic_functions, statistic_functions_names):\n print(\"{}({}): {}\".format(function_name,\", \".join(features),\"%.2f\" % function(*values)))\n\n\ndef deep_copy_data(data):\n return {feature : data[feature].copy() for feature in data}\n","repo_name":"yuval-belelovsky/HW1_INTRO_DS","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14101762372","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport sys\nsys.path.insert(0, '../src/files/')\n\nimport pyrfid\n\nproject = u'PyRfid'\nmaster_doc = 'index'\nauthor = 'Philipp Meisberger '\ncopyright = '2015-{}, {}'.format(datetime.date.today().year, author)\nversion = pyrfid.__version__\nrelease = version\nexclude_patterns = [\n '_build',\n 'Thumbs.db',\n '.DS_Store'\n]\nextensions = [\n 'sphinx.ext.napoleon',\n]\nautoclass_content = \"both\"\nautodoc_mock_imports = [\"serial\"]\nhtml_theme = \"sphinx_rtd_theme\"\n","repo_name":"philippmeisberger/pyrfid","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"82"} +{"seq_id":"1098348021","text":"\nimport rl\nimport rl.core\nimport keras\nfrom keras.layers import *\nfrom keras.models import Model\nfrom keras.models import model_from_json\nfrom keras.utils import CustomObjectScope\n\nimport os\nimport pickle\n\nfrom .common import *\n\n\n#---------------------------------------------------\n# Rainbow\n#---------------------------------------------------\nclass Rainbow(rl.core.Agent):\n def __init__(self,\n input_shape,\n input_type,\n nb_actions,\n memory,\n action_policy,\n optimizer,\n\n metrics=[],\n image_model=None, # imegeモデルを指定\n input_sequence=4, # 入力フレーム数\n dense_units_num=512, # Dense層のユニット数\n enable_dueling_network=True, # dueling network有効フラグ\n dueling_network_type=DuelingNetwork.AVERAGE, # dueling networkで使うアルゴリズム\n lstm_type=LstmType.NONE, # LSTM有効フラグ\n lstm_units_num=512, # LSTMのユニット数\n lstm_ful_input_length=1, # ステートフルLSTMの入力数\n\n # burn-in有効時の設定\n burnin_length=4, # burn-in期間\n enable_rescaling=False, # rescalingを有効にするか\n rescaling_epsilon=0.001, # rescalingの定数\n priority_exponent=0.9, # シーケンス長priorityを計算する際のη\n \n # train関係\n batch_size=32, # batch_size\n memory_warmup_size=50000, # 初期メモリー確保用step数(学習しない)\n target_model_update=500, # target networkのupdate間隔\n enable_double_dqn=True, # DDQN有効フラグ\n action_interval=4, # アクションを実行する間隔\n train_interval=4, # 学習間隔\n gamma=0.99, # Q学習の割引率\n reward_multisteps=3, # multistep reward\n\n processor=None\n ):\n super(Rainbow, self).__init__(processor)\n self.compiled = False # super()\n\n #--- check\n if lstm_type == LstmType.STATEFUL:\n self.burnin_length = burnin_length\n else:\n self.burnin_length = 0\n\n assert memory.capacity > batch_size, \"Memory capacity is small.(Larger than batch size)\"\n assert memory_warmup_size > batch_size, \"Warmup steps is few.(Larger than batch size)\"\n\n if image_model is None:\n assert input_type == InputType.VALUES\n else:\n assert input_type == InputType.GRAY_2ch or input_type == InputType.GRAY_3ch or input_type == InputType.COLOR\n\n # 画像入力の制約\n # LSTMを使う場合: 画像は(w,h,ch)で入力できます。\n # LSTMを使わない場合：\n # input_sequenceが1：全て使えます。\n # input_sequenceが1以外：GRAY_2ch のみ使えます。\n if lstm_type == LstmType.NONE and input_sequence != 1:\n assert (input_type == InputType.GRAY_2ch), \"input_iimage can use GRAY_2ch.\"\n\n #---\n self.input_shape = input_shape\n self.image_model = image_model\n self.input_type = input_type\n\n self.nb_actions = nb_actions\n self.input_sequence = input_sequence\n self.memory_warmup_size = memory_warmup_size\n self.target_model_update = target_model_update\n self.action_interval = action_interval\n self.train_interval = train_interval\n self.gamma = gamma\n self.batch_size = batch_size\n assert reward_multisteps > 0, \"'reward_multisteps' is 1 or more.\"\n self.reward_multisteps = reward_multisteps\n self.dense_units_num = dense_units_num\n\n self.lstm_units_num = lstm_units_num\n self.enable_rescaling = enable_rescaling\n self.rescaling_epsilon = rescaling_epsilon\n self.priority_exponent = priority_exponent\n self.lstm_type = lstm_type\n\n self.optimizer = optimizer\n self.metrics = metrics\n\n self.memory = memory\n self.action_policy = action_policy\n \n self.lstm_ful_input_length = lstm_ful_input_length\n \n self.enable_double_dqn = enable_double_dqn\n self.enable_dueling_network = enable_dueling_network\n self.dueling_network_type = dueling_network_type\n \n self.model = self.build_compile_model() # Q network\n model_json = self.model.to_json()\n self.target_model = model_from_json(model_json)\n self.action_policy.compile(model_json)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.lstm = self.model.get_layer(\"lstm\")\n self.target_lstm = self.target_model.get_layer(\"lstm\")\n\n self.compiled = True # super\n\n self.local_step = 0\n\n\n def reset_states(self): # override\n self.repeated_action = 0\n \n if self.lstm_type == LstmType.STATEFUL:\n multi_len = self.reward_multisteps + self.lstm_ful_input_length - 1\n self.recent_actions = [ 0 for _ in range(multi_len + 1)]\n self.recent_rewards = [ 0 for _ in range(multi_len)]\n self.recent_rewards_multistep = [ 0 for _ in range(self.lstm_ful_input_length)]\n tmp = self.burnin_length + self.input_sequence + multi_len\n self.recent_observations = [\n np.zeros(self.input_shape) for _ in range(tmp)\n ]\n tmp = self.burnin_length + multi_len + 1\n self.recent_observations_wrap = [\n [np.zeros(self.input_shape) for _ in range(self.input_sequence)] for _ in range(tmp)\n ]\n\n # hidden_state: [(batch_size, lstm_units_num), (batch_size, lstm_units_num)]\n tmp = self.burnin_length + multi_len + 1+1\n self.model.reset_states()\n self.recent_hidden_states = [\n [K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])] for _ in range(tmp)\n ]\n \n else:\n self.recent_actions = [ 0 for _ in range(self.reward_multisteps+1)]\n self.recent_rewards = [ 0 for _ in range(self.reward_multisteps)]\n self.recent_rewards_multistep = 0\n self.recent_observations = [\n np.zeros(self.input_shape) for _ in range(self.input_sequence + self.reward_multisteps)\n ]\n\n def build_compile_model(self):\n\n if self.lstm_type == LstmType.STATEFUL:\n # input(batch_size, timesteps, shape)\n c = input_ = Input(batch_shape=(self.batch_size, self.input_sequence) + self.input_shape)\n else:\n # input(input_sequence, shape)\n c = input_ = Input(shape=(self.input_sequence,) + self.input_shape)\n \n \n if self.image_model is None:\n # input not image\n if self.lstm_type == LstmType.NONE:\n c = Flatten()(c)\n else:\n c = TimeDistributed(Flatten())(c)\n else:\n # input image\n if self.lstm_type == LstmType.NONE:\n enable_lstm = False\n if self.input_type == InputType.GRAY_2ch:\n # (input_seq, w, h) ->(w, h, input_seq)\n c = Permute((2, 3, 1))(c)\n\n elif self.lstm_type == LstmType.STATELESS or self.lstm_type == LstmType.STATEFUL:\n enable_lstm = True\n if self.input_type == InputType.GRAY_2ch:\n # (time steps, w, h) -> (time steps, w, h, ch)\n c = Reshape((self.input_sequence, ) + self.input_shape + (1,) )(c)\n \n else:\n raise ValueError('lstm_type is not undefined')\n c = self.image_model.create_image_model(c, enable_lstm)\n\n # lstm layer\n if self.lstm_type == LstmType.STATELESS:\n c = LSTM(self.lstm_units_num, name=\"lstm\")(c)\n elif self.lstm_type == LstmType.STATEFUL:\n c = LSTM(self.lstm_units_num, stateful=True, name=\"lstm\")(c)\n\n # dueling network\n if self.enable_dueling_network:\n # value\n v = Dense(self.dense_units_num, activation=\"relu\")(c)\n v = Dense(1, name=\"v\")(v)\n\n # advance\n adv = Dense(self.dense_units_num, activation='relu')(c)\n adv = Dense(self.nb_actions, name=\"adv\")(adv)\n\n # 連結で結合\n c = Concatenate()([v,adv])\n if self.dueling_network_type == DuelingNetwork.AVERAGE:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.mean(a[:, 1:], axis=1, keepdims=True), output_shape=(self.nb_actions,))(c)\n elif self.dueling_network_type == DuelingNetwork.MAX:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:] - K.max(a[:, 1:], axis=1, keepdims=True), output_shape=(self.nb_actions,))(c)\n elif self.dueling_network_type == DuelingNetwork.NAIVE:\n c = Lambda(lambda a: K.expand_dims(a[:, 0], -1) + a[:, 1:], output_shape=(self.nb_actions,))(c)\n else:\n raise ValueError('dueling_network_type is not undefined')\n else:\n c = Dense(self.dense_units_num, activation=\"relu\")(c)\n c = Dense(self.nb_actions, activation=\"linear\", name=\"adv\")(c)\n \n model = Model(input_, c)\n model.compile(loss=clipped_error_loss, optimizer=self.optimizer, metrics=self.metrics)\n self.compiled = True # super\n\n return model\n\n def compile(self, optimizer, metrics=[]): # override\n self.compiled = True # super\n\n def save_weights(self, filepath, overwrite=False, save_memory=False): # override\n if overwrite or not os.path.isfile(filepath):\n d = {\n \"weights\": self.model.get_weights(),\n \"policy\": self.action_policy.get_weights(),\n \"step\": self.local_step,\n }\n with open(filepath, 'wb') as f:\n pickle.dump(d, f)\n \n # memory\n if save_memory:\n d = self.memory.get_memorys()\n with open(filepath + \".mem\", 'wb') as f:\n pickle.dump(d, f)\n\n\n def load_weights(self, filepath, load_memory=False): # override\n if not os.path.isfile(filepath):\n return\n with open(filepath, 'rb') as f:\n d = pickle.load(f)\n self.model.set_weights(d[\"weights\"])\n self.target_model.set_weights(d[\"weights\"])\n self.action_policy.set_weights(d[\"policy\"])\n self.local_step = d[\"step\"]\n\n # memory\n if load_memory:\n filepath = filepath + \".mem\"\n if os.path.isfile(filepath):\n with open(filepath, 'rb') as f:\n d = pickle.load(f)\n self.memory.set_memorys(d)\n\n def forward(self, observation): # override\n # observation\n self.recent_observations.pop(0)\n self.recent_observations.append(observation)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.recent_observations_wrap.pop(0)\n self.recent_observations_wrap.append(self.recent_observations[-self.input_sequence:])\n\n # tmp\n self._qvals = None\n self._state1 = self.recent_observations[-self.input_sequence:]\n self._state1_np = np.asarray(self._state1)\n\n # 学習(次の状態が欲しいのでforwardで学習)\n if self.training:\n self.forward_train()\n\n # 状態の更新\n if self.lstm_type == LstmType.STATEFUL:\n self.lstm.reset_states(self.recent_hidden_states[-1])\n\n # hidden_state を更新しつつQ値も取得\n state = self._state1_np\n pred_state = np.full((self.batch_size,)+state.shape, state) # batchサイズ分増やす\n self._qvals = self.model.predict(pred_state, batch_size=self.batch_size)[0]\n \n hidden_state = [K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])]\n self.recent_hidden_states.pop(0)\n self.recent_hidden_states.append(hidden_state)\n \n # フレームスキップ(action_interval毎に行動を選択する)\n action = self.repeated_action\n if self.step % self.action_interval == 0:\n\n # 行動を決定\n if self.training:\n # training中は action policyに従う\n action = self.action_policy.select_action(self)\n else:\n # テスト中またはNoisyNet中の場合\n action = np.argmax(self.get_qvals())\n\n # リピート用\n self.repeated_action = action\n \n # アクション保存\n self.recent_actions.pop(0)\n self.recent_actions.append(action)\n \n return action\n \n\n def get_qvals(self):\n if self.lstm_type == LstmType.STATEFUL:\n return self._qvals\n else:\n if self._qvals is None:\n self._qvals = self.model.predict(\n self._state1_np[np.newaxis,:], batch_size=1)[0]\n return self._qvals\n\n def get_state(self):\n return self._state1_np\n\n def get_prev_state(self):\n if self.lstm_type == LstmType.STATEFUL:\n observation = np.asarray(self.recent_observations_wrap[-self.reward_multisteps-1])\n action = self.recent_actions[-self.reward_multisteps-1]\n reward = self.recent_rewards_multistep[-self.reward_multisteps]\n else:\n observation = np.asarray(self.recent_observations[:self.input_sequence])\n action = self.recent_actions[0]\n reward = self.recent_rewards_multistep\n return (observation, action, reward)\n\n # 長いのでこちらに\n def forward_train(self):\n \n if self.lstm_type == LstmType.STATEFUL:\n self.memory.add((\n self.recent_observations_wrap[:],\n self.recent_actions[0:self.lstm_ful_input_length],\n self.recent_rewards_multistep[:],\n self.recent_hidden_states[0]))\n\n else:\n self.memory.add((\n self.recent_observations[:self.input_sequence],\n self.recent_actions[0],\n self.recent_rewards_multistep, \n self._state1))\n\n # 初期のReplay Memoryの確保、学習しない。\n if len(self.memory) <= self.memory_warmup_size:\n return\n \n # 学習の更新間隔\n if self.step % self.train_interval != 0:\n return\n\n # memory から優先順位に基づき状態を取得\n (indexes, batchs, weights) = self.memory.sample(self.batch_size, self.local_step)\n \n # 学習(長いので関数化)\n if self.lstm_type == LstmType.STATEFUL:\n self.train_model_ful(indexes, batchs, weights)\n else:\n self.train_model(indexes, batchs, weights)\n\n # ノーマルの学習\n def train_model(self, indexes, batchs, weights):\n state0_batch = []\n action_batch = []\n reward_batch = []\n state1_batch = []\n for i, batch in enumerate(batchs):\n state0_batch.append(batch[0])\n action_batch.append(batch[1])\n reward_batch.append(batch[2])\n state1_batch.append(batch[3])\n state0_batch = np.asarray(state0_batch)\n state1_batch = np.asarray(state1_batch)\n \n # 更新用に現在のQネットワークを出力(Q network)\n state0_qvals = self.model.predict(state0_batch, self.batch_size)\n\n if self.enable_double_dqn:\n # TargetNetworkとQNetworkのQ値を出す\n state1_qvals_model = self.model.predict(state1_batch, self.batch_size)\n state1_qvals_target = self.target_model.predict(state1_batch, self.batch_size)\n else:\n # 次の状態のQ値を取得(target_network)\n state1_qvals_target = self.target_model.predict(state1_batch, self.batch_size)\n\n for i in range(self.batch_size):\n if self.enable_double_dqn:\n action = state1_qvals_model[i].argmax() # modelからアクションを出す\n maxq = state1_qvals_target[i][action] # Q値はtarget_modelを使って出す\n else:\n maxq = state1_qvals_target[i].max()\n \n # priority計算\n q0 = state0_qvals[i][action_batch[i]]\n td_error = reward_batch[i] + (self.gamma ** self.reward_multisteps) * maxq - q0\n priority = abs(td_error)\n \n # Q値の更新\n state0_qvals[i][action_batch[i]] += td_error * weights[i]\n\n # priorityを更新\n self.memory.update(indexes[i], batchs[i], priority)\n\n # 学習\n self.model.train_on_batch(state0_batch, state0_qvals)\n \n\n # ステートフルLSTMの学習\n def train_model_ful(self, indexes, batchs, weights):\n\n hidden_s0 = []\n hidden_s1 = []\n for batch in batchs:\n # batchサイズ分あるけどすべて同じなので0番目を取得\n hidden_s0.append(batch[3][0][0])\n hidden_s1.append(batch[3][1][0])\n hidden_states = [np.asarray(hidden_s0), np.asarray(hidden_s1)]\n\n # init hidden_state\n self.lstm.reset_states(hidden_states)\n self.target_lstm.reset_states(hidden_states)\n\n # predict\n hidden_states_arr = []\n if self.burnin_length == 0:\n hidden_states_arr.append(hidden_states)\n state_batch_arr = []\n model_qvals_arr = []\n target_qvals_arr = []\n prioritys = [ [] for _ in range(self.batch_size)]\n for seq_i in range(self.burnin_length + self.reward_multisteps + self.lstm_ful_input_length):\n\n # state\n state_batch = [ batch[0][seq_i] for batch in batchs ]\n state_batch = np.asarray(state_batch)\n \n # hidden_state更新およびQ値取得\n model_qvals = self.model.predict(state_batch, self.batch_size)\n target_qvals = self.target_model.predict(state_batch, self.batch_size)\n\n # burnin-1\n if seq_i < self.burnin_length-1:\n continue\n hidden_states_arr.append([K.get_value(self.lstm.states[0]), K.get_value(self.lstm.states[1])])\n\n # burnin\n if seq_i < self.burnin_length:\n continue\n\n state_batch_arr.append(state_batch)\n model_qvals_arr.append(model_qvals)\n target_qvals_arr.append(target_qvals)\n\n # train\n for seq_i in range(self.lstm_ful_input_length):\n\n # state0 の Qval (multistep前)\n state0_qvals = model_qvals_arr[seq_i]\n \n # batch\n for batch_i in range(self.batch_size):\n\n # maxq\n if self.enable_double_dqn:\n action = model_qvals_arr[seq_i+self.reward_multisteps][batch_i].argmax() # modelからアクションを出す\n maxq = target_qvals_arr[seq_i+self.reward_multisteps][batch_i][action] # Q値はtarget_modelを使って出す\n else:\n maxq = target_qvals_arr[seq_i+self.reward_multisteps][batch_i].max()\n\n # priority\n batch_action = batchs[batch_i][1][seq_i]\n q0 = state0_qvals[batch_i][batch_action]\n reward = batchs[batch_i][2][seq_i]\n td_error = reward + (self.gamma ** self.reward_multisteps) * maxq - q0\n priority = abs(td_error)\n prioritys[batch_i].append(priority)\n\n # Q値の更新\n state0_qvals[batch_i][batch_action] += td_error * weights[batch_i]\n\n # train\n self.lstm.reset_states(hidden_states_arr[seq_i])\n self.model.train_on_batch(state_batch_arr[seq_i], state0_qvals)\n \n # priority update\n for batch_i, batch in enumerate(batchs):\n priority = self.priority_exponent * np.max(prioritys[batch_i]) + (1-self.priority_exponent) * np.average(prioritys[batch_i])\n self.memory.update(indexes[batch_i], batch, priority)\n \n\n def backward(self, reward, terminal): # override\n # terminal は env が終了状態ならTrue\n self.local_step += 1\n if not self.training:\n return []\n \n # 報酬の保存\n self.recent_rewards.pop(0)\n self.recent_rewards.append(reward)\n\n # multi step learning の計算\n _tmp = 0\n for i in range(-self.reward_multisteps, 0):\n r = self.recent_rewards[i]\n _tmp += r * (self.gamma ** i)\n \n # rescaling\n if self.enable_rescaling:\n _tmp = rescaling(_tmp)\n\n if self.lstm_type == LstmType.STATEFUL:\n self.recent_rewards_multistep.pop(0)\n self.recent_rewards_multistep.append(_tmp)\n else:\n self.recent_rewards_multistep = _tmp\n\n # 一定間隔でtarget modelに重さをコピー\n if self.step % self.target_model_update == 0:\n self.target_model.set_weights(self.model.get_weights())\n\n return []\n \n @property\n def layers(self): #override\n return self.model.layers[:]\n","repo_name":"pocokhc/r2d2","sub_path":"src/rainbow.py","file_name":"rainbow.py","file_ext":"py","file_size_in_byte":21453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19631667837","text":"import time\r\nimport serial\r\nimport tkinter as tk\r\nfrom pandas import DataFrame\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\r\nimport threading\r\nfrom matplotlib.animation import FuncAnimation\r\nfrom itertools import count\r\nimport pandas as pd\r\n\r\n\r\nread = [0,0,0,0]\r\nmain_read_1 = [] \r\nmain_read_2 = [] \r\n\r\n\r\n\r\n\r\n#plt.style.use('seaborn')\r\naxcolor = 'lightgoldenrodyellow'\r\n\r\nx_vals = []\r\ny_vals = []\r\n \r\nindex = count()\r\n\r\nfig1, (ax1,ax2) = plt.subplots(nrows=2,ncols=1)\r\ncounter_1 = 0\r\ncounter_2 = 0\r\ncc1 = 0\r\ncc2 = 0\r\n\r\n\r\nx1 = []\r\nx2 = []\r\n\r\ny1 = []\r\ny2 = []\r\nchan1_id = 0\r\nchan2_id = 0\r\nchan1_lable = str()\r\nchan2_lable = str()\r\nstart = 0 \r\ndef animate(i):\r\n global read\r\n global main_read_1\r\n global main_read_2\r\n global cc1\r\n global cc2\r\n global chan1_id\r\n global chan2_id\r\n global chan1_lable\r\n global chan2_lable\r\n global start\r\n global counter_1 \r\n global counter_2 \r\n if start == 1 :\r\n if len(main_read_1) > cc1 :\r\n cc1 = len(main_read_1)\r\n \r\n ax1.cla()\r\n \r\n ax1.set_axisbelow(True)\r\n ax1.minorticks_on()\r\n ax1.grid(which='major', linestyle='-', linewidth='0.5', color='red')\r\n ax1.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\r\n ax2.axvline(0.0,0,1, linestyle='dashed') \r\n ax1.plot(x1, main_read_1, label= chan1_lable)\r\n ax1.legend()\r\n ax1.set_ylabel('time')\r\n if counter_1 < 500 :\r\n ax1.set_xlim([0,550]) \r\n else :\r\n ax1.set_xlim(max(0,counter_1 - 500),(counter_1 + 50))\r\n ax1.set_ylim([0,400]) \r\n \r\n if len(main_read_2) > cc2 :\r\n cc2 = len(main_read_2)\r\n ax2.cla()\r\n ax2.set_axisbelow(True)\r\n\r\n ax2.minorticks_on()\r\n \r\n ax2.grid(which='major', linestyle='-', linewidth='0.5', color='green')\r\n ax2.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\r\n \r\n ax2.plot(x2, main_read_2, label= chan2_lable)\r\n ax2.legend()\r\n ax2.set_xlabel('time')\r\n if counter_2 < 250 :\r\n ax2.set_xlim([0,300]) \r\n else :\r\n ax2.set_xlim(max(0,counter_2 - 250),(counter_2 + 50))\r\n ax2.set_ylim([0,400])\r\n \r\ndef start_Stop () :\r\n global start\r\n if start == 0 :\r\n start = 1 \r\n else :\r\n start = 0 \r\n \r\n \r\n \r\ndef enter () :\r\n global chan1_id\r\n global chan2_id\r\n global chan1_lable\r\n global chan2_lable\r\n global start\r\n global enterButton\r\n global l1\r\n global l2\r\n global l3\r\n global l4\r\n global send_ID\r\n global send_DATA\r\n global ser\r\n chan1_id = int(chan_1_ID.get())\r\n chan2_id = int(chan_2_ID.get())\r\n chan1_lable = str(chan_1_lable.get())\r\n chan2_lable = str(chan_2_lable.get())\r\n chan_1_ID.destroy()\r\n chan_2_ID.destroy()\r\n chan_1_lable.destroy()\r\n chan_2_lable.destroy()\r\n enterButton.grid_remove ()\r\n l1.grid_remove ()\r\n l2.grid_remove ()\r\n l3.grid_remove ()\r\n l4.grid_remove ()\r\n \r\n \r\n send_ID = tk.Entry(Title1 , width = 30)\r\n send_ID.grid(row = 1 , column = 3)\r\n SEND_ID_LABEL = tk.Label(Title1, text = 'ID', font =('Verdana', 10)) \r\n SEND_ID_LABEL.grid(row = 1 , column = 1)\r\n\r\n send_DATA = tk.Entry(Title1 , width = 30)\r\n send_DATA.grid(row = 3 , column = 3)\r\n send_DATA_LABEL = tk.Label(Title1, text = 'DATA', font =('Verdana', 10)) \r\n send_DATA_LABEL.grid(row = 3 , column = 1)\r\n\r\n sendButton = tk.Button(Title1,text = \"SEND\" ,command = send_func , bg = 'green',width = 15)\r\n sendButton.grid(row = 4 , column = 3)\r\n \r\n sendButton = tk.Button(Title1,text = \"SEND\" ,command = send_func , bg = 'green',width = 15)\r\n sendButton.grid(row = 4 , column = 3)\r\n \r\n startstopButton = tk.Button(Title1,text = \"start / stop\" ,command = start_Stop , bg = 'red',width = 20)\r\n startstopButton.grid(row = 6 , column = 3)\r\n \r\n ser = serial.Serial('COM9',115200) \r\n \r\n if ser.is_open:\r\n print(\"\\n Port Open Success\")\r\n \r\n ser.write((chan1_id).to_bytes(2, byteorder='big'))\r\n ser.write((chan2_id).to_bytes(2, byteorder='big'))\r\n\r\n\r\n \r\n start = 1 ;\r\n\r\n \r\n \r\ndef send_func () :\r\n global send_ID\r\n global send_DATA\r\n global ser\r\n _id = int(send_ID.get())\r\n _data = int(send_DATA.get())\r\n print(_id)\r\n print(_data)\r\n ser.write((_id).to_bytes(2, byteorder='big'))\r\n ser.write((_data).to_bytes(2, byteorder='little'))\r\n\r\n\r\ndef function (i): \r\n global counter_1 \r\n global counter_2 \r\n \r\n global read\r\n global main_read_1\r\n global main_read_2\r\n global can_1_data\r\n global can_2_data\r\n\r\n global chan1_id\r\n global chan2_id\r\n global start\r\n global ser\r\n global root\r\n while start == 0 :\r\n time.sleep(1)\r\n while 1 :\r\n while (ser.inWaiting()>0):\r\n for n in range(4):\r\n read[n]=int.from_bytes(ser.read(1), \"big\")\r\n can_id = (read[1] << 8 )| ((read[0]) & 0xff )\r\n can_id_data = (read[3] << 8 )| ((read[2]) & 0xff ) \r\n if start == 1 :\r\n if ( can_id == chan1_id):\r\n can_1_data = can_id_data\r\n counter_1+=1 \r\n x1.append(counter_1)\r\n main_read_1.append(can_id_data)\r\n elif (can_id == chan2_id) :\r\n can_2_data = can_id_data \r\n counter_2+=1 \r\n x2.append(counter_2)\r\n main_read_2.append(can_id_data)\r\n \r\n \r\n \r\n\r\nroot= tk.Tk() \r\nroot.geometry(\"1000x500+200+100\")\r\n\r\ni=0\r\nwhile i< 10:\r\n\troot.columnconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\nwhile i<10:\r\n\troot.rowconfigure(i,minsize='10m')\r\n\ti+=1\r\n\r\nfig1.set_size_inches(6,5)\r\n\r\n\r\n\r\n#loginButton =tkinter.Button(root,text = \"LOG IN\" ,command = CheckID , bg = 'green',width = 15).grid(row = 4 , column = 4)\r\n#l1 = tkinter.Label(root, text = 'ID', font =('Verdana', 10)) \r\n#l1.grid(row = 3 , column = 3)\r\n\r\nTitle = tk.Frame(root, width=400, height=400, bd=4, relief=\"ridge\")\r\nTitle.grid(row=0, column=0)\r\n\r\nTitle1 = tk.Frame(root, width=400, height=400, relief=\"ridge\")\r\nTitle1.grid(row=0, column=1)\r\ni=0\r\nwhile i< 10:\r\n\tTitle1.columnconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\nwhile i<10:\r\n\tTitle1.rowconfigure(i,minsize='10m')\r\n\ti+=1\r\ni=0\r\n \r\nchan_1_ID = tk.Entry(Title1 , width = 30)\r\nchan_1_ID.grid(row = 0 , column = 3)\r\nl1 = tk.Label(Title1, text = 'ID 1', font =('Verdana', 10)) \r\nl1.grid(row = 0 , column = 1)\r\n\r\n \r\nchan_1_lable = tk.Entry(Title1 , width = 30)\r\nchan_1_lable.grid(row = 1 , column = 3)\r\nl2 = tk.Label(Title1, text = 'label 1', font =('Verdana', 10)) \r\nl2.grid(row = 1 , column = 1)\r\n\r\nchan_2_ID = tk.Entry(Title1 , width = 30)\r\nchan_2_ID.grid(row = 4 , column = 3)\r\nl3 = tk.Label(Title1, text = 'ID 2', font =('Verdana', 10)) \r\nl3.grid(row = 4 , column = 1)\r\n\r\n\r\nchan_2_lable = tk.Entry(Title1 , width = 30)\r\nchan_2_lable.grid(row = 5 , column = 3)\r\nl4 = tk.Label(Title1, text = 'label 2', font =('Verdana', 10)) \r\nl4.grid(row = 5 , column = 1)\r\n\r\nenterButton =tk.Button(Title1,text = \"enter\" ,command = enter , bg = 'green',width = 15)\r\nenterButton.grid(row = 6 , column = 3)\r\n\r\n\r\nbar1 = FigureCanvasTkAgg(fig1, Title)\r\nbar1.get_tk_widget().grid(row = 0 , column = 0)\r\n \r\nx = threading.Thread(target=function, args=(1,))\r\nx.start() \r\n\r\nani = FuncAnimation(fig1, animate, interval=50)\r\n\r\nroot.mainloop()\r\n \r\n\r\n \r\n","repo_name":"Amralmasry/can_analyzer_stm32f446re","sub_path":"CAN_ANALYZER_PYTHON.py","file_name":"CAN_ANALYZER_PYTHON.py","file_ext":"py","file_size_in_byte":7633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"33062340266","text":"import time\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import Session, sessionmaker\nimport json\n\nfrom viewes import task_01, task_01_sql, task_02, task_02_sql\n\ncfg = json.load(open(\"./config.json\"))\nDB_INFO = cfg['db']\n\nengine = create_engine(\n f'postgresql://{DB_INFO[\"user\"]}:{DB_INFO[\"password\"]}@{DB_INFO[\"host\"]}:{DB_INFO[\"port\"]}/{DB_INFO[\"name\"]}',\n pool_pre_ping=True)\n\nSession = sessionmaker(bind=engine)\n\n\ndef menu():\n choices = \\\n '''\n 1 - Найти все отделы, в которых работает более 10 сотрудников.\n 2 - Найти сотрудников, которые не выходят с рабочего места в течение всего рабочего дня.\n 3 - Найти все отделы, в которых есть сотрудники, опоздавшие в определенную дату. Дату передавать с клавиатуры\n -1- Завершить работу.\n '''\n print(choices)\n\n\nQUERIES = [1, 2, 3, 4]\n\n\ndef main():\n is_work = True\n while is_work:\n menu()\n action = input()\n try:\n action = int(action)\n except:\n print(\"Invalid input actions. Only nums.\")\n else:\n if action == -1:\n print(\"End of work\")\n break\n else:\n if action in QUERIES:\n session = Session()\n if action == 1:\n res = task_01(session)\n if action == 2:\n res = task_01_sql(session)\n if action == 3:\n res = task_02(session)\n if action == 4:\n res = task_02_sql(session)\n print(res)\n session.commit()\n else:\n print(\"Error input action\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Flash1ee/db-5th-sem-bmstu","sub_path":"labs/rk_03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5765290978","text":"from django.urls import path\n\nfrom .views import *\n\nurlpatterns = [\n path('', PostList.as_view(), name='home'),\n path('about/', about, name='about'),\n path('post//', PostDetail.as_view(), name='post_detail'),\n path('add/', PostCreate.as_view(), name='post_create'),\n\n path('contact/', contact, name='contact'),\n\n path('login/', LoginUser.as_view(), name='login'),\n path('logout/', logout_user, name='logout'),\n path('register/', RegisterUser.as_view(), name='register'),\n]","repo_name":"ZhArtem/TulaHack2023","sub_path":"posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"5673951856","text":"import copy\nimport sys\n\nsys.stdin = open(\"./input.txt\", \"r\")\n\ndirections = [(0,-1),(1,-1),(1,0),(1,1),(0,1),(-1,1),(-1,0),(-1,-1)]\nN, M, K = map(int, sys.stdin.readline().split())\n\nboards = {}\nfireballs = {}\nlast_id = 0\nfor i in range(M):\n r, c, m, s, d = map(int, sys.stdin.readline().split())\n fireballs[i] = [r,c,m,s,d]\n last_id = i+1\n try: boards[(r,c)].append(i)\n except: boards[(r,c)] = [i]\n\ndef move_fireballs(fireballs):\n tmp_boards = {}\n tmp_fireballs = {}\n for id,f in fireballs.items():\n r,c,m,s,d = f\n print(id, '번째 파이어볼...', r,c, m, s, d)\n sx, sy = (directions[d][1] * s), (directions[d][0] * s)\n nx, ny = (r + sx + N) % N, (c + sy + N) % N\n x,y = nx, ny\n\n print(r, c, '->', x, y)\n try: tmp_boards[(x,y)].append(id)\n except: tmp_boards[(x,y)] = [id]\n tmp_fireballs[id] = [x,y,m,s,d]\n return tmp_boards, tmp_fireballs\n\nresult_dt, result_df = [0,2,4,6], [1,3,5,7]\ndef asemble_fireballs(boards, fireballs, last_id):\n tmp_boards = copy.deepcopy(boards)\n tmp_fireballs = copy.deepcopy(fireballs)\n for loc, fbs in boards.items():\n print(fbs)\n x, y = loc\n if len(fbs)>1 : # 볼이 두개이상일 때\n tmp_boards[(x,y)].clear()\n print('==after clear, 파이어볼 합치기 시작. === ', x, y)\n print(tmp_boards)\n total_w = 0 # 총 질량\n total_s = 0 # 총 속력\n check_d = -1 # 방향 체크. 0 = 짝, 1 = 홀\n is_D_flag = True\n print(fireballs)\n for f in fbs:\n r, c, m, s, d = fireballs.pop(f)\n tmp_fireballs.pop(f)\n total_w += m\n total_s += s\n if check_d == -1:\n check_d = d%2\n elif check_d != d%2:\n is_D_flag = False\n result_w = int(total_w/5)\n result_s = int(total_s/len(fbs))\n print(result_s, result_w)\n if result_w > 0: # 파이어볼이 소멸 되지 않았을 때.\n for i in range(4):\n if is_D_flag: # 모두 짝 or 홀\n tmp_fireballs[last_id] = [x, y, result_w, result_s, result_dt[i]]\n tmp_boards[(x,y)].append(last_id)\n last_id+=1\n else: # 모두 짝 or 홀\n tmp_fireballs[last_id] = [x, y, result_w, result_s, result_df[i]]\n tmp_boards[(x,y)].append(last_id)\n last_id+=1\n print(tmp_fireballs)\n print('tmp_board: ',tmp_boards)\n print('board: ', boards)\n return tmp_boards, tmp_fireballs, last_id\n\nfor turn in range(K):\n print('==============================', turn,'==============================')\n print(boards)\n print(fireballs)\n boards, fireballs = move_fireballs(fireballs)\n boards, fireballs, last_id = asemble_fireballs(boards,fireballs, last_id)\n\nresult = 0\nfor f in fireballs.values():\n _,_,m,_,_ = f\n result+=m\nprint(result)\n\n\n","repo_name":"jimilee/Cotemuseum","sub_path":"pre/re_마법사_상어와_파이어볼.py","file_name":"re_마법사_상어와_파이어볼.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"44194803048","text":"from typing import List\n\nimport cv2\nimport numpy as np\n\nfrom infer.data_class import DetectResult, BBox, ModelConfig\nfrom infer.inferencer import TensorRTInferencer\n\n\nclass YOLOV5Detector(TensorRTInferencer):\n def __init__(self, model_config: ModelConfig):\n # noinspection PyUnresolvedReferences\n self.num_classes = self.num_classes # 检测种类\n\n filters = (self.num_classes + 5) * 3\n self.output_shapes = [\n (1, 3, 80, 80, self.num_classes+5),\n (1, 3, 40, 40, self.num_classes+5),\n (1, 3, 20, 20, self.num_classes+5)\n ]\n self.strides = np.array([8., 16., 32.])\n anchors = np.array([\n [[10, 13], [16, 30], [33, 23]],\n [[30, 61], [62, 45], [59, 119]],\n [[116, 90], [156, 198], [373, 326]],\n ])\n self.nl = len(anchors)\n self.no = self.num_classes + 5 # outputs per anchor\n self.na = len(anchors[0])\n a = anchors.copy().astype(np.float32)\n a = a.reshape(self.nl, -1, 2)\n self.anchors = a.copy()\n self.anchor_grid = a.copy().reshape(self.nl, 1, -1, 1, 1, 2)\n\n super().__init__(model_config=model_config)\n\n def pre_process(self, img):\n img = cv2.resize(img, (640, 640))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n # img = img.transpose((2, 0, 1)).astype(np.float16)\n img = img.transpose((2, 0, 1)).astype(np.float32)\n img /= 255.0\n return img\n\n def sigmoid_v(self, array):\n return np.reciprocal(np.exp(-array) + 1.0)\n\n def make_grid(self, nx, ny):\n \"\"\"\n Create scaling tensor based on box location\n Source: https://github.com/ultralytics/yolov5/blob/master/models/yolo.py\n Arguments\n nx: x-axis num boxes\n ny: y-axis num boxes\n Returns\n grid: tensor of shape (1, 1, nx, ny, 80)\n \"\"\"\n nx_vec = np.arange(nx)\n ny_vec = np.arange(ny)\n yv, xv = np.meshgrid(ny_vec, nx_vec)\n grid = np.stack((yv, xv), axis=2)\n grid = grid.reshape(1, 1, ny, nx, 2)\n return grid\n\n def post_process(self, outputs, conf_thres=0.001):\n \"\"\"\n Transforms raw output into boxes, confs, classes\n Applies NMS thresholding on bounding boxes and confs\n Parameters:\n output: raw output tensor\n Returns:\n boxes: x1,y1,x2,y2 tensor (dets, 4)\n confs: class * obj prob tensor (dets, 1)\n classes: class type tensor (dets, 1)\n \"\"\"\n scaled = []\n grids = []\n for out in outputs:\n out = self.sigmoid_v(out)\n _, _, width, height, _ = out.shape\n grid = self.make_grid(width, height)\n grids.append(grid)\n scaled.append(out)\n z = []\n for out, grid, stride, anchor in zip(scaled, grids, self.strides, self.anchor_grid):\n _, _, width, height, _ = out.shape\n out[..., 0:2] = (out[..., 0:2] * 2. - 0.5 + grid) * stride\n out[..., 2:4] = (out[..., 2:4] * 2) ** 2 * anchor\n\n out = out.reshape((1, 3 * width * height, self.num_classes+5))\n z.append(out)\n pred = np.concatenate(z, 1)\n xc = pred[..., 4] > conf_thres\n pred = pred[xc]\n return self.nms(pred)\n\n def xywh2xyxy(self, x):\n # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\n y = np.zeros_like(x)\n y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x\n y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y\n y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x\n y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y\n return y\n\n def non_max_suppression(self, boxes, confs, classes, iou_thres=0.6):\n x1 = boxes[:, 0]\n y1 = boxes[:, 1]\n x2 = boxes[:, 2]\n y2 = boxes[:, 3]\n areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n order = confs.flatten().argsort()[::-1]\n keep = []\n while order.size > 0:\n i = order[0]\n keep.append(i)\n xx1 = np.maximum(x1[i], x1[order[1:]])\n yy1 = np.maximum(y1[i], y1[order[1:]])\n xx2 = np.minimum(x2[i], x2[order[1:]])\n yy2 = np.minimum(y2[i], y2[order[1:]])\n w = np.maximum(0.0, xx2 - xx1 + 1)\n h = np.maximum(0.0, yy2 - yy1 + 1)\n inter = w * h\n ovr = inter / (areas[i] + areas[order[1:]] - inter)\n inds = np.where(ovr <= iou_thres)[0]\n order = order[inds + 1]\n boxes = boxes[keep]\n confs = confs[keep]\n classes = classes[keep]\n return boxes, confs, classes\n\n def nms(self, pred, iou_thres=0.6):\n boxes = self.xywh2xyxy(pred[..., 0:4])\n # 原仓库https://github.com/SeanAvery/yolov5-tensorrt/blob/master/python/lib/Processor.py\n # 没有下面这一行，置信度取得是class的置信度，这里给乘上obj的置信度，防止置信度都是1\n pred[:, 5:] *= pred[:, 4:5]\n confs = np.amax(pred[:, 5:], 1, keepdims=True)\n classes = np.argmax(pred[:, 5:], axis=-1)\n return self.non_max_suppression(boxes, confs, classes)\n\n def infer(self, image: np.ndarray = None) -> List[DetectResult]:\n super().prepare()\n shape_orig_WH = (image.shape[1], image.shape[0])\n resized = self.pre_process(image)\n # outputs = self.inference(resized)\n np.copyto(self.inputs[0].host, resized.flatten())\n outputs = self.do_inference()\n # reshape from flat to (1, 3, x, y, 85)\n reshaped = []\n for output, shape in zip(outputs, self.output_shapes):\n reshaped.append(output.reshape(shape))\n boxes, confs, classes = self.post_process(reshaped)\n detect_results = []\n for box, conf, category in zip(boxes, confs, classes):\n x_scale, y_scale = image.shape[1] / 640, image.shape[0] / 640\n detect_results.append(DetectResult(bbox=BBox(ltx=round(box[0]*x_scale),\n lty=round(box[1]*y_scale),\n rbx=round(box[2]*x_scale),\n rby=round(box[3]*y_scale)),\n score=float(conf),\n category=int(category)))\n return detect_results\n\n","repo_name":"xuguangzong/ToolScript","sub_path":"post_processing/yolov5.py","file_name":"yolov5.py","file_ext":"py","file_size_in_byte":6455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"1074791608","text":"from rest_framework.response import Response\nfrom waves.interfaces.use_cases.create_sample_patients_use_case_interface import CreateSamplePatientsUseCaseInterface\nfrom waves.repositories.create_sample_patients_data_access import CreateSamplePatientsDataAccess\n\n\nclass CreateSamplePatientsUseCase(CreateSamplePatientsUseCaseInterface):\n def __init__(self, user_id):\n self._user_id = user_id\n\n def run(self):\n create_sample_data_data_access = CreateSamplePatientsDataAccess(self._user_id)\n patients_entities = create_sample_data_data_access.create_sample_patients()\n parsed_patients = {}\n index = 0\n for patient in patients_entities:\n parsed_patients[index] = patient.__dict__\n index += 1\n return Response(data=parsed_patients, status=Response.status_code)\n","repo_name":"alberturria/PerformAppServer","sub_path":"waves/use_cases/create_sample_patients_use_case.py","file_name":"create_sample_patients_use_case.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"72935617227","text":"print('Bem vindo(a) ao controle de colaboradores Sabrina Bueno Prata Fernandes!')\nid_global = 0 # Definição da variável de id's\nlista_colaboradores = [] # Definição da lista onde serão armazenados os colaboradores\n\n# Função para cadastrar um colaborador\ndef cadastrar_colaborador(id):\n global id_global\n print(80 * '*')\n print(25 * '-', ' MENU CADASTRAR COLABORADOR ', 25 * '-')\n print('Id do colaborador {}'.format(id))\n #Inputs dos dados do colaborador sendo cadastrado\n nome = input(\"Por favor, digite o Nome do colaborador:\")\n setor = input(\"Por favor, digite o Setor do colaborador: \")\n salario = float(input(\"Por favor, digite o Salário do colaborador: \"))\n #Criação de um dicionário que armazena os dados cadastrados\n colaborador = {\n \"id\": id,\n \"nome\": nome,\n \"setor\": setor,\n \"salario\": salario\n }\n\n lista_colaboradores.append(colaborador)\n id_global += 1\n\n#Função que consulta a lista_colaboradores e retorna o colaborador\ndef print_colaborador(colaborador):\n print('Id:{}'.format(colaborador[\"id\"]))\n print('nome:{}'.format(colaborador[\"nome\"]))\n print('setor:{}'.format(colaborador[\"setor\"]))\n print('salario:{}'.format(colaborador[\"salario\"]))\n\n#Função que consulta todos os colaboradores cadastrados na lista_colaboradores mostrando cada colaborador ao chamar a função print_colaborador(colaborador)\ndef consultar_todos():\n global lista_colaboradores\n for colaborador in lista_colaboradores:\n print_colaborador(colaborador)\n\n# Função que consulta os colaboradores fazendo uma busca pelo id correspondente\ndef consultar_por_id():\n global lista_colaboradores\n id = int(input('Digite o id do colaborador:')) #Recebe o valor do id que deve ser apresentado\n for colaborador in lista_colaboradores:\n if colaborador[\"id\"] == id: #Define que se o id do cadastro for igual ao que deve ser apresentado deve chamar a função print_colaborador(colaborador)\n print_colaborador(colaborador)\n return\n print('Id não encontrado')\n\n# Função que consulta os colaboradores fazendo uma busca pelo setor correspondente\ndef consultar_por_setor():\n global lista_colaboradores\n setor = input('Digite o setor dos colaboradores:') #Recebe o valor do setor que deve ser apresentado\n vazio = True\n for colaborador in lista_colaboradores:\n if colaborador[\"setor\"] == setor: #Define que se o setor do cadastro for igual ao que deve ser apresentado deve chamar a função print_colaborador(colaborador)\n print_colaborador(colaborador)\n vazio = False\n if vazio:\n print('Setor vazio')\n\n# Função que realiza consulta no cadastro de colaboradores\ndef consultar_colaborador():\n while True:\n #Menu apresentado ao usuário\n print(80 * '*')\n print(25 * '-', ' MENU CONSULTAR COLABORADOR ', 25 * '-')\n print(\"CONSULTAR COLABORADOR\")\n print(\"1. Consultar Todos\")\n print(\"2. Consultar por ID\")\n print(\"3. Consultar por Setor\")\n print(\"4. Retornar ao Menu\")\n opcao = input(\"Digite sua opção: \") #Recebe a opção deseado pelo usuário\n\n #Verifica a opção recebida e chama a função corresponte\n if opcao == '1':\n consultar_todos()\n\n elif opcao == '2':\n consultar_por_id()\n\n elif opcao == '3':\n consultar_por_setor()\n\n elif opcao == '4':\n break\n\n else:\n print(\"Opção inválida.\")\n\n#Função que remove o colaborados da lista existente\ndef remover_colaborador():\n global lista_colaboradores\n print(80 * '*')\n print(26 * '-', ' MENU REMOVER COLABORADOR ', 26 * '-')\n print('Digite o id do colaborador a ser removido')\n id = int(input('id'))\n\n # Função para verificar se o colaborador deve ser removido\n def check(colaborador):\n return colaborador[\"id\"] != id\n lista_colaboradores = filter(check, lista_colaboradores) #Mantém apenas os colaboradores com id diferente do inserido para remoção através do filter\n\n#Função que apresenta o menu principal\ndef main_menu():\n global id_global\n while True:\n #Print do menu\n print(80 * '*')\n print(31 * '-', ' MENU PRINCIPAL ', 31 * '-')\n print('Escolha a opção desejada:')\n print('1 - Cadastrar colaborador')\n print('2 - Consultar Colaborador(es)')\n print('3 - Remover Colaborador')\n print('4 - Sair')\n op = int(input('Opção:'))\n #Verifica a opção selecionada e chama a função correspondente\n if op == 1:\n cadastrar_colaborador(id_global+1)\n elif op == 2:\n consultar_colaborador()\n elif op == 3:\n remover_colaborador()\n elif op == 4:\n print('Encerrando o programa...')\n break\n else:\n print('Opção inválida, selecione um número correspondente a ação desejada.')\n\nmain_menu()\n","repo_name":"sabrinapratafernandes/praticando_python_faculdade","sub_path":"pratica4.py","file_name":"pratica4.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"27623485758","text":"class Solution:\n def getHint(self, secret: str, guess: str) -> str:\n a = 0\n b = 0\n\n map = [0 for i in range(10)]\n\n for i in range(len(secret)):\n map[ord(secret[i]) - ord('0')] += 1\n\n for i in range(len(secret)):\n ch = ord(secret[i]) - ord('0')\n if ch == ord(guess[i]) - ord('0'):\n a += 1\n map[ch] -= 1\n\n for i in range(len(secret)):\n ch1 = ord(secret[i]) - ord('0')\n ch2 = ord(guess[i]) - ord('0')\n if ch1 != ch2 and map[ch2] > 0:\n b += 1\n map[ch2] -= 1\n\n return f'{a}A{b}B'\n\n\ns = Solution()\nsecret = \"1807\"\nguess = \"7810\"\nprint(s.getHint(secret, guess))\nprint(s.getHint(\"1123\", \"0111\"))\n","repo_name":"srinathalla/python","sub_path":"algo/string/BullsAndCows.py","file_name":"BullsAndCows.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"30980457809","text":"# -*- coding: utf-8 -*-\n# --------------------------------------------------------------------------------- #\n# Software de Observaciones Sintéticas S.O.S.\n# Line fitting functions\n#\n# Marcial Becerril, @ 19 January 2022\n# Latest Revision: 19 Jan 2022, 19:12 GMT\n#\n# For all kind of problems, requests of enhancements and bug reports, please\n# write to me at:\n#\n# mbecerrilt92@gmail.com\n# mbecerrilt@inaoep.mx\n#\n# --------------------------------------------------------------------------------- #\n\n\nimport numpy as np\n\nfrom matplotlib import colors\nfrom matplotlib.pyplot import *\nfrom scipy.optimize import curve_fit\n\nfrom PyQt5 import QtCore, QtWidgets, uic, QtGui\nfrom PyQt5.QtCore import Qt, QObject, QThread\nfrom PyQt5.QtWidgets import QApplication, QWidget, QMessageBox\nfrom PyQt5.QtGui import QPixmap, QIcon\n\nimport sos\nfrom .misc.line_functions import *\nfrom .misc.print_msg import *\nfrom .misc.table_model import *\n\nfrom scipy.signal import find_peaks\nfrom scipy.signal import savgol_filter\nfrom scipy import sparse\nfrom scipy.sparse.linalg import spsolve\nfrom scipy.integrate import simps\n\nfrom datetime import datetime\n\nfrom matplotlib.backends.backend_qt4agg import(\n FigureCanvasQTAgg as FigureCanvas,\n NavigationToolbar2QT as NavigationToolbar)\n\n\n\nclass BaselineSpecWindow(QWidget):\n \"\"\"\n Substract baselines\n Parameters\n ----------\n x : array\n y : array\n method : array\n Method of baseline substraction:\n 1. line. Get the best fit line that fit several points\n 2. polynomial N-degree. Best fit with a polynomial function of N-degree\n 3. bls. BLS Algorithm\n ----------\n \"\"\"\n # Signal to update data\n signal_baseline = QtCore.pyqtSignal(str)\n\n def __init__(self):\n\n super(BaselineSpecWindow, self).__init__()\n\n uic.loadUi(\"./sos/res/gui/baseline.ui\", self)\n\n # Initialisation of variables\n self.inter = False\n self.selPointsFlag = False\n self.selRegionFlag = False\n self.method = None\n self.flag_apply = False\n self.iter_points = []\n\n # Assign buttons\n self.cancelButton.mousePressEvent = self.close_widget\n # Activate interaction\n self.interactButton.mousePressEvent = self.activate_interactive\n # Type of selection\n self.choosePointsButton.mousePressEvent = self.act_points_selection\n self.removePointsButton.mousePressEvent = self.act_region_selection\n # Selection method\n self.linealButton.mousePressEvent = self.linear_selection\n self.polyButton.mousePressEvent = self.poly_selection\n self.blsButton.mousePressEvent = self.bls_selection\n # Clear button\n self.clearButton.mousePressEvent = self.reset_canvas\n # Apply button\n self.applyButton.mousePressEvent = self.apply_baseline_correction\n # Accept button\n self.acceptButton.mousePressEvent = self.accept_baseline_correction\n\n #cid = self.f1.figure.canvas.mpl_connect('button_press_event', self.resDraw)\n\n\n def load_init_params(self, fig, ax, x, y, name, save):\n # Load initial params\n self.x = x\n self.y = y\n\n # File name\n self.nameLabel.setText(name)\n\n # Initialise corrected data array\n self.data_corrected = self.y.copy()\n\n # Get figure\n self.fig = fig\n self.fig.subplots_adjust(left=0.12, bottom=0.12, right=0.98,\n top=0.98, wspace=None, hspace=None)\n self.ax = ax \n \n # Save figure?\n self.save = save\n\n # Update plot\n self._addmpl(self.fig)\n\n # Initial plot\n self.initial_plot()\n\n\n def close_widget(self, event):\n # Disable graphs\n self.close()\n\n\n def activate_interactive(self, event):\n # Interactive activation\n self.inter = not self.inter\n if self.inter:\n if self.selPointsFlag or self.selRegionFlag:\n self._onclick_xy = self.fig.canvas.mpl_connect('button_press_event', self._onclick)\n else:\n msg('Choose one selection mode', 'warn')\n self.inter = not self.inter\n return\n\n icon_img = './sos/res/icons/int_sel.png'\n else:\n if self.selPointsFlag or self.selRegionFlag:\n self.fig.canvas.mpl_disconnect(self._onclick_xy)\n\n icon_img = './sos/res/icons/int.png'\n\n self.interactButton.setIcon(QIcon(icon_img))\n\n\n def act_points_selection(self, event):\n # Points selection activated\n self.selection_settings(True)\n self._update_selected_plot(self.iter_points)\n\n\n def act_region_selection(self, event):\n # Region selection activated\n self.selection_settings(False)\n self._update_selected_plot(self.iter_points)\n\n\n def linear_selection(self, event):\n # Linear baseline method\n self.baseline_method('linear')\n\n\n def poly_selection(self, event):\n # Linear baseline method\n self.baseline_method('poly')\n\n\n def bls_selection(self, event):\n # Linear baseline method\n self.baseline_method('bls')\n\n\n def baseline_method(self, method):\n\n self.method = method\n\n if self.method == 'linear':\n linear = './sos/res/icons/lineal_icon_sel.png'\n poly = './sos/res/icons/poly_curve.png' \n bls = './sos/res/icons/bls_icon.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(False)\n self.lamdbaBLSEdit.setEnabled(False)\n self.pBLSEdit.setEnabled(False)\n self.iterBLSEdit.setEnabled(False)\n\n elif self.method == 'poly':\n linear = './sos/res/icons/lineal_icon.png'\n poly = './sos/res/icons/poly_curve_sel.png' \n bls = './sos/res/icons/bls_icon.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(True)\n self.lamdbaBLSEdit.setEnabled(False)\n self.pBLSEdit.setEnabled(False)\n self.iterBLSEdit.setEnabled(False)\n\n elif self.method == 'bls':\n linear = './sos/res/icons/lineal_icon.png'\n poly = './sos/res/icons/poly_curve.png' \n bls = './sos/res/icons/bls_icon_sel.png' \n # Disable the other functions\n self.nDegreeBox.setEnabled(False)\n self.lamdbaBLSEdit.setEnabled(True)\n self.pBLSEdit.setEnabled(True)\n self.iterBLSEdit.setEnabled(True)\n\n self.linealButton.setIcon(QIcon(linear))\n self.polyButton.setIcon(QIcon(poly))\n self.blsButton.setIcon(QIcon(bls))\n\n\n def selection_settings(self, ptsFlag):\n # Grpah Selection Configuration\n self.selPointsFlag = ptsFlag\n self.selRegionFlag = not self.selPointsFlag\n\n if self.selPointsFlag:\n points = './sos/res/icons/choosePoints_sel.png'\n region = './sos/res/icons/removePoints.png'\n else:\n points = './sos/res/icons/choosePoints.png'\n region = './sos/res/icons/removePoints_sel.png'\n\n self.choosePointsButton.setIcon(QIcon(points))\n self.removePointsButton.setIcon(QIcon(region))\n\n\n def apply_baseline_correction(self, event):\n # Baseline correction\n if self.method == 'bls':\n l = float(self.lamdbaBLSEdit.text())\n p = float(self.pBLSEdit.text())\n n = int(float((self.iterBLSEdit.text())))\n self.iterBLSEdit.setText(str(n))\n y_baseline = baseline_als_optimized(self.y, l, p, niter=n)\n \n else:\n points = self.iter_points\n points = np.sort(points)\n\n if self.selPointsFlag:\n x_filtered = []\n y_filtered = []\n # Extracting data [points]\n for i in range(len(points)):\n x_filtered.append(self.x[points[i]])\n y_filtered.append(self.y[points[i]])\n\n elif self.selRegionFlag:\n # Extracting data [region]\n adquire_data = False\n x_mask = [True]*len(self.x)\n y_mask = [True]*len(self.y)\n for i in range(len(points)):\n if adquire_data:\n x_mask[points[i-1]:points[i]] = [False]*(points[i]-points[i-1])\n y_mask[points[i-1]:points[i]] = [False]*(points[i]-points[i-1]) \n adquire_data = not adquire_data\n\n x_filtered = np.array(self.x)[x_mask]\n y_filtered = np.array(self.y)[y_mask]\n\n else:\n msg('Choose one selection mode', 'warn')\n return\n\n x_baseline = self.x\n\n if self.method == 'linear':\n y_baseline = poly_baseline(x_filtered, y_filtered, 1, x_baseline)\n\n elif self.method == 'poly':\n degree = self.nDegreeBox.value()\n y_baseline = poly_baseline(x_filtered, y_filtered, degree, x_baseline)\n \n # Set flag\n self.flag_apply = True\n\n data_corrected = self.y - y_baseline\n\n # Update data with baseline substrated\n self.data_corrected = data_corrected\n\n self._update_plot(y_baseline, data_corrected)\n\n\n def accept_baseline_correction(self, event):\n # Applying baseline correction\n if not self.flag_apply:\n self.apply_baseline_correction(event)\n \n #self.signal_baseline.emit(self.kind)\n\n if self.save:\n now = datetime.now()\n name = now.strftime(\"%d-%m-%Y_%H-%M-%S\")\n self.fig.savefig('fig_'+name+'_bl.png')\n\n self.close()\n\n\n def _onclick(self, event):\n \"\"\"\n On click event to select lines\n \"\"\"\n if event.inaxes == self.ax:\n # Left-click\n if event.button == 1:\n ix, iy = event.xdata, event.ydata\n # Add peaks\n xarray = np.where(self.x>ix)[0]\n if len(xarray) > 0:\n xpos = xarray[0]\n else:\n xpos = len(self.x)-1\n self.iter_points.append(xpos)\n\n self.flag_apply = False\n\n # Right-click\n elif event.button == 3:\n ix, iy = event.xdata, event.ydata\n popt = []\n # Remove points\n # Define threshold\n thresh = 5*np.mean(np.diff(self.x))\n xlines = np.where((ix >= (np.array(self.x)[self.iter_points] - thresh)) & \n (ix < (np.array(self.x)[self.iter_points] + thresh)))[0]\n\n try:\n if len(xlines) > 0:\n x_min = np.argmin(np.abs((np.array(self.x)[np.array(self.iter_points)[xlines]] - ix)))\n if self.selRegionFlag:\n self.iter_points.remove(self.iter_points[xlines[x_min]])\n else:\n ylines = np.where((iy >= (np.array(self.y)[self.iter_points] - thresh)) & \n (iy < (np.array(self.y)[self.iter_points] + thresh)))\n\n if len(ylines) > 0:\n y_min = np.argmin(np.abs((np.array(self.y)[np.array(self.iter_points)[ylines]] - iy)))\n self.iter_points.remove(self.iter_points[xlines[y_min]])\n \n self.flag_apply = False\n except:\n pass\n\n # Update plot\n self._update_selected_plot(self.iter_points)\n\n\n def _update_selected_plot(self, points):\n \"\"\"\n Update selected Points/Region in the canvas\n \"\"\"\n self.ax.clear()\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.plot(self.x, self.y, 'k')\n for i in range(len(points)):\n if self.selPointsFlag:\n self.ax.plot(self.x[points[i]], self.y[points[i]], 'r+')\n elif self.selRegionFlag:\n self.ax.axvline(self.x[points[i]], color='r', linewidth=1)\n\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def _update_plot(self, baseline, data_corrected):\n \"\"\"\n Update baseline in the canvas\n \"\"\"\n self.ax.clear()\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.plot(self.x, self.y, 'k', linewidth=0.75)\n self.ax.plot(self.x, baseline, 'c-.', linewidth=0.75)\n self.ax.plot(self.x, data_corrected, 'r')\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def initial_plot(self):\n \"\"\"\n Initial plot\n \"\"\"\n self.ax.clear()\n self.ax.plot(self.x, self.y, 'k')\n\n # Label axes\n ux_label = self.ux\n if self.ux:\n ux_label = '['+ux_label+']'\n uy_label = self.uy\n if self.uy:\n uy_label = '['+uy_label+']'\n\n self.ax.set_xlabel(r''+ux_label)\n self.ax.set_ylabel(r'Temperature '+uy_label)\n\n self.ax.grid()\n\n self.fig.canvas.draw_idle()\n\n\n def reset_canvas(self, event):\n # Restart to initial plot\n self.initial_plot()\n\n self.iter_points = []\n\n\n def _addmpl(self, fig):\n \n self.canvas = FigureCanvas(fig)\n self.plotLayout.addWidget(self.canvas)\n self.canvas.draw()\n self.toolbar = NavigationToolbar(self.canvas,\n self, coordinates=True)\n self.plotLayout.addWidget(self.toolbar)\n\n\n def _rmmpl(self):\n self.plotLayout.removeWidget(self.canvas)\n self.canvas.close()\n self.plotLayout.removeWidget(self.toolbar)\n self.toolbar.close()","repo_name":"MarcialX/MUSpipe","sub_path":"inter_functions.py","file_name":"inter_functions.py","file_ext":"py","file_size_in_byte":14339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"6524726228","text":"import random\nimport numpy as np\nimport torch\nimport logging\n\n\ndef set_seed(seed: int = 42, n_gpu: int = 0):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n if n_gpu > 0:\n torch.cuda.manual_seed_all(seed)\n\n\ndef get_logger(file_name: str, logger_name: str = \"dialogue\") -> logging.Logger:\n root = logging.getLogger(logger_name)\n root.setLevel(logging.DEBUG)\n formatter = logging.Formatter(\"%(asctime)s | %(message)s\")\n file_handler = logging.FileHandler(file_name)\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(formatter)\n root.addHandler(file_handler)\n return root\n","repo_name":"MalinML/EmpathySeeker","sub_path":"posts_classifier/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"14448175192","text":"# Convert from Python to JSON\n# json.dumps() method can convert a Python object into a JSON string. \n# Syntax:\n\n# json.dumps(dict, indent)\n# It takes two parameters:\n\n# dictionary – name of dictionary which should be converted to JSON object.\n# indent – defines the number of units for indentation\n\n\n\n\n\n\n\n\n\n\n\n\n##dumps() method is used to store the python objct to json file in a string formate\n\nimport json\n\na={9: 3}\nmystring = json.dumps(a)\nprint(mystring)","repo_name":"asmonorizimik/python_json","sub_path":"dumps.py","file_name":"dumps.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"24384486265","text":"import sys\nfrom Bio import Entrez\n\ndef entry_count(org, start, end):\n Entrez.email = \"noahflynn@wustl.edu\"\n\n term = '%s[Organism] AND (%s[Publication Date] : %s[Publication Date])' % (org, start, end)\n\n handle = Entrez.esearch(db=\"nucleotide\", term=term)\n record = Entrez.read(handle)\n return record[\"Count\"]\n\ndef main():\n with open(sys.argv[1]) as f:\n data = f.read()\n data = data.split()\n count = entry_count(data[0], data[1], data[2])\n print(count)\n\nmain()\n","repo_name":"nrflynn2/Algorithm-Challenges","sub_path":"Rosalind/Armory/GBK.py","file_name":"GBK.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41804350778","text":"\n\n\"\"\"\n\nTODO: In Fill Room, replace npc search with simpler Sub-Class search\nTODO: Eliminate need to pass character to the npcs.\n\n\"\"\"\n\nimport time as time\nimport random as random\nimport threading as threading\nimport textwrap as textwrap\nimport logging as logging\n\nfrom app.main import config, items, enemies, actions, world, mixins, objects, shops, npcs\n\n\nwrapper = textwrap.TextWrapper(width=config.TEXT_WRAPPER_WIDTH)\n\nall_npcs = mixins.npcs\n\nlock = threading.Lock()\nlogging.basicConfig(level=logging.DEBUG,\n format='[%(levelname)s] (%(threadName)-10s) %(message)s',\n )\n \n\nclass MapTile(mixins.DataFileMixin):\n def __init__(self, x, y, area_name: str, room_name: str, room_number: int):\n\n self._area_data = self.get_area_by_name(area_name)\n self._room_data = self._area_data[room_name]\n\n self.x = x\n self.y = y\n self.character = None\n self.room_name = self._room_data['name']\n self.area = self._room_data['area']\n self._room_number = room_number\n self.description = self._room_data['description']\n self._is_shop = self._room_data['shop']\n self._shop_items = self._room_data['shop_items']\n self.objects = []\n self.items = []\n self.npcs = []\n self.enemies = []\n self.spawn = self._room_data['spawn']\n self.hidden = []\n self.room_filled = False\n self.shop_filled = False\n\n def modify_player(self):\n raise NotImplementedError()\n\n def adjacent_moves_enemy(self, area):\n moves = []\n if world.tile_exists(x=self.x, y=self.y - 1, area=self.area):\n moves.append(actions.MoveNorthEnemy())\n if world.tile_exists(x=self.x, y=self.y + 1, area=self.area):\n moves.append(actions.MoveSouthEnemy())\n if world.tile_exists(x=self.x + 1, y=self.y, area=self.area):\n moves.append(actions.MoveEastEnemy())\n if world.tile_exists(x=self.x - 1, y=self.y, area=self.area):\n moves.append(actions.MoveWestEnemy())\n return moves\n\n def obvious_exits(self):\n \"\"\"Returns all of the available actions in this room.\"\"\"\n moves = []\n if world.tile_exists(x=self.x, y=self.y - 1, area=self.area):\n moves.append(\"north\")\n if world.tile_exists(x=self.x, y=self.y + 1, area=self.area):\n moves.append(\"south\")\n if world.tile_exists(x=self.x + 1, y=self.y, area=self.area):\n moves.append(\"east\")\n if world.tile_exists(x=self.x - 1, y=self.y, area=self.area):\n moves.append(\"west\")\n obvious = []\n if len(moves) == 0:\n obvious = 'None'\n return \"Obvious exits: {}\".format(obvious)\n for move in moves:\n obvious.append(move)\n obvious = ', '.join(obvious)\n return \"Obvious exits: {}\".format(obvious)\n\n def all_objects(self):\n all_objects = []\n if len(self.items) + len(self.npcs) + len(self.objects) + len(self.enemies) == 0:\n return \"\"\n for char in self.npcs:\n all_objects.append(char.name)\n for char in self.enemies:\n all_objects.append(char.name)\n for item in self.items:\n all_objects.append(item.name)\n for object in self.objects:\n all_objects.append(object.name)\n if len(all_objects) > 1:\n all_objects_output = ', '.join(all_objects[:-1])\n all_objects_output = all_objects_output + ', and ' + all_objects[-1]\n else:\n all_objects_output = all_objects[0]\n return \"You also see {}.\".format(all_objects_output)\n \n def all_object_handles(self):\n all_object_handles = []\n if len(self.items) + len(self.npcs) + len(self.objects) + len(self.enemies) == 0:\n return \"\"\n for char in self.npcs:\n all_object_handles.append(char.name)\n for char in self.enemies:\n all_object_handles.append(char.name)\n for item in self.items:\n all_object_handles.append(item.name)\n for object in self.objects:\n all_object_handles.append(object.name)\n return all_object_handles\n\n def fill_room(self, character):\n if not self.room_filled:\n for category in self._room_data['objects']:\n for object in self._room_data['objects'][category]:\n try:\n self.objects.append(objects.create_object(object_category=category, object_name=object, room=self))\n except:\n print(\"WARNING: Could not create object \" + object.name + \" in room \" + self.room_name)\n for category in self._room_data['items']:\n for item in self._room_data['items'][category]:\n try:\n self.items.append(items.create_item(item_category=category, item_name=item))\n except:\n print(\"WARNING: Could not create item \" + item.name + \" in room \" + self.room_name)\n for npc in self._room_data['npcs']:\n try:\n self.npcs.append(npcs.create_npc(npc_category=npc, npc_name=npc, character=character, room=self))\n self.npcs[-1].start()\n except:\n print(\"WARNING: Could not create npc \" + npc.name + \" in room \" + self.room_name)\n for door in self._room_data['hidden']['doors']:\n try:\n self.hidden.append(objects.Door(object_name=door, room=self))\n except:\n print(\"WARNING: Could not create hidden door \" + door.name + \" in room \" + self.room_name)\n for npc in self._room_data['hidden']['npcs']:\n try:\n self.hidden.append(npcs.create_npc(npc_category=npc, npc_name=npc, character=character, room=self))\n self.hidden[-1].start()\n except:\n print(\"WARNING: Could not create hidden npc \" + npc.name + \" in room \" + self.room_name)\n for category in self._room_data['hidden']['items']:\n for item in self._room_data['hidden']['items'][category]:\n try:\n self.hidden.append(items.create_item(item_category=category, item_name=item))\n except:\n print(\"WARNING: Could not create hidden item \" + item.name + \" in room \" + self.room_name)\n self.room_filled = True\n \n def fill_shop(self):\n if not self.shop_filled:\n self.shop = shops.Shop(shop_name=self.area, shop_items=self.shop_items)\n self.shop.write_shop_menu() \n self.shop_filled = True\n \n @property\n def room_number(self):\n with lock:\n return self._room_number\n @room_number.setter\n def room_number(self, room_number):\n with lock:\n self._room_number = room_number\n \n @property \n def is_shop(self):\n with lock:\n return self._is_shop\n \n @property\n def shop(self):\n with lock:\n return self._shop\n @shop.setter\n def shop(self, shop):\n with lock:\n self._shop = shop\n \n @property\n def shop_items(self):\n with lock:\n return self._shop_items\n\n def add_object(self, object):\n with lock:\n self.objects.append(object)\n return\n\n def add_hidden_object(self, object):\n with lock:\n self.hidden.append(object)\n return\n\n def remove_object(self, object):\n with lock:\n self.objects.remove(object)\n return\n\n def remove_hidden_object(self, object):\n with lock:\n self.hidden.remove(object)\n return\n\n def add_item(self, item):\n with lock:\n self.items.append(item)\n return\n\n def remove_item(self, item):\n with lock:\n self.items.remove(item)\n return\n\n def add_hidden_item(self, item):\n with lock:\n self.hidden.append(item)\n return\n\n def remove_hidden_item(self, item):\n with lock:\n self.hidden.remove(item)\n return\n\n def add_npc(self, npc):\n with lock:\n self.npcs.append(npc)\n return\n\n def add_hidden_npc(self, npc):\n with lock:\n self.hidden.append(npc)\n return\n\n def remove_npc(self, npc):\n with lock:\n self.npcs.remove(npc)\n return\n\n def remove_hidden_npc(self, npc):\n with lock:\n self.hidden.remove(npc)\n return\n\n def add_enemy(self, enemy):\n with lock:\n self.enemies.append(enemy)\n return\n\n def remove_enemy(self, enemy):\n with lock:\n self.enemies.remove(enemy)\n return\n\n\n def intro_text(self):\n intro_text = \"\"\"\\\n[{}, {}] \n{}\n{}\n{}\\\n \"\"\".format(self.area,\n self.room_name,\n wrapper.fill(text=self.description),\n self.obvious_exits(),\n self.all_objects())\n return intro_text\n\n def spawn_generator(self, character):\n return NotImplementedError()\n\n def search_room(self):\n pass\n\n def run(self, character):\n return NotImplementedError()\n\n\nclass Town(MapTile):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n def spawn_generator(self, character):\n pass\n\n def run(self, character):\n pass\n\n\nclass Dochas(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasGrounds(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasLeatherworks(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass DochasSmallHouse(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n \n \nclass DochasWeaponsmith(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\nclass EdgewoodForest(MapTile):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n def spawn_generator(self, character):\n area_rooms = world.area_rooms(self.area)\n while character.area == self.area.replace(\" \", \"\"):\n time.sleep(5)\n area_enemies = world.area_enemies(self.area)\n if len(area_enemies) < 1:\n area_rooms = {keys: value for keys, value in area_rooms.items() if value is not None}\n spawn_room_coords = random.choice(list(area_rooms))\n if random.randint(0,100) > 50:\n spawn_room = world.tile_exists(x=spawn_room_coords[0], y=spawn_room_coords[1], area=self.area)\n spawn_room.enemies.append(\n enemies.Enemy(enemy_name=self._room_data['spawn'][0],\n target=character,\n room=spawn_room,\n location_x=spawn_room_coords[0],\n location_y=spawn_room_coords[1],\n area=self.area))\n spawn_room.enemies[-1].start()\n\n\n def run(self, character):\n spawn_thread = threading.Thread(target=self.spawn_generator, args=(character,))\n spawn_thread.setDaemon(True)\n spawn_thread.start()\n\n\nclass Field(Town):\n def __init__(self, x, y, area_name, room_name, room_number):\n super().__init__(x=x, y=y, area_name=area_name, room_name=room_name, room_number=room_number)\n\n\n","repo_name":"turpenar/dion2","sub_path":"app/main/tiles.py","file_name":"tiles.py","file_ext":"py","file_size_in_byte":12353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"8817109424","text":"iterator = 1\n\n\ndef print_dict(myDict, iterator, symbol=\"\"):\n iterator += 1\n for k, v in myDict.items():\n print(symbol, k, \": \", end=\"\")\n if type(v) == type(myDict):\n print(\"\\n\", end=\"\")\n print_dict(v, iterator, (iterator * \"\\t\"))\n else:\n print(v)\n\n\npeople = {1: {'name': 'John', 'age': '27', 'sex': 'Male'},\n 2: {'name': 'Marie', 'age': '22', 'sex': 'Female'},\n 3: {'name': 'Peter', 'age': '29', 'sex': 'Male',\n 'parents': {\"mam\": \"Gohar\", \"father\": \"Smbat\", \"brother\": \"Alik\"}},\n 4: {'name': 'Peter', 'age': '29', 'sex': 'Male'}}\n\nprint_dict(people, iterator)\n","repo_name":"matevosyanmher/python","sub_path":"QA_Automation/Homework_8_Classes/printDictionary.py","file_name":"printDictionary.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"18010351205","text":"# https://leetcode.com/problems/network-delay-time/description/\n# MEDIUM\n# Tags: graphlc, heaplc, minheaplc, djikstralc, #743\n\n# GIVEN:\n # a network of n nodes, labeled from 1 to n\n # array, times, a list of travel times as directed edges times[i] = (ui, vi, wi), where ui is the source node, vi is the target node, and wi is the time it takes for a signal to travel from source to target\n\n# TASK:\n # We will send a signal from a given node k\n # Return the minimum time it takes for all the n nodes to receive the signal\n # If it is impossible for all the n nodes to receive the signal, return -1\n\n# EXAMPLES:\n# Input: times = [[2,1,1],[2,3,1],[3,4,1]], n = 4, k = 2\n# Output: 2\n\n# Input: times = [[1,2,1]], n = 2, k = 1\n# Output: 1\n\n# Input: times = [[1,2,1]], n = 2, k = 2\n# Output: -1\n\n###########################################################################################################\n\n# ✅ ALGORITHM: DJIKSTRA'S ALGORITHM\n# Create a min heap that pops out the node in the path with the min total time\n# Once we have visited all n nodes, we can return this min total time\n\n# TIME COMPLEXITY: O(E log V)\n # each push/pop operation is O(log V)\n # in the worst case, we can push to heap E times (1 for each edge)\n # -> Overall TC = O(E log V)\n# SPACE COMPLEXITY: O(V^2)\n # worst case: every node is connected to every other node -> SC = O(V^2)\n\nfrom collections import defaultdict\nfrom heapq import heappop, heappush\n\ndef networkDelayTime(times, n, k):\n # build adjacency list of destination nodes and times\n graph = defaultdict(set)\n for src, dest, time in times:\n graph[src].add((dest, time))\n \n # graph[i] = { source_node: (\n # (destination_node1, time1), \n # (destination_node2, time2),\n # ...\n # )\n # }\n \n heap = [ (0, k) ] # add source node to heap\n visited = set()\n\n while heap:\n total_time, node = heappop(heap) # pop the node in the path with minimum total time\n visited.add(node)\n\n if len(visited) == n: # if we visited all nodes, i.e. all nodes have received signal\n return total_time # this total_time is the min. time needed visit all nodes\n\n for neighbor, time in graph[node]: # for each neighbor of current node,\n if neighbor not in visited:\n heappush(heap, (total_time + time, neighbor)) # visit neighbor by adding to heap\n \n return -1 # if no min. total time has been returned, it means we can't visit all nodes -> return -1","repo_name":"SlaveToJavascript/LeetCode","sub_path":"Heaps/NetworkDelayTime.py","file_name":"NetworkDelayTime.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"19105471725","text":"import json\n\ndef load_json(file_name):\n \"\"\"Загружает посты из файла в список.\"\"\"\n \"\"\"Если файл не найден или в неподходящем формате, происходит логирование ошибки и\n возвращается пустой список\"\"\"\n with open(file_name, encoding='UTF-8') as file:\n return json.load(file)\n\ndef load_posts():\n \"\"\"загружает все наши посты\"\"\"\n data = load_json(\"data/posts.json\")\n for post in data:\n post['short'] = post['content'][:post['content'].find(\" \", 100)]\n return data\n\ndef load_comments(post_pk):\n \"\"\"возвращаем комментарии\"\"\"\n data = load_json('data/comments.json')\n return [comment for comment in data if comment['post_id'] == post_pk]\n\ndef load_posts_user_id(pk):\n \"\"\"загружает все наши посты\"\"\"\n data = load_posts()\n for post in data:\n if post['pk'] == pk:\n return post\n return 'нет поста с таким номером!'\n\ndef get_comments_by_post_id(post_id):\n \"\"\"возвращает комментарии определенного поста. \"\"\"\n for comment in load_posts():\n if comment[\"post_id\"] in post_id:\n return comment\n return 'у поста нет комментария!'\n\ndef search_for_posts(text):\n \"\"\" возвращает список постов по ключевому слову\"\"\"\n data = load_posts()\n post_filter = []\n for post in data:\n if text.lower() in post['content'].lower():\n post_filter.append(post)\n return post_filter\n\ndef search_for_name(name):\n \"\"\" возвращает список имени и что написал этот человек\"\"\"\n data = load_posts()\n post_filter = []\n for post in data:\n if name.lower() == post['poster_name'].lower():\n post_filter.append(post)\n return post_filter\n\n","repo_name":"melchoir1/course_work3","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"41402381385","text":"import logging\nfrom collections import defaultdict\nfrom itertools import combinations\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\n\nlogger = logging.getLogger(__name__)\n\n\nclass PDAG:\n \"\"\"\n Class for dealing with partially directed graph i.e.\n graphs that contain both directed and undirected edges.\n \"\"\"\n\n def __init__(\n self,\n nodes: list = [],\n dir_edges: list[tuple] = [],\n undir_edges: list[tuple] = [],\n ):\n self._nodes = set(nodes)\n self._undir_edges = set()\n self._dir_edges = set()\n self._parents = defaultdict(set)\n self._children = defaultdict(set)\n self._neighbors = defaultdict(set)\n self._undirected_neighbors = defaultdict(set)\n\n for dir_edge in dir_edges:\n self._add_dir_edge(*dir_edge)\n for unir_edge in undir_edges:\n self._add_undir_edge(*unir_edge)\n\n def _add_dir_edge(self, i, j):\n self._nodes.add(i)\n self._nodes.add(j)\n self._dir_edges.add((i, j))\n\n self._neighbors[i].add(j)\n self._neighbors[j].add(i)\n\n self._children[i].add(j)\n self._parents[j].add(i)\n\n def _add_undir_edge(self, i, j):\n self._nodes.add(i)\n self._nodes.add(j)\n self._undir_edges.add((i, j))\n\n self._neighbors[i].add(j)\n self._neighbors[j].add(i)\n\n self._undirected_neighbors[i].add(j)\n self._undirected_neighbors[j].add(i)\n\n def children(self, node: str) -> set:\n if node in self._children.keys():\n return self._children[node]\n else:\n return set()\n\n def parents(self, node: str) -> set:\n if node in self._parents.keys():\n return self._parents[node]\n else:\n return set()\n\n def neighbors(self, node: str) -> set:\n if node in self._neighbors.keys():\n return self._neighbors[node]\n else:\n return set()\n\n def undir_neighbors(self, node: str) -> set:\n if node in self._undirected_neighbors.keys():\n return self._undirected_neighbors[node]\n else:\n return set()\n\n def is_adjacent(self, i, j):\n \"\"\"Return True if the graph contains an directed\n or undirected edge between i and j\"\"\"\n return any(\n (\n (j, i) in self.dir_edges or (j, i) in self.undir_edges,\n (i, j) in self.dir_edges or (i, j) in self.undir_edges,\n )\n )\n\n def is_clique(self, potential_clique: set) -> bool:\n \"\"\"\n Check every pair of node X potential_clique is adjacent.\n \"\"\"\n return all(self.is_adjacent(i, j) for i, j in combinations(potential_clique, 2))\n\n @classmethod\n def from_pandas(cls, pd_amat: pd.DataFrame):\n assert pd_amat.shape[0] == pd_amat.shape[1]\n nodes = pd_amat.columns\n\n all_connections = []\n start, end = np.where(pd_amat != 0)\n for idx, _ in enumerate(start):\n all_connections.append(\n (pd_amat.columns[start[idx]], pd_amat.columns[end[idx]])\n )\n\n temp = [set(i) for i in all_connections]\n temp2 = [arc for arc in all_connections if temp.count(set(arc)) > 1]\n undir_edges = [tuple(item) for item in set(frozenset(item) for item in temp2)]\n\n dir_edges = [edge for edge in all_connections if edge not in temp2]\n\n return PDAG(nodes=nodes, dir_edges=dir_edges, undir_edges=undir_edges)\n\n def remove_node(self, node):\n \"\"\"Remove a node from the graph\"\"\"\n self._nodes.remove(node)\n\n self._dir_edges = {\n (i, j) for i, j in self._dir_edges if i != node and j != node\n }\n\n self._undir_edges = {\n (i, j) for i, j in self._undir_edges if i != node and j != node\n }\n\n for child in self._children[node]:\n self._parents[child].remove(node)\n self._neighbors[child].remove(node)\n\n for parent in self._parents[node]:\n self._children[parent].remove(node)\n self._neighbors[parent].remove(node)\n\n for u_nbr in self._undirected_neighbors[node]:\n self._undirected_neighbors[u_nbr].remove(node)\n self._neighbors[u_nbr].remove(node)\n\n self._parents.pop(node, \"I was never here\")\n self._children.pop(node, \"I was never here\")\n self._neighbors.pop(node, \"I was never here\")\n self._undirected_neighbors.pop(node, \"I was never here\")\n\n def to_dag(self) -> nx.DiGraph:\n \"\"\"\n Algorithm as described in Chickering (2002):\n\n 1. From PDAG P create DAG G containing all directed edges from P\n 2. Repeat the following: Select node v in P s.t.\n i. v has no outgoing edges (children) i.e. \\\\(ch(v) = \\\\emptyset \\\\)\n\n ii. \\\\(neigh(v) \\\\neq \\\\emptyset\\\\)\n Then \\\\( (pa(v) \\\\cup (neigh(v) \\\\) form a clique.\n For each v that is in a clique and is part of an undirected edge in P\n i.e. w - v, insert a directed edge w -> v in G.\n Remove v and all incident edges from P and continue with next node.\n Until all nodes have been deleted from P.\n\n Returns:\n nx.DiGraph: DAG that belongs to the MEC implied by the PDAG\n \"\"\"\n\n pdag = self.copy()\n\n dag = nx.DiGraph()\n dag.add_nodes_from(pdag.nodes)\n dag.add_edges_from(pdag.dir_edges)\n\n if pdag.num_undir_edges == 0:\n return dag\n else:\n while pdag.nnodes > 0:\n # find node with (1) no directed outgoing edges and\n # (2) the set of undirected neighbors is either empty or\n # undirected neighbors + parents of X are a clique\n found = False\n for node in pdag.nodes:\n children = pdag.children(node)\n neighbors = pdag.neighbors(node)\n pdag._undirected_neighbors[node]\n parents = pdag.parents(node)\n potential_clique_members = neighbors.union(parents)\n\n is_clique = pdag.is_clique(potential_clique_members)\n\n if not len(children) and (not len(neighbors) or is_clique):\n found = True\n # add all edges of node as outgoing edges to dag\n for edge in pdag.undir_edges:\n if node in edge:\n incident_node = set(edge) - {node}\n dag.add_edge(*incident_node, node)\n\n pdag.remove_node(node)\n break\n\n if not found:\n logger.warning(\"PDAG not extendible: Random DAG on skeleton drawn.\")\n\n dag = nx.from_pandas_adjacency(\n self._amat_to_dag(), create_using=nx.DiGraph\n )\n\n break\n\n return dag\n\n @property\n def adjacency_matrix(self) -> pd.DataFrame:\n amat = pd.DataFrame(\n np.zeros([self.nnodes, self.nnodes]),\n index=self.nodes,\n columns=self.nodes,\n )\n for edge in self.dir_edges:\n amat.loc[edge[0], edge[1]] = 1\n for edge in self.undir_edges:\n amat.loc[edge[0], edge[1]] = amat.loc[edge[1], edge[0]] = 1\n return amat\n\n def _amat_to_dag(self) -> pd.DataFrame:\n \"\"\"Transform the adjacency matrix of an PDAG to the adjacency\n matrix of a SOME DAG in the Markov equivalence class.\n\n Returns:\n pd.DataFrame: DAG, a member of the MEC.\n \"\"\"\n pdag_amat = self.adjacency_matrix.to_numpy()\n\n p = pdag_amat.shape[0]\n skel = pdag_amat + pdag_amat.T\n skel[np.where(skel > 1)] = 1\n permute_ord = np.random.choice(a=p, size=p, replace=False)\n skel = skel[:, permute_ord][permute_ord]\n\n for i in range(1, p):\n for j in range(0, i + 1):\n if skel[i, j] == 1:\n skel[i, j] = 0\n\n i_ord = np.sort(permute_ord)\n skel = skel[:, i_ord][i_ord]\n return pd.DataFrame(\n skel,\n index=self.adjacency_matrix.index,\n columns=self.adjacency_matrix.columns,\n )\n\n def vstructs(self):\n vstructs = set()\n for node in self._nodes:\n for p1, p2 in combinations(self._parents[node], 2):\n if p1 not in self._parents[p2] and p2 not in self._parents[p1]:\n vstructs.add((p1, node))\n vstructs.add((p2, node))\n return vstructs\n\n def copy(self):\n \"\"\"Return a copy of the graph\"\"\"\n return PDAG(\n nodes=self._nodes, dir_edges=self._dir_edges, undir_edges=self._undir_edges\n )\n\n def show(self):\n \"\"\"Plot PDAG.\"\"\"\n graph = self.to_networkx()\n pos = nx.circular_layout(graph)\n nx.draw(graph, pos=pos, with_labels=True)\n\n def to_networkx(self) -> nx.MultiDiGraph:\n \"\"\"Convert to networkx graph.\n\n Returns:\n nx.MultiDiGraph: Graph with directed and undirected edges.\n \"\"\"\n nx_pdag = nx.MultiDiGraph(self.dir_edges)\n for edge in self.undir_edges:\n nx_pdag.add_edge(*edge)\n nx_pdag.add_edge(*edge[::-1])\n\n return nx_pdag\n\n @property\n def nodes(self):\n return sorted(list(self._nodes))\n\n @property\n def nnodes(self):\n return len(self._nodes)\n\n @property\n def num_undir_edges(self):\n return len(self._undir_edges)\n\n @property\n def num_dir_edges(self):\n return len(self._dir_edges)\n\n @property\n def num_adjacencies(self):\n return self.num_undir_edges + self.num_edges\n\n @property\n def undir_edges(self):\n return list(self._undir_edges)\n\n @property\n def dir_edges(self):\n return list(self._dir_edges)\n","repo_name":"boschresearch/causalAssembly","sub_path":"causalAssembly/pdag.py","file_name":"pdag.py","file_ext":"py","file_size_in_byte":10048,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"82"} +{"seq_id":"21252616272","text":"import os\nimport time\nfrom dotenv import load_dotenv\nimport telebot\n\n# Load .env\nload_dotenv()\n\nAPI_TOKEN = os.environ['API_TOKEN']\nCHAT_ID = os.environ['CHAT_ID']\nFILE_PATH = os.environ['FILE_PATH']\n\n# Init Telegram Bot\ntb = telebot.TeleBot(API_TOKEN, parse_mode=None)\n\n\ndef reading_log_files(filename):\n with open(filename, \"r\") as f:\n data = f.read().splitlines()\n return data\n\n\ndef log_generator(filename, period=15):\n data = reading_log_files(filename)\n while True:\n time.sleep(period)\n new_data = reading_log_files(filename)\n yield new_data[len(data):]\n data = new_data\n\n\nif __name__ == '__main__':\n x = log_generator(FILE_PATH)\n for lines in x:\n # lines will be a list of new lines added at the end\n # print(lines)\n for message in lines:\n if message:\n tb.send_message(CHAT_ID, message)\n","repo_name":"edtk/log-to-telegram","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"19567533028","text":"from pprint import pprint\r\n\r\nimport requests\r\n\r\nTOKEN = \" \"\r\n\r\n\r\nclass YandexDisk:\r\n\r\n def __init__(self, token):\r\n self.token = token\r\n\r\n def get_headers(self):\r\n return {\r\n 'Content-Type': 'application/json',\r\n 'Authorization': 'OAuth {}'.format(self.token)\r\n }\r\n\r\n def get_files_list(self):\r\n files_url = 'https://akabab.github.io/superhero-api/api/all.json'\r\n headers = {\r\n 'Content-Type': 'application/json',\r\n 'Authorization': 'OAuth {}'.format(self.token)\r\n }\r\n # headers = self.get_headers()\r\n response = requests.get(files_url, headers=headers)\r\n\r\n\r\n heroes = []\r\n for hero in response.json():\r\n # print(hero)\r\n if hero['name'] in ['Hulk', 'Captain America', 'Thanos']:\r\n heroes.append(\r\n {'id': hero['id'], 'name': hero['name'], 'intelligence': hero['powerstats']['intelligence']})\r\n # print(heroes)\r\n max_intelligence = max(heroes, key=lambda x: x['intelligence'])['name']\r\n print(f'Самый умный среди супергероев - {max_intelligence}')\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n ya = YandexDisk(token=TOKEN)\r\nya.get_files_list()\r\n","repo_name":"AndreyPotapovAndrey/Hero","sub_path":"Hero.py","file_name":"Hero.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"3147148774","text":"import pymongo\n\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient[\"itingen\"]\n\n\n\"\"\"\n This returns an array of venues / pevents / tevents. Iterate over them simply like so:\n\n for venue in venues:\n print(venue)\n\"\"\"\nvenues = mydb[\"venues\"].find()\npevents = mydb[\"pevents\"].find()\ntevents = mydb[\"tevents\"].find()\n\n\n","repo_name":"maxxliu/ItinGen","sub_path":"app/helpers/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"31253898998","text":"import random\nimport string\n\n\ndef read_file(path):\n \"\"\"\n (str) -> (list)\n Reads data of battle field.\n True if there is ship by current coordinates.\n \"\"\"\n try:\n with open(path, 'r', encoding='UTF-8', errors='ignore') as file:\n field = file.read().split('\\n')\n field = [row + ' ' * (10 - len(row)) for row in field]\n return field\n except FileNotFoundError:\n print(\"There is no file with such name.\")\n\n\ndef is_valid(field):\n \"\"\"\n (list) -> (bool)\n Returns if field is right.\n If not, then AssertionError.\n \"\"\"\n def right_ship(crd):\n \"\"\"\n (tuple) -> (bool, size)\n Returns if ship is correct and size of this ship.\n \"\"\"\n if (has_ship(field, (crd[0] - 1, crd[1])) or\n has_ship(field, (crd[0] + 1, crd[1]))) and \\\n (has_ship(field, (crd[0], crd[1] - 1)) or\n has_ship(field, (crd[0], crd[1] + 1))):\n print(\"Invalid shape of ships.\")\n return (False, [0, 0])\n return (True, ship_size(field, crd))\n\n assert len(field) == 10, \"Invalid size of field.\"\n for row in field:\n assert len(row) == 10, \"Invalid size of field.\"\n for el in row:\n assert el in [' ', '*', 'X'], \"Invalid chars in field\"\n\n # Checking ships in field\n ships = {1: 0, 2: 0, 3: 0, 4: 0}\n field = new_field(field)\n for i in range(10):\n for j in range(10):\n try:\n ship_info = right_ship((i, j))\n if not ship_info[0]:\n return False\n if ship_info[1] != [0, 0]:\n # Increase number of ships with such size\n ships[max(ship_info[1])] += 1\n except KeyError:\n return False\n return ships == {1: 4, 2: 6, 3: 6, 4: 4}\n\n\ndef new_field(field):\n \"\"\"\n (list) -> (list)\n Creates new field, more comfortable for further process.\n \"\"\"\n field = [[el == ('*' or 'X') for el in row] for row in field]\n return field\n\n\ndef normal_coordinates(crd):\n \"\"\"\n (tuple) -> (tuple)\n Returns coordinates with first coordinate as int.\n \"\"\"\n if not isinstance(crd[0], int):\n return (ord(crd[0]) - ord('A'), crd[1] - 1)\n else:\n return crd\n\n\ndef valid_coordinates(crd):\n \"\"\"\n (tuple) -> (bool)\n Returns if coordinates are possible.\n \"\"\"\n crd = normal_coordinates(crd)\n return (0 <= crd[0] < 10) and (0 <= crd[1] < 10)\n\n\ndef has_ship(field, crd):\n \"\"\"\n (list, tuple) -> bool\n Returns if by there coordinates is ship.\n \"\"\"\n crd = normal_coordinates(crd)\n return field[crd[0]][crd[1]]\n\n\ndef ship_size(field, crd, pre_crd=(-1, -1)):\n \"\"\"\n (list, tuple) -> (int)\n Returns size of ship be coordinates.\n \"\"\"\n crd = normal_coordinates(crd)\n size = [0, 0]\n if valid_coordinates(crd) and has_ship(field, crd):\n if (crd[0] - 1, crd[1]) != pre_crd:\n size[1] += ship_size(field, (crd[0] - 1, crd[1]), crd)[1]\n if (crd[0] + 1, crd[1]) != pre_crd:\n size[1] += ship_size(field, (crd[0] + 1, crd[1]), crd)[1]\n if (crd[0], crd[1] - 1) != pre_crd:\n size[0] += ship_size(field, (crd[0], crd[1] - 1), crd)[0]\n if (crd[0], crd[1] + 1) != pre_crd:\n size[0] += ship_size(field, (crd[0], crd[1] + 1), crd)[0]\n size[0] += 1\n size[1] += 1\n return size\n\n\ndef field_to_str(field):\n \"\"\"\n (list) -> (str)\n Returns string of field.\n Used for printing.\n \"\"\"\n if isinstance(field[0], str):\n return '\\n'.join(field)\n # First line of number coordinates\n line = ' ' + ' '.join([str(x) for x in range(1, 11)]) + '\\n'\n for i, row in enumerate(field):\n # Letter coordinate\n line += string.ascii_uppercase[i] + ' '\n for el in row:\n if el:\n line += '* '\n else:\n line += '_ '\n line += '\\n'\n return line\n\n\ndef generate_field():\n \"\"\"\n () -> (list)\n Generating field for sea battle.\n \"\"\"\n def new_ship(size, number):\n \"\"\"\n (int) -> changed field.\n Put new ship into field.\n \"\"\"\n def is_new_ship(crd):\n \"\"\"\n (tuple) -> bool\n In this coordinates can be new ship 1*1.\n \"\"\"\n for i in range(crd[0] - 1, crd[0] + 2):\n for j in range(crd[1] - 1, crd[1] + 2):\n if field[i][j]:\n return False\n return True\n\n def continue_ship(crd, size, side):\n \"\"\"\n (tuple, int, str) -> bool\n Returns if ship with such size and side can be continued.\n \"\"\"\n # Down\n if side:\n if (10 - crd[0]) >= size:\n for i in range(crd[0] + 1, crd[0] + size + 2):\n for j in range(crd[1] - 1, crd[1] + 2):\n if field[i][j]:\n return False\n else:\n return False\n # Right\n else:\n if (10 - crd[1]) >= size:\n for i in range(crd[0] - 1, crd[0] + 2):\n for j in range(crd[1] + 1, crd[1] + size + 2):\n if field[i][j]:\n return False\n else:\n return False\n return True\n\n def new_ship_into_field(point, side, size):\n \"\"\"\n (list) -> changed field.\n \"\"\"\n ship = [point]\n # Down\n if side:\n for i in range(1, size):\n ship.append((point[0] + i, point[1]))\n else:\n for i in range(1, size):\n ship.append((point[0], point[1] + i))\n\n for part in ship:\n field[part[0]][part[1]] = (number, not side, size)\n\n point = (random.randint(1, 10), random.randint(1, 10))\n if is_new_ship(point):\n # 0 -- right, 1 -- down\n side = random.randint(0, 1)\n if continue_ship(point, size - 1, side):\n new_ship_into_field(point, side, size)\n else:\n side = not side\n if continue_ship(point, size - 1, side):\n new_ship_into_field(point, side, size)\n else:\n new_ship(size, number)\n else:\n new_ship(size, number)\n\n field = [[False] * 12 for i in range(12)]\n number = 1\n # For each size put ship\n for size in range(4, 0, -1):\n number_of_ships = 5 - size\n for i in range(number_of_ships):\n new_ship(size, number)\n number += 1\n\n # Cut from frame\n field = field[1: -1]\n field = list(map(lambda x: x[1: -1], field))\n return field\n","repo_name":"sofiia-tesliuk/SeaBattle","sub_path":"task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":6893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"32641156206","text":"#!/usr/bin/env python\n\nimport json\nimport subprocess\nimport sys\n\nwith open(sys.argv[1], 'r') as fh:\n api = json.load(fh)\n\nnew_api = {\n \"$schema\": api[\"$schema\"],\n \"type\": \"Object\",\n \"title\": \"EChartsOption\",\n \"properties\": api[\"option\"][\"properties\"]\n}\n\n\ndef clean(value):\n return value.strip(\"'\").rstrip(\"'\")\n\n\ndef iterate(array):\n for i, value in enumerate(array):\n if isinstance(value, dict):\n walk(value)\n elif isinstance(value, str):\n array[i] = clean(value)\n elif isinstance(value, list):\n iterate(value)\n\n\ndef walk(node):\n for key, value in node.items():\n if isinstance(value, dict):\n walk(value)\n elif isinstance(value, list):\n iterate(value)\n elif isinstance(value, str):\n node[key] = clean(value)\n\n\nwalk(new_api)\n\ntmpfile = sys.argv[1] + '.clean'\nwith open(tmpfile, 'w') as fh:\n json.dump(new_api, fh, indent=4)\n\nproc = subprocess.run(f\"\"\"\n datamodel-codegen\n --class-name EChartsOption\n --base-class ezcharts.plots._base.BaseModel\n --use-schema-description --reuse-model\n --input {tmpfile} --input-file-type jsonschema\"\"\".split(),\n capture_output=True)\n\n# make some changes to the models\nmodel = proc.stdout.decode()\n\nlaundry_list = dict(\n dataset=dict(\n find=\"dataset: Optional[Dataset] = Field(\",\n replace=\"dataset: Optional[Union[List[Dataset], Dataset]] = Field(\"),\n grid=dict(\n find=\"grid: Optional[Grid] = Field(\",\n replace=\"grid: Optional[Union[List[Grid], Grid]] = Field(\"),\n xaxis=dict(\n find=\"xAxis: Optional[XAxis] = Field(\",\n replace=\"xAxis: Optional[Union[List[XAxis], XAxis]] = Field(\"),\n yaxis=dict(\n find=\"yAxis: Optional[YAxis] = Field(\",\n replace=\"yAxis: Optional[Union[List[YAxis], YAxis]] = Field(\"),\n renderitem=dict(\n find=\"renderItem: Optional[RenderItem] = Field(\",\n replace=\"renderItem: Optional[JSCode] = Field(\"),\n imports=dict(\n find=\"from __future__ import annotations\",\n replace=\"\"\"from __future__ import annotations\\n\n from ezcharts.plots.util import JSCode\"\"\"))\n\nfor k, v in laundry_list.items():\n model = model.replace(v['find'], v['replace'])\n\nwith open(\"ezcharts/plots/_model.py\", 'w') as fh:\n fh.write(\"# flake8: noqa\\n\")\n fh.write(model)\n","repo_name":"epi2me-labs/ezcharts","sub_path":"generate-model.py","file_name":"generate-model.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"82"} +{"seq_id":"74101074189","text":"# guiscrape.py\n\nfrom tkinter import *\nfrom tkinter import ttk, filedialog, messagebox\nimport base64\nimport json\nimport os\nfrom bs4 import BeautifulSoup\nimport requests\n\nconfig = {} # This will be our memory\n\n# Scaning the given url:\ndef fetch_url():\n url = _url.get()\n config['images'] = []\n _images.set(()) # initialized as empty tuple\n try:\n page = requests.get(url)\n except requests.RequestException as rex:\n _statusbar(str(rex))\n else:\n soup = BeautifulSoup(page.content, 'html.parser')\n images = fetch_images(soup, url)\n if images:\n _images.set(tuple(img['name'] for img in images))\n _statusbar('Images found: {}'.format(len(images)))\n else:\n _statusbar('No images found')\n config['images'] = images\n\n# Scaning all the img objects in the page:\ndef fetch_images(soup, base_url):\n images = []\n for img in soup.findAll('img'):\n src = img.get('src')\n img_url = (\n '{base_url}//{src}'.format(base_url=base_url, src=src))\n name = img_url.split('/')[-1]\n images.append(dict(name=name, url=img_url))\n return images\n\n# function for saving the images:\ndef save():\n if not config.get('images'):\n _alert('No images to save')\n return\n\t\t\n if _save_method.get() == 'img':\n dirname = filedialog.askdirectory(mustexist=True)\n _save_images(dirname)\n else:\n filename = filedialog.asksaveasfilename(\n initialfile='images.json',\n filetypes=[('JSON','.json')])\n _save_json(filename)\n\n# Saving the choosen images in selected path:\ndef _save_images(dirname):\n if dirname and config.get('images'):\n for img in config['images']:\n img_data = requests.get(img['url']).content\n filename = os.path.join(dirname, img['name'])\n with open(filename, 'wb') as f:\n f.write(img_data)\n _alert('Done')\n\t\n# Saving choosen images like a .json file:\t\ndef _save_json(filename):\n if filename and config.get('images'):\n data = {}\n for img in config['images']:\n img_data = requests.get(img['url']).content\n b64_img_data = base64.b64encode(img_data)\n str_img_data = b64_img_data.decode('utf-8')\n data[img['name']] = str_img_data\n\t\t\t\n with open(filename, 'w') as ijson:\n ijson.write(json.dumps(data))\n _alert('Done')\n\t\n# Status Bar:\t\ndef _statusbar(arg):\n _status_msg.set(arg)\n\n# Alert function:\ndef _alert(msg):\n messagebox.showinfo(message=msg)\t\t\n\n\n# The gui:\nif __name__ == '__main__':\n\n# Defining the window:\n _root = Tk()\n _root.title('Scrape app')\n# make _root window resizable:\n _root.columnconfigure(0, weight=1)\n _root.rowconfigure(0, weight=1)\n# set default size of the window:\n _root.geometry(\"720x480\")\n# Define mainframe, where all the objects will be:\n _mainframe = ttk.Frame(_root, padding='5 5 5 5')\n _mainframe.grid(row=0, column=0, sticky=(E, W, N, S))\n# make _mainframe resizable:\n _mainframe.columnconfigure(0, weight=1)\n _mainframe.rowconfigure(0, weight=1)\n _mainframe.rowconfigure(1, weight=1)\n _mainframe.rowconfigure(2, weight=1)\n\n# Url frame, where you write the url for scrapping:\n _url_frame = ttk.LabelFrame(_mainframe, text='URL', padding='5 5 5 5')\n _url_frame.grid(row=0, column=0, sticky=(E, W))\n# make _url_frame resizable:\n _url_frame.columnconfigure(0, weight=1)\n #_url_frame.columnconfigure(1, weight=1)\n _url_frame.rowconfigure(0, weight=1)\n\n\n _url = StringVar()\n _url.set('http://localhost:8000')\n _url_entry = ttk.Entry(\n _url_frame, width=40, textvariable=_url)\n _url_entry.grid(row=0, column=0, sticky=(E, W, S, N), padx=5)\n _fetch_btn = ttk.Button(\n _url_frame, text='Fetch info', command=fetch_url)\n _fetch_btn.grid(row=0, column=1, sticky=W, padx=5)\n\n# Define the frame, where list of all images will be: \n _img_frame = ttk.LabelFrame(\n _mainframe, text='Content', padding='9 0 0 0')\n _img_frame.grid(row=1, column=0, sticky=(N, S, E, W))\n _img_frame.columnconfigure(0, weight=1)\n _img_frame.rowconfigure(0, weight=1)\n \n _images = StringVar()\n _img_listbox = Listbox(\n _img_frame, listvariable=_images, height=6, width=25)\n _img_listbox.grid(row=0, column=0, sticky=(E, W, S, N), pady=5)\n # here ^ ^ ^ we make the listbox expandable.\n _scrollbar = ttk.Scrollbar(\n _img_frame, orient=VERTICAL, command=_img_listbox.yview)\n _scrollbar.grid(row=0, column=1, sticky=(S, N), pady=6)\n _img_listbox.configure(yscrollcommand=_scrollbar.set)\n\n# Define frame for the radio buttons(buttons to select save format for the images):\n _radio_frame = ttk.Frame(_img_frame)\n _radio_frame.grid(row=0, column=2, sticky=(N, S, W, E))\n# simple lable with text for the radio buttons:\n _choice_lbl = ttk.Label(\n _radio_frame, text='Choose how to save images')\n _choice_lbl.grid(row=0, column=0, padx=5, pady=5)\n########\n _save_method = StringVar()\n _save_method.set('img')\n _img_only_radio = ttk.Radiobutton(\n _radio_frame, text='As Images', variable=_save_method, value='img')\n _img_only_radio.grid(\n row=1, column=0, padx=5, pady=2, sticky=W)\n _img_only_radio.configure(state='normal')\n##########\n _json_radio = ttk.Radiobutton(\n _radio_frame, text='As JSON', variable=_save_method, value='json')\n _json_radio.grid(row=2, column=0, padx=5, pady=2, sticky=W) \n############\n _scrape_btn = ttk.Button(\n _mainframe, text='Scrape!', command=save)\n _scrape_btn.grid(row=2, column=0, sticky=E, pady=5)\n\n# Define the frame for the StatusBar:\n _status_frame= ttk.Frame(\n _root, relief='sunken', padding='2 2 2 2')\n _status_frame.grid(row=1, column=0, sticky=(E, W, S))\n#######\n _status_msg = StringVar()\n _status_msg.set('Type a URL to start scraping...')\n _status = ttk.Label(\n _status_frame, textvariable=_status_msg, anchor=W)\n _status.grid(row=0, column=0, sticky=(E, W))\n##########\n\n _root.mainloop()\n","repo_name":"L37sg0/web-scrapper","sub_path":"guiscrape.py","file_name":"guiscrape.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"16808591012","text":"import mdp\nimport observation_table\nimport models\n\nimport sympy\nimport numpy as np\nimport time\n\nstart = time.time()\n#m = models.get_chain(3)\n#m = models.get_float_n(3)\nm = models.get_test1()\nprint(m.to_dot())\nprint(f\"MDP has {len(m.states)} states\")\nassert m.check()\nconfig1 = { 'linear_close': True, 'linear_hypothesis': True, 'tries': 100, 'max_observation_length': 5, 'cex': 'all_suffixes'}\n\ntable = observation_table.ObservationTable(m, m.observation_mapping, config1)\n\nh = table.learn_mdp()\nassert m.try_find_counter_example(h, 100, 15) is None\ntable.print_observation_table()\nprint(h.to_dot())\nprint(f\"learned mdp has {len(h.states)} states\")\n#h = table.create_hypothesis()\n#cex = m.try_find_counter_example(h)\nend = time.time()\nprint(\"took {}ms\".format((end-start)*1000))\nprint(table.stats)\n","repo_name":"Marckvdv/learnmdp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"25372660451","text":"import unittest\nfrom unittest.mock import MagicMock\n\nfrom ....pintaformas.core.tipos import Color\nfrom ....pintaformas.core.control_general.control_general import ControlGeneral\nfrom ....pintaformas.core.control_general.realizador import Realizador\nfrom ....pintaformas.core.control_general.gestor_seleccion import GestorSeleccion\nfrom ....pintaformas.core.control_general.variables_estado import VariableDeEstado\n\n\nclass TestRealizadorSeleccionarColor(unittest.TestCase):\n '''\n Comprueba la correcta comunicacion entre el Realizador y el AreaMuestrasColores\n para que se muestre el color seleccionado\n '''\n def setUp(self) -> None:\n self.vista = MagicMock()\n gestor_cursor = MagicMock()\n color_seleccionado = VariableDeEstado()\n control_cambios= MagicMock()\n estado= MagicMock()\n self.gestor_seleccion = GestorSeleccion(self.vista, estado, control_cambios)\n control_general = ControlGeneral(\n gestor_cursor = gestor_cursor,\n gestor_seleccion=self.gestor_seleccion, gestor_seleccion_circulo=MagicMock(),dibujador_en_documento=MagicMock(),vista=self.vista\n )\n self.realizador = Realizador(control_general, control_cambios, MagicMock())\n self.vista.areas.color_seleccionado.set_color_seleccionado = MagicMock()\n\n\n def test_realizador_seleccionar_color(self) -> None:\n '''realizador.seleccionar_color modifica el color_seleccionado y la muestra visible en area_muestras_colores'''\n COLOR_A_SELECCIONAR = Color((50, 50, 50))\n self.realizador.seleccionar_color(COLOR_A_SELECCIONAR)\n self.assertEqual(self.gestor_seleccion.color_pluma, COLOR_A_SELECCIONAR)\n self.assertEqual(self.vista.areas.color_seleccionado.color_seleccionado, COLOR_A_SELECCIONAR)\n\n\n\n\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"gulliver-madrid/pintaformas","sub_path":"src/tests/core/realizador/test_realizador_seleccionar_color.py","file_name":"test_realizador_seleccionar_color.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"82"} +{"seq_id":"493845926","text":"import tensorflow as tf\n\n\nclass ContinuousBinaryTreeConvLayer(tf.keras.layers.Layer):\n def __init__(self, feature_size, output_size):\n super().__init__()\n self.feature_size = feature_size\n self.output_size = output_size\n self.w_t = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_t\"\n )\n self.w_l = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_l\"\n )\n self.w_r = self.add_weight(\n shape=(feature_size, output_size), initializer=\"random_normal\", trainable=True, name=\"w_r\"\n )\n self.b = self.add_weight(shape=(output_size,), initializer=\"random_normal\", trainable=True, name=\"b\")\n\n def call(self, inputs):\n # nodes is shape (batch_size x max_tree_size x feature_size)\n nodes = inputs[0]\n # children is shape (batch_size x max_tree_size x max_children)\n children = inputs[1]\n children_vectors = self.children_tensor(nodes, children)\n nodes = tf.expand_dims(nodes, axis=2)\n tree_tensor = tf.concat([nodes, children_vectors], axis=2, name='trees')\n c_t = self.eta_t(children)\n c_r = self.eta_r(children, c_t)\n c_l = self.eta_l(children, c_t, c_r)\n\n coef = tf.stack([c_t, c_r, c_l], axis=3, name='coef')\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n\n # reshape for matrix multiplication\n x = batch_size * max_tree_size\n y = max_children + 1\n result = tf.reshape(tree_tensor, (x, y, self.feature_size))\n coef = tf.reshape(coef, (x, y, 3))\n result = tf.matmul(result, coef, transpose_a=True)\n result = tf.reshape(result, (batch_size, max_tree_size, 3, self.feature_size))\n\n # output is (batch_size, max_tree_size, output_size)\n w = tf.stack([self.w_t, self.w_r, self.w_l], axis=0)\n result = tf.tensordot(result, w, [[2, 3], [0, 1]])\n\n # output is (batch_size, max_tree_size, output_size)\n return tf.nn.relu(result + self.b, name='conv')\n\n def children_tensor(self, nodes, children):\n \"\"\"Build the children tensor from the input nodes and child lookup.\"\"\"\n max_children = tf.shape(children)[2]\n batch_size = tf.shape(nodes)[0]\n num_nodes = tf.shape(nodes)[1]\n\n # replace the root node with the zero vector so lookups for the 0th\n # vector return 0 instead of the root vector\n # zero_vecs is (batch_size, num_nodes, 1)\n zero_vecs = tf.zeros((batch_size, 1, self.feature_size))\n # vector_lookup is (batch_size x num_nodes x feature_size)\n vector_lookup = tf.concat([zero_vecs, nodes[:, 1:, :]], axis=1)\n # children is (batch_size x num_nodes x num_children x 1)\n children = tf.expand_dims(children, axis=3)\n # prepend the batch indices to the 4th dimension of children\n # batch_indices is (batch_size x 1 x 1 x 1)\n batch_indices = tf.reshape(tf.range(0, batch_size), (batch_size, 1, 1, 1))\n # batch_indices is (batch_size x num_nodes x num_children x 1)\n batch_indices = tf.tile(batch_indices, [1, num_nodes, max_children, 1])\n # children is (batch_size x num_nodes x num_children x 2)\n children = tf.concat([batch_indices, children], axis=3)\n # output will have shape (batch_size x num_nodes x num_children x feature_size)\n # NOTE: tf < 1.1 contains a bug that makes backprop not work for this!\n return tf.gather_nd(vector_lookup, children, name='children')\n\n def eta_t(self, children):\n \"\"\"Compute weight matrix for how much each vector belongs to the 'top'\"\"\"\n # children is shape (batch_size x max_tree_size x max_children)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n # eta_t is shape (batch_size x max_tree_size x max_children + 1)\n return tf.tile(tf.expand_dims(tf.concat(\n [tf.ones((max_tree_size, 1)), tf.zeros((max_tree_size, max_children))],\n axis=1), axis=0,\n ), [batch_size, 1, 1], name='coef_t')\n\n def eta_r(self, children, t_coef):\n \"\"\"Compute weight matrix for how much each vector belogs to the 'right'\"\"\"\n # children is shape (batch_size x max_tree_size x max_children)\n children = tf.cast(children, tf.float32)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n max_children = tf.shape(children)[2]\n\n # num_siblings is shape (batch_size x max_tree_size x 1)\n num_siblings = tf.cast(\n tf.math.count_nonzero(children, axis=2, keepdims=True),\n dtype=tf.float32\n )\n # num_siblings is shape (batch_size x max_tree_size x max_children + 1)\n num_siblings = tf.tile(\n num_siblings, [1, 1, max_children + 1], name='num_siblings'\n )\n # creates a mask of 1's and 0's where 1 means there is a child there\n # has shape (batch_size x max_tree_size x max_children + 1)\n mask = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.minimum(children, tf.ones(tf.shape(children)))],\n axis=2, name='mask'\n )\n\n # child indices for every tree (batch_size x max_tree_size x max_children + 1)\n child_indices = tf.multiply(tf.tile(\n tf.expand_dims(\n tf.expand_dims(\n tf.range(-1.0, tf.cast(max_children, tf.float32), 1.0, dtype=tf.float32),\n axis=0\n ),\n axis=0\n ),\n [batch_size, max_tree_size, 1]\n ), mask, name='child_indices')\n\n # weights for every tree node in the case that num_siblings = 0\n # shape is (batch_size x max_tree_size x max_children + 1)\n singles = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.fill((batch_size, max_tree_size, 1), 0.5),\n tf.zeros((batch_size, max_tree_size, max_children - 1))],\n axis=2, name='singles')\n\n # eta_r is shape (batch_size x max_tree_size x max_children + 1)\n return tf.where(\n tf.equal(num_siblings, 1.0),\n # avoid division by 0 when num_siblings == 1\n singles,\n # the normal case where num_siblings != 1\n tf.multiply((1.0 - t_coef), tf.divide(child_indices, num_siblings - 1.0)),\n name='coef_r'\n )\n\n def eta_l(self, children, coef_t, coef_r):\n \"\"\"Compute weight matrix for how much each vector belongs to the 'left'\"\"\"\n children = tf.cast(children, tf.float32)\n batch_size = tf.shape(children)[0]\n max_tree_size = tf.shape(children)[1]\n # creates a mask of 1's and 0's where 1 means there is a child there\n # has shape (batch_size x max_tree_size x max_children + 1)\n mask = tf.concat(\n [tf.zeros((batch_size, max_tree_size, 1)),\n tf.minimum(children, tf.ones(tf.shape(children)))],\n axis=2,\n name='mask'\n )\n\n # eta_l is shape (batch_size x max_tree_size x max_children + 1)\n return tf.multiply(\n tf.multiply((1.0 - coef_t), (1.0 - coef_r)), mask, name='coef_l'\n )","repo_name":"S-wald/tbcnn-tf2","sub_path":"classifier/layers/ContinuousBinaryTreeConvLayer.py","file_name":"ContinuousBinaryTreeConvLayer.py","file_ext":"py","file_size_in_byte":7432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"82"} +{"seq_id":"17094271614","text":"import logging\nimport hashlib\nimport random\nimport time\nfrom core import db\nfrom core.database.postgres.user import OAuthUserCredentials\n\nq = db.query\n\nlogg = logging.getLogger(__name__)\n\nlogg.info(\"imported core.oauth\")\n\n\ndef verify_request_signature(req_path, params):\n # we generate the signature from the shared secret, the request path and all sorted parameters\n # as described in the upload api documentation\n _p = params.copy()\n\n fmsg = \"verify_request_signature going to return False: \"\n if 'user' not in _p or 'sign' not in _p:\n logg.info(fmsg + \"'user' or 'sign' parameter missing in request\")\n return False\n\n oauth_user = _p.get('user')\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == oauth_user).count()\n if oauth_user_credentials_count < 1:\n logg.info(fmsg + \"no oauth user credentials known for oauth_user %r\", oauth_user)\n return False\n if oauth_user_credentials_count > 1:\n logg.info(fmsg + \"multiple oauth user credentials stored for oauth_user %r\", oauth_user)\n return False\n oauth_user_credentials = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == oauth_user).one()\n workingString = oauth_user_credentials.oauth_key\n\n #try:\n # workingString = \"\"\n # for n in [h for h in tree.getRoot('home').getChildren() if h.get('system.oauthuser') == params.get('user')]:\n # workingString = n.get('system.oauthkey')\n # break\n #except:\n # return False\n\n workingString += req_path\n\n # remove signature form parameters before we calculate the test signature\n signature = _p['sign']\n del _p['sign']\n\n keylist = sorted(_p.keys())\n\n isFirst = True\n\n for oneKey in keylist:\n oneValue = _p[oneKey]\n if not isFirst:\n workingString += '&'\n else:\n isFirst = False\n workingString += '{}={}'.format(oneKey,\n oneValue)\n testSignature = hashlib.md5(workingString).hexdigest()\n return (testSignature == signature)\n\n\ndef get_oauth_key_for_user(user):\n login_name = user.login_name\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == login_name).filter(OAuthUserCredentials.user_id == user.id).count()\n if oauth_user_credentials_count == 0:\n oauthkey = ''\n elif oauth_user_credentials_count == 1:\n # retrieve that key\n oauth_user_credentials = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == login_name).filter(OAuthUserCredentials.user_id == user.id).one()\n oauthkey = oauth_user_credentials.oauth_key\n else:\n oauthkey = ''\n pass #raise exception? should not happen: unique constraint on column oauth_user\n return oauthkey\n\n\ndef generate_new_oauth_key_for_user(user):\n s = db.session\n\n generated_key = hashlib.md5(str(time.time()) + str(''.join(str(random.randint(0, 9)) for i in range(40)))).hexdigest()[0:15] # generate key\n\n user_login_name = user.login_name\n\n oauth_user_credentials_count = q(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == user_login_name).filter(OAuthUserCredentials.user_id == user.id).count()\n if oauth_user_credentials_count == 0:\n oauth_user_credentials = OAuthUserCredentials(oauth_user=user_login_name, oauth_key=generated_key, user_id=user.id)\n s.add(oauth_user_credentials)\n s.commit()\n elif oauth_user_credentials_count == 1:\n oauth_user_credentials = s.query(OAuthUserCredentials).filter(OAuthUserCredentials.oauth_user == unicode(user_login_name)).filter(OAuthUserCredentials.user_id == user.id)\n oauth_user_credentials.update({'oauth_key': generated_key})\n s.commit()\n else:\n pass #raise exception? should not happen: unique constraint on column oauth_user\n\n return generated_key","repo_name":"mediatum/mediatum","sub_path":"core/oauth.py","file_name":"oauth.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"82"} +{"seq_id":"16791708571","text":"import numpy as np\n\n\ndic_file = open('entity_dic', 'r', encoding='utf-8')\nwords = dic_file.read().split('\\n')\ndic_entity = np.array(words)\ndic_entity.sort()\n\n\ndef search(word):\n idx = dic_entity.searchsorted(word)\n\n if idx == dic_entity.shape[0]:\n return -1\n if word == dic_entity[idx]:\n return idx\n return -1\n\n\nN = 50\n\n#검색에 사용될 문서\nfile = open('wiki_morph', 'r', encoding='utf-8')\ndocs = file.read().split('\\a')\n\n#[전체 키워드의 개수, 최대 저장 개수]\n#해당 문서에서의 빈도수를 나타내는 배열\nindexer_frequency = np.zeros(shape=[dic_entity.shape[0], N], dtype=np.int32)\n#문서의 번호를 저장하는 배열\nindexer_pointer = np.zeros(shape=[dic_entity.shape[0], N], dtype=np.int32)\n\n#해당 키워드가 총 몇개의 문서에서 등장했는지 저장하는 배열\ncount = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n\nfor i in range(len(docs) - 1):\n frequency = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n checker = np.zeros(shape=[dic_entity.shape[0]], dtype=np.int32)\n vocab_list = []\n\n if i % 50 == 0:\n print(i, '/', len(docs))\n\n doc = docs[i].split('\\t')[1]\n\n TK = doc.split(' ')\n\n for k in range(len(TK)):\n idx = search(TK[k])\n\n if idx != -1:\n frequency[idx] += 1\n if checker[idx] == 0:\n checker[idx] = 1\n vocab_list.append(idx)\n\n for k in vocab_list:\n if frequency[k] > 0:\n point = np.array(indexer_frequency[k], dtype=np.int32).argmin()\n indexer_frequency[k, point] = frequency[k]\n indexer_pointer[k, point] = i\n\n count += checker\n\nnp.save('indexer_pointer', indexer_pointer)\nnp.save('indexer_frequency', indexer_frequency)\nnp.save('indexer_count', count)\n\n\nfile.close()\n","repo_name":"delosyCho/simple_search","sub_path":"Indexer.py","file_name":"Indexer.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"18074176655","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"This module provides functions to sample from a random distribution\n\nAll functions in here should be callable in a similar way as rand() or randn() that are part of brian2\n\nPlease note: brian2 uses a specific random generator in the 'randomkit', so any seed you set in brian2 will not necessarily\napply here, depending on which rng is used. (The truncated randn uses brian2's rand, so it's fine, gamma however uses another one)\nIf you need a seed, it may be easy to implement though. (TODO!)\n\nPlease also note that, currently, there is a bug in brian2 (issue #988) that does not allow you to use the several functions\nwith the same dependencies for the same variable (but this probably happens only rarely).\n\nI used brian2/input/binomial.py as a template\n\"\"\"\n# @author: alpha\n\nimport numpy as np\nimport os\nfrom scipy.stats import truncnorm\nfrom brian2 import DEFAULT_FUNCTIONS\n\nfrom brian2 import check_units\n\nimport matplotlib.pyplot as plt\nfrom brian2 import NeuronGroup, prefs, set_device, run, ms, mV, seed\n\nfrom brian2 import Nameable, Function\nfrom brian2.utils.stringtools import replace\n\n\ndef _randn_trunc_generate_cpp_code(lower, upper, name):\n # C++ implementation\n cpp_code = '''\n float %NAME%(const int _vectorisation_idx) {\n float retVal = 0; \n do {retVal = _randn(_vectorisation_idx);\n } while ((retVal > %UPPER%) || (retVal < %LOWER%));\n return retVal;\n }\n '''\n cpp_code = replace(cpp_code, {'%NAME%': name, '%UPPER%': str(upper), '%LOWER%': str(lower)})\n cpp_code = cpp_code.replace('inf','std::numeric_limits::max()')\n dependencies = {'_randn': DEFAULT_FUNCTIONS['randn']}\n return {'support_code': cpp_code}, dependencies\n\n\nclass Randn_trunc(Function, Nameable):\n \"\"\"\n Sample from a truncated Gaussian\n We are using this in core/groups to add mismatch.\n In python it wraps truncnorm.rvs(lower, upper, size=N)\n\n refer to the example below\n \"\"\"\n implementations = {\n 'cpp': _randn_trunc_generate_cpp_code,\n }\n\n @check_units(lower=1, upper=1)\n def __init__(self, lower, upper, name='_randn_trunc*'):\n Nameable.__init__(self, name)\n\n def sample_function(vectorisation_idx):\n try:\n N = len(vectorisation_idx)\n except TypeError:\n N = int(vectorisation_idx)\n return truncnorm.rvs(lower, upper, size=N)\n\n try:\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False,\n auto_vectorise=True)\n except TypeError as e:\n # this is necessary for backward compatibility with brian2 < 2.3, as the argument auto_vectorise\n # does not exist\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False)\n\n self.implementations.add_implementation('numpy', sample_function)\n\n for target, func in Randn_trunc.implementations.items():\n code, dependencies = func(lower=lower, upper=upper, name=self.name)\n # print('target:', target, '\\ncode: ', code, '\\ndependencies: ', dependencies, '\\nname:', self.name)\n self.implementations.add_implementation(target, code,\n dependencies=dependencies,\n name=self.name)\n\n\ndef _rand_gamma_generate_cpp_code(alpha, beta, name):\n # C++ implementation\n cpp_code = '''\n std::mt19937 rng(std::random_device{}());\n // Not ideal, but probably good enough for us:\n // https://codereview.stackexchange.com/questions/109260/seed-stdmt19937-from-stdrandom-device \n // Would be good to seed the rng with a random number from brian2, so the brian2 seed affects the rng here.\n float %NAME%(const int _vectorisation_idx) {\n std::gamma_distribution distribution(%ALPHA%,1/%BETA%);\n float retVal = distribution(rng);\n \treturn retVal;\n }\n '''\n cpp_code = replace(cpp_code, {'%NAME%': name, '%BETA%': str(beta), '%ALPHA%': str(alpha)})\n dependencies = {}\n return {'support_code': cpp_code}, dependencies\n\n\nclass Rand_gamma(Function, Nameable):\n \"\"\"\n Sample from a gamma distribution.\n Refer to the example below.\n \"\"\"\n prefs.codegen.cpp.headers += ['']\n\n implementations = {\n 'cpp': _rand_gamma_generate_cpp_code,\n }\n\n @check_units(alpha=1, beta=1)\n def __init__(self, alpha, beta, name='_rand_gamma*'):\n Nameable.__init__(self, name)\n\n def sample_function(vectorisation_idx):\n try:\n N = len(vectorisation_idx)\n except TypeError:\n N = int(vectorisation_idx)\n f = -1 if beta < 0 else 1\n if N == 1:\n return f * np.random.gamma(alpha, scale=f / beta)\n else:\n return f * np.random.gamma(alpha, scale=f / beta, size=N)\n\n try:\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False,\n auto_vectorise=True)\n except TypeError:\n # this is necessary for backward compatibility with brian2 < 2.3, as the argument auto_vectorise\n # does not exist\n Function.__init__(self, pyfunc=lambda: sample_function(1),\n arg_units=[], return_unit=1, stateless=False)\n\n self.implementations.add_implementation('numpy', sample_function)\n\n for target, func in Rand_gamma.implementations.items():\n code, dependencies = func(alpha=alpha, beta=beta, name=self.name)\n self.implementations.add_implementation(target, code,\n dependencies=dependencies,\n name=self.name)\n\n\nif __name__ == '__main__':\n # Some examples how to use the gamma sampling\n # And some different parametrizations\n\n n_samples = 10000\n\n # outside of brian2\n rand_gamma = Rand_gamma(2, 2)\n\n gamma_samples = [rand_gamma() for _ in range(n_samples)]\n\n plt.figure()\n _ = plt.hist(gamma_samples, 50)\n plt.show()\n\n # brian2 with numpy codegen\n prefs.codegen.target = \"numpy\"\n\n ng = NeuronGroup(n_samples, 'testvar : 1')\n ng.testvar = 'rand_gamma()'\n\n plt.figure()\n plt.hist(ng.testvar, 50)\n plt.show()\n\n # keep std constant\n gamma_samples = [[Rand_gamma(alpha, np.sqrt(alpha))() for _ in range(n_samples)] for alpha in range(1, 20, 2)]\n plt.figure()\n _ = plt.hist(gamma_samples, 500, histtype='step')\n plt.show()\n\n print(np.mean(gamma_samples, 1))\n print(np.std(gamma_samples, 1))\n\n # set mean and std like in normal dist\n std = 0.2 * mV\n mu = -0.4 * mV #\n alpha = (1 / std ** 2) * mu ** 2\n beta = (1 / std ** 2) * mu * 1000 * mV\n gamma_samples = [Rand_gamma(alpha, beta)() * 1000 for _ in range(n_samples)]\n plt.figure()\n _ = plt.hist(gamma_samples, 500, histtype='step', density=True)\n plt.show()\n\n print(np.mean(gamma_samples))\n print(np.std(gamma_samples))\n print(np.var(gamma_samples))\n\n # %% It also works in standalone mode:\n standaloneDir = os.path.expanduser('~/gamma_standalone')\n set_device('cpp_standalone', directory=standaloneDir, build_on_run=True)\n seed(42) # does not affect sampling from gamma distribution!\n\n\n ng = NeuronGroup(n_samples, '''\n testvar : 1\n testvar2 : 1''', name = 'ng_test')\n ng.namespace.update({\n 'rand_gamma': Rand_gamma(4.60, -10750.0),\n 'randn_trunc': Randn_trunc(-1.5,1.5)\n })\n ng.testvar = 'rand_gamma()'\n ng.testvar2 = '5*randn_trunc()'\n\n run(10 * ms)\n\n plt.figure()\n plt.title('rand_gamma')\n plt.hist(ng.testvar, 50, histtype='step')\n plt.show()\n\n plt.figure()\n plt.title('randn_trunc')\n plt.hist(ng.testvar2, 50, histtype='step')\n plt.show()\n","repo_name":"russelljjarvis/teili","sub_path":"teili/tools/random_sampling.py","file_name":"random_sampling.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"9120759306","text":"import scrapy\nimport re\nimport unidecode\n\n# running: scrapy crawl indeed -o indeed_data.json\n\nclass IndeedSpider(scrapy.Spider):\n\tname = 'indeed'\t\n\t\n\t# def start_requests(self):\n\t\t\n\tstart_urls = [\n\t\t'https://www.indeed.co.in/jobs-in-Bangalore,-Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Mumbai%2C+Maharashtra',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Suratkal%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Delhi',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Trivandrum%2C+Kerala',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Chennai%2C+Tamil+Nadu',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Pune%2C+Maharashtra',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Mangalore%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Manipal%2C+Karnataka',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Bekal%2C+Kerala',\n\t\t'https://www.indeed.co.in/jobs?q=&l=Goa'\n\t]\n\n\t\t# for url in start_urls:\n\t\t\t# yield scrapy.Request(url=url, callback=self.parse)\n\t\t# \tnext_page = response.xpath(\"//span[@class='np']/../../@href\").extract_first()\n\t\t# \tif next_page is not None:\n\t\t# \t\tnext_page_link = response.urljoin(next_page)\n\t\t# \t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\t\t\t\t\n\n\tdef parse(self, response):\n\t\tfor job in response.xpath(\"//div[contains(@class,'jobsearch-SerpJobCard') and contains(@class,'unifiedRow')]\"):\n\t\t\t\n\t\t\t##### COMPANY NAME\n\t\t\tcompany=job.xpath(\"normalize-space(.//span[@class='company']/text())\") \n\t\t\t# print(\"company:\", company.extract_first())\n\t\t\tif company.extract_first() == '':\n\t\t\t\tcompany=job.xpath(\"normalize-space(.//a[@class='turnstileLink']/text())\")\n\n\t\t\t##### SALARY\n\t\t\tsalary_data=job.xpath(\"normalize-space(.//span[contains(@class, 'salary')])\")\n\t\t\tsalary=salary_data.re(r'[₹][A-Za-z0-9,]+')\n\t\t\tif salary:\n\t\t\t\tif len(salary)==2:\n\t\t\t\t\tlow_sal=float(re.sub('[,₹]','',salary[0]))\n\t\t\t\t\thigh_sal=float(re.sub('[,₹]','',salary[1]))\n\t\t\t\t\tsalary=(low_sal+high_sal)/2\n\t\t\t\t\t# print(\"Salary averaged!\")\n\t\t\t\telse:\n\t\t\t\t\tsalary=salary[0]\n\t\t\t\t\tsalary=int(re.sub('[,₹]','',salary))\n\t\t\t\t# print(salary)\n\t\t\t\tif salary_data.re(r'[mM][oO][nN][tT][hH]'):\n\t\t\t\t\t# print(\"\\nMonthly salary detected!\")\n\t\t\t\t\tsalary=12*salary\n\n\t\t\t##### DESCRIPTION\n\t\t\tdesc_data=job.xpath(\".//div[contains(@class,'summary')]/ul/li\")\n\t\t\t# print(\"----------------------\")\n\t\t\t# print(desc_data)\n\t\t\t# print(len(desc_data))\n\t\t\tdesc=\"\"\n\t\t\tfor point in desc_data:\n\t\t\t\tdesc+=(\" \"+point.xpath(\"normalize-space(.//text())\").extract_first())\n\t\t\t# print(desc)\n\n\n\t\t\t##### LINK\n\t\t\tlink_data=job.xpath(\"//a[contains(@class,'jobtitle') and contains(@class,'turnstileLink')]/@href\").extract_first()\n\t\t\tlink=re.sub(r'(https:\\/\\/www\\.indeed\\.co\\.in)','',link_data)\n\t\t\tlink='https://www.indeed.co.in'+link\n\t\t\t\n\n\n\t\t\tyield {\n\t\t\t\t'TITLE' : job.xpath(\"normalize-space(.//div[@class='title']/a/text())\").extract_first(),\n\t\t\t\t'COMPANY' : company.extract_first(),\n\t\t\t\t'LOCATION' : job.xpath(\"normalize-space(.//div[contains(@class,'location')])\").extract_first(),\n\t\t\t\t'SALARY' : salary,\n\t\t\t\t'DESCRIPTION' : unidecode.unidecode(desc),\n\t\t\t\t'LINK' : link,\n\n\t\t\t}\n\n\t\tnext_page = response.xpath(\"//div[@class='pagination']/a[position()=last()]/@href\").extract_first()\n\t\tif next_page is not None:\n\t\t\tnext_page_link = response.urljoin(next_page)\n\t\t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\n# running: scrapy crawl tj -o tj_data.json\n\nclass TJSpider(scrapy.Spider):\n\tname = 'tj'\t\n\t\n\tstart_urls = [\n\t\t#'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Bengaluru/%20Bangalore&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Mangalore&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Goa&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Kerala&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1',\n\t\t'https://www.timesjobs.com/candidate/job-search.html?from=submit&searchType=personalizedSearch&txtLocation=Delhi&luceneResultSize=25&postWeek=60&pDate=Y&sequence=1&startPage=1'\n\n\t]\t\t\t\t\n\n\n\n\n\tdef parse(self, response):\n\t\tif response.xpath(\"//div[contains(@class,'no-jobs-found')]\"):\n\t\t\tprint('End of pagination!')\n\t\telse:\n\t\t\tfor job in response.xpath(\"//ul/li[contains(@class,'clearfix')]\"):\n\t\t\t\t\n\t\t\t\t##### SALARY\n\t\t\t\tsalary_data=job.xpath(\"normalize-space(.//i[contains(@class,'rupee')]/../text())\")\n\t\t\t\tsalary=salary_data.re(r'[0-9]*[.]*[0-9]+')\n\t\t\t\tif salary:\n\t\t\t\t\tif len(salary)==2:\n\t\t\t\t\t\t#low_sal=float(re.sub('[,₹]','',salary[0]))\n\t\t\t\t\t\t#high_sal=float(re.sub('[,₹]','',salary[1]))\n\t\t\t\t\t\tlow_sal=float(salary[0])*100000\n\t\t\t\t\t\thigh_sal=float(salary[1])*100000\n\t\t\t\t\t\tsalary=(low_sal+high_sal)/2\n\t\t\t\t\t\t# print(\"Salary averaged!\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tsalary=salary[0]\n\t\t\t\t\t\tsalary=float(salary)*100000\n\t\t\t\t\t# print(salary)\n\n\t\t\t\t##### DESCRIPTION\n\t\t\t\tdesc=re.sub('(Job Description:)|(More Details)', '', job.xpath(\"normalize-space(.//ul[contains(@class,'list-job-dtl')]/li)\").extract_first())\n\t\t\t\t\n\t\t\t\tyield {\n\t\t\t\t\t'TITLE' : job.xpath(\"normalize-space(.//header[contains(@class,'clearfix')]/h2/a/text())\").extract_first(),\n\t\t\t\t\t'COMPANY' : job.xpath(\"normalize-space(.//header[contains(@class,'clearfix')]/h3/text())\").extract_first(),\n\t\t\t\t\t'LOCATION' : job.xpath(\"normalize-space(.//ul[contains(@class,'top-jd-dtl')]/li[position()=last()]/span/text())\").extract_first(),\n\t\t\t\t\t'SALARY' : salary,\n\t\t\t\t\t'DESCRIPTION': unidecode.unidecode(desc),\n\t\t\t\t\t'LINK': job.xpath(\".//header[contains(@class,'clearfix')]/h2/a/@href\").extract_first()\n\t\t\t\t\t# 'AGE OF POSTING' : job.xpath(\"normalize-space(.//span[@class='date '])\").extract_first()\n\t\t\t\t}\n\t\t\t\t\n\n\t\t\t\t# extracting the page number from url, incrementing it and injecting it back into the next url\n\t\t\tcurrent_url=response.request.url\n\t\t\tmatches = re.finditer(r\"(?<=sequence=).[0-9]*\", current_url)\n\t\t\tmatches=list(enumerate(matches))\n\t\t\tpgno=int(matches[0][1].group())\n\t\t\tpgno+=1\n\t\t\tnext_url=re.sub('(?<=sequence=).[0-9]*',str(pgno),current_url)\n\t\t\tprint(\"---------------------------------------------------------ENTERING PAGE\", pgno)\n\t\t\tnext_page_link = response.urljoin(next_url)\n\t\t\tyield scrapy.Request(url=next_page_link, callback=self.parse)\n\n\n\n\n\n\n\t\n\n\t\t\n\t","repo_name":"CinnamonRolls1/job-searcher-webapp","sub_path":"scraper/scraper/spiders/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":6410,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"38946518978","text":"from good_morning.calibrations.ultility import get_target, readout_convertor\n\nfrom core_tools.utility.variable_mgr.var_mgr import variable_mgr\nfrom core_tools.sweeps.pulse_lib_wrappers.PSB_exp import run_qubit_exp\nfrom core_tools.sweeps.sweeps import scan_generic\nfrom core_tools.job_mgnt.job_mgmt import job_wrapper\n\nfrom dev_V2.six_qubit_QC_v2.system import six_dot_sample\nfrom dev_V2.Elzerman_2_qubits_clean.TRIG import mk_TRIG\nfrom dev_V2.six_qubit_QC_v2.VAR import variables\n\nimport pulse_lib.segments.utility.looping as lp\nimport matplotlib.pyplot as plt\n\nimport qcodes as qc\nimport scipy as sp\nimport numpy as np\n\n@job_wrapper\ndef PSB12_calibration(sweep_range=0.5, plot=False):\n gates, _311113, ST_anti_12, ST_anti_12_tc_high, ST_anti_56, ST_anti_56_tc_high, vSD1_threshold, vSD2_threshold = variables()\n \n anticrossing = list(ST_anti_12)\n anticrossing[1] = lp.linspace(anticrossing[1] - sweep_range/2,anticrossing[1] + sweep_range/2, 20, axis=0, name='vP1', unit='mV') \n anticrossing[3] = lp.linspace(anticrossing[3] - sweep_range/2,anticrossing[3] + sweep_range/2, 20, axis=1, name='vP2', unit='mV') \n anticrossing =tuple(anticrossing)\n\n var_mgr = variable_mgr()\n\n s = six_dot_sample(qc.Station.default.pulse)\n \n s.add(s.init12, anti_crossing = anticrossing)\n s.add(s.pre_pulse)\n\n s.add(s.wait(10000)) \n s.add(s.q1.X90)\n # s.add(s.wait(50e3)) \n s.add(s.read12, anti_crossing = anticrossing)\n\n # s.add(s.rand_read12, anti_crossing = anticrossing)\n \n s.n_rep = 500\n sequence, minstr, name = run_qubit_exp(f'PSB12_calibration_SLOW', s.sequencer)\n\n qc.Station.default.MW_source.on() \n ds_on = scan_generic(sequence, minstr, name=name).run()\n\n # qc.Station.default.MW_source.off()\n # ds_off = scan_generic(sequence, minstr, name=name).run()\n\n # x = ds_on('read12').x()\n # y = ds_on('read12').y()\n\n # contrast = np.where(ds_on('read12')()>0.9,0,ds_on('read12')()) - np.where(ds_off('read12')()>0.9,0,ds_off('read12')())\n # contrast = sp.ndimage.filters.gaussian_filter(contrast, [2,2], mode='constant')\n # if plot:\n # plt.imshow(contrast)\n \n # var_mgr.PSB_12_P2 = round(x[np.where(contrast == contrast.max())[0][0]], 2)\n # var_mgr.PSB_12_P1 = round(y[np.where(contrast == contrast.max())[1][0]], 2)\n\n # print(f\"Selected point\\n\\tvP1 :: {var_mgr.PSB_12_P1}\\n\\tvP2 :: {var_mgr.PSB_12_P2}\")\n\n # qc.Station.default.MW_source.on() \n\n@job_wrapper\ndef PSB56_calibration(sweep_range=0.5, plot=False):\n gates, _311113, ST_anti_12, ST_anti_12_tc_high, ST_anti_56, ST_anti_56_tc_high, vSD1_threshold, vSD2_threshold = variables()\n \n anticrossing = list(ST_anti_56)\n anticrossing[9] = lp.linspace(anticrossing[9] - sweep_range/2,anticrossing[9] + sweep_range/2, 20, axis=1, name='vP5', unit='mV') \n anticrossing[11] = lp.linspace(anticrossing[11] - sweep_range/2,anticrossing[11] + sweep_range/2, 20, axis=0, name='vP6', unit='mV')\n anticrossing =tuple(anticrossing)\n\n var_mgr = variable_mgr()\n\n s = six_dot_sample(qc.Station.default.pulse)\n \n s.add(s.init56, anti_crossing = anticrossing)\n s.add(s.pre_pulse)\n\n s.add(s.wait(10000)) \n s.add(s.q6.X90)\n # s.add(s.wait(50e3)) \n s.add(s.read56, anti_crossing = anticrossing)\n \n s.n_rep = 500\n sequence, minstr, name = run_qubit_exp(f'PSB56_calibration_SLOW', s.sequencer)\n\n qc.Station.default.MW_source.on() \n ds_on = scan_generic(sequence, minstr, name=name).run()\n\n # qc.Station.default.MW_source.off() \n # ds_off = scan_generic(sequence, minstr, name=name).run()\n\n # x = ds_on('read56').x()\n # y = ds_on('read56').y() \n # contrast = np.where(ds_on('read56')()>0.9,0,ds_on('read56')()) - np.where(ds_off('read56')()>0.9,0,ds_off('read56')())\n # contrast = sp.ndimage.filters.gaussian_filter(contrast, [2,2], mode='constant')\n # if plot:\n # plt.imshow(contrast)\n\n # var_mgr.PSB_56_P5 = round(x[np.where(contrast == contrast.max())[0][0]],2)\n # var_mgr.PSB_56_P6 = round(y[np.where(contrast == contrast.max())[1][0]],2)\n # print(f\"Selected point\\n\\tvP5 :: {var_mgr.PSB_56_P5}\\n\\tvP6 :: {var_mgr.PSB_56_P6}\")\n\n # qc.Station.default.MW_source.on()\n","repo_name":"bundseth/good_morning_scripts","sub_path":"good_morning/calibrations/PSB_calib.py","file_name":"PSB_calib.py","file_ext":"py","file_size_in_byte":4220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"20569689824","text":"inp = input().lower()\ncnt = {}\nfor x in inp:\n if not 'a'<=x<='z':continue\n if x not in cnt:cnt[x]=1\n else:cnt[x]+=1\ns = []\nfor x in cnt:\n s.append([-cnt[x],x])\ns.sort()\nfor x in s:\n print(x[1]+' -> '+str(-x[0]))\n","repo_name":"petchluvsyou/2110101-grader","sub_path":"08_Dict_21.py","file_name":"08_Dict_21.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"12957721417","text":"# Public imports\n\n# Private imports\n\n# Look ma, no imports!\n\nclass Graph:\n\tdef __init__(self, n = 2):\n\t\tassert n > 0\n\n\t\tself._g = [[0 for x in range(n)] for x in range(n)]\n\n\tdef addNode(self):\n\t\tfor row in self._g:\n\t\t\trow.append(0);\n\n\t\tself._g.append([0 for x in range(len(self._g[0]))])\n\n\t\treturn len(self._g) - 1\n\t\n\tdef connect(self, n, m):\n\t\tassert n != m\n\t\tassert n < len(self._g)\n\t\tassert m < len(self._g)\n\n\t\tself._g[n][m] = 1\n\t\tself._g[m][n] = 1\n\n\tdef getDOTRepresentation(self, complete = True, readable = False, includeNodeIDs = True, degreeAsLabel = True):\n\t\tresult = \"\"\n\n\t\tif complete:\n\t\t\tresult = \"graph G {\"\n\t\t\tif readable:\n\t\t\t\tresult += \"\\n\"\n\n\t\tfor rowI in range(len(self._g)):\n\t\t\tfor colI in range(rowI + 1, len(self._g)):\n\t\t\t\tif self._g[rowI][colI] == 1:\n\t\t\t\t\tif readable:\n\t\t\t\t\t\tresult += \"\\t\"\n\n\t\t\t\t\tresult += \"{} -- {};\".format(rowI, colI)\n\n\t\t\t\t\tif readable:\n\t\t\t\t\t\tresult += \"\\n\"\n\n\t\tif includeNodeIDs or degreeAsLabels:\n\t\t\tfor v in range(len(self._g)):\n\t\t\t\tif readable:\n\t\t\t\t\tresult += \"\\t\"\n\n\t\t\t\tif not degreeAsLabel:\n\t\t\t\t\tresult += \"{};\".format(v)\n\t\t\t\telse:\n\t\t\t\t\tresult += \"{} [label = {}];\".format(v, self.getNeighbourCount(v))\n\n\t\t\t\tif readable:\n\t\t\t\t\tresult += \"\\n\"\n\n\t\tif complete:\n\t\t\tresult += \"}\"\n\n\t\treturn result\n\n\tdef dumpToFile(self, dotFile = \"\", colors = [], colorscheme = \"\"):\n\t\tresult = \"\"\n\n\t\tif colors:\n\t\t\tif colorscheme != \"\":\n\t\t\t\tcolors = [\"/{}/{}\".format(colorscheme, x) for x in colors]\n\n\t\t\tresult = self.getColorFilledDOTRepresentation(colors)\n\t\telse:\n\t\t\tresult = self.getDOTRepresentation()\n\n\t\twith open(dotFile, \"w\") as f:\n\t\t\tf.write(result)\n\t\n\tdef getDegreeColorFilledDOTRepresentation(self, readable = False, degreeAsLabel = True, colorscheme = \"__none__\", colorMax = -1):\n\t\tmaxNeighourCount = self.getMaxNeighbourCount() \n\n\t\tif colorscheme == \"__none__\":\n\t\t\tcolors = ['\"{}{}\"'.format('grey', floor(sum(row) / maxNeighourCount * 100)) for row in self._g]\n\t\telse:\n\t\t\tcolors = ['\"/{}/{}\"'.format(colorscheme, 1 + floor(sum(row) / maxNeighourCount * (colorMax - 1))) for row in self._g]\n\n\t\treturn self.getColorFilledDOTRepresentation(colors, degreeAsLabel, True)\n\n\tdef getColorFilledDOTRepresentation(self, colors, degreeAsLabel = True, readable = False):\n\t\tresult = \"graph G {\"\n\t\tif readable:\n\t\t\tresult += \"\\n\"\n\n\t\tresult += self.getDOTRepresentation(False, readable, degreeAsLabel = True)\n\n\t\tfor v, c in enumerate(colors):\n\t\t\tif readable:\n\t\t\t\tresult += \"\\t\"\n\t\t\t\n\t\t\tresult += \"{} [style = filled, fillcolor = {}];\".format(v, c)\n\n\t\t\tif readable:\n\t\t\t\tresult += \"\\n\"\n\n\t\tresult += \"}\"\n\n\t\treturn result\n\n\tdef getNodeAmount(self):\n\t\treturn len(self._g)\n\n\tdef getNeighbourCount(self, v):\n\t\treturn sum(self._g[v])\n\n\tdef getMaxNeighbourCount(self):\n\t\treturn max([sum(row) for row in self._g])\n\n\tdef getNeighbours(self, v):\n\t\treturn [i for i, x in enumerate(self._g[v]) if x == 1]\n\n\tdef getDegreeSortedNodes(self):\n\t\tnodes = [(v, self.getNeighbours(v)) for v in range(self.getNodeAmount())]\n\t\tnodes = sorted(nodes, key=lambda pair: pair[1])\n\t\treturn [p[0] for p in nodes]\n\n\tdef isConnected(self):\n\t\tassert len(self._g) > 0\n\n\t\tvisited = [False] * len(self._g)\n\t\ttoVisit = [0]\n\n\t\twhile toVisit:\n\t\t\tcurrV = toVisit.pop()\n\t\t\tvisited[currV] = True\n\t\t\ttoVisit += [x for x in self.getNeighbours(currV) if not visited[x]]\n\n\t\treturn sum(visited) == self.getNodeAmount()\n\n\tdef getMaxDegreeNode(self):\n\t\tmaxNode = -1\n\t\tmaxDegree = -1\n\t\tfor v in range(self.getNodeAmount()):\n\t\t\tif self.getNeighbourCount(v) > maxDegree:\n\t\t\t\tmaxNode = v\n\t\t\t\tmaxDegree = self.getNeighbourCount(v)\n\t\t\n\t\treturn maxNode\n\n\tdef deepClone(self):\n\t\tg = Graph(self.getNodeAmount())\n\n\t\tfor f in range(self.getNodeAmount()):\n\t\t\tfor s in range(f + 1, self.getNodeAmount()):\n\t\t\t\tif self._g[f][s] == 1:\n\t\t\t\t\tg.connect(f, s)\n\n\t\treturn g\n","repo_name":"bobismijnnaam/graphsim","sub_path":"supergraphs.py","file_name":"supergraphs.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"16154167810","text":"from pacotes import funcoes\n\n\ndef area(l, c):\n print(f'A área do seu terro de {l}m x {c}m é de {l * c}m².')\n\n\nwhile True:\n l = float(input('Digite a largura do terreno: '))\n c = float(input('Digite o comprimento do terreno: '))\n area(l, c)\n while True:\n resp = funcoes.simnao('Deseja continuar? (S/N) ')\n if resp in 'SN':\n break\n if resp == 'N':\n break\n","repo_name":"sauliiin/Python-from-Padawan-to-Jedi","sub_path":"105.py","file_name":"105.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"43772346607","text":"'''\n1990 : 3의 배수 판별하기\n자연수 n이 입력되면 3의 배수인지 아닌지 판별하시오.\n3의 배수이면 1을 출력하고, 아니면 0을 출력한다.\n'''\nn = int(input())\nif(n%3==0):\n print(1)\nelse:\n print(0)","repo_name":"minhyeonlee/algorithm-python","sub_path":"codeUp/codeUpBasic/1990.py","file_name":"1990.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73115644106","text":"import os.path\nimport sys\nimport logging\nimport re\nfrom modules.BeautifulSoup import BeautifulSoup\n\nCURRENTDIR = os.path.dirname(__file__)\nCITYCONFIG = os.path.join(CURRENTDIR, 'cities.xml')\n\nNAMES_DICT = None\nIDS_DICT = None\n\ndef city_names():\n if not NAMES_DICT:\n load_data()\n \n return NAMES_DICT\n\ndef city_ids():\n if not IDS_DICT:\n load_data()\n \n return IDS_DICT\n\ndef load_data():\n global NAMES_DICT\n global IDS_DICT\n\n NAMES_DICT = {}\n IDS_DICT = {}\n\n markup = BeautifulSoup(open(CITYCONFIG, 'r'))\n\n for node in markup.findAll('city'):\n city_id = node['id']\n city_name = node.text\n\n NAMES_DICT[city_name] = city_id\n IDS_DICT[city_id] = city_name\n\n","repo_name":"yrlihuan/tuan-site-fetcher","sub_path":"extractor/cityutil.py","file_name":"cityutil.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28248480268","text":"'''\nCreate an empty dictionary. Then, welcome the user and give the user a menu with the following options:\n\nAdd a key-value pair to the dictionary.\nRemove a key-value pair from the dictionary.\nQuit.\nMake sure that each choice does what's intended, then return to this main menu and do it again.\n\nPrint the dictionary after each choice is done!\n'''\n\n'''\nWelcome to SmartDictionary!\nCurrently, the dictionary is empty.\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? a\nWhat key do you want to add? name\nWhat is the value for this key? Oakland Tech\nCurrently the dictionary is as follows: {'name': 'Oakland Tech'}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? a\nWhat key do you want to add? school_type\nWhat is the value for this key? High School\nCurrently the dictionary is as follows: {'name': 'Oakland Tech', 'school_type': 'High School}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? r\nWhat key do you want to remove? name\nCurrently the dictionary is as follows: {'school_type': 'High School'}\n\nDo you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? q\n'''\n\ndict = {}\n\nprint(\"Welcome to SmartDictionary\")\nprint(\"Currently, the dictionary is empty.\")\nprint()\nwhile True:\n action = input(\"Do you want to (a)dd a kv pair, (r)emove a kv pair, or (q)uit? \")\n if action == \"q\":\n break\n elif action == \"a\":\n key = input(\"What key do you want to add? \")\n value = input(\"What is the value for this key? \")\n dict[key] = value\n elif action == \"r\":\n key = input(\"What key do you want to remove? \")\n del dict[key]\n else:\n continue\n print(f\"Currently the dictionary is as follows: {dict}\")\n","repo_name":"NotMyPersonalAccount/DualEnrollmentAssignments","sub_path":"module_08/interactive_dictionary.py","file_name":"interactive_dictionary.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"4994369078","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 19 23:18:57 2020\n\n@author: elif.ayvali\n\"\"\"\nimport numpy as np\nimport random\nfrom collections import namedtuple, deque\n\nfrom network import deep_Q_net, dueling_Q_net\n\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nBUFFER_SIZE = int(1e4) # replay buffer size\nBATCH_SIZE = 64 # minibatch size\nGAMMA = 0.99 # discount factor\nTAU = 1e-3 # for soft update of target parameters\nLR = 5e-4 # learning rate \nUPDATE_EVERY = 4 # how often to update the network\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nclass Agent():\n \"\"\"Interacts with and learns from the environment.\"\"\"\n\n def __init__(self, state_size, action_size, seed, learning_alg='vanilla_deep_Q_learning'):\n \"\"\"Initialize an Agent object.\n \n Params\n ======\n state_size (int): dimension of each state\n action_size (int): dimension of each action\n seed (int): random seed\n \"\"\"\n self.state_size = state_size\n self.action_size = action_size\n self.seed = random.seed(seed)\n self.learning_alg=learning_alg\n \n # Q-Network\n if self.learning_alg=='deep_Q_learning':\n self.qnetwork_local=deep_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=deep_Q_net(state_size, action_size, seed).to(device)\n\n print('...Running DQN')\n elif self.learning_alg=='double_deep_Q_learning':\n self.qnetwork_local=deep_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=deep_Q_net(state_size, action_size, seed).to(device)\n print('...Running double DQN')\n elif self.learning_alg=='dueling_deep_Q_learning':\n self.qnetwork_local=dueling_Q_net(state_size, action_size, seed).to(device)\n self.qnetwork_target=dueling_Q_net(state_size, action_size, seed).to(device) \n print('...Running dueling DQN')\n else:\n print('Invalid Algorithm Type') \n \n print('Network Architecture', self.qnetwork_local)\n self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)\n\n # Replay memory\n self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)\n # Initialize time step (for updating every UPDATE_EVERY steps)\n self.t_step = 0\n \n def step(self, state, action, reward, next_state, done):\n # Save experience in replay memory\n self.memory.add(state, action, reward, next_state, done)\n \n self.t_step = (self.t_step + 1) % UPDATE_EVERY\n # Learn every UPDATE_EVERY time steps:\n if self.t_step == 0:\n # If enough samples are available in memory, get random subset and learn\n if len(self.memory) > BATCH_SIZE:\n experiences = self.memory.sample() #returns torch datatype\n self.learn(experiences, GAMMA) #update value parameters\n\n def act(self, state, eps=0.):\n \"\"\"Returns actions for given state as per current policy.\n \n Params\n ======\n state (array_like): current state\n eps (float): epsilon, for epsilon-greedy action selection\n \"\"\"\n #Convert state to torch structure\n state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n #Evaluate the current network to get action values for the state\n self.qnetwork_local.eval()#This is equivalent with self.train(False)\n with torch.no_grad():\n action_values = self.qnetwork_local(state)\n self.qnetwork_local.train()\n\n # Epsilon-greedy action selection\n #For windows system, action type should be int32 to play nice with Unity\n if random.random() > eps:\n \n greedy_action=np.argmax(action_values.cpu().data.numpy())\n return greedy_action.astype(np.int32)\n else:\n random_action=random.choice(np.arange(self.action_size))\n return random_action.astype(np.int32)\n\n def learn(self, experiences, gamma):\n \"\"\"Update value parameters using given batch of experience tuples.\n\n Params\n ======\n experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples \n gamma (float): discount factor \n \"\"\"\n #states: (batchsize x statesize), actions:(batchsize x 1), rewards: (batchsize x 1)\n states, actions, rewards, next_states, dones = experiences\n if self.learning_alg==\"deep_Q_learning\":\n #qnetwork_target(next_states): Get max predicted Q values (for next states) from target model (batchsize x actionsize)\n #qnetwork_target(next_states).max(1)# (1 x batch size) returns two tensors: max value in each batch(row), the column index at which the max value is found.\n #qnetwork_target(next_states).max(1)[0]) # (1 x batch size) gets the max value in each batch \n #qnetwork_target(next_states).max(1)[0].unsqueeze(1) converts it to (bathsize x 1) \n #qnetwork_target(next_states).max(1)[0].unsqueeze(1).detach() detaches the output from the computational graph to ensure that these values don’t update the target network when loss.backward() and optimizer.step() are called\n #Q_target weights should not change during learning phase and should be updated periodically by swapping local network weights\n #select greedy actions using target network and use target network to evaluate its q-value\n Q_greedy = self.qnetwork_target(next_states).max(1)[0].unsqueeze(1).detach() #batchsize x 1 \n # ------Compute Q targets for current states------ : \n elif self.learning_alg==\"double_deep_Q_learning\" or self.learning_alg==\"dueling_deep_Q_learning\":\n #select the greedy action using online network\n greedy_actions = self.qnetwork_local(next_states).max(1)[1].unsqueeze(1).detach()#(bathsize x 1) column index at which the max value is found\n #get the q-values of the selected greedy actions using the target network\n Q_greedy = self.qnetwork_target(next_states).gather(1, greedy_actions) \n\n Q_target = rewards + (gamma * Q_greedy * (1 - dones))#If it is the last episode (dones=1) only reward is used \n\n # -----Get expected Q values from local model------:\n #self.qnetwork_local(states): batch size x action_size \n #Get the Q-values for the actions that the agent actually took, gather() function gets this subset \n Q_est = self.qnetwork_local(states).gather(1, actions)\n # Compute loss\n loss = F.mse_loss(Q_est, Q_target)\n # Minimize the loss\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n # -------Udate target network --_____________-------:\n self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU) \n \n \n def soft_update(self, local_model, target_model, tau):\n \"\"\"Soft update model parameters.\n θ_target = τ*θ_local + (1 - τ)*θ_target\n\n Params\n ======\n local_model (PyTorch model): weights will be copied from\n target_model (PyTorch model): weights will be copied to\n tau (float): interpolation parameter \n \"\"\"\n for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n\n\nclass ReplayBuffer:\n \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n\n def __init__(self, action_size, buffer_size, batch_size, seed):\n \"\"\"Initialize a ReplayBuffer object.\n\n Params\n ======\n action_size (int): dimension of each action\n buffer_size (int): maximum size of buffer\n batch_size (int): size of each training batch\n seed (int): random seed\n \"\"\"\n self.action_size = action_size\n self.memory = deque(maxlen=buffer_size) \n self.batch_size = batch_size\n self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n self.seed = random.seed(seed)\n \n def add(self, state, action, reward, next_state, done):\n \"\"\"Add a new experience to memory.\"\"\"\n e = self.experience(state, action, reward, next_state, done)#define a new tuple\n self.memory.append(e)\n \n def sample(self):\n \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n experiences = random.sample(self.memory, k=self.batch_size)\n\n states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)\n rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n \n return (states, actions, rewards, next_states, dones)\n\n def __len__(self):\n \"\"\"Return the current size of internal memory.\"\"\"\n return len(self.memory)\n \n ","repo_name":"eayvali/DeepRL","sub_path":"DQN DDQN Dueling/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":9534,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"32144283522","text":"from __future__ import print_function, absolute_import, division\n\nfrom pils import retry\nimport boto3\n\nfrom .util import _boto_tags_to_dict\nfrom .replacement_policy import ReplacementPolicy\n\n# Any ASG that has a tag with this key will be handled by spotnik.\nSPOTNIK_TAG_KEY = \"spotnik\"\n\n\nclass Spotnik(object):\n def __init__(self, region_name, asg, logger=None):\n self.asg = asg\n self.asg_name = asg['AutoScalingGroupName']\n\n self.ec2_client = boto3.client('ec2', region_name=region_name)\n self.asg_client = boto3.client('autoscaling', region_name=region_name)\n\n self.logger = logger\n\n def describe_instance(self, instance_id):\n response = self.ec2_client.describe_instances(InstanceIds=[instance_id])\n return response['Reservations'][0]['Instances'][0]\n\n def describe_launch_configuration(self, launch_config_name):\n response = self.asg_client.describe_launch_configurations(\n LaunchConfigurationNames=[launch_config_name])\n return response['LaunchConfigurations'][0]\n\n def get_pending_spot_resources(self):\n self.logger.info(\"Searching pending resources of ASG\")\n response = self.ec2_client.describe_spot_instance_requests(Filters=[\n {'Name': 'tag-value', 'Values': [self.asg_name]}])\n requests = response['SpotInstanceRequests']\n\n for request in requests:\n if request['State'] not in ('open', 'active'):\n continue\n\n instance_id = request.get('InstanceId')\n if instance_id is None:\n return request, None\n\n details = self.describe_instance(instance_id)\n state = details['State']['Name']\n self.logger.info(\"Found spot instance %s which is in state %s.\", instance_id, state)\n if state == 'running':\n return request, instance_id\n return request, None\n return None, None\n\n def tag_new_instance(self, new_instance_id, old_instance):\n self.ec2_client.create_tags(Resources=[new_instance_id],\n Tags=[old_instance['Tags']])\n\n @staticmethod\n def get_spotnik_asgs(region_name):\n client = boto3.client('autoscaling', region_name=region_name)\n asgs = client.describe_auto_scaling_groups()['AutoScalingGroups']\n spotnik_asgs = []\n for asg in asgs:\n tags = asg['Tags']\n tag_keys = [tag['Key'] for tag in tags]\n if SPOTNIK_TAG_KEY in tag_keys:\n spotnik_asgs.append(asg)\n return spotnik_asgs\n\n def attach_spot_instance(self, spot_instance_id, spot_request):\n instance_id = _boto_tags_to_dict(spot_request['Tags'])['spotnik-will-replace']\n\n self.logger.info(\"attaching: %r detaching: %r\", spot_instance_id, instance_id)\n\n # If the ASG is already at its MaxSize, we cannot attach a new instance.\n # So either\n # - temporarily increase the MaxSize with AUTOSCALING.update_auto_scaling_group()\n # or\n # - detach the old instance before attaching the new one\n current_max_size = self.asg['MaxSize']\n self.asg_client.update_auto_scaling_group(\n AutoScalingGroupName=self.asg_name,\n MaxSize=current_max_size + 1)\n self.asg_client.attach_instances(InstanceIds=[spot_instance_id],\n AutoScalingGroupName=self.asg_name)\n try:\n self.asg_client.detach_instances(InstanceIds=[instance_id],\n AutoScalingGroupName=self.asg_name,\n ShouldDecrementDesiredCapacity=True)\n except Exception:\n self.logger.exception(\n \"Could not detach instance %r, I'll assume it was terminated \"\n \"by the ASG. Therefore, I will terminate spot instance %r, \"\n \"which was supposed to replace it. Original backtrace:\",\n instance_id, spot_instance_id)\n self.ec2_client.terminate_instances(InstanceIds=[spot_instance_id])\n else:\n self.ec2_client.terminate_instances(InstanceIds=[instance_id])\n\n self.asg_client.update_auto_scaling_group(\n AutoScalingGroupName=self.asg_name, MaxSize=current_max_size)\n\n def untag_spot_request(self, spot_request):\n # Remove tags so that self.get_pending_spot_resources() does not find\n # this spot request again.\n self.ec2_client.delete_tags(Resources=[spot_request['SpotInstanceRequestId']],\n Tags=[{'Key': SPOTNIK_TAG_KEY}])\n\n def make_spot_request(self):\n policy = ReplacementPolicy(self.asg, self)\n if not policy.is_replacement_needed():\n return\n\n launch_specification, replaced_instance_details, bid_price = policy.decide_replacement()\n\n response = self.ec2_client.request_spot_instances(\n DryRun=False, SpotPrice=bid_price,\n LaunchSpecification=launch_specification)\n\n spot_request_id = response['SpotInstanceRequests'][0]['SpotInstanceRequestId']\n self.logger.info(\"New spot request %r was created\", spot_request_id)\n\n tags = [\n {'Key': SPOTNIK_TAG_KEY, 'Value': self.asg['AutoScalingGroupName']},\n {'Key': 'spotnik-will-replace', 'Value': replaced_instance_details['InstanceId']}]\n self.tag_spot_request(spot_request_id, tags)\n\n @retry(attempts=3, delay=3)\n def tag_spot_request(self, spot_request_id, tags):\n self.ec2_client.create_tags(Resources=[spot_request_id], Tags=tags)\n","repo_name":"Scout24/spotnik","sub_path":"src/main/python/spotnik/spotnik.py","file_name":"spotnik.py","file_ext":"py","file_size_in_byte":5638,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"28273709771","text":"from splinter import Browser\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\nimport time\n\n\ndef init_browser():\n executable_path = {\"executable_path\": \"chromedriver.exe\"}\n return Browser(\"chrome\", **executable_path, headless=False)\n\n\ndef scrape():\n browser = init_browser()\n\n # visit mars new site\n url = \"https://mars.nasa.gov/news\"\n browser.visit(url)\n\n time.sleep(2)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n # get news title\n headline = soup.find_all(\"div\", class_=\"content_title\")\n news_headline = headline[1].text\n\n # get paragraph text\n pargs = soup.find_all(\"div\", class_=\"article_teaser_body\")\n parg_text = pargs[0].text\n\n # visit image url\n pic_url = \"https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars\"\n browser.visit(pic_url)\n\n time.sleep(1)\n\n # click through links to find image\n browser.links.find_by_partial_text(\"FULL IMAGE\")\n time.sleep(2)\n browser.links.find_by_partial_text(\"more info\")\n time.sleep(2)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n # extract image link\n image = soup.find(\"figure\", class_=\"lede\")\n image_url = image.find(\"a\")[\"href\"]\n featured_image_url = \"https://www.jpl.nasa.gov\" + image_url\n\n # get mars facts\n facts_url = \"https://space-facts.com/mars/\"\n tables = pd.read_html(facts_url)\n\n # visit mars hemispheres html\n mars_url = \"https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars\"\n browser.visit(mars_url)\n\n browser.links.find_by_partial_text(\"Cerberus\")\n\n time.sleep(1)\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n base_url = \"https://astrogeology.usgs.gov\"\n\n # extract image links\n cerberus = soup.find(\"div\", class_=\"downloads\")\n cerberus_link = cerberus.find(\"img\")[\"src\"]\n cerberus_url = base_url + cerberus_link\n\n browser.back()\n time.sleep(1)\n\n browser.links.find_by_partial_text(\"Schiaparelli\")\n time.sleep(2)\n\n schiaparelli = soup.find(\"div\", class_=\"downloads\")\n schiaparelli_link = schiaparelli.find(\"img\")[\"src\"]\n schiaparelli_url = base_url + schiaparelli_link\n\n browser.back()\n time.sleep(2)\n\n browser.links.find_by_partial_text(\"Syrtis\")\n time.sleep(2)\n\n syrtis = soup.find(\"div\", class_=\"downloads\")\n syrtis_link = syrtis.find(\"img\")[\"src\"]\n syrtis_url = base_url + syrtis_link\n\n browser.links.find_by_partial_text(\"Valles\")\n time.sleep(2)\n\n valles = soup.find(\"div\", class_=\"downloads\")\n valles_link = valles.find(\"img\")[\"src\"]\n valles_url = base_url + valles_link\n\n hemisphere_image_urls = [\n {\"title\": \"Valles Marineris Hemisphere\", \"img_url\": {\"valles_url\"}},\n {\"title\": \"Cerberus Hemisphere\", \"img_url\": {\"cerberus_url\"}},\n {\"title\": \"Schiaparelli Hemisphere\", \"img_url\": {\"schiaparelli_url\"}},\n {\"title\": \"Syrtis Major Hemisphere\", \"img_url\": {\"syrtis_url\"}},\n ]\n\n mars_data = {\n \"Headline\": news_headline,\n \"Paragraph Text\": parg_text,\n \"Featured Image\": featured_image_url,\n \"Mars Facts\": tables,\n \"Hemispheres\": hemisphere_image_urls,\n }\n\n browser.quit()\n\n return mars_data\n","repo_name":"sjplaza/web-scraping-challenge","sub_path":"Missions_to_Mars/scrape_nasa.py","file_name":"scrape_nasa.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"73309258184","text":"############### IMPORT NECESSARY RECOURSES ###############\n# Throughout this document, several libraries are used #\n# in order to import necessary functions to make graphs, #\n# communicate with SQL, and filter data. #\n##########################################################\n\nimport psycopg2 as pg2 # import psycopg2 to communicate with 'Receipt_Project_v3.0' database\nimport matplotlib.pyplot as plt # import matplotlib to display data\nimport numpy as np # import numpy to restructure data\nimport re # import re regular expressions for data filter\nimport seaborn as sns # import seaborn for graph styles\nimport os # import os to delete past graph images\n\n\n\n############### IMPORT DATA FROM 'Receipt_Project_v3.0' ###############\n# In order to 'decongest' the file, all data relevant to the #\n# different plots will be declared as global variables. #\n#######################################################################\n\n##### CREATE LINK BETWEEN PYTHON AND SQL SERVER #####\n\nreceipt_project = pg2.connect( # connect python to 'Receipt_Project_v3.0' database\n host='localhost',\n database='Receipt_Project_v3.0',\n user='',\n password=''\n)\nreceiptProjectCursor = receipt_project.cursor() # create cursor to input commands\n\n##### GRAB 'total_spent' FROM 'Receipt_Project_v3.0' #####\n\nreceiptProjectCursor.execute( # grab 'total_spent' from 'base_fields_table' for data set\n 'select total_spent from base_fields_table order by purchase_date'\n)\npurchaseTotals = list(np.asarray(receiptProjectCursor.fetchall()).flatten()) # store contents in 'purchaseTotals'\n\n##### RESTRUCTURE 'purchaseTotals' ARRAY #####\n\nfor i in range(len(purchaseTotals)): # restructure 'purchaseTotals' to remove null values and special characters\n\n if (purchaseTotals[i] == None): # replace null values with 0\n purchaseTotals[i] = 0\n\n else: # pass for all non-null values\n pass\n\n purchaseTotals[i] = float(re.sub( # filter 'purchaseTotals' to remove special characters\n '[@_!$%^#&*()<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(purchaseTotals[i]),\n count=len(str(purchaseTotals[i]))\n ))\n\n##### CLOSE CONNECTION TO 'Receipt_Project_v3.0' #####\n\nreceiptProjectCursor.close() # stop communication with 'Receipt_Project_v3.0' database\nreceipt_project.close() # close the connection to prevent data leaks\n\n\n\n############### 'makeMovingAverage' FUNCTION ###############\n# The makeMovingAverage function is used to create a #\n# moving average based on provided parameters. #\n############################################################\n\ndef makeMovingAverage(x, w):\n\n return np.convolve(x, np.ones(w), 'same') / w # return moving average\n\n\n\n############### 'makeQuarterlyMovingAverage' FUNCTION ###############\n# The makeQuarterlyMovingAverage function creates a model that is #\n# used to analyze how average daily spending has changed in the #\n# past year. #\n#####################################################################\n\ndef makeQuarterlyMovingAverage():\n\n ##### CREATE 'quarterlyMovingAverageGraph' RESOURCES #####\n\n quarterlyTotals = purchaseTotals[-90:] # grab last 90 days of data from 'purchaseTotals'\n\n quarterlyMovingAverage = makeMovingAverage(quarterlyTotals, int(np.sqrt(len(quarterlyTotals))) * 2) # create 'quarterlyMovingAverage'\n\n quarterlyMovingAverageIndex = [] # create empty array as 'quarterlyMovingAverageIndex' index\n\n for i in range(len(quarterlyMovingAverage)): # fill empty array to length of 'quarterlyMovingAverage'\n quarterlyMovingAverageIndex.append(i + 1)\n\n quarterlyMovingAverageTrend = makeMovingAverage(quarterlyMovingAverage, int(np.sqrt(len(quarterlyMovingAverage))) * 4) # create 'quarterlyMovingAverageTrend'\n\n quarterlyAverage = \" Rolling Average Trend:\"\n\n ##### PLOT 'quarterlyMovingAverageGraph' GRAPH #####\n\n sns.set( # configure 'quarterlyMovingAverageGraph' graph\n rc={'axes.facecolor': '#292D2E', 'figure.facecolor': '#292D2E', 'grid.color': '#395B64',\n 'axes.edgecolor': '#292D2E', 'text.color': '#A5C9CA', 'xtick.color': '#A5C9CA',\n 'ytick.color': '#A5C9CA', 'figure.figsize':(5.5, 3.5)}\n )\n\n quarterlyMovingAverageGraph = sns.lineplot( # create 'quarterlyMovingAverageGraph' average line\n quarterlyMovingAverageIndex, quarterlyMovingAverage, color='#A5C9CA'\n ).set(title=quarterlyAverage)\n\n quarterlyMovingAverageGraph = sns.lineplot( # create 'quarterlyMovingAverageGraph' trend line\n quarterlyMovingAverageIndex, quarterlyMovingAverageTrend, color='#E7F6F2'\n )\n\n if ( # if 'quarterlyMovingAverage' negative, proceed\n\n (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2)) < 0\n ):\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverage' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverage[-1:]), text=(\"-$\" + str(-1 * (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2)))),\n color='#A5C9CA', size=8\n )\n\n else: # if 'quarterlyMovingAverage' positive, proceed\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverage' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverage[-1:]), text=(\"$\" + str(round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverage[-1:]),\n count=len(str(quarterlyMovingAverage))\n )), 2))),\n color='#A5C9CA', size=8\n )\n\n if ( # if 'quarterlyMovingAverageTrend' negative, proceed\n\n (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2)) < 0\n ):\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverageTrend' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverageTrend[-1:]), text=(\"-$\" + str(-1 * (round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2)))),\n color='#E7F6F2', size=8\n )\n\n else: # if 'quarterlyMovingAverageTrend' positive, proceed\n\n quarterlyMovingAverageGraph.annotate( # annotate 'quarterlyMovingAverageTrend' line\n xy=(max(quarterlyMovingAverageIndex), quarterlyMovingAverageTrend[-1:]), text=(\"$\" + str(round(float(re.sub(\n '[@_!$%^#&*()\\[\\]<>?/\\|}{~:;¿§«»ω⊙¤°℃℉€¥£¢¡®©_+]', \"\", str(quarterlyMovingAverageTrend[-1:]),\n count=len(str(quarterlyMovingAverageTrend))\n )), 2))),\n color='#E7F6F2', size=8\n )\n\n plt.legend( # create legend\n labels=[\"Avg.\", \"Trend\"],\n fontsize=8,\n loc='upper left'\n )\n\n ##### SAVE 'quarterlyMovingAverageGraph.png' FILE #####\n\n if os.path.exists('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png'): # if file path exists, delete 'quarterlyMovingAverageGraph.png'\n os.remove('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png')\n\n else: # if file path does not exist, pass\n pass\n\n plt.savefig('/Users/matthewbeck/Desktop/Projects/Receipt_Project_v3.0/quarterlyMovingAverageGraph.png') # save 'quarterlyMovingAverageGraph' graph as 'quarterlyMovingAverageGraph.png'\n\n plt.cla() # clear 'quarterlyMovingAverageGraph' when complete","repo_name":"MTBProgramming/Receipt_Analyzer-v3.0","sub_path":"make_quarterly_moving_average.py","file_name":"make_quarterly_moving_average.py","file_ext":"py","file_size_in_byte":8009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"36234958537","text":"import os\nfrom pathlib import Path\nimport tempfile\nimport shutil\n\nimport maia\n\nmesh_dir = Path(maia.__file__).parent.parent/'share/meshes'\nsample_mesh_dir = Path(maia.__file__).parent.parent/'share/sample_meshes'\npytest_output_prefix = 'pytest_out'\n\ndef create_collective_tmp_dir(comm):\n \"\"\"\n Create a unique temporary directory and return its path\n \"\"\"\n if comm.Get_rank()==0:\n tmp_test_dir = tempfile.mkdtemp()\n else:\n tmp_test_dir = \"\"\n return Path(comm.bcast(tmp_test_dir,root=0))\n\ndef rm_collective_dir(path, comm):\n \"\"\"\n Remove a directory from its path\n \"\"\"\n comm.barrier()\n if comm.Get_rank() == 0:\n shutil.rmtree(path)\n comm.barrier()\n\nclass collective_tmp_dir:\n \"\"\"\n Context manager creating a tmp dir in parallel and removing it at the\n exit\n \"\"\"\n def __init__(self, comm):\n self.comm = comm\n def __enter__(self):\n self.path = create_collective_tmp_dir(self.comm)\n return self.path\n def __exit__(self, type, value, traceback):\n rm_collective_dir(self.path, self.comm)\n\ndef create_pytest_output_dir(comm):\n \"\"\"\n Create (in parallel) a directory named from the name of the current\n test runned by pytest and prefixed by module variable pytest_output_prefix.\n Return the name of this directory\n \"\"\"\n test_name = os.environ.get('PYTEST_CURRENT_TEST').split('::')[-1].split()[0]\n out_dir = Path(pytest_output_prefix)/test_name\n if comm.Get_rank() == 0:\n if not out_dir.exists():\n out_dir.mkdir(parents=True)\n comm.barrier()\n return out_dir\n\n","repo_name":"onera/Maia","sub_path":"maia/utils/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"} +{"seq_id":"27828248544","text":"from kuantum.kyber.utils.constants import PARAMS_K_512, PARAMS_K_768, PARAMS_K_1024, POLY_BYTES\nfrom kuantum.kyber.utils.num_type import uint16, int16, byte\nfrom kuantum.kyber.IDCPA import IDCPA\nfrom Crypto.Hash import SHA3_256, SHA3_512, SHAKE256\nfrom Crypto.Random import get_random_bytes\n\nPOLY_VEC_BYTES_K512 = 2 * POLY_BYTES\nPOLY_VEC_BYTES_K768 = 3 * POLY_BYTES\nPOLY_VEC_BYTES_K1024 = 4 * POLY_BYTES\n\nIDCPA_PK_BYTES_512 = POLY_VEC_BYTES_K512 + 32\nIDCPA_PK_BYTES_768 = POLY_VEC_BYTES_K768 + 32\nIDCPA_PK_BYTES_1024 = POLY_VEC_BYTES_K1024 + 32\n\nIDCPA_SK_BYTES_512 = 2 * POLY_BYTES\nIDCPA_SK_BYTES_768 = 3 * POLY_BYTES\nIDCPA_SK_BYTES_1024 = 4 * POLY_BYTES\n\nKYBER_SK_BYTES_512 = POLY_VEC_BYTES_K512 + ((POLY_VEC_BYTES_K512 + 32) + 2 * 32)\nKYBER_SK_BYTES_768 = POLY_VEC_BYTES_K768 + ((POLY_VEC_BYTES_K768 + 32) + 2 * 32)\nKYBER_SK_BYTES_1024 = POLY_VEC_BYTES_K1024 + ((POLY_VEC_BYTES_K1024 + 32) + 2 * 32)\n\n\nclass Kyber:\n\n def __init__(self, level):\n self.type = level\n if level == 'kyber512':\n self.k = PARAMS_K_512\n if level == 'kyber768':\n self.k = PARAMS_K_768\n if level == 'kyber1024':\n self.k = PARAMS_K_1024\n self.idcpa = IDCPA(level)\n\n def gen_keypair(self):\n keys = self.idcpa.idcpa_gen_keypair()\n pk = keys['public_key']\n sk = keys['secret_key']\n\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in pk]))\n h_pk = md.digest()\n h_pk = [byte(x) for x in h_pk]\n z = get_random_bytes(32)\n z = [byte(x) for x in z]\n\n kyber_keys = {\n 'public_key': pk,\n 'secret_key': sk[:] + pk[:] + h_pk[:] + z[:]\n }\n\n return kyber_keys\n\n def encrypt(self, public_key, msg=None):\n if msg is not None and len(msg) != 32:\n raise ValueError('Message must be 32 bytes long')\n if msg is None:\n msg = get_random_bytes(32)\n\n # hash msg with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in msg]))\n h_msg = md.digest()\n h_msg = [byte(x) for x in h_msg]\n\n # hash public key with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in public_key]))\n h_pk = md.digest()\n h_pk = [byte(x) for x in h_pk]\n\n # hash h_msg and h_pk with SHA3-512\n md512 = SHA3_512.new()\n md512.update(bytearray([x & 0xff for x in h_msg + h_pk]))\n h_msg_pk = md512.digest()\n h_msg_pk = [byte(x) for x in h_msg_pk]\n\n kr1 = h_msg_pk[:32]\n kr2 = [h_msg_pk[i + 32] for i in range(0, len(h_msg_pk) - 32)]\n\n # generate ciphertext\n ct = self.idcpa.idcpa_enc(public_key, h_msg, kr2)\n\n # hash cypher text with SHA-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in ct]))\n h_ct = md.digest()\n h_ct = [byte(x) for x in h_ct]\n\n # hash kr1 and h_ct with SHAKE-256\n md_shake = SHAKE256.new()\n md_shake.update(bytearray([x & 0xff for x in kr1 + h_ct]))\n shared_secret = md_shake.read(32)\n shared_secret = [byte(x) for x in shared_secret]\n\n return {\n 'ciphertext': ct,\n 'shared_secret': shared_secret\n }\n\n def decrypt(self, cipher_text, private_key):\n idcpa_private_key = None\n idcpa_public_key = None\n if self.k == 2:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_512]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_512:IDCPA_SK_BYTES_512 + IDCPA_PK_BYTES_512]\n h = private_key[KYBER_SK_BYTES_512 - 2 * 32:KYBER_SK_BYTES_512 - 32]\n z = private_key[KYBER_SK_BYTES_512 - 32:]\n\n if self.k == 3:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_768]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_768:IDCPA_SK_BYTES_768 + IDCPA_PK_BYTES_768]\n h = private_key[KYBER_SK_BYTES_768 - 2 * 32:KYBER_SK_BYTES_768 - 32]\n z = private_key[KYBER_SK_BYTES_768 - 32:]\n\n if self.k == 4:\n idcpa_private_key = private_key[0: IDCPA_SK_BYTES_1024]\n idcpa_public_key = private_key[IDCPA_SK_BYTES_1024:IDCPA_SK_BYTES_1024 + IDCPA_PK_BYTES_1024]\n h = private_key[KYBER_SK_BYTES_1024 - 2 * 32:KYBER_SK_BYTES_1024 - 32]\n z = private_key[KYBER_SK_BYTES_1024 - 32:]\n\n # idcpa decrypt\n msg = self.idcpa.idcpa_dec(cipher_text, idcpa_private_key)\n\n # hash msg + pk_h with SHA3-512\n md = SHA3_512.new()\n md.update(bytearray([x & 0xff for x in msg + h]))\n h_msg_pk = md.digest()\n h_msg_pk = [byte(x) for x in h_msg_pk]\n k = h_msg_pk[:32]\n r = h_msg_pk[-32:]\n\n # idcpa encrypt\n ct = self.idcpa.idcpa_enc(idcpa_public_key, msg, r)\n\n # hash ct with SHA3-256\n md = SHA3_256.new()\n md.update(bytearray([x & 0xff for x in cipher_text]))\n h_ct = md.digest()\n h_ct = [byte(x) for x in h_ct]\n\n if ct == cipher_text:\n temp_buf = k + h_ct\n else:\n temp_buf = z[:] + h_ct\n\n # hash temp_buf with SHAKE-256\n md_shake = SHAKE256.new()\n md_shake.update(bytearray([x & 0xff for x in temp_buf]))\n shared_secret = md_shake.read(32)\n return [byte(x) for x in shared_secret]\n","repo_name":"rakshit087/kuantum","sub_path":"kuantum/kyber/Kyber.py","file_name":"Kyber.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"22498675201","text":"# Verilen listenin içindeki elemanları tersine döndüren bir fonksiyon yazın. Eğer listenin içindeki elemanlar da liste içeriyorsa onların elemanlarını da tersine döndürün. Örnek olarak:\n# input: [[1, 2], [3, 4], [5, 6, 7]]\n# output: [[[7, 6, 5], [4, 3], [2, 1]]\n\nl = [[1, 2], [3, 4], [5, 6, 7]]\nnewList = []\n\ndef Reverse(l):\n for x in l:\n if isinstance(x, list):\n x.reverse()\n newList.append(x)\n else:\n newList.append(x)\n newList.reverse()\n return newList\n\n\n","repo_name":"zaferna/Patika.dev","sub_path":"Reversed_List.py","file_name":"Reversed_List.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"22141386890","text":"\"\"\"Faça um Programa que leia 4 notas, mostre as notas e a média na tela.\"\"\"\n\ndef lervetor(vetor):\n media = sum(vetor) / len(vetor)\n print(f\"Média:, {media:.2f}\")\n \nnotas = [5,8,9,7]\nnotas2 = [5,8,9,7,10]\nnotas3 = [5,8,9,7,10,11]\n\n\nlervetor(notas)\nlervetor(notas2)\nlervetor(notas3)","repo_name":"TassioSales/Python_Brasil_exercicios","sub_path":"4 - ExerciciosListas/Exercicio_3.py","file_name":"Exercicio_3.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"} +{"seq_id":"29390342221","text":"class Person: \n # class attributes\n name = \"kibria\"\n\n # instance attributes\n def __init__(this, age, id): \n this.age = age\n this.id = id \n \n # instance method\n def address(this, add): \n return \"I live in {} and I am {} years old\" .format(add, this.age) \n\nobj1 = Person(22, 522)\n\n# access the class attributes\n# print(obj1.__class__.name) \nprint(obj1.name) \n\n# access_the_instance_attributes\n# print(obj1.age, obj1.id) \nprint(\"I am {} years old\".format(obj1.age))\nprint(\"My id is {}\".format(obj1.id)) \n\n# call our instance method\na = obj1.address(\"Dhaka\")\nprint(a) ","repo_name":"himelhrh/Python","sub_path":"46. OOP/1. basic.py","file_name":"1. basic.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"70469638984","text":"from typing import List\nimport torch\nfrom torch import nn\nfrom torch import Tensor\n\nfrom face2anime.modules.up_down import Encoder, Decoder\n\n\nclass BaseGenerator(nn.Module):\n def __init__(self,\n img_channels: int,\n channels: int = 32,\n block: str = \"Residual\",\n n_layer_blocks: int = 1,\n channel_multipliers: List[int] = [1, 2, 4],\n attention: str = \"SelfAttention\"):\n \n super().__init__()\n \n self.encoder = Encoder(in_channels=img_channels,\n channels=channels,\n block=block,\n n_layer_blocks=n_layer_blocks,\n channel_multipliers=channel_multipliers,\n attention=attention)\n \n self.decoder = Decoder(out_channels=img_channels,\n channels=channels,\n block=block,\n n_layer_blocks=n_layer_blocks,\n channel_multipliers=channel_multipliers,\n attention=attention)\n\n def forward(self, x: Tensor):\n x = self.encoder(x)\n x = self.decoder(x)\n return x\n \n\nif __name__ == \"__main__\":\n x = torch.randn(2, 3, 32, 32)\n generator = BaseGenerator(img_channels=3)\n out = generator(x)\n\n print('***** Generator *****')\n print('Input:', x.shape)\n print('Output:', out.shape)","repo_name":"hoang1007/face2anime","sub_path":"face2anime/modules/generators/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"20928551798","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"MUONEFF\")\n\nprocess.load(\"MuonAnalysis.TagAndProbe.MuonPerformanceESSource_cfi\")\n\nprocess.poolDBESSource.connect = 'sqlite_file:MuonPhysicsPerformance7TeV.db'\n\nprocess.load (\"MuonAnalysis.TagAndProbe.MuonPerformanceESProducer_cfi\")\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(100)\n)\n\nprocess.source = cms.Source(\"PoolSource\",\n fileNames = cms.untracked.vstring(\n '/store/relval/CMSSW_3_6_0/RelValJpsiMM/GEN-SIM-RECO/START36_V4-v1/0013/D4B634F3-8149-DF11-9056-002618943939.root'\n )\n )\n\nprocess.demo2 = cms.EDAnalyzer('MuTestPerformanceFW_ES',\n outfilename = cms.untracked.string('EfficiencyCorrectionPlots.root'),\n UseAbsEtaVals = cms.bool(True),\n AlgoNames = cms.vstring(\n 'GlobalMuon_Data_CaloMuonProbe_JPsi',\n 'HLT_Mu3_Data_CaloMuonProbe_JPsi',\n ))\n\nprocess.p = cms.Path(process.demo2)\n\n#print process.dumpPython()\n\n\n\n","repo_name":"cms-analysis/MuonAnalysis-TagAndProbe","sub_path":"test/performanceDB/test_ReadbackFromDB7TeV.py","file_name":"test_ReadbackFromDB7TeV.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"71072054985","text":"# To support both python 2 and python 3\nfrom __future__ import division, print_function, unicode_literals\n# list of points\nimport numpy as np\nimport matplotlib.pyplot as plt\n#thu vien dung de tinh toan khoang cach trong matrix\nfrom scipy.spatial.distance import cdist\nfrom matplotlib.backends.backend_pdf import PdfPages\nnp.random.seed(22)\n\nmeans = [[2, 2], [4, 2]]\ncov = [[.7, 0], [0, .7]]\nN = 20\n# dung de ve ngau nhien tu cac mau da bien\nX0 = np.random.multivariate_normal(means[0], cov, N) # each row is a data point\nX1 = np.random.multivariate_normal(means[1], cov, N)\n\nwith PdfPages('data.pdf') as pdf:\n plt.plot(X0[:, 0], X0[:, 1], 'bs', markersize = 8, alpha = 1)\n plt.plot(X1[:, 0], X1[:, 1], 'ro', markersize = 8, alpha = 1)\n plt.axis('equal')\n plt.ylim(0, 4)\n plt.xlim(0, 5)\n\n # hide tikcs\n cur_axes = plt.gca()\n cur_axes.axes.get_xaxis().set_ticks([])\n cur_axes.axes.get_yaxis().set_ticks([])\n\n plt.xlabel('$x_1$', fontsize = 20)\n plt.ylabel('$x_2$', fontsize = 20)\n pdf.savefig()\n # plt.savefig('logistic_2d.png', bbox_inches='tight', dpi = 300)\n plt.show()\n","repo_name":"Clapboiz/Scientific-research","sub_path":"Marchine learning/Code_py/soft_margin_svm.py","file_name":"soft_margin_svm.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"38420099251","text":"import requests\n\n# 阻塞io\n# html = requests.get(\"http://www.baidu.com\")\n# print(html.encoding)\n# print(html.status_code)\n# html.encoding = html.apparent_encoding\n# print(html.text)\n\nimport socket\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nhost = \"www.baidu.com\"\nclient.connect((host, 80)) # 阻塞IO， cpu空闲\nclient.send(\"GET {} HTTP/1.1\\r\\nHost:{}\\r\\nConnection:close\\r\\n\\r\\n\".format(\"/\", host).encode(\"utf8\"))\n\ndata = b\"\"\nwhile True:\n d = client.recv(1024)\n if d:\n data += d\n else:\n break\n\ndata = data.decode(\"utf-8\")\nprint(data)\n","repo_name":"YeBax/tornado_overview","sub_path":"chapter01/blockod_test.py","file_name":"blockod_test.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"74013686664","text":"\"\"\"Implements forward and reverse trapezoidal corrections.\"\"\"\nimport warnings\nimport numpy as np\nimport xarray as xr\n\nimport numba\n\nfrom typing import Any, Callable, Dict, List\n\nfrom arpes.trace import Trace, traceable\nfrom arpes.utilities import normalize_to_spectrum\n\nfrom .base import CoordinateConverter\nfrom .core import convert_coordinates\n\n__all__ = [\"apply_trapezoidal_correction\"]\n\n\n@numba.njit(parallel=True)\ndef _phi_to_phi(energy, phi, phi_out, l_fermi, l_volt, r_fermi, r_volt):\n \"\"\"Performs reverse coordinate interpolation using four angular waypoints.\n\n Args:\n energy: The binding energy in the corrected coordinate space\n phi: The angle in the corrected coordinate space\n phi_out: The array to populate with the measured phi angles\n l_fermi: The measured phi coordinate of the left edge of the hemisphere's range\n at the Fermi level\n l_volt: The measured phi coordinate of the left edge of the hemisphere's range\n at a binding energy of 1 eV (eV = -1.0)\n r_fermi: The measured phi coordinate of the right edge of the hemisphere's range\n at the Fermi level\n r_volt: The measured phi coordinate of the right edge of the hemisphere's range\n at a binding energy of 1 eV (eV = -1.0)\n \"\"\"\n for i in numba.prange(len(phi)):\n l = l_fermi - energy[i] * (l_volt - l_fermi)\n r = r_fermi - energy[i] * (r_volt - r_fermi)\n\n # These are the forward equations, we can just invert them below\n # c = (phi[i] - l) / (r - l)\n # phi_out[i] = l_fermi + c * (r_fermi - l_fermi)\n\n dac_da = (r - l) / (r_fermi - l_fermi)\n phi_out[i] = (phi[i] - l_fermi) * dac_da + l\n\n\n@numba.njit(parallel=True)\ndef _phi_to_phi_forward(energy, phi, phi_out, l_fermi, l_volt, r_fermi, r_volt):\n \"\"\"The inverse transform to ``_phi_to_phi``. See that function for details.\"\"\"\n for i in numba.prange(len(phi)):\n l = l_fermi - energy[i] * (l_volt - l_fermi)\n r = r_fermi - energy[i] * (r_volt - r_fermi)\n\n # These are the forward equations\n c = (phi[i] - l) / (r - l)\n phi_out[i] = l_fermi + c * (r_fermi - l_fermi)\n\n\nclass ConvertTrapezoidalCorrection(CoordinateConverter):\n \"\"\"A converter for applying the trapezoidal correction to ARPES data.\"\"\"\n\n def __init__(self, *args: Any, corners: List[Dict[str, float]], **kwargs: Any):\n super().__init__(*args, **kwargs)\n self.phi = None\n\n # we normalize the corners so that they are equivalent to four corners at the Fermi level\n # and one volt below.\n c1, c2, c3, c4 = sorted(corners, key=lambda x: x[\"phi\"])\n c1, c2 = sorted([c1, c2], key=lambda x: x[\"eV\"])\n c3, c4 = sorted([c3, c4], key=lambda x: x[\"eV\"])\n\n # now, corners are in\n # (c1, c2, c3, c4) = (LL, UL, LR, UR) order\n\n left_per_volt = (c1[\"phi\"] - c2[\"phi\"]) / (c1[\"eV\"] - c2[\"eV\"])\n left_phi_fermi = c2[\"phi\"] - c2[\"eV\"] * left_per_volt\n left_phi_one_volt = left_phi_fermi - left_per_volt\n\n right_per_volt = (c3[\"phi\"] - c4[\"phi\"]) / (c3[\"eV\"] - c4[\"eV\"])\n right_phi_fermi = c3[\"phi\"] - c4[\"eV\"] * right_per_volt\n right_phi_one_volt = right_phi_fermi - right_per_volt\n\n self.corner_angles = (\n left_phi_fermi,\n left_phi_one_volt,\n right_phi_fermi,\n right_phi_one_volt,\n )\n\n def get_coordinates(self, *args, **kwargs):\n return self.arr.indexes\n\n def conversion_for(self, dim: str) -> Callable:\n def with_identity(*args, **kwargs):\n return self.identity_transform(dim, *args, **kwargs)\n\n return {\n \"phi\": self.phi_to_phi,\n }.get(dim, with_identity)\n\n def phi_to_phi(self, binding_energy: np.ndarray, phi: np.ndarray, *args: Any, **kwargs: Any):\n if self.phi is not None:\n return self.phi\n self.phi = np.zeros_like(phi)\n _phi_to_phi(binding_energy, phi, self.phi, *self.corner_angles)\n return self.phi\n\n def phi_to_phi_forward(\n self, binding_energy: np.ndarray, phi: np.ndarray, *args: Any, **kwargs: Any\n ):\n phi_out = np.zeros_like(phi)\n _phi_to_phi_forward(binding_energy, phi, phi_out, *self.corner_angles)\n return phi_out\n\n\n@traceable\ndef apply_trapezoidal_correction(\n data: xr.DataArray, corners: List[Dict[str, float]], trace: Trace = None\n) -> xr.DataArray:\n \"\"\"Applies the trapezoidal correction to data in angular units by linearly interpolating slices.\n\n Shares some code with standard coordinate conversion, i.e. to momentum, because you can think of\n this as performing a coordinate conversion between two angular coordinate sets, the measured angles\n and the true angles.\n\n Args:\n data: The xarray instances to perform correction on\n corners: These don't actually have to be corners, but are waypoints of the conversion. Use points near the Fermi\n level and near the bottom of the spectrum just at the edge of recorded angular region.\n trace: A trace instance which can be used to enable execution tracing and debugging. Pass ``True`` to enable.\n\n\n Returns:\n The corrected data.\n \"\"\"\n trace(\"Normalizing to spectrum\")\n\n if isinstance(data, dict):\n warnings.warn(\n \"Treating dict-like data as an attempt to forward convert a single coordinate.\"\n )\n converter = ConvertTrapezoidalCorrection(None, [], corners=corners)\n result = dict(data)\n result[\"phi\"] = converter.phi_to_phi_forward(\n np.array([data[\"eV\"]]), np.array([data[\"phi\"]])\n )[0]\n return result\n\n if isinstance(data, xr.Dataset):\n warnings.warn(\n \"Remember to use a DataArray not a Dataset, attempting to extract spectrum and copy attributes.\"\n )\n attrs = data.attrs.copy()\n data = normalize_to_spectrum(data)\n data.attrs.update(attrs)\n\n original_coords = data.coords\n\n trace(\"Determining dimensions.\")\n if \"phi\" not in data.dims:\n raise ValueError(\"The data must have a phi coordinate.\")\n trace(\"Replacing dummy coordinates with index-like ones.\")\n removed = [d for d in data.dims if d not in [\"eV\", \"phi\"]]\n data = data.transpose(*([\"eV\", \"phi\"] + removed))\n converted_dims = data.dims\n\n restore_index_like_coordinates = {r: data.coords[r].values for r in removed}\n new_index_like_coordinates = {r: np.arange(len(data.coords[r].values)) for r in removed}\n data = data.assign_coords(**new_index_like_coordinates)\n\n converter = ConvertTrapezoidalCorrection(data, converted_dims, corners=corners)\n converted_coordinates = converter.get_coordinates()\n\n trace(\"Calling convert_coordinates\")\n result = convert_coordinates(\n data,\n converted_coordinates,\n {\n \"dims\": data.dims,\n \"transforms\": dict(zip(data.dims, [converter.conversion_for(d) for d in data.dims])),\n },\n trace=trace,\n )\n\n trace(\"Reassigning index-like coordinates.\")\n result = result.assign_coords(**restore_index_like_coordinates)\n result = result.assign_coords(\n **{c: v for c, v in original_coords.items() if c not in result.coords}\n )\n result = result.assign_attrs(data.attrs)\n return result\n","repo_name":"chstan/arpes","sub_path":"arpes/utilities/conversion/trapezoid.py","file_name":"trapezoid.py","file_ext":"py","file_size_in_byte":7325,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"81"} +{"seq_id":"15847413655","text":"import numpy as np\nfrom tqdm import tqdm\n\nfrom pysmt.shortcuts import Symbol, Int, get_model\nfrom pysmt.shortcuts import And, Or\nfrom pysmt.shortcuts import GE, LE, Equals\nfrom pysmt.shortcuts import Plus, Times\nfrom pysmt.typing import INT\n\nfrom itertools import combinations, permutations, product\nfrom scipy.special import factorial\nfrom utils import score_orders2\n\n\ndef smt_to_word(smt_solution, indicator, dice_names, d):\n n = len(dice_names)\n bit_array = []\n for i in range(n):\n bit_array.append(\n [int(smt_solution.get_py_value(indicator[i][jj])) for jj in range(n * d)]\n )\n char_list = [\"\" for i in range(n * d)]\n for x, row in zip(dice_names, bit_array):\n for i in range(n * d):\n if row[i] == 1:\n char_list[i] = x\n return \"\".join(char_list)\n\n\n# ============================================================================\n\ndice_names = \"abcd\"\nn = len(dice_names)\nd = 6\nk = 3\n\nrow_lut = {(x,): i for i, x in enumerate(sorted(dice_names))}\n\nindicator = [\n [Symbol(x + \"%i_ind\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nindicator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(1))) for x in indicator[i]]) for i in range(n)]\n)\n\naccumulator = [\n [Symbol(x + \"%i_acc\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nconstraint = [\n [Equals(accumulator[i][j], Plus(indicator[i][: j + 1])) for j in range(n * d)]\n for i in range(n)\n]\n\naccumulators = [accumulator]\nconstraints = [constraint]\nrow_luts = [row_lut]\n\nfor m in range(2, k + 1):\n keys = sorted(list(permutations(sorted(dice_names), m)))\n row_lut = {x: i for i, x in enumerate(keys)}\n accumulator = []\n constraint = []\n for i, x in enumerate(keys):\n mask = indicator[row_luts[0][x[-1:]]]\n j = row_luts[-1][x[:-1]]\n temp = [accumulators[-1][j][jj] * mask[jj] for jj in range(n * d)]\n accumulator.append(\n [Symbol(\"\".join(x) + \"%i_acc\" % jj, INT) for jj in range(n * d)]\n )\n constraint.append(\n [Equals(accumulator[i][jj], Plus(temp[: jj + 1])) for jj in range(n * d)]\n )\n accumulators.append(accumulator)\n constraints.append(constraint)\n row_luts.append(row_lut)\n\n\nindicator_columns = [[indicator[i][jj] for i in range(n)] for jj in range(n * d)]\nindicator_constraints = And(\n [Equals(Plus(indicator_columns[jj]), Int(1)) for jj in range(n * d)]\n)\nsymmetry_constraints = Equals(indicator[0][0], Int(1))\n\ntarget_constraints = []\nfor i in range(k):\n target_vars = [x[-1] for x in accumulators[i]]\n target_val = d ** (i + 1) // factorial((i + 1), exact=True)\n target_constraints.append(And([Equals(x, Int(target_val)) for x in target_vars]))\ntarget_constraints = And(target_constraints)\n\nproblem_constraints = And(sum(sum(constraints, []), []))\nformula = And(\n indicator_domains,\n indicator_constraints,\n problem_constraints,\n target_constraints,\n symmetry_constraints,\n)\n\nmodel = get_model(formula)\nif model:\n print(model)\nelse:\n print(\"No solution found\")\n\nfor i in range(n):\n print([model[indicator[i][jj]] for jj in range(n * d)])\n\ntest = smt_to_word(model, indicator, dice_names, d)\nprint(test)\nscore_orders2(test, k)\n\n# ============================================================================\n\n# n = 19\n# # dice_names = [\"D%i\" % i for i in range(n)]\n# dice_names = \"abcdefghijklmnopqrs\"\n\nn = 19\ndice_names = \"abcdefghijklmnopqrs\" # tuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%\"\n\ndice_pairs = list(permutations(dice_names, 2))\nd = 3\n\nk = 2\n\nrow_lut = {(x,): i for i, x in enumerate(sorted(dice_names))}\n\nindicator = [\n [Symbol(x + \"%i_ind\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\nindicator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(1))) for x in indicator[i]]) for i in range(n)]\n)\n\naccumulator = [\n [Symbol(x + \"%i_acc\" % i, INT) for i in range(n * d)] for x in sorted(dice_names)\n]\naccumulator_domains = And(\n [And([And(GE(x, Int(0)), LE(x, Int(d))) for x in indicator[i]]) for i in range(n)]\n)\n\nconstraint = [\n [Equals(accumulator[i][j], Plus(indicator[i][: j + 1])) for j in range(n * d)]\n for i in range(n)\n]\n\naccumulators = [accumulator]\nconstraints = [constraint]\nrow_luts = [row_lut]\n\nindicator_columns = [[indicator[i][jj] for i in range(n)] for jj in range(n * d)]\nindicator_constraints = And(\n [Equals(Plus(indicator_columns[jj]), Int(1)) for jj in range(n * d)]\n)\nsymmetry_constraints = Equals(indicator[0][0], Int(1))\n\nscore = d ** 2 // 2 + 1\nmask_index = sorted([x for x in set(np.arange(1, n) ** 2 % n)])\nmask = [1 if (i + 1) in mask_index else 0 for i in range(n - 1)]\ntemp = [score if mask[i] else d ** 2 - score for i in range(n - 1)]\nS = [[temp[(j - i) % (n - 1)] for j in range(n - 1)] for i in range(n)]\nscores = {p: s for p, s in zip(dice_pairs, sum(S, [])) if s == score}\n\ntarget_constraints = []\ntarget_vars = [x[-1] for x in accumulators[0]]\ntarget_val = d\ntarget_constraints.append(And([Equals(x, Int(target_val)) for x in target_vars]))\nfor key, target_val in scores.items():\n i, j = row_lut[key[:1]], row_lut[key[-1:]]\n target_constraints.append(\n GE(\n Plus([Times(accumulator[i][jj], indicator[j][jj]) for jj in range(n * d)]),\n Int(target_val),\n )\n )\ntarget_constraints = And(target_constraints)\n\n\nproblem_constraints = And(sum(sum(constraints, []), []))\nformula = And(\n indicator_domains,\n indicator_constraints,\n accumulator_domains,\n problem_constraints,\n target_constraints,\n symmetry_constraints,\n)\n\nmodel = get_model(formula)\nif model:\n print(model)\nelse:\n print(\"No solution found\")\n\nfor i in range(n):\n print([model[indicator[i][jj]] for jj in range(n * d)])\n\ntest = smt_to_word(model, indicator, dice_names, d)\nprint(test)\ncounts = score_orders2(test, k)\nfor s in scores:\n print(s, scores[s], counts[s])\n","repo_name":"michaelpatrickpurcell/permutation-fair-dice","sub_path":"SMT_search.py","file_name":"SMT_search.py","file_ext":"py","file_size_in_byte":5904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"38913098418","text":"from firedrake import *\nfrom firedrake.petsc import PETSc\nfrom firedrake import COMM_WORLD\n\ntry:\n import matplotlib.pyplot as plt\n\n plt.rcParams[\"contour.corner_mask\"] = False\n plt.close(\"all\")\nexcept:\n warning(\"Matplotlib not imported\")\n\nnx, ny = 20, 20\nquads = True\nmesh = UnitSquareMesh(nx, ny, quadrilateral=quads)\n\ndegree = 1\nk_plus = 0\nprimal_family = \"DQ\" if quads else \"DG\"\nU = FunctionSpace(mesh, primal_family, degree + k_plus)\nV = VectorFunctionSpace(mesh, \"CG\", degree + k_plus)\nLagrangeElement = FiniteElement(\"Lagrange\", mesh.ufl_cell(), degree)\nC0TraceElement = LagrangeElement[\"facet\"]\nT = FunctionSpace(mesh, C0TraceElement)\nW = U * T\n\n# Trial and test functions\nsolution = Function(W)\nu, lambda_h = split(solution)\nv, mu_h = TestFunction(W)\n\n# Mesh entities\nn = FacetNormal(mesh)\nx, y = SpatialCoordinate(mesh)\n\n# Exact solution and source term projection\np_exact = sin(2 * pi * x) * sin(2 * pi * y)\nsol_exact = Function(U).interpolate(p_exact)\nsol_exact.rename(\"Exact pressure\", \"label\")\nsigma_e = Function(V, name=\"Exact velocity\")\nsigma_e.project(-grad(p_exact))\nsource_expr = div(-grad(p_exact))\nf = Function(U).interpolate(source_expr)\n\n# BCs\np_boundaries = Constant(0.0)\nbc_multiplier = DirichletBC(W.sub(1), p_boundaries, \"on_boundary\")\n\n# DG parameter\ns = Constant(1.0)\nbeta = Constant(32.0)\nh = CellDiameter(mesh)\nh_avg = avg(h)\n\n# Classical term\na = dot(grad(u), grad(v)) * dx\nL = f * v * dx\n# Hybridization terms\na += s * dot(grad(v), n)(\"+\") * (u(\"+\") - lambda_h(\"+\")) * dS\na += -dot(grad(u), n)(\"+\") * (v(\"+\") - mu_h(\"+\")) * dS\na += (beta / h_avg) * (u(\"+\") - lambda_h(\"+\")) * (v(\"+\") - mu_h(\"+\")) * dS\n# Boundary terms\n# a += -dot(vel_projected, n) * v * ds # How to set this bc??\na += (beta / h) * (u - p_boundaries) * v * ds # is this necessary?\nL += s * dot(grad(v), n) * p_boundaries * ds\n\nF = a - L\n\n# Solving SC below\nPETSc.Sys.Print(\"*******************************************\\nSolving...\\n\")\nparams = {\n \"snes_type\": \"ksponly\",\n \"mat_type\": \"matfree\",\n \"pmat_type\": \"matfree\",\n \"ksp_type\": \"preonly\",\n \"pc_type\": \"python\",\n # Use the static condensation PC for hybridized problems\n # and use a direct solve on the reduced system for lambda_h\n \"pc_python_type\": \"firedrake.SCPC\",\n \"pc_sc_eliminate_fields\": \"0\",\n \"condensed_field\": {\n \"ksp_type\": \"preonly\",\n \"pc_type\": \"lu\",\n \"pc_factor_mat_solver_type\": \"mumps\",\n },\n}\n\nproblem = NonlinearVariationalProblem(F, solution, bcs=bc_multiplier)\nsolver = NonlinearVariationalSolver(problem, solver_parameters=params)\nsolver.solve()\n\nPETSc.Sys.Print(\"Solver finished.\\n\")\n\n# Gathering solution\nu_h, lambda_h = solution.split()\nu_h.rename(\"Solution\", \"label\")\n\n# Post-processing solution\nsigma_h = Function(V, name=\"Projected velocity\")\nsigma_h.project(-grad(u_h))\n\n# Plotting velocity field exact solution\nfig, axes = plt.subplots()\ncollection = quiver(sigma_e, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.title(\"Exact solution for velocity\")\nplt.savefig(\"exact_velocity.png\")\n# plt.show()\n\n# Plotting pressure field exact solution\nfig, axes = plt.subplots()\ncollection = tripcolor(sol_exact, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\naxes.set_xlim([0, 1])\naxes.set_ylim([0, 1])\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.title(\"Exact solution for pressure\")\nplt.savefig(\"exact_pressure.png\")\n# plt.show()\n\n# Plotting velocity field numerical solution\nfig, axes = plt.subplots()\ncollection = quiver(sigma_h, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"solution_velocity.png\")\n# plt.show()\n\n# Plotting pressure field numerical solution\nfig, axes = plt.subplots()\ncollection = tripcolor(u_h, axes=axes, cmap='coolwarm')\nfig.colorbar(collection)\naxes.set_xlim([0, 1])\naxes.set_ylim([0, 1])\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"solution_pressure.png\")\n# plt.show()\n\n\nprint(\"\\n*** DoF = %i\" % W.dim())\n","repo_name":"volpatto/firedrake_scripts","sub_path":"scripts/2D/ldgc_poisson_2D.py","file_name":"ldgc_poisson_2D.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"} +{"seq_id":"35384942665","text":"# -*- coding: utf-8 -*-\nimport os\n\nfrom Products.CMFCore.utils import getToolByName\nfrom collective.iamisearch import _\nfrom collective.iamisearch.interfaces import IIAmFolder\nfrom collective.iamisearch.interfaces import IISearchFolder\nfrom plone.dexterity.interfaces import IDexterityFTI\n\nfrom Products.CMFPlone.interfaces import INonInstallable\nfrom collective.taxonomy.factory import registerTaxonomy\nfrom collective.taxonomy.interfaces import ITaxonomy\nfrom plone import api\nfrom plone.app.dexterity.behaviors.exclfromnav import IExcludeFromNavigation\nfrom plone.app.multilingual import api as api_lng\nfrom plone.i18n.normalizer.interfaces import IIDNormalizer\nfrom zope.component import getUtility, queryUtility\nfrom zope.i18n.interfaces import ITranslationDomain\nfrom zope.interface import alsoProvides\nfrom zope.interface import implementer\nfrom zope.schema.interfaces import IVocabularyFactory\nfrom zope.i18n import translate\n\n\n@implementer(INonInstallable)\nclass HiddenProfiles(object):\n def getNonInstallableProfiles(self):\n \"\"\"Hide uninstall profile from site-creation and quickinstaller.\"\"\"\n return [\"collective.iamisearch:uninstall\"]\n\n\ndef post_install(context):\n \"\"\"Post install script\"\"\"\n # creation of taxonomies\n\n language_tool = api.portal.get_tool(\"portal_languages\")\n langs = language_tool.supported_langs\n current_lang = api.portal.get_default_language()[:2]\n\n taxonomies_collection = [\"I am\", \"I search\"]\n data_iam = {\n \"taxonomy\": \"iam\",\n \"field_title\": translate(_(\"I am\"), target_language=current_lang),\n \"field_description\": \"\",\n \"default_language\": \"fr\",\n }\n\n data_isearch = {\n \"taxonomy\": \"isearch\",\n \"field_title\": translate(_(\"I search\"), target_language=current_lang),\n \"field_description\": \"\",\n \"default_language\": \"fr\",\n }\n\n faced_config = {\n \"I am\": \"/faceted/config/iam_folder_{0}.xml\",\n \"I search\": \"/faceted/config/isearch_folder_{0}.xml\",\n }\n\n provided_interfaces = {\"I am\": IIAmFolder, \"I search\": IISearchFolder}\n\n # install taxonomy\n portal = api.portal.get()\n sm = portal.getSiteManager()\n iam_item = \"collective.taxonomy.iam\"\n isearch_item = \"collective.taxonomy.isearch\"\n utility_iam = sm.queryUtility(ITaxonomy, name=iam_item)\n utility_isearch = sm.queryUtility(ITaxonomy, name=isearch_item)\n\n # stop installation if already\n if utility_iam and utility_isearch:\n enable_taxonomies_content_type()\n return\n\n create_taxonomy_object(data_iam)\n create_taxonomy_object(data_isearch)\n\n # remove taxonomy test\n item = \"collective.taxonomy.test\"\n utility = sm.queryUtility(ITaxonomy, name=item)\n if utility:\n utility.unregisterBehavior()\n sm.unregisterUtility(utility, ITaxonomy, name=item)\n sm.unregisterUtility(utility, IVocabularyFactory, name=item)\n sm.unregisterUtility(utility, ITranslationDomain, name=item)\n\n enable_taxonomies_content_type()\n # creation of two collections by language\n\n container = api.portal.get().get(current_lang)\n if container is None:\n container = api.portal.get()\n for taxonomy_collection in taxonomies_collection:\n title = taxonomy_collection\n translate_title = translate(_(title), target_language=current_lang)\n normalizer = getUtility(IIDNormalizer)\n new_id = normalizer.normalize(translate_title)\n if normalizer.normalize(title) not in container:\n new_obj = api.content.create(\n type=\"Folder\", title=translate_title, container=container\n )\n alsoProvides(new_obj, provided_interfaces[taxonomy_collection])\n if new_obj.id != new_id:\n api.content.rename(new_obj, new_id=new_id)\n try:\n nav = IExcludeFromNavigation(new_obj)\n except:\n pass\n if nav:\n nav.exclude_from_nav = True\n new_obj.reindexObject()\n _activate_dashboard_navigation(\n new_obj, faced_config[taxonomy_collection].format(current_lang)\n )\n for lang in langs:\n if lang != current_lang:\n translated_obj = translation_folderish(new_obj, lang, title)\n alsoProvides(\n translated_obj, provided_interfaces[taxonomy_collection]\n )\n _activate_dashboard_navigation(\n translated_obj, faced_config[taxonomy_collection].format(lang)\n )\n\n\ndef create_taxonomy_object(data):\n taxonomy = registerTaxonomy(\n api.portal.get(),\n name=data[\"taxonomy\"],\n title=data[\"field_title\"],\n description=data[\"field_description\"],\n default_language=data[\"default_language\"],\n )\n\n del data[\"taxonomy\"]\n taxonomy.registerBehavior(**data)\n\n\ndef translation_folderish(obj, lang, title):\n translated_obj = api_lng.translate(obj, lang)\n translate_title = translate(_(title), target_language=lang)\n normalizer = getUtility(IIDNormalizer)\n new_id = normalizer.normalize(translate_title)\n translated_obj.title = translate_title\n if translated_obj.id != new_id:\n api.content.rename(translated_obj, new_id=new_id)\n try:\n nav = IExcludeFromNavigation(translated_obj)\n except:\n pass\n if nav:\n nav.exclude_from_nav = True\n translated_obj.reindexObject()\n return translated_obj\n\n\ndef _activate_dashboard_navigation(context, config_path=\"\"):\n subtyper = context.restrictedTraverse(\"@@faceted_subtyper\")\n if subtyper.is_faceted:\n return\n subtyper.enable()\n context.unrestrictedTraverse(\"@@faceted_exportimport\").import_xml(\n import_file=open(os.path.dirname(__file__) + config_path)\n )\n\n\ndef enable_taxonomies_content_type():\n portal_types = getToolByName(api.portal.get(), \"portal_types\")\n types = portal_types.listContentTypes()\n for type in types:\n add_behavior(type, \"collective.taxonomy.generated.iam\")\n add_behavior(type, \"collective.taxonomy.generated.isearch\")\n\n\ndef add_behavior(type_name, behavior_name):\n \"\"\"Add a behavior to a type\"\"\"\n fti = queryUtility(IDexterityFTI, name=type_name)\n if not fti:\n return\n behaviors = list(fti.behaviors)\n if behavior_name not in behaviors:\n behaviors.append(behavior_name)\n fti._updateProperty(\"behaviors\", tuple(behaviors))\n\n\ndef uninstall(context):\n \"\"\"Uninstall script\"\"\"\n # Do something at the end of the uninstallation of this package.\n","repo_name":"affinitic/collective.iamisearch","sub_path":"src/collective/iamisearch/setuphandlers.py","file_name":"setuphandlers.py","file_ext":"py","file_size_in_byte":6611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"28047257679","text":"from dataclasses import asdict, dataclass\nfrom typing import Dict, Type\n\n\n@dataclass\nclass InfoMessage:\n \"\"\"Информационное сообщение о тренировке.\"\"\"\n training_type: str\n duration: float\n distance: float\n speed: float\n calories: float\n\n MESSAGE = (\n 'Тип тренировки: {training_type}; '\n 'Длительность: {duration:.3f} ч.; '\n 'Дистанция: {distance:.3f} км; '\n 'Ср. скорость: {speed:.3f} км/ч; '\n 'Потрачено ккал: {calories:.3f}.')\n\n def get_message(self) -> str:\n return self.MESSAGE.format(**asdict(self))\n\n\nclass Training:\n \"\"\"Базовый класс тренировки.\"\"\"\n\n M_IN_KM: float = 1000\n LEN_STEP: float = 0.65\n DURATION_IN_MINUTS_COEFF: float = 60\n\n def __init__(self,\n action: float,\n duration: float,\n weight: float,\n ) -> None:\n self.action = action\n self.duration = duration\n self.weight = weight\n\n def get_distance(self) -> float:\n \"\"\"Получить дистанцию в км.\"\"\"\n return self.action * self.LEN_STEP / self.M_IN_KM\n\n def get_mean_speed(self) -> float:\n \"\"\"Получить среднюю скорость движения.\"\"\"\n return self.get_distance() / self.duration\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий.\"\"\"\n raise NotImplementedError(\n 'расход калорий расчитывается '\n 'в дочернем классе ', self.__class__.__name__)\n\n def show_training_info(self) -> InfoMessage:\n \"\"\"Вернуть информационное сообщение о выполненной тренировке.\"\"\"\n return InfoMessage(\n self.__class__.__name__,\n self.duration,\n self.get_distance(),\n self.get_mean_speed(),\n self.get_spent_calories())\n\n\nclass Running(Training):\n \"\"\"Тренировка: бег.\"\"\"\n COEFF_MEAN_SPEED_1: float = 18\n COEFF_MEAN_SPEED_2: float = 20\n LEN_STEP: float = 0.65\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для бега.\"\"\"\n return ((self.COEFF_MEAN_SPEED_1\n * self.get_mean_speed()\n - self.COEFF_MEAN_SPEED_2)\n * self.weight\n / self.M_IN_KM * self.duration * self.DURATION_IN_MINUTS_COEFF)\n\n\nclass SportsWalking(Training):\n \"\"\"Тренировка: спортивная ходьба.\"\"\"\n\n LEN_STEP: float = 0.65\n COEFF_WEIGHT_CALORIES_1: float = 0.035\n COEFF_CALORIES_CONST_2: float = 2\n COEFF_WEIGHT_CALORIES_3: float = 0.029\n\n def __init__(\n self,\n action: float,\n duration: float,\n weight: float,\n height: float) -> None:\n super().__init__(action, duration, weight)\n self.height = height\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для ходьбы.\"\"\"\n\n return ((\n self.COEFF_WEIGHT_CALORIES_1\n * self.weight + (self.get_mean_speed()\n ** self.COEFF_CALORIES_CONST_2\n // self.height)\n * self.COEFF_WEIGHT_CALORIES_3\n * self.weight)\n * self.duration * self.DURATION_IN_MINUTS_COEFF)\n\n\nclass Swimming(Training):\n \"\"\"Тренировка: плавание.\"\"\"\n\n LEN_STEP: float = 1.38\n COEFF_CALORIES_CONST_SWM_1: float = 1.1\n COEFF_CALORIES_WEIGHT_SWM_2: float = 2\n\n def __init__(\n self,\n action: float,\n duration: float,\n weight: float,\n length_pool: float,\n count_pool: float) -> None:\n super().__init__(action, duration, weight)\n self.length_pool = length_pool\n self.count_pool = count_pool\n\n def get_mean_speed(self) -> float:\n \"\"\"Получить среднюю скорость движения для плавания.\"\"\"\n return (self.length_pool * self.count_pool\n / self.M_IN_KM / self.duration)\n\n def get_spent_calories(self) -> float:\n \"\"\"Получить количество затраченных калорий для плавания.\"\"\"\n return ((\n self.get_mean_speed() + self.COEFF_CALORIES_CONST_SWM_1)\n * self.COEFF_CALORIES_WEIGHT_SWM_2 * self.weight)\n\n\ndef read_package(workout_type: str, data: list) -> Training:\n \"\"\"Прочитать данные полученные от датчиков.\"\"\"\n\n district_sport: Dict[str, Type[Training]] = {\n 'SWM': Swimming,\n 'RUN': Running,\n 'WLK': SportsWalking\n }\n\n if workout_type not in district_sport:\n raise KeyError(\n 'ключа', workout_type, 'я не знаю,'\n 'ведите другой код тренeровки')\n return district_sport[workout_type](*data)\n\n\ndef main(training: Training) -> None:\n \"\"\"Главная функция.\"\"\"\n print(training.show_training_info().get_message())\n\n\nif __name__ == '__main__':\n packages = [\n ('SWM', [720, 1, 80, 25, 40]),\n ('RUN', [15000, 1, 75]),\n ('WLK', [9000, 1, 75, 180]),\n ]\n\n for workout_type, data in packages:\n main(read_package(workout_type, data))\n","repo_name":"AndreyKatyshev/fitness-tracker-module","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"41262361327","text":"# -*- coding: utf-8 -*-\n# Created on Fri Mar 23 2018 17:3:14\n# Author: WuLC\n# EMail: liangchaowu5@gmail.com\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n# serializing just returns the result of preorder traversal\n# deserializing builds the result of inorder traversal from the result of preorder traversal, then build the tree with these two results\nclass Codec:\n def serialize(self, root):\n \"\"\"Encodes a tree to a single string.\n \n :type root: TreeNode\n :rtype: str\n \"\"\"\n # preorder traversal\n vals, stack = [], []\n curr = root\n while curr or len(stack)>0:\n if curr == None:\n curr = stack.pop().right\n else:\n vals.append(curr.val)\n stack.append(curr)\n curr = curr.left\n return ' '.join(map(str, vals))\n\n def deserialize(self, data):\n \"\"\"Decodes your encoded data to tree.\n \n :type data: str\n :rtype: TreeNode\n \"\"\"\n def build(pre, ino):\n if len(pre) == 0:\n return None\n val = pre[0]\n idx = ino.index(val)\n root = TreeNode(val)\n root.left = build(pre[1:1+idx], ino[:idx])\n root.right = build(pre[1+idx:], ino[idx+1:])\n return root\n preorder = map(int, data.split())\n inorder = sorted(preorder)\n return build(preorder, inorder)\n \n\n# Your Codec object will be instantiated and called as such:\n# codec = Codec()\n# codec.deserialize(codec.serialize(root))","repo_name":"WuLC/LeetCode","sub_path":"Algorithm/Python/449. Serialize and Deserialize BST.py","file_name":"449. Serialize and Deserialize BST.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"81"} +{"seq_id":"40911111880","text":"\"\"\"\nlista1 = [1,2,3,7,4,8,8,9,9,7,10,11,20,1,8,1,1]\nlista2 = ['Lucas', \"É um bosta\", \"Vida zuada\"]\nlista3 = []\nfor lista3 in range(0,10):\n print(lista1+lista2)\n\"\"\"\n\nimport random\nfor x in range(0,100):\n lista = [x+1]\nprint (f\"Lista = [{random.randint(0,100)}]\")\n","repo_name":"lucaslk122/Programas-python","sub_path":"listas.py","file_name":"listas.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"1990139411","text":"import mysql.connector\nimport os\nimport subprocess\nimport requests\nimport json\nimport platform\nimport time\n\n#mysqlconnector\nmydb = mysql.connector.connect(\n host='192.168.43.1',\n database='ControlPanel',\n user='root',\n password='root'\n)\n\n#hostname\nimport socket\ngethostname = socket.gethostname()\n\n# ipAddress\ndef getIP():\n endpoint = 'https://ipinfo.io/json'\n response = requests.get(endpoint, verify = True)\n\n if response.status_code != 200:\n return 'Status:', response.status_code, 'Problem with the request. Exiting.'\n exit()\n\n data = response.json()\n return data['ip']\n#get my ip\n# my_ip = getIP()\n# print my ip\n# print(my_ip)\n\n# os\ndef os():\n os = platform.system()\n\n\n\n\n# time.sleep(2)\n\n#insert\ndef insert(gethostbyname,my_ip,os):\n mycursor = mydb.cursor()\n sql = \"INSERT INTO victims (hostname,ipaddress,operatingsystem) VALUES (%s, %s, %s)\"\n val = (gethostname,my_ip,os)\n mycursor.execute(sql, val)\n mydb.commit()\n\n# time.sleep(3)\n\n#shell\ndef shell(gethostname):\n while True:\n #fetch command\n mycursor = mydb.cursor()\n mycursor.execute(\"SELECT command FROM victims where hostname='\"+gethostname+\"'\")\n myresult = mycursor.fetchall()\n \n\n\n for commandMysql in myresult:\n\n for i in commandMysql:\n commandStr = str(commandMysql)\n length=len(commandStr)\n length = length -3\n # print(length)\n # print(command[2:length])\n command = commandStr[2:length]\n # print(command)\n print(command[3:])\n \n \n \n # cd change directory\n if command[:2] == \"cd\" and len(command) > 1:\n try:\n os.chdir(command[3:])\n commandresult = command[3:]\n except:\n commandresult = \"failed to cd \" + command[3:]\n continue\n \n \n\n #start\n elif command[:5] == \"start\":\n subprocess.Popen(command[:6])\n\n #virus\n elif command[:5] == \"virus\":\n virus=0\n while(virus<25):\n subprocess.Popen(command[:6])\n virus = virus +1\n \n \n else:\n commandresult = subprocess.getoutput(command)\n\n # print(command)\n\n #upload result\n mycursor = mydb.cursor()\n sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' WHERE hostname='\"+gethostname+\"'\"\n # if command[:2] == \"cd\":\n # sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' , command='' WHERE hostname='\"+gethostname+\"'\"\n # else:\n # sql = \"UPDATE victims SET commandresult = '\"+commandresult+\"' WHERE hostname='\"+gethostname+\"'\"\n mycursor.execute(sql)\n mydb.commit()\n\n\n# insert(gethostbyname,my_ip,os)\nshell(gethostname)\n","repo_name":"surzatine/mw","sub_path":"mw/rsDBMS/rsdatabase.py","file_name":"rsdatabase.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"35412006841","text":"#!/usr/bin/env python3\n\"\"\"\nSimulate two counter-flow jets of reactants shooting into each other. This\nsimulation differs from the similar premixed_counterflow_flame.py example as the\nlatter simulates a jet of reactants shooting into products.\n\nRequires: cantera >= 2.5.0\n\"\"\"\n\nimport cantera as ct\nimport numpy as np\nimport sys\nimport pandas as pd\nimport os\nimport time\n\nprint('\\n*** Computation of premixed counter-flow twin flames ***\\n\\n')\n\n# Select the reaction mechanism\nmec = 'chemicalMechanism/kee.xml'\ngas = ct.Solution(mec)\n\nTmax_s = []\nSc_s = []\nSd_s = []\nstrain_s = []\nmassFlux_s = []\n\n#Set input velocity\naxial_velocity = np.linspace(1,5,10)\n\nphi = 1.\nfolderName ='{:.2f}'.format(phi)\n\npath = './counterFlowResults/CH4/' + folderName\nif not os.path.isdir(path):\n os.makedirs(path)\n print(\"created folder : \", path)\nelse:\n print(path, \" folder already exists.\")\n\nprint('Path to Save: ' + path) \ntime.sleep(6) \n\nfor i in range(0,axial_velocity.size):\n # Create a CH4/Air premixed mixture with equivalence at room\n # temperature and pressure.\n fuel = 'CH4'\n gas.set_equivalence_ratio(phi, fuel, {'O2':1.0, 'N2':3.76})\n gas.TP = 300, ct.one_atm\n\n # Domain half-width of 2.5 cm, meaning the whole domain is 5 cm wide\n width = 0.025\n\n # Done with initial conditions\n # Compute the mass flux, as this is what the Flame object requires\n massFlux = gas.density * axial_velocity[i] # units kg/m2/s\n \n # Create the flame object\n oppFlame = ct.CounterflowTwinPremixedFlame(gas, width=width)\n oppFlame.max_grid_points = 5e4\n\n # Uncomment the following line to use a Multi-component formulation. Default is\n # mixture-averaged\n #oppFlame.transport_model = 'Multi'\n #oppFlame.soret_enabled=True\n #oppFlame.transport_model = 'UnityLewis'\n oppFlame.transport_model = 'Mix'\n \n oppFlame.reactants.mdot = massFlux\n oppFlame.set_refine_criteria(ratio=2, slope=0.02, curve=0.02, prune=0.00)\n\n oppFlame.show_solution()\n oppFlame.solve(loglevel = 1, auto=True)\n T_max = np.max(oppFlame.T)\n\n if T_max < 500:\n print(\"\\n** Flame extinction\\ \" )\n break\n \n print(\"Peak temperature: {0:.1f} K\".format(T_max))\n print(\"Mass flux: {0:.4f} Kg/m2s\".format(massFlux))\n\n list_species = ['CH4','O2','CO','CO2',\\\n 'H2O','OH','CH2O','H2O2','HO2','HCO']\n\n #list_species = ['H2','O2','H2O','OH','H2O2','HO2']\n\n df = pd.DataFrame()\n df['x'] = oppFlame.grid\n df['rho'] = oppFlame.density\n df['T'] = oppFlame.T\n df['velocity'] = oppFlame.velocity\n for species in list_species:\n df[species] = oppFlame.Y[gas.species_index(species),:]\n\n for species in list_species:\n df['wdot' + species] = oppFlame.net_production_rates[gas.species_index(species),:]*gas.molecular_weights[gas.species_index(species)] \n\n for species in list_species:\n df['diff' + species] = oppFlame.mix_diff_coeffs_mass[gas.species_index(species),:] \n\n df['alpha'] = oppFlame.thermal_conductivity/(oppFlame.cp_mass*oppFlame.density)\n df['k'] = oppFlame.thermal_conductivity\n df['Qdot'] = abs(oppFlame.heat_release_rate)\n\n #df['Z_C'] = oppFlame.elemental_mass_fraction('C')\n df['Z_O'] = oppFlame.elemental_mass_fraction('O')\n df['Z_H'] = oppFlame.elemental_mass_fraction('H')\n df['Z_N'] = oppFlame.elemental_mass_fraction('N')\n \n fileName = '{:.3f}'.format( axial_velocity[i] ) \n df.to_csv(path + '/' + fileName, index = False)\n \n","repo_name":"RafaelMeier/MarksteinComp","sub_path":"premixedCounterflow.py","file_name":"premixedCounterflow.py","file_ext":"py","file_size_in_byte":3497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"27604854898","text":"import numpy as np\n\n\nxyz = []\n\ndef error_calc(netlist,noisy_sim,map_list,_init,qubits, ii):\n\n factor_2q = 1.0 # Not used\n scale_factor = 5 # error-rates in near-term devices are quite high.\n # scale_factor scales down the actual error-rates.\n # use scale_factor = 1 for actual error-rates\n\n ''' Copied from IBM quantum experience web-account at https://quantum-computing.ibm.com/\n update if needed as the device error-rates drift with time '''\n\n # single qubit gate error rates\n p1q = [0.005113333288104, 0.012195725568826, 0.013547471030001, 0.002832526375781,\n 0.004751385258102, 0.004215774885966, 0.002749144623865, 0.004224698881771,\n 0.003090993115084, 0.006667456457925, 0.002981703921251, 0.03996271061982,\n 0.008279604984066, 0.010766647756086]\n p1q = [(1/scale_factor)*x for x in p1q]\n # p1q swapper:\n for kk in range(len(qubits)):\n temp = p1q[qubits[kk]]\n p1q[qubits[kk]] = p1q[map_list[kk]]\n p1q[map_list[kk]] = temp\n\n\n # Two qubit gate error rates\n\n p2q = {\n \"1_0\": 0.04,\n \"2_1\": 0.13,\n \"3_2\": 0.08,\n \"4_3\": 0.04,\n \"10_4\": 0.04,\n \"5_4\": 0.05,\n \"6_5\": 0.06,\n \"9_5\": 0.05,\n \"8_6\": 0.03,\n \"8_7\": 0.03,\n \"9_8\": 0.04,\n \"10_9\": 0.04,\n \"11_3\": 0.14,\n \"11_10\": 0.1,\n \"12_11\": 0.15,\n \"12_2\": 0.07,\n \"13_1\": 0.16,\n \"13_12\": 0.04,\n }\n for key in p2q:\n p2q[key] = (1/scale_factor) * p2q[key]\n ## p2q swapper\n for key in p2q:\n old_key = key\n key_splt = key.split(\"_\")\n\n if int(key_splt[0]) in qubits and int(key_splt[1]) in qubits:\n new_key_0 = map_list[ qubits.index( int(key_splt[0]) ) ]\n new_key_1 = map_list[ qubits.index( int(key_splt[1]) ) ]\n\n if new_key_0 > new_key_1:\n new_key = str(new_key_0) + \"_\" + str(new_key_1)\n else:\n new_key = str(new_key_1) + \"_\" + str(new_key_0)\n\n temp = p2q[old_key]\n try:\n p2q[old_key] = p2q[new_key]\n p2q[new_key] = temp\n except:\n print(old_key)\n print(new_key)\n print(\"Key Error; Doing nothing\")\n\n\n def apply_error_gate(prob_in, identifier, _control, _target):\n\n if identifier == 1:\n p = p1q[qubits[_init.index(_target)]]\n prob_out = prob_in * (1-p)\n\n elif identifier == 2:\n\n min = []\n max = []\n\n if qubits[_init.index(_control)] > qubits[_init.index(_target)]:\n max = qubits[_init.index(_control)]\n min = qubits[_init.index(_target)]\n else:\n max = qubits[_init.index(_target)]\n min = qubits[_init.index(_control)]\n\n\n coupling = str(max)+ \"_\" + str(min)\n\n try:\n p = (factor_2q)*p2q[coupling]\n prob_out = prob_in * (1-p)\n except:\n\n return 4000 # any greater than 1 value shall do\n exit()\n\n\n\n return prob_out\n\n\n\n prob = 1.0\n for i in range(len(netlist)):\n line = netlist[i]\n if line[0] =='C':\n _gate = 'cnot'\n elif line[0] =='U':\n if line[1] =='3':\n _gate = 'u3'\n elif line[1] =='2':\n _gate = 'u2'\n elif line[1] == '1':\n _gate = 'u1'\n\n if _gate == 'u3':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n _param.append(float(x[1]))\n _param.append(float(x[2]))\n\n _control = 0\n\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[3][0:len(x[3])-2])\n else:\n _target = int(x[3][0:len(x[3])-1])\n\n\n elif _gate == 'u2':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(np.pi/2)\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n _param.append(float(x[1]))\n\n _control = 0\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[2][0:len(x[2])-2])\n else:\n _target = int(x[2][0:len(x[2])-1])\n\n\n\n elif _gate == 'u1':\n _param = []\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n x = line.split(\",\")\n _param.append(0)\n _param.append(0)\n _param.append(float(line[line.find(\"(\")+1 : line.find(\",\")]))\n\n _control = 0\n\n\n if line[len(line) - 1 ] == \"\\n\":\n _target = int(x[1][0:len(x[1])-2])\n else:\n _target = int(x[1][0:len(x[1])-1])\n\n\n\n elif _gate == 'cnot':\n line = line.replace(\"Autograd ArrayBox with value \",\"\")\n _param = 100\n _control = int(line[line.find(\"(\")+1 : line.find(\",\")])\n _target = int(line[line.find(\",\")+2 : line.find(\")\")])\n\n\n if noisy_sim == 1:\n\n if _gate == 'cnot':\n prob = apply_error_gate(prob, 2, int(_control), int(_target))\n if prob > 1:\n return 4000\n elif _gate == 'u3' or _gate == 'u2': # u1 in IBM machine is noiseless\n prob = apply_error_gate(prob, 1, int(_control), int(_target))\n\n return prob\n","repo_name":"debjyoti0891/quantum-chain","sub_path":"qure/error_rate_calculator.py","file_name":"error_rate_calculator.py","file_ext":"py","file_size_in_byte":5237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"70683182024","text":"import web \n\nimport WebApp\nimport os\nimport json\n\t\t\nclass AppManager(WebApp.WebApp):\n\t#mediadir = 'media/'\n\tmediadir = os.path.expanduser('~')\n\tdef __init__(self):\n\t\tpass\n\tdef fileList(self,folder = \"\"):\n\t\t#mediadir='media/'\n\t\tmediadir= self.mediadir\n\t\tl = os.listdir(mediadir+folder)\n\t\tfiles=[]\n\t\tfor f in l:\n\t\t\tif(os.path.isfile(mediadir+folder+f)):\n\t\t\t\tfiles.append({'name':f,'type':os.path.splitext(f)[1][1:]})\n\t\t\telse:\n\t\t\t\tfiles.append({'name':f,'type':'folder'})\n\t\treturn json.dumps(files);\n\t\t\t\t\n\n\t\t\n\tdef dirList(self,folder = \"\"):\n\t\t#mediadir='media/'\n\t\tmediadir = self.mediadir\n\t\treturn filter(os.path.isdir, os.listdir(mediadir+folder)) \n\t\t\n\tdef appList(self):\n\t\tappsdir='./apps'\n\t\tls = os.listdir(appsdir) \n\t\tapps = {}\n\t\tfor f in ls:\n\t\t\tif(os.path.isdir(appsdir+'/'+f)):\n\t\t\t\tapp = f\n\t\t\t\tif (self.instanciable(app)): apps[app]=self.instanceList(app)\n\t\t\t\telse: apps[app]=None\n\t\t#print json.dumps(apps)\n\t\treturn json.dumps(apps)\n\tdef close(self,app=None,id=None):\n\t\t#del WebApp.selfies[app][id]\n\t\traise web.seeother('/'+app+'/'+id+'/close?app='+app+'&id='+id)\n\tdef instanceList(self,app):\n\t\tif app in WebApp.selfies and WebApp.selfies[app]!=None : return WebApp.selfies[app].keys()\n\t\telse: return []\n\tdef echo(self,message = None):\n\t\treturn 'received '+str(message)\n\tdef instanciable(self,app):\n\t\treturn app in WebApp.selfies and WebApp.selfies[app]!=None\n\tdef HTML(self):\n\t\treturn \"\"\"

Welcome to ApplePi

Remotely use your Pi with this app system.

\n\t\t\t

Start selecting an application from menu on the left!

\n\t\t\t

PS: This is just a prototype, if you want to help, contribute (writing code), please contact me at spocchio@gmail.com

\n
\"\"\"\n","repo_name":"spocchio/ApplePi","sub_path":"apps/AppManager/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"} +{"seq_id":"28625623729","text":"from acpmf import AcSharedMemory\n\nfrom datetime import datetime\nimport json\nimport math\nimport os\nimport sys\n\n# colors:\nRED = (1, 0, 0, 1)\nGREEN = (0, 1, 0, 1)\nWHITE = (0, 1, 0, 1)\nGREY_30 = (0.3, 0.3, 0.3, 1)\nGREY_60 = (0.6, 0.6, 0.6, 1)\n\n\nclass Session(object):\n '''\n Represent a racing sessions, stores laps, etc.\n '''\n def __init__(self, ac=None, acsys=None):\n '''\n We pass ac and acsys so we don't have to import them here,\n that way we can test the code without AC modules\n '''\n self.ac = ac\n self.acsys = acsys\n self.app_path = os.path.dirname(os.path.realpath(__file__))\n self.ui = None\n self.current_lap = None\n self.best_lap = None\n self.trackname = ''\n self.carname = ''\n self.app_size_x = 0\n self.app_size_y = 0\n self.save_data = False\n self.start_time = datetime.now()\n self.current_data = {}\n self.delta = 0.0 # Time since last data update\n self.freq = 0.5\n self.laps = [] # This is only used when running outside of AC\n self.zoom = 1.0 # Current zoom level\n\n def _best_lap_path(self):\n '''\n Returns the path to the best lap JSON file\n Create the best lap directory if it doesn't already exists\n '''\n if not (self.trackname and self.carname):\n return None\n\n dirpath = os.path.join(self.app_path, 'best-laps', self.trackname)\n try:\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n except Exception as e:\n self.console('Can\\'t create directories \"%s\": %s' % (dirpath, e))\n return None\n\n return os.path.join(dirpath, '%s.json' % self.carname)\n\n def load_best_lap(self):\n '''\n Checks if a best lap for the current track/car exists and loads it\n '''\n path = self._best_lap_path()\n\n if not os.path.exists(path):\n return\n\n try:\n f = open(path)\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (path, e))\n return\n\n self.best_lap = Lap(self, 0)\n data = json.loads(f.read())\n self.best_lap.json_loads(data)\n f.close()\n\n def console(self, msg):\n '''\n Prints to AC console if available or to terminal if in test mode\n '''\n if self.ac:\n self.ac.console(msg)\n else:\n sys.stdout.write('%s\\n' % msg)\n\n def new_best_lap(self):\n '''\n Save the current lap as new best lap\n '''\n self.best_lap = self.current_lap\n\n path = self._best_lap_path()\n if not path:\n return\n\n try:\n f = open(path, 'w')\n except Exception as e:\n self.console('Can\\'t open file \"%s\" for writing: %s' % (path, e))\n return\n\n f.write(self.best_lap.json_dumps() + '\\n')\n f.close()\n\n def new_lap(self, count, drop=False):\n '''\n Create a new lap, save best lap if previous lap was faster\n than current best\n if drop is True we don't save the current lap or use it to compare with best lap\n '''\n def is_best_lap(new, best):\n '''\n Returns True if 'new' lap is faster than 'best'\n '''\n if not new:\n return False\n if not best:\n return True\n if best.invalid and not new.invalid:\n return True\n if not best.invalid and new.invalid:\n return False\n if new.laptime < best.laptime:\n return True\n return False\n\n if not drop:\n # Check if current_lap is faster than previous best\n if is_best_lap(self.current_lap, self.best_lap):\n self.new_best_lap()\n\n # Save the current lap to file if necessary\n if self.save_data:\n self.export_data()\n\n # Create new lap\n self.current_lap = Lap(self, count)\n\n def _get_wheels_lock(self):\n wheel_angular_speed = self.current_data['wheel_angular_speed']\n tyre_radius = self.current_data['tyre_radius']\n current_speed = abs(self.current_data['current_speed'])\n\n # Calculate the wheel speed:\n # Angular_speed (radians) * radius = m/s converted to km/h\n wheel_speed = [abs(speed) * radius * 3600 / 1000 for speed, radius in zip(wheel_angular_speed, tyre_radius)]\n\n # Calculate the locking ratio\n if current_speed > 1:\n lock_ratios = [1 - w / current_speed for w in wheel_speed]\n else:\n # The car is stopped, we ignore wheel lock\n lock_ratios = [0 for w in wheel_speed]\n\n return lock_ratios\n\n def update_data(self, deltaT):\n '''\n Called by acUpdate, updates internal data\n '''\n # Check if we're in a new lap\n lap_count = self.ac.getCarState(0, self.acsys.CS.LapCount)\n lap_time = self.ac.getCarState(0, self.acsys.CS.LapTime)\n if lap_time < self.current_lap.laptime:\n splits = self.ac.getLastSplits(0)\n self.ac.console('split: %s' % splits)\n if all(split > 0 for split in splits):\n # If we have splits then the last lap was complete\n self.new_lap(lap_count)\n else:\n # Not all splits are valid, there was a restart, etc. we can drop\n # the previous lap\n self.new_lap(lap_count, drop=True)\n\n # Update the status of the current lap\n self.current_lap.invalid = self.ac.getCarState(0, self.acsys.CS.LapInvalidated)\n self.current_lap.laptime = self.ac.getCarState(0, self.acsys.CS.LapTime)\n # Save some current data for rendering\n self.current_data['current_speed'] = self.ac.getCarState(0, self.acsys.CS.SpeedKMH)\n self.current_data['tyre_radius'] = self.ac.getCarState(0, self.acsys.CS.TyreRadius)\n self.current_data['wheel_angular_speed'] = self.ac.getCarState(0, self.acsys.CS.WheelAngularSpeed)\n\n acshm = AcSharedMemory(7)\n acshm.readValue(\"physics\", \"heading\")\n self.current_data['heading'] = math.pi - acshm.shm[\"physics\"].memStruct[\"heading\"][\"val\"]\n # wheelSlip is currently unused, left here for reference\n # acshm.readValue(\"physics\", \"wheelSlip\")\n # self.current_data['wheels_slip'] = [ acshm.shm[\"physics\"].memStruct[\"wheelSlip\"][\"val\"]\n\n # We only update the rest of the data every FREQ seconds to\n # prevent filling up the memory with data points\n self.delta += deltaT\n if self.delta < self.freq:\n return\n self.delta = 0\n\n # Get the current car's position and add it to current lap\n position = self.ac.getCarState(0, self.acsys.CS.WorldPosition)\n point = Point(*position)\n point.speed = self.ac.getCarState(0, self.acsys.CS.SpeedKMH)\n point.gas = self.ac.getCarState(0, self.acsys.CS.Gas)\n point.brake = self.ac.getCarState(0, self.acsys.CS.Brake)\n point.clutch = self.ac.getCarState(0, self.acsys.CS.Clutch)\n point.gear = self.ac.getCarState(0, self.acsys.CS.Gear)\n\n # If we have a best lap get the speed at the closest point\n if self.best_lap:\n closest_point = self.best_lap.closest_point(point)\n if closest_point:\n point.best_speed = closest_point.speed\n\n self.current_lap.points.append(point)\n\n def render_tyres_slip(self):\n '''\n Render the tyres slip widget\n '''\n # Get the tyres slip ratio\n lock_ratios = self._get_wheels_lock()\n\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[0], fade_in=True))\n self.ac.glQuad(380, 30, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[1], fade_in=True))\n self.ac.glQuad(390, 30, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[2], fade_in=True))\n self.ac.glQuad(380, 50, 5, 10)\n self.ac.glColor4f(*get_color_from_ratio(lock_ratios[3], fade_in=True))\n self.ac.glQuad(390, 50, 5, 10)\n\n def render(self):\n '''\n Renders the widget\n '''\n heading = self.current_data['heading']\n\n if self.best_lap:\n self.best_lap.render(self.current_lap.last_point, heading, GREY_60)\n self.ac.setText(self.ui.labels['best_lap_time_val'], '%s' % self.best_lap.human_laptime())\n\n self.current_lap.render(self.current_lap.last_point, heading)\n\n last_point = self.current_lap.last_point\n if not last_point:\n return\n current_speed = self.current_data['current_speed']\n current_speed_val_label = self.ui.labels['current_speed_val']\n best_speed_val_label = self.ui.labels['best_speed_val']\n self.ac.setText(current_speed_val_label, \"{0}\".format(round(current_speed, 1)))\n\n # Print the speed of the closest point of the best lap if any\n if last_point.best_speed is not None:\n self.ac.setText(best_speed_val_label, \"{0}\".format(round(last_point.best_speed, 1)))\n if last_point.best_speed > current_speed + 2: # +2 is to avoid flickering\n self.ac.setFontColor(current_speed_val_label, *RED)\n elif last_point.best_speed < current_speed - 2:\n self.ac.setFontColor(current_speed_val_label, *GREEN)\n else:\n self.ac.setFontColor(current_speed_val_label, *WHITE)\n\n self.render_tyres_slip()\n\n def zoom_in(self):\n '''\n Increase the current map zoom level\n '''\n self.zoom *= 1.2\n\n def zoom_out(self):\n '''\n Decrease the current map zoom level\n '''\n self.zoom /= 1.2\n\n def json_dumps(self):\n '''\n Returns a JSON representation of the Session\n '''\n return json.dumps({\n 'trackname': self.trackname,\n 'carname': self.carname,\n })\n\n def export_data(self):\n '''\n Export the Session data to a file in the plugin's directory\n Returns the path to the file\n '''\n target_dir = os.path.join(self.app_path, 'exports')\n\n # Create the export directory if it doesn't already exists\n if not os.path.exists(target_dir):\n os.mkdir(target_dir)\n\n filename = '%s-%s-%s.json' % (self.start_time.strftime('%Y-%m-%d-%H-%M-%S'),\n self.trackname, self.carname)\n\n try:\n f = open(os.path.join(target_dir, filename), 'a')\n except Exception as e:\n self.console('Can\\'t open file \"%s\" for writing: %s' % (filename, e))\n return\n\n # Check the position in the file, if we're at 0 then the file\n # is new and write the session headers\n if f.tell() == 0:\n f.write(self.json_dumps() + '\\n')\n\n # Write the current lap to file\n f.write(self.current_lap.json_dumps() + '\\n')\n f.close()\n\n self.console('Saved lap %d to file %s.' % (self.current_lap.count,\n filename))\n\n def import_data(self, filename):\n '''\n Import a session from file. This is not meant to be called in AC\n '''\n try:\n f = open(filename)\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (filename, e))\n return\n\n # Read session_data\n data = f.readline()\n data = json.loads(data)\n for key, value in data.items():\n setattr(self, key, value)\n\n # Read laps data\n for i, line in enumerate(f):\n lap = Lap(self, i)\n data = json.loads(line)\n lap.json_loads(data)\n self.laps.append(lap)\n\n\nclass Point(object):\n def __init__(self, x, y, z, s=0, g=0, b=0, c=0, r=0):\n self.x = round(x, 2)\n self.y = round(y, 2)\n self.z = round(z, 2)\n self.speed = round(s, 2) # Speed in Km/h\n self.gas = g\n self.brake = b\n self.clutch = c\n self.gear = r\n self.best_speed = None # Speed at the closet point\n # of the best lap if any\n self.start = False # Used to start a new line when rendering\n self.end = False # Used to end a line when rendering\n\n # List of attributes, and their JSON keys\n self.keys = (\n ('x', 'x'),\n ('y', 'y'),\n ('z', 'z'),\n ('speed', 's'),\n ('gas', 'g'),\n ('brake', 'b'),\n ('clutch', 'c'),\n ('gear', 'r'),\n )\n\n def __repr__(self):\n return 'x: %f, z: %f' % (self.x, self.z)\n\n def equal_coords(self, point):\n '''\n Return trues if the given point has the same x, y and z coordinates\n '''\n return self.x == point.x and self.y == point.y and self.z == point.z\n\n def dumps(self, previous=None):\n '''\n Returns a dict representation of the Point that can be passed to JSON\n If 'previous' is given only dump the data which has changed since\n '''\n result = {}\n for key, shortname in self.keys:\n if not previous or \\\n (previous and getattr(previous, key) != getattr(self, key)):\n result[shortname] = getattr(self, key)\n\n return result\n\n\nclass Line(object):\n '''\n A line is a series of point, used to represent a lap or circuit\n '''\n def __init__(self, session):\n self.session = session # Reference to the current session\n self.points = []\n\n @property\n def last_point(self):\n '''\n Returns the last point from the line\n '''\n try:\n return self.points[-1]\n except IndexError:\n # This can happen before the first lap is recorded, but also happens\n # \"randomly\" at given points on the track...\n # so we check if we actually have points.\n # Note that this is a dirty hack and it SHOULDN'T work! (but it does)\n if self.points:\n return self.points[-1]\n return None\n\n def normalise(self, reference_point, heading):\n '''\n Return a normalised version of the points based on the widget\n size, zoom level, the given reference point and current heading\n '''\n result = []\n\n if not reference_point:\n # We don't have any data yet\n return []\n\n # Calculate the shift to fit the points within the widget\n if reference_point.x > self.session.app_size_x / 2:\n diff_x = -(reference_point.x - self.session.app_size_x / 2)\n else:\n diff_x = self.session.app_size_x / 2 - reference_point.x\n if reference_point.z > self.session.app_size_y / 2:\n diff_z = -(reference_point.z - self.session.app_size_y / 2)\n else:\n diff_z = self.session.app_size_y / 2 - reference_point.z\n\n # Shift the points, only keep the one that actually fit\n # in the widget\n out = False # Whether or not the last point was outside the widget\n for point in self.points:\n # Rotate the point by 'heading' rad around the center (last point)\n x = math.cos(heading) * (point.x - reference_point.x) - math.sin(heading) * (point.z - reference_point.z) + reference_point.x\n z = math.sin(heading) * (point.x - reference_point.x) + math.cos(heading) * (point.z - reference_point.z) + reference_point.z\n\n x = x + diff_x\n y = point.y # We ignore y for now\n z = z + diff_z\n\n # Zoom in/out point:\n # Takes the difference between the coordinate and the center point,\n # multiply it by the zoom ratio and add to center coordinate\n x = self.session.app_size_x / 2 + (x - self.session.app_size_x / 2) * self.session.zoom\n z = self.session.app_size_y / 2 + (z - self.session.app_size_y / 2) * self.session.zoom\n\n if x > self.session.app_size_x or x < 0 or \\\n z > self.session.app_size_y or z < 0:\n out = True\n if result:\n result[-1].end = True\n continue\n\n p = Point(x, y, z)\n p.speed = point.speed\n p.best_speed = point.best_speed\n if out:\n p.start = True\n out = False\n\n result.append(p)\n\n return result\n\n def render(self, reference_point, heading, color=None):\n '''\n Renders the lap using the given color (default to grey)\n '''\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n for point in self.normalise(reference_point, heading):\n if point.start:\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n self.session.ac.glVertex2f(point.x, point.z)\n\n if color:\n self.session.ac.glColor4f(*color)\n else:\n self.session.ac.glColor4f(*GREY_30)\n\n if point.end:\n self.session.ac.glEnd()\n\n self.session.ac.glEnd()\n\n def svg_path(self):\n '''\n Returns a SVG path version of the line\n '''\n path = 'M %f,%f' % (self.points[0].x, self.points[0].z)\n\n for point in self.points:\n path += ' L %f,%f' % (point.x, point.z)\n\n return path\n\n def write_svg(self, filename, title=''):\n '''\n Write a SVG path of the line to filename\n '''\n data = '''\n\n %s\n\n \n\n''' % (title, self.svg_path())\n\n try:\n f = open(filename, 'w')\n except Exception as e:\n self.console('Can\\'t open file \"%s\": %s' % (filename, e))\n\n f.write(data)\n f.close()\n\n def closest_point(self, ref_point):\n '''\n Returns the point from the line closest to the given point\n '''\n distance = None\n closest = None\n for point in self.points:\n d = (point.x - ref_point.x) ** 2 + \\\n (point.y - ref_point.y) ** 2 + \\\n (point.z - ref_point.z) ** 2\n d = abs(d)\n\n if distance is None or d < distance:\n distance = d\n closest = point\n\n return closest\n\n\nclass Lap(Line):\n def __init__(self, session, count):\n Line.__init__(self, session)\n self.count = count\n self.invalid = 0\n self.laptime = 0\n\n def human_laptime(self):\n '''\n Returns the laptime under the format: m:s.ms\n '''\n s, ms = divmod(self.laptime, 1000)\n m, s = divmod(s, 60)\n return '%d:%d.%d' % (m, s, ms)\n\n def __repr__(self):\n return '%d: %s%s' % (self.count, self.human_laptime(),\n '*' if self.invalid else '')\n\n def render(self, reference_point, heading, color=None):\n '''\n Renders the lap, if no color is given we use green for fast sectors\n and red for slow sectors, and all green if no best_speed is available\n '''\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n for point in self.normalise(reference_point, heading):\n if point.start:\n self.session.ac.glBegin(self.session.acsys.GL.LineStrip)\n\n self.session.ac.glVertex2f(point.x, point.z)\n\n if color:\n self.session.ac.glColor4f(*color)\n elif point.best_speed is not None:\n if point.best_speed > point.speed + 2:\n self.session.ac.glColor4f(*RED)\n elif point.best_speed < current_speed - 2:\n self.session.ac.glColor4f(*WHITE)\n else:\n self.session.ac.glColor4f(*GREEN)\n else:\n self.session.ac.glColor4f(*GREEN)\n\n if point.end:\n self.session.ac.glEnd()\n\n self.session.ac.glEnd()\n\n def json_dumps(self):\n '''\n Returns a JSON representation of the Lap\n '''\n points = []\n for i, point in enumerate(self.points):\n # We check if we have a previous point to only dump\n # the data which has changed\n if i > 0:\n points.append(point.dumps(self.points[i - 1]))\n else:\n points.append(point.dumps())\n\n return json.dumps({\n 'count': self.count,\n 'invalid': self.invalid,\n 'laptime': self.laptime,\n 'points': points,\n })\n\n def json_loads(self, data):\n '''\n Update the lap with the given JSON data\n '''\n if 'invalid' in data:\n self.invalid = data['invalid']\n if 'laptime' in data:\n self.laptime = data['laptime']\n\n previous = {}\n for point_data in data['points']:\n\n # Update the previous point_data with the current one, updated\n # data will overwrite the old one, data which hasn't changed\n # will be kept\n previous.update(point_data)\n\n point = Point(**previous)\n self.points.append(point)\n\n\nclass Track(object):\n def __init__(self, session, name):\n self.session = session\n # TODO: load track from file if available,\n # set inner and outer track line as Line()\n\n\ndef get_color_from_ratio(ratio, fade_in=False, mode='yr'):\n '''\n Return a color based on ratio\n Ratios greater than 1 are considered as 1\n If fade_in then ratio also affects alpha channel from 0 to 1\n Modes:\n yr: yellow to red\n gr: green to red\n '''\n if ratio > 1:\n ratio = 1\n if fade_in:\n alpha = ratio\n else:\n alpha = 1\n\n if mode == 'gr':\n if ratio <= 0.5:\n return (ratio * 2, 1, 0, alpha)\n else:\n return (1, 1 - (ratio - 0.5) * 2, 0, alpha)\n\n # Default to mode 'yr'\n return (1, 1 - ratio, 0, alpha)\n","repo_name":"mathiasuk/racingline","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":22317,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"30089831169","text":"\"\"\"The lookin integration light platform.\"\"\"\nfrom __future__ import annotations\n\nfrom collections.abc import Callable, Coroutine\nfrom datetime import timedelta\nimport logging\nfrom typing import Any, cast\n\nfrom aiolookin import Remote\nfrom aiolookin.models import UDPCommandType, UDPEvent\n\nfrom homeassistant.components.light import COLOR_MODE_ONOFF, LightEntity\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.update_coordinator import DataUpdateCoordinator\n\nfrom .const import DOMAIN\nfrom .entity import LookinPowerEntity\nfrom .models import LookinData\n\nLOGGER = logging.getLogger(__name__)\n\n\nasync def async_setup_entry(\n hass: HomeAssistant,\n config_entry: ConfigEntry,\n async_add_entities: AddEntitiesCallback,\n) -> None:\n \"\"\"Set up the light platform for lookin from a config entry.\"\"\"\n lookin_data: LookinData = hass.data[DOMAIN][config_entry.entry_id]\n entities = []\n\n for remote in lookin_data.devices:\n if remote[\"Type\"] != \"03\":\n continue\n uuid = remote[\"UUID\"]\n\n def _wrap_async_update(\n uuid: str,\n ) -> Callable[[], Coroutine[None, Any, Remote]]:\n \"\"\"Create a function to capture the uuid cell variable.\"\"\"\n\n async def _async_update() -> Remote:\n return await lookin_data.lookin_protocol.get_remote(uuid)\n\n return _async_update\n\n coordinator = DataUpdateCoordinator(\n hass,\n LOGGER,\n name=f\"{config_entry.title} {uuid}\",\n update_method=_wrap_async_update(uuid),\n update_interval=timedelta(\n seconds=60\n ), # Updates are pushed (fallback is polling)\n )\n await coordinator.async_refresh()\n device: Remote = coordinator.data\n\n entities.append(\n LookinLightEntity(\n uuid=uuid,\n device=device,\n lookin_data=lookin_data,\n coordinator=coordinator,\n )\n )\n\n async_add_entities(entities)\n\n\nclass LookinLightEntity(LookinPowerEntity, LightEntity):\n \"\"\"A lookin IR controlled light.\"\"\"\n\n _attr_supported_color_modes = {COLOR_MODE_ONOFF}\n _attr_color_mode = COLOR_MODE_ONOFF\n\n def __init__(\n self,\n uuid: str,\n device: Remote,\n lookin_data: LookinData,\n coordinator: DataUpdateCoordinator,\n ) -> None:\n \"\"\"Init the light.\"\"\"\n super().__init__(coordinator, uuid, device, lookin_data)\n self._attr_is_on = False\n\n @property\n def _remote(self) -> Remote:\n return cast(Remote, self.coordinator.data)\n\n async def async_turn_on(self, **kwargs: Any) -> None:\n \"\"\"Turn on the light.\"\"\"\n await self._async_send_command(self._power_on_command)\n self._attr_is_on = True\n self.async_write_ha_state()\n\n async def async_turn_off(self, **kwargs: Any) -> None:\n \"\"\"Turn off the light.\"\"\"\n await self._async_send_command(self._power_off_command)\n self._attr_is_on = False\n self.async_write_ha_state()\n\n def _update_from_status(self, status: str) -> None:\n \"\"\"Update media property from status.\n\n 1000\n 0 - 0/1 on/off\n \"\"\"\n if len(status) != 4:\n return\n state = status[0]\n\n self._attr_is_on = state == \"1\"\n\n def _async_push_update(self, event: UDPEvent) -> None:\n \"\"\"Process an update pushed via UDP.\"\"\"\n LOGGER.debug(\"Processing push message for %s: %s\", self.entity_id, event)\n self._update_from_status(event.value)\n self.coordinator.async_set_updated_data(self._remote)\n self.async_write_ha_state()\n\n async def _async_push_update_device(self, event: UDPEvent) -> None:\n \"\"\"Process an update pushed via UDP.\"\"\"\n LOGGER.debug(\"Processing push message for %s: %s\", self.entity_id, event)\n await self.coordinator.async_refresh()\n self._attr_name = self._remote.name\n\n async def async_added_to_hass(self) -> None:\n \"\"\"Call when the entity is added to hass.\"\"\"\n self.async_on_remove(\n self._lookin_udp_subs.subscribe_event(\n self._lookin_device.id,\n UDPCommandType.ir,\n self._uuid,\n self._async_push_update,\n )\n )\n self.async_on_remove(\n self._lookin_udp_subs.subscribe_event(\n self._lookin_device.id,\n UDPCommandType.data,\n self._uuid,\n self._async_push_update_device,\n )\n )\n","repo_name":"neojski/home-assistant-core","sub_path":"homeassistant/components/lookin/light.py","file_name":"light.py","file_ext":"py","file_size_in_byte":4709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"} +{"seq_id":"42570758490","text":"\"\"\"Core contraction tree data structure and methods.\n\"\"\"\nimport math\nimport random\nimport warnings\nimport operator\nimport itertools\nimport functools\nimport collections\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nfrom autoray import do\n\nfrom .utils import (\n compute_size_by_dict,\n deprecated,\n get_symbol,\n groupby,\n inputs_output_to_eq,\n interleave,\n is_valid_node,\n MaxCounter,\n node_from_seq,\n node_from_single,\n node_get_single_el,\n node_supremum,\n prod,\n unique,\n)\nfrom .parallel import (\n can_scatter,\n maybe_leave_pool,\n maybe_rejoin_pool,\n parse_parallel_arg,\n scatter,\n submit,\n)\nfrom .hypergraph import get_hypergraph\nfrom .scoring import (\n DEFAULT_COMBO_FACTOR,\n get_score_fn,\n CompressedStatsTracker,\n)\nfrom .contract import make_contractor\nfrom .plot import (\n plot_contractions_alt,\n plot_contractions,\n plot_hypergraph,\n plot_tree_ring,\n plot_tree_rubberband,\n plot_tree_span,\n plot_tree_tent,\n)\n\n\ndef cached_node_property(name):\n \"\"\"Decorator for caching information about nodes.\"\"\"\n\n def wrapper(meth):\n @functools.wraps(meth)\n def getter(self, node):\n try:\n return self.info[node][name]\n except KeyError:\n self.info[node][name] = value = meth(self, node)\n return value\n\n return getter\n\n return wrapper\n\n\ndef union_it(bs):\n \"\"\"Non-variadic version of various set type unions.\"\"\"\n b0, *bs = bs\n return b0.union(*bs)\n\n\ndef legs_union(legs_seq):\n \"\"\"Combine a sequence of legs into a single set of legs, summing their\n appearances.\n \"\"\"\n new_legs, *rem_legs = legs_seq\n new_legs = new_legs.copy()\n for legs in rem_legs:\n for ix, ix_count in legs.items():\n new_legs[ix] = new_legs.get(ix, 0) + ix_count\n return new_legs\n\n\ndef legs_without(legs, ind):\n \"\"\"Discard ``ind`` from legs to create a new set of legs.\"\"\"\n new_legs = legs.copy()\n new_legs.pop(ind, None)\n return new_legs\n\n\ndef get_with_default(k, obj, default):\n return obj.get(k, default)\n\n\n@dataclass(order=True, frozen=True)\nclass SliceInfo:\n inner: bool\n ind: str\n size: int\n project: Optional[int]\n\n @property\n def sliced_range(self):\n if self.project is None:\n return range(self.size)\n else:\n return [self.project]\n\n\ndef get_slice_strides(sliced_inds):\n \"\"\"Compute the 'strides' given the (ordered) dictionary of sliced indices.\n \"\"\"\n slice_infos = list(sliced_inds.values())\n nsliced = len(slice_infos)\n strides = [1] * nsliced\n # backwards cumulative product\n for i in range(nsliced - 2, -1, -1):\n strides[i] = strides[i + 1] * slice_infos[i + 1].size\n return strides\n\n\nclass ContractionTree:\n \"\"\"Binary tree representing a tensor network contraction.\n\n Parameters\n ----------\n inputs : sequence of str\n The list of input tensor's indices.\n output : str\n The output indices.\n size_dict : dict[str, int]\n The size of each index.\n track_childless : bool, optional\n Whether to dynamically keep track of which nodes are childless. Useful\n if you are 'divisively' building the tree.\n track_flops : bool, optional\n Whether to dynamically keep track of the total number of flops. If\n ``False`` You can still compute this once the tree is complete.\n track_write : bool, optional\n Whether to dynamically keep track of the total number of elements\n written. If ``False`` You can still compute this once the tree is\n complete.\n track_size : bool, optional\n Whether to dynamically keep track of the largest tensor so far. If\n ``False`` You can still compute this once the tree is complete.\n\n Attributes\n ----------\n children : dict[node, tuple[node]]\n Mapping of each node to two children.\n info : dict[node, dict]\n Information about the tree nodes. The key is the set of inputs (a\n set of inputs indices) the node contains. Or in other words, the\n subgraph of the node. The value is a dictionary to cache information\n about effective 'leg' indices, size, flops of formation etc.\n \"\"\"\n\n def __init__(\n self,\n inputs,\n output,\n size_dict,\n track_childless=False,\n track_flops=False,\n track_write=False,\n track_size=False,\n ):\n self.inputs = inputs\n self.output = output\n\n if not isinstance(next(iter(size_dict.values()), 1), int):\n # make sure we are working with python integers to avoid overflow\n # comparison errors with inf etc.\n self.size_dict = {k: int(v) for k, v in size_dict.items()}\n else:\n self.size_dict = size_dict\n\n self.N = len(self.inputs)\n\n # create the index representation for each input: an ordered mapping of\n # each index to the number of times it has appeared on children. By\n # also tracking the total number of appearances one can efficiently\n # and locally compute which indices should be kept or contracted\n self.inputs_legs = []\n self.appearances = {}\n for term in self.inputs:\n legs = {}\n for ix in term:\n legs[ix] = legs.get(ix, 0) + 1\n self.appearances[ix] = self.appearances.get(ix, 0) + 1\n self.inputs_legs.append(legs)\n self.output_legs = dict.fromkeys(self.output)\n # adding output appearances ensures these are never contracted away,\n # N.B. if after this step every appearance count is exactly 2,\n # then there are no 'hyper' indices in the contraction\n for ix in self.output_legs:\n self.appearances[ix] = self.appearances.get(ix, 0) + 1\n\n # check for single term simplifications, these are treated as a simple\n # preprocessing step that only is taken into account during actual\n # contraction, and are not represented in the binary tree\n preprocessing = []\n for i, (term, legs) in enumerate(zip(inputs, self.inputs_legs)):\n is_simplifiable = (\n # repeated indices (diag or traces)\n (len(term) != len(legs))\n or\n # reduced indices (summed immediately)\n any(\n ix_count == self.appearances[ix]\n for ix, ix_count in legs.items()\n )\n )\n if is_simplifiable:\n # compute the simplified legs\n new_legs = {\n ix: ix_count\n for ix, ix_count in legs.items()\n if ix_count != self.appearances[ix]\n }\n # modify the input legs as if these were the inputs\n self.inputs_legs[i] = new_legs\n # add a preprocessing step to the list of contractions\n eq = f\"{''.join(term)}->{''.join(new_legs)}\"\n preprocessing.append((i, eq))\n self.preprocessing = tuple(preprocessing)\n\n # mapping of parents to children - the core binary tree object\n self.children = {}\n\n # information about all the nodes\n self.info = {}\n\n # ... which we can fill in already for final / top node i.e.\n # the collection of all nodes\n self.root = node_supremum(self.N)\n self.info[self.root] = {\n \"legs\": self.output_legs,\n \"size\": compute_size_by_dict(self.output, size_dict),\n }\n\n # whether to keep track of dangling nodes/subgraphs\n self.track_childless = track_childless\n if self.track_childless:\n # the set of dangling nodes\n self.childless = {self.root}\n\n # running largest_intermediate and total flops\n self._track_flops = track_flops\n if track_flops:\n self._flops = 0\n\n self._track_write = track_write\n if track_write:\n self._write = 0\n\n self._track_size = track_size\n if track_size:\n self._sizes = MaxCounter()\n\n # container for caching subtree reconfiguration condidates\n self.already_optimized = dict()\n\n # info relating to slicing (base constructor is always unsliced)\n self.multiplicity = 1\n self.sliced_inds = {}\n self.sliced_inputs = frozenset()\n\n # cache for compiled contraction cores\n self.contraction_cores = {}\n\n def set_state_from(self, other):\n \"\"\"Set the internal state of this tree to that of ``other``.\"\"\"\n # immutable properties\n for attr in (\n \"appearances\",\n \"inputs\",\n \"multiplicity\",\n \"N\",\n \"output\",\n \"preprocessing\",\n \"root\",\n \"size_dict\",\n \"sliced_inputs\",\n ):\n setattr(self, attr, getattr(other, attr))\n\n # mutable properties\n for attr in (\n \"children\",\n \"contraction_cores\",\n \"inputs_legs\",\n \"output_legs\",\n \"sliced_inds\",\n ):\n setattr(self, attr, getattr(other, attr).copy())\n\n # dicts of mutable\n for attr in (\"info\", \"already_optimized\"):\n setattr(\n self,\n attr,\n {k: v.copy() for k, v in getattr(other, attr).items()},\n )\n\n self.track_childless = other.track_childless\n if other.track_childless:\n self.childless = other.childless.copy()\n\n self._track_flops = other._track_flops\n if other._track_flops:\n self._flops = other._flops\n\n self._track_write = other._track_write\n if other._track_write:\n self._write = other._write\n\n self._track_size = other._track_size\n if other._track_size:\n self._sizes = other._sizes.copy()\n\n def copy(self):\n \"\"\"Create a copy of this ``ContractionTree``.\"\"\"\n tree = object.__new__(self.__class__)\n tree.set_state_from(self)\n return tree\n\n @property\n def nslices(self):\n \"\"\"Simple alias for how many independent contractions this tree\n represents overall.\n \"\"\"\n return self.multiplicity\n\n @property\n def nchunks(self):\n \"\"\"The number of 'chunks' - determined by the number of sliced output\n indices.\n \"\"\"\n return prod(\n si.size for si in self.sliced_inds.values() if not si.inner\n )\n\n def node_to_terms(self, node):\n \"\"\"Turn a node -- a frozen set of ints -- into the corresponding terms\n -- a sequence of sets of str corresponding to input indices.\n \"\"\"\n return map(self.inputs_legs.__getitem__, node)\n\n def gen_leaves(self):\n \"\"\"Generate the nodes representing leaves of the contraction tree, i.e.\n of size 1 each corresponding to a single input tensor.\n \"\"\"\n return map(node_from_single, range(self.N))\n\n @classmethod\n def from_path(\n cls,\n inputs,\n output,\n size_dict,\n *,\n path=None,\n ssa_path=None,\n check=False,\n **kwargs,\n ):\n \"\"\"Create a (completed) ``ContractionTree`` from the usual inputs plus\n a standard contraction path or 'ssa_path' - you need to supply one.\n \"\"\"\n if int(path is None) + int(ssa_path is None) != 1:\n raise ValueError(\n \"Exactly one of ``path`` or ``ssa_path`` must be \" \"supplied.\"\n )\n\n if ssa_path is not None:\n path = ssa_path\n\n tree = cls(inputs, output, size_dict, **kwargs)\n nodes = list(tree.gen_leaves())\n\n for p in path:\n if ssa_path is not None:\n merge = [nodes[i] for i in p]\n else:\n merge = [nodes.pop(i) for i in sorted(p, reverse=True)]\n nodes.append(tree.contract_nodes(merge, check=check))\n\n return tree\n\n @classmethod\n def from_info(cls, info, **kwargs):\n \"\"\"Create a ``ContractionTree`` from an ``opt_einsum.PathInfo`` object.\n \"\"\"\n return cls.from_path(\n inputs=info.input_subscripts.split(\",\"),\n output=info.output_subscript,\n size_dict=info.size_dict,\n path=info.path,\n **kwargs,\n )\n\n @classmethod\n def from_eq(cls, eq, size_dict, **kwargs):\n \"\"\"Create a empty ``ContractionTree`` directly from an equation and set\n of shapes.\n\n Parameters\n ----------\n eq : str\n The einsum string equation.\n size_dict : dict[str, int]\n The size of each index.\n \"\"\"\n lhs, output = eq.split(\"->\")\n inputs = lhs.split(\",\")\n return cls(inputs, output, size_dict, **kwargs)\n\n def get_eq(self):\n \"\"\"Get the einsum equation corresponding to this tree. Note that this\n is the total (or original) equation, so includes indices which have\n been sliced.\n\n Returns\n -------\n eq : str\n \"\"\"\n return inputs_output_to_eq(self.inputs, self.output)\n\n def get_shapes(self):\n \"\"\"Get the shapes of the input tensors corresponding to this tree.\n\n Returns\n -------\n shapes : tuple[tuple[int]]\n \"\"\"\n return tuple(\n tuple(self.size_dict[ix] for ix in term) for term in self.inputs\n )\n\n def get_inputs_sliced(self):\n \"\"\"Get the input indices corresponding to a single slice of this tree,\n i.e. with sliced indices removed.\n\n Returns\n -------\n inputs : tuple[tuple[str]]\n \"\"\"\n return tuple(\n tuple(ix for ix in term if ix not in self.sliced_inds)\n for term in self.inputs\n )\n\n def get_output_sliced(self):\n \"\"\"Get the output indices corresponding to a single slice of this tree,\n i.e. with sliced indices removed.\n\n Returns\n -------\n output : tuple[str]\n \"\"\"\n return tuple(ix for ix in self.output if ix not in self.sliced_inds)\n\n def get_eq_sliced(self):\n \"\"\"Get the einsum equation corresponding to a single slice of this\n tree, i.e. with sliced indices removed.\n\n Returns\n -------\n eq : str\n \"\"\"\n return inputs_output_to_eq(\n self.get_inputs_sliced(),\n self.get_output_sliced()\n )\n\n def get_shapes_sliced(self):\n \"\"\"Get the shapes of the input tensors corresponding to a single slice\n of this tree, i.e. with sliced indices removed.\n\n Returns\n -------\n shapes : tuple[tuple[int]]\n \"\"\"\n return tuple(\n tuple(\n self.size_dict[ix] for ix in term if ix not in self.sliced_inds\n )\n for term in self.inputs\n )\n\n @classmethod\n def from_edge_path(\n cls, edge_path, inputs, output, size_dict, check=False, **kwargs\n ):\n \"\"\"Create a ``ContractionTree`` from an edge elimination ordering.\"\"\"\n tree = cls(inputs, output, size_dict, **kwargs)\n nodes = list(tree.gen_leaves())\n\n for e in edge_path:\n # filter out the subgraph induced by edge `e` (generally a pair)\n new_terms, merge = [], []\n for node in nodes:\n term = union_it(tree.node_to_terms(node))\n if e in term:\n merge.append(node)\n else:\n new_terms.append(node)\n\n # contract the subgraph\n if merge:\n nodes = new_terms + [tree.contract_nodes(merge, check=check)]\n\n # make sure we are generating a full contraction tree\n nt = len(nodes)\n if nt > 1:\n # this seems to happen when the initial contraction contains a\n # scalar? Or disconnected subgraphs?\n warnings.warn(\n f\"Ended up with {nt} nodes - contracting all remaining.\"\n )\n tree.contract_nodes(nodes, check=check)\n\n return tree\n\n def _add_node(self, node, check=False):\n if check:\n if len(self.info) > 2 * self.N - 1:\n raise ValueError(\"There are too many children already.\")\n if len(self.children) > self.N - 1:\n raise ValueError(\"There are too many branches already.\")\n if not is_valid_node(node):\n raise ValueError(\"{} is not a valid node.\".format(node))\n\n self.info.setdefault(node, dict())\n\n def _remove_node(self, node):\n \"\"\"Remove ``node`` from this tree and update the flops and maximum size\n if tracking them respectively. Inplace operation.\n \"\"\"\n if self._track_flops:\n self._flops -= self.get_flops(node)\n\n if self._track_write and len(node) > 1:\n # only non-leaf nodes contribute to write\n self._write -= self.get_size(node)\n\n if self._track_size:\n self._sizes.discard(self.get_size(node))\n\n del self.info[node]\n del self.children[node]\n\n @cached_node_property(\"legs\")\n def get_legs(self, node):\n \"\"\"Get the effective 'outer' indices for the collection of tensors\n in ``node``.\n \"\"\"\n if len(node) == 1:\n return self.inputs_legs[node_get_single_el(node)]\n try:\n involved = self.get_involved(node)\n except KeyError:\n involved = legs_union(self.node_to_terms(node))\n\n return {\n ix: ix_count\n for ix, ix_count in involved.items()\n if ix_count < self.appearances[ix]\n }\n\n @cached_node_property(\"involved\")\n def get_involved(self, node):\n \"\"\"Get all the indices involved in the formation of subgraph ``node``.\n \"\"\"\n if len(node) == 1:\n return {}\n sub_legs = map(self.get_legs, self.children[node])\n return legs_union(sub_legs)\n\n @cached_node_property(\"removed\")\n def get_removed(self, node):\n \"\"\"Get the indices that will be removed by the creation of ``node``.\"\"\"\n involved = self.get_involved(node)\n legs = self.get_legs(node)\n return {\n ix: ix_count for ix, ix_count in involved.items() if ix not in legs\n }\n\n @cached_node_property(\"size\")\n def get_size(self, node):\n \"\"\"Get the tensor size of ``node``.\"\"\"\n return compute_size_by_dict(self.get_legs(node), self.size_dict)\n\n @cached_node_property(\"flops\")\n def get_flops(self, node):\n \"\"\"Get the FLOPs for the pairwise contraction that will create\n ``node``.\n \"\"\"\n if len(node) == 1:\n return 0\n involved = self.get_involved(node)\n return compute_size_by_dict(involved, self.size_dict)\n\n @cached_node_property(\"can_dot\")\n def get_can_dot(self, node):\n \"\"\"Get whether this contraction can be performed as a dot product (i.e.\n with ``tensordot``), or else requires ``einsum``, as it has indices\n that don't appear exactly twice in either the inputs or the output.\n \"\"\"\n l, r = self.children[node]\n sp, sl, sr = map(self.get_legs, (node, l, r))\n\n srl_symmdiff = sl.copy()\n for ix, ix_count in sr.items():\n if ix in srl_symmdiff:\n srl_symmdiff.pop(ix)\n else:\n srl_symmdiff[ix] = ix_count\n\n return srl_symmdiff == sp\n\n @cached_node_property(\"inds\")\n def get_inds(self, node):\n \"\"\"Get the indices of this node - an ordered string version of\n ``get_legs`` that starts with ``tree.inputs`` and maintains the order\n they appear in each contraction 'ABC,abc->ABCabc', to match tensordot.\n \"\"\"\n # NB: self.inputs and self.output contain the full (unsliced) indices\n # thus we filter even the input legs and output legs\n\n if len(node) == 1:\n return \"\".join(self.inputs_legs[node_get_single_el(node)])\n\n if len(node) == self.N:\n return \"\".join(self.output_legs)\n\n legs = self.get_legs(node)\n l_inds, r_inds = map(self.get_inds, self.children[node])\n # the filter here takes care of contracted indices\n return \"\".join(\n unique(filter(legs.__contains__, itertools.chain(l_inds, r_inds)))\n )\n\n @cached_node_property(\"tensordot_axes\")\n def get_tensordot_axes(self, node):\n \"\"\"Get the ``axes`` arg for a tensordot ocontraction that produces\n ``node``. The pairs are sorted in order of appearance on the left\n input.\n \"\"\"\n l_inds, r_inds = map(self.get_inds, self.children[node])\n l_axes, r_axes = [], []\n for i, ind in enumerate(l_inds):\n j = r_inds.find(ind)\n if j != -1:\n l_axes.append(i)\n r_axes.append(j)\n return tuple(l_axes), tuple(r_axes)\n\n @cached_node_property(\"tensordot_perm\")\n def get_tensordot_perm(self, node):\n \"\"\"Get the permutation required, if any, to bring the tensordot output\n of this nodes contraction into line with ``self.get_inds(node)``.\n \"\"\"\n l_inds, r_inds = map(self.get_inds, self.children[node])\n # the target output inds\n p_inds = self.get_inds(node)\n # the tensordot output inds\n td_inds = \"\".join(sorted(p_inds, key=f\"{l_inds}{r_inds}\".find))\n if td_inds == p_inds:\n return None\n return tuple(map(td_inds.find, p_inds))\n\n @cached_node_property(\"einsum_eq\")\n def get_einsum_eq(self, node):\n \"\"\"Get the einsum string describing the contraction that produces\n ``node``, unlike ``get_inds`` the characters are mapped into [a-zA-Z],\n for compatibility with ``numpy.einsum`` for example.\n \"\"\"\n l, r = self.children[node]\n l_inds, r_inds, p_inds = map(self.get_inds, (l, r, node))\n # we need to map any extended unicode characters into ascii\n char_mapping = {\n ord(ix): get_symbol(i)\n for i, ix in enumerate(unique(itertools.chain(l_inds, r_inds)))\n }\n return f\"{l_inds},{r_inds}->{p_inds}\".translate(char_mapping)\n\n def get_centrality(self, node):\n try:\n return self.info[node][\"centrality\"]\n except KeyError:\n self.compute_centralities()\n return self.info[node][\"centrality\"]\n\n def total_flops(self, dtype=None):\n \"\"\"Sum the flops contribution from every node in the tree.\n\n Parameters\n ----------\n dtype : {'float', 'complex', None}, optional\n Scale the answer depending on the assumed data type.\n \"\"\"\n if self._track_flops:\n C = self.multiplicity * self._flops\n\n else:\n self._flops = 0\n for node, _, _ in self.traverse():\n self._flops += self.get_flops(node)\n\n self._track_flops = True\n C = self.multiplicity * self._flops\n\n if dtype is None:\n return C\n\n if \"float\" in dtype:\n return 2 * C\n\n if \"complex\" in dtype:\n return 8 * C\n\n def total_write(self):\n \"\"\"Sum the total amount of memory that will be created and operated on.\n \"\"\"\n if not self._track_write:\n self._write = 0\n for node, _, _ in self.traverse():\n self._write += self.get_size(node)\n\n self._track_write = True\n\n return self.multiplicity * self._write\n\n def total_cost(self, factor=DEFAULT_COMBO_FACTOR, combine=sum):\n t = 0\n for p in self.children:\n f = self.get_flops(p)\n w = self.get_size(p)\n t += combine((f, factor * w))\n return self.multiplicity * t\n\n def max_size(self):\n \"\"\"The size of the largest intermediate tensor.\"\"\"\n if self._track_size:\n return self._sizes.max()\n\n self._sizes = MaxCounter()\n for node, _, _ in self.traverse():\n self._sizes.add(self.get_size(node))\n\n self._track_size = True\n return self._sizes.max()\n\n def peak_size(self, order=None):\n \"\"\"Get the peak concurrent size of tensors needed - this depends on the\n traversal order, i.e. the exact contraction path, not just the\n contraction tree.\n \"\"\"\n tot_size = sum(self.get_size(node) for node in self.gen_leaves())\n peak = tot_size\n for p, l, r in self.traverse(order=order):\n tot_size -= self.get_size(l)\n tot_size -= self.get_size(r)\n tot_size += self.get_size(p)\n peak = max(peak, tot_size)\n return peak\n\n def contract_stats(self):\n \"\"\"Simulteneously compute the total flops, write and size of the\n contraction tree. This is more efficient than calling each of the\n individual methods separately. Once computed, each quantity is then\n automatically tracked.\n\n Returns\n -------\n stats : dict[str, int]\n The total flops, write and size.\n \"\"\"\n if not (self._track_flops and self._track_write and self._track_size):\n self._flops = self._write = 0\n self._sizes = MaxCounter()\n\n for node, _, _ in self.traverse():\n self._flops += self.get_flops(node)\n node_size = self.get_size(node)\n self._write += node_size\n self._sizes.add(node_size)\n\n self._track_flops = self._track_write = self._track_size = True\n\n return {\n \"flops\": self.multiplicity * self._flops,\n \"write\": self.multiplicity * self._write,\n \"size\": self._sizes.max(),\n }\n\n def arithmetic_intensity(self):\n \"\"\"The ratio of total flops to total write - the higher the better for\n extracting good computational performance.\n \"\"\"\n return self.total_flops(dtype=None) / self.total_write()\n\n def contraction_scaling(self):\n \"\"\"This is computed simply as the maximum number of indices involved\n in any single contraction, which will match the scaling assuming that\n all dimensions are equal.\n \"\"\"\n return max(len(self.get_involved(node)) for node in self.info)\n\n def contraction_cost(self, log=None):\n \"\"\"Get the total number of scalar operations ~ time complexity.\"\"\"\n C = float(self.total_flops(dtype=None))\n if log is not None:\n C = math.log(C, log)\n return C\n\n def contraction_width(self, log=2):\n \"\"\"Get log2 of the size of the largest tensor.\"\"\"\n W = self.max_size()\n if log is not None:\n W = math.log(W, log)\n return W\n\n def compressed_contract_stats(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n ):\n hg = self.get_hypergraph(accel=\"auto\")\n\n # conversion between tree nodes <-> hypergraph nodes during contraction\n tree_map = dict(zip(self.gen_leaves(), range(hg.get_num_nodes())))\n\n tracker = CompressedStatsTracker(hg, chi)\n\n for p, l, r in self.traverse(order):\n li = tree_map[l]\n ri = tree_map[r]\n\n tracker.update_pre_step()\n\n if compress_late:\n tracker.update_pre_compress(hg, li, ri)\n # compress just before we contract tensors\n hg.compress(chi=chi, edges=hg.get_node(li))\n hg.compress(chi=chi, edges=hg.get_node(ri))\n tracker.update_post_compress(hg, li, ri)\n\n tracker.update_pre_contract(hg, li, ri)\n pi = tree_map[p] = hg.contract(li, ri)\n tracker.update_post_contract(hg, pi)\n\n if not compress_late:\n # compress as soon as we can after contracting tensors\n tracker.update_pre_compress(hg, pi)\n hg.compress(chi=chi, edges=hg.get_node(pi))\n tracker.update_post_compress(hg, pi)\n\n tracker.update_post_step()\n\n return tracker\n\n def total_flops_compressed(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n dtype=None,\n ):\n \"\"\"Estimate the total flops for a compressed contraction of this tree\n with maximum bond size ``chi``. This includes basic estimates of the\n ops to perform contractions, QRs and SVDs.\n \"\"\"\n if dtype is not None:\n raise ValueError(\n \"Can only estimate cost in terms of \"\n \"number of abstract scalar ops.\"\n )\n\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).flops\n\n def total_write_compressed(\n self, chi, order=\"surface_order\", compress_late=False, accel=\"auto\"\n ):\n \"\"\"Compute the total size of all intermediate tensors when a\n compressed contraction is performed with maximum bond size ``chi``,\n ordered by ``order``. This is relevant maybe for time complexity and\n e.g. autodiff space complexity (since every intermediate is kept).\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).write\n\n def total_cost_compressed(\n self,\n chi,\n order=\"surface_order\",\n compress_late=False,\n factor=DEFAULT_COMBO_FACTOR,\n ):\n return self.total_flops_compressed(\n chi=chi, order=order, compress_late=compress_late\n ) + factor * self.total_write_compressed(\n chi=chi, order=order, compress_late=compress_late\n )\n\n def max_size_compressed(\n self, chi, order=\"surface_order\", compress_late=False\n ):\n \"\"\"Compute the maximum sized tensor produced when a compressed\n contraction is performed with maximum bond size ``chi``, ordered by\n ``order``. This is close to the ideal space complexity if only\n tensors that are being directly operated on are kept in memory.\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).max_size\n\n def peak_size_compressed(\n self, chi, order=\"surface_order\", compress_late=False, accel=\"auto\"\n ):\n \"\"\"Compute the peak size of combined intermediate tensors when a\n compressed contraction is performed with maximum bond size ``chi``,\n ordered by ``order``. This is the practical space complexity if one is\n not swapping intermediates in and out of memory.\n \"\"\"\n return self.compressed_contract_stats(\n chi=chi,\n order=order,\n compress_late=compress_late,\n ).peak_size\n\n contraction_cost_compressed = total_cost_compressed\n\n def contraction_width_compressed(\n self, chi, order=\"surface_order\", compress_late=False\n ):\n \"\"\"Compute log2 of the maximum sized tensor produced when a compressed\n contraction is performed with maximum bond size ``chi``, ordered by\n ``order``.\n \"\"\"\n return math.log2(self.max_size_compressed(chi, order, compress_late))\n\n def contract_nodes_pair(self, x, y, check=False):\n \"\"\"Contract node ``x`` with node ``y`` in the tree to create a new\n parent node.\n \"\"\"\n parent = x.union(y)\n\n # make sure info entries exist for all (default dict)\n for node in (x, y, parent):\n self._add_node(node, check=check)\n\n # enforce left ordering of 'heaviest' subtrees\n nx, ny = len(x), len(y)\n hx, hy = hash(x), hash(y)\n\n # deterministically break ties\n if (nx, hx) > (ny, hy):\n lr = (x, y)\n else:\n lr = (y, x)\n\n self.children[parent] = lr\n\n if self.track_childless:\n self.childless.discard(parent)\n if x not in self.children and nx > 1:\n self.childless.add(x)\n if y not in self.children and ny > 1:\n self.childless.add(y)\n\n if self._track_flops:\n self._flops += self.get_flops(parent)\n if self._track_write:\n self._write += self.get_size(parent)\n if self._track_size:\n self._sizes.add(self.get_size(parent))\n\n return parent\n\n def contract_nodes(\n self,\n nodes,\n optimize=\"auto-hq\",\n check=False,\n extra_opts=None,\n ):\n \"\"\"Contract an arbitrary number of ``nodes`` in the tree to build up a\n subtree. The root of this subtree (a new intermediate) is returned.\n \"\"\"\n if len(nodes) == 1:\n return next(iter(nodes))\n\n if len(nodes) == 2:\n return self.contract_nodes_pair(*nodes, check=check)\n\n from .interface import find_path\n\n # create the bottom and top nodes\n grandparent = union_it(nodes)\n self._add_node(grandparent, check=check)\n for node in nodes:\n self._add_node(node, check=check)\n\n # if more than two nodes need to find the path to fill in between\n # \\\n # GN <- 'grandparent'\n # / \\\n # ?????????\n # ????????????? <- to be filled with 'temp nodes'\n # / \\ / / \\\n # N0 N1 N2 N3 N4 <- ``nodes``, or, subgraphs\n # / \\ / / \\\n path_inputs = [tuple(self.get_legs(x)) for x in nodes]\n path_output = tuple(self.get_legs(grandparent))\n\n path = find_path(\n path_inputs,\n path_output,\n self.size_dict,\n optimize=optimize,\n **(extra_opts or {}),\n )\n\n # now we have path create the nodes in between\n temp_nodes = list(nodes)\n for p in path:\n to_contract = [temp_nodes.pop(i) for i in sorted(p, reverse=True)]\n temp_nodes.append(self.contract_nodes(to_contract, check=check))\n\n (parent,) = temp_nodes\n\n if check:\n # final remaining temp input should be the 'grandparent'\n assert parent == grandparent\n\n return parent\n\n def is_complete(self):\n \"\"\"Check every node has two children, unless it is a leaf.\"\"\"\n too_many_nodes = len(self.info) > 2 * self.N - 1\n too_many_branches = len(self.children) > self.N - 1\n\n if too_many_nodes or too_many_branches:\n raise ValueError(\"Contraction tree seems to be over complete!\")\n\n queue = [self.root]\n while queue:\n x = queue.pop()\n if len(x) == 1:\n continue\n try:\n queue.extend(self.children[x])\n except KeyError:\n return False\n\n return True\n\n def get_default_order(self):\n return \"dfs\"\n\n def _traverse_ordered(self, order):\n \"\"\"Traverse the tree in the order that minimizes ``order(node)``, but\n still constrained to produce children before parents.\n \"\"\"\n from bisect import bisect\n\n if order == \"surface_order\":\n order = self.surface_order\n\n seen = set()\n queue = [self.root]\n scores = [order(self.root)]\n\n while len(seen) != len(self.children):\n i = 0\n while i < len(queue):\n node = queue[i]\n if node not in seen:\n for child in self.children[node]:\n if len(child) > 1:\n # insert child into queue by score + before parent\n score = order(child)\n ci = bisect(scores[:i], score)\n scores.insert(ci, score)\n queue.insert(ci, child)\n # parent moves extra place to right\n i += 1\n seen.add(node)\n i += 1\n\n for node in queue:\n yield (node, *self.children[node])\n\n def traverse(self, order=None):\n \"\"\"Generate, in order, all the node merges in this tree. Non-recursive!\n This ensures children are always visited before their parent.\n\n Parameters\n ----------\n order : None or callable, optional\n How to order the contractions within the tree. If a callable is\n given (which should take a node as its argument), try to contract\n nodes that minimize this function first.\n\n Returns\n -------\n generator[tuple[node]]\n The bottom up ordered sequence of tree merges, each a\n tuple of ``(parent, left_child, right_child)``.\n\n See Also\n --------\n descend\n \"\"\"\n if order is None:\n order = self.get_default_order()\n\n if order != \"dfs\":\n yield from self._traverse_ordered(order=order)\n return\n\n ready = set(self.gen_leaves())\n queue = [self.root]\n\n while queue:\n node = queue[-1]\n l, r = self.children[node]\n\n # both node's children are ready -> we can yield this contraction\n if (l in ready) and (r in ready):\n ready.add(queue.pop())\n yield node, l, r\n continue\n\n if r not in ready:\n queue.append(r)\n if l not in ready:\n queue.append(l)\n\n def descend(self, mode=\"dfs\"):\n \"\"\"Generate, from root to leaves, all the node merges in this tree.\n Non-recursive! This ensures parents are visited before their children.\n\n Parameters\n ----------\n mode : {'dfs', bfs}, optional\n How expand from a parent.\n\n Returns\n -------\n generator[tuple[node]\n The top down ordered sequence of tree merges, each a\n tuple of ``(parent, left_child, right_child)``.\n\n See Also\n --------\n traverse\n \"\"\"\n queue = [self.root]\n while queue:\n if mode == \"dfs\":\n parent = queue.pop(-1)\n elif mode == \"bfs\":\n parent = queue.pop(0)\n l, r = self.children[parent]\n yield parent, l, r\n if len(l) > 1:\n queue.append(l)\n if len(r) > 1:\n queue.append(r)\n\n def get_subtree(self, node, size, search=\"bfs\"):\n \"\"\"Get a subtree spanning down from ``node`` which will have ``size``\n leaves (themselves not necessarily leaves of the actual tree).\n\n Parameters\n ----------\n node : node\n The node of the tree to start with.\n size : int\n How many subtree leaves to aim for.\n search : {'bfs', 'dfs', 'random'}, optional\n How to build the tree:\n\n - 'bfs': breadth first expansion\n - 'dfs': depth first expansion (largest nodes first)\n - 'random': random expansion\n\n Returns\n -------\n sub_leaves : tuple[node]\n Nodes which are subtree leaves.\n branches : tuple[node]\n Nodes which are between the subtree leaves and root.\n \"\"\"\n # nodes which are subtree leaves\n branches = []\n\n # actual tree leaves - can't expand\n real_leaves = []\n\n # nodes to expand\n queue = [node]\n\n while (len(queue) + len(real_leaves) < size) and queue:\n if search == \"bfs\":\n p = queue.pop(0)\n elif search == \"dfs\":\n p = queue.pop(-1)\n elif search == \"random\":\n p = queue.pop(random.randint(0, len(queue) - 1))\n\n if len(p) == 1:\n real_leaves.append(p)\n continue\n\n # the left child is always >= in weight that right child\n # if we append it last then ``.pop(-1)`` above perform the\n # depth first search sorting by node subgraph size\n l, r = self.children[p]\n\n queue.append(r)\n queue.append(l)\n branches.append(p)\n\n # nodes at the bottom of the subtree\n sub_leaves = queue + real_leaves\n\n return tuple(sub_leaves), tuple(branches)\n\n def remove_ind(self, ind, project=None, inplace=False):\n \"\"\"Remove (i.e. by default slice) index ``ind`` from this contraction\n tree, taking care to update all relevant information about each node.\n \"\"\"\n tree = self if inplace else self.copy()\n\n # make sure all flops and size information has been populated\n tree.contract_stats()\n\n d = tree.size_dict[ind]\n\n for node, node_info in tree.info.items():\n # if ind doesn't feature in this node (contraction) nothing to do\n involved = tree.get_involved(node)\n\n # inputs can have leg indices that are not involved so\n legs = tree.get_legs(node)\n\n if (ind not in involved) and (ind not in legs):\n continue\n\n # else update all the relevant information about this node\n node_info[\"involved\"] = legs_without(involved, ind)\n removed = tree.get_removed(node)\n\n # update information regarding node indices sets\n if ind in legs:\n # removing indices changes both flops and size of node\n node_info[\"legs\"] = legs_without(legs, ind)\n\n old_size = tree.get_size(node)\n tree._sizes.discard(old_size)\n new_size = old_size // d\n tree._sizes.add(new_size)\n node_info[\"size\"] = new_size\n\n if len(node) > 1:\n # only non-leaf nodes contribute to write\n tree._write += -old_size + new_size\n else:\n # removing indices only changes flops\n node_info[\"removed\"] = legs_without(removed, ind)\n\n old_flops = tree.get_flops(node)\n new_flops = old_flops // d\n node_info[\"flops\"] = new_flops\n tree._flops += -old_flops + new_flops\n\n if len(node) == 1:\n # its a leaf - corresponding input will be sliced\n i = node_get_single_el(node)\n tree.sliced_inputs = tree.sliced_inputs | frozenset([i])\n tree.inputs_legs[i] = legs_without(tree.inputs_legs[i], ind)\n elif len(node) == tree.N:\n # root node\n tree.output_legs = legs_without(tree.output_legs, ind)\n\n # delete info we can't change\n for k in (\n \"inds\",\n \"einsum_eq\",\n \"can_dot\",\n \"tensordot_axes\",\n \"tensordot_perm\",\n ):\n tree.info[node].pop(k, None)\n\n if project is None:\n # we are slicing the index\n si = SliceInfo(ind not in tree.output, ind, d, None)\n tree.multiplicity = tree.multiplicity * d\n else:\n si = SliceInfo(ind not in tree.output, ind, 1, project)\n\n # update the ordered slice information dictionary, but maintain the\n # order such that output sliced indices always appear first ->\n # enforced by the dataclass SliceInfo ordering\n tree.sliced_inds = {\n si.ind: si for si in sorted((*tree.sliced_inds.values(), si))\n }\n\n tree.already_optimized.clear()\n tree.contraction_cores.clear()\n\n return tree\n\n remove_ind_ = functools.partialmethod(remove_ind, inplace=True)\n\n def calc_subtree_candidates(self, pwr=2, what=\"flops\"):\n candidates = list(self.children)\n\n if what == \"size\":\n weights = [self.get_size(x) for x in candidates]\n\n elif what == \"flops\":\n weights = [self.get_flops(x) for x in candidates]\n\n max_weight = max(weights)\n\n # can be bigger than numpy int/float allows\n weights = [float(w / max_weight) ** (1 / pwr) for w in weights]\n\n # sort by descending score\n candidates, weights = zip(\n *sorted(zip(candidates, weights), key=lambda x: -x[1])\n )\n\n return list(candidates), list(weights)\n\n def subtree_reconfigure(\n self,\n subtree_size=8,\n subtree_search=\"bfs\",\n weight_what=\"flops\",\n weight_pwr=2,\n select=\"max\",\n maxiter=500,\n seed=None,\n minimize=\"flops\",\n optimize=None,\n inplace=False,\n progbar=False,\n ):\n \"\"\"Reconfigure subtrees of this tree with locally optimal paths.\n\n Parameters\n ----------\n subtree_size : int, optional\n The size of subtree to consider. Cost is exponential in this.\n subtree_search : {'bfs', 'dfs', 'random'}, optional\n How to build the subtrees:\n\n - 'bfs': breadth-first-search creating balanced subtrees\n - 'dfs': depth-first-search creating imbalanced subtrees\n - 'random': random subtree building\n\n weight_what : {'flops', 'size'}, optional\n When assessing nodes to build and optimize subtrees from whether to\n score them by the (local) contraction cost, or tensor size.\n weight_pwr : int, optional\n When assessing nodes to build and optimize subtrees from, how to\n scale their score into a probability: ``score**(1 / weight_pwr)``.\n The larger this is the more explorative the algorithm is when\n ``select='random'``.\n select : {'max', 'min', 'random'}, optional\n What order to select node subtrees to optimize:\n\n - 'max': choose the highest score first\n - 'min': choose the lowest score first\n - 'random': choose randomly weighted on score -- see\n ``weight_pwr``.\n\n maxiter : int, optional\n How many subtree optimizations to perform, the algorithm can\n terminate before this if all subtrees have been optimized.\n seed : int, optional\n A random seed (seeds python system random module).\n minimize : {'flops', 'size'}, optional\n Whether to minimize with respect to contraction flops or size.\n inplace : bool, optional\n Whether to perform the reconfiguration inplace or not.\n progbar : bool, optional\n Whether to show live progress of the reconfiguration.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # ensure these have been computed and thus are being tracked\n tree.contract_stats()\n\n scorer = get_score_fn(minimize)\n\n if optimize is None:\n from .pathfinders.path_basic import OptimalOptimizer\n\n opt = OptimalOptimizer(\n minimize=scorer.get_dynamic_programming_minimize()\n )\n else:\n opt = optimize\n\n node_cost = getattr(scorer, \"cost_local_tree_node\", lambda _: 2)\n\n # different caches as we might want to reconfigure one before other\n self.already_optimized.setdefault(minimize, set())\n already_optimized = self.already_optimized[minimize]\n\n if seed is not None:\n random.seed(seed)\n\n candidates, weights = self.calc_subtree_candidates(\n pwr=weight_pwr, what=weight_what\n )\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n r = 0\n try:\n while candidates and r < maxiter:\n if select == \"max\":\n i = 0\n elif select == \"min\":\n i = -1\n elif select == \"random\":\n (i,) = random.choices(\n range(len(candidates)), weights=weights\n )\n\n weights.pop(i)\n sub_root = candidates.pop(i)\n\n # get a subtree to possibly reconfigure\n sub_leaves, sub_branches = tree.get_subtree(\n sub_root, size=subtree_size, search=subtree_search\n )\n\n sub_leaves = frozenset(sub_leaves)\n\n # check if its already been optimized\n if sub_leaves in already_optimized:\n continue\n\n # else remove the branches, keeping track of current cost\n current_cost = node_cost(tree, sub_root)\n for node in sub_branches:\n if minimize == \"size\":\n current_cost = max(current_cost, node_cost(tree, node))\n else:\n current_cost += node_cost(tree, node)\n tree._remove_node(node)\n\n # make the optimizer more efficient by supplying accurate cap\n opt.cost_cap = max(2, current_cost)\n\n # and reoptimize the leaves\n tree.contract_nodes(sub_leaves, optimize=opt)\n already_optimized.add(sub_leaves)\n\n r += 1\n\n if progbar:\n pbar.update()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n # if we have reconfigured simply re-add all candidates\n candidates, weights = tree.calc_subtree_candidates(\n pwr=weight_pwr, what=weight_what\n )\n finally:\n if progbar:\n pbar.close()\n\n # invalidate any compiled contractions\n tree.contraction_cores.clear()\n\n return tree\n\n subtree_reconfigure_ = functools.partialmethod(\n subtree_reconfigure, inplace=True\n )\n\n def subtree_reconfigure_forest(\n self,\n num_trees=8,\n num_restarts=10,\n restart_fraction=0.5,\n subtree_maxiter=100,\n subtree_size=10,\n subtree_search=(\"random\", \"bfs\"),\n subtree_select=(\"random\",),\n subtree_weight_what=(\"flops\", \"size\"),\n subtree_weight_pwr=(2,),\n parallel=\"auto\",\n parallel_maxiter_steps=4,\n minimize=\"flops\",\n progbar=False,\n inplace=False,\n ):\n \"\"\"'Forested' version of ``subtree_reconfigure`` which is more\n explorative and can be parallelized. It stochastically generates\n a 'forest' reconfigured trees, then only keeps some fraction of these\n to generate the next forest.\n\n Parameters\n ----------\n num_trees : int, optional\n The number of trees to reconfigure at each stage.\n num_restarts : int, optional\n The number of times to halt, prune and then restart the\n tree reconfigurations.\n restart_fraction : float, optional\n The fraction of trees to keep at each stage and generate the next\n forest from.\n subtree_maxiter : int, optional\n Number of subtree reconfigurations per step.\n ``num_restarts * subtree_maxiter`` is the max number of total\n subtree reconfigurations for the final tree produced.\n subtree_size : int, optional\n The size of subtrees to search for and reconfigure.\n subtree_search : tuple[{'random', 'bfs', 'dfs'}], optional\n Tuple of options for the ``search`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_select : tuple[{'random', 'max', 'min'}], optional\n Tuple of options for the ``select`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_weight_what : tuple[{'flops', 'size'}], optional\n Tuple of options for the ``weight_what`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n subtree_weight_pwr : tuple[int], optional\n Tuple of options for the ``weight_pwr`` kwarg of\n :meth:`ContractionTree.subtree_reconfigure` to randomly sample.\n parallel : 'auto', False, True, int, or distributed.Client\n Whether to parallelize the search.\n parallel_maxiter_steps : int, optional\n If parallelizing, how many steps to break each reconfiguration into\n in order to evenly saturate many processes.\n minimize : {'flops', 'size', ..., Objective}, optional\n Whether to minimize the total flops or maximum size of the\n contraction tree.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the subtree reconfiguration inplace.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # candidate trees\n num_keep = max(1, int(num_trees * restart_fraction))\n\n # how to rank the trees\n score = get_score_fn(minimize)\n\n # set up the initial 'forest' and parallel machinery\n pool = parse_parallel_arg(parallel)\n is_scatter_pool = can_scatter(pool)\n if is_scatter_pool:\n is_worker = maybe_leave_pool(pool)\n # store the trees as futures for the entire process\n forest = [scatter(pool, tree)]\n maxiter = subtree_maxiter // parallel_maxiter_steps\n else:\n forest = [tree]\n maxiter = subtree_maxiter\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm(total=num_restarts)\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n try:\n for _ in range(num_restarts):\n # on the next round take only the best trees\n forest = itertools.cycle(forest[:num_keep])\n\n # select some random configurations\n saplings = [\n {\n \"tree\": next(forest),\n \"maxiter\": maxiter,\n \"minimize\": minimize,\n \"subtree_size\": subtree_size,\n \"subtree_search\": random.choice(subtree_search),\n \"select\": random.choice(subtree_select),\n \"weight_pwr\": random.choice(subtree_weight_pwr),\n \"weight_what\": random.choice(subtree_weight_what),\n }\n for _ in range(num_trees)\n ]\n\n if pool is None:\n forest = [_reconfigure_tree(**s) for s in saplings]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n elif not is_scatter_pool:\n forest_futures = [\n submit(pool, _reconfigure_tree, **s) for s in saplings\n ]\n forest = [f.result() for f in forest_futures]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n else:\n # submit in smaller steps to saturate processes\n for _ in range(parallel_maxiter_steps):\n for s in saplings:\n s[\"tree\"] = submit(pool, _reconfigure_tree, **s)\n\n # compute scores remotely then gather\n forest_futures = [s[\"tree\"] for s in saplings]\n res_futures = [\n submit(pool, _get_tree_info, t) for t in forest_futures\n ]\n res = [\n {\"tree\": tree_future, **res_future.result()}\n for tree_future, res_future in zip(\n forest_futures, res_futures\n )\n ]\n\n # update the order of the new forest\n res.sort(key=score)\n forest = [r[\"tree\"] for r in res]\n\n if progbar:\n pbar.update()\n if pool is None:\n d = _describe_tree(forest[0])\n else:\n d = submit(pool, _describe_tree, forest[0]).result()\n pbar.set_description(d, refresh=False)\n\n finally:\n if progbar:\n pbar.close()\n\n if is_scatter_pool:\n tree.set_state_from(forest[0].result())\n maybe_rejoin_pool(is_worker, pool)\n else:\n tree.set_state_from(forest[0])\n\n return tree\n\n subtree_reconfigure_forest_ = functools.partialmethod(\n subtree_reconfigure_forest, inplace=True\n )\n\n def slice(\n self,\n target_size=None,\n target_overhead=None,\n target_slices=None,\n temperature=0.01,\n minimize=\"flops\",\n allow_outer=True,\n max_repeats=16,\n inplace=False,\n ):\n \"\"\"Slice this tree (turn some indices into indices which are explicitly\n summed over rather than being part of contractions). The indices are\n stored in ``tree.sliced_inds``, and the contraction width updated to\n take account of the slicing. Calling ``tree.contract(arrays)`` moreover\n which automatically perform the slicing and summation.\n\n Parameters\n ----------\n target_size : int, optional\n The target number of entries in the largest tensor of the sliced\n contraction. The search algorithm will terminate after this is\n reached.\n target_slices : int, optional\n The target or minimum number of 'slices' to consider - individual\n contractions after slicing indices. The search algorithm will\n terminate after this is breached.\n target_overhead : float, optional\n The target increase in total number of floating point operations.\n For example, a value of ``2.0`` will terminate the search just\n before the cost of computing all the slices individually breaches\n twice that of computing the original contraction all at once.\n temperature : float, optional\n How much to randomize the repeated search.\n minimize : {'flops', 'size', ..., Objective}, optional\n Which metric to score the overhead increase against.\n allow_outer : bool, optional\n Whether to allow slicing of outer indices.\n max_repeats : int, optional\n How many times to repeat the search with a slight randomization.\n inplace : bool, optional\n Whether the remove the indices from this tree inplace or not.\n\n Returns\n -------\n ContractionTree\n\n See Also\n --------\n SliceFinder, ContractionTree.slice_and_reconfigure\n \"\"\"\n from .slicer import SliceFinder\n\n tree = self if inplace else self.copy()\n\n sf = SliceFinder(\n tree,\n target_size=target_size,\n target_overhead=target_overhead,\n target_slices=target_slices,\n temperature=temperature,\n minimize=minimize,\n allow_outer=allow_outer,\n )\n\n ix_sl, _ = sf.search(max_repeats)\n for ix in ix_sl:\n tree.remove_ind_(ix)\n\n return tree\n\n slice_ = functools.partialmethod(slice, inplace=True)\n\n def slice_and_reconfigure(\n self,\n target_size,\n step_size=2,\n temperature=0.01,\n minimize=\"flops\",\n allow_outer=True,\n max_repeats=16,\n reconf_opts=None,\n progbar=False,\n inplace=False,\n ):\n \"\"\"Interleave slicing (removing indices into an exterior sum) with\n subtree reconfiguration to minimize the overhead induced by this\n slicing.\n\n Parameters\n ----------\n target_size : int\n Slice the tree until the maximum intermediate size is this or\n smaller.\n step_size : int, optional\n The minimum size reduction to try and achieve before switching to a\n round of subtree reconfiguration.\n temperature : float, optional\n The temperature to supply to ``SliceFinder`` for searching for\n indices.\n minimize : {'flops', 'size', ..., Objective}, optional\n The metric to minimize when slicing and reconfiguring subtrees.\n max_repeats : int, optional\n The number of slicing attempts to perform per search.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the slicing and reconfiguration inplace.\n reconf_opts : None or dict, optional\n Supplied to\n :meth:`ContractionTree.subtree_reconfigure` or\n :meth:`ContractionTree.subtree_reconfigure_forest`, depending on\n `'forested'` key value.\n \"\"\"\n tree = self if inplace else self.copy()\n\n reconf_opts = {} if reconf_opts is None else dict(reconf_opts)\n minimize = get_score_fn(minimize)\n reconf_opts.setdefault(\"minimize\", minimize)\n forested_reconf = reconf_opts.pop(\"forested\", False)\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n try:\n while tree.max_size() > target_size:\n tree.slice_(\n temperature=temperature,\n target_slices=step_size,\n minimize=minimize,\n allow_outer=allow_outer,\n max_repeats=max_repeats,\n )\n if forested_reconf:\n tree.subtree_reconfigure_forest_(**reconf_opts)\n else:\n tree.subtree_reconfigure_(**reconf_opts)\n\n if progbar:\n pbar.update()\n pbar.set_description(_describe_tree(tree), refresh=False)\n finally:\n if progbar:\n pbar.close()\n\n return tree\n\n slice_and_reconfigure_ = functools.partialmethod(\n slice_and_reconfigure, inplace=True\n )\n\n def slice_and_reconfigure_forest(\n self,\n target_size,\n step_size=2,\n num_trees=8,\n restart_fraction=0.5,\n temperature=0.02,\n max_repeats=32,\n minimize=\"flops\",\n allow_outer=True,\n parallel=\"auto\",\n progbar=False,\n inplace=False,\n reconf_opts=None,\n ):\n \"\"\"'Forested' version of :meth:`ContractionTree.slice_and_reconfigure`.\n This maintains a 'forest' of trees with different slicing and subtree\n reconfiguration attempts, pruning the worst at each step and generating\n a new forest from the best.\n\n Parameters\n ----------\n target_size : int\n Slice the tree until the maximum intermediate size is this or\n smaller.\n step_size : int, optional\n The minimum size reduction to try and achieve before switching to a\n round of subtree reconfiguration.\n num_restarts : int, optional\n The number of times to halt, prune and then restart the\n tree reconfigurations.\n restart_fraction : float, optional\n The fraction of trees to keep at each stage and generate the next\n forest from.\n temperature : float, optional\n The temperature at which to randomize the sliced index search.\n max_repeats : int, optional\n The number of slicing attempts to perform per search.\n parallel : 'auto', False, True, int, or distributed.Client\n Whether to parallelize the search.\n progbar : bool, optional\n Whether to show live progress.\n inplace : bool, optional\n Whether to perform the slicing and reconfiguration inplace.\n reconf_opts : None or dict, optional\n Supplied to\n :meth:`ContractionTree.slice_and_reconfigure`.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n tree = self if inplace else self.copy()\n\n # candidate trees\n num_keep = max(1, int(num_trees * restart_fraction))\n\n # how to rank the trees\n score = get_score_fn(minimize)\n\n # set up the initial 'forest' and parallel machinery\n pool = parse_parallel_arg(parallel)\n is_scatter_pool = can_scatter(pool)\n if is_scatter_pool:\n is_worker = maybe_leave_pool(pool)\n # store the trees as futures for the entire process\n forest = [scatter(pool, tree)]\n else:\n forest = [tree]\n\n if progbar:\n import tqdm\n\n pbar = tqdm.tqdm()\n pbar.set_description(_describe_tree(tree), refresh=False)\n\n next_size = tree.max_size()\n\n try:\n while True:\n next_size //= step_size\n\n # on the next round take only the best trees\n forest = itertools.cycle(forest[:num_keep])\n\n saplings = [\n {\n \"tree\": next(forest),\n \"target_size\": next_size,\n \"step_size\": step_size,\n \"temperature\": temperature,\n \"max_repeats\": max_repeats,\n \"reconf_opts\": reconf_opts,\n \"allow_outer\": allow_outer,\n }\n for _ in range(num_trees)\n ]\n\n if pool is None:\n forest = [\n _slice_and_reconfigure_tree(**s) for s in saplings\n ]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n\n elif not is_scatter_pool:\n # simple pool with no pass by reference\n forest_futures = [\n submit(pool, _slice_and_reconfigure_tree, **s)\n for s in saplings\n ]\n forest = [f.result() for f in forest_futures]\n res = [{\"tree\": t, **_get_tree_info(t)} for t in forest]\n\n else:\n forest_futures = [\n submit(pool, _slice_and_reconfigure_tree, **s)\n for s in saplings\n ]\n\n # compute scores remotely then gather\n res_futures = [\n submit(pool, _get_tree_info, t) for t in forest_futures\n ]\n res = [\n {\"tree\": tree_future, **res_future.result()}\n for tree_future, res_future in zip(\n forest_futures, res_futures\n )\n ]\n\n # we want to sort by flops, but also favour sampling as\n # many different sliced index combos as possible\n # ~ [1, 1, 1, 2, 2, 3] -> [1, 2, 3, 1, 2, 1]\n res.sort(key=score)\n res = list(\n interleave(\n groupby(lambda r: r[\"sliced_ind_set\"], res).values()\n )\n )\n\n # update the order of the new forest\n forest = [r[\"tree\"] for r in res]\n\n if progbar:\n pbar.update()\n if pool is None:\n d = _describe_tree(forest[0])\n else:\n d = submit(pool, _describe_tree, forest[0]).result()\n pbar.set_description(d, refresh=False)\n\n if res[0][\"size\"] <= target_size:\n break\n\n finally:\n if progbar:\n pbar.close()\n\n if is_scatter_pool:\n tree.set_state_from(forest[0].result())\n maybe_rejoin_pool(is_worker, pool)\n else:\n tree.set_state_from(forest[0])\n\n return tree\n\n slice_and_reconfigure_forest_ = functools.partialmethod(\n slice_and_reconfigure_forest, inplace=True\n )\n\n def compressed_reconfigure(\n self,\n minimize,\n order_only=False,\n max_nodes=\"auto\",\n max_time=None,\n local_score=None,\n exploration_power=0,\n best_score=None,\n progbar=False,\n inplace=False,\n ):\n \"\"\"Reconfigure this tree according to ``peak_size_compressed``.\n\n Parameters\n ----------\n chi : int\n The maximum bond dimension to consider.\n order_only : bool, optional\n Whether to only consider the ordering of the current tree\n contractions, or all possible contractions, starting with the\n current.\n max_nodes : int, optional\n Set the maximum number of contraction steps to consider.\n max_time : float, optional\n Set the maximum time to spend on the search.\n local_score : callable, optional\n A function that assigns a score to a potential contraction, with a\n lower score giving more priority to explore that contraction\n earlier. It should have signature::\n\n local_score(step, new_score, dsize, new_size)\n\n where ``step`` is the number of steps so far, ``new_score`` is the\n score of the contraction so far, ``dsize`` is the change in memory\n by the current step, and ``new_size`` is the new memory size after\n contraction.\n exploration_power : float, optional\n If not ``0.0``, the inverse power to which the step is raised in\n the default local score function. Higher values favor exploring\n more promising branches early on - at the cost of increased memory.\n Ignored if ``local_score`` is supplied.\n best_score : float, optional\n Manually specify an upper bound for best score found so far.\n progbar : bool, optional\n If ``True``, display a progress bar.\n inplace : bool, optional\n Whether to perform the reconfiguration inplace on this tree.\n\n Returns\n -------\n ContractionTree\n \"\"\"\n from .experimental.path_compressed_branchbound import (\n CompressedExhaustive,\n )\n\n if max_nodes == \"auto\":\n if max_time is None:\n max_nodes = max(10_000, self.N**2)\n else:\n max_nodes = float(\"inf\")\n\n opt = CompressedExhaustive(\n minimize=minimize,\n local_score=local_score,\n max_nodes=max_nodes,\n max_time=max_time,\n exploration_power=exploration_power,\n best_score=best_score,\n progbar=progbar,\n )\n opt.setup(self.inputs, self.output, self.size_dict)\n opt.explore_path(self.get_path_surface(), restrict=order_only)\n\n # rtree = opt.search(self.inputs, self.output, self.size_dict)\n\n opt.run(self.inputs, self.output, self.size_dict)\n ssa_path = opt.ssa_path\n # ssa_path = opt(self.inputs, self.output, self.size_dict)\n rtree = self.__class__.from_path(\n self.inputs,\n self.output,\n self.size_dict,\n ssa_path=ssa_path,\n )\n if inplace:\n self.set_state_from(rtree)\n rtree = self\n rtree.set_surface_order_from_path(ssa_path)\n return rtree\n\n compressed_reconfigure_ = functools.partialmethod(\n compressed_reconfigure, inplace=True\n )\n\n def windowed_reconfigure(\n self,\n minimize,\n order_only=False,\n window_size=20,\n max_iterations=100,\n max_window_tries=1000,\n score_temperature=0.0,\n queue_temperature=1.0,\n scorer=None,\n queue_scorer=None,\n seed=None,\n inplace=False,\n progbar=False,\n **kwargs,\n ):\n from .pathfinders.path_compressed import WindowedOptimizer\n\n wo = WindowedOptimizer(\n self.inputs,\n self.output,\n self.size_dict,\n minimize=minimize,\n ssa_path=self.get_ssa_path(),\n seed=seed,\n )\n\n wo.refine(\n window_size=window_size,\n max_iterations=max_iterations,\n order_only=order_only,\n max_window_tries=max_window_tries,\n score_temperature=score_temperature,\n queue_temperature=queue_temperature,\n scorer=scorer,\n queue_scorer=queue_scorer,\n progbar=progbar,\n **kwargs,\n )\n ssa_path = wo.get_ssa_path()\n\n rtree = self.__class__.from_path(\n self.inputs,\n self.output,\n self.size_dict,\n ssa_path=ssa_path,\n )\n\n if inplace:\n self.set_state_from(rtree)\n rtree = self\n rtree.set_surface_order_from_path(ssa_path)\n\n return rtree\n\n windowed_reconfigure_ = functools.partialmethod(\n windowed_reconfigure, inplace=True\n )\n\n def flat_tree(self, order=None):\n \"\"\"Create a nested tuple representation of the contraction tree like::\n\n ((0, (1, 2)), ((3, 4), ((5, (6, 7)), (8, 9))))\n\n Such that the contraction will progress like::\n\n ((0, (1, 2)), ((3, 4), ((5, (6, 7)), (8, 9))))\n ((0, 12), (34, ((5, 67), 89)))\n (012, (34, (567, 89)))\n (012, (34, 56789))\n (012, 3456789)\n 0123456789\n\n Where each integer represents a leaf (i.e. single element node).\n \"\"\"\n tups = dict(zip(self.gen_leaves(), range(self.N)))\n\n for parent, l, r in self.traverse(order=order):\n tups[parent] = tups[l], tups[r]\n\n return tups[self.root]\n\n def get_leaves_ordered(self):\n \"\"\"Return the list of leaves as ordered by the contraction tree.\n\n Returns\n -------\n tuple[frozenset[str]]\n \"\"\"\n if not self.is_complete():\n raise ValueError(\"Can't order the leaves until tree is complete.\")\n\n return tuple(\n nd\n for nd in itertools.chain.from_iterable(self.traverse())\n if len(nd) == 1\n )\n\n def get_path(self, order=None):\n \"\"\"Generate a standard path from the contraction tree.\"\"\"\n path = []\n terms = list(self.gen_leaves())\n\n for parent, l, r in self.traverse(order=order):\n i, j = sorted((terms.index(l), terms.index(r)))\n terms.pop(j)\n terms.pop(i)\n path.append((i, j))\n terms.append(parent)\n\n return tuple(path)\n\n path = deprecated(get_path, \"path\", \"get_path\")\n\n def get_numpy_path(self, order=None):\n \"\"\"Generate a path compatible with the `optimize` kwarg of\n `numpy.einsum`.\n \"\"\"\n return [\"einsum_path\", *self.get_path(order=order)]\n\n def get_ssa_path(self, order=None):\n \"\"\"Generate a ssa path from the contraction tree.\"\"\"\n ssa_path = []\n pos = dict(zip(self.gen_leaves(), range(self.N)))\n\n for parent, l, r in self.traverse(order=order):\n i, j = sorted((pos[l], pos[r]))\n ssa_path.append((i, j))\n pos[parent] = len(ssa_path) + self.N - 1\n\n return tuple(ssa_path)\n\n ssa_path = deprecated(get_ssa_path, \"ssa_path\", \"get_ssa_path\")\n\n def surface_order(self, node):\n return (len(node), self.get_centrality(node))\n\n def set_surface_order_from_path(self, ssa_path):\n o = {}\n nodes = list(self.gen_leaves())\n for j, p in enumerate(ssa_path):\n l, r = (nodes[i] for i in p)\n p = l.union(r)\n nodes.append(p)\n o[p] = j\n\n self.surface_order = functools.partial(\n get_with_default, obj=o, default=float(\"inf\")\n )\n\n def get_path_surface(self):\n return self.get_path(order=self.surface_order)\n\n path_surface = deprecated(\n get_path_surface, \"path_surface\", \"get_path_surface\"\n )\n\n def get_ssa_path_surface(self):\n return self.get_ssa_path(order=self.surface_order)\n\n ssa_path_surface = deprecated(\n get_ssa_path_surface, \"ssa_path_surface\", \"get_ssa_path_surface\"\n )\n\n def get_spans(self):\n \"\"\"Get all (which could mean none) potential embeddings of this\n contraction tree into a spanning tree of the original graph.\n\n Returns\n -------\n tuple[dict[frozenset[int], frozenset[int]]]\n \"\"\"\n ind_to_term = collections.defaultdict(set)\n for i, term in enumerate(self.inputs):\n for ix in term:\n ind_to_term[ix].add(i)\n\n def boundary_pairs(node):\n \"\"\"Get nodes along the boundary of the bipartition represented by\n ``node``.\n \"\"\"\n pairs = set()\n for ix in self.get_removed(node):\n # for every index across the contraction\n l1, l2 = ind_to_term[ix]\n\n # can either span from left to right or right to left\n pairs.add((l1, l2))\n pairs.add((l2, l1))\n\n return pairs\n\n # first span choice is any nodes across the top level bipart\n candidates = [\n {\n # which intermedate nodes map to which leaf nodes\n \"map\": {self.root: node_from_single(l2)},\n # the leaf nodes in the spanning tree\n \"spine\": {l1, l2},\n }\n for l1, l2 in boundary_pairs(self.root)\n ]\n\n for _, l, r in self.descend():\n for child in (r, l):\n # for each current candidate check all the possible extensions\n for _ in range(len(candidates)):\n cand = candidates.pop(0)\n\n # don't need to do anything for\n if len(child) == 1:\n candidates.append(\n {\n \"map\": {child: child, **cand[\"map\"]},\n \"spine\": cand[\"spine\"].copy(),\n }\n )\n\n for l1, l2 in boundary_pairs(child):\n if (l1 in cand[\"spine\"]) or (l2 not in cand[\"spine\"]):\n # pair does not merge inwards into spine\n continue\n\n # valid extension of spanning tree\n candidates.append(\n {\n \"map\": {\n child: node_from_single(l2),\n **cand[\"map\"],\n },\n \"spine\": cand[\"spine\"] | {l1, l2},\n }\n )\n\n return tuple(c[\"map\"] for c in candidates)\n\n def compute_centralities(self, combine=\"mean\"):\n \"\"\"Compute a centrality for every node in this contraction tree.\"\"\"\n hg = self.get_hypergraph(accel=\"auto\")\n cents = hg.simple_centrality()\n\n for i, leaf in enumerate(self.gen_leaves()):\n self.info[leaf][\"centrality\"] = cents[i]\n\n combine = {\n \"mean\": lambda x, y: (x + y) / 2,\n \"sum\": lambda x, y: (x + y),\n \"max\": max,\n \"min\": min,\n }.get(combine, combine)\n\n for p, l, r in self.traverse(\"dfs\"):\n self.info[p][\"centrality\"] = combine(\n self.info[l][\"centrality\"], self.info[r][\"centrality\"]\n )\n\n def get_hypergraph(self, accel=False):\n \"\"\"Get a hypergraph representing the uncontracted network (i.e. the\n leaves).\n \"\"\"\n return get_hypergraph(self.inputs, self.output, self.size_dict, accel)\n\n def reset_contraction_indices(self):\n \"\"\"Reset all information regarding the explicit contraction indices\n ordering.\n \"\"\"\n # delete all derived information\n for node in self.children:\n for k in (\n \"inds\",\n \"einsum_eq\",\n \"can_dot\",\n \"tensordot_axes\",\n \"tensordot_perm\",\n ):\n self.info[node].pop(k, None)\n\n # invalidate any compiled contractions\n self.contraction_cores.clear()\n\n def sort_contraction_indices(\n self,\n priority=\"flops\",\n make_output_contig=True,\n make_contracted_contig=True,\n reset=True,\n ):\n \"\"\"Set explicit orders for the contraction indices of this self to\n optimize for one of two things: contiguity in contracted ('k') indices,\n or contiguity of left and right output ('m' and 'n') indices.\n\n Parameters\n ----------\n priority : {'flops', 'size', 'root', 'leaves'}, optional\n Which order to process the intermediate nodes in. Later nodes\n re-sort previous nodes so are more likely to keep their ordering.\n E.g. for 'flops' the mostly costly contracton will be process last\n and thus will be guaranteed to have its indices exactly sorted.\n make_output_contig : bool, optional\n When processing a pairwise contraction, sort the parent contraction\n indices so that the order of indices is the order they appear\n from left to right in the two child (input) tensors.\n make_contracted_contig : bool, optional\n When processing a pairwise contraction, sort the child (input)\n tensor indices so that all contracted indices appear contiguously.\n reset : bool, optional\n Reset all indices to the default order before sorting.\n \"\"\"\n if reset:\n self.reset_contraction_indices()\n\n if priority == \"flops\":\n nodes = sorted(\n self.children.items(), key=lambda x: self.get_flops(x[0])\n )\n elif priority == \"size\":\n nodes = sorted(\n self.children.items(), key=lambda x: self.get_size(x[0])\n )\n elif priority == \"root\":\n nodes = ((p, (l, r)) for p, l, r in self.traverse())\n elif priority == \"leaves\":\n nodes = ((p, (l, r)) for p, l, r in self.descend())\n else:\n raise ValueError(priority)\n\n for p, (l, r) in nodes:\n p_inds, l_inds, r_inds = map(self.get_inds, (p, l, r))\n\n if make_output_contig and len(p) != self.N:\n # sort indices by whether they appear in the left or right\n # whether this happens before or after the sort below depends\n # on the order we are processing the nodes\n # (avoid root as don't want to modify output)\n\n def psort(ix):\n # group by whether in left or right input\n return (r_inds.find(ix), l_inds.find(ix))\n\n p_inds = \"\".join(sorted(p_inds, key=psort))\n self.info[p][\"inds\"] = p_inds\n\n if make_contracted_contig:\n # sort indices by:\n # 1. if they are going to be contracted\n # 2. what order they appear in the parent indices\n # (but ignore leaf indices)\n if len(l) != 1:\n\n def lsort(ix):\n return (r_inds.find(ix), p_inds.find(ix))\n\n l_inds = \"\".join(sorted(self.get_legs(l), key=lsort))\n self.info[l][\"inds\"] = l_inds\n\n if len(r) != 1:\n\n def rsort(ix):\n return (p_inds.find(ix), l_inds.find(ix))\n\n r_inds = \"\".join(sorted(self.get_legs(r), key=rsort))\n self.info[r][\"inds\"] = r_inds\n\n # invalidate any compiled contractions\n self.contraction_cores.clear()\n\n def print_contractions(self, sort=None, show_brackets=True):\n \"\"\"Print each pairwise contraction, with colorized indices (if\n `colorama` is installed), and other information.\n \"\"\"\n try:\n from colorama import Fore\n\n RESET = Fore.RESET\n GREY = Fore.WHITE\n PINK = Fore.MAGENTA\n RED = Fore.RED\n BLUE = Fore.BLUE\n GREEN = Fore.GREEN\n except ImportError:\n RESET = GREY = PINK = RED = BLUE = GREEN = \"\"\n\n entries = []\n\n for i, (p, l, r) in enumerate(self.traverse()):\n p_legs, l_legs, r_legs = map(self.get_legs, [p, l, r])\n p_inds, l_inds, r_inds = map(self.get_inds, [p, l, r])\n\n # print sizes and flops\n p_flops = self.get_flops(p)\n p_sz, l_sz, r_sz = (\n math.log2(self.get_size(node)) for node in [p, l, r]\n )\n # print whether tensordottable\n if self.get_can_dot(p):\n type_msg = \"tensordot\"\n perm = self.get_tensordot_perm(p)\n if perm is not None:\n # and whether indices match tensordot\n type_msg += \"+perm\"\n else:\n type_msg = \"einsum\"\n\n pa = \"\".join(\n PINK + f\"({ix})\"\n if (ix in l_legs) and (ix in r_legs)\n else GREEN + f\"({ix})\"\n if ix in r_legs\n else BLUE + ix\n for ix in p_inds\n ).replace(f\"){GREEN}(\", \"\")\n la = \"\".join(\n PINK + f\"[{ix}]\"\n if (ix in p_legs) and (ix in r_legs)\n else RED + f\"[{ix}]\"\n if ix in r_legs\n else BLUE + ix\n for ix in l_inds\n ).replace(f\"]{RED}[\", \"\")\n ra = \"\".join(\n PINK + f\"[{ix}]\"\n if (ix in p_legs) and (ix in l_legs)\n else RED + f\"[{ix}]\"\n if ix in l_legs\n else GREEN + ix\n for ix in r_inds\n ).replace(f\"]{RED}[\", \"\")\n\n entries.append(\n (\n p,\n f\"{GREY}({i}) cost: {RESET}{p_flops:.1e} \"\n f\"{GREY}widths: {RESET}{l_sz:.1f},{r_sz:.1f}->{p_sz:.1f} \"\n f\"{GREY}type: {RESET}{type_msg}\\n\"\n f\"{GREY}inputs: {la},{ra}{RESET}->\\n\"\n f\"{GREY}output: {pa}\\n\",\n )\n )\n\n if sort == \"flops\":\n entries.sort(key=lambda x: self.get_flops(x[0]), reverse=True)\n if sort == \"size\":\n entries.sort(key=lambda x: self.get_size(x[0]), reverse=True)\n\n entries.append((None, f\"{RESET}\"))\n\n o = \"\\n\".join(entry for _, entry in entries)\n print(o)\n\n # --------------------- Performing the Contraction ---------------------- #\n\n def get_contractor(\n self,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n implementation=None,\n autojit=False,\n ):\n \"\"\"Get a reusable function which performs the contraction corresponding\n to this tree, cached.\n\n Parameters\n ----------\n tree : ContractionTree\n The contraction tree.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`, the order in which\n to perform the pairwise contractions given by the tree.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n strip_exponent : bool, optional\n If ``True``, the function will strip the exponent from the output\n array and return it separately.\n implementation : str or tuple[callable, callable], optional\n What library to use to actually perform the contractions. Options\n are:\n\n - None: let cotengra choose.\n - \"autoray\": dispatch with autoray, using the ``tensordot`` and\n ``einsum`` implementation of the backend.\n - \"cotengra\": use the ``tensordot`` and ``einsum`` implementation\n of cotengra, which is based on batch matrix multiplication. This\n is faster for some backends like numpy, and also enables\n libraries which don't yet provide ``tensordot`` and ``einsum`` to\n be used.\n - \"cuquantum\": use the cuquantum library to perform the whole\n contraction (not just individual contractions).\n - tuple[callable, callable]: manually supply the ``tensordot`` and\n ``einsum`` implementations to use.\n\n autojit : bool, optional\n If ``True``, use :func:`autoray.autojit` to compile the contraction\n function.\n\n Returns\n -------\n fn : callable\n The contraction function, with signature ``fn(*arrays)``.\n \"\"\"\n key = (\n autojit,\n order,\n prefer_einsum,\n strip_exponent,\n implementation,\n )\n try:\n fn = self.contraction_cores[key]\n except KeyError:\n fn = self.contraction_cores[key] = make_contractor(\n tree=self,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n implementation=implementation,\n autojit=autojit,\n )\n\n return fn\n\n def contract_core(\n self,\n arrays,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n check_zero=False,\n backend=None,\n implementation=None,\n autojit=False,\n progbar=False,\n ):\n \"\"\"Contract ``arrays`` with this tree. The order of the axes and\n output is assumed to be that of ``tree.inputs`` and ``tree.output``,\n but with sliced indices removed. This functon contracts the core tree\n and thus if indices have been sliced the arrays supplied need to be\n sliced as well.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n backend : str, optional\n What library to use for ``einsum`` and ``transpose``, will be\n automatically inferred from the arrays if not given.\n autojit : bool, optional\n Whether to use ``autoray.autojit`` to jit compile the expression.\n progbar : bool, optional\n Show progress through the contraction.\n \"\"\"\n fn = self.get_contractor(\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent is not False,\n implementation=implementation,\n autojit=autojit,\n )\n result = fn(\n *arrays,\n check_zero=check_zero,\n backend=backend,\n progbar=progbar,\n )\n\n # handle exponent outside of potential jit\n if isinstance(strip_exponent, dict):\n result, exponent = result\n if \"exponent\" not in strip_exponent:\n # set the exponent (e.g. first slice)\n strip_exponent[\"exponent\"] = exponent\n else:\n # match the exponent (e.g. subsequent slices)\n target = strip_exponent[\"exponent\"]\n result = result * 10 ** (exponent - target)\n\n return result\n\n def slice_key(self, i, strides=None):\n \"\"\"Get the combination of sliced index values for overall slice ``i``.\n\n Parameters\n ----------\n i : int\n The overall slice index.\n\n Returns\n -------\n key : dict[str, int]\n The value each sliced index takes for slice ``i``.\n \"\"\"\n if strides is None:\n strides = get_slice_strides(self.sliced_inds)\n\n key = {}\n for (ind, info), stride in zip(self.sliced_inds.items(), strides):\n if info.project is None:\n key[ind] = i // stride\n i %= stride\n else:\n # size is 1 and i doesn't change\n key[ind] = info.project\n\n return key\n\n def slice_arrays(self, arrays, i):\n \"\"\"Take ``arrays`` and slice the relevant inputs according to\n ``tree.sliced_inds`` and the dynary representation of ``i``.\n \"\"\"\n temp_arrays = list(arrays)\n\n # e.g. {'a': 2, 'd': 7, 'z': 0}\n locations = self.slice_key(i)\n\n for c in self.sliced_inputs:\n # the indexing object, e.g. [:, :, 7, :, 2, :, :, 0]\n selector = tuple(\n locations.get(ix, slice(None)) for ix in self.inputs[c]\n )\n # re-insert the sliced array\n temp_arrays[c] = temp_arrays[c][selector]\n\n return temp_arrays\n\n def contract_slice(self, arrays, i, **kwargs):\n \"\"\"Get slices ``i`` of ``arrays`` and then contract them.\"\"\"\n return self.contract_core(self.slice_arrays(arrays, i), **kwargs)\n\n def gather_slices(self, slices, backend=None, progbar=False):\n \"\"\"Gather all the output contracted slices into a single full result.\n If none of the sliced indices appear in the output, then this is a\n simple sum - otherwise the slices need to be partially summed and\n partially stacked.\n \"\"\"\n if progbar:\n import tqdm\n\n slices = tqdm.tqdm(slices, total=self.multiplicity)\n\n output_pos = {\n ix: i for i, ix in enumerate(self.output) if ix in self.sliced_inds\n }\n\n if not output_pos:\n # we can just sum everything\n return functools.reduce(operator.add, slices)\n\n # first we sum over non-output sliced indices\n chunks = {}\n for i, s in enumerate(slices):\n key_slice = self.slice_key(i)\n key = tuple(key_slice[ix] for ix in output_pos)\n try:\n chunks[key] = chunks[key] + s\n except KeyError:\n chunks[key] = s\n\n # then we stack these summed chunks over output sliced indices\n def recursively_stack_chunks(loc, rem):\n if not rem:\n return chunks[loc]\n arrays = [\n recursively_stack_chunks(loc + (d,), rem[1:])\n for d in self.sliced_inds[rem[0]].sliced_range\n ]\n axes = output_pos[rem[0]] - len(loc)\n return do(\"stack\", arrays, axes, like=backend)\n\n return recursively_stack_chunks((), tuple(output_pos))\n\n def gen_output_chunks(\n self, arrays, with_key=False, progbar=False, **contract_opts\n ):\n \"\"\"Generate each output chunk of the contraction - i.e. take care of\n summing internally sliced indices only first. This assumes that the\n ``sliced_inds`` are sorted by whether they appear in the output or not\n (the default order). Useful for performing some kind of reduction over\n the final tensor object like ``fn(x).sum()`` without constructing the\n entire thing.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n with_key : bool, optional\n Whether to yield the output index configuration key along with the\n chunk.\n progbar : bool, optional\n Show progress through the contraction chunks.\n\n Yields\n ------\n chunk : array\n A chunk of the contracted result.\n key : dict[str, int]\n The value each sliced output index takes for this chunk.\n \"\"\"\n # consecutive slices of size ``stepsize`` all belong to the same output\n # block because the sliced indices are sorted output first\n stepsize = prod(\n si.size for si in self.sliced_inds.values() if si.inner\n )\n\n if progbar:\n import tqdm\n\n it = tqdm.trange(self.nslices // stepsize)\n else:\n it = range(self.nslices // stepsize)\n\n for o in it:\n chunk = self.contract_slice(arrays, o * stepsize, **contract_opts)\n\n if with_key:\n output_key = {\n ix: x\n for ix, x in self.slice_key(o * stepsize).items()\n if ix in self.output\n }\n\n for j in range(1, stepsize):\n i = o * stepsize + j\n chunk = chunk + self.contract_slice(arrays, i, **contract_opts)\n\n if with_key:\n yield chunk, output_key\n else:\n yield chunk\n\n def contract(\n self,\n arrays,\n order=None,\n prefer_einsum=False,\n strip_exponent=False,\n check_zero=False,\n backend=None,\n implementation=\"auto\",\n autojit=False,\n progbar=False,\n ):\n \"\"\"Contract ``arrays`` with this tree. This function takes *unsliced*\n arrays and handles the slicing, contractions and gathering. The order\n of the axes and output is assumed to match that of ``tree.inputs`` and\n ``tree.output``.\n\n Parameters\n ----------\n arrays : sequence of array\n The arrays to contract.\n order : str or callable, optional\n Supplied to :meth:`ContractionTree.traverse`.\n prefer_einsum : bool, optional\n Prefer to use ``einsum`` for pairwise contractions, even if\n ``tensordot`` can perform the contraction.\n strip_exponent : bool, optional\n If ``True``, eagerly strip the exponent (in log10) from\n intermediate tensors to control numerical problems from leaving the\n range of the datatype. This method then returns the scaled\n 'mantissa' output array and the exponent separately.\n check_zero : bool, optional\n If ``True``, when ``strip_exponent=True``, explicitly check for\n zero-valued intermediates that would otherwise produce ``nan``,\n instead terminating early if encounteredand returning\n ``(0.0, 0.0)``.\n backend : str, optional\n What library to use for ``tensordot``, ``einsum`` and\n ``transpose``, it will be automatically inferred from the input\n arrays if not given.\n autojit : bool, optional\n Whether to use the 'autojit' feature of `autoray` to compile the\n contraction expression.\n progbar : bool, optional\n Whether to show a progress bar.\n\n Returns\n -------\n output : array\n The contracted output, it will be scaled if\n ``strip_exponent==True``.\n exponent : float\n The exponent of the output in base 10, returned only if\n ``strip_exponent==True``.\n\n See Also\n --------\n contract_core, contract_slice, slice_arrays, gather_slices\n \"\"\"\n if isinstance(self.inputs[0], set) or isinstance(self.output, set):\n warnings.warn(\"The inputs or output of this tree are not ordered.\")\n\n if not self.sliced_inds:\n return self.contract_core(\n arrays,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n check_zero=check_zero,\n backend=backend,\n implementation=implementation,\n autojit=autojit,\n progbar=progbar,\n )\n\n if strip_exponent:\n # first slice will set the exponent for others to match\n strip_exponent = {}\n\n slices = (\n self.contract_slice(\n arrays,\n i,\n order=order,\n prefer_einsum=prefer_einsum,\n strip_exponent=strip_exponent,\n check_zero=check_zero,\n backend=backend,\n implementation=implementation,\n autojit=autojit,\n )\n for i in range(self.multiplicity)\n )\n\n result = self.gather_slices(slices, backend=backend, progbar=progbar)\n\n if strip_exponent:\n return result, strip_exponent[\"exponent\"]\n\n return result\n\n def contract_mpi(self, arrays, comm=None, root=None, **kwargs):\n \"\"\"Contract the slices of this tree and sum them in parallel -\n *assuming* we are already running under MPI.\n\n Parameters\n ----------\n arrays : sequence of array\n The input (unsliced arrays)\n comm : None or mpi4py communicator\n Defaults to ``mpi4py.MPI.COMM_WORLD`` if not given.\n root : None or int, optional\n If ``root=None``, an ``Allreduce`` will be performed such that\n every process has the resulting tensor, else if an integer e.g.\n ``root=0``, the result will be exclusively gathered to that\n process using ``Reduce``, with every other process returning\n ``None``.\n kwargs\n Supplied to :meth:`~cotengra.ContractionTree.contract_slice`.\n \"\"\"\n if not set(self.sliced_inds).isdisjoint(set(self.output)):\n raise NotImplementedError(\n \"Sliced and output indices overlap - currently only a simple \"\n \"sum of result slices is supported currently.\"\n )\n\n if comm is None:\n from mpi4py import MPI\n\n comm = MPI.COMM_WORLD\n\n if self.multiplicity < comm.size:\n raise ValueError(\n f\"Need to have more slices than MPI processes, but have \"\n f\"{self.multiplicity} and {comm.size} respectively.\"\n )\n\n # round robin compute each slice, eagerly summing\n result_i = None\n for i in range(comm.rank, self.multiplicity, comm.size):\n # note: fortran ordering is needed for the MPI reduce\n x = do(\"asfortranarray\", self.contract_slice(arrays, i, **kwargs))\n if result_i is None:\n result_i = x\n else:\n result_i += x\n\n if root is None:\n # everyone gets the summed result\n result = do(\"empty_like\", result_i)\n comm.Allreduce(result_i, result)\n return result\n\n # else we only sum reduce the result to process ``root``\n if comm.rank == root:\n result = do(\"empty_like\", result_i)\n else:\n result = None\n comm.Reduce(result_i, result, root=root)\n return result\n\n plot_ring = plot_tree_ring\n plot_tent = plot_tree_tent\n plot_span = plot_tree_span\n plot_rubberband = plot_tree_rubberband\n plot_contractions = plot_contractions\n plot_contractions_alt = plot_contractions_alt\n\n @functools.wraps(plot_hypergraph)\n def plot_hypergraph(self, **kwargs):\n hg = self.get_hypergraph(accel=False)\n hg.plot(**kwargs)\n\n def __repr__(self):\n s = \"<{}(N={}, branches={}, complete={})>\"\n return s.format(\n self.__class__.__name__,\n self.N,\n len(self.children),\n self.is_complete(),\n )\n\n\ndef _reconfigure_tree(tree, *args, **kwargs):\n return tree.subtree_reconfigure(*args, **kwargs)\n\n\ndef _slice_and_reconfigure_tree(tree, *args, **kwargs):\n return tree.slice_and_reconfigure(*args, **kwargs)\n\n\ndef _get_tree_info(tree):\n stats = tree.contract_stats()\n stats[\"sliced_ind_set\"] = frozenset(tree.sliced_inds)\n return stats\n\n\ndef _describe_tree(tree):\n stats = tree.contract_stats()\n return (\n f\"log2[SIZE]: {math.log2(stats['size']):.2f} \"\n f\"log10[FLOPs]: {math.log10(stats['flops']):.2f}\"\n )\n\n\nclass ContractionTreeCompressed(ContractionTree):\n \"\"\"A contraction tree for compressed contractions. Currently the only\n difference is that this defaults to the 'surface' traversal ordering.\n \"\"\"\n\n @classmethod\n def from_path(\n cls,\n inputs,\n output,\n size_dict,\n *,\n path=None,\n ssa_path=None,\n check=False,\n **kwargs,\n ):\n \"\"\"Create a (completed) ``ContractionTreeCompressed`` from the usual\n inputs plus a standard contraction path or 'ssa_path' - you need to\n supply one. This also set the default 'surface' traversal ordering to\n be the initial path.\n \"\"\"\n if int(path is None) + int(ssa_path is None) != 1:\n raise ValueError(\n \"Exactly one of ``path`` or ``ssa_path`` must be \" \"supplied.\"\n )\n\n if path is not None:\n from .pathfinders.path_basic import linear_to_ssa\n\n ssa_path = linear_to_ssa(path)\n\n tree = cls(inputs, output, size_dict, **kwargs)\n terms = list(tree.gen_leaves())\n\n for p in ssa_path:\n merge = [terms[i] for i in p]\n terms.append(tree.contract_nodes(merge, check=check))\n\n tree.set_surface_order_from_path(ssa_path)\n\n return tree\n\n def get_default_order(self):\n return \"surface_order\"\n\n total_flops = ContractionTree.total_flops_compressed\n total_write = ContractionTree.total_write_compressed\n total_cost = ContractionTree.total_cost_compressed\n max_size = ContractionTree.max_size_compressed\n peak_size = ContractionTree.peak_size_compressed\n contraction_cost = ContractionTree.contraction_cost_compressed\n contraction_width = ContractionTree.contraction_width_compressed\n\n total_flops_exact = ContractionTree.total_flops\n total_write_exact = ContractionTree.total_write\n total_cost_exact = ContractionTree.total_cost\n max_size_exact = ContractionTree.max_size\n peak_size_exact = ContractionTree.peak_size\n\n def get_contractor(self, *_, **__):\n raise NotImplementedError(\n \"`cotengra` doesn't implement compressed contraction itself. \"\n \"If you want to use compressed contractions, you need to use \"\n \"`quimb` and the `TensorNetwork.contract_compressed` method, \"\n \"with e.g. `optimize=tree.get_path()`.\"\n )\n\n\nclass ContractionTreeMulti(ContractionTree):\n def set_varmults(self, varmults):\n self._varmults = varmults\n\n def get_varmults(self):\n return self._varmults\n\n def set_numconfigs(self, numconfigs):\n self._numconfigs = numconfigs\n\n def get_numconfigs(self):\n return self._numconfigs\n\n\nclass PartitionTreeBuilder:\n \"\"\"Function wrapper that takes a function that partitions graphs and\n uses it to build a contraction tree. ``partition_fn`` should have\n signature:\n\n def partition_fn(inputs, output, size_dict,\n weight_nodes, weight_edges, **kwargs):\n ...\n return membership\n\n Where ``weight_nodes`` and ``weight_edges`` decsribe how to weight the\n nodes and edges of the graph respectively and ``membership`` should be a\n list of integers of length ``len(inputs)`` labelling which partition\n each input node should be put it.\n \"\"\"\n\n def __init__(self, partition_fn):\n self.partition_fn = partition_fn\n\n def build_divide(\n self,\n inputs,\n output,\n size_dict,\n random_strength=0.01,\n cutoff=10,\n parts=2,\n parts_decay=0.5,\n sub_optimize=\"auto\",\n super_optimize=\"auto-hq\",\n check=False,\n **partition_opts,\n ):\n tree = ContractionTree(inputs, output, size_dict, track_childless=True)\n rand_size_dict = jitter_dict(size_dict, random_strength)\n\n dynamic_imbalance = (\"imbalance\" in partition_opts) and (\n \"imbalance_decay\" in partition_opts\n )\n if dynamic_imbalance:\n imbalance = partition_opts.pop(\"imbalance\")\n imbalance_decay = partition_opts.pop(\"imbalance_decay\")\n else:\n imbalance = imbalance_decay = None\n\n dynamic_fix = partition_opts.get(\"fix_output_nodes\", None) == \"auto\"\n\n while tree.childless:\n tree_node = next(iter(tree.childless))\n subgraph = tuple(tree_node)\n subsize = len(subgraph)\n\n # skip straight to better method\n if subsize <= cutoff:\n tree.contract_nodes(\n [node_from_single(x) for x in subgraph],\n optimize=sub_optimize,\n check=check,\n )\n continue\n\n # relative subgraph size\n s = subsize / tree.N\n\n # let the target number of communities depend on subgraph size\n parts_s = max(int(s**parts_decay * parts), 2)\n\n # let the imbalance either rise or fall\n if dynamic_imbalance:\n if imbalance_decay >= 0:\n imbalance_s = s**imbalance_decay * imbalance\n else:\n imbalance_s = 1 - s**-imbalance_decay * (1 - imbalance)\n partition_opts[\"imbalance\"] = imbalance_s\n\n if dynamic_fix:\n # for the top level subtree (s==1.0) we partition the outputs\n # nodes first into their own bi-partition\n parts_s = 2\n partition_opts[\"fix_output_nodes\"] = s == 1.0\n\n # partition! get community membership list e.g.\n # [0, 0, 1, 0, 1, 0, 0, 2, 2, ...]\n inputs = tuple(map(tuple, tree.node_to_terms(subgraph)))\n output = tuple(tree.get_legs(tree_node))\n membership = self.partition_fn(\n inputs,\n output,\n rand_size_dict,\n parts=parts_s,\n **partition_opts,\n )\n\n # divide subgraph up e.g. if we enumerate the subgraph index sets\n # (0, 1, 2, 3, 4, 5, 6, 7, 8, ...) ->\n # ({0, 1, 3, 5, 6}, {2, 4}, {7, 8})\n new_subgs = tuple(\n map(node_from_seq, separate(subgraph, membership))\n )\n\n if len(new_subgs) == 1:\n # no communities found - contract all remaining\n tree.contract_nodes(\n tuple(map(node_from_single, subgraph)),\n optimize=sub_optimize,\n check=check,\n )\n continue\n\n # update tree structure with newly contracted subgraphs\n tree.contract_nodes(\n new_subgs, optimize=super_optimize, check=check\n )\n\n if check:\n assert tree.is_complete()\n\n return tree\n\n def build_agglom(\n self,\n inputs,\n output,\n size_dict,\n random_strength=0.01,\n groupsize=4,\n check=False,\n sub_optimize=\"greedy\",\n **partition_opts,\n ):\n tree = ContractionTree(inputs, output, size_dict, track_childless=True)\n rand_size_dict = jitter_dict(size_dict, random_strength)\n leaves = tuple(tree.gen_leaves())\n for node in leaves:\n tree._add_node(node, check=check)\n output = tuple(tree.output)\n\n while len(leaves) > groupsize:\n parts = max(2, len(leaves) // groupsize)\n\n inputs = [tuple(tree.get_legs(node)) for node in leaves]\n membership = self.partition_fn(\n inputs,\n output,\n rand_size_dict,\n parts=parts,\n **partition_opts,\n )\n leaves = [\n tree.contract_nodes(group, check=check, optimize=sub_optimize)\n for group in separate(leaves, membership)\n ]\n\n if len(leaves) > 1:\n tree.contract_nodes(leaves, check=check, optimize=sub_optimize)\n\n if check:\n assert tree.is_complete()\n\n return tree\n\n def trial_fn(self, inputs, output, size_dict, **partition_opts):\n return self.build_divide(inputs, output, size_dict, **partition_opts)\n\n def trial_fn_agglom(self, inputs, output, size_dict, **partition_opts):\n return self.build_agglom(inputs, output, size_dict, **partition_opts)\n\n\ndef jitter(x, strength):\n return x * (1 + strength * random.expovariate(1.0))\n\n\ndef jitter_dict(d, strength):\n return {k: jitter(v, strength) for k, v in d.items()}\n\n\ndef separate(xs, blocks):\n \"\"\"Partition ``xs`` into ``n`` different list based on the corresponding\n labels in ``blocks``.\n \"\"\"\n sorter = collections.defaultdict(list)\n for x, b in zip(xs, blocks):\n sorter[b].append(x)\n x_b = list(sorter.items())\n x_b.sort()\n return [x[1] for x in x_b]\n","repo_name":"jcmgray/cotengra","sub_path":"cotengra/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":113267,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"81"} +{"seq_id":"39552567906","text":"\"\"\"\nDevelopment Server\n\"\"\"\nfrom flask_cors import CORS\n\nfrom app import app\n\nif __name__ == '__main__':\n cors = CORS(app, resources={r\"/api/*\": {\"origins\": \"*\"}})\n\n app.run(\n debug=app.config['DEBUG'],\n host=app.config['LISTEN_HOST_DEV'],\n port=app.config['LISTEN_PORT_DEV']\n )\n","repo_name":"unbyte/we-are-fine","sub_path":"run-development.py","file_name":"run-development.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"} +{"seq_id":"21883706238","text":"import random\nimport asyncio\nfrom test_framework.util import waitForAsync, assert_equal, assert_raises_async\nfrom test_framework.test_framework import BitcoinTestFramework\nfrom test_framework.loginit import logging\nfrom test_framework.electrumutil import compare, bitcoind_electrum_args, \\\n address_to_scripthash, sync_electrum_height, ElectrumConnection\nfrom test_framework.nodemessages import COIN, CTransaction, ToHex, CTxIn, COutPoint\nfrom test_framework.connectrum.exc import ElectrumErrorResponse\n\n\nclass ElectrumBlockchainAddress(BitcoinTestFramework):\n \"\"\"\n Basic blockchain.address.* testing, mostly to check that the function\n handle an address correctly. The blockchain.scripthash.* equivalents are\n more thoroughly tested.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.setup_clean_chain = True\n self.num_nodes = 1\n self.extra_args = [bitcoind_electrum_args()]\n\n def run_test(self):\n n = self.nodes[0]\n\n n.generate(200)\n\n async def async_tests():\n await self.test_get_frist_use(n)\n cli = ElectrumConnection()\n await cli.connect()\n await self.test_invalid_args(cli)\n await self.test_get_balance(n, cli)\n await self.test_get_history(n, cli)\n await self.test_list_unspent(n, cli)\n loop = asyncio.get_event_loop()\n loop.run_until_complete(async_tests())\n\n def setup_network(self, dummy = None):\n self.nodes = self.setup_nodes()\n\n async def test_invalid_args(self, cli):\n from test_framework.connectrum.exc import ElectrumErrorResponse\n error_code = \"-32602\"\n\n hash_param_methods = (\n \"blockchain.address.get_balance\",\n \"blockchain.address.get_history\",\n \"blockchain.address.listunspent\")\n\n for method in hash_param_methods:\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n method, \"invalidaddress\")\n\n async def test_get_balance(self, n, cli):\n addr = n.getnewaddress()\n balance = 11.42\n txhash = n.sendtoaddress(addr, balance)\n\n async def check_address(address, unconfirmed = 0, confirmed = 0):\n res = await cli.call(\"blockchain.address.get_balance\", addr)\n\n return res[\"unconfirmed\"] == unconfirmed * COIN \\\n and res[\"confirmed\"] == confirmed * COIN\n\n await waitForAsync(10, lambda: check_address(addr, unconfirmed = balance))\n n.generate(1)\n await waitForAsync(10, lambda: check_address(addr, confirmed = balance))\n\n async def sendtoaddr(self, n, cli, addr, amount):\n utxo = n.listunspent().pop()\n inputs = [{\n \"txid\": utxo[\"txid\"],\n \"vout\": utxo[\"vout\"]}]\n outputs = {\n addr: utxo['amount'],\n }\n tx = n.createrawtransaction(inputs, outputs)\n signed = n.signrawtransaction(tx)\n txid = await cli.call(\"blockchain.transaction.broadcast\", signed['hex'])\n return txid\n\n async def test_get_frist_use(self, n):\n cli = ElectrumConnection()\n await cli.connect()\n\n # New address that has never received coins. Should return an error.\n addr = n.getnewaddress()\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n \"blockchain.address.get_first_use\", addr)\n await assert_raises_async(\n ElectrumErrorResponse,\n cli.call,\n \"blockchain.scripthash.get_first_use\", address_to_scripthash(addr))\n\n # Send coin to the new address\n txid = await self.sendtoaddr(n, cli, addr, 1)\n\n # Wait for electrum server to see the utxo.\n async def wait_for_utxo():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n if len(utxo) == 1:\n return utxo\n return None\n utxo = await waitForAsync(10, wait_for_utxo)\n\n # Observe that get_first_use returns the tx when it's in the mempool\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n res2 = await cli.call(\"blockchain.scripthash.get_first_use\",\n address_to_scripthash(addr))\n assert_equal(res, res2)\n assert_equal(\n \"0000000000000000000000000000000000000000000000000000000000000000\",\n res['block_hash'])\n assert_equal(0, res['height'])\n assert_equal(txid, res['tx_hash'])\n\n # Confirm tx, observe that block height and gets set.\n n.generate(1)\n sync_electrum_height(n)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n res2 = await cli.call(\"blockchain.scripthash.get_first_use\",\n address_to_scripthash(addr))\n assert_equal(res, res2)\n assert_equal(n.getbestblockhash(), res['block_hash'])\n assert_equal(n.getblockcount(), res['height'])\n assert_equal(txid, res['tx_hash'])\n\n # Send another tx, observe that the first one is till returned.\n txid2 = await self.sendtoaddr(n, cli, addr, 2)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n assert_equal(txid, res['tx_hash'])\n\n # Also when the second tx is confirmed, the first is returned.\n n.generate(1)\n sync_electrum_height(n)\n res = await cli.call(\"blockchain.address.get_first_use\", addr)\n assert_equal(txid, res['tx_hash'])\n\n async def test_list_unspent(self, n, cli):\n addr = n.getnewaddress()\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n assert_equal(0, len(utxo))\n\n txid = n.sendtoaddress(addr, 21)\n async def fetch_utxo():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n if len(utxo) > 0:\n return utxo\n return None\n\n utxo = await waitForAsync(10, fetch_utxo)\n assert_equal(1, len(utxo))\n\n assert_equal(0, utxo[0]['height'])\n assert_equal(txid, utxo[0]['tx_hash'])\n assert_equal(21 * COIN, utxo[0]['value'])\n assert(utxo[0]['tx_pos'] in [0, 1])\n\n n.generate(1)\n async def wait_for_confheight():\n utxo = await cli.call(\"blockchain.address.listunspent\", addr)\n return len(utxo) == 1 and utxo[0]['height'] == n.getblockcount()\n await waitForAsync(10, wait_for_confheight)\n\n\n async def test_get_history(self, n, cli):\n addr = n.getnewaddress()\n txid = n.sendtoaddress(addr, 11)\n async def fetch_history():\n h = await cli.call(\"blockchain.address.get_history\", addr)\n if len(h) > 0:\n return h\n return None\n history = await waitForAsync(10, fetch_history)\n assert_equal(1, len(history))\n\n UNCONFIRMED_HEIGHT = 0\n assert_equal(UNCONFIRMED_HEIGHT, history[0]['height'])\n assert_equal(txid, history[0]['tx_hash'])\n\n n.generate(1)\n async def wait_for_confheight():\n h = await cli.call(\"blockchain.address.get_history\", addr)\n return len(h) == 1 and h[0]['height'] == n.getblockcount()\n await waitForAsync(10, wait_for_confheight)\n\nif __name__ == '__main__':\n ElectrumBlockchainAddress().main()\n","repo_name":"BitcoinUnlimited/BitcoinUnlimited","sub_path":"qa/rpc-tests/electrum_blockchain_address.py","file_name":"electrum_blockchain_address.py","file_ext":"py","file_size_in_byte":7306,"program_lang":"python","lang":"en","doc_type":"code","stars":453,"dataset":"github-code","pt":"81"} +{"seq_id":"23102486136","text":"\"\"\"\nUm funcionário de uma empresa recebe aumento salarial anualmente: Sabe-se que:\nEsse funcionário foi contratado em 1995, com salário inicial de R$ 1.000,00;\nEm 1996 recebeu aumento de 1,5% sobre seu salário inicial;\nA partir de 1997 (inclusive), os aumentos salariais sempre correspondem ao dobro do percentual do ano anterior.\nFaça um programa que determine o salário atual desse funcionário.\nApós concluir isto, altere o programa permitindo que o usuário digite o salário inicial do funcionário.\n\"\"\"\n# SOLUÇÃO 01\n\nano_entrada = 1996\naumento = 0.015\nsalario_inicial = 1_000\nsalario_atual = salario_inicial\nwhile True:\n try:\n ano_atual = int(input('Digite o ano atual: '))\n if ano_atual > 1996:\n break\n except ValueError:\n print('Você digitou um valor inválido, tente novamente.')\n\nfor _ in range(1997, ano_atual + 1, 1):\n salario_atual = salario_atual * (1 + aumento)\n aumento = aumento * 2\n\nprint(f'O salário atual do funcionário é de {\"%.2f\" % salario_atual} RS')\n","repo_name":"SOLRAC32/Exercicios_resolvidos_pythonbrasil","sub_path":"3 - Estruturas de Repetição/EXERCICIO 38.py","file_name":"EXERCICIO 38.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"24345475961","text":"#！/usr/bin/python\n#此句用于指定脚本执行的py版本\n# -*- coding: UTF-8 -*-\nimport time\nimport tornado.ioloop\nimport tornado.web\nfrom mysqldb import *\nfrom func import *\n\nclass MainHandler(tornado.web.RequestHandler):\n @gen.coroutine\n def get(self):\n # gen.sleep(10)\n self.write(\"Hello, world\")\n\n#登录接口\nclass login(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n user_name = self.get_argument('user_name')\n password = self.get_argument('password')\n\n sql = \"SELECT id,agent_phone,agent_level_id FROM wz_agent WHERE agent_phone=%s AND agent_password=%s\"\n try:\n datas = yield executesql(sql,(user_name,password))\n if not datas:\n return returnJson(self, 0, msg='账号密码错误')\n print(user_name, password, datas)\n token = create_Token(datas[0]['id'], datas[0]['agent_phone'], datas[0]['agent_level_id'])\n endtime = int(time.time()) + 2000 # 截止日期\n adddata = {'user_id': datas[0]['id'], 'token': token, 'expires_in': endtime}\n print(adddata)\n add('wz_token', adddata)\n except:\n return returnJson(self, 0, msg='用户不存在')\n\n\n\n # print(token)\n returnJson(self,1,data={'token':token})\n\n#获取商户列表接口\nclass getMerchantList(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n agent_id = self.get_argument('agent_id')\n sql = \"SELECT id,mer_name from wz_merchant WHERE mer_parent_agent = %s\"\n try:\n datas = yield executesql(sql,agent_id)\n if not datas:\n return returnJson(self, 0, msg='该代理还没有商户列表')\n except:\n return returnJson(self, 0, msg='出现异常错误')\n print(datas)\n dic = {'merchant_list':datas}\n return returnJson(self, 1, data=dic)\n\n#获取交易流水接口\nclass getOrderList(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n user_id = self.get_argument('user_id') #用户ID\n user_type = self.get_argument('user_type') #用户类别 1代理商 2商户\n trans_time = self.get_argument('trans_time') #交易时间 2018-\n order_type = self.get_argument('order_type') #排序方式 1升序，2降序\n if order_type == '1':\n order_type = 'asc'\n else:\n order_type = 'desc'\n if user_type == '2':\n chip_sql = \"WHERE merchant_id = %s AND DATE_FORMAT(time,'%%Y-%%m-%%d') = %s order by time {}\".format(order_type)\n sql = \"SELECT id,order_no from wz_order {} \".format(chip_sql)\n print(sql)\n try:\n datas = yield executesql(sql,(user_id,'2018-04-03'))\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='该代理还没有交易流水记录')\n except:\n return returnJson(self, 0, msg='出现异常错误')\n dic = {'order_list': datas}\n # print(datas)\n\n return returnJson(self, 1, data=dic)\n\n# 添加商户接口\nclass addMerchant(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header('Content-type', 'x-www-form-urlencoded;charset=utf-8')\n\n @gen.coroutine\n def post(self):\n data = self.request.arguments\n try:\n datas = yield add('wz_merchant',data)\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='添加失败')\n except:\n print(111)\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n\n# 获取商户资料接口\nclass getMerchnatInfoById(tornado.web.RequestHandler):\n def set_default_headers(self):\n self.set_header('Content-type', 'x-www-form-urlencoded;charset=utf-8')\n\n @gen.coroutine\n def post(self):\n merchant_id = self.get_argument('merchant_id') # 商户ID\n\n try:\n where = [\"WHERE id = %s\",[merchant_id]]\n datas = yield query('wz_merchant','*',where)\n print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass updateMerchantById(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n merchant_id = self.get_argument('merchant_id') # 商户ID\n merchant_logo = self.get_argument('merchant_logo') # 商户logo\n merchant_name = self.get_argument('merchant_name')\n # print(merchant_logo)\n try:\n where = [\"WHERE id = %s\",[merchant_id]]\n res = {'mer_name':merchant_name,'mer_logo': merchant_logo}\n datas = yield update('wz_merchant',res,where)\n # print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass resetPassword(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n userid = self.get_argument('userid') # 用户ID\n phone = self.get_argument('phone') # 手机号\n # old_password = self.get_argument('old_password') #原密码\n new_password = self.get_argument('new_password') #新密码\n try:\n where = [\"WHERE id = %s and agent_phone = %s\",[userid,phone]]\n res = {'agent_password':new_password}\n datas = yield update('wz_agent',res,where)\n # print(datas)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取商户资料接口\nclass countMoneyById(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n agent_id = self.get_argument('agent_id') # 代理商ID\n startDate = self.get_argument('startDate') # 起时间\n endDate = self.get_argument('endDate') # 终时间\n\n try:\n where = [\"WHERE agent_id = %s and (time > %s and time < %s)\",[agent_id,startDate,endDate]]\n datas = yield query('wz_agent_profit','*',where)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\n# 获取提现记录接口\nclass getTradeHistory(tornado.web.RequestHandler):\n @gen.coroutine\n def post(self):\n\n agent_id = self.get_argument('agent_id') # 代理商ID\n trade_type = self.get_argument('trade_type') # 交易类型(昝定义 1正在提现 2提现完成)\n startDate = self.get_argument('startDate') # 起时间\n endDate = self.get_argument('endDate') # 终时间\n\n try:\n where = [\"WHERE agent_id = %s and status = %s and (pub_time > %s and pub_time < %s)\",[agent_id,trade_type,startDate,endDate]]\n datas = yield query('wz_agent_tx','*',where)\n if not datas:\n return returnJson(self, 0, msg='获取失败，请重试')\n except:\n return returnJson(self, 0, msg='出现异常错误，请重试')\n\n dic = {'merchant_info': datas}\n return returnJson(self, 1, data=dic)\n\napplication = tornado.web.Application([\n\n (r\"/getTradeHistory\", getTradeHistory),\n (r\"/countMoneyById\", countMoneyById),\n (r\"/resetPassword\", resetPassword),\n (r\"/updateMerchantById\", updateMerchantById),\n (r\"/getMerchnatInfoById\", getMerchnatInfoById),\n (r\"/addMerchant\", addMerchant),\n (r\"/getOrderList\", getOrderList),\n (r\"/getMerchantList\", getMerchantList),\n (r\"/login\", login),\n (r\"/\", MainHandler),\n])\napplication.add_handlers(r\"^(www/.)?a/.com$\", [(r\"/\", MainHandler)])\nif __name__ == \"__main__\":\n application.listen(8000)\n # application.listen(8001)\n # application.listen(8002)\n # application.listen(8003)\n tornado.ioloop.IOLoop.instance().start()","repo_name":"SmTime/Bankapi","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":8545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"} +{"seq_id":"2727637969","text":"import sys\nfrom os import listdir\nfrom os.path import isfile, join\n\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.stats.power import TTestIndPower\n\nfrom utils.performance_utils import *\nfrom utils.vis_utils import plot_rewards, plot_boxplot\nfrom utils.performance_utils import cohend, welch_ttest\n\ndef avg_rwd_last_t_episodes(rewards, t=100):\n\treturn np.mean(rewards[:, -t:])# - np.var(rewards[:, -t:])\n\ndef preprocess_np_arrays(rewards):\n\treturn [np.stack(r, axis=0) for r in rewards]\n\ndef max_streaks(rewards, threshold, streak_size):\n\tstreaks = []\n\tfor rwd in rewards:\n\t\tmax_len = 0\n\t\tinit_streak = 0\n\t\tcurrent_len = 0\n\t\tcurrent_init = len(rwd) // 10\n\t\tfor i in range(current_init, len(rwd)):\n\t\t\tif rwd[i] >= threshold:\n\t\t\t\tcurrent_len += 1\n\t\t\tif current_len > max_len:\n\t\t\t\tmax_len = current_len\n\t\t\t\tinit_streak = current_init\n\t\t\tif rwd[i] < threshold:\n\t\t\t\tcurrent_init = i\n\t\t\t\tcurrent_len = 0\n\t\tif max_len < streak_size:\n\t\t\tstreaks.append([len(rwd), max_len])\n\t\telse:\n\t\t\tstreaks.append([init_streak, max_len])\n\treturn np.array(streaks)\n\ndef test(x, y, alpha=0.05):\n\tt, p = stats.ttest_ind(x, y, equal_var=False)\n\tdf = welch_ttest(np.array(x), np.array(y))\n\tdecision = \"aceptada\" if p >= alpha else \"rechazada\"\n\teffect = cohend(y, x)\n\t# analysis = TTestIndPower()\n\t# power = analysis.solve_power(effect, power=None, nobs1=len(x), ratio=1.0, alpha=alpha)\n\treturn [t, df, p, decision, effect]\n\ndef local_results_to_html(filename, test_table, plot_path):\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\tname = filename[:-4].replace(\".\", \"\")\n\tsplited_name = name.strip().split(\"_\")\n\ttext = \"\"\n\tdelta = float(splited_name[-1]) / (float(splited_name[11]) * float(splited_name[7]))\n\ttext += f\"

Ambiente: {splited_name[0]}

\"\n\ttext += f\"

Porcentaje de modificación: {splited_name[1]} {splited_name[2]}

\"\n\ttext += f\"

Tipo de estructura: {' '.join(splited_name[3:6])}

\"\n\ttext += f\"

N: {splited_name[7]}

\"\n\ttext += f\"

Simulaciones: {splited_name[9]}

\"\n\ttext += f\"

Episodios: {splited_name[11]}

\"\n\ttext += f\"

Delta: {delta}

\"\n\theader = [\"Algoritmo\", \"M\", \"SD\", \"t\", \"df\", \"p\", \"H0\", \"d de Cohen\"]\n\ttext += array_to_html_table(header, test_table)\n\t# text += f\"\\n\\n![plot]({plot_path})\"\n\ttext += f''\n\treturn text\n\ndef printable_list(row):\n\treturn [ f\"{cell:.2f}\" if type(cell) != str and type(cell) != int else cell for cell in row]\n\ndef push_into_storage(storage, num, i, episodes):\n\tlabels = [\"Q-learning\", \"Q-learning + estructura completa\", \\\n \"Q-learning + estructura parcial\", \"Q-learning + estructura incorrecta\"]\n\ts = len(episodes[:, 0])\n\tstorage[\"Algoritmo\"] = np.concatenate((storage[\"Algoritmo\"], [labels[i]]), axis=None)\n\tstorage[\"N\"] = np.concatenate((storage[\"N\"], [num]), axis=None)\n\tstorage[\"Episodio\"] = np.concatenate((storage[\"Episodio\"], np.mean(episodes[:, 0])), axis=None)\n\ndef push_into_global_table(args, row, general_table):\n\t\"\"\"\n\targs[0]: env\n\targs[1]: pmod\n\targs[2]: delta\n\targs[3]: num\n\targs[4]: param\n\t\"\"\"\n\tgeneral_table[\"Ambiente\"].append(args[0])\n\tgeneral_table[\"Parametro\"].append(args[1] if args[4] == \"pmod\" else args[2])\n\tgeneral_table[\"N\"].append(args[3])\n\tgeneral_table[\"Algoritmo\"].append(row[0])\n\tgeneral_table[\"M\"].append(row[1])\n\tgeneral_table[\"STD\"].append(row[2])\n\tgeneral_table[\"t\"].append(row[3])\n\tgeneral_table[\"df\"].append(row[4])\n\tgeneral_table[\"p\"].append(row[5])\n\tgeneral_table[\"H0\"].append(row[6])\n\tgeneral_table[\"Cohend\"].append(row[7])\n\ndef run_tests(results_storage, general_table, labels, threshold, rewards, streak_size, mod=50, *args):\n\t\"\"\"\n\targs[0]: env\n\targs[1]: pmod\n\targs[2]: delta\n\targs[3]: num\n\targs[4]: param\n\t\"\"\"\n\tnum = args[3]\n\tvanilla_q_streak = max_streaks(rewards[0], threshold, streak_size) * mod\n\tpush_into_storage(results_storage, num, 0, vanilla_q_streak)\n\ttable = []\n\tfor i in range(len(rewards)):\n\t\tcausal_streak = max_streaks(rewards[i], threshold, streak_size) * mod\n\t\tif i > 0:\n\t\t\tpush_into_storage(results_storage, num, i, causal_streak)\n\t\tprintable_row = printable_list([labels[i], np.mean(causal_streak[:, 0]), np.std(causal_streak[:, 0])]\\\n\t\t\t\t\t\t\t\t\t+ test(causal_streak[:, 0], vanilla_q_streak[:, 0]))\n\t\tpush_into_global_table(args, printable_row, general_table)\n\t\ttable.append(printable_row)\n\treturn table\n\ndef create_storage():\n\tresults_storage = dict(deterministic={}, stochastic={})\n\tresults_storage[\"deterministic\"] = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tresults_storage[\"stochastic\"] = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tfor env in results_storage:\n\t\tfor struct in results_storage[env]:\n\t\t\tresults_storage[env][struct] = dict(N=[], Algoritmo=[], Episodio=[])\n\treturn results_storage\n\ndef get_args(filename):\n\tsplited_name = filename.strip().split(\"_\")\n\tenv = splited_name[0]\n\tstruct = \"_\".join(splited_name[3:6])\n\tnum = splited_name[7]\n\tpmod = splited_name[2]\n\tdelta = float(splited_name[-1][:-4]) / (float(splited_name[11]) * float(splited_name[7]))\n\treturn struct, int(num), env, pmod, delta\n\ndef plot_mat(mat, base_dir_plots, name, mod):\n\tlabels = [\"Q-learning\", \"Q-learning + estructura completa\", \\\n \"Q-learning + estructura parcial\", \"Q-learning + estructura incorrecta\"]\n\tmean_vectors, std_dev_vectors = compute_mean_and_std_dev(mat)\n\tx_axis = mod * (np.arange(len(mean_vectors[0])))\n\tplot_path = join(base_dir_plots, name)\n\tplot_rewards(x_axis, mean_vectors, std_dev_vectors, labels, plot_path, filetype=\"png\")\n\treturn plot_path + \".png\"\n\ndef save_str_to_doc(filename, string):\n\twith open(filename, \"w\") as f:\n\t\tf.writelines(string)\n\ndef call_boxplotting(memory, struct, env):\n\tplot_path = join(base_dir_plots, f\"boxplot_{env}_{struct}\")\n\tdf = pd.DataFrame.from_dict(memory[env][struct])\n\tplot_boxplot(df, \"N\", \"Episodio\", \"Algoritmo\", plot_path)\n\treturn plot_path + \".png\"\n\ndef create_html_tables(global_table):\n\thtml_str = \"\"\n\tfor struct in global_table:\n\t\thtml_str += f\"

{struct}

\"\n\t\tdf = pd.DataFrame.from_dict(global_table[struct])\n\t\tdf = df.sort_values(by=[\"Ambiente\", \"N\", \"Parametro\", \"Algoritmo\"]).reset_index(drop=True)\n\t\thtml_str += df.to_html()\n\treturn html_str\n\n\ndef create_general_table():\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\ttable = dict(one_to_one={}, many_to_one={}, one_to_many={})\n\tfor struct in table:\n\t\ttable[struct] = dict(Ambiente=[], N=[], Parametro=[],\\\n\t\t\t\t\t\t\t\t\t\t\t\tAlgoritmo=[], M=[], STD=[],\\\n\t\t\t\t\t\t\t\t\t\t\t\tt=[], df=[], p=[], H0=[], Cohend=[])\n\treturn table\n\ndef plot_boxes(memory):\n\thtml_str = \"\"\n\tfor env in memory:\n\t\tfor struct in memory[env]:\n\t\t\thtml_str += f\"

{env} {struct}

\"\n\t\t\thtml_str += f''\n\treturn html_str\n\ndef process_dir(input_directory, output_file_name, plot_dir, labels, mod_tests, mod_plot, experiment_name, size=10, t=50, param=\"delta\"):\n\t\"\"\"\n\tdocstring\n\t\"\"\"\n\tfiles_list = sorted([f for f in listdir(input_directory) if isfile(join(input_directory, f))])\n\tmemory = create_storage()\n\tgeneral_table = create_general_table()\n\thtml_str = f\"

{experiment_name}

\"\n\thtml_str += f\"

Tamaño racha: {size}\"\n\tfor filepath in files_list:\n\t\tname = filepath[:-4].replace(\".\", \"\")\n\t\trewards = preprocess_np_arrays(transform_to_modulated_matrix(read_mat_from_file(join(input_directory, filepath)), mod=mod_tests))\n\t\tsolving_threshold = avg_rwd_last_t_episodes(rewards[0], t)\n\t\tstruct, num, env, pmod, delta = get_args(filepath)\n\t\ttable = run_tests(memory[env][struct], general_table[struct], labels, solving_threshold, rewards, size, mod_tests, env, pmod, delta, num, param)\n\t\tplot_path = plot_mat(transform_to_modulated_matrix(read_mat_from_file(join(input_directory, filepath)), mod=mod_plot), plot_dir, name, mod=mod_plot)\n\t\thtml_str += local_results_to_html(filepath, table, plot_path)\n\thtml_str += \"

Número de episodios en alcanzar racha de recompensas

\"\n\thtml_str += plot_boxes(memory)\n\thtml_str += create_html_tables(general_table)\n\tsave_str_to_doc(output_file_name, html_str)\n\nif __name__ == \"__main__\":\n\tinput_directory = sys.argv[1]\n\toutput_file_name = sys.argv[2]\n\texperiment_name = sys.argv[3]\n\tbase_dir_plots = sys.argv[4]\n\tmod_plots = int(sys.argv[5])\n\tmod_tests = int(sys.argv[6])\n\tstreak_size = int(sys.argv[7])\n\tt = int(sys.argv[8])\n\tparam = sys.argv[9]\n\tlabels = [\"$Q_1$\", \"$Q_2$\", \"$Q_3$\", \"$Q_4$\"]\n\tprocess_dir(input_directory, output_file_name, base_dir_plots, labels, mod_tests, mod_plots, experiment_name, streak_size, t, param)","repo_name":"ivanfeliciano/causal_rl","sub_path":"guided_q_learning/rewards_analizer.py","file_name":"rewards_analizer.py","file_ext":"py","file_size_in_byte":8390,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"} +{"seq_id":"1429290639","text":"import pandas as pd\nimport numpy as np\n\ndef LabelingDatasetUsingCycles(path2csv, OutputFilename, SaveToCsv = True, Verbose=False):\n df = pd.read_csv(path2csv)\n df.drop(['Unnamed: 0'], axis=1, inplace=True)\n df.drop(['Materia', 'Repetidor', 'Calificacion', 'Faltas', 'Partial', 'Genero', 'Turno', 'Especialidad', 'Group'], axis=1, inplace = True)\n df_pivot = pd.pivot_table(df, index=['Id Unico'], columns=['Cycle'], values=['Semester'], aggfunc=np.max).copy()\n df_pivot.reset_index(col_level=1, inplace= True)\n df_pivot.columns = df_pivot.columns.droplevel()\n df_pivot['Abandono'] = ''\n df_pivot['Ultimo Ciclo'] = ''\n NoRows = df_pivot.shape[0]\n NoColumns = df_pivot.shape[1]\n if Verbose: print(\"No Rows: \" + str(NoRows) + \", No Columns: \" + str(NoColumns))\n \n for i in range(NoRows):\n Abandono = 'Si'\n if(not np.isnan(df_pivot.iloc[i][-3])):\n #print(\"Row = \" + str(i) + \", Column = 5\")\n Abandono = 'No'\n LastSemester = df_pivot.columns[-3]\n else:\n for j in range(NoColumns-3, 0, -1): # Removing 'No. Control' and 'Abandono' columns and checking from last to first cycle\n #print(\"Row = \" + str(i) + \", Column = \" + str(j))\n if not np.isnan(df_pivot.iloc[i][j]):\n LastSemester = df_pivot.columns[j]\n if df_pivot.iloc[i][j] == 6:\n Abandono = 'No'\n break\n #print(\" Abandono? \" + Abandono)\n df_pivot.iloc[i, -2] = Abandono # This is the 'Abandono' Column\n df_pivot.iloc[i, -1] = LastSemester # This is the 'Abandono' Column\n \n\n if Verbose:\n NoDropped = df_pivot[df_pivot['Abandono']=='No'].shape[0]\n Dropped = df_pivot[df_pivot['Abandono']=='Si'].shape[0]\n print(\"The Number of Alumni that Dropped the studies is: {}\".format(Dropped))\n print(\"The Number of Alumni that No Dropped the studies is: {}\".format(NoDropped))\n print(\"The porcentage of Alumni that dropped the studies is: {}\".format(Dropped/(NoDropped+Dropped)))\n \n if SaveToCsv: # if SaveToCsv is True\n if Verbose: print(\"Saving the dataframe in: {}\".format(OutputFilename))\n df_pivot.to_csv(OutputFilename, encoding='utf-8-sig')\n\nif __name__ == \"__main__\": # This main is just to setup some variables before running the script if we \n # run it with \"double click\" or with python