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

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# Exercício Python 031: Desenvolva um programa que pergunte a distância de uma viagem em Km. Calcule o preço da passagem, # cobrando R$0,50 por Km para viagens de até 200Km e R$0,45 parta viagens mais longas. distancia = float(input("Qual a distância da viagem, em quilômetros? ")) preco = distancia * 0.45 if distancia > 200 else distancia * 0.50 print("Você vai pagar R$ {:.2f} por uma viagem de {} quilômetros.".format(preco, int(distancia)))

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from django.conf import settings from django.contrib.auth.models import AbstractUser from django.db import models from django.urls import reverse from django.utils.translation import ugettext_lazy as _ class User(AbstractUser): # WARNING! """ Some officially supported features of Crowdbotics Dashboard depend on the initial state of this User model (Such as the creation of superusers using the CLI or password reset in the dashboard). Changing, extending, or modifying this model may lead to unexpected bugs and or behaviors in the automated flows provided by Crowdbotics. Change it at your own risk. This model represents the User instance of the system, login system and everything that relates with an `User` is represented by this model. """ name = models.CharField( null=True, blank=True, max_length=255, )

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""" https://youtrack.jetbrains.com/issue/PY-43916 """ s = "a=b,c=d" opts = dict([opt.split("=", 1) for opt in s.split(",")]) print(opts)

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import os import numpy as np import tensorflow as tf import nnutil as nn from .base_model import BaseModel class AdversarialTransformer(BaseModel): def __init__(self, name, shape): super(AdversarialTransformer, self).__init__(name) self._shape = shape self._transformer = None self._discriminator = None @property def input_shape(self): return self._shape @property def output_shape(self): return self._shape @property def layers(self): return self._transformer.layers def transformer_network(self, params): raise NotImplementedError def discriminator_network(self, params): raise NotImplementedError def features_placeholder(self, batch_size=1): return { 'source': tf.placeholder(dtype=tf.float32, shape=(batch_size,) + self._shape, name='source'), 'target': tf.placeholder(dtype=tf.float32, shape=(batch_size,) + self._shape, name='target') } def loss_function(self, tgt_image, synth_image, params): step = tf.train.get_global_step() # Sample weights, so that easy samples weight less sample_bias = params.get('sample_bias', 0.0) sample_bias_step = params.get('sample_bias_step', 0) # Regularizer weight regularizer = params.get('regularizer', 0.0) regularizer_step = params.get('regularizer_step', 0) # Calculate total loss function with tf.name_scope('losses'): sample_loss = tf.norm(nn.util.flatten(synth_image - tgt_image), ord=2, axis=1) # TODO: perform importance sampling here model_loss = tf.reduce_mean(sample_loss) regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) regularization_dampening = tf.sigmoid(tf.cast(step - regularizer_step, dtype=tf.float32) / 10.0) total_loss = model_loss + regularizer * regularization_dampening * sum([l for l in regularization_losses]) tf.summary.scalar("model_loss", model_loss) return total_loss def model_fn(self, features, labels, mode, params, config): src_image = features['source'] tgt_image = features['target'] step = tf.train.get_global_step() training = (mode == tf.estimator.ModeKeys.TRAIN) self._transformer = nn.layers.Segment(self.transformer_network(params), name="transformer") self._discriminator = nn.layers.Segment(self.transformer_network(params), name="discriminator") synth_image = self._transformer.apply(src_image, training=training) if mode == tf.estimator.ModeKeys.PREDICT: return self.prediction_estimator_spec(src_image, synth_image, params, config) loss = self.loss_function(tgt_image, synth_image, params) # Configure the training and eval phases if mode == tf.estimator.ModeKeys.TRAIN: return self.training_estimator_spec(loss, src_image, synth_image, tgt_image, params, config) else: return self.evaluation_estimator_spec(loss, src_image, synth_image, tgt_image, params, config) def training_estimator_spec(self, loss, src_image, synth_image, tgt_image, params, config): step = tf.train.get_global_step() learning_rate = params.get('learning_rate', 0.0001) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0, beta2=0.9) # Manually apply gradients. We want the gradients for summaries. We need # to apply them manually in order to avoid having duplicate gradient ops. gradients = optimizer.compute_gradients(loss) # Make sure we update averages on each training step extra_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_ops): train_op = optimizer.apply_gradients(gradients, global_step=step) nn.summary.image_transformation( "transformation", src_image[0, :], synth_image[0, :]) nn.summary.image_transformation( "truth", tgt_image[0, :], synth_image[0, :]) nn.summary.layers("layer_summary_{}".format(self._transformer.name), layers=self._transformer.layers, gradients=gradients, activations=self._transformer.layer_activations) nn.summary.layers("layer_summary_{}".format(self._discriminator.name), layers=self._discriminator.layers, gradients=gradients, activations=self._discriminator.layer_activations) training_hooks = [] return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.TRAIN, loss=loss, training_hooks=training_hooks, train_op=train_op) def evaluation_estimator_spec(self, loss, src_image, synth_image, tgt_image, params, config): eval_metric_ops = {} evaluation_hooks = [] # Make sure we run update averages on each training step extra_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(extra_ops): loss = tf.identity(loss) eval_dir = os.path.join(config.model_dir, "eval") evaluation_hooks.append( nn.train.EvalSummarySaverHook( output_dir=eval_dir, summary_op=tf.summary.merge_all() ) ) return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.EVAL, loss=loss, evaluation_hooks=evaluation_hooks, eval_metric_ops=eval_metric_ops) def prediction_estimator_spec(self, src_image, synth_image, params, config): predictions = { "synth": synth_image } exports = {} return tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.PREDICT, predictions=predictions, export_outputs=exports)

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""" In JavaScript, there are two types of comments: 1. Single-line comments start with `//` 2. Multi-line or inline comments start with `/*` and end with `*/` The input will be a sequence of `//`, `/*` and `*/`. **Every`/*` must have a `*/` that immediately follows it**. To add, there can be **no single-line comments in between multi-line comments** in between the `/*` and `*/`. Create a function that returns `True` if comments are properly formatted, and `False` otherwise. ### Examples comments_correct("//////") ➞ True # 3 single-line comments: ["//", "//", "//"] comments_correct("/**//**////**/") ➞ True # 3 multi-line comments + 1 single-line comment: # ["/*", "*/", "/*", "*/", "//", "/*", "*/"] comments_correct("///*/**/") ➞ False # The first /* is missing a */ comments_correct("/////") ➞ False # The 5th / is single, not a double // ### Notes N/A """ def comments_correct(txt): if len(txt)%2 !=0: return False chunks = [] for n in range(0,len(txt)-2,2): chunks.append(txt[n:n+2]) for i in range(len(chunks)-1): if chunks[i] == '/*' and chunks[i+1] != '*/': return False return True

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import cv2 import numpy as np # 全局变量 # 第几张图片 0 第一张 1 第二张 img_flag = 0 # 第一张图片 def on_EVENT_LBUTTONDOWN1(event, x, y,flags, param): # 点击三次，获得三个位置的坐标，销毁窗口 if event == cv2.EVENT_LBUTTONDOWN: xy = "%d,%d" % (x, y) a.append(x) b.append(y) cv2.circle(img1, (x, y), 1, ( 0, 0,255), thickness=4) cv2.putText(img1, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1) cv2.imshow("image1", img1) # 第二张图片 def on_EVENT_LBUTTONDOWN2(event, x, y,flags, param): # 点击三次，获得三个位置的坐标，销毁窗口 if event == cv2.EVENT_LBUTTONDOWN: xy = "%d,%d" % (x, y) a1.append(x) b1.append(y) cv2.circle(img2, (x, y), 1, (255, 0, 0), thickness=4) cv2.putText(img2, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1) cv2.imshow("image2", img2) # 获取同名点对 def get_same_point(img_flag): # 第一张图片 cv2.namedWindow("image1") cv2.setMouseCallback("image1", on_EVENT_LBUTTONDOWN1) cv2.imshow("image1", img1) cv2.waitKey(0) cv2.destroyAllWindows() # 第二张图片 cv2.namedWindow("image2") cv2.setMouseCallback("image2", on_EVENT_LBUTTONDOWN2) cv2.imshow("image2", img2) cv2.waitKey(0) cv2.destroyAllWindows() # print(a) # print(b) # print(a1) # print(b1) len_1 = len(a) img_sq_1 = np.ones((len_1,3),dtype='int') img_sq_2 = np.ones((len_1,2), dtype='int') img_sq_1[:,0] = a[:] img_sq_1[:,1] = b[:] img_sq_2[:,0] = a1[:] img_sq_2[:,1] = b1[:] print(img_sq_1) print(img_sq_2) return img_sq_1,img_sq_2 if __name__ == '__main__': # 分表保存第一张图片与第二张图片的同名点对 # 记录同名点对 # 第一张图片 a,b = [], [] # 第二张图片 a1, b1 = [], [] img1 = cv2.imread(r'T:\imgs\XL4\klcc_a.png') img2 = cv2.imread(r"T:\imgs\XL4\klcc_b.png") img1_copy = img1[:] img2_copy = img2[:] # img_sq_1, img_sq_2 = get_same_point(img_flag) # 获取同点对 img_sq_1,img_sq_2 = get_same_point(img_flag) """ [[318 250 1] [153 318 1] [344 351 1]] [[243 270] [ 74 342] [272 369]] # 为了避免重复获取同点对，所以直接获取，后期删了 X = np.mat([[318,250,1],[153,318,1],[344,351,1]]) U = np.mat([[243,270],[ 74,342],[272,369]]) """ X = np.mat(img_sq_1) U = np.mat(img_sq_2) # 前A A = np.dot(X.I,U) print(A) # 因为当时画图的时候，图像已经被修改，所以，恢复原图像 img1 = img1_copy[:] img2 = img2_copy[:] M1,N1 = img1.shape[0],img1.shape[1] M2, N2 = img2.shape[0], img2.shape[1] img1_cnt = img1[:] img2_cnt = img2[:] # 建立一个大型图片 # 确定变换后的图片坐标 # 变换后图片的坐标 X save_img2 = [] for x in range(M1): for y in range(N1): cnt_sq = np.array([x, y, 1]).dot(A) cnt_sq = [int(cnt_sq.tolist()[0][0]),int(cnt_sq.tolist()[0][1])] save_img2.append(cnt_sq) # 参考图片 U save_img1 = [] for x in range(M2): for y in range(N2): save_img1.append([x,y]) save_img1 = np.array(save_img1) # 找变换后的图片的最小坐标 save_img2=np.array(save_img2) min_h = np.min(save_img2,axis=1) # 记录x,y 最小坐标 x_min,y_min = min_h[0],min_h[1] img3 = np.zeros([1000,1000, 3], np.uint8) save_img1_1 = save_img1[:] save_img2_1 = save_img2[:] if x_min<0: cnt = abs(x_min) for i in range(len(save_img1)): save_img1[i][0]+=cnt for i in range(len(save_img2)): save_img2[i][0]+=cnt if y_min<0: cnt = abs(y_min) for i in range(len(save_img1)): save_img1[i][1]+=cnt for i in range(len(save_img2)): save_img2[i][1]+=cnt # print(save_img1_1) # print(save_img2_1) for i in range(len(save_img1)): try: img3[save_img1_1[i][0],save_img1_1[i][1]]=img1[save_img1[i][0],save_img1[i][1]] except: # img3[save_img1_1[i][0], save_img1_1[i][1]] = img1[save_img1[i][0]-1, save_img1[i][1]-1] continue for i in range(len(save_img2)): try: img3[save_img2_1[i][0],save_img2_1[i][1]]=img2[save_img2[i][0],save_img2[i][1]] except: #img3[save_img1_1[i][0], save_img1_1[i][1]] = img1[save_img2[i][0]-1, save_img2[i][1]-1] continue cv2.imshow("3",img3) cv2.waitKey(0)

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from django import forms from empinfo.models import Employee class EmployeeForm(forms.ModelForm): class Meta: model = Employee fields = "__all__"

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""" Compare two linked list head could be None as well for empty list Node is defined as return back the head of the linked list in the below method. """ class Node(object): def __init__(self, data=None, next_node=None): self.data = data self.next = next_node def CompareLists(headA, headB): count = 0 while headA != None or headB != None: if (headA == None and headB != None) or (headA != None and headB == None): return 0 if headA.data != headB.data: count = count + 1 headA = headA.next headB = headB.next if count == 0: return 1 else: return 0

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#! /usr/bin/env python # -*- coding: utf-8 -*- """ Created on Tue Oct 22 12:00:00 2013 @author: sampfeiffer """ import roslib; roslib.load_manifest('example_sm_pkg') import rospy import sys from std_msgs.msg import String class myNode(): def __init__(self, argument_one): # my class variables self.my_variable_string = "I'm a string yo!" self.subs = rospy.Subscriber('my_topic_to_subscribe', String, self.myCallback) self.pub = rospy.Publisher('my_topic_to_publish', String, latch=True) self.myMethod() def myCallback(self, data): rospy.loginfo("Received from topic data!") self.myMethod() def myMethod(self): rospy.loginfo("Using the method!") publish_this_thing = String("I'm the content of a string!") self.pub.publish(publish_this_thing) if __name__ == '__main__': rospy.init_node('node_name') if len(sys.argv) < 2: print "Error, we need an arg!" rospy.loginfo("No args given, closing...") exit() node = myNode("this is an argument") rospy.spin()

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from Report import Report class ProjectsByStaffReportFactory(object): def __init__(self, context, **kw): self.context = context self.params = kw def getReport(self): # create and fill the report name = "Projects by Staff Report" report = Report(name) report.setReportHeaders(( name,),) report.setTableHeaders((( 'Staff Name', 'Staff position', 'Focal Area', 'Project Title', 'Project Cycle Stage', 'Last milestone action', 'Actual date', 'Project Grant ', ),)) # XXX Implement this # report.setTableRows() # report.setTableTotals([]) # report.setReportFooters() return report

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .sub_resource import SubResource class LoadBalancingRule(SubResource): """A loag balancing rule for a load balancer. All required parameters must be populated in order to send to Azure. :param id: Resource Identifier. :type id: str :param frontend_ip_configuration: A reference to frontend IP addresses. :type frontend_ip_configuration: ~azure.mgmt.network.v2015_06_15.models.SubResource :param backend_address_pool: A reference to a pool of DIPs. Inbound traffic is randomly load balanced across IPs in the backend IPs. :type backend_address_pool: ~azure.mgmt.network.v2015_06_15.models.SubResource :param probe: The reference of the load balancer probe used by the load balancing rule. :type probe: ~azure.mgmt.network.v2015_06_15.models.SubResource :param protocol: Required. The transport protocol for the external endpoint. Possible values are 'Udp' or 'Tcp'. Possible values include: 'Udp', 'Tcp' :type protocol: str or ~azure.mgmt.network.v2015_06_15.models.TransportProtocol :param load_distribution: The load distribution policy for this rule. Possible values are 'Default', 'SourceIP', and 'SourceIPProtocol'. Possible values include: 'Default', 'SourceIP', 'SourceIPProtocol' :type load_distribution: str or ~azure.mgmt.network.v2015_06_15.models.LoadDistribution :param frontend_port: Required. The port for the external endpoint. Port numbers for each rule must be unique within the Load Balancer. Acceptable values are between 1 and 65534. :type frontend_port: int :param backend_port: The port used for internal connections on the endpoint. Acceptable values are between 1 and 65535. :type backend_port: int :param idle_timeout_in_minutes: The timeout for the TCP idle connection. The value can be set between 4 and 30 minutes. The default value is 4 minutes. This element is only used when the protocol is set to TCP. :type idle_timeout_in_minutes: int :param enable_floating_ip: Configures a virtual machine's endpoint for the floating IP capability required to configure a SQL AlwaysOn Availability Group. This setting is required when using the SQL AlwaysOn Availability Groups in SQL server. This setting can't be changed after you create the endpoint. :type enable_floating_ip: bool :param provisioning_state: Gets the provisioning state of the PublicIP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. :type provisioning_state: str :param name: The name of the resource that is unique within a resource group. This name can be used to access the resource. :type name: str :param etag: A unique read-only string that changes whenever the resource is updated. :type etag: str """ _validation = { 'protocol': {'required': True}, 'frontend_port': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'frontend_ip_configuration': {'key': 'properties.frontendIPConfiguration', 'type': 'SubResource'}, 'backend_address_pool': {'key': 'properties.backendAddressPool', 'type': 'SubResource'}, 'probe': {'key': 'properties.probe', 'type': 'SubResource'}, 'protocol': {'key': 'properties.protocol', 'type': 'str'}, 'load_distribution': {'key': 'properties.loadDistribution', 'type': 'str'}, 'frontend_port': {'key': 'properties.frontendPort', 'type': 'int'}, 'backend_port': {'key': 'properties.backendPort', 'type': 'int'}, 'idle_timeout_in_minutes': {'key': 'properties.idleTimeoutInMinutes', 'type': 'int'}, 'enable_floating_ip': {'key': 'properties.enableFloatingIP', 'type': 'bool'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'etag': {'key': 'etag', 'type': 'str'}, } def __init__(self, *, protocol, frontend_port: int, id: str=None, frontend_ip_configuration=None, backend_address_pool=None, probe=None, load_distribution=None, backend_port: int=None, idle_timeout_in_minutes: int=None, enable_floating_ip: bool=None, provisioning_state: str=None, name: str=None, etag: str=None, **kwargs) -> None: super(LoadBalancingRule, self).__init__(id=id, **kwargs) self.frontend_ip_configuration = frontend_ip_configuration self.backend_address_pool = backend_address_pool self.probe = probe self.protocol = protocol self.load_distribution = load_distribution self.frontend_port = frontend_port self.backend_port = backend_port self.idle_timeout_in_minutes = idle_timeout_in_minutes self.enable_floating_ip = enable_floating_ip self.provisioning_state = provisioning_state self.name = name self.etag = etag

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rs = [(x - l, x + l) for x, l in (map(int, input().split()) for _ in range(int(input())))] rs.sort(key=lambda x: x[1]) last = - 10 ** 9 ans = 0 for l, r in rs: if last <= l: ans += 1 last = r print(ans)

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import datetime from django.views.generic import View from django.contrib import messages from django.shortcuts import render, redirect, reverse from django.http import Http404 from rooms import models as room_models from reviews import forms as review_forms from . import models class CreateError(Exception): pass def create(request, room, year, month, day): try: date_obj = datetime.datetime(year, month, day) room = room_models.Room.objects.get(pk=room) models.BookedDay.objects.get(day=date_obj, reservation__room=room) raise CreateError() except (room_models.Room.DoesNotExist, CreateError): messages.error(request, "Can't Reserve That Room") return redirect(reverse("core:home")) except models.BookedDay.DoesNotExist: reservation = models.Reservation.objects.create( guest=request.user, room=room, check_in=date_obj, check_out=date_obj + datetime.timedelta(days=1), ) return redirect(reverse("reservations:detail", kwargs={"pk": reservation.pk})) class ReservationDetailView(View): def get(self, *args, **kwargs): pk = kwargs.get("pk") reservation = models.Reservation.objects.get_or_none(pk=pk) if not reservation or ( reservation.guest != self.request.user and reservation.room.host != self.request.user ): raise Http404() form = review_forms.CreateReviewForm() return render( self.request, "reservations/detail.html", {"reservation": reservation, "form": form}, ) def edit_reservation(request, pk, verb): reservation = models.Reservation.objects.get_or_none(pk=pk) if not reservation or ( reservation.guest != request.user and reservation.room.host != request.user ): raise Http404() if verb == "confirm": reservation.status = models.Reservation.STATUS_CONFIRMED elif verb == "cancel": reservation.status = models.Reservation.STATUS_CANCELED models.BookedDay.objects.filter(reservation=reservation).delete() reservation.save() messages.success(request, "Reservation Updated") return redirect(reverse("reservations:detail", kwargs={"pk": reservation.pk}))

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''' Given a m x n matrix, if an element is 0, set its entire row and column to 0. Do it in-place. Example 1: Input: [ [1,1,1], [1,0,1], [1,1,1] ] Output: [ [1,0,1], [0,0,0], [1,0,1] ] Example 2: Input: [ [0,1,2,0], [3,4,5,2], [1,3,1,5] ] Output: [ [0,0,0,0], [0,4,5,0], [0,3,1,0] ] Follow up: A straight forward solution using O(mn) space is probably a bad idea. A simple improvement uses O(m + n) space, but still not the best solution. Could you devise a constant space solution? ''' class Solution(object): def setZeroes(self, matrix): """ :type matrix: List[List[int]] :rtype: void Do not return anything, modify matrix in-place instead. """ row = set() col = set() for i in range(0,len(matrix)): for j in range(0,len(matrix[0])): if matrix[i][j] == 0: row.add(i) col.add(j) row = list(row) col = list(col) for i in range(0,len(row)): matrix[row[i]] = [0 for _ in range(0,len(matrix[0]))] for j in range(0,len(col)): for jj in range(len(matrix)): matrix[jj][col[j]] = 0

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#create csv file by this programs import csv with open("example1.csv",'w')as obj: field = ["name","salary"] writer = csv.DictWriter(obj, fieldnames=field) writer.writeheader() writer.writerow({'name':'bob','salary':10000}) writer.writerow({'name':'sem','salary':40000}) writer.writerow({'name':'kamlesh','salary':30000}) writer.writerow({'name':'vishal','salary':50000})

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# Copyright 2020 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. def RunOtherPresubmit(function_name, input_api, output_api): # Apply the PRESUBMIT for components/policy/resources to run the syntax check. presubmit_path = ( input_api.change.RepositoryRoot() + \ '/components/policy/resources/PRESUBMIT.py') presubmit_content = input_api.ReadFile(presubmit_path) global_vars = {} exec (presubmit_content, global_vars) return global_vars[function_name](input_api, output_api) def CheckChangeOnUpload(input_api, output_api): return RunOtherPresubmit("CheckChangeOnUpload", input_api, output_api) def CheckChangeOnCommit(input_api, output_api): return RunOtherPresubmit("CheckChangeOnCommit", input_api, output_api)

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/MovieApp/migrations/0004_auto_20210610_1948.py

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# Generated by Django 3.2.4 on 2021-06-10 14:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('MovieApp', '0003_images'), ] operations = [ migrations.CreateModel( name='M_lang', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('language', models.CharField(max_length=256)), ], ), migrations.AddField( model_name='movie', name='M_lang', field=models.ManyToManyField(to='MovieApp.M_lang'), ), ]

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from setuptools import setup setup( name='BasicApp', app=['main.py'], options=dict(py2app=dict( argv_emulation=True, )), )

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prdx/PoliteScrapper

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from bs4 import BeautifulSoup import os import pickle files = [f for f in os.listdir('.') if os.path.isfile(f) and f.endswith(".xml")] outlinks = {} inlinks = {} def generate_outlink_file(): print("Generating outlinks file ...") # Generate outlinks for xml in files: try: with open(xml, "rb") as f: soup = BeautifulSoup(f, "lxml") url = soup.id.value.text outlinks[url] = soup.outlinks.value.text.split(",") except Exception as e: print("Error processing: " + xml) print(e) os.rename(xml, xml + ".fail") # Dump the outlinks with open("../output/outlinks.p", "wb") as out: pickle.dump(outlinks, out, protocol=pickle.HIGHEST_PROTOCOL) print("Done generating outlinks file ...") print("Outlinks size: " + str(len(outlinks)) + " urls") def generate_inlink_file(): print("Generating inlinks file ...") # Generate inlinks for key in outlinks: for url in outlinks[key]: try: inlinks[url].append(key) except KeyError: inlinks[url] = [key] except Exception as e: print("Error processing: " + key) print(e) # Dump the inlinks with open("../output/inlinks.p", "wb") as out: pickle.dump(inlinks, out, protocol=pickle.HIGHEST_PROTOCOL) print("Inlinks size: " + str(len(inlinks)) + " urls") print("Done inlinks file ...") generate_outlink_file() generate_inlink_file()

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#!/usr/bin/env python -*- coding: utf-8 -*- # # Python Word Sense Disambiguation (pyWSD) # # Copyright (C) 2014 alvations # URL: # For license information, see LICENSE.md from distutils.core import setup setup( name='pywsd', version='0.1', packages=['pywsd',], long_description='Python Implementations of Word Sense Disambiguation (WSD) technologies', )

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ndina/webdev2019

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""" Django settings for library project. Generated by 'django-admin startproject' using Django 1.8.7. For more information on this file, see https://docs.djangoproject.com/en/1.8/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.8/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'u2iir6bmw(y%pu*23y%sm1u#8y#o7_qchko#=r*_rtqy_-ge+e' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'django.middleware.security.SecurityMiddleware', ) ROOT_URLCONF = 'library.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'library.wsgi.application' # Database # https://docs.djangoproject.com/en/1.8/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Internationalization # https://docs.djangoproject.com/en/1.8/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.8/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static')

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gabriellaec/desoft-analise-exercicios

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# Está função é para calcular a velocidade média def velocidade_media (d,t): velocidade_media = d / t return velocidade_média

[ "you@example.com" ]

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import pulumi import pulumi_aws as aws sns = aws.ses.EventDestination("sns", configuration_set_name=aws_ses_configuration_set["example"]["name"], enabled=True, matching_types=[ "bounce", "send", ], sns_destination={ "topic_arn": aws_sns_topic["example"]["arn"], })

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class Solution(object): def smallestFromLeaf(self, root): self.result = "~" def dfs(node, A): if node: A.append(chr(node.val + ord('a'))) if not node.left and not node.right: self.result = min(self.result, "".join(reversed(A))) dfs(node.left, A) dfs(node.right, A) A.pop() dfs(root, []) return self.result

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huangyingw@gmail.com

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1,096

py

#!/usr/bin/env python __author__ = 'Bill' def check_pancakes(n): """(check_pancakes): function to test for all face up :param n: the pancakes string """ for ch in n: if ch == '-': return False return True def flip_pancakes(n): """(flip_pancakes): function to flip pancakes :param n: the pancakes string """ n = list(n) dict = {'+':'-', '-':'+'} first = n[0] i = 0 for ch in n: if ch != first: break i += 1 for j in xrange(i): n[j] = dict[first] n = "".join(n) return n from misc import input_, output_ num_cases, cases = input_('B-large.in') Results = [] for case in cases: case = case.rstrip('\n') i = 0 face_up = check_pancakes(case) if face_up == True: Results.append(i) else: while check_pancakes(case) == False: case = flip_pancakes(case) i += 1 Results.append(i) output_(Results, 'Revenge_of_the_pancakes_large.out')

[ "[dhuo@tcd.ie]" ]

[dhuo@tcd.ie]

56749342e68294136dbbbacb342a3d9b2f01f30b

18b3ad3b0e1f7f10969738251e1201d01dfbc6bf

/backup_files/samplepy/passbyvalue.py

26689727f2944f32dee1688daef3ff1dc4632725

[]

no_license

sahthi/backup2

11d509b980e731c73733b1399a8143780779e75a

16bed38f0867fd7c766c2a008c8d43b0660f0cb0

refs/heads/master

2020-03-21T12:39:56.890129

2018-07-09T08:12:46

138,565,151

0

null

UTF-8

Python

false

180

py

def changeme(mylist): mylist = [1,2,3,4 ] print "values inside the function",mylist return mylist = [10,20,30] changeme(mylist) print"values outside the function ",mylist

[ "siddamsetty.sahithi@votarytech.com" ]

siddamsetty.sahithi@votarytech.com

057695d4910d814affa1cef49fbca93b9b520c88

df690ac0484ff04cb63f71f528a9d0a0e557d6a3

/.history/ws_20210608130810.py

59216ed4c38672800e718b0909e4e451e853a45b

[]

no_license

khanhdk0000/Mqtt-Web-Socket

437777c740c68d4197353e334f6fe6a629094afd

4f9e49a3817baa9ebc4e4f8dcffc21b6ea9d0134

refs/heads/master

2023-06-20T17:08:09.447381

2021-06-08T17:42:37

375,090,458

0

null

UTF-8

Python

false

3,612

py

from flask import Flask, jsonify, request from flask_sock import Sock import time app = Flask(__name__) sock = Sock(app) import threading BROKER = 'io.adafruit.com' USER = 'khanhdk0000' PASSWORD = 'aio_FfID10QWNVSKUC2j15nLtOSeckin' TOPIC = 'khanhdk0000/feeds/' LIGHT = 'light' SOUND = 'sound' TEMP = 'temp' LCD = 'iot_led' BUZZER = 'buzzer' ######## # USER = 'CSE_BBC' # PASSWORD = 'aio_FfID10QWNVSKUC2j15nLtOSeckin' # TOPIC = 'CSE_BBC/feeds/' # USER1 = 'CSE_BBC1' # PASSWORD1 = 'aio_FfID10QWNVSKUC2j15nLtOSeckin' # TOPIC1 = 'CSE_BBC1/feeds/' # LIGHT = 'bk-iot-light' # SOUND = 'bk-iot-sound' # TEMP = 'bk-iot-temp-humid' # LCD = 'bk-iot-lcd' # BUZZER = 'bk-iot-speaker' resLight = '"id":"13","name":"LIGHT","data":"0","unit":""' prevLight = resLight resTemp = '"id":"7","name":"SOUND","data":"0","unit":""' prevTemp = resTemp resSound = '"id":"12","name":"TEMP-HUMID","data":"0","unit":""' prevSound = resSound def mqttGet(user, password,topic,device): import paho.mqtt.client as mqtt def on_connect(client, userdata, flags, rc): print("Connected with result code "+str(rc)) if rc == 0: print('good') else: print('no good') def on_disconnect(client, userdata, flags, rc=0): print("Disconnected result code " + str(rc)) def on_message(client, userdata, message): if device == LIGHT: global resLight message = str(message.payload.decode("utf-8")) print(message) resLight = message elif device == TEMP: global resTemp message = str(message.payload.decode("utf-8")) print(message) resTemp = message elif device == SOUND: global resSound message = str(message.payload.decode("utf-8")) print(message) resSound = message client = mqtt.Client() client.username_pw_set(username=user,password=password) client.on_connect = on_connect client.on_disconnect = on_disconnect client.on_message = on_message client.connect(BROKER, 1883, 60) client.subscribe(topic) client.loop_forever() t1 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + LIGHT, LIGHT)) t1.start() t2 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + TEMP, TEMP)) t2.start() t3 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + SOUND, SOUND)) t3.start() def mqttPost(topic, user,pass,payload): import paho.mqtt.publish as publish publish.single(topic,hostname="io.adafruit.com",auth={"username":user, "password":pass},payload = payload) @sock.route('/light') def light(ws): global resLight, prevLight while True: if prevLight == resLight: continue else: ws.send(resLight) prevLight = resLight @sock.route('/sound') def sound(ws): global resSound, prevSound while True: if prevSound == resSound: continue else: ws.send(resSound) prevSound = resSound @sock.route('/temp') def temp(ws): global resTemp, prevTemp while True: if prevTemp == resTemp: continue else: ws.send(resTemp) prevTemp = resTemp @app.route('/postlcd', methods=["POST"]) def testpost(): input_json = request.get_json(force=True) domain = input_json['data'] print('receive data', domain) mqttPost(TOPIC+LCD, U) return 'yea:' + domain if __name__ == '__main__': app.run(debug=True)

[ "khanhtran28092000@gmail.com" ]

khanhtran28092000@gmail.com

09f47ffa874febc1dd80bb23531d909ac281739b

694c187c8a00bee8c670c1690170099bad9b16b3

/hindex.py

edded2784cbd958ce569e1997c2a49c5589810d0

[]

no_license

ajayvenkat10/Competitive

301f220b6d296f7e34328f192c43c4d7ef208cb1

14f2ecebe10eb19f72cc412dd0c414b3b1de9b4d

refs/heads/master

2022-11-20T14:31:33.590099

2020-07-23T15:39:14

281,599,951

0

null

UTF-8

Python

false

450

py

t = int(input()) for _ in range(t): n = int(input()) arr = list(map(int, input().split())) final = [1] val = 2 for i in range(1,len(arr)): count = 0 for j in range(i+1): if(arr[j] >= val): count += 1 if(count>=val): final.append(val) val += 1 else: final.append(val-1) print("Case #%d: " % (_+1) , end="") print(*final)

[ "37923623+ajayvenkat10@users.noreply.github.com" ]

37923623+ajayvenkat10@users.noreply.github.com

6452090ca100845c839848f14ac2d04f85352f4d

934235f70a390a3ba0d7b464cddd10872f31cda3

/rango/server/.history/tango_with_django/rango/admin_20210103130028.py

361f6ca167ae05dc1771706293718383039c718e

[]

no_license

deji100/Projects

6919041ba23e77a5c74e5ab7692bfcee38ececcb

17e64d954d1d7805be57ec5d8d4344e4944889e6

refs/heads/master

2023-04-30T05:25:03.143303

2021-05-20T15:00:43

338,844,691

0

null

UTF-8

Python

false

521

py

from django.contrib import admin from .models import Category, Page, User # Register your models here. class PageInline(admin.StackedInline): list_display = ('title', 'category', 'url') # fields = ('title', 'url', 'category') model class CategoryAdmin(admin.ModelAdmin): list_display = ('name', 'views', 'likes') # prepopulated_fields = {'slug': ('name',)} inlines = [PageInline] admin.site.register(Category, CategoryAdmin) admin.site.register(Page, PageAdmin) admin.site.register(User)

[ "68882568+deji100@users.noreply.github.com" ]

68882568+deji100@users.noreply.github.com

0ea09ec878674f42ce2fb633727af303b0ff9662

830398bc5ae951b153ff695a40be7239742bc73e

/exercises/parse_dhcp_snooping.py

27114f9e94d30bcea5c6296a1383f9c2e461987f

[]

no_license

dmikos/pyneng

ff67f1d617a97d73103a7785a7bf86140e7baa82

543fb0d9fc63a2afee45d2465af3a4c3966e4a86

refs/heads/master

2021-01-25T14:56:44.181140

2018-04-23T04:31:00

123,739,447

0

1

null

UTF-8

Python

false

669

py

# -*- coding: utf-8 -*- import re #'00:09:BB:3D:D6:58 10.1.10.2 86250 dhcp-snooping 10 FastEthernet0/1' regex = re.compile('(?P<mac>\S+) +(?P<ip>\S+) +\d+ +\S+ +(?P<vlan>\d+) +(?P<port>\S+)') result = [] with open('dhcp_snooping.txt') as data: for line in data: match = regex.search(line) if match: result.append(match.groupdict()) print('К коммутатору подключено {} устройства'.format(len(result))) for num, comp in enumerate(result, 1): print('Параметры устройства {}:'.format(num)) for key in comp: print('{:10}: {:10}'.format(key,comp[key]))

[ "dkostinov@gmail.com" ]

dkostinov@gmail.com

8435baa0b8beaab331ff8904a8889f896a8d23c0

9ae6ce54bf9a2a86201961fdbd5e7b0ec913ff56

/google/ads/googleads/v9/services/services/third_party_app_analytics_link_service/transports/__init__.py

502d5cf2169f355fb53779b340f3900e0e913770

[ "Apache-2.0" ]

permissive

GerhardusM/google-ads-python

73b275a06e5401e6b951a6cd99af98c247e34aa3

676ac5fcb5bec0d9b5897f4c950049dac5647555

refs/heads/master

2022-07-06T19:05:50.932553

2022-06-17T20:41:17

207,535,443

0

Apache-2.0

2019-09-10T10:58:55

null

UTF-8

Python

false

1,141

py

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict from typing import Dict, Type from .base import ThirdPartyAppAnalyticsLinkServiceTransport from .grpc import ThirdPartyAppAnalyticsLinkServiceGrpcTransport # Compile a registry of transports. _transport_registry = ( OrderedDict() ) # type: Dict[str, Type[ThirdPartyAppAnalyticsLinkServiceTransport]] _transport_registry["grpc"] = ThirdPartyAppAnalyticsLinkServiceGrpcTransport __all__ = ( "ThirdPartyAppAnalyticsLinkServiceTransport", "ThirdPartyAppAnalyticsLinkServiceGrpcTransport", )

[ "noreply@github.com" ]

GerhardusM.noreply@github.com

ce7050ab38a7683c7b476a80901ac6beac9d0799

4fbd844113ec9d8c526d5f186274b40ad5502aa3

/algorithms/python3/maximize_distance_to_closest_person.py

37e744aa546a7f515c70e1f156bc63f0f499ee8d

[]

no_license

capric8416/leetcode

51f9bdc3fa26b010e8a1e8203a7e1bcd70ace9e1

503b2e303b10a455be9596c31975ee7973819a3c

refs/heads/master

2022-07-16T21:41:07.492706

2020-04-22T06:18:16

null

0

null

UTF-8

Python

false

1,203

py

# !/usr/bin/env python # -*- coding: utf-8 -*- """ In a row of seats, 1 represents a person sitting in that seat, and 0 represents that the seat is empty. There is at least one empty seat, and at least one person sitting. Alex wants to sit in the seat such that the distance between him and the closest person to him is maximized. Return that maximum distance to closest person. Example 1: Input: [1,0,0,0,1,0,1] Output: 2 Explanation: If Alex sits in the second open seat (seats[2]), then the closest person has distance 2. If Alex sits in any other open seat, the closest person has distance 1. Thus, the maximum distance to the closest person is 2. Example 2: Input: [1,0,0,0] Output: 3 Explanation: If Alex sits in the last seat, the closest person is 3 seats away. This is the maximum distance possible, so the answer is 3. Note: 1 <= seats.length <= 20000 seats contains only 0s or 1s, at least one 0, and at least one 1. """ """ ==================== body ==================== """ class Solution: def maxDistToClosest(self, seats): """ :type seats: List[int] :rtype: int """ """ ==================== body ==================== """

[ "capric8416@gmail.com" ]

capric8416@gmail.com

a4a27e3eb0c39273105293f96a89dc9b05e6f10a

b6a84594f8c29d968014faaddd49abeb7537a5fc

/python/1040.moving-stones-until-consecutive-ii.py

799deed3b361b4636ffa827b1e859308649b708d

[]

no_license

nickyfoto/lc

8a6af3df114e693e265d0ede03f4d4e1283e010e

3633b4df3e24968057c7d684689b931c5a8032d3

refs/heads/master

2020-09-16T19:23:07.765917

2020-06-07T17:18:06

223,866,098

0

null

UTF-8

Python

false

1,913

py

# # @lc app=leetcode id=1040 lang=python3 # # [1040] Moving Stones Until Consecutive II # # https://leetcode.com/problems/moving-stones-until-consecutive-ii/description/ # # algorithms # Medium (52.07%) # Likes: 152 # Dislikes: 231 # Total Accepted: 4.5K # Total Submissions: 8.7K # Testcase Example: '[7,4,9]' # # On an infinite number line, the position of the i-th stone is given by # stones[i]. Call a stone an endpoint stone if it has the smallest or largest # position. # # Each turn, you pick up an endpoint stone and move it to an unoccupied # position so that it is no longer an endpoint stone. # # In particular, if the stones are at say, stones = [1,2,5], you cannot move # the endpoint stone at position 5, since moving it to any position (such as 0, # or 3) will still keep that stone as an endpoint stone. # # The game ends when you cannot make any more moves, ie. the stones are in # consecutive positions. # # When the game ends, what is the minimum and maximum number of moves that you # could have made? Return the answer as an length 2 array: answer = # [minimum_moves, maximum_moves] # # # # Example 1: # # # Input: [7,4,9] # Output: [1,2] # Explanation: # We can move 4 -> 8 for one move to finish the game. # Or, we can move 9 -> 5, 4 -> 6 for two moves to finish the game. # # # # Example 2: # # # Input: [6,5,4,3,10] # Output: [2,3] # We can move 3 -> 8 then 10 -> 7 to finish the game. # Or, we can move 3 -> 7, 4 -> 8, 5 -> 9 to finish the game. # Notice we cannot move 10 -> 2 to finish the game, because that would be an # illegal move. # # # # Example 3: # # # Input: [100,101,104,102,103] # Output: [0,0] # # # # # # Note: # # # 3 <= stones.length <= 10^4 # 1 <= stones[i] <= 10^9 # stones[i] have distinct values. # # # # # # # # # # @lc code=start class Solution: def numMovesStonesII(self, stones): pass # @lc code=end

[ "nickyfoto@gmail.com" ]

nickyfoto@gmail.com

9adf50d27141869fb0693ddeb11ca31431191545

bd93fa910151c278be8249055bc084e5a5c35a6a

/Python/DjangoTest2/booktest/models.py

3735ebb85498b072da8a92f26cec7a80e612790c

[]

no_license

ahojcn/practice-code

bd81595b80239cd2550183093566bd536a83ed3f

b65f4e76271479269463e92fd3fd41585c2ac792

refs/heads/master

2021-07-10T14:15:08.036592

2020-07-09T11:32:16

153,059,349

2

null

UTF-8

Python

false

704

py

from django.db import models # Create your models here. # 创建模型 class BookInfo(models.Model): """图书模型类""" # 图书名 btitle = models.CharField(max_length=20) # 出版日期 bpub_date = models.DateField() def __str__(self): return self.btitle class HeroInfo(models.Model): """书中的英雄人物类""" # 英雄名 hname = models.CharField(max_length=20) # 性别 Boolean 类型，默认 False 代表男 hgender = models.BooleanField(default=False) # 备注 hcomment = models.CharField(max_length=128) # 关系 hbook = models.ForeignKey(BookInfo, on_delete=None) def __str__(self): return self.hname

[ "hanoi_ahoj@icloud.com" ]

hanoi_ahoj@icloud.com

564d81d0051cf261ea8cf3a8060afb2cc81c2406

718f4a6f53da14dbd79031928900a26c4de65ccb

/optimize_NMDA_KIN2.py

bf70c8a044f4ff48aa4d86895cd5e68a6e41e55f

[]

no_license

neurosutras/CA1Sim

ff37e5ae96cc00d923bbcf333d75842c34156b5b

9a5796e5de9b9be477d61837c164fcbccbe3c8ce

refs/heads/master

2023-04-08T01:39:09.559475

2022-01-13T20:20:45

29,497,263

4

3

null

UTF-8

Python

false

7,046

py

__author__ = 'Aaron D. Milstein' from specify_cells import * from plot_results import * import scipy.optimize as optimize import random """ This simulation uses scipy.optimize to iterate through NMDA_KIN mechanism parameters to fit target EPSP kinetics. """ #morph_filename = 'EB1-early-bifurcation.swc' morph_filename = 'EB2-late-bifurcation.swc' #mech_filename = '043015 pas_exp_scale kdr ka_scale ih_sig_scale - EB2' #mech_filename = '072515 optimized basal ka_scale dend_sh_ar_nas - EB2' mech_filename = '102915 interim dendritic excitability' def synaptic_kinetics_error(x, plot=0): """ :param x: list of parameters :param plot: int or bool: method can be called manually to compare actual to target and fit waveforms :return: float: Error """ spike_times = h.Vector([equilibrate]) for i, syn in enumerate(stim_syn_list): syn.target(syn_type).kon = x[0] syn.target(syn_type).koff = x[1] syn.target(syn_type).CC = x[2] syn.target(syn_type).CO = x[3] syn.target(syn_type).Beta = x[4] syn.target(syn_type).Alpha = x[5] syn.source.play(spike_times) sim.run(v_init) t = np.array(sim.tvec) g = np.array(sim.rec_list[0]['vec']) interp_t = np.arange(0, duration, 0.001) interp_g = np.interp(interp_t, t, g) """ Rc = np.interp(interp_t, t, np.array(sim.rec_list[1]['vec'])) Ro = np.interp(interp_t, t, np.array(sim.rec_list[2]['vec'])) Rb = np.interp(interp_t, t, np.array(sim.rec_list[3]['vec'])) Ro_peak = np.max(Ro) Ro_peak_loc = np.where(Ro == Ro_peak)[0][0] Rc_max = Ro_peak + Rc[Ro_peak_loc] + Rb[Ro_peak_loc] """ start, end = time2index(interp_t, equilibrate, duration) y = interp_g[start:end] interp_t = interp_t[start:end] interp_t -= interp_t[0] amp = np.max(y) t_peak = np.where(y == amp)[0][0] y /= amp rise_10 = np.where(y[0:t_peak] >= 0.1)[0][0] rise_90 = np.where(y[0:t_peak] >= 0.9)[0][0] rise_tau = interp_t[rise_90] - interp_t[rise_10] decay_90 = np.where(y[t_peak:] <= 0.9)[0][0] decay_10 = np.where(y[t_peak:] <= 0.1)[0] if decay_10.any(): decay_tau = interp_t[decay_10[0]] - interp_t[decay_90] else: decay_tau = 1000. # large error if trace has not decayed to 10% in 1 second result = {'rise_tau': rise_tau, 'decay_tau': decay_tau} # , 'Rc_max': Rc_max} spike_times = h.Vector([equilibrate + i * 10. for i in range(5)]) for i, syn in enumerate(stim_syn_list): syn.source.play(spike_times) sim.run(v_init) for i, syn in enumerate(stim_syn_list): syn.source.play(h.Vector()) t = np.array(sim.tvec) g = np.array(sim.rec_list[0]['vec']) interp_t = np.arange(0, duration, 0.001) interp_g = np.interp(interp_t, t, g) start, end = time2index(interp_t, equilibrate, duration) yf = interp_g[start:end] interp_t = interp_t[start:end] interp_t -= interp_t[0] facil_amp = np.max(yf) result['facilitation'] = facil_amp / amp yf /= amp Err = 0. for target in result: Err += ((target_val[target] - result[target])/target_range[target])**2. print('[kon, koff, CC, CO, Beta, Alpha]: [%.3f, %.3f, %.3f, %.3f, %.3f, %.3f], Error: %.3E, Rise: %.3f, Decay: ' '%.3f, facilitation: %.2f' % (x[0], x[1], x[2], x[3], x[4], x[5], Err, rise_tau, decay_tau, result['facilitation'])) if plot: plt.plot(interp_t, y) plt.plot(interp_t, yf) plt.show() plt.close() return Err equilibrate = 250. # time to steady-state duration = 1250. v_init = -67. num_syns = 1 cell = CA1_Pyr(morph_filename, mech_filename, full_spines=True) cell.zero_na() syn_type = 'NMDA_KIN2' sim = QuickSim(duration) # look for a trunk bifurcation trunk_bifurcation = [trunk for trunk in cell.trunk if len(trunk.children) > 1 and trunk.children[0].type == 'trunk' and trunk.children[1].type == 'trunk'] # get where the thickest trunk branch gives rise to the tuft if trunk_bifurcation: # follow the thicker trunk trunk = max(trunk_bifurcation[0].children[:2], key=lambda node: node.sec(0.).diam) trunk = (node for node in cell.trunk if cell.node_in_subtree(trunk, node) and 'tuft' in (child.type for child in node.children)).next() else: trunk = (node for node in cell.trunk if 'tuft' in (child.type for child in node.children)).next() tuft = (child for child in trunk.children if child.type == 'tuft').next() trunk = trunk_bifurcation[0] #sim.append_rec(cell, trunk, loc=1., description='trunk vm') spine_list = [] spine_list.extend(trunk.spines) for spine in spine_list: syn = Synapse(cell, spine, [syn_type], stochastic=0) local_random = random.Random() local_random.seed(0) stim_syn_list = [spine_list[i].synapses[0] for i in local_random.sample(range(len(spine_list)), num_syns)] for i, syn in enumerate(stim_syn_list): syn.target(syn_type).mg = 0.1 #syn.target(syn_type).gmax = 0.005 sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_g') sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Rc') sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Ro') sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Rb') #the target values and acceptable ranges target_val = {'rise_tau': 3., 'decay_tau': 75., 'Rc_max': 0.6, 'facilitation': 1.3} # extrapolating from Chen...Murphy and Harnett...Magee, Popescu et al. target_range = {'rise_tau': 0.1, 'decay_tau': .5, 'Rc_max': 0.01, 'facilitation': 0.01} #the initial guess and bounds #x = [kon, koff, CC, CO, Beta, Alpha) #x0 = [10., .02, 1., 0.1, 0.04, 0.09] #x0 = [26.414, 1.903, 3.185, 5.119, 0.274, 0.0299] #x0 = [44.35, 2.46, 10.34, 1.06, 0.40, 0.045] x0 = [85.47, 0.68, 9.48, 2.56, 0.72, 0.078] xmin = [10., .01, .1, .1, .01, .01] xmax = [100., 10., 20., 20., 1., 1.] #x1 = [1099.70, 0.07, 1.70, 14.12, 4.64, 0.19] # old NMDA_KIN2, unrealistic kon x1 = [68.74, 1.43, 5.86, 3.32, 0.270, 0.034] mytakestep = Normalized_Step(x0, xmin, xmax) minimizer_kwargs = dict(method=null_minimizer) """ result = optimize.basinhopping(synaptic_kinetics_error, x0, niter=720, niter_success=200, disp=True, interval=20, minimizer_kwargs=minimizer_kwargs, take_step=mytakestep) synaptic_kinetics_error(result.x, plot=1) polished_result = optimize.minimize(synaptic_kinetics_error, result.x, method='Nelder-Mead', options={'ftol': 1e-3, 'xtol': 1e-3, 'disp': True}) """ polished_result = optimize.minimize(synaptic_kinetics_error, x0, method='Nelder-Mead', options={'ftol': 1e-3, 'xtol': 1e-3, 'disp': True}) synaptic_kinetics_error(polished_result.x, plot=1) #synaptic_kinetics_error(x1, plot=1)

[ "neurosutras@gmail.com" ]

neurosutras@gmail.com

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/cc_mcc_seq/SNVINDEL/tmp/3.1_tumor_minus_normal_exome_somatic_number/1_tumor_minus_normal_somatic.py

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[]

no_license

arvin580/SIBS

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refs/heads/master

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def tumor_minus_normal_to_somatic(tumorFile,normalFile,oFile) : dict_Normal=dict() ouFile=open(oFile,'w') inFile=open(normalFile) for line in inFile : line=line.strip() fields=line.split('\t') k='\t'.join(fields[1:-1]) dict_Normal[k]=1 inFile.close() inFile=open(tumorFile) for line in inFile : line=line.strip() fields=line.split('\t') k='\t'.join(fields[1:-1]) if k not in dict_Normal : ouFile.write(line+'\n') ouFile.close() tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC10A','sum_snp34.exome_summary.pass012.ICC10B','sum_snp.exome_summary.pass012.ICC10') tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC4A','sum_snp34.exome_summary.pass012.ICC4B','sum_snp.exome_summary.pass012.ICC4') tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC5A','sum_snp34.exome_summary.pass012.ICC5B','sum_snp.exome_summary.pass012.ICC5') tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC9A','sum_snp34.exome_summary.pass012.ICC9B','sum_snp.exome_summary.pass012.ICC9') tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC10A','sum_snp34.exome_summary.pass012.CHC10B','sum_snp.exome_summary.pass012.CHC10') tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC5A','sum_snp34.exome_summary.pass012.CHC5B','sum_snp.exome_summary.pass012.CHC5') tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC6A','sum_snp34.exome_summary.pass012.CHC6B','sum_snp.exome_summary.pass012.CHC6') tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC7A','sum_snp34.exome_summary.pass012.CHC7B','sum_snp.exome_summary.pass012.CHC7')

[ "sunhanice@gmail.com" ]

sunhanice@gmail.com

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/python/codeup/solved/_1068.py

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TERADA-DANTE/algorithm

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n = int(input()) if 90 <= n: print('A') elif 70 <= n: print('B') elif 40 <= n: print('C') elif 0 <= n: print('D')

[ "55175301+TERADA-DANTE@users.noreply.github.com" ]

55175301+TERADA-DANTE@users.noreply.github.com

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/CMGTools/H2TauTau/prod/25aug_corrMC/up/mc/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0_1377544840/HTT_24Jul_newTES_manzoni_Up_Jobs/Job_225/run_cfg.py

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[]

no_license

rmanzoni/HTT

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refs/heads/master

2016-09-06T05:55:52.602604

2014-02-20T16:35:34

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null

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py

import FWCore.ParameterSet.Config as cms import os,sys sys.path.append('/afs/cern.ch/user/m/manzoni/summer13/CMGTools/CMSSW_5_3_9/src/CMGTools/H2TauTau/prod/25aug_corrMC/up/mc/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0_1377544840/HTT_24Jul_newTES_manzoni_Up_Jobs') from base_cfg import * process.source = cms.Source("PoolSource", noEventSort = cms.untracked.bool(True), inputCommands = cms.untracked.vstring('keep *', 'drop cmgStructuredPFJets_cmgStructuredPFJetSel__PAT'), duplicateCheckMode = cms.untracked.string('noDuplicateCheck'), fileNames = cms.untracked.vstring('/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2006.root', '/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2007.root', '/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2008.root', '/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2009.root', '/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_201.root') )

[ "riccardo.manzoni@cern.ch" ]

riccardo.manzoni@cern.ch

fc8f7fd662fe988e7f5f65c94869efdafc5af3eb

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/dojoassignments/python/django/full_stack_django/login_and_registration/apps/login_registration_app/migrations/0001_initial.py

2e5994f927a8fa2ce9b4a5d96fd6c594f3453aa5

[]

no_license

mtjhartley/codingdojo

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refs/heads/master

2022-12-14T23:06:11.927445

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# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2017-06-20 19:02 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('first_name', models.CharField(max_length=255)), ('last_name', models.CharField(max_length=255)), ('email', models.CharField(max_length=255)), ('password', models.CharField(max_length=45)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), ]

[ "mtjhartley@gmail.com" ]

mtjhartley@gmail.com

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/samples/openapi3/client/petstore/python-experimental/petstore_api/model/animal.py

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permissive

Januson/openapi-generator

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refs/heads/master

2022-10-19T04:16:38.042495

2022-04-23T08:42:21

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2020-02-06T10:12:38

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py

# coding: utf-8 """ OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 import typing # noqa: F401 from frozendict import frozendict # noqa: F401 import decimal # noqa: F401 from datetime import date, datetime # noqa: F401 from frozendict import frozendict # noqa: F401 from petstore_api.schemas import ( # noqa: F401 AnyTypeSchema, ComposedSchema, DictSchema, ListSchema, StrSchema, IntSchema, Int32Schema, Int64Schema, Float32Schema, Float64Schema, NumberSchema, UUIDSchema, DateSchema, DateTimeSchema, DecimalSchema, BoolSchema, BinarySchema, NoneSchema, none_type, Configuration, Unset, unset, ComposedBase, ListBase, DictBase, NoneBase, StrBase, IntBase, Int32Base, Int64Base, Float32Base, Float64Base, NumberBase, UUIDBase, DateBase, DateTimeBase, BoolBase, BinaryBase, Schema, _SchemaValidator, _SchemaTypeChecker, _SchemaEnumMaker ) class Animal( DictSchema ): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ _required_property_names = set(( 'className', )) className = StrSchema color = StrSchema @classmethod @property def _discriminator(cls): return { 'className': { 'Cat': Cat, 'Dog': Dog, } } def __new__( cls, *args: typing.Union[dict, frozendict, ], className: className, color: typing.Union[color, Unset] = unset, _configuration: typing.Optional[Configuration] = None, **kwargs: typing.Type[Schema], ) -> 'Animal': return super().__new__( cls, *args, className=className, color=color, _configuration=_configuration, **kwargs, ) from petstore_api.model.cat import Cat from petstore_api.model.dog import Dog

[ "noreply@github.com" ]

Januson.noreply@github.com

fd96964145fbc06b436ee1ecbbf561c15f201c00

caf192dbc1ca90fee18bb4ce170d37eb14870ec5

/Chapter-5/7. Caesar cipher.py

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[]

no_license

Dfredude/PythonZelle

858b00f5eacce841173c64b3cecd978dedbeb145

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refs/heads/main

2023-08-30T21:45:57.070344

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py

def main(): #Get plaintext(p_text) and key(x) from the user p_text = input("Enter the message you'd like encrypted.\n") key = eval(input("What's the key? : ")) p_text = p_text.lower() #Create string of letters table = "abcdefghijklmnopqrstuvwxyz" #Convert plaintext to ciphertext(c_text) using cipher loop c_text = "" for ch in p_text: c_text = c_text + (table[((ord(ch)) - 97) + key % 52]) print("Your encoded message is {0}.".format(c_text)) main()

[ "dominguezlucio@outlook.com" ]

dominguezlucio@outlook.com

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/dolo/compiler/function_compiler_numexpr.py

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permissive

christophe-gouel/dolo

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refs/heads/master

2020-12-24T09:31:19.389548

2018-01-04T20:42:19

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from __future__ import division from dolo.symbolic.derivatives import DerivativesTree from dolo.symbolic.symbolic import TSymbol from dolo.compiler.function_compiler import compile_multiargument_function as compile_multiargument_function_regular DerivativesTree.symbol_type = TSymbol def compile_multiargument_function(equations, args_list, args_names, parms, fname='anonymous_function', diff=True, return_text=False, order='rows'): return compile_multiargument_function_regular(equations, args_list, args_names, parms, fname=fname, diff=diff, return_text=return_text, use_numexpr=True, order=order) if __name__ == '__main__': import sympy from pprint import pprint [w,x,y,z,t] = vars = sympy.symbols('w, x, y, z, t') [a,b,c,d] = parms = sympy.symbols('a, b, c, d') [k_1,k_2] = s_sym = sympy.symbols('k_1, k_2') [x_1,x_2] = x_sym = sympy.symbols('x_1, x_2') args_list = [ s_sym, x_sym ] from sympy import exp eqs = [ x + y*k_2 + z*exp(x_1 + t), (y + z)**0.3, z, (k_1 + k_2)**0.3, k_2**x_1 ] sdict = {s:eqs[i] for i,s in enumerate(vars) } from dolo.misc.triangular_solver import solve_triangular_system order = solve_triangular_system(sdict, return_order=True) ordered_vars = [ v for v in order ] ordered_eqs = [ eqs[vars.index(v)] for v in order ] pprint(ordered_vars) pprint(ordered_eqs) import numpy floatX = numpy.float32 s0 = numpy.array( [2,5], dtype=floatX) x0 = numpy.array( [2,2], dtype=floatX) p0 = numpy.array( [4,3], dtype=floatX) N = 2000 s1 = numpy.column_stack( [s0]*N ) x1 = numpy.column_stack( [x0]*N ) p1 = numpy.array( [4,3, 6, 7], dtype=floatX ) # f = create_fun() # # test = f(s1,x1,p0) # print(test) args_names = ['s','x'] # # solution = solve_triangular_system(sdict) vals = [sympy.sympify(solution[v]) for v in ordered_vars] from dolo.compiler.compiling import compile_multiargument_function as numpy_compiler from dolo.compiler.compiling_theano import compile_multiargument_function as theano_compiler f_numexpr = compile_multiargument_function( vals, args_list, args_names, parms ) f_numpy = numpy_compiler( vals, args_list, args_names, parms ) f_theano = theano_compiler( vals, args_list, args_names, parms ) n_exp = 1000 import time r = time.time() for i in range(n_exp): res_numexpr = f_numexpr(s1,x1,p1) # res = numpy.row_stack(res) s = time.time() print('Time (numexpr) : '+ str(s-r)) r = time.time() for i in range(n_exp): res_theano = f_theano(s1,x1,p1) # res = numpy.row_stack(res) s = time.time() print('Time (theano) : '+ str(s-r)) r = time.time() for i in range(n_exp): res_numpy = f_numpy(s1,x1,p1) # res = numpy.row_stack(res) s = time.time() print('Time (numpy) : '+ str(s-r)) print( abs(res_numpy - res_theano).max() ) print( abs(res_numexpr - res_numpy).max() )

[ "pablo.winant@gmail.com" ]

pablo.winant@gmail.com

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/azure-mgmt-network/azure/mgmt/network/v2018_02_01/models/outbound_nat_rule.py

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refs/heads/master

2023-08-12T20:36:24.284497

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MIT

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from .sub_resource import SubResource class OutboundNatRule(SubResource): """Outbound NAT pool of the load balancer. All required parameters must be populated in order to send to Azure. :param id: Resource ID. :type id: str :param allocated_outbound_ports: The number of outbound ports to be used for NAT. :type allocated_outbound_ports: int :param frontend_ip_configurations: The Frontend IP addresses of the load balancer. :type frontend_ip_configurations: list[~azure.mgmt.network.v2018_02_01.models.SubResource] :param backend_address_pool: Required. A reference to a pool of DIPs. Outbound traffic is randomly load balanced across IPs in the backend IPs. :type backend_address_pool: ~azure.mgmt.network.v2018_02_01.models.SubResource :param provisioning_state: Gets the provisioning state of the PublicIP resource. Possible values are: 'Updating', 'Deleting', and 'Failed'. :type provisioning_state: str :param name: The name of the resource that is unique within a resource group. This name can be used to access the resource. :type name: str :param etag: A unique read-only string that changes whenever the resource is updated. :type etag: str """ _validation = { 'backend_address_pool': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'allocated_outbound_ports': {'key': 'properties.allocatedOutboundPorts', 'type': 'int'}, 'frontend_ip_configurations': {'key': 'properties.frontendIPConfigurations', 'type': '[SubResource]'}, 'backend_address_pool': {'key': 'properties.backendAddressPool', 'type': 'SubResource'}, 'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'etag': {'key': 'etag', 'type': 'str'}, } def __init__(self, **kwargs): super(OutboundNatRule, self).__init__(**kwargs) self.allocated_outbound_ports = kwargs.get('allocated_outbound_ports', None) self.frontend_ip_configurations = kwargs.get('frontend_ip_configurations', None) self.backend_address_pool = kwargs.get('backend_address_pool', None) self.provisioning_state = kwargs.get('provisioning_state', None) self.name = kwargs.get('name', None) self.etag = kwargs.get('etag', None)

[ "lmazuel@microsoft.com" ]

lmazuel@microsoft.com

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/test/webapi/controllers/test_wmts.py

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permissive

rabaneda/xcube

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refs/heads/master

2020-11-24T00:11:17.107630

2020-02-11T10:11:34

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MIT

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2019-12-13T16:14:50

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import os import unittest from test.webapi.helpers import get_res_test_dir, new_test_service_context from xcube.webapi.controllers.wmts import get_wmts_capabilities_xml class WmtsControllerTest(unittest.TestCase): def test_get_wmts_capabilities_xml(self): self.maxDiff = None with open(os.path.join(get_res_test_dir(), 'WMTSCapabilities.xml')) as fp: expected_capabilities = fp.read() ctx = new_test_service_context() capabilities = get_wmts_capabilities_xml(ctx, 'http://bibo') print(80 * '=') print(capabilities) print(80 * '=') self.assertEqual(expected_capabilities.replace(' ', ''), capabilities.replace(' ', ''))

[ "norman.fomferra@gmail.com" ]

norman.fomferra@gmail.com

a21dfa9182883f7045cd35880f722f3d9a36a0ab

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/tests/terraform/checks/resource/azure/test_SynapseWorkspaceEnablesDataExfilProtection.py

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[ "Apache-2.0" ]

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bridgecrewio/checkov

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refs/heads/main

2023-08-31T06:57:21.990147

2023-08-30T23:01:47

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import unittest from pathlib import Path from checkov.runner_filter import RunnerFilter from checkov.terraform.checks.resource.azure.SynapseWorkspaceEnablesDataExfilProtection import check from checkov.terraform.runner import Runner class TestSynapseWorkspaceEnablesDataExfilProtection(unittest.TestCase): def test(self): # given test_files_dir = Path(__file__).parent / "example_SynapseWorkspaceEnablesDataExfilProtection" # when report = Runner().run(root_folder=str(test_files_dir), runner_filter=RunnerFilter(checks=[check.id])) # then summary = report.get_summary() passing_resources = { "azurerm_synapse_workspace.pass", } failing_resources = { "azurerm_synapse_workspace.fail", "azurerm_synapse_workspace.fail2", } passed_check_resources = {c.resource for c in report.passed_checks} failed_check_resources = {c.resource for c in report.failed_checks} self.assertEqual(summary["passed"], 1) self.assertEqual(summary["failed"], 2) self.assertEqual(summary["skipped"], 0) self.assertEqual(summary["parsing_errors"], 0) self.assertEqual(summary["resource_count"], 3) # 3 unknown self.assertEqual(passing_resources, passed_check_resources) self.assertEqual(failing_resources, failed_check_resources) if __name__ == "__main__": unittest.main()

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bridgecrewio.noreply@github.com

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/pcat2py/class/235864d6-5cc5-11e4-af55-00155d01fe08.py

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phnomcobra/PCAT2PY

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#!/usr/bin/python ################################################################################ # 235864d6-5cc5-11e4-af55-00155d01fe08 # # Justin Dierking # justindierking@hardbitsolutions.com # phnomcobra@gmail.com # # 10/24/2014 Original Construction ################################################################################ class Finding: def __init__(self): self.output = [] self.is_compliant = False self.uuid = "235864d6-5cc5-11e4-af55-00155d01fe08" def check(self, cli): # Initialize Compliance self.is_compliant = False # Execute command and parse capture standard output stdout = cli.system("ls -l /etc/group") # Split output lines self.output = stdout.split('\n') # Process standard output lineNumber = 0 for line in self.output: lineNumber += 1 if len(line.strip()) > 0: subStrings = line.split(' ') if subStrings[3] == "root": self.is_compliant = True return self.is_compliant def fix(self, cli): cli.system("chgrp root /etc/group")

[ "phnomcobra@gmail.com" ]

phnomcobra@gmail.com

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/nlu/components/sentence_detectors/deep_sentence_detector/deep_sentence_detector.py

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ahmedlone127/nlu

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refs/heads/master

2023-02-09T05:10:29.631583

2022-05-20T15:16:33

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from sparknlp.annotator import * class SentenceDetectorDeep: @staticmethod def get_default_model(): return SentenceDetectorDLModel\ .pretrained()\ .setInputCols(["document"]) \ .setOutputCol("sentence") @staticmethod def get_pretrained_model(name,lang, bucket=None): return SentenceDetectorDLModel.pretrained(name,lang,bucket) \ .pretrained() \ .setInputCols(["document"]) \ .setOutputCol("sentence") # # # @staticmethod # def get_trainable_model(): # return SentenceDetectorDLApproach \ # .setInputCol("document") \ # .setOutputCol("sentence")

[ "christian.kasim.loan@gmail.com" ]

christian.kasim.loan@gmail.com

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/archive-20200922/uncategorized/quick_palindrome_check.py

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[]

no_license

clarkngo/python-projects

fe0e0aa02896debe82d1e9de84b1ae7d00932607

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refs/heads/master

2021-07-02T10:45:31.242041

2020-10-25T08:59:23

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# function which return reverse of a string def reverse(s): return s[::-1] def isPalindrome(s): # Calling reverse function rev = reverse(s) # Checking if both string are equal or not if (s == rev): return True return False # Driver code s = "malayalam" ans = isPalindrome(s) if ans == 1: print("Yes") else: print("No")

[ "clarkngo@gmail.com" ]

clarkngo@gmail.com

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/homeassistant/components/airzone/coordinator.py

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permissive

konnected-io/home-assistant

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refs/heads/dev

2023-05-11T08:57:41.891518

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"""The Airzone integration.""" from __future__ import annotations from datetime import timedelta import logging from typing import Any from aioairzone.exceptions import AirzoneError from aioairzone.localapi import AirzoneLocalApi import async_timeout from homeassistant.core import HomeAssistant from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed from .const import AIOAIRZONE_DEVICE_TIMEOUT_SEC, DOMAIN SCAN_INTERVAL = timedelta(seconds=60) _LOGGER = logging.getLogger(__name__) class AirzoneUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]): """Class to manage fetching data from the Airzone device.""" def __init__(self, hass: HomeAssistant, airzone: AirzoneLocalApi) -> None: """Initialize.""" self.airzone = airzone super().__init__( hass, _LOGGER, name=DOMAIN, update_interval=SCAN_INTERVAL, ) async def _async_update_data(self) -> dict[str, Any]: """Update data via library.""" async with async_timeout.timeout(AIOAIRZONE_DEVICE_TIMEOUT_SEC): try: await self.airzone.update() except AirzoneError as error: raise UpdateFailed(error) from error return self.airzone.data()

[ "noreply@github.com" ]

konnected-io.noreply@github.com

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/load_model.py

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[]

no_license

Guya-LTD/profanity-detector

59dbcb2e3e2fe4eba29cd49f5f028c48413f035f

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refs/heads/main

2023-02-11T18:26:59.205036

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UTF-8

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import numpy as np import joblib def _get_profane_prob(prob): return prob[1] def predict(lang, texts): vectorizer = joblib.load(lang + '/vectorizer.joblib') model = joblib.load(lang + '/model.joblib') return model.predict(vectorizer.transform(texts)) def predict_prob(lang, texts): vectorizer = joblib.load(lang + '/vectorizer.joblib') model = joblib.load(lang + '/model.joblib') return np.apply_along_axis(_get_profane_prob, 1, model.predict_proba(vectorizer.transform(texts)))

[ "simonbelete@gmail.com" ]

simonbelete@gmail.com

e6ea4e632b0b731721851c7db5ec5498ae307b76

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/python/wait_functions_test_py3.py

02cab39f268b7e1880b29bbcbcffa372099fe449

[ "MIT" ]

permissive

glenn-edgar/chain_flow

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refs/heads/master

2021-01-02T22:41:30.066536

2017-09-05T19:34:57

99,368,944

0

null

UTF-8

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false

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py

from py_cf_py3.chain_flow_py3 import CF_Base_Interpreter def test_function_1( cf_handle, chainObj, parameters, event ): print("test function 1 ",event) def wait_test_function( cf_handle, chainObj, parameters, event ): print("event",event) return_value = False if event["name"] == "INIT": parameters.append(0) if event["name"] == "TIME_TICK": parameters[-1] = parameters[-1] +1 if parameters[-1] >= parameters[1]: return_value = True return return_value cf = CF_Base_Interpreter() cf.define_chain("Chain_1", False) # wait_tod cf.insert.log("Chain 1 started") cf.insert.wait_tod( "*","*","*",15 ) # wait for 15 seconds cf.insert.one_step( test_function_1) cf.insert.log("Chain 1 is reset") cf.insert.reset( ) cf.define_chain("Chain_2",False) # wait_tod_ge wait_tod_le cf.insert.log("Chain 2 started") cf.insert.wait_tod_ge( "*","*","*",45 ) # wait for 15 seconds cf.insert.check_event( test_function_1, "TIME_TICK" ) cf.insert.wait_tod_le( "*","*","*",15) # wait for 15 seconds cf.insert.reset( ) cf.define_chain("Chain_3",False) #wait_event_count cf.insert.log("Chain 3 started") cf.insert.wait_event_count(count = 10) cf.insert.one_step( test_function_1) cf.insert.reset() cf.define_chain("Chain_4",True) # wait_function cf.insert.log("Chain 4 has started") cf.insert.wait_function(wait_test_function, 10 ) cf.insert.log("Chain 4 is ended ") cf.insert.reset() cf.execute()

[ "glenn-edgar@onyxengr.com" ]

glenn-edgar@onyxengr.com

d82a7c81e00fa27c5ad59a4fc4811c1928d2518e

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/taobao-sdk/top/api/rest/TmallProductSpecsGetRequest.py

b7150211c3b1b6c63e9ce9e9c0ee66bd56c5f336

[]

no_license

cash2one/language-Python

e332ecfb4e9321a11407b29987ee64d44e552b15

8adb4f2fd2f023f9cc89b4edce1da5f71a3332ab

refs/heads/master

2021-06-16T15:15:08.346420

2017-04-20T02:44:16

112,173,361

1

0

null

2017-11-27T09:08:57

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UTF-8

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356

py

''' Created by auto_sdk on 2014.02.28 ''' from top.api.base import RestApi class TmallProductSpecsGetRequest(RestApi): def __init__(self,domain='gw.api.taobao.com',port=80): RestApi.__init__(self,domain, port) self.cat_id = None self.product_id = None self.properties = None def getapiname(self): return 'tmall.product.specs.get'

[ "a@ie9.org" ]

a@ie9.org

ce502221c2081beadd2ed01aa5ddd02cf7cf7901

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/data_structures_and_algorithms_in_python/ch04/power_fast.py

c7f98d650facb9e5b5bb39c4db5cd09f1ee64c4c

[]

no_license

timothyshull/python_reference_code

692a7c29608cadfd46a6cc409a000023e95b9458

f3e2205dd070fd3210316f5f470d371950945028

refs/heads/master

2021-01-22T20:44:07.018811

2017-03-17T19:17:22

85,346,735

0

null

UTF-8

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false

267

py

def power(x, n): if n == 0: return 1 else: partial = power(x, n // 2) # rely on truncated division result = partial * partial if n % 2 == 1: # if n odd, include extra factor of x result *= x return result

[ "timothyshull@gmail.com" ]

timothyshull@gmail.com

e8fb8b6c7a2c7ba04314e431ec618dd22761941e

612325535126eaddebc230d8c27af095c8e5cc2f

/src/build/android/pylib/utils/device_dependencies.py

c448396fbc0ab0c74370a723afeb7c9fb47be053

[ "BSD-3-Clause" ]

permissive

TrellixVulnTeam/proto-quic_1V94

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feee14d96ee95313f236e0f0e3ff7719246c84f7

refs/heads/master

2023-04-01T14:36:53.888576

2019-10-17T02:23:04

null

0

null

UTF-8

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false

3,315

py

# Copyright 2016 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import os import re from pylib import constants _BLACKLIST = [ re.compile(r'.*OWNERS'), # Should never be included. re.compile(r'.*\.crx'), # Chrome extension zip files. re.compile(r'.*\.so'), # Libraries packed into .apk. re.compile(r'.*Mojo.*manifest\.json'), # Some source_set()s pull these in. re.compile(r'.*\.py'), # Some test_support targets include python deps. re.compile(r'.*\.stamp'), # Stamp files should never be included. # Some test_support targets include python deps. re.compile(r'.*\.mojom\.js'), # Chrome external extensions config file. re.compile(r'.*external_extensions\.json'), # Exists just to test the compile, not to be run. re.compile(r'.*jni_generator_tests'), # v8's blobs get packaged into APKs. re.compile(r'.*natives_blob.*\.bin'), re.compile(r'.*snapshot_blob.*\.bin'), ] def DevicePathComponentsFor(host_path, output_directory): """Returns the device path components for a given host path. This returns the device path as a list of joinable path components, with None as the first element to indicate that the path should be rooted at $EXTERNAL_STORAGE. e.g., given '$CHROMIUM_SRC/foo/bar/baz.txt' this would return [None, 'foo', 'bar', 'baz.txt'] This handles a couple classes of paths differently than it otherwise would: - All .pak files get mapped to top-level paks/ - Anything in the output directory gets mapped relative to the output directory rather than the source directory. e.g. given '$CHROMIUM_SRC/out/Release/icu_fake_dir/icudtl.dat' this would return [None, 'icu_fake_dir', 'icudtl.dat'] Args: host_path: The absolute path to the host file. Returns: A list of device path components. """ if host_path.startswith(output_directory): if os.path.splitext(host_path)[1] == '.pak': return [None, 'paks', os.path.basename(host_path)] rel_host_path = os.path.relpath(host_path, output_directory) else: rel_host_path = os.path.relpath(host_path, constants.DIR_SOURCE_ROOT) device_path_components = [None] p = rel_host_path while p: p, d = os.path.split(p) if d: device_path_components.insert(1, d) return device_path_components def GetDataDependencies(runtime_deps_path): """Returns a list of device data dependencies. Args: runtime_deps_path: A str path to the .runtime_deps file. Returns: A list of (host_path, device_path) tuples. """ if not runtime_deps_path: return [] with open(runtime_deps_path, 'r') as runtime_deps_file: rel_host_files = [l.strip() for l in runtime_deps_file if l] output_directory = constants.GetOutDirectory() abs_host_files = [ os.path.abspath(os.path.join(output_directory, r)) for r in rel_host_files] filtered_abs_host_files = [ host_file for host_file in abs_host_files if not any(blacklist_re.match(host_file) for blacklist_re in _BLACKLIST)] return [(f, DevicePathComponentsFor(f, output_directory)) for f in filtered_abs_host_files]

[ "2100639007@qq.com" ]

2100639007@qq.com

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/Code/CodeRecords/2158/60876/250371.py

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[]

no_license

AdamZhouSE/pythonHomework

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ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

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UTF-8

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537

py

string=input() index=0 while string[index]==' ': index+=1 temp="" if string[index]=='-': temp+="-" index+=1 while index<len(string) and string[index].isdigit(): temp+=string[index] index+=1 if int(temp)<-2**(31): print( -2**(31)) else: print(temp) elif not string[index].isdigit(): print(0) else: while index<len(string) and string[index].isdigit(): temp+=string[index] index+=1 if int(temp)>2**31-1: print(2**31-1) else: print(temp)

[ "1069583789@qq.com" ]

1069583789@qq.com

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/py/py_0198_ambiguous_numbers.py

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[ "MIT" ]

permissive

lcsm29/project-euler

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refs/heads/main

2023-07-04T11:45:24.374841

2021-08-07T08:20:41

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py

# Solution of; # Project Euler Problem 198: Ambiguous Numbers # https://projecteuler.net/problem=198 # # A best approximation to a real number $x$ for the denominator bound $d$ is a # rational number $\frac r s$ (in reduced form) with $s \le d$, so that any # rational number $\frac p q$ which is closer to $x$ than $\frac r s$ has $q > # d$. Usually the best approximation to a real number is uniquely determined # for all denominator bounds. However, there are some exceptions, e. g. $\frac # 9 {40}$ has the two best approximations $\frac 1 4$ and $\frac 1 5$ for the # denominator bound $6$. We shall call a real number $x$ ambiguous, if there # is at least one denominator bound for which $x$ possesses two best # approximations. Clearly, an ambiguous number is necessarily rational. How # many ambiguous numbers $x=\frac p q, 0 < x < \frac 1 {100}$, are there whose # denominator $q$ does not exceed $10^8$? # # by lcsm29 http://github.com/lcsm29/project-euler import timed def dummy(n): pass if __name__ == '__main__': n = 1000 i = 10000 prob_id = 198 timed.caller(dummy, n, i, prob_id)

[ "lcsm29@outlook.com" ]

lcsm29@outlook.com

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/examples/data/Assignment_4/bgrtej001/piglatin.py

8a3ae80180102da97b97c2eee4594a3e8512b2c3

[]

no_license

MrHamdulay/csc3-capstone

479d659e1dcd28040e83ebd9e3374d0ccc0c6817

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refs/heads/master

2021-03-12T21:55:57.781339

2014-09-22T02:22:22

22,372,174

0

null

UTF-8

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false

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#Tejasvin Bagirathi #Assignment 4, Question 3 def toPigLatin(s): wrdno = 1 new = "" for i in range(len(s)): if s[i] == " ": wrdno+=1 string = s.split(" ") for i in range(wrdno): wrd = string[i] #If word starts with vowel if wrd[0] in "aeiou": wrd = wrd + "way" if wrdno == i: new += wrd else: new += wrd + " " else: k = 0 for c in wrd[:]: if c not in "aeiou": k+=1 else: break wrd = wrd[k:len(wrd)] + "a" + wrd[0:k] + "ay" if wrdno == i: new += wrd else: new += wrd + " " return new def toEnglish(s): sentence=s.split() newsentence="" for word in range(len(sentence)): if sentence[word][-3:]=="way": newsentence+=sentence[word][:-3]+" " elif sentence[word][-2:]=="ay": nWord=sentence[word][:-2] aPos=nWord.rfind("a") newsentence+=nWord[aPos+1:]+nWord[:aPos]+" " return(newsentence)

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import os import platform from os import getcwd from os.path import relpath, splitdrive from pathlib import Path from shutil import which import pytest from modflow_devtools.markers import requires_exe from modflow_devtools.misc import set_dir from flopy.utils.flopy_io import line_parse, relpath_safe def test_line_parse(): """t027 test line_parse method in MNW2 Package class""" # ensure that line_parse is working correctly # comment handling line = line_parse("Well-A -1 ; 2a. WELLID,NNODES") assert line == ["Well-A", "-1"] @requires_exe("mf6") @pytest.mark.parametrize("scrub", [True, False]) @pytest.mark.parametrize("use_paths", [True, False]) def test_relpath_safe(function_tmpdir, scrub, use_paths): if ( platform.system() == "Windows" and splitdrive(function_tmpdir)[0] != splitdrive(getcwd())[0] ): if use_paths: assert ( Path(relpath_safe(function_tmpdir)) == function_tmpdir.absolute() ) assert relpath_safe(Path(which("mf6"))) == str( Path(which("mf6")).absolute() ) else: assert ( Path(relpath_safe(str(function_tmpdir))) == function_tmpdir.absolute() ) assert relpath_safe(which("mf6")) == str( Path(which("mf6")).absolute() ) else: if use_paths: assert Path( relpath_safe(function_tmpdir, function_tmpdir.parent) ) == Path(function_tmpdir.name) assert ( Path( relpath_safe( function_tmpdir, function_tmpdir.parent.parent ) ) == Path(function_tmpdir.parent.name) / function_tmpdir.name ) assert relpath_safe(Path(which("mf6"))) == relpath( Path(which("mf6")), Path(getcwd()) ) else: assert Path( relpath_safe(str(function_tmpdir), str(function_tmpdir.parent)) ) == Path(function_tmpdir.name) assert ( Path( relpath_safe( str(function_tmpdir), str(function_tmpdir.parent.parent), ) ) == Path(function_tmpdir.parent.name) / function_tmpdir.name ) assert relpath_safe(which("mf6")) == relpath( which("mf6"), getcwd() ) # test user login obfuscation with set_dir("/"): try: login = os.getlogin() if use_paths: p = relpath_safe(Path.home(), scrub=scrub) else: p = relpath_safe(str(Path.home()), scrub=scrub) if login in str(Path.home()) and scrub: assert "***" in p assert login not in p except OSError: # OSError is possible in CI, e.g. 'No such device or address' pass

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# Generated by Django 2.1.5 on 2019-02-27 19:02 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('credentials', '0007_auto_20190128_1630'), ] operations = [ migrations.AddField( model_name='aboutme', name='resume', field=models.FileField(default='Arnold_Resume.pdf', upload_to=''), ), ]

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import matplotlib.pyplot as plt import matplotlib.dates as mdates import matplotlib.ticker as mticker from matplotlib.finance import candlestick_ohlc from matplotlib import style import numpy as np import urllib import datetime as dt #style.use('ggplot') style.use('fivethirtyeight') MA1 = 10 MA2 = 30 # Will give the moving average def moving_average(values, window): weights = np.repeat(1.0, window)/window smas = np.convolve(values, weights, 'valid') return smas def high_minus_low(highs, lows): return highs-lows # fmt - format def bytespdate2num(fmt, encoding='utf-8'): strconverter = mdates.strpdate2num(fmt) def bytesconverter(b): a = b.decode(encoding) return strconverter(a) return bytesconverter def graph_data(stock): fig = plt.figure() # ax1 is a subplot ax1 = plt.subplot2grid((6,1),(0,0), rowspan=1, colspan=1) plt.title(stock) ax2 = plt.subplot2grid((6,1),(1,0), rowspan=4, colspan=1) plt.xlabel('Date') plt.ylabel('Price') ax3 = plt.subplot2grid((6,1),(5,0), rowspan=1, colspan=1) stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv' source_code = urllib.request.urlopen(stock_price_url).read().decode() stock_data = [] split_source = source_code.split('\n') for line in split_source: split_line = line.split(',') if len(split_line) == 6: if 'values' not in line and 'labels' not in line: stock_data.append(line) date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data, delimiter = ',', unpack = True, converters={0: bytespdate2num('%Y%m%d')}) x = 0 y = len(date) # OHLC : open high low close ohlc = [] while x < y: append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x] ohlc.append(append_me) x += 1 ma1 = moving_average(closep, MA1) ma2 = moving_average(closep, MA2) start = len(date[MA2-1:]) h_l = list(map(high_minus_low, highp, lowp)) ax1.plot_date(date, h_l, '-') candlestick_ohlc(ax2, ohlc, width=0.4, colorup='g', colordown='r') for label in ax2.xaxis.get_ticklabels(): label.set_rotation(45) ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) ax2.xaxis.set_major_locator(mticker.MaxNLocator(10)) ax2.grid(True) bbox_props = dict(boxstyle='larrow', fc='w', ec='k', lw=1) # to display the last price ax2.annotate(str(closep[-1]), (date[-1], closep[-1]), xytext = (date[-1]+4, closep[-1]), bbox=bbox_props) ax3.plot(date[-start:], ma1[-start:]) ax3.plot(date[-start:], ma2[-start:]) #plt.legend() plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0) plt.show() graph_data('ebay')

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "${prefix}/include".split(';') if "${prefix}/include" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp;nodelet;sensor_msgs;tf;pcl_ros;pcl_conversions;geometry_msgs;message_filters".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-llaser_ortho_projector".split(';') if "-llaser_ortho_projector" != "" else [] PROJECT_NAME = "laser_ortho_projector" PROJECT_SPACE_DIR = "/home/nhamtung/TungNV/MyKitAgv/catkin_ws/install" PROJECT_VERSION = "0.3.2"

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# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """run function for softplus""" import numpy as np from tests.common.tensorio import compare_tensor from akg.utils import kernel_exec as utils from tests.common.test_op import softplus from tests.common.gen_random import random_gaussian from tests.common.base import get_rtol_atol def softplus_run(shape, dtype, attrs): mod = utils.op_build_test(softplus.softplus, [shape], [dtype], kernel_name="softplus", attrs=attrs) expect, inputs, output = gen_data(dtype, shape) output = utils.mod_launch(mod, (inputs, output), expect=expect) rtol, atol = get_rtol_atol("softplus", dtype) TestCase_Result = compare_tensor( output, expect, rtol=rtol, atol=atol, equal_nan=False) return inputs, output, expect, TestCase_Result def gen_data(dtype, shape): inputs = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype) expect = np.log1p(np.exp(-np.abs(inputs))) + np.maximum(inputs, 0) output = np.full(shape, np.nan, dtype) return expect, inputs, output

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mary-tano/python-programming

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# Лаборатория Франкенштейна from monsterlab import * # Основная программа Frank = Monster("Фрэнки", "необычный") Frank.show() Albert = GMonster("Альберт", "задумчивый") Albert.show() Sigmund = SMonster("Зигмунд", "веселый") Sigmund.show()

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class Solution: def reverse(self, x: 'int') -> 'int': if x > float('inf') or x < float('-inf'): return 0 sign = 1 if x < 0: sign = -1 xstr = str(x) if -1 == sign: xstr = xstr[1:] xstr = xstr[::-1] skip_cnt = 0 for ch in xstr: if ch != '0': break skip_cnt += 1 res = xstr[skip_cnt:] if '' == res: return 0 if -1 == sign: res = '-' + res return int(res) x = 123 #x = -123 #x = 120 #x = 901000 x = 1534236469 # 0 sol = Solution() print(sol.reverse(x))

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''' ============================================================ http://rosalind.info/problems/qrtd Given: A list containing n taxa (n<=2000) and two unrooted binary trees T1 and T2 on the given taxa. Both T1 and T2 are given in Newick format. Return: The quartet distance dq(T1,T2). ============================================================ ''' # From http://rosalind.info/problems/qrtd/solutions/. # Need to get the rest of his libraries import time from rosalind import rostree def qrtd(fp): taxa = next(fp).split() t1_str = next(fp) t2_str = next(fp) taxa_id = dict((s,i) for i, s in enumerate(taxa)) all_taxa = set(xrange(len(taxa))) start_time = time.time() def build_tree(t_str): T = rostree.read_newick_str(t_str) #T = make_unrooted_binary(T) for node in T.walk(order=T.POSTORDER): if node.is_leaf: node.id = taxa_id[node.val] node.nodes = set([node.id]) node.rest = all_taxa - node.nodes else: node.nodes = reduce(set.union, map(attrgetter('nodes'), node.children), set()) node.rest = all_taxa - node.nodes # special case to walk unroot tree; the first node is also a leaf node T.id = taxa_id[T.val] T.nodes = set([T.id]) T.rest = all_taxa - T.nodes return T T1 = build_tree(t1_str) T2 = build_tree(t2_str) # link T2 nodes to T1. Mind the special case for root node. id_2_T1 = dict((node.id,node) for node in T1.walk(type=T1.LEAF)) id_2_T1[T1.id] = T1 for node in T2.walk(type=T1.LEAF): node.t1_node = id_2_T1[node.id] T2.t1_node = id_2_T1[T2.id] N = len(taxa) print 'N=',N count = 0 for i, v1 in enumerate(T1.walk(type=T1.INODE)): if v1 is T1: continue if i % 10 == 0: print 'T1 %3d %s' % (time.time() - start_time, i) for A_node in T1.walk(exclude_node=v1): A_node.color = 1 for B_node in v1.left.walk(): B_node.color = 2 for C_node in v1.right.walk(): C_node.color = 3 A1 = v1.rest B1 = v1.left.nodes C1 = v1.right.nodes for v2 in T2.walk(order=T2.POSTORDER): if v2 is T2: pass elif v2.is_leaf: v2.a1 = 0 v2.b1 = 0 v2.c1 = 0 c = v2.t1_node.color if c == 1: v2.a1 = 1 elif c == 2: v2.b1 = 1 else: v2.c1 = 1 else: B = v2.left C = v2.right a1b2 = B.a1 a1c2 = C.a1 a1a2 = len(A1) - a1b2 - a1c2 b1b2 = B.b1 b1c2 = C.b1 b1a2 = len(B1) - b1b2 - b1c2 c1b2 = B.c1 c1c2 = C.c1 c1a2 = len(C1) - c1b2 - c1c2 # rememeber under v2, how many of them intersect with A1, B1 and C1 v2.a1 = a1b2 + a1c2 v2.b1 = b1b2 + b1c2 v2.c1 = c1b2 + c1c2 # 3x3=9 different orientation for T12 and T2, # times in each case two ways to pair B and C from each tree count += a1a2 * (a1a2-1) / 2 * (b1b2 * c1c2 + b1c2 * c1b2) count += a1b2 * (a1b2-1) / 2 * (b1a2 * c1c2 + b1c2 * c1a2) count += a1c2 * (a1c2-1) / 2 * (b1a2 * c1b2 + b1b2 * c1a2) count += b1a2 * (b1a2-1) / 2 * (a1b2 * c1c2 + a1c2 * c1b2) count += b1b2 * (b1b2-1) / 2 * (a1a2 * c1c2 + a1c2 * c1a2) count += b1c2 * (b1c2-1) / 2 * (a1a2 * c1b2 + a1b2 * c1a2) count += c1a2 * (c1a2-1) / 2 * (a1b2 * b1c2 + a1c2 * b1b2) count += c1b2 * (c1b2-1) / 2 * (a1a2 * b1c2 + a1c2 * b1a2) count += c1c2 * (c1c2-1) / 2 * (a1a2 * b1b2 + a1b2 * b1a2) print N * (N - 1) * (N- 2) * (N - 3) / 12 - count if __name__ == "__main__": print qrtd('rosalind_qrtd_sample.dat') #print qrtd('rosalind_qrtd.dat')

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eduardogpg/wamadeusV1

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('usuarios', '0002_auto_20141215_1710'), ] operations = [ migrations.AddField( model_name='usuario', name='correo', field=models.EmailField(default=' ', max_length=50), preserve_default=False, ), ]

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#!/usr/bin/python # -*- coding: UTF-8 -*- if __name__ == '__main__': a = [] for i in range(10): a.append([]) #创建十行 for j in range(10): a[i].append(0) #每行创建i列 # 杨辉三角边界都为1 for i in range(10): a[i][0] = 1 a[i][i] = 1 # 杨辉三角定义，下一行数值为上一行数与上一行前面数之和(除边界） for i in range(2,10): for j in range(1,i): a[i][j] = a[i - 1][j-1] + a[i - 1][j] from sys import stdout for i in range(10): for j in range(i + 1): stdout.write(str(a[i][j])) stdout.write(' ') print

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from astropy.io import fits import numpy as np # The 'dataset.py' module has been extended to give the dataset a name and metadata. class Dataset: def __init__(self, data, noise_map, name=None): """A class containing the data and noise-map of a 1D line dataset. Parameters ---------- data : np.ndarray The array of the data, in arbitrary units. noise_map : np.ndarray An array describing the RMS standard deviation error in each data pixel, in arbitrary units. """ self.data = data self.noise_map = noise_map # The name of the dataset is used by the aggregator, to determine the name of the file the dataset is saved as # and so that when using the aggregator you can know which dataset you are manipulating. self.name = name if name is str else "dataset" @property def xvalues(self): return np.arange(self.data.shape[0]) @classmethod def from_fits(cls, data_path, noise_map_path, name=None): """Load the data and noise-map of a 1D line dataset from ``.fits`` files. Parameters ---------- data_path : str The path on your hard-disk to the ``.fits`` file of the data. noise_map_path : str The path on your hard-disk to the ``.fits`` file of the noise-map. """ data_hdu_list = fits.open(data_path) noise_map_hdu_list = fits.open(noise_map_path) data = np.array(data_hdu_list[0].data) noise_map = np.array(noise_map_hdu_list[0].data) return Dataset(data=data, noise_map=noise_map, name=name) class MaskedDataset: def __init__(self, dataset, mask): """ A masked dataset, which is an image, noise-map and mask. Parameters ---------- dataset: im.Dataset The dataset (the image, noise-map, etc.) mask: msk.Mask2D The 1D mask that is applied to the dataset. """ self.dataset = dataset self.mask = mask self.data = dataset.data * np.invert(mask) self.noise_map = dataset.noise_map * np.invert(mask) @property def xvalues(self): return np.arange(self.data.shape[0]) def signal_to_noise_map(self): return self.data / self.noise_map def with_left_trimmed(self, data_trim_left): if data_trim_left is None: return self # Here, we use the existing masked dataset to create a trimmed dataset. data_trimmed = self.dataset.data[data_trim_left:] noise_map_trimmed = self.dataset.noise_map[data_trim_left:] dataset_trimmed = Dataset(data=data_trimmed, noise_map=noise_map_trimmed) mask_trimmed = self.mask[data_trim_left:] return MaskedDataset(dataset=dataset_trimmed, mask=mask_trimmed) def with_right_trimmed(self, data_trim_right): if data_trim_right is None: return self # We do the same as above, but removing data to the right. data_trimmed = self.dataset.data[:-data_trim_right] noise_map_trimmed = self.dataset.noise_map[:-data_trim_right] dataset_trimmed = Dataset(data=data_trimmed, noise_map=noise_map_trimmed) mask_trimmed = self.mask[:-data_trim_right] return MaskedDataset(dataset=dataset_trimmed, mask=mask_trimmed)

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from seleniumbase import BaseCase class MyTestClass(BaseCase): def test_create_tour(self): self.open("https://www.google.com/maps/@42.3591234,-71.0915634,15z") self.wait_for_element("#searchboxinput", timeout=20) self.wait_for_element("#minimap", timeout=20) self.wait_for_element("#zoom", timeout=20) # Create a website tour using the DriverJS library # Same as: self.create_driverjs_tour() self.create_tour(theme="driverjs") self.add_tour_step( "🗺️ Welcome to Google Maps 🗺️", "html", title="✅ SeleniumBase Tours 🌎", ) self.add_tour_step( "You can type a location into this Search box.", "#searchboxinput" ) self.add_tour_step( "Then click here to view it on the map.", "#searchbox-searchbutton", alignment="bottom", ) self.add_tour_step( "Or click here to get driving directions.", "#searchbox-directions", alignment="bottom", ) self.add_tour_step( "Use this button to get a Satellite view.", "div.widget-minimap-shim", alignment="right", ) self.add_tour_step( "Click here to zoom in.", "#widget-zoom-in", alignment="left" ) self.add_tour_step( "Or click here to zoom out.", "#widget-zoom-out", alignment="left" ) self.add_tour_step( "Use the Menu button for more options.", ".searchbox-hamburger-container", alignment="right", ) self.add_tour_step( "Or click here to see more Google apps.", '[title="Google apps"]', alignment="left", ) self.add_tour_step( "Thanks for using SeleniumBase Tours", "html", title="🚃 End of Guided Tour 🚃", ) self.export_tour() # The default name for exports is "my_tour.js" self.play_tour(interval=0) # If interval > 0, autoplay after N seconds

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############################################################### # NATS-Bench (arxiv.org/pdf/2009.00437.pdf), IEEE TPAMI 2021 # ############################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 # ############################################################### # Usage: python exps/NATS-Bench/draw-correlations.py # ############################################################### import os, gc, sys, time, scipy, torch, argparse import numpy as np from typing import List, Text, Dict, Any from shutil import copyfile from collections import defaultdict, OrderedDict from copy import deepcopy from pathlib import Path import matplotlib import seaborn as sns matplotlib.use('agg') import matplotlib.pyplot as plt import matplotlib.ticker as ticker lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) from config_utils import dict2config, load_config from nats_bench import create from log_utils import time_string def get_valid_test_acc(api, arch, dataset): is_size_space = api.search_space_name == 'size' if dataset == 'cifar10': xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False) test_acc = xinfo['test-accuracy'] xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False) valid_acc = xinfo['valid-accuracy'] else: xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False) valid_acc = xinfo['valid-accuracy'] test_acc = xinfo['test-accuracy'] return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc) def compute_kendalltau(vectori, vectorj): # indexes = list(range(len(vectori))) # rank_1 = sorted(indexes, key=lambda i: vectori[i]) # rank_2 = sorted(indexes, key=lambda i: vectorj[i]) # import pdb; pdb.set_trace() coef, p = scipy.stats.kendalltau(vectori, vectorj) return coef def compute_spearmanr(vectori, vectorj): coef, p = scipy.stats.spearmanr(vectori, vectorj) return coef if __name__ == '__main__': parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.') parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.') args = parser.parse_args() save_dir = Path(args.save_dir) api = create(None, 'tss', fast_mode=True, verbose=False) indexes = list(range(1, 10000, 300)) scores_1 = [] scores_2 = [] for index in indexes: valid_acc, test_acc, _ = get_valid_test_acc(api, index, 'cifar10') scores_1.append(valid_acc) scores_2.append(test_acc) correlation = compute_kendalltau(scores_1, scores_2) print('The kendall tau correlation of {:} samples : {:}'.format(len(indexes), correlation)) correlation = compute_spearmanr(scores_1, scores_2) print('The spearmanr correlation of {:} samples : {:}'.format(len(indexes), correlation)) # scores_1 = ['{:.2f}'.format(x) for x in scores_1] # scores_2 = ['{:.2f}'.format(x) for x in scores_2] # print(', '.join(scores_1)) # print(', '.join(scores_2)) dpi, width, height = 250, 1000, 1000 figsize = width / float(dpi), height / float(dpi) LabelSize, LegendFontsize = 14, 14 fig, ax = plt.subplots(1, 1, figsize=figsize) ax.scatter(scores_1, scores_2 , marker='^', s=0.5, c='tab:green', alpha=0.8) save_path = '/Users/xuanyidong/Desktop/test-temp-rank.png' fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png') plt.close('all')

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models # Create your models here. class User(models.Model): name = models.CharField(max_length=255)

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# -*- coding: utf-8 -*- # # Labs documentation build configuration file, created by # sphinx-quickstart on Tue Jun 24 15:49:19 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Labs' copyright = u'2014, Marcus Ottosson' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.0.1' # The full version, including alpha/beta/rc tags. release = '0.0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'Labsdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'Labs.tex', u'Labs Documentation', u'Marcus Ottosson', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'labs', u'Labs Documentation', [u'Marcus Ottosson'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'Labs', u'Labs Documentation', u'Marcus Ottosson', 'Labs', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False

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# Copyright 2014 Facebook, Inc. # You are hereby granted a non-exclusive, worldwide, royalty-free license to # use, copy, modify, and distribute this software in source code or binary # form for use in connection with the web services and APIs provided by # Facebook. # As with any software that integrates with the Facebook platform, your use # of this software is subject to the Facebook Developer Principles and # Policies [http://developers.facebook.com/policy/]. This copyright 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 facebook_business.adobjects.abstractobject import AbstractObject """ This class is auto-generated. For any issues or feature requests related to this class, please let us know on github and we'll fix in our codegen framework. We'll not be able to accept pull request for this class. """ class AdCampaignFrequencyControlSpecs( AbstractObject, ): def __init__(self, api=None): super(AdCampaignFrequencyControlSpecs, self).__init__() self._isAdCampaignFrequencyControlSpecs = True self._api = api class Field(AbstractObject.Field): event = 'event' interval_days = 'interval_days' max_frequency = 'max_frequency' _field_types = { 'event': 'string', 'interval_days': 'unsigned int', 'max_frequency': 'unsigned int', } @classmethod def _get_field_enum_info(cls): field_enum_info = {} return field_enum_info

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#!/home/elmartin913/workspace/app/PanDjango/pdenv/bin/python3 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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import time import pandas as pd import pickle import os import numpy as np SLURM_OUT = "../slurm_out" from subprocess import Popen import time print "a" out_overall = pickle.load(open('../data/input/all_regions.pkl','r')) num_trials=25 print "b" K = 3 for train_region in ["SanDiego"]: if train_region=="Austin": NUM_HOMES_MAX = 45 elif train_region=="SanDiego": NUM_HOMES_MAX = len(out_overall['SanDiego']) else: NUM_HOMES_MAX = len(out_overall['Boulder']) NUM_HOMES_MAX=20 for test_region in ["Austin"]: if train_region!=test_region: TRANSFORMATIONS = ["None","DD","DD-percentage","median-aggregate-percentage", "median-aggregate",'regional','regional-percentage'] else: TRANSFORMATIONS = ["None"] train_df = out_overall[train_region] test_df = out_overall[test_region] test_df=test_df[(test_df.full_agg_available==1)&(test_df.md_available==1)] NUM_HOMES_MIN=4 for num_homes in range(NUM_HOMES_MIN, NUM_HOMES_MAX, 2): for transform in TRANSFORMATIONS: #for transform in ["None","DD","DD-percentage"]: #for transform in ["median-aggregate-percentage"]: print transform print "*"*40 count = 0 #for appliance in ["dw",'hvac','fridge','wm','mw','ec','wh','oven']: for appliance in ["hvac"]: if appliance=="hvac": month_min, month_max = 5, 11 else: month_min, month_max = 1, 13 count+= 1 #for appliance in ["hvac","fridge","dr","wm"]: test_df = test_df.ix[test_df[['%s_%d' %(appliance,month) for month in range(month_min, month_max)]].dropna().index] for test_home in test_df.index: #for appliance in ["mw"]: if len(test_df.ix[test_home][['%s_%d' %(appliance, m) for m in range(month_min, month_max)]].dropna())==0: # Appliance data not present for this homes..let's save some time continue print appliance, test_home, count, len(test_df.index), K, transform, train_region, test_region OFILE = "%s/%d_%s_%s_%d_%s_%s.out" % (SLURM_OUT, num_homes, train_region[0], test_region[0], test_home, appliance[0], transform[0] ) EFILE = "%s/%d_%s_%s_%d_%s_%s.err" % (SLURM_OUT, num_homes, train_region[0], test_region[0], test_home, appliance, transform ) SLURM_SCRIPT = "%d_%s_%s_%d_%s_%s.pbs" % (num_homes, train_region[0], test_region[0], test_home, appliance[:2], transform) CMD = 'python ../new_experiments/create_inequalities_subset_kdd.py %s %s %d %s %s %d %d %d' % (train_region, test_region, test_home, appliance, transform, K, num_homes, num_trials) lines = [] lines.append("#!/bin/sh\n") lines.append('#SBATCH --time=0-05:0:00\n') lines.append('#SBATCH --mem=16\n') lines.append('#SBATCH -o '+'"' +OFILE+'"\n') lines.append('#SBATCH -e '+'"' +EFILE+'"\n') lines.append(CMD+'\n') with open(SLURM_SCRIPT, 'w') as f: f.writelines(lines) command = ['sbatch', SLURM_SCRIPT] Popen(command) #os.remove(SLURM_SCRIPT) print "Now sleeping.." import time time.sleep(40) time.sleep(400) time.sleep(1200)

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from django.contrib import messages from django.contrib.auth.mixins import LoginRequiredMixin from django.shortcuts import redirect from django.views.generic import DetailView from app.models import Request, Avaliacao class AvaliacaoView(LoginRequiredMixin, DetailView): template_name = 'loja/avaliacao_cliente.html' login_url = '/define/login/' model = Request context_object_name = 'pedido_obj' def get(self, request, *args, **kwargs): return super(AvaliacaoView, self).get(request, *args, **kwargs) def add_avaliacao(request): data = request.POST pedido = Request.objects.get(id=data['pedido']) if 'comentario' and 'nota' in data: aval = Avaliacao(cliente=pedido.cliente, estabelecimento=pedido.estabelecimento, nota=data['nota'], comentario=data['comentario']) aval.save() else: messages.error(request, 'Insira uma nota e um comentario') return redirect('/avaliacao/pedido/' + str(data['pedido'])) messages.success(request, 'Avaliacao Realizada com Sucesso') return redirect('/acompanhar-pedido/' + str(data['pedido']))

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import numpy as np from emotion_clf.emotion import load_clf, vectorize, emotions clf = load_clf('emotion_clf/clf2.pkl') def associate_emojis(face_landmarks): emotions_probs = predict_probabilities(face_landmarks) emoji = map_emoji(emotions_probs) return emoji def predict_probabilities(face_landmarks: dict): landmarks = [] for points in face_landmarks.values(): landmarks.extend(points) vector = vectorize(landmarks) data = np.array([vector]) res = clf.predict_proba(data)[0] probs = {} for i, e in enumerate(emotions): probs[e] = res[i] return probs def map_emoji(emotions_prob: dict): emojis = { '😡': { 'anger': 10, }, '😒': { 'contempt': 10, }, '😣': { 'disgust': 10, }, '😱': { 'fear': 10, }, '😀': { 'happiness': 10, }, '😢': { 'sadness': 10, }, '😮': { 'surprise': 10, }, } max_s = None result = '🌚' for emoji, ems in emojis.items(): s = sum([ems.get(e, 1) * emotions_prob[e] for e in emotions]) if not max_s or s > max_s: max_s = s result = emoji return result

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import asyncio from typing import Any, Dict import httpx import pytest from asgi_lifespan import LifespanManager from fastapi import status from chapter9.chapter9_app_external_api import app, external_api class MockExternalAPI: mock_data = { "data": [ { "employee_age": 61, "employee_name": "Tiger Nixon", "employee_salary": 320800, "id": 1, "profile_image": "", } ], "status": "success", "message": "Success", } async def __call__(self) -> Dict[str, Any]: return MockExternalAPI.mock_data @pytest.fixture(scope="session") def event_loop(): loop = asyncio.get_event_loop() yield loop loop.close() @pytest.fixture async def test_client(): app.dependency_overrides[external_api] = MockExternalAPI() async with LifespanManager(app): async with httpx.AsyncClient(app=app, base_url="http://app.io") as test_client: yield test_client @pytest.mark.asyncio async def test_get_employees(test_client: httpx.AsyncClient): response = await test_client.get("/employees") assert response.status_code == status.HTTP_200_OK json = response.json() assert json == MockExternalAPI.mock_data

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# long variable name this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = 0 this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = 1 # with a comment this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = [ 1, 2, 3 ] this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function() this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function( arg1, arg2, arg3 ) this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function( [1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3 ) # long function name normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying() normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying( arg1, arg2, arg3 ) normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying( [1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3 ) # long arguments normal_name = normal_function_name( "but with super long string arguments that on their own exceed the line limit so there's no way it can ever fit", "eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs", this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it=0, ) # output # long variable name this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = ( 0 ) this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = ( 1 ) # with a comment this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = [ 1, 2, 3 ] this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = ( function() ) this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function( arg1, arg2, arg3 ) this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function( [1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3 ) # long function name normal_name = ( but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying() ) normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying( arg1, arg2, arg3 ) normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying( [1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3 ) # long arguments normal_name = normal_function_name( "but with super long string arguments that on their own exceed the line limit so there's no way it can ever fit", "eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs", this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it=0, )

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"""Functions used by server.""" import time from . import rpc from .constants import MAX_QUEUE_SIZE def start_server(server_id, ip_config, num_servers, num_clients, server_state, \ max_queue_size=MAX_QUEUE_SIZE, net_type='socket'): """Start DGL server, which will be shared with all the rpc services. This is a blocking function -- it returns only when the server shutdown. Parameters ---------- server_id : int Current server ID (starts from 0). ip_config : str Path of IP configuration file. num_servers : int Server count on each machine. num_clients : int Total number of clients that will be connected to the server. Note that, we do not support dynamic connection for now. It means that when all the clients connect to server, no client will can be added to the cluster. server_state : ServerSate object Store in main data used by server. max_queue_size : int Maximal size (bytes) of server queue buffer (~20 GB on default). Note that the 20 GB is just an upper-bound because DGL uses zero-copy and it will not allocate 20GB memory at once. net_type : str Networking type. Current options are: 'socket'. """ assert server_id >= 0, 'server_id (%d) cannot be a negative number.' % server_id assert num_servers > 0, 'num_servers (%d) must be a positive number.' % num_servers assert num_clients >= 0, 'num_client (%d) cannot be a negative number.' % num_clients assert max_queue_size > 0, 'queue_size (%d) cannot be a negative number.' % max_queue_size assert net_type in ('socket'), 'net_type (%s) can only be \'socket\'' % net_type # Register signal handler. rpc.register_sig_handler() # Register some basic services rpc.register_service(rpc.CLIENT_REGISTER, rpc.ClientRegisterRequest, rpc.ClientRegisterResponse) rpc.register_service(rpc.SHUT_DOWN_SERVER, rpc.ShutDownRequest, None) rpc.register_service(rpc.GET_NUM_CLIENT, rpc.GetNumberClientsRequest, rpc.GetNumberClientsResponse) rpc.register_service(rpc.CLIENT_BARRIER, rpc.ClientBarrierRequest, rpc.ClientBarrierResponse) rpc.set_rank(server_id) server_namebook = rpc.read_ip_config(ip_config, num_servers) machine_id = server_namebook[server_id][0] rpc.set_machine_id(machine_id) ip_addr = server_namebook[server_id][1] port = server_namebook[server_id][2] rpc.create_sender(max_queue_size, net_type) rpc.create_receiver(max_queue_size, net_type) # wait all the senders connect to server. # Once all the senders connect to server, server will not # accept new sender's connection print("Wait connections non-blockingly...") rpc.receiver_wait(ip_addr, port, num_clients, blocking=False) rpc.set_num_client(num_clients) # Recv all the client's IP and assign ID to clients addr_list = [] client_namebook = {} for _ in range(num_clients): # blocked until request is received req, _ = rpc.recv_request() assert isinstance(req, rpc.ClientRegisterRequest) addr_list.append(req.ip_addr) addr_list.sort() for client_id, addr in enumerate(addr_list): client_namebook[client_id] = addr for client_id, addr in client_namebook.items(): client_ip, client_port = addr.split(':') # TODO[Rhett]: server should not be blocked endlessly. while not rpc.connect_receiver(client_ip, client_port, client_id): time.sleep(1) if rpc.get_rank() == 0: # server_0 send all the IDs for client_id, _ in client_namebook.items(): register_res = rpc.ClientRegisterResponse(client_id) rpc.send_response(client_id, register_res) # main service loop while True: req, client_id = rpc.recv_request() res = req.process_request(server_state) if res is not None: if isinstance(res, list): for response in res: target_id, res_data = response rpc.send_response(target_id, res_data) elif isinstance(res, str) and res == 'exit': break # break the loop and exit server else: rpc.send_response(client_id, res)

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#! /usr/bin/env python # -*- coding:utf-8 -*- """ ------------------------------------- File name: Py09_dropout.py Author: Ruonan Yu Date: 18-1-30 ------------------------------------- """ import torch import matplotlib.pyplot as plt from torch.autograd import Variable import torch.nn as nn torch.manual_seed(1) N_SAMPLES = 20 N_HIDDEN = 300 LR = 0.001 # fake data # training data x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) y = x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) x, y = Variable(x), Variable(y) # test data test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) test_y = test_x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True) # overfitting network net_overfitting = nn.Sequential( nn.Linear(1, N_HIDDEN), nn.ReLU(), nn.Linear(N_HIDDEN, N_HIDDEN), nn.ReLU(), nn.Linear(N_HIDDEN, 1) ) # dropout network net_dropouted = nn.Sequential( nn.Linear(1, N_HIDDEN), nn.Dropout(0.5), # drop 50% of neuron nn.ReLU(), nn.Linear(N_HIDDEN, N_HIDDEN), nn.Dropout(0.5), # drop 50% of neuron nn.ReLU(), nn.Linear(N_HIDDEN, 1) ) print(net_overfitting) print(net_dropouted) # training optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=LR) optimizer_drop = torch.optim.Adam(net_dropouted.parameters(), lr=LR) loss_func = nn.MSELoss() plt.ion() for t in range(500): pred_ofit = net_overfitting(x) pred_drop = net_dropouted(x) loss_ofit = loss_func(pred_ofit, y) loss_drop = loss_func(pred_drop, y) optimizer_ofit.zero_grad() optimizer_drop.zero_grad() loss_ofit.backward() loss_drop.backward() optimizer_ofit.step() optimizer_drop.step() if t % 10 == 0: # 每10步画一次图 # 将神经网络转换test形式，画好图之后改回训练形式 net_overfitting.eval() net_dropouted.eval() # 因为drop网络在train的时候和test的时候参数不一样 plt.cla() test_pred_ofit = net_overfitting(test_x) test_pred_drop = net_dropouted(test_x) plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', alpha=0.5, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test') plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting') plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)') plt.text(0, -1.2, r'$overfitting loss=%.4f$' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 10, 'color': 'red'}) plt.text(0, -1.5, r'$dropout loss=%.4f$' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 10, 'color': 'red'}) plt.legend(loc='upper left') plt.ylim((-2.5, 2.5)) plt.pause(0.1) # 将两个网络改回train形式 net_overfitting.train() net_dropouted.train() plt.ioff() plt.show()

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""" Write a Python program to remove existing indentation from all of the lines in a given text.""" import textwrap sample_text = ''' Python is a widely used high-level, general-purpose, interpreted, dynamic programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java. ''' text = textwrap.dedent(sample_text) print(text)

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from django.contrib import admin # Register your models here. from pokedex.models import Specie @admin.register(Specie) class SpecieAdmin(admin.ModelAdmin): pass

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# Generated by Django 3.1 on 2020-08-25 13:49 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ("rpi_manager", "0001_initial"), ] operations = [ migrations.CreateModel( name="Ph", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("date", models.DateTimeField()), ("value", models.FloatField()), ( "rpi", models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to="rpi_manager.rpi", ), ), ], ), ]

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import numpy as np import matplotlib.pyplot as plt #Changing the default size #fig_size = plt.rcParams["figure.figsize"] #fig_size[0] = 20 #fig_size[1] = 16 #plt.rcParams["figure.figsize"] = fig_size imax = 2001 imax = int( input("Enter imax ") ) length = 2.0 #-1<=x<=1 dx = length/(imax-1) u = np.ndarray((imax),dtype=np.float64) un = np.ndarray((imax),dtype=np.float64) ud1 = np.zeros_like(u) ud1n = np.zeros_like(u) ud2 = np.zeros_like(u) ud2n = np.zeros_like(u) x = np.ndarray((imax),dtype=np.float64) ''' for i in range(imax): x[i] = i*dx u[i] = 0.0 un[i] =0.0 if x[i] >= 4.0 and x[i] <= 6.0: u[i] = 1.0 un[i]=1.0 ''' u[:] = 0.0 un[:] = 0.0 #multiple wave profile for i in range(imax): x[i] = -1.0 + i*dx if x[i] >=-0.8 and x[i] <=-0.6: u[i] = np.exp( -np.log(2.0)*(x[i]+0.7)**2 / 0.0009 ) un[i] = u[i] elif x[i] >=-0.5 and x[i] <=-0.2: u[i] = 1.0 un[i] = u[i] elif x[i] >=0.0 and x[i] <=0.2: u[i] = 1.0 - abs(10.0*x[i] - 1.0) un[i] = u[i] elif x[i] >=0.4 and x[i] <=0.6: u[i] = np.sqrt( 1.0 - 100.0*(x[i] - 0.5)**2 ) un[i] = u[i] #Initiate derivatives value for i in range( 1, imax-1 ): ud1[i] = 0.5*(u[i+1] - u[i-1])/dx for i in range( 1, imax-1 ): ud2[i] = 0.5*(ud1[i+1] - ud1[i-1])/dx dt = np.float64(input("Enter dt, dx=%s\n "%dx )) elapsed = 10.0 itermax = int( elapsed/dt )-int(elapsed/2.0) #adjusted timestep; don't know why print("Maximum iteration: ", itermax) c = 1.0 c = float(input("Enter c, +1.0 or -1.0 ")) alpha = c*dt/dx eps = 1.0e-6 uexact = np.zeros_like(u) ''' #calculating exact solution for i in range(imax): r1 = itermax*dt + 4.0 r2 = r1 + (6.0 - 4.0) #did this on purpose, a reminder if x[i] >=r1 and x[i] <= r2: uexact[i] = 1.0 ''' uexact[:] = u[:] #matrix A up = -np.sign(c) A = np.array( [ [ (up*dx)**5, (up*dx)**4, (up*dx)**3], [5.0*(up*dx)**4, 4.0*(up*dx)**3, 3.0*(up*dx)**2], [20.0*(up*dx)**3, 12.0*(up*dx)**2, 6.0*up*dx] ] ) coef = np.array( [0.0, 0.0, 0.0] ) b = np.array( [0.0, 0.0, 0.0] ) xx = -c*dt steps = 1 eps = 1.0e-8 phi = np.zeros_like(u) for iter in range(itermax): for i in range(1,imax-1): up = -np.sign(c) iup = i + int(up) xx = -c*dt b[0] = ( u[iup] - u[i] ) -0.5*ud2[i]*dx*dx - ud1[i]*up*dx b[1] = ( ud1[iup] - ud1[i] ) - ud2[i]*up*dx b[2] = ud2[iup] - ud2[i] coef = np.linalg.solve(A, b) a0 = coef[0] a1 = coef[1] a2 = coef[2] a3 = ud2[i]*0.5 a4 = ud1[i] #limiter udif = ( u[iup] - u[i] )/dx*up #minmod limiter ratio = (u[i] - u[i-1]) / (u[i+1] - u[i] + eps) phi0 = min(10.0*dx, ratio) #default is 1.0 phi[iup] = max(0.0, phi0) #phi[iup] = 0.0 #van Leer (continuous function) #very diffusive #ratio = (u[i] - u[i-1]) / (u[i+1] - u[i] + eps) #phi[iup] = (ratio + abs(ratio)) / (1.0 + ratio) #un[i] = a0*xx**5 + a1*xx**4 + a2*xx**3 + a3*xx**2 + a4*xx + u[i] un[i] = u[i] + (1.0-phi[iup])*(a4*xx + a3*xx**2 + a2*xx**3 + a1*xx**4 + a0*xx**5) \ + phi[iup]*(udif*xx) ud1n[i] = (1.0 - phi[iup])*( 5.0*a0*xx**4 + 4.0*a1*xx**3 + 3.0*a2*xx**2 + 2.0*a3*xx \ + ud1[i] ) + phi[iup]*udif # weight 0.98, 0.01 is the least diffusive #putting weight only on the first derivative #un[i] = u[i] + (1.0 - phi[iup])*(a4*xx) + a3*xx**2 + a2*xx**3 + a1*xx**4 + a0*xx**5 \ # + phi[iup]*(udif*xx) #ud1n[i] = 5.0*a0*xx**4 + 4.0*a1*xx**3 + 3.0*a2*xx**2 + 2.0*a3*xx \ # + (1.0 - phi[iup])*ud1[i] + phi[iup]*udif #the second derivative is not affected ud2n[i] = 20.0*a0*xx**3 + 12.0*a1*xx**2 + 6.0*a2*xx + ud2[i] #update periodic BC u[0] = un[imax-2] ud1[0] = ud1n[imax-2] ud2[0] = ud2n[imax-2] u[imax-1] = un[imax-2] ud1[imax-1] = ud1n[imax-2] ud2[imax-1] = ud2n[imax-2] for i in range(1, imax-1): u[i] = un[i] ud1[i] = ud1n[i] ud2[i] = ud2n[i] #update periodic BC #u[imax-1] = un[imax-2] #ud1[imax-1] = ud1n[imax-2] #ud2[imax-1] = ud2n[imax-2] #u[0] = un[imax-2] #ud1[0] = ud1n[imax-2] #ud2[0] = ud2n[imax-2] ''' #update u[:] = un[:] ud1[:] = ud1n[:] ud2[:] = ud2n[:] ''' #if iter%steps == 0: # num = str(iter) # filename = "./data1D/f"+num.zfill(5)+".csv" # fp = open(filename, "w") # fp.write("x, u\n") # for i in range(imax): # str1 = str(x[i]) # str2 = str(u[i]) # comma = "," # nextline = "\n" # strall = str1+comma+str2+nextline # fp.write(strall) # fp.close() current = iter*dt + dt display = "t = %.4f"%(current) phi[:] = 0.0 current = iter*dt + dt display = "t = %.4f"%(current) #plt.axis([0.0, 10.0, -0.5, 1.5 ] ) plt.axis([-2.0, 2.0, -0.5, 1.5 ] ) plt.title(display) plt.ylabel("U") plt.xlabel("x") plt.plot(x,u,'bo-') plt.pause(0.001) plt.clf() #clear drawing filename = "final.png" #plt.axis([0.0, 10.0, -0.5, 1.5 ] ) plt.axis([-2.0, 2.0, -0.5, 1.5 ] ) plt.plot(x,u, 'bo-', x, uexact,'kv-') plt.title(display) plt.ylabel("U") plt.xlabel("x") plt.savefig(filename) plt.show() #plt.show(block=False) #plt.pause(3) #plt.close() filename = "cip5-final.csv" fp = open(filename, "w") fp.write("x, u\n") for i in range(imax): str1 = str(x[i]) str2 = str(u[i]) comma = "," nextline = "\n" strall = str1+comma+str2+nextline fp.write(strall) fp.close()

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Jaamunozr/Archivos-python

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import os import pylab as pl from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter import numpy as np #------------------------------------------------------------------------------ os.system("clear") fig = pl.figure() axx = Axes3D(fig) raiz=np.sqrt ln=np.log puntoX=float(0) puntoY=float(0) #puntoX=float(input("Seleccione la coordenada en X donde desea calcular el potencial: ")) #puntoY=float(input("Seleccione la coordenada en Y donde desea calcular el potencial: ")) print("Calculando ...") #------------------------------------------------------------------------------ Xa = np.arange(-10, 10, 0.1) #Rango de coordenadas de X Ya = np.arange(-10, 10, 0.1) #Rango de coordenadas de Y l = 2 #Longitud del electrodo [m] rho= 100 #Resistividad de terrreno [Ohm/m] Ik=200 #Corriente de falla [A] (Total) Rad=0.01 #Radio del electrodo [m] Electrodos=8 #Número de electrodos Pos1=4 #Posición 1 en Y para analisis de grafica 2D Pos2=0 #Posición 2 en Y para analisis de grafica 2D #------------------------------------------------------------------------------ #Posición de los electrodos #------------------------------------------------------------------------------ P=np.array([ [-4,-4], #Electrodo A [0,-4], #Electrodo B [4,-4], #Electrodo C [-4,0], #Electrodo D [4,0], #Electrodo E [-4,4], #Electrodo F [0,4], #Electrodo G [4,4] #Electrodo H ]) #------------------------------------------------------------------------------ E=Electrodos-1 ik=Ik/Electrodos Vt=np.zeros((np.count_nonzero(Xa),np.count_nonzero(Ya))) m=np.zeros((Electrodos,1)) V=np.zeros((Electrodos,1)) k=0 m2=np.zeros((Electrodos,1)) V2=np.zeros((Electrodos,1)) #------------------------------------------------------------------------------ #Cálculo del punto ingresado #------------------------------------------------------------------------------ i=0 while i<=E: m2[i][0] =round(raiz((((P[i][0])-puntoX)**2)+(((P[i][1])-puntoY)**2)),4) o,u=((P[i][0])-puntoX),((P[i][1])-puntoY) if ((o ==0) and (u==0)) or (m2[i][0]==0): #print("Elementos de matriz",k,t, "x,y",P[i][0],P[i][1],"punto de eje",X,Y ) m2[i][0]=Rad V2[i][0] =ln((l+raiz((m2[i][0])**2+l**2))/(m2[i][0])) i += 1 Vt2=(np.sum(V2)*(rho*ik))/(2*np.pi*l) print("El potencial en el punto (",puntoX,",",puntoY,"), es de",round(Vt2,3),"[V]") #------------------------------------------------------------------------------ #Cálculo de la malla #------------------------------------------------------------------------------ Vxy = [1] * (np.count_nonzero(Ya)) while k<np.count_nonzero(Ya): Y=round(Ya[k],3) t=0 while t<np.count_nonzero(Xa): X=round(Xa[t],3) i=0 while i<=E: m[i][0] =round(raiz((((P[i][0])-X)**2)+(((P[i][1])-Y)**2)),4) o,u=((P[i][0])-X),((P[i][1])-Y) if ((o ==0) and (u==0)) or (m[i][0]==0): #print("Elementos de matriz",k,t, "x,y",P[i][0],P[i][1],"punto de eje",X,Y ) m[i][0]=Rad V[i][0] =ln((l+raiz((m[i][0])**2+l**2))/(m[i][0])) i += 1 Vt[k][t]=np.sum(V) if Y==Pos1: Vxa=Vt[k] if Y==Pos2: Vxb=Vt[k] if (Y==X) and ((X-Y)==0): Vxy[k]=Vt[k][t]*(rho*ik)/(2*np.pi*l) t +=1 k +=1 Vtt=(Vt*(rho*ik))/(2*np.pi*l) Vxa=(Vxa*(rho*ik))/(2*np.pi*l) Vxb=(Vxb*(rho*ik))/(2*np.pi*l) aa=np.where(np.amax(Vtt) == Vtt) print ("Valor máximo de tensión (GPR):",round(Vtt[::].max(),3),"[V], en posición: (",round(Xa[aa[0][0]],2),",",round(Ya[aa[1][0]],2),")") bb=np.where(np.amin(Vtt) == Vtt) print("Valor de Resistencia de puesta a tierra:", (round(Vtt[::].max(),3)/Ik), "[Ohm]") #print ("Valor mínimo de tensión:",round(Vtt[::].min(),3),"[V], en posición: (",round(Xa[bb[0][0]],2),",",round(Ya[bb[1][0]],2),")") print ("Número de elementos de Vt:",np.count_nonzero(Vtt)) #------------------------------------------------------------------------------ # GRAFICAS 3D #------------------------------------------------------------------------------ # Configurar una figura dos veces más alta que ancha #fig = plt.figure(figsize=plt.figaspect(0.2)) #fig = plt.figure(4,figsize=(6,4.5)) #(Ancho, alto) #fig.suptitle('Potencial') fig = plt.figure(figsize=plt.figaspect(2.)) fig.suptitle('A tale of 2 subplots') # Primera imagen a imprimir ax = fig.add_subplot(2, 2, 1, projection='3d') X, Y = np.meshgrid(Xa, Ya) #surf = ax.plot_surface(X, Y, Vtt, cmap = cm.get_cmap("jet"))#, antialiased=False) surf = ax.plot_surface(X, Y, Vtt, rstride=1, cstride=1, linewidth=0, antialiased=False) ax.set_zlim(300, 1800) #fig.colorbar(surf) #------------------------------------------------------------------------------ #Graficas en 2D #------------------------------------------------------------------------------ x1=Xa ax = fig.add_subplot(2, 2, 2) ax.plot(x1, Vxa, color="blue", linewidth=1.0, linestyle="-") ax.title.set_text('Eje X1 vs V') ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5) ax.set_ylabel('Grafica 1') ax = fig.add_subplot(2, 2, 3) ax.plot(x1, Vxb, color="red", linewidth=1.0, linestyle="-") ax.title.set_text('Eje X2 vs V') ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5) ax.set_ylabel('Grafica 2') ax = fig.add_subplot(2, 2, 4) ax.plot(x1, Vxy, color="green", linewidth=1.0, linestyle="-") ax.title.set_text('Eje X,Y vs V') ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5) ax.set_ylabel('Grafica 3') plt.pause(25) pl.savefig('tierras.pdf')

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#!/usr/bin/env python ############################################################################## # Copyright 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. ############################################################################## from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import json import os from utils.devices import devices as devices_dict class Devices(object): def __init__(self, filename=None): if filename: # if the user provides filename, we will load it. assert os.path.isfile(filename), \ "Device file {} does not exist".format(filename) with open(filename, "r") as f: self.devices = json.load(f) else: # otherwise read from internal self.devices = devices_dict self._elaborateDevices() def getFullNames(self, devices): names = devices.split(",") new_names = [self.devices[name]["name"] if name in self.devices else name for name in names] return ",".join(new_names) def getAbbrs(self, abbr): if abbr in self.devices: device = self.devices[abbr] if "abbr" in device: return device["abbr"] return None def _elaborateDevices(self): device_abbr = [] for name, _ in self.devices.items(): device = self.devices[name] assert "name" in device, \ "Field name is required in devices" assert device["name"] == name, \ "Device key ({}) and name ({})".format(name, device["name"]) + \ " do not match" if "abbr" in device: assert isinstance(device["abbr"], list), \ "Abbreviations for {} needs to be a list".format(name) for abbr in device["abbr"]: device_abbr.append((device, abbr)) for device_abbr_pair in device_abbr: self._elaborateOneDevice(device_abbr_pair[0], device_abbr_pair[1]) def _elaborateOneDevice(self, device, abbr): assert abbr not in self.devices, "Abbreviation " + \ "{} is already specified in the device list".format(abbr) self.devices[abbr] = device

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/src/collective/iptvusp/tests/test_uspvideo.py

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simplesconsultoria/collective.iptvusp

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# -*- coding: utf-8 -*- import unittest2 as unittest from zope.component import createObject from zope.component import queryUtility from plone.app.testing import TEST_USER_ID from plone.app.testing import setRoles from plone.dexterity.interfaces import IDexterityFTI from plone.app.dexterity.behaviors.exclfromnav import IExcludeFromNavigation from collective.iptvusp.content import IUSPVideo from collective.iptvusp.testing import INTEGRATION_TESTING class CoverIntegrationTestCase(unittest.TestCase): layer = INTEGRATION_TESTING def setUp(self): self.portal = self.layer['portal'] setRoles(self.portal, TEST_USER_ID, ['Manager']) self.portal.invokeFactory('Folder', 'test-folder') setRoles(self.portal, TEST_USER_ID, ['Member']) self.folder = self.portal['test-folder'] self.folder.invokeFactory('iptvusp.uspvideo', 'c1', template_layout='Layout A') self.c1 = self.folder['c1'] def test_adding(self): self.assertTrue(IUSPVideo.providedBy(self.c1)) def test_fti(self): fti = queryUtility(IDexterityFTI, name='iptvusp.uspvideo') self.assertNotEqual(None, fti) def test_schema(self): fti = queryUtility(IDexterityFTI, name='iptvusp.uspvideo') schema = fti.lookupSchema() self.assertEqual(IUSPVideo, schema) def test_factory(self): fti = queryUtility(IDexterityFTI, name='iptvusp.uspvideo') factory = fti.factory new_object = createObject(factory) self.assertTrue(IUSPVideo.providedBy(new_object)) def test_exclude_from_navigation_behavior(self): self.assertTrue(IExcludeFromNavigation.providedBy(self.c1))

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# -*- coding: utf-8 -*- import numpy as np from sklearn import svm samples_w1 = np.array([[-3.0, 0.5, 2.9, -0.1, -4.0, -1.3, -3.4, -4.1, -5.1, 1.9], [-2.9, 8.7, 2.1, 5.2, 2.2, 3.7, 6.2, 3.4, 1.6, 5.1]]).T samples_w2 = np.array([[-2.0, -8.9, -4.2, -8.5, -6.7, -0.5, -5.3, -8.7, -7.1, -8.0], [-8.4, 0.2, -7.7, -3.2, -4.0, -9.2, -6.7, -6.4, -9.7, -6.3]]).T def transform_data(data): # return 1 x1 x2 x1**2 x2**2 x1x2 return np.hstack((np.ones((data.shape[0], 1)), data, data**2, (data[:, 0] * data[:, 1])[:, np.newaxis])) def main(): # set misclassification penalty to a large enough value trans_samples_w1 = transform_data(samples_w1) trans_samples_w2 = transform_data(samples_w2) # data = np.vstack((trans_samples_w1[0, :], trans_samples_w2[0, :])) # labels = [0, 1] # res = svm.SVC(C=1e10, kernel="linear").fit(data, labels) # m = np.sqrt(res.coef_[0].dot(res.coef_[0])) # margin1 = (res.coef_.dot(trans_samples_w1[0,:]) + res.intercept_) / m # margin2 = (res.coef_.dot(trans_samples_w2[0,:]) + res.intercept_) / m # print "margin of w1 {}: {}; margin of w2 {}: {}".format(trans_samples_w1[0, :], margin1, # trans_samples_w2[0, :], margin2) for num in range(1, samples_w1.shape[0]+1): data = np.vstack((trans_samples_w1[:num, :], trans_samples_w2[:num, :])) labels = np.hstack((np.zeros(num), np.ones(num))) res = svm.SVC(C=1e10, kernel="linear").fit(data, labels) print "sample number: {}, coef: {}, b: {}, margin: {}".format(num*2, res.coef_, res.intercept_, np.sqrt(1/(res.coef_[0].dot(res.coef_[0])))) if __name__ == "__main__": main()

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# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, List, Optional, Union import torch from torch import nn from torch.nn import functional as F from torchmetrics import IoU from flash.core.classification import ClassificationTask from flash.core.data.io.input import DataKeys from flash.core.data.io.output_transform import OutputTransform from flash.core.registry import FlashRegistry from flash.core.utilities.imports import _KORNIA_AVAILABLE from flash.core.utilities.isinstance import _isinstance from flash.core.utilities.types import ( LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE, OUTPUT_TRANSFORM_TYPE, OUTPUT_TYPE, ) from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES from flash.image.segmentation.heads import SEMANTIC_SEGMENTATION_HEADS from flash.image.segmentation.output import SegmentationLabels if _KORNIA_AVAILABLE: import kornia as K class SemanticSegmentationOutputTransform(OutputTransform): def per_sample_transform(self, sample: Any) -> Any: resize = K.geometry.Resize(sample[DataKeys.METADATA]["size"][-2:], interpolation="bilinear") sample[DataKeys.PREDS] = resize(sample[DataKeys.PREDS]) sample[DataKeys.INPUT] = resize(sample[DataKeys.INPUT]) return super().per_sample_transform(sample) class SemanticSegmentation(ClassificationTask): """``SemanticSegmentation`` is a :class:`~flash.Task` for semantic segmentation of images. For more details, see :ref:`semantic_segmentation`. Args: num_classes: Number of classes to classify. backbone: A string or model to use to compute image features. backbone_kwargs: Additional arguments for the backbone configuration. head: A string or (model, num_features) tuple to use to compute image features. head_kwargs: Additional arguments for the head configuration. pretrained: Use a pretrained backbone. loss_fn: Loss function for training. optimizer: Optimizer to use for training. lr_scheduler: The LR scheduler to use during training. metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics` package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict containing a combination of the aforementioned. In all cases, each metric needs to have the signature `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.IOU`. learning_rate: Learning rate to use for training. multi_label: Whether the targets are multi-label or not. output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs. output_transform: :class:`~flash.core.data.io.output_transform.OutputTransform` use for post processing samples. """ output_transform_cls = SemanticSegmentationOutputTransform backbones: FlashRegistry = SEMANTIC_SEGMENTATION_BACKBONES heads: FlashRegistry = SEMANTIC_SEGMENTATION_HEADS required_extras: str = "image" def __init__( self, num_classes: int, backbone: Union[str, nn.Module] = "resnet50", backbone_kwargs: Optional[Dict] = None, head: str = "fpn", head_kwargs: Optional[Dict] = None, pretrained: Union[bool, str] = True, loss_fn: LOSS_FN_TYPE = None, optimizer: OPTIMIZER_TYPE = "Adam", lr_scheduler: LR_SCHEDULER_TYPE = None, metrics: METRICS_TYPE = None, learning_rate: float = 1e-3, multi_label: bool = False, output: OUTPUT_TYPE = None, output_transform: OUTPUT_TRANSFORM_TYPE = None, ) -> None: if metrics is None: metrics = IoU(num_classes=num_classes) if loss_fn is None: loss_fn = F.cross_entropy # TODO: need to check for multi_label if multi_label: raise NotImplementedError("Multi-label not supported yet.") super().__init__( model=None, loss_fn=loss_fn, optimizer=optimizer, lr_scheduler=lr_scheduler, metrics=metrics, learning_rate=learning_rate, output=output or SegmentationLabels(), output_transform=output_transform or self.output_transform_cls(), ) self.save_hyperparameters() if not backbone_kwargs: backbone_kwargs = {} if not head_kwargs: head_kwargs = {} if isinstance(backbone, nn.Module): self.backbone = backbone else: self.backbone = self.backbones.get(backbone)(**backbone_kwargs) self.head: nn.Module = self.heads.get(head)( backbone=self.backbone, num_classes=num_classes, pretrained=pretrained, **head_kwargs ) self.backbone = self.head.encoder def training_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().training_step(batch, batch_idx) def validation_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().validation_step(batch, batch_idx) def test_step(self, batch: Any, batch_idx: int) -> Any: batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET]) return super().test_step(batch, batch_idx) def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any: batch_input = batch[DataKeys.INPUT] batch[DataKeys.PREDS] = super().predict_step(batch_input, batch_idx, dataloader_idx=dataloader_idx) return batch def forward(self, x) -> torch.Tensor: res = self.head(x) # some frameworks like torchvision return a dict. # In particular, torchvision segmentation models return the output logits # in the key `out`. if _isinstance(res, Dict[str, torch.Tensor]): res = res["out"] return res @classmethod def available_pretrained_weights(cls, backbone: str): result = cls.backbones.get(backbone, with_metadata=True) pretrained_weights = None if "weights_paths" in result["metadata"]: pretrained_weights = list(result["metadata"]["weights_paths"]) return pretrained_weights @staticmethod def _ci_benchmark_fn(history: List[Dict[str, Any]]): """This function is used only for debugging usage with CI.""" assert history[-1]["val_iou"] > 0.2

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no_license

Aasthaengg/IBMdataset

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MOD = 1000000007 n = int(input()) s1 = input() s2 = input() if s1[0] == s2[0]: ans = 3 i = 1 prev = 1 else: ans = 6 i = 2 prev = 2 while i<n: if s1[i] == s2[i]: i += 1 if prev == 1: ans *= 2 else: prev = 1 else: i += 2 if prev == 1: ans *= 2 prev = 2 else: ans *= 3 ans %= MOD print(ans)

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66529651+Aastha2104@users.noreply.github.com

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/Scorm.py

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Gamboua/zope-migration

34e6b27962859352fe08a4277a8215b36b01889c

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refs/heads/master

2020-12-25T14:49:22.173420

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import paramiko import os from scp import SCPClient from config import * from Command import Command import random, string class Scorm: def __init__(self, scorm, course): self.course = course self.scp = None self.type = 'scorm' self.section = 0 self.folder = self.get_if_exists('folder', scorm) self.title = self.get_if_exists('title', scorm) def get_if_exists(self, parameter, json): return json.get(parameter) if parameter in json else None def scorm_add(self): self.scorm_import_folder() zip_name = self.scorm_zip() Command.command_execute(Command.activity_create_command( options=self.get_scorm_options(zip_name), type=self.type, id=self.course.id )) def get_scorm_options(self, name): params = [] if self.section is not None: params.append('--section %s' % self.section) if self.title: params.append('--name "%s"' % self.title) params.append('--filepath /tmp/%s.zip' % name) return ' '.join(params) def scorm_zip(self): name = ''.join(random.choice(string.ascii_letters) for x in range(8)) os.chdir(self.folder) os.system('zip -r /tmp/%s *' % name) os.chdir(os.path.dirname(os.path.abspath(__file__))) return name def scorm_import_folder(self): client = paramiko.SSHClient() client.load_system_host_keys() client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) client.connect(REMOTE_SCORM_SERVER, REMOTE_SCORM_PORT, REMOTE_SCORM_USER) scp = SCPClient(client.get_transport()) if not os.path.isdir('/opt/zope298/courses'): os.makedirs('/opt/zope298/courses') scp.get( self.folder, '/opt/zope298/courses/', recursive=True ) scp.close()

[ "gabriel.bonfim@4linux.com.br" ]

gabriel.bonfim@4linux.com.br

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/accounts/managers.py

42d3aae755fba1358031994ccd3a06d4ca8dcdd1

[]

no_license

mfonism/django-inqueerstigate

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af5420bf8adf6aa89533cd1462d9eeed6e8c88db

refs/heads/main

2023-05-26T12:59:55.774989

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from django.contrib.auth.models import BaseUserManager class UserManager(BaseUserManager): use_in_migrations = True def _create_user(self, email, password, **extra_fields): if not email: raise ValueError("The given email must be set") email = self.normalize_email(email) user = self.model(email=email, **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, email, password=None, **extra_fields): extra_fields.setdefault("is_staff", False) extra_fields.setdefault("is_superuser", False) return self._create_user(email, password, **extra_fields) def create_superuser(self, email, password, **extra_fields): extra_fields.setdefault("is_staff", True) extra_fields.setdefault("is_superuser", True) if extra_fields.get("is_staff") is not True: raise ValueError("Superuser must have is_staff=True.") if extra_fields.get("is_superuser") is not True: raise ValueError("Superuser must have is_superuser=True.") return self._create_user(email, password, **extra_fields)

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mfonetimfon@gmail.com

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[ "Apache-2.0" ]

permissive

russellgao/algorithm

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2023-03-28T03:00:02.370660

2021-03-28T10:56:38

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# Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None # 迭代 def sortList(head: ListNode) -> ListNode: head_len = 0 invc = 1 h = head while h : head_len += 1 h = h.next result = ListNode(0) result.next = head while invc <= head_len : pre = result h = result.next while h : h1 ,i = h , invc while i and h : i -= 1 h = h.next if i : break h2, i = h, invc while i and h : i -= 1 h = h.next c1, c2 = invc, invc-i while c1 and c2 : if h1.val > h2.val : pre.next = h2 h2 = h2.next c2 -= 1 else : pre.next = h1 h1 = h1.next c1 -= 1 pre = pre.next pre.next = h1 if c1 else h2 while c1 > 0 or c2 > 0 : pre = pre.next c1 -= 1 c2 -= 1 pre.next = h invc <<= 1 return result.next if __name__ == "__main__" : node = ListNode(4) node.next = ListNode(2) node.next.next = ListNode(1) node.next.next.next = ListNode(3) node.next.next.next.next = ListNode(5) result = sortList(node) while result : print(result.val) result = result.next print()

[ "gaoweizong@hd123.com" ]

gaoweizong@hd123.com

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[]

no_license

zhaoyuzhi/HSGAN

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refs/heads/main

2023-08-03T10:06:05.195187

2023-07-27T14:21:54

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import os import numpy as np # ---------------------------------------- # PATH processing # ---------------------------------------- def check_path(path): if not os.path.exists(path): os.makedirs(path) def get_files(path): # read a folder, return the complete path ret = [] for root, dirs, files in os.walk(path): for filespath in files: ret.append(os.path.join(root, filespath)) return ret def get_jpgs(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: ret.append(filespath) return ret def get_mats(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: if filespath[-3:] == 'mat': ret.append(os.path.join(root, filespath)) return ret def get_mats_name(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: if filespath[-3:] == 'mat': ret.append(filespath.split('.')[0]) return ret def get_bmps(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: if filespath[-3:] == 'bmp': ret.append(os.path.join(root, filespath)) return ret def get_pairs_name(path): # read a folder, return the image name ret = [] for root, dirs, files in os.walk(path): for filespath in files: if filespath[-3:] == 'mat': ret.append(filespath.split('.')[0]) return ret # ---------------------------------------- # PATH processing # ---------------------------------------- def text_readlines(filename): # Try to read a txt file and return a list.Return [] if there was a mistake. try: file = open(filename, 'r') except IOError: error = [] return error content = file.readlines() # This for loop deletes the EOF (like \n) for i in range(len(content)): content[i] = content[i][:len(content[i])-1] file.close() return content def text_save(content, filename, mode = 'a'): # save a list to a txt # Try to save a list variable in txt file. file = open(filename, mode) for i in range(len(content)): file.write(str(content[i]) + '\n') file.close() def savetxt(name, loss_log): np_loss_log = np.array(loss_log) np.savetxt(name, np_loss_log)

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permissive

erdogant/pypiplot

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import pypiplot # print(pypiplot.__version__) # print(dir(Pypiplot)) from pypiplot import Pypiplot # %% Update all libraries to date. pp = Pypiplot(username='erdogant', repo_type=['owner', 'fork']) pp.update() results = pp.stats() pp.plot_year(vmin=700) pp.plot() pp.plot_year() # %% Top 10 best repos pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/') # Get download statistics pp.stats() # Get top 10 repo=pp.results['data'].sum().sort_values()[-10:].index.values # Get stats for the top10 pp.stats(repo=repo) # Plot pp.plot() # pp.plot_year() # pp.plot_cal() # path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap_full.html' pp.plot_heatmap(vmin=10, vmax=2000, cmap='interpolateOranges', path=path) # %% Plot # Init pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/') # Get download statistics results = pp.stats() # Store svg on github.io # path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html' path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html' path = 'C://temp/pypi_heatmap.html' pp.plot_year(path=path, vmin=700) # Store all repo info in github.io pp.plot(legend=False) # %% D3blocks pp = Pypiplot(username='d3blocks') pp.update(repo=['d3blocks']) pp.stats(repo='d3blocks') pp.plot() # %% pp = Pypiplot(username='erdogant') pp.stats(repo='distfit') pp.plot_year() pp.plot(vmin=25) # %% Update single repo pp.update(repo=['bnlearn']) pp.update(repo='bnlearn') results = pp.stats(repo=['distfit','pca', 'bnlearn']) pp.plot(legend=True) # %% Get some stats results = pp.stats(repo=['df2onehot','pca','bnlearn','ismember','thompson']) pp.plot(legend=True) # %% pp = Pypiplot(username='erdogant') pp.stats(repo='distfit') pp.plot_year() pp.plot(vmin=25) pp.stats(repo='worldmap') pp.plot_year() pp.stats(repo='hnet') pp.plot_year() pp.stats(repo='ismember') pp.plot_year() pp.stats(repo='flameplot') pp.plot_year() pp.stats(repo='pca') pp.plot_year() pp.stats() pp.stats(repo=['df2onehot','clustimage','bnlearn','distfit','pypickle','clusteval','findpeaks', 'kaplanmeier','pca','colourmap']) pp.results['data'].rolling(window=30).mean().plot(figsize=(15,10)) plt.grid(True) plt.xlabel('Time') plt.ylabel('Average nr. download based on a rolling window of 30 days') # pp.results['data'].cumsum().plot() pp.plot_year(vmin=100) pp.plot(vmin=25) pp.results['data'].cumsum().plot() # %% Plot bnlearn results = pp.stats(repo='bnlearn') pp.plot_year() # %% pp.update() results = pp.stats() pp.plot_year(vmin=700) pp.plot(vmin=25) # %% Plot # Init pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/') # Get download statistics results = pp.stats() # Store svg on github.io path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html' path = 'C://temp/pypi_heatmap.html' pp.plot_year(path=path, vmin=700) # Store all repo info in github.io path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap_repos.html' pp.plot(path=path, vmin=100) # %% from pypiplot import Pypiplot # results = pp.stats() pp.stats(repo=['df2onehot','clustimage','bnlearn','distfit','pypickle','clusteval','findpeaks', 'kaplanmeier','colourmap']) pp.plot_cal(method='mean', vmin=100) pp.plot(method='mean') # %%

[ "erdogant@gmail.com" ]

erdogant@gmail.com

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/Tensorflow/CNN/莫烦python02.py

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[]

no_license

HotView/PycharmProjects

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61393fe5ba781a8c1216a5cbe7e0d06149a10190

refs/heads/master

2020-06-02T07:41:53.608742

2019-11-13T08:31:57

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import tensorflow as tf import numpy as np import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("data",one_hot=True) def add_layer(inputs,in_size,out_size,activaion_function = None): Weights = tf.Variable(tf.random_normal([in_size,out_size])) biases = tf.Variable(tf.zeros([1,out_size])+0.1) Wx_plus_b = tf.matmul(inputs,Weights)+biases if activaion_function is None: outputs = Wx_plus_b else: outputs =activaion_function(Wx_plus_b) return outputs def compute_accuracy(v_xs,v_ys): global prediction y_pre = sess.run(prediction,feed_dict={xs:v_xs}) correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys}) return result xs = tf.placeholder(tf.float32,[None,784]) ys = tf.placeholder(tf.float32,[None,10]) # add output layer prediction = add_layer(xs,784,10,activaion_function=tf.nn.softmax) # error crosss_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(crosss_entropy) sess =tf.Session() sess.run(tf.initialize_all_variables()) for i in range(5000): batch_xs,batch_ys = mnist.train.next_batch(100) sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys}) if i%50==0: print(compute_accuracy(mnist.test.images,mnist.test.labels))

[ "864773190@qq.com" ]

864773190@qq.com

6567d0f8b19425ebfd1cd990c73c0e2498f971f2

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/solution/accounting.py

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[]

no_license

tessajules/underpaid-customers-HB-homework

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refs/heads/master

2021-05-28T22:11:25.106565

2015-04-10T02:56:36

null

0

null

UTF-8

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py

MELON_COST = 1.00 def melon_payment_calculator(payment_data): """Calculate cost of melons and determine who has underpaid.""" payment_data = open(payment_data) for line in payment_data: order = line.split('|') customer_name = order[1] customer_first = customer_name.split(" ")[0] customer_melons = float(order[2]) customer_paid = float(order[3]) customer_expected = customer_melons * MELON_COST if customer_expected < customer_paid: print customer_name, "paid %.2f, expected %.2f" % ( customer_paid, customer_expected) print customer_first, "has overpaid for their melons." elif customer_expected > customer_paid: print customer_name, "paid %.2f, expected %.2f" % ( customer_paid, customer_expected) print customer_first, "has underpaid for their melons." melon_payment_calculator("customer-orders.txt")

[ "info@hackbrightacademy.com" ]

info@hackbrightacademy.com

ea8bb3f37fef6e37cd9f9274f22db69548ed5b99

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/xcp2k/classes/_program_run_info36.py

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[]

no_license

Roolthasiva/xcp2k

66b2f30ebeae1a946b81f71d22f97ea4076e11dc

fc3b5885503c6f6dc549efeb4f89f61c8b6b8242

refs/heads/master

2022-12-23T06:03:14.033521

2020-10-07T08:01:48

null

0

null

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Python

false

695

py

from xcp2k.inputsection import InputSection from xcp2k.classes._each343 import _each343 class _program_run_info36(InputSection): def __init__(self): InputSection.__init__(self) self.Section_parameters = None self.Add_last = None self.Common_iteration_levels = None self.Filename = None self.Log_print_key = None self.EACH = _each343() self._name = "PROGRAM_RUN_INFO" self._keywords = {'Add_last': 'ADD_LAST', 'Common_iteration_levels': 'COMMON_ITERATION_LEVELS', 'Filename': 'FILENAME', 'Log_print_key': 'LOG_PRINT_KEY'} self._subsections = {'EACH': 'EACH'} self._attributes = ['Section_parameters']

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

acc4aef5d2a6eb365488380fe43780058d19a3d6

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/sdBs/AllRun/pg_1623+386/sdB_PG_1623+386_lc.py

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tboudreaux/SummerSTScICode

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2021-01-20T18:07:44.723496

2016-08-08T16:49:53

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py

from gPhoton.gAperture import gAperture def main(): gAperture(band="NUV", skypos=[246.351292,38.505214], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_PG_1623+386 /sdB_PG_1623+386_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3) if __name__ == "__main__": main()

[ "thomas@boudreauxmail.com" ]

thomas@boudreauxmail.com