Reset23/the-stack-v2-python · Datasets at Hugging Face

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import cv2 import os cam = cv2.VideoCapture(0) #cam.set(3, 640) # set video width #cam.set(4, 480) # set video height face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') # For each person, enter one numeric face id face_id = input('\n enter user id end press <return> ==> ') print("\n [INFO] Initializing face capture. Look the camera and wait ...") # Initialize individual sampling face count count = 0 while(True): ret, img = cam.read() # img = cv2.flip(img, -1) # flip video image vertically gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_detector.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in faces: cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2) count += 1 # Save the captured image into the datasets folder cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w]) # cv2.imshow('image', img) k = cv2.waitKey(100) & 0xff # Press 'ESC' for exiting video if k == 27: break elif count >= 15: # Take 30 face sample and stop video break # Do a bit of cleanup print("\n [INFO] Exiting Program and cleanup stuff") cam.release() cv2.destroyAllWindows()

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import time import numpy as np import image_data_pipeline import ni import np_tif #import thorlabs from pco import pco_edge_camera_child_process import pickle def main(): # This incantation is forced on us so the IDP won't print everything twice: import logging import multiprocessing as mp logger = mp.log_to_stderr() logger.setLevel(logging.INFO) # Set parameters for IDP (Image Data Pipeline) num_buffers_needed = 200 image_height_pixels = 128 image_width_pixels = 380 # Set parameters for DAQ (analog out card) num_daq_channels = 3 daq_rate = 8e5 ############################################################## # Set exposure parameters for camera and laser illumination: # ############################################################## green_AOM_mV = [ 0, 60, 69, 82, 92, 103, 114, 127, 151, 174, 212, 247, 300, ] #calibrated green_powers = [ '0mW', '25mW', '50mW', '100mW', '150mW', '225mW', '300mW', '400mW', '600mW', '800mW', '1100mW', '1300mW', '1500mW', ] red_AOM_mV = [ 0, 100, 131, 158, 186, 219, 269, ] #calibrated red_powers = [ '0mW', '50mW', '100mW', '150mW', '200mW', '250mW', '300mW', ] angle_string = '117_5' # Set laser pulse duration VERY SHORT green_pulse_duration_pixels = 1 red_pulse_duration_pixels = 1 # Set green pulse train repetition time short enough to # thermally stabilize the sample green_rep_time_us = 600 green_rep_time_pixels = int(np.ceil( green_rep_time_us * 1e-6 * daq_rate)) # how many red laser shots in an exposure? pulses_per_exposure = 25 # you don't want red light leaking into next exposure so set this to # 1 if you're imaging 720 nm. # set to zero if you're looking for depletion, because you need # every green pulse matched with a red for that measurement less_red_pulses = 1 desired_effective_exposure_time_pixels = (green_rep_time_pixels * pulses_per_exposure) assert desired_effective_exposure_time_pixels > 0 #define red/green pulse delays red_start_pixel_array = np.array([-2, -1, 0, 1, 2]) num_delays = red_start_pixel_array.shape[0] print('Red/green delay (us) =', red_start_pixel_array / daq_rate * 1e6) # number of exposures should be the first dimension of the idp buffer num_reps = num_buffers_needed num_exposures = ( num_delays * len(green_powers) * len(red_powers) ) # actual roll time is 640 us, which should be a multiple of # green_rep_time_us, but may not always be this only works for the # current field of view height 128 pixels 10 us per line, rolling is # symmetrical around middle of chip rolling_time_us = 640 #experimentally determined for this field of view rolling_time_pixels = int(np.ceil( rolling_time_us * 1e-6 * daq_rate)) extra_time_after_roll_pixels = (green_rep_time_pixels - rolling_time_pixels % green_rep_time_pixels) effective_exposure_time_pixels = (extra_time_after_roll_pixels + desired_effective_exposure_time_pixels) # reminder: negative delay values (red before green) are only valid if the # camera roll finishes before the red pulse gets there assert extra_time_after_roll_pixels > -min(red_start_pixel_array) set_exposure_time_pixels = (rolling_time_pixels + effective_exposure_time_pixels) # set exposure time must be an integer multiple of green rep time assert (set_exposure_time_pixels % green_rep_time_pixels) == 0 set_exposure_time_us = int(np.ceil( set_exposure_time_pixels / daq_rate * 1e6)) # Initialize the IDP: idp = image_data_pipeline.Image_Data_Pipeline( num_buffers=num_buffers_needed, buffer_shape=(num_exposures, image_height_pixels, image_width_pixels), camera_child_process=pco_edge_camera_child_process) assert idp.buffer_shape[0] == num_exposures # Initialize the DAQ: daq = ni.PCI_6733( num_channels=num_daq_channels, rate=daq_rate, verbose=True) assert daq.rate == daq_rate try: # Apply camera settings: idp.display.set_intensity_scaling('median_filter_autoscale') idp.apply_camera_settings( trigger='external_trigger', exposure_time_microseconds = set_exposure_time_us, region_of_interest ={'bottom': 1088, 'top': 961, 'left': 841, 'right': 1220}, preframes=0) # UNCOMMON COMMAND: the daq voltage string can get very long, so # Andy wrote a new part of pco.py that adjusts the set timeout # for waiting for the FIRST camera trigger (Oct 4, 2016) idp.camera.commands.send(('set_first_trigger_timeout_seconds', {'first_trigger_timeout_seconds': 3})) assert idp.camera.commands.recv() == 3 # clear command queue # Figure out some basic timing information: This is what the # camera thinks it's doing. Is it what we want it to do? exposure_time_us = idp.camera.get_setting('exposure_time_microseconds') print('I want exposure time to be (us)',set_exposure_time_us) print('Exposure time actually is (us)',exposure_time_us) assert exposure_time_us == set_exposure_time_us rolling_time_us = idp.camera.get_setting('rolling_time_microseconds') rolling_time_jitter_us = 15 #experimentally measured and also in spec rolling_time_us += rolling_time_jitter_us pulse_tail_us = 25 #experimentally measured response of buffer amp and AOM print("\nCamera exposure time:", exposure_time_us, "(us)\n") print("\nCamera rolling time:", rolling_time_us, "(us)\n") effective_exposure_us = exposure_time_us - rolling_time_us print("\nCamera effective exposure:", effective_exposure_us, "(us)\n") # Calculate DAQ voltages for rep_num in range(num_reps): exposure_number = 0 for [red_voltage_num, my_red_voltage_mV] in enumerate(red_AOM_mV): for [green_voltage_num, my_green_voltage_mV] in enumerate(green_AOM_mV): # Set voltages to play on analog out card green_voltage = my_green_voltage_mV/1000 red_voltage = my_red_voltage_mV/1000 trig_voltage = 3 # time between exposures must be greater than camera trigger # jitter and a multiple of the green rep time # trigger jitter is about 10 us time_between_exposures_pixels = 2 * green_rep_time_pixels camera_rep_time_pixels = (set_exposure_time_pixels + time_between_exposures_pixels) camera_rep_time_us = ( camera_rep_time_pixels / daq_rate * 1e6) voltages_delay_scan = np.zeros( (camera_rep_time_pixels * num_delays, num_daq_channels)) # green laser pulses on for the duration of the daq play green_chunk = np.zeros(green_rep_time_pixels) green_chunk[0:green_pulse_duration_pixels] = green_voltage voltages_delay_scan[:,1] = np.tile( green_chunk, int( voltages_delay_scan.shape[0]/green_rep_time_pixels)) # camera trigger duration should be 3us or greater trigger_duration_us = 3 trigger_duration_pixels = int(np.ceil( trigger_duration_us / 1e6 * daq_rate)) # loop used to define camera trigger and red laser pulse # voltages for which_exposure in range(num_delays): cursor = which_exposure * camera_rep_time_pixels # Camera triggers: voltages_delay_scan[ cursor:cursor + trigger_duration_pixels, 0] = ( trig_voltage) # Red laser pulses red_start_pixel = ( red_start_pixel_array[which_exposure % num_delays]) red_series_start = (cursor + rolling_time_pixels + extra_time_after_roll_pixels + red_start_pixel) red_chunk = np.zeros(green_rep_time_pixels) red_chunk[0:red_pulse_duration_pixels] = red_voltage red_exposure_array = np.tile(red_chunk, ( pulses_per_exposure - less_red_pulses)) voltages_delay_scan[ red_series_start:( red_series_start + red_exposure_array.shape[0]), 2 ] = red_exposure_array if exposure_number == 0: all_voltages = voltages_delay_scan else: all_voltages = np.append( all_voltages,voltages_delay_scan, axis=0) exposure_number += 1 # Put it all together idp.load_permission_slips( num_slips=1, file_saving_info=[ {'filename': ( 'STE_darkfield_power_delay_scan_' + str(rep_num) + '.tif'), 'channels': num_delays, 'slices': num_exposures/num_delays, }]) daq.play_voltages(all_voltages, block=True) finally: # Shut everything down. This can be important! daq.close() idp.close() if __name__ == '__main__': main()

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/main/urls.py

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from django.contrib import admin from django.urls import path from . import views admin.site.site_header = "Martian Pins administration" app_name = "main" urlpatterns = [ path("", views.index, name="index"), path("terms", views.terms, name="terms"), path("pins/hash/", views.hash_pin, name="hash_pin"), path("pins/upload/", views.upload_pin, name="upload_pin"), path("pins/rm/<int:pin_id>", views.rm_pin, name="rm_pin"), ]

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"""time_display_assignment URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.10/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.conf.urls import url, include 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls')) """ from django.conf.urls import url, include from django.contrib import admin urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^', include('apps.timedisplay.urls')) ]

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#!/Users/trananhquan/Desktop/ARTIFICIAL INTELLIGENCE/Proj2/Project/bin/python #-*- coding:utf-8 -*- ## ## lbx.py ## ## Created on: Jan 9, 2017 ## Author: Alexey S. Ignatiev ## E-mail: aignatiev@ciencias.ulisboa.pt ## """ =============== List of classes =============== .. autosummary:: :nosignatures: LBX ================== Module description ================== This module implements a prototype of the LBX algorithm for the computation of a *minimal correction subset* (MCS) and/or MCS enumeration. The LBX abbreviation stands for *literal-based MCS extraction* algorithm, which was proposed in [1]_. Note that this prototype does not follow the original low-level implementation of the corresponding MCS extractor available `online <https://reason.di.fc.ul.pt/wiki/doku.php?id=lbx>`_ (compared to our prototype, the low-level implementation has a number of additional heuristics used). However, it implements the LBX algorithm for partial MaxSAT formulas, as described in [1]_. .. [1] Carlos Mencia, Alessandro Previti, Joao Marques-Silva. *Literal-Based MCS Extraction*. IJCAI 2015. pp. 1973-1979 The implementation can be used as an executable (the list of available command-line options can be shown using ``lbx.py -h``) in the following way: :: $ xzcat formula.wcnf.xz p wcnf 3 6 4 1 1 0 1 2 0 1 3 0 4 -1 -2 0 4 -1 -3 0 4 -2 -3 0 $ lbx.py -d -e all -s glucose3 -vv formula.wcnf.xz c MCS: 1 3 0 c cost: 2 c MCS: 2 3 0 c cost: 2 c MCS: 1 2 0 c cost: 2 c oracle time: 0.0002 Alternatively, the algorithm can be accessed and invoked through the standard ``import`` interface of Python, e.g. .. code-block:: python >>> from pysat.examples.lbx import LBX >>> from pysat.formula import WCNF >>> >>> wcnf = WCNF(from_file='formula.wcnf.xz') >>> >>> lbx = LBX(wcnf, use_cld=True, solver_name='g3') >>> for mcs in lbx.enumerate(): ... lbx.block(mcs) ... print(mcs) [1, 3] [2, 3] [1, 2] ============== Module details ============== """ # #============================================================================== from __future__ import print_function import collections import getopt from math import copysign import os from pysat.formula import CNFPlus, WCNFPlus from pysat.solvers import Solver, SolverNames import re from six.moves import range import sys # #============================================================================== class LBX(object): """ LBX-like algorithm for computing MCSes. Given an unsatisfiable partial CNF formula, i.e. formula in the :class:`.WCNF` format, this class can be used to compute a given number of MCSes of the formula. The implementation follows the LBX algorithm description in [1]_. It can use any SAT solver available in PySAT. Additionally, the "clause :math:`D`" heuristic can be used when enumerating MCSes. The default SAT solver to use is ``m22`` (see :class:`.SolverNames`). The "clause :math:`D`" heuristic is disabled by default, i.e. ``use_cld`` is set to ``False``. Internal SAT solver's timer is also disabled by default, i.e. ``use_timer`` is ``False``. :param formula: unsatisfiable partial CNF formula :param use_cld: whether or not to use "clause :math:`D`" :param solver_name: SAT oracle name :param use_timer: whether or not to use SAT solver's timer :type formula: :class:`.WCNF` :type use_cld: bool :type solver_name: str :type use_timer: bool """ def __init__(self, formula, use_cld=False, solver_name='m22', use_timer=False): """ Constructor. """ # bootstrapping the solver with hard clauses self.oracle = Solver(name=solver_name, bootstrap_with=formula.hard, use_timer=use_timer) self.solver = solver_name # adding native cardinality constraints (if any) as hard clauses # this can be done only if the Minicard solver is in use if isinstance(formula, WCNFPlus) and formula.atms: assert self.oracle.supports_atmost(), \ '{0} does not support native cardinality constraints. Make sure you use the right type of formula.'.format(solver_name) for atm in formula.atms: self.oracle.add_atmost(*atm) self.topv = formula.nv # top variable id self.soft = formula.soft self.sels = [] self.ucld = use_cld # mappings between internal and external variables VariableMap = collections.namedtuple('VariableMap', ['e2i', 'i2e']) self.vmap = VariableMap(e2i={}, i2e={}) # at this point internal and external variables are the same for v in range(1, formula.nv + 1): self.vmap.e2i[v] = v self.vmap.i2e[v] = v for cl in self.soft: sel = cl[0] if len(cl) > 1 or cl[0] < 0: self.topv += 1 sel = self.topv self.oracle.add_clause(cl + [-sel]) self.sels.append(sel) def __del__(self): """ Destructor. """ self.delete() def __enter__(self): """ 'with' constructor. """ return self def __exit__(self, exc_type, exc_value, traceback): """ 'with' destructor. """ self.delete() def delete(self): """ Explicit destructor of the internal SAT oracle. """ if self.oracle: self.oracle.delete() self.oracle = None def add_clause(self, clause, soft=False): """ The method for adding a new hard of soft clause to the problem formula. Although the input formula is to be specified as an argument of the constructor of :class:`LBX`, adding clauses may be helpful when *enumerating* MCSes of the formula. This way, the clauses are added incrementally, i.e. *on the fly*. The clause to add can be any iterable over integer literals. The additional Boolean parameter ``soft`` can be set to ``True`` meaning the the clause being added is soft (note that parameter ``soft`` is set to ``False`` by default). :param clause: a clause to add :param soft: whether or not the clause is soft :type clause: iterable(int) :type soft: bool """ # first, map external literals to internal literals # introduce new variables if necessary cl = list(map(lambda l: self._map_extlit(l), clause if not len(clause) == 2 or not type(clause[0]) == list else clause[0])) if not soft: if not len(clause) == 2 or not type(clause[0]) == list: # the clause is hard, and so we simply add it to the SAT oracle self.oracle.add_clause(cl) else: # this should be a native cardinality constraint, # which can be used only together with Minicard assert solver_name in SolverNames.minicard or \ solver_name in SolverNames.gluecard3 or \ solver_name in SolverNames.gluecard4, \ '{0} does not support native cardinality constraints'.format(solver_name) self.oracle.add_atmost(cl, clause[1]) else: self.soft.append(cl) # soft clauses should be augmented with a selector sel = cl[0] if len(cl) > 1 or cl[0] < 0: self.topv += 1 sel = self.topv self.oracle.add_clause(cl + [-sel]) self.sels.append(sel) def compute(self, enable=[]): """ Compute and return one solution. This method checks whether the hard part of the formula is satisfiable, i.e. an MCS can be extracted. If the formula is satisfiable, the model computed by the SAT call is used as an *over-approximation* of the MCS in the method :func:`_compute` invoked here, which implements the LBX algorithm. An MCS is reported as a list of integers, each representing a soft clause index (the smallest index is ``1``). An optional input parameter is ``enable``, which represents a sequence (normally a list) of soft clause indices that a user would prefer to enable/satisfy. Note that this may result in an unsatisfiable oracle call, in which case ``None`` will be reported as solution. Also, the smallest clause index is assumed to be ``1``. :param enable: a sequence of clause ids to enable :type enable: iterable(int) :rtype: list(int) """ self.setd = [] self.satc = [False for cl in self.soft] # satisfied clauses self.solution = None self.bb_assumps = [] # backbone assumptions self.ss_assumps = [] # satisfied soft clause assumptions if self.oracle.solve(assumptions=[self.sels[cl_id - 1] for cl_id in enable]): # hard part is satisfiable => there is a solution self._filter_satisfied(update_setd=True) self._compute() self.solution = list(map(lambda i: i + 1, filter(lambda i: not self.satc[i], range(len(self.soft))))) return self.solution def enumerate(self): """ This method iterates through MCSes enumerating them until the formula has no more MCSes. The method iteratively invokes :func:`compute`. Note that the method does not block the MCSes computed - this should be explicitly done by a user. """ done = False while not done: mcs = self.compute() if mcs != None: yield mcs else: done = True def block(self, mcs): """ Block a (previously computed) MCS. The MCS should be given as an iterable of integers. Note that this method is not automatically invoked from :func:`enumerate` because a user may want to block some of the MCSes conditionally depending on the needs. For example, one may want to compute disjoint MCSes only in which case this standard blocking is not appropriate. :param mcs: an MCS to block :type mcs: iterable(int) """ self.oracle.add_clause([self.sels[cl_id - 1] for cl_id in mcs]) def _satisfied(self, cl, model): """ Given a clause (as an iterable of integers) and an assignment (as a list of integers), this method checks whether or not the assignment satisfies the clause. This is done by a simple clause traversal. The method is invoked from :func:`_filter_satisfied`. :param cl: a clause to check :param model: an assignment :type cl: iterable(int) :type model: list(int) :rtype: bool """ for l in cl: if len(model) < abs(l) or model[abs(l) - 1] == l: # either literal is unassigned or satisfied by the model return True return False def _filter_satisfied(self, update_setd=False): """ This method extracts a model provided by the previous call to a SAT oracle and iterates over all soft clauses checking if each of is satisfied by the model. Satisfied clauses are marked accordingly while the literals of the unsatisfied clauses are kept in a list called ``setd``, which is then used to refine the correction set (see :func:`_compute`, and :func:`do_cld_check`). Optional Boolean parameter ``update_setd`` enforces the method to update variable ``self.setd``. If this parameter is set to ``False``, the method only updates the list of satisfied clauses, which is an under-approximation of a *maximal satisfiable subset* (MSS). :param update_setd: whether or not to update setd :type update_setd: bool """ model = self.oracle.get_model() setd = set() for i, cl in enumerate(self.soft): if not self.satc[i]: if self._satisfied(cl, model): self.satc[i] = True self.ss_assumps.append(self.sels[i]) else: setd = setd.union(set(cl)) if update_setd: self.setd = sorted(setd) def _compute(self): """ The main method of the class, which computes an MCS given its over-approximation. The over-approximation is defined by a model for the hard part of the formula obtained in :func:`compute`. The method is essentially a simple loop going over all literals unsatisfied by the previous model, i.e. the literals of ``self.setd`` and checking which literals can be satisfied. This process can be seen a refinement of the over-approximation of the MCS. The algorithm follows the pseudo-code of the LBX algorithm presented in [1]_. Additionally, if :class:`LBX` was constructed with the requirement to make "clause :math:`D`" calls, the method calls :func:`do_cld_check` at every iteration of the loop using the literals of ``self.setd`` not yet checked, as the contents of "clause :math:`D`". """ # unless clause D checks are used, test one literal at a time # and add it either to satisfied of backbone assumptions i = 0 while i < len(self.setd): if self.ucld: self.do_cld_check(self.setd[i:]) i = 0 if self.setd: # if may be empty after the clause D check if self.oracle.solve(assumptions=self.ss_assumps + self.bb_assumps + [self.setd[i]]): # filtering satisfied clauses self._filter_satisfied() else: # current literal is backbone self.bb_assumps.append(-self.setd[i]) i += 1 def do_cld_check(self, cld): """ Do the "clause :math:`D`" check. This method receives a list of literals, which serves a "clause :math:`D`" [2]_, and checks whether the formula conjoined with :math:`D` is satisfiable. .. [2] Joao Marques-Silva, Federico Heras, Mikolas Janota, Alessandro Previti, Anton Belov. *On Computing Minimal Correction Subsets*. IJCAI 2013. pp. 615-622 If clause :math:`D` cannot be satisfied together with the formula, then negations of all of its literals are backbones of the formula and the LBX algorithm can stop. Otherwise, the literals satisfied by the new model refine the MCS further. Every time the method is called, a new fresh selector variable :math:`s` is introduced, which augments the current clause :math:`D`. The SAT oracle then checks if clause :math:`(D \\vee \\neg{s})` can be satisfied together with the internal formula. The :math:`D` clause is then disabled by adding a hard clause :math:`(\\neg{s})`. :param cld: clause :math:`D` to check :type cld: list(int) """ # adding a selector literal to clause D # selector literals for clauses D currently # cannot be reused, but this may change later self.topv += 1 sel = self.topv cld.append(-sel) # adding clause D self.oracle.add_clause(cld) if self.oracle.solve(assumptions=self.ss_assumps + self.bb_assumps + [sel]): # filtering satisfied self._filter_satisfied(update_setd=True) else: # clause D is unsatisfiable => all literals are backbones self.bb_assumps.extend([-l for l in cld[:-1]]) self.setd = [] # deactivating clause D self.oracle.add_clause([-sel]) def _map_extlit(self, l): """ Map an external variable to an internal one if necessary. This method is used when new clauses are added to the formula incrementally, which may result in introducing new variables clashing with the previously used *clause selectors*. The method makes sure no clash occurs, i.e. it maps the original variables used in the new problem clauses to the newly introduced auxiliary variables (see :func:`add_clause`). Given an integer literal, a fresh literal is returned. The returned integer has the same sign as the input literal. :param l: literal to map :type l: int :rtype: int """ v = abs(l) if v in self.vmap.e2i: return int(copysign(self.vmap.e2i[v], l)) else: self.topv += 1 self.vmap.e2i[v] = self.topv self.vmap.i2e[self.topv] = v return int(copysign(self.topv, l)) def oracle_time(self): """ Report the total SAT solving time. """ return self.oracle.time_accum() # #============================================================================== def parse_options(): """ Parses command-line options. """ try: opts, args = getopt.getopt(sys.argv[1:], 'de:hs:v', ['dcalls', 'enum=', 'help', 'solver=', 'verbose']) except getopt.GetoptError as err: sys.stderr.write(str(err).capitalize() + '\n') usage() sys.exit(1) dcalls = False to_enum = 1 solver = 'm22' verbose = 0 for opt, arg in opts: if opt in ('-d', '--dcalls'): dcalls = True elif opt in ('-e', '--enum'): to_enum = str(arg) if to_enum != 'all': to_enum = int(to_enum) elif opt in ('-h', '--help'): usage() sys.exit(0) elif opt in ('-s', '--solver'): solver = str(arg) elif opt in ('-v', '--verbose'): verbose += 1 else: assert False, 'Unhandled option: {0} {1}'.format(opt, arg) return dcalls, to_enum, solver, verbose, args # #============================================================================== def usage(): """ Prints help message. """ print('Usage:', os.path.basename(sys.argv[0]), '[options] file') print('Options:') print(' -d, --dcalls Try to bootstrap algorithm') print(' -e, --enum=<string> How many solutions to compute') print(' Available values: [1 .. all] (default: 1)') print(' -h, --help') print(' -s, --solver SAT solver to use') print(' Available values: g3, g4, lgl, mcb, mcm, mpl, m22, mc, mgh (default = m22)') print(' -v, --verbose Be verbose') # #============================================================================== if __name__ == '__main__': dcalls, to_enum, solver, verbose, files = parse_options() if type(to_enum) == str: to_enum = 0 if files: # reading standard CNF, WCNF, or (W)CNF+ if re.search('cnf[p|+]?(\.(gz|bz2|lzma|xz))?$', files[0]): if re.search('\.wcnf[p|+]?(\.(gz|bz2|lzma|xz))?$', files[0]): formula = WCNFPlus(from_file=files[0]) else: # expecting '*.cnf[,p,+].*' formula = CNFPlus(from_file=files[0]).weighted() with LBX(formula, use_cld=dcalls, solver_name=solver, use_timer=True) as mcsls: for i, mcs in enumerate(mcsls.enumerate()): if verbose: print('c MCS:', ' '.join([str(cl_id) for cl_id in mcs]), '0') if verbose > 1: cost = sum([formula.wght[cl_id - 1] for cl_id in mcs]) print('c cost:', cost) if to_enum and i + 1 == to_enum: break mcsls.block(mcs) print('c oracle time: {0:.4f}'.format(mcsls.oracle_time()))

[ "trananhquanttbpvn@gmail.com" ]

trananhquanttbpvn@gmail.com

27b6bcf7074e83cbd289142a3e9781b9bb8031bf

0a22dca130804854d892d7f5d33510c4f1aed160

/queue.py

b2acadae2f00d54c3eb5e00b5cb6a68d6dd6034b

[]

no_license

abossard/interview_code

7c9b80c94de649589f4d4910a9f5c5064fd79095

3fd4ba450a2423f7c345e57c6d765512ec3ba7c2

refs/heads/master

2021-01-21T22:58:30.914500

2014-03-19T22:51:06

null

0

null

UTF-8

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810

py

class Queue(object): a = list() b = list() def enqueue(self, data): tmp = self.b.pop() if self.b else None while tmp: self.a.append(tmp) tmp = self.b.pop() if self.b else None self.a.append(data) tmp = self.a.pop() if self.a else None while tmp: self.b.append(tmp) tmp = self.a.pop() if self.a else None def dequeue(self): return self.b.pop() if self.b else None def size(self): return len(self.b) def __unicode__(self): return self.b q = Queue() q.enqueue('first') q.enqueue('second1') q.enqueue('second2') q.enqueue('second3') q.enqueue('last') print q.dequeue() print q.dequeue() print q.dequeue() print q.dequeue() print q.dequeue()

[ "abossard@gmail.com" ]

abossard@gmail.com

ef21c363df2cf73f2d3e12e9a5bf03fdd55c5ad3

eb2367cd85d8616751cc588d3531bf0fae420871

/hellowebapp/urls.py

924e902cb229eb1d06dde5f9e7dca1882b3f5f4f

[]

no_license

Shai1436/Collection-app

169c2c0d394c6b151b9a488ff7ba2b4d0ce171b3

1f20d558ffacd07700760bf18f85a5d7e008023e

refs/heads/master

2021-01-21T10:30:27.833898

2017-05-21T02:58:18

91,694,618

0

null

UTF-8

Python

false

2,323

py

from django.contrib.auth.views import ( password_reset, password_reset_done, password_reset_confirm, password_reset_complete ) from django.conf.urls import url, include from django.contrib import admin from django.views.generic import ( TemplateView, RedirectView ) from collection import views from collection.backends import MyRegistrationView urlpatterns = [ url(r'^$', views.index, name='home'), # The new URL entries we're adding: url(r'^about/$', TemplateView.as_view(template_name='about.html'), name='about'), url(r'^contact/$', TemplateView.as_view(template_name='contact.html'), name='contact'), url(r'^profiles/(?P<slug>[-\w]+)/$', views.profile_detail, name='profile_detail'), url(r'^things/(?P<slug>[-\w]+)/edit/$', views.edit_profile, name='edit_profile'), url(r'^accounts/register/$', MyRegistrationView.as_view(), name='registration_register'), url(r'^accounts/create_profile/$', views.create_profile, name='registration_create_profile'), # our new browse flow url(r'^things/$', RedirectView.as_view(pattern_name='browse', permanent=True)), url(r'^browse/$', RedirectView.as_view(pattern_name='browse', permanent=True)), url(r'^browse/name/$', views.browse_by_name, name='browse'), url(r'^browse/name/(?P<initial>[-\w]+)/$', views.browse_by_name, name='browse_by_name'), url(r'^accounts/password/reset/$', password_reset, {'template_name': 'registration/password_reset_form.html'}, name="password_reset"), url(r'^accounts/password/reset/done/$', password_reset_done, {'template_name': 'registration/password_reset_done.html'}, name="password_reset_done"), url(r'^accounts/password/reset/(?P<uidb64>[0-9A-Za-z]+)-(?P<token>.+)/$', password_reset_confirm, {'template_name': 'registration/password_reset_confirm.html'}, name="password_reset_confirm"), url(r'^accounts/password/done/$', password_reset_complete, {'template_name': 'registration/password_reset_complete.html'}, name="password_reset_complete"), url(r'^accounts/', include('registration.backends.simple.urls')), url(r'^admin/', admin.site.urls), ]

[ "shahrukh.haider35@gmail.com" ]

shahrukh.haider35@gmail.com

1d15e2a81dc7faddc9fd019cf75d10f62e17e236

28c3137a00ca91c14b08211a6621404e52d1a9e3

/scripts/State Space Simulator/apparent_wind

95a991e5442c823c42baac6b89ba09ba1b72481e

[]

no_license

brunopinto900/sailboat_auto_navigation

0241df00f12132b3265d6baaf68b4643de5631f4

02bece9fc18a5bc87751b928724d44f350679034

refs/heads/main

2023-03-25T17:11:26.699931

2021-03-20T22:11:37

349,806,874

1

null

UTF-8

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3,818

#!/usr/bin/python # Simulate the heading state import rospy from std_msgs.msg import Float32 from sailboat.msg import Vector3, Wind, Vector2 import math, time, mpmath import numpy as np from sailboat.cfg import windConfig from dynamic_reconfigure.server import Server #Nao esquecer dynamic config class apparent_wind_simulation(): def __init__(self): # Initializes the node rospy.init_node("apparent_wind_simulation", anonymous=True) def wind_callback(config, level): self.wind_speed = config.TWS self.wind_direction_north = config.TWA return config srv = Server(windConfig, wind_callback) self.sample_time = rospy.get_param("simulation/sample_time") # rate at which this node runs self.node = rospy.Rate(self.sample_time) # wait function to use inside threads # Publishers (heading) self.set_apparent_wind = rospy.Publisher('apparent_wind', Wind, queue_size = 10) self.apparent_wind = Wind() # Subscribers (angular_velocity) rospy.Subscriber('heading', Float32, self.get_heading) self.heading = rospy.get_param("scenario/initial_state/heading") rospy.Subscriber('linear_velocity', Vector2, self.get_linear_velocity) self.linear_velocity = Vector2() # True Wind speed and direction self.true_wind_speed = rospy.get_param("scenario/true_wind/speed") self.true_wind_angle = rospy.get_param("scenario/true_wind/angle") # from the north #Noise self.wind_angle_noise_range = rospy.get_param('model/noise/wind_angle_noise_range') self.wind_speed_noise_range = rospy.get_param('model/noise/wind_speed_noise_range') rospy.loginfo("Wind direction simulated") self.calculate_apparent_wind() # function responsible to publish, created here after all initializations def get_heading(self, data): self.heading = data.data def get_linear_velocity(self, data): # velocity in the boat reference system self.linear_velocity.x = data.x self.linear_velocity.y = data.y speed = mpmath.hypot(self.linear_velocity.x,self.linear_velocity.y) heading = math.degrees(math.atan2(self.linear_velocity.y, self.linear_velocity.x) ) self.linear_velocity.x = speed * math.cos(math.radians(heading - self.heading)) self.linear_velocity.y = speed * math.sin(math.radians(heading - self.heading)) def calculate_apparent_wind(self): # every sample_time seconds, it executes this loop while not rospy.is_shutdown(): if self.wind_angle_noise_range: noise_angle = np.random.normal(scale= self.wind_angle_noise_range) else: noise_angle = 0 if self.wind_speed_noise_range: noise_speed = np.random.normal(scale= self.wind_speed_noise_range) else: noise_speed = 0 angle = (self.true_wind_angle + noise_angle - self.heading) % 360 true_wind_vector = ((self.true_wind_speed + noise_speed)* math.cos(math.radians(angle)), (self.true_wind_speed + noise_speed)* math.sin(math.radians(angle)),) #alterar sinal menos apparent_wind_vector = (-self.linear_velocity.x + true_wind_vector[0], #alterar sinal menos nas velocidades -self.linear_velocity.y + true_wind_vector[1],) self.apparent_wind.speed = mpmath.hypot(apparent_wind_vector[0],apparent_wind_vector[1]) self.apparent_wind.angle = ( math.degrees(math.atan2(apparent_wind_vector[1], apparent_wind_vector[0]))) % 360 self.set_apparent_wind.publish(self.apparent_wind) self.node.sleep() if __name__ == '__main__': try: apparent_wind_simulation() except rospy.ROSInterruptException: pass

[ "bruno@DESKTOP-TSTEU1E.localdomain" ]

bruno@DESKTOP-TSTEU1E.localdomain

f7bf373ffaca6f71f7cd4fc4ed4c868ca26266be

05ee3f8d8f66657ee8e62f85edecb36b2bb98786

/udemy-design-patterns-in-python/geomatric_shapes.py

e580d93df99538ce82199ee022b1251375172d03

[]

no_license

zahedul/python-practice

b490fa6f16db048a8ab107671faf33508008eb15

3b101e44b69fd065044e3e4e88062bdb7b53fa93

refs/heads/master

2023-04-14T19:58:13.758234

2021-05-12T09:31:32

260,423,836

0

null

UTF-8

Python

false

1,079

py

class GraphicObject: def __init__(self, color=None): self.color = color self.children = [] self._name = 'Group' @property def name(self): return self._name def _print(self, items, depth): items.append('*' * depth) if self.color: items.append(self.color) items.append(f"{self.name}\n") for child in self.children: child._print(items, depth + 1) def __str__(self): items = [] self._print(items, 0) return "".join(items) class Circle(GraphicObject): @property def name(self): return "Circle" class Square(GraphicObject): @property def name(self): return "Square" if __name__ == "__main__": drawing = GraphicObject() drawing._name = "My Drawing" drawing.children.append(Square('Red')) drawing.children.append(Circle('Yellow')) group = GraphicObject() group.children.append(Circle('Blue')) group.children.append(Square('Blue')) drawing.children.append(group) print(drawing)

[ "z.alam015@gmail.com" ]

z.alam015@gmail.com

94cece20c7eab62186905a05f4b938605bcee510

f07a42f652f46106dee4749277d41c302e2b7406

/Data Set/bug-fixing-4/c03373921d734b99f5b462090aa7a70158844266-<__init__>-bug.py

ef3c2834ce746add31f043dba3be28fe76c02351

[]

no_license

wsgan001/PyFPattern

e0fe06341cc5d51b3ad0fe29b84098d140ed54d1

cc347e32745f99c0cd95e79a18ddacc4574d7faa

refs/heads/main

2023-08-25T23:48:26.112133

2021-10-23T14:11:22

null

0

null

UTF-8

Python

false

902

py

def __init__(self): self.module_arg_spec = dict(default_rules=dict(type='list', elements='dict', options=rule_spec), location=dict(type='str'), name=dict(type='str', required=True), purge_default_rules=dict(type='bool', default=False), purge_rules=dict(type='bool', default=False), resource_group=dict(required=True, type='str'), rules=dict(type='list', elements='dict', options=rule_spec), state=dict(type='str', default='present', choices=['present', 'absent'])) self.default_rules = None self.location = None self.name = None self.purge_default_rules = None self.purge_rules = None self.resource_group = None self.rules = None self.state = None self.tags = None self.client = None self.nsg_models = None self.results = dict(changed=False, state=dict()) super(AzureRMSecurityGroup, self).__init__(self.module_arg_spec, supports_check_mode=True)

[ "dg1732004@smail.nju.edu.cn" ]

dg1732004@smail.nju.edu.cn

d2cb00feb1adee6e4816e40c8e4f588943c24ee9

121d2ab76d137d51c8c085eb8b15420d75708269

/text11/text/写日记.py

a4ce9478847a4ab77b16770650ab669b108d9e51

[]

no_license

yogaxmj/xmj

94381fe36f65f5f0ed2291dac793665e15296f4d

75aa6fd5895ed9cfa94ae60effbe04f4d178408b

refs/heads/master

2022-09-20T01:12:21.530090

2020-06-02T14:01:12

268,051,433

0

null

UTF-8

Python

false

223

py

import time now = time.strftime("%y-%m-%d %H:%M:%M") text = input("输入") with open("D:\日记.text","a",encoding="utf8") as f: f.write(now+"\n") f.write(text+"\n") f.write("-------------\n" ) #文件写入

[ "2941671445@qq.com" ]

2941671445@qq.com

616d4da0e2ce0322fb87506d85ca26e033c11674

fd36faed2a313bd506f4c0ce8fec9331b03cfcc6

/1. Array/13. merge two sorted arrays without extra space.py

cd49382be9e2c465a01a781892812bda13888f46

[]

no_license

Vivekyadv/450-DSA

6f760604ec462d38058a5ea0f2faa5f17680a0aa

f03cd763f11ed801d07de93cfe4b6b2e03235e1b

refs/heads/master

2023-07-13T12:59:04.587801

2021-08-13T17:15:23

393,289,315

0

null

UTF-8

Python

false

1,512

py

# Given two sorted arrays, merge them and return sorted array without using extra space def solve(a, b): for i in range(len(a)): if a[i] > b[0]: a[i], b[0] = b[0], a[i] b.sort() return a, b a = [1,4,7,8,10] b = [2,3,9] print(solve(a,b)) # extend 1st array with 2nd array and compare from the end def merge(arr1, arr2, m, n): i = m-1 j = n-1 arr1.extend(arr2) k = len(arr1)-1 while i >= 0 and j >= 0: if arr1[i] > arr2[j]: arr1[k] = arr1[i] i -= 1 k -= 1 elif arr1[i] < arr2[j]: arr1[k] = arr2[j] j -= 1 k -= 1 while i >= 0: arr1[k] = arr1[i] i -= 1 k -= 1 while j >= 0: arr1[k] = arr2[j] j -= 1 k -= 1 return arr1 # Method: All elements in Arr 1 must be smaller than all elements in Arr2 # Algorithm # 1. Take two pointers -> i = m-1 and j = 0 # 2. if a[i] > b[j] -> swap(a[i], b[j]) and i -= 1, j += 1 # 3. if not, that means all elements in a is smaller than all elements in b. # So break the loop # 4. Now arrays might be unsorted so sort them def merge(a, b, m, n): i = m-1 j = 0 while i >= 0 and j < n: if a[i] >= b[j]: a[i], b[j] = b[j], a[i] i -= 1 j += 1 else: break a.sort() b.sort() return a, b a = [5, 8, 10, 12, 13, 15] b = [2,6,7,9,11] print(merge(a, b, len(a), len(b)))

[ "vivek.yadav2611@gmail.com" ]

vivek.yadav2611@gmail.com

d79e7c1a7553c893d22772c74e536301c7ba6ca3

bdf11484e889086d7cef919658a6ab04e01d204b

/codevent/2019/03/Coordinates.py

e915ac7b52c163488db8ef2e8d97b8f269407d4b

[ "Unlicense" ]

permissive

paralleldynamic/challenges

e0fd5180a0b1050336eabe9e9706ba9692dc8c97

9fa92c39da909ba95cf3815d8083e963d490e774

refs/heads/master

2023-02-22T20:47:52.394764

2023-02-08T15:58:45

226,190,985

0

null

UTF-8

Python

false

1,650

py

from functools import reduce directions = { 'U': (0, 1), 'D': (0, -1), 'R': (1, 0), 'L': (-1, 0) } def walk(path:list, origin:tuple = (0, 0)): coordinates = [origin] for turn in path: d = turn[0] steps = int(turn[1:]) i = 0 while i < steps: coordinates.append( ((directions[d][0] * 1) + coordinates[-1][0] , (directions[d][1] * 1) + coordinates[-1][1])) i += 1 return coordinates def walk_until(path:list, destination:tuple = (0,0)): step = 0 for step in range(len(path)): if path[step] == destination: return step return False def find_intersections(coordinates): return reduce(set.intersection, map(set,coordinates)) def closest_intersection(paths): coordinates = [] for path in paths: coordinates.append(walk(path)) manhattan_distance = lambda x: abs(x[0]) + abs(x[1]) intersections = find_intersections(coordinates) intersections.discard((0,0)) #min([manhattan_distance(i) for i in intersections])) return min(intersections, key=manhattan_distance), intersections, coordinates if __name__ == '__main__': from sys import argv wires = [] with open(argv[1], 'r') as f: for r in f: wires.append(r.strip().split(',')) mapped = closest_intersection(wires) closest, intersections, paths = mapped[0], mapped[1], mapped[2] distances = [] for intersect in intersections: steps = [] for path in paths: steps.append(walk_until(path, intersect)) distances.append(steps) print(min(map(sum, distances)))

[ "spearspa@gmail.com" ]

spearspa@gmail.com

c57922c3990c40a764ae5fd90a742f2d922e539d

68b526f6958e8bf0b99f6d08c02bf1bbdf651bd2

/pages/migrations/0001_initial.py

8263cacbc70bcf857b740e6729335d33ed850fbf

[]

no_license

vvojvoda/conference-web

5f053dca4c1e0e39efa01cd8413e6d707abf1d05

362e558b6c980823cc57c53a5d16253401318d4d

refs/heads/master

2020-04-01T18:18:31.914116

2016-05-20T08:51:43

31,506,926

0

null

2015-03-01T18:59:14

null

UTF-8

Python

false

820

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('flatpages', '0001_initial'), ] operations = [ migrations.CreateModel( name='Page', fields=[ ('flatpage_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='flatpages.FlatPage')), ('meta_description', models.TextField(help_text=b'Used for og:description')), ('hero_type', models.CharField(default=b'main', max_length=20, choices=[(b'main', b'Main'), (b'blog', b'Blog'), (b'cfp', b'CFP')])), ], options={ }, bases=('flatpages.flatpage',), ), ]

[ "ivan.habunek@gmail.com" ]

ivan.habunek@gmail.com

c12756b4fcc336d69f3d57e4f574233a4d87dc96

1924f93ead8754a5fc881d1f0d5743b9f1c0165a

/first_project/first_app/migrations/0001_initial.py

db8233e569381c32474594f432a14e72cf85a879

[]

no_license

mbclark1995/first_django_app

682751a43b9f45e98ae54d0ed051e4022b745dc6

78689d4abd613ab05d5d7bd41ece6c5080ea7752

refs/heads/master

2022-06-14T06:16:48.963138

2020-05-07T21:27:58

261,954,235

0

null

UTF-8

Python

false

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py

# Generated by Django 3.0.5 on 2020-04-30 04:20 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Topic', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('top_name', models.CharField(max_length=264, unique=True)), ], ), migrations.CreateModel( name='Webpage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=264, unique=True)), ('url', models.URLField(unique=True)), ('topic', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='first_app.Topic')), ], ), migrations.CreateModel( name='AccessRecord', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date', models.DateField()), ('name', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='first_app.Webpage')), ], ), ]

[ "mbclark1995@gmail.com" ]

mbclark1995@gmail.com

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/7_input_and_while()/exercises.py

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[]

no_license

Balowen/python_crashcourse

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refs/heads/master

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#7_1 rental car # car = input("What car would you like to rent? ") # # print("Let me see if I have a " + car + ".") #7-2 restaurant seating # group_size = input("How many people are in your dinner group? ") # group_size = int(group_size) # # if group_size > 8: # print("You have to wait for a table.") # else: # print("Table is ready.") #7_3 multiples of ten # number = input("Give me a number: ") # number = int(number) # # if number % 10 == 0: # print("The number " + str(number) + " is a multiple of 10.") # else: # print("The number " + str(number) + " is not a multiple of 10.") #7_4 pizza toppings # prompt = "Enter a pizza topping: " # prompt += "\n(Type 'quit' to close the program.) " # # while True: # topping = input(prompt) # # if topping == 'quit': # break # else: # print("I'll add " + topping + " to your pizza.") #7-5 movie tickets # prompt = "Enter your age: " # prompt += "\n(Type 'quit' to close the program.) " # # active = True # while active: # age = input(prompt) # # if age == 'quit': # active = False # break # # age = int(age) # # if age <= 3: # print("Your ticket is free.") # elif age <= 12: # print("Your ticket costs $10.") # else: # print("Your ticket costs $15.") # #7-8 Deli sandwich_orders = ['kurczakowa', 'pastrami', 'z szynkom', 'pastrami', 'gyros', 'vege', 'pastrami'] finished_sandwiches = [] print("We currently don't have any pastrami left.") while 'pastrami' in sandwich_orders: sandwich_orders.remove('pastrami') while sandwich_orders: current_sandwich = sandwich_orders.pop() print("I made your " + current_sandwich) finished_sandwiches.append(current_sandwich) print("\nList of made sandwiches:") for sandwich in finished_sandwiches: print(sandwich)

[ "thebalowen@gmail.com" ]

thebalowen@gmail.com

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rafaelperazzo/programacao-web

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2021-01-12T14:06:25.773146

2017-12-22T16:05:45

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# -*- coding: utf-8 -*- I1=float(input('digite um valor I: ')) I2=int(I1) I3=I1-I2 print('I2 é %d '%I2) print('I3 é %f '%I3)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

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/Chapter4/4-22.py

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[]

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TheXJT/python_study

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2021-05-04T10:27:51.760751

2019-01-14T09:36:34

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import re re_numbers_str=re.compile(r'\d+') re_words_str=re.compile(r'\w+') re_numbers_bytes=re.compile(rb'\d+') re_words_bytes=re.compile(rb'\w+') text_str = ("Ramanujan saw \u0be7\u0bed\u0be8\u0bef" " as 1729 = 13 + 123 = 93 + 103.") text_bytes=text_str.encode('utf-8') print('Text',repr(text_str),sep='\n ') print('Numbers') print(' str :',re_numbers_str.findall(text_str)) print(' bytes:',re_numbers_bytes.findall(text_bytes)) print('Words') print(' str :',re_words_str.findall(text_str)) print(' bytes:',re_words_bytes.findall(text_bytes))

[ "1251284167@qq.com" ]

1251284167@qq.com

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/latte/monkey_patches/frappe/utils/nestedset.py

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sunnyakaxd/latte

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refs/heads/master

2023-06-11T10:25:31.217047

2021-07-06T06:40:19

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2021-07-06T06:26:49

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import frappe from frappe import local from frappe.utils import nestedset import random def patched_update_add_node(doc, parent, parent_field): print('Parent=', parent, not not parent) if not parent: parent_lft = frappe.db.sql(f''' select rgt from `tab{doc.doctype}` where name != %(name)s order by rgt desc limit 1 ''', { 'name': doc.name, }) if parent_lft: parent_lft = parent_lft[0][0] else: parent_lft = -10.0**12 parent_rgt = 10.0**12 else: parent_lft, parent_rgt = frappe.db.get_value(doc.doctype, parent, ['lft', 'rgt']) add_child(doc.doctype, doc.name, parent_lft, parent_rgt) def patched_update_move_node(doc, parent_field): ''' is called when op!=p so just add_child under p with doc's details ''' parent_rgt = frappe.db.get_value(doc.doctype, doc.get(parent_field), 'rgt') tree_lft = local.db.sql(f''' select rgt from `tab{doc.doctype}` where rgt < %(parent_rgt)s order by rgt desc limit 1 ''', { 'parent_rgt': parent_rgt, })[0][0] add_child(doc.doctype, doc.name, tree_lft, parent_rgt) # called in the on_update method def update_nsm(doc): # get fields, data from the DocType opf = 'old_parent' pf = "parent_" + frappe.scrub(doc.doctype) if hasattr(doc,'nsm_parent_field'): pf = doc.nsm_parent_field if hasattr(doc,'nsm_oldparent_field'): opf = doc.nsm_oldparent_field p, op = doc.get(pf) or None, doc.get(opf) or None # has parent changed (?) or parent is None (root) if not doc.lft and not doc.rgt: patched_update_add_node(doc, p or '', pf) elif op != p: patched_update_move_node(doc, pf) # set old parent doc.set(opf, p) frappe.db.set_value(doc.doctype, doc.name, opf, p or '', update_modified=False) doc.reload() def add_child(doctype, docname, left, right): print('add_child running for', doctype, docname, left, right) PRECISION = -9 safe_left = left + 10**PRECISION safe_right = right - 10**PRECISION lft = _getsafeval(safe_left, safe_right, retry=True) rgt = _getsafeval(lft, safe_right, retry=True) frappe.log_error(f'''{doctype=} {docname=} {left=} {right=} {lft=} {rgt=}''', title=f'Add child for {doctype}') if lft == rgt: frappe.throw(f'''INVALID VALUES FOR {lft=} {rgt=}''') frappe.db.sql(f""" update `tab{doctype}` set lft = %(lft)s , rgt = %(rgt)s where name = %(docname)s """, { 'lft': lft, 'rgt': rgt, 'docname': docname, }, debug=1) for descendent in get_immediate_descendents(doctype, docname): add_child(doctype, descendent.name, lft, rgt) def get_immediate_descendents(doctype, docname): return frappe.get_all(doctype, filters={ f'parent_{frappe.scrub(doctype)}': docname }) def _getsafeval(left, right, retry=False): ''' Returns a mid value in the given range param retry: ----------- default: False. if set to true, ensures that mid value does not equal left/right ''' PRECISION = -9 inf_loop_safe_counter = 4 if retry: while inf_loop_safe_counter > 0: inf_loop_safe_counter -= 1 mid = random.uniform(left, right) if mid == left or mid == right: safe_left = left + 10**PRECISION safe_right = right - 10**PRECISION return _getsafeval(safe_left, safe_right) else: return mid return random.uniform(left, right) def rebuild_node(doctype, parent, left, parent_field): """ reset lft, rgt and recursive call for all children """ # the right value of this node is the left value + 1 right = left+1 # get all children of this node result = local.db.sql(f"SELECT name FROM `tab{doctype}` WHERE `{parent_field}`=%s", (parent)) for r in result: right = rebuild_node(doctype, r[0], right, parent_field) # we've got the left value, and now that we've processed # the children of this node we also know the right value local.db.sql(f""" UPDATE `tab{doctype}` SET lft=%s, rgt=%s WHERE name=%s """, (left, right, parent)) #return the right value of this node + 1 return right+1 # nestedset.update_move_node = patched_update_move_node # nestedset.update_nsm = update_nsm # nestedset.update_add_node = patched_update_add_node # nestedset.rebuild_node = rebuild_node

[ "himanshu.mishra@elastic.run" ]

himanshu.mishra@elastic.run

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/6_7.py

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rursvd/pynumerical

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2021-01-22T20:49:09.154726

2017-08-19T07:50:34

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from numpy import array A = array([[1,2,3],[4,5,6],[7,8,9]]) mult = A * 10.0 divi = A/2.0 print('A*10 = ',mult) print('A/2 = ',divi)

[ "noreply@github.com" ]

rursvd.noreply@github.com

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/web_flask/5-number_template.py

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no_license

bean710/AirBnB_clone_v2

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refs/heads/master

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#!/usr/bin/python3 """This module starts a simple flask server""" from flask import Flask from flask import render_template app = Flask(__name__) @app.route("/", strict_slashes=False) def hello_world(): """Sends simple text""" return ("Hello HBNB!") @app.route("/hbnb", strict_slashes=False) def hello_hbnb(): """Sends simple text""" return ("HBNB") @app.route("/c/<text>", strict_slashes=False) def dyn_text(text): """Returns dynamic text""" return ("C {}".format(text.replace("_", " "))) @app.route("/python/<text>", strict_slashes=False) @app.route("/python/", strict_slashes=False, defaults={"text": "is_cool"}) def opt_text(text="is_cool"): """Returns optional dynamic text""" return("Python {}".format(text.replace("_", " "))) @app.route("/number/<int:n>", strict_slashes=False) def is_num(n): """Returns if n is a number""" return ("{} is a number".format(n)) @app.route("/number_template/<int:n>", strict_slashes=False) def html_num(n): """Returns an HTML template""" return (render_template("5-number.html", name=n)) if __name__ == "__main__": app.run(host="0.0.0.0", port="5000")

[ "keener4christ@gmail.com" ]

keener4christ@gmail.com

fffd473437f434f2c87334c2be9ee615692c4749

cec8eb058a730185b1a3845aaf1fa743a3b41074

/sql/user_detail.py

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[]

no_license

chinatian/python-orm

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refs/heads/master

2020-12-24T14:36:06.505669

2019-03-29T03:15:21

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#-*- coding: utf-8 -*- import MySQLdb for d in range(16): db=MySQLdb.connect( host="localhost", user="root", passwd="sa", db="platform_test_%d" % d, use_unicode=True, charset="utf8") for t in range(16): c = db.cursor() c.execute(""" CREATE TABLE `user_detail_%d` ( `id` BIGINT NOT NULL, `user_id` BIGINT NOT NULL, `field_name` VARCHAR(32) NOT NULL, `field_value` VARCHAR(127) NULL, `updated_at` DATETIME NOT NULL, `created_at` TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP, PRIMARY KEY (`id`), INDEX `user_id_index` (`user_id` ASC) ) """ % t) c.execute("INSERT INTO seq (id, tb) VALUES (%s, %s)", (16*t+d, 'user_detail_%d' % t)) c.close() db.commit() db.close()

[ "tian_2200@163.com" ]

tian_2200@163.com

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/quiz_brain.py

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[]

no_license

khadada/Quiz_game

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refs/heads/master

2023-09-05T05:58:25.146725

2021-11-09T14:30:50

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class QuizBrain: def __init__(self,question_list): self.question_list = question_list self.question_number = 0 self.score = 0 self.name = input("What's your name: ").title() def next_question(self): current_question = self.question_list[self.question_number] self.question_number += 1 question = current_question.question print('-'*40) print(f'The difficulty of this quiz is: {current_question.difficulty}') print('-'*40) number_list = 1 print(f"Q.{self.question_number}: {question} ") print(f'Choose the correct answer: ') for choice in current_question.generete_list: print(f'[{number_list}]: {choice}. ') number_list += 1 print(f'pssssssssss correct answer is: {current_question.correct_answer}') # for testing my code remove it later on user_answer = input('Type your answer here: -> ').title() self.check_correct_answer(user_answer,current_question.correct_answer) def has_question_left(self): return self.question_number < len(self.question_list) def check_correct_answer(self,user_answer,correct_answer): if user_answer == correct_answer.title(): print(f'You get it right!') self.score += 1 else: print(f'Your answer is wrong!') print(f'Your score: {self.score} / {len(self.question_list)}')

[ "lkhadada@gmail.com" ]

lkhadada@gmail.com

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/intro_to_data_science/practice_1/playing_with_pandas.py

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[]

no_license

wmorris75/Python_Data_Science

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refs/heads/master

2021-01-23T06:01:08.988390

2017-06-02T15:06:24

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import pandas as pd ''' The following code is to help you play with the concept of Series in Pandas. You can think of Series as an one-dimensional object that is similar to an array, list, or column in a database. By default, it will assign an index label to each item in the Series ranging from 0 to N, where N is the number of items in the Series minus one. Please feel free to play around with the concept of Series and see what it does *This playground is inspired by Greg Reda's post on Intro to Pandas Data Structures: http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/ ''' # Change False to True to create a Series object if False: series = pd.Series(['Dave', 'Cheng-Han', 'Udacity', 42, -1789710578]) print series ''' You can also manually assign indices to the items in the Series when creating the series ''' # Change False to True to see custom index in action if True: series = pd.Series(['Dave', 'Cheng-Han', 359, 9001], index=['Instructor', 'Curriculum Manager', 'Course Number', 'Power Level']) print series ''' You can use index to select specific items from the Series ''' # Change False to True to see Series indexing in action if True: series = pd.Series(['Dave', 'Cheng-Han', 359, 9001], index=['Instructor', 'Curriculum Manager', 'Course Number', 'Power Level']) print series['Instructor'] print "" print series[['Instructor', 'Curriculum Manager', 'Course Number']] ''' You can also use boolean operators to select specific items from the Series ''' # Change False to True to see boolean indexing in action if True: cuteness = pd.Series([1, 2, 3, 4, 5], index=['Cockroach', 'Fish', 'Mini Pig', 'Puppy', 'Kitten']) print cuteness > 3 print "" print cuteness[cuteness > 3]

[ "wmorris@charterschoolsusa.com" ]

wmorris@charterschoolsusa.com

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/PYQT/pyqt_3_message_box.py

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[ "MIT" ]

permissive

dogancantorun8/python-application

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2023-04-15T11:20:56.187416

2021-04-27T11:50:04

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# -*- coding: utf-8 -*- """ Created on Sun Mar 14 18:26:00 2021 @author: Doğancan """ #Butona basıldığında message box çıksın istersem: import sys from PyQt5.QtWidgets import * from PyQt5.QtCore import * class MainWindow(QWidget): def __init__(self): super().__init__() self.pushButtonOk = QPushButton('Ok', self) self.pushButtonOk.move(100, 100) self.pushButtonOk.resize(QSize(100, 100)) self.pushButtonOk.clicked.connect(self.buttonOkHandler) def buttonOkHandler(self): mb = QMessageBox(self) #QMessageBox(None) yazarsam alt pencereden bağımsız çıkar mb.setWindowTitle('Test') mb.setText('Are you sure?') mb.exec() #her message box için exec yazılmalı app = QApplication(sys.argv) mainWindow = MainWindow() mainWindow.show() app.exec() ##diyalog penceresi çıktığında standart bazı butonlar çıksın istersem: import sys from PyQt5.QtWidgets import * from PyQt5.QtCore import * class MainWindow(QWidget): def __init__(self): super().__init__() self.pushButtonOk = QPushButton('Ok', self) self.pushButtonOk.move(100, 100) self.pushButtonOk.resize(QSize(100, 100)) self.pushButtonOk.clicked.connect(self.buttonOkHandler) def buttonOkHandler(self): mb = QMessageBox(None) mb.setWindowTitle('Test') #içteki pencere başlığı mb.setText('Are you sure?')#messageboxtaki iç yazı mb.setStandardButtons(QMessageBox.Yes|QMessageBox.No|QMessageBox.Cancel) mb.setDefaultButton(QMessageBox.No) #defaultta hangisi seçili olsun istersem bu bloğu yazıyorum result = mb.exec() #hangi butonla çıktığımı anlamak için bu if bloğunu yazdım if result == QMessageBox.Yes: print('Yes') elif result == QMessageBox.No: print('No') else: print('Cancel') app = QApplication(sys.argv) mainWindow = MainWindow() mainWindow.show() app.exec() #Yukarıdaki messagebox için daha kolay bir yöntemi aşağıdaki gibi izleyebilirim import sys from PyQt5.QtWidgets import * from PyQt5.QtCore import * class MainWindow(QWidget): def __init__(self): super().__init__() self.pushButtonOk = QPushButton('Ok', self) self.pushButtonOk.move(100, 100) self.pushButtonOk.resize(QSize(100, 100)) self.pushButtonOk.clicked.connect(self.buttonOkHandler) def buttonOkHandler(self): result = QMessageBox.information(None, 'Test', 'Are youe sure?', QMessageBox.Yes|QMessageBox.No|QMessageBox.Cancel, QMessageBox.Cancel) if result == QMessageBox.Yes: print('Yes') elif result == QMessageBox.No: print('No') else: print('Cancel') app = QApplication(sys.argv) mainWindow = MainWindow() mainWindow.show() app.exec()

[ "48547417+dogancantorun8@users.noreply.github.com" ]

48547417+dogancantorun8@users.noreply.github.com

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/3_retrainfixedrlrlam/noRegu/log5.py

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[]

no_license

Richardych/HW_recommendationsys

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refs/heads/master

2021-09-07T14:57:53.427276

2018-02-24T12:21:51

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null

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import logging logger = logging.getLogger('mylogger5') logger.setLevel(logging.DEBUG) fh = logging.FileHandler('monitor5.log') fh.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fh.setFormatter(formatter) ch.setFormatter(formatter) logger.addHandler(fh) logger.addHandler(ch)

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yuchaohui@megvii.com

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[ "Apache-2.0" ]

permissive

mtreinish/pysubunit

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refs/heads/master

2021-08-23T12:59:10.423987

2017-12-04T23:30:11

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# -*- coding: utf-8 -*- # # pysubunit documentation build configuration file # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.viewcode', ] # Enable todos in the output todo_include_todos = True # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'pysubunit' copyright = u'2017, Matthew Treinish' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = 'trunk' # The full version, including alpha/beta/rc tags. release = 'trunk' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". #html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'pysubunitdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'pysubunit.tex', u'pysubunit Documentation', u'Matthew Treinish', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'pysubunit', u'pysubunit Documentation', [u'Matthew Treinish'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'pysubunit', u'pysubunit Documentation', u'pysubunit Contributors', 'pysubunit', '', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote'

[ "mtreinish@kortar.org" ]

mtreinish@kortar.org

45b40923152a8285e97189d52f1fbb9fb92ec503

fdf3b7185d3aae13106491329a00b34171fb4def

/post_bot.py

c45f5f1f55dc48875c7379488935d56a9492fa11

[ "MIT" ]

permissive

lacymorrow/rbot

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refs/heads/master

2021-05-08T02:22:15.898002

2019-10-20T02:24:04

108,056,243

1

0

MIT

2019-10-20T02:24:05

2017-10-24T00:25:47

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py

###### # Todo: # Language processing # Don't self repost on same sub # Post Image # Video/gif(v) import os import time import datetime import random import requests # Required in requirements.txt import gensim import nltk import praw nltk.download('punkt') # import configuration files import config import subs ## ## 1) REPOST OLD, POPULAR POSTS FROM SUBS EVERY <delay> ## 2) REPOST RELEVANT POPULAR COMMENT TO A REPOSTED QUESTION # Check new posts in popular subs for something similar to a popular repost. Find a high comment from the original, paraphrase, and repost # Find a hot post not on all and xrosspost to similar sub ########### (first sub on sidebar?) # check hottest posts of all-time, repost as new ########### Paraphrase ########### Change ownership # random subreddits # rply to comments # If True, will CREATE LIVE REDDIT POSTS SEND_FLAG = True # Config SORT_DATE = 'month' # 'week', 'month', etc. via reddit POST_DELAY = 10 # seconds ; 600 === 10 mins SEARCH_LIMIT = 50 # Posts within subs that are searched Max 1000 AGE_THRESHOLD = 30 # Default 25 days (considered a month); 20-25 is active EXECUTION_TOTAL = 500 # TOTAL APPLICATION RUN COUNT BANNED_WORDS = ['[OC]', 'OC', 'test'] # PERSONAL_SUBS = ['test', 'me_irl', 'lgbqt',] # 'askreddit', 'askwomen', 'askmen'] # ACTIVE_SUBS = ['test'] ACTIVE_SUBS = subs.most_subscribed_subs # NEWS SUBS NEWS_SUBS = ['usanews', 'worldnews', 'poitics', 'neutralpolitics', 'unceensorednews'] NO_REPOST_SUBS = ['nottheonion', 'personalfinance'] # 'trees' = 1 karman # 'Futurology' = 5 day old # 'technology' = 5 days old # 'youtubehaiku' = 5 daays old WAIT_SUBS = ['trees'] BANNED_SUBS = ['pics', 'gifs', 'nosleep', 'bodyweightfitness', 'CrappyDesign', 'Overwatch', 'personalfinance' #trolling/plagarism 'hearthstone', 'tattoos', 'pokemon', 'futurology', 'technology', 'worldnews', 'youtubehaiku'] + WAIT_SUBS preface = '' # Skip banned subs was = [x.lower() for x in ACTIVE_SUBS] wns = [x.lower() for x in NEWS_SUBS] wbs = [x.lower() for x in BANNED_SUBS] working_active_subs = [x for x in was if x not in wbs] working_news_subs = [x for x in was if x not in wbs] # Other Subs to consider to widen the net ## print "subscribed to " str(subscribed) ## print "most subscribers" + str(subs.most_subscribed_subs) ## print "most activity" + str(subs.most_active_subs) # Monitors incoming submissions # Get the newest submissions from a subreddit ('all') # for submission in r.subreddit('askreddit').stream.submissions(): # print(submission) def bot_login(): print "Logging in..." r = praw.Reddit(username = config.username, password = config.password, client_id = config.client_id, client_secret = config.client_secret, user_agent = config.user_agent) return r def run_bot(r): # random.choice(ACTIVE_SUBS) # number is how far from top post to use (0 is default/top) ## Todo, active sub may be repeated/ not removed active_sub = random.choice(working_active_subs) news_sub = random.choice(working_news_subs) print '\n ** RUNNING **\n' print 'TARGETING ACTIVE SUB: ' + active_sub print 'TARGETING NEWS SUB: ' + news_sub # get top OLD posts from working subs (default ALL TIME) (POSTS that stood test of time) posts = [] # posts old enough to consider for reposting top_posts = r.subreddit(active_sub).top(SORT_DATE, limit=SEARCH_LIMIT) for post in top_posts: if post.shortlink: # get post age delta post.age = datetime.date.today() - datetime.date.fromtimestamp(post.created) # print max(post.age.days, key=lambda item: item[1]) # check if post is old enough to warrant a repost (don't repost brand new posts, better for xposts) if(int(post.age.days) > AGE_THRESHOLD): post = restyle_post(post) print post.shortlink posts.append(post) # # # repost top stories immediately # news_posts = r.subreddit(news_sub).new() # for post in news_posts: # if post.shortlink: # post = restyle_post(post) # posts.append(post) # optionally, randomize; create_posts(r, posts[:-1]) # POST def create_posts(r, posts): for post in posts: # Truncate post title in console pretty_post_title = pretty_text(post.title) try: # check if link or text post if post.selftext: # text post print "\n\n********Text Post:\n" + post.subreddit.display_name + "\nPost Title: \n" + post.title if(SEND_FLAG): submission = r.subreddit(post.subreddit.display_name).submit(preface + post.title, post.selftext) print "URL: " + submission.url sleep(POST_DELAY) else: debug('No data will be sent.') elif post.url: # link post print "\n\n********Link Post: \n" + post.subreddit.display_name + "\nPost Title: " + pretty_post_title + "\nURL: " + post.url if(SEND_FLAG): submission = r.subreddit(post.subreddit.display_name).submit(preface + post.title, url=post.url ) print "URL: " + submission.url sleep(POST_DELAY) else: debug('No data will be sent.') else: debug("NNAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA!!!! \nyou broke me...") print "******\nReposting: " + pretty_post_title + "\nAge: " + str(post.age.days) + " days \nSubreddit: " + post.subreddit.display_name # credits print "#####" + str(post.author) # We've been banned except APIException.PRAW as e: s = str(e) print "APIException PRAW: " + s if "SUBREDDIT_NOTALLOWED: 'you aren't allowed to post there.'" in s: print "Private Sub" next_post_delay = int(filter(str.isdigit, s)) if not str(next_post_delay.isdigit()): exit(1) sleep(next_post_delay) # Auto Sleep the delay provided except praw.exceptions.APIException as e: s = str(e) print "APIException PRAW: " + s if "SUBREDDIT_NOTALLOWED" in s: print "Private Sub" next_post_delay = int(filter(str.isdigit, s)) if not str(type(next_post_delay) is int): exit(1) sleep(next_post_delay) except AttributeError as e: print "AttributeError: " + str(e) exit(1) except AssertionError as e: print "AssertionError: " + str(e) exit(1) except Exception as e: print "Error" + e exit(1) except: print "Uncaught Error" exit(1) def restyle_post(post): for word in BANNED_WORDS: post.title = post.title.replace(word, '') return post def pretty_text(text): return (text[:75] + '...') if len(text) > 75 else text def sleep(mins): if mins > 11: mins = 2 #prevent seconds parsed as minutes if mins == 1: mins = 2 print "Sleep " + str(mins) + " minutes..." time.sleep(mins * 60) def debug(msg): print "DEBUGGING....\n" + msg print "NOT SENDING LIVE DATA" execution_count = 0 r = bot_login() while execution_count < EXECUTION_TOTAL: print "### Iteration ", str(execution_count) + " ###\n" run_bot(r) execution_count += 1 # get subscribed subreddits # subscribed = list(r.user.subreddits(limit=None))

[ "me@lacymorrow.com" ]

me@lacymorrow.com

ac8a5bedf7559d486b7c1d9412b4342e3d4234d6

a72e17b0f45297d403160ab1ae7569c1fbe2dd52

/inventarios/SaveInventario.py

7720823cc32601849a49e6a6e4e6a0e327971488

[]

no_license

mmejia/systra

649f2ffde5d62ecbec0e1e4d865be7f0fbbeb7f9

fcee7e7421422631a38c3a023a00939e6d7f88dc

refs/heads/master

2020-12-24T18:03:37.347805

2014-01-23T02:50:20

null

0

null

UTF-8

Python

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py

from django.db import IntegrityError, transaction from json import dumps, loads, JSONEncoder from inventarios.models import * import logging from datetime import date log = logging.getLogger(__name__) class SaveInventario: def __init__(self,request): inventario_str=request.POST.get('data') self.inv= loads(inventario_str) def do(self): inve= Inventario() inventario= self.inv fi=inventario.get('folio_del_inventario',0) if fi== 0 or fi =='': fi=0 inve.folio_inventario=fi f_inf=inventario.get('folio_infraccion',0) if f_inf == 0 or f_inf == '': f_inf=0 inve.folio_infraccion=f_inf fa= inventario.get('folio_accidente',0) if fa == 0 or fa== '': fa=0 inve.folio_accidente=fa inve.delegacion=inventario.get('delegacion',"") inve.comandancia=inventario.get('comandancia',"") inve.turno=inventario.get('turno',"") inve.motivo_de_recojimiento=inventario.get('motivo_de_recojimiento',"") inve.tipo_servicio=inventario.get('tipo_servicio',"") inve.folio_agente=inventario.get('folio_agente',"") inve.nombre_agente=inventario.get('nombre_agente',"") inve.fecha_incid=inventario.get('fecha_incidente') inve.fecha_captura=date.today() inve.pension_hora=inventario.get('hora_entrada') inve.pension_fecha=inventario.get('fecha_entrada') inve.calle1=inventario.get('calle1',"") inve.calle2=inventario.get('calle2',"") inve.colonia=inventario.get('colonia',"") inve.interior=inventario.get('num_interior',"") inve.exterior=inventario.get('exterior',"") inve.depositado_en=inventario.get('depositado_en',"") inve.autoridad_competente=inventario.get('autoridad_competente',"") inve.clas_vehiculo=inventario.get('clas_vehiculo',"") inve.tipo=inventario.get('tipoveh',"") inve.marca=inventario.get('marca',"") inve.submarca=inventario.get('submarca',"") inve.veh_modelo=inventario.get('modelo',"") inve.veh_color=inventario.get('color',"") inve.veh_color_detalle=inventario.get('detalle',"") inve.veh_placas=inventario.get('placas',"") inve.veh_serie=inventario.get('serie',"") inve.numero_economico=inventario.get('numero_economico',"") inve.infractor_nombre=inventario.get('conductor_nombre',"") inve.infractor_apepaterno=inventario.get('apellido_paterno',"") inve.infractor_apematerno=inventario.get('apellido_materno',"") inve.pension_lugar=inventario.get('lugar',"") inve.pension_recibido_por=inventario.get('recibido_por',"") inve.pension_transportado_por=inventario.get('vehiculo_transportado_por',"") inve.pension_transportado_pension=inventario.get('vehiculo_transportado_pension',"") inve.pension_transportado_unidad_transito=inventario.get('vehiculo_transportado_unidad_transito',"") inve.usuario=inventario.get('usuario',"") inve.agencia_mp=inventario.get('mp',0) inve.activo=1 inve.corporacion=inventario.get('corporacion',"") l =Inventario.objects.filter(folio_inventario__exact=inve.folio_inventario) lista= list(l) if len(lista)>0: res='{"ERROR":' + '"SE INTENTA AGREGAR UN INVENTARIO QUE SU FOLIO YA EXISTE"}' return res frente= inventario.get('frente',None) if frente !=None: id= frente.get('id',0) nombre=frente.get('nombre','') inve.frente=id else: inve.frente=0 posterior=inventario.get('posterior',None) if posterior !=None: id= posterior.get('id',0) nombre=posterior.get('nombre','') inve.posterior=id else: inve.posterior=0 inve.save() res= "{\"id\":\"" + str(inve.id) + "\"}" return res

[ "mmejia_mmm@hotmail.com" ]

mmejia_mmm@hotmail.com

f2d39ed43e60f086c5bb5287b29282c5a8d53124

cf455d62eeb4a1e4364b87067d0c9c9040242339

/performance_model/utils.py

241f53ba44797148727e25021baeec9168bdcfb1

[]

no_license

WenqiJiang/SIGMOD-2022-submission-Fanns

fb3c33336197a9e732f901106166e9e3e1284fb7

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refs/heads/main

2023-06-12T12:46:57.835530

2021-07-01T23:14:14

381,019,262

3

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UTF-8

Python

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10,087

py

""" Helper functions & Unit test """ from constants import * def max_of_two(a, b): if a >= b: return a else: return b def max_of_three(a, b, c): if a >= b: if a >= c: return a else: return c else: if b >= c: return b else: return c def get_bottleneck(perf_resource_dict_list, PE_num_list): """ Given a list of stages (each stage is a perf_resource_dict), return (a) which stage (ID in the list) is the bottleneck (b) the overall accelerator QPS """ min_QPS = 9999999999 min_QPS_ID = 0 for i, perf_resource_dict in enumerate(perf_resource_dict_list): if perf_resource_dict["QPS"] * PE_num_list[i] < min_QPS: min_QPS = perf_resource_dict["QPS"] * PE_num_list[i] min_QPS_ID = i assert min_QPS != 9999999999 accelerator_QPS = min_QPS return min_QPS_ID, accelerator_QPS def resource_consumption_A_less_than_B( perf_resource_dict_list_A, PE_num_list_A, perf_resource_dict_list_B, PE_num_list_B): consumed_HBM_bank_A = 0 consumed_BRAM_18K_A = 0 consumed_DSP48E_A = 0 consumed_FF_A = 0 consumed_LUT_A = 0 consumed_URAM_A = 0 consumed_HBM_bank_B = 0 consumed_BRAM_18K_B = 0 consumed_DSP48E_B = 0 consumed_FF_B = 0 consumed_LUT_B = 0 consumed_URAM_B = 0 for i, perf_resource_dict in enumerate(perf_resource_dict_list_A): consumed_HBM_bank_A = consumed_HBM_bank_A + perf_resource_dict["HBM_bank"] * PE_num_list_A[i] consumed_BRAM_18K_A = consumed_BRAM_18K_A + perf_resource_dict["BRAM_18K"] * PE_num_list_A[i] consumed_DSP48E_A = consumed_DSP48E_A + perf_resource_dict["DSP48E"] * PE_num_list_A[i] consumed_FF_A = consumed_FF_A + perf_resource_dict["FF"] * PE_num_list_A[i] consumed_LUT_A = consumed_LUT_A + perf_resource_dict["LUT"] * PE_num_list_A[i] consumed_URAM_A = consumed_URAM_A + perf_resource_dict["URAM"] * PE_num_list_A[i] for i, perf_resource_dict in enumerate(perf_resource_dict_list_B): consumed_HBM_bank_B = consumed_HBM_bank_B + perf_resource_dict["HBM_bank"] * PE_num_list_B[i] consumed_BRAM_18K_B = consumed_BRAM_18K_B + perf_resource_dict["BRAM_18K"] * PE_num_list_B[i] consumed_DSP48E_B = consumed_DSP48E_B + perf_resource_dict["DSP48E"] * PE_num_list_B[i] consumed_FF_B = consumed_FF_B + perf_resource_dict["FF"] * PE_num_list_B[i] consumed_LUT_B = consumed_LUT_B + perf_resource_dict["LUT"] * PE_num_list_B[i] consumed_URAM_B = consumed_URAM_B + perf_resource_dict["URAM"] * PE_num_list_B[i] # Priority: LUT is the most important one if consumed_LUT_A < consumed_LUT_B: return True else: return False def fit_resource_constraints(perf_resource_dict_list, PE_num_list, count_shell=False): """ Given a list of stages (each stage is a perf_resource_dict), return whether it is within the resource constraint """ consumed_HBM_bank = 0 consumed_BRAM_18K = 0 consumed_DSP48E = 0 consumed_FF = 0 consumed_LUT = 0 consumed_URAM = 0 for i, perf_resource_dict in enumerate(perf_resource_dict_list): consumed_HBM_bank = consumed_HBM_bank + perf_resource_dict["HBM_bank"] * PE_num_list[i] consumed_BRAM_18K = consumed_BRAM_18K + perf_resource_dict["BRAM_18K"] * PE_num_list[i] consumed_DSP48E = consumed_DSP48E + perf_resource_dict["DSP48E"] * PE_num_list[i] consumed_FF = consumed_FF + perf_resource_dict["FF"] * PE_num_list[i] consumed_LUT = consumed_LUT + perf_resource_dict["LUT"] * PE_num_list[i] consumed_URAM = consumed_URAM + perf_resource_dict["URAM"] * PE_num_list[i] if count_shell: consumed_HBM_bank += shell_consumption["HBM_bank"] consumed_BRAM_18K += shell_consumption["BRAM_18K"] consumed_DSP48E += shell_consumption["DSP48E"] consumed_FF += shell_consumption["FF"] consumed_LUT += shell_consumption["LUT"] consumed_URAM += shell_consumption["URAM"] if consumed_HBM_bank <= MAX_HBM_bank and consumed_BRAM_18K <= MAX_BRAM_18K and \ consumed_DSP48E <= MAX_DSP48E and consumed_FF <= MAX_FF and \ consumed_LUT < MAX_LUT and consumed_URAM < MAX_URAM: return True else: return False def get_resource_consumption(perf_resource_dict_list, PE_num_list, count_shell=False): """ Given a list of stages (each stage is a perf_resource_dict), return the resource consumption dictionary """ consumed_HBM_bank = 0 consumed_BRAM_18K = 0 consumed_DSP48E = 0 consumed_FF = 0 consumed_LUT = 0 consumed_URAM = 0 for i, perf_resource_dict in enumerate(perf_resource_dict_list): consumed_HBM_bank = consumed_HBM_bank + perf_resource_dict["HBM_bank"] * PE_num_list[i] consumed_BRAM_18K = consumed_BRAM_18K + perf_resource_dict["BRAM_18K"] * PE_num_list[i] consumed_DSP48E = consumed_DSP48E + perf_resource_dict["DSP48E"] * PE_num_list[i] consumed_FF = consumed_FF + perf_resource_dict["FF"] * PE_num_list[i] consumed_LUT = consumed_LUT + perf_resource_dict["LUT"] * PE_num_list[i] consumed_URAM = consumed_URAM + perf_resource_dict["URAM"] * PE_num_list[i] perf_resource_dict = dict() if not count_shell: perf_resource_dict["HBM_bank"] = consumed_HBM_bank perf_resource_dict["BRAM_18K"] = consumed_BRAM_18K perf_resource_dict["DSP48E"] = consumed_DSP48E perf_resource_dict["FF"] = consumed_FF perf_resource_dict["LUT"] = consumed_LUT perf_resource_dict["URAM"] = consumed_URAM else: perf_resource_dict["HBM_bank"] = consumed_HBM_bank + shell_consumption["HBM_bank"] perf_resource_dict["BRAM_18K"] = consumed_BRAM_18K + shell_consumption["BRAM_18K"] perf_resource_dict["DSP48E"] = consumed_DSP48E + shell_consumption["DSP48E"] perf_resource_dict["FF"] = consumed_FF + shell_consumption["FF"] perf_resource_dict["LUT"] = consumed_LUT + shell_consumption["LUT"] perf_resource_dict["URAM"] = consumed_URAM + shell_consumption["URAM"] return perf_resource_dict def get_utilization_rate(perf_resource_dict): utilization_rate = dict() utilization_rate["BRAM_18K"] = "{}%".format(perf_resource_dict["BRAM_18K"] / TOTAL_BRAM_18K * 100) utilization_rate["DSP48E"] = "{}%".format(perf_resource_dict["DSP48E"] / TOTAL_DSP48E * 100) utilization_rate["FF"] = "{}%".format(perf_resource_dict["FF"] / TOTAL_FF * 100) utilization_rate["LUT"] = "{}%".format(perf_resource_dict["LUT"] / TOTAL_LUT * 100) utilization_rate["URAM"] = "{}%".format(perf_resource_dict["URAM"] / TOTAL_URAM * 100) return utilization_rate def unit_test(): """ Print the options of each function unit """ print("\nget_priority_queue_info:\n") perf_resource_dict = get_priority_queue_info(queue_len=32, N_insertion=8192) print(perf_resource_dict) print("\nget_bitonic_sort_16_info:\n") perf_resource_dict = get_bitonic_sort_16_info(N_insertion=1e8/8192/64*32) print(perf_resource_dict) print("\nget_parallel_merge_32_to_16_info:\n") perf_resource_dict = get_parallel_merge_32_to_16_info(N_insertion=1e8/8192/64*32) print(perf_resource_dict) option_list = get_options_preprocessing_OPQ() print("\nget_options_preprocessing_OPQ:\n") for option in option_list: print(option) print("\nget_options_stage_1_cluster_distance_computation:\n") nlist_options = [2**10, 2**11, 2**12, 2**13, 2**14, 2**15, 2**16, 2**17, 2**18] for nlist in nlist_options: option_list = get_options_stage_1_cluster_distance_computation(nlist) print("nlist={}".format(nlist)) for option in option_list: print(option) print("\nget_options_stage_2_select_Voronoi_cells:\n") option_list = get_options_stage_2_select_Voronoi_cells(nlist=8192, nprobe=32) for option in option_list: print(option) print("\nget_options_stage_3_distance_LUT_construction:\n") option_list = get_options_stage_3_distance_LUT_construction(nprobe=32) for option in option_list: print(option) print("\nget_options_stage_4_distance_estimation_by_LUT:\n") option_list = get_options_stage_4_distance_estimation_by_LUT( PE_num=63, nprobe=32, N_compute_per_nprobe=int(1e8/8192/63)) for option in option_list: print(option) # for a small amount of number being scanned, hierachical priority queue is not # really an option print("\nget_options_stage_5_sort_reduction:\n") # for small number of Voronoi cells, only 2 level is required print("nlist=8192, nprobe=32, nstreams=64") option_list = get_options_stage_5_sort_reduction( input_stream_num=64, N_insertion_per_stream=int(1e8/8192*32/64)) for option in option_list: print(option) # for large number of Voronoi cells, 4 level is required print("nlist=262144, nprobe=32, nstreams=64") option_list = get_options_stage_5_sort_reduction( input_stream_num=64, N_insertion_per_stream=int(1e8/262144*32/64)) for option in option_list: print(option) # try different stream num print("nlist=8192, nprobe=32, nstreams=48") option_list = get_options_stage_5_sort_reduction( input_stream_num=48, N_insertion_per_stream=int(1e8/8192*32/64)) for option in option_list: print(option) print("nlist=8192, nprobe=32, nstreams=32") option_list = get_options_stage_5_sort_reduction( input_stream_num=32, N_insertion_per_stream=int(1e8/8192*32/64)) for option in option_list: print(option) print("nlist=8192, nprobe=32, nstreams=16") option_list = get_options_stage_5_sort_reduction( input_stream_num=16, N_insertion_per_stream=int(1e8/8192*32/64)) for option in option_list: print(option)

[ "wejiang@r630-03.ethz.ch" ]

wejiang@r630-03.ethz.ch

bdb48e86eb17c48178b7ed1e9f44a9a4d7f89034

40d84c19e1e5ef0d12656e5288ad5c9667d52f41

/accounts/tests/test_auth_api.py

c14d7ee3eb71f1c573a3120e8f63f311c5bd3488

[]

no_license

Michael-Spirit/StarNavi

be6b727dfe689308c9db7497c05810bc2ee7f7d3

0a1e2994d9a7e96837c9a0842977658d507cbb00

refs/heads/master

2023-01-08T12:08:58.028321

2019-10-15T14:10:06

215,303,284

0

null

2023-01-04T22:47:05

2019-10-15T13:16:24

Vue

UTF-8

Python

false

1,236

py

from rest_framework.test import APITestCase from rest_framework.reverse import reverse from accounts.tests.factories import UserFactory, TEST_USER_PASSWORD from allauth.account.models import EmailAddress class TestAuth(APITestCase): def test_registration(self): data = { 'email': 'test@mail.com', 'password1': 'test-password123', 'password2': 'test-password123' } response = self.client.post(reverse('rest_register'), data=data) self.assertEqual(response.status_code, 201) def test_login_success(self): user = UserFactory() data = { 'email': user.email, 'password': TEST_USER_PASSWORD } response = self.client.post(reverse('rest_login'), data=data) self.assertIsNotNone(response.data['token']) def test_login_unverified_email(self): user = UserFactory() EmailAddress.objects.filter(email=user.email).update(verified=False) data = { 'email': user.email, 'password': TEST_USER_PASSWORD } response = self.client.post(reverse('rest_login'), data=data) self.assertContains(response, data['email'], status_code=200)

[ "MSpiridonov94@gmail.com" ]

MSpiridonov94@gmail.com

e75f7bb601e0c23858b263f18e3f92a930c7e303

0f80565b3d15fbb493dfda1f8522600b6bb69c74

/BasicPolling/BasicPolling/urls.py

60cd34f9519a8cb754a8235fd8f779f9b77ff134

[]

no_license

chimpansiets/BasicPolling

ccbdabd817bb07919f346bdc15972f49a779adaf

6ad6ad27dfd3fb09b26de8240afe15134bbb5a5d

refs/heads/main

2023-03-24T09:42:27.990896

2021-03-23T09:07:14

350,048,103

0

null

UTF-8

Python

false

806

py

"""BasicPolling URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('polls/', include('polls.urls')), path('admin/', admin.site.urls), ]

[ "svoort1@live.nl" ]

svoort1@live.nl

f00b018ae4c08271e7b5558c629a0ab780474a45

a42be7826de86dd3f961b0486a30f8305ceb2b94

/src/common/Molecule.py

6a108463d0f20645886fa6a94ae9bca790ac96e4

[ "MIT" ]

permissive

catenate15/Pilgrim

9199bc50714c60abbc7f97e239a2cbee1e48b573

5dd261c0f3fb89732a8a57681d7f6cc3a41a8085

refs/heads/master

2022-08-04T04:54:22.513185

2020-05-25T20:16:14

null

0

null

UTF-8

Python

false

23,606

py

#!/usr/bin/python3.6 ''' --------------------------- Licensing and Distribution --------------------------- Program name: Pilgrim Version : 2020.2 License : MIT/x11 Copyright (c) 2020, David Ferro Costas (david.ferro@usc.es) and Antonio Fernandez Ramos (qf.ramos@usc.es) 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. --------------------------- *----------------------------------* | Module : common | | Sub-module : Molecule | | Last Update: 2020/02/03 (Y/M/D) | | Main Author: David Ferro-Costas | *----------------------------------* This module contains the Molecule class ''' #--------------------------------------------------# import os import numpy as np #--------------------------------------------------# import common.fncs as fncs import common.partfns as pf import common.internal as intl import common.Exceptions as Exc #--------------------------------------------------# from common.criteria import EPS_IC from common.dicts import dpt_im from common.files import read_gtsfile from common.files import read_fchk from common.files import read_gauout from common.files import write_gtsfile from common.files import write_xyz, write_molden from common.pgs import get_pgs #--------------------------------------------------# from common.physcons import AMU from common.physcons import KCALMOL from common.physcons import EV from common.physcons import ANGSTROM from common.physcons import H2CM #--------------------------------------------------# class Molecule(): # Initialization method def __init__(self,label=None): self._label = label # Unidimensional self._mform = "-" self._mu = None self._ch = None self._mtp = None self._V0 = None self._pgroup = None self._rotsigma = None self._nel = None # number of electrons self._rtype = None self._linear = None # Multi-dimensional self._atnums = None self._symbols = None self._masses = None self._les = None # list of electronic states self._itensor = None self._imoms = None self._rotTs = None # Arrays of importance self._xcc = None self._gcc = None self._Fcc = None self._xms = None self._gms = None self._Fms = None # related to frequencies self._fscal = 1.0 self._nvdof = None self._cczpe = None self._ccfreqs = None self._ccFevals = None self._ccFevecs = None self._iczpe = None self._icfreqs = None self._icFevals = None self._icFevecs = None # other stuff for very particular occasion self._gts = None def __str__(self): return self._mform def setvar(self,xcc=None,gcc=None,Fcc=None,\ atonums=None,symbols=None,masses=None,\ ch=None,mtp=None, V0=None,\ pgroup=None,rotsigma=None,\ fscal=None,les=None): if xcc is not None: self._xcc = xcc if gcc is not None: self._gcc = gcc if Fcc is not None: self._Fcc = Fcc if atonums is not None: self._atnums = atonums if symbols is not None: self._symbols = symbols if masses is not None: self._masses = masses if ch is not None: self._ch = int(ch) if mtp is not None: self._mtp = int(mtp) if V0 is not None: self._V0 = V0 if pgroup is not None: self._pgroup = pgroup if rotsigma is not None: self._rotsigma = rotsigma if fscal is not None: self._fscal = fscal if les is not None: self._les = les def genderivates(self): self._mform = fncs.get_molformula(self._symbols) self._natoms = len(self._atnums) self._mass = sum(self._masses) self._nel = sum(self._atnums)-self._ch if self._les is None: self._les = [ (self._mtp,0.0) ] def prepare(self): # check atnums if self._atnums is not None and type(self._atnums[0]) == str: self._symbols = list(self._atnums) # check symbols if self._symbols is not None and type(self._symbols[0]) == int: self._atnums = list(self._symbols) # Get both atnums and symbols if None if self._atnums is None: self._atnums = fncs.symbols2atonums(self._symbols) if self._symbols is None: self._symbols = fncs.atonums2symbols(self._atnums) # check masses if self._masses is None: self._masses = fncs.atonums2masses(self._atnums) # derivated magnitudes self.genderivates() # check Fcc if self._Fcc not in (None,[]) and len(self._Fcc) != 3*self._natoms: self._Fcc = fncs.lowt2matrix(self._Fcc) def calc_pgroup(self,force=False): calculate = False if force : calculate = True if self._pgroup is None: calculate = True if self._rotsigma is None: calculate = True if calculate: self._pgroup,self._rotsigma = get_pgs(self._atnums,self._masses,self._xcc) def remove_frozen(self): frozen = fncs.detect_frozen(self._Fcc,self._natoms) if len(frozen) == 0: return [],[] # coordinates and symbols of frozen moiety bN = [at in frozen for at in range(self._natoms)] b3N = [at in frozen for at in range(self._natoms) for ii in range(3)] frozen_xcc = np.array(self._xcc)[b3N] frozen_symbols = np.array(self._symbols)[bN] # now system is just the flexible moiety bN = [at not in frozen for at in range(self._natoms)] b3N = [at not in frozen for at in range(self._natoms) for ii in range(3)] self._xcc = np.array(self._xcc)[b3N].tolist() self._symbols = np.array(self._symbols)[bN].tolist() self._atnums = np.array(self._atnums)[bN].tolist() self._masses = np.array(self._masses)[bN].tolist() self._pgroup = None self._rotsigma = None # Gradient and hessian if self._gcc is not None and len(self._gcc) != 0: self._gcc = np.array(self._gcc)[b3N].tolist() if self._Fcc is not None and len(self._Fcc) != 0: n3 = self._natoms*3 self._Fcc = [[self._Fcc[idx1][idx2] for idx1 in range(n3) if b3N[idx1]]\ for idx2 in range(n3) if b3N[idx2]] # set origin for frozen moiety com = fncs.get_com(self._xcc,self._masses) frozen_xcc = fncs.set_origin(frozen_xcc,com) # prepare system self.prepare() return frozen_xcc, frozen_symbols def mod_masses(self,masses): self._masses = list(masses) self._mass = sum(self._masses) # re-calculate point group self.calc_pgroup(force=True) def apply_imods(self,imods,imasses): ''' example: imods = ["H2(4,5)","C13(all_C)"] imasses = {"H2":2.0141/AMU, "C13":13.0034/AMU} ''' if imods is None: return for imod in imods: isymbol = imod.split("(")[0] if isymbol in imasses.keys(): imass = imasses[isymbol] elif isymbol in dpt_im.keys(): imass = dpt_im[isymbol] else: exception = Exc.WrongInIsomass exception._var = isymbol raise exception atoms = imod.split("(")[1].split(")")[0] if "all_" in atoms: atype = atoms.split("all_")[1].strip() for idx,symbol in enumerate(self._symbols): if symbol == atype: self._masses[idx] = imass else: list_of_atoms = [] for atom in atoms.split(","): if "-" in atom: at1,atn = atom.split("-") list_of_atoms += range(int(at1),int(atn)+1) else: list_of_atoms.append(int(atom)) list_of_atoms = sorted(list(set(list_of_atoms))) for idx in list_of_atoms: self._masses[idx-1] = imass # re-calculate total mass and point group self.mod_masses(self._masses) def setup(self,mu=1.0/AMU,projgrad=False): self._mu = mu # shift to center of mass and reorientate molecule idata = (self._xcc,self._gcc,self._Fcc,self._masses) self._xcc, self._gcc, self._Fcc = fncs.center_and_orient(*idata) # symmetry self.calc_pgroup(force=False) # Generate mass-scaled arrays self._xms = fncs.cc2ms_x(self._xcc,self._masses,self._mu) self._gms = fncs.cc2ms_g(self._gcc,self._masses,self._mu) self._Fms = fncs.cc2ms_F(self._Fcc,self._masses,self._mu) #-------------# # Atomic case # #-------------# if self._natoms == 1: self._nvdof = 0 self._linear = False #self._xms = list(self._xcc) #self._gms = list(self._gcc) #self._Fms = list(self._Fcc) self._ccfreqs = [] self._ccFevals = [] self._ccFevecs = [] #----------------# # Molecular case # #----------------# else: # Calculate inertia self._itensor = fncs.get_itensor_matrix(self._xcc,self._masses) self._imoms, self._rotTs, self._rtype, self._linear = \ fncs.get_itensor_evals(self._itensor) # Vibrational degrees of freedom if self._linear: self._nvdof = 3*self._natoms - 5 else : self._nvdof = 3*self._natoms - 6 # calculate frequencies if self._Fcc is None : return if len(self._Fcc) == 0 : return if self._ccfreqs is not None: return v0 = self._gms if projgrad else None data = fncs.calc_ccfreqs(self._Fcc,self._masses,self._xcc,self._mu,v0=v0) self._ccfreqs, self._ccFevals, self._ccFevecs = data # Scale frequencies self._ccfreqs = fncs.scale_freqs(self._ccfreqs,self._fscal) def get_imag_main_dir(self): ic, fwsign = intl.ics_idir(self._xcc,self._symbols,\ self._masses,self._ccfreqs,self._ccFevecs) return ic, fwsign def icfreqs(self,ics,bool_pg=False): #----------------# # Molecular case # #----------------# if self._natoms != 1: ituple = (self._Fcc,self._masses,self._xcc,self._gcc,ics,bool_pg) self._icfreqs, self._icFevals, self._icFevecs = intl.calc_icfreqs(*ituple) #-------------# # Atomic case # #-------------# else: self._icfreqs = [] self._icFevals = [] self._icFevecs = [] # scale frequencies self._icfreqs = [freq*self._fscal for freq in self._icfreqs] def ana_freqs(self,case="cc"): if case == "cc": # Keep record of imaginary frequencies if self._ccFevecs is not None: self._ccimag = [ (frq,self._ccFevecs[idx]) for idx,frq in enumerate(self._ccfreqs)\ if frq < 0.0] else: self._ccimag = [ (frq,None) for idx,frq in enumerate(self._ccfreqs)\ if frq < 0.0] # Calculate zpe self._cczpes = [fncs.afreq2zpe(frq) for frq in self._ccfreqs] self._cczpe = sum(self._cczpes) self._ccV1 = self._V0 + self._cczpe if case == "ic": # Keep record of imaginary frequencies if self._icFevecs is not None: self._icimag = [ (frq,self._icFevecs[idx]) for idx,frq in enumerate(self._icfreqs)\ if frq < 0.0] else: self._icimag = [ (frq,None) for idx,frq in enumerate(self._icfreqs)\ if frq < 0.0] # Calculate zpe self._iczpes = [fncs.afreq2zpe(frq) for frq in self._icfreqs] self._iczpe = sum(self._iczpes) self._icV1 = self._V0 + self._iczpe def clean_freqs(self,case="cc"): # select case if case == "cc": freqs = self._ccfreqs else : freqs = self._icfreqs # keep track of those to save keep = [] for idx,freq in enumerate(freqs): if abs(fncs.afreq2cm(freq)) < EPS_IC: continue keep.append(idx) # keep only those > EPS_IC if case == "cc": self._ccfreqs = [self._ccfreqs[idx] for idx in keep] if self._ccFevals is not None: self._ccFevals = [self._ccFevals[idx] for idx in keep] if self._ccFevecs is not None: self._ccFevecs = [self._ccFevecs[idx] for idx in keep] if case == "ic": self._icfreqs = [self._icfreqs[idx] for idx in keep] if self._icFevals is not None: self._icFevals = [self._icFevals[idx] for idx in keep] if self._icFevecs is not None: self._icFevecs = [self._icFevecs[idx] for idx in keep] def deal_lowfq(self,lowfq={},case="cc"): # for Cartesian Coordinates if case == "cc": # frequencies were not projected along MEP if self._nvdof - len(self._ccfreqs) == 0: for idx,newfreq in lowfq.items(): self._ccfreqs[idx] = max(self._ccfreqs[idx],newfreq) # frequencies were projected along MEP elif self._nvdof - len(self._ccfreqs) == 1: for idx,newfreq in lowfq.items(): self._ccfreqs[idx-1] = max(self._ccfreqs[idx-1],newfreq) # for Internal Coordinates elif case == "ic": # frequencies were not projected along MEP if self._nvdof - len(self._icfreqs) == 0: for idx,newfreq in lowfq.items(): self._icfreqs[idx] = max(self._icfreqs[idx],newfreq) # frequencies were projected along MEP elif self._nvdof - len(self._icfreqs) == 1: for idx,newfreq in lowfq.items(): self._icfreqs[idx-1] = max(self._icfreqs[idx-1],newfreq) def calc_pfns(self,temps,case="cc",fmode=0): ''' fmode = -1 or 0 (0 is default) ''' # Calculate translational partition function (per unit volume) ph_tra = np.array([pf.pf_partinbox(self._mass,T) for T in temps]) # Calculate rotational partition function (Rigid-Rotor) if self._natoms > 1: pf_rot = np.array([pf.pf_rigidrotor(self._imoms,T,self._rotsigma) for T in temps]) else: pf_rot = np.array([1.0 for T in temps]) # Calculate vibrational partition function (Harmonic-Oscillator) if self._nvdof != 0: # remove freq if required nf = self._nvdof + fmode if case == "cc": afreqs = list(self._ccfreqs) if case == "ic": afreqs = list(self._icfreqs) while len(afreqs) > nf: afreqs = afreqs[1:] # Calculate vib pfn pf_vib = np.array([pf.pf_harmosc(afreqs,T,imag=1E10) for T in temps]) else: pf_vib = np.array([1.0 for T in temps]) # Calculate electronic partition function pf_ele = np.array([pf.pf_electr(self._les,T) for T in temps]) # Total partition function qtot = ph_tra * pf_rot * pf_vib * pf_ele if case == "cc": return qtot, self._ccV1, (ph_tra,pf_rot,pf_vib,pf_ele) if case == "ic": return qtot, self._icV1, (ph_tra,pf_rot,pf_vib,pf_ele) def info_string(self,ib=0): root_mass = sum(fncs.symbols2masses(self._symbols)) string = "Molecular formula : %s\n"%self._mform string += "Number of atoms : %i\n"%self._natoms string += "Number of electrons : %i\n"%self._nel string += "Vibrational DOFs : %i\n"%self._nvdof string += "Charge : %i\n"%self._ch string += "Multiplicity : %i\n"%self._mtp string += "Electronic energy (V0): %.8f hartree\n"%self._V0 string += "Total mass [root] : %.4f amu\n"%(root_mass *AMU) string += "Total mass : %.4f amu\n"%(self._mass*AMU) if self._pgroup is not None: string += "Point group symmetry : %s\n"%(self._pgroup) if self._rotsigma is not None: string += "Rotational sym num : %i\n"%(self._rotsigma) string += "Cartesian coordinates (Angstrom):\n" for at,symbol in enumerate(self._symbols): mass = self._masses[at]*AMU x,y,z = fncs.xyz(self._xcc,at) x *= ANGSTROM y *= ANGSTROM z *= ANGSTROM string += " %2s %+10.6f %+10.6f %+10.6f [%7.3f amu]\n"%(symbol,x,y,z,mass) try: str2 = "Moments and product of inertia (au):\n" if len(self._imoms) == 1: str2 += " %+10.3E\n"%self._imoms[0] if len(self._imoms) == 3: prodinert = self._imoms[0]*self._imoms[1]*self._imoms[2] dataline = (self._imoms[0],self._imoms[1],self._imoms[2],prodinert) str2 += " %+10.3E %+10.3E %+10.3E [%10.3E]\n"%dataline string += str2 except: pass try: str2 = "Vibrational frequencies [1/cm] (scaled by %.3f):\n"%self._fscal for idx in range(0,len(self._ccfreqs),6): str2 += " %s\n"%(" ".join("%8.2f"%fncs.afreq2cm(freq) \ for freq in self._ccfreqs[idx:idx+6])) if len(self._ccfreqs) != 0: string += str2 except: pass try: str2 = "Vibrational zero-point energies [kcal/mol]:\n" for idx in range(0,len(self._cczpes),6): str2 += " %s\n"%(" ".join("%8.2f"%(zpe*KCALMOL) \ for zpe in self._cczpes[idx:idx+6])) zpe_au = self._cczpe zpe_kcal = self._cczpe * KCALMOL zpe_eV = self._cczpe * EV zpe_cm = self._cczpe * H2CM str2 += "Vibrational zero-point energy: %+14.8f hartree = \n"%zpe_au str2 += " %+14.2f kcal/mol = \n"%zpe_kcal str2 += " %+14.2f eV = \n"%zpe_eV str2 += " %+14.2f cm^-1 \n"%zpe_cm str2 += "V0 + zero-point energy (V1) : %+14.8f hartree\n"%self._ccV1 if self._cczpe != 0.0: string += str2 except: pass # add blank spaces string = "\n".join([" "*ib+line for line in string.split("\n")]) return string #=======================================# # Set variables from external files # #=======================================# def set_from_gts(self,gtsfile): if not os.path.exists(gtsfile): return # read file self._gts = gtsfile xcc,atonums,ch,mtp,E,gcc,Fcc,masses,pgroup,rotsigma,freq_list = read_gtsfile(self._gts) # set variables self.setvar(xcc=xcc,gcc=gcc,Fcc=Fcc) self.setvar(atonums=atonums,masses=masses) self.setvar(ch=ch,mtp=mtp,V0=E,pgroup=pgroup,rotsigma=rotsigma) # Prepare system self.prepare() # only for developers: freq list if freq_list is not None and len(freq_list) != 0: self._ccfreqs = freq_list def set_from_fchk(self,fchk): if not os.path.exists(fchk): return # read file xcc, atonums, ch, mtp, E, gcc, Fcc, masses, calclevel = read_fchk(fchk) # set variables self.setvar(xcc=xcc,gcc=gcc,Fcc=Fcc) self.setvar(atonums=atonums,masses=masses) self.setvar(ch=ch,mtp=mtp,V0=E) # Prepare system self.prepare() def set_from_gauout(self,gauout): if not os.path.exists(gauout): return # read file xcc, atonums, ch, mtp, E, gcc, Fcc, masses, calclevel = read_gauout(gauout) # set variables self.setvar(xcc=xcc,gcc=gcc,Fcc=Fcc) self.setvar(atonums=atonums,masses=masses) self.setvar(ch=ch,mtp=mtp,V0=E) # Prepare system self.prepare() #=======================================# # Generation of different kind of files # #=======================================# def genfile_xyz(self,filename): try : write_xyz(filename,self._xcc,self._symbols) except: return 0 return 1 #---------------------------------------# def genfile_molden(self,filename): try : write_molden(filename,self._xcc,self._symbols,self._ccfreqs,self._ccFevecs) except: return 0 return 1 #---------------------------------------# def genfile_gts(self,filename,level=""): write_gtsfile(self._xcc,self._atnums,self._ch,self._mtp,\ self._V0,self._pgroup,self._rotsigma,self._gcc,\ self._Fcc,filename,level=level) try : write_gtsfile(self._xcc,self._atnums,self._ch,self._mtp,\ self._V0,self._pgroup,self._rotsigma,self._gcc,\ self._Fcc,filename,level=level) except: return 0 return 1 #=======================================#

[ "cathedralpkg@gmail.com" ]

cathedralpkg@gmail.com

77eb8e1fa8124381406bb991d72844b5849b75c9

5cfaa7709237e4fd0bdc2f00a5bde6b063324580

/tests/test_features.py

09359dca7a96226ce0d9d9edf939634cf73beaaa

[]

no_license

khamv/ml_investment

6f1f0fe0e0cc4a7fc0b84524b519b21a4582a85a

9251847dcd5e88427106eed3c9952902210d645c

refs/heads/main

2023-03-22T05:52:12.537206

2021-03-17T20:24:15

null

0

null

UTF-8

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13,636

py

import pytest import hashlib import pandas as pd import numpy as np from data import SF1Data from features import calc_series_stats, QuarterlyFeatures, BaseCompanyFeatures,\ QuarterlyDiffFeatures, FeatureMerger, \ DailyAggQuarterFeatures from utils import load_json config = load_json('config.json') @pytest.mark.parametrize( ["series", "norm", "expected"], [([10, 0, 1], False, {'_mean': 3.6666666666666665, '_median': 1.0, '_max': 10.0, '_min': 0.0, '_std': 4.4969125210773475}), ([10, -30, 1, 4, 15.2], False, {'_mean': 0.039999999999999855, '_median': 4.0, '_max': 15.2, '_min': -30.0, '_std': 15.798936673080249}), ([1], False, {'_mean': 1.0, '_median': 1.0, '_max': 1.0, '_min': 1.0, '_std': 0.0} ), ([10, -30, 1, 4, 15.2], True, {'_mean': 0.0039999999999999855, '_median': .4, '_max': 1.52, '_min': -3.0, '_std': 1.5798936673080249})] ) def test_calc_series_stats(series, norm, expected): result = calc_series_stats(series, norm=norm) assert type(result) == dict assert len(result) == len(expected) assert result.keys() == expected.keys() for key in result: assert np.isclose(result[key], expected[key]) if norm == False: np.random.seed(0) np.random.shuffle(series) result = calc_series_stats(series, norm=norm) for key in result: assert np.isclose(result[key], expected[key]) def test_calc_series_stats_nans(): assert calc_series_stats([np.nan, 10, 0, 1]) == calc_series_stats([10, 0, 1]) assert calc_series_stats([None, 10, 0, 1]) == calc_series_stats([10, 0, 1]) assert calc_series_stats([10, 0, np.nan, 1]) == calc_series_stats([10, 0, 1]) result = calc_series_stats([]) for key in result: assert np.isnan(result[key]) result = calc_series_stats([np.nan, None]) for key in result: assert np.isnan(result[key]) def int_hash(text): return int(hashlib.md5(text.encode('utf-8')).hexdigest()[:8], 16) class Data: def __init__(self, columns, cat_columns=None, tickers=None): self.columns = columns self.cat_columns = cat_columns self.tickers = tickers def load_quarterly_data(self, tickers, quarter_count=None): size=50 df = pd.DataFrame() df['ticker'] = tickers * size df['date'] = np.nan np.random.seed(int_hash(str(tickers))) for col in self.columns: df[col] = np.random.uniform(-1e5, 1e5, size) return df def load_daily_data(self, tickers): size=500 df = pd.DataFrame() df['ticker'] = tickers * size df['date'] = np.datetime64('now') np.random.seed(int_hash(str(tickers))) for col in self.columns: df[col] = np.random.uniform(-1e5, 1e5, size) return df def load_base_data(self): df = pd.DataFrame() df['ticker'] = self.tickers for col in self.cat_columns: np.random.seed(0) df[col] = np.random.randint(-2, 2, len(self.tickers)) return df class TestQuarterlyFeatures: @pytest.mark.parametrize( ["tickers", "columns", "quarter_counts", "max_back_quarter"], [(['AAPL', 'TSLA'], ['ebit'], [2], 10), (['NVDA', 'TSLA'], ['ebit'], [2, 4], 5), (['AAPL', 'NVDA', 'TSLA', 'WORK'], ['ebit', 'debt'], [2, 4, 10], 10), (['AAPL', 'ZLG'], ['ebit', 'debt'], [2, 4, 10], 5)] ) def test_calculate(self, tickers, columns, quarter_counts, max_back_quarter): fc = QuarterlyFeatures(columns=columns, quarter_counts=quarter_counts, max_back_quarter=max_back_quarter) loaders = [Data(columns), SF1Data(config['sf1_data_path'])] for data_loader in loaders: X = fc.calculate(data_loader, tickers) assert type(X) == pd.DataFrame assert 'ticker' in X.index.names assert 'date' in X.index.names if type(data_loader) == Data: assert X.shape[0] == max_back_quarter * len(tickers) else: assert X.shape[0] <= max_back_quarter * len(tickers) assert X.shape[1] == 2 * len(calc_series_stats([])) * \ len(columns) * len(quarter_counts) # Minimum can not be lower with reduction of quarter_count sorted_quarter_counts = np.sort(quarter_counts) for col in columns: for k in range(len(sorted_quarter_counts) - 1): lower_count = sorted_quarter_counts[k] higher_count = sorted_quarter_counts[k + 1] l_col = 'quarter{}_{}_min'.format(lower_count, col) h_col = 'quarter{}_{}_min'.format(higher_count, col) assert (X[h_col] <= X[l_col]).min() # Maximum can not be higher with reduction of quarter_count sorted_quarter_counts = np.sort(quarter_counts) for col in columns: for k in range(len(sorted_quarter_counts) - 1): lower_count = sorted_quarter_counts[k] higher_count = sorted_quarter_counts[k + 1] l_col = 'quarter{}_{}_max'.format(lower_count, col) h_col = 'quarter{}_{}_max'.format(higher_count, col) assert (X[h_col] >= X[l_col]).min() std_cols = [x for x in X.columns if '_std' in x] for col in std_cols: assert X[col].min() >= 0 for col in columns: for count in quarter_counts: min_col = 'quarter{}_{}_min'.format(count, col) max_col = 'quarter{}_{}_max'.format(count, col) mean_col = 'quarter{}_{}_mean'.format(count, col) median_col = 'quarter{}_{}_median'.format(count, col) assert (X[max_col] >= X[min_col]).min() assert (X[max_col] >= X[mean_col]).min() assert (X[max_col] >= X[median_col]).min() assert (X[mean_col] >= X[min_col]).min() assert (X[median_col] >= X[min_col]).min() class TestQuarterlyDiffFeatures: @pytest.mark.parametrize( ["tickers", "columns", "compare_quarter_idxs", "max_back_quarter"], [(['AAPL', 'TSLA'], ['ebit'], [1], 10), (['NVDA', 'TSLA'], ['ebit'], [1, 4], 5), (['AAPL', 'NVDA', 'TSLA', 'WORK'], ['ebit', 'debt'], [1, 4, 10], 10), (['AAPL', 'ZLG'], ['ebit', 'debt'], [1, 4, 10], 5)] ) def test_calculate(self, tickers, columns, compare_quarter_idxs, max_back_quarter): fc = QuarterlyDiffFeatures(columns=columns, compare_quarter_idxs=compare_quarter_idxs, max_back_quarter=max_back_quarter) loaders = [Data(columns), SF1Data(config['sf1_data_path'])] for data_loader in loaders: X = fc.calculate(data_loader, tickers) assert type(X) == pd.DataFrame assert 'ticker' in X.index.names assert 'date' in X.index.names if type(data_loader) == Data: assert X.shape[0] == max_back_quarter * len(tickers) else: assert X.shape[0] <= max_back_quarter * len(tickers) assert X.shape[1] == len(compare_quarter_idxs) * len(columns) class WrapData: def __init__(self, data_loader, tickers): self.data_loader = data_loader self.tickers = tickers def load_base_data(self): df = pd.DataFrame() df['ticker'] = self.tickers df = pd.merge(df, self.data_loader.load_base_data(), how='left') return df class TestBaseCompanyFeatures: @pytest.mark.parametrize( ["tickers", "cat_columns"], [(['AAPL', 'TSLA'], ['sector']), (['NVDA', 'TSLA'], ['sector', 'sicindustry']), (['AAPL', 'NVDA', 'TSLA', 'WORK'], ['sector', 'sicindustry']), (['AAPL', 'ZLG'], ['sector', 'sicindustry'])] ) def test_calculate(self, tickers, cat_columns): loaders = [Data(columns=[], cat_columns=cat_columns, tickers=tickers), SF1Data(config['sf1_data_path'])] for data_loader in loaders[:]: fc = BaseCompanyFeatures(cat_columns=cat_columns) X = fc.calculate(data_loader, tickers) assert type(X) == pd.DataFrame assert 'ticker' in X.index.names base_data = data_loader.load_base_data() for col in cat_columns: assert len(base_data[col].unique()) ==\ len(fc.col_to_encoder[col].classes_) # Reuse fitted after first calculate fc for col in cat_columns: assert col in fc.col_to_encoder new_X = fc.calculate(data_loader, tickers) for col in cat_columns: assert (new_X[col] == X[col]).min() wd = WrapData(data_loader, tickers) new_X = fc.calculate(wd, tickers) for col in cat_columns: assert (new_X[col] == X[col]).min() class TestFeatureMerger: @pytest.mark.parametrize( "tickers", [['AAPL', 'TSLA'], ['NVDA', 'TSLA'], ['AAPL', 'NVDA', 'TSLA', 'WORK'], ['AAPL', 'ZLG']] ) def test_calculate(self, tickers): data_loader = SF1Data(config['sf1_data_path']) fc1 = QuarterlyFeatures(columns=['ebit'], quarter_counts=[2], max_back_quarter=10) fc2 = QuarterlyDiffFeatures(columns=['ebit', 'debt'], compare_quarter_idxs=[1, 4], max_back_quarter=10) fc3 = BaseCompanyFeatures(cat_columns=['sector', 'sicindustry']) X1 = fc1.calculate(data_loader, tickers) X2 = fc2.calculate(data_loader, tickers) X3 = fc3.calculate(data_loader, tickers) fm1 = FeatureMerger(fc1, fc2, on=['ticker', 'date']) Xm1 = fm1.calculate(data_loader, tickers) fm2 = FeatureMerger(fc1, fc3, on='ticker') Xm2 = fm2.calculate(data_loader, tickers) assert Xm1.shape[0] == X1.shape[0] assert Xm2.shape[0] == X1.shape[0] assert Xm1.shape[1] == X1.shape[1] + X2.shape[1] assert Xm2.shape[1] == X1.shape[1] + X3.shape[1] assert (Xm1.index == X1.index).min() assert (Xm2.index == X1.index).min() new_cols = Xm1.columns[:X1.shape[1]] old_cols = X1.columns for nc, oc in zip(new_cols, old_cols): assert (Xm1[nc] == X1[oc]).min() new_cols = Xm2.columns[:X1.shape[1]] old_cols = X1.columns for nc, oc in zip(new_cols, old_cols): assert (Xm2[nc] == X1[oc]).min() class TestDailyAggQuarterFeatures: @pytest.mark.parametrize( ["tickers", "columns", "agg_day_counts", "max_back_quarter"], [(['AAPL', 'TSLA'], ['marketcap'], [100], 10), (['NVDA', 'TSLA'], ['marketcap'], [100, 200], 5), (['AAPL', 'NVDA', 'TSLA', 'WORK'], ['marketcap', 'pe'], [50, 200], 10), (['AAPL', 'ZLG'], ['marketcap', 'pe'], [50, 200], 5)] ) def test_calculate(self, tickers, columns, agg_day_counts, max_back_quarter): fc = DailyAggQuarterFeatures(columns=columns, agg_day_counts=agg_day_counts, max_back_quarter=max_back_quarter) data_loader = SF1Data(config['sf1_data_path']) X = fc.calculate(data_loader, tickers) assert type(X) == pd.DataFrame assert 'ticker' in X.index.names assert 'date' in X.index.names assert X.shape[0] <= max_back_quarter * len(tickers) assert X.shape[1] == len(calc_series_stats([])) * \ len(columns) * len(agg_day_counts) for col in columns: for count in agg_day_counts: min_col = 'days{}_{}_min'.format(count, col) max_col = 'days{}_{}_max'.format(count, col) mean_col = 'days{}_{}_mean'.format(count, col) median_col = 'days{}_{}_median'.format(count, col) assert (X[max_col] >= X[min_col]).min() assert (X[max_col] >= X[mean_col]).min() assert (X[max_col] >= X[median_col]).min() assert (X[mean_col] >= X[min_col]).min() assert (X[median_col] >= X[min_col]).min()

[ "fartuk@pop-os.localdomain" ]

fartuk@pop-os.localdomain

1e5ba37e5632d9af328796803bb460d84fe8af12

cb0e7d6493b23e870aa625eb362384a10f5ee657

/solutions/python3/1330.py

a628f22bd8f3702332e9e5888502f2c30940ce64

[]

no_license

sweetpand/LeetCode-1

0acfa603af254a3350d457803449a91322f2d1a7

65f4ef26cb8b2db0b4bf8c42bfdc76421b479f94

refs/heads/master

2022-11-14T07:01:42.502172

2020-07-12T12:25:56

279,088,171

1

0

null

2020-07-12T15:03:20

2020-07-12T15:03:19

null

UTF-8

Python

false

567

py

class Solution: def maxValueAfterReverse(self, nums: List[int]) -> int: mini = float('inf') maxi = float('-inf') for a, b in zip(nums, nums[1:]): mini = min(mini, max(a, b)) maxi = max(maxi, min(a, b)) diff = max(0, (maxi - mini) * 2) for a, b in zip(nums, nums[1:]): headDiff = -abs(a - b) + abs(nums[0] - b) tailDiff = -abs(a - b) + abs(nums[-1] - a) diff = max(diff, headDiff, tailDiff) return sum(abs(a - b) for a, b in zip(nums, nums[1:])) + diff

[ "walkccray@gmail.com" ]

walkccray@gmail.com

44e407c8917cf9421fad88dac6961fbb22179b78

fef6ed0cee947f753cf8a34b244726bfdeedb079

/exercises/05_basic_scripts/task_5_2a.py

1fbea70ee085898ee942deef5240481f9c528704

[]

no_license

mirsadm82/pyneng-examples-exercises-en

c11bc3d4181c7e79807bfab46587ecb5a182d54a

9a5b34bc460005d8b04dfd636790bf53a1916efc

refs/heads/main

2023-05-10T22:26:43.348567

2021-06-01T12:18:54

2021-06-01T12:20:20

360,168,419

0

null

UTF-8

Python

false

2,386

py

# -*- coding: utf-8 -*- """ Task 5.2a Copy and modify the script from task 5.2 so that, if the user entered a host address rather than a network address, convert the host address to a network address and print the network address and mask, as in task 5.2. An example of a network address (all host bits are equal to zero): * 10.0.1.0/24 * 190.1.0.0/16 Host address example: * 10.0.1.1/24 - host from network 10.0.1.0/24 * 10.0.5.195/28 - host from network 10.0.5.192/28 If the user entered the address 10.0.1.1/24, the output should look like this: Network: 10 0 1 0 00001010 00000000 00000001 00000000 Mask: /24 255 255 255 0 11111111 11111111 11111111 00000000 Check the script work on different host/mask combinations, for example: 10.0.5.195/28, 10.0.1.1/24 Hint: The network address can be calculated from the binary host address and the netmask. If the mask is 28, then the network address is the first 28 bits host addresses + 4 zeros. For example, the host address 10.1.1.195/28 in binary will be: bin_ip = "00001010000000010000000111000011" Then the network address will be the first 28 characters from bin_ip + 0000 (4 because in total there can be 32 bits in the address, and 32 - 28 = 4) 00001010000000010000000111000000 Restriction: All tasks must be done using the topics covered in this and previous chapters. """ net = input("Enter the IP network in the format e.g. 10.1.1.0/24: ") ip, mask = net.split("/") ip_list = ip.split(".") mask = int(mask) oct1, oct2, oct3, oct4 = [ int(ip_list[0]), int(ip_list[1]), int(ip_list[2]), int(ip_list[3]), ] bin_ip_str = "{:08b}{:08b}{:08b}{:08b}".format(oct1, oct2, oct3, oct4) bin_network_str = bin_ip_str[:mask] + "0" * (32 - mask) net1, net2, net3, net4 = [ int(bin_network_str[0:8], 2), int(bin_network_str[8:16], 2), int(bin_network_str[16:24], 2), int(bin_network_str[24:32], 2), ] bin_mask = "1" * mask + "0" * (32 - mask) m1, m2, m3, m4 = [ int(bin_mask[0:8], 2), int(bin_mask[8:16], 2), int(bin_mask[16:24], 2), int(bin_mask[24:32], 2), ] ip_output = """ Network: {0:<8} {1:<8} {2:<8} {3:<8} {0:08b} {1:08b} {2:08b} {3:08b}""" mask_output = """ Mask: /{0} {1:<8} {2:<8} {3:<8} {4:<8} {1:08b} {2:08b} {3:08b} {4:08b} """ print(ip_output.format(net1, net2, net3, net4)) print(mask_output.format(mask, m1, m2, m3, m4))

[ "mirsad.muratagic@gmail.com" ]

mirsad.muratagic@gmail.com

0dd4cf4810bd0213389b3262df85aa7df66ccec0

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/Detect emotions of your favorite toons/Final_model.py

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import keras from keras.layers import Conv2D, MaxPooling2D, BatchNormalization from keras.layers import Dropout, Flatten, Dense from keras.models import Sequential model = Sequential() model.add(Conv2D(16, (3, 3), input_shape = (128, 128, 3),kernel_initializer='normal', activation='relu')) model.add(Conv2D(16, (3, 3),activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(32, (3, 3), border_mode='valid',activation='relu')) model.add(Conv2D(32, (3, 3),activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(128, (3, 3), border_mode='valid',activation='relu')) model.add(Conv2D(128, (3, 3),activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.4)) model.add(Conv2D(128, (3, 3), border_mode='valid',activation='relu')) model.add(Conv2D(128, (3, 3),activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.4)) model.add(Flatten()) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(5,activation='softmax')) ada = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False) sgd = keras.optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss="mean_squared_error", optimizer=sgd, metrics=['accuracy']) model.compile(loss="categorical_crossentropy", optimizer='adam', metrics=['accuracy']) from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator(shear_range = 0.2, channel_shift_range = 0.2, zoom_range = 0.2, rotation_range=10, validation_split=0.9, horizontal_flip = True) test_datagen = ImageDataGenerator(validation_split=0.9) training_set = train_datagen.flow_from_directory('Train', target_size = (128, 128), batch_size = 16, shuffle=True, seed=101, #save_to_dir='Augumented/Train', #save_format='jpeg', interpolation='nearest', class_mode = 'categorical') test_set = test_datagen.flow_from_directory('Test', target_size = (128, 128), batch_size = 16, shuffle=True, seed=101, #save_to_dir='Augumented/Test', #save_format='jpeg', interpolation='nearest', class_mode = 'categorical') from keras.callbacks import EarlyStopping from keras.callbacks import ModelCheckpoint es = EarlyStopping(monitor='val_accuracy', mode='max', patience=20) mc = ModelCheckpoint('best_model.h5', monitor='val_accuracy', mode='max', save_best_only=True) model.fit_generator(training_set, steps_per_epoch = 228, epochs = 100, validation_data = test_set, validation_steps = 70, class_weight={0:5,4:9,2:15,3:15,1:15}, callbacks=[es,mc]) from keras.models import load_model saved_model = load_model('best_model.h5') from sklearn.externals import joblib joblib.dump(model, 'Detect_emotions_of_your_favorite_cartoon.joblib') d=training_set.class_indices d= {v:k for k,v in d.items()} import numpy as np import pandas as pd from keras.preprocessing import image test_predict=pd.read_csv('Test.csv') for n in test_predict['Frame_ID']: test_image = image.load_img('predict/'+n, target_size = (128, 128)) test_image = image.img_to_array(test_image) test_image = np.expand_dims(test_image, axis = 0) result = saved_model.predict(test_image) training_set.class_indices if result[0][0] >= 0.5: prediction = 'Unknown' elif result[0][1] >= 0.5: prediction = 'angry' elif result[0][2] >= 0.5: prediction = 'happy' elif result[0][3] >= 0.5: prediction = 'sad' else: prediction = 'surprised' test_predict.loc[test_predict['Frame_ID']==n,'Results']=prediction test_predict.to_csv(r'Test_final.csv')

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VigneshwaraChinnadurai.noreply@github.com

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## ## 0. Install python3+ and the requests library if required ## 1. Get an account at SecurityTrails.com (free ones are available for infrequent users) ## 2. Get your APIKEY from the the dash board and replace the text "Your Security Trails APIKey here" below ## 3. Replace the text "addYourDomain.com" below with the domain you want to block ## 4. The domains with "0.0.0.0 on the front will be added to a text file in your working directory (domain.com.txt) ## 5. Add the content of the output file to a new or existing blocklist ## 6. Repeat from step 3 for each domain ## import requests import os domain = "addYourDomain.com" url = "https://api.securitytrails.com/v1/domain/"+domain+"/subdomains" querystring = {"children_only":"false"} headers = { "Accept": "application/json", "APIKEY": "Your Security Trails APIKey here" } response = requests.request("GET", url, headers=headers, params=querystring) lstSubs = response.json()["subdomains"] count = 0 with open (os.getcwd()+"\\"+domain+".txt", "w") as f: f.write("0.0.0.0 " + domain + "\n") for sub in lstSubs: f.write("0.0.0.0 " + sub + "." + domain+"\n") count = count + 1 print ("Blocklist with "+str(count)+" domains written to: "+f.name)

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FreeLike76/kpi_pa_5

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from tkinter import * class Gui: def __init__(self): self.root = Tk() self.root.geometry("340x440") headerFont = ("Times New Roman", 16, "bold") simpleFont = ("Times New Roman", 16) playerLabel = Label(self.root, text="Player", width=8, height=1, font=headerFont) playerLabel.grid(row=0, column=0) totalLabel = Label(self.root, text=":", width=4, height=1, font=headerFont) totalLabel.grid(row=0, column=1) botLabel = Label(self.root, text="AI", width=8, height=1, font=headerFont) botLabel.grid(row=0, column=2) self.playerHist = Text(self.root, width=8, height=9, font=simpleFont, state="disabled") self.playerHist.grid(row=1, column=0) self.scoreHist = Text(self.root, width=4, heigh=9, font=headerFont, state="disabled") self.scoreHist.grid(row=1, column=1) self.botHist = Text(self.root, width=8, height=9, font=simpleFont, state="disabled") self.botHist.grid(row=1, column=2) self.playerButtonRoll = Button(self.root, text="Roll Dice", font=simpleFont, width=21) self.playerButtonRoll.grid(row=2, column=0, columnspan=3) self.playerButtonAccept = Button(self.root, width=21, font=simpleFont, text="Accept Roll", state="disabled") self.playerButtonAccept.grid(row=3, column=0, columnspan=3) def pushPlayerHist(self, array): self.playerHist.configure(state="normal") self.playerHist.insert(END, "{}-{}-{}-{}-{}" .format(array[0], array[1], array[2], array[3], array[4])+'\n') self.playerHist.configure(state="disabled") def pushBotHist(self, array): self.botHist.configure(state="normal") self.botHist.insert(END, "{}-{}-{}-{}-{}" .format(array[0], array[1], array[2], array[3], array[4])+'\n') self.botHist.configure(state="disabled") def pushBotNewLine(self, amount): self.botHist.configure(state="normal") for i in range(0, amount): self.botHist.insert(END, " - - - - -\n") self.botHist.configure(state="disabled") def pushScoreHist(self, score, rollCount): scoreStr = "" for i in range(1, rollCount): scoreStr += " - -\n" if score > 0: scoreStr += " +" elif score == 0: scoreStr += " " else: scoreStr += " " scoreStr += "{}\n".format(score) self.scoreHist.configure(state="normal") self.scoreHist.insert(END, scoreStr) self.scoreHist.configure(state="disabled")

[ "dmytro.geleshko01@gmail.com" ]

dmytro.geleshko01@gmail.com

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/first_file.py

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chenyipeng1/507

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2020-03-28T23:35:57.054821

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for i in range(0,5): print (i) a = 10

[ "chenyipeng@chendeMacBook-Pro.local" ]

chenyipeng@chendeMacBook-Pro.local

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Ashutos-h/python-L10

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refs/heads/master

2020-03-19T06:41:57.274817

2018-06-04T15:36:11

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#Answer 1 from threading import * from time import * def display(): print("Starting thread:") sleep(5) print("Ending Thread") t=Thread(target=display) t.start()

[ "ashhutoshsharma@gmail.com" ]

ashhutoshsharma@gmail.com

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import unittest from cumulusci.core import template_utils class TemplateUtils(unittest.TestCase): def test_string_generator(self): x = 100 y = template_utils.StringGenerator(lambda: str(x)) assert str(y) == "100" x = 200 assert str(y) == "200" def test_faker_library(self): fake = template_utils.FakerTemplateLibrary() assert fake.first_name assert "example.com" in fake.email(domain="example.com") def test_faker_languages(self): fake = template_utils.FakerTemplateLibrary("no_NO") assert fake.first_name assert "example.com" in fake.email(domain="example.com") def test_format_str(self): assert template_utils.format_str("abc") == "abc" assert template_utils.format_str("{{abc}}", {"abc": 5}) == "5" assert len(template_utils.format_str("{{fake.first_name}}")) assert "15" in template_utils.format_str( "{{fake.first_name}} {{count}}", {"count": 15} ) assert "15" in template_utils.format_str( "{{fake.first_name}} {{count}}", {"count": "15"} ) assert ( template_utils.format_str("{% raw %}{}{% endraw %}", {"count": "15"}) == "{}" ) def test_format_str_languages(self): norwegian_faker = template_utils.FakerTemplateLibrary("no_NO") val = template_utils.format_str( "{{vikingfake.first_name}} {{abc}}", {"abc": 5, "vikingfake": norwegian_faker}, ) assert "5" in val def cosmopolitan_faker(language): return template_utils.FakerTemplateLibrary(language) val = template_utils.format_str( "{{fakei18n('ne_NP').first_name}} {{abc}}", {"abc": 5, "fakei18n": cosmopolitan_faker, "type": type}, ) assert "5" in val

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pprescod@salesforce.com

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""" corporate directory [shichang-scz.rhcloud.com foobar]\> python manage-dev.py syncdb CommandError: One or more models did not validate: foo.person: Accessor for field 'contact' clashes with related field 'PersonalContact.person'. Add a related_name argument to the definition for 'contact'. foo.person: Reverse query name for field 'contact' clashes with related field 'PersonalContact.person'. Add a related_name argument to the definition for 'contact'. """ """ # # organization contact directory # from django.db import models class Address(models.Model): line1 = models.CharField(max_length=50, blank=False) line2 = models.CharField(max_length=50, blank=True, null=False) city = models.CharField(max_length=50, blank=False) state = models.CharField(max_length=2, blank=False) zip_code = models.CharField(max_length=32, blank=False) country = models.CharField(max_length=3, blank=False) def __unicode__(self): return u'%s, %s %s' % (self.line1, self.city, self.state) class PersonalContact(Address): home_phone = models.CharField(max_length=32, blank=True, null=False) work_phone = models.CharField(max_length=32, blank=True, null=False) mobile = models.CharField(max_length=32, blank=True, null=False) email = models.EmailField(blank=True, null=False) #max_length=75, url = models.URLField(blank=True, null=False) #max_length=200 notes = models.TextField(blank=True, null=False) class BusinessContact(Address): phone = models.CharField(max_length=32, blank=True, null=False) email = models.EmailField(blank=True, null=False) #max_length=75, url = models.URLField(blank=True, null=False) #max_length=200 notes = models.TextField(blank=True, null=False) # # entity - in nature how we define a person # data ownership - organization # therefore, a real person may have multiple records in the system # # API edit not allowed # class Person(models.Model): GENDER = ( ('', 'No Reply'), ('M', 'Male'), ('F', 'Female'), ('O', 'Other') ) first_name = models.CharField(max_length=30, blank=False) last_name = models.CharField(max_length=30, blank=False) middle_name = models.CharField("middle and initials", max_length=30, blank=True, null=False) gender = models.CharField(max_length=1, choices=GENDER, blank=True, null=False) dob = models.DateField("birth date", blank=True, null=True) # # OneToOneField => ForeignKey(model, unique=True) # so if intention is, each one shall have its own record, then use OneToOne # if intention is, multiple may share the same, use ForeignKey # # each person shall provide and maintain one's own birth place record # birth_place = models.OneToOneField(Address, blank=True, null=True) ssn = models.CharField(max_length=9, blank=True, null=False) # # up to date best contact information # contact = models.OneToOneField(PersonalContact, blank=True, null=True) def __unicode__(self): return u"%s %s" % (self.first_name, self.last_name) class Organization(models.Model): name = models.CharField(max_length=50, unique=True, blank=False) description = models.TextField(blank=True, null=False) # #legal/public # contact = models.OneToOneField(BusinessContact, blank=False) def __unicode__(self): return u'%s' % self.name class OfficeType(models.Model): class Meta: unique_together = ('sym', 'organization') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) organization = models.ForeignKey(Organization, blank=False) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) class DepartmentType(models.Model): class Meta: unique_together = ('sym', 'organization') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) organization = models.ForeignKey(Organization, blank=False) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) # # POST - per organization settings # class Position(models.Model): class Meta: unique_together = ('sym', 'organization') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) organization = models.ForeignKey(Organization, blank=False) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) class Level(models.Model): class Meta: unique_together = ('sym', 'position') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) position = models.ForeignKey(Position) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) class Title(models.Model): class Meta: unique_together = ('sym', 'position') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) position = models.ForeignKey(Position) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) #full-time, contract, consultant ... class EmploymentType(models.Model): class Meta: unique_together = ('sym', 'organization') name = models.CharField(max_length=50, unique=True, blank=False, null=False) sym = models.CharField(max_length=32, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) organization = models.ForeignKey(Organization, blank=False) def __unicode__(self): return u'%s - %s' % (self.sym, self.name) class Office(models.Model): class Meta: unique_together = ('name', 'organization') name = models.CharField(max_length=50, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) organization = models.ForeignKey(Organization, blank=False, null=True) office_type = models.ForeignKey(OfficeType, blank=False) contact = models.OneToOneField(BusinessContact) work_hours = models.CharField(max_length=250, blank=True, null=False) weekend_hours = models.CharField(max_length=250, blank=True, null=False) holiday_hours = models.CharField(max_length=250, blank=True, null=False) # # regional primary office or head quarter # is_primary = models.BooleanField(default=False) def __unicode__(self): return u'%s - %s' % (self.name, self.organization.name) class Department(models.Model): class Meta: unique_together = ('name', 'office') name = models.CharField(max_length=50, unique=True, blank=False, null=False) description = models.TextField(blank=True, null=False) office = models.ForeignKey(Office, blank=False, null=True) department_type = models.ForeignKey(DepartmentType, blank=False) contact = models.OneToOneField(BusinessContact) work_hours = models.CharField(max_length=250, blank=True, null=False) weekend_hours = models.CharField(max_length=250, blank=True, null=False) holiday_hours = models.CharField(max_length=250, blank=True, null=False) def __unicode__(self): return u'%s - %s' % (self.name, self.office.name) class Worker(models.Model): class Meta: # # the same person may be associated with mutliple departments # unique_together = ('person', 'department') # # corporate owned 'Person' record # access to person record requires: department | department.office | department.office.organization # and function permission # person = models.ForeignKey(Person) # # contact information given to and shared by this employment # contact = models.OneToOneField(PersonalContact) department = models.ForeignKey(Department) start_date = models.DateField(blank=True, null=True) conversion_date = models.DateField(blank=True, null=True) end_date = models.DateField(blank=True, null=True) position = models.ForeignKey(Position) title = models.ForeignKey(Title) level = models.ForeignKey(Level) employment = models.ForeignKey(EmploymentType) """

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import sublime_plugin import sublime def getValue(key, defaultValue): SETTINGS = sublime.load_settings("Software.sublime_settings") # log("Got value!") return SETTINGS.get(key, defaultValue) def setValue(key, value): SETTINGS = sublime.load_settings("Software.sublime_settings") # log("Set value!") return SETTINGS.set(key, value)

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from mim.utils import exit_with_error try: import torch import torch.nn as nn import torch.nn.functional as F from mmcls.models.builder import BACKBONES except ImportError: exit_with_error('Please install mmcls, mmcv, torch to run this example.') class SEBlock(nn.Module): """Squeeze Excitation Block the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. Args: input_channels: down/up sampling channels internal_neurons: internal sampling channels """ def __init__(self, input_channels, internal_neurons): super(SEBlock, self).__init__() self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons, kernel_size=1, stride=1, bias=True) self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels, kernel_size=1, stride=1, bias=True) self.input_channels = input_channels def forward(self, inputs): x = F.avg_pool2d(inputs, kernel_size=inputs.size(3)) x = self.down(x) x = F.relu(x) x = self.up(x) x = torch.sigmoid(x) x = x.view(-1, self.input_channels, 1, 1) return inputs * x def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1): result = nn.Sequential() result.add_module( 'conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False)) result.add_module('bn', nn.BatchNorm2d(num_features=out_channels)) return result class RepVGGBlock(nn.Module): """RepVGG BLock Module Args: in_channels: input channels out_channels: output channels kernel_size: kernel size use_se: use SEBlock or not """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', use_se=False): super(RepVGGBlock, self).__init__() assert kernel_size == 3, "kernel size only 33 or 11" assert padding == 1, "this padding to keep the input feature map \ size equal to the output size" padding11 = padding - kernel_size // 2 if use_se: self.se = SEBlock(out_channels, internal_neurons=out_channels // 16) else: self.se = nn.Identity() self.nonlinearity = nn.ReLU() self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) \ if out_channels == in_channels and stride == 1 else None self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups) self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding11, groups=groups) def forward(self, inputs): if self.rbr_identity is None: id_out = 0 else: id_out = self.rbr_identity(inputs) return self.nonlinearity( self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)) @BACKBONES.register_module() class RepVGG(nn.Module): """VGG backbone Example: model = RepVGG(numclasses = 1000, num_blocks=[4, 6, 16, 1], width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map) use model.. Args: num_blocks: Depth of RepVGG, from [4, 6, 16, 1] . width_multiplier : stage width ,from [2.5, 2.5, 2.5, 5] ,default None override_groups_map:.... ,default None use_se: use SEBlock or not ,default False """ def __init__(self, num_blocks, num_classes, use_se=False, width_multiplier=None, override_groups_map=None): super(RepVGG, self).__init__() assert len(width_multiplier) == 4, " " self.override_groups_map = override_groups_map or dict() assert 0 not in self.override_groups_map, " " self.use_se = use_se self.cur_layer_idx = 1 self.in_planes = min(64, int(64 * width_multiplier[0])) self.stage0 = RepVGGBlock(in_channels=3, out_channels=self.in_planes, kernel_size=3, stride=2, padding=1, use_se=self.use_se) self.stage1 = self._make_stage(int(64 * width_multiplier[0]), num_blocks[0], stride=2) self.stage2 = self._make_stage(int(128 * width_multiplier[1]), num_blocks[1], stride=2) self.stage3 = self._make_stage(int(256 * width_multiplier[2]), num_blocks[2], stride=2) self.stage4 = self._make_stage(int(512 * width_multiplier[3]), num_blocks[3], stride=2) self.gap = nn.AdaptiveAvgPool2d(output_size=1) self.linear = nn.Linear(int(512 * width_multiplier[3]), num_classes) def _make_stage(self, planes, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) blocks = [] for stride in strides: cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1) blocks.append( RepVGGBlock(in_channels=self.in_planes, out_channels=planes, kernel_size=3, stride=stride, padding=1, groups=cur_groups, use_se=self.use_se)) self.in_planes = planes self.cur_layer_idx += 1 return nn.Sequential(*blocks) def forward(self, x): assert x.shape[1] == 3, "first input channel equal 3" out = self.stage0(x) out = self.stage1(out) out = self.stage2(out) out = self.stage3(out) out = self.stage4(out) out = self.gap(out) out = out.view(out.size(0), -1) out = self.linear(out) return out

[ "bofeng1997@gamil.com" ]

bofeng1997@gamil.com

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/code/Wikipedia Dataset XML to html page converter/WikiExtractor.py

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[]

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nilayc2012/Topic-Recommendation-System-using-LDA

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refs/heads/master

2020-07-04T08:40:41.161229

2017-05-05T07:53:38

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#!/usr/bin/env python # -*- coding: utf-8 -*- # ============================================================================= # Version: 2.66 (Oct 29, 2016) # Author: Giuseppe Attardi (attardi@di.unipi.it), University of Pisa # # Contributors: # Antonio Fuschetto (fuschett@aol.com) # Leonardo Souza (lsouza@amtera.com.br) # Juan Manuel Caicedo (juan@cavorite.com) # Humberto Pereira (begini@gmail.com) # Siegfried-A. Gevatter (siegfried@gevatter.com) # Pedro Assis (pedroh2306@gmail.com) # Wim Muskee (wimmuskee@gmail.com) # Radics Geza (radicsge@gmail.com) # orangain (orangain@gmail.com) # Seth Cleveland (scleveland@turnitin.com) # # ============================================================================= # Copyright (c) 2011-2016. Giuseppe Attardi (attardi@di.unipi.it). # ============================================================================= # This file is part of Tanl. # # Tanl is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License, version 3, # as published by the Free Software Foundation. # # Tanl 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 General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ============================================================================= """Wikipedia Extractor: Extracts and cleans text from a Wikipedia database dump and stores output in a number of files of similar size in a given directory. Each file will contain several documents in the format: <doc id="" revid="" url="" title=""> ... </doc> Template expansion requires preprocesssng first the whole dump and collecting template definitions. """ from __future__ import unicode_literals, division import numpy as np import sys import argparse import bz2 import codecs import cgi import fileinput import logging import os.path import re # TODO use regex when it will be standard import time from io import StringIO from multiprocessing import Queue, Process, Value, cpu_count from timeit import default_timer PY2 = sys.version_info[0] == 2 if PY2: from urllib import quote from htmlentitydefs import name2codepoint from itertools import izip as zip, izip_longest as zip_longest range = xrange # Overwrite by Python 3 name chr = unichr # Overwrite by Python 3 name text_type = unicode else: from urllib.parse import quote from html.entities import name2codepoint from itertools import zip_longest text_type = str # =========================================================================== # Program version version = '2.66' ## PARAMS #################################################################### ## # Defined in <siteinfo> # We include as default Template, when loading external template file. knownNamespaces = set(['Template']) ## # Keys for Template and Module namespaces templateKeys = set(['10', '828']) ## # The namespace used for template definitions # It is the name associated with namespace key=10 in the siteinfo header. templateNamespace = '' templatePrefix = '' ## # The namespace used for module definitions # It is the name associated with namespace key=828 in the siteinfo header. moduleNamespace = '' ## # Recognize only these namespaces in links # w: Internal links to the Wikipedia # wiktionary: Wiki dictionary # wikt: shortcut for Wiktionary # acceptedNamespaces = ['w', 'wiktionary', 'wikt'] ## # Drop these elements from article text # discardElements = [ 'gallery', 'timeline', 'noinclude', 'pre', 'table', 'tr', 'td', 'th', 'caption', 'div', 'form', 'input', 'select', 'option', 'textarea', 'ul', 'li', 'ol', 'dl', 'dt', 'dd', 'menu', 'dir', 'ref', 'references', 'img', 'imagemap', 'source', 'small', 'sub', 'sup', 'indicator' ] # This is obtained from <siteinfo> urlbase = '' ## # Filter disambiguation pages filter_disambig_pages = False filter_disambig_page_pattern = re.compile("{{disambig(uation)?(\|[^}]*)?}}") ## # page filtering logic -- remove templates, undesired xml namespaces, and disambiguation pages def keepPage(ns, page): if ns != '0': # Aritcle return False # remove disambig pages if desired if filter_disambig_pages: for line in page: if filter_disambig_page_pattern.match(line): return False return True def get_url(uid): return "%s?curid=%s" % (urlbase, uid) # ========================================================================= # # MediaWiki Markup Grammar # https://www.mediawiki.org/wiki/Preprocessor_ABNF # xml-char = %x9 / %xA / %xD / %x20-D7FF / %xE000-FFFD / %x10000-10FFFF # sptab = SP / HTAB # ; everything except ">" (%x3E) # attr-char = %x9 / %xA / %xD / %x20-3D / %x3F-D7FF / %xE000-FFFD / %x10000-10FFFF # literal = *xml-char # title = wikitext-L3 # part-name = wikitext-L3 # part-value = wikitext-L3 # part = ( part-name "=" part-value ) / ( part-value ) # parts = [ title *( "|" part ) ] # tplarg = "{{{" parts "}}}" # template = "{{" parts "}}" # link = "[[" wikitext-L3 "]]" # comment = "" # unclosed-comment = "', re.DOTALL) # Match <nowiki>...</nowiki> nowiki = re.compile(r'<nowiki>.*?</nowiki>') # Match ignored tags ignored_tag_patterns = [] def ignoreTag(tag): left = re.compile(r'<%s\b.*?>' % tag, re.IGNORECASE | re.DOTALL) # both <ref> and <reference> right = re.compile(r'</\s*%s>' % tag, re.IGNORECASE) ignored_tag_patterns.append((left, right)) for tag in ignoredTags: ignoreTag(tag) # Match selfClosing HTML tags selfClosing_tag_patterns = [ re.compile(r'<\s*%s\b[^>]*/\s*>' % tag, re.DOTALL | re.IGNORECASE) for tag in selfClosingTags ] # Match HTML placeholder tags placeholder_tag_patterns = [ (re.compile(r'<\s*%s(\s*| [^>]+?)>.*?<\s*/\s*%s\s*>' % (tag, tag), re.DOTALL | re.IGNORECASE), repl) for tag, repl in placeholder_tags.items() ] # Match preformatted lines preformatted = re.compile(r'^ .*?$') # Match external links (space separates second optional parameter) externalLink = re.compile(r'\[\w+[^ ]*? (.*?)]') externalLinkNoAnchor = re.compile(r'\[\w+[&\]]*\]') # Matches bold/italic bold_italic = re.compile(r"'''''(.*?)'''''") bold = re.compile(r"'''(.*?)'''") italic_quote = re.compile(r"''\"([^\"]*?)\"''") italic = re.compile(r"''(.*?)''") quote_quote = re.compile(r'""([^"]*?)""') # Matches space spaces = re.compile(r' {2,}') # Matches dots dots = re.compile(r'\.{4,}') # ====================================================================== class Template(list): """ A Template is a list of TemplateText or TemplateArgs """ @classmethod def parse(cls, body): tpl = Template() # we must handle nesting, s.a. # {{{1|{{PAGENAME}}} # {{{italics|{{{italic|}}} # {{#if:{{{{{#if:{{{nominee|}}}|nominee|candidate}}|}}}| # start = 0 for s, e in findMatchingBraces(body, 3): tpl.append(TemplateText(body[start:s])) tpl.append(TemplateArg(body[s + 3:e - 3])) start = e tpl.append(TemplateText(body[start:])) # leftover return tpl def subst(self, params, extractor, depth=0): # We perform parameter substitutions recursively. # We also limit the maximum number of iterations to avoid too long or # even endless loops (in case of malformed input). # :see: http://meta.wikimedia.org/wiki/Help:Expansion#Distinction_between_variables.2C_parser_functions.2C_and_templates # # Parameter values are assigned to parameters in two (?) passes. # Therefore a parameter name in a template can depend on the value of # another parameter of the same template, regardless of the order in # which they are specified in the template call, for example, using # Template:ppp containing "{{{{{{p}}}}}}", {{ppp|p=q|q=r}} and even # {{ppp|q=r|p=q}} gives r, but using Template:tvvv containing # "{{{{{{{{{p}}}}}}}}}", {{tvvv|p=q|q=r|r=s}} gives s. # logging.debug('&*ssubst tpl %d %s', extractor.frame.length, '', depth, self) if depth > extractor.maxParameterRecursionLevels: extractor.recursion_exceeded_3_errs += 1 return '' return ''.join([tpl.subst(params, extractor, depth) for tpl in self]) def __str__(self): return ''.join([text_type(x) for x in self]) class TemplateText(text_type): """Fixed text of template""" def subst(self, params, extractor, depth): return self class TemplateArg(object): """ parameter to a template. Has a name and a default value, both of which are Templates. """ def __init__(self, parameter): """ :param parameter: the parts of a tplarg. """ # the parameter name itself might contain templates, e.g.: # appointe{{#if:{{{appointer14|}}}|r|d}}14| # 4|{{{{{subst|}}}CURRENTYEAR}} # any parts in a tplarg after the first (the parameter default) are # ignored, and an equals sign in the first part is treated as plain text. # logging.debug('TemplateArg %s', parameter) parts = splitParts(parameter) self.name = Template.parse(parts[0]) if len(parts) > 1: # This parameter has a default value self.default = Template.parse(parts[1]) else: self.default = None def __str__(self): if self.default: return '{{{%s|%s}}}' % (self.name, self.default) else: return '{{{%s}}}' % self.name def subst(self, params, extractor, depth): """ Substitute value for this argument from dict :param params: Use :param extractor: to evaluate expressions for name and default. Limit substitution to the maximun :param depth:. """ # the parameter name itself might contain templates, e.g.: # appointe{{#if:{{{appointer14|}}}|r|d}}14| paramName = self.name.subst(params, extractor, depth + 1) paramName = extractor.transform(paramName) res = '' if paramName in params: res = params[paramName] # use parameter value specified in template invocation elif self.default: # use the default value defaultValue = self.default.subst(params, extractor, depth + 1) res = extractor.transform(defaultValue) # logging.debug('subst arg %d %s -> %s' % (depth, paramName, res)) return res class Frame(object): def __init__(self, title='', args=[], prev=None): self.title = title self.args = args self.prev = prev self.depth = prev.depth + 1 if prev else 0 def push(self, title, args): return Frame(title, args, self) def pop(self): return self.prev def __str__(self): res = '' prev = this.prev while prev: if res: res += ', ' res += '(%s, %s)' % (prev.title, prev.args) prev = prev.prev return '<Frame [' + res + ']>' # ====================================================================== substWords = 'subst:|safesubst:' class Extractor(object): """ An extraction task on a article. """ ## # Whether to preserve links in output keepLinks = False ## # Whether to preserve section titles keepSections = True ## # Whether to preserve lists keepLists = False ## # Whether to output HTML instead of text toHTML = False ## # Whether to expand templates expand_templates = True ## ## Whether to escape doc content escape_doc = False ## # Print the wikipedia article revision print_revision = False ## # Minimum expanded text length required to print document min_text_length = 0 def __init__(self, id, revid, title, lines): """ :param id: id of page. :param title: tutle of page. :param lines: a list of lines. """ self.id = id self.revid = revid self.title = title self.text = ''.join(lines) self.magicWords = MagicWords() self.frame = Frame() self.recursion_exceeded_1_errs = 0 # template recursion within expand() self.recursion_exceeded_2_errs = 0 # template recursion within expandTemplate() self.recursion_exceeded_3_errs = 0 # parameter recursion self.template_title_errs = 0 def extract(self, out): """ :param out: a memory file. """ logging.info('%s\t%s', self.id, self.title) url = get_url(self.id) header='<!DOCTYPE html><html><head><title>%s</title></head><body>\n' % (self.title) # Separate header from text with a newline. header += '<h1>' + self.title + '</h1>\n' header +="<a href='https://en.wikipedia.org/"+url+"'>Wikipedia Link</a>\n <p>" # https://www.mediawiki.org/wiki/Help:Magic_words self.magicWords['PAGENAME'] = self.title self.magicWords['FULLPAGENAME'] = self.title self.magicWords['CURRENTYEAR'] = time.strftime('%Y') self.magicWords['CURRENTMONTH'] = time.strftime('%m') self.magicWords['CURRENTDAY'] = time.strftime('%d') self.magicWords['CURRENTHOUR'] = time.strftime('%H') self.magicWords['CURRENTTIME'] = time.strftime('%H:%M:%S') text = self.text self.text = '' # save memory # # @see https://doc.wikimedia.org/mediawiki-core/master/php/classParser.html # This does the equivalent of internalParse(): # # $dom = $this->preprocessToDom( $text, $flag ); # $text = $frame->expand( $dom ); # text = self.transform(text) text = self.wiki2text(text) text = compact(self.clean(text)) footer = "\n </p></body></html>\n" if out == sys.stdout: # option -a or -o - header = header.encode('utf-8') out.write(header) for line in text: # option -a or -o - if out == sys.stdout: line = line.encode('utf-8') out.write(line) out.write('\n'); out.write(footer) errs = (self.template_title_errs, self.recursion_exceeded_1_errs, self.recursion_exceeded_2_errs, self.recursion_exceeded_3_errs) if any(errs): logging.warn("Template errors in article '%s' (%s): title(%d) recursion(%d, %d, %d)", self.title, self.id, *errs) def transform(self, wikitext): """ Transforms wiki markup. @see https://www.mediawiki.org/wiki/Help:Formatting """ # look for matching <nowiki>...</nowiki> res = '' cur = 0 for m in nowiki.finditer(wikitext, cur): res += self.transform1(wikitext[cur:m.start()]) + wikitext[m.start():m.end()] cur = m.end() # leftover res += self.transform1(wikitext[cur:]) return res def transform1(self, text): """Transform text not containing <nowiki>""" if Extractor.expand_templates: # expand templates # See: http://www.mediawiki.org/wiki/Help:Templates return self.expand(text) else: # Drop transclusions (template, parser functions) return dropNested(text, r'{{', r'}}') def wiki2text(self, text): # # final part of internalParse().) # # $text = $this->doTableStuff( $text ); # $text = preg_replace( '/(^|\n)-----*/', '\\1<hr />', $text ); # $text = $this->doDoubleUnderscore( $text ); # $text = $this->doHeadings( $text ); # $text = $this->replaceInternalLinks( $text ); # $text = $this->doAllQuotes( $text ); # $text = $this->replaceExternalLinks( $text ); # $text = str_replace( self::MARKER_PREFIX . 'NOPARSE', '', $text ); # $text = $this->doMagicLinks( $text ); # $text = $this->formatHeadings( $text, $origText, $isMain ); # Drop tables # first drop residual templates, or else empty parameter |} might look like end of table. text = dropNested(text, r'{{', r'}}') text = dropNested(text, r'{\|', r'\|}') # Handle bold/italic/quote if self.toHTML: text = bold_italic.sub(r'<b>\1</b>', text) text = bold.sub(r'<b>\1</b>', text) text = italic.sub(r'<i>\1</i>', text) else: text = bold_italic.sub(r'\1', text) text = bold.sub(r'\1', text) text = italic_quote.sub(r'"\1"', text) text = italic.sub(r'"\1"', text) text = quote_quote.sub(r'"\1"', text) # residuals of unbalanced quotes text = text.replace("'''", '').replace("''", '"') # replace internal links text = replaceInternalLinks(text) # replace external links text = replaceExternalLinks(text) # drop MagicWords behavioral switches text = magicWordsRE.sub('', text) # ############### Process HTML ############### # turn into HTML, except for the content of <syntaxhighlight> res = '' cur = 0 for m in syntaxhighlight.finditer(text): res += unescape(text[cur:m.start()]) + m.group(1) cur = m.end() text = res + unescape(text[cur:]) return text def clean(self, text): """ Removes irrelevant parts from :param: text. """ # Collect spans spans = [] # Drop HTML comments for m in comment.finditer(text): spans.append((m.start(), m.end())) # Drop self-closing tags for pattern in selfClosing_tag_patterns: for m in pattern.finditer(text): spans.append((m.start(), m.end())) # Drop ignored tags for left, right in ignored_tag_patterns: for m in left.finditer(text): spans.append((m.start(), m.end())) for m in right.finditer(text): spans.append((m.start(), m.end())) # Bulk remove all spans text = dropSpans(spans, text) # Drop discarded elements for tag in discardElements: text = dropNested(text, r'<\s*%s\b[^>/]*>' % tag, r'<\s*/\s*%s>' % tag) if not self.toHTML: # Turn into text what is left (&nbsp;) and <syntaxhighlight> text = unescape(text) # Expand placeholders for pattern, placeholder in placeholder_tag_patterns: index = 1 for match in pattern.finditer(text): text = text.replace(match.group(), '%s_%d' % (placeholder, index)) index += 1 text = text.replace('<<', '«').replace('>>', '»') ############################################# # Cleanup text text = text.replace('\t', ' ') text = spaces.sub(' ', text) text = dots.sub('...', text) text = re.sub(' (,:\.\)\]»)', r'\1', text) text = re.sub('(\[\(«) ', r'\1', text) text = re.sub(r'\n\W+?\n', '\n', text, flags=re.U) # lines with only punctuations text = text.replace(',,', ',').replace(',.', '.') if Extractor.toHTML: text = cgi.escape(text) return text # ---------------------------------------------------------------------- # Expand templates maxTemplateRecursionLevels = 30 maxParameterRecursionLevels = 10 # check for template beginning reOpen = re.compile('(?<!{){{(?!{)', re.DOTALL) def expand(self, wikitext): """ :param wikitext: the text to be expanded. Templates are frequently nested. Occasionally, parsing mistakes may cause template insertion to enter an infinite loop, for instance when trying to instantiate Template:Country {{country_{{{1}}}|{{{2}}}|{{{2}}}|size={{{size|}}}|name={{{name|}}}}} which is repeatedly trying to insert template 'country_', which is again resolved to Template:Country. The straightforward solution of keeping track of templates that were already inserted for the current article would not work, because the same template may legally be used more than once, with different parameters in different parts of the article. Therefore, we limit the number of iterations of nested template inclusion. """ # Test template expansion at: # https://en.wikipedia.org/wiki/Special:ExpandTemplates # https://it.wikipedia.org/wiki/Speciale:EspandiTemplate res = '' if self.frame.depth >= self.maxTemplateRecursionLevels: self.recursion_exceeded_1_errs += 1 return res # logging.debug('%*s<expand', self.frame.depth, '') cur = 0 # look for matching {{...}} for s, e in findMatchingBraces(wikitext, 2): res += wikitext[cur:s] + self.expandTemplate(wikitext[s + 2:e - 2]) cur = e # leftover res += wikitext[cur:] # logging.debug('%*sexpand> %s', self.frame.depth, '', res) return res def templateParams(self, parameters): """ Build a dictionary with positional or name key to expanded parameters. :param parameters: the parts[1:] of a template, i.e. all except the title. """ templateParams = {} if not parameters: return templateParams # logging.debug('%*s<templateParams: %s', self.frame.length, '', '|'.join(parameters)) # Parameters can be either named or unnamed. In the latter case, their # name is defined by their ordinal position (1, 2, 3, ...). unnamedParameterCounter = 0 # It's legal for unnamed parameters to be skipped, in which case they # will get default values (if available) during actual instantiation. # That is {{template_name|a||c}} means parameter 1 gets # the value 'a', parameter 2 value is not defined, and parameter 3 gets # the value 'c'. This case is correctly handled by function 'split', # and does not require any special handling. for param in parameters: # Spaces before or after a parameter value are normally ignored, # UNLESS the parameter contains a link (to prevent possible gluing # the link to the following text after template substitution) # Parameter values may contain "=" symbols, hence the parameter # name extends up to the first such symbol. # It is legal for a parameter to be specified several times, in # which case the last assignment takes precedence. Example: # "{{t|a|b|c|2=B}}" is equivalent to "{{t|a|B|c}}". # Therefore, we don't check if the parameter has been assigned a # value before, because anyway the last assignment should override # any previous ones. # FIXME: Don't use DOTALL here since parameters may be tags with # attributes, e.g. <div class="templatequotecite"> # Parameters may span several lines, like: # {{Reflist|colwidth=30em|refs= # <ref name="Goode">Title</ref> # The '=' might occurr within an HTML attribute: # "<ref name=value" # but we stop at first. m = re.match(' *([^=]*?) *?=(.*)', param, re.DOTALL) if m: # This is a named parameter. This case also handles parameter # assignments like "2=xxx", where the number of an unnamed # parameter ("2") is specified explicitly - this is handled # transparently. parameterName = m.group(1).strip() parameterValue = m.group(2) if ']]' not in parameterValue: # if the value does not contain a link, trim whitespace parameterValue = parameterValue.strip() templateParams[parameterName] = parameterValue else: # this is an unnamed parameter unnamedParameterCounter += 1 if ']]' not in param: # if the value does not contain a link, trim whitespace param = param.strip() templateParams[str(unnamedParameterCounter)] = param # logging.debug('%*stemplateParams> %s', self.frame.length, '', '|'.join(templateParams.values())) return templateParams def expandTemplate(self, body): """Expands template invocation. :param body: the parts of a template. :see http://meta.wikimedia.org/wiki/Help:Expansion for an explanation of the process. See in particular: Expansion of names and values http://meta.wikimedia.org/wiki/Help:Expansion#Expansion_of_names_and_values For most parser functions all names and values are expanded, regardless of what is relevant for the result. The branching functions (#if, #ifeq, #iferror, #ifexist, #ifexpr, #switch) are exceptions. All names in a template call are expanded, and the titles of the tplargs in the template body, after which it is determined which values must be expanded, and for which tplargs in the template body the first part (default) [sic in the original doc page]. In the case of a tplarg, any parts beyond the first are never expanded. The possible name and the value of the first part is expanded if the title does not match a name in the template call. :see code for braceSubstitution at https://doc.wikimedia.org/mediawiki-core/master/php/html/Parser_8php_source.html#3397: """ # template = "{{" parts "}}" # Templates and tplargs are decomposed in the same way, with pipes as # separator, even though eventually any parts in a tplarg after the first # (the parameter default) are ignored, and an equals sign in the first # part is treated as plain text. # Pipes inside inner templates and tplargs, or inside double rectangular # brackets within the template or tplargs are not taken into account in # this decomposition. # The first part is called title, the other parts are simply called parts. # If a part has one or more equals signs in it, the first equals sign # determines the division into name = value. Equals signs inside inner # templates and tplargs, or inside double rectangular brackets within the # part are not taken into account in this decomposition. Parts without # equals sign are indexed 1, 2, .., given as attribute in the <name> tag. if self.frame.depth >= self.maxTemplateRecursionLevels: self.recursion_exceeded_2_errs += 1 # logging.debug('%*sEXPAND> %s', self.frame.depth, '', body) return '' logging.debug('%*sEXPAND %s', self.frame.depth, '', body) parts = splitParts(body) # title is the portion before the first | title = parts[0].strip() title = self.expand(title) # SUBST # Apply the template tag to parameters without # substituting into them, e.g. # {{subst:t|a{{{p|q}}}b}} gives the wikitext start-a{{{p|q}}}b-end # @see https://www.mediawiki.org/wiki/Manual:Substitution#Partial_substitution subst = False if re.match(substWords, title, re.IGNORECASE): title = re.sub(substWords, '', title, 1, re.IGNORECASE) subst = True if title in self.magicWords.values: ret = self.magicWords[title] logging.debug('%*s<EXPAND %s %s', self.frame.depth, '', title, ret) return ret # Parser functions. # For most parser functions all names and values are expanded, # regardless of what is relevant for the result. The branching # functions (#if, #ifeq, #iferror, #ifexist, #ifexpr, #switch) are # exceptions: for #if, #iferror, #ifexist, #ifexp, only the part that # is applicable is expanded; for #ifeq the first and the applicable # part are expanded; for #switch, expanded are the names up to and # including the match (or all if there is no match), and the value in # the case of a match or if there is no match, the default, if any. # The first argument is everything after the first colon. # It has been evaluated above. colon = title.find(':') if colon > 1: funct = title[:colon] parts[0] = title[colon + 1:].strip() # side-effect (parts[0] not used later) # arguments after first are not evaluated ret = callParserFunction(funct, parts, self) logging.debug('%*s<EXPAND %s %s', self.frame.depth, '', funct, ret) return ret title = fullyQualifiedTemplateTitle(title) if not title: self.template_title_errs += 1 return '' redirected = redirects.get(title) if redirected: title = redirected # get the template if title in templateCache: template = templateCache[title] elif title in templates: template = Template.parse(templates[title]) # add it to cache templateCache[title] = template del templates[title] else: # The page being included could not be identified logging.debug('%*s<EXPAND %s %s', self.frame.depth, '', title, '') return '' logging.debug('%*sTEMPLATE %s: %s', self.frame.depth, '', title, template) # tplarg = "{{{" parts "}}}" # parts = [ title *( "|" part ) ] # part = ( part-name "=" part-value ) / ( part-value ) # part-name = wikitext-L3 # part-value = wikitext-L3 # wikitext-L3 = literal / template / tplarg / link / comment / # line-eating-comment / unclosed-comment / # xmlish-element / *wikitext-L3 # A tplarg may contain other parameters as well as templates, e.g.: # {{{text|{{{quote|{{{1|{{error|Error: No text given}}}}}}}}}}} # hence no simple RE like this would work: # '{{{((?:(?!{{{).)*?)}}}' # We must use full CF parsing. # the parameter name itself might be computed, e.g.: # {{{appointe{{#if:{{{appointer14|}}}|r|d}}14|}}} # Because of the multiple uses of double-brace and triple-brace # syntax, expressions can sometimes be ambiguous. # Precedence rules specifed here: # http://www.mediawiki.org/wiki/Preprocessor_ABNF#Ideal_precedence # resolve ambiguities like this: # {{{{ }}}} -> { {{{ }}} } # {{{{{ }}}}} -> {{ {{{ }}} }} # # :see: https://en.wikipedia.org/wiki/Help:Template#Handling_parameters params = parts[1:] # Order of evaluation. # Template parameters are fully evaluated before they are passed to the template. # :see: https://www.mediawiki.org/wiki/Help:Templates#Order_of_evaluation if not subst: # Evaluate parameters, since they may contain templates, including # the symbol "=". # {{#ifexpr: {{{1}}} = 1 }} params = [self.transform(p) for p in params] # build a dict of name-values for the parameter values params = self.templateParams(params) # Perform parameter substitution. # Extend frame before subst, since there may be recursion in default # parameter value, e.g. {{OTRS|celebrative|date=April 2015}} in article # 21637542 in enwiki. self.frame = self.frame.push(title, params) instantiated = template.subst(params, self) value = self.transform(instantiated) self.frame = self.frame.pop() logging.debug('%*s<EXPAND %s %s', self.frame.depth, '', title, value) return value # ---------------------------------------------------------------------- # parameter handling def splitParts(paramsList): """ :param paramsList: the parts of a template or tplarg. Split template parameters at the separator "|". separator "=". Template parameters often contain URLs, internal links, text or even template expressions, since we evaluate templates outside in. This is required for cases like: {{#if: {{{1}}} | {{lc:{{{1}}} | "parameter missing"}} Parameters are separated by "|" symbols. However, we cannot simply split the string on "|" symbols, since these also appear inside templates and internal links, e.g. {{if:| |{{#if:the president| |{{#if:| [[Category:Hatnote templates|A{{PAGENAME}}]] }} }} }} We split parts at the "|" symbols that are not inside any pair {{{...}}}, {{...}}, [[...]], {|...|}. """ # Must consider '[' as normal in expansion of Template:EMedicine2: # #ifeq: ped|article|[http://emedicine.medscape.com/article/180-overview|[http://www.emedicine.com/ped/topic180.htm#{{#if: |section~}} # as part of: # {{#ifeq: ped|article|[http://emedicine.medscape.com/article/180-overview|[http://www.emedicine.com/ped/topic180.htm#{{#if: |section~}}}} ped/180{{#if: |~}}] # should handle both tpl arg like: # 4|{{{{{subst|}}}CURRENTYEAR}} # and tpl parameters like: # ||[[Category:People|{{#if:A|A|{{PAGENAME}}}}]] sep = '|' parameters = [] cur = 0 for s, e in findMatchingBraces(paramsList): par = paramsList[cur:s].split(sep) if par: if parameters: # portion before | belongs to previous parameter parameters[-1] += par[0] if len(par) > 1: # rest are new parameters parameters.extend(par[1:]) else: parameters = par elif not parameters: parameters = [''] # create first param # add span to last previous parameter parameters[-1] += paramsList[s:e] cur = e # leftover par = paramsList[cur:].split(sep) if par: if parameters: # portion before | belongs to previous parameter parameters[-1] += par[0] if len(par) > 1: # rest are new parameters parameters.extend(par[1:]) else: parameters = par # logging.debug('splitParts %s %s\nparams: %s', sep, paramsList, str(parameters)) return parameters def findMatchingBraces(text, ldelim=0): """ :param ldelim: number of braces to match. 0 means match [[]], {{}} and {{{}}}. """ # Parsing is done with respect to pairs of double braces {{..}} delimiting # a template, and pairs of triple braces {{{..}}} delimiting a tplarg. # If double opening braces are followed by triple closing braces or # conversely, this is taken as delimiting a template, with one left-over # brace outside it, taken as plain text. For any pattern of braces this # defines a set of templates and tplargs such that any two are either # separate or nested (not overlapping). # Unmatched double rectangular closing brackets can be in a template or # tplarg, but unmatched double rectangular opening brackets cannot. # Unmatched double or triple closing braces inside a pair of # double rectangular brackets are treated as plain text. # Other formulation: in ambiguity between template or tplarg on one hand, # and a link on the other hand, the structure with the rightmost opening # takes precedence, even if this is the opening of a link without any # closing, so not producing an actual link. # In the case of more than three opening braces the last three are assumed # to belong to a tplarg, unless there is no matching triple of closing # braces, in which case the last two opening braces are are assumed to # belong to a template. # We must skip individual { like in: # {{#ifeq: {{padleft:|1|}} | { | |  }} # We must resolve ambiguities like this: # {{{{ }}}} -> { {{{ }}} } # {{{{{ }}}}} -> {{ {{{ }}} }} # {{#if:{{{{{#if:{{{nominee|}}}|nominee|candidate}}|}}}|...}} # {{{!}} {{!}}} # Handle: # {{{{{|safesubst:}}}#Invoke:String|replace|{{{1|{{{{{|safesubst:}}}PAGENAME}}}}}|%s+%([^%(]-%)$||plain=false}} # as well as expressions with stray }: # {{{link|{{ucfirst:{{{1}}}}}} interchange}}} if ldelim: # 2-3 reOpen = re.compile('[{]{%d,}' % ldelim) # at least ldelim reNext = re.compile('[{]{2,}|}{2,}') # at least 2 else: reOpen = re.compile('{{2,}|\[{2,}') reNext = re.compile('{{2,}|}{2,}|\[{2,}|]{2,}') # at least 2 cur = 0 while True: m1 = reOpen.search(text, cur) if not m1: return lmatch = m1.end() - m1.start() if m1.group()[0] == '{': stack = [lmatch] # stack of opening braces lengths else: stack = [-lmatch] # negative means [ end = m1.end() while True: m2 = reNext.search(text, end) if not m2: return # unbalanced end = m2.end() brac = m2.group()[0] lmatch = m2.end() - m2.start() if brac == '{': stack.append(lmatch) elif brac == '}': while stack: openCount = stack.pop() # opening span if openCount == 0: # illegal unmatched [[ continue if lmatch >= openCount: lmatch -= openCount if lmatch <= 1: # either close or stray } break else: # put back unmatched stack.append(openCount - lmatch) break if not stack: yield m1.start(), end - lmatch cur = end break elif len(stack) == 1 and 0 < stack[0] < ldelim: # ambiguous {{{{{ }}} }} #yield m1.start() + stack[0], end cur = end break elif brac == '[': # [[ stack.append(-lmatch) else: # ]] while stack and stack[-1] < 0: # matching [[ openCount = -stack.pop() if lmatch >= openCount: lmatch -= openCount if lmatch <= 1: # either close or stray ] break else: # put back unmatched (negative) stack.append(lmatch - openCount) break if not stack: yield m1.start(), end - lmatch cur = end break # unmatched ]] are discarded cur = end def findBalanced(text, openDelim=['[['], closeDelim=[']]']): """ Assuming that text contains a properly balanced expression using :param openDelim: as opening delimiters and :param closeDelim: as closing delimiters. :return: an iterator producing pairs (start, end) of start and end positions in text containing a balanced expression. """ openPat = '|'.join([re.escape(x) for x in openDelim]) # pattern for delimiters expected after each opening delimiter afterPat = {o: re.compile(openPat + '|' + c, re.DOTALL) for o, c in zip(openDelim, closeDelim)} stack = [] start = 0 cur = 0 # end = len(text) startSet = False startPat = re.compile(openPat) nextPat = startPat while True: next = nextPat.search(text, cur) if not next: return if not startSet: start = next.start() startSet = True delim = next.group(0) if delim in openDelim: stack.append(delim) nextPat = afterPat[delim] else: opening = stack.pop() # assert opening == openDelim[closeDelim.index(next.group(0))] if stack: nextPat = afterPat[stack[-1]] else: yield start, next.end() nextPat = startPat start = next.end() startSet = False cur = next.end() # ---------------------------------------------------------------------- # Modules # Only minimal support # FIXME: import Lua modules. def if_empty(*rest): """ This implements If_empty from English Wikipedia module: <title>Module:If empty</title> <ns>828</ns> <text>local p = {} function p.main(frame) local args = require('Module:Arguments').getArgs(frame, {wrappers = 'Template:If empty', removeBlanks = false}) -- For backwards compatibility reasons, the first 8 parameters can be unset instead of being blank, -- even though there's really no legitimate use case for this. At some point, this will be removed. local lowestNil = math.huge for i = 8,1,-1 do if args[i] == nil then args[i] = '' lowestNil = i end end for k,v in ipairs(args) do if v ~= '' then if lowestNil < k then -- If any uses of this template depend on the behavior above, add them to a tracking category. -- This is a rather fragile, convoluted, hacky way to do it, but it ensures that this module's output won't be modified -- by it. frame:extensionTag('ref', '[[Category:Instances of Template:If_empty missing arguments]]', {group = 'TrackingCategory'}) frame:extensionTag('references', '', {group = 'TrackingCategory'}) end return v end end end return p </text> """ for arg in rest: if arg: return arg return '' # ---------------------------------------------------------------------- # String module emulation # https://it.wikipedia.org/wiki/Modulo:String def functionParams(args, vars): """ Build a dictionary of var/value from :param: args. Parameters can be either named or unnamed. In the latter case, their name is taken fron :param: vars. """ params = {} index = 1 for var in vars: value = args.get(var) if value is None: value = args.get(str(index)) if value is None: value = '' else: index += 1 params[var] = value return params def string_sub(args): params = functionParams(args, ('s', 'i', 'j')) s = params.get('s', '') i = int(params.get('i', 1) or 1) # or handles case of '' value j = int(params.get('j', -1) or -1) if i > 0: i -= 1 # lua is 1-based if j < 0: j += 1 if j == 0: j = len(s) return s[i:j] def string_len(args): params = functionParams(args, ('s')) s = params.get('s', '') return len(s) def string_find(args): params = functionParams(args, ('source', 'target', 'start', 'plain')) source = params.get('source', '') pattern = params.get('target', '') start = int('0'+params.get('start', 1)) - 1 # lua is 1-based plain = int('0'+params.get('plain', 1)) if source == '' or pattern == '': return 0 if plain: return source.find(pattern, start) + 1 # lua is 1-based else: return (re.compile(pattern).search(source, start) or -1) + 1 # ---------------------------------------------------------------------- # Module:Roman # http://en.wikipedia.org/w/index.php?title=Module:Roman # Modulo:Numero_romano # https://it.wikipedia.org/wiki/Modulo:Numero_romano def roman_main(args): """Convert first arg to roman numeral if <= 5000 else :return: second arg.""" num = int(float(args.get('1'))) # Return a message for numbers too big to be expressed in Roman numerals. if 0 > num or num >= 5000: return args.get('2', 'N/A') def toRoman(n, romanNumeralMap): """convert integer to Roman numeral""" result = "" for integer, numeral in romanNumeralMap: while n >= integer: result += numeral n -= integer return result # Find the Roman numerals for numbers 4999 or less. smallRomans = ( (1000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I") ) return toRoman(num, smallRomans) # ---------------------------------------------------------------------- modules = { 'convert': { 'convert': lambda x, u, *rest: x + ' ' + u, # no conversion }, 'If empty': { 'main': if_empty }, 'String': { 'sub': string_sub, 'len': string_len, 'find': string_find }, 'Roman': { 'main': roman_main }, 'Numero romano': { 'main': roman_main } } # ---------------------------------------------------------------------- # variables class MagicWords(object): """ One copy in each Extractor. @see https://doc.wikimedia.org/mediawiki-core/master/php/MagicWord_8php_source.html """ names = [ '!', 'currentmonth', 'currentmonth1', 'currentmonthname', 'currentmonthnamegen', 'currentmonthabbrev', 'currentday', 'currentday2', 'currentdayname', 'currentyear', 'currenttime', 'currenthour', 'localmonth', 'localmonth1', 'localmonthname', 'localmonthnamegen', 'localmonthabbrev', 'localday', 'localday2', 'localdayname', 'localyear', 'localtime', 'localhour', 'numberofarticles', 'numberoffiles', 'numberofedits', 'articlepath', 'pageid', 'sitename', 'server', 'servername', 'scriptpath', 'stylepath', 'pagename', 'pagenamee', 'fullpagename', 'fullpagenamee', 'namespace', 'namespacee', 'namespacenumber', 'currentweek', 'currentdow', 'localweek', 'localdow', 'revisionid', 'revisionday', 'revisionday2', 'revisionmonth', 'revisionmonth1', 'revisionyear', 'revisiontimestamp', 'revisionuser', 'revisionsize', 'subpagename', 'subpagenamee', 'talkspace', 'talkspacee', 'subjectspace', 'subjectspacee', 'talkpagename', 'talkpagenamee', 'subjectpagename', 'subjectpagenamee', 'numberofusers', 'numberofactiveusers', 'numberofpages', 'currentversion', 'rootpagename', 'rootpagenamee', 'basepagename', 'basepagenamee', 'currenttimestamp', 'localtimestamp', 'directionmark', 'contentlanguage', 'numberofadmins', 'cascadingsources', ] def __init__(self): self.values = {'!': '|'} def __getitem__(self, name): return self.values.get(name) def __setitem__(self, name, value): self.values[name] = value switches = ( '__NOTOC__', '__FORCETOC__', '__TOC__', '__TOC__', '__NEWSECTIONLINK__', '__NONEWSECTIONLINK__', '__NOGALLERY__', '__HIDDENCAT__', '__NOCONTENTCONVERT__', '__NOCC__', '__NOTITLECONVERT__', '__NOTC__', '__START__', '__END__', '__INDEX__', '__NOINDEX__', '__STATICREDIRECT__', '__DISAMBIG__' ) magicWordsRE = re.compile('|'.join(MagicWords.switches)) # ---------------------------------------------------------------------- # parser functions utilities def ucfirst(string): """:return: a string with just its first character uppercase We can't use title() since it coverts all words. """ if string: return string[0].upper() + string[1:] else: return '' def lcfirst(string): """:return: a string with its first character lowercase""" if string: if len(string) > 1: return string[0].lower() + string[1:] else: return string.lower() else: return '' def fullyQualifiedTemplateTitle(templateTitle): """ Determine the namespace of the page being included through the template mechanism """ if templateTitle.startswith(':'): # Leading colon by itself implies main namespace, so strip this colon return ucfirst(templateTitle[1:]) else: m = re.match('([^:]*)(:.*)', templateTitle) if m: # colon found but not in the first position - check if it # designates a known namespace prefix = normalizeNamespace(m.group(1)) if prefix in knownNamespaces: return prefix + ucfirst(m.group(2)) # The title of the page being included is NOT in the main namespace and # lacks any other explicit designation of the namespace - therefore, it # is resolved to the Template namespace (that's the default for the # template inclusion mechanism). # This is a defense against pages whose title only contains UTF-8 chars # that are reduced to an empty string. Right now I can think of one such # case - <C2><A0> which represents the non-breaking space. # In this particular case, this page is a redirect to [[Non-nreaking # space]], but having in the system a redirect page with an empty title # causes numerous problems, so we'll live happier without it. if templateTitle: return templatePrefix + ucfirst(templateTitle) else: return '' # caller may log as error def normalizeNamespace(ns): return ucfirst(ns) # ---------------------------------------------------------------------- # Parser functions # see http://www.mediawiki.org/wiki/Help:Extension:ParserFunctions # https://github.com/Wikia/app/blob/dev/extensions/ParserFunctions/ParserFunctions_body.php class Infix: """Infix operators. The calling sequence for the infix is: x |op| y """ def __init__(self, function): self.function = function def __ror__(self, other): return Infix(lambda x, self=self, other=other: self.function(other, x)) def __or__(self, other): return self.function(other) def __rlshift__(self, other): return Infix(lambda x, self=self, other=other: self.function(other, x)) def __rshift__(self, other): return self.function(other) def __call__(self, value1, value2): return self.function(value1, value2) ROUND = Infix(lambda x, y: round(x, y)) from math import floor, ceil, pi, e, trunc, exp, log as ln, sin, cos, tan, asin, acos, atan def sharp_expr(extr, expr): """Tries converting a lua expr into a Python expr.""" try: expr = extr.expand(expr) expr = re.sub('(?<![!<>])=', '==', expr) # negative lookbehind expr = re.sub('mod', '%', expr) # no \b here expr = re.sub('\bdiv\b', '/', expr) expr = re.sub('\bround\b', '|ROUND|', expr) return text_type(eval(expr)) except: return '<span class="error">%s</span>' % expr def sharp_if(extr, testValue, valueIfTrue, valueIfFalse=None, *args): # In theory, we should evaluate the first argument here, # but it was evaluated while evaluating part[0] in expandTemplate(). if testValue.strip(): # The {{#if:}} function is an if-then-else construct. # The applied condition is: "The condition string is non-empty". valueIfTrue = extr.expand(valueIfTrue.strip()) # eval if valueIfTrue: return valueIfTrue elif valueIfFalse: return extr.expand(valueIfFalse.strip()) # eval return "" def sharp_ifeq(extr, lvalue, rvalue, valueIfTrue, valueIfFalse=None, *args): rvalue = rvalue.strip() if rvalue: # lvalue is always evaluated if lvalue.strip() == rvalue: # The {{#ifeq:}} function is an if-then-else construct. The # applied condition is "is rvalue equal to lvalue". Note that this # does only string comparison while MediaWiki implementation also # supports numerical comparissons. if valueIfTrue: return extr.expand(valueIfTrue.strip()) else: if valueIfFalse: return extr.expand(valueIfFalse.strip()) return "" def sharp_iferror(extr, test, then='', Else=None, *args): if re.match('<(?:strong|span|p|div)\s(?:[^\s>]*\s+)*?class="(?:[^"\s>]*\s+)*?error(?:\s[^">]*)?"', test): return extr.expand(then.strip()) elif Else is None: return test.strip() else: return extr.expand(Else.strip()) def sharp_switch(extr, primary, *params): # FIXME: we don't support numeric expressions in primary # {{#switch: comparison string # | case1 = result1 # | case2 # | case4 = result2 # | 1 | case5 = result3 # | #default = result4 # }} primary = primary.strip() found = False # for fall through cases default = None rvalue = None lvalue = '' for param in params: # handle cases like: # #default = [http://www.perseus.tufts.edu/hopper/text?doc=Perseus...] pair = param.split('=', 1) lvalue = extr.expand(pair[0].strip()) rvalue = None if len(pair) > 1: # got "=" rvalue = extr.expand(pair[1].strip()) # check for any of multiple values pipe separated if found or primary in [v.strip() for v in lvalue.split('|')]: # Found a match, return now return rvalue elif lvalue == '#default': default = rvalue rvalue = None # avoid defaulting to last case elif lvalue == primary: # If the value matches, set a flag and continue found = True # Default case # Check if the last item had no = sign, thus specifying the default case if rvalue is not None: return lvalue elif default is not None: return default return '' # Extension Scribunto: https://www.mediawiki.org/wiki/Extension:Scribunto def sharp_invoke(module, function, args): functions = modules.get(module) if functions: funct = functions.get(function) if funct: return str(funct(args)) return '' parserFunctions = { '#expr': sharp_expr, '#if': sharp_if, '#ifeq': sharp_ifeq, '#iferror': sharp_iferror, '#ifexpr': lambda *args: '', # not supported '#ifexist': lambda *args: '', # not supported '#rel2abs': lambda *args: '', # not supported '#switch': sharp_switch, '#language': lambda *args: '', # not supported '#time': lambda *args: '', # not supported '#timel': lambda *args: '', # not supported '#titleparts': lambda *args: '', # not supported # This function is used in some pages to construct links # http://meta.wikimedia.org/wiki/Help:URL 'urlencode': lambda string, *rest: quote(string.encode('utf-8')), 'lc': lambda string, *rest: string.lower() if string else '', 'lcfirst': lambda string, *rest: lcfirst(string), 'uc': lambda string, *rest: string.upper() if string else '', 'ucfirst': lambda string, *rest: ucfirst(string), 'int': lambda string, *rest: str(int(string)), } def callParserFunction(functionName, args, extractor): """ Parser functions have similar syntax as templates, except that the first argument is everything after the first colon. :return: the result of the invocation, None in case of failure. :param: args not yet expanded (see branching functions). https://www.mediawiki.org/wiki/Help:Extension:ParserFunctions """ try: # https://it.wikipedia.org/wiki/Template:Str_endswith has #Invoke functionName = functionName.lower() if functionName == '#invoke': module, fun = args[0].strip(), args[1].strip() logging.debug('%*s#invoke %s %s %s', extractor.frame.depth, '', module, fun, args[2:]) # special handling of frame if len(args) == 2: # find parameters in frame whose title is the one of the original # template invocation templateTitle = fullyQualifiedTemplateTitle(module) if not templateTitle: logging.warn("Template with empty title") params = None frame = extractor.frame while frame: if frame.title == templateTitle: params = frame.args break frame = frame.prev else: params = [extractor.transform(p) for p in args[2:]] # evaluates them params = extractor.templateParams(params) ret = sharp_invoke(module, fun, params) logging.debug('%*s<#invoke %s %s %s', extractor.frame.depth, '', module, fun, ret) return ret if functionName in parserFunctions: # branching functions use the extractor to selectively evaluate args return parserFunctions[functionName](extractor, *args) except: return "" # FIXME: fix errors return "" # ---------------------------------------------------------------------- # Expand using WikiMedia API # import json # def expand(text): # """Expand templates invoking MediaWiki API""" # text = urlib.urlencodew(text.encode('utf-8')) # base = urlbase[:urlbase.rfind('/')] # url = base + "/w/api.php?action=expandtemplates&format=json&text=" + text # exp = json.loads(urllib.urlopen(url)) # return exp['expandtemplates']['*'] # ---------------------------------------------------------------------- # Extract Template definition reNoinclude = re.compile(r'<noinclude>(?:.*?)</noinclude>', re.DOTALL) reIncludeonly = re.compile(r'<includeonly>|</includeonly>', re.DOTALL) # These are built before spawning processes, hence thay are shared. templates = {} redirects = {} # cache of parser templates # FIXME: sharing this with a Manager slows down. templateCache = {} def define_template(title, page): """ Adds a template defined in the :param page:. @see https://en.wikipedia.org/wiki/Help:Template#Noinclude.2C_includeonly.2C_and_onlyinclude """ global templates global redirects # title = normalizeTitle(title) # check for redirects m = re.match('#REDIRECT.*?\[\[([^\]]*)]]', page[0], re.IGNORECASE) if m: redirects[title] = m.group(1) # normalizeTitle(m.group(1)) return text = unescape(''.join(page)) # We're storing template text for future inclusion, therefore, # remove all <noinclude> text and keep all <includeonly> text # (but eliminate <includeonly> tags per se). # However, if <onlyinclude> ... </onlyinclude> parts are present, # then only keep them and discard the rest of the template body. # This is because using <onlyinclude> on a text fragment is # equivalent to enclosing it in <includeonly> tags **AND** # enclosing all the rest of the template body in <noinclude> tags. # remove comments text = comment.sub('', text) # eliminate <noinclude> fragments text = reNoinclude.sub('', text) # eliminate unterminated <noinclude> elements text = re.sub(r'<noinclude\s*>.*$', '', text, flags=re.DOTALL) text = re.sub(r'<noinclude/>', '', text) onlyincludeAccumulator = '' for m in re.finditer('<onlyinclude>(.*?)</onlyinclude>', text, re.DOTALL): onlyincludeAccumulator += m.group(1) if onlyincludeAccumulator: text = onlyincludeAccumulator else: text = reIncludeonly.sub('', text) if text: if title in templates: logging.warn('Redefining: %s', title) templates[title] = text # ---------------------------------------------------------------------- def dropNested(text, openDelim, closeDelim): """ A matching function for nested expressions, e.g. namespaces and tables. """ openRE = re.compile(openDelim, re.IGNORECASE) closeRE = re.compile(closeDelim, re.IGNORECASE) # partition text in separate blocks { } { } spans = [] # pairs (s, e) for each partition nest = 0 # nesting level start = openRE.search(text, 0) if not start: return text end = closeRE.search(text, start.end()) next = start while end: next = openRE.search(text, next.end()) if not next: # termination while nest: # close all pending nest -= 1 end0 = closeRE.search(text, end.end()) if end0: end = end0 else: break spans.append((start.start(), end.end())) break while end.end() < next.start(): # { } { if nest: nest -= 1 # try closing more last = end.end() end = closeRE.search(text, end.end()) if not end: # unbalanced if spans: span = (spans[0][0], last) else: span = (start.start(), last) spans = [span] break else: spans.append((start.start(), end.end())) # advance start, find next close start = next end = closeRE.search(text, next.end()) break # { } if next != start: # { { } nest += 1 # collect text outside partitions return dropSpans(spans, text) def dropSpans(spans, text): """ Drop from text the blocks identified in :param spans:, possibly nested. """ spans.sort() res = '' offset = 0 for s, e in spans: if offset <= s: # handle nesting if offset < s: res += text[offset:s] offset = e res += text[offset:] return res # ---------------------------------------------------------------------- # WikiLinks # May be nested [[File:..|..[[..]]..|..]], [[Category:...]], etc. # Also: [[Help:IPA for Catalan|[andora]]] def replaceInternalLinks(text): """ Replaces internal links of the form: [[title |...|label]]trail with title concatenated with trail, when present, e.g. 's' for plural. See https://www.mediawiki.org/wiki/Help:Links#Internal_links """ # call this after removal of external links, so we need not worry about # triple closing ]]]. cur = 0 res = '' for s, e in findBalanced(text): m = tailRE.match(text, e) if m: trail = m.group(0) end = m.end() else: trail = '' end = e inner = text[s + 2:e - 2] # find first | pipe = inner.find('|') if pipe < 0: title = inner label = title else: title = inner[:pipe].rstrip() # find last | curp = pipe + 1 for s1, e1 in findBalanced(inner): last = inner.rfind('|', curp, s1) if last >= 0: pipe = last # advance curp = e1 label = inner[pipe + 1:].strip() res += text[cur:s] + makeInternalLink(title, label) + trail cur = end return res + text[cur:] # the official version is a method in class Parser, similar to this: # def replaceInternalLinks2(text): # global wgExtraInterlanguageLinkPrefixes # # the % is needed to support urlencoded titles as well # tc = Title::legalChars() + '#%' # # Match a link having the form [[namespace:link|alternate]]trail # e1 = re.compile("([%s]+)(?:\\|(.+?))?]](.*)" % tc, re.S | re.D) # # Match cases where there is no "]]", which might still be images # e1_img = re.compile("([%s]+)\\|(.*)" % tc, re.S | re.D) # holders = LinkHolderArray(self) # # split the entire text string on occurrences of [[ # iterBrackets = re.compile('[[').finditer(text) # m in iterBrackets.next() # # get the first element (all text up to first [[) # s = text[:m.start()] # cur = m.end() # line = s # useLinkPrefixExtension = self.getTargetLanguage().linkPrefixExtension() # e2 = None # if useLinkPrefixExtension: # # Match the end of a line for a word that is not followed by whitespace, # # e.g. in the case of "The Arab al[[Razi]]", "al" will be matched # global wgContLang # charset = wgContLang.linkPrefixCharset() # e2 = re.compile("((?>.*[^charset]|))(.+)", re.S | re.D | re.U) # if self.mTitle is None: # raise MWException(__METHOD__ + ": \self.mTitle is null\n") # nottalk = not self.mTitle.isTalkPage() # if useLinkPrefixExtension: # m = e2.match(s) # if m: # first_prefix = m.group(2) # else: # first_prefix = false # else: # prefix = '' # useSubpages = self.areSubpagesAllowed() # for m in iterBrackets: # line = text[cur:m.start()] # cur = m.end() # # TODO: Check for excessive memory usage # if useLinkPrefixExtension: # m = e2.match(e2) # if m: # prefix = m.group(2) # s = m.group(1) # else: # prefix = '' # # first link # if first_prefix: # prefix = first_prefix # first_prefix = False # might_be_img = False # m = e1.match(line) # if m: # page with normal label or alt # label = m.group(2) # # If we get a ] at the beginning of m.group(3) that means we have a link that is something like: # # [[Image:Foo.jpg|[http://example.com desc]]] <- having three ] in a row fucks up, # # the real problem is with the e1 regex # # See bug 1300. # # # # Still some problems for cases where the ] is meant to be outside punctuation, # # and no image is in sight. See bug 2095. # # # if label and m.group(3)[0] == ']' and '[' in label: # label += ']' # so that replaceExternalLinks(label) works later # m.group(3) = m.group(3)[1:] # # fix up urlencoded title texts # if '%' in m.group(1): # # Should anchors '#' also be rejected? # m.group(1) = str_replace(array('<', '>'), array('&lt', '&gt'), rawurldecode(m.group(1))) # trail = m.group(3) # else: # m = e1_img.match(line): # if m: # # Invalid, but might be an image with a link in its caption # might_be_img = true # label = m.group(2) # if '%' in m.group(1): # m.group(1) = rawurldecode(m.group(1)) # trail = "" # else: # Invalid form; output directly # s += prefix + '[[' + line # continue # origLink = m.group(1) # # Dont allow internal links to pages containing # # PROTO: where PROTO is a valid URL protocol these # # should be external links. # if (preg_match('/^(?i:' + self.mUrlProtocols + ')/', origLink)) { # s += prefix + '[[' + line # continue # } # # Make subpage if necessary # if useSubpages: # link = self.maybeDoSubpageLink(origLink, label) # else: # link = origLink # noforce = origLink[0] != ':' # if not noforce: # # Strip off leading ':' # link = link[1:] # nt = Title::newFromText(self.mStripState.unstripNoWiki(link)) # if nt is None: # s += prefix + '[[' + line # continue # ns = nt.getNamespace() # iw = nt.getInterwiki() # if might_be_img { # if this is actually an invalid link # if (ns == NS_FILE and noforce) { # but might be an image # found = False # while True: # # look at the next 'line' to see if we can close it there # next_line = iterBrakets.next() # if not next_line: # break # m = explode(']]', next_line, 3) # if m.lastindex == 3: # # the first ]] closes the inner link, the second the image # found = True # label += "[[%s]]%s" % (m.group(0), m.group(1)) # trail = m.group(2) # break # elif m.lastindex == 2: # # if there is exactly one ]] that is fine, we will keep looking # label += "[[{m[0]}]]{m.group(1)}" # else: # # if next_line is invalid too, we need look no further # label += '[[' + next_line # break # if not found: # # we couldnt find the end of this imageLink, so output it raw # # but dont ignore what might be perfectly normal links in the text we ve examined # holders.merge(self.replaceInternalLinks2(label)) # s += "{prefix}[[%s|%s" % (link, text) # # note: no trail, because without an end, there *is* no trail # continue # } else: # it is not an image, so output it raw # s += "{prefix}[[%s|%s" % (link, text) # # note: no trail, because without an end, there *is* no trail # continue # } # wasblank = (text == '') # if wasblank: # text = link # else: # # Bug 4598 madness. Handle the quotes only if they come from the alternate part # # [[Lista d''e paise d''o munno]] . <a href="...">Lista d''e paise d''o munno</a> # # [[Criticism of Harry Potter|Criticism of ''Harry Potter'']] # # . <a href="Criticism of Harry Potter">Criticism of <i>Harry Potter</i></a> # text = self.doQuotes(text) # # Link not escaped by : , create the various objects # if noforce and not nt.wasLocalInterwiki(): # # Interwikis # if iw and mOptions.getInterwikiMagic() and nottalk and ( # Language::fetchLanguageName(iw, None, 'mw') or # in_array(iw, wgExtraInterlanguageLinkPrefixes)): # # Bug 24502: filter duplicates # if iw not in mLangLinkLanguages: # self.mLangLinkLanguages[iw] = True # self.mOutput.addLanguageLink(nt.getFullText()) # s = rstrip(s + prefix) # s += strip(trail, "\n") == '' ? '': prefix + trail # continue # if ns == NS_FILE: # if not wfIsBadImage(nt.getDBkey(), self.mTitle): # if wasblank: # # if no parameters were passed, text # # becomes something like "File:Foo.png", # # which we dont want to pass on to the # # image generator # text = '' # else: # # recursively parse links inside the image caption # # actually, this will parse them in any other parameters, too, # # but it might be hard to fix that, and it doesnt matter ATM # text = self.replaceExternalLinks(text) # holders.merge(self.replaceInternalLinks2(text)) # # cloak any absolute URLs inside the image markup, so replaceExternalLinks() wont touch them # s += prefix + self.armorLinks( # self.makeImage(nt, text, holders)) + trail # else: # s += prefix + trail # continue # if ns == NS_CATEGORY: # s = rstrip(s + "\n") # bug 87 # if wasblank: # sortkey = self.getDefaultSort() # else: # sortkey = text # sortkey = Sanitizer::decodeCharReferences(sortkey) # sortkey = str_replace("\n", '', sortkey) # sortkey = self.getConverterLanguage().convertCategoryKey(sortkey) # self.mOutput.addCategory(nt.getDBkey(), sortkey) # s += strip(prefix + trail, "\n") == '' ? '' : prefix + trail # continue # } # } # # Self-link checking. For some languages, variants of the title are checked in # # LinkHolderArray::doVariants() to allow batching the existence checks necessary # # for linking to a different variant. # if ns != NS_SPECIAL and nt.equals(self.mTitle) and !nt.hasFragment(): # s += prefix + Linker::makeSelfLinkObj(nt, text, '', trail) # continue # # NS_MEDIA is a pseudo-namespace for linking directly to a file # # @todo FIXME: Should do batch file existence checks, see comment below # if ns == NS_MEDIA: # # Give extensions a chance to select the file revision for us # options = [] # descQuery = False # Hooks::run('BeforeParserFetchFileAndTitle', # [this, nt, &options, &descQuery]) # # Fetch and register the file (file title may be different via hooks) # file, nt = self.fetchFileAndTitle(nt, options) # # Cloak with NOPARSE to avoid replacement in replaceExternalLinks # s += prefix + self.armorLinks( # Linker::makeMediaLinkFile(nt, file, text)) + trail # continue # # Some titles, such as valid special pages or files in foreign repos, should # # be shown as bluelinks even though they are not included in the page table # # # # @todo FIXME: isAlwaysKnown() can be expensive for file links; we should really do # # batch file existence checks for NS_FILE and NS_MEDIA # if iw == '' and nt.isAlwaysKnown(): # self.mOutput.addLink(nt) # s += self.makeKnownLinkHolder(nt, text, array(), trail, prefix) # else: # # Links will be added to the output link list after checking # s += holders.makeHolder(nt, text, array(), trail, prefix) # } # return holders def makeInternalLink(title, label): colon = title.find(':') if colon > 0 and title[:colon] not in acceptedNamespaces: return '' if colon == 0: # drop also :File: colon2 = title.find(':', colon + 1) if colon2 > 1 and title[colon + 1:colon2] not in acceptedNamespaces: return '' if Extractor.keepLinks: return '<a href="%s">%s</a>' % (quote(title.encode('utf-8')), label) else: return label # ---------------------------------------------------------------------- # External links # from: https://doc.wikimedia.org/mediawiki-core/master/php/DefaultSettings_8php_source.html wgUrlProtocols = [ 'bitcoin:', 'ftp://', 'ftps://', 'geo:', 'git://', 'gopher://', 'http://', 'https://', 'irc://', 'ircs://', 'magnet:', 'mailto:', 'mms://', 'news:', 'nntp://', 'redis://', 'sftp://', 'sip:', 'sips:', 'sms:', 'ssh://', 'svn://', 'tel:', 'telnet://', 'urn:', 'worldwind://', 'xmpp:', '//' ] # from: https://doc.wikimedia.org/mediawiki-core/master/php/Parser_8php_source.html # Constants needed for external link processing # Everything except bracket, space, or control characters # \p{Zs} is unicode 'separator, space' category. It covers the space 0x20 # as well as U+3000 is IDEOGRAPHIC SPACE for bug 19052 EXT_LINK_URL_CLASS = r'[^][<>"\x00-\x20\x7F\s]' ANCHOR_CLASS = r'[^][\x00-\x08\x0a-\x1F]' ExtLinkBracketedRegex = re.compile( '\[(((?i)' + '|'.join(wgUrlProtocols) + ')' + EXT_LINK_URL_CLASS + r'+)' + r'\s*((?:' + ANCHOR_CLASS + r'|\[\[' + ANCHOR_CLASS + r'+\]\])' + r'*?)\]', re.S | re.U) # A simpler alternative: # ExtLinkBracketedRegex = re.compile(r'\[(.*?)\](?!])') EXT_IMAGE_REGEX = re.compile( r"""^(http://|https://)([^][<>"\x00-\x20\x7F\s]+) /([A-Za-z0-9_.,~%\-+&;#*?!=()@\x80-\xFF]+)\.((?i)gif|png|jpg|jpeg)$""", re.X | re.S | re.U) def replaceExternalLinks(text): """ https://www.mediawiki.org/wiki/Help:Links#External_links [URL anchor text] """ s = '' cur = 0 for m in ExtLinkBracketedRegex.finditer(text): s += text[cur:m.start()] cur = m.end() url = m.group(1) label = m.group(3) # # The characters '<' and '>' (which were escaped by # # removeHTMLtags()) should not be included in # # URLs, per RFC 2396. # m2 = re.search('&(lt|gt);', url) # if m2: # link = url[m2.end():] + ' ' + link # url = url[0:m2.end()] # If the link text is an image URL, replace it with an <img> tag # This happened by accident in the original parser, but some people used it extensively m = EXT_IMAGE_REGEX.match(label) if m: label = makeExternalImage(label) # Use the encoded URL # This means that users can paste URLs directly into the text # Funny characters like ö aren't valid in URLs anyway # This was changed in August 2004 s += makeExternalLink(url, label) # + trail return s + text[cur:] def makeExternalLink(url, anchor): """Function applied to wikiLinks""" if Extractor.keepLinks: return '<a href="%s">%s</a>' % (quote(url.encode('utf-8')), anchor) else: return anchor def makeExternalImage(url, alt=''): if Extractor.keepLinks: return '<img src="%s" alt="%s">' % (url, alt) else: return alt # ---------------------------------------------------------------------- # match tail after wikilink tailRE = re.compile('\w+') syntaxhighlight = re.compile('<syntaxhighlight .*?>(.*?)</syntaxhighlight>', re.DOTALL) # skip level 1, it is page name level section = re.compile(r'(==+)\s*(.*?)\s*\1') listOpen = {'*': '<ul>', '#': '<ol>', ';': '<dl>', ':': '<dl>'} listClose = {'*': '</ul>', '#': '</ol>', ';': '</dl>', ':': '</dl>'} listItem = {'*': '<li>%s</li>', '#': '<li>%s</<li>', ';': '<dt>%s</dt>', ':': '<dd>%s</dd>'} def compact(text): """Deal with headers, lists, empty sections, residuals of tables. :param text: convert to HTML. """ page = [] # list of paragraph headers = {} # Headers for unfilled sections emptySection = False # empty sections are discarded listLevel = [] # nesting of lists for line in text.split('\n'): if not line: continue # Handle section titles m = section.match(line) if m: title = m.group(2) lev = len(m.group(1)) # header level if Extractor.toHTML: page.append("<h%d>%s</h%d>" % (lev, title, lev)) if title and title[-1] not in '!?': title += '.' # terminate sentence. headers[lev] = title # drop previous headers for i in list(headers.keys()): if i > lev: del headers[i] emptySection = True listLevel = [] continue # Handle page title elif line.startswith('++'): title = line[2:-2] if title: if title[-1] not in '!?': title += '.' page.append(title) # handle indents elif line[0] == ':': # page.append(line.lstrip(':*#;')) continue # handle lists elif line[0] in '*#;:': i = 0 # c: current level char # n: next level char for c, n in zip_longest(listLevel, line, fillvalue=''): if not n or n not in '*#;:': # shorter or different if c: if Extractor.toHTML: page.append(listClose[c]) listLevel = listLevel[:-1] continue else: break # n != '' if c != n and (not c or (c not in ';:' and n not in ';:')): if c: # close level if Extractor.toHTML: page.append(listClose[c]) listLevel = listLevel[:-1] listLevel += n if Extractor.toHTML: page.append(listOpen[n]) i += 1 n = line[i - 1] # last list char line = line[i:].strip() if line: # FIXME: n is '"' if Extractor.keepLists: # emit open sections items = sorted(headers.items()) for i, v in items: page.append(v) headers.clear() # FIXME: use item count for #-lines bullet = '1. ' if n == '#' else '- ' page.append('{0:{1}s}'.format(bullet, len(listLevel)) + line) elif Extractor.toHTML: page.append(listItem[n] % line) elif len(listLevel): page.append(line) if Extractor.toHTML: for c in reversed(listLevel): page.append(listClose[c]) listLevel = [] # Drop residuals of lists elif line[0] in '{|' or line[-1] == '}': continue # Drop irrelevant lines elif (line[0] == '(' and line[-1] == ')') or line.strip('.-') == '': continue elif len(headers): if Extractor.keepSections: items = sorted(headers.items()) for i, v in items: page.append(v) headers.clear() page.append(line) # first line emptySection = False elif not emptySection: # Drop preformatted if line[0] != ' ': # dangerous page.append(line) return page def handle_unicode(entity): numeric_code = int(entity[2:-1]) if numeric_code >= 0x10000: return '' return chr(numeric_code) # ------------------------------------------------------------------------------ # Output class NextFile(object): """ Synchronous generation of next available file name. """ filesPerDir = 9223372036854775807 def __init__(self, path_name): self.path_name = path_name self.dir_index = -1 self.file_index = -1 def __next__(self): self.file_index = (self.file_index + 1) % NextFile.filesPerDir if self.file_index == 0: self.dir_index += 1 dirname = self._dirname() if not os.path.isdir(dirname): os.makedirs(dirname) return self._filepath() next = __next__ def _dirname(self): char1 = self.dir_index % 26 char2 = self.dir_index // 26 % 26 return os.path.join(self.path_name, '%c%c' % (ord('A') + char2, ord('A') + char1)) def _filepath(self): return '%s/wiki_%02d.html' % (self._dirname(), self.file_index) class OutputSplitter(object): """ File-like object, that splits output to multiple files of a given max size. """ def __init__(self, nextFile, max_file_size=0, compress=True): """ :param nextFile: a NextFile object from which to obtain filenames to use. :param max_file_size: the maximum size of each file. :para compress: whether to write data with bzip compression. """ self.nextFile = nextFile self.compress = compress self.max_file_size = max_file_size self.file = self.open(next(self.nextFile)) def reserve(self, size): #if self.file.tell() + size > self.max_file_size: self.close() self.file = self.open(next(self.nextFile)) def write(self, data): self.reserve(len(data)) self.file.write(data) def close(self): self.file.close() def open(self, filename): if self.compress: return bz2.BZ2File(filename + '.bz2', 'w') else: return open(filename, 'wb') # ---------------------------------------------------------------------- # READER tagRE = re.compile(r'(.*?)<(/?\w+)[^>]*>(?:([^<]*)(<.*?>)?)?') # 1 2 3 4 def load_templates(file, output_file=None): """ Load templates from :param file:. :param output_file: file where to save templates and modules. """ global templateNamespace, templatePrefix templatePrefix = templateNamespace + ':' global moduleNamespace, modulePrefix modulePrefix = moduleNamespace + ':' if output_file: output = codecs.open(output_file, 'wb', 'utf-8') for page_count, page_data in enumerate(pages_from(file)): id, revid, title, ns, page = page_data if not output_file and (not templateNamespace or not moduleNamespace): # do not know it yet # reconstruct templateNamespace and moduleNamespace from the first title if ns in templateKeys: colon = title.find(':') if colon > 1: if ns == '10': templateNamespace = title[:colon] templatePrefix = title[:colon + 1] elif ns == '828': moduleNamespace = title[:colon] modulePrefix = title[:colon + 1] if ns in templateKeys: text = ''.join(page) define_template(title, text) # save templates and modules to file if output_file: output.write('<page>\n') output.write(' <title>%s</title>\n' % title) output.write(' <ns>%s</ns>\n' % ns) output.write(' <id>%s</id>\n' % id) output.write(' <text>') for line in page: output.write(line) output.write(' </text>\n') output.write('</page>\n') if page_count and page_count % 100000 == 0: logging.info("Preprocessed %d pages", page_count) if output_file: output.close() logging.info("Saved %d templates to '%s'", len(templates), output_file) def pages_from(input): """ Scans input extracting pages. :return: (id, revid, title, namespace key, page), page is a list of lines. """ # we collect individual lines, since str.join() is significantly faster # than concatenation page = [] id = None ns = '0' last_id = None revid = None inText = False redirect = False title = None for line in input: line = line.decode('utf-8') if '<' not in line: # faster than doing re.search() if inText: page.append(line) continue m = tagRE.search(line) if not m: continue tag = m.group(2) if tag == 'page': page = [] redirect = False elif tag == 'id' and not id: id = m.group(3) elif tag == 'id' and id: revid = m.group(3) elif tag == 'title': title = m.group(3) elif tag == 'ns': ns = m.group(3) #elif tag == 'redirect': # redirect = True elif tag == 'text': if m.lastindex == 3 and line[m.start(3)-2] == '/': # self closing # <text xml:space="preserve" /> continue inText = True line = line[m.start(3):m.end(3)] page.append(line) if m.lastindex == 4: # open-close inText = False elif tag == '/text': if m.group(1): page.append(m.group(1)) inText = False elif inText: page.append(line) elif tag == '/page': if id != last_id and not redirect: yield (id, revid, title, ns, page) last_id = id ns = '0' id = None revid = None title = None page = [] def process_dump(input_file, template_file, out_file, file_size, file_compress, process_count): """ :param input_file: name of the wikipedia dump file; '-' to read from stdin :param template_file: optional file with template definitions. :param out_file: directory where to store extracted data, or '-' for stdout :param file_size: max size of each extracted file, or None for no max (one file) :param file_compress: whether to compress files with bzip. :param process_count: number of extraction processes to spawn. """ global urlbase global knownNamespaces global templateNamespace, templatePrefix global moduleNamespace, modulePrefix if input_file == '-': input = sys.stdin else: input = fileinput.FileInput(input_file, openhook=fileinput.hook_compressed) # collect siteinfo for line in input: line = line.decode('utf-8') m = tagRE.search(line) if not m: continue tag = m.group(2) if tag == 'base': # discover urlbase from the xml dump file # /mediawiki/siteinfo/base base = m.group(3) urlbase = base[:base.rfind("/")] elif tag == 'namespace': knownNamespaces.add(m.group(3)) if re.search('key="10"', line): templateNamespace = m.group(3) templatePrefix = templateNamespace + ':' elif re.search('key="828"', line): moduleNamespace = m.group(3) modulePrefix = moduleNamespace + ':' elif tag == '/siteinfo': break if Extractor.expand_templates: # preprocess template_load_start = default_timer() if template_file: if os.path.exists(template_file): logging.info("Preprocessing '%s' to collect template definitions: this may take some time.", template_file) # can't use with here:' file = fileinput.FileInput(template_file, openhook=fileinput.hook_compressed) load_templates(file) file.close() else: if input_file == '-': # can't scan then reset stdin; must error w/ suggestion to specify template_file raise ValueError("to use templates with stdin dump, must supply explicit template-file") logging.info("Preprocessing '%s' to collect template definitions: this may take some time.", input_file) load_templates(input, template_file) input.close() input = fileinput.FileInput(input_file, openhook=fileinput.hook_compressed) template_load_elapsed = default_timer() - template_load_start logging.info("Loaded %d templates in %.1fs", len(templates), template_load_elapsed) # process pages logging.info("Starting page extraction from %s.", input_file) extract_start = default_timer() # Parallel Map/Reduce: # - pages to be processed are dispatched to workers # - a reduce process collects the results, sort them and print them. maxsize = 10 * process_count # output queue output_queue = Queue(maxsize=maxsize) if out_file == '-': out_file = None worker_count = max(1, process_count) # load balancing max_spool_length = 10000 spool_length = Value('i', 0, lock=False) # reduce job that sorts and prints output reduce = Process(target=reduce_process, args=(output_queue, spool_length, out_file, file_size, file_compress)) reduce.start() # initialize jobs queue jobs_queue = Queue(maxsize=maxsize) # start worker processes logging.info("Using %d extract processes.", worker_count) workers = [] for i in range(worker_count): extractor = Process(target=extract_process, args=(i, jobs_queue, output_queue)) extractor.daemon = True # only live while parent process lives extractor.start() workers.append(extractor) # fp_i = open('output.npy', 'rb') # arr = np.load(fp_i); #sorted_arr=arr[:,0].sort() ignored_list=[] with open('ignored_docs.txt') as f: for line in f: ignored_list.append(int(line)); count=1; listcount=0; # Mapper process page_num = 0 for page_data in pages_from(input): if count != ignored_list[listcount]: id, revid, title, ns, page = page_data if keepPage(ns, page): # slow down delay = 0 if spool_length.value > max_spool_length: # reduce to 10% while spool_length.value > max_spool_length/10: time.sleep(10) delay += 10 if delay: logging.info('Delay %ds', delay) job = (id, revid, title, page, page_num) jobs_queue.put(job) # goes to any available extract_process page_num += 1 page = None # free memory else: listcount=listcount+1 count=count+1 input.close() # signal termination for _ in workers: jobs_queue.put(None) # wait for workers to terminate for w in workers: w.join() # signal end of work to reduce process output_queue.put(None) # wait for it to finish reduce.join() extract_duration = default_timer() - extract_start extract_rate = page_num / extract_duration logging.info("Finished %d-process extraction of %d articles in %.1fs (%.1f art/s)", process_count, page_num, extract_duration, extract_rate) # ---------------------------------------------------------------------- # Multiprocess support def extract_process(i, jobs_queue, output_queue): """Pull tuples of raw page content, do CPU/regex-heavy fixup, push finished text :param i: process id. :param jobs_queue: where to get jobs. :param output_queue: where to queue extracted text for output. """ out = StringIO() # memory buffer while True: job = jobs_queue.get() # job is (id, title, page, page_num) if job: id, revid, title, page, page_num = job try: e = Extractor(*job[:4]) # (id, revid, title, page) page = None # free memory e.extract(out) text = out.getvalue() except: text = '' logging.exception('Processing page: %s %s', id, title) output_queue.put((page_num, text)) out.truncate(0) out.seek(0) else: logging.debug('Quit extractor') break out.close() report_period = 10000 # progress report period def reduce_process(output_queue, spool_length, out_file=None, file_size=0, file_compress=True): """Pull finished article text, write series of files (or stdout) :param output_queue: text to be output. :param spool_length: spool length. :param out_file: filename where to print. :param file_size: max file size. :param file_compress: whether to compress output. """ if out_file: nextFile = NextFile(out_file) output = OutputSplitter(nextFile, file_size, file_compress) else: output = sys.stdout if PY2 else sys.stdout.buffer if file_compress: logging.warn("writing to stdout, so no output compression (use an external tool)") interval_start = default_timer() # FIXME: use a heap spool = {} # collected pages next_page = 0 # sequence numbering of page while True: if next_page in spool: output.write(spool.pop(next_page).encode('utf-8')) next_page += 1 # tell mapper our load: spool_length.value = len(spool) # progress report if next_page % report_period == 0: interval_rate = report_period / (default_timer() - interval_start) logging.info("Extracted %d articles (%.1f art/s)", next_page, interval_rate) interval_start = default_timer() else: # mapper puts None to signal finish pair = output_queue.get() if not pair: break page_num, text = pair spool[page_num] = text # tell mapper our load: spool_length.value = len(spool) # FIXME: if an extractor dies, process stalls; the other processes # continue to produce pairs, filling up memory. if len(spool) > 200: logging.debug('Collected %d, waiting: %d, %d', len(spool), next_page, next_page == page_num) if output != sys.stdout: output.close() # ---------------------------------------------------------------------- # Minimum size of output files minFileSize = 200 * 1024 def main(): global urlbase, acceptedNamespaces, filter_disambig_pages global templateCache parser = argparse.ArgumentParser(prog=os.path.basename(sys.argv[0]), formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__) parser.add_argument("input", help="XML wiki dump file") groupO = parser.add_argument_group('Output') groupO.add_argument("-o", "--output", default="text", help="directory for extracted files (or '-' for dumping to stdout)") groupO.add_argument("-b", "--bytes", default="1M", help="maximum bytes per output file (default %(default)s)", metavar="n[KMG]") groupO.add_argument("-c", "--compress", action="store_true", help="compress output files using bzip") groupP = parser.add_argument_group('Processing') groupP.add_argument("--html", action="store_true", help="produce HTML output, subsumes --links") groupP.add_argument("-l", "--links", action="store_true", help="preserve links") groupP.add_argument("-s", "--sections", action="store_true", help="preserve sections") groupP.add_argument("--lists", action="store_true", help="preserve lists") groupP.add_argument("-ns", "--namespaces", default="", metavar="ns1,ns2", help="accepted namespaces in links") groupP.add_argument("--templates", help="use or create file containing templates") groupP.add_argument("--no-templates", action="store_false", help="Do not expand templates") groupP.add_argument("-r", "--revision", action="store_true", default=Extractor.print_revision, help="Include the document revision id (default=%(default)s)") groupP.add_argument("--min_text_length", type=int, default=Extractor.min_text_length, help="Minimum expanded text length required to write document (default=%(default)s)") groupP.add_argument("--filter_disambig_pages", action="store_true", default=filter_disambig_pages, help="Remove pages from output that contain disabmiguation markup (default=%(default)s)") default_process_count = cpu_count() - 1 parser.add_argument("--processes", type=int, default=default_process_count, help="Number of processes to use (default %(default)s)") groupS = parser.add_argument_group('Special') groupS.add_argument("-q", "--quiet", action="store_true", help="suppress reporting progress info") groupS.add_argument("--debug", action="store_true", help="print debug info") groupS.add_argument("-a", "--article", action="store_true", help="analyze a file containing a single article (debug option)") groupS.add_argument("-v", "--version", action="version", version='%(prog)s ' + version, help="print program version") args = parser.parse_args() Extractor.keepLinks = args.links Extractor.keepSections = args.sections Extractor.keepLists = args.lists Extractor.toHTML = args.html Extractor.print_revision = args.revision Extractor.min_text_length = args.min_text_length if args.html: Extractor.keepLinks = True Extractor.expand_templates = args.no_templates filter_disambig_pages = args.filter_disambig_pages try: power = 'kmg'.find(args.bytes[-1].lower()) + 1 file_size = int(args.bytes[:-1]) * 1024 ** power if file_size < minFileSize: raise ValueError() except ValueError: logging.error('Insufficient or invalid size: %s', args.bytes) return if args.namespaces: acceptedNamespaces = set(args.namespaces.split(',')) FORMAT = '%(levelname)s: %(message)s' logging.basicConfig(format=FORMAT) logger = logging.getLogger() if not args.quiet: logger.setLevel(logging.INFO) if args.debug: logger.setLevel(logging.DEBUG) input_file = args.input if not Extractor.keepLinks: ignoreTag('a') # sharing cache of parser templates is too slow: # manager = Manager() # templateCache = manager.dict() if args.article: if args.templates: if os.path.exists(args.templates): with open(args.templates) as file: load_templates(file) file = fileinput.FileInput(input_file, openhook=fileinput.hook_compressed) # fp_i = open('output.npy', 'rb') # arr = np.load(fp_i); #sorted_arr=arr[:,0].sort() ignored_list=[] with open('ignored_docs.txt') as f: for line in f: ignored_list.append(int(line)); count=1; listcount=0; reduced_page_data=[] for page_data in pages_from(file): if count != ignored_list[listcount]: reduced_page_data.append(page_data) else: listcount=listcount+1 count=count+1 for page_data in reduced_page_data: id, revid, title, ns, page = page_data Extractor(id, revid, title, page).extract(sys.stdout) file.close() return output_path = args.output if output_path != '-' and not os.path.isdir(output_path): try: os.makedirs(output_path) except: logging.error('Could not create: %s', output_path) return process_dump(input_file, args.templates, output_path, file_size, args.compress, args.processes) if __name__ == '__main__': main()

[ "nilay.chakraborty@rutgers.edu" ]

nilay.chakraborty@rutgers.edu

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/versionmonitor/models.py

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[]

no_license

SammyVimes/versionmonitor

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refs/heads/master

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py

from versionmonitor.model.application import * from versionmonitor.model.project import * from versionmonitor.model.usr import *

[ "samvimes@yandex.ru" ]

samvimes@yandex.ru

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[]

no_license

fatmaoz7/GlobalAIHubPythonCourse

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2023-04-02T00:18:12.209147

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# -*- coding: utf-8 -*- """ Created on Fri Apr 9 21:41:36 2021 @author: fatma oz """ students={} students_with_passing_grade={} #creating empty dictionaries i=1; n=5 students_grade_list_sorted=[] while i<=n: name=input("Please enter student name") #getting inputs from user midterm=float(input("Please enter midterm grade of student")) project=float(input("Please enter project grade of student")) final=float(input("Please enter final grade of student")) students[name]=midterm,project,final #placing into dictionary passing_grade=students[name][0]*0.3+students[name][1]*0.3+students[name][2]*0.4; students_with_passing_grade[name]="Passing grade", passing_grade i+=1 # passingGrades=[passing_grade for passing_grade in students_with_passing_grade.items()] students_grade_list_sorted.append(students_with_passing_grade[name][1]) print(students_with_passing_grade) print(students) # print(passingGrades) print(sorted(students_grade_list_sorted,reverse=True))

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/feedforward/modelsv3/test_rejected.py

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[]

no_license

nigroup/binaural_audition

e157693f5a3b524d9c9f79d00663a785ab348f7c

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refs/heads/master

2021-06-06T04:05:00.243227

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import os from sys import exit from sys import path import pdb from tqdm import tqdm import tensorflow as tf import numpy as np import train_utils as tr_utils # import plotting as plot import hyperparams import cnn_model import dataloader import ldataloader import settings import test_compare def test_ldl(ldl_buffer_rows): h = hyperparams.Hyperparams() hyp = h.getworkingHyperparams() import time from tqdm import tqdm TRAIN_FOLDS = [1, 2, 3, 4, 5, 6] train_loader = ldataloader.LineDataLoader('train', h.LABEL_MODE, TRAIN_FOLDS, h.TRAIN_SCENES, ldl_timesteps=settings.ldl_timesteps, ldl_blocks_per_batch=hyp["ldl_blocks_per_batch"], ldl_overlap=settings.ldl_overlap, ldl_buffer_rows=ldl_buffer_rows, epochs=1, features=h.NFEATURES, classes=h.NCLASSES, seed_by_epoch=False, seed=time.time()) _, d_timesteps, __ = test_compare.measure_directly(train_loader) batches = 0 while (True): batches = batches + 1 _x, _y = train_loader.next_batch() if _x is None: break ''' print("batches: " + str(batches)) print("Memory:" + str(train_loader.buffer_add_memory)) print("Timestepsy:" + str(train_loader.buffer_add_timesteps)) print("Buffer Iterations:" + str(train_loader.buffer_add_iterations)) #print(train_loader.buffer_x.shape) print(train_loader.buffer_x.shape) print("Buffer Timesteps:" + str(train_loader.buffer_add_timesteps)) print("Real Timesteps:" + str(d_timesteps)) print("bad:") print(train_loader.bad_sliced_lines) print("good") print(train_loader.good_sliced_lines) print("lin self.count_line_positions_taken_total" + str(train_loader.count_line_positions_taken_total)) print("max position sum" + str(train_loader.add_max_positions)) print("slices y" + str(train_loader.check_slices_y)) print("slices x" + str(train_loader.check_slices_xy)) print("count batches: " + str(train_loader.count_batches)) ''' slices_with_all_data = (d_timesteps / train_loader.buffer_x.shape[0]) /25 slices_in_buffer = train_loader.add_max_positions / 25 loss_due_to_buffer = 1 - (slices_in_buffer/slices_with_all_data) loss_due_to_rejeceted_data = (train_loader.bad_sliced_lines / slices_in_buffer) #print(slices_with_all_data) #print(loss_due_to_buffer) #print(loss_due_to_rejeceted_data) return slices_with_all_data, loss_due_to_buffer, loss_due_to_rejeceted_data if __name__ == '__main__': iterations_per_block = 10 data = np.zeros((3,iterations_per_block,3)) for e, block_row in tqdm(enumerate(np.array([8, 16,32]))): for i in range(0,iterations_per_block): data[e,i,:] = test_ldl(block_row) np.save("rejected_data.npy", data) pdb.set_trace()

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sangampatel/pythonproject

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a = int(input()) b = int(input()) c = a/b print(int(c)) print(float(c))

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IgorEngControle/djnago

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""" Django settings for tutorial project. Generated by 'django-admin startproject' using Django 2.2.6. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '+zxj0xk)-8r@d1*(757&ef*ui_4h4xnwe)k5_)-_z)xv+7%1+9' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'accounts', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'tutorial.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'tutorial.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

[ "igor@tacoma.dcc.ufmg.br" ]

igor@tacoma.dcc.ufmg.br

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/lambda.py

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no_license

joyvai/Python-

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refs/heads/master

2021-01-16T23:37:55.719482

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# lambda is key word which is used for annonymous function. # Small anonymous functions can be created with the lambda keyword def make_incrementor(n): return lambda x:x+n f = make_incrementor(42) print f(0) print f(2) a.sort(key = lambda pair: pair[0]) print a

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joyvai.noreply@github.com

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/data acquisition/env_vars.py

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davidanagy/Spotify-Song-Suggester-DS

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refs/heads/master

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SPOTIFYUSERNAME = 'basslaughter' #your spotify username CLIENT_ID = '63594c9b2f99411a8cbd18df04851fc4' #set at your developer account CLIENT_SECRET = '096168b2bd1f4378ae410726955c9ed8' #set at your developer account REDIRECT_URI = 'http://google.com/' #set at your developer account, usually "http://localhost:8000" SCOPE = 'user-library-read' # or else GENIUS_CLIENT_ACCESS_TOKEN = 'p0TX__gy-p5bD4UG4D0ASZ3tiLvVNqlD0iStYgOPR3OPakUewNwHlMBkodfkCv1V'

[ "zwarshavsky@gmail.com" ]

zwarshavsky@gmail.com

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/ch06_iteration/iteration.py

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no_license

suptaphilip/how-to-think-like-a-computer-scientist

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refs/heads/master

2021-01-10T21:19:46.799334

2012-08-08T20:17:37

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def sqrt(n): approx = n / 2.0 better = (approx + n / approx) / 2.0 while better != approx: approx = better better = (approx + n / approx) / 2.0 print better return approx def print_triangular_numbers(n): result = 0 index = 1 while index <= n: result += index print '%-5d %-5d' % (index, result) index += 1

[ "helena.josol@gmail.com" ]

helena.josol@gmail.com

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/main.py

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no_license

Guybachar210/python-pi

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refs/heads/master

2020-03-13T06:15:45.300351

2018-04-25T13:11:32

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#!/usr/bin/python3 //This is a comment print("guy")

[ "guy.bachar@mail.huji.ac.il" ]

guy.bachar@mail.huji.ac.il

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/linker_tests/link_pre_489/PysamTestModule_link_pre_489/__init__.py

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pysam-developers/pysam

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from PysamTestModule_link_pre_489.BuildRead import build_read all = ["build_read"]

[ "jacobs@bioinoformed.com" ]

jacobs@bioinoformed.com

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/main.py

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matsui23/blogz

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refs/heads/master

2020-04-01T15:58:53.049625

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from flask import Flask, request, redirect, render_template, session, flash, url_for from flask_sqlalchemy import SQLAlchemy app = Flask(__name__) app.config['DEBUG'] = True app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://blogz:pass@localhost:8889/blogz' app.config['SQLALCHEMY_ECHO'] = True db = SQLAlchemy(app) app.secret_key = 'secretkey' class Blog(db.Model): id = db.Column(db.Integer, primary_key=True) title = db.Column(db.String(120)) body = db.Column(db.String(600)) owner_id = db.Column(db.Integer, db.ForeignKey('user.id')) def __init__(self, title, body, owner): self.title = title self.body = body self.owner = owner class User(db.Model): id = db.Column(db.Integer, primary_key=True) username = db.Column(db.String(120)) password = db.Column(db.String(120)) posts = db.relationship('Blog', backref = 'owner') def __init__(self, username, password): self.username = username self.password = password @app.before_request def require_login(): print('()()()()()()()()()()()()()()()') allowed_routes = ['login', 'register'] if request.endpoint not in allowed_routes and 'email' not in session: return redirect('/login') @app.route('/', methods=['POST', 'GET']) def index(): title = 'Build-a-blog' if request.method == 'POST': return render_template('blog.html') if request.method == 'GET': titles = Blog.query.all() return render_template('blog.html', titles = titles) @app.route('/blog', methods=['POST','GET']) def blog(): title = 'Build-a-blog' if request.method == 'POST': return render_template('blog.html') if request.method == 'GET': current_user = session['email'] print('================') print(session['email']) print('================') titles = Blog.query.all() #titles_id = Blog.query.get(id) print('++++++++++++++++++++++++++++') users = User.query.all() #print(titles_id) print(users) print('++++++++++++++++++++++++++++') return render_template('blog.html', titles = titles, current_user = current_user, users = users) @app.route('/newpost', methods=['POST', 'GET']) def newpost(): owner = User.query.filter_by(username = session['email']).first() if request.method == 'POST': title = request.form['title'] post = request.form['post'] error_title = False error_post = False if title == '': error_title = True print(error_title) return render_template('newpost.html', error_title = error_title, error_post = error_post, title = title, post = post) if post == '': print(error_post) error_post = True return render_template('newpost.html', error_title = error_title, error_post = error_post, title = title, post = post) new_post = Blog(title, post, owner) db.session.add(new_post) db.session.commit() current_id = new_post.id user_id = new_post.owner_id return redirect('/post?id={0}&user_id={1}'.format(current_id, user_id)) titles = Blog.query.all() return render_template('blog.html', titles = titles, user_id = user_id) if request.method == 'GET': return render_template('newpost.html') @app.route('/post', methods = ['POST', 'GET']) def see_post(): # if request.method == 'POST' # pass print('----------------------') if request.method == 'GET': current_id = request.args.get('id') user_id = request.args.get('user_id') post = Blog.query.get(current_id) user = User.query.get(user_id) # need to query for the id to pass to the html return render_template('post.html', post = post, user = user) @app.route('/register', methods = ['POST', 'GET']) def register(): print('-------------------------------') if request.method == 'GET': print('-------------------------------') return render_template('register.html') if request.method == 'POST': email = request.form['email'] password = request.form['password'] verify = request.form['verify'] duplicate_user = False error_email = False error_password = False error_pass_match = False existing_user = User.query.filter_by(username = email).first() if existing_user: duplicate_user = True return render_template('register.html', email = email, duplicate_user = duplicate_user, error_email = error_email, error_password = error_password, error_pass_match = error_pass_match) if email =='' and password == '' and verify == '': error_email = True error_password = True return render_template('register.html', email = email, duplicate_user = duplicate_user, error_email = error_email, error_password = error_password, error_pass_match = error_pass_match) if password == '' or verify =='': error_password = True return render_template('register.html', email = email, duplicate_user = duplicate_user, error_email = error_email, error_password = error_password, error_pass_match = error_pass_match) if password != verify: error_pass_match = True return render_template('register.html', email = email, duplicate_user = duplicate_user, error_email = error_email, error_password = error_password, error_pass_match = error_pass_match) if not existing_user and password == verify and password: new_user = User(email, password) db.session.add(new_user) db.session.commit() session['email'] = email print('-----------------------') return redirect('/blog') else: print('***********************') return redirect('/register') @app.route('/login', methods = ['POST', 'GET']) def login(): print('-------------------------------') if request.method == 'GET': print('-------------------------------') return render_template('login.html') if request.method == 'POST': email = request.form['email'] password = request.form['password'] error_email = False error_password = False error_email_empty = False error_password_empty = False user = User.query.filter_by(username = email).first() if user and user.password == password: session['email'] = email return redirect('/newpost') if email == '' and password == '': error_email_empty = True error_password_empty = True return render_template('login.html', email = email, error_email = error_email, error_password = error_password, error_email_empty = error_email_empty, error_password_empty = error_password_empty) if not user: error_email = True return render_template('login.html', email = email, error_email = error_email, error_password = error_password, error_email_empty = error_email_empty, error_password_empty = error_password_empty) if user.password != password: error_password = True return render_template('login.html', email = email, error_email = error_email, error_password = error_password, error_email_empty = error_email_empty, error_password_empty = error_password_empty) return render_template('login.html') @app.route('/logout') def logout(): print('----------------------------') print(session['email']) print('----------------------------') del session['email'] return redirect('/') @app.route('/userlist', methods = ['POST', 'GET']) def display_users(): if request.method == 'GET': users = User.query.all() return render_template('user_list.html', users = users) @app.route('/userposts', methods = ['POST', 'GET']) def display_users_posts(): if request.method == 'GET': owner_id = request.args.get('id') print('**********************') print(owner_id) print('**********************') posts = Blog.query.filter_by(owner_id = owner_id) print('[][][][[][][][][]') print(posts) print('[][][][[][][][][]') return render_template('users_posts.html', posts = posts) if __name__ == '__main__': app.run()

[ "mattgamedev2313@gmail.com" ]

mattgamedev2313@gmail.com

cdec30010c2923fa9b8c544e8bdf200916434ee6

5cc4f84574e98c3182baa8049e307a54f0ce0eba

/Image.py

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[ "MIT" ]

permissive

leonardodalcin/cvbootstrap

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refs/heads/master

2020-03-28T03:59:13.325705

2018-09-06T14:46:10

147,687,057

0

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UTF-8

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py

from datetime import datetime from matplotlib import pyplot as plt import cv2 import os class Image: image = None def show(self): plt.imshow(self.image, cmap="gray", interpolation='bicubic') plt.show() def save(self): print("Saving photo") now = datetime.now() dirName = now.strftime("%d-%m-%Y") fileName = now.strftime("%X") if not os.path.exists(dirName): os.makedirs(dirName) cv2.imwrite(dirName + "/" + fileName + ".png", self.image) def rotate(self, degrees): (height, width) = self.image.shape[:2] center = (height / 2, width / 2) rotationMatrix = cv2.getRotationMatrix2D(center, degrees, scale=1) self.image = cv2.warpAffine(self.image, rotationMatrix, (width, height)) def __init__(self, image = None, path = None): if (path): self.image = cv2.imread(path, 0) else: self.image = image

[ "noreply@github.com" ]

leonardodalcin.noreply@github.com

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1b3454e03d5f07bd9739c8fa51aef53576234970

/publish_preflight/forms.py

812367022ef16f4da49c7b8f7faa99228bbe4966

[ "BSD-3-Clause" ]

permissive

cityofaustin/wagtail-publish-preflight

6b4f171713b31b39fbda3cecf128b38c8278790b

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refs/heads/master

2022-12-15T13:31:29.861322

2019-12-23T19:13:24

221,550,274

1

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BSD-3-Clause

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UTF-8

Python

false

2,727

py

from wagtail.admin.forms import WagtailAdminPageForm from wagtail.core.models import Page, PageRevision from django.core.exceptions import ValidationError from wagtail.admin import messages import logging class PublishPreflightForm(WagtailAdminPageForm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def clean(self): """ ways to limit scope: changed fields, then exclude fields that are required self.changed_data = list of fields changed self[field_name].data or as_text (might be useful for streamfields) looks like this is working, atm tho it just wont let you publish any empty fields :-D """ def check_for_empties(): """ adds an error to each field if it is empty """ if hasattr(self.instance, 'fields_required_for_publish'): errors_for_empties = { field_name: try_adding_error_to_field( field_name, field_value) for (field_name, field_value) in self.data.items() if (len(field_value) == 0 or field_value == 'null') and field_name in self.instance.fields_required_for_publish } def try_adding_error_to_field(field_name, field_value): try: self.add_error(field_name, f'{field_name} is empty!') except ValueError as e: logging.error(e) try: field_value.non_form_errors().append( f'{field_name} not selected!') self.add_error(None, f'{field_name} is missing!') except AttributeError as e: logging.error(e) pass pass def check_for_missing_relations(): relations = self.formsets # relation_value.cleaned_data if hasattr(self.instance, 'fields_required_for_publish'): errors_for_missing_relations = { relation_name: try_adding_error_to_field( relation_name, relation_value) for (relation_name, relation_value) in relations.items() if not relation_value.forms and relation_name in self.instance.fields_required_for_publish } cleaned_data = super().clean() if 'action-publish' in self.data: # TODO: we'll probably want a good way to check a managed subset all_keys = list(self.fields.keys()) check_all = check_for_empties() missing_relations = check_for_missing_relations() return cleaned_data

[ "ericandrewsherman@gmail.com" ]

ericandrewsherman@gmail.com

f188de2692770cbb987a93c8b00f449e44b9a4d7

df9f069a0186cf38df8a592ca561c5cb4eb02aef

/src/Indeed.py

f80663d7f132d040ffdbefb9eaba85b3cee71925

[]

no_license

SeekrLabs/JobScraper

6f018d6915f9cb30454e5c905117647f7df07426

3e490b5e22d3593f3951dbd62aabd57c1dde107c

refs/heads/master

2020-07-30T08:59:42.171708

2019-08-29T19:18:08

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UTF-8

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import json import botocore.vendored.requests as requests import time from bs4 import BeautifulSoup import boto3 import datetime # Run this once a day, it gets completely refreshed once per day BASE_LINK = 'https://www.indeed.ca/jobs?l=Toronto,+ON&sort=date&fromage=1&limit=50' sqs = boto3.resource('sqs') QUEUE_MESSAGE_SIZE = 50 queue = sqs.get_queue_by_name( QueueName='JobsIngestionQueue' ) def scrape(event, context): scrape_start_time = int(time.time()) search = IndeedSearch(BASE_LINK, scrape_start_time) num_pages = event['pages'] early_stop = event['ads_per_page'] job_ads = [] for _ in range(num_pages): results = search.process_visit_link(early_stop) job_ads += [vars(res) for res in results] search.update_visit_link() print("Sending message to SQS queue.") sqs_batch_send_message(job_ads) def sqs_batch_send_message(content_list): if content_list == []: return num_batches = len(content_list) // 50 + 1 for i in range(num_batches): batch_send = content_list[i * num_batches: i * num_batches + 50] queue.send_message( MessageBody=json.dumps(batch_send) ) class IndeedSearch: # base_link is the base query without indexing the pages of the search # visit_link is indexed pages, it's updated def __init__(self, base_link, scrape_start_time): self.base_link = base_link self.num_ads = 0 self.visit_link = base_link + '&start=' + str(self.num_ads) self.scrape_start_time = scrape_start_time def update_visit_link(self): self.num_ads += 20 self.visit_link = self.base_link + '&start=' + str(self.num_ads) def process_visit_link(self, ads_to_visit=999): jobs_data = [] print('Issuing GET: ' + self.visit_link) search_query = requests.get(self.visit_link) print('GET Success, Parsing...') search_soup = BeautifulSoup(search_query.text, 'html.parser') print('Finding advertisement cards...') ad_card_soups = search_soup.find_all('div', {'class': 'jobsearch-SerpJobCard'}) print('Found ' + str(len(ad_card_soups)) + ' ad cards.') for ad_card_soup in ad_card_soups: job_ad = IndeedJobAd(ad_card_soup, self.scrape_start_time) valid_card = job_ad.extract_card() if valid_card: jobs_data.append(job_ad) if len(jobs_data) > ads_to_visit: break # Visiting each link in ad card for ad in jobs_data: ad.visit_link_to_extract_description() return jobs_data class IndeedJobAd: # Constants BASE_INDEED = 'https://www.indeed.com' # Initalize with a BeautifulSoup Card element def __init__(self, ad_soup, scrape_start_time): self.ad_soup = ad_soup self.scrape_start_time = scrape_start_time # Returns false if Job Posting is sponsored def extract_card(self): title_soup = find_element_from_soup(self.ad_soup, [{'el': 'a', 'tag': 'class', 'attr': 'jobtitle'}]) metadata_soup = find_element_from_soup(self.ad_soup, [{'el': 'div', 'tag': 'class', 'attr': 'sjcl'}]) post_date_soup = find_element_from_soup(self.ad_soup, [{'el': 'span', 'tag': 'class', 'attr': 'date'}]) del self.ad_soup if title_soup: self.title = title_soup.text.strip() self.url = self.BASE_INDEED + title_soup['href'] self.source = 'INDEED' if self.url.startswith('/pagead'): return False if metadata_soup: company_soup = find_element_from_soup(metadata_soup, [{'el': 'span', 'tag': 'class', 'attr': 'company'}]) location_soup = find_element_from_soup(metadata_soup, [{'el': 'span', 'tag': 'class', 'attr': 'location'}, {'el': 'div', 'tag': 'class', 'attr': 'location'}]) if company_soup: self.company = company_soup.text.strip() if location_soup: self.location = location_soup.text.strip() if post_date_soup: self.get_post_date_and_time(post_date_soup.text.strip()) return True def visit_link_to_extract_description(self): if self.url: job_url = self.BASE_INDEED + self.url print('Issuing GET: ' + job_url) job_response = requests.get(job_url) print('GET Success, Parsing...') specific_job_soup = BeautifulSoup(job_response.text, 'html.parser') description = find_element_from_soup(specific_job_soup, [{'el': 'div', 'tag': 'class', 'attr': 'jobsearch-JobComponent-description'}]) if description: self.description = str(description) def get_post_date_and_time(self, post_time): post_time_epoch = self.scrape_start_time if 'hour' in post_time: num_hours = int(post_time[0:2].strip()) post_time_epoch -= num_hours * 60 * 60 elif 'minute' in post_time: num_hours = int(post_time[0:2].strip()) post_time_epoch -= num_hours * 60 self.post_date = datetime.datetime.utcfromtimestamp(post_time_epoch).strftime('%Y-%m-%d %H:%M:%S') def find_element_from_soup(soup, specs): for spec in specs: print('Looking for ' + spec['el'] + ' ' + spec['tag'] + ' ' + spec['attr'] + '... Found if not otherwise stated.') result = soup.find(spec['el'], {spec['tag'], spec['attr']}) if result: return result print('NOT FOUND ' + specs[0]['attr'] + '... ' + str(soup.attrs)) return None

[ "waltonwang1922@gmail.com" ]

waltonwang1922@gmail.com

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/ActSent/src/pythoncode/stat/filterRelation.py

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[]

no_license

yulongp/ActSent

5525f81dc7ba92d24826cd6fc09e33a41c383bbe

d2e521ee85093712ab2dbddf3edb90da80ebc1b0

refs/heads/master

2016-09-02T00:13:46.036602

2014-12-10T15:51:57

null

0

null

UTF-8

Python

false

596

py

#!/usr/bin/python # -*- coding: utf-8 -*- ''' Created on 2014年11月25日 @author: Yulong Pei ''' fin = open('E:/Courses/11742/data/Sentiment/tweet/user_filter.txt', 'r') users = set() for line in fin.readlines(): users.add(line.strip()) fin.close() print len(users) fin = open('E:/Courses/11742/data/Sentiment/relation.txt', 'r') fout = open('E:/Courses/11742/data/Sentiment/tweet/relation_filter.txt', 'w') for line in fin.readlines(): tmp = line.strip().split() if tmp[0] in users and tmp[1] in users: fout.write(line) fin.close() fout.close()

[ "Think@Think-PC" ]

Think@Think-PC

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7c731762df05f017e464801c1aa0ad34c5cde722

/2ndLeariningPhase/prepareTrainingData.py

5d3246e30f3eb05b19131d01c7108a441e7ce8e5

[]

no_license

WaleedRagheb/tmv-dmem

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0a63f1397dae9e1c26272b512bb72032598dd8c1

refs/heads/master

2023-01-02T04:39:06.372426

2020-10-30T09:10:50

308,577,400

0

null

UTF-8

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false

7,048

py

# -*- coding: utf-8 -*- """ Created on Mon Jan 22 12:50:10 2018 @author: HP """ import sys sys.path.insert(0, r'..\ChunkDealer') import chunksProcessor import sys sys.path.insert(0, r'..\DeepModeling') import testScoring import gensim import numpy as np import os ############################################################## def pos_neg_count(prob,prob_Thr_Diff): pos_cntr = 0 neg_cntr = 0 for index, row in prob.iterrows(): pos_Prob = row[0] neg_Prob = row[1] diff = np.abs((pos_Prob-neg_Prob)) if diff >= prob_Thr_Diff: if pos_Prob > neg_Prob: pos_cntr = pos_cntr + 1 else: neg_cntr = neg_cntr + 1 return pos_cntr, neg_cntr # print(row['c1'], row['c2']) ############################################################## def giveDecision(prob, prob_Thr_Diff): cnt_p,cnt_n = pos_neg_count(prob, prob_Thr_Diff) sum_P_N = cnt_p + cnt_n if sum_P_N == 0: return 0, 0 return cnt_p/sum_P_N, cnt_n/sum_P_N ############################################################## prob_Thr_Diff = 0.0 for numberOfChunks in range(1,11): if numberOfChunks == 0: continue for model_ChunkN in range(10,11): if model_ChunkN == 0: continue Positive_chunkDir = r'C:\Users\HP\Downloads\clpsych16-data\2018\training\task1\eRisk@CLEF2018-task1-releasedtrainingdata\eRisk 2018 - training\2017 train\positive_examples_anonymous_chunks' Negative_chunkDir = r'C:\Users\HP\Downloads\clpsych16-data\2018\training\task1\eRisk@CLEF2018-task1-releasedtrainingdata\eRisk 2018 - training\2017 train\negative_examples_anonymous_chunks' model_Positive_path = r'../DeepModeling/Models_arch_6-plus/Positive/Chunk_' + str(model_ChunkN) + r'_300_c_40.word2vec' model_Negative_path = r'../DeepModeling/Models_arch_6-plus/Negative/Chunk_' + str(model_ChunkN) + r'_300_c_40.word2vec' sDic_pos = chunksProcessor.process_chunk_N_NotAcc(Positive_chunkDir,numberOfChunks) sDic_neg = chunksProcessor.process_chunk_N_NotAcc(Negative_chunkDir,numberOfChunks) modelPositive = gensim.models.Word2Vec.load(model_Positive_path) modelNegative = gensim.models.Word2Vec.load(model_Negative_path) if not os.path.exists(r'.\TempRes\Positive'): os.makedirs(r'.\TempRes\Positive') if not os.path.exists(r'.\TempRes\Negative'): os.makedirs(r'.\TempRes\Negative') fileName_pos = r'.\TempRes\Positive\Initial Results_' + str(numberOfChunks) + '_' + str(model_ChunkN) + '.txt' fileName_neg = r'.\TempRes\Negative\Initial Results_' + str(numberOfChunks) + '_' + str(model_ChunkN) + '.txt' for k, v_txt in sDic_pos.items(): sents = testScoring.txtToSen(v_txt) sentsList = list(sents) if len(sentsList) < 1: with open(fileName_pos, "a") as f: f.write(k + "\t None\n") continue prob = testScoring.docprob(sentsList,[modelPositive,modelNegative]) # d_subj should be 0,1,2 cnt_p, cnt_n = giveDecision(prob, prob_Thr_Diff) with open(fileName_pos, "a") as f: f.write(k + "\t " + str(cnt_p) + "\t " + str(cnt_n) + "\n") ################################################################### for k, v_txt in sDic_neg.items(): sents = testScoring.txtToSen(v_txt) sentsList = list(sents) if len(sentsList) < 1: with open(fileName_neg, "a") as f: f.write(k + "\t None\n") continue prob = testScoring.docprob(sentsList,[modelPositive,modelNegative]) # d_subj should be 0,1,2 cnt_p, cnt_n = giveDecision(prob, prob_Thr_Diff) with open(fileName_neg, "a") as f: f.write(k + "\t " + str(cnt_p) + "\t " + str(cnt_n) + "\n") ####################################################################### from os import listdir from os.path import isfile, join import numpy as np import re import matplotlib.pyplot as plt res_matrix_pos = np.zeros((10,10,83,2),float) subj_ID_array = [] resultsPath =r'.\TempRes\Positive' onlyfiles = [f for f in listdir(resultsPath) if isfile(join(resultsPath, f))] for res_f_name in onlyfiles: print(res_f_name) m = re.search('_(.+?)\.',res_f_name) chunk_idx, model_idx = m.group(1).split('_') subj_ID_array = np.loadtxt(resultsPath + '\\' + res_f_name, delimiter='\t', usecols=(0), unpack=True,dtype=np.str) try: Pos_Neg_mtrx = np.loadtxt(resultsPath + '\\' + res_f_name, delimiter='\t', usecols=(1, 2), unpack=True).transpose() except IndexError: Pos_Neg_mtrx = np.zeros((83,2),float) with open(resultsPath + '\\' + res_f_name) as fi: l_ctr = 0 for line in fi: fields = line.split('\t') if len(fields) > 2: Pos_Neg_mtrx[l_ctr][0] = fields[1] Pos_Neg_mtrx[l_ctr][1] = fields[2] l_ctr += 1 res_matrix_pos[int(chunk_idx)-1,int(model_idx)-1,:,:] = Pos_Neg_mtrx ############################################################################ res_matrix_neg = np.zeros((10,10,403,2),float) subj_ID_array = [] resultsPath =r'.\TempRes\Negative' onlyfiles = [f for f in listdir(resultsPath) if isfile(join(resultsPath, f))] for res_f_name in onlyfiles: print(res_f_name) m = re.search('_(.+?)\.',res_f_name) chunk_idx, model_idx = m.group(1).split('_') subj_ID_array = np.loadtxt(resultsPath + '\\' + res_f_name, delimiter='\t', usecols=(0), unpack=True,dtype=np.str) try: Pos_Neg_mtrx = np.loadtxt(resultsPath + '\\' + res_f_name, delimiter='\t', usecols=(1, 2), unpack=True).transpose() except IndexError: Pos_Neg_mtrx = np.zeros((403,2),float) with open(resultsPath + '\\' + res_f_name) as fi: l_ctr = 0 for line in fi: fields = line.split('\t') if len(fields) > 2: Pos_Neg_mtrx[l_ctr][0] = fields[1] Pos_Neg_mtrx[l_ctr][1] = fields[2] l_ctr += 1 res_matrix_neg[int(chunk_idx)-1,int(model_idx)-1,:,:] = Pos_Neg_mtrx with open(r'.\TempRes\TrainingData.csv', 'a') as trainFile: for itr in range(402): lstToWrite = res_matrix_neg[:,9,itr,0] for ii in range(len(lstToWrite)): trainFile.write(str(lstToWrite[ii]) + ",") trainFile.write("NEG\n") for itr in range(82): lstToWrite = res_matrix_pos[:,9,itr,0] for ii in range(len(lstToWrite)): trainFile.write(str(lstToWrite[ii]) + ",") trainFile.write("POS\n") trainFile.close()

[ "azmy1@hotmail.com" ]

azmy1@hotmail.com

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/04/skuska.py

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while True: rodne_cislo = input('Zadaj rodne cislo: ') datum = rodne_cislo[0:6] poradie = rodne_cislo[7:11] if rodne_cislo == datum + "/" + poradie: print('Spravne zadane rodne cislo.') break print("Zle zadane rodne cislo. Zadaj rodne cislo: ")

[ "katrinkarusnak@gmail.com" ]

katrinkarusnak@gmail.com

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/venv/lib/python3.9/site-packages/keyring/errors.py

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permissive

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import sys __metaclass__ = type class KeyringError(Exception): """Base class for exceptions in keyring""" class PasswordSetError(KeyringError): """Raised when the password can't be set.""" class PasswordDeleteError(KeyringError): """Raised when the password can't be deleted.""" class InitError(KeyringError): """Raised when the keyring could not be initialised""" class KeyringLocked(KeyringError): """Raised when the keyring failed unlocking""" class NoKeyringError(KeyringError, RuntimeError): """Raised when there is no keyring backend""" class ExceptionRaisedContext: """ An exception-trapping context that indicates whether an exception was raised. """ def __init__(self, ExpectedException=Exception): self.ExpectedException = ExpectedException self.exc_info = None def __enter__(self): self.exc_info = object.__new__(ExceptionInfo) return self.exc_info def __exit__(self, *exc_info): self.exc_info.__init__(*exc_info) return self.exc_info.type and issubclass( self.exc_info.type, self.ExpectedException ) class ExceptionInfo: def __init__(self, *info): if not info: info = sys.exc_info() self.type, self.value, _ = info def __bool__(self): """ Return True if an exception occurred """ return bool(self.type) __nonzero__ = __bool__

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liam@storskegg.org

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c = (200, 100, 50, 20, 10, 5, 2, 1) def updated_path(path, i): if path is None: path = (0,) * len(c) return tuple(x + 1 if j == i else x for j, x in enumerate(path)) solution_set = set() def ways_to_make(y, tab=0, previous_coin=None, path=None): if y <= 0: return for i, coin in enumerate(c): if previous_coin is not None: if coin > previous_coin: continue if coin == y: # print '%s%s' % ('\t'*tab, coin) solution_set.add(updated_path(path, i)) elif coin < y: # print '%s%s' % ('\t'*tab, coin) ways_to_make(y - coin, tab + 1, coin, updated_path(path, i)) ways_to_make(200) print len(solution_set)

[ "amir.sadoughi@gmail.com" ]

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#!/usr/bin/env python from __future__ import print_function import argparse import os import cv2 from vizdoom import * parser = argparse.ArgumentParser( description='record human training data in CIG') parser.add_argument('--config', type=str, default='/home/sliay/Documents/ViZDoom/scenarios/cig.cfg') parser.add_argument('--map', type=str, default='map01') parser.add_argument('--bots', type=int, default=7) parser.add_argument('--frame-skip', type=int, default=4) parser.add_argument('--ip', type=str, default=None) parser.add_argument('--dir', type=str, required=True) args = parser.parse_args() save_dir = os.path.join(args.dir, 'screens') os.makedirs(save_dir) f_frag = open(os.path.join(save_dir, 'frags.txt'), 'w') f_health = open(os.path.join(save_dir, 'health.txt'), 'w') f_ammo = open(os.path.join(save_dir, 'ammo.txt'), 'w') f_action = open(os.path.join(save_dir, 'action.txt'), 'w') frame_skip = args.frame_skip game = DoomGame() game.load_config(args.config) game.set_doom_map(args.map) # Limited deathmatch. # game.set_doom_map("map02") # Full deathmatch. game.add_game_args("-host 1 -deathmatch +sv_spawnfarthest 1 " "+timelimit 10.0 +sv_forcerespawn 1 +sv_noautoaim 1 +sv_respawnprotect 1 +sv_nocrouch 1 " "+viz_respawn_delay 2 +viz_nocheat 1 ") if args.ip is not None: game.add_game_args("-join " + args.ip + " ") game.add_game_args("+name AI +colorset 0") game.set_screen_format(ScreenFormat.RGB24) # game.set_screen_format(ScreenFormat.ARGB32) # game.set_screen_format(ScreenFormat.GRAY8) # game.set_screen_format(ScreenFormat.BGR24) # game.set_screen_format(ScreenFormat.RGBA32) # game.set_screen_format(ScreenFormat.BGRA32) # game.set_screen_format(ScreenFormat.ABGR32) # Raw Doom buffer with palette's values. This one makes no sense in particular # game.set_screen_format(ScreenFormat.DOOM_256_COLORS) # Sets resolution for all buffers. game.set_screen_resolution(ScreenResolution.RES_640X480) # Enables depth buffer. game.set_depth_buffer_enabled(True) # Enables labeling of in game objects labeling. game.set_labels_buffer_enabled(True) # Enables buffer with top down map of he current episode/level . game.set_automap_buffer_enabled(True) game.set_automap_mode(AutomapMode.OBJECTS) game.set_automap_rotate(False) game.set_automap_render_textures(False) game.set_render_hud(True) game.set_render_minimal_hud(False) game.set_mode(Mode.ASYNC_SPECTATOR) game.init() print("Episode Started") print('Available Buttons:', game.get_available_buttons()) game.send_game_command("removebots") for i in range(args.bots): game.send_game_command("addbot") # Play until the game (episode) is over. cnt = 0 while not game.is_episode_finished(): s = game.get_state() if cnt % frame_skip == 0: cv2.imwrite(os.path.join(save_dir, 'screen_%05d.png' % cnt), s.screen_buffer) cnt += 1 game.advance_action() if game.is_player_dead(): game.respawn_player() print("Frags:", game.get_game_variable(GameVariable.FRAGCOUNT)) print("Health:", game.get_game_variable(GameVariable.HEALTH)) print("Ammo:", game.get_game_variable(GameVariable.AMMO5)) print("Performed Action:", game.get_last_action()) f_frag.write('%d\n' % game.get_game_variable(GameVariable.FRAGCOUNT)) f_health.write('%d\n' % game.get_game_variable(GameVariable.HEALTH)) f_ammo.write('%d\n' % game.get_game_variable(GameVariable.AMMO5)) action = game.get_last_action() for i in range(len(action)): if i != len(action)-1: f_action.write('%f ' % action[i]) else: f_action.write('%f\n' % action[i]) print("Episode finished.") print("************************") f_frag.close() f_health.close() f_ammo.close() f_action.close() game.close()

[ "lisiyi.zju@gmail.com" ]

lisiyi.zju@gmail.com

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/src/com/zobar/rosalind/GC.py

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zo-bar/rosalind

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2021-04-26T03:47:02.052410

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''' Created on Feb 28, 2013 @author: Zoya ''' from __future__ import division import io def gcCount(dna): stream = io.StringIO(unicode(dna)); nchar = stream.read(1) gc = 0 total = 0 while nchar: if nchar.upper() == 'C' or nchar.upper() == 'G': gc += 1 total += 1 elif nchar.upper() == 'A' or nchar.upper() == 'T': total += 1 nchar = stream.read(1) result = 100 * gc / total return result def gcCountLines(fileName): bestGCcontent = 0 bestID = 0 with open(fileName) as inputFile: # skip first > inputFile.read(1) dnaID = inputFile.readline() while dnaID: char = 'strat' dna = '' while char: char = inputFile.read(1) if char == '>': break dna = dna + char gcContent = gcCount(dna) if bestGCcontent < gcContent: bestGCcontent = gcContent bestID = dnaID dnaID = inputFile.readline() print "%s%f" % (bestID, bestGCcontent) gcCountLines("data/rosalind_gc.txt")

[ "zoya.barabanova@gmail.com" ]

zoya.barabanova@gmail.com

741c7ed84e776d3b296496e8a03cb9505ed6c707

ae8680fdf9c0ccef372a28c6e21c5bc88b759e28

/user/views.py

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[]

no_license

LevchenCoM/django-hotels

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refs/heads/master

2020-04-02T15:41:25.522697

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from django.shortcuts import render, redirect from django.contrib import admin from django.contrib.auth.decorators import login_required from django.contrib.auth.models import User from django.contrib.auth import login, authenticate from django.contrib.auth.forms import UserCreationForm from django import forms from django.http import HttpResponse def sign_up(request): user_creation_form = UserCreationForm() if request.method=='POST': user_creation_form = UserCreationForm(request.POST) # user_form = UserSignUpForm(request.POST) if user_creation_form.is_valid(): username=user_creation_form.cleaned_data['username'] password=user_creation_form.cleaned_data['password1'] new_user=User.objects.create_user(username=username,password=password) login(request, authenticate(username=username,password=password)) return redirect('/home') return render(request, "user_auth/sign-up.html", {'user_form':user_creation_form}) # def sign_in(request): # # user_form=UserSignUpForm() # # # user_form = UserCreationForm() # # dir(user_form) # # if request.method=='POST': # # user_form = UserSignUpForm(request.POST) # # # user_form = UserCreationForm(request.POST) # # if user_form.is_valid(): # # username=user_form.cleaned_data['username'] # # password=user_form.cleaned_data['password1'] # # new_user=User.objects.create_user(username=username,password=password) # # login(request, authenticate(username=username,password=password)) # # return redirect('/home') # return render(request, "user_auth/sign-up.html", {'user_form':user_form})

[ "qupadlive@gmail.com" ]

qupadlive@gmail.com

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/blog/migrations/0003_posts_status.py

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p3dr0migue1/back_and_beyond

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refs/heads/main

2022-12-10T09:22:54.475648

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# -*- coding: utf-8 -*- # Generated by Django 1.9 on 2015-12-22 08:52 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0002_auto_20151220_2103'), ] operations = [ migrations.AddField( model_name='posts', name='status', field=models.IntegerField(choices=[(1, b'Draft'), (2, b'Published'), (3, b'Private'), (4, b'Archived')], default=1), ), ]

[ "pedro.miguel@live.co.uk" ]

pedro.miguel@live.co.uk

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LEVON-332125/python-homework

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# Write following program: calculate how much money you # should pay for 1500$ bank loan if annual percentage is 16% value = 1500 percent = 16 years_total = (value * percent)/100 print(years_total)

[ "levon.yeghshatyan1994@gmail.com" ]

levon.yeghshatyan1994@gmail.com

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# coding: utf-8 """ Yapily API To access endpoints that require authentication, use your application key and secret created in the Dashboard (https://dashboard.yapily.com) # noqa: E501 The version of the OpenAPI document: 0.0.359 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from yapily.configuration import Configuration class Site(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_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. """ openapi_types = { 'identification': 'str', 'name': 'str' } attribute_map = { 'identification': 'Identification', 'name': 'Name' } def __init__(self, identification=None, name=None, local_vars_configuration=None): # noqa: E501 """Site - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._identification = None self._name = None self.discriminator = None if identification is not None: self.identification = identification if name is not None: self.name = name @property def identification(self): """Gets the identification of this Site. # noqa: E501 :return: The identification of this Site. # noqa: E501 :rtype: str """ return self._identification @identification.setter def identification(self, identification): """Sets the identification of this Site. :param identification: The identification of this Site. # noqa: E501 :type: str """ self._identification = identification @property def name(self): """Gets the name of this Site. # noqa: E501 :return: The name of this Site. # noqa: E501 :rtype: str """ return self._name @name.setter def name(self, name): """Sets the name of this Site. :param name: The name of this Site. # noqa: E501 :type: str """ self._name = name def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_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 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, Site): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, Site): return True return self.to_dict() != other.to_dict()

[ "simonbarnett@emerge-it.co.uk" ]

simonbarnett@emerge-it.co.uk

985a0181777a667c351bdcecfb2f163f8ca20c93

21b719411b6a6a18e169eab506c95b8e4c2ca0de

/mysite/main/admin.py

ab004f6a11edf10b6fbf3fe07ededabb02b087c6

[]

no_license

Techdavee/Myfiles

5525d1f93dbcc7961b12725ddb91b54223853cf7

1bfc755e0c1332f69006679393b76e5b08258bb1

refs/heads/main

2023-04-29T20:40:48.225158

2021-02-04T15:04:41

null

0

null

UTF-8

Python

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py

from django.contrib import admin from .models import Tutorial from tinymce.widgets import TinyMCE from django.db import models # Register your models here. class TutorialAdmin(admin.ModelAdmin): fieldsets = [ ("Title/date", {"fields": ["tutorial_title", "tutorial_published"]}), ("Content", {"fields":["tutorial_content"]}) ] formfields_overrides = { models.TextField: {'widget': TinyMCE()}, } admin.site.register(Tutorial, TutorialAdmin)

[ "noreply@github.com" ]

Techdavee.noreply@github.com

fbe74fd7cfe43e90db48ece7aa26a52868248dc4

9d6706d64836ca032101fb4a01a5a5b3762e9778

/school_summary.py

35123ead34007c2030fdd47d979a628c58b8b556

[]

no_license

johnwchoo/school_district_analysis

a34b2bb325d5d0b441916fc0d1eed1cb904158c2

691a14550d299350fc519a933f7c7e64508e34dd

refs/heads/master

2021-02-14T02:58:04.087393

2020-03-03T23:07:13

244,464,691

0

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Python

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10,283

py

#School Summary #Create an overview table that summarizes key metrics about each school, including: #School Name + #School Type + #Total Students + #Total School Budget + #Per Student Budget + #Average Math Score + #Average Reading Score + #% Passing Math + #% Passing Reading + #Overall Passing Rate (Average of the above two) # Set Up ------------------------------------------------------------------------------------ import pandas as pd import numpy as np school_data_to_load = "Resources/schools_complete.csv" student_data_to_load = "Resources/students_complete.csv" school_data = pd.read_csv(school_data_to_load) student_data = pd.read_csv(student_data_to_load) school_data_complete = pd.merge(student_data, school_data, how="left", on=["school_name", "school_name"]) # School Name ------------------------------------------------------------------------------------ #skip # School Type ------------------------------------------------------------------------------------ school_type = school_data.set_index(['school_name'])['type'] #print(school_type) # Total Students per school ------------------------------------------------------------------------------------ total_students_per_school = school_data_complete["school_name"].value_counts() #print(students_per_school) # Total School & Student Budget ------------------------------------------------------------------------------------ per_school_budget = school_data_complete.groupby(['school_name']).mean()['budget'] per_student_budget = per_school_budget/ total_students_per_school #print(per_student_budget) # Average Math Score per School ------------------------------------------------------------------------------------ avg_math_score_per_school = school_data_complete.groupby(['school_name']).mean()['math_score'] #print(avg_math_score_per_school) # Average Reading Score per School ------------------------------------------------------------------------------------ avg_reading_score_per_school = school_data_complete.groupby(['school_name']).mean()['reading_score'] #print(avg_reading_score_per_school) #% Passing Math ------------------------------------------------------------------------------------ school_passing_math = school_data_complete[(school_data_complete["math_score"] > 70)] per_school_passing_math = school_passing_math.groupby(["school_name"]).count()["student_name"] / total_students_per_school * 100 # 1 - total students passing math # 2 - total students passing math grouped by school name and turned into percentage #% Passing Reading ------------------------------------------------------------------------------------ school_passing_reading = school_data_complete[(school_data_complete["reading_score"] > 70)] per_school_passing_reading = school_passing_reading.groupby(["school_name"]).count()["student_name"] / total_students_per_school * 100 #% Overall Passing Rate ------------------------------------------------------------------------------------ overall_passing_rate = (per_school_passing_reading + per_school_passing_math)/ 2 #print(overall_passing_rate) # School Summary ------------------------------------------------------------------------------------ per_school_summary = pd.DataFrame({"School Type": school_type, "Total Students": total_students_per_school, "Total School Budget": per_school_budget, "Per Student Budget": per_student_budget, "Average Math Score": avg_math_score_per_school, "Average Reading Score": avg_reading_score_per_school, "% Passing Math": per_school_passing_math, "% Passing Reading": per_school_passing_reading, "% Overall Passing Rate": overall_passing_rate}) #print(per_school_summary) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #Top Performing Schools (By Passing Rate) ------------------------------------------------------------------------------------ top_schools = per_school_summary.sort_values(["% Overall Passing Rate"], ascending=False) #Bottom Performing Schools (By Passing Rate) ------------------------------------------------------------------------------------ bottom_schools = per_school_summary.sort_values(["% Overall Passing Rate"], ascending=True) #Math Scores by Grade ------------------------------------------------------------------------------------ ninth_graders = school_data_complete[(school_data_complete["grade"] == "9th")] tenth_graders = school_data_complete[(school_data_complete["grade"] == "10th")] eleventh_graders = school_data_complete[(school_data_complete["grade"] == "11th")] twelfth_graders = school_data_complete[(school_data_complete["grade"] == "12th")] ninth_graders_scores = ninth_graders.groupby(["school_name"]).mean()["math_score"] tenth_graders_scores = tenth_graders.groupby(["school_name"]).mean()["math_score"] eleventh_graders_scores = eleventh_graders.groupby(["school_name"]).mean()["math_score"] twelfth_graders_scores = twelfth_graders.groupby(["school_name"]).mean()["math_score"] #categorize by grade #Group each grade by school scores_by_grade = pd.DataFrame({"9th": ninth_graders_scores, "10th": tenth_graders_scores, "11th": eleventh_graders_scores, "12th": twelfth_graders_scores}) #Reading Scores by Grade ------------------------------------------------------------------------------------ ninth_graders = school_data_complete[(school_data_complete["grade"] == "9th")] tenth_graders = school_data_complete[(school_data_complete["grade"] == "10th")] eleventh_graders = school_data_complete[(school_data_complete["grade"] == "11th")] twelfth_graders = school_data_complete[(school_data_complete["grade"] == "12th")] # Group each by school name ninth_graders_scores = ninth_graders.groupby(["school_name"]).mean()["reading_score"] tenth_graders_scores = tenth_graders.groupby(["school_name"]).mean()["reading_score"] eleventh_graders_scores = eleventh_graders.groupby(["school_name"]).mean()["reading_score"] twelfth_graders_scores = twelfth_graders.groupby(["school_name"]).mean()["reading_score"] scores_by_grade = pd.DataFrame({"9th": ninth_graders_scores, "10th": tenth_graders_scores, "11th": eleventh_graders_scores, "12th": twelfth_graders_scores}) #****Scores by School Spending ------------------------------------------------------------------------------------ spending_bins = [0, 250, 500, 750] group_names = ['<250','250-500','500-750'] per_school_summary["Spending Ranges (Per Student)"] = pd.cut(per_student_budget, spending_bins, labels=group_names) print(per_school_summary) spending_math_scores = per_school_summary.groupby(["Spending Ranges (Per Student)"]).mean()["Average Math Score"] spending_reading_scores = per_school_summary.groupby(["Spending Ranges (Per Student)"]).mean()["Average Reading Score"] spending_passing_math = per_school_summary.groupby(["Spending Ranges (Per Student)"]).mean()["% Passing Math"] spending_passing_reading = per_school_summary.groupby(["Spending Ranges (Per Student)"]).mean()["% Passing Reading"] overall_passing_rate = (spending_math_scores + spending_reading_scores) / 2 spending_summary = pd.DataFrame({"Average Math Score" : spending_math_scores, "Average Reading Score": spending_reading_scores, "% Passing Math": spending_passing_math, "% Passing Reading": spending_passing_reading, "% Overall Passing Rate": overall_passing_rate}) #print(spending_summary) #****Scores by School Size ------------------------------------------------------------------------------------ size_bins = [0, 1000, 2000, 5000] label_names = ["Small (<1000)", "Medium (1000-2000)", "Large (2000-5000)"] # Categorize the spending based on the bins per_school_summary["School Size"] = pd.cut(per_school_summary["Total Students"], size_bins, labels=label_names) # Calculate the scores based on bins size_math_scores = per_school_summary.groupby(["School Size"]).mean()["Average Math Score"] size_reading_scores = per_school_summary.groupby(["School Size"]).mean()["Average Reading Score"] size_passing_math = per_school_summary.groupby(["School Size"]).mean()["% Passing Math"] size_passing_reading = per_school_summary.groupby(["School Size"]).mean()["% Passing Reading"] overall_passing_rate = (size_passing_math + size_passing_reading) / 2 # Assemble into data frame size_summary = pd.DataFrame({"Average Math Score" : size_math_scores, "Average Reading Score": size_reading_scores, "% Passing Math": size_passing_math, "% Passing Reading": size_passing_reading, "% Overall Passing Rate": overall_passing_rate}) #print(size_summary) #print(per_school_summary) #****Scores by School Type ------------------------------------------------------------------------------------ type_math_score = per_school_summary.groupby(['School Type']).mean()["Average Math Score"] type_reading_score = per_school_summary.groupby(['School Type']).mean()["Average Reading Score"] type_passing_math = per_school_summary.groupby(['School Type']).mean()['% Passing Math'] type_passing_reading = per_school_summary.groupby(['School Type']).mean()['% Passing Reading'] overall_passing_rate = (type_passing_math + type_passing_reading) / 2 type_summary = pd.DataFrame({"Average Math Score": type_math_score, "Average Reading Score": type_reading_score, "% Passing Math": type_passing_math, "% Passing Reading": type_passing_reading, "% Overall Passing Rate": overall_passing_rate}) print(type_summary)

[ "johnwchoo@gmail.com" ]

johnwchoo@gmail.com

cc97825789e2626dee928f0438ffb14d11c4338f

6782c1b6b1a472846417b82c0089a935ba0cd13b

/examples/node2vec/multi_class.py

e01f7b39d0697941b2337f7c6d218828090f75f8

[ "Apache-2.0" ]

permissive

Liwb5/PGL

291d7d23a2f56060de8afffb6180476c86a81279

1253335c602709fb93061d8a64b2e4ba1049bfff

refs/heads/main

2022-04-26T10:17:27.937508

2022-04-19T12:20:33

216,797,608

1

0

Apache-2.0

2020-10-09T07:49:14

2019-10-22T11:31:31

Python

UTF-8

Python

false

9,706

py

# Copyright (c) 2020 PaddlePaddle Authors. 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 argparse import time import os import math import glob import numpy as np import paddle from easydict import EasyDict as edict import pgl import yaml from paddle.optimizer import Adam import tqdm from pgl.utils.logger import log from sklearn.metrics import f1_score from dataset import ShardedDataset def load(name): if name == 'cora': dataset = pgl.dataset.CoraDataset() elif name == "pubmed": dataset = pgl.dataset.CitationDataset("pubmed", symmetry_edges=True) elif name == "citeseer": dataset = pgl.dataset.CitationDataset("citeseer", symmetry_edges=True) elif name == "BlogCatalog": dataset = pgl.dataset.BlogCatalogDataset() else: raise ValueError(name + " dataset doesn't exists") dataset.graph.indegree() dataset.graph.outdegree() dataset.graph = dataset.graph.to_mmap() return dataset class Model(paddle.nn.Layer): def __init__(self, num_nodes, embed_size=16, num_classes=39): super(Model, self).__init__() self.num_nodes = num_nodes embed_init = paddle.nn.initializer.Uniform( low=-1. / math.sqrt(embed_size), high=1. / math.sqrt(embed_size)) emb_attr = paddle.ParamAttr(name="node_embedding") self.emb = paddle.nn.Embedding( num_nodes, embed_size, weight_attr=emb_attr) self.linear = paddle.nn.Linear(embed_size, num_classes) def forward(self, node_ids): node_emb = self.emb(node_ids) node_emb.stop_gradient = True logits = self.linear(node_emb) return logits def node_classify_generator(graph, all_nodes=None, batch_size=512, epoch=1, shuffle=True): if all_nodes is None: all_nodes = np.arange(graph.num_nodes) def batch_nodes_generator(shuffle=shuffle): perm = np.arange(len(all_nodes), dtype=np.int64) if shuffle: np.random.shuffle(perm) start = 0 while start < len(all_nodes): yield all_nodes[perm[start:start + batch_size]] start += batch_size def wrapper(): for _ in range(epoch): for batch_nodes in batch_nodes_generator(): # batch_nodes_expanded = np.expand_dims(batch_nodes, # -1).astype(np.int64) batch_labels = graph.node_feat['group_id'][batch_nodes].astype( np.float32) yield [batch_nodes, batch_labels] return wrapper def topk_f1_score(labels, probs, topk_list=None, average="macro", threshold=None): assert topk_list is not None or threshold is not None, "one of topklist and threshold should not be None" if threshold is not None: preds = probs > threshold else: preds = np.zeros_like(labels, dtype=np.int64) for idx, (prob, topk) in enumerate(zip(np.argsort(probs), topk_list)): preds[idx][prob[-int(topk):]] = 1 return f1_score(labels, preds, average=average) def train(model, data_loader, optim, log_per_step=1000, threshold=0.3): model.train() total_loss = 0. total_sample = 0 bce_loss = paddle.nn.BCEWithLogitsLoss() test_probs_vals, test_labels_vals, test_topk_vals = [], [], [] for batch, (node, labels) in enumerate(data_loader): num_samples = len(node) node = paddle.to_tensor(node) labels = paddle.to_tensor(labels) logits = model(node) probs = paddle.nn.functional.sigmoid(logits) loss = bce_loss(logits, labels) loss.backward() optim.step() optim.clear_grad() topk = labels.sum(-1) test_probs_vals.append(probs.numpy()) test_labels_vals.append(labels.numpy()) test_topk_vals.append(topk.numpy()) total_loss += loss.numpy()[0] * num_samples total_sample += num_samples test_probs_array = np.concatenate(test_probs_vals) test_labels_array = np.concatenate(test_labels_vals) test_topk_array = np.concatenate(test_topk_vals) test_macro_f1 = topk_f1_score(test_labels_array, test_probs_array, test_topk_array, "macro", threshold) test_micro_f1 = topk_f1_score(test_labels_array, test_probs_array, test_topk_array, "micro", threshold) test_loss_val = total_loss / total_sample log.info("Train Loss: %f " % test_loss_val + "Train Macro F1: %f " % test_macro_f1 + "Train Micro F1: %f " % test_micro_f1) return total_loss / total_sample @paddle.no_grad() def test(model, data_loader, log_per_step=1000, threshold=0.3): model.eval() total_loss = 0. total_sample = 0 bce_loss = paddle.nn.BCEWithLogitsLoss() test_probs_vals, test_labels_vals, test_topk_vals = [], [], [] for batch, (node, labels) in enumerate(data_loader): num_samples = len(node) node = paddle.to_tensor(node) labels = paddle.to_tensor(labels) logits = model(node) probs = paddle.nn.functional.sigmoid(logits) loss = bce_loss(logits, labels) topk = labels.sum(-1) test_probs_vals.append(probs.numpy()) test_labels_vals.append(labels.numpy()) test_topk_vals.append(topk.numpy()) total_loss += loss.numpy()[0] * num_samples total_sample += num_samples test_probs_array = np.concatenate(test_probs_vals) test_labels_array = np.concatenate(test_labels_vals) test_topk_array = np.concatenate(test_topk_vals) test_macro_f1 = topk_f1_score(test_labels_array, test_probs_array, test_topk_array, "macro", threshold) test_micro_f1 = topk_f1_score(test_labels_array, test_probs_array, test_topk_array, "micro", threshold) test_loss_val = total_loss / total_sample log.info("\t\tTest Loss: %f " % test_loss_val + "Test Macro F1: %f " % test_macro_f1 + "Test Micro F1: %f " % test_micro_f1) return test_loss_val, test_macro_f1, test_micro_f1 def load_from_files(model_dir): files = glob.glob( os.path.join(model_dir, "node_embedding_txt", "node_embedding.block*.txt")) emb_table = dict() for filename in files: for line in open(filename): key, value = line.strip(",\n").split("\t") key = int(key) value = [float(v) for v in value.split(",")] emb_table[key] = value emb_list = [emb_table[key] for key in range(len(emb_table))] emb_arr = np.array(emb_list, dtype=np.float32) emb_arr = emb_arr[:, :(emb_arr.shape[1] - 3) // 3] return {'emb.weight': emb_arr} def main(args): if not args.use_cuda: paddle.set_device("cpu") if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() dataset = load(args.dataset) graph = dataset.graph model = Model(graph.num_nodes, args.embed_size, dataset.num_groups) model = paddle.DataParallel(model) batch_size = len(dataset.train_index) train_steps = int(len(dataset.train_index) / batch_size) * args.epoch scheduler = paddle.optimizer.lr.PolynomialDecay( learning_rate=args.multiclass_learning_rate, decay_steps=train_steps, end_lr=0.0001) optim = Adam(learning_rate=scheduler, parameters=model.parameters()) if args.load_from_static: model.set_state_dict(load_from_files("./model")) else: model.set_state_dict(paddle.load("model.pdparams")) train_data_loader = node_classify_generator( graph, dataset.train_index, batch_size=batch_size, epoch=1) test_data_loader = node_classify_generator( graph, dataset.test_index, batch_size=batch_size, epoch=1) best_test_macro_f1 = -1 for epoch in tqdm.tqdm(range(args.epoch)): train_loss = train(model, train_data_loader(), optim) test_loss, test_macro_f1, test_micro_f1 = test(model, test_data_loader()) best_test_macro_f1 = max(best_test_macro_f1, test_macro_f1) log.info("Best test macro f1 is %s." % best_test_macro_f1) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Deepwalk') parser.add_argument( "--dataset", type=str, default="BlogCatalog", help="dataset (cora, pubmed, BlogCatalog)") parser.add_argument("--use_cuda", action='store_true', help="use_cuda") parser.add_argument( "--conf", type=str, default="./config.yaml", help="config file for models") parser.add_argument("--epoch", type=int, default=1000, help="Epoch") parser.add_argument( "--load_from_static", action='store_true', help="use_cuda") args = parser.parse_args() # merge user args and config file config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader)) config.update(vars(args)) main(config)

[ "weiyue.su@gmail.com" ]

weiyue.su@gmail.com

a1624b94b297bc87a48ad16e0cdcb21d81e82e0b

cc32a607aedba612412dab77886fda45f9796abb

/erfelijkheid.py

5f7a37594cd0ea9125f529909152085f9576bafe

[ "MIT" ]

permissive

Smartz-Wesley/Smartz-Test

59fe0f7c092f631ed3689862634f07a715f6aab7

8b34849ec8933a9715cf3c28d81bd211c3dc30e4

refs/heads/master

2020-03-15T16:18:55.279708

2018-05-13T10:09:06

132,232,185

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class Person (): def lopen(self): print("rechtervoet, linkervoer, rechtervoet, wat was het ook alweer...") def praten(self): print("Het enige dat ik kan is praten") def werken(self): print("Ben ik aan het werk?") class Wesley (Person): def usa(self): print("Oh say can you see....") class Jeffrey (Person): def antiajax(self): print("Bah, bah Ajax: joden, joden") class Tim (Person): def best(self): print("Zoals mijn grote voorbeeld altijd aangaf, ik ben de beste!") class Dennis (Person): def fortnite(self): print("Van het weekend zal ik eens gaan spelen") w = Wesley() j = Jeffrey() t = Tim() d = Dennis() w.lopen() w.praten() w.werken() w.usa()

[ "w.speetgens@smartz.eu" ]

w.speetgens@smartz.eu

61a09c18c8b630245e154ae30f0dcbb89579d77a

33c674fb0209804aa715276201c9966e2b050ab3

/wksp/devel/bin/view_frames

76057371ca6f360e6f00925d656e9fe8905003ac

[]

no_license

ese-519/teamcaravan-rpi

74d4d45adb00b5acc48e0f7f3f6689ee4d477001

88fd59055758b0c39c0e00816e2bc3e06be695b3

refs/heads/master

2016-08-11T07:09:00.863406

2015-12-09T16:42:39

46,686,630

0

null

UTF-8

Python

false

291

#!/usr/bin/env python # -*- coding: utf-8 -*- # creates a relay to a python script source file, acting as that file. # The purpose is that of a symlink with open("/home/ubuntu/hemo_code/new_code/wksp/src/geometry/tf/scripts/groovy_compatibility/view_frames", 'r') as fh: exec(fh.read())

[ "avanaken92@gmail.com" ]

avanaken92@gmail.com

1ce95d475c7088d7b805a6c7e62d93c4986fce10

cfb474d8b9aa6fd268d322e0aeae8598d0d40383

/user/logsitic.py

d8022176535ce4f98a2f59df6adb9ed9a5ce7d8b

[]

no_license

aomenxinpujing/swiper

b90fd6f439e3afdf0d0c89160b7b83b7b6e1c8fc

af16546ce15cfa68e9c416e9acc5d3ea25f3577d

refs/heads/master

2020-05-03T02:21:22.655293

2019-04-02T14:06:02

178,367,068

0

1

null

2019-04-02T14:06:03

2019-03-29T08:44:43

Python

UTF-8

Python

false

629

py

import os from urllib.parse import urljoin from django.conf import settings from lib import qiniuyun from swiper import config from commen.keys import ICON_KEY from user.models import User from worker import celery_app @celery_app.task def upload_qn(uid,icon): filename = ICON_KEY % uid filepath = os.path.join(settings.BASE_DIR, settings.MEDIA_ROOT, filename) with open(filepath, 'wb') as fp: for chunk in icon.chunks(): fp.write(chunk) qiniuyun.upload_qiniu(filename,filepath) user = User.objects.get(id=uid) user.avatar = urljoin(config.QINIU_CLOUD_URL, filename) user.save()

[ "noreply@github.com" ]

aomenxinpujing.noreply@github.com

e2347380c65993b481e6ce5d1830967a3fdc5e84

ef4e6257040fde6dc73898c967f8d7005f293a20

/DS6.py

8580174f69860b4ef0785661ce4bf38cf2b3b099

[]

no_license

AnandKrishnamoorthy1/ML-and-NLP-Basics

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refs/heads/master

2020-08-25T06:43:49.375453

2019-12-19T16:08:06

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# -*- coding: utf-8 -*- """ Created on Sun Apr 29 10:26:06 2018 @author: anand """ from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression load_data=datasets.load_boston() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) model=LinearRegression() model.fit(X_train,y_train) model_coeff=pd.DataFrame(data={'Features':load_data.feature_names,'Coefficients':model.coef_}) """ print('Coefficients:') print(model_coeff) print('Intercept:') print(model.intercept_) """ predict_val=model.predict(X_test) price_compare=pd.DataFrame(data={'Actual Value':y_test,'Predicted Value':predict_val}) print(price_compare.round(1)) predict_train_val=model.predict(X_train) price_compare=pd.DataFrame(data={'Actual Value':y_train,'Predicted Value':predict_train_val}) print(price_compare.round(1)) print('Model Train Score: ',model.score(X_train,y_train).round(2)) print('Model Test Score: ',model.score(X_test,y_test).round(2)) ##################################################################### from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge from sklearn.preprocessing import MinMaxScaler load_data=datasets.load_boston() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) scaler=MinMaxScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) model=Ridge(alpha=2) model.fit(X_train_scaled,y_train) model_coeff=pd.DataFrame(data={'Features':load_data.feature_names,'Coefficients':model.coef_}) print('Coefficients:') print(model_coeff) print('Intercept:') print(model.intercept_) predict_val=model.predict(X_test_scaled) price_compare=pd.DataFrame(data={'Actual Value':y_test,'Predicted Value':predict_val}) print(price_compare.round(1)) print('Model Train Score: ',model.score(X_train_scaled,y_train).round(2)) print('Model Test Score: ',model.score(X_test_scaled,y_test).round(2)) ##################################################################### from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import Lasso from sklearn.preprocessing import MinMaxScaler load_data=datasets.load_boston() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) scaler=MinMaxScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) model=Lasso(alpha=.2) model.fit(X_train_scaled,y_train) model_coeff=pd.DataFrame(data={'Features':load_data.feature_names,'Coefficients':model.coef_}) print('Coefficients:') print(model_coeff) print('Intercept:') print(model.intercept_) predict_val=model.predict(X_test_scaled) price_compare=pd.DataFrame(data={'Actual Value':y_test,'Predicted Value':predict_val}) print(price_compare.round(1)) print('Model Train Score: ',model.score(X_train_scaled,y_train).round(2)) print('Model Test Score: ',model.score(X_test_scaled,y_test).round(2)) from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge from sklearn.preprocessing import PolynomialFeatures load_data=datasets.load_boston() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) poly=PolynomialFeatures() X_train_scaled=poly.fit_transform(X_train) X_test_scaled=poly.fit_transform(X_test) model=Ridge(alpha=5) model.fit(X_train_scaled,y_train) #model_coeff=pd.DataFrame(data={'Features':load_data.feature_names,'Coefficients':model.coef_}) """ print('Coefficients:') print(model_coeff) print('Intercept:') print(model.intercept_) """ predict_val=model.predict(X_test_scaled) print('Model Train Score Polynomial: ',model.score(X_train_scaled,y_train).round(2)) print('Model Test Score Polynomial: ',model.score(X_test_scaled,y_test).round(2)) ##################################################################### from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression load_data=datasets.load_breast_cancer() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) model=LogisticRegression(C=2) model.fit(X_train,y_train) predict_val=model.predict(X_test) price_compare=pd.DataFrame(data={'Actual Value':y_test,'Predicted Value':predict_val}) print(price_compare.round(1)) print('Model Train Score: ',model.score(X_train,y_train).round(2)) print('Model Test Score: ',model.score(X_test,y_test).round(2)) ##################################################################### from sklearn import datasets import pandas as pd from sklearn.model_selection import train_test_split from sklearn.svm import LinearSVC load_data=datasets.load_breast_cancer() X=pd.DataFrame(data=load_data.data,columns=load_data.feature_names) Y=load_data.target X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0) model=LinearSVC(C=5) model.fit(X_train,y_train) predict_val=model.predict(X_test) price_compare=pd.DataFrame(data={'Actual Value':y_test,'Predicted Value':predict_val}) print(price_compare.round(1)) print('Model Train Score: ',model.score(X_train,y_train).round(2)) print('Model Test Score: ',model.score(X_test,y_test).round(2)) #####################################################################

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AnandKrishnamoorthy1.noreply@github.com

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/NumPy_TutorialPoint.py

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sireeshadaruvuri/Python-NumPy

f1536c04805b0757d19c1ea820b0391a009e76ae

a357d3b347850b3715956cde25559c37dd0f1d07

refs/heads/master

2022-12-22T22:08:05.238517

2020-09-18T23:18:12

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#array syntax import numpy as np a = np.array([1,2,3,4]) print(a) #two dimensions (result shows with two square brackets) b = np.array([[1,2,3],[4,5,6]]) print(b) #ndmin specifies the minimum dimensions of resultant array c = np.array([1,2,3,4], ndmin=2) #If I give ndmin = 3(3 dimensions)here it gives result with 3 square brackets print(c) #dtype is a desired datatype of an array a = np.array([1,2,3,4], dtype=complex) print(a) a = np.array([1,2,3,4], dtype=str) print(a) #Data Type Objects (dtype) d = np.dtype(np.float) print(d) ##int8, int16, int32, int64 can be replaced by equivalent string 'i1', 'i2','i4', etc. dt = np.dtype('i8') print(dt) # using endian notation dt = np.dtype('>i4') print(dt) dt = np.dtype([('age',np.int8)]) print(dt) ## first create structured data type #In case of structured type, the names of fields, # data type of each field and part of the memory block taken by each field. dto = np.dtype([('name','S5')]) ax = np.array([('Siri'),('purni')],dtype=dto) print(ax) print(ax['name']) #The following examples define a structured data type called student with a string field 'name', # an integer field 'age' and a float field 'marks' dt = np.dtype([('name','S10'),('age','i8'),('marks','f8')]) a = np.array([('siri',20,30),('purni',10,50)], dtype=dt) print(a) #--------------------------------------------- #array attributes a = np.array([[1,2,3,4],[3,4,5,6]]) print(a.shape) #array can be reshaped using a.shape function a.shape = (4,2) print(a) #NumPy also provides a reshape function to resize an array. b = a.reshape(4,2) print(b) #ndarray.ndim #This array attribute returns the number of array dimensions. a = np.arange(24) print(a) print(a.ndim) b = a.reshape(4,2,3) print(b) b = a.reshape(2,4,3) print(b) print(b.ndim) #numpy.itemsize This array attribute returns the length of each element of array in bytes. c = np.array([1,2,3,4,5],dtype=np.int8) print(c.itemsize) #numpy.flags #The ndarray object has the following attributes. Its current values are returned by this function. print(c.flags) #numpy.empty #The following code shows an example of an empty array x = np.empty([3,2],dtype=np.int8) print(x) #np.zeros x = np.zeros((5,),dtype=np.int) print(x) #convert a list to ndarray(n dimentional array) using asarray function x = [1,2,3,4] a = np.asarray(x,dtype=int) print(a) #convert a tuple x = (1,2,3,4) a = np.asarray(x,dtype=float) print(a) #list of typles x = [(1,2,3,4),(4,5,6,8)] a = np.asarray(x,dtype=int) print(a) #numpy.frombuffer #This function interprets a buffer as one-dimensional array. """ s = 'Hello World' a = np.frombuffer(s,dtype=int) print(a) """ a = np.arange(20) print(a[1:20:2]) print(a[1:]) print(a[:5:2]) a = np.array([[1,2,3,4],[12,8,9,20],[34,56,78,90]]) print(a[1:]) print(a[:1]) print(a[1,3]) print(a) #Advanced slicing of arrays #column1 print(a[...,1] ) #column3 print(a[...,3]) #row1 print(a[1,...]) #from column 1 print(a[...,1:]) print(a) b = a[[0,1,2],[0,1,0]] #it takes as 0,0 and 1,1 and 2,0 print(b) #---------- x = np.array([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],[ 9, 10, 11]]) print(x) rows = np.array([[0, 0], [3, 3]]) cols = np.array([[0, 2], [0, 2]]) y = x[rows, cols] print(y) #----------- #advanced indexing for column x = np.array([[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8],[ 9, 10, 11]]) print('after slicing array becomes') print(x[1:4]) print(x[1:3]) print(x[1:2,1:3]) print(x[1:4,1:3]) print(x[1,2]) print(x[1:4,[1,2]]) #boolean print('greater than 5 are ', +x[x>5]) #remove NaN numbers(Not a Number) using ~ (complement operator) y = np.array([np.nan,1,2,np.nan,4,5]) print('elements in y are', +y) print(y[~np.isnan(y)]) #to filterout non complex elements z = np.array([1, 2+6j, 5, 6+7j]) print(z[np.iscomplex(z)]) #self-exercise of indexing and advanced indexing n = np.array([[5,4,10,15,6],[20,25,16,18,21],[23,32,42,45,46],[48,50,55,60,62]]) print(n) print(n[0,2]) print(n[0:2]) print(n[2:3]) print(n[1,4]) print(n[2,1]) print(n[3,2]) print(n[3,4]) print(n[1:]) print(n[:1]) print(n[1,]) print(n[...,1]) print(n[...,2]) print(n[...,3]) print(n[...,1:3]) print(n[[1,1,1],[0,1,2]],n[[2,2,2],[0,2,2]]) a = np.array([[1,1,1],[2,2,2]]) b = np.array([[0,1,2],[0,2,2]]) c = n[a,b] print(c)

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sireeshadaruvuri.noreply@github.com

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/craft-demo/.venv_python/bin/easy_install

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kashyapa/interview-prep

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2023-07-28T13:12:49.515299

2021-09-06T14:33:25

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#!/Users/schandra2/Developer/coding-problems/craft-demo/.venv_python/bin/python # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "schandra2@godaddy.com" ]

schandra2@godaddy.com

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/old/6th sem/competetive_c/COMPILER/test_modules/testCases.py

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no_license

anant-pushkar/competetive_programming_codes

398a39c85a761c8d242f42f368933239a438ac06

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2021-01-20T11:57:07.528790

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import testTemplate ''' class customTester(testInstance): def __init__(self , inStr , outStr , dec): testInstance.__init__(self , inStr , outStr , dec) def test(self,txt,ref): #write test logic here ''' def getTests(): tests = [] suite=testTemplate.testSuite("Sample Test Suite1") testcase = testTemplate.regexTester("<<>>" , "4" , "sample1") suite.add(testcase) testcase = testTemplate.regexTester("><" , "0" , "sample2") suite.add(testcase) testcase = testTemplate.regexTester("<>>>" , "2" , "sample3") suite.add(testcase) tests.append(suite) return tests

[ "anantpushkar009@gmail.com" ]

anantpushkar009@gmail.com

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/data/utils.py

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no_license

arryon/ml2014

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refs/heads/master

2020-06-06T02:05:13.268466

2013-10-22T17:39:37

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import numpy as np def get_largest_std(nested_list): tuples = [(np.std(a),idx) for idx,a in zip(range(len(nested_list)),nested_list)] _max = max(tuples) return nested_list[_max[1]]

[ "arryon@gmail.com" ]

arryon@gmail.com

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/app/recipy/tests/test_tags_api.py

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permissive

Zoki92/Vue-recipe-api-frontend

470a8ba0fc96f0788fddd9e24c9e86d6e393ab5c

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refs/heads/master

2020-05-19T23:12:44.846021

2019-05-17T19:04:54

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from django.contrib.auth import get_user_model from django.urls import reverse from django.test import TestCase from rest_framework import status from rest_framework.test import APIClient from core.models import Tag, Recipe from recipy.serializers import TagSerializer TAGS_URL = reverse('recipy:tag-list') class PublicTagsApiTests(TestCase): # Test the publicly available tags API def setUp(self): self.client = APIClient() def test_login_required(self): # Test that login is required for retrieving tags res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateTagsApiTests(TestCase): # Test the authorized user tags API def setUp(self): self.user = get_user_model().objects.create_user( 'test@test.com', '123testing' ) self.client = APIClient() self.client.force_authenticate(self.user) def test_retrieve_tags(self): # Test retrieving tags Tag.objects.create(user=self.user, name='Vegan') Tag.objects.create(user=self.user, name='Dessert') res = self.client.get(TAGS_URL) tags = Tag.objects.all().order_by('-name') serializer = TagSerializer(tags, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_tags_limited_to_user(self): # Test that tags returned are for the authenticated user user2 = get_user_model().objects.create_user( 'other@test.com', '123test123' ) Tag.objects.create(user=user2, name="Fruity") tag = Tag.objects.create(user=self.user, name="Comfort Food") res = self.client.get(TAGS_URL) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data[0]['name'], tag.name) def test_create_tag_successful(self): # Test creating a new tag payload = {'name': 'Test tag'} self.client.post(TAGS_URL, payload) exists = Tag.objects.filter( user=self.user, name=payload['name'] ).exists() self.assertTrue(exists) def test_create_tag_invalid(self): # Test creating a new tag with invalid payload payload = {'name': ''} res = self.client.post(TAGS_URL, payload) self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_retrieve_tags_assigned_to_recipes(self): # Test filtering tags by those assigned to recipes tag1 = Tag.objects.create(user=self.user, name='Breakfast') tag2 = Tag.objects.create(user=self.user, name='Lunch') recipe = Recipe.objects.create( title='Coriander eggs on toast', time_minutes=10, price=5.00, user=self.user ) recipe.tags.add(tag1) res = self.client.get(TAGS_URL, {'assigned_only': 1}) serializer1 = TagSerializer(tag1) serializer2 = TagSerializer(tag2) self.assertIn(serializer1.data, res.data) self.assertNotIn(serializer2.data, res.data)

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zoranstoilov@yahoo.com

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/QUESTION_3.PY

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gauripatil20/DEBUGGING

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2023-04-11T04:11:00.233625

2021-04-18T13:00:13

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# Apne simple se message ko aise change karna jisse koi dusra insaan usse samajh na paye usse encryption bolte hai. Encryption karne ke bhot sare ways hote hai. Hum cipher wheel use karenge. Cipher wheel mei hum her character ko kissi number se aage shift kar dete hai. Jaise: Hum iss cihper wheel mein her character ki value ko 2 se increase kar denge. Aisa karne ke liye hum chars aur shifted_chars array ka use karenge. Example: # chars = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'] # shifted_chars = ['c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','a','b'] # Topics covered * function returning the wrong value # argument passed to the function but never used. # for loop itterates over wrong string # semantic/syntactic problems in if/else # Neeche yeh program diya hua hai, isko sahi kar ke ek nayi file mein submit karo. # Cipher wheel with a function for finding an element in a list # find_in_list function defined here but not called def find_in_list(query, mainlist): # this function is used to find the position of the "query" in the "mainlist". If "query" is in the list then it returns its position, otherwise it returns None mainlist_len = len(mainlist) range_for_loop = range(mainlist_len) index =None for i in range_for_loop: element = mainlist[i] if element == query: index = i return index # this should return the position of the "query" in the "mainlist" chars = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'] shifted_chars = ['c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','a','b'] # encrypt_message function defined here but not called def encrypted_message(plain_msg): # this fucnction takes "plain_msg" as an argument and print/return the encrypted message. The "plain_msg" is tranfered into "encrypted_msg" using "shifted_chars" list. Example, if plain_msg = "ng" then n => p, g => i and hence encrypted_msg = "pi" encrypted_msg = "" for character in plain_msg: # for character in msg if character in chars: chars_index = find_in_list(character,chars) new_char = shifted_chars[chars_index] encrypted_msg = encrypted_msg + new_char else: encrypted_msg = encrypted_msg + character return encrypted_msg # decrypt_message function defined here but not called def decrypt_message(encrypted_msg): # this fucnction takes "encrypted_msg" as an argument and print/return the encrypted message. The "encrypted_msg" is tranfered into "decrypted_msg" using "shifted_chars" list. Example, if encrypted_msg = "pi" then p => n, i => g and hence decrypted_msg = "ng" decrypted_msg = "" for character in encrypted_msg: if character in shifted_chars: char_index = find_in_list(character, shifted_chars) new_char = chars[char_index] decrypted_msg = decrypted_msg + new_char else: decrypted_msg = decrypted_msg + character return decrypted_msg # methods should return or print the new messages. ############################################### Code starts from here ################################################## flag = True while flag == True: choice = input("What do you want to do? .Encrypt a message 2. Decrypt a message Enter `e` or `d` respectively!") if choice == 'e': plain_message = input("Enter message to encrypt??") print(encrypted_message(plain_message)) elif choice == 'd': encrypted_msg = input("Enter message to decrypt?") print(decrypt_message(encrypted_msg)) play_again = input("Do you want to try agian or Do you want to exit? (Y/N)") if play_again == 'Y': continue elif play_again == 'N': break

[ "you@example.com" ]

you@example.com

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/segmentation/RTDA/scripts/train.py

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ydwisroad/imageprocessingpytorch

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2023-07-29T05:05:11.145832

2022-02-21T23:32:03

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Jupyter Notebook

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py

import os import torch import torch.optim as optim from tqdm import tqdm from utils.scheduler import PolyLR from utils.losses import CrossEntropy2d, FocalLoss, get_target_tensor, DiceLoss, get_target_tensor_mc from torch.nn import BCEWithLogitsLoss from scripts.eval import validate from models.utils import save_model, load_model, save_da_model, load_da_model, freeze_model import torch.nn.functional as F from dataset.utils import source_to_target, source_to_target_np import torch.backends.cudnn as cudnn from dataset.utils import find_dataset_using_name from torch.utils import data import copy import numpy as np from PIL import Image import torch.cuda.amp as amp palette = [128, 64, 128, # Road, 0 244, 35, 232, # Sidewalk, 1 70, 70, 70, # Building, 2 102, 102, 156, # Wall, 3 190, 153, 153, # Fence, 4 153, 153, 153, # pole, 5 250, 170, 30, # traffic light, 6 220, 220, 0, # traffic sign, 7 107, 142, 35, # vegetation, 8 152, 251, 152, # terrain, 9 70, 130, 180, # sky, 10 220, 20, 60, # person, 11 255, 0, 0, # rider, 12 0, 0, 142, # car, 13 0, 0, 70, # truck, 14 0, 60, 100, # bus, 15 0, 80, 100, # train, 16 0, 0, 230, # motor-bike, 17 119, 11, 32] # bike, 18] zero_pad = 256 * 3 - len(palette) for i in range(zero_pad): palette.append(0) def colorize_mask(mask): # mask: numpy array of the mask new_mask = Image.fromarray(mask.astype(np.uint8)).convert('P') new_mask.putpalette(palette) return new_mask def compute_ce_weights(num_classes, predictions): z = np.zeros((num_classes,)) predictions = predictions.clone().detach().cpu().numpy() for i, (label) in enumerate(predictions): y = label # y = y.detach().cpu().numpy() mask = (y >= 0) & (y < num_classes) labels = y[mask].astype(np.uint8) count_l = np.bincount(labels, minlength=num_classes) z += count_l total_frequency = np.sum(z) class_weights = [] for frequency in z: class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) class_weights.append(class_weight) ret = np.array(class_weights) return ret def train_source_only(args, model, source_train_loader, val_loader, metrics, iter_counter, visualizer): start_iter = 0 train_loss = 0.0 scaler = amp.GradScaler() # Define optimizer optimizer = optim.SGD(model.parameters(), lr=args.seg_lr, momentum=args.momentum, weight_decay=args.weight_decay) # Define scheduler scheduler = PolyLR(optimizer, max_iters=args.max_iters, power=args.power) # Define loss criterion if args.seg_loss == 'focal': criterion_seg = FocalLoss(num_class=args.num_classes, ignore_label=args.ignore_index) elif args.seg_loss == 'dice': criterion_seg = DiceLoss(num_classes=args.num_classes, ignore_index=args.ignore_index) else: criterion_seg = CrossEntropy2d(ignore_label=args.ignore_index) # Resume model if continuing training if args.continue_train: model, optimizer, scheduler, start_iter = load_model(args, model, optimizer, scheduler) # Start training source_train_loader_it = iter(source_train_loader) iter_counter.record_training_start(start_iter) for i_iter in tqdm(iter_counter.training_steps()): # Set model to train model.train() # Get images/labels and move them to GPUs try: images, labels, _, _ = next(source_train_loader_it) except: source_train_loader_it = iter(source_train_loader) images, labels, _, _ = next(source_train_loader_it) iter_counter.record_one_epoch() if args.use_st: mean = torch.reshape(torch.from_numpy(args.mean), (1, 3, 1, 1)) B, C, H, W = images.shape mean = mean.repeat(B, 1, H, W) images = images - mean images, labels = images, labels = images.to(args.gpu_ids[0], dtype=torch.float32), labels.to(args.gpu_ids[0], dtype=torch.long) # Zero-grad the optimizer optimizer.zero_grad() # Train source with amp.autocast(): preds, preds_sup1, preds_sup2 = model(images) loss1 = criterion_seg(preds, labels) loss2 = criterion_seg(preds_sup1, labels) loss3 = criterion_seg(preds_sup2, labels) loss_seg = loss1 + loss2 + loss3 # Update gradients scaler.scale(loss_seg).backward() # Update model scaler.step(optimizer) if scheduler is not None: scheduler.step() scaler.update() # Update logging information train_loss += loss_seg.item() # Print losses if iter_counter.needs_printing(): # Print log and visualize on tensorboard visualizer.info(f'Training loss at iter {iter_counter.total_steps_so_far}: {train_loss / args.print_freq}') visualizer.add_scalar('Training_Loss', train_loss / args.print_freq, iter_counter.total_steps_so_far) train_loss = 0.0 # Validation phase if iter_counter.needs_validating(): # Set model to eval visualizer.info('Validating model at step %d' % iter_counter.total_steps_so_far) validate(args, model, val_loader, metrics, visualizer, i_iter) # Save model if iter_counter.needs_saving(): save_model(args, model, optimizer, scheduler, iter_counter) iter_counter.record_one_iteration() iter_counter.record_training_end() save_model(args, model, optimizer, scheduler, iter_counter) validate(args, model, val_loader, metrics, visualizer, i_iter) def train_da(args, model, model_d, source_train_loader, target_train_loader, val_loader, metrics, iter_counter, visualizer): if args.ft is not None: model = load_model(args, model, ft=args.ft) validate(args, model, val_loader, metrics, visualizer, 0) exit() # Initialize variables cudnn.benchmark = True cudnn.enabled = True scaler = amp.GradScaler() start_iter = 0 parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 metrics.reset() # Define optimizers optimizer = optim.SGD(model.parameters(), lr=args.seg_lr, momentum=args.momentum, weight_decay=args.weight_decay) optimizer_d = optim.Adam(model_d.parameters(), lr=args.d_lr, betas=(args.beta1_d, args.beta2_d)) # Define schedulers scheduler = PolyLR(optimizer, max_iters=args.max_iters, power=args.power) scheduler_d = PolyLR(optimizer_d, max_iters=args.max_iters, power=args.power) # Define losses criterion if args.seg_loss == 'focal': criterion_seg = FocalLoss(num_class=args.num_classes, ignore_label=args.ignore_index) elif args.seg_loss == 'dice': criterion_seg = DiceLoss(num_classes=args.num_classes, ignore_index=args.ignore_index) else: criterion_seg = CrossEntropy2d(ignore_label=args.ignore_index) criterion_d = BCEWithLogitsLoss() # Resume model if continuing training if args.continue_train: model, model_d, model_d2, optimizer, optimizer_d, \ optimizer_d2, scheduler, scheduler_d, scheduler_d2, start_iter = load_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d ) # Start training iter_counter.record_training_start(start_iter) source_train_loader_it = iter(source_train_loader) target_train_loader_it = iter(target_train_loader) visualizer.info(f'Lambda: {args.lambda_adv} at epoch: {iter_counter.total_epochs()}') iter_counter.record_training_start(start_iter) for i_iter in tqdm(iter_counter.training_steps()): # Set model to train model.train() model_d.train() # Zero-grad the optimizers optimizer.zero_grad() optimizer_d.zero_grad() # Get source/target images and labels and move them to GPUs try: source_images, source_labels, _, _ = next(source_train_loader_it) except: source_train_loader_it = iter(source_train_loader) source_images, source_labels, _, _ = next(source_train_loader_it) try: target_images, target_labels, _, name = next(target_train_loader_it) except: target_train_loader_it = iter(target_train_loader) target_images, target_labels, _, name = next(target_train_loader_it) #validate(args, model, val_loader, metrics, visualizer, i_iter) #model.train() if args.use_st: src_in_trg = source_to_target(source_images, target_images, L=0.01) mean = torch.reshape(torch.from_numpy(args.mean), (1, 3, 1, 1)) B, C, H, W = source_images.shape mean = mean.repeat(B, 1, H, W) source_images = src_in_trg.clone() - mean target_images = target_images - mean source_images, source_labels = source_images.to(args.gpu_ids[0], dtype=torch.float32), source_labels.to( args.gpu_ids[0], dtype=torch.long) target_images, target_labels = target_images.to(args.gpu_ids[0], dtype=torch.float32), target_labels.to( args.gpu_ids[0], dtype=torch.long) # TRAIN SCENE PARSER # Don't accumulate gradients in discriminator for param in model_d.parameters(): param.requires_grad = False # Train Source if args.model == "bisenetv2": with amp.autocast(): spreds = model(source_images) loss_seg_source = criterion_seg(spreds, source_labels) else: with amp.autocast(): spreds, spreds_sup1, spreds_sup2 = model(source_images) loss1 = criterion_seg(spreds, source_labels) loss2 = criterion_seg(spreds_sup1, source_labels) loss3 = criterion_seg(spreds_sup2, source_labels) loss_seg_source = loss1 + loss2 + loss3 scaler.scale(loss_seg_source).backward() # Train Target if args.model == "bisenetv2": with amp.autocast(): tpreds = model(target_images) loss_seg_source = criterion_seg(spreds, source_labels) if args.ssl == "ssl" or args.ssl == "ssl_st": loss_seg_target = criterion_seg(tpreds, target_labels) else: loss_seg_target = 0.0 else: with amp.autocast(): tpreds, tpreds_sup1, tpreds_sup2 = model(target_images) if args.ssl == "ssl" or args.ssl == "ssl_st": losst1 = criterion_seg(tpreds, target_labels) losst2 = criterion_seg(tpreds_sup1, target_labels) losst3 = criterion_seg(tpreds_sup2, target_labels) loss_seg_target = losst1 + losst2 + losst3 else: loss_seg_target = 0.0 # Fool the discriminator with amp.autocast(): d_output = model_d(F.softmax(tpreds, dim=1)) loss_fool = criterion_d(d_output, get_target_tensor(d_output, "source").to(args.gpu_ids[0], dtype=torch.float)) loss_target = loss_fool * args.lambda_adv + loss_seg_target scaler.scale(loss_target).backward() # TRAIN DISCRIMINATOR for param in model_d.parameters(): param.requires_grad = True source_predictions = spreds.detach() target_predictions = tpreds.detach() with amp.autocast(): d_output_source = model_d(F.softmax(source_predictions, dim=1)) target_tensor = get_target_tensor(d_output_source, "source") source_d_loss = criterion_d(d_output_source, target_tensor.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(source_d_loss).backward() with amp.autocast(): d_output_target = model_d(F.softmax(target_predictions, dim=1)) target_tensor = get_target_tensor(d_output_target, "target") target_d_loss = criterion_d(d_output_target, target_tensor.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(target_d_loss).backward() scaler.step(optimizer) scaler.step(optimizer_d) if scheduler is not None: scheduler.step() scheduler_d.step() scaler.update() # Update logging information parser_source_loss += loss_seg_source.item() if loss_seg_target != 0.0: parser_target_loss += loss_seg_target.item() else: parser_target_loss += loss_seg_target parser_d_loss += loss_fool.item() discriminator_source_loss += source_d_loss.item() discriminator_target_loss += target_d_loss.item() # Print losses if iter_counter.needs_printing(): # Print log and visualize on tensorboard visualizer.info( f'Parser source loss at iter {iter_counter.total_steps_so_far}: {parser_source_loss / args.print_freq}') visualizer.add_scalar('Parser_Source_Loss', parser_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser target loss at iter {iter_counter.total_steps_so_far}: {parser_target_loss / args.print_freq}') visualizer.add_scalar('Parser_Target_Loss', parser_target_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser discriminator loss at iter {iter_counter.total_steps_so_far}: {parser_d_loss / args.print_freq}') visualizer.add_scalar('Parser_Discriminator_Loss', parser_d_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Source loss at iter {iter_counter.total_steps_so_far}: {discriminator_source_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Source_Loss', discriminator_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Target loss at iter {iter_counter.total_steps_so_far}: {discriminator_target_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Target_Loss', discriminator_target_loss / args.print_freq, iter_counter.total_steps_so_far) parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 # Validation phase if iter_counter.needs_validating(): # Set model to eval visualizer.info('Validating model at step %d' % iter_counter.total_steps_so_far) validate(args, model, val_loader, metrics, visualizer, i_iter) # Save model if iter_counter.needs_saving(): save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter) iter_counter.record_one_iteration() iter_counter.record_training_end() visualizer.info('End training') save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter) validate(args, model, val_loader, metrics, visualizer, i_iter) def train_mcda(args, model, model_d, source_train_loader, target_train_loader, val_loader, metrics, iter_counter, visualizer): # Initialize variables cudnn.benchmark = True cudnn.enabled = True scaler = amp.GradScaler() start_iter = 0 parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 metrics.reset() # Define optimizers optimizer = optim.SGD(model.parameters(), lr=args.seg_lr, momentum=args.momentum, weight_decay=args.weight_decay) optimizer_d = optim.Adam(model_d.parameters(), lr=args.d_lr, betas=(args.beta1_d, args.beta2_d)) # Define schedulers scheduler = PolyLR(optimizer, max_iters=args.max_iters, power=args.power) scheduler_d = PolyLR(optimizer_d, max_iters=args.max_iters, power=args.power) # Define losses criterion if args.seg_loss == 'focal': criterion_seg = FocalLoss(num_class=args.num_classes, ignore_label=args.ignore_index) else: criterion_seg = CrossEntropy2d(ignore_label=args.ignore_index) criterion_d = BCEWithLogitsLoss() # Resume model if continuing training if args.continue_train: model, model_d, model_d2, optimizer, optimizer_d, \ optimizer_d2, scheduler, scheduler_d, scheduler_d2, start_iter = load_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d ) # Start training iter_counter.record_training_start(start_iter) source_train_loader_it = iter(source_train_loader) target_train_loader_it = iter(target_train_loader) visualizer.info(f'Lambda: {args.lambda_adv} at epoch: {iter_counter.total_epochs()}') iter_counter.record_training_start(start_iter) for i_iter in tqdm(iter_counter.training_steps()): # Set model to train model.train() model_d.train() # Zero-grad the optimizers optimizer.zero_grad() optimizer_d.zero_grad() # Get source/target images and labels and move them to GPUs try: source_images, source_labels, _, _ = next(source_train_loader_it) except: source_train_loader_it = iter(source_train_loader) source_images, source_labels, _, _ = next(source_train_loader_it) try: target_images, target_labels, _, name = next(target_train_loader_it) except: target_train_loader_it = iter(target_train_loader) target_images, target_labels, _, name = next(target_train_loader_it) validate(args, model, val_loader, metrics, visualizer, i_iter) model.train() source_images, source_labels = source_images.to(args.gpu_ids[0], dtype=torch.float32), source_labels.to( args.gpu_ids[0], dtype=torch.long) target_images, target_labels = target_images.to(args.gpu_ids[0], dtype=torch.float32), target_labels.to( args.gpu_ids[0], dtype=torch.long) # TRAIN SCENE PARSER # Don't accumulate gradients in discriminator for param in model_d.parameters(): param.requires_grad = False # Train Source with amp.autocast(): spreds, spreds_sup1, spreds_sup2 = model(source_images) loss1 = criterion_seg(spreds, source_labels) loss2 = criterion_seg(spreds_sup1, source_labels) loss3 = criterion_seg(spreds_sup2, source_labels) loss_seg_source = loss1 + loss2 + loss3 scaler.scale(loss_seg_source).backward() # Train Target with amp.autocast(): tpreds, _, _ = model(target_images) # tsp = tpreds.clone().detach() #_, tsp = tsp.max(dim=1) # tsp = tsp.cpu().numpy() if args.use_weights: classes_weights = compute_ce_weights(args.num_classes, source_labels) classes_weights = (classes_weights - np.min(classes_weights)) / (np.max(classes_weights) - np.min(classes_weights)) classes_weights = torch.from_numpy(classes_weights.astype(np.float32)).to(args.gpu_ids[0]) loss_seg_target = 0.0 # Fool the discriminator d_output = model_d(F.softmax(tpreds, dim=1)) target_tensor = get_target_tensor_mc(d_output, "source").to(args.gpu_ids[0], dtype=torch.long) if args.use_weights: loss_fool = criterion_seg(d_output, target_tensor, weight=classes_weights) else: loss_fool = criterion_seg(d_output, target_tensor) loss_target = loss_fool * args.lambda_adv + loss_seg_target scaler.scale(loss_target).backward() # TRAIN DISCRIMINATOR for param in model_d.parameters(): param.requires_grad = True source_predictions = spreds.detach() target_predictions = tpreds.detach() with amp.autocast(): d_output_source = model_d(F.softmax(source_predictions, dim=1)) target_tensor = get_target_tensor_mc(d_output_source, "source") source_d_loss = criterion_seg(d_output_source, target_tensor.to(args.gpu_ids[0], dtype=torch.long)) / 2 scaler.scale(source_d_loss).backward() with amp.autocast(): d_output_target = model_d(F.softmax(target_predictions, dim=1)) target_tensor = get_target_tensor_mc(d_output_target, "target") target_d_loss = criterion_seg(d_output_target, target_tensor.to(args.gpu_ids[0], dtype=torch.long)) / 2 scaler.scale(target_d_loss).backward() scaler.step(optimizer) scaler.step(optimizer_d) if scheduler is not None: scheduler.step() scheduler_d.step() scaler.update() # Update logging information parser_source_loss += loss_seg_source.item() if loss_seg_target != 0.0: parser_target_loss += loss_seg_target.item() else: parser_target_loss += loss_seg_target parser_d_loss += loss_fool.item() discriminator_source_loss += source_d_loss.item() discriminator_target_loss += target_d_loss.item() # Print losses if iter_counter.needs_printing(): # Print log and visualize on tensorboard visualizer.info( f'Parser source loss at iter {iter_counter.total_steps_so_far}: {parser_source_loss / args.print_freq}') visualizer.add_scalar('Parser_Source_Loss', parser_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser target loss at iter {iter_counter.total_steps_so_far}: {parser_target_loss / args.print_freq}') visualizer.add_scalar('Parser_Target_Loss', parser_target_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser discriminator loss at iter {iter_counter.total_steps_so_far}: {parser_d_loss / args.print_freq}') visualizer.add_scalar('Parser_Discriminator_Loss', parser_d_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Source loss at iter {iter_counter.total_steps_so_far}: {discriminator_source_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Source_Loss', discriminator_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Target loss at iter {iter_counter.total_steps_so_far}: {discriminator_target_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Target_Loss', discriminator_target_loss / args.print_freq, iter_counter.total_steps_so_far) parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 # Validation phase if iter_counter.needs_validating(): # Set model to eval visualizer.info('Validating model at step %d' % iter_counter.total_steps_so_far) validate(args, model, val_loader, metrics, visualizer, i_iter) # Save model if iter_counter.needs_saving(): save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter) iter_counter.record_one_iteration() iter_counter.record_training_end() visualizer.info('End training') save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter) validate(args, model, val_loader, metrics, visualizer, i_iter) def train_dda(args, model, model_d, model_d_sp, source_train_loader, target_train_loader, val_loader, metrics, iter_counter, visualizer): if args.ft is not None: model = load_model(args, model, ft=args.ft) # Initialize variables cudnn.benchmark = True cudnn.enabled = True scaler = amp.GradScaler() start_iter = 0 parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 metrics.reset() # Define optimizers optimizer = optim.SGD(model.parameters(), lr=args.seg_lr, momentum=args.momentum, weight_decay=args.weight_decay) optimizer_d = optim.Adam(model_d.parameters(), lr=args.d_lr, betas=(args.beta1_d, args.beta2_d)) optimizer_d_sp = optim.Adam(model_d_sp.parameters(), lr=args.d_lr, betas=(args.beta1_d, args.beta2_d)) # Define schedulers scheduler = PolyLR(optimizer, max_iters=args.max_iters, power=args.power) scheduler_d = PolyLR(optimizer_d, max_iters=args.max_iters, power=args.power) scheduler_d_sp = PolyLR(optimizer_d_sp, max_iters=args.max_iters, power=args.power) # Define losses criterion if args.seg_loss == 'focal': criterion_seg = FocalLoss(num_class=args.num_classes, ignore_label=args.ignore_index) elif args.seg_loss == 'dice': criterion_seg = DiceLoss(num_classes=args.num_classes, ignore_index=args.ignore_index) else: criterion_seg = CrossEntropy2d(ignore_label=args.ignore_index) criterion_d = BCEWithLogitsLoss() # Resume model if continuing training if args.continue_train: model, model_d, model_d_sp, optimizer, optimizer_d, \ optimizer_d_sp, scheduler, scheduler_d, scheduler_d_sp, start_iter = load_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, model_d_sp, optimizer_d_sp, scheduler_d_sp) # Start training iter_counter.record_training_start(start_iter) source_train_loader_it = iter(source_train_loader) target_train_loader_it = iter(target_train_loader) visualizer.info(f'Lambda: {args.lambda_adv} at epoch: {iter_counter.total_epochs()}') iter_counter.record_training_start(start_iter) for i_iter in tqdm(iter_counter.training_steps()): # Set model to train model.train() model_d.train() model_d_sp.train() # Zero-grad the optimizers optimizer.zero_grad() optimizer_d.zero_grad() optimizer_d_sp.zero_grad() # Get source/target images and labels and move them to GPUs try: source_images, source_labels, _, _ = next(source_train_loader_it) except: source_train_loader_it = iter(source_train_loader) source_images, source_labels, _, _ = next(source_train_loader_it) try: target_images, target_labels, _, name = next(target_train_loader_it) except: target_train_loader_it = iter(target_train_loader) target_images, target_labels, _, name = next(target_train_loader_it) validate(args, model, val_loader, metrics, visualizer, i_iter) model.train() source_images, source_labels = source_images.to(args.gpu_ids[0], dtype=torch.float32), source_labels.to( args.gpu_ids[0], dtype=torch.long) target_images, target_labels = target_images.to(args.gpu_ids[0], dtype=torch.float32), target_labels.to( args.gpu_ids[0], dtype=torch.long) # TRAIN SCENE PARSER # Don't accumulate gradients in discriminator for param in model_d.parameters(): param.requires_grad = False for param in model_d_sp.parameters(): param.requires_grad = False # Train Source with amp.autocast(): spreds, spreds_sup1, spreds_sup2, ssp = model(source_images) loss1 = criterion_seg(spreds, source_labels) loss2 = criterion_seg(spreds_sup1, source_labels) loss3 = criterion_seg(spreds_sup2, source_labels) loss_seg_source = loss1 + loss2 + loss3 scaler.scale(loss_seg_source).backward() # Train Target with amp.autocast(): tpreds, _, _, tsp = model(target_images) loss_seg_target = 0.0 # Fool the discriminator d_output = model_d(F.softmax(tpreds, dim=1)) loss_fool = criterion_d(d_output, get_target_tensor(d_output, "source").to(args.gpu_ids[0], dtype=torch.float)) output_sp = model_d_sp(F.softmax(tsp, dim=1)) loss_fool_sp = criterion_d(output_sp, get_target_tensor(output_sp, "source").to(args.gpu_ids[0], dtype=torch.float)) loss_target = loss_fool * args.lambda_adv + loss_fool_sp * args.lambda_adv + loss_seg_target scaler.scale(loss_target).backward() # TRAIN DISCRIMINATOR for param in model_d.parameters(): param.requires_grad = True for param in model_d_sp.parameters(): param.requires_grad = True source_predictions = spreds.detach() target_predictions = tpreds.detach() source_predictions_sp = ssp.detach() target_predictions_sp = tsp.detach() with amp.autocast(): d_output_source = model_d(F.softmax(source_predictions, dim=1)) target_tensor = get_target_tensor(d_output_source, "source") source_d_loss = criterion_d(d_output_source, target_tensor.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(source_d_loss).backward() with amp.autocast(): d_output_target = model_d(F.softmax(target_predictions, dim=1)) target_tensor = get_target_tensor(d_output_target, "target") target_d_loss = criterion_d(d_output_target, target_tensor.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(target_d_loss).backward() with amp.autocast(): d_output_source_sp = model_d_sp(F.softmax(source_predictions_sp, dim=1)) target_tensor_sp = get_target_tensor(d_output_source_sp, "source") source_d_loss_sp = criterion_d(d_output_source_sp, target_tensor_sp.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(source_d_loss_sp).backward() with amp.autocast(): d_output_target_sp = model_d_sp(F.softmax(target_predictions_sp, dim=1)) target_tensor_sp = get_target_tensor(d_output_target_sp, "target") target_d_loss_sp = criterion_d(d_output_target_sp, target_tensor_sp.to(args.gpu_ids[0], dtype=torch.float)) / 2 scaler.scale(target_d_loss_sp).backward() scaler.step(optimizer) scaler.step(optimizer_d) scaler.step(optimizer_d_sp) if scheduler is not None: scheduler.step() scheduler_d.step() scheduler_d_sp.step() scaler.update() # Update logging information parser_source_loss += loss_seg_source.item() if loss_seg_target != 0.0: parser_target_loss += loss_seg_target.item() else: parser_target_loss += loss_seg_target parser_d_loss += loss_fool.item() discriminator_source_loss += source_d_loss.item() discriminator_target_loss += target_d_loss.item() # Print losses if iter_counter.needs_printing(): # Print log and visualize on tensorboard visualizer.info( f'Parser source loss at iter {iter_counter.total_steps_so_far}: {parser_source_loss / args.print_freq}') visualizer.add_scalar('Parser_Source_Loss', parser_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser target loss at iter {iter_counter.total_steps_so_far}: {parser_target_loss / args.print_freq}') visualizer.add_scalar('Parser_Target_Loss', parser_target_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Parser discriminator loss at iter {iter_counter.total_steps_so_far}: {parser_d_loss / args.print_freq}') visualizer.add_scalar('Parser_Discriminator_Loss', parser_d_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Source loss at iter {iter_counter.total_steps_so_far}: {discriminator_source_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Source_Loss', discriminator_source_loss / args.print_freq, iter_counter.total_steps_so_far) visualizer.info( f'Discriminator Target loss at iter {iter_counter.total_steps_so_far}: {discriminator_target_loss / args.print_freq}') visualizer.add_scalar('Discriminator_Target_Loss', discriminator_target_loss / args.print_freq, iter_counter.total_steps_so_far) parser_source_loss = 0.0 parser_target_loss = 0.0 parser_d_loss = 0.0 discriminator_source_loss = 0.0 discriminator_target_loss = 0.0 # Validation phase if iter_counter.needs_validating(): # Set model to eval visualizer.info('Validating model at step %d' % iter_counter.total_steps_so_far) validate(args, model, val_loader, metrics, visualizer, i_iter) # Save model if iter_counter.needs_saving(): save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter, model_d_sp, optimizer_d_sp, scheduler_d_sp) iter_counter.record_one_iteration() iter_counter.record_training_end() visualizer.info('End training') save_da_model(args, model, model_d, optimizer, optimizer_d, scheduler, scheduler_d, iter_counter, model_d_sp, optimizer_d_sp, scheduler_d_sp) validate(args, model, val_loader, metrics, visualizer, i_iter) def ssl(args, model, target_train_loader, visualizer): if args.ft is not None: model = load_model(args, model, ft=args.ft) model.eval() target_train_loader_it = iter(target_train_loader) index = 0 for i_iter in range(len(target_train_loader)): target_images, _, _, target_names = next(target_train_loader_it) if index % 100 == 0: visualizer.info(f'Processed {index} images') image_name = [] predicted_label = np.zeros((target_images.shape[0], 1024, 2048)) predicted_prob = np.zeros((target_images.shape[0], 1024, 2048)) for index, (timage, tname) in enumerate(zip(target_images, target_names)): if timage is not None: timage = timage.unsqueeze(0) timage = timage.to(args.gpu_ids[0], dtype=torch.float32) predictions = model(timage) target_predictions = predictions[0].unsqueeze(0) output = torch.nn.functional.softmax(target_predictions, dim=1) output = torch.nn.functional.upsample(output, (1024, 2048), mode='bilinear', align_corners=True).cpu().data[0].numpy() output = output.transpose(1, 2, 0) label, prob = np.argmax(output, axis=2), np.max(output, axis=2) predicted_label[index] = label.copy() predicted_prob[index] = prob.copy() image_name.append(tname) # sceglie le threshold guardando il batch thres = [] for i in range(19): x = predicted_prob[predicted_label == i] if len(x) == 0: thres.append(0) continue x = np.sort(x) thres.append(x[np.int(np.round(len(x) * 0.5))]) thres = np.array(thres) thres[thres > 0.9] = 0.9 # crea le pseudo labels for idx in range(target_images.shape[0]): name = image_name[idx] label = predicted_label[idx] prob = predicted_prob[idx] # creare la label di conseguenza for i in range(19): label[(prob < thres[i]) * (label == i)] = 255 output = np.asarray(label, dtype=np.uint8) mask_img = colorize_mask(output) output = Image.fromarray(output) name = name.split('/')[-1] os.makedirs("Cityscapes/pseudolabels", exist_ok=True) os.makedirs("Cityscapes/pseudolabels_rgb", exist_ok=True) output.save('%s/%s' % ("Cityscapes/pseudolabels", name)) mask_img.save('%s/%s' % ("Cityscapes/pseudolabels_rgb", name)) def ssl_st(args, model, source_train_loader, target_train_loader, visualizer): if args.ft is not None: model = load_model(args, model, ft=args.ft) model.eval() source_train_loader_it = iter(source_train_loader) target_train_loader_it = iter(target_train_loader) index = 0 for i_iter in range(len(target_train_loader)): try: source_images, source_labels, _, _ = next(source_train_loader_it) except: source_train_loader_it = iter(source_train_loader) source_images, source_labels, _, _ = next(source_train_loader_it) target_images, _, _, target_names = next(target_train_loader_it) if index % 100 == 0: visualizer.info(f'Processed {index} images') target_in_source = target_images.clone() for cnt, (trg_img, src_img) in enumerate(zip(target_images, source_images)): trg_in_src = torch.from_numpy(source_to_target_np(trg_img, src_img, L=0.01)) target_in_source[cnt] = trg_in_src.clone() target_images[cnt] = trg_img.clone() mean = torch.reshape(torch.from_numpy(args.mean), (1, 3, 1, 1)) B, C, H, W = target_images.shape mean = mean.repeat(B, 1, H, W) target_in_source = target_in_source - mean target_images = target_images - mean image_name = [] predicted_label = np.zeros((target_images.shape[0], 1024, 2048)) predicted_prob = np.zeros((target_images.shape[0], 1024, 2048)) predicted_label_st = np.zeros((target_images.shape[0], 1024, 2048)) predicted_prob_st = np.zeros((target_images.shape[0], 1024, 2048)) for index, (timage, image_st, tname) in enumerate(zip(target_images, target_in_source, target_names)): if timage is not None: timage = timage.unsqueeze(0) image_st = image_st.unsqueeze(0) img = torch.cat((timage, image_st), dim=0) img = img.to(args.gpu_ids[0], dtype=torch.float32) predictions = model(img) target_predictions, target_predictions_st = predictions[0].unsqueeze(0), predictions[1].unsqueeze(0) output = torch.nn.functional.softmax(target_predictions, dim=1) output = torch.nn.functional.upsample(output, (1024, 2048), mode='bilinear', align_corners=True).cpu().data[0].numpy() output = output.transpose(1, 2, 0) label, prob = np.argmax(output, axis=2), np.max(output, axis=2) predicted_label[index] = label.copy() predicted_prob[index] = prob.copy() image_name.append(tname) output_st = torch.nn.functional.softmax(target_predictions_st, dim=1) output_st = torch.nn.functional.upsample(output_st, (1024, 2048), mode='bilinear', align_corners=True).cpu().data[0].numpy() output_st = output_st.transpose(1, 2, 0) label_st, prob_st = np.argmax(output_st, axis=2), np.max(output_st, axis=2) predicted_label_st[index] = label_st.copy() predicted_prob_st[index] = prob_st.copy() # sceglie le threshold guardando il batch thres, thres_st = [], [] for i in range(19): cont, cont_st = False, False x = predicted_prob[predicted_label == i] x_st = predicted_prob_st[predicted_label_st == i] if len(x) == 0: thres.append(0) cont = True if len(x_st) == 0: thres_st.append(0) cont_st = True if not cont: x = np.sort(x) thres.append(x[np.int(np.round(len(x) * 0.5))]) if not cont_st: x_st = np.sort(x_st) thres_st.append(x_st[np.int(np.round(len(x_st) * 0.5))]) thres, thres_st = np.array(thres), np.array(thres_st) for cls in range(len(thres)): thres[cls] = max(thres[cls], thres_st[cls]) thres[thres > 0.9] = 0.9 # crea le pseudo labels for idx in range(target_images.shape[0]): name = image_name[idx] label = predicted_label[idx] prob = predicted_prob[idx] label_st = predicted_label_st[idx] prob_st = predicted_prob_st[idx] # max label_new = np.maximum(label, label_st) prob_new = np.maximum(prob, prob_st) # creare la label di conseguenza for i in range(19): label_new[(prob_new < thres[i]) * (label_new == i)] = 255 output = np.asarray(label_new, dtype=np.uint8) mask_img = colorize_mask(output) output = Image.fromarray(output) name = name.split('/')[-1] os.makedirs("Cityscapes/pseudolabels_st", exist_ok=True) os.makedirs("Cityscapes/pseudolabels_st_rgb", exist_ok=True) output.save('%s/%s' % ("Cityscapes/pseudolabels_st", name)) mask_img.save('%s/%s' % ("Cityscapes/pseudolabels_st_rgb", name))

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'''test_style - test coding style confirmation of CGAT code =========================================================== :Author: Andreas Heger :Release: $Id$ :Date: |today| :Tags: Python Purpose ------- This script runs pep8 on all scripts in the CGAT code collection. This script is best run within nosetests:: nosetests tests/test_style.py ''' import pep8 import glob import os from nose.tools import ok_ # DIRECTORIES to examine for python modules/scripts EXPRESSIONS = ( ('tests', 'tests/*.py'), ('scripts', 'scripts/*.py'), ('optic', 'scripts/optic/*.py'), ('gpipe', 'scripts/gpipe/*.py'), ('CGAT', 'CGAT/*.py'), ('CGATPipelines', 'CGATPipelines/*.py'), ('trackers', 'CGATPipelines/pipeline_docs/*/trackers/*.py')) # Codes to ignore in the pep8 BaseReport IGNORE = set(('E101', # indentation contains mixed spaces and tabs 'E201', # whitespace after '(' 'E202', # whitespace before ')' 'E122', # continuation line missing indentation or outdented 'E265', # block comment should start with '# ' 'E501', # line too long (82 > 79 characters) 'E502', # the backslash is redundant between brackets 'E731', # do not assign a lambda expression, use a def 'W191', 'W291', 'W293', 'W391', 'W503', # line break before binary operator 'W601', 'W602', 'files', 'directories', 'physical lines', 'logical lines',)) def check_style(filename): '''check style of filename. ''' p = pep8.StyleGuide(quiet=True) report = p.check_files([filename]) # count errors/warning excluding # those to ignore take = [y for x, y in report.counters.items() if x not in IGNORE] found = ['%s:%i' % (x, y) for x, y in report.counters.items() if x not in IGNORE] total = sum(take) ok_(total == 0, 'pep8 style violations in %s: %s' % (filename, ','.join(found))) def test_style(): '''test style of scripts ''' for label, expression in EXPRESSIONS: files = glob.glob(expression) files.sort() for f in files: if os.path.isdir(f): continue check_style.description = os.path.abspath(f) yield(check_style, os.path.abspath(f))

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import random from difflib import SequenceMatcher from string import ascii_uppercase, digits, punctuation import numpy import regex def pred_to_dict(text, pred, prob): res = {"company": ("", 0), "date": ("", 0), "address": ("", 0), "total": ("", 0)} keys = list(res.keys()) seps = [0] + (numpy.nonzero(numpy.diff(pred))[0] + 1).tolist() + [len(pred)] for i in range(len(seps) - 1): pred_class = pred[seps[i]] - 1 if pred_class == -1: continue new_key = keys[pred_class] new_prob = prob[seps[i] : seps[i + 1]].max() if new_prob > res[new_key][1]: res[new_key] = (text[seps[i] : seps[i + 1]], new_prob) return {k: regex.sub(r"[\t\n]", " ", v[0].strip()) for k, v in res.items()} def compare_truth(pred_dict, truth_dict): ratio = 0 for k in truth_dict.keys(): ratio += SequenceMatcher(None, truth_dict[k], pred_dict[k]).ratio() return ratio / len(truth_dict.keys()) def robust_padding(texts, labels): maxlen = max(len(t) for t in texts) for i, text in enumerate(texts): if len(text) == maxlen: continue pad_before = random.randint(0, maxlen - len(text)) pad_after = maxlen - pad_before - len(text) texts[i] = random_string(pad_before) + text + random_string(pad_after) labels[i] = numpy.pad( labels[i], (pad_before, pad_after), "constant", constant_values=0 ) def random_string(n): if n == 0: return "" x = random.random() if x > 0.5: pad = " " * n elif x > 0.3: pad = "".join(random.choices(digits + " \t\n", k=n)) elif x > 0.2: pad = "".join(random.choices(ascii_uppercase + " \t\n", k=n)) elif x > 0.1: pad = "".join(random.choices(ascii_uppercase + digits + " \t\n", k=n)) else: pad = "".join( random.choices(ascii_uppercase + digits + punctuation + " \t\n", k=n) ) return pad if __name__ == "__main__": pred = {"a": "qwertyuiop", "b": "asdfghjkl", "c": "zxcvbnm"} truth = {"a": "qwertyu iop", "b": "ascfghjkl ", "c": ""} print(compare_truth(pred, truth))

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import os import operator index_unigram= {} # Inverted Index for Unigrams """index_bigram={} # Inverted Index for Bigrams index_trigram={} # Inverted Index for Trigrams unigram_tf={} # Unigrams term frequencies bigram_tf={} # Bigrams term frequencies trigram_tf={} # Trigrams term frequencies unigram_df={} # Unigrams Document frequencies bigram_df={} # Bigrams Document frequencies trigram_df={} # Trigrams Document frequencies uni_tokens={} # Unigrams- no. of tokens in each document bi_tokens={} # Bigrams- no. of tokens in each document tri_tokens={} # Trigrams- no. of tokens in each document""" # fetch corups from the below path docs_source_path = "D:\IR\dummypro\corpus" # filenamelist contains the list of raw HTML file names filenamelist = os.listdir(docs_source_path) # to generate an inverted index def create_inverted_index(): for filename in filenamelist: content=[] name_of_file=filename.split('.txt') docID=name_of_file[0] print docID f = open(docs_source_path + '\\' + filename, 'r+') raw_data=f.read() content=raw_data.split() ## Unigram-Inverted-Index generation unigram_token=[] for c in content: #if c not in unigram_token: # to generate the number of tokens in each document #unigram_token.append(c) if index_unigram.has_key(c): # when this term occurs for the first time in a particular document if docID not in index_unigram[c]: index_unigram[c].update({docID:1}) else: index_unigram[c][docID]+=1 # if term does not exist in the dictionary yet else: index_unigram[c]={docID:1} #uni_tokens[docID]=len(unigram_token) #Bigram-Inverted_Index creation """ bigrams_token=[] for i,c in enumerate(content): next=i+1 if next < len(content): term=c+' '+content[i+1] if term not in bigrams_token: # to generate the number of tokens in each document bigrams_token.append(term) if index_bigram.has_key(term): # when this term occurs for the first time in a particular document if docID not in index_bigram[term]: index_bigram[term].update({docID:1}) else: index_bigram[term][docID]+=1 # if term does not exist in the dictionary yet else: index_bigram[term]={docID:1} bi_tokens[docID]=len(bigrams_token) #Trigram_Inverted_Index creation trigram_token=[] for i,c in enumerate(content): next=i+2 if next < len(content): term=c+' '+content[i+1]+' '+content[i+2] if term not in trigram_token: # to generate the number of tokens in each document trigram_token.append(term) if index_trigram.has_key(term): # when this term occurs for the first time in a particular document if docID not in index_trigram[term]: index_trigram[term].update({docID:1}) else: index_trigram[term][docID]+=1 # if term does not exist in the dictionary yet else: index_trigram[term]={docID:1} tri_tokens[docID]=len(trigram_token) # to generate the term frequency and document frequency table def generate_tf_df(index_gram,gram_tf,gram_df): for key,value in index_gram.iteritems(): term_frequency=0 docString="" i=0 for k,v in value.iteritems(): i+=1 term_frequency+=v docString+=k if(i<len(value)): docString+=" " no_of_docs=len(value) # dictionary to hold the term frequencies gram_tf[key]=term_frequency # dictionary to hold the document frequencies gram_df[key]={docString:no_of_docs} # to sort and write the term frequency table to a file def generate_tf_table(gram_tf,nof): sort_dict=sorted(sorted(gram_tf.iteritems()), key=operator.itemgetter(1), reverse=True) f=open(nof,"w+") for k,v in sort_dict: f.write(str(k)+" -------------> "+str(v)+"\n") f.close() # to sort and write document frequency table to file def generate_df_table(gram_df,nof): sort_dict=sorted(gram_df.iteritems(), key=operator.itemgetter(0)) f=open(nof,"w+") for k,v in sort_dict: df_value="" for key,value in v.iteritems(): df_value=str(key)+" -> "+str(value) final_string=str(k)+" ------------> "+df_value+"\n" f.write(final_string) f.close()""" # to write the inverted index to a file def write_index_to_file(dict_gram,nof): f=open(nof,"w+") for k,v in dict_gram.iteritems(): value_term="" i=0 for key,val in v.iteritems(): i+=1 value_term+="("+str(key)+","+str(val)+")" if(i<len(v)): value_term+="," final_term=str(k)+" -----> "+value_term+"\n" f.write(final_term) f.close() """# to write ti no. of tokens dictionary to file def write_tokens_to_file(grams_tokens,nof): token_string="" f=open(nof,"w+") for k,v in grams_tokens.iteritems(): token_string+=str(k)+"->"+str(v)+"\n" f.write(token_string) f.close()""" def _mainindexer(): create_inverted_index() write_index_to_file(index_unigram,"Task_2_Index_Unigram.txt") """write_index_to_file(index_bigram,"Task_2_Index_Bigram.txt") write_index_to_file(index_trigram,"Task_2_Index_Trigram.txt") write_tokens_to_file(uni_tokens,"Task_2_Unigram_tokens.txt") write_tokens_to_file(bi_tokens,"Task_2_Bigram_tokens.txt") write_tokens_to_file(tri_tokens,"Task_2_Trigram_tokens.txt") generate_tf_df(index_unigram,unigram_tf,unigram_df) generate_tf_df(index_bigram,bigram_tf,bigram_df) generate_tf_df(index_trigram,trigram_tf,trigram_df) generate_tf_table(unigram_tf,"Task 3_Unigram_TF.txt") generate_tf_table(bigram_tf,"Task 3_Bigram_TF.txt") generate_tf_table(trigram_tf,"Task 3_Trigram_TF.txt") generate_df_table(unigram_df,"Task 3_Unigram_DF.txt") generate_df_table(bigram_df,"Task 3_Bigram_DF.txt") generate_df_table(trigram_df,"Task 3_Trigram_DF.txt")""" _mainindexer()

[ "rachna_reddy93@yahoo.in" ]

rachna_reddy93@yahoo.in

d8f3e2e8da6c3e45068109576849095485f36831

21790d64e67a705e3cba12facc7a5132fd4b42db

/ex7.py

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jiaoweiming215/ABOUT-OF-PYTHON

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# format string #-*-coding:utf-8-*- import time; ticks = time.time() localtime = time.localtime(time.time()) print "时间:",ticks print"NOw",localtime

[ "jiaoweimng215@163.com" ]

jiaoweimng215@163.com

100c7b9715f2d3d1d5e5fcd0b8a906106e8912a9

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/AlphaZero_Gomoku/mcts_alphaZero.py

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[]

no_license

RanZhu1989/Py.RL

cec5e6abdad5988fa1fe5f61112728bc5b0e3334

9abb025ced8fc7e51f61790301efd7aee31826cb

refs/heads/master

2022-12-07T10:38:16.095433

2020-08-27T14:47:41

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# -*- coding: utf-8 -*- """ Monte Carlo Tree Search in AlphaGo Zero style, which uses a policy-value network to guide the tree search and evaluate the leaf nodes @author: Junxiao Song """ import numpy as np import copy def softmax(x): probs = np.exp(x - np.max(x)) probs /= np.sum(probs) return probs class TreeNode(object): """A node in the MCTS tree.""" def __init__(self, parent, prior_p): self._parent = parent self._children = {} self._n_visits = 0 self._Q = 0 self._u = 0 self._P = prior_p def select(self, c_puct): """Return: A tuple of (action, next_node)""" return max(self._children.items(), key=lambda act_node: act_node[1].get_value(c_puct)) def get_value(self, c_puct): self._u = (c_puct * self._P * np.sqrt(self._parent._n_visits) / (1 + self._n_visits)) return self._Q + self._u def expand(self, action_priors): for action, prob in action_priors: if action not in self._children: self._children[action] = TreeNode(self, prob) def update(self, leaf_value): self._n_visits += 1 self._Q += 1.0*(leaf_value - self._Q) / self._n_visits def update_recursive(self, leaf_value): if self._parent: self._parent.update_recursive(-leaf_value) self.update(leaf_value) def is_leaf(self): return self._children == {} def is_root(self): return self._parent is None class MCTS(object): """An implementation of Monte Carlo Tree Search.""" def __init__(self, policy_value_fn, c_puct=5, n_playout=10000): self._root = TreeNode(None, 1.0) self._policy = policy_value_fn self._c_puct = c_puct self._n_playout = n_playout def _playout(self, state): """完整的执行选择、扩展评估和回传更新等步骤""" node = self._root # 选择 while(1): if node.is_leaf(): break action, node = node.select(self._c_puct) state.do_move(action) # 扩展及评估 action_probs, leaf_value = self._policy(state) end, winner = state.game_end() if not end: node.expand(action_probs) else: if winner == -1: # 平局 leaf_value = 0.0 else: leaf_value = ( 1.0 if winner == state.get_current_player() else -1.0 ) # 回传更新 node.update_recursive(-leaf_value) def get_move_probs(self, state, temp=1e-3): for n in range(self._n_playout): state_copy = copy.deepcopy(state) self._playout(state_copy) act_visits = [(act, node._n_visits) for act, node in self._root._children.items()] acts, visits = zip(*act_visits) act_probs = softmax(1.0/temp * np.log(np.array(visits) + 1e-10)) return acts, act_probs def update_with_move(self, last_move): if last_move in self._root._children: self._root = self._root._children[last_move] self._root._parent = None else: self._root = TreeNode(None, 1.0) # def __str__(self): # return "MCTS" class MCTSPlayer(object): """AI player based on MCTS""" def __init__(self, policy_value_function, c_puct=5, n_playout=2000, is_selfplay=0): self.mcts = MCTS(policy_value_function, c_puct, n_playout) self._is_selfplay = is_selfplay def get_action(self, board, temp=1e-3, return_prob=0): sensible_moves = board.availables move_probs = np.zeros(board.width*board.height) if len(sensible_moves) > 0: acts, probs = self.mcts.get_move_probs(board, temp) move_probs[list(acts)] = probs if self._is_selfplay: move = np.random.choice( acts, p=0.75*probs + 0.25*np.random.dirichlet(0.3*np.ones(len(probs))) ) # 更新根节点，复用搜索子树 self.mcts.update_with_move(move) else: move = np.random.choice(acts, p=probs) # 重置根节点 self.mcts.update_with_move(-1) location = board.move_to_location(move) print("AI move: %d,%d\n" % (location[0], location[1])) if return_prob: return move, move_probs else: return move else: print("WARNING: the board is full") def set_player_ind(self, p): self.player = p def reset_player(self): self.mcts.update_with_move(-1) def __str__(self): return "MCTS {}".format(self.player)

[ "gemina_cat@163.com" ]

gemina_cat@163.com

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/FILTER.py

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dasing/CBIR

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2021-08-16T10:34:17.802624

2017-11-19T16:16:22

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import numpy as np import cv2 from PARAMETER import * def buildFilters(): filters = [] ksize = 31 orientation = 4 scale = 4 bandWidth = 4 for theta in np.arange(0, np.pi, np.pi/orientation): for sigma in np.arange(1, scale+1, 1 ): for lamda in np.arange(0, np.pi, np.pi/bandWidth): params = {'ksize':(ksize, ksize), 'sigma':sigma, 'theta':theta, 'lambd':lamda, 'gamma':0.02, 'psi':0, 'ktype': cv2.CV_32F} kern = cv2.getGaborKernel(**params) kern /= 1.5*kern.sum() filters.append(kern) return filters def buildRandomMatrix( featureDim ): reducedDim = int(round(featureDim * PROJECT_RATIO)) size = reducedDim * featureDim genMatrix = np.random.normal( 0, 0.1, size ).reshape((reducedDim, featureDim)) # print("reducedDim is {}".format(reducedDim)) # print("size is {}".format(size)) # print(genMatrix) return genMatrix

[ "f2283689@gmail.com" ]

f2283689@gmail.com

ad482575e6fc07931a01a486236fdad23538b5d3

58a9984d8ceb274ddd2e35a4c1416be22fb3081f

/cnn_baseline.py

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[]

no_license

KaranKaur/mnist

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refs/heads/master

2020-03-28T07:10:21.621632

2018-09-08T00:50:29

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# from keras.datasets import mnist import matplotlib.pyplot as plt import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils import pandas as pd from sklearn.model_selection import train_test_split # load the data files def plot_figure(): plt.subplot(221) plt.imshow(X_train[0], cmap=plt.get_cmap('gray')) plt.subplot(222) plt.imshow(X_train[1], cmap=plt.get_cmap('gray')) plt.subplot(223) plt.imshow(X_train[2], cmap=plt.get_cmap('gray')) plt.subplot(224) plt.imshow(X_train[3], cmap=plt.get_cmap('gray')) plt.show() np.random.seed(7) def get_data(): #read training data mnist_train = pd.read_csv('.') x_train = mnist_train.iloc[:, 1:] y_train = mnist_train.iloc[:, 0] #read test data mnist_test = pd.read_csv('.') x_test = mnist_test.iloc[:, :] return x_train.values, y_train.values, x_test.values def baseline_model(): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(1, 28, 28))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model def main(): X_train, Y_train, X_test = get_data() print(X_train.shape) # reshape to be [samples][pixels][width][height] x_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') x_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') # normalize x_train /= 255 x_test /= 255 y_train = np_utils.to_categorical(y_train) num_classes = y_train.shape[1] model = baseline_model() model.fit(x_train, y_train,batch_size=128, nb_epoch=10, verbose=1) #scores = model.evaluate(x_test, y_test, verbose=0) #print("CNN Error: %.2f%%" % (100 - scores[1] * 100)) print('Fitted the model!') predict = model.predict_classes(x_test, batch_size=128, verbose=1) return predict if __name__ == '__main__': predictions = main() df = pd.DataFrame(predictions) df.to_csv("predictions_base_cnn.csv") print('saved the predictions!')

[ "kkaur89@umd.edu" ]

kkaur89@umd.edu

a96134858b3badb524e61aed9c77e9b7fcefa628

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/crypto/PhaseStream 3/find_aes_blocks.py

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[]

no_license

NU-Cyber-Clinic/Cyber-Apocalypse-2021

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2023-04-11T01:42:08.483075

2021-04-28T15:58:15

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from bitstring import BitArray # CTR explainer: https://pycryptodome.readthedocs.io/en/latest/src/util/util.html#crypto-util-counter-module test = b"No right of private conversation was enumerated in the Constitution. I don't suppose it occurred to anyone at the time that it could be prevented." test_bin = BitArray(test).bin encrypted = "464851522838603926f4422a4ca6d81b02f351b454e6f968a324fcc77da30cf979eec57c8675de3bb92f6c21730607066226780a8d4539fcf67f9f5589d150a6c7867140b5a63de2971dc209f480c270882194f288167ed910b64cf627ea6392456fa1b648afd0b239b59652baedc595d4f87634cf7ec4262f8c9581d7f56dc6f836cfe696518ce434ef4616431d4d1b361c" encrypted_bin = BitArray(hex=encrypted).bin print(test_bin) print(encrypted_bin) print() print() print() block = [] test_block = [] for i in range(146*8): block.append( '1' if test_bin[i] != encrypted_bin[i] else '0') block_hex = BitArray(bin=''.join(block)).hex print(block_hex)

[ "54325599+cosmin-bianu@users.noreply.github.com" ]

54325599+cosmin-bianu@users.noreply.github.com

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2023-08-17T18:12:30.886054

2021-10-17T07:17:48

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# %% [markdown] # ## Optimizer: Adam + lr scheduling # Inverse square root 排程對於訓練 Transformer 時的穩定性很重要，後來也用在 RNN 上。 # 根據底下公式來更新 learning rate，前期線性增長，後期根據更新步數方根的倒數來遞減。 # $$lrate = d_{\text{model}}^{-0.5}\cdot\min({step\_num}^{-0.5},{step\_num}\cdot{warmup\_steps}^{-1.5})$$ # code [source](https://nlp.seas.harvard.edu/2018/04/03/attention.html) class NoamOpt: "Optim wrapper that implements rate." def __init__(self, model_size, factor, warmup, optimizer): self.optimizer = optimizer self._step = 0 self.warmup = warmup self.factor = factor self.model_size = model_size self._rate = 0 @property def param_groups(self): return self.optimizer.param_groups def multiply_grads(self, c): """Multiplies grads by a constant *c*.""" for group in self.param_groups: for p in group['params']: if p.grad is not None: p.grad.data.mul_(c) def step(self): "Update parameters and rate" self._step += 1 rate = self.rate() for p in self.param_groups: p['lr'] = rate self._rate = rate self.optimizer.step() def rate(self, step=None): "Implement `lrate` above" if step is None: step = self._step return 0 if not step else self.factor * (self.model_size**(-0.5) * min( step**(-0.5), step * self.warmup**(-1.5)))

[ "silencejiang12138@gmail.com" ]

silencejiang12138@gmail.com

ad008fd9022a57c2af0750b1b2aba2d6d91e46b7

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/igep_qa/helpers/common.py

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permissive

eballetbo/igep_qa

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refs/heads/master

2020-03-29T13:16:30.695466

2014-02-19T14:53:51

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#!/usr/bin/env python """ This provides various generic helper functions and classes """ import fcntl import mmap import os import struct import socket def is_in_path(name): """ Return True if name refers to an existing file in path, otherwise returns False. Keyword arguments: - name: The path name. """ for p in os.environ["PATH"].split(os.pathsep): if os.path.exists(os.path.join(p, name)): return True # File not found in path return False def is_nfsroot(): """ Return True if rootfs is mounted via nfs, otherwise returns False """ fd = open("/proc/mounts", "r") content = fd.read() if content.find("/ nfs") == -1 : return False return True def get_hwaddr(ifname): """ Return the MAC address for a specific local interface. Keyword arguments: - ifname: The interface name, e.g. eth0, wlan0. """ try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) info = fcntl.ioctl(s.fileno(), 0x8927, struct.pack('256s', ifname[:15])) return ''.join(['%02x:' % ord(char) for char in info[18:24]])[:-1] # exception except: return "" class QCpuinfo: """ Helper class to parse the /proc/cpuinfo On Linux systems various information about the CPU ( or CPUs ) in the computer can be gleaned from /proc/cpuinfo. Known issues: 1. This class doesn't work as expected with more than one core """ def __init__(self): """ Parse /proc/cpuinfo file and store into map. """ fd = open("/proc/cpuinfo", "r") self.data = { "processor" : "0" } lines = fd.readlines() for line in lines: keymap = line.split(": ") if len(keymap) == 2: key = keymap[0].strip("\t") value = keymap[1].rstrip("\n") self.data[key] = value fd.close() def __getitem__(self, index): """ Overload [] operator, access to the data like a mamp Example 1: Is easy to get the "Processor" value with, cpu = TCpuinfo() print cpu["Processor"] The result should be like this ARMv7 Processor rev 2 (v7l) """ return self.data[index] class QCmdline: """ Helper class to parse the /proc/cmdline On Linux systems information about the kernel command line in the computer can be gleaned from /proc/cmdline. """ def __init__(self): fd = open("/proc/cmdline", "r") self.cmdline = fd.read() fd.close() def checkparam(self, param): """ Check for the existence of a kernel parameter Return True if parameter is in cmdline, otherwise returns False Keyword arguments: - param: The paramater to be found. """ if (self.cmdline.find(param) == -1) : return False return True class QDeviceTree: """ Helper class to parse the /proc/device-tree On Linux systems information about device-tree in the computer can be gleaned from /proc/device-tree directory. """ def compatible(self, name): """ Check for the existence of a kernel parameter Return True if name is in compatible, otherwise returns False Keyword arguments: - name: The compatible string to be found. """ fd = open("/proc/device-tree/compatible", "r") return (name in fd.read()) class QMmap: """ Simple helper class to read/write from/to any location in memory References: http://www.lartmaker.nl/lartware/port/devmem2.c """ MAP_MASK = mmap.PAGESIZE - 1 WORD = 4 def read(self, addr): """ Read from any location in memory Returns the readed value in hexadecimal format Keyword arguments: - addr: The memory address to be readed. """ fd = os.open("/dev/mem", os.O_RDWR | os.O_SYNC) # Map one page mm = mmap.mmap(fd, mmap.PAGESIZE, mmap.MAP_SHARED, mmap.PROT_WRITE | mmap.PROT_READ, offset=addr & ~self.MAP_MASK) mm.seek(addr & self.MAP_MASK) retval = struct.unpack('I', mm.read(self.WORD)) mm.close() os.close(fd) return "%08X" % retval[0]

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eballetbo@iseebcn.com

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""" Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). """ # from torch.utils.serialization import load_lua from torch.utils.data import DataLoader from core.networks import Vgg16 from torch.autograd import Variable from torch.optim import lr_scheduler from torchvision import transforms # from data import ImageFilelist, ImageFolder import torch import torch.nn as nn import os import math import torchvision.utils as vutils import yaml import numpy as np import torch.nn.init as init import time # from datasets.mnist2colormnist.MNIST_Color import MNISTDataset # from datasets.cure_tsr.CURE_TSR_Datasets import CURE_StreetSigns # from datasets.loaders import get_model_loaders # Methods # get_config : load yaml file # eformat : # write_2images : save output image # prepare_sub_folder : create checkpoints and images folders for saving outputs # write_one_row_html : write one row of the html file for output images # write_html : create the html file. # write_loss # slerp # get_slerp_interp # get_model_list # load_vgg16 # load_inception # vgg_preprocess # get_scheduler # weights_init # def get_all_data_loaders(conf): # train_A, train_B, test_A, test_B = get_model_loaders(conf) # # return train_A, train_B, test_A, test_B def get_config(config): with open(config, 'r') as stream: return yaml.load(stream, Loader=yaml.FullLoader) def eformat(f, prec): s = "%.*e"%(prec, f) mantissa, exp = s.split('e') # add 1 to digits as 1 is taken by sign +/- return "%se%d"%(mantissa, int(exp)) def __write_images(image_outputs, display_image_num, file_name): image_outputs = [images.expand(-1, 3, -1, -1) for images in image_outputs] # expand gray-scale images to 3 channels image_tensor = torch.cat([images[:display_image_num] for images in image_outputs], 0) image_grid = vutils.make_grid(image_tensor.data, nrow=display_image_num, padding=0, normalize=True) vutils.save_image(image_grid, file_name, nrow=1) def write_2images(image_outputs, display_image_num, image_directory, postfix): n = len(image_outputs) __write_images(image_outputs[0:n//2], display_image_num, '%s/gen_a2b_%s.jpg' % (image_directory, postfix)) __write_images(image_outputs[n//2:n], display_image_num, '%s/gen_b2a_%s.jpg' % (image_directory, postfix)) def prepare_sub_folder(output_directory): image_directory = os.path.join(output_directory, 'images') if not os.path.exists(image_directory): print("Creating directory: {}".format(image_directory)) os.makedirs(image_directory) checkpoint_directory = os.path.join(output_directory, 'checkpoints') if not os.path.exists(checkpoint_directory): print("Creating directory: {}".format(checkpoint_directory)) os.makedirs(checkpoint_directory) return checkpoint_directory, image_directory def write_one_row_html(html_file, iterations, img_filename, all_size): html_file.write("<h3>iteration [%d] (%s)</h3>" % (iterations,img_filename.split('/')[-1])) html_file.write(""" <p><a href="%s"> <img src="%s" style="width:%dpx"> </a><br> <p> """ % (img_filename, img_filename, all_size)) return def write_html(filename, iterations, image_save_iterations, image_directory, all_size=1536): html_file = open(filename, "w") html_file.write(''' <!DOCTYPE html> <html> <head> <title>Experiment name = %s</title> <meta http-equiv="refresh" content="30"> </head> <body> ''' % os.path.basename(filename)) html_file.write("<h3>current</h3>") write_one_row_html(html_file, iterations, '%s/gen_a2b_train_current.jpg' % (image_directory), all_size) write_one_row_html(html_file, iterations, '%s/gen_b2a_train_current.jpg' % (image_directory), all_size) for j in range(iterations, image_save_iterations-1, -1): if j % image_save_iterations == 0: write_one_row_html(html_file, j, '%s/gen_a2b_test_%08d.jpg' % (image_directory, j), all_size) write_one_row_html(html_file, j, '%s/gen_b2a_test_%08d.jpg' % (image_directory, j), all_size) write_one_row_html(html_file, j, '%s/gen_a2b_train_%08d.jpg' % (image_directory, j), all_size) write_one_row_html(html_file, j, '%s/gen_b2a_train_%08d.jpg' % (image_directory, j), all_size) html_file.write("</body></html>") html_file.close() def write_loss(iterations, trainer, train_writer): members = [attr for attr in dir(trainer) \ if not callable(getattr(trainer, attr)) and not attr.startswith("__") and ('loss' in attr or 'grad' in attr or 'nwd' in attr)] for m in members: train_writer.add_scalar(m, getattr(trainer, m), iterations + 1) def slerp(val, low, high): """ original: Animating Rotation with Quaternion Curves, Ken Shoemake https://arxiv.org/abs/1609.04468 Code: https://github.com/soumith/dcgan.torch/issues/14, Tom White """ omega = np.arccos(np.dot(low / np.linalg.norm(low), high / np.linalg.norm(high))) so = np.sin(omega) return np.sin((1.0 - val) * omega) / so * low + np.sin(val * omega) / so * high def get_slerp_interp(nb_latents, nb_interp, z_dim): """ modified from: PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot https://github.com/ptrblck/prog_gans_pytorch_inference """ latent_interps = np.empty(shape=(0, z_dim), dtype=np.float32) for _ in range(nb_latents): low = np.random.randn(z_dim) high = np.random.randn(z_dim) # low + np.random.randn(512) * 0.7 interp_vals = np.linspace(0, 1, num=nb_interp) latent_interp = np.array([slerp(v, low, high) for v in interp_vals], dtype=np.float32) latent_interps = np.vstack((latent_interps, latent_interp)) return latent_interps[:, :, np.newaxis, np.newaxis] # Get model list for resume def get_model_list(dirname, key): if os.path.exists(dirname) is False: return None gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if os.path.isfile(os.path.join(dirname, f)) and key in f and ".pt" in f] if gen_models is None: return None gen_models.sort() last_model_name = gen_models[-1] return last_model_name def load_vgg16(model_dir): """ Use the model from https://github.com/abhiskk/fast-neural-style/blob/master/neural_style/utils.py """ if not os.path.exists(model_dir): os.mkdir(model_dir) if not os.path.exists(os.path.join(model_dir, 'vgg16.weight')): if not os.path.exists(os.path.join(model_dir, 'vgg16.t7')): os.system('wget https://www.dropbox.com/s/76l3rt4kyi3s8x7/vgg16.t7?dl=1 -O ' + os.path.join(model_dir, 'vgg16.t7')) vgglua = load_lua(os.path.join(model_dir, 'vgg16.t7')) vgg = Vgg16() for (src, dst) in zip(vgglua.parameters()[0], vgg.parameters()): dst.data[:] = src torch.save(vgg.state_dict(), os.path.join(model_dir, 'vgg16.weight')) vgg = Vgg16() vgg.load_state_dict(torch.load(os.path.join(model_dir, 'vgg16.weight'))) return vgg def load_inception(model_path): state_dict = torch.load(model_path) model = inception_v3(pretrained=False, transform_input=True) model.aux_logits = False num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, state_dict['fc.weight'].size(0)) model.load_state_dict(state_dict) for param in model.parameters(): param.requires_grad = False return model def vgg_preprocess(batch): tensortype = type(batch.data) (r, g, b) = torch.chunk(batch, 3, dim = 1) batch = torch.cat((b, g, r), dim = 1) # convert RGB to BGR batch = (batch + 1) * 255 * 0.5 # [-1, 1] -> [0, 255] mean = tensortype(batch.data.size()).cuda() mean[:, 0, :, :] = 103.939 mean[:, 1, :, :] = 116.779 mean[:, 2, :, :] = 123.680 batch = batch.sub(Variable(mean)) # subtract mean return batch def get_scheduler(optimizer, hyperparameters, iterations=-1): if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant': scheduler = None # constant scheduler elif hyperparameters['lr_policy'] == 'step': scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'], gamma=hyperparameters['gamma'], last_epoch=iterations) else: return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy']) return scheduler def weights_init(init_type='gaussian'): def init_fun(m): classname = m.__class__.__name__ if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'): # print m.__class__.__name__ if init_type == 'gaussian': init.normal_(m.weight.data, 0.0, 0.02) elif init_type == 'xavier': init.xavier_normal_(m.weight.data, gain=math.sqrt(2)) elif init_type == 'kaiming': init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': init.orthogonal_(m.weight.data, gain=math.sqrt(2)) elif init_type == 'default': pass else: assert 0, "Unsupported initialization: {}".format(init_type) if hasattr(m, 'bias') and m.bias is not None: init.constant_(m.bias.data, 0.0) return init_fun class Timer: def __init__(self, msg): self.msg = msg self.start_time = None def __enter__(self): self.start_time = time.time() def __exit__(self, exc_type, exc_value, exc_tb): print(self.msg % (time.time() - self.start_time)) def pytorch03_to_pytorch04(state_dict_base, trainer_name): def __conversion_core(state_dict_base, trainer_name): state_dict = state_dict_base.copy() if trainer_name == 'MUNIT': for key, value in state_dict_base.items(): if key.endswith(('enc_content.model.0.norm.running_mean', 'enc_content.model.0.norm.running_var', 'enc_content.model.1.norm.running_mean', 'enc_content.model.1.norm.running_var', 'enc_content.model.2.norm.running_mean', 'enc_content.model.2.norm.running_var', 'enc_content.model.3.model.0.model.1.norm.running_mean', 'enc_content.model.3.model.0.model.1.norm.running_var', 'enc_content.model.3.model.0.model.0.norm.running_mean', 'enc_content.model.3.model.0.model.0.norm.running_var', 'enc_content.model.3.model.1.model.1.norm.running_mean', 'enc_content.model.3.model.1.model.1.norm.running_var', 'enc_content.model.3.model.1.model.0.norm.running_mean', 'enc_content.model.3.model.1.model.0.norm.running_var', 'enc_content.model.3.model.2.model.1.norm.running_mean', 'enc_content.model.3.model.2.model.1.norm.running_var', 'enc_content.model.3.model.2.model.0.norm.running_mean', 'enc_content.model.3.model.2.model.0.norm.running_var', 'enc_content.model.3.model.3.model.1.norm.running_mean', 'enc_content.model.3.model.3.model.1.norm.running_var', 'enc_content.model.3.model.3.model.0.norm.running_mean', 'enc_content.model.3.model.3.model.0.norm.running_var', )): del state_dict[key] else: def __conversion_core(state_dict_base): state_dict = state_dict_base.copy() for key, value in state_dict_base.items(): if key.endswith(('enc.model.0.norm.running_mean', 'enc.model.0.norm.running_var', 'enc.model.1.norm.running_mean', 'enc.model.1.norm.running_var', 'enc.model.2.norm.running_mean', 'enc.model.2.norm.running_var', 'enc.model.3.model.0.model.1.norm.running_mean', 'enc.model.3.model.0.model.1.norm.running_var', 'enc.model.3.model.0.model.0.norm.running_mean', 'enc.model.3.model.0.model.0.norm.running_var', 'enc.model.3.model.1.model.1.norm.running_mean', 'enc.model.3.model.1.model.1.norm.running_var', 'enc.model.3.model.1.model.0.norm.running_mean', 'enc.model.3.model.1.model.0.norm.running_var', 'enc.model.3.model.2.model.1.norm.running_mean', 'enc.model.3.model.2.model.1.norm.running_var', 'enc.model.3.model.2.model.0.norm.running_mean', 'enc.model.3.model.2.model.0.norm.running_var', 'enc.model.3.model.3.model.1.norm.running_mean', 'enc.model.3.model.3.model.1.norm.running_var', 'enc.model.3.model.3.model.0.norm.running_mean', 'enc.model.3.model.3.model.0.norm.running_var', 'dec.model.0.model.0.model.1.norm.running_mean', 'dec.model.0.model.0.model.1.norm.running_var', 'dec.model.0.model.0.model.0.norm.running_mean', 'dec.model.0.model.0.model.0.norm.running_var', 'dec.model.0.model.1.model.1.norm.running_mean', 'dec.model.0.model.1.model.1.norm.running_var', 'dec.model.0.model.1.model.0.norm.running_mean', 'dec.model.0.model.1.model.0.norm.running_var', 'dec.model.0.model.2.model.1.norm.running_mean', 'dec.model.0.model.2.model.1.norm.running_var', 'dec.model.0.model.2.model.0.norm.running_mean', 'dec.model.0.model.2.model.0.norm.running_var', 'dec.model.0.model.3.model.1.norm.running_mean', 'dec.model.0.model.3.model.1.norm.running_var', 'dec.model.0.model.3.model.0.norm.running_mean', 'dec.model.0.model.3.model.0.norm.running_var', )): del state_dict[key] return state_dict state_dict = dict() state_dict['a'] = __conversion_core(state_dict_base['a'], trainer_name) state_dict['b'] = __conversion_core(state_dict_base['b'], trainer_name) return state_dict

[ "arobey1@seas.upenn.edu" ]

arobey1@seas.upenn.edu

982cd66cd05486ce807cf58a7913b9abf4656f7a

dc2f1cf081b1c08907e2572ac9a484574b841ca5

/test_polygon_rect.py

435b2c17d882c260bc5adba99396cdc4f58a5f2a

[]

no_license

meekrob/merge-overlapping-svg-paths

52ede1136a771d7ed6cadfbb25d77b2942253a0b

04375132b7834494d8f900d531764cc451320e24

refs/heads/master

2020-09-27T18:34:05.522538

2019-12-08T21:04:14

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#!/usr/bin/env python3 def list_to_svg_path_d(l): command = 'm' d = '' for point in l: d += "%s %.2f,%.2f " % (command,point[0],point[1]) command = 'l' return d + 'z' def merge_rect_into_polygon(rect, polygon): # for intersecting points between the rect and polygon, replace with the rect's non-intersecting points match_point = None points_to_insert = [] for corner in rect: try: i = polygon.index( corner ) print('found:', corner, "==", polygon[i]) except: points_to_insert.append( corner ) continue if match_point == None: match_point = i print('match_point:', match_point) polygon.pop(i) for new_point in points_to_insert: polygon.insert( i, new_point ) i += 1 return polygon rect1 = [ (0,0), (0,1), (1,1), (1,0) ] rect2 = [ (1,0), (1,1), (2,1), (2,0) ] rect3 = [ (0,1), (0,2), (1,2), (1,1) ] rect4 = [ (1,1), (1,2), (2,2), (2,1) ] p = rect1 print("polygon", p) print("###############\n") print("rect2", rect2) p = merge_rect_into_polygon(rect2, p) print("polygon", p) print("###############\n") print("rect3", rect3) p = merge_rect_into_polygon(rect3, p) print("polygon", p) print("###############\n") print("rect4", rect4) p = merge_rect_into_polygon(rect4, p) print("polygon", p) print( list_to_svg_path_d(p) )

[ "davidcking.inbox@gmail.com" ]

davidcking.inbox@gmail.com

100eb310ce1ac3e3f2486d6d9618a3bf5a705b8e

beab958fd74261f2598818b44666d4613de65774

/test_create_negative.py

748f78374128c19f7f12e6b8fc62d5056d3d0b47

[ "Apache-2.0" ]

permissive

ArtemVavilov88/test2-Se-Python-14

cb3ead45f144a1b08dea6fcd66a1764572534f00

522791b4fcde1d26b8e56244b10ebaf2e7146e57

refs/heads/master

2021-01-13T00:53:54.313505

2015-11-18T17:28:06

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import unittest from selenium import webdriver from selenium.webdriver.common.keys import Keys class php4dvd_create_film_negtive(unittest.TestCase): def setUp(self): self.driver = webdriver.Firefox() self.driver.implicitly_wait(20) def test_create_film_negative(self): driver = self.driver driver.get("http://localhost/php4dvd/") driver.find_element_by_id("username").clear() driver.find_element_by_id("username").send_keys("admin") driver.find_element_by_name("password").clear() driver.find_element_by_name("password").send_keys("admin") driver.find_element_by_name("submit").click() driver.find_element_by_link_text("Add movie").click() driver.find_element_by_name("imdbid").send_keys("0003") driver.find_element_by_name("aka").send_keys("Test-information") driver.find_element_by_name("year").send_keys("2015") driver.find_element_by_name("duration").send_keys("0000") driver.find_element_by_id("own_no").click() driver.find_element_by_name("plots").send_keys("test-test") driver.find_element_by_id("submit").click() #checking error message getText_error = driver.find_element_by_tag_name("label").text print getText_error def tearDown(self): self.driver.quit() if __name__=="__main__": unittest.main()

[ "avlov88@gmail.com" ]

avlov88@gmail.com

4bc7aecd3876eb2d91e7d6880e5c13737562dca5

e6e20cd07e12679fd3085ab5d9a75cb60353a96a

/apps/login_app/migrations/0004_auto_20170927_1201.py

6a51b738cfa5e227fa02e22f97e87c1c0f39e65e

[]

no_license

sneihaasawant/Friends

47b948658b6706ca1a7963c882423ad3d63befab

c74c85995b982b5e968142cb09853574849414d2

refs/heads/master

2021-08-27T23:01:46.158186

2017-12-10T16:21:11

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# -*- coding: utf-8 -*- # Generated by Django 1.11.5 on 2017-09-27 19:01 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('login_app', '0003_user_friends_with'), ] operations = [ migrations.AlterField( model_name='user', name='friends_with', field=models.ManyToManyField(blank=True, null=True, related_name='_user_friends_with_+', to='login_app.User'), ), ]

[ "sneha.s.sawant25@gmail.com" ]

sneha.s.sawant25@gmail.com

1d1b0213e352a561d37417353719711b31bd3de4

b51535db8031ce51babcc1d86073ff8e74504ef2

/primenumber.py

82078f0aba6378c1115906dabe6f1a7bd2bfd52f

[]

no_license

chriskok/PythonLearningWorkspace

d5750b3cbf651538e58b97ae3f9889f157a3ca33

2852f47b5eb6e52d549ded0324418b2d492cef29

refs/heads/master

2021-01-24T08:44:22.412024

2018-03-19T14:15:13

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py

# Note, prime can only be divided by 1 and itself # 5 is prime because only divided by 1 and 5 - positive factor # 6 is not a prime, divide by 1,2,3,6 # use a for loop and check if modulus == 0 True def is_prime(num): for i in range(2, num): if (num % i) == 0: return False return True def get_prime(max_number): list_of_primes = [] for num1 in range(2, max_number): if is_prime(num1): list_of_primes.append(num1) return list_of_primes # Ask the user to type in the maximum prime max_prime = input("Insert max prime: ") max_prime = int(max_prime) primes_list = get_prime(max_prime) for prime in primes_list: print(prime)

[ "ckok@purdue.edu" ]

ckok@purdue.edu