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ml6team

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the-stack-smol-python

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ML6 Team 83

import datetime import random import csv import sys book_titles = [ 'Advanced Deep Learning with Keras', 'Hands-On Machine Learning for Algorithmic Trading', 'Architects of Intelligence', 'Deep Reinforcement Learning Hands-On', 'Natural Language Processing with TensorFlow', 'Hands-On Reinforcement Learning with Python', 'Brave New World', 'The Grapes of Wrath', 'For Whom The Bell Tolls', 'To Kill A Mocking Bird', 'The Great Gatsby', 'The Catcher in the Rye', 'Farenheit 451', 'Pride and Prejudice', '1984', 'Animal Farm: A Fairy Story', 'Paul Clifford', 'The Talisman', ] users = ['alice', 'bob', 'carol', 'david'] review_content_rating = [ ('Fascinating work.', 5), ('Great Read.', 5), ('Inspiring.', 5), ('I can''t put this one down..', 5), ('Interesting book.', 4), ('Quite good.', 4), ('Fair.', 3), ('Interesting.', 3), ('Definitely just for the fans.', 2), ('A tad mediocre.', 2), ('Not my cup of tea.', 1), ] csvfile = sys.stdout csvwriter = csv.writer(csvfile) # , delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) header = ['review_content', 'review_rating', 'review_date_created', 'review_date_edited', 'review_creator', 'review_book'] csvwriter.writerow(header) for user in users: time_now = datetime.datetime.now() - datetime.timedelta(days=60) for book_title in book_titles: if random.random()<0.5: continue review_content, review_rating = random.choice(review_content_rating) time_now += datetime.timedelta(days=random.randint(0, 10)) + datetime.timedelta(seconds=int(random.random()*24*3600)) review_date_created = time_now.strftime('%Y-%m-%d %H:%M:%S') review_date_edited = (time_now + datetime.timedelta(seconds=int(random.random()*24*3600))).strftime('%Y-%m-%d %H:%M:%S') csvwriter.writerow([review_content, review_rating, review_date_created, review_date_edited, user, book_title]) """ content:Review,,,,, review_content,review_rating,review_date_created,review_date_edited,review_creator,review_book A must read for all,5,2020-01-04 16:31:40.376237,2020-01-04 16:31:40.376237,peterjones@test.com,Advanced Deep Learning with Keras An ok read,3,2020-01-04 16:31:40.376237,2020-01-04 16:31:40.376237,marksandler@test.com,Advanced Deep Learning with Keras """

PacktWorkshops/The-Django-Workshop

Old Code Backup/chapters9,10,11/_newChapter10/Exercise01/bookr/reviews/management/commands/DjangoWorkshopReviewsData.py

Python

from rate_limiter.Limit import Limit from rate_limiter.Parser import Parser from rate_limiter.LimitProcessor import LimitProcessor from rate_limiter.LogLine import LogLine from typing import Dict, List from datetime import datetime class IpRateLimiter: # used to store unban time. Also used to maintain what is currently banned ipToUnbanTimeMap: List logLineList: List[LogLine] limitProcessorList: List[LimitProcessor] def __init__(self, logLineList, limitProcessorList) -> None: self.logLineList = logLineList self.limitProcessorList = limitProcessorList self.ipToUnbanTimeMap = {} def processNewBan(self, newIpToUnbanTimeMap, currTime): for ip in newIpToUnbanTimeMap: if ip not in self.ipToUnbanTimeMap: # new ban. Need to print print("{0},BAN,{1}".format(int(currTime.timestamp()), ip)) self.ipToUnbanTimeMap[ip] = newIpToUnbanTimeMap[ip] else: self.ipToUnbanTimeMap[ip] = max( self.ipToUnbanTimeMap[ip], newIpToUnbanTimeMap[ip] ) def needsToBeUnbanned(self, ip: str, currTime: datetime): toUnban = False if currTime >= self.ipToUnbanTimeMap[ip]: toUnban = True return toUnban def unbanPassedIPs(self, currTime: datetime): toUnban = [] for ip in self.ipToUnbanTimeMap: if self.needsToBeUnbanned(ip, currTime): toUnban.append(ip) for ip in toUnban: print("{0},UNBAN,{1}".format(int(currTime.timestamp()), ip)) # print("{0},UNBAN,{1}".format(self.ipToUnbanTimeMap[ip].timestamp(), ip)) self.ipToUnbanTimeMap.pop(ip) def run(self): for line in self.logLineList: currTime = line.getTime() # evict expired entries from each processor window for limitProcessor in self.limitProcessorList: limitProcessor.evictExpired(currTime) # check all banned ips if they need to be unbanned self.unbanPassedIPs(currTime) # process new request in limit processors for limitProcessor in self.limitProcessorList: newBanMap = limitProcessor.processNewRequest(line) self.processNewBan(newBanMap, currTime) if self.logLineList and self.ipToUnbanTimeMap: for ip in self.ipToUnbanTimeMap: print( "{0},UNBAN,{1}".format( int(self.ipToUnbanTimeMap[ip].timestamp()), ip ) ) class IpRateLimiterBuilder: filePath = "" fileParser: Parser = None limitList: List[Limit] = [] def addFile(self, parser: Parser, filePath: str): self.filePath = filePath self.fileParser = parser return self def addLimit(self, limit: Limit): self.limitList.append(limit) return self def build(self): logLineList = self.fileParser.parse(self.filePath) limitProcessorList: List[LimitProcessor] = [] for limit in self.limitList: limitProcessorList.append(LimitProcessor(limit)) return IpRateLimiter(logLineList, limitProcessorList)

v-shash/ackerman

rate-limiter/rate_limiter/IPRateLimiter.py

Python

import numpy as np import tensorflow as tf from pyuvdata import UVData, UVCal, UVFlag from . import utils import copy import argparse import itertools import datetime from pyuvdata import utils as uvutils from .utils import echo from .utils import PBARS from . import cal_utils from . import modeling import re OPTIMIZERS = { "Adadelta": tf.optimizers.Adadelta, "Adam": tf.optimizers.Adam, "Adamax": tf.optimizers.Adamax, "Ftrl": tf.optimizers.Ftrl, "Nadam": tf.optimizers.Nadam, "SGD": tf.optimizers.SGD, "RMSprop": tf.optimizers.RMSprop, "Adagrad": tf.optimizers.Adagrad } def chunk_fg_comp_dict_by_nbls(fg_model_comps_dict, use_redundancy=False, grp_size_threshold=5): """ Order dict keys in order of number of baselines in each group chunk fit_groups in fg_model_comps_dict into chunks where all groups in the same chunk have the same number of baselines in each group. Parameters ---------- fg_model_comps_dict: dict dictionary with keys that are tuples of tuples of 2-tuples (thats right, 3 levels) in the first level, each tuple represents a 'modeling group' visibilities in each modeling group are represented by a set of basis vectors that span all baselines in that group with elements raveled by baseline and then frequency. Each tuple in the modeling group is a 'redundant group' representing visibilities that we will represent with identical component coefficients each element of each 'redundant group' is a 2-tuple antenna pair. Our formalism easily accomodates modeling visibilities as redundant or non redundant (one simply needs to make each redundant group length 1). use_redundancy: bool, optional If False, break fitting groups with the same number of baselines in each redundant sub_group into different fitting groups with no redundancy in each redundant subgroup. This is to prevent fitting groups with single redundant groups of varying lengths from being lumped into different chunks increasing the number of chunks has a more significant impact on run-time then increasing the number of baselines in each chunk. default is False. Returns: fg_model_comps_dict_chunked: dict dictionary where each key is a 2-tuple (nbl, nvecs) referring to the number of baselines in each vector and the number of vectors. Each 2-tuple points to a dictionary where each key is the fitting group in fg_comps_dict that includes nbl baselines. Each key in the referenced dict points to an (nred_grps * nfreqs x nvecs) numpy.ndarray describing the modeling components for each fitting group in the chunk. """ chunked_keys = {} maxvecs = {} fg_model_comps_dict = copy.deepcopy(fg_model_comps_dict) if not use_redundancy: # We can remove redundancies for fitting groups of baselines that have the same # number of elements in each redundant group. keys_with_redundancy = list(fg_model_comps_dict.keys()) for fit_grp in keys_with_redundancy: rlens = np.asarray([len(red_grp) for red_grp in fit_grp]) # only break up groups with small numbers of group elements. if np.allclose(rlens, np.mean(rlens)) and len(rlens) < grp_size_threshold: # split up groups. modeling_vectors = fg_model_comps_dict.pop(fit_grp) for rednum in range(int(rlens[0])): fit_grp_new = tuple([(red_grp[rednum],) for red_grp in fit_grp]) fg_model_comps_dict[fit_grp_new] = modeling_vectors for fit_grp in fg_model_comps_dict: nbl = 0 for red_grp in fit_grp: for ap in red_grp: nbl += 1 if nbl in chunked_keys: chunked_keys[nbl].append(fit_grp) if fg_model_comps_dict[fit_grp].shape[1] > maxvecs[nbl]: maxvecs[nbl] = fg_model_comps_dict[fit_grp].shape[1] else: chunked_keys[nbl] = [fit_grp] maxvecs[nbl] = fg_model_comps_dict[fit_grp].shape[1] fg_model_comps_dict_chunked = {} for nbl in chunked_keys: fg_model_comps_dict_chunked[(nbl, maxvecs[nbl])] = {k: fg_model_comps_dict[k] for k in chunked_keys[nbl]} return fg_model_comps_dict_chunked def tensorize_fg_model_comps_dict( fg_model_comps_dict, ants_map, nfreqs, use_redundancy=False, dtype=np.float32, notebook_progressbar=False, verbose=False, grp_size_threshold=5, ): """Convert per-baseline model components into a Ndata x Ncomponent tensor Parameters ---------- fg_model_comps_dict: dict dictionary where each key is a 2-tuple (nbl, nvecs) referring to the number of baselines in each vector and the number of vectors. Each 2-tuple points to a dictionary where each key is the fitting group in fg_comps_dict that includes nbl baselines. Each key in the referenced dict points to an (nred_grps * nfreqs x nvecs) numpy.ndarray describing the modeling components for each fitting group in the chunk. ants_map: dict mapping integers to integers map between each antenna number to a unique index between 0 and Nants_data (typically the index of each antenna in ants_map) nfreqs: int, optional number of frequency channels dtype: numpy.dtype tensor data types default is np.float32 Returns ------- fg_model_comps: list list of tf.Tensor objects where each tensor has shape (nvecs, ngrps, nbls, nfreqs) where nbls varies from tensor to tensor. Fitting groups with vectors that span nbls are lumped into the same modeling tensor along the ngrps axis. nvecs is chosen in chunk_fg_comp_dict_by_nbls to be the maximum number of vectors representing any of the ngrps baseline grps which means that many rows in nvecs will be zero. For example, if we are modeling with vectors that all span nbls=1 baseline and using delay-modes to model our data then nvecs will equal the largest number of delay modes necessary to model the wedge on all baselines even though the short baselines are described by far fewer modes on short baselines, most of the rows along the vector dimension will therefor be zero. This is wasteful of memory but it allows us to take advantage of the fast dense matrix operations on a GPU. corr_inds: list list of list of lists of 2-tuples. Hierarchy of lists is chunk group baseline - (int 2-tuple) """ echo( f"{datetime.datetime.now()} Computing foreground components matrices...\n", verbose=verbose, ) # chunk foreground components. fg_model_comps_dict = chunk_fg_comp_dict_by_nbls( fg_model_comps_dict, use_redundancy=use_redundancy, grp_size_threshold=grp_size_threshold ) fg_model_comps = [] corr_inds = [] for nbls, nvecs in fg_model_comps_dict: ngrps = len(fg_model_comps_dict[(nbls, nvecs)]) modeling_matrix = np.zeros((nvecs, ngrps, nbls, nfreqs)) corr_inds_chunk = [] for grpnum, modeling_grp in enumerate(fg_model_comps_dict[(nbls, nvecs)]): corr_inds_grp = [] nbl = 0 for rgrpnum, red_grp in enumerate(modeling_grp): nred = len(red_grp) for ap in red_grp: i, j = ants_map[ap[0]], ants_map[ap[1]] corr_inds_grp.append((i, j)) vecslice = slice(0, fg_model_comps_dict[(nbls, nvecs)][modeling_grp].shape[1]) compslice = slice(rgrpnum * nfreqs, (rgrpnum + 1) * nfreqs) dslice = slice(nbl * nfreqs, (nbl + 1) * nfreqs) modeling_matrix[vecslice, grpnum, nbl] = fg_model_comps_dict[(nbls, nvecs)][modeling_grp][ compslice ].T nbl += 1 corr_inds_chunk.append(corr_inds_grp) fg_model_comps.append(tf.convert_to_tensor(modeling_matrix, dtype=dtype)) corr_inds.append(corr_inds_chunk) return fg_model_comps, corr_inds def tensorize_data( uvdata, corr_inds, ants_map, polarization, time, data_scale_factor=1.0, weights=None, nsamples_in_weights=False, dtype=np.float32, ): """Convert data in uvdata object to a tensor Parameters ---------- uvdata: UVData object UVData object containing data, flags, and nsamples to tensorize. corr_inds: list list of list of lists of 2-tuples. Hierarchy of lists is chunk group baseline - (int 2-tuple) ants_map: dict mapping integers to integers map between each antenna number to a unique index between 0 and Nants_data (typically the index of each antenna in ants_map) polarization: str pol-str of gain to extract. time: float time of data to convert to tensor. data_scale_factor: float, optional overall scaling factor to divide tensorized data by. default is 1.0 weights: UVFlag object, optional UVFlag weights object containing weights to use for data fitting. default is None -> use nsamples * ~flags if nsamples_in_weights or ~flags if not nsamples_in_weights nsamples_in_weights: bool, optional If True and weights is None, generate weights proportional to nsamples. default is False. dtype: numpy.dtype data-type to store in tensor. default is np.float32 Returns ------- data_r: list of tf.Tensor objects list of tf.Tensor objects. Each tensor has shape (ngrps, nbls, nfreqs) where ngrps, nbls are the dimensions of each sublist in corr_inds and contain the real components of the baselines specified by these 2-tuples. data_i: list of tf.Tensor objects list of tf.Tensor objects. Each tensor has shape (ngrps, nbls, nfreqs) where ngrps, nbls are the dimensions of each sublist in corr_inds and contain the imag components of the baselines specified by these 2-tuples. wgts: tf.Tensor object list of tf.Tensor objects. Each tensor has shape (ngrps, nbls, nfreqs) where ngrps, nbls are the dimensions of each sublist in corr_inds and contain the weights of the baselines specified by these 2-tuples. """ ants_map_inv = {ants_map[i]: i for i in ants_map} dshape = (uvdata.Nants_data, uvdata.Nants_data, uvdata.Nfreqs) data_r = np.zeros(dshape, dtype=dtype) data_i = np.zeros_like(data_r) wgts = np.zeros_like(data_r) wgtsum = 0.0 for chunk in corr_inds: for fitgrp in chunk: for (i, j) in fitgrp: ap = ants_map_inv[i], ants_map_inv[j] bl = ap + (polarization,) dinds1, dinds2, pol_ind = uvdata._key2inds(bl) if len(dinds1) > 0: dinds = dinds1 conjugate = False pol_ind = pol_ind[0] else: dinds = dinds2 conjugate = True pol_ind = pol_ind[1] dind = dinds[np.where(np.isclose(uvdata.time_array[dinds], time, rtol=0.0, atol=1e-7))[0][0]] data = uvdata.data_array[dind, 0, :, pol_ind].squeeze() iflags = ~uvdata.flag_array[dind, 0, :, pol_ind].squeeze() nsamples = uvdata.nsample_array[dind, 0, :, pol_ind].squeeze() data /= data_scale_factor if conjugate: data = np.conj(data) data_r[i, j] = data.real.astype(dtype) data_i[i, j] = data.imag.astype(dtype) if weights is None: wgts[i, j] = iflags if nsamples_in_weights: wgts[i, j] *= nsamples else: if ap in weights.get_antpairs(): dinds = weights.antpair2ind(*ap) else: dinds = weights.antpair2ind(*ap[::-1]) dind = dinds[np.where(np.isclose(weights.time_array[dinds], time, atol=1e-7, rtol=0.0))[0][0]] polnum = np.where( weights.polarization_array == uvutils.polstr2num(polarization, x_orientation=weights.x_orientation) )[0][0] wgts[i, j] = weights.weights_array[dind, 0, :, polnum].astype(dtype) * iflags if nsamples_in_weights: wgts[i, j] *= nsamples wgtsum += np.sum(wgts[i, j]) data_r = tf.convert_to_tensor(data_r, dtype=dtype) data_i = tf.convert_to_tensor(data_i, dtype=dtype) wgts = tf.convert_to_tensor(wgts / wgtsum, dtype=dtype) nchunks = len(corr_inds) data_r = [tf.gather_nd(data_r, corr_inds[cnum]) for cnum in range(nchunks)] data_i = [tf.gather_nd(data_i, corr_inds[cnum]) for cnum in range(nchunks)] wgts = [tf.gather_nd(wgts, corr_inds[cnum]) for cnum in range(nchunks)] return data_r, data_i, wgts def renormalize(uvdata_reference_model, uvdata_deconv, gains, polarization, time, additional_flags=None): """Remove arbitrary phase and amplitude from deconvolved model and gains. Parameters ---------- uvdata_reference_model: UVData object Reference model for "true" visibilities. uvdata_deconv: UVData object "Deconvolved" data solved for in self-cal loop. gains: UVCal object Gains solved for in self-cal loop. polarization: str Polarization string to compute phase and amplitude correction for. additional_flags: np.ndarray Any additional flags you wish to use for excluding data from normalization fed as an np.ndarray with same shape as uvdata_reference_model and uvdata_deconv. default is None -> Only exclude data in flags from reference model and deconv from determinging normalization. Returns ------- N/A: Modifies uvdata_deconv and gains in-place. """ # compute and multiply out scale-factor accounting for overall amplitude and phase degeneracy. polnum_data = np.where( uvdata_deconv.polarization_array == uvutils.polstr2num(polarization, x_orientation=uvdata_deconv.x_orientation) )[0][0] bltsel = np.isclose(uvdata_deconv.time_array, time, atol=1e-7, rtol=0.0) selection = ( ~uvdata_deconv.flag_array[bltsel, :, :, polnum_data] & ~uvdata_reference_model.flag_array[bltsel, :, :, polnum_data] ) if additional_flags is not None: selection = selection & ~additional_flags[bltsel, :, :, polnum_data] data_ratio = ( uvdata_reference_model.data_array[bltsel, :, :, polnum_data][selection] / uvdata_deconv.data_array[bltsel, :, :, polnum_data][selection] ) data_ratio[~np.isfinite(data_ratio)] = np.nan scale_factor_phase = np.angle(np.nanmean(data_ratio)) scale_factor_abs = np.sqrt(np.nanmean(np.abs(data_ratio) ** 2.0)) scale_factor = scale_factor_abs # * np.exp(1j * scale_factor_phase) Need to figure this out later. uvdata_deconv.data_array[bltsel, :, :, polnum_data] *= scale_factor polnum_gains = np.where( gains.jones_array == uvutils.polstr2num(polarization, x_orientation=uvdata_deconv.x_orientation) )[0][0] gindt = np.where(np.isclose(gains.time_array, time, atol=1e-7, rtol=0.0))[0][0] gains.gain_array[:, :, :, gindt, polnum_gains] *= (scale_factor) ** -0.5 def tensorize_gains(uvcal, polarization, time, dtype=np.float32): """Helper function to extract gains into fitting tensors. Parameters ---------- uvcal: UVCal object UVCal object holding gain data to tensorize. polarization: str pol-str of gain to extract. time: float JD of time to convert to tensor. dtype: numpy.dtype dtype of tensors to output. Returns ------- gains_re: tf.Tensor object. tensor object holding real component of gains for time_index and polarization shape is Nant x Nfreq gains_im: tf.Tensor object. tensor object holding imag component of gains for time_index and polarization shape is Nant x Nfreq """ polnum = np.where(uvcal.jones_array == uvutils.polstr2num(polarization, x_orientation=uvcal.x_orientation))[0][0] gindt = np.where(np.isclose(uvcal.time_array, time, atol=1e-7, rtol=0.0))[0][0] gains_re = tf.convert_to_tensor(uvcal.gain_array[:, 0, :, gindt, polnum].squeeze().real, dtype=dtype) gains_im = tf.convert_to_tensor(uvcal.gain_array[:, 0, :, gindt, polnum].squeeze().imag, dtype=dtype) return gains_re, gains_im def yield_fg_model_array( nants, nfreqs, fg_model_comps, fg_coeffs, corr_inds, ): """Compute tensor foreground model. Parameters ---------- nants: int number of antennas in data to model. freqs: int number of frequencies in data to model. fg_model_comps: list list of fg modeling tf.Tensor objects representing foreground modeling vectors. Each tensor is (nvecs, ngrps, nbls, nfreqs) fg_coeffs: list list of fg modeling tf.Tensor objects representing foreground modeling coefficients. Each tensor is (nvecs, ngrps, 1, 1) corr_inds: list list of list of lists of 2-tuples. Hierarchy of lists is chunk group baseline - (int 2-tuple) Returns ------- model: tf.Tensor object nants x nants x nfreqs model of the visibility data """ model = np.zeros((nants, nants, nfreqs)) nchunks = len(fg_model_comps) for cnum in range(nchunks): ngrps = fg_model_comps[cnum].shape[1] gchunk = tf.reduce_sum(fg_coeffs[cnum] * fg_model_comps[cnum], axis=0).numpy() for gnum in range(ngrps): for blnum, (i, j) in enumerate(corr_inds[cnum][gnum]): model[i, j] = gchunk[gnum, blnum] return model def fit_gains_and_foregrounds( g_r, g_i, fg_r, fg_i, data_r, data_i, wgts, fg_comps, corr_inds, use_min=False, tol=1e-14, maxsteps=10000, optimizer="Adamax", freeze_model=False, verbose=False, notebook_progressbar=False, dtype=np.float32, graph_mode=False, n_profile_steps=0, profile_log_dir="./logdir", sky_model_r=None, sky_model_i=None, model_regularization=None, graph_args_dict=None, **opt_kwargs, ): """Run optimization loop to fit gains and foreground components. Parameters ---------- g_r: tf.Tensor object. tf.Tensor object holding real parts of gains. g_i: tf.Tensor object. tf.Tensor object holding imag parts of gains. fg_r: list list of tf.Tensor objects. Each has shape (nvecs, ngrps, 1, 1) tf.Tensor object holding foreground coeffs. fg_i: list list of tf.Tensor objects. Each has shape (nvecs, ngrps, 1, 1) tf.Tensor object holding imag coeffs. data_r: list list of tf.Tensor objects. Each has shape (ngrps, nbls, nfreqs) real part of data to fit. data_i: list list of tf.Tensor objects. Each has shape (ngrps, nbls, nfreqs) imag part of data to fit. wgts: list list of tf.Tensor objects. Each has shape (ngrps, nbls, nfreqs) fg_comps: list: list of tf.Tensor objects. Each has shape (nvecs, ngrps, nbls, nfreqs) represents vectors to be used in modeling visibilities. corr_inds: list list of list of lists of 2-tuples. Hierarchy of lists is chunk group baseline - (int 2-tuple) use_min: bool, optional if True, use the value that minimizes the loss function regardless of where optimization loop ended up (prevents overshooting due to excess momentum) tol: float, optional halt optimization loop once the loss changes by less then this value. default is 1e-14 maxsteps: int, optional maximum number of opt.minimize calls before halting. default is 10000 optimizer: string Name of optimizer. See OPTIMIZERS dictionary which contains optimizers described in https://www.tensorflow.org/api_docs/python/tf/keras/optimizers default is 'Adamax' freeze_model: bool, optional Only optimize loss function wrt gain variables. This is effectively traditional model-based calibration with sky_model as the model (but projected onto the foreground basis vectors). default is False. verbose: bool, optional lots of text output default is False. notebook_progressbar: bool, optional use progress bar optimized for notebook output. default is False. graph_mode: bool, optional if True, compile gradient update step in graph mode to speed up runtime by ~2-3x. I've found that this helps on CPUs but on GPUs it actually increases runtime by a similar factor. n_profile_steps: bool, optional number of steps to run profiling on default is 0. profile_log_dir: str, optional directory to save profile logs to default is './logdir' sky_model_r: list of tf.Tensor objects, optional chunked tensors containing model in same format as data_r sky_model_i: list of tf.Tensor objects, optional chunked tensors containing model in the same format as data_i model_regularization: str, optional type of model regularization to perform. Currently support "sum" where the sums of real and imaginary parts (across all bls and freqs) are constrained to be the same as the sum of real and imag parts of data. opt_kwargs: kwarg dict additional kwargs for tf.opt.Optimizer(). See tensorflow docs. Returns ------- g_r_opt: tf.Tensor object real part of optimized gains. g_i_opt: tf.Tensor object imag part of optimized gains. fg_r_opt: tf.Tensor object real part of foreground coeffs. fg_i_opt: tf.Tensor object. imag part of optimized foreground coeffs. fit_history: dict dictionary containing fit history for each time-step and polarization in the data with fields: 'loss_history': list of values of the loss function in each minimization iteration. """ if graph_args_dict is None: graph_args_dict = {} # initialize the optimizer. echo(f"Using {str(dtype)} precision.") echo(f"{datetime.datetime.now()} Provided the following opt_kwargs") for k in opt_kwargs: echo(f"{k}: {opt_kwargs[k]}") opt = OPTIMIZERS[optimizer](**opt_kwargs) # set up history recording fit_history = {"loss": []} min_loss = 9e99 nants = g_r.shape[0] nfreqs = g_r.shape[1] ant0_inds = [] ant1_inds = [] nchunks = len(fg_comps) # build up list of lists of ant0 and ant1 for gather ops for cnum in range(nchunks): ant0_chunk = [] ant1_chunk = [] ngrps = len(corr_inds[cnum]) for gnum in range(ngrps): ant0_grp = [] ant1_grp = [] for cpair in corr_inds[cnum][gnum]: ant0_grp.append(cpair[0]) ant1_grp.append(cpair[1]) ant0_chunk.append(ant0_grp) ant1_chunk.append(ant1_grp) ant0_inds.append(ant0_chunk) ant1_inds.append(ant1_chunk) g_r = tf.Variable(g_r) g_i = tf.Variable(g_i) if not freeze_model: fg_r = [tf.Variable(fgr) for fgr in fg_r] fg_i = [tf.Variable(fgi) for fgi in fg_i] vars = [g_r, g_i] + fg_r + fg_i else: vars = [g_r, g_i] echo( f"{datetime.datetime.now()} Performing gradient descent on {np.prod(g_r.shape)} complex gain parameters...", verbose=verbose, ) if not freeze_model: echo( f"Performing gradient descent on total of {int(np.sum([fgr.shape[0] * fgr.shape[1] for fgr in fg_r]))} complex foreground parameters", verbose=verbose, ) echo( f"Foreground Parameters grouped into chunks of shape ((nvecs, ngrps): nbls) {[str(fgr.shape[:2]) + ':' + str(dc.shape[1]) for fgr, dc in zip(fg_r, data_r)]}", verbose=verbose, ) if model_regularization == "sum": prior_r_sum = tf.reduce_sum( tf.stack([tf.reduce_sum(sky_model_r[cnum] * wgts[cnum]) for cnum in range(nchunks)]) ) prior_i_sum = tf.reduce_sum( tf.stack([tf.reduce_sum(sky_model_i[cnum] * wgts[cnum]) for cnum in range(nchunks)]) ) def loss_function(): return mse_chunked_sum_regularized( g_r=g_r, g_i=g_i, fg_r=fg_r, fg_i=fg_i, fg_comps=fg_comps, nchunks=nchunks, data_r=data_r, data_i=data_i, wgts=wgts, ant0_inds=ant0_inds, ant1_inds=ant1_inds, dtype=dtype, prior_r_sum=prior_r_sum, prior_i_sum=prior_i_sum, ) else: def loss_function(): return mse_chunked( g_r=g_r, g_i=g_i, fg_r=fg_r, fg_i=fg_i, fg_comps=fg_comps, nchunks=nchunks, data_r=data_r, data_i=data_i, wgts=wgts, ant0_inds=ant0_inds, ant1_inds=ant1_inds, dtype=dtype, ) def train_step_code(): with tf.GradientTape() as tape: loss = loss_function() grads = tape.gradient(loss, vars) opt.apply_gradients(zip(grads, vars)) return loss if graph_mode: @tf.function(**graph_args_dict) def train_step(): return train_step_code() else: def train_step(): return train_step_code() if n_profile_steps > 0: echo(f"{datetime.datetime.now()} Profiling with {n_profile_steps}. And writing output to {profile_log_dir}...") tf.profiler.experimental.start(profile_log_dir) for step in PBARS[notebook_progressbar](range(n_profile_steps)): with tf.profiler.experimental.Trace("train", step_num=step): train_step() tf.profiler.experimental.stop() echo( f"{datetime.datetime.now()} Building Computational Graph...\n", verbose=verbose, ) loss = train_step() echo( f"{datetime.datetime.now()} Performing Gradient Descent. Initial MSE of {loss:.2e}...\n", verbose=verbose, ) for step in PBARS[notebook_progressbar](range(maxsteps)): loss = train_step() fit_history["loss"].append(loss.numpy()) if use_min and fit_history["loss"][-1] < min_loss: # store the g_r, g_i, fg_r, fg_i values that minimize loss # in case of overshoot. min_loss = fit_history["loss"][-1] g_r_opt = g_r.value() g_i_opt = g_i.value() if not freeze_model: fg_r_opt = [fgr.value() for fgr in fg_r] fg_i_opt = [fgi.value() for fgi in fg_i] if step >= 1 and np.abs(fit_history["loss"][-1] - fit_history["loss"][-2]) < tol: echo( f"Tolerance thresshold met with delta of {np.abs(fit_history['loss'][-1] - fit_history['loss'][-2]):.2e}. Terminating...\n ", verbose=verbose, ) break # if we dont use use_min, then the last # visited set of parameters will be used # to set the ML params. if not use_min: min_loss = fit_history["loss"][-1] g_r_opt = g_r.value() g_i_opt = g_i.value() if not freeze_model: fg_r_opt = [fgr.value() for fgr in fg_r] fg_i_opt = [fgi.value() for fgi in fg_i] else: fg_r_opt = fg_r fg_i_opt = fg_i echo( f"{datetime.datetime.now()} Finished Gradient Descent. MSE of {min_loss:.2e}...\n", verbose=verbose, ) return g_r_opt, g_i_opt, fg_r_opt, fg_i_opt, fit_history def insert_model_into_uvdata_tensor( uvdata, time, polarization, ants_map, red_grps, model_r, model_i, scale_factor=1.0, ): """Insert fitted tensor values back into uvdata object for tensor mode. Parameters ---------- uvdata: UVData object uvdata object to insert model data into. time: float JD of time to insert. polarization: str polarization to insert. ants_map: dict mapping integers to integers map between each antenna number to a unique index between 0 and Nants_data (typically the index of each antenna in ants_map) red_grps: list of lists of int 2-tuples a list of lists of 2-tuples where all antenna pairs within each sublist are redundant with eachother. Assumes that conjugates are correctly taken. model_r: np.ndarray an Nants_data x Nants_data x Nfreqs np.ndarray with real parts of data model_i: np.ndarray an Nants_data x Nants_data x Nfreqs np.ndarray with imag parts of model scale_factor: float, optional overall scaling factor to divide tensorized data by. default is 1.0 Returns ------- N/A: Modifies uvdata inplace. """ antpairs_data = uvdata.get_antpairs() polnum = np.where( uvdata.polarization_array == uvutils.polstr2num(polarization, x_orientation=uvdata.x_orientation) )[0][0] for red_grp in red_grps: for ap in red_grp: i, j = ants_map[ap[0]], ants_map[ap[1]] if ap in antpairs_data: dinds = uvdata.antpair2ind(ap) dinds = dinds[np.where(np.isclose(time, uvdata.time_array[dinds], atol=1e-7, rtol=0.0))[0][0]] model = model_r[i, j] + 1j * model_i[i, j] else: dinds = uvdata.antpair2ind(ap[::-1]) dinds = dinds[np.where(np.isclose(time, uvdata.time_array[dinds], atol=1e-7, rtol=0.0))[0][0]] model = model_r[i, j] - 1j * model_i[i, j] uvdata.data_array[dinds, 0, :, polnum] = model * scale_factor def insert_gains_into_uvcal(uvcal, time, polarization, gains_re, gains_im): """Insert tensorized gains back into uvcal object Parameters ---------- uvdata: UVData object uvdata object to insert model data into. time: float JD of time to insert. polarization: str polarization to insert. gains_re: dict with int keys and tf.Tensor object values dictionary mapping i antenna numbers to Nfreq 1d tf.Tensor object representing the real component of the complex gain for antenna i. gains_im: dict with int keys and tf.Tensor object values dictionary mapping j antenna numbers to Nfreq 1d tf.Tensor object representing the imag component of the complex gain for antenna j. Returns ------- N/A: Modifies uvcal inplace. """ polnum = np.where(uvcal.jones_array == uvutils.polstr2num(polarization, x_orientation=uvcal.x_orientation))[0][0] gindt = np.where(np.isclose(uvcal.time_array, time, atol=1e-7, rtol=0.0))[0][0] for ant_index in range(uvcal.Nants_data): uvcal.gain_array[ant_index, 0, :, gindt, polnum] = ( gains_re[ant_index].numpy() + 1j * gains_im[ant_index].numpy() ) def tensorize_fg_coeffs( data, wgts, fg_model_comps, notebook_progressbar=False, verbose=False, ): """Initialize foreground coefficient tensors from uvdata and modeling component dictionaries. Parameters ---------- data: list list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs) representing data wgts: list list of tf.Tensor objects, each with shape (ngrps, nbls, nfreqs) representing weights. fg_model_comps: list list of fg modeling tf.Tensor objects representing foreground modeling vectors. Each tensor is (nvecs, ngrps, nbls, nfreqs) see description in tensorize_fg_model_comps_dict docstring. notebook_progressbar: bool, optional use progress bar optimized for notebook output. default is False. verbose: bool, optional lots of text output default is False. Returns ------- fg_coeffs_re: tf.Tensor object 1d tensor containing real parts of coeffs for each modeling vector. ordering is over foreground modeling vector per redundant group and then redundant group in the order of groups appearing in red_grps fg_coeffs_im: tf.Tensor object 1d tensor containing imag parts of coeffs for each modeling vector. ordering is over foreground modeling vector per redundant group and then redundant group in the order of groups appearing in red_grps """ echo( f"{datetime.datetime.now()} Computing initial foreground coefficient guesses using linear-leastsq...\n", verbose=verbose, ) fg_coeffs = [] nchunks = len(data) binary_wgts = [ tf.convert_to_tensor(~np.isclose(wgts[cnum].numpy(), 0.0), dtype=wgts[cnum].dtype) for cnum in range(nchunks) ] for cnum in PBARS[notebook_progressbar](range(nchunks)): # set up linear leastsq fg_coeff_chunk = [] ngrps = data[cnum].shape[0] ndata = data[cnum].shape[1] * data[cnum].shape[2] nvecs = fg_model_comps[cnum].shape[0] # pad with zeros for gnum in range(ngrps): nonzero_rows = np.where( np.all(np.isclose(fg_model_comps[cnum][:, gnum].numpy().reshape(nvecs, ndata), 0.0), axis=1) )[0] if len(nonzero_rows) > 0: nvecs_nonzero = np.min(nonzero_rows) else: nvecs_nonzero = nvecs # solve linear leastsq fg_coeff_chunk.append( tf.reshape( tf.linalg.lstsq( tf.transpose(tf.reshape(fg_model_comps[cnum][:, gnum], (nvecs, ndata)))[:, :nvecs_nonzero], tf.reshape(data[cnum][gnum] * binary_wgts[cnum][gnum], (ndata, 1)), ), (nvecs_nonzero,), ) ) # pad zeros at the end back up to nvecs. fg_coeff_chunk[-1] = tf.pad(fg_coeff_chunk[-1], [(0, nvecs - nvecs_nonzero)]) # add two additional dummy indices to satify broadcasting rules. fg_coeff_chunk = tf.reshape(tf.transpose(tf.stack(fg_coeff_chunk)), (nvecs, ngrps, 1, 1)) fg_coeffs.append(fg_coeff_chunk) echo( f"{datetime.datetime.now()} Finished initial foreground coefficient guesses...\n", verbose=verbose, ) return fg_coeffs def get_auto_weights(uvdata, delay_extent=25.0): """ inverse variance weights from interpolated autocorrelation data Parameters ---------- uvdata: UVData object UVData object containing autocorrelation data to use for computing inverse noise weights. offset: float, optional Fit autocorrelation to delay components with this width. Returns ------- data_weights: UVFlag object UFlag in flag-mode where flags contain original data flags and weights contain autocorr weights. """ dpss_components = modeling.yield_dpss_model_comps_bl_grp(0.0, uvdata.freq_array[0], offset=delay_extent) data_weights = UVFlag(uvdata, mode="flag") data_weights.weights_array = np.zeros(uvdata.data_array.shape) # compute autocorrelation weights auto_fit_dict = {} bls = uvdata.get_antpairpols() for bl in bls: if bl[0] == bl[1]: d_wf = uvdata.get_data(bl) w_wf = ~uvdata.get_flags(bl) auto_fit_dict[bl] = [] for ds, fs in zip(d_wf, w_wf): # fit autocorr waterfall to DPSS modes. nunflagged = np.count_nonzero(fs) amat = tf.convert_to_tensor(dpss_components[fs]) dvec = tf.reshape(tf.convert_to_tensor(ds[fs].real), (nunflagged, 1)) model = dpss_components @ tf.linalg.lstsq(amat, dvec).numpy().squeeze() auto_fit_dict[bl].append(model) auto_fit_dict[bl] = np.atleast_2d(np.asarray(auto_fit_dict[bl])) # from autocorrelation fits, weights for bl in bls: smooth_weights = 1.0 / (auto_fit_dict[bl[0], bl[0], bl[-1]] * auto_fit_dict[bl[1], bl[1], bl[-1]]) smooth_weights *= ~uvdata.get_flags(bl) dinds = data_weights.antpair2ind(*bl[:2]) polnum = np.where( data_weights.polarization_array == uvutils.polstr2num(bl[-1], x_orientation=data_weights.x_orientation) )[0][0] data_weights.weights_array[dinds, 0, :, polnum] = smooth_weights return data_weights def calibrate_and_model_tensor( uvdata, fg_model_comps_dict, gains=None, freeze_model=False, optimizer="Adamax", tol=1e-14, maxsteps=10000, include_autos=False, verbose=False, sky_model=None, dtype=np.float32, use_min=False, use_redundancy=False, notebook_progressbar=False, correct_resid=False, correct_model=True, weights=None, nsamples_in_weights=True, graph_mode=False, grp_size_threshold=5, n_profile_steps=0, profile_log_dir="./logdir", model_regularization="sum", init_guesses_from_previous_time_step=False, skip_threshold=0.5, use_model_snr_weights=False, **opt_kwargs, ): """Perform simultaneous calibration and foreground fitting using tensors. Parameters ---------- uvdata: UVData object uvdata objet of data to be calibrated. fg_model_comps_dict: dictionary dictionary with keys that are tuples of tuples of 2-tuples (thats right, 3 levels) in the first level, each tuple represents a 'modeling group' visibilities in each modeling group are represented by a set of basis vectors that span all baselines in that group with elements raveled by baseline and then frequency. Each tuple in the modeling group is a 'redundant group' representing visibilities that we will represent with identical component coefficients each element of each 'redundant group' is a 2-tuple antenna pair. Our formalism easily accomodates modeling visibilities as redundant or non redundant (one simply needs to make each redundant group length 1). values are real numpy arrays with size (Ngrp * Nfreqs) * Ncomponents gains: UVCal object UVCal with initial gain estimates. There many smart ways to obtain initial gain estimates but this is beyond the scope of calamity (for example, firstcal, logcal, sky-based cal). Users can determine initial gains with their favorite established cal algorithm. default is None -> start with unity gains. WARNING: At the present, the flags in gains are not propagated/used! Make sure flags in uvdata object! freeze_model: bool, optional Only optimize loss function wrt gain variables. This is effectively traditional model-based calibration with sky_model as the model (but projected onto the foreground basis vectors). default is False. optimizer: string Name of optimizer. See OPTIMIZERS dictionary which contains optimizers described in https://www.tensorflow.org/api_docs/python/tf/keras/optimizers default is 'Adamax' tol: float, optional halting condition for optimizer loop. Stop loop when the change in the cost function falls below tol. default is 1e-14 maxsteps: int, optional maximum number of opt.minimize calls before halting. default is 10000 include_autos: bool, optional include autocorrelations in fitting. default is False. verbose: bool, optional generate lots of text. default is False. sky_model: UVData object, optional a sky-model to use for initial estimates of foreground coeffs and to set overall flux scale and phases. Note that this model is not used to obtain initial gain estimates. These must be provided through the gains argument. dtype: numpy dtype, optional the float precision to be used in tensorflow gradient descent. runtime scales roughly inversely linear with precision. default is np.float32 use_min: bool, optional If True, use the set of parameters that determine minimum as the ML params If False, use the last set of parameters visited by the optimization loop. use_redundancy: bool, optional if true, solve for one set of foreground coeffs per redundant baseline group instead of per baseline. notebook_progressbar: bool, optional use progress bar optimized for notebook output. default is False. red_tol: float, optional tolerance for determining baselines redundant (meters) default is 1.0 correct_resid: bool, optional if True, gain correct residual. default is False correct_model: bool, optional if True, gain correct model. default is False weights: UVFlag object, optional. UVFlag weights object containing weights to use for data fitting. default is None -> use nsamples * ~flags if nsamples_in_weights or ~flags if not nsamples_in_weights nsamples_in_weights: bool, optional If True and weights is None, generate weights proportional to nsamples. default is True. graph_mode: bool, optional if True, compile gradient update step in graph mode to speed up runtime by ~2-3x. I've found that this helps on CPUs but on GPUs it actually increases runtime by a similar factor. n_profile_steps: bool, optional number of steps to run profiling on default is 0. profile_log_dir: str, optional directory to save profile logs to default is './logdir' model_regularization: str, optional option to regularize model supported 'post_hoc', 'sum' default is 'post_hoc' which sets sum of amps equal and sum of phases equal. init_guesses_from_previous_time_step: bool, optional if True, then use foreground coeffs and gains from previous time-step to initialize gains for next time step. skip_threshold: float, optional if less then this fraction of data is unflagged on a particular poltime, flag the entire poltime. opt_kwargs: kwarg_dict kwargs for tf.optimizers Returns ------- model: UVData object uvdata object containing model of the foregrounds resid: UVData object uvdata object containing resids which are the data minus the model with gains multiplied and then with the gains divided out. gains: UVCal object uvcal object containing estimates of the gain solutions. These solutions are not referenced to any sky model and are likely orders of fit_history: dictionary containing fit history with fields: 'loss_history': list of values of the loss function in each minimization iteration. """ antpairs_data = uvdata.get_antpairs() if not include_autos: antpairs_data = set([ap for ap in antpairs_data if ap[0] != ap[1]]) uvdata = uvdata.select(inplace=False, bls=[ap for ap in antpairs_data]) resid = copy.deepcopy(uvdata) model = copy.deepcopy(uvdata) model.data_array[:] = 0.0 model.flag_array[:] = False # get redundant groups red_grps = [] for fit_grp in fg_model_comps_dict.keys(): for red_grp in fit_grp: red_grps.append(red_grp) if gains is None: echo( f"{datetime.datetime.now()} Gains are None. Initializing gains starting with unity...\n", verbose=verbose, ) gains = cal_utils.blank_uvcal_from_uvdata(uvdata) if sky_model is None and model_regularization is not None: echo( f"{datetime.datetime.now()} Sky model is None. Initializing from data...\n", verbose=verbose, ) sky_model = cal_utils.apply_gains(uvdata, gains) else: sky_model = sky_model.select(inplace=False, bls=[ap for ap in antpairs_data]) fit_history = {} ants_map = {ant: i for i, ant in enumerate(gains.ant_array)} # generate tensors to hold foreground components. fg_model_comps, corr_inds = tensorize_fg_model_comps_dict( fg_model_comps_dict=fg_model_comps_dict, ants_map=ants_map, dtype=dtype, nfreqs=sky_model.Nfreqs, verbose=verbose, notebook_progressbar=notebook_progressbar, use_redundancy=use_redundancy, grp_size_threshold=grp_size_threshold, ) echo( f"{datetime.datetime.now()}Finished Converting Foreground Modeling Components to Tensors...\n", verbose=verbose, ) # delete fg_model_comps_dict. It can take up a lot of memory. del fg_model_comps_dict # loop through polarization and times. for polnum, pol in enumerate(uvdata.get_pols()): echo( f"{datetime.datetime.now()} Working on pol {pol}, {polnum + 1} of {uvdata.Npols}...\n", verbose=verbose, ) fit_history_p = {} first_time = True for time_index, time in enumerate(np.unique(uvdata.time_array)): echo( f"{datetime.datetime.now()} Working on time {time_index + 1} of {uvdata.Ntimes}...\n", verbose=verbose, ) bltsel = np.isclose(uvdata.time_array, time, atol=1e-7, rtol=0.0) frac_unflagged = np.count_nonzero(~uvdata.flag_array[bltsel, 0, :, polnum]) / ( uvdata.Nbls * uvdata.Nfreqs ) # check that fraction of unflagged data > skip_threshold. if frac_unflagged >= skip_threshold: rmsdata = np.sqrt( np.mean( np.abs(uvdata.data_array[bltsel, 0, :, polnum][~uvdata.flag_array[bltsel, 0, :, polnum]]) ** 2.0 ) ) echo(f"{datetime.datetime.now()} Tensorizing data...\n", verbose=verbose) data_r, data_i, wgts = tensorize_data( uvdata, corr_inds=corr_inds, ants_map=ants_map, polarization=pol, time=time, data_scale_factor=rmsdata, weights=weights, nsamples_in_weights=nsamples_in_weights, dtype=dtype, ) if sky_model is not None: echo(f"{datetime.datetime.now()} Tensorizing sky model...\n", verbose=verbose) sky_model_r, sky_model_i, _ = tensorize_data( sky_model, corr_inds=corr_inds, ants_map=ants_map, polarization=pol, time=time, data_scale_factor=rmsdata, weights=weights, dtype=dtype, ) else: sky_model_r, sky_model_i = None, None if first_time or not init_guesses_from_previous_time_step: first_time = False echo(f"{datetime.datetime.now()} Tensorizing Gains...\n", verbose=verbose) g_r, g_i = tensorize_gains(gains, dtype=dtype, time=time, polarization=pol) # generate initial guess for foreground coeffs. echo( f"{datetime.datetime.now()} Tensorizing Foreground coeffs...\n", verbose=verbose, ) fg_r = tensorize_fg_coeffs( data=data_r, wgts=wgts, fg_model_comps=fg_model_comps, verbose=verbose, notebook_progressbar=notebook_progressbar, ) fg_i = tensorize_fg_coeffs( data=data_i, wgts=wgts, fg_model_comps=fg_model_comps, verbose=verbose, notebook_progressbar=notebook_progressbar, ) if use_model_snr_weights: wgts_model = [fg_model(fgr, fgi, fgc) for fgr, fgi, fgc in zip(fg_r, fg_i, fg_model_comps)] wgts = [(tf.square(wm[0]) + tf.square(wm[1])) * w for wm, w in zip(wgts_model, wgts)] del wgts_model # renormalize wgts_sum = np.sum([np.sum(w) for w in wgts]) wgts = [w / wgts_sum for w in wgts] (g_r, g_i, fg_r, fg_i, fit_history_p[time_index],) = fit_gains_and_foregrounds( g_r=g_r, g_i=g_i, fg_r=fg_r, fg_i=fg_i, data_r=data_r, data_i=data_i, wgts=wgts, fg_comps=fg_model_comps, corr_inds=corr_inds, optimizer=optimizer, use_min=use_min, freeze_model=freeze_model, notebook_progressbar=notebook_progressbar, verbose=verbose, tol=tol, dtype=dtype, maxsteps=maxsteps, graph_mode=graph_mode, n_profile_steps=n_profile_steps, profile_log_dir=profile_log_dir, sky_model_r=sky_model_r, sky_model_i=sky_model_i, model_regularization=model_regularization, **opt_kwargs, ) # insert into model uvdata. insert_model_into_uvdata_tensor( uvdata=model, time=time, polarization=pol, ants_map=ants_map, red_grps=red_grps, model_r=yield_fg_model_array( fg_model_comps=fg_model_comps, fg_coeffs=fg_r, corr_inds=corr_inds, nants=uvdata.Nants_data, nfreqs=uvdata.Nfreqs, ), model_i=yield_fg_model_array( fg_model_comps=fg_model_comps, fg_coeffs=fg_i, corr_inds=corr_inds, nants=uvdata.Nants_data, nfreqs=uvdata.Nfreqs, ), scale_factor=rmsdata, ) # insert gains into uvcal insert_gains_into_uvcal( uvcal=gains, time=time, polarization=pol, gains_re=g_r, gains_im=g_i, ) else: echo( f"{datetime.datetime.now()}: Only {frac_unflagged * 100}-percent of data unflagged. Skipping...\n", verbose=verbose, ) flag_poltime(resid, time=time, polarization=pol) flag_poltime(gains, time=time, polarization=pol) flag_poltime(model, time=time, polarization=pol) fit_history[polnum] = "skipped!" # normalize on sky model if we use post-hoc regularization if not freeze_model and model_regularization == "post_hoc" and np.any(~model.flag_array[bltsel]): renormalize( uvdata_reference_model=sky_model, uvdata_deconv=model, gains=gains, polarization=pol, time=time, additional_flags=uvdata.flag_array, ) fit_history[polnum] = fit_history_p model_with_gains = cal_utils.apply_gains(model, gains, inverse=True) if not correct_model: model = model_with_gains resid.data_array -= model_with_gains.data_array resid.data_array[model_with_gains.flag_array] = 0.0 # set resid to zero where model is flagged. resid.data_array[uvdata.flag_array] = 0.0 # also set resid to zero where data is flagged. if correct_resid: resid = cal_utils.apply_gains(resid, gains) return model, resid, gains, fit_history def flag_poltime(data_object, time, polarization): if isinstance(data_object, UVData): bltsel = np.isclose(data_object.time_array, time, atol=1e-7, rtol=0.0) polnum = np.where( data_object.polarization_array == uvutils.polstr2num(polarization, x_orientation=data_object.x_orientation) )[0][0] data_object.flag_array[bltsel, :, :, polnum] = True data_object.data_array[bltsel, :, :, polnum] = 0.0 elif isinstance(data_object, UVCal): polnum = np.where( data_object.jones_array == uvutils.polstr2num(polarization, x_orientation=data_object.x_orientation) )[0][0] gindt = np.where(np.isclose(data_object.time_array, time, atol=1e-7, rtol=0.0))[0][0] data_object.gain_array[:, 0, :, gindt, polnum] = 1.0 data_object.flag_array[:, 0, :, gindt, polnum] = True else: raise ValueError("only supports data_object that is UVCal or UVData.") def calibrate_and_model_mixed( uvdata, horizon=1.0, min_dly=0.0, offset=0.0, ant_dly=0.0, include_autos=False, verbose=False, red_tol=1.0, red_tol_freq=0.5, n_angle_bins=200, notebook_progressbar=False, use_redundancy=False, use_tensorflow_to_derive_modeling_comps=False, eigenval_cutoff=1e-10, dtype_matinv=np.float64, require_exact_angle_match=True, angle_match_tol=1e-3, grp_size_threshold=5, model_comps_dict=None, save_dict_to=None, **fitting_kwargs, ): """Simultaneously solve for gains and model foregrounds with a mix of DPSS vectors for baselines with no frequency redundancy and simple_cov components for groups of baselines that have some frequency redundancy. Parameters ---------- uvdata: UVData object. dataset to calibrate and filter. horizon: float, optional fraction of baseline delay length to model with dpss modes unitless. default is 1. min_dly: float, optional minimum delay to model with dpss models. in units of ns. default is 0. offset: float optional offset off of horizon wedge to include in dpss delay range. in units of ns. default is 0. ant_dly: float, optional intrinsic chromaticity of each antenna element in units of ns. default is 0. include_autos: bool, optional if true, include autocorrelations in fitting. default is False. verbose: bool, optional lots of text output default is False. red_tol: float, optional tolerance for treating baselines as redundant (meters) default is 1.0 red_tol_freq: float, optional tolerance for treating two baselines as having some frequency redundancy. When frequency redundancy exists, baselines will be modeled jointly. n_angle_bins: int, optional number of angular bins to use between -pi and pi to compare baselines default is 200 notebook_progressbar: bool, optional if True, show graphical notebook progress bar that looks good in jupyter. default is False. use_redundancy: bool, optional If True, model all baselines within each redundant group with the same components If False, model each baseline within each redundant group with sepearate components. default is False. use_tensorflow_to_derive_modeling_comps: bool, optional Use tensorflow methods to derive multi-baseline modeling components. recommended if you have a GPU with enough memory to perform spectral decomposition of multi-baseline covariance matrices. eigenval_cutoff: float, optional threshold of eigenvectors to include in modeling components. dtype_matinv: numpy.dtype, optional data type to use for deriving modeling components. default is np.float64 (need higher precision for cov-mat like calculation) grp_size_threshold: int, optional groups with number of elements less then this value are split up into single baselines. default is 5. model_comps_dict: dict, optional dictionary mapping fitting groups to numpy.ndarray see modeling.yield_mixed_comps for more specifics. default is None -> compute fitting groups automatically. save_dict_to: str, optional save model_comps_dict to hdf5 container if True default is False. fitting_kwargs: kwarg dict additional kwargs for calibrate_and_model_tensor. see docstring of calibrate_and_model_tensor. Returns ------- model: UVData object uvdata object containing DPSS model of intrinsic foregrounds. resid: UVData object uvdata object containing residuals after subtracting model times gains and applying gains. gains: UVCal object uvcal object containing fitted gains. fit_history: dictionary containing fit history for each time-step and polarization in the data with fields: 'loss_history': list of values of the loss function in each minimization iteration. """ # get fitting groups fitting_grps, blvecs, _, _ = modeling.get_uv_overlapping_grps_conjugated( uvdata, red_tol=red_tol, include_autos=include_autos, red_tol_freq=red_tol_freq, n_angle_bins=n_angle_bins, notebook_progressbar=notebook_progressbar, require_exact_angle_match=require_exact_angle_match, angle_match_tol=angle_match_tol, ) if model_comps_dict is None: model_comps_dict = modeling.yield_mixed_comps( fitting_grps, blvecs, uvdata.freq_array[0], eigenval_cutoff=eigenval_cutoff, use_tensorflow=use_tensorflow_to_derive_modeling_comps, ant_dly=ant_dly, horizon=horizon, offset=offset, min_dly=min_dly, verbose=verbose, dtype=dtype_matinv, notebook_progressbar=notebook_progressbar, grp_size_threshold=grp_size_threshold, ) if save_dict_to is not None: np.save(save_dict_to, model_comps_dict) (model, resid, gains, fitted_info,) = calibrate_and_model_tensor( uvdata=uvdata, fg_model_comps_dict=model_comps_dict, include_autos=include_autos, verbose=verbose, notebook_progressbar=notebook_progressbar, use_redundancy=use_redundancy, **fitting_kwargs, ) return model, resid, gains, fitted_info def calibrate_and_model_dpss( uvdata, horizon=1.0, min_dly=0.0, offset=0.0, include_autos=False, verbose=False, red_tol=1.0, notebook_progressbar=False, fg_model_comps_dict=None, **fitting_kwargs, ): """Simultaneously solve for gains and model foregrounds with DPSS vectors. Parameters ---------- uvdata: UVData object. dataset to calibrate and filter. horizon: float, optional fraction of baseline delay length to model with dpss modes unitless. default is 1. min_dly: float, optional minimum delay to model with dpss models. in units of ns. default is 0. offset: float optional offset off of horizon wedge to include in dpss delay range. in units of ns. default is 0. include_autos: bool, optional if true, include autocorrelations in fitting. default is False. verbose: bool, optional lots of text output default is False. red_tol: float, optional tolerance for treating baselines as redundant (meters) default is 1.0 notebook_progressbar: bool, optional use progress bar optimized for notebook output. default is False. fg_model_comps_dict: dict, optional dictionary containing precomputed foreground model components. Currently only supported if use_redundancy is False. fitting_kwargs: kwarg dict additional kwargs for calibrate_and_model_pbl. see docstring of calibrate_and_model_pbl. Returns ------- model: UVData object uvdata object containing DPSS model of intrinsic foregrounds. resid: UVData object uvdata object containing residuals after subtracting model times gains and applying gains. gains: UVCal object uvcal object containing fitted gains. fit_history: dictionary containing fit history for each time-step and polarization in the data with fields: 'loss_history': list of values of the loss function in each minimization iteration. """ dpss_model_comps_dict = modeling.yield_pbl_dpss_model_comps( uvdata, horizon=horizon, min_dly=min_dly, offset=offset, include_autos=include_autos, red_tol=red_tol, notebook_progressbar=notebook_progressbar, verbose=verbose, ) (model, resid, gains, fitted_info,) = calibrate_and_model_tensor( uvdata=uvdata, fg_model_comps_dict=dpss_model_comps_dict, include_autos=include_autos, verbose=verbose, notebook_progressbar=notebook_progressbar, **fitting_kwargs, ) return model, resid, gains, fitted_info def fg_model(fg_r, fg_i, fg_comps): vr = tf.reduce_sum(fg_r * fg_comps, axis=0) vi = tf.reduce_sum(fg_i * fg_comps, axis=0) return vr, vi def data_model(g_r, g_i, fg_r, fg_i, fg_comps, ant0_inds, ant1_inds): gr0 = tf.gather(g_r, ant0_inds) gr1 = tf.gather(g_r, ant1_inds) gi0 = tf.gather(g_i, ant0_inds) gi1 = tf.gather(g_i, ant1_inds) grgr = gr0 * gr1 gigi = gi0 * gi1 grgi = gr0 * gi1 gigr = gi0 * gr1 vr, vi = fg_model(fg_r, fg_i, fg_comps) model_r = (grgr + gigi) * vr + (grgi - gigr) * vi model_i = (gigr - grgi) * vr + (grgr + gigi) * vi return model_r, model_i def mse(model_r, model_i, data_r, data_i, wgts): return tf.reduce_sum((tf.square(data_r - model_r) + tf.square(data_i - model_i)) * wgts) def mse_chunked(g_r, g_i, fg_r, fg_i, fg_comps, nchunks, data_r, data_i, wgts, ant0_inds, ant1_inds, dtype=np.float32): cal_loss = [tf.constant(0.0, dtype) for cnum in range(nchunks)] # now deal with dense components for cnum in range(nchunks): model_r, model_i = data_model( g_r, g_i, fg_r[cnum], fg_i[cnum], fg_comps[cnum], ant0_inds[cnum], ant1_inds[cnum] ) cal_loss[cnum] += mse(model_r, model_i, data_r[cnum], data_i[cnum], wgts[cnum]) return tf.reduce_sum(tf.stack(cal_loss)) def mse_chunked_sum_regularized( g_r, g_i, fg_r, fg_i, fg_comps, nchunks, data_r, data_i, wgts, ant0_inds, ant1_inds, prior_r_sum, prior_i_sum, dtype=np.float32, ): cal_loss = [tf.constant(0.0, dtype) for cnum in range(nchunks)] model_i_sum = [tf.constant(0.0, dtype) for cnum in range(nchunks)] model_r_sum = [tf.constant(0.0, dtype) for cnum in range(nchunks)] # now deal with dense components for cnum in range(nchunks): model_r, model_i = data_model( g_r, g_i, fg_r[cnum], fg_i[cnum], fg_comps[cnum], ant0_inds[cnum], ant1_inds[cnum] ) # compute sum of real and imag parts x weights for regularization. model_r_sum[cnum] += tf.reduce_sum(model_r * wgts[cnum]) model_i_sum[cnum] += tf.reduce_sum(model_i * wgts[cnum]) cal_loss[cnum] += mse(model_r, model_i, data_r[cnum], data_i[cnum], wgts[cnum]) return ( tf.reduce_sum(tf.stack(cal_loss)) + tf.square(tf.reduce_sum(tf.stack(model_r_sum)) - prior_r_sum) + tf.square(tf.reduce_sum(tf.stack(model_i_sum)) - prior_i_sum) ) def read_calibrate_and_model_dpss( input_data_files, input_model_files=None, input_gain_files=None, resid_outfilename=None, gain_outfilename=None, model_outfilename=None, fitted_info_outfilename=None, x_orientation="east", clobber=False, bllen_min=0.0, bllen_max=np.inf, bl_ew_min=0.0, ex_ants=None, select_ants=None, gpu_index=None, gpu_memory_limit=None, precision=32, use_autocorrs_in_weights=False, **calibration_kwargs, ): """ Driver function for using calamity with DPSS modeling. Parameters ---------- input_data_files: list of strings or UVData object. list of paths to input files to read in and calibrate. input_model_files: list of strings or UVData object, optional list of paths to model files for overal phase/amp reference. Default is None -> use input files as model for overall phase and amplitude calibration. input_gain_files: list of strings or UVCal object, optional list of paths to gain files to use as initial guesses for calibration. resid_outfilename: str, optional path for file to write residuals. default is None -> don't write out residuals. gain_outfilename: str, optional path to gain calfits to write fitted gains. default is None -> don't write out gains. model_outfilename, str, optional path to file to write model output. default is None -> Don't write model. fitting_info_outfilename, str, optional string to pickel fitting info to. n_output_chunks: int optional split up outputs into n_output_chunks chunked by time. default is None -> write single output file. bllen_min: float, optional select all baselines with length greater then this value [meters]. default is 0.0 bllen_max: float, optional select only baselines with length less then this value [meters]. default is np.inf. bl_ew_min: float, optional select all baselines with EW projected length greater then this value [meters]. default is 0.0 gpu_index: int, optional limit visible GPUs to be the index of this GPU. default: None -> all GPUs are visible. gpu_memory_limit: float, optional GiB of memory on GPU that can be used. default None -> all memory available. use_autocorrs_in_weights: bool, optional if True, use smooth fits to autocorrelations as inverse variance weights. default is False. calibration_kwargs: kwarg dict see kwrags for calibration_and_model_dpss() Returns ------- model_fit: UVData object uvdata object containing DPSS model of intrinsic foregrounds. resid_fit: UVData object uvdata object containing residuals after subtracting model times gains and applying gains. gains_fit: UVCal object uvcal object containing fitted gains. fit_info: dictionary containing fit history for each time-step and polarization in the data with fields: 'loss_history': list of values of the loss function in each minimization iteration. """ gpus = tf.config.list_physical_devices("GPU") if gpu_index is not None: # See https://www.tensorflow.org/guide/gpu if gpus: if gpu_memory_limit is None: tf.config.set_visible_devices(gpus[gpu_index], "GPU") else: tf.config.set_logical_device_configuration( gpus[gpu_index], [tf.config.LogicalDeviceConfiguration(memory_limit=gpu_memory_limit * 1024)] ) logical_gpus = tf.config.list_logical_devices("GPU") print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU") if isinstance(input_data_files, str): input_data_files = [input_data_files] if isinstance(input_data_files, list): uvd = UVData() uvd.read(input_data_files) else: uvd = input_data_files if use_autocorrs_in_weights: weights = get_auto_weights(uvd) else: weights = None utils.select_baselines( uvd, bllen_min=bllen_min, bllen_max=bllen_max, bl_ew_min=bl_ew_min, ex_ants=ex_ants, select_ants=select_ants ) if isinstance(input_model_files, str): input_model_files = [input_model_files] if input_model_files is not None: if isinstance(input_model_files, list): uvd_model = UVData() uvd_model.read(input_model_files) else: uvd_model = input_model_files else: uvd_model = None if uvd_model is not None: utils.select_baselines(uvd, bllen_min=bllen_min, bllen_max=bllen_max, bl_ew_min=bl_ew_min) if isinstance(input_gain_files, str): input_gain_files = [input_gain_files] if input_gain_files is not None: if isinstance(input_gain_files, list): uvc = UVCal() uvc.read_calfits(input_gain_files) else: uvc = input_gain_files else: uvc = None # run calibration with specified GPU device. dtype = {32: np.float32, 64: np.float64}[precision] if gpu_index is not None and gpus: with tf.device(f"/device:GPU:{gpus[gpu_index].name[-1]}"): model_fit, resid_fit, gains_fit, fit_info = calibrate_and_model_dpss( uvdata=uvd, sky_model=uvd_model, gains=uvc, dtype=dtype, weights=weights, **calibration_kwargs ) else: model_fit, resid_fit, gains_fit, fit_info = calibrate_and_model_dpss( uvdata=uvd, sky_model=uvd_model, gains=uvc, dtype=dtype, weights=weights, **calibration_kwargs ) if resid_outfilename is not None: resid_fit.write_uvh5(resid_outfilename, clobber=clobber) if gain_outfilename is not None: gains_fit.x_orientation = x_orientation gains_fit.write_calfits(gain_outfilename, clobber=clobber) if model_outfilename is not None: model_fit.write_uvh5(model_outfilename, clobber=clobber) # don't write fitting_info_outfilename for now. fit_info["calibration_kwargs"] = calibration_kwargs fit_info["calibration_kwargs"]["dtype"] = dtype # don't write fitting_info_outfilename for now. return model_fit, resid_fit, gains_fit, fit_info def input_output_parser(): ap = argparse.ArgumentParser() sp = ap.add_argument_group("Input and Output Arguments.") sp.add_argument("--input_data_files", type=str, nargs="+", help="paths to data files to calibrate.", required=True) sp.add_argument( "--input_model_files", type=str, nargs="+", help="paths to model files to set overal amplitude and phase." ) sp.add_argument("--input_gain_files", type=str, nargs="+", help="paths to gains to use as a staring point.") sp.add_argument("--resid_outfilename", type=str, default=None, help="postfix for resid output file.") sp.add_argument("--model_outfilename", type=str, default=None, help="postfix for foreground model file.") sp.add_argument("--gain_outfilename", type=str, default=None, help="path for writing fitted gains.") sp.add_argument("--clobber", action="store_true", default="False", help="Overwrite existing outputs.") sp.add_argument("--x_orientation", default="east", type=str, help="x_orientation of feeds to set in output gains.") sp.add_argument( "--bllen_min", default=0.0, type=float, help="minimum baseline length to include in calibration and outputs." ) sp.add_argument( "--bllen_max", default=np.inf, type=float, help="maximum baseline length to include in calbration and outputs." ) sp.add_argument( "--bl_ew_min", default=0.0, type=float, help="minimum EW baseline component to include in calibration and outputs.", ) sp.add_argument( "--ex_ants", default=None, type=int, nargs="+", help="Antennas to exclude from calibration and modeling." ) sp.add_argument( "--select_ants", default=None, type=int, nargs="+", help="Antennas to select exclusively for calibration and modeling.", ) sp.add_argument("--gpu_index", default=None, type=int, help="Index of GPU to run on (if on a multi-GPU machine).") sp.add_argument("--gpu_memory_limit", default=None, type=int, help="Limit GPU memory use to this many GBytes.") sp.add_argument("--precision", default=32, type=int, help="Number of bits to keep track of.") return ap def fitting_argparser(): ap = input_output_parser() sp = ap.add_argument_group("General Fitting Arguments.") sp.add_argument( "--tol", type=float, default=1e-14, help="Stop gradient descent after cost function converges to within this value.", ) sp.add_argument( "--optimizer", type=str, default="Adamax", help="First order optimizer to use for gradient descent." ) sp.add_argument("--maxsteps", type=int, default=10000, help="Max number of steps to iterate during optimization.") sp.add_argument("--verbose", default=False, action="store_true", help="lots of text ouputs.") sp.add_argument( "--use_min", default=False, action="store_true", help="Use params for mimimum cost function derived. Otherwise, use the params last visited by the descent. Avoids momentum overshoot.", ) sp.add_argument( "--use_redundancy", default=False, action="store_true", help="Model redundant visibilities with the same set of foreground parameters.", ) sp.add_argument( "--correct_model", default=True, action="store_true", help="Remove gain effects from foreground model." ) sp.add_argument( "--correct_resid", default=False, action="store_true", help="Apply fitted gains to the fitted residuals." ) sp.add_argument( "--graph_mode", default=False, action="store_true", help="Pre-compile computational graph before running gradient descent. Not reccomended for GPUs.", ) sp.add_argument( "--init_guesses_from_previous_time_step", default=False, action="store_true", help="initialize gain and foreground guesses from previous time step when calibrating multiple times.", ) sp.add_argument("--learning_rate", type=float, default=1e-2, help="gradient descent learning rate.") sp.add_argument( "--red_tol", type=float, default=1.0, help="Tolerance for determining redundancy between baselines [meters]." ) sp.add_argument( "--skip_threshold", type=float, default=0.5, help="Skip and flag time/polarization if more then this fractionf of data is flagged.", ) sp.add_argument("--model_regularization", type=str, default="post_hoc") sp.add_argument( "--nsamples_in_weights", default=False, action="store_true", help="Weight contributions to MSE by nsamples." ) sp.add_argument( "--use_model_snr_weights", default=False, action="store_true", help="If True, weight contributions to MSE as proportional to SNR.", ) sp.add_argument( "--use_autocorrs_in_weights", default=False, action="store_true", help="If True, use autocorrelations to derive relative SNR weights.", ) return ap def dpss_fit_argparser(): ap = fitting_argparser() sp = ap.add_argument_group("DPSS Specific Fitting Arguments.") sp.add_argument("--horizon", default=1.0, type=float, help="Fraction of horizon delay to model with DPSS modes.") sp.add_argument("--min_dly", default=0.0, type=float, help="Minimum delay [ns] to model with DPSS modes.") sp.add_argument( "--offset", default=0.0, type=float, help="Offset from horizon delay [ns] to model with DPSS modes." ) return ap

aewallwi/calamity

calamity/calibration.py

Python

import os SQLALCHEMY_DATABASE_URI = os.environ.get('DB_HOST') SECRET_KEY = os.environ.get('SECRET_KEY') SQLALCHEMY_TRACK_MODIFICATIONS = False

cojok/python_play_around

flask_qa/flask_qa/settings.py

Python

""" Script for processing image (Pre OCR) """ import cv2 import numpy as np import sys import os.path if len(sys.argv) != 3: print "%s input_file output_file" % (sys.argv[0]) sys.exit() else: input_file = sys.argv[1] output_file = sys.argv[2] if not os.path.isfile(input_file): print "No such file '%s'" % input_file sys.exit() DEBUG = 0 # Determine pixel intensity # Apparently human eyes register colors differently. # TVs use this formula to determine # pixel intensity = 0.30R + 0.59G + 0.11B def ii(xx, yy): global img, img_y, img_x if yy >= img_y or xx >= img_x: #print "pixel out of bounds ("+str(y)+","+str(x)+")" return 0 pixel = img[yy][xx] return 0.30 * pixel[2] + 0.59 * pixel[1] + 0.11 * pixel[0] # A quick test to check whether the contour is # a connected shape def connected(contour): first = contour[0][0] last = contour[len(contour) - 1][0] return abs(first[0] - last[0]) <= 1 and abs(first[1] - last[1]) <= 1 # Helper function to return a given contour def c(index): global contours return contours[index] # Count the number of real children def count_children(index, h_, contour): # No children if h_[index][2] < 0: return 0 else: #If the first child is a contour we care about # then count it, otherwise don't if keep(c(h_[index][2])): count = 1 else: count = 0 # Also count all of the child's siblings and their children count += count_siblings(h_[index][2], h_, contour, True) return count # Quick check to test if the contour is a child def is_child(index, h_): return get_parent(index, h_) > 0 # Get the first parent of the contour that we care about def get_parent(index, h_): parent = h_[index][3] while not keep(c(parent)) and parent > 0: parent = h_[parent][3] return parent # Count the number of relevant siblings of a contour def count_siblings(index, h_, contour, inc_children=False): # Include the children if necessary if inc_children: count = count_children(index, h_, contour) else: count = 0 # Look ahead p_ = h_[index][0] while p_ > 0: if keep(c(p_)): count += 1 if inc_children: count += count_children(p_, h_, contour) p_ = h_[p_][0] # Look behind n = h_[index][1] while n > 0: if keep(c(n)): count += 1 if inc_children: count += count_children(n, h_, contour) n = h_[n][1] return count # Whether we care about this contour def keep(contour): return keep_box(contour) and connected(contour) # Whether we should keep the containing box of this # contour based on it's shape def keep_box(contour): xx, yy, w_, h_ = cv2.boundingRect(contour) # width and height need to be floats w_ *= 1.0 h_ *= 1.0 # Test it's shape - if it's too oblong or tall it's # probably not a real character if w_ / h_ < 0.1 or w_ / h_ > 10: if DEBUG: print "\t Rejected because of shape: (" + str(xx) + "," + str(yy) + "," + str(w_) + "," + str(h_) + ")" + \ str(w_ / h_) return False # check size of the box if ((w_ * h_) > ((img_x * img_y) / 5)) or ((w_ * h_) < 15): if DEBUG: print "\t Rejected because of size" return False return True def include_box(index, h_, contour): if DEBUG: print str(index) + ":" if is_child(index, h_): print "\tIs a child" print "\tparent " + str(get_parent(index, h_)) + " has " + str( count_children(get_parent(index, h_), h_, contour)) + " children" print "\thas " + str(count_children(index, h_, contour)) + " children" if is_child(index, h_) and count_children(get_parent(index, h_), h_, contour) <= 2: if DEBUG: print "\t skipping: is an interior to a letter" return False if count_children(index, h_, contour) > 2: if DEBUG: print "\t skipping, is a container of letters" return False if DEBUG: print "\t keeping" return True # Load the image orig_img = cv2.imread(input_file) # Add a border to the image for processing sake img = cv2.copyMakeBorder(orig_img, 50, 50, 50, 50, cv2.BORDER_CONSTANT) # Calculate the width and height of the image img_y = len(img) img_x = len(img[0]) if DEBUG: print "Image is " + str(len(img)) + "x" + str(len(img[0])) #Split out each channel blue, green, red = cv2.split(img) # Run canny edge detection on each channel blue_edges = cv2.Canny(blue, 200, 250) green_edges = cv2.Canny(green, 200, 250) red_edges = cv2.Canny(red, 200, 250) # Join edges back into image edges = blue_edges | green_edges | red_edges # Find the contours contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) hierarchy = hierarchy[0] if DEBUG: processed = edges.copy() rejected = edges.copy() # These are the boxes that we are determining keepers = [] # For each contour, find the bounding rectangle and decide # if it's one we care about for index_, contour_ in enumerate(contours): if DEBUG: print "Processing #%d" % index_ x, y, w, h = cv2.boundingRect(contour_) # Check the contour and it's bounding box if keep(contour_) and include_box(index_, hierarchy, contour_): # It's a winner! keepers.append([contour_, [x, y, w, h]]) if DEBUG: cv2.rectangle(processed, (x, y), (x + w, y + h), (100, 100, 100), 1) cv2.putText(processed, str(index_), (x, y - 5), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255)) else: if DEBUG: cv2.rectangle(rejected, (x, y), (x + w, y + h), (100, 100, 100), 1) cv2.putText(rejected, str(index_), (x, y - 5), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255)) # Make a white copy of our image new_image = edges.copy() new_image.fill(255) boxes = [] # For each box, find the foreground and background intensities for index_, (contour_, box) in enumerate(keepers): # Find the average intensity of the edge pixels to # determine the foreground intensity fg_int = 0.0 for p in contour_: fg_int += ii(p[0][0], p[0][1]) fg_int /= len(contour_) if DEBUG: print "FG Intensity for #%d = %d" % (index_, fg_int) # Find the intensity of three pixels going around the # outside of each corner of the bounding box to determine # the background intensity x_, y_, width, height = box bg_int = \ [ # bottom left corner 3 pixels ii(x_ - 1, y_ - 1), ii(x_ - 1, y_), ii(x_, y_ - 1), # bottom right corner 3 pixels ii(x_ + width + 1, y_ - 1), ii(x_ + width, y_ - 1), ii(x_ + width + 1, y_), # top left corner 3 pixels ii(x_ - 1, y_ + height + 1), ii(x_ - 1, y_ + height), ii(x_, y_ + height + 1), # top right corner 3 pixels ii(x_ + width + 1, y_ + height + 1), ii(x_ + width, y_ + height + 1), ii(x_ + width + 1, y_ + height) ] # Find the median of the background # pixels determined above bg_int = np.median(bg_int) if DEBUG: print "BG Intensity for #%d = %s" % (index_, repr(bg_int)) # Determine if the box should be inverted if fg_int >= bg_int: fg = 255 bg = 0 else: fg = 0 bg = 255 # Loop through every pixel in the box and color the # pixel accordingly for x in range(x_, x_ + width): for y in range(y_, y_ + height): if y >= img_y or x >= img_x: if DEBUG: print "pixel out of bounds (%d,%d)" % (y, x) continue if ii(x, y) > fg_int: new_image[y][x] = bg else: new_image[y][x] = fg # blur a bit to improve ocr accuracy new_image = cv2.blur(new_image, (2, 2)) cv2.imwrite(output_file, new_image) if DEBUG: cv2.imwrite('edges.png', edges) cv2.imwrite('processed.png', processed) cv2.imwrite('rejected.png', rejected)

sheeshmohsin/insta_hack

panverification/panapp/process_image.py

Python

from django.forms import ( CheckboxSelectMultiple, EmailInput, FileInput, HiddenInput, NumberInput, PasswordInput, Textarea, TextInput, URLInput, ) from django.utils.safestring import mark_safe from .bootstrap import get_bootstrap_setting, get_field_renderer, get_form_renderer, get_formset_renderer from .exceptions import BootstrapError from .text import text_value from .utils import add_css_class, render_tag FORM_GROUP_CLASS = "form-group" def render_formset(formset, **kwargs): """Render a formset to a Bootstrap layout.""" renderer_cls = get_formset_renderer(**kwargs) return renderer_cls(formset, **kwargs).render() def render_formset_errors(formset, **kwargs): """Render formset errors to a Bootstrap layout.""" renderer_cls = get_formset_renderer(**kwargs) return renderer_cls(formset, **kwargs).render_errors() def render_form(form, **kwargs): """Render a form to a Bootstrap layout.""" renderer_cls = get_form_renderer(**kwargs) return renderer_cls(form, **kwargs).render() def render_form_errors(form, type="all", **kwargs): """Render form errors to a Bootstrap layout.""" renderer_cls = get_form_renderer(**kwargs) return renderer_cls(form, **kwargs).render_errors(type) def render_field(field, **kwargs): """Render a field to a Bootstrap layout.""" renderer_cls = get_field_renderer(**kwargs) return renderer_cls(field, **kwargs).render() def render_label(content, label_for=None, label_class=None, label_title=""): """Render a label with content.""" attrs = {} if label_for: attrs["for"] = label_for if label_class: attrs["class"] = label_class if label_title: attrs["title"] = label_title return render_tag("label", attrs=attrs, content=content) def render_button( content, button_type=None, button_class="btn-primary", size="", href="", name=None, value=None, title=None, extra_classes="", id="", ): """Render a button with content.""" attrs = {} classes = add_css_class("btn", button_class) size = text_value(size).lower().strip() if size == "xs": classes = add_css_class(classes, "btn-xs") elif size == "sm" or size == "small": classes = add_css_class(classes, "btn-sm") elif size == "lg" or size == "large": classes = add_css_class(classes, "btn-lg") elif size == "md" or size == "medium": pass elif size: raise BootstrapError(f'Parameter "size" should be "xs", "sm", "lg" or empty ("{size}" given).') if button_type: if button_type not in ("submit", "reset", "button", "link"): raise BootstrapError( f'Parameter "button_type" should be "submit", "reset", "button", "link" or empty ("{button_type}" given).' ) if button_type != "link": attrs["type"] = button_type classes = add_css_class(classes, extra_classes) attrs["class"] = classes if href: tag = "a" if button_type and button_type != "link": raise BootstrapError(f'Button of type "{button_type}" is not allowed a "href" parameter.') attrs["href"] = href # Specify role for link with button appearance attrs.setdefault("role", "button") else: tag = "button" if id: attrs["id"] = id if name: attrs["name"] = name if value: attrs["value"] = value if title: attrs["title"] = title return render_tag(tag, attrs=attrs, content=mark_safe(content)) def render_field_and_label(field, label, field_class="", label_for=None, label_class="", layout="", **kwargs): """Render a field with its label.""" if layout == "horizontal": if not label_class: label_class = get_bootstrap_setting("horizontal_label_class") if not field_class: field_class = get_bootstrap_setting("horizontal_field_class") if not label: label = mark_safe("&#160;") label_class = add_css_class(label_class, "control-label") html = field if field_class: html = f'<div class="{field_class}">{html}</div>' if label: html = render_label(label, label_for=label_for, label_class=label_class) + html return html def render_form_group(content, css_class=FORM_GROUP_CLASS): """Render a Bootstrap form group.""" return f'<div class="{css_class}">{content}</div>' def is_widget_with_placeholder(widget): """ Return whether this widget should have a placeholder. Only text, text area, number, e-mail, url, password, number and derived inputs have placeholders. """ return isinstance(widget, (TextInput, Textarea, NumberInput, EmailInput, URLInput, PasswordInput))

Natureshadow/django-bootstrap4

src/bootstrap4/forms.py

Python

""" Revision ID: 0304a_merge Revises: 0304_remove_org_to_service, 0303a_merge Create Date: 2019-07-29 16:18:27.467361 """ # revision identifiers, used by Alembic. revision = "0304a_merge" down_revision = ("0304_remove_org_to_service", "0303a_merge") branch_labels = None import sqlalchemy as sa from alembic import op def upgrade(): pass def downgrade(): pass

cds-snc/notification-api

migrations/versions/0304a_merge.py

Python

import argparse import os.path from os import walk import sys import xml.etree.ElementTree from lib.config import get_env_var from lib.transifex import (pull_source_files_from_transifex, should_use_transifex, pull_xtb_without_transifex, combine_override_xtb_into_original) from lib.grd_string_replacements import get_override_file_path PRESEARCH_SOURCE_ROOT = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) def parse_args(): parser = argparse.ArgumentParser(description='Push strings to Transifex') parser.add_argument('--source_string_path', nargs=1) return parser.parse_args() def check_args(): transifex_info = (get_env_var('TRANSIFEX_USERNAME') and get_env_var('TRANSIFEX_PASSWORD') or get_env_var('TRANSIFEX_API_KEY')) message = 'TRANSIFEX_USERNAME and TRANSIFEX_PASSWORD or '\ 'TRANSIFEX_API_KEY must be set' assert transifex_info, message def main(): args = parse_args() check_args() source_string_path = os.path.join(PRESEARCH_SOURCE_ROOT, args.source_string_path[0]) filename = os.path.basename(source_string_path).split('.')[0] if should_use_transifex(source_string_path, filename): print('Transifex: ', source_string_path) pull_source_files_from_transifex(source_string_path, filename) else: print('Local: ', source_string_path) override_path = get_override_file_path(source_string_path) print('Transifex override: ', override_path) override_filename = os.path.basename(override_path).split('.')[0] override_exists = os.path.exists(override_path) if override_exists: pull_source_files_from_transifex(override_path, override_filename) pull_xtb_without_transifex(source_string_path, PRESEARCH_SOURCE_ROOT) if override_exists: combine_override_xtb_into_original(source_string_path) if __name__ == '__main__': sys.exit(main())

mamylinx/presearch-core

script/pull-l10n.py

Python

# Copyright (c) 2019, Xilinx # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import pynq from pynq import pmbus from pynq import Device import numpy as np import pandas as pd import time import argparse # Set up data acquisition using PYNQ's PMBus API def setup_power_recording(): rails = pmbus.get_xrt_sysfs_rails() #We create a recorder monitoring the three rails that have power measurement on Alveo. #Total board power is obtained by summing together the PCI Express and Auxilliary 12V rails. #While some current is also drawn over the PCIe 5V rail this is negligible compared to the 12V rails and isn't recorded. #We also measure the VCC_INT power which is the primary supply to the FPGA. recorder = pmbus.DataRecorder(rails["12v_aux"].power, rails["12v_pex"].power, rails["vccint"].power) return recorder # ## Synthetic Throughput Test # We execute inference of a configurable-size batch of images, without data movement. We measure the latency, throughput, and power def benchmark_synthetic(bs, nreps): ibuf = pynq.allocate((bs,3,224,224), dtype=np.int8, target=ol.bank0) obuf = pynq.allocate((bs,5), dtype=np.uint32, target=ol.bank0) # Start power monitoring pwr_rec = setup_power_recording() pwr_rec.record(0.1) total_duration = time.monotonic() for i in range(nreps): accelerator.call(ibuf, obuf, fcbuf, bs) total_duration = time.monotonic() - total_duration # Stop the power monitoring pwr_rec.stop() latency = total_duration/nreps fps = int((nreps/total_duration)*bs) # Aggregate board/fpga power into a Pandas dataframe f = pwr_rec.frame powers = pd.DataFrame(index=f.index) powers['board_power'] = f['12v_aux_power'] + f['12v_pex_power'] powers['fpga_power'] = f['vccint_power'] return fps, latency, powers if __name__== "__main__": parser = argparse.ArgumentParser(description='ResNet50 inference with FINN and PYNQ on Alveo') parser.add_argument('--xclbin', type=str, default='resnet50.xclbin', help='Accelerator image file (xclbin)') parser.add_argument('--fcweights', type=str, default='fcweights.csv', help='FC weights file (CSV)') parser.add_argument('--shell', type=str, default='xilinx_u250_xdma_201830_2', help='Name of compatible shell') parser.add_argument('--bs', type=int, default=1, help='Batch size (images processed per accelerator invocation)') parser.add_argument('--reps',type=int, default=100, help='Number of batches to run') args = parser.parse_args() # discover a compatible shell if there are multiple devices = Device.devices if len(devices) > 1: for i in range(len(devices)): print("{}) {}".format(i, devices[i].name)) if devices[i].name == args.shell: print("Compatible shell found, using device",i) Device.active_device = devices[i] break ol=pynq.Overlay(args.xclbin) accelerator=ol.resnet50_1 #allocate a buffer for FC weights, targeting the Alveo DDR Bank 0 fcbuf = pynq.allocate((1000,2048), dtype=np.int8, target=ol.bank0) # Load the weight from a CSV file and push them to the accelerator buffer: fcweights = np.genfromtxt(args.fcweights, delimiter=',', dtype=np.int8) #csv reader erroneously adds one extra element to the end, so remove, then reshape fcweights = fcweights[:-1].reshape(1000,2048) fcbuf[:] = fcweights #Move the data to the Alveo DDR fcbuf.sync_to_device() fps, latency, power = benchmark_synthetic(args.bs,args.reps) print("Throughput:",fps,"FPS") print("Latency:",round(latency*1000,2),"ms") print("FPGA Power:",round(power.mean()['fpga_power'],2),"Watts") print("Board Power:",round(power.mean()['board_power'],2),"Watts")

Tobi-Alonso/ResNet50-PYNQ

host/synth_bench_power.py

Python

def get_workout(day): if day == 'Monday': return 'Chest+biceps' elif day == 'Tuesday': return 'Back+triceps' elif day == 'Wednesday': return 'Core' elif day == 'Thursday': return 'Legs' elif day == 'Friday': return 'Shoulders' elif day in ('Saturday', 'Sunday'): return 'Rest' raise ValueError('Not a day') # use a dict to sort it out workwouts = { 'Monday': 'Chest+biceps', 'Tuesday': 'Back+triceps', 'Wednesday': 'Core', 'Thursday': 'Legs', 'Friday': 'Shoulders', 'Saturday': 'Rest', 'Sunday': 'Rest' } # one other way days = 'Monday Tuesday Wednesday Thursday Friday Saturday Sunday'.split() routines = 'Chest+biceps Back+triceps Core Legs Shoulders Rest Rest'.split() workouts2 = dict(zip(days, routines)) print(workouts2) def get_workout(day): routine = workwouts.get(day) if routine is None: raise ValueError('Not a day') return routine print(get_workout('Monday'))

pruty20/100daysofcode-with-python-course

days/34-36-refactoring/refactoring_yo.py

Python

#!/usr/bin/env python # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- from codecs import open from setuptools import setup, find_packages VERSION = "0.1.0" CLASSIFIERS = [ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'License :: OSI Approved :: MIT License', ] DEPENDENCIES = [] with open('README.md', 'r', encoding='utf-8') as f: README = f.read() with open('HISTORY.rst', 'r', encoding='utf-8') as f: HISTORY = f.read() setup( name='anf-preview', version=VERSION, description='Provides a preview for upcoming Azure NetApp Files (ANF) features.', long_description='An Azure CLI Extension for Azure NetApp Files (ANF) preview features.', license='MIT', author='Microsoft Corporation', author_email='azpycli@microsoft.com', url='https://github.com/Azure/azure-cli-extensions/tree/master/src/anf-preview', classifiers=CLASSIFIERS, packages=find_packages(exclude=["tests"]), package_data={'azext_anf_preview': ['azext_metadata.json']}, install_requires=DEPENDENCIES )

XiangyuL-Microsoft/azure-cli-extensions

src/anf-preview/setup.py

Python

# Copyright (c) 2014 Alex Meade. All rights reserved. # Copyright (c) 2014 Clinton Knight. All rights reserved. # Copyright (c) 2015 Tom Barron. All rights reserved. # Copyright (c) 2016 Mike Rooney. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import sys from oslo_log import log as logging from oslo_utils import excutils import six from cinder import exception from cinder.i18n import _ from cinder import utils from cinder.volume.drivers.netapp.dataontap.client import api as netapp_api from cinder.volume.drivers.netapp import utils as na_utils LOG = logging.getLogger(__name__) DELETED_PREFIX = 'deleted_cinder_' MAX_SIZE_FOR_A_LUN = '17555678822400' @six.add_metaclass(utils.TraceWrapperMetaclass) class Client(object): def __init__(self, **kwargs): host = kwargs['hostname'] username = kwargs['username'] password = kwargs['password'] api_trace_pattern = kwargs['api_trace_pattern'] self.connection = netapp_api.NaServer( host=host, transport_type=kwargs['transport_type'], port=kwargs['port'], username=username, password=password, api_trace_pattern=api_trace_pattern) self.ssh_client = self._init_ssh_client(host, username, password) def _init_ssh_client(self, host, username, password): return netapp_api.SSHUtil( host=host, username=username, password=password) def _init_features(self): """Set up the repository of available Data ONTAP features.""" self.features = na_utils.Features() def get_ontap_version(self, cached=True): """Gets the ONTAP version.""" if cached: return self.connection.get_ontap_version() ontap_version = netapp_api.NaElement("system-get-version") result = self.connection.invoke_successfully(ontap_version, True) version_tuple = result.get_child_by_name( 'version-tuple') or netapp_api.NaElement('none') system_version_tuple = version_tuple.get_child_by_name( 'system-version-tuple') or netapp_api.NaElement('none') generation = system_version_tuple.get_child_content("generation") major = system_version_tuple.get_child_content("major") return '%(generation)s.%(major)s' % { 'generation': generation, 'major': major} def get_ontapi_version(self, cached=True): """Gets the supported ontapi version.""" if cached: return self.connection.get_api_version() ontapi_version = netapp_api.NaElement('system-get-ontapi-version') res = self.connection.invoke_successfully(ontapi_version, False) major = res.get_child_content('major-version') minor = res.get_child_content('minor-version') return major, minor def _strip_xml_namespace(self, string): if string.startswith('{') and '}' in string: return string.split('}', 1)[1] return string def check_is_naelement(self, elem): """Checks if object is instance of NaElement.""" if not isinstance(elem, netapp_api.NaElement): raise ValueError('Expects NaElement') def create_lun(self, volume_name, lun_name, size, metadata, qos_policy_group_name=None): """Issues API request for creating LUN on volume.""" path = '/vol/%s/%s' % (volume_name, lun_name) space_reservation = metadata['SpaceReserved'] initial_size = size ontap_version = self.get_ontap_version() # On older ONTAP versions the extend size is limited to its # geometry on max_resize_size. In order to remove this # limitation we create the LUN with its maximum possible size # and then shrink to the requested size. if ontap_version < '9.5': initial_size = MAX_SIZE_FOR_A_LUN # In order to create a LUN with its maximum size (16TB), # the space_reservation needs to be disabled space_reservation = 'false' params = {'path': path, 'size': str(initial_size), 'ostype': metadata['OsType'], 'space-reservation-enabled': space_reservation} version = self.get_ontapi_version() if version >= (1, 110): params['use-exact-size'] = 'true' lun_create = netapp_api.NaElement.create_node_with_children( 'lun-create-by-size', **params) if qos_policy_group_name: lun_create.add_new_child('qos-policy-group', qos_policy_group_name) try: self.connection.invoke_successfully(lun_create, True) except netapp_api.NaApiError as ex: with excutils.save_and_reraise_exception(): LOG.error("Error provisioning volume %(lun_name)s on " "%(volume_name)s. Details: %(ex)s", {'lun_name': lun_name, 'volume_name': volume_name, 'ex': ex}) if ontap_version < '9.5': self.do_direct_resize(path, six.text_type(size)) if metadata['SpaceReserved'] == 'true': self.set_lun_space_reservation(path, True) def set_lun_space_reservation(self, path, flag): """Sets the LUN space reservation on ONTAP.""" lun_modify_space_reservation = ( netapp_api.NaElement.create_node_with_children( 'lun-set-space-reservation-info', **{ 'path': path, 'enable': str(flag)})) self.connection.invoke_successfully(lun_modify_space_reservation, True) def destroy_lun(self, path, force=True): """Destroys the LUN at the path.""" lun_destroy = netapp_api.NaElement.create_node_with_children( 'lun-destroy', **{'path': path}) if force: lun_destroy.add_new_child('force', 'true') self.connection.invoke_successfully(lun_destroy, True) seg = path.split("/") LOG.debug("Destroyed LUN %s", seg[-1]) def map_lun(self, path, igroup_name, lun_id=None): """Maps LUN to the initiator and returns LUN id assigned.""" lun_map = netapp_api.NaElement.create_node_with_children( 'lun-map', **{'path': path, 'initiator-group': igroup_name}) if lun_id: lun_map.add_new_child('lun-id', lun_id) try: result = self.connection.invoke_successfully(lun_map, True) return result.get_child_content('lun-id-assigned') except netapp_api.NaApiError as e: code = e.code message = e.message LOG.warning('Error mapping LUN. Code :%(code)s, Message: ' '%(message)s', {'code': code, 'message': message}) raise def unmap_lun(self, path, igroup_name): """Unmaps a LUN from given initiator.""" lun_unmap = netapp_api.NaElement.create_node_with_children( 'lun-unmap', **{'path': path, 'initiator-group': igroup_name}) try: self.connection.invoke_successfully(lun_unmap, True) except netapp_api.NaApiError as e: exc_info = sys.exc_info() LOG.warning("Error unmapping LUN. Code :%(code)s, Message: " "%(message)s", {'code': e.code, 'message': e.message}) # if the LUN is already unmapped if e.code == '13115' or e.code == '9016': pass else: six.reraise(*exc_info) def create_igroup(self, igroup, igroup_type='iscsi', os_type='default'): """Creates igroup with specified args.""" igroup_create = netapp_api.NaElement.create_node_with_children( 'igroup-create', **{'initiator-group-name': igroup, 'initiator-group-type': igroup_type, 'os-type': os_type}) self.connection.invoke_successfully(igroup_create, True) def add_igroup_initiator(self, igroup, initiator): """Adds initiators to the specified igroup.""" igroup_add = netapp_api.NaElement.create_node_with_children( 'igroup-add', **{'initiator-group-name': igroup, 'initiator': initiator}) self.connection.invoke_successfully(igroup_add, True) def do_direct_resize(self, path, new_size_bytes, force=True): """Resize the LUN.""" seg = path.split("/") LOG.info("Resizing LUN %s directly to new size.", seg[-1]) lun_resize = netapp_api.NaElement.create_node_with_children( 'lun-resize', **{'path': path, 'size': new_size_bytes}) if force: lun_resize.add_new_child('force', 'true') self.connection.invoke_successfully(lun_resize, True) def get_lun_geometry(self, path): """Gets the LUN geometry.""" geometry = {} lun_geo = netapp_api.NaElement("lun-get-geometry") lun_geo.add_new_child('path', path) try: result = self.connection.invoke_successfully(lun_geo, True) geometry['size'] = result.get_child_content("size") geometry['bytes_per_sector'] = result.get_child_content( "bytes-per-sector") geometry['sectors_per_track'] = result.get_child_content( "sectors-per-track") geometry['tracks_per_cylinder'] = result.get_child_content( "tracks-per-cylinder") geometry['cylinders'] = result.get_child_content("cylinders") geometry['max_resize'] = result.get_child_content( "max-resize-size") except Exception as e: LOG.error("LUN %(path)s geometry failed. Message - %(msg)s", {'path': path, 'msg': six.text_type(e)}) return geometry def get_volume_options(self, volume_name): """Get the value for the volume option.""" opts = [] vol_option_list = netapp_api.NaElement("volume-options-list-info") vol_option_list.add_new_child('volume', volume_name) result = self.connection.invoke_successfully(vol_option_list, True) options = result.get_child_by_name("options") if options: opts = options.get_children() return opts def move_lun(self, path, new_path): """Moves the LUN at path to new path.""" seg = path.split("/") new_seg = new_path.split("/") LOG.debug("Moving LUN %(name)s to %(new_name)s.", {'name': seg[-1], 'new_name': new_seg[-1]}) lun_move = netapp_api.NaElement("lun-move") lun_move.add_new_child("path", path) lun_move.add_new_child("new-path", new_path) self.connection.invoke_successfully(lun_move, True) def get_iscsi_target_details(self): """Gets the iSCSI target portal details.""" raise NotImplementedError() def get_fc_target_wwpns(self): """Gets the FC target details.""" raise NotImplementedError() def get_iscsi_service_details(self): """Returns iscsi iqn.""" raise NotImplementedError() def check_iscsi_initiator_exists(self, iqn): """Returns True if initiator exists.""" raise NotImplementedError() def set_iscsi_chap_authentication(self, iqn, username, password): """Provides NetApp host's CHAP credentials to the backend.""" raise NotImplementedError() def get_lun_list(self): """Gets the list of LUNs on filer.""" raise NotImplementedError() def get_igroup_by_initiators(self, initiator_list): """Get igroups exactly matching a set of initiators.""" raise NotImplementedError() def _has_luns_mapped_to_initiator(self, initiator): """Checks whether any LUNs are mapped to the given initiator.""" lun_list_api = netapp_api.NaElement('lun-initiator-list-map-info') lun_list_api.add_new_child('initiator', initiator) result = self.connection.invoke_successfully(lun_list_api, True) lun_maps_container = result.get_child_by_name( 'lun-maps') or netapp_api.NaElement('none') return len(lun_maps_container.get_children()) > 0 def has_luns_mapped_to_initiators(self, initiator_list): """Checks whether any LUNs are mapped to the given initiator(s).""" for initiator in initiator_list: if self._has_luns_mapped_to_initiator(initiator): return True return False def get_lun_by_args(self, **args): """Retrieves LUNs with specified args.""" raise NotImplementedError() def get_performance_counter_info(self, object_name, counter_name): """Gets info about one or more Data ONTAP performance counters.""" api_args = {'objectname': object_name} result = self.connection.send_request('perf-object-counter-list-info', api_args, enable_tunneling=False) counters = result.get_child_by_name( 'counters') or netapp_api.NaElement('None') for counter in counters.get_children(): if counter.get_child_content('name') == counter_name: labels = [] label_list = counter.get_child_by_name( 'labels') or netapp_api.NaElement('None') for label in label_list.get_children(): labels.extend(label.get_content().split(',')) base_counter = counter.get_child_content('base-counter') return { 'name': counter_name, 'labels': labels, 'base-counter': base_counter, } else: raise exception.NotFound(_('Counter %s not found') % counter_name) def delete_snapshot(self, volume_name, snapshot_name): """Deletes a volume snapshot.""" api_args = {'volume': volume_name, 'snapshot': snapshot_name} self.connection.send_request('snapshot-delete', api_args) def create_cg_snapshot(self, volume_names, snapshot_name): """Creates a consistency group snapshot out of one or more flexvols. ONTAP requires an invocation of cg-start to first fence off the flexvols to be included in the snapshot. If cg-start returns success, a cg-commit must be executed to finalized the snapshot and unfence the flexvols. """ cg_id = self._start_cg_snapshot(volume_names, snapshot_name) if not cg_id: msg = _('Could not start consistency group snapshot %s.') raise exception.VolumeBackendAPIException(data=msg % snapshot_name) self._commit_cg_snapshot(cg_id) def _start_cg_snapshot(self, volume_names, snapshot_name): snapshot_init = { 'snapshot': snapshot_name, 'timeout': 'relaxed', 'volumes': [ {'volume-name': volume_name} for volume_name in volume_names ], } result = self.connection.send_request('cg-start', snapshot_init) return result.get_child_content('cg-id') def _commit_cg_snapshot(self, cg_id): snapshot_commit = {'cg-id': cg_id} self.connection.send_request('cg-commit', snapshot_commit) def get_snapshot(self, volume_name, snapshot_name): """Gets a single snapshot.""" raise NotImplementedError() @utils.retry(exception.SnapshotIsBusy) def wait_for_busy_snapshot(self, flexvol, snapshot_name): """Checks for and handles a busy snapshot. If a snapshot is busy, for reasons other than cloning, an exception is raised immediately. Otherwise, wait for a period of time for the clone dependency to finish before giving up. If the snapshot is not busy then no action is taken and the method exits. """ snapshot = self.get_snapshot(flexvol, snapshot_name) if not snapshot['busy']: LOG.debug("Backing consistency group snapshot %s available for " "deletion.", snapshot_name) return else: LOG.debug("Snapshot %(snap)s for vol %(vol)s is busy, waiting " "for volume clone dependency to clear.", {"snap": snapshot_name, "vol": flexvol}) raise exception.SnapshotIsBusy(snapshot_name=snapshot_name) def mark_snapshot_for_deletion(self, volume, snapshot_name): """Mark snapshot for deletion by renaming snapshot.""" return self.rename_snapshot( volume, snapshot_name, DELETED_PREFIX + snapshot_name) def rename_snapshot(self, volume, current_name, new_name): """Renames a snapshot.""" api_args = { 'volume': volume, 'current-name': current_name, 'new-name': new_name, } return self.connection.send_request('snapshot-rename', api_args)

sapcc/cinder

cinder/volume/drivers/netapp/dataontap/client/client_base.py

Python

from typing import Optional from data_structures.singly_linked_list_node import SinglyLinkedListNode def rotate_list(head: Optional[SinglyLinkedListNode], amount: int) -> Optional[SinglyLinkedListNode]: if not head: return None if not head.next: return head current = head number = 1 while current.next: number += 1 current = current.next current.next = head current = head for _ in range((number - amount) % number - 1): current = current.next new_head = current.next current.next = None return new_head

ahcode0919/python-ds-algorithms

singly_linked_lists/rotate_list.py

Python

#/* # * Player - One Hell of a Robot Server # * Copyright (C) 2004 # * Andrew Howard # * # * # * This library is free software; you can redistribute it and/or # * modify it under the terms of the GNU Lesser General Public # * License as published by the Free Software Foundation; either # * version 2.1 of the License, or (at your option) any later version. # * # * This library is distributed in the hope that it will be useful, # * but WITHOUT ANY WARRANTY; without even the implied warranty of # * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # * Lesser General Public License for more details. # * # * You should have received a copy of the GNU Lesser General Public # * License along with this library; if not, write to the Free Software # * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # */ # Desc: Test the camera # Author: Andrew Howard # Date: 15 Sep 2004 # CVS: $Id: test_camera.py 8114 2009-07-24 11:28:20Z thjc $ from playerc import * def test_camera(client, index, context): """Basic test of the camera interface.""" camera = playerc_camera(client, index) if camera.subscribe(PLAYERC_OPEN_MODE) != 0: raise playerc_error_str() for i in range(10): while 1: id = client.read() if id == camera.info.id: break if context: print context, print "camera: [%14.3f] [%d %d %d %d]" % \ (camera.info.datatime, camera.width, camera.height, camera.depth, camera.image_size), print # Save the image filename = 'camera_%03d.ppm' % i print 'camera: saving [%s] (only works for RGB888)' % filename test_camera_save(camera, filename); camera.unsubscribe() return def test_camera_save(camera, filename): """Save a camera image. Assumes the image is RGB888""" file = open(filename, 'w+'); assert(file) # Write ppm header file.write('P6\n%d %d\n%d\n' % (camera.width, camera.height, 255)) # TODO: ? # Write image data file.write(camera.image) return

parasol-ppl/PPL_utils

physicalrobots/player/client_libs/libplayerc/bindings/python/test/test_camera.py

Python

from cwltool.main import main from .util import get_data def test_missing_cwl_version(): """No cwlVersion in the workflow.""" assert main([get_data('tests/wf/missing_cwlVersion.cwl')]) == 1 def test_incorrect_cwl_version(): """Using cwlVersion: v0.1 in the workflow.""" assert main([get_data('tests/wf/wrong_cwlVersion.cwl')]) == 1

jayvdb/cwltool

tests/test_cwl_version.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Launches PODs""" import json import math import time from datetime import datetime as dt from typing import Optional, Tuple import pendulum import tenacity from kubernetes import client, watch from kubernetes.client.models.v1_pod import V1Pod from kubernetes.client.rest import ApiException from kubernetes.stream import stream as kubernetes_stream from requests.exceptions import BaseHTTPError from airflow.exceptions import AirflowException from airflow.kubernetes.kube_client import get_kube_client from airflow.kubernetes.pod_generator import PodDefaults from airflow.settings import pod_mutation_hook from airflow.utils.log.logging_mixin import LoggingMixin from airflow.utils.state import State def should_retry_start_pod(exception: Exception): """Check if an Exception indicates a transient error and warrants retrying""" if isinstance(exception, ApiException): return exception.status == 409 return False class PodStatus: """Status of the PODs""" PENDING = 'pending' RUNNING = 'running' FAILED = 'failed' SUCCEEDED = 'succeeded' class PodLauncher(LoggingMixin): """Launches PODS""" def __init__( self, kube_client: client.CoreV1Api = None, in_cluster: bool = True, cluster_context: Optional[str] = None, extract_xcom: bool = False, ): """ Creates the launcher. :param kube_client: kubernetes client :param in_cluster: whether we are in cluster :param cluster_context: context of the cluster :param extract_xcom: whether we should extract xcom """ super().__init__() self._client = kube_client or get_kube_client(in_cluster=in_cluster, cluster_context=cluster_context) self._watch = watch.Watch() self.extract_xcom = extract_xcom def run_pod_async(self, pod: V1Pod, **kwargs): """Runs POD asynchronously""" pod_mutation_hook(pod) sanitized_pod = self._client.api_client.sanitize_for_serialization(pod) json_pod = json.dumps(sanitized_pod, indent=2) self.log.debug('Pod Creation Request: \n%s', json_pod) try: resp = self._client.create_namespaced_pod( body=sanitized_pod, namespace=pod.metadata.namespace, **kwargs ) self.log.debug('Pod Creation Response: %s', resp) except Exception as e: self.log.exception('Exception when attempting to create Namespaced Pod: %s', json_pod) raise e return resp def delete_pod(self, pod: V1Pod): """Deletes POD""" try: self._client.delete_namespaced_pod( pod.metadata.name, pod.metadata.namespace, body=client.V1DeleteOptions() ) except ApiException as e: # If the pod is already deleted if e.status != 404: raise @tenacity.retry( stop=tenacity.stop_after_attempt(3), wait=tenacity.wait_random_exponential(), reraise=True, retry=tenacity.retry_if_exception(should_retry_start_pod), ) def start_pod(self, pod: V1Pod, startup_timeout: int = 120): """ Launches the pod synchronously and waits for completion. :param pod: :param startup_timeout: Timeout for startup of the pod (if pod is pending for too long, fails task) :return: """ resp = self.run_pod_async(pod) curr_time = dt.now() if resp.status.start_time is None: while self.pod_not_started(pod): self.log.warning("Pod not yet started: %s", pod.metadata.name) delta = dt.now() - curr_time if delta.total_seconds() >= startup_timeout: raise AirflowException("Pod took too long to start") time.sleep(1) def monitor_pod(self, pod: V1Pod, get_logs: bool) -> Tuple[State, V1Pod, Optional[str]]: """ Monitors a pod and returns the final state, pod and xcom result :param pod: pod spec that will be monitored :param get_logs: whether to read the logs locally :return: Tuple[State, Optional[str]] """ if get_logs: read_logs_since_sec = None last_log_time = None while True: logs = self.read_pod_logs(pod, timestamps=True, since_seconds=read_logs_since_sec) for line in logs: timestamp, message = self.parse_log_line(line.decode('utf-8')) last_log_time = pendulum.parse(timestamp) self.log.info(message) time.sleep(1) if not self.base_container_is_running(pod): break self.log.warning('Pod %s log read interrupted', pod.metadata.name) if last_log_time: delta = pendulum.now() - last_log_time # Prefer logs duplication rather than loss read_logs_since_sec = math.ceil(delta.total_seconds()) result = None if self.extract_xcom: while self.base_container_is_running(pod): self.log.info('Container %s has state %s', pod.metadata.name, State.RUNNING) time.sleep(2) result = self._extract_xcom(pod) self.log.info(result) result = json.loads(result) while self.pod_is_running(pod): self.log.info('Pod %s has state %s', pod.metadata.name, State.RUNNING) time.sleep(2) remote_pod = self.read_pod(pod) return self._task_status(remote_pod), remote_pod, result def parse_log_line(self, line: str) -> Tuple[str, str]: """ Parse K8s log line and returns the final state :param line: k8s log line :type line: str :return: timestamp and log message :rtype: Tuple[str, str] """ split_at = line.find(' ') if split_at == -1: raise Exception(f'Log not in "{{timestamp}} {{log}}" format. Got: {line}') timestamp = line[:split_at] message = line[split_at + 1 :].rstrip() return timestamp, message def _task_status(self, event): self.log.info('Event: %s had an event of type %s', event.metadata.name, event.status.phase) status = self.process_status(event.metadata.name, event.status.phase) return status def pod_not_started(self, pod: V1Pod): """Tests if pod has not started""" state = self._task_status(self.read_pod(pod)) return state == State.QUEUED def pod_is_running(self, pod: V1Pod): """Tests if pod is running""" state = self._task_status(self.read_pod(pod)) return state not in (State.SUCCESS, State.FAILED) def base_container_is_running(self, pod: V1Pod): """Tests if base container is running""" event = self.read_pod(pod) status = next(iter(filter(lambda s: s.name == 'base', event.status.container_statuses)), None) if not status: return False return status.state.running is not None @tenacity.retry(stop=tenacity.stop_after_attempt(3), wait=tenacity.wait_exponential(), reraise=True) def read_pod_logs( self, pod: V1Pod, tail_lines: Optional[int] = None, timestamps: bool = False, since_seconds: Optional[int] = None, ): """Reads log from the POD""" additional_kwargs = {} if since_seconds: additional_kwargs['since_seconds'] = since_seconds if tail_lines: additional_kwargs['tail_lines'] = tail_lines try: return self._client.read_namespaced_pod_log( name=pod.metadata.name, namespace=pod.metadata.namespace, container='base', follow=True, timestamps=timestamps, _preload_content=False, **additional_kwargs, ) except BaseHTTPError as e: raise AirflowException(f'There was an error reading the kubernetes API: {e}') @tenacity.retry(stop=tenacity.stop_after_attempt(3), wait=tenacity.wait_exponential(), reraise=True) def read_pod_events(self, pod): """Reads events from the POD""" try: return self._client.list_namespaced_event( namespace=pod.metadata.namespace, field_selector=f"involvedObject.name={pod.metadata.name}" ) except BaseHTTPError as e: raise AirflowException(f'There was an error reading the kubernetes API: {e}') @tenacity.retry(stop=tenacity.stop_after_attempt(3), wait=tenacity.wait_exponential(), reraise=True) def read_pod(self, pod: V1Pod): """Read POD information""" try: return self._client.read_namespaced_pod(pod.metadata.name, pod.metadata.namespace) except BaseHTTPError as e: raise AirflowException(f'There was an error reading the kubernetes API: {e}') def _extract_xcom(self, pod: V1Pod): resp = kubernetes_stream( self._client.connect_get_namespaced_pod_exec, pod.metadata.name, pod.metadata.namespace, container=PodDefaults.SIDECAR_CONTAINER_NAME, command=['/bin/sh'], stdin=True, stdout=True, stderr=True, tty=False, _preload_content=False, ) try: result = self._exec_pod_command(resp, f'cat {PodDefaults.XCOM_MOUNT_PATH}/return.json') self._exec_pod_command(resp, 'kill -s SIGINT 1') finally: resp.close() if result is None: raise AirflowException(f'Failed to extract xcom from pod: {pod.metadata.name}') return result def _exec_pod_command(self, resp, command): if resp.is_open(): self.log.info('Running command... %s\n', command) resp.write_stdin(command + '\n') while resp.is_open(): resp.update(timeout=1) if resp.peek_stdout(): return resp.read_stdout() if resp.peek_stderr(): self.log.info(resp.read_stderr()) break return None def process_status(self, job_id, status): """Process status information for the JOB""" status = status.lower() if status == PodStatus.PENDING: return State.QUEUED elif status == PodStatus.FAILED: self.log.error('Event with job id %s Failed', job_id) return State.FAILED elif status == PodStatus.SUCCEEDED: self.log.info('Event with job id %s Succeeded', job_id) return State.SUCCESS elif status == PodStatus.RUNNING: return State.RUNNING else: self.log.error('Event: Invalid state %s on job %s', status, job_id) return State.FAILED

kevin0120/airflow

airflow/providers/cncf/kubernetes/utils/pod_launcher.py

Python

# -*- coding: utf-8 -*- # Generated by Django 1.11.11 on 2019-08-25 10:18 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Brand', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=20, verbose_name='名称')), ('logo', models.ImageField(upload_to='', verbose_name='Logo图片')), ('first_letter', models.CharField(max_length=1, verbose_name='品牌首字母')), ], options={ 'verbose_name': '品牌', 'verbose_name_plural': '品牌', 'db_table': 'tb_brand', }, ), migrations.CreateModel( name='GoodsCategory', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=10, verbose_name='名称')), ('parent', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='subs', to='goods.GoodsCategory', verbose_name='父类别')), ], options={ 'verbose_name': '商品类别', 'verbose_name_plural': '商品类别', 'db_table': 'tb_goods_category', }, ), migrations.CreateModel( name='GoodsChannel', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('url', models.CharField(max_length=50, verbose_name='频道页面链接')), ('sequence', models.IntegerField(verbose_name='组内顺序')), ('category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='goods.GoodsCategory', verbose_name='顶级商品类别')), ], options={ 'verbose_name': '商品频道', 'verbose_name_plural': '商品频道', 'db_table': 'tb_goods_channel', }, ), migrations.CreateModel( name='GoodsChannelGroup', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=20, verbose_name='频道组名')), ], options={ 'verbose_name': '商品频道组', 'verbose_name_plural': '商品频道组', 'db_table': 'tb_channel_group', }, ), migrations.CreateModel( name='SKU', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=50, verbose_name='名称')), ('caption', models.CharField(max_length=100, verbose_name='副标题')), ('price', models.DecimalField(decimal_places=2, max_digits=10, verbose_name='单价')), ('cost_price', models.DecimalField(decimal_places=2, max_digits=10, verbose_name='进价')), ('market_price', models.DecimalField(decimal_places=2, max_digits=10, verbose_name='市场价')), ('stock', models.IntegerField(default=0, verbose_name='库存')), ('sales', models.IntegerField(default=0, verbose_name='销量')), ('comments', models.IntegerField(default=0, verbose_name='评价数')), ('is_launched', models.BooleanField(default=True, verbose_name='是否上架销售')), ('default_image', models.ImageField(blank=True, default='', max_length=200, null=True, upload_to='', verbose_name='默认图片')), ('category', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='goods.GoodsCategory', verbose_name='从属类别')), ], options={ 'verbose_name': '商品SKU', 'verbose_name_plural': '商品SKU', 'db_table': 'tb_sku', }, ), migrations.CreateModel( name='SKUImage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('image', models.ImageField(upload_to='', verbose_name='图片')), ('sku', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='goods.SKU', verbose_name='sku')), ], options={ 'verbose_name': 'SKU图片', 'verbose_name_plural': 'SKU图片', 'db_table': 'tb_sku_image', }, ), migrations.CreateModel( name='SKUSpecification', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ], options={ 'verbose_name': 'SKU规格', 'verbose_name_plural': 'SKU规格', 'db_table': 'tb_sku_specification', }, ), migrations.CreateModel( name='SpecificationOption', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('value', models.CharField(max_length=20, verbose_name='选项值')), ], options={ 'verbose_name': '规格选项', 'verbose_name_plural': '规格选项', 'db_table': 'tb_specification_option', }, ), migrations.CreateModel( name='SPU', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=50, verbose_name='名称')), ('sales', models.IntegerField(default=0, verbose_name='销量')), ('comments', models.IntegerField(default=0, verbose_name='评价数')), ('desc_detail', models.TextField(default='', verbose_name='详细介绍')), ('desc_pack', models.TextField(default='', verbose_name='包装信息')), ('desc_service', models.TextField(default='', verbose_name='售后服务')), ('brand', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='goods.Brand', verbose_name='品牌')), ('category1', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='cat1_spu', to='goods.GoodsCategory', verbose_name='一级类别')), ('category2', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='cat2_spu', to='goods.GoodsCategory', verbose_name='二级类别')), ('category3', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='cat3_spu', to='goods.GoodsCategory', verbose_name='三级类别')), ], options={ 'verbose_name': '商品SPU', 'verbose_name_plural': '商品SPU', 'db_table': 'tb_spu', }, ), migrations.CreateModel( name='SPUSpecification', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('create_time', models.DateTimeField(auto_now_add=True, verbose_name='创建时间')), ('update_time', models.DateTimeField(auto_now=True, verbose_name='更新时间')), ('name', models.CharField(max_length=20, verbose_name='规格名称')), ('spu', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='specs', to='goods.SPU', verbose_name='商品SPU')), ], options={ 'verbose_name': '商品SPU规格', 'verbose_name_plural': '商品SPU规格', 'db_table': 'tb_spu_specification', }, ), migrations.AddField( model_name='specificationoption', name='spec', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='options', to='goods.SPUSpecification', verbose_name='规格'), ), migrations.AddField( model_name='skuspecification', name='option', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='goods.SpecificationOption', verbose_name='规格值'), ), migrations.AddField( model_name='skuspecification', name='sku', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='specs', to='goods.SKU', verbose_name='sku'), ), migrations.AddField( model_name='skuspecification', name='spec', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='goods.SPUSpecification', verbose_name='规格名称'), ), migrations.AddField( model_name='sku', name='spu', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='goods.SPU', verbose_name='商品'), ), migrations.AddField( model_name='goodschannel', name='group', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='goods.GoodsChannelGroup', verbose_name='频道组名'), ), ]

sxfang32/meiduo_29

meiduo_mall/meiduo_mall/apps/goods/migrations/0001_initial.py

Python

""" Low-level wrapper for PortMidi library Copied straight from Grant Yoshida's portmidizero, with slight modifications. """ import sys from ctypes import (CDLL, CFUNCTYPE, POINTER, Structure, c_char_p, c_int, c_long, c_uint, c_void_p, cast, create_string_buffer, byref) import ctypes.util dll_name = '' if sys.platform == 'darwin': dll_name = ctypes.util.find_library('libportmidi.dylib') elif sys.platform in ('win32', 'cygwin'): dll_name = 'portmidi.dll' else: dll_name = 'libportmidi.so' lib = CDLL(dll_name) null = None false = 0 true = 1 # portmidi.h # From portmidi.h PM_HOST_ERROR_MSG_LEN = 256 def get_host_error_message(): """Return host error message.""" buf = create_string_buffer(PM_HOST_ERROR_MSG_LEN) lib.Pm_GetHostErrorText(buf, PM_HOST_ERROR_MSG_LEN) return buf.raw.decode().rstrip('\0') PmError = c_int # PmError enum pmNoError = 0 pmHostError = -10000 pmInvalidDeviceId = -9999 pmInsufficientMemory = -9989 pmBufferTooSmall = -9979 pmBufferOverflow = -9969 pmBadPtr = -9959 pmBadData = -9994 pmInternalError = -9993 pmBufferMaxSize = -9992 lib.Pm_Initialize.restype = PmError lib.Pm_Terminate.restype = PmError PmDeviceID = c_int PortMidiStreamPtr = c_void_p PmStreamPtr = PortMidiStreamPtr PortMidiStreamPtrPtr = POINTER(PortMidiStreamPtr) lib.Pm_HasHostError.restype = c_int lib.Pm_HasHostError.argtypes = [PortMidiStreamPtr] lib.Pm_GetErrorText.restype = c_char_p lib.Pm_GetErrorText.argtypes = [PmError] lib.Pm_GetHostErrorText.argtypes = [c_char_p, c_uint] pmNoDevice = -1 class PmDeviceInfo(Structure): _fields_ = [("structVersion", c_int), ("interface", c_char_p), ("name", c_char_p), ("is_input", c_int), ("is_output", c_int), ("opened", c_int)] PmDeviceInfoPtr = POINTER(PmDeviceInfo) lib.Pm_CountDevices.restype = c_int lib.Pm_GetDefaultOutputDeviceID.restype = PmDeviceID lib.Pm_GetDefaultInputDeviceID.restype = PmDeviceID PmTimestamp = c_long PmTimeProcPtr = CFUNCTYPE(PmTimestamp, c_void_p) NullTimeProcPtr = cast(null, PmTimeProcPtr) # PmBefore is not defined lib.Pm_GetDeviceInfo.argtypes = [PmDeviceID] lib.Pm_GetDeviceInfo.restype = PmDeviceInfoPtr lib.Pm_OpenInput.restype = PmError lib.Pm_OpenInput.argtypes = [PortMidiStreamPtrPtr, PmDeviceID, c_void_p, c_long, PmTimeProcPtr, c_void_p] lib.Pm_OpenOutput.restype = PmError lib.Pm_OpenOutput.argtypes = [PortMidiStreamPtrPtr, PmDeviceID, c_void_p, c_long, PmTimeProcPtr, c_void_p, c_long] lib.Pm_SetFilter.restype = PmError lib.Pm_SetFilter.argtypes = [PortMidiStreamPtr, c_long] lib.Pm_SetChannelMask.restype = PmError lib.Pm_SetChannelMask.argtypes = [PortMidiStreamPtr, c_int] lib.Pm_Abort.restype = PmError lib.Pm_Abort.argtypes = [PortMidiStreamPtr] lib.Pm_Close.restype = PmError lib.Pm_Close.argtypes = [PortMidiStreamPtr] PmMessage = c_long class PmEvent(Structure): _fields_ = [("message", PmMessage), ("timestamp", PmTimestamp)] PmEventPtr = POINTER(PmEvent) lib.Pm_Read.restype = PmError lib.Pm_Read.argtypes = [PortMidiStreamPtr, PmEventPtr, c_long] lib.Pm_Poll.restype = PmError lib.Pm_Poll.argtypes = [PortMidiStreamPtr] lib.Pm_Write.restype = PmError lib.Pm_Write.argtypes = [PortMidiStreamPtr, PmEventPtr, c_long] lib.Pm_WriteShort.restype = PmError lib.Pm_WriteShort.argtypes = [PortMidiStreamPtr, PmTimestamp, c_long] lib.Pm_WriteSysEx.restype = PmError lib.Pm_WriteSysEx.argtypes = [PortMidiStreamPtr, PmTimestamp, c_char_p] # porttime.h # PtError enum PtError = c_int ptNoError = 0 ptHostError = -10000 ptAlreadyStarted = -9999 ptAlreadyStopped = -9998 ptInsufficientMemory = -9997 PtTimestamp = c_long PtCallback = CFUNCTYPE(PmTimestamp, c_void_p) lib.Pt_Start.restype = PtError lib.Pt_Start.argtypes = [c_int, PtCallback, c_void_p] lib.Pt_Stop.restype = PtError lib.Pt_Started.restype = c_int lib.Pt_Time.restype = PtTimestamp

EnjoyLifeFund/macHighSierra-py36-pkgs

mido/backends/portmidi_init.py

Python

# -*- coding: utf-8 -*- from __future__ import absolute_import, print_function from niftynet.layer.base_layer import TrainableLayer from niftynet.layer.convolution import ConvolutionalLayer as Conv from niftynet.layer.downsample import DownSampleLayer as Down from niftynet.layer.residual_unit import ResidualUnit as ResUnit class DownBlock(TrainableLayer): def __init__(self, n_output_chns=4, kernel_size=3, downsample_kernel_size=2, downsample_stride=2, acti_func='relu', w_initializer=None, w_regularizer=None, type_string='bn_acti_conv', name='res-downsample'): super(TrainableLayer, self).__init__(name=name) self.n_output_chns = n_output_chns self.kernel_size = kernel_size self.downsample_kernel_size = downsample_kernel_size self.downsample_stride = downsample_stride self.acti_func = acti_func self.conv_param = {'w_initializer': w_initializer, 'w_regularizer': w_regularizer} self.type_string = type_string def layer_op(self, inputs, is_training=True): """ Consists of:: (inputs)--conv_0-o-conv_1--conv_2-+-(conv_res)--down_sample-- | | o----------------o conv_0, conv_res is also returned for feature forwarding purpose """ conv_0 = Conv(n_output_chns=self.n_output_chns, kernel_size=self.kernel_size, acti_func=self.acti_func, with_bias=False, feature_normalization='batch', **self.conv_param)(inputs, is_training) conv_res = ResUnit(n_output_chns=self.n_output_chns, kernel_size=self.kernel_size, acti_func=self.acti_func, type_string=self.type_string, **self.conv_param)(conv_0, is_training) conv_down = Down('Max', kernel_size=self.downsample_kernel_size, stride=self.downsample_stride)(conv_res) return conv_down, conv_0, conv_res

BRAINSia/NiftyNet

niftynet/layer/downsample_res_block.py

Python

#!/usr/bin/env python3 # -*- encoding=utf-8 -*- # description: # author:jack # create_time: 2018/9/17 """ desc:pass """ class __init__: pass if __name__ == '__main__': pass

Mryan2005/bot-sdk-python

dueros/directive/Base/__init__.py

Python

from tensorflow.keras.applications import MobileNetV2 from tensorflow.keras.callbacks import EarlyStopping from tensorflow.keras.callbacks import LearningRateScheduler from tensorflow.keras.layers import Activation from tensorflow.keras.layers import Dense from tensorflow.keras.layers import GlobalAveragePooling2D from tensorflow.keras.layers import Input from tensorflow.keras.layers.experimental.preprocessing import Rescaling from tensorflow.keras.layers.experimental.preprocessing import Resizing from tensorflow.keras.models import Model from tensorflow.keras.optimizers import Adam from tf_utils.callbacks import schedule_fn2 from tf_utils.dogsCatsDataAdvanced import DOGSCATS IMAGENET_SIZE = 224 IMAGENET_DEPTH = 3 IMAGENET_SHAPE = (IMAGENET_SIZE, IMAGENET_SIZE, IMAGENET_DEPTH) def build_model(img_shape, num_classes) -> Model: base_model = MobileNetV2( include_top=False, weights="imagenet", input_shape=IMAGENET_SHAPE ) num_layers = len(base_model.layers) print(f"Number of layers in the base model: {num_layers}") fine_tune_at = num_layers - 10 for layer in base_model.layers[:fine_tune_at]: layer.trainable = False input_img = Input(shape=img_shape) x = Rescaling(scale=2.0, offset=-1.0)(input_img) x = Resizing(height=IMAGENET_SIZE, width=IMAGENET_SIZE)(x) x = base_model(x) x = GlobalAveragePooling2D()(x) x = Dense(units=num_classes)(x) y_pred = Activation("softmax")(x) model = Model( inputs=[input_img], outputs=[y_pred] ) model.summary() return model if __name__ == "__main__": """ Best model from chapter 9_2: 0.9034 accuracy Best model from chapter 9_7: 0.9614 accuracy """ data = DOGSCATS() train_dataset = data.get_train_set() val_dataset = data.get_val_set() test_dataset = data.get_test_set() img_shape = data.img_shape num_classes = data.num_classes # Global params epochs = 100 model = build_model( img_shape, num_classes ) opt = Adam(learning_rate=5e-4) model.compile( loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"] ) lrs_callback = LearningRateScheduler( schedule=schedule_fn2, verbose=1 ) es_callback = EarlyStopping( monitor="val_loss", patience=30, verbose=1, restore_best_weights=True ) model.fit( train_dataset, verbose=1, epochs=epochs, callbacks=[lrs_callback, es_callback], validation_data=val_dataset, ) scores = model.evaluate( val_dataset, verbose=0 ) print(f"Scores: {scores}")

thisisjako/UdemyTF

Chapter9_AdvancedDL/Chapter9_7_AdvancedTechniques2/dogsCatsTransferLearning.py

Python

# IDLSave - a python module to read IDL 'save' files # Copyright (c) 2010 Thomas P. Robitaille # Many thanks to Craig Markwardt for publishing the Unofficial Format # Specification for IDL .sav files, without which this Python module would not # exist (http://cow.physics.wisc.edu/~craigm/idl/savefmt). # This code was developed by with permission from ITT Visual Information # Systems. IDL(r) is a registered trademark of ITT Visual Information Systems, # Inc. for their Interactive Data Language software. # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. __all__ = ['readsav'] import struct import numpy as np from numpy.compat import asstr import tempfile import zlib import warnings # Define the different data types that can be found in an IDL save file DTYPE_DICT = {1: '>u1', 2: '>i2', 3: '>i4', 4: '>f4', 5: '>f8', 6: '>c8', 7: '|O', 8: '|O', 9: '>c16', 10: '|O', 11: '|O', 12: '>u2', 13: '>u4', 14: '>i8', 15: '>u8'} # Define the different record types that can be found in an IDL save file RECTYPE_DICT = {0: "START_MARKER", 1: "COMMON_VARIABLE", 2: "VARIABLE", 3: "SYSTEM_VARIABLE", 6: "END_MARKER", 10: "TIMESTAMP", 12: "COMPILED", 13: "IDENTIFICATION", 14: "VERSION", 15: "HEAP_HEADER", 16: "HEAP_DATA", 17: "PROMOTE64", 19: "NOTICE", 20: "DESCRIPTION"} # Define a dictionary to contain structure definitions STRUCT_DICT = {} def _align_32(f): '''Align to the next 32-bit position in a file''' pos = f.tell() if pos % 4 != 0: f.seek(pos + 4 - pos % 4) return def _skip_bytes(f, n): '''Skip `n` bytes''' f.read(n) return def _read_bytes(f, n): '''Read the next `n` bytes''' return f.read(n) def _read_byte(f): '''Read a single byte''' return np.uint8(struct.unpack('>B', f.read(4)[:1])[0]) def _read_long(f): '''Read a signed 32-bit integer''' return np.int32(struct.unpack('>l', f.read(4))[0]) def _read_int16(f): '''Read a signed 16-bit integer''' return np.int16(struct.unpack('>h', f.read(4)[2:4])[0]) def _read_int32(f): '''Read a signed 32-bit integer''' return np.int32(struct.unpack('>i', f.read(4))[0]) def _read_int64(f): '''Read a signed 64-bit integer''' return np.int64(struct.unpack('>q', f.read(8))[0]) def _read_uint16(f): '''Read an unsigned 16-bit integer''' return np.uint16(struct.unpack('>H', f.read(4)[2:4])[0]) def _read_uint32(f): '''Read an unsigned 32-bit integer''' return np.uint32(struct.unpack('>I', f.read(4))[0]) def _read_uint64(f): '''Read an unsigned 64-bit integer''' return np.uint64(struct.unpack('>Q', f.read(8))[0]) def _read_float32(f): '''Read a 32-bit float''' return np.float32(struct.unpack('>f', f.read(4))[0]) def _read_float64(f): '''Read a 64-bit float''' return np.float64(struct.unpack('>d', f.read(8))[0]) class Pointer(object): '''Class used to define pointers''' def __init__(self, index): self.index = index return class ObjectPointer(Pointer): '''Class used to define object pointers''' pass def _read_string(f): '''Read a string''' length = _read_long(f) if length > 0: chars = _read_bytes(f, length) _align_32(f) chars = asstr(chars) else: chars = '' return chars def _read_string_data(f): '''Read a data string (length is specified twice)''' length = _read_long(f) if length > 0: length = _read_long(f) string_data = _read_bytes(f, length) _align_32(f) else: string_data = '' return string_data def _read_data(f, dtype): '''Read a variable with a specified data type''' if dtype == 1: if _read_int32(f) != 1: raise Exception("Error occurred while reading byte variable") return _read_byte(f) elif dtype == 2: return _read_int16(f) elif dtype == 3: return _read_int32(f) elif dtype == 4: return _read_float32(f) elif dtype == 5: return _read_float64(f) elif dtype == 6: real = _read_float32(f) imag = _read_float32(f) return np.complex64(real + imag * 1j) elif dtype == 7: return _read_string_data(f) elif dtype == 8: raise Exception("Should not be here - please report this") elif dtype == 9: real = _read_float64(f) imag = _read_float64(f) return np.complex128(real + imag * 1j) elif dtype == 10: return Pointer(_read_int32(f)) elif dtype == 11: return ObjectPointer(_read_int32(f)) elif dtype == 12: return _read_uint16(f) elif dtype == 13: return _read_uint32(f) elif dtype == 14: return _read_int64(f) elif dtype == 15: return _read_uint64(f) else: raise Exception("Unknown IDL type: %i - please report this" % dtype) def _read_structure(f, array_desc, struct_desc): ''' Read a structure, with the array and structure descriptors given as `array_desc` and `structure_desc` respectively. ''' nrows = array_desc['nelements'] columns = struct_desc['tagtable'] dtype = [] for col in columns: if col['structure'] or col['array']: dtype.append(((col['name'].lower(), col['name']), np.object_)) else: if col['typecode'] in DTYPE_DICT: dtype.append(((col['name'].lower(), col['name']), DTYPE_DICT[col['typecode']])) else: raise Exception("Variable type %i not implemented" % col['typecode']) structure = np.recarray((nrows, ), dtype=dtype) for i in range(nrows): for col in columns: dtype = col['typecode'] if col['structure']: structure[col['name']][i] = _read_structure(f, struct_desc['arrtable'][col['name']], struct_desc['structtable'][col['name']]) elif col['array']: structure[col['name']][i] = _read_array(f, dtype, struct_desc['arrtable'][col['name']]) else: structure[col['name']][i] = _read_data(f, dtype) # Reshape structure if needed if array_desc['ndims'] > 1: dims = array_desc['dims'][:int(array_desc['ndims'])] dims.reverse() structure = structure.reshape(dims) return structure def _read_array(f, typecode, array_desc): ''' Read an array of type `typecode`, with the array descriptor given as `array_desc`. ''' if typecode in [1, 3, 4, 5, 6, 9, 13, 14, 15]: if typecode == 1: nbytes = _read_int32(f) if nbytes != array_desc['nbytes']: warnings.warn("Not able to verify number of bytes from header") # Read bytes as numpy array array = np.frombuffer(f.read(array_desc['nbytes']), dtype=DTYPE_DICT[typecode]) elif typecode in [2, 12]: # These are 2 byte types, need to skip every two as they are not packed array = np.frombuffer(f.read(array_desc['nbytes']*2), dtype=DTYPE_DICT[typecode])[1::2] else: # Read bytes into list array = [] for i in range(array_desc['nelements']): dtype = typecode data = _read_data(f, dtype) array.append(data) array = np.array(array, dtype=np.object_) # Reshape array if needed if array_desc['ndims'] > 1: dims = array_desc['dims'][:int(array_desc['ndims'])] dims.reverse() array = array.reshape(dims) # Go to next alignment position _align_32(f) return array def _read_record(f): '''Function to read in a full record''' record = {'rectype': _read_long(f)} nextrec = _read_uint32(f) nextrec += _read_uint32(f) * 2**32 _skip_bytes(f, 4) if not record['rectype'] in RECTYPE_DICT: raise Exception("Unknown RECTYPE: %i" % record['rectype']) record['rectype'] = RECTYPE_DICT[record['rectype']] if record['rectype'] in ["VARIABLE", "HEAP_DATA"]: if record['rectype'] == "VARIABLE": record['varname'] = _read_string(f) else: record['heap_index'] = _read_long(f) _skip_bytes(f, 4) rectypedesc = _read_typedesc(f) if rectypedesc['typecode'] == 0: if nextrec == f.tell(): record['data'] = None # Indicates NULL value else: raise ValueError("Unexpected type code: 0") else: varstart = _read_long(f) if varstart != 7: raise Exception("VARSTART is not 7") if rectypedesc['structure']: record['data'] = _read_structure(f, rectypedesc['array_desc'], rectypedesc['struct_desc']) elif rectypedesc['array']: record['data'] = _read_array(f, rectypedesc['typecode'], rectypedesc['array_desc']) else: dtype = rectypedesc['typecode'] record['data'] = _read_data(f, dtype) elif record['rectype'] == "TIMESTAMP": _skip_bytes(f, 4*256) record['date'] = _read_string(f) record['user'] = _read_string(f) record['host'] = _read_string(f) elif record['rectype'] == "VERSION": record['format'] = _read_long(f) record['arch'] = _read_string(f) record['os'] = _read_string(f) record['release'] = _read_string(f) elif record['rectype'] == "IDENTIFICATON": record['author'] = _read_string(f) record['title'] = _read_string(f) record['idcode'] = _read_string(f) elif record['rectype'] == "NOTICE": record['notice'] = _read_string(f) elif record['rectype'] == "DESCRIPTION": record['description'] = _read_string_data(f) elif record['rectype'] == "HEAP_HEADER": record['nvalues'] = _read_long(f) record['indices'] = [_read_long(f) for _ in range(record['nvalues'])] elif record['rectype'] == "COMMONBLOCK": record['nvars'] = _read_long(f) record['name'] = _read_string(f) record['varnames'] = [_read_string(f) for _ in range(record['nvars'])] elif record['rectype'] == "END_MARKER": record['end'] = True elif record['rectype'] == "UNKNOWN": warnings.warn("Skipping UNKNOWN record") elif record['rectype'] == "SYSTEM_VARIABLE": warnings.warn("Skipping SYSTEM_VARIABLE record") else: raise Exception("record['rectype']=%s not implemented" % record['rectype']) f.seek(nextrec) return record def _read_typedesc(f): '''Function to read in a type descriptor''' typedesc = {'typecode': _read_long(f), 'varflags': _read_long(f)} if typedesc['varflags'] & 2 == 2: raise Exception("System variables not implemented") typedesc['array'] = typedesc['varflags'] & 4 == 4 typedesc['structure'] = typedesc['varflags'] & 32 == 32 if typedesc['structure']: typedesc['array_desc'] = _read_arraydesc(f) typedesc['struct_desc'] = _read_structdesc(f) elif typedesc['array']: typedesc['array_desc'] = _read_arraydesc(f) return typedesc def _read_arraydesc(f): '''Function to read in an array descriptor''' arraydesc = {'arrstart': _read_long(f)} if arraydesc['arrstart'] == 8: _skip_bytes(f, 4) arraydesc['nbytes'] = _read_long(f) arraydesc['nelements'] = _read_long(f) arraydesc['ndims'] = _read_long(f) _skip_bytes(f, 8) arraydesc['nmax'] = _read_long(f) arraydesc['dims'] = [_read_long(f) for _ in range(arraydesc['nmax'])] elif arraydesc['arrstart'] == 18: warnings.warn("Using experimental 64-bit array read") _skip_bytes(f, 8) arraydesc['nbytes'] = _read_uint64(f) arraydesc['nelements'] = _read_uint64(f) arraydesc['ndims'] = _read_long(f) _skip_bytes(f, 8) arraydesc['nmax'] = 8 arraydesc['dims'] = [] for d in range(arraydesc['nmax']): v = _read_long(f) if v != 0: raise Exception("Expected a zero in ARRAY_DESC") arraydesc['dims'].append(_read_long(f)) else: raise Exception("Unknown ARRSTART: %i" % arraydesc['arrstart']) return arraydesc def _read_structdesc(f): '''Function to read in a structure descriptor''' structdesc = {} structstart = _read_long(f) if structstart != 9: raise Exception("STRUCTSTART should be 9") structdesc['name'] = _read_string(f) predef = _read_long(f) structdesc['ntags'] = _read_long(f) structdesc['nbytes'] = _read_long(f) structdesc['predef'] = predef & 1 structdesc['inherits'] = predef & 2 structdesc['is_super'] = predef & 4 if not structdesc['predef']: structdesc['tagtable'] = [_read_tagdesc(f) for _ in range(structdesc['ntags'])] for tag in structdesc['tagtable']: tag['name'] = _read_string(f) structdesc['arrtable'] = {tag['name']: _read_arraydesc(f) for tag in structdesc['tagtable'] if tag['array']} structdesc['structtable'] = {tag['name']: _read_structdesc(f) for tag in structdesc['tagtable'] if tag['structure']} if structdesc['inherits'] or structdesc['is_super']: structdesc['classname'] = _read_string(f) structdesc['nsupclasses'] = _read_long(f) structdesc['supclassnames'] = [ _read_string(f) for _ in range(structdesc['nsupclasses'])] structdesc['supclasstable'] = [ _read_structdesc(f) for _ in range(structdesc['nsupclasses'])] STRUCT_DICT[structdesc['name']] = structdesc else: if not structdesc['name'] in STRUCT_DICT: raise Exception("PREDEF=1 but can't find definition") structdesc = STRUCT_DICT[structdesc['name']] return structdesc def _read_tagdesc(f): '''Function to read in a tag descriptor''' tagdesc = {'offset': _read_long(f)} if tagdesc['offset'] == -1: tagdesc['offset'] = _read_uint64(f) tagdesc['typecode'] = _read_long(f) tagflags = _read_long(f) tagdesc['array'] = tagflags & 4 == 4 tagdesc['structure'] = tagflags & 32 == 32 tagdesc['scalar'] = tagdesc['typecode'] in DTYPE_DICT # Assume '10'x is scalar return tagdesc def _replace_heap(variable, heap): if isinstance(variable, Pointer): while isinstance(variable, Pointer): if variable.index == 0: variable = None else: if variable.index in heap: variable = heap[variable.index] else: warnings.warn("Variable referenced by pointer not found " "in heap: variable will be set to None") variable = None replace, new = _replace_heap(variable, heap) if replace: variable = new return True, variable elif isinstance(variable, np.core.records.recarray): # Loop over records for ir, record in enumerate(variable): replace, new = _replace_heap(record, heap) if replace: variable[ir] = new return False, variable elif isinstance(variable, np.core.records.record): # Loop over values for iv, value in enumerate(variable): replace, new = _replace_heap(value, heap) if replace: variable[iv] = new return False, variable elif isinstance(variable, np.ndarray): # Loop over values if type is np.object_ if variable.dtype.type is np.object_: for iv in range(variable.size): replace, new = _replace_heap(variable.item(iv), heap) if replace: variable.itemset(iv, new) return False, variable else: return False, variable class AttrDict(dict): ''' A case-insensitive dictionary with access via item, attribute, and call notations: >>> d = AttrDict() >>> d['Variable'] = 123 >>> d['Variable'] 123 >>> d.Variable 123 >>> d.variable 123 >>> d('VARIABLE') 123 ''' def __init__(self, init={}): dict.__init__(self, init) def __getitem__(self, name): return super(AttrDict, self).__getitem__(name.lower()) def __setitem__(self, key, value): return super(AttrDict, self).__setitem__(key.lower(), value) __getattr__ = __getitem__ __setattr__ = __setitem__ __call__ = __getitem__ def readsav(file_name, idict=None, python_dict=False, uncompressed_file_name=None, verbose=False): """ Read an IDL .sav file. Parameters ---------- file_name : str Name of the IDL save file. idict : dict, optional Dictionary in which to insert .sav file variables. python_dict : bool, optional By default, the object return is not a Python dictionary, but a case-insensitive dictionary with item, attribute, and call access to variables. To get a standard Python dictionary, set this option to True. uncompressed_file_name : str, optional This option only has an effect for .sav files written with the /compress option. If a file name is specified, compressed .sav files are uncompressed to this file. Otherwise, readsav will use the `tempfile` module to determine a temporary filename automatically, and will remove the temporary file upon successfully reading it in. verbose : bool, optional Whether to print out information about the save file, including the records read, and available variables. Returns ------- idl_dict : AttrDict or dict If `python_dict` is set to False (default), this function returns a case-insensitive dictionary with item, attribute, and call access to variables. If `python_dict` is set to True, this function returns a Python dictionary with all variable names in lowercase. If `idict` was specified, then variables are written to the dictionary specified, and the updated dictionary is returned. Examples -------- >>> from os.path import dirname, join as pjoin >>> import scipy.io as sio >>> from scipy.io import readsav Get the filename for an example .sav file from the tests/data directory. >>> data_dir = pjoin(dirname(sio.__file__), 'tests', 'data') >>> sav_fname = pjoin(data_dir, 'array_float32_1d.sav') Load the .sav file contents. >>> sav_data = readsav(sav_fname) Get keys of the .sav file contents. >>> print(sav_data.keys()) dict_keys(['array1d']) Access a content with a key. >>> print(sav_data['array1d']) [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] """ # Initialize record and variable holders records = [] if python_dict or idict: variables = {} else: variables = AttrDict() # Open the IDL file f = open(file_name, 'rb') # Read the signature, which should be 'SR' signature = _read_bytes(f, 2) if signature != b'SR': raise Exception(f"Invalid SIGNATURE: {signature}") # Next, the record format, which is '\x00\x04' for normal .sav # files, and '\x00\x06' for compressed .sav files. recfmt = _read_bytes(f, 2) if recfmt == b'\x00\x04': pass elif recfmt == b'\x00\x06': if verbose: print("IDL Save file is compressed") if uncompressed_file_name: fout = open(uncompressed_file_name, 'w+b') else: fout = tempfile.NamedTemporaryFile(suffix='.sav') if verbose: print(f" -> expanding to {fout.name}") # Write header fout.write(b'SR\x00\x04') # Cycle through records while True: # Read record type rectype = _read_long(f) fout.write(struct.pack('>l', int(rectype))) # Read position of next record and return as int nextrec = _read_uint32(f) nextrec += _read_uint32(f) * 2**32 # Read the unknown 4 bytes unknown = f.read(4) # Check if the end of the file has been reached if RECTYPE_DICT[rectype] == 'END_MARKER': fout.write(struct.pack('>I', int(nextrec) % 2**32)) fout.write(struct.pack('>I', int((nextrec - (nextrec % 2**32)) / 2**32))) fout.write(unknown) break # Find current position pos = f.tell() # Decompress record rec_string = zlib.decompress(f.read(nextrec-pos)) # Find new position of next record nextrec = fout.tell() + len(rec_string) + 12 # Write out record fout.write(struct.pack('>I', int(nextrec % 2**32))) fout.write(struct.pack('>I', int((nextrec - (nextrec % 2**32)) / 2**32))) fout.write(unknown) fout.write(rec_string) # Close the original compressed file f.close() # Set f to be the decompressed file, and skip the first four bytes f = fout f.seek(4) else: raise Exception(f"Invalid RECFMT: {recfmt}") # Loop through records, and add them to the list while True: r = _read_record(f) records.append(r) if 'end' in r: if r['end']: break # Close the file f.close() # Find heap data variables heap = {} for r in records: if r['rectype'] == "HEAP_DATA": heap[r['heap_index']] = r['data'] # Find all variables for r in records: if r['rectype'] == "VARIABLE": replace, new = _replace_heap(r['data'], heap) if replace: r['data'] = new variables[r['varname'].lower()] = r['data'] if verbose: # Print out timestamp info about the file for record in records: if record['rectype'] == "TIMESTAMP": print("-"*50) print(f"Date: {record['date']}") print(f"User: {record['user']}") print(f"Host: {record['host']}") break # Print out version info about the file for record in records: if record['rectype'] == "VERSION": print("-"*50) print(f"Format: {record['format']}") print(f"Architecture: {record['arch']}") print(f"Operating System: {record['os']}") print(f"IDL Version: {record['release']}") break # Print out identification info about the file for record in records: if record['rectype'] == "IDENTIFICATON": print("-"*50) print(f"Author: {record['author']}") print(f"Title: {record['title']}") print(f"ID Code: {record['idcode']}") break # Print out descriptions saved with the file for record in records: if record['rectype'] == "DESCRIPTION": print("-"*50) print(f"Description: {record['description']}") break print("-"*50) print("Successfully read %i records of which:" % (len(records))) # Create convenience list of record types rectypes = [r['rectype'] for r in records] for rt in set(rectypes): if rt != 'END_MARKER': print(" - %i are of type %s" % (rectypes.count(rt), rt)) print("-"*50) if 'VARIABLE' in rectypes: print("Available variables:") for var in variables: print(f" - {var} [{type(variables[var])}]") print("-"*50) if idict: for var in variables: idict[var] = variables[var] return idict else: return variables

ikamensh/scipy

scipy/io/idl.py

Python

from typing import List, Optional, Any, Dict from checkov.common.graph.checks_infra.enums import Operators from .equals_attribute_solver import EqualsAttributeSolver class NotEqualsAttributeSolver(EqualsAttributeSolver): operator = Operators.NOT_EQUALS def __init__(self, resource_types: List[str], attribute: Optional[str], value: Any) -> None: super().__init__(resource_types=resource_types, attribute=attribute, value=value) def _get_operation(self, vertex: Dict[str, Any], attribute: Optional[str]) -> bool: return not super()._get_operation(vertex, attribute)

0xflotus/checkov

checkov/common/checks_infra/solvers/attribute_solvers/not_equals_attribute_solver.py

Python

#!/usr/bin/env python """ These tests only check whether plots are created, not that they look correct! """ import unittest import os import sys from glob import glob import numpy as np import matador.cli.dispersion from matador.scrapers import res2dict, magres2dict from matador.hull import QueryConvexHull from matador.plotting.battery_plotting import plot_voltage_curve from matador.plotting.pdf_plotting import plot_pdf from matador.plotting.pxrd_plotting import plot_pxrd from matador.plotting.magres_plotting import plot_magres from .utils import MatadorUnitTest REAL_PATH = "/".join(os.path.realpath(__file__).split("/")[:-1]) + "/" ROOT_DIR = os.getcwd() try: import matplotlib # noqa matplotlib.use("Agg") MATPLOTLIB_PRESENT = True except ImportError: MATPLOTLIB_PRESENT = False try: import ternary # noqa TERNARY_PRESENT = True except ImportError: TERNARY_PRESENT = False try: import _tkinter # noqa except Exception: MATPLOTLIB_PRESENT = False @unittest.skipIf(not MATPLOTLIB_PRESENT, "Skipping plotting tests.") class SpectralPlotTests(unittest.TestCase): """ Test Dispersion script. """ def test_pdis_plot(self): """ Test combined spectral plots. """ os.chdir(REAL_PATH + "/data/dispersion") expected_file = "K3P-OQMD_4786-CollCode25550_spectral.png" if os.path.isfile(expected_file): os.remove(expected_file) sys.argv = [ "dispersion", "K3P-OQMD_4786-CollCode25550", "--png", "-scale", "10", "-interp", "2", "-pw", "-5", "5", "--gap", "--preserve_kspace_distance", "--figsize", "10", "10", ] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc file_exists = os.path.isfile(expected_file) if file_exists: os.remove(expected_file) os.chdir(ROOT_DIR) if errored: raise error self.assertTrue(file_exists) def test_dos_only(self): """ Test combined spectral plots. """ os.chdir(REAL_PATH + "/data/dispersion") expected_file = "K3P-OQMD_4786-CollCode25550_spectral.png" if os.path.isfile(expected_file): os.remove(expected_file) sys.argv = [ "dispersion", "K3P-OQMD_4786-CollCode25550", "--png", "--dos_only", "--figsize", "10", "10", ] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc file_exists = os.path.isfile(expected_file) if file_exists: os.remove(expected_file) os.chdir(ROOT_DIR) if errored: raise error self.assertTrue(file_exists) def test_multiseed(self): """ Test plotting two seed bandstructures on top of each other. """ os.chdir(REAL_PATH + "/data/bands_files") expected_file = "KPSn_spectral.png" sys.argv = [ "dispersion", "KPSn", "KPSn_2", "--dos_only", "--cmap", "viridis", "--png", "--band_reorder", "--labels", "PBE, LDA", "--figsize", "10", "10", "--colours", "green", "red" ] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc file_exists = os.path.isfile(expected_file) if file_exists: os.remove(expected_file) os.chdir(ROOT_DIR) if errored: raise error self.assertTrue(file_exists) def test_x11_no_fail(self): """ Test combined spectral plots. """ os.chdir(REAL_PATH + "/data/dispersion") sys.argv = [ "dispersion", "K3P-OQMD_4786-CollCode25550", "--dos_only", "--cmap", "viridis", "--figsize", "10", "10", ] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc os.chdir(ROOT_DIR) if errored: raise error def test_phonon_dispersion(self): """ Test phonon dispersion plot. """ os.chdir(REAL_PATH + "/data/phonon_dispersion") expected_file = "K3P_spectral.png" if os.path.isfile(expected_file): os.remove(expected_file) sys.argv = [ "dispersion", "K3P", "--png", "-ph", "--colours", "grey", "green", "blue", "--figsize", "10", "10", ] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc file_exists = os.path.isfile(expected_file) if file_exists: os.remove(expected_file) os.chdir(ROOT_DIR) if errored: raise error self.assertTrue(file_exists) def test_phonon_ir(self): """ Test phonon IR/Raman plot. """ os.chdir(REAL_PATH + "/data/phonon_ir") expected_file = "h-BN_IRR_ir.svg" if os.path.isfile(expected_file): os.remove(expected_file) sys.argv = ["dispersion", "h-BN_IRR", "--svg", "-ir", "--figsize", "5", "5"] errored = False try: matador.cli.dispersion.main() except Exception as exc: errored = True error = exc file_exists = os.path.isfile(expected_file) if file_exists: os.remove(expected_file) os.chdir(ROOT_DIR) if errored: raise error self.assertTrue(file_exists) def test_projector_scraping(self): from matador.plotting.spectral_plotting import _parse_projectors_list self.assertEqual( _parse_projectors_list("K"), [ ("K", "s", None), ("K", "p", None), ("K", "d", None), ("K", "f", None), ("K", None, None), ], ) self.assertEqual( _parse_projectors_list("K,P"), [ ("K", "s", None), ("K", "p", None), ("K", "d", None), ("K", "f", None), ("K", None, None), ("P", "s", None), ("P", "p", None), ("P", "d", None), ("P", "f", None), ("P", None, None), ], ) self.assertEqual( _parse_projectors_list("K,P:s"), [ ("K", "s", None), ("K", "p", None), ("K", "d", None), ("K", "f", None), ("K", None, None), ("P", "s", None), ], ) self.assertEqual( _parse_projectors_list("123:x,P:s"), [("123", "x", None), ("P", "s", None)] ) @unittest.skipIf(not MATPLOTLIB_PRESENT, "Skipping plotting tests.") class HullPlotTests(MatadorUnitTest): """ Tests for plotting phase diagrams. """ def test_binary_hull_plot(self): """ Test plotting binary hull. """ expected_files = ["KP_hull_simple.svg"] cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0] QueryConvexHull( cursor=cursor, elements=["K", "P"], svg=True, hull_cutoff=0.0, plot_kwargs={"plot_fname": "KP_hull_simple", "svg": True}, ) for expected_file in expected_files: self.assertTrue(os.path.isfile(expected_file)) def test_binary_battery_plots(self): """ Test plotting binary hull. """ expected_files = ["KP_hull.png", "KP_voltage.png", "KP_volume.png"] cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0] QueryConvexHull( cursor=cursor, elements=["K", "P"], no_plot=False, png=True, quiet=False, voltage=True, labels=True, label_cutoff=0.05, hull_cutoff=0.1, volume=True, plot_kwargs={"colour_by_source": True}, ) for expected_file in expected_files: self.assertTrue(os.path.isfile(expected_file)) def test_voltage_labels(self): expected_files = ["KP_voltage.png"] cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0] hull = QueryConvexHull( cursor=cursor, species="KP", no_plot=True, voltage=True, labels=True ) plot_voltage_curve(hull.voltage_data, labels=True, savefig=expected_files[0]) for expected_file in expected_files: self.assertTrue(os.path.isfile(expected_file)) @unittest.skipIf(not TERNARY_PRESENT, "Skipping as python-ternary not found") def test_ternary_hull_plot(self): """ Test plotting ternary hull. """ expected_files = ["KSnP_hull.png", "KSnP_voltage.png"] for expected_file in expected_files: if os.path.isfile(expected_file): os.remove(expected_file) res_list = glob(REAL_PATH + "data/hull-KPSn-KP/*.res") self.assertEqual( len(res_list), 87, "Could not find test res files, please check installation...", ) cursor = [res2dict(res)[0] for res in res_list] QueryConvexHull( cursor=cursor, elements=["K", "Sn", "P"], no_plot=False, png=True, quiet=False, voltage=True, labels=True, label_cutoff=0.05, hull_cutoff=0.1, capmap=True, ) self.assertTrue(os.path.isfile(expected_file)) for expected_file in expected_files: os.remove(expected_file) def test_beef_hull_plot(self): """ Test plotting BEEF hull. """ from matador.hull import EnsembleHull from matador.scrapers import castep2dict expected_file = "KP_beef_hull.svg" cursor, s = castep2dict(REAL_PATH + "data/beef_files/*.castep", db=False) self.assertEqual(len(s), 0) beef_hull = EnsembleHull( cursor, "_beef", elements=["K", "P"], num_samples=10, energy_key="total_energy_per_atom", parameter_key="thetas", ) beef_hull.plot_hull(svg=True) self.assertTrue(os.path.isfile(expected_file)) def test_td_hull_plot(self): from matador.hull.hull_temperature import TemperatureDependentHull from matador.scrapers import castep2dict cursor, s = castep2dict(REAL_PATH + "data/castep_phonon_files/*.castep", db=False) td_hull = TemperatureDependentHull(cursor=cursor, energy_key="total_energy_per_atom") td_hull.plot_hull(plot_fname="td_hull", png=True) self.assertTrue(os.path.isfile("td_hull.png")) @unittest.skipIf(not MATPLOTLIB_PRESENT, "Skipping plotting tests.") class FingerprintPlotTests(MatadorUnitTest): """ Test ability to plot PDF and PXRDs. """ def test_pdf_plot(self): structure = res2dict( REAL_PATH + "data/res_files/KPSn-OQMD_123456.res", as_model=True )[0] plot_pdf(structure, png=True) self.assertTrue(os.path.isfile("K7PSn_pdf.png")) plot_pdf([structure, structure], filename="test_pdf", rmax=5, png=True) self.assertTrue(os.path.isfile("test_pdf.png")) def test_pxrd_plot(self): structure = res2dict( REAL_PATH + "data/res_files/KPSn-OQMD_123456.res", as_model=True )[0] plot_pxrd(structure, png=True) self.assertTrue(os.path.isfile("K7PSn_pxrd.png")) plot_pdf([structure, structure], filename="test_pxrd", png=True) self.assertTrue(os.path.isfile("test_pxrd.png")) @unittest.skipIf(not MATPLOTLIB_PRESENT, "Skipping plotting tests.") class MagresPlotTests(MatadorUnitTest): """ Test ability to plot magres data. """ def test_magres_plot(self): magres, f = magres2dict(REAL_PATH + "data/magres_files/*P*.magres", as_model=True) plot_magres( magres, species="P", savefig="magres_P.pdf", line_kwargs={"c": "green"}, ) self.assertTrue(os.path.isfile("magres_P.pdf")) plot_magres( magres, species="Li", broadening_width=0, magres_key="chemical_shift_aniso", savefig="magres_Li.png", signal_labels=["NaP", "LiP"], line_kwargs=[{"lw": 3}, {"ls": "--"}], ) self.assertTrue(os.path.isfile("magres_Li.png")) with self.assertRaises(RuntimeError): plot_magres(magres, species=None) with self.assertRaises(RuntimeError): plot_magres(magres, species="K") @unittest.skipIf(not MATPLOTLIB_PRESENT, "Skipping plotting tests.") class ConvergencePlotTest(unittest.TestCase): """ Test the ability to read convergence data and make plots. """ def setUp(self): os.chdir(REAL_PATH + "/data/convergence/") def tearDown(self): os.chdir(ROOT_DIR) def test_scraping_and_plotting(self): from matador.plotting.convergence_plotting import ( get_convergence_data, get_convergence_files, combine_convergence_data, get_convergence_values, ) from matador.plotting.convergence_plotting import plot_cutoff_kpt_grid kpt_files = get_convergence_files("completed_kpts") cutoff_files = get_convergence_files("completed_cutoff") kpt_data = get_convergence_data( kpt_files, conv_parameter="kpoints_mp_spacing", species=["Li"] ) cutoff_data = get_convergence_data( cutoff_files, conv_parameter="cut_off_energy", species=["Li"] ) data = combine_convergence_data(kpt_data, cutoff_data) self.assertEqual( data["Li-bcc"]["kpoints_mp_spacing"]["kpoints_mp_spacing"], [0.1, 0.07] ) self.assertEqual(data["Li-bcc"]["cut_off_energy"]["cut_off_energy"], [300, 400]) values, parameters = get_convergence_values( data["Li-bcc"], "cut_off_energy", "formation_energy_per_atom", log=True ) self.assertEqual(parameters.tolist(), [300.0, 400.0]) self.assertAlmostEqual(values.tolist()[0], 0.7291198427497395, places=6) self.assertEqual(values.tolist()[1], -np.inf) self.data = data expected_files = ["conv.svg"] for expected_file in expected_files: if os.path.isfile(expected_file): os.remove(expected_file) plot_cutoff_kpt_grid(self.data, svg=True) for file in expected_files: self.assertTrue(os.path.isfile(file)) for expected_file in expected_files: os.remove(expected_file)

AJMGroup/matador

tests/test_plotting.py

Python

def is_dark(wf): if not wf.alfred_env.get('theme_background'): return True rgb = [int(x) for x in wf.alfred_env['theme_background'][5:-6].split(',')] return (0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2]) / 255 < 0.5 def get_icon(wf, name): name = '%s-dark' % name if is_dark(wf) else name return "icons/%s.png" % name def search_key_for_action(action): return u'{}'.format(action['name']) def search_key_for_project(project): return u'{} {} {}'.format(project['id'], project['name'], project['description']) def search_key_for_aliased_activities(activity): return u'{} {} {}'.format(activity['name'], activity['project']['name'], activity['alias'])

r0x73/alfred-zebra

src/helpers.py

Python

# -*- coding: utf-8 -*- # # Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Resource definitions for cloud platform apis.""" import enum BASE_URL = 'https://cloudbilling.googleapis.com/v1/' DOCS_URL = 'https://cloud.google.com/billing/' class Collections(enum.Enum): """Collections for all supported apis.""" BILLINGACCOUNTS = ( 'billingAccounts', '{+name}', { '': 'billingAccounts/{billingAccountsId}', }, [u'name'], True ) PROJECTS = ( 'projects', 'projects/{projectsId}', {}, [u'projectsId'], True ) def __init__(self, collection_name, path, flat_paths, params, enable_uri_parsing): self.collection_name = collection_name self.path = path self.flat_paths = flat_paths self.params = params self.enable_uri_parsing = enable_uri_parsing

bshaffer/google-cloud-sdk

lib/googlecloudsdk/third_party/apis/cloudbilling/v1/resources.py

Python

# coding: utf-8 """ Laserfiche API Welcome to the Laserfiche API Swagger Playground. You can try out any of our API calls against your live Laserfiche Cloud account. Visit the developer center for more details: <a href=\"https://developer.laserfiche.com\">https://developer.laserfiche.com</a><p><strong>Build# : </strong>650780</p> # noqa: E501 OpenAPI spec version: 1-alpha Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class GetEdocWithAuditReasonRequest(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'audit_reason_id': 'int', 'comment': 'str' } attribute_map = { 'audit_reason_id': 'auditReasonId', 'comment': 'comment' } def __init__(self, audit_reason_id=None, comment=None): # noqa: E501 """GetEdocWithAuditReasonRequest - a model defined in Swagger""" # noqa: E501 self._audit_reason_id = None self._comment = None self.discriminator = None if audit_reason_id is not None: self.audit_reason_id = audit_reason_id if comment is not None: self.comment = comment @property def audit_reason_id(self): """Gets the audit_reason_id of this GetEdocWithAuditReasonRequest. # noqa: E501 The reason id for this audit event. # noqa: E501 :return: The audit_reason_id of this GetEdocWithAuditReasonRequest. # noqa: E501 :rtype: int """ return self._audit_reason_id @audit_reason_id.setter def audit_reason_id(self, audit_reason_id): """Sets the audit_reason_id of this GetEdocWithAuditReasonRequest. The reason id for this audit event. # noqa: E501 :param audit_reason_id: The audit_reason_id of this GetEdocWithAuditReasonRequest. # noqa: E501 :type: int """ self._audit_reason_id = audit_reason_id @property def comment(self): """Gets the comment of this GetEdocWithAuditReasonRequest. # noqa: E501 The comment for this audit event. # noqa: E501 :return: The comment of this GetEdocWithAuditReasonRequest. # noqa: E501 :rtype: str """ return self._comment @comment.setter def comment(self, comment): """Sets the comment of this GetEdocWithAuditReasonRequest. The comment for this audit event. # noqa: E501 :param comment: The comment of this GetEdocWithAuditReasonRequest. # noqa: E501 :type: str """ self._comment = comment def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(GetEdocWithAuditReasonRequest, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, GetEdocWithAuditReasonRequest): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

Layer8Err/laserfiche-api

laserfiche_api/models/get_edoc_with_audit_reason_request.py

Python

from django.core.urlresolvers import reverse from django.http import HttpResponseRedirect from allauth.socialaccount.helpers import render_authentication_error from allauth.socialaccount.providers.oauth.client import (OAuthClient, OAuthError) from allauth.socialaccount.helpers import complete_social_login from allauth.socialaccount import providers from allauth.socialaccount.models import SocialToken, SocialLogin class OAuthAdapter(object): def complete_login(self, request, app): """ Returns a SocialLogin instance """ raise NotImplementedError def get_provider(self): return providers.registry.by_id(self.provider_id) class OAuthView(object): @classmethod def adapter_view(cls, adapter): def view(request, *args, **kwargs): self = cls() self.request = request self.adapter = adapter() return self.dispatch(request, *args, **kwargs) return view def _get_client(self, request, callback_url): provider = self.adapter.get_provider() app = provider.get_app(request) scope = ' '.join(provider.get_scope()) parameters = {} if scope: parameters['scope'] = scope for param in request.GET: if param.startswith('auth_param_'): try: parameters['auth_params'][param[11:]] = request.GET.get(param) except KeyError:parameters['auth_params'] = {param[11:]: request.GET.get(param)} if 'redirect_account_url' in request.GET: request.session['redirect_account_url'] = request.GET['redirect_account_url'] client = OAuthClient(request, app.key, app.secret, self.adapter.request_token_url, self.adapter.access_token_url, self.adapter.authorize_url, request.GET.get('callback_url', callback_url), parameters=parameters, disable_ssl_certificate_validation=True) return client class OAuthLoginView(OAuthView): def dispatch(self, request): callback_url = reverse(self.adapter.provider_id + "_callback") # TODO: Can't this be moved as query param into callback? # Tried but failed somehow, needs further study... request.session['oauth_login_state'] \ = SocialLogin.marshall_state(request) client = self._get_client(request, callback_url) try: return client.get_redirect() except OAuthError: return render_authentication_error(request) class OAuthCallbackView(OAuthView): def dispatch(self, request): """ View to handle final steps of OAuth based authentication where the user gets redirected back to from the service provider """ login_done_url = reverse(self.adapter.provider_id + "_callback") client = self._get_client(request, login_done_url) if not client.is_valid(): if 'denied' in request.GET: return HttpResponseRedirect(reverse('socialaccount_login_cancelled')) extra_context = dict(oauth_client=client) return render_authentication_error(request, extra_context) app = self.adapter.get_provider().get_app(request) try: access_token = client.get_access_token() token = SocialToken(app=app, token=access_token['oauth_token'], token_secret=access_token['oauth_token_secret']) login = self.adapter.complete_login(request, app, token) token.account = login.account login.token = token login.state = SocialLogin.unmarshall_state \ (request.session.pop('oauth_login_state', None)) login.redirect_account_url = request.session.pop('redirect_account_url', None) return complete_social_login(request, login) except OAuthError: return render_authentication_error(request)

rawjam/django-allauth

allauth/socialaccount/providers/oauth/views.py

Python

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs __all__ = [ 'GetMachineLearningComputeResult', 'AwaitableGetMachineLearningComputeResult', 'get_machine_learning_compute', ] @pulumi.output_type class GetMachineLearningComputeResult: """ Machine Learning compute object wrapped into ARM resource envelope. """ def __init__(__self__, identity=None, location=None, name=None, properties=None, sku=None, tags=None, type=None): if identity and not isinstance(identity, dict): raise TypeError("Expected argument 'identity' to be a dict") pulumi.set(__self__, "identity", identity) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if properties and not isinstance(properties, dict): raise TypeError("Expected argument 'properties' to be a dict") pulumi.set(__self__, "properties", properties) if sku and not isinstance(sku, dict): raise TypeError("Expected argument 'sku' to be a dict") pulumi.set(__self__, "sku", sku) if tags and not isinstance(tags, dict): raise TypeError("Expected argument 'tags' to be a dict") pulumi.set(__self__, "tags", tags) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter def identity(self) -> Optional['outputs.IdentityResponse']: """ The identity of the resource. """ return pulumi.get(self, "identity") @property @pulumi.getter def location(self) -> Optional[str]: """ Specifies the location of the resource. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> str: """ Specifies the name of the resource. """ return pulumi.get(self, "name") @property @pulumi.getter def properties(self) -> Any: """ Compute properties """ return pulumi.get(self, "properties") @property @pulumi.getter def sku(self) -> Optional['outputs.SkuResponse']: """ The sku of the workspace. """ return pulumi.get(self, "sku") @property @pulumi.getter def tags(self) -> Optional[Mapping[str, str]]: """ Contains resource tags defined as key/value pairs. """ return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> str: """ Specifies the type of the resource. """ return pulumi.get(self, "type") class AwaitableGetMachineLearningComputeResult(GetMachineLearningComputeResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetMachineLearningComputeResult( identity=self.identity, location=self.location, name=self.name, properties=self.properties, sku=self.sku, tags=self.tags, type=self.type) def get_machine_learning_compute(compute_name: Optional[str] = None, resource_group_name: Optional[str] = None, workspace_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetMachineLearningComputeResult: """ Use this data source to access information about an existing resource. :param str compute_name: Name of the Azure Machine Learning compute. :param str resource_group_name: Name of the resource group in which workspace is located. :param str workspace_name: Name of Azure Machine Learning workspace. """ __args__ = dict() __args__['computeName'] = compute_name __args__['resourceGroupName'] = resource_group_name __args__['workspaceName'] = workspace_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:machinelearningservices/v20200901preview:getMachineLearningCompute', __args__, opts=opts, typ=GetMachineLearningComputeResult).value return AwaitableGetMachineLearningComputeResult( identity=__ret__.identity, location=__ret__.location, name=__ret__.name, properties=__ret__.properties, sku=__ret__.sku, tags=__ret__.tags, type=__ret__.type)

test-wiz-sec/pulumi-azure-nextgen

sdk/python/pulumi_azure_nextgen/machinelearningservices/v20200901preview/get_machine_learning_compute.py

Python

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import torch import os, json, hashlib from torch.autograd import Function from http import client as http_client import antares_custom_op def generate_antares_expression(antares_ir, inputs): input_dict, kwargs = {}, {} for i in range(len(inputs)): input_dict['input%d' % i] = { 'dtype': str(inputs[i].dtype).split('.')[1], 'shape': list(inputs[i].shape) } kwargs['input%d' % i] = inputs[i] input_dict = json.dumps(input_dict) return '- einstein_v2("%s", input_dict=%s)' % (antares_ir.replace('"', '\\"'), input_dict) def fetch_and_compile_antares_kernel(expression, expr_hash, server_addr): print('+ [Antares Op]', expression) h = http_client.HTTPConnection(server_addr, timeout=10) try: h.request('GET', '/', headers={'COMPUTE_V1': expression}) except: raise Exception("Failed to contact with Antares server: %s (not started?)" % server_addr) res = h.getresponse() if res.status != 200: raise Exception("Fail to get server response, reason: %s" % res.reason) source = res.read().decode() try: meta_bgn = source.index('///') + len('///') except: raise Exception("Illegal syntax for Antares expression: %s" % expression) meta_pos = source.index(':', meta_bgn) meta_end = source.index('\n', meta_pos) meta_inputs = source[meta_bgn:meta_pos].split(',') meta_outputs = source[meta_pos + 1:meta_end].split(',') code_name = 'Antares' + expr_hash source_path = '/tmp/antares_torch_%s.cc.kernel.cu' % code_name # Compile Kernel object with open(source_path, 'w') as fp: fp.write(source) return source, source_path, expr_hash, meta_inputs, meta_outputs ''' class CustomFunction(Function): @staticmethod def forward(ctx, inputs, attributes): outputs = antares_custom_op.forward(inputs, *attributes) return outputs ''' class CustomOp(torch.nn.Module): def __init__(self, server_addr='localhost:8880'): super(CustomOp, self).__init__() self.server_addr = server_addr self.ops = dict() def forward(self, antares_ir, inputs): antares_expr = generate_antares_expression(antares_ir, inputs) expr_hash = hashlib.sha256(antares_expr.encode()).hexdigest() if expr_hash in self.ops: attributes = self.ops[expr_hash] else: attributes = fetch_and_compile_antares_kernel(antares_expr, expr_hash, self.server_addr) self.ops[expr_hash] = attributes outputs = antares_custom_op.forward(inputs, *attributes) return outputs

kdtree/antares

frameworks/antares/pytorch/custom_op.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdklive.endpoint import endpoint_data class ApplyRecordTokenRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'live', '2016-11-01', 'ApplyRecordToken','live') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId) def get_AppId(self): return self.get_query_params().get('AppId') def set_AppId(self,AppId): self.add_query_param('AppId',AppId)

Explorer1092/aliyun-openapi-python-sdk

aliyun-python-sdk-live/aliyunsdklive/request/v20161101/ApplyRecordTokenRequest.py

Python

""" A simple class to help with paging result sets """ import logging from flask import request, url_for, Markup __author__ = 'Stephen Brown (Little Fish Solutions LTD)' log = logging.getLogger(__name__) class Pager(object): """ Standard Pager used on back end of website. When viewing page 234 of 1000, the following page links will be displayed: 1, 134, 184, 232, 233, 234, 235, 236, 284, 334, 1000 """ def __init__(self, page_size, page_number, query): self.page_size = page_size try: self.page_number = int(page_number) except ValueError: self.page_number = 1 if self.page_number < 1: self.page_number = 1 self.query = query # Do the paging here self.total_items = query.count() self.total_pages = (self.total_items - 1) // page_size + 1 if self.page_number > self.total_pages: self.page_number = self.total_pages self.offset = self.page_size * (self.page_number - 1) if self.offset < 0: self.offset = 1 self.items = query[self.offset:self.offset + self.page_size] def get_first_item_from_next_page(self): if self.has_next: return self.query[self.offset + self.page_size] return None def get_last_item_from_previous_page(self): if self.has_prev: return self.query[self.offset - 1] return None @property def has_prev(self): return self.page_number > 1 @property def has_next(self): return self.page_number < self.total_pages @property def prev(self): return self.page_number - 1 @property def next(self): return self.page_number + 1 @property def page_link_numbers(self): pages = [1] if self.total_pages <= 1: return pages if self.page_number > 103: pages.append(self.page_number - 100) if self.page_number > 53: pages.append(self.page_number - 50) if self.page_number > 3: pages.append(self.page_number - 2) if self.page_number > 2: pages.append(self.page_number - 1) if self.page_number != 1 and self.page_number != self.total_pages: pages.append(self.page_number) if self.page_number < self.total_pages - 1: pages.append(self.page_number + 1) if self.page_number < self.total_pages - 2: pages.append(self.page_number + 2) if self.page_number < self.total_pages - 52: pages.append(self.page_number + 50) if self.page_number < self.total_pages - 102: pages.append(self.page_number + 100) pages.append(self.total_pages) return pages @property def empty(self): return self.total_pages == 0 def get_full_page_url(self, page_number, scheme=None): """Get the full, external URL for this page, optinally with the passed in URL scheme""" args = dict( request.view_args, _external=True, ) if scheme is not None: args['_scheme'] = scheme if page_number != 1: args['page'] = page_number return url_for(request.endpoint, **args) def get_canonical_url(self, scheme=None): """Get the canonical page URL""" return self.get_full_page_url(self.page_number, scheme=scheme) def render_prev_next_links(self, scheme=None): """Render the rel=prev and rel=next links to a Markup object for injection into a template""" output = '' if self.has_prev: output += '<link rel="prev" href="{}" />\n'.format(self.get_full_page_url(self.prev, scheme=scheme)) if self.has_next: output += '<link rel="next" href="{}" />\n'.format(self.get_full_page_url(self.next, scheme=scheme)) return Markup(output) def render_canonical_link(self, scheme=None): """Render the rel=canonical link to a Markup object for injection into a template""" return Markup('<link rel="canonical" href="{}" />'.format(self.get_canonical_url(scheme=scheme))) def render_seo_links(self, scheme=None): """Render the rel=canonical, rel=prev and rel=next links to a Markup object for injection into a template""" out = self.render_prev_next_links(scheme=scheme) if self.total_pages == 1: out += self.render_canonical_link(scheme=scheme) return out @property def first_item_number(self): """ :return: The first "item number", used when displaying messages to the user like "Displaying items 1 to 10 of 123" - in this example 1 would be returned """ return self.offset + 1 @property def last_item_number(self): """ :return: The last "item number", used when displaying messages to the user like "Displaying items 1 to 10 of 123" - in this example 10 would be returned """ n = self.first_item_number + self.page_size - 1 if n > self.total_items: return self.total_items return n class SimplePager(Pager): """ Uses the same api as above, but displays a range of continuous page numbers. If you are on page 6 of 10 the following page numbers will be displayed: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 """ def __init__(self, page_size, page_number, query, max_pages=12): """ :param max_pages: The maximum number of page links to display """ super().__init__(page_size, page_number, query) self.max_pages = max_pages @property def page_link_numbers(self): start = self.page_number - self.max_pages // 2 + 1 if start < 1: start = 1 end = start + self.max_pages - 1 if end > self.total_pages: end = self.total_pages if start > 1: start = end - self.max_pages + 1 return range(start, end + 1) class InMemoryPager(Pager): """ Use this when you absolutely have to load everything and page in memory. You can access all of the items through the all_items attribute after initialising this object """ def __init__(self, page_size, page_number, query): self.page_size = page_size try: self.page_number = int(page_number) except ValueError: self.page_number = 1 if self.page_number < 1: self.page_number = 1 self.query = query # Load everything self.all_items = query.all() # Do the paging here self.total_items = len(self.all_items) self.total_pages = (self.total_items - 1) // page_size + 1 if self.page_number > self.total_pages: self.page_number = self.total_pages self.offset = self.page_size * (self.page_number - 1) if self.offset < 0: self.offset = 1 self.items = self.all_items[self.offset:self.offset + self.page_size] class ViewAllPager(object): """ Uses the same API as pager, but lists all items on a single page. This is to allow easy implementation of a "view all" function on a listing page """ def __init__(self, query): self.page_number = 1 self.query = query # Do the paging here self.total_items = query.count() self.page_size = self.total_items self.total_pages = 1 self.offset = 0 self.items = query.all() @property def has_prev(self): return False @property def has_next(self): return False @property def prev(self): return self.page_number - 1 @property def next(self): return self.page_number + 1 @property def page_link_numbers(self): return [1]

michaelwalkerfl/littlefish

build/lib/littlefish/pager.py

Python

"""Support for TPLink HS100/HS110/HS200 smart switch.""" import logging import time from pyHS100 import SmartDeviceException, SmartPlug from homeassistant.components.switch import ( ATTR_CURRENT_POWER_W, ATTR_TODAY_ENERGY_KWH, SwitchDevice, ) from homeassistant.const import ATTR_VOLTAGE import homeassistant.helpers.device_registry as dr from homeassistant.helpers.typing import HomeAssistantType from . import CONF_SWITCH, DOMAIN as TPLINK_DOMAIN from .common import async_add_entities_retry PARALLEL_UPDATES = 0 _LOGGER = logging.getLogger(__name__) ATTR_TOTAL_ENERGY_KWH = "total_energy_kwh" ATTR_CURRENT_A = "current_a" async def async_setup_platform(hass, config, add_entities, discovery_info=None): """Set up the platform. Deprecated. """ _LOGGER.warning( "Loading as a platform is no longer supported, " "convert to use the tplink component." ) def add_entity(device: SmartPlug, async_add_entities): """Check if device is online and add the entity.""" # Attempt to get the sysinfo. If it fails, it will raise an # exception that is caught by async_add_entities_retry which # will try again later. device.get_sysinfo() async_add_entities([SmartPlugSwitch(device)], update_before_add=True) async def async_setup_entry(hass: HomeAssistantType, config_entry, async_add_entities): """Set up switches.""" await async_add_entities_retry( hass, async_add_entities, hass.data[TPLINK_DOMAIN][CONF_SWITCH], add_entity ) return True class SmartPlugSwitch(SwitchDevice): """Representation of a TPLink Smart Plug switch.""" def __init__(self, smartplug: SmartPlug): """Initialize the switch.""" self.smartplug = smartplug self._sysinfo = None self._state = None self._available = False # Set up emeter cache self._emeter_params = {} self._mac = None self._alias = None self._model = None self._device_id = None @property def unique_id(self): """Return a unique ID.""" return self._device_id @property def name(self): """Return the name of the Smart Plug.""" return self._alias @property def device_info(self): """Return information about the device.""" return { "name": self._alias, "model": self._model, "manufacturer": "TP-Link", "connections": {(dr.CONNECTION_NETWORK_MAC, self._mac)}, "sw_version": self._sysinfo["sw_ver"], } @property def available(self) -> bool: """Return if switch is available.""" return self._available @property def is_on(self): """Return true if switch is on.""" return self._state def turn_on(self, **kwargs): """Turn the switch on.""" self.smartplug.turn_on() def turn_off(self, **kwargs): """Turn the switch off.""" self.smartplug.turn_off() @property def device_state_attributes(self): """Return the state attributes of the device.""" return self._emeter_params def update(self): """Update the TP-Link switch's state.""" try: if not self._sysinfo: self._sysinfo = self.smartplug.sys_info self._mac = self.smartplug.mac self._model = self.smartplug.model if self.smartplug.context is None: self._alias = self.smartplug.alias self._device_id = self._mac else: self._alias = [ child for child in self.smartplug.sys_info["children"] if child["id"] == self.smartplug.context ][0]["alias"] self._device_id = self.smartplug.context if self.smartplug.context is None: self._state = self.smartplug.state == self.smartplug.SWITCH_STATE_ON else: self._state = [ child for child in self.smartplug.sys_info["children"] if child["id"] == self.smartplug.context ][0]["state"] == 1 if self.smartplug.has_emeter: emeter_readings = self.smartplug.get_emeter_realtime() self._emeter_params[ATTR_CURRENT_POWER_W] = "{:.2f}".format( emeter_readings["power"] ) self._emeter_params[ATTR_TOTAL_ENERGY_KWH] = "{:.3f}".format( emeter_readings["total"] ) self._emeter_params[ATTR_VOLTAGE] = "{:.1f}".format( emeter_readings["voltage"] ) self._emeter_params[ATTR_CURRENT_A] = "{:.2f}".format( emeter_readings["current"] ) emeter_statics = self.smartplug.get_emeter_daily() try: self._emeter_params[ATTR_TODAY_ENERGY_KWH] = "{:.3f}".format( emeter_statics[int(time.strftime("%e"))] ) except KeyError: # Device returned no daily history pass self._available = True except (SmartDeviceException, OSError) as ex: if self._available: _LOGGER.warning( "Could not read state for %s: %s", self.smartplug.host, ex ) self._available = False

ABOTlegacy/home-assistant

homeassistant/components/tplink/switch.py

Python

import cpboard import periphery import pytest import smbus import sys def pytest_addoption(parser): group = parser.getgroup('i2cslave') group.addoption("--bus", dest='i2cbus', type=int, help='I2C bus number') group.addoption("--serial-wait", default=20, dest='serial_wait', type=int, help='Number of milliseconds to wait before checking board output (default: 20ms)') group.addoption("--smbus-timeout", default=True, dest='smbus_timeout', type=bool, help='Use SMBUS timeout limit (default: True)') @pytest.fixture(scope='session') def board(request): board = cpboard.CPboard.from_try_all(request.config.option.boarddev) board.open() board.repl.reset() return board class I2C: def __init__(self, bus): self.bus = periphery.I2C('/dev/i2c-%d' % bus) def __enter__(self): return self def __exit__(self, t, value, traceback): self.close() def close(self): self.bus.close() def transfer(self, address, messages): #__tracebackhide__ = True # Hide this from pytest traceback self.bus.transfer(address, messages) Message = periphery.I2C.Message def read(self, address, n): data = [0] * n msgs = [I2C.Message(data, read=True)] self.transfer(address, msgs) return msgs[0].data def write(self, address, data): msgs = [I2C.Message(data)] self.transfer(address, msgs) def write_read(self, address, data, n): recv = [0] * n msgs = [I2C.Message(data), I2C.Message(recv, read=True)] self.transfer(address, msgs) return msgs[1].data @pytest.fixture def i2cbus(request): return I2C(request.config.option.i2cbus)

notro/cp-smbusslave

tests/i2cslave/conftest.py

Python

from typing import Dict # TODO consolidate some of these imports from vyper.semantics.types.user.struct import StructDefinition from vyper.semantics.types.value.address import AddressDefinition from vyper.semantics.types.value.array_value import BytesArrayDefinition from vyper.semantics.types.value.bytes_fixed import Bytes32Definition from vyper.semantics.types.value.numeric import Uint256Definition CONSTANT_ENVIRONMENT_VARS: Dict[str, Dict[str, type]] = { "block": { "coinbase": AddressDefinition, "difficulty": Uint256Definition, "number": Uint256Definition, "gaslimit": Uint256Definition, "basefee": Uint256Definition, "prevhash": Bytes32Definition, "timestamp": Uint256Definition, }, "chain": {"id": Uint256Definition}, "msg": { "data": BytesArrayDefinition, "gas": Uint256Definition, "sender": AddressDefinition, "value": Uint256Definition, }, "tx": {"origin": AddressDefinition}, } MUTABLE_ENVIRONMENT_VARS: Dict[str, type] = { "self": AddressDefinition, } def get_constant_vars() -> Dict: """ Get a dictionary of constant environment variables. """ result = {} for name, members in CONSTANT_ENVIRONMENT_VARS.items(): members = {k: v(is_immutable=True) for k, v in members.items()} result[name] = StructDefinition(name, members, is_immutable=True) return result def get_mutable_vars() -> Dict: """ Get a dictionary of mutable environment variables (those that are modified during the course of contract execution, such as `self`). """ return {name: type_(is_immutable=True) for name, type_ in MUTABLE_ENVIRONMENT_VARS.items()}

GDGSNF/vyper

vyper/semantics/environment.py

Python

from django.core.exceptions import ValidationError from django.utils.translation import ugettext_lazy as _ import datetime # for checking renewal date range. from django import forms class RenewBookForm(forms.Form): """Form for a librarian to renew books.""" renewal_date = forms.DateField( help_text="Enter a date between now and 4 weeks (default 3).") def clean_renewal_date(self): data = self.cleaned_data['renewal_date'] # Check date is not in past. if data < datetime.date.today(): raise ValidationError(_('Invalid date - renewal in past')) # Check date is in range librarian allowed to change (+4 weeks) if data > datetime.date.today() + datetime.timedelta(weeks=4): raise ValidationError( _('Invalid date - renewal more than 4 weeks ahead')) # Remember to always return the cleaned data. return data class ReturnBookForm(forms.Form): """Form for a librarian to renew books.""" return_date = forms.DateField( help_text="Enter a date between borrow date and today.") penalty = forms.IntegerField( help_text="Penalty (in IDR).", initial=0) def clean_return_date(self): data = self.cleaned_data['return_date'] # Check date is not in future. if data > datetime.date.today(): raise ValidationError(_('Invalid date - return in future')) return data

PMPL-Arieken/django-locallibrary-tutorial

catalog/forms.py

Python

#!/usr/bin/env python # Import modules import numpy as np import sklearn from sklearn.preprocessing import LabelEncoder import pickle from sensor_stick.srv import GetNormals from sensor_stick.features import compute_color_histograms from sensor_stick.features import compute_normal_histograms from visualization_msgs.msg import Marker from sensor_stick.marker_tools import * from sensor_stick.msg import DetectedObjectsArray from sensor_stick.msg import DetectedObject from sensor_stick.pcl_helper import * import rospy import tf from geometry_msgs.msg import Pose from std_msgs.msg import Float64 from std_msgs.msg import Int32 from std_msgs.msg import String from pr2_robot.srv import * from rospy_message_converter import message_converter import yaml # Helper function to get surface normals def get_normals(cloud): get_normals_prox = rospy.ServiceProxy('/feature_extractor/get_normals', GetNormals) return get_normals_prox(cloud).cluster # Helper function to create a yaml friendly dictionary from ROS messages def make_yaml_dict(test_scene_num, arm_name, object_name, pick_pose, place_pose): yaml_dict = {} yaml_dict["test_scene_num"] = test_scene_num.data yaml_dict["arm_name"] = arm_name.data yaml_dict["object_name"] = object_name.data yaml_dict["pick_pose"] = message_converter.convert_ros_message_to_dictionary(pick_pose) yaml_dict["place_pose"] = message_converter.convert_ros_message_to_dictionary(place_pose) return yaml_dict # Helper function to output to yaml file def send_to_yaml(yaml_filename, dict_list): data_dict = {"object_list": dict_list} with open(yaml_filename, 'w') as outfile: yaml.dump(data_dict, outfile, default_flow_style=False) # Callback function for your Point Cloud Subscriber def pcl_callback(pcl_msg): # Exercise-2 TODOs: # TODO: Convert ROS msg to PCL data cloud = ros_to_pcl(pcl_msg) # TODO: Statistical Outlier Filtering # creating a statistical outlier filter object for reducing noise outlier_filter = cloud.make_statistical_outlier_filter() # Set the number of neighboring points to analyze for any given point outlier_filter.set_mean_k(10) # Set threshold scale factor x = 0.5 # Any point with a mean distance larger than global (mean distance+x*std_dev) will be considered outlier outlier_filter.set_std_dev_mul_thresh(x) # Finally call the filter function cloud_filtered = outlier_filter.filter() # TODO: Voxel Grid Downsampling # Create a VoxelGrid filter object for our input point cloud vox = cloud_filtered.make_voxel_grid_filter() # Choose a voxel (also known as leaf) size LEAF_SIZE = 0.01 # Set the voxel (or leaf) size vox.set_leaf_size(LEAF_SIZE, LEAF_SIZE, LEAF_SIZE) # Call the filter function to obtain the resultant downsampled point cloud cloud_filtered = vox.filter() # TODO: PassThrough Filter # Create a PassThrough filter object. passthrough = cloud_filtered.make_passthrough_filter() # Assign axis and range to the passthrough filter object. filter_axis = 'z' passthrough.set_filter_field_name(filter_axis) axis_min = 0.6 axis_max = 1.1 passthrough.set_filter_limits(axis_min, axis_max) # Finally use the filter function to obtain the resultant point cloud. cloud_filtered = passthrough.filter() #PassThrough Filter for removing outliers in y direction # Create a PassThrough filter object. passthrough = cloud_filtered.make_passthrough_filter() # Assign axis and range to the passthrough filter object. filter_axis = 'y' passthrough.set_filter_field_name(filter_axis) axis_min = -0.4 axis_max = 0.4 passthrough.set_filter_limits(axis_min, axis_max) # Finally use the filter function to obtain the resultant point cloud. cloud_filtered = passthrough.filter() # TODO: RANSAC Plane Segmentation # Create the segmentation object seg = cloud_filtered.make_segmenter() # Set the model you wish to fit seg.set_model_type(pcl.SACMODEL_PLANE) seg.set_method_type(pcl.SAC_RANSAC) # Max distance for a point to be considered fitting the model # Experiment with different values for max_distance # for segmenting the table max_distance = 0.03 seg.set_distance_threshold(max_distance) # Call the segment function to obtain set of inlier indices and model coefficients inliers, coefficients = seg.segment() # TODO: Extract inliers and outliers cloud_table = cloud_filtered.extract(inliers, negative=False) cloud_objects = cloud_filtered.extract(inliers, negative=True) # TODO: Euclidean Clustering white_cloud = XYZRGB_to_XYZ(cloud_objects) tree = white_cloud.make_kdtree() # Create a cluster extraction object ec = white_cloud.make_EuclideanClusterExtraction() # Set tolerances for distance threshold # as well as minimum and maximum cluster size (in points) ec.set_ClusterTolerance(0.05) ec.set_MinClusterSize(10) ec.set_MaxClusterSize(25000) # Search the k-d tree for clusters ec.set_SearchMethod(tree) # Extract indices for each of the discovered clusters cluster_indices = ec.Extract() # TODO: Create Cluster-Mask Point Cloud to visualize each cluster separately #Assign a color corresponding to each segmented object in scene cluster_color = get_color_list(len(cluster_indices)) color_cluster_point_list = [] for j, indices in enumerate(cluster_indices): for i, indice in enumerate(indices): color_cluster_point_list.append([white_cloud[indice][0], white_cloud[indice][1], white_cloud[indice][2], rgb_to_float(cluster_color[j])]) #Create new cloud containing all clusters, each with unique color cluster_cloud = pcl.PointCloud_PointXYZRGB() cluster_cloud.from_list(color_cluster_point_list) # TODO: Convert PCL data to ROS messages ros_cluster_cloud = pcl_to_ros(cluster_cloud) # TODO: Publish ROS messages pcl_cluster_pub.publish(ros_cluster_cloud) # Exercise-3 TODOs: # Classify the clusters! (loop through each detected cluster one at a time) detected_objects_labels = [] detected_objects = [] for index, pts_list in enumerate(cluster_indices): # Grab the points for the cluster pcl_cluster = cloud_objects.extract(pts_list) # convert pcl to ros ros_cluster = pcl_to_ros(pcl_cluster) # Compute the associated feature vector chists = compute_color_histograms(ros_cluster, using_hsv=True) normals = get_normals(ros_cluster) nhists = compute_normal_histograms(normals) feature = np.concatenate((chists, nhists)) # Make the prediction prediction = clf.predict(scaler.transform(feature.reshape(1,-1))) label = encoder.inverse_transform(prediction)[0] detected_objects_labels.append(label) # Publish a label into RViz label_pos = list(white_cloud[pts_list[0]]) label_pos[2] += .4 object_markers_pub.publish(make_label(label,label_pos, index)) # Add the detected object to the list of detected objects. do = DetectedObject() do.label = label do.cloud = ros_cluster detected_objects.append(do) # Publish the list of detected objects detected_objects_pub.publish(detected_objects) # Suggested location for where to invoke your pr2_mover() function within pcl_callback() # Could add some logic to determine whether or not your object detections are robust # before calling pr2_mover() try: pr2_mover(detected_objects) except rospy.ROSInterruptException: pass # function to load parameters and request PickPlace service def pr2_mover(object_list): # TODO: Initialize variables object_name = [] object_group = [] TEST_SCENE_NUM = Int32() OBJECT_NAME = String() WHICH_ARM = String() PICK_POSE = Pose() PLACE_POSE = Pose() labels = [] centroids = [] # to be list of tuples (x, y, z) # Store labels and their centroids in lists for object in object_list: labels.append(object.label) points_arr = ros_to_pcl(object.cloud).to_array() centroids.append(np.mean(points_arr, axis=0)[:3]) left_position = None right_position = None dict_list = [] # TODO: Get/Read parameters object_list_param = rospy.get_param('/object_list') drop_box_param = rospy.get_param('/dropbox') # Get left and right box positions for i in range(len(drop_box_param)): if(drop_box_param[i]['name'] == 'left'): left_position = drop_box_param[i]['position'] else: right_position = drop_box_param[i]['position'] # TODO: Rotate PR2 in place to capture side tables for the collision map # TODO: Loop through the pick list for i in range(len(object_list_param)): # Get object name from pick list OBJECT_NAME.data = object_list_param[i]['name'] # Specify the test scene number TEST_SCENE_NUM.data = 2 # Get index of object from stored list obj_idx = labels.index(object_list_param[i]['name']) # Stop if object was not detected in the scene if(obj_idx == -1): rospy.loginfo('Object not detected') return # TODO: Get the PointCloud for a given object and obtain it's centroid# Calculate centroids centroid = centroids[obj_idx] # Cast centroids to native Python float type and assign centroid to pick_pose PICK_POSE.position.x = np.asscalar(centroid[0]) PICK_POSE.position.y = np.asscalar(centroid[1]) PICK_POSE.position.z = np.asscalar(centroid[2]) # TODO: Assign the arm to be used for pick_place if(object_list_param[i]['group'] == 'red'): WHICH_ARM.data = 'left' PLACE_POSE.position.x = left_position[0] PLACE_POSE.position.y = left_position[1] PLACE_POSE.position.z = left_position[2] else: WHICH_ARM.data = 'right' PLACE_POSE.position.x = right_position[0] PLACE_POSE.position.y = right_position[1] PLACE_POSE.position.z = right_position[2] # TODO: Create a list of dictionaries (made with make_yaml_dict()) for later output to yaml format yaml_dict = make_yaml_dict(TEST_SCENE_NUM, OBJECT_NAME, WHICH_ARM, PICK_POSE, PLACE_POSE) dict_list.append(yaml_dict) # Wait for 'pick_place_routine' service to come up rospy.wait_for_service('pick_place_routine') try: pick_place_routine = rospy.ServiceProxy('pick_place_routine', PickPlace) # TODO: Insert your message variables to be sent as a service request resp = pick_place_routine(TEST_SCENE_NUM, OBJECT_NAME, WHICH_ARM, PICK_POSE, PLACE_POSE) print ("Response: ",resp.success) except rospy.ServiceException, e: print "Service call failed: %s"%e # TODO: Output your request parameters into output yaml file # send_to_yaml('src/RoboND-Perception-Project/pr2_robot/config/output_3.yaml',dict_list) # print('yaml file saved') if __name__ == '__main__': # TODO: ROS node initialization rospy.init_node('pick_and_place_main') # TODO: Create Subscribers pcl_sub = rospy.Subscriber("/pr2/world/points", pc2.PointCloud2, pcl_callback, queue_size=1) # TODO: Create Publishers pcl_cluster_pub = rospy.Publisher("/pcl_cluster", PointCloud2, queue_size=1) object_markers_pub = rospy.Publisher("/object_markers", Marker, queue_size=1) detected_objects_pub = rospy.Publisher("/detected_objects", DetectedObjectsArray, queue_size=1) # TODO: Load Model From disk model = pickle.load(open('model.sav', 'rb')) clf = model['classifier'] encoder = LabelEncoder() encoder.classes_ = model['classes'] scaler = model['scaler'] # Initialize color_list get_color_list.color_list = [] # TODO: Spin while node is not shutdown while not rospy.is_shutdown(): rospy.spin()

navinrahim/RoboND-Perception-Project

pr2_robot/scripts/project_run.py

Python

from flask import render_template, request, redirect, url_for, flash from datetime import datetime as dt from app.forms import AdicionarQuarto, VerificarDisponibilidade from app.models import Rooms, Hotels, User, Reservation, Status from app import db def adicionar_quarto(user_id): form_reserva = VerificarDisponibilidade() user = User.query.filter_by(id=user_id).first() form = AdicionarQuarto() if user.profile not in ['admin', 'gerente']: return '<h1>Erro! Você não pode acessar este conteúdo!</h1>' if user.hotel_id is None: hoteis = Hotels.query.order_by(Hotels.created_at) form.hotel_id.choices = [(hotel.id, hotel.name) for hotel in hoteis if hotel.user_id == user_id] else: hoteis = Hotels.query.filter_by(id=user.hotel_id).order_by(Hotels.created_at) form.hotel_id.choices = [(hotel.id, hotel.name) for hotel in hoteis] if request.method == 'POST': if form.validate_on_submit(): room = Rooms.query.filter_by(hotel_id=form.hotel_id.data, number=form.number.data).first() if room is None: room = Rooms(number=form.number.data, hotel_id=form.hotel_id.data, name=form.name.data, short_description=form.short_description.data, kind=form.kind.data, phone_extension=form.phone_extension.data, price=float(form.price.data.replace('.','').replace(',','.')), guest_limit=form.guest_limit.data) db.session.add(room) db.session.commit() flash('Quarto cadastrado com sucesso!', 'success') else: flash('Quarto já existe...', 'danger') return redirect(url_for('ocupacao_quartos_endpoint', id=form.hotel_id.data)) return render_template('adicionar_quartos.html', form=form, hoteis=hoteis, user=user, titulo='Adicionar quarto', form_reserva=form_reserva ) def ocupacao_quartos(id, user_id): form_reserva = VerificarDisponibilidade() user = User.query.filter_by(id=user_id).first() hotel = Hotels.query.get_or_404(id) if hotel.user_id != user_id and user.hotel_id != hotel.id: return '<h1>Erro! Você não pode acessar este conteúdo!</h1>' quartos = Rooms.query.filter_by(hotel_id=id).order_by(Rooms.number) reservas = Reservation.query.order_by(Reservation.id) hoje = dt.strptime(dt.today().strftime('%Y-%m-%d'), '%Y-%m-%d') status_reservas = [(r.room_id, (r.check_in <= hoje <= r.check_out)) for r in reservas if r.status == Status.ATIVO] status_reservas = [status for status in status_reservas if status[1] is True] status_reservas = dict(set(status_reservas)) return render_template('ocupacao_quartos.html', quartos=quartos, form_reserva=form_reserva, status_reservas=status_reservas ) def deletar_quarto(id_quarto, user_id): user = User.query.filter_by(id=user_id).first() quarto = Rooms.query.get_or_404(id_quarto) id_hotel = quarto.hotel_id hotel = Hotels.query.get_or_404(id_hotel) if hotel.user_id != user_id and user.hotel_id != hotel.id or user.profile not in ['admin', 'gerente']: return '<h1>Erro! Você não pode acessar este conteúdo!</h1>' db.session.delete(quarto) db.session.commit() flash('Quarto deletado com sucesso!', 'success') return redirect(f'/ocupacao-quartos/{id_hotel}') def editar_quarto(quarto_id, user_id): form_reserva = VerificarDisponibilidade() form = AdicionarQuarto() user = User.query.filter_by(id=user_id).first() user_id_room = Rooms \ .query.filter_by(id=quarto_id) \ .join(Hotels, Rooms.hotel_id == Hotels.id).add_columns(Hotels.user_id).add_columns(Hotels.id) if [i.user_id for i in user_id_room][0] != user_id and user.hotel_id != [i for i in user_id_room][0][2]: return '<h1>Erro! Você não pode acessar este conteúdo!</h1>' if user.hotel_id is None: hoteis = Hotels.query.order_by(Hotels.created_at) form.hotel_id.choices = [(hotel.id, hotel.name) for hotel in hoteis if hotel.user_id == user_id] else: hoteis = Hotels.query.filter_by(id=user.hotel_id).order_by(Hotels.created_at) form.hotel_id.choices = [(hotel.id, hotel.name) for hotel in hoteis] if form.validate_on_submit(): if request.method == 'POST': to_update = Rooms.query.get_or_404(quarto_id) to_update.hotel_id = request.form['hotel_id'] to_update.number = request.form['number'] to_update.name = request.form['name'] to_update.short_description = request.form['short_description'] to_update.kind = request.form['kind'] to_update.phone_extension = request.form['phone_extension'] to_update.price = float(request.form['price'].replace('.','').replace(',','.')) to_update.guest_limit = request.form['guest_limit'] db.session.commit() flash('Quarto editado com sucesso!', 'success') return redirect(url_for('ocupacao_quartos_endpoint', id=request.form['hotel_id'])) room = Rooms.query.filter_by(id=quarto_id).first() form.hotel_id.default = room.hotel_id form.process() form.number.data = room.number form.name.data = room.name form.short_description.data = room.short_description form.kind.data = room.kind form.phone_extension.data = room.phone_extension form.price.data = str(room.price).replace('.',',') form.guest_limit.data = room.guest_limit return render_template('adicionar_quartos.html', form=form, user=user, quarto=room, titulo='Editar quarto', form_reserva=form_reserva )

ES-UFABC/Grupo-17

app/scripts/ocupacao_quartos.py

Python

#!/usr/bin/env python3 # # Copyright 2016 WebAssembly Community Group participants # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import contextlib import os import json import shutil import signal import subprocess import sys import tempfile # Get signal names from numbers in Python # http://stackoverflow.com/a/2549950 SIGNAMES = dict((k, v) for v, k in reversed(sorted(signal.__dict__.items())) if v.startswith('SIG') and not v.startswith('SIG_')) class Error(Exception): pass class Executable(object): def __init__(self, exe, *before_args, **kwargs): self.exe = exe self.before_args = list(before_args) self.after_args = [] self.basename = kwargs.get('basename', os.path.basename(exe)).replace('.exe', '') self.error_cmdline = kwargs.get('error_cmdline', True) self.stdout_handle = self._ForwardHandle(kwargs.get('forward_stdout')) self.stderr_handle = self._ForwardHandle(kwargs.get('forward_stderr')) self.verbose = False def _ForwardHandle(self, forward): return None if forward else subprocess.PIPE def _RunWithArgsInternal(self, *args, **kwargs): cmd = [self.exe] + self.before_args + list(args) + self.after_args cmd_str = ' '.join(cmd) if self.verbose: print(cmd_str) if self.error_cmdline: err_cmd_str = cmd_str.replace('.exe', '') else: err_cmd_str = self.basename stdout = '' stderr = '' error = None try: process = subprocess.run(cmd, check=False, text=True, stdout=self.stdout_handle, stderr=self.stderr_handle, **kwargs) stdout = process.stdout stderr = process.stderr if process.returncode < 0: # Terminated by signal signame = SIGNAMES.get(-process.returncode, '<unknown>') error = Error('Signal raised running "%s": %s\n%s' % (err_cmd_str, signame, stderr)) elif process.returncode > 0: error = Error('Error running "%s" (%d):\n%s' % (err_cmd_str, process.returncode, stderr)) except OSError as e: error = Error('Error running "%s": %s' % (err_cmd_str, str(e))) return stdout, stderr, error def RunWithArgsForStdout(self, *args, **kwargs): stdout, stderr, error = self._RunWithArgsInternal(*args, **kwargs) if error: raise error return stdout def RunWithArgs(self, *args, **kwargs): stdout, stderr, error = self._RunWithArgsInternal(*args, **kwargs) if stdout: sys.stdout.write(stdout) if error: raise error def AppendArg(self, arg): self.after_args.append(arg) def AppendOptionalArgs(self, option_dict): for option, value in option_dict.items(): if value: if value is True: self.AppendArg(option) else: self.AppendArg('%s=%s' % (option, value)) @contextlib.contextmanager def TempDirectory(out_dir, prefix=None): if out_dir: out_dir_is_temp = False if not os.path.exists(out_dir): os.makedirs(out_dir) else: out_dir = tempfile.mkdtemp(prefix=prefix) out_dir_is_temp = True try: yield out_dir finally: if out_dir_is_temp: shutil.rmtree(out_dir) def ChangeExt(path, new_ext): return os.path.splitext(path)[0] + new_ext def ChangeDir(path, new_dir): return os.path.join(new_dir, os.path.basename(path)) def Hexdump(data): DUMP_OCTETS_PER_LINE = 16 DUMP_OCTETS_PER_GROUP = 2 p = 0 end = len(data) lines = [] while p < end: line_start = p line_end = p + DUMP_OCTETS_PER_LINE line = '%07x: ' % p while p < line_end: for i in range(DUMP_OCTETS_PER_GROUP): if p < end: line += '%02x' % data[p] else: line += ' ' p += 1 line += ' ' line += ' ' p = line_start for i in range(DUMP_OCTETS_PER_LINE): if p >= end: break x = data[p] if x >= 32 and x < 0x7f: line += '%c' % x else: line += '.' p += 1 line += '\n' lines.append(line) return lines def GetModuleFilenamesFromSpecJSON(json_filename): with open(json_filename) as json_file: json_data = json.load(json_file) return [m['filename'] for m in json_data['commands'] if 'filename' in m]

ChristianGutman/wabt

test/utils.py

Python

import pandas as pd import os from sklearn.model_selection import train_test_split from sklearn.metrics import roc_curve, auc import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import precision_recall_curve from sklearn.metrics import average_precision_score import time #Criteo's CTR Prediction Challenge #Creating a list of the numerical and categorical variables intnames = [] catnames = [] for i in range(13): intnames += ['i'+ str(i+1)] for i in range(26): catnames += ['c'+ str(i+1)] colnames = ['clicked'] + intnames + catnames #Load Data (500,000 rows) and name columns ds = pd.read_csv("train.txt", nrows=500000, sep='\t', header=None, names = colnames) #Basic info of dataset ds.info() ds['clicked'].mean() #Number of categories per each category variable categoriesPerVariable = {} for var in catnames: varList = ds[var].tolist() varUnique = set(varList) print(var, len(varUnique)) categoriesPerVariable[var] = len(varUnique) catnamesFinal = [] #Delete variables with more than 100 categories for var in categoriesPerVariable: if categoriesPerVariable[var] > 100: ds = ds.drop(var, 1) print(var, 'DELETED') else: catnamesFinal += [var] ds.info() #Create dummy variables: for var in catnamesFinal: ds = pd.concat([ds, pd.get_dummies(ds[var], prefix = var, prefix_sep = '_')], axis=1) ds = ds.drop(var, axis=1) print('Created dummy variables for: ', var) ds.shape #Creating train and test datasets y = ds.clicked x_cols = set(ds.columns) x_cols.remove('clicked') X = ds[list(x_cols)] #Train, test and Validation Sets (60%, 20%, 20%) X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6) X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, train_size=0.5) #More Preprocessing # - Fill NaN values in X_train, X_test, X_val with the mean of X_train X_train[intnames] = X_train[intnames].fillna(X_train[intnames].mean()) X_test[intnames] = X_test[intnames].fillna(X_train[intnames].mean()) X_val[intnames] = X_val[intnames].fillna(X_train[intnames].mean()) #Dataset with PCA #Choosing the number of components from sklearn.decomposition import PCA for e in range(10): pca1 = PCA(n_components=e) pca1.fit(X_train) exp_var = 0 for i in pca1.explained_variance_ratio_: exp_var += i print(e, round(exp_var,3)) pca = PCA(n_components=5) pca.fit(X_train) X_train_PCA = pd.DataFrame(pca.transform(X_train)) X_test_PCA = pd.DataFrame(pca.transform(X_test)) X_val_PCA = pd.DataFrame(pca.transform(X_val)) ''' ########################################### # FUNCTIONS ########################################### ''' def ROCcurve(y_pred, y_test): # Compute ROC curve and ROC area for each class n_classes = y_pred.shape[1] fpr = dict() tpr = dict() roc_auc = dict() y_test1 = [] for index, row in y_test.iteritems(): if row == 0: y_test1 += [[1,0]] else: y_test1 += [[0,1]] y_test1 = np.array(y_test1) for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_test1[:,i], y_pred[:,i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y_test1.ravel(), y_pred.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) return fpr, tpr, roc_auc #Precision Recall Curve def precision_recall(y_test, y_pred): ydp = [] for i in range(len(y_pred)): ydp+= [y_pred[i][1]] precision, recall, _ = precision_recall_curve(y_test, ydp) return precision, recall def ypd(y_pred): ydp = [] for i in range(len(y_pred)): ydp+= [y_pred[i][1]] return ydp #Return all the Algorithm Curves Info def algorithmCurvesInfo(alg_name, y_pred, y_test): algDict = {} algDict['alg_name'] = alg_name algDict['fpr'], algDict['tpr'], \ algDict['roc_auc'] = ROCcurve(y_pred, y_test) algDict['precision'], algDict['recall'], = precision_recall(y_test, y_pred) algDict['average_precision'] = average_precision_score(y_test, ypd(y_pred)) return algDict #PLOT ROC CURVE def plotROC(alg_fpr_tpr_rocDict, color_paletteList, tuple_size, path_name): colors = [] for p in color_paletteList: for c in plt.get_cmap(p).colors: colors += [c] #Dict with key --> name of algorithm (dict) #Each algorithm dict: # - fpr # - tpr # - roc_auc # - alg_name: algorithm name to be shown plt.figure(figsize=tuple_size) col = 0 for al in alg_fpr_tpr_rocDict: fpr = alg_fpr_tpr_rocDict[al]['fpr'] tpr = alg_fpr_tpr_rocDict[al]['tpr'] roc_auc = alg_fpr_tpr_rocDict[al]['roc_auc'] alg_name = alg_fpr_tpr_rocDict[al]['alg_name'] plt.plot(fpr[1], tpr[1], color= colors[col], alpha = 0.7, label= alg_name + ' (area = %0.3f)' % roc_auc[1]) col += 1 plt.plot([0, 1], [0, 1], color='black', linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.0]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC curves per each algorithm') plt.legend(loc="lower right") plt.savefig(path_name + '_ROC-AUC.png') plt.show() #Plot the Precision-Recall Curve def plotPrecisionRecall(alg_pre_recDict, color_paletteList, tuple_size, path_name): colors = [] for p in color_paletteList: for c in plt.get_cmap(p).colors: colors += [c] col = 0 plt.figure(figsize=tuple_size) for al in alg_pre_recDict: recall = alg_pre_recDict[al]['recall'] precision = alg_pre_recDict[al]['precision'] average_precision = alg_pre_recDict[al]['average_precision'] alg_name = alg_pre_recDict[al]['alg_name'] ''' plt.step(recall, precision, color=colors[col], alpha=0.8, where='post', \ label= alg_name + ' (area = %0.3f)'.format(average_precision)) ''' plt.plot(recall, precision, color=colors[col], alpha=0.8, \ label= alg_name + ' (area = %0.3f)' % average_precision) col += 1 plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([0.0, 1.0]) plt.xlim([0.0, 1.0]) plt.legend(loc="upper right") plt.title('Precision-Recall curve for CLICKED') plt.savefig(path_name + '_PrecisionRecall.png') plt.show() #Algorithm Process Automation def algorithmAutomat(algorithm, X_train, y_train, X_test, name): algDict = {} train_s = time.time() algorithm.fit(X_train, y_train) train_e = time.time() pred_s = time.time() y_pred = algorithm.predict_proba(X_test) pred_e = time.time() algDict = algorithmCurvesInfo(name, y_pred, y_test) algDict['train_time'] = round(train_e - train_s,2) algDict['predict_time'] = round(pred_e - pred_s,2) algDict['model'] = algorithm print(name + ' Prediction calculated') print('Elapsed time: ' + str(round(pred_e-train_s,2)) + ' seconds') return algDict #Algorithm Validation Prediction and Curves def algorithmValidation(model, X_validation, y_validation, name): algDict = {} start = time.time() y_pred = model.predict_proba(X_validation) end = time.time() algDict = algorithmCurvesInfo(name, y_pred, y_validation) algDict['prediction_time'] = end - start print(name + ' Prediction calculated') return algDict ''' ########################################### # ALGORITHMS ########################################### ''' #Path where I will save the ROC and Precision-Recall curves path = os.getcwd() + '/graphs/' #Dictionaries to save algorithms' results for dataset with and without PCA algs = {} algsPCA = {} ###################################### # Logistic Regression ###################################### from sklearn.linear_model import LogisticRegression #Parameter tuning options regularizers = ['l1', 'l2'] C = [0.001,0.01,0.1,1,10,100,1000] algs['lr'] = {} for r in regularizers: for c in C: #Algorithm name based on tuning options name = 'LogReg_' + str(r) + '_' + str(c) logreg = LogisticRegression(penalty = r, C = c ,random_state = 0) algs['lr'][name] = algorithmAutomat(logreg, X_train, y_train, X_test, name) algsPCA['lr'] = {} for r in regularizers: for c in C: name = 'LogRegPCA_' + str(r) + '_' + str(c) logreg = LogisticRegression(penalty = r, C = c ,random_state = 0) algsPCA['lr'][name] = algorithmAutomat(logreg, X_train_PCA, y_train, X_test_PCA, name) #Plots plotROC(algs['lr'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LR') plotROC(algsPCA['lr'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LRpca') plotPrecisionRecall(algs['lr'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LR') plotPrecisionRecall(algsPCA['lr'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LRpca') ###################################### # Random Forest ###################################### from sklearn.ensemble import RandomForestClassifier n_estim = [2, 10, 50, 100, 1000] max_d = [None, 2, 5, 10, 50] algsPCA['rf'] = {} for n in n_estim: for m in max_d: name = 'RandForPCA_est' + str(n) + '_depth' + str(m) rf = RandomForestClassifier(n_estimators = n, max_depth=m, random_state=0) algsPCA['lr'][name] = algorithmAutomat(rf, X_train_PCA, y_train, X_test_PCA, name) algs['rf'] = {} for n in n_estim: for m in max_d: name = 'RandFor_est' + str(n) + '_depth' + str(m) rf = RandomForestClassifier(n_estimators = n, max_depth=m, random_state=0) algs['rf'][name] = algorithmAutomat(rf, X_train, y_train, X_test, name) plotROC(algs['rf'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'RF') plotROC(algsPCA['rf'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'RFpca') plotPrecisionRecall(algs['rf'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'RF') plotPrecisionRecall(algsPCA['rf'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'RFpca') ###################################### # K-nearest neighbors ###################################### from sklearn.neighbors import KNeighborsClassifier algsPCA['knn'] = {} for k in [5, 10, 20, 50, 100, 200]: name = 'KNN_PCA_' + str(k) knn = KNeighborsClassifier(n_neighbors=k) algsPCA['knn'][name] = algorithmAutomat(knn, X_train_PCA, y_train, X_test_PCA, name) algs['knn'] = {} for k in [5, 10, 20, 50, 100, 200]: name = 'KNN_' + str(k) knn = KNeighborsClassifier(n_neighbors=k) algs['knn'][name] = algorithmAutomat(knn, X_train, y_train, X_test, name) plotROC(algs['knn'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'KNN') plotROC(algs['knn']['knn'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'KNNpca') plotPrecisionRecall(algs['knn'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'KNN') plotPrecisionRecall(algs['knn']['knn'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'KNNpca') ###################################### # Naive Bayes ###################################### from sklearn.naive_bayes import GaussianNB algsPCA['nbayes'] = {} algs['nbayes'] = {} gnb = GaussianNB() name = 'NaiveBayes_PCA' algsPCA['nbayes'][name] = algorithmAutomat(gnb, X_train_PCA, y_train, X_test_PCA, name) algs['nbayes'] = {} gnb = GaussianNB() name = 'NaiveBayes' algs['nbayes'][name] = algorithmAutomat(gnb, X_train, y_train, X_test, name) plotROC(algs['nbayes'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'NB') plotPrecisionRecall(algs['nbayes'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'NB') plotROC(algsPCA['nbayes'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'NB') plotPrecisionRecall(algsPCA['nbayes'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'NBpca') ###################################### # AdaBoost ###################################### from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier n_estim = [2, 10, 50] max_d = [2, 10, 50, None] algsPCA['adab'] = {} for n in n_estim: for m in max_d: name = 'AdaBoost_PCA_est' + str(n) + '_depth' + str(m) bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=m), algorithm="SAMME", n_estimators=n) algsPCA['adab'][name] = algorithmAutomat(bdt, X_train_PCA, y_train, X_test_PCA, name) algs['adab'] = {} for n in n_estim: for m in max_d: name = 'AdaBoost_est' + str(n) + '_depth' + str(m) bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=m), algorithm="SAMME", n_estimators=n) algs['adab'][name] = algorithmAutomat(bdt, X_train, y_train, X_test, name) plotROC(algs['adab'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'AB') plotROC(algsPCA['adab'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'ABpca') plotPrecisionRecall(algs['adab'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'AB') plotPrecisionRecall(algsPCA['adab'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'ABpca') ###################################### # Linear Discriminant Analysis ###################################### from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA algsPCA['lda'] = {} lda = LDA() name = 'LDA_PCA' algsPCA['lda'] [name] = algorithmAutomat(lda, X_train_PCA, y_train, X_test_PCA, name) algs['lda'] = {} lda = LDA() name = 'LDA' algs['lda'][name] = algorithmAutomat(lda, X_train, y_train, X_test, name) plotROC(algs['lda'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LDA') plotPrecisionRecall(algs['lda'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LDA') plotROC(algsPCA['lda'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LDA') plotPrecisionRecall(algsPCA['lda'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'LDApca') ###################################### # Gradient Boosting ###################################### from sklearn.ensemble import GradientBoostingClassifier learning_rate = [0.1, 1] max_depth = [3,5] loss = ['deviance', 'exponential'] algsPCA['gradbo'] = {} for l in learning_rate: for m in max_depth: for lo in loss: name = 'GradBoost_PCA_lr' + str(l) + '_depth' + str(m) + '_loss-' + lo gbc = GradientBoostingClassifier(learning_rate = l, max_depth = m, loss = lo) algsPCA['gradbo'][name] = algorithmAutomat(gbc, X_train_PCA, y_train, X_test_PCA, name) algs['gradbo'] = {} for l in learning_rate: for m in max_depth: for lo in loss: name = 'GradBoost_lr' + str(l) + '_depth' + str(m) + '_loss-' + lo gbc = GradientBoostingClassifier(learning_rate = l, max_depth = m, loss = lo) algs['gradbo'][name] = algorithmAutomat(gbc, X_train, y_train, X_test, name) plotROC(algs['gradbo'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'GB') plotROC(algsPCA['gradbo'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'GBpca') plotPrecisionRecall(algs['gradbo'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'GB') plotPrecisionRecall(algsPCA['gradbo'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'GBpca') ###################################### # NEURAL NETWORKS: MLP ###################################### #File mlp_train.py included in this repository #This file contains the needed functions import mlp_train as mlp from keras.utils import to_categorical #Creating dummy variables for the response variable (needed in Neural Nets) y_train_cat = to_categorical(y_train) y_test_cat = to_categorical(y_test) algs['mlp'] = {} #Load model trained in MLP.py #Parameters: baseName = 'MLP_' batch_size = 200 epochs = 20 optimizer = ['adam', 'rmsprop'] denseLayers = [2, 3] layerNeurons = [200, 500] dropout = [True, False] dropoutRate = 0.3 n_classes = 2 for o in optimizer: for d in denseLayers: for do in dropout: for ln in layerNeurons: name, y_pred_mlp, train_time = mlp.NeuralNetProcess(baseName, o, batch_size, epochs, d, ln, do, dropoutRate, X_train, X_test, y_train_cat, y_test_cat, n_classes) algs['mlp'][name] = algorithmCurvesInfo(name, y_pred_mlp, y_test) algs['mlp'][name]['train_time'] = train_time algs['mlp'][name]['predict_time'] = None plotROC(algs['mlp'], ['Set1', 'Set2', 'Set3', 'Set1'], (10,8), path + 'MLP') plotPrecisionRecall(algs['mlp'], ['Set1', 'Set2', 'Set3', 'Set1'], (10,8), path + 'MLP') ''' ########################################### # TEST EXPORTING SUMMARIES ########################################### ''' #Exporting summary info to csv file headers = ['HAS_PCA', 'ALGORITHM', 'ALG_NAME', 'TRAIN_TIME', 'PREDICT_TIME', 'AVERAGE_PRECISION', 'ROC_AUC'] rows = [] for a in algsPCA: print('---------------',a) for k in algsPCA[a]: row = [] row += ['PCA'] row += [a] row += [k] row += [str(algsPCA[a][k]['train_time'])] row += [str(algsPCA[a][k]['predict_time'])] row += [str(algsPCA[a][k]['average_precision'])] row += [str(algsPCA[a][k]['roc_auc'][1])] rows += [row] for a in algs: print('---------------',a) for k in algs[a]: row = [] row += ['REG'] row += [a] row += [k] row += [str(algs[a][k]['train_time'])] row += [str(algs[a][k]['predict_time'])] row += [str(algs[a][k]['average_precision'])] row += [str(algs[a][k]['roc_auc'][1])] rows += [row] csvfile = ', '.join(headers) for r in rows: csvfile += '\n' + ', '.join(r) f = open(os.getcwd() + "\\algorithmsDataset.csv",'w') f.write(csvfile) f.close() ''' ########################################### # VALIDATION MODELS ########################################### ''' #Select best tuned model for each algorithm and store the list #Established a limit_train_time and limit_predict_time. bestAlgs = {} limitTrainTime = 400 limitPredictTime = 200 bestAlgs['PCA'] = {} for a in algsPCA: balg = '' roc = 0 for k in algsPCA[a]: if algsPCA[a][k]['roc_auc'][1] > roc and algsPCA[a][k]['train_time'] < limitTrainTime and algsPCA[a][k]['predict_time'] < limitPredictTime: roc = algsPCA[a][k]['roc_auc'][1] balg = k bestAlgs['PCA'][balg] = roc bestAlgs['REG'] = {} for a in algs: balg = '' roc = 0 for k in algs[a]: if algs[a][k]['roc_auc'][1] > roc and algs[a][k]['train_time'] < limitTime and algs[a][k]['predict_time'] < limitPredictTime: roc = algs[a][k]['roc_auc'][1] balg = k bestAlgs['REG'][balg] = roc ''' ########################################### # VALIDATION PREDICTIONS ########################################### ''' #Predict results using the validation set for each selected model VALalgs = {} VALalgs['PCA'] = {} for k in bestAlgs['PCA']: print(k) name = 'LogRegPCA_l1_100' VALalgs['PCA'][name] = algorithmValidation(algsPCA['lr'][name]['model'], X_val_PCA, y_val, name) name = 'RandForPCA_est100_depth10' VALalgs['PCA'][name] = algorithmValidation(algsPCA['rf'][name]['model'], X_val_PCA, y_val, name) name = 'KNN_PCA_100' VALalgs['PCA'][name] = algorithmValidation(algsPCA['knn'][name]['model'], X_val_PCA, y_val, name) name = 'NaiveBayes_PCA' VALalgs['PCA'][name] = algorithmValidation(algsPCA['nbayes'][name]['model'], X_val_PCA, y_val, name) name = 'AdaBoost_PCA_est50_depth10' VALalgs['PCA'][name] = algorithmValidation(algsPCA['adab'][name]['model'], X_val_PCA, y_val,name) name = 'GradBoost_PCA_lr0.1_depth5_loss-deviance' VALalgs['PCA'][name] = algorithmValidation(algsPCA['gradbo'][name]['model'], X_val_PCA, y_val, name) name = 'LDA_PCA' VALalgs['PCA'][name] = algorithmValidation(algsPCA['lda'][name]['model'], X_val_PCA, y_val, name) VALalgs['REG'] = {} for k in bestAlgs['REG']: print(k) name = 'LogReg_l1_0.1' VALalgs['REG'][name] = algorithmValidation(algs['lr'][name]['model'], X_val, y_val, name) name = 'RandFor_est100_depth50' VALalgs['REG'][name] = algorithmValidation(algs['rf'][name]['model'], X_val, y_val, name) name = 'KNN_100' VALalgs['REG'][name] = algorithmValidation(algs['knn'][name]['model'], X_val, y_val, name) name = 'NaiveBayes' VALalgs['REG'][name] = algorithmValidation(algs['nbayes'][name]['model'], X_val, y_val, name) name = 'AdaBoost_est50_depth10' VALalgs['REG'][name] = algorithmValidation(algs['adab'][name]['model'], X_val, y_val, name) name = 'GradBoost_lr0.1_depth5_loss-deviance' VALalgs['REG'][name] = algorithmValidation(algs['gradbo'][name]['model'], X_val, y_val, name) name = 'LDA' VALalgs['REG'][name] = algorithmValidation(algs['lda'][name]['model'], X_val, y_val, name) name = 'MLP_rmsprop_b200_e20_DL2_200_drop-False_0.3' bestModelPath = os.getcwd() + '/NNbestModel/' bestModelPathLoss = bestModelPath + 'model_loss_' + name + '.hdf5' y_pred_mlp, prediction_time = mlp.NeuralNetPredict(bestModelPathLoss, X_val) VALalgs['REG'][name] = algorithmCurvesInfo(name, y_pred_mlp, y_val) VALalgs['REG'][name]['prediction_time'] = prediction_time #Plot & Save plotROC(VALalgs['PCA'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'Val_PCA') plotROC(VALalgs['REG'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'Val_REG') plotPrecisionRecall(VALalgs['PCA'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'Val_PCA') plotPrecisionRecall(VALalgs['REG'], ['Set1', 'Set2', 'Set3'], (10,8), path + 'Val_REG') ''' ########################################### # VALIDATION EXPORTING SUMMARIES ########################################### ''' #Exporting validation set data to csv headers = ['HAS_PCA', 'ALGORITHM', 'ALG_NAME', 'TRAIN_TIME', 'PREDICT_TIME', 'AVERAGE_PRECISION', 'ROC_AUC'] val_rows = [] for a in algsPCA: for k in algsPCA[a]: if k in VALalgs['PCA']: print('---------------',a) row = [] row += ['PCA'] row += [a] row += [k] row += [str(algsPCA[a][k]['train_time'])] row += [str(VALalgs['PCA'][k]['prediction_time'])] row += [str(VALalgs['PCA'][k]['average_precision'])] row += [str(VALalgs['PCA'][k]['roc_auc'][1])] val_rows += [row] for a in algs: for k in algs[a]: if k in VALalgs['REG']: print('---------------',a) row = [] row += ['REG'] row += [a] row += [k] row += [str(algs[a][k]['train_time'])] row += [str(VALalgs['REG'][k]['prediction_time'])] row += [str(VALalgs['REG'][k]['average_precision'])] row += [str(VALalgs['REG'][k]['roc_auc'][1])] val_rows += [row] csvfile = ', '.join(headers) for r in val_rows: csvfile += '\n' + ', '.join(r) f = open(os.getcwd() + "\\algorithmsValidationDataset.csv",'w') f.write(csvfile) f.close()

alvarodemig/Algorithms-Performance-Comparison

AlgPerformComparison.py

Python

from dataclasses import dataclass, field from typing import List @dataclass class Doc: class Meta: name = "doc" elem: List[str] = field( default_factory=list, metadata={ "type": "Element", "namespace": "", "pattern": r"\]", } )

tefra/xsdata-w3c-tests

output/models/ms_data/regex/re_i45_xsd/re_i45.py

Python

""" Train LearnedPDReconstructor on 'lodopab'. """ import numpy as np from dival import get_standard_dataset from dival.measure import PSNR from dival.reconstructors.learnedpd_reconstructor import LearnedPDReconstructor from dival.reference_reconstructors import ( check_for_params, download_params, get_hyper_params_path) from dival.util.plot import plot_images IMPL = 'astra_cuda' LOG_DIR = './logs/lodopab_learnedpd' SAVE_BEST_LEARNED_PARAMS_PATH = './params/lodopab_learnedpd' dataset = get_standard_dataset('lodopab', impl=IMPL) ray_trafo = dataset.get_ray_trafo(impl=IMPL) test_data = dataset.get_data_pairs('test', 100) reconstructor = LearnedPDReconstructor( ray_trafo, log_dir=LOG_DIR, save_best_learned_params_path=SAVE_BEST_LEARNED_PARAMS_PATH) #%% obtain reference hyper parameters if not check_for_params('learnedpd', 'lodopab', include_learned=False): download_params('learnedpd', 'lodopab', include_learned=False) hyper_params_path = get_hyper_params_path('learnedpd', 'lodopab') reconstructor.load_hyper_params(hyper_params_path) #%% train reconstructor.train(dataset) #%% evaluate recos = [] psnrs = [] for obs, gt in test_data: reco = reconstructor.reconstruct(obs) recos.append(reco) psnrs.append(PSNR(reco, gt)) print('mean psnr: {:f}'.format(np.mean(psnrs))) for i in range(3): _, ax = plot_images([recos[i], test_data.ground_truth[i]], fig_size=(10, 4)) ax[0].set_xlabel('PSNR: {:.2f}'.format(psnrs[i])) ax[0].set_title('LearnedPDReconstructor') ax[1].set_title('ground truth') ax[0].figure.suptitle('test sample {:d}'.format(i))

MBaltz/dival

dival/examples/ct_train_learnedpd.py

Python

import numpy as np import pytest from agents.common import BoardPiece, NO_PLAYER, PLAYER1, PLAYER2, pretty_print_board, initialize_game_state, \ string_to_board, apply_player_action, connected_four, check_connect_topleft_bottomright def test_initialize_game_state(): ret = initialize_game_state() assert isinstance(ret, np.ndarray) assert ret.dtype == BoardPiece assert ret.shape == (6, 7) assert np.all(ret == NO_PLAYER) def test_output_pretty_print_board(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) ret = pretty_print_board(initialBoard) assert ret != '' def test_empty_pretty_print_board(): initialBoard = np.ndarray(shape=(7, 6), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) ret = pretty_print_board(initialBoard) assert ret == '\n|==============|\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '|==============|\n' \ '|0 1 2 3 4 5 6 |' def test_player1_pretty_print_board(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(PLAYER1) ret = pretty_print_board(initialBoard) assert ret == '\n|==============|\n' \ '|X X X X X X X |\n' \ '|X X X X X X X |\n' \ '|X X X X X X X |\n' \ '|X X X X X X X |\n' \ '|X X X X X X X |\n' \ '|X X X X X X X |\n' \ '|==============|\n' \ '|0 1 2 3 4 5 6 |' def test_player2_pretty_print_board(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(PLAYER2) ret = pretty_print_board(initialBoard) assert ret == '\n|==============|\n' \ '|O O O O O O O |\n' \ '|O O O O O O O |\n' \ '|O O O O O O O |\n' \ '|O O O O O O O |\n' \ '|O O O O O O O |\n' \ '|O O O O O O O |\n' \ '|==============|\n' \ '|0 1 2 3 4 5 6 |' def test_precision_pretty_print_board(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[0,0] = PLAYER1 ret = pretty_print_board(initialBoard) assert ret == '\n|==============|\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '|X |\n' \ '|==============|\n' \ '|0 1 2 3 4 5 6 |' def test_dimensions_pretty_print_board(): initialBoard = np.ndarray(shape=(7, 6), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) with pytest.raises(ValueError): ret = pretty_print_board(initialBoard) def test_invalid_piece_pretty_print_board(): initialBoard = np.ndarray(shape=(7, 6), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[0, 0] = 60 with pytest.raises(ValueError): ret = pretty_print_board(initialBoard) def test_string_to_board(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) print = '\n|==============|\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '| |\n' \ '|==============|\n' \ '|0 1 2 3 4 5 6 |' ret = string_to_board(print) assert ret.all() == initialBoard.all() def test_drop_piece(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) ret = apply_player_action(initialBoard, 0, PLAYER1) drop_board = np.ndarray(shape=(6, 7), dtype=BoardPiece) drop_board.fill(NO_PLAYER) drop_board[0,5] = 1 print(ret) assert ret.all() == drop_board.all() def test_connected_four_false(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) assert connected_four(initialBoard, PLAYER1, 5) == False def test_connected_four_true(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(PLAYER1) assert connected_four(initialBoard, PLAYER1, 5) == True def test_connected_four_row_true(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[5, 1] = 1 initialBoard[5, 2] = 1 initialBoard[5, 3] = 1 print(initialBoard) assert connected_four(initialBoard, PLAYER1, 0) == True def test_connected_four_row_false(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[5, 1] = 1 initialBoard[5, 3] = 1 print(initialBoard) with pytest.raises(AssertionError): assert connected_four(initialBoard, PLAYER1, 0) == True def test_connected_four_BL_TR_true(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 initialBoard[2, 3] = 1 print(initialBoard) assert connected_four(initialBoard, PLAYER1, 0) == True def test_connected_four_BL_TR_false(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 print(initialBoard) with pytest.raises(AssertionError): assert connected_four(initialBoard, PLAYER1, 0) == True def test_connected_four_BR_TL_true(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 5] = 1 initialBoard[4, 4] = 1 initialBoard[3, 3] = 1 initialBoard[2, 2] = 1 print(initialBoard) assert connected_four(initialBoard, PLAYER1, 5) == True def test_connected_four_BR_TL_false(): initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 5] = 1 initialBoard[4, 4] = 1 initialBoard[2, 2] = 1 assert connected_four(initialBoard, PLAYER1, 5) == False def test_diagonal_check_BLTR_true(): from agents.common import diagonal_check initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 initialBoard[2, 3] = 1 print(initialBoard) assert diagonal_check(initialBoard, PLAYER1, 0, 5, 1, -1) == True def test_diagonal_check_TLBR_YX_true(): from agents.common import diagonal_check initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 4] = 1 initialBoard[4, 3] = 1 initialBoard[3, 2] = 1 initialBoard[2, 1] = 1 print(initialBoard) assert diagonal_check(initialBoard, PLAYER1, 4, 5, -1, -1) == True def test_TLBR_YX_true(): from agents.common import check_connect_topleft_bottomright initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 4] = 1 initialBoard[4, 3] = 1 initialBoard[3, 2] = 1 initialBoard[2, 1] = 1 print(initialBoard) assert check_connect_topleft_bottomright(initialBoard, PLAYER1, 4, 0) == True def test_diagonal_check_TLBR_XY_true(): from agents.common import diagonal_check initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 6] = 1 initialBoard[4, 5] = 1 initialBoard[3, 4] = 1 initialBoard[2, 3] = 1 print(initialBoard) assert diagonal_check(initialBoard, PLAYER1, 6, 5, -1, -1) == True def test_TLBR_XY_true(): from agents.common import check_connect_topleft_bottomright initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 6] = 1 initialBoard[4, 5] = 1 initialBoard[3, 4] = 1 initialBoard[2, 3] = 1 print(initialBoard) assert check_connect_topleft_bottomright(initialBoard, PLAYER1, 6, 0) def test_BL_TR_true(): from agents.common import check_connect_topright_bottomleft initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 initialBoard[2, 3] = 1 print(initialBoard) assert check_connect_topright_bottomleft(initialBoard, PLAYER1, 0, 0) == True def test_BL_TR_false(): from agents.common import check_connect_topright_bottomleft initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 print(initialBoard) assert check_connect_topright_bottomleft(initialBoard, PLAYER1, 0, 0) == False def test_end_state_win(): from agents.common import check_end_state, GameState from agents.common import check_connect_topright_bottomleft initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) initialBoard[5, 0] = 1 initialBoard[4, 1] = 1 initialBoard[3, 2] = 1 initialBoard[2, 3] = 1 assert check_end_state(initialBoard, PLAYER1, 0) == GameState.IS_WIN def test_end_state_still_playing(): from agents.common import check_end_state, GameState initialBoard = np.ndarray(shape=(6, 7), dtype=BoardPiece) initialBoard.fill(NO_PLAYER) assert check_end_state(initialBoard, PLAYER1, 0) == GameState.STILL_PLAYING def test_end_state_draw(): from agents.common import check_end_state, GameState x = np.zeros((6, 7), dtype=int) x.fill(2) x[1::2, ::2] = 1 x[::2, 1::2] = 1 print(x) assert check_end_state(x, PLAYER1, 1) == GameState.IS_DRAW def test_diagonal_neg(): #str = "|==============|\n|O |\n|X O |\n|O X O |\n|X X O O X |\n|O X O X X |\n|X O X X O |\n|==============|\n|0 1 2 3 4 5 6 |" #board = string_to_board(str) board = np.zeros((6, 7), dtype=int) board[0, 0] = PLAYER2 board[1, 1] = PLAYER2 board[2, 2] = PLAYER2 board[3, 3] = PLAYER2 assert check_connect_topleft_bottomright(board, PLAYER2, 2, 3) == True

Sibimobon/Connect4

tests/test_common.py

Python

def types(): from .config_result import GraphenePipelineConfigValidationResult from .config import ( GrapheneEvaluationErrorReason, GrapheneEvaluationStack, GrapheneEvaluationStackEntry, GrapheneEvaluationStackListItemEntry, GrapheneEvaluationStackPathEntry, GrapheneFieldNotDefinedConfigError, GrapheneFieldsNotDefinedConfigError, GrapheneMissingFieldConfigError, GrapheneMissingFieldsConfigError, GraphenePipelineConfigValidationError, GraphenePipelineConfigValidationInvalid, GrapheneRunConfigValidationInvalid, GraphenePipelineConfigValidationValid, GrapheneRuntimeMismatchConfigError, GrapheneSelectorTypeConfigError, ) from .logger import GrapheneLogger from .mode import GrapheneMode from .pipeline_errors import GrapheneInvalidSubsetError, GrapheneConfigTypeNotFoundError from .pipeline_ref import GraphenePipelineReference, GrapheneUnknownPipeline from .pipeline_run_stats import ( GraphenePipelineRunStatsSnapshot, GrapheneRunStatsSnapshotOrError, GrapheneRunStatsSnapshot, ) from .pipeline import ( GrapheneAsset, GrapheneIPipelineSnapshot, GraphenePipeline, GraphenePipelinePreset, GraphenePipelineRun, GrapheneRunOrError, GrapheneRun, ) from .resource import GrapheneResource from .snapshot import GraphenePipelineSnapshot, GraphenePipelineSnapshotOrError from .status import GrapheneRunStatus from .subscription import ( GraphenePipelineRunLogsSubscriptionFailure, GraphenePipelineRunLogsSubscriptionPayload, GraphenePipelineRunLogsSubscriptionSuccess, ) return [ GrapheneAsset, GrapheneConfigTypeNotFoundError, GrapheneEvaluationErrorReason, GrapheneEvaluationStack, GrapheneEvaluationStackEntry, GrapheneEvaluationStackListItemEntry, GrapheneEvaluationStackPathEntry, GrapheneFieldNotDefinedConfigError, GrapheneFieldsNotDefinedConfigError, GrapheneInvalidSubsetError, GrapheneIPipelineSnapshot, GrapheneLogger, GrapheneMissingFieldConfigError, GrapheneMissingFieldsConfigError, GrapheneMode, GraphenePipeline, GraphenePipelineConfigValidationError, GraphenePipelineConfigValidationInvalid, GrapheneRunConfigValidationInvalid, GraphenePipelineConfigValidationResult, GraphenePipelineConfigValidationValid, GraphenePipelinePreset, GraphenePipelineReference, GraphenePipelineRun, GraphenePipelineRunLogsSubscriptionFailure, GraphenePipelineRunLogsSubscriptionPayload, GraphenePipelineRunLogsSubscriptionSuccess, GrapheneRunOrError, GraphenePipelineRunStatsSnapshot, GrapheneRunStatsSnapshotOrError, GrapheneRunStatsSnapshot, GrapheneRunStatus, GraphenePipelineSnapshot, GraphenePipelineSnapshotOrError, GrapheneResource, GrapheneRuntimeMismatchConfigError, GrapheneRun, GrapheneSelectorTypeConfigError, GrapheneUnknownPipeline, ]

kbd/dagster

python_modules/dagster-graphql/dagster_graphql/schema/pipelines/__init__.py

Python

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, TYPE_CHECKING from azure.core.configuration import Configuration from azure.core.pipeline import policies from azure.mgmt.core.policies import ARMHttpLoggingPolicy, AsyncARMChallengeAuthenticationPolicy if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from azure.core.credentials_async import AsyncTokenCredential VERSION = "unknown" class MultiapiServiceClientConfiguration(Configuration): """Configuration for MultiapiServiceClient. Note that all parameters used to create this instance are saved as instance attributes. :param credential: Credential needed for the client to connect to Azure. :type credential: ~azure.core.credentials_async.AsyncTokenCredential :keyword api_version: Api Version. The default value is "3.0.0". Note that overriding this default value may result in unsupported behavior. :paramtype api_version: str """ def __init__( self, credential: "AsyncTokenCredential", **kwargs: Any ) -> None: super(MultiapiServiceClientConfiguration, self).__init__(**kwargs) api_version = kwargs.pop('api_version', "3.0.0") # type: str if credential is None: raise ValueError("Parameter 'credential' must not be None.") self.credential = credential self.api_version = api_version self.credential_scopes = kwargs.pop('credential_scopes', ['https://management.azure.com/.default']) kwargs.setdefault('sdk_moniker', 'multiapi-sample/{}'.format(VERSION)) self._configure(**kwargs) def _configure( self, **kwargs: Any ) -> None: self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs) self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs) self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs) self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs) self.http_logging_policy = kwargs.get('http_logging_policy') or ARMHttpLoggingPolicy(**kwargs) self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs) self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs) self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs) self.authentication_policy = kwargs.get('authentication_policy') if self.credential and not self.authentication_policy: self.authentication_policy = AsyncARMChallengeAuthenticationPolicy(self.credential, *self.credential_scopes, **kwargs)

changlong-liu/autorest.python

docs/samples/specification/multiapi/generated/azure/multiapi/sample/v3/aio/_configuration.py

Python

def factorial(n): if n == 0: return 1.0 else: return float(n) * factorial(n-1) def taylor_exp(n): return [1.0/factorial(i) for i in range(n)] def taylor_sin(n): res = [] for i in range(n): if i % 2 == 1: res.append((-1)**((i-1)/2)/float(factorial(i))) else: res.append(0.0) return res def benchmark(): taylor_exp(500) taylor_sin(500) if __name__ == '__main__': benchmark()

UW-HPC/Parallelizing-Python-Workshop

lab4/taylor.py

Python

""" Support for TopoJSON was added in OGR 1.11 to the `GeoJSON` driver. Starting at GDAL 2.3 support was moved to the `TopoJSON` driver. """ import fiona from fiona.env import GDALVersion import os import pytest from collections import OrderedDict gdal_version = GDALVersion.runtime() driver = "TopoJSON" if gdal_version.at_least((2, 3)) else "GeoJSON" has_driver = driver in fiona.drvsupport.supported_drivers.keys() @pytest.mark.skipif(not gdal_version.at_least((1, 11)), reason="Requires GDAL >= 1.11") @pytest.mark.skipif(not has_driver, reason="Requires {} driver".format(driver)) def test_read_topojson(data_dir): """Test reading a TopoJSON file The TopoJSON support in GDAL is a little unpredictable. In some versions the geometries or properties aren't parsed correctly. Here we just check that we can open the file, get the right number of features out, and that they have a geometry and some properties. See GH#722. """ with fiona.open(os.path.join(data_dir, "example.topojson"), "r") as collection: features = list(collection) assert len(features) == 3, "unexpected number of features" for feature in features: assert isinstance(feature["properties"], OrderedDict) assert len(feature["properties"]) > 0 assert feature["geometry"]["type"] in {"Point", "LineString", "Polygon"}

HirniMeshram1/Fiona

tests/test_topojson.py

Python

# -*- coding: utf-8 -*- # Copyright (c) 2020, Youssef Restom and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe from frappe.model.document import Document from frappe.desk.doctype.notification_log.notification_log import ( enqueue_create_notification, ) class ExtraNotificationLog(Document): def after_insert(self): self.make_notification_log() def make_notification_log(self): alert_doc = frappe.get_doc(self.doctype_name, self.doc_name) users = [] owner_email = frappe.get_value("User", alert_doc.owner, "email") if owner_email: users.append(owner_email) modified_by_email = frappe.get_value("User", alert_doc.modified_by, "email") if modified_by_email: users.append(modified_by_email) if len(users) == 0: return notification_doc = { "type": "Share", "document_type": self.doctype_name, "subject": self.subject, "document_name": self.doc_name, "from_user": frappe.session.user, } enqueue_create_notification(users, notification_doc)

Govind-Jangid/erpnext_telegram

erpnext_telegram_integration/extra_notifications/doctype/extra_notification_log/extra_notification_log.py

Python

import os import sys import logging from typing import Optional, List from datetime import datetime from pythonjsonlogger import jsonlogger from . import dirs from .decorators import deprecated # NOTE: Will be removed in a future version since it's not compatible with running a multi-service process # TODO: prefix with `_` log_file_path = None @deprecated def get_log_file_path() -> Optional[str]: # pragma: no cover """DEPRECATED: Use get_latest_log_file instead.""" return log_file_path def setup_logging( name: str, testing=False, verbose=False, log_stderr=True, log_file=False, log_file_json=False, ): # pragma: no cover root_logger = logging.getLogger() root_logger.setLevel(logging.DEBUG if verbose else logging.INFO) root_logger.handlers = [] if log_stderr: root_logger.addHandler(_create_stderr_handler()) if log_file: root_logger.addHandler( _create_file_handler(name, testing=testing, log_json=log_file_json) ) def excepthook(type_, value, traceback): root_logger.exception("Unhandled exception", exc_info=(type_, value, traceback)) # call the default excepthook if log_stderr isn't true (otherwise it'll just get duplicated) if not log_stderr: sys.__excepthook__(type_, value, traceback) sys.excepthook = excepthook def _get_latest_log_files(name, testing=False) -> List[str]: # pragma: no cover """Returns a list with the paths of all available logfiles for `name` sorted by latest first.""" log_dir = dirs.get_log_dir(name) files = filter(lambda filename: name in filename, os.listdir(log_dir)) files = filter( lambda filename: "testing" in filename if testing else "testing" not in filename, files, ) return [os.path.join(log_dir, filename) for filename in sorted(files, reverse=True)] def get_latest_log_file(name, testing=False) -> Optional[str]: # pragma: no cover """ Returns the filename of the last logfile with `name`. Useful when you want to read the logfile of another TimeBench service. """ last_logs = _get_latest_log_files(name, testing=testing) return last_logs[0] if last_logs else None def _create_stderr_handler() -> logging.Handler: # pragma: no cover stderr_handler = logging.StreamHandler(stream=sys.stderr) stderr_handler.setFormatter(_create_human_formatter()) return stderr_handler def _create_file_handler( name, testing=False, log_json=False ) -> logging.Handler: # pragma: no cover log_dir = dirs.get_log_dir(name) # Set logfile path and name global log_file_path # Should result in something like: # $LOG_DIR/aw-server_testing_2017-01-05T00:21:39.log file_ext = ".log.json" if log_json else ".log" now_str = str(datetime.now().replace(microsecond=0).isoformat()).replace(":", "-") log_name = name + "_" + ("testing_" if testing else "") + now_str + file_ext log_file_path = os.path.join(log_dir, log_name) fh = logging.FileHandler(log_file_path, mode="w") if log_json: fh.setFormatter(_create_json_formatter()) else: fh.setFormatter(_create_human_formatter()) return fh def _create_human_formatter() -> logging.Formatter: # pragma: no cover return logging.Formatter( "%(asctime)s [%(levelname)-5s]: %(message)s (%(name)s:%(lineno)s)", "%Y-%m-%d %H:%M:%S", ) def _create_json_formatter() -> logging.Formatter: # pragma: no cover supported_keys = [ "asctime", # 'created', "filename", "funcName", "levelname", # 'levelno', "lineno", "module", # 'msecs', "message", "name", "pathname", # 'process', # 'processName', # 'relativeCreated', # 'thread', # 'threadName' ] def log_format(x): """Used to give JsonFormatter proper parameter format""" return ["%({0:s})".format(i) for i in x] custom_format = " ".join(log_format(supported_keys)) return jsonlogger.JsonFormatter(custom_format)

minhlt9196/activeseconds-aw-core

aw_core/log.py

Python

from txgcv.base.algorithm import Algorithm from txgcv.base.parameter import Parameter __all__ = ["Algorithm", "Parameter"]

dongyaoli10x/txgcv

txgcv/base/__init__.py

Python

# ----------------------------------------------------------------------------- # Task 002 print(''' Задача 002: =========== Каждый следующий элемент ряда Фибоначчи получается при сложении двух предыдущих. Начиная с 1 и 2, первые 10 элементов будут: 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ... \x1b[33m Найдите сумму всех четных элементов ряда Фибоначчи, которые не превышают четыре миллиона. \x1b[0m ''') # Способ 1. Механический. Перебор. пока Единственный def fib_sum(limit): """ Считает сумму положительных чисел Фибаначи, которые меньше указанного значения """ u1 = 1 u2 = 2 summ = 2 _u = u1 + u2 while _u < limit: if _u % 2 == 0: summ += _u u1 = u2 u2 = _u _u = u1 + u2 return summ x = 4000000 result = fib_sum(x) print("{:,}".format(result)) # сгенерированный ряд # >>> [1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, # 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, # 121393, 196418, 317811, 514229, 832040, 1346269, # 2178309, 3524578]

z1365/euler

t002-fibanatchi.py

Python

import setuptools with open("README.md", "r") as fh: long_description = fh.read() with open("python/requirements.txt", "r") as fh: install_requires = fh.read().splitlines() setuptools.setup( name="fastquant", version="0.1.3.16", author="Lorenzo Ampil", author_email="lorenzo.ampil@gmail.com", description="Bringing data driven investments to the mainstream", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/enzoampil/fastquant", packages=setuptools.find_packages(where="./python", exclude=["docs", "tests"]), package_dir={"": "python"}, package_data={"fastquant": ["python/data/*"]}, include_package_data=True, scripts=["python/scripts/get_disclosures", "python/scripts/update_cache"], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", ], install_requires=install_requires, )

beatobongco/fastquant

setup.py

Python

# -*- coding: utf-8 -*- from bs4 import BeautifulSoup import requests import requests.exceptions from urlparse import urlsplit from collections import deque import re def crawl(url): new_urls = deque(["http://{}".format(url)]) processed_urls = set() emails = [] while len(new_urls): url = new_urls.popleft() processed_urls.add(url) parts = urlsplit(url) base_url = "{0.scheme}://{0.netloc}".format(parts) path = url[:url.rfind('/') + 1] if '/' in parts.path else url print "Processing {}".format(url) try: response = requests.get(url) except (requests.exceptions.MissingSchema, \ requests.exceptions.ConnectionError): continue new_emails = re.findall(r"\w+[.|\w]\w+@\w+[.]\w+[.|\w+]\w+", \ response.text, re.I) for addr in new_emails: emails.append(addr) soup = BeautifulSoup(response.text) for anchor in soup.find_all('a'): link = anchor.attrs["href"] if "href" in anchor.attrs else '' if link.startswith('/'): link = base_url + link elif not link.startswith("http"): link = path + link if not link in new_urls and not link in processed_urls: new_urls.append(link) with open("emails.txt", 'w') as f: f.write('\n'.join(emails))

vesche/snippets

autocapstone/email_crawl.py

Python

from gensim.models import word2vec print("Learning Word2Vec embeddings") tok_file = 'data/preprocessed/lemmatized.txt' sentences = word2vec.LineSentence(tok_file) model = word2vec.Word2Vec(sentences=sentences, size=10, window=5, workers=3, min_count=1) model.wv.save_word2vec_format('models/vejica_word2vec.emb') print("Saved Word2Vec format")

gregorkrz/vejice

scripts/experiments/learn_word2vec.py

Python

########################################################################## # # Copyright (c) 2012, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above # copyright notice, this list of conditions and the following # disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with # the distribution. # # * Neither the name of John Haddon nor the names of # any other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import IECore import Gaffer import GafferUI ## This class forms the base class for all uis which manipulate PathFilters. class PathFilterWidget( GafferUI.Widget ) : def __init__( self, topLevelWidget, pathFilter, **kw ) : GafferUI.Widget.__init__( self, topLevelWidget, **kw ) self.__pathFilter = pathFilter self.__pathFilterChangedConnection = self.__pathFilter.changedSignal().connect( Gaffer.WeakMethod( self.__pathFilterChanged ) ) ## Returns the PathFilter object this UI represents. def pathFilter( self ) : return self.__pathFilter ## Must be implemented by subclasses to update the UI when the filter # changes in some way. def _updateFromPathFilter( self ) : raise NotImplementedError def __pathFilterChanged( self, pathFilter ) : assert( pathFilter is self.__pathFilter ) self._updateFromPathFilter() ## Creates a PathFilterWidget instance for the specified pathFilter. Returns None # if no suitable widget exists. @classmethod def create( cls, pathFilter ) : visible = True with IECore.IgnoredExceptions( KeyError ) : visible = pathFilter.userData()["UI"]["visible"] if not visible : return None c = pathFilter.__class__ while c is not None : creator = cls.__typesToCreators.get( c, None ) if creator is not None : return creator( pathFilter ) c = c.__bases__[0] if c.__bases__ else None return None ## Registers a subclass of PathFilterWidget to be used with a specific pathFilter type. @classmethod def registerType( cls, pathFilterClass, widgetCreationFunction ) : cls.__typesToCreators[pathFilterClass] = widgetCreationFunction __typesToCreators = {} class BasicPathFilterWidget( PathFilterWidget ) : def __init__( self, pathFilter ) : self.__checkBox = GafferUI.BoolWidget( str( pathFilter ) ) PathFilterWidget.__init__( self, self.__checkBox, pathFilter ) self.__stateChangedConnection = self.__checkBox.stateChangedSignal().connect( Gaffer.WeakMethod( self.__stateChanged ) ) self._updateFromPathFilter() def _updateFromPathFilter( self ) : label = str( self.pathFilter() ) with IECore.IgnoredExceptions( KeyError ) : label = self.pathFilter().userData()["UI"]["label"] self.__checkBox.setText( label ) invertEnabled = False with IECore.IgnoredExceptions( KeyError ) : invertEnabled = self.pathFilter().userData()["UI"]["invertEnabled"] self.__checkBox.setState( self.pathFilter().getEnabled() is not invertEnabled ) def __stateChanged( self, checkBox ) : invertEnabled = False with IECore.IgnoredExceptions( KeyError ) : invertEnabled = self.pathFilter().userData()["UI"]["invertEnabled"] self.pathFilter().setEnabled( checkBox.getState() is not invertEnabled ) PathFilterWidget.registerType( Gaffer.PathFilter, BasicPathFilterWidget )

cedriclaunay/gaffer

python/GafferUI/PathFilterWidget.py

Python

from sympy.core.backend import sin, cos, tan, pi, symbols, Matrix, zeros, S from sympy.physics.mechanics import (Particle, Point, ReferenceFrame, RigidBody, Vector) from sympy.physics.mechanics import (angular_momentum, dynamicsymbols, inertia, inertia_of_point_mass, kinetic_energy, linear_momentum, outer, potential_energy, msubs, find_dynamicsymbols, Lagrangian) from sympy.physics.mechanics.functions import gravity, center_of_mass from sympy.physics.vector.vector import Vector from sympy.utilities.pytest import raises Vector.simp = True q1, q2, q3, q4, q5 = symbols('q1 q2 q3 q4 q5') N = ReferenceFrame('N') A = N.orientnew('A', 'Axis', [q1, N.z]) B = A.orientnew('B', 'Axis', [q2, A.x]) C = B.orientnew('C', 'Axis', [q3, B.y]) def test_inertia(): N = ReferenceFrame('N') ixx, iyy, izz = symbols('ixx iyy izz') ixy, iyz, izx = symbols('ixy iyz izx') assert inertia(N, ixx, iyy, izz) == (ixx * (N.x | N.x) + iyy * (N.y | N.y) + izz * (N.z | N.z)) assert inertia(N, 0, 0, 0) == 0 * (N.x | N.x) raises(TypeError, lambda: inertia(0, 0, 0, 0)) assert inertia(N, ixx, iyy, izz, ixy, iyz, izx) == (ixx * (N.x | N.x) + ixy * (N.x | N.y) + izx * (N.x | N.z) + ixy * (N.y | N.x) + iyy * (N.y | N.y) + iyz * (N.y | N.z) + izx * (N.z | N.x) + iyz * (N.z | N.y) + izz * (N.z | N.z)) def test_inertia_of_point_mass(): r, s, t, m = symbols('r s t m') N = ReferenceFrame('N') px = r * N.x I = inertia_of_point_mass(m, px, N) assert I == m * r**2 * (N.y | N.y) + m * r**2 * (N.z | N.z) py = s * N.y I = inertia_of_point_mass(m, py, N) assert I == m * s**2 * (N.x | N.x) + m * s**2 * (N.z | N.z) pz = t * N.z I = inertia_of_point_mass(m, pz, N) assert I == m * t**2 * (N.x | N.x) + m * t**2 * (N.y | N.y) p = px + py + pz I = inertia_of_point_mass(m, p, N) assert I == (m * (s**2 + t**2) * (N.x | N.x) - m * r * s * (N.x | N.y) - m * r * t * (N.x | N.z) - m * r * s * (N.y | N.x) + m * (r**2 + t**2) * (N.y | N.y) - m * s * t * (N.y | N.z) - m * r * t * (N.z | N.x) - m * s * t * (N.z | N.y) + m * (r**2 + s**2) * (N.z | N.z)) def test_linear_momentum(): N = ReferenceFrame('N') Ac = Point('Ac') Ac.set_vel(N, 25 * N.y) I = outer(N.x, N.x) A = RigidBody('A', Ac, N, 20, (I, Ac)) P = Point('P') Pa = Particle('Pa', P, 1) Pa.point.set_vel(N, 10 * N.x) raises(TypeError, lambda: linear_momentum(A, A, Pa)) raises(TypeError, lambda: linear_momentum(N, N, Pa)) assert linear_momentum(N, A, Pa) == 10 * N.x + 500 * N.y def test_angular_momentum_and_linear_momentum(): """A rod with length 2l, centroidal inertia I, and mass M along with a particle of mass m fixed to the end of the rod rotate with an angular rate of omega about point O which is fixed to the non-particle end of the rod. The rod's reference frame is A and the inertial frame is N.""" m, M, l, I = symbols('m, M, l, I') omega = dynamicsymbols('omega') N = ReferenceFrame('N') a = ReferenceFrame('a') O = Point('O') Ac = O.locatenew('Ac', l * N.x) P = Ac.locatenew('P', l * N.x) O.set_vel(N, 0 * N.x) a.set_ang_vel(N, omega * N.z) Ac.v2pt_theory(O, N, a) P.v2pt_theory(O, N, a) Pa = Particle('Pa', P, m) A = RigidBody('A', Ac, a, M, (I * outer(N.z, N.z), Ac)) expected = 2 * m * omega * l * N.y + M * l * omega * N.y assert linear_momentum(N, A, Pa) == expected raises(TypeError, lambda: angular_momentum(N, N, A, Pa)) raises(TypeError, lambda: angular_momentum(O, O, A, Pa)) raises(TypeError, lambda: angular_momentum(O, N, O, Pa)) expected = (I + M * l**2 + 4 * m * l**2) * omega * N.z assert angular_momentum(O, N, A, Pa) == expected def test_kinetic_energy(): m, M, l1 = symbols('m M l1') omega = dynamicsymbols('omega') N = ReferenceFrame('N') O = Point('O') O.set_vel(N, 0 * N.x) Ac = O.locatenew('Ac', l1 * N.x) P = Ac.locatenew('P', l1 * N.x) a = ReferenceFrame('a') a.set_ang_vel(N, omega * N.z) Ac.v2pt_theory(O, N, a) P.v2pt_theory(O, N, a) Pa = Particle('Pa', P, m) I = outer(N.z, N.z) A = RigidBody('A', Ac, a, M, (I, Ac)) raises(TypeError, lambda: kinetic_energy(Pa, Pa, A)) raises(TypeError, lambda: kinetic_energy(N, N, A)) assert 0 == (kinetic_energy(N, Pa, A) - (M*l1**2*omega**2/2 + 2*l1**2*m*omega**2 + omega**2/2)).expand() def test_potential_energy(): m, M, l1, g, h, H = symbols('m M l1 g h H') omega = dynamicsymbols('omega') N = ReferenceFrame('N') O = Point('O') O.set_vel(N, 0 * N.x) Ac = O.locatenew('Ac', l1 * N.x) P = Ac.locatenew('P', l1 * N.x) a = ReferenceFrame('a') a.set_ang_vel(N, omega * N.z) Ac.v2pt_theory(O, N, a) P.v2pt_theory(O, N, a) Pa = Particle('Pa', P, m) I = outer(N.z, N.z) A = RigidBody('A', Ac, a, M, (I, Ac)) Pa.potential_energy = m * g * h A.potential_energy = M * g * H assert potential_energy(A, Pa) == m * g * h + M * g * H def test_Lagrangian(): M, m, g, h = symbols('M m g h') N = ReferenceFrame('N') O = Point('O') O.set_vel(N, 0 * N.x) P = O.locatenew('P', 1 * N.x) P.set_vel(N, 10 * N.x) Pa = Particle('Pa', P, 1) Ac = O.locatenew('Ac', 2 * N.y) Ac.set_vel(N, 5 * N.y) a = ReferenceFrame('a') a.set_ang_vel(N, 10 * N.z) I = outer(N.z, N.z) A = RigidBody('A', Ac, a, 20, (I, Ac)) Pa.potential_energy = m * g * h A.potential_energy = M * g * h raises(TypeError, lambda: Lagrangian(A, A, Pa)) raises(TypeError, lambda: Lagrangian(N, N, Pa)) def test_msubs(): a, b = symbols('a, b') x, y, z = dynamicsymbols('x, y, z') # Test simple substitution expr = Matrix([[a*x + b, x*y.diff() + y], [x.diff().diff(), z + sin(z.diff())]]) sol = Matrix([[a + b, y], [x.diff().diff(), 1]]) sd = {x: 1, z: 1, z.diff(): 0, y.diff(): 0} assert msubs(expr, sd) == sol # Test smart substitution expr = cos(x + y)*tan(x + y) + b*x.diff() sd = {x: 0, y: pi/2, x.diff(): 1} assert msubs(expr, sd, smart=True) == b + 1 N = ReferenceFrame('N') v = x*N.x + y*N.y d = x*(N.x|N.x) + y*(N.y|N.y) v_sol = 1*N.y d_sol = 1*(N.y|N.y) sd = {x: 0, y: 1} assert msubs(v, sd) == v_sol assert msubs(d, sd) == d_sol def test_find_dynamicsymbols(): a, b = symbols('a, b') x, y, z = dynamicsymbols('x, y, z') expr = Matrix([[a*x + b, x*y.diff() + y], [x.diff().diff(), z + sin(z.diff())]]) # Test finding all dynamicsymbols sol = {x, y.diff(), y, x.diff().diff(), z, z.diff()} assert find_dynamicsymbols(expr) == sol # Test finding all but those in sym_list exclude_list = [x, y, z] sol = {y.diff(), x.diff().diff(), z.diff()} assert find_dynamicsymbols(expr, exclude=exclude_list) == sol # Test finding all dynamicsymbols in a vector with a given reference frame d, e, f = dynamicsymbols('d, e, f') A = ReferenceFrame('A') v = d * A.x + e * A.y + f * A.z sol = {d, e, f} assert find_dynamicsymbols(v, reference_frame=A) == sol # Test if a ValueError is raised on supplying only a vector as input raises(ValueError, lambda: find_dynamicsymbols(v)) def test_gravity(): N = ReferenceFrame('N') m, M, g = symbols('m M g') F1, F2 = dynamicsymbols('F1 F2') po = Point('po') pa = Particle('pa', po, m) A = ReferenceFrame('A') P = Point('P') I = outer(A.x, A.x) B = RigidBody('B', P, A, M, (I, P)) forceList = [(po, F1), (P, F2)] forceList.extend(gravity(g*N.y, pa, B)) l = [(po, F1), (P, F2), (po, g*m*N.y), (P, g*M*N.y)] for i in range(len(l)): for j in range(len(l[i])): assert forceList[i][j] == l[i][j] # This function tests the center_of_mass() function # that was added in PR #14758 to compute the center of # mass of a system of bodies. def test_center_of_mass(): a = ReferenceFrame('a') m = symbols('m', real=True) p1 = Particle('p1', Point('p1_pt'), S(1)) p2 = Particle('p2', Point('p2_pt'), S(2)) p3 = Particle('p3', Point('p3_pt'), S(3)) p4 = Particle('p4', Point('p4_pt'), m) b_f = ReferenceFrame('b_f') b_cm = Point('b_cm') mb = symbols('mb') b = RigidBody('b', b_cm, b_f, mb, (outer(b_f.x, b_f.x), b_cm)) p2.point.set_pos(p1.point, a.x) p3.point.set_pos(p1.point, a.x + a.y) p4.point.set_pos(p1.point, a.y) b.masscenter.set_pos(p1.point, a.y + a.z) point_o=Point('o') point_o.set_pos(p1.point, center_of_mass(p1.point, p1, p2, p3, p4, b)) expr = 5/(m + mb + 6)*a.x + (m + mb + 3)/(m + mb + 6)*a.y + mb/(m + mb + 6)*a.z assert point_o.pos_from(p1.point)-expr == 0

Abhi58/sympy

sympy/physics/mechanics/tests/test_functions.py

Python

import cv2 import numpy as np import matplotlib.pyplot as plt def Canny(img): # Gray scale def BGR2GRAY(img): b = img[:, :, 0].copy() g = img[:, :, 1].copy() r = img[:, :, 2].copy() # Gray scale out = 0.2126 * r + 0.7152 * g + 0.0722 * b out = out.astype(np.uint8) return out # Gaussian filter for grayscale def gaussian_filter(img, K_size=3, sigma=1.3): if len(img.shape) == 3: H, W, C = img.shape gray = False else: img = np.expand_dims(img, axis=-1) H, W, C = img.shape gray = True ## Zero padding pad = K_size // 2 out = np.zeros([H + pad * 2, W + pad * 2, C], dtype=np.float) out[pad : pad + H, pad : pad + W] = img.copy().astype(np.float) ## prepare Kernel K = np.zeros((K_size, K_size), dtype=np.float) for x in range(-pad, -pad + K_size): for y in range(-pad, -pad + K_size): K[y + pad, x + pad] = np.exp( - (x ** 2 + y ** 2) / (2 * sigma * sigma)) #K /= (sigma * np.sqrt(2 * np.pi)) K /= (2 * np.pi * sigma * sigma) K /= K.sum() tmp = out.copy() # filtering for y in range(H): for x in range(W): for c in range(C): out[pad + y, pad + x, c] = np.sum(K * tmp[y : y + K_size, x : x + K_size, c]) out = np.clip(out, 0, 255) out = out[pad : pad + H, pad : pad + W] out = out.astype(np.uint8) if gray: out = out[..., 0] return out # sobel filter def sobel_filter(img, K_size=3): if len(img.shape) == 3: H, W, C = img.shape else: H, W = img.shape # Zero padding pad = K_size // 2 out = np.zeros((H + pad * 2, W + pad * 2), dtype=np.float) out[pad : pad + H, pad : pad + W] = img.copy().astype(np.float) tmp = out.copy() out_v = out.copy() out_h = out.copy() ## Sobel vertical Kv = [[1., 2., 1.],[0., 0., 0.], [-1., -2., -1.]] ## Sobel horizontal Kh = [[1., 0., -1.],[2., 0., -2.],[1., 0., -1.]] # filtering for y in range(H): for x in range(W): out_v[pad + y, pad + x] = np.sum(Kv * (tmp[y : y + K_size, x : x + K_size])) out_h[pad + y, pad + x] = np.sum(Kh * (tmp[y : y + K_size, x : x + K_size])) out_v = np.clip(out_v, 0, 255) out_h = np.clip(out_h, 0, 255) out_v = out_v[pad : pad + H, pad : pad + W] out_v = out_v.astype(np.uint8) out_h = out_h[pad : pad + H, pad : pad + W] out_h = out_h.astype(np.uint8) return out_v, out_h def get_edge_angle(fx, fy): # get edge strength edge = np.sqrt(np.power(fx.astype(np.float32), 2) + np.power(fy.astype(np.float32), 2)) edge = np.clip(edge, 0, 255) fx = np.maximum(fx, 1e-10) #fx[np.abs(fx) <= 1e-5] = 1e-5 # get edge angle angle = np.arctan(fy / fx) return edge, angle def angle_quantization(angle): angle = angle / np.pi * 180 angle[angle < -22.5] = 180 + angle[angle < -22.5] _angle = np.zeros_like(angle, dtype=np.uint8) _angle[np.where(angle <= 22.5)] = 0 _angle[np.where((angle > 22.5) & (angle <= 67.5))] = 45 _angle[np.where((angle > 67.5) & (angle <= 112.5))] = 90 _angle[np.where((angle > 112.5) & (angle <= 157.5))] = 135 return _angle def non_maximum_suppression(angle, edge): H, W = angle.shape _edge = edge.copy() for y in range(H): for x in range(W): if angle[y, x] == 0: dx1, dy1, dx2, dy2 = -1, 0, 1, 0 elif angle[y, x] == 45: dx1, dy1, dx2, dy2 = -1, 1, 1, -1 elif angle[y, x] == 90: dx1, dy1, dx2, dy2 = 0, -1, 0, 1 elif angle[y, x] == 135: dx1, dy1, dx2, dy2 = -1, -1, 1, 1 if x == 0: dx1 = max(dx1, 0) dx2 = max(dx2, 0) if x == W-1: dx1 = min(dx1, 0) dx2 = min(dx2, 0) if y == 0: dy1 = max(dy1, 0) dy2 = max(dy2, 0) if y == H-1: dy1 = min(dy1, 0) dy2 = min(dy2, 0) if max(max(edge[y, x], edge[y + dy1, x + dx1]), edge[y + dy2, x + dx2]) != edge[y, x]: _edge[y, x] = 0 return _edge def hysterisis(edge, HT=100, LT=30): H, W = edge.shape # Histeresis threshold edge[edge >= HT] = 255 edge[edge <= LT] = 0 _edge = np.zeros((H + 2, W + 2), dtype=np.float32) _edge[1 : H + 1, 1 : W + 1] = edge ## 8 - Nearest neighbor nn = np.array(((1., 1., 1.), (1., 0., 1.), (1., 1., 1.)), dtype=np.float32) for y in range(1, H+2): for x in range(1, W+2): if _edge[y, x] < LT or _edge[y, x] > HT: continue if np.max(_edge[y-1:y+2, x-1:x+2] * nn) >= HT: _edge[y, x] = 255 else: _edge[y, x] = 0 edge = _edge[1:H+1, 1:W+1] return edge # grayscale gray = BGR2GRAY(img) # gaussian filtering gaussian = gaussian_filter(gray, K_size=5, sigma=1.4) # sobel filtering fy, fx = sobel_filter(gaussian, K_size=3) # get edge strength, angle edge, angle = get_edge_angle(fx, fy) # angle quantization angle = angle_quantization(angle) # non maximum suppression edge = non_maximum_suppression(angle, edge) # hysterisis threshold out = hysterisis(edge, 100, 30) return out def Hough_Line_step1(edge): ## Voting def voting(edge): H, W = edge.shape drho = 1 dtheta = 1 # get rho max length rho_max = np.ceil(np.sqrt(H ** 2 + W ** 2)).astype(np.int) # hough table hough = np.zeros((rho_max * 2, 180), dtype=np.int) # get index of edge ind = np.where(edge == 255) ## hough transformation for y, x in zip(ind[0], ind[1]): for theta in range(0, 180, dtheta): # get polar coordinat4s t = np.pi / 180 * theta rho = int(x * np.cos(t) + y * np.sin(t)) # vote hough[rho + rho_max, theta] += 1 out = hough.astype(np.uint8) return out # voting out = voting(edge) return out # Read image img = cv2.imread("thorino.jpg").astype(np.float32) # Canny edge = Canny(img) # Hough out = Hough_Line_step1(edge) out = out.astype(np.uint8) # Save result #cv2.imwrite("out.jpg", out) cv2.imshow("result", out) cv2.waitKey(0) cv2.destroyAllWindows()

lusi1990/ImageProcessing100Wen

Question_41_50/answers_py/answer_44.py

Python

""" timedelta support tools """ import numpy as np from pandas._libs.tslibs import NaT from pandas._libs.tslibs.timedeltas import Timedelta, parse_timedelta_unit from pandas.core.dtypes.common import is_list_like from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries from pandas.core.arrays.timedeltas import sequence_to_td64ns def to_timedelta(arg, unit=None, errors="raise"): """ Convert argument to timedelta. Timedeltas are absolute differences in times, expressed in difference units (e.g. days, hours, minutes, seconds). This method converts an argument from a recognized timedelta format / value into a Timedelta type. Parameters ---------- arg : str, timedelta, list-like or Series The data to be converted to timedelta. The character M by itself, e.g. '1M', is treated as minute, not month. The characters Y and y are treated as the mean length of the Gregorian calendar year - 365.2425 days or 365 days 5 hours 49 minutes 12 seconds. unit : str, optional Denotes the unit of the arg for numeric `arg`. Defaults to ``"ns"``. Possible values: * 'W' * 'D' / 'days' / 'day' * 'hours' / 'hour' / 'hr' / 'h' * 'm' / 'minute' / 'min' / 'minutes' / 'T' * 'S' / 'seconds' / 'sec' / 'second' * 'ms' / 'milliseconds' / 'millisecond' / 'milli' / 'millis' / 'L' * 'us' / 'microseconds' / 'microsecond' / 'micro' / 'micros' / 'U' * 'ns' / 'nanoseconds' / 'nano' / 'nanos' / 'nanosecond' / 'N' .. versionchanged:: 1.1.0 Must not be specified when `arg` context strings and ``errors="raise"``. errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception. - If 'coerce', then invalid parsing will be set as NaT. - If 'ignore', then invalid parsing will return the input. Returns ------- timedelta64 or numpy.array of timedelta64 Output type returned if parsing succeeded. See Also -------- DataFrame.astype : Cast argument to a specified dtype. to_datetime : Convert argument to datetime. convert_dtypes : Convert dtypes. Examples -------- Parsing a single string to a Timedelta: >>> pd.to_timedelta('1 days 06:05:01.00003') Timedelta('1 days 06:05:01.000030') >>> pd.to_timedelta('15.5us') Timedelta('0 days 00:00:00.000015500') Parsing a list or array of strings: >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan']) TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015500', NaT], dtype='timedelta64[ns]', freq=None) Converting numbers by specifying the `unit` keyword argument: >>> pd.to_timedelta(np.arange(5), unit='s') TimedeltaIndex(['0 days 00:00:00', '0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03', '0 days 00:00:04'], dtype='timedelta64[ns]', freq=None) >>> pd.to_timedelta(np.arange(5), unit='d') TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None) """ if unit is not None: unit = parse_timedelta_unit(unit) if errors not in ("ignore", "raise", "coerce"): raise ValueError("errors must be one of 'ignore', 'raise', or 'coerce'}") if unit in {"Y", "y", "M"}: raise ValueError( "Units 'M', 'Y', and 'y' are no longer supported, as they do not " "represent unambiguous timedelta values durations." ) if arg is None: return arg elif isinstance(arg, ABCSeries): values = _convert_listlike(arg._values, unit=unit, errors=errors) return arg._constructor(values, index=arg.index, name=arg.name) elif isinstance(arg, ABCIndexClass): return _convert_listlike(arg, unit=unit, errors=errors, name=arg.name) elif isinstance(arg, np.ndarray) and arg.ndim == 0: # extract array scalar and process below arg = arg.item() elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1: return _convert_listlike(arg, unit=unit, errors=errors) elif getattr(arg, "ndim", 1) > 1: raise TypeError( "arg must be a string, timedelta, list, tuple, 1-d array, or Series" ) if isinstance(arg, str) and unit is not None: raise ValueError("unit must not be specified if the input is/contains a str") # ...so it must be a scalar value. Return scalar. return _coerce_scalar_to_timedelta_type(arg, unit=unit, errors=errors) def _coerce_scalar_to_timedelta_type(r, unit="ns", errors="raise"): """Convert string 'r' to a timedelta object.""" try: result = Timedelta(r, unit) except ValueError: if errors == "raise": raise elif errors == "ignore": return r # coerce result = NaT return result def _convert_listlike(arg, unit=None, errors="raise", name=None): """Convert a list of objects to a timedelta index object.""" if isinstance(arg, (list, tuple)) or not hasattr(arg, "dtype"): # This is needed only to ensure that in the case where we end up # returning arg (errors == "ignore"), and where the input is a # generator, we return a useful list-like instead of a # used-up generator arg = np.array(list(arg), dtype=object) try: value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0] except ValueError: if errors == "ignore": return arg else: # This else-block accounts for the cases when errors='raise' # and errors='coerce'. If errors == 'raise', these errors # should be raised. If errors == 'coerce', we shouldn't # expect any errors to be raised, since all parsing errors # cause coercion to pd.NaT. However, if an error / bug is # introduced that causes an Exception to be raised, we would # like to surface it. raise from pandas import TimedeltaIndex value = TimedeltaIndex(value, unit="ns", name=name) return value

cdeil/pandas

pandas/core/tools/timedeltas.py

Python

from functools import reduce from itertools import chain from typing import Optional, Set import pandas as pd from sqlalchemy import ( func, or_, orm, sql, ) import fiber from fiber.condition.base import _BaseCondition from fiber.database import ( compile_sqla, read_with_progress, ) from fiber.database import get_engine from fiber.database.table import Table def _case_insensitive_like(column: str, value): return func.upper(column).like(value.upper()) def _multi_like_clause(column: str, value_or_values): values = ( [value_or_values] if isinstance(value_or_values, str) else value_or_values ) return reduce( or_, [_case_insensitive_like(column, value) for value in values] ) class _DatabaseCondition(_BaseCondition): """ The DatabaseCondition adds functionality to the BaseCondition which is needed to run queries against a database. It also allows to combine SQL Statements into one to optimize performance. It should only be used by developers and not by end-users. It builds the basis for specific conditions like Diagnosis, VitalSign, ... It should be possible to use this for other databases that use MRNs by adjusting the engine. Problems one would need to look into is that database conditions of different DBs are only combined as BaseConditions in ``__and__``, ``__or__``. """ engine = get_engine() def __init__( self, mrns: Optional[Set[str]] = None, dimensions: Optional[Set[str]] = None, clause=None, data_columns=None, **kwargs ): """ Args: mrns: Set of MRN-Strings for which the condition is true. children: List of child conditions which were combined with an operator. operator: String representing the combination of the child condition (e.g. ``_BaseCondition.AND``) dimensions: A set of tables that need to be joined on the ``base_table`` clause: The SQLAlchemy clause of the current condition to select patients. data_columns: The SQLAlchemy data_columns that should be returned when ``.get_data()`` is called. """ super().__init__(**kwargs) self.dimensions = dimensions or set() # sql.true() acts as an 'empty' initializer for the clause self._clause = sql.true() if clause is None else clause self.data_columns = data_columns or [] @property def base_table(self) -> Table: """ Must be set by subclasses to the database table which the class uses to select patients and data. This is also used to optimize queries on the same table. """ raise NotImplementedError @property def _default_columns(self): """ Must be set by subclasses. This should return an array of columns which are in the result table of ``._create_query()``. These columns will be returned by default when ``.get_data()`` is called. """ raise NotImplementedError @property def mrn_column(self): """ Must be set by subclasses. This is used to specify the column in the result table of ``._create_query()`` which is holding the MRNs. """ raise NotImplementedError @property def age_column(self): """ Must be set by subclasses. This is used to specify the column in the result table of ``._create_query()`` which is holding the age in days. """ raise NotImplementedError @property def data_columns(self): """ Returns columns which are in the result table of ``._create_query()``. These columns will be returned when ``.get_data()`` is called. """ return [ str(col) for col in (self._specified_columns or self._default_columns) ] @data_columns.setter def data_columns(self, value): self._specified_columns = value @property def clause(self): """ Returns the clause of the current condition or runs ``._create_clause()`` to create it. """ # TODO recursively create clause of combinable conditions if not isinstance(self._clause, sql.elements.True_): return self._clause else: return self._create_clause() def _create_clause(self): """ Should be overwritten by subclasses to create a SQLAlchemy clause based on the defined condition. It is used to select the correct patients. """ return sql.true() def _create_query(self) -> orm.Query: """ Must be implemented by subclasses to return an instance of a SQLAlchemy query which only returns MRNs. This query should yield all medical record numbers in the ``base_table`` of the condition. It uses the ``.clause`` to select the relevant patients. This query is also used by other function which change the selected columns to get data about the patients. """ raise NotImplementedError def _fetch_mrns(self, limit: Optional[int] = None): """Fetches MRNs from the results of ``._create_query()``.""" q = self._create_query() if limit: q = q.limit(limit) mrn_df = read_with_progress(q.statement, self.engine) if mrn_df.empty: mrn_df = pd.DataFrame(columns=['medical_record_number']) assert len(mrn_df.columns) == 1, '_create_query must return only MRNs' result = set( mrn for mrn in mrn_df.iloc[:, 0] ) return result def _fetch_data(self, included_mrns: Optional[Set] = None, limit: Optional[int] = None): """ Fetches the data defined with ``.data_columns`` for each patient defined by this condition and via ``included_mrns`` from the results of ``._create_query()``. """ q = self._create_query() if included_mrns: q = q.filter(self.mrn_column.in_(included_mrns)) if limit: q = q.limit(limit) q = q.with_entities(*self.data_columns).distinct() result = read_with_progress( q.statement, self.engine, silent=bool(included_mrns)) return result def example_values(self): """ Returns ten values of the current condition. Example: >>> Patient(gender='Female', religion='Hindu').example_values() """ return self.get_data(limit=10) def values_per(self, *columns: Set[str]): """ Counts occurence of unique values in the specified columns. """ return self._grouped_count('*', *columns, label='values') def patients_per(self, *columns: Set[str]): """ Counts distinct patients for unique values in the specified columns. """ return self._grouped_count( self.mrn_column.distinct(), *columns, label='patients' ) def _grouped_count(self, count_column: str, *columns: Set[str], label: Optional[str] = None): if not columns: raise ValueError('Supply one or multiple columns as arguments.') q = self._create_query() q = q.group_by( *columns ).with_entities( *columns ).order_by( func.count(count_column).label((label or 'count')).desc() ) return read_with_progress(q.statement, self.engine) def distinct(self, *columns: Set[str]): """Returns distinct values based on the specified ``columns``""" if not columns: raise ValueError('Supply one or multiple columns as arguments.') q = self._create_query() q = q.with_entities(*columns).distinct() return read_with_progress(q.statement, self.engine) def to_dict(self): obj_dict = super().to_dict() if self._specified_columns: obj_dict['data_columns'] = self.data_columns return obj_dict @classmethod def from_dict(cls: _BaseCondition, obj_dict: dict): obj = super().from_dict(obj_dict) if 'data_columns' in obj_dict: obj.data_columns = obj_dict['data_columns'] return obj def __or__(self, other: _BaseCondition): """ The _DatabaseCondition optimizes the SQL statements for ``|`` by combining the clauses of condition which run on the same database table. This is done via the ``.base_table`` attribute. """ if ( self.base_table == other.base_table and not (self._mrns or other._mrns) ): unique_columns = list(dict.fromkeys( chain(self.data_columns, other.data_columns) )) return self.__class__( dimensions=self.dimensions | other.dimensions, clause=self.clause | other.clause, data_columns=unique_columns, children=[self, other], operator=_BaseCondition.OR, ) else: return _BaseCondition( mrns=self.get_mrns() | other.get_mrns(), children=[self, other], operator=_BaseCondition.OR, ) def __and__(self, other: _BaseCondition): # The SQL queries could theoretically be combined for AND as well, by # running them as subqueries and joining on the MRNs return self.__class__( mrns=self.get_mrns() & other.get_mrns(), dimensions=self.dimensions | other.dimensions, children=[self, other], operator=_BaseCondition.AND, ) def __repr__(self): """Shows the running query or the resulting MRNs""" if self._mrns: return f'{self.__class__.__name__}: {len(self.get_mrns())} mrns' else: clause = ( compile_sqla(self.clause, self.engine) if fiber.config.VERBOSE else '...' ) return ( f'{self.__class__.__name__} ' f'({clause})' )

hpi-dhc/fiber

fiber/condition/database.py

Python

# # PySNMP MIB module PAN-ENTITY-EXT-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///mnt/d/data/MIBS/text_mibs/paloalto/PAN-ENTITY-EXT-MIB # Produced by pysmi-0.3.4 at Wed Feb 10 13:07:35 2021 # On host QS-IL-COSTAY platform Linux version 5.4.72-microsoft-standard-WSL2 by user coye # Using Python version 3.8.5 (default, Jul 28 2020, 12:59:40) # ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ValueSizeConstraint, SingleValueConstraint, ValueRangeConstraint, ConstraintsIntersection, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsIntersection", "ConstraintsUnion") entPhysicalIndex, = mibBuilder.importSymbols("ENTITY-MIB", "entPhysicalIndex") panModules, = mibBuilder.importSymbols("PAN-GLOBAL-REG", "panModules") NotificationGroup, ObjectGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ObjectGroup", "ModuleCompliance") MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, Counter32, ModuleIdentity, Integer32, Bits, TimeTicks, IpAddress, ObjectIdentity, Gauge32, NotificationType, Unsigned32, Counter64, iso = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter32", "ModuleIdentity", "Integer32", "Bits", "TimeTicks", "IpAddress", "ObjectIdentity", "Gauge32", "NotificationType", "Unsigned32", "Counter64", "iso") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") panEntityMIBModule = ModuleIdentity((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7)) panEntityMIBModule.setRevisions(('2012-11-05 11:06',)) if mibBuilder.loadTexts: panEntityMIBModule.setLastUpdated('201211051106Z') if mibBuilder.loadTexts: panEntityMIBModule.setOrganization('Palo Alto Networks') panEntityMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1)) panEntityMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2)) panEntityChassisGroup = ObjectIdentity((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 1)) if mibBuilder.loadTexts: panEntityChassisGroup.setStatus('current') panEntityFRUModuleGroup = ObjectIdentity((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 2)) if mibBuilder.loadTexts: panEntityFRUModuleGroup.setStatus('current') panEntityFanTrayGroup = ObjectIdentity((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 3)) if mibBuilder.loadTexts: panEntityFanTrayGroup.setStatus('current') panEntityPowerSupplyGroup = ObjectIdentity((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 4)) if mibBuilder.loadTexts: panEntityPowerSupplyGroup.setStatus('current') panEntityTotalPowerAvail = MibScalar((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 1, 1), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntityTotalPowerAvail.setStatus('current') panEntityTotalPowerUsed = MibScalar((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 1, 2), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntityTotalPowerUsed.setStatus('current') panEntityFRUModuleTable = MibTable((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 2, 1), ) if mibBuilder.loadTexts: panEntityFRUModuleTable.setStatus('current') panEntityFRUModuleEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 2, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex")) if mibBuilder.loadTexts: panEntityFRUModuleEntry.setStatus('current') panEntryFRUModulePowerUsed = MibTableColumn((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 2, 1, 1, 1), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntryFRUModulePowerUsed.setStatus('current') panEntryFRUModuleNumPorts = MibTableColumn((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 2, 1, 1, 2), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntryFRUModuleNumPorts.setStatus('current') panEntityFanTrayTable = MibTable((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 3, 1), ) if mibBuilder.loadTexts: panEntityFanTrayTable.setStatus('current') panEntityFanTrayEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 3, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex")) if mibBuilder.loadTexts: panEntityFanTrayEntry.setStatus('current') panEntryFanTrayPowerUsed = MibTableColumn((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 3, 1, 1, 1), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntryFanTrayPowerUsed.setStatus('current') panEntityPowerSupplyTable = MibTable((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 4, 1), ) if mibBuilder.loadTexts: panEntityPowerSupplyTable.setStatus('current') panEntityPowerSupplyEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 4, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex")) if mibBuilder.loadTexts: panEntityPowerSupplyEntry.setStatus('current') panEntryPowerSupplyPowerCapacity = MibTableColumn((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 1, 4, 1, 1, 1), Integer32()).setMaxAccess("readonly") if mibBuilder.loadTexts: panEntryPowerSupplyPowerCapacity.setStatus('current') panEntityMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 1)) panEntityMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 2)) panEntityMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 1, 1)).setObjects(("PAN-ENTITY-EXT-MIB", "panEntityMIBChassisGroup"), ("PAN-ENTITY-EXT-MIB", "panEntityMIBFRUModuleGroup"), ("PAN-ENTITY-EXT-MIB", "panEntityMIBFanTrayGroup"), ("PAN-ENTITY-EXT-MIB", "panEntityMIBPowerSupplyGroup")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): panEntityMIBCompliance = panEntityMIBCompliance.setStatus('current') panEntityMIBChassisGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 2, 1)).setObjects(("PAN-ENTITY-EXT-MIB", "panEntityTotalPowerAvail"), ("PAN-ENTITY-EXT-MIB", "panEntityTotalPowerUsed")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): panEntityMIBChassisGroup = panEntityMIBChassisGroup.setStatus('current') panEntityMIBFRUModuleGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 2, 2)).setObjects(("PAN-ENTITY-EXT-MIB", "panEntryFRUModulePowerUsed"), ("PAN-ENTITY-EXT-MIB", "panEntryFRUModuleNumPorts")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): panEntityMIBFRUModuleGroup = panEntityMIBFRUModuleGroup.setStatus('current') panEntityMIBFanTrayGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 2, 3)).setObjects(("PAN-ENTITY-EXT-MIB", "panEntryFanTrayPowerUsed")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): panEntityMIBFanTrayGroup = panEntityMIBFanTrayGroup.setStatus('current') panEntityMIBPowerSupplyGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 25461, 1, 1, 7, 2, 2, 4)).setObjects(("PAN-ENTITY-EXT-MIB", "panEntryPowerSupplyPowerCapacity")) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): panEntityMIBPowerSupplyGroup = panEntityMIBPowerSupplyGroup.setStatus('current') mibBuilder.exportSymbols("PAN-ENTITY-EXT-MIB", panEntityMIBPowerSupplyGroup=panEntityMIBPowerSupplyGroup, panEntityMIBConformance=panEntityMIBConformance, panEntityPowerSupplyEntry=panEntityPowerSupplyEntry, panEntityPowerSupplyGroup=panEntityPowerSupplyGroup, panEntityTotalPowerAvail=panEntityTotalPowerAvail, panEntityMIBCompliances=panEntityMIBCompliances, panEntryFRUModuleNumPorts=panEntryFRUModuleNumPorts, panEntityFanTrayGroup=panEntityFanTrayGroup, panEntryFanTrayPowerUsed=panEntryFanTrayPowerUsed, panEntryPowerSupplyPowerCapacity=panEntryPowerSupplyPowerCapacity, panEntityFanTrayEntry=panEntityFanTrayEntry, panEntityMIBFanTrayGroup=panEntityMIBFanTrayGroup, panEntityFanTrayTable=panEntityFanTrayTable, panEntityMIBCompliance=panEntityMIBCompliance, panEntityFRUModuleEntry=panEntityFRUModuleEntry, panEntityMIBFRUModuleGroup=panEntityMIBFRUModuleGroup, panEntityMIBModule=panEntityMIBModule, panEntityMIBChassisGroup=panEntityMIBChassisGroup, panEntityMIBObjects=panEntityMIBObjects, panEntityFRUModuleGroup=panEntityFRUModuleGroup, panEntityFRUModuleTable=panEntityFRUModuleTable, panEntityTotalPowerUsed=panEntityTotalPowerUsed, PYSNMP_MODULE_ID=panEntityMIBModule, panEntityChassisGroup=panEntityChassisGroup, panEntityPowerSupplyTable=panEntityPowerSupplyTable, panEntryFRUModulePowerUsed=panEntryFRUModulePowerUsed, panEntityMIBGroups=panEntityMIBGroups)

QualiSystems/cloudshell-firewall-panos

cloudshell/firewall/paloalto/panos/mibs/PAN-ENTITY-EXT-MIB.py

Python

from setuptools import setup, find_packages # declare these here since we use them in multiple places _tests_require = [ 'pytest', 'pytest-cov', 'flake8', ] setup( # package info name='cheapskate_bal', description='Cheapskate labs single/dual plane balancer', version='0.0.2', url='http://your/url/here', author='Kevin Powell', author_email='kevin@kevinpowell.guru', packages=find_packages(exclude=['tests', 'tests.*']), # scripts to install to usr/bin entry_points={ 'console_scripts': [ 'csbal=cheapskate_bal.cli:csbal_process', 'csbal_s=cheapskate_bal.cli:csbal_single', 'csbal_dinit=cheapskate_bal.cli:csbal_dual_init', 'csbal_d=cheapskate_bal.cli:csbal_dual_iter' ] }, # run time requirements # exact versions are in the requirements.txt file install_requires=[], # need this for setup.py test setup_requires=[ 'pytest-runner', ], # needs this if using setuptools_scm # use_scm_version=True, # test dependencies tests_require=_tests_require, extras_require={ # this allows us to pip install .[test] for all test dependencies 'test': _tests_require, } )

kevinpowell/balancer

cheapskate_bal/setup.py

Python

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Run this script to train a QR-DQN agent in the selected environment """ from cnn_deepmind import CNNDeepmind_Multihead from eqrdqn import QRDQN from atari_wrappers import make_atari, wrap_deepmind import pickle import numpy as np import matplotlib.pyplot as plt env = make_atari("BreakoutNoFrameskip-v4",noop=True) env = wrap_deepmind(env, episode_life=True) nb_steps = 12500000 agent = QRDQN( env, CNNDeepmind_Multihead, n_quantiles=200, kappa=0, prior = 0.0001, replay_start_size=50000, replay_buffer_size=1000000, initial_exploration_rate=1, final_exploration_rate=0.01, final_exploration_step=1000000, gamma=0.99, update_target_frequency=10000, minibatch_size=32, learning_rate=5e-5, adam_epsilon=0.01/32, update_frequency=4, logging=True) agent.learn(timesteps=nb_steps, verbose=True) agent.save()

uncharted-technologies/risk-and-uncertainty

Archive/main/Atari/train_atari.py

Python

from ..commands.help import HelpCommand from ..commands.exit import ExitCommand from ..commands.purchase import PurchaseCommand class CommandState: """ The __state value should not be accessed directly, instead the get() method should be used. """ __state = { 'commands': { 'help': HelpCommand.execute, 'exit': ExitCommand.execute, 'purchase': PurchaseCommand.execute, }, } @classmethod def get(cls, key): return cls.__state.get(key)

itsSayantan/pyshop

src/state/CommandState.py

Python

#!/bin/bash from pkg_resources import require require('numpy') require('h5py') import sys, os import numpy as np import h5py cxifilenames = sys.argv[2:] output_dims = tuple() print "Using CXI file for dims: ", cxifilenames[0] with h5py.File(cxifilenames[0], 'r') as cxi: output_dtype = cxi['entry_1']['data_1']['data'].dtype output_dims = (len(cxifilenames), cxi['entry_1']['data_3']['data'].shape[0] * 2, cxi['entry_1']['data_3']['data'].shape[1]) print output_dims, output_dtype dset = np.zeros(shape=output_dims, dtype = output_dtype) for i, cxi_file in enumerate(cxifilenames): with h5py.File(cxi_file, 'r') as cxi: print cxi_file cxi_dset = cxi['entry_1']['data_3']['data'] offset = (i, 0, 0) print " ", offset, cxi_dset.shape dset[offset[0], offset[1]:cxi_dset.shape[0]+offset[1], offset[2]:cxi_dset.shape[1]+offset[2]] = cxi_dset cxi_dset = cxi['entry_1']['data_4']['data'] offset = (i, output_dims[1]/2, 0) print " ", offset, cxi_dset.shape dset[offset[0], offset[1]:cxi_dset.shape[0]+offset[1], offset[2]:cxi_dset.shape[1]+offset[2]] = cxi_dset print "Large dataset created: ", dset print "min/max/mean value: ", dset.min(), dset.max(), dset.mean() print "Raising data values by (turning into unsigned dataset): ", abs(dset.min()) unsigned_dset = np.array(dset + abs(dset.min()), dtype=np.uint16) print "Creating file: ", sys.argv[1] out = h5py.File(sys.argv[1], 'w') print "Creating dataset in output file" out_dset = out.create_dataset('data', data = unsigned_dset) print "Done. Closing file" out.close()

ulrikpedersen/benchpress

cxi2hdf5.py

Python

#from sql_gen.sql_gen.filters import * class Prompter(object): def __init__(self, template_source): self.template_source = template_source def get_prompts(self): result=[] for undeclared_var in self.template_source.find_undeclared_variables(): result.append(Prompt(undeclared_var,self.template_source.get_filters(undeclared_var))) return result def build_context(self): prompts = self.get_prompts() context ={} for prompt in prompts: prompt.populate_value(context) return context class Prompt: def __init__(self, variable_name, filter_list): self.variable_name =variable_name self.filter_list = filter_list def get_diplay_text(self): self.display_text = self.variable_name for template_filter in self.filter_list: self.display_text = template_filter.apply(self.display_text); return self.display_text+": " def populate_value(self,context): var =raw_input(self.get_diplay_text()) if var: context[self.variable_name] = var

vecin2/em-dev-tools

build/lib.linux-x86_64-2.7/sql_gen/sql_gen/prompter.py

Python

import logging import os import select import socket from typing import Union, List log = logging.getLogger(__name__) class Receiver: def __init__(self, irc_socket: socket.socket, socket_timeout: int) -> None: self._irc_socket = irc_socket self._socket_timeout = socket_timeout try: self._tc = int(os.popen('stty size', 'r').read().split()[1]) except IndexError: log.warning("term columns could not be ascertained") self._tc = 80 @property def irc_socket(self) -> socket.socket: return self._irc_socket @irc_socket.setter def irc_socket(self, new_socket: socket.socket) -> None: self._irc_socket = new_socket def receive_msg(self) -> Union[str, List[str]]: # Timeout when connection is lost self._irc_socket.setblocking(False) ready = select.select([self._irc_socket], [], [], self._socket_timeout) ircmsg = "" if ready[0]: try: ircmsg = self._irc_socket.recv(2048).decode("UTF-8") except (OSError, UnicodeDecodeError) as e: log.error(e) return "ERROR" ircmsgs = ircmsg.split('\r\n') if len(ircmsgs) > 1 and not ircmsgs[len(ircmsgs) - 1]: del ircmsgs[len(ircmsgs) - 1] sepmsg = "ircmsg:" for ircmsg in ircmsgs: log.info("%s %s", sepmsg, "-" * (self._tc - len(sepmsg) - 30)) log.info(ircmsg) self._irc_socket.setblocking(True) return ircmsgs

LoLei/ircbot

src/receiver/receiver.py

Python

# Copyright 2011 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import functools import netaddr from oslo_serialization import jsonutils import six from nova import exception from nova.i18n import _ from nova import utils # Constants for the 'vif_type' field in VIF class VIF_TYPE_OVS = 'ovs' VIF_TYPE_IVS = 'ivs' VIF_TYPE_DVS = 'dvs' VIF_TYPE_IOVISOR = 'iovisor' VIF_TYPE_BRIDGE = 'bridge' VIF_TYPE_802_QBG = '802.1qbg' VIF_TYPE_802_QBH = '802.1qbh' VIF_TYPE_HW_VEB = 'hw_veb' VIF_TYPE_HYPERV = 'hyperv' VIF_TYPE_HOSTDEV = 'hostdev_physical' VIF_TYPE_IB_HOSTDEV = 'ib_hostdev' VIF_TYPE_MIDONET = 'midonet' VIF_TYPE_VHOSTUSER = 'vhostuser' VIF_TYPE_VROUTER = 'vrouter' VIF_TYPE_OTHER = 'other' VIF_TYPE_TAP = 'tap' VIF_TYPE_MACVTAP = 'macvtap' VIF_TYPE_AGILIO_OVS = 'agilio_ovs' VIF_TYPE_BINDING_FAILED = 'binding_failed' VIF_TYPE_VIF = 'vif' # Constants for dictionary keys in the 'vif_details' field in the VIF # class VIF_DETAILS_PORT_FILTER = 'port_filter' VIF_DETAILS_OVS_HYBRID_PLUG = 'ovs_hybrid_plug' VIF_DETAILS_PHYSICAL_NETWORK = 'physical_network' VIF_DETAILS_BRIDGE_NAME = 'bridge_name' VIF_DETAILS_OVS_DATAPATH_TYPE = 'datapath_type' # The following constant defines an SR-IOV related parameter in the # 'vif_details'. 'profileid' should be used for VIF_TYPE_802_QBH VIF_DETAILS_PROFILEID = 'profileid' # The following constant defines an SR-IOV and macvtap related parameter in # the 'vif_details'. 'vlan' should be used for VIF_TYPE_HW_VEB or # VIF_TYPE_MACVTAP VIF_DETAILS_VLAN = 'vlan' # The following three constants define the macvtap related fields in # the 'vif_details'. VIF_DETAILS_MACVTAP_SOURCE = 'macvtap_source' VIF_DETAILS_MACVTAP_MODE = 'macvtap_mode' VIF_DETAILS_PHYS_INTERFACE = 'physical_interface' # Constants for vhost-user related fields in 'vif_details'. # Sets mode on vhost-user socket, valid values are 'client' # and 'server' VIF_DETAILS_VHOSTUSER_MODE = 'vhostuser_mode' # vhost-user socket path VIF_DETAILS_VHOSTUSER_SOCKET = 'vhostuser_socket' # Specifies whether vhost-user socket should be plugged # into ovs bridge. Valid values are True and False VIF_DETAILS_VHOSTUSER_OVS_PLUG = 'vhostuser_ovs_plug' # Specifies whether vhost-user socket should be used to # create a fp netdevice interface. VIF_DETAILS_VHOSTUSER_FP_PLUG = 'vhostuser_fp_plug' # Specifies whether vhost-user socket should be used to # create a vrouter netdevice interface # TODO(mhenkel): Consider renaming this to be contrail-specific. VIF_DETAILS_VHOSTUSER_VROUTER_PLUG = 'vhostuser_vrouter_plug' # Constants for dictionary keys in the 'vif_details' field that are # valid for VIF_TYPE_TAP. VIF_DETAILS_TAP_MAC_ADDRESS = 'mac_address' # Open vSwitch datapath types. VIF_DETAILS_OVS_DATAPATH_SYSTEM = 'system' VIF_DETAILS_OVS_DATAPATH_NETDEV = 'netdev' # Define supported virtual NIC types. VNIC_TYPE_DIRECT and VNIC_TYPE_MACVTAP # are used for SR-IOV ports VNIC_TYPE_NORMAL = 'normal' VNIC_TYPE_DIRECT = 'direct' VNIC_TYPE_MACVTAP = 'macvtap' VNIC_TYPE_DIRECT_PHYSICAL = 'direct-physical' VNIC_TYPE_BAREMETAL = 'baremetal' VNIC_TYPE_VIRTIO_FORWARDER = 'virtio-forwarder' # Define list of ports which needs pci request. # Note: The macvtap port needs a PCI request as it is a tap interface # with VF as the lower physical interface. # Note: Currently, VNIC_TYPE_VIRTIO_FORWARDER assumes a 1:1 # relationship with a VF. This is expected to change in the future. VNIC_TYPES_SRIOV = (VNIC_TYPE_DIRECT, VNIC_TYPE_MACVTAP, VNIC_TYPE_DIRECT_PHYSICAL, VNIC_TYPE_VIRTIO_FORWARDER) # Define list of ports which are passthrough to the guest # and need a special treatment on snapshot and suspend/resume VNIC_TYPES_DIRECT_PASSTHROUGH = (VNIC_TYPE_DIRECT, VNIC_TYPE_DIRECT_PHYSICAL) # Constants for the 'vif_model' values VIF_MODEL_VIRTIO = 'virtio' VIF_MODEL_NE2K_PCI = 'ne2k_pci' VIF_MODEL_PCNET = 'pcnet' VIF_MODEL_RTL8139 = 'rtl8139' VIF_MODEL_E1000 = 'e1000' VIF_MODEL_E1000E = 'e1000e' VIF_MODEL_NETFRONT = 'netfront' VIF_MODEL_SPAPR_VLAN = 'spapr-vlan' VIF_MODEL_LAN9118 = 'lan9118' VIF_MODEL_SRIOV = 'sriov' VIF_MODEL_VMXNET = 'vmxnet' VIF_MODEL_VMXNET3 = 'vmxnet3' VIF_MODEL_ALL = ( VIF_MODEL_VIRTIO, VIF_MODEL_NE2K_PCI, VIF_MODEL_PCNET, VIF_MODEL_RTL8139, VIF_MODEL_E1000, VIF_MODEL_E1000E, VIF_MODEL_NETFRONT, VIF_MODEL_SPAPR_VLAN, VIF_MODEL_LAN9118, VIF_MODEL_SRIOV, VIF_MODEL_VMXNET, VIF_MODEL_VMXNET3, ) # these types have been leaked to guests in network_data.json LEGACY_EXPOSED_VIF_TYPES = ( VIF_TYPE_BRIDGE, VIF_TYPE_DVS, VIF_TYPE_HW_VEB, VIF_TYPE_HYPERV, VIF_TYPE_OVS, VIF_TYPE_TAP, VIF_TYPE_VHOSTUSER, VIF_TYPE_VIF, ) # Constant for max length of network interface names # eg 'bridge' in the Network class or 'devname' in # the VIF class NIC_NAME_LEN = 14 class Model(dict): """Defines some necessary structures for most of the network models.""" def __repr__(self): return jsonutils.dumps(self) def _set_meta(self, kwargs): # pull meta out of kwargs if it's there self['meta'] = kwargs.pop('meta', {}) # update meta with any additional kwargs that may exist self['meta'].update(kwargs) def get_meta(self, key, default=None): """calls get(key, default) on self['meta'].""" return self['meta'].get(key, default) class IP(Model): """Represents an IP address in Nova.""" def __init__(self, address=None, type=None, **kwargs): super(IP, self).__init__() self['address'] = address self['type'] = type self['version'] = kwargs.pop('version', None) self._set_meta(kwargs) # determine version from address if not passed in if self['address'] and not self['version']: try: self['version'] = netaddr.IPAddress(self['address']).version except netaddr.AddrFormatError: msg = _("Invalid IP format %s") % self['address'] raise exception.InvalidIpAddressError(msg) def __eq__(self, other): keys = ['address', 'type', 'version'] return all(self[k] == other[k] for k in keys) def __ne__(self, other): return not self.__eq__(other) def is_in_subnet(self, subnet): if self['address'] and subnet['cidr']: return (netaddr.IPAddress(self['address']) in netaddr.IPNetwork(subnet['cidr'])) else: return False @classmethod def hydrate(cls, ip): if ip: return cls(**ip) return None class FixedIP(IP): """Represents a Fixed IP address in Nova.""" def __init__(self, floating_ips=None, **kwargs): super(FixedIP, self).__init__(**kwargs) self['floating_ips'] = floating_ips or [] if not self['type']: self['type'] = 'fixed' def add_floating_ip(self, floating_ip): if floating_ip not in self['floating_ips']: self['floating_ips'].append(floating_ip) def floating_ip_addresses(self): return [ip['address'] for ip in self['floating_ips']] @staticmethod def hydrate(fixed_ip): fixed_ip = FixedIP(**fixed_ip) fixed_ip['floating_ips'] = [IP.hydrate(floating_ip) for floating_ip in fixed_ip['floating_ips']] return fixed_ip def __eq__(self, other): keys = ['address', 'type', 'version', 'floating_ips'] return all(self[k] == other[k] for k in keys) def __ne__(self, other): return not self.__eq__(other) class Route(Model): """Represents an IP Route in Nova.""" def __init__(self, cidr=None, gateway=None, interface=None, **kwargs): super(Route, self).__init__() self['cidr'] = cidr self['gateway'] = gateway # FIXME(mriedem): Is this actually used? It's never set. self['interface'] = interface self._set_meta(kwargs) @classmethod def hydrate(cls, route): route = cls(**route) route['gateway'] = IP.hydrate(route['gateway']) return route class Subnet(Model): """Represents a Subnet in Nova.""" def __init__(self, cidr=None, dns=None, gateway=None, ips=None, routes=None, **kwargs): super(Subnet, self).__init__() self['cidr'] = cidr self['dns'] = dns or [] self['gateway'] = gateway self['ips'] = ips or [] self['routes'] = routes or [] self['version'] = kwargs.pop('version', None) self._set_meta(kwargs) if self['cidr'] and not self['version']: self['version'] = netaddr.IPNetwork(self['cidr']).version def __eq__(self, other): keys = ['cidr', 'dns', 'gateway', 'ips', 'routes', 'version'] return all(self[k] == other[k] for k in keys) def __ne__(self, other): return not self.__eq__(other) def add_route(self, new_route): if new_route not in self['routes']: self['routes'].append(new_route) def add_dns(self, dns): if dns not in self['dns']: self['dns'].append(dns) def add_ip(self, ip): if ip not in self['ips']: self['ips'].append(ip) def as_netaddr(self): """Convenient function to get cidr as a netaddr object.""" return netaddr.IPNetwork(self['cidr']) @classmethod def hydrate(cls, subnet): subnet = cls(**subnet) subnet['dns'] = [IP.hydrate(dns) for dns in subnet['dns']] subnet['ips'] = [FixedIP.hydrate(ip) for ip in subnet['ips']] subnet['routes'] = [Route.hydrate(route) for route in subnet['routes']] subnet['gateway'] = IP.hydrate(subnet['gateway']) return subnet class Network(Model): """Represents a Network in Nova.""" def __init__(self, id=None, bridge=None, label=None, subnets=None, **kwargs): super(Network, self).__init__() self['id'] = id self['bridge'] = bridge self['label'] = label self['subnets'] = subnets or [] self._set_meta(kwargs) def add_subnet(self, subnet): if subnet not in self['subnets']: self['subnets'].append(subnet) @classmethod def hydrate(cls, network): if network: network = cls(**network) network['subnets'] = [Subnet.hydrate(subnet) for subnet in network['subnets']] return network def __eq__(self, other): keys = ['id', 'bridge', 'label', 'subnets'] return all(self[k] == other[k] for k in keys) def __ne__(self, other): return not self.__eq__(other) class VIF8021QbgParams(Model): """Represents the parameters for a 802.1qbg VIF.""" def __init__(self, managerid, typeid, typeidversion, instanceid): super(VIF8021QbgParams, self).__init__() self['managerid'] = managerid self['typeid'] = typeid self['typeidversion'] = typeidversion self['instanceid'] = instanceid class VIF8021QbhParams(Model): """Represents the parameters for a 802.1qbh VIF.""" def __init__(self, profileid): super(VIF8021QbhParams, self).__init__() self['profileid'] = profileid class VIF(Model): """Represents a Virtual Interface in Nova.""" def __init__(self, id=None, address=None, network=None, type=None, details=None, devname=None, ovs_interfaceid=None, qbh_params=None, qbg_params=None, active=False, vnic_type=VNIC_TYPE_NORMAL, profile=None, preserve_on_delete=False, **kwargs): super(VIF, self).__init__() self['id'] = id self['address'] = address self['network'] = network or None self['type'] = type self['details'] = details or {} self['devname'] = devname self['ovs_interfaceid'] = ovs_interfaceid self['qbh_params'] = qbh_params self['qbg_params'] = qbg_params self['active'] = active self['vnic_type'] = vnic_type self['profile'] = profile self['preserve_on_delete'] = preserve_on_delete self._set_meta(kwargs) def __eq__(self, other): keys = ['id', 'address', 'network', 'vnic_type', 'type', 'profile', 'details', 'devname', 'ovs_interfaceid', 'qbh_params', 'qbg_params', 'active', 'preserve_on_delete'] return all(self[k] == other[k] for k in keys) def __ne__(self, other): return not self.__eq__(other) def fixed_ips(self): if self['network']: return [fixed_ip for subnet in self['network']['subnets'] for fixed_ip in subnet['ips']] else: return [] def floating_ips(self): return [floating_ip for fixed_ip in self.fixed_ips() for floating_ip in fixed_ip['floating_ips']] def labeled_ips(self): """Returns the list of all IPs The return value looks like this flat structure:: {'network_label': 'my_network', 'network_id': 'n8v29837fn234782f08fjxk3ofhb84', 'ips': [{'address': '123.123.123.123', 'version': 4, 'type: 'fixed', 'meta': {...}}, {'address': '124.124.124.124', 'version': 4, 'type': 'floating', 'meta': {...}}, {'address': 'fe80::4', 'version': 6, 'type': 'fixed', 'meta': {...}}] """ if self['network']: # remove unnecessary fields on fixed_ips ips = [IP(**ip) for ip in self.fixed_ips()] for ip in ips: # remove floating ips from IP, since this is a flat structure # of all IPs del ip['meta']['floating_ips'] # add floating ips to list (if any) ips.extend(self.floating_ips()) return {'network_label': self['network']['label'], 'network_id': self['network']['id'], 'ips': ips} return [] def is_hybrid_plug_enabled(self): return self['details'].get(VIF_DETAILS_OVS_HYBRID_PLUG, False) def is_neutron_filtering_enabled(self): return self['details'].get(VIF_DETAILS_PORT_FILTER, False) def get_physical_network(self): phy_network = self['network']['meta'].get('physical_network') if not phy_network: phy_network = self['details'].get(VIF_DETAILS_PHYSICAL_NETWORK) return phy_network @classmethod def hydrate(cls, vif): vif = cls(**vif) vif['network'] = Network.hydrate(vif['network']) return vif def get_netmask(ip, subnet): """Returns the netmask appropriate for injection into a guest.""" if ip['version'] == 4: return str(subnet.as_netaddr().netmask) return subnet.as_netaddr()._prefixlen class NetworkInfo(list): """Stores and manipulates network information for a Nova instance.""" # NetworkInfo is a list of VIFs def fixed_ips(self): """Returns all fixed_ips without floating_ips attached.""" return [ip for vif in self for ip in vif.fixed_ips()] def floating_ips(self): """Returns all floating_ips.""" return [ip for vif in self for ip in vif.floating_ips()] @classmethod def hydrate(cls, network_info): if isinstance(network_info, six.string_types): network_info = jsonutils.loads(network_info) return cls([VIF.hydrate(vif) for vif in network_info]) def wait(self, do_raise=True): """Wait for asynchronous call to finish.""" # There is no asynchronous call for this class, so this is a no-op # here, but subclasses may override to provide asynchronous # capabilities. Must be defined here in the parent class so that code # which works with both parent and subclass types can reference this # method. pass def json(self): return jsonutils.dumps(self) class NetworkInfoAsyncWrapper(NetworkInfo): """Wrapper around NetworkInfo that allows retrieving NetworkInfo in an async manner. This allows one to start querying for network information before you know you will need it. If you have a long-running operation, this allows the network model retrieval to occur in the background. When you need the data, it will ensure the async operation has completed. As an example: def allocate_net_info(arg1, arg2) return call_neutron_to_allocate(arg1, arg2) network_info = NetworkInfoAsyncWrapper(allocate_net_info, arg1, arg2) [do a long running operation -- real network_info will be retrieved in the background] [do something with network_info] """ def __init__(self, async_method, *args, **kwargs): super(NetworkInfoAsyncWrapper, self).__init__() self._gt = utils.spawn(async_method, *args, **kwargs) methods = ['json', 'fixed_ips', 'floating_ips'] for method in methods: fn = getattr(self, method) wrapper = functools.partial(self._sync_wrapper, fn) functools.update_wrapper(wrapper, fn) setattr(self, method, wrapper) def _sync_wrapper(self, wrapped, *args, **kwargs): """Synchronize the model before running a method.""" self.wait() return wrapped(*args, **kwargs) def __getitem__(self, *args, **kwargs): fn = super(NetworkInfoAsyncWrapper, self).__getitem__ return self._sync_wrapper(fn, *args, **kwargs) def __iter__(self, *args, **kwargs): fn = super(NetworkInfoAsyncWrapper, self).__iter__ return self._sync_wrapper(fn, *args, **kwargs) def __len__(self, *args, **kwargs): fn = super(NetworkInfoAsyncWrapper, self).__len__ return self._sync_wrapper(fn, *args, **kwargs) def __str__(self, *args, **kwargs): fn = super(NetworkInfoAsyncWrapper, self).__str__ return self._sync_wrapper(fn, *args, **kwargs) def __repr__(self, *args, **kwargs): fn = super(NetworkInfoAsyncWrapper, self).__repr__ return self._sync_wrapper(fn, *args, **kwargs) def wait(self, do_raise=True): """Wait for asynchronous call to finish.""" if self._gt is not None: try: # NOTE(comstud): This looks funky, but this object is # subclassed from list. In other words, 'self' is really # just a list with a bunch of extra methods. So this # line just replaces the current list (which should be # empty) with the result. self[:] = self._gt.wait() except Exception: if do_raise: raise finally: self._gt = None

Alex-Sizov/nova

nova/network/model.py

Python

import bs4 import requests import operator res = requests.get("http://dollarrupee.in/") soup = bs4.BeautifulSoup(res.text, "lxml") rate = soup.select(".item-page p strong") rupee = float(rate[0].text) print("Today's rate: $1 = ₹{}".format(rupee)) choice = int(input( "\nWhat do you want to convert?\n1. Dollars to Rupees\n2. Rupees to Dollars\n")) if(choice == 1): amount = int(input("Enter amount in USD:\n")) print("Today's conversion: ${} = ₹{} (approx.)".format( amount, round(amount * rupee))) elif(choice == 2): amount = int(input("Enter amount in INR:\n")) print("Today's conversion: ₹{} = ${} (approx.)".format( amount, round(amount / rupee))) else: print("Bad choice!")

urmilshroff/netscraper

rupee.py

Python

# -*- coding: utf-8 -*- """ """ from ill import api tn = api.request_document('8236596') print(tn) #api.download_papers() #NOT YET IMPLEMENTED #Not downloaded #api.delete_online_papers(api.downloaded_paper_ids) #main.fill_form('610035') print('Done with the request')

ScholarTools/ill_filler

ill_filler_quick_testing.py

Python

from __future__ import unicode_literals, division, absolute_import from builtins import * # noqa pylint: disable=unused-import, redefined-builtin import pytest from flexget.event import fire_event from flexget.manager import Session from flexget.plugins.modify.variables import Variables @pytest.mark.usefixtures('tmpdir') class TestVariablesFromFile(object): config = """ variables: __tmp__/variables.yml tasks: test_variable_from_file: mock: - { title: 'test', location: 'http://mock'} if: - '{? test_variable ?}': accept test_variables_alongside_jinja: mock: - title: title 1 entry_var: foo set: a_field: first {?bar_var?} then {{entry_var|default("shouldn't happen")}} {{fake_field|default("end")}} accept_all: yes """ @pytest.mark.filecopy('variables.yml', '__tmp__/variables.yml') def test_variable_from_file(self, execute_task, manager): task = execute_task('test_variable_from_file') assert len(task.accepted) == 1 @pytest.mark.filecopy('variables.yml', '__tmp__/variables.yml') def test_variables_alongside_jinja(self, execute_task): task = execute_task('test_variables_alongside_jinja') assert len(task.accepted) == 1 assert task.accepted[0]['a_field'] == 'first bar then foo end' class TestVariablesFromConfig(): config = """ variables: mock_entry_list: - title: a - title: b integer: 2 tasks: test_int_var: mock: - title: a - title: b - title: c accept_all: yes limit_new: "{? integer ?}" test_var_mock: mock: "{? mock_entry_list ?}" """ def test_complex_var(self, execute_task): task = execute_task('test_var_mock') assert len(task.all_entries) == 2 assert task.all_entries[1]['title'] == 'b' def test_int_var(self, execute_task): task = execute_task('test_int_var') assert len(task.all_entries) == 3 assert len(task.accepted) == 2 class TestVariablesFromDB(object): config = """ variables: yes tasks: test_variable_from_db: mock: - { title: 'test', location: 'http://mock'} if: - '{? test_variable_db ?}': accept """ def test_variable_from_db(self, execute_task, manager): with Session() as session: s = Variables(variables={'test_variable_db': True}) session.add(s) fire_event('manager.before_config_validate', manager.config, manager) task = execute_task('test_variable_from_db') assert len(task.accepted) == 1

Daeymien/Flexget

flexget/tests/test_variables.py

Python

# -*- coding: utf-8 -*- """ Created on Mon Jun 11 15:00:26 2018 @author: Alex # reads and parses local html """ #%% Import libraries from bs4 import BeautifulSoup import pandas as pd import numpy as np import codecs import os import re import pickle import nltk from nltk.stem.wordnet import WordNetLemmatizer import spacy from gensim import models import pickle from sklearn.feature_extraction import stop_words #%% Read in saved html # read in saved html back in def read_local_html(blog_folder,blog_num): # make filename filename = blog_folder + 'kd_blog' + str(blog_num).zfill(4) + '.html' # read in file f = codecs.open(filename, 'r', 'utf-8') # parse file soup = BeautifulSoup(f.read(), 'html.parser') return soup #%% def get_article_str(soup): # Titles title = soup.title.text # Tag data tag = soup.find_all('div', class_ = 'tag-data') tags = tag[0].text tags = tags.replace('Tags: ','') # Paragraphs paras = soup.find_all('p') # The first paragraph always contains a description of the article description = paras[0].text # Get main text main_text = "" # remove second paragraph if it just contains author name if "By " not in paras[1].text: main_text = paras[1].text for i in range(2,len(paras)): # These if statements remove later paragraphs if they don't contain the main text of the article if i > len(paras)-5 and "Bio" in paras[i].text: continue elif i > len(paras)-5 and "Original" in paras[i].text: continue elif i > len(paras)-5 and "Related" in paras[i].text: continue elif i > len(paras)-5 and "disqus" in paras[i].text: continue elif i > len(paras)-5 and "Pages" in paras[i].text: continue else: main_text = main_text + ' ' + paras[i].text # Create an article string article_str = title + '. ' + tags + '. ' + description + ' ' + main_text return article_str #%% def clean_article(article_str): # lowercase article_str = article_str.lower() #Remove any non alphanumeric characters that are no end-of-sentence punctuation article_str = re.sub('[^a-z\s\.\?\!]+','', article_str) # Replace ? with . article_str = re.sub('\?','.', article_str) # Replace ! with . article_str = re.sub('\!','.', article_str) # Replace more than one whitespace with one whitespace article_str = re.sub('\s+',' ', article_str) # Remove trailing whitespace article_str = re.sub("\s+(?!\S)", "",article_str) # Remove preceding whitespace article_str = re.sub("(?<!\S)\s+", "",article_str) # Replace funny words from lemmatization article_str = re.sub("datum","data",article_str) article_str = re.sub("learn\s","learning",article_str) article_str = re.sub("miss","missing",article_str) return article_str #%% Split each blog post into sentences def get_sentences(article_str): # lowercase article_str = article_str.lower() #Remove any non alphanumeric characters article_str = re.sub('[^a-z\s\.]+','', article_str) article_str = re.sub('\s+',' ', article_str) # Split doc into sentences sent_text = nltk.sent_tokenize(article_str) # Split sentences into words tokenized_sentences = [] for sentence in sent_text: # remove periods sentence = re.sub('\.','', sentence) # tokenize tokenized_sentences.append(nltk.word_tokenize(sentence)) return tokenized_sentences #%% def lemmatize(cleaned_article): nlp = spacy.load('en', disable=['parser', 'ner']) doc = nlp(article_str) lemma_article = " ".join([token.lemma_ if token.lemma_ not in ['-PRON-'] else '' for token in doc]) cleaned_lemma = clean_article(lemma_article) return cleaned_lemma #%% Extract phrases from all the documents def phrase_extractor(doc_sents): ''' doc_sents is a list where each element is a list with elements corresponding to individual sentences of a document ''' # rename some functions Phraser = models.phrases.Phraser Phrases = models.phrases.Phrases # Generate list of sentences sentence_stream = sum(doc_sents, []) # Generate bigrams common_terms = ["of", "with", "without", "and", "or", "the", "a", "as"] phrases = Phrases(sentence_stream, common_terms=common_terms) bigram = Phraser(phrases) # Generate trigrams trigram = Phrases(bigram[sentence_stream]) # Generate output output_strs = [] for idx in range(0,len(doc_sents)): doc = doc_sents[idx] output_doc = list(trigram[doc]) output_str = sum(output_doc,[]) output_strs.append(' '.join(output_str)) return output_strs #%% Loop through all the blog posts blog_folder = 'C:\\Users\\Alex\\Documents\\GitHub\\insight-articles-project\\data\\raw\\kd_blogs\\' os.chdir(blog_folder) num_blog_posts = len(os.listdir(blog_folder)) documents = [] num_skipped = 0 blogs_included = [] doc_sents = [] for blog_num in range(1,num_blog_posts+1): try: # Parse html soup = read_local_html(blog_folder,blog_num) article_str = get_article_str(soup) cleaned_article = clean_article(article_str) lemma_article = lemmatize(cleaned_article) # Extract sentences for phrase extraction tokenized_sentences = get_sentences(lemma_article) doc_sents.append(tokenized_sentences) # Meta data blogs_included.append(blog_num) except: print('Blog ' + str(blog_num) + ' skipped') num_skipped += 1 documents = phrase_extractor(doc_sents) #documents.append(cleaned_article) # Save documents processed_data_folder = 'C:\\Users\\Alex\\Documents\\GitHub\\insight-articles-project\\data\\processed\\' filename = processed_data_folder + 'kd_docs' with open(filename, 'wb') as fp: pickle.dump((documents,blogs_included), fp) ''' filename = processed_data_folder + 'doc_sents' with open(filename, 'wb') as fp: pickle.dump(doc_sents, fp) '''

avbatchelor/insight-articles-project

src/scraping/read_and_parse.py

Python

""" .. module: lemur.domains.models :platform: Unix :copyright: (c) 2018 by Netflix Inc., see AUTHORS for more :license: Apache, see LICENSE for more details. .. moduleauthor:: Kevin Glisson <kglisson@netflix.com> """ from sqlalchemy import Column, Integer, String, Boolean, Index from lemur.database import db class Domain(db.Model): __tablename__ = "domains" __table_args__ = ( Index( "ix_domains_name_gin", "name", postgresql_ops={"name": "gin_trgm_ops"}, postgresql_using="gin", ), ) id = Column(Integer, primary_key=True) name = Column(String(256), index=True) sensitive = Column(Boolean, default=False) def __repr__(self): return "Domain(name={name})".format(name=self.name)

DZ521111/lemur

lemur/domains/models.py

Python

import cv2 import os import numpy as np from image_processor import process_image from processor_properties import ProcessorProperties import time class Camera: def __init__(self): self.cap = cv2.VideoCapture(0) def snapshot(self): ret, frame = self.cap.read() return frame if __name__ == '__main__': camera = Camera() while True: frame = camera.snapshot() props = ProcessorProperties() # props.brightness_factor.update(1.5) # props.contrast_factor.update(1.5) # props.scaling_factor.update(3.0) frame = process_image(frame, props) cv2.imshow('image', frame) k = cv2.waitKey(1) & 0xFF if k == ord('q'): break elif k == ord('s'): timestr = time.strftime("%Y%m%d-%H%M%S") image_path = os.path.join("testimgs", "%s.jpg" % timestr) cv2.imwrite(image_path, frame) print "save %s" % image_path

korrawat/athack-susan

camera_skeleton.py

Python

# encoding: utf-8 """Placeholder-related objects. Specific to shapes having a `p:ph` element. A placeholder has distinct behaviors depending on whether it appears on a slide, layout, or master. Hence there is a non-trivial class inheritance structure. """ from pptx.enum.shapes import MSO_SHAPE_TYPE, PP_PLACEHOLDER from pptx.oxml.shapes.graphfrm import CT_GraphicalObjectFrame from pptx.oxml.shapes.picture import CT_Picture from pptx.shapes.autoshape import Shape from pptx.shapes.graphfrm import GraphicFrame from pptx.shapes.picture import Picture from pptx.util import Emu class _InheritsDimensions(object): """ Mixin class that provides inherited dimension behavior. Specifically, left, top, width, and height report the value from the layout placeholder where they would have otherwise reported |None|. This behavior is distinctive to placeholders. :meth:`_base_placeholder` must be overridden by all subclasses to provide lookup of the appropriate base placeholder to inherit from. """ @property def height(self): """ The effective height of this placeholder shape; its directly-applied height if it has one, otherwise the height of its parent layout placeholder. """ return self._effective_value("height") @height.setter def height(self, value): self._element.cy = value @property def left(self): """ The effective left of this placeholder shape; its directly-applied left if it has one, otherwise the left of its parent layout placeholder. """ return self._effective_value("left") @left.setter def left(self, value): self._element.x = value @property def shape_type(self): """ Member of :ref:`MsoShapeType` specifying the type of this shape. Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case. Read-only. """ return MSO_SHAPE_TYPE.PLACEHOLDER @property def top(self): """ The effective top of this placeholder shape; its directly-applied top if it has one, otherwise the top of its parent layout placeholder. """ return self._effective_value("top") @top.setter def top(self, value): self._element.y = value @property def width(self): """ The effective width of this placeholder shape; its directly-applied width if it has one, otherwise the width of its parent layout placeholder. """ return self._effective_value("width") @width.setter def width(self, value): self._element.cx = value @property def _base_placeholder(self): """ Return the layout or master placeholder shape this placeholder inherits from. Not to be confused with an instance of |BasePlaceholder| (necessarily). """ raise NotImplementedError("Must be implemented by all subclasses.") def _effective_value(self, attr_name): """ The effective value of *attr_name* on this placeholder shape; its directly-applied value if it has one, otherwise the value on the layout placeholder it inherits from. """ directly_applied_value = getattr(super(_InheritsDimensions, self), attr_name) if directly_applied_value is not None: return directly_applied_value return self._inherited_value(attr_name) def _inherited_value(self, attr_name): """ Return the attribute value, e.g. 'width' of the base placeholder this placeholder inherits from. """ base_placeholder = self._base_placeholder if base_placeholder is None: return None inherited_value = getattr(base_placeholder, attr_name) return inherited_value class _BaseSlidePlaceholder(_InheritsDimensions, Shape): """Base class for placeholders on slides. Provides common behaviors such as inherited dimensions. """ @property def is_placeholder(self): """ Boolean indicating whether this shape is a placeholder. Unconditionally |True| in this case. """ return True @property def shape_type(self): """ Member of :ref:`MsoShapeType` specifying the type of this shape. Unconditionally ``MSO_SHAPE_TYPE.PLACEHOLDER`` in this case. Read-only. """ return MSO_SHAPE_TYPE.PLACEHOLDER @property def _base_placeholder(self): """ Return the layout placeholder this slide placeholder inherits from. Not to be confused with an instance of |BasePlaceholder| (necessarily). """ layout, idx = self.part.slide_layout, self._element.ph_idx return layout.placeholders.get(idx=idx) def _replace_placeholder_with(self, element): """ Substitute *element* for this placeholder element in the shapetree. This placeholder's `._element` attribute is set to |None| and its original element is free for garbage collection. Any attribute access (including a method call) on this placeholder after this call raises |AttributeError|. """ element._nvXxPr.nvPr._insert_ph(self._element.ph) self._element.addprevious(element) self._element.getparent().remove(self._element) self._element = None class BasePlaceholder(Shape): """ NOTE: This class is deprecated and will be removed from a future release along with the properties *idx*, *orient*, *ph_type*, and *sz*. The *idx* property will be available via the .placeholder_format property. The others will be accessed directly from the oxml layer as they are only used for internal purposes. Base class for placeholder subclasses that differentiate the varying behaviors of placeholders on a master, layout, and slide. """ @property def idx(self): """ Integer placeholder 'idx' attribute, e.g. 0 """ return self._sp.ph_idx @property def orient(self): """ Placeholder orientation, e.g. ST_Direction.HORZ """ return self._sp.ph_orient @property def ph_type(self): """ Placeholder type, e.g. PP_PLACEHOLDER.CENTER_TITLE """ return self._sp.ph_type @property def sz(self): """ Placeholder 'sz' attribute, e.g. ST_PlaceholderSize.FULL """ return self._sp.ph_sz class LayoutPlaceholder(_InheritsDimensions, Shape): """ Placeholder shape on a slide layout, providing differentiated behavior for slide layout placeholders, in particular, inheriting shape properties from the master placeholder having the same type, when a matching one exists. """ @property def _base_placeholder(self): """ Return the master placeholder this layout placeholder inherits from. """ base_ph_type = { PP_PLACEHOLDER.BODY: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.BITMAP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.CENTER_TITLE: PP_PLACEHOLDER.TITLE, PP_PLACEHOLDER.ORG_CHART: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.DATE: PP_PLACEHOLDER.DATE, PP_PLACEHOLDER.FOOTER: PP_PLACEHOLDER.FOOTER, PP_PLACEHOLDER.MEDIA_CLIP: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.OBJECT: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.PICTURE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.SLIDE_NUMBER: PP_PLACEHOLDER.SLIDE_NUMBER, PP_PLACEHOLDER.SUBTITLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TABLE: PP_PLACEHOLDER.BODY, PP_PLACEHOLDER.TITLE: PP_PLACEHOLDER.TITLE, }[self._element.ph_type] slide_master = self.part.slide_master return slide_master.placeholders.get(base_ph_type, None) class MasterPlaceholder(BasePlaceholder): """ Placeholder shape on a slide master. """ class NotesSlidePlaceholder(_InheritsDimensions, Shape): """ Placeholder shape on a notes slide. Inherits shape properties from the placeholder on the notes master that has the same type (e.g. 'body'). """ @property def _base_placeholder(self): """ Return the notes master placeholder this notes slide placeholder inherits from, or |None| if no placeholder of the matching type is present. """ notes_master = self.part.notes_master ph_type = self.element.ph_type return notes_master.placeholders.get(ph_type=ph_type) class SlidePlaceholder(_BaseSlidePlaceholder): """ Placeholder shape on a slide. Inherits shape properties from its corresponding slide layout placeholder. """ class ChartPlaceholder(_BaseSlidePlaceholder): """Placeholder shape that can only accept a chart.""" def insert_chart(self, chart_type, chart_data): """ Return a |PlaceholderGraphicFrame| object containing a new chart of *chart_type* depicting *chart_data* and having the same position and size as this placeholder. *chart_type* is one of the :ref:`XlChartType` enumeration values. *chart_data* is a |ChartData| object populated with the categories and series values for the chart. Note that the new |Chart| object is not returned directly. The chart object may be accessed using the :attr:`~.PlaceholderGraphicFrame.chart` property of the returned |PlaceholderGraphicFrame| object. """ rId = self.part.add_chart_part(chart_type, chart_data) graphicFrame = self._new_chart_graphicFrame( rId, self.left, self.top, self.width, self.height ) self._replace_placeholder_with(graphicFrame) return PlaceholderGraphicFrame(graphicFrame, self._parent) def _new_chart_graphicFrame(self, rId, x, y, cx, cy): """ Return a newly created `p:graphicFrame` element having the specified position and size and containing the chart identified by *rId*. """ id_, name = self.shape_id, self.name return CT_GraphicalObjectFrame.new_chart_graphicFrame( id_, name, rId, x, y, cx, cy ) class PicturePlaceholder(_BaseSlidePlaceholder): """Placeholder shape that can only accept a picture.""" def insert_picture(self, image_file): """Return a |PlaceholderPicture| object depicting the image in `image_file`. `image_file` may be either a path (string) or a file-like object. The image is cropped to fill the entire space of the placeholder. A |PlaceholderPicture| object has all the properties and methods of a |Picture| shape except that the value of its :attr:`~._BaseSlidePlaceholder.shape_type` property is `MSO_SHAPE_TYPE.PLACEHOLDER` instead of `MSO_SHAPE_TYPE.PICTURE`. """ pic = self._new_placeholder_pic(image_file) self._replace_placeholder_with(pic) return PlaceholderPicture(pic, self._parent) def _new_placeholder_pic(self, image_file): """ Return a new `p:pic` element depicting the image in *image_file*, suitable for use as a placeholder. In particular this means not having an `a:xfrm` element, allowing its extents to be inherited from its layout placeholder. """ rId, desc, image_size = self._get_or_add_image(image_file) shape_id, name = self.shape_id, self.name pic = CT_Picture.new_ph_pic(shape_id, name, desc, rId) pic.crop_to_fit(image_size, (self.width, self.height)) return pic def _get_or_add_image(self, image_file): """ Return an (rId, description, image_size) 3-tuple identifying the related image part containing *image_file* and describing the image. """ image_part, rId = self.part.get_or_add_image_part(image_file) desc, image_size = image_part.desc, image_part._px_size return rId, desc, image_size class PlaceholderGraphicFrame(GraphicFrame): """ Placeholder shape populated with a table, chart, or smart art. """ @property def is_placeholder(self): """ Boolean indicating whether this shape is a placeholder. Unconditionally |True| in this case. """ return True class PlaceholderPicture(_InheritsDimensions, Picture): """ Placeholder shape populated with a picture. """ @property def _base_placeholder(self): """ Return the layout placeholder this picture placeholder inherits from. """ layout, idx = self.part.slide_layout, self._element.ph_idx return layout.placeholders.get(idx=idx) class TablePlaceholder(_BaseSlidePlaceholder): """Placeholder shape that can only accept a table.""" def insert_table(self, rows, cols): """Return |PlaceholderGraphicFrame| object containing a `rows` by `cols` table. The position and width of the table are those of the placeholder and its height is proportional to the number of rows. A |PlaceholderGraphicFrame| object has all the properties and methods of a |GraphicFrame| shape except that the value of its :attr:`~._BaseSlidePlaceholder.shape_type` property is unconditionally `MSO_SHAPE_TYPE.PLACEHOLDER`. Note that the return value is not the new table but rather *contains* the new table. The table can be accessed using the :attr:`~.PlaceholderGraphicFrame.table` property of the returned |PlaceholderGraphicFrame| object. """ graphicFrame = self._new_placeholder_table(rows, cols) self._replace_placeholder_with(graphicFrame) return PlaceholderGraphicFrame(graphicFrame, self._parent) def _new_placeholder_table(self, rows, cols): """ Return a newly added `p:graphicFrame` element containing an empty table with *rows* rows and *cols* columns, positioned at the location of this placeholder and having its same width. The table's height is determined by the number of rows. """ shape_id, name, height = self.shape_id, self.name, Emu(rows * 370840) return CT_GraphicalObjectFrame.new_table_graphicFrame( shape_id, name, rows, cols, self.left, self.top, self.width, height )

Adriyst/python-pptx

pptx/shapes/placeholder.py

Python

# ######################################################################### # Copyright (c) , UChicago Argonne, LLC. All rights reserved. # # # # See LICENSE file. # # ######################################################################### """ This tools auto eliminate aliens from CDI experiment data. It is configuration driven. """ import numpy as np import sys import os from sklearn.cluster import DBSCAN import tifffile as tif from time import time import cohere.src_py.utilities.utils as ut __author__ = "Kenly Pelzer, Ross Harder" __copyright__ = "Copyright (c) 2021, UChicago Argonne, LLC." __docformat__ = 'restructuredtext en' __all__ = ['get_asymmetry', 'analyze_clusters', 'crop_center', 'save_arr', 'save_arrays', 'auto_alien1', 'filter_aliens', 'remove_blocks', 'remove_aliens'] def get_asymmetry(arr): """ Returns asymmetry of an array. Parameters ---------- arr : ndarray an array to find asymmetry Returns ------- ndarray an array capturing asymmetry of original array """ arr_rev = arr[::-1, ::-1, ::-1] denom = (arr + arr_rev) / 2.0 denom_nz = np.where(denom == 0, 1.0, denom) asym = np.where(denom > 0.0, abs(arr - arr_rev) / denom_nz, 0.0) # asym only assigned to non-zero intensity points in the passed array return np.where(arr > 0, asym, 0) # add output of absolute cluster size. def analyze_clusters(arr, labels, nz): """ Analyzes clusters and returns characteristics in arrays. Parameters ---------- arr : ndarray the analyzed array labels: arr cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1. nz : tuple tuple of arrays, each array containing indices of elements in arr that are non-zero along one axis. Returns ------- tuple tuple containing the following arrays: nlabels # number of labels, i.e clusters labels_arr # array with label for each non zero point rel_cluster_size # array with cluster size divided by max cluster size for each # non zero point cluster_avg # array with cluster average for each non zero point noise_arr # array with points that are non zero but not in cluster no_noise # array with noise poits set to 0 label_counts # tuple of two arrays: First is label number, second is number of # occurances of that label (size of cluster) cluster_avg_asym # array with average asymmetry of a points in cluster asymmetry # array of asymmetry with regard to entire array cluster_size # array with cluster size for each non zero point """ labels_arr = np.zeros_like(arr) noise_arr = np.zeros_like(arr) cluster_size = np.zeros_like(arr) cluster_avg = np.zeros_like(arr).astype(np.float32) cluster_avg_asym = np.zeros_like(arr).astype(np.float32) asymmetry = get_asymmetry(arr) # label_counts is tuple of two arrays. First is label number, second is number of occurances of that label (size of cluster). label_counts = np.unique(labels, return_counts=True) # nz and labels are the same length. so the indicies given by nz will be set # to their corresponding cluster number (includes noise pts). labels_arr[nz] = labels # this selects the nz indicies where labels=-1 (noise) noise_pts = tuple([nz[n][labels == -1] for n in range(3)]) no_noise = arr # move the points labeled noise into their own array # remove the noise out of arr (no_noise is copy of arr) noise_arr[noise_pts] = arr[noise_pts] no_noise[noise_pts] = 0 nlabels = len(label_counts[0]) # print("processing labels") # loop over the labels (clusters). label_counts[0] is the unique labels for n in range(1, nlabels): # print(" %i %i "%(label_counts[0][n],label_counts[1][n]), end='\r') # the nth label from the first array of the label_counts tuple n_lab = label_counts[0][n] # the indicies of the points belonging to label n cluspts = tuple([nz[d][labels == n_lab] for d in range(3)]) # the second array of the label_counts tuple is the number of points # with that label. So put those into an array. cluster_size[cluspts] = label_counts[1][n] # compute the average intensity of each cluster and write into an array. cluster_avg[cluspts] = np.sum(arr[cluspts]) / cluspts[0].size # compute average asym of each cluster and store in array. cluster_avg_asym[cluspts] = np.sum(asymmetry[cluspts]) / cluspts[0].size # print(" %i %i %f %f "%(label_counts[0][n],label_counts[1][n],np.sum(asymmetry[cluspts]),cluspts[0].size), end='\n') # print("largest clus size", cluster_size.max()) # compute relative cluster sizes to largest (main) cluster. rel_cluster_size = cluster_size / cluster_size.max() # return all of these arrays return ( nlabels, labels_arr, rel_cluster_size, cluster_avg, noise_arr, no_noise, label_counts, cluster_avg_asym, asymmetry, cluster_size) def crop_center(arr): """ Finds max element in array and crops the array to be symetrical with regard to this point in each direction. Parameters ---------- arr : ndarray an array Returns ------- centered : ndarray an array symetrical in all dimensions around the max element of input array """ shape = arr.shape # This tells us the point of highest intensity, which we will use as the center for inversion operations center = np.unravel_index(np.argmax(arr, axis=None), shape) # clip the largest possible cuboid putting the point of highest intensity at the center principium = [] finis = [] for i in range(len(shape)): half_shape = min(center[i], shape[i] - center[i] - 1) principium.append(center[i] - half_shape) finis.append(center[i] + half_shape + 1) centered = arr[principium[0]:finis[0], principium[1]:finis[1], principium[2]:finis[2]] return centered def save_arr(arr, dir, fname): """ Saves an array in 'tif' format file. Parameters ---------- arr : ndarray an array to save dir : str directory to save the file to fname : str file name Returns ------- nothing """ if dir is not None: full_name = os.path.join(dir, fname) else: full_name = fname # save in the current dir tif.imsave(full_name, arr.transpose().astype(np.float32)) def save_arrays(arrs, iter, thresh, eps, dir): """ Saves multiple arrays in 'tif' format files. Determines file name from given parameters: iteration, threshold, and eps. Parameters ---------- arr : tuple a tuple of arrays to save iter, thresh, eps : str, str, str parameters: iteration, threshold, and eps, to deliver file name from dir : str directory to save the file to Returns ------- nothing """ save_arr(arrs[1], dir, "db%d_%3.2f_labels_arr%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[2], dir, "db%d_%3.2f_rel_clustersizes%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[3], dir, "db%d_%3.2f_clusteravg%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[4], dir, "db%d_%3.2f_noise%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[5], dir, "db%d_%3.2f_no_noise%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[7], dir, "db%d_%3.2f_clusteravgasym%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[8], dir, "db%d_%3.2f_asym%3.2f.tif" % (iter, thresh, eps)) save_arr(arrs[9], dir, "db%d_%3.2f_abs_clustersizes%3.2f.tif" % (iter, thresh, eps)) def auto_alien1(data, config, data_dir=None): """ Removes aliens from experimental CDI data using iterative algorithm and returns the result. The algorithm follows the steps: 1. Initialization: - initialize variables with the configuration parameters - crop the data array around maximum element to it's biggest size - sets points below threshold value to 0 - finds non-zero elements of the data array and keeps them as tuples of indices 2. Iteration loop, runs until number of clasters remains unchanged - runs DBSCAN algorithm on the non-zero and returns clasters labels - analyzes the results to find relative clusters sizes, and clusters average asymmetry, and other characteristics - removes alien clusters, i.e. the ones with relative cluster size below configured size threshold and with average asymmetry over configured asymmetry threshold - go back to the loop using the non-zero elements of alien removed array to the DBSCAN 3. If configured, add final step to apply gaussian convolusion to the result and use it as a filter with configured sigma as threshold Parameters ---------- data : ndarray an array with experiment data config : Object configuration object providing access to configuration parameters data_dir : str a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured Returns ------- cuboid : ndarray data array with removed aliens """ try: size_threshold = config.AA1_size_threshold except AttributeError: size_threshold = 0.01 except Exception as e: print ('error parsing AA1_size_threshold ', str(e)) try: asym_threshold = config.AA1_asym_threshold except AttributeError: asym_threshold = 1.75 except Exception as e: print ('error parsing AA1_asym_threshold ', str(e)) try: min_pts = config.AA1_min_pts except AttributeError: min_pts = 5 except Exception as e: print ('error parsing AA1_min_pts ', str(e)) try: eps = config.AA1_eps except AttributeError: eps = 1.1 except Exception as e: print ('error parsing AA1_eps ', str(e)) try: threshold = config.AA1_amp_threshold except AttributeError: print ('AA1_amp_threshold parameter not configured, not removing aliens') return data except Exception as e: print ('error parsing AA1_amp_threshold ', str(e)) try: save_arrs = config.AA1_save_arrs if save_arrs: save_dir = os.path.join(data_dir, 'alien_analysis') if not os.path.exists(save_dir): os.makedirs(save_dir) except AttributeError: save_arrs = False except Exception as e: print ('error parsing save_arrs ', str(e)) try: expandcleanedsig = config.AA1_expandcleanedsigma except AttributeError: expandcleanedsig = 0.0 except Exception as e: print ('error parsing expandcleanedsig ', str(e)) cuboid = crop_center(data) cuboid = np.where(cuboid >= threshold, cuboid, 0) if (save_arrs): save_arr(cuboid, save_dir, "db%3.2f_cuboid%3.2f.tif" % (threshold, eps)) save_arr(cuboid[::-1, ::-1, ::-1], save_dir, "db%3.2f_cuboidrev%3.2f.tif" % (threshold, eps)) # the non_zero is a tuple of arrays, each array containing indices of elements that are non-zero along one axis. non_zero = cuboid.nonzero() # https://scikit-learn.org/stable/modules/clustering.html#dbscan # labels is same size as input arr with a cluster label per point iter = 0 nclusters = 0 finished = False while ( not finished ): non_zero = cuboid.nonzero() # print("running db", iter) labels = DBSCAN(eps=eps, metric='euclidean', min_samples=min_pts, n_jobs=-1).fit_predict( np.array(non_zero).transpose().astype(np.float32)) # print("running analyze_clusters", iter) arrs = analyze_clusters(cuboid, labels, non_zero) if (save_arrs): save_arrays(arrs, iter, threshold, eps, save_dir) if nclusters == arrs[0]: finished = True nclusters = arrs[0] if iter == 0: # keep values for all iterations rel_cluster_size = arrs[2] cluster_avg_asym = arrs[7] # print("cleaning cuboid", iter) cuboid = np.where(np.logical_and(rel_cluster_size < size_threshold, cluster_avg_asym > asym_threshold), 0.0, cuboid) # print("iter", iter, nclusters) iter += 1 if (expandcleanedsig > 0): cuboid = np.where(cuboid > 0, 1.0, 0.0) sig = [expandcleanedsig, expandcleanedsig, 1.0] cuboid = ut.gauss_conv_fft(cuboid, sig) no_thresh_cuboid = crop_center(data) cuboid = np.where(cuboid > 0.1, no_thresh_cuboid, 0.0) return cuboid def remove_blocks(data, config_map): """ Sets to zero given alien blocks in the data array. Parameters ---------- data : ndarray an array with experiment data config : Object configuration object providing access to configuration parameters Returns ------- data : ndarray data array with zeroed out aliens """ try: aliens = config_map.aliens for alien in aliens: # The ImageJ swaps the x and y axis, so the aliens coordinates needs to be swapped, since ImageJ is used # to find aliens data[alien[0]:alien[3], alien[1]:alien[4], alien[2]:alien[5]] = 0 except AttributeError: print ('aliens parameter not configured') except Exception as e: print ('did not remove aliens, error in aliens removal ', str(e)) return data def filter_aliens(data, config_map): """ Sets to zero points in the data array defined by a file. Parameters ---------- data : ndarray an array with experiment data config : Object configuration object providing access to configuration parameters Returns ------- data : ndarray data array with zeroed out aliens """ try: alien_file = config_map.alien_file if os.path.isfile(alien_file): mask = np.load(alien_file) for i in range(len(mask.shape)): if mask.shape[i] != data.shape[i]: print ('exiting, mask must be of the same shape as data:', data.shape) return data = np.where((mask==1), data, 0.0) except AttributeError: print ('alien_file parameter not configured') except Exception as e: print ('did not remove aliens, error in aliens removal ', str(e)) return data def remove_aliens(data, config_map, data_dir=None): """ Finds which algorithm is cofigured to remove the aliens and applies it to clean the data. Parameters ---------- data : ndarray an array with experiment data config : Object configuration object providing access to configuration parameters data_dir : str a directory where 'alien_analysis' subdirectory will be created to save results of analysis if configured Returns ------- data : ndarray data array without aliens """ try: algorithm = config_map.alien_alg if algorithm == 'block_aliens': data = remove_blocks(data, config_map) elif algorithm == 'alien_file': data = filter_aliens(data, config_map) elif algorithm == 'AutoAlien1': data = auto_alien1(data, config_map, data_dir) elif algorithm != 'none': print('not supported alien removal algorithm', algorithm) except AttributeError: pass except Exception as e: print ('did not remove aliens, error in aliens removal, error: ', str(e)) return data ## https://stackoverflow.com/questions/51503672/decorator-for-timeit-timeit-method/51503837#51503837 #from functools import wraps #from time import time # #def measure(func): # @wraps(func) # def _time_it(*args, **kwargs): # start = int(round(time() * 1000)) # try: # return func(*args, **kwargs) # finally: # end_ = int(round(time() * 1000)) - start # print(f"Total execution time: {end_ if end_ > 0 else 0} ms") #

AdvancedPhotonSource/cdisupp

scripts/alien_tools.py

Python

#!/usr/bin/env python2 import rospy from gnss_status_viewer import Status from nmea_msgs.msg import Sentence import sys import copy # Previous and current Status prev = None curr = None def print_current_status(status): """ Prints the current status :param status: :return: """ print(status) # Move to the beginning of the previous line for i in range(str(status).count('\n') + 1): sys.stdout.write('\033[F') def nmea_cb(msg): global prev global curr if prev is None: prev = Status(msg.sentence) return curr = Status(msg.sentence) if not curr.is_gga: return if prev != curr: status_change = Status.get_status_change(prev, curr) [ rospy.loginfo(s) for s in status_change ] n = max(map(lambda line: len(line), status_change)) print(' ' * n) print_current_status(curr) prev = copy.deepcopy(curr) rospy.init_node('gnss_status_viewer_node') rospy.Subscriber('nmea_sentence', Sentence, nmea_cb) rospy.spin()

naoki-mizuno/gnss_status_viewer

nodes/gnss_status_viewer_node.py

Python

''' Generate uv position map of 300W_LP. ''' import os, sys import numpy as np import scipy.io as sio import random as ran from skimage.transform import SimilarityTransform from skimage import io, util import skimage.transform from time import time import cv2 import matplotlib.pyplot as plt sys.path.append('..') import face3d from face3d import mesh from face3d.morphable_model import MorphabelModel def process_uv(uv_coords, uv_h = 256, uv_w = 256): uv_coords[:,0] = uv_coords[:,0]*(uv_w - 1) uv_coords[:,1] = uv_coords[:,1]*(uv_h - 1) uv_coords[:,1] = uv_h - uv_coords[:,1] - 1 uv_coords = np.hstack((uv_coords, np.zeros((uv_coords.shape[0], 1)))) # add z return uv_coords def run_posmap_300W_LP(bfm, image_path, mat_path, save_folder, uv_h = 256, uv_w = 256, image_h = 256, image_w = 256): # 1. load image and fitted parameters image_name = image_path.strip().split('/')[-1] image = io.imread(image_path)/255; [h, w, c] = image.shape; info = sio.loadmat(mat_path); pose_para = info['Pose_Para'].T.astype(np.float32); shape_para = info['Shape_Para'].astype(np.float32); exp_para = info['Exp_Para'].astype(np.float32); # 2. generate mesh; # generate shape vertices = bfm.generate_vertices(shape_para, exp_para); # transform mesh s = pose_para[-1, 0]; angles = pose_para[:3, 0]; t = pose_para[3:6, 0]; transformed_vertices = bfm.transform_3ddfa(vertices, s, angles, t) projected_vertices = transformed_vertices.copy() # using stantard camera & orth projection as in 3DDFA image_vertices = projected_vertices.copy() image_vertices[:,1] = h - image_vertices[:,1] - 1 # 3. crop image with key points kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32) left = np.min(kpt[:, 0]) right = np.max(kpt[:, 0]) top = np.min(kpt[:, 1]) bottom = np.max(kpt[:, 1]) center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0]) old_size = (right - left + bottom - top)/2 size = int(old_size*1.5) # random pertube. you can change the numbers marg = old_size*0.1 t_x = np.random.rand()*marg*2 - marg t_y = np.random.rand()*marg*2 - marg center[0] = center[0]+t_x; center[1] = center[1]+t_y size = size*(np.random.rand()*0.2 + 0.9) # crop and record the transform parameters src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]); DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]]); tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS); # transform face position(image vertices) along with 2d facial image angle = np.random.rand() * 90 - 45; rows, cols = image.shape[0], image.shape[1]; # rotation around center center = np.array((cols, rows)) / 2. - 0.5; tform1 = SimilarityTransform(translation=center); tform2 = SimilarityTransform(rotation=np.deg2rad(angle)); tform3 = SimilarityTransform(translation=-center); rotate_transform = tform3 + tform2 + tform1; tform = rotate_transform + tform; opt = ran.randint(1,2); cropped_image = skimage.transform.warp(image, tform.inverse, output_shape=(image_h, image_w)); position = image_vertices.copy() position[:, 2] = 1 position = np.dot(position, tform.params.T) position[:, 2] = image_vertices[:, 2]*tform.params[0, 0] # scale z position[:, 2] = position[:, 2] - np.min(position[:, 2]) # translate z # 4. uv position map: render position in uv space uv_position_map = mesh.render.render_colors(uv_coords, bfm.full_triangles, position, uv_h, uv_w, c = 3) #cv2.imshow('image', cropped_image); #cv2.waitKey(0); #cv2.destroyAllWindows(); # 5. save files io.imsave('{}\{}'.format(save_folder, image_name), np.squeeze(cropped_image)); np.save('{}\{}'.format(save_folder, image_name.replace('jpg', 'npy')), uv_position_map); io.imsave('{}\{}'.format('results/uv_maps/', image_name.replace('.jpg', '_posmap.jpg')), (uv_position_map)/abs(uv_position_map.max())); # only for show #cv2.imwrite(image_name[:-4]+'_posmap.jpg',uv_position_map); # --verify #import cv2 uv_texture_map_rec = cv2.remap(cropped_image, uv_position_map[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR,borderMode= cv2.BORDER_CONSTANT,borderValue=(0)); #io.imsave('{}\{}'.format(save_folder, image_name.replace('.jpg', '_tex.jpg')), np.squeeze(uv_texture_map_rec)); #Save fitted face on position map (texture). if __name__ == '__main__': save_folder = 'results/' if not os.path.exists(save_folder): os.mkdir(save_folder) # set para uv_h = uv_w = 256 image_h = image_w = 256; # load uv coords global uv_coords uv_coords = face3d.morphable_model.load.load_uv_coords('Data/BFM/Out/BFM_UV.mat') uv_coords = process_uv(uv_coords, uv_h, uv_w) ''' Save LFPW Testing as well, only the first 8. ''' # load bfm bfm = MorphabelModel('Data/BFM/Out/BFM.mat') # run content = []; print('Running'); s =0; f=0; types = ['AFW', 'HELEN', 'LFPW', 'IBUG', 'LFPW_Test']; for i in types: print(i); with open(i+'_Data.txt', 'r') as fileRead: content = [file.rstrip('\n') for file in fileRead]; s=0; print(len(content)); for filename in content: #print(filename) #if(s==8 and i is 'LFPW_Test'): # break if(s%500 ==0): print(str(s) +'/' +str(len(content))) image_path = 'Data/BFM/300W_LP/'+ i+'/'+filename+'.jpg'; mat_path = 'Data/BFM/300W_LP/'+i+'/'+filename+'.mat'; if(i is 'LFPW_Test'): image_path = 'Data/BFM/300W_LP/'+ 'LFPW'+'/'+filename+'.jpg'; mat_path = 'Data/BFM/300W_LP/'+'LFPW'+'/'+filename+'.mat'; run_posmap_300W_LP(bfm, image_path, mat_path, save_folder) s+=1; print(s+f) print(f)

Nnemr/PRNet

get_300WLP_maps.py

Python

from comet_ml import Experiment experiment = Experiment(api_key="oda8KKpxlDgWmJG5KsYrrhmIV", project_name="consensusnet") import numpy as np from keras.models import Model from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, Input from keras.layers import Conv1D, MaxPooling1D, Conv2D import sys module_path = '/home/diplomski-rad/consensus-net/src/python/dataset/' if module_path not in sys.path: print('Adding dataset module.') sys.path.append(module_path) import dataset X_train = np.load('../dataset-n3-X-reshaped-train.npy') X_validate = np.load('../dataset-n3-X-reshaped-validate.npy') y_train = np.load('../dataset-n3-y-reshaped-train.npy') y_validate = np.load('../dataset-n3-y-reshaped-validate.npy') example_shape = X_train.shape[1:] input_layer = Input(shape=example_shape) conv_1 = Conv2D(filters=10, kernel_size=3, padding='same', activation='relu')(input_layer) bn_1 = BatchNormalization()(conv_1) conv_2 = Conv2D(filters=10, kernel_size=3, padding='same', activation='relu')(bn_1) drop_1 = Dropout(0.25)(conv_2) flatten = Flatten()(drop_1) predictions = Dense(4, activation='softmax')(flatten) model = Model(input_layer, predictions) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) batch_size = 10000 epochs = 50 model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_validate, y_validate))

ajuric/consensus-net

experiments/karla/diplomski-rad/blade/pb/datasets/n3-all-indels/finished-experiments/model-n3-indel-5.py

Python

# Generated by Django 3.1.1 on 2022-01-06 23:38 import django.contrib.auth.models import django.contrib.auth.validators from django.db import migrations, models import django.utils.timezone import uuid class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0012_alter_user_first_name_max_length'), ] operations = [ migrations.CreateModel( name='ControleOTP', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True)), ('email', models.EmailField(max_length=254)), ('codigo', models.CharField(max_length=6)), ], ), migrations.CreateModel( name='User', fields=[ ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('username', models.CharField(error_messages={'unique': 'A user with that username already exists.'}, help_text='Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.', max_length=150, unique=True, validators=[django.contrib.auth.validators.UnicodeUsernameValidator()], verbose_name='username')), ('first_name', models.CharField(blank=True, max_length=150, verbose_name='first name')), ('last_name', models.CharField(blank=True, max_length=150, verbose_name='last name')), ('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')), ('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')), ('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')), ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True)), ('email', models.EmailField(max_length=254)), ('codigo', models.CharField(max_length=6)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': 'user', 'verbose_name_plural': 'users', 'abstract': False, }, managers=[ ('objects', django.contrib.auth.models.UserManager()), ], ), ]

JGabriel-AbreuM/Pokedex

pokedex/accounts/migrations/0001_initial.py

Python

# coding=utf-8 # Copyright 2020 The Edward2 Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Ensemble on ImageNet. This script only performs evaluation, not training. We recommend training ensembles by launching independent runs of `deterministic.py` over different seeds. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import app from absl import flags from absl import logging import edward2 as ed import deterministic_model # local file import import utils # local file import import numpy as np import tensorflow.compat.v2 as tf flags.DEFINE_integer('per_core_batch_size', 512, 'Batch size per TPU core/GPU.') flags.DEFINE_integer('seed', 0, 'Random seed.') flags.DEFINE_string('data_dir', None, 'Path to training and testing data.') flags.mark_flag_as_required('data_dir') flags.DEFINE_string('checkpoint_dir', None, 'The directory where the model weights are stored.') flags.mark_flag_as_required('checkpoint_dir') flags.DEFINE_string('output_dir', '/tmp/imagenet', 'The directory where to save predictions.') flags.DEFINE_string('alexnet_errors_path', None, 'Path to AlexNet corruption errors file.') flags.DEFINE_integer('num_bins', 15, 'Number of bins for ECE computation.') # Accelerator flags. flags.DEFINE_bool('use_gpu', True, 'Whether to run on GPU or otherwise TPU.') flags.DEFINE_integer('num_cores', 1, 'Number of TPU cores or number of GPUs.') flags.DEFINE_string('tpu', None, 'Name of the TPU. Only used if use_gpu is False.') FLAGS = flags.FLAGS # Number of images in eval dataset. IMAGENET_VALIDATION_IMAGES = 50000 NUM_CLASSES = 1000 def ensemble_negative_log_likelihood(labels, logits): """Negative log-likelihood for ensemble. For each datapoint (x,y), the ensemble's negative log-likelihood is: ``` -log p(y|x) = -log sum_{m=1}^{ensemble_size} exp(log p(y|x,theta_m)) + log ensemble_size. ``` Args: labels: tf.Tensor of shape [...]. logits: tf.Tensor of shape [ensemble_size, ..., num_classes]. Returns: tf.Tensor of shape [...]. """ labels = tf.cast(labels, tf.int32) logits = tf.convert_to_tensor(logits) ensemble_size = float(logits.shape[0]) nll = tf.nn.sparse_softmax_cross_entropy_with_logits( tf.broadcast_to(labels[tf.newaxis, ...], tf.shape(logits)[:-1]), logits) return -tf.reduce_logsumexp(-nll, axis=0) + tf.math.log(ensemble_size) def gibbs_cross_entropy(labels, logits): """Average cross entropy for ensemble members (Gibbs cross entropy). For each datapoint (x,y), the ensemble's Gibbs cross entropy is: ``` GCE = - (1/ensemble_size) sum_{m=1}^ensemble_size log p(y|x,theta_m). ``` The Gibbs cross entropy approximates the average cross entropy of a single model drawn from the (Gibbs) ensemble. Args: labels: tf.Tensor of shape [...]. logits: tf.Tensor of shape [ensemble_size, ..., num_classes]. Returns: tf.Tensor of shape [...]. """ labels = tf.cast(labels, tf.int32) logits = tf.convert_to_tensor(logits) nll = tf.nn.sparse_softmax_cross_entropy_with_logits( tf.broadcast_to(labels[tf.newaxis, ...], tf.shape(logits)[:-1]), logits) return tf.reduce_mean(nll, axis=0) def main(argv): del argv # unused arg if not FLAGS.use_gpu: raise ValueError('Only GPU is currently supported.') if FLAGS.num_cores > 1: raise ValueError('Only a single accelerator is currently supported.') tf.enable_v2_behavior() tf.random.set_seed(FLAGS.seed) tf.io.gfile.makedirs(FLAGS.output_dir) batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size dataset_test = utils.ImageNetInput( is_training=False, data_dir=FLAGS.data_dir, batch_size=FLAGS.per_core_batch_size, use_bfloat16=False).input_fn() test_datasets = {'clean': dataset_test} corruption_types, max_intensity = utils.load_corrupted_test_info() for name in corruption_types: for intensity in range(1, max_intensity + 1): dataset_name = '{0}_{1}'.format(name, intensity) test_datasets[dataset_name] = utils.load_corrupted_test_dataset( name=name, intensity=intensity, batch_size=FLAGS.per_core_batch_size, drop_remainder=True, use_bfloat16=False) model = deterministic_model.resnet50(input_shape=(224, 224, 3), num_classes=NUM_CLASSES) logging.info('Model input shape: %s', model.input_shape) logging.info('Model output shape: %s', model.output_shape) logging.info('Model number of weights: %s', model.count_params()) # Search for checkpoints from their index file; then remove the index suffix. ensemble_filenames = tf.io.gfile.glob(os.path.join(FLAGS.checkpoint_dir, '**/*.index')) ensemble_filenames = [filename[:-6] for filename in ensemble_filenames] ensemble_size = len(ensemble_filenames) logging.info('Ensemble size: %s', ensemble_size) logging.info('Ensemble number of weights: %s', ensemble_size * model.count_params()) logging.info('Ensemble filenames: %s', str(ensemble_filenames)) checkpoint = tf.train.Checkpoint(model=model) # Write model predictions to files. num_datasets = len(test_datasets) for m, ensemble_filename in enumerate(ensemble_filenames): checkpoint.restore(ensemble_filename) for n, (name, test_dataset) in enumerate(test_datasets.items()): filename = '{dataset}_{member}.npy'.format(dataset=name, member=m) filename = os.path.join(FLAGS.output_dir, filename) if not tf.io.gfile.exists(filename): logits = [] test_iterator = iter(test_dataset) for _ in range(steps_per_eval): features, _ = next(test_iterator) # pytype: disable=attribute-error logits.append(model(features, training=False)) logits = tf.concat(logits, axis=0) with tf.io.gfile.GFile(filename, 'w') as f: np.save(f, logits.numpy()) percent = (m * num_datasets + (n + 1)) / (ensemble_size * num_datasets) message = ('{:.1%} completion for prediction: ensemble member {:d}/{:d}. ' 'Dataset {:d}/{:d}'.format(percent, m + 1, ensemble_size, n + 1, num_datasets)) logging.info(message) metrics = { 'test/negative_log_likelihood': tf.keras.metrics.Mean(), 'test/gibbs_cross_entropy': tf.keras.metrics.Mean(), 'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(), 'test/ece': ed.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), } corrupt_metrics = {} for name in test_datasets: corrupt_metrics['test/nll_{}'.format(name)] = tf.keras.metrics.Mean() corrupt_metrics['test/accuracy_{}'.format(name)] = ( tf.keras.metrics.SparseCategoricalAccuracy()) corrupt_metrics['test/ece_{}'.format( name)] = ed.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins) # Evaluate model predictions. for n, (name, test_dataset) in enumerate(test_datasets.items()): logits_dataset = [] for m in range(ensemble_size): filename = '{dataset}_{member}.npy'.format(dataset=name, member=m) filename = os.path.join(FLAGS.output_dir, filename) with tf.io.gfile.GFile(filename, 'rb') as f: logits_dataset.append(np.load(f)) logits_dataset = tf.convert_to_tensor(logits_dataset) test_iterator = iter(test_dataset) for step in range(steps_per_eval): _, labels = next(test_iterator) # pytype: disable=attribute-error logits = logits_dataset[:, (step*batch_size):((step+1)*batch_size)] labels = tf.cast(tf.reshape(labels, [-1]), tf.int32) negative_log_likelihood = tf.reduce_mean( ensemble_negative_log_likelihood(labels, logits)) per_probs = tf.nn.softmax(logits) probs = tf.reduce_mean(per_probs, axis=0) if name == 'clean': gibbs_ce = tf.reduce_mean(gibbs_cross_entropy(labels, logits)) metrics['test/negative_log_likelihood'].update_state( negative_log_likelihood) metrics['test/gibbs_cross_entropy'].update_state(gibbs_ce) metrics['test/accuracy'].update_state(labels, probs) metrics['test/ece'].update_state(labels, probs) else: corrupt_metrics['test/nll_{}'.format(name)].update_state( negative_log_likelihood) corrupt_metrics['test/accuracy_{}'.format(name)].update_state( labels, probs) corrupt_metrics['test/ece_{}'.format(name)].update_state( labels, probs) message = ('{:.1%} completion for evaluation: dataset {:d}/{:d}'.format( (n + 1) / num_datasets, n + 1, num_datasets)) logging.info(message) corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics, corruption_types, max_intensity, FLAGS.alexnet_errors_path) total_results = {name: metric.result() for name, metric in metrics.items()} total_results.update(corrupt_results) logging.info('Metrics: %s', total_results) if __name__ == '__main__': app.run(main)

mhavasi/edward2

baselines/imagenet/ensemble.py

Python

from collections import defaultdict from .tree import Tree from .visitors import Transformer_InPlace from .common import ParserConf from .lexer import Token, PatternStr from .parsers import earley from .grammar import Rule, Terminal, NonTerminal def is_discarded_terminal(t): return t.is_term and t.filter_out def is_iter_empty(i): try: _ = next(i) return False except StopIteration: return True class WriteTokensTransformer(Transformer_InPlace): def __init__(self, tokens): self.tokens = tokens def __default__(self, data, children, meta): # if not isinstance(t, MatchTree): # return t if not getattr(meta, 'match_tree', False): return Tree(data, children) iter_args = iter(children) to_write = [] for sym in meta.orig_expansion: if is_discarded_terminal(sym): t = self.tokens[sym.name] if not isinstance(t.pattern, PatternStr): raise NotImplementedError("Reconstructing regexps not supported yet: %s" % t) to_write.append(t.pattern.value) else: x = next(iter_args) if isinstance(x, list): to_write += x else: if isinstance(x, Token): assert Terminal(x.type) == sym, x else: assert NonTerminal(x.data) == sym, (sym, x) to_write.append(x) assert is_iter_empty(iter_args) return to_write class MatchTree(Tree): pass class MakeMatchTree: def __init__(self, name, expansion): self.name = name self.expansion = expansion def __call__(self, args): t = MatchTree(self.name, args) t.meta.match_tree = True t.meta.orig_expansion = self.expansion return t class Reconstructor: def __init__(self, parser): # XXX TODO calling compile twice returns different results! tokens, rules, _grammar_extra = parser.grammar.compile() self.write_tokens = WriteTokensTransformer({t.name:t for t in tokens}) self.rules = list(self._build_recons_rules(rules)) def _build_recons_rules(self, rules): expand1s = {r.origin for r in rules if r.options and r.options.expand1} aliases = defaultdict(list) for r in rules: if r.alias: aliases[r.origin].append( r.alias ) rule_names = {r.origin for r in rules} nonterminals = {sym for sym in rule_names if sym.name.startswith('_') or sym in expand1s or sym in aliases } for r in rules: recons_exp = [sym if sym in nonterminals else Terminal(sym.name) for sym in r.expansion if not is_discarded_terminal(sym)] # Skip self-recursive constructs if recons_exp == [r.origin]: continue sym = NonTerminal(r.alias) if r.alias else r.origin yield Rule(sym, recons_exp, alias=MakeMatchTree(sym.name, r.expansion)) for origin, rule_aliases in aliases.items(): for alias in rule_aliases: yield Rule(origin, [Terminal(alias)], alias=MakeMatchTree(origin.name, [NonTerminal(alias)])) yield Rule(origin, [Terminal(origin.name)], alias=MakeMatchTree(origin.name, [origin])) def _match(self, term, token): if isinstance(token, Tree): return Terminal(token.data) == term elif isinstance(token, Token): return term == Terminal(token.type) assert False def _reconstruct(self, tree): # TODO: ambiguity? callbacks = {rule: rule.alias for rule in self.rules} # TODO pass callbacks through dict, instead of alias? parser = earley.Parser(ParserConf(self.rules, callbacks, tree.data), self._match, resolve_ambiguity=True) unreduced_tree = parser.parse(tree.children) # find a full derivation assert unreduced_tree.data == tree.data res = self.write_tokens.transform(unreduced_tree) for item in res: if isinstance(item, Tree): for x in self._reconstruct(item): yield x else: yield item def reconstruct(self, tree): return ''.join(self._reconstruct(tree))

larryk85/eosio.depman

lark/reconstruct.py

Python

"""py.test fixtures for Pyramid. http://pyramid.readthedocs.org/en/latest/narr/testing.html """ import datetime as datetime_module import logging import os import pkg_resources import pytest import webtest from dcicutils.qa_utils import notice_pytest_fixtures, MockFileSystem from pyramid.request import apply_request_extensions from pyramid.testing import DummyRequest from pyramid.threadlocal import get_current_registry, manager as threadlocal_manager from snovault import DBSESSION, ROOT, UPGRADER from snovault.elasticsearch import ELASTIC_SEARCH, create_mapping from snovault.util import generate_indexer_namespace_for_testing from .conftest_settings import make_app_settings_dictionary from .. import main from ..loadxl import load_all """ README: * This file contains application level fixtures and hooks in the server/data fixtures present in other files. * There are "app" based fixtures that rely only on postgres, "es_app" fixtures that use both postgres and ES (for search/ES related testing) """ @pytest.fixture(autouse=True) def autouse_external_tx(external_tx): pass @pytest.fixture(scope='session') def app_settings(request, wsgi_server_host_port, conn, DBSession): # noQA - We didn't choose the fixture name. notice_pytest_fixtures(request, wsgi_server_host_port, conn, DBSession) settings = make_app_settings_dictionary() settings['auth0.audiences'] = 'http://%s:%s' % wsgi_server_host_port settings[DBSESSION] = DBSession return settings INDEXER_NAMESPACE_FOR_TESTING = generate_indexer_namespace_for_testing('cgap') @pytest.fixture(scope='session') def es_app_settings(wsgi_server_host_port, elasticsearch_server, postgresql_server, aws_auth): settings = make_app_settings_dictionary() settings['create_tables'] = True settings['persona.audiences'] = 'http://%s:%s' % wsgi_server_host_port # 2-tuple such as: ('localhost', '5000') settings['elasticsearch.server'] = elasticsearch_server settings['sqlalchemy.url'] = postgresql_server settings['collection_datastore'] = 'elasticsearch' settings['item_datastore'] = 'elasticsearch' settings['indexer'] = True settings['indexer.namespace'] = INDEXER_NAMESPACE_FOR_TESTING # use aws auth to access elasticsearch if aws_auth: settings['elasticsearch.aws_auth'] = aws_auth return settings def pytest_configure(): logging.basicConfig(format='%(message)s') logging.getLogger('sqlalchemy.engine').setLevel(logging.WARNING) class Shorten(logging.Filter): max_len = 500 def filter(self, record): if record.msg == '%r': record.msg = record.msg % record.args record.args = () if len(record.msg) > self.max_len: record.msg = record.msg[:self.max_len] + '...' return True logging.getLogger('sqlalchemy.engine.base.Engine').addFilter(Shorten()) @pytest.yield_fixture def threadlocals(request, dummy_request, registry): notice_pytest_fixtures(request, dummy_request, registry) threadlocal_manager.push({'request': dummy_request, 'registry': registry}) yield dummy_request threadlocal_manager.pop() class MyDummyRequest(DummyRequest): def remove_conditional_headers(self): pass def _get_registry(self): if self._registry is None: return get_current_registry() return self._registry def _set_registry(self, registry): self.__dict__['registry'] = registry def _del_registry(self): self._registry = None registry = property(_get_registry, _set_registry, _del_registry) @pytest.fixture def dummy_request(root, registry, app): request = app.request_factory.blank('/dummy') request.root = root request.registry = registry request._stats = {} request.invoke_subrequest = app.invoke_subrequest apply_request_extensions(request) return request @pytest.fixture(scope='session') def app(app_settings): """ WSGI application level functional testing. """ return main({}, **app_settings) @pytest.fixture(scope='session') def es_app(es_app_settings, **kwargs): """ App that uses both Postgres and ES - pass this as "app" argument to TestApp. Pass all kwargs onto create_mapping """ app = main({}, **es_app_settings) create_mapping.run(app, **kwargs) return app @pytest.fixture def registry(app): return app.registry @pytest.fixture def elasticsearch(registry): return registry[ELASTIC_SEARCH] @pytest.fixture def upgrader(registry): return registry[UPGRADER] @pytest.fixture def root(registry): return registry[ROOT] # Available Fixtures # ------------------ # # ################## +-----------------------------------------+----------------------------------------------------+ # ################## | Basic Application | Application with ES + Postgres | # ################## +-----------------------+-----------------+---------------------------+------------------------+ # ################## | JSON content | HTML content | JSON content | HTML content | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # Anonymous User | anontestapp | anonhtmltestapp | anon_es_testapp | anon_html_es_testapp | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # System User | testapp | htmltestapp | es_testapp | html_es_testapp | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # Authenticated User | authenticated_testapp | ----- | authenticated_es_testapp | ----- | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # Submitter User | submitter_testapp | ----- | ----- | ----- | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # Indexer User | ----- | ----- | indexer_testapp | ----- | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # Embed User | embed_testapp | ----- | ----- | ----- | # -------------------+-----------------------+-----------------+---------------------------+------------------------+ # # TODO: Reconsider naming to have some underscores interspersed for better readability. # e.g., html_testapp rather than htmltestapp, and especially anon_html_test_app rather than anonhtmltestapp. # -kmp 03-Feb-2020 @pytest.fixture def anontestapp(app): """TestApp for anonymous user (i.e., no user specified), accepting JSON data.""" environ = { 'HTTP_ACCEPT': "application/json" } return webtest.TestApp(app, environ) @pytest.fixture def anonhtmltestapp(app): """TestApp for anonymous (not logged in) user, accepting text/html content.""" environ = { 'HTTP_ACCEPT': 'text/html' } test_app = webtest.TestApp(app, environ) return test_app @pytest.fixture def anon_es_testapp(es_app): """ TestApp simulating a bare Request entering the application (with ES enabled) """ environ = { 'HTTP_ACCEPT': 'application/json' } return webtest.TestApp(es_app, environ) @pytest.fixture def anon_html_es_testapp(es_app): """TestApp with ES + Postgres for anonymous (not logged in) user, accepting text/html content.""" environ = { 'HTTP_ACCEPT': 'text/html' } return webtest.TestApp(es_app, environ) @pytest.fixture(scope="session") def testapp(app): """TestApp for username TEST, accepting JSON data.""" environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST' } return webtest.TestApp(app, environ) @pytest.fixture def htmltestapp(app): """TestApp for TEST user, accepting text/html content.""" environ = { 'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST', } test_app = webtest.TestApp(app, environ) return test_app @pytest.fixture(scope='session') def es_testapp(es_app): """ TestApp with ES + Postgres. Must be imported where it is needed. """ environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST', } return webtest.TestApp(es_app, environ) @pytest.fixture def html_es_testapp(es_app): """TestApp with ES + Postgres for TEST user, accepting text/html content.""" environ = { 'HTTP_ACCEPT': 'text/html', 'REMOTE_USER': 'TEST', } return webtest.TestApp(es_app, environ) @pytest.fixture def authenticated_testapp(app): """TestApp for an authenticated, non-admin user (TEST_AUTHENTICATED), accepting JSON data.""" environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED', } return webtest.TestApp(app, environ) @pytest.fixture def authenticated_es_testapp(es_app): """ TestApp for authenticated non-admin user with ES """ environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_AUTHENTICATED', } return webtest.TestApp(es_app, environ) @pytest.fixture def submitter_testapp(app): """TestApp for a non-admin user (TEST_SUBMITTER), accepting JSON data.""" environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST_SUBMITTER', } return webtest.TestApp(app, environ) @pytest.fixture def indexer_testapp(es_app): """ Indexer testapp, meant for manually triggering indexing runs by posting to /index. Always uses the ES app (obviously, but not so obvious previously) """ environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'INDEXER', } return webtest.TestApp(es_app, environ) @pytest.fixture def embed_testapp(app): """TestApp for user EMBED, accepting JSON data.""" environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'EMBED', } return webtest.TestApp(app, environ) @pytest.fixture def wsgi_app(wsgi_server): """TestApp for WSGI server.""" return webtest.TestApp(wsgi_server) class WorkbookCache: """ Caches whether or not we have already provisioned the workbook. """ done = None @classmethod def initialize_if_needed(cls, es_app): if not cls.done: cls.done = cls.make_fresh_workbook(es_app) @classmethod def make_fresh_workbook(cls, es_app): environ = { 'HTTP_ACCEPT': 'application/json', 'REMOTE_USER': 'TEST', } testapp = webtest.TestApp(es_app, environ) # Just load the workbook inserts # Note that load_all returns None for success or an Exception on failure. load_res = load_all(testapp, pkg_resources.resource_filename('encoded', 'tests/data/workbook-inserts/'), []) if isinstance(load_res, Exception): raise load_res elif load_res: raise RuntimeError("load_all returned a true value that was not an exception.") testapp.post_json('/index', {}) return True @pytest.fixture(scope='session') def workbook(es_app): """ Loads a bunch of data (tests/data/workbook-inserts) into the system on first run (session scope doesn't work). """ WorkbookCache.initialize_if_needed(es_app) @pytest.yield_fixture def mocked_file_system(): with MockFileSystem(auto_mirror_files_for_read=True).mock_exists_open_remove(): yield

dbmi-bgm/cgap-portal

src/encoded/tests/conftest.py

Python

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from azure.mgmt.core.exceptions import ARMErrorFormat from msrest import Serializer from .. import models as _models from .._vendor import _convert_request if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, Optional, TypeVar T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() # fmt: off def build_get_method_local_valid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest api_version = "2.0" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/azurespecials/apiVersion/method/string/none/query/local/2.0') # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_method_local_null_request( **kwargs # type: Any ): # type: (...) -> HttpRequest api_version = kwargs.pop('api_version', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/azurespecials/apiVersion/method/string/none/query/local/null') # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if api_version is not None: query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_path_local_valid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest api_version = "2.0" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/azurespecials/apiVersion/path/string/none/query/local/2.0') # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_swagger_local_valid_request( **kwargs # type: Any ): # type: (...) -> HttpRequest api_version = "2.0" accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/azurespecials/apiVersion/swagger/string/none/query/local/2.0') # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) # fmt: on class ApiVersionLocalOperations(object): """ApiVersionLocalOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azurespecialproperties.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def get_method_local_valid( self, **kwargs # type: Any ): # type: (...) -> None """Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed. :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_method_local_valid_request( template_url=self.get_method_local_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) get_method_local_valid.metadata = {"url": "/azurespecials/apiVersion/method/string/none/query/local/2.0"} # type: ignore @distributed_trace def get_method_local_null( self, api_version=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> None """Get method with api-version modeled in the method. pass in api-version = null to succeed. :param api_version: This should appear as a method parameter, use value null, this should result in no serialized parameter. :type api_version: str :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_method_local_null_request( api_version=api_version, template_url=self.get_method_local_null.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) get_method_local_null.metadata = {"url": "/azurespecials/apiVersion/method/string/none/query/local/null"} # type: ignore @distributed_trace def get_path_local_valid( self, **kwargs # type: Any ): # type: (...) -> None """Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed. :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_path_local_valid_request( template_url=self.get_path_local_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) get_path_local_valid.metadata = {"url": "/azurespecials/apiVersion/path/string/none/query/local/2.0"} # type: ignore @distributed_trace def get_swagger_local_valid( self, **kwargs # type: Any ): # type: (...) -> None """Get method with api-version modeled in the method. pass in api-version = '2.0' to succeed. :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) request = build_get_swagger_local_valid_request( template_url=self.get_swagger_local_valid.metadata["url"], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.Error, pipeline_response) raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) get_swagger_local_valid.metadata = {"url": "/azurespecials/apiVersion/swagger/string/none/query/local/2.0"} # type: ignore

Azure/autorest.python

test/azure/legacy/Expected/AcceptanceTests/AzureSpecials/azurespecialproperties/operations/_api_version_local_operations.py

Python

import os from glob import glob from tqdm import tqdm from pathlib import Path # from kaggle_isic_2020.lib import dirs # Doesn't work on unix, why? # Test source_dir = "/home/common/datasets/SIIM-ISIC_2020_Melanoma/jpeg/test/" dest_dir = "/home/common/datasets/SIIM-ISIC_2020_Melanoma/jpeg/test_compact/" # dirs.create_folder(dest_dir) file_list = glob(source_dir+"*.jpg") print("Converting image files...") for file_path in tqdm(file_list): file_name = Path(file_path).name os.system("convert \""+file_path+"[512x]\" -set filename:base \"%[basename]\" \""+dest_dir+"/%[filename:base].jpg\"")

olavosamp/kaggle_isic_2020

dataset_stats/convert_dataset_test_set.py

Python

#!/usr/bin/env python # -*- coding: utf-8 -*- import cv2 import pdb import numpy as np from os.path import join from ops import l2_dist_360 from MeanOverlap import MeanOverlap def catData(totalData, newData): """ Concat data from scratch """ if totalData is None: totalData = newData[np.newaxis].copy() else: totalData = np.concatenate((totalData, newData[np.newaxis])) return totalData def score(Agent, seq1, seq2, _full=True): """ Calculate IoU """ acc = 0.0 total_num = 0 MO = MeanOverlap(Agent.W, Agent.H) for batch in xrange(Agent.batch_size): for i in xrange(Agent.n_frames): if not _full and np.sum(seq2[batch][i]) == 0: continue acc += MO.IOU((seq1[batch, i, 0], seq1[batch, i, 1]), (seq2[batch, i, 0], seq2[batch, i, 1])) total_num += 1 return (acc / total_num) if total_num != 0 else 0 #(n_frames*batch_size) def printAcc(threshold, targetFrNum, totalFrNum): """ Fetch accuracy and print out """ print "Acc is:" for th in threshold: print ("%d" %(th)), print for i, types in enumerate(targetFrNum): print i if i < 4 else (i-4), for j, th in enumerate(threshold): print ("%.5f" %(types[j] / (totalFrNum if totalFrNum > 0 else 1))), print def cal_accuracy(Agent, pred, gt, targetFrNum, totalFrNum): """ Calculate and return accuracy """ if np.sum(gt) == 0: return targetFrNum, totalFrNum l2_dist = l2_dist_360(pred, gt, Agent.W) l2_dist = np.tile(l2_dist,(len(Agent.threshold), 1)) """ if l2_dist(10 x 50) <= thres(1 x 10), then targetFrNum(8types x 10thres) += 1 """ thres = np.sum(l2_dist <= np.tile(np.reshape(Agent.threshold, (-1, 1)), (1, l2_dist.shape[-1])), axis=1) center = np.array([Agent.W/2, Agent.H/2]) for th, i in enumerate(thres): if np.min(np.linalg.norm(gt - center, axis=1)) > 100: targetFrNum[i,th] += 1 else: targetFrNum[i+4,th] += 1 totalFrNum += 1 return targetFrNum, totalFrNum def load_batch_data(Agent, path, num_batch, _copy=False, _augment=False): """ Load batch data from path and normalize them, use copy to preserve raw data """ data = np.load(join(path, 'pruned_roisavg/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:Agent.n_input] oracle_viewangle = np.load(join(path, 'label/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_classes+1] one_hot_labels = np.load(join(path, 'onehot/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection] hof = np.load(join(path, 'hof/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:Agent.n_bin_size] box_center = np.load(join(path, 'divide_area_pruned_boxes/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:] img = np.zeros((Agent.batch_size), dtype=np.float16) if _augment is True: data, oracle_viewangle, box_center = augment_data(data, oracle_viewangle, box_center) if _copy is True: box = np.copy(box_center) gt = np.copy(oracle_viewangle)[:, :, :2] else: box = None gt = None box_center[:,:,:,0] = (box_center[:,:,:,0]/Agent.W + box_center[:,:,:,2]/Agent.W)/2 box_center[:,:,:,1] = (box_center[:,:,:,1]/Agent.H + box_center[:,:,:,3]/Agent.H)/2 box_center = box_center[:, :, :, :2] oracle_viewangle[:,:,0] = oracle_viewangle[:,:,0]/Agent.W oracle_viewangle[:,:,1] = oracle_viewangle[:,:,1]/Agent.H oracle_viewangle = oracle_viewangle[:, :, :2] return data, one_hot_labels, oracle_viewangle, box_center, hof, img, box, gt def visual_gaze(Agent, img_name, gt, pred, alphas, box): """ [Deprecated] Draw and plot visual gaze contains boxes, gt gazes, and prediction """ print Agent.img_path + img_name + '.jpg' img = cv2.imread(Agent.img_path + img_name + '.jpg',3) if img is None: print 'No image is found.' return 1 img = cv2.resize(img, (int(W),int(H))) W = Agent.W H = Agent.H # Box idx = 0 transparent = 0.90 for xmin, ymin, xmax, ymax in box.astype(np.int32): if xmax > W: xmax = int(W) if ymax > H: ymax = int(H) print xmin, ymin, xmax, ymax, alphas[idx] #if alphas[idx] > 0.0: cv2.rectangle(img,(xmin, ymin),(xmax, ymax), (255,255,255), 2) img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*0.95 + np.ones((ymax-ymin,xmax-xmin,3))*0.05 cv2.putText(img, ("{0:.2f}").format(alphas[idx]), (int((xmax+xmin)/2)+1 , int((ymax+ymin)/2)+1), cv2.FONT_HERSHEY_SIMPLEX, 1.50, (0,0,0), 2) cv2.putText(img, ("{0:.2f}").format(alphas[idx]), (int((xmax+xmin)/2) , int((ymax+ymin)/2)), cv2.FONT_HERSHEY_SIMPLEX, 1.50, (255,255,255), 2) idx += 1 # Predicted gaze ll = 3 # Desire gaze color = [(255, 0, 0), (0,255,0),(0,255,255),(0,0,255)] # Green, Yellow, Red i = 2 u, v = gt.astype(np.int32) img[v-ll:v+ll,u-ll:u+ll,1] = 255 cv2.circle(img,(u,v),10,color[i],2) # desize gaze centers xmin = u - int(W/4) if u > W/4 else 0 xmax = u + int(W/4) if u < 3*W/4 else int(W) ymin = v - int(H/4) if v > H/4 else 0 ymax = v + int(H/4) if v < 3*H/4 else int(H) cv2.rectangle(img,(xmin, ymin),(xmax, ymax), color[i], 2) img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*transparent + \ np.tile(np.array([clr for clr in color[i]])*(1-transparent),(ymax-ymin,xmax-xmin,1)) print ("gt: ({}, {})").format(u, v) # Predicted gaze i = 0 u, v = int(pred[0]), int(pred[1]) img[v-ll:v+ll,u-ll:u+ll,2] = 255 cv2.circle(img,(u,v),10,(255,0,0),2) # predicted gaze center xmin = u - int(W/4) if u > W/4 else 0 xmax = u + int(W/4) if u < 3*W/4 else int(W) ymin = v - int(H/4) if v > H/4 else 0 ymax = v + int(H/4) if v < 3*H/4 else int(H) cv2.rectangle(img,(xmin, ymin),(xmax, ymax), color[i], 2) img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*transparent + \ np.tile(np.array([clr for clr in color[i]])*(1-transparent),(ymax-ymin,xmax-xmin,1)) print ("pred: ({}, {})").format(u, v) img = cv2.resize(img, (800,400)) if Agent._save_img: cv2.imwrite(save_path+img_name+'.jpg', img) else: cv2.imshow("gaze", img) key = cv2.waitKey(0) & 0xFF if key == 27: return -1 elif key == ord('q'): return -2 elif key == ord('s'): return -3 elif key == ord('c'): Agent._save_img = not Agent._save_img return 0 else: return 0

remega/OF

Deep360Pilot-CVPR17-tf1.2/util.py

Python

# -*- coding: utf-8 -*- # Copyright (C) 2012-2015, Code for America # This is open source software, released under a standard 3-clause # BSD-style license; see the file LICENSE for details. import os import datetime import re from flask import Flask, render_template, request, abort, redirect, url_for, make_response, session, flash from werkzeug.contrib.atom import AtomFeed import requests import iso8601 import pytz import updater import open311tools __version__ = '1.0.2' # Config DEFAULT_CONFIG_PATH = os.path.join(os.path.dirname(__file__), 'configuration.py') # Quick-start config. You should really put something in # ./configuration.py or set the SRTRACKER_CONFIGURATION env var instead. DEBUG = True OPEN311_SERVER = 'http://localhost:5000' OPEN311_API_KEY = '' PASSWORD_PROTECTED = False SECRET_KEY = 'please_please_change_this!' app = Flask(__name__) @app.before_request def password_protect(): # don't password-protect images (for e-mail!) if app.config['PASSWORD_PROTECTED'] and not request.path.startswith('/static/img'): auth = request.authorization if not auth or auth.password != app.config['PASSWORD']: # Tell the browser to do basic auth return make_response( 'Could not verify your access level for that URL.\n' 'You have to login with proper credentials', 401, {'WWW-Authenticate': 'Basic realm="Login Required"'}) #-------------------------------------------------------------------------- # ROUTES #-------------------------------------------------------------------------- @app.route("/", defaults={'page': 1, 'service_code': ''}) @app.route("/<int:page>", defaults={'service_code': ''}) @app.route("/<int:page>/<service_code>") def index(page, service_code): if 'filter' in request.args: service_code = request.args['filter'] url = '%s/requests.json' % app.config['OPEN311_SERVER'] recent_sr_timeframe = app.config.get('RECENT_SRS_TIME') # If SRS_PAGE_SIZE is set, use paging. Otherwise, fall back to a non-paged list from MAX_RECENT_SRS page_size = app.config.get('SRS_PAGE_SIZE') paged = page_size > 0 if not paged: page_size = app.config.get('MAX_RECENT_SRS', 50) page = 1 services_list = open311tools.services(app.config['OPEN311_SERVER'], app.config['OPEN311_API_KEY']) service_name = '' for service in services_list: if service_code == service['service_code']: service_name = service['service_name'] break if not service_name: service_code = '' params = { 'extensions': 'true', 'page_size': page_size, 'page': page, 'service_code': service_code } if recent_sr_timeframe: start_datetime = datetime.datetime.utcnow() - datetime.timedelta(seconds=recent_sr_timeframe) params['start_date'] = start_datetime.isoformat() + 'Z' if app.config['OPEN311_API_KEY']: params['api_key'] = app.config['OPEN311_API_KEY'] r = requests.get(url, params=params) if r.status_code != 200: app.logger.error('OPEN311: Failed to load recent requests from Open311 server. Status Code: %s, Response: %s', r.status_code, r.text) service_requests = None else: # need to slice with page_size in case an endpoint doesn't support page_size its API (it's non-standard) service_requests = r.json[:page_size] # we might receive SRs that were updated in the future (!); pretend like those updates were just now. # fixes https://github.com/codeforamerica/srtracker/issues/80 now = datetime.datetime.utcnow() for sr in service_requests: if 'updated_datetime' in sr: # parse and ensure the date is naive for comparison to utcnow updated = iso8601.parse_date(sr['updated_datetime']) \ .astimezone(pytz.utc).replace(tzinfo=None) sr['updated_datetime'] = min(now, updated) return render_app_template('index.html', service_requests = service_requests, page = page, services_list = services_list, service_code = service_code, service_name = service_name) @app.route("/requests/") def request_search(): if 'request_id' in request.args: return redirect(url_for('show_request', request_id=request.args['request_id'])) else: abort(404) @app.route("/requests/<request_id>", methods=["GET", "POST"]) def show_request(request_id): request_id = request_id.lstrip('#') # receive subscription form_errors = [] submitted_email = None if request.method == 'POST': submitted_email = request.form.get('update_email') if submitted_email: success = subscribe_to_sr(request_id, submitted_email) if not success: form_errors.append('Please use a valid e-mail address.') # TODO: Should probably use Three or something nice for this... url = '%s/requests/%s.json' % (app.config['OPEN311_SERVER'], request_id) params = {'extensions': 'true', 'legacy': 'false'} if app.config['OPEN311_API_KEY']: params['api_key'] = app.config['OPEN311_API_KEY'] r = requests.get(url, params=params) if r.status_code == 404: # TODO: how to generalize this? # Chicago's SR IDs are always \d\d-\d{8}, if we get just digits, reformat and try again request_id_digits = re.sub(r'\D', '', request_id) if len(request_id_digits) == 8: # Try prepending the year if it's only 8 digits request_id_digits = datetime.date.today().strftime('%y') + request_id_digits if len(request_id_digits) == 10: reformatted = '%s-%s' % (request_id_digits[:2], request_id_digits[2:]) if reformatted != request_id: return redirect(url_for('show_request', request_id=reformatted)) # It would be nice to log this for analytical purposes (what requests are being checked that we can't show?) # but that would be better done through GA or KISS Metrics than through server logging services = open311tools.services(app.config['OPEN311_SERVER'], app.config['OPEN311_API_KEY']) return render_app_template('error_no_sr.html', request_id=request_id, services=services), 404 elif r.status_code != 200: app.logger.error('OPEN311: Error (not 404) loading data for SR %s', request_id) return render_app_template('error_311_api.html', request_id=request_id), 500 srs = r.json if srs: sr = fixup_sr(srs[0], request_id) if 'requested_datetime' in sr: sr['requested_datetime'] = iso8601.parse_date(sr['requested_datetime']) # sometimes an SR doesn't include notes even though there should always be an "opened" note if 'notes' not in sr: sr['notes'] = [] relevant_notes = 0 for note in sr['notes']: note['datetime'] = iso8601.parse_date(note['datetime']) if note['type'] in ('follow_on', 'follow_on_created', 'activity', 'closed'): relevant_notes += 1 # add follow-on closure data, fix types, etc, etc by_id = {} follow_on_open_count = 0 follow_on_close_count = 0 for note in sr['notes']: if note['type'] in ('follow_on', 'follow_on_created', 'follow_on_closed'): note_sr_id = note['extended_attributes']['service_request_id'] # old-style is just "follow_on" for everything related to follow-ons # new-style is "follow_on_created" and "follow_on_closed" # update old notes so templates don't get crazy complicated :( if note['type'] == 'follow_on_created' or note['description'].endswith('Created'): note['type'] = 'follow_on_created' follow_on_open_count += 1 by_id[note_sr_id] = note elif note['type'] == 'follow_on_closed' or note['description'].endswith('Closed'): follow_on_close_count += 1 note['type'] = 'follow_on_closed' if note_sr_id in by_id: original = by_id[note_sr_id] original['extended_attributes']['closed_datetime'] = note['datetime'] # if we hit any follow_on_opened notes if follow_on_open_count > 0: # remove the notes that claim the request is closed sr['notes'] = [n for n in sr['notes'] if not n['type'] == 'closed'] # set the request to open sr['status'] = 'open' # if we hit as many follow_on_closed as follow_on_opened notes, then request is really closed if follow_on_open_count == follow_on_close_count: # set the request status to closed sr['status'] = 'closed' tmp_note = {} # add a closing note tmp_note['type'] = 'closed' tmp_note['summary'] = 'Request Completed' # this is brittle, but shouldn't break tmp_datetime = sorted([n['extended_attributes']['closed_datetime'] for n in by_id.values()]) # set the closed datetime to be the datetime of the last-closed follow-on tmp_note['datetime'] = tmp_datetime[0] # add the extra note sr['notes'].append(tmp_note) # if there's no activity yet, show 'under review' if relevant_notes == 0: sr['notes'].append({ 'type': 'activity', 'summary': 'Under review by %s staff' % sr.get('agency_responsible', '') }) subscribed = False if sr['status'] == 'open' and session.get('addr', None): # TODO: when subscription service supports more than e-mail, # we should probably be able to show all your subscriptions here subscribed = updater.subscription_exists(request_id, 'email', session.get('addr', '')) # test media # sr['media_url'] = sr['media_url'] or 'http://farm5.staticflickr.com/4068/4286605571_c1a1751fdc_n.jpg' body = render_app_template('service_request.html', sr=sr, subscribed=subscribed, errors=form_errors, submitted_email=submitted_email) return (body, 200, None) else: return render_app_template('error_no_sr.html', request_id=request_id), 404 @app.route("/subscribe/<request_id>", methods=["POST"]) def subscribe(request_id): email = request.form.get('update_email') if email: success = subscribe_to_sr(request_id, email) if not success: flash('Please use a valid e-mail address.', 'error') return redirect(url_for('show_request', request_id=request_id)) @app.route("/unsubscribe/<subscription_key>", methods=["GET", "POST"]) def unsubscribe(subscription_key): subscription = updater.subscription_for_key(subscription_key) if subscription: sr_id = subscription.sr_id updater.unsubscribe_with_key(subscription_key) destination = url_for('show_request', request_id=sr_id) else: destination = url_for('index') flash(u'You‘ve been unsubscribed from this service request. You will no longer receive e-mails when it is updated.') return redirect(destination) #-------------------------------------------------------------------------- # SYNDICATION #-------------------------------------------------------------------------- @app.route('/recent.atom') def recent_feed(): atom_size = app.config.get('ATOM_SIZE', 25) url = '%s/requests.json' % app.config['OPEN311_SERVER'] recent_sr_timeframe = app.config.get('RECENT_SRS_TIME') params = { 'extensions': 'true', 'page_size': atom_size } if recent_sr_timeframe: start_datetime = datetime.datetime.utcnow() - datetime.timedelta(seconds=recent_sr_timeframe) params['start_date'] = start_datetime.isoformat() + 'Z' if app.config['OPEN311_API_KEY']: params['api_key'] = app.config['OPEN311_API_KEY'] r = requests.get(url, params=params) if r.status_code != 200: app.logger.error('OPEN311: Failed to load recent requests from Open311 server. Status Code: %s, Response: %s', r.status_code, r.text) service_requests = None else: # need to slice with atom_size in case an endpoint doesn't support page_size service_requests = r.json[:atom_size] # generate feed feed = AtomFeed('Recently Updated Service Requests', feed_url=request.url, url=request.url_root) if service_requests: for sr in service_requests: if 'service_request_id' in sr: sr['requested_datetime'] = iso8601.parse_date(sr['requested_datetime']) sr['updated_datetime'] = iso8601.parse_date(sr['updated_datetime']) title = '%s #%s' % (sr['service_name'], sr['service_request_id']) # in principle, this could be the result of a templating operation body = sr.get('description','') if body: body += '<br /><br />' body += sr['address'] feed.add(title, unicode(body), content_type='html', author=sr['agency_responsible'], url=url_for('show_request', request_id=sr['service_request_id']), updated=sr['updated_datetime'], published=sr['requested_datetime']) return feed.get_response() #-------------------------------------------------------------------------- # ERRORS #-------------------------------------------------------------------------- @app.errorhandler(404) def page_not_found(error): return render_app_template('error_404.html'), 404 @app.errorhandler(500) def generic_error(error): return render_app_template('error_generic.html'), 500 #-------------------------------------------------------------------------- # FILTERS #-------------------------------------------------------------------------- # Friendly time by Sean Vieira (http://flask.pocoo.org/snippets/33/) @app.template_filter() def friendly_time(dt, past_="ago", future_="from now", default="just now"): """ Returns string representing "time since" or "time until" e.g. 3 days ago, 5 hours from now etc. """ if dt is None: return '' if isinstance(dt, basestring): dt = iso8601.parse_date(dt) # ensure the date is naive for comparison to utcnow if dt.tzinfo: dt = dt.astimezone(pytz.utc).replace(tzinfo=None) now = datetime.datetime.utcnow() if now > dt: diff = now - dt dt_is_past = True else: diff = dt - now dt_is_past = False periods = ( (diff.days / 365, "year", "years"), (diff.days / 30, "month", "months"), (diff.days / 7, "week", "weeks"), (diff.days, "day", "days"), (diff.seconds / 3600, "hour", "hours"), (diff.seconds / 60, "minute", "minutes"), (diff.seconds, "second", "seconds"), ) for period, singular, plural in periods: if period: return "%d %s %s" % (period, singular if period == 1 else plural, past_ if dt_is_past else future_) return default state_pattern = re.compile(r'\b(\w\w)(,?\s*\d{5}(?:-\d{4})?)?$') @app.template_filter() def title_address(address): '''Slightly improved title() method for address strings Makes sure state abbreviations are upper-case.''' titled = address.title() titled = state_pattern.sub(lambda match: match.group(1).upper() + (match.group(2) or ''), titled) return titled #-------------------------------------------------------------------------- # UTILITIES #-------------------------------------------------------------------------- def render_app_template(template, **kwargs): '''Add some goodies to all templates.''' if 'config' not in kwargs: kwargs['config'] = app.config if '__version__' not in kwargs: kwargs['__version__'] = __version__ return render_template(template, **kwargs) def fixup_sr(sr, request_id=None): ''' Fix up an SR to try and ensure some basic info. (In Chicago's API, any field can be missing, even if it's required.) ''' remove_blacklisted_fields(sr) if 'service_request_id' not in sr: sr['service_request_id'] = request_id or sr.get('token', 'UNKNOWN') if 'status' not in sr: sr['status'] = 'open' if 'service_name' not in sr: sr['service_name'] = 'Miscellaneous Services' return sr def remove_blacklisted_fields(sr): blacklist = app.config.get('SR_FIELD_BLACKLIST') if blacklist: for field in blacklist: if field in sr: del sr[field] def subscribe_to_sr(request_id, email): # validate e-mail match = re.match(r'[A-Z0-9._%+\-]+@[A-Z0-9.\-]+\.[A-Z]{2,4}$', email, re.IGNORECASE) if match: key = updater.subscribe(request_id, 'email', email) if key: # TODO: should we use the subscription key instead? session['addr'] = email session.permanent = True return True else: app.logger.error('Error creating a subscription for %s on %s', email, request_id) return False #-------------------------------------------------------------------------- # INIT #-------------------------------------------------------------------------- if __name__ == "__main__": app.config.from_object(__name__) # we want to support a nice fallback, so use from_pyfile directly instead of from_envvar config_path = os.path.abspath(os.environ.get('SRTRACKER_CONFIGURATION', DEFAULT_CONFIG_PATH)) if os.path.isfile(config_path): app.config.from_pyfile(config_path) else: app.logger.warn('''YOU ARE USING THE QUICK-START CONFIG, WHICH IS NOT RECOMMENDED. PUT SOMETHING IN "./configuration.py" OR SET THE "SRTRACKER_CONFIGURATION" ENV VAR INSTEAD.''') port = int(os.environ.get('PORT', 5100)) app.run(host='0.0.0.0', port=port)

codeforamerica/srtracker

app.py

Python

# Copyright 2015 moco_beta # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import unittest from janome.sysdic import all_fstdata, entries, mmap_entries, connections, chardef, unknowns from janome.dic import ( SystemDictionary, MMapSystemDictionary, UserDictionary, CompiledUserDictionary, FILE_USER_FST_DATA, FILE_USER_ENTRIES_DATA ) from janome.progress import SimpleProgressIndicator, logger as p_logger # TODO: better way to find package... parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.insert(0, parent_dir) class TestDictionary(unittest.TestCase): def test_system_dictionary_ipadic(self): sys_dic = SystemDictionary(all_fstdata(), entries(), connections, chardef.DATA, unknowns.DATA) self.assertEqual(7, len(sys_dic.lookup('形態素'.encode('utf-8')))) self.assertEqual(1, sys_dic.get_trans_cost(0, 1)) self.assertEqual({'HIRAGANA': []}, sys_dic.get_char_categories('は')) self.assertEqual({'KATAKANA': []}, sys_dic.get_char_categories('ハ')) self.assertEqual({'KATAKANA': []}, sys_dic.get_char_categories('ﾊ')) self.assertEqual({'KANJI': []}, sys_dic.get_char_categories('葉')) self.assertEqual({'ALPHA': []}, sys_dic.get_char_categories('C')) self.assertEqual({'ALPHA': []}, sys_dic.get_char_categories('Ｃ')) self.assertEqual({'SYMBOL': []}, sys_dic.get_char_categories('#')) self.assertEqual({'SYMBOL': []}, sys_dic.get_char_categories('＃')) self.assertEqual({'NUMERIC': []}, sys_dic.get_char_categories('5')) self.assertEqual({'NUMERIC': []}, sys_dic.get_char_categories('５')) self.assertEqual({'KANJI': [], 'KANJINUMERIC': ['KANJI']}, sys_dic.get_char_categories('五')) self.assertEqual({'GREEK': []}, sys_dic.get_char_categories('Γ')) self.assertEqual({'CYRILLIC': []}, sys_dic.get_char_categories('Б')) self.assertEqual({'DEFAULT': []}, sys_dic.get_char_categories('𠮷')) self.assertEqual({'DEFAULT': []}, sys_dic.get_char_categories('한')) self.assertTrue(sys_dic.unknown_invoked_always('ALPHA')) self.assertFalse(sys_dic.unknown_invoked_always('KANJI')) self.assertTrue(sys_dic.unknown_grouping('NUMERIC')) self.assertFalse(sys_dic.unknown_grouping('KANJI')) self.assertEqual(2, sys_dic.unknown_length('HIRAGANA')) def test_property_types(self): sys_dic = SystemDictionary(all_fstdata(), entries(), connections, chardef.DATA, unknowns.DATA) # entry in the system dictionary entry = sys_dic.lookup('すもも'.encode('utf8'))[0] self.assertTrue(type(entry[1]) is str) self.assertTrue(type(entry[0]) is int) self.assertTrue(type(entry[2]) is int) self.assertTrue(type(entry[3]) is int) self.assertTrue(type(entry[4]) is int) entry_extra = sys_dic.lookup_extra(entry[0]) self.assertTrue(type(entry_extra[0]) is str) self.assertTrue(type(entry_extra[1]) is str) self.assertTrue(type(entry_extra[2]) is str) self.assertTrue(type(entry_extra[3]) is str) self.assertTrue(type(entry_extra[4]) is str) self.assertTrue(type(entry_extra[5]) is str) # unknown entry entry = sys_dic.unknowns.get(u'HIRAGANA')[0] self.assertTrue(type(entry[3]) is str) self.assertTrue(type(entry[0]) is int) self.assertTrue(type(entry[1]) is int) self.assertTrue(type(entry[2]) is int) # mmap dict etnry mmap_dic = MMapSystemDictionary(all_fstdata(), mmap_entries(), connections, chardef.DATA, unknowns.DATA) entry = mmap_dic.lookup(u'すもも'.encode('utf8'))[0] self.assertTrue(type(entry[1]) is str) self.assertTrue(type(entry[0]) is int) self.assertTrue(type(entry[2]) is int) self.assertTrue(type(entry[3]) is int) self.assertTrue(type(entry[4]) is int) entry_extra = mmap_dic.lookup_extra(entry[0]) self.assertTrue(type(entry_extra[0]) is str) self.assertTrue(type(entry_extra[1]) is str) self.assertTrue(type(entry_extra[2]) is str) self.assertTrue(type(entry_extra[3]) is str) self.assertTrue(type(entry_extra[4]) is str) self.assertTrue(type(entry_extra[5]) is str) # entry in the user defined dictionary user_dic = UserDictionary(user_dict=os.path.join(parent_dir, 'tests/user_ipadic.csv'), enc='utf8', type='ipadic', connections=connections) entry = user_dic.lookup('東京スカイツリー'.encode('utf8'))[0] self.assertTrue(type(entry[1]) is str) self.assertTrue(type(entry[0]) is int) self.assertTrue(type(entry[2]) is int) self.assertTrue(type(entry[3]) is int) self.assertTrue(type(entry[4]) is int) def test_system_dictionary_cache(self): sys_dic = SystemDictionary(all_fstdata(), entries(), connections, chardef.DATA, unknowns.DATA) self.assertEqual(11, len(sys_dic.lookup('小書き'.encode('utf8')))) self.assertEqual(11, len(sys_dic.lookup('小書き'.encode('utf8')))) self.assertEqual(11, len(sys_dic.lookup('小書きにしました'.encode('utf8')))) self.assertEqual(10, len(sys_dic.lookup('みんなと'.encode('utf8')))) self.assertEqual(10, len(sys_dic.lookup('みんなと'.encode('utf8')))) self.assertEqual(2, len(sys_dic.lookup('叩く'.encode('utf8')))) self.assertEqual(2, len(sys_dic.lookup('叩く'.encode('utf8')))) def test_user_dictionary(self): # create user dictionary from csv user_dic = UserDictionary(user_dict=os.path.join(parent_dir, 'tests/user_ipadic.csv'), enc='utf8', type='ipadic', connections=connections) self.assertEqual(1, len(user_dic.lookup('東京スカイツリー'.encode('utf8')))) # save compiled dictionary dic_dir = os.path.join(parent_dir, 'tests/userdic') user_dic.save(to_dir=os.path.join(parent_dir, 'tests/userdic')) self.assertTrue(os.path.exists(os.path.join(dic_dir, FILE_USER_FST_DATA))) self.assertTrue(os.path.exists(os.path.join(dic_dir, FILE_USER_ENTRIES_DATA))) # load compiled dictionary compiled_user_dic = CompiledUserDictionary(dic_dir, connections=connections) self.assertEqual(1, len(compiled_user_dic.lookup('とうきょうスカイツリー駅'.encode('utf8')))) def test_user_dictionary_with_progress(self): # create user dictionary from csv with progress indicator progress_indicator = SimpleProgressIndicator(update_frequency=1.0) with self.assertLogs(logger=p_logger) as cm: # create user dictionary large_user_dic = UserDictionary( user_dict=os.path.join(parent_dir, 'tests/user_ipadic.csv'), enc='utf8', type='ipadic', connections=connections, progress_handler=progress_indicator) entry_count = len(large_user_dic.entries) # output for each entry and for complete (entry_count + 1) self.assertEqual((entry_count + 1) * 2, len(cm.output)) # reset after complete self.assertIsNone(progress_indicator.value) for i in range(0, (entry_count + 1) * 2): if i < entry_count: # progress for reading csv self.assertIn('Reading user dictionary from CSV', cm.output[i]) self.assertIn(f'{i + 1}/{entry_count}', cm.output[i]) elif i == entry_count: # on compete loading csv self.assertIn(f'{entry_count}/{entry_count}', cm.output[i]) elif i < entry_count * 2 + 1: # progress for create_minimum_transducer self.assertIn('Running create_minimum_transducer', cm.output[i]) self.assertIn(f'{i - entry_count}/{entry_count}', cm.output[i]) elif i == entry_count * 2 + 1: # on compete loading create_minimum_transducer self.assertIn(f'{entry_count}/{entry_count}', cm.output[i]) # same result as without progress indicator self.assertEqual(1, len(large_user_dic.lookup('東京スカイツリー'.encode('utf8')))) def test_simplified_user_dictionary(self): # create user dictionary from csv user_dic = UserDictionary(user_dict=os.path.join(parent_dir, 'tests/user_simpledic.csv'), enc='utf8', type='simpledic', connections=connections) self.assertEqual(1, len(user_dic.lookup('東京スカイツリー'.encode('utf8')))) # save compiled dictionary dic_dir = os.path.join(parent_dir, 'tests/userdic_simple') user_dic.save(to_dir=os.path.join(parent_dir, 'tests/userdic_simple')) self.assertTrue(os.path.exists(os.path.join(dic_dir, FILE_USER_FST_DATA))) self.assertTrue(os.path.exists(os.path.join(dic_dir, FILE_USER_ENTRIES_DATA))) # load compiled dictionary compiled_user_dic = CompiledUserDictionary(dic_dir, connections=connections) self.assertEqual(1, len(compiled_user_dic.lookup('とうきょうスカイツリー駅'.encode('utf8')))) def test_simplified_user_dictionary_with_progress(self): # create simplified user dictionary from csv with progress indicator progress_indicator = SimpleProgressIndicator(update_frequency=1.0) with self.assertLogs(logger=p_logger) as cm: # create user dictionary large_user_dic = UserDictionary( user_dict=os.path.join(parent_dir, 'tests/user_simpledic.csv'), enc='utf8', type='simpledic', connections=connections, progress_handler=progress_indicator) entry_count = len(large_user_dic.entries) # output for each entry and for complete (entry_count + 1) self.assertEqual((entry_count + 1) * 2, len(cm.output)) # value is reset after complete self.assertIsNone(progress_indicator.value) for i in range(0, (entry_count + 1) * 2): if i < entry_count: # progress for reading csv self.assertIn('Reading user dictionary from CSV', cm.output[i]) self.assertIn(f'{i + 1}/{entry_count}', cm.output[i]) elif i == entry_count: # on compete loading csv self.assertIn(f'{entry_count}/{entry_count}', cm.output[i]) elif i < entry_count * 2 + 1: # progress for create_minimum_transducer self.assertIn('Running create_minimum_transducer', cm.output[i]) self.assertIn(f'{i - entry_count}/{entry_count}', cm.output[i]) elif i == entry_count * 2 + 1: # on compete loading create_minimum_transducer self.assertIn(f'{entry_count}/{entry_count}', cm.output[i]) # same result as without progress indicator self.assertEqual(1, len(large_user_dic.lookup('東京スカイツリー'.encode('utf8')))) if __name__ == '__main__': unittest.main()

mocobeta/janome

tests/test_dic.py

Python

import sys import time from selenium import webdriver from selenium.webdriver.common.keys import Keys import smtplib import logging logger = logging.getLogger("crawler") logger.setLevel(logging.DEBUG) fh = logging.FileHandler('sc_appointment_check.log') fh.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')) logger.addHandler(fh) def gmail_login(): server = smtplib.SMTP("smtp.gmail.com", 587) server.ehlo() server.starttls() server.login(sys.argv[3], sys.argv[4]) return server def verify_gmail(): try: server = gmail_login() server.close() except StandardError as e: print (e) logger.error(e) exit() def notify_user(month): FROM = sys.argv[3] TO = sys.argv[3] SUBJECT = "[SC Application] Vacancy found in %s" % month TEXT = "Go to below address to catch the slot: https://eappointment.ica.gov.sg/ibook/index.do" message = """ From: %(FROM)s To: %(TO)s Subject: %(SUBJECT)s %(TEXT) """ % locals() try: server = gmail_login() server.sendmail(FROM, TO, message) server.close() except StandardError as r: print "failed to send mail %s" % r logger.info("failed to send mail %s" % r) exit() def go_to_query_page(driver): driver.get("https://eappointment.ica.gov.sg/ibook/index.do") driver.switch_to_frame(driver.find_element_by_name("bottomFrame")); driver.switch_to_frame(driver.find_element_by_name("mainFrame")); driver.find_element_by_name("apptDetails.apptType").send_keys("Singapore Citizen Application") driver.find_element_by_name("apptDetails.identifier1").send_keys(sys.argv[1]) driver.find_element_by_name("apptDetails.identifier2").send_keys(sys.argv[2]) driver.find_element_by_name("Submit").send_keys(Keys.ENTER) def contains_released_dates(driver): days = driver.find_elements_by_css_selector("td[class^='cal_']") return any(day.get_attribute("class") in ("cal_AF", "cal_AD") for day in days) def get_month(driver): year = int(driver.find_element_by_name("calendar.calendarYearStr").get_attribute("value")) month = int(driver.find_element_by_name("calendar.calendarMonthStr").get_attribute("value")) + 1 return "%d%.2d" % (year, month) def check_free_slots(driver): days = driver.find_elements_by_css_selector("td[class^='cal_']") current_month = get_month(driver) if any("cal_AD" in day.get_attribute("class") for day in days): logger.info("Slots found in %s" % current_month) notify_user(current_month) def check(): driver = webdriver.Chrome() go_to_query_page(driver) while contains_released_dates(driver): check_free_slots(driver) driver.execute_script("doNextMth(document.forms[0]);") logger.info("Checked until %s, no available slots found." % get_month(driver)) driver.close() if __name__ == "__main__": if len(sys.argv) < 5: print("Please refer to the readme file for proper usage.") else: verify_gmail() retry_interval = sys.argv[5] if len(sys.argv) > 5 else 60 while True: check() time.sleep(retry_interval)

jzhang-cloud/e-appointment-checker

sc_appointment_check.py

Python

# -*- coding: utf-8 -*- """Top-level package for PrecisionMapper.""" import requests from requests import ConnectionError from datetime import datetime from bs4 import BeautifulSoup __author__ = """Thibault Ducret""" __email__ = 'hello@tducret.com' __version__ = '0.0.2' _DEFAULT_BEAUTIFULSOUP_PARSER = "html.parser" _SIGNIN_URL = "https://www.precisionmapper.com/users/sign_in" _SURVEYS_URL = "https://www.precisionmapper.com/surveys" _SHARED_SURVEYS_URL = "https://www.precisionmapper.com/shared_surveys" _DEFAULT_DATE = "2000-01-01T00:00:00.000Z" _RFC3339_DATE_FORMAT = '%Y-%m-%dT%H:%M:%S.%fZ' _SHORT_DATE_FORMAT = '%d/%m/%Y %H:%M' _AUTHENTICITY_TOKEN_SELECTOR = 'meta["name"="csrf-token"]' _SURVEYS_SELECTOR = "#surveysList .tableCellWrap" _SURVEY_NAME_SELECTOR = "div.surveyName > a['href']" _SURVEY_LOCATION_SELECTOR = "div.cellWrap.locationWrapper > span" _SURVEY_DATE_SELECTOR = "div.cellWrap.surveyDateRow .date" _SURVEY_IMG_NB_AND_SIZE_SELECTOR = "div.surveyotherDetails > span" _SURVEY_SENSOR_SELECTOR = ".surveySensorWrap" _SURVEY_URL_SELECTOR = "div.surveyName > a['href']" class Client(object): """ Do the requests with the servers """ def __init__(self): self.session = requests.session() self.headers = { 'authority': 'www.precisionmapper.com', 'origin': 'https://www.precisionmapper.com', 'user-Agent': 'Mozilla/5.0 (Macintosh; \ Intel Mac OS X 10_13_5) AppleWebKit/537.36 (KHTML, like Gecko) \ Chrome/67.0.3396.99 Safari/537.36', 'referer': 'https://www.precisionmapper.com\ /users/sign_in', } def _get(self, url, expected_status_code=200): ret = self.session.get(url=url, headers=self.headers) if (ret.status_code != expected_status_code): raise ConnectionError( 'Status code {status} for url {url}\n{content}'.format( status=ret.status_code, url=url, content=ret.text)) return ret def _post(self, url, post_data, expected_status_code=200, allow_redirects=True): ret = self.session.post(url=url, headers=self.headers, data=post_data, allow_redirects=allow_redirects) if (ret.status_code != expected_status_code): raise ConnectionError( 'Status code {status} for url {url}\n{content}'.format( status=ret.status_code, url=url, content=ret.text)) return ret class Survey(object): """ Class for a drone survey (mission) """ def __init__( self, id, name, url, date, sensor="", location="", image_nb=0, size="0 MB", thumbnail="", altitude_in_m=0, resolution_in_cm=0, area_in_ha=0, drone_platform=""): if type(id) != int: raise TypeError("id must be an int, not a "+str(type(id))) self.id = id if type(image_nb) != int: raise TypeError("image_nb must be an int, not a " + str(type(image_nb))) self.image_nb = image_nb self.date = date try: self.date_obj = _rfc_date_str_to_datetime_object(self.date) except: raise TypeError("date must respect the format \ YYYY-MM-DDTHH:MM:SS.sssZ, received : "+date) self.name = name self.drone_platform = drone_platform self.sensor = sensor self.location = location self.date_str = _datetime_object_to_short_date_str(self.date_obj) self.size = size self.thumbnail = thumbnail self.altitude_in_m = altitude_in_m self.resolution_in_cm = resolution_in_cm self.area_in_ha = area_in_ha def __str__(self): return('[{name}] ({location} - {date}) : {image_nb} images, \ {size}, sensor : {sensor}, id : {id}'.format( name=self.name, location=self.location, date=self.date_str, image_nb=self.image_nb, size=self.size, sensor=self.sensor, id=self.id)) def __repr__(self): return("Survey(id={}, name={})".format(self.id, self.name)) class PrecisionMapper(object): """ Class for the communications with precisionmapper.com """ def __init__(self, login, password): self.login = login self.password = password self.client = Client() def __str__(self): return(repr(self)) def __repr__(self): return("PrecisionMapper(login={})".format(self.login)) def get_authenticity_token(self, url=_SIGNIN_URL): """ Returns an authenticity_token, mandatory for signing in """ res = self.client._get(url=url, expected_status_code=200) soup = BeautifulSoup(res.text, _DEFAULT_BEAUTIFULSOUP_PARSER) selection = soup.select(_AUTHENTICITY_TOKEN_SELECTOR) try: authenticity_token = selection[0].get("content") except: raise ValueError( "authenticity_token not found in {} with {}\n{}".format( _SIGNIN_URL, _AUTHENTICITY_TOKEN_SELECTOR, res.text)) return authenticity_token def sign_in(self): authenticity_token = self.get_authenticity_token() post_data = {"utf8": "✓", "authenticity_token": authenticity_token, "return": "", "login[username]": self.login, "login[password]": self.password, "commit": "Log In"} res = self.client._post(url=_SIGNIN_URL, post_data=post_data, expected_status_code=302, allow_redirects=False) # "allow_redirects = False" because we don't want to load the # <survey> page right now => better performance return res def get_surveys(self, url=_SURVEYS_URL): """ Function to get the surveys for the account """ res = self.client._get(url=url, expected_status_code=200) soup = BeautifulSoup(res.text, _DEFAULT_BEAUTIFULSOUP_PARSER) surveys_soup = soup.select(_SURVEYS_SELECTOR) survey_list = [] for survey_soup in surveys_soup: survey_name = _css_select(survey_soup, _SURVEY_NAME_SELECTOR) try: url = survey_soup.select(_SURVEY_URL_SELECTOR)[0]["href"] except: raise ValueError("Cannot get URL for the survey \ with css selector {}".format(_SURVEY_URL_SELECTOR)) try: id = int(url.split("survey_id=")[1].split("&")[0]) except: raise ValueError("Cannot extract id from URL {}".format( url)) survey_location = _css_select(survey_soup, _SURVEY_LOCATION_SELECTOR) try: survey_epoch = int(survey_soup.select( _SURVEY_DATE_SELECTOR)[0]["epoch"]) survey_date_obj = datetime.fromtimestamp(survey_epoch) survey_date = _datetime_object_to_rfc_date_str(survey_date_obj) except: raise ValueError("Cannot get date for the survey \ with css selector {}".format(_SURVEY_DATE_SELECTOR)) survey_img_nb_and_size = survey_soup.select( _SURVEY_IMG_NB_AND_SIZE_SELECTOR) try: survey_img_nb = survey_img_nb_and_size[0].text survey_img_nb = int(survey_img_nb.split(" ")[0]) except: raise ValueError("Cannot get or convert image number, \ survey_img_nb_and_size = {}".format(survey_img_nb_and_size)) try: survey_size = survey_img_nb_and_size[1].text except: raise ValueError("Cannot get survey size, \ survey_img_nb_and_size = {}".format(survey_img_nb_and_size)) sensor = _css_select(survey_soup, _SURVEY_SENSOR_SELECTOR) survey = Survey( id=id, name=survey_name, url=url, date=survey_date, location=survey_location, image_nb=survey_img_nb, size=survey_size, sensor=sensor) survey_list.append(survey) return survey_list def get_shared_surveys(self, url=_SHARED_SURVEYS_URL): return self.get_surveys(url=url) def _css_select(soup, css_selector): """ Returns the content of the element pointed by the CSS selector, or an empty string if not found """ selection = soup.select(css_selector) if len(selection) > 0: if hasattr(selection[0], 'text'): retour = selection[0].text.strip() else: retour = "" else: retour = "" return retour def _datetime_object_to_rfc_date_str(datetime_obj): """ Returns a date string to the RFC 3339 standard """ return datetime_obj.strftime(_RFC3339_DATE_FORMAT) def _rfc_date_str_to_datetime_object(rfc_date_str): """ Returns a date string to the RFC 3339 standard """ return datetime.strptime(rfc_date_str, _RFC3339_DATE_FORMAT) def _datetime_object_to_short_date_str(datetime_obj): """ Returns a short date string """ return datetime_obj.strftime(_SHORT_DATE_FORMAT)

tducret/precisionmapper-python

precisionmapper/__init__.py

Python

from __future__ import absolute_import, unicode_literals import sys from subprocess import CalledProcessError import pytest from virtualenv.info import PY2 from virtualenv.seed.wheels.acquire import download_wheel, pip_wheel_env_run from virtualenv.seed.wheels.embed import BUNDLE_FOLDER, get_embed_wheel from virtualenv.seed.wheels.util import discover_wheels def test_pip_wheel_env_run_could_not_find(session_app_data, mocker): mocker.patch("virtualenv.seed.wheels.acquire.from_bundle", return_value=None) with pytest.raises(RuntimeError, match="could not find the embedded pip"): pip_wheel_env_run([], session_app_data) def test_download_wheel_bad_output(mocker, for_py_version, session_app_data): """if the download contains no match for what wheel was downloaded, pick one that matches from target""" distribution = "setuptools" p_open = mocker.MagicMock() mocker.patch("virtualenv.seed.wheels.acquire.Popen", return_value=p_open) p_open.communicate.return_value = "", "" p_open.returncode = 0 embed = get_embed_wheel(distribution, for_py_version) as_path = mocker.MagicMock() available = discover_wheels(BUNDLE_FOLDER, "setuptools", None, for_py_version) as_path.iterdir.return_value = [i.path for i in available] result = download_wheel(distribution, "=={}".format(embed.version), for_py_version, [], session_app_data, as_path) assert result.path == embed.path def test_download_fails(mocker, for_py_version, session_app_data): p_open = mocker.MagicMock() mocker.patch("virtualenv.seed.wheels.acquire.Popen", return_value=p_open) p_open.communicate.return_value = "out", "err" p_open.returncode = 1 as_path = mocker.MagicMock() with pytest.raises(CalledProcessError) as context: download_wheel("pip", "==1", for_py_version, [], session_app_data, as_path), exc = context.value if PY2: assert exc.output == "outerr" else: assert exc.output == "out" assert exc.stderr == "err" assert exc.returncode == 1 assert [ sys.executable, "-m", "pip", "download", "--progress-bar", "off", "--disable-pip-version-check", "--only-binary=:all:", "--no-deps", "--python-version", for_py_version, "-d", str(as_path), "pip==1", ] == exc.cmd

MShaffar19/virtualenv

tests/unit/seed/wheels/test_acquire.py

Python

# Copyright European Organization for Nuclear Research (CERN) # # Licensed under the Apache License, Version 2.0 (the "License"); # You may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # # Authors: # - Mario Lassnig, <mario.lassnig@cern.ch>, 2012-2015, 2017 # - Vincent Garonne, <vincent.garonne@cern.ch>, 2012-2013 # - Thomas Beermann, <thomas.beermann@cern.ch>, 2014 # - Hannes Hansen, <hannes.jakob.hansen@cern.ch>, 2019 # - Ruturaj Gujar <ruturaj.gujar23@gmail.com>, 2019 # # PY3K COMPATIBLE import hashlib import os import six from base64 import b64encode from re import match from sqlalchemy.exc import IntegrityError from rucio.common import exception from rucio.core.account import account_exists from rucio.db.sqla import models from rucio.db.sqla.constants import IdentityType from rucio.db.sqla.session import read_session, transactional_session @transactional_session def add_identity(identity, type, email, password=None, session=None): """ Creates a user identity. :param identity: The identity key name. For example x509 DN, or a username. :param type: The type of the authentication (x509, gss, userpass, ssh, saml) :param email: The Email address associated with the identity. :param password: If type==userpass, this sets the password. :param session: The database session in use. """ if type == IdentityType.USERPASS and password is None: raise exception.IdentityError('You must provide a password!') new_id = models.Identity() new_id.update({'identity': identity, 'identity_type': type, 'email': email}) if type == IdentityType.USERPASS and password is not None: salt = os.urandom(255) # make sure the salt has the length of the hash if six.PY3: decoded_salt = b64encode(salt).decode() salted_password = ('%s%s' % (decoded_salt, password)).encode() else: salted_password = '%s%s' % (salt, str(password)) password = hashlib.sha256(salted_password).hexdigest() # hash it new_id.update({'salt': salt, 'password': password, 'email': email}) try: new_id.save(session=session) except IntegrityError as e: if match('.*IntegrityError.*1062.*Duplicate entry.*for key.*', e.args[0]): raise exception.Duplicate('Identity pair \'%s\',\'%s\' already exists!' % (identity, type)) raise exception.DatabaseException(str(e)) @transactional_session def del_identity(identity, type, session=None): """ Deletes a user identity. :param identity: The identity key name. For example x509 DN, or a username. :param type: The type of the authentication (x509, gss, userpass, saml). :param session: The database session in use. """ id = session.query(models.Identity).filter_by(identity=identity, identity_type=type).first() if id is None: raise exception.IdentityError('Identity (\'%s\',\'%s\') does not exist!' % (identity, type)) id.delete(session=session) @transactional_session def add_account_identity(identity, type, account, email, default=False, password=None, session=None): """ Adds a membership association between identity and account. :param identity: The identity key name. For example x509 DN, or a username. :param type: The type of the authentication (x509, gss, userpass, ssh, saml). :param account: The account name. :param email: The Email address associated with the identity. :param default: If True, the account should be used by default with the provided identity. :param password: Password if type is userpass. :param session: The database session in use. """ if not account_exists(account, session=session): raise exception.AccountNotFound('Account \'%s\' does not exist.' % account) id = session.query(models.Identity).filter_by(identity=identity, identity_type=type).first() if id is None: add_identity(identity=identity, type=type, email=email, password=password, session=session) id = session.query(models.Identity).filter_by(identity=identity, identity_type=type).first() iaa = models.IdentityAccountAssociation(identity=id.identity, identity_type=id.identity_type, account=account) try: iaa.save(session=session) except IntegrityError: raise exception.Duplicate('Identity pair \'%s\',\'%s\' already exists!' % (identity, type)) @read_session def get_default_account(identity, type, session=None): """ Retrieves the default account mapped to an identity. :param identity: The identity key name. For example, x509DN, or a username. :param type: The type of the authentication (x509, gss, userpass, saml). :param session: The database session to use. :returns: The default account name, None otherwise. """ tmp = session.query(models.IdentityAccountAssociation).filter_by(identity=identity, identity_type=type, is_default=True).first() if tmp is None: raise exception.IdentityError('There is no default account for identity (%s, %s)' % (identity, type)) return tmp.account @transactional_session def del_account_identity(identity, type, account, session=None): """ Removes a membership association between identity and account. :param identity: The identity key name. For example x509 DN, or a username. :param type: The type of the authentication (x509, gss, userpass, saml). :param account: The account name. :param session: The database session in use. """ aid = session.query(models.IdentityAccountAssociation).filter_by(identity=identity, identity_type=type, account=account).first() if aid is None: raise exception.IdentityError('Identity (\'%s\',\'%s\') does not exist!' % (identity, type)) aid.delete(session=session) @read_session def list_identities(session=None, **kwargs): """ Returns a list of all identities. :param session: The database session in use. returns: A list of all identities. """ id_list = [] for id in session.query(models.Identity).order_by(models.Identity.identity): id_list.append((id.identity, id.identity_type)) return id_list @read_session def list_accounts_for_identity(identity, type, session=None): """ Returns a list of all accounts for an identity. :param identity: The identity key name. For example x509 DN, or a username. :param type: The type of the authentication (x509, gss, userpass, saml). :param session: The database session in use. returns: A list of all accounts for the identity. """ account_list = [] for account, in session.query(models.IdentityAccountAssociation.account).filter_by(identity=identity, identity_type=type): account_list.append(account) return account_list

Pranay144/rucio

lib/rucio/core/identity.py

Python

import pygame import os from pygame.locals import * import config import game import engine import menu from random import randint import _fighter from pygame_functions import * class Scenario: def __init__(self, game, scenario): self.game = game self.scenario = scenario pygame.mixer.music.stop() music = engine.Music("mkt") music.play() music.volume(0.5) def setScenario(self, scenario): if scenario == 9: scenario = randint(1, 8) #self.scene = pygame.image.load('../res/Background/Scenario'+str(scenario)+'.png') #self.game.getDisplay().blit(self.scene, (0, 0)) #pygame.display.update() #screenSize(800, 500,"pyKombat",None,None,True) # FullScreen screenSize(800, 500,"pyKombat") # Minimized setBackgroundImage('../res/Background/Scenario'+str(scenario)+'.png') self.judge(scenario) def judge(self,scenario): [player1,player2] = self.addFigther(scenario) player1.act() player2.act() nextFrame1 = clock() nextFrame2 = clock() hitCounter = 0 while True: aux1 = player1.fight(clock(),nextFrame1) nextFrame1 = aux1 aux2 = player2.fight(clock(),nextFrame2) nextFrame2 = aux2 x1 = player1.getX() x2 = player2.getX() #print(x1, x2, x2-x1) # caso encostem na tela if player1.getX() < 20: player1.setX(20) if player2.getX() < 20: player2.setX(20) if player1.getX() > (800-20): player1.setX(800-20) if player2.getX() > (800-20): player2.setX(800-20) if(collide(player1.currentSprite(),player2.currentSprite())): # caso só encostem if ( (player1.isWalking() or player1.isJumping()) and (player2.isDancing() or player2.isCrouching() or player2.isWalking()) ) or ((player2.isWalking() or player2.isJumping()) and (player1.isDancing() or player1.isCrouching() or player2.isWalking()) ) or (player1.isWalking() and player2.isWalking()) or (player1.isJumping() and player2.isJumping()) or (player1.isDancing() and player2.isDancing()) or (player2.isSpecialMove() and player1.ishitSpecial()): player1.setX(x1-15) if not player2.isSpecialMove() :player2.setX(x2+15) else: player1.setX(x1-25) # caso houve soco fraco: if ( player1.isApunching() and (player2.isWalking() or player2.isDancing() or player2.isApunching() or player2.ishitSpecial()) ) or ( player2.isApunching() and (player1.isWalking() or player1.isDancing() or player1.isApunching()) ): if player1.isApunching(): player2.takeHit("Apunching") if player2.isApunching(): player1.takeHit("Apunching") print("socofraco") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve soco forte: if ( player1.isBpunching() and (player2.isWalking() or player2.isDancing() or player2.isBpunching()) ) or ( player2.isBpunching() and (player1.isWalking() or player1.isDancing() or player1.isBpunching()) ): if player1.isBpunching(): player2.takeHit("Bpunching") if player2.isBpunching(): player1.takeHit("Bpunching") print("socoforte") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve chute fraco: if ( player1.isAkicking() and (player2.isWalking() or player2.isDancing() or player2.isAkicking() or player2.isCrouching()) and not player2.isBblocking() ) or ( player2.isAkicking() and (player1.isWalking() or player1.isDancing() or player1.isAkicking() or player1.isCrouching() and not player1.isBblocking()) ): if player1.isAkicking(): player2.takeHit("Akicking") if player2.isAkicking(): player1.takeHit("Akicking") print("chutefraco") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve chute forte: if ( player1.isBkicking() and (player2.isWalking() or player2.isDancing() or player2.isBkicking()) ) or ( player2.isBkicking() and (player1.isWalking() or player1.isDancing() or player1.isBkicking()) ): if player1.isBkicking(): player2.takeHit("Bkicking") if player2.isBkicking(): player1.takeHit("Bkicking") print("chuteforte") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve bloqueio em pé: if ( (player1.isApunching() or player1.isBpunching() or player1.isDpunching() or player1.isAkicking() or player1.isBkicking() ) and player2.isAblocking() ) or ( (player2.isApunching() or player2.isBpunching() or player1.isDpunching() or player2.isAkicking() or player2.isBkicking() ) and player1.isAblocking() ): if player1.isAblocking(): player1.takeHit("Ablocking") if player2.isAblocking(): player2.takeHit("Ablocking") engine.Sound("block").play() player1.setX(x1-12) player2.setX(x2+12) print("ablock") # caso houve soco ou chute agachado fraco em alguém em pé: if ( ((player1.isCpunching() or player1.isCkicking() ) and not player2.isCrouching() and not player2.isBblocking() ) or ((player2.isCpunching() or player2.isCkicking() ) and not player1.isCrouching() and not player1.isBblocking() ) ): # falta adicionar o Bblock if player1.isCpunching() or player1.isCkicking(): player2.takeHit("Cpunching") if player2.isCpunching() or player2.isCkicking(): player1.takeHit("Cpunching") print("socofraco!!!!!!!") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve soco agachado forte em alguém em pé: if ( (player1.isDpunching() and (not player2.isAblocking() and not player2.isBblocking()) ) or player2.isDpunching() and (not player1.isAblocking() and not player1.isBblocking()) ): if player1.isDpunching(): player2.takeHit("Bkicking") if player2.isDpunching(): player1.takeHit("Bkicking") print("socofraco$#$") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve chute agachado forte em alguém em pé: if ( player1.isDkicking() or player2.isDkicking() ): if player1.isDkicking(): player2.takeHit("Dkicking") if player2.isDkicking(): player1.takeHit("Dkicking") print("socofraco") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve soco ou chute agachado fraco em alguém agachado: if ( ( (player1.isCpunching() or player1.isCkicking()) and player2.isCrouching() and not player2.isBblocking() ) or ( (player2.isCpunching() or player2.isCkicking()) and player1.isCrouching() and not player1.isBblocking() ) ): if player1.isCpunching() or player1.isCkicking(): player2.takeDownHit("Ehit") if player2.isCpunching() or player2.isCkicking(): player1.takeDownHit("Ehit") print("socofraco**") engine.Sound("Hit0").play() if hitCounter == 0: engine.Sound().roundHit() hitCounter = (hitCounter+1) % 5 # caso houve bloqueio agachado: if ( (player1.isCpunching() or player1.isDpunching() or player1.isAkicking() or player1.isCkicking() ) and player2.isBblocking() ) or ( (player2.isCpunching() or player2.isDpunching() or player2.isAkicking() or player2.isCkicking() ) and player1.isBblocking() ): if player1.isBblocking(): player1.takeDownHit("Bblocking") if player2.isBblocking(): player2.takeDownHit("Bblocking") engine.Sound("block").play() player1.setX(x1-12) player2.setX(x2+12) print("bblock") # caso houve special if ( player1.isSpecialMove() and (player2.isWalking() or player2.isDancing()) ) or ( player2.isSpecialMove() and (player1.isWalking() or player1.isDancing()) ): if player1.isSpecialMove() and collide(player1.getProjectile().getProjectileSprite(), player2.currentSprite()): # and collide(projetil,player2) player2.takeHit("special") if player2.isSpecialMove(): # and collide(projetil,player1) player1.takeHit("special") print("special") for event in pygame.event.get(): if event.type == QUIT: pygame.quit() if keyPressed("backspace"): pygame.quit() if keyPressed("esc"): self.goBack(player1,player2) def addFigther(self,scenario): player1 = _fighter.Fighter(0,scenario) # 0: subzero player2 = _fighter.Fighter(1,scenario) # 1: scorpion return player1,player2 def goBack(self,player1,player2): player1.killPlayer() player2.killPlayer() del(player1) del(player2) sound = engine.Sound("back") sound.play() pygame.mixer.music.stop() music = engine.Music("intro") music.play() music.volume(0.5) menu.ScenarioMenu() def collide(sprite1,sprite2): return pygame.sprite.collide_mask(sprite1,sprite2)

Lewiscowles1986/pyKombat

.history/src/fightScene_20190422211023.py

Python

from fastapi import HTTPException, status from sqlalchemy.orm import Session from blog import hashing, models, schemas def create(request: schemas.User, db: Session): new_user = models.User(name=request.name, email=request.email, password=hashing.Hash.bcrypt(request.password)) db.add(new_user) db.commit() db.refresh(new_user) return new_user def get_one(id: int, db: Session): user = db.query(models.User).filter(models.User.id == id).first() if not user: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f'User with the id {id} is not found.') return user def bulk_load(data, db: Session): for i in data: new_post = models.User(name=i[0], email=i[1], password=hashing.Hash.bcrypt(i[2])) db.add(new_post) db.commit() db.refresh(new_post) return len(data)

cristian-rincon/blog-api

app/blog/repository/user.py

Python

# -*- coding: utf-8 -* __author__ = 'shawn' t = (1,) print t t = (1) print t t = (1, 2, 3) print t

BeeBubble/SnakeRace

Liao Xue Feng Py2 Edu/tuple.py

Python

try: from django.utils.unittest import TestCase except ImportError: from django.test import TestCase try: from django.utils import unittest except ImportError: import unittest from mock import Mock import string from evennia.server.portal import irc from twisted.conch.telnet import IAC, WILL, DONT, SB, SE, NAWS, DO from twisted.test import proto_helpers from twisted.trial.unittest import TestCase as TwistedTestCase from .telnet import TelnetServerFactory, TelnetProtocol from .portal import PORTAL_SESSIONS from .suppress_ga import SUPPRESS_GA from .naws import DEFAULT_HEIGHT, DEFAULT_WIDTH from .ttype import TTYPE, IS from .mccp import MCCP from .mssp import MSSP from .mxp import MXP from .telnet_oob import MSDP, MSDP_VAL, MSDP_VAR class TestIRC(TestCase): def test_plain_ansi(self): """ Test that printable characters do not get mangled. """ irc_ansi = irc.parse_ansi_to_irc(string.printable) ansi_irc = irc.parse_irc_to_ansi(string.printable) self.assertEqual(irc_ansi, string.printable) self.assertEqual(ansi_irc, string.printable) def test_bold(self): s_irc = "\x02thisisatest" s_eve = r'|hthisisatest' self.assertEqual(irc.parse_ansi_to_irc(s_eve), s_irc) self.assertEqual(s_eve, irc.parse_irc_to_ansi(s_irc)) def test_italic(self): s_irc = "\x02thisisatest" s_eve = r'|hthisisatest' self.assertEqual(irc.parse_ansi_to_irc(s_eve), s_irc) def test_colors(self): color_map = (("\0030", r'|w'), ("\0031", r'|X'), ("\0032", r'|B'), ("\0033", r'|G'), ("\0034", r'|r'), ("\0035", r'|R'), ("\0036", r'|M'), ("\0037", r'|Y'), ("\0038", r'|y'), ("\0039", r'|g'), ("\00310", r'|C'), ("\00311", r'|c'), ("\00312", r'|b'), ("\00313", r'|m'), ("\00314", r'|x'), ("\00315", r'|W'), ("\00399,5", r'|[r'), ("\00399,3", r'|[g'), ("\00399,7", r'|[y'), ("\00399,2", r'|[b'), ("\00399,6", r'|[m'), ("\00399,10", r'|[c'), ("\00399,15", r'|[w'), ("\00399,1", r'|[x')) for m in color_map: self.assertEqual(irc.parse_irc_to_ansi(m[0]), m[1]) self.assertEqual(m[0], irc.parse_ansi_to_irc(m[1])) def test_identity(self): """ Test that the composition of the function and its inverse gives the correct string. """ s = r'|wthis|Xis|gis|Ma|C|complex|*string' self.assertEqual(irc.parse_irc_to_ansi(irc.parse_ansi_to_irc(s)), s) class TestTelnet(TwistedTestCase): def setUp(self): super(TestTelnet, self).setUp() factory = TelnetServerFactory() factory.protocol = TelnetProtocol factory.sessionhandler = PORTAL_SESSIONS factory.sessionhandler.portal = Mock() self.proto = factory.buildProtocol(("localhost", 0)) self.transport = proto_helpers.StringTransport() self.addCleanup(factory.sessionhandler.disconnect_all) def test_mudlet_ttype(self): self.transport.client = ["localhost"] self.transport.setTcpKeepAlive = Mock() d = self.proto.makeConnection(self.transport) # test suppress_ga self.assertTrue(self.proto.protocol_flags["NOGOAHEAD"]) self.proto.dataReceived(IAC + DONT + SUPPRESS_GA) self.assertFalse(self.proto.protocol_flags["NOGOAHEAD"]) self.assertEqual(self.proto.handshakes, 7) # test naws self.assertEqual(self.proto.protocol_flags['SCREENWIDTH'], {0: DEFAULT_WIDTH}) self.assertEqual(self.proto.protocol_flags['SCREENHEIGHT'], {0: DEFAULT_HEIGHT}) self.proto.dataReceived(IAC + WILL + NAWS) self.proto.dataReceived([IAC, SB, NAWS, '', 'x', '', 'd', IAC, SE]) self.assertEqual(self.proto.protocol_flags['SCREENWIDTH'][0], 120) self.assertEqual(self.proto.protocol_flags['SCREENHEIGHT'][0], 100) self.assertEqual(self.proto.handshakes, 6) # test ttype self.assertTrue(self.proto.protocol_flags["FORCEDENDLINE"]) self.assertFalse(self.proto.protocol_flags["TTYPE"]) self.assertTrue(self.proto.protocol_flags["ANSI"]) self.proto.dataReceived(IAC + WILL + TTYPE) self.proto.dataReceived([IAC, SB, TTYPE, IS, "MUDLET", IAC, SE]) self.assertTrue(self.proto.protocol_flags["XTERM256"]) self.assertEqual(self.proto.protocol_flags["CLIENTNAME"], "MUDLET") self.proto.dataReceived([IAC, SB, TTYPE, IS, "XTERM", IAC, SE]) self.proto.dataReceived([IAC, SB, TTYPE, IS, "MTTS 137", IAC, SE]) self.assertEqual(self.proto.handshakes, 5) # test mccp self.proto.dataReceived(IAC + DONT + MCCP) self.assertFalse(self.proto.protocol_flags['MCCP']) self.assertEqual(self.proto.handshakes, 4) # test mssp self.proto.dataReceived(IAC + DONT + MSSP) self.assertEqual(self.proto.handshakes, 3) # test oob self.proto.dataReceived(IAC + DO + MSDP) self.proto.dataReceived([IAC, SB, MSDP, MSDP_VAR, "LIST", MSDP_VAL, "COMMANDS", IAC, SE]) self.assertTrue(self.proto.protocol_flags['OOB']) self.assertEqual(self.proto.handshakes, 2) # test mxp self.proto.dataReceived(IAC + DONT + MXP) self.assertFalse(self.proto.protocol_flags['MXP']) self.assertEqual(self.proto.handshakes, 1) # clean up to prevent Unclean reactor self.proto.nop_keep_alive.stop() self.proto._handshake_delay.cancel() return d

Antrare/evennia

evennia/server/portal/tests.py

Python

#========================================================================== # # Copyright Insight Software Consortium # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0.txt # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #==========================================================================*/ # new features introduced by itk module # each new feature use a name in lower case clrLine = "\033[2000D\033[K" def auto_not_in_place( v=True ) : """Force it to not run in place """ import itkConfig itkConfig.NotInPlace = v def auto_progress( progressType = 1 ): """Set up auto progress report progressType: 1 or True -> auto progress be used in a terminal 2 -> simple auto progress (without special characters) 0 or False -> disable auto progress """ import itkConfig if progressType == True or progressType == 1 : itkConfig.ImportCallback = terminal_import_callback itkConfig.ProgressCallback = terminal_progress_callback elif progressType == 2 : itkConfig.ImportCallback = simple_import_callback itkConfig.ProgressCallback = simple_progress_callback elif progressType == False or progressType == 0 : itkConfig.ImportCallback = None itkConfig.ProgressCallback = None else: raise ValueError("Invalid auto progress type: "+repr(progressType)) def terminal_progress_callback(name, p): """Display the progress of an object and clean the display once complete This function can be used with itkConfig.ProgressCallback """ import sys print >> sys.stderr, clrLine+"%s: %f" % (name, p), if p == 1 : print >> sys.stderr, clrLine, def terminal_import_callback(name, p): """Display the loading of a module and clean the display once complete This function can be used with itkConfig.ImportCallback """ import sys print >> sys.stderr, clrLine+"Loading %s..." % name, if p == 1 : print >> sys.stderr, clrLine, def simple_import_callback(name, p): """Print a message when a module is loading This function can be used with itkConfig.ImportCallback """ import sys if p == 0: print >> sys.stderr, "Loading %s..." % name, elif p == 1 : print >> sys.stderr, "done" def simple_progress_callback(name, p): """Print a message when an object is running This function can be used with itkConfig.ProgressCallback """ import sys if p == 0 : print >> sys.stderr, "Running %s..." % name, elif p == 1 : print >> sys.stderr, "done" def force_load(): """force itk to load all the submodules""" import itk for k in dir(itk): getattr(itk, k) import sys def echo(object, f=sys.stderr) : """Print an object is f If the object has a method Print(), this method is used. repr(object) is used otherwise """ print >> f, object del sys def size(imageOrFilter) : """Return the size of an image, or of the output image of a filter This method take care of updating the needed informations """ # we don't need the entire output, only its size imageOrFilter.UpdateOutputInformation() img = output(imageOrFilter) return img.GetLargestPossibleRegion().GetSize() def physical_size(imageOrFilter) : """Return the physical size of an image, or of the output image of a filter This method take care of updating the needed informations """ from __builtin__ import range # required because range is overladed in this module spacing_ = spacing(imageOrFilter) size_ = size(imageOrFilter) result = [] for i in range(0, spacing_.Size()): result.append( spacing_.GetElement(i) * size_.GetElement(i) ) return result def spacing(imageOrFilter) : """Return the spacing of an image, or of the output image of a filter This method take care of updating the needed informations """ # we don't need the entire output, only its size imageOrFilter.UpdateOutputInformation() img = output(imageOrFilter) return img.GetSpacing() def origin(imageOrFilter) : """Return the origin of an image, or of the output image of a filter This method take care of updating the needed informations """ # we don't need the entire output, only its size imageOrFilter.UpdateOutputInformation() img = output(imageOrFilter) return img.GetOrigin() def index(imageOrFilter) : """Return the index of an image, or of the output image of a filter This method take care of updating the needed informations """ # we don't need the entire output, only its size imageOrFilter.UpdateOutputInformation() img = output(imageOrFilter) return img.GetLargestPossibleRegion().GetIndex() def region(imageOrFilter) : """Return the region of an image, or of the output image of a filter This method take care of updating the needed informations """ # we don't need the entire output, only its size imageOrFilter.UpdateOutputInformation() img = output(imageOrFilter) return img.GetLargestPossibleRegion() def strel(dim, radius=1) : """A method to create a ball structuring element """ import itk import sys # print >> sys.stderr, "strel() is deprecated and will be removed in the next release" return itk.FlatStructuringElement[dim].Ball(radius) # return an image from itkTemplate import image, output def template(cl) : """Return the template of a class (or of the class of an object) and its parameters template() returns a tuple with 2 elements: - the first one is the itkTemplate object - the second is a tuple containing the template parameters """ from itkTemplate import itkTemplate return itkTemplate.__class_to_template__[class_(cl)] def ctype(s) : """Return the c type corresponding to the string passed in parameter The string can contain some extra spaces. see also itkCType """ from itkTypes import itkCType ret = itkCType.GetCType(" ".join(s.split())) if ret == None : raise KeyError("Unrecognized C type '%s'" % s) return ret def class_(obj) : """Return a class from an object Often in itk, the __class__ is not what the user is expecting. class_() should do a better job """ import inspect if inspect.isclass(obj) : # obj is already a class ! return obj else : return obj.__class__ def range(imageOrFilter) : """Return the range of values in a image of in the output image of a filter The minimum and maximum values are returned in a tuple: (min, max) range() take care of updating the pipeline """ import itk img = output(imageOrFilter) img.UpdateOutputInformation() img.Update() # don't put that calculator in the automatic pipeline tmp_auto_pipeline = auto_pipeline.current auto_pipeline.current = None comp = itk.MinimumMaximumImageCalculator[img].New(Image=img) auto_pipeline.current = tmp_auto_pipeline comp.Compute() return (comp.GetMinimum(), comp.GetMaximum()) def write(imageOrFilter, fileName, compression=False): """Write a image or the output image of a filter to filename The writer is instantiated with the image type of the image in parameter (or, again, with the output image of the filter in parameter) """ import itk img = output(imageOrFilter) img.UpdateOutputInformation() # don't put that writer in the automatic pipeline tmp_auto_pipeline = auto_pipeline.current auto_pipeline.current = None writer = itk.ImageFileWriter[img].New(Input=img, FileName=fileName, UseCompression=compression) auto_pipeline.current = tmp_auto_pipeline writer.Update() def index_to_physical_point( imageOrFilter, idx ): """Get the pysical point in an image from an index imageOrFilter is the image where the physical point must be computed idx is the index used to compute the physical point. It can be a continuous index. """ import sys print >> sys.stderr, "WrapITK warning: itk.index_to_physical_point() is deprecated. The coresponding templated method is now available in itk::ImageBase." from __builtin__ import range # required because range is overladed in this module # get the image if needed img = output( imageOrFilter ) dim = img.GetImageDimension() o = origin( img ) s = spacing( img ) # use the typemaps to really get a continuous index import itk idx = itk.ContinuousIndex[ itk.D, dim ]( idx ) # create the output object p = itk.Point[ itk.D, dim ]() for i in range( 0, dim ): p[i] = s[i] * idx[i] + o[i] return p def physical_point_to_continuous_index( imageOrFilter, p ): """Get the continuous index in an image from the physical point imageOrFilter is the image where the physical point must be computed p is the point used to compute the index """ import sys print >> sys.stderr, "WrapITK warning: itk.index_to_physical_point() is deprecated. The coresponding templated method is now available in itk::ImageBase." from __builtin__ import range # required because range is overladed in this module # get the image if needed img = output( imageOrFilter ) dim = img.GetImageDimension() o = origin( img ) s = spacing( img ) # use the typemaps to really get a point import itk p = itk.Point[ itk.D, dim ]( p ) # create the output object idx = itk.ContinuousIndex[ itk.D, dim ]() for i in range( 0, dim ): idx.SetElement( i, ( p[i] - o[i] ) / s[i] ) return idx def physical_point_to_index( imageOrFilter, p ): """Get the index in an image from the physical point image is the image where the physical point must be computed p is the point used to compute the index """ import sys print >> sys.stderr, "WrapITK warning: itk.physical_point_to_index() is deprecated. The coresponding templated method is now available in itk::ImageBase." from __builtin__ import range # required because range is overladed in this module # get the image if needed img = output( imageOrFilter ) dim = img.GetImageDimension() o = origin( img ) s = spacing( img ) # use the typemaps to really get a point import itk p = itk.Point[ itk.D, dim ]( p ) # create the output object idx = itk.Index[ dim ]() for i in range( 0, dim ): idx.SetElement( i, int( round( ( p[i] - o[i] ) / s[i] ) ) ) return idx def search( s, case_sensitive=False): #, fuzzy=True): """Search for a class name in the itk module. """ s = s.replace(" ", "") if not case_sensitive: s = s.lower() import itk names = dir(itk) names.sort() # exact match first if case_sensitive: res = [n for n in names if s == n] else: res = [n for n in names if s == n.lower()] # then exact match inside the name if case_sensitive: res += [n for n in names if s in n and s != n] else: res += [n for n in names if s in n.lower() and s != n.lower()] # if fuzzy: # try: # # everything now requires editdist # import editdist # if case_sensitive: # res.sort(key=lambda x: editdist.distance(x, s)) # else: # res.sort(key=lambda x: (editdist.distance(x.lower(), s), x)) # except: # pass return res def set_inputs( newItkObject, args=[], kargs={} ): """Set the inputs of the given objects, according to the non named or the named parameters in args and kargs This function tries to assign all the non named parameters in the input of the newItkObject - the first non named parameter in the first input, etc. The named parameters are used by calling the method with the same name prefixed by 'Set'. set_inputs( obj, kargs={'Threshold': 10} ) calls obj.SetThreshold(10) This is the function use in the enhanced New() method to manage the inputs. It can be used to produce a similar behavior: def SetInputs(self, *args, **kargs): import itk itk.set_inputs(self, *args, **kargs) """ # try to get the images from the filters in args args = [output(arg) for arg in args] # args without name are filter used to set input image # # count SetInput calls to call SetInput, SetInput2, SetInput3, ... # usefull with filter which take 2 input (or more) like SubstractImageFiler # Ex: substract image2.png to image1.png and save the result in result.png # r1 = itk.ImageFileReader.US2.New(FileName='image1.png') # r2 = itk.ImageFileReader.US2.New(FileName='image2.png') # s = itk.SubtractImageFilter.US2US2US2.New(r1, r2) # itk.ImageFileWriter.US2.New(s, FileName='result.png').Update() try : for setInputNb, arg in enumerate(args) : methodName = 'SetInput%i' % (setInputNb+1) if methodName in dir(newItkObject) : # first try to use methods called SetInput1, SetInput2, ... # those method should have more chances to work in case of multiple # input types getattr(newItkObject, methodName)(arg) else : # no method called SetInput? # try with the standard SetInput(nb, input) newItkObject.SetInput(setInputNb, arg) except TypeError, e : # the exception have (at least) to possible reasons: # + the filter don't take the input number as first argument # + arg is an object of wrong type # # if it's not the first input, re-raise the exception if setInputNb != 0 : raise e # it's the first input, try to use the SetInput() method without input number newItkObject.SetInput(args[0]) # but raise an exception if there is more than 1 argument if len(args) > 1 : raise TypeError('Object accept only 1 input.') except AttributeError : # There is no SetInput() method, try SetImage # but before, check the number of inputs if len(args) > 1 : raise TypeError('Object accept only 1 input.') methodList = ['SetImage', 'SetInputImage'] methodName = None for m in methodList: if m in dir(newItkObject): methodName = m if methodName : getattr(newItkObject, methodName)(args[0]) else: raise AttributeError('No method found to set the input.') # named args : name is the function name, value is argument(s) for attribName, value in kargs.iteritems() : # use Set as prefix. It allow to use a shorter and more intuitive # call (Ex: itk.ImageFileReader.UC2.New(FileName='image.png')) than with the # full name (Ex: itk.ImageFileReader.UC2.New(SetFileName='image.png')) if attribName not in ["auto_progress", "template_parameters"] : attrib = getattr(newItkObject, 'Set' + attribName) attrib(value) def show(input, **kargs) : """display an image """ import itk img = output(input) if img.GetImageDimension() == 3 and "show3D" in dir(itk): return itk.show3D(input, **kargs) else : # print "2D not supported yet, use the 3D viewer." return show2D(input, **kargs) class show2D : """Display a 2D image """ def __init__(self, imageOrFilter, Label=False, Title=None) : import tempfile, itk, os # get some data from the environment command = os.environ.get("WRAPITK_SHOW2D_COMMAND", "imagej %(image)s -run 'View 100%%' -eval 'rename(\"%(title)s\")' &") label_command = os.environ.get("WRAPITK_SHOW2D_LABEL_COMMAND", "imagej %(image)s -run 'View 100%%' -eval 'rename(\"%(title)s\")' -run '3-3-2 RGB' &") compress = os.environ.get("WRAPITK_SHOW2D_COMPRESS", "true").lower() in ["on", "true", "yes", "1"] extension = os.environ.get("WRAPITK_SHOW2D_EXTENSION", ".tif") # use the tempfile module to get a non used file name and to put # the file at the rignt place self.__tmpFile__ = tempfile.NamedTemporaryFile(suffix=extension) # get an updated image img = output(imageOrFilter) img.UpdateOutputInformation() img.Update() if Title == None: # try to generate a title s = img.GetSource() if s: s = itk.down_cast(s) if hasattr(img, "GetSourceOutputIndex"): o = '[%s]' % img.GetSourceOutputIndex() elif hasattr(img, "GetSourceOutputName"): o = '[%s]' % img.GetSourceOutputName() else: o = "" Title = "%s%s" % (s.__class__.__name__, o) else: Title = img.__class__.__name__ try: import IPython.ipapi ip = IPython.ipapi.get() if ip != None: names = [] ref = imageOrFilter if s: ref = s for n, v in ip.user_ns.iteritems(): if isinstance(v, itk.LightObject) and v == ref: names.append(n) if names != []: Title = ", ".join(names)+" - "+Title except ImportError: # just do nothing pass # change the LabelMaps to an Image, so we can look at them easily if 'LabelMap' in dir(itk) and img.GetNameOfClass() == 'LabelMap': # retreive the biggest label in the label map maxLabel = img.GetNthLabelObject( img.GetNumberOfLabelObjects() - 1 ).GetLabel() # search for a filter to convert the label map label_image_type = sorted( [params[1] for params in itk.LabelMapToLabelImageFilter.keys() if params[0] == class_(img) and itk.NumericTraits[itk.template(params[1])[1][0]].max() >= maxLabel ] )[0] convert = itk.LabelMapToLabelImageFilter[ img, label_image_type ].New( img ) convert.Update() img = convert.GetOutput() # this is a label image - force the parameter Label = True write(img, self.__tmpFile__.name, compress) # now run imview import os if Label: os.system( label_command % {"image":self.__tmpFile__.name, "title": Title} ) else: os.system( command % {"image":self.__tmpFile__.name, "title": Title} ) #tmpFile.close() class templated_class: """This class is used to mimic the behavior of the templated C++ classes. It is used that way: class CustomClass: # class definition here CustomClass = templated_class(CustomClass) customObject = CustomClass[template, parameters].New() The template parameters are passed to the custom class constructor as a named parameter 'template_parameters' in a tuple. The custom class may implement a static method check_template_parameters(parameters) which should raise an exception if the template parameters provided are not suitable to instantiate the custom class. """ def __init__(self, cls): """cls is the custom class """ self.__cls__ = cls self.__templates__ = {} def New(self, *args, **kargs): """Use the parameters to infer the types of the template parameters. """ # extract the types from the arguments to instantiate the class import itk types = tuple(itk.class_(o) for o in args) return self[types].New(*args, **kargs) def __getitem__(self, template_parameters): """Return a pair class-template parameters ready to be instantiated. The template parameters may be validated if the custom class provide the static method check_template_parameters(parameters). """ if not isinstance(template_parameters, tuple): template_parameters = (template_parameters,) return templated_class.__templated_class_and_parameters__(self, template_parameters) def check_template_parameters(self, template_parameters): """Check the template parameters passed in parameter. """ # this method is there mainly to make possible to reuse it in the custom class # constructor after having used templated_class(). Without that, the following # example doesn't work: # # class CustomClass: # def __init__(self, *args, **kargs): # template_parameters = kargs["template_parameters"] # CustomClass.check_template_parameters(template_parameters) # # other init stuff # def check_template_parameters(template_parameters): # # check, really # pass # CustomClass = templated_class(CustomClass) # self.__cls__.check_template_parameters(template_parameters) def add_template(self, name, params): if not isinstance(params, list) and not isinstance(params, tuple): params = (params,) params = tuple(params) val = self[params] self.__templates__[params] = val setattr(self, name, val) def add_image_templates(self, *args): import itk if args == []: return combinations = [[t] for t in args[0]] for types in args[1:]: temp = [] for t in types: for c in combinations: temp.append(c+[t]) combinations = temp for d in itk.DIMS: for c in combinations: parameters = [] name = "" for t in c: parameters.append( itk.Image[t, d] ) name += "I"+t.short_name+str(d) self.add_template(name, tuple(parameters)) class __templated_class_and_parameters__: """Inner class used to store the pair class-template parameters ready to instantiate. """ def __init__(self, templated_class, template_parameters): self.__templated_class__ = templated_class self.__template_parameters__ = template_parameters if "check_template_parameters" in dir(templated_class.__cls__): templated_class.__cls__.check_template_parameters(template_parameters) def New(self, *args, **kargs): """A New() method to mimic the ITK default behavior, even if the class doesn't provide any New() method. """ kargs["template_parameters"] = self.__template_parameters__ if "New" in dir(self.__templated_class__.__cls__): obj = self.__templated_class__.__cls__.New(*args, **kargs) else: obj = self.__templated_class__.__cls__(*args, **kargs) setattr(obj, "__template_parameters__", self.__template_parameters__) setattr(obj, "__templated_class__", self.__templated_class__) return obj def __call__(self, *args, **kargs): return self.New(*args, **kargs) def keys(self): return self.__templates__.keys() # everything after this comment is for dict interface # and is a copy/paste from DictMixin # only methods to edit dictionary are not there def __iter__(self): for k in self.keys(): yield k def has_key(self,key): try: value=self[key] except KeyError: return False return True def __contains__(self,key): return self.has_key(key) # third level takes advantage of second level definitions def iteritems(self): for k in self: yield (k,self[k]) def iterkeys(self): return self.__iter__() # fourth level uses definitions from lower levels def itervalues(self): for _,v in self.iteritems(): yield v def values(self): return [v for _,v in self.iteritems()] def items(self): return list(self.iteritems()) def get(self,key,default=None): try: return self[key] except KeyError: return default def __len__(self): return len(self.keys()) class pipeline: """A convenient class to store the reference to the filters of a pipeline With this class, a method can create a pipeline of several filters and return it without losing the references to the filters in this pipeline. The pipeline object act almost like a filter (it has a GetOutput() method) and thus can be simply integrated in another pipeline. """ def __init__( self, *args, **kargs ): self.clear() self.input = None set_inputs( self, args, kargs ) def connect( self, filter ): """Connect a new filter to the pipeline The output of the first filter will be used as the input of this one and the filter passed as parameter will be added to the list """ if self.GetOutput() != None: set_inputs(filter, [self.GetOutput()] ) self.append( filter ) def append( self, filter ): """Add a new filter to the pipeline The new filter will not be connected. The user must connect it. """ self.filters.append( filter ) def clear( self ): """Clear the filter list """ self.filters = [] def GetOutput( self, index=0 ): """Return the output of the pipeline If another output is needed, use pipeline.filters[-1].GetAnotherOutput() instead of this method, subclass pipeline to implement another GetOutput() method, or use expose() """ if len(self.filters) == 0: return self.GetInput() else : filter = self.filters[-1] if hasattr(filter, "__getitem__"): return filter[index] try: return filter.GetOutput(index) except: if index == 0: return filter.GetOutput() else: raise ValueError("Index can only be 0 on that object") def SetInput( self, input ): """Set the input of the pipeline """ if len(self.filters) != 0: set_inputs(self.filters[0], [input]) self.input = input def GetInput( self ): """Get the input of the pipeline """ return self.input def Update( self ): """Update the pipeline """ if len(self.filters) > 0: return self.filters[-1].Update() def UpdateLargestPossibleRegion( self ): """Update the pipeline """ if len(self.filters) > 0: return self.filters[-1].UpdateLargestPossibleRegion() def UpdateOutputInformation( self ): if "UpdateOutputInformation" in dir(self.filters[-1]): self.filters[-1].UpdateOutputInformation() else: self.Update() def __len__(self): if len(self.filters) == 0: return 1 else: return self.filters[-1].GetNumberOfOutputs() def __getitem__(self, item): return self.GetOutput( item ) def __call__(self, *args, **kargs): set_inputs( self, args, kargs ) self.UpdateLargestPossibleRegion() return self def expose(self, name, new_name=None, position=-1): """Expose an attribute from a filter of the minipeline. Once called, the pipeline instance has a new Set/Get set of methods to access directly the corresponding method of one of the filter of the pipeline. Ex: p.expose( "Radius" ) p.SetRadius( 5 ) p.GetRadius( 5 ) By default, the attribute usable on the pipeline instance has the same name than the one of the filter, but it can be changed by providing a value to new_name. The last filter of the pipeline is used by default, but another one may be used by giving its position. Ex: p.expose("Radius", "SmoothingNeighborhood", 2) p.GetSmoothingNeighborhood() """ if new_name == None: new_name = name src = self.filters[position] ok = False set_name = "Set" + name if set_name in dir(src): setattr(self, "Set" + new_name, getattr(src, set_name)) ok = True get_name = "Get" + name if get_name in dir(src): setattr(self, "Get" + new_name, getattr(src, get_name)) ok = True if not ok: raise RuntimeError("No attribute %s at position %s." % (name, position)) class auto_pipeline(pipeline): current = None def __init__(self, *args, **kargs): pipeline.__init__(self, *args, **kargs) self.Start() def Start(self): auto_pipeline.current = self def Stop(self): auto_pipeline.current = None def down_cast(obj): """Down cast an itkLightObject (or a object of a subclass) to its most specialized type. """ import itk, itkTemplate className = obj.GetNameOfClass() t = getattr(itk, className) if isinstance(t, itkTemplate.itkTemplate): for c in t.values(): try: return c.cast(obj) except: # fail silently for now pass raise RuntimeError("Can't downcast to a specialization of %s" % className) else: return t.cast(obj) def attribute_list( i, name ): """Returns a list of the specified attributes for the objects in the image. i: the input LabelImage name: the attribute name """ import itk i = itk.output(i) relabel = itk.StatisticsRelabelLabelMapFilter[i].New(i, Attribute=name, ReverseOrdering=True, InPlace=False) relabel.UpdateLargestPossibleRegion() r = relabel.GetOutput() l = [] for i in range(1, r.GetNumberOfLabelObjects()+1): l.append( r.GetLabelObject(i).__getattribute__("Get"+name)() ) return l def attributes_list( i, names ): """Returns a list of the specified attributes for the objects in the image. i: the input LabelImage name: the attribute name """ import itk i = itk.output(i) relabel = itk.StatisticsRelabelLabelMapFilter[i].New(i, Attribute=names[0], ReverseOrdering=True, InPlace=False) relabel.UpdateLargestPossibleRegion() r = relabel.GetOutput() l = [] for i in range(1, r.GetNumberOfLabelObjects()+1): attrs = [] for name in names : attrs.append( r.GetLabelObject(i).__getattribute__("Get"+name)() ) l.append( tuple( attrs ) ) return l def attribute_dict( i, name ): """Returns a dict with the attribute values in keys and a list of the corresponding objects in value i: the input LabelImage name: the name of the attribute """ import itk i = itk.output(i) relabel = itk.StatisticsRelabelLabelMapFilter[i].New(i, Attribute=name, ReverseOrdering=True, InPlace=False) relabel.UpdateLargestPossibleRegion() r = relabel.GetOutput() d = {} for i in range(1, r.GetNumberOfLabelObjects()+1): lo = r.GetLabelObject(i) v = lo.__getattribute__("Get"+name)() l = d.get( v, [] ) l.append( lo ) d[v] = l return d def number_of_objects( i ): """Returns the number of objets in the image. i: the input LabelImage """ import itk i.UpdateLargestPossibleRegion() i = itk.output(i) return i.GetNumberOfLabelObjects() def ipython_kw_matches(text): """Match named ITK object's named parameters""" import IPython.ipapi, itk, re, inspect, itkTemplate regexp = re.compile(r''' '.*?' | # single quoted strings or ".*?" | # double quoted strings or \w+ | # identifier \S # other characters ''', re.VERBOSE | re.DOTALL) ip = IPython.ipapi.get() if "." in text: # a parameter cannot be dotted return [] # 1. find the nearest identifier that comes before an unclosed # parenthesis e.g. for "foo (1+bar(x), pa", the candidate is "foo". # Use get_endidx() to find the indentifier at the cursor position tokens = regexp.findall(ip.IP.Completer.get_line_buffer()[:ip.IP.Completer.get_endidx()]) tokens.reverse() iterTokens = iter(tokens); openPar = 0 for token in iterTokens: if token == ')': openPar -= 1 elif token == '(': openPar += 1 if openPar > 0: # found the last unclosed parenthesis break else: return [] # 2. Concatenate dotted names ("foo.bar" for "foo.bar(x, pa" ) ids = [] isId = re.compile(r'\w+$').match while True: try: ids.append(iterTokens.next()) if not isId(ids[-1]): ids.pop(); break if not iterTokens.next() == '.': break except StopIteration: break # lookup the candidate callable matches either using global_matches # or attr_matches for dotted names if len(ids) == 1: callableMatches = ip.IP.Completer.global_matches(ids[0]) else: callableMatches = ip.IP.Completer.attr_matches('.'.join(ids[::-1])) argMatches = [] for callableMatch in callableMatches: # drop the .New at this end, so we can search in the class members if callableMatch.endswith(".New"): callableMatch = callableMatch[:-4] try: object = eval(callableMatch, ip.IP.Completer.namespace) if isinstance(object, itkTemplate.itkTemplate): # this is a template - lets grab the first entry to search for the methods object = object.values()[0] namedArgs = [] if isinstance(object, itk.LightObject) or (inspect.isclass(object) and issubclass(object, itk.LightObject)): namedArgs = [n[3:] for n in dir(object) if n.startswith("Set")] except Exception, e: print e continue for namedArg in namedArgs: if namedArg.startswith(text): argMatches.append("%s=" %namedArg) return argMatches # install progress callback and custom completer if we are in ipython interpreter try: import itkConfig, IPython.ipapi if IPython.ipapi.get(): IPython.ipapi.get().IP.Completer.matchers.insert(0, ipython_kw_matches) itkConfig.ProgressCallback = terminal_progress_callback # some cleanup del itkConfig, IPython except ImportError: # fail silently pass # now loads the other modules we may found in the same directory import os.path, sys directory = os.path.dirname(__file__) moduleNames = [name[:-len('.py')] for name in os.listdir(directory) if name.endswith('.py') and name != '__init__.py'] for name in moduleNames: # there should be another way - I don't like to much exec -, but the which one ?? exec "from %s import *" % name # some cleaning del directory, os, sys, moduleNames, name

CapeDrew/DCMTK-ITK

Wrapping/WrapITK/Languages/Python/itkExtras/__init__.py

Python

from mwb_help import deck_is_available, create_deck,\ model_is_available, add_model, add_note from scraper_lxml import get_note_default, get_note_simple class AnkiDutchDeck(): def __init__(self, deck_name=None): if deck_name is None: deck_name = 'tidbits' if not deck_is_available(deck_name): create_deck(deck_name) self.deck_name = deck_name self.default_model_name = 'dutch_default' self.simple_model_name = 'dutch_simple' if not model_is_available(self.default_model_name): self.add_model_default() if not model_is_available(self.simple_model_name): self.add_model_simple() def add_model_default(self): model_name = 'dutch_default' note_fields = ['Dutch', 'Misc', 'Explanations', 'Examples'] card_templates = [{'Front': '{{Dutch}}', 'Back' : '{{Misc}}<hr><hr>{{Explanations}}'}, {'Front': '{{Dutch}}<hr>{{Misc}}', 'Back' : '{{Explanations}}<hr><hr>{{Examples}}'}, {'Front': '{{Explanations}}', 'Back' : '{{Dutch}}<hr>{{Misc}}<hr><hr>{{Examples}}'}, {'Front': '{{Examples}}', 'Back' : '{{Dutch}}<hr>{{Misc}}<hr><hr>{{Explanations}}'}] add_model(model_name, note_fields, card_templates) def add_model_simple(self): model_name = 'dutch_simple' note_fields = ['Dutch', 'Explanations'] card_templates = [{'Front': '{{Dutch}}', 'Back' : '{{Explanations}}'}, {'Front': '{{Explanations}}', 'Back' : '{{Dutch}}'}] add_model(model_name, note_fields, card_templates) def add_note_default(self, note_fields): add_note(note_fields, self.deck_name, self.default_model_name) def add_note_simple(self, note_fields): add_note(note_fields, self.deck_name, self.simple_model_name) def add_note_from_word(self, word, output_file=None): is_default_model = True try: note_fields = get_note_default(word) if note_fields is None: print(f'"{word}" not found in mijnwoordenbook') if output_file: with open(output_file, 'a') as f: f.write('\n') f.write(word) return except: is_default_model = False note_fields = get_note_simple(word) try: if is_default_model: self.add_note_default(note_fields) else: self.add_note_simple(note_fields) except: print(f'"{word}" already exists in deck {self.deck_name}') def add_note_from_list(self, word_list, output_file=None): [self.add_note_from_word(w, output_file) for w in word_list] if __name__ == '__main__': import argparse parser = argparse.ArgumentParser(description='file containing word list') parser.add_argument('-f', '--file', help='select file containing word list') parser.add_argument('-l', '--list', help='input word list, separated by comma') parser.add_argument('-o', '--output', help='output unfound words to file') args = parser.parse_args() word_list = [] if args.file is not None: words = open(args.file).read().split('\n') word_list.extend(words) if args.list is not None: words = args.list.strip().split(',') word_list.extend(words) # word_list = ['hhhsss', 'duits', 'alsjeblieft', 'waterpokken'] if "" in word_list: word_list.remove("") ADD = AnkiDutchDeck() ADD.add_note_from_list(word_list, args.output)

gaganpreet/learning-dutch

add_cards.py

Python

#!/usr/bin/env python from distutils.core import setup import setuptools with open("README.md", "r") as fh: long_description = fh.read() setup( name='pyzkaccess', description='Python interface to ZKTeco ZKAccess C3-100/200/400 controllers', version='0.2', author='Igor Derkach', author_email='gosha753951@gmail.com', url='https://github.com/bdragon300/pyzkaccess', license='Apache 2.0', python_requires='>=3.5', packages=setuptools.find_packages(exclude=['tests', 'docs']), long_description=long_description, long_description_content_type='text/markdown', classifiers=[ 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Operating System :: Microsoft :: Windows', 'Development Status :: 5 - Production/Stable', 'License :: OSI Approved :: Apache Software License', 'Intended Audience :: Developers', 'Topic :: System :: Hardware' ], # Also tox.ini install_requires=[ 'wrapt', 'pydantic' ], )

cybrnode/pyzkaccess

setup.py

Python

from genomics_demo.dna import DNA import pytest def test_bad_sequence_raises_error(): with pytest.raises(ValueError): DNA('ATB') def test_complimentary_sequence_works(): assert DNA('GTC').complimentary_sequence == DNA('CAG') assert DNA('ATC').complimentary_sequence == DNA('TAG') assert DNA('GTC').complimentary_sequence == DNA('CAG') assert DNA('ATC').complimentary_sequence == DNA('TAG') def test_gc_content(): assert DNA('ATTTATGGCC').gc_content == 0.4 assert DNA('AGGTATGGCC').gc_content == 0.6 assert DNA('ATAT').gc_content == 0 def test_triplets(): assert DNA('AAA').split_DNA_triplets == ['AAA'] assert DNA('AAATTTGGG').split_DNA_triplets == ['AAA','TTT','GGG'] assert DNA('AAAT').split_DNA_triplets == ['AAA','T'] def test_find_start(): assert DNA('ATGGG').find_first_start_site == 0 assert DNA('CCCCATG').find_first_start_site == 4 with pytest.raises(TypeError): DNA('GGG').find_first_start_site assert DNA('GTC').compliment == DNA('CAG') assert DNA('ATC').compliment == DNA('TAG') def test_find_start_codons(): """New test to test the function to find start codons""" assert DNA('ATGGTACATGCGA').find_start_codons() == [0, 7] def test_transcribe(): assert DNA('GTC').transcribe() == 'GAC' assert DNA('ATC').transcribe() == 'GAU' #def test_gc_content_sequence_works(): # assert DNA('GC').gc_content > 0.5 def test_is_gc_rich(): assert DNA('GTGT').gc_content() == 0.5 # length = len(sequence) # c_count = sequence.upper().count('C') # g_count = sequence.upp def test_gc_content(): assert DNA('ATTTATGGCC').gc_content == 0.4 assert DNA('AGGTATGGCC').gc_content == 0.6 assert DNA('ATAT').gc_content == 0

nickdelgrosso/genomics_workshop_demo

tests/test_dna.py

Python

import json def read_cfg(model_repository): pass

drunkcoding/model-inference

service/server_cfg.py

Python